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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/. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A systematic investigation of low-lying levels of nuclides in the mass 150 region has been undertaken at iThemba LABS. An extensive set of data on the low-lying, positive-parity bands in the nuclides between N = 88 and 92 and Sm to Yb has been obtained from γ-γ coincidence measurements following fusion-evaporation reactions optimized of the population of low-spin states. The energies and electromagnetic properties of the so-called β- and γ-bands of nuclei in this region have been compared with the solutions of a five dimensional collective Hamiltonian for quadrupole vibrational and rotational degrees of freedom, with moments-of-inertia and mass parameters determined by constrained self-consistent relativistic mean-field calculations using the PC-F1 relativistic functional. Some of the results of this comparison are presented here.

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. [ABSTRACT FROM AUTHOR]

Stable and radiogenic isotopes represent powerful tools for reconstructing ecological and environmental patterns in ancient ecosystems. The Cretaceous of North America preserves a diverse record of fossil vertebrates well‐suited to analysis using these proxies, contained within many well‐sampled and stratigraphically well‐characterized intervals. Multiple hypotheses have been offered to explain the diverse assemblages of megaherbivores that co‐occurred in the relatively restricted available landmass here, including various forms of niche‐partitioning related to habitat preference, dietary specialization and feeding height stratification. Here we analyse the 87Sr/86Sr, δ13C and δ18O of bioapatite samples obtained from a range of herbivores, faunivores and endemic taxa, from a spatiotemporally‐constrained and intensively‐sampled site in the upper Oldman Formation, to test if megaherbivores partitioned their niches based on spatial patterns of occupation and resource‐use. We also compare measured strontium values to regional 87Sr/86Sr data to assess biogeographical range sizes, habitat breadth and migration potential. We find that hadrosaurs had broad ranges, whereas ankylosaurs and ceratopsids were more spatially restricted. The 87Sr/86Sr ranges of hadrosaurs are much wider and do not overlap with those of other ornithischians, potentially related to dietary differences driven by a combination of feeding height‐stratification and habitat breadth differences. Ankylosaurs and ceratopsids overlapped extensively in 87Sr/86Sr, δ13C and δ18O, indicating overlap in the same habitats and intake of similar resources, and suggesting more complex spatiotemporal variation in resource‐use patterns, fine‐scale dietary differences, and/or sufficient resource‐availability to reduce the degree of competition given this theoretical niche overlap. Additional analyses integrating ecomorphological proxies may elucidate these patterns further. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Glioma is the main type of malignant brain tumor in adults, and the status of isocitrate dehydrogenase (IDH) mutation highly affects the diagnosis, treatment, and prognosis of gliomas. Radiographic medical imaging provides a noninvasive platform for sampling both inter and intralesion heterogeneity of gliomas, and previous research has shown that the IDH genotype can be predicted from the fusion of multimodality radiology images. The features of medical images and IDH genotype are vital for medical treatment; however, it still lacks a multitask framework for the segmentation of the lesion areas of gliomas and the prediction of IDH genotype. In this paper, we propose a novel three-dimensional (3D) multitask deep learning model for segmentation and genotype prediction (SGPNet). The residual units are also introduced into the SGPNet that allows the output blocks to extract hierarchical features for different tasks and facilitate the information propagation. Our model reduces 26.6% classification error rates comparing with previous models on the datasets of Multimodal Brain Tumor Segmentation Challenge (BRATS) 2020 and The Cancer Genome Atlas (TCGA) gliomas' databases. Furthermore, we first practically investigate the influence of lesion areas on the performance of IDH genotype prediction by setting different groups of learning targets. The experimental results indicate that the information of lesion areas is more important for the IDH genotype prediction. Our framework is effective and generalizable, which can serve as a highly automated tool to be applied in clinical decision making. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Novel core-shell structured CoNi2S4@polydopamine nanocomposites have been successfully developed as advanced electrode materials for supercapacitors. The CoNi2S4 nanoparticles are tightly wrapped by polydopamine, forming a hierarchical core/shell network, which benefits the improvement of the electrochemical properties. The electrochemical performance of the as-prepared materials had been investigated by cyclic voltammetry and galvanostatic charge/discharge tests. Specific capacitances of CoNi2S4 and CoNi2S4@polydopamine are 425 and 725 F g−1 at 10 A g−1 respectively. Electrochemical impedance spectroscopy results suggest that the coating of polydopamine can reduce the charge transfer resistance and increase the diffusion rate of the electrolytic ion efficiently. The superior pseudocapacitive performance of CoNi2S4@polydopamine can be ascribed to the unique core-shell heterostructure and the combined contribution of the electrochemical active CoNi2S4 and the conductive polydopamine. [ABSTRACT FROM AUTHOR]

Development of antimicrobial peptides (AMPs) as highly effective and selective anticancer agents would represent great progress in cancer treatment. Here we show that myristoyl-CM4, a new synthetic analog generated by N-myristoylation of AMPs CM4, had anticancer activity against MCF-7, MDA-MB-231, MX-1 breast cancer cells (IC50 of 3–6 μM) and MDA-MB-231 xenograft tumors. The improved activity was attributed to the effect of myristoyl on the cell membrane. Flow cytometry and confocal laser scanning microscopy results showed that N-myristoylation significantly increased the membrane affinity toward breast cancer cells and also effectively mediated cellular entry. Despite increasing cytotoxicity against HEK293 and NIH3T3 cells and erythrocytes associated with its anticancer activity, myristoyl-CM4 maintained a certain selectivity toward breast cancer cells. Accordingly, the membrane affinity toward breast cancer cells was two to threefold higher than that of normal cells. Glycosylation analysis showed that sialic acid-containing oligosaccharides (including O -mucin and gangliosides) were important targets for myristoyl-CM4 binding to breast cancer cells. After internalization, co-localization analysis revealed that myristoyl-CM4 targeted mitochondria and induced mitochondrial dysfunction, including alterations in mitochondrial transmembrane potential, reactive oxygen species (ROS) generation and cytochrome c release. Activation of caspase 9, caspase 3 and cleavage of PARP were observed in MX-1, MCF-7, and MDA-MB-231 cells after myristoyl-CM4 treatment. The current work indicates that increasing hydrophobicity by myristoylation to modulate peptide-membrane interactions and then target mitochondria is a good strategy to develop AMPs as anticancer agents in the future. [ABSTRACT FROM AUTHOR]

Liquefaction residue of Heishan bituminous coal (HLR) was subject to two hydroconversion reactions under 5 MPa initial pressure of hydrogen at 300°C for 3 h, without catalyst and with acid supported catalyst (ASC), respectively. The reaction products were analyzed with gas chromatography/mass spectrometer (GC/MS). The results show that 222 organic compounds were detected totally in the products and they can be divided into alkanes, aromatic hydrocarbons (AHCs), phenols, ketones, ethers, and other species (OSs). The yield of hydroconversion over the ASC is much higher than that without catalyst. The most abundant products are aromatic hydrocarbons in the reaction products from both catalytic and noncatalytic reactions of HLR. The yield of aromatic hydrocarbons in the reaction product from hydroconversion with the ACS is considerably higher than that from hydroconversion without a catalyst. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • Pb isotope ratios were strikingly similar in diverse animals from western Canada. • The bioaccumulated Pb originated primarily from atmospheric aerosols. • Natural geogenic sources contributed little to bioaccumulated Pb. • Pb isotopes suggest aerosol Pb is highly bioavailable. • Fossil fuels may be important sources of bioavailable Pb to fish and wildlife. [ABSTRACT FROM AUTHOR]

This paper proposes a new gravitational search algorithm to solve the unit commitment (UC) problem, which is integrated binary gravitational search algorithm (BGSA) with the Lambda-iteration method. The proposed method is enhanced by priority list based on the unit characteristics and heuristic search strategies to repair the spinning reserve and minimum up/down time constraints. Furthermore, local mutation strategies are applied to improve the performance of BGSA. The implementation of the proposed method for UC problem consists of three stages. Firstly, the BGSA based on priority list is applied for solution unit scheduling when neglecting minimum up/down time constraints. Secondly, heuristic search strategies are used to handle minimum up/down time constraints and decommit excess spinning reserve units. Thirdly, local mutation strategies are raised to avoid premature convergence of the algorithm and prevent it from trapping into local optima. Finally, Lambda-iteration method is adopted to solve economic load dispatch based on the obtained unit schedule. The feasibility and effectiveness of the proposed method is verified by the systems with the number of units in the range of 10–100 and the results are compared with those of other methods reported in literatures. The results clearly show that the proposed method gives better quality solutions than other methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Immobilization of phosphonium ionic liquid (IL) onto activated carbon (AC) was synthesized via grafting and impregnated methods, and the modified materials were analyzed via Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction, thermal gravity analysis, scanning electron microscope, pore structure and CO2/N2 adsorption selectivity. The effect of the gas flow rate (100–500 mL/min) and adsorption pressure (0.2–0.6 MPa) on the dynamic adsorption behavior of mixture gas containing 15 vol.% CO2 and 85 vol.% N2 was explained using a breakthrough method. By analyzing the breakthrough curves, the adsorption capacity was determined. The results show that surface functionalization of activated carbon with phosphonium ionic liquid is conducive to improving CO2/N2 selectivity, especially ionic liquid-impregnated film. The different adsorption behaviors of impregnated and grafted adsorbents are observed under various conditions. The grafted AC had better CO2 adsorption and mass transfer due to a lower blockage of pores by ionic liquid. [ABSTRACT FROM AUTHOR]

Adsorption and diffusion of water vapor in phosphonium ionic liquid modified silica gel were studied, aiming to reduce the loading of water vapor in porous materials. The modified silica gel was prepared through a grafting method and characterized by FTIR, thermal gravity analysis and X-ray photoelectron spectroscopy. N2 sorption isotherms at −196 °C and CO2 sorption isotherms at 0 °C were also measured to analyzee the porosity. Water vapor adsorption equilibriums at 25 °C up to 30 mbar were tested. The results indicate that the ionic liquids (ILs) phase acts as a protecting film which decreases water vapor adsorption. The improvement of water-resistant performance is also attributed to the decrease of micro-porosity and silanol groups on the silica surface. Diffusion behavior of water vapor on modified silica was determined on the basis of the adsorption equilibrium. The effective diffusivity of water vapor in modified silica is almost the same as in bare silica and decreases with the increasing of water vapor loading. [ABSTRACT FROM AUTHOR]

Abstract When, how, and following which paths hominins created the innovations that allowed them to colonize regions of the planet that were not suited to their thermal physiology is still a matter of inquiry. In this paper, we elaborate a theoretical framework to investigate the origin and diversification of bone needles, summarize the evidence for their emergence, create a large database of their morphometric and stylistic characters, and present results of the study of an exceptionally well-preserved collection of needles from Shuidonggou Locality 12 (SDG12), a site located in the Ningxia Hui Autonomous Region, Northern China, dated to ca. 11.2 ka BP. Bone needles are reported from 271 sites and 355 archaeological layers. Revision of the evidence shows they represent an original cultural innovation that emerged in Eurasia between 45–40 ka BP. Size differences between the earliest known specimens, found in Siberia and China, indicate needles may have been invented independently in these two regions. Needles from Eastern Europe may represent either an independent invention or a geographic extension of earlier Siberian and Caucasian sewing traditions. In Western Europe, needles appear during the Solutrean. The wider size range characteristic of Magdalenian specimens supports the idea that needles of different sizes were used in a variety of tasks. In China, the robust sub-circular needles found at sites dated between 35–25 ka BP are followed, between 26–23 ka BP, by small flat needles, which may represent an innovation associated with the microblades/microcores toolkit. At SDG12, technological, functional, and morphometric analyses of finished needles and manufacturing by-products identify two previously undetected reduction sequences for the production of needles of different size and, probably, function. The bone needles found at Paleoindian sites are the smallest and reflect a never previously achieved mastery in the production of such tools. [ABSTRACT FROM AUTHOR]

B-cell activating factor of the TNF family (BAFF) has been documented to act as a critical factor in the development of aggressive B lymphocytes and autoimmune diseases. However, the effect of various cytokines on BAFF-elicited neoplastic B-lymphoid cells is not known. In this study, we exhibited that administration of human soluble BAFF (hsBAFF), IL-2, IL-4, IFN-γ, or TNF-α alone increased cell viability and survival in Raji cells concentration-dependently, yet a more robust viability/survival was seen in the cells co-treatment of IL-2, IL-4, IFN-γ, or TNF-α with hsBAFF, respectively. Further research revealed that both Erk1/2 and S6K1 signaling pathways were essential for IL-2, IL-4, IFN-γ, or TNF-α enhancement of the viability/survival in the hsBAFF-stimulated cells, as inhibition of Erk1/2 with U0126 or down-regulation of Erk1/2, or blockage of S6K1 with rapamycin or silencing S6K1, or silencing S6K1/Erk1/2, respectively, reduced the cell viability/survival in the cells treated with/without hsBAFF ± IL-2, IL-4, IFN-γ, or TNF-α. These findings indicate that IL-2, IL-4, IFN-γ or TNF-α enhances BAFF-stimulated cell viability/survival by activating Erk1/2 and S6K1 signaling in neoplastic B-lymphoid cells. Our data suggest that modulation of IL-2, IL-4, IFN-γ and/or TNF-α levels, or inhibitors of Erk1/2 or S6K1 may be a new approach to prevent BAFF-induced aggressive B-cell malignancies. [ABSTRACT FROM AUTHOR]

Metabolism in host cells can be modulated after viral infection, favoring viral survival or clearance. Here, we report that lipid droplet (LD) synthesis in host cells can be modulated by yin yang 1 (YY1) after porcine reproductive and respiratory syndrome virus (PRRSV) infection, resulting in active antiviral activity. As a ubiquitously distributed transcription factor, there was increased expression of YY1 upon PRRSV infection both in vitro and in vivo . YY1 silencing promoted the replication of PRRSV, whereas YY1 overexpression inhibited PRRSV replication. PRRSV infection led to a marked increase in LDs, while YY1 knockout inhibited LD synthesis, and YY1 overexpression enhanced LD accumulation, indicating that YY1 reprograms PRRSV infection-induced intracellular LD synthesis. We also showed that the viral components do not colocalize with LDs during PRRSV infection, and the effect of exogenously induced LD synthesis on PRRSV replication is nearly lethal. Moreover, we demonstrated that YY1 affects the synthesis of LDs by regulating the expression of lipid metabolism genes. YY1 negatively regulates the expression of fatty acid synthase (FASN) to weaken the fatty acid synthesis pathway and positively regulates the expression of peroxisome proliferator-activated receptor gamma (PPARγ) to promote the synthesis of LDs, thus inhibiting PRRSV replication. These novel findings indicate that YY1 plays a crucial role in regulating PRRSV replication by reprogramming LD synthesis. Therefore, our study provides a novel mechanism of host resistance to PRRSV and suggests potential new antiviral strategies against PRRSV infection.IMPORTANCEPorcine reproductive and respiratory virus (PRRSV) has caused incalculable economic damage to the global pig industry since it was first discovered in the 1980s. However, conventional vaccines do not provide satisfactory protection. It is well known that viruses are parasitic pathogens, and the completion of their replication life cycle is highly dependent on host cells. A better understanding of host resistance to PRRSV infection is essential for developing safe and effective strategies to control PRRSV. Here, we report a crucial host antiviral molecule, yin yang 1 (YY1), which is induced to be expressed upon PRRSV infection and subsequently inhibits virus replication by reprogramming lipid droplet (LD) synthesis through transcriptional regulation. Our work provides a novel antiviral mechanism against PRRSV infection and suggests that targeting YY1 could be a new strategy for controlling PRRSV.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Xie, Wang, Sheng, Zhang, Cao and Fu.)

Measuring conditional relatedness, the degree of relation between a pair of genes in a certain condition, is a basic but difficult task in bioinformatics, as traditional co-expression analysis methods rely on co-expression similarities, well known with high false positive rate. Complement with prior-knowledge similarities is a feasible way to tackle the problem. However, classical combination machine learning algorithms fail in detection and application of the complex mapping relations between similarities and conditional relatedness, so a powerful predictive model will have enormous benefit for measuring this kind of complex mapping relations. To this need, we propose a novel deep learning model of convolutional neural network with a fully connected first layer, named fully convolutional neural network (FCNN), to measure conditional relatedness between genes using both co-expression and prior-knowledge similarities. The results on validation and test datasets show FCNN model yields an average 3.0% and 2.7% higher accuracy values for identifying gene-gene interactions collected from the COXPRESdb, KEGG, and TRRUST databases, and a benchmark dataset of Xiao-Yong et al. research, by grid-search 10-fold cross validation, respectively. In order to estimate the FCNN model, we conduct a further verification on the GeneFriends and DIP datasets, and the FCNN model obtains an average of 1.8% and 7.6% higher accuracy, respectively. Then the FCNN model is applied to construct cancer gene networks, and also calls more practical results than other compared models and methods. A website of the FCNN model and relevant datasets can be accessed from https://bmbl.bmi.osumc.edu/FCNN., (Copyright © 2019 Wang, Zhang, Yang, Yang, Tian and Ma.)

The gut microbiota, including probiotics and pathogenic microorganisms, is involved in ulcerative colitis (UC) by regulating pathogenic microorganisms and the production of intestinal mucosal antibodies. Huangqin decoction (HQD), a traditional Chinese formula chronicled in the Shanghan lun, has been recognized as an effective drug for UC, owing to its anti-inflammatory and anti-oxidative properties. In the present study, we investigated whether HQD ameliorates dextran sulphate sodium (DSS)-induced colitis through alteration of the gut microbiota. We found that HQD significantly inhibited colitis, alleviating the loss of body weight, disease activity index, colon shortening, tissue injury, and inflammatory cytokine changes induced by DSS treatment. Principal component analysis and principal co-ordinate analysis showed an obvious difference among the groups, with increased diversity in the DSS and DSS+HQD groups. Linear discriminant analysis effect size was used to determine differences between the groups. The relative abundance of Lactococcus was higher in the DSS+HQD group than in the DSS group, whereas Desulfovibrio and Helicobacter were decreased. Furthermore, the protective effect of HQD was attenuated only in antibiotic-treated mice. In conclusion, our results suggest that HQD could ameliorate DSS-induced inflammation through alteration of the gut microbiota.