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The study of food consumption, diet, and related concepts is motivated by diverse goals, including understanding why food consumption impacts our health, and why we eat the foods we do. These varied motivations can make it challenging to define and measure consumption, as it can be specified across nearly infinite dimensions-from micronutrients to carbon footprint to food preparation. This challenge is amplified by the dynamic nature of food consumption processes, with the underlying phenomena of interest often based on the nature of repeated interactions with food occurring over time. This complexity underscores a need to not only improve how we measure food consumption but is also a call to support theoreticians in better specifying what, how, and why food consumption occurs as part of processes, as a prerequisite step to rigorous measurement. The purpose of this Perspective article is to offer a framework, the consumption process framework, as a tool that researchers in a theoretician role can use to support these more robust definitions of consumption processes. In doing so, the framework invites theoreticians to be a bridge between practitioners who wish to measure various aspects of food consumption and methodologists who can develop measurement protocols and technologies that can support measurement when consumption processes are clearly defined. In the paper we justify the need for such a framework, introduce the consumption process framework, illustrate the framework via a use case, and discuss existing technologies that enable the use of this framework and, by extension, more rigorous study of consumption. This consumption process framework demonstrates how theoreticians could fundamentally shift how food consumption is defined and measured towards more rigorous study of what, how, and why food is eaten as part of dynamic processes and a deeper understanding of linkages between behavior, food, and health.

PDF file - 226K, Relative Abundance of Indicator Bacterial Phylotypes in Mice Fecal bacteria of age- and litter-matched SPF-N and SPF-S mice (n = 4-5 for each type) were subjected to an indicator analysis.

PDF file - 197K, Phyla Legend.

PDF file - 94K, Effect of Oral Administration of L. johnsonii on Genotoxicity and Inflammation.

PDF file - 551K, Relative Abundance of OTU of CM and RM Mice by Genotype and Intestinal Region.

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PDF file - 61K, Relative Abundance of Phyla of CM and RM Mice by Genotype and Intestinal Region.

PDF file - 369K, Reverse PCR primers used in the Illumina-based high throughput sequence analysis of bacterial rRNA genes.

Purpose It remains unclear why individuals living in disadvantaged neighborhoods have shorter non-small cell lung cancer (NSCLC) survival. It is possible that living in these deprived areas is linked with increased risk of developing aggressive NSCLC biology. Here, we explored the association of somatic KRAS mutations, which are associated with shorter survival in NSCLC patients, and 11 definitions of neighborhood disadvantage spanning socioeconomic and structural environmental elements. Methods We analyzed data from 429 NSCLC patients treated at a Comprehensive Cancer Center from 2015 to 2018. Data were abstracted from medical records and each patient’s home address was used to assign publicly available indices of neighborhood disadvantage. Prevalence Ratios (PRs) for the presence of somatic KRAS mutations were estimated using modified Poisson regression models adjusted for age, sex, smoking status, race/ethnicity, educational attainment, cancer stage, and histology. Results In the NSCLC cohort, 29% had KRAS mutation-positive tumors. We found that five deprivation indices of socioeconomic disadvantage were associated with KRAS mutation. A one decile increase in several of these socioeconomic disadvantage indices was associated with a 1.06 to 1.14 increased risk of KRAS mutation. Measures of built structural environment were not associated with KRAS mutation status. Conclusion Socioeconomic disadvantage at the neighborhood level is associated with higher risk of KRAS mutation while disadvantage related to built environmental structural measures was inversely associated. Our results indicate not only that neighborhood disadvantage may contribute to aggressive NSCLC biology, but the pathways linking biology to disadvantage are likely operating through socioeconomic-related stress.

Limited previous work has identified a relationship between exposure to ambient air pollution and aggressive somatic lung tumor mutations. More work is needed to confirm this relationship, especially using spatially resolved air pollution. We aimed to quantify the association between different air pollution metrics and aggressive tumor biology. Among patients treated at City of Hope Comprehensive Cancer Center in Duarte, CA (2013–2018), three non-small cell lung cancer somatic tumor mutations, TP53, KRAS, and KRAS G12C/V, were documented. PM2.5 exposure was assessed using state-of-the art ensemble models five and ten years before lung cancer diagnosis. We also explored the role of NO2 using inverse-distance-weighting approaches. We fitted logistic regression models to estimate odds ratio (OR) and their 95% confidence intervals (CIs). Among 435 participants (median age: 67, female: 51%), an IQR increase in NO2 exposure (3.5 μg/m3) five years before cancer diagnosis was associated with an increased risk in TP53 mutation (OR, 95% CI: 1.30, 0.99–1.71). We found an association between highly-exposed participants to PM2.5 (>12 μg/m3) five and ten years before cancer diagnosis and TP53 mutation (OR, 95% CI: 1.61, 0.95–2.73; 1.57, 0.93–2.64, respectively). Future studies are needed to confirm this association and better understand how air pollution impacts somatic profiles and the molecular mechanisms through which they operate.

Background Potato taste defect (PTD) of coffee is characterized by a raw potato like smell that leads to a lower quality taste in the brewed coffee, and harms the commercial value of some East African coffees. Although several causes for PTD have been proposed, none of them have been confirmed. Recently, high throughput sequencing techniques and bioinformatic analysis have shown great potential for identifying putative causal agents of plant diseases. Toward the goal of determining the cause of PTD, we examined raw coffee beans from Rwanda exhibiting varying PTD scores using an Illumina-based sequence analysis of the fungal rRNA ITS region. Results Six fungal amplicon sequence variants (ASVs) with high relative abundances correlated with coffee taste scores. Four of these ASVs exhibited negative correlations – Aspergillus versicolor, Penicillium cinnamopurpureum, Talaromyces radicus, and Thermomyces lanuginosus – indicating that they might be causing PTD. Two of these fungi exhibited positive correlations – Kazachstania humilis and Clavispora lusitaniae – indicating that they might be inhibiting organisms that cause PTD. Conclusions This study addressed PTD causality from a new angle by examining fungi with high throughput sequencing. To our knowledge, this is the first study characterizing fungi associated with PTD, providing candidates for both causality and biocontrol.

This study endeavored to test two hypotheses: (i) Hyalorbilia oviparasitica and related species (H. oviparasitica clade) will be detected in sugar beet fields in California’s Imperial Valley and (ii) the population densities of these fungi will increase in the presence of their nematode host (Heterodera schachtii) and a host plant of this nematode. The H. oviparasitica clade includes potent hyperparasites of sedentary stages of plant-parasitic nematodes, which have the ability to substantially suppress the population densities of several endoparasitic nematode species. In this study, 21 of 25 Imperial Valley soils cropped to sugar beets harbored members of the H. oviparasitica clade. The population densities of these fungi increased, on average, approximately 10,000-fold in the presence of H. schachtii and its host Swiss Chard over one nematode generation. An rRNA ITS sequence analysis showed that members of the H. oviparasitica clade were the dominant group of fungi associated with H. schachtii females derived from these soils. These results provide evidence supporting the hypotheses. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Objective We used global positioning system (GPS) derived indicators and ecological momentary assessment (EMA) to assess real-time relationships between life-space, mood, and social interactions among older adults with and without HIV. Methods Participants (People with HIV [PWH] n=54, HIV- n=34) completed smartphone-based EMA surveys assessing mood and social interactions four times/day for 14-days. Participants’ smartphones were GPS enabled. Mixed-effects models analyzed concurrent and lagged associations among life-space and mood. Results PWH spent more time at home (79% vs 70%, z=-2.08; p=.04) and reported lower mean happiness (3.2 vs 3.7; z=2.63; p=.007). More daily social interactions were associated with higher ratings of real-time happiness (b=0.12; t=5.61; p< 0.001). Prior day social interactions (b=0.15; t=7.3; p Conclusions Interventions targeting social interaction may help increase positive mood in isolated older adults.

Nematophagous fungi, mostly belonging to the phylum Ascomycota, have raised great attention because of their potential use against plant-pathogenic nematodes. In this investigation, entomopathogenic fungi were collected in Taiwan and isolated and identified based on molecular phylogenetic analyses of six nuclear loci including LSU, ITS, TEF1, RPB1, RPB2, and TUB2 combined with morphological data. Two novel fungal taxa are described in the present study, namely, Paraboeremia taiwanensis (Pleosporales) and Polycephalomyces elaphomyceticola (Hypocreales), respectively. Both species showed the ability to parasitize eggs of the rice root-knot nematode, Meloidogyne graminicola, in in vitro assays. Size ranges of conidiomata and conidia distinguish Paraboeremia taiwanensis from other currently known Paraboeremia species. Polycephalomyces elaphomyceticola differs from other Polycephalomyces species by producing single stromata, smaller perithecia, awl-shaped phialides, and monomorphic conidia. It was encountered as a parasite on Elaphomyces muricatus. Both examined strains grew and sporulated well on artificial media. Pathogenicity testing showed that they were capable of parasitizing eggs of Meloidogyne graminicola. In addition, our study elucidated the teleomorph of Polycephalomyces agaricus for the first time.

Understanding diverse characteristics of human mobility provides profound knowledge of urban dynamics and complexity. Human movements are recorded in a variety of data sources, and each describes unique mobility characteristics. Revealing similarity and difference in various data sources facilitates grasping comprehensive human mobility patterns. This study introduces a new method to measure similarities on two origin–destination (OD) matrices by spatially extending an image‐assessment tool, the structural similarity index (SSIM). The new measurement, spatially weighted SSIM (SpSSIM), utilizes weight matrices to overcome the SSIM sensitivity issue by explicitly defining spatial adjacency. To evaluate SpSSIM, we compared performances between SSIM and SpSSIM with resampling the orders of OD pairs and conducted bootstrapping to test the statistical significance of SpSSIM. As a case study, we compared OD matrices generated from three data sources in San Diego County, CA: U.S. Census‐based Longitudinal Employer–Household Dynamics Origin–Destination employment statistics, Twitter, and Instagram. The case study demonstrated that SpSSIM was able to capture similarities of mobility patterns between datasets that varied by distance. Some regions showed local dissimilarity while the global index indicated they were similar. The results enhance the understanding of complex mobility patterns from various datasets, including social media.

The spatial diffusion of information is a process governed by the flow of interpersonal communication. The emergence of the Internet and especially social media platforms has reshaped this process and previous research has studied how online social networks contribute to the diffusion of information. Understanding such processes can help devise methods to maximize or control the reach of information or even identify upcoming events and social movements. Yet activities in cyberspace are still confined to physical locations and this geographic connection tends to be overlooked. In this research, we focus on geographic regions instead of individuals and study how the underlying hierarchical structure of regions relates to their response to the information. We examined the top 30 populated cities and metropolitan areas in the U.S. and retrieved Twitter data related to two selected topics from these regions, the 2015 Nepal Earthquake and the #JesuisCharlie hashtag in response to the Paris attacks on the Charlie Hebdo offices. We analyzed the similarity among regions of their response using multiple statistical methods and three urban classifications. Our results indicate that the diffusion of information is impacted by the hierarchy of urban regions and that the Twitter responses act more similar when the populated regions are positioned at the same level in the urban hierarchy.

As cannabis use is being legalized in an increasing number of states, it is important to understand the changing dynamic of the risk in cannabis use disorder (CUD). Shape-based time-series clustering was used to identify ZIP Code Tabulation Areas (ZCTAs) with similar changing pattern in CUD over time. We conducted a cross-sectional logistic regression analysis to investigate the most recent ZCTA socio-demographic characteristics in relation to the changing CUD rates. The emergency discharge rates generally increased during 2010-2016. Increase during 2017-2019 was found in Sacramento and Santa Barbara County. Approximately 13% of ZCTAs showed an increasing trend of hospitalization discharge during 2017-2019. Males and non-Hispanic Black had larger increase than other groups during 2017-2019. The recent growing trend was found associated with greater racial diversity and rural ZCTAs. The findings from this study hold promise for local public health officials to adjust the cannabis intervention strategies in target districts and improve overall health outcomes.

Active travel (AT) provides an opportunity to alleviate the physical inactivity and climate crises contributing to the global chronic disease burden, including cardiovascular diseases (CVD). Though AT shows promising links to reduced CVD risk, prior studies relied on self-reported AT assessment. In the present study, device-measured and self-reported AT were compared across population subgroups and relationships with CVD risk biomarkers were evaluated for both measures. The study recruited an ethnically diverse sample (N = 602, mean age 59 years, 42% Hispanic/Latino ethnicity) from neighborhoods that varied by walkability and food access. AT was assessed using concurrently collected accelerometer and GPS data and self-report data from a validated survey. Relationships with body mass index (BMI), triglycerides, high-density lipoprotein (HDL) cholesterol, blood pressure (BP), and moderate-to-vigorous physical activity (MVPA) were modeled using multivariable linear regression. Devices captured more AT than did self-report. We found differences in AT measures by population subgroups, including race, ethnicity, education, income, vehicle access, and walkability. Men had more accelerometer-measured MVPA, though women self-reported more daily minutes. Both device and survey AT measures were positively associated with total accelerometer-measured MVPA, though the relationship was stronger with device-measured AT. Device-measured AT was associated with lower BMI. No other CVD risk biomarker was associated with either AT measure. No effect modification by Hispanic/Latino ethnicity was detected. Further studies with device-based measures are warranted to better understand the relationship between AT and cardiovascular health.

Time-weighted spatial averaging approaches (TWSA) are an increasingly utilized method for calculating exposure using global positioning system (GPS) mobility data for health-related research. They can provide a time-weighted measure of exposure, or dose, to various environments or health hazards. However, little work has been done to compare existing methodologies, nor to assess how sensitive these methods are to mobility data inputs (e.g., walking vs driving), the type of environmental data being assessed as the exposure (e.g., continuous surfaces vs points of interest), and underlying point-pattern clustering of participants (e.g., if a person is highly mobile vs predominantly stationary). Here we contrast three TWSA approaches that have been previously used or recently introduced in the literature: Kernel Density Estimation (KDE), Density Ranking (DR), and Point Overlay (PO). We feed GPS and accelerometer data from 602 participants through each method to derive time-weighted activity spaces, comparing four mobility behaviors: all movement, stationary time, walking time, and in-vehicle time. We then calculate exposure values derived from the various TWSA activity spaces with four environmental layer data types (point, line, area, surface). Similarities and differences across TWSA derived exposures for the sample and between individuals are explored, and we discuss interpretation of TWSA outputs providing recommendations for researchers seeking to apply these methods to health-related studies.

Introduction: Exposure to air pollution disproportionately affects racial/ethnic minorities that could contribute to health inequalities including metabolic disorders. However, most existing studies used a static assessment of air pollution exposure (mostly using the residential address) and do not account for activity space when modelling exposure to air pollution. The aim of this study is to understand how exposure to air pollution impacts metabolic disorders biomarkers, how this effect differs according to ethnicity, and for the first time compare these findings with two methods of exposure assessment: dynamic and static measures. Methods: Among the Community of Mine study, a cross-sectional study conducted in San Diego County, insulin resistance, diabetes, hypertension, obesity, dyslipidemia, and metabolic syndrome (MetS) were assessed. Exposure to air pollution (PM2.5, NO2, traffic) was calculated using static measures around the home, and dynamic measures of mobility derived from Global Positioning Systems (GPS) traces using kernel density estimators to account for exposure variability across space and time. Associations of air pollution with metabolic disorders were quantified using generalized estimating equation models to account for the clustered nature of the data. Results: Among 552 participants (mean age 58.7 years, 42% Hispanic/Latino), Hispanics/Latinos had a higher exposure to PM2.5 compared to non-Hispanics using static measures. In contrast, Hispanics/Latinos had less exposure to PM2.5 using dynamic measures. For all participants, higher dynamic exposure to PM2.5 and NO2 was associated with increased insulin resistance and cholesterol levels, and increased risk of obesity, dyslipidemia and MetS (RR 1.17, 95% CI: 1.07–1.28; RR 1.21, 95% CI: 1.12–1.30, respectively). The association between dynamic PM2.5 exposure and MetS differed by Hispanic/Latino ethnicity. Conclusion: These results highlight the importance of considering people's daily mobility in assessing the impact of air pollution on health.

Plant disease and pests are the third largest source of the loss rate of crops worldwide after the loss causing by natural disaster and transportation process. Plant-parasitic nematodes (PPN) are one of the main plant pests and cause over 100 million US dollars of agricultural loss worldwide every year. In general, identifying species of nematodes from their morphological characters is the first step for a nematologist. However, limited professionals cannot fulfill the huge demand. A fast identification system for nematodes applying imaging classification methods would be a potential solution to amend the practical needs. In recent years, the rise of deep learning brought a significant breakthrough in object detection. In this study, deep learning algorithms were applied to develop the plant-parasitic nematode (PPN) image identification system. The experimental dataset included four genera (e.g., Aphelenchoides, Bursaphelenchus, Meloidogyne, and Pratylenchus) and 10 species of common PPNs worldwide. In addition, one free-living nematodes, Caenorhabditis elegans, and two entomopathogenic nematodes, Heterocephalobellus sp. and Metarhabditis amsactae, were also collected as the control group for the recognition of the non-plant-parasitic nematodes. In total, 9483 images of nematode images were acquired. Faster Region-based Convolutional Neural Network (Faster RCNN) architecture was selected to develop the PPN identification model. As a result, the model with the ResNet-101 structure achieved the highest mean average precision (mAP) of 0.9018.

Including pest resistance elements against the major local concern is naturally important in the breeding process. Oat (Avena sativa L.) has been recently reintroduced into Taiwan as a winter alternative forage crop, and its agronomic performance has been evaluated at different locations in the country. This study examined the resistance to root-knot nematode, Meloidogyne graminicola, in four oat (Avena sativa L.) breeding lines of mass planting potential for winter in Taiwan. The host attraction level to the nematode, and the penetration and reproduction ability of nematode towards host roots were evaluated by chemotaxis assay, root staining assay, root galling, and nematode extraction. Based on the gall index (GI) and multiplication factor (R), the resistance of each oat line was evaluated. At 24 h postinoculation, second-stage juvenile (J2) nematodes appeared most attracted by oat breeding lines UFRGS136104-3 and UFRGS136119-2. The number of J2s successfully penetrated into the two breeding lines were also high. However, at 40 days postinoculation, observation of the oats in the newly developed culture bag nematode-inoculation system revealed that the amount of root galls and 2nd generation nematodes were significantly higher in line LA08085BS-T2 than in other lines. In sum, oat breeding line UFRGS136104-3 was highly resistant to M. graminicola by inhibiting the gall formation and nematode reproduction, while UFRGS136106-3 and UFRGS136119-2 showed relatively weak resistance and oat line LA08085BS-T2 would be a moderately susceptible host to M. graminicola, with high numbers of root gall formation. The outcome of this study provides ground information for nematode-resistant oat cultivar breeding.

Older persons with human immunodeficiency virus (HIV) (PWH) are particularly susceptible to life-space restrictions. The aims of this study included: 1) using global positioning system (GPS) derived indicators as an assessment of time spent at home among older adults with and without HIV; 2) using ecological momentary assessment (EMA) to examine real-time relationships between life-space, mood (happiness, sadness, anxious), fatigue, and pain; and 3) determining if number of daily social interactions moderated the effect of life-space on mood.Eighty-eight older adults (PWH n = 54, HIV-negative n = 34) completed smartphone-based EMA surveys assessing mood, fatigue, pain, and social interactions four times per day for two weeks. Participants' smartphones were GPS enabled throughout the study. Mixed-effects regression models analyzed concurrent and lagged associations among life-space and behavioral indicators of health.PWH spent more of their time at home (79% versus 70%, z = -2.08; p = 0.04) and reported lower mean happiness (3.2 versus 3.7; z = 2.63; p = 0.007) compared to HIV-negative participants. Controlling for covariates, more daily social interactions were associated with higher ratings of real-time happiness (b = 0.12; t = 5.61; df = 1087.9; p0.001). Similar findings were seen in lagged analyses: prior day social interactions (b = 0.15; t = 7.3; df = 1024.9; p0.0001) and HIV status (b = -0.48; t = -2.56; df = 1026.8; p = 0.01) attenuated the effect of prior day time spent at home on happiness.Accounting for engagement in social interactions reduced the significant effect of time spent at home and lower happiness. Interventions targeting social isolation within the context of constricted life-space may be beneficial for increasing positive mood in older adults, and especially relevant to older PWH.

Meloidogyne enterolobii, an aggressive plant-parasitic nematode, has been causing great yield loss worldwide in recent years. With no resistant Chinese cabbage cultivar available currently, a biological control strategy is needed to offer an eco-friendly option for sustainable farming. In this study, the nematode suppression efficacy of two newly isolated fungi, Paraboeremia taiwanensis and Samsoniella sp., were evaluated against M. enterolobii and compared to the known biological control agents Hyalorbilia oviparasitica strain DoUCR50 and Purpureocillium lilacinum strain 251 (PL251). Both P. taiwanensis and Samsoniella sp. reduced 29%&ndash, 63% disease severity as effectively as the commercial product PL251 on Chinese cabbage in greenhouse trails. The in vitro egg infection rate was 47.83% by P. taiwanensis and 47.50% for Samsoniella sp., respectively. A special protocol for scanning electron microscope observation of the fungi-infected nematodes was established in this study, and the egg parasitism of the four fungi against M. enterolobii was further confirmed. For all fungi examined in this study, fungal hyphae were seen apparently penetrating into M. enterolobii eggs without destructive damage of the overall outer eggshell and the hyphae continued to grow within eggs after 6 days of infection. The results of this study imply a similar egg-parasitism mechanism for P. taiwanensis, Samsoniella sp., H. oviparasitica DoUCR50, and P. lilacinum PL251. It further enlightens the application potential of nematophagous fungi as biocontrol agents against plant-parasitic nematodes in vegetable crop management.

As the most abundant animals on earth, nematodes are a dominant component of the soil community. They play critical roles in regulating biogeochemical cycles and vegetation dynamics within and across landscapes and are an indicator of soil biological activity. Here, we present a comprehensive global dataset of soil nematode abundance and functional group composition. This dataset includes 6,825 georeferenced soil samples from all continents and biomes. For geospatial mapping purposes these samples are aggregated into 1,933 unique 1-km pixels, each of which is linked to 73 global environmental covariate data layers. Altogether, this dataset can help to gain insight into the spatial distribution patterns of soil nematode abundance and community composition, and the environmental drivers shaping these patterns., This research was supported by a grant from DOB Ecology to T.W.C., a grant from the Netherlands Organization for Scientific Research (grant 016.Veni.181.078) to S.G., grants from NSF (OPP 1115245, 1341736, 0840979) to B.J.A., by a Ramon y Cajal fellow award (RYC-2016-19939) to R.C.H., a grant from UNEP & Global Environment Facility to J.E.C., grants from NERC’s Soil Security Programme to R.D.B. (NE/M017028/1) T.C. (NE/M017036/1), a grant from FAPEMIG/FAPESP/VALE S.A.(CRA-RDP-00136-10) to L.B.C., through the strategic programme UID/BIA/04050/2013 (POCI-01-0145-FEDER-007569) awarded to S.R.C., a grant from CNPq PROTAX (562346/2010-4) to J.M.d.C.C., a grant from DFG (CRC990) to V.K. and S.S., a grant from the MSHE of Russia (AAAA-A17-117112850234-5) to A.A.K., grants from the Chinese Academy of Sciences (XDB15010402) and the National Natural Science Foundation of China (41877047) to Q.L., grants from the National Natural Science Foundation of China (31330011, 31170484) to W.L., grants from NERC (NE/ M017036/1) to M.M., grants from the Spanish Ministry of Innovation (CGL2009-14686-C02-01/02, CGL2013- 43675-P) to J.A.R.M., grant from the Spanish Ministry of Innovation (RYC-2016-19939) to R.C.H., grants from NSF (DEB-0450537, DEB-1145440) to P.M., T.O.P. and K. Powers, grants from the German Academic Exchange Service (PKZ 91540366) and NAFOSTED (106.05–2017.330) to T.A.D.N., by an ARC Discovery project (DP150104199) to U.N.N., by the National Key Research and Development Program of China (2016YFC0502101) and the National Natural Science Foundation of China (31370632) to K. Pan, a ERC Research Council Advanced grant (ERC-Adv 323020 SPECIALS) to W.H.v.d.P, a grant from the Natural Environment Research Council (NERC) to D.G.W., a grant from BAPHIQ (106AS-9.5.1-BQ-B3) to J.-i.Y., a grant from the Russian Foundation for Basic Research (18-29-05076) to A.V.T. The James Hutton Institute receives financial support from the Scottish Government Rural and Environment Science and Analytical Services (RESAS) division. Investigations in Northwest Russia were carried out under state order for IB KarRC RAS and are partially supported by the Russian Foundation for Basic Research (18-34-00849).

Soil organisms are a crucial part of the terrestrial biosphere. Despite their importance for ecosystem functioning, few quantitative, spatially explicit models of the active belowground community currently exist. In particular, nematodes are the most abundant animals on Earth, filling all trophic levels in the soil food web. Here we use 6,759 georeferenced samples to generate a mechanistic understanding of the patterns of the global abundance of nematodes in the soil and the composition of their functional groups. The resulting maps show that 4.4 ± 0.64 × 1020 nematodes (with a total biomass of approximately 0.3 gigatonnes) inhabit surface soils across the world, with higher abundances in sub-Arctic regions (38% of total) than in temperate (24%) or tropical (21%) regions. Regional variations in these global trends also provide insights into local patterns of soil fertility and functioning. These high-resolution models provide the first steps towards representing soil ecological processes in global biogeochemical models and will enable the prediction of elemental cycling under current and future climate scenarios., This research was supported by a grant from DOB Ecology to T.W.C., a grant from the Netherlands Organization for Scientific Research (grant 016.Veni.181.078) to S.G., grants from NSF (OPP 1115245, 1341736, 0840979) to B.J.A., by a Ramon y Cajal fellow award (RYC-2016-19939) to R.C.H., a grant from UNEP & Global Environment Facility to J.E.C., a grant from NERC (NE/M017036/1) to T.C., a grant from FAPEMIG/FAPESP/VALE S.A.(CRA-RDP-00136-10) to L.B.C., through the strategic programme UID/BIA/04050/2013 (POCI-01-0145-FEDER-007569) awarded to S.R.C., a grant from CNPq PROTAX (562346/2010-4) to J.M.d.C.C., a grant from DFG (CRC990) to V.K. and S.S., a grant from the MSHE of Russia (AAAA-A17-117112850234-5) to A.A.K., grants from the Chinese Academy of Sciences (XDB15010402) and the National Natural Science Foundation of China (41877047) to Q.L., grants from the National Natural Science Foundation of China (31330011, 31170484) to W.L., grants from NERC (NE/M017036/1) to M.M., grants from the Spanish Ministry of Innovation (CGL2009-14686-C02-01/ 02, CGL2013-43675-P) to J.A.R.M., grants from NSF (DEB-0450537, DEB-1145440) to P.M., T.O.P. and K. Powers, grants from the German Academic Exchange Service (PKZ 91540366) and NAFOSTED (106.05 – 2017.330) to T.A.D.N., by an ARC Discovery project (DP150104199) to U.N.N., by the National Key Research and Development Program of China (2016YFC0502101) and the National Natural Science Foundation of China (31370632) to K. Pan, a grant from the Natural Environment Research Council (NERC) to D.G.W., a grant from BAPHIQ (106AS-9.5.1-BQ-B3) J.-i.Y. The James Hutton Institute receives financial support from the Scottish Government Rural and Environment Science and Analytical Services (RESAS) division. Investigations in northwest Russia were carried out under state order for IB KarRC RAS and are partially supported by the Russian Foundation for Basic Research (18-34-00849). We thank E. Clark and A. Orgiazzi for review of the manuscript; and R. Bouharroud, Z. Ferji, L. Jackson and E. Mzough for providing data.

Microbial community structure and composition in the plant vascular endosphere has not been studied extensively especially in the context of vascular diseases. Pierce’s disease (PD) of grapevine is caused by Xylella fastidiosa, a xylem-limited bacterium. In PD-impacted vineyards, there are observations of vines that remain asymptomatic despite being under high disease pressure. Because these vines are clonal, we hypothesized that the microbial community inhabiting the grapevine vascular endosphere is a major contributor to the disease escape phenotype. We used a next-generation sequencing Illumina MiSeq-based platform to characterize the bacterial and fungal microbiome residing in the cane endosphere of grapevine that displayed severely symptomatic, to mildly symptomatic or asymptomatic disease phenotypes. Our results provide evidence that the endophytic grapevine microbial community is composed primarily of Proteobacteria and Ascomycota with Pseudomonodales and Pleosporales as the main bacterial and fungal orders, respectively. Endophytic Pseudomonas fluorescens and Achromobacter xylosoxidans showed significant negative correlations with X. fastidiosa titer. Our data suggest that the clustering of bacterial communities appeared to be driven by the abundance of both P. fluorescens and X. fastidiosa. P. fluorescens emerged as a potential driver of the disease-escape phenotype and a promising biological control agent of PD.

During puberty, a woman’s breasts are vulnerable to environmental damage (“window of vulnerability”). Early exposure to environmental carcinogens, endocrine disruptors, and unhealthy foods (refined sugar, processed fats, food additives) are hypothesized to promote molecular damage that increases breast cancer risk. However, prospective human studies are difficult to perform and effective interventions to prevent these early exposures are lacking. It is difficult to prevent environmental exposures during puberty. Specifically, young women are repeatedly exposed to media messaging that promotes unhealthy foods. Young women living in disadvantaged neighborhoods experience additional challenges including a lack of access to healthy food and exposure to contaminated air, water, and soil. The purpose of this review is to gather information on potential exposures during puberty. In future directions, this information will be used to help elementary/middle-school girls to identify and quantitate environmental exposures and develop cost-effective strategies to reduce exposures.

Eating is a health-related behavior that could be intervened upon in the moment using smartphone technology, however predicting eating events using sensor data is challenging. We evaluate multiple machine learning algorithms for predicting eating and food purchasing events in a pilot sample of free living individuals. Data was collected with accelerometer, GPS device, and body-worn cameras for a week from 81 individuals. Raw minute-level features from sensors and engineered features including temporal and environmental context were included in the models. The Gradient Boosting model performed best for predicting eating, and the RBF-SVM model best predicted food purchasing. Time and context engineered features were important contributors to predicting eating and food purchasing events. This study provides a promising start in integrating body-worn sensor data, time components, and environmental contextual data into food-related behavior prediction for use in smartphone interventions.

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Introduction: Lung cancer is the leading cause of cancer-related deaths in the US. While smoking remains the most common cause for non-small cell lung cancer (NSCLC), less research exists on the role of adverse social conditions and neighborhood socioeconomic status (nSES) in the development of specific NSCLC somatic mutations. Neighborhood conditions can be influenced by economic, housing, education, transportation, and environmental factors. Understanding the relationship between NSCLC biology and nSES is vital in addressing disproportionately high rates of NSCLC incidence and poor outcomes among marginalized communities in the US. While neighborhood deprivation indices have been linked to aggressive biology in other cancers, the link to somatic mutations in NSCLC is unknown. The purpose of this analysis is to identify relationships between exposure to adverse nSES conditions and somatic KRAS mutations, a marker of aggressive NSCLC biology. Methods: We analyzed NSCLC patients who received care at the City of Hope Comprehensive Cancer Center in Duarte, CA from 2015-2018 and received somatic KRAS testing of their lung tumor tissue as a part of their care. The patients' medical record was abstracted for data including demographics and disease characteristics. The patients' home address was geocoded and assigned a census tract-level exposure to several indices of nSES conditions including the California Health Places Index (HPI), the US Department of Housing and Urban Development Labor Market Engagement Index (LME), and the Area Deprivation Index (ADI). We used several logistic regressions to model the presence of somatic KRAS mutations in relation to exposure to various adverse nSES conditions. Odds ratios (ORs) were adjusted for age at diagnosis, sex, cigarette smoking, race/ethnicity, insurance status, cancer stage, cancer histology, and exposure to PM2.5. Results: Of 426 eligible NSCLC patients, 28% tested positive for a KRAS mutation, 54% were non-Hispanic White, 66% were stage IV, and 83% had adenocarcinomas. The risk of a KRAS mutation was significantly elevated among patients living in places with lower HPI scores (OR 1.92, 95% CI: 1.11 - 3.32) and lower LME (OR 1.16, 95% CI: 1.05 - 1.28). In adjusted models, PM2.5, ADI, and other nSES indices were not significantly associated with risk of a KRAS mutation. Conclusions: We found that NSCLC patients living in deprived neighborhood conditions as measured by the California HPI and LME were more likely to have a somatic KRAS mutation. This relationship shows that nSES may be an important determinant of aggressive NSCLC biology. However, our results also indicate that deprivation is a complex mixture of domains and regionally specific influences. These findings indicate that future research may need to consider locally specific nSES measures as a dynamic marker of neighborhood conditions in addition to nationally derived indexes such as the ADI. Citation Format: Sam Wing, Ernesto Sosa, Tarik Benmarhnia, Jiue-An Yang, Stacy Gray, Marta Jankowska, Loretta Erhunmwunsee. Association of neighborhood socioeconomic status and aggressive non-small cell lung cancer biology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 784.

Disease resistance is one of the most successful strategies in crop protection. For example, the implementation of PI 88788 type resistance, which contains high copy numbers of Resistance to Heterodera glycines 1 (rhg1) loci, into the commercial soybean varieties of the United States has significantly reduced the yield losses caused by soybean cyst nematode (SCN, H. glycines). Vegetable soybean, or edamame, has become a major exporting agricultural product in Taiwan with an annual revenue over $80 million USD since 2017. Several local varieties have been developed to fulfill the market needs such as the traits of flavor and sweetness. However, it remains unclear if the historical breeding programs ever incorporated rhg1 resistance into the varieties of Taiwan. This study applied the TaqMan qPCR method to measure the fluorescent signals specific to the rhg1 locus on the chromosome 18 of soybean, and the ratio of VIC and FAM signals were analyzed to predict the rhg1 copy number in the 21 soybean varieties of Taiwan. The results indicated the copy number and the single nucleotide polymorphisms of the 21 soybean varieties were identical to the susceptible soybean variety ‘Williams 82’. As importation of soybean will be continuously needed to fulfill the market and because SCN is absent in the soybean fields of Taiwan, lacking rhg1 resistance in the local soybean varieties may put the edamame industry at risk and early implementation of SCN resistance in the breeding program, alongside the application of quarantine regulations, will be the key to maintain the SCN-free status and to sustain the edamame industry in Taiwan.