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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.

Accumulating evidence links cardiometabolic health with social and environmental neighborhood exposures, which may contribute to health inequities. We examined whether environmental characteristics were individually or jointly associated with insulin resistance, hypertension, obesity, type 2 diabetes, and metabolic syndrome in San Diego County, CA. As part of the Community of Mine Study, cardiometabolic outcomes of insulin resistance, hypertension, BMI, diabetes, and metabolic syndrome were collected in 570 participants. Seven census tract level characteristics of participants' residential environment were assessed and grouped as follows: economic, education, health care access, neighborhood conditions, social environment, transportation, and clean environment. Generalized estimating equation models were performed, to take into account the clustered nature of the data and to estimate β or relative risk (RR) and 95 % confidence intervals (CIs) between each of the seven environmental characteristics and cardiometabolic outcomes. Quantile g-computation was used to examine the association between the joint effect of a simultaneous increase in all environmental characteristics and cardiometabolic outcomes. Among 570 participants (mean age 58.8 ± 11 years), environmental economic, educational and health characteristics were individually associated with insulin resistance, diabetes, obesity, and metabolic syndrome. In the mixture analyses, a joint quartile increase in all environmental characteristics (i.e., improvement) was associated with decreasing insulin resistance (β, 95 %CI: -0.09, -0.18-0.01)), risk of diabetes (RR, 95 %CI: 0.59, 0.36-0.98) and obesity (RR, 95 %CI: 0.81, 0.64-1.02). Environmental characteristics synergistically contribute to cardiometabolic health and independent analysis of these determinants may not fully capture the potential health impact of social and environmental determinants of health.

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 .

As food intake patterns become less structured, different methods of dietary assessment may be required to capture frequently omitted snacks, smaller meals, and the time of day when they are consumed. Incorporating sensors that passively and objectively detect eating behavior may assist in capturing these eating occasions into dietary assessment methods. The aim of this study was to identify and collate sensor-based technologies that are feasible for dietitians to use to assist with performing dietary assessments in real-world practice settings. A scoping review was conducted using the PRISMA extension for scoping reviews (PRISMA-ScR) framework. Studies were included if they were published between January 2016 and December 2021 and evaluated the performance of sensor-based devices for identifying and recording the time of food intake. Devices from included studies were further evaluated against a set of feasibility criteria to determine whether they could potentially be used to assist dietitians in conducting dietary assessments. The feasibility criteria were, in brief, consisting of an accuracy ≥80%; tested in settings where subjects were free to choose their own foods and activities; social acceptability and comfort; a long battery life; and a relatively rapid detection of an eating episode. Fifty-four studies describing 53 unique devices and 4 device combinations worn on the wrist (n = 18), head (n = 16), neck (n = 9), and other locations (n = 14) were included. Whilst none of the devices strictly met all feasibility criteria currently, continuous refinement and testing of device software and hardware are likely given the rapidly changing nature of this emerging field. The main reasons devices failed to meet the feasibility criteria were: an insufficient or lack of reporting on battery life (91%), the use of a limited number of foods and behaviors to evaluate device performance (63%), and the device being socially unacceptable or uncomfortable to wear for long durations (46%). Until sensor-based dietary assessment tools have been designed into more inconspicuous prototypes and are able to detect most food and beverage consumption throughout the day, their use will not be feasible for dietitians in practice settings.

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.

Preterm birth (

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.

Cognitive regions and places are notoriously difficult to represent in geographic information science and systems. The exact delineation of cognitive regions is challenging insofar as borders are vague, membership within the regions varies non-monotonically, and raters cannot be assumed to assess membership consistently and homogeneously. In a study published in this journal in 2014, researchers devised a novel grid-based task in which participants rated the membership of individual cells in a given region and contrasted this approach to a standard boundary-drawing task. Specifically, the authors assessed the vague cognitive regions of Northern California and Southern California. The boundary between these cognitive regions was found to have variable width, and region membership peaked not at the most northern or southern cells but at substantially less extreme latitudes. The authors thus concluded that region membership is about attitude, not just latitude. In the present work, we reproduce this study by approaching it from a computational fourth-paradigm perspective, i.e., by the synthesis of high volumes of heterogeneous data from various sources. We compare the regions which we identify to those from the human-participants study of 2014, identifying differences and commonalities. Our results show a significant positive correlation to those in the original study. Beyond the extracted regions themselves, we compare and contrast the empirical and analytical approaches of these two methods, one a conventional human-participants study and the other an application of increasingly popular data-synthesis-driven research methods in GIScience.

Pierce's disease (PD) of grapevine is caused by the bacterium Xylella fastidiosa. In this study, an integrated approach was applied to assess PD susceptibility among different Vitis vinifera cultivars that incorporated disease severity, bacterial pathogen abundance and loss of stem xylem hydraulic conductivity. It was hypothesized that levels of PD susceptibility in V. vinifera can be attributed in part to the host anatomical features that are shaped by its pedigree background. Two popular wine grape cultivars were initially selected from the occidentalis group, Merlot and Cabernet Sauvignon, and one from the orientalis group, Thompson Seedless. The more recently bred table grape cultivar Scarlet Royal, that has mixed pedigree parentage, was also included. PD susceptibility was compared to the known PD resistant b43-17 V. arizonica/candicans wild grape species from North America. The data indicated that Thompson Seedless was ranked as the most susceptible to PD because it significantly exhibited the most severe disease symptoms at 12 weeks post-inoculation and hosted the highest X. fastidiosa titre of the cultivars, and lost over 90% of its stem hydraulic conductivity. In contrast, the other three cultivars displayed less susceptibility to PD. The way in which the xylem anatomy could impact PD susceptibility in V. vinifera cultivars is discussed, together with how grape pedigrees and their cognate centre of domestication may have influenced xylem anatomical features. This work provides a reference framework to further test the hypothesis that V. vinifera cultivars with wide xylem vessels may be more prone to PD decline.

Research concerning how the built environment impacts behaviors linked to breast cancer has become of increased interest to public health researchers. Physical activity is a modifiable behavioral factor that can reduce breast cancer risk, cancer recurrence risk, and improve treatment effects by targeting biological mechanisms such as insulin resistance and inflammation. In order to improve population levels of PA, especially in vulnerable groups such as cancer survivors, it is essential to incorporate multiple levels of influence into interventions and analyses, including environmental contexts in which behaviors occur. In order to best measure and intervene on such behaviors, exposure and behavioral sciences have seen a rise in the use of more sensitive and accurate measurement methodologies, primarily using sensors like Global Positioning Systems (GPS) and accelerometers. When coupled with Geographic Information Science data, these three data sources result in dynamic exposure measures that can assess what environments individuals are exposed, where, for how long, and during what behaviors. In this chapter we present three examples of studies that have utilized GPS, accelerometry, and GIS data to better understand cancer risk, disparities, and related behaviors. The goal of the examples is to illustrate decision points that must be made when utilizing GPS data, demonstrate possible processing approaches, and highlight how GPS data can be used in conjunction with GIS and accelerometry to better understand behaviors within specific contexts.

This chapter describesa Spatial Web Automatic Reasoning and Mapping System (SWARMS) for visualizing and analyzing space-time dimensions of information landscape represented by a social media channel—Twitter. SWARMS utilizes computer programming and Twitter Search APIs to retrieve tweets by searching keywords from the Twitter database. Two case studies were conducted to analyze the spatial information landscape: the 2012 U.S. Presidential Election and 2012 summer movies. The two case studies were selected because these events can have a reality check by comparing to the actual election results and the movie box office revenue. Our preliminary spatial analysis indicates that there is correlation and geographic linkage between cyberspace communications and the real-world events. However, some cyberspace representation maps or information landscapes may be distorted from reality to degrees that depend on the media communication channels and varies by topics. As a pilot study of mapping cyberspace to real space, this chapter presents two case studies on visualizing information landscape in cyberspace and also addresses some limitations and suggestions for future research in this domain.

The volume, velocity, and variety of data that are now becoming available allow us to study urban environments based on human behaviour with a spatial, temporal, and thematic granularity that was not achievable until now. Such data-driven approaches open up additional, complementary perspectives on how urban systems function, especially if they are based on user-generated content (UGC). While the data sources, such as social media, introduce specific biases, they also open up new possibilities for scientists and the broader public. For instance, they provide answers to questions that previously could only be addressed by complex simulations or extensive human-participant surveys. Unfortunately, many of the required data sets are locked in data silos that are accessible only via restricted APIs. Even if these data could be fully accessed, their naϊve processing and visualization would surpass the abilities of modern computer architectures. Finally, the established place schemata used to study urban spaces differ substantially from UGC-based point-of-interest (POI) schemata. In this work, we present a multi-granular, data-driven, and theory-informed approach that addresses the key issues outlined above by introducing a theoretical and technical framework to interactively explore the pulse of a city based on social media. RESUME: Le volume, la rapidite et la variete des donnees maintenant disponibles permettent d’etudier les milieux urbains en fonction du comportement humain a un niveau spatial, temporel et granulaire thematique sans precedent. De telles methodes axees sur les donnees, et surtout celles fondees sur le contenu genere par l’utilisateur (CGU), proposent d’autres perspectives complementaires sur le fonctionnement des systemes urbains. Les sources de donnees (p. ex., medias sociaux) introduisent des biais precis, mais elles offrent de nouvelles possibilites aux scientifiques et au grand public (p. ex., en repondant a des questions qui, auparavant, necessitaient des simulations complexes ou de vastes enquetes aupres de participants humains). Malheureusement, beaucoup d’ensembles de donnees necessaires sont enfermes dans des reserves de donnees accessibles seulement par l’entremise d’interfaces de programmation d’applications (API) restreintes. Meme si l’on pouvait avoir pleinement accesa ces donnees, leur traitement et leur visualisation naϊfs depasseraient les capacites des architectures informatiques modernes. Enfin, le schema des lieux etabli que l’on utilise pour etudier les espaces urbains est considerablement different du schema des points d’interet (PI) fondes sur le CGU. L’article presente une methode multigranulaire fondee sur la theorie et les donnees qui abordent les principales difficultes soulevees precedemment en introduisant un cadre theorique et technique pour explorer de facon interactive le pouls d’une ville en fonction des medias sociaux.

Geographers and scientists can collect and analyze social media and Big Data via smartphones, sensors, and mobile devices with locational contents, such as global positioning system tags, check-ins, place names, and user location profiles. The dynamic characteristics of social media and Big Data offer geographers research opportunities for examining and modeling human behaviors, communications, and movements. To discuss this emerging research themes in the field of geography and GIScience, a series of special paper sessions were organized at AAG annual meetings in 2015 and 2016, Human Dynamics in the Mobile Age: Linking Physical and Virtual Spaces and Symposium on Human Dynamics Research: Social Media and Big Data. This short viewpoint paper first reports on a summary of papers presented in these AAG sessions. Then we discuss the current state-of-the-arts in human dynamics research and highlight their key concepts, opportunities, and challenges.

Aphelenchoides besseyi is a seed-borne plant-parasitic nematode pathogen that causes rice white-tip disease worldwide. For quarantine purposes at custom border inspection and in field surveillance, a rapid, highly specific, and sensitive loop-mediated isothermal amplification (LAMP) assay for the nematode detection was developed. The assay targets the sequence variable region of the mitochondrial cytochrome oxidase subunit I (COI) gene of the nematode. It is capable of distinguishing the A. besseyi rice pathotype from the closely related A. besseyi nest fern pathotype. The assay is highly sensitive, with the detection minimum limit at 103 copy numbers/µl cloned target plasmid or a single nematode crude DNA. A minimum of 35 min is needed for the assay to complete, and the detection results can be rapidly determined by the color change of hydroxynaphathol blue dye or by the indication bar on a specific lateral flow dipstick. The application potential of this assay on rice seeding was demonstrated at the sensitivity level of five nematodes in single rice seedling. This is the first LAMP assay for A. besseyi rice race detection using a mitochondria gene as marker. This assay is well-suited for phytosanitary examination and seedling screening purposes in nurseries.

Pratylenchus vulnus was discovered in nematode-distributing fields from symptomatic seedling roots and corresponding rhizosphere soil on strawberry farms in Taiwan. Microscopic measurements and scanning electron microscope observations of both sexes of the nematode coincided with the general morphological descriptions of the species. Four different types of female tail termini were observed, including pointed, digitate, smooth and tapering. Molecular analysis of the ribosomal RNA sequence (SSU, ITS and LSU regions) and the mitochondria COI gene sequences confirmed the species identification. Phylogenetic analysis suggested no specific geographic linkage of the Taiwan population to other previously reported populations.

Barley yellow dwarf virus is a group of positive-sense single stranded RNA viruses with many species belonging to the family Luteoviridae and known to cause significant yield losses of cereal crops worldwide (Irwin and Thresh 1990). Oat (Avena sativa L.), a temperate cereal crop usually grown as forage in subtropical regions, has been recently reintroduced into Taiwan as a local winter forage source. In April 2017, yellowing, V-shape reddening, and purple symptoms were observed on 7.9% of the mature leaves of the oat plants grown in the experimental field of National Taiwan University (NTU) in Taipei city, Taiwan. We collected 37 leaf samples with suspected virus-infection symptoms in the experimental field for virus screening. The samples were expressing varying degrees of yellowing, browning, or typical barley yellow dwarf virus (BYDV)-associated V-shape reddening and purple symptoms. An additional five samples with no symptoms were also collected. These 37 samples belong to 22 oat lines, including two registered cultivars and 20 unreleased breeding lines. Some lines were replicated in the field and therefore were sampled more than once. A specific reverse transcription polymerase chain reaction assay (RT-PCR) for BYDV, covering the coat protein (CP) gene and partial RNA-dependent RNA polymerase regions, was performed with the primers Shu-F (5′-TACGGTAAGTGCCCAACTCC-3′) and Yan-R (5′-TGTTGAGGAGTCTACCTATTTG-3′) on the extracted total RNA of the samples (Malmstrom and Shu 2004). Among the examined samples, only the three expressing typical BYDV symptoms tested positive, whereas the other 34 samples with solely yellowing or browning symptoms and the five symptomless samples tested negative. The samples belonged to the cultivar Swan and two breeding lines, 15-39 and 16-47; the seeds of the three symptomatic oats were all imported from the United States. We further cross-examined the three positive samples with another RT-PCR assay, using specific primer pairs targeting the BYDV CP gene (for strains PAV, MAV, and GAV): Luteo1F (5′-TTCGGMSARTGGTTGTGGTCCA-3′) and YanR-new (5′-TGTTGAGGAGTCTACCTATTTNG-3′) (Mustafayev et al. 2013; Svanella-Dumas et al. 2013). The electrophoresis analysis of the RT-PCR amplicons revealed the expected 545-bp fragment from all. The amplicons were later cloned into pCR2.1-TOPO (Invitrogen, Carlsbad, CA) for sequencing analysis. All of the sequences obtained were identical (GenBank accession nos. MF618251, MK361042, and MK361043) and showed 99% similarity to the BYDV-PAV isolates from the United States (accession nos. EF521840 and DQ115532) in GenBank. Based on the phylogenetic analysis of CP gene sequences, the BYDV-PAV Taiwan isolates and these two U.S. isolates were in the same cluster. To further confirm the results, the BYDV-PAV specific enzyme-linked immunosorbent assay on the three symptomatic samples, using the commercialized kit (Agdia, Elkhart, IN), also aligned with the prior assay and showed positive reaction results. To our knowledge, this is the first report of BYDV-PAV in Taiwan. The phylogeny analysis of the virus suggests the BYDV-PAV might have been introduced through the seeds, which had gone through one cycle of multiplication at the border between Idaho and Oregon states prior to growing in Taiwan. Because the BYDV infection on oat presented a threat to cereal crop production in Taiwan, all oat plants in the NTU experimental field were eliminated immediately to prevent the spread of the disease. Agricultural agencies were informed of the incidence, and oat growers were educated to identify the BYDV symptoms on oat. Further field surveys of BYDV on the alleged vector insects will be carried to better understand the disease biology in Taiwan and to provide information on the disease system in the Southeast Asia region.

We introduce a new research framework for analyzing the spatial distribution of web pages and social media (Twitter) messages with related contents, called Visualizing Information Space in Ontologi...

We introduce a new method for visualizing and analyzing information landscapes of ideas and events posted on public web pages through customized web-search engines and keywords. This research integrates GIScience and web-search engines to track and analyze public web pages and their web contents with associated spatial relationships. Web pages searched by clusters of keywords were mapped with real-world coordinates (by geolocating their Internet Protocol addresses). The resulting maps represent web information landscapes consisting of hundreds of populated web pages searched by selected keywords. By creating a Spatial Web Automatic Reasoning and Mapping System prototype, researchers can visualize the spread of web pages associated with specific keywords, concepts, ideas, or news over time and space. These maps may reveal important spatial relationships and spatial context associated with selected keywords. This approach may provide a new research direction for geographers to study the diffusion of human t...

Situation awareness plays an important role in disaster response and emergency management. Displaying real-time location-based social media messages along with videos, pictures, and hashtags during a disaster event could help first responders improve their situation awareness . A geo-targeted event observation (Geo) Viewer was developed for monitoring real-time social media messages in target areas with four major functions: (1) real-time display of geo-tagged tweets within the target area; (2) interactive mapping functions; (3) spatial, text, and temporal search functions using keywords, spatial boundaries, or dates; and (4) manual labeling and text-tagging of messages. Different from traditional web GIS maps, the user interface design of GeoViewer provides the interactive display of multimedia content and maps. The front-end user interface to visualize and query tweets is built with open source programming libraries using server-side MongoDB. GeoViewer is built for assisting emergency responses and disaster management tasks by tracking disaster event impacts, recovery activities, and residents’ needs in the target region.

The multilevel model of meme diffusion conceptualizes how mediated messages diffuse over time and space. As a pilot application of implementing the meme diffusion, we developed the social media analytics and research testbed to monitor Twitter messages and track the diffusion of information in and across different cities and geographic regions. Social media analytics and research testbed is an online geo-targeted search and analytics tool, including an automatic data processing procedure at the backend and an interactive frontend user interface. Social media analytics and research testbed is initially designed to facilitate (1) searching and geo-locating tweet topics and terms in different cities and geographic regions; (2) filtering noise from raw data (such as removing redundant retweets and using machine learning methods to improve precision); (3) analyzing social media data from a spatiotemporal perspective; and (4) visualizing social media data in diagnostic ways (such as weekly and monthly trends, trend maps, top media, top retweets, top mentions, or top hashtags). Social media analytics and research testbed provides researchers and domain experts with a tool that can efficiently facilitate the refinement, formalization, and testing of research hypotheses or questions. Three case studies (flu outbreaks, Ebola epidemic, and marijuana legalization) are introduced to illustrate how the predictions of meme diffusion can be examined and to demonstrate the potentials and key functions of social media analytics and research testbed.

Preterm birth (PTB, < 37 weeks’ gestation) may impose lifelong sequelae or death. Fresno County reports the highest rate of PTB in California. A place-based approach investigating local risk factors for PTB may provide important opportunities for intervention and prevention. In this study, we examine risk and protective factors for PTB in rural, suburban, and urban Fresno County, California. The sample was drawn from Fresno County, California singleton births 2007-2012 (n = 81,021). Multivariate models of maternal risk and protective factors for PTB were stratified by rural, suburban, and urban residence. Women with diabetes, hypertension, infection, fewer than three prenatal care visits, previous PTB, interpregnancy interval less than six months, or were of Black race/ethnicity were at increased risk of PTB. The risk of PTB was highest for women residing in rural locations with preeclampsia superimposed on preexisting hypertension (adjusted relative risk (aRR) 5.7, 95% confidence interval (CI) 4.4-7.4). For women living in urban residences, maternal birth in Mexico and overweight body mass index (BMI) offered protection from PTB (aRRs 0.9), whereas participation in the Women, Infants and Children program was protective for women in either urban or rural residences (aRRs 0.8). Public insurance