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In recent years, due to the diversity of fire scenes in ports and ships, the problem of fire command is complicated. In the case of power failure, the original command platform and fire extinguishing system will not be able to be used normally, or the fire extinguishing resources carried by the ship when it is on fire cannot be used. In the traditional firefighting ability research, there is no clear measure of firefighting ability, resulting in inaccurate calculation results. By corresponding to the quantity and composition of resources for the study of fire extinguishing capability, the combination of the fire dynamics simulation software PyroSim2020 and a calculation model that converts its resources into the total amount and flow of fire extinguishing agent can be provided. Based on the analytic hierarchy process (AHP), a firefighting demand grade calculation and judgment model was established, which included five factors: fire scale, combustion material characteristics, foam extinguishing agent performance, firefighting object characteristics, and external factors. It concluded that the demand assignment values of each index element were 2, 5, 6, 9, and 10, and further proposed the grade judgment criteria for the calculation results of comprehensive fire demand. Through the quantitative simulation of firefighting demand based on the fire scenario and calculation model test of the consumption prediction of cooperative firefighting equipment, it can also provide a strategic reference for related cooperative fire rescue. [ABSTRACT FROM AUTHOR]

This study presents a bi-objective optimization model for the Green Vehicle-Routing Problem in cold chain logistics, with a focus on symmetric distance matrices, aiming to minimize total costs, including carbon emissions, while maximizing customer satisfaction. To address this complex challenge, we developed a Stage-Specific Multi-Objective Five-Element Cycle Optimization algorithm (MOFECO-SS), which dynamically adjusts optimization strategies across different stages of the process, thereby enhancing overall efficiency. Extensive comparative analyses with existing algorithms demonstrate that MOFECO-SS consistently outperforms in solving the multi-objective optimization model, particularly in reducing total costs and carbon emissions while maintaining high levels of customer satisfaction. The symmetric nature of the distance matrix further aids in achieving balanced and optimized route planning. The results highlight that MOFECO-SS offers decision-makers flexible route planning options that balance cost efficiency with environmental sustainability, ultimately improving the effectiveness of cold chain logistics operations. [ABSTRACT FROM AUTHOR]

This paper presents a novel algorithm named Five-element Cycle Integrated Mutation Optimization (FECOIMO) for solving the Traveling Thief Problem (TTP). The algorithm introduces a five-element cycle structure that integrates various mutation operations to enhance both global exploration and local exploitation capabilities. In experiments, FECOIMO was extensively tested on 39 TTP instances of varying scales and compared with five common metaheuristic algorithms: Enhanced Simulated Annealing (ESA), Improved Grey Wolf Optimization Algorithm (IGWO), Improved Whale Optimization Algorithm (IWOA), Genetic Algorithm (GA), and Profit-Guided Coordination Heuristic (PGCH). The experimental results demonstrate that FECOIMO outperforms the other algorithms across all instances, particularly excelling in large-scale instances. The results of the Friedman test show that FECOIMO significantly outperforms other algorithms in terms of average solution, maximum solution, and solution standard deviation. Additionally, although FECOIMO has a longer execution time, its complexity is comparable to that of other algorithms, and the additional computational overhead in solving complex optimization problems translates into better solutions. Therefore, FECOIMO has proven its effectiveness and robustness in handling complex combinatorial optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Multi-objective Five-element Cycle Optimization algorithm based on Multi-strategy fusion (MOFECO-MS) to address the Bi-objective Traveling Thief Problem (BITTP), an extension of the Traveling Thief Problem that incorporates two conflicting objectives. The novelty of our approach lies in a unique individual selection strategy coupled with an innovative element update mechanism rooted in the Five-element Cycle Model. To balance global exploration and local exploitation, the algorithm categorizes the population into distinct groups and applies crossover operations both within and between these groups, while also employing a mutation operator for local searches on the best individuals. This coordinated approach optimizes parameter settings and enhances the search capabilities of the algorithm. Extensive experiments were conducted on nine BITTP instances, comparing MOFECO-MS against eight state-of-the-art multi-objective optimization algorithms. The results show that MOFECO-MS excels in both Hypervolume (HV) and Spread (SP) indicators, while also maintaining a high level of Pure Diversity (PD). Overall, MOFECO-MS outperformed the other algorithms in most instances, demonstrating its superiority and robustness in solving complex multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

This study investigated the antibacterial effects of S-nitroso-N-acetylcysteine (SNAC) and sodium nitrite (NaNO2) against Escherichia coli and their application in beef sausages. Both SNAC and NaNO2 demonstrated pH-responsive antibacterial activity, with SNAC showing greater efficacy than NaNO2 (p < 0.05) at the same pH (3, 5, and 7). The reactive oxygen species (ROS) and reactive nitrogen species (RNS) induced in E. coli by SNAC were significantly higher than those induced by NaNO2 (p < 0.05), and both ROS and RNS values increased as the pH decreased. In addition, a lower pH led to more pores on the E. coli cell surface and increased membrane permeability, resulting in a more pronounced inhibitory effect. When applied to a beef sausage, SNAC-treated sausages had significantly lower total colony counts and carbonyl content compared to NaNO2-treated ones (p < 0.05). Consequently, SNAC shows great potential as a replacement for NaNO2 in meat products. [ABSTRACT FROM AUTHOR]

Land surface water is a key part in the global ecosystem balance and hydrological cycle. Remote sensing has become an effective tool for its spatio-temporal monitoring. However, remote sensing results exemplified in so-called water indices are subject to several limitations. This paper proposes a new and effective water index called the Sentinel Multi-Band Water Index (SMBWI) to extract water bodies in complex environments from Sentinel-2 satellite imagery. Individual tests explore the effectiveness of the SMBWI in eliminating interference of various special interfering cover features. The Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) method and confusion matrix along with the derived accuracy evaluation indicators are used to provide a threshold reference when extracting water bodies and evaluate the accuracy of the water body extraction results, respectively. The SMBWI and eight other commonly used water indices are qualitatively and quantitatively compared through vision and accuracy evaluation indicators, respectively. Here, the SMBWI is proven to be the most effective at suppressing interference of buildings and their shadows, cultivated lands, vegetation, clouds and their shadows, alpine terrain with bare ground and glaciers when extracting water bodies. The overall accuracy in all tests was consistently greater than 96.5%. The SMBWI is proven to have a high ability to identify mixed pixels of water and non-water, with the lowest total error among nine water indices. Most notably, better results are obtained when extracting water bodies under interfering environments of cover features. Therefore, we propose that our novel and robust water index, the SMBWI, is ready to be used for mapping land surface water with high accuracy. [ABSTRACT FROM AUTHOR]

Multivariate regression is a fundamental supervised chemometric method for developing the relationship between the independent variables and quantitative response, and it has been widely applied for data analysis in many research fields. In this study, we propose an effective method for the quantitative determination of target compounds in traditional Chinese medicine, specifically Mongolia, using excitation-emission matrix (EEM) spectra with partial overlap. The accuracy and reliability of the established model have been validated, demonstrating that the proposed method can realize the accurate quantitative analysis purpose. In order to facilitate the calculation easier, the authors have developed a friendly graphical user interface (GUI). The GUI offers the procedures for data imputation, model establishment, model optimization and results presentation. [ABSTRACT FROM AUTHOR]

The advancement of an intelligent automobile sound switching system has the potential to elevate the market standing of automotive products, with the pivotal prerequisite being the selection of automobile sounds based on the driver's subjective perception. The subjective responses of diverse individuals to sounds can be objectively manifested through EEG signals. Therefore, EEG signals are employed herein to attain the recognition of automobile sounds. A subjective evaluation and EEG signal acquisition experiment are designed involving the stimulation of three distinct types of automobile sounds, namely comfort, power, and technology sounds, and a comprehensive database of EEG signals corresponding to these three sound qualities is established. Then, a specific transfer learning model based on a convolutional neural network (STL-CNN) is formulated, where the method of training the upper layer parameters with the fixed bottom weights is proposed to adaptively extract the EEG features related to automobile sounds. These improvements contribute to improving the generalization ability of the model and realizing the recognition of automobile sounds fused with EEG signals. The results of the comparison with traditional support vector machine (SVM) and convolutional neural network (CNN) models demonstrate that the accuracy of the test set of the STL-CNN model reaches 91.5%. Moreover, its comprehensive performance, coupled with the ability to adapt to individual differences, surpasses that of both SVM and CNN models. The demonstrated method in the recognition of automobile sounds based on EEG signals is of significance for the future implementation of switching driving sound modes fused with EEG signals. [ABSTRACT FROM AUTHOR]

In the context of peptide drug development, glycosylation plays a pivotal role. Accordingly, L-type peptides were synthesized predicated upon the PD-1/PD-L1 blocker DPPA-1. Subsequent glycosylation resulted in the production of two distinct glycopeptides, D-glu-LPPA-1 and D-gal-LPPA-1, by using D-glucose (D-glu) and D-galactose (D-gal), respectively, during glycosylation. Both glycopeptides significantly inhibited the interaction between PD-1 and PD-L1, and the measured half maximal inhibitory concentrations (IC50s) were 75.5 μM and 101.9 μM for D-glu-LPPA-1 and D-gal-LPPA-1, respectively. Furthermore, D-gal-LPPA-1 displayed a pronounced ability to restore T-cell functionality. In an MC38 tumor-bearing mouse model, D-gal-LPPA-1 demonstrated a significant inhibitory effect. Notably, D-gal-LPPA-1 substantially augmented the abundance and functionality of CD8+ T cells in the tumor microenvironment. Additionally, in the lymph nodes and spleens, D-gal-LPPA-1 significantly increased the proportion of CD8+ T cells secreting interferon-gamma (IFN-γ). These strong findings position D-gal-LPPA-1 as a potent enhancer of the antitumor immune response in MC38 tumor-bearing mice, underscoring its potential as a formidable PD-1/PD-L1 blocking agent. [ABSTRACT FROM AUTHOR]

This work aimed to find new inhibitors of the CYP3A4 and JAK3 enzymes, which are significant players in autoimmune diseases such as rheumatoid arthritis. Advanced computer-aided drug design techniques, such as pharmacophore and 3D-QSAR modeling, were used. Two strong 3D-QSAR models were created, and their predictive power was validated by the strong correlation (R2 values > 80%) between the predicted and experimental activity. With an ROC value of 0.9, a pharmacophore model grounded in the DHRRR hypothesis likewise demonstrated strong predictive ability. Eight possible inhibitors were found, and six new inhibitors were designed in silico using these computational models. The pharmacokinetic and safety characteristics of these candidates were thoroughly assessed. The possible interactions between the inhibitors and the target enzymes were made clear via molecular docking. Furthermore, MM/GBSA computations and molecular dynamics simulations offered insightful information about the stability of the binding between inhibitors and CYP3A4 or JAK3. Through the integration of various computational approaches, this study successfully identified potential inhibitor candidates for additional investigation and efficiently screened compounds. The findings contribute to our knowledge of enzyme–inhibitor interactions and may help us create more effective treatments for autoimmune conditions like rheumatoid arthritis. [ABSTRACT FROM AUTHOR]

In order to effectively prevent fire accidents and improve fire management capability, this paper describes the independent designs and builds of an experimental low-cost particle image velocimetry platform for a propane jet combustion flame using traditional mutual correlation theory. The particle image velocimetry (PIV) algorithm is written based on MATLAB software, allowing it to realise image preprocessing, multi-level grid window deformation inter-correlation calculations, and other functions. Fluid flow velocity and vorticity are used as entry points to study the flame combustion mechanism. The air flow field and vorticity above the propane jet flame are analysed. The results show that, from the level of fluid flow velocity, the maximum fluid flow velocity in the test area does not exceed 0.23 m/s, and the maximum transverse fluid flow velocity is close to 0.15 m/s. Additionally, the longitudinal fluid flow velocity is opposite the upper and lower portions of the longitudinal flow velocity, and there is a swirling phenomenon in the propane flame jet. From the vorticity level, the closer to the centre of the jet in the vortex plane, the faster the air flow speed, and simultaneously, in the upper and lower parts of the vortex, the air flow travels in the opposite direction and is of equal size. The particle image velocimetry platform that was independently designed in this study can efficiently characterise the dynamic flow field and the flow characteristics of complex combustion chambers, simultaneously ensuring high efficiency and reducing research costs. It provides a measurement method and experimental basis for the development of fire extinguishing equipment and numerical simulation, while also helping us to carry out a series of subsequent studies on fire extinguishing mechanisms. [ABSTRACT FROM AUTHOR]

Owing to the rapid expansion of data science, data-driven methods have emerged as a dominant trend in chiller fault detection and diagnosis (FDD). Most of these methods prioritize feature selection to achieve optimal diagnostic performance. However, on-site research indicates a common installation of a limited number of sensors, coupled with a necessity to minimize diagnostic costs. This discrepancy between existing research's feature selection principles and the current on-site sensor installation status presents a significant challenge. To facilitate the practical implementation of data-driven methods in real chiller units, this study addresses a critical question: under the constraint of limited on-site sensor installations, what is the optimal performance achievable by data-driven methods and their improved versions? To answer this, only features derived from commonly installed sensors on field chillers are chosen as indicators for typical chiller faults. The FDD performance of six frequently used data-driven methods, namely, back-propagation neural network, convolutional neural network, support vector machine, support vector data description, Bayesian network, and random forest, along with their improved versions, is comprehensively evaluated and validated using experimental data, considering four evaluation metrics. The conclusions drawn in this paper provide valuable insights for users/manufacturers with limited or no budget, detailing the best achievable diagnostic performance for each typical fault and offering guidance for those aiming to further enhance FDD performance. [ABSTRACT FROM AUTHOR]

Neuroblastoma (NB) is the most common extracranial solid tumor that affects developing nerve cells in the fetus, infants, and children. miR-124 is a microRNA (miRNA) enriched in neuronal tissues, and VAMP3 (vesicle-associated membrane protein 3) has been reported to be an miR-124 target, although the relationship between NB and miR-124 or VAMP3 is unknown. Our current work identified that miR-124 levels are high in NB cases and that elevated miR-124 correlates with worse NB outcomes. Conversely, depressed VAMP3 correlates with worse NB outcomes. To investigate the mechanisms by which miR-124 and VAMP3 regulate NB, we altered miR-124 or VAMP3 expression in human NB cells and observed that increased miR-124 and reduced VAMP3 stimulated cell proliferation and suppressed apoptosis, while increased VAMP3 had the opposite effects. Genome-wide mRNA expression analyses identified gene and pathway changes which might explain the NB cell phenotypes. Together, our studies suggest that miR-124 and VAMP3 could be potential new markers of NB and targets of NB treatments. [ABSTRACT FROM AUTHOR]

Phthalic acid esters (PAEs), which are widespread environmental contaminants, can be efficiently biodegraded, mediated by enzymes such as hydrolases. Despite great advances in the characterization of PAE hydrolases, which are the most important enzymes in the process of PAE degradation, their molecular catalytic mechanism has rarely been systematically investigated. Acinetobacter sp. LUNF3, which was isolated from contaminated soil in this study, demonstrated excellent PAE degradation at 30 °C and pH 5.0–11.0. After sequencing and annotating the complete genome, the gene dphAN1, encoding a novel putative PAE hydrolase, was identified with the conserved motifs catalytic triad (Ser201-Asp295-His325) and oxyanion hole (H127GGG130). DphAN1 can hydrolyze DEP (diethyl phthalate), DBP (dibutyl phthalate) and BBP (benzyl butyl phthalate). The high activity of DphAN1 was observed under a wide range of temperature (10–40 °C) and pH (6.0–9.0). Moreover, the metal ions (Fe2+, Mn2+, Cr2+ and Fe3+) and surfactant TritonX-100 significantly activated DphAN1, indicating a high adaptability and tolerance of DphAN1 to these chemicals. Molecular docking revealed the catalytic triad, oxyanion hole and other residues involved in binding DBP. The mutation of these residues reduced the activity of DphAN1, confirming their interaction with DBP. These results shed light on the catalytic mechanism of DphAN1 and may contribute to protein structural modification to improve catalytic efficiency in environment remediation. [ABSTRACT FROM AUTHOR]

Feature data refer to direct measurements of specific features, while feature residuals represent the deviations between these measurements and their corresponding benchmark values. Both types of information offer unique insights into the system's behavior. However, conventional diagnostic systems often struggle to effectively integrate and utilize both types of information concurrently. To address this limitation and improve diagnostic performance, a hybrid method based on the Bayesian network (BN) is proposed. This method enables the parallel fusion of feature residuals and feature data within a unified diagnostic model, and a comprehensive framework for developing this hybrid method is also given. In the hybrid BN, the symptom layer consists of residual nodes representing feature residuals and data nodes representing measured feature data. By applying the proposed method to two chillers and comparing it with state-of-the-art existing methods, we demonstrate its effectiveness and superiority. The results highlight that the proposed method not only accommodates the absence of either type of information but also leverages both of them to enhance diagnostic performance. Compared to using a single type of node, the hybrid method achieves a maximum improvement of 24.5% in diagnostic accuracy, with significant enhancements in F-measure observed for refrigerant leakage fault (34.5%) and excessive lubricant fault (32.8%), respectively. [ABSTRACT FROM AUTHOR]

Obesity is a serious public health problem. According to statistics, there are millions of obese people worldwide. Research studies have discovered a complex and intricate relationship between the gut microbiota and obesity. Probing and summarizing the relationship between intestinal microbes and obesity has important guiding significance for the accurate control of the research direction and expanding the choice of obesity treatment methods. We used bibliometric analysis to analyze the published literature with the intention to reveal the research hotspots and development trends on the effects of intestinal microbes on obesity from a visualization perspective, both qualitatively and quantitatively. The results showed that current research is focusing on related mechanisms of the effects of intestinal microbes on obesity and therapeutic methods for obesity. Several noteworthy hotspots within this field have garnered considerable attention and are expected to remain the focal points of future research. Of particular interest are the mechanisms by which intestinal microbes potentially regulate obesity through metabolite interactions, as well as the role of microbiomes as metabolic markers of obesity. These findings strongly suggest that gut microbes continue to be a key target in the quest for effective obesity treatments. Co-operation and communication between countries and institutions should be strengthened to promote development in this field to benefit more patients with obesity. [ABSTRACT FROM AUTHOR]

Tumors are often polyclonal due to copy number alteration (CNA) events. Through the CNA profile, we can understand the tumor heterogeneity and consistency. CNA information is usually obtained through DNA sequencing. However, many existing studies have shown a positive correlation between the gene expression and gene copy number identified from DNA sequencing. With the development of spatial transcriptome technologies, it is urgent to develop new tools to identify genomic variation from the spatial transcriptome. Therefore, in this study, we developed CVAM, a tool to infer the CNA profile from spatial transcriptome data. Compared with existing tools, CVAM integrates the spatial information with the spot's gene expression information together and the spatial information is indirectly introduced into the CNA inference. By applying CVAM to simulated and real spatial transcriptome data, we found that CVAM performed better in identifying CNA events. In addition, we analyzed the potential co-occurrence and mutual exclusion between CNA events in tumor clusters, which is helpful to analyze the potential interaction between genes in mutation. Last but not least, Ripley's K-function is also applied to CNA multi-distance spatial pattern analysis so that we can figure out the differences of different gene CNA events in spatial distribution, which is helpful for tumor analysis and implementing more effective treatment measures based on spatial characteristics of genes. [ABSTRACT FROM AUTHOR]

In the present study, an integrated framework for automatic detection, segmentation, and measurement of road surface cracks is proposed. First, road images are captured, and crack regions are detected based on the fifth version of the You Only Look Once (YOLOv5) algorithm; then, a modified Residual Unity Networking (Res-UNet) algorithm is proposed for accurate segmentation at the pixel level within the crack regions; finally, a novel crack surface feature quantification algorithm is developed to determine the pixels of crack in width and length, respectively. In addition, a road crack dataset containing complex environmental noise is produced. Different shooting distances, angles, and lighting conditions are considered. Validated through the same dataset and compared with You Only Look at CoefficienTs ++ (YOLACT++) and DeepLabv3+, the proposed method shows higher accuracy for crack segmentation under complex backgrounds. Specifically, the crack damage detection based on the YOLOv5 method achieves a mean average precision of 91%; the modified Res-UNet achieves 87% intersection over union (IoU) when segmenting crack pixels, 6.7% higher than the original Res-UNet; and the developed crack surface feature algorithm has an accuracy of 95% in identifying the crack length and a root mean square error of 2.1 pixels in identifying the crack width, with the accuracy being 3% higher in length measurement than that of the traditional method. [ABSTRACT FROM AUTHOR]

Phthalate esters (PAEs) are a ubiquitous kind of environmental endocrine that disrupt chemicals, causing environmental and health issues. EstJ6 is an effective phthalate-degrading hydrolase, and its mutant with a combination of three non-conservative distal mutations has an improved activity against PAEs with unknown molecular mechanisms. Herein, we attempt to fill the significant gap between distal mutations and the activity of this enzyme using computational approaches. We found that mutations resulted in a redistribution of the enzyme's preexisting conformational states and dynamic changes of key functional regions, especially the lid over the active site. The outward motion of the lid upon the mutations made it easier for substrates or products to enter or exit. Additionally, a stronger substrate binding affinity and conformational rearrangements of catalytic reaction-associated residues in the mutant, accompanied by the strengthened communication within the protein, could synergistically contribute to the elevated catalytic efficiency. Finally, an attempt was made to improve the thermostability of EstJ6 upon introducing a distal disulfide bond between residues A23 and A29, and the simulation results were as expected. Together, our work explored the allosteric effects caused by distal mutations, which could provide insights into the rational design of esterases for industrial applications in the future. [ABSTRACT FROM AUTHOR]

Ischemia-reperfusion injury (IRI) often occurs in the process of kidney transplantation, which significantly impacts the subsequent treatment and prognosis of patients. The prognosis of patients with different subtypes of IRI is quite different. Therefore, in this paper, the gene expression data of multiple IRI samples were downloaded from the GEO database, and a double Laplacian orthogonal non-negative matrix factorization (DL-ONMF) algorithm was proposed to classify them. In this algorithm, various regularization constraints are added based on the non-negative matrix factorization algorithm, and the prior information is fused into the algorithm from different perspectives. The connectivity information between different samples and features is added to the algorithm by Laplacian regularization constraints on samples and features. In addition, orthogonality constraints on the basis matrix and coefficient matrix obtained by the algorithm decomposition are added to reduce the influence of redundant samples and redundant features on the results. Based on the DL-ONMF algorithm for clustering, two PRGs-related IRI isoforms were obtained in this paper. The results of immunoassays showed that the immune microenvironment was different among PRGS-related IRI types. Based on the differentially expressed PRGs between subtypes, we used LASSO and SVM-RFE algorithms to construct a diagnostic model related to renal transplantation. ROC analysis showed that the diagnostic model could predict the outcome of renal transplant patients with high accuracy. In conclusion, this paper presents an algorithm, DL-ONMF, which can identify subtypes with different disease characteristics. Comprehensive bioinformatic analysis showed that pyroptosis might affect the outcome of kidney transplantation by participating in the immune response of IRI. [ABSTRACT FROM AUTHOR]

The parathyroid hormone type 1 receptor (PTH1R) acts as a canonical class B G protein-coupled receptor, regulating crucial functions including calcium homeostasis and bone formation. The identification and development of PTH1R non-peptide allosteric modulators have obtained widespread attention. It has been found that a negative allosteric modulator (NAM) could inhibit the activation of PTH1R, but the implied mechanism remains unclear. Herein, extensive molecular dynamics simulations together with multiple analytical approaches are utilized to unravel the mechanism of PTH1R allosteric inhibition. The results suggest that the binding of NAM destabilizes the structure of the PTH1R–PTH–spep/qpep (the C terminus of Gs/Gq proteins) complexes. Moreover, the presence of NAM weakens the binding of PTH/peps (spep and qpep) and PTH1R. The intra- and inter-molecular couplings are also weakened in PTH1R upon NAM binding. Interestingly, compared with our previous study of the positive allosteric effects induced by extracellular Ca2+, the enhanced correlation between the PTH and G-protein binding sites is significantly reduced by the replacement of this negative allosteric regulator. Our findings might contribute to the development of new therapeutic agents for diseases caused by the abnormal activation of PTH1R. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of sensors in the Internet of things (IoT) environment has resulted in the continuous generation of massive and rich data in Location-Based Social Networks (LBSN). In LBSN, the next point-of-interest (POI) recommendation has become an important task, which provides the best POI recommendation according to the user's recent check-in sequences. However, all existing methods for the next POI recommendation only focus on modeling the correlation between POIs based on users' check-in sequences but ignore the significant fact that the next POI recommendation is a time-subtle recommendation task. In view of the fact that the attention mechanism does not comprehensively consider the influence of the user's trajectory sequences, time information, social relations and geographic information of Point-of-Interest (POI) in the next POI recommendation field, a Context Geographical-Temporal-Social Awareness Hierarchical Attention Network (CGTS-HAN) model is proposed. The model extracts context information from the user's trajectory sequences and designs a Geographical-Temporal-Social attention network and a common attention network for learning dynamic user preferences. In particular, a bidirectional LSTM model is used to capture the temporal influence between POIs in a user's check-in trajectory. Moreover, In the context interaction layer, a feedforward neural network is introduced to capture the interaction between users and context information, which can connect multiple context factors with users. Then an embedded layer is added after the interaction layer, and three types of vectors are established for each POI to represent its sign-in trend so as to solve the heterogeneity problem between context factors. Finally reconstructs the objective function and learns model parameters through a negative sampling algorithm. The experimental results on Foursquare and Yelp real datasets show that the AUC, precision and recall of CGTS-HAN are better than the comparison models, which proves the effectiveness and superiority of CGTS-HAN. [ABSTRACT FROM AUTHOR]

Epidermal growth factor receptor (EGFR) is an intensively focused target for anti-tumor compounds used in non-small cell lung cancer (NSCLC) therapy. Compared to the classical activating mutations, there are still many uncommon EGFR mutations associated with poorer responses to EGFR inhibitors. A detailed understanding of the molecular basis for multiple EGFR mutants exhibiting diverse responses to inhibitors is of critical importance for related drug development. Herein, we explored the molecular determinants contributing to the distinct responses of EGFR with a single rare mutation (G719S) or combined mutations (G719S/L858R and G719S/l861Q) to Gefitinib (IRE). Our results indicated that interactions, formed within the tetrad of residues S768 (in the αC-helix), D770 (in the αC-β4 loop), Y827 (in the αE-helix), and R831 (in the catalytic loop), play an important role in the stability of αC-helix and the maintenance of K745–E762 salt bridge in the absence of IRE, which are weakened in the EGFRG719S system and enhanced in the EGFRG719S/L858R system upon IRE binding. Besides, the introduced hydrogen bonds by the co-occurring mutation partner also contribute to the stability of αC-helix. The work done for inhibitor dissociation suggests that IRE exhibits a stronger binding affinity to EGFRG719S/L858R mutant. Together, these findings provide a deeper understanding of minor mutations, which is essential for drug development targeting EGFR with less common mutations. [ABSTRACT FROM AUTHOR]

The growing complex user intention gap and information overload are obstacles for users to access the desired content. User interactions and the involved content indicate rich evidence of users' interests. It is required to investigate interaction characters over user interest and information distribution, and this alleviates information overload for personalized recommendation. Therefore, this work explores user interests with interactions and visual information from users' historical records for image recommendation. This paper introduces cross-modal manifold propagation (CMP) for personalized image recommendation. CMP investigates the trend of user preferences by propagating users' historical records along with users' interest distribution, which produces personalized interest-aware image candidates according to user interests. CMP simultaneously leverages visual distribution to spread users' visual records relying on the dense semantic visual manifold. Visual manifold propagation estimates detailed semantic-level user-image correlations for ranking candidate images in recommendations. In the proposed CMP, both user interest manifold and images' visual manifold compensate each other in propagating users' records to predict users' interaction. Experimental results illustrate the effectiveness of collaborative user-image propagation of CMP for personalized image recommendation. Performance improved by more than 20 % compared to that of existing baselines. [ABSTRACT FROM AUTHOR]

Swarms and assemblies are ubiquitous in nature and they can perform complex collective behaviors and cooperative functions that they cannot accomplish individually. In response to light, some colloidal particles (CPs), including light active and passive CPs, can mimic their counterparts in nature and organize into complex structures that exhibit collective functions with remote controllability and high temporospatial precision. In this review, we firstly analyze the structural characteristics of swarms and assemblies of CPs and point out that light-controlled swarming and assembly of CPs are generally achieved by constructing light-responsive interactions between CPs. Then, we summarize in detail the recent advances in light-controlled swarming and assembly of CPs based on the interactions arisen from optical forces, photochemical reactions, photothermal effects, and photoisomerizations, as well as their potential applications. In the end, we also envision some challenges and future prospects of light-controlled swarming and assembly of CPs. With the increasing innovations in mechanisms and control strategies with easy operation, low cost, and arbitrary applicability, light-controlled swarming and assembly of CPs may be employed to manufacture programmable materials and reconfigurable robots for cooperative grasping, collective cargo transportation, and micro- and nanoengineering. [ABSTRACT FROM AUTHOR]

In this work, we for the first time have proposed and fabricated a self-propelled Janus foam motor for on-the-fly oil absorption on water by simply loading camphor/stearic acid (SA) mixture as fuels into one end of the SA-modified polyvinyl alcohol (PVA) foam. The as-fabricated Janus foam motors show an efficient Marangoni effect-based self-propulsion on water for a long lifetime due to the effective inhibition of the rapid release of camphor by the hydrophobic SA in the fuel mixture. Furthermore, they can automatically search, capture, and absorb oil droplets on the fly, and then be spontaneously self-assembled after oil absorption due to the self-propulsion of the motors as well as the attractive capillary interactions between the motors and oil droplets. This facilitates the subsequent collection of the motors from water after the treatment. Since the as-developed Janus foam motors can effectively integrate intriguing behaviors of the self-propulsion, efficient oil capture, and spontaneous self-assembly, they hold great promise for practical applications in water treatment. [ABSTRACT FROM AUTHOR]

Islatravir (MK-8591) is a nucleoside reverse transcriptase translocation inhibitor in development for the treatment and prevention of HIV-1. The potential for islatravir to interact with commonly co-prescribed medications was studied in vitro. Elimination of islatravir is expected to be balanced between adenosine deaminase–mediated metabolism and renal excretion. Islatravir did not inhibit uridine diphosphate glucuronosyltransferase 1A1 or cytochrome p450 (CYP) enzymes CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, or 3A4, nor did it induce CYP1A2, 2B6, or 3A4. Islatravir did not inhibit hepatic transporters organic anion transporting polypeptide (OATP) 1B1, OATP1B3, organic cation transporter (OCT) 1, bile salt export pump (BSEP), multidrug resistance-associated protein (MRP) 2, MRP3, or MRP4. Islatravir was neither a substrate nor a significant inhibitor of renal transporters organic anion transporter (OAT) 1, OAT3, OCT2, multidrug and toxin extrusion protein (MATE) 1, or MATE2K. Islatravir did not significantly inhibit P-glycoprotein and breast cancer resistance protein (BCRP); however, it was a substrate of BCRP, which is not expected to be of clinical significance. These findings suggest islatravir is unlikely to be the victim or perpetrator of drug-drug interactions with commonly co-prescribed medications, including statins, diuretics, anti-diabetic drugs, proton pump inhibitors, anticoagulants, benzodiazepines, and selective serotonin reuptake inhibitors. [ABSTRACT FROM AUTHOR]

Low-coherent fiber-optic sensors combined with neural network algorithms were designed to carry out a load-sensitizing spherical bearing. Four sensing fibers were wound around the outside of the pot support of the spherical bearing uniformly deployed from upper to bottom. The upper three were configured in a distributed way to respond to the applied load as a function of the three strain sensors. The bottom one was employed as a temperature compensation sensor. A loading experiment was implemented to test the performance of the designed system. The results showed that there was a hysteresis in all the three sensors between loading and unloading process. The neural network algorithm is proposed to set up a function of the three sensors, treated as a set of input vectors to establish the input-output relationship between the applied loads and the constructed input vectors, in order to overcome the hysteresis existing in each sensor. An accuracy of 6% for load sensing was approached after temperature compensation. [ABSTRACT FROM AUTHOR]

The present study aimed to investigate the change of intestinal mucosa proteins, especially the alteration of intestinal drug metabolizing enzymes (IDMEs) following 14-day simulated microgravity. Morey–Holton tail-suspension analog was used to simulate microgravity. Intestinal mucosa proteins of rats were determined by label-free quantitative proteomic strategy. A total of 335 differentially expressed proteins (DEPs) were identified, 190 DEPs were upregulated, and 145 DEPs were downregulated. According to bioinformatic analysis, most of DEPs exhibited hydrolase, oxidoreductase, transferase, ligase, or lyase catalytic activity. DEPs were mainly enriched in metabolic pathways, including metabolism of amino acid, glucose, and carbon. Moreover, 11 of DEPs were involved in exogenous drug and xenobiotics metabolism. Owing to the importance of IDMEs for the efficacy and safety of oral drugs, the expression of cytochrome P450 1A2 (CYP1A2), CYP2D1, CYP3A2, CYP2E1, alcohol dehydrogenase 1 (ADH1), and glutathione S-transferase mu 5 (GSTM5) in rat intestine mucosa was determined by Western-blot. The activity of ADH, aldehyde dehydrogenase (ALDH) and GST was evaluated. Compared with control rats, the expression of CYP1A2, CYP2D1, CYP3A2, and ADH1 in the simulated microgravity (SMG) group of rats were dramatically decreased by 33.16%, 21.93%, 48.49%, and 22.83%, respectively. GSTM5 was significantly upregulated by 53.14% and CYP2E1 expression did not show a dramatical change in SMG group rats. Moreover, 14-day SMG reduced ADH activity, while ALDH and GST activities was not altered remarkably. It could be concluded that SMG dramatically affected the expression and activity of some IDMEs, which might alter the efficacy or safety of their substrate drugs under microgravity. The present study provided some preliminary information on IDMEs under microgravity. It revealed the potential effect of SMG on intestinal metabolism, which may be helpful to understand the intestinal health of astronauts and medication use. [ABSTRACT FROM AUTHOR]

The wide voltage design methodology has been widely employed in the state-of-the-art circuit design with the advantage of remarkable power reduction and energy efficiency enhancement. However, the timing verification issue for multiple PVT (process–voltage–temperature) corners rises due to unacceptable analysis effort increase for multiple supply voltage nodes. Moreover, the foundry-provided timing libraries in the traditional STA (static timing analysis) approach are only available for the nominal supply voltage with limited voltage scaling, which cannot support timing verification for low voltages down to near- or sub-threshold voltages. In this paper, a learning-based approach for wide voltage design is proposed where feature engineering is performed to enhance the correlation among PVT corners based on a dilated CNN (convolutional neural network) model, and an ensemble model is utilized with two-layer stacking to improve timing prediction accuracy. The proposed method was verified with a commercial RISC (reduced instruction set computer) core under the supply voltage nodes ranging from 0.5 V to 0.9 V. Experimental results demonstrate that the prediction error is limited by 4.9% and 7.9%, respectively, within and across process corners for various working temperatures, which achieves up to 4.4× and 3.9× precision enhancement compared with related learning-based methods. [ABSTRACT FROM AUTHOR]

Path delay variation becomes a serious concern in advanced technology, especially for multi-corner conditions. Plenty of timing analysis methods have been proposed to solve the issue of path delay variation, but they mainly focus on every single corner and are based on a characterized timing library, which neglects the correlation among multiple corners, resulting in a high characterization effort for all required corners. Here, a novel prediction framework is proposed for path delay variation by employing a learning-based method using back propagation (BP) regression. It can be used to solve the issue of path delay variation prediction under a single corner. Moreover, for multi-corner conditions, the proposed framework can be further expanded to predict corners that are not included in the training set. Experimental results show that the proposed model outperforms the traditional Advanced On-Chip Variation (AOCV) method with 1.4X improvement for the prediction of path delay variation for single corners. Additionally, while predicting new corners, the maximum error is 4.59%, which is less than current state-of-the-art works. [ABSTRACT FROM AUTHOR]

The past decades have witnessed the rapid development in soft, stretchable, and biocompatible devices for applications in biomedical monitoring, personal healthcare, and human–machine interfaces. In particular, the design of soft devices in optics has attracted tremendous interests attributed to their distinct advantages such as inherent electrical safety, high stability in long-term operation, potential to be miniaturized, and free of electromagnetic interferences. As the alternatives to conventional rigid optical waveguides, considerable efforts have been made to develop light-guiding devices by using various transparent and elastic polymers, which offer desired physiomechanical properties and enable wearable/implantable applications in optical sensing, diagnostics, and therapy. Here, we review recent progress in soft and stretchable optical waveguides and sensors, including advanced structural design, fabrication strategies, and functionalities. Furthermore, the potential applications of those optical devices for various wearable and biomedical applications are discussed. It is expected that the newly emerged soft and stretchable optical technologies will provide a safe and reliable alternative to next-generation, smart wearables and healthcare devices. [ABSTRACT FROM AUTHOR]

We previously reported that expression of matrix metalloproteinase-9 (MMP-9) mRNA and protein was upregulated during 1,2-dichloroethane (1,2-DCE) induced brain edema in mice. We also found that the p38 mitogen-activated protein kinase (p38 MAPK) signaling pathway resulted in MMP-9 overexpression and nuclear factor-κB (NF-κB) activation in mice treated with 1,2-DCE. In this study, we further hypothesized that inflammatory reactions mediated by the p38 MAPK/ NF-κB signaling pathway might be involved in MMP-9 overexpression, blood–brain barrier (BBB) disruption and edema formation in the brain of 1,2-DCE-intoxicated mice. Our results revealed that subacute poisoning by 1,2-DCE upregulates protein levels of glial fibrillary acidic protein (GFAP), ionized calcium-binding adapter molecule 1 (Iba-1), interleukin-1β (IL-1β), vascular cell adhesion molecule-1 (VCAM-1), intercellular adhesion molecule-1 (ICAM-1), inducible nitric oxide synthase (iNOS) and p-p65 in mouse brains. Pretreatment with an inhibitor against p38 MAPK attenuates these changes. Moreover, pretreatment with an inhibitor against NF-κB attenuates alterations in brain water content, pathological indications notable in brain edema, as well as mRNA and protein expression on levels of MMP-9, VCAM-1, ICAM-1, iNOS, and IL-1β, tight junction proteins (TJs), GFAP and Iba-1 in the brain of 1,2-DCE-intoxicated mice. Furthermore, pretreatment with an inhibitor against MMP-9 obstructs the decrease of TJs in the brain of 1,2-DCE-intoxicated mice. Lastly, pretreatment with an antagonist against the IL-1β receptor also attenuates changes in protein levels of p-p38 MAPK, p-p65, p-IκB, VCAM -1, ICAM-1, IL-1β, and Iba-1 in the brain of 1,2-DCE-intoxicated-mice. Taken together, findings from the current study indicate that the p38 MAPK/ NF-κB signaling pathway might be involved in the activation of glial cells, and the overproduction of proinflammatory factors, which might induce inflammatory reactions in the brain of 1,2-DCE-intoxicated mice that leads to brain edema. [ABSTRACT FROM AUTHOR]

The near-threshold design is widely employed in the energy-efficient circuits, but it suffers from a high sensitivity to process variation, which leads to 2X delay variation due to temperature effects. Hence, it is not negligible. In this paper, we propose an analytical model for gate delay variation considering temperature effects in the near-threshold region. The delay variation model is constructed based on the log-skew-normal distribution by moment matching. Moreover, to deal with complex gates, a multi-variate threshold voltage approximation approach of stacked transistors is proposed. Also, three delay metrics (delay variability, ± 3 σ percentile points) are quantified and have a comparison with other known works. Experimental results show that the maximum of delay variability is 5% compared with Monte Carlo simulation and improves 5X in stacked gates compared with lognormal distribution. Additionally, it is worth mentioning that, the proposed model exhibits excellent advantages on − 3 σ and stacked gates, which improves 5X–10X in accuracy compared with other works. [ABSTRACT FROM AUTHOR]

Six examples of 2-(1-arylimino)ethyl-9-arylimino-5,6,7,8-tetrahydrocycloheptapyridine-cobalt(II) chloride complexes, [2-(1-ArN)C2H3-9-ArN-5,6,7,8-C5H8C5H3N]CoCl2, (Ar = 2-(C5H9)-6-MeC6H3Co1, 2-(C6H11)-6-MeC6H3Co2, 2-(C8H15)-6-MeC6H3Co3, 2-(C5H9)-4,6-Me2C6H2Co4, 2-(C6H11)-4,6-Me2C6H2Co5, and 2-(C8H15)-4,6-Me2C6H2Co6), were synthesized by the direct reaction of the corresponding ortho-cycloalkyl substituted carbocyclic-fused bis(arylimino)pyridines (L1–L6) and cobalt(II) chloride in ethanol with good yields. All the synthesized ligands (L1–L6) and their corresponding cobalt complexes (Co1–Co6) were fully characterized by FT-IR, 1H/13C-NMR spectroscopy and elemental analysis. The crystal structure of Co2 and Co3 revealed that the ring puckering of both the ortho-cyclohexyl/cyclooctyl substituents and the one pyridine-fused seven-membered ring; a square-based pyramidal geometry is conferred around the metal center. On treatment with either methylaluminoxane (MAO) or modified methylaluminoxane (MMAO), all the six complexes showed high activities (up to 4.09 × 106 g of PE mol−1 (Co) h−1) toward ethylene polymerization at temperatures between 20 °C and 70 °C with the catalytic activities correlating with the type of ortho-cycloalkyl substituent: Cyclopentyl (Co1 and Co4) > cyclohexyl (Co2 and Co5) > cyclooctyl (Co3 and Co6) for either R = H or Me and afforded strictly linear polyethylene (Tm > 130 °C). The narrow unimodal distributions of the resulting polymers are consistent with single-site active species for the precatalyst. Furthermore, compared to the previously reported cobalt analogues, the titled precatalysts exhibited good thermo-stability (up to 70 °C) and possessed longer lifetime along with a higher molecular weight of PE (Mw: 9.2~25.3 kg mol−1). [ABSTRACT FROM AUTHOR]

Building information modelling (BIM) has been adopted in the construction industry. The success of BIM implementation relies on the accurate building information stored in BIM models. However, building information in BIM models can be inaccurate, out-of-date, or missing in real-world projects. 3D laser scanning has been leveraged to capture the accurate as-is conditions of buildings and create as-is BIM models of buildings; this is known as the scan-to-BIM process. Although industry practitioners and researchers have implemented and studied the scan-to-BIM process, there is no framework that systematically defines and discusses the key steps and considerations in the process. This study proposes an application-oriented framework for scan-to-BIM, which describes the four major steps of a scan-to-BIM process and their relationships. The framework is oriented towards the specific BIM application to be implemented using the created as-is BIM, and includes four steps: (1) identification of information requirements, (2) determination of required scan data quality, (3) scan data acquisition, and (4) as-is BIM reconstruction. Two illustrative examples are provided to demonstrate the feasibility of the proposed scan-to-BIM framework. Furthermore, future research directions within the scan-to-BIM framework are suggested. [ABSTRACT FROM AUTHOR]

As a major marine pollution source, oil spills largely threaten the sustainability of the coastal environment. Polarimetric synthetic aperture radar remote sensing has become a promising approach for marine oil spill detection since it could effectively separate crude oil and biogenic look-alikes. However, on the sea surface, the signal to noise ratio of Synthetic Aperture Radar (SAR) backscatter is usually low, especially for cross-polarized channels. In practice, it is necessary to combine the refined detail of slick-sea boundary derived from the co-polarized channel and the highly accurate crude slick/look-alike classification result obtained based on the polarimetric information. In this paper, the architecture for oil spill detection based on polarimetric SAR is proposed and analyzed. The performance of different polarimetric SAR filters for oil spill classification are compared. Polarimetric SAR features are extracted and taken as the input of Staked Auto Encoder (SAE) to achieve high accurate classification between crude oil, biogenic slicks, and clean sea surface. A post-processing method is proposed to combine the classification result derived from SAE and the refined boundary derived from VV channel power image based on special density thresholding (SDT). Experiments were conducted on spaceborne fully polarimetric SAR images where both crude oil and biogenic slicks were presented on the sea surface. [ABSTRACT FROM AUTHOR]

Ricinoleic acid (RA) is an important raw material for plasticizers, emulsifiers, and nanomaterials. In this work, a green and efficient method was developed for RA production. Results showed that Lipozyme TLIM can be used as a novel biocatalyst to catalyze the hydrolysis of castor oil (CO) for RA preparation. Response surface methodology (RSM) was used to evaluate and optimize the effects of reaction variables on the hydrolysis of CO. Reaction conditions were optimized as follows: 41.3 °C, enzyme load 8.9%, 39.2 h, and 40:1 molar ratio of water to oil. Under these optimized reaction variables, the maximum hydrolysis ratio of CO (96.2 ± 1.5%) was obtained. The effect of hydrolysis variables on the reaction was as follows: enzyme load > hydrolysis time > temperature. In conclusion, this is a green, simple, and efficient method for RA preparation and can provide a good alternative method for RA industrial production. [ABSTRACT FROM AUTHOR]

This work aimed to find new inhibitors of the CYP3A4 and JAK3 enzymes, which are significant players in autoimmune diseases such as rheumatoid arthritis. Advanced computer-aided drug design techniques, such as pharmacophore and 3D-QSAR modeling, were used. Two strong 3D-QSAR models were created, and their predictive power was validated by the strong correlation (R 2 values > 80%) between the predicted and experimental activity. With an ROC value of 0.9, a pharmacophore model grounded in the DHRRR hypothesis likewise demonstrated strong predictive ability. Eight possible inhibitors were found, and six new inhibitors were designed in silico using these computational models. The pharmacokinetic and safety characteristics of these candidates were thoroughly assessed. The possible interactions between the inhibitors and the target enzymes were made clear via molecular docking. Furthermore, MM/GBSA computations and molecular dynamics simulations offered insightful information about the stability of the binding between inhibitors and CYP3A4 or JAK3. Through the integration of various computational approaches, this study successfully identified potential inhibitor candidates for additional investigation and efficiently screened compounds. The findings contribute to our knowledge of enzyme-inhibitor interactions and may help us create more effective treatments for autoimmune conditions like rheumatoid arthritis.

The parathyroid hormone type 1 receptor (PTH1R) acts as a canonical class B G protein-coupled receptor, regulating crucial functions including calcium homeostasis and bone formation. The identification and development of PTH1R non-peptide allosteric modulators have obtained widespread attention. It has been found that a negative allosteric modulator (NAM) could inhibit the activation of PTH1R, but the implied mechanism remains unclear. Herein, extensive molecular dynamics simulations together with multiple analytical approaches are utilized to unravel the mechanism of PTH1R allosteric inhibition. The results suggest that the binding of NAM destabilizes the structure of the PTH1R-PTH-spep/qpep (the C terminus of Gs/Gq proteins) complexes. Moreover, the presence of NAM weakens the binding of PTH/peps (spep and qpep) and PTH1R. The intra- and inter-molecular couplings are also weakened in PTH1R upon NAM binding. Interestingly, compared with our previous study of the positive allosteric effects induced by extracellular Ca 2+ , the enhanced correlation between the PTH and G-protein binding sites is significantly reduced by the replacement of this negative allosteric regulator. Our findings might contribute to the development of new therapeutic agents for diseases caused by the abnormal activation of PTH1R.