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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 promotion of the EPC model in public construction projects is a priority for reform in the Chinese construction industry. This study integrates the push–pull–mooring (PPM) model with the stimulus-organism-response (SOR) framework to explore the influencing factors and action mechanisms of adopting the EPC model of the owners of public construction projects in China. An online questionnaire was sent to public construction project owners who have participated in EPC projects. Partial least squares-structural equation modeling (PLS-SEM) was employed to analyze the survey data. The results identified that the push factors for public construction project owners to the adoption of the EPC model include owners' management ability, owner's knowledge about EPC, and the matching degree between the owner's management philosophy and EPC model; the pull factor is the performance pressure of the project; and the mooring factors include subjective norms, the maturity degree of the EPC, and the support environment. In addition, perceived usefulness, cognitive, and adoption attitude play a mediating role in the relationship between external stimuli and adoption behavior. Overall, this study enhances the understanding of Chinese public project owners' behavior in adopting the EPC model and provides a theoretical basis for policy recommendations to promote the adoption of the EPC model by public construction project owners in China. [ABSTRACT FROM AUTHOR]

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]

In dairy goat farming, increasing the female kid rate is beneficial to milk production and is, therefore, economically beneficial to farms. Our previous study demonstrated that alkaline incubation enriched the concentration of X-chromosome-bearing sperm; however, the mechanism by which pH affects the motility of X-chromosome-bearing sperm remains unclear. In this study, we explored this mechanism by incubating dairy goat sperm in alkaline dilutions, examining the pattern of changes in sperm internal pH and Ca2+ concentrations and investigating the role of the sAC/cAMP/PKA pathway in influencing sperm motility. The results showed that adding a calcium channel inhibitor during incubation resulted in a concentration-dependent decrease in the proportion of spermatozoa with forward motility, and the sperm sAC protein activity was positively correlated with the calcium ion concentration (r = 0.9972). The total motility activity, proportion of forward motility, and proportion of X-chromosome-bearing sperm decreased (p < 0.05) when cAMP/PKA protease activity was inhibited. Meanwhile, the enrichment of X-chromosome-bearing sperm by pH did not affect the sperm capacitation state. These results indicate that alkaline dilution incubation reduces Ca2+ entry into X-sperm and the motility was slowed down through the sAC/cAMP/PKA signaling pathway, providing a theoretical foundation for further optimization of the sex control method. [ABSTRACT FROM AUTHOR]

Largemouth bass virus (LMBV) is a major viral pathogen in largemouth bass culture, usually causing high mortality and heavy economic losses. Accurate and early detection of LMBV is crucial for diagnosis and control of the diseases caused by LMBV. Previously, we selected the specific aptamers, LA38 and LA13, targeting LMBV by systematic evolution of ligands by exponential enrichment (SELEX). In this study, we further generated truncated LA38 and LA13 (named as LA38s and LA13s) with high specificity and affinities and developed an aptamer-based sandwich enzyme-linked apta-sorbent assay (ELASA) for LMBV diagnosis. The sandwich ELASA showed high specificity and sensitivity for the LMBV detection, without cross reaction with other viruses. The detection limit of the ELASA was as low as 1.25 × 102 LMBV-infected cells, and the incubation time of the lysate and biotin labeled aptamer was as short as 10 min. The ELASA could still detect LMBV infection in spleen lysates at dilutions of 1/25, with good consistency of qRT-PCR. For the fish samples collected from the field, the sensitivity of ELASA was 13.3% less than PCR, but the ELASA was much more convenient and less time consuming. The procedure of ELASA mainly requires washing and incubation, with completion in approximately 4 h. The sandwich ELASA offers a useful tool to rapidly detect LMBV rapidly, contributing to control and prevention of LMBV infection. [ABSTRACT FROM AUTHOR]

Accurate maps of regional surface water features are integral for advancing ecologic, atmospheric and land development studies. The only comprehensive surface water feature map of Alaska is the National Hydrography Dataset (NHD). NHD features are often digitized representations of historic topographic map blue lines and may be outdated. Here we test deep learning methods to automatically extract surface water features from airborne interferometric synthetic aperture radar (IfSAR) data to update and validate Alaska hydrographic databases. U-net artificial neural networks (ANN) and high-performance computing (HPC) are used for supervised hydrographic feature extraction within a study area comprised of 50 contiguous watersheds in Alaska. Surface water features derived from elevation through automated flow-routing and manual editing are used as training data. Model extensibility is tested with a series of 16 U-net models trained with increasing percentages of the study area, from about 3 to 35 percent. Hydrography is predicted by each of the models for all watersheds not used in training. Input raster layers are derived from digital terrain models, digital surface models, and intensity images from the IfSAR data. Results indicate about 15 percent of the study area is required to optimally train the ANN to extract hydrography when F1-scores for tested watersheds average between 66 and 68. Little benefit is gained by training beyond 15 percent of the study area. Fully connected hydrographic networks are generated for the U-net predictions using a novel approach that constrains a D-8 flow-routing approach to follow U-net predictions. This work demonstrates the ability of deep learning to derive surface water feature maps from complex terrain over a broad area. [ABSTRACT FROM AUTHOR]

MicroRNA-21 (miR-21) is an oncomiR and significantly upregulated in a wide range of cancers. It is strongly involved in apoptosis and oncogenesis, since most of its reported targets are tumor suppressors. Recently, miR-21 was found to be correlated with the pathogenesis of autoimmune diseases and may play an essential role in regulating autoimmune responses. In particular, miR-21 promotes Th17 cell differentiation, which mediates the development of multiple autoimmune diseases. In this article, we review the current research on the mechanisms that regulate miR-21 expression, the potential of miR-21 as a diagnostic biomarker for autoimmune disease and the mechanisms by which miR-21 promotes the development of autoimmune disease. We also discussed the therapeutic potential of targeting miR-21 in treating patients with autoimmune disease.