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The source vessel noise is a very common noise type in offshore seismic surveys. The state‐of‐art deep learning‐based methods provide an end‐to‐end framework for seismic data denoising. The denoising performance of a pretrained network is, however, highly dependent on the completeness of the training set. When training a denoising network with only field data, especially for attenuating erratic noise, it is hard to obtain a noise‐free data as the training target for the network. Transfer learning, by combining the synthetic and field data, is an alternative solution for improving the generalization capabilities of the network, although being able to model such erratic noise represents also a challenge. Although the denoising results by traditional methods are not accurate enough for creating a complete training set, the features in residual noise by subtracting the denoised data from noisy data are enough for the network to learn. Considering the aforementioned factors, we develop a deep learning‐based workflow for the attenuation of the erratic source vessel noise from ocean bottom node 4C data. Instead of using denoising results directly, we use the conventional methods to extract noise and add them to the high signal‐to‐ratio region of the field data. The created noisy dataset is different from the original noisy data in noise regions; thus, the pretrained network can also be used for predicting the same original data. The denoising results of synthetic and field data all show that even the network is trained on a noisy labelled dataset, we still can obtain high signal‐to‐noise ratio denoising result. Besides, when compare with the results by filtering‐based methods, our proposed method can attenuate the vessel noise more effectively and preserve the near offsets reflections. [ABSTRACT FROM AUTHOR]

Blended acquisition, which allows multiple sources almost simultaneously fired, has become an effective way for accelerating seismic data acquisition. In order to use conventional processing methods for imaging, deblending is necessary for this special acquisition. Convolutional neural network‐based deblending methods provide a novel end‐to‐end framework for source separation. We proposed a field‐data‐based augmentation method that uses shuffled deblending noise as the features to be learned and take the inaccurate labels as the output of the network. Synthetic data experiments show that a network trained on data set with the proposed data augmentation method has higher accuracy for deblending even if the labelled data are noisy. Besides, 2D discrete wavelet transform, which has the advantage of multiscale decomposition and dimensionality reduction, is introduced to accelerate the computation of the network. The data augmentation method for data set generation and the computational speedup method for network training/predicting are also applied to field data. The results from synthetic and field data all confirm the performance of our methods. [ABSTRACT FROM AUTHOR]

Distracted behavior detection is an important task in computer-assisted driving. Although deep learning has made significant progress in this area, it is still difficult to meet the requirements of the real-time analysis and processing of massive data by relying solely on local computing power. To overcome these problems, this paper proposes a driving distraction detection method based on cloud–fog computing architecture, which introduces scalable modules and a model-driven optimization based on greedy pruning. Specifically, the proposed method makes full use of cloud–fog computing to process complex driving scene data, solves the problem of local computing resource limitations, and achieves the goal of detecting distracted driving behavior in real time. In terms of feature extraction, scalable modules are used to adapt to different levels of feature extraction to effectively capture the diversity of driving behaviors. Additionally, in order to improve the performance of the model, a model-driven optimization method based on greedy pruning is introduced to optimize the model structure to obtain a lighter and more efficient model. Through verification experiments on multiple driving scene datasets such as LDDB and Statefarm, the effectiveness of the proposed driving distraction detection method is proved. [ABSTRACT FROM AUTHOR]

The hydroxyl group was discovered to promote gold catalyzed hydrofluorination of alkynes via hydrogen bonding interaction. Based on this strategy, propargyl alcohols could be hydrofluorinated smoothly using Et3N·3HF under acidic additive-free conditions, which provided a straightforward alternative protocol for the synthesis of 3-fluoroallyl alcohols. [ABSTRACT FROM AUTHOR]

Diabetic foot ulcer (DFU) is one of the most serious complication of diabetes mellitus with the characteristic of long-term non-healing wound, which often leads to amputation and eventual death. In response to the refractory wound, medical dressings are considered helpful. However, traditional dressings often have the disadvantages of limited efficacy and difficulty in long-term maintenance, therefore there is an urgent need to develop novel dressing supplements. A variety of biomaterials have been used as wound dressings to promote wound repair. In particular, the injectable hydrogels with 3D structures similar to soft tissue have excellent biocompatibility, which can fill the irregular wound to play the effect of moisturizing, anti-infection, etc. On this basis, the injectable hydrogels can also be loaded with carriers with certain physiological and pharmacological effects, such as inorganic nanoparticles, drugs, and bioactive carriers, to achieve functions of antibacterial, anti-inflammatory, antioxidant, promoting collagen deposition, and vascular regeneration. Therefore they can promote wound healing well and have a great prospect in wound dressing. This review describes the mechanisms of DFU, summarizes the research progress of injectable carrier hydrogel systems in DFU repair, evaluates the advantages and disadvantages of various materials and the therapeutic principles of carriers, and proposes the challenges and development prospects of injectable carrier hydrogel. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have identified that single-nucleotide polymorphisms (SNPs) rs1258267 in CHAT and rs3753841 in COL11A1 are associated with primary angle-closure glaucoma (PACG). The purpose of the study was to evaluate the association of CHAT rs1258267 and COL11A1 rs3753841 with PACG. A comprehensive electronic database search was performed to include eligible studies, published from October 2010 to March 2022. By calculating summary odds ratios (ORs) and 95% confidence intervals (CI) under five genetic models, the risk of PACG related to these two SNPs could be estimated. Heterogeneity was measured with a Chi-square-based Q statistic test and the I2 statistic. By the Z test, we analyzed the overall effect of OR. We used funnel plots and Begg's funnel plots to evaluate the publication bias of included studies. The meta-analysis was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist. There were eighteen studies associating CHAT rs1258267 with PACG indicating evidently decreased PACG risk in five genetic models. Thirty studies were included to demonstrate a notable increase in the risk of PACG-carrying COL11A1 rs3753841 genotypes. Subgroup analyses showed that the association of CHAT rs1258267 and COL11A1 rs3753841 with PACG was obvious in Asians, while no evidence was found to confirm this connection in Caucasians. This meta-analysis suggests that CHAT rs1258267 G/A polymorphisms could bring about a decreased risk of PACG susceptibility and COL11A1 rs3753841 G/A polymorphisms could cause an increased risk. These effects mainly manifest in Asians. [ABSTRACT FROM AUTHOR]

The supervised neural network-based method provides an effective way for seismic data denoising. The noise level of seismic erratic noise, i.e., outlier, varies from traces, time windows, and shot gathers. The networks with popular structures may damage reflections because the network cannot learn the exact location of the noises. In order to accommodate the characteristics of erratic noise, we propose using an attention-based network that focuses more on noisy regions. The network outputs an attention map, which shows the spatial distribution probability of erratic noise and a raw noise that may have leakage reflections. To train the network with two kinds of outputs, we use two different loss functions to optimize the network. The erratic noises in the specified area can be extracted by multiplying the attention map and the raw noise, and the reflections in areas without noise are preserved. For generating the training set, we proposed a shuffled noise strategy, which starts from the inaccurate denoised data with the conventional method. Only the noisy data are available in the whole denoising workflow. The network can learn the noise features effectively with the shuffle noise strategy and can achieve better denoising effect than that based on the conventional method. In addition, the denoising capability of the network can be controlled manually by filtering the attention map with probability, and the denoising effect can be further improved through recovering the leaked signals. Synthetic and field data examples show that the proposed method has potential for field erratic noise attenuation. [ABSTRACT FROM AUTHOR]

[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]

The key to promoting the EPC (Engineering, Procurement, Construction) model in China's public construction projects is to alter the path dependence of a project owner's choice of project delivery model (PDM). This study uses evolutionary game theory to discuss the mechanism of government incentives as an external motivation to alter path dependence in the PDM. In addition, a cellular automata simulation to examine the influence of various government incentives on the project owner's choice. The results show that the combination of subsidies and penalties can produce the best incentive. Subsidies are more effective at promoting PDM institutional change, whereas penalties are more effective at preventing PDM institutional change from anti-recession effects. Based on our results, we propose that the Chinese government should take active subsidy measures at the initial stage of EPC promotion, and adopt a dynamic incentive strategy of continuously reducing subsidies and increasing penalties according to the improvement of the development degree of EPC model. [ABSTRACT FROM AUTHOR]

The efficient use of livestock and poultry manure waste has become a global challenge, with microorganisms playing an important role. To investigate the impact of novel ammonifying microorganism cultures (NAMC) on microbial community dynamics and carbon and nitrogen metabolism, five treatments [5% (v/w) sterilized distilled water, Amm-1, Amm-2, Amm-3, and Amm-4] were applied to cow manure compost. Inoculation with NAMC improved the structure of bacterial and fungal communities, enriched the populations of the functional microorganisms, enhanced the role of specific microorganisms, and promoted the formation of tight modularity within the microbial network. Further functional predictions indicated a significant increase in both carbon metabolism (CMB) and nitrogen metabolism (NMB). During the thermophilic phase, inoculated NAMC treatments boosted carbon metabolism annotation by 10.55%–33.87% and nitrogen metabolism annotation by 26.69%–63.11. Structural equation modeling supported the NAMC-mediated enhancement of NMB and CMB. In conclusion, NAMC inoculation, particularly with Amm-4, enhanced the synergistic interaction between bacteria and fungi. This collaboration promoted enzymatic catabolic and synthetic processes, resultng in positive feedback loops with the endogenous microbial community. Understanding these mechanisms not only unravels how ammonifying microorganisms influence microbial communities but also paves the way for the development of the composting industry and global waste management practices. [Display omitted] • Novel ammonifying microorganism cultures (NAMC) increased the stability of the composting • Inoculation with NAMC improved the structure of bacterial and fungal communities. • Inoculation with NAMC enhanced the complementary effects of bacteria and fungi in compost • Inoculated NAMC promoted carbon metabolism and nitrogen metabolism in composting. [ABSTRACT FROM AUTHOR]

North American bat species have been undergoing extreme population declines due to the White‐Nose Syndrome (WNS) epidemic caused by the spread of its pathogen, Pseudogymnoascus destructans. Existing models that represent the spread of the disease are limited in their scalability for use in management decisions or lacked the sophistication necessary to capture the complexity of WNS spread. Grounded in the theory of geoexpression, we exploit the latent structure of geographical process concurrency by implementing our modeling software using a concurrent Entity Component System architecture. We demonstrate our model's computational tractability for millions of individual bats. This work is significant because it lays the foundation for the use of advanced cyberinfrastructure and cyberGIS to address challenges in geographical wildlife epidemiology that can be understood using dynamic geographical Individual‐Based Models. [ABSTRACT FROM AUTHOR]

This paper surveys the rapid development of the Internet of Things, the massive data streams that are only now beginning to be generated from it, and the resulting opportunities and challenges that these data streams bring to geographic information analysis. These challenges arise because streaming data volumes cannot bt subjected to analysis using the standard repertoire of methods that have been designed to analyze static geospatial datasets. New approaches are needed, not to supplant, but to supplement, these existing tools. A focus is placed on the concept of data velocity (fast data) and its effects on sampling and inference. Innovative data ingestion strategies based on principles related to reservoir sampling and sketching are described. Dynamic temporal data flows present significant challenges to load balancing in distributed (e.g. cloud) parallel environments, even at exascale levels of performance. Further advances in the exploitation of data locality based on geographical concepts, as well as advanced processing methods based on edge and approximate computing, require further elucidation. Concepts are illustrated using a database compiled from a distributed sensor network of mobile radioactivity detectors. [ABSTRACT FROM AUTHOR]

Abstract The lysosomal cysteine protease cathepsin C plays a pivotal role in regulation of inflammatory and immune responses. However, the function of fish cathepsin C in virus replication remains largely unknown. In this study, cathepsin C gene (Ec-CC) was cloned and characterized from orange-spotted grouper, Epinephelus coioides. The full-length Ec-CC cDNA was composed of 2077 bp. It contained an open reading frame (ORF) of 1374 bp and encoded a 458-amino acid protein which shared 89% identity to cathepsin C from bicolor damselfish (Stegastes partitus). Amino acid alignment analysis showed that Ec-CC contained an N-terminal signal peptide, the propeptide region and the mature peptide. RT-PCR analysis showed that Ec-CC transcript was expressed in all the examined tissues which abundant in spleen and head kidney. After challenged with Singapore grouper iridovirus (SGIV) stimulation, the relative expression of EC-CC was significantly increased at 24 h post-infection. Subcellular localization analysis revealed that Ec-CC was distributed mainly in the cytoplasm. Further studies showed that overexpression of Ec-CC in vitro significantly delayed the cytopathic effect (CPE) progression evoked by SGIV and inhibited the viral genes transcription. Moreover, overexpression of Ec-CC significantly increased the expression of proinflammatory cytokines during SGIV infection. Taken together, our results demonstrated that Ec-CC might play a functional role in SGIV infection by regulating the inflammation response. Highlights • Cathepsin C gene from Epinephelus coioides (Ec-CC) was identified and characterized. • The expression of Ec-CC was up-regulated in the spleen of grouper challenged with SGIV. • Overexpression of Ec-CC significantly inhibited SGIV replication. • Overexpression of Ec-CC regulated inflammation response during SGIV infection. [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]

We present a novel framework called geoexpression for representing geographic process concurrency and discuss its implications for geographic dynamic modeling. Unapt representations of geographic process concurrency can lead to non‐deterministic geographic dynamic modeling outcomes, excessive cognitive burdens when reasoning about how concurrent processes interact, and even inconsistent results. After demonstrating the importance of geographic process concurrency, we examine how the characteristics of geographic process concurrency are missing from other state‐of‐the‐art geographic process representations. The geoexpression framework adopts Petri networks to allow researchers to understand how to relate geographic process concurrency to observed modeling patterns. Of interest are the real‐world geographic patterns formed by concurrent geographic processes. [ABSTRACT FROM AUTHOR]

Tracking spatial and temporal trends of events (e.g. disease outbreaks and natural disasters) is important for situation awareness and timely response. Social media, with increasing popularity, provide an effective way to collect event-related data from massive populations and thus a significant opportunity to dynamically monitor events as they emerge and evolve. While existing research has demonstrated the value of social media as sensors in event detection, estimating potential time spans and influenced areas of an event from social media remains challenging. Challenges include the unstable volumes of available data, the spatial heterogeneity of event activities and social media data, and the data sparsity. This paper describes a systematic approach to detecting potential spatiotemporal patterns of events by resolving these challenges through several interrelated strategies: using kernel density estimation for smoothed social media intensity surfaces; utilizing event-unrelated social media posts to help map relative event prevalence; and normalizing event indicators based on historical fluctuation. This approach generates event indicator maps and significance maps explaining spatiotemporal variations of event prevalence to identify space-time regions with potentially abnormal event activities. The approach has been applied to detect influenza activity patterns in the conterminous US usingTwitterdata. A set of experiments demonstrated that our approach produces high-resolution influenza activity maps that could be explained by available ground truth data. [ABSTRACT FROM PUBLISHER]

The mathematical modelling of a rotating tapered Timoshenko beam with preset and pre-twist angles is constructed. The partial differential equations governing the six degrees, i.e., three displacements in the axial, flapwise, and edgewise directions and three cross-sectional angles of torsion, flapwise bending, and edgewise bending, are obtained by the Euler angle descriptions. The power series method is then used to investigate the natural frequencies and the corresponding complex mode functions. It is found that all the natural frequencies are increased by the centrifugal stiffening except the twist frequency, which is slightly decreased. The tapering ratio increases the first transverse, torsional, and axial frequencies, while decreases the second transverse frequency. Because of the pre-twist, all the directions are gyroscopically coupled with the phase differences among the six degrees. [ABSTRACT FROM AUTHOR]

Psoriasis is a chronic inflammatory disorder of the skin. Accumulating evidence indicates that the Rel gene, a member of the NF-κB family, is a risk factor for the disease. We sought to investigate whether psoriasis can be prevented by directly targeting the Rel gene transcript, i.e., the c-Rel mRNA. Using chemically-modified c-Rel specific siRNA (siRel) and poly(ethylene glycol)-b-poly( l -lysine)-b-poly( l -leucine) (PEG-PLL-PLLeu) micelles, we successfully knocked down the expression of c-Rel, and showed that the expression of cytokine IL-23, a direct target of c-Rel that can drive the development of IL-17-producing T cells, was markedly inhibited. More importantly, treating mice with siRel not only prevented but also ameliorated imiquimod (IMQ)-induced psoriasis. Mechanistic studies showed that siRel treatment down-regulated the expression of multiple inflammatory cytokines. Taken together, these results indicate that the susceptibility gene Rel can be targeted to treat and prevent psoriasis. [ABSTRACT FROM AUTHOR]

Epidemic agent-based models ( ABMs) simulate individuals in artificial societies that are capable of movement, interaction, and transmitting disease among themselves. ABMs have been used to study the spread of disease at various spatial and temporal scales ranging from small communities to the world, over days, months, and years. The representations of space and time often vary between different epidemic ABMs and can be influenced by factors such as the size of a modeled population, computational requirements, population environments, and disease-related data. The influence that the representations of space and time have on epidemic ABMs is difficult to assess. Here we show that the finest representations of space and time-termed spatial and temporal granularities ( STGs)-in a parsimonious ABM affect speed, intensity, and spatial spread of a synthetic disease. Specifically, we found disease spread faster and more intensely as spatial granularity is coarsened, whereas disease spread slower and less intensely as temporal granularity is coarsened in a parsimonious ABM. Our study is the first to use the same epidemic ABM to examine the influence of STGs. Our results demonstrate that STGs influence ABM dynamics including early disease burnout and that an interrelationship exists between the coarsening of STGs and the speed and intensity at which disease spreads. Our parsimonious ABM is extended based on a structured community model and we found STGs also influence ABM dynamics in a more realistic context that includes hierarchical movement. Broadly, our study serves as a basis for further inquiry toward the influence of space-time representations on more realistic models that include multiscale mobility, routine movements (e.g., commuting), and heterogeneous population distributions. [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]

Known as an effective heuristic for finding optimal or near-optimal solutions to difficult optimization problems, a genetic algorithm (GA) is inherently parallel for exploiting high performance and parallel computing resources for randomized iterative evolutionary computation. It remains to be a significant challenge, however, to devise parallel genetic algorithms (PGAs) that can scale to massively parallel computer architecture (also known as the mainstream supercomputer architecture) primarily because: (1) a common PGA design adopts synchronized migration, which becomes increasingly costly as more processor cores are involved in global synchronization; and (2) asynchronous PGA design and associated performance evaluation are intricate due to the fact that PGA is a type of stochastic algorithm and the amount of computation work needed to solve a problem is not simply dependent on the problem size. To address the challenge, this paper describes a scalable coarse-grained PGA–PGAP, for a well-known NP -hard optimization problem: Generalized Assignment Problem (GAP). Specifically, an asynchronous migration strategy is developed to enable efficient deme interactions and significantly improve the overlapping of computation and communication. Buffer overflow and its relationship with migration parameters were investigated to resolve the issues of observed message buffer overflow and the loss of good solutions obtained from migration. Two algorithmic conditions were then established to detect these issues caused by communication delays and improper configuration of migration parameters and, thus, guide the dynamic tuning of PGA parameters to detect and avoid these issues. A set of computational experiments is designed to evaluate the scalability and numerical performance of PGAP. These experiments were conducted for large GAP instances on multiple supercomputers as part of the National Science Foundation Extreme Science and Engineering Discovery Environment (XSEDE). Results showed that, PGAP exhibited desirable scalability by achieving low communication cost when using up to 16,384 processor cores. Near-linear and super-linear speedups on large GAP instances were obtained in strong scaling tests. Desirable scalability to both population size and the number of processor cores were observed in weak scaling tests. The design strategies applied in PGAP are applicable to general asynchronous PGA development. [ABSTRACT FROM AUTHOR]

Computational and data sciences are transforming the entire science enterprise. In the arena of GIS, this is represented by the emergence of cyberGIS. We provide an overview of applying the cyberGIS approach to spatial analysis for health studies. We emphasize that cyberGIS is not just aserviceto traditional spatial analyses, but itself is an alternative approach to problem solving. Some fundamental and profound distinctions of cyberGIS approaches in health-GIS include the following: (1) they may greatly reduce the reliance on models or assumptions, and instead seek actual empirical evidence through mining a large amount of data orvirtualempirical evidence generated through computation; (2) they tend to be non-parametric and tend to generate local solution; (3) they are scalable to high-resolution and less aggregated data; (4) they tend to be stochastic rather than deterministic; and (5) with these approaches, the large amount of data may not be only from input data-sets, but also from analytical workflows. We described the kernel ratio estimation for local intensity estimation, therestrictedandcontrolledMonte Carlo for data disaggregation, andunrestrictedandcontrolledMonte Carlo for statistical significance evaluation as examples of the cyberGIS approaches in health-GIS. [ABSTRACT FROM PUBLISHER]

CyberGIS – defined as cyberinfrastructure-based geographic information systems (GIS) – has emerged as a new generation of GIS representing an important research direction for both cyberinfrastructure and geographic information science. This study introduces a 5-year effort funded by the US National Science Foundation to advance the science and applications of CyberGIS, particularly for enabling the analysis of big spatial data, computationally intensive spatial analysis and modeling (SAM), and collaborative geospatial problem-solving and decision-making, simultaneously conducted by a large number of users. Several fundamental research questions are raised and addressed while a set of CyberGIS challenges and opportunities are identified from scientific perspectives. The study reviews several key CyberGIS software tools that are used to elucidate a vision and roadmap for CyberGIS software research. The roadmap focuses on software integration and synthesis of cyberinfrastructure, GIS, and SAM by defining several key integration dimensions and strategies. CyberGIS, based on this holistic integration roadmap, exhibits the following key characteristics: high-performance and scalable, open and distributed, collaborative, service-oriented, user-centric, and community-driven. As a major result of the roadmap, two key CyberGIS modalities – gateway and toolkit – combined with a community-driven and participatory approach have laid a solid foundation to achieve scientific breakthroughs across many geospatial communities that would be otherwise impossible. [ABSTRACT FROM AUTHOR]

Parallel spatially explicit agent-based models (SE-ABM) exploit high-performance and parallel computing to simulate spatial dynamics of complex geographic systems. The integration of parallel SE-ABM with CyberGIS could facilitate straightforward access to massive computational resources and geographic information systems to support pre- and post-simulation analysis and visualization. However, to benefit from CyberGIS integration, parallel SE-ABM must overcome the challenge of communication management for orchestrating many processor cores in parallel computing environments. This paper examines and addresses this challenge by describing a generic framework for the management of inter-processor communication to enable parallel SE-ABM to scale to high-performance parallel computers. The framework synthesizes four interrelated components: agent grouping, rectilinear domain decomposition, a communication-aware load-balancing strategy, and entity proxies. The results of a series of computational experiments based on a template agent-based model demonstrate that parallel computational efficiency diminishes as inter-processor communication increases, particularly when scaling a fixed-size model to thousands of processor cores. Therefore, effective communication management is crucial. The communication framework is shown to efficiently scale up to 2048 cores, demonstrating its ability to effectively scale to thousands of processor cores to support the simulation of billions of agents. In a simulated scenario, the communication-aware load-balancer reduced both overall simulation time and communication percentage improving overall computational efficiency. By examining and addressing inter-processor communication challenges, this research enables parallel SE-ABM to efficiently use high-performance computing resources, which reduces the barriers for synergistic integration with CyberGIS. [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]

To investigate whether enzyme production can be enhanced in the Trichoderma reesei industrial hyperproducer strain RUT C30 by manipulation of cellulase regulation, the positive regulator Xyr1 was constitutively expressed under the control of the strong T. reesei pdc promoter, resulting in significantly enhanced cellulase activity in the transformant during growth on cellulose. In addition, constitutive expression of xyr1 combined with downregulation of the negative regulator encoding gene ace1 further increased cellulase and xylanase activities. Compared with RUT C30, the resulting transformant exhibited 103, 114, and 134 % greater total secreted protein levels, filter paper activity, and CMCase activity, respectively. Surprisingly, strong increases in xyr1 basal expression levels resulted in very high levels of CMCase activity during growth on glucose. These findings demonstrate the feasibility of improving cellulase production by modifying regulator expression, and suggest an attractive new single-step approach for increasing total cellulase productivity in T. reesei. [ABSTRACT FROM AUTHOR]

In the present study, a homemade probe-based nanometric morphology measurement system is proposed, which can be easily integrated with other probes, such as a diamond probe and an electrochemical electrode. In this system, an intermittent-contact mode is adopted, which is based on a set of micro-force servo modules. The micro-force serve module is mainly composed of a piezoelectric ceramic transducer, a capacitive displacement sensor, an excitation piezoelectric ceramic ring, and a four-beam spring. The four-beam spring integrated with a diamond probe is driven by the excitation piezoelectric ceramic ring. The mechanical structure and the control system of the measurement system are also designed. The vibration amplitude and the resolution of a normal load are calibrated during the engagement process under open-loop control. Moreover, the optimal values for parameters P, I, and D are obtained for the closed-loop measurement. The performance of the developed system is verified by measuring a standard sample. The measured depths agree well with the results obtained by commercial atomic force microscopy. The developed system can be used to measure nanostructures with high precision. [ABSTRACT FROM AUTHOR]

Agent-based models (ABM) allow for the bottom-up simulation of dynamics in complex adaptive spatial systems through the explicit representation of pattern–process interactions. This bottom-up simulation, however, has been identified as both data- and computing-intensive. While cyberinfrastrucutre provides such support for intensive computation, the appropriate management and use of cyberinfrastructure (CI)-enabled computing resources for ABM raise a challenging and intriguing issue. To gain insight into this issue, in this article we present a service-oriented simulation framework that supports spatially explicit agent-based modeling within a CI environment. This framework is designed at three levels: intermodel, intrasimulation, and individual. Functionalities at these levels are encapsulated into services, each of which is an assembly of new or existing services. Services at the intermodel and intrasimulation levels are suitable for generic ABM; individual-level services are designed specifically for modeling intelligent agents. The service-oriented simulation framework enables the integration of domain-specific functionalities for ABM and allows access to high-performance and distributed computing resources to perform simulation tasks that are often computationally intensive. We used a case study to investigate the utility of the framework in enabling agent-based modeling within a CI environment. We conducted experiments using supercomputing resources on the TeraGrid – a key element of the US CI. It is indicated that the service-oriented framework facilitates the leverage of CI-enabled resources for computationally intensive agent-based modeling. [ABSTRACT FROM AUTHOR]

Cyberinfrastructure (CI) integrates distributed information and communication technologies for coordinated knowledge discovery. The purpose of this article is to develop a CyberGIS framework for the synthesis of CI, geographic information systems (GIS), and spatial analysis (broadly including spatial modeling). This framework focuses on enabling computationally intensive and collaborative geographic problem solving. The article describes new trends in the development and use of CyberGIS while illustrating particular CyberGIS components. Spatial middleware glues CyberGIS components and corresponding services while managing the complexity of generic CI middleware. Spatial middleware, tailored to GIS and spatial analysis, is developed to capture important spatial characteristics of problems through the spatially explicit representation of computing, data, and communication intensity (collectively termed computational intensity), which enables GIS and spatial analysis to locate, allocate, and use CI resources effectively and efficiently. A CyberGIS implementation—GISolve—is developed to systematically integrate CI capabilities, including high-performance and distributed computing, data management and visualization, and virtual organization support. Currently, GISolve is deployed on the National Science Foundation TeraGrid, a key element of the U.S. and worldwide CI. A case study, motivated by an application in which geographic patterns of the impact of global climate change on large-scale crop yields are examined in the United States, focuses on assessing the computational performance of GISolve on resolving the computational intensity of a widely used spatial interpolation analysis that is conducted in a collaborative fashion. Computational experiments demonstrate that GISolve achieves a high-performance, distributed, and collaborative CyberGIS implementation. [ABSTRACT FROM AUTHOR]

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]

Cyberinfrastructure (CI) represents the integrated information and communication technologies for distributed information processing and coordinated knowledge discovery, and is promising to revolutionize how science and engineering are conducted in the twenty-first century. The value of bridging CI and GIScience is significant to advance CI and benefit GIScience research and education, particularly in distributed geographic information processing (DGIP). This article presents a holistic framework that bridges CI and GIScience by integrating CI capabilities to empower GIScience research and education and establish generic DGIP services supported by CI. The framework, the TeraGrid GIScience Gateway, is based on a CI science gateway approach developed on the National Science Foundation (NSF) TeraGrid - a key element of US and world CI. This gateway develops a unifying service-oriented framework with respect to its architecture, design, and implementation as well as its integration with the TeraGrid. The functions of the gateway focus on enabling parallel and distributed processing for geographical analysis, managing the complexity of TeraGrid software environment, and establishing a Web-based GIS for the GIScience community to gain shared and collaborative access to TeraGrid-based geospatial processing services. The gateway implementation uses Web 2.0 technologies to create a highly configurable and interactive multiuser environment. Two case studies, Bayesian geostatistical modeling and a spatial statistic [image omitted] for detecting local clustering, are used to demonstrate the gateway functions and user environment. The service transformation for these analyses is applied to create a shared, decentralized, and collaborative geographical analysis environment in which GIScience community users can contribute new analysis services and reuse existing gateway services. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical approach that has been developed to capture the computational intensity and computing resource requirements of geographical data and analysis methods. These requirements are then transformed into a common framework, a grid-based representation of a spatial computational domain, which supports the efficient use of emerging cyberinfrastructure environments. Two key types of transformational functions (data-centric and operation-centric) are identified and their relationships are explained. The application of the approach is illustrated using two geographical analysis methods: inverse distance weighted interpolation and the [image omitted] spatial statistic. We describe the underpinnings of these two methods, present their conventional sequential algorithms, and then address their latent parallelism based on a spatial computational domain representation. Through the application of this theoretical approach, the development of domain decomposition methods is decoupled from specific high-performance computer architectures and task scheduling implementations, which makes the design of generic parallel processing solutions feasible for geographical analyses. [ABSTRACT FROM AUTHOR]

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]

Spatial interpolation is widely used in geographical information systems to create continuous surfaces from discrete data points. The creation of such surfaces, however, can involve considerable computation, especially when large problems are addressed, because of the need to search for neighbors on which to base interpolation calculations. Computational Grids provide the computing resources to tackle spatial interpolation in a timely way. The objective of this paper is to investigate the use of domain decomposition for a distributed inverse-distance-weighted spatial interpolation algorithm; the algorithm runs using the Globus Toolkit (GT) in a heterogeneous Grid computing environment. The interpolation algorithm is modified for implementation in the Grid by using a quadtree to spatially index and adaptively decompose the interpolation problem to balance processing loads. In addition, the GT allows the distributed algorithm to couple multiple machines, potentially of different architectures, to dynamically schedule the decomposed sub-problems through Globus services and protocols (e.g., resource management, data transfer). Experiments are conducted to test how well this distributed IDW interpolation algorithm scales to heterogeneous grid computing environments using irregularly distributed geographical data sets. [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]

Understanding urban heat exposure dynamics is critical for public health, urban management, and climate change resilience. Near real-time analysis of urban heat enables quick decision-making and timely resource allocation, thereby enhancing the well-being of urban residents, especially during heatwaves or electricity shortages. To serve this purpose, we develop a cyberGIS framework to analyze and visualize human sentiments of heat exposure dynamically based on near real-time location-based social media (LBSM) data. Large volumes and low-cost LBSM data, together with a content analysis algorithm based on natural language processing are used effectively to generate near real-time heat exposure maps from human sentiments on social media at both city and national scales with km spatial resolution and census tract spatial unit. We conducted a case study to visualize and analyze human sentiments of heat exposure in Chicago and the United States in September 2021. Enabled with high-performance computing, dynamic visualization of heat exposure is achieved with fine spatiotemporal scales while heat exposure detected from social media data can be used to understand heat exposure from a human perspective and allow timely responses to extreme heat. Near real-time and high spatial resolution mapping of human sentiments of heat exposure with Twitter data An integrated cyberGIS and machine learning framework for visualizing heat exposure with Twitter data Human sentiment of heat exposure mapping in the City of Chicago and the United States [ABSTRACT FROM AUTHOR]

Ocean‐bottom node exploration has developed rapidly in marine seismic exploration. However, due to no illumination for the seafloor, and the observation system of the 'less traces but more shots' mode, the conventional surface‐related multiples elimination meets challenges for the application in the ocean‐bottom node exploration. This paper develops a new three‐dimensional surface‐related multiples elimination technique for ocean‐bottom node data. First, a three‐dimensional Kirchhoff pre‐stack time mirror migration algorithm is proposed to realize the accurate imaging for the seabed. Second, the time domain Kirchhoff demigration method is used to construct the contribution traces for multiples prediction. Finally, the surface multiples can be predicted based on the equation derived in the paper. Model tests and field data processing prove that our method can accurately predict the surface‐related multiples for ocean‐bottom node data and be helpful to the multiple elimination, which shows that the proposed technique has potential in actual ocean‐bottom node data processing. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including geographic information systems (GIS), cyberinfrastructure, and high-performance computing of spatial modeling.

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]

Numerous scientific disciplines have witnessed tremendous growth in the amount of spatial data produced over the past decade. To handle the volume and velocity of such data, researchers have embraced distributed systems, which partition data among multiple nodes to provide scalability and high availability. Previous work on partitioning large spatiotemporal data focuses on bulk-ingestion and static partitioning, hence is unable to handle dynamic data and querying workloads which is common for real-time data. In this paper we develop GeoBalance as a workload-aware partitioning approach for spatiotemporal data that can adapt partitions on-the-fly without disrupting the data ingestion/retrieval process. GeoBalance employs a spatial evolutionary algorithm to incrementally tune the partitions according to a geo-aware partitioning fitness function. In addition, we perform a rolling migration from one partitioning scheme to another to ensure that data ingestion and retrieval is not compromised during the partition change period. We conduct multiple experiments using a write-intensive hybrid workload of Twitter data and random hotspots, to demonstrate that the GeoBalance partitioning approach outperforms statically defined partitions and other partitioning algorithms such as k-d tree. [ABSTRACT FROM AUTHOR]

For a potential application of FK506 in the treatment of acute kidney failure only the FKBP12 binding capability of the compound is required, while the immunosuppressive activity via calcineurin binding is considered as a likely risk to the patients. The methoxy groups at C13 and C15 are thought to have significant influence on the immunosuppressive activity of the molecule. Consequently, FK506 analogs with different functionalities at C13 and C15 were generated by targeted CRISPR editing of the AT domains in module 7 and 8 of the biosynthetic assembly line in Streptomyces tsukubaensis. In addition, the corresponding FK520 (C21 ethyl derivative of FK506) analogs could be obtained by media adjustments. The compounds were tested for their bioactivity in regards to FKBP12 binding, BMP potentiation and calcineurin sparing. 15‐desmethoxy FK506 was superior to the other tested analogs as it did not inhibit calcineurin but retained high potency towards FKBP12 binding and BMP potentiation. [ABSTRACT FROM AUTHOR]

Timely identification of disaster‐prone neighborhoods and examination of disparity in disaster exposure are critical for policymakers to plan efficient disaster management strategies. Many studies have investigated racial, ethnic, and geographic disparities and populations most vulnerable to disasters. However, little attention has been paid to the development of easily accessible and reusable tools to enable: (1) the prompt identification of vulnerable neighborhoods; and (2) the examination of social disparity in disaster impact. In this research, we have developed a visual analytics tool that allows users to: (1) delineate neighborhoods based on their selection of variables; and (2) explore which neighborhoods are susceptible to the impacts of disasters based on specific socioeconomic and demographic characteristics. Through an exploration of COVID‐19 data in the case study, we revealed that the tool can provide new insights into the identification of vulnerable neighborhoods that need immediate attention for disaster control, management, and relief. [ABSTRACT FROM AUTHOR]

Detecting irregularly shaped spatial clusters within heterogeneous point processes is challenging because the number of potential clusters with different sizes and shapes can be enormous. This research develops a novel method, expansion-based spatial clustering for inhomogeneous point processes (ESCIP), for detecting spatial clusters of any shape within a heterogeneous point process in the context of analyzing spatial big data. Statistical testing is used to find core points—points with neighboring areas that have significantly more cases than the expectation—and an expansion approach is developed to find irregularly shaped clusters by connecting nearby core points. Instead of employing a brute-force search for all potential clusters, as done in the spatial scan statistics, this approach only requires testing a small neighboring area for each potential core point. Moreover, spatial indexing is leveraged to speed up the search for nearby points and the expansion of clusters. The proposed method is implemented with Poisson and Bernoulli models and evaluated for large spatial data sets. Experimental results show that ESCIP can detect irregularly shaped spatial clusters from millions of points with high efficiency. It is also demonstrated that the method outperforms the spatial scan statistics on the flexibility of cluster shapes and computational performance. Furthermore, ESCIP ensures that every subset of a detected cluster is statistically significant and contiguous. Key Words: cyberGIS, spatial algorithm, spatial analysis, spatial clustering. [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]

Ranaviruses are nucleoplasmic large DNA viruses that can cause major economic losses in the aquaculture industry and pose a severe threat to global ecological diversity. The available literature demonstrates that classifiable members of the genus Ranavirus enter cells via multiple and complicated routes. Here, we demonstrated the underlying cellular entry mechanism of soft-shelled turtle iridovirus (STIV) using green fluorescence tagged recombinant virus. Treatment with chlorpromazine, sucrose, ethyl-isopropyl amiloride, chloroquine or bafilomycin A1 all significantly decreased STIV infection, suggesting that STIV uses clathrin-mediated endocytosis and macropinocytosis to enter cells via a pH-dependent pathway. Depletion of cellular cholesterol with methyl-β-cyclodextrin significantly inhibited STIV entry, but neither filipin III nor nystatin did, suggesting that STIV entry was cholesterol dependent but caveola independent. Treatment with dynasore, genistein, ML-7 or cytochalasin D all significantly inhibited STIV infection, indicating that Rac GTPase and myosin II activity were required for the macropinocytosis-like pathway as well as actin polymerization. Our findings suggest that the molecular events involved in STIV entry are not identical to those of other ranavirus isolates. Our results also extend our understanding of the molecular mechanism of iridovirus entry and pathogenesis. [ABSTRACT FROM AUTHOR]

The article presents information on the function of cyberinfrastructure in empowering ecological and environmental research. Cyberinfrastructure describes integrated information and communication technologies for distributed information processing and coordinated knowledge discovery, which promises to revolutionize the way that science and engineering are done in the 21st century and beyond. The National Science Foundation's TeraGrid--a key element of the U.S. and world cyberinfrastructure--has facilitated the development and operation of more than two dozen science and engineering gateways. Coupling cyberinfrastructure and geographical information system can facilitate computational thinking to analyze massive quantities of spatiotemporal data rapidly and economically.

The black sea bass, Centropristis striata, is a potential candidate for commercial aquaculture. Due to inadequate removal of nitrogen in its breeding environment, C. striata exhibits increased nitrate concentration, which can cause acute toxicity, including energy metabolism damage and tissue damage. Therefore, RNA-seq technology was applied to characterize genes associated with toxicity tolerance under nitrate stress. The nitrate treatment caused significant changes in a total of 8920 genes, of which 2949 genes were up-regulated and 5971 genes were down-regulated. It was found that significantly enriched GO terms and KEGG were associated with blood microparticles, inhibitors of enzyme activity, and complement and coagulation cascade pathways. Furthermore, through bioinformatics analysis, it was found that these different pathways obtained in GO and KEGG enrichment analysis were mostly related to the immune and inflammatory response of fish. This study expands our understanding of the mechanism of nitrate stress affecting the liver function of C. striata. [ABSTRACT FROM AUTHOR]