Horticulture

In eastern Chester County, the rural zoning is typically one house per two acres, with only a few municipalities requiring one house per 10 acres. Chester County’s master plan seeks to acknowledge and support agricultural economic planning through a variety of measures that focus economic development efforts on farm-related businesses, promote agritourism, transition younger farmers into employment, and allow construction of farm labor housing. The county has committed its own staff for agricultural economic development and local food marketing within the county, while offering county facilities as host sites for farmers markets. The county plan also recognizes the synergy between agricultural land uses and alternate energy or emerging bio-fuel markets- but does not go so far as to encourage model siting legislation for these industries. Going further than agricultural economic planning, Chester County ties its county plan to food security planning. In the effort to keep farms viable, plastic plant pot the county makes a commitment to work with the Delaware Valley Regional Planning Commission in regional food system planning.

The County plans recognize that nearly 25% of the county is food insecure and encourages local farms and citizens to volunteer in the local food bank’s gleaning program .Farming networks are represented by 754 unique geographic locations with 1087 connections between these nodes. The total farm network reach on average was 44 km, drawn to the east with a magnitude of 89 km, indicating that the majority of farming networks are drawn to the urban market of Philadelphia and surrounding suburbs . Despite using methods which were expected to reveal more direct-to-consumer relationships, findings are dominated by sales to wholesalers, representing medium and large-scale grocery chains, food hubs, and produce aggregators . Farm sales to farmers’ markets, CSAs and Buyer’s Clubs are the next most prominent networks. The longest reaching networks are farmers’ markets, farm-to farm sales, wholesale distribution, and sales to restaurants . Farm participation in farmers’ markets exhibits the direct network with the longest average reach, with farm employees travelling nearly 60 km to visit farmers’ markets, predominantly located in the south east toward urban populations in Philadelphia. Conversely, the most common relationship in farm to-farm networks are those where farms located in more rural western settings partner with Chester County farms for farm-gate sales. For example, one farm outside the county supplied milk to a Chester County farm that made cheese, which it sold further from its farm gate and to local wholesale distributors.

The reach of farmers’ markets and restaurants contrasts those of the CSA and institutional sales, which are roughly half the distance and oriented more toward surrounding suburbs. The most proximate networks are those for byproduct, educational visits and the county food bank, showing that these networks may rely more on proximity of resources and social contacts. Farm byproducts, such as compost and spent grain generally move away from urban areas toward rural land. Similarly, the gardening and gleaning programs organized by Chester County Food Bank are proximate in space.Social network mapping of Chester County farm networks by the ten network-type codes indicates the degree to which various farm relationships are intertwined . Based on the network connections, the food bank plays an important role in linking volunteer groups to educational farm visits. Many of the farms involved in the food bank’s gleaning program are centrally located in the social network, and are connected to numerous other networking typologies. For example, farms that participate in the food bank’s gleaning programs are also likely to host educational visits from the same institutions that participate in the gardening program for the food bank. The Force Atlas layout of the social network draws apart disparate nodes based on their network coding. From this layout, we see that many farms specialize in one network type, be it CSA sales, sales to wholesale distributors, or participating in multiple different farmers’ markets.

One can also see threads that run centrally to the social network, such as farm-to-farm and farm-to restaurant sales. This view also allows us to see overlap in networks. Every CSA node has a link to a farmers’ market, but the opposite is not true. Likewise, many farms that specialize in wholesale markets also sell through farmers’ markets.Interviews with key agricultural and food policy experts helped to verify the social network findings and provide explanations. Many interviewees emphasized how networks evolve over time and in relation to one another, adding a time-component to this analysis. Chester County has had a long history of direct-to consumer sales. Interviewees agreed that the proximity of suburbs, particularly wealthy suburbs, aided in the establishment of farm-to-market networks throughout the region.These statements indicate that the geographic distance-decay function of social networks built around food marketing. Indeed, Anthony’s assertion that farms involved with CSAs “bring people to the farm” is visualized with the social network map where numerous farms involved in CSAs also host educational visits that tend to be geographically proximate in nature . The central role of the food bank in purveying directly from farms and coordinating on-farm volunteer efforts may also help explain the breadth of Chester County farming networks. Larry Welsch, director of the Chester County Food Bank, notes that the food bank currently has a fleet of over 3000 volunteers, which “flock” to volunteer opportunities on farms after school and on the weekends. The size and willingness of this volunteer base speaks to Chester County’s wealth but also the draw of agritourism. Through the volunteer participation in the gleaning program, the farms generate goodwill and donate excess food to the food bank. Larry Welsch, asserts that the gleaning allows farms to showcase the good work they do to volunteers and further build their market potential for agritourism activities beyond volunteer days. As a result, farms involved in the gleaning program get practice and market exposure, helping them to later operate on-farm agritourism events, CSAs, and farmers’ market stands to further their market base and generate more profit per pound of product sold. Indeed, plastic planter pot the social network mapping indicates that participating food bank farms use multiple networks that are all highly localized geographically . Welsch noted that the majority of the forty farms that participate in the gleaning program are incapable of contiguous expansion and surrounded on all sides by urban and suburban land-uses. The network analysis in this research captures only 11 farms currently involved in the food bank gleaning program . Welsch also noted that many of the participating farms are located in southwest Chester County, the headquarters of the Food Bank before it moved to its more central location in 2010. Though the northwestern portion of Chester County has large, contiguous blocks of farmland, few of these farms participate in food bank programs. The food bank readily leverages geographically and socially proximate networks. Welsch attributes the success of gleaning program with spawning the more recent “raised-bed” program, in which local churches, businesses, schools or residents grow produce for the Food Bank. The Food Bank now has 546 gardens at 129 sites, including 49 schools, up from a total of 25 in 2009. From this rapid success, the Food Bank launched a greenhouse initiative, providing schools with high tunnels so that students can grow food year-round for their cafeterias. The school presence spurred the development of curriculums for healthy eating, farming and nutrition in elementary and middle schools with high tunnels.

Staff have pioneered cooking classes and lunch-time tastings of fresh food, such as frozen squash popsicles, in order to introduce children to vegetables that they grow and try to persuade school catering companies to source locally and provide more fresh food. All of these programs make use of the same networks to facilitate food donations through gardening and gleaning along with farm visits for educational purposes with the aim of promoting healthy eating for low-income Chester County residents. Chester County interviewees agreed that the limits to farm networks were not based on farmer will or consumer demand, but land-use regulation. As Marilyn Anthony stated, “The barriers to entry-it’s policy, regulation. Many of those things are controlled by small groups-whether that’s county commissioners or land conservation groups. They can change the language in their easements, but that doesn’t happen easily.” Moreover, zoning regulations “can be counter-intuitive, irrational, arbitrary. A lot of it is really outdated. It’s based on false assumptions of agriculture.” These sentiments are supported in recent studies, such as the Green Space Alliance Commission’s report on “Transforming Open Space,” which highlights zoning language as an obstacle for the transformation of vacant land . Zoning restrictions apply not only to the farm parcel, but to traffic regulation. As Anthony explains, “you may be farming in an area that is zoned agricultural, but it may not be able to have any retail or commerce on that site, so you would have ag zoning but not commercial. And you may not be able to conduct retail or have a farm store. There may be ordinance restrictions on traffic, so you may not be able to have parking for 20 cars- or it’s a two-lane road and they don’t want that level of traffic on it.” Such land-use regulations would limit the ability for farms to host any network which brings users to the farm, such as: education tours, gleaning volunteers, CSA pick-up locations, or roadside stands. Restrictive land-use regulations may force farms into a long-distance network typology characterized primarily by wholesale marketing. Interviewees noted that farms struggle not only with land-use regulations at the farm, but also variations in state and county level land-use regulations encountered en route to the market. Matthew Wiess works for Farm-to-City, a Philadelphia-based nonprofit which helps farmers navigate urban market regulations while also helping communities who would like to open a farmers’ market in their neighborhood. Farm-to-City manages over 20 farmers markets in Philadelphia, but does not work with New Jersey farms or farmers’ markets due to the numerous differing county and state health regulations. Wiess notes that the chief concern for farmers’ market managers is the cost of street closure permits and various approval processes for new farmers market citation. Philadelphia has an ordinance allowing farmers’ markets, but to put a new site on the ordinance, the city council member in the proposed district has to introduce and pass new legislation. Weiss notes that the demand for farm-city connections is as much as urbanite driven as farmer-driven. At the time of the interview, Farm-toCity had a waiting list of 40 farms for farmers’ markets and over 20 applications to open new farmers’ markets throughout the city. The waitlist speaks to both an abundance of supply and demand, but forming the connection for each farm network is difficult due to land-use regulations and public service limitations in access to restrooms, parking and water. Moreover, Farm-to-City likes to see desire by neighbors for the market in the form of resident petitions. Some residents may not want the traffic, noise or commercial activity that a farmers’ market brings. Bryan Snyder, one of the original founders of Buy Fresh Buy Local, a national local food marketing campaign that started out of Pennsylvania, goes further in asserting that more local networks could be had if there were higher quality public receiving points in urban areas. The farm-to-city network requires infrastructure; ironically, an infrastructure that most cities had until shortly after the 1950s when many central covered farmers markets were removed for public health reasons . As recently as 1918, a majority of cities in the United States with populations over 30,000 had a municipal food market where local and fresh produce was hocked to urbanites .As Hinrichs supposed, CSAs and farmers’ markets appear to connect over differing geographies as represented by the generalized reach diagram . Namely, CSA markets are more proximal . Yet, this research shows that CSAs and farmers’ market networks cluster socially ; and both marketing typologies are not well interwoven in other food system networks. This finding begs the question: are direct markets embedded socially at the local level? The social network analysis reveals the important role that the food bank plays in convening many of the farms involved at this nexus of networks . Interviews and review of the comprehensive plan corroborate the social embeddedness of the food bank in land-use policy and food planning in Chester County.

Hypothetically, an area is at higher risk of disease transmission if it is more likely to experience interactions between feral pig and domestic pigs raised outdoors, as these outdoor based pigs can serve as a conduit for disease spread from wildlife to humans. Locations at greatest risk for increased contact between both swine populations are those regions that contain feral pig suitable habitat located near outdoor-raised domestic pig premises, especially those OPO with relatively low levels of bio-security., , Contact between feral pigs and outdoor-raised pigs in California has been documented, as feral pigs are attracted to agricultural regions for food, water and mates., , There is enormous value in identifying agricultural regions with a higher probability of feral pig contact, because these areas could benefit from targeted cost-effective disease surveillance and risk-mitigation strategies to prevent disease transmission. Predicting suitable habitat for feral pigs in combination with spatially characterizing the distribution of OPO can provide an important tool to ascertain possible high-risk areas of contact at the feral-domestic pig interface and identify future disease spillover areas.

Species distribution modeling methods have been widely used in ecological studies and are becoming popular for use in epidemiological investigations of disease transmission between wildlife and livestock. Maximum Entropy , which is one type of SDM, plastic planters bulk allows usage of presence-only data for the species of interest . In combination with biologically-appropriate covariate factors, MaxEnt is able to spatially predict the probability of suitable habitat for a species for a chosen spatial unit . These two parallel trends of expanding feral pig populations and a resurgence of raising domestic swine outside has important implications for disease transmission, which could negatively impact both public health and California’s agricultural industry. To the best of our knowledge, there are no maps characterizing where suitable feral pig habitat overlaps with domestic pigs raised outdoors at the farm-level in California. The overall objective of this study entailed spatially identifying potential high-risk areas of disease transmission between these two swine populations. This objective was achieved by a three-step process: 1) predicting suitable feral pig habitat in California using MaxEnt; 2) mapping the spatial distribution of OPO in California; and 3) identifying high-risk regions where there is spatial overlap between feral pig suitable habitat and OPOs, as potential disease transmission areas.

MaxEnt is an established SDM method that produces an output prediction map containing estimates of the relative probability of suitable habitat areas for the species of interest within each pixel, using presence-only points and predictor rasters . For feral pig presence data, we obtained feral pig hunting tags from 2012-19 that were cleaned and recorded with GPS coordinates by the California Department of Fish and Wildlife . Hunters in California are voluntarily asked to report feral pig harvest locations by submitting hunting tags to CDFW. Using hunting records for presence-points of feral pigs or wild boars has been used in previous studies. CDFW 2012-19 feral pig hunting tags totaled 5,148 after removing duplicates. Due to the large amount of data points, hunting tags were also manually filtered by year as a way to decrease the abundance of points before running models to reduce sampling bias and increase model stability, as suggested by previous analyses of MaxEnt. Publicly available predictor layers online, including biotic and abiotic , were included in variable selection steps, see Table 2.1. These predictors were chosen based on known feral pig behaviors, habitat and food preferences.For instance, AVGMODIS was the annual maximum green vegetation fraction combined with 12 years of normalized difference vegetation index data and relates to food and shrub cover for feral pigs.

Other variables included elevation, as feral pigs may prefer specific altitudes, and nineteen environmental variables from the WorldClim set of 30 year trend climatic factors. Examples of environmental variables used from the WorldClim site included BIO6, which is the minimum temperature of the coldest month, BIO13 which represents precipitation of the wettest month and BIO15 which is the coefficient of variation for seasonal precipitation.MaxEnt models were built in R Statistical Software version 0.98.110253 ©. The following R packages were used to run MaxEnt: dismo, sp, and raster. MaxEnt settings were chosen based on previously published literature and included using 25 random test points, 15 replicates, 5000 maximum iterations and the 10-percentile training for the threshold rule. , , A regularization multiplier of 1 through 5 was assessed to avoid overfitting and the default 1 was determined to be the optimal setting for the final model. Logistic values for output was used as well as cross validation, which separates presence points into 80% training and 20% testing data , using k-fold sub-sampling to fit a model. The relative contribution of each variable in a MaxEnt model was assessed comparing both percent contribution and permutation of importance, averaged over the number of iterations run and ascertained by jackknife tests. Predictors for the final model were assessed using a backward variable selection approach: variables remained at each step if their percent contribution or permutation importance was approximately 10% or more. The response curves generated within MaxEnt showed the predicted probability of suitable feral pig habitat for each individual variable, changing per each level of the predictor. MaxEnt model performance was assessed using the area under the curve of the receiver operator characteristic , averaged over the number of chosen replicate runs. AUC reflects a model’s prediction ability, on a scale of 0 to 1.00, with 0.50 representing random chance. While AUC is a standard diagnostic method to evaluate MaxEnt models, some authors suggest calibrating the AUC , which removes spatial sorting bias  by using point-wise distance sampling. A ssb close to 1 indicates no spatial sorting bias, whereas a ssb close to 0 suggests a large spatialbias, and the need to use AUCc. The final model was chosen based on the highest AUCc, collection pot relative to other models. The feral pig-domestic pig risk map was built by overlapping California OPO locations with the final MaxEnt feral pig suitable habitat raster. Between 2014-2019, a list of California OPOs was compiled through various sources advisors, web-based searches . GPS coordinates for all OPO were identified using Google Earth Pro v7.3.3. Additionally, an online survey that contained an interactive map component was built with Survey 123 v3.6. The survey contained 29 questions that consisted mainly of multiple choice questions, with a few open ended questions about the number of animals raised . The survey included questions regarding biosecurity practices, swine health and feral pig presence. This online survey was announced electronically to swine related groups and organizations or conducted in-person at events, such as agricultural fairs. The survey instrument and protocols were reviewed and exempted by the Institutional Review Board of the University of California-Davis . To build a risk map for California, the final MaxEnt model predicting suitable habitat for feral pigs was overlapped with the location of OPOs to categorize areas at greatest risk for disease transmission, due to contact between these two swine populations, and characterize risk at the farm-level. The underlying assumption presumed that direct or indirect contact between feral pigs and domestic pigs raised outdoors is a risk for disease transmission. The probability of suitable habitat for feral pigs was extracted from the final MaxEnt model for each OPO location, using the Sample Raster Value tool in QGIS and added to the OPO shape file. Then the Kernel Density tool in QGIS was used to make the risk map, matching the 270m x 270m resolution of the MaxEnt model and using the MaxEnt model probabilities as weights.

Additionally, we used a radius of 5 km at each OPO location, which was an extrapolated average estimate from US based studies that measured home range of feral pigs, understanding that home ranges vary depending on age and gender of animal, as well as resource availability. The Kernel Density map was overlaid with the final MaxEnt model. The final MaxEnt model was chosen based on the highest AUCc of 89.7, relative to other models . Probability values that predict suitable habitat were divided into five equal interval categories: minimal ; low ; moderate ; high ; and extremely high , with 0.87 being the highest predicted probability in the final MaxEnt model. Areas with the highest likelihood of suitable feral pig habitat in California included the north coast from Mendocino County all the way south along the coast to Santa Barbara County, and counties that border these coastal counties . Additionally, suitable habitat areas included the foothills of the Sierra mountains, from Shasta County south to Tulare County. Least likely suitable habitat included the Central Valley and eastern counties of California, from the most northern county of Modoc all the way to Imperial County in the south.Five variables were identified as significant in predicting suitable feral pig habitat in the final model based on 2017 hunting tags . The five significant variables were AVGMODIS, Elevation, BIO6, BIO13 and BIO15. All five variables provided approximately 10% or more percent contribution and permutation importance to the final model. . The jackknife test results provided more information regarding the importance of each variable in the final model . For example, BIO15 was the variable with the highest gain when used alone and elevation had the most information that was not available in the other variables. The response curves for the significant five variables indicated the predicted suitability range of each variable for feral pigs . For instance, feral pigs are predicted to prefer vegetative cover of at least 60% or more. The risk map reflects areas at greatest risk for contact between feral swine and domestic pigs raised outdoor and subsequent potential disease transmission . Areas with the most risk for contact between these two swine populations are denoted in orange or red, with sharper colors representing denser clustering of OPO. The counties with the highest likelihood of suitable feral pig habitat and densest clustering of OPO included: Sonoma, Marin, Napa, Yolo, Nevada, Mendocino and Lake counties. Areas at lowest risk include the full eastern edge of California, which includes the Cascadian and Sierra Nevada Mountain ranges as well as deserts in the south. Table 2 categorizes the distribution of OPO at each level of probable suitable feral pig habitat using the final MaxEnt model levels. The results indicated that 49.18% of OPO are located near extremely high or highly suitable feral pig habitat.In this study, we built a feral pig suitable habitat prediction model for California using MaxEnt at a fine scale of 270m x270m. Significant predictors of suitable feral habitat included precipitation, minimum temperature, elevation, and percentage of vegetation. Additionally, this study overlapped MaxEnt predicted suitable feral pig habitat and outdoor-raised pig operations to create a risk map for potential disease transmission in California at the feral pig-domestic pig interface. To the best of our knowledge, this is one of the first studies that identified areas at risk for feral and domestic pig contact in California. Although previous studies discussed the possibility of feral pig populations spreading disease to outdoor-raised pigs at the county level, to our knowledge, this is the first study to predict risk at the farm-level in California. Since the exact location of most feral pig populations is unknown, species distribution predictive methods like MaxEnt are important to understand where feral pigs could potentially interface with domestic swine raised outside, either currently or in the future. Our final MaxEnt prediction model provides a more informative picture of suitable habitat for feral pigs than previous studies, which only showed single presence points or reported feral pigs at the county level, even if only one feral pig was identified in that county. For instance, although previous county-level maps indicated that all California counties except for Imperial County harbored feral pigs, our MaxEnt model shows almost no suitable habitat in an additional five counties: Modoc, Mono, Alpine, Lassen and Inyo. This result may indicate that few feral pigs have been seen in those counties. Additionally, the final MaxEnt model was based on a fine spatial scale and indicated heterogenous suitable habitat, not a uniform distribution, for each county, which is compatible with the fact that feral pigs need shrub cover and food to survive, which would not be found in cities or deserts.

The grapem1355 candidate maps on chromosome 3, exactly on thefirst exon of its target , in a region where another two isoforms of the same gene are located . The last target of this miRNA candidate, codes a cinnamyl alcohol dehydrogenase known to be involved in the lignin biosynthesis . Other novel vvi-miRNA candidates seem to be involved in cell proliferation and in chloroplasts-related functions . Furthermore, for the new vvi-miRC482b candidate, besides the already known involvement of this miRNA family with disease resistance also predicted here, one predicted target encodes an anthocyanin 5-aromatic acyltransferase-like protein known to be involved in the biosynthesis of anthocyanin in different species . As for the conserved known vvi-miRNAs, most of the well-established examples of miR-targets, such as miR156-SPB, miR166-HD-ZIP, miR171-GRAS, miR172-AP2, confirmed in several plant species and already predicted in grapevine, blueberry containers were also predicted here. We studied miRNA profile of accumulation in the different samples.

Using their normalized abundance , i.e., their relative cloning frequency, we set an empirical cut off value equal to at least 10 TP4M in both biological replicates to consider a miRNA as expressed in a given library. Also, a miRNA was considered specific when it was expressed in one or more libraries of a unique cultivar, unique environment or unique developmental stage. According to our established cut off, 175 miRNAs were classified as expressed in at least one of our libraries . The libraries constructed from Sangiovese berries at bunch closure collected in Bolgheri showed only 24 expressed miRNAs . For all the other libraries, expressed miRNAs ranged from 76 to 148 . We found very few miRNAs specific to a given condition. The number of specific miRNAs for each cultivar, developmental stage and environment is reported in Figures 8A–C, respectively. Thirty-nine vvi-miRNAs were highly expressed in almost all libraries [21 ubiquitous plus 18 expressed in all libraries except in Bol_SG_bc ], whereas other miRNAs had different accumulation patterns. The normalized expression values of miRNAs were subjected to hierarchical clustering and represented in a heat map . To examine the relatedness among cultivars, environments and developmental stages, we generated a correlation dendrogram . The dendrogram shows, as already suggested by the heatmaps, that a fundamental dichotomy emerges between ripened and green berries. The most evident pattern of expression is observed when comparing different developmental stages, and confirm previous observation of miRNA modulation during fruit ripening .

For example, some members of the vvi-miRNA156 family were highly expressed in all ripened berries, but weakly or not expressed in green berries. Differently, vvi-miR396a-3p and vvi-miR396b-3p showed the opposite profile. Similarly, vvi-miR172d, vvi-miR166b-5p, vvi-miR166f-5p, and vvi-miR396d-5p were highly expressed in green berries but weakly expressed in ripened berries and the members of the vvi-miR319 family showed a gradient of decreasing abundance from pea size to harvest.To gain statistical evidence of miRNA differential expression driven by the environment and/or genotype, we made pairwise comparisons, keeping constant the developmental stage, and evaluating the miRNA modulation among vineyards or between cultivars . The analyses reveal that some miRNAs are differentially expressed between the two genotypes grown in the same environment, but also that a number of miRNAs are modulated by the environment. In particular the number of differentially expressed miRNAs is higher in ripened berries , while no miRNAs are differentially expressed at bunch closure stage . In details, 14 reads are differentially expressed at pea size stage, in at least one comparison, corresponding to 6 distinct miRNA families; 27 reads are modulated at 19 ◦Brix stage, corresponding to 12 miRNA families and 35 reads are differentially expressed in berries at harvest, corresponding to 12 miRNA families. It is worth noting that 4 of the 6 families modulated in the berries at pea size, are still present among the miRNAs differentially expressed in the berries sampled at 19 ◦Brix and at harvest , even though not always in the same comparisons. Some of the modulated miRNAs, both novel and known are intriguingly connected to berry development and secondary metabolism, even though most of the modulated families are still uncharacterized, or with targets not clearly involved in berry ripening and development, and deserve further studies to fully understand their biological roles. Using high throughput sequencing coupled with robust bio-informatics pipelines we analyzed small RNAs derived from the berries of Cabernet Sauvignon and Sangiovese, grown sideby-side in three vineyards, representative of different grapevine cultivation areas in Italy . We obtained nearly 750 MB reads comprising a significant proportion of small RNAs.

The size distribution profiles of our libraries were in general consistent with previous reports in berry grapevine, where the 21-nt class was more abundant than the 24-nt class . Our analysis revealed dynamic features of the regulatory network mediated by miRNAs and other small RNAs, at the basis of genotype-environment interactions. Plants evolved a series of pathways that generate small RNAs of different sizes with dedicated functions . Although the various small RNA classes have been intensively studied, we are still far from understanding how many small RNA pathways exist, and how they are connected . Additionally, new classes of small non-coding RNAs continue to be discovered and many studies demonstrate a substantial redundancy and cross-talk between known small RNA pathways . Estimating the exact percentage of the plant genome covered by small RNA-generating loci still remains a challenge. By applying static cluster analysis, we investigated small RNA abundances across the genome, identifying 4408 small RNAs producing hotspots. We analyzed their expression in different cultivars, environments and developmental stages, highlighting that the majority of the considered small RNA producing regions was modulated in different conditions. This suggests a strong influence of small RNAs in the response to environment in grapevine berries. Only 462 small RNA-generating loci, corresponding to about 10% of the total, were expressed in all the analyzed libraries, possibly involved in essential biological pathways. Comparing the two cultivars, we observed, with few exceptions, that Cabernet Sauvignon berries have a higher number of expressed sRNA-generating loci than Sangiovese berries when collected in the same conditions . Considering the fact that small RNAs are implicated in the regulation of gene expression in several processes , the higher number of small RNAs expressed in Cabernet Sauvignon compared to Sangiovese berries may reflect a buffering effect of small RNAs influencing grapevine response to diverse growing environments. We believe that these characteristics may have contributed to the wide diffusion of Cabernet Sauvignon, allowing its wide cultivation in almost all wine producing countries. This is not the case for Sangiovese whose cultivation is more restricted. It is worth noting that Sangiovese is considered a very unsettled grapevine cultivar , showing a wide range of variability in response to year, clone and bunch exposure . Differently, Cabernet Sauvignon is a cultivars showing less inter-annual differences in terms, for example, best indoor plant pots of concentration of secondary metabolites . To better evaluate varietal differences in response to the environment, we calculated the CS/SG ratio for the small RNA producing hotspots in the three vineyards. A region on chromosome 4 showed a 390-fold change in the small RNA abundance, when comparing Cabernet vs. Sangiovese . Most of the reads produced in this region are 21 nt long and are also phased in intervals of 21 nt from both strands, typical of a phased locus . The gene in this locus, also known as VvRD22g, encodes a BURP domain containing protein, involved in an ABA-mediated abiotic stress response, which persists still after long periods of stress . The small RNAs profile suggests that the locus is regulated by phased siRNAs similarly to the mechanisms already described for PPR, NB-LRR, and MYB gene families . This is a clear example of GxE interactions since the BURP domain gene modulates phased siRNAs production in the two cultivars only when grown in Riccione. When removing the threshold of minimum cluster abundance set to 5 HNA, in the CS/SG ratio, a high number of clusters with fold change greater than 50 was found, where one of the libraries has 0 HNA and the other any number greater than 30 HNA. This fact suggests a very strong modulation of the expression of small RNAs between the two cultivars, which is more or less pronounced depending on the vineyard where the berries were cultivated. A similar situation was observed comparing the expression level of small RNAs between reciprocal hybrids of Solanum lycopersicum and S. pimpinellifolium .

The ripening process of grapevine berries is highly affected by the environment and we observed the impact of the environment on the ripening process in the expression of small RNAs. The most relevant observation is that Riccione is very peculiar in relation to the activation of sRNA hotspots, as indicated by the high number of Riccione specific clusters and by the extreme modification it induces in the CS/SG ratio : in Riccione in fact this ratio decreases in green berries and increases in ripened berries, and this is not observed in any other vineyard; in addition to this the already discussed example of BURP domain gene, is observed in Riccione, as well. Riccione is the most diverse environment when compared to Montalcino and Bolgheri. Riccione is located at the Adriatic coast and has a temperate sub-littoral climate, while Montalcino and Bolgheri are both located in Tuscany with typically Mediterranean climate. Moreover, both cultivars show a peculiar profile of small RNA loci during berries ripening, in Riccione. The expression of small RNA loci in Cabernet Sauvignon berries drastically changed during development, especially when collected in Riccione , not only in the number of active loci but also in the different genic or intergenic disposition: ripened berries have a 2.6-fold increase in small RNA loci active in genic regions. Differently, when Sangiovese is grown in Riccione, there is a very high number of small RNA loci active in green berries, mainly associated to transposable elements that remains almost stable during development although the proportion of intergenic loci is reduced. Sangiovese berries collected in Montalcino show a 2.5- fold increase of small RNA producing loci during development. Differences during berry development between the cultivars may explain their different behavior in different environments, and the characteristics of each vineyard may favor one or other variety according to their demands. For example, Sangiovese needs a long growing season with sufficient warmth to fully ripen . Consequently, cooler environments will require a reprograming of Sangiovese gene expression in order to achieve ripening. Other factors such as composition of soil, level of humidity, photo period and density of cultivation may be exerting the same influence on the ripening of the berries triggering the activation of different small RNA loci.Applying a conservative pipeline to the analysis of our 48 small RNA libraries, we recognized 89 known and annotated grapevine miRNAs. In addition, when compared to previous reports in grapevine we identified 7 completely novel miRNAs plus 26 homologous to other plant species, but novel to grapevine. This is a remarkable number considering the stringency of our pipeline and that our study is based only on four developmental stages of berries. The outline of miRNA accumulation across samples is different from that of sRNA-producing loci. While the expression of sRNA-generating regions allows distinguishing very well between ripened and green berries and also between cultivars , the accumulation of miRNAs shows a clear distinction only between ripened and green berries, and when the berries were green, we observe a further dichotomy separating the two cultivars and the two green developmental stages. The same pattern of miRNA accumulation among green and ripened berries of grapevine was observed when we described the miRNA expression atlas of Vitis vinifera . Comparing the distribution of miRNAs expressed throughout our samples, we found a set of 39 miRNAs ubiquitous or nearly ubiquitous to all the libraries, and very few miRNAs specific of a cultivar, vineyard or developmental stage. All these 39 miRNAs belong to known vvi-miRNA families. With few exceptions, the same set of miRNAs was also found expressed in all the small RNA libraries constructed with different tissues of the grapevine cv. Corvina , where the population of expressed miRNAs appears highly variable apart from a well-defined group of miRNAs, probably related to the basal metabolism. These findings are also consistent with previous report in grapevine where a small number of known tissue-specific miRNAs was described .

The Berry phases of bosonic and fermionic coherent sates and the special cases with a 1D Hilbert space are summarized in the Appendix. The classical approach to the central limit theorem and the accuracy of approximations for independent random variables rely heavily on Fourier transform methods. However, the use of Fourier methods is highly limited without an independence structure, which makes it far less possible to capture the explicit bounds for the accuracy of approximations. In 1972, Charles Stein introduced a novel technique, now known as Stein’s method, for normal approximation. The method works for both independent and dependent random variables. The method also provides bounds of approximation accuracy. Extensive applications of Stein’s method to obtain uniform and non-uniform Berry–Esseen-type bounds for independent and dependent random variables can be found in, for example, Diaconis , Baldi et al. , Barbour , Dembo and Rinott , Goldstein and Reinert , Chen and Shao , Chatterjee , Nourdin and Peccati and Chen and Fang . In addition to the traditional study of Berry–Esseen bounds, container growing raspberries new developments to Stein’s method have triggered a series of research on Cramér-type moderate deviations, which address the relative error of two tail probabilities. See, for example, Raič , Chen et al. and Shao and Zhou , among others.

Various extensions of Stein’s idea have been applied to many other probability approximations, most notably to Poisson, Poisson process, compound Poisson, binomial approximations and more recently to multivariate, combinatorial and discretized normal approximations. Stein’s method has also found diverse applications in a wide range of fields, see for example,Arratia et al. , Barbour et al. and Chen . Expositions of Stein’s method and its applications in normal and other distributional approximations can be found in Diaconis and Holmes , Barbour and Chen . We also refer to Chen et al. a thorough coverage of the method’s fundamentals and recent developments in both theory and applications. The paper is organized as follows. In the next section, we give a brief review on recent developments on Stein’s method. In Section 3, we present the main results in this paper, the Berry–Esseen bounds and Cramér type moderate deviations for Studentized nonlinear statistics. Applications to Studentized U-statistics and L-statistics are discussed in Section 4. The proofs of the main results are in Section 5, while other technical proofs are postponed to Appendix. Vitis vinifera grapevines originated approximately 65 million years ago from Eurasia and have been cultivated for at least the last 8000 years for its fruits that are crushed to make wine. Grapevines are now grown throughout the world in many kinds of environments. Grape berry development is a complex process involving three developmental phases and multiple hormones.

It is in the latter ripening phase that many compounds involved in flavor and aromas are synthesized, conjugated or catabolized. Most of these compounds reside in the skin of the berry and seem to develop in the very last stages of berry development. Aroma and flavor are important sensory components of wine. They are derived from multiple classes of compounds in grapes including important volatile compounds from the grape and from yeast metabolism during grape fermentation. Each grape cultivar produces a unique set of volatile and flavor compounds at varying concentration that represents its wine typicity or typical cultivar characteristics. Esters and terpenes are volatile compound chemical classes largely responsible for the fruity and floral aromas in wines. Esters are largely produced during yeast fermentation from grape-derived products such as aliphatic alcohols and aldehydes. Grape lipoxygenases are thought to provide the six carbon precursors from fatty acids for the synthesis of the fruity aroma, hexyl acetate, in yeast during wine fermentation. Terpenes mostly originate from the grapes and are found in both the free and bound forms. Both plant fatty acid and terpenoid metabolism pathways are very sensitive to the environment. Climate has large effects on berry development and composition.

Besides grape genetics other factors may influence metabolite composition including the local grape berry microbiome, the soil type and the rootstock. While there is evidence that rootstock can affect fruit composition and transcript abundance, this effect appears to be minor relative to other environmental factors. Many cultural practices used by the grape grower may directly or indirectly affect the environment sensed by the grapevine . Temperature and light are major contributors to “terroir”. Terroir refers to the environmental effects on grapes and how it contributes distinctive characteristics to the typicity of a wine . The terroir term includes biotic and abiotic factors, soil environments as well as the viticultural practices. In the present work, we will use the term “place” to address all of the above except for the viticultural practices. Recently, a transcriptomic approach was used to elucidate the common gene subnetworks of the late stages of berry development when grapes are normally harvested at their peak maturity. One of the major sub-networks associated with ripening involved autophagy, catabolism, RNA splicing, proteolysis, chromosome organization and the circadian clock. An integrated model was constructed to link light sensing with the circadian clock highlighting the importance of the light environment on berry development. In this report, in order to get a better understanding of how much of the gene expression in Cabernet Sauvignon berry skin could be attributed to environmental influences, we tested the hypothesis that there would be significant differences in gene expression during the late stages of Cabernet Sauvignon berry ripening between two widely different locations: one in Reno, NV, USA and the other in Bordeaux, France . The analysis revealed a core set of genes that did not depend on location, climate, vineyard management, grafting and soil properties. Also, the analysis revealed key genes that are differentially expressed between the two locations. Some of these differences were linked to the effects of temperature and other environmental factors known to affect aromatic and other quality-trait-associated pathways. Many gene families were differentially expressed and may provide useful levers for the vine grower to adjust berry composition. Among others, these families encompassed genes involved in amino acid and phenylpropanoid metabolism, as well as aroma and flavor synthesis.To test the hypothesis that the transcript abundance of grape berries during the late stages of ripening differed in two locations with widely different environmental conditions, we compared the transcript abundance of grape berry skins in BOD and RNO. The vineyards were originally planted in RNO in 2004 and in BOD in 2009. The RNO vines were grown on their own roots, whereas the BOD vines were grafted on to SO4 rootstock. A vertical shoot positioning trellis design was used in both locations. There were a number environmental variables that differed between the two locations. BOD is located at a slightly more northern latitude than RNO. This resulted in slightly longer day lengths in BOD at the beginning of harvest and slightly shorter at the end of harvest . On the final harvest dates, blueberries in pots the day length differed between RNO and BOD by about 30 min. RNO had warmer average monthly maximum temperatures than that in BOD, but minimum September temperatures were cooler in RNO . Thus, RNO had a larger average daily day/night temperature differential of 20 °C, whereas BOD had a smaller average daily day/ night temperature differential of 10 °C during the harvest periods. RNO had warmer day temperatures by about6 °C and cooler night temperatures by about 4 °C than that of BOD. The RNO vineyard location was much drier than the BOD vineyard location . The monthly precipitation totals for RNO in September were 2.03 mm whereas it was 65.5 mm in BOD; the average relative humidities were 34 and 74% for RNO and BOD, respectively.

The soil at the RNO vineyard was a deep sandy loam with a pH of 6.7; the BOD vineyard was a gravelly soil with a pH of 6.2. No pathogens, nutrient deficiencies or toxicity symptoms were observed on or in the vines at either site.The analysis of transcript profiles of Cabernet Sauvignon grapes harvested in RNO in September of 2012 was previously described. Individual berry skins were separated immediately from the whole berry and the individual total soluble solids level of the berry, which is mostly composed of sugars, was determined. The Cabernet Sauvignon berry skins from BOD were harvested in a similar manner as the RNO berry skins. The berry skins in BOD were harvested from midway in September, 2013 until the first week of October . The berry skins were separated and the °Brix analyzed in the same manner as that in RNO. Grapes were harvested at a lower °Brix range in BOD than in RNO because fruit maturity for making wine is typically reached in the BOD region at a lower sugar level. Transcript abundance of the RNA-Seq reads from both RNO and BOD was estimated using Salmon software with the assembly and gene model annotation of Cabernet Sauvignon. The TPM were computed for each gene from each experimental replicate from berry skins at different sugar levels ranging from 19 to 26°Brix . Principal component analysis of the transcriptomic data showed clear grouping of experimental replicates with the largest separation by location = 51% variance and then °Brix = 22% variance of the berry skin samples . To get different perspectives of the data, three approaches were used to further analyze the transcriptomic data. One focused on expression at one similar sugar level in both locations. Another identified a common set of genes whose transcript abundance changed in both locations. And the third one was a more comprehensive network analysis using all of the sugar levels and the two locations. We chose two very similar sugar levels to determine the differential gene expression between the two locations, since sugar levels were not exactly the same at harvest. We identified 5528 differentially expressed genes between the two locations in approach 1 at the sugar level closest to the 22°Brix level using DESeq2. DEGs will refer to this set of differentially expressed genes throughout this manuscript. Gene set enrichment analysis using topGO determined the top gene ontology categories for biological processes for these 5528 genes . Based on the number of genes identified, the top GO categories were cellular metabolic process , biosynthetic process , and response to stimulus . Other important and highly significant categories were response to stress and developmental process . The relationship between the top 25 GO categories can be seen in Additional file 4. We use the term “significantly” throughout this text to mean statistically significant at or below a padj-value of 0.05. Amongst the top stimulus subcategories with the largest number of genes were response to abiotic stimulus , response to endogenous stimulus , response to external stimulus , and biotic stimulus . Some other significant environmental stimuli GO categories included response to light stimulus , response to osmotic stress , and response to temperature stimulus . In approach 2, we examined which gene expression was changing with °Brix level in both locations to identify a common set of genes differentially expressed during berry development with very different environmental conditions. The significant differences in transcript abundance in each location was determined with DESeq2 using the lowest °Brix sampling as the control. For example, the control sample in RNO was the lowest sugar sampling at 20 °Brix; the transcript abundance of the three higher °Brix samplings were compared to the transcript abundance of the control. The genes that had significantly different transcript abundance relative to control in at least one of the comparisons were identified in RNO and BOD. These gene lists were compared and the common gene set consisting of 1985 genes for both locations was determined . Comparing this common gene list to the DEGs from approach 1 identified 907 genes that were common to both sets, indicating that this subset was differentially expressed between the locations at 22°Brix. The other 1078 genes did not differ significantly between locations. This 1078 gene subset list can be found in Additional file 5 . The GO categories most enriched in this gene set included response to inorganic substance, response to abiotic stimulus and drug metabolic process. In approach 3, using a more powerful approach to finely distinguish the expression data for all sugar levels, Weighted Gene Coexpression Network Analysis identified gene sets common to and different gene expression profiles between BOD and RNO. All expressed genes for all °Brix levels were used in this analysis.

The first intrinsic two dimensional ferromagnets were discovered in 2017, so I think it’s safe to say that our field hasn’t yet come particularly close to identifying all possible two dimensional magnets. It’s hard to do an accurate accounting of all of the so-far discovered two dimensional magnets, and it is certainly the case that many of these are are not Chern magnets. But of the two dimensional magnets we have found, a surprisingly large fraction are intrinsic Chern magnets. We know of eight intrinsic Chern magnets stable in the absence of an applied magnetic field in the published literature so far. These are presented, along with a few of their basic properties, in Table 8.1. We have discussed several of these materials in this thesis, but we have also skipped a few,including the only currently known intrinsic Chern magnet in an atomic crystal, i.e., not on a moir´e superlattice: MnBi2Te4. These other materials all also represent areas of active research. Of the Chern magnets we know about, 2/8 have C < 0 with B > 0, so that property might be quite common. Indeed, there’s no particular reason to expect the B > 0 ground state to have one sign of the Chern number over the other as far as I know. It’s worth mentioning that if we ever find one, a room temperature Chern magnet with C < 0 for B > 0 would also have extremely large ∆m, large pots plastic and will therefore likely be switchable, since ∆m increases linearly with EGap.

It is also the case that two of these materials have been observed to be switchable with pulses of electric current, although it is not yet clear if the tBLG/hBN and ABMoTe2/WSe2 Chern magnets share a common current-switching mechanism, or if their respective mechanisms would generalize well to large gap Chern magnets. I think it’s clear that we are in the early days of the study of this class of material systems, and without discovering more Chern magnets there is little we can say with much generality. All of this is to say that I don’t think it’s crazy to expect to discover Chern magnets at much higher energy scales than we have already encountered, and that should we ever find such a system, there are a variety of intriguing technological applications for which this class of material systems could be exploited. I have put some effort into making this thesis a self-contained explanation of the background, details, and impact of the instrumentation and research work I participated in during my PhD. More can always be said, of course, and there exist a few articles targeted at a general physics audience discussing these discoveries in the context of the field written by other authors. They are referenced at the end of the Curriculum Vitae at the beginning of this thesis, and they are worth reading if you are interested in more of the details of these experiments and their implications for the field. Chern magnets were predicted to exist in the 1980s and realized for the first time in the form of doped topological insulators in 2013.

The first intrinsic Chern magnets were discovered in 2018. I hope I’ve convinced the reader that there are reasons to study this class of materials beyond a simple desire to catalogue all possible phases of matter. The phenomenology of intrinsic Chern magnets turned out to be very rich and may one day add something to a wide variety of electronic technologies, including low dissipation, electronically switchable magnetic memories and electronic metrology. Over the course of my PhD, four nanoSQUID microscopes were proposed, and construction began in some form on all of them. By the time I left we had finished three of these microscopes. The first nanoSQUID microscope we completed was inserted into a bath of liquid helium and could operate at 4 K. The CrI3 magnetic imaging campaign was performed in this system. The second nanoSQUID microscope had a pumped He-4 evaporative cooling pot, and could reach temperatures of 1.5 K. The tBLG/hBN Chern magnet transport measurements, the tBLG/hBN Chern magnet imaging measurements, and the AB-MoTe2/WeSe2 Chern magnet imaging measurements were all performed in this system. The third nanoSQUID microscope had a closed cycle He-3 sorption pump cooling system, and could reach 300 mK. The fourth and final microscope remains under construction, and is designed to operate inside of a dilution refrigerator. Pictures of several of these microscopes are shown in Fig. 8.6. Acoustic isolation chambers and the 300 mK system are not shown. All nanoSQUIDs have liquid He-4 baths for primary stage cooling, and all are mounted on several thousand pound vibration isolation tables floating on air legs to protect the nanoSQUID sensors from mechanical and acoustic shocks close to the surface.

The nanoSQUID sensor circuit is fairly simple, with only one important non-standard circuit element in it, other than the nanoSQUID itself of course. This is the series SQUID array amplifier. Current is forced into the nanoSQUID sensor in parallel with a shunt resistor of comparable resistance to the nanoSQUID sensor in the voltage state, which is generally a few Ohms. Current through the nanoSQUID side of the circuit is inductively coupled to a series of identical SQUIDs. These SQUIDs in series generate a large voltage, which is detected at room temperature. Current is forced through a feedback coil to maintain constant flux through the SQUIDs in series. This allows the circuit to maintain sensitivity over a wide range of currents . This current amplification circuit has good current sensitivity and enormous dynamics range, easily able to accommodate the several hundred µA necessary to reach the critical current of the nanoSQUID sensor. There are a lot of things that make scanning probe microscopy tough relative to other techniques for performing microscopy. One particularly challenging issue is navigation of the sensor to the sample. Those experienced with optical imaging might be spoiled by a contrast mechanism that is sensitive to a ton of different phenomena- the nanoSQUID can only see local gradients in magnetic field and temperature, and those are rare unless you have intentionally built structures and devices that generate them for use in navigation. In particular, large thermal gradients and variations in local magnetic field aren’t general properties of surfaces, square planter pots so it’s very easy to blunder a nanoSQUID sensor into a surface without ever seeing it coming! Experiments are thus much safer and more expedient if we can provide the nanoSQUID sensor with topographic feedback- i.e., some way of detecting surfaces without crashing into them and destroying the sensor. We did this using shear force microscopy, which is a form of atomic force microscopy, or AFM. There is nothing particularly atomic about this contrast mechanism in the nanoSQUID microscope- we don’t have nearly that much resolution- but it is incredibly useful for navigation because it allows us to safely and reliably detect surfaces without destroying the SQUID. Researchers and companies building scanning tunneling microscopes will often accomplish this by gluing their sensor, which is a microscopic metallic wire, onto a piezoelectric tuning fork and then exciting the tuning fork at its resonant frequency. This is a good strategy, but it must be modified for use with the nanoSQUID sensor, because the nanoSQUID sensor is considerably more massive thanscanning tunneling microscope wires, so it cannot be glued onto the tuning fork without destroying its quality factor. We preserve the tuning fork’s quality factor by instead pressing a piezoelectric tuning fork against the side of the nanoSQUID sensor and performing shear force microscopy instead of tapping mode microscopy. The glass micropipettes serving as substrates for the nanoSQUID sensors are so thin that they bend easily when pressed agains the tuning fork, and this keeps them in mechanical contact with the fork. An optical microscope image of a nanoSQUID sensor pressed against a tuning fork is shown in Fig. 8.8A, and the resonant frequency of the piezoelectrically driven tuning fork is shown in Fig. 8.8B, with a fit to a Butterworth Van-Dyke model. A phase-locked loop and PID feedback system together allow us to approach the surface with the nanoSQUID sensor, detect it without crashing into it and destroying the tip, and maintain feedback while scanning.

Schematics of this assembly are shown in Fig. 8.9. A calibration of the scan range and height of the nanoSQUID AFM is shown in Fig. 8.10, with a comparison to a Bruker Icon AFM displayed as well. An image of these assemblies mounted on the microscope and ready to scan is provided in Fig. 8.11. By far the most common experimental campaign for the nanoSQUID microscope during my time in Andrea’s lab involved being handed a sample fabricated primarily for transport or capacitance measurements, with little consideration afforded to the viability or ease of a scanning probe microscopy campaign on the sample. I think this is fairly common in scanning probe microscopy, and it often means that we need to get sensors to samples without much in the way of navigation infrastructure. For this reason the vast majority of nanoSQUID microscopy campaigns start with thermal navigation. Before cooling down the nanoSQUID microscope, an attempt is made to align the nanoSQUID sensor with the heterostructure under an optical microscope, but the nanoSQUID sensor often still starts several hundred microns away from the sample. Once the system is cold, we generally proceed by injecting a few mBar of helium gas into the sample chamber. This facilitates thermal transport between the nanoSQUID sensor and the sample. We then run an AC current through the sample, heating it and generating an AC temperature distribution. The nanoSQUID sensors are excellent thermometers as well as magnetometers, so we can use this thermal gradient to navigate to the sample. An image of the resulting distribution of temperature over the device is shown in Fig. 8.13A. Some of the details are described in a later section, but in summary this technique works surprisingly well- we can usually find samples even several millimeters away from the nanoSQUID sensor using this technique. Once the nanoSQUID is reasonably close to the sample, it is usually necessary to pump out the heat exchange gas before attempting magnetic imaging, since thermal contrast can produce large backgrounds. After the heat exchange gas is removed, further navigation must proceed by imaging the magnetic fields produced by applied current through the Biot-Savart effect, as illustrated in Fig. 8.13B. Thermal navigation does not work for all systems. In the simplest case in which other techniques are necessary, current cannot be driven through magnetic insulators, so if you want to find them with the nanoSQUID you must arrange for some navigation technique other than flowing current through the sample. There are a variety of solutions to this problem, and perhaps the simplest is fabricating an additional device adjacent to the one you’d like to investigate and running current through that instead. There are reasons you might want to avoid this- some samples are so unstable in air and moisture that it makes sense to avoid photolithography on heterostructures entirely- and for these situations, I’m going to discuss ferromagnetic navigation. We start by generating a photolithography mask containing a large array of microscopic QR codes, as illustrated in Fig. 8.14A. These QR codes and the associated sample area with contact wires is shown in Fig. 8.14, and a chip with this pattern deposited onto it is shown in Fig. 8.14C, D. The GDSII patterns for these QR codes were generated procedurally using the GDSPy python package, and all of the associated software is available on Github, including a few different QR code designs, here: https://github.com/afylab/QR-Code-Generator. These patterns and wires are composed of 2 nm of Cr , 10-60 nm of permalloy, which is a nickel/iron alloy, and50 nm of Au, to prevent extensive oxidation of the permalloy and to facilitate electronic transport through the wires and easy wirebonding. NanoSQUID images of the magnetic field distributions above these patterns are shown in Fig. 8.14E, with line-by-line subtraction illustrating the visability of the QR code in Fig. 8.14F. Navigation of the nanoSQUID sensor to the chromium iodide flake was performed using these patterns, and an optical image of the scan region for that device is shown in Fig. 8.14G,H.

For this reason, orbital magnetism does not need spin-orbit coupling to support hysteresis, and it can couple to a much wider variety of physical phenomena than spin magnetism can- indeed, anything that affects the electronic band structure or real space wave function is fair game. For this reason we can expect to encounter many of the phenomena we normally associate with spin-orbit coupling in orbital magnets that do not possess it. I would also like to talk briefly about magnetic moments. It has already been said that magnetic moments in orbital magnets come from center-of-mass angular momentum of electrons, which makes them in some ways simpler and less mysterious than magnetic moments derived from electron spin. However, I didn’t tell you how to compute the angular momentum of an electronic band, only that it can be done. It is a somewhat more involved process to do at any level of generality than I’m willing to attempt here- it is described briefly in a later chapter- but suffice to say that it depends on details of band structure and interaction effects, which themselves depend on electron density and, drainage gutter in two dimensional materials, ambient conditions like displacement field. For this reason we can expect the magnitude of the magnetic moment of the valley degree of freedom to be much more sensitive to variables we can control than the magnetic moment of the electron spin, which is almost always close to 1 µB.

In particular, the magnetization of an orbital magnet can be vanishingly small, or it can increase far above the maximum possible magnetization of a spin ferromagnet of 1 µB per electron. Under a very limited and specific set of conditions we can precisely calculate the contribution of the orbital magnetic moment to the magnetization, and that will be discussed in detail later as well. Finally, I want to talk briefly about coercive fields. The more perceptive readers may have already noticed that we have broken the argument we used to understand magnetic inversion in spin magnets. The valley degree of freedom is a pair of electronic bands, and is thus bound to the two dimensional crystalline lattice- there is no sense in which we can continuously cant it into the plane while performing magnetic inversion. But of course, we have to expect that it is possible to apply a large magnetic field, couple to the magnetic moment of the valley µ, and eventually reach an energy µ · BC = EI at which magnetic inversion occurs. But what can we use for the Ising anisotropy energy EI ? It turns out that this model survives in the sense that we can make up a constant for EI and use it to understand some basic features of the coercive fields of orbital magnets, but where EI comes from in these systems remains somewhat mysterious. It is likely that it represents the difference in energy between the valley polarized ground state and some minimal-energy path through the spin and valley degenerate subspace, involving hybridized or intervalley coherent states in the intermediate regime. But we don’t need to understand this aspect of the model to draw some useful insights from it, as we will see later.

Real magnets are composed of constituent magnetic moments that can be modelled as infinitesimal circulating currents, or charges with finite angular momentum. It can be shown that the magnetic fields generated by the sum total of a uniform two dimensional distribution of these circulating currents- i.e., by a region of uniform magnetization- is precisely equivalent to the magnetic field generated by the current travelling around the edge of that two dimensional uniformly magnetized region through the Biot-Savart law. It turns out that this analogy is complete; it is also the case that a two dimensional region of uniform magnetization also experiences the same forces and torques in a magnetic field as an equivalent circulating current. The converse is also true- circulating currents can be modelled as two dimensional regions of uniform magnetization. The two pictures in fact are precisely equivalent. This is illustrated in Fig. 2.9. It is possible to prove this rigorously, but I will not do so here. One can say that in general, every phenomenon that produces a chiral current can be equivalently understood as a magnetization. All of the physical phenomena are preserved, although they need to be relabeled: Chiral edge currents are uniform magnetizations, and bulk gradients in magnetization are variations in bulk current current density. The details of this situation aren’t important; the lesson that is important is quite simple. In classical physics, we know how charged particles respond to local magnetic and electric fields. These rules are enough to completely explain the phenomena. This is apparently not the case in quantum mechanical systems. We can certainly attempt to describe systems this way, but in a wide variety of situations our models would be wrong, as in this one.

There is no point in this experiment at which an electron interacted with a magnetic field through the Lorentz force, and yet it turns out to be true that the magnetic field impacts the kinematics of electrons participating in the experiment. In a landmark result published in 1984, Michael Berry showed that our understanding of a variety of systems- including crystalline systems in condensed matter theory- suffered from a close analog of this misunderstanding. Researchers have since gone back to fix this oversight, plastic gutter and this led to the introduction of Berry curvature in condensed matter systems. Every crystal is defined by a periodic electric potential profile. In two dimensional crystals this is a scalar function of two dimensions over the lattice in real space. Let us switch our focus to momentum space. The periodic electric potential in real space produces a set of functions over momentum space E that define quantum states that electrons within the crystal can occupy. A correctly executed attempt to account for the effects of the Berry phase in crystalline systems produces a new vector-valued function over momentum space Ω that affects the kinematics of electrons in electronic bands. In two dimensional systems Ω is always oriented out-of-plane, but it can be positively or negatively oriented. We call this function the Berry curvature, and it must be accounted for to correctly explain a vast array of electronic phenomena, including electronic transport in metals, electronic transport in insulators, and angular momentum and magnetization in magnetic systems. In the same way that the Berry phase impacts the kinematics of free electrons moving through a two slit interferometer, Berry curvature impacts the kinematics of electrons moving through a crystal. You’ll often hear people describe Berry curvature as a ‘magnetic field in momentum space.’ You already know how electrons with finite velocity in an ambient magnetic field acquire momentum transverse to their current momentum vector. We call this the Lorentz force. Well, electronswith finite momentum in ‘ambient Berry curvature’ acquire momentum transverse to their current momentum vector. The difference is that magnetic fields vary in real space, and we like to look at maps of their real space distribution. Magnetic fields do not ‘vary in momentum space,’ at non-relativistic velocities they are strictly functions of position, not of momentum. Berry curvature does not vary in real space within a crystal. It does, however, vary in momentum space; it is strictly a function of momentum within a band. And of course Berry curvature impacts the kinematics of electrons in crystals. Condensed matter physicists love to say that particular phenomena are ‘quantum mechanical’ in nature. Of course this is a rather poorly-defined description of a phenomenon; all phenomena in condensed matter depend on quantum mechanics at some level. Sometimes this means that a phenomenon relies on the existence of a discrete spectrum of energy eigenstates. At other times it means that the phenomenon relies on the existence of the mysterious internal degree of freedom wave functions are known to have: the quantum phase. I hope it is clear that Berry curvature and all its associated phenomena are the latter kind of quantum mechanical effect. Berry curvature comes from the evolution of an electron’s quantum phase through the Brillouin zone of a crystal in momentum space. It impacts the kinematics of electrons for the same reason it impacts interferometry experiments on free electrons; the quantum phase has gauge freedom and is thus usually safely neglected, but relative quantum phase does not, so whenever coherent wave functions are being interfered with each other, scattered off each other, or made to match boundary conditions in a ‘standing wave,’ as in a crystal, we can expect the kinematics of electrons to be affected.

We will shortly encounter a variety of surprising and fascinating consequences of the presence of this new property of a crystal. Berry curvature is not present in every crystal- in some crystals there exist symmetries that prevent it from arising- but it is very common, and many materials with which the reader is likely familiar have substantial Berry curvature, including transition metal magnets, many III-V semiconductors, and many elemental heavy metals. It is a property of bands in every number of dimensions, although the consequences of finite Berry curvature vary dramatically for systems with different numbers of dimensions. A plot of the Berry curvature in face-centered cubic iron is presented in the following reference: We will not be discussing this material in any amount of detail, the only point I’d like you to take away from it is that Berry curvature is really quite common. For reasons that have already been extensively discussed, we will focus on Berry curvature in two dimensional systems. This equation is telling us that in systems with significant Berry curvature, applying an electric field will produce current density transverse to that electric field. It is illustrated in Fig. 3.1 for an isolated electron in a specific Bloch state. Berry curvature has a few general properties that are worth knowing. Kramers’ pairs- i.e., pairs of spin subbands related by time reversal symmetry- must have opposite Berry curvature. As a result, systems that don’t break time reversal symmetry cannot have any net current flow as a result of Eq. 3.3. The equation still applies, but each spin-polarized current density is precisely balanced by the other spin polarization’s contribution. This does not mean that Berry curvature has no consequences in such systems; in these systems, spin concentrates on opposite sides of the system, transverse to the applied electric field. This state of affairs is known as the spin Hall effect, and it is illustrated in Fig. 3.2A,B. In the presence of magnetism, electrons can occupy states with unbalanced Berry curvature, and as a result Eq. 3.3 produces a net current density. The resulting electron accumulation transverse to the applied electric field produces a transverse voltage called the Hall voltage. It is often useful to put contacts on the edges of devices in order to probe this voltage, as illustrated in Fig. 3.1. Of course, the sign of this voltage is a spontaneously broken symmetry, and it follows the magnetization of the magnetic order, as illustrated in Fig. 3.2C-F. It is possible for magnetic insulators to form in systems with bands that have finite net Berry curvature . This produces an extremely special situation, and the bulk of this thesis will be devoted to probing and understanding the properties of these kinds of systems. They are called Chern magnets.Several chapters of this thesis focus on the properties of a particular class of magnetic insulator that can exist in two dimensional crystals. These materials share many of the same properties with the magnetic insulators described in Chapter 2. They can have finite magnetization at zero field, and this property is often accompanied by magnetic hysteresis. The spectrum of quantum states available in the bulk of the crystal is gapped, and as a result they are bulk electrical and thermal insulators. They have magnetic domain walls that can move around in response to the application of an external magnetic field, or alternatively be pinned to structural disorder. And of course they emit magnetic fields which can be detected by magnetometers.

Overall, including a practical amount of red raspberry in the diet regularly is a low-calorie dietary strategy that improves gut microbiota composition and function in individuals with prediabetes and insulin resistance resulting in improvements in metabolic health. With a sustained emphasis on the role of gut microbiota in nutrition research, advances in our understanding of food-gut dynamics will provide new insights about the role of nuts and berries in human health and performance. Although research on a specific nut or berry provides insight into bio-activity and potential mechanisms of action, such focus also creates the potential for fragmentation because the search for overall dietary patterns is not addressed. The composition of fruits and nuts differ at the molecular level, and a broader view assessing similarities in chemistry and health benefits is critical for translational research as well as for messaging purposes. For example, blueberries, strawberries, pomegranate, walnuts, and grapes all have reported benefits for cardiovascular health, driven largely by the presence of similar polyphenols, which are present at varying quantities in each of these foods . Although health professionals and consumers often hear messaging on a single berry or nut, snap clamps ABS pvc pipe clip the potential benefits of increasing consumption of the broader category may be obscured or lost.

This challenges the ability to maintain consistent messaging and align better with translatable dietary guidance. Future interventions that combine nuts and berries with one or more other foods within a food matrix at dietary achievable doses and in more diverse populations are warranted. To date, multi-omics technologies have provided valuable insights into exposure-disease relationships. Coupled with artificial intelligence, predictive modeling and continuous, personalized monitoring, these data-intensive outcomes can provide further insights about the health benefits associated with regular intake of nuts or berries. One of the challenges of similar foods being studied in differing formats and by various research groups is the utility of the data as a combined set. Differences in test materials and experimental designs make integration of data difficult. The proper curation of combined data, whether physiologic, metabolomic, or genomic, is critical to ensure that combined datasets provide synergy, statistical power, and enhanced usefulness.The cardiometabolic benefits from regular consumption of nuts or berries are widely reported and include improved vascular function, reduction of cardiovascular disease risk factors, improved insulin sensitivity, and reduced risk of type 2 diabetes mellitus. Antioxidant and anti-inflammatory capacity and activity have also been noted. Metabolic outcomes may be context-specific and related to the physiologic state of the individual and host microbiome composition, among other factors.

Examples include findings of ellagitannin and ellagic acid rich foods resulting in differential responses in healthy individuals compared to those with prediabetes, who are dependent on gut microbial-derived metabolite profiles. Many factors contribute to inter individual variability in response to diet that can extend to context-specific aspects influencing the magnitude of health benefits and reinforces the importance for further research aimed at advancing discoveries in precision nutrition. Additional health outcomes related to nut or berry intake are outlined below.Adding nuts or berries to the daily diet may be advantageous for weight management for several physiological reasons. One is that these foods produce feelings of satiety, helping to reduce the desire to consume calorie-rich snacks that are low in vitamins, minerals, and fibers, ultimately improving body composition over time. A second possibility is due to urolithins, secondary metabolites produced from ellagitannins in nuts and berries. Urolithins increase the activation of the adenosine monophosphate-activated protein kinase pathway, resulting in anti-obesogenic properties in vitro and in animal models. AMPK increases fatty acid oxidation and decreases triglyceride accumulation. Phosphorylation of AMPK may also decrease cholesterol synthesis and lipogenesis by down regulating 3-hydroxy-3-methylglutaryl coenzyme A reductase activity and sterol regulatory-element binding protein expression.

In clinical studies exploring the relationship between food and body composition, the incorporation of nuts and berries into the diet was associated with weight loss or maintenance.Regular consumption of nuts or berries has been reported to support brain health and cognitive function, motor control, mood, and executive function at physiologically relevant intakes. Middle-aged and older adults experienced improvements in balance, gait, and memory, and children experienced higher executive function and positive affect after acute and regular intake of both strawberries and blueberries. These beneficial effects may be the result of direct effects on brain signaling or indirect effects through oxidant defense and anti-inflammatory properties of polyphenols and other bioactive compounds in nuts and berry foods. The gut-brain axis is an emerging area of research. Most studies are preclinical in nature using animal models but are suggestive of a significant role of gut microbial-derived ellagitannin metabolites on brain health and neuroprotection.The influence of nuts and berries on skin health and appearance is an emerging area of research. Regular intake of almonds, a good source of fatty acids and polyphenols, has been associated with a significant decrease in facial hyperpigmentation and wrinkle severity. A walnut protein hydrolysate administered to rats exposed to ultraviolet radiation significantly reduced skin photoaging and enhanced skin elasticity. Supplementation with ellagic acid, a compound found in many berries, prevented ultraviolet B -related inflammation and collagen degradation related to skin wrinkling and aging in a murine model. More human studies, using objective measures of skin wrinkles, skin elasticity and response to low-dose UVB radiation exposure are warranted. Monitoring skin responses to a UVB radiation challenge has been used as a marker of whole-body antioxidant status in response to almond consumption. The response to a UVB challenge has also been used to monitor oxidant defenses and changes in skin microbiome following the intake of pomegranate juice.Age-related macular degeneration is the third leading cause of vision loss worldwide. Anthocyanins, carotenoids, flavonoids, and vitamins C and E, found in many berries, have been shown to reduce risk of eye-related diseases. Goji berries, containing the highest amount of zeaxanthin of any known food, hold particular promise since this compound binds to receptors in the macula to offer protection from blue and ultraviolet light. Regular supplementation with 28 g/d of goji berries for 3 mo increased macular pigment optical density, a biomarker for AMD, as well as the skin carotenoid index. Nuts may also be protective against AMD since they are a rich source of vitamin E and essential fatty acids. Regular intake of nuts has been associated with a reduced risk and slower progression of AMD in 2 epidemiological studies, thought to be due to the beneficial role of polyunsaturated fatty acids.Identification of new cultivars with traits desirable for growers, processors, and consumers is a continuous effort. As researchers continue to produce new varieties by both conventional and molecular-driven approaches, assessing these varieties for nutritional value is a challenge. A combination of broad targeted and untargeted metabolomic approaches, along with defined functional phenotyping could be used for rapid screening and defining of mechanistic pathways associated with health. However, consumer preferences for new cultivars are often driven by size and appearance of the berry or nut and flavor, greenhouse snap clamps rather than its nutritional value. This would further confirm the need to balance improvements to nutritional profiles with enhancement of consumer-driven traits, maintaining the marketable nature of the berries and nuts.

Biomedical research, particularly for clinical studies, is expensive and resource intensive. Although the USDA competitive grants program offers funding for outstanding research projects, budget limitations favor animal or in vitro study proposals. Compelling pilot data is needed to be competitive for clinical studies funded by the USDA or NIH, so many researchers submit their initial ideas to commodity groups representing specific nuts or berries. Commodity groups represent farmers, processors, and distributors and have been instrumental in supporting fundamental and applied research focused on their specific berry or nut. The perception that studies funded by nut and berry commodity groups are inherently biased in favor of the test food is an issue sometimes raised by critics, journalists, and the general public. As in all nutrition research, ethical considerations regarding the structure of research questions, hypotheses, study design, outcome measures, interpretation of data, and conclusions must be rigorously considered. The food and beverage industries have played a key role in providing funds and supporting nutrition research on individual foods and beverages, including berries and nuts. Although this draws scrutiny regarding scientific integrity and data reporting, collaboration between academia and industry compared to exclusive corporate funding may help offset some of these concerns. For example, in multiple reported studies, matching funds were also provided by non-industry sources, including institutional and federal agencies. In other cases, while the food industry provided the test agents, key research personnel and staff were not supported by the same funding source. The academia-industry collaboration has also led to the formation of scientific advisory committees that evaluate and recommend proposals for funding, a peer review process that helps ensure rigorous study designs, data reporting, and dissemination of results. Human studies of sufficient statistical power are expensive, labor-intensive efforts requiring sophisticated and costly laboratory equipment and supplies. In order for research proposals to be competitive for funding from the USDA or NIH, pilot data is required, and for nuts and berries, the only realistic source of funding for these exploratory trials is from industry sources. Critics of industry support for nutrition research have yet to propose realistic alternatives for funding needed to generate initial data. Further, ongoing industry funding of nuts and berries research has yielded important insights into the molecular and physiological understanding of mechanisms of action. Without industry support, provided in an ethical and transparent manner, advances in our understanding of the role of nuts and berries in a healthy dietary pattern would be limited. A risk-of-bias study of 5675 journal articles used in systematic reviews published between 1930 and 2015, representing a wide variety of nutrition topics, concluded that ROB domains started to significantly decrease after 1990, and particularly after 2000. Another study examined the incidence of favorable outcomes reported in studies funded by the food industry in the 10 most-cited nutrition and dietetics journals in 2018. Of the 1461 articles included in the analysis, 196 reported industry support, with processed food and dietary supplement manufacturers supporting 68% of the studies included. Studies supported by any nut or berry commodity group were not considered due to an incidence lower than 3% of qualifying articles. Studies with food industry support reported favorable results in 56% of their articles, compared to 10% of articles with no industry involvement. The authors offer a number of suggestions to help minimize real or perceived bias, calling on research institutions to enforce strict, regularly updated, and transparent oversight of all research projects involving industry. Suggestions in support of research transparency and integrity have also been advanced from guidelines adapted from the International Life Sciences Institute North America. This served as the basis for the development of consensus guiding principles for public-private partnerships developed by a group of representatives from academia, scientific societies and organizations, industry scientists, and the USDA, NIH, US Centers for Disease Control, and the US Food and Drug Administration. These provisions include full disclosure of funding and confirmation of no direct industry involvement in the study design, data and statistical analyses, and interpretation of the results and only minimal, if any, involvement of industry coauthor, often given as a courtesy to acknowledge funding and logistical support by the investigators with no intellectual involvement by the study sponsor. This is in contrast to industry-initiated research, where the industry office or commodity group sets predetermined research objectives, provides intellectual collaboration, and often has input on the study design, interpretation of results, and decisions regarding publication. Although some critics may argue that repeated industry funding in support of research groups that report favorable results on a particular nut or berry shows a bias toward positive outcomes, other interpretations are also possible. First, few labs have the infrastructure, detailed methodology and analytical equipment, and trained personnel to conduct clinical studies in an efficient and timely manner. Industry funded studies conducted at major universities have layers of review and accountability within their organizations to guard against malfeasance, and while these layers may not focus directly on precise elements of research design and interpretation of results, faculty members at such institutions generally have a level of integrity and accountability, knowing that administrative review exists. Calls for industry-funded research are often broad in scope, which allows researchers to generate proposals, research questions, and hypotheses that do not have preconceived outcomes.

Some volatiles have been lost during domestication and breeding as a combined result of negative selection and linkage drag in tomato and watermelon . Likewise, gain and loss of terpene compounds during strawberry domestication and its genetic causes have been investigated . Recent advances in sequencing technology and analytical approaches have opened new opportunities to understand the chemistry and genetics of fruit flavor. Genome-wide association studies have revealed loci for flavor in a variety of fruit crops . Meanwhile, genomes-wide expression quantitative trait loci studies have the capability to bridge the gaps between GWAS signals and their underlying causative genes. Integration of GWAS and eQTL studies has led to discovery of a master metabolite regulator in tomato and a flesh-color-determining gene in melon . Long-read sequencing now allows assembly of genomes with high contiguity, and when coupled with parental short-read data , the two haplotypes of a heterozygous individual can be fully resolved. Phased assemblies have improved variant discovery, plant pot with drainage especially for large structural variants . The extent, diversity and impact of SVs increasingly are being studied in horticultural crops and have been shown to alter fruit flavor, fruit shape and sex determination .

Great opportunity exists to coherently integrate these multi-omics resources for the discovery of flavor genes. Garden strawberry is an allo-octoploid species with highly palatable non-climacteric fruit . It increasingly has been utilized as a model for Rosaceae fruit crops genomics and flavor research as a result of its short generation time, wide cultivation and high value. Through exploration of spatiotemporal changes in gene expression and homolog search, several flavor genes have been cloned and validated, including an alcohol dehydrogenase and several alcohol acyltransferases for esters, a nerolidol synthase 1 for terpenes and a quinone oxidoreductase for furaneol. Recently, QTL studies and transcriptome data analyses for strawberry volatiles using biparental crosses have detected QTL and causative genes for mesifurane and gamma-decalactone . Nevertheless, low mapping resolution and a lack of subgenome-specific markers have hampered further characterization of causal genes underlying other QTL. This problem recently was addressed by the development of 50K Fana SNP array using probe DNA sequences physically anchored to the octoploid ‘Camarosa’ genome . High heterozygosity combined with an allopolyploid genome presents difficulties for resolving causative genes and their haplotypes. To further the goal of discovering causative genes affecting flavor in strawberry, association studies with larger sample sizes and additional genetic resources such as eQTL and additional genomes are required.

Furthermore, these resources must span the breadth of natural variation in breeding germplasm. Here we present multi-omics resources consisting of an eQTL study representing the genetic diversity of strawberry breeding programs in the US, phased genome assemblies of a highly- flavored University of Florida breeding selection, a structural variation map in octoploid strawberry and a volatile GWAS of 305 individuals. These are combined to leverage the extensive metabolomic, genomic and regulatory complexity in strawberry for the discovery of natural variation in genes affecting flavor. Ultimately, the functional alleles identified will be selected in breeding to achieve superior flavor.The eQTL population consisted of 196 genotypes including 133 newly sequenced accessions . The University of Florida genotypes were grown at GCREC and collected in the spring of 2020 and 2021. The University of California-Davis collection of diverse selections from multiple breeding programs were grown at either Santa Maria CA or Oxnard CA, for day-neutral and short-day accessions, respectively, and collected in the spring of 2021. Four UC genotypes were collected at both sites to ensure sequencing and SNP quality. Total RNA was extracted from a bulk of three fully ripe fruits using a Spectrum™ Plant Total RNA Kit , after flash freezing in liquid nitrogen. Illumina 150-bp pair-end sequencing was performed on the Illumina NovoSeq platform by Novogene Co. . On average, 6.9 Gb of sequence data were obtained for each sample. Raw RNA-Seq data of 63 samples from previous published studies were retrieved from the NCBI SRA database . In order to quantify gene expression, short reads were trimmed for adapter sequences and low-quality reads with TRIMMOMATIC v.0.39 and aligned against the reference genome using STAR v.2.7.6a in the two-pass mode .

Only unique aligned reads were scored by HTSEQ v.0.11.2 in the union mode with the ‘–nonunique none’ flag supplied with the latest Fragaria_ananassa_v1.0.a2 annotation . All count files were compiled in R and normalized with the DESEQ package . To generate the marker dataset for eQTL mapping, SNPs and InDels were called using the mpileup and call commands. Markers were further hard-filtered using BCFTOOLS with the following steps: individual calls with lower than sequencing depth of three were set to missing using + setGT plugin; marker sites with quality < 30, missing rate > 0.3, heterozygous call rate > 0.98, minor allele frequency < 0.05, or number of alternative alleles > 1 were purged; the filtered markers were imported and analyzed in R, and only markers showing more than three matched calls in four duplicated sample pairs were retained. A total of 491 896 markers passed the three stages of filtering. The missing calls were imputed, and all calls were phased using BEAGLE v.5.2 using the default settings . The eQTL mapping was performed for 62 181 fruit expressed genes using the filtered markers. Linear mixed models implemented in GEMMA were used for association analysis . The relationship matrix was computed in GEMMA and supplied to explain relationship within populations, and the top five principal components with a total of 25.0% variance explained were imported as covariates to reduce effects from population stratification to signify the genetic variance underlying the target traits. The Bonferroni corrected 5% significance threshold was used, determined the by number of LD-pruned markers . The approach to define an eQTL was similar to that used in previous studies . Briefly, we first clustered all significant markers with distance < 100 kb and purged clusters with fewer than three markers. The lead marker with lowest P-value was used to identify the eQTL, and boundaries of eQTL were defined as the furthest flanking significant markers. Clusters in LD were merged and boundaries were updated. The longest distance between cis-eQTL boundaries and eGene boundaries was limitedto 500 kb. Because a substantial number of regulatory elements were found for fruit-expressed genes, a structural variant map would greatly facilitate the identification of potential causative SVs underlying the regulatory elements. To construct an SV map, pot with drainage holes we first assembled a phased genome of an UF accession. The genome of FL 15.89-25 was assembled into 1480 and 672 phased contigs with N50 of 12.8 and 12.4 Mb, respectively , with similar contiguity to other recent high-quality octoploid strawberry genomes . A Kmer-based approach revealed 97.1% and 99.2% completeness for the haploid assemblies based on parental Illumina short reads, which were corroborated by 98.1% and 98% completeness of the BUSCO eudicots odb10 genes . Phasing quality was evaluated by parent-specific Kmers; the average switching error and hamming error were 0.19% and 0.18% for the F12 haploid assembly , comparable to phased genomes in other species . The phased contigs were scaffolded into pseudochromosomes based on alignment to the ‘Camarosa’ reference genome, with 96.0% and 92.8% of phased contigs placed on 28 pseudochromosomes for the respective F12 and Bea haploid assemblies , consistent withprevious flow cytometry estimations . There were only 88 and 79 gaps in the final scaffolds, averaging 3.14 and 2.82 per chromosome for the respective F12 and Bea assemblies . Scaffolding quality was evaluated by a linkage map and public Hi-C data . High collinearity was observed between haplotypes . The FL 15.89-25 assemblies and three additional haploid assemblies were utilized to explore SV diversity in garden strawberry. These geographically and genetically diverse accessions empowered the discovery of SVs across all chromosomes except for a large portion of Chr 4B which may be under strong purifying selection .

Individual haplotypes had between 31 574 and 60 453 SVs relative to the PHASE1 assembly of ‘Royal Royce’ , with the WONG haplotype harboring the most SVs, consistent with the larger genetic distance of Asian populations to North American populations . Insertions and deletions were the most common SV types, together consisting of 88.3– 94.1% of SVs. All SVs across haplotypes werethen merged into a nonredundant set of SVs . In total, 56 342 deletions, 60 983 insertions, 12 016 translocations, 166 interspersed duplications, 236 tandem duplications and 137 inversions were identified. Unlike the SV composition of a tomato population in which the majority of SVs were singletons , an average of 62.6% strawberry SVs were shared by at least two haplotypes . We observed a gradually reduced number of new SVs every time a new haplotype was merged , suggesting this SV map surveys a substantial portion of SV diversity in cultivated strawberry. The majority of SVs were < 1 kb , whereas only 3.3% were > 10 kb . Structural variations were present extensively in exons , introns and promoter regions . Transposable elements were rich resources of SVs. We identified 34 379 deletions overlapped with TEs, especially inverted tandem repeats and long terminal repeats , consisting of 61.0% of total deletions, significantly higher than the genome-wide TE percentage of 38.42% .In order to investigate the genetic control of fruit volatiles, we performed volatile phenotyping and SNP array genotyping with 49 330 markers on a panel of 305 accessions from the UF strawberry breeding program, with 59 individuals overlapped with the eQTL panel . A total of 97 volatiles including esters, terpenes, aldehydes, alcohols, acids, ketones and lactones were quantified . Based on relationships among volatiles, we identified at least five clusters belonging to the same chemical class or biosynthetic pathway, including clusters of eight aldehydes, three ethyl esters, three hexanoic acid derivatives, seven medium-chain esters and three terpenes . Generally high narrow-sense heritability was observed across volatiles , ranging from 0.212 to 0.916, with a mean of 0.660. The highest value of h2 was found for mesifurane and the lowest for octanoic acid, ethyl ester . The lead SNP effects varied from 0.27 to 2.44 , with the largest effect for methyl anthranilate . Two hotspots which contained multiple signals of volatiles belonging to the same class or pathway were found for medium chain esters and for terpenes , which also were detected to in previous studies and reflected in chemical relationships . Our GWAS results confirmed previous homoeologous group assignments for these volatile QTL and clarified their subgenome and physical positions. The SNP AX-166515537 was the lead SNP for three esters, and a 14 Mb region on Chr 6A shared signals for six medium-chain esters. An LD analysis revealed three linkage blocks . The distal region of Chr 3C was associated with six volatiles including five terpenes . This 3.1-Mb region did not display clear LD block separation . Two significant markers for medium-chain ester hotspot and methyl thiolacetate were tested for their predictability of flavor characteristics . Some abundant volatiles including: 2-hexenal, -; butanoic acid, 2-methyl-; and pentanal were associated with multiple DNA variants , suggesting polygenic inheritance. Pentanal was associated with threeIn this study we leveraged eQTL, GWAS and haplotype-resolved genome assemblies of a heterozygous octoploid to identify allelic variation in flavor genes and their regulatory elements. Finetuning of metabolomic traits such as amylose content in rice and sugar content in wild strawberry recently were made possible via CRISPR-Cas9 gene-editing technology. Similar approaches can be taken in cultivated strawberry for flavor improvement, but not before the biosynthetic genes responsible for metabolites production and their regulatory elements are identified. Our pipeline has proven to be effective in identification of novel causal mutations for flavor genes responsible for natural variation in volatile content and can be further applied to various metabolomic and morphological aspects of strawberry fruit such as anthocyanin biosynthesis , sugar content and fruit firmness. These findings also will help breeders to select for genomic variants underlying volatiles important to flavor. New markers can be designed from regulatory regions of key aroma volatiles, including multiple medium-chain volatiles shown to improve strawberry flavor and consumer liking , methyl thioacetate contributing to overripe flavor and methyl anthranilate imparting grape flavor . In the present study, a new functional HRM marker for mesifurane was developed and tested in multiple populations . These favorable alleles of volatiles can be pyramided to improve overall fruit flavor via marker assisted selection. Strawberry also shares common volatiles with a variety of fruit crops.

Limited prior research that has looked at the effects of multiple soil management practices indicates that metrics for soil health are a product of both inherent soil properties and dynamic soil properties . Whether available soil indicators could translate these soil properties and processes when management systems are complex remains unclear. As an added layer of complexity, field variability is hard to distinguish from management-induced changes in soil properties . To address this challenge, prior studies have suggested increasing samples, the number of sites, and sampling strategies that account for spatial and temporal variability ; however, as farmers themselves expressed in this study, such an approach requires additional time and resources, and may not increase their utility—at least to farmers—in the end. In this sense, farmer knowledge may serve as an important mechanism for ground-truthing soil health assessments, particularly when management is synergistic and does not rely heavily on organic fertilizers. As emphasized by our results above, farmer involvement in soil health assessment studies is imperative to better converge soil indicators with farmer knowledge of their soil. Lastly, our results also highlight the utility of incorporating information about nitrogen-based fertilizer application on sampled field sites, round plastic pot particularly when assessing soil indicators on working farms with a large variation in the quantity of N-based fertilizers applied .

Farms on the low end of additional organic fertilizer application showed minimal differences between farmer selected fields for soil fertility, particularly in terms of soil inorganic nitrogen —which suggests that differences in soil fertility in fields with more circular nutrient use may be less detectable using commonly available soil indicators. This cursory finding here corroborated farmer observations touched on in the previous section above, and requires further investigation to see if similar trends extend to other organic systems. Here, we have identified several gaps in the utility of commonly available indicators for soil fertility among a unique group of organic farmers in Yolo County, California using interviews with farmers and field surveys. Our study highlights that if available soil indicators are to be considered effective by farmers, they must be grounded in farmers’ realities. Moving forward, working in collaboration with farmers to close this continued gap in soil health research will be essential in order to ground widely available soil indicators in real working farms with unique management systems and variable, local soil conditions. This approach is particularly needed among organic farms that do not rely extensively on nitrogen-based organic fertilizers and additional nutrient input to supply their fertility, as available soil indicators do not adequately reflect farmers’ descriptive metrics for soil fertility.

Moreover, our research elevates concerns that currently available soil indicators used in soil health and fertility assessments may not fully capture the complex plant-microbe-soil interactions that regulate soil fertility, particularly on organic farms that use minimal organic fertilizer application. Moving forward, additional studies that pursue a deeper dive into nutrient dynamics across a gradient of management and varying nitrogen-based fertilizer input is needed. Overall, the strong overlap between farmer knowledge in this study and ongoing soil health research speaks to the opportunity to further engage with farmers in developing useful indicators for soil health and fertility that are better calibrated to local contexts and draw on local farmer knowledge. A deeper investigation of farmers knowledge systems, in particular farmer understanding of soil function in connection with crop productivity, soil health, and soil fertility, represents a critical path forward for this research arena. Additionally, we recommend placing greater emphasis on developing descriptive indicators for soil health and fertility in collaboration with farmers that are better integrated with ongoing qualitative soil health and fertility metrics. These descriptive indicators should not be developed in isolation to ongoing research on soil health and fertility assessment, but rather as an integrated research process among scientists, farmers, and extension agents—importantly, with scientists as listeners working toward a shared language. Ants benefit plants . Humans have known this for quite a long time. In fact, ants were described as biological control agents in China around 304 AD . Surveys of tropical forests show that up to one third of all woody plants have evolved ant-attracting rewards .

Some plants provide domatia as ant housing structures, while others attract ants to their tissues with extra-floral nectaries. Some plants are hosts to honeydew-producing hemipterans that excrete honeydew, a sugary substance consumed by ants. Still other plants are simply substrates for ant foraging. The majority of studies conducted across these ant–plant groups show that ants benefit plants by removal of herbivores . Nonetheless, in many agroecosystems, the benefits of pest control services by ants are not recognized. Agricultural managers often view them as pests or annoyances to agricultural production because some ants tend honeydew-producing insects that can damage crops . However, a review of the literature on ant-hemipteran associations suggests that even these associations benefit plants indirectly because ants remove other, more damaging herbivores . Regardless, the literature lacks studies investigating ant–plant interactions in agroecosystems. Here, we broadly survey the pest control services provided by a suite of ant species to better understand the role of ant defense of coffee. Coffee is a tropical crop that occurs as an understory shrub in its native range, and coffee plants are therefore often grown under a canopy of shade trees in agroforestry systems in some parts of the world . This canopy layer provides plantatsions with a forest-like vegetation structure that can help maintain biodiversity . Ant biodiversity is high in many coffee plantations and ants attack and prey on many coffee pests, including the coffee berry borer . For example, Azteca instabilis F. Smith is a competitively dominant ant that aggressively patrols arboreal territories in high densities and previous research has found that it impacts the CBB . Some laboratory and observational field studies have found that Pseudomyrmex spp., Procryptocerus hylaeus Kempf, and Pheidole spp. may limit the CBB . However, other field experiments have not found ants to be biological control agents of the CBB . Further, round pot the pest control effects of many ant species on the CBB have not yet been evaluated and it could be that previously documented effects are specific to only a few species. Natural ant pest control of the CBB is particularly important because chemical insecticides used to control CBB are not always effective. This lack of effectiveness is in part because the CBB lifecycle takes place largely hidden within coffee berries and also because the CBB has developed insecticide resistance . Several of the stages of the CBB life cycle make it vulnerable to attack by ants . First, the CBB hatches from eggs within the coffee berry, where it consumes the seeds . Small ants may enter the berry through the beetle entrance hole and predate the larvae and adults inside . Second, old berries infested with the CBB may not be harvested because they often turn black and remain on the coffee branches or may fall to the ground . These old infested berries may act as a population reservoir of borer populations and ant predation at this stage could be very important for limiting CBB populations in the next season. Third, as adult borers disperse to colonize new berries, ants may prevent them from entering new berries . To date, no field experiment has specifically investigated how coffee-foraging ants limit CBB colonization of berries. Here, we studied the abilities of eight ant species to prevent colonization of berries by the CBB. We hypothesized that only species with high activity on branches would limit CBB colonization of berries. We show that six of eight ant species limit CBB colonization of berries and that the effect of ants is independent of ant activity on branches. This study is the first field experiment to provide evidence that a diverse group of ant species limits the CBB from colonizing coffee berries.Our goal was to capture a broad survey of the ant species that occupy the coffee vegetation in the coffee plantation.

Within the plantation, five Crematogaster spp. forage in the coffee, however field identification at the time was not reliable therefore taxonomic resolution for Crematogaster spp. remained at the genus level. For P. simplex and P. ejectus it was not always possible to find occupied bushes by observation of ant foraging. Instead, for P. simplex and P. ejectus, we determined occupation by removing all dead twigs on the coffee bush and searching these for ant nests within the hollow branches . We reattached the nested hollow branch to a living branch with thin wire and treated these bushes as bushes occupied by P. simplex or P. ejectus. To test the effects of each ant on CBB colonization of berries, we performed an ant exclusion experiment. We surveyed bushes occupied by one of the eight target ant species. We excluded coffee bushes with few branches to control for the size of the foraging area of each ant species. On each bush, we searched for two branches of equal age and position and roughly the same number of coffee berries . On each branch, we removed all berries that had CBB entrance holes. We then removed all ants from one branch and applied tangle foot to the base of the branch near the coffee trunk. On the second branch, we left ants to forage freely . To estimate ant activity, we counted the total number of ants foraging on the stem, leaves, and berries of each branch for 1-min including those that travelled onto the branch during the 1-min survey. We also counted ants on exclusion branches after the experiment and if a branch had more than one ant individual present, we excluded the bush from analysis . To release CBB onto control and treatment branches, we created a leaf platform to aid their chances of encountering berries. The leaf was wedged between the branch stem and a cluster of berries to create a platform surrounding the cluster . A coffee leaf was used as a platform because artificial structures attract attention from many ant species. After waiting several minutes to ensure normal ant activity, we released 20 CBBs on the leaf platforms of the control and exclusion branches. After 24 h, we counted the number of berries per branch that had CBBs inside entrance holes. We did not count partially bored holes in berries, nor CBBs that had bored into twigs and leaves. Multiple bored entrance holes per berry were only counted as one bored berry. We modified the experiment slightly for P. simplex and P. ejectus because of the difficulty in locating these species within a bush using visual cues . For these two species, we used the living branch to which the nest was attached to as the control branch . This was done because we wanted to make sure that ants were actively foraging on control branches after the disturbance of removing nests. To statistically analyze experimental data, we opted to use linear mixed models instead of paired t tests because mixed models allow inclusions of experimental non-independencies through the incorporation of covariates. We included bush as a random effect in the model to pair control and exclusion branches within each bush. Ant species and treatment and the species 9 treatment interaction were included as fixed effects in the model. To control for differences between each branch and bush, we included the number of berries per branch, the number of berries in contact with the leaf platform, and the number of worker ants per branch as covariates in the model. We performed type III F tests of significance for main effects with maximum likelihood to estimate the fixed effect parameters and variance of random effects . We removed non-significant factors from models and compared nested and null models with likelihood ratio tests to determine the best-fit model. We also compared ant activity across different species to determine if this factor might correlate with berries bored and vary across ant species. To determine if ant activity correlated with the number of coffee berries bored, we limited the dataset to only control branches and used a generalized linear model with a Poisson log-link function because data did not meet the assumptions of normality. To determine if ant activity varied by species, we again limited the dataset to only control branches and used ANOVA with Tukey’s HSD analysis.

Farm Type I consistently showed the highest values for total soil N, total organic C, POXC, and soil protein, which suggests sites in this farm type had higher soil quality compared to Farm Type II and III; similarly, Farm Type II consistently showed intermediate values for all four indicators for soil organic matter. Lastly, Farm Type III consistently showed the lowest values across all four indicators, which suggests sites in this latter farm type had lower soil quality compared to the other two farm types. These initial results highlight the usefulness of establishing farm typologies based on indicators for soil organic matter as a novel approach to study gradients in soil quality on organic farms. The three farm types generated based on soil organic matter levels served as a key starting point for further analysis of the role of management in relation to soil quality. Accordingly, not only were the three farm types identified in this study significantly different based on indicators for soil organic matter levels, but the farm types also aligned with general trends in management among sites, 10 liter drainage collection pot which indicated a link between soil organic matter levels and management.

In particular, as the four indicators for soil organic matter collectively serve as a proxy for soil quality, our results suggest that soil quality indicators may show responsiveness to the impacts of short-term management. In our study, crop diversity, crop rotational complexity, and tillage emerged as the strongest drivers of farm type differences, as shown by LDA coefficients . These results also coincided with average values for management variables compared across all three farm types , though variables for ICLS and cover crop application overlapped considerably across all three farms. These cursory findings extend results from ongoing work from others , including a recent 4-year study by Sprunger et al. —which focused on organic corn systems in the Midwest. Sprunger et al. likewise reported strong links between soil metrics such as total N, total C, soil protein, and POXC—and on-farm management practices, such as crop rotation patterns, manure and cover crop application, and tillage. While extensive work has been done on organic corn and grain systems in the midwestern region of the US, our study provides new insight on the applicability of these common soil metrics in entirely different organic farming systems and climate regions—specifically on high-value vegetable farms operating in the dry, hot Mediterranean climates of northern California.

Our results also underscore the usefulness of on-farm interviews in developing management variables that are potentially linked to soil indicators . Whereas most previous studies have frequently utilized mail-in surveys that rely on binary responses from farmers to understand management , our study, following Guthman and others, highlights the uneven gradient in management practices that exists among organic farms and the importance of in-depth interviews . For example, rather than simply noting the presence or absence of tillage at a field site, our study accounted for the number of tillage passes per season that a farmer implemented on a particular field site, which required soliciting a range of responses from each farmer to create a congruent metric across all field sites. As displayed in Table 6, the mean values for frequency of tillage and crop abundance differed across the three farm types in our study; these management variables strongly separated Farm Type I from the other two farm types and weakly correlated with soil quality. On the other hand, crop rotational complexity generally separated all three farm types, but did not correlate with increasing soil quality. These results suggest that while certain management practices may increase soil organic matter pools as frequency decreases, some management practices may require finding a “sweet spot” to achieve higher soil organic matter levels.

Relatedly, the implementation of ICLS did not appear to be as strong of a source of differentiation among the three farm types. One reason for this weak link between soil organic matter levels and ICLS may be due to the lack of a temporal component in the development of this soil metric. For example, some farms may have recently rotated livestock on their fields, while other farms may not have rotated livestock for several years on that particular field; our metric does not capture such spatial and temporal differences. Though limited studies on organic systems in California currently exist, previous studies in the midwestern US have found that the integration of livestock does increase organic matter levels on-farm ; however, based on our results, crop diversity, crop rotational complexity, and frequency of tillage present stronger influences than cover crop application and ICLS in differentiating working organic farms—at least in this particular context. While management is undoubtedly an important driver of soil organic matter levels, our findings also suggest that soil texture may play a more significant role than management in determining levels of SOM than originally considered. Though management explained 18% of the variance among the three farm types, further analysis showed that soil textural class was the more dominant factor as shown in Figure 5; in fact, soil texture class was 44% greater than management in explaining the three farm types. This important result from our study complements parallel findings from Sprunger et al. , who also determined that soil textural class, rather than management, explained the largest amount of variation among the soil indicators they measured on their midwestern US-based organic corn systems . Our combined findings provide an initial indication that regardless of the organic system— ie, crop, climate, and/or geography—soil texture is the more dominant determinant of soil indicators for soil quality rather than the diverse management practices applied to these systems .This broader finding is significant because it supports emergent research that suggests that while management certainly contributes to soil quality, inherent characteristics of the soil in a given field may place limits on achievable organic matter levels on organic farms . Based on our findings, it is evident that even along minimal gradients in soil texture class, organic matter levels strongly differ. Soil texture is known to be a strong control on soil organic matter dynamics across diverse ecological systems—not just agricultural systems—in part because organic compounds, particularly those derived from soil microbes, are among those capable of stabilization by physical and chemical mechanisms, including aggregation, sorption on mineral surfaces, and entrapment within fine pores . At a fundamental level, 10 liter drainage pot soils with greater amounts of clay tend to stabilize SOM on surfaces more than soils with high sand and/or silt content , as clay particles provide greater surface area through organo-mineral associations than other particle sizes . For example, it has been shown in numerous previous studies that as clay content increases, the relative abundance of total soil N also increases . Further other studies have shown that soil texture and structure can influence SOM chemistry, and therefore, SOM stabilization . Our study takes previous research in agricultural contexts one step further to show that while management is important to consider, soil texture may be the more dominant factor; however, based on our results, it is still unclear which direction soil texture may be driving SOM. Nonetheless, our results highlight that contextualizing management in the native soil texture is essential to understand the limits of management imposed by pre-existing constraints of the soil. In practice, current emphasis in on-farm soil health research and quality assessments tends to focus on the importance of changing management to build healthy soils and improve soil quality without explicit consideration for soil texture .

In this study, the gradient of soil textures across the farm fields sites was relatively limited and even so—soil texture still explained a significant component of the variance observed compared to management. Given this outcome, our findings here reinforce the importance of using soil texture as a starting point for evaluating soil quality. Knowing the soil textural class of different fields may help farmers determine the management practices that have greatest potential for improving soil quality on farms with even small variances in soil textures; soil texture class may also help farmers better contextualize results of their soil health tests. Our study suggests that moving forward, soil texture should be more explicitly considered when making management recommendations to improve soil quality on organic farms. That said, understanding the interactive effects between management and soil texture continues to be a gap in on-farm research and soil health assessment. Future studies might build on our approach and examine whether applying a similar suite of indicators to capture soil organic matter levels may yield similar connections with management in other organic farming contexts in California—and elsewhere in the US. Our study provides a potentially widely applicable method for developing a functional understanding of soil organic matter in complex agricultural landscapes. In this sense, the overall significance of the results of the cluster analysis highlights the efficacy of developing typologies to provide a useful tool for understanding the complexity of working agricultural landscapes. Importantly, the development of farm typologies allowed for additional analysis of other soil indicators for N cycling an availability—by using the farm types as a central tool for further investigation.Though the range of gross N cycling rates from this study are comparable to N cycling values reported from previous studies in organic agricultural systems , we found that farm types did not have significantly different gross N mineralization and nitrification rates—contrary to our initial hypothesis and despite that farm types strongly differentiated based on soil organic matter levels. These hypotheses were in part based on prior work with organic farms in this region that reported instances where inorganic N pools were low—well below established soil nitrate threshold sufficiency values—but that the crops themselves showed high production of, and sufficient N . Fields in which this trend was observed had the highest levels of soil C, and so in this previous study, it was hypothesized that higher rates of N production explained this observed trend. However, nitrogen bio-availability for crops is not just a function of the gross production of inorganic N by microbes but is also influenced by physical soil characteristics within the rhizosphere, such as the local soil structure and mineralogy, plant root structure and associated mycorrhizal pathways, as well as accessibility of water to plants and soil microbes . These variable conditions in the rhizosphere are not captured by measuring N cycling rates but still directly influence bio-availability of N. For these reasons, the N cycling results of this study may not follow prior findings from Bowles et al. . Still, we did observe an influence of soil organic matter levels on N cycling, particularly in terms of gross nitrification rates. As shown in the Linear Mixed Model results in Table 12, SOM indicators do appear to have an influence in predicting gross nitrification rates , even as the proportion of variation explained is modest . This slight trend is also evident in the boxplots . The weak but significant link between soil organic matter levels and gross nitrification rates is important to highlight because these results suggest that building soil organic matter presents one way to increase nitrification rates and potentially crop N availability. Because the plant-soil-microbe N cycling system is strongly influenced by soil water content and soil structure, it is possible that gross N cycling indicators lack the responsiveness that SOM indicators exhbiti especially in scenarios where improved soil quality allows for crops to continue accessing soil microsites with available N . Similarly, crops with more abundant and active mycorrhizal community associations can extend into smaller Ncontaining aggregates that may be otherwise locked up for crops with less root proliferation and hyphal associations. Additionally, it is also possible that changing microbial community composition in the soil may lead to greater immobilization of N, locking up available N but not necessarily impacting gross production of N. These plant-soil-microbe interactions that control availability of N may not be detectable solely by measuring gross N flows. While not significant, SOM indicators were also selected in the development of the LMM for gross mineralization rates as well. These results are congruent with previous research looking across ecosystem types that reported a relationship between N cycling rates and SOM indicators. For example, a meta-analysis published by Booth et al. that examined woody, grass, and agricultural ecosystems found a strong positive relationship between indicators for SOM and gross N mineralization.