Enhancing the USDA FAS Crop Forecasting System Using SMAP L3 Soil Moisture Observations

One of the U.S. Department of Agriculture-Foreign Agricultural Services (USDA-FAS) mission objectives is to provide current information on global crop supply and demand estimates. Crop growth and development is especially susceptible to the amount of water present in the root-zone portion of the soil profile. Therefore, accurate knowledge of the root-zone soil moisture (RZSM) is an essential for USDA-FAS global crop assessments. This paper focusses on the possibility of enhancing the USDA-FAS's RZSM estimates through the integration of passive-based soil moisture observations derived from the Soil Moisture Active Passive (SMAP) mission into the USDA-FAS Palmer model. Lag-correlation analysis, which explores the agreement between changes in RZSM and crop status indicated that the satellite-based observations can enhance the model-only estimates

Examining Rapid Onset Drought Development Using the Thermal Infrared–Based Evaporative Stress Index

Reliable indicators of rapid drought onset can help to improve the effectiveness of drought early warning systems. In this study, the evaporative stress index (ESI), which uses remotely sensed thermal infrared imagery to estimate evapotranspiration (ET), is compared to drought classifications in the U.S. Drought Monitor (USDM) and standard precipitation-based drought indicators for several cases of rapid drought development that have occurred across the United States in recent years. Analysis of meteorological time series from the North American Regional Reanalysis indicates that these events are typically characterized by warm air temperature and low cloud cover anomalies, often with high winds and dewpoint depressions that serve to hasten evaporative depletion of soil moisture reserves. Standardized change anomalies depicting the rate at which various multiweek ESI composites changed over different time intervals are computed to more easily identify areas experiencing rapid changes in ET. Overall, the results demonstrate that ESI change anomalies can provide early warning of incipient drought impacts on agricultural systems, as indicated in crop condition reports collected by the National Agricultural Statistics Service. In each case examined, large negative change anomalies indicative of rapidly drying conditions were either coincident with the introduction of drought in the USDM or lead the USDM drought depiction by several weeks, depending on which ESI composite and time-differencing interval was used. Incorporation of the ESI as a data layer used in the construction of the USDM may improve timely depictions of moisture conditions and vegetation stress associated with flash drought events

Microsatellite Instability in Mouse Models of Colorectal Cancer

Microsatellite instability (MSI) is caused by DNA mismatch repair deficiency and is an important prognostic and predictive biomarker in colorectal cancer but relatively few studies have exploited mouse models in the study of its clinical utility. Furthermore, most previous studies have looked at MSI in the small intestine rather than the colon of mismatch repair deficient Msh2-knockout (KO) mice. Here we compared Msh2-KO, p53-KO, and wild type (WT) mice that were treated with the carcinogen azoxymethane (AOM) and the nonsteroidal anti-inflammatory drug sulindac or received no treatment. The induced tumors and normal tissue specimens from the colon were analysed with a panel of five mononucleotide repeat markers. MSI was detected throughout the normal colon in untreated Msh2-KO mice and this involved contraction of the repeat sequences compared to WT. The markers with longer mononucleotide repeats (37–59) were the most sensitive for MSI while the markers with shorter repeats (24) showed only minor change. AOM exposure caused further contraction of the Bat37 and Bat59 repeats in the distal colon of Msh2-KO mice which was reversed by sulindac. Thus AOM-induced carcinogenesis is associated with increased instability of mononucleotide repeats in the colon of Msh2-KO mice but not in WT or p53-KO mice. Chemoprevention of these tumors by sulindac treatment reversed or prevented the increased MSI

Earth Observations and Integrative Models in Support of Food and Water Security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries

Of gastro and the gold standard: evaluation and policy implications of norovirus test performance for outbreak detection

<p>Abstract</p> <p>Background</p> <p>The norovirus group (NVG) of caliciviruses are the etiological agents of most institutional outbreaks of gastroenteritis in North America and Europe. Identification of NVG is complicated by the non-culturable nature of this virus, and the absence of a diagnostic gold standard makes traditional evaluation of test characteristics problematic.</p> <p>Methods</p> <p>We evaluated 189 specimens derived from 440 acute gastroenteritis outbreaks investigated in Ontario in 2006–07. Parallel testing for NVG was performed with real-time reverse-transcriptase polymerase chain reaction (RT²-PCR), enzyme immunoassay (EIA) and electron microscopy (EM). Test characteristics (sensitivity and specificity) were estimated using latent class models and composite reference standard methods. The practical implications of test characteristics were evaluated using binomial probability models.</p> <p>Results</p> <p>Latent class modelling estimated sensitivities of RT²-PCR, EIA, and EM as 100%, 86%, and 17% respectively; specificities were 84%, 92%, and 100%; estimates obtained using a composite reference standard were similar. If all specimens contained norovirus, RT²-PCR or EIA would be associated with > 99.9% likelihood of at least one test being positive after three specimens tested. Testing of more than 5 true negative specimens with RT²-PCR would be associated with a greater than 50% likelihood of a false positive test.</p> <p>Conclusion</p> <p>Our findings support the characterization of EM as lacking sensitivity for NVG outbreaks. The high sensitivity of RT²-PCR and EIA permit identification of NVG outbreaks with testing of limited numbers of clinical specimens. Given risks of false positive test results, it is reasonable to limit the number of specimens tested when RT²-PCR or EIA are available.</p

Sequence analysis of the Epstein-Barr virus (EBV) BRLF1 gene in nasopharyngeal and gastric carcinomas

<p>Abstract</p> <p>Background</p> <p>Epstein-Barr virus (EBV) has a biphasic infection cycle consisting of a latent and a lytic replicative phase. The product of immediate-early gene BRLF1, Rta, is able to disrupt the latency phase in epithelial cells and certain B-cell lines. The protein Rta is a frequent target of the EBV-induced cytotoxic T cell response. In spite of our good understanding of this protein, little is known for the gene polymorphism of BRLF1.</p> <p>Results</p> <p>BRLF1 gene was successfully amplified in 34 EBV-associated gastric carcinomas (EBVaGCs), 57 nasopharyngeal carcinomas (NPCs) and 28 throat washings (TWs) samples from healthy donors followed by PCR-direct sequencing. Fourteen loci were found to be affected by amino acid changes, 17 loci by silent nucleotide changes. According to the phylogenetic tree, 5 distinct subtypes of BRLF1 were identified, and 2 subtypes BR1-A and BR1-C were detected in 42.9% (51/119), 42.0% (50/119) of samples, respectively. The distribution of these 2 subtypes among 3 types of specimens was significantly different. The subtype BR1-A preferentially existed in healthy donors, while BR1-C was seen more in biopsies of NPC. A silent mutation A/G was detected in all the isolates. Among 3 functional domains, the dimerization domain of Rta showed a stably conserved sequence, while DNA binding and transactivation domains were detected to have multiple mutations. Three of 16 CTL epitopes, NAA, QKE and ERP, were affected by amino acid changes. Epitope ERP was relatively conserved; epitopes NAA and QKE harbored more mutations.</p> <p>Conclusions</p> <p>This first detailed investigation of sequence variations in BRLF1 gene has identified 5 distinct subtypes. Two subtypes BR1-A and BR1-C are the dominant genotypes of BRLF1. The subtype BR1-C is more frequent in NPCs, while BR1-A preferentially presents in healthy donors. BR1-C may be associated with the tumorigenesis of NPC.</p

Synergistic integration of optical and microwave satellite data for crop yield estimation

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index (EVI) derived from MODIS and Vegetation Optical Depth (VOD) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or descriptor. Our second approach avoids summarizing statistics and uses machine learning to combine full time series of EVI and VOD. This study considers two statistical methods, a regularized linear regressionand its nonlinear extension called kernel ridge regression to directly estimate the county-level surveyed total production, as well as individual yields of the major crops grown in the region: corn, soybean and wheat. The study area includes the US Corn Belt, and we use agricultural survey data from the National Agricultural Statistics Service (USDA-NASS) for year 2015 for quantitative assessment. Results show that (1) the proposed EVI-VOD lag metric correlates well with crop yield and outperforms common single-sensor metrics for crop yield estimation; (2) the statistical (machine learning) models working directly with the time series largely improve results compared to previously reported estimations; (3) the combined exploitation of information from the optical and microwave data leads to improved predictions over the use of single sensor approaches with coefficient of determination R 2 ≥ 0.76; (4) when models are used for within-season forecasting with limited time information, crop yield prediction is feasible up to four months before harvest (models reach a plateau in accuracy); and (5) the robustness of the approach is confirmed in a multi-year setting, reaching similar performances than when using single-year data. In conclusion, results confirm the value of using both EVI and VOD at the same time, and the advantage of using automatic machine learning models for crop yield/production estimation