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BackgroundMajor depression disorder (MDD) is a devastating neuropsychiatric disease, and one of the leading causes of suicide. Ferroptosis, an iron-dependent form of regulated cell death, plays a pivotal role in numerous diseases. The study aimed to construct and validate a gene signature for diagnosing MDD based on ferroptosis-related genes (FRGs) and further explore the biological functions of these genes in MDD.MethodsThe datasets were downloaded from the Gene Expression Omnibus (GEO) database and FRGs were obtained from the FerrDb database and other literatures. Least absolute shrinkage and selection operator (LASSO) regression and stepwise logistic regression were performed to develop a gene signature. Receiver operating characteristic (ROC) curves were utilized to assess the diagnostic power of the signature. Gene ontology (GO) enrichment analysis was used to explore the biological roles of these diagnostic genes, and single sample gene set enrichment analysis (ssGSEA) algorithm was used to evaluate immune infiltration in MDD. Animal model of depression was constructed to validate the expression of the key genes.ResultsEleven differentially expressed FRGs were identified in MDD patients compared with healthy controls. A signature of three FRGs (ALOX15B, RPLP0, and HP) was constructed for diagnosis of MDD. Afterwards, ROC analysis confirmed the signature’s discriminative capacity (AUC = 0.783, 95% CI = 0.719–0.848). GO enrichment analysis revealed that the differentially expressed genes (DEGs) related to these three FRGs were mainly involved in immune response. Furthermore, spearman correlation analysis demonstrated that these three FRGs were associated with infiltrating immune cells. ALOX15B and HP were significantly upregulated and RPLP0 was significantly downregulated in peripheral blood of the lipopolysaccharide (LPS)-induced depressive model.ConclusionOur results suggest that the novel FRG signature had a good diagnostic performance for MDD, and these three FRGs correlated with immune infiltration in MDD.

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors originating from chromaffin cells, holding significant clinical importance due to their capacity for excessive catecholamine secretion and associated cardiovascular complications. Roughly 80% of cases are associated with genetic mutations. Based on the functionality of these mutated genes, PPGLs can be categorized into distinct molecular clusters: the pseudohypoxia signaling cluster (Cluster-1), the kinase signaling cluster (Cluster-2), and the WNT signaling cluster (Cluster-3). A pivotal factor in the pathogenesis of PPGLs is hypoxia-inducible factor-2α (HIF2α), which becomes upregulated even under normoxic conditions, activating downstream transcriptional processes associated with pseudohypoxia. This adaptation provides tumor cells with a growth advantage and enhances their ability to thrive in adverse microenvironments. Moreover, pseudohypoxia disrupts immune cell communication, leading to the development of an immunosuppressive tumor microenvironment. Within Cluster-1a, metabolic perturbations are particularly pronounced. Mutations in enzymes associated with the tricarboxylic acid (TCA) cycle, such as succinate dehydrogenase (SDHx), fumarate hydratase (FH), isocitrate dehydrogenase (IDH), and malate dehydrogenase type 2 (MDH2), result in the accumulation of critical oncogenic metabolic intermediates. Notable among these intermediates are succinate, fumarate, and 2-hydroxyglutarate (2-HG), which promote activation of the HIFs signaling pathway through various mechanisms, thus inducing pseudohypoxia and facilitating tumorigenesis. SDHx mutations are prevalent in PPGLs, disrupting mitochondrial function and causing succinate accumulation, which competitively inhibits α-ketoglutarate-dependent dioxygenases. Consequently, this leads to global hypermethylation, epigenetic changes, and activation of HIFs. In FH-deficient cells, fumarate accumulation leads to protein succination, impacting cell function. FH mutations also trigger metabolic reprogramming towards glycolysis and lactate synthesis. IDH1/2 mutations generate D-2HG, inhibiting α-ketoglutarate-dependent dioxygenases and stabilizing HIFs. Similarly, MDH2 mutations are associated with HIF stability and pseudohypoxic response. Understanding the intricate relationship between metabolic enzyme mutations in the TCA cycle and pseudohypoxic signaling is crucial for unraveling the pathogenesis of PPGLs and developing targeted therapies. This knowledge enhances our comprehension of the pivotal role of cellular metabolism in PPGLs and holds implications for potential therapeutic advancements.

Abstract We still do not have an effective treatment for Alzheimer's disease (AD) despite it being the most common cause of dementia and impaired cognitive function. Thus, research endeavors are directed toward identifying AD biomarkers and targets. In this regard, we designed a computational method that exploits multiple hub gene ranking methods and feature selection methods with machine learning and deep learning to identify biomarkers and targets. First, we used three AD gene expression datasets to identify 1/ hub genes based on six ranking algorithms (Degree, Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC), Betweenness Centrality (BC), Closeness Centrality, and Stress Centrality), 2/ gene subsets based on two feature selection methods (LASSO and Ridge). Then, we developed machine learning and deep learning models to determine the gene subset that best distinguishes AD samples from the healthy controls. This work shows that feature selection methods achieve better prediction performances than the hub gene sets. Beyond this, the five genes identified by both feature selection methods (LASSO and Ridge algorithms) achieved an AUC = 0.979. We further show that 70% of the upregulated hub genes (among the 28 overlapping hub genes) are AD targets based on a literature review and six miRNA (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, hsa-mir-26a-5p, hsa-mir-93-5p, hsa-mir-155-5p) and one transcription factor, JUN, are associated with the upregulated hub genes. Furthermore, since 2020, four of the six microRNA were also shown to be potential AD targets. To our knowledge, this is the first work showing that such a small number of genes can distinguish AD samples from healthy controls with high accuracy and that overlapping upregulated hub genes can narrow the search space for potential novel targets.

Abstract The procollagen C-protease enhancer (PCOLCE) has been identified to influence tumor growth and metastasis in multiple cancers. However, the relationship between PCOLCE activity and the progression of gliomas remains largely unknown. Glioma RNA-seq data were derived from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas databases for analysis. Kaplan–Meier survival curve, clinical characterization correlation, univariate and multivariate Cox, and receiver operating characteristic curve analyses were performed to assess the prognostic role of PCOLCE. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to determine the functions or pathways associated with PCOLCE. The ESTIMATE and CIBERSORT algorithms, Spearman’s rank correlation analysis, and Tumor Immune Estimation Resource (TIMER) databases were used to explore the relationship between PCOLCE and immune infiltration. Correlation analysis between PCOLCE, related genes, and immune cell markers was conducted using the TIMER database. Immunophenoscore assays were performed to determine differential PCOLCE expression levels in glioma. The sensitivity of multi-drugs were determined to explore potential chemotherapeutic agents in between PCOLCE. Compared to normal brain tissue, PCOLCE expression was increased in glioma and correlated with shorter overall survival (OS). Furthermore, significant differences were observed in the immune scores and immune cell infiltration levels. PCOLCE is positively associated with immune checkpoints and many immune markers. Additionally, PCOLCE expression was higher in gliomas with higher IPS Z-scores in CGGA. High expression of PCOLCE increased sensitivity to multiple chemotherapy agents in CGGA (P

Abstract Background and aims Monocytes/macrophages play important roles in inflammatory bowel disease and depression, but few studies had focused on the change of monocytes/macrophages in ulcerative colitis (UC) patients with psychiatric disorders. Methods UC patients were divided into two groups based on the Hospital Anxiety and Depression Scale (HADS). Demographic and clinical data were captured. Peripheral blood samples and intestinal biopsies were collected for the analysis of monocyte immunophenotype, phagocytic function, and CD4 + T cell differentiation. Transmission electron microscopy was used to observe the ultrastructure of intestinal macrophages. Results A total of 139 UC patients were included. 37.41% and 32.37% of UC patients had symptoms of anxiety and depression. In patients with symptoms of anxiety/depression, mayo score, platelet count, erythrocyte sedimentation rate, and endoscopic score, histological scores were significantly higher than those in UC patients without. In patients with symptoms of anxiety/depression, the percentages of CD14 + + CD16 + monocytes and CD14 + CD16++ monocytes were higher, and the phagocytosis was decreased. Patients with symptoms of anxiety/depression had more CD68 + cells and higher M1/M2 ratios in the intestine mucosal layer compared to those without. Conclusions Monocytes and intestinal macrophages from UC patients with anxiety/depression tended to polarize to pro-inflammatory subtypes, and their function was also impaired.

This study comprehensively benchmarks 18 state-of-the-art methods for cellular deconvolution of spatial transcriptomics and provide decision-tree-style guidelines and recommendations for method selection.

Abstract Background Genome-wide association studies have identified numerous human host genetic risk variants that play a substantial role in the host immune response to SARS-CoV-2. Although these genetic risk variants significantly increase the severity of COVID-19, their influence on body systems is poorly understood. Therefore, we aim to interpret the biological mechanisms and pathways associated with the genetic risk factors and immune responses in severe COVID-19. We perform a deep analysis of previously identified risk variants and infer the hidden interactions between their molecular networks through disease mapping and the similarity of the molecular functions between constructed networks. Results We designed a four-stage computational workflow for systematic genetic analysis of the risk variants. We integrated the molecular profiles of the risk factors with associated diseases, then constructed protein–protein interaction networks. We identified 24 protein–protein interaction networks with 939 interactions derived from 109 filtered risk variants in 60 risk genes and 56 proteins. The majority of molecular functions, interactions and pathways are involved in immune responses; several interactions and pathways are related to the metabolic and cardiovascular systems, which could lead to multi-organ complications and dysfunction. Conclusions This study highlights the importance of analyzing molecular interactions and pathways to understand the heterogeneous susceptibility of the host immune response to SARS-CoV-2. We propose new insights into pathogenicity analysis of infections by including genetic risk information as essential factors to predict future complications during and after infection. This approach may assist more precise clinical decisions and accurate treatment plans to reduce COVID-19 complications.

While the doubly fed induction generator (DFIG) is widely applied in the new power system, its decoupled control and fluctuation of output power have led to serious problems in frequency stability. Inertia response control (IRC) of DFIG has attracted significant attention, while its impact has not been clearly researched. In this study, a low-order model of DFIG focusing on frequency stability is proposed. Firstly, the simplified model of DFIG considering the IRC is introduced through Taylor series expansion. The frequency response model considering the energy storage system (ESS) is proposed. Taking the rate of change of frequency (RoCoF) and steady-state frequency deviation (SFD) as the constraint, an analytical solution for the maximum proportion of DFIGs is deduced to reflect the impact of IRC on the system frequency stability. Finally, the accuracy of the proposed method and the effectiveness of IRC is verified on the modified IEEE-9 system.

Abstract The aim of this study was to quantitatively analyze the mechanical change of spinal segments (disc, muscle, and ligament) at various postures under microgravity using a full-body musculoskeletal modeling approach. Specifically, in the lumbar spine, the vertebra were modeled as rigid bodies, the intervertebral discs were modeled as 6-degree-of-freedom joints with linear force-deformation relationships, the disc swelling pressure was deformation dependent, the ligaments were modeled as piecewise linear elastic materials, the muscle strength was dependent on its functional cross-sectional area. The neutral posture and the “fetal tuck” posture in microgravity (short as “Neutral 0G” and “Fetal Tuck 0G”, in our simulation, the G constant was set to 0 for simulating microgravity), and for comparison, the relaxed standing posture in 1G and 0G gravity (short as “Neutral 1G” and “Standing 0G”) were simulated. Compared to values at Neutral 1G, the mechanical response in the lower spine changed significantly at Neutral 0G. For example, the compressive forces on lumbar discs decreased 62–70%, the muscle forces decreased 55.7–92.9%, while disc water content increased 7.0–10.2%, disc height increased 2.1–3.0%, disc volume increased 6.4–9.3%, and ligament forces increased 59.5–271.3% at Neutral 0G. The fetal tuck 0G reversed these changes at Neutral 0G back toward values at Neutral 1G, with magnitudes much larger than those at Neutral 1G. Our results suggest that microgravity has significant influences on spinal biomechanics, alteration of which may increase the risks of disc herniation and degeneration, muscle atrophy, and/or ligament failure.

Starch is a vital component of wheat grain and flour, characterized by two distinct granule types: A-type starch (AS) with granules larger than 10 µm in diameter, and B-type starch (BS) with granules measuring no more than 10 µm in diameter. This review comprehensively evaluates the isolation, purification, and biosynthesis processes of these types of granules. In addition, a comparative analysis of the structure and properties of AS and BS is presented, encompassing chemical composition, molecular, crystalline and morphological structures, gelatinization, pasting and digestive properties. The variation in size distribution of granules leads to differences in physicochemical properties of starch, influencing the formation of polymeric proteins, secondary and micro-structures of gluten, chemical and physical interactions between gluten and starch, and water absorption and water status in dough system. Thus, starch size distribution affects the quality of dough and final products. In this review, we summarize the up-to-date knowledge of AS and BS, and propose the possible strategies to enhance wheat yield and quality through coordinated breeding efforts. This review serves as a valuable reference for future advancements in wheat breeding.

Cyber-physical-social systems (CPSS) have recently gained attention from researchers due to their combination of cyber, physical, and social spaces. Modeling and Analysis of Real-Time and Embedded systems (MARTE) is a Unified Modeling Language (UML) extension profile that supports the specification, design, and verification of Real-Time Embedded Systems (RTES). While MARTE Statecharts can assist in describing CPS, it does not model the uncertainty within a CPSS environment. To enhance the accuracy of CPSS analysis, we propose the stohMCharts (stochastic hybrid MARTE statecharts) modelling framework as an extension of MARTE statecharts for modelling and analyzing stochastic hybrid systems. stohMCharts can model CPSS in a unified manner. Additionally, based on the mapping rules and algorithms, we have developed a tool to convert models built in stohMChart language into Networks Stochastic Hybrid Automata (NSHA) which can be verified by statistical model checker UPPAAL-SMC. We demonstrate the efficiency and accuracy of the framework by applying it to one autonomous driving scenarios.

Abstract Background This study aimed to assess changes in quality of sleep (QoS) in isolated metastatic patients with spinal cord compression following two different surgical treatments and identify potential contributing factors associated with QoS improvement. Methods We reviewed 49 patients with isolated spinal metastasis at our spinal tumor center between December 2017 and May 2021. Total en bloc spondylectomy (TES) and palliative surgery with postoperative stereotactic radiosurgery (PSRS) were performed on 26 and 23 patients, respectively. We employed univariate and multivariate analyses to identify the potential prognostic factors affecting QoS. Results The total Pittsburgh Sleep Quality Index (PSQI) score improved significantly 6 months after surgery. Univariate analysis indicated that age, pain worsening at night, decrease in visual analog scale (VAS), increase in Eastern Cooperative Oncology Group performance score (ECOG-PS), artificial implant in focus, and decrease in epidural spinal cord compression (ESCC) scale values were potential contributing factors for QoS. Multivariate analysis indicated that the ESCC scale score decreased as an independent prognostic factor. Conclusions Patients with spinal cord compression caused by the metastatic disease had significantly improved QoS after TES and PSRS treatment. Moreover, a decrease in ESCC scale value of > 1 was identified as a favorable contributing factor associated with PSQI improvement. In addition, TES and PSRS can prevent recurrence by achieving efficient local tumor control to improve indirect sleep. Accordingly, timely and effective surgical decompression and recurrence control are critical for improving sleep quality.

Abstract Background Pyroptosis is a unique inflammatory‐related cell death process, and inflammation is considered to be a key factor in hepatocellular carcinoma (HCC). However, the pyroptosis landscape in HCC has not been thoroughly studied. Methods Clinical data and RNA sequencing data of HCC patients were collected from The Cancer Genome Atlas database, and differentially expressed genes (DEGs) associated with pyroptosis were discovered. The relationship between DEGs and prognosis was studied. Using The Cancer Genome Atlas cohort, a least absolute shrinkage and selection operator regression model was built on the basis of pyroptosis‐related DEGs, which was then verified by the Gene Expression Omnibus (GEO) cohort. Functional enrichment analysis and immunological states were also studied between distinct risk subgroups. Finally, the potential tumor suppressive function of NLRP6 in HCC was analyzed. Results In total, 26 pyroptosis‐related DEGs were identified. Consensus clustering results showed patients with high levels of pyroptosis were associated with higher tumor stage, grade, and poor prognosis. The least absolute shrinkage and selection operator risk model was built using six genes linked with prognosis (GSDMC, GSDME, NOD2, NLRP6, CASP8, and SCAF11). Risk score was an independent risk factor that suggested shortened overall survival in both the training cohort (HR: 3.52, 95% CI: 1.351–9.193) and validation cohort (HR: 3.31, 95% CI: 1.435–7.617). Meanwhile, the low‐risk population had higher immunological activity. We also found a novel potential tumor suppressor gene NLRP6, which may negatively regulate the E2F and MYC pathways and be associated with higher immune cell infiltration levels, which lead to better prognosis. Conclusions This study revealed the pyroptosis landscape of HCC and provided a promising clinical prognosis evaluation model. Additionally, some new targets related to prognosis such as NLRP6 are proposed for further study.

Abstract Glioblastoma multiforme (GBM) is the most lethal primary brain tumor with a poor median survival of less than 15 months. However, clinical strategies and effective therapies are limited. Here, we found that the second-generation small molecule multi-CDK inhibitor AT7519 is a potential drug for GBM treatment according to high-throughput screening via the Approved Drug Library and Clinical Compound Library (2718 compounds). We found that AT7519 significantly inhibited the cell viability and proliferation of U87MG, U251, and patient-derived primary GBM cells in a dose-dependent manner. Furthermore, AT7519 also inhibited the phosphorylation of CDK1/2 and arrested the cell cycle at the G1-S and G2-M phases. More importantly, AT7519 induced intrinsic apoptosis and pyroptosis via caspase-3-mediated cleavage of gasdermin E (GSDME). In the glioblastoma intracranial and subcutaneous xenograft assays, tumor volume was significantly reduced after treatment with AT7519. In summary, AT7519 induces cell death through multiple pathways and inhibits glioblastoma growth, indicating that AT7519 is a potential chemical available for GBM treatment.

Purpose – This paper studies the political economy of the endogenous urban–rural divide in two dimensions: labor market and provision of public goods. Design/methodology/approach – This paper gives a dual-sector model endogenously depending on the consumption of public goods (club goods), the number of rural–urban migrants and the tax rate (transfer payments). Findings – According to the research findings in this paper, the constraints on the participation of rural residents portray the rural residents' bargaining power, and in the game between the urban elites and the rural residents, tax rates depend on the preferences of the urban elites and the constraints urban elites and the rural residents jointly face. Therefore, the urban elites have to set tax rates deviating from the most preferred ones. The model in this paper can explain a series of empirical findings and yield new theoretical findings for empirical testing. Originality/value – Significantly, the paper finds that the increase in agricultural productivity will lead to industrialization, accompanied by the disintegration of the dual-sector model. However, though the increase in industrial productivity can accelerate industrialization, it will further expand the urban–rural divide.

The North China Craton (NCC) is one of the oldest cratons on earth. Several important tectonic transformations of Mesozoic-Cenozoic tectonic regime led to the destruction of the North China craton. The knowledge of crustal structure can provide important constraints for the formation and evolution of cratons. New maps of sediment thickness, crustal thickness (H) and vP/vS (κ) in the central and western NCC were obtained using sequential H-κ stacking. P-wave receiver functions are calculated using teleseismic waveform data recorded by 405 stations from ChinArray project. Benefiting from the densely distribution of temporary seismic stations, our results reveal details of the crustal structure in the study area. The thickness of sedimentary layer in North China ranges from 0–6.4 km, and the thickest sedimentary layer is in Ordos block and its surroundings (about 2.8–6 km); The thickness of sedimentary layer in the Mongolia fold belt and Yinshan orogenic belt is relatively thin (less than 1 km). The crustal thickness of the study area varies between 27–48 km, of which the crust of the North China Plain is about 30–33 km, the central NCC is about 33–40 km, and the Ordos block is 40–48 km thick. The average vP/vS ratios in the study area is mostly between 1.66 and 1.90, and that in the Yanshan-Taihang mountain fold belt is between 1.70 and 1.85, and that in the Ordos block is between 1.65 and 1.90, with an average value of 1.77, indicating the absence of a thick basaltic lower crust. The obvious negative correlation between crustal thickness and average vP/vS ratio within Ordos and Central Asia orogenic belt may be related to magmatic underplating during the crustal formation. There is no significant correlation between the crustal thickness and the vP/vS ratio in the Lüliang-Taihang mountain fold belt, which may be related to the multiple geological processes such as underplating and crustal extension and thinning in this area. The lack of correlation between crust thickness and topography in the central orogenic belt and the North China Basin indicates the topography of these areas are controlled not only by crustal isostatic adjustment but also by the lithospheric mantle processes.

The most widely applied doubly-fed induction generator (DFIG) leads to insufficient frequency regulation ability of power system. The virtual inertia control can adjust the active power of DFIG according to frequency fluctuation. However, the frequent changes in power flow and operating point bring great challenges to traditional virtual inertia controller with fixed parameters. Direct Heuristic Dynamic Programming (Direct HDP) based control parameter regulator is proposed in this paper to obtain the optimal control parameters when the operation condition changes. Compared with the initial parameters, the response has been significantly improved after adaptive optimization by the proposed method when the system operating point changes.

The role of enhancer de-regulation in anti-tumor immunity remains to be explored. Here, the authors suggest that ablation of MLL3 and MLL4, two enhancer-associated H3K4 monomethyltransferases, increases tumor immunogenicity and promotes anti-tumor T cell response.

Abstract Neoplasms originating from thymic T-cell progenitors and post-thymic mature T-cell subsets account for a minority of lymphoproliferative neoplasms. These T-cell derived neoplasms, while molecularly and genetically heterogeneous, exploit transcription factors and signaling pathways that are critically important in normal T-cell biology, including those implicated in antigen-, costimulatory-, and cytokine-receptor signaling. The transcription factor GATA-3 regulates the growth and proliferation of both immature and mature T cells and has recently been implicated in T-cell neoplasms, including the most common mature T-cell lymphoma observed in much of the Western world. Here we show that GATA-3 is a proto-oncogene across the spectrum of T-cell neoplasms, including those derived from T-cell progenitors and their mature progeny, and further define the transcriptional programs that are GATA-3 dependent, which include therapeutically targetable gene products. The discovery that p300-dependent acetylation regulates GATA-3 mediated transcription by attenuating DNA binding has novel therapeutic implications. As most patients afflicted with GATA-3 driven T-cell neoplasms will succumb to their disease within a few years of diagnosis, these findings suggest opportunities to improve outcomes for these patients.

The accurate annotation of transcription start sites (TSSs) and their usage are critical for the mechanistic understanding of gene regulation in different biological contexts. To fulfill this, specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner, and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic sequences. Most of these computational tools cast the problem as a binary classification task on a balanced dataset, thus resulting in drastic false positive predictions when applied on the genome scale. Here, we present DeeReCT-TSS, a deep learning-based method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing data. We show that by effectively incorporating these two sources of information, DeeReCT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell types. Furthermore, we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types, which enables the identification of cell type-specific TSSs. Finally, we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin states. The source code for DeeReCT-TSS is available at https://github.com/JoshuaChou2018/DeeReCT-TSS_release and https://ngdc.cncb.ac.cn/biocode/tools/BT007316.

Integrative analyses of single-cell datasets are facing new challenges as data size and complexity grow. Here the authors present SCALEX, which projects cells from different datasets into a common latent space, allowing accurate online integration as well as cross-referencing with atlas-scale data.

Abstract Background Circulating levels of amino acids were associated with blood pressure (BP) in observational studies. However, the causation of such associations has been hypothesized but is difficult to prove in human studies. Here, we aimed to use two-sample Mendelian randomization analyses to evaluate the potential causal associations of circulating levels of amino acids with BP and risk of hypertension. Methods We generated genetic instruments for circulating levels of nine amino acids by conducting meta-analyses of genome-wide association study (GWAS) in UK Biobank participants with metabolomic data (n = 98,317) and another published metabolomics GWAS (n = 24,925). Data on the associations of the genetic variants with BP and hypertension were obtained in the UK Biobank participants without metabolomic data (n = 286,390). The causal effects were estimated using inverse-variance weighted method. Results Significant evidence consistently supported the causal effects of increased branched-chain amino acids (BCAAs, i.e., leucine, isoleucine, and valine) levels on higher BP and risk of hypertension (all P

With the widespread adoption of flexible stealth materials (FSMs) in radar stealth application, understanding their electromagnetic scattering information has become increasingly crucial. However, acquiring the electromagnetic scattering properties of FSMs solely through measurement can be challenging. Hence, this study proposes an electromagnetic scattering model for FSMs based on random undulating units formed by sweeping two parabola functions. Specifically, the multi-layer fast multipole method (MLFMM) is applied to calculate the model’s monostatic RCS within 8–12 GHz, enabling a comprehensive analysis of FSM’s electromagnetic scattering characteristics (ESCs). We fabricate an FSM sample for experimental validation and conduct meticulous measurements alongside theoretical calculations. Impressively, the calculated results exhibit relatively good agreement with the measured data, signifying our model’s reliability. Furthermore, by leveraging the proposed model and the MLFMM, we delve into the influence of various electromagnetic parameters, the undulating angle, and the size of undulation units on the ESCs of FSM. Our numerical results demonstrate that the ESCs of FSM exhibit regular variations in response to the changes in these parameters. In addition, we meticulously examine the interaction between electromagnetic waves and the material, further enriching our understanding of the observed results. This research provides valuable references for ESC investigation of flexible stealth material and radar stealth experimental design.

BackgroundThyroid nodules (TN) are localized morphological changes in the thyroid gland and can be benign or malignant.ObjectiveThe present study investigates the relationships between biochemical markers in serum (s) and their homologs in washout (w) after fine-needle aspiration biopsy (FNAB) of the TN of interest and their correlation with cytology specimen findings.MethodsWe investigated the relationships between serum biochemical markers nitric oxide (NO), thyroglobulin (TG), and calcitonin (CT), their homologs in washout after FNAB of the TN of interest, and cytology findings of biopsy samples classified according to the Bethesda system for thyroid cytopathology in this study, which included 86 subjects.ResultsWashout TG (TGw) level positively correlates with the cytology finding of the biopsy. A higher level of TGw correlates with higher categories of the Bethesda classification and indicates a higher malignant potential. The levels of serum NO (NOs), serum TG (TGs), serum CT (CTs), and washout CT (CTw) do not correlate with the cytology finding of the biopsy, and the higher levels of washout NO (NOw) correspond to the more suspicious ultrasound findings.ConclusionThe findings of our study suggest that TGw and NOw could be used as potential predictors of malignancy in TN.

Dental mesenchymal stem cells (DMSCs) are multipotent progenitor cells that can differentiate into multiple lineages including odontoblasts, osteoblasts, chondrocytes, neural cells, myocytes, cardiomyocytes, adipocytes, endothelial cells, melanocytes, and hepatocytes. Odontoblastic differentiation of DMSCs is pivotal in dentinogenesis, a delicate and dynamic process regulated at the molecular level by signaling pathways, transcription factors, and posttranscriptional and epigenetic regulation. Mutations or dysregulation of related genes may contribute to genetic diseases with dentin defects caused by impaired odontoblastic differentiation, including tricho-dento-osseous (TDO) syndrome, X-linked hypophosphatemic rickets (XLH), Raine syndrome (RS), hypophosphatasia (HPP), Schimke immuno-osseous dysplasia (SIOD), and Elsahy-Waters syndrome (EWS). Herein, recent progress in the molecular regulation of the odontoblastic differentiation of DMSCs is summarized. In addition, genetic syndromes associated with disorders of odontoblastic differentiation of DMSCs are discussed. An improved understanding of the molecular regulation and related genetic syndromes may help clinicians better understand the etiology and pathogenesis of dentin lesions in systematic diseases and identify novel treatment targets.

Abstract Background Williams syndrome is an autosomal dominant multisystem disorder caused by a 1.5–1.8 Mb deletion on chromosome 7q11.23. It is characterized by facial deformations, cardiovascular abnormalities, developmental delays, gastrointestinal manifestations, and endocrine disorders. Case description A 1‐year‐old child presenting with developmental delays, special facial features, gastrointestinal bleeding, renal calcium deposition, and hypotonia was admitted to the hospital for “hypercalcemia and gastrointestinal bleeding.” Genetic testing showed a deletion mutation in the 7q11.23 region. Currently, the child receiving treatment to promote calcium excretion and rehabilitation training, but hypercalcemia has recurred. Conclusion The clinical phenotype of Williams syndrome is complex, and different severities, characterized by developmental delays, facial deformities, cardiovascular abnormalities, gastrointestinal symptoms and endocrine disorders, should be considered in children. The syndrome may require thorough genetic testing for diagnosis and early intervention treatment to improve patient quality of life.

Senescence is an inevitable biological process. Disturbances in glucose and lipid metabolism are essential features of cellular senescence. Given the important roles of these types of metabolism, we review the evidence for how key metabolic enzymes influence senescence and how senescence-related secretory phenotypes, autophagy, apoptosis, insulin signaling pathways, and environmental factors modulate glucose and lipid homeostasis. We also discuss the metabolic alterations in abnormal senescence diseases and anti-cancer therapies that target senescence through metabolic interventions. Our work offers insights for developing pharmacological strategies to combat senescence and cancer.

Abstract 18F‐Fluorodeoxyglucose positron emission tomography (18F‐FDG PET) is widely employed to reveal metabolic abnormalities linked to Parkinson's disease (PD) at a systemic level. However, the individual metabolic connectome details with PD based on 18F‐FDG PET remain largely unknown. To alleviate this issue, we derived a novel brain network estimation method for individual metabolic connectome, that is, Jensen‐Shannon Divergence Similarity Estimation (JSSE). Further, intergroup difference between the individual's metabolic brain network and its global/local graph metrics was analyzed to investigate the metabolic connectome's alterations. To further improve the PD diagnosis performance, multiple kernel support vector machine (MKSVM) is conducted for identifying PD from normal control (NC), which combines both topological metrics and connection. Resultantly, PD individuals showed higher nodal topological properties (including assortativity, modularity score, and characteristic path length) than NC individuals, whereas global efficiency and synchronization were lower. Moreover, 45 most significant connections were affected. Further, consensus connections in occipital, parietal, and frontal regions were decrease in PD while increase in subcortical, temporal, and prefrontal regions. The abnormal metabolic network measurements depicted an ideal classification in identifying PD of NC with an accuracy up to 91.84%. The JSSE method identified the individual‐level metabolic connectome of 18F‐FDG PET, providing more dimensional and systematic mechanism insights for PD.

Carbon fiber epoxy resin (CFRP) is gradually used as the space satellite antenna material, thermal control coatings (TCCs) are prepared on the surface to ensure the stable operation. However, the traditional TCCs have no wave transmission capability. The hollow glass microspheres (HGMs) were used to prepare the coating in our work, and ZrO2 was coated on its surface (Zr-HGM) to improve the solar reflectivity. The Zr-HGM powders were obtained by a homogeneous precipitation method with an appropriate reaction time of 4 h. By investigating the optical and dielectric properties, we found that the solar absorption of the coating with Zr-HGM was reduced by half compared to the coating with the original HGM. The microwave transmittances were all above 96.5% from 2 to 18 GHz. Our findings have proposed a preparation method for a new CFRP-based TCC, which will ensure the stable operation of the satellite antenna.

ObjectivesTo screen out the predictors and establish a prediction model of positive fine needle aspiration biopsy (FNAB) in the Chinese Guidelines for Malignant Risk Stratification of Thyroid Nodule Ultrasound (C-TIRADS) 4 thyroid nodules, and this nomogram can help clinicians evaluate the risk of positive FNAB and determine if FNAB is necessary.MethodsWe retrospectively analyzed data from 547 patients who had C-TIRADS 4 thyroid nodules and underwent fine-needle aspiration biopsy (FNAB) at the Second Affiliated Hospital of Chongqing Medical University between November 30, 2021 and September 5, 2022. Patients who met our inclusion criteria were divided into two groups based on positive or negative FNAB results. We compared their ultrasound (US) features, BRAF V600E status, thyroid function, and other general characteristics using univariate and multivariate logistic regression analyses to identify independent predictors. These predictors were then used to construct a nomogram. The calibration plot, area under the curve (AUC), and decision curve analysis were employed to evaluate the calibration, discrimination, and clinical utility of the prediction model.ResultsOut of 547 patients, 39.3% (215/547) had a positive result on fine-needle aspiration biopsy (FNAB), while 60.7% (332/547) had a negative result. Univariate logistic regression analysis revealed no significant differences in TPOAb, TgAb, TSH, Tg, nodule location, sex, or solid status between the two groups (P>0.05). However, age, nodule size, internal or surrounding blood flow signal, microcalcifications, aspect ratio, morphology, and low echo showed significant differences (P1) and morphology (irregular), were identified. A nomogram was established based on the above 5 predictors, and the nomogram can be used as a complementary basis to help clinicians make decisions on FNAB of C-TI-RADS 4 thyroid nodules.

After the metabolic syndrome and its components, thyroid disorders represent the most common endocrine disorders, with increasing prevalence in the last two decades. Thyroid dysfunctions are distinguished by hyperthyroidism, hypothyroidism, or inflammation (thyroiditis) of the thyroid gland, in addition to the presence of thyroid nodules that can be benign or malignant. Thyroid cancer is typically detected via an ultrasound (US)-guided fine-needle aspiration biopsy (FNAB) and cytological examination of the specimen. This approach has significant limitations due to the small sample size and inability to characterize follicular lesions adequately. Due to the rapid advancement of high-throughput molecular biology techniques, it is now possible to identify new biomarkers for thyroid neoplasms that can supplement traditional imaging modalities in postoperative surveillance and aid in the preoperative cytology examination of indeterminate or follicular lesions. Here, we review current knowledge regarding biomarkers that have been reliable in detecting thyroid neoplasms, making them valuable tools for assessing the efficacy of surgical procedures or adjunctive treatment after surgery. We are particularly interested in providing an up-to-date and systematic review of emerging biomarkers, such as mRNA and non-coding RNAs, that can potentially detect thyroid neoplasms in clinical settings. We discuss evidence for miRNA, lncRNA and circRNA dysregulation in several thyroid neoplasms and assess their potential for use as diagnostic and prognostic biomarkers.

Introduction: Clear cell renal cell carcinoma (ccRCC) is associated with unfavorable clinical outcomes. To identify viable therapeutic targets, a comprehensive understanding of intratumoral heterogeneity is crucial. In this study, we conducted bioinformatic analysis to scrutinize single-cell RNA sequencing data of ccRCC tumor and para-tumor samples, aiming to elucidate the intratumoral heterogeneity in the ccRCC tumor microenvironment (TME).Methods: A total of 51,780 single cells from seven ccRCC tumors and five para-tumor samples were identified and grouped into 11 cell lineages using bioinformatic analysis. These lineages included tumor cells, myeloid cells, T-cells, fibroblasts, and endothelial cells, indicating a high degree of heterogeneity in the TME. Copy number variation (CNV) analysis was performed to compare CNV frequencies between tumor and normal cells. The myeloid cell population was further re-clustered into three major subgroups: monocytes, macrophages, and dendritic cells. Differential expression analysis, gene ontology, and gene set enrichment analysis were employed to assess inter-cluster and intra-cluster functional heterogeneity within the ccRCC TME.Results: Our findings revealed that immune cells in the TME predominantly adopted an inflammatory suppression state, promoting tumor cell growth and immune evasion. Additionally, tumor cells exhibited higher CNV frequencies compared to normal cells. The myeloid cell subgroups demonstrated distinct functional properties, with monocytes, macrophages, and dendritic cells displaying diverse roles in the TME. Certain immune cells exhibited pro-tumor and immunosuppressive effects, while others demonstrated antitumor and immunostimulatory properties.Conclusion: This study contributes to the understanding of intratumoral heterogeneity in the ccRCC TME and provides potential therapeutic targets for ccRCC treatment. The findings emphasize the importance of considering the diverse functional roles of immune cells in the TME for effective therapeutic interventions.

BackgroundPhotodynamic therapy (PDT) promotes significant tumor regression and extends the lifetime of patients. The actual operation of PDT often relies on the subjective judgment of experienced neurosurgeons. Patients can benefit more from precisely targeting PDT’s key operating zones.MethodsWe used magnetic resonance imaging scans and created 3D digital models of patient anatomy. Multiple images are aligned and merged in STL format. Neurosurgeons use HoloLens to import reconstructions and assist in PDT execution. Also, immunohistochemistry was used to explore the association of hyperperfusion sites in PDT of glioma with patient survival.ResultsWe constructed satisfactory 3D visualization of glioma models and accurately localized the hyperperfused areas of the tumor. Tumor tissue taken in these areas was rich in CD31, VEGFA and EGFR that were associated with poor prognosis in glioma patients. We report the first study using MR technology combined with PDT in the treatment of glioma. Based on this model, neurosurgeons can focus PDT on the hyperperfused area of the glioma. A direct benefit was expected for the patients in this treatment.ConclusionUsing the Mixed Reality technique combines multimodal imaging signatures to adjuvant glioma PDT can better exploit the vascular sealing effect of PDT on glioma.

Parkinson's disease (PD) is the second most common neurodegenerative disease, with an increasing prevalence as the population ages, posing a serious threat to human health, but the pathogenesis remains uncertain. Acanthopanax senticosus (Rupr. et Maxim.) Harms (ASH) (aqueous ethanol extract), a Chinese herbal medicine, provides obvious and noticeable therapeutic effects on PD. To further investigate the ASH's mechanism of action in treating PD, the structural and functional gut microbiota, as well as intestinal metabolite before and after ASH intervention in the PD mice model, were examined utilizing metagenomics and fecal metabolomics analysis. α-syn transgenic mice were randomly divided into a model and ASH groups, with C57BL/6 mice as a control. The ASH group was gavaged with ASH (45.5 mg/kg/d for 20d). The time of pole climbing and autonomous activity were used to assess motor ability. The gut microbiota's structure, composition, and function were evaluated using Illumina sequencing. Fecal metabolites were identified using UHPLC-MS/MS to construct intestinal metabolites. The findings of this experiment demonstrate that ASH may reduce the climbing time of PD model mice while increasing the number of autonomous movements. The results of metagenomics analysis revealed that ASH could up-regulated Firmicutes and down-regulated Actinobacteria at the phylum level, while Clostridium was up-regulated and Akkermansia was down-regulated at the genus level; it could also recall 49 species from the phylum Firmicutes, Actinobacteria, and Tenericutes. Simultaneously, metabolomics analysis revealed that alpha-Linolenic acid metabolism might be a key metabolic pathway for ASH to impact in PD. Furthermore, metagenomics function analysis and metabolic pathway enrichment analysis revealed that ASH might influence unsaturated fatty acid synthesis and purine metabolism pathways. These metabolic pathways are connected to ALA, Palmitic acid, Adenine, and 16 species of Firmicutes, Actinobacteria, and Tenericutes. Finally, these results indicate that ASH may alleviate the movement disorder of the PD model, which may be connected to the regulation of gut microbiota structure and function as well as the modulation of metabolic disorders by ASH.

BackgroundCardiac resynchronization therapy (CRT) is a major device therapy used to treat patients suffering from heart failure (HF) and electrical asynchrony. It can improve HF symptoms, reduce HF hospitalization time, and improve long-term survival in HF with and without implantable cardioverter (ICD) therapy. However, the benefit of defibrillator therapy in CRT-eligible patients with nonischemic cardiomyopathy (NICM) remains unknown. As a result, we conducted a systematic review and meta-analysis to compare clinical outcomes in patients with NICM and HF who were treated with implantable CRT defibrillators (CRT-D) vs. a CRT pacemaker (CRT-P) alone.MethodsWe searched the electronic databases PubMed, Embase, and Cochrane for all studies comparing CRT-D vs. CRT-P treatment in patients with NICM. The time frame was from 1990 to September 2022. All-cause mortality and cardiovascular mortality were the primary clinical outcomes of interest to us. To pool adjusted hazard ratios (HRs) and 95% confidence intervals (CIs), a random-effects model with inverse variance was used.ResultsA pooled meta-analysis included two randomized controlled trials (RCTs), each with 1,200 CRT-eligible patients with NICM (592 with CRT-D and 608 with CRT-P) and nine cohort studies representing 27,568 CRT-eligible patients with NICM (16,196 with CRT-D and 11,372 with CRT-P). The adjusted HR for all-cause mortality for CRT-D vs. CRT-P was 0.90 (95% CI, 0.81-0.99). In a subgroup analysis of two RCTs and nine cohort studies, the adjusted HR for all-cause mortality was 0.72 (95% CI, 0.43–1.19) and HR 0.92 (95% CI, 0.83–1.03) for CRT-D vs. CRT-P, respectively.ConclusionWith the addition of defibrillation leads, we found a significantly lower risk of all-cause mortality in patients with NICM, but this association was not found in subgroup analyses of RCTs and observational studies.

Two coupled climate models that participated in the CMIP6 project (Coupled Model Intercomparison Project Phase 6), the Earth System Model of Chinese Academy of Sciences version 2 (CAS-ESM2-0), and the Nanjing University of Information Science and Technology Earth System Model version 3 (NESM3) were assessed in terms of the impact of four new sea ice parameterization schemes. These four new schemes are related to air–ice heat flux, radiation penetration and absorption, melt ponds, and ice–ocean flux, respectively. To evaluate the effectiveness of these schemes, key sea ice variables with and without these new schemes, such as sea ice concentration (SIC) and sea ice thickness (SIT), were compared against observation and reanalysis products from 1980 to 2014. The simulations followed the design of historical experiments within the CMIP6 framework. The results revealed that both models demonstrated improvements in simulating Arctic SIC and SIT when the new parameterization schemes were implemented. The model bias of SIC in some marginal sea ice zones of the Arctic was reduced, especially during March. The SIT was increased and the transpolar gradient of SIT was reproduced. The changes in spatial patterns of SIC and SIT after adding new schemes bear similarities between the two coupled models. This suggests that the new schemes have the potential for broad application in climate models for simulation and future climate scenario projection, especially for those with underestimated SIT.

To uncover the internal mechanisms of various drought stress intensities affecting the soluble sugar content in organs and its regulation by endogenous abscisic acid (ABA), we selected the saplings of Platycladus orientalis, a typical tree species in the Beijing area, as our research subject. We investigated the correlation between tree soluble sugars and endogenous ABA in the organs (comprised of leaf, branch, stem, coarse root, and fine root) under two water treatments. One water treatment was defined as T1, which stopped watering until the potted soil volumetric water content (SWC) reached the wilting coefficient and then rewatered the sapling. The other water treatment, named T2, replenished 95% of the total water loss of one potted sapling every day and irrigated the above-mentioned sapling after its SWC reached the wilt coefficients. The results revealed that (1) the photosynthetic physiological parameters of P. orientalis were significantly reduced (p < 0.05) under fast and slow drought processes. The photosynthetic physiological parameters of P. orientalis in the fast drought–rehydration treatment group recovered faster relative to the slow drought–rehydration treatment group. (2) The fast and slow drought treatments significantly (p < 0.05) increased the ABA and soluble sugar contents in all organs. The roots of the P. orientalis exhibited higher sensitivity in ABA and soluble sugar content to changes in soil moisture dynamics compared to other organs. (3) ABA and soluble sugar content of P. orientalis showed a significant positive correlation (p < 0.05) under fast and slow drought conditions. During the rehydration stage, the two were significantly correlated in the T2 treatment (p < 0.05). In summary, soil drought rhythms significantly affected the photosynthetic parameters, organ ABA, and soluble sugar content of P. orientalis. This study elucidates the adaptive mechanisms of P. orientalis plants to drought and rehydration under the above-mentioned two water drought treatments, offering theoretical insights for selecting and cultivating drought-tolerant tree species.

Synthetic aperture radar (SAR) ship detection and classification has gained unprecedented attention due to its important role in maritime transportation. Many deep learning-based detectors and classifiers have been successfully applied and achieved great progress. However, ships in SAR images present discrete and multi-centric features, and their scattering characteristics and edge information are sensitive to variations in target attitude angles (TAAs). These factors pose challenges for existing methods to obtain satisfactory results. To address these challenges, a novel target attitude angle-guided network (TAG-Net) is proposed in this article. The core idea of TAG-Net is to leverage TAA information as guidance and use an adaptive feature-level fusion strategy to dynamically learn more representative features that can handle the target imaging diversity caused by TAA. This is achieved through a TAA-aware feature modulation (TAFM) module. It uses the TAA information and foreground information as prior knowledge and establishes the relationship between the ship scattering characteristics and TAA information. This enables a reduction in the intra-class variability and highlights ship targets. Additionally, considering the different requirements of the detection and classification tasks for the scattering information, we propose a layer-wise attention-based task decoupling detection head (LATD). Unlike general deep learning methods that use shared features for both detection and classification tasks, LATD extracts multi-level features and uses layer attention to achieve feature decoupling and select the most suitable features for each task. Finally, we introduce a novel salient-enhanced feature balance module (SFB) to provide richer semantic information and capture the global context to highlight ships in complex scenes, effectively reducing the impact of background noise. A large-scale ship detection dataset (LSSDD+) is used to verify the effectiveness of TAG-Net, and our method achieves state-of-the-art performance.

Archery ranks among the sports with a high incidence of upper extremity injuries, particularly affecting the drawing shoulder and elbow, as well as inducing stress on the lower back. This study seeks to bridge the gap by integrating real-time human motion with biomechanical software to enhance the ergonomics of archers. Thirteen participants were involved in four tasks, using different bows with varied draw weights and shooting distances. Through the application of advanced integrative technology, this study highlights the distinct postures adopted by both males and females, which indicate the biomechanical differences between genders. Additionally, an analysis of the correlation between exposed spinal forces and these adopted postures provides insights into injury risk assessment during the key archery movements. The findings of this study have the potential to significantly enhance the application of training methodologies and the design of assistive devices. These improvements are geared towards mitigating injury risks and enhancing the overall performance of archers.

Fibroblast activation protein (FAP) is a serine protease characterized by its high expression in cancer-associated fibroblasts (CAFs) and near absence in adult normal tissues and benign lesions. This unique expression pattern positions FAP as a prospective biomarker for targeted tumor radiodiagnosis and therapy. The advent of FAP-based radiotheranostics is anticipated to revolutionize cancer management. Among various types of FAP ligands, peptides and antibodies have shown advantages over small molecules, exemplifying prolonged tumor retention in human volunteers. Within its scope, this review summarizes the recent research progress of the FAP radiopharmaceuticals based on antibodies and peptides in tumor imaging and therapy. Additionally, it incorporates insights from recent studies, providing valuable perspectives on the clinical utility of FAP-targeted radiopharmaceuticals.

Optimizing jamming strategies is crucial for enhancing the performance of cognitive jamming systems in dynamic electromagnetic environments. The emergence of frequency-agile radars, capable of changing the carrier frequency within or between pulses, poses significant challenges for the jammer to make intelligent decisions and adapt to the dynamic environment. This paper focuses on researching intelligent jamming decision-making algorithms for Intra-Pulse Frequency Agile Radar using deep reinforcement learning. Intra-Pulse Frequency Agile Radar achieves frequency agility at the sub-pulse level, creating a significant frequency agility space. This presents challenges for traditional jamming decision-making methods to rapidly learn its changing patterns through interactions. By employing Gated Recurrent Units (GRU) to capture long-term dependencies in sequence data, together with the attention mechanism, this paper proposes a GA-Dueling DQN (GRU-Attention-based Dueling Deep Q Network) method for jamming frequency selection. Simulation results indicate that the proposed method outperforms traditional Q-learning, DQN, and Dueling DQN methods in terms of jamming effectiveness. It exhibits the fastest convergence speed and reduced reliance on prior knowledge, highlighting its significant advantages in jamming the subpulse-level frequency-agile radar.

Addressing climate change by carbon management is critical to achieving the goal of net zero carbon emissions by 2050. In this work, we examined the electrochemically-driven recovery of CO _2 during alkaline solvent regeneration for solvent-based direct air capture. A mathematical model was developed by incorporating carbonate chemistry with water electrolysis to predict the energy consumption per unit of CO _2 released. The predicted results were consistent with the experimental data, in which the experimental work was achieved by characterizing alkalinity and carbon loading values of solvent collected from a flow carbonate electrolyzer. Through this study, we learned that minimizing the energy expended on CO _2 release can be achieved by using an anolyte with a lower alkalinity, increasing the electric charge input to the electrolyzer, and reducing the ohmic resistance of the electrolyzer. Furthermore, using a supporting electrolyte, e.g., Na _2 SO _4 in the present work, effectively compensates for the higher ohmic resistance from using an anolyte with a lower alkalinity.

Fu tea is receiving increasing attention for its specific aroma, flavor, and dramatic functional benefits. Herein, we explored the effects and underlying mechanisms of Fu loose tea (FLT), Fu brick tea (FBT), and diet pills (orlistat) on a high-fat diet (HFD)-induced obesity. The results indicated that FLT and FBT administration effectively inhibited weight gain, glucose metabolic dysregulation, fat accumulation in organs, hepatic and kidney injury, and oxidative stress induced by HFD. Additionally, FLT and FBT treatments improved the lipid profiles and reduced the production of proinflammatory cytokines by regulating the expression levels of lipid metabolism- and inflammation-related genes. Furthermore, FLT and FBT ameliorated the gut microbiota dysbiosis in HFD-mice in a dose-dependent relationship by increasing the abundance of family Verrucomicrobiaceae and genus Akkermansia and Turicibacter and simultaneously reducing the abundance of family Erysipelotrichaceae and genus Bifidobacterium; in contrast, orlistat did not exert a regulatory effect on gut microbiota similar to FLT and FBT to improve HFD-induced obesity. KEGG analysis of gut microbiota annotation revealed that “metabolism” was the most enriched category. This study further provides a theoretical basis for FLT and FBT to be potential supplements to alleviate diet-induced obesity.

In this study, all-cellulose nanocomposite (ACNC) was successfully prepared through a green and sustainable approach by using corn stalk as raw material, water as regeneration solvent, and recyclable two-component ionic liquid/DMSO as the solvent to dissolve cellulose. The morphology and structural properties of ACNC were determined by scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, and X-ray diffraction analysis, indicating homogeneity and good crystallinity. In addition, a comprehensive characterization of ACNC showed that CNF not only improved the thermal stability and mechanical characteristics of ACNC, but also significantly improved the oxygen barrier performance. The ACNC prepared in this work has a good appearance, smooth surface, and good optical transparency, which provides a potential application prospect for converting cellulose wastes such as corn straws into biodegradable packaging materials and electronic device encapsulation materials.

This paper presents a phase retrieval algorithm that incorporates sparsity priors into total variation and framelet regularization. The proposed algorithm exploits the sparsity priors in both the gradient domain and the spatial distribution domain to impose desirable characteristics on the reconstructed image. We utilize structured illuminated patterns in holography, consisting of three light fields. The theoretical and numerical analyses demonstrate that when the illumination pattern parameters are non-integers, the three diffracted data sets are sufficient for image restoration. The proposed model is solved using the alternating direction multiplier method. The numerical experiments confirm the theoretical findings of the lighting mode settings, and the algorithm effectively recovers the object from Gaussian and salt–pepper noise.

The lack of high-spectral and high-resolution remote sensing data is impeding the differentiation of various fruit tree species that share comparable spectral and spatial features, especially for evergreen broadleaf trees in tropical and subtropical areas. Here, we propose a novel decision tree approach to map the spatial distribution of fruit trees at a 10 m spatial resolution based on the growth stage features extracted from Sentinel-1A (S-1A) time-series synthetic aperture radar (SAR) data. This novel method was applied to map the spatial distribution of fruit trees in Maoming City, which is known for its vast cultivation of fruit trees, such as litchi, citrus, and longan. The results showed that the key to extracting information on the distribution of fruit trees lies in the fact that the fruit ripening and expansion period attenuates the information on the vegetation of fruit trees, a characteristic of the reproductive period. Under VH polarization, different fruit tree growth stage traits were more separable and easier to distinguish. The optimal features, such as Hv (high valley value of the 14 May, 26 May, and 7 June SAR data), Tb (difference between the 7 June and 14 January SAR data), Cr (high valley value of the 13 July, 25 July, and 6 August SAR data), and Lo (high valley value of the 23 September, 17 October, and 11 November SAR data), were constructed based on the optimal window. The thresholds for these features were set to 1, 1, 1.5, and 1, respectively. The classification model can effectively distinguish different fruit trees and extract distribution information with overall accuracy (OA) of 90.34% and a Kappa coefficient of 0.84. The proposed method extracts the spatial distribution information of different fruit trees more accurately and provides a reference for the extraction of more tropical and subtropical species.

The daylighting environment in university gymnasiums affects daily teaching and sports training. However, direct sunlight, glare, and indoor overheating in summer are common problems. Semi-transparent photovoltaic glass can solve these issues by replacing shading facilities, blocking solar radiation, and generating electricity. This study examines the influence of different types of CdTe semi-transparent film photovoltaic glass on the daylighting environment of six typical university gymnasium skylights. The optimal types of CdTe semi-transparent film photovoltaic glass are determined by dynamic daylighting performance metrics DA, DAcon, DAmax, and UDI. The results show that, for instance, centralized rectangular skylights benefit from the 50–60% transmittance type, while centralized X-shaped skylights require the 70–80% transmittance type to enhance indoor daylighting. The research results offer specific recommendations based on skylight shapes and photovoltaic glass types and can provide a reference for the daylighting design of university gymnasium buildings with different forms of photovoltaic skylights in the future.

The ground surface temperature (GST) serves as a crucial indicator for understanding land-atmosphere mass and energy exchange. The shift from manual measurement to automated station for GST in China after 2002 introduced inconsistencies at certain stations, potentially distorting research findings. Here, daily automatedly observed GST from 2003 to 2017 at 615 selected meteorological stations were updated by constructing linear regression model based on manually observed air temperature (AT) and GST from 1960 to 2002. Then, the spatiotemporal variations of GST from 1960 to 2017 and its driving factors were investigated. Results indicated that: (1) the AT-GST linear regression model could effectively mitigate the inconsistency caused by the change of GST observation methods, enhancing data reliability. (2) GST in China showed little change from 1960–1980, but increased significantly across all regions from 1980 to 2000, with the increase rate slowed down except in the Qinghai–Tibet plateau (QTP) and southwest China after 2000. Notable GST increase is concentrated in colder regions, including the QTP, northeast (NEC), and northwest China (NWC). (3) Evapotranspiration (ET) and vapor pressure deficit were the primary drivers of annual GST variations at the regional scale, while their contributions to GST variations exhibited notable seasonal variability. Our findings could offer valuable scientific insights for addressing climate change, enhancing surface environmental models, and safeguarding ecological environments.