iCRBP-LKHA: Large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites.

Circular RNAs (circRNAs) play vital roles in transcription and translation. Identification of circRNA-RBP (RNA-binding protein) interaction sites has become a fundamental step in molecular and cell biology. Deep learning (DL)-based methods have been proposed to predict circRNA-RBP interaction sites and achieved impressive identification performance. However, those methods cannot effectively capture long-distance dependencies, and cannot effectively utilize the interaction information of multiple features. To overcome those limitations, we propose a DL-based model iCRBP-LKHA using deep hybrid networks for identifying circRNA-RBP interaction sites. iCRBP-LKHA adopts five encoding schemes. Meanwhile, the neural network architecture, which consists of large kernel convolutional neural network (LKCNN), convolutional block attention module with one-dimensional convolution (CBAM-1D) and bidirectional gating recurrent unit (BiGRU), can explore local information, global context information and multiple features interaction information automatically. To verify the effectiveness of iCRBP-LKHA, we compared its performance with shallow learning algorithms on 37 circRNAs datasets and 37 circRNAs stringent datasets. And we compared its performance with state-of-the-art DL-based methods on 37 circRNAs datasets, 37 circRNAs stringent datasets and 31 linear RNAs datasets. The experimental results not only show that iCRBP-LKHA outperforms other competing methods, but also demonstrate the potential of this model in identifying other RNA-RBP interaction sites.

MLRR-ATV: A Robust Manifold Nonnegative LowRank Representation with Adaptive Total-Variation Regularization for scRNA-seq Data Clustering.

Since genomics was proposed, the exploration of genes has been the focus of research. The emergence of single-cell RNA sequencing (scRNA-seq) technology makes it possible to explore gene expression at the single-cell level. Due to the limitations of sequencing technology, the data contains a lot of noise. At the same time, it also has the characteristics of highdimensional and sparse. Clustering is a common method of analyzing scRNA-seq data. This paper proposes a novel singlecell clustering method called Robust Manifold Nonnegative LowRank Representation with Adaptive Total-Variation Regularization (MLRR-ATV). The Adaptive Total-Variation (ATV) regularization is introduced into Low-Rank Representation (LRR) model to reduce the influence of noise through gradient learning. Then, the linear and nonlinear manifold structures in the data are learned through Euclidean distance and cosine similarity, and more valuable information is retained. Because the model is non-convex, we use the Alternating Direction Method of Multipliers (ADMM) to optimize the model. We tested the performance of the MLRRATV model on eight real scRNA-seq datasets and selected nine state-of-the-art methods as comparison methods. The experimental results show that the performance of the MLRRATV model is better than the other nine methods.

MEFFGRN: Matrix enhancement and feature fusion-based method for reconstructing the gene regulatory network of epithelioma papulosum cyprini cells by spring viremia of carp virus infection.

Gene regulatory networks (GRNs) are crucial for understanding organismal molecular mechanisms and processes. Construction of GRN in the epithelioma papulosum cyprini (EPC) cells of cyprinid fish by spring viremia of carp virus (SVCV) infection helps understand the immune regulatory mechanisms that enhance the survival capabilities of cyprinid fish. Although many computational methods have been used to infer GRNs, specialized approaches for predicting the GRN of EPC cells following SVCV infection are lacking. In addition, most existing methods focus primarily on gene expression features, neglecting the valuable network structural information in known GRNs. In this study, we propose a novel supervised deep neural network, named MEFFGRN (Matrix Enhancement- and Feature Fusion-based method for Gene Regulatory Network inference), to accurately predict the GRN of EPC cells following SVCV infection. MEFFGRN considers both gene expression data and network structure information of known GRN and introduces a matrix enhancement method to address the sparsity issue of known GRN, extracting richer network structure information. To optimize the benefits of CNN (Convolutional Neural Network) in image processing, gene expression and enhanced GRN data were transformed into histogram images for each gene pair respectively. Subsequently, these histograms were separately fed into CNNs for training to obtain the corresponding gene expression and network structural features. Furthermore, a feature fusion mechanism was introduced to comprehensively integrate the gene expression and network structural features. This integration considers the specificity of each feature and their interactive information, resulting in a more comprehensive and precise feature representation during the fusion process. Experimental results from both real-world and benchmark datasets demonstrate that MEFFGRN achieves competitive performance compared with state-of-the-art computational methods. Furthermore, study findings from SVCV-infected EPC cells suggest that MEFFGRN can predict novel gene regulatory relationships.

Synthesis of [60]Fullerene-Fused Lactones via Carboxylic Acid Group-Directed C-H Bond Activation and Further Retro Baeyer-Villiger Reaction.

An efficient palladium-catalyzed reaction of [60]fullerene with benzoic acids via carboxylic acid group-directed C-H bond activation is achieved. The obtained [60]fullerene-fused lactones can undergo a retro Baeyer-Villiger reaction to provide [60]fullerene-fused ketones via apparent reduction in the presence of triflic acid. A representative ketone product obtained by the reduction reaction can be employed as an overcoating layer for the electron-transporting layer in an n-type perovskite solar cell.

The effect of extracellular polymeric substances on MICP solidifying rare earth slags and stabilizing Th and U.

Microbially induced carbonate precipitation (MICP) has been used to cure rare earth slags (RES) containing radionuclides (e.g. Th and U) and heavy metals with favorable results. However, the role of microbial extracellular polymeric substances (EPS) in MICP curing RES remains unclear. In this study, the EPS of Lysinibacillus sphaericus K-1 was extracted for the experiments of adsorption, inducing calcium carbonate (CaCO3) precipitation and curing of RES. The role of EPS in in MICP curing RES and stabilizing radionuclides and heavy metals was analyzed by evaluating the concentration and morphological distribution of radionuclides and heavy metals, and the compressive strength of the cured body. The results indicate that the adsorption efficiencies of EPS for Th (IV), U (VI), Cu2+, Pb2+, Zn2+, and Cd2+ were 44.83%, 45.83%, 53.7%, 61.3%, 42.1%, and 77.85%, respectively. The addition of EPS solution resulted in the formation of nanoscale spherical particles on the microorganism surface, which could act as an accumulating skeleton to facilitate the formation of CaCO3. After adding 20 mL of EPS solution during the curing process (Treat group), the maximum unconfined compressive strength (UCS) of the cured body reached 1.922 MPa, which was 12.13% higher than the CK group. The contents of exchangeable Th (IV) and U (VI) in the cured bodies of the Treat group decreased by 3.35% and 4.93%, respectively, compared with the CK group. Therefore, EPS enhances the effect of MICP curing RES and reduces the potential environmental problems that may be caused by radionuclides and heavy metals during the long-term sequestration of RES.

Protein Profiles and Novel Molecular Biomarkers of Schizophrenia Based on 4D-DIA Proteomics.

Schizophrenia is a severe psychological disorder. The current diagnosis mainly relies on clinical symptoms and lacks laboratory evidence, which makes it very difficult to make an accurate diagnosis especially at an early stage. Plasma protein profiles of schizophrenia patients were obtained and compared with healthy controls using 4D-DIA proteomics technology. Furthermore, 79 DEPs were identified between schizophrenia and healthy controls. GO functional analysis indicated that DEPs were predominantly associated with responses to toxic substances and platelet aggregation, suggesting the presence of metabolic and immune dysregulation in patients with schizophrenia. KEGG pathway enrichment analysis revealed that DEPs were primarily enriched in the chemokine signaling pathway and cytokine receptor interactions. A diagnostic model was ultimately established, comprising three proteins, namely, PFN1, GAPDH and ACTBL2. This model demonstrated an AUC value of 0.972, indicating its effectiveness in accurately identifying schizophrenia. PFN1, GAPDH and ACTBL2 exhibit potential as biomarkers for the early detection of schizophrenia. The findings of our studies provide novel insights into the laboratory-based diagnosis of schizophrenia.

Synthesis of C3'-Foused Aryl/Penta-1,4-Dien-3-One/Amine Hybrids as HSP90C-Terminal Inhibitors.

24 C3'-focused hybrids of aryl/penta-1,4-dien-3-one/amine (APDA) were designed and synthesized. Of these hybrids, 2 n demonstrated improved antiproliferative effects on HER2-positive breast cancer cells (SKBr3 and BT474) and triple-negative breast cancer (TNBC) cells (MDA-MB-231 and MDA-MB-468) with IC50 values ranging from 7.45 to 10.75 µM, but less toxicity to normal breast cells MCF-10A than the first generation of hybrid 1. Additionally, 2 n retained its ability to inhibit HSP90C-terminus, leading to the degradation of HSP90 client proteins HER2, EGFR, pAKT, AKT, and CDK4, without inducing a heat-shock response. Notably, 2 n also demonstrated improved thermostability compared to 1 and maintained in vitro metabolic stability in simulated intestinal fluid. These findings will provide a scientific basis for developing HSP90C-terminal inhibitors in the future.

Inference of gene regulatory networks based on directed graph convolutional networks.

Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose Directed Graph Convolutional neural network-based method for GRN inference (DGCGRN). To better understand and process the directed graph structure data of GRN, a directed graph convolutional neural network is conducted which retains the structural information of the directed graph while also making full use of neighbor node features. The local augmentation strategy is adopted in graph neural network to solve the problem of poor prediction accuracy caused by a large number of low-degree nodes in GRN. In addition, for real data such as E.coli, sequence features are obtained by extracting hidden features using Bi-GRU and calculating the statistical physicochemical characteristics of gene sequence. At the training stage, a dynamic update strategy is used to convert the obtained edge prediction scores into edge weights to guide the subsequent training process of the model. The results on synthetic benchmark datasets and real datasets show that the prediction performance of DGCGRN is significantly better than existing models. Furthermore, the case studies on bladder uroepithelial carcinoma and lung cancer cells also illustrate the performance of the proposed model.

An inversion method for calculating formation and borehole parameters in X-ray lithology density logging.

The use of X-ray sources in place of the 137Cs sources used in traditional lithology density logging methods has become a new trend in the development of nuclear logging techniques. How to eliminate the effects of drilling fluids or mudcake in the measurement process is a key question that determines the accuracy of measurement. In order to reduce the effects of mudcake and improve the accuracy of measurement of formation parameters, this paper presents an inversion method that can accurately calculate formation and borehole parameters and is suitable for X-ray lithology density logging. The general process of this inversion method is described below. First, a response model for broad-beam attenuation during X-ray lithology density logging is derived. Subsequently, the responses of four detectors under various formation and borehole conditions are studied by means of Monte Carlo simulation, and the energy spectra measured by each detector are divided into four energy windows (ranges) depending on the correlation with formation parameters. Finally, accurate values of formation and borehole parameters are obtained through iterative inversion using the Levenberg-Marquardt (LM) algorithm. The results of this study show that compared with previously established analysis methods, the inversion method based on forward modeling can effectively improve the accuracy of measurement of formation density and lithology index during X-ray lithology density logging, reduce the influence of the borehole environment, and overcome the deficiencies of data processing techniques based on the spine and ribs plot.

Interface Passivation of a Pyridine-Based Bifunctional Molecule for Inverted Perovskite Solar Cells.

Organic-inorganic hybrid perovskite solar cells (PSCs) have recently been demonstrated to be promising renewable harvesters because of their prominent photovoltaic power conversion efficiency (PCE), although their stability and efficiency still have not reached commercial criteria. Trouble-oriented analyses showcase that defect reduction among the grain boundaries and interfaces in the prepared perovskite polycrystalline films is a practical strategy, which has prompted researchers to develop functional molecules for interface passivation. Herein, the pyridine-based bifunctional molecule dimethylpyridine-3,5-dicarboxylate (DPDC) was employed as the interface between the electron-transport layer and perovskite layer, which achieved a champion PCE of 21.37% for an inverted MAPbI3-based PSC, which was greater than 18.64% for the control device. The mechanistic studies indicated that the significantly improved performance was mainly attributed to the remarkably enhanced fill factor with a value greater than 83%, which was primarily due to the nonradiative recombination suppression offered by the passivation effect of DPDC. Moreover, the promoted carrier mobility together with the enlarged crystal size contributed to a higher short-circuit current density. In addition, an increase in the open-circuit voltage was also observed in the DPDC-treated PSC, which benefited from the improved work function for reducing the energy loss during carrier transport. Furthermore, the DPDC-treated PSC showed substantially enhanced stability, with an over 80% retention rate of its initial PCE value over 300 h even at a 60% relative humidity level, which was attributed to the hydrophobic nature of the DPDC molecule and effective defect passivation. This work is expected not only to serve as an effective strategy for using a pyridine-based bifunctional molecule to passivate perovskite interfaces to enhance photovoltaic performance but also to shed light on the interface passivation mechanism.

[Mechanism of Tougu Xiaotong Capsules in delaying degeneration of osteoarthritis by regulating cholesterol metabolism in chondrocytes through lncRNA MALAT1].

From the perspective of lncRNA MALAT1 regulating cholesterol metabolism in chondrocytes, this paper explores the effect and mechanism of Tougu Xiaotong Capsules(TGXTC) in delaying the degeneration of osteoarthritis. After one week of adaptive feeding, 48(8-week-old) C57BL/6 mice were randomly divided into a blank group(12 mice) and a model group(36 mice) by random number table method. The mice in the model group were anesthetized by inhalation of 5% isoflurane, and the OA model was induced by Hulth method. The experiment randomly divided the mice into a model group(12 mice), a drug-positive group(taururso-deoxycholic acid)(12 mice), and a TGXTC group(12 mice). The drug-positive group was given 500 mg·kg~(-1) taurodeoxycholic acid by intragastric administration. TGXTC group was given TGXTC 368 mg·kg~(-1) by gavage. The blank group and model group were given the same amount of normal saline for four weeks. After the intervention, the mice in each group were killed under anesthesia, and the knee cartilage tissue was separated and collected. The morphologic changes of knee cartilage were observed. The level of lncRNA MALAT1 in the cartilage tissue was detected by real-time PCR. The protein expressions of ABCA1, ApoA1, LXRß, CHOP, and caspase-3 in mouse articular cartilage were detected by Western blot. Lentivirus-coated plasmid was used to transfect mouse chondrocytes with sh-MALAT1. The gene levels of lncRNA MALAT1 in mouse chondrocytes transfected with sh-MALAT1 were detected by real-time PCR. Western blot was used to detect the effect of TGXTC on the protein content of ABCA1, ApoA1, LXRß, CHOP, and caspase-3 in thapsigargin(TG)-induced mouse chondrocytes after lncRNA MALAT1 knockdown. Flow cytometry was used to detect the effect of TGXTC on apoptosis of TG-induced mouse chondrocytes after lncRNA MALAT1 knockdown. The results of HE and saffranine O staining showed that compared with the model group, the structure of the cartilage layer was basically intact; the damage degree of joint structure was significantly improved, and the cartilage matrix was significantly enhanced by saffranine O staining in the TGXTC group and drug-positive group. Compared with the model group, the lncRNA MALAT1 level was significantly decreased in the TGXTC group and drug-positive group. Compared with the model group, the protein content of ABCA1, ApoA1, and LXRß was significantly increased, while that of CHOP and caspase-3 in the TGXTC group and drug-positive group significantly decreased. Compared with the TG group, the lncRNA MALAT1 level in the TG+sh-MALAT1 group was decreased. The lncRNA MALAT1 level in the TG+sh-MA-LAT1+TGXTC group was increased compared with the TG+TGXTC group. Western blot results showed that compared with the model group, protein expressions of ABCA1, ApoA1, LXRß, CHOP, and caspase-3 in the TGXTC group were significantly decreased, after lncRNA MALAT1 knockdown, the regulation and apoptosis of ABCA1, ApoA1, LXRß, CHOP, and caspase-3 in TG-induced mouse chondrocytes were weakened by TGXTC. TGXTC can improve the disorder of cholesterol metabolism in OA chondrocytes and delay OA degeneration, which is closely related to the regulation of lncRNA MALAT1.

The component and structure of interpersonal trust.

Prior research has identified trust trait, trust expectation, trust risk and trust behavior as integral components of interpersonal trust. However, there still lack an in-depth exploration of the structural relationships among these integral components-how these integral components collectively constitute interpersonal trust. The current study innovatively proposed that interpersonal trust is anchored by individual trust trait, mediated by the dynamic equilibrium between trust risk and trust expectation, and culminates in trust behavior as the outcome. Interpersonal trust results from the synergistic interplay of individual and environmental factors. We called such structural relationships as the pyramid structure model of interpersonal trust, and proved its rationality by empirical evidence.

UsIL-6: An unbalanced learning strategy for identifying IL-6 inducing peptides by undersampling technique.

BACKGROUND AND OBJECTIVE: Interleukin-6 (IL-6) is the critical factor of early warning, monitoring, and prognosis in the inflammatory storm of COVID-19 cases. IL-6 inducing peptides, which can induce cytokine IL-6 production, are very important for the development of diagnosis and immunotherapy. Although the existing methods have some success in predicting IL-6 inducing peptides, there is still room for improvement in the performance of these models in practical application. METHODS: In this study, we proposed UsIL-6, a high-performance bioinformatics tool for identifying IL-6 inducing peptides. First, we extracted five groups of physicochemical properties and sequence structural information from IL-6 inducing peptide sequences, and obtained a 636-dimensional feature vector, we also employed NearMiss3 undersampling method and normalization method StandardScaler to process the data. Then, a 40-dimensional optimal feature vector was obtained by Boruta feature selection method. Finally, we combined this feature vector with extreme randomization tree classifier to build the final model UsIL-6. RESULTS: The AUC value of UsIL-6 on the independent test dataset was 0.87, and the BACC value was 0.808, which indicated that UsIL-6 had better performance than the existing methods in IL-6 inducing peptide recognition. CONCLUSIONS: The performance comparison on independent test dataset confirmed that UsIL-6 could achieve the highest performance, best robustness, and most excellent generalization ability. We hope that UsIL-6 will become a valuable method to identify, annotate and characterize new IL-6 inducing peptides.

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

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

DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding.

The inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addition, the GRNs of real species are always large graphs with direction and high sparsity, which impede the advancement of GRN inference. Therefore, DeepFGRN builds a node bidirectional representation module to capture the directed graph embedding representation of the GRN. Specifically, the source and target generators are designed to learn the low-dimensional dense embedding of the source and target neighbors of a gene, respectively. An adversarial learning strategy is applied to iteratively learn the real neighbors of each gene. In addition, because the expression profiles of genes with regulatory associations are correlative, a correlation analysis module is designed. Specifically, this module not only fully extracts gene expression features, but also captures the correlation between regulators and target genes. Experimental results show that DeepFGRN has a competitive capability for both GRN and FGRN inference. Potential biomarkers and therapeutic drugs for breast cancer, liver cancer, lung cancer and coronavirus disease 2019 are identified based on the candidate FGRNs, providing a possible opportunity to advance our knowledge of disease treatments.

Dendrobium officinale phenolic extract maintains proteostasis by regulating autophagy in a Caenorhabditis elegans model of Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disease, and accumulating evidence suggested that proteostatic imbalance is a key feature of the disease. Traditional Chinese medicine exhibits a multi-target therapeutic effect, making it highly suitable for addressing protein homeostasis imbalance in AD. Dendrobium officinale is a traditional Chinese herbs commonly used as tonic agent in China. In this study, we investigated protection effects of D. officinale phenolic extract (SH-F) and examined its underlying mechanisms by using transgenic Caenorhabditis elegans models. We found that treatment with SH-F (50 µg/mL) alleviated Aß and tau protein toxicity in worms, and also reduced aggregation of polyglutamine proteins to help maintain proteostasis. RNA sequencing results showed that SH-F treatment significantly affected the proteolytic process and autophagy-lysosomal pathway. Furthermore, we confirmed that SH-F showing maintainance of proteostasis was dependent on bec-1 by qRT-PCR analysis and RNAi methods. Finally, we identified active components of SH-F by LC-MS method, and found the five major compounds including koaburaside, tyramine dihydroferulate, N-p-trans-coumaroyltyramine, naringenin and isolariciresinol are the main bioactive components responsible for the anti-AD activity of SH-F. Our findings provide new insights to develop a treatment strategy for AD by targeting proteostasis, and SH-F could be an alternative drug for the treatment of AD.

Direct C-H Sulfuration: Synthesis of Disulfides, Dithiocarbamates, Xanthates, Thiocarbamates and Thiocarbonates.

In light of the important biological activities and widespread applications of organic disulfides, dithiocarbamates, xanthates, thiocarbamates and thiocarbonates, the continual persuit of efficient methods for their synthesis remains crucial. Traditionally, the preparation of such compounds heavily relied on intricate multi-step syntheses and the use of highly prefunctionalized starting materials. Over the past two decades, the direct sulfuration of C-H bonds has evolved into a straightforward, atom- and step-economical method for the preparation of organosulfur compounds. This review aims to provide an up-to-date discussion on direct C-H disulfuration, dithiocarbamation, xanthylation, thiocarbamation and thiocarbonation, with a special focus on describing scopes and mechanistic aspects. Moreover, the synthetic limitations and applications of some of these methodologies, along with the key unsolved challenges to be addressed in the future are also discussed. The majority of examples covered in this review are accomplished via metal-free, photochemical or electrochemical approaches, which are in alignment with the overraching objectives of green and sustainable chemistry. This comprehensive review aims to consolidate recent advancements, providing valuable insights into the dynamic landscape of efficient and sustainable synthetic strategies for these crucial classes of organosulfur compounds.

Intestinal malrotation complicated with gastric cancer: A case report.

BACKGROUND: Intestinal malrotation is a congenital defect of embryonic development caused by various teratogenic factors. In this condition, the intestinal tube, along with the superior mesenteric artery serving as the axis for the counterclockwise movement, is incomplete or abnormally rotated due to incomplete attachment of the mesentery and abnormal intestinal tube position. Such a case is usually asymptomatic and thus difficult to detect. Therefore, similar variant malformations are only found during an operation required for other abdominal diseases. CASE SUMMARY: An elderly male patient was admitted to the hospital due to gastric cancer. An abdominal computed tomography (CT) scan with contrast revealed that the ascending and descending colon were parallel on the right side of the abdominal cavity, while the sigmoid colon extended into the right iliac fossa, allowing the diagnosis of congenital midgut malrotation. Following thorough preoperative preparation, the patient underwent laparoscopic radical gastrectomy to treat his gastric cancer. Intraoperatively, an exploration of the abdominal cavity uncovered the absence of the transverse colon. The distal colon at the hepatic flexure, along with the ascending colon, extended into the right iliac fossa, where it continued as the sigmoid colon. As planned, the laparoscopic radical gastrectomy was performed, and the patient was discharged from the hospital 7 d after the surgery. CONCLUSION: Asymptomatic intestinal malrotation is best detected by CT, requiring no treatment but possibly interfering with the treatment of other diseases.