Haplotype-resolved T2T genome assemblies and pangenome graph of pear reveal diverse patterns of allele-specific expression and genomic basis of fruit quality traits.

Hybrid crops often exhibit increased yield and greater resilience, yet the genomic mechanism(s) underlying hybrid vigor or heterosis remain unclear, hindering our ability to predict the expression of phenotypic traits in hybrid breeding. Here, we generated haplotype-resolved T2T genome assemblies of two pear hybrid varieties 'Yuluxiangli' (YLX) and 'Hongxiangsu' (HXS) that share the same maternal parent, but differ in their paternal parents. We then used these assemblies to explore genome-scale landscape of allele-specific expression and create a pangenome graph for pear. Allele specific expression (ASE) was observed for close to 6000 genes in both hybrid cultivars. A subset of ASEGs related to fruit quality including sugar, organic acid and cuticular wax were identified, suggesting their important contributions to heterosis. Specifically, Ma1, a gene regulating fruit acidity, was absent in the paternal haplotypes of HXS and YLX. Further, a pangenome graph was built based on our assemblies and eight published pear genomes. Resequencing data for 139 cultivated pear genotypes (including 97 genotypes sequenced here) were subsequently aligned to the pangenome graph, revealing numerous SV hotspots and selective sweeps during pear diversification. As predicted, the Ma1 allele was found to be absent in varieties with low organic acid content, an association that was functionally validated by Ma1 over-expression in pear fruit and calli. Overall, the results unraveled contributions of allele-specific expression to heterosis involving fruit quality and provided a robust pangenome reference for high resolution allele discovery and association mapping.

An Intersubject Brain-Computer Interface Based on Domain-Adversarial Training of Convolutional Neural Network for Online Attention Decoding.

Attention decoding plays a vital role in daily life, where electroencephalography (EEG) has been widely involved. However, training a universally effective model for everyone is impractical due to substantial interindividual variability in EEG signals. To tackle the above challenge, we propose an end-to-end brain-computer interface (BCI) framework, including temporal and spatial one-dimensional (1D) convolutional neural network and domain-adversarial training strategy, namely DA-TSnet. Specifically, DA-TSnet extracts temporal and spatial features of EEG, while it is jointly supervised by task loss and domain loss. During training, DA-TSnet aims to maximize the domain loss while simultaneously minimizing the task loss. We conduct an offline analysis, simulate online experiments on a self-collected dataset of 85 subjects, and real online experiments on 22 subjects. Main results: DA-TSnet achieves a leave-one-subject-out (LOSO) cross-validation (CV) classification accuracy of 89.40% ± 9.96%, outperforming several state-of-the-art attention EEG decoding methods. In simulated online experiments, DA-TSnet achieves an outstanding accuracy of 88.07% ± 11.22%. In real online experiments, it achieves an average accuracy surpassing 86%. Significance: An end-to-end network framework does not rely on elaborate preprocessing and feature extraction steps, which saves time and human workload. Moreover, our framework utilizes domain-adversarial training neural network (DANN) to tackle the challenge posed by the high interindividual variability in EEG signals, which has significant reference value for handling other EEG signal decoding issues. Last, the performance of the DA-TSnet framework in offline and online experiments underscores its potential to facilitate more reliable applications.

Design and Experiment of an Unoccupied Control System for a Tracked Grain Vehicle.

In order to enhance crop harvesting efficiency, an automatic-driving tracked grain vehicle system was designed. Based on the harvester chassis, we designed the mechanical structure of a tracked grain vehicle with a loading capacity of 4.5 m3 and a grain unloading hydraulic system. Using the BODAS hydraulic controller, we implemented the design of an electronic control system that combines the manual and automatic operation of the chassis walking mechanism and grain unloading mechanism. We utilized a hybrid A* algorithm to plan the traveling path of the tracked grain vehicle, and the path-tracking controller of the tracked grain vehicle was designed by combining fuzzy control and pure pursuit algorithms. Leveraging binocular vision technology and semantic segmentation technology, we designed an automatic grain unloading control system with functions of grain tank recognition and grain unloading regulation control. Finally, we conducted experiments on automatic grain unloading control and automatic navigation control in the field. The results showed that both the precision of the path-tracking control and the automatic unloading system meet the requirements for practical unoccupied operations of the tracked grain vehicle.

Precise detection of awareness in disorders of consciousness using deep learning framework.

Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-state functional MRI (rs-fMRI) for evaluating patients with DOC, this study seeks to explore the conjunction of deep learning techniques and rs-fMRI in precisely detecting awareness in DOC. We initiated our research with a benchmark dataset comprising 140 participants, including 76 unresponsive wakefulness syndrome (UWS), 25 minimally conscious state (MCS), and 39 Controls, from three independent sites. We developed a cascade 3D EfficientNet-B3-based deep learning framework tailored for discriminating MCS from UWS patients, referred to as "DeepDOC", and compared its performance against five state-of-the-art machine learning models. We also included an independent dataset consists of 11 DOC patients to test whether our model could identify patients with cognitive motor dissociation (CMD), in which DOC patients were behaviorally diagnosed unconscious but could be detected conscious by brain computer interface (BCI) method. Our results demonstrate that DeepDOC outperforms the five machine learning models, achieving an area under curve (AUC) value of 0.927 and accuracy of 0.861 for distinguishing MCS from UWS patients. More importantly, DeepDOC excels in CMD identification, achieving an AUC of 1 and accuracy of 0.909. Using gradient-weighted class activation mapping algorithm, we found that the posterior cortex, encompassing the visual cortex, posterior middle temporal gyrus, posterior cingulate cortex, precuneus, and cerebellum, as making a more substantial contribution to classification compared to other brain regions. This research offers a convenient and accurate method for detecting covert awareness in patients with MCS and CMD using rs-fMRI data.

Nerolidol reduces depression-like behavior in mice and suppresses microglia activation by down-regulating DNA methyltransferase 1.

Modern medicine has unveiled that essential oil made from Aquilaria possesses sedative and hypnotic effects. Among the chemical components in Aquilaria, nerolidol, a natural sesquiterpene alcohol, has shown promising effects. This study aimed to unravel the potential of nerolidol in treating depression. Chronic unpredictable mild stress (CUMS) was utilized to induce depression-like behavior in mice, and open field test, sucrose preference, and tail suspension test was conducted. The impacts of nerolidol on the inflammatory response, microglial activation, and DNA methyltransferase 1 (DNMT1) were assessed. To study the regulatory role of DNMT1, lipopolysaccharide (LPS) was used to treat BV2 cells, followed by the evaluation of cell viability and DNMT1 level. Additionally, the influence of DNMT1 overexpression on BV2 cell activation was determined. Behavioral analysis revealed that nerolidol reduced depression-like behavior in mice. Nerolidol reduced the levels of inflammatory factors and microglial activation caused by CUMS. Nerolidol treatment was found to reduce DNMT1 levels in mouse brain tissue and it also decrease the LPS-induced increase in DNMT1 levels in BV2 cells. DNMT1 overexpression reversed the impacts of nerolidol on the inflammation response and cell activation. This study underscores the potential of nerolidol in reducing CUMS-induced depressive-like behavior and inhibiting microglial activation by downregulating DNMT1. These findings offer valuable insights into the potential of nerolidol as a therapeutic option for depression.

Impact of anemia on clinical outcomes in patients with acute heart failure: A systematic review and meta-analysis.

Anemia and acute heart failure (AHF) frequently coexist. Several published studies have investigated the association of anemia with all-cause mortality and all-cause heart failure events in AHF patients, but their findings remain controversial. This study is intended to evaluate the relationship between anemia and AHF. We systematically searched PubMed, Medline, the Cochrane Library, Embase, and Elsevier's ScienceDirect databases until July 30, 2023, and selected prospective or retrospective cohort studies to evaluate anemia for AHF. A total of nine trials involving 29 587 AHF patients were eventually included. Pooled analyses demonstrated anemia is associated with a higher risk of all-cause heart failure event rate (OR: 1.82, 95% CI: 1.58-2.10, p < .01) and all-cause mortality, both for short-term (30 days) all-cause mortality (OR: 1.91, 95% CI: 1.31-2.79, p < .01) and long-term (1 year) all-cause mortality (OR: 1.72, 95% CI: 1.27-2.32, p < .01). The evidence from this meta-analysis suggested that anemia may be an independent risk factor for all-cause mortality and all-cause heart failure events in patients with AHF and might emphasize the importance of anemia correction before discharge.

RpS25 is required for sperm elongation and individualization during Drosophila spermatogenesis.

Ribosomal protein 25 (RPS25) has been related to male fertility diseases in humans. However, the role of RPS25 in spermatogenesis has yet to be well understood. RpS25 is evolutionarily highly conserved from flies to humans through sequence alignment and phylogenetic tree construction. In this study, we found that RpS25 plays a critical role in Drosophila spermatogenesis and its knockdown leads to male sterility. Examination of each stage of spermatogenesis from RpS25-knockdown flies showed that RpS25 was not required for initial germline cell divisions, but was required for spermatid elongation and individualization. In RpS25-knockdown testes, the average length of cyst elongation was shortened, the spermatid nuclei bundling was disrupted, and the assembly of individualization complex from actin cones failed, resulting in the failure of mature sperm production. Our data revealed an essential role of RpS25 during Drosophila spermatogenesis through regulating spermatid elongation and individualization.

ST-SCGNN: A Spatio-Temporal Self-Constructing Graph Neural Network for Cross-Subject EEG-Based Emotion Recognition and Consciousness Detection.

In this paper, a novel spatio-temporal self-constructing graph neural network (ST-SCGNN) is proposed for cross-subject emotion recognition and consciousness detection. For spatio-temporal feature generation, activation and connection pattern features are first extracted and then combined to leverage their complementary emotion-related information. Next, a self-constructing graph neural network with a spatio-temporal model is presented. Specifically, the graph structure of the neural network is dynamically updated by the self-constructing module of the input signal. Experiments based on the SEED and SEED-IV datasets showed that the model achieved average accuracies of 85.90% and 76.37%, respectively. Both values exceed the state-of-the-art metrics with the same protocol. In clinical besides, patients with disorders of consciousness (DOC) suffer severe brain injuries, and sufficient training data for EEG-based emotion recognition cannot be collected. Our proposed ST-SCGNN method for cross-subject emotion recognition was first attempted in training in ten healthy subjects and testing in eight patients with DOC. We found that two patients obtained accuracies significantly higher than chance level and showed similar neural patterns with healthy subjects. Covert consciousness and emotion-related abilities were thus demonstrated in these two patients. Our proposed ST-SCGNN for cross-subject emotion recognition could be a promising tool for consciousness detection in DOC patients.

Few-shot EEG sleep staging based on transductive prototype optimization network.

Electroencephalography (EEG) is a commonly used technology for monitoring brain activities and diagnosing sleep disorders. Clinically, doctors need to manually stage sleep based on EEG signals, which is a time-consuming and laborious task. In this study, we propose a few-shot EEG sleep staging termed transductive prototype optimization network (TPON) method, which aims to improve the performance of EEG sleep staging. Compared with traditional deep learning methods, TPON uses a meta-learning algorithm, which generalizes the classifier to new classes that are not visible in the training set, and only have a few examples for each new class. We learn the prototypes of existing objects through meta-training, and capture the sleep features of new objects through the "learn to learn" method of meta-learning. The prototype distribution of the class is optimized and captured by using support set and unlabeled high confidence samples to increase the authenticity of the prototype. Compared with traditional prototype networks, TPON can effectively solve too few samples in few-shot learning and improve the matching degree of prototypes in prototype network. The experimental results on the public SleepEDF-2013 dataset show that the proposed algorithm outperform than most advanced algorithms in the overall performance. In addition, we experimentally demonstrate the feasibility of cross-channel recognition, which indicates that there are many similar sleep EEG features between different channels. In future research, we can further explore the common features among different channels and investigate the combination of universal features in sleep EEG. Overall, our method achieves high accuracy in sleep stage classification, demonstrating the effectiveness of this approach and its potential applications in other medical fields.

Research progress of single-cell sequencing in tuberculosis.

Tuberculosis is a major infectious disease caused by Mycobacterium tuberculosis infection. The pathogenesis and immune mechanism of tuberculosis are not clear, and it is urgent to find new drugs, diagnosis, and treatment targets. A useful tool in the quest to reveal the enigmas related to Mycobacterium tuberculosis infection and disease is the single-cell sequencing technique. By clarifying cell heterogeneity, identifying pathogenic cell groups, and finding key gene targets, the map at the single cell level enables people to better understand the cell diversity of complex organisms and the immune state of hosts during infection. Here, we briefly reviewed the development of single-cell sequencing, and emphasized the different applications and limitations of various technologies. Single-cell sequencing has been widely used in the study of the pathogenesis and immune response of tuberculosis. We review these works summarizing the most influential findings. Combined with the multi-molecular level and multi-dimensional analysis, we aim to deeply understand the blank and potential future development of the research on Mycobacterium tuberculosis infection using single-cell sequencing technology.

An EEG-based attention recognition method: fusion of time domain, frequency domain, and non-linear dynamics features.

Introduction: Attention is a complex cognitive function of human brain that plays a vital role in our daily lives. Electroencephalogram (EEG) is used to measure and analyze attention due to its high temporal resolution. Although several attention recognition brain-computer interfaces (BCIs) have been proposed, there is a scarcity of studies with a sufficient number of subjects, valid paradigms, and reliable recognition analysis across subjects. Methods: In this study, we proposed a novel attention paradigm and feature fusion method to extract features, which fused time domain features, frequency domain features and nonlinear dynamics features. We then constructed an attention recognition framework for 85 subjects. Results and discussion: We achieved an intra-subject average classification accuracy of 85.05% ± 6.87% and an inter-subject average classification accuracy of 81.60% ± 9.93%, respectively. We further explored the neural patterns in attention recognition, where attention states showed less activation than non-attention states in the prefrontal and occipital areas in α, ß and θ bands. The research explores, for the first time, the fusion of time domain features, frequency domain features and nonlinear dynamics features for attention recognition, providing a new understanding of attention recognition.

STGATE: Spatial-temporal graph attention network with a transformer encoder for EEG-based emotion recognition.

Electroencephalogram (EEG) is a crucial and widely utilized technique in neuroscience research. In this paper, we introduce a novel graph neural network called the spatial-temporal graph attention network with a transformer encoder (STGATE) to learn graph representations of emotion EEG signals and improve emotion recognition performance. In STGATE, a transformer-encoder is applied for capturing time-frequency features which are fed into a spatial-temporal graph attention for emotion classification. Using a dynamic adjacency matrix, the proposed STGATE adaptively learns intrinsic connections between different EEG channels. To evaluate the cross-subject emotion recognition performance, leave-one-subject-out experiments are carried out on three public emotion recognition datasets, i.e., SEED, SEED-IV, and DREAMER. The proposed STGATE model achieved a state-of-the-art EEG-based emotion recognition performance accuracy of 90.37% in SEED, 76.43% in SEED-IV, and 76.35% in DREAMER dataset, respectively. The experiments demonstrated the effectiveness of the proposed STGATE model for cross-subject EEG emotion recognition and its potential for graph-based neuroscience research.

Eliminating or Shortening the Calibration for a P300 Brain-Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study.

A brain-computer interface (BCI) measures and analyzes brain activity and converts it into computer commands to control external devices. Traditional BCIs usually require full calibration, which is time-consuming and makes BCI systems inconvenient to use. In this study, we propose an online P300 BCI spelling system with zero or shortened calibration based on a convolutional neural network (CNN) and big electroencephalography (EEG) data. Specifically, three methods are proposed to train CNNs for the online detection of P300 potentials: (i) training a subject-independent CNN with data collected from 150 subjects; (ii) adapting the CNN online via a semisupervised learning/self-training method based on unlabeled data collected during the user's online operation; and (iii) fine-tuning the CNN with a transfer learning method based on a small quantity of labeled data collected before the user's online operation. Note that the calibration process is eliminated in the first two methods and dramatically shortened in the third method. Based on these methods, an online P300 spelling system is developed. Twenty subjects participated in our online experiments. Average accuracies of 89.38%, 94.00% and 93.50% were obtained by the subject-independent CNN, the self-training-based CNN and the transfer learning-based CNN, respectively. These results demonstrate the effectiveness of our methods, and thus, the convenience of the online P300-based BCI system is substantially improved.

A novel semi-supervised meta learning method for subject-transfer brain-computer interface.

The brain-computer interface (BCI) provides a direct communication pathway between the human brain and external devices. However, the models trained for existing subjects perform poorly on new subjects, which is termed the subject calibration problem. In this paper, we propose a semi-supervised meta learning (SSML) method for subject-transfer calibration. The proposed SSML learns a model-agnostic meta learner with existing subjects and then fine-tunes the meta learner in a semi-supervised learning manner, i.e. using a few labelled samples and many unlabelled samples of the target subject for calibration. It is significant for BCI applications in which labelled data are scarce or expensive while unlabelled data are readily available. Three different BCI paradigms are tested: event-related potential detection, emotion recognition and sleep staging. The SSML achieved classification accuracies of 0.95, 0.89 and 0.83 in the benchmark datasets of three paradigms. The runtime complexity of SSML grows linearly as the number of samples of target subject increases so that is possible to apply it in real-time systems. This study is the first attempt to apply semi-supervised model-agnostic meta learning methodology for subject calibration. The experimental results demonstrated the effectiveness and potential of the SSML method for subject-transfer BCI applications.

Identifying patients with cognitive motor dissociation using resting-state temporal stability.

Using task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness as healthy controls, which was defined as cognitive motor dissociation (CMD). However, existing task-dependent approaches might fail when CMD patients have cognitive function (e.g., attention, memory) impairments, in which patients with covert awareness cannot perform a specific task accurately and are thus wrongly considered unconscious, which leads to false-negative findings. Recent studies have suggested that sustaining a stable functional organization over time, i.e., high temporal stability, is crucial for supporting consciousness. Thus, temporal stability could be a powerful tool to detect the patient's cognitive functions (e.g., consciousness), while its alteration in the DOC and its capacity for identifying CMD were unclear. The resting-state fMRI (rs-fMRI) study included 119 participants from three independent research sites. A sliding-window approach was used to investigate global and regional temporal stability, which measured how stable the brain's functional architecture was across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset, where patients underwent rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. Our results showed that global and regional temporal stability was impaired in DOC patients, especially in regions of the cingulo-opercular task control network, default-mode network, fronto-parietal task control network, and salience network. Using temporal stability as the feature, the SVM model not only showed good performance in the first dataset (accuracy = 90%), but also good generalizability in the second dataset (accuracy = 84%). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91%). Our preliminary findings suggested that temporal stability could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of consciousness.

Landscape of Exhausted T Cells in Tuberculosis Revealed by Single-Cell Sequencing.

Tuberculosis, a contagious bacterial infection caused by Mycobacterium tuberculosis, is a substantial global health problem, impacting millions of lives annually. Exhausted T-cell signatures are critical for predicting clinical responses to tuberculosis infection. To obtain a panoramic transcriptional profile of T cells, we performed single-cell RNA-sequencing analysis of CD4+ T and CD8+ T cells isolated from peripheral blood mononuclear cells of healthy individuals and patients with tuberculosis. We identified seven subsets in CD8+ T cells and eight subsets in CD4+ T cells and elucidated the transcriptomic landscape changes and characteristics of each subset. We further investigated the cell-to-cell relationship of each subgroup of the two cell types. Different signature genes and pathways of exhausted CD4+ and CD8+ T cells were examined. We identified 12 genes with potential associations of T-cell exhaustion after tuberculosis infection. We also identified five genes as potential exhaustion marker genes. The CD8-EX3 subcluster in CD8+ T-exhausted cells was identified as an exhaustion-specific subcluster. The identified gene module further clarified the key factors influencing CD8+ T cell exhaustion. These data provide new insights into T-cell signatures in tuberculosis-exhausted populations. IMPORTANCE Identifying the changes in immune cells in response to infection can provide a better understanding of the effects of Mycobacterium tuberculosis on the host immune system. We performed single-cell RNA-sequencing analysis of CD4+ T and CD8+ T cells isolated from peripheral blood mononuclear cells of healthy individuals and patients with tuberculosis to reveal the cellular characteristics. Different signature genes and pathways of exhausted CD4+ and CD8+ T cells were examined. These will facilitate a more comprehensive understanding of the onset and underlying mechanism of T-cell exhaustion during active Mtb infection.

Efficacy and safety of thrombolytic therapy for portal venous system thrombosis: A systematic review and meta-analysis.

BACKGROUND AND AIMS: The role of thrombolytic therapy in patients with portal venous system thrombosis (PVST) remains ambiguous. This study aimed to systematically collect available evidence and evaluate the efficacy and safety of thrombolysis for PVST. METHODS: Eligible studies were searched via PubMed, EMBASE, and Cochrane Library databases. Among the cohort studies, meta-analyses were performed to assess the outcomes of PVST patients receiving thrombolysis. Pooled proportions were calculated. Among the case reports and case series, logistic regression analyses were performed to identify the risk factors for outcomes of PVST patients receiving thrombolysis. Odds ratios (ORs) were calculated. RESULTS: Among the 2134 papers initially identified, 29 cohort studies and 131 case reports or case series were included. Based on the cohort studies, the pooled rates of overall response to thrombolytic therapy, complete recanalization of PVST, bleeding events during thrombolysis, further bowel resection, thrombosis recurrence, and 30-day mortality were 93%, 58%, 18%, 3%, 1%, and 4%, respectively. Based on the case reports and case series, acute pancreatitis (OR = 0.084), history of liver transplantation (OR = 13.346), and interval between onset of symptoms and initiation of thrombolysis ≤14 days (OR = 3.105) were significantly associated with complete recanalization of PVST; acute pancreatitis (OR = 6.556) was significantly associated with further bowel resection; but no factors associated with the overall response to thrombolytic therapy, bleeding events during thrombolysis, thrombosis recurrence, and 30-day mortality were identified or could be calculated. CONCLUSION: Early initiation of thrombolysis should be effective for the treatment of PVST. But its benefits for PVST secondary to acute pancreatitis are weakened.

Efficacy and Safety of PD-1/PD-L1 Inhibitors in Advanced Hepatocellular Carcinoma: A Systematic Review and Meta-analysis.

INTRODUCTION: Programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) inhibitors have been increasingly employed for the treatment of various cancers in clinical practice. This study aimed to systematically evaluate the efficacy and safety of PD-1/PD-L1 inhibitors for advanced hepatocellular carcinoma (HCC). METHODS: PubMed, EMBASE, Cochrane library, Web of Science, and Abstracts of American Society of Clinical Oncology proceedings databases were searched. Objective response rate (ORR), disease control rate (DCR), median progression-free survival (PFS), median overall survival (OS), and incidence of adverse events (AEs) and drug withdrawal were pooled. Odds ratio (OR) and hazard ratio (HR) were calculated to analyze the difference in the ORR, DCR, PFS, and OS between groups. RESULTS: Among the 14,902 initially identified papers, 98 studies regarding use of PD-1/PD-L1 inhibitors in advanced HCC were included. Based on different criteria of response in solid tumors, the pooled ORR, DCR, and median PFS was 16-36%, 54-74%, and 4.5-6.8 months, respectively. The pooled median OS was 11.9 months. Compared to multitarget tyrosine kinase inhibitors (TKIs), PD-1/PD-L1 inhibitors monotherapy significantly increased ORR (OR 2.73, P < 0.00001) and OS (HR 0.97, P = 0.05), and PD-1/PD-L1 inhibitors combined with TKIs significantly increased ORR (OR 3.17, P < 0.00001), DCR (OR 2.44, P < 0.00001), PFS (HR 0.58, P < 0.00001), and OS (HR 0.58, P < 0.00001). The pooled incidence of all-grade AEs, grade ≥ 3 AEs, and drug withdrawal was 71%, 25%, and 7%, respectively. CONCLUSION: On the basis of the present systematic review and meta-analysis, PD-1/PD-L1 inhibitors should be the preferred treatment choice for advanced HCC owing to their higher antitumor effect and improved outcomes.

AGL-Net: An Efficient Neural Network for EEG-Based Driver Fatigue Detection.

BACKGROUND: In recent years, road traffic safety has become a prominent issue due to the worldwide proliferation of vehicles on roads. The challenge of driver fatigue detection involves balancing the efficiency and accuracy of the detection process. While various detection methods are available, electroencephalography (EEG) is considered the gold standard due to its high precision in terms of detecting fatigue. However, deep learning models for EEG-based fatigue detection are limited by their large numbers of parameters and low computational efficiency levels, making it difficult to implement them on mobile devices. METHODS: To overcome this challenge, an attention-based Ghost-LSTM neural network (AGL-Net) is proposed for EEG-based fatigue detection in this paper. AGL-Net utilizes an attention mechanism to focus on relevant features and incorporates Ghost bottlenecks to efficiently extract spatial EEG fatigue information. Temporal EEG fatigue features are extracted using a long short-term memory (LSTM) network. We establish two types of models: regression and classification models. In the regression model, we use linear regression to obtain regression values. In the classification model, we classify features based on the predicted values obtained from regression. RESULTS: AGL-Net exhibits improved computational efficiency and a more lightweight design than existing deep learning models, as evidenced by its floating-point operations per second (FLOPs) and Params values of 2.67 M and 103,530, respectively. Furthermore, AGL-Net achieves an average accuracy of approximately 87.3% and an average root mean square error (RMSE) of approximately 0.0864 with the Shanghai Jiao Tong University (SJTU) Emotion EEG Dataset (SEED)-VIG fatigued driving dataset, indicating its advanced performance capabilities. CONCLUSIONS: The experiments conducted with the SEED-VIG dataset demonstrate the feasibility and advanced performance of the proposed fatigue detection method. The effectiveness of each AGL-Net module is verified through thorough ablation experiments. Additionally, the implementation of the Ghost bottleneck module greatly enhances the computational efficiency of the model. Overall, the proposed method has higher accuracy and computational efficiency than prior fatigue detection methods, demonstrating its considerable practical application value.

A Potential Prognosis Indicator Based on P300 Brain-Computer Interface for Patients with Disorder of Consciousness.

For patients with disorders of consciousness, such as unresponsive wakefulness syndrome (UWS) patients and minimally conscious state (MCS) patients, their long treatment cycle and high cost commonly put a heavy burden on the patient's family and society. Therefore, it is vital to accurately diagnose and predict consciousness recovery for such patients. In this paper, we explored the role of the P300 signal based on an audiovisual BCI in the classification and prognosis prediction of patients with disorders of consciousness. This experiment included 18 patients: 10 UWS patients and 8 MCS- patients. At the three-month follow-up, we defined patients with an improved prognosis (from UWS to MCS-, from UWS to MCS+, or from MCS- to MCS+) as "improved patients" and those who stayed in UWS/MCS as "not improved patients". First, we compared and analyzed different types of patients, and the results showed that the P300 detection accuracy rate of "improved" patients was significantly higher than that of "not improved" patients. Furthermore, the P300 detection accuracy of traumatic brain injury (TBI) patients was significantly higher than that of non-traumatic brain injury (NTBI, including acquired brain injury and cerebrovascular disease) patients. We also found that there was a positive linear correlation between P300 detection accuracy and CRS-R score, and patients with higher P300 detection accuracy were likely to achieve higher CRS-R scores. In addition, we found that the patients with higher P300 detection accuracies tend to have better prognosis in this audiovisual BCI. These findings indicate that the detection accuracy of P300 is significantly correlated with the level of consciousness, etiology, and prognosis of patients. P300 can be used to represent the preservation level of consciousness in clinical neurophysiology and predict the possibility of recovery in patients with disorders of consciousness.