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Abstract Hypertrophic cardiomyopathy (HCM) is the most common cardiovascular disease that is inherited from a single gene. Its clinical manifestations range from asymptomatic mutant gene carriers to patients with severe left ventricular effluent tract obstruction and end‐stage HCM with motor restriction. In this case, we present a patient with the main presentation of heart failure and ST‐segment elevation of the lateral wall, as determined by electrocardiogram. The patient was finally diagnosed with HCM because of genetic testing and the presentation of extensive myocardial fibrosis with reduced systolic function on cardiac magnetic resonance imaging. The patient's clinical findings, electrocardiogram, and cardiac magnetic resonance imaging were different from those of typical patients with HCM.

The role of protein arginine methylation in serine metabolism of cancer cells in hepatocellular carcinoma (HCC) remains to be explored. Here, the authors show that phosphoglycerate dehydrogenase (PHGDH) is activated by PRMT1-mediated R236 methylation, promoting serine synthesis, redox homeostasis and HCC growth.

In the process of butt welding, achieving real-time monitoring of butt weld penetration is of great significance for improving forming quality. Currently, weld penetration detection methods based on weld pool vision are easily disturbed by arc lights. In this study, an innovative approach for butt weld penetration detection using infrared sensing and improved histograms of oriented gradients (HOG) is proposed. First, an improved HOG algorithm was studied to extract the temperature field distribution features of the welding heat-affected zone (HAZ) around the weld pool. Second, the feasibility of using the improved HOG feature to detect weld penetration was analysed, and a weld penetration detection model was established. Finally, the generalisation performance of the proposed weld penetration detection algorithm was confirmed, and the influence of the location and size of the detection area on the accuracy of the detection algorithm was analysed. The testing results indicate that the proposed detection algorithm exhibits high precision and excellent generalisation performance, and enhances the means of monitoring of butt weld penetration.

Since Electroencephalogram (EEG) is resistant to camouflage, it has been a reliable data source for objective emotion recognition. EEG is naturally multi-rhythm and multi-channel, based on which we can extract multiple features for further processing. In EEG-based emotion recognition, it is important to investigate whether there exist some common features shared by different emotional states, and the specific features associated with each emotional state. However, such fundamental problem is ignored by most of the existing studies. To this end, we propose a Joint label-Common and label-Specific Features Exploration (JCSFE) model for semi-supervised cross-session EEG emotion recognition in this paper. To be specific, JCSFE imposes the $\ell _{\text {2,1}}$ -norm on the projection matrix to explore the label-common EEG features and simultaneously the $\ell _{{1}}$ -norm is used to explore the label-specific EEG features. Besides, a graph regularization term is introduced to enforce the data local invariance property, i.e., similar EEG samples are encouraged to have the same emotional state. Results obtained from the SEED-IV and SEED-V emotional data sets experimentally demonstrate that JCSFE not only achieves superior emotion recognition performance in comparison with the state-of-the-art models but also provides us with a quantitative method to identify the label-common and label-specific EEG features in emotion recognition.

Electroencephalography (EEG)-based motor imagery (MI) is one of brain computer interface (BCI) paradigms, which aims to build a direct communication pathway between human brain and external devices by decoding the brain activities. In a traditional way, MI BCI replies on a single brain, which suffers from the limitations, such as low accuracy and weak stability. To alleviate these limitations, multi-brain BCI has emerged based on the integration of multiple individuals’ intelligence. Nevertheless, the existing decoding methods mainly use linear averaging or feature integration learning from multi-brain EEG data, and do not effectively utilize coupling relationship features, resulting in undesired decoding accuracy. To overcome these challenges, we proposed an EEG-based multi-brain MI decoding method, which utilizes coupling feature extraction and few-shot learning to capture coupling relationship features among multi-brains with only limited EEG data. We performed an experiment to collect EEG data from multiple persons who engaged in the same task simultaneously and compared the methods on the collected data. The comparison results showed that our proposed method improved the performance by 14.23% compared to the single-brain mode in the 10-shot three-class decoding task. It demonstrated the effectiveness of the proposed method and usability of the method in the context of only small amount of EEG data available.

Abstract Introduction Case of double primary cancer of the prostate and rectum is rare, prostate cancer involving the postoperative intestinal anastomotic mucosal tissue is even rarer. Case presentation We report a case of rectal cancer discovered 1 year after a diagnosis of prostate cancer and a tumour in the postoperative anastomotic intestinal mucosal tissue involving prostatic adenocarcinoma at 1 year after the diagnosis of rectal cancer. Due to the poor differentiation of both prostate and rectal cancers, there are some pitfalls in the diagnosis of intestinal mucosal lesions at an anastomosis. The lack of an accurate diagnosis of a tumour in anastomosis intestinal mucosal tissue will affect treatment and patient survival. Conclusions The pathologists should have a detailed understanding of the patient's medical history and carefully observe the histopathological morphology and, if necessary, immunohistochemistry or other techniques should be used to assist in the pathological diagnosis and avoid both misdiagnosis and missed diagnosis.

BackgroundVentricular septal rupture (VSR) is a serious complication occurring after myocardial infarction (MI). Cardiogenic shock (CS) is a common complication of VSR and an important factor affecting its prognosis. CS can occur in either an immediate or delayed manner after VSR; however, studies on the risk factors associated with immediate or delayed CS are scarce.MethodsWe retrospectively studied patients diagnosed with VSR after MI and admitted to the West China Hospital between September 2009 and August 2023. Demographic data, medical history, physical examination results, electrocardiograms, and echocardiographic and hematological data were extracted from electronic medical records or archived records. CS was defined as hypotension (

PurposeTo investigate the effect of transcutaneous cervical vagus nerve stimulation (tcVNS) on motor cortex excitability in healthy adults.MethodTwenty eight healthy subjects were assigned to receive real and sham tcVNS for 30 min. The interval between the real and sham conditions was more than 24 h, and the sequence was random. The central and peripheral motor-evoked potential (MEP) of the right first dorsal interosseous (FDI) muscle was measured by transcranial magnetic stimulation (TMS) before and after stimulation. MEP latency, MEP amplitude and rest motor threshold (rMT) were analyzed before and after stimulation.ResultsMEP amplitude, MEP latency and rMT had significant interaction effect between time points and conditions (p 0.05).ConclusiontcVNS could improve motor cortex excitability in healthy adults.

Stiff person syndrome (SPS) is a rare central nervous system disorder associated with malignancies. In this review, we retrieved information from PubMed, up until August 2023, using various search terms and their combinations, including SPS, stiff person syndrome spectrum disorders (SPSSDs), paraneoplastic, cancer, and malignant tumor. Data from peer-reviewed journals printed in English were organized to explain the possible relationships between different carcinomas and SPSSD subtypes, as well as related autoantigens. From literature searching, it was revealed that breast cancer was the most prevalent carcinoma linked to SPSSDs, followed by lung cancer and lymphoma. Furthermore, classic SPS was the most common SPSSD subtype, followed by stiff limb syndrome and progressive encephalomyelitis with rigidity and myoclonus. GAD65 was the most common autoantigen in patients with cancer and SPSSDs, followed by amphiphysin and GlyR. Patients with cancer subtypes might have multiple SPSSD subtypes, and conversely, patients with SPSSD subtypes might have multiple carcinoma subtypes. The first aim of this review was to highlight the complex nature of the relationships among cancers, autoantigens, and SPSSDs as new information in this field continues to be generated globally. The adoption of an open-minded approach to updating information on new cancer subtypes, autoantigens, and SPSSDs is recommended to renew our database. The second aim of this review was to discuss SPS animal models, which will help us to understand the mechanisms underlying the pathogenesis of SPS. In future, elucidating the relationship among cancers, autoantigens, and SPSSDs is critical for the early prediction of cancer and discovery of new therapeutic modalities.

Since Electroencephalogram (EEG) is resistant to camouflage and contains abundant neurophysiological information, it shows significant superiorities in objective emotion recognition, making EEG-based emotion recognition become a hot research field in brain-computer interface research. However, EEG is generally non-stationary and has a low signal-to-noise ratio, which is difficult to analyze. Inspired by the consensus that exploring a discriminative subspace representation usually helps to capture the semantic information of EEG data, in this paper we propose a Graph Adaptive Semi-supervised Discriminative Subspace Learning (GASDSL) model for EEG-based emotion recognition. GASDSL aims to explore a discriminative subspace in which the intra-class scatter decreases while the inter-class separability increases. The adaptive maximum entropy graph construction and semi-supervised subspace emotional state prediction are adopted to mediate the discriminative subspace learning. Extensive comparative studies on the SEED-IV and SEED-V datasets depict that 1) GASDSL achieved satisfactory emotion recognition accuracy compared with other semi-supervised learning models, 2) the discriminative abilities of both the learned maximum entropy graph and subspace are improved as the model iterates, and 3) the features extracted from the Gamma band, the left/right temporal, prefrontal, and (central) parietal lobes contributed more to emotion recognition based on the spatial-frequency pattern analysis results.

Leukemia is a fatal disease that requires the counting of White Blood Cells (WBCs) in bone marrow for diagnosis. However, bone marrow blood contains many types of leukocytes, some of which have subtle differences. To address this issue, we propose the WBC-GLAformer model, which comprises three parts: Low-level Feature Extractor (LFE), Global–Local Attention based Encoder (GLAE), and Discrimination Part Select (DPS). The LFE uses a convolutional neural network (CNN) to tokenize patches from the extracted low-level features. The GLAE combines the ability of the CNN to extract local features with the ability of the transformer to extract global features, thereby enriching the features of leukocyte images. The DPS improves the accuracy of leukocyte classification by selecting the discriminative regions. Our method achieves state-of-the-art results in the bone marrow leukocyte fine-grained classification dataset. Experimental results demonstrate that the model has good generalization on different datasets and is more robust to the optimizer. And visualization results show that the model can effectively focus on the discriminative parts of different cells. Code is available at https://github.com/ywj1/WBC-GLAformer

Background: Clinically useful predictors for risk stratification of long-term survival may assist in selecting patients for endovascular abdominal aortic aneurysm (EVAR) procedures. This study aimed to analyze the prognostic significance of peroperative novel systemic inflammatory markers (SIMs), including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), hemoglobin-to-red cell distribution width ratio (HRR), systemic immune-inflammatory index (SIII), and systemic inflammatory response index (SIRI), for long-term mortality in EVAR. Methods: A retrospective analysis was performed on 147 consecutive patients who underwent their first EVAR procedure at the Department of Vascular Surgery, Beijing Hospital. The patients were divided into the mortality group (n = 37) and the survival group (n = 110). The receiver operating characteristic curves were used to ascertain the threshold value demonstrating the most robust connection with mortality. The Kaplan–Meier survival analysis was performed between each SIM and mortality. The relationship between SIMs and survival was investigated using restricted cubic splines and multivariate Cox regression analysis. Results: The study included 147 patients, with an average follow-up duration of 34.28 ± 22.95 months. Deceased patients showed significantly higher NLR (p < 0.001) and reduced HRR (p < 0.001). The Kaplan–Meier estimates of mortality were considerably greater in the higher-NLR group (NLR >2.77) and lower-HRR group (HRR

To optimize the evacuation process of rail transit passenger flows, the influence of the feeder bus network on bus demand is pivotal. This study first examines the transportation mode preferences of rail transit station passengers and addresses the feeder bus network’s optimization challenge within a three-dimensional framework, incorporating an elastic mechanism. Consequently, a strategic planning model is developed. Subsequently, a multi-objective optimization model is constructed to simultaneously increase passenger numbers and decrease both travel time costs and bus operational expenses. Due to the NP-hard nature of this optimization problem, we introduce an enhanced non-dominated sorting genetic algorithm, INSGA-II. This algorithm integrates innovative encoding and decoding rules, adaptive parameter adjustment strategies, and a combination of crowding distance and distribution entropy mechanisms alongside an external elite archive strategy to enhance population convergence and local search capabilities. The efficacy of the proposed model and algorithm is corroborated through simulations employing standard test functions and instances. The results demonstrate that the INSGA-II algorithm closely approximates the true Pareto front, attaining Pareto optimal solutions that are uniformly distributed. Additionally, an increase in the fleet size correlates with greater passenger volumes and higher operational costs, yet it substantially lowers the average travel cost per customer. An optimal fleet size of 11 vehicles is identified. Moreover, expanding feeder bus routes enhances passenger counts by 18.03%, raises operational costs by 32.33%, and cuts passenger travel time expenses by 21.23%. These findings necessitate revisions to the bus timetable. Therefore, for a bus network with elastic demand, it is essential to holistically optimize the actual passenger flow demand, fleet size, bus schedules, and departure frequencies.

Bio-inspired thin-wall structures with excellent mechanical properties, high-energy absorption capabilities, and a desirable lightweight level have been extensively applied to the passive safety protection of transportation and aerospace. Collaboration matching and the selection of optional structures with different bionic principles considering the multiple attribute evaluation index and engineering preference information have become an urgent problem. This paper proposes a parameter reduction-based indifference threshold-based attribute ratio analysis method under an interval-valued neutrosophic soft set (IVNS-SOFT) to obtain the weight vector of an evaluation indicator system for the selection of bionic thin-wall structures, which can avoid the problem of an inadequate subjective evaluation and reduce redundant parameters. An IVNS-SOFT-based multi-attributive border approximation area comparison (MABAC) method is proposed to obtain an optimal alternative, which can quantify uncertainty explicitly and handle the uncertain and inconsistent information prevalent in the expert system. Subsequently, an application of five bio-inspired thin-wall structures is applied to demonstrate that this proposed method is valid and practical. Comparative analysis, sensitivity analysis, and discussion are conducted in this research. The results show that this study provides an effective tool for the selection of bionic thin-wall structures.

Label distribution learning is a novel machine learning paradigm to deal with label ambiguity, which is the generalization of the traditional single-label learning and multi-label learning paradigms. Though label distribution learning has attracted a lot of attentions recently, data sets with label distributions rather than logical labels have always been scarce and most of the existing models put emphasis mainly on the supervised learning, neglecting the utilization of unlabeled samples. In this paper, we propose a semi-supervised Label Distribution Learning model with label Correlations and data Manifold exploration (sLDLCM). On one hand, sLDLCM is a semi-supervised extension of existing label distribution learning models, which can effectively make use of both labeled and unlabeled data for better capturing the underlying data properties; on the other hand, sLDLCM jointly estimates the label distributions of unlabeled samples and the other model variables. Besides, both label correlations and local data manifold are explored in sLDLCM. Extensive experiments are conducted on six real-world data sets including two facial expression, one movie rating, two bioinformatics and one visual sentiment analysis data sets. Comparative studies demonstrate that the proposed sLDLCM model achieves better performance the state-of-the-arts in terms of five evaluation metrics.

Cancer-associated fibroblasts (CAFs) are a predominant and critical component of the tumour microenvironment. Here, the authors integrate and analyse single-cell RNA-seq data of CAFs across 10 common solid cancer types, identifying their plasticity and interactions with other cell types.

In this work, a macro-micro VOF-PF (Volume of Fluid-Phase field) model is established to simulate the heat transfer, dendrite growth and Nb segregation during the laser IN718 deposition. According to calculated heat transfers, G increases, while Vp decreases, and the corresponding G×Vp decreases from top to bottom in the molten pool. The calculated the primary dendrite arm spacing (PDAS) using the PF model increases from top to bottom. Compared with other models, the calculated PDAS using the PF model considering the liquid-metal flow is closest to experiment measurements. During the dendrite growth, Nb is enriched in the interdendritic region, and finally is distributed in the droplet shape. The calculated actual solute distribution coefficient Cmin/Cmax decreases from 0.584 to 5.548 with the decrease of G×Vp, which follows the equilibrium solidification theory. According to dc/dy=G/ml, the liquidus slope ml is calculated as 0.05322wt.%/μm very close to the theoretical value 0.05394wt.%/μm. The simulated Nb concentrations in the matrix γ and Laves phases are 3.97wt.%, 13.3wt.% respectively, which are in good agreements with experimental 3.7wt.% and 13wt.%.

Global environmental problems have become increasingly prominent, and China, as one of the world’s major powers, should take action. China promises to achieve a “carbon peak” by 2030, and carbon dioxide emissions will no longer increase, and will gradually decrease after reaching the peak. To achieve “carbon neutrality” by 2060, all the carbon dioxide emissions will be offset by tree planting, energy saving and emission reduction. The optimization of pipeline energy consumption is also associated with it. In recent years, the transportation mode of the QT oil pipeline has changed from normal temperature transportation to heating transportation. The energy consumption of this transportation method mainly comes from heating furnaces and pumps. In order to reduce energy consumption and find a suitable pipeline operation plan, this article optimizes and analyzes the transformed QT oil pipeline under the premise of ensuring safe production. Based on programming software, this article establishes a corresponding mathematical model of energy consumption of the QT oil pipeline, and uses artificial bee colony algorithm, invasive weed algorithm and optimization algorithm based on biogeography to solve the model. The article innovatively introduces the speed of the variable frequency pump as a variable to study the energy consumption optimization problem of the oil pipeline, analyze the practical application of the oil pipeline of the QT oil pipeline, and obtains the best plan for the optimized operation of the oil pipeline of the QT oil pipeline It provided research basis and played a role in promoting the country’s dual-carbon goals.

The transmission line maintenance is usually carried out by live-line maintenance, and equipotential live-line maintenance is one of the primary forms. In this process, the linemen need to enter the high potential with the help of conductive gloves or a potential transfer rod. This process is called the potential transfer. Because this process is accompanied by sizeable potential transfer current and transient energy, it may threaten linemen’s safety. Hence, the research on this process is of great significance. This paper improves the traditional transient model by introducing the arc model. The finite element method calculates the capacitance distribution between conductors by establishing the three-dimensional models of different postures and positions that the lineman may adopt. Then, considering the influence of the arc model on the process of the potential transfer, the potential transfer current and transient energy under different linemen’s postures and positions are calculated. Finally, the optimal mode of potential transfer operation for linemen in the process of potential transfer is proposed, which provides a theoretical reference for the safety protection of personnel in the process of the potential transfer.

Long air gaps are the main external insulation medium of overhead transmission lines. When there are floating conductors in the air gap, the gap’s configuration changes, and the combined air gaps is formed. We built an experimental platform to obtain the breakdown characteristics of a 2 m rod–plane air gap with a rod floating conductor in different positions under negative lightning impulse. The results show that as the distance between the floating conductor and the high voltage conductor changes, the breakdown voltage (BV) and timing change. Moreover, the breakdown voltage increases with the length of sub-gap 1, showing a trend of first increasing, then decreasing, and then increasing, which is different from the trend under positive lightning impulse. The reason for this phenomenon is inferred from the analysis to be related to the polarity effect.

Based on the 16 scenes GF-1 satellite multi-spectral remote sensing images, through the adoption of data processing methods including orthorectification, geometric rectification, data fusion and image mosaic, integrated with field surveys, the remote sensing interpretation signs for the inland wetland types have been built, and the remote sensing survey of inland wetlands in Yadong region has been initiated, with six types of inland wetlands recognized in Yadong region, namely permanent rivers, seasonal rivers, lakes, salt lakes, alpine meadows, and inundated land. The spatial distribution characteristics and the spreading rules of these wetlands have also been revealed. Based on full understanding of the overall characteristics of the inland wetlands in the Yadong region, using the three phases of TM images acquired in 1989, 2003 and 2008 as well as the PMS2 data gathered by GF-1 in 2014, and the wide-range data (WFV3) gathered by GF-1 in 2020. As to the typical salt lakes, a long- time salt lakes transition study was carried out. The results show that the typical salt lakes in Yadong have been shrinking in the past three decades. The average annual shrinkage of Duoqing Co (Co means lake in Tibetan) was stronger than that of Gala Co, which are respective 87.30 hectares (usually short as ha; 1 ha equals to 0.01 km2) /a and 24.20 ha/a; the shrinkage degree of Gala Co was higher than that of Duoqing Co, shrank by 59.27% and 35.73% respectively. Based on the remote sensing survey results and an integrated analysis of the predecessors’ researchers, the reason for the shrinkage of the salt lakes is more inclined to geological factors. Geological process is manifested by a series of extensional faults at the bottom of the lake basin generated from tectonic activities, providing fluid infiltration channels, and inducing the eventual leakage of lake water to the lower strata. The result provides an important instance for understanding the evolution characteristics of wetlands and salt lakes in specific environment of the Tibetan Plateau.©2022 China Geology Editorial Office.

Abstract. Multiple sclerosis (MS) and experimental autoimmune encephalomyelitis (EAE), a pathologically similar disease used to model MS in rodents, are typical CD4+ T cell-dominated autoimmune diseases. CD4+ interleukin (IL)17+ T cells (Th17 cells) have been well studied and have shown that they play a critical role in the pathogenesis of MS/EAE. However, studies have suggested that CD8+IL17+ T cells (Tc17 cells) have a similar phenotype and cytokine and transcription factor profiles to those of Th17 cells and have been found to be crucial in the pathogenesis of autoimmune diseases, including MS/EAE, psoriasis, type I diabetes, rheumatoid arthritis, and systemic lupus erythematosus. However, the evidence for this is indirect and insufficient. Therefore, we searched for related publications and attempted to summarize the current knowledge on the role of Tc17 cells in the pathogenesis of MS/EAE, as well as in the pathogenesis of other autoimmune diseases, and to find out whether Tc17 cells or Th17 cells play a more critical role in autoimmune disease, especially in MS and EAE pathogenesis, or whether the interaction between these two cell types plays a critical role in the development of the disease.

Alzheimer’s disease (AD) is the most common chronic neurodegenerative disease worldwide. It causes cognitive dysfunction, such as aphasia and agnosia, and mental symptoms, such as behavioral abnormalities; all of which place a significant psychological and economic burden on the patients’ families. No specific drugs are currently available for the treatment of AD, and the current drugs for AD only delay disease onset and progression. The pathophysiological basis of AD involves abnormal deposition of beta-amyloid protein (Aβ), abnormal tau protein phosphorylation, decreased activity of acetylcholine content, glutamate toxicity, autophagy, inflammatory reactions, mitochondria-targeting, and multi-targets. The US Food and Drug Administration (FDA) has approved five drugs for clinical use: tacrine, donepezil, carbalatine, galantamine, memantine, and lecanemab. We have focused on the newer drugs that have undergone clinical trials, most of which have not been successful as a result of excessive clinical side effects or poor efficacy. Although aducanumab received rapid approval from the FDA on 7 June 2021, its long-term safety and tolerability require further monitoring and confirmation. In this literature review, we aimed to explore the possible pathophysiological mechanisms underlying the occurrence and development of AD. We focused on anti-Aβ and anti-tau drugs, mitochondria-targeting and multi-targets, commercially available drugs, bottlenecks encountered in drug development, and the possible targets and therapeutic strategies for future drug development. We hope to present new concepts and methods for future drug therapies for AD.

IntroductionProlonged exposure of train drivers to thermal discomfort can lead to occupational safety and health (OSH) risks, causing physical and mental injuries. Traditional method of treating human skin as a wall surface fail to observe accurate skin temperature changes or obtain human thermal comfort that adapts to the thermal environment.MethodsThis study employs the Stolwijk human thermal regulation model to investigate and optimize the thermal comfort of train drivers. To minimize the time-consuming design optimization, a pointer optimization algorithm based on radial basis function (RBF) approximation was utilized to optimize the train cab ventilation system design and enhance drivers’ thermal comfort. The train driver thermal comfort model was developed using Star-CCM+ and 60 operating conditions were sampled using an Optimal Latin Hypercube Design (Opt LHD).Results and DiscussionWe analyzed the effects of air supply temperature, air supply volume, air supply angle, solar radiation intensity and solar altitude angle on the local thermal sensation vote (LTSV) and overall thermal sensation vote (OTSV) of the train driver. Finally, the study obtained the optimal air supply parameters for the Heating Ventilation and Air Conditioning (HVAC) in the train cabin under extreme summer conditions, effectively improving the thermal comfort of the driver.