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Abstract Alcohol use disorder (AUD) is a profound psychiatric condition marked by disrupted connectivity among distributed brain regions, indicating impaired functional integration. Previous connectome studies utilizing functional magnetic resonance imaging (fMRI) have predominantly focused on undirected functional connectivity, while the specific alterations in directed effective connectivity (EC) associated with AUD remain unclear. To address this issue, this study utilized multivariate pattern analysis (MVPA) and spectral dynamic causal modeling (DCM). We recruited 32 abstinent men with AUD and 30 healthy controls (HCs) men, and collected their resting-state fMRI data. A regional homogeneity (ReHo)-based MVPA method was employed to classify AUD and HC groups, as well as predict the severity of addiction in AUD individuals. The most informative brain regions identified by the MVPA were further investigated using spectral DCM. Our results indicated that the ReHo-based support vector classification (SVC) exhibits the highest accuracy in distinguishing individuals with AUD from HCs (classification accuracy: 98.57%). Additionally, our results demonstrated that ReHo-based support vector regression (SVR) could be utilized to predict the addiction severity (alcohol use disorders identification test, AUDIT, R 2 = 0.38; Michigan alcoholism screening test, MAST, R 2 = 0.29) of patients with AUD. The most informative brain regions for the prediction include left pre-SMA, right dACC, right LOFC, right putamen, and right NACC. These findings were validated in an independent data set (35 patients with AUD and 36 HCs, Classification accuracy: 91.67%; AUDIT, R 2 = 0.17; MAST, R 2 = 0.20). The results of spectral DCM analysis indicated that individuals with AUD exhibited decreased EC from the left pre-SMA to the right putamen, from the right dACC to the right putamen, and from the right LOFC to the right NACC compared to HCs. Moreover, the EC strength from the right NACC to left pre-SMA and from the right dACC to right putamen mediated the relationship between addiction severity (MAST scores) and behavioral measures (impulsive and compulsive scores). These findings provide crucial evidence for the underlying mechanism of impaired self-control, risk assessment, and impulsive and compulsive alcohol consumption in individuals with AUD, providing novel causal insights into both diagnosis and treatment.

Abstract Insole blanking production technology plays a vital role in contemporary machining and manufacturing industries. Existing insole blanking production models have limitations because most robots are required to accurately position the workpiece to a predetermined location, and special auxiliary equipment is usually required to ensure the precise positioning of the robot. In this paper, we present an adaptive blanking robotic system for different lighting environments, which consists of an industrial robot arm, an RGB-D camera configuration, and a customized insole blanking table and mold. We introduce an innovative edge detection framework that utilizes color features and morphological parameters optimized through particle swarm optimization (PSO) techniques to Adaptive recognition of insole edge contours. A path planning framework based on FSPS-BIT* is also introduced, which integrates the BIT* algorithm with the FSPS algorithm for efficient path planning of the robotic arm.

Context Achyranthes bidentata Blume (ABB), a plant of Amaranthaceae family, has been one of the more commonly used phytomedicine remedies for thousands of years, and recent studies have highlighted the efficacy of its extracts in the treatment of osteoporosis. Nonetheless, a thorough analysis of its benefits is currently absent.Objective This meta-analysis assessed the effects of ABB root extract (ABBRE) on osteoporotic rats and provides a rationale for future clinical studies.Methods Searches were conducted in seven different Chinese and English databases, and the search period was from their establishment to January 2024. This study was registered in PROSPERO (CRD42023418917). Selected research regarding the ABBRE treatment of osteoporotic rats, and the corresponding research has distinctly reported outcomes, and the data on the bone mineral density (BMD), bone histomorphometrics, biomechanical parameters, and bone biochemical markers of osteoporotic rats can be extracted.Results Through screening, 11 studies met the eligibility requirements for inclusion, in which 222 animals were studied. The treatment group with ABBRE exhibited increased bone mineral density (standardized mean difference [SMD] = 1.64, 95% CI = 0.52 to 2.77). Based on subgroup analysis, the greatest increase in bone mineral density was observed when the dose of ABBRE was ≤ 400 mg/kg/day and the duration of treatment was ≤ 12 weeks.Conclusions ABBRE is a phytomedicine that can effectively promote the enhancement of bone mineral density and ease osteoporosis. It can be developed into a new alternative therapy by conducting experiments and clinical studies on larger samples.

Abstract Long-term exposure to hyperglycemic conditions leads to β-cell dysfunction, particularly mitochondrial dysfunction, and inflammatory and oxidative stress responses, which are considered the primary causes of β-cell death and the hallmarks of diabetes. Plant-active ingredients may play a key role in glycemic control. Epigallocatechin gallate (EGCG) is a characteristic catechin derived from tea that possesses anti-diabetic properties. Nonetheless, its underlying mechanisms remain elusive. Herein, the protective role of EGCG on high glucose (33 mM)-induced pancreatic beta cell dysfunction and its possible molecular mechanisms were investigated. Briefly, MIN6 cells were treated with glucose and EGCG (10 µM, 20 µM, and 40 µM) for 48 h. Our results revealed that EGCG dose-dependently restored mitochondrial membrane potential and concomitantly alleviated cell apoptosis. Mechanistically, the expression level of apoptotic protein BAX and Dynamic related protein 1 (DRP1) was significantly downregulated following EGCG treatment, whereas that of the anti-apoptotic protein BCL-2 was significantly upregulated. Taken together, EGCG alleviated high glucose-induced pancreatic beta cell dysfunction by targeting the DRP1-related mitochondrial apoptosis pathway and thus can serve as a nutritional intervention for the preservation of beta cell dysfunction in patients with type 2 diabetes mellitus.

Optic neuritis (ON) is a common cause of acute visual loss due to optic neuropathy in adults. Although typical demyelinating ON usually recovers, some forms of autoimmune inflammatory ON may result in permanent disability. The relatively recent discovery of novel autoantibodies and their roles in the pathogenesis of ON, including aquaporin 4 (AQP4) in neuromyelitis optica spectrum disorders (NMOSD) and myelin oligodendrocyte glycoprotein (MOG) antibody in MOG-associated disease (MOGAD) has led to paradigm shifts in both the acute and chronic management of ON. We performed a comprehensive literature review to assess current and upcoming perspectives on ON by searching for contemporary articles (from 2015 to 2024) for articles that reported updates in diagnostic tools and methods, such as antibody testing. This monograph reviews these recent advances in the evaluation and management of ON.

Abstract Background Inflammation and endothelial barrier dysfunction are the major pathophysiological changes in acute respiratory distress syndrome (ARDS). Sphingosine-1-phosphate receptor 3 (S1PR3), a G protein-coupled receptor, has been found to mediate inflammation and endothelial cell (EC) integrity. However, the function of S1PR3 in ARDS has not been fully elucidated. Methods We used a murine lipopolysaccharide (LPS)-induced ARDS model and an LPS- stimulated ECs model to investigate the role of S1PR3 in anti-inflammatory effects and endothelial barrier protection during ARDS. Results We found that S1PR3 expression was increased in the lung tissues of mice with LPS-induced ARDS. TY-52156, a selective S1PR3 inhibitor, effectively attenuated LPS-induced inflammation by suppressing the expression of proinflammatory cytokines and restored the endothelial barrier by repairing adherens junctions and reducing vascular leakage. S1PR3 inhibition was achieved by an adeno-associated virus in vivo and a small interfering RNA in vitro. Both the in vivo and in vitro studies demonstrated that pharmacological or genetic inhibition of S1PR3 protected against ARDS by inhibiting the NF-κB pathway and improving mitochondrial oxidative phosphorylation. Conclusions S1PR3 inhibition protects against LPS-induced ARDS via suppression of pulmonary inflammation and promotion of the endothelial barrier by inhibiting NF-κB and improving mitochondrial oxidative phosphorylation, indicating that S1PR3 is a potential therapeutic target for ARDS.

Internet of Things (IoT) technologies are increasingly implemented in buildings as the cost-effective smart sensing infrastructure of building automation systems (BASs). They are also dispersed computing resources for novel distributed optimal control approaches. However, wireless communication networks are critical to fulfill these tasks with the performance influenced by inherent uncertainties in networks, e.g., unpredictable occurrence of link failures. Centralized and hierarchical distributed approaches are vulnerable against link failure, while the robustness of fully distributed approaches depends on the algorithms adopted. This study therefore proposes a fully distributed robust optimal control approach for air-conditioning systems considering uncertainties of communication link in IoT-enabled BASs. The distributed algorithm is adopted that agents know their out-neighbors only. Agents directly coordinate with the connected neighbors for global optimization. Tests are conducted to test and validate the proposed approach by comparing with existing approaches, i.e., the centralized, the hierarchical distributed and the fully distributed approaches. Results show that different approaches are vulnerable against to uncertainties of communication link to different extents. The proposed approach always guarantees the optimal control performance under normal conditions and conditions with link failures, verifying its high robustness. It also has low computation complexity and high optimization efficiency, thus applicable on IoT-enabled BASs.

Abstract To investigate how cell elongation impacts extracellular electron transfer (EET) of electroactive microorganisms (EAMs), the division of model EAM Shewanella oneidensis (S. oneidensis) MR‐1 is engineered by reducing the formation of cell divisome. Specially, by blocking the translation of division proteins via anti‐sense RNAs or expressing division inhibitors, the cellular length and output power density are all increased. Electrophysiological and transcriptomic results synergistically reveal that the programmed cell elongation reinforces EET by enhancing NADH oxidation, inner‐membrane quinone pool, and abundance of c‐type cytochromes. Moreover, cell elongation enhances hydrophobicity due to decreased cell‐surface polysaccharide, thus facilitates the initial surface adhesion stage during biofilm formation. The output current and power density all increase in positive correction with cellular length. However, inhibition of cell division reduces cell growth, which is then restored by quorum sensing‐based dynamic regulation of cell growth and elongation phases. The QS‐regulated elongated strain thus enables a cell length of 143.6 ± 40.3 µm (72.6‐fold of that of S. oneidensis MR‐1), which results in an output power density of 248.0 ± 10.6 mW m−2 (3.41‐fold of that of S. oneidensis MR‐1) and exhibits superior potential for pollutant treatment. Engineering cellular length paves an innovate avenue for enhancing the EET of EAMs.

PIK3CA gene encodes the p110α catalytic subunit of PI3K, which regulates the PI3K/AKT/mTOR signaling pathway. PIK3CA gene mutation is one of the most common mutations in colorectal cancer (CRC), affecting about 15%–20% of CRC patients. PIK3CA gene mutation leads to the persistent activation of the PI3K/AKT/mTOR signaling pathway, which promotes the proliferation, invasion, metastasis, and drug resistance of CRC. This article provides a summary of the key detection methods for PIK3CA gene mutation, and provides an introduction to the existing colorectal cancer treatments and their practical applications in the clinic. Besides, this article summarizes the role and mechanism of PIK3CA gene mutation in the occurrence and development of CRC. It also explores the relationship between PIK3CA gene mutation and the clinical features and prognosis of CRC. This article focuses on the influence and mechanism of PIK3CA gene mutation on the targeted therapy and immunotherapy of CRC, and discusses the potential value and future direction of PIK3CA gene mutation in the personalized therapy of CRC. We aim to provide new perspectives and ideas for the precise diagnosis and treatment of CRC.

ABSTRACT Background High‐intensity focused ultrasound (HIFU) is a promising minimally invasive treatment for liver cancer; however, its efficacy is often limited by the attenuation of ultrasonic energy. This study investigates the effectiveness of B7‐H3‐targeted microbubbles (T‐MBs) in enhancing HIFU ablation of liver cancer and explores their potential for clinical translation. Methods T‐MBs and isotype control microbubbles (I‐MBs) were synthesized through the conjugation of biotinylated anti‐B7‐H3 antibody and isotype control antibody to the microbubble surface, respectively. Contrast‐enhanced ultrasound imaging was performed to compare the accumulation of T‐MBs and I‐MBs in liver cancer at various time points. The efficacy of T‐MBs in enhancing HIFU treatment was evaluated by measuring the immediate tumor ablation rate and long‐term tumor growth suppression. Additionally, the induced antitumor immune response was assessed through cytokine quantification in serum and tumor tissue, along with immunofluorescence staining conducted on days 1, 3, and 7 post‐treatment. Results T‐MBs demonstrated superior liver cancer‐specific accumulation, characterized by higher concentrations and prolonged retention compared to I‐MBs. The combination of T‐MBs with HIFU resulted in significantly enhanced tumor ablation rates and superior tumor growth suppression. Post‐treatment analysis revealed a gradual uptick in cytokine levels within the tumor microenvironment, along with progressive infiltration of antitumor immune cells. Conclusion T‐MBs effectively enhance the therapeutic efficacy of HIFU for liver cancer treatment while simultaneously promoting an antitumor immune response. These findings provide a strong experimental foundation for the clinical translation of ultrasound molecular imaging combined with HIFU as a novel approach for tumor therapy.

BackgroundSugar consumption has increased dramatically around the world, and at the same time, the prevalence of mental illnesses such as depression and anxiety continues to increase. While previous research has explored the impact of various dietary factors on mental health, the specific impact of dietary sugar consumption on the risk of depression and anxiety disorders remains elusive. This study aimed to comprehensively assess this relationship through a systematic review and meta-analysis.MethodsPubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Network (CNKI), and WangFang were systematically searched for studies of the association between total dietary sugar intake and risk of depression and/or anxiety. The articles that meet the criteria are screened and included in the systematic review, and the data are extracted after assessing their quality. Stata 18.0 software was used for the meta-analysis.ResultsForty studies with 1,212,107 participants were included in the analysis. Results showed that sugar intake increased the risk of depression by 21% (OR = 1.21, 95% CI: 1.14, 1.27), while the overall association between sugar intake and anxiety risk was not statistically significant (OR = 1.11, 95% CI: 0.93, 1.28). Despite high heterogeneity (I2 = 99.7%), the results were statistically significant (p 10,000). Women have a higher risk of depression than men (OR = 1.19, 95% CI: 1.04, 1.35). Among the different exposure measures, the Food Frequency Questionnaire (FFQ) showed the most significant effect (OR = 1.32, 95% CI: 1.08, 1.67, I2 = 99.7%, p

User clustering is crucial for tapping the flexibility of the load side and realizing dynamic management of power loads in new power system. K-means method is widely used in clustering analysis due to its simplicity, high efficiency, and scalability, but it needs to specify the number of clusters in advance, and is sensitive to the initial clustering centers. The current initialization method does not take into account the neighborhood distribution of the data points, and the direct use of data that has undergone dimensionality reduction processing leads to inaccurate selection of the initial clustering centers. To address the above problems, a new K-means improvement method that takes into account the initialization problem and the adaptive determination of the number of clusters: K-means clustering method based on nearest-neighbor density matrix is proposed in this paper. The method improves the efficiency of nearest neighbor search by building a K-D tree, and enhances the performance of unsupervised classification by utilizing the adaptive selection strategy of the number of clusters and the initial clustering centers selection algorithm. The proposed method is applied to real datasets, and its effectiveness is assessed by calculating three clustering evaluation metrics of the clustering results in comparison with several existing initialization and clustering methods. The experimental results show that the method proposed in this paper has higher stability and better clustering performance than existing clustering methods.

The Chinese cordyceps, a unique parasitic complex of Thitarodes/Hepialus ghost moths and Ophiocordyceps sinensis fungus in the Tibetan Plateau, is a highly valuable biological resource for medicine and health foods in Asian countries. Efficient system for artificial cultivation of Chinese cordyceps relies on understanding the gene functions involved in the induction of growing blastospores into hyphae in the larval hemolymph of insect host, during O. sinensis infection. Transcriptome analysis and ribonucleic acid interference (RNA interference) method were employed to identify the key differentially expressed genes and to demonstrate their functions in Thitarodes xiaojinensis. Key larval genes critical for O. sinensis blastospore development or filamentation were identified. Nine of the 20 top upregulated genes encoded cuticles proteins, indicating that these proteins highly activated when the larval hemolymph was full of blastospores. Small interfering RNA (siRNA) knockdown of five larval genes such as Flightin, larval cuticle protein LCP-30, 26-hydroxylase (CYP18A1), cuticle protein 18.6, isoform B, and probable chitinase 3 significantly stimulated the dimorphic transition from blastospores to prehyphae in O. sinensis in the larval hemolymph after 120 h after injection. The expressions of these genes determined by quantitative real-time PCR were suppressed in various levels from 38.64% to 91.54%, compared to the controls. These results demonstrated that injection of the siRNAs of key upregulated genes into the larval hemolymph containing high load of blastospores caused the gene silence in T. xiaojinensis larvae and induced the fungal transition from blastospores to prehyphae, providing novel knowledge on the regulation of O. sinensis fungal dimorphism by Thitarodes host and cues for further study of Thitarodes biology and commercial cultivation of Chinese cordyceps.

In this review, we explore the effects of food additives on intestinal health. Food additives, such as preservatives, antioxidants and colorants, are widely used to improve food quality and extend shelf life. However, their effects on intestinal microecology May pose health risks. Starting from the basic functions of food additives and the importance of intestinal microecology, we analyze in detail how additives affect the diversity of intestinal flora, oxidative stress and immune responses. Additionally, we examine the association between food additives and intestinal disorders, including inflammatory bowel disease and irritable bowel syndrome, and how the timing, dosage, and individual differences affect the body’s response to additives. We also assess the safety and regulatory policies of food additives and explore the potential of natural additives. Finally, we propose future research directions, emphasizing the refinement of risk assessment methods and the creation of safer, innovative additives.

Carbon black (CB) has wide range of industrial applications, including in the manufacturing of automobile tires, rubber products, inks, and plastics. To improve the properties of the target products and establish recycling systems, it must be fully characterized. However, characterization of CB is challenging owing to its structural complexity and the limitation of conventionally used experimental techniques, especially for surface structures at the nanoscale. In this study, we characterized the surface structures of two commercial CB via atomic force and scanning tunneling microscopy. Analysis of well-dispersed aggregates on atomically flat solid surfaces revealed primary particles of diverse sizes. The particle surfaces lacked edges, grooves, and steps that should be observed between stacked graphene sheets, which contradicts the widely accepted crystallite model. Observed images suggest that the graphene sheets exhibit a size distribution, inferring that multiple non-uniformly sized small graphene sheets are stacked turbostratically, with each sheet displaying a localized curvature rather than the ideal planar form. Varying size of sheets and curvature indicate the presence of a decent number of edges terminated with hydrogen and oxygen-containing functional groups. This interpretation was corroborated by conventional spectroscopic techniques: Raman spectroscopy, X-ray photoelectron spectroscopy, temperature-programmed desorption, and infrared absorption spectroscopy.

Invasive Emerald Ash Borer (EAB) damage pose significant challenges for sustainable forest management, necessitating accurate mapping of damaged ash trees. Traditional change detection methods, using time-series imagery, are essential for monitoring forest disturbances but complicated by abnormal fluctuations in original time-series features. Tree phenology also complicates this process by masking the reflectance characteristics indicative of EAB infestation. To address these challenges, we propose an improved change detection method integrating patterns from host tree phenology and EAB life history. This improved method includes: (1) select the indices with time stability to enhance detection reliability by partial least squares method (PLS); (2) correction on negative change values before and positive change values after the phenological peak based on known patterns of tree phenology and EAB life history. Result confirms that this method effectively reflects the seasonal growth and decline dynamics of ash trees, revealing the impacts of phenology and EAB infestation. EAB-damaged trees exhibited slower growth in May and premature decline in July compared with healthy tree, with the damage severity influencing the rate of leaf decline. This proposed method achieved an overall accuracy of 53.4%-76.7% across different months for ash trees with health, light and severe damage. This study highlights the capabilities of integrating pest life history and phenology in change detection method and provide a new method to monitor individual tree health across large areas by high-resolution satellite imagery.

Abstract In this paper, a theoretical analysis of how the excitation conditions affect the sapphire fiber Fabry-Perot interferometer (SFPI) visibility was performed. The conditions were considered, in which an SFPI was excited by a single-mode fiber (SMF), a multimode fiber (MMF), and a fiber collimator. The finite difference method (FDM) was used to realize the numerical solution of the modal electric fields, and then, the modal excited distributions in the sapphire fiber and the SFPI visibility were calculated. The results showed that different numbers of modes were excited in sapphire fibers under different excitation conditions and finally affected the fringe visibility of the SFPI. The fiber collimator excited the fewest modes and the visibility remained at the highest level. Finally, an experiment was performed, and the experimental results agreed well with the theoretical results.

Abstract Background Vasculogenic mimicry (VM), when microvascular channels are formed by cancer cells independent of endothelial cells, often occurs in deep hypoxic areas of tumors and contributes to the aggressiveness and metastasis of triple-negative breast cancer (TNBC) cells. However, well-developed VM inhibitors exhibit inadequate efficacy due to their low drug utilization rate and limited deep penetration. Thus, a cost-effective VM inhibition strategy needs to be designed for TNBC treatment. Results Herein, we designed a low-intensity focused ultrasound (LIFU) and matrix metalloproteinase-2 (MMP-2) dual-responsive nanoplatform termed PFP@PDM-PEG for the cost-effective and efficient utilization of the drug disulfiram (DSF) as a VM inhibitor. The PFP@PDM-PEG nanodroplets effectively penetrated tumors and exhibited substantial accumulation facilitated by PEG deshielding in a LIFU-mediated and MMP-2-sensitive manner. Furthermore, upon exposure to LIFU irradiation, DSF was released controllably under ultrasound imaging guidance. This secure and controllable dual-response DSF delivery platform reduced VM formation by inhibiting COL1/pro-MMP-2 activity, thereby significantly inhibiting tumor progression and metastasis. Conclusions Considering the safety of the raw materials, controlled treatment process, and reliable repurposing of DSF, this dual-responsive nanoplatform represents a novel and effective VM-based therapeutic strategy for TNBC in clinical settings. Graphical Abstract

Abstract Network embedding aspires to learn a low‐dimensional vector of each node in networks, which can apply to diverse data mining tasks. In real‐life, many networks include rich attributes and temporal information. However, most existing embedding approaches ignore either temporal information or network attributes. A self‐attention based architecture using higher‐order weights and node attributes for both static and temporal attributed network embedding is presented in this article. A random walk sampling algorithm based on higher‐order weights and node attributes to capture network topological features is presented. For static attributed networks, the algorithm incorporates first‐order to k‐order weights, and node attribute similarities into one weighted graph to preserve topological features of networks. For temporal attribute networks, the algorithm incorporates previous snapshots of networks containing first‐order to k‐order weights, and nodes attribute similarities into one weighted graph. In addition, the algorithm utilises a damping factor to ensure that the more recent snapshots allocate a greater weight. Attribute features are then incorporated into topological features. Next, the authors adopt the most advanced architecture, Self‐Attention Networks, to learn node representations. Experimental results on node classification of static attributed networks and link prediction of temporal attributed networks reveal that our proposed approach is competitive against diverse state‐of‐the‐art baseline approaches.

Abstract Background Diabetes patients are at higher risk for mortality than the general population; however, little is known about whether the excess mortality risk associated with diabetes could be mitigated or nullified via controlling for risk factors. Methods We included 18,535 diabetes patients and 91,745 matched individuals without diabetes without baseline cancer or cardiovascular disease (CVD), followed up from 2006 to 2021. The main exposure was the number of optimized risk factors including glycated hemoglobin

Ultra-low tidal volume (ULT) is an appealing alternative for severe acute respiratory distress syndrome (ARDS) patients with the aim to alleviate excess lung stress and strain. A recent article showed that ULT without extracorporeal carbon dioxide removal did not improve prognosis in moderate-to-severe coronavirus disease 2019-related ARDS patients. However, several reasons should be considered before drawing the definite conclusion about the ULT strategy in severe ARDS.

Background To evaluate the new preoperative Changgung classification (CC) system of portal vein thrombosis (PVT) in choosing suitable operative procedures to reconstruct portal veins during liver transplantation (LT) in patients with portal vein cavernous transformation (PVCT).Methods This retrospective observational study analyzed data from allograft LTs performed for various liver diseasesResults The study included 22 males and 4 females with LT indications comprising cirrhosis (n = 9), hepatocellular carcinoma (n = 12), PVCT (n = 2), liver failure from fulminant hepatitis B (n = 1), dysfunction of transplanted liver (n = 1), and chronic rejection of transplanted liver (n = 1). Patients were classified according to Yerdel (21 Yerdel II and 5 Yerdel III) and CC (C1–C5). In total 16 simple operations were performed on C1–C3 cases and 9 complex operations on C4–C5 cases, with one additional simple operation. The distribution according to the Yerdel classification was 16 simple and 5 complex operations in Yerdel II cases and 1 simple and 4 complex operations in Yerdel III cases. The median follow-up time was 27.5 months with overall one-year and three-year OS rates of 88.1% and 83.9% for the cohort. Specifically, the one-year OS rates for patients classified as C1-3 vs. C4-5 were 93.3% and 80.0%, while the three-year OS rates were 86.7% and 80.0%, respectively (p = 0.526).Conclusion The CC proposed in this study shows comparable potential to the Yerdel classification in preoperatively identifying the need for complex surgical techniques in LT patients with PVCT and may also have predictive power for the survival benefits following LT.

Summary: Systemic sclerosis (SSc) is an autoimmune disease affecting multiple tissues. The underlying causes and mechanisms of subcutaneous adipose tissue (SAT) loss in SSc remain unclear. Recent studies have highlighted the role of microRNAs in adipogenesis. Our study found that miR-4769-3p was upregulated in SSc patients and its silencing promoted SAT recovery in bleomycin-induced SSc mice, suggesting that miR-4769-3p might affect adipogenesis in SSc. Manipulating miR-4769-3p expression in 3T3-L1 cells revealed that its inhibition enhanced adipogenesis, while its overexpression weakened it. Further investigations showed that miR-4769-3p bound to 3′UTR of ubiquitin-specific protease-18 (USP18), inhibiting its expression, while USP18 interacted with voltage-dependent anion channel-2 (VDAC2), both of which were reduced in SSc. Silencing either USP18 or VDAC2 attenuated adipogenesis. Notably, USP18 inhibited VDAC2 ubiquitination and degradation, whereas miR-4769-3p reversed the VDAC2-induced elevation of adipogenesis, suggesting that miR-4769-3p inhibited adipogenesis by negatively regulating the USP18/VDAC2 pathway, providing a potential therapeutic target for SSc.

Abstract Regulating nuclear export precisely is essential for maintaining mRNA homeostasis and impacts tumor progression. However, the mechanisms governing nuclear mRNA export remain poorly elucidated. Herein, it is revealed that the enhanced hypoxic long no‐ncoding RNA (lncRNA prostate cancer associated transcript 6 (PCAT6) in breast cancer (BC) promotes the nuclear export of m6A‐modified mRNAs, bolstering breast cancer stem cells (BCSCs) stemness and doxorubicin resistance. Clinically, hypoxic PCAT6 correlates with malignant BC features and poor prognosis. Mechanically, PCAT6 functions as a scaffold between interferon‐stimulated gene 15 (ISG15) and heterogeneous nuclear ribonucleoprotein A2/B1 (hnRNPA2B1), leading to ISGylation of hnRNPA2B1, thus protecting hnRNPA2B1 from ubiquitination‐mediated proteasomal degradation. Interestingly, as an m6A reader, hnRNPA2B1 selectively mediates m6A‐tagged mRNAs nuclear export via the Aly/REF export factor (ALYREF)/ nuclear RNA export factor 1 (NXF1) complex, which promotes stemness‐related genes expression. HnRNPA2B1 knockdown or mRNA export inhibition can result in the retention of nuclear m6A‐tagged mRNA associated with stemness maintenance, which suppresses BCSCs self‐renewal and effectively improves the efficacy of doxorubicin therapy. These findings demonstrate the pivotal role of m6A‐modified mRNA nuclear export in BC progression, highlighting that the inhibition of m6A‐tagged mRNA and its nuclear export is a potential therapeutic strategy for the amelioration of cancer chemotherapy.

Summary: Background: Understanding the impact of CYP2D6 metabolism on paroxetine, a widely used antidepressant, is essential for precision dosing. Methods: We conducted an 8-week, multi-center, single-drug, 2-week wash period prospective cohort study in 921 Chinese Han patients with depressive or anxiety disorders (ChiCTR2000038462). We performed CYP2D6 genotyping (single nucleotide variant and copy number variant) to derive the CYP2D6 activity score and evaluated paroxetine treatment outcomes including steady-state concentration, treatment efficacy, and adverse reaction. CYP2D6 metabolizer status was categorized into poor metabolizers (PMs), intermediate metabolizers (IMs), extensive metabolizers (EMs), and ultrarapid metabolizers (UMs). The influence of CYP2D6 metabolic phenotype on paroxetine treatment outcomes was examined using multiple regression analysis and cross-ethnic meta-analysis. The therapeutic reference range of paroxetine was estimated by receiver operating characteristic (ROC) analyses. Findings: After adjusting for demographic factors, the steady-state concentrations of paroxetine in PMs, IMs, and UMs were 2.50, 1.12, and 0.39 times that of EMs, with PM and UM effects being statistically significant (multiple linear regression, P = 0.03 and P = 0.04). Sex and ethnicity influenced the comparison between IMs and EMs. Moreover, poor efficacy of paroxetine was associated with UM, and a higher risk of developing adverse reactions was associated with lower CYP2D6 activity score. Lastly, cross-ethnic meta-analysis suggested dose adjustments for PMs, IMs, EMs, and UMs in the East Asian population to be 35%, 40%, 143%, and 241% of the manufacturer's recommended dose, and 62%, 68%, 131%, and 159% in the non-East Asian population. Interpretation: Our findings advocate for precision dosing based on the CYP2D6 metabolic phenotype, with sex and ethnicity being crucial considerations in this approach. Funding: National Natural Science Foundation of China; Academy of Medical Sciences Research Unit.

The detection of fine-grained objects in remote sensing images has been recognized as a challenging issue, which cannot be well addressed by the existing deep learning-based methods due to their inadaptability to multi-scale objects, slow convergence speed, and limitations to scarce datasets. To cope with the above issue, we propose a deep convolutional neural network (CNN)-based detection method for multi-scale fine-grained objects in complex remote sensing scenarios. Specifically, we adopt a deep CNN with a residual structure as the backbone network, to extract deep-level details from the image. Besides, we introduce a multi-scale region generation network to overcome the limitations of fixed receptive field convolution kernels and enable multi-scale object detection. Lastly, we replace fully connected layers in the fully convolutional region classification network with $1\times 1$ convolutional layer to enhance detection efficiency and detection speed. To overcome the limitation of scarce datasets, we conducted experiments on the FAIR1M dataset, which is currently the largest fine-grained object detection dataset in the remote sensing field. Simulation results show that the proposed detection method achieves the highest average precision (35.86%) among all benchmarks and outperforms the classic Faster R-CNN-based method by 3.44%. Furthermore, our method demonstrates significantly improved detection speed compared to the Faster R-CNN-based methods.

Abstract The recent observations of exotic quantum phenomena in AV3Sb5 (A = K, Rb, Cs) kagome superconductors have attracted significant attention in materials physics. Here, we propose an innovative two-frequencies laser model for ultrafast control of transient structural distortions. Using first-principles density functional theory in conjunction with the perturbative regime of nonlinear phononics, we investigate the nonharmonic potential energy, the crystal lattice dynamics and the topological properties of CsV3Sb5. We find that driving two infrared-active phonons of different frequencies promotes the desired Raman phonon vibrations, in which the displacement of Sb atoms is closely related to superconductivity. We demonstrate that the dimensional crossover and the topological nontrivial to trivial state transition of superconductor CsV3Sb5 can be triggered by ultrafast optical control. This work can be applied to other layered quantum materials and provide guidance for experiments related to photoinduced topology and superconductivity.

Dendroctonus valens is one of the main invasive pests in China, causing serious economic and ecological damage. Early detection and control of D. valens can help prevent further outbreaks. Based on unmanned aerial vehicle (UAV) thermal infrared and hyperspectral data, we compared the spectral characteristics of Pinus sylvestris var. mongolica in three states (healthy, early-infested, and dead), and constructed a classification model based on the random forest algorithm using four spectral datasets (reflectance, first derivative, second derivative, and spectral vegetation index) and one temperature parameter dataset. Our results indicated that the spectral differences between healthy and early-infested trees mainly occur in the near-infrared region, with dead trees showing different characteristics. While it was effective to distinguish healthy from early-infested trees using spectral data alone, the addition of a temperature parameter further improved classification accuracy across all datasets. The combination of the spectral vegetation index and temperature parameter achieved the highest accuracy at 93.75%, which is 3.13% higher than using the spectral vegetation index alone. This combination also significantly improved early detection precision by 13.89%. Our findings demonstrated the applicability of UAV-based thermal infrared and combined hyperspectral datasets in monitoring D. valens early-infested trees, providing important technical support for the scientific prevention and control of D. valens.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impaired social interactions and verbal communication, accompanied by symptoms of restricted and repetitive patterns of behavior or interest. Over the past 30 years, the morbidity of ASD has increased in most areas of the world. Although the pathogenesis of ASD is not fully understood, it has been associated with over 1000 genes or genomic loci, indicating the importance and complexity of the genetic mechanisms involved. This review focuses on the synaptic pathology of ASD and particularly on genetic variants involved in synaptic structure and functions. These include SHANK, NLGN, NRXN, FMR1, and MECP2 as well as other potentially novel genes such as CHD8, CHD2, and SYNGAP1 that could be core elements in ASD pathogenesis. Here, we summarize several pathological pathways supporting the hypothesis that synaptic pathology caused by genetic mutations may be the pathogenic basis for ASD.

The quality of corn kernels is crucial for their nutritional value, making the enhancement of kernel quality a primary objective of contemporary corn breeding efforts. This study utilized 260 corn inbred lines as research materials and assessed three traits associated with grain quality. A genome-wide association study (GWAS) was conducted using the best linear unbiased estimator (BLUE) for quality traits, resulting in the identification of 23 significant single nucleotide polymorphisms (SNPs). Additionally, nine genes associated with grain quality traits were identified through gene function annotation and prediction. Furthermore, a total of 697 quantitative trait loci (QTL) related to quality traits were compiled from 27 documents, followed by a meta-QTL analysis that revealed 40 meta-QTL associated with these traits. Among these, 19 functional genes and reported candidate genes related to quality traits were detected. Three significant SNPs identified by GWAS were located within the intervals of these QTL, while the remaining eight significant SNPs were situated within 2 Mb of the QTL. In summary, the findings of this study provide a theoretical framework for analyzing the genetic basis of corn grain quality-related traits and for enhancing corn quality.

Biologically inspired jumping robots exhibit exceptional movement capabilities and can quickly overcome obstacles. However, the stability and accuracy of jumping movements are significantly compromised by rapid changes in posture. Here, we propose a stable jumping control algorithm for a locust-inspired jumping robot based on deep reinforcement learning. The algorithm utilizes a training framework comprising two neural network modules (actor network and critic network) to enhance training performance. The framework can control jumping by directly mapping the robot’s observations (robot position and velocity, obstacle position, target position, etc.) to its joint torques. The control policy increases randomness and exploration by introducing an entropy term to the policy function. Moreover, we designed a stage incentive mechanism to adjust the reward function dynamically, thereby improving the robot’s jumping stability and accuracy. We established a locus-inspired jumping robot platform and conducted a series of jumping experiments in simulation. The results indicate that the robot could perform smooth and non-flip jumps, with the error of the distance from the target remaining below 3%. The robot consumed 44.6% less energy to travel the same distance by jumping compared with walking. Additionally, the proposed algorithm exhibited a faster convergence rate and improved convergence effects compared with other classical algorithms.

Accurate modelling of occupancy patterns is critical for reliable estimation of building stock energy demand, which is a key input for the design of district energy systems. Aiming to investigate the suitability of different occupancy-modelling approaches for the design of district energy systems, the present study examines a set of standard-based schedules (from the UK National Calculation Methodology), a widely used stochastic occupancy model, and a novel energy-data-driven occupancy model. To this end, a dynamic energy model of a higher education office building developed within a stock model of London’s Bloomsbury district serves as a testbed to implement the occupancy models, explore their implications for the estimation of annual and peak heating and cooling demand, and extrapolate the findings to the computationally demanding building stock stimulations. Furthermore, the simulations were conducted in two years before and after the COVID-19 pandemic to examine the implications of hybrid working patterns after the pandemic. From the results, the energy-data-driven model demonstrated superior performance in annual heating demand estimations, with errors of ±2.5% compared to 14% and 7% for the standard-based and stochastic models. For peak heating demand, the models performed rather similarly, with the data-driven model showing 28% error compared to 29.5% for both the standard-based and stochastic models in 2019. In cooling demand estimations, the data-driven model yielded noticeably higher annual cooling demand and lower peak cooling demand estimations as compared with the standard-based and stochastic occupancy models. Given the adopted building-modelling approach, these findings can be extended to district-level investigations and inform the decision on the choice of occupancy models for building stock energy simulation.

In this paper, a three-layer structure of silver particle (AgNP)-dielectric-metal is proposed and constructed based on the characteristics of AgNPs that can excite LSPR (Localized Surface Plasmon Resonance) in free space. In order to overcome the problem of AgNPs easily oxidizing in the air, this paper synthesizes AgNPs using the improved Tollens method and effectively suppresses the coffee-ring effect by changing the solution evaporation conditions, so that the distribution of AgNPs in the deposition area is relatively uniform. The structure proposed in this paper takes advantage of the flexibility of nanoparticle application. The AgNPs deposited on the dielectric layer can effectively localize energy and regulate the LSPR of the device well. The structure can not only achieve precise regulation of the LSPR resonance peak of AgNPs but also can be used as a SERS substrate.

Background Individuals with diabetes have a significantly higher risk of developing chronic kidney disease (CKD) and higher levels of social isolation and loneliness compared with those without diabetes. Recently, the American Heart Association highlighted the importance of considering social determinants of health (SDOH) in conjunction with traditional risk factors in patients with diabetes.Aims To investigate the associations of loneliness and social isolation with incident CKD risk in patients with diabetes in the UK Biobank.Methods A total of 18 972 patients with diabetes were included in this prospective study. Loneliness and Social Isolation Scales were created based on self-reported factors. An adjusted Cox proportional hazard model was used to investigate the associations of loneliness and social isolation with CKD risk among patients with diabetes. The relative importance in predicting CKD was also calculated alongside traditional risk factors.Results During a median follow-up of 10.8 years, 1127 incident CKD cases were reported. A higher loneliness scale, but not social isolation, was significantly associated with a 25% higher risk of CKD, independent of traditional risk factors, among patients with diabetes. Among the individual loneliness factors, the sense of feeling lonely emerged as the primary contributing factor to the elevated risk of CKD. Compared with individuals not experiencing feelings of loneliness, those who felt lonely exhibited a 22% increased likelihood of developing CKD. In addition, feeling lonely demonstrated greater relative importance of predicting CKD compared with traditional risk factors such as body mass index, smoking, physical activity and diet.Conclusions This study indicates the significant relationship between loneliness and CKD risk among patients with diabetes, highlighting the need to address SDOH in preventing CKD in this population.

Abstract Phosphate is an important fertilizer in agriculture and electrochemical sensors based on cobalt (Co) are promising for measurement of phosphate. The deposition of Co onto conductive substrates to form a nano/microstructured Co layer with rich active sites is a typical strategy to improve detection performances. However, the widely used substrates show smooth surfaces, which cannot provide extra active sites. Herein, a high‐performance phosphate sensor is fabricated by generating Co nanoparticles (NPs) on a hierarchically porous laser‐induced graphene (LIG) matrix with a large surface area. Specifically, a Co layer is first electroplated on the LIG matrix. Nevertheless, the pores of LIG will be blocked by the dense Co layer so the performances of the fabricated LIG‐Co sensor exhibit limited enhancements. Therefore, a “nearly all etching (NAE)” technique is developed to transform the dense Co layer to dispersive Co NPs and re‐expose the pores of LIG, both increasing the active sites. Compared to the LIG‐Co sensor, the LIG‐Co NP sensor displays overall superiority. The response time, sensitivity, and linearity are enhanced from ≈60 to ≈10 s, from 0.012 to 0.069 V dec−1, and from 0.973 to 0.998, respectively. The LIG‐Co NP sensor also shows a decent anti‐interference capability.

IntroductionThe composition and structure of natural soil are very complex, leading to the difficult contact between hydrophobic organic compounds and degrading-bacteria in contaminated soil, making pollutants hard to be removed from the soil. Several researches have reported the bacterial migration in unsaturated soil mediated by fungal hyphae, but bacterial movement in soil of different particle sizes or in heterogeneous soil was unclear. The remediation of contaminated soil enhanced by hyphae still needs further research.MethodsIn this case, the migration and biodegradation of Diaphorobacter sp. LW2 in soil was investigated in presence of Pythium ultimum.ResultsHyphae could promote the growth and migration of LW2 in culture medium. It was also confirmed that LW2 was able to migrate in the growth direction and against the growth direction along hyphae. Mediated by hyphae, motile strain LW2 translocated over 3 cm in soil with different particle size (CS1, 1.0–2.0 mm; CS2, 0.5–1.0mm; MS, 0.25–0.5 mm and FS,

Learning, an important activity for both human and animals, has long been a focal point of research. During the learning process, subjects assimilate not only their own information but also information from others, a phenomenon known as social learning. While numerous studies have explored the impact of social feedback as a reward/punishment during learning, few studies have investigated whether social feedback facilitates or inhibits the learning of environmental rewards/punishments. This study aims to test the effects of social feedback on economic feedback and its cognitive processes by using the Iowa Gambling Task (IGT). One hundred ninety-two participants were recruited and categorized into one non-social feedback group and four social feedback groups. Participants in the social feedback groups were informed that after the outcome of each choice, they would also receive feedback from an online peer. This peer was a fictitious entity, with variations in identity (novice or expert) and feedback type (random or effective). The Outcome-Representation Learning model (ORL model) was used to quantify the cognitive components of learning. Behavioral results showed that both the identity of the peer and the type of feedback provided significantly influenced the deck selection, with effective social feedback increasing the ratio of chosen good decks. Results in the ORL model showed that the four social feedback groups exhibited lower learning rates for gain and loss compared to the nonsocial feedback group, which suggested, in the social feedback groups, the impact of the recent outcome on the update of value decreased. Parameters such as forgetfulness, win frequency, and deck perseverance in the expert-effective feedback group were significantly higher than those in the non-social feedback and expert-random feedback groups. These findings suggest that individuals proactively evaluate feedback providers and selectively adopt effective feedback to enhance learning.

The heavy metal(loid)s (HMs) in soils can be accumulated by crops grown, which is accompanied by crop ingestion into the human body and then causes harm to human health. Hence, the health risks posed by HMs in three crops for different populations were assessed using Health risk assessment (HRA) model coupled with Monte Carlo simulation. Results revealed that Zn had the highest concentration among three crops; while Ni was the main polluting element in maize and soybean, and As in rice. Non-carcinogenic risk for all populations through rice ingestion was at an “unacceptable” level, and teenagers suffered higher risk than adults and children. All populations through ingestion of three crops might suffer Carcinogenic risk, with the similar order of Total carcinogenic risk (TCR): TCRAdults > TCRTeenagers > TCRChildren. As and Ni were identified as priority control HMs in this study area due to their high contribution rates to health risks. According to the HRA results, the human health risk was associated with crop varieties, HM species, and age groups. Our findings suggest that only limiting the Maximum allowable intake rate is not sufficient to prevent health risks caused by crop HMs, thus more risk precautions are needed.

Low temperature storage is widely used in the storage and transportation of postharvest fruits and vegetables. However, the negative effects of chilling injury (CI) on certain fruits and vegetables cannot be ignored. Therefore, efficient CI prevention technologies were used for reducing CI. This paper expounds the mechanisms of CI, common symptoms of CI and its impacts on the quality of fruits and vegetables, and summarizes the application of CI prevention technology. CI control methods are mainly classified into physical treatments (hot shock, near-freezing storage, high relative humidity storage, light-proof storage, and electromagnetic field), chemical treatments (melatonin, 1-methylcyclopropene, astragalus polysaccharides, γ-aminobutyric acid, 24-epibrassinolide, methyl jasmonate, trisodium phosphate, glycine betaine, and salicylic acid, etc.), coating treatments (sodium alginate, chitosan, carboxymethyl cellulose and aloe vera gel, etc.) and their combined treatments. These treatments have enhanced antioxidant activity, enzyme activity, membrane system integrity, and energy levels, thereby reducing the CI of fruits and vegetables.

Abstract A primary challenge of high-throughput imaging flow cytometry (IFC) is to analyze the vast amount of imaging data, especially in applications where ground truth labels are unavailable or hard to obtain. We present an unsupervised deep embedding algorithm, the Deep Convolutional Autoencoder-based Clustering (DCAEC) model, to cluster label-free IFC images without any prior knowledge of input labels. The DCAEC model first encodes the input images into the latent representations and then clusters based on the latent representations. Using the DCAEC model, we achieve a balanced accuracy of 91.9% for human white blood cell (WBC) clustering and 97.9% for WBC/leukemia clustering using the 3D IFC images and 3D DCAEC model. Above all, although no human recognizable features can separate the clusters of cells with protein localization, we demonstrate the fused DCAEC model can achieve a cluster balanced accuracy of 85.3% from the label-free 2D transmission and 3D side scattering images. To reveal how the neural network recognizes features beyond human ability, we use the gradient-weighted class activation mapping method to discover the cluster-specific visual patterns automatically. Evaluation results show that the automatically identified salient image regions have strong cluster-specific visual patterns for different clusters, which we believe is a stride for the interpretable neural network for cell analysis with high-throughput IFCs.

Abstract Background Most of superficial soft-tissue masses are benign tumors, and very few are malignant tumors. However, persistent growth, of both benign and malignant tumors, can be painful and even life-threatening. It is necessary to improve the differential diagnosis performance for superficial soft-tissue masses by using deep learning models. This study aimed to propose a new ultrasonic deep learning model (DLM) system for the differential diagnosis of superficial soft-tissue masses. Methods Between January 2015 and December 2022, data for 1615 patients with superficial soft-tissue masses were retrospectively collected. Two experienced radiologists (radiologists 1 and 2 with 8 and 30 years’ experience, respectively) analyzed the ultrasound images of each superficial soft-tissue mass and made a diagnosis of malignant mass or one of the five most common benign masses. After referring to the DLM results, they re-evaluated the diagnoses. The diagnostic performance and concerns of the radiologists were analyzed before and after referring to the results of the DLM results. Results In the validation cohort, DLM-1 was trained to distinguish between benign and malignant masses, with an AUC of 0.992 (95% CI: 0.980, 1.0) and an ACC of 0.987 (95% CI: 0.968, 1.0). DLM-2 was trained to classify the five most common benign masses (lipomyoma, hemangioma, neurinoma, epidermal cyst, and calcifying epithelioma) with AUCs of 0.986, 0.993, 0.944, 0.973, and 0.903, respectively. In addition, under the condition of the DLM-assisted diagnosis, the radiologists greatly improved their accuracy of differential diagnosis between benign and malignant tumors. Conclusions The proposed DLM system has high clinical application value in the differential diagnosis of superficial soft-tissue masses.

Abstract Background As an emerging standard of care for portal vein cavernous transformation (PVCT), Meso-Rex bypass (MRB) has been complicated and variated. The study aim was to propose a new classification of PVCT to guide MRB operations. Methods Demographic data, the extent of extrahepatic PVCT, surgical methods for visceral side revascularization, intraoperative blood loss, operating time, changes in visceral venous pressure before and after MRB, postoperative complications and the condition of bypass vessels after MRB were extracted retrospectively from the medical records of 19 patients. Results The median age of the patients (13 males and 6 females) was 32.5 years, while two patients were underage. Causes of PVCT can be summarized as follows: thrombophilia such as dysfunction of antithrombin III or proteins C; secondary to abdominal surgeries; secondary to abdominal infection or traumatic intestinal obstruction, and unknown causes. Intraoperatively, the median operation time was 9.5 h (7–13 h), and the intraoperative blood loss was 300 mL (100-1,600 mL). Ten cases used autologous blood vessels while 10 used allogeneic blood vessels. The vascular anastomosis was divided into the following types according to the site and approach: Type (T) 1-PV pedicel type, T2-confluence type, T3-major visceral vascular type; and T4-collateral visceral vascular type. Furthermore, the visceral venous pressure before and after MRB dropped significantly from 36 cmH2O (28–44) to 24.5 cmH2O (15–31) (P

Alzheimer’s disease (AD) is characterized by progressive cognitive decline and late-stage neurobehavioral issues marked by amyloid-beta plaques and Tau protein tangles. This study aims to investigate Fibulin-1(FBLN1) gene expression in the hippocampal tissue of Presenilin-1/Presenilin-2 conditional double-knockout (DKO) and double-transgenic (DTG) mice, using single-cell sequencing and experimental methods to verify abnormal methylation status and correlation with AD. Genomic DNA from DKO and DTG mice was used for genotyping. Reduced Representation Bisulfite Sequencing (RRBS) identified 10 genes with abnormal methylation changes, with protein–protein interaction (PPI) analysis highlighting five core genes, including FBLN1. Single-cell sequencing, RT-PCR, and Western blotting (WB) were used to analyze FBLN1 mRNA and protein levels in the hippocampal tissues of early-stage and mid-stage AD DKO, DTG, and CBAC57 mice. RRBS identified 10 genes with abnormal methylation, with PPI highlighting five core genes. Single-cell sequencing showed significant FBLN1 expression in AD groups. RT-PCR and WB indicated elevated FBLN1 mRNA and protein levels in mid-stage AD DKO and DTG mice compared to CBAC57 mice, with no differences in early-stage AD DKO and CBAC57 mice. RRBS revealed hypomethylation of the FBLN1 gene in mid-stage AD DKO mice. Elevated FBLN1 expression in AD models suggests an age-dependent neurodegenerative mechanism independent of amyloid-beta deposition. This study enhances our understanding of AD’s epigenetic mechanisms, which will aid targeted diagnosis, treatment, and prognosis.

Graphene-based frameworks suffer from a low quantum capacitance due to graphene’s Dirac point at the Fermi level. This theoretical study investigated the effect structural defects, nitrogen and boron doping, and surface epoxy/hydroxy groups have on the electronic structure and capacitance of graphene. Density functional theory calculations reveal that the lowest energy configurations for nitrogen or boron substitutional doping occur when the dopant atoms are segregated. This elucidates why the magnetic transition for nitrogen doping is experimentally only observed at higher doping levels. We also highlight that the lowest energy configuration for a single vacancy defect is magnetic. Joint density functional theory calculations show that the fixed band approximation becomes increasingly inaccurate for electrolytes with lower dielectric constants. The introduction of structural defects rather than nitrogen or boron substitutional doping, or the introduction of adatoms leads to the largest increase in density of states and capacitance around graphene’s Dirac point. However, the presence of adatoms or substitutional doping leads to a larger shift of the potential of zero charge away from graphene’s Dirac point.

Brown cotton is a major cultivar of naturally colored cotton, and brown cotton fibers (BCFs) are widely utilized as raw materials for textile industry production due to their advantages of being green and dyeing-pollution-free. However, the mechanisms underlying the pigmentation in fibers are still poorly understood, which significantly limits their extensive applications in related fields. In this study, we conducted a multidimensional comparative analysis of the transcriptomes and metabolomes between brown and white fibers at different developmental periods to identify the key genes and pathways regulating the pigment deposition. The transcriptomic results indicated that the pathways of flavonoid biosynthesis and phenylpropanoid biosynthesis were significantly enriched regulatory pathways, especially in the late development periods of fiber pigmentation; furthermore, the genes distributed in the pathways of PAL, CHS, F3H, DFR, ANR, and UFGT were identified as significantly up-regulated genes. The metabolic results showed that six metabolites, namely (−)-Epigallocatechin, Apiin, Cyanidin-3-O-glucoside, Gallocatechin, Myricetin, and Poncirin, were significantly accumulated in brown fibers but not in white fibers. Integrative analysis of the transcriptomic and metabolomic data demonstrated a possible regulatory network potentially regulating the pigment deposition, in which three MYB transcription factors promote the expression levels of flavonoid biosynthesis genes, thereby inducing the content increase in (−)-Epigallocatechin, Cyanidin-3-O-glucoside, Gallocatechin, and Myricetin in BCFs. Our findings provide new insights into the pigment deposition mechanism in BCFs and offer references for genetic engineering and breeding of colored cotton materials.

In the IRS-aided THz communication system, in order to break the channel sparsity limitation on the system spatial multiplexing gain, a hybrid beam forming architecture with wide-spaced multi-subarrays at the transceiver/transmitter side was proposed and the transmission scheme of IRS multi-partition-aided THz multi-subarrays was designed.Firstly, a non-convex objective function with multivariate coupling and non-convex constraints was constructed based on the principle of maximizing spectrum efficiency.Then, the optimization problem was decoupled into two easy-to-solve sub-problems, namely, the reflection coefficient matrix design of IRS and the hybrid beamforming matrix design at the transceiver/receiver.Finally, the Riemannian manifold optimization algorithm was used to calculate the reflection coefficient matrix of IRS, and the closed solution of the hybrid beamforming matrix design at the transceiver/receiver was obtained through mathematical derivation.Simulation results show that compared with the baseline scheme, the proposed scheme can achieve better spectrum efficiency.

Abstract Fire carbon emissions over Equatorial Asia (EQAS) play a critical role in the global carbon cycle. Most regional fire emissions (89.0%) occur in the dry season, but how changes in the dry-season length affect the fire emissions remains poorly understood. Here we show that, the length of the EQAS dry season has decreased significantly during 1979–2021, and the delayed dry season onset (5.4 ± 1.6 (± one standard error) days decade−1) due to increased precipitation (36.4 ± 9.1 mm decade−1) in the early dry season is the main reason. The dry season length is strongly correlated with the length of the fire season. Increased precipitation during the early dry season led to a significant reduction (May: −0.7 ± 0.4 Tg C decade−1; August: −12.9 ± 6.7 Tg C decade−1) in fire carbon emissions during the early and peak fire season. Climate models from the Coupled Model Intercomparison Project Phase 6 project a continued decline in future dry season length in EQAS under medium and high-emission scenarios, implying further reductions in fire carbon emissions.

Abstract Background I kappa B kinase interacting protein, a highly conserved gene, has rarely been reported in cancer. According to previous study, IKBIP has only been shown to promote malignant progression of glioma. In other malignant tumors, few reports have examined the function of IKBIP, especially in papillary renal cell carcinoma. Therefore, the molecular profiles and clinical prognostic values of the IKBIP in papillary renal cell carcinoma remain undetermined. Methods Several bioinformatic platforms and Immunohistochemistry were used to clarify the expression and prognostic values of IKBIP in Papillary renal cell carcinoma. Results In this study, GEPIA and TIMER platform were used to identify mRNA expression of IKBIP in papillary renal cell carcinoma. And our results revealed that IKBIP mRNA expression was up-regulated in papillary renal cell carcinoma than in its corresponding normal tissues. In addition, high mRNA expression levels of IKBIP were correlated with age, pathological stage, pathological T stage and pathological N stage. Moreover, High IKBIP mRNA expression was negatively correlated with overall survival (OS) and disease-free survival (DFS) in patients of papillary renal cell carcinoma. Besides, Multivariate analysis indicated that IKBIP mRNA expression was an independent prognostic factor for patients of papillary renal cell carcinoma. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed IKBIP co-expressed genes were enriched in homologous recombination, DNA replication, cell cycle, Mismatch repair, Fanconi anemia pathway, P53 signaling pathway and nucleotide excision repair. And Immunohistochemical profile showed that protein expression of IKBIP was higher in papillary renal cell carcinoma than adjacent normal tissue. Conclusions Overall, our findings reveled that IKBIP may act as a novel and potential tumor factor to accelerate papillary renal cell carcinoma progression, meanwhile, IKBIP could serve as a promising target for treating papillary renal cell carcinoma.

ObjectiveTo investigate natural outcome of moderate- and high-risk human papillomavirus (HPV) infection and influencing factors of persistent infection among Kazak women in Xinjiang Uygur Autonomous Region (Xinjiang). MethodsA baseline survey was conducted among 2 000 Kazakh women (25 – 64 years old) recruited using cluster sampling at Tacheng region of Xinjiang during June – August 2018. Relevant information of the women were collected with questionnaire interview, routine gynaecological examination, laboratory test, and biopsy if required. A year later, follow-up detections of HPV infection, cell cycle protein-dependent kinase inhibitor (P16), liquid-based cytology (LBC) were performed among 10% of the women negative for HPV and all the women positive for HPV infection at the baseline survey. An end follow-up survey was conducted among all the participants of the study two years after the baseline survey. SPSS 21.0 statistical software was used to analyze influencing factors for natural regression and persistence of HPV infection. ResultsIn the baseline survey, totally 291 HPV-positive women were detected, with 208 monotypic infections (including 164, 14, and 30 high-, moderate-, and low-risk HPV infections) and 83 multiple infections (80, 16, and 33 high-, moderate-, and low-risk HPV infections). At one-year follow-up detection among 193 baseline moderate- high-risk HPV positive women, 92 (47.67%) were found having persistent infection (with top five strains of HPV33, HPV35, HPV58, HPV18 and HPV16 in baseline detection) and 101 (52.33%) were observed having HPV clearance for strains of HPV45, HPV73, HPV82, and HPV83 in the baseline detection; while at the end follow-up among 186 baseline moderate- high-risk HPV positive women, 77 (41.40%) had persistent infection (with top five strains of HPV35, HPV16, HPV33, HPV18 and HPV59 in baseline detection) and 109 (58.60%) showed HPV clearance. The results of logistic regression analysis revealed that the age of ≤ 20 years at first sex (odds ratio [OR] = 1.85, 95% confidence interval [95%CI]: 1.12 – 3.03), having syphilis infection (OR = 3.09, 95%CI: 1.15 – 8.32), and being menopause (OR = 3.09, 95%CI: 1.91 – 5.00) were risk factors for persistent moderate- and high-risk HPV infection during the two-year follow-up period. ConclusionPersistent moderate- and high-risk HPV infection are more likely to occur for infections of HPV35/HPV16/HPV33/HPV18/HPV59 and main risk factors for the persistent infection include early age at first sex, syphilis infection and menopause among Kazakh women in Xinjiang.