Month: October 2024

Future research projects could also benefit from a larger sample size to analyze farm-level differences and whole genome sequencing of positive isolates to ascertain any genetic relatedness between livestock species or between farms, as well as presence of antimicrobial resistance genes. In Chapter 2, the risk map determined regions at highest risk for contact between feral pigs and outdoor-raised pigs, and these areas will be important to target surveillance and outreach, in the case of future disease transmission between these two swine groups. This project can be expanded nationwide to create awareness of high-risk contact and potential disease transmission areas, to protect both public health and agriculture in the US. For example, this risk map could be used to plan surveillance programs to prevent transmission of imported diseases to the US, such as African Swine Fever that was recently detected in the Dominican Republic, or prevent the spread of reemerging diseases like pseudorabies, which was detected in feral pig populations in Mendocino County, CA in 2015. A 2015 report by the European Food Safety Authority concluded that surveillance programs will be key in preventing the introduction and spread of ASF in North America and other naïve countries. Additionally, if feral or outdoor-based domestic swine disease location data were readily accessible in the US, additional risk maps could predict the spread of specific pathogens. Disease risk maps are useful to support decision making for agencies focused on wildlife management and conservation as well as animal and public health.

Currently, USDA collects data through surveys for swine operations only in the top 16 producing pork states in the US, and California is not included, raspberry grow in pots which reduces available data for disease risk models. However, the swine NAHMS is due to be conducted again in 2021 with a larger focus on small-scale swine operators. Another avenue for future research entails using covariate rasters from specific years or decades to predict the distribution of a feral pigs. Few SDM or MaxEnt comparison projects have compared temporality of covariate rasters for model prediction accuracy. Shifts in weather patterns, as well as the dynamics of large fires, will most likely be exacerbated by climate change and will affect wildlife movements and locations in the future, which may indicate the importance of choosing temporally-specific variables for model building. Additionally, if climate change and wildfires accelerate in California, current static rasters may become inaccurate in predicting future suitable habitat. California’s annual precipitation levels fluctuate between drought and excessive precipitation associated with El Niño and La Niña events. Climatologists predict volatility of rainfall patterns and temperature for California, which may affect suitable habitat for wild mammals such as feral pigs, and emphasizes the need for dynamic climate rasters. Both the MaxEnt model and risk map in Chapter 2 are limited because they are static maps that used fixed layers as their foundation; consequently, they do not incorporate dynamic events. Future species distribution models could incorporate temporal environmental patterns into models, due to dynamic changes over time, especially in regions like California where climate change and large wildfires can affect the distribution of certain species.

For instance, Snow et al used temporally dynamic prediction models and examined three decades from 1982 to 2012 to report evidence of feral pig expansion due to climate change. Also, feral pigs in Canada build “pigloos” to be able to survive the harsh Canadian winters, expanding their habitat northward. A 2015 study on climate change affecting wild boars in Europe also reported that milder winters allow for expanded abundance of these mammals.. Additionally, feral pigs may migrate seasonally due to food and water availability in California, therefore future projects could incorporate migration patterns and develop risk maps for specific time periods as Lee et al conducted with waterfowl species, although these data are not available yet for most predictors in California. Building real time dynamic risk maps that incorporate remote sensing data, such as satellite information, could be the next step in predicting high-risk disease transmission areas, as built previously for avian influenza by the California Waterfowl tracker. However, tracking birds may be easier than collaring feral pigs. Chapter 3 combined aspects of Chapters 1 and 2, by determining the prevalence of STEC in feral pigs that reside near domestic swine raised outdoors and predicted possible areas of contact between these two swine populations. Both multilevel logistic models in Chapters 1 and 3 identified outdoor-raised livestock with access to wild areas, such as wetlands or forests, as a significant risk factor for the presence of STEC in samples. One possible pathway for shared pathogens in wild areas may be wildlife contaminating food or water, which are then consumed by livestock. Additionally, Chapter 3 results could be improved upon by using WGS to analyze relatedness between feral pig and domestic pig samples. Although the pathway for pathogen spillover can be bi-directional and temporality may be unclear, identifying clusters of shared indicator pathogens is an important next step in analyzing disease risks from feral pigs in California and nationwide. Chapters 2 and 3 analyzed feral pig populations and their risk to farms that raised domestic swine outdoors.

In Chapter 3, 45.45% of survey respondents observed feral pig presence on their farm, with 36.36% stating that feral pigs had direct contact with their domestic pigs in pastures, pens or barns. These results match the risk map from Chapter 2, which overlapped predicted suitable habitat for feral pigs and OPO locations and showed that 49.18% of the 305 OPO identified in California overlapped with suitable feral pig habitat, indicating that spillover of an emerging or transboundary disease is possible, given the correct drivers. We know human or livestock encroaching into tropical forests are drivers for zoonotic diseases such as COVID-19 or Nipah Virus, usually with an intermediate host such as bats. Although emerging zoonotic diseases in many cases originate at the interface of wildlife-livestock-humans, the US is not considered a hot spot for zoonotic disease outbreaks according to Daszak and the EcoHealth Alliance , yet zoonotic pathogen outbreaks can still occur. Possible drivers of disease spillover in the US between feral and domestic pigs raised outdoor include density of animals, shared natural areas between domestic and wildlife and increasing contact between these two growing swine populations. As the number of DSSF farms continues to grow, continued evaluation of risk factors and agricultural management practices that are unique to these small operations will identify additional risk mitigation strategies and develop extension outreach materials to keep food safe from farm to fork and protect California’s agricultural economy. Additionally, as the two parallel trends nationwide of expanding feral pig populations and outdoor-based domestic swine continues, disease surveillance of feral pigs located near outdoor-raised domestic swine is key in preventing transmission of emerging or reemerging pathogens in the future.Encouraged by consumer preference for local foods and willingness to pay more than double the price for local products , both large and small-scale farming is increasingly turning to direct markets through you-pick operations, farm stands, farmers’ markets, 30 planter pot and Community Supported Agriculture . Currently, nearly 7% of U.S. farms are involved with direct marketing with an 8% increase in sales since 2007 . The federal government began tracking the number of farmers markets in 1994 and CSAs in 2007. The number of farmers’ markets has more than doubled in the past decade, rising to 8284 in 2014 from 3706 in 2004 . Local food is also increasingly promoted through food hubs and sales to restaurants and grocery stores . Numerous practitioners of planning, land-use management, policy and economic development encourage local food programming . ‘Buy Local’ campaigns have been codified in every state with branding and are buoyed through formal and informal economic development support in comprehensive planning documents. With its growing popularity, the local food movement is expected to change both consumers and farmers. The movement often emphasizes ‘weak social ties’ created through food as bringing together novel constituents for political persuasion which combines purchasing power with the ‘soft power’ of a social movement. Where markets should emphasize the highest financial returns, economic sociologists have noted their non-economic logic , terming them ‘embedded’ in both geographies and social value systems . Hinrichs states that part of what direct marketing producers sell is “social connection. Local embeddedness itself then becomes some of the value added in the farmers’ market experience” .

Embeddedness describes the noneconomic logic of how markets yoke together two separate geographies through shared economies and social values . This research asks: what is the extent and orientation of embeddedness in the local food system? First, a literature review demonstrates the current understanding in the field and the need for new methodologies to help test theories of embeddedness within local food systems. Namely, the local food movement is expected to transmit values through proximate economic and social networks. But which communities are connected, and across which local marketing strategies? In response to this question, I pilot a method for mapping the local food system socially and spatially. Document review and program director interviews help to verify and explain the findings as well as their consequences for food systems planning and economic development.Local food activists have reconceptualized food supply chains as a means of spatially distributing social values by leveraging economic capital. The values encompassed by the food system are exemplified by the over 300 different labelling schemes which promote fair labor, sustainable land-use, and animal welfare practices to name a few . Yet, only a few global corporations control distribution, connecting consumers to producers . This bottleneck in supply chains reveals an important lever for altering geographies and financing shared value systems. Renting et al. asserts that shortening the supply chain by decreasing the number of intermediaries involved in production, distribution, processing and purchasing should clarify the values and geographies involved. In sum, geographically explicit, personal relationships between producers and consumers are expected to raise awareness about social, economic, and environmental effects of food consumption by tightening feedback loops which concentrate economic and social capital toward values-based goals . Hinrichs cautions that even the shortest supply chains, such as direct marketing from farms to consumers, can have varied power structures. Namely, farmers often travel to cities for farmers’ markets, while consumers travel to farms in which they own a share of the commodities produced in the CSA model. Hinrichs asserts that while both supply chain typologies emphasize direct, local consumer relationships with farmers, the resulting geo-social embeddedness of the network and the values it promotes will fundamentally differ. In addition, local values-based supply chains are not limited to direct-marketing. Nearly 50,000 farms in 2012 sold some or all of their products directly to retail outlets such as restaurants, grocery stores, schools, hospitals, or other businesses that in turn sold to consumers . Intermediaries between farms and consumers can also play important roles in food system-based social change. For example, chefs, like Alice Waters of Chez Panisse in California, are often seen as the forefront of the local food movement where they change consumer demand for certain types of local food. In the process, their search for ingredients resulted in direct contracts with farmers to grow specific products using agroecological methods . Similarly, farm-to-school programming is conceptualized as a means of encouraging healthy eating, transferring farming education to the next generation, and preserving local farming land-uses . Sonnino finds that school food reform in the UK gave small producers access to new income streams while offering students food that is more nutritious. Similar rationales underpin the motivations behind promoting regional food hubs . Planning practitioners have also noted that public procurement anti-hunger efforts that champion local food have had a successful track record of protecting farmland, spurring rural economic development and increasing urban food security in Canada and Belo Horizonte, Brazil . Most importantly, the geo-social embeddedness of food systems may not be driven solely by food purchases. In addition to supplying food, farms serve numerous socio-ecologic functions for urban users and nearby communities . In 2012, over 33,000 farms listed income from agritourism and recreational services such as farm tours, hayrides, school visits, and other activities . A review of the mission and vision statements from 130 nationally accredited farmland preservation agencies notes that ecosystem, social and cultural services are among the top reasons for preserving farmland, ranking far above food supply .

All these findings highlight the need for further investigation to identify high-risk areas for disease spread in the event of a future disease outbreak in California or nationwide. My overall thesis goal entailed evaluating the risk of STEC on DSSF and the risk of disease transmission in the spatial overlap of feral pigs and domestic swine raised outdoors in California. In Chapter 1, we collected fecal samples from cattle, goats, sheep and pigs raised on DSSF to estimate the prevalence of STEC on these unique operations. I used a multilevel logistic regression model to assess the association between risk factors and STEC presence in fecal samples. In Chapter 2, I used a species distribution modeling method Maximum Entropy to predict suitable habitat for feral pigs in California. Then I overlapped this MaxEnt model with the location of over 300 OPO in California to create a risk map that identified areas most at risk for disease transmission between these two growing swine populations. The increasing potential for contact between domestic swine raised outdoors and feral pigs provides an opportunity for the widespread transmission of diseases throughout California, as each pig could serve as a link in the transfer of pathogens between wildlife, livestock and humans. Additionally, the transmission of diseases to domestic pigs raised outside could negatively impact the sustainability of California’s agriculture economy. In Chapter 3, I designed a study to evaluate the prevalence of STEC in six counties at highest risk for contact between feral pigs and OPO, large plastic garden pots based on the risk map built in Chapter 2. I collected fecal samples from both feral pigs and domestic pigs raised outdoors and used a multilevel logistic regression model to assess risk factors associated with the presence of STEC in samples.

The results of these last two chapters fill critical information gaps regarding the epidemiology of STEC harbored in outdoor-raised pigs on DSSF located near suitable feral pig habitat. The increasing number of diversified small-scale farms that raise outdoor-based livestock in the United States , reflects growing consumer demand for sustainably-produced local foods, including animal products such as meat and eggs. California is the top producer of agricultural products in the US and also leads the country in organic food sales, which includes products from DSSF However, there is a lack of science-based information characterizing the risk factors associated with the prevalence of food borne pathogens, such as Shiga toxin-producing Escherichia coli , in livestock raised on DSSF. Diversified farms are most often small-scale and raise a combination of livestock and numerous types of produce or multiple livestock species, with the intent of selling specialized animal products directly to consumers. Some diversified farms integrate livestock and crop production by using their animals to graze crop residues or cover crops before planting a field to fresh produce. Grazing improves soil fertility and provides farm owners with another source of revenue through fiber or meat products. Many consumers perceive produce grown on small-scale farms and/or meat raised on pasture as more “natural” or safer than food grown on large-scale conventional farms or meat animals raised in confinement systems. However, livestock are asymptomatic reservoirs for food borne pathogens and without adequate mitigation strategies, these pathogenic microorganisms may enter into the food supply. Livestock are intermittent shedders of enteric pathogens and shedding may increase under certain conditions, such as during periods of stress , due to certain management practices or seasonally.

Foodborne pathogens survive in the soil for extended periods of time and can spread to humans directly through contact with livestock or indirectly via contaminated food or water.For instance, cattle grazing uphill from a produce field was likely the causative factor for the 2019 E. coli O157:H7 romaine lettuce outbreak. STEC remains one of the top enteric pathogens associated with food borne outbreaks in the US. The top seven STEC O-serogroups that cause the most severe illness in humans have been traced to consumption of produce consumed raw, such as spinach, tomatoes and melons. Fresh produce consumed raw , which has been contaminated by livestock or wildlife feces containing STEC, can become a vehicle for these pathogens to enter the food supply. The aim of this study is to a) describe the unique characteristics of DSSF, b) estimate the prevalence of STEC in livestock raised on DSSF and c) evaluate the association between risk factors and the presence of STEC in livestock raised on DSSF located in California. Farms were visited twice between May 2015 and June 2016, once during each of the following periods: summer/autumn or winter/spring, which reflect California growing seasons and the seasonality of STEC shedding. Livestock species sampled in this study included dairy and beef cattle, dairy and meat goats, pigs and sheep. Sample sizes were calculated using Epitools based on the total number of animals on each farm, with an assumed STEC prevalence of 5% and 10% precision error, stratified by each livestock species. Individual fresh fecal samples were collected from the ground. Samplers wore gloves and placed approximately 50- 100 grams of feces into each sterile whirl-pak bag . Bags were immediately placed into plastic coolers containing ice packs, transported to the laboratory at the end of the sampling day and most samples processed within 24 hrs.

STEC was isolated from fecal samples as described previously with modifications. In brief, 10 grams of fecal material was placed in 90 ml Tryptic Soy Broth and homogenized before and after. Samples were then incubated for 2 hrs at 25°C with 100 rpm agitation, followed by 8 hrs at 42°C with 100 rpm agitation, and held overnight at 6°C, using a Multitron programmable shaking incubator . For detecting E. coli O157:H7, immunomagnetic separation using Dynal anti-E. coli O157 beads was performed on TSB enrichment broths with the automated Dynal Bead Retriever per the manufacturer’s instructions. After incubation and washing, 50 µL of the resuspended beads were plated onto Rainbow agar O157 with novobiocin and tellurite . Fifty µL of the resuspended beads were also plated onto MacConkey II Agar using sorbitol supplemented with potassium tellurite and Cefixime ; plates were streaked for isolation and incubated for 24 hrs at 37°C. Suspect E. coli O157:H7 isolates were confirmed using traditional PCR for the rfbE gene. To detect non-O157 STEC, 1 mL of pre-enrichment broth was incubated in mEHEC selective media for 12 hrs at 42°C followed by plating and incubating on Chromagar STEC . Up to 8 presumptive STEC positive colonies were confirmed for the presence of stx1 and/or stx2 genes by real-time PCR. Confirmed STEC isolates were then characterized for virulence genes using conventional PCR. After PCR testing, one colony from each positive sample was submitted to the Pennsylvania State University E. coli Reference Center to confirm O-serogroups.A 41-question questionnaire, raspberry plant pot consisting of mostly closed-ended questions, was administered to farm owners at the end of the study period. The questionnaire included sections regarding farm demographics, animal health, farm management practices, and water sources . Variables analyzed for model building included risk factors from the farmer questionnaire, sample day factors and variables that were created using known information about each farm, for instance, whether a farm raised multiple types of livestock or if they integrated livestock within produce fields before planting. Variables from the questionnaire included whether farmers allowed different livestock species to share the same barn and if livestock had contact with wild areas . Weather data from the nearest California Irrigation Management Information System weather station within a similar microclimate, provided environmental factors for possible model inclusion . Also, USDA plant hardiness zones, which are based on the average annual minimum winter temperature, were included as a proxy for the many microclimates in California . Only three zones were necessary to categorize our participating farms: zone 7b , 9a and 9b .Descriptive statistics, were calculated for all data. STEC was estimated for the overall study and per livestock species . Generalized linear mixed models were used to assess the association between STEC presence in fecal samples and risk factors. The binary outcome of interest was whether each fecal sample was STEC positive or negative. Univariate analysis assessed the distribution of variables. During bivariate analysis, variables with low variability, small cell sizes , or large standard errors were either modified, collapsed if appropriate, or discarded from model building. Correlation of numeric variables was measured with Spearman’s rank correlation coefficient; those variables that were correlated 0.80 or more were highlighted during the model-building phase to evaluate for multicollinearity issues. To identify possible confounders, each variable was evaluated using a directed acyclic graph and then added to the model to determine whether the variable affected the odds ratios of the other variables by more than 10%. The glmer function was used from the lme4 package in R to build models, with farm added as a random effect to account for possible farm-level clustering effects when analyzing individual samples.

Variance inflation factors identified possible multicollinearity and variables in the model that had a VIF over 5 were assessed for removal. Top models were compared, and a final model chosen based on the lowest Akaike Information Criterion , smallest deviance, relative to the other models. Intraclass correlation . Diagnostic plots from the DHARMa package in R were used to assess the final model and included fitted vs binned residuals, a Q-Q plot and the Kolmogorov-Smirnov test statistic. Odds ratios and 95% confidence intervals were calculated for variables in the final model. All data analysis was performed using R Statistical Software version 1.4.1036 ©.Of the 16 participating farms, two were not included in model building, as their questionnaires were not completed, leaving a total of 502 fecal samples for model building. The mean, median and range of selected numeric variables assessed for inclusion during model building are shown in Table 1.4. Farms in this study ranged from two to 500 acres and had been farming two to 30 years. Stocking rate was calculated by dividing the total number of livestock, excluding poultry, by the total number of farm acres . Selected categorial variables, stratified by whether they were STEC positive or negative are presented in Table 1.5.P -values were reported for chi-square test or Fisher’s Exact test if cell sizes were less than five. For instance, 28.99% of positive samples came from farms that mixed livestock species within a barn, whilst only 15.70% of negative samples came from farms with shared barns . Moreover, 72.46% of positive samples were from farms that allow livestock to have contact with bordering wild areas .This is one of the first studies to describe the unique characteristics of diversified small scale farms in California, while ascertaining significant associations between risk factors and the prevalence of STEC. This study detected STEC on more than half of the enrolled farms and in all the livestock species sampled. Moreover, O-serogroups isolated in this study included ones that cause serve illness in humans, including O157:H7, O26, O103 and O111. Significant risk factors associated with the presence of STEC included the daily maximum temperature, whether multiple livestock species shared a barn, the livestock source of the collected fecal sample, and whether livestock had contact with wild areas.Overall STEC prevalence measured for the 16 farms in this study was 13.62%. Six of the 16 farms had 0% STEC prevalence; however, due to the intermittent shedding of STEC which may be affected by many factors, this result does not necessarily indicate that they are free from STEC. Although STEC prevalence in livestock raised on large farms has been measured frequently in past studies, evaluation of STEC prevalence and associated risk factors estimated on DSSF is less common. However, a study conducted by USDA-APHIS collected fecal samples from dairy cows in 21 states and stratified E. coli O157:H7 prevalence between large dairies and small dairies and reported that small ranches had 29.4% E. coli O157:H7 and large dairies had 53.9% prevalence. Although this USDA-APHIS study indicated that small farms have less E. coli O157:H7 than large farms, the 29.4% prevalence they detected on small dairies is still larger than the 18.18% we identified in dairy cattle. Risk factors for the transmission of food borne pathogens on large farms may be different, especially if they only raise one crop or livestock type, instead of a diversity of species. One of our studies published in 2018 measured a 4.17% STEC prevalence in sheep raised on a mixed crop-livestock organic farm in California, which was lower than the 13.4% prevalence in sheep identified in this current study.

Reliable markers could aid in the decision of when to harvest the grapes. “Optimum” maturity is a judgement call and will ultimately depend on the winemaker’s or grower’s specific goals or preferences. A combination of empirical factors can be utilized including °Brix, total acidity, berry tasting in the mouth for aroma and tannins, seed color, etc. °Brix or total soluble solids by itself may not be the best marker for berry ripening as it appears to be uncoupled from berry maturity by temperature. Phenylpropanoid metabolism, including anthocyanin metabolism, is also highly sensitive to both abiotic and biotic stresses and may not be a good indicator of full maturity. Thus, color may not be a good indicator either. Specific developmental signals from the seed or embryo, such as those involved with auxin and ABA signaling, may provide more reliable markers for berry ripening in diverse environments, but will not be useful in seedless grapes. Aromatic compounds may also be reliable markers but they will need to be generic, developmental markers that are not influenced by the environment. This study revealed many genes that are not reliable markers because they were expressed differently in different environments. One candidate marker that is noteworthy is ATG18G . Its transcript abundance increased and was relatively linear with increasing °Brix and these trends were offset at the two locations relative to their level of putative fruit maturity . ATG18G is required for the autophagy process and maybe important during the fruit ripening phase.

It was found to be a hub gene in a gene subnetwork associated with fruit ripening and chloroplast degradation. Further testing will be required to know if it is essential for fruit ripening and whether its transcript abundanceis influenced by abiotic and biotic stresses in grape berry skins.The ultimate function of a fruit is to produce fully mature seeds in order to reproduce another generation of plants. The ripe berry exhibits multiple traits that signal to other organisms when the fruit is ready for consumption and seed dispersal. In this study, we show that there were large differences in transcript abundance in grape skins in two different locations with different environments, plastic seedling pots confirming our original hypothesis. We also identified a set of DEGs with common profiles in the two locations. The observations made in this study provide lists of such genes and generated a large number of hypotheses to be tested in the future. WGCNA was particularly powerful and enhanced our analyses. The transcript abundance during the late stages of berry ripening was very dynamic and may respond to many of the environmental and developmental factors identified in this study. Functional analysis of the genes and GO enrichment analysis were very useful tools to elucidate these factors. Some of the factors identified were temperature, moisture, light and biotic stress. The results of this study indicated that berries still have a “sense of place” during the late stages of berry ripening. Future studies are required to follow up on these observations. It appears that fruit ripening is very malleable. Manipulation of the canopy may offer a powerful lever to adjust gene expression and berry composition, since these parameters are strongly affected by light and temperature.

The ability of a genotype to produce different phenotypes as a function of environmental cues is known as phenotypic plasticity . Phenotypic plasticity is considered one of the main processes by which plants, as sessile organisms, can face and adapt to the spatio-temporal variation of environmental factors . Grapevine berries are characterized by high phenotypic plasticity and a genotype can present variability within berries, among berriesin a cluster, and among vines . Berry phenotypic traits, such as the content of sugars, acids, phenolic, anthocyanins, and flavor compounds, are the result of cultivar and environmental influences , and often strong G × E interactions . Although grapevine plasticity in response to environmental conditions and viticulture practices may provide advantages related to the adaptation of a cultivar to specific growing conditions, it may also cause irregular ripening and large inter-seasonal fluctuations , which are undesirable characteristics for wine making . Due to its complex nature, the study of phenotypic plasticity is challenging and the mechanisms by which the genes affecting plastic responses operate are poorly characterized . In fact it is often difficult to assess the performance of different phenotypes in different environments . It has been suggested that genetic and epigenetic regulation of gene expression might be at the basis of phenotypic plasticity through the activation of alternative gene pathways or multiple genes . Epigenetics has been proposed as crucial in shaping plant phenotypic plasticity, putatively explaining the rapid and reversible alterations in gene expression in response to environmental changes. This fine-tuning of gene expression can be achieved through DNA methylation, histone modifications and chromatin remodeling . Small non-coding RNAs are ubiquitous and adjustable repressors of gene expression across a broad group of eukaryotic species and are directly involved in controlling, in a sequence specific manner, multiple epigenetic phenomena such as RNA-directed DNA methylation and chromatin remodeling and might play a role in genotype by environment interactions.

In plants, small ncRNAs are typically 20–24 nt long RNA molecules and participate in a wide series of biological processes controlling gene expression via transcriptional and post-transcriptional regulation . Moreover, small RNAs have been recently shown to play an important role in plants environmental plasticity . Fruit maturation, the process that starts with fruit-set and ends with fruit ripening , has been largely investigated in fleshy fruits such as tomato and grapevine. These studies highlighted, among others, the vast transcriptomic reprogramming underlying the berry ripening process , the extensive plasticity of berry maturation in the context of a changing environment , and the epigenetic regulatory network which contributes to adjust gene expression to internal and external stimuli . In particular, small RNAs, and especially microRNAs , are involved, among others, in those biological processes governing fruit ripening . In this work, we assessed the role of small ncRNAs in the plasticity of grapevine berries development, by employing next-generation sequencing. We focused on two cultivars of Vitis vinifera, Cabernet Sauvignon, and Sangiovese, collecting berries at four different developmental stages in three Italian vineyards, diversely located. First, we described the general landscape of small RNAs originated from hotspots present along the genome, examining their accumulation according to cultivars, environments and developmental stages. Subsequently, we analyzed miRNAs, identifying known and novel miRNA candidates and their distribution profiles in the various samples. Based on the in silico prediction of their targets, we suggest the potential involvement of this class of small RNAs in GxE interactions. The results obtained provide insights into the complex molecular machinery that connects the genotype and the environment.RNA extraction was performed as described in Kullan et al. . Briefly, total RNA was extracted from 200 mg of ground berries pericarp tissue using 1 ml of Plant RNA Isolation Reagent following manufacturer’s recommendations. The small RNA fraction was isolated from the total RNA using the mirPremier R microRNA Isolation kit and dissolved in DEPC water. All the steps suggested in the technical bulletin for small RNA isolation of plant tissues were followed except the “Filter Lysate” step, which was omitted. The quality and quantity of small RNAs were evaluated by a NanoDrop 1000 spectrometer , and their integrity assessed by an Agilent 2100 Bioanalyzer using a small RNA chip according to the manufacturer’s instructions. Small RNA libraries were prepared using the TruSeq Small RNA Sample Preparation Kit , following all manufacturers’ instructions. Forty-eight bar-coded small RNA libraries were constructed starting from 50 ng of small RNAs. The quality of each library was assessed using an Agilent DNA 1000 chip for the Agilent 2100 Bioanalyzer. Libraries were grouped in pools with six libraries each . The pools of libraries were sequenced on an Illumina Hiseq 2000 at IGA Technology Services . The sequencing data were submitted to GEO–NCBI under the accession number GSE85611. A miRNA was considered as “expressed” only when the values of both biological replicates were greater than or equal to the threshold set at 10 TP4M. We defined a miRNA as “vineyard-, cultivar-, or stage-specific” when it was expressed only in a given vineyard, cultivar or one specific developmental stage. Differentially expressed miRNAs were identified using the CLCbio Genomics Workbench using multiple comparison analysis. We loaded the total raw redundant reads from our 48 libraries in the CLCbio package and trimmed the adaptors, considering only reads between 18 and 34 nt.

We annotated miRNAs against the user defined database, comprehending our set of 122 MIRNA loci and their corresponding mature sequences. For each library, container size for raspberries the total counts of read perfectly mapping to the miRNA precursors was considered as the input of the expression analysis. Given the main focus of our work, we aimed at identifying miRNAs differentially expressed between the two cultivars in the same environment and developmental stage , or between the three vineyards in the same cultivar and in the same developmental stage . For this reason, we considered each developmental stage and we performed the Empirical Analysis of digital gene expression , an implementation of the “Exact Test” present in the EdgeR Bioconductor package, as implemented in CLCbio software and estimating tagwise dispersion with pairwise comparisons and setting the significance threshold to FDR-adjusted p ≤ 0.05. The normalized reads of all miRNAs identified in this study and also the cluster abundances obtained from the static clustering analysis were submitted to another adhoc normalization [log10 or log10 ] for correlation analysis. This normalization was chosen because of the enormous range of abundance values that produced a logunimodal distribution and may cause significant biases in the correlation analysis when performed using TP4M or HNA values. A unity was then added to the abundance value due to the presence of zero entries. After this addition, a value of zero still corresponds to zero of the log10 function, thus making consistent the comparisons among profiles. The dendrogram was generated using the function hclust and the Pearson correlation was calculated using the function cor in R, based on the log10 or log10 values for miRNAs and sRNA-generating loci respectively. Pearson’s correlation coefficients were converted into distance coefficients to define the height of the dendrogram. Heat maps were produced using MeV based on TP4M values of miRNAs abundance. The Venn diagrams were produced using the function vennDiagram in R, based on the miRNA list for each cultivar, environment and developmental stage.Small RNA libraries were constructed and sequenced for 48 samples of grapevine berries . We obtained a total of 752,020,195 raw redundant reads . After adaptors trimming, 415,910,891 raw clean reads were recovered, ranging from 18 to 34 nt in length . Eliminating the reads mapping to rRNA, tRNA, snRNA, and snoRNA sequences, 199,952,950 reads represented by 20,318,708 distinct sequences, i.e., non-redundant sequences found in the 48 libraries , were perfectly mapped to the V. vinifera PN40024 reference genome . The libraries were analyzed to assess the size distributions of mapped reads. Distinct peaks at 21- and 24-nt were observed in all the libraries. Consistent with previous reports in grapevine and other plant species , the 21- nt peak was the highest, comprising a higher proportion of redundant reads, whereas the 24-nt peak was less abundant. A few exceptions regarding the highest peak in the small RNA size profile were observed: Ric_SG_ps had the highest peak at 24- nt whereas Mont_CS_ps and Mont_SG_bc did not show a clear difference between the 21- and the 24-nt peak. Using the Pearson coefficients we observed a strong association between the replicates as indicated by the high coefficients . To facilitate access and utilization of these data, we have incorporated the small RNAs into a website . This website provides a summary of the library information, including samples metadata, mapped reads, and GEO accession numbers. It also includes pages for data analysis, such as quick summary of the abundances of annotated microRNAs from grapevine or other species. Small RNA-related tools are available, for example target prediction for user-specified small RNA sequences and matching criteria. Finally, and perhaps most importantly, a customized browser allows users to examine specific loci for the position, abundance, length, and genomic context of matched small RNAs; with this information, coupled with the target prediction output, users can develop and assess hypotheses about whether there is evidence for small RNA-mediated regulation of grapevine loci of interest.

Using k-means clustering, VviERF6L1 fell within Cluster 8 with 369 transcripts, including five additional VviERF6 paralogs. The top GO categories associated with Cluster 8 were genes associated with terpenoid metabolism and pigment biosynthesis . Other interesting flavor associated categories included fatty acid and alcohol metabolism . Representative transcripts from Cluster 8 that were correlated with the transcript abundance profile of VviERF6L1 can be seen in Figure 4. These are ACC oxidase, which is involved in ethylene biosynthesis; a lipoxygenase, part of a fatty acid degradation pathway giving rise to flavor alcohols such as hexenol; α-expansin 1, a cell wall loosening enzyme involved in fruit softening, and two terpene synthases, which produce important terpenes that contribute to Cabernet Sauvignon flavor and aroma. The high similarity of these transcript profiles indicates that ethylene biosynthesis and signaling may be involved in the production of grape aroma. Supporting this argument, two recent studies have shown that a tomato ERF TF , falling in the same ERF IX subfamily, has a strong effect on ethylene signaling and fruit ripening. The transcript abundance of AtERF6 in Arabidopsis is strongly increased by ethylene, which is triggered by the MKK9/MPK3/MPK6 pathway. The transcript abundance of VviMKK9 in the Cabernet Sauvignon berries was higher in the skin than the pulp, but there were no significant differences for VviMPK3 or VviMPK6 . This is not too surprising since AtMKK9 activates AtMPK3 and AtMPK6 by phosphorylation. In addition, planting blueberries in a pot the transcript abundance of AtERF6 in Arabidopsis increases with ROS, SA, cold, pathogens, and water deficit.

Additional circumstantial evidence for ethylene signaling in the late stages of berry ripening was that the transcript abundance of many VviERF TFs was significantly affected by berry ripening and/or tissue . The transcript abundance of 129 members from the berries was determined to be above background noise levels on the microarray . The expression profiles of the 92 significantly affected AP2/ERF super-family members were separated into six distinct clusters by hierarchical clustering and indicated that this super-family had a complex response during berry ripening . The 12 members of Cluster 1 responded similarly in both the skin and pulp, gradually decreasing with increasing °Brix with a large decrease in transcript abundance at the 36.7 °Brix level. Cluster 2 with 14 members, including 8 members of the VviERF6 clade, had much higher transcript abundance in the skin with a sharp peak at 23.2 °Brix. Cluster 3 had similar profiles in both the skin and pulp with a peak abundance at 25° Brix. Cluster 4 with 7 members was a near mirror image of cluster 2, with a sharp valley for transcript abundance in the skin between 23 and 25 °Brix. Cluster 5 had 36 members with a steady increase in transcript abundance in the pulp but no substantial increase in the skin until 36.7 °Brix. Finally, in Cluster 6, there were 13 members with a higher transcript abundance in skins compared to pulp. Their transcript abundance increased with increasing °Brix level, but decreased in the skin. The transcript abundance of important components of the ethylene signaling pathway characterized in Arabidopsis and presumed to be functional in grape were also affected by °Brix level and tissue . Three different ethylene receptors, VviETR1, VviETR2, and VviEIN4 decreased with °Brix level in the skin, however there was very little or no change in the pulp. Likewise, VviCTR1, another negative regulator of ethylene signaling that interacts with the ethylene receptors, decreased between 22.6 and 23.2 °Brix in both the skin and the pulp. The transcript abundance of the positive regulator, VviEIN2, peaked at 25 °Brix in both the skin and the pulp.

AtEIN2 is negatively regulated by AtCTR1 and when it is released from repression, turns on AtEIN3 and the ethylene signaling pathway downstream. The transcript abundance of VviEIN3 increased with °Brix level, peaking at 25 °Brix in the skin, and was much higher than in the pulp. Although more subtle, its profile was very similar to VviERF6L1. Derepression of the negative regulators and the increase in positive regulators indicated that ethylene signaling was stimulated during this late stage of berry ripening.The transcript abundance of many of the genes involved in the isoprenoid biosynthesis pathway peaked between 23 and 25 °Brix level, particularly in the skin; this stimulation of transcript abundance continued in both the carotenoid and terpenoid biosynthesis pathways . DXP synthase is a key regulatory step in isoprenoid biosynthesis and its profile was similar to VviERF6L1; its transcript abundance was correlated with the transcript abundance of several terpene synthases in the terpenoid biosynthesis pathway . About 50% of the putative 69 functional terpene synthases in the Pinot Noir reference genome have been functionally characterized. Another 20 genes may be functional but need further functional validation or checking for sequencing and assembly errors. On the NimbleGen Grape Whole-Genome array there are 110 probe sets representing transcripts of functional, partial and psuedo terpene synthases in Pinot Noir . It is uncertain how many may be functional in Cabernet Sauvignon. There were 34 probe sets that significantly changed with °Brix or the °Brix and Tissue interaction effect; 20 of these are considered functional genes in Pinot Noir. Terpene synthases are separated into 4 subfamilies in the Pinot Noir reference genome; they use a variety of substrates and produce a variety of terpenes. Many of these enzymes produce more than one terpene. The top 8 transcripts that peaked in the skin at the 23.2 to 25 °Brix stages were also much higher in the skin relative to pulp .

Five of the eight probesets match four functionally-classified genes in Pinot Noir ; these terpene synthases clustered very closely with VviTPS54, a functionally annotated – Linalool/- Nerolidol synthase. VviTPS58, a -geranyl linalool synthase, was also in the cluster. The other two probesets match partial terpene synthase sequences in the Pinot Noir reference genome. The transcript abundance of genes involved with carotenoid metabolism also changed at different °Brix levels and with tissue type . CCDs are carotenoid cleavage dioxgenases and are involved in norisoprenoid biosynthesis. The transcript abundance of VviCCD1 changed significantly with °Brix level and was higher in skin than pulp, except at 36.7 °Brix. Likewise, the transcript abundance of VviCCD4a and VviCCD4b changed signficantly with °Brix level, but was higher in the pulp than the skin. The transcript abundance of VviCCD4c significantly increased with °Brix level, but there were no significant differences between tissues. VviCCD1 and VviCCD4 produce β- and α-ionone , geranylacetone , and 6-methyl-5-hepten-2-one in grapes. There were no significant effects on the transcript abundance of VviCCD7. The transcript abundance of VviCCD8 significantly increased with°Brix level and was higher in pulp than skin. Phytoene synthase, raspberries in pots which was also increased in the skin compared to the pulp , and VviCCD1, have been associated with β-ionone and β-damascenone biosynthesis. Other important grape flavors are derived from the fatty acid metabolism pathway and lead to the production of aromatic alcohols and esters. The transcript abundance of many genes associated with fatty acid biosynthesis and catabolism changed with °Brix level . In particular the transcript abundance of a number of genes were correlated with the transcript abundance of VviERF6L1 including VviACCase, Acetyl-CoA carboxylase; KAS III ; VviOAT, ; VviFAD8; ; VviLOX2 and VviHPL . The transcript abundance of alcohol dehydrogenases was affected by tissue and °Brix level . Some ADHs are associated with the production of hexenol and benzyl alcohol.Methoxypyrazines give herbaceous/bell pepper aromas. They are synthesized early in berry development and gradually diminish to very low levels at maturity. Nevertheless, humans can detect very low concentrations of these aroma compounds. Four enzymes, VviOMT1, VviOMT2, VviOMT3 and VviOMT4 , synthesize methoxypyrazines. The transcript abundance of VviOMT1 was higher in the pulp than the skin . In addition, the transcript abundance of VviOMT1 decreased significantly with °Brix level in the pulp. There were no significant differences in the trancript abundance in the skin or pulp for VviOMT2, VviOMT3 or VviOMT4 . There was a high correlation of the transcript abundance of VviOMT1 in the pulp with 2-isobutyl-3-methoxypyrazine concentrations in the berries . The transcript abundance of VviOMT2, VviOMT3, or VviOMT4 in either skin or pulp was not correlated with IBMP concentrations . This is consistent with the suggestion that the pulp is the main contributor of IBMP in the berry. Our data indicated that VviOMT1 in the pulp may contribute to the IBMP concentration in these berries.Orthologs of RIN and SPL tomato transcription factors, which are known to be very important fruit ripening trancription factors, were at much higher transcript levels in the skin and decline with °Brix level . The transcript abundance of the VviNOR ortholog in grape was higher in the pulp and increased slightly to peak at 25 °Brix. In addition, the transcript abundance of VviRAP2.3, an inhibitor of ripening in tomato , decreased in the skin with a valley at 23.2 °Brix; it belongs to Cluster 4 of the AP2/ERF super-family . Of particular interest was VviWRKY53 [UniProt: F6I6B1], which had a very similar transcript profile as VviERF6L1 .

AtWRKY53 is a TF that promotes leaf senescence and is induced by hydrogen peroxide. This is the first report we know of implicating WRKY53 in fruit ripening . AtERF4 induces AtWRKY53 and leaf senescence, so the interactions between WRKY and ERF TFs are complex. WRKY TFs bind to the WBOX elements in promoters and VviERF6L1 has a number of WBOX elements in its promoter . In addition, AtMEKK1 regulates AtWRKY53 and the transcript abundance of VviMEKK1 peaked at 23.2 °Brix in the skin as well. Interestingly, the transcript abundance of both VviERF4 and VviERF8, whose orthologs in Arabidopsis promote leaf senescence, were at their highest level of transcript abundance at the lowest °Brix levels examined in this study .This study focused on the very late stages of the mature Cabernet Sauvignon berry when fruit flavors are known to develop. Cabernet Sauvignon is an important red wine cultivar, originating from the Bordeaux region of France. It is now grown in many countries. Wines made from Cabernet Sauvignon are dark red with flavors of dark fruit and berries. They also can contain herbaceous characters such as green bell pepper flavor that are particulary prevalent in under ripe grapes. Grape flavor is complex consisting not only of many different fruit descriptors, but descriptors that are frequently made up of a complex mixture of aromatic compounds. For example, black currant flavor, in part, can be attributed to 1,8-cineole, 3-methyl-1-butanol, ethyl hexanoate, 2- methoxy-3-isopropylpyrazine, linalool, 4-terpineol, and β- damascenone and major components of raspberry flavor can be attributed to α- and β-ionone, α- and β- phellandrene, linalool, β-damascenone, geraniol, nerol and raspberry ketone. Some common volatile compounds found in the aroma profiles of these dark fruits and berries include benzaldehyde, 1-hexanol, 2-heptanol, hexyl acetate, β-ionone, β-damascenone, linalool, and α-pinene. In a study of Cabernet Sauvignon grapes and wines in Australia, Cabernet Sauvignon berry aromas were associated with trans-geraniol and 2-pentyl furan and Cabernet Sauvignon flavor was associated with 3-hexenol, 2-heptanol, heptadienol and octanal. In another comprehensive study of 350 volatiles of Cabernet Sauvignon wines from all over Australia, the factors influencing sensory attributes were found to be complex; in part, norisoprenoids and δ − and γ-lactones were associated with sweet and fruity characteristics and red berry and dried fruit aromas were correlated with ethyl and acetate esters. In Cabernet Sauvignon wines from the USA, sensory attributes were complex also and significantly affected by alcohol level of the wine. Linalool and hexyl acetate were postitively associated with berry aroma and IBMP was positively correlated with green bell pepper aroma. In France, β-damascenone was found to contribute to Cabernet Sauvignon wine aroma. Thus, flavor development in berries and wines is very complex, being affected by a large number of factors including genetics, chemistry, time and environment. In this paper we begin to examine the changes in transcript abundance that may contribute to flavor development. We show that the transcript abundance of many genes involved in fatty acid, carotenoid, isoprenoid and terpenoid metabolism was increased in the skin and peaked at the °Brix levels known to have the highest fruit flavors . Many of these are involved in the production of dark fruit flavors such as linalool synthases, carotenoid dioxygenases and lipoxygenases. These genes serve as good candidates for berry development and flavor markers during ripening.

This was widely assumed to be the case at the time of the system’s discovery. There is now substantial evidence that this system instead forms a valley coherent state stabilized by its spin order, which would require a new mechanism for generating the Berry curvature necessary to produce a Chern magnet. In general I think it is fair to say that the details of the microscopic mechanism responsible for producing the Chern magnet in this system are not yet well understood. In light of the differences between these two systems, there was no particular reason to expect the same phenomena in MoTe2/WSe2 as in tBLG/hBN. As will shortly be explained, current-switching of the magnetic order was indeed found in MoTe2/WSe2. The fact that we find current-switching of magnetic order in both the tBLG/hBN Chern magnet and the AB-MoTe2/WSe2 Chern magnet is interesting. It may suggest that the phenomenon is a simple consequence of the presence of a finite Chern number; i.e., that it is a consequence of a local torque exerted by the spin/valley Hall effect, which is itself a simple consequence of the spin Hall effect and finite Berry curvature. These ideas will be discussed in the following sections. In spin torque magnetic memories, electrically actuated spin currents are used to switch a magnetic bit. Typically, these require a multi-layer geometry including both a free ferromagnetic layer and a second layer providing spin injection. For example, spin may be injected by a nonmagnetic layer exhibiting a large spin Hall effect, a phenomenon known as spin-orbit torque. Here, we demonstrate a spin-orbit torque magnetic bit in a single two-dimensional system with intrinsic magnetism and strong Berry curvature. We study AB-stacked MoTe2/WSe2, plant pots with drainage which hosts a magnetic Chern insulator at a carrier density of one hole per moir´e superlattice site. We observe hysteretic switching of the resistivity as a function of applied current.

The real space pattern of domain reversals aligns with spin accumulation measured near the high Berry curvature Hubbard band edges. This suggests that intrinsic spin or valley Hall torques drive the observed current-driven magnetic switching in both MoTe2/WSe2 and other moir´e materials. The switching current density is significantly less than those reported in other platforms, suggesting moir´e heterostructures are a suitable platform for efficient control of magnetic order. To support a magnetic Chern insulator and thus exhibit a quantized anomalous Hall effect, a two dimensional electron system must host both spontaneously broken time-reversal symmetry and bands with finite Chern numbers. This makes Chern magnets ideal substrates upon which to engineer low-current magnetic switches, because the same Berry curvature responsible for the finite Chern number also produces spin or valley Hall effects that may be used to effect magnetic switching. Recently, moir´e heterostructures emerged as a versatile platform for realizing intrinsic Chern magnets. In these systems, two layers with mismatched lattices are combined, producing a long-wavelength moir´e pattern that reconstructs the single particle band structure within a reduced superlattice Brillouin zone. In certain cases, moir´e heterostructures host superlattice minibands with narrow bandwidth, placing them in a strongly interacting regime whereCoulomb repulsion may lead to one or more broken symmetries. In several such systems, the underlying bands have finite Chern numbers, setting the stage for the appearance of anomalous Hall effects when combined with time-reversal symmetry breaking. Notably, in twisted bilayer graphene low current magnetic switching has been observed, though consensus does not exist on the underlying mechanism. Current switching may be correlated precisely with magnetic structure.

To examine the metastable magnetic domain structure of the system under applied current, we use tuning-fork based gradient magnetometry where a magnetic signal is produced by modulating the tip position. Figure 6.5c shows the change in magnetization relative to the zero current state for ISD = 670 nA, well above the threshold current. The images in Figs. 6.5c-d are acquired over the scan range depicted by the dashed box in Fig. 6.4f. Above the threshold, a magnetic do- main a few µm2 in size is inverted relative to the ground state on one side of the device. Reversing the current flips the side hosting the reversed domain . We conclude that the current switching corresponds to the reversal of magnetic domains, with the inverted domains appearing on opposite edges for opposing directions of applied DC current. This is confirmed by the fact that the required switching current increases dramatically as a function of the applied magnetic field, which increases the energy cost of an inverted magnetic domain. The correspondence between magnetic dynamics and resistivity may be probed in detail using current modulation magnetometry, which examines the magnetic response, δBI to a small AC current. Figs. 6.5e and f show δBI , measured near the right and left edges of the device, respectively, for the same range of VBG, VT G, and VSD as Fig. 6.5a-b. The local δBI signal shows a single sharp dip feature on the right side of the device for ISD > 0 and on the left side for ISD < 0, but no signal for the opposite signs. These features correlate precisely with the current switching features observed in transport, as evidenced by overlaying a fit to the local δBI dip on the transport data in Fig. 6.5a-b. The δBI dips may be understood as a consequence of current-driven domain wall motion. As established above, applied current drives nucleation of minority magnetization domains.

Once these domains are nucleated, increasing the current magnitude is expected to enlarge them through domain wall motion. Where domain walls are weakly pinned, a small increase in the current δI drives a correspondingly small motion δx of the domain wall, producing a change in the local magnetic field δBI characterized by a sharp negative peak at the domain wall position . We may then use this mechanism to map out the microscopic evolution of domains with current. Fig. 6.5h shows a spatial map of δBI , measured at three different values of ISD corresponding to distinct features in the transport data. Evidently, the domain wall moves from its nucleation site on the device boundary towards the device bulk. Local measurements of δBI as a function of ISD show that this motion is itself characterized by threshold behavior, corresponding to the domain wall rapidly moving between stable pinning sites. A full correspondence of transport features and local domain dynamics is presented in the associated publication. The symmetry of the observed magnetic switching is suggestive of a spin or valley Hall effect driven mechanism. The bulk nature of the spin Hall torque mechanism means that similar phenomena should manifest not only in the growing class of intrinsic Chern magnets, but in all metals combining strong Berry curvature and broken time-reversal symmetry, including crystalline graphite multi-layers. Research into charge-to-spin current transduction has identified a set of specific issues restricting the efficiency of spin torque switching of magnetic order. Spin current is not necessarily conserved, and as a result a wide variety of spin current sinks exist within typical spin torque devices. Extensive evidence indicates that in many spin torque systems a significant fraction of the spin current is destroyed or reflected at the spin-orbit material/magnet boundary. In addition, the transition metals used as magnetic bits in traditional spin-orbit torque devices are electrically quite conductive, and can thus shunt current around the spin-orbit material, preventing it from generating spin current. These issues are entirely circumvented here through the use of a material that combines a spin Hall effect with magnetism, plastic plants pots and as a result of these effects this spin Hall torque device has better current-switching efficiency than any known spin torque device. We started this discussion with a favorable comparison of the impact of disorder on the ABMoTe2/WSe2 Chern magnet to graphene-based Chern magnets. I’m sure the reader was just as disappointed as we were to see the dramatic disorder landscape on display in Fig. 6.4E, which presents a map of the magnetization in the AB-MoTe2/WSe2 Chern magnet. This is not a refutation of our original claims; it remains true that the repeatability of the fabrication protocol of the AB-MoTe2/WSe2 Chern magnet is unambiguously much better than that of tBLG/hBN, or even tMBG. It is also easy to lose track of the scale of these images- the tBLG/hBN Chern magnet was only a few square microns, whereas this sample supports a Chern magnet that is almost a hundred square microns in area.

The presence of these ‘holes’ in the magnetization of this Chern magnet is not a result of strong twist angle disorder. We have so far discussed a variety of phenomena realized in gate-tunable exfoliated heterostructures. In all cases, these phenomena were accessible experimentally because of the presence of a moir´e superlattice, which gave us access to electronic bands that could be completely filled or depleted at will using an electrostatic gate. We will next be discussing an atomic crystal without a moir´e superlattice. This material does not have flat bands, and we will have no hope of completely filling or depleting any of the bands in the system. Instead, it has features in its band structure that lend themselves to interaction-driven phenomena, specifically flat-bottomed bands satisfying the Stoner criterion. The material we will be studying is an allotrope of three-layer graphene called ABC trilayer graphene. In addition to a variety of other interesting phases, this material supports both spin and orbital magnetism. We will discuss why this is the case, and we will study the ABC trilayer magnets using the nanoSQUID microscope. As in three dimensional crystals, many two dimensional crystals have multiple allotropes that are stable under different conditions. Trilayer graphene is such a material. We label multilayer graphene allotropes using letters that refer to the relative positions of atoms of different layers, projected onto the two dimensional plane. We have already encountered ABA trilayer graphene in the introduction, and this material has atoms in the third layer aligned to atoms in the first layer. At room temperature and pressure the ABA stacking order is preferred, but trilayer graphene has a metastable allotrope, ABC trilayer graphene, that can either be prepared or found naturally occurring. In ABC trilayer graphene atoms in the third layer are aligned neither with the first nor with the second layer. ABC trilayer graphene has band structure that differs significantly from ABA trilayer graphene, and these differences have important consequences for its properties. The band structure of ABC trilayer graphene at two different displacement fields is illustrated in Fig. 7.1. In the absence of a displacement field, the system is metallic at all electron densities. When a large displacement field is applied to the system, it becomes a band insulator when the Fermi level is tuned between the two resulting bands. This is the regime of displacement field that we will be discussing. ABC trilayer graphene has extremely weak spin-orbit coupling, so the spin degree of freedom is present and more or less completely orthogonal to electronic degrees of freedom, contributing only a twofold degeneracy to the band structure. Just like most other allotropes of graphene, ABC trilayer graphene has valley degeneracy, and this produces an overall fourfold energetic degeneracy of its band structure. This is illustrated in Fig. 7.2. As is abundantly clear from these plots, the bandspresent in ABC trilayer graphene are not flat; they have extremely large bandwidths. However, the bands do satisfy the flat-bottomed band condition, and as a result we can expect these systems to be able to spin- and valley-polarize without paying significant kinetic energy costs. A schematic of the device we will discuss is presented in Fig. 7.3A. This device allows us to perform several different experiments: we can tune the electron density and displacement field in the ABC trilayer graphene layer, we can measure in-plane electronic transport , and we can measure the out-of-plane capacitive conductivity as a function of electron density and displacement field. Data extracted from this contrast mechanism is presented in Fig. 7.3B. This dataset is restricted to the hole band; i.e., the bottom band in all of the plots we have so far encountered. Sharp features in this dataset correspond to spontaneous symmetry breaking; these features are marked with the numbers and .

There are situations in which this is a valuable tool, and we will look at some DC magnetometry data shortly, but in practice our nanoSQUID sensors often suffer from 1/f noise, spoiling our sensitivity for signals at low or zero frequency. One of the primary advantages of the technique is its sensitivity, and to make the best of the sensor’s sensitivity we must measure magnetic fields at finite frequencies. We have already discussed how we can use electrostatic gates to change the electron density and band structure of two dimensional crystals. We will discuss shortly a variety of gate-tunable phenomena with magnetic signatures that appear in these systems. It follows, of course, that we can modulate the magnetic fields emitted by these electronic phases and phenomena by modulating the voltages applied to the electrostatic gates we use to stabilize these phases. This is illustrated in Fig. 1.15C: an AC voltage is applied to the bottom gate relative to the two dimensional crystal, and the local magnetic field is sampled at the same frequency by the SQUID. Electrons carry a degree of freedom that we have not yet extensively discussed: spin. Electron spin is a fundamentally quantum mechanical property; it can be more or less understood using analogies to classical physics, but it also has some properties that don’t have simple classical analogues. Spin can be understood as a quantized unit of angular momentum that an electron can never be rid of. Although an electron is, as far as we know, a point particle, this unit of angular momentum couples to charge and produces a quantized electron magnetic moment, which we call the Bohr magneton, µB. Electron spins both couple to and emit local magnetic fields, drainage for plants in pots and they are orthogonal to the electronic wave function- changing an electron’s wave function will not under normal circumstances influence its spin, and vice versa.

Electrons are fermions; they obey the Pauli exclusion principle, which states that no two electrons can be placed into the same quantum state. The simplest consequence the existence of electron spin has is the fact that electronic wave functions can fit two electrons instead of one, because an electron can have either an ‘up’ spin or a ‘down’ spin. We say that electron spin produces an energetic degeneracy, because each electronic wave function can thus support two electrons. Electron spin is not the only degree of freedom that can produce energetic degeneracies; we will discuss a different one later. All of the above arguments apply for electron spin in condensed matter systems as well, and we can expect every electronic band to support both spin ‘up’ and spin ‘down’ electrons. These arguments say nothing about interactions between electrons, and all of the physical laws we normally expect to encounter still apply. In particular, electrons of opposite spin can occupy the same wave function, but a pair of electrons have like charges, so they repel each other. There is thus an energetic cost to putting two electrons with opposite spin into the same wave function, and this cost can be quite large. This consideration is outside the realm of the physical models we have so far discussed, because electronic bands in the simplest possible picture are independent of the extent to which they are filled. We are introducing an effect that will violate this assumption; the energies of electronic bands may now change in response to the extent to which they are filled. In particular, when an electronic wave function is completely filled with one spin species , it will remain possible to add additional electrons with opposite spins, but there will be an additional energetic cost to doing so. It is important to be precise about the fact that the displacement of the unfavorable spin species upward in energy occurs after the wave function is filled with its first spin.

As a result, which spin species gets displaced upward in energy is arbitrary, and is determined by the spin polarization of the first electron we loaded into our wave function. This is an example of a ‘spontaneously broken symmetry,’ because before the addition of that first electron, the two spin species were energetically degenerate, and after the band is completely filled with both electron species, they will again be energetically degenerate. All of the above arguments apply to localized electronic wave functions and do not say anything specific about condensed matter systems, which involve many separate atoms that each support their own wave functions. A similar but somewhat subtler argument applies to electronic wave functions on adjacent atoms in condensed matter systems. When electronic wave functions on two adjacent atoms overlap, the structure of the delocalized electronic band that will emerge from them when they hybridize depends strongly on their relative spin polarization. When electrons on adjacent atoms have the same spin, the Pauli exclusion principle will prevent them from overlapping, thus minimizing their Coulomb interaction energy. When electrons on adjacent atoms have opposite spins, the Pauli exclusion principle doesn’t apply, because the two electrons are already in different quantum states, and they can overlap. This produces a larger interaction energy for arrangements wherein electrons on adjacent atoms have antialigned spins . Like all qualitative rules there are exceptions wherein other energetic contributions are more important, but this argument applies to a wide variety of condensed matter systems. These systems are known as ‘ferromagnets.’ They have interaction-driven displacements of minority spin bands, are at least partially spin polarized, and have electron spins that are largely aligned with each other. Both of these energy scales, the ‘same-site interaction’ and the ‘exchange interaction’ respectively, can be quite large in real condensed matter systems. The presence of these effects can produce a variety of phenomena.

The displacement of a spin subband upward in energy can produce partially spin-polarized metals , fully spin-polarized metals which we call ‘half-metals’ , and spin-polarized insulators which we call ‘magnetic insulators’ . Examples of each of these kinds of systems are known in nature, and all of these phenomena represent manifestations of magnetism. In principle one must perform calculations to determine whether magnetism will occur in any specific system. In practice there exist good rules of thumb for making qualitative predictions. Same-site interactions and exchange interactions minimize energy by minimizing the number of minority spin species present in a crystal, and putting the electrons that would otherwise have occupied minority spin states into majority spin states. Of course, this process always requires that the system pay an additional energetic cost in kinetic energy, because those previously unoccupied majority spin states started out above the Fermi level. The competition between these energy scales determines whether magnetism will occur in any particular material. It follows that systems with a multitude of quantum states with very similar energies in their band structure will be more likely to form magnets; to put it more precisely, growing raspberries in pots we are looking for situations in which, near the Ferm level at least, E = C, where C is some constant. We can say that under these circumstances, the energies of electrons in the crystal are independent of their momenta. We can also say that we have encountered a large local maximum or even a singularity in the density of states. We sometimes call this the ‘flat-bottomed band condition,’ or just the ‘flat band condition’ , and it can be made quantitative in the form of the Stoner criterion. Magnetism is perhaps the simplest phenomenon that can be understood in this context, but it turns out that this argument applies very generally, and physicists expect to find a variety of interesting phenomena dependent on electron interactions whenever we encounter these situations. It is important to be specific about what we mean by a flat band here: we expect to encounter magnetism whenever an electronic band is locally flat- it is fine for the band to have very high bandwidth as long as it has a region with E ≈ C. These systems will tend to produce magnetic metals. When we encounter bands that are truly flat- i.e., they have both weak dispersion and small bandwidths- we are more likely to encounter magnetic insulators, as illustrated in Fig. 2.3. The fact that spins in ferromagnetic condensed matter systems are also aligned with each other does not affect this argument, and indeed there exist systems in both theory and experiment wherein electron spins align both with each other and with an applied magnetic field, smoothly and collectively following the direction of an applied magnetic field even as it varies. This is of course incompatible with ferromagnetic hysteresis, so we will need to mix in additional physics to explain this phenomenon. We have already discussed the fact the electron spins are orthogonal degrees of freedom from electronic wave functions, and do not couple to electric fields. This was something of an oversimplification. It is true in electrostatics problems, but in the relativistic limit- when electrons are moving at a non-negligible fraction of the speed of light- in their rest frames they experience static electric fields as large magnetic fields, as illustrated in Fig. 2.4.

Most electrons in condensed matter systems are not moving at relativistic velocities. However, in the outermost valence shells of very large atoms , electrons can end up in such high angular momentum states that their velocities become relativistic. We can thus expect electrons in bands formed from orbitals supported by heavy atoms to respond to local electric potential variations as if they provide a local magnetic field. This phenomenon is known as spin-orbit coupling, and it provides a mechanism through which the energy of an electron spin can couple to the electrostatic environment inside of an atomic lattice. Predicting the global minima in energy as a function of spin orientation is very challenging, but it is often true that a discrete set of minima exist, and of course they must obey the symmetries of the atomic lattice. For this reason in many magnetic materials there is a discrete set of magnetic ground states defined by axes along which the electron spin can point. It is very often the case that there exist two global minima in energy that are antiparrallel along an axis of high symmetry; when this is the case, we say that the system is an Ising ferromagnet. The axis along which the ground state spin orientation points is called the ‘easy axis.’ This is the origin of magnetic hysteresis in ferromagnets. According to the model of ferromagnetism we have so far developed, all of the spins in a ferromagnetic crystal are always aligned. When we apply a small magnetic field antialigned with the magnetization of our ferromagnet, nothing will occur at first. When the magnitude of the magnetic field is increased past BC , all of the spins will suddenly rotate into alignment with the applied magnetic field. The simplest way is through polycrystallinity; in magnets composed of many microscopic domains, the magnetocrystalline anisotropy axes vary locally within the crystal, producing local variations in BC . In large,highly magnetized magnets, magnetic fields generated by the crystal itself can couple to its own magnetic domains . The resulting dispersion in individual domains’ coercive fields makes magnetization hysteresis loops of macroscopic samples rather smooth, instead of instantaneous at a well defined coercive field BC . However, the qualitative properties of the model apply rather well to individual domains, which do in many systems flip all together and rather suddenly at a well-defined, albeit local, BC . For this reason careful study of the detailed structure of magnetization curves of macroscopic samples often reveals a multitude of sharp steps in magnetization, corresponding to instantaneous repolarization of tiny, monocrystalline domains. This phenomenon is known as Barkhausen noise. In summary, the model we have built is very simple, and it requires both very clean samples and a lot of information about microscopic crystalline properties to provide insights into the behaviors of real spin ferromagnets. That said, there will be many situations in which it will have some utility in understanding the phenomena we encounter. We are now ready to discuss a real magnetic system. Chromium iodide is a two dimensional magnetic insulator.

Since the OOP component of the magnetization vector is parallel to the momentum transfer Q, it is not responsive in PNR. This is consistent with the observed PMA in the VSM measurements . These results collectively suggest that the more pronounced strain at the film/substrate interface leads to a higher OOP magnetic anisotropy and hence a lower measured IP MSLD. The observed depth-dependent magnetization configuration is a result of the competition between the anisotropy energy and the Zeeman energy. Thus, under the IP configuration in the PNR experiments in Fig. 2c, with reduced IP external field, the Zeeman energy becomes insufficient to compete with the interfacial-strain-enhanced magnetic anisotropy term, giving rise to a restoration of a more OOPoriented magnetization vector in the bottom layer. This magnetically soft layer is also responsible for the near-zero field kink in OOP M in Fig. 2b, where only a small OOP external field is needed for magnetic switching. To completely flip the magnetically harder top layer in the OOP configuration, though, a much higher coercive field is required . This is indeed consistent with the observation of a larger IP magnetization preserved in the top layer under reduced IP external field in Fig. 2c. This scenario is further substantiated by the lower magnetization observed for t = 6 u.c. with stronger strain measured at 5 and 60 K under 1 T IP magnetic field . The salient structural and magnetic features pave the way for an in-depth investigation of the magneto-transport responses in Cr2Te3 thin films.The 2020–2025 Dietary Guidelines for Americans encourages the intake of a variety of plant-based foods including nuts and berries.

With the goal of increasing current knowledge on nuts and berries, as well as addressing research challenges and opportunities, the Nuts and Berries Conference: Pathways to Oxidant Defense, Vascular Function, 25 liter square pot and Gut Microbiome Changes was held on 5 to 6 May, 2022 at the University of California, Davis. Tree nuts and berries were selected as the focus of the conference for their unique composition, bioactivity, and multitude of associated health-promoting qualities. With over 50 different edible nut species and hundreds of berry varietals, the following were selected for the purpose of the conference and this review: walnuts, almonds, hazelnuts, cashews, pecans, pistachios, strawberries, blueberries, raspberries, and blackberries. Tree nuts and berries are significant commodities in the United States. The total value of tree nuts grown in California in 2021 was estimated at $8.961 billion. The total value of berries grown in California in 2021 was approximately $3.667 billion. With over two-thirds of US tree nuts and berries grown in California, the agricultural land-grant institution of the University of California, Davis was the appropriate location to convene this conference of leading researchers, registered dietitians, community partners, and industry representatives. Regular tree nut and berry consumption is associated with a decreased risk for the development of cardiovascular disease along with favorable effects on brain and gut health. Tree nuts provide protein and fiber and monounsaturated and polyunsaturated fatty acids, along with vitamins, minerals, and bio-active carotenoids, phytosterols, phenolics and flavonoids, and lignan and tannins, such as the condensed proanthocyanidins and hydrolysable ellagitannins. Berries are also a significant source of fiber and vitamin C, along with bioactive carotenoids, phenolics, including proanthocyanins and ellagitannins, and anthocyanins that provide berry color. Moreover, berries provide flavan-3-ols in quantities up to 37 mg/100 g serving , which would contribute to a recently proposed daily recommended intake level of 400 to 600 mg/d.

Although research results to date have been promising, mechanisms of action in general, and for vascular and gut health specifically, have yet to be fully defined. More data are needed that can be generalized to diverse population groups as well as for modeling of precision nutrition recommendations. This paper will review the progress and challenges of current nut and berry research and suggest future directions for the field.Many different study designs have been used to assess the effects of nuts and berries on cardiometabolic health. The strengths and limitations of various clinical nutrition study designs have been addressed elsewhere. A summary of the past 5 y of studies on nuts and berries on outcome measures of cardiovascular and gut health is presented in Tables 4, 5, 6 7, 8, 9 and Tables 10, 11, 12, 13, respectively. Eligible studies consisted of clinical human trials in children, adolescents, and adults published within the last 5 y , exploring associations between the consumption of nuts and berries and associated biomarkers of interest. Two long-term intervention trials, the PREDIMED and the COcoa Supplement and Multivitamin Outcomes Study , published in 2018 and 2022, respectively, provide examples of study designs that could be useful for future planning. The PREDIMED dietary intervention trial provides the strongest evidence to date that incorporation of nuts into a healthy Mediterranean dietary pattern in individuals ages 55 to 80 y old for 4.8 y can reduce risk of cardiovascular events by 28%. The COSMOS trial demonstrated that the daily intake of monomeric and polymeric flavanols from cocoa in older adults reduces risk for cardiovascular morbidity and mortality. Although the COSMOS study utilized a flavanol supplement compared to a whole food, it is a case study to support the need for larger trials with clinical outcomes based on the use of multi-site data of surrogate outcomes from dietary interventions that use randomized, double-blind controlled trials in crossover or parallel-arm study designs for studies of nuts or berries.

A common study design for whole foods is the replacement of the test food with a nutritionally matched, isocaloric substitute. However, matching nutritional content can be a challenge because food processing, such as blending berries and roasting nuts, causes a disruption to the nutrient matrix, potentially changing the bio-availability of key nutrients. For nuts, controls often include the complete omission of the nut of interest. For berry research, a number of considerations exist that are alternative to consuming the whole food. One is the use of freeze-dried berry powders as the test product, controlled with an isocaloric powder either lower or devoid of potential bio-actives. Attempts have been made to mask the control powders, but issues such as product color, texture, scent, and mouth feel are challenging to completely match. Although this approach is similar to a classical pharmaceutical trial design, blinding study personnel and participants is challenging, thus creating both performance and detection bias. Additionally, freeze-dried berry powders can have a different food matrix compared to the whole food, which could influence outcome measures as well as limit generalizability to the whole fruit. This can aid in participant masking, but the total amount of test product provided can be limiting, and large intakes of control gelatin capsules have resulted in adverse effects. A third option can be examining 2 or more intake levels, with or without a true control group. Finally, the use of macro- and micronutrient matched gummies with similar amounts of calories, sugars, and fiber, but devoid of other bio-actives, is a novel option for use as a comparative control. In all of these approaches, the potential bio-activity of the control itself must be considered. For example, isocaloric control powders that are lowin polyphenols may still have a considerable amount of fiber in order to obtain similar mouth feel and texture, but the fiber content may have effects on lipid metabolism and the microbiome, which could influence outcome measures. Multiple cultivars of berries exist, some of which have differences in the content of bio-active ingredients, thus limiting comparison and extrapolation of results. For nuts, walnuts contain a variety of phenolic acids, catechins, gallon pot and flavonoids, most of which have been reported to possess bio-activity. Significant differences in the concentration of 16 phenolic compounds were identified when comparing black and English walnuts. More than 50 cultivars of strawberries exist in the United States. To help reduce the potential experimental variability created with the use of different cultivars, the California Strawberry Commission has produced a freeze-dried test material that utilizes a composite of genotypes to produce a powder that is characterized for its macro- and micro-nutrients and bio-active components. The US Highbush Blueberry Council also provides a powder that is a 50/50 mixture of 2 cultivars. A limitation of this approach is that the standardized mixture may contain varieties with reduced or low bioactivity. However, the advantage of this approach is that the composite represents the “market basket” available to consumers and allows comparison of results from studies conducted among different research groups and generalizability of results to a broader berry application actually used by consumers. In addition to cultivar differences, factors such as climate and seasonal differences due to heat, sunlight, and rainfall can contribute additional variability. Given the above, the characterization of bio-actives within these foods is critical. New analytical equipment and techniques have increased the precision of food composition compared to analyses performed decades ago. Current advances in the development of nutrition databases have been reviewed elsewhere.

For example, databases such as that from the USDA Food Central could be strengthened if the date of the analyses was included, along with the protocols used and the number of samples analyzed. Linking resources from repositories detailing data, such as chemical composition and bio-activity, will help both plant scientists and health professionals to make accurate and timely recommendations and guide future research.Free-living populations have differences in background diets that can influence their responses to the intake of test foods, potentially creating significant variation in baseline measurements. This variability presents a challenge when elucidating clinically relevant effects, especially if unknown a priori, where statistical significance can be masked by combining and analyzing groups together. Inter individual variability may be mitigated by increasing sample size as well as using a crossover design, but challenges in recruitment, retention, and budget constraints exist. One way to help minimize experimental variability is through a run-in period to identify participants who may be differentially metabolizing bio-active phenolics or with the goal of minimizing or removing potentially confounding metabolites from circulation prior to the intervention. However, study designs that employ highly controlled settings, strict inclusion and exclusion criteria, extended washout periods that alter background diets, and ask participants to follow an atypical consumption pattern does not reflect “normal” life and may have limited applicability to the general population. Another useful model that also has limitations is the provision of nuts or berries in amounts and duration that are greater than normally consumed. Feeding relatively high amounts of nuts or berries for a limited period of time has been employed to demonstrate proof-of-concept and provide a basis for further exploration for changes in physiology, cognitive performance, and gut microbiome profiles. Subsequent study designs must be realistic, guided by the USDA FoodCentral database for portion size. These trial designs should also use a duration that is realistically achievable by consumers, whose food purchasing behavior can be influenced by cost, access, and seasonal availability of the food. Studies using average daily portion sizes typically require intervention periods of months, which present challenges regarding participant compliance and retention and cost of the study. In a review of 231 reports on berries and health, approximately 70% of studies used interventions of less than 3 mo or contained less than 50 participants. Meeting the challenge of conducting long-term studies using amounts of foods in a typical diet, with a representative sample of participants, requires a significant commitment of resources. The health and functional levels of participants are other factors that influence study designs and outcomes. For example, studies on cognitive performance with both nuts and berries have assessed effects among those both with and without cognitive impairments. In such studies, short-term interventions may show little or no response after the addition of nuts or berries to the diet. Although the net change may not be statistically significant, this model does not address the ability of the food to prevent decline, which would require long-term testing. Further, an individual with cognitive impairments might demonstrate favorable responses compared to baseline measures following nut or berry intake but may still not reach the level of performance of a healthy individual. In both instances, neither change from baseline, nor absolute values of performance, fully captures the beneficial cognitive response.