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Metal smelting have brought severe metal(loid)s contamination to the soil. Spatial distribution and pollution source analysis for soil metal(loid)s in an abandoned lead/zinc smelter were studied. The results showed that soil was contaminated heavily with metal(loid)s. The mean of lead (Pb), arsenic (As), cadmium (Cd), mercury (Hg) and antimony (Sb) content in topsoil is 9.7, 8.2, 5.0, 2.3, and 1.2 times higher than the risk screening value for soil contamination of development land of China (GB36600-2018), respectively. Cd is mainly enriched in the 0-6 m depth of site soil while As and Pb mainly deposited in the 0-4 m layer. The spatial distribution of soil metal(loid)s is significantly correlated with the pollution source in the different functional areas of smelter. As, Hg, Sb, Pb and copper (Cu) were mainly distributed in pyrometallurgical area, while Cd, thallium (Tl) and zinc (Zn) was mainly existed in both hydrometallurgical area and raw material storage area. Soil metal(loid)s pollution sources in the abandoned smelter are mainly contributed to the anthropogenic sources, accounting for 84.5%. Specifically, Pb, Tl, As, Hg, Sb and Cu mainly from atmospheric deposition (55.9%), Cd and Zn mainly from surface runoff (28.6%), While nickel (Ni) mainly comes from parent material (15.5%). The results clarified the spatial distribution and their sources in different functional areas of the smelter, providing a new thought for the risk prevention and control of metal(loid)s in polluted site soil.

Information storage and security are the building blocks of the information age. Optical information storage is a low-cost, robust, and high-capacity storage technique. Discovering new optical information storage media and technique with beyond binary capacity and enhanced security is in demand. Here we demonstrate a beyond-binary, "burn after read" and rewritable multiplexing optical information and encryption method via Ce:YAG trapping states. We find that the thermo-luminescence (TL) intensity of Ce:YAG trapping states is writing light power dependent, enabling multi-gray-level information storage and encoding. We present a new strategy to encode multiplexing information in a single pixel, increasing the storage capability. Meanwhile, the "burn after read" feature protects the data from secondary leakage. This method has wide applications in data storage, anti-counterfeiting, and encryption.

This paper focuses on the blow-up solutions of the space-time fractional equations with Riemann–Liouville type nonlinearity in arbitrary-dimensional space. Using the Banach mapping principle and the test function method, we establish the local well-posedness and overcome the difficulties caused by the fractional operators to obtain the blow-up results. Furthermore, we get the precise lifespan of blow-up solutions under special initial conditions.

User authentication plays a critical role in access control of a man-machine system, where the knowledge factor like a Personal Identification Number (PIN) constitutes the most widely-used authentication element. However, knowledge factors are usually vulnerable to the spoofing attack. Recently, the inheritance factor like fingerprints emerges as an efficient alternative resilient to malicious users, but it normally requires special equipment. To this end, we propose WiPass, a device-free authentication system only leveraging the pervasive WiFi infrastructure to explore keystroke dynamics (manner and rhythm of keystrokes) captured by the Channel State Information (CSI) to recognize legitimate users while rejecting spoofers. However, it remains an open challenge to characterize the behavioral features hidden in the human subtle motions like keystrokes. Therefore, we build a signal enhancement model using Ricean distribution to amplify user keystroke dymanics and a hybrid learning model for user authentication, which consists of two parts, i.e., CNN-based feature extraction and SVM-based classification. The former relies on visualizing the channel responses into time-series images to learn the behavioral features of keystrokes in energy and spectrum domains, while the latter exploits such behavioral features for user authentication. We prototype WiPass on the low-cost off-the-shelf WiFi devices and verify its performance. Empirical results show that WiPass achieves on average 92.1% authentication accuracy, 5.9% false accept rate, and 6.3% false reject rate in three real environments.

Deep video compression is attracting increasing attention from both deep learning and video processing community. Recent learning-based approaches follow the hybrid coding paradigm to perform pixel space operations for reducing redundancy along both spatial and temporal dimentions, which leads to inaccurate motion estimation or less effective motion compensation. In this work, we propose a feature-space video coding framework (FVC), which performs all major operations (i.e., motion estimation, motion compression, motion compensation and residual compression) in the feature space. Specifically, a new deformable compensation module, which consists of motion estimation, motion compression and motion compensation, is proposed for more effective motion compensation. In our deformable compensation module, we first perform motion estimation in the feature space to produce the motion information (i.e., the offset maps). Then the motion information is compressed by using the auto-encoder style network. After that, we use the deformable convolution operation to generate the predicted feature for motion compensation. Finally, the residual information between the feature from the current frame and the predicted feature from the deformable compensation module is also compressed in the feature space. Motivated by the conventional codecs, in which the blocks with different sizes are used for motion estimation, we additionally propose two new modules called resolution-adaptive motion coding (RaMC) and resolution-adaptive residual coding (RaRC) to automatically cope with different types of motion and residual patterns at different spatial locations. Comprehensive experimental results demonstrate that our proposed framework achieves the state-of-the-art performance on three benchmark datasets including HEVC, UVG and MCL-JCV.

Background. Both inflammatory factors and immune response play important roles in the pathogenesis of intervertebral disc degeneration (IDD). However, a comprehensive analysis of interaction between inflammatory response-associated genes (IRGs) and immune microenvironment in patients with IDD remains lacking. Hence, the current research is aimed at investigating the correlations between IRG signatures and immune cells in the progression of IDD. Methods. The expression profiles (GSE27494 and GSE41883) and IRGs were downloaded from the Gene Expression Omnibus (GEO) database and Molecular Signature Database (MSigDB), respectively. Weighted gene coexpression network analysis (WGCNA) and differential expression analysis were used to identify the pivotal modules and common differentially expressed genes (DEGs) associated with IDD. Subsequently, we retrieved differentially expressed IRGs (DE-IRGs) by intersecting IRGs and DEGs for enrichment analysis. Next, LASSO regression analyses were performed to screen optimal marker genes for IDD prediction. Additionally, we validated differences DE-IRGs between IDD patients and controls in GSE150408. Finally, the infiltration alteration of immune cells was evaluated by the CIBERSORT, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. Results. A total of 10 upregulated differentially expressed inflammatory genes were identified that were obviously related to progression of IDD. Functional analysis results revealed that DE-IRGs were mainly enriched in signaling pathways TNF, IL-17, NOD-like receptor, and NF-kappa B pathway. A five-gene signature that consisted of IL-1β, LIF, LYN, NAMPT, and SLC7A2 was constructed by the LASSO Cox regression. IL1B, LYN, and NAMPT were further validated as optimal candidate genes in the pathophysiology of IDD. In addition, there was a remarkable immune cell infiltration difference between the healthy and IDD groups. The proportions for dendritic cells activated, mast cells activated, and neutrophils in the IDD group were significantly higher than those in the normal group, while the proportion of some cells was lower than that of the normal group, such as T cell CD4 memory resting, NK cells activated, and macrophage M0. Furthermore, correlation analysis indicated IL-1β, LYN, and NAMPT were closely implicated with immune cell infiltration in IDD development. Conclusions. We explored an association between inflammatory response-associated signature and immune infiltration in IDD and validated that IL-1β, LYN, and NAMPT might serve as biomarkers and therapeutic targets for IDD in the future.

Microplastics (MP) and nanoplastics (NP) exist in the disposable plastic take-away containers. This study aims to determine the gut and oral microbiota alterations in the individuals frequently and occasionally consuming take-away food in disposable plastic containers (TFDPC), and explore the effect of micro/nanoplastics (MNP) reduction on gut microbiota in mice. TFDPC consumption are associated with greater presences of gastrointestinal dysfunction and cough. Both occasional and frequent consumers have altered gut and oral microbiota, and their gut diversity and evenness are greater than those of non-TFDPC consuming cohort. Multiple gut and oral bacteria are associated with TFDPC consumers, among which intestinal Collinsella and oral Thiobacillus are most associated with the frequent consumers, while intestinal Faecalibacterium is most associated with the occasional consumers. Although some gut bacteria associated with the mice treated with 500 µg NP and 500 µg MP are decreased in the mice treated with 200 µg NP, the gut microbiota of the three MNP groups are all different from the control group. This study demonstrates that TFDPC induces gut and oral microbiota alterations in the consumers, and partial reduction of the size and amount of MNP cannot rectify the MNP-induced gut microbial dysbiosis.

Histone deacetylases (HDACs) were highlighted as a novel category of anticancer targets. Several HDACs inhibitors were approved for therapeutic use in cancer treatment. Comparatively, receptor-dependent 4D-QSAR, LQTA-QSAR, is a new approach which generates conformational ensemble profiles of compounds by molecular dynamics simulations at binding site of enzyme. This work describes a receptor-dependent 4D-QSAR studies on hydroxamate-based HDACs inhibitors. The 4D-QSAR model was generated by multiple linear regression method of QSARINS. Leave-N-out cross-validation (LNO) and Y-randomization were performed to analysis of the independent test set and to verify the robustness of the model. Best 4D-QSAR model showed the following statistics: R2 = 0.8117, Q2LOO = 0.6881, Q2LNO = 0.6830, R2Pred = 0.884. The results may be used for further virtual screening and design for novel HDACs inhibitors. The receptor dependent 4D-QSAR model was developed for the hydroxamate derivatives as HDAC inhibitors by making use of molecular dynamics simulation to obtain conformational ensemble profile for each compound. The multiple linear regression method was used to generate 4D-QSAR model with the suitable predictive ability and the excellent statistical parameters.

Graphical abstract The hypoxia microenvironment induces the expression of HIF1 and EPAS1 in GCA. During tumor development, the EPAS1 suppresses the expression of HIF1, resulting in the dominant form of HIFs switching from HIF1 to EPAS1. LncRNA ZFAS1 binds to EPAS1, facilitating the HIF1 suppression, EPAS1 up-regulation, and the switching from HIF1 to EPAS1., LncRNA (Long non-coding RNA) ZFAS1 (zinc finger antisense 1) functions as the oncogene in multiple cancers, including gastric cancer. However, its function and underlying mechanism in the GCA (gastric cardia adenocarcinoma), the most aggressive type of gastric cancer, remain unknown. We demonstrated here that the LncRNA ZFAS1 was up-regulated in GCA tissues. Furthermore, the elevated level of ZFAS1 was significantly associated with the GCA metastasis and cancer recurrence. It was also demonstrated to be an independent prognostic indicator of disease-free survival and overall survival for GCA patients. RNA sequencing showed that the up-regulated ZFAS1 was tightly associated with the down-regulated hypoxia inducible factor 1 (HIF1) and up-regulated EPAS1 (Endothelial PAS domain protein 1, also known as HIF2). In vitro studies showed that the ZFAS1 could bind to EPAS1, enhance its abilities to epigenetically silence the HIF1, and promote its own expression in GCA cell lines. In the animal model, co-delivering the EPAS1 and the ZFAS1 antisense oligos could significantly boost up their therapeutic effects on tumor growth. Thus, targeting ZFAS1 and EPAS1 might be an alternative therapeutic option in GCA.

We develop a methodology for comparing agent-based models that are developed for the same domain, but may differ in the data sets (e.g., geographical regions) to which they are applied, and in the structure of the model. Our approach is to learn a response surface in the common parameter space of the models and compare the regions corresponding to qualitatively different behaviors in the models. As an example, we develop an active learning algorithm to learn phase shift boundaries in contagion processes in order to compare two agent-based models of rooftop solar panel adoption developed for different regions. We present results for 2D and 3D subspaces of the parameter space, though the approach scales to higher dimensions as well.

To date, the underlying mechanisms involved intervertebral disc degeneration (IDD) remain unclear, which has hindered the development of molecular biological therapy for IDD. Autophagy is vital for intracellular quality control and metabolic balance in intervertebral disc cells. Hence, autophagy homeostasis is important. Emerging evidence has implicated vitamin D (VD) and the vitamin D receptor (VDR) in IDD progression because of their effects on different autophagy steps. However, the results of clinical trials in which VD supplementation was assessed as a treatment for IDD are controversial. Furthermore, experimental studies on the interplay between VD/VDR and autophagy are still in their infancy. In view of the significance of the crosstalk between VD/VDR and autophagy components, this review focuses on the latest research on VD/VDR modulation in autophagy and investigates the possible regulatory mechanisms. This article will deepen our understanding of the relationship between VD/VDR and autophagy and suggests novel strategies for IDD prevention and treatment.

Objective This study was carried out to explore the potential involvement of miR‐125a‐5p in the oncogenic effects of EphA2, TAZ, and TEAD2 and the activity of the Hippo signaling pathway in gastric cancer progression. Methods In vitro transfection of miR‐125a‐5p mimics or inhibitors, qRT‐PCR, colony formation assays, and cell invasion assays were used to assess the effect of miR‐125a‐5p on the growth and invasion in gastric cancer (GC). Male nude mice bearing tumors derived from human GC cells were used for evaluating the effects of miR‐125a‐5p on tumor growth. Luciferase reporter assay, immunofluorescence, immunohistochemistry, qRT‐PCR, and immunoblotting were performed to explore the role of miR‐125a‐5p in the epithelial‐mesenchymal transition (EMT) and association among miR‐125a‐5p, EphA2, TAZ, and TEAD2 in GC cells. Results MiR‐125a‐5p enhanced GC cell viability and invasion in vitro, whereas inhibition of miR‐125a‐5p using a specific inhibitor and antagomir suppressed cancer cell invasion and tumor growth. Moreover, inhibition of miR‐125a‐5p reversed EMT in vitro. miR‐125a‐5p upregulated the expression of EphA2, TAZ, and TEAD2, promoted TAZ nuclear translocation, and induced changes in the activity of the Hippo pathway by enhancing the expression of TAZ target genes. Finally, miR‐125a‐5p was overexpressed in late‐stage GCs, and positive correlations were observed with its targets EphA2, TAZ, and TEAD2. Conclusion miR‐125a‐5p can promote GC growth and invasion by upregulating the expression of EphA2, TAZ, and TEAD2., miR‐125a‐5p may function as a powerful tumor promoter, activate the expression of its target genes EphA2, TAZ, and TEAD2, and promote cell growth, invasion, and epithelial‐mesenchymal transition in certain types of gastric cancer cells through activation of the Hippo pathway. miR‐125a‐5p may thus serve as a potential therapeutic target in the treatment of advanced gastric cancer cells.

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We develop a methodology for comparing two or more agent-based models that are developed for the same domain, but may differ in the particular data sets (e.g., geographical regions) to which they are applied, and in the structure of the model. Our approach is to learn a response surface in the common parameter space of the models and compare the regions corresponding to qualitatively different behaviors in the models. As an example, we develop an active learning algorithm to learn phase transition boundaries in contagion processes in order to compare two agent-based models of rooftop solar panel adoption.

Improper network protocol implementations usually bring about serious consequences. Therefore, network protocol security testing has become a hot area of research in network and information security. Popular vulnerability discovery techniques include static analysis, dynamic analysis, fuzzing and so on. With the increasing scale and complexity of software, fuzzing has incomparable advantages over other vulnerability discovery techniques and becomes the most common and effective one in network protocol security testing. Firstly, this paper introduces the basic principle and classification of network protocol fuzzing. Then this paper briefly shows the basic process of network protocol fuzzing and the fundamental structure of a network protocol fuzzing system. Next, this paper describes research advances of network protocol fuzzing techniques and machine learning techniques applied to network protocol fuzzing in details. Finally, this paper concludes some existing problems of network protocol fuzzing techniques and discusses future research directions.

In this article, we analyzed the impact mechanism of Internet finance on the efficiency of regional financial resource allocation. A data envelopment analysis–Malmquist model is used to measure the...

Stateful network protocol fuzzing is one of the essential means for ensuring network communication security. However, the existing methods have problems, including frequent auxiliary message interaction, no in-depth state-space exploration, and high shares of invalid interaction time. To this end, we propose SATFuzz, a stateful network protocol fuzzing framework. SATFuzz first prioritizes the states identified by the status codes in response messages, then randomly selects a state to test among the high-priority states, and determines its corresponding optimal test sequence, which is composed of the minimum pre-lead sequence, the test case, and the fittest post-end sequence. Finally, SATFuzz uses a quasi-recurrent neural network (QRNN) to filter the test cases before performing interaction, and only the optimal test sequence, including the valid test case, can be fed to the protocol entity. To verify the proposed framework, we conduct extensive experiments with the state-of-the-art fuzzer on two popular protocols. The results show that the vulnerability discovery efficiency of the proposed approach increases by at least 1.48 times (at most by 3.06 times), making it superior to the rival methods. This not only confirms the effectiveness of SATFuzz in terms of improving the vulnerability discovery efficiency but also shows that SATFuzz has significant advantages.

MicroRNAs (miRNAs) from mesenchymal stem cells (MSC)-derived extracellular vesicles (MSCs-EVs), including exosomes, are known to participate in different diseases. However, the function of miR-301b-3p from MSCs-EVs on the chemoresistance of gastric cancer (GC) cells remains poorly characterized. Thus, we aim to explore the role of MSCs-EVs-derived miR-301b-3p in multidrug resistance of GC cells.Cisplatin (DDP)/vincristine (VCR)-resistant and sensitive GC clinical samples were harvested to detect expression of miR-301b-3p and thioredoxin interacting protein (TXNIP). MSCs were respectively transfected with miR-301b-3p oligonucleotides and/or TXNIP plasmids to extract the EVs, which were then co-cultured with multidrug-resistant GC cells. Then, P-glycoprotein (P-gp) and multidrug resistance-associated protein (MRP), IC50, proliferation, migration, and apoptosis of resistant GC cells were determined. The tumor growth was observed in nude mice. Targeting relationship between miR-301b-3p and TXNIP was confirmed.miR-301b-3p was upregulated, and TXNIP was downregulated in DDP/VCR-resistant GC tissues and cells. MSC-EVs induced drug resistance, proliferation, and migration and inhibited apoptosis of DDP/VCR-resistant GC cells in vitro, as well as facilitated tumor growth in vivo. Inhibition of miR-301b-3p or upregulation of TXNIP reversed the promoting effect of MSC-EVs on DDP/VCR resistant GC cells to DDP/VCR resistance and malignant behaviors. The effects of MSC-EVs carrying miR-301b-3p inhibition on DDP/VCR-resistant GC cells were reversed by TXNIP downregulation. TXNIP was confirmed as a target gene of miR-301b-3p.miR-301b-3p from MSCs-EVs inhibits TXNIP to promote multidrug resistance of GC cells, providing a novel insight for chemotherapy in GC.

Learning based video compression attracts increasing attention in the past few years. The previous hybrid coding approaches rely on pixel space operations to reduce spatial and temporal redundancy, which may suffer from inaccurate motion estimation or less effective motion compensation. In this work, we propose a feature-space video coding network (FVC) by performing all major operations (i.e., motion estimation, motion compression, motion compensation and residual compression) in the feature space. Specifically, in the proposed deformable compensation module, we first apply motion estimation in the feature space to produce motion information (i.e., the offset maps), which will be compressed by using the auto-encoder style network. Then we perform motion compensation by using deformable convolution and generate the predicted feature. After that, we compress the residual feature between the feature from the current frame and the predicted feature from our deformable compensation module. For better frame reconstruction, the reference features from multiple previous reconstructed frames are also fused by using the non-local attention mechanism in the multi-frame feature fusion module. Comprehensive experimental results demonstrate that the proposed framework achieves the state-of-the-art performance on four benchmark datasets including HEVC, UVG, VTL and MCL-JCV., Comment: CVPR2021(oral)

Understanding the factors that contribute to the adoption of rooftop solar panels can help design appropriate policies to convert potential adopters into actual adopters. This paper aims to predict the adoption of rooftop solar panels in the rural regions of Virginia using demographic features of households. A novel feature of this work is that it uses a decision-adjusted framework, which optimizes the specific goals of the study. Another novel feature is that we construct indicator features to improve the model performance. Traditional logistic regression models and decision-adjusted logistic regression models are compared. Results show that decision-adjusted models are better than traditional logistic regression models, and that indicator features can improve the performance of predictive models. The sum of true positive and true negative rates of the decision-adjusted model is 1.5973, while for the traditional logistic regression model it is only 1.

Chlorinated polyfluoroalkyl ether sulfonates (trade name F-53B) has been detected in various environmental matrices, and reported to be equally or more toxic than perfluorooctane sulfonate. Efficient sorptive removal of F-53B from water by two types of layered double hydroxides (LDHs), NO3−-LDH and sodium dodecyl sulfate modified NO3−-LDH (SDS-LDH), was demonstrated in this study. Both LDHs removed F-53B in several minutes and had sorption capacities of over 860 mg/g. SDS-LDH exhibited a greater F-53B uptake than NO3−-LDH under the influence of different solution chemistry, including pH 3-11, or in the presence of competing anions or co-contaminants, primarily due to the higher surface areas and the presence of SDS for SDS-LDH. Batch experiments, structural characterization, molecular dynamics simulations, and density functional theory calculations were combined to explore the sorption mechanisms, which mainly include ion exchange (specifically, O-H⋯O/F hydrogen bond), C-F/Cl⋯H hydrogen bond, and micellar sorption (occurring at high initial F-53B concentrations). Accordingly, we propose to improve the sorption performance of LDHs by increasing their surface areas and modifying LDHs to produce more hydrogen bond sites, as well as exfoliating LDHs into two dimensional nanosheets to eliminate the steric hindrance for the micellar formation of F-53B or other per- and polyfluoroalkyl substances.

Aims Bufothionine had been used for gastric cancer (GC) treatment, and this study managed to uncover the underlying mechanisms. Materials and methods Cell proliferation was determined by CCK-8 assay and colony formation assay. Flow cytometry (FCM) and TUNEL assay were used to measure cell apoptosis ratio. Intracellular ROS was measured by DCFH-DA probes. qRT-PCR was used to determine miRNAs levels. Western Blot was performed to probe proteins. Dual-luciferase reporter gene system was employed to validate the binding sites of miR-133a-3p and 3′UTR regions of IGF1R mRNA. Immunohistochemistry (IHC) was used to determine the expressions of Ki-67 in mice tumor tissues. Key findings Bufothionine inhibited cell viability, triggered ER stress and promoted ROS production in GC cells, and both ER stress inhibitor Salburinal (Sal) and ROS scavenger (NAC) abrogated Bufothionine induced GC cell death. Besides, miR-133a-3p was upregulated by Bufothionine, and Bufothionine-induced cell death was enhanced by miR-133a-3p overexpression while alleviated by miR-133a-3p knockdown. Furthermore, miR-133a-3p inactivated PI3K/Akt signal pathway by sponging IGF1R, and Bufothionine inhibited insulin-like growth factor 1 receptor (IGF1R) and inactivated PI3K/Akt cascade by upregulating miR-133a-3p. Notably, the promoting effects of overexpressed miR-133a-3p on Bufothionine-induced GC cell death were abrogated by overexpressing IGF1R, and aggravated by the PI3K/Akt cascade inhibitor (LY294002). Significance Bufothionine promoted GC cell death by triggering miR-133a-3p/IGF1R/PI3K/Akt axis mediated ER stress and ROS production.

Group or collective identity is an individual’s cognitive, moral, and emotional connection with a broader community, category, practice, or institution. There are many different contexts in which collective identity operates, and a host of application domains where collective identity is important. Collective identity is studied across myriad academic disciplines. Consequently, there is interest in understanding the collective identity formation process. In laboratory and other settings, collective identity is fostered through priming a group of human subjects. However, there have been no works in developing agent-based models for simulating collective identity formation processes. Our focus is understanding a game that is designed to produce collective identity within a group. To study this process, we build an online game platform; perform and analyze controlled laboratory experiments involving teams; build, exercise, and evaluate network-based agent-based models; and form and evaluate hypotheses about collective identity. We conduct these steps in multiple abductive iterations of experiments and modeling to improve our understanding of collective identity as this looping process unfolds. Our work serves as an exemplar of using abductive looping in the social sciences. Findings on collective identity include the observation that increased team performance in the game, resulting in increased monetary earnings for all players, did not produce a measured increase in collective identity among them.

This paper considers a variety of social, demographic, spatial, and structural features of the households to determine factors that are influential in predicting the adoption of rooftop solar panels in rural regions of Virginia. A novel feature of this work is that it synthesizes anonymized data from different sources to make it usable under one common architecture and to provide a geospatial context. Three different sets of models are used which add features in a modular manner. Results show that the demographics and neighborhood level features influence the likelihood of adoption but the social network based features do not.

In the learning based video compression approaches, it is an essential issue to compress pixel-level optical flow maps by developing new motion vector (MV) encoders. In this work, we propose a new framework called Resolution-adaptive Flow Coding (RaFC) to effectively compress the flow maps globally and locally, in which we use multi-resolution representations instead of single-resolution representations for both the input flow maps and the output motion features of the MV encoder. To handle complex or simple motion patterns globally, our frame-level scheme RaFC-frame automatically decides the optimal flow map resolution for each video frame. To cope different types of motion patterns locally, our block-level scheme called RaFC-block can also select the optimal resolution for each local block of motion features. In addition, the rate-distortion criterion is applied to both RaFC-frame and RaFC-block and select the optimal motion coding mode for effective flow coding. Comprehensive experiments on four benchmark datasets HEVC, VTL, UVG and MCL-JCV clearly demonstrate the effectiveness of our overall RaFC framework after combing RaFC-frame and RaFC-block for video compression.

Objectives The anti-PD-1/PD-L1 therapy has been demonstrated safe and effective for cancer patients. However, our previous data showed that it had no obvious effects on gastric cardia adenocarcinoma (GCA). Thus, we investigated how the expression level of the PD-L1 was affected by the anti-PD-1 therapy, because it has been demonstrated that the PD-L1 level affects the therapeutic efficient of the anti-PD-1 therapy. Materials and methods The mRNA and protein levels of PD-L1 in the GCA tissues and corresponding normal tissues were determined by qPCR and ELISA. Promoter methylation was analyzed by bisulfite sequencing. Finally the methylation of PD-L1 promoter was confirmed in the mice. Results The level of PD-L1 was up-regulated in the GCA tissues when compared to the adjacent non-tumor tissues. The anti-PD1 therapy could reduce the PD-L1 levels in patients with cancer recurrence. The promoter of PD-L1 was more hypermethylated in the secondary GCA after the anti-PD-1 therapy when compared with the adjacent non-tumor tissues or the primary GCA without the anti-PD-1 therapy. Furthermore, the promoter methylation of PD-L1 could be induced by the anti-PD-1 therapy in the mice model. Finally, the anti-PD-1 plus DNA hypomethylating agent azacytidine could significantly suppressed the tumor growth better than the anti-PD-1 therapy. Conclusions Here we demonstrated that the unresponsiveness of GCA to the anti-PD-1 therapy might result from the promoter methylation and down-regulation of PD-L1. The anti-PD-1 plus azacytidine might be a more promising approach to treat GCA.

Anagram games (i.e., word construction games in which players use letters to form words) have been researched for some 60 years. Games with individual players are the subject of over 20 published investigations. Moreover, there are many popular commercial anagram games such as Scrabble. Recently, cooperative team play of anagram games has been studied experimentally. With all of the experimental work and the popularity of such games, it is somewhat surprising that very little modeling of anagram games has been done to predict player behavior/actions in them. We devise a cooperative group anagram game and develop an agent-based modeling and simulation framework to capture player interactions of sharing letters and forming words. Our primary goals are to understand, quantitatively predict, and explain individual and aggregate group behavior, through simulations, to inform the design of a group anagram game experimental platform.

Silicon carbide and nanostructured ferritic alloy (SiC-NFA) composites have the potential to maintain the outstanding irradiation resistance and enhance the mechanical integrity for nuclear cladding. By introducing a carbon reaction barrier on NFA (C@NFA), SiC-C@NFA composites are investigated in order to reduce the reaction between SiC and NFA in this work. The densities of the spark plasma sintered (SPS) SiC-C@NFA composites show an increasing trend with the SiC content to almost 100% dense. Although the SiC phase is absent, the Vickers hardness of the composites reaches 436-638 kgf/mm2. The reaction leads to two types of regions in the composites, the NFA matrix and the micro-sized carbon enrich region, both of which contain extra Si element. The SiC-C@NFA composites show much improved microstructures and phases compared to the SiC-NFA composites without any coating, and the effectiveness of the carbon barrier is further verified based on the phase diagram and Gibbs free energy analysis. The SPS sintered SiC-C@NFA composites offer a new promising system for nuclear cladding.

The irrational NH3 emission routinely poses a significant threat to human health and environmental protection even at low dose. In addition, high miniaturization and low power-consumption has been the critical requirements of Internet of Things. To meet these demands, it is greatly pressing to develop a novel gas sensor with the capability to detect trace NH3 without external heating or light-irradiation elements. In this work, the organic conducting conjugated polymer PEDOT:PSS was combined with inorganic nitrogen-doped transition metal carbides and nitrides (N-MXene Ti3C2T x ) for chemiresistive NH3 sensing at room temperature (20 oC). By means of the organic–inorganic n–p heterojunctions via the synergistic effect, the results show that the composite film sensor with the optimal mass ratio of 1:0.5 between N-MXene and PEDOT:PSS components delivered favorable NH3 sensing performance than individual N-MXene or PEDOT:PSS counterparts in terms of higher response and quicker response/recovery speeds under 20 oC@36%RH air. Besides, decent repeatability, stability and selectivity were demonstrated. The incorporated N atoms served as excellent electron donors to promote the electron-transfer reactions and augment the sorption sites. Simultaneously, partial oxidation of MXene brought about some TiO2 nanoparticles which acted as spacers to widen the interlayer spacing and probably suppress the MXene restacking during the film deposition, thus favoring the gas diffusion/penetration within the sensing layer and then a quick reaction kinetic. The modulation of consequent build-in field within the heterojunctions was responsible for the reversible NH3 sensing. In addition, pre-adsorbed water molecules facilitated to establish a swift adsorption/desorption balance. The proposed strategy expanded the application range of MXene based composite materials and enrich the current sensing mechanisms of NH3 gas sensors.

BiOI powder was first supported on Ni foam to form BiOI/Ni foam. The Bi4O5I2-Bi5O7I/Ni foam was obtained by calcining the BiOI/Ni foam at 400 °C for 1 h with a ramp rate of 10 °C min−1, which demonstrated excellent photocatalytic performance in the removal of RhB from water. Approximately 99% of RhB was decomposed over Bi4O5I2-Bi5O7I/Ni foam under irradiation of a 500 W xenon lamp for 5 h, 41% higher than that over BiOI powder calcined under the same conditions. The removal rates of total organic carbon and total organic nitrogen were about 47% and 38%, respectively. Degradation rate of RhB only decreased by 2% after the Bi4O5I2-Bi5O7I/Ni foam was used for 5 rounds. Photo-induced electrons flowed from Bi4O5I2 to Bi5O7I, to Ni foam, while photo-generated holes migrated in the reverse direction. The rapid migration of photo-induced electrons and holes on the interface of Bi4O5I2-Bi5O7I/Ni foam oppositely enhanced its photocatalytic activity for better degradation of RhB.

Squid pens were subjected to alkali hydrolysis to extract chitin and chitosan. Proteins present in the alkaline extraction wastewater were recovered at pH 3, 4, 5 and 6, and were subjected to hydrolysis by trypsin, pepsin and a bacterial protease called HT for 1, 2, 4 and 24h. Hydrolysis of the extracted proteins with either trypsin or HT generated more antioxidant activity than hydrolysis with pepsin. Higher ACE-inhibitory activity was generated in the trypsin and pepsin hydrolysates than in the HT hydrolysate. Squid pen protein recovered from chitosan processing waste alkaline solution can be a potential source of bioactive peptides for addition to foods. The antioxidant and ACE-inhibitory activities of the extracted proteins were initially low and increased upon incubation with the proteases. Pepsin generated significantly lower (P0.05) antioxidant activities compared to trypsin and HT, while trypsin and pepsin hydrolysates exhibited higher ACE-inhibitory activity than HT (P0.05).