Month: October 2024

The geographical range, orientation and power dynamics involved in such non-food functions have yet to be assessed . In short, the many highly-valued social and ecological services that farms provide have not been defined spatially or related to marketing practices, though it is these very orientations that are important to theories of localization and its role in the practices of farmland preservation and management.Last, production, relationships and proximity do not necessarily beget mutually beneficial feedback loops between environmental and social justice objectives. Food insecurity in farm workers is more than triple the national household average in multiple areas of the country . Naturally, markets will gravitate toward more wealthy and powerful communities that are better positioned to help farmers achieve their end goals of profitability and secure farm tenure. Indeed, there is evidence that many direct marketing networks target consumers in the wealthiest neighborhoods. Farms involved in direct marketing are more likely to be located in the Northeast or the West Coast, near densely populated urban markets in areas with high median home values . Schupp finds that farmers markets locate in areas where the neighborhood population has attained higher education levels and a higher percentage identify as white than the national average. Direct market customers are more likely to bemiddle-aged, middle-income or above, well-educated, suburban women . However, different types of local food marketing, blueberry in pot beyond direct marketing through farmers’ markets, may differ significantly in demographics of clientele, economics and geographies.

While the local food movement grows, so does demand for food assistance. As the federal government removed welfare programs, non-profit food banks have rapidly grown in number since the 1980s . Today, one in seven Americans rely on food banks to feed their families . To meet the needs, food banks source from nearby farmers, distributors and retailers, and they are increasingly sourcing fresh, local food . Indeed, the market embeddedness that enabled Belo Horizonte, Brazil to achieve food security for all its citizens may be differently oriented spatially and socially than a food system that localized with the objective of influencing production practices. Empiric research on the embeddedness of food supply is growing to help understand how such theories play out in practice. Penker shows the alternate routes for grain from harvest to mill to bakery with unique social and geographic distinctions between whole meal and standard bread chains. Moragues-Faus and Sonnino review three olive oil producers and their sourcing regions to show the socio-spatial place-making in branding. This research will be the first to explore multiple sales and donation practices in relation to one another. The aim of the research is to identify the geo-socially embedded intersections and deviations in the local food system.The research is not exclusively focused on consumers and their relation to farms, but rather on the interplay of a variety of immediate relationships with farms around sales, visits, and donations, referred to collectively as networks. Geo-social network findings are triangulated using comprehensive planning documents and expert interviews. To start, the methods section will provide a description of the case study region and its relevance the research questions raised in the above literature review to help make sense of the methods employed, how networks were coded, and profiles of interviewees selected.

This study focuses on Chester County, PA due to its long history of direct marketing local food. Located in the northeast, near high to median home values in close proximity to large urban markets of New York, Philadelphia and Washington D.C , Chester County has similar characteristics to what the literature defines as the average landscape involved in direct marketing which both grounds this study and broadens its application to similar cases. The county has historically held widely spaced towns and villages surrounded by new growth forest, livestock operations, row crops, horse farms, and mushroom farming activities . Farms face economic pressure from the housing market. Having added 70,000 people from 2000 to 2010, Chester County has the highest population growth rate of any county in Pennsylvania and ranks second in Pennsylvania, only after adjacent Lancaster County, for farm production. Because of heavy development pressure, agricultural land-uses face continual competition from the residential housing market. The 2007 Census of Agriculture reported a 10 percent decline in the number of farms and 14 percent decline in farm acres from the previous census in 2002.Food insecurity is actively tackled by the local food movement. One in 20 of the 500,000 Chester county residents receive Supplemental Nutrition Assistance Programs compared to one in seven for the state . The food bank, which has been in operation for over 80 years, started its gleaning program in 1996 with the help of state Senator Andy Dinniman and the newly hired Larry Welsch, the Chester County Food Bank’s current director.

The concept of gleaning is based on the Biblical description of scavenging for food left in harvested fields. Some farmers’ crops are earmarked for the food bank while others make their leftovers available to be picked by volunteers. Chester County Food Bank has become a national leader in purveying local, fresh food by harnessing the goodwill of a large volunteer base and generous farming community. The food bank supplies fresh, local food through a variety of programs: gleaning, urban gardening, and school-based high-tunnel greenhouses. In addition, the food bank runs several outreach programs whose education and social networking aims dovetail with gleaning program farms. The Chester County Food Bank ranks sixth nationwide in the percentage of fresh food it disperses, with over twenty-two percent of the 2,000,000 pounds of food distributed being fresh, according to a study by the University of Pennsylvania . This amount does not include the many pounds of fresh food grown in raised beds at food cupboard sites and distributed directly to the community without being transferred through the food bank.No comprehensive list of farms and their market connections currently exists in Chester County. I employed a cross-sectional design to create a novel database, which required a range of sources. Farm and market data was gathered from civic documents, market promotion material, media, farm website listings, county farm listings, Local Harvest affiliates, and buyer associations. Farm managers were queried with an IRB-approved electronic questionnaire to identify their geographic coordinates, raw products and direct sale/donation markets. Non-local products sold through the farm and processed products are not included in this study. In turn, markets were queried by an email, which asked them to identify other direct sale farms in a double verified snow-ball sampling technique. Market and farm locations are geocoded by latitude and longitude based on the exact address. The geographic location of farms and markets were virtually site checked using Google Street View imagery from 2007 to 2015 to verify the location. CSA member purchases are coded at the zipcode level to protect client confidentiality. CSA members were not queried to verify zipcode or network connection. This technique allowed the researcher to capture direct farm networks within, moving into or going from Chester County. Email surveys were sent to 700 farms and 2000 markets/users, and responses from 117 farms and 637 unique users/ markets confirmed network connection.This research takes a broader approach in accounting for any immediate relationship with a farm, plastic planters wholesale including sales to distributors and wholesale grocers, donations of unprocessed food, and visits to farms. Relationships, including donations, sales and farm visits, are referred to collectively throughout as “network.” Networks trace the connections formed through the sale or donation of raw product and services produced by the farm to their first point of sale/donation to customers, institutions, and distributors. In this way, the research encompasses a range of the immediate interactions with farms to assess the spatial distribution and typologies of networks in which farms engage in relation to one another. A priori coding is based on theoretical considerations. Informed by the theory of local food’s embeddedness , this study parses direct marketing networks by their social construct. For example, farms can market directly to consumers through Community Supported Agriculture or farmers’ markets. Farmer’s markets are seasonal and represent a direct connection for consumers with the farmer where the farmer usually travels to an urban or suburban location. CSA and Buyer Club networks bring the product and end consumer in contact through drop-off/pick-up locations. Thus, CSAs constitute a different socio-spatial type of farm network when compared to farmers’ markets, but not buyers clubs, and are coded thus. Wholesale networks represent purchases by larger-volume distributors and grocery stores which act as intermediaries between farms and end-users. Institutions are largescale buyers which, like the smaller-scale restaurants, represent a steady relationship between the purveyor and farmer to cater to consumer demand.

Agricultural byproduct, farm-to-farm sales, and educational visits are also noted as important networks between farmers, farms and their communities. School trips to farms bring students to the farm and represent regional knowledge networks captured in the ‘education visits’ variable. Farm-to-farm sales represent the agricultural social networks involved in sales of raw products.The generated network map is an under-estimate of a county’s farm networks for a variety of reasons. Some categories of farm networks are not captured in this data. Many farms allow online purchases through their own website or a crowd-sourcing website. Farms also sell directly from their farm gate. These sales and connections are not documented in this study. Larger direct distribution networks were not captured in this study mainly because large suppliers did not respond to the query nor do they list their outlets online. Conversely, many smaller-scale suppliers readily listed market outlets on their websites and confirmed them in the research query. Additionally, the online query method limited the response to farms whose networks could be verified by email correspondence. Farms that only listed phone numbers were not contacted. For example, numerous Amish farms were not included in this study due to inability to reach the farmers via email. Conversely, many farmers’ markets list Amish farmers asprominent suppliers. This study does not include non-food producing farms, thereby omitting many fiber alpaca farms, greenhouse nurseries, and horse farms that play a vital role in supporting food-producing farms through the sale and purchase of ancillary products such as horse manure for mushroom substrate. The size of the farm and product sold are not noted. Seller, buyer, and market manager characteristics, which may be highly relevant to the social and geographical nature of supply chains were not noted in this study. Further, coding the type of network is imperfect. Some farms sell through supermarkets that they run from their farm gate. Many retail establishments may operate a cafe through which they serve locally-sourced farm products. In these instances, the duplicated forms of retail were noted. For example, if a farmers’ market is operated from the parking lot of a grocery store that uses some of the food in its on-site cafe, food sold through the farmers’ market is coded as a farmers’ market and restaurant though the primary venue use is for wholesale.The 2009 Chester County Comprehensive Plan is divided into urban, suburban, and rural landscape visions which seek to isolate active farming areas from residential developments while connecting these land-uses through local food marketing . In essence, the planning regulations seek to divorce producers from users physically, while promoting their connections socially. Some agricultural activities are included within the suburban landscape vision. Community Supported Agriculture , small specialized farms and nurseries, community gardens, and farmers markets in suburban areas are meant to “provide residents with fresh locally grown food.” The rural landscape vision has three components: small villages that make up rural centers, a rural landscape of scenic vistas without active farming, and an agricultural landscape . The agricultural landscape is largely located in western Chester County, where the character is similar to the large agricultural area in Lancaster and Berks Counties as opposed to the nearby Philadelphia metropolitan urban area. Agricultural production is diverse, including dairy production, horses and other livestock, poultry, mushrooms, nurseries, orchards, and field crops. This landscape is not planned to accommodate future projected growth, and is dominated by a concentration of active farms, Agricultural Security Areas, large clusters of land permanently protected by agricultural easements, and areas with municipal commitment to adopt effective agricultural zoning.

A study that screened livestock at the California state fair in 2005, which usually hosts livestock raised on small farms or in backyards, observed a 3% prevalence of E. coli O157:H7 in pigs, but did not find O157:H7 in any other livestock samples including dairy cows, whereas our study identified O157:H7 in cattle but not pigs. A 2002 study that also collected fecal samples at fairs in three states, identified an E. coli O157:H7 prevalence of 11.4% in cattle, 1.2% in swine and 3.6% in sheep and goats, whereas we measured a 5.31% E. coli O157:H7 in all cattle . Differing STEC prevalence in these aforementioned studies may reflect different management practices on farms or other climate or animal-level factors. Additionally, since ruminants are the main reservoirs for STEC, our results indicating that STEC prevalence in swine is lower comparatively than the other sampled species is in agreement with previous research, however, pigs are still a livestock species of public health concern, as they harbor E. coli O157:H7 as indicated by many studies. Our model results also indicated that cattle and sheep are a significant factor in STEC presence on farms, as compared to goats and pigs. However, differences in location, laboratory methods and sampling methods make comparison between studies challenging. More than half of the identified O-serogroups in this study are on CDC’s list of the top 7 STEC of concern for public health, including six O157:H7, twenty-two O26, nine O103 and one O111. Stx2, blueberry production which is the more virulent form of the Shiga toxin gene that has been implicated in severe human disease, was identified in 16.46% of O-serogroups; 13.92% contained both stx1 and stx2.

The eaeA gene, which allows STEC bacteria to attach to human host cells, was detected in 55.69% of positive STEC samples, contrary to a study conducted by Dewbury et al, which rarely discovered eaeA in their non-O157 isolates from cattle fecal samples. The ehxA gene, which is reported in severe human cases of STEC, was detected in 88.61% of the positive isolates . Compared to a study conducted by Djordjevic et al in adult sheep and lambs, they detected stx1, stx2 and ehxA in 78.2% of their positive serogroups, versus our study which only identified those three genes in 1.27% of positive serogroups. However, they reported 0.8% of their serogroups had just stx2 and ehxA genes, whereas in this current study, 11.39% of the positive isolates contained these two virulence genes. The pathogenic STEC O-serogroups, genes and virulence factors identified in this study highlight the need for continued studies on DSSF, as well as outreach to stakeholders regarding pre-harvest food safety risks and development of onfarm mitigation strategies. Significant risk factors identified by the final mixed effect model included daily maximum temperature °C. The data in our study ranged from 11.7°C – 39.80°C. An experiment that measured the decline of E. coli O157:H7 in inoculated manure at four temperatures, 7°C, 16°C, 23°C and 33°C, reported that E. coli O157:H7 declined significantly faster in manure at 23°C and 33°C, than at 7°C and 16°C, for both oscillating and constant temperatures. This study confirms our model result, which suggested that as the daily maximum temperature increased, the odds of finding STEC in a fecal sample was less likely. A study by Franklin et al also identified daily maximum temperature as a significant risk factor, when conducting a study of STEC in wild ungulates in Colorado.

They detected a positive association between temperature and STEC presence in fecal samples, whereas our model identified a negative association with the daily maximum temperature. However, the range of daily maximum temperatures displayed in their analysis were narrower than our recorded daily maximum temperatures, which may account for this difference. Although many studies indicate that STEC sheds more in summer months, California microclimates differ from each other and from the majority of seasons in other states. California valleys and foothills experience low humidity and temperatures above 37.78°C in the summer and autumn, which may affect STEC shedding from livestock raised on California farms located in different microclimates. For instance, to compensate for the numerous microclimates in California in our study on Campylobacter spp., which included the same farms included in this current STEC study, we divided the California summer season into Coastal and Inland and season was a significant risk factor in that final multilevel logistic regression model. Interestingly, our Campylobacter study also found a significant association between presence of Campylobacter spp. and a farm owning swine, with 13.76% prevalence of Campylobacter spp. measured in pigs raised outdoors. Difference in climate conditions between states in the US reveal a need to report the full range of temperatures and other environmental factors measured for studies estimating the effect of weather on food borne pathogen shedding in livestock. For instance, a study that collected samples from conventional dairy and beef cattle in Michigan revealed that high average temperatures measured one to five days before sampling had a 2.5 times greater odds of STEC than lower temperatures, which differs from our study results that suggested that STEC survival is less likely at higher maximum temperatures.

Michigan results contradict ours, however the highest maximum daily temperature measured in our study is not a temperature normally observed in many areas of the US. The range of daily maximum temperatures for the Michigan study was 22.78 – 32.2°C, with one 36.11°C outlier. Additionally, our study included winter temperatures, while their study was only conducted in summer . Extreme temperature, heat index or humidity values observed in different parts of the world may affect conclusions and interpretations of results, especially between studies. Stanford et al reported the effects of severe weather events on STEC shedding in Canadian cattle. Although they also observed that STEC prevalence increased when ambient temperatures were higher than 28.9°C, a separate finding indicated that the O-serogroup O26 had a significant reduction in prevalence during extreme heat in July and August. Almost 28% of the O-serogroups in our study were O26, and the final model results may have been influenced by this strain. The ways that different non-O157 STEC strains react to varying environmental conditions, such as temperature or humidity, may account for variations in results between studies. Moreover, changes in the host species during various temperature fluctuations or extreme weather events should also be studied. For instance, Dawson et al measured behavioral changes in cattle during increased temperatures, as a possible driver of changes in STEC prevalence, such as increased water consumption or change in grazing habits. Their simulation results indicated that higher summer temperatures may encourage more resting by cattle in crowded areas, such as under shade trees, which can lead to direct transmission of STEC. Since the aforementioned studies differ in conclusions regarding the direction of environmental effects on STEC shedding in livestock, this risk factor needs further investigation, as perhaps there are underlying mechanisms accounting for the difference between results, including microclimates or animal level factors. Our multi-variable model also indicated that livestock sharing a barn with other animals resulted in 3.5 greater odds of a positive STEC sample. Multiple livestock housed in a barn could share pathogens by cross-contamination of food or water troughs or persistence of STEC in a barn environment that may not be subjected to regular cleaning. Other studies have indicated that STEC persists for long periods of time in barns or on surfaces within the farm environment. For instance, blueberry in container one study swabbed multiple barn surfaces at a dairy ranch and measured 14.9% – 19.1% STEC in samples from cattle or calf feeders, and 11.3% – 18% on other surfaces. Another study implicated water troughs as harboring E. coli O157:H7, and inferred that shared water troughs play a key role in the persistence and maintenance of continued E. coli O157:H7 infections in cattle. A British study reported that housed beef cattle shed more STEC than unhoused and suggested that this may be due to shared water sources or feeding bins and an accumulation of pathogens in a shared environment. Finally, the last significant risk factor from the multi-variable final model indicated that livestock in contact with wild areas, such as forests or wetlands, have a higher likelihood of STEC presence in their feces. Wildlife, including feral pigs, deer, rodents and birds are known reservoirs of STEC. 

A study conducted in California identified a low prevalence of E. coli O157:H7 in rodents , however, they did not test for non-O157 STEC in samples, which may have a higher prevalence in rodents. A 2016 published study discovered the stx2 gene in over twenty percent of Canada geese fecal samples and seven percent of nearby water samples from Lake Eric bordering Ohio, USA. A case-control study conducted after 15 human cases of E. coli O157:H7, identified the source of STEC as those who ate fresh strawberries contaminated by deer feces. Livestock that graze in wild areas may be exposed to indirect sources of STEC, for instance through environmental contamination of soil or water, or because wildlife that live in these bordering wild areas enter agricultural areas and contaminate the pastures grazed by farm animals. Limitations of this study include the small sample size of farms that were convenience sampled, so the model results are not generalizable to other regions and farms. Moreover, because we collected the freshest fecal samples available and did not randomize sample collection, we may have added bias to the study results. Unmeasured variables that should be included in future studies include the age of the animal and whether livestock have direct or indirect contact with neighboring livestock. Although a majority of commercial swine production in the United States occurs indoors with high levels of biosecurity, the US is currently experiencing a return to raising domestic pigs outdoors. Before the 1950s, most swine operations in the US were small scale family farms and either a hybrid of indoor/outdoor or solely outdoor-based. Beginning in the 1960s, commercial swine production began transitioning to indoor systems, based on goals to increase efficiency and reduce swine disease transmission as well as a public health mandate to decrease human trichinosis cases.However, consumer demand for sustainable or pasture-raised animal products within the past few decades has revived traditional methods of raising swine outdoors or on pasture . While primarily considered a niche production method in the US, outdoor-raised pig operations are broadly distributed throughout California. A challenge in raising pigs outdoors is the possibility of these animals interacting with wildlife disease reservoirs, such as feral pigs, and the associated risk of zoonotic and/or swine pathogen transmission Both domestic and feral pigs share the same genus and species and can be reservoirs for zoonotic pathogens ,Also, swine diseases eradicated in conventional indoor-raised herds have been documented in feral swine in California and contact between feral pigs and outdoor-raised swine herds is a risk factor for the reintroduction of these diseases to domestic herds in the US. For example, a 2016 human case of brucellosis in New York state was traced to a feral pig intrusion event on a pasture-raised pig farm. Brucella suis was then transmitted to domestic pigs raised outdoors in 13 other states through animal sales. Feral pigs could also play a significant role in the transmission and maintenance of transboundary animal diseases introduced to North America., For instance, African Swine Fever is actively spreading in eastern Europe, with wild boars transmitting this devastating disease between and within countries. Similarly, wild boars abet the transmission of ASF in South Korea, spreading the virus to outdoor-raised swine. And most recently, ASF was identified in domestic swine in the Dominican Republic, which is the closest to the US that ASF as spread in this century. During the past few decades, feral pig populations have greatly increased in the US from 17 to 41 states. California has one of the largest and widest geographic distributions of feral pigs and this invasive species has the broadest habitat range of any large mammal except humans, which is in part due to their ability to adapt to a diverse range of ecological habitats and their opportunistic omnivore diet. Feral pig population distribution and abundance is dynamic yet has not been documented at fine spatial units. Previous presence maps reported feral pigs for an entire county, even if there had only been a single occurrence recorded countywide.

In order to investigate whether the overall distribution and accumulation of small RNA is affected by the interaction between different V. vinifera genotypes [Cabernet Sauvignon and Sangiovese ] and environments [Bolgheri , Montalcino and Riccione ], we investigated the regions in the grapevine genome from where a high number of small RNAs were being produced , by applying a proximity-based pipeline to group and quantify clusters of small RNAs as described by Lee et al. . The nuclear grapevine genome was divided in 972,413 adjacent, non-overlapping, fixed-size windows or clusters. To determine the small RNA cluster abundance, we summed the hits-normalized-abundance values of all the small RNAs mapping to each of the 500 bp clusters, for each library . To reduce the number of false positives, we considered a cluster as expressed when the cluster abundance was greater than the threshold for a given library, eliminating regions where few small RNAs were generated, possibly by chance. Libraries from bunch closure, representing green berries, and 19 ◦Brix representing ripened berries, where used in this analysis. From the 972,413 clusters covering the whole grapevine genome, 4408 were identified as expressed in at least one sample. As showed in Figure 1, CS-derived libraries have a higher number of expressed clusters when compared to SG-derived libraries of the same developmental stage and from the same vineyard. The exceptions were the Sangiovese green berries collected in Riccione and Sangiovese ripened berries collected in Montalcino, big plastic pots which have a higher number of expressed clusters than the respective CS ones.

When Cabernet berries were green, a higher number of sRNA-generating regions were found active in Bolgheri than in Montalcino and Riccione. Differently, ripened berries had the highest number of sRNAproducing regions expressed in Riccione, while Bolgheri and Montalcino show a similar level of expressed clusters . Sangiovese green berries instead show the highest number of active sRNA-generating regions in Riccione, and this number is twice the number found in Bolgheri and Montalcino that is similar. Ripened berries collected in Montalcino and Riccione show almost the same high level of sRNA-generating clusters, whereas those collected in Bogheri present a lower number . We also noted that when cultivated in Bolgheri, neither Cabernet Sauvignon or Sangiovese change dramatically the number of expressed clusters during ripening, while in Riccione Cabernet Sauvignon shows a 2-fold increase of sRNAproducing clusters, which is not observed in Sangiovese. Next, the small RNA-generating clusters were characterized on the basis of the genomic regions where they map, i.e., genic, intergenic and transposable elements. In general, when the berries were green, the numbers of sRNA-generating loci located in genic and intergenic regions were roughly equal in all environments and for both cultivars, except for Sangiovese berries collected in Riccione, which show a slight intergenic disposition of sRNA-producing regions . Differently, in ripened berries on average 65% of the sRNA-generating loci were in genic regions, indicating a strong genic disposition of the sRNA-producing clusters . The shift of sRNA-producing clusters from intergenic to mostly genic is more pronounced in Cabernet Sauvignon berries collected in Riccione, with an increase of approximately 20% of expressed clusters in genic regions when berries pass from the green to the ripened stage.

When comparing the clusters abundance among libraries, we found that 462 clusters were expressed in all libraries. The remaining 3946 expressed clusters were either shared among groups of libraries or specific to unique libraries. Interestingly, 1335 of the 4408 expressed clusters were specific to Riccione-derived libraries . The other two environments showed a much lower percentage of specific clusters, 263 and 140 in Bolgheri and Montalcino respectively . Comparing the expressed clusters between cultivars or developmental stages, we did not observe a similar discrepancy of specific clusters toward one cultivar or developmental stage; roughly the same proportion of specific clusters was found for each cultivar and for each developmental stage . Among the 1335 specific clusters of Riccione, 605 were specific to Cabernet Sauvignon ripened berries of and 499 to Sangiovese green berries. Other smaller groups of expressed clusters were identified as specific to one cultivar, one developmental stage or also one cultivar in a specific developmental stage. When comparing the expressed clusters with the presence of transposable elements annotated in the grapevine genome , we noticed that approximately 23% of the sRNA-generating regions were TE-associated. Sangiovese green berries from Riccione have the highest proportion of TE-associated expressed clusters, while Cabernet Sauvignon ripened berries also from Riccione show the lowest proportion of TE associated expressed clusters. Sangiovese berries have the highest percentage of expressed clusters located in TE when cultivated in Riccione, compared to the other two vineyards. Interestingly, Cabernet Sauvignon berries show the lowest proportion of TE-associated clusters when growing in Riccione , independently from the berry stage. In all the libraries, Long Terminal Repeat retrotransposons were the most represented TE. More specifically, the gypsy family was the LTR class associated with the highest number of sRNA hotspots. The other classes of TE associated with the sRNA-generating regions can be visualized in Figure 3B.To determine the global relationship of small RNA-producing loci in the different environments, cultivars and developmental stages, we performed a hierarchical clustering analysis. As showed in Figure 4, the libraries clearly clustered according to the developmental stage and cultivar and not according to the environments.

Ripened and green berries had their profile of sRNA-generating loci clearly distinguished from each other. Inside each branch of green and ripened samples, Cabernet Sauvignon and Sangiovese were also well separated, indicating that, the cultivar and the stage of development in which the berries were sampled modulate the outline of sRNA-producing loci more than the environment. Notwithstanding the evidence that developmental stage and variety have the strongest effect in terms of distinguishing samples clustering, we were interested to verify the environmental influence on small RNA loci expression in the two cultivars. Thus, for each sRNA-generating cluster we calculated the ratio between cluster abundance in Cabernet Sauvignon and Sangiovese in each environment and developmental stage, thereby revealing the genomic regions with regulated clusters, considering a 2-fold change threshold, a minimum abundance of 5 HNA in each library and a minimum sum of abundance of 30 HNA . Figure 5 shows how different environments affect the production of small RNAs. In Bolgheri, regardless the developmental stage there were many clusters with a very high abundance level in Cabernet Sauvignon . In Montalcino and even more in Riccione we also observed differences between the expressions of clusters in the two cultivars, with ripened and green berries showing an almost opposite profile in terms of number of clusters more expressed in Cabernet Sauvignon or Sangiovese. When the berries were green, in Montalcino Cabernet Sauvignon shows the highest number of up-regulated clusters, while in Riccione, Sangiovese has the highest number of up-regulated clusters. The opposite behavior was noticed in ripened berries, with Sangiovese having the highest number of up-regulated clusters in Montalcino and Cabernet Sauvignon in Riccione . Notably, we observed a small percentage of regulated clusters exhibiting at least a 10-fold higher abundance of small RNA in Cabernet Sauvignon or Sangiovese when compared to each other . An examination of those clusters showed that a substantial difference could exist between the cultivars, depending on the vineyard and the developmental stage. For example, in Riccione, a cluster matching a locus encoding a BURP domain-containing protein showed a fold change of 390 when comparing green berries of Sangiovese with Cabernet Sauvignon. The small RNAs mapping in this region were mainly 21-nt and produced from both strands . Similarly, growing berries in containers the majority of the highly differentially expressed clusters showed a similar profile: strong bias toward 21-nt sRNAs and a low strand bias. These findings suggest that these small RNAs might be the product of RDR polymerase activity rather than degradation products of mRNAs. We applied a pipeline adapted from Jeong et al. and Zhai et al. to identify annotated vvi-miRNAs, their variants, novel species-specific candidates and, when possible, the complementary 3p or 5p sequences. Starting from 25,437,525 distinct sequences from all the 48 libraries, the first filter of the pipeline removed sequences matching t/r/sn/snoRNAs as well as those that did not meet the threshold of 30 TP4M in at least one library or, conversely, that mapped in more than 20 loci of the grapevine genome . Only sequences 18–26-nt in length were retained. Overall, 27,332 sequences, including 56 known vvi-miRNAs, passed through this first filter and were subsequently analyzed by a modified version of miREAP as described by Jeong et al. . miREAP identified 1819 miRNA precursors producing 1108 unique miRNA candidates, including 47 known vvi-miRNA. Next, the sequences were submitted to the third filter to evaluate the single-strand and abundance bias retrieving only one or two most abundant miRNA sequence for each precursor previously identified.

A total of 150 unique miRNA corresponding to 209 precursors were identified as candidate miRNAs. Among these 209 candidate precursors, 61 belonged to 31 known vvi-miRNA that passed all the filters and 148 were identified as putatively novel miRNA candidates. To certify that they were novel candidates rather than variants of known vvi-miRNAs we compared their sequences and coordinates with the miRNAs registered in miRBase . In order to reduce false positives and the selection of siRNA-like miRNAs, we considered only 20, 21, and 22 nt candidates whose stemloop structures were manually evaluated . Eventually, 26 miRNAs homologous to other plant species were identified with high confidence. Twenty-two were new members of nine known V. vinifera families, whereas the other four belong to three families not yet described in grapevine . For 16 homologs we were able to retrieve also the complementary sequence. Finally, excluding these 26 miRNAs and other si-RNA like miRNAs, we identified 7 completely novel bona fide miRNAs. Apart from the 61 known vvi-miRNAs identified by the pipeline, we searched the dataset for others known vvi-miRNAs eliminated throughout the pipeline, looking for isomiRs that were actually more abundant than the annotated sequences. Their complementary 3p or 5p sequence was also retrieved when possible. Hence 89 known vvi-miRNAs were identified in at least one of our libraries . Among the known vvi-miRNAs identified, 24 had an isomiR more abundant than the annotated sequence and 4 have the complementary sequence as the most abundant sequence mapping to their precursor. We found 16 vvi-miRNA isomiRs that were either longer or shorter than the annotated sequence, 7 vvi-miRNAs that mapped in the precursor in a position shifted with respect to the annotated ones and one miRNA that contains a nucleotide gap when compared to the annotated sequence . An extreme case of shifted position was found in vvi-miRNA169c, where the annotated sequence had only 5 TP4M when summing its individual abundance in the 48 libraries. Another sequence, shifted 16 bp as compared to its annotated position on the precursor had an abundance sum of 1921 TP4M, and was retained together with the annotated sequence, and named vvi-miRNA169c.1. For 36 of the 48 V. vinifera miRNA families deposited in miRBase we found at least one member. An in silico prediction of miRNA targets was performed for the 191 mature miRNAs here identified. Using the miRferno tool , and considering only targets predicted with high stringency, 1192 targets were predicted for 143 miRNAs, including six completely novel vvi-miRNA candidates . Two novel candidates seem to be involved in the regulation of important secondary metabolites biosynthesis. Among the six targets predicted for grape-m1191, the TT12 gene is known to be involved in the vacuolar accumulation of proanthocyanidins in grapevine . For grape-m1355, 12 targets were predicted and all of them are involved in secondary metabolism pathways. Nine targets code a bifunctional dihydroflavonol 4-reductase that is responsible for the production of anthocyanins , catalyzing the first step in the conversion of dihydroflavonols to anthocyanins . Another targeted gene codes a phenylacetaldehyde reductase which, in tomato, was demonstrated to catalyze the last step in the synthesis of the aroma volatile 2-phenylethanol, important for the aroma and flavor . Still this same miRNA candidate was predicted to target with high confidence a cinnamoyl reductaselike protein that is part of polyphenol biosynthetic pathway .

A broader range of studies from different cultivars, locations and environments are needed to determine a common set of genes involved in berry and flavor development. A similar study was conducted on the production of volatile aromas in Cabernet Sauvignon berries across many developmental stages, including a detailed analysis of the °Brix levels that was surveyed in this study. They found that the production of alcohol volatiles from the lipoxygenase pathway dominated in the later stages of berry ripening and suggested that the activity of alcohol dehydrogenases also could play an important role. The abundance of the transcript of VviOMT1 decreased in the pulp with increasing °Brix level and was correlated with IBMP concentrations in the late stages of berry development in this study. Both OMT1 and OMT3 have been shown to synthesize IBMP. Furthermore, the transcript abundance of each gene has been correlated with IBMP concentration, but the transcript abundance of each gene cannot fully account for the total IBMP present in all genotypes and conditions. OMT3 was found to be the major genetic determinant for this trait in two independent studies. Nevertheless, it is possible that OMT1 may contribute to the IBMP concentration, blueberries in containers growing because OMT1 can synthesize IBMP and it is located at the edge of a QTL significantly contributing to this trait.

Furthermore, the majority of IBHP , the precursor for the OMT1 and OMT3 biosynthesis of IBMP, is produced in the pulp of the berry complicating the factors that influence IBMP concentration. Our results raise questions that require additional research to clarify this relationship of transcript abundance to IBMP concentration, including determination of the rates of biosynthesis and catabolism, enzyme activities, volatilization of IBMP from the berry, as well as the concentrations of substrates for the enzymes involve. There are a number of other transcriptomic ripening studies in grapes and other fruit species. Many of these have compared broad developmental stages with partial genome microarrays. One study compared transcriptomic responses of the lates stages of ripening of whole berries of Chardonnay. This study used a different microarray platform with only about half of the genome represented on the array. In this study, 12 genes were found to be differentially expressed in each of the 3 different stages investigated. There were approximately another 50 genes that were differentially expressed at one stage versus another. Several genes were proposed as good candidates for markers of ripeness and these were also examined in Cabernet Sauvignon berries using qPCR. Several of these candidate genes are consistent with our results in the present study. They include CCD4a , a late embryogenesis abundant protein , a dirigent-like protein , and an S-adenosyl-L-methionine:salicylic acid carboxyl methyltransferase . Of these, the transcript expression of SAMT was found to be temperature insensitive. Like the previous study, the present study focused on very close stages in the mature berry when fruit flavors are known to develop. In contrast to the previous study on Chardonnay, there were massive changes in the transcript abundance in hundreds of GO categories over this narrow window of ripening.

This may in part be due to using six biological replicates rather than the standard three, which probably improved the detection of significantly changing transcripts. In addition, we used a different threshold level for statistical significance and an improved microarray platform, which was able to detect double the number of transcripts. In the present study, many differences were found between the skin and the pulp, °Brix levels and the interaction of tissue and °Brix. Important fruit ripening processes were affected including ethylene signaling, senescence, volatile aroma production, lipid metabolism and cell wall softening. These data indicate that fruit ripening in the late stages of maturity is a very dynamic and active process.Ethylene is involved in climacteric fruit ripening with a CO2 burst preceding the rise in ethylene. In tomato, this occurs at the time the seeds become mature in the mature green fruit stage. At this stage, tomato fruits become sensitive to ethyene and can continue through the ripening stage. Prior to the mature breaker stage, ethylene cannot promote tomato ripening to full ripeness. In non-climacteric fruit, there is no respiratory burst of CO2 and the ripening of most non-climacteric fruits was thought not to respond significantly to an extra application of ethylene. However, recently some non-climacteric fruit such as strawberry, bell pepper and grape have been found to produce a small amount of ethylene and appear to have responses to ethylene at certain stages. In the study of grapes, this peak was observed just before the start of veraison, followed by decreases in ethylene concentrations for several weeks afterwards; the late mature stages of ripening were not examined. Ethylene action is dependent upon ethylene concentration and ethylene sensitivity or signaling. In this study, there were clear and significant changes in transcript abundance of genes involved in ethylene signaling and biosynthesis in the late stages of berry ripening. Seeds become fully mature at this time .

Perhaps there is a signal from the seeds when they become mature that allows the fruit to ripen and senesce? Perhaps small amounts of ethylene are produced or there is a change in sensitivity to ethylene? Seymour et al. suggested the response of EIN3 might be a common signaling mechanism for both climacteric and non-climacteric fruit. The responses of VviEIN3 in this study and in a pepper fruit ripening study are consistent with this hypothesis. In addition, the transcript abundance of VviEIN3 in grape is very responsive to ethylene and the ethylene inhibitor, MCP. There are many other factors other than fruit development that can influence ethylene signaling. Could chilling of the fruit or other aspects of the processing of the grapes influence these responses? Could there be some influence of other abiotic or biotic stresses? These are questions that can only be addressed in future studies with additional experiments that are designed to answer these questions.The Berry phase reveals geometric information of quantum wave functions via their phases acquired after an adiabatic cyclic process, and its concept has laid the foundation for understanding many topological properties of materials. For example, the ground state fits the description as the limit of a statistical ensemble at zero temperature. At finite temperatures, the density matrix describes thermal properties of a quantum system by associating a thermal distribution to all the states of the system. Therefore, it is an important task to generalize the Berry phase to the realm of mixed quantum states. There have been several approaches to address this problem, among which the Uhlmann phase has attracted much attention recently since it has been shown to exhibit topological phase transitions at finite temperatures in several 1D, 2D, and spin-j systems. A key feature of those systems is the discontinuous jumps of the Uhlmann phase at the critical temperatures, signifying the changes of the underlying Uhlmann holonomy as the system traverses a loop in the parameter space. However, due to the complexity of the mathematical structure and physical interpretation, the knowledge of the Uhlmann phase is far less than that of the Berry phase in the literature. Moreover, planting blueberries in containers only a handful of models allow analytical results of the Uhlmann phase to be obtained. The Berry phase is purely geometric in the sense that it does not depend on any dynamical effect during the time evolution of the quantum system of interest. Therefore, the theory of the Berry phase can be constructed in a purely mathematical manner. As a generalization, the Uhlmann phase of density matrices was built in an almost parallel way from a mathematical point of view and shares many geometric properties with the Berry phase. We will first summarize both the Berry and Uhlmann phases using a fiber-bundle language to highlight their geometric properties. Next, we will present the analytic expressions of the Uhlmann phases of bosonic and fermionic coherent states and show that their values approach the corresponding Berry phases as temperature approaches zero. Both types of coherent states are useful in the construction of path integrals of quantum fields. While any number of bosons are allowed in a single state, the Pauli exclusion principle restricts the fermion number of a single state to be zero or one. Therefore, complex numbers are used in the bosonic coherent states while Grassmann numbers are used in the fermionic coherent states. The bosonic coherent states are also used in quantum optics to describe radiation from a classical source . Moreover, the Berry phases of coherent states can be found in the literature , and we summarize the results in Appendix A. Our exact results of the Uhlmann phases of bosonic and fermionic coherent states suggest that they indeed carry geometric information, as expected by the concept of holonomy and analogy to the Berry phase.

We will show that the Uhlmann phases of both cases decrease smoothly with temperature without a finite-temperature transition, in contrast to some examples with finite-temperature transitions in previous studies. As temperature drops to zero, the Uhlmann phases of bosonic and fermionic coherent state approach the corresponding Berry phases. Our results of the coherent states, along with earlier observations , suggest the Uhlmann phase reduce to the corresponding Berry phase in the zero-temperature limit. The correspondence is nontrivial because the Uhlmann phase requires full-rank density matrices, which cannot be satisfied only by the ground state at zero temperature. Moreover, the fiber bundle for density matrices in Uhlmann’s theory is a trivial one, but the fiber bundle for wavevfunctions in the theory of Berry phase needs not be trivial. A similar question on why the Uhlmann phase agrees with the Berry phase in certain systems as temperature approaches zero was asked in Ref. without an answer. In the last part of the paper, we present a detailed analysis of the Uhlmann phase at low temperatures to search for direct relevance with the Berry phase. With the clues from the previous examples, we present a conditional proof of the correspondence by focusing on systems allowing analytic treatments of the path-ordering operations. Before showing the results, we present a brief comparison between the Uhlmann phase and another frequently mentioned geometrical phase for mixed quantum states proposed in Refs., which was originally introduced for unitary evolution but later extended to nonunitary evolution. This geometrical phase was inspired by a generalization of the Mach-Zehnder interferometry in optics and was named accordingly as the interferometric phase. It has a different formalism with a more intuitive physical picture and has been measured in experiments. In general situations, the interferometric phase can be expressed as the argument of a weighted sum of the Berry phase factors from each individual eigenstate. Thus, its relation to the Berry phase is obvious. However, the concise topological meaning of the interferometric phase is less transparent since it is not directly connected to the holonomy of the underlying bundle as the Uhlmann phase does. The reason has been discussed in a previous comparison between the two geometrical phases. The interferometric phase relies solely on the evolution of the system state while the Uhlmann phase is influenced by the changes of both the system and ancilla, which result in the Uhlmann holonomy. Although Uhlmann’s approach can be cast into a formalism parallel to that of the Berry phase as we will explain shortly, its exact connection to the Berry phase is still unclear. The Uhlmann-Berry correspondence discussed below will offer an insight into this challenging problem. The rest of the paper is organized as follows. In Sec. II, we first present concise frameworks based on geometry for the Berry and Uhlmann phases, using a fiber-bundle language. In Sec. III, we derive the analytic expressions of theUhlmann phases of bosonic and fermionic coherent states and analyze their temperature dependence. Additionally, the Uhlmann phase of a three-level system is also presented. Importantly, the Uhlmann phases of both types of coherent states and the three-level system are shown to approach the respective Berry phases as temperature approaches zero. In Sec. IV, we propose the generality of the correspondence between the Uhlmann and Berry phases in the zerotemperature limit and give a conditional proof. In Sec. V, we discuss experimental implications and propose a protocol for simulating and measuring the Uhlmann phase of bosonic coherent states. Sec. VI concludes out work.

The right side of this plot, labelled with an electron density of zero, corresponds to charge neutrality in this system and lies in the gap of the band insulator. Therefore and both correspond to situations in which the hole band is very slightly filled. The valley and spin subbands of ABC trilayer graphene are presented in schematic form in Fig. 7.4A in the absence of electronic interactions. When we tune the Fermi level into these bands and activate interactions, we cannot produce a gap- the bandwidths of these bands are far too high- but we can produce full spin or valley polarization, as illustrated in Fig. 7.4B. The precise situations in which we find this system at and are presented in Fig. 7.4C and D; these situations correspond repsectively to full spin polarization but no valley polarization in and full spin and valley polarization in . Valley polarization couples strongly to transport, generating a large anomalous Hall effect and ferromagnetic hysteresis, as presented in Fig. 7.4E. Although these magnets occur in an atomic crystal, they are composed entirely of electrons we have forced into the system with an electrostatic gate, and as a result we can expect their magnetizations to be considerably smaller than fully spin-polarized atomic crystals. We will use the nanoSQUID microscope to image these magnetic phases. An optical image of the ABC trilayer graphene device used to produce data for the publications is presented in Fig. 7.5A. A black dashed line outlines the region we will be imaging using the nanoSQUID microscope. A nanoSQUID image of this region using AC bottom gate contrast is presented in Fig. 7.5B. This magnetic image was taken in the same phase in which we observe magnetic hysteresis, as presented in Fig. 7.4E. Clearly the system is quite magnetized; we also see evidence of internal disorder, blueberry pot likely corresponding to bubbles between layers of the heterostructure. We can park the SQUID over a corner of the device and extract a density- and displacement field-tuned phase diagram of the magnetic field generated by the magnetization of the device; this is presented in Fig. 7.5C.

Electronic transport data of the same region is presented in Fig. 7.5D. The spin magnet has only a weak impact on electronic transport, but the valley ferromagnet couples extremely strongly to electrical resistance. The system also supports a pair of superconductors, including a spin-polarized one; these phases are subjects ofcontinued study. Capacitance data over the same region of phase space is presented in Fig. 7.5E. ABC trilayer graphene is the first atomic crystal known to support purely orbital magnetism. Other related systems have since been discovered to host similar phenomena, including bilayer graphene. Our understanding of these magnetic phases is very far from complete, and we expect to encounter more surprises as our magnetic imaging campaign on this class of materials continues. The first systems with nonzero Chern numbers to be discovered were systems with quantum Hall effects. Quantum Hall insulators behave a lot like Chern magnets but are generally realized at much higher magnetic fields, and Berry curvature in these systems comes from the applied magnetic field, not from band structure. The fact that resistance in these materials is an intrinsic property and not an extrinsic one had implications for metrology that were immediately obvious to the earliest researchers that encountered the phenomenon. All of these devices have resistances that depend only on fundamental physical constants, so a resistance standard composed of these materials need not obey any particular geometric constraints, and can thus be easily replicated. The case for quantum Hall resistance standards was strong enough for the the National Institute for Standards and Technology to rapidly adopt them, and today the Ohm is defined by a graphene quantum Hall resistance standard at NIST. There are some downsides to the quantum Hall resistance standard. The modern voltage standard is a superconducting integrated circuit known as the Josephson voltage standard; it uses Shapiro steps to relate the absolute size of a set of voltage steps to a frequency standard. Because the voltage standard and resistance standard are independently fixed to physical phenomena, current standards are necessarily defined by the relationship between these two different standards. Unfortunately, the superconducting integrated circuits used as Josephson voltage standards must be operated in very low ambient magnetic field, because large magnetic fields destroy superconductivity.

This makes them incompatible with the graphene quantum Hall resistance standard, which must operate in large magnetic fields, generally B > 5T. This is a surmountable problem- in practice it is handled by storing the two standards in different cryostats, or with significant magnetic shielding between them- but the significant distance separating the standards reduces the precision with which the current standard can be defined with respect to our current resistance and voltage standards. One possible way to resolve this conflict is to replace the quantum Hall resistance standard with a Chern magnet resistance standard. Chern magnets show quantized anomalous Hall effects at low or zero magnetic field, meaning they can be installed in very close proximity to Josephson voltage standards in calibration cryostats. Unfortunately, doped topological insulators have such small band gaps that even at the base temperatures of dilution fridges, there is enough thermal activation of electrons into the bulk to limit the precision of quantization of the quantized anomalous Hall effect in these systems. This made the class of Chern magnets discovered in 2013 unsuitable as replacements for the graphene quantum Hall resistance standard. Since intrinsic Chern magnets have now been discovered, and are observed to have band gaps considerably exceeding those of doped topological insulators, it might make sense to replace the graphene quantum Hall resistance standard with an intrinsic Chern magnet resistance standard. The ease of replication of the fabrication process of MoTe2/WSe2 makes that material particularly intriguing as a candidate material for a new resistance standard, but over the past few years new intrinsic Chern magnets have been discovered almost every year, so we may soon be discussing much better materials for this application. In any case, it seems possible and perhaps even likely that Chern magnets will supplant quantum Hall systems as resistance standards in the near future.For decades, magnetic memories dominated information storage technology. Magnetic storage media are robust, do not require continuous access to power, survive high temperatures and extreme radiation environments, and are relatively cheap to manufacture. Hard drives, cassette tapes, floppy disks, and other legacy technologies leveraged the many advantages of magnetic information storage to fuel an explosion in affordable information storage, facilitating mass market access to movies, music, and personal computing.

Many of these technologies were in widespread use until quite recently . Since the heyday of these technologies, however, nursery pots magnetic information storage has fallen out of favor, for one simple reason: magnetic bits cannot be easily written electronically. Legacy mag- netic storage media address magnetic bits mechanically, which limits their maximum speed; modern flash memories can access data much faster precisely because each bit can be written and read electronically. Of course, that fact didn’t take away the many advantages of magnetic memories, and magnetic memories still persist in a variety of niche applications that depend particularly strongly on one of these advantages. Many computers destined to spend their lives in space still use hard drives, and sensors designed to operate over a wide range of temperatures and with intermittent access to power often use non-volatile magnetic memories as well. This has led researchers to search for phenomena and device architectures that allow magnetic order to be switched either with electrical currents or electrostatic gates. Until recently, the best technology available capable of electronic switching of magnetism used spin-orbit torques. In a spin-orbit torque device, current through a system with a strong spin Hall effect pumps spin into a separate magnet, which is eventually inverted by the torque exerted by those spins. This technology has matured considerably over the past few years, producing a cascade of new records for low current density magnetic switching and even a few consumer products in the memory market. The exotic orbital magnet in twisted bilayer graphene was found to be switchable with extremely small pulses of current, and the resulting current-switchable magnetic bits displaced previouslyrealized spin-orbit torque devices as the ultimate limit in low-current control of magnetism. A flurry of theoretical investigation of these systems followed, dedicated primarily to identifying and generalizing the mechanism underlying current control of magnetism in these systems. A few years later, AB-MoTe2/WSe2 joined twisted bilayer graphene, with a similarly small magnetic switching current. In the intervening time, a new phenomenon had been observed- switching of a Chern magnet with an electrostatic gate, in twisted monolayer/bilayer graphene. All of these phenomena represent newly discovered and now more or less well understood mechanisms for controlling magnetic bits electronically, and by the performance metrics used in the literature they reign supreme. Several electronic switching phenomena known in intrinsic Chern magnets are summarized in Fig. 8.3. Chern magnets differ from the magnetic materials used in more traditional magnetic memories in a wide variety of intriguing ways other than their electronic switch ability. Chern magnets are not metals and thus don’t have the same limitations as metallic magnetic memories. For example, the resistance of a Chern magnet is independent of its size, depending only on fundamental physical constants. This makes the resistance of a Chern magnet completely insensitive to miniaturization. Dissipation does occur in Chern magnets, but it occurs only at the contacts to the Chern magnet, so once electrons enter the crystal they can undergo very long range transport completely free of dissipation. Chern magnets are atomically thin in the out-of-plane direction, and of course if they are separated by insulators they can easily be stacked to increase magnetic bit density. Chern magnets are two dimensional materials, and two dimensional materials already have small radiation cross-sections relative to three dimensional crystals like silicon, but the conduction path through a Chern magnet is both one dimensional and topologically protected, so it is overwhelmingly likely that Chern magnet memories would be even more radiation hard than the thin semiconducting films that form the current state of the art. All of these ideas make Chern magnets interesting candidates as substrates for magnetic memories of the distant future. Of course none of these ideas have been implemented in technologies yet, and that is because intrinsic Chern magnets have only been realized at fairly low temperatures . All of the magnetic memory applications we’ve discussed depend critically on the discovery of intrinsic Chern magnets at considerably higher temperatures, and ideally room temperature. The Chern number is just a property of a band and does not come with an energy scale, so there is no reason to expect to encounter Chern bands only at low temperatures. Indeed, bands with finite Chern numbers have been shown to support quantized Hall effects in graphene quantum Hall devices at room temperature and high magnetic fields, as illustrated in Fig. 8.5A,B. The energy scale in a Chern magnet is set by the band gap produced by magnetic interactions. So if we’d like to know what the maximum temperature at which we can expect to find Chern magnets is, we need tothink about the energy scales of known magnets. Magnetism is an interaction-driven electronic phase, and interaction-driven phases almost always melt at sufficiently high temperatures. However, among interaction-driven electronic phases ferromagnetism is particularly stable. Many common transition metals, including iron, cobalt, and nickel, support ferromagnetism into the range 600-1200 K, and all of these have found applications in a variety of electronic technologies as a result. These are of course all three dimensional crystals, and Chern magnets are two dimensional crystals. So the next question we can ask is: do two dimensional magnets exist with Curie temperatures as high as room temperature? The answer turns out to be yes, as illustrated in Fig. 8.5C,D. This magnetic system appears not to be a Chern magnet, unfortunately, but the point is that there is nothing in particular stopping a Chern magnet with a Curie temperature above 300 K from existing.

Systems like chromium iodide have properties that are easy to understand in the context of the models we have so far discussed: strong on site interactions and exchange interactions produce full spin polarization, an interaction-driven band gap, and aligned magnetic moments within a single layer. As a result, these systems are electrical insulators. They support magnetic domain dynamics, and there is a temperature TC above which they cease to be magnetized , although they remain insulators far above that temperature. Extremely weak out-of-plane bonds produce highly anisotropic cleavage planes and make it relatively easy to prepare atomically thin crystals mechanically. As in other systems, this does not mean we will always be studying monolayers of the material. Bilayers, trilayers, four-layer crystals, and even thicker flakes can all have properties that differ significantly from those of a monolayer, often for reasons that we can understand, and CrI3 is no exception. Although it isn’t particularly relevant to the physics of magnetism, it’s worth mentioning that all of the chromium halides are highly unstable compounds, and decompose in a matter of seconds when exposed to air or moisture. These materials are difficult to study under normal circumstances, but two dimensional crystalline samples can be prepared inside of an inert-atmosphere glovebox. They can also be sandwiched, plant pot with drainage or ‘encapsulated,’ between other two dimensional crystals. Two dimensional crystals are so flat that this process produces an air- and water-proof barrier and protects the encapsulated crystal from degradation in atmosphere, facilitating easy measurements with tools like the nanoSQUID.

The crystalline structure of CrI3, projected onto a two-dimensional crystal, is visible in Fig. 2.6A. Unlike graphene, CrI3 has two different kinds of atoms in its unit cell; the chromium atoms are responsible for the magnetic moments producing magnetism. CrI3 has fairly strong spin-orbit coupling, and thus strong Ising anisotropy, with magnetic moments pointing out-of-plane . Most of the other chromium halides also support magnetic order, although the precise nature of each of their ground states differs somewhat. Both CrI3 and CrBr3 have ferromagnetic in-plane interactions and strong Ising anisotropy, but CrI3 has antiferromagnetic out-of-plane interactions, meaning that in the magnetic ground state of the crystal adjacent layers have their spins antialigned . Interestingly, CrCl3 also seems to have ferromagnetic in-plane interactions, but it is likely that it is not an Ising or easy-axis magnet, and instead has its spins pointed in the in-plane direction and thus free to rotate. It is evidently the case that although these systems are structurally very similar and all have strong spin-orbit coupling, their magnetic interactions and magnetocrystalline anisotropies vary wildly in response to modest differences in their electronic structure. As a result of all of the arguments discussed previously in this chapter, a CrI3 monolayer has finite magnetization even in the absence of an applied magnetic field, and its magnetic order experiences hysteresis in response to variations in the applied magnetic field, as illustrated in Fig. 2.6D . Antiferromagnetic interactions between adjacent layers in CrI3 mix in an interesting factor that can be easily understood: flakes with an even number of layers have no net magnetization in the absence of an applied magnetic field, but develop finite magnetization at higher magnetic fields as the applied magnetic field overwhelms interlayer interactions and realigns each layer in turn with the ambient magnetic field .

Layer realignments are close analogues of magnetic phase transitions we have already discussed, and they support magnetic hysteresis as well. We will be studying the magnetic phase transition highlighted in yellow with the nanoSQUID microscope. An optical microscope image of a large four-layer CrI3 sample and a much smaller CrI3 monolayer is shown in Fig. 2.6F; this sample has been encapsulated in hBN for protection in atmosphere. We will use this system to get a taste of what the nanoSQUID microscope is capable of. We will use the nanoSQUID to image the region outlined with a white box in Fig. 2.6F. We will be imaging magnetic order across the magnetic phase transition highlighted in yellow in Fig. 2.6E, starting at B = 720 mT and thus in a state in which the four-layer CrI3 sample has finite magnetization and ending at B = 540 mT, in a state in which the four-layer CrI3 sample has no net magnetization. We thus expect the four-layer CrI3 flake to have a finite net magnetization in the first image, and we expect an antiferromagnetic domain with no net magnetization to consume the magnetized region by the final image. The nanoSQUID sensor used to generate these images was about 80 nm in diameter, and was about 100 nm from the surface of the device, producing an imaging resolution of about 100 nm. A characterization of the SQUID used in this imaging campaign is available in Fig. 1.7. The fully magnetized state can be seen in Fig. 2.7A. The magnetic fields generated by the four-layer crystal are comparable to those emitted by the smaller monolayer, at right. In both flakes, the magnetic order is riven with linear defects, which we attribute to wrinkles or cracks in the two dimensional crystals. We can see in Fig. 2.7B that as we decrease the magnetic field, antiferromagnetic domains spread in from the edges of the flake, destroying the magnetization nearthe edges of the two dimensional crystal.

This process continues in Fig. 2.7C, but it is clear that the linear defects present in the magnetic order stop and redirect ferromagnet/anti-ferromagnet domain walls. These defects protect a small patch of magnetization at the center of the flake as the magnetic field continues to decrease in Fig. 2.7D. In Fig. 2.7E, even this internal patch of magnetized material is overwhelmed, and the entire four-layer flake has completed its phase transition to antiferromagnetic order. The monolayer remains fully magnetized. Chromium iodide is a very simple magnetic system, at least at the level of its macroscopic magnetic properties, but there are still a few conclusions we can draw from our nanoSQUID imaging campaign. First, although it is true that CrI3 supports magnetic hysteresis, it does not behave as a single macrospin, instead supporting rich domain dynamics dominated by internal structural disorder. For this reason we cannot expect to learn anything about the energy scale of magnetoelectric anisotropy from the coercive field. This puts CrI3 in a very large class of magnets that includes almost all large polycrystalline samples of transition metal magnets. We will later on discuss several magnets for which the macrospin approximation is more or less valid. Second, it is apparently the case that magnetic domains cost the least energy to nucleate near the edges of the sample. This isn’t surprising, since this region of the crystal experiences the weakest exchange interactions because the nucleated domain is not completely surrounded by the metastable magnetization state, but it is a nice sanity check for our understanding of these systems. Finally, growing blueberries in pots the fact that regions of high disorder remain highly magnetized even in the antiferromagnetic ground state may provide a hint towards the nature of internal disorder in this system. Other experiments have shown that regions of high strain in CrI3 become highly magnetized, so it could be that these one dimensional defects are wrinkles in the two dimensional crystal. We have thus used the nanoSQUID microscope to image magnetic domain dynamics in a two dimensional chromium iodide crystal with approximately 100 nm resolution at magnetic fields as high as 720 mT. This system is a relatively simple one, an uncomplicated magnetic insulator, without the physical phenomena that will form the scientific focus of this thesis. I think it’s useful to illustrate the capabilites of the nanoSQUID microscopy technique under ideal circumstances, i.e. in a system with high magnetization and strong internal disorder, but also to distinguish the physics of magnetism from the physics of Berry curvature, orbital magnetism, and Chern numbers. These phenomenatogether will form the main focus of this thesis, and we will discuss all of them in the next chapter. Let us take a closer look at the crystalline structure of graphene, armed with the knowledge we have gained about spontaneously broken symmetries and magnetism. We have already discussed how each atomic orbital of each atom contributes a band to the crystalline band structure, although of course the orbitals hybridize to produce new, decocalized quantum states. And of course we know that because either spin species can occupy a band, in reality each band can accommodate two electrons. It contains two different atoms, but those atoms have almost precisely the same environment- in fact, the only difference between them is the fact that the distribution of atoms with which they are surrounded is inverted. We can say that graphene has inversion symmetry, and furthermore that there exists a pair of different atoms that are swapped by inversion in real space and time reversal symmetry in momentum space.

Because these two atoms have almost exactly the same environment, they produce bands that are also strikingly similar. In particular, they produce pairs of bands that are related both by inversion symmetry and time reversal symmetry. These are not the same bands, but they do have precisely the same density of states at every energy. As a result, these bands are in practice energetically degenerate. This means that all of the phenomena associated with spontaneously broken symmetry can apply to this pair of bands, which together form a new degree of freedom. For reasons having to do with the shape of graphene’s band structure, we often call this new degree of freedom the ‘valley degeneracy.’ We have already seen how spin degeneracy produced magnetism. This is now joined by the valley degeneracy, so in graphene we can expect to encounter both of these twofold degeneracies, together producing a fourfold degeneracy. Every graphene band can thus accommodate four electrons. There is one more important point to make about the valley degeneracy. I mentioned in passing that these two states can be related to each other by time reversal symmetry in momentum space. In practice, this means that if the function describing one valley’s band structure is E, then we can immediately say that the other valley’s band structure is E. Suppose we found a set of conditions under which one of the bands in a graphene allotrope had finite angular momentum in its ground state. This is actually not so uncommon, so far as physical phenomena go- many atomic orbitals have finite angular momentum, and in condensed matter systems they can hybridize to form delocalized bands with finite angular momentum. The above condition tells us that we can then expect to find another band with equal energies and equal and opposite angular momenta, and thus magnetic moments. These are precisely the conditions satisfied by the electron spin degree of freedom that allowed it to produce magnetism! So, under these circumstances- i.e., assuming we can find conditions under which a band in graphene has finite anguluar momentum in its ground state, strong electronic interactions, and a flat-bottomed or flat band- we can expect to find a new form of magnetism, dubbed by theorists ‘orbital magnetism,’ wherein center of mass angular momentum coupling to the electron charge is responsible for the magnetic moment, instead of electron spin. There are many important corollaries of the arguments we’ve just discussed, and many more of them will appear later, but there are a few I’d like to focus some special attention on. We discussed earlier how the orientation of electron spin generally does not interact with electronic band structure unless we invoke relativistic effects in the form of spin-orbit coupling. Carbon atoms are extremely light, and as a result the energy scale of spin-orbit coupling in graphene is quite low. For this reason condensed matter researchers in the distant past na¨ıvely expected not to find magnetic hysteresis ingraphene systems. The type of magnet proposed here does not invoke spin-orbit coupling; in fact, it does not even invoke spin. Instead, the two symmetry-broken states are themselves electronic bands that live on the crystal, and they differ from each other in both momentum space and real space.

Dietary interventions require the incorporation of foods into an individual’s eating pattern, which may present a number of challenges. One is the creation of boredom with eating the same food on a regular basis. Second is that the caloric load of the test nut or berry may displace the intake of other nutrient-dense foods. These factors may make compliance for the entire study duration an issue, particularly if the intervention is weeks or months in duration. A third challenge involves compliance. In berry research studies, compliance is often not reported, or the reported range of intake is so variable that it is hard to discern the significance of the results. In addition to compliance, dietary patterns are an important consideration needed for the interpretation of results because individuals do not eat a single food in the absence of other foods. Background or habitual intake is often not addressed in nutritional trials. The potential variability in habitual dietary intake of participants is often a confounding factor in nutrition research. Dietary assessment methods, with 24-h recalls, 3-d food records, and food frequency questionnaires, gallon nursery pot all have limitations. These subjective measures may also not accurately capture the potential for nutrient-nutrient interactions that may alter polyphenolic or other bioactive components attributed to nut and berry consumption. Further complicating this issue is the observation that study designs utilizing longer-term interventions or that require the intake of a large amount of the test food are more likely to result in over reporting food intake due to fear that participants may be dismissed from the intervention.

Innovations in dietary assessment methodology using “smart” eyeglasses or other image-based technologies have been proposed to address this issue. Assessing the relationship between the intake of nutrients and bio-actives from a whole food product to physiologic responses is difficult, as a multitude of processes are affected, including regulation of vascular function, provision of oxidant defense, and changes in gut microbiome profiles and subsequent output of secondary metabolites. Additionally, bio-actives from nuts and berries can interact with each other as well as other dietary components to alter bio-availability and health-promoting properties . For example, intake of dietary fats in conjunction with berries has been demonstrated to increase carotenoid bio-availability.Results could also be confounded by dietary changes made by participants in addition to incorporation of the test nut or berry. Habitual dietary intake is often measured through food frequency questionnaires or repeated 24-h dietary recalls. However, these subjective measures may not accurately capture the potential for nutrient-nutrient interactions that may alter polyphenolic or other bio-active components attributed to nut and berry consumption. Further complicating this issue is the observation that study designs utilizing longer-term interventions or that require the intake of a large amount of the test food are more likely to result in over reporting food intake due to fear that participants may be dismissed from the intervention. Expanding the scope of populations to be studied is another key area for future research. Most clinical trials using nuts and berries have been conducted in middle-aged or older Caucasian adults with one or more cardiometabolic risk factors. Whether these results extend to other population groups is either inferred or unknown. Future research would benefit from extending the study populations to include those from other racial and ethnic groups.

This is particularly important in order to address the current NIH research initiative in precision nutrition and health, the “Nutrition for Precision Health powered by the All of Us Research Program”. The inclusion of biological females in clinical nutrition trials is imperative, yet the current literature includes predominantly male participants. Because many studies on nuts and berries focus on cardiometabolic outcomes, the unique aspects of female physiology must be considered. For example, vascular function fluctuates with the phase of the menstrual cycle, which has largely been ignored in most past studies. More studies are also needed in young children as well as in young adults up to about the age of 40. A pilot study reported a correlation between blueberry supplementation and acute positive effects on memory and executive function in 7- to 10-y old children. A large study among pregnant women-infant dyads reported positive protective neuropsychological effects on long-term cognitive development in children at 1, 5, and 8 y of age when nuts were consumed during gestation. Finally, translation of research results is challenging when considering socioeconomic status , particularly when food items are not accessible or affordable. Barriers to participation in clinical research studies among those of low SES include a low interest in clinical trials, inefficient or inadequate explanation of the study in culturally appropriate terms, participants’ distrust of biomedical research, and participant burden, including lack of transportation or the inability to prioritize participation in research over work obligations.Like many other dietary studies, research on nuts and berry studies often use acute studies evaluating postprandial effects. However, either a lack of or successful demonstration of benefits does not necessarily predict a similar outcome over extended periods of intake. Depending on the outcome measure, detectable effects may take weeks or months for the intervention. Only a limited number of studies exist assessing the impact of nut or berry intake on the incidence or severity of diseases or metabolic dysfunction, which require durations of months or years.

Precision nutrition evaluates an individual’s unique biological characteristics such as genotype and phenotype, including DNA expression, influences of the gut microbiome, and metabolic response to specific foods or dietary patterns, as well as dietary habits and external factors influencing outcomes such as social determinants of health, to determine the most effective dietary strategies to improve health and prevent disease. Understanding the sources of inter individual variability that contribute to metabolic heterogeneity and applying mathematical modeling and computational algorithms will be essential to refining dietary recommendations. Several recent publications comprehensively review research gaps and study design considerations in the field of precision nutrition and specifically concerning phenolic-rich plant foods. Precision nutrition will lead to important discoveries pertaining to inter individual responsiveness to the intake of nuts and berries. Ultimately, this information can be applied via targeted recommendations to individuals and groups for achievable and sustainable dietary intake of nuts and berries to promote optimal health. The incorporation of bio-monitoring technologies into study designs may also be used for precision nutrition. Current and emerging mobile devices can provide continuous data collection in free-living populations with minimal participant burden. The study of nuts and berries would be enhanced with the use of devices that can capture real-time physiological outputs at home that reflect normal living conditions. Further collaborative efforts in the fields of bioengineering and artificial intelligence hold promise for advancing the understanding of benefits from nuts or berries. An emerging personal bio-monitoring technology is the Precision Health Toilet, which collects and evaluates human urine and stool, which are then analyzed using artificial intelligence to determine flow rate and volume of urine, as well as fecal analysis via the Bristol Stool Scale. A second type of toilet seat, the Heart Seat, has recently been approved by the US Food and Drug Administration for home use to monitor heart rate and oxygen saturation, greenhouse ABS snap clamp with future plans to add sensors that monitor systolic and diastolic blood pressure. Assessment of metabolites in the excreta seems like a feasible goal for future development, which may be useful, for example in the detection of urinary and fecal metabolites that can reflect the metabolism of ellagic acid to urolithins and of -epicatechin to γ-valerolactone. A third example is an ingestible capsule containing a biological photosensor that can detect gut inflammation. Bioluminescence can be monitored from bacteria that have been engineered to illuminate when they come into contact with a molecule for which they have been coded, such as urolithins from berries or lipid-sensitive metabolites from nuts. Finally, another type of ingestible capsule has recently been detailed that collects samples from multiple regions of the human intestinal tract during normal digestion. This device has been used to explore the role of the gut microbiome in physiology and disease, with novel findings that intestinal and stool metabolomes differ dramatically.

The ability of nut or berry intake to alter such metabolomes, and their association with changes in physiological function and health outcomes, would be an interesting area for future research. Although these technologies are still in their infancy, they have promise to further precision nutrition research efforts on nuts and berries. Research addressing the issue of “responders” compared with “nonresponders” is important in understanding the metabolic discrepancies in many studies on nuts and berries. For example, platelet aggregation phenotypes can vary significantly by individual responsiveness to oxylipins, bioactive lipid mediators derived from polyunsaturated fatty acids present in nuts as well as in extra virgin olive oil. Variations in circulating metabolites and microvascular function following the intake of freeze-dried strawberry powder have been reported. Those individuals producing increased nitrate and nitrite levels showed favorable changes in function whereas those showing no change in nitrate or nitrite levels did not . Another example is illustrated by a recent letter in response to a systematic review of almond intake and inflammatory biomarkers. The letter notes that while the review included amounts of almonds ranging from 10 to 113 g/d, favorable responses only occurred at intake of <60 g/d. Further, the authors note that although the review reports beneficial effects of almond intake on reduction in C-reactive protein and interleukin-6, subgroup analyses showed that the effects on these 2 outcomes were not significant among those with obesity or who were rated as unhealthy prior to the intervention. Characterizing participants according to precision nutrition, including the use of genetic phenotyping to identify target genes that may result in “responders” and “non-responders” prior to enrollment may be helpful for clinical trials but does not reflect responses in a free-living population. Furthermore, in addition to physiological variations, sociobehavioral differences among individuals that may modulate responses to berries and nuts must also considered. Nonetheless, innovative precision nutrition models that can identify inter individual differences would be useful in defining mechanisms of action and potentially who would benefit the most from regular nut or berry consumption. Plasma and serum concentrations are useful to identify the bio-availability and bio-efficacy of key nutrients and phytochemicals found in nuts and berries [133]. Some compounds, such as small molecular weight polyphenols, are first absorbed in their native state in the small intestine. Other polyphenols can be bio-transformed via the host microbiota to a second set of compounds that are subsequently absorbed and confer additional bio-activity beyond that obtained from the parent molecules. Monitoring both host and microbial metabolites in the blood and urine, and those that may accumulate in tissues of interest such as the liver and gastrointestinal epithelium, among other tissues, would be useful in understanding the dynamics of nut and berry bio-activity and specific association with site of actions. Broader application of orthogonal approaches that combine untargeted with targeted metabolomic platforms and combined with the use of advanced informatics will support new understanding about the absorption, distribution, metabolism, and excretion of compounds found in nuts and berries. For example, the UC Davis West Coast Metabolomics Center conducts both targeted and untargeted assays that assess plasma microbial metabolites using a biogenic amine panel that identifies and quantifies acylcarnitines, trimethylamine N-oxide, cholines, betaines, nucleotides and nucleosides, methylated and acetylated amines, di- and oligo-peptides, and a number of microbially modified food-derived metabolites. Some interindividual differences in response to nut or berry intake have been attributed to the composition of the gut microbiome. For example, ellagitannins are polyphenolic compounds present in strawberries, raspberries, and walnuts that are metabolized by gut bacteria into an array of urolithins. The production of urolithins relies on the capacity of specific microbes, Gordonibacter pamelaeae and Gordonibacterurolithinfaciens. Urolithins may decrease symptoms of chronic metabolic diseases, including inflammation and dyslipidemia. Following a single intake of red raspberries, individuals with prediabetes and insulin resistance had lower concentrations of circulating urolithins compared to levels found in those who were metabolically healthy, a result related to gut microbiome composition. In the same population, consuming red raspberries for 4 wk improved hepatic insulin resistance and total and LDL cholesterol in the prediabetes group, and the effects were related to decreased R. gnavus and increased E. eligins.