Diamond-Like Carbon Depositing on the Surface of Polylactide Membrane for Prevention of Adhesion Formation During Tendon Repair.

Post-traumatic peritendinous adhesion presents a significant challenge in clinical medicine. This study proposes the use of diamond-like carbon (DLC) deposited on polylactic acid (PLA) membranes as a biophysical mechanism for anti-adhesion barrier to encase ruptured tendons in tendon-injured rats. The results indicate that PLA/DLC composite membrane exhibits more efficient anti-adhesion effect than PLA membrane, with histological score decreasing from 3.12 ± 0.27 to 2.20 ± 0.22 and anti-adhesion effectiveness increasing from 21.61% to 44.72%. Mechanistically, the abundant C=O bond functional groups on the surface of DLC can reduce reactive oxygen species level effectively; thus, the phosphorylation of NF-κB and M1 polarization of macrophages are inhibited. Consequently, excessive inflammatory response augmented by M1 macrophage-originated cytokines including interleukin-6 (IL-6), interleukin-1ß (IL-1ß), and tumor necrosis factor-α (TNF-α) is largely reduced. For biocompatibility evaluation, PLA/DLC membrane is slowly absorbed within tissue and displays prolonged barrier effects compared to traditional PLA membranes. Further studies show the DLC depositing decelerates the release of degradation product lactic acid and its induction of macrophage M2 polarization by interfering esterase and PLA ester bonds, which further delays the fibrosis process. It was found that the PLA/DLC membrane possess an efficient biophysical mechanism for treatment of peritendinous adhesion.

CFViSA: A comprehensive and free platform for visualization and statistics in omics-data.

INTRODUCTION: The rapid growth of omics technologies has led to the use of bioinformatics as a powerful tool for unravelling scientific puzzles. However, the obstacles of bioinformatics are compounded by the complexity of data processing and the distinct nature of omics data types, particularly in terms of visualization and statistics. OBJECTIVES: We developed a comprehensive and free platform, CFViSA, to facilitate effortless visualization and statistical analysis of omics data by the scientific community. METHODS: CFViSA was constructed using the Scala programming language and utilizes the AKKA toolkit for the web server and MySQL for the database server. The visualization and statistical analysis were performed with the R program. RESULTS: CFViSA integrates two omics data analysis pipelines (microbiome and transcriptome analysis) and an extensive array of 79 analysis tools spanning simple sequence processing, visualization, and statistics available for various omics data, including microbiome and transcriptome data. CFViSA starts from an analysis interface, paralleling a demonstration full course to help users understand operating principles and scientifically set the analysis parameters. Once analysis is conducted, users can enter the task history interface for figure adjustments, and then a complete series of results, including statistics, feature tables and figures. All the graphic layouts were printed with necessary statistics and a traceback function recording the options for analysis and visualization; these statistics were excluded from the five competing methods. CONCLUSION: CFViSA is a user-friendly bioinformatics cloud platform with detailed guidelines for integrating functions in multi-omics analysis with real-time visualization adjustment and complete series of results provision. CFViSA is available at http://www.cloud.biomicroclass.com/en/CFViSA/.

Employing Atomic Force Microscopy (AFM) for Microscale Investigation of Interfaces and Interactions in Membrane Fouling Processes: New Perspectives and Prospects.

Membrane fouling presents a significant challenge in the treatment of wastewater. Several detection methods have been used to interpret membrane fouling processes. Compared with other analysis and detection methods, atomic force microscopy (AFM) is widely used because of its advantages in liquid-phase in situ 3D imaging, ability to measure interactive forces, and mild testing conditions. Although AFM has been widely used in the study of membrane fouling, the current literature has not fully explored its potential. This review aims to uncover and provide a new perspective on the application of AFM technology in future studies on membrane fouling. Initially, a rigorous review was conducted on the morphology, roughness, and interaction forces of AFM in situ characterization of membranes and foulants. Then, the application of AFM in the process of changing membrane fouling factors was reviewed based on its in situ measurement capability, and it was found that changes in ionic conditions, pH, voltage, and even time can cause changes in membrane fouling morphology and forces. Existing membrane fouling models are then discussed, and the role of AFM in predicting and testing these models is presented. Finally, the potential of the improved AFM techniques to be applied in the field of membrane fouling has been underestimated. In this paper, we have fully elucidated the potentials of the improved AFM techniques to be applied in the process of membrane fouling, and we have presented the current challenges and the directions for the future development in an attempt to provide new insights into this field.

Predicting cell types with supervised contrastive learning on cells and their types.

Single-cell RNA-sequencing (scRNA-seq) is a powerful technique that provides high-resolution expression profiling of individual cells. It significantly advances our understanding of cellular diversity and function. Despite its potential, the analysis of scRNA-seq data poses considerable challenges related to multicollinearity, data imbalance, and batch effect. One of the pivotal tasks in single-cell data analysis is cell type annotation, which classifies cells into discrete types based on their gene expression profiles. In this work, we propose a novel modeling formalism for cell type annotation with a supervised contrastive learning method, named SCLSC (Supervised Contrastive Learning for Single Cell). Different from the previous usage of contrastive learning in single cell data analysis, we employed the contrastive learning for instance-type pairs instead of instance-instance pairs. More specifically, in the cell type annotation task, the contrastive learning is applied to learn cell and cell type representation that render cells of the same type to be clustered in the new embedding space. Through this approach, the knowledge derived from annotated cells is transferred to the feature representation for scRNA-seq data. The whole training process becomes more efficient when conducting contrastive learning for cell and their types. Our experiment results demonstrate that the proposed SCLSC method consistently achieves superior accuracy in predicting cell types compared to five state-of-the-art methods. SCLSC also performs well in identifying cell types in different batch groups. The simplicity of our method allows for scalability, making it suitable for analyzing datasets with a large number of cells. In a real-world application of SCLSC to monitor the dynamics of immune cell subpopulations over time, SCLSC demonstrates a capability to discriminate cell subtypes of CD19+ B cells that were not present in the training dataset.

An update of clinical value of circulating tumor DNA in esophageal cancer: a systematic review and meta-analysis.

BACKGROUND: Esophageal cancer (EC) is a deadly disease with limited therapeutic options. Although circulating tumor DNA (ctDNA) could be a promising tool in this regard, the availiable evidence is limited. We performed a systematic review and meta-analysis to summarize the clinical applicability of the next-generation sequencing (NGS) and droplet digital polymerase chain reaction (ddPCR) technology on the ctDNA detection of the EC and listed the current challenges. METHODS: We systematically searched MEDLINE (via PubMed), Embase (via OVID), ISI Web of Science database and Cochrane Library from January, 2000 to April, 2023. Progression-free survival (PFS) and overall survival (OS) were set as primary outcome endpoints. Pathologic response was evaluated by tumor regression grade (TRG), according to the eighth edition of the American Joint Committee on Cancer (AJCC). Major pathologic regression (MPR) was defined as TRG 1 and 2. The MPR was set as secondary endpoint. Hazard rate (HR) and associated 95% CI were used as the effect indicators the association between ctDNA and prognosis of EC. MPR rates were also calculated. Fixed-effect model (Inverse Variance) or random-effect model (Mantel-Haenszel method) was performed depending on the statistically heterogeneity. RESULTS: Twenty-two studies, containing 1144 patients with EC, were included in this meta-analysis. The results showed that OS (HR = 3.87; 95% CI, 2.86-5.23) and PFS (HR = 4.28; 95% CI, 3.34-5.48) were shorter in ctDNA-positive patients. In the neoadjuvant therapy, the sensitivity analysis showed the clarified HR of ctDNA-positive was 1.13(95% CI, 1.01-1.28). We also found that TP53, NOTCH1, CCND1 and CNKN2A are the most frequent mutation genes. CONCLUSIONS: Positive ctDNA is associated with poor prognosis, which demonstrated clinical value of ctDNA. Longitudinal ctDNA monitoring showed potential prognostic value in the neoadjuvant therapy. In an era of precision medicine, ctDNA could be a promising tool to individualize treatment planning and to improve outcomes in EC. PROSPERO REGISTRATION NUMBER: CRD42023412465.

Correction: Microbial Gene Ontology informed deep neural network for microbe functionality discovery in human diseases.

[This corrects the article DOI: 10.1371/journal.pone.0290307.].

Investigation of the BERT model on nucleotide sequences with non-standard pre-training and evaluation of different k-mer embeddings.

MOTIVATION: In recent years, pre-training with the transformer architecture has gained significant attention. While this approach has led to notable performance improvements across a variety of downstream tasks, the underlying mechanisms by which pre-training models influence these tasks, particularly in the context of biological data, are not yet fully elucidated. RESULTS: In this study, focusing on the pre-training on nucleotide sequences, we decompose a pre-training model of Bidirectional Encoder Representations from Transformers (BERT) into its embedding and encoding modules to analyze what a pre-trained model learns from nucleotide sequences. Through a comparative study of non-standard pre-training at both the data and model levels, we find that a typical BERT model learns to capture overlapping-consistent k-mer embeddings for its token representation within its embedding module. Interestingly, using the k-mer embeddings pre-trained on random data can yield similar performance in downstream tasks, when compared with those using the k-mer embeddings pre-trained on real biological sequences. We further compare the learned k-mer embeddings with other established k-mer representations in downstream tasks of sequence-based functional prediction. Our experimental results demonstrate that the dense representation of k-mers learned from pre-training can be used as a viable alternative to one-hot encoding for representing nucleotide sequences. Furthermore, integrating the pre-trained k-mer embeddings with simpler models can achieve competitive performance in two typical downstream tasks. AVAILABILITY AND IMPLEMENTATION: The source code and associated data can be accessed at https://github.com/yaozhong/bert_investigation.

Interfacial Polymerization Depth Mediated by the Shuttle Effect Regulating the Application Performance of Pesticide-Loaded Microcapsules.

The highly water-soluble nematicide fosthiazate is anticipated to undergo microencapsulation in order to enhance its retention around plant roots and mitigate leaching into groundwater. However, the underlying mechanism governing the influence of hydrophilicity of the microcapsule (MC) core on the evolution of the microcapsule shell remains unclear, posing challenges for encapsulating water-soluble core materials. This study elucidates the microlevel formation mechanism of microcapsules by investigating the impact of interfacial mass transfer on shell formation and proposes a method for regulating the structure of shells. The study reveals that enhancing the hydrophilicity of the core enhances the shuttle effect between the oil and aqueous phase, expands the region of polymerization reactions, and forms a loose and thick shell. The thickness of the microcapsule shell prepared using solvent oil 150# (MCs-SOL) measures only 264 nm, while that of the microcapsules prepared using propylene glycol diacetate and solvent oil 150# at a ratio of 2:1 (MCs-P2S1) is 5.2 times greater. The enhanced compactness of the shell reduced the release rate of microcapsules and the leaching distance of fosthiazate in soil, thereby mitigating the risk of leaching loss and facilitating the distribution of active ingredients within crop roots. The MCs-SOL had a limited leaching distance measurement of 8 cm and exhibited a satisfactory efficacy of 87.3% in controlling root galling nematodes. The thickness and compactness of the MCs shell can be regulated by manipulating the interfacial shuttle effect, providing a promising approach to enhancing utilization efficiency while mitigating potential environmental risks.

Development and validation of a predictive model for the diagnosis of bladder tumors using narrow band imaging.

PURPOSE: At present, the prediction of bladder tumor nature during cystoscopy is partially dependent on the clinician's own experience. Subjective factors may lead to excessive biopsy or delayed treatment. The purpose of our study is to establish a reliable model for predicting the nature of bladder tumors using narrow band imaging. METHODS: From November 2021 to November 2022, the clinical data of 231 patients who required a cystoscopy were prospectively collected at our center. Cystoscopy was performed in 219 eligible patients, in which both tumor and vascular morphology characteristics were recorded. Pathological results were used as the diagnostic standard. A logistic regression analysis was used to screen out factors related to tumor pathology. Bootstrap resampling was used for internal validation. A total of 71 patients from four other centers served as an external validation cohort. RESULTS: The following diagnostic factors were identified: tumor morphology (cauliflower-like or algae-like lesions), vascular morphology (dotted or circumferential vessels), tumor boundary (clear or unclear), and patients' symptoms (gross hematuria) and were included in the prediction model. The internal validation results showed that the area under the curve was 0.94 (95% CI 0.92-0.97), and the P value from the goodness-of-fit test was 0.97. After external validation, the results showed the area under the curve was 0.89 (95% CI 0.82-0.97) and the P value of the goodness-of-fit test was 0.24. CONCLUSION: A diagnostic prediction nomogram was established for bladder cancer. The verification results showed that the prediction model has good prediction performance.

Microbial Gene Ontology informed deep neural network for microbe functionality discovery in human diseases.

The human microbiome plays a crucial role in human health and is associated with a number of human diseases. Determining microbiome functional roles in human diseases remains a biological challenge due to the high dimensionality of metagenome gene features. However, existing models were limited in providing biological interpretability, where the functional role of microbes in human diseases is unexplored. Here we propose to utilize a neural network-based model incorporating Gene Ontology (GO) relationship network to discover the microbe functionality in human diseases. We use four benchmark datasets, including diabetes, liver cirrhosis, inflammatory bowel disease, and colorectal cancer, to explore the microbe functionality in the human diseases. Our model discovered and visualized the novel candidates' important microbiome genes and their functions by calculating the important score of each gene and GO term in the network. Furthermore, we demonstrate that our model achieves a competitive performance in predicting the disease by comparison with other non-Gene Ontology informed models. The discovered candidates' important microbiome genes and their functions provide novel insights into microbe functional contribution.

A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients.

OBJECTIVE: Based on ultrasound (US) images, this study aimed to detect and quantify calcifications of thyroid nodules, which are regarded as one of the most important features in US diagnosis of thyroid cancer, and to further investigate the value of US calcifications in predicting the risk of lymph node metastasis (LNM) in papillary thyroid cancer (PTC). METHODS: Based on the DeepLabv3+ networks, 2992 thyroid nodules in US images were used to train a model to detect thyroid nodules, of which 998 were used to train a model to detect and quantify calcifications. A total of 225 and 146 thyroid nodules obtained from two centers, respectively, were used to test the performance of these models. A logistic regression method was used to construct the predictive models for LNM in PTCs. RESULTS: Calcifications detected by the network model and experienced radiologists had an agreement degree of above 90%. The novel quantitative parameters of US calcification defined in this study showed a significant difference between PTC patients with and without cervical LNM (p < 0.05). The calcification parameters were beneficial to predicting the LNM risk in PTC patients. The LNM prediction model using these calcification parameters combined with patient age and other US nodular features showed a higher specificity and accuracy than the calcification parameters alone. CONCLUSIONS: Our models not only detect the calcifications automatically, but also have value in predicting cervical LNM risk of PTC patients, thereby making it possible to investigate the relationship between calcifications and highly invasive PTC in detail. CLINICAL RELEVANCE STATEMENT: Due to the high association of US microcalcifications with thyroid cancers, our model will contribute to the differential diagnosis of thyroid nodules in daily practice. KEY POINTS: • We developed an ML-based network model for automatically detecting and quantifying calcifications within thyroid nodules in US images. • Three novel parameters for quantifying US calcifications were defined and verified. • These US calcification parameters showed value in predicting the risk of cervical LNM in PTC patients.

Zero-shot-capable identification of phage-host relationships with whole-genome sequence representation by contrastive learning.

Accurately identifying phage-host relationships from their genome sequences is still challenging, especially for those phages and hosts with less homologous sequences. In this work, focusing on identifying the phage-host relationships at the species and genus level, we propose a contrastive learning based approach to learn whole-genome sequence embeddings that can take account of phage-host interactions (PHIs). Contrastive learning is used to make phages infecting the same hosts close to each other in the new representation space. Specifically, we rephrase whole-genome sequences with frequency chaos game representation (FCGR) and learn latent embeddings that 'encapsulate' phages and host relationships through contrastive learning. The contrastive learning method works well on the imbalanced dataset. Based on the learned embeddings, a proposed pipeline named CL4PHI can predict known hosts and unseen hosts in training. We compare our method with two recently proposed state-of-the-art learning-based methods on their benchmark datasets. The experiment results demonstrate that the proposed method using contrastive learning improves the prediction accuracy on known hosts and demonstrates a zero-shot prediction capability on unseen hosts. In terms of potential applications, the rapid pace of genome sequencing across different species has resulted in a vast amount of whole-genome sequencing data that require efficient computational methods for identifying phage-host interactions. The proposed approach is expected to address this need by efficiently processing whole-genome sequences of phages and prokaryotic hosts and capturing features related to phage-host relationships for genome sequence representation. This approach can be used to accelerate the discovery of phage-host interactions and aid in the development of phage-based therapies for infectious diseases.

DCiPatho: deep cross-fusion networks for genome scale identification of pathogens.

Pathogen detection from biological and environmental samples is important for global disease control. Despite advances in pathogen detection using deep learning, current algorithms have limitations in processing long genomic sequences. Through the deep cross-fusion of cross, residual and deep neural networks, we developed DCiPatho for accurate pathogen detection based on the integrated frequency features of 3-to-7 k-mers. Compared with the existing state-of-the-art algorithms, DCiPatho can be used to accurately identify distinct pathogenic bacteria infecting humans, animals and plants. We evaluated DCiPatho on both learned and unlearned pathogen species using both genomics and metagenomics datasets. DCiPatho is an effective tool for the genomic-scale identification of pathogens by integrating the frequency of k-mers into deep cross-fusion networks. The source code is publicly available at https://github.com/LorMeBioAI/DCiPatho.

Comparison of neoadjuvant immunotherapy versus routine neoadjuvant therapy for patients with locally advanced esophageal cancer: A systematic review and meta-analysis.

Background: The neoadjuvant use of immune checkpoint inhibitor combined with chemotherapy (nICT) or chemoradiotherapy (nICRT) in locally advanced esophageal cancer (EC) is currently an area of active ongoing research. Therefore, we carried out a comprehensive meta-analysis to compare the efficacy and safety of the new strategy with routine neoadjuvant strategy, which included neoadjuvant chemotherapy (nCT) and neoadjuvant chemoradiotherapy (nCRT). Patients and methods: MEDLINE (via PubMed), Embase (via OVID), ISI Web of Science database and Cochrane Library were included. And, all of them were searched for eligible studies between January, 2000 and February, 2023. The pathological complete response (pCR) and major pathological response (MPR) were primary outcome of our study. The second outcome of interest was R0 resection rate. Odds ratio (OR) and associated 95% CI were used as the effect indicators comparing the safety and efficiency of the neoadjuvant immunotherapy with the routine neoadjuvant therapy. Fixed-effect model (Inverse Variance) or random-effect model (Mantel-Haenszel method) was performed depending on the statistically heterogeneity. Results: There were eight trials with 652 patients were included in our meta-analysis. The estimated pCR rate was higher in the neoadjuvant immunotherapy group (OR =1.86; 95% CI, 1.25-2.75; I2 = 32.8%, P=0.166). The different results were found in the esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) subgroups, the estimated OR was 2.35 (95%CI, 1.00-2.72; I2 = 30.9%, P=0.215) in the EAC subgroup, and 2.35 (95% CI, 1.20-4.54; I2 = 45.3%, P=0.161) in the ESCC subgroup, respectively. The neoadjuvant immunotherapy also showed the advantage in the MPR rates (OR =2.66; 95% CI, 1.69-4.19; I2 = 24.3%, P=0.252). There was no obvious difference between the neoadjuvant immunotherapy and routine neoadjuvant therapy with respect to surgical resection rate, R0 resection rate, surgical delay rate; while more treatment-related adverse events were observed for the neoadjuvant immunotherapy for pneumonitis/pneumonia (OR=3.46, 95% CI, 1.31-9.16; I2 = 67.3%, P=0.005) and thyroid dysfunction (OR=4.69, 95% CI, 1.53-14.36; I2 = 56.5%, P=0.032). Conclusion: The pooled correlations indicated that the neoadjuvant immunotherapy (both nICT and nICRT) could significantly increase the rates of pCR and MPR, compared with routine neoadjuvant therapy (both nCT and nCRT) in the treatment of locally advanced EC. The neoadjuvant immunotherapy and routine neoadjuvant therapy were with acceptable toxicity. However, randomized studies with larger groups of patients need to performed to confirm these results. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42020155802.

An endogenous amniotic fluid-derived 10-amino acid peptide improves lung development and hyperoxia injury.

Background: Bronchopulmonary dysplasia (BPD) is a chronic lung disease that occurs in preterm infants and lacks effective treatment. We aim to reveal the relationship between amniotic fluid (AF) peptides and lung development by analyzing the differences in the composition of AF peptides at different gestational periods, thus providing a new means of prevention and treatment for BPD. Methods: Based on the stages of lung development, we collected AF by amniocentesis in two different gestational periods, using the 25th week of pregnancy as the cut-off. We conducted a peptide omics analysis of these AF samples using liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Additionally, we verified the regulatory effects of hyperoxia and the peptide COL5A2 on BPD-related cells [(mouse lung epithelial (MLE-12) cells] by 5-Ethynyl-2'-deoxyuridine (EdU) staining, JC-1 staining, flow cytometry, and reactive oxygen species (ROS) assay. Results: There were 131 differentially expressed peptides, including 85 up-regulated and 46 down-regulated [fold change (FC) ≥1.2 or ≤1/1.2, P<0.05], in the ≥25 weeks' gestation group compared to the <25 weeks' gestation group. Further bioinformatics analysis revealed that the precursor proteins of the differentially expressed peptides between these two groups were involved in the regulation of the developmental process, anatomical structure development, and other biological processes, suggesting that these differential peptides may play a key role in lung development. We found peptide COL5A2 with the sequence GPPGEPGPPG and verified the regulatory effects of COL5A2 on the proliferation, apoptosis, cell viability, and ROS levels of MLE-12 cells by cell assays. Conclusions: In this study, peptidomic studies using AF from different gestational periods revealed that peptides in AF may be involved in lung development. They could be used in the future to assist in the postnatal development of preterm infants and provide new therapeutic prospects for BPD.

How and why does time matter - A comparison of fouling caused by organic substances on membranes over adsorption durations.

This study investigated the effect of time on the severity of adsorptive fouling on polyvinylidene fluoride (PVDF) membrane surface. Sodium alginate (SA), bovine serum albumin (BSA), and humic acid (HA) were selected as representative membrane foulants. We examined the fouling behavior of these three selected model foulants over different adsorption durations (i.e., ~2300 and ~20,000 s). The fouling experiments were performed under conditions with and without the presence of Ca2+. For the SA-Ca2+ system, a longer adsorption duration slightly increased adsorption amount of SA but sharply reduced the reversibility (from 86.8 % to 12.9 %). For BSA-Ca2+, extended time did not change the deposition amount of BSA on the membrane surface, but led to more residual BSA after cleaning (reversibility decreased from 11.3 % to 4.5 %). Similarly, in the HA-Ca2+ system, adsorption duration barely influenced the adsorption amount of HA, while reduced its reversibility from 39.4 to 32.2 %. Therefore, time duration significantly influenced the amount and reversibility of membrane fouling depending on their chemical property. Corresponding results can be well reflected by a selected mathematical model. Further investigation on relevant mechanisms was conducted, quartz crystal microbalance with dissipation (QCM-D) and atomic force microscope (AFM) measurements indicated that longer adsorption duration resulted in more compacted fouling layer and stronger foulant-membrane interaction force. Our results suggest that time (adsorption duration) plays an important role in determining the reversibility of membrane fouling, while the severity is related to the inherent characteristics of foulants.

Effect of Precipitates on the Mechanical Performance of 7005 Aluminum Alloy Plates.

In this study, the strength, elongation, and fatigue properties of 7005 aluminum alloy plates with different configurations of precipitates were investigated by means of tensile tests, fatigue tests, and microstructural observation. We found that the number and size of GP zones in an alloy plate matrix increased and the distribution was more uniform after the aging time was extended from 1 h to 4 h at 120 °C, which led to a rise in both strength and elongation of alloy plates with the extending aging time. The fatigue life of the alloy plates shortened slightly at first, then significantly prolonged, and then shortened again with the aging time extending from 1 h to 192 h and a fatigue stress level of 185 MPa and stress ratio (R) = 0. After aging at 120 °C for 96 h, the precipitates in the alloy plate matrix were almost all metastable η'-phase particles, which had the optimal aging strengthening effect on the alloy matrix, and the degree of mismatch between the α-Al matrix and second-phase particles was the smallest; the fatigue crack initiation and propagation resistances were the largest, leading to the best fatigue performance of alloy plates, and the fatigue life of the aluminum plate was the longest, up to 1.272 × 106 cycles. When the aging time at 120 °C was extended to 192 h, there were a small number of equilibrium η phases in the aluminum plates that were completely incoherent with the matrix and destroyed the continuity of the aluminum matrix, easily causing stress concentration. As a result, the fatigue life of alloy plates was shortened to 9.422 × 105 cycles.

Identification of bacteriophage genome sequences with representation learning.

MOTIVATION: Bacteriophages/phages are the viruses that infect and replicate within bacteria and archaea, and rich in human body. To investigate the relationship between phages and microbial communities, the identification of phages from metagenome sequences is the first step. Currently, there are two main methods for identifying phages: database-based (alignment-based) methods and alignment-free methods. Database-based methods typically use a large number of sequences as references; alignment-free methods usually learn the features of the sequences with machine learning and deep learning models. RESULTS: We propose INHERIT which uses a deep representation learning model to integrate both database-based and alignment-free methods, combining the strengths of both. Pre-training is used as an alternative way of acquiring knowledge representations from existing databases, while the BERT-style deep learning framework retains the advantage of alignment-free methods. We compare INHERIT with four existing methods on a third-party benchmark dataset. Our experiments show that INHERIT achieves a better performance with the F1-score of 0.9932. In addition, we find that pre-training two species separately helps the non-alignment deep learning model make more accurate predictions. AVAILABILITY AND IMPLEMENTATION: The codes of INHERIT are now available in: https://github.com/Celestial-Bai/INHERIT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Case Report: A Novel Intronic Mutation in AIFM1 Associated With Fatal Encephalomyopathy and Mitochondrial Disease in Infant.

Background: The AIFM1 gene is located on chromosome Xq26.1 and encodes a flavoprotein essential for nuclear disassembly in apoptotic cells. Mutations in this gene can cause variable clinical phenotypes, but genotype-phenotype correlations of AIFM1-related disorder have not yet been fully determined because of the clinical scarcity. Case Presentation: We describe a 4-month-old infant with mitochondrial encephalopathy, carrying a novel intronic variant in AIFM1 (NM_004208.4: c.1164 + 5G > A). TA cloning of the complementary DNA (cDNA) and Sanger sequencing revealed the simultaneous presence of an aberrant transcript with exon 11 skipping (89 bp) and a normal transcript through analysis of mRNA extracted from the patient's fibroblasts, which is consistent with direct RNA sequencing results. Conclusion: We verified the pathogenic effect of the AIFM1 c.1164 + 5G > A splicing variant, which disturbed normal mRNA splicing. Our findings expand the mutation spectrum of AIFM1 and point out the necessity of intronic sequence analysis and the importance for integrative functional studies in the interpretation of sequence variants.

A Versatile Hydrophilic and Antifouling Coating Based on Dopamine Modified Four-Arm Polyethylene Glycol by One-Step Synthesis Method.

A versatile hydrophilic and antifouling coating was designed and prepared based on catechol-modified four-arm polyethylene glycol. The dopamine (DA) molecules were grafted onto the end of the four-arm polyethylene glycol carboxyl (4A-PEG-COOH) through the amidation reaction, which was proven by 1H NMR and FTIR analysis, assisting the strong adhesion of PEG on the surface of various types of materials, including metallic, inorganic, and polymeric materials. The reduction of the water contact angle and the bacteria-repellent and protein-repellent effects indicated that the coating had good hydrophilicity and antifouling performance. Raman spectroscopy analysis demonstrated the affinity between the polymeric surface and water, which further confirmed the hydrophilicity of the coating. Finally, in vitro cytotoxicity assay demonstrated good biocompatibility of the coating layer.