Participatory mapping to address neighborhood level data deficiencies for food security assessment in Southeastern Virginia, USA.

BACKGROUND: Food is not equitably available. Deficiencies and generalizations limit national datasets, food security assessments, and interventions. Additional neighborhood level studies are needed to develop a scalable and transferable process to complement national and internationally comparative data sets with timely, granular, nuanced data. Participatory geographic information systems (PGIS) offer a means to address these issues by digitizing local knowledge. METHODS: The objectives of this study were two-fold: (i) identify granular locations missing from food source and risk datasets and (ii) examine the relation between the spatial, socio-economic, and agency contributors to food security. Twenty-nine subject matter experts from three cities in Southeastern Virginia with backgrounds in food distribution, nutrition management, human services, and associated research engaged in a participatory mapping process. RESULTS: Results show that publicly available and other national datasets are not inclusive of non-traditional food sources or updated frequently enough to reflect changes associated with closures, expansion, or new programs. Almost 6 percent of food sources were missing from publicly available and national datasets. Food pantries, community gardens and fridges, farmers markets, child and adult care programs, and meals served in community centers and homeless shelters were not well represented. Over 24 km2 of participant identified need was outside United States Department of Agriculture low income, low access areas. Economic, physical, and social barriers to food security were interconnected with transportation limitations. Recommendations address an international call from development agencies, countries, and world regions for intervention methods that include systemic and generational issues with poverty, incorporate non-traditional spaces into food distribution systems, incentivize or regulate healthy food options in stores, improve educational opportunities, increase data sharing. CONCLUSIONS: Leveraging city and regional agency as appropriate to capitalize upon synergistic activities was seen as critical to achieve these goals, particularly for non-traditional partnership building. To address neighborhood scale food security needs in Southeastern Virginia, data collection and assessment should address both environment and utilization issues from consumer and producer perspectives including availability, proximity, accessibility, awareness, affordability, cooking capacity, and preference. The PGIS process utilized to facilitate information sharing about neighborhood level contributors to food insecurity and translate those contributors to intervention strategies through discussion with local subject matter experts and contextualization within larger scale food systems dynamics is transferable.

Virtual engagement of under-resourced communities: Lessons learned during the COVID-19 pandemic for creating crisis-resistant research infrastructure.

The COVID-19 pandemic led to an increased need to conduct research and community engagement using digital methods. Unfortunately, the shift away from in-person research activities can make it difficult to engage and recruit participants from under-resourced communities that lack adequate digital infrastructure. At the beginning of the pandemic, our team recognized that imminent lockdowns would significantly disrupt ongoing engagement with low-income housing resident community partners and that we would ultimately bear responsibility if that occurred. This manuscript outlines the development of methods designed to create capacity for virtual engagement with a community advisory board that were subsequently applied to a longitudinal mixed-methods study. We describe how our experience engaging low-income housing residents during the height of the pandemic influenced the approach and offer guidelines useful for engaging under-resourced communities regardless of setting. Of these, a strong commitment to providing technology, unlimited data connectivity, and basic digital literacy training/technical support is most important. While each of these is essential and failure in any one area will reduce overall effectiveness of the effort, providing adequate technical support while maintaining ongoing relationships with community members is the most important and resource-intensive.

On the Fourier transform of a quantitative trait: Implications for compressive sensing.

This paper explores the genotype-phenotype relationship. It outlines conditions under which the dependence of a quantitative trait on the genome might be predictable, based on measurement of a limited subset of genotypes. It uses the theory of real-valued Boolean functions in a systematic way to translate trait data into the Fourier domain. Important trait features, such as the roughness of the trait landscape or the modularity of a trait have a simple Fourier interpretation. Ruggedness at a gene location corresponds to high sensitivity to mutation, while a modular organization of gene activity reduces such sensitivity. Traits where rugged loci are rare will naturally compress gene data in the Fourier domain, leading to a sparse representation of trait data, concentrated in identifiable, low-level coefficients. This Fourier representation of a trait organizes epistasis in a form which is isometric to the trait data. As Fourier matrices are known to be maximally incoherent with the standard basis, this permits employing compressive sensing techniques to work from data sets that are relatively small-sometimes even of polynomial size-compared to the exponentially large sets of possible genomes. This theory provides a theoretical underpinning for systematic use of Boolean function machinery to dissect the dependency of a trait on the genome and environment.

Development of a Risk Behavior Diagnosis Scale for E-cigarette Use.

Objectives: Adolescent use of electronic cigarettes has risen dramatically, prompting concerns about the health effects. There is need for brief measures to assess adolescents' perceived threat and efficacy related to e-cigarette use and cessation. A 12-item Likert-type scale was modeled after the Risk Behavior Diagnosis Scale and designed to assess threat (ie, severity and susceptibility of threat) and efficacy (ie, self-efficacy and response efficacy) as they relate to e-cigarette use. Methods: The scale was administered online to a developmental sample of 674 adolescents to examine internal consistency and factor structure. Participants (52.1% female, M age = 14.6) were representative of the surrounding community (60% non-Hispanic white; 27% non-Hispanic black; 8% Hispanic). Results: Factor analysis and Velicer's minimum average partial test revealed 2 factors (as expected), which explained 68% of the variance. Analyses revealed strong internal consistency, with Cronbach's alpha of .93 overall and alphas of .92 and .87 for threat and efficacy subscales, respectively. The measure also exhibited good convergent and discriminant validity with other constructs. Conclusions: The measure demonstrates strong preliminary reliability and validity for a developmental sample of adolescents.

Triggering an unexpected earthquake in an uncoupled subduction zone.

In the 1970s, the Shumagin Islands region of the Alaska subduction zone was identified as a seismic gap expected to host a future great [moment magnitude (M w) ≥8.0] earthquake. More recent geodetic data indicate that this region is weakly coupled, and the geologic record shows little evidence of past large events. From July to October 2020, a series of earthquakes occurred in this region, raising the possibility of greater coupling. The initial M w 7.8 thrust faulting earthquake straddled the eastern edge of the Shumagin Gap and was followed by an M w 7.6 strike-slip earthquake within the Shumagin Gap. Stress modeling indicates that this strike-slip earthquake is in fact favored if the Shumagin Gap has low coupling, whereas a highly coupled Shumagin Gap inhibits that type and location of earthquake. The initial thrust earthquake and its afterslip enhanced the strike-slip loading within the subducting slab, helping to trigger the October event.

Food Footprint as a Measure of Sustainability for Grazing Dairy Farms.

Livestock productions require significant resources allocation in the form of land, water, energy, air, and capital. Meanwhile, owing to increase in the global demand for livestock products, it is wise to consider sustainable livestock practices. In the past few decades, footprints have emerged as indicators for sustainability assessment. In this study, we are introducing a new footprint measure to assess sustainability of a grazing dairy farm while considering carbon, water, energy, and economic impacts of milk production. To achieve this goal, a representative farm was developed based on grazing dairy practices surveys in the State of Michigan, USA. This information was incorporated into the Integrated Farm System Model (IFSM) to estimate the farm carbon, water, energy, and economic impacts and associated footprints for ten different regions in Michigan. A multi-criterion decision-making method called VIKOR was used to determine the overall impacts of the representative farms. This new measure is called the food footprint. Using this new indicator, the most sustainable milk production level (8618 kg/cow/year) was identified that is 19.4% higher than the average milk production (7215 kg/cow/year) in the area of interest. In addition, the most sustainable pasture composition was identified as 90% tall fescue with 10% white clover. The methodology introduced here can be adopted in other regions to improve sustainability by reducing water, energy, and environmental impacts of grazing dairy farms, while maximizing the farm profit and productions.

Evaluation of the effectiveness of conservation practices under implementation site uncertainty.

Agricultural nonpoint source pollution is the leading source of water quality degradation in United States, which has led to the development of programs that aim to mitigate this pollution. One common approach to mitigating nonpoint source pollution is the use of best management practices (BMPs). However, it can be challenging to evaluate the effectiveness of implemented BMPs due to polices that limit data sharing. In this study, the uncertainty introduced by data sharing limitations is quantified through the use of a watershed model. Results indicated that BMP implementation improved the overall water quality in the region (up to ∼15% pollution reduction) and that increasing the area of BMP implementation resulted in higher pollution reduction. However, the model outputs also indicated that uncertainty caused by data sharing limitations resulted in variabilities ranging from -160% to 140%. This shows the importance of data sharing among agencies to better guide current and future conservation programs.

Evaluating the significance of wetland restoration scenarios on phosphorus removal.

Freshwater resources are vital for human and natural systems. However, anthropogenic activities, such as agricultural practices, have led to the degradation of the quality of these limited resources through pollutant loading. Agricultural Best Management Practices (BMPs), such as wetlands, are recommended as a valuable solution for pollutant removal. However, evaluation of their long-term impacts is difficult and requires modeling since performing in-situ monitoring is expensive and not feasible at the watershed scale. In this study, the impact of natural wetland implementation on total phosphorus reduction was evaluated both at the subwatershed and watershed levels. The study area is the Saginaw River Watershed, which is largest watershed in Michigan. The phosphorus reduction performances of four different wetland sizes (2, 4, 6, and 8 ha) were evaluated within this study area by implementing one wetland at a time in areas identified to have the highest potential for wetland restoration. The subwatershed level phosphorus loads were obtained from a calibrated Soil and Water Assessment Tool (SWAT) model. These loads were then incorporated into a wetland model (System for Urban Stormwater Treatment and Analysis IntegratioN-SUSTAIN) to evaluate phosphorus reduction at the subwatershed level and then the SWAT model was again used to route phosphorus transport to the watershed outlet. Statistical analyses were performed to evaluate the spatial impact of wetland size and placement on phosphorus reduction. Overall, the performance of 2 ha wetlands in total phosphorus reduction was significantly lower than the larger sizes at both the subwatershed and watershed levels. Regarding wetland implementation sites, wetlands located in headwaters and downstream had significantly higher phosphorus reduction than the ones located in the middle of the watershed. More specifically, wetlands implemented at distances ranging from 200 to 250 km and 50-100 km from the outlet had the highest impact on phosphorus reduction at the subwatershed and watershed levels, respectively. A multi criteria decision making (MCDM) method named VIKOR was successfully executed to identify the most suitable wetland size and location for each subwatershed considering the phosphorus reduction and economic cost associated with wetland implementation. The methods introduced in this study can be easily applied to other watersheds for selection and placement of wetlands while considering environmental benefits and economic costs.

Development and evaluation of a comprehensive drought index.

Droughts are known as the world's costliest natural disasters impacting a variety of sectors. Despite their wide range of impacts, no universal drought definition has been defined. The goal of this study is to define a universal drought index that considers drought impacts on meteorological, agricultural, hydrological, and stream health categories. Additionally, predictive drought models are developed to capture both categorical (meteorological, hydrological, and agricultural) and overall impacts of drought. In order to achieve these goals, thirteen commonly used drought indices were aggregated to develop a universal drought index named MASH. The thirteen drought indices consist of four drought indices from each meteorological, hydrological, and agricultural categories, and one from the stream health category. Cluster analysis was performed to find the three closest indices in each category. Then the closest drought indices were averaged in each category to create the categorical drought score. Finally, the categorical drought scores were simply averaged to develop the MASH drought index. In order to develop predictive drought models for each category and MASH, the ReliefF algorithm was used to rank 90 variables and select the best variable set. Using the best variable set, the adaptive neuro-fuzzy inference system (ANFIS) was used to develop drought predictive models and their accuracy was examined using the 10-fold cross validation technique. The models' predictabilities ranged from R2 = 0.75 for MASH to R2 = 0.98 for the hydrological drought model. The results of this study can help managers to better position resources to cope with drought by reducing drought impacts on different sectors.

Optimization of bioenergy crop selection and placement based on a stream health indicator using an evolutionary algorithm.

The emission of greenhouse gases continues to amplify the impacts of global climate change. This has led to the increased focus on using renewable energy sources, such as biofuels, due to their lower impact on the environment. However, the production of biofuels can still have negative impacts on water resources. This study introduces a new strategy to optimize bioenergy landscapes while improving stream health for the region. To accomplish this, several hydrological models including the Soil and Water Assessment Tool, Hydrologic Integrity Tool, and Adaptive Neruro Fuzzy Inference System, were linked to develop stream health predictor models. These models are capable of estimating stream health scores based on the Index of Biological Integrity. The coupling of the aforementioned models was used to guide a genetic algorithm to design watershed-scale bioenergy landscapes. Thirteen bioenergy managements were considered based on the high probability of adaptation by farmers in the study area. Results from two thousand runs identified an optimum bioenergy crops placement that maximized the stream health for the Flint River Watershed in Michigan. The final overall stream health score was 50.93, which was improved from the current stream health score of 48.19. This was shown to be a significant improvement at the 1% significant level. For this final bioenergy landscape the most often used management was miscanthus (27.07%), followed by corn-soybean-rye (19.00%), corn stover-soybean (18.09%), and corn-soybean (16.43%). The technique introduced in this study can be successfully modified for use in different regions and can be used by stakeholders and decision makers to develop bioenergy landscapes that maximize stream health in the area of interest.

Application of risk-based multiple criteria decision analysis for selection of the best agricultural scenario for effective watershed management.

Effective watershed management requires the evaluation of agricultural best management practice (BMP) scenarios which carefully consider the relevant environmental, economic, and social criteria involved. In the Multiple Criteria Decision-Making (MCDM) process, scenarios are first evaluated and then ranked to determine the most desirable outcome for the particular watershed. The main challenge of this process is the accurate identification of the best solution for the watershed in question, despite the various risk attitudes presented by the associated decision-makers (DMs). This paper introduces a novel approach for implementation of the MCDM process based on a comparative neutral risk/risk-based decision analysis, which results in the selection of the most desirable scenario for use in the entire watershed. At the sub-basin level, each scenario includes multiple BMPs with scores that have been calculated using the criteria derived from two cases of neutral risk and risk-based decision-making. The simple additive weighting (SAW) operator is applied for use in neutral risk decision-making, while the ordered weighted averaging (OWA) and induced OWA (IOWA) operators are effective for risk-based decision-making. At the watershed level, the BMP scores of the sub-basins are aggregated to calculate each scenarios' combined goodness measurements; the most desirable scenario for the entire watershed is then selected based on the combined goodness measurements. Our final results illustrate the type of operator and risk attitudes needed to satisfy the relevant criteria within the number of sub-basins, and how they ultimately affect the final ranking of the given scenarios. The methodology proposed here has been successfully applied to the Honeyoey Creek-Pine Creek watershed in Michigan, USA to evaluate various BMP scenarios and determine the best solution for both the stakeholders and the overall stream health.

Ecohydrological modeling for large-scale environmental impact assessment.

Ecohydrological models are frequently used to assess the biological integrity of unsampled streams. These models vary in complexity and scale, and their utility depends on their final application. Tradeoffs are usually made in model scale, where large-scale models are useful for determining broad impacts of human activities on biological conditions, and regional-scale (e.g. watershed or ecoregion) models provide stakeholders greater detail at the individual stream reach level. Given these tradeoffs, the objective of this study was to develop large-scale stream health models with reach level accuracy similar to regional-scale models thereby allowing for impacts assessments and improved decision-making capabilities. To accomplish this, four measures of biological integrity (Ephemeroptera, Plecoptera, and Trichoptera taxa (EPT), Family Index of Biotic Integrity (FIBI), Hilsenhoff Biotic Index (HBI), and fish Index of Biotic Integrity (IBI)) were modeled based on four thermal classes (cold, cold-transitional, cool, and warm) of streams that broadly dictate the distribution of aquatic biota in Michigan. The Soil and Water Assessment Tool (SWAT) was used to simulate streamflow and water quality in seven watersheds and the Hydrologic Index Tool was used to calculate 171 ecologically relevant flow regime variables. Unique variables were selected for each thermal class using a Bayesian variable selection method. The variables were then used in development of adaptive neuro-fuzzy inference systems (ANFIS) models of EPT, FIBI, HBI, and IBI. ANFIS model accuracy improved when accounting for stream thermal class rather than developing a global model.

Continuing megathrust earthquake potential in Chile after the 2014 Iquique earthquake.

The seismic gap theory identifies regions of elevated hazard based on a lack of recent seismicity in comparison with other portions of a fault. It has successfully explained past earthquakes (see, for example, ref. 2) and is useful for qualitatively describing where large earthquakes might occur. A large earthquake had been expected in the subduction zone adjacent to northern Chile, which had not ruptured in a megathrust earthquake since a M âˆ¼8.8 event in 1877. On 1 April 2014 a M 8.2 earthquake occurred within this seismic gap. Here we present an assessment of the seismotectonics of the March-April 2014 Iquique sequence, including analyses of earthquake relocations, moment tensors, finite fault models, moment deficit calculations and cumulative Coulomb stress transfer. This ensemble of information allows us to place the sequence within the context of regional seismicity and to identify areas of remaining and/or elevated hazard. Our results constrain the size and spatial extent of rupture, and indicate that this was not the earthquake that had been anticipated. Significant sections of the northern Chile subduction zone have not ruptured in almost 150 years, so it is likely that future megathrust earthquakes will occur to the south and potentially to the north of the 2014 Iquique sequence.

Inhibition of LTi cell development by CD25 blockade is associated with decreased intrathecal inflammation in multiple sclerosis.

Genetic polymorphisms in the interleukin-2 receptor α (IL-2Rα) chain (CD25) locus are associated with several human autoimmune diseases, including multiple sclerosis (MS). Blockade of CD25 by the humanized monoclonal antibody daclizumab decreases MS-associated inflammation but has surprisingly limited direct inhibitory effects on activated T cells. The present study describes unexpected effects of daclizumab therapy on innate lymphoid cells (ILCs). The number of circulating retinoic acid receptor-related orphan receptor γt-positive ILCs, which include lymphoid tissue inducer (LTi) cells, was found to be elevated in untreated MS patients compared to healthy subjects. Daclizumab therapy not only decreased numbers of ILCs but also modified their phenotype away from LTi cells and toward a natural killer (NK) cell lineage. Mechanistic studies indicated that daclizumab inhibited differentiation of LTi cells from CD34⁺ hematopoietic progenitor cells or c-kit⁺ ILCs indirectly, steering their differentiation toward immunoregulatory CD56(bright) NK cells through enhanced intermediate-affinity IL-2 signaling. Because adult LTi cells may retain lymphoid tissue-inducing capacity or stimulate adaptive immune responses, we indirectly measured intrathecal inflammation in daclizumab-treated MS patients by quantifying the cerebrospinal fluid chemokine (C-X-C motif) ligand 13 and immunoglobulin G index. Both of these inflammatory biomarkers were inhibited by daclizumab treatment. Our study indicates that ILCs are involved in the regulation of adaptive immune responses, and their role in human autoimmunity should be investigated further, including their potential as therapeutic targets.

Transglutaminase-1 gene mutations in autosomal recessive congenital ichthyosis: summary of mutations (including 23 novel) and modeling of TGase-1.

Autosomal recessive congenital ichthyosis (ARCI) is a heterogeneous group of rare cornification diseases. Germline mutations in TGM1 are the most common cause of ARCI in the United States. TGM1 encodes for the TGase-1 enzyme that functions in the formation of the cornified cell envelope. Structurally defective or attenuated cornified cell envelop have been shown in epidermal scales and appendages of ARCI patients with TGM1 mutations. We review the clinical manifestations as well as the molecular genetics of ARCI. In addition, we characterized 115 TGM1 mutations reported in 234 patients from diverse racial and ethnic backgrounds (Caucasion Americans, Norwegians, Swedish, Finnish, German, Swiss, French, Italian, Dutch, Portuguese, Hispanics, Iranian, Tunisian, Moroccan, Egyptian, Afghani, Hungarian, African Americans, Korean, Japanese and South African). We report 23 novel mutations: 71 (62%) missense; 20 (17%) nonsense; 9 (8%) deletion; 8 (7%) splice-site, and 7 (6%) insertion. The c.877-2A>G was the most commonly reported TGM1 mutation accounting for 34% (147 of 435) of all TGM1 mutant alleles reported to date. It had been shown that this mutation is common among North American and Norwegian patients due to a founder effect. Thirty-one percent (36 of 115) of all mutations and 41% (29 of 71) of missense mutations occurred in arginine residues in TGase-1. Forty-nine percent (35 of 71) of missense mutations were within CpG dinucleotides, and 74% (26/35) of these mutations were C>T or G>A transitions. We constructed a model of human TGase-1 and showed that all mutated arginines that reside in the two beta-barrel domains and two (R142 and R143) in the beta-sandwich are located at domain interfaces. In conclusion, this study expands the TGM1 mutation spectrum and summarizes the current knowledge of TGM1 mutations. The high frequency of mutated arginine codons in TGM1 may be due to the deamination of 5' methylated CpG dinucleotides.