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Abstract Objective Sarcopenia is a geriatric syndrome that occurs with age and is characterized by a gradual decline in muscle mass, power, and functionality. It serves as a prominent contributor to frailty, disability, and mortality among older individuals. Currently, no standardized global guidelines exist for the diagnosis of sarcopenia. This study aimed to establish the correlation between sarcopenia and the constitutions of traditional Chinese medicine (TCM), considering the connection between physical functioning and sarcopenia. Methods A total of 1441 participants in this study were diagnosed with sarcopenia. The Asian Working Group for Sarcopenia (AWGS) proposed a sarcopenia definition algorithm. To determine the constitution of each participant, classification and determination standards were used in traditional Chinese medicine. This study evaluated the demographics, lifestyles, and self‐reported medical history of individuals diagnosed with sarcopenia through a self‐administered questionnaire. The constitution of the participants was determined using TCM classification and determination standards. Subsequently, we analyzed the results of univariate analysis and multivariate regression and constructed a receiver operating characteristic (ROC) curve. Results Participants who were diagnosed with sarcopenia had substantially lower original Neutral constitution scores (P

Summary: Irisin, an exercise-induced myokine, is known to be able to regulate bone metabolism. However, the underlying mechanisms regarding the effects of irisin on senile osteoporosis have not been fully elucidated. Here, we demonstrated that irisin can inhibit bone mass loss and bone microarchitecture alteration in senile osteoporosis mouse model. In addition, irisin has effects on bone remodeling that is in favor of bone formation. Remarkably, irisin induced autophagy in osteocytes demonstrated by increased LC3-positive osteocytes, and increased autophagy-related genes and proteins. In vitro analysis revealed that Irisin can prevent mitochondrial oxidative damage. Furthermore, irisin can obviously induce osteocyte mitophagy and increased phosphorylation of Ampk and Ulk1. Inhibition of Ampk signaling recapitulated the biological effect of irisin loss, accompanied by the markedly lower expression of Ulk1. Taken together, our findings show that irisin reduces age-related bone loss by inducing osteocyte mitophagy via Ampk-dependent activation of Ulk1.

Abstract Childhood obesity not only has a negative impact on a child's health but is also a significant risk factor for adult obesity and related metabolic disorders, making it a major global public health concern. Recent studies have revealed the crucial role of gut microbiota in the occurrence and development of obesity, in addition to genetic and lifestyle factors. In this study, we recruited 19 normal-weight children and 47 children with varying degrees of obesity. A questionnaire survey was conducted to inquire about the family background, lifestyle habits and dietary composition of the 66 children. Findings indicate that fathers of obese children tend to be obese themselves, while children with highly educated mothers are more likely to maintain a normal weight. Furthermore, overweight children tend to spend more time on electronic devices and less time on physical activities compared to their normal-weight counterparts. Obese children exhibit significant differences in breakfast and dinner dietary composition when compared to children with normal weight. Additionally, the gut microbiota of these 66 children was analyzed using 16S rRNA sequencing. Analysis of gut microbiota composition showed similar compositions among children with varying degrees of obesity, but significant differences were observed in comparison to normal-weight children. Obese children exhibited a reduced proportion of Bacteroidota and an increased proportion of Firmicutes, resulting in an elevated Firmicutes/Bacteroidota ratio. Moreover, Actinobacteriota were found to be increased in the gut microbiota of children with varying degrees of obesity. PICRUSt analysis indicated significant metabolic differences in the microbiota functions between obese and normal-weight children, suggesting the composition of gut microbiota could be a crucial factor contributing to obesity. These findings provide valuable insights for the treatment of childhood obesity.

ObjectiveSarcopenia is a gradually advancing systemic disorder affecting skeletal muscles, primarily distinguished by diminished muscle mass and functional decline. As of present, a universally accepted diagnostic criterion for sarcopenia has yet to be established. From the perspective of the constitution theory in traditional Chinese medicine (TCM), the Yin-deficiency constitution is believed to have a significant correlation with the development of sarcopenia. The primary objective of this study was to examine the potential association between sarcopenia and Yin-deficiency constitution.MethodsThe present study is a cross-sectional analysis. The Asian Working Group for Sarcopenia (AWGS) recommended a diagnostic criterion for sarcopenia. A total of 141 participants over 50 years of age were diagnosed with sarcopenia. To determine the constitution of each patient, classification and determination standards were used in traditional Chinese medicine. In this study, a combination of logistic regression and propensity score matching (PSM) was employed to analyze a dataset comprising 1,372 eligible observations. The diagnostic efficacy of the test in distinguishing sarcopenia was assessed through receiver operating characteristic (ROC) curve analysis.ResultsThe relationship between Yin-deficiency constitution and sarcopenia was examined using logistic regression analysis. In the crude model, the odds ratio (OR) was found to be 3.20 (95% confidence interval [CI]: 1.70–6.03). After adjusting for various confounding factors, including gender, sex, 6 m walking test/(m/s), SMI, and maximum grip strength/kg, the OR increased to 9.70 (95% CI: 3.20–69.38). The associations between seven other biased traditional Chinese medicine (TCM) constitutions and sarcopenia were not found to be statistically significant in the fully adjusted model. The propensity score matching (PSM) analysis yielded consistent results with the logistic regression analysis. Receiver operating characteristic (ROC) curve analysis showed that the AUC of the Yin-deficiency constitution combined with age and gender reached 0.707.ConclusionAmong the nine TCM constitutions examined, the Yin-deficiency constitution demonstrates an independent association with sarcopenia. Yin-deficiency constitution may serve as a potential risk factor for the development of sarcopenia. To establish a causal relationship, further experimental investigations are warranted. The diagnostic performance of sarcopenia is effectively demonstrated by the Yin-deficiency constitution combined with age and gender.

Abstract. Background:. Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts. Methods:. We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models. Results:. The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749). Conclusions:. The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population. Trial Registration:. Chictr.org, ChiCTR 1800018895.

Alternative polyadenylation (APA) plays important roles in modulating mRNA stability, translation, and subcellular localization, and contributes extensively to shaping eukaryotic transcriptome complexity and proteome diversity. Identification of poly(A) sites (pAs) on a genome-wide scale is a critical step toward understanding the underlying mechanism of APA-mediated gene regulation. A number of established computational tools have been proposed to predict pAs from diverse genomic data. Here we provided an exhaustive overview of computational approaches for predicting pAs from DNA sequences, bulk RNA sequencing (RNA-seq) data, and single-cell RNA sequencing (scRNA-seq) data. Particularly, we examined several representative tools using bulk RNA-seq and scRNA-seq data from peripheral blood mononuclear cells and put forward operable suggestions on how to assess the reliability of pAs predicted by different tools. We also proposed practical guidelines on choosing appropriate methods applicable to diverse scenarios. Moreover, we discussed in depth the challenges in improving the performance of pA prediction and benchmarking different methods. Additionally, we highlighted outstanding challenges and opportunities using new machine learning and integrative multi-omics techniques, and provided our perspective on how computational methodologies might evolve in the future for non-3′ untranslated region, tissue-specific, cross-species, and single-cell pA prediction.

Metastatic prostate cancer can be difficult to biopsy and characterise. Here, the authors use cell-free DNA methylation analysis to illustrate changes in hypermethylation in metastatic disease.

To address the problem that traditional spectral clustering algorithms cannot obtain the complete structural information of networks, this paper proposes a spectral clustering community detection algorithm, PMIK-SC, based on the point-wise mutual information (PMI) graph kernel. The kernel is constructed according to the point-wise mutual information between nodes, which is then used as a proximity matrix to reconstruct the network and obtain the symmetric normalized Laplacian matrix. Finally, the network is partitioned by the eigendecomposition and eigenvector clustering of the Laplacian matrix. In addition, to determine the number of clusters during spectral clustering, this paper proposes a fast algorithm, BI-CNE, for estimating the number of communities. For a specific network, the algorithm first reconstructs the original network and then runs Monte Carlo sampling to estimate the number of communities by Bayesian inference. Experimental results show that the detection speed and accuracy of the algorithm are superior to other existing algorithms for estimating the number of communities. On this basis, the spectral clustering community detection algorithm PMIK-SC also has high accuracy and stability compared with other community detection algorithms and spectral clustering algorithms.

The molecular mechanism underlying the DNA hypermethylation phenotype observed in the SPOP-mutant prostate cancers is unclear. Here, the authors show that mutant SPOP induces global aberrant DNA methylation patterns through GLP/G9a and renders prostate cancer cells susceptible to DNA demethylating agents.

Estradiol (E2) has been proven to be effective in treating perimenopausal depression (PD); however, the downstream signaling pathways have not been fully elucidated. Transient receptor potential channels 6 (TRPC6) plays a vital role in promoting neuronal development and the formation of excitatory synapses. At present, we found that the serum levels of E2 and brain-derived neurotrophic factor (BDNF) declined significantly in the women with PD compared to perimenopausal women, which was accompanied by a clear reduction in TRPC6 levels. To further reveal the effects of TRPC6 on neuronal survival and excitability, the PD-like rat model was established by the total removal of left ovary and 80% removal of right ovary followed by 21 days of the chronic unpredictable mild stress. Intragastric administration of E2 (2 mg/kg), intraperitoneal injection of BDNF/TrB signaling pathway inhibitor (K252a, 100 μg/kg) and TRPC6 agonist (OAG, 0.6 mg/kg), and intracerebroventricular infusion of anti-BDNF antibody for blocking BDNF (0.5 μg/24 μl/rat) daily for 21 days were conducted. The levels of BDNF and TRPC6 in rat serum were lower in PD rats compared to the control rats; the depression-like behavior was induced, the neuronal death rate in the hippocampus increased, and the thickness of postsynaptic density (PSD) and the number of asymmetric synapses decreased significantly in the PD group. E2 treatment greatly upregulated the serum levels of BDNF and TRPC6, the neuronal excitability indicated by an elevation in the PSD thickness and the numbers of asymmetric synapses, and these actions were reversed by K252a; co-administration of TRPC6 agonist and K252a improved neuronal degeneration and increased the neuronal excitability induced in the E2-treated PD rats. K252a or anti-BDNF antibody inhibited the increased neuronal BDNF and TRPC6 expression in E2-treated PD rats; co-treatment of TRPC6 agonist and anti-BDNF antibody reduced neuronal death and increased the BDNF and TRPC6 expression in the hippocampal CA1 neurons in the E2-treated PD rats. These results suggest that the neuroprotective role of E2 in PD is closely related to enhance the activity of BDNF/TRPC6 pathway and is helpful to provide new prevention and strategies.

Many deep learning (DL) models have shown exceptional promise in radar-based human activity recognition (HAR) area. For radar-based HAR, the raw data is generally converted into a 2-D spectrogram by using short-time Fourier transform (STFT). All the existing DL methods treat the spectrogram as an optical image, and thus the corresponding architectures such as 2-D convolutional neural networks (2D-CNNs) are adopted in those methods. These 2-D methods that ignore temporal characteristics ordinarily lead to a complex network with a huge amount of parameters but limited recognition accuracy. In this paper, for the first time, the radar spectrogram is treated as a time sequence with multiple channels. Hence, we propose a DL model composed of 1-D convolutional neural networks (1D-CNNs) and long short-term memory (LSTM). The experiments results show that the proposed model can extract spatio-temporal characteristics of the radar data and thus achieves the best recognition accuracy and relatively low complexity compared to the existing 2D-CNN methods.

Abstract Background Single-cell RNA-sequencing (scRNA-seq) is fast becoming a powerful tool for profiling genome-scale transcriptomes of individual cells and capturing transcriptome-wide cell-to-cell variability. However, scRNA-seq technologies suffer from high levels of technical noise and variability, hindering reliable quantification of lowly and moderately expressed genes. Since most downstream analyses on scRNA-seq, such as cell type clustering and differential expression analysis, rely on the gene-cell expression matrix, preprocessing of scRNA-seq data is a critical preliminary step in the analysis of scRNA-seq data. Results We presented scNPF, an integrative scRNA-seq preprocessing framework assisted by network propagation and network fusion, for recovering gene expression loss, correcting gene expression measurements, and learning similarities between cells. scNPF leverages the context-specific topology inherent in the given data and the priori knowledge derived from publicly available molecular gene-gene interaction networks to augment gene-gene relationships in a data driven manner. We have demonstrated the great potential of scNPF in scRNA-seq preprocessing for accurately recovering gene expression values and learning cell similarity networks. Comprehensive evaluation of scNPF across a wide spectrum of scRNA-seq data sets showed that scNPF achieved comparable or higher performance than the competing approaches according to various metrics of internal validation and clustering accuracy. We have made scNPF an easy-to-use R package, which can be used as a versatile preprocessing plug-in for most existing scRNA-seq analysis pipelines or tools. Conclusions scNPF is a universal tool for preprocessing of scRNA-seq data, which jointly incorporates the global topology of priori interaction networks and the context-specific information encapsulated in the scRNA-seq data to capture both shared and complementary knowledge from diverse data sources. scNPF could be used to recover gene signatures and learn cell-to-cell similarities from emerging scRNA-seq data to facilitate downstream analyses such as dimension reduction, cell type clustering, and visualization.

Diabetic retinopathy (DR) is an important cause of blindness worldwide. However, DR is hard to be detected in the early stages, and the diagnostic procedure can be time-consuming even for the experienced experts. Therefore, a computer-aided diagnosis method based on deep learning algorithms is proposed to automatedly diagnose the referable diabetic retinopathy by classifying color retinal fundus photographs into two grades. In this paper, a novel convolutional neural network model with the Siamese-like architecture is trained with a transfer learning technique. Different from the previous works, the proposed model accepts binocular fundus images as inputs and learns their correlation to help to make a prediction. In the case with a training set of only 28 104 images and a test set of 7024 images, an area under the receiver operating curve of 0.951 is obtained by the proposed binocular model, which is 0.011 higher than that obtained by the existing monocular model. To further verify the effectiveness of the binocular design, a binocular model for five-class DR detection is also trained and evaluated on a 10% validation set. The result shows that it achieves a kappa score of 0.829 which is higher than that of the existing non-ensemble model.

In this paper, we present a novel one-dimensional deep convolutional neural network (1D-DCNN) with a multi-label-way-based algorithm for comprehensively and automatically extracting features and classifying mixture gases. Although a number of pattern recognition methods have been used to analyze the mixed gases, the performances of these methods highly depend on the hand-crafted feature engineering. By contrast, the proposed implementation, based on one-dimensional convolution, is capable of automatically extracting features and distinguishing the individual component of binary mixture gases composed of ethylene, CO, and methane. To the best of our knowledge, the proposed 1D-DCNN algorithm is first applied in the mixture gases' recognition. In addition, the proposed 1D-DCNN with multi-label way not only significantly reduces the label dimension but also quantifies the probability of each component in mixed gases. Compared with the conventional pattern recognition algorithms including support vector machine, artificial neural network, k-nearest neighbor, and random forest, the proposed 1D-DCNN exhibits a higher recognition accuracy (96.30%) based on our extensive experimental results using ten-fold cross validation.

Fast recognition of flammable and toxic gas species within very short response time is a challenging task for the gas sensing devices adopted in a wide range of applications. The recognition accuracies of the previous implementations are always constrained by the limited feature or dynamic information extracted from the short transient gas response curves. In order to address this issue, in this paper, we propose a novel hybrid approach with both convolutional and recurrent neural networks combined, which is based on the long short-term memory module. Featuring the capability of learning the correlations of time-series data, the proposed deep learning method is well-suited for extracting the valuable transient feature contained in the very beginning of the response curve. As a result, within a response time as short as 0.5 s, the proposed implementation is capable of recognizing the gas species with an accuracy of 84.06%. In addition, the aforesaid accuracy can be further improved by increasing the response time with the step of 0.5 s. According to our extensive experimental results, the recognition accuracy can be elevated up to 98.28% at the response time of 4 s, where it typically needs 40 s for the response curve to achieve saturation. The reported accuracy dramatically outperforms the previous algorithms, including gradient tree boosting (GTB), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and linear discriminant analysis (LDA). Moreover, various standard drift related experiments are conducted, of which the results validate our proposed algorithm's superior robustness for the wide range of real-life applications.

Abstract Background Alternative polyadenylation (APA) has emerged as a pervasive mechanism that contributes to the transcriptome complexity and dynamics of gene regulation. The current tsunami of whole genome poly(A) site data from various conditions generated by 3′ end sequencing provides a valuable data source for the study of APA-related gene expression. Cluster analysis is a powerful technique for investigating the association structure among genes, however, conventional gene clustering methods are not suitable for APA-related data as they fail to consider the information of poly(A) sites (e.g., location, abundance, number, etc.) within each gene or measure the association among poly(A) sites between two genes. Results Here we proposed a computational framework, named PASCCA, for clustering genes from replicated or unreplicated poly(A) site data using canonical correlation analysis (CCA). PASCCA incorporates multiple layers of gene expression data from both the poly(A) site level and gene level and takes into account the number of replicates and the variability within each experimental group. Moreover, PASCCA characterizes poly(A) sites in various ways including the abundance and relative usage, which can exploit the advantages of 3′ end deep sequencing in quantifying APA sites. Using both real and synthetic poly(A) site data sets, the cluster analysis demonstrates that PASCCA outperforms other widely-used distance measures under five performance metrics including connectivity, the Dunn index, average distance, average distance between means, and the biological homogeneity index. We also used PASCCA to infer APA-specific gene modules from recently published poly(A) site data of rice and discovered some distinct functional gene modules. We have made PASCCA an easy-to-use R package for APA-related gene expression analyses, including the characterization of poly(A) sites, quantification of association between genes, and clustering of genes. Conclusions By providing a better treatment of the noise inherent in repeated measurements and taking into account multiple layers of poly(A) site data, PASCCA could be a general tool for clustering and analyzing APA-specific gene expression data. PASCCA could be used to elucidate the dynamic interplay of genes and their APA sites among various biological conditions from emerging 3′ end sequencing data to address the complex biological phenomenon.

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

Authentication is a crucial security service for the wireless sensor networks (WSNs) in versatile domains. The deployment of WSN devices in the untrusted open environment and the resource-constrained nature make the on-chip authentication an open challenge. The strong physical unclonable function (PUF) came in handy as light-weight authentication security primitive. In this paper, we present the first ring oscillator (RO) based strong physical unclonable function (PUF) with high resilience to both the electromagnetic (EM) side-channel attack and the support vector machine (SVM) modelling attack. By employing an RO based PUF architecture with the current starved inverter as the delay cell, the oscillation power is significantly reduced to minimize the emitted EM signal, leading to greatly enhanced immunity to the EM side-channel analysis attack. In addition, featuring superior reconfigurability due to the conspicuously simplified circuitries, the proposed implementation is capable of withstanding the SVM modelling attack by generating and comparing a large number of RO frequency pairs. The reported experimental results validate the prototype of a 9-stage RO PUF fabricated using standard 65 nm complementary-metal-oxide-semiconductor (CMOS) process. Operating at the supply voltage of 1.2 V and the frequency of 100 KHz, the fabricated RO PUF occupies a compact silicon area of 250 μ m 2 and consumes a power as low as 5.16 μ W per challenge-response pair (CRP). Furthermore, the uniqueness and the worst-case reliability are measured to be 50.17% and 98.30% for the working temperature range of −40∼120 ∘ C and the supply voltage variation of ±2%, respectively. Thus, the proposed PUF is applicable for the low power, low cost and secure WSN communications.

In this paper, an approach that can fast classify the data from the electronic nose is presented. In this approach the gradient tree boosting algorithm is used to classify the gas data and the experiment results show that the proposed gradient tree boosting algorithm achieved high performance on this classification problem, outperforming other algorithms as comparison. In addition, electronic nose we used only requires a few seconds of data after the gas reaction begins. Therefore, the proposed approach can realize a fast recognition of gas, as it does not need to wait for the gas reaction to reach steady state.