MMGAT: a graph attention network framework for ATAC-seq motifs finding.

BACKGROUND: Motif finding in Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data is essential to reveal the intricacies of transcription factor binding sites (TFBSs) and their pivotal roles in gene regulation. Deep learning technologies including convolutional neural networks (CNNs) and graph neural networks (GNNs), have achieved success in finding ATAC-seq motifs. However, CNN-based methods are limited by the fixed width of the convolutional kernel, which makes it difficult to find multiple transcription factor binding sites with different lengths. GNN-based methods has the limitation of using the edge weight information directly, makes it difficult to aggregate the neighboring nodes' information more efficiently when representing node embedding. RESULTS: To address this challenge, we developed a novel graph attention network framework named MMGAT, which employs an attention mechanism to adjust the attention coefficients among different nodes. And then MMGAT finds multiple ATAC-seq motifs based on the attention coefficients of sequence nodes and k-mer nodes as well as the coexisting probability of k-mers. Our approach achieved better performance on the human ATAC-seq datasets compared to existing tools, as evidenced the highest scores on the precision, recall, F1_score, ACC, AUC, and PRC metrics, as well as finding 389 higher quality motifs. To validate the performance of MMGAT in predicting TFBSs and finding motifs on more datasets, we enlarged the number of the human ATAC-seq datasets to 180 and newly integrated 80 mouse ATAC-seq datasets for multi-species experimental validation. Specifically on the mouse ATAC-seq dataset, MMGAT also achieved the highest scores on six metrics and found 356 higher-quality motifs. To facilitate researchers in utilizing MMGAT, we have also developed a user-friendly web server named MMGAT-S that hosts the MMGAT method and ATAC-seq motif finding results. CONCLUSIONS: The advanced methodology MMGAT provides a robust tool for finding ATAC-seq motifs, and the comprehensive server MMGAT-S makes a significant contribution to genomics research. The open-source code of MMGAT can be found at https://github.com/xiaotianr/MMGAT , and MMGAT-S is freely available at https://www.mmgraphws.com/MMGAT-S/ .

GNNMF: a multi-view graph neural network for ATAC-seq motif finding.

BACKGROUND: The Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) utilizes the Transposase Tn5 to probe open chromatic, which simultaneously reveals multiple transcription factor binding sites (TFBSs) compared to traditional technologies. Deep learning (DL) technology, including convolutional neural networks (CNNs), has successfully found motifs from ATAC-seq data. Due to the limitation of the width of convolutional kernels, the existing models only find motifs with fixed lengths. A Graph neural network (GNN) can work on non-Euclidean data, which has the potential to find ATAC-seq motifs with different lengths. However, the existing GNN models ignored the relationships among ATAC-seq sequences, and their parameter settings should be improved. RESULTS: In this study, we proposed a novel GNN model named GNNMF to find ATAC-seq motifs via GNN and background coexisting probability. Our experiment has been conducted on 200 human datasets and 80 mouse datasets, demonstrated that GNNMF has improved the area of eight metrics radar scores of 4.92% and 6.81% respectively, and found more motifs than did the existing models. CONCLUSIONS: In this study, we developed a novel model named GNNMF for finding multiple ATAC-seq motifs. GNNMF built a multi-view heterogeneous graph by using ATAC-seq sequences, and utilized background coexisting probability and the iterloss to find different lengths of ATAC-seq motifs and optimize the parameter sets. Compared to existing models, GNNMF achieved the best performance on TFBS prediction and ATAC-seq motif finding, which demonstrates that our improvement is available for ATAC-seq motif finding.

Impact of rural soundscape on environmental restoration: An empirical study based on the Taohuayuan Scenic Area in Changde, China.

Previous studies on environmental restorative effects have mainly focused on visual landscapes, and less on the influence of soundscapes on restorative, but soundscapes play a crucial role in restorative environments, especially rural soundscapes, but there is insufficient existing theoretical evidence on the subject. Therefore, this study aims to investigate the influence of Rural Soundscape Perception on Environmental Restoration Perception, and introduces two affective variables, tourism nostalgia and place attachment, to explore the mechanism of Rural Soundscape Perception on Environmental Restoration Perception, as well as the moderating role of the number of trips is also discussed. Based on the theory of restorative environment, this study took the Taohuayuan Scenic Spot in Changde, Hunan Province, China, as the case site, and selected the rural soundscape in the area as the research object; a total of 506 valid data were collected through questionnaire surveys, and structural equation modeling was used to validate the collected data. It was found that rural soundscape perception had a significant positive effect on tourism nostalgia, place attachment, and environmental restoration perception. The results also showed that tourism nostalgia and place attachment mediated the relationship between rural soundscape perception and environmental restoration perception. Additionally, the results revealed that the number of trips did not play a moderating role in the structural relationship between rural soundscape perception and environmental restoration perception. Last, the results of the study shed light on the complex influence path of "rural soundscape perception→tourism nostalgia→place attachment→environmental restoration perception", which provides a new perspective for understanding the mechanism of the rural environment to people's health, and also has a certain guiding significance for the landscape planning of rural tourism sites.

A Survey of Deep Learning for Detecting miRNA-Disease Associations: Databases, Computational Methods, Challenges, and Future Directions.

MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease pathogenesis. Traditional laboratory experiments are expensive and time-consuming. Computational models have enabled systematic large-scale prediction of potential MDAs, greatly improving the research efficiency. With recent advances in deep learning, it has become an attractive and powerful technique for uncovering novel MDAs. Consequently, numerous MDA prediction methods based on deep learning have emerged. In this review, we first summarize publicly available databases related to miRNAs and diseases for MDA prediction. Next, we outline commonly used miRNA and disease similarity calculation and integration methods. Then, we comprehensively review the 48 existing deep learning-based MDA computation methods, categorizing them into classical deep learning and graph neural network-based techniques. Subsequently, we investigate the evaluation methods and metrics that are frequently used to assess MDA prediction performance. Finally, we discuss the performance trends of different computational methods, point out some problems in current research, and propose 9 potential future research directions. Data resources and recent advances in MDA prediction methods are summarized in the GitHub repository https://github.com/sheng-n/DL-miRNA-disease-association-methods.

Natural compound Sanggenon C inhibits porcine reproductive and respiratory syndrome virus replication in piglets.

Porcine reproductive and respiratory syndrome virus is one of the main pathogens threatening the global pig industry, and there is still a lack of effective therapeutic drugs. Sanggenon C is a flavanone Diels-Alder adduct compound extracted from the root bark of the mulberry genus, which has blood pressure-reducing, anti-atherosclerotic, anti-oxidative, and anti-inflammatory effects. In our previous study, Sanggenon C was confirmed to significantly inhibit PRRSV replication in vitro. However, its antiviral potential to inhibit PRRSV infection in vivo has not been evaluated in piglets. Here, the antiviral effect of Sanggenon C was evaluated in PRRSV-challenged piglets based on assessments of rectal temperature, viral load, pathological changes of lung tissue and secretion of inflammatory cytokines. The results showed that Sanggenon C treatment relieved the clinical symptoms, reduced the viral loads in the lungs and bloods, alleviated the pathological damage of lung tissue, decreased the secretion of inflammatory cytokines, and shorten the excretion time of virus from the oral and nasal secretions and feces of piglets after PRRSV infection. The results indicated that Sanggenon C is a promising anti-PRRSV drug, which provides a new strategy for the prevention and control of PRRS in clinical practice.

PromGER: Promoter Prediction Based on Graph Embedding and Ensemble Learning for Eukaryotic Sequence.

Promoters are DNA non-coding regions around the transcription start site and are responsible for regulating the gene transcription process. Due to their key role in gene function and transcriptional activity, the prediction of promoter sequences and their core elements accurately is a crucial research area in bioinformatics. At present, models based on machine learning and deep learning have been developed for promoter prediction. However, these models cannot mine the deeper biological information of promoter sequences and consider the complex relationship among promoter sequences. In this work, we propose a novel prediction model called PromGER to predict eukaryotic promoter sequences. For a promoter sequence, firstly, PromGER utilizes four types of feature-encoding methods to extract local information within promoter sequences. Secondly, according to the potential relationships among promoter sequences, the whole promoter sequences are constructed as a graph. Furthermore, three different scales of graph-embedding methods are applied for obtaining the global feature information more comprehensively in the graph. Finally, combining local features with global features of sequences, PromGER analyzes and predicts promoter sequences through a tree-based ensemble-learning framework. Compared with seven existing methods, PromGER improved the average specificity of 13%, accuracy of 10%, Matthew's correlation coefficient of 16%, precision of 4%, F1 score of 6%, and AUC of 9%. Specifically, this study interpreted the PromGER by the t-distributed stochastic neighbor embedding (t-SNE) method and SHAPley Additive exPlanations (SHAP) value analysis, which demonstrates the interpretability of the model.

Selective Fluorosulfonylation of Thianthrenium Salts Enabled by Electrochemistry.

A practical electrochemically driven method for fluorosulfonylation of both aryl and alkyl thianthrenium salts has been disclosed. The strategy does not need external redox reagents or metal catalysts. In combination with C-H thianthrenation of aromatics, this method provides a new tool for the site-selective fluorosulfonylation of drugs.

Visual preference of plant features in different living environments using eye tracking and EEG.

Plants play a very important role in landscape construction. In order to explore whether different living environment will affect people's preference for the structural features of plant organs, this study examined 26 villagers and 33 college students as the participants, and pictures of leaves, flowers and fruits of plants as the stimulus to conduct eye-tracking and EEG detection experiments. We found that eye movement indicators can explain people's visual preferences, but they are unable to find differences in preferences between groups. EEG indicators can make up for this deficiency, which further reveals the difference in psychological and physiological responses between the two groups when viewing stimuli. The final results show that the villagers and the students liked leaves best, preferring aciculiform and leathery leaves; solitary, purple and capitulum flowers; and medium-sized, spathulate, black and pear fruits. In addition, it was found that the overall attention of the villagers when watching stimuli was far lower than that of the students, but the degree of meditation was higher. With regard to eye movement and EEG, the total duration of fixations is highly positively correlated with the number of fixations, and the average pupil size has a weak negative correlation with attention. On the contrary, the average duration of fixations has a weak positive correlation with meditation. Generally speaking, we believe that Photinia×fraseri, Metasequoia glyptostroboides, Photinia serratifolia, Koelreuteria bipinnata and Cunninghamia lanceolata are superior landscape building plants in rural areas and on campuses; Pinus thunbergii, Myrica rubra, Camellia japonica and other plants with obvious features and bright colours are also the first choice in rural landscapes; and Yulania biondii, Cercis chinensis, Hibiscus mutabilis and other plants with simple structures are the first choice in campus landscapes. This study is of great significance for selecting plants for landscape construction and management according to different environments and local conditions.

Predicting miRNA-disease associations based on multi-view information fusion.

MicroRNAs (miRNAs) play an important role in various biological processes and their abnormal expression could lead to the occurrence of diseases. Exploring the potential relationships between miRNAs and diseases can contribute to the diagnosis and treatment of complex diseases. The increasing databases storing miRNA and disease information provide opportunities to develop computational methods for discovering unobserved disease-related miRNAs, but there are still some challenges in how to effectively learn and fuse information from multi-source data. In this study, we propose a multi-view information fusion based method for miRNA-disease association (MDA)prediction, named MVIFMDA. Firstly, multiple heterogeneous networks are constructed by combining the known MDAs and different similarities of miRNAs and diseases based on multi-source information. Secondly, the topology features of miRNAs and diseases are obtained by using the graph convolutional network to each heterogeneous network view, respectively. Moreover, we design the attention strategy at the topology representation level to adaptively fuse representations including different structural information. Meanwhile, we learn the attribute representations of miRNAs and diseases from their similarity attribute views with convolutional neural networks, respectively. Finally, the complicated associations between miRNAs and diseases are reconstructed by applying a bilinear decoder to the combined features, which combine topology and attribute representations. Experimental results on the public dataset demonstrate that our proposed model consistently outperforms baseline methods. The case studies further show the ability of the MVIFMDA model for inferring underlying associations between miRNAs and diseases.

Synthesis of CNT@CoS/NiCo Layered Double Hydroxides with Hollow Nanocages to Enhance Supercapacitors Performance.

One of the key factors to improve electrochemical properties is to find exceptional electrode materials. In this work, the nickel-cobalt layered double hydroxide (CNT@CoS/NiCo-LDH) with the structure of a hollow nanocage was prepared by etching CNT@CoS with zeolitic imidazolate framework-67 (ZIF-67) as a template. The results show that the addition of nickel has a great influence on the structure, morphology and chemical properties of materials. The prepared material CNT@CoS/NiCo-LDH-100 (C@CS/NCL-100) inherited the rhombic dodecahedral shape of ZIF-67 well and the CNTs were evenly interspersed among the rhombic dodecahedrons. The presence of CNTs improved the conductivity and surface area of the samples. The C@CS/NCL-100 demonstrates a high specific capacitance of 2794.6 F·g-1 at 1 A·g-1. Furthermore, as an assemble device, the device of C@CS/NCL-100 as a positive electrode exhibits a relatively high-energy density of 35.64 Wh·kg-1 at a power density of 750 W·kg-1 Further, even at the high-power density of 3750 W·kg-1, the energy density can still retain 26.38 Wh·kg-1. Hence, the superior performance of C@CS/NCL-100 can be ascribed to the synergy among CNTs, CoS and NiCo LDH, as well as the excellent three-dimensional structure obtained by used ZIF-67 as a template.

MMGraph: a multiple motif predictor based on graph neural network and coexisting probability for ATAC-seq data.

MOTIVATION: Transcription factor binding sites (TFBSs) prediction is a crucial step in revealing functions of transcription factors from high-throughput sequencing data. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) provides insight on TFBSs and nucleosome positioning by probing open chromatic, which can simultaneously reveal multiple TFBSs compare to traditional technologies. The existing tools based on convolutional neural network (CNN) only find the fixed length of TFBSs from ATAC-seq data. Graph neural network (GNN) can be considered as the extension of CNN, which has great potential in finding multiple TFBSs with different lengths from ATAC-seq data. RESULTS: We develop a motif predictor called MMGraph based on three-layer GNN and coexisting probability of k-mers for finding multiple motifs from ATAC-seq data. The results of the experiment which has been conducted on 88 ATAC-seq datasets indicate that MMGraph has achieved the best performance on area of eight metrics radar score of 2.31 and could find 207 higher-quality multiple motifs than other existing tools. AVAILABILITY AND IMPLEMENTATION: MMGraph is wrapped in Python package, which is available at https://github.com/zhangsq06/MMGraph.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Resveratrol induces autophagy impeding BAFF-stimulated B-cell proliferation and survival by inhibiting the Akt/mTOR pathway.

Therapeutically targeting B cells has received great attention in the treatment of B-cell malignancies and autoimmune diseases. The B-cell activating factor (BAFF) is critical to the survival of normal and neoplastic B cells, and excess production of BAFF contributes to autoimmune diseases. Resveratrol, a natural polyphenolic compound, has a positive effect on the treatment of autoimmune diseases. However, how resveratrol affects BAFF-stimulated B-cell proliferation and survival is poorly understood. Here, we show that resveratrol increased autophagosome formation and ATG5/LC3-II levels and decreased p62 level, promoting autophagic flux/autophagy and thereby suppressing the basal or human soluble BAFF (hsBAFF)-stimulated proliferation and survival of normal and B-lymphoid (Raji) cells. This is supported by the findings that inhibition of autophagy with 3-methyladenine (3-MA, an inhibitor of Vps34) or ATG5 shRNA attenuates resveratrol-induced autophagy and -reduced proliferation/viability in B-cells. Inhibition of mTOR with rapamycin or knockdown of mTOR potentiated resveratrol-induced autophagy and inhibition of hsBAFF-stimulated B-cell proliferation/viability, while overexpression of wild-type mTOR conferred resistance to the actions of resveratrol. Similarly, inhibition of Akt with Akt inhibitor X or ectopic expression of dominant negative Akt reinforced resveratrol-induced autophagy and inhibition of hsBAFF-stimulated B-cell proliferation/viability, whereas expression of constitutively active Akt conferred resistance to the actions of resveratrol. Taken together, these results indicate that resveratrol induces autophagy impeding BAFF-stimulated proliferation and survival via blocking the Akt/mTOR signaling pathway in normal and neoplastic B cells. Our findings highlight that resveratrol has a great potential for prevention and treatment of excessive BAFF-elicited aggressive B-cell disorders and autoimmune diseases.

Isotopic evidence for bioaccumulation of aerosol lead in fish and wildlife of western Canada.

Lead (Pb) is a toxic element which is released as a result of anthropogenic activities, and Pb stable isotope ratios provide a means to distinguish sources and transport pathways in receiving environments. In this study, isotopes of bioaccumulated Pb (204Pb, 206Pb, 207Pb, 208Pb) were examined for diverse terrestrial and aquatic biota from three areas in western Canada: (a) otter, marten, gulls, terns, and wood frogs in the Alberta Oil Sands Region (AOSR), (b) fish, plankton, and gulls of Great Slave Lake (Yellowknife, Northwest Territories), and (c) wolverine from the Yukon. Aquatic and terrestrial biota from different habitats and a broad geographic area showed a remarkable similarity in their Pb isotope composition (grand mean ± 1 standard deviation: 206Pb/207Pb = 1.189 ± 0.007, 208Pb/207Pb = 2.435 ± 0.009, n = 116). Comparisons with Pb isotope ratios of local sources and environmental receptors showed that values in biota were most similar to those of atmospheric Pb, either measured in local aerosols influenced by industrial activities in the AOSR or in lichens (an aerosol proxy) near Yellowknife and in the Yukon. Biotic Pb isotope ratios were different from those of local geogenic Pb. Although the Pb isotope measurements could not unambiguously identify the specific anthropogenic sources of atmospheric Pb in biota, initial evidence points to the importance of fossil fuels currently used in transportation and power generation. Further research should characterize bioavailable chemical species of Pb in aerosols and important emission sources in western Canada.

Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data.

Identifying cis-regulatory motifs from genomic sequencing data (e.g. ChIP-seq and CLIP-seq) is crucial in identifying transcription factor (TF) binding sites and inferring gene regulatory mechanisms for any organism. Since 2015, deep learning (DL) methods have been widely applied to identify TF binding sites and predict motif patterns, with the strengths of offering a scalable, flexible and unified computational approach for highly accurate predictions. As far as we know, 20 DL methods have been developed. However, without a clear and systematic assessment, users will struggle to choose the most appropriate tool for their specific studies. In this manuscript, we evaluated 20 DL methods for cis-regulatory motif prediction using 690 ENCODE ChIP-seq, 126 cancer ChIP-seq and 55 RNA CLIP-seq data. Four metrics were investigated, including the accuracy of motif finding, the performance of DNA/RNA sequence classification, algorithm scalability and tool usability. The assessment results demonstrated the high complementarity of the existing DL methods. It was determined that the most suitable model should primarily depend on the data size and type and the method's outputs.

Minimum Distribution Support Vector Clustering.

Support vector clustering (SVC) is a boundary-based algorithm, which has several advantages over other clustering methods, including identifying clusters of arbitrary shapes and numbers. Leveraged by the high generalization ability of the large margin distribution machine (LDM) and the optimal margin distribution clustering (ODMC), we propose a new clustering method: minimum distribution for support vector clustering (MDSVC), for improving the robustness of boundary point recognition, which characterizes the optimal hypersphere by the first-order and second-order statistics and tries to minimize the mean and variance simultaneously. In addition, we further prove, theoretically, that our algorithm can obtain better generalization performance. Some instructive insights for adjusting the number of support vector points are gained. For the optimization problem of MDSVC, we propose a double coordinate descent algorithm for small and medium samples. The experimental results on both artificial and real datasets indicate that our MDSVC has a significant improvement in generalization performance compared to SVC.

LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning.

Long noncoding RNA (lncRNA) plays a crucial role in many critical biological processes and participates in complex human diseases through interaction with proteins. Considering that identifying lncRNA-protein interactions through experimental methods is expensive and time-consuming, we propose a novel method based on deep learning that combines raw sequence composition features, hand-designed features and structure features, called LGFC-CNN, to predict lncRNA-protein interactions. The two sequence preprocessing methods and CNN modules (GloCNN and LocCNN) are utilized to extract the raw sequence global and local features. Meanwhile, we select hand-designed features by comparing the predictive effect of different lncRNA and protein features combinations. Furthermore, we obtain the structure features and unifying the dimensions through Fourier transform. In the end, the four types of features are integrated to comprehensively predict the lncRNA-protein interactions. Compared with other state-of-the-art methods on three lncRNA-protein interaction datasets, LGFC-CNN achieves the best performance with an accuracy of 94.14%, on RPI21850; an accuracy of 92.94%, on RPI7317; and an accuracy of 98.19% on RPI1847. The results show that our LGFC-CNN can effectively predict the lncRNA-protein interactions by combining raw sequence composition features, hand-designed features and structure features.

Metformin prevents BAFF activation of Erk1/2 from B-cell proliferation and survival by impeding mTOR-PTEN/Akt signaling pathway.

B-cell activating factor (BAFF) is an essential cytokine for B-cell maturation, differentiation and survival, and excess BAFF induces aggressive or neoplastic B-cell disorders and contributes to development of autoimmune diseases. Metformin, an anti-diabetic drug, has recently garnered a great attention due to its anti-proliferative and immune-modulatory features. However, little is known regarding the effect of metformin on BAFF-stimulated B cells. Here, we show that metformin attenuated human soluble BAFF (hsBAFF)-induced cell proliferation and survival by blocking the Erk1/2 pathway in normal and B-lymphoid (Raji) cells. Pretreatment with U0126, knockdown of Erk1/2, or expression of dominant negative MKK1 strengthened metformin's inhibition of hsBAFF-activated Erk1/2 and B-cell proliferation/viability, whereas expression of constitutively active MKK1 rendered high resistance to metformin. Further investigation found that overexpression of wild type PTEN or ectopic expression of dominant negative Akt potentiated metformin's suppression of hsBAFF-induced Erk1/2 activation and proliferation/viability in Raji cells, implying a PTEN/Akt-dependent mechanism involved. Furthermore, we noticed that metformin hindered hsBAFF-activated mTOR pathway in B cells. Inhibition of mTOR with rapamycin or knockdown of mTOR enhanced metformin's suppression of hsBAFF-induced phosphorylation of S6K1, PTEN, Akt, and Erk1/2, as well as B-cell proliferation/viability. These results indicate that metformin prevents BAFF activation of Erk1/2 from cell proliferation and survival by impeding mTOR-PTEN/Akt signaling pathway in normal and neoplastic B-lymphoid cells. Our findings support that metformin has a great potential for prevention of excessive BAFF-induced aggressive B-cell malignancies and autoimmune diseases.

NCResNet: Noncoding Ribonucleic Acid Prediction Based on a Deep Resident Network of Ribonucleic Acid Sequences.

Noncoding RNA (ncRNA) is a kind of RNA that plays an important role in many biological processes, diseases, and cancers, while cannot translate into proteins. With the development of next-generation sequence technology, thousands of novel RNAs with long open reading frames (ORFs, longest ORF length > 303 nt) and short ORFs (longest ORF length ≤ 303 nt) have been discovered in a short time. How to identify ncRNAs more precisely from novel unannotated RNAs is an important step for RNA functional analysis, RNA regulation, etc. However, most previous methods only utilize the information of sequence features. Meanwhile, most of them have focused on long-ORF RNA sequences, but not adapted to short-ORF RNA sequences. In this paper, we propose a new reliable method called NCResNet. NCResNet employs 57 hybrid features of four categories as inputs, including sequence, protein, RNA structure, and RNA physicochemical properties, and introduces feature enhancement and deep feature learning policies in a neural net model to adapt to this problem. The experiments on benchmark datasets of 8 species shows NCResNet has higher accuracy and higher Matthews correlation coefficient (MCC) compared with other state-of-the-art methods. Particularly, on four short-ORF RNA sequence datasets, specifically mouse, Saccharomyces cerevisiae, zebrafish, and cow, NCResNet achieves greater than 10 and 15% improvements over other state-of-the-art methods in terms of accuracy and MCC. Meanwhile, for long-ORF RNA sequence datasets, NCResNet also has better accuracy and MCC than other state-of-the-art methods on most test datasets. Codes and data are available at https://github.com/abcair/NCResNet.

Rapamycin inhibits B-cell activating factor (BAFF)-stimulated cell proliferation and survival by suppressing Ca2+-CaMKII-dependent PTEN/Akt-Erk1/2 signaling pathway in normal and neoplastic B-lymphoid cells.

B-cell activating factor (BAFF) is a crucial survival factor for B cells, and excess BAFF contributes to development of autoimmune diseases. Recent studies have shown that rapamycin can prevent BAFF-induced B-cell proliferation and survival, but the underlying mechanism remains to be elucidated. Here we found that rapamycin inhibited human soluble BAFF (hsBAFF)-stimulated cell proliferation by inducing G1-cell cycle arrest, which was through downregulating the protein levels of CDK2, CDK4, CDK6, cyclin A, cyclin D1, and cyclin E. Rapamycin reduced hsBAFF-stimulated cell survival by downregulating the levels of anti-apoptotic proteins (Mcl-1, Bcl-2, Bcl-xL and survivin) and meanwhile upregulating the levels of pro-apoptotic proteins (BAK and BAX). The cytostatic and cytotoxic effects of rapamycin linked to its attenuation of hsBAFF-elevated intracellular free Ca2+ ([Ca2+]i). In addition, rapamycin blocked hsBAFF-stimulated B-cell proliferation and survival by preventing hsBAFF from inactivating PTEN and activating the Akt-Erk1/2 pathway. Overexpression of wild type PTEN or ectopic expression of dominant negative Akt potentiated rapamycin's suppression of hsBAFF-induced Erk1/2 activation and proliferation/viability in Raji cells. Interestingly, PP242 (mTORC1/2 inhibitor) or Akt inhibitor X, like rapamycin (mTORC1 inhibitor), reduced the basal or hsBAFF-induced [Ca2+]i elevations. Chelating [Ca2+]i with BAPTA/AM, preventing [Ca2+]i elevation using EGTA, 2-APB or verapamil, inhibiting CaMKII with KN93, or silencing CaMKII strengthened rapamycin's inhibitory effects. The results indicate that rapamycin inhibits BAFF-stimulated B-cell proliferation and survival by blunting mTORC1/2-mediated [Ca2+]i elevations and suppressing Ca2+-CaMKII-dependent PTEN/Akt-Erk1/2 signaling pathway. Our finding underscores that rapamycin may be exploited for prevention of excessive BAFF-induced aggressive B-cell malignancies and autoimmune diseases.

Selection rules of electromagnetic transitions for chirality-parity violation in atomic nuclei.

The nuclear Chirality-Parity (ChP) violation, a simultaneous breaking of chiral and reflection symmetries in the intrinsic frame, is investigated with a reflection-asymmetric triaxial particle rotor model. A new symmetry for an ideal ChP violation system is found and the corresponding selection rules of the electromagnetic transitions are derived. The fingerprints for the ChP violation including the nearly degenerate quartet bands and the selection rules of the electromagnetic transitions are provided. These fingerprints are examined for ChP quartet bands by taking a two-j shell h11/2 and d5/2 with typical energy spacing for A = 130 nuclei.