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[Display omitted] • Nitrogen-fixing bacteria were isolated and identified in cow dung compost. • Inoculant use improved the hygienisation and maturity of compost. • Inoculation reduced NH 3 and N 2 O emissions. • Inoculant increased the abundance of Ureibacillus and Ruminofilibacter. Excavating nitrogen-fixing bacteria with high-temperature tolerance is essential for the efficient composting of animal dung. In this study, two strains of thermophilic nitrogen-fixing bacteria, NF1 (Bacillus subtilis) and NF2 (Azotobacter chroococcum), were added to cow dung compost both individually (NF1, NF2) and mixed together (NF3; mixing NF1 and NF2 at a ratio of 1:1). The results showed that NF1, NF2, and NF3 inoculants increased the total Kjeldahl nitrogen level by 38.43%–55.35%, prolonged the thermophilic period by 1–13 d, increased the seed germination index by 17.81%, and the emissions of NH 3 and N 2 O were reduced by 25.11% and 42.75%, respectively. Microbial analysis showed that Firmicutes were the predominant bacteria at the thermophilic stage, whereas Chloroflexi, Proteobacteria, and Bacteroidetes were the predominant bacteria at the mature stage. These results confirmed that the addition of the isolated strains to cow dung composting improved the bacterial community structure and benefited nitrogen retention. [ABSTRACT FROM AUTHOR]

Abstract Chemokines comprise a group of small molecular weight (6–14 kDa) cytokines; chemokine receptors are a superfamily of seven transmembrane domain G-coupled receptors. Both chemokines and their receptors have important roles in immune surveillance, inflammation, and development. Recently, 9 CXC chemokine ligands (CXCLs) and 8 CXC chemokine receptors (CXCRs) were identified and cloned from orange-spotted grouper (Epinephelus coioides) and annotated by phylogenetic and syntenic analyses. We detected mRNA transcripts for CXCLs and CXCRs in healthy tissues of E. coioides. Our data show that CXCL genes are highly expressed in the spleen, kidney and liver and that CXCR genes are ubiquitously expressed, rather than being expressed only in immune organs. Analysis of gene expression after Singapore grouper iridovirus infection indicated that CXCL and CXCR genes are regulated in a gene-specific manner. CXCL8 and CXCL12a were significantly upregulated in the spleen, kidney and liver of resistant fish, indicating potential roles in immunity against the pathogen. Additionally, CXCR4a was upregulated in all three organs in resistant fish, suggesting that CXCL8 or CXCL12a may participate in the immune response via interaction with CXCR4a. In addition, the new orange-spotted grouper receptor CXCR1b was found to be upregulated in the spleen and kidney of resistant fish, indicating that this receptor plays an important role in immune responses to viral infection. These results are valuable for comparative immunological studies and provide insight into the roles of these genes in viral infection. Highlights • 9 CXC chemokine ligands and 8 CXC chemokine receptors were identified and characterized in orange-spotted grouper genome. • There are 2 CXCR1 (a/b) isoforms in orange-spotted grouper have been proposed. • It showed a gene-specific expression manner after SGIV infection. • CXCL8, CXCL12a, CXCR1b, CXCR3.3 and CXCR4a might play significant roles in orange-spotted grouper immune responses. [ABSTRACT FROM AUTHOR]

Biomass supply chain optimization aims to facilitate large-scale production of biofuels by improving the efficiency and effectiveness of biomass feedstock provision. Most existing models are not web based, limited by the accessibility for real-world applications. A CyberGIS-enabled biomass supply chain decision support platform was developed to improve model accessibility and computational performance. The platform includes four major components: BioScope optimization model, GISolve middleware, high-performance cyberinfrastructure, and an interactive web interface. The workflow and functions of each component are provided to illustrate the development and usage of the platform. Case studies and associated system performance have been evaluated to demonstrate the utility of the CyberGIS-enabled decision support platform. Through leveraging cyberinfrastructure resources and interactive web-based interface, the platform enables solving complex biomass supply chain optimization problems. The improved computational performance could provide responsive decision support for group-based applications. [ABSTRACT FROM AUTHOR]

Highlights: [•] Silencing of cre1 resulted in derepression of cellulase gene expression. [•] Silencing of cre1 led to significantly enhanced enzyme production capability. [•] CREI acted as a repressor of xyr1 transcription under inducing conditions. [•] RNAi is a feasible method for improving cellulase productivity in Trichoderma koningii. [Copyright &y& Elsevier]

Abstract: Cyberinfrastructure science and engineering gateways have become an important modality to connect science and engineering communities and cyberinfrastructure. The use of cyberinfrastructure through gateways is fundamental to the advancement of science and engineering. However, learning science gateway technologies and developing science gateways remain a significant challenge, given that science gateway technologies are still actively evolving and often include a number of sophisticated components. A geosciences gateway must be designed to accommodate legacy methods that geoscientists use in conventional computational tools. The research described in this paper establishes an open-source toolkit—SimpleGrid for learning and developing science gateways based on a service-oriented architecture using a component-based approach that allows flexible separation and integration of the components between geocomputation applications and cyberinfrastructure. The design and implementation of SimpleGrid is based on the National Science Foundation TeraGrid—a key element of the U.S. and world cyberinfrastructure. This paper illustrates our experience of using SimpleGrid and a spatial interpolation method in a tutorial to teach TeraGrid science gateways. [Copyright &y& Elsevier]

Considerable progress has been made in exploiting the enormous amount of genomic and genetic information for the identification of potential targets for drug discovery and development. New tools that incorporate pathway information have been developed for gene expression data mining to reflect differences in pathways in normal and disease states. In addition, forward and reverse genetics used in a high-throughput mode with full-length cDNA and RNAi libraries enable the direct identification of components of signaling pathways. The discovery of the regulatory function of microRNAs highlights the importance of continuing the investigation of the genome with sophisticated tools. Furthermore, epigenetic information including DNA methylation and histone modifications that mediate important biological processes add to the possibilities to identify novel drug targets and patient populations that will benefit from new therapies. [Copyright &y& Elsevier]

Surface water is an irreplaceable resource for human survival and environmental sustainability. Accurate, finely detailed cartographic representations of hydrologic streamlines are critically important in various scientific domains, such as assessing the quantity and quality of present and future water resources, modeling climate changes, evaluating agricultural suitability, mapping flood inundation, and monitoring environmental changes. Conventional approaches to detecting such streamlines cannot adequately incorporate information from the complex three-dimensional (3D) environment of streams and land surface features. Such information is vital to accurately delineate streamlines. In recent years, high accuracy lidar data has become increasingly available for deriving both 3D information and terrestrial surface reflectance. This study develops an attention U-net model to take advantage of high-accuracy lidar data for finely detailed streamline detection and evaluates model results against a baseline of multiple traditional machine learning methods. The evaluation shows that the attention U-net model outperforms the best baseline machine learning method by an average F1 score of 11.25% and achieves significantly better smoothness and connectivity between classified streamline channels. These findings suggest that our deep learning approach can harness high-accuracy lidar data for fine-scale hydrologic streamline detection, and in turn produce desirable benefits for many scientific domains. • A deep learning model for incorporating multi-scale remote sensing information is created. • A novel application of the model for fine-scale hydrologic streamline detection is developed. • An innovative streamline detection method for fully harnessing LiDAR data is presented. [ABSTRACT FROM AUTHOR]

Spatiotemporal uncertainties in the collection of crop residues pose great challenges to the development of a long-term and economic biomass-to-biofuel supply chain network (BSCN). A multiperiod stochastic programming (SP) model considering uncertain collectible corn stover removal and farmer participation rates is developed. The SP model is compared with the deterministic programming for the expected scenario (DPES) model to provide decision-making support for BSCN in two different periods. With the statistical results of separate deterministic programming models for each scenario generated randomly based on the normal distribution as a reference, the economic performance of the SP and DPES models is compared in the model development period and then confirmed in the model validation period. A county-level case study with a 10-year development and a 3-year validation period is applied. The economic performance of the SP model is comparable to that of the DPES model in the development period, and the SP model achieves much higher cost savings in the validation period. Although biomass transportation cost is the most unstable cost component, the variation in bioethanol production cost is largely consistent with that in biomass purchase cost. The SP model demonstrates stronger robustness to uncertainty than the DPES model. [ABSTRACT FROM AUTHOR]

Emergency evacuation is a critical response to deadly disasters such as hurricanes, floods, and earthquakes, etc. However, mass emergency evacuation itself is a complex process that sometimes could lead to chaotic situations and unintended consequences. In many emergency scenarios, mass evacuation is necessary to cope with severe public threats within tight spatiotemporal ranges. To better understand complex phenomena like mass evacuation, and study possible consequences, agent-based models (ABMs) have been widely developed in previous work. Existing models simulate individual behaviors, posing computational challenges when applied to large geographic areas and sophisticated behaviors. A key strategy for resolving such computational challenges is to partition transportation networks into smaller regions and resolve corresponding computational costs by taking advantage of advanced cyberinfrastructure and cyberGIS. In this study, a novel network partition algorithm is developed to improve the scalability of agent-based modeling of mass evacuation based on a cutting-edge cyberGIS-enabled computational framework that exploits the spatial movement patterns of emergency evacuation. Specifically, the algorithm is termed as Voronoi Clustering based on Target-Shift, or ViCTS. It is enlightened by network Voronoi diagrams and designed to resolve computational scalability challenges caused by the unique characteristics of evacuation traffic. We conducted a set of computational experiments with real street network data in various evacuation scenarios to test the effectiveness and efficiency of the algorithm. Computational experiments show that ViCTS outperforms a widely used network partition algorithm for microscopic traffic simulation in terms of achieving optimal computational performance by balancing computational loads and reducing communications across high-performance parallel computing resources. • Conventional network partition algorithms result in suboptimal computational performance. • Voronoi diagrams and target-shifting proximity are employed to resolve computational challenges. • The ViTCS algorithm achieves optimal computational performance, and enables scalable agent-based modeling of mass evacuation. [ABSTRACT FROM AUTHOR]

[Display omitted] • Newly ammonifying microorganisms cultures were inoculated into cow manure compost. • AM increased the count and viability of microbial cells during composting. • Amm-4 enhanced the ammonification, sequestration, and conversion of nitrogen. • Provides a novel approach to manure management, and environmental pollution control. It is essential to reduce nitrogen losses and to improve nitrogen conversion during organic waste composting because of environmental protection and sustainable development. To reveal newly domesticated ammonifying microorganisms (AM) cultures on the ammonification and nitrogen conversion during the composting, the screened microbial agents were inoculated at 5 % concentration (in weight basis) into cow manure compost under five different treatments: sterilized distilled water (Control), Amm-1 (mesophilic fungus-F1), Amm-2 (mesophilic bacterium-Z1), Amm-3 (thermotolerant bacterium-Z2), and Amm-4 (consortium: F1, Z1, and Z2), and composted for 42 days. Compared to control, AM inoculation prolonged the thermophilic phases to 9–19 days, increased the content of NH 4 +-N to 1.60–1.96 g/kg in the thermophilic phase, reduced N 2 O and NH 3 emissions by 22.85–61.13 % and 8.45–23.29 %, increased total Kjeldahl nitrogen, and improved cell count and viability by 12.09–71.33 % and 66.71–72.91 %. AM was significantly associated with different nitrogen and microbial compositions. The structural equation model (SEM) reveals NH 4 +-N is the preferable nitrogen for the majority of bacterial and fungal growth and that AM is closely associated with the conversion between NH 3 and NH 4 +-N. Among the treatments, inoculation with Amm-4 was more effective, as it significantly enhanced the driving effect of the critical microbial composition on nitrogen conversion and accelerated nitrogen ammonification and sequestration. This study provided new concepts for the dynamics of microbial in the ammonification process of new AM bacterial agents in cow manure compost, and an understanding of the ecological mechanism underlying the ammonification process and its contribution to nitrogen (N) cycling from the perspective of microbial communities. [ABSTRACT FROM AUTHOR]

Abstract Cystatin C is an endogenous inhibitor of cysteine proteases and widely exist in organisms. Several studies in mammals have showed that Cystatin C plays critical role in the immune defense against microorganisms. It is also well known that some fish Cystatin C have important immune regulation functions in inflammatory responses. However, the function of fish Cystatin C in virus infection as well as its underlying molecular mechanisms remain to be elucidated. In the present study, a Cystatin C gene termed Ec-CysC was identified from orange-spotted grouper, Epinephelus coioides. The full-length of Ec-CysC cDNA was 817 bp with a 387 bp open reading frame (ORF) that encoded a 129-amino acid (aa) protein, including 18-aa signal peptide and 111-aa mature polypeptide. The deduced amino acid of Ec-CysC shared three conserved domains containing Glycine at the N-terminus region, QVVAG motif in the middle and PW motif near the C-terminus region. Transcription analysis of the Ec-CysC gene showed its expression in all twelve examined tissues including liver, spleen, kidney, brain, intestine, heart, skin, muscle, fin, stomach, gill and head kidney. Its expression following stimulation with Singapore grouper iridovirus (SGIV) was further tested in spleen, the relative expression of Ec-CysC was significantly up-regulated at 12 h post-infection. The subcellular localization experiment revealed that Ec-CysC was mainly distributed in the cytoplasm in Grouper Spleen (GS) cells. In vitro , Overexpression of Ec-CysC in GS cells significantly reduced the expression of viral genes, namely, ORF162, ORF049 and ORF072. Meanwhile, we found that overexpression of Ec-CysC resulted in upward trend of expression of inflammatory cytokines TNF-a, IL-1β and IL8 during SGIV infection. Further, SGIV-inducible apoptosis and Caspase-3 activity were also weakened by overexpression Ec-CysC in fathead minnow (FHM) cells. These results indicated that Ec-CysC might have a deeper involvement in fish immune defense, and played important roles in inflammation and apoptosis induced by SGIV. Highlights • Cystatin C gene from Epinephelus coioides (Ec-CysC) was identified and characterized. • The transcription of Ec-CysC was up-regulated in the spleen of grouper stimulated with SGIV. • Overexpression of Ec-CysC significantly inhibited SGIV infection in GS cells. • Overexpression of Ec-CysC suppressed SGIV induced apoptosis in FHM cells. [ABSTRACT FROM AUTHOR]

Abstract In this study, the transcriptional response of grouper to Singapore grouper iridovirus (SGIV) stimulation was characterized using RNA sequencing. Transcriptome sequencing of three test groups in the grouper was performed using the Illumina MiSeq platform. The three test groups were a control group, which was injected with PBS buffer; a high-susceptible (HS) group, which died shortly after the SGIV injection; and a high-resistance (HR) group, which survived the SGIV injection. In total, 38,253 unigenes were generated. When the HS group was compared with the control group, 885 unigenes were upregulated and 487 unigenes were downregulated. When the HR and control groups were compared, 1114 unigenes were upregulated and 420 were downregulated, and when the HR and HS groups were compared, 1010 unigenes were upregulated and 375 were downregulated. In the KEGG analysis, two immune-related pathways, the p53 and peroxisome proliferator-activated receptor pathways, were detected with highly significant enrichment. In addition, 7465 microsatellites and 22,1569 candidate single nucleotide polymorphisms were identified from our transcriptome data. The results suggested several pathways that are associated with traits of disease susceptibility or disease resistance, and provided extensive information about novel gene sequences, gene expression profiles, and genetic markers. This may contribute to vaccine research and a breeding program against SGIV infection in grouper. Highlights • This study suggested several pathways associated with traits of disease susceptibility or disease resistance in grouper. • This study offered information about novel gene sequences, gene expression profiles, and genetic markers in grouper. • This may contribute to vaccine research and a breeding program against SGIV infection in grouper. [ABSTRACT FROM AUTHOR]

Decision making in biomass supply chain management is subject to uncertainties in a number of factors such as biomass yield, procurement prices, market demands, transportation costs, and processing technologies. To better understand such uncertainties requires statistical analysis and data-intensive computing enabled by cyberGIS (aka geographic information science and systems based on advanced cyberinfrastructure and e-science). Therefore, we have developed a cyberGIS approach to optimize biomass supply chains under uncertainties. Our approach (1) designs optimal biomass supply chains from regional to national scale with flexible spatial selection of study areas; (2) performs uncertainty and sensitivity analysis to quantify how various sources of uncertainty in the biomass supply chain contribute to the variation of optimal results; and (3) provides users with online geodesign features. This approach has been implemented as a decision support system through integration of data management, mathematical modeling, uncertainty and sensitivity analysis, scenario analysis, and result representation and visualization. An optimization modeling analysis of 7000 scenarios using Monte Carlo methods has been conducted to quantify the uncertainty and sensitivity impact of various input factors on ethanol production costs and optimal biomass supply chain configurations in Illinois, United States. The results from uncertainty analysis showed that the minimal ethanol production costs range from $2.30 to $3.43 gal −1 , considering uncertainties from biomass supply, transportation, and processing. The results of sensitivity analysis demonstrated that biomass-ethanol conversion rate was the most influential factor to ethanol production costs while the optimal biomass supply chain infrastructure was sensitive to changes in biomass yield, raw biomass transportation cost, and logistics loss rate. Leveraging high performance computing power through cutting-edge cyberGIS software, what-if scenario analysis has been evaluated to make decisions in case of unexpected events occurring in the supply chain operations. [ABSTRACT FROM AUTHOR]

Dairy goats are the goats bred with the ability to produce large quantities of milk, and the increase of the female kid rate of breeding dairy goats is beneficial for milk production and economic benefits of dairy goat farms. Our previous study revealed that regulating the pH of dairy goat semen diluent to 6.2 or 7.4 respectively, the proportion of X chromosome bearing sperm (X-sperm) in the up and down layers of the tube after incubation was significantly higher than that of Y chromosome bearing sperm (Y-sperm) i.e. enriched X-sperm. In this study, fresh dairy goat semen collected in different seasons was diluted in different pH solutions to calculate the number and rate of X-sperm and to measure the functional parameters of enriched sperm. The artificial insemination experiments were performed with enriched X-sperm. The mechanisms of regulating the pH of diluent affecting sperm enrichment were further studied. The results showed that the proportion of enriched X-sperm in pH 6.2 and 7.4 diluents of sperm collected in different seasons showed no significantly different, but were significantly higher than that of the control group (pH 6.8). The in vitro functional parameters of X-sperm enriched in pH 6.2 and 7.4 diluent solution were not significantly different from those of the control group (P > 0.05). After artificial insemination with X-sperm enriched in pH7.4 diluent, the proportion of female offspring was significantly higher than that of the control group. It was found that the regulating pH of the diluent affected sperm mitochondrial activity and glucose uptake capacity via phosphorylating NF-κB and GSK3α/β proteins. The motility activity of X-sperm was enhanced under acidic conditions and weakened under alkaline conditions, which was conducive to the effective enrichment of X-sperm. This study demonstrated that the number and proportion of X-sperm enriched using pH 7.4 diluent were elevated, and the proportion of female kids was increased. This technology can be used for the reproduction and production of dairy goats in farms at large scales. • Regulating the pH of semen diluent could effectively enrich X-sperm. • The quality of X-sperm enriched was not altered after using alkaline diluent. • Diluent pH alters motility activity by affecting the production of sperm energy. [ABSTRACT FROM AUTHOR]

Abstract: Under the notion of sustainable development, a heuristic method named as the Boundary-based Fast Genetic Algorithm (BFGA) is developed to search for optimal solutions to a land use allocation problem with multiple objectives and constraints. Plans are obtained based on the trade-off among economic benefit, environmental and ecological benefit, social equity including Gross Domestic Product (GDP), conversion cost, geological suitability, ecological suitability, accessibility, Not In My Back Yard (NIMBY) influence, compactness, and compatibility. These objectives and constraints are formulated into a Multi-objective Optimization of Land Use (MOLU) model based on a reference point method (i.e. goal programming). This paper demonstrates that the BFGA is effective by offering the possibility of searching over tens of thousands of plans for trade-off sets of non-dominated plans. This paper presents an application of the model to the Tongzhou Newtown in Beijing, China. The results clearly evince the potential of the model in a planning support process by generating suggested near-optimal planning scenarios considering multi-objectives with different preferences. [Copyright &y& Elsevier]

Recent decades have witnessed a massive increase in the volume and quality of hydrologic data available to aid water resources decision makers, managers, and scientists. This has been accompanied by exponential growth in both desktop and cloud computing, as well as data storage capabilities. As a result, there are abundant opportunities to drastically change how water data is collected, managed, disseminated, and analyzed – which should ultimately have significant positive impacts on water science, engineering, and management. We are at the cusp of a new era in water data science which brings with it many exciting technological and scientific challenges and opportunities. Many of these challenges are alleviated and opportunities are multiplied when hydrology is viewed as a "team sport" rather than as an individual activity. These factors formed the motivation for the development of the HydroShare open-source software and the hydroshare.org operational system. This retrospective paper reviews a decade of HydroShare development and operation by presenting the general architecture, functionality, key contributions of the project to earth science and cyberinfrastructure research, current usage metrics, and future directions. • We present a review of the design and development of HydroShare for publishing water data and models. • The design and current functionality of this open source and open access software system is presented. • We review specific scientific and technical contributions of the project in both information science and modeling. • Current usage statistics and future directions of the system are discussed. [ABSTRACT FROM AUTHOR]

Rain-fed corn system has varied optimal environmental requirements by growth phases and regions. Understanding spatiotemporal characteristics of such requirements are important to ensure food security. To capture the stage-variant growing requirements, we develop and compare statistical models with various spatial and temporal resolutions to quantify the relationships between corn yield and meteorological factors. Multilinear regression models are trained using cross-sectional datasets pooled at three magnitudes (state, district, county) with temperature and precipitation related predictors according to three temporal resolutions (growing season, fixed month, growing phase). The models are applied to the U.S. Corn Belt for the time period of 1981–2016. The results show that average corn yield variation explained by meteorological factors can be improved to 50.2% at the agricultural district scale with growth phase resolution from ~30% at the state-level with growing season resolution. The results reveal that corn yield is most sensitive to extreme heat stress during the grain filling phase. From a spatial perspective, the northern counties in the U.S. Corn Belt are less limited by precipitation resources but are more vulnerable to extreme heat. The spatiotemporal explicit statistic modeling approach quantifies the impact and adaptation potential of changing the planting date for production. Appropriate adaptions by changing plant dates can increase the potential of corn production by 0.87 million Mg year−1 in the Corn Belt. Unlabelled Image • Develop and compare statistical models with various spatiotemporal resolutions. • Agricultural district and growth phase are practical resolutions for modeling. • Reveal spatial patterns of corn yield changes. • Evaluate the potential of early planting on mitigating climate stress. [ABSTRACT FROM AUTHOR]

Creating online data repositories that follow Findable, Accessible, Interoperable, and Reusable (FAIR) principles has been a significant focus in the research community to address the reproducibility crisis facing many computational fields, including environmental modeling. However, less work has focused on another reproducibility challenge: capturing modeling software and computational environments needed to reproduce complex modeling workflows. Containerization technology offers an opportunity to address this need, and there are a growing number of strategies being put forth that leverage containerization to improve the reproducibility of environmental modeling. This research compares ten such approaches using a hydrologic model application as a case study. For each approach, we use both quantitative and qualitative metrics for comparing the different strategies. Based on the results, we discuss challenges and opportunities for containerization in environmental modeling and recommend best practices across both research and educational use cases for when and how to apply the different containerization-based strategies. • Different approaches for reproducible environmental modeling are evaluated. • The approaches emphasize the use of containerization technologies. • The SUMMA hydrologic model is used for evaluating the approaches. • Results are used to recommend best practices for computational reproducibility. • Best practices are recommended for different common modeling use cases. [ABSTRACT FROM AUTHOR]

Building cyberinfrastructure for the reuse and reproducibility of large-scale hydrologic modeling studies requires overcoming a number of data management and software architecture challenges. The objective of this research is to advance the cyberinfrastructure needed to overcome some of these challenges to make such computational hydrologic studies easier to reuse and reproduce. We present novel cyberinfrastructure capable of integrating HydroShare (an online data repository), CyberGIS-Jupyter for Water and high performance computing (HPC) resources (computational environments), and the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrologic modeling framework through its application programming interface for orchestrating model runs. The cyberinfrastructure is demonstrated for a complex computational modeling study on a contiguous United States dataset. We present and discuss key capabilities of the cyberinfrastructure including (1) containerization for portability across compute environments, (2) Globus for large data transfers, (3) a Jupyter gateway to HPC environments, and (4) Jupyter notebooks for capturing the modeling workflows. • Presents novel cyberinfrastructure for complex hydrologic modeling studies. • Focuses on the challenges introduced by computationally and data intensive studies. • Uses Globus for large data transfers between scientific cloud services. • Leverages containerization for model portability across compute environments. • Combines model APIs and Jupyter notebooks to document modeling workflows. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, many patients could not receive timely healthcare services due to limited availability and access to healthcare resources and services. Previous studies found that access to intensive care unit (ICU) beds saves lives, but they overlooked the temporal dynamics in the availability of healthcare resources and COVID-19 cases. To fill this gap, our study investigated daily changes in ICU bed accessibility with an enhanced two-step floating catchment area (E2SFCA) method in the state of Texas. Along with the increased temporal granularity of measurements, we uncovered two phenomena: 1) aggravated spatial inequality of access during the pandemic, and 2) the retrospective relationship between insufficient ICU bed accessibility and the high case-fatality ratio of COVID-19 in rural areas. Our findings suggest that those locations should be supplemented with additional healthcare resources to save lives in future pandemic scenarios. • Spatial accessibility to healthcare was suppressed and changed dynamically due to surges of confirmed COVID-19 cases. • Daily changes in accessibility to ICU beds indicated a retrospective relationship with the COVID-19 case-fatality ratio. • Inequality of access was aggravated, particularly during the early outbreaks of confirmed COVID-19 cases due to the limited ICU bed allocation. [ABSTRACT FROM AUTHOR]

Accurate and timely spatial classification of crop types based on remote sensing data is important for both scientific and practical purposes. Spatially explicit crop-type information can be used to estimate crop areas for a variety of monitoring and decision-making applications such as crop insurance, land rental, supply-chain logistics, and financial market forecasting. However, there is no publically available spatially explicit in-season crop-type classification information for the U.S. Corn Belt (a landscape predominated by corn and soybean). Instead, researchers and decision-makers have to wait until four to six months after harvest to have such information from the previous year. The state-of-the-art research on crop-type classification has been shifted from relying on only spectral features of single static images to combining together spectral and time-series information. While Landsat data have a desirable spatial resolution for field-level crop-type classification, the ability to extract temporal phenology information based on Landsat data remains a challenge due to low temporal revisiting frequency and inevitable cloud contamination. To address this challenge and generate accurate, cost-effective, and in-season crop-type classification, this research uses the USDA's Common Land Units (CLUs) to aggregate spectral information for each field based on a time-series Landsat image data stack to largely overcome the cloud contamination issue while exploiting a machine learning model based on Deep Neural Network (DNN) and high-performance computing for intelligent and scalable computation of classification processes. Experiments were designed to evaluate what information is most useful for training the machine learning model for crop-type classification, and how various spatial and temporal factors affect the crop-type classification performance in order to derive timely crop type information. All experiments were conducted over Champaign County located in central Illinois, and a total of 1322 Landsat multi-temporal scenes including all the six optical spectral bands spanning from 2000 to 2015 were used. Computational experiments show the inclusion of temporal phenology information and evenly distributed spatial training samples in the study domain improves classification performance. The shortwave infrared bands show notably better performance than the widely used visible and near-infrared bands for classifying corn and soybean. In comparison with USDA's Crop Data Layer (CDL), this study found a relatively high Overall Accuracy (i.e. the number of the corrected classified fields divided by the number of the total fields) of 96% for classifying corn and soybean across all CLU fields in the Champaign County from 2000 to 2015. Furthermore, our approach achieved 95% Overall Accuracy by late July of the concurrent year for classifying corn and soybean. The findings suggest the methodology presented in this paper is promising for accurate, cost-effective, and in-season classification of field-level crop types, which may be scaled up to large geographic extents such as the U.S. Corn Belt. [ABSTRACT FROM AUTHOR]

Mammalian studies have shown that the nuclear transcription factor Y (NFYC) regulates the expression of major histocompatibility complex (MHC) by binding to CCAAT-box on promoters. However, few studies have focused on the regulatory mechanisms of NFYC in MHC pathway in fish. To explore the transcriptional regulatory mechanism of MHCIa in fish, we characterized NFYC and MHCIa of red-spotted grouper (Epinephelus akaara) (named EaNFYC and EaMHCIa, respectively). The EaNFYC genome sequence is 13,796 bp and contains 1,065 bp open reading frame. It is composed of ten exons and nine introns and encode a 354 amino acid sequence. The putative EaNFYC protein sequence shared 67.2–99.4% identity to vertebrate NFYC and possesses a typically conserved domain (histone- or haem-associated protein 5 domain (HAP5)) at the N-terminus. Transcripts of both EaNFYC and EaMHCIa were ubiquitously expressed in all detect tissues, and higher mRNA levels were detected in immune-relevant tissues (middle-kidney). EaNFYC expression increased after treatment with polyinosinic: polycytidylic acid, lipopolysaccharide, nervous necrosis virus, zymosan A, and Singapore grouper iridovirus. Analysis of subcellular localization indicated that EaNFYC was localized at the cell nucleus only. Furthermore, overexpression of EaNFYC significantly stimulated the expression of EaMHCIa , interferon signalling molecules and inflammatory cytokine. The region −878 bp to +82 bp of EaMHCIa promoter was identified to be the core promoter which EaNFYC take effect on. Additionally, point mutations and electrophoretic mobility shift assays verified that NFYC activate MHCIa expression by binding at the M1 and M2 binding sites that do not contain CCAAT-box. These results contribute to elucidating the function of fish NFYC on MHC transcriptional mechanisms, and provide the first evidence of positive regulation of MHCIa expression by NFYC in fish. • NFYC and MHCIa of E. akaara are functionally characterized. • Expression of these two genes is the highest in kidney. • NFYC activate MHCIa expression by binding with no CCAAT-box on its promoter. [ABSTRACT FROM AUTHOR]

Drainage network analysis is fundamental to understanding the characteristics of surface hydrology. Based on elevation data, drainage network analysis is often used to extract key hydrological features like drainage networks and streamlines. Limited by raster-based data models, conventional drainage network algorithms typically allow water to flow in 4 or 8 directions (surrounding grids) from a raster grid. To resolve this limitation, this paper describes a new vector-based method for drainage network analysis that allows water to flow in any direction around each location. The method is enabled by rapid advances in Light Detection and Ranging (LiDAR) remote sensing and high-performance computing. The drainage network analysis is conducted using a high-density point cloud instead of Digital Elevation Models (DEMs) at coarse resolutions. Our computational experiments show that the vector-based method can better capture water flows without limiting the number of directions due to imprecise DEMs. Our case study applies the method to Rowan County watershed, North Carolina in the US. After comparing the drainage networks and streamlines detected with corresponding reference data from US Geological Survey generated from the Geonet software, we find that the new method performs well in capturing the characteristics of water flows on landscape surfaces in order to form an accurate drainage network. • Vector-based drainage system analysis that enables water to flow in any direction. • Drainage system analysis and streamline detection based on fine-resolution LiDAR data. • High-performance computing for resolving computational intensity of fine-scale drainage network analysis. [ABSTRACT FROM AUTHOR]

In this study, a spatiotemporal estimation method based on Funk singular value decomposition (F-SVD) that considers the spatiotemporal correlation of rainfall is proposed to improve estimations from gauge observations. Hourly rainfall data of several flood events are selected to verify the proposed method by comparing with Inverse Distance Weighting (IDW) and Ordinary Kriging (OK) in Hanjiang basin, China. The results show that (1) F-SVD has the best performance in rainfall estimation, the larger the amount of rainfall event, the greater the improvement of F-SVD method as compared to OK and IDW; (2) through the combination/integration with F-SVD, the accuracy of IDW and OK can be greatly improved. Therefore, F-SVD can be employed as a practical method to estimate rainfall spatial distribution, which is essential data for regional hydrological modelling and water resource analysis. • A spatiotemporal estimation method based on F-SVD is proposed to estimate rainfall using gauge observation. • F-SVD is utilized to decompose the spatiotemporal matrix consisted of rainfall data. • F-SVD has higher accuracy and lower uncertainty compared to OK and IDW. • Through combination with F-SVD, the accuracy of IDW and OK can be greatly improved. • It is a practical method to process data for regional hydrological modelling. [ABSTRACT FROM AUTHOR]

Iridoviruses are well-known as causative agents of serious systemic diseases, and have been identified in more than 100 fish species worldwide in the last few decades. Singapore grouper iridovirus (SGIV) is a devastating viral pathogen of grouper aquaculture, and has caused substantial economic losses in China and Southeast Asia. However, there are currently no effective therapies against SGIV. Thus, rapid and sensitive diagnosis of SGIV infection is urgently needed for controlling virus infection. Lateral flow biosensors (LFBs) are widely designed for point-of-care use due to their properties of low cost, and simple and rapid operation. In the current study, we established and characterized a novel LFB combined with two previously screened DNA aptamers (Q2 and Q3) against SGIV-infected cells for SGIV detection. One aptamer was used for target isolation and the other was used for the strand displacement amplification reaction. The LFB rapidly detected SGIV infection in vitro and in vivo , with high specificity and sensitivity. It could specifically recognize SGIV-infected cells, other than other-virus-infected cells or uninfected cells. This biosensor detected SGIV infection in a number-dependent manner in SGIV-infected cells as low as 5 × 104 /mL. The LFB method did not need sophisticated manipulation and equipment, when compared to conventional PCR. It only needed shorter time for detection, with the complete process needing no more than 90 min. This is the first developed LFB combined with aptamers for detecting marine large DNA virus, and it could be a widely applicable tool in the grouper aquaculture industry. • It is the first time to detect iridoviruses by using LFB combined with aptamers instead of antibodies. • The limit of this LFB detection was as low as 5 × 104/mL SGIV-infected cells. • The detection process using LFB can be completed in less than 90 min. • This LFB works well and exhibits great potential application in the aquaculture industry. [ABSTRACT FROM AUTHOR]

Cyberinfrastructure needs to be advanced to enable open and reproducible environmental modeling research. Recent efforts toward this goal have focused on advancing online repositories for data and model sharing, online computational environments along with containerization technology and notebooks for capturing reproducible computational studies, and Application Programming Interfaces (APIs) for simulation models to foster intuitive programmatic control. The objective of this research is to show how these efforts can be integrated to support reproducible environmental modeling. We present first the high-level concept and general approach for integrating these three components. We then present one possible implementation that integrates HydroShare (an online repository), CUAHSI JupyterHub and CyberGIS-Jupyter for Water (computational environments), and pySUMMA (a model API) to support open and reproducible hydrologic modeling. We apply the example implementation for a hydrologic modeling use case to demonstrate how the approach can advance reproducible environmental modeling through the seamless integration of cyberinfrastructure services. • New approaches are needed to support open and reproducible environmental modeling. • Efforts should focus on integrating existing cyberinfrastructure to build new systems. • Our focus is on integrating repositories, computational environments, and model APIs. • An example implementation is shown using HydroShare, JupyterHub, and pySUMMA. • We demonstrate how the approach fosters reproducibility using a modeling case study. [ABSTRACT FROM AUTHOR]