FSCME: A Feature Selection Method Combining Copula Correlation and Maximal Information Coefficient by Entropy Weights.

Feature selection is a critical component of data mining and has garnered significant attention in recent years. However, feature selection methods based on information entropy often introduce complex mutual information forms to measure features, leading to increased redundancy and potential errors. To address this issue, we propose FSCME, a feature selection method combining Copula correlation (Ccor) and the maximum information coefficient (MIC) by entropy weights. The FSCME takes into consideration the relevance between features and labels, as well as the redundancy among candidate features and selected features. Therefore, the FSCME utilizes Ccor to measure the redundancy between features, while also estimating the relevance between features and labels. Meanwhile, the FSCME employs MIC to enhance the credibility of the correlation between features and labels. Moreover, this study employs the Entropy Weight Method (EWM) to evaluate and assign weights to the Ccor and MIC. The experimental results demonstrate that FSCME yields a more effective feature subset for subsequent clustering processes, significantly improving the classification performance compared to the other six feature selection methods. The source codes of the FSCME are available online at https://github.com/CDMBlab/FSCME.

Combining EEG Features and Convolutional Autoencoder for Neonatal Seizure Detection.

Neonatal epilepsy is a common emergency phenomenon in neonatal intensive care units (NICUs), which requires timely attention, early identification, and treatment. Traditional detection methods mostly use supervised learning with enormous labeled data. Hence, this study offers a semi-supervised hybrid architecture for detecting seizures, which combines the extracted electroencephalogram (EEG) feature dataset and convolutional autoencoder, called Fd-CAE. First, various features in the time domain and entropy domain are extracted to characterize the EEG signal, which helps distinguish epileptic seizures subsequently. Then, the unlabeled EEG features are fed into the convolutional autoencoder (CAE) for training, which effectively represents EEG features by optimizing the loss between the input and output features. This unsupervised feature learning process can better combine and optimize EEG features from unlabeled data. After that, the pre-trained encoder part of the model is used for further feature learning of labeled data to obtain its low-dimensional feature representation and achieve classification. This model is performed on the neonatal EEG dataset collected at the University of Helsinki Hospital, which has a high discriminative ability to detect seizures, with an accuracy of 92.34%, precision of 93.61%, recall rate of 98.74%, and F1-score of 95.77%, respectively. The results show that unsupervised learning by CAE is beneficial to the characterization of EEG signals, and the proposed Fd-CAE method significantly improves classification performance.

Coronary computed tomography angiography-derived total coronary plaque burden associated with subsequent cardiovascular outcomes following percutaneous coronary intervention.

OBJECTIVE: To investigate the association of coronary plaque burden variables derived from coronary computed tomography angiography (CCTA) before patients underwent their first percutaneous coronary intervention (PCI) procedure and major adverse cardiovascular events (MACEs) after PCI. METHODS: Patients who underwent CCTA before their first PCI were included retrospectively. A radiologist and a cardiologist analyzed CCTA images on a dedicated workstation. The coronary plaque burden variables included total plaque volume, total percent atheroma volume, volumes and fractions of total low-attenuation plaque, total fibrous plaque, and total calcified plaque. The primary outcomes were MACEs, a composite of all-cause death, nonfatal myocardial infarction, nonfatal stroke, and unscheduled coronary revascularization. RESULTS: A total of 230 patients were included in the final analysis. During a median follow-up of 4.8 years, 67 MACEs occurred. Total plaque volume, total percent atheroma volume, volumes of total low-attenuation plaque and total fibrous plaque but not their fractions were independent predictors for MACEs. Compared with the first tertiles, the hazard ratio of the third tertile of total plaque volume, total percent atheroma volume, total low-attenuation plaque volume, and total fibrous plaque volume were 2.06 (95% CI: 1.03-4.15), 2.15 (95% CI: 1.02-4.51), 3.04 (95% CI: 1.45-6.36), and 2.23 (95% CI: 1.11-4.46), respectively. Neither total calcified plaque volume nor fraction was associated with MACEs independently. CONCLUSION: Selected pre-PCI CCTA-derived variables, including total percent atheroma volume, volumes of total plaque, total low-attenuation plaque and total fibrous plaque, were significantly associated with MACEs after PCI, suggesting that CCTA before PCI reveals the residual risk after revascularization. CLINICAL RELEVANCE STATEMENT: The coronary plaque burden variables derived from coronary computed tomography angiography before percutaneous coronary intervention are independently associated with major adverse cardiovascular events, which could be instrumental in optimizing patient management. KEY POINTS: Coronary plaque burden is associated with cardiovascular events in patients with coronary artery disease. Selected total plaque burden variables derived from coronary computed tomography angiography before percutaneous coronary intervention were associated with poor prognosis. Routine coronary computed tomography angiography before percutaneous coronary intervention might be helpful in reducing future risks.

The effect of deep and awake extubation on emergence agitation after nasal surgery: a randomized controlled trial.

BACKGROUND: Post-anesthetic emergence agitation is common after general anesthesia and may cause adverse consequences, such as injury as well as respiratory and circulatory complications. Emergence agitation after general anesthesia occurs more frequently in nasal surgery than in other surgical procedures. This study aimed to assess the occurrence of emergence agitation in patients undergoing nasal surgery who were extubated under deep anesthesia or when fully awake. METHODS: A total of 202 patients (18-60 years, American Society of Anesthesiologists classification: I-II) undergoing nasal surgery under general anesthesia were randomized 1:1 into two groups: a deep extubation group (group D) and an awake extubation group (group A). The primary outcome was the incidence of emergence agitation. The secondary outcomes included number of emergence agitations, sedation score, vital signs, and incidence of adverse events. RESULTS: The incidence of emergence agitation was lower in group D than in group A (34.7% vs. 72.8%; p < 0.001). Compared to group A, patients in group D had lower Richmond Agitation-Sedation Scale scores, higher Ramsay sedation scores, fewer agitation episodes, and lower mean arterial pressure when extubated and 30 min after surgery, whereas these indicators did not differ 90 min after surgery. There was no difference in the incidence of adverse events between the two groups. CONCLUSIONS: Extubation under deep anesthesia can significantly reduce emergence agitation after nasal surgery under general anesthesia without increasing the incidence of adverse events. TRIAL REGISTRATION: Registered in Clinicaltrials.gov (NCT04844333) on 14/04/2021.

Role and molecular mechanism of Salvia miltiorrhiza associated with chemical compounds in the treatment of diabetes mellitus and its complications: A review.

Diabetes mellitus (DM) is one of the most prevalent diseases worldwide, greatly impacting patients' quality of life. This article reviews the progress in Salvia miltiorrhiza, an ancient Chinese plant, for the treatment of DM and its associated complications. Extensive studies have been conducted on the chemical composition and pharmacological effects of S miltiorrhiza, including its anti-inflammatory and antioxidant activities. It has demonstrated potential in preventing and treating diabetes and its consequences by improving peripheral nerve function and increasing retinal thickness in diabetic individuals. Moreover, S miltiorrhiza has shown effectiveness when used in conjunction with angiotensin-converting enzyme inhibitors, angiotensin receptor blockers (ARBs), and statins. The safety and tolerability of S miltiorrhiza have also been thoroughly investigated. Despite the established benefits of managing DM and its complications, further research is needed to determine appropriate usage, dosage, long-term health benefits, and safety.

[Relationship Between Tim-3 and Galectin-9 Expression Levels, Clinical Pathological Characteristics, and Prognosis in Patients After Radical Resection of Colorectal Cancer]. / ç»“ç›´è‚ ç™Œæ ¹æ²»æœ¯åŽæ‚£è€ Tim-3、galectin-9è¡¨è¾¾æ°´å¹³ä¸Žå ¶ä¸´åºŠç— ç†ç‰¹å¾åŠé¢„åŽçš„å ³ç³».

Objective: Some colorectal cancer patients still face high recurrence rates and poor prognoses even after they have undergone the surgical treatment of radical resection. Identifying potential biochemical markers and therapeutic targets for the prognostic evaluation of patients undergoing radical resection of colorectal cancer is crucial for improving their clinical outcomes. Recently, it has been reported that the T cell immunoglobulin and mucin domain protein 3 (Tim-3) and its ligand galactose lectin 9 (galectin-9) play crucial roles in immune dysfunction caused by various tumors, such as colorectal cancer. However, their expressions, biological functions, and prognostic value in colorectal cancer are still unclear. This study aims to investigate the relationship between Tim-3 and galectin-9 expression levels and the clinicopathological characteristics and prognosis of patients undergoing radical resection of colorectal cancer. Methods: A total of 171 patients who underwent radical resection of colorectal cancer at Chengdu Fifth People's Hospital between February 2018 and March 2019 were selected. Immunohistochemistry was performed to assess the expression levels of Tim-3 and galectin-9 in the cancer tissue samples and the paracancerous tissue samples of the patients. The relationship between Tim-3 and galectin-9 expression levels and the baseline clinical parameters of the patients was analyzed accordingly. Kaplan-Meier analysis was performed to assess the association between Tim-3 and galectin-9 expression levels and the relapse-free survival (RFS) and the overall survival (OS) of colorectal cancer patients. Cox regression analysis was conducted to identify factors associated with adverse prognosis in the patients. Results: The immunohistochemical results showed that the high expression levels of Tim-3 and galectin-9 were observed in 70.18% (120/171) and 32.16% (55/171), respectively, of the colorectal cancer tissues, whereas the low expression levels were 29.82% (51/171) and 67.84% (116/171), respectively. Furthermore, the expression score of Tim-3 was significantly higher in colorectal cancer tissues than that in the paracancerous tissues, while the expression score of galectin-9 was lower than that in the paracancerous tissues (P<0.05). Further analysis revealed that the expression of Tim-3 and galectin-9 was associated with the depth of tumor infiltration, vascular infiltration, and clinical staging (P<0.05). During the follow-up period of 14-63 months, 7 out of 171 patients were lost to follow-up. Among the remaining patients, 49 and 112 cases presented abnormally low expression of Tim-3 and galectin-9, respectively, whereas 115 and 52 cases presented high expression of Tim-3 and galectin-9, respectively. Kaplan-Meier survival analysis demonstrated that patients with high Tim-3 expression in colorectal cancer tissues had significantly lower RFS and OS than those with low expression did (RFS: log-rank=22.66, P<0.001; OS: log-rank=19.71, P<0.001). Conversely, patients with low galectin-9 expression had significantly lower RFS and OS than those with high expression did (RFS: log-rank=19.45, P<0.001; OS: log-rank=22.24, P<0.001). Cox multivariate analysis indicated that TNM stage Ⅲ (HR=2.26, 95% CI: 1.20-5.68), high expression of Tim-3 (HR=0.80, 95% CI: 0.33-0.91), and low expression of galectin-9 (HR=1.80, 95% CI: 1.33-4.70) were independent risk factors affecting RFS and OS in patients (P<0.05). Conclusion: Aberrant expression of Tim-3 and galectin-9 is observed in colorectal cancer tissues. High expression of Tim-3 and low expression of galectin-9 are closely associated with adverse clinico-pathological characteristics and prognosis. They are identified as independent influencing factors that may trigger adverse prognostic events in patients. These findings suggest that Tim-3 and galectin-9 have potential as new therapeutic targets and clinical indicators.

Deep Self-Reconstruction Fusion Similarity Hashing for the Diagnosis of Alzheimer's Disease on Multi-Modal Data.

The pathogenesis of Alzheimer's disease (AD) is extremely intricate, which makes AD patients almost incurable. Recent studies have demonstrated that analyzing multi-modal data can offer a comprehensive perspective on the different stages of AD progression, which is beneficial for early diagnosis of AD. In this paper, we propose a deep self-reconstruction fusion similarity hashing (DS-FSH) method to effectively capture the AD-related biomarkers from the multi-modal data and leverage them to diagnose AD. Given that most existing methods ignore the topological structure of the data, a deep self-reconstruction model based on random walk graph regularization is designed to reconstruct the multi-modal data, thereby learning the nonlinear relationship between samples. Additionally, a fused similarity hash based on anchor graph is proposed to generate discriminative binary hash codes for multi-modal reconstructed data. This allows sample fused similarity to be effectively modeled by a fusion similarity matrix based on anchor graph while modal correlation can be approximated by Hamming distance. Especially, extracted features from the multi-modal data are classified using deep sparse autoencoders classifier. Finally, experiments conduct on the AD Neuroimaging Initiative database show that DS-FSH outperforms comparable methods of AD classification. To conclude, DS-FSH identifies multi-modal features closely associated with AD, which are expected to contribute significantly to understanding of the pathogenesis of AD.

Investigating the mechanisms of drug resistance and prognosis in ovarian cancer using single-cell RNA sequencing and bulk RNA sequencing.

Ovarian cancer stands as a prevalent malignancy within the realm of gynecology, and the emergence of resistance to chemotherapeutic agents remains a pivotal impediment to both prognosis and treatment. Through a single-cell level investigation, we scrutinize the drug resistance and mitotic activity of the core tumor cells in ovarian cancer. Our study revisits the interrelationships and temporal trajectories of distinct epithelial cells (EPCs) subpopulations, while identifying genes associated with ovarian cancer prognosis. Notably, our findings establish a strong association between the drug resistance of EPCs and oxidative phosphorylation pathways. Subsequently, through subpopulation and temporal trajectory analysis, we confirm the intermediate position of EPCs subpopulation C0. Furthermore, we delve into the immunological functions and differentially expressed genes associated with the prognosis of C0, shedding light on the potential for constructing novel ovarian cancer prognosis models and identifying new therapeutic targets.

Comparing lycopene's impact on mortality in adults with or without obesity.

Objective: Given lycopene's anti-inflammatory and antioxidant properties, we investigated its mortality impact in individuals with and without obesity, confirming distinct effects. Methods: This study analyzes the National Health and Nutrition Examination Survey (NHANES) data from 2003-2006 and 2017-2018, linking lycopene levels to all-cause and cardiovascular mortality. Using various statistical methods, three models are sequentially adjusted for confounders, investigating the lycopene-outcome relationship. Results: We studied 11 737 adults for 162 months and found 1537 all-cause deaths (13.1%) and 443 cardiovascular deaths (3.8%). For those without obesity, serum lycopene had an "L" shape relationship with all-cause mortality, being harmful at very low levels but protective above a certain threshold. It consistently protects against cardiovascular mortality. In individuals with obesity, the relationship with all-cause mortality formed a "U" shape, with increased risk at very low and very high lycopene levels and protection in the middle range. Cardiovascular mortality showed a similar pattern in individuals with obesity. Interestingly, dietary lycopene intake had protective effects in both groups. Conclusion: This study reveals that lycopene exhibits distinct associations with all-cause and cardiovascular mortality in populations with or without obesity, emphasizing the importance of considering individual health profiles when assessing its benefits.

Diagnosis-Guided Deep Subspace Clustering Association Study for Pathogenetic Markers Identification of Alzheimer's Disease Based on Comparative Atlases.

The roles of brain region activities and genotypic functions in the pathogenesis of Alzheimer's disease (AD) remain unclear. Meanwhile, current imaging genetics methods are difficult to identify potential pathogenetic markers by correlation analysis between brain network and genetic variation. To discover disease-related brain connectome from the specific brain structure and the fine-grained level, based on the Automated Anatomical Labeling (AAL) and human Brainnetome atlases, the functional brain network is first constructed for each subject. Specifically, the upper triangle elements of the functional connectivity matrix are extracted as connectivity features. The clustering coefficient and the average weighted node degree are developed to assess the significance of every brain area. Since the constructed brain network and genetic data are characterized by non-linearity, high-dimensionality, and few subjects, the deep subspace clustering algorithm is proposed to reconstruct the original data. Our multilayer neural network helps capture the non-linear manifolds, and subspace clustering learns pairwise affinities between samples. Moreover, most approaches in neuroimaging genetics are unsupervised learning, neglecting the diagnostic information related to diseases. We presented a label constraint with diagnostic status to instruct the imaging genetics correlation analysis. To this end, a diagnosis-guided deep subspace clustering association (DDSCA) method is developed to discover brain connectome and risk genetic factors by integrating genotypes with functional network phenotypes. Extensive experiments prove that DDSCA achieves superior performance to most association methods and effectively selects disease-relevant genetic markers and brain connectome at the coarse-grained and fine-grained levels.

Study of multi-dimensional problems arising in wave propagation using a hybrid scheme.

Many scientific phenomena are linked to wave problems. This paper presents an effective and suitable technique for generating approximation solutions to multi-dimensional problems associated with wave propagation. We adopt a new iterative strategy to reduce the numerical work with minimum time efficiency compared to existing techniques such as the variational iteration method (VIM) and homotopy analysis method (HAM) have some limitations and constraints within the development of recurrence relation. To overcome this drawback, we present a Sawi integral transform ( S T) for constructing a suitable recurrence relation. This recurrence relation is solved to determine the coefficients of the homotopy perturbation strategy (HPS) that leads to the convergence series of the precise solution. This strategy derives the results in algebraic form that are independent of any discretization. To demonstrate the performance of this scheme, several mathematical frameworks and visual depictions are shown.

KFDAE: CircRNA-Disease Associations Prediction Based on Kernel Fusion and Deep Auto-Encoder.

CircRNA has been proved to play an important role in the diseases diagnosis and treatment. Considering that the wet-lab is time-consuming and expensive, computational methods are viable alternative in these years. However, the number of circRNA-disease associations (CDAs) that can be verified is relatively few, and some methods do not take full advantage of dependencies between attributes. To solve these problems, this paper proposes a novel method based on Kernel Fusion and Deep Auto-encoder (KFDAE) to predict the potential associations between circRNAs and diseases. Firstly, KFDAE uses a non-linear method to fuse the circRNA similarity kernels and disease similarity kernels. Then the vectors are connected to make the positive and negative sample sets, and these data are send to deep auto-encoder to reduce dimension and extract features. Finally, three-layer deep feedforward neural network is used to learn features and gain the prediction score. The experimental results show that compared with existing methods, KFDAE achieves the best performance. In addition, the results of case studies prove the effectiveness and practical significance of KFDAE, which means KFDAE is able to capture more comprehensive information and generate credible candidate for subsequent wet-lab.

A Clustering Method for Single-Cell RNA-Seq Data Based on Automatic Weighting Penalty and Low-Rank Representation.

Advances in high-throughput single-cell RNA sequencing (scRNA-seq) technology have provided more comprehensive biological information on cell expression. Clustering analysis is a critical step in scRNA-seq research and provides clear knowledge of the cell identity. Unfortunately, the characteristics of scRNA-seq data and the limitations of existing technologies make clustering encounter a considerable challenge. Meanwhile, some existing methods treat different features equally and ignore differences in feature contributions, which leads to a loss of information. To overcome limitations, we introduce a weighted distance constraint into the construction of the similarity graph and combine the similarity constraint. We propose the Joint Automatic Weighting Similarity Graph and Low-rank Representation (JAGLRR) clustering method. Evaluating the contributions of each feature and assigning various weight values can increase the significance of valuable features while decreasing the interference of redundant features. The similarity constraint allows the model to generate a more symmetric affinity matrix. Benefitting from that affinity matrix, JAGLRR recovers the original linear relationship of the data more accurately and obtains more discriminative information. The results on simulated datasets and 8 real datasets show that JAGLRR outperforms 11 existing comparison methods in clustering experiments, with higher clustering accuracy and stability.

Assessing the relationship between gut microbiota and endometriosis: a bidirectional two-sample mendelian randomization analysis.

BACKGROUND: An increasing body of observational studies have indicated an association between gut microbiota and endometriosis. However, the causal relationship between them is not yet clear. In this study, we employed Mendelian randomization method to investigate the causal relationship between 211 gut microbiota taxa and endometriosis. METHODS: Independent genetic loci significantly associated with the relative abundance of 211 gut microbiota taxa, based on predefined thresholds, were extracted as instrumental variables. The primary analytical approach employed was the IVW method. Effect estimates were assessed primarily using the odds ratio and 95% confidence intervals. Supplementary analyses were conducted using MR-Egger regression, the weighted median method, the simple mode and the weighted mode method to complement the IVW results. In addition, we conducted tests for heterogeneity, horizontal pleiotropy, sensitivity analysis, and MR Steiger to assess the robustness of the results and the strength of the causal relationships. RESULTS: Based on the IVW method, we found that the family Prevotellaceae, genus Anaerotruncus, genus Olsenella, genus Oscillospira, and order Bacillales were identified as risk factors for endometriosis, while class Melainabacteria and genus Eubacterium ruminantium group were protective factors. Additionally, no causal relationship was observed between endometriosis and gut microbiota. Heterogeneity tests, pleiotropy tests, and leave-one-out sensitivity analyses did not detect any significant heterogeneity or pleiotropic effects. CONCLUSIONS: Our MR study has provided evidence supporting a potential causal relationship between gut microbiota and endometriosis, and it suggests the absence of bidirectional causal effects. These findings could potentially offer new insights for the development of novel strategies for the prevention and treatment of endometriosis.

HGSMDA: miRNA-Disease Association Prediction Based on HyperGCN and Sørensen-Dice Loss.

Biological research has demonstrated the significance of identifying miRNA-disease associations in the context of disease prevention, diagnosis, and treatment. However, the utilization of experimental approaches involving biological subjects to infer these associations is both costly and inefficient. Consequently, there is a pressing need to devise novel approaches that offer enhanced accuracy and effectiveness. Presently, the predominant methods employed for predicting disease associations rely on Graph Convolutional Network (GCN) techniques. However, the Graph Convolutional Network algorithm, which is locally aggregated, solely incorporates information from the immediate neighboring nodes of a given node at each layer. Consequently, GCN cannot simultaneously aggregate information from multiple nodes. This constraint significantly impacts the predictive efficacy of the model. To tackle this problem, we propose a novel approach, based on HyperGCN and Sørensen-Dice loss (HGSMDA), for predicting associations between miRNAs and diseases. In the initial phase, we developed multiple networks to represent the similarity between miRNAs and diseases and employed GCNs to extract information from diverse perspectives. Subsequently, we draw into HyperGCN to construct a miRNA-disease heteromorphic hypergraph using hypernodes and train GCN on the graph to aggregate information. Finally, we utilized the Sørensen-Dice loss function to evaluate the degree of similarity between the predicted outcomes and the ground truth values, thereby enabling the prediction of associations between miRNAs and diseases. In order to assess the soundness of our methodology, an extensive series of experiments was conducted employing the Human MicroRNA Disease Database (HMDD v3.2) as the dataset. The experimental outcomes unequivocally indicate that HGSMDA exhibits remarkable efficacy when compared to alternative methodologies. Furthermore, the predictive capacity of HGSMDA was corroborated through a case study focused on colon cancer. These findings strongly imply that HGSMDA represents a dependable and valid framework, thereby offering a novel avenue for investigating the intricate association between miRNAs and diseases.

Integrin ß5 subunit regulates hyperglycemia-induced vascular endothelial cell apoptosis through FoxO1-mediated macroautophagy.

BACKGROUND: Hyperglycemia frequently induces apoptosis in endothelial cells and ultimately contributes to microvascular dysfunction in patients with diabetes mellitus (DM). Previous research reported that the expression of integrins as well as their ligands was elevated in the diseased vessels of DM patients. However, the association between integrins and hyperglycemia-induced cell death is still unclear. This research was designed to investigate the role played by integrin subunit ß5 (ITGB5) in hyperglycemia-induced endothelial cell apoptosis. METHODS: We used leptin receptor knockout (Lepr-KO) ( db / db ) mice as spontaneous diabetes animal model. Selective deletion of ITGB5 in endothelial cell was achieved by injecting vascular targeted adeno-associated virus via tail vein. Besides, we also applied small interfering RNA in vitro to study the mechanism of ITGB5 in regulating high glucose-induced cell apoptosis. RESULTS: ITGB5 and its ligand, fibronectin, were both upregulated after exposure to high glucose in vivo and in vitro . ITGB5 knockdown alleviated hyperglycemia-induced vascular endothelial cell apoptosis and microvascular rarefaction in vivo.In vitro analysis revealed that knockdown of either ITGB5 or fibronectin ameliorated high glucose-induced apoptosis in human umbilical vascular endothelial cells (HUVECs). In addition, knockdown of ITGB5 inhibited fibronectin-induced HUVEC apoptosis, which indicated that the fibronectin-ITGB5 interaction participated in high glucose-induced endothelial cell apoptosis. By using RNA-sequencing technology and bioinformatic analysis, we identified Forkhead Box Protein O1 (FoxO1) as an important downstream target regulated by ITGB5. Moreover, we demonstrated that the excessive macroautophagy induced by high glucose can contribute to HUVEC apoptosis, which was regulated by the ITGB5-FoxO1 axis. CONCLUSION: The study revealed that high glucose-induced endothelial cell apoptosis was positively regulated by ITGB5, which suggested that ITGB5 could potentially be used to predict and treat DM-related vascular complications.

Joint L2,p-norm and random walk graph constrained PCA for single-cell RNA-seq data.

The development and widespread utilization of high-throughput sequencing technologies in biology has fueled the rapid growth of single-cell RNA sequencing (scRNA-seq) data over the past decade. The development of scRNA-seq technology has significantly expanded researchers' understanding of cellular heterogeneity. Accurate cell type identification is the prerequisite for any research on heterogeneous cell populations. However, due to the high noise and high dimensionality of scRNA-seq data, improving the effectiveness of cell type identification remains a challenge. As an effective dimensionality reduction method, Principal Component Analysis (PCA) is an essential tool for visualizing high-dimensional scRNA-seq data and identifying cell subpopulations. However, traditional PCA has some defects when used in mining the nonlinear manifold structure of the data and usually suffers from over-density of principal components (PCs). Therefore, we present a novel method in this paper called joint L2,p-norm and random walk graph constrained PCA (RWPPCA). RWPPCA aims to retain the data's local information in the process of mapping high-dimensional data to low-dimensional space, to more accurately obtain sparse principal components and to then identify cell types more precisely. Specifically, RWPPCA combines the random walk (RW) algorithm with graph regularization to more accurately determine the local geometric relationships between data points. Moreover, to mitigate the adverse effects of dense PCs, the L2,p-norm is introduced to make the PCs sparser, thus increasing their interpretability. Then, we evaluate the effectiveness of RWPPCA on simulated data and scRNA-seq data. The results show that RWPPCA performs well in cell type identification and outperforms other comparison methods.

Multi-Kernel Graph Attention Deep Autoencoder for MiRNA-Disease Association Prediction.

Accumulating evidence indicates that microRNAs (miRNAs) can control and coordinate various biological processes. Consequently, abnormal expressions of miRNAs have been linked to various complex diseases. Recognizable proof of miRNA-disease associations (MDAs) will contribute to the diagnosis and treatment of human diseases. Nevertheless, traditional experimental verification of MDAs is laborious and limited to small-scale. Therefore, it is necessary to develop reliable and effective computational methods to predict novel MDAs. In this work, a multi-kernel graph attention deep autoencoder (MGADAE) method is proposed to predict potential MDAs. In detail, MGADAE first employs the multiple kernel learning (MKL) algorithm to construct an integrated miRNA similarity and disease similarity, providing more biological information for further feature learning. Second, MGADAE combines the known MDAs, disease similarity, and miRNA similarity into a heterogeneous network, then learns the representations of miRNAs and diseases through graph convolution operation. After that, an attention mechanism is introduced into MGADAE to integrate the representations from multiple graph convolutional network (GCN) layers. Lastly, the integrated representations of miRNAs and diseases are input into the bilinear decoder to obtain the final predicted association scores. Corresponding experiments prove that the proposed method outperforms existing advanced approaches in MDA prediction. Furthermore, case studies related to two human cancers provide further confirmation of the reliability of MGADAE in practice.

Application of Yang homotopy perturbation transform approach for solving multi-dimensional diffusion problems with time-fractional derivatives.

In this paper, we aim to present a powerful approach for the approximate results of multi-dimensional diffusion problems with time-fractional derivatives. The fractional order is considered in the view of the Caputo fractional derivative. In this analysis, we develop the idea of the Yang homotopy perturbation transform method (YHPTM), which is the combination of the Yang transform (YT) and the homotopy perturbation method (HPM). This robust scheme generates the solution in a series form that converges to the exact results after a few iterations. We show the graphical visuals in two-dimensional and three-dimensional to provide the accuracy of our developed scheme. Furthermore, we compute the graphical error to demonstrate the close-form analytical solution in the comparison of the exact solution. The obtained findings are promising and suitable for the solution of multi-dimensional diffusion problems with time-fractional derivatives. The main advantage is that our developed scheme does not require assumptions or restrictions on variables that ruin the actual problem. This scheme plays a significant role in finding the solution and overcoming the restriction of variables that may cause difficulty in modeling the problem.

Obesity, birth weight, and lifestyle factors for frailty: a Mendelian randomization study.

Obesity, birth weight and lifestyle factors have been found associated with the risk of frailty in observational studies, but whether these associations are causal is uncertain. We conducted a two-sample Mendelian randomization study to investigate the associations. Genetic instruments associated with the exposures at the genome-wide significance level (p < 5 × 10-8) were selected from corresponding genome-wide association studies (n = 143,677 to 703,901 individuals). Summary-level data for the frailty index were obtained from the UK Biobank (n = 164,610) and Swedish TwinGene (n = 10,616). The ß of the frailty index was 0.15 (p = 3.88 × 10-9) for 1 standard deviation increase in the prevalence of smoking initiation, 0.19 (p = 3.54 × 10-15) for leisure screen time, 0.13 (p = 5.26 × 10-7) for body mass index and 0.13 (p = 1.80 × 10-4) for waist circumference. There was a suggestive association between genetically predicted higher birth weight and moderate-to-vigorous intensity physical activity with the decreased risk of the frailty index. We observed no causal association between genetically predicted age of smoking initiation and alcoholic drinks per week with the frailty index. This study supports the causal roles of smoking initiation, leisure screen time, overall obesity, and abdominal obesity in frailty. The possible association between higher birth weight, proper physical activity and a decreased risk of frailty needs further confirmation.