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Background Case Presentation Conclusion Congenital lumbar facet joint defect is a rare congenital developmental disorder with only a few reported cases in the literature, primarily affecting the L5‐S1 segments. This study reports the first case of a defect in the left L3 inferior articular process; and presents a review of the existing literature on the subject, proposes a classification system, and validates the inter‐observer and intra‐observer reliability of this classification system.A 14‐year‐old boy presented to our orthopedic clinic with persistent lower back pain for 1 month. Imaging analysis, including CT scans, 3D reconstruction, and MRI, revealed a congenital lumbar facet joint defect at the L3 level, which has not been reported. Conservative treatment resulted in a significant improvement in his symptoms, and he is currently under follow‐up care.Congenital defect of the lumbar facet joint is a rare spinal condition. This article reports the first patient with a defect in the left L3 inferior articular process and conducts a comprehensive literature review, proposing a classification of articular process defects into five types. The two most common types are Types B and C. We have demonstrated that this system is reliable and reproducible and have described the treatment of each type. [ABSTRACT FROM AUTHOR]

In this paper, we consider a finite element approximation of an optimal control problem governed by a time fractional wave equation. We first prove the well-posedness and regularity of the optimal control problem. Furthermore, we discuss the fully discrete scheme and analyze its stability and a priori error estimate. Numerical experiments are given to illustrate the theoretical findings. • The weak solution to the time fractional wave equation is studied under low regular data. • The well-posedness and regularity of the optimal control problem are proven. • Numerical schemes exist the first-order and second-order accuracy in time and space respectively. [ABSTRACT FROM AUTHOR]

Despite limited research on refractory and/or endocrine therapy failure in elderly metastatic breast cancer (MBC) patients, a prior study showed that low-dose oral cyclophosphamide (CY) can improve the overall survival rate of MBC patients, possibly through the immunoregulation of regulatory T cells (Tregs). We preliminarily investigated the combination of endocrine therapy (ET) with oral low-dose CY as salvage therapy in elderly patients via peripheral blood regulatory T-cell analyses. In addition, we evaluated the associations of tumor tertiary lymphoid structures (TLSs) with therapeutic outcomes. HR+/HER2− advanced breast cancer patients who received low-dose CY combined with ET or ET only from April 2015 to August 2021 were enrolled in this retrospective study. The primary outcome was the clinical control rate (CCR), and the secondary outcome was progression-free survival (PFS). Circulating T lymphocyte subpopulations represented by Tregs were monitored during treatment by flow cytometry methods. TLSs wereconfirmed by hematoxylin–eosin staining of pretreatment specimens, and CD3, CD4, and Foxp3 were detected using Opal multicolor immunofluorescence. A total of 85 patients who received CY + ET and 50 patients who received ET only were enrolled, the percentage of patients who received CCR was 73% (62/85) vs. 70% (45/50), and the objective response rate (ORR) was 28% (24/85) vs. 24% (12/50). No deaths occurred during the study period. The mean PFS time was 13 vs. 11 months (P = 0.03). In the CY + ET group, decreases in CD4+/CD25+/Foxp3+ T cells (P < 0.001) were favorable for both clinical control and prolonged PFS (P < 0.001). Compared with patients without TLSs, those with TLSs were more likely to have better clinical control and PFS (mean time = 6 months), and a greater number of Treg cells during TLS pretreatment correlated with longer PFS (P = 0.043). Oral low-dose CY combined with standard ET exerts immunological effects by decreasing Treg levels to achieve improved clinical responses. Moreover, patients with TLSs might benefit more from such therapy than those without TLSs, and a high Treg cell count in TLSs before treatment predicts better therapeutic efficacy. [ABSTRACT FROM AUTHOR]

Cuproptosis is a novel programmed cell death pathway that is initiated by direct binding of copper to lipoylated tricarboxylic acid (TCA) cycle proteins. Recent studies have demonstrated that cuproptosis-related genes regulate tumorigenesis. However, the potential role and clinical significance of cuproptosis-related long noncoding RNAs (lncRNAs) in hepatocellular carcinoma (HCC) have not been established. We performed a bioinformatics analyses of RNA-sequencing data of HCC patients extracted from The Cancer Genome Atlas (TCGA) dataset to identify and validate a cuproptosis-related lncRNA prognostic signature. Furthermore, we analyzed the clinical significance of the prognostic signature of cuproptosis-related lncRNA in predicting the immunotherapeutic efficacy and the status of the tumor immune microenvironment. The RNA-sequencing data, genomic mutations, and clinical information were downloaded for 374 HCC samples and 50 normal liver samples from TCGA-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset. Co-expression analysis of Gene-lncRNA pairs with 49 known cuproptosis-related prognostic genes was used to define cuproptosis-related prognostic lncRNAs. We performed the LASSO algorithm and univariate and multivariate Cox regression analysis, respectively, to gradually identify the prognostic risk models of cuproptosis-related lncRNA based on the TCGA-LIHC dataset. Subsequently, the predictive performance of the model was evaluated using receiver operation characteristic (ROC) curves, Kaplan–Meier survival curves, and prognostic nomogram. The analysis of gene-lncRNA co-expression with 49 known cuproptosis-related genes identified 1359 cuproptosis-related lncRNAs in the TCGA-LIHC data set. A prognostic model was constructed with nine cuproptosis-related prognostic lncRNAs (AC007998.3, AC003086.1, AC009974.2, IQCH-AS1, LINC0256 1, AC105345.1, ZFPM2-AS1, AL353708.1 and WAC-AS1) using LASSO regression and Cox regression analyses. Risk scores were calculated for all HCC patient samples based on the four cuproptosis-related lncRNA prognostic models. All HCC patients were divided into high-risk and low-risk subgroups according to a 1:1 ratio column. The Kaplan–Meier survival curve analysis showed that the overall survival rate (OS) of the high-risk group patients was significantly lower than that of the low-risk group. The principal component analysis (PCA) confirmed that the prognostic lncRNA model accurately distinguished between high- and low-risk HCC patients. Furthermore, regression analysis as well as ROC curves confirmed the prognostic value of the risk score. A nomogram with risk scores and other clinicopathological characteristics was constructed. The nomogram accurately predicted the probability of 1-, 3-, and 5-year OS in HCC patients. Tumor mutation burden (TMB) scores were higher for high-risk patients than for low-risk patients. HCC patients in the low-risk group showed lower TIDE scores and greater sensitivity to antitumor drugs than those in the high-risk group. Tumor immune responses and tumor immune cell infiltration were significantly different between the high-risk and low-risk groups of patients with HCC. Our study identified a 9-cuproptosis-related lncRNA signature that accurately predicted prognosis, immunotherapeutic efficacy, and the status of the tumor immune microenvironment in HCC patients. Therefore, this cuproptosis-related lncRNA risk model is a potential prognostic biometric feature in HCC and shows high clinical value in identifying HCC patients who are potentially responsive to immunotherapy. [ABSTRACT FROM AUTHOR]

Background: Survival prediction using high-dimensional molecular data is a hot topic in the field of genomics and precision medicine, especially for cancer studies. Considering that carcinogenesis has a pathway-based pathogenesis, developing models using such group structures is a closer mimic of disease progression and prognosis. Many approaches can be used to integrate group information; however, most of them are single-model methods, which may account for unstable prediction. Methods: We introduced a novel survival stacking method that modeled using group structure information to improve the robustness of cancer survival prediction in the context of high-dimensional omics data. With a super learner, survival stacking combines the prediction from multiple sub-models that are independently trained using the features in pre-grouped biological pathways. In addition to a non-negative linear combination of sub-models, we extended the super learner to non-negative Bayesian hierarchical generalized linear model and artificial neural network. We compared the proposed modeling strategy with the widely used survival penalized method Lasso Cox and several group penalized methods, e.g., group Lasso Cox, via simulation study and real-world data application. Results: The proposed survival stacking method showed superior and robust performance in terms of discrimination compared with single-model methods in case of high-noise simulated data and real-world data. The non-negative Bayesian stacking method can identify important biological signal pathways and genes that are associated with the prognosis of cancer. Conclusions: This study proposed a novel survival stacking strategy incorporating biological group information into the cancer prognosis models. Additionally, this study extended the super learner to non-negative Bayesian model and ANN, enriching the combination of sub-models. The proposed Bayesian stacking strategy exhibited favorable properties in the prediction and interpretation of complex survival data, which may aid in discovering cancer targets. [ABSTRACT FROM AUTHOR]

Handwriting difficulty is a defining feature of Chinese developmental dyslexia (DD) due to the complex structure and dense information contained within compound characters. Despite previous attempts to use deep neural network models to extract handwriting features, the temporal property of writing characters in sequential order during dictation tasks has been neglected. By combining transfer learning of convolutional neural network (CNN) and positional encoding with the temporal-sequential encoding of long short-term memory (LSTM) and attention mechanism, we trained and tested the model with handwriting images of 100,000 Chinese characters from 1064 children in Grades 2–6 (DD = 483; Typically Developing [TD] = 581). Using handwriting features only, the best model reached 83.2% accuracy, 79.2% sensitivity, 86.4% specificity, and 91.2% AUC. With grade information, the best model achieved 85.0% classification accuracy, 83.3% sensitivity, 86.4% specificity, and 89.7% AUC. These findings suggest the potential of utilizing machine learning technology to identify children at risk for dyslexia at an early age. [ABSTRACT FROM AUTHOR]

Summary: Increasing studies suggest that the biased retention of stress‐related transcription factors (TFs) after whole‐genome duplications (WGDs) could rewire gene transcriptional networks, facilitating plant adaptation to challenging environments. However, the role of posttranscriptional factors (e.g. RNA‐binding proteins, RBPs) following WGDs has been largely ignored.Uncovering thousands of RBPs in 21 representative angiosperm species, we integrate genomic, transcriptomic, regulatomic, and paleotemperature datasets to unravel their evolutionary trajectories and roles in adapting to challenging environments.We reveal functional enrichments of RBP genes in stress responses and identify their convergent retention across diverse angiosperms from independent WGDs, coinciding with global cooling periods. Numerous RBP duplicates derived from WGDs are then identified as cold‐induced. A significant overlap of 29 orthogroups between WGD‐derived and cold‐induced RBP genes across diverse angiosperms highlights a correlation between WGD and cold stress. Notably, we unveil an orthogroup (Glycine‐rich RNA‐binding Proteins 7/8, GRP7/8) and relevant TF duplicates (CCA1/LHY, RVE4/8, CBF2/4, etc.), co‐retained in different angiosperms post‐WGDs. Finally, we illustrate their roles in rewiring circadian and cold‐regulatory networks at both transcriptional and posttranscriptional levels during global cooling.Altogether, we underline the adaptive evolution of RBPs in angiosperms after WGDs during global cooling, improving our understanding of plants surviving periods of environmental turmoil. [ABSTRACT FROM AUTHOR]

Plant growth and development can be significantly impacted by drought stress. Plants will adjust the synthesis and accumulation of secondary metabolites to improve survival in times of water constraint. Simultaneously, drought stress can lead to modifications in the DNA methylation status of plants, and these modifications can directly impact gene expression and product synthesis by changing the DNA methylation status of functional genes involved in secondary metabolite synthesis. However, further research is needed to fully understand the extent to which DNA methylation modifies the content of secondary metabolites to mediate plants' responses to drought stress, as well as the underlying mechanisms involved. Our study found that in Eleutherococcus senticosus (E. senticosus), moderate water deprivation significantly decreased DNA methylation levels throughout the genome and at the promoters of EsFPS, EsSS, and EsSE. Transcription factors like EsMYB-r1, previously inhibited by DNA methylation, can re-bind to the EsFPS promotor region following DNA demethylation. This process promotes gene expression and, ultimately, saponin synthesis and accumulation. The increased saponin levels in E. senticosus acted as antioxidants, enhancing the plant's adaptability to drought stress. [ABSTRACT FROM AUTHOR]

Malicious domains provide malware with covert communication channels which poses a severe threat to cybersecurity. Despite the continuous progress in detecting malicious domains with various machine learning algorithms, maintaining up‐to‐date various samples with fine‐labeled data for training is difficult. To handle these issues and improve the detection accuracy, a novel malicious domain detection method named MDND‐SS‐PO is proposed that combines semi‐supervised learning and parameter optimization. The contributions of the study are as follows. First, the method extracts the statistical features of the IP address, TTL value, the NXDomain record, and the domain name query characteristics to discriminate Domain‐Flux and Fast‐Flux domain names simultaneously. Second, an improved DBSCAN based on the neighborhood division is designed to cluster labeled data and unlabeled data with low time consumption. Then, based on the clustering hypothesis, unlabeled data is tagged with pseudo‐label according to the cluster results, which aims to train a supervised classifier effectively. Finally, Gaussian process regression is used to optimize parameter settings of the algorithm. And the Silhouette index and F1 score are introduced to evaluate the optimization results. Experimental results show that the proposed method achieved a precise detection performance of 0.885 when the ratio of labeled data is 5%. [ABSTRACT FROM AUTHOR]

Knowledge of aerosol radiative effects in the Tibetan Plateau (TP) is limited due to the lack of reliable aerosol optical properties, especially the single scattering albedo (SSA). We firstly reported in situ measurement of SSA in Lhasa using a cavity enhanced albedometer (CEA) at λ = 532 nm from 22nd May to 11th June 2021. Unexpected strong aerosol absorbing ability was observed with an average SSA of 0.69. Based on spectral absorptions measured by Aethalometer (AE33), black carbon (BC) was found to be the dominated absorbing species, accounting for about 83% at λ = 370 nm, followed by primary and secondary brown carbon (BrCpri and BrCsec). The average direct aerosol radiative forcing at the top of atmosphere (DARFTOA) was 2.83 W/m2, indicating aerosol warming effect on the Earth‐atmosphere system. Even though aerosol loading is low, aerosol heating effect plays a significant role on TP warming due to strong absorbing ability. Plain Language Summary: The Tibetan Plateau (TP) has experienced rapid warming over the past decades, but the key factors affecting TP climate change haven't yet been clearly understood. Aerosol single scattering albedo (SSA) is a key optical parameter determining aerosol warming or cooling effect; however, reliable SSA measurement is scarce in TP. This study firstly reported in situ measurement of SSA in Lhasa and explored the direct radiative effect of aerosol on TP warming. Strong aerosol absorption, mainly contributed by black carbon (BC), was observed with an average SSA value of 0.69 in this city. Besides Lhasa, other sites over TP were also reported with low SSA (≤0.77) from surface measurement. The strong aerosol absorption could cause heating effect on the Earth‐atmosphere system. To relieve TP warming, reasonable pollutant emission control strategies should be taken urgently to weaken aerosol absorbing ability. Key Points: Unexpected low aerosol single scattering albedo was observed in Lhasa via in situ measurement of multiple optical parameters simultaneouslyBlack carbon was the dominant contributor (∼83%) to aerosol absorption at 370 nm, followed by primary and secondary brown carbonThe strong absorption in Lhasa exerted positive direct aerosol radiative forcing (warming effect) at the top of atmosphere [ABSTRACT FROM AUTHOR]

Diabetic cardiomyopathy (DCM) is a major factor in the development of heart failure. Mitochondria play a crucial role in regulating insulin resistance, oxidative stress, and inflammation, which affect the progression of DCM. Regular exercise can induce altered non-coding RNA (ncRNA) expression, which subsequently affects gene expression and protein function. The mechanism of exercise-induced mitochondrial-related non-coding RNA network in the regulation of DCM remains unclear. This study seeks to construct an innovative exercise-induced mitochondrial-related ncRNA network. Bioinformatic analysis of RNA sequencing data from an exercise rat model identified 144 differentially expressed long non-coding RNA (lncRNA) with cutoff criteria of p< 0.05 and fold change ≥1.0. GSE6880 and GSE4745 were the differentially expressed mRNAs from the left ventricle of DCM rat that downloaded from the GEO database. Combined with the differentially expressed mRNA and MitoCarta 3.0 dataset, the mitochondrial located gene Pdk4 was identified as a target gene. The miRNA prediction analysis using miRanda and TargetScan confirmed that 5 miRNAs have potential to interact with the 144 lncRNA. The novel lncRNA-miRNA-Pdk4 network was constructed for the first time. According to the functional protein association network, the newly created exercise-induced ncRNA network may serve as a promising diagnostic marker and therapeutic target, providing a fresh perspective to understand the molecular mechanism of different exercise types for the prevention and treatment of diabetic cardiomyopathy. [ABSTRACT FROM AUTHOR]

Cancer is one of the leading causes of deaths worldwide. Survival analysis and prediction of cancer patients is of great significance for their precision medicine. The robustness and interpretability of the survival prediction models are important, where robustness tells whether a model has learned the knowledge, and interpretability means if a model can show human what it has learned. In this paper, we propose a robust and interpretable model SurvConvMixer, which uses pathways customized gene expression images and ConvMixer for cancer short-term, mid-term and long-term overall survival prediction. With ConvMixer, the representation of each pathway can be learned respectively. We show the robustness of our model by testing the trained model on absolutely untrained external datasets. The interpretability of SurvConvMixer depends on gradient-weighted class activation mapping (Grad-Cam), by which we can obtain the pathway-level activation heat map. Then wilcoxon rank-sum tests are conducted to obtain the statistically significant pathways, thereby revealing which pathways the model focuses on more. SurvConvMixer achieves remarkable performance on the short-term, mid-term and long-term overall survival of lung adenocarcinoma, lung squamous cell carcinoma and skin cutaneous melanoma, and the external validation tests show that SurvConvMixer can generalize to external datasets so that it is robust. Finally, we investigate the activation maps generated by Grad-Cam, after wilcoxon rank-sum test and Kaplan–Meier estimation, we find that some survival-related pathways play important role in SurvConvMixer. [ABSTRACT FROM AUTHOR]

Background: High-dimensional omics data are increasingly utilized in clinical and public health research for disease risk prediction. Many previous sparse methods have been proposed that using prior knowledge, e.g., biological group structure information, to guide the model-building process. However, these methods are still based on a single model, offen leading to overconfident inferences and inferior generalization. Results: We proposed a novel stacking strategy based on a non-negative spike-and-slab Lasso (nsslasso) generalized linear model (GLM) for disease risk prediction in the context of high-dimensional omics data. Briefly, we used prior biological knowledge to segment omics data into a set of sub-data. Each sub-model was trained separately using the features from the group via a proper base learner. Then, the predictions of sub-models were ensembled by a super learner using nsslasso GLM. The proposed method was compared to several competitors, such as the Lasso, grlasso, and gsslasso, using simulated data and two open-access breast cancer data. As a result, the proposed method showed robustly superior prediction performance to the optimal single-model method in high-noise simulated data and real-world data. Furthermore, compared to the traditional stacking method, the proposed nsslasso stacking method can efficiently handle redundant sub-models and identify important sub-models. Conclusions: The proposed nsslasso method demonstrated favorable predictive accuracy, stability, and biological interpretability. Additionally, the proposed method can also be used to detect new biomarkers and key group structures. [ABSTRACT FROM AUTHOR]

The population of cancer survivors has markedly increased due to the rapid improvements in cancer treatment. However, cancer survivors experience accelerated aging, which leads to chronic diseases and other age-related conditions, such as frailty. Those conditions may persist years after cancer diagnosis and treatment. Cellular senescence, a hallmark of aging, is one of the mechanisms that contribute to accelerated aging in cancer survivors. Several aging measures, including measures based on clinical markers and biomarkers, have been proposed to estimate the aging process, and some of them have shown associations with mortality and frailty in cancer survivors. Several anti-aging interventions, including lifestyle changes and anti-aging drugs, have been proposed. Future research, particularly in large-scale studies, is needed to determine the efficiency of these aging measures and anti-aging interventions before considering their application in clinics. This review focuses on the mechanisms of cellular senescence and accelerated aging in cancer survivors, assessment of the aging process using clinical markers and biomarkers, and the high prevalence of frailty in that population, as well as possible opportunities for anti-aging interventions. A deeper understanding of aging measures and anti-aging interventions in cancer survivors will contribute to the development of effective strategies to mitigate accelerated aging in cancer survivors and improve their quality of life. [ABSTRACT FROM AUTHOR]

The development of information technology has created conducive conditions for the digital economy. The digital economy is regarded as a critical pathway for transforming traditional economic models. Green total factor productivity serves as an indicator for assessing the quality of economic development. During pivotal periods of economic transition, the digital economy and green total factor productivity have emerged as two prominent themes for achieving sustainable economic development. But the impact of digital economy on green total factor productivity is less discussed. Innovation environment refers to a confluence of conditions shaped by factors such as talent, funding, cultural atmosphere and government policies, all of which collectively support innovative activities within a region. The institutional environment encompasses the aggregate of economic, political, social, and legal rules. Currently, there is little discussion on bringing innovation environment and institutional environment into the impact of digital economy on green total factor productivity. To fill the research gap, this paper adopts the Slack based measure-Directional distance function model and Malmquist-Luenberger productivity index to measure green total factor productivity in each region based on the panel data collected from 30 provinces in China from 2004 to 2019. Generalized Method of Moments method is constructed to carry out an empirical study on the impact of digital economy on green total factor productivity. This paper constructs a panel threshold model with innovation environment and institutional environment as threshold variables. In further analysis, this paper employs panel quantile regression for the empirical analysis of the impact of the digital economy on green total factor productivity. Further analysis elucidates the evident disparities in the influence of the digital economy on green total factor productivity at various levels. The research results can provide a guide for discussing the green value of the digital economy and its role in fostering the development of a green economy. [ABSTRACT FROM AUTHOR]

The rise in wearable electronics has witnessed the advancement of self-healable wires, which are capable of recovering mechanical and electrical properties upon structural damage. However, their highly fluctuating electrical resistances in the range of hundreds to thousands of ohms under dynamic conditions such as bending, pressing, stretching and tremoring may seriously degrade the precision and continuity of the resulting electronic devices, thus severely hindering their wearable applications. Here, we report a new family of self-healable wires with high strengths and stable electrical conductivities under dynamic conditions, inspired by mechanical–electrical coupling of the myelinated axon in nature. Our self-healable wire based on mechanical–electrical coupling between the structural and conductive components has significantly improved the electrical stability under dynamic scenarios, enabling precise monitoring of human health status and daily activities, even in the case of limb tremors from simulated Parkinson's disease. Our mechanical–electrical coupling strategy opens a new avenue for the development of dynamically stable electrodes and devices toward real-world wearable applications. [ABSTRACT FROM AUTHOR]

Backgroud: The tumor immune microenvironment influences the efficiency of concurrent chemoradiotherapy (CCRT) in high-grade glioma (HGG). This study investigated peripheral blood T lymphocyte subsets as clinical indicators of therapeutic response and prognosis in pediatric high-grade glioma (pHGG). Methods: This retrospective study included 77 patients with postoperative pHGG who were treated concurrently with temozolomide and external beam radiotherapy between January 1, 2012, and December 31, 2018. The median follow-up was 26 (range: 5-106) months. Peripheral venous blood samples were collected before and after CCRT. The proportions of peripheral blood T lymphocytes and their association with treatment outcome and survival were determined. Results: Sixty-four (83.1%) patients achieved complete remission, partial remission, and stable disease, and 13 (16.9%) patients had progressive disease. Higher CD3+ T cell, CD4+ T cell, and CD8+ CD28+ T cell ratios were predictive of better response, while a higher CD8+ CD28− T cell ratio was predictive of poorer response. Binary logistic regression analysis showed that the CD8+ CD28+ T cell ratio was a significant independent predictor of CCRT response (odds ratio [OR] = 53.521, 95% confidence interval [CI] = 4.294-667.119, P =.002). Univariate and multivariate analysis of prognostic factors associated with survival showed that the CD8+ CD28+ T lymphocyte ratio was a significant independent predictor of progression-free survival (hazard ratio [HR] = 1.80, 95% CI = 1.06-3.08, P =.03), but none of the subsets were significantly associated with overall survival. Conclusion: Peripheral blood T lymphocytes have potential as predictors of CCRT response and prognosis in pHGG. [ABSTRACT FROM AUTHOR]

Cytospora (Cytosporaceae, Diaporthales) is a fungal genus that usually inhabits plants as endophytes, saprobes, as well as pathogens. Species of this genus are characterized by possessing allantoid hyaline conidia and ascospores. Samples with typical Cytospora canker symptoms on Prunus davidiana, P. padus and Salix sp. were collected in Tibet and Xinjiang, China. Species were identified using both morphological and molecular approaches of combined loci of internal transcribed spacer region rDNA (ITS), the partial actin (act) region, RNA polymerase II second largest subunit (rpb2), the translation elongation factor 1-alpha (tef1) gene and the partial be-ta-tubulin (tub2) gene. Six isolates in the present study formed three distinct clades from previously known species. Cytospora hejingensis sp. nov. from Salix sp., C. jilongensis sp. nov. from P. davidiana and C. kunsensis from P. padus were proposed herein. The current study improves the understanding of species concept in Cytospora. [ABSTRACT FROM AUTHOR]

Synchronous reluctance machines are considered a cost-effective solution for several industrial applications and present potential efficiency benefits compared to induction motors. In industrial applications, power supply oscillations can lead to short-term disturbances that can affect the drive operation; therefore, the control must be robust to guarantee high efficiency and service continuity. The focus of this study was to identify the speed boundaries considering different values of applied DC-link voltage, taking into account the highly nonlinear magnetic behavior of the machine and its cross-coupling characteristics. In addition, a comprehensive carrier-based implementation of a pulse width modulation strategy was proposed to achieve optimal efficiency in both the machine and converter, which is essential in the presence of "weak" grids. The proposed technique was demonstrated to meet the desired reference torque and rated speed, even during DC-link voltage drops (up to 7.4% of the rated voltage). The proposed methodology was experimentally validated on a 90 kW SynRM drive with a broader modulation range and higher efficiency. This work considered several different supply voltage levels to assess the stability and continuity of torque output and further proved the proposed method. [ABSTRACT FROM AUTHOR]

Optical encryption based on single-pixel imaging (SPI) has made great advances with the introduction of deep learning. However, the use of deep neural networks usually requires a long training time, and the networks need to be retrained once the target scene changes. With this in mind, we propose an SPI encryption scheme based on an attention-inserted physics-driven neural network. Here, an attention module is used to encrypt the single-pixel measurement value sequences of two images, together with a sequence of cryptographic keys, into a one-dimensional ciphertext signal to complete image encryption. Then, the encrypted signal is fed into a physics-driven neural network for high-fidelity decoding (i.e., decryption). This scheme eliminates the need for pre-training the network and gives more freedom to spatial modulation. Both simulation and experimental results have demonstrated the feasibility and eavesdropping resistance of this scheme. Thus, it will lead SPI-based optical encryption closer to intelligent deep encryption. [ABSTRACT FROM AUTHOR]

Camelina neglecta is a new diploid Brassicaceae species, which has great research value because of its close relationship with the hexaploid oilseed crop Camelina sativa. Here, we report a chromosome-level assembly of C. neglecta with a total length of 210 Mb. By adopting PacBio sequencing and Hi-C technology, the C. neglecta genome was assembled into 6 chromosomes with scaffold N50 of 29.62 Mb. C. neglecta has undergone the whole-genome triplication (γ) shared among eudicots and two whole-genome duplications (α and β) shared by crucifers, but it has not undergone a specific whole-genome duplication event. By synteny analysis between C. neglecta and C. sativa, we successfully used the method of calculating Ks to distinguish the three subgenomes of C. sativa and determined that C. neglecta was closest to the first subgenome (SG1) of C. sativa. Further, transcriptomic analysis revealed the key genes associated with seed oil biosynthesis and its transcriptional regulation, including SAD, FAD2, FAD3, FAE1, ABI3, WRI1 and FUS3 displaying high expression levels in C. neglecta seeds. The high representability of C. neglecta as a model species for Camelina-based biotechnology research has been demonstrated for the first time. In particular, floral Agrobacterium tumefaciens infiltration-based transformation of C. neglecta, leading to overexpression of CvLPAT2, CpDGAT1 and CvFatB1 transgenes, was demonstrated for medium-chain fatty acid accumulation in C. neglecta seed oil. This study provides an important genomic resource and establishes C. neglecta as a new model for oilseed biotechnology research. [ABSTRACT FROM AUTHOR]

Magnetic–plasmonic nanoparticles (NPs) have attracted great interest in many fields because they can exhibit more physical and chemical properties than individual magnetic or plasmonic NPs. In this work, we synthesized Au- or Ag-decorated Fe3O4 nanoparticles coated with PEI (Fe3O4-PEI-M (M = Au or Ag) NPs) using a simple method. The influences of the plasmonic metal NPs' (Au or Ag) coating density on the magnetic and plasmonic properties of the Fe3O4-PEI-M (M = Au or Ag) NPs were investigated, and the density of the plasmonic metal NPs coated on the Fe3O4 NPs surfaces could be adjusted by controlling the polyethyleneimine (PEI) concentration. It showed that the Fe3O4-PEI-M (M = Au or Ag) NPs exhibited both magnetic and plasmonic properties. When the PEI concentration increased from 5 to 35 mg/mL, the coating density of the Au or Ag NPs on the Fe3O4 NPs surfaces increased, the corresponding magnetic intensity became weaker, and the plasmonic intensity was stronger. At the same time, the plasmonic resonance peak of the Fe3O4-PEI-M (M = Au or Ag) NPs was red shifted. Therefore, there was an optimal coverage of the plasmonic metal NPs on the Fe3O4 NPs surfaces to balance the magnetic and plasmonic properties when the PEI concentration was between 15 and 25 mg/mL. This result can guide the application of the Fe3O4-M (M = Au or Ag) NPs in the biomedical field. [ABSTRACT FROM AUTHOR]

In recent years, deep learning-based classification methods for hyperspectral images (HSIs) have gained widespread popularity in fields such as agriculture, environmental monitoring, and geological exploration. This is owing to their ability to automatically extract features and deliver outstanding performance. This study provides a new Dilated Spectral–Spatial Gaussian Transformer Net (DSSGT) model. The DSSGT model incorporates dilated convolutions as shallow feature extraction units, which allows for an expanded receptive field while maintaining computational efficiency. We integrated transformer architecture to effectively capture feature relationships and generate deep fusion features, thereby enhancing classification accuracy. We used consecutive dilated convolutional layers to extract joint low-level spectral–spatial features. We then introduced Gaussian Weighted Pixel Embedding blocks, which leverage Gaussian weight matrices to transform the joint features into pixel-level vectors. By combining the features of each pixel with its neighbouring pixels, we obtained pixel-level representations that are more expressive and context-aware. The transformed vector matrix was fed into the transformer encoder module, enabling the capture of global dependencies within the input data and generating higher-level fusion features with improved expressiveness and discriminability. We evaluated the proposed DSSGT model using five hyperspectral image datasets through comparative experiments. Our results demonstrate the superior performance of our approach compared to those of current state-of-the-art methods, providing compelling evidence of the DSSGT model's effectiveness. [ABSTRACT FROM AUTHOR]

Background: Patent foramen ovale (PFO) is associated with transient ischemia attack (TIA) or stroke, paradoxical embolism, and migraines. PFO closure decreases the recurrent incidence of cerebral ischemic events and reduces the incidence of syncope in adults. However, whether PFO is associated with syncope in pediatric patients has not been investigated. Methods: 1001 pediatric patients (aged 4 to 17 years, mean age 10.31 ± 2.61 years, 519 males) who complained of unexplained syncope, palpitation, headache, dizziness and chest pain and were hospitalized in the Syncope Ward, The Second Xiangya Hospital, Central South University between January 2013 and April 2022 were recruited. Children with definite etiology of syncope, neurological, cardiogenic, psychological and other system diseases were excluded. PFO was measured by transthoracic echocardiography and right-heart contrast echocardiography was performed to identify the presence of right-to-left shunting. The demographic data and medical records were retrospectively reviewed and analyzed. Results: 276 cases were included in the simple syncope group, 379 cases in the headache/dizziness group, 265 cases in the chest pain group, and 81 cases in the palpitation group. The incidence of PFO between the four groups was insignificant (4.71%, 4.74%, 4.15%, 6.17%, respectively, P = 0.903). Multivariate Logistic regression demonstrated that PFO is not associated with the increased risk of syncope (P = 0.081). Conclusion: PFO may not increase the risk of syncope in pediatric patients. Further study may include a large and multicenter sample to investigate the association between PFO and unexplained syncope. [ABSTRACT FROM AUTHOR]

The recently discovered kagome superconductors offer a promising platform for investigating intertwined orders and novel states, including topology, superconductivity, charge density waves, and more. The interplay among these orders can spontaneously break rotational symmetry, giving rise to exotic phenomena such as nematicity or even nematic superconductivity. Here we present our findings on the two-fold symmetric superconductivity in thin-flake RbV3Sb5 in response to direction-dependent in-plane magnetic fields, in contrast to the inherent six-fold structural symmetry of the crystal lattice. The two-fold symmetry was evidenced through a combination of magnetoresistance transport experiments, critical magnetic field measurements, and observations of anisotropic superconducting gaps. Additionally, by altering the experimental configuration, we also detected the presence of six-fold symmetric components superimposed on the two-fold symmetry at the boundary between normal and superconducting states. Our results underscore the correlation-driven symmetry-breaking phenomena and emphasize the potential of this correlated kagome family as a promising platform for investigating intertwined orders, including unconventional superconductivity. Kagome materials provide a platform to investigate the interplay between a range of ordered phases such as charge density waves, superconductivity, as well as non-trivial topological properties. Here, the authors observed the two-fold symmetric superconductivity in RbV3Sb5, which suggests the presence of unconventional superconductivity involved with nematic states and spin-orbit-parity coupled superconductivity. [ABSTRACT FROM AUTHOR]

The microwave absorption performance of high-entropy alloys (HEAs) can be improved by reducing the reflection coefficient of electromagnetic waves and broadening the absorption frequency band. The present work prepared flaky irregular-shaped Al1.5Co4Fe2Cr and Al1.5Co4Fe2Cr@rGO alloy powders by mechanical alloying (MA) at different rotational speeds. It was found that the addition of trace amounts of reduced graphene oxide (rGO) had a favorable effect on the impedance matching, reflection loss (RL), and effective absorbing bandwidth (EAB) of the Al1.5Co4Fe2Cr@rGO HEA composite powders. The EAB of the alloy powders prepared at 300 rpm increased from 2.58 GHz to 4.62 GHz with the additive, and the RL increased by 2.56 dB. The results showed that the presence of rGO modified the complex dielectric constant of HEA powders, thereby enhancing their dielectric loss capability. Additionally, the presence of lamellar rGO intensified the interfacial reflections within the absorber, facilitating the dissipation of electromagnetic waves. The effect of the ball milling speed on the defect concentration of the alloy powders also affected its wave absorption performance. The samples prepared at 350 rpm had the best wave absorption performance, with an RL of −16.23 and −17.28 dB for a thickness of 1.6 mm and EAB of 5.77 GHz and 5.43 GHz, respectively. [ABSTRACT FROM AUTHOR]

Cytoplasmic Dynein is a multiple-subunit macromolecular motor protein involved in the transport process of cells. The Dynein intermediate chain (DIC) is one of the subunits of Dynein-1. In our previous studies, we showed that Pt-DIC may play an important role in the nuclear deformation of spermiogenesis in Portunus trituberculatus. Lamin B is essential for maintaining nuclear structure and functions. Surprisingly, Pt-Lamin B was expressed not only in the perinucleus but also in the pro-acrosome during spermiogenesis in P. trituberculatus. Studies have also shown that Dynein-1 can mediate the transport of Lamin B in mammals. Thus, to study the relationship of Pt-DIC and Pt-Lamin B in the spermatogenesis of P. trituberculatus, we knocked down the Pt-DIC gene in P. trituberculatus by RNAi. The results showed that the distribution of Pt-DIC and Pt-Lamin B in spermiogenesis was abnormal, and the colocalization was weakened. Moreover, we verified the interaction of Pt-DIC and Pt-Lamin B via coimmunoprecipitation. Therefore, our results suggested that both Pt-DIC and Pt-Lamin B were involved in the spermatogenesis of P. trituberculatus, and one of the functions of Dynein-1 is to mediate the transport of Lamin B in the spermiogenesis of P. trituberculatus. [ABSTRACT FROM AUTHOR]

Crystalline phase engineering is a prominent strategy for synergistically optimizing the surface–body phases of a catalyst. In this work, TiO2 nanosheets assembled into nanotubes (TNSTs) with two phases, anatase and rutile, were firstly synthesized via crystal engineering by simple thermal annealing. These were subsequently loaded with Ru nanoparticles, with a mean size of 5.0 nm, to create the efficient benzene hydrogenation catalyst Ru/TNSTs. The well-designed nanosheet-tube structure boasts a large specific surface area and excellent transmission channels, which effectively prevents the agglomeration and deactivation of loaded Ru nanoparticles, as well as promoting the internal diffusion in the reaction process of benzene hydrogenation to cyclohexene. Furthermore, titanium dioxide nanosheet-tubes contain numerous Ti3+ defects, which not only improves the overall conversion rate of cyclohexene but also enhances the suppression of cyclohexene adsorption. Most importantly, the titanium dioxide with its two-phase composition of 75 wt% anatase and 25 wt% rutile increases the ratio of electron deficiencies of Ru and promotes cyclohexene desorption. These synergistic properties enhance the selectivity and efficiency of the Ru/TNSTs catalysts, resulting in excellent performance in the hydrogenation of benzene to cyclohexene. In particular, the Ru/TNSTs-4 catalyst (annealed for 4 h), under the specific conditions of 140 °C temperature and 5 MPa hydrogen pressure for the hydrogenation process, achieves a 95% initial selectivity and 51% yield of cyclohexene in the reaction, outperforming most supported Ru-based catalysts. This work may provide new perspectives for designing efficient benzene hydrogenation catalysts via crystalline phase engineering. [ABSTRACT FROM AUTHOR]

Extreme dry and wet events can result in significant crop yield losses. However, the impact of consecutive occurrence of dry and wet extremes on crop yield remains unclear. Here, we investigate the hotspots of compound dry and wet (CDW) extremes across global rice croplands and their impacts on rice yield. We identify a significant increasing trend in the frequency of CDW extremes during 1981–2016. The risk of yield loss caused by CDW extremes can be twice as high as the risk from individual wet and dry extremes. Furthermore, we find that global rice croplands face a 43% higher risk of rice yield loss due to dry‐to‐wet extremes compared to wet‐to‐dry extremes. Our findings provide new insights into the sustainability of global rice production and food security in the face of compound hydrological extremes. Plain Language Summary: It is widely recognized that compound events may exert larger impacts on crop production compared to individual extremes. Here, we investigate the consecutive occurrence of dry and wet (CDW) extremes during the rice‐growing season and estimate their impacts on rice yield. We observe a significant increase in the frequency of CDW extremes across global rice croplands during the rice‐growing season from 1981 to 2016. The CDW extremes exert a larger impact on rice yield loss compared to individual wet and dry extremes. The CDW extremes, characterized by longer durations of both dry and wet extremes and faster transitions between them, have an even more adverse influence on rice yield. The risk of yield loss caused by CDW extremes is 200% higher than the risk from individual wet and dry extremes. Furthermore, global rice croplands face a 43% higher risk of yield loss due to dry‐to‐wet extremes than wet‐to‐dry extremes. Key Points: A significant increasing trend in the frequency of compound dry and wet (CDW) extremes was observed across global rice croplandsThe risk of rice yield loss caused by CDW extremes can be twice as high as the risk from individual wet and dry extremesGlobal rice croplands face a 43% higher risk of rice yield loss caused by dry‐to‐wet extremes compared to wet‐to‐dry extremes [ABSTRACT FROM AUTHOR]

This research proposed to retrospectively analyze 20 years of clinical data and investigate the relationship between demographic factors and syncopal symptom in pediatric vasovagal syncope. A total of 2513 children, 1124 males and 1389 females, age range 3–18 years, who presented to Department of Pediatric Cardiovasology, Children's Medical Center, The Second Xiangya Hospital, Central South University with unexplained syncope or pre-syncope and were diagnosed with vasovagal syncope were retrospectively collected and divided into syncope group (n = 1262) and pre-syncope group (n = 1251). (1) Females had a 36% increased risk of syncope compared to males, a 27% increased risk of syncope for every 1-year increase in age, and a 2% decreased risk of syncope for every 1 cm increase in height. (2) A non-linear relationship between age, height, weight and syncope was observed. When age > 10.67 years, the risk of syncope increases by 45% for each 1-year increase in age; when height < 146 cm, the risk of syncope decreases by 4% for each 1 cm increase in height; when weight < 28.5 kg, the risk of syncope decreases by 10% for each 1 kg increase in weight. Demographic factors are strongly associated with syncopal symptom in pediatric vasovagal syncope and can help to predict the risk. [ABSTRACT FROM AUTHOR]

The multifunctional linker molecules are crucial for the bio-orthogonal reaction for proteomic target profiling. Herein, we wish to present a novel type of biotin-based tetra-functional bio-orthogonal linkers 3a–3h named BPPA which, possessing a unique photolabile phenacyl ester motif, were readily prepared in 85–90% yields by a simple and green one-step protocol from commercially available and inexpensive reagents of biotin acids and 4'-ethynyl/azido 2-bromoacetophenones. The typical click reaction of BPPA linkers 3a and 3e via copper-catalyzed azide–alkyne cycloaddition (CuAAC) took place easily, resulting in the corresponding BPPA-triazole adducts 4a and 4b in nearly quantitative yields. A further cleavability evaluation of 4a and 4b demonstrated that the expected C-O bond detachment could be accomplished efficiently and rapidly by UV irradiation or by ammonia hydrolysis, respectively, resulting in the residual (hydroxyl)acetylphenyl triazole fragment supposed to be attached to proteins during biological manipulations. The BPPA linkers, with dual clickable options of either the terminal azide or alkyne clickable group, exhibit high potentials for various CuAAC-oriented bio-orthogonal reactions. [ABSTRACT FROM AUTHOR]

Ground-based synthetic aperture radar (GBSAR) is widely used in mountains, mines, and other areas because it can get the sub-millimeter deformation information of monitoring scenes. This technology plays a vital role in safeguarding production operations, providing accurate disaster projections, and facilitating timely early warning dissemination. However, the moving target's defocus/displaced signal will mask the image of GBSAR, which affects the accuracy of deformation inversion. Hence, the detection of moving targets in GBSAR imagery is essential. An algorithm for moving target detection based on refocusing is proposed in this paper to address this problem. The algorithm establishes a two-dimensional parameter search space for squint angle and relative speed. Based on the parameter searching, the improved Range Doppler (RD) algorithm is used for refocusing. The optimal 2D parameters are searched via an algorithm combining the entropy minimization principle and the enlightend search. The presence of a moving target in the observation area is determined based on whether there is an optimal parameter to minimize the entropy value of the refocused image. This approach enables the detection of moving targets in GBSAR imagery. The proposed method is verified by the synthetic data. [ABSTRACT FROM AUTHOR]

To avoid wrinkling and cross-sectional distortion in thin-walled tube bending, a novel bending process called tube press bending under gas internal pressure (abbreviated as TGPB) is proposed. In the TGPB process, internal pressure serves a supportive function and generates an additional tensile stress, which can reduce the axial compressive stress on the inner side of the bend, avoiding the occurrence of wrinkles and cross-sectional distortions. Compared with the previous tube bending technologies, the support pressure inside the tube will not change suddenly with the volume change of the tube cavity due to the compressibility of gas. It is suitable for thin-walled tube bending with large, variable axial curvatures and high strength as the shape of the target part is determined by the die cavity. A theoretical prediction model of critical support pressure for TGPB without wrinkling defects is established. The critical support pressure, wall thickness, and section distortion of tubes with different curvatures bent by TGPB are analyzed through FE simulations and experiments, and tubes with different material properties are also done through FE simulations. The results indicate that the tube can be stably bent to the desired shape under the predicted critical support pressure, which is significantly affected by the bending radius and material characteristics, increases with strength coefficient, and decreases with bending radius and strain hardening exponent. A high-strength steel prebending part with complex variable axial curvatures has been successfully formed by TGPB technology under a support pressure of 5 MPa; the minimum and maximum bending radii of which are 846 mm and 2373 mm, respectively. [ABSTRACT FROM AUTHOR]

The emergence of abrupt shift from drought to downpour has attracted widespread attention in recent years, with particularly disastrous consequences in low‐income regions. However, the spatiotemporal evolution and poverty exposure to such drought‐to‐downpour events remain poorly understood. Here, we investigate the connection between poverty and drought‐to‐downpour events based on three data products and climate models on a global scale. We find that the drought‐to‐downpour events increased by 24%–48% in the poorest 20% of the world's population from 1980 to 2010. The drought‐to‐downpour events do not appear to be occurring more frequently in most regions globally, just affecting regions with higher poverty rates more frequently, especially in African countries. The exposure inequality remains under future socioeconomic pathways, with a nearly fivefold increase in the exposure for the poorer populations. Poverty exposure to more frequent drought‐to‐downpour events demands greater support for climate adaptation in low‐income countries to reduce poverty and inequality. Plain Language Summary: Many regions have suffered greatly from recent occurrences of abrupt shift from drought to downpour, suggesting that the emerging threat is a global challenge. Such drought‐to‐downpour events pose challenges to water infrastructures in developed countries, let alone those poor countries with limited adaptation capacity and resources. However, the connection between the drought‐to‐downpour events and poverty incidence remains poorly understood. Here, we show that such drought‐to‐downpour events experienced by the poorest 20% of the world's population increased significantly by 24%–48% from 1980 to 2010. Such a significant increase, however, is not observed for the remaining wealthiest 80%. The drought‐to‐downpour events do not appear to be occurring more frequently in most global regions, just affecting regions with higher poverty rates more frequently, especially in African countries. Climate projections show that such inequality would remain in a warming climate. Our results highlight the urgency to provide greater support for climate adaptation in low‐income countries to reduce poverty and inequality. Key Points: The connection between poverty incidence and drought‐to‐downpour weather whiplashes is uncovered on a global scaleThe drought‐to‐downpour events increased by 24%–48% in the poorest 20% of the world's population from 1980 to 2010The drought‐to‐downpour events do not appear to be occurring more frequently in most global regions, just affecting regions with higher poverty rates more frequently [ABSTRACT FROM AUTHOR]

Network embedding is a technique used to learn a low-dimensional vector representation for each node in a network. This method has been proven effective in network mining tasks, especially in the area of recommendation systems. The real-world scenarios often contain rich attribute information that can be leveraged to enhance the performance of representation learning methods. Therefore, this article proposes an attribute network embedding recommendation method based on self-attention mechanism (AESR) that caters to the recommendation needs of users with little or no explicit feedback data. The proposed AESR method first models the attribute combination representation of items and then uses a self-attention mechanism to compactly embed the combination representation. By representing users as different anchor vectors, the method can efficiently learn their preferences and reconstruct them with few learning samples. This achieves accurate and fast recommendations and avoids data sparsity problems. Experimental results show that AESR can provide personalized recommendations even for users with little explicit feedback information. Moreover, the attribute extraction of documents can effectively improve recommendation accuracy on different datasets. Overall, the proposed AESR method provides a promising approach to recommendation systems that can leverage attribute information for better performance. [ABSTRACT FROM AUTHOR]

The treatment of ulcerative colitis (UC) is still an intractable medical problem. Polysaccharides are promising candidates for the treatment of UC and have received widespread attention in recent years. The objective of this study was to explore the protective effect and underlying mechanism of dandelion polysaccharide (DP) on dextran sulfate sodium (DSS)‐induced colitis in mice. Our results showed that oral administration of DP could dramatically alleviate colonic lesions, as evidenced by reduced DAI scores, shortening of colon length, and ameliorating pathologic abnormalities in colons. Additionally, the expressions of pro‐inflammatory factors (TNF‐α, IL‐1β, and IL‐6) and the infiltration of inflammation‐regulation cells, marked by myeloperoxidase and F4/80, were also inhibited after DP treatment. Moreover, DP treatment also markedly suppressed the nuclear translocation of NF‐κB‐p65 and the activation of the NLRP3 inflammasome. Furthermore, DP also activated the Nrf2/HO‐1 pathway and reduced the oxidative stress induced by DSS. Overall, these results suggest that DP could be a promising novel therapeutic approach for the treatment of UC. [ABSTRACT FROM AUTHOR]

The past decade has witnessed remarkable economic development, marked by rapid industrialization and urbanization across Asian regions. This surge in economic activity has led to significant emissions, resulting in alarming levels of air pollution. Our study comprehensively assessed the spatial and temporal trends of key pollutants, namely nitrogen dioxide (NO2), sulfur dioxide (SO2), and aerosol (using aerosol optical depth (AOD) at 550 nm as an indicator), from 2011 to 2021. The data sources utilized include OMI onboard the Aura satellite for NO2 and SO2, as well as MODIS onboard Terra and Aqua satellites for AOD. The results from spatial and temporal trend analyses of the three parameters show that there is a clear declining trend over China and Republic of Korea (e.g., NO2 is declining with an overall rate of −7.8 × 1012 molecules/cm2/year over China) due to the strict implementation of air pollution control policies. However, it is essential to note that both countries still grapple with substantial pollution levels, with proportions exceeding 0.5, indicating that air quality is improving but has not yet reached a safe threshold. In contrast, South Asian regions, including Bangladesh, Pakistan, and India, are experiencing an increasing trend (e.g., NO2 is increasing with an overall rate of 1.2 × 1012 molecules/cm2/year in Bangladesh), primarily due to the lack of rigorous air pollution control policies. The average emissions of NO2 and SO2 were remarkably higher in winter than in summer. Notably, the identified hotspots are statistically significant and predominantly coincide with densely populated areas, such as the North China Plain (NCP). Furthermore, this study underscores the pivotal role of sector-wise emissions in air quality monitoring and improvement. Different cities are primarily influenced by emissions from specific sectors, emphasizing the need for targeted pollution control measures. The findings presented in this research contribute valuable insights to the air quality monitoring and improvement efforts in East and South Asian regions. [ABSTRACT FROM AUTHOR]

A few spiking neural network (SNN)-based classifiers have been proposed for hyperspectral images (HSI) classification to alleviate the higher computational energy cost problem. Nevertheless, due to the lack of ability to distinguish boundaries, the existing SNN-based HSI classification methods are very prone to falling into the Hughes phenomenon. The confusion of the classifier at the class boundary is particularly obvious. To remedy these issues, we propose a boundary-aware deformable spiking residual neural network (BDSNN) for HSI classification. A deformable convolution neural network plays the most important role in realizing the boundary-awareness of the proposed model. To the best of our knowledge, this is the first attempt to combine the deformable convolutional mechanism and the SNN-based model. Additionally, spike-element-wise ResNet is used as a fundamental framework for going deeper. A temporal channel joint attention mechanism is introduced to filter out which channels and times are critical. We evaluate the proposed model on four benchmark hyperspectral data sets—the IP, PU, SV, and HU data sets. The experimental results demonstrate that the proposed model can obtain a comparable classification accuracy with state-of-the-art methods in terms of overall accuracy (OA), average accuracy (AA), and statistical kappa (κ) coefficient. The ablation study results prove the effectiveness of the introduction of the deformable convolutional mechanism for BDSNN's boundary-aware characteristic. [ABSTRACT FROM AUTHOR]

Innovation drive differs from investment drive and resource drive in that it focuses on knowledge and skills to promote productivity growth. By integrating technical standards within the framework of an innovation-driven development system in this work, theoretical implications for this development strategy may be revealed. Following our theoretical study, we built a PECM utilizing China's inter-provincial panel data from 2007 to 2020 to investigate the long and short-term relationships between standardization, R&D, and innovation-driven development. The following are the key findings: First, both standardization and R&D are the nation's critical engines of innovation-driven development. Second, standardization has the greatest impact on TFP through improving technical efficiency, whereas R&D drives both technical development and technical efficiency improvement. Third, while the influence of technical standard drafters' production scale on scale efficiency was insignificant from 2007 to 2013, it became substantial after 2014 with China's macroeconomic reform of "transforming the mode and changing the structure." [ABSTRACT FROM AUTHOR]

The stress caused by calcium–magnesium–alumino–silicate (CMAS) corrosion is a critical factor in thermal barrier failure of thermal barrier coatings (TBCs). For the service safety of TBCs, it is important to characterize the stress inside TBCs during CMAS corrosion using a nondestructive and accurate method. In this study, photoluminescence spectroscopy technology was applied to characterize the stress in TBCs during CMAS corrosion. First, TBC specimens containing yttrium–aluminum–garnet doped with trace Ce3+ ions (YAG:Ce3+)/yttrium oxide partially stabilized zirconia double‐ceramic‐layer were prepared by atmospheric plasma spraying. Then, CMAS corrosion experiments were performed using the TBC specimens, and a mechanical model was derived based on Ce3+ photoluminescence spectroscopy to investigate the stress in the TBCs. Finally, the microstructure, extent of CMAS corrosion and stress field in TBC specimens, was characterized. The results reveal that the penetration of CMAS leads to local stress concentration and a nonlinear stress distribution from the outside surface to the inside of the YAG:Ce3+ layer. In addition, an increase in corrosion time, temperature, and CMAS concentration can significantly influence the evolution of the stress field in TBCs. [ABSTRACT FROM AUTHOR]

With the continuous development of surface observation methods and technologies, we can acquire multiple sources of data more effectively in the same geographic area. The quality and availability of these data have also significantly improved. Consequently, how to better utilize multi-source data to represent ground information has become an important research question in the field of geoscience. In this paper, a novel model called multi-modal transformer cascaded fusion net (MMTCFN) is proposed for fusion and classification of multi-modal remote sensing data, Hyperspectral Imagery (HSI) and LiDAR data. Feature fusion and feature extraction are the two stages of the model. First, in the feature extraction stage, a three-branch cascaded Convolutional Neural Network (CNN) framework is employed to fully leverage the advantages of convolutional operators in extracting shallow-level local features. Based on this, we generated multi-modal long-range integrated deep features utilizing the transformer-based vectorized pixel group transformer (VPGT) module during the feature fusion stage. In the VPGT block, we designed a vectorized pixel group embedding that preserves the global features extracted from the three branches in a non-overlapping multi-space manner. Moreover, we introduce the DropKey mechanism into the multi-head self-attention (MHSA) to alleviate overfitting caused by insufficient training samples. Finally, we employ a probabilistic decision fusion strategy to integrate multiple class estimations, assigning a specific category to each pixel. This model was experimented on three HSI-LiDAR datasets with balanced and unbalanced training samples. The proposed model outperforms the other seven SOTA approaches in terms of OA performance, proving the superiority of MMTCFN for the HSI-LiDAR classification task. [ABSTRACT FROM AUTHOR]

Aluminum alloys are widely used in transportation and aerospace industry because of their high strength-weight ratio and great formability. Compared with tuning grain refinement and solid solution, regulating precipitation strengthening through heat treatment is the most effective method to improve the yield strength of aluminum alloys. However, for the existing models, 3D precipitates are still simplified to 2D shape in aluminum alloys, and this trend causes the low accurate microstructure design. To address this issue, we develop a novel probability-dependent statistical model to predict the strength of aluminum alloys, considering the statistical distribution of the precipitate size and the relative spatial position of dislocations and precipitates. Compared with the classical model, the yield strength calculated from the current model is in good agreement with the experimental measurements, and the prediction accuracy is improved from 84.9% to 95.15%. In addition, the optimal size of precipitate is obtained for maximizing the strengthening effect. Our model not only provides a useful tool for the design of high-strength aluminum alloys, but also give a promising way to maximize the strength by changing the size distribution of the precipitate through a reasonable heat treatment process. [ABSTRACT FROM AUTHOR]

With the aim of promoting environmental sustainability and enhancing transport efficiency, battery-powered connected and automated vehicles (B-CAVs) are employed to replace diesel-powered ones in horizontal transport systems (HTSs) of container terminals. The operational efficiency of an HTS can be increased by the cooperation of B-CAVs. However, it is time-consuming to charge them. Therefore, their battery management becomes a critical issue of a transport schedule. To run container terminals more economically and efficiently, this work proposes an integrated scheduling approach to B-CAVs tasks' dispatch and route planning, where battery management is taken into account. An integer programming model is constructed with the goal of minimizing the total travel distance. Then, a sustainable charging policy is designed to ensure the consistent transport capacity of an HTS. Furthermore, a congestion-free path plan-based improved genetic algorithm is presented to obtain a near-optimal plan for dispatching B-CAVs to perform transporting and charging operations. A series of experiments are carried out to verify the effectiveness and efficiency of our approach. [ABSTRACT FROM AUTHOR]

The impact of solute Al on the penetration of Cu atoms into the Fe grain boundary (GB) in the Fe–Cu embrittlement system was investigated by performing molecular dynamics simulations. Furthermore, the first principle density functional theory calculation was also employed to determine the binding properties and the electronic structure of GBs doped with solute atoms. The inhibition mechanism of liquid metal embrittlement (LME) cracks in the Fe–Cu system by Al was analyzed at the atomic scale by conducting simulations and calculations. The results show that the diffusion rate of Al along with the GB direction was much higher than that of Cu, and the preferential penetration and segregation of Al atoms acted as a barrier layer. Moreover, the addition of Al reduced the potential energy of Cu atoms, thus stabilizing their motion. These factors significantly inhibited the diffusion of Cu atoms along with the GB direction. The binding property of Fe GB doped with a high concentration of Cu was severely deteriorated, while the Al doping improved the atomic bonding. GBs with higher binding properties are less susceptible to fracture and groove under the application of tensile stress. This paper presents a novel perspective on the inhibition mechanism of LME cracks by doping a third component to inhibit the penetration of embrittlement atoms. [ABSTRACT FROM AUTHOR]

Seasonal freezing depth change is important in many environmental science and engineering applications. However, such changes are rare at region scales, especially over China, in the long time series. In this study, we evaluated the annual changes in seasonal maximum freezing depth (MFD) over China from 1971 to 2020 using an ensemble-modeling method based on support vector machine regression (SVMR) with 600 repetitions. Remote sensing data and climate data were input variables used as predictors. The models were trained using a large amount of annual measurement data from 600 meteorological stations. The main reason for using SVMR here was because it has been shown to perform better than random forests (RF), k-nearest neighbors (KNN), and generalized linear regression (GLR) in these cases. The prediction results were generally consistent with the observed MFD values. Cross validation showed that the model performed well on training data and had a better spatial generalization ability. The results show that the freezing depth of seasonally frozen ground in China decreased year by year. The average MFD was reduced by 3.64 cm, 7.59 cm, 5.54 cm, and 5.58 cm, in the 1980s, 1990s, 2000s, and 2010s, respectively, compared with the decade before. In the last 50 years, the area occupied by the freezing depth ranges of 0–40 cm, 40–60 cm, 60–80 cm, 80–100 cm, and 120–140 cm increased by 99,300 square kilometers, 146,200 square kilometers, 130,300 square kilometers, 115,600 square kilometers, and 83,800 square kilometers, respectively. In addition to the slight decrease in freezing depth range of 100–120 cm, the reduced area was 29,500 square kilometers. Freezing depth ranges greater than 140 cm showed a decreasing trend. The freezing depth range of 140–160 cm, which was the lowest range, decreased by 89,700 square kilometers. The 160–180 cm range decreased by 120,500 square kilometers, and the 180–200 cm range decreased by 161,500 square kilometers. The freezing depth range greater than 200 cm, which was the highest reduction range, decreased by 174,000 square kilometers. Considering the lack of data on the change in MFD of seasonally frozen ground in China in recent decades, machine learning provides an effective method for studying meteorological data and reanalysis data in order to predict the changes in MFD. [ABSTRACT FROM AUTHOR]

This study was conducted to evaluate the oligomeric proanthocyanidins (OPC) in alleviating the detrimental effects of intestinal health caused by dietary histamine in juvenile American eels (Anguilla rostrata). A total of 480 fish with a similar body weight of 10.84 ± 0.16 g were randomly divided into four groups, and there were the control group fed a basal diet, the HIS group fed a diet with a high level of histamine (534 mg/kg), the H + OPC I group fed the high histamine diet with 300 mg/kg OPC, and the H + OPC II group fed the high histamine diet with 600 mg/kg OPC, respectiviely. After the fish were fed the trial diets for 77 days, the intestinal samples were taken, and the related parameters of intestinal health were analyzed. Dietary 300 mg/kg or 600 mg/kg OPC could reverse the decreased activities of lipase, protease, and glutathione peroxidase and the level of total antioxidant capacity, the increased intestinal malondialdehyde and D-lactate acid levels and the activity of diamine oxidase in serum, and the decreased villus height caused by a high level of dietary histamine. There were no significant differences above all the indices between the H + OPC I group and the H + OPC II group. The higher relative abundances of potentially pathogenic bacteria were induced by the high level of dietary histamine. Dietary 300 mg/kg OPC might increase the relative abundance of the potential probiotics and inhibit the colonization of intestinal pathogenic bacteria of juvenile American eels exposed to the stress of high dietary hisatamine. The intestinal health status of the H + OPC groups was similar to that of the control group. These results suggested that dietary 300 mg/kg OPC might alleviate the detrimental effects of dietary 534 mg/kg histamine on the intestine health of juvenile American eels by increasing the activity of digestive enzymes, improving the antioxidative potential and barrier function, and beneficially modulating the intestinal microbiota. Dietary 600 mg/kg OPC could not exert further improvement in growth performance and the intestinal health of juvenile American eels. [ABSTRACT FROM AUTHOR]

A subtraction procedure is proposed to evaluate the logarithmic singularity for matrix‐friendly layered medium Green's functions. In this subtraction procedure, an integral of the Bessel function combined with the exponential function and power function, where the sum of the power of kρ$k_{\rho }$ and the order of the Bessel function is −1, is designed to fit the oscillation of the tail integral. After subtracting the designed integral, the integrand of the remainder decreases rapidly. Accordingly, the remainder can be calculated without finding any surface poles or steepest descent path, while the subtracted part can be calculated analytically. Several numerical examples are provided to reveal the feasibility and the accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

The abrupt alternation between hot and wet extremes can lead to more severe societal impacts than isolated extremes. However, despite an understanding of hot and wet extremes separately, their temporally compounding characteristics are not well examined yet. Our study presents a comprehensive assessment of successive heat‐pluvial and pluvial‐heat events globally. We find that these successive extremes within a week occur every 6–7 years on average within warm seasons during 1956–2015, about 15% more often than would be expected by chance, and that they have a significant increase in frequency of about 22% per decade due to warming. We further investigate the role of vapor pressure deficit (VPD) and find that heat‐pluvial (pluvial‐heat) events are linked to negative (positive) VPD anomalies. Our results are statistically significant based on moving‐blocks bootstrap resampling and field significance tests, highlighting these methods' importance in robustly identifying compound events under autocorrelation and multiple‐testing conditions. Plain Language Summary: In recent years, the world has experienced various clustered weather and climate extremes, which are highly disruptive to humans and society. However, current knowledge on the risk of successive occurrence of hot (humid heat, including the effects of both temperature and humidity) and wet (pluvial flooding, usually caused by extreme rainfall) extremes remains unclear. In this study, we present a comprehensive assessment of the two types of interacting hot and wet extremes: humid heat extremes followed by pluvial flooding (heat‐pluvial) and extreme pluvials followed by humid heat (pluvial‐heat). We find that these events have increased significantly in most regions of the world for the last three decades, which can be associated with the warming effect. Importantly, we identify that the vapor pressure deficit plays an important but varying role in the abrupt alternation between heat and pluvial events. We emphasize the importance of using reliable statistical tests to ensure the validity of the results for complex compound events. Our analysis highlights the need for policymakers and stakeholders to develop adaptation strategies to cope with overlapping vulnerabilities due to compound hot and wet extremes, especially in areas prone to both such as West Australia, South America and Sub‐Saharan Africa. Key Points: Temporally compounding heat and pluvial events occur about 15% more often than would be expected by chanceIncreases in hot‐wet compound events have largely been linked to warmingVapor‐pressure‐deficit anomalies are a signature of heat‐pluvial versus pluvial‐heat sequences, a conclusion drawn from field significance tests [ABSTRACT FROM AUTHOR]