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Surface ablation temperature and linear ablation rate are two crucial indicators for ceramic coatings under ultrahigh temperatures service, yet the results collection of such two indicators in the process is difficult due to the long‐period material preparation and the high‐cost test. In this work, four kinds of machine learning models are applied to predict the above two indicators. The Random Forest (RF) model exhibits a high accuracy of 87% in predicting surface ablation temperature, while a low accuracy of 60% in linear ablation rate. To optimize the model, the novel features are constructed based on the original features by the sum of the importance weights in the model. Thereafter, the importance of the newly constructed features increases significantly, and the accuracy of the optimized RF model is improved by 11%, exceeding 70% in accuracy. By validation with available data and experiments, the optimized model demonstrates precise predictions of the target variables. [ABSTRACT FROM AUTHOR]

Introduction: Hypoxia responses are critical for myriad physiological and pathological processes, such as development, tissue repair, would healing, and tumorigenesis. microRNAs (miRNAs) are a class of small non-coding RNAs that exert their functions by inhibiting the expression of their target genes, and miR-210 is the miRNA universally and most conspicuously upregulated by hypoxia in mammalian systems. For its relationship to hypoxia, miR-210 has been studied extensively, yet no consensus exists on the roles and mechanisms of miR-210 in human physiological processes or diseases, and we know little about genuine miR-210 target genes in humans. Methods: To better investigate the functions and mechanisms of human miR-210, therefore, we derived the human miR-210 gene knockout (KO) 293T cell lines using the CRISPR/Cas9 technology. We then examined the cellular phenotypes and gene expression profiles of 293T cells under normoxia and hypoxia conditions. Results and Discussion: We found that the loss of miR-210 altered a variety of cellular phenotypes including proliferation and apoptosis. Subsequent global gene expression analyses identified plausible mechanisms underlying these phenotypic changes in 293T cells. In particular, we showed that miR-210 might target the expression of BNIP3L as a potential mechanism to suppress apoptosis. Surprisingly, the mRNA levels of most previously reported miR-210 target genes were not induced upon miR-210 KO, suggesting a need to reexamining and studying human miR-210 functions directly and comprehensively. Thus, our work established a human cellular system and opportunity to unravel the complexity of the regulatory networks by miR-210. [ABSTRACT FROM AUTHOR]

Itraconazole (ITZ), a broad-spectrum triazole antifungal agent, exhibits remarkable pharmacodynamic and pharmacokinetic properties. However, the low solubility of ITZ significantly reduces its oral bioavailability. Furthermore, it has been reported that this medication can result in dose-related adverse effects. Therefore, the objective of this study was to enhance the solubility of ITZ through the utilization of various polymers and to manufacture personalized and programmable release ITZ tablets. Five different polymers were selected as water-soluble carriers. Thirty percent w/w ITZ was mixed with seventy percent w/w of the polymers, which were then extruded. A series of physical and chemical characterization studies were conducted, including DSC, PXRD, PLM, and in vitro drug release studies. The results demonstrated that ITZ was dispersed within the polymers, forming ASDs that markedly enhanced its solubility and dissolution rate. Consequently, soluplus® was employed as the polymer for the extrusion of ITZ-loaded filaments, which were subsequently designed and printed. The in vitro drug release studies indicated that the release of ITZ could be regulated by modifying the 3D structure design. Overall, this study found that the combination of HME and 3D printing technologies could represent an optimal approach for the development of personalized and precise drug delivery dosages. [ABSTRACT FROM AUTHOR]

Cervical cancer is the fourth most common cancer among women globally. Its development is closely linked to accelerated cell cycle progression and the inhibition of apoptosis in cervical cancer tissues. Gefitinib has demonstrated efficacy in inhibiting cervical cancer cells, and the 1,2,3‐triazole structure is widely recognized for its role in inducing mitochondrial apoptosis in tumor cells. In this study, we employed click chemistry to modify the structure of gefitinib, leading to the synthesis of 16 derivatives containing the 1,2,3‐triazole moiety. These compounds were evaluated for their in vitro activity against Hela cells. Among them, compound 3p exhibited the most promising anticancer activity, with an IC50 value of 4.09 ± 0.54 μM. Compound 3p significantly inhibited Hela cell colony formation in a dose‐dependent manner, accompanied by noticeable morphological changes. Further investigations revealed that 3p induced apoptosis and caused G2/M phase cell cycle arrest in Hela cells. Western blot analysis showed that 3p increased the Bax/Bcl‐2 ratio and elevated the levels of cleaved caspase‐3 and PARP1, indicating that apoptosis was mediated through the mitochondrial pathway. Additionally, 3p inhibited indoleamine 2,3‐dioxygenase 1 (IDO1) enzymatic activity, and molecular docking studies revealed a strong interaction between 3p and the IDO1 active site, suggesting that IDO1 may be a potential target. In conclusion, compound 3p shows promise as a potential therapeutic agent for cervical cancer. [ABSTRACT FROM AUTHOR]

Objective: To explore the impacts of different types of physical exercise on health outcomes of individuals with hypertensive disorders of pregnancy (HDPs). Methods: Forty individuals with HDPs admitted to a tertiary hospital providing maternal and pediatric care between July 2023 and March 2024 were enrolled in this prospective randomized controlled clinical study and completed a ≥4-week intervention. Data were collected before the intervention and before delivery. Participants were assigned randomly to control (no exercise intervention), aerobic exercise (AE), resistance training (RT), and AE + RT groups. All participants downloaded a mobile health-education app for gestational hypertension developed by our research group. Exercise videos in the app guided participants' performance of different types of exercise. General information; physical activity and sleep quality data; morning blood pressure, lipid profiles, and urinary micro-albumin/creatinine ratios; serum soluble fms-like tyrosine kinase 1 (sFlt-1), placental growth factor (PlGF), and advanced oxidation protein product (AOPP) concentrations; and pregnancy outcome data were collected and compared among groups. Results: After the intervention, the physical activity status, sleep quality, morning blood pressure, lipid profiles, urinary micro-albumin/creatinine ratios, and pregnancy outcomes differed significantly among all groups comparing with control (all p <.05). In the three exercise groups, the serum sFlt-1, PlGF, and AOPPs levels improved significantly (all p <.05). All differences were most pronounced in the AE + RT group. Limitations: The study period was relatively short. The further long-term follow-up research is needed. A larger sample size study is also needed. Conclusions: The study results suggest that AE + RT interventions are beneficial for individuals with HDPs in clinical settings, and could be implemented with careful consideration of individuals' specific conditions. [ABSTRACT FROM AUTHOR]

The multifaceted regulation of the chiroptical properties and self‐assembly behaviors of chiral fluorescent polymers is of great significance yet remains challenging to achieve. Herein, a series of novel salen‐based chiral fluorescent polymers with aggregation‐induced emission and varied substitution manners were facilely and efficiently synthesized. Multiple factors were systematically investigated on the chiroptical properties and self‐assembly performance of these polymers, which include molecular structures, solvent environments, metal coordination, and liquid crystal (LC) assemblies. Sutle change in the solvent composition can lead to diverse assembly morphologies of all these chiral polymers, from single‐handed helical fibers, helical toroids or loops, to spherical structures, consequently leading to an aggregation‐reduced circular dichroism (CD) phenomenon. The polymers bearing salen units show highly selective and reversible coordination with Zn2+ and can also induce multiple responses in the absorption, luminescence, CD, and circularly polarized luminescence (CPL) of these chiral fluorescent polymers via a coordination‐ and dissociation‐initiated self‐assembly tuning. Furthermore, a small amount of the chiral fluorescent polymer in can induce achiral nematic 4‐cyano‐4′‐n‐pentylbiphenyl (5CB) to form ordered chiral nematic LC phase with significant improvement in their CD and CPL signal. The absolute absorption and luminescent dissymmetry factors of the resulting supramolecular assemblies can reach the order of 10−1. [ABSTRACT FROM AUTHOR]

Urinary tract infections (UTIs), primarily caused by uropathogenic Escherichia coli (UPEC), have high morbidity and recurrence rates. Resistance to levofloxacin hydrochloride (LEV), a commonly used treatment for UTIs, is increasingly problematic, exacerbated by biofilm formation mediated by interactions between cyclic di-GMP (c-di-GMP or CDG) and YcgR. In this study, we identified three caffeoylquinic acid compounds from Pyrrosia lingua—chlorogenic acid (CGA), sibiricose A5 (Si-A5), and 3-O-caffeoylquinic acid methyl ester (CAM)—that target YcgR through molecular docking. Biological assays revealed that combining these compounds with levofloxacin hydrochloride significantly enhanced antibacterial activity against standard UPEC strains in a concentration-dependent manner and clinically isolated UPEC strains. Notably, chlorogenic acid and sibiricose A5, when used with levofloxacin hydrochloride, enhanced intracellular c-di-GMP levels and swimming motility, significantly reduced YcgR gene expression, and effectively inhibited biofilm formation of UPEC at multiple time points. Additionally, molecular dynamics simulations elucidated the strong binding of these compounds to YcgR, underscoring the critical roles of residues, such as Arg118 and Asp145. This research serves as a foundation for tackling antibiotic resistance and developing innovative therapeutics for UTIs. [ABSTRACT FROM AUTHOR]

Functional traits of individuals and not just species can regulate ecological interactions with biotic and abiotic environments, yet how individual-level functional diversity (FDind) mediates the environmental effects on community properties (e.g., interaction patterns, connectivity and productivity) remains largely unexplored. Here, we assembled 4432 individuals from 30 macrophyte species across 26 lakes in south China and measured six functional traits -shoot height, specific leaf area, lamina thickness, leaf dry mass content, stem diameter and stem dry mass content—for each individual. We estimated FDind for macrophyte community in each plot using trait probability density framework. Path analysis revealed that functional richness promoted negative interactions (the absolute value of negative: positive cohesion as proxy of community interaction patterns) and thus community productivity at lower total phosphorus (TP) and shallower water, while functional divergence/redundancy reduced negative interaction and thus community productivity at shallower water, lower TP and higher altitude. Functional evenness decreased community productivity at higher TP and deeper water. Functional dissimilarity reduced community connectivity (total cohesion) and thus community productivity at deeper water and higher altitude. Our results highlight that FDind could modify the environmental effects (from local to regional) on the interaction patterns, network connectivity and productivity of macrophyte community. [ABSTRACT FROM AUTHOR]

Background: Neural stem cells (NSCs) are considered to be the most promising cell type for cell replacement therapy in neurodegenerative diseases. However, their low neuronal differentiation ratio impedes their application in such conditions. Elucidating the molecular mechanism of NSC differentiation may provide the necessary experimental basis for expanding their application. Previous studies have indicated that POU3F4 can induce neuronal differentiation of NSCs, this study aims to underly the possible exact mechanism of POU3F4 on the NSC differentiation and development. Methods: NSCs were isolated and cultured from the hippocampus of neonatal mice. The frozen hippocampal sections were prepared for immunohistochemical staining. Synaptic development was assessed using electron microscopy. High-throughput sequencing was employed to analyze the gene expression profile following the overexpression of Brn4. Gene expression levels were determined through Western blotting and qRT-PCR. Cell cycle and differentiation were evaluated using flow cytometry and immunofluorescent staining. Results: It was found that POU3F4 promoted the neuronal differentiation of hippocampal NSCs and synapse development, and inhibited NSC proliferation. POU3F4-deficient mice exhibited impairments in learning and memory. RNA sequencing and ChIP assays confirmed that Gli1 was downstream of POU3F4. Loss and gain function experiments indicated that Gli1 mediated POU3F4 promoting neuronal differentiation and synapse development. Forced expression of Gli1 in hippocampus improved learning and memory function of animal models. Conclusions: The results suggest that POU3F4 and Gli1 promote neuronal differentiation and synaptic development of NSCs, and that Gli1 partially mediates the effects of POU3F4. [ABSTRACT FROM AUTHOR]

Extensive use of the neonicotinoid insecticides acetamiprid (ACE) and flonicamid (FLO) in agriculture poses severe environmental and ecological risks. Microbial remediation is considered a feasible approach to address these issues. Many ACE-and FLO-degrading microorganisms have been isolated and characterized, but few reviews have concentrated on the underlying degradation mechanisms. In this review, we describe the microbial degradation pathways of ACE and FLO and assess the toxicity of ACE, FLO and their metabolites. Especially, we focus on the enzymes involved in degradation of ACE and FLO, including cytochrome P450s, nitrile hydratases, amidases, and nitrilases. Those studies reviewed here further our understanding of the enzymatic mechanisms of microbial degradation of ACE and FLO, and aid in the application of microbes to remediate environmental ACE and FLO contamination. [ABSTRACT FROM AUTHOR]

Ultra-low-voltage design brings considerable outcomes in power reduction and energy efficiency improvement at the cost of performance degradation and uncertainty. Conventional standard cell design methodology cannot guarantee optimal performance for subthreshold operations due to the lack of consideration of process variation. In this paper, an effective subthreshold cell sizing method is proposed to minimize the worst-case propagation delay by deriving the optimal pMOS-to-nMOS width ratio (β) analytically, which reveals the relation between the minimal worst-case delay and the process parameters and provides distinct guidance for standard cell library design. The proposed method demonstrated good agreement with the Monte Carlo SPICE simulation results and was validated at the cell level and the circuit level. At the cell level, the logic cells designed with the proposed method show at least 8.6% and 7.4% improvement, on average, for worst-case delay and energy-delay product (EDP), respectively, with an additional 3.2% energy overhead compared to the prior approaches. At the circuit level, the proposed method improves the worst-case performance and worst-case EDP of the ring oscillator by at least 15.5% and 15.0%, respectively, with a 0.9% energy penalty. Moreover, the ISCAS'89 and OpenCores circuits synthesized with the optimized cells achieve at least 6.6% worst-case performance enhancement, 6.9% power reduction, and 9.4% area saving. [ABSTRACT FROM AUTHOR]

The WHO's classification of renal cell carcinoma (RCC) has identified loss of SMARCB1 as one of the driven mutations. Despite intensive postoperative interventions, the prognosis for SMARCB1-deficient medullary RCC remains poor, indicating insufficiency in current therapy. Herein, we reported the treatment outcomes of five patients with metastatic SMARCB1-deficient medullary RCC and molecular correlates. Four patients were treated with first-line immune checkpoint inhibitors (ICI) plus tyrosine kinase inhibitors (TKI) combination therapy with a median PFS (mPFS) of 12.3 months. Transcriptomic analysis revealed enrichment of immune-related pathways in SMARCB1-deficient medullary RCC compared to clear-cell and papillary RCC. Multiple immunofluorescence (mIF) revealed the association between the formation of mature tertiary lymphoid structures (TLSs) and the favorable response to ICI-based combination therapy. In conclusion, ICI-based combination therapy showed promising anti-tumor activity in SMARCB1-deficient medullary RCC patients. The presence of mature tertiary TLSs may partially elucidate the mechanism underlying treatment response. [ABSTRACT FROM AUTHOR]

Sea buckthorn pomace is often discarded as a by-product during the sea buckthorn processing stage. Consequently, its richness in dietary fiber is usually overlooked. In this study, soluble dietary fiber (SDF) and insoluble dietary fiber (IDF) were extracted from sea buckthorn pomace using ultrasound combined with the enzyme method. The optimal values of the independent variable were determined by a combinatorial design and a response surface optimization test with SDF/IDF as the dependent variable, prepared as follows: 5% enzyme addition, ultrasonic power of 380 W, enzymatic time of 30 min, and alcoholic precipitation liquid ratio of 4:1. Under these conditions, the SDF/IDF ratio was 17.07%. The structural characterization and hypoglycemic activity of the two dietary fibers were then compared. The results show that two dietary fibers have respective structures and functional groups of fibers. SDF was less crystalline than IDF, and its structure was looser. Furthermore, the hypoglycemic activity of SDF was significantly better than IDF's (p < 0.05). The glucose adsorption capacity of SDF was 1.08–1.12 times higher than that of IDF. SDF inhibited α-amylase and α-glucosidase by 1.76 and 4.71 times more than IDF, respectively. These findings provide a reference for improving the utilization of sea buckthorn processing by-products. [ABSTRACT FROM AUTHOR]

Middle East respiratory syndrome coronavirus (MERS-CoV) and the pangolin MERS-like coronavirus MjHKU4r-CoV-1 employ dipeptidyl peptidase 4 (DPP4) as an entry receptor. MjHKU4r-CoV-1 could infect transgenic mice expressing human DPP4. To understand the mechanism of MjHKU4r-CoV-1 entry into cells, we determined the crystal structures of the receptor binding domain (RBD) of MjHKU4r-CoV-1 spike protein bound to human DPP4 (hDPP4) and Malayan pangolin DPP4 (MjDPP4), respectively. The overall hDPP4-binding mode of MjHKU4r-CoV-1 RBD is similar to that of MERS-CoV RBD. MjHKU4r-CoV-1 RBD shows higher binding affinity to hDPP4 compared to the bat MERS-like coronavirus Ty-BatCoV-HKU4. Via swapping residues between MjHKU4r-CoV-1 RBD and Ty-BatCoV-HKU4 RBD, we identified critical determinants on MjHKU4r-CoV-1 that are responsible for virus usage of hDPP4. Our study suggests that MjHKU4r-CoV-1 is more adapted to the human receptor compared to the bat HKU4 coronavirus and highlights the potential of virus emergence into the human population. Author summary: MjHKU4r-CoV-1 is a MERS-like coronavirus isolated in Malayan pangolins. As a pangolin coronavirus that evolves from its counterparts in bats, MjHKU4r-CoV-1 could use bat, pangolin or human dipeptidyl peptidase 4 (DPP4) as an entry receptor. MjHKU4r-CoV-1 is infectious in human organs and human DPP4 transgenic mice as well and therefore shows the risk of infecting humans. However, the mechanism of receptor recognition by MjHKU4r-CoV-1 is not clear, which is important for evaluating the zoonotic potential of the virus. Here, we showed that MjHKU4r-CoV-1 entered human DPP4 expressing cells more efficiently than its bat progenitor, Ty-BatCoV-HKU4. We further determined the crystal structures of MjHKU4r-CoV-1 receptor binding domains (RBDs) complexed with human DPP4 and pangolin DPP4, respectively. Based on structure guided mutagenesis studies, we identified key residues on MjHKU4r-CoV-1 that determined the virus preference for human DPP4 receptor. Therefore, our study implies that the pangolin coronavirus MjHKU4r-CoV-1 have a risk of emergence into the human population. Future surveillance studies of pangolins and other animals are necessary to determine the transmission chains and zoonotic potential of MjHKU4r-CoV-1. [ABSTRACT FROM AUTHOR]

Excess cholesterol accumulation induces the accumulation of foam cells, eventually accelerating atherosclerosis progress. Historically, the mechanisms of macrophage-derived foam cells have attracted attention because of their central role in plaque development, which was challenged by lineage tracing in union with single-cell sequencing (sc-seq). Accumulated studies have uncovered how vascular smooth muscle cells (VSMCs) proliferate and migrate to the vascular intima and accumulate, then transform into foam cells induced by surplus lipids, finally accounting for 30% to 70% of the total foam cells within the plaque of both mice and humans. Therefore, the mechanisms of VSMC-derived foam cells have received increasing attention. The review intends to summarize the transformation mechanism of VSMCs into foam cells induced by oxidized low-density lipoproteins (ox-LDL) in atherosclerosis. [ABSTRACT FROM AUTHOR]

Motivation Molecular docking is an invaluable computational tool with broad applications in computer-aided drug design and enzyme engineering. However, current molecular docking tools are typically implemented in languages such as C++ for calculation speed, which lack flexibility and user-friendliness for further development. Moreover, validating the effectiveness of external scoring functions for molecular docking and screening within these frameworks is challenging, and implementing more efficient sampling strategies is not straightforward. Results To address these limitations, we have developed an open-source molecular docking framework, OpenDock, based on Python and PyTorch. This framework supports the integration of multiple scoring functions; some can be utilized during molecular docking and pose optimization, while others can be used for post-processing scoring. In terms of sampling, the current version of this framework supports simulated annealing and Monte Carlo optimization. Additionally, it can be extended to include methods such as genetic algorithms and particle swarm optimization for sampling docking poses and protein side chain orientations. Distance constraints are also implemented to enable covalent docking, restricted docking or distance map constraints guided pose sampling. Overall, this framework serves as a valuable tool in drug design and enzyme engineering, offering significant flexibility for most protein–ligand modelling tasks. Availability and implementation OpenDock is publicly available at: https://github.com/guyuehuo/opendock. [ABSTRACT FROM AUTHOR]

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]

As strategies to build a strong maritime nation are widely implemented, the importance of island ports has been increasingly highlighted, and their fire safety issues are also receiving more attention. However, at present, research on the construction of fire protection systems has mostly been focused on the interior of cities, and less research has been carried out on the linear firefighting needs of island ports. In particular, island ports are characterized by firefighting characteristics such as inconsistent mission objectives and coordinated ship-to-shore rescues, when compared to cities. In this study, a model for the siting of harbor fire stations on a cluster of distant, isolated islands is proposed, considering the costs associated with building, maintaining, and rescuing fire stations on the basis of coverage. At the same time, the loss costs associated with the response time are considered to construct an arc segment demand siting model under ship–shore synergy conditions with the objective of minimizing the total cost. The Sea Island Group is taken as an example for the analysis of the constructed site selection model. The firefighting needs and ship–shore cooperative firefighting and rescue scenarios for eight islands in the island group are the main considerations, and the model is solved using a genetic algorithm. It is concluded that the establishment of five island harbor fire stations in the Sea Island Group can balance the cost of rescue with the cost of construction. Thus, the benefits of island fire stations can be improved, satisfying coverage rates while minimizing the associated costs. [ABSTRACT FROM AUTHOR]

Lung adenocarcinoma (LUAD) is the most common lung cancer, accounting for 19.4% of all cancer deaths. Our previous study discovered that INTS7 expression was upregulated in LUAD, while the precise mechanism by which INTS7 exerts pro-cancer effects remains unknown. In our study, shRNA was used to knockdown the expression of INTS7 in A549 cells. Cancer behaviors in vitro were determined by CCK8 and transwell assays. Xenograft mice models were constructed to detect the tumorigenesis in vivo. Immunofluorescence and toluidine blue staining were used to test the immune infiltration. Bioinformatics analysis was adopted to predict the potential signaling pathways and construct INTS7-derived genomic prognostic model. Western blot was utilized to confirm the molecular pathways. In total, downregulation of INTS7 suppressed proliferation, invasion and migration of A549 cells, as well as tumor growth. Bioinformatics and western blot analysis indicated that p38MAPK pathway participated in the regulatory mechanism of INTS7. Moreover, INTS7 expression was negatively correlated with infiltration of memory B cells and mast cells, while positively correlated with infiltration of macrophages M2. A nomogram, including INTS7-derived risk score, was used to estimate individual's survival probability. Generally, our findings provided comprehensive understanding of the molecular mechanisms about INTS7, and targeting INTS7 may represent a potential therapy for LUAD. [ABSTRACT FROM AUTHOR]

Hydrogel-based delivery systems have now emerged as a pivotal platform for addressing chronic tissue defects, leveraging their innate capacity to suppress pathogenic infections and facilitate expedited tissue regeneration. In this work, an injectable hydrogel dressing, termed AgNPs-dermal matrix hydrogel (Ag@ADMH), has been designed to expedite the healing process of wounds afflicted with methicillin-resistant Staphylococcus aureus (MRSA), featuring sustained antibacterial efficacy. The synthesis of the hydrogel dressing entailed a self-assembly process of collagen fibers within an acellular dermal matrix to construct a three-dimensional scaffold, encapsulated with plant polyphenol-functionalized silver nanoparticles (AgNPs). The Ag@ADMH demonstrated exceptional biocompatibility, and enables a sustained release of AgNPs, ensuring prolonged antimicrobial activity. Moreover, the in vitro RT-qPCR analysis revealed that compared with ADMH, Ag@ADMH diminish the expression of iNOS while augmenting CD206 expression, thereby mitigating the inflammatory response and fostering wound healing. Especially, the Ag@ADMH facilitated a reduction in M1 macrophage polarization, as evidenced by a significant decrement in the M1 polarization trend and an enhanced M2/M1 ratio in dermal matrix hydrogels laden with AgNPs, corroborated by confocal microscopy and flow cytometry analyses of macrophage phenotypes. The in vivo assessments indicated that Ag@ADMH minimized fibrous capsule formation. In a full-thickness skin defect model of MRSA infection, the formulation significantly attenuated the inflammatory response by reducing MPO and CD68 expression levels, concurrently promoting collagen synthesis and CD34 expression, pivotal for vasculogenesis, thereby accelerating the resolution of MRSA-infected wounds. These attributes underscore the injectable extracellular matrix hydrogel as a formidable strategy for the remediation and regeneration of infected wounds. [ABSTRACT FROM AUTHOR]

A photo-induced electron donor–acceptor (EDA) complex enabled tandem reaction of alkyne ketones via a radical difluoroalkylation/cyclization cascade sequence is reported. The EDA complex plays a key role, and the C – Br bond homolysis process may also be involved for this transformation. Varieties of difluoroalkyl-substituted thiofavones can be smoothly assembled in moderate to good yields under photocatalyst-, metal- and oxidant-free conditions, thus offering potential applications for pharmaceutical research. [ABSTRACT FROM AUTHOR]

Background: Lipid metabolism dysregulation is a key characteristic of hepatocellular carcinoma (HCC) onset and progression. Elevated expression of immunoglobulin (Ig), especially the Igκ free light chain with a unique Vκ4-1/Jκ3 rearrangement in cancer cells, is linked to increased malignancy and has been implicated in colon cancer tumorigenesis. However, the role of Igκ in HCC carcinogenesis remains unclear. The aim of this study was to elucidate the pivotal roles of hepatocyte-derived Igκ in HCC development. Methods: The rearrangement sequence and expression level of hepatocyte-derived Igκ in HCC cells were determined via RT-PCR, Sanger sequencing, immunohistochemistry, and western blot analysis. The function of Igκ in HCC tumorigenesis was assessed by silencing Igκ using siRNA or gRNA in various HCC cell lines. To assess the role of Igκ in HCC pathogenesis in vivo, a mouse model with hepatocyte-specific Igκ knockout and diethylnitrosamine (DEN) and carbon tetrachloride (CCL4)-induced HCC was utilized. The molecular mechanism by which Igκ affects HCC tumorigenesis was investigated through multiomics analyses, quantitative real-time PCR, immunoprecipitation, mass spectrometry, immunofluorescence, and metabolite detection. Results: We confirmed that Igκ, especially Vκ4-1/Jκ3-Igκ, is highly expressed in human HCC cells. Igκ depletion inhibited HCC cell proliferation and migration in vitro, and hepatocyte-specific Igκ deficiency ameliorated HCC progression in mice with DEN and CCL4-induced HCC in vivo. Mechanistically, Vκ4-1/Jκ3-Igκ interacts with electron transfer flavoprotein subunit α (ETFA), delaying its protein degradation. Loss of Igκ led to a decrease in the expression of mitochondrial respiratory chain complexes III and IV, resulting in aberrant fatty acid β-oxidation (FAO) and lipid accumulation, which in turn inhibited HCC cell proliferation and migration. Conclusion: Our findings indicate that the Igκ/ETFA axis deregulates fatty acid β-oxidation, contributing to HCC progression, which suggests that targeting fatty acid metabolism may be an effective HCC treatment strategy. The results of this study suggest that hepatocyte-derived Vκ4-1/Jκ3-Igκ may serve as a promising therapeutic target for HCC. [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]

Predicting the binding of ligands to the human proteome via reverse‐docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off‐target effects or toxic side effects of drugs. In this study, we constructed 11 reverse docking pipelines by integrating site prediction tools (PointSite and SiteMap), docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, and OnionNet‐SFCT), and then thoroughly benchmarked their predictive capabilities. The results show that the Glide_SFCT (PS) pipeline exhibited the best target prediction performance based on the atomic structure models in AlphaFold2 human proteome. It achieved a success rate of 27.8% when considering the top 100 ranked prediction. This pipeline effectively narrows the range of potential targets within the human proteome, laying a foundation for drug target prediction, off‐target assessment, and toxicity prediction, ultimately boosting drug development. By facilitating these critical aspects of drug discovery and development, our work has the potential to ultimately accelerate the identification of new therapeutic agents and improve drug safety. [ABSTRACT FROM AUTHOR]

Bruton tyrosine kinase inhibitor (BTKi) combined with rituximab-based chemotherapy benefits diffuse large B-cell lymphoma (DLBCL) patients. However, drug resistance is the major cause of relapse and death of DLBCL. In this study, we conducted a comprehensive analysis BTKi-resistance related genes (BRRGs) and established a 10-gene (CARD16, TRIP13, PSRC1, CASP1, PLBD1, CARD6, CAPG, CACNA1A, CDH15, and NDUFA4) signature for early identifying high-risk DLBCL patients. The resistance scores based on the BRRGs signature were associated with prognosis. Furthermore, we developed a nomogram incorporating the BRRGs signature, which demonstrated excellent performance in predicting the prognosis of DLBCL patients. Notably, tumor immune microenvironment, biological pathways, and chemotherapy sensitivity were different between high- and low-resistance score groups. Additionally, we identified TRIP13 as a key gene in our model. TRIP13 was found to be overexpressed in DLBCL and BTKi-resistant DLBCL cell lines, knocking down TRIP13 suppresses cell proliferation, promotes cell apoptosis, and enhances the apoptosis effect of BTKi on DLBCL cells by regulating the Wnt/β-catenin pathway. In conclusion, our study presents a novel BRRGs signature that could serve as a promising prognostic marker in DLBCL, and TRIP13 might be a potential therapeutic target for resistant DLBCL. [ABSTRACT FROM AUTHOR]

Rapid socio-economic development has led to many water environmental issues in small watersheds such as non-compliance with water quality standards, complex pollution sources, and difficulties in water environment management. To achieve a quantitative evaluation of water quality, identify pollution sources, and implement refined management in small watersheds, this study collected monthly seven water quality indexes of four monitoring points from 2010 to 2023, and ten water quality indexes of 23 sampling points in the Shiting River and Mianyuan River which are tributaries of the Tuojiang River Basin. Then, water quality evaluation and pollution source analysis were conducted from both temporal and spatial perspectives using the Water Quality Index (WQI) method, the Absolute Principal Component Scores/Multiple Linear Regression (APCS-MLR) method, and the Positive Matrix Factorization (PMF) receptor modeling technique. The results indicated that except for total nitrogen (TN), the concentrations of other water quality indexes exhibited a decreasing trend, and all were divided into two obvious stages before and after 2016. Furthermore, the proportion of water quality grade of Good and above increased from 73.96 to 84.94% from 2010–2015 to 2016–2023, and the water quality grade of Good and above from upstream to downstream dropped from 100 to 23.33%. From the temporal scale, four and five pollution sources were identified in the first and second stages, respectively. The distinct TN pollutant is mainly affected by agricultural non-point sources (NPS), whose impact is enhanced from 17.76 to 78.31%. Total phosphorus (TP) was affected by the phosphorus chemical industry, whose contribution gradually weakened from 50.8 to 24.9%. From a spatial perspective, four and five pollution sources were identified in the upstream and downstream, respectively. Therefore, even though there are some limitations due to the data availability of water monitory and hydrology data, the proposed research framework of this study can be applied to the water environmental management of other similar watersheds. [ABSTRACT FROM AUTHOR]

Purpose: To develop a deep neural network system for the automatic segmentation and risk stratification prediction of gastrointestinal stromal tumors (GISTs). Methods: A total of 980 ultrasound (US) images from 245 GIST patients were retrospectively collected. These images were randomly divided (6:2:2) into a training set, a validation set, and an internal test set. Additionally, 188 US images from 47 prospective GIST patients were collected to evaluate the segmentation and diagnostic performance of the model. Five deep learning‐based segmentation networks, namely, UNet, FCN, DeepLabV3+, Swin Transformer, and SegNeXt, were employed, along with the ResNet 18 classification network, to select the most suitable network combination. The performance of the segmentation models was evaluated using metrics such as the intersection over union (IoU), Dice similarity coefficient (DSC), recall, and precision. The classification performance was assessed based on accuracy and the area under the receiver operating characteristic curve (AUROC). Results: Among the compared models, SegNeXt‐ResNet18 exhibited the best segmentation and classification performance. On the internal test set, the proposed model achieved IoU, DSC, precision, and recall values of 82.1, 90.2, 91.7, and 88.8%, respectively. The accuracy and AUC for GIST risk prediction were 87.4 and 92.0%, respectively. On the external test set, the segmentation models exhibited IoU, DSC, precision, and recall values of 81.0, 89.5, 92.8, and 86.4%, respectively. The accuracy and AUC for GIST risk prediction were 86.7 and 92.5%, respectively. Conclusion: This two‐stage SegNeXt‐ResNet18 model achieves automatic segmentation and risk stratification prediction for GISTs and demonstrates excellent segmentation and classification performance. [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]

Disulfide bonds, covalently formed by sulfur atoms in cysteine residues, play a crucial role in protein folding and structure stability. Considering their significance, artificial disulfide bonds are often introduced to enhance protein thermostability. Although an increasing number of tools can assist with this task, significant amounts of time and resources are often wasted owing to inadequate consideration. To enhance the accuracy and efficiency of designing disulfide bonds for protein thermostability improvement, we initially collected disulfide bond and protein thermostability data from extensive literature sources. Thereafter, we extracted various sequence‐ and structure‐based features and constructed machine‐learning models to predict whether disulfide bonds can improve protein thermostability. Among all models, the neighborhood context model based on the Adaboost‐DT algorithm performed the best, yielding "area under the receiver operating characteristic curve" and accuracy scores of 0.773 and 0.714, respectively. Furthermore, we also found AlphaFold2 to exhibit high superiority in predicting disulfide bonds, and to some extent, the coevolutionary relationship between residue pairs potentially guided artificial disulfide bond design. Moreover, several mutants of imine reductase 89 (IR89) with artificially designed thermostable disulfide bonds were experimentally proven to be considerably efficient for substrate catalysis. The SS‐bond data have been integrated into an online server, namely, ThermoLink, available at guolab.mpu.edu.mo/thermoLink. [ABSTRACT FROM AUTHOR]

Melatonin (Mt) functions as a growth regulator and multifunctional signaling molecule in plants, thereby playing a crucial role in promoting growth and orchestrating protective responses to various abiotic stresses. However, the mechanism whereby exogenous Mt protects Lonicera japonica Thunb. (L. japonica) against salt stress has not been fully elucidated. Therefore, this study aimed to elucidate how exogenous Mt alleviates sodium chloride (NaCl) stress in L. japonica seedlings. Salt-sensitive L. japonica seedlings were treated with an aqueous solution containing 150 mM of NaCl and aqueous solutions containing various concentrations of Mt. The results revealed that treatment of NaCl-stressed L. japonica seedlings with a 60 µM aqueous solution of Mt significantly enhanced vegetative plant growth by scavenging reactive oxygen species and thus reducing oxidative stress. The latter was evidenced by decreases in electrical conductivity and malondialdehyde (MDA) concentrations. Moreover, Mt treatment led to increases in the NaCl-stressed L. japonica seedlings' total chlorophyll content, soluble sugar content, and flavonoid content, demonstrating that Mt treatment improved the seedlings' tolerance of NaCl stress. This was also indicated by the NaCl-stressed L. japonica seedlings exhibiting marked increases in the activities of antioxidant enzymes (superoxide dismutase, peroxidase, catalase, and ascorbate peroxidase) and in photosynthetic functions. Furthermore, Mt treatment of NaCl-stressed L. japonica seedlings increased their expression of phenylalanine ammonia-lyase 1 (PAL1), phenylalanine ammonia-lyase 2 (PAL2), calcium-dependent protein kinase (CPK), cinnamyl alcohol dehydrogenase (CAD), flavanol synthase (FLS), and chalcone synthase (CHS). In conclusion, our results demonstrate that treatment of L. japonica seedlings with a 60 µM aqueous solution of Mt significantly ameliorated the detrimental effects of NaCl stress in the seedlings. Therefore, such treatment has substantial potential for use in safeguarding medicinal plant crops against severe salinity. [ABSTRACT FROM AUTHOR]

Objectives: Hepatitis B is a liver disease caused by Hepatitis B virus (HBV) infection and is highly prevalent in China. To better understand the epidemiological characteristics of hepatitis B in China and develop effective disease control strategies, we employed temporal and spatial statistical methods. Methods: We obtained HBV incidence data from the Public Health Science Data Center of the Chinese Center for Disease Control and Prevention for the years 2006 to 2018. Using Geographic Information System (GIS) and SaTScan scanning technology, we conducted spatial autocorrelation analysis and spatiotemporal scan analysis to create a map and visualize the distribution of hepatitis B incidence. Results: While hepatitis B incidence rebounded in 2011 and 2017, the overall incidence in China decreased.In the trend analysis by item, the incidence varies from high to low. The global spatial autocorrelation analysis revealed a clustered distribution, and the Moran index analysis of spatial autocorrelation within local regions identified five provinces as H-H clusters (hot spots), while one province was an L-L cluster (cold spot). Spatial scan analysis identified 11 significant spatial clusters. Conclusions: We found significant clustering in the spatial distribution of hepatitis B incidence and positive spatial correlation of hepatitis B incidence in China. We also identified high-risk times and regional clusters of hepatitis B incidence. [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]

Observational studies have revealed associations between various dietary factors and skin conditions. However, the causal relationship between diet and skin condition is still unknown. Data on 17 dietary factors were obtained from the UK Biobank. Data on four skin conditions were derived from the UK Biobank and another large-scale GWAS study. Genetic predictions suggested that the intake of oily fish was associated with a lower risk of skin aging (OR: 0.962, P = 0.036) and skin pigmentation (OR: 0.973, P = 0.033); Tea intake was associated with a lower risk of skin pigmentation (OR: 0.972, P = 0.024); Salad/raw vegetables intake was associated with a lower risk of keratinocyte skin cancer (OR: 0.952, P = 0.007). Coffee intake was associated with increased risk of skin aging (OR: 1.040, P = 0.028); Pork intake was associated with increased risk of skin aging (OR: 1.134, P = 0.020); Beef intake was associated with increased risk of cutaneous melanoma (OR: 1.013, P = 0.016); Champagne plus white wine intake was associated with increased risk of cutaneous melanoma (OR: 1.033, P = 0.004); Bread intake was associated with increased risk of keratinocyte skin cancer (OR: 1.026, P = 0.013). Our study results indicate causal relationships between genetically predicted intake of oily fish, tea, salad/raw vegetables, coffee, pork, beef, champagne plus white wine, and bread and skin conditions. [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]

The automated classification of rock images is of paramount importance in geological analysis, as it serves as the foundational criterion for the categorization of rock lithology. Despite recent advancements in leveraging deep learning technologies to enhance the efficiency and precision of image classification, a crucial aspect has been overlooked: these methods face a performance bottleneck when attempting to apply it directly to rock classification methods. To address this limitation, we propose a multiple granularity Spatial disorder Hierarchical residual Network (SHN). This approach involves learning from objects annotated at different levels, thereby facilitating the transfer of hierarchical knowledge across levels. By enabling lower-level classes to inherit pertinent attributes from higher-level superclasses, our method aims to capture the intricate hierarchical relationships among different rock types. Especially, we introduce a multi-granularity spatial disorder module to aid neural networks in discerning discriminative details across various scales. This module enables processed images to exhibit region independence, compelling the network to adeptly identify discriminative local regions at diverse granularity levels and extract pertinent features. Furthermore, in light of the absence of a comprehensive rock dataset, this study amassed 4,227 rock images of diverse compositions from various places, culminating in the creation of a robust rock dataset for classification. Rigorous experimentation on this dataset yielded highly promising results, demonstrating the effectiveness of our proposed method in addressing the challenges of rock image classification. [ABSTRACT FROM AUTHOR]

The construction of olefin‐linked chiral covalent organic frameworks (COFs) with high crystallinity is highly desirable while remains great challenge due to the poor reversibility of the formation reaction for the olefin linkages during the in situ structural self‐healing process. Herein, we successfully synthesized two sets of enantiomeric olefin‐linked COFs. The chiral catalytic groups are uniformly distributed on the pore walls of COFs, resulting in the full exposure of catalytic sites to the reactants in asymmetric catalysis. The as‐prepared (R)/(S)‐CCOF8 exhibits excellent catalytic performance with exceeding 99 % enantiomeric excess in the enantioselective electrophilic amination reaction. Moreover, the heterogeneous chiral catalysts are conveniently recycled and could maintain the performance after ten catalytic cycles. Our findings expand the scope to construct stable and crystalline chiral COFs for the asymmetric catalysis. [ABSTRACT FROM AUTHOR]

The construction of olefin‐linked chiral covalent organic frameworks (COFs) with high crystallinity is highly desirable while remains great challenge due to the poor reversibility of the formation reaction for the olefin linkages during the in situ structural self‐healing process. Herein, we successfully synthesized two sets of enantiomeric olefin‐linked COFs. The chiral catalytic groups are uniformly distributed on the pore walls of COFs, resulting in the full exposure of catalytic sites to the reactants in asymmetric catalysis. The as‐prepared (R)/(S)‐CCOF8 exhibits excellent catalytic performance with exceeding 99 % enantiomeric excess in the enantioselective electrophilic amination reaction. Moreover, the heterogeneous chiral catalysts are conveniently recycled and could maintain the performance after ten catalytic cycles. Our findings expand the scope to construct stable and crystalline chiral COFs for the asymmetric catalysis. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM) is an incurable hematological malignancy with poor survival. Accumulating evidence reveals that lactylation modification plays a vital role in tumorigenesis. However, research on lactylation-related genes (LRGs) in predicting the prognosis of MM remains limited. Differentially expressed LRGs (DELRGs) between MM and normal samples were investigated from the Gene Expression Omnibus database. Univariate Cox regression and LASSO Cox regression analysis were applied to construct gene signature associated with overall survival. The signature was validated in two external datasets. A nomogram was further constructed and evaluated. Additionally, Enrichment analysis, immune analysis, and drug chemosensitivity analysis between the two groups were investigated. qPCR and immunofluorescence staining were performed to validate the expression and localization of PFN1. CCK-8 and flow cytometry were performed to validate biological function. A total of 9 LRGs (TRIM28, PPIA, SOD1, RRP1B, IARS2, RB1, PFN1, PRCC, and FABP5) were selected to establish the prognostic signature. Kaplan–Meier survival curves showed that high-risk group patients had a remarkably worse prognosis in the training and validation cohorts. A nomogram was constructed based on LRGs signature and clinical characteristics, and showed excellent predictive power by calibration curve and C-index. Moreover, biological pathways, immunologic status, as well as sensitivity to chemotherapy drugs were different between high- and low-risk groups. Additionally, the hub gene PFN1 is highly expressed in MM, knocking down PFN1 induces cell cycle arrest, suppresses cell proliferation and promotes cell apoptosis. In conclusion, our study revealed that LRGs signature is a promising biomarker for MM that can effectively early distinguish high-risk patients and predict prognosis. [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]

Natural redox‐regulated channel proteins often utilize disulfide bonds as redox sensors for adaptive regulation of channel conformations in response to diverse physiological environments. In this study, we developed novel synthetic ion channels capable of reversibly switching their ion‐transport capabilities by incorporating multiple disulfide bonds into artificial systems. X‐ray structural analysis and electrophysiological experiments demonstrated that these disulfide‐bridged molecules possess well‐defined tubular cavities and can be efficiently inserted into lipid bilayers to form artificial ion channels. More importantly, the disulfide bonds in these molecules serve as redox‐tunable switches to regulate the formation and disruption of ion‐permeation pathways, thereby achieving a transition in the transmembrane transport process between the ON and OFF states. [ABSTRACT FROM AUTHOR]

Natural redox‐regulated channel proteins often utilize disulfide bonds as redox sensors for adaptive regulation of channel conformations in response to diverse physiological environments. In this study, we developed novel synthetic ion channels capable of reversibly switching their ion‐transport capabilities by incorporating multiple disulfide bonds into artificial systems. X‐ray structural analysis and electrophysiological experiments demonstrated that these disulfide‐bridged molecules possess well‐defined tubular cavities and can be efficiently inserted into lipid bilayers to form artificial ion channels. More importantly, the disulfide bonds in these molecules serve as redox‐tunable switches to regulate the formation and disruption of ion‐permeation pathways, thereby achieving a transition in the transmembrane transport process between the ON and OFF states. [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]

Protein–ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein–ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein–ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein–ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel. [ABSTRACT FROM AUTHOR]

Graph databases have received increased interests as many applications are handled as graph problems. Shortest distance queries are one of the fundamental operations and have been studied for recent years. To ensure the data and query privacy, researchers have introduced some secure graph encryption schemes which support the shortest distance queries on a large-scale graph database. Unfortunately, most of them only provide an approximate result by pre-computing and storing a distance oracle. To provide the exact shortest path, our solution employs a distributed two-trapdoor public-key crypto-system to perform addition and comparison operations over ciphertexts. The detailed security analysis indicates that our scheme achieves semantically secure under DDH assumption and the experiments are performed on various real-world database and random database. The experimental result shows the feasibility of our scheme. [ABSTRACT FROM AUTHOR]