New Alpiniamide-Type Polyketide with Antibiofilm Activities from the Marine-Derived Streptomyces sp. ZS-A65.

Two new alpiniamide-type polyketides, alpiniamides H-I (1-2), in addition to four recognized compounds, were discovered in Streptomyces sp. ZSA65 derived from the marine sediments. The planar structure and absolute configuration of alpiniamides H-I were elucidated using a combination of 1D, 2D NMR, HRESIMS data analysis, Mosher's method and ECD calculations. The antibiofilm and antibacterial activities against P. aeruginosa were evaluated using the microdilution method. Notably, Compound 2 exhibited strong antibiofilm property.

Identifying Antitubercular Peptides via Deep Forest Architecture with Effective Feature Representation.

Tuberculosis (TB) is a severe disease caused by Mycobacterium tuberculosis that poses a significant threat to human health. The emergence of drug-resistant strains has made the global fight against TB even more challenging. Antituberculosis peptides (ATPs) have shown promising results as a potential treatment for TB. However, conventional wet lab-based approaches to ATP discovery are time-consuming and costly and often fail to discover peptides with desired properties. To address these challenges, we propose a novel machine learning-based framework called ATPfinder that can significantly accelerate the discovery of ATP. Our approach integrates various efficient peptide descriptors and utilizes the deep forest algorithm to construct the model. This neural network-like cascading structure can effectively process and mine features without complex hyperparameter tuning. Our experimental results show that ATPfinder outperforms existing ATP prediction tools, achieving state-of-the-art performance with an accuracy of 89.3% and an MCC of 0.70. Moreover, our framework exhibits better robustness than baseline algorithms commonly used for other sequence analysis tasks. Additionally, the excellent interpretability of our model can assist researchers in understanding the critical features of ATP. Finally, we developed a downloadable desktop application to simplify the use of our framework for researchers. Therefore, ATPfinder can facilitate the discovery of peptide drugs and provide potential solutions for TB treatment. Our framework is freely available at https://github.com/lantianyao/ATPfinder/ (data sets and code) and https://awi.cuhk.edu.cn/dbAMP/ATPfinder.html (software).

DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning.

Fungal infections have become a significant global health issue, affecting millions worldwide. Antifungal peptides (AFPs) have emerged as a promising alternative to conventional antifungal drugs due to their low toxicity and low propensity for inducing resistance. In this study, we developed a deep learning-based framework called DeepAFP to efficiently identify AFPs. DeepAFP fully leverages and mines composition information, evolutionary information, and physicochemical properties of peptides by employing combined kernels from multiple branches of convolutional neural network with bi-directional long short-term memory layers. In addition, DeepAFP integrates a transfer learning strategy to obtain efficient representations of peptides for improving model performance. DeepAFP demonstrates strong predictive ability on carefully curated datasets, yielding an accuracy of 93.29% and an F1-score of 93.45% on the DeepAFP-Main dataset. The experimental results show that DeepAFP outperforms existing AFP prediction tools, achieving state-of-the-art performance. Finally, we provide a downloadable AFP prediction tool to meet the demands of large-scale prediction and facilitate the usage of our framework by the public or other researchers. Our framework can accurately identify AFPs in a short time without requiring significant human and material resources, and hence can accelerate the development of AFPs as well as contribute to the treatment of fungal infections. Furthermore, our method can provide new perspectives for other biological sequence analysis tasks.

Conjunctive Analyses of BSA-Seq and BSR-Seq to Identify Candidate Genes Controlling the Black Lemma and Pericarp Trait in Barley.

Black barley seeds are a health-beneficial diet resource because of their special chemical composition and antioxidant properties. The black lemma and pericarp (BLP) locus was mapped in a genetic interval of 0.807 Mb on chromosome 1H, but its genetic basis remains unknown. In this study, targeted metabolomics and conjunctive analyses of BSA-seq and BSR-seq were used to identify candidate genes of BLP and the precursors of black pigments. The results revealed that five candidate genes, purple acid phosphatase, 3-ketoacyl-CoA synthase 11, coiled-coil domain-containing protein 167, subtilisin-like protease, and caffeic acid-O-methyltransferase, of the BLP locus were identified in the 10.12 Mb location region on the 1H chromosome after differential expression analysis, and 17 differential metabolites, including the precursor and repeating unit of allomelanin, were accumulated in the late mike stage of black barley. Phenol nitrogen-free precursors such as catechol (protocatechuic aldehyde) or catecholic acids (caffeic, protocatechuic, and gallic acids) may promote black pigmentation. BLP can manipulate the accumulation of benzoic acid derivatives (salicylic acid, 2,4-dihydroxybenzoic acid, gallic acid, gentisic acid, protocatechuic acid, syringic acid, vanillic acid, protocatechuic aldehyde, and syringaldehyde) through the shikimate/chorismite pathway other than the phenylalanine pathway and alter the metabolism of the phenylpropanoid-monolignol branch. Collectively, it is reasonable to infer that black pigmentation in barley is due to allomelanin biosynthesis in the lemma and pericarp, and BLP regulates melanogenesis by manipulating the biosynthesis of its precursors.

Accelerating the Discovery of Anticancer Peptides through Deep Forest Architecture with Deep Graphical Representation.

Cancer is one of the leading diseases threatening human life and health worldwide. Peptide-based therapies have attracted much attention in recent years. Therefore, the precise prediction of anticancer peptides (ACPs) is crucial for discovering and designing novel cancer treatments. In this study, we proposed a novel machine learning framework (GRDF) that incorporates deep graphical representation and deep forest architecture for identifying ACPs. Specifically, GRDF extracts graphical features based on the physicochemical properties of peptides and integrates their evolutionary information along with binary profiles for constructing models. Moreover, we employ the deep forest algorithm, which adopts a layer-by-layer cascade architecture similar to deep neural networks, enabling excellent performance on small datasets but without complicated tuning of hyperparameters. The experiment shows GRDF exhibits state-of-the-art performance on two elaborate datasets (Set 1 and Set 2), achieving 77.12% accuracy and 77.54% F1-score on Set 1, as well as 94.10% accuracy and 94.15% F1-score on Set 2, exceeding existing ACP prediction methods. Our models exhibit greater robustness than the baseline algorithms commonly used for other sequence analysis tasks. In addition, GRDF is well-interpretable, enabling researchers to better understand the features of peptide sequences. The promising results demonstrate that GRDF is remarkably effective in identifying ACPs. Therefore, the framework presented in this study could assist researchers in facilitating the discovery of anticancer peptides and contribute to developing novel cancer treatments.

Holistic similarity-based prediction of phosphorylation sites for understudied kinases.

Phosphorylation is an essential mechanism for regulating protein activities. Determining kinase-specific phosphorylation sites by experiments involves time-consuming and expensive analyzes. Although several studies proposed computational methods to model kinase-specific phosphorylation sites, they typically required abundant experimentally verified phosphorylation sites to yield reliable predictions. Nevertheless, the number of experimentally verified phosphorylation sites for most kinases is relatively small, and the targeting phosphorylation sites are still unidentified for some kinases. In fact, there is little research related to these understudied kinases in the literature. Thus, this study aims to create predictive models for these understudied kinases. A kinase-kinase similarity network was generated by merging the sequence-, functional-, protein-domain- and 'STRING'-related similarities. Thus, besides sequence data, protein-protein interactions and functional pathways were also considered to aid predictive modelling. This similarity network was then integrated with a classification of kinase groups to yield highly similar kinases to a specific understudied type of kinase. Their experimentally verified phosphorylation sites were leveraged as positive sites to train predictive models. The experimentally verified phosphorylation sites of the understudied kinase were used for validation. Results demonstrate that 82 out of 116 understudied kinases were predicted with adequate performance via the proposed modelling strategy, achieving a balanced accuracy of 0.81, 0.78, 0.84, 0.84, 0.85, 0.82, 0.90, 0.82 and 0.85, for the 'TK', 'Other', 'STE', 'CAMK', 'TKL', 'CMGC', 'AGC', 'CK1' and 'Atypical' groups, respectively. Therefore, this study demonstrates that web-like predictive networks can reliably capture the underlying patterns in such understudied kinases by harnessing relevant sources of similarities to predict their specific phosphorylation sites.

KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites.

The purpose of this work is to enhance KinasePhos, a machine learning-based kinase-specific phosphorylation site prediction tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProtKB, the GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models demonstrated their effectiveness compared with other prediction tools. For example, the prediction of sites phosphorylated by the protein kinase B, casein kinase 2, and protein kinase A families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the SHapley Additive exPlanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned to provide comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/download.html or https://github.com/tom-209/KinasePhos-3.0-executable-file.

Edaravone Dexborneol Downregulates Neutrophil Extracellular Trap Expression and Ameliorates Blood-Brain Barrier Permeability in Acute Ischemic Stroke.

Background: Our previous work has shown that inflammatory processes play a detrimental role in the pathophysiology of acute ischemic stroke (AIS). Neutrophil extracellular traps (NETs) have been recognized as a key contributor to the proinflammatory response in AIS and could aggravate blood-brain barrier (BBB) damage. Recently, experimental and clinical researches showed that Edaravone Dexborneol (Eda.B), which is comprised of two active ingredients, Edaravone and (+)-Borneol, was effective in treatment of AIS. However, it is not clear whether the effects of Eda.B against AIS are related to NETs and BBB permeability. Methods: Experiment 1 was to detect the effects of Eda.B in AIS patients. Serum samples of volunteers and AIS patients were collected before and 3 days after Edaravone Dexborneol treatment. Markers of NETs and occludin were detected by ELISA kit. Experiment 2 was to explore the effects of Eda.B on experimental stroke mice. Male C57BL/6 mice were subjected to distal middle cerebral artery occlusion (MCAO) and treated with vehicle, Eda.B, or DeoxyribonueleaseI (DNase I). After stroke, the neurobehavioral tests, infarct volume, and cerebral blood flow evaluation were determined. Leakage of Evans blue was to assess the integrity of BBB. Western blot, real-time quantitative polymerase chain reaction (RT-qPCR), and immunofluorescence were used to examine the expression of NETs and tight junction- (TJ-) associated proteins. Results: Eda.B significantly improved neurological function and cerebral blood flow but reduced infarct volume after experimental stroke. Eda.B downregulated level of NETs in serum samples of AIS patients and tissue samples of MCAO mouse cortex. Eda.B and DNase I alleviated BBB permeability by upregulating TJ-associated proteins. Conclusion: NETs are related to the early stage of AIS. Eda.B exerted neuroprotective effects and ameliorated BBB permeability after AIS.

A Multi-Mode Switching Variational Bayesian Adaptive Kalman Filter Algorithm for the SINS/PNS/GMNS Navigation System of Pelagic Ships.

The ocean-going environment is complex and changeable with great uncertainty, which poses a huge challenge to the navigation ability of ships working in the pelagic ocean. In this paper, in an attempt to deal with the complex uncertain interference that the environment may bring to the strap-down inertial navigation system/polarization navigation system/geomagnetic navigation system (SINS/PNS/GMNS) integrated navigation system, the multi-mode switching variational Bayesian adaptive Kalman filter (MMS-VBAKF) algorithm is proposed. To be more specific, to identify the degrees of measurement interference more effectively, we design an interference evaluation and multi-mode switching mechanism using the original polarization information and geomagnetic information. Through this mechanism, the interference to the SINS/PNS/GMNS navigation system is divided into three cases. In case of slight interference (case SI), the variational Bayesian method is adopted directly to solve the problem that the statistical characteristics of measurement noise are unknown. By the fixed-point iteration mechanism, the statistical properties of the measurement noise and the system states can be estimated adaptively in real time. In case of interference-tolerance (case TI), the estimation of the statistical characteristics of measurement noise need to weigh the measurement information at the moment and a priori value information comprehensively. In case of excessive interference (case EI), the SINS/PNS/GMNS integrated navigation system will perform mode switching and filtering system reconstruction in advance. Then, the information fusion and navigation states estimation can be completed. Consequently, the reliability, robustness, and accuracy of the SINS/PNS/GMNS integrated navigation system can be guaranteed. Finally, the effectiveness of the algorithm is illustrated by the simulation experiments.

Complement C3a Enhances the Phagocytic Activity of B Cells Through C3aR in a Fish.

The complement system is an important part of the immune system of teleost fish. Besides, teleost B cells possess both phagocytic activity and adaptive humoral immune function, unlike mammalian B1 cells with phagocytic activity and B2 cells specific to adaptive humoral immunity. However, the cross talk between complement system and phagocytic B cells in teleost fish still requires elucidation. Here, we show that, unlike tetrapods with a single C3 gene, nine C3 genes were identified from the grass carp (Ctenopharyngodon idella) genome, named C3.1-C3.9. Expression analysis revealed that C3.1 is the dominant C3 molecule in grass carp, for its expression was significantly higher than that of the other C3 molecules both at the mRNA and protein levels. The C3a fragment of C3.1 (C3a.1) was determined after the conserved C3 convertase cleavage site. Structural analysis revealed that C3a.1 consists of four α-helixes, with the C-terminal region forming a long α-helix, which is the potential functional region. Interestingly, we found that the recombinant GST-C3a.1 protein and the C-terminal α-helix peptide of C3a.1 both could significantly enhance the phagocytic activity of IgM+ B cells. Further study revealed that the C3a receptor (C3aR) was highly expressed in grass carp IgM+ B cells, and the phagocytosis-stimulating activity of C3a.1 could be dramatically inhibited by the anti-C3aR antibodies, indicating that C3a.1 performed the stimulating function through C3aR on IgM+ B cells. Taken together, our study not only uncovered the novel phagocytosis-stimulating activity of C3a, but also increased our knowledge of the cross talk between complement system and phagocytic B cells in teleost fish.

dbPTM in 2022: an updated database for exploring regulatory networks and functional associations of protein post-translational modifications.

Protein post-translational modifications (PTMs) play an important role in different cellular processes. In view of the importance of PTMs in cellular functions and the massive data accumulated by the rapid development of mass spectrometry (MS)-based proteomics, this paper presents an update of dbPTM with over 2 777 000 PTM substrate sites obtained from existing databases and manual curation of literature, of which more than 2 235 000 entries are experimentally verified. This update has manually curated over 42 new modification types that were not included in the previous version. Due to the increasing number of studies on the mechanism of PTMs in the past few years, a great deal of upstream regulatory proteins of PTM substrate sites have been revealed. The updated dbPTM thus collates regulatory information from databases and literature, and merges them into a protein-protein interaction network. To enhance the understanding of the association between PTMs and molecular functions/cellular processes, the functional annotations of PTMs are curated and integrated into the database. In addition, the existing PTM-related resources, including annotation databases and prediction tools are also renewed. Overall, in this update, we would like to provide users with the most abundant data and comprehensive annotations on PTMs of proteins. The updated dbPTM is now freely accessible at https://awi.cuhk.edu.cn/dbPTM/.

Estimating Quasiperiodic Disturbance With Unknown Frequency via Expectation-Maximization.

This article is concerned with a quasiperiodic disturbance estimation problem for dynamic control systems without prior knowledge on frequency. As a major challenge of our work, the quasiperiodic disturbance to be treated is always submerged by untargeted waves, leading to complicated coupling between disturbance separation and frequency identification. Existing approaches on quasiperiodic disturbance rejection have circumvented, rather than overcome, this challenge by assuming either a known frequency or a measurable disturbance signal. In this work, an expectation-maximization (EM) framework is proposed where disturbance signal separation and frequency identification are carried out in an iterative manner. In the E-step, the expected log-likelihood function is evaluated via reconstruction of the quasiperiodic signal based on the latest frequency estimate; and in the M-step, the frequency estimate is updated by maximizing the log-likelihood function obtained in the E-step. To facilitate recursive frequency estimation, an online EM algorithm is also developed based on the forward-only smoothing techniques. Furthermore, we show that the proposed method can be easily extended to deal with nonlinear system models and time-varying frequencies.

CircNet 2.0: an updated database for exploring circular RNA regulatory networks in cancers.

Circular RNAs (circRNAs), which are single-stranded RNA molecules that have individually formed into a covalently closed continuous loop, act as sponges of microRNAs to regulate transcription and translation. CircRNAs are important molecules in the field of cancer diagnosis, as growing evidence suggests that they are closely related to pathological cancer features. Therefore, they have high potential for clinical use as novel cancer biomarkers. In this article, we present our updates to CircNet (version 2.0), into which circRNAs from circAtlas and MiOncoCirc, and novel circRNAs from The Cancer Genome Atlas database have been integrated. In total, 2732 samples from 37 types of cancers were integrated into CircNet 2.0 and analyzed using several of the most reliable circRNA detection algorithms. Furthermore, target miRNAs were predicted from the full-length circRNA sequence using three reliable tools (PITA, miRanda and TargetScan). Additionally, 384 897 experimentally verified miRNA-target interactions from miRTarBase were integrated into our database to facilitate the construction of high-quality circRNA-miRNA-gene regulatory networks. These improvements, along with the user-friendly interactive web interface for data presentation, search, and visualization, showcase the updated CircNet database as a powerful, experimentally validated resource, for providing strong data support in the biomedical fields. CircNet 2.0 is currently accessible at https://awi.cuhk.edu.cn/∼CircNet.

dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data.

The last 18 months, or more, have seen a profound shift in our global experience, with many of us navigating a once-in-100-year pandemic. To date, COVID-19 remains a life-threatening pandemic with little to no targeted therapeutic recourse. The discovery of novel antiviral agents, such as vaccines and drugs, can provide therapeutic solutions to save human beings from severe infections; however, there is no specifically effective antiviral treatment confirmed for now. Thus, great attention has been paid to the use of natural or artificial antimicrobial peptides (AMPs) as these compounds are widely regarded as promising solutions for the treatment of harmful microorganisms. Given the biological significance of AMPs, it was obvious that there was a significant need for a single platform for identifying and engaging with AMP data. This led to the creation of the dbAMP platform that provides comprehensive information about AMPs and facilitates their investigation and analysis. To date, the dbAMP has accumulated 26 447 AMPs and 2262 antimicrobial proteins from 3044 organisms using both database integration and manual curation of >4579 articles. In addition, dbAMP facilitates the evaluation of AMP structures using I-TASSER for automated protein structure prediction and structure-based functional annotation, providing predictive structure information for clinical drug development. Next-generation sequencing (NGS) and third-generation sequencing have been applied to generate large-scale sequencing reads from various environments, enabling greatly improved analysis of genome structure. In this update, we launch an efficient online tool that can effectively identify AMPs from genome/metagenome and proteome data of all species in a short period. In conclusion, these improvements promote the dbAMP as one of the most abundant and comprehensively annotated resources for AMPs. The updated dbAMP is now freely accessible at http://awi.cuhk.edu.cn/dbAMP.

Perceptions of patients with chronic obstructive pulmonary disease towards telemedicine: A qualitative systematic review.

BACKGROUND: There are some qualitative studies on the views of patients with chronic obstructive pulmonary disease (COPD) on telemedicine, however, there are few related qualitative systematic reviews. OBJECTIVES: To systematically review and synthesize qualitative studies involving the perceptions of patients with COPD about telemedicine to understand patients' attitudes and expectations for telemedicine and determine the obstacles and stimulus in the use of telemedicine. METHODS: We searched PubMed, Web of Science, MEDLINE, Embase and CINAHL for articles published from January 2000 to December 2020. The data were analysed using thematic synthesis. RESULTS: We included 20 articles involving 19 studies and 301 patients, and we identified four themes: perceived ease of use, perceived usefulness, perceived difficulty of use, and perceived uselessness. We found that although patients have different views on telemedicine, most of them have a positive attitude towards it. CONCLUSIONS: The synthesis of views will help us determine the factors that promote or hinder the application of telemedicine and guide the design and implementation of telemedicine in the future.

The Cost-Effectiveness of Pulmonary Rehabilitation for COPD in Different Settings: A Systematic Review.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) has high morbidity and mortality rates. COPD impairs body functioning, reduces quality of life, and creates a great economic burden for society. Pulmonary rehabilitation (PR) has become an important nonpharmacological treatment for COPD. This paper systematically reviews economic evaluations of PR in COPD patients in different settings. OBJECTIVES: We aimed to understand the cost-effectiveness of PR in different settings for COPD to provide economic evidence for decision-makers. METHODS: We searched eight databases from their inception to 23 November 2019. The results were presented in terms of an incremental cost-effectiveness ratio (ICER), and the decision uncertainty was expressed by cost-effectiveness acceptability curves (CEACs). We used the Consensus on Health Economic Criteria to assess study quality. RESULTS: This review included ten studies that matched the selection criteria. Five studies compared PR with usual care in primary healthcare or outpatient departments. Two studies compared community-based PR with hospital PR or usual care. In the other studies, PR was mainly carried out at home. Compared with usual care, PR was cost-effective in primary healthcare institutions or outpatient departments. According to CEACs, community-based PR had a 50% probability of cost-effectiveness at £30,000/quality-adjusted life year (QALY) compared with hospital PR in the UK. Based on the ICER, community-based PR was "moderately" cost-effective, with a ratio of €32,425/QALY compared with usual care in the Netherlands. Home-based PR was dominant compared with usual care, and tele-rehabilitation was dominant compared with traditional home PR. CONCLUSIONS: PR conducted in different settings can potentially be cost-effective, as measured using QALY or the Chronic Respiratory Questionnaire (CRQ).