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Abstract Background Flies are acknowledged as vectors of diseases transmitted through mechanical means and represent a significant risk to human health. The study aimed to determine the prevalence of enteropathogens carried by flies in Pudong New Area to inform strategies for preventing and controlling flies. Methods Samples were collected from various locations in the area using cage trapping techniques between April and November 2021, encompassing various habitats such as parks, residential areas, restaurants, and farmers’ markets. The main fly species were identified using cryomicrography and taxonomic enumeration, with 20 samples per tube collected from different habitats. Twenty-five enteropathogens were screened using GI_Trial v3 TaqManTM microbial arrays. Results A total of 3,875 flies were collected from 6,400 placements, resulting in an average fly density of 0.61 flies per cage. M. domestica were the most common species at 39.85%, followed by L. sericata at 16.57% and B. peregrina at 13.14%. Out of 189 samples, 93 tested positive for enteropathogens, with nine different pathogens being found. 12.70% of samples exclusively had parasites, a higher percentage than those with only bacteria or viruses. The study found that M. domestica had fewer enteropathogens than L. sericata and B. peregrina, which primarily harbored B. hominis instead of bacteria and viruses such as E. coli, Astrovirus, and Sapovirus. During spring testing, all three fly species exhibited low rates of detecting enteropathogens. M. domestica were found in residential areas with the highest number of pathogen species, totaling six. In contrast, L. sericata and B. peregrina were identified in farmers’ markets with the highest number of pathogen species, totaling six and seven, respectively. Conclusions Flies have the potential to serve as vectors for the transmission of enteropathogens, thereby posing a substantial risk to public health.

Abstract The COVID-19 pandemic has had profound adverse effects on public health and society, with increased mobility contributing to the spread of the virus and vulnerable populations, such as those with pre-existing health conditions, at a higher risk of COVID-19 mortality. However, the specific spatial and temporal impacts of health conditions and mobility on COVID-19 mortality have yet to be fully understood. In this study, we utilized the Geographical and Temporal Weighted Regression (GTWR) model to assess the influence of mobility and health-related factors on COVID-19 mortality in the United States. The model examined several significant factors, including demographic and health-related factors, and was compared with the Multiscale Geographically Weighted Regression (MGWR) model to evaluate its performance. Our findings from the GTWR model reveal that human mobility and health conditions have a significant spatial impact on COVID-19 mortality. Additionally, our study identified different patterns in the association between COVID-19 and the explanatory variables, providing insights to policymakers for effective decision-making.

Abstract With the rapid development of the Internet-of-Things (IoT), a massive amount of transient data is transmitted in edge networks. Transient data are highly time-sensitive, such as monitoring data generated by industrial devices. Due to their inefficiency, traditional caching strategies in edge networks are inadequate for handling transient data. Thus, to improve the efficiency of transient data caching, we construct a freshness model of transient data and propose a maximum entropy Actor–Critic-based caching strategy, TD-MEAC-which can improve the freshness of cached data and reduce the long-term caching cost. Simulation results show that the proposed TD-MEAC achieves a higher cache hit rate and maintains a higher average freshness of cached transient data compared with the existing DRL and baseline caching strategies.

Stress is a crucial factor affecting social decision-making. However, its impacts on the behavioral and neural processes of females’ unfairness decision-making remain unclear. Combining computational modeling and functional near-infrared spectroscopy (fNIRS), this study attempted to illuminate the neurocomputational signature of unfairness decision-making in females. We also considered the effect of trait stress coping styles. Forty-four healthy young females (20.98 ± 2.89 years) were randomly assigned to the stress group (n = 21) and the control group (n = 23). Acute psychosocial stress was induced by the Trier Social Stress Test (TSST), and participants then completed the one-shot ultimatum game (UG) as responders. The results showed that acute psychosocial stress reduced the adaptability to fairness and lead to more random decision-making responses. Moreover, in the stress group, a high level of negative coping style predicted more deterministic decision. fNIRS results showed that stress led to an increase of oxy-hemoglobin (HbO) peak in the right temporoparietal junction (rTPJ), while decreased the activation of left middle temporal gyrus (lMTG) when presented the moderately unfair (MU) offers. This signified more involvement of the mentalization and the inhibition of moral processing. Moreover, individuals with higher negative coping scores showed more deterministic decision behaviors under stress. Taken together, our study emphasizes the role of acute psychosocial stress in affecting females’ unfairness decision-making mechanisms in social interactions, and provides evidences for the “tend and befriend” pattern based on a cognitive neuroscience perspective.

Ulcerative colitis (UC) is a kind of inflammatory bowel condition characterized by inflammation within the mucous membrane, rectal bleeding, diarrhea, and pain experienced in the abdominal region. Existing medications for UC have limited treatment efficacy and primarily focus on symptom relief. Limonium bicolor (LB), an aquatic traditional Chinese medicine (TCM), exerts multi-targeted therapeutic effects with few side effects and is used to treat anemia and hemostasis. Nevertheless, the impact of LB on UC and its mechanism of action remain unclear. Therefore, the objective of this study was to investigate the anti-inflammatory effects and mechanism of action of ethanol extract of LB (LBE) in lipopolysaccharide-induced RAW 264.7 macrophages and dextran sulfate sodium (DSS)-induced UC. The results showed that LBE suppressed the secretion of cytokines in LPS-stimulated RAW 264.7 cells in a dose-dependent manner. LBE had protective effects against DSS-induced colitis in mice, decreased the disease activity index (DAI) score, alleviated symptoms, increased colon length, and improved histological characteristics, thus having protective effects against DSS-induced colitis in mice. In addition, it reversed disturbances in the abundance of proteobacteria and probiotics such as Lactobacillus and Blautia in mice with DSS-induced UC. Based on the results of network pharmacology analysis, we identified four main compounds in LBE that are associated with five inflammatory genes (Ptgs2, Plg, Ppar-γ, F2, and Gpr35). These results improve comprehension of the biological activity and functionality of LB and may facilitate the development of LB-based compounds for the treatment of UC.

Visual object tracking tasks often struggle with utilizing inter-frame correlation information and handling challenges like local occlusion, deformations, and background interference. To address these issues, this paper proposes a spatio-temporal interactive fusion (STIF) based visual object tracking method. The goal is to fully utilize spatio-temporal background information, enhance feature representation for object recognition, improve tracking accuracy, adapt to object changes, and reduce model drift. The proposed method incorporates feature-enhanced networks in both temporal and spatial dimensions. It leverages spatio-temporal background information to extract salient features that contribute to improved object recognition and tracking accuracy. Additionally, the model’s adaptability to object changes is enhanced, and model drift is minimized. A spatio-temporal interactive fusion network is employed to learn a similarity metric between the memory frame and the query frame by utilizing feature enhancement. This fusion network effectively filters out stronger feature representations through the interactive fusion of information. The proposed tracking method is evaluated on four challenging public datasets. The results demonstrate that the method achieves state-of-the-art (SOTA) performance and significantly improves tracking accuracy in complex scenarios affected by local occlusion, deformations, and background interference. Finally, the method achieves a remarkable success rate of 78.8% on TrackingNet, a large-scale tracking dataset.

Most popular Siamese network trackers implement similarity learning by convolutional intercorrelation using the output features of the last layer of the feature extraction network. The extracted image features, although having high semantic information, lack edge as well as some detail information, which is not conducive to meet some challenges in tracking tasks. In this paper, a simple Siamese network tracking based on feature enhancement is proposed to address the above problems. The method applies the output features of the last three layers of the feature extraction network together, which are used to supplement some detailed information and to augment the target information features by a self-attentive mechanism. At the same time, a classification enhancement module is designed in the back-end classification Section to assist in classifying target information more accurately. Finally, this method is experimented on four challenging datasets, namely GOT-10k, UAV123, OTB100 and LaSOT, and the experiments show that the proposed method in this paper outperforms some existing state-of-the-art target trackers. And the method in this paper weighs the tracking accuracy and speed in the model structure design, which both improves the tracker tracking performance and can run at a speed of about 61 fps on such large data as GOT-10k.

Multiple-point geostatistics (MPS) has more advantages than two-point geostatistics in reproducing the continuity of geobodies in subsurface reservoir modeling. For fluvial reservoir modeling, the more continuous a channel, the more consistent it is with geological knowledge in general, and fluvial continuity is also of paramount importance when simulating fluid flow. Based on the pixel-based MPS algorithm Snesim, this study proposes a method that utilizes multiple search trees (MSTs) to enhance simulation continuity in 2D fluvial reservoir modeling. The objective of the MST method is to capture complete data events from a training image (TI), which aims to achieve enhanced continuity in fluvial reservoir sublayer modeling. By resorting to search neighborhoods based on their proximity to the central node of the data template, multiple data templates that correspond to the MSTs will be generated. Here, four data templates were generated by arranging the relative search neighborhood coordinates in ascending and descending order with respect to the central node. Parallel computing was tried for the construction of the search trees. This work calculated the conditional probability distribution function (CPDF) of the simulating nodes by averaging the CPDFs derived from the MSTs, and double retrieval was employed to filter out the search trees that possessed an inaccurate local CPDF for the simulating nodes. In addition, the connected component labeling (CCL) method was introduced to evaluate the simulation continuity in MPS. The results indicated that the MST method can enhance the simulation continuity of the Snesim algorithm by reproducing the fine connectivity of channel facies in 2D fluvial reservoir modeling.

An a priori model for multipoint statistics (MPS) modeling approaches is a training image. Before using MPS modeling, it must be determined whether the training images satisfy the spatial statistical stationarity. Modeling can be performed using the regular MPS approach if a training image is stationary. Otherwise, an enhanced method of nonstationary modeling is required. For instance, partition-based nonstationary modeling is an option. This study proposes a nonstationary evaluation metric based on pattern tile distances. It is possible to more accurately quantify the characteristics of the various distributions of spatial structure features in the entire space and achieve the goal of quantitatively evaluating the nonstationary metrics of training images by quantifying the distances of lower-level subpatterns in the pattern. Furthermore, an automatic partitioning approach based on pattern tile discrepancy is proposed for nonstationary training images to avoid the subjective and inefficient issues of manual partitioning when the training images cannot meet the stationary requirement of MPS modeling.

A reservoir is the space and gathering place of underground oil and gas reservoirs. Lithofacies proportion is the key parameter of a reservoir. Due to the heterogeneity of geological bodies, the multi-solution of seismic data and the limited and uneven distribution of logging data, it is unclear how to determine the lithofacies proportion in the study area directly from the available data. The mean is a way to make a quick estimate, but the mean cannot quantify the uncertainty of the lithofacies proportion using the available data. In this paper, a new method for quantifying uncertainty and determining the value of the three levels of the facies proportion is proposed. Taking sandstone proportion as an example, a normality test was carried out to verify the applicability of the sandstone proportion data to this method, and then the mean and variance of samples were calculated. Based on the confidence interval theory, the uncertainty of the mean value and the range of sandstone proportion were determined, that is, the optimistic value and the pessimistic value of the sandstone proportion. The results show that this method provides a new reference for quantifying values for the three levels of the uncertainty, and it uses the petrographic proportion as an example to provide the data basis for subsequent modeling and reservoir research.

AbstractA new modeling method is proposed to address the shortcomings of conventional geological modeling methods in simultaneously characterizing the geometry, stacking patterns, and well data of the channel bar in braided river. The channel bar model is reconstructed based on the target body, which is defined by the channel bar’s geometric parameters points, lines, and surfaces. In the simulation process, the distributions of sandstone and mudstone are simulated first. Then, the channel bars at the well are simulated based on the channel bars identified on the well and the thickness of the sandstone, the priori geometries, and sizes of the channel bars. It is judged whether the proportion of the simulated channel bars has reached the global proportion. If not, a randomly selected point between wells is used as a virtual well, and the sandstone thickness is used to determine whether it is a single or composite channel bar. The process is repeated until the proportion of channel bars reaches the set proportion. Take the N gas field as an example. The analysis of the geometry, variance function, thickness distribution histogram, and volume distribution histogram of the simulation results shows that the new method can better characterize the channel bar’s geometry, sizes, and stacking patterns than the multipoint geostatistical modeling method Snesim.

The tumor suppressor p53 is the central hub of a molecular network, which controls cell proliferation and death, and also plays an important role in the occurrence and development of liver fibrosis. The abundant post-translational processing and modification endow the functional diversity of p53. Considering the relationship between p53 and liver fibrosis, drug intervention targeting p53 or management of p53 regulation might be effective strategies to treat liver fibrosis. Here, we systematically discuss the regulation of p53 in different liver cells (hepatocytes, immune cells, HSCs, etc) and the role of p53 in the development of liver fibrosis, and propose possible interventions to prevent the pathogenic processes of liver fibrosis.

Microalgae extracts have a wide range of uses in the field of healthcare and nutrition. However, the use of microalgae extracts in anti-inflammatory properties and their mechanism of action have not yet been fully studied. Here, we show that extracts from Chlorella sp. WZ13 (CSE-WZ13) dose-dependently reduced nitrite production, inhibited the expression of inducible nitric oxide synthase (iNOS) protein, and decreased the production of the gene and inflammatory cytokines tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6), in lipopolysaccharide (LPS)-stimulated RAW264.7 cells. Using high-content imaging analysis, it was found that CSE-WZ13 inhibited the translocation of nuclear factor kappa B (NF-κB) from the cytoplasm to the nucleus. CSE-WZ13 also exerted anti-inflammatory effects in an ear edema mouse model induced by 12-O-tetradecanoylphorbol-13-acetate (TPA). CSE-WZ13 inhibited edema by 36.17% and 25.66% at a dose of 0.3 and 0.1 mg/ear, respectively. Histological analysis showed that topical application of CSE-WZ13 decreased TPA-induced inflammatory cell infiltration. Our results indicate that CSE-WZ13 may be a useful candidate for the purpose of decreasing inflammation.

Huazhi-Rougan (HZRG) formula is a Traditional Chinese medicine prescription, and has been widely used to treat non-alcoholic fatty liver disease (NAFLD) and its progressive form non-alcoholic steatohepatitis (NASH). However, the anti-NASH effects and the underlying mechanisms of HZRG have not yet been characterized. Here we showed that 4-week HZRG treatment alleviated methionine-choline-deficiency (MCD) diet-induced NASH in C57BL/6J mice, as evidenced by the improvement of hepatic steatosis and inflammation, as well as the decrease of serum levels of alanine and aspartate transaminases. Fecal 16S rDNA sequencing indicated that HZRG reduced the enrichment of pathogenic bacteria and increased the abundance of bacteria gena that are involved in bile acid (BA) conversation. The alteration of fecal and serum BA profile suggested that HZRG enhanced fecal BA excretion, and reduced the reabsorption of toxic secondary BA species (LCA, DCA, HCA). We further analyzed the BA receptors and transporters, and found that HZRG inhibited the expression of ileal bile acid transporter, and organic solute transporter subunit β, and increased the expression of intestinal tight junction proteins (ZO-1, Occludin, Claudin-2). The modulation of gut dysbiosis and BA profile, as well as the improvement of the intestinal environment, may contribute to the decrease of the p-65 subunit of NF-κB phosphorylation, liver F4/80 positive macrophages, inflammatory cytokine IL-1β and TNF-α expression. In conclusion, HZRG treatment enhances fecal BA excretion via inhibiting BA transporters, modulates BA profiles, gut dysbiosis as well as the intestinal environment, thus contributing to the beneficial effect of HZRG on NASH mice.

Vitamin D deficiency is a common phenomenon in non-alcoholic fatty liver disease (NAFLD) and the progressive non-alcoholic steatohepatitis (NASH). Jiangzhi Granule (JZG) formula is a Traditional Chinese medicine prescription, and has been found effective against NAFLD/NASH. Here we showed that vitamin D deficiency could accelerate NASH development, and reduce vitamin D receptor (VDR) expression. JZG treatment alleviated high-fat vitamin D deficient (HF-VDD) diet-induced NASH in C57BL/6 J mice, and up-regulated both the liver and intestinal VDR expression independent of 1,25-dihydroxy-vitamin D3 level. We analyzed the fecal BA profile using liquid chromatography coupled with triple quadrupole mass spectrometry (UPLC-TQMS) -based metabolomics, and found that JZG modulated fecal BA profile, predominantly increased the ratio of secondary BA species, as well as the expression of tight junction proteins Zona occludens 1(ZO-1) and occludin in the colon. In vitro experiment further confirmed the representative secondary BA species lithocholic acid (LCA) and keto-LCA upregulated the expression of and ZO-1 through VDR in LPS-stressed Caco-2 cells. Our results identified the endogenous VDR activation by JZG through modulating BA species in vitamin D deficiency-related NASH mice, thus providing evidence for the clinical application of JZG in treating NASH.

Porcine reproductive and respiratory syndrome virus (PRRSV) is a significant threat to the pig industry in China. However, the epidemiological characteristics of PRRSV after the outbreak of African swine fever in China were not thoroughly investigated. In the present study, the serological and epidemiological investigations of PRRSV in pigs from the Hunan and Hebei provinces of China were assessed. The results showed that 73.12% (95% CI 71.74–74.49) of pigs were positive for PRRSV-special antibody by enzyme-linked immunosorbent assay. Out of 5799 samples, 482 (8.31%, 95% CI 7.60–9.02) samples were positive for PRRSV nucleic acids. The positive rates of PRRSV in healthy pigs from farms and slaughterhouses were 2.27% (47/2072) and 7.70% (217/2818), which were lower than that in diseased pigs (23.98%, 218/909). Furthermore, the full-length OFR5 gene sequences of 43 PRRSV strains were sequenced and analysed. Phylogenetic analysis revealed that 43 isolates were classified into three lineages, namely lineage 1 (n = 24), lineage 8 (n = 15), and lineage 3 (n = 4). Lineage 1 could be further divided into sublineage 1.5 (n = 2) and sublineage 1.8 (n = 22), and lineage 8 was classified into sublineage 8.1 (n = 3) and sublineage 8.7 (n = 12). Collectively, our findings revealed the severe prevalence of PRRSV in the Hunan and Hebei provinces, where sublineage 1.8 and sublineage 8.7 predominated. The present study provides the update information of the epidemiological and genetic characteristics of PRRSV in the investigated regions, which will be beneficial for PRRS control.

AbstractThe proportion of different lithofacies is essential to determine the net-gross ratio, which directly affects the calculation of oil and gas reserves. Under the condition of few wells, the reliability of different lithofacies proportions obtained using well data is poor because it is difficult to estimate the uncertainty variation range of different lithofacies proportions. The present study overcomes this problem using an uncertainty evaluation method for reservoir lithofacies proportion. First, different lithofacies proportions of the reservoir are determined using the well and seismic data and are considered as the most likely estimated. Based on the lithofacies proportion, the lithofacies models of the study area are developed. Second, uniform and random methods are applied to sample the lithofacies models, and then, the probability distribution functions of different lithofacies proportions are derived. Third, the three levels of lithofacies proportions in the study area are calculated using the method of quantile and discretization of the probability distribution. Finally, we take M gas field as a sample and compare the test results of the fixed and random well structures. The results show that the sampling results of the fixed well structure model significantly deviate under the influence of the extension direction of the channel and well pattern distribution. Therefore, it is suggested to use random well structure to determine the uncertainty distribution range of lithofacies proportion.

Fructose, especially industrial fructose (sucrose and high fructose corn syrup) is commonly used in all kinds of beverages and processed foods. Liver is the primary organ for fructose metabolism, recent studies suggest that excessive fructose intake is a driving force in non-alcoholic fatty liver disease (NAFLD). Dietary fructose metabolism begins at the intestine, along with its metabolites, may influence gut barrier and microbiota community, and contribute to increased nutrient absorption and lipogenic substrates overflow to the liver. Overwhelming fructose and the gut microbiota-derived fructose metabolites (e.g., acetate, butyric acid, butyrate and propionate) trigger the de novo lipogenesis in the liver, and result in lipid accumulation and hepatic steatosis. Fructose also reprograms the metabolic phenotype of liver cells (hepatocytes, macrophages, NK cells, etc.), and induces the occurrence of inflammation in the liver. Besides, there is endogenous fructose production that expands the fructose pool. Considering the close association of fructose metabolism and NAFLD, the drug development that focuses on blocking the absorption and metabolism of fructose might be promising strategies for NAFLD. Here we provide a systematic discussion of the underlying mechanisms of dietary fructose in contributing to the development and progression of NAFLD, and suggest the possible targets to prevent the pathogenetic process.

AbstractTraining image (TI) is important for multipoint statistics simulation method (MPS), since it captures the spatial geological pattern of target reservoir to be modeled. Generally, one optimal TI is selected before applying MPS by evaluating the similarities between many TIs and the well interpretations of target reservoir. In this paper, we propose a new training image optimization approach based on the convolutional neural network (CNN). First, candidate TIs were randomly sampled several times to obtain the sample dataset. Then, the CNN was used to conduct transfer learning for all samples, and finally, the optimal TI of the conditioning well data is selected through the trained CNN model. By taking advantage of the strong learning ability of CNN in image feature recognition, the proposed method can automatically identify differences in spatial features between the conditioning well data and the samples of the training image. Hence, it effectively resolves the difficulty of spatial matching between discrete datapoints and grid structures. We demonstrated the applicability of our model via 2D and 3D training image selection examples. The proposed methods effectively selected the appropriate TI, and then the pretreatment techniques for improving the accuracy of continuous TI selection were achieved. Moreover, the proposed method was successfully applied to training image selection of a discrete fracture network model. Finally, sensitivity analysis was carried out to show that sufficient conditioning data volume can reduce the uncertainty of the optimization results. By comparing with the improved MDevD method, the advantages of the new method are verified in terms of efficiency and reliability.

Geological factors are key elements to control shale gas enrichment and influence the accurate estimation of shale gas reserve. However, the impact of the main geological factors, such as porosity, mineralogy, and organic matter, on marine shale gas enrichment and reserve calculation has not yet been fully clarified. Herein, we measured gas adsorption, porosity, mineralogical composition, and total organic carbon content of the marine shale samples from the Jiaoshiba area of Fuling gas field in Sichuan Basin, South China, and investigate the relationships between the geological factors and the adsorbed gas content. The results show that adsorbed gas content is positively correlated with total organic carbon and porosity; the larger specific surface area of samples with more clay minerals essentially contributes to shale gas enrichment. Additionally, the sealing of faults imposes a significant impact on shale gas accumulation. The probability volume method was applied to calculate the shale gas reserve. The reserves of P90 (the most pessimistic reserve), P50 (the most likely reserve), and P10 (the most optimistic reserve) were calculated, respectively, which provides useful information to reduce the risk in shale gas development.

As climate change increases flood risk, there is growing recognition that the multiple plans cities adopt often work at cross purposes and encourage development in areas at risk of current and future flooding. There have been calls for a more coordinated approach to planning for current and future flood risks, but is planning rising to the challenge? We apply the Plan Integration for Resilience Scorecard (PIRS) methodology to spatially evaluate policies that would impact physical vulnerability to flooding in the network of different city plans that shape land use and steer development in Boston, MA and Fort Lauderdale, FL. Unlike previous applications of this approach, we look at how the plan network is changing over time. Between 2015 and 2019, attention on climate change grew in both cities, as did the number of plans and policies across the network with the potential to decrease physical vulnerability. New types of plans, such as climate change adaptation plans, play an important role in reducing flood risks. However, plans perpetuate past disparities in policy attention. As the first study to analyze how city networks of plans and their potential impact on resilience evolve over time, this work has important implications for planning scholarship and practice as well as hazard and climate change governance more broadly.

The randomness and complexity of urban traffic scenes make it a difficult task for self-driving cars to detect drivable areas. Inspired by human driving behaviors, we propose a novel method of drivable area detection for self-driving cars based on fusing pixel information from a monocular camera with spatial information from a light detection and ranging (LIDAR) scanner. Similar to the bijection of collineation, a new concept called co-point mapping, which is a bijection that maps points from the LIDAR scanner to points on the edge of the image segmentation, is introduced in the proposed method. Our method positions candidate drivable areas through self-learning models based on the initial drivable areas that are obtained by fusing obstacle information with superpixels. In addition, a fusion of four features is applied in order to achieve a more robust performance. In particular, a feature called drivable degree (DD) is proposed to characterize the drivable degree of the LIDAR points. After the initial drivable area is characterized by the features obtained through self-learning, a Bayesian framework is utilized to calculate the final probability map of the drivable area. Our approach introduces no common hypothesis and requires no training steps; yet it yields a state-of-art performance when tested on the ROAD-KITTI benchmark. Experimental results demonstrate that the proposed method is a general and efficient approach for detecting drivable area. Keywords: Drivable area, Self-driving, Data fusion, Co-point mapping

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