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Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.

EEG-based automatic emotion recognition can help brain-inspired robots in improving their interactions with humans. This paper presents a novel framework for emotion recognition using multi-channel electroencephalogram (EEG). The framework consists of a linear EEG mixing model and an emotion timing model. Our proposed framework considerably decomposes the EEG source signals from the collected EEG signals and improves classification accuracy by using the context correlations of the EEG feature sequences. Specially, Stack AutoEncoder (SAE) is used to build and solve the linear EEG mixing model and the emotion timing model is based on the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The framework was implemented on the DEAP dataset for an emotion recognition experiment, where the mean accuracy of emotion recognition achieved 81.10% in valence and 74.38% in arousal, and the effectiveness of our framework was verified. Our framework exhibited a better performance in emotion recognition using multi-channel EEG than the compared conventional approaches in the experiments.

Pancreatic cancer is characterized by a grim prognosis and is regarded as one of the most formidable malignancies. Among the genes exhibiting high expression in different tumor tissues, ITGA2 stands out as a promising candidate for cancer therapy. The promotion of cancer in pancreatic cancer is not effective. The objective of this study is to assess the presence of ITGA2, EMT and PD-L1 in pancreatic cancer. We examined the expression of ITGA2, MET, E-cadherin, PD-L1, CD4, and CD8 proteins in 62 pancreatic cancer tissue samples using multi-tissue immunofluorescence and immunohistochemistry techniques. Functional assays, such as the cell migration assay and transwell assay, were used to determine the biological role of ITGA2 in pancreatic cancer. The relationship of ITGA2,EMT and PD-L1 were examined using Western blot analysis and RT-qPCR assay. In our study, we observed the expression of ITGA2, E-cadherin, and PD-L1 in both tumor and stroma tissues of pancreatic cancer. Additionally, a positive correlation between ITGA2, E-cadherin, and PD-L1 in the tumor region (r=0.559, P<0.001 and r=0.511, P<0.001), and PD-L1 in the stroma region (r=0.512, P<0.001).The expression levels of ITGA2, CD4, and CD8 were found to be higher in pancreatic cancer tissues compared to adjacent tissues (P < 0.05). Additionally, ITGA2 was negatively correlated with CD4 and CD8 (r = -0.344, P < 0.005 and r = -0.398, P < 0.005).Furthermore, ITGA2, CD4, and CD8 were found to be correlated with the survival time of patients (P < 0.05). Blocking ITGA2 inhibited the proliferation and invasion ability of pancreatic cancer cells significantly, Additionally, sh-ITGA2 can down-regulate the expression of EMT and PD-L1. We identified a novel mechanism in which ITGA2 plays a crucial role in the regulation of pancreatic cancer growth and invasion. This mechanism involves the upregulation of MET and PD-L1 expression in pancreatic cancer cells. Additionally, we found that increased expression of ITGA2 is associated with a poor prognosis in pancreatic cancer patients. Furthermore, ITGA2 also affects immune regulation in these patients. Therefore, targeting ITGA2 is an effective method to enhance the efficacy of checkpoint immunotherapy and prohibiting tumor growth against pancreatic cancer.

Dwarf apple is one of the most important forms of garden economy, which has become a new engine for rural revitalization. The effective detection of coloring and ripening apples in complex environments is important for the sustainable development of smart agricultural operations. Addressing the issues of low detection efficiency in the greenhouse and the challenges associated with deploying complex target detection algorithms on low-cost equipment, we propose an enhanced lightweight model rooted in YOLOv7. Firstly, we enhance the model training performance by incorporating the Squeeze-and-Excite attention mechanism, which can enhance feature extraction capability. Then, an SCYLLA-IoU (SIoU) loss function is introduced to improve the ability of extracting occluded objects in complex environments. Finally, the model was simplified by introducing depthwise separable convolution and adding a ghost module after up-sampling layers. The improved YOLOv7 model has the highest AP value, which is 10.00%, 5.61%, and 6.00% higher compared to YOLOv5, YOLOv7, and YOLOX, respectively. The improved YOLOv7 model has an MAP value of 95.65%, which provides higher apple detection accuracy compared to other detection models and is suitable for potted dwarf anvil apple identification and detection.

The exploitation of natural gas hydrates (NGHs) reduces the reservoir strength and increases the effective stress, which may trigger stratum settlement and submarine landslides. In particular, commercial-scale production requires the exploitation of NGHs through multiple wells at the same time, which increases the submarine landslide risk. Therefore, it is necessary to study the influences of well layouts on the stability of submarine slopes in the exploitation of NGHs. To this end, a thermo-fluid–solid multifield coupling model that considers the phase change of NGHs in the exploitation process was established. Considering the finite element strength reduction method, an evaluation model was built to analyze the slope stability in the multiwell exploitation of NGHs with the depressurization method. The results show that because NGH decomposition reduces the reservoir strength, the reservoir is compressed overall, and plastic yield zones first occur in the NGH decomposition zone and the slope toe. Finally, a coalesced plastic yield zone propagates throughout the slope. When exploiting NGHs, slope stability is enhanced with increasing well spacing in a multiwell pattern. The linear well layout along the slope dip direction is more conducive to maintaining slope stability than that perpendicular to the slope dip direction. The slope stability decreases with increasing well spacing density but increases with decreasing slope dip. The influence of well layout on submarine slope stability during natural gas hydrate development is studied, which provides a basis for well layouts during gas hydrate exploitation.

目的 运用两样本孟德尔随机化(TSMR)评估原发性硬化性胆管炎(PSC)与结直肠癌(CRC)之间的关联。 方法 PSC与CRC相关的单核苷酸多态性(SNP) 数据分别来自芬兰生物银行及英国生物银行，对基于全基因组关联研究(GWAS)的所有汇总数据进行二次数据分析，选择与PSC密切关联的遗传位点作为工具变量，分别以孟德尔随机化Egger回归法、中位数加权法、IVW随机效应模型、最大似然比法、线性中位数加权法、IVW radial法、IVW固定效应模型七种方法做TSMR，以OR值评价PSC和CRC风险之间的因果关系。 结果 基因预测的PSC对CRC存在正向因果关系，以IVW固定效应模型为例，遗传决定的患PSC患者发生CRC风险增加(OR=1.002 243，95%CI：1.001 319~1.003 167)。TSMR结果不存在异质性(P=0.87)，无水平多效性(P=0.95)。本次所选PSC的3个工具变量为强工具变量(F=11.86)。 结论 TSMR发现PSC具有与CRC风险相关的遗传证据。无论是否合并炎症性肠病，在PSC患者中积极进行肠镜筛查或可有利于CRC的早期发现与及时干预。

Ovarian cancer (OC) and cervical cancer (CC) are the leading causes of death among women. Therefore, identifying markers for early detection and treatment is critical. CDK1 governs the G2/M transition of the cell cycle and is a significant regulatory protein of the cycle. RO-3306 and UBE2C are related to CDK1 expression and might jointly facilitate the development of OC. CDK1 and CDK2 phosphorylate MLK3, which plays an important role in the invasion and proliferation of OC cells. Furthermore, miR-490-3P targets CDK1 and restrains the growth of ovarian tumors. CDK1 also plays a crucial part in the progression of CC. For instance, CDK1 overexpression can rescue the effect of RCC1 knockdown, which is involved in key processes, such as cytoplasmic transport, on G1 cell cycle progression. Using bioinformatics analysis, we evaluated the functional enrichment and role of the co-expressed gene CDK1 in these two cancers and its impact on their prognoses. First, we screened public datasets for OC- and CC-associated DEGs and identified intersecting genes. Enrichment analyses of these genes revealed key biological pathways and processes. We then generated protein-protein interaction networks to identify central genes and important gene modules. Additional enrichment analyses revealed that cell cycle regulation and germ cell maturation were the primary processes regulated by these core genes. We also examined the function of CDK1 in OC and CC, demonstrating its overexpression and its association with particular immunological cell infiltration patterns. Furthermore, CDK1 mutational burden, copy number variation, and patient survival analyses indicated that CDK1 may be a useful prognostic marker. Finally, immunohistochemical examination confirmed the expression of some candidate genes in clinical samples. These findings shed light on the molecular causes of OC and CC and will aid the identification of novel targets for future research regarding these cancers, including their diagnosis and treatment.

In developing countries, decision-making regarding old rural houses significantly relies on expert site investigations, which are criticized for being resource-demanding. This paper aims to construct an efficient Bayesian classifier for house safety and habitability risk evaluations, enabling people with none-civil-engineering backgrounds to make judgements comparable with experts so that house risk levels can be checked regularly at low costs. An initial list of critical risk factors for house safety and habitability was identified with a literature review and verified by expert discussions, field surveys, and Pearson’s Chi-square test of independence with 864 questionnaire samples. The model was constructed according to the causal mechanism between the verified factors and quantified using Bayesian belief network parameter learning. The model reached relatively high accuracy rates, ranging from 91.3% to 100.0% under different situations, including crosschecks with unused expert judgement samples with full input data, crosschecks with unused expert judgement samples with missing input data, and those involving local residents’ judgement. Model sensitivity analyses revealed walls; purlins and roof trusses; and foundations as the three most critical factors for safety and insulation and waterproofing; water and electricity; and fire safety for habitability. The identified list of critical factors contributes to the rural house evaluation and management strategies for developing countries. In addition, the established Bayesian classifier enables regular house checks on a regular and economical basis.

G-protein-coupled receptors (GPCRs) are the most diverse membrane proteins in eukaryotes that transmit chemical signals to the cell. GPCRs are considered the largest family of targets for approved drugs on the market. Sphingosine 1-phosphate receptor 1 (S1P1), a widely expressed GPCR protein in many cell types, performs signal transduction function not only in the monomeric state but also in the dimeric or complex oligomeric states. However, the detail of the S1P1 assembly is still not clearly understood. Cholesterol is believed to affect S1P1 dimerization, but the details of the cholesterol-binding sites with S1P1 are not well understood. In this project, we employ the recently developed Protein AssociatioN Energy Landscape (PANEL) method to investigate S1P1 dimer formation in cholesterol containing membrane. The results of this study can be used to drive theoretical and experimental trials in the study of GPCR oligomers, as well as in the study of transmembrane protein - protein interactions in general.

The equivariant CR minimal immersions from the round 3-sphere S3 into the complex projective space CPn have been classified by the third author explicitly (Li in J Lond Math Soc 68:223–240, 2003). In this paper, by employing the equivariant condition which implies that the induced metric is left-invariant and that all geometric properties of S3=SU(2) endowed with a left-invariant metric can be expressed in terms of the structure constants of the Lie algebra su(2), we establish an extended classification theorem for equivariant CR minimal immersions from the 3-sphere S3 into CPn without the assumption of constant sectional curvatures.