TBK1-stabilized ZNF268a recruits SETD4 to methylate TBK1 for efficient interferon signaling.

Sufficient activation of interferon signaling is critical for the host to fight against invading viruses, in which post-translational modifications have been demonstrated to play a pivotal role. Here, we demonstrate that the human KRAB-zinc finger protein ZNF268a is essential for virus-induced interferon signaling. We find that cytoplasmic ZNF268a is constantly degraded by lysosome and thus remains low expressed in resting cell cytoplasm. Upon viral infection, TBK1 interacts with cytosolic ZNF268a to catalyze the phosphorylation of Serine 178 of ZNF268a, which prevents the degradation of ZNF268a, resulting in the stabilization and accumulation of ZNF268a in the cytoplasm. Furthermore, we provide evidence that stabilized ZNF268a recruits the lysine methyltransferase SETD4 to TBK1 to induce the mono-methylation of TBK1 on lysine 607, which is critical for the assembly of the TBK1 signaling complex. Notably, ZNF268 S178 is conserved among higher primates but absent in rodents. Meanwhile, rodent TBK1 607th aa happens to be replaced by arginine, possibly indicating a species-specific role of ZNF268a in regulating TBK1 during evolution. These findings reveal novel functions of ZNF268a and SETD4 in regulating antiviral interferon signaling.

CEEM: a Chemically Explainable Deep Learning Platform for Identifying Compounds with Low Effective Mass.

The semiconductor industry occupies a crucial position in the fields of integrated circuits, energy, and communication systems. Effective mass (mE ), which is closely related to electron transition, thermal excitation, and carrier mobility, is a key performance indicator of semiconductor. However, the highly neglected mE is onerous to measure experimentally, which seriously hinders the evaluation of semiconductor properties and the understanding of the carrier migration mechanisms. Here, a chemically explainable effective mass predictive platform (CEEM) is constructed by deep learning, to identify n-type and p-type semiconductors with low mE . Based on the graph network, a versatile explainable network is innovatively designed that enables CEEM to efficiently predict the mE of any structure, with the area under the curve of 0.904 for n-type semiconductors and 0.896 for p-type semiconductors, and derive the most relevant chemical factors. Using CEEM, the currently largest mE database is built that contains 126 335 entries and screens out 466 semiconductors with low mE for transparent conductive materials, photovoltaic materials, and water-splitting materials. Moreover, a user-friendly and interactive CEEM web is provided that supports query, prediction, and explanation of mE . CEEM's high efficiency, accuracy, flexibility, and explainability open up new avenues for the discovery and design of high-performance semiconductors.

Hydrocarbon Transportation in Heterogeneous Shale Pores by Molecular Dynamic Simulation.

Shale oil in China is widely distributed and has enormous resource potential. The pores of shale are at the nanoscale, and traditional research methods encounter difficulty in accurately describing the fluid flow mechanism, which has become a bottleneck restricting the industrial development of shale oil in China. To clarify the distribution and migration laws of fluid microstructure in shale nanopores, we constructed a heterogeneous inorganic composite shale model and explored the fluid behavior in different regions of heterogeneous surfaces. The results revealed the adsorption capacity for alkanes in the quartz region was stronger than that in the illite region. When the aperture was small, solid-liquid interactions dominated; as the aperture increased, the bulk fluid achieved a more uniform and higher flow rate. Under conditions of small aperture/low temperature/low pressure gradient, the quartz region maintained a negative slip boundary. Illite was more hydrophilic than quartz; when the water content was low, water molecules formed a "liquid film" on the illite surface, and the oil flux percentages in the illite and quartz regions were 87% and 99%, respectively. At 50% water content, the adsorbed water in the illite region reached saturation, the quartz region remained unsaturated, and the difference in the oil flux percentage of the two regions decreased. At 70% water content, the adsorbed water in the two regions reached a fully saturated state, and a layered structure of "water-two-phase region-water" was formed in the heterogeneous nanopore. This study is of great significance for understanding the occurrence characteristics and flow mechanism of shale oil within inorganic nanopores.

On-Board Unit (OBU)-Supported Longitudinal Driving Behavior Monitoring Using Machine Learning Approaches.

Driving behavior recognition can provide an important reference for the intelligent vehicle industry and probe vehicle-based traffic estimation. The identification of driving behavior using mobile sensing techniques such as smartphone- and vehicle-mounted terminals has gained significant attention in recent years. The present work proposed the monitoring of longitudinal driving behavior using a machine learning approach with the support of an on-board unit (OBU). Specifically, based on velocity, three-axis acceleration and three-axis angular velocity data were collected by a mobile vehicle terminal OBU; through the process of data preprocessing and feature extraction, seven machine learning algorithms, including support vector machine (SVM), random forest (RF), k-nearest neighbor algorithm (KNN), logistic regression (LR), BP neural network (BPNN), decision tree (DT), and the Naive Bayes (NB), were applied to implement the classification and monitoring of the longitudinal driving behavior of probe vehicles. The results show that the three classifiers SVM, RF and DT achieved good performances in identifying different longitudinal driving behaviors. The outcome of the present work could contribute to the fields of traffic management and traffic safety, providing important support for the realization of intelligent transport systems and the improvement of driving safety.

[Expression of Tyrosine Kinase Receptor 2 in Oral Squamous Cell Carcinoma and the Effect on Cell Proliferation and Migration and Epithelial-Mesenchymal Transition Process].

Objective: To study the expression of tyrosine kinase receptor 2 (Tie2) in oral squamous cell carcinoma (OSCC) and its effect on cell proliferation and migration and the epithelial-mesenchymal transition (EMT) process. Methods: Immunohistochemistry (IHC) tests were conducted to examine the expression of Tie2 in OSCC tissues and normal oral mucosa tissues. Western blot was performed to examine the expression of Tie2 in dysplastic oral keratinocyte (DOK) cell line and OSCC cell lines, and the cell line with high Tie2 expression was selected as the experimental cell line. The Tie2-silenced lentiviral vector was successfully transfected onto the experimental cell line for subsequent experiments. Cell proliferation and cloning abilities were examined with CCK-8 and clone formation assays. Cell migration ability was examined with scratch and Transwell assays. The remodeling ability of cytoskeletal F-actin and the expressions of E-cadherin and N-cadherin were examined with confocal laser scanning microscope. Western blot was performed to examine the expression of EMT-related signature proteins, including E-cadherin, N-cadherin, and vimentin, and the expression of the protein kinase B (AKT) and extracellular signal-regulated kinase (ERK). Results: IHC results showed that the Tie2-positive rate of the OSCC group (74.5%) was significantly higher than that of the control group (19.4%) ( P<0.0001). The expression of Tie2 was higher in HSC-4 and SCC-9 cell lines compared to that in DOK cells. The lentiviral shRNA-162 group showed the best silencing effect, which was used as the experimental group and applied in subsequent experiments. Compared with those of the control group, the proliferation, cloning and migration capacities of the cells of the experimental group were significantly reduced. Furthermore, the green fluorescence intensity of the cytoskeleton F-actin was reduced, the number of filamentous pseudopods at the leading edge of the cells decreased and their length was shortened, and the expression of E-cadherin was significantly increased, while the expression of N-cadherin and vimentin was significantly reduced in the experimental group in comparison with those of the control group. The expression of p-AKT and p-ERK proteins decreased, while AKT and ERK protein expression increased. Conclusion: Tie2 was highly expressed in most OSCC cells. Silencing Tie2 can inhibit the proliferation, cloning, and migration ability of OSCC cells, inhibit F-actin remodeling, and alter the expression of its EMT-related signature proteins by regulating AKT and ERK signaling pathway, which suggests that Tie2 may be involved in the growth, metastasis and EMT process of OSCC.

A Carbon-Negative Hydrogen Production Strategy: CO2 Selective Capture with H2 Production.

We reported a strategy of carbon-negative H2 production in which CO2 capture was coupled with H2 evolution at ambient temperature and pressure. For this purpose, carbonate-type Cux Mgy Fez layered double hydroxide (LDH) was preciously constructed, and then a photocatalysis reaction of interlayer CO3 2- reduction with glycerol oxidation was performed as driving force to induce the electron storage on LDH layers. With the participation of pre-stored electrons, CO2 was captured to recover interlayer CO3 2- in presence of H2 O, accompanied with equivalent H2 production. During photocatalysis reaction, Cu0.6 Mg1.4 Fe1 exhibited a decent CO evolution amount of 1.63 mmol g-1 and dihydroxyacetone yield of 3.81 mmol g-1 . In carbon-negative H2 production process, it showed an exciting CO2 capture quantity of 1.61 mmol g-1 and H2 yield of 1.44 mmol g-1 . Besides, this system possessed stable operation capability under simulated flu gas condition with negligible performance loss, exhibiting application prospect.

iEnhancer-XG: interpretable sequence-based enhancers and their strength predictor.

MOTIVATION: Enhancers are non-coding DNA fragments with high position variability and free scattering. They play an important role in controlling gene expression. As machine learning has become more widely used in identifying enhancers, a number of bioinformatic tools have been developed. Although several models for identifying enhancers and their strengths have been proposed, their accuracy and efficiency have yet to be improved. RESULTS: We propose a two-layer predictor called 'iEnhancer-XG.' It comprises a one-layer predictor (for identifying enhancers) and a second classifier (for their strength) and uses 'XGBoost' as a base classifier and five feature extraction methods, namely, k-Spectrum Profile, Mismatch k-tuple, Subsequence Profile, Position-specific scoring matrix (PSSM) and Pseudo dinucleotide composition (PseDNC). Each method has an independent output. We place the feature vector matrix into the ensemble learning for fusion. This experiment involves the method of 'SHapley Additive explanations' to provide interpretability for the previous black box machine learning methods and improve their credibility. The accuracies of the ensemble learning method are 0.811 (first layer) and 0.657 (second layer). The rigorous 10-fold cross-validation confirms that the proposed method is significantly better than existing technologies. AVAILABILITY AND IMPLEMENTATION: The source code and dataset for the enhancer predictions have been uploaded to https://github.com/jimmyrate/ienhancer-xg. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MSA-MOT: Multi-Stage Association for 3D Multimodality Multi-Object Tracking.

Three-dimensional multimodality multi-object tracking has attracted great attention due to the use of complementary information. However, such a framework generally adopts a one-stage association approach, which fails to perform precise matching between detections and tracklets, and, thus, cannot robustly track objects in complex scenes. To address this matching problem caused by one-stage association, we propose a novel multi-stage association method, which consists of a hierarchical matching module and a customized track management module. Specifically, the hierarchical matching module defines the reliability of the objects by associating multimodal detections, and matches detections with trajectories based on the reliability in turn, which increases the utilization of true detections, and, thus, guides accurate association. Then, based on the reliability of the trajectories provided by the matching module, the customized track management module sets maximum missing frames with differences for tracks, which decreases the number of identity switches of the same object and, thus, further improves the association accuracy. By using the proposed multi-stage association method, we develop a tracker called MSA-MOT for the 3D multi-object tracking task, alleviating the inherent matching problem in one-stage association. Extensive experiments are conducted on the challenging KITTI benchmark, and the results show that our tracker outperforms the previous state-of-the-art methods in terms of both accuracy and speed. Moreover, the ablation and exploration analysis results demonstrate the effectiveness of the proposed multi-stage association method.

GTAD: Graph and Temporal Neural Network for Multivariate Time Series Anomaly Detection.

The rapid development of smart factories, combined with the increasing complexity of production equipment, has resulted in a large number of multivariate time series that can be recorded using sensors during the manufacturing process. The anomalous patterns of industrial production may be hidden by these time series. Previous LSTM-based and machine-learning-based approaches have made fruitful progress in anomaly detection. However, these multivariate time series anomaly detection algorithms do not take into account the correlation and time dependence between the sequences. In this study, we proposed a new algorithm framework, namely, graph attention network and temporal convolutional network for multivariate time series anomaly detection (GTAD), to address this problem. Specifically, we first utilized temporal convolutional networks, including causal convolution and dilated convolution, to capture temporal dependencies, and then used graph neural networks to obtain correlations between sensors. Finally, we conducted sufficient experiments on three public benchmark datasets, and the results showed that the proposed method outperformed the baseline method, achieving detection results with F1 scores higher than 95% on all datasets.

A Novel Deep Transfer Learning Method for Intelligent Fault Diagnosis Based on Variational Mode Decomposition and Efficient Channel Attention.

In recent years, deep learning has been applied to intelligent fault diagnosis and has achieved great success. However, the fault diagnosis method of deep learning assumes that the training dataset and the test dataset are obtained under the same operating conditions. This condition can hardly be met in real application scenarios. Additionally, signal preprocessing technology also has an important influence on intelligent fault diagnosis. How to effectively relate signal preprocessing to a transfer diagnostic model is a challenge. To solve the above problems, we propose a novel deep transfer learning method for intelligent fault diagnosis based on Variational Mode Decomposition (VMD) and Efficient Channel Attention (ECA). In the proposed method, the VMD adaptively matches the optimal center frequency and finite bandwidth of each mode to achieve effective separation of signals. To fuse the mode features more effectively after VMD decomposition, ECA is used to learn channel attention. The experimental results show that the proposed signal preprocessing and feature fusion module can increase the accuracy and generality of the transfer diagnostic model. Moreover, we comprehensively analyze and compare our method with state-of-the-art methods at different noise levels, and the results show that our proposed method has better robustness and generalization performance.

Deep Transfer Network with Multi-Space Dynamic Distribution Adaptation for Bearing Fault Diagnosis.

Domain adaptation-based bearing fault diagnosis methods have recently received high attention. However, the extracted features in these methods fail to adequately represent fault information due to the versatility of the work scenario. Moreover, most existing adaptive methods attempt to align the feature space of domains by calculating the sum of marginal distribution distance and conditional distribution distance, without considering variable cross-domain diagnostic scenarios that provide significant cues for fault diagnosis. To address the above problems, we propose a deep convolutional multi-space dynamic distribution adaptation (DCMSDA) model, which consists of two core components: two feature extraction modules and a dynamic distribution adaptation module. Technically, a multi-space structure is proposed in the feature extraction module to fully extract fault features of the marginal distribution and conditional distribution. In addition, the dynamic distribution adaptation module utilizes different metrics to capture distribution discrepancies, as well as an adaptive coefficient to dynamically measure the alignment proportion in complex cross-domain scenarios. This study compares our method with other advanced methods, in detail. The experimental results show that the proposed method has excellent diagnosis performance and generalization performance. Furthermore, the results further demonstrate the effectiveness of each transfer module proposed in our model.

hnRNPA2B1-Mediated Extracellular Vesicles Sorting of miR-122-5p Potentially Promotes Lung Cancer Progression.

Extracellular vesicles (EVs) released by tumor cells play important roles on the remodeling of the tumor-stromal environment and on promoting tumor metastasis. Our earlier studies revealed that miR-122-5p, a type of small non-coding RNA, was dysregulated in non-small cell lung cancer (NSCLC) cell-derived EVs. In this study, we found that miR-122-5p was selectively sorted and secreted into lung cancer EVs through binding to RNA-binding protein hnRNPA2B1. In addition, we found that hnRNPA2B1 interacted with miR-122-5p through the EXO-motif. The delivering of lung cancer EVs-miR-122-5p promoted the migration of liver cells, which may play roles in establishing a pre-metastatic micro-environment and hepatic metastasis of lung cancer. Importantly, our findings revealed the molecular mechanism that RNA-binding protein controls the selective sorting of tumor-derived EV miR-122-5p, which potentially promotes lung cancer progression.

Real-Time Object Tracking via Adaptive Correlation Filters.

Although correlation filter-based trackers (CFTs) have made great achievements on both robustness and accuracy, the performance of trackers can still be improved, because most of the existing trackers use either a sole filter template or fixed features fusion weight to represent a target. Herein, a real-time dual-template CFT for various challenge scenarios is proposed in this work. First, the color histograms, histogram of oriented gradient (HOG), and color naming (CN) features are extracted from the target image patch. Then, the dual-template is utilized based on the target response confidence. Meanwhile, in order to solve the various appearance variations in complicated challenge scenarios, the schemes of discriminative appearance model, multi-peaks target re-detection, and scale adaptive are integrated into the proposed tracker. Furthermore, the problem that the filter model may drift or even corrupt is solved by using high confidence template updating technique. In the experiment, 27 existing competitors, including 16 handcrafted features-based trackers (HFTs) and 11 deep features-based trackers (DFTs), are introduced for the comprehensive contrastive analysis on four benchmark databases. The experimental results demonstrate that the proposed tracker performs favorably against state-of-the-art HFTs and is comparable with the DFTs.

A Multiscale Recognition Method for the Optimization of Traffic Signs Using GMM and Category Quality Focal Loss.

Effective traffic sign recognition algorithms can assist drivers or automatic driving systems in detecting and recognizing traffic signs in real-time. This paper proposes a multiscale recognition method for traffic signs based on the Gaussian Mixture Model (GMM) and Category Quality Focal Loss (CQFL) to enhance recognition speed and recognition accuracy. Specifically, GMM is utilized to cluster the prior anchors, which are in favor of reducing the clustering error. Meanwhile, considering the most common issue in supervised learning (i.e., the imbalance of data set categories), the category proportion factor is introduced into Quality Focal Loss, which is referred to as CQFL. Furthermore, a five-scale recognition network with a prior anchor allocation strategy is designed for small target objects i.e., traffic sign recognition. Combining five existing tricks, the best speed and accuracy tradeoff on our data set (40.1% mAP and 15 FPS on a single 1080Ti GPU), can be achieved. The experimental results demonstrate that the proposed method is superior to the existing mainstream algorithms, in terms of recognition accuracy and recognition speed.

Effect and Mechanism of Rhein-praseodymium Complex on Intestinal Uric Acid Excretion in Rats with Renal Injury and Hyperuricemia.

BACKGROUND: Hyperuricemia (HUA) is a disease characterized by excessive uric acid production and/or insufficient uric acid excretion caused by abnormal purine metabolism in the human body. Uric acid deposition caused by hyperuricemia can cause complications, such as kidney damage. The current therapeutic drugs for HUA are not very targeted and usually have specific toxic side effects. OBJECTIVES: This study aimed to synthesize a compound using rhein and praseodymium, which can effectively help hyperuricemia patients with kidney injury to excrete uric acid through the intestine and preliminarily explore its intestinal excretion mechanism. METHODS: The natural active ingredient rhein and rare earth metal praseodymium were used to synthesize Rh-Pr. The possible chemical structure of Rh-Pr was deduced by UV, IR, 1H-NMR, conductivity method, and thermogravity analysis. Adenine (100 mg/kg) and ethambutol hydrochloride (250 mg/kg) were administered by gavage for three weeks to establish the hyperuricemia rat model of renal injury. Serum uric acid (UA), creatinine (Cr), urea nitrogen (BUN), and uric acid concentration in urine and feces were detected by biochemical methods. The protein expression levels of GLUT9, ABCG2, and MRP4 in the jejunum, ileum, and colon of rats were detected by Western Blotting. RESULTS: According to the characterization, the chemical composition formula of the complex is Pr(C15H7O6)3·2H2O. In vivo, activity tests showed that Rh-Pr could enhance the intestinal uric acid excretion level of rats, upregulate the expression of ABCG2 protein in the jejunum and ileum, down-regulate the expression of GLUT9 protein in the ileum and colon, and also had a good recovery effect on serum uric acid, creatinine, and urea nitrogen levels. CONCLUSION: Rh-Pr is different from other drugs in that it promotes intestinal uric acid excretion and has a renal recovery effect. It reduces the patient's kidney burden and is significant for hyperuricemia patients with kidney injury.

A risk entropy approach for linking pesticides and soil bacterial communities.

Pesticides play a vital role in ensuring modern agricultural production, but also adversely affecting soil health. Microorganisms are the cornerstone of soil ecology, however, to date, there are few unified standards to measure the risk of soil pesticide residues to soil microbial community. To compensate for this gap, we collected soil samples from 55 orchards and monitored and risk-assessed 165 pesticides to microbial community in the soil. Results showed that a total of 137 pesticides were detected in all samples. Pesticide residues significantly influenced the microbial diversity and community structure in orchard soils, particularly fungicides and herbicides. The risk entropy of each pesticide was calculated in all samples and it was found that 60% of the samples had a "pesticide risk" (Risk quotient > 0.01), where the relative abundance significantly increased in 43 genera and significantly decreased in 111 genera (p < 0.05). Through multiple screens, we finally identified Bacillus and Sphingomonas as the most abundant sensitive genera under pesticide perturbation. The results showed that despite the complexity of the effects of pesticide residues on soils health, we could reveal them by identifying changes in soil bacterial, especially by the differences of microbial biomarkers abundance. The present study could provide new insights into the research strategy for pesticide pollution on soil microbial communities. ENVIRONMENTAL IMPLICATION: The risk of pesticide residues in soil needs to be quantified and standardized. We believe that microorganisms can be used as a marker to indicate soil pesticide residue risk. For this end, we investigated the residues of 165 pesticides in 55 orchard soil samples, calculated pesticide risk entropy and their effects on the soil microbial community. Through multiple analyzing and screening, we ultimately identified that, out of the 154 detected biomarkers, Bacillus and Sphingomonas were the most abundant sensitive genera under pesticide perturbation, which have the potential to be used as key biomarkers of soil microbiomes induced by pesticide perturbation.

Expression of hypoxia-inducible factor 1α mRNA in hearts and lungs of broiler chickens with ascites syndrome induced by excess salt in drinking water.

Hypoxia-inducible factor 1 (HIF-1) is a ubiquitously expressed heterodimeric transcription factor that mediates adaptive responses to hypoxia in all nucleated cells of metazoan organisms. Hypoxia-inducible factor 1α is involved in the pathogenesis of pulmonary hypertension in humans and animals, but whether HIF-1α is associated with the development of pulmonary hypertension syndrome (also known as ascites syndrome, AS) in broiler chickens has not been determined. In the present paper we addressed this issue by measuring the expression of HIF-1α mRNA in hearts and lungs of broiler chickens with AS induced by excess salt in drinking water. We conducted 2 experiments. The first experiment was used to observe the effects of excess salt on AS incidence. The results indicated that total incidence (20%) of AS in excess salt group (receiving 0.3% NaCl in drinking water) was much higher compared with the control group (receiving tap water) over a 43-d time course (P < 0.05). In the second experiment, we determined mean pulmonary arterial pressure (mPAP), ascites heart index (AHI), and expression of HIF-1α mRNA in lungs and hearts of broiler chickens after the excess salt treatment. Our results showed that excess salt induced pulmonary hypertension (indicated by higher mPAP) and right ventricular hypertrophy (greater ascites heart index) in broiler chickens. Meanwhile, the expression levels of HIF-1α mRNA in lungs and hearts were significantly increased at different time points in the excess salt group compared with the control group. Linear correlation analysis showed that the expression of HIF-1α mRNA in lungs was significantly positively correlated with mPAP (correlation coefficient = 0.79, P < 0.001), demonstrating that expression of HIF-1α mRNA was gradually increased in the excess salt group with the increase of pulmonary arterial pressure. In addition, the ascitic chickens showed significantly higher transcriptional levels of HIF-1α in hearts and lungs, compared with the age-matched healthy chickens, respectively. Our findings hinted that HIF-1α might be associated with the development of AS induced by excess salt in drinking water in broiler chickens.

Experimental Study on the Ethanol-Assisted Catalytic Steam Reforming of Endothermic Hydrocarbon Fuel.

Thermal protection is a critical problem in the development of hypersonic aircraft. To enhance the thermal protection capability of hydrocarbon fuel, the ethanol-assisted catalytic steam reforming of endothermic hydrocarbon fuel was proposed. The result shows that the total heat sink can be significantly improved by the endothermic reactions of ethanol. A higher water/ethanol ratio can promote the steam reforming of ethanol and further increase the chemical heat sink. The addition of 10 wt % ethanol at 30 wt % water content can improve the total heat sink by 8-17% at 300-550 °C, which is caused by the heat absorption by phase transition and chemical reactions of ethanol. The reaction region of thermal cracking moves backward, resulting in the suppression of thermal cracking. Meanwhile, the addition of ethanol can inhibit the coke deposition and increase the working temperature upper limit of the active thermal protection.

Conditional normalizing flow for multivariate time series anomaly detection.

Multivariate time series data is becoming increasingly ubiquitous in various fields such as servers, industrial applications, and healthcare. However, detecting anomalies in such data is challenging due to its complex time-dependent, high-dimensional, and label scarcity. Aiming at this problem, this paper proposes an Attention Factorization Normalizing Flow (AFNF) algorithm for unsupervised multivariate time series anomaly detection. Our hypothesis is that anomalies are in a low-density region of the distribution. To transform the complex density of high-dimensional time series into a simple evaluable conditional density, we propose a time series factorization strategy and parameterize the conditional information generated by factorization in the time and attribute dimensions using an attention mechanism. Moreover, to compensate for the lack of temporal information due to the permutation invariance attention mechanism, a adjacency contrasting approach is proposed to model the local invariance of the time series. To provide long-term location information, a learnable global location encoding is introduced. Conditional normalizing flows are applied to evaluate the conditional probability of the observations. Finally, through extensive experiments on three real data sets, our method yielded the best results and its effectiveness in density estimation and anomaly detection is demonstrated.

Atmospheric antibiotic resistome driven by air pollutants.

The atmosphere is an important reservoir and habitat for antibiotic resistance genes (ARGs) and is a main pathway to cause potential health risks through inhalation and ingestion. However, the distribution characteristics of ARGs in the atmosphere and whether they were driven by atmospheric pollutants remain unclear. We annotated 392 public air metagenomic data worldwide and identified 1863 ARGs, mainly conferring to tetracycline, MLS, and multidrug resistance. We quantified these ARG's risk to human health and identified their principal pathogenic hosts, Burkholderia and Staphylococcus. Additionally, we found that bacteria in particulate contaminated air carry more ARGs than in chemically polluted air. This study revealed the influence of typical pollutants in the global atmosphere on the dissemination and risk of ARGs, providing a theoretical basis for the prevention and mitigation of the global risks associated with ARGs.