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Purposes In order to obtain additional microstructural information on the ConMoS(n＝1-5) cluster and establish a theoretical foundation for subsequent investigations of its electronic properties and catalytic performance, the atomic interaction relationships and frontier orbital compositions of the cluster are investigated. Methods To accomplish this, density functional theory with the B3LYP functional and def2-TZVP basis set is adopted to optimize the cluster configuration and perform theoretical analysis under various spin multiplicities with the Gaussian09 software package. Findings The results demonstrate the existence of 21 stable configurations. AIM theory, electron localization function, and deformation density are analyzed to obtain information on the degree of internal interaction and electron density transfer trends of the cluster. The findings indicate that covalent bonding interactions between Co, Mo, and S atoms are strong in the internal atom interactions of the cluster, with S atoms exhibiting high localization and low probability of electron escape from the domain. In contrast, Co and Mo atoms exhibit strong delocalization and are likely to exchange electrons with the outside electrons. Through Hirshfeld analysis of atomic contribution rates, it can be observed that metal atoms are the primary contributors to the HOMO and LUMO orbitals, with Mo and Co atoms dominating the frontier orbitals. Overall, covalent bonding is the primary atomic interaction within the cluster, but the strength of these interactions varies. Mo and Co atoms should be the focus in chemical reactions of the cluster.

To delve into the microscopic property variations of the ConMoP(n = 1 ~ 5) cluster, this study employs density functional theory at the B3LYP/def2‐TZVP quantum chemistry level. The paper conducts a comprehensive theoretical analysis of the clusters, examining parameters such as bond lengths, bond angles, electronic localization function (ELF), interaction region indicator function (IRI), independent gradient model based on the Hirshfeld partition, deformation density, electronic space range, chemical hardness and softness, polarizability, and dipole moment. The optimization process reveals 16 stable configurations for the ConMoP(n = 1 ~ 5) cluster, predominantly taking on a steric form. As the cluster size increases, the electronic delocalization of Co atoms intensifies, while the electronic properties of Mo atoms remain relatively stable. Notably, P atoms maintain a high degree of electronic delocalization. The interatomic forces within the clusters predominantly exhibit covalent bonding, with no evident repulsive effects observed between atoms. The bonding between metal atoms in the clusters leans toward a preference for covalent interaction. The degree of dispersion in the clusters increases with their growth, particularly evident in configuration 5‐a, which displays a broader spatial distribution. Configuration 3‐a demonstrates the most favorable activity, showcasing a relatively stable structure. Overall, configuration 3‐a emerges as a focal point for catalytic reactions. This article provides a more comprehensive analysis of the ConMoP(n = 1 ~ 5) cluster, yielding diverse results and enhancing the theoretical understanding of this cluster. [ABSTRACT FROM AUTHOR]

Context: Hydrogen has emerged as a promising clean energy carrier, underscoring the imperative need to comprehend its adsorption mechanisms. This study delves into the magnetic and electronic properties of Co-Mo-P clusters, aiming to unveil their catalytic potential in hydrogen production. Employing density functional theory (DFT), we optimized cluster configurations and scrutinized their magnetic behaviors. Our investigation unveiled 16 stable configurations of the ConMoP (n = 1 ~ 5) cluster, predominantly in steric forms. The magnetic attributes were primarily ascribed to the d orbitals of Co metal atoms, with Co3MoP exhibiting exceptional magnetic characteristics. Analysis of density of state diagrams revealed the prevalence of spin-up α-electrons in d orbitals, while spin-down β-electrons attenuated overall magnetic properties. Localized orbital (LOL) analysis highlighted stable covalent bonds within the clusters, affirming their catalytic potential. Orbital delocalization index (ODI) analysis revealed diverse spatial distribution ranges for orbitals across different configurations, suggesting a progressive attenuation of off-domain properties with increasing cluster size. Furthermore, infrared spectroscopy unveiled distinct vibrational peaks in various configurations, indicative of unique infrared activities. These findings contribute to a nuanced theoretical understanding of Co-Mo-P clusters and pave the path for future research aimed at augmenting their catalytic efficiency in hydrogen production. This study underscores the viability of Co-Mo-P clusters as alternatives to conventional Pt catalysts, offering insights into the design of novel materials for sustainable energy applications. Further research is warranted to explore the behavior of the Co-Mo-P system under diverse reaction conditions, fostering advancements in materials and energy science. Methods: In this study, we harnessed the ConMoP (n = 1 ~ 5) cluster as a simulation platform for probing the local structure of the material. Our aim was to scrutinize the magnetism, electronic characteristics influenced by the varying metal atoms within these clusters. A systematic exploration involved incrementing the number of metal atoms and expanding the cluster size to elucidate the corresponding property variations. Density functional theory (DFT) calculations were pivotal to our methodology, employing the B3LYP hybrid functional implemented in the Gaussian 16 software package. The ConMoP (n = 1 ~ 5) cluster underwent optimization calculations and vibrational analysis at the def2-tzvp quantization level, yielding optimized configurations with diverse spin multiplet degrees. To comprehensively characterize and visually represent the stability, electronic features, and catalytic attributes of these configurations, we employed a suite of computational tools. Specifically, quantum chemistry software GaussView and wave function analysis software Multiwfn played integral roles. Through the integrated use of these computational tools, we acquired valuable insights into the magnetism, electronic characteristics of the ConMoP (n = 1 ~ 5) cluster, shedding light on their dependency on distinct metal atoms. [ABSTRACT FROM AUTHOR]

Stem cells hold promise for treating hair loss. Here an in vitro mouse model was developed using outer root sheaths (ORSs) isolated from hair follicles for studying stem cell-mediated dermal papillary regeneration.

Context: The investigation of the stability, electronic properties, and catalytic activity of clusters ConMoP holds significant applications and implications in catalyst design, materials science, energy conversion and storage, and environmental protection. The study aims to delve into the unique features of the clusters ConMoP(n = 1 ~ 5), aiming to drive advancements in these related fields. The results obtained from the analysis revealed the stable configurations of the ten clusters, primarily characterized by steric structures. Furthermore, the energy of the clusters was found to increase continuously during growth, as indicated by calculations of atomic fragmentation energy and atomic binding energy. The researchers conducted an analysis of the Natural Population Analysis(NPA) charge, which revealed that Co atoms acted as electron donors, while P and Mo atoms acted as electron acceptors within the clusters. Additionally, an examination of the electrostatic potential indicated that Co and Mo atoms displayed nucleophilic tendencies, while P atoms exhibited electrophilic characteristics. Moreover, the density of states curves, HOMO and LUMO orbitals, and Kooperman's theorem were applied to the clusters ConMoP(n = 1 ~ 5).Through this study, a deeper understanding of the properties and behavior of clusters ConMoP has been achieved, shedding light on their potential as catalysts. The findings contribute to the existing knowledge of these clusters and provide a basis for further research and exploration in this field. Methods: In this study, we employed the clusters ConMoP(n = 1 ~ 5) to simulate the local structure of the material, enabling us to investigate the stability, electronic properties, and catalytic properties influenced by the metal atoms. By systematically increasing the number of metal atoms and expanding the cluster size, we explored the variations in these properties. Density functional theory (DFT) calculations were performed using the B3LYP hybrid functional implemented in the Gaussian09 software package. The clusters ConMoP(n = 1 ~ 5) underwent optimization calculations and vibrational analysis at the def2-tzvp quantization level, resulting in optimized configurations with different spin multiplet degrees. For data characterization and graphical representation of the stability, electronic properties, and catalytic properties of the optimized configurations, we utilized a range of computational tools. Specifically, the quantum chemistry software GaussView, wave function analysis software Multiwfn were employed. Through the comprehensive utilization of these computational tools, we gained valuable insights into the stability, electronic properties, and catalytic properties of the clusters ConMoP(n = 1 ~ 5) and their dependence on different metal atoms. [ABSTRACT FROM AUTHOR]

The target of text‐based person re‐identification (Re‐ID) is to retrieve the corresponding image of a person through the given text information. However, due to the homogeneous variety and modality heterogeneity, it is challenging to simultaneously learn both global‐level and local‐level cross‐modal features and align them in the same embedding space without additional networks. To address these problems, an effective multi‐level cross‐modality learning (MCL) framework for language and vision person Re‐ID is proposed. More specifically, a multi‐branch feature extraction (MFE) module is designed to comprehensively map both global and partial semantic information for the visual and textual embedding at the same time, capturing the intra‐class semantic relationships in multi‐granularities. Besides, a cross‐modal alignment (CA) module is devised to match the multi‐grained representations and reduce the inter‐class gap from global‐level to partial‐level. Extensive experiments conducted on the CUHK‐PEDES and ICFG‐PEDES datasets suggest that this method outperforms the state‐of‐the‐art models. [ABSTRACT FROM AUTHOR]

Hetian sheep is a breed of sheep unique to the Hetian area of Xinjiang whose wool is used for producing blankets. Individual differences and hair follicle density are the key factors affecting wool production. Therefore, this study aimed to assess the Hetian sheep having different wool densities to statistically analyze the wool traits and hair follicle parameters. Furthermore, the transcriptome sequencing analysis was performed on the skins with different wool densities. The results showed that wool quantity and total hair follicle density of the high wool density sheep was significantly higher than low wool density sheep. The sheepskin with high wool density was found to grow more and finer wool than sheepskin with low wool density. A total of 1,452 differentially expressed genes were screened from the two sets of samples, including 754 upregulated and 698 downregulated genes. The differentially expressed genes were involved in the TGF-β/BMP and MAPK signaling pathways related to hair growth. Eleven differentially expressed genes belonging to the KAPs and KIFs might affect the fineness of the wool. The key genes, like the TNF, MAP2K2, INHBA, FST, PTPN11, MAP3K7, KIT, and BMPR1A, were found to probably affect the growth and density of the wool. The qPCR verified eight genes related to the MAPK pathway whose gene expression trends were consistent with the transcriptome sequencing results. This study furnishes valuable resources for enhancing the quality and production of wool in the Hetian sheep. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence (AI) in medicine has increased rapidly with respect to tasks including disease detection/diagnosis, risk stratification, and prognosis prediction. With recent advances in computing power and algorithms, AI has shown promise in taking advantage of vast electronic health data and imaging studies to supplement clinicians. Machine learning and deep learning are the most widely used AI methodologies for medical research and have been applied in pancreatobiliary diseases for which diagnosis and treatment selection are often complicated and require joint consideration of data from multiple sources. The aim of this review is to provide a concise introduction of the major AI methodologies and the current landscape of AI research in pancreatobiliary diseases. [ABSTRACT FROM AUTHOR]

The CXC chemokines belong to a family which includes 17 different CXC members. Accumulating evidence suggests that CXC chemokines regulate tumor cell proliferation, invasion, and metastasis in various types of cancers by influencing the tumor microenvironment. The different expression profiles and specific function of each CXC chemokine in head and neck squamous cell carcinoma (HNSCC) are not yet clarified. In our work, we analyzed the altered expression, interaction network, and clinical data of CXC chemokines in patients with HNSCC by using the following: the Oncomine dataset, cBioPortal, Metascape, String analysis, GEPIA, and the Kaplan–Meier plotter. The transcriptional level analysis suggested that the mRNA levels of CXCL1, CXCL2, CXCL3, CXCL5, CXCL6, CXCL8, CXCL9, CXCL10, CXCL11, and CXCL13 increased in HNSCC tissue samples when compared to the control tissue samples. The expression levels of CXCL9, CXCL10, CXCL11, CXCL12, and CXCL14 were associated with various tumor stages in HNSCC. Clinical data analysis showed that high transcription levels of CXCL2, CXCL3, and CXCL12, were linked with low relapse-free survival (RFS) in HNSCC patients. On the other hand, high CXCL14 levels predicted high RFS outcomes in HNSCC patients. Meanwhile, increased gene transcription levels of CXCL9, CXCL10, CXCL13, CXCL14, and CXCL17 were associated with a higher overall survival (OS) advantage in HNSCC patients, while high levels of CXCL1, and CXCL8 were associated with poor OS in all HNSCC patients. This study implied that CXCL1, CXCL2, CXCL3, CXCL8, and CXCL12 could be used as prognosis markers to identify low survival rate subgroups of patients with HNSCC as well as be potential suitable therapeutic targets for HNSCC patients. Additionally, CXCL9, CXCL10, CXCL13, CXCL14, and CXCL17 could be used as functional prognosis biomarkers to identify better survival rate subgroups of patients with HNSCC. [ABSTRACT FROM AUTHOR]

Based on hyperspectral imaging technology, rapid and efficient prediction of soil moisture content (SMC) can provide an essential basis for the formulation of precise agricultural programs (e.g., forestry irrigation and environmental management). To build an efficient inversion model of SMC, this paper collected 117 cultivated soil samples from the Chair Hill area and tested them using the GaiaSorter hyperspectral sorter. The collected soil reflectance dataset was preprocessed by wavelet transform, before the combination of competitive adaptive reweighted sampling algorithm and successive projections algorithm (CARS-SPA) was used to select the bands optimally. Seven wavelengths of 695, 711, 736, 747, 767, 778, and 796 nm were selected and used as the factors of the SMC inversion model. The popular linear regression algorithm was employed to construct this model. The result indicated that the inversion model established by the multiple linear regression algorithm (the predicted R2 was 0.83 and the RMSE was 0.0078) was feasible and highly accurate, indicating it could play an important role in predicting SMC of cultivated soils over a large area for agricultural irrigation and remote monitoring of crop yields. [ABSTRACT FROM AUTHOR]