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The common walnut (Juglans regia) is one of the most economically important nut trees cultivated worldwide. Despite its importance, no comprehensive evaluation of walnut tree population genetics has been undertaken across the range where it originated, Central Asia. In this study, we investigated the genetic diversity and population structure of 1082 individuals from 46 populations across Central Asia. We found moderate genetic diversity of J. regia across Central Asia, with 46 populations clustered into three groups with a weak relationship between genetic and geographic distance. Our findings reveal that the western Himalaya might be the core region of common walnut genetic diversity in Central Asia and that, except for two populations in Gongliu Wild Walnut Valley, humans might have introduced walnut populations to Xinjiang, China. The observed distribution of the genetic landscape has probably been affected by historical climate fluctuation, breeding system, and prolonged anthropogenic activity. We propose the conservation of the core genetic diversity resources in the western Himalaya and pay special attention to populations from Gongliu in Xinjiang. These findings enhance our understanding of the genetic variation throughout the distribution range of J. regia in Central Asia, which will provide a key prerequisite for evidence-based conservation and management.

Abstract Background Interventional therapy, in conjunction with tyrosine kinase inhibitors (TKIs), has shown promising outcomes for treating hepatocellular carcinoma (HCC) with portal vein tumor thrombosis (PVTT). With the advent of immunotherapy, the combined use of immune checkpoint inhibitors (ICIs) has attracted great attention due to their potential effectiveness in advanced HCC. This study aims to compare the efficacy and safety of a triple therapy regimen (Interventional therapy, TKIs and ICIs, IT-TKI-ICI) with a dual therapy regimen (Interventional therapy and TKIs, IT-TKI) in the treatment of HCC and PVTT (HCC-PVTT). Methods A comprehensive search was carried out in PubMed, Web of Science, Embase, Scopus, and the Cochrane Library databases. Primary outcome measures were overall survival (OS) and progression-free survival (PFS), while secondary outcomes included tumor response rate, adverse event incidence as well as downstaging surgery rate. Statistical analysis was conducted using Revman 5.4 software. Results The meta-analysis finally included 6 cohort studies. The triple therapy group demonstrated significantly prolonged OS and PFS compared to the dual therapy group. Meanwhile, the former exhibited significantly higher rates of objective response rate (ORR), disease control rate (DCR) and better downstaging effects with a higher salvage surgery rate without significantly increasing adverse events. Conclusion In comparison to dual therapy, the triple therapy with interventional therapy, TKIs, and ICIs demonstrates superior efficacy and equivalent safety for HCC-PVTT.

Abstract In the post-pandemic era, there is a need for medical professionals with creativity, clinical expertise, and social responsibility. The Chinese government issued a directive to enhance the “Five Characteristics” perceptions of medical students, incorporating moral integrity and adeptness in saving lives and aiding the injured, embracing a compassionate approach to medical practice, possessing the knowledge essential for academic distinction, mastering technical expertise, and the artistry of applying scientific methodologies. The purpose of this study was to investigate the opinions related to ethics and professional behaviors of medical students at one Chinese medical school using a validated 19-item survey instrument and analyze its influencing factors. We conducted a survey in a medical university in Anhui Province, China, and collected 1966 valid questionnaires using the “Five Characteristics Rating Scale”. The results indicated high perceptions of “Five Characteristics” among Chinese medical students (85.42 ± 8.727). Lower-grade-year medical students (86.59 ± 7.888) had higher “Five Characteristics” perceptions than upper-grade-year medical students (84.29 ± 9.327, P

The precipitation reaches its maximum and is most concentrated during the period of the first flood season in South China around the Dragon Boat Festival every year, which is usually referred to as"Dragon-boat Precipitation". The Dragon-boat Precipitation causes serious floods and exerts a serious impact on the production of early rice. It is urgent to study the frequency and intensity of rainstorms during the Dragon-boat Precipitation in South China for disaster prevention and reduction. Using daily precipitation data from 192 national meteorological observation stations in South China from 1961 to 2022, the spatiotemporal characteristics and variation of precipitation during the Dragon-boat Precipitation period in South China were analyzed with different methods, such as EOF analysis, linear trend analysis, and M-K mutation analysis. A comprehensive index representing the intensity of the Dragon-boat Precipitation rainstorm events in South China is constructed. Then, it is used to analyze the characteristics and variations of the number and intensity of the rainstorm events during the Dragon-boat Precipitation period in South China in the recent 62 years, as well as the annual index of the Dragon-boat Precipitation intensity. The results indicate that the consistent increase of the annual intensity index is the main characteristic of the Dragon-boat Precipitation in South China. During the Dragon-boat Precipitation period in the recent 62 years, there were 229 rainstorm events in South China, with an average of 3.7 times per year. In South China, the average precipitation and the number of rainstorm events did not change significantly, but the annual intensity index of Dragon-boat precipitation increased significantly at a rate of 1.2·(10 a)-1. The assessment showed that the strongest Dragon-boat precipitation rainstorm event in South China in the past 62 years would occur during June 4-21, 2022. The annual variation shows that there are four peaks of Dragon-boat Precipitation in South China, which occurred in 2022, 2020, 2008, and 2006.

Traditional biomass boiler operations predominantly rely on manual control, with adjustments made based on operator experience. This approach not only demands considerable manpower but also leads to suboptimal resource utilization. Addressing the specific operational dynamics of biomass boilers, this study introduces a controller designed around the STM32 microcontroller. This device employs a modular architecture and incorporates cost-effective chips and peripheral components. To enhance energy efficiency and the overall user experience, the controller utilizes pulse width modulation pulse modulation alongside a fuzzy PID control strategy. Capable of toggling between manual and automatic modes for local operations, it also supports data logging directly on the device or in the cloud. Tailored to meet diverse user requirements, the controller facilitates remote monitoring and management. It employs a strategic control mechanism for various motors, ensuring precise hot water temperature measurements within the boiler and enabling data uploads to cloud platforms. This not only conserves energy and reduces labor costs but also promotes environmental sustainability and operational efficiency. Through ongoing refinement and rigorous testing, the controller has achieved a level of maturity that underscores its readiness for widespread implementation.

Endocytosis represents a category of regulated active transport mechanisms. These encompass clathrin-dependent and -independent mechanisms, as well as fluid phase micropinocytosis and macropinocytosis, each demonstrating varying degrees of specificity and capacity. Collectively, these mechanisms facilitate the internalization of cargo into cellular vesicles. Pregnancy is one such physiological state during which endocytosis may play critical roles. A successful pregnancy necessitates ongoing communication between maternal and fetal cells at the maternal-fetal interface to ensure immunologic tolerance for the semi-allogenic fetus whilst providing adequate protection against infection from pathogens, such as viruses and bacteria. It also requires transport of nutrients across the maternal-fetal interface, but restriction of potentially harmful chemicals and drugs to allow fetal development. In this context, trogocytosis, a specific form of endocytosis, plays a crucial role in immunological tolerance and infection prevention. Endocytosis is also thought to play a significant role in nutrient and toxin handling at the maternal-fetal interface, though its mechanisms remain less understood. A comprehensive understanding of endocytosis and its mechanisms not only enhances our knowledge of maternal-fetal interactions but is also essential for identifying the pathogenesis of pregnancy pathologies and providing new avenues for therapeutic intervention.

Load redistribution attacks (LRAs) are a type of false data injection attack that disrupts the normal operation of the power grid by redistributing load. While most LRAs in the literature are based on the DC model, this paper proposes an LRA in the AC model using incomplete network information. To defend against the proposed LRA, the paper proposes using Moving Target Defense (MTD) to actively invalidate the attackers’ knowledge. A zero-sum defense-attack game is formulated between MTD and LRA to select an MTD operating range optimally, considering the trade-off between attack detection effectiveness and the extra operation cost. The paper defines a new payoff function as an expected incremental operation cost, consisting of the defense cost, attack cost, and attack mitigation capability. A Nash Equilibrium of the game provides optimal strategies for selecting the MTD operating range. Simulation results on the modified IEEE 14-bus system demonstrate the effectiveness of MTD in detecting the proposed LRA. The paper shows that MTD not only detects ongoing LRAs but also prevents the construction of such attacks by using the proposed game theoretical framework. The proposed approach highlights the role of MTD in enhancing the cybersecurity of power grids against LRAs.

The process of energy decarbonization in island power systems is accelerated due to the swift integration of inverter-based renewable energy resources (IBRs). The unique features of such systems, including rapid frequency changes resulting from potential generation outages or imbalances due to the unpredictability of renewable power, pose a significant challenge in maintaining the frequency nadir without external support. This paper presents a unit commitment (UC) model with data-driven frequency nadir constraints, including either frequency nadir or minimum inertia requirements, helping to limit frequency deviations after significant generator outages. The constraints are formulated using a linear regression model that takes advantage of real-world, year-long generation scheduling and dynamic simulation data. The efficacy of the proposed UC model is verified through a year-long simulation in an actual island power system using historical weather data. The alternative minimum inertia constraint, derived from actual system operation assumptions, is also evaluated. Findings demonstrate that the proposed frequency nadir constraint notably improves the system’s frequency nadir under high photovoltaic (PV) penetration levels, albeit with a slight increase in generation costs, when compared to the alternative minimum inertia constraint.

On-orbit geometric calibration is key to improving the geometric positioning accuracy of high-resolution optical remote sensing satellite data. Grouped calibration with geometric consistency (GCGC) is proposed in this paper for the Jilin1-KF01B satellite, which is the world’s first satellite capable of providing 150-km swath width and 0.5-m resolution data. To ensure the geometric accuracy of high-resolution image data, the GCGC method conducts grouped calibration of the time delay integration charge-coupled device (TDI CCD). Each group independently calibrates the exterior orientation elements to address the multi-time synchronization issues between imaging processing system (IPS). An additional inter-chip geometric positioning consistency constraint is used to enhance geometric positioning consistency in the overlapping areas between adjacent CCDs. By combining image simulation techniques associated with spectral bands, the calibrated panchromatic data are used to generate simulated multispectral reference band image as control data, thereby enhancing the geometric alignment consistency between panchromatic and multispectral data. Experimental results show that the average seamless stitching accuracy of the basic products after calibration is better than 0.6 pixels, the positioning accuracy without ground control points(GCPs) is better than 20 m, the band-to-band registration accuracy is better than 0.3 pixels, the average geometric alignment consistency between panchromatic and multispectral data are better than 0.25 multispectral pixels, the geometric accuracy with GCPs is better than 2.1 m, and the geometric alignment consistency accuracy of multi-temporal data are better than 2 m. The GCGC method significantly improves the quality of image data from the Jilin1-KF01B satellite and provide important references and practical experience for the geometric calibration of other large-swath high-resolution remote sensing satellites.

BackgroundMalnutrition, despite being a common complication, is often neglected in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). The objective of this study was to develop a simplified nutritional prognostic score to accurately predict mortality in HBV-ACLF patients.MethodsIn this multicenter retrospective study, clinical data from 530 HBV-ACLF patients were used to create a new prognostic score, which was then validated in two external cohorts (n = 229 and 248).ResultsFour independent factors were significantly associated with 28-day mortality in HBV-ACLF patients, forming a novel prognostic score (ALTA score = 0.187 × age—0.849 × lymphocyte count—2.033 × total cholesterol—0.148 × albumin—0.971). Notably, the AUROC of ALTA score for 28/90-day mortality (0.950/0.967) were significantly higher than those of three other ACLF prognostic scores (COSSH-ACLF II, 0.864/0.734; MELD, 0.525/0.488; MELD-Na, 0.546/0.517; all P < 0.001), and three known nutritional scores (CONUT, 0.739/0.861; OPNI, 0.279/0.157; NRS-2002, 0.322/0.286; all P < 0.001). The prediction error rates of ALTA score for 28-day mortality were significantly lower than COSSH-ACLF II (7.3%), MELD (14.4%), MELD-Na (12.7%), CONUT (9.0%), OPNI (30.6%), and NRS2002 (34.1%) scores. Further classifying ALTA score into two strata, the hazard ratios of mortality at 28/90 days were notably increased in the high-risk groups compared to the low-risk group (15.959 and 5.740). These results were then validated in two external cohorts.ConclusionALTA, as a simplified nutritional prognostic score for HBV-ACLF, demonstrates superiority over the COSSH-ACLF II and other scores in predicting short-term mortality among HBV-ACLF patients. Therefore, it may be used to guide clinical management, particularly in primary care settings.

Summary: The Hunga Tonga-Hunga Ha’apai (HTHH) volcano eruption received worldwide attention due to its magnitude and potential effects on environment and climate. However, the operational sulfur dioxide (SO2) products mis-estimated SO2 emissions under volcanic conditions due to large uncertainties in the assumptions of SO2 plume altitude. That might have occurred in previous volcanic eruptions and misled understanding of the evolution of sulfate aerosols in the atmosphere and their impact on global climate. Here, we simultaneously retrieved the volcanic SO2 and its plume altitude from the Troposphere Monitoring Instrument (TROPOMI) and the Environment Monitoring Instrument-2 (EMI-2), exploring the SO2 burden, distribution, and evolution from January 14 to 17. We captured multiple eruptions with the second eruption emitting far more SO2 than the first. Total emissions exceeded 900 kt, significantly higher than those from operational products. Our inferred emission fluxes and injection heights offer valuable references for climate modeling and submarine volcano studies.

Abstract Background The gut microbiome and fecal metabolites have been found to influence sarcopenia, but whether there are potential bacteria that can alleviate sarcopenia has been under‐investigated, and the molecular mechanism remains unclear. Methods To investigate the relationships between the gut microbiome, fecal metabolites and sarcopenia, subjects were selected from observational multi‐ethnic study conducted in Western China. Sarcopenia was diagnosed according to the criteria of the Asian Working Group for Sarcopenia 2014. The gut microbiome was profiled by shotgun metagenomic sequencing. Untargeted metabolomic analysis was performed to analyse the differences in fecal metabolites. We investigated bacterium with the greatest relative abundance difference between healthy individuals and sarcopenia patients, and the differences in metabolites associated with the bacteria, to verify its effects on muscle mass and function in a mouse model. Results The study included 283 participants (68.90% females, mean age: 66.66 years old) with and without sarcopenia (141 and 142 participants, respectively) and from the Han (98 participants), Zang (88 participants) and Qiang (97 participants) ethnic groups. This showed an overall reduction (15.03% vs. 20.77%, P = 0.01) of Prevotella copri between the sarcopenia and non‐sarcopenia subjects across the three ethnic groups. Functional characterization of the differential bacteria showed enrichment (odds ratio = 15.97, P = 0.0068) in branched chain amino acid (BCAA) metabolism in non‐sarcopenia group. A total of 13 BCAA and their derivatives have relatively low levels in sarcopenia. In the in vivo experiment, we found that the blood BCAA level was higher in the mice gavaged with live P. copri (LPC) (P

Abstract Background Alternative splicing (AS) of mRNA plays an important roles in transcriptome diversity, involving regulation of plant growth and stress response. Understanding the variation of AS events underlying GWAS loci in a crop population can provide insight into the molecular mechanisms of complex agronomic traits. To date, genome-wide association studies relating AS events to agronomic traits have rarely been conducted at the population level in crops. Results Here, a pipeline was constructed to identify candidate AS events related to complex traits. Firstly, ovule transcriptome data were used to characterize intron retention (IR), the predominant type of AS in plants, on a genome-wide scale. This was done in a natural population consisting of 279 upland cotton lines. Secondly, splice quantitative trait locus (sQTL) analysis was carried out, which yielded a total of 2295 sQTLs involving 1607 genes. Of these, 14.25% (n = 427) were cis-regulatory loci. Integration with expression quantitative trait loci (eQTL) revealed that 53 (21.4%) cis-sGenes were regulated by both cis-sQTLs and cis-eQTLs. Finally, co-localization analysis integrated with GWAS loci in this population showed 32 cis-QTLs to be co-located with genetic regulatory loci related to fiber yield and quality traits, indicating that sQTLs are likely to participate in regulating cotton fiber yield and quality. An in-depth evaluation confirmed that differences in the IR rates of sQTL-regulated candidate genes such as GhLRRK1 and GhGC1 are associated with lint percentage (LP), which has potential in breeding applications. Conclusion This study provides a clue that AS of mRNA has an impact on crop yield, along with functional sQTLs are new genetic resources for cotton precision breeding.

Abstract Candida albicans deploys a variety of mechanisms such as morphological switch and elicitor release to promote virulence. However, the intricate interactions between the fungus and the host remain poorly understood, and a comprehensive inventory of fungal virulence factors has yet to be established. In this study, we identified a C. albicans secretory effector protein Sce1, whose induction and secretion are associated with vagina‐simulative conditions and chlamydospore formation. Sequence alignment showed that Sce1 belongs to a Pir family in C. albicans, which is conserved across several fungi and primarily characterized as a β‐glucan binding protein in the Saccharomyces cerevisiae. Mechanically, Sce1 is primarily localized to the cell wall in a cleaved form as an alkali‐labile β‐1,3‐glucan binding protein and plays a role in masking β‐glucan in acidic environments and chlamydospores, a feature that might underline C. albicans' ability to evade host immunity. Further, a cleaved short form of Sce1 protein could be released into extracellular compartments and presented in bone marrow‐derived macrophages infected with chlamydospores. This cleaved short form of Sce1 also demonstrated a unique ability to trigger the caspases‐8/9‐dependent apoptosis in various host cells. Correspondingly, genetic deletion of SCE1 led to dampened vaginal colonization of C. albicans and diminished fungal virulence during systemic infection. The discovery of Sce1 as a versatile virulence effector that executes at various compartments sheds light on the fungus–host interactions and C. albicans pathogenesis.

Abstract Anisotropy is a manifestation of lowered symmetry in material systems that have profound fundamental and technological implications. For van der Waals magnets, the two-dimensional (2D) nature greatly enhances the effect of in-plane anisotropy. However, electrical manipulation of such anisotropy as well as demonstration of possible applications remains elusive. In particular, in-situ electrical modulation of anisotropy in spin transport, vital for spintronics applications, has yet to be achieved. Here, we realized giant electrically tunable anisotropy in the transport of second harmonic thermal magnons (SHM) in van der Waals anti-ferromagnetic insulator CrPS4 with the application of modest gate current. Theoretical modeling found that 2D anisotropic spin Seebeck effect is the key to the electrical tunability. Making use of such large and tunable anisotropy, we demonstrated multi-bit read-only memories (ROMs) where information is inscribed by the anisotropy of magnon transport in CrPS4. Our result unveils the potential of anisotropic van der Waals magnons for information storage and processing.

BackgroundRecent studies have suggested a relationship between gut microbiota and non-alcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH). However, the nature and direction of this potential causal relationship are still unclear. This study used two-sample Mendelian randomization (MR) to clarify the potential causal links.MethodsSummary-level Genome-Wide Association Studies (GWAS) statistical data for gut microbiota and NAFLD/NASH were obtained from MiBioGen and FinnGen respectively. The MR analyses were performed mainly using the inverse-variance weighted (IVW) method, with sensitivity analyses conducted to verify the robustness. Additionally, reverse MR analyses were performed to examine any potential reverse causal associations.ResultsOur analysis, primarily based on the IVW method, strongly supports the existence of causal relationships between four microbial taxa and NAFLD, and four taxa with NASH. Specifically, associations were observed between Enterobacteriales (P =0.04), Enterobacteriaceae (P =0.04), Lachnospiraceae UCG-004 (P =0.02), and Prevotella9 (P =0.04) and increased risk of NAFLD. Dorea (P =0.03) and Veillonella (P =0.04) could increase the risks of NASH while Oscillospira (P =0.04) and Ruminococcaceae UCG-013 (P=0.005) could decrease them. We also identified that NAFLD was found to potentially cause an increased abundance in Holdemania (P =0.007) and Ruminococcus2 (P =0.002). However, we found no evidence of reverse causation in the microbial taxa associations with NASH.ConclusionThis study identified several specific gut microbiota that are causally related to NAFLD and NASH. Observations herein may provide promising theoretical groundwork for potential prevention and treatment strategies for NAFLD and its progression to NASH in future.

Due to increasing environmental concerns and demand for clean energy resources, photovoltaic (PV) systems are becoming more prevalent. Considering that in several instances, customers pay for both energy and power, PV installations not only must reduce the customers’ energy purchases but also lower their peak demand for maximum financial benefits. However, in many cases, the peak demand does not coincide with the peak of photovoltaic generation. To address this issue, excess energy generated during low-demand periods can be stored in a battery, which can then be used to meet peak demand. Determining the optimal size of photovoltaic and battery components while ensuring system performance and financial benefits is significantly challenging. This study proposes a novel statistical methodology for optimizing PV-battery system size. In the proposed method, the PV-battery system must meet peak demand thresholds with a specific probability. Further, cost and benefit functions are used for financial evaluation. Finally, Monte Carlo simulations, developed using time series clustering and a Bayesian model are utilized to assess system performance and financial feasibility.

(1) Background: In digital-technology-assisted nasal defect reconstruction methods, a crucial step involves utilizing computer-aided design to virtually reconstruct the nasal defect’s complete morphology. However, current digital methods for virtual nasal defect reconstruction have yet to achieve efficient, precise, and personalized outcomes. In this research paper, we propose a novel approach for reconstructing external nasal defects based on the Facial Mesh Generation Network (FMGen-Net), aiming to enhance the levels of automation and personalization in virtual reconstruction. (2) Methods: We collected data from 400 3D scans of faces with normal morphology and combined the structured 3D face template and the Meshmonk non-rigid registration algorithm to construct a structured 3D facial dataset for training FMGen-Net. Guided by defective facial data, the trained FMGen-Net automatically generated an intact 3D face that was similar to the defective face, and maintained a consistent spatial position. This intact 3D face served as the 3D target reference face (3D-TRF) for nasal defect reconstruction. The reconstructed nasal data were extracted from the 3D-TRF based on the defective area using reverse engineering software. The ‘3D surface deviation’ between the reconstructed nose and the original nose was calculated to evaluate the effect of 3D morphological restoration of the nasal defects. (3) Results: In the simulation experiment of 20 cases involving full nasal defect reconstruction, the ‘3D surface deviation’ between the reconstructed nasal data and the original nasal data was 1.45 ± 0.24 mm. The reconstructed nasal data, constructed from the personalized 3D-TRF, accurately reconstructed the anatomical morphology of nasal defects. (4) Conclusions: This paper proposes a novel method for the virtual reconstruction of external nasal defects based on the FMGen-Net model, achieving the automated and personalized construction of the 3D-TRF and preliminarily demonstrating promising clinical application potential.

The SAND algorithm is a family of lightweight AND-RX block ciphers released by DCC in 2022. Our research focuses on assessing the security of SAND with a quantum computation model. This paper presents the first quantum implementation of SAND (including two versions of SAND, SAND-64 and SAND-128). Considering the depth-times-width metric, the quantum circuit implementation of the SAND algorithm demonstrates a relatively lower consumption of quantum resources than that of the quantum implementations of existing lightweight algorithms. A generalized Grover-based brute-force attack framework was implemented and employed to perform attacks on two versions of the SAND algorithm. This framework utilized the g-database algorithm, which considered different plaintext–ciphertext pairs in a unified manner, reducing quantum resource consumption. Our findings indicate that the SAND-128 algorithm achieved the NIST security level I, while the SAND-64 algorithm fell short of meeting the requirements of security level I.

Phyllostachys nigra has green young culms (S1) and purple black mature culms (S4). Anthocyanins are the principal pigment responsible for color presentation in ornamental plants. We employ a multi-omics approach to investigate the regulatory mechanisms of anthocyanins in Ph. nigra. Firstly, we found that the pigments of the culm of Ph. nigra accumulated only in one to four layers of cells below the epidermis. The levels of total anthocyanins and total flavonoids gradually increased during the process of bamboo culm color formation. Metabolomics analysis indicated that the predominant pigment metabolites observed were petunidin 3-O-glucoside and malvidin O-hexoside, exhibiting a significant increase of up to 9.36-fold and 13.23-fold, respectively, during pigmentation of Ph. nigra culm. Transcriptomics sequencing has revealed that genes involved in flavonoid biosynthesis, phenylpropanoid biosynthesis, and starch and sucrose metabolism pathways were significantly enriched, leading to color formation. A total of 62 differentially expressed structural genes associated with anthocyanin synthesis were identified. Notably, PnANS2, PnUFGT2, PnCHI2, and PnCHS1 showed significant correlations with anthocyanin metabolites. Additionally, certain transcription factors such as PnMYB6 and PnMYB1 showed significant positive or negative correlations with anthocyanins. With the accumulation of sucrose, the expression of PnMYB6 is enhanced, which in turn triggers the expression of anthocyanin biosynthesis genes. Based on these findings, we propose that these key genes primarily regulate the anthocyanin synthesis pathway in the culm and contribute to the accumulation of anthocyanin, ultimately resulting in the purple-black coloration of Ph. nigra.

Background: Neuroendocrine neoplasms (NENs) are tumors that originate from secretory cells of the diffuse endocrine system and typically produce bioactive amines or peptide hormones. This paper describes the development and validation of a predictive model of the risk of lymph node metastasis among gastric NEN patients based on machine learning platform. Methods: In this investigation, data from 1256 patients were used, of whom 119 patients from the First Affiliated Hospital of Soochow University in China and 1137 cases from the surveillance epidemiology and end results (SEER) database were combined. Six machine learning algorithms, including the logistic regression model (LR), random forest (RF), decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), were used to build the predictive model. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results: Among the 1256 patients with gastric NENs, 276 patients (21.97 %) developed lymph node metastasis. T stage, tumor size, degree of differentiation, and sex were predictive factors of lymph node metastasis. The RF model achieved the best predictive performance among the six machine learning models, with an AUC, accuracy, sensitivity, and specificity of 0.81, 0.78, 0.76, and 0.82, respectively. Conclusion: The RF model provided the best prediction and can help physicians determine the lymph node metastasis risk of gastric NEN patients to formulate individualized medical strategies.

This study aims at generation of a novel artificial bee colony algorithm using surrogate finite element method with neural network technique. In this paper, theory of surrogate finite element method with physics-informed neural networks (PINNs) are generated and applied to deal with the geometrically nonlinear optimization problem of size, shape and topology for single-layer domes. In the artificial bee colony algorithm, the feedforward neural network is used to surrogate finite element analyses. Three numerical examples of 10-bar truss, Lamella dome, and Kiewit dome are carried out to verify feasibility and accuracy of the proposed method. Results of the present study are in good agreement with ones from literature. It is indicated that optimization processes can be considerably accelerated using the modified algorithm. That is, using the neural network surrogate-based models could significantly increase computational efficiency of structural optimum design for single-layer domes.

Summary: The dissection of a gene regulatory network (GRN) that complements the genome-wide association study (GWAS) locus and the crosstalk underlying multiple agronomical traits remains a major challenge. In this study, we generate 558 transcriptional profiles of lint-bearing ovules at one day post-anthesis from a selective core cotton germplasm, from which 12,207 expression quantitative trait loci (eQTLs) are identified. Sixty-six known phenotypic GWAS loci are colocalized with 1,090 eQTLs, forming 38 functional GRNs associated predominantly with seed yield. Of the eGenes, 34 exhibit pleiotropic effects. Combining the eQTLs within the seed yield GRNs significantly increases the portion of narrow-sense heritability. The extreme gradient boosting (XGBoost) machine learning approach is applied to predict seed cotton yield phenotypes on the basis of gene expression. Top-ranking eGenes (NF-YB3, FLA2, and GRDP1) derived with pleiotropic effects on yield traits are validated, along with their potential roles by correlation analysis, domestication selection analysis, and transgenic plants.

BackgroundPreoperative assessment of the presence of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) remains difficult. We aimed to develop a practical prediction model based on preoperative pathological data and inflammatory or nutrition-related indicators.MethodsThis study retrospectively analyzed the clinicopathological characteristics of 1,061 patients with EGC who were randomly divided into the training set and validation set at a ratio of 7:3. In the training set, we introduced the least absolute selection and shrinkage operator (LASSO) algorithm and multivariate logistic regression to identify independent risk factors and construct the nomogram. Both internal validation and external validation were performed by the area under the receiver operating characteristic curve (AUC), C-index, calibration curve, and decision curve analysis (DCA).ResultsLNM occurred in 162 of 1,061 patients, and the rate of LNM was 15.27%. In the training set, four variables proved to be independent risk factors (p < 0.05) and were incorporated into the final model, including depth of invasion, tumor size, degree of differentiation, and platelet-to-lymphocyte ratio (PLR). The AUC values were 0.775 and 0.792 for the training and validation groups, respectively. Both calibration curves showed great consistency in the predictive and actual values. The Hosmer–Lemeshow (H-L) test was carried out in two cohorts, showing excellent performance with p-value >0.05 (0.684422, 0.7403046). Decision curve analysis demonstrated a good clinical benefit in the respective set.ConclusionWe established a preoperative nomogram including depth of invasion, tumor size, degree of differentiation, and PLR to predict LNM in EGC patients and achieved a good performance.

Background & Aims: Liver contains high frequency of group 1 innate lymphoid cells (ILC), which are composed of comparable number of type 1 ILC (ILC1) and natural killer (NK) cells in steady state. Little is known about whether and how the interaction between ILC1 and NK cells affects the development of alcoholic liver disease. Methods: A mouse model of chronic alcohol abuse plus single-binge (Gao-Binge model) was established. The levels of alanine aminotransferase/aspartate aminotransferase, hepatic lipid, and inflammatory cytokines or neutrophils were measured to evaluate the degree of liver injury, steatosis, and inflammation. Flow cytometric analysis, cell depletion, or adoptive transfer were used to interrogate the interaction between ILC1 and NK cells. Results: Upon chronic alcohol consumption, NK cells, but not ILC1, underwent apoptosis, resulting in ILC1 dominance among group 1 ILC. Interleukin (IL) 17A expression was up-regulated, and increased IL17A was mainly derived from liver ILC1 after chronic alcohol feeding. Either depletion of ILC1 or neutralization of IL17A could significantly attenuate liver steatosis, inflammation, and injury in alcohol-fed mice. In contrast, normalization of the ILC1/NK cells ratio through NK cells transfer or expanding NK cells had a significant hepatoprotection against alcohol-induced steatohepatitis. Furthermore, NK cell-derived interferon gamma exerted a protective function via inhibiting IL17A production by liver ILC1 during alcoholic steatohepatitis. Conclusions: This is the first study showing that the interplay between liver ILC1 and NK cells occurs and drives the development of alcoholic steatohepatitis. Our findings support further exploration of liver ILC1 or NK cells as a therapeutic target for the treatment of alcohol-associated liver disease.

Molecular glues can specifically induce aggregation between two or more proteins to modulate biological functions. In recent years, molecular glues have been widely used as protein degraders. In addition, however, molecular glues play a variety of vital roles, such as complex stabilization, interactome modulation and transporter inhibition, enabling challenging therapeutic targets to be druggable and offering an exciting novel approach for drug discovery. Since most molecular glues are identified serendipitously, exploration of their systematic discovery and rational design are important. In this review, representative examples of molecular glues with various physiological functions are divided into those mediating homo-dimerization, homo-polymerization and hetero-dimerization according to their aggregation modes, and we attempt to elucidate their mechanisms of action. In particular, we aim to highlight some biochemical techniques typically exploited within these representative studies and classify them in terms of three stages of molecular glue development: starting point, optimization and identification.

Abstract Objective Detecting dysthyroid optic neuropathy (DON) in the early stages is vital for clinical decision-making. The aim of this study was to determine the feasibility of using an optic-nerve-based radiomics nomogram on water-fat imaging for detecting DON. Methods This study included 104 orbits (83 in the training cohort) from 59 DON patients and 131 orbits (80 in the training cohort) from 69 thyroid-associated ophthalmopathy (TAO) without DON patients. Radiomic features were extracted from the optic-nerve T2-weighted water-fat images for each patient. Selected radiomics features were retrained to construct the radiomic signature model and calculate the radiomic score (Rad-score). The conventional MRI evaluation model was constructed based on apical crowding sign, optic-nerve stretching sign and muscle index. The radiomics nomogram model combining the Rad-score and conventional MRI evaluation factors was then developed. Predictive performance of the three models was assessed using ROC curves. Results Eight radiomics features from water-fat imaging were selected to build the radiomics signature. The radiomics nomogram (based on Rad-score, apical crowding sign and optic-nerve stretching sign) had superior diagnostic performance than did the conventional MRI evaluation model (AUC in the training set: 0.92 vs 0.80, the validation set:0.88 vs 0.75). Decision curve analysis confirmed the clinical usefulness of the radiomics nomogram. Conclusions This optic-nerve-based radiomics nomogram showed better diagnostic performance than conventional MRI evaluation for differentiating DON from TAO without DON. The changes of the optic-nerve itself may deserve more consideration in the clinical decision-making process.

High-throughput chromosome conformation capture (Hi-C) technology has been applied to explore the chromatin interactions and shed light on the biological functions of three-dimensional genomic features. However, it remains challenging to guarantee the high quality of Hi-C library in plants and hence the reliable capture of chromatin structures, especially loops, due to insufficient fragmentation and low efficiency of proximity ligations. To overcome these deficiencies, we optimized the parameters of the Hi-C protocol, principally the cross-linking agents and endonuclease fragmentation strategy. The double cross-linkers (FA+DSG) and double restriction enzymes (DpnII+DdeI) were utilized. Thus, a systematic in situ Hi-C protocol was designed using plant tissues embedded with comprehensive quality controls to monitor the library construction. This upgraded method, termed Hi-C 3.0, was applied to cotton leaves for trial. In comparison with the conventional Hi-C 2.0, Hi-C 3.0 can obtain more than 50% valid contacts at a given sequencing depth to improve the signal-to-noise ratio. Hi-C 3.0 can furthermore enhance the capturing of loops almost as twice as that of Hi-C 2.0. In addition, Hi-C 3.0 showed higher efficiency of compartment detection and identified compartmentalization more accurately. In general, Hi-C 3.0 contributes to the advancement of the Hi-C method in plants by promoting its capability on decoding the chromatin organization.

Laparoscopic cholecystectomy is used to treat cholecystitis and cholelithiasis. Because the high risk of the surgery prevents novice doctors from practicing it on real patients, VR-based surgical simulation has been developed to simulate surgical procedures to train surgeons without patients, cadavers, or animals. In this study, we propose a real-time system designed to provide plausible visual and tactile simulation of the main surgical procedures. To achieve this, the physical properties of organs are modeled by particles, and cluster-based shape matching is used to simulate soft deformation. The haptic interaction between tools and soft tissue is modeled as a collision between a capsule and particles. Constraint-based haptic rendering is used to generate feedback force and the non-penetrating position of the virtual tool. The proposed system can simulate the major steps of laparoscopic cholecystectomy, such as the anatomy of Calot’s triangle, clipping of the cystic duct and biliary artery, disjunction of the cystic duct and biliary artery, and separation of the gallbladder bed. The experimental results show that haptic rendering can be performed at a high frequency (> 900 Hz), whereas mesh skinning and graphics rendering can be performed at 60 frames per second (fps).

Objective To analyze the risk factors of postoperative complications after craniofacial flap repair, and to establish a risk prediction model. Methods A total of 120 patients who underwent skin flap repair from October 2017 to September 2020 were selected. Univariate and logistic multivariate regression analyses were used to screen the risk factors for complications after skin flap repair, and a personalized predictive nomogram model was established. Results The incidence of postoperative complications was 25.83%. The main complications were flap vascular crisis, infection, flap avulsion, subcutaneous hematoma and edema. Logistic multivariate regression analysis revealed that the risk factors for complications after skin flap repair included venous end-to-side anastomosis (OR=3.766, 95% CI: 1.587~8.938), excessive intraoperative blood loss (OR=2.889, 95% CI: 1.089~7.668), smoking history (OR=4.697), 95% CI: 1.558~14.161), physician′s surgical skill (OR=3.062, 95% CI: 1.183~7.922) and improper flap design (OR=2.399, 95% CI: 0.861~6.686) (P

IntroductionExtra spindle pole bodies like 1 (ESPL1) are required to continue the cell cycle, and its primary role is to initiate the final segregation of sister chromatids. Although prior research has revealed a link between ESPL1 and the development of cancer, no systematic pan-cancer analysis has been conducted. Combining multi-omics data with bioinformatics, we have thoroughly described the function of ESPL1 in cancer. In addition, we examined the impact of ESPL1 on the proliferation of numerous cancer cell lines. In addition, the connection between ESPL1 and medication sensitivity was verified using organoids obtained from colorectal cancer patients. All these results confirm the oncogene nature of ESPL1.MethodsHerein, we downloaded raw data from numerous publicly available databases and then applied R software and online tools to explore the association of ESPL1 expression with prognosis, survival, tumor microenvironment, tumor heterogeneity, and mutational profiles. To validate the oncogene nature of ESPL1, we have performed a knockdown of the target gene in various cancer cell lines to verify the effect of ESPL1 on proliferation and migration. In addition, patients’ derived organoids were used to verify drug sensitivity.ResultsThe study found that ESPL1 expression was markedly upregulated in tumorous tissues compared to normal tissues, and high expression of ESPL1 was significantly associated with poor prognosis in a range of cancers. Furthermore, the study revealed that tumors with high ESPL1 expression tended to be more heterogeneous based on various tumor heterogeneity indicators. Enrichment analysis showed that ESPL1 is involved in mediating multiple cancer-related pathways. Notably, the study found that interference with ESPL1 expression significantly inhibited the proliferation of tumor cells. Additionally, the higher the expression of ESPL1 in organoids, the greater the sensitivity to PHA-793887, PAC-1, and AZD7762.DiscussionTaken together, our study provides evidence that ESPL1 may implicate tumorigenesis and disease progression across multiple cancer types, highlighting its potential utility as both a prognostic indicator and therapeutic target.

IntroductionExisting dynamical models can explain the transmigration mechanisms involved in seizures but are limited to a single modality. Combining models with networks can reproduce scaled epileptic dynamics. And the structure and coupling interactions of the network, as well as the heterogeneity of both the node and network activities, may influence the final state of the network model.MethodsWe built a fully connected network with focal nodes prominently interacting and established a timescale separated epileptic network model. The factors affecting epileptic network seizure were explored by varying the connectivity patterns of focal network nodes and modulating the distribution of network excitability.ResultsThe whole brain network topology as the brain activity foundation affects the consistent delayed clustering seizure propagation. In addition, the network size and distribution heterogeneity of the focal excitatory nodes can influence seizure frequency. With the increasing of the network size and averaged excitability level of focal network, the seizure period decreases. In contrast, the larger heterogeneity of excitability for focal network nodes can lower the functional activity level (average degree) of focal network. There are also subtle effects of focal network topologies (connection patterns of excitatory nodes) that cannot be ignored along with non-focal nodes.DiscussionUnraveling the role of excitatory factors in seizure onset and propagation can be used to understand the dynamic mechanisms and neuromodulation of epilepsy, with profound implications for the treatment of epilepsy and even for the understanding of the brain.

This study used three-stage data envelopment analysis (DEA) and Tobit regression models to comprehensively evaluate the performance levels, trends, and differences of marine ecological welfare (MEW) in China, and explore its influencing factors to derive relevant policy suggestions. The evaluation results indicate that the three-stage DEA model performed better on MEW than the traditional DEA model by effectively eliminating the influence of random errors and external environmental factors. However, it underestimated the coastal MEW performance of the country. The southern marine economic circle showed an overall improvement, whereas the overall northern marine economic circle did not improve much; the eastern marine economic circle was stable. Moreover, the external environmental factors significantly impacted the performance of MEW, and the value of average MEW performance of each province changed to some extent under the influence of the slack variables of resource consumption and environmental consumption input. Other factors that significantly influenced China’s coastal MEW performance were the level of economic developments on land and government environmental regulations, which correlated positively. Meanwhile, inadequate development momentum in marine research and technology or poor achievement transformation rate negatively influenced it. Finally, countermeasures and suggestions were proposed based on the results of empirical analysis.

Abstract The high energy consumption of data centres (DCs) has recently become a significant problem with the rapid development of DCs worldwide. This work studies the daily energy management of a data centre microgrid (DCM). The energy management problem is formulated as a two‐stage stochastic mixed‐integer linear programing (MILP) model that accounts for workload schedules, cooling resources, uncertainties of onsite renewable generation, and electricity price. An energy management scheme that co‐optimises workloads and waste heat is proposed to minimise the operating cost. Furthermore, the potential for waste heat recovery and reuse is also considered. The simulation results illustrate that the proposed model can reduce the operating cost of DCM by the co‐optimisation of workloads and waste heat while meeting the temperature requirements for server clusters.

Tropical cyclones (TCs) are often accompanied by heavy precipitation, which may lead to natural disasters and a serious threat to life and property. However, they also provide indispensable water resources. Studying the temporal and spatial characteristics of TC precipitation is of great importance for TC precipitation forecasting, TC disaster mitigation, and water resource utilization. Guangdong is one of the most frequently and severely TC-affected provinces in China. Due to the different methods used to identify TC precipitation, the conclusions offered by the existing studies are often inconsistent. Moreover, their analyses of the spatiotemporal characteristics of TC precipitation in Guangdong are not sufficiently thorough. In this study, we first selected the historical TCs that affected Guangdong from 1961 to 2020, using an objective separation method for TC wind and rain, based on the observation data from 86 national meteorological stations in Guangdong Province. From these observations covering the past 60 years, the temporal and spatial variations in TC precipitation in Guangdong for four different periods, namely the first rainy season (FRS), the second rainy season (SRS), the non-rainy season (NRS), and over the whole year (WY), were then explored using statistical analysis and multiple cluster methods. The results show that TC frequencies in the four periods all showed a decreasing trend. TC precipitation also showed a decreasing trend in the SRS and NRS, as well as for the WY, but showed a slightly increasing trend in the FRS. Both TC frequency and TC precipitation showed an apparent inter-annual fluctuation and a quasi-periodic pattern. The spatial distribution of TC precipitation in the four periods all showed a decreasing trend from the coastal to the inland stations, but the western coastal areas had higher TC precipitation values than the eastern coastal areas for the SRS, NRS, and WY periods. The spatial variations of TC precipitation in Guangdong in the four periods of the last six decades were quite similar, exhibiting three primary spatial modes and six patterns. Among them, the spatial distribution of TC precipitation being less than normal across the whole province is the most common pattern. The 86 stations can be classified into six groups when using the spatial clustering method and into four groups when using the time-series clustering method. Stations with higher TC precipitation and large inter-annual fluctuations are often distributed in the coastal areas, while stations with less precipitation and small inter-annual fluctuations are distributed in inland areas. However, the primary areas that are affected by TCs may vary in the different periods.

Abstract. Serving as the interface between the fetal and maternal environments during gestation, the placenta plays critical roles in the protection of the developing fetus and the maintenance of maternal health. The placenta is primarily derived from the embryonic trophectoderm which differentiates into various subtypes of trophoblast cells through villous and extravillous pathways. The interactions among trophoblasts and multiple decidual cells and immune cells at the maternal-fetal interface fundamentally form the functional units of the placenta, which are responsible for blood perfusion and maternal-fetal material exchange, immune tolerance, and the regulation of pregnancy adaptation. Defects in placental development and functional maintenance are in tight association with adverse pregnancy outcomes such as preeclampsia. In this article, we review recent advances on human trophoblast cell differentiation and the construction of placental functional units and discuss the placental and maternal factors that may contribute to the occurrence of preeclampsia.

Dynamic tariffs such as time-of-use (TOU) were introduced to motivate consumers to change the operation of their behind-the-meter (BTM) resources to reduce their power consumption during peak demand times. Recently, export rates have been implemented that reimburse consumers for electricity exported to the grid at a rate less than the retail rate to reduce reverse power flow in distribution power systems with high photovoltaic penetrations. Export rates are expected to be employed more widely, and therefore it is important to understand their impact on consumers, the operation of BTM resources, and the distribution grid. This paper presents a home energy management system (HEMS) capable of optimally scheduling BTM resources under a tariff with export rates. The proposed HEMS is formulated as a multi-objective model predictive control problem. In the paper, we analyze the performance of the proposed HEMS under an export rate including the operation of each BTM resource and the impact on the cost and comfort of homeowners. We also compare the performance of the proposed HEMS under an export rate with the HEMS under a TOU rate. Simulation results are presented for a small power system with several homes that employ the proposed HEMS to respond to an export rate. The simulation results show that the estimated energy cost savings for houses with HEMS and energy storage under an export rate is 20% compared to the baseline scenario with no HEMS. The results also demonstrate that the proposed HEMS responding to an export rate increases the self-consumption of buildings more than HEMS responding to a TOU rate.

Background: Liver resection involves surgical removal of a portion of the liver. It is used to treat liver tumors and liver injuries. The complexity and high-risk nature of this surgery prevents novice doctors from practicing it on real patients. Virtual surgery simulation was developed to simulate surgical procedures to enable medical professionals to be trained without requiring a patient, a cadaver, or an animal. Therefore, there is a strong need for the development of a liver resection surgery simulation system. We propose a real-time simulation system that provides realistic visual and tactile feedback for hepatic parenchymal transection. Methods: The tetrahedron structure and cluster-based shape matching are used for physical model construction, topology update of a three-dimensional liver model soft deformation simulation, and haptic rendering acceleration. During the liver parenchyma separation simulation, a tetrahedral mesh is used for surface triangle subdivision and surface generation of the surgical wound. The shape-matching cluster is separated via component detection on an undirected graph constructed using the tetrahedral mesh. Results: In our system, cluster-based shape matching is implemented on a GPU, whereas haptic rendering and topology updates are implemented on a CPU. Experimental results show that haptic rendering can be performed at a high frequency (> 900 Hz), whereas mesh skinning and graphics rendering can be performed at 45 fps. The topology update can be executed at an interactive rate (> 10 Hz) on a single CPU thread. Conclusion: We propose an interactive hepatic parenchymal transection simulation method based on a tetrahedral structure. The tetrahedral mesh simultaneously supports physical model construction, topology update, and haptic rendering acceleration.

Abstract Moving target defence (MTD) has been gaining traction to thwart false data injection attacks against state estimation (SE) in the power grid. MTD actively perturbs the reactance of transmission lines equipped with distributed flexible AC transmission system (D‐FACTS) devices to falsify the attacker's knowledge about the system configuration. However, the existing literature has not systematically studied what influences the detection effectiveness of MTD and how it can be improved based on the topology analysis. These problems are tackled here from the perspective of an MTD plan in which the D‐FACTS placement is determined. We first exploit the relation between the rank of the composite matrix and the detecting effectiveness. Then, we rigorously derive upper and lower bounds on the attack detecting probability of MTDs with a given rank of the composite matrix. Furthermore, we analyse existing planning methods and highlight the importance of bus coverage by D‐FACTS devices. To improve the detection effectiveness, we propose a novel graph theory–based planning algorithm to retain the maximum rank of the composite matrix while covering all necessary buses. Comparative results on multiple systems show the high detecting effectiveness of the proposed algorithm in both DC‐ and AC‐SE.

Background: Mitophagy has been found to play a significant part in the cancer process in a growing number of studies in recent years. However, there is still a lack of study on mitophagy-related genes’ (MRGs) prognostic potential and clinical significance in hepatocellular carcinoma (HCC).Methods: We employed bioinformatics and statistical knowledge to examine the transcriptome data of HCC patients in the TCGA and GEO databases, with the goal of constructing a multigene predictive model. Then, we separated the patients into high- and low-risk groups based on the score. The model’s dependability was determined using principal components analysis (PCA), survival analysis, independent prognostic analysis, and receiver operating characteristic (ROC) analysis. Following that, we examined the clinical correlations, pharmacological treatment sensitivity, immune checkpoint expression, and immunological correlations between patients in high and low risk groups. Finally, we evaluated the variations in gene expression between high- and low-risk groups and further analyzed the network core genes using protein-protein interaction network analysis.Results: Prognostic models were built using eight genes (OPTN, ATG12, CSNK2A2, MFN1, PGAM5, SQSTM1, TOMM22, TOMM5). During validation, the prognostic model demonstrated high reliability, indicating that it could accurately predict the prognosis of HCC patients. Additionally, we discovered that typical HCC treatment medicines had varying impacts on patients classified as high or low risk, and that individuals classified as high risk are more likely to fail immunotherapy. Additionally, the high-risk group expressed more immunological checkpoints. The immunological status of patients in different risk categories varies as well, and patients with a high-risk score have a diminished ability to fight cancer. Finally, PPI analysis identified ten related genes with potential for research.Conclusion: Our prognostic model had good and reliable predictive ability, as well as clinical diagnosis and treatment guiding significance. Eight prognostic MRGs and ten network core genes merited further investigation.

Automated indoor environmental control is a research topic that is beginning to receive much attention in smart home automation. All machine learning models proposed to date for this purpose have relied on reinforcement learning using simple metrics of comfort as reward signals. Unfortunately, such indicators do not take into account individual preferences and other elements of human perception. This research explores an alternative (albeit closely related) paradigm called imitation learning. In the proposed architecture, machine learning models are trained with tabular data pertaining to environmental control activities of the real occupants of a residential unit. This eliminates the need for metrics that explicitly quantify human perception of comfort. Moreover, this article introduces the recently proposed deep attentive tabular neural network (TabNet) into smart home research by incorporating TabNet-based components within its overall framework. TabNet has consistently outperformed all other popular machine learning models in a variety of other application domains, including gradient boosting, which was previously considered ideal for learning from tabular data. The results obtained herein strongly suggest that TabNet is the best choice for smart home applications. Simulations conducted using the proposed architecture demonstrate its effectiveness in reproducing the activity patterns of the home unit’s actual occupants.

The purpose of this study is to investigate the role that a deep learning approach could play in computational mechanics. In this paper, a convolutional neural network technique based on modified loss function is proposed as a surrogate of the finite element method (FEM). Several surrogate-based physics-informed neural networks (PINNs) are developed to solve representative boundary value problems based on elliptic partial differential equations (PDEs). According to the authors’ knowledge, the proposed method has been applied for the first time to solve boundary value problems with elliptic partial differential equations as the governing equations. The results of the proposed surrogate-based approach are in good agreement with those of the conventional FEM. It is found that modification of the loss function could improve the prediction accuracy of the neural network. It is demonstrated that to some extent, the deep learning approach could replace the conventional numerical method as a significant surrogate model.

This paper focuses on the life of Liu Shu (1520–1657), a woman warrior who lived through the social and political turmoil of the violent dynastic transition from the Ming (1368–1644) dynasty to the Qing (1636–1911) dynasty. Drawing on the writings of Liu Shu and on different versions of accounts of her life written by male literati in different time periods, this paper intends to reveal the multiplicity and complexity of how a woman could exert her agency and interact with the dominant structure. Commonly, women’s agency has been understood as resistance against the male-dominant patriarchal system. However, recently, scholars such as Saba Mahmood have problematized universalizing overtly resistant acts against the patriarchal society to bring radical changes as demonstrations of women’s agency. These scholars argue that this approach fails to recognize that through their autonomy and while living a life of self-fulfillment, women have the capacity to reproduce, sustain, or subtly change the social norms and views of values that justify and support the patriarchal structure. In light of these scholars’ studies, this paper explores Liu Shu’s engagement in political and military activities and her Buddhist practices to analyze how she transgressed established gender norms in order to uphold rather than reject the virtues promoted by patriarchal ideology. The paper also discusses how conflicting demands on women by a male-centered society in the drastic dynastic transition enabled her to negotiate with or challenge the dominant structure. It also considers how her disobedient acts were accepted and applauded by some male literati to address their own agenda in different cultural, historical, and political contexts.

To investigate the issue of multi-entry bus priority at intersections, an intelligent priority control method based on deep reinforcement learning was constructed in the bus network environment. Firstly, a dimension reduction method for the state vector based on the key lane was proposed, which contains characteristic parameters such as the bus states, the flow states, and the signal timing. Secondly, a control action method that can adjust phase sequence and phase green time at the same time was proposed under the constraints of maximum green and minimum green. Furthermore, a reward function, which can be uniformly converted into the number of standard cars, was established focusing on the indexes such as the busload and maximum waiting time. Finally, through building an experimental environment based on SUMO simulation, a real-time bus signal priority control method based on deep reinforcement learning was constructed. The results show that the algorithm can effectively reduce the waiting time of buses without affecting overall traffic efficiency. The findings can provide a theoretical basis for the signal control method considering bus priority and improve the operation efficiency of public transport.

Based on the daily precipitation data at 192 surface meteorological stations in South China from 1961 to 2019, the standard for regional rainstorm events in South China is defined, and all regional rainstorm events in South China are objectively and quantitatively assessed. The intensity series of rainstorm events in South China during a whole year, the first and the second flood seasons are determined. The climatic characteristics and changes of its frequency and comprehensive intensity index were analyzed. Based on NCEP/NCAR reanalysis data, the characteristics of atmospheric circulation in strong and weak years of rainstorm events in the first and second flood season in South China are studied by using correlation and composite analysis methods. The results show that there are 1196 regional rainstorms in South China in recent 59 years, with an average of 20.3 events per year. 82.4% of them occurred in the flood season from April to September. The number of annual regional rainstorm events in South China increased by 0.45/(10a)-1. The annual intensity index of regional rainstorm in South China increased significantly at a rate of 1.148·(10 a)-1, especially in the second flood season. In the first flood season in South China, the westerly jet in upper levels intensifies, East Asian Trough in the southern part of Japan strengthened, and the subtropical high strengthened westward. Additionally, the low level South China was located at the intersection of the North-South airflow, so the rainstorm intensity in South China increased in the first flood season. In the second flood season of South China, the westerly jet in upper levels intensifies, the geopotential height of East Asia in the middle and middle latitudes increases, and the cyclonic circulation in the South China Sea and the Western Pacific increases. The sea level pressure in the North Pacific decreases in the middle and low latitudes, which is conducive to the formation of typhoons in the South China Sea and the Western Pacific. The rainstorm intensity in South China increases in the second flood season.

PurposeThe impact of surgery on non-functional pancreatic neuroendocrine tumors (NF-PNETs) ≤2 cm is controversial. This study sought to demonstrate the impact of surgery on the prognosis of NF-PNETs ≤2 cm with different biological behaviors.MethodsPatients with NF-PNETs ≤2 cm from 2004 to 2015 in the Surveillance, Epidemiology, and End Results database were included in this study. An inverse probability of treatment weighting (IPTW) method was used to reduce the selection bias. Kaplan–Meier survival analysis and Cox proportional hazards regression were used to evaluate the effect of surgery on the prognosis.ResultsIn the IPTW-adjusted Cox proportional hazards regression analysis, surgery improved the cancer-specific survival (CSS) in the overall cohort (hazard ratio [HR], 0.187; 95% confidence interval [CI], 0.102–0.343; p

Residential customers use more than one-quarter of the electricity in the world. Optimally managing home energy consumption is an effective way of easing the operational challenges facing the electric grid with increasing solar photovoltaics (PV). This paper studies the impact of the future proliferation of home energy management systems (HEMS) in the presence of PV on large-scale distribution systems. First, we present a stochastic HEMS model that minimizes residential customers’ thermal discomfort and energy costs under uncertainty. The HEMS model schedules the optimal operations of residential appliances in the presence of PV within a mixed-integer linear programming-based model predictive control framework that links the proposed HEMS to a quasi-steady-state time-series simulation tool. Extensive simulations are conducted for a stand-alone residential home using two tariff structures and for 1977 homes on an 8,500-node distribution feeder. Simulation results quantify the impact of the future proliferation of HEMS on the large-scale distribution system with PV.

Vascular smooth muscle cell (VSMC) phenotypic transition represents the fundamental pathophysiological alteration in the vascular remodeling process during the initiation and progression of cardiovascular diseases. Recent studies have revealed that Icariside II (ICS-II), a flavonol glycoside derived from the traditional Chinese medicine Herba Epimedii, exhibited therapeutic effects in various cardiovascular diseases. However, the therapeutic efficacy and underlying mechanisms of ICS-II regarding VSMC phenotypic transition were unknown. In this study, we investigated the therapeutic effects of ICS-Ⅱ on vascular remodeling with a rat’s balloon injury model in vivo. The label-free proteomic analysis was further implemented to identify the differentially expressed proteins (DEPs) after ICS-II intervention. Gene ontology and the pathway enrichment analysis were performed based on DEPs. Moreover, platelet-derived growth factor (PDGF-BB)-induced primary rat VSMC was implemented to verify the restoration effects of ICS-II on the VSMC contractile phenotype. Results showed that ICS-II could effectively attenuate the vascular remodeling process, promote SMA-α protein expression, and inhibit OPN expression in vivo. The proteomic analysis identified 145 differentially expressed proteins after ICS-II intervention. Further, the bioinformatics analysis indicated that the focal adhesion signaling pathway was enriched in the ICS-II group. In vitro studies showed that ICS-II suppressed VSMC proliferation and migration, and promoted VSMC contractile phenotype by modulating the focal adhesion signaling pathway. Taken together, our results suggest that ICS-II attenuates the vascular remodeling process and restores the VSMC contractile phenotype by promoting the focal adhesion pathway.

Abstract Background Previous studies have found that patients with subclinical interstitial lung disease (ILD) are highly susceptible to developing radiation pneumonitis (RP) after thoracic radiation therapy. In the present study we aimed to evaluate the incidence of and risk factors for RP after thoracic intensity-modulated radiation therapy in lung cancer patients with subclinical ILD. Methods We retrospectively analyzed data from lung cancer patients with subclinical ILD who were treated with thoracic intensity-modulated radiation therapy with a prescribed dose of ≥ 50 Gy in our institution between January 2016 and December 2017. Results Eighty-seven consecutive lung cancer patients with subclinical ILD were selected for the study. The median follow-up period was 14.0 months. The cumulative incidence of grades ≥ 2 and ≥ 3 RP at one year was 51.0% and 20.9%, respectively. In the multivariate analysis, a mean lung dose ≥ 12 Gy was a significant risk factor for grade ≥ 2 RP (p = 0.049). Chemotherapy with gemcitabine in the past, V5 ≥ 50%, and subclinical ILD involving ≥ 25% of the lung volume were significantly associated with grade ≥ 3 RP (p = 0.046, p = 0.040, and p = 0.024, respectively). Conclusion Mean lung dose is a significant risk factor for grade ≥ 2 RP. Lung cancer patients who have received chemotherapy with gemcitabine in the past, V5 ≥ 50%, and those with subclinical ILD involving ≥ 25% of lung volume have an increased risk of grade ≥ 3 RP in lung cancer patients with subclinical ILD.