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.

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.

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.

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.

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.

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.

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.

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.

Process coupling of CO2 reduction and 5-HMF oxidation mediated by defect-enriched layered double hydroxides.

Aiming at the comprehensive utilization of waste carbon resources and renewable carbon resources, we put forward the photocatalytic coupling process of CO2 reduction and 5-hydroxymethylfurfural (5-HMF) oxidation mediated by the anionic compound of layered double hydroxides (LDHs). Specifically, a ZnNiFe-LDH was synthesized by co-precipitation method, during which CO2 was stored between LDH layers in the form of carbonate. Then, a certain amount of metal vacancies were introduced into LDH nanosheets by selectively etching Zn2+ ions. ICP-AES, EPR and XPS showed that the concentration of Zn vacancies gradually increased with the etching time prolonging, which thus optimized the electronic structure of LDH layers. Under the catalysis of the electron-rich metal cations and hydroxyl groups on the layers, the interlayer carbonate was in situ reduced into CO coupled accompanied with the 5-HMF oxidation to 2.5-furandiformaldehyde (DFF). Compared with the unetched ZnNiFe-LDHs, the CO and DFF yields over the LDHs etched for 3 h were increased by 2.84 and 2.82 times under UV-vis irradiation with a density of 500 mW cm-2. Finally, combined with isotope-labeled 13CO2 experiments and in situ FTIR characterization, we revealed the possible coupling mechanism and defect-induced performance enhancement mechanism.

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.

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.

Reference values of the carotid elastic modulus using shear wave elastography and arterial stiffness change in coronary slow flow.

PURPOSE: Shear wave elastography (SWE) accurately and sensitively evaluates arterial wall stiffness by quantifying the elastic modulus (EM); however, the absence of reference values has precluded its widespread clinical application. This prospective cohort study aimed to establish reference values for the carotid EM using SWE; investigate the main determinants of the EM; and evaluate EM changes in coronary slow flow (CSF), which is characterized by delayed coronary opacification without evident obstructive lesion in epicardial coronary artery on angiography. METHOD: This study enrolled 169 healthy volunteers and 30 patients with CSF. The carotid maximum EM (EMmax), mean EM, and minimum EM were measured using SWE. CSF was diagnosed by thrombolysis in the myocardial infarction frame count during coronary angiography. RESULTS: No differences were found in the EM between the left and right carotid arteries and between men and women. Multiple linear regression analysis revealed that age was independently correlated with the EMmax, which progressively increased with age. Moreover, smoking had an independent influence on the EM after adjusting for age; smokers had higher EM than non-smokers. Age-specific reference values for the carotid EM were established. The EM was higher in patients with CSF than in controls after adjusting for age and smoking status. CONCLUSIONS: This study first established the reference values for the carotid EM using SWE. Age and smoking status were the main determinants of the EM. Patients with CSF had high EM. SWE can effectively and noninvasively evaluate arterial stiffness in patients with CSF.

Advanced aqueous sodium-ion capacitors based on Ni0.25Mn0.75O nanoparticles encapsulated in electrospinning carbon nanofibers.

Manganese oxides are promising cathode material candidates with appropriate positive potential windows for low-cost and safe aqueous sodium-ion capacitors (ASICs). However, their low electrical conductivity issue and the lack of advanced binder-free manganese oxide-based electrodes severely restrict their practical capacitance and application in flexible ASICs. Here, Ni0.25Mn0.75O (NMO) nanoparticles uniformly encapsulated in carbon nanofiber films with excellent flexibility are fabricated by electrospinning and subsequent carbonization. The uniformly amorphous carbon layer enhances the conductivity, avoids dissolution and alleviates the volume or stress change of NMO during ion intercalation or mechanical deformation. More importantly, compared with the flexible electrodes prepared by traditional methods, electrospinning materials can be directly used as binder-free electrodes, which can effectively simplify the process and improve the energy density. Finally, a 2.4 V flexible quasi-solid-state ASIC device is integrated, which exhibits a high energy density of 5.95 mWh cm-3, a high power density of 670 mW cm-3 and an outstanding stability of 1000 cycles. This work offers an effective materials engineering strategy for high-performance binder-free NMO-based cathodes and advanced flexible ASICs.

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.

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.

Light-Induced Structural Dynamic Evolution of Pt Single Atoms for Highly Efficient Photocatalytic CO2 Reduction.

Revealing the structural evolution of the real active site during photocatalysis is very important for understanding the catalytic mechanism, but it remains a great challenge. By employing single atoms (SAs) as the mechanism research platform, we investigated the variation of the SA structure under light and the corresponding reaction pathway controlment mechanism. In particular, taking the defect anchoring strategy, Pt SAs are anchored on the metal ion vacancy-rich ZnNiTi layered double hydroxide-etched (ZnNiTi-LDHs-E) support. It is proved by CO-Fourier transform infrared and X-ray absorption fine structure characterization methods that the Pt SAs could gain photoelectrons to form cationic Pt(IV), electron-rich Pt(II), and near-neutral Ptδ+ species at different light intensities. By in situ inducing the above different Pt SAs in photocatalytic CO2 reduction, a dramatic product distribution is observed: (1) under weak light, Pt(IV) SAs cannot activate CO, so CO cannot be further transformed into hydrocarbons; (2) under the moderate light, electron-rich Pt(II) SAs could cooperate with adjacent LDH surface sites (Ni2+/Ti4+) to open up the C-C coupling route for C2H6 generation; and (3) Pt SAs in the state of near-neutral Ptδ+ could directly hydrogenate CO into CH4. This work reveals the structural evolution of Pt SAs in photocatalysis and the corresponding effect on catalytic performance, which provides a new idea for the construction of highly efficient photocatalysts.

MILNP: Plant lncRNA-miRNA Interaction Prediction Based on Improved Linear Neighborhood Similarity and Label Propagation.

Knowledge of the interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) is the basis of understanding various biological activities and designing new drugs. Previous computational methods for predicting lncRNA-miRNA interactions lacked for plants, and they suffer from various limitations that affect the prediction accuracy and their applicability. Research on plant lncRNA-miRNA interactions is still in its infancy. In this paper, we propose an accurate predictor, MILNP, for predicting plant lncRNA-miRNA interactions based on improved linear neighborhood similarity measurement and linear neighborhood propagation algorithm. Specifically, we propose a novel similarity measure based on linear neighborhood similarity from multiple similarity profiles of lncRNAs and miRNAs and derive more precise neighborhood ranges so as to escape the limits of the existing methods. We then simultaneously update the lncRNA-miRNA interactions predicted from both similarity matrices based on label propagation. We comprehensively evaluate MILNP on the latest plant lncRNA-miRNA interaction benchmark datasets. The results demonstrate the superior performance of MILNP than the most up-to-date methods. What's more, MILNP can be leveraged for isolated plant lncRNAs (or miRNAs). Case studies suggest that MILNP can identify novel plant lncRNA-miRNA interactions, which are confirmed by classical tools. The implementation is available on https://github.com/HerSwain/gra/tree/MILNP.