Deciphering the genetic architecture of human brain structure and function: a brief survey on recent advances of neuroimaging genomics.

Brain imaging genomics is an emerging interdisciplinary field, where integrated analysis of multimodal medical image-derived phenotypes (IDPs) and multi-omics data, bridging the gap between macroscopic brain phenotypes and their cellular and molecular characteristics. This approach aims to better interpret the genetic architecture and molecular mechanisms associated with brain structure, function and clinical outcomes. More recently, the availability of large-scale imaging and multi-omics datasets from the human brain has afforded the opportunity to the discovering of common genetic variants contributing to the structural and functional IDPs of the human brain. By integrative analyses with functional multi-omics data from the human brain, a set of critical genes, functional genomic regions and neuronal cell types have been identified as significantly associated with brain IDPs. Here, we review the recent advances in the methods and applications of multi-omics integration in brain imaging analysis. We highlight the importance of functional genomic datasets in understanding the biological functions of the identified genes and cell types that are associated with brain IDPs. Moreover, we summarize well-known neuroimaging genetics datasets and discuss challenges and future directions in this field.

MorbidGCN: prediction of multimorbidity with a graph convolutional network based on integration of population phenotypes and disease network.

Exploring multimorbidity relationships among diseases is of great importance for understanding their shared mechanisms, precise diagnosis and treatment. However, the landscape of multimorbidities is still far from complete due to the complex nature of multimorbidity. Although various types of biological data, such as biomolecules and clinical symptoms, have been used to identify multimorbidities, the population phenotype information (e.g. physical activity and diet) remains less explored for multimorbidity. Here, we present a graph convolutional network (GCN) model, named MorbidGCN, for multimorbidity prediction by integrating population phenotypes and disease network. Specifically, MorbidGCN treats the multimorbidity prediction as a missing link prediction problem in the disease network, where a novel feature selection method is embedded to select important phenotypes. Benchmarking results on two large-scale multimorbidity data sets, i.e. the UK Biobank (UKB) and Human Disease Network (HuDiNe) data sets, demonstrate that MorbidGCN outperforms other competitive methods. With MorbidGCN, 9742 and 14 010 novel multimorbidities are identified in the UKB and HuDiNe data sets, respectively. Moreover, we notice that the selected phenotypes that are generally differentially distributed between multimorbidity patients and single-disease patients can help interpret multimorbidities and show potential for prognosis of multimorbidities.

Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey.

Advances in high-throughput experimental technologies promote the accumulation of vast number of biomedical data. Biomedical link prediction and single-cell RNA-sequencing (scRNA-seq) data imputation are two essential tasks in biomedical data analyses, which can facilitate various downstream studies and gain insights into the mechanisms of complex diseases. Both tasks can be transformed into matrix completion problems. For a variety of matrix completion tasks, matrix factorization has shown promising performance. However, the sparseness and high dimensionality of biomedical networks and scRNA-seq data have raised new challenges. To resolve these issues, various matrix factorization methods have emerged recently. In this paper, we present a comprehensive review on such matrix factorization methods and their usage in biomedical link prediction and scRNA-seq data imputation. Moreover, we select representative matrix factorization methods and conduct a systematic empirical comparison on 15 real data sets to evaluate their performance under different scenarios. By summarizing the experimental results, we provide general guidelines for selecting matrix factorization methods for different biomedical matrix completion tasks and point out some future directions to further improve the performance for biomedical link prediction and scRNA-seq data imputation.

Surface Acoustic Wave Sensors for Wireless Temperature Measurements above 1200 Degree Celsius.

High-temperature wireless sensing is crucial for monitoring combustion chambers and turbine stators in aeroengines, where surface temperatures can reach up to 1200 °C. Surface Acoustic Wave (SAW) temperature sensors are an excellent choice for these measurements. However, at extreme temperatures, they face issues such as agglomeration and recrystallization of electrodes, leading to loss of conductivity and reduced quality factor, hindering effective wireless signal transmission. This study develops an LGS SAW sensor with a Pt-10%Rh/Zr/Pt-10%Rh/Zr/Pt-10%Rh/Zr multilayer composite electrode structure to address these challenges. We demonstrate that the sensor can achieve wireless temperature measurements from room temperature to 1200 °C with an accuracy of 1.59%. The composite electrodes excite a quasi-shear wave on the LGS substrate, maintaining a Q-factor of 3526 at room temperature, providing an initial assurance for the strength of the wireless interrogation echo signal. The sensor operates stably for 2.18 h at 1200 °C before adhesion loss between the composite electrode and the substrate causes a sudden increase in resonant frequency. This study highlights the durability of the proposed electrode materials and structure at extreme temperatures and suggests future research to improve adhesion and extend the sensor's lifespan, thereby enhancing the reliability and effectiveness of high-temperature wireless sensing in aerospace applications.

HMGN2 represses gene transcription via interaction with transcription factors Lef-1 and Pitx2 during amelogenesis.

The chromatin-associated high mobility group protein N2 (HMGN2) cofactor regulates transcription factor activity through both chromatin and protein interactions. Hmgn2 expression is known to be developmentally regulated, but the post-transcriptional mechanisms that regulate Hmgn2 expression and its precise roles in tooth development remain unclear. Here, we demonstrate that HMGN2 inhibits the activity of multiple transcription factors as a general mechanism to regulate early development. Bimolecular fluorescence complementation, pull-down, and coimmunoprecipitation assays show that HMGN2 interacts with the transcription factor Lef-1 through its HMG-box domain as well as with other early development transcription factors, Dlx2, FoxJ1, and Pitx2. Furthermore, EMSAs demonstrate that HMGN2 binding to Lef-1 inhibits its DNA-binding activity. We found that Pitx2 and Hmgn2 associate with H4K5ac and H3K4me2 chromatin marks in the proximal Dlx2 promoter, demonstrating Hmgn2 association with open chromatin. In addition, we demonstrate that microRNAs (miRs) mir-23a and miR-23b directly target Hmgn2, promoting transcriptional activation at several gene promoters, including the amelogenin promoter. In vivo, we found that decreased Hmgn2 expression correlates with increased miR-23 expression in craniofacial tissues as the murine embryo develops. Finally, we show that ablation of Hmgn2 in mice results in increased amelogenin expression because of increased Pitx2, Dlx2, Lef-1, and FoxJ1 transcriptional activity. Taken together, our results demonstrate both post-transcriptional regulation of Hmgn2 by miR-23a/b and post-translational regulation of gene expression by Hmgn2-transcription factor interactions. We conclude that HMGN2 regulates tooth development through its interaction with multiple transcription factors.

Profiling transcriptional heterogeneity of epithelium, fibroblasts, and immune cells in esophageal squamous cell carcinoma by single-cell RNA sequencing.

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignancies with complex tumor microenvironment (TME) which has been proven to be associated with therapeutic failure or resistance. A deeper understanding of the complex TME and cellular heterogeneity is urgently needed in ESCC. Here, we generated single-cell RNA sequencing (scRNA-seq) of 25 796 immune and 8197 non-immune cells from three primary tumor and paired normal samples in ESCC patients. The results revealed intratumoral and intertumoral epithelium heterogeneity and tremendously differences in tumor and normal epithelium. The infiltration of myofibroblasts, one subtype of fibroblasts, might play important roles in the progression of ESCC. We also found that some differentially expressed genes and markers in epithelium and fibroblast subtypes showed prognostic values for ESCC. Diverse cell subtypes of T cells and myeloid cells were identified, including tumor-enriched HAVCR2+ CD4+ T cells with significantly exhausted signature. The epithelium and myeloid cells had more frequent cell-cell communication compared with epithelium and T cells. Taken together, this study provided in-depth insights into the cellular heterogeneity of TME in ESCC and highlighted potential therapeutic targets including for immunotherapy.

STAB: a spatio-temporal cell atlas of the human brain.

The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.

A graph regularized generalized matrix factorization model for predicting links in biomedical bipartite networks.

MOTIVATION: Predicting potential links in biomedical bipartite networks can provide useful insights into the diagnosis and treatment of complex diseases and the discovery of novel drug targets. Computational methods have been proposed recently to predict potential links for various biomedical bipartite networks. However, existing methods are usually rely on the coverage of known links, which may encounter difficulties when dealing with new nodes without any known link information. RESULTS: In this study, we propose a new link prediction method, named graph regularized generalized matrix factorization (GRGMF), to identify potential links in biomedical bipartite networks. First, we formulate a generalized matrix factorization model to exploit the latent patterns behind observed links. In particular, it can take into account the neighborhood information of each node when learning the latent representation for each node, and the neighborhood information of each node can be learned adaptively. Second, we introduce two graph regularization terms to draw support from affinity information of each node derived from external databases to enhance the learning of latent representations. We conduct extensive experiments on six real datasets. Experiment results show that GRGMF can achieve competitive performance on all these datasets, which demonstrate the effectiveness of GRGMF in prediction potential links in biomedical bipartite networks. AVAILABILITY AND IMPLEMENTATION: The package is available at https://github.com/happyalfred2016/GRGMF. CONTACT: leouyang@szu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Reference Pose Generation for Long-term Visual Localization via Learned Features and View Synthesis.

Visual Localization is one of the key enabling technologies for autonomous driving and augmented reality. High quality datasets with accurate 6 Degree-of-Freedom (DoF) reference poses are the foundation for benchmarking and improving existing methods. Traditionally, reference poses have been obtained via Structure-from-Motion (SfM). However, SfM itself relies on local features which are prone to fail when images were taken under different conditions, e.g., day/night changes. At the same time, manually annotating feature correspondences is not scalable and potentially inaccurate. In this work, we propose a semi-automated approach to generate reference poses based on feature matching between renderings of a 3D model and real images via learned features. Given an initial pose estimate, our approach iteratively refines the pose based on feature matches against a rendering of the model from the current pose estimate. We significantly improve the nighttime reference poses of the popular Aachen Day-Night dataset, showing that state-of-the-art visual localization methods perform better (up to 47%) than predicted by the original reference poses. We extend the dataset with new nighttime test images, provide uncertainty estimates for our new reference poses, and introduce a new evaluation criterion. We will make our reference poses and our framework publicly available upon publication.

Remediation of hexavalent chromium in column by green synthesized nanoscale zero-valent iron/nickel: Factors, migration model and numerical simulation.

In this work, green tea extracts synthesized nanoscale zero-valent iron/nickel (GT-nZVI/Ni) was prepared and the Cr(VI) contaminated soil column was remediated by GT-nZVI/Ni suspension. The influence factors including the concentration, pH value and flow rate of GT-nZVI/Ni suspension were studied. Under the conditions of pH = 4, concentration of 0.15 g/L and flow rate of 1.25 mL/h, GT-nZVI/Ni suspension had the best reduction and immobilization effect on Cr(VI) in the soil column. Na+ and Ca2+ can promote the immobilization of Cr (VI) in soil, while humic acid weakened the immobilization of Cr (VI). After GT-nZVI/Ni is injected into the soil column, the content of weak acid extractable and reduced chromium is significantly reduced, and the toxic hazard of hexavalent chromium in the soil is greatly reduced. The 1D-CDE model was used to fit the breakthrough curves of Fe(tot), Fe(aq) and Fe(0), and the migration of GT-nZVI/Ni in Cr(VI) contaminated soil was simulated and predicted. Compared with the inert solute Cl-, the breakthrough curves of Fe (tot), Fe (aq) and Fe (0) in Cr (VI) contaminated soil column were significantly lagged, with delay coefficients of 2.465, 2.322 and 3.288, respectively. The reaction of GT-nZVI/Ni with Cr (VI) led to the decrease of Fe mobility. Finally, the outflow concentration of Fe (tot) was 0.064 g/L, and the loss was mainly due to reaction and retention in the soil. About 57.89% of GT-nZVI/Ni was retained in the soil.

The Pitx2:miR-200c/141:noggin pathway regulates Bmp signaling and ameloblast differentiation.

The mouse incisor is a remarkable tooth that grows throughout the animal's lifetime. This continuous renewal is fueled by adult epithelial stem cells that give rise to ameloblasts, which generate enamel, and little is known about the function of microRNAs in this process. Here, we describe the role of a novel Pitx2:miR-200c/141:noggin regulatory pathway in dental epithelial cell differentiation. miR-200c repressed noggin, an antagonist of Bmp signaling. Pitx2 expression caused an upregulation of miR-200c and chromatin immunoprecipitation assays revealed endogenous Pitx2 binding to the miR-200c/141 promoter. A positive-feedback loop was discovered between miR-200c and Bmp signaling. miR-200c/141 induced expression of E-cadherin and the dental epithelial cell differentiation marker amelogenin. In addition, miR-203 expression was activated by endogenous Pitx2 and targeted the Bmp antagonist Bmper to further regulate Bmp signaling. miR-200c/141 knockout mice showed defects in enamel formation, with decreased E-cadherin and amelogenin expression and increased noggin expression. Our in vivo and in vitro studies reveal a multistep transcriptional program involving the Pitx2:miR-200c/141:noggin regulatory pathway that is important in epithelial cell differentiation and tooth development.

The LIM homeodomain transcription factor LHX6: a transcriptional repressor that interacts with pituitary homeobox 2 (PITX2) to regulate odontogenesis.

LHX6 is a LIM-homeobox transcription factor expressed during embryogenesis; however, the molecular mechanisms regulating LHX6 transcriptional activities are unknown. LHX6 and the PITX2 homeodomain transcription factor have overlapping expression patterns during tooth and craniofacial development, and in this report, we demonstrate new transcriptional mechanisms for these factors. PITX2 and LHX6 are co-expressed in the oral and dental epithelium and epithelial cell lines. Lhx6 expression is increased in Pitx2c transgenic mice and decreased in Pitx2 null mice. PITX2 activates endogenous Lhx6 expression and the Lhx6 promoter, whereas LHX6 represses its promoter activity. Chromatin immunoprecipitation experiments reveal endogenous PITX2 binding to the Lhx6 promoter. LHX6 directly interacts with PITX2 to inhibit PITX2 transcriptional activities and activation of multiple promoters. Bimolecular fluorescence complementation assays reveal an LHX6·PITX2 nuclear interaction in living cells. LHX6 has a dominant repressive effect on the PITX2 synergistic activation with LEF-1 and ß-catenin co-factors. Thus, LHX6 acts as a transcriptional repressor and represses the expression of several genes involved in odontogenesis. We have identified specific defects in incisor, molar, mandible, bone, and root development and late stage enamel formation in Lhx6 null mice. Amelogenin and ameloblastin expression is reduced and/or delayed in the Lhx6 null mice, potentially resulting from defects in dentin deposition and ameloblast differentiation. Our results demonstrate that LHX6 regulates cell proliferation in the cervical loop and promotes cell differentiation in the anterior region of the incisor. We demonstrate new molecular mechanisms for LHX6 and an interaction with PITX2 for normal craniofacial and tooth development.

Protein inhibitors of activated STAT (Pias1 and Piasy) differentially regulate pituitary homeobox 2 (PITX2) transcriptional activity.

Protein inhibitors of activated STAT (Pias) proteins can act independent of sumoylation to modulate the activity of transcription factors and Pias proteins interacting with transcription factors can either activate or repress their activity. Pias proteins are expressed in many tissues and cells during development and we asked if Pias proteins regulated the pituitary homeobox 2 (PITX2) homeodomain protein, which modulates developmental gene expression. Piasy and Pias1 proteins are expressed during craniofacial/tooth development and directly interact and differentially regulate PITX2 transcriptional activity. Piasy and Pias1 are co-expressed in craniofacial tissues with PITX2. Yeast two-hybrid, co-immunoprecipitation and pulldown experiments demonstrate Piasy and Pias1 interactions with the PITX2 protein. Piasy interacts with the PITX2 C-terminal tail to attenuate its transcriptional activity. In contrast, Pias1 interacts with the PITX2 C-terminal tail to increase PITX2 transcriptional activity. The E3 ligase activity associated with the RING domain in Piasy is not required for the attenuation of PITX2 activity, however, the RING domain of Pias1 is required for enhanced PITX2 transcriptional activity. Bimolecular fluorescence complementation assays reveal PITX2 interactions with Piasy and Pias1 in the nucleus. Piasy represses the synergistic activation of PITX2 with interacting co-factors and Piasy represses Pias1 activation of PITX2 transcriptional activity. In contrast, Pias1 did not affect the synergistic interaction of PITX2 with transcriptional co-factors. Last, we demonstrate that Pias proteins form a complex with PITX2 and Lef-1, and PITX2 and ß-catenin. Lef-1, ß-catenin, and Pias interactions with PITX2 provide new molecular mechanisms for the regulation of PITX2 transcriptional activity and the activity of Pias proteins.

Construction of store-operated calcium entry-related gene signature for predicting prognosis and indicates immune microenvironment infiltration in stomach adenocarcinomas.

Gastric adenocarcinoma (STAD) is the most prevalent malignancy of the human digestive system and the fourth leading cause of cancer-related death. Calcium pools, especially Ca2+ entry (SOCE) for storage operations, play a crucial role in maintaining intracellular and extracellular calcium balance, influencing cell activity, and facilitating tumor progression. Nevertheless, the prognostic and immunological value of SOCE in STAD has not been systematically studied. The objective of this study was to develop a risk model for SOCE signature and to explore its correlation with clinical characteristics, prognosis, tumor microenvironment (TME), as well as response to immunotherapy, chemotherapy, and targeted drugs. We used the TCGA, GEO (GSE84437 and GSE159929), cBioPortal and TIMER databases to acquire mRNA expression data for STAD, along with patient clinical indicators, single-cell sequencing data, genomic variation information, and correlations of immune cell infiltration. An analysis of SOCE genes based on tumor vs. normal tissue comparisons, pan-cancer dimension, single-cell sequencing, DNA mutation, and copy number variation revealed that SOCE genes significantly impact the survival of STAD patients and are differentially involved in the immune response. SOCE co-expressed genes were identified by Pearson analysis, and subsequently protein-protein interaction (PPI) and gene function enrichment analysis indicated that coexpressed genes were associated with multicellular pathways. Based on TCGA and GSE84437 datasets, a multifactor Cox proportional hazard regression analysis was conducted to identify SOCE co-expressed genes associated with overall survival in STAD patients. Several mRNA prognostic genes, including PTPRJ, GPR146, LTBP3, FBLN1, EFEMP2, ADAMTS7 and LBH, were identified, which could be used as effective prognostic predictors for STAD patients. In both training and test datasets, receiver operating characteristic (ROC) curves were utilized to evaluate and illustrate the predictive capability of this characteristic in forecasting overall survival of STAD patients. The qPCR and human protein atlas (HPA) were employed to assess mRNA expression and protein levels. Furthermore, the ESTIMATE, TIMER, CIBERSORT, QUANTISEQ, MCPCOUNTER, xCell and EPIC algorithms were utilized to perform immune score and analyze immune cell infiltration. It effectively revealed the difference in prognosis and immune cell infiltration in TME between high-risk and low-risk groups based on the risk signature associated with SOCE. Notably, significant differences in tumor immune dysfunction and rejection (TIDE) scores between the two groups, suggesting that patients in the low-risk group may exhibit a more favorable response to ICIS treatment. The GDSC database and R packages for predictive analysis were utilized to analyze responses to chemotherapy and targeted drugs in high-risk and low-risk groups. In summary, the 7-gene signature associated with SOCE serves as a significant biomarker for evaluating the TME and predicting the prognosis of STAD patients. In addition, it may provide valuable insights for developing effective immunotherapy and chemotherapy regiments for patients with STAD.

Tight Junction Component Occludin Binds to FIP5 to Regulate Endosome Trafficking and Mitotic Spindle Function.

The genetic basis of vertebrate emergence during metazoan evolution has remained largely unknown. Understanding vertebrate-specific genes, such as the tight junction protein Occludin (Ocln), may help answer this question. Here, it is shown that mammary glands lacking Ocln exhibit retarded epithelial branching, owing to reduced cell proliferation and surface expansion. Interestingly, Ocln regulates mitotic spindle orientation and function, and its loss leads to a range of defects, including prolonged prophase and failed nuclear and/or cytoplasmic division. Mechanistically, Ocln binds to the RabGTPase-11 adaptor FIP5 and recruits recycling endosomes to the centrosome to participate in spindle assembly and function. FIP5 loss recapitulates Ocln null, leading to prolonged prophase, reduced cell proliferation, and retarded epithelial branching. These results identify a novel role in OCLN-mediated endosomal trafficking and potentially highlight its involvement in mediating membranous vesicle trafficking and function, which is evolutionarily conserved and essential.

A new simplified risk assessment model enhances postoperative prophylaxis of venous thromboembolism in Chinese adult patients with inguinal hernia (CHAT-3): a prospective, multicenter, randomized controlled trial.

BACKGROUND: Venous thromboembolism (VTE) significantly affects the prognosis of surgical patients with inguinal hernia. The complex Caprini score, commonly used for postoperative VTE risk assessment, poses practical challenges for surgeons in clinical settings. METHODS: The CHAT-3 trial, a prospective, multicenter, randomized controlled trial, compared a simple three-factor model to assess VTE risk against routine practices in postinguinal hernia surgery (IHS) patients. The patients were randomly assigned (1:1) to the intervention or control arm. The intervention group used the three-factor model to identify patients at moderate or high risk of VTE for subsequent prophylaxis according to clinical guidelines. Both groups were followed for 4 weeks, with randomization implemented using computer-generated sequences. The primary outcome measured was the rate of VTE prophylaxis. Secondary outcomes included time spent on VTE risk assessment (surgeon self-reported), postoperative D-dimer trends, perioperative VTE occurrence, bleeding events, and the net clinical benefit. RESULTS: Of the 1109 participants, 508 in the experimental group and 601 in the control group completed follow-up. The three-factor model showed higher VTE prophylaxis rates in all patients (pharmacologic prophylaxis: 26.2 vs. 6.00%, P <0.001) and particularly in those at high risk (pharmacologic prophylaxis: 57.3 vs. 9.50%, P <0.001). The experimental group significantly reduced VTE risk assessment time compared to the Caprini score (1.39±0.55 min vs. 5.73±1.35 min, P <0.001). The experimental group had lower D-dimer levels (0.26±0.73 mg/l vs. 0.35±0.55 mg/l, P =0.028). In the experimental group, the patients did not experience an increased risk of VTE (0 vs. 1.66%, P =0.268) and bleeding (1.18 vs. 0.67%, P =0.558) compared to the controls. There was no significant difference in net clinical benefit, which combined VTE and bleeding events, between the experimental and control groups (1.18 vs. 0.83%, P =0.559). CONCLUSION: Applying the simple three-factor model in perioperative VTE management could quickly identify the patient with a high risk of VTE and improve the prophylaxis rate of perioperative VTE.

Facilitation or inhibition? Impact of CEO's financial background on industrial AI transformation of manufacturing companies.

Introduction: The purpose of this paper is to empirically test the impact of CEO's financial background on industrial AI transformation of manufacturing enterprises based on upper echelons theory and imprinting theory. Methods: The paper preliminarily takes listed manufacturing companies in Shanghai and Shenzhen stock markets that are affiliated to enterprise groups from 2014 to 2020 as samples, and manually collects and collates datas of CEO's financial background and industrial AI transformation. The research hypotheses are tested by stata 15.0 software. Results: It is found that CEO's financial background significantly inhibits the industrial AI transformation of manufacturing enterprises, and when the CEO works part-time in the parent company, it will strengthen the negative impact of CEO's financial background on industrial AI transformation. Further research shows that enterprise financialization plays a partial intermediary role between CEO's financial background and industrial AI transformation; Compared with private enterprise groups, the inhibiting effect of CEO financial background on industrial AI transformation is stronger in state-owned enterprise groups; CEOs with non-banking financial background have a stronger inhibitory effect on industrial AI transformation. Discussion: Firstly, based on the process of making business decisions, it verifies and clarifies the action mechanism of CEO's financial background on industrial AI transformation through internal driving mechanism, which expands the research horizon of industrial AI transformation, and further applies the Imprinting Theory in biology to the research of business decision-making, which forms a beneficial complement to the relevant research on economic consequences of CEO's financial background. Secondly, different from the research of single independent company, this paper focuses on the special situation of parent-subsidiary corporate governance, and explores the mechanism of action, deepening the research on the synergy of enterprise groups. Finally, this paper further explores the influence of CEO's financial background on industrial AI transformation, which is conducive to a deeper understanding of the heterogeneity of managers except manpower and capital factors in the industrial AI transformation practice of manufacturing enterprises, and provides a new idea and a more comprehensive analysis perspective for industrial AI transformation.

Stromal Interaction Molecule 1 (STIM1) is a Potential Prognostic Biomarker and Correlates with Immune Infiltrates in Solid Tumors.

Increasing evidence has shown that stromal interaction molecule 1 (STIM1), a key subunit of store-operated Ca2+ entry (SOCE), is closely associated with tumor growth, development, and metastasis. However, there is no report of a comprehensive assessment of STIM1 in pan-cancer. This study aimed to perform a general analysis of STIM1 in human tumors, including its molecular characteristics, functional mechanisms, clinical significance, and immune infiltrates correlation based on pan-cancer data from The Cancer Genome Atlas (TCGA). Gene expression analysis was investigated using TCGA RNA-seq data, the Tumor Immune Estimation Resource (TIMER). Phosphorylation analysis was undertaken using the Clinical Proteomic Tumor Analysis Consortium (CP-TAC) and the PhosphoNET database. Genetic alterations of STIM1 were analyzed using cBioPortal. Prognostic analysis was via the R package "survival" function and the Kaplan-Meier plotter. Functional enrichment analysis was via by the R package "cluster Profiler" function. The association between STIM1 and tumor-infiltrating immune cells and immune markers was by the R package "GSVA" function and TIMER. STIM1 was differentially expressed and associated with distinct clinical stages in multiple tumors. The phosphorylation of STIM1 at S673 is highly expressed in clear cell renal carcinoma and lung adenocarcinoma tumors compared to normal tissues. STIM1 genetic alterations correlate with poor prognosis in several tumors, including ovarian cancer and lung squamous cell carcinomas. High STIM1 expression is associated with good or poor prognosis across diverse tumors. Overall survival (OS) analysis indicated that STIM1 is a favorable prognostic factor for patients with BRCA, KIRC, LIHC, LUAD, OV, SARC, and UCEC, and is a risk prognostic factor for BLCA, KIRP, STAD, and UVM. There is a close correlation between STIM1 expression and immune cell infiltration, immune-regulated genes, chemokines, and immune checkpoints in a variety of tumors. STIM1 functions differently in diverse tumors, playing an oncogenic or antitumor role. Moreover, It may serve as a prognostic biomarker and an immunotherapy target across multiple tumors.

Balance dysfunction in large yellow croaker in response to ocean acidification.

Large yellow croaker (Larimichthys crocea) is a coastal-dwelling soniferous, commercially important fish species that is sensitive to sound. An understanding of how ocean acidification might affect its auditory system is therefore important for its long-term viability and management as a fisheries resource. We tested the effects of ocean acidification with four CO2 treatments (440 ppm (control), 1000 ppm, 1800 ppm, and 3000 ppm) on the inner ear system of this species. After exposure to acidified water for 50 d, the impacts on the perimeter and mass of the sagitta, asteriscus, and lapillus otoliths were determined. In the acidified water treatments, the shape of sagittal otoliths became more irregular, and the surface became rougher. Similar sound frequency ranges triggered startle responses of fish in all treatments. In the highest CO2 treatment (3000 ppm CO2), significant asymmetry of the left and right lapillus perimeter and weight was apparent. Moreover, in the higher CO2 treatments (1800 ppm and 3000 ppm CO2), the fish were unable to maintain a balanced dorsal-up posture and tilted to one side. This result suggested that the balance functions of the inner ear might be affected by ocean acidification, which may threaten large yellow croaker individuals and populations. The molecular response to acidification was analyzed by RNA-Seq. The differentially expressed genes (DEGs) between right and left sensory epithelia of the utricle in each CO2 treatment group were identified. In higher CO2 concentration groups, nervous system function and regulation of bone mineralization pathways were enriched with DEGs. The comparative transcriptome analyses provide valuable molecular information about how the inner ear system responds to an acidified environment.

Improving Alzheimer's Disease Diagnosis With Multi-Modal PET Embedding Features by a 3D Multi-Task MLP-Mixer Neural Network.

Positron emission tomography (PET) with fluorodeoxyglucose (FDG) or florbetapir (AV45) has been proved effective in the diagnosis of Alzheimer's disease. However, the expensive and radioactive nature of PET has limited its application. Here, employing multi-layer perceptron mixer architecture, we present a deep learning model, namely 3-dimensional multi-task multi-layer perceptron mixer, for simultaneously predicting the standardized uptake value ratios (SUVRs) for FDG-PET and AV45-PET from the cheap and widely used structural magnetic resonance imaging data, and the model can be further used for Alzheimer's disease diagnosis based on embedding features derived from SUVR prediction. Experiment results demonstrate the high prediction accuracy of the proposed method for FDG/AV45-PET SUVRs, where we achieved Pearson's correlation coefficients of 0.66 and 0.61 respectively between the estimated and actual SUVR and the estimated SUVRs also show high sensitivity and distinct longitudinal patterns for different disease status. By taking into account PET embedding features, the proposed method outperforms other competing methods on five independent datasets in the diagnosis of Alzheimer's disease and discriminating between stable and progressive mild cognitive impairments, achieving the area under receiver operating characteristic curves of 0.968 and 0.776 respectively on ADNI dataset, and generalizes better to other external datasets. Moreover, the top-weighted patches extracted from the trained model involve important brain regions related to Alzheimer's disease, suggesting good biological interpretability of our proposed method."