A review of methods for predicting DNA N6-methyladenine sites.

Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA site prediction. With the continuous development of technology, traditional techniques with the high costs and low efficiencies are gradually being replaced by computer methods. Computer methods that are widely used can be divided into two categories: traditional machine learning and deep learning methods. We first list some existing experimental methods for predicting the 6mA site, then analyze the general process from sequence input to results in computer methods and review existing model architectures. Finally, the results were summarized and compared to facilitate subsequent researchers in choosing the most suitable method for their work.

Brown adipose tissue-derived exosomes improve polycystic ovary syndrome in mice via STAT3/GPX4 signaling pathway.

Polycystic ovary syndrome (PCOS) is associated with impaired adipose tissue physiology. Elevated brown adipose tissue (BAT) mass or activity has shown potential in the treatment of PCOS. In this study, we aimed to investigate whether BAT-derived exosomes (BAT-Exos), as potential biomarkers of BAT activity, exert similar benefits as BAT in the treatment of PCOS. PCOS was induced in female C57BL/6J mice orally administered 1 mg/kg of letrozole for 21 days. Subsequently, the animals underwent transplantation with BAT or administered BAT-Exos (200 µg) isolated from young healthy mice via the tail vein; healthy female mice were used as controls. The results indicate that BAT-Exos treatment significantly reduced body weight and improved insulin resistance in PCOS mice. In addition, BAT-Exos improved ovulation function by reversing the acyclicity of the estrous cycle, decreasing circulating luteinizing hormone and testosterone, recovering ovarian performance, and improving oocyte quality, leading to a higher pregnancy rate and litter size. Furthermore, western blotting revealed reduced expression of signal transducer and activator of transcription 3 (STAT3) and increased expression of glutathione peroxidase 4 (GPX4) in the ovaries of mice in the BAT-Exos group. To further explore the role of the STAT3/GPX4 signaling pathway in PCOS mice, we treated the mice with an intraperitoneal injection of 5 mg/kg stattic, a STAT3 inhibitor. Consistent with BAT-Exos treatment, the administration of stattic rescued letrozole-induced PCOS phenotypes. These findings suggest that BAT-Exos treatment might be a potential therapeutic strategy for PCOS and that the STAT3/GPX4 signaling pathway is a critical therapeutic target for PCOS.

Spring Viremia of Carp Virus N Protein Negatively Regulates IFN Induction through Autophagy-Lysosome-Dependent Degradation of STING.

Fish possess a powerful IFN system to defend against aquatic virus infections. Nevertheless, spring viremia of carp virus (SVCV) causes large-scale mortality in common carp and significant economic losses to aquaculture. Therefore, it is necessary to investigate the strategies used by SVCV to escape the IFN response. In this study, we show that the SVCV nucleoprotein (N protein) negatively regulates cellular IFN production by degrading stimulator of IFN genes (STING) via the autophagy-lysosome-dependent pathway. First, overexpression of N protein inhibited the IFN promoter activation induced by polyinosinic-polycytidylic acid and STING. Second, the N protein associated with STING and experiments using a dominant-negative STING mutant demonstrated that the N-terminal transmembrane domains of STING were indispensable for this interaction. Then, the N protein degraded STING in a dose-dependent and autophagy-lysosome-dependent manner. Intriguingly, in the absence of STING, individual N proteins could not elicit host autophagic flow. Furthermore, the autophagy factor Beclin1 was found to interact with the N protein to attenuate N protein-mediated STING degradation after beclin1 knockdown. Finally, the N protein remarkably weakened STING-enhanced cellular antiviral responses. These findings reveal that SVCV uses the host autophagic process to achieve immune escape, thus broadening our understanding of aquatic virus pathogenesis.

Deep learning based method for predicting DNA N6-methyladenosine sites.

DNA N6 methyladenine (6mA) plays an important role in many biological processes, and accurately identifying its sites helps one to understand its biological effects more comprehensively. Previous traditional experimental methods are very labor-intensive and traditional machine learning methods also seem to be somewhat insufficient as the database of 6mA methylation groups becomes progressively larger, so we propose a deep learning-based method called multi-scale convolutional model based on global response normalization (CG6mA) to solve the prediction problem of 6mA site. This method is tested with other methods on three different kinds of benchmark datasets, and the results show that our model can get more excellent prediction results.

Rapid and Green Fabrication of Nanozyme with Geminal CuN3O Configuration for Efficient Catecholase-Mimicking.

Fabrication of nanozyme with catecholase-like catalytic activity faces the great challenge of merging outstanding activity with low cost as well as simple, rapid, and low-energy-consumed production, restricting its industrial applications. Herein, an inexpensive yet robust nanozyme (i.e., DT-Cu) via simple one-step coordination between diaminotriazole (DT) and CuSO4 within 1 h in water at room temperature is constructed. The asymmetric dicopper site with CuN3O configuration for each copper as well as Cu─O bond length of ≈1.83 Å and Cu···Cu distance of ≈3.5 Å in DT-Cu resemble those in catechol oxidase (CO), which ensure its prominent intrinsic activity, outperforming most CO-mimicking nanozymes and artificial homogeneous catalysts. The use of inexpensive DT/CuSO4 in this one-pot strategy endows DT-Cu with only ≈20% cost of natural CO per activity unit. During catalysis, O2 experienced a 4e-dominated reduction process accompanied by the formation of 1O2 and H2O2 intermediates and the product of H2O. Benefiting from the low cost as well as the distinctive structure and superior intrinsic activity, DT-Cu presents potential applications ranging from biocatalysis to analytical detection of biomolecules such as epinephrine and beyond.

Zebrafish TMEM47 is an effective blocker of IFN production during RNA and DNA virus infection.

IMPORTANCE: Mitochondrial antiviral signaling protein (MAVS) and stimulator of interferon (IFN) genes (STING) are key adaptor proteins required for innate immune responses to RNA and DNA virus infection. Here, we show that zebrafish transmembrane protein 47 (TMEM47) plays a critical role in regulating MAVS- and STING-triggered IFN production in a negative feedback manner. TMEM47 interacted with MAVS and STING for autophagic degradation, and ATG5 was essential for this process. These findings suggest the inhibitory function of TMEM47 on MAVS- and STING-mediated signaling responses during RNA and DNA virus infection.

Zebrafish MAP2K7 Simultaneously Enhances Host IRF7 Stability and Degrades Spring Viremia of Carp Virus P Protein via Ubiquitination Pathway.

During viral infection, host defensive proteins either enhance the host immune response or antagonize viral components directly. In this study, we report on the following two mechanisms employed by zebrafish mitogen-activated protein kinase kinase 7 (MAP2K7) to protect the host during spring viremia of carp virus (SVCV) infection: stabilization of host IRF7 and degradation of SVCV P protein. In vivo, map2k7+/- (map2k7-/- is a lethal mutation) zebrafish showed a higher lethality, more pronounced tissue damage, and more viral proteins in major immune organs than the controls. At the cellular level, overexpression of map2k7 significantly enhanced host cell antiviral capacity, and viral replication and proliferation were significantly suppressed. Additionally, MAP2K7 interacted with the C terminus of IRF7 and stabilized IRF7 by increasing K63-linked polyubiquitination. On the other hand, during MAP2K7 overexpression, SVCV P proteins were significantly decreased. Further analysis demonstrated that SVCV P protein was degraded by the ubiquitin-proteasome pathway, as the attenuation of K63-linked polyubiquitination was mediated by MAP2K7. Furthermore, the deubiquitinase USP7 was indispensable in P protein degradation. These results confirm the dual functions of MAP2K7 during viral infection. IMPORTANCE Normally, during viral infection, host antiviral factors individually modulate the host immune response or antagonize viral components to defense infection. In the present study, we report that zebrafish MAP2K7 plays a crucial positive role in the host antiviral process. According to the weaker antiviral capacity of map2k7+/- zebrafish than that of the control, we find that MAP2K7 reduces host lethality through two pathways, as follows: enhancing K63-linked polyubiquitination to promote host IRF7 stability and attenuating K63-mediated polyubiquitination to degrade the SVCV P protein. These two mechanisms of MAP2K7 reveal a special antiviral response in lower vertebrates.

MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network.

Accurate identification of transcription factor binding sites is of great significance in understanding gene expression, biological development and drug design. Although a variety of methods based on deep-learning models and large-scale data have been developed to predict transcription factor binding sites in DNA sequences, there is room for further improvement in prediction performance. In addition, effective interpretation of deep-learning models is greatly desirable. Here we present MAResNet, a new deep-learning method, for predicting transcription factor binding sites on 690 ChIP-seq datasets. More specifically, MAResNet combines the bottom-up and top-down attention mechanisms and a state-of-the-art feed-forward network (ResNet), which is constructed by stacking attention modules that generate attention-aware features. In particular, the multi-scale attention mechanism is utilized at the first stage to extract rich and representative sequence features. We further discuss the attention-aware features learned from different attention modules in accordance with the changes as the layers go deeper. The features learned by MAResNet are also visualized through the TMAP tool to illustrate that the method can extract the unique characteristics of transcription factor binding sites. The performance of MAResNet is extensively tested on 690 test subsets with an average AUC of 0.927, which is higher than that of the current state-of-the-art methods. Overall, this study provides a new and useful framework for the prediction of transcription factor binding sites by combining the funnel attention modules with the residual network.

Search for Cosmic-Ray Boosted Sub-MeV Dark-Matter-Electron Scattering in PandaX-4T.

We report the first search for the elastic scatterings between cosmic-ray boosted sub-MeV dark matter (DM) and electrons in the PandaX-4T liquid xenon experiment. Sub-MeV DM particles can be accelerated by scattering with electrons in the cosmic rays and produce detectable electron recoil signals in the detector. Using the commissioning data from PandaX-4T of 0.63 tonne·year exposure, we set new constraints on DM-electron scattering cross sections for DM masses ranging from 10 eV/c^{2} to 3 keV/c^{2}.

Searching for Two-Neutrino and Neutrinoless Double Beta Decay of

^{134}Xe is a candidate isotope for neutrinoless double beta decay (0νßß) search. In addition, the two-neutrino case (2νßß) allowed by the standard model of particle physics has not yet been observed. With the 656-kg natural xenon in the fiducial volume of the PandaX-4T detector, which contains 10.4% of ^{134}Xe, and its initial 94.9-day exposure, we have established the most stringent constraints on 2νßß and 0νßß of ^{134}Xe half-lives, with limits of 2.8×10^{22} yr and 3.0×10^{23} yr at 90% confidence level, respectively. The 2νßß (0νßß) limit surpasses the previously reported best result by a factor of 32 (2.7), highlighting the potential of large monolithic natural xenon detectors for double beta decay searches.

Cloning, heterologous expression, and molecular characterization of a highly active and stable non-specific endonuclease from Pseudomonas fluorescens.

Non-specific endonucleases can be used for the digestion of nucleic acids because they hydrolyze DNA/RNA into 3-5 base pairs (bp) length oligonucleotide fragments without strict selectivity. In this work, a novel non-specific endonuclease from Pseudomonas fluorescens (PfNuc) with high activities for both DNA and RNA was successfully cloned and expressed in Escherichia coli. The production of PfNuc in flask scale could be achieved to 1.73 × 106 U/L and 4.82 × 106 U/L for DNA and RNA by investigation of the culture and induction conditions. The characterization of PfNuc indicated that it was Mg2+-dependent and the catalytic activity was enhanced by 3.74 folds for DNA and 1.06 folds for RNA in the presence of 5 mM Mg2+. The specific activity of PfNuc for DNA was 1.44 × 105 U/mg at pH 8.0 and 40 °C, and 3.93 × 105 U/mg for RNA at pH 8.5 and 45 °C. The Km of the enzyme for both DNA and RNA was close to 43 µM. The Vmax was 6.40 × 105 U/mg and 1.11 × 106 U/mg for DNA and RNA, respectively. There was no observed activity loss when PfNuc was stored at 4 °C and - 20 °C after 28 days or 10 repeated freeze-thaw cycles at - 80 °C. Molecular docking revealed that PfNuc formed 17 and 19 hydrogen bonds with single-stranded RNA and double-stranded DNA, respectively. These results could explain the high activity and stability of PfNuc, suggesting its great potential applications in the industry and clinic.

Zebrafish CERKL Enhances Host TBK1 Stability and Simultaneously Degrades Viral Protein via Ubiquitination Modulation.

In the viral infection process, host gene function is usually reported as either defending the host or assaulting the virus. In this study, we demonstrated that zebrafish ceramide kinase-like (CERKL) mediates protection against viral infection via two distinct mechanisms: stabilization of TANK-binding kinase 1 (TBK1) through impairing K48-linked ubiquitination and degradation of spring viremia of carp virus (SVCV) P protein by dampening K63-linked ubiquitination, resulting in an improvement of the host immune response and a decline in viral activity in epithelioma papulosum cyprini (EPC) cells. On SVCV infection, ifnφ1 expression was increased or blunted by CERKL overexpression or knockdown, respectively. Subsequently, we found that CERKL localized in the cytoplasm, where it interacted with TBK1 and enhanced its stability by impeding the K48-linked polyubiquitination; meanwhile, the antiviral capacity of TBK1 was significantly potentiated by CERKL. In contrast, CERKL also interacted with and degraded SVCV P protein to disrupt its function in viral proliferation. Further mechanism analysis revealed K63-linked deubiquitination is the primary means of CERKL-mediated SVCV P protein degradation. Taken together, our study reveals a novel mechanism of fish defense against viral infection: the single gene cerkl is both a shield for the host and a spear against the virus, which strengthens resistance.

Bowel function, quality of life, and mental health of patients with high-grade intraepithelial neoplasia or T1 colorectal cancer after organ-preserving versus organ-resection surgeries: a cross-sectional study at a Chinese tertiary care center.

BACKGROUND: This study aimed to determine the postoperative intestinal functioning, quality of life (QoL), and psychological well-being of patients treated either with organ-preserving surgery (OPS) or organ-resection surgery (ORS) for high-grade intraepithelial neoplasia (HIN) or T1 colorectal cancer (CRC). METHODS: This cross-sectional study was conducted at a single tertiary care center. In total, 175 eligible individuals with T1 CRC or HIN were divided into the OPS (n = 103) or ORS (n = 72) group based on whether the relevant segment of the intestine was preserved or resected. Intestinal function was evaluated using low anterior resection syndrome (LARS) scores. QoL was evaluated using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC-QLQ)-C30 and EORTC-QLQ-CR29. Psychological status was evaluated using the Fear of Progression Questionnaire-Short Form and the Self-rating Anxiety and Depression scales. Propensity score matching (PSM) was used to minimize the influence of potential confounders. RESULTS: Overall, 130 of 175 patients (74.29%) responded to the questionnaires; 56 and 74 were in the ORS and OPS groups, respectively. Thirty-five patient pairs were successfully matched through PSM. The mild and severe LARS rates were significantly higher in the ORS group than in the OPS group (P < 0.001). The EORTC-QLQ-C30 and EORTC-QLQ-CR29 scores revealed significantly better physical, role, and emotional functioning and an overall improved state of health (with multiple reduced symptom scores) in the OPS group than in the ORS group (P < 0.05). Significantly more patients were depressed in the ORS group than in the OPS group (P = 0.034), whereas anxiety or fear of disease progression did not differ significantly between the groups. CONCLUSIONS: OPS for the treatment of HIN or T1 CRC was found to be more advantageous for patients in terms of improved intestinal function, QoL, and psychological status than was ORS.

Mix-VIO: A Visual Inertial Odometry Based on a Hybrid Tracking Strategy.

In this paper, we proposed Mix-VIO, a monocular and binocular visual-inertial odometry, to address the issue where conventional visual front-end tracking often fails under dynamic lighting and image blur conditions. Mix-VIO adopts a hybrid tracking approach, combining traditional handcrafted tracking techniques with Deep Neural Network (DNN)-based feature extraction and matching pipelines. The system employs deep learning methods for rapid feature point detection, while integrating traditional optical flow methods and deep learning-based sparse feature matching methods to enhance front-end tracking performance under rapid camera motion and environmental illumination changes. In the back-end, we utilize sliding window and bundle adjustment (BA) techniques for local map optimization and pose estimation. We conduct extensive experimental validations of the hybrid feature extraction and matching methods, demonstrating the system's capability to maintain optimal tracking results under illumination changes and image blur.

[The research development of programmed cell death in rheumatoid arthritis].

The etiology of rheumatoid arthritis (RA), a chronic inflammatory systemic disease, remains unclear. It is characterized by symmetrical and invasive joint inflammation, primarily affecting distal small joints such as those in the hands and feet. This inflammation can lead to joint deformity and loss of function, and often accompanied by involvement of extra-articular organs like the lungs and heart. Currently, anti-rheumatic drugs only provide symptom improvement but have toxic side effects that require optimization. Therefore, it is crucial to thoroughly analyze the mechanisms underlying RA development for the identification of new drug targets. Programmed cell death (PCD) has been extensively studied in recent years and proved to be one of the key pathogenic factors in RA. Dysregulation of PCD is particularly evident in synoviocytes, immune cells, and osteocytes. This review summarizes various forms of PCD including apoptosis, NETosis, autophagy, pyroptosis, necroptosis, ferroptosis, cuproptosis, as well as their regulatory roles in fibroblast synoviocytes, immune cells and osteocytes. These findings hold significant theoretical implications for optimizing clinical treatment options for RA and developing new target drugs.

PScL-HDeep: image-based prediction of protein subcellular location in human tissue using ensemble learning of handcrafted and deep learned features with two-layer feature selection.

Protein subcellular localization plays a crucial role in characterizing the function of proteins and understanding various cellular processes. Therefore, accurate identification of protein subcellular location is an important yet challenging task. Numerous computational methods have been proposed to predict the subcellular location of proteins. However, most existing methods have limited capability in terms of the overall accuracy, time consumption and generalization power. To address these problems, in this study, we developed a novel computational approach based on human protein atlas (HPA) data, referred to as PScL-HDeep, for accurate and efficient image-based prediction of protein subcellular location in human tissues. We extracted different handcrafted and deep learned (by employing pretrained deep learning model) features from different viewpoints of the image. The step-wise discriminant analysis (SDA) algorithm was applied to generate the optimal feature set from each original raw feature set. To further obtain a more informative feature subset, support vector machine-based recursive feature elimination with correlation bias reduction (SVM-RFE + CBR) feature selection algorithm was applied to the integrated feature set. Finally, the classification models, namely support vector machine with radial basis function (SVM-RBF) and support vector machine with linear kernel (SVM-LNR), were learned on the final selected feature set. To evaluate the performance of the proposed method, a new gold standard benchmark training dataset was constructed from the HPA databank. PScL-HDeep achieved the maximum performance on 10-fold cross validation test on this dataset and showed a better efficacy over existing predictors. Furthermore, we also illustrated the generalization ability of the proposed method by conducting a stringent independent validation test.

Improving protein fold recognition using triplet network and ensemble deep learning.

Protein fold recognition is a critical step toward protein structure and function prediction, aiming at providing the most likely fold type of the query protein. In recent years, the development of deep learning (DL) technique has led to massive advances in this important field, and accordingly, the sensitivity of protein fold recognition has been dramatically improved. Most DL-based methods take an intermediate bottleneck layer as the feature representation of proteins with new fold types. However, this strategy is indirect, inefficient and conditional on the hypothesis that the bottleneck layer's representation is assumed as a good representation of proteins with new fold types. To address the above problem, in this work, we develop a new computational framework by combining triplet network and ensemble DL. We first train a DL-based model, termed FoldNet, which employs triplet loss to train the deep convolutional network. FoldNet directly optimizes the protein fold embedding itself, making the proteins with the same fold types be closer to each other than those with different fold types in the new protein embedding space. Subsequently, using the trained FoldNet, we implement a new residue-residue contact-assisted predictor, termed FoldTR, which improves protein fold recognition. Furthermore, we propose a new ensemble DL method, termed FSD_XGBoost, which combines protein fold embedding with the other two discriminative fold-specific features extracted by two DL-based methods SSAfold and DeepFR. The Top 1 sensitivity of FSD_XGBoost increases to 74.8% at the fold level, which is ~9% higher than that of the state-of-the-art method. Together, the results suggest that fold-specific features extracted by different DL methods complement with each other, and their combination can further improve fold recognition at the fold level. The implemented web server of FoldTR and benchmark datasets are publicly available at http://csbio.njust.edu.cn/bioinf/foldtr/.

Association between emphysema and other pulmonary computed tomography patterns in COVID-19 pneumonia.

To evaluate the chest computed tomography (CT) findings of patients with Corona Virus Disease 2019 (COVID-19) on admission to hospital. And then correlate CT pulmonary infiltrates involvement with the findings of emphysema. We analyzed the different infiltrates of COVID-19 pneumonia using emphysema as the grade of pneumonia. We applied open-source assisted software (3D Slicer) to model the lungs and lesions of 66 patients with COVID-19, which were retrospectively included. we divided the 66 COVID-19 patients into the following two groups: (A) 12 patients with less than 10% emphysema in the low-attenuation area less than -950 Hounsfield units (%LAA-950), (B) 54 patients with greater than or equal to 10% emphysema in %LAA-950. Imaging findings were assessed retrospectively by two authors and then pulmonary infiltrates and emphysema volumes were measured on CT using 3D Slicer software. Differences between pulmonary infiltrates, emphysema, Collapsed, affected of patients with CT findings were assessed by Kruskal-Wallis and Wilcoxon test, respectively. Statistical significance was set at p < 0.05. The left lung (A) affected left lung 20.00/affected right lung 18.50, (B) affected left lung 13.00/affected right lung 11.50 was most frequently involved region in COVID-19. In addition, collapsed left lung, (A) collapsed left lung 4.95/collapsed right lung 4.65, (B) collapsed left lung 3.65/collapsed right lung 3.15 was also more severe than the right one. There were significant differences between the Group A and Group B in terms of the percentage of CT involvement in each lung region (p < 0.05), except for the inflated affected total lung (p = 0.152). The median percentage of collapsed left lung in the Group A was 20.00 (14.00-30.00), right lung was 18.50 (13.00-30.25) and the total was 19.00 (13.00-30.00), while the median percentage of collapsed left lung in the Group B was 13.00 (10.00-14.75), right lung was 11.50 (10.00-15.00) and the total was 12.50 (10.00-15.00). The percentage of affected left lung is an independent predictor of emphysema in COVID-19 patients. We need to focus on the left lung of the patient as it is more affected. The people with lower levels of emphysema may have more collapsed segments. The more collapsed segments may lead to more serious clinical feature.

Search for Light Dark Matter with Ionization Signals in the PandaX-4T Experiment.

We report the search results of light dark matter through its interactions with shell electrons and nuclei, using the commissioning data from the PandaX-4T liquid xenon detector. Low energy events are selected to have an ionization-only signal between 60 to 200 photoelectrons, corresponding to a mean nuclear recoil energy from 0.77 to 2.54 keV and electronic recoil energy from 0.07 to 0.23 keV. With an effective exposure of 0.55 tonne·year, we set the most stringent limits within a mass range from 40 MeV/c^{2} to 10 GeV/c^{2} for pointlike dark matter-electron interaction, 100 MeV/c^{2} to 10 GeV/c^{2} for dark matter-electron interaction via a light mediator, and 3.2 to 4 GeV/c^{2} for dark matter-nucleon spin-independent interaction. For DM interaction with electrons, our limits are closing in on the parameter space predicted by the freeze-in and freeze-out mechanisms in the early Universe.

Search for Light Dark Matter from the Atmosphere in PandaX-4T.

We report a search for light dark matter produced through the cascading decay of η mesons, which are created as a result of inelastic collisions between cosmic rays and Earth's atmosphere. We introduce a new and general framework, publicly accessible, designed to address boosted dark matter specifically, with which a full and dedicated simulation including both elastic and quasielastic processes of Earth attenuation effect on the dark matter particles arriving at the detector is performed. In the PandaX-4T commissioning data of 0.63 tonne·year exposure, no significant excess over background is observed. The first constraints on the interaction between light dark matter generated in the atmosphere and nucleus through a light scalar mediator are obtained. The lowest excluded cross section is set at 5.9×10^{-37} cm^{2} for a dark matter mass of 0.1 MeV/c^{2} and mediator mass of 300 MeV/c^{2}. The lowest upper limit of η to the dark matter decay branching ratio is 1.6×10^{-7}.