Microalgae produce copious amounts of structurally diverse polysaccharides, some are bound within cells and cell walls, while others are secreted into the surrounding seawater. A fraction of the secreted polysaccharides assembles into particles promoting aggregation and in turn formation of aggregates increases the export of carbon into the deep ocean via sinking. However, specific polysaccharides contributing to particle formation and carbon export remain unknown. Here, we studied microalgae polysaccharide composition in a system of reduced complexity consisting of lab grown monospecific cultures of the centric diatom species Thalassiosira weissflogii and Chaetoceros socialis. We followed the abundance and dynamics of five specific polysaccharide types in the dissolved and particulate organic matter (DOM and POM) for two weeks. Polysaccharides were detected using monoclonal antibodies (mAbs) specific for β‐1,4‐mannan, β‐1,4‐xylan, arabinogalactan, and two fucose‐containing sulfated polysaccharide (FCSP) epitopes. Additionally, glycan composition of all samples was analyzed by monosaccharide analysis. The time series revealed polysaccharides partition differently between the dissolved and particulate carbon pools. β‐1,4‐xylan and β‐1,4‐mannan were mainly present in POM, possibly as cell wall polymers, while FCSPs were found in both DOM and POM. The data showed that the main glycan component secreted by diatoms was fucose‐containing polysaccharide, which accumulated in DOM over time. Roller tank experiments were used to induce aggregate formation finding FCSP transitioned from DOM to POM under aggregating conditions. These results suggest that diatom‐secreted FCSPs are involved in the formation of aggregates, which promote the formation of particles, and potentially carbon export.

Background: Gestational diabetes mellitus (GDM) is a serious health concern that affects pregnant women worldwide and can lead to adverse pregnancy outcomes. Early detection of high-risk individuals and the implementation of appropriate treatment can enhance these outcomes. Methods: We conducted a study on a cohort of 3467 pregnant women during their pregnancy, with a total of 5649 clinical and biochemical records collected. We utilized this dataset as our training dataset to develop a web server called GDMPredictor. The GDMPredictor utilizes advanced machine learning techniques to predict the risk of GDM in pregnant women. We also personalize treatment recommendations based on essential biochemical indicators, such as A1MG, BMG, CysC, CO2, TBA, FPG, and CREA. Our assessment of GDMPredictor's effectiveness involved training it on the dataset of 3467 pregnant women and measuring its ability to predict GDM risk using an AUC and auPRC. Results: GDMPredictor demonstrated an impressive level of precision by achieving an AUC score of 0.967. To tailor our treatment recommendations, we use the GDM risk level to identify higher risk candidates who require more intensive care. The GDMPredictor can accept biochemical indicators for predicting the risk of GDM at any period from 1 to 24 weeks, providing healthcare professionals with an intuitive interface to identify high-risk patients and give optimal treatment recommendations. Conclusions: The GDMPredictor presents a valuable asset for clinical practice, with the potential to change the management of GDM in pregnant women. Its high accuracy and efficiency make it a reliable tool for doctors to improve patient outcomes. Early identification of high-risk individuals and tailored treatment can improve maternal and fetal health outcomes http://www.bioinfogenetics.info/GDM/.

Head and neck squamous cell carcinoma (HNSCC) represents the sixth most common cancer worldwide, with pathologists routinely analyzing histological slides to diagnose cancer by evaluating cellular heterogeneity, a process that remains time-consuming and labor-intensive. Although no previous studies have systematically applied deep learning techniques to automate HNSCC TNM staging and overall stage prediction from digital histopathology slides, we developed an inception-ResNet34 convolutional neural network model (HNSC-Classifier) trained on 791 whole slide images (WSIs) from 500 HNSCC patients sourced from The Cancer Genome Atlas (TCGA) Head and Neck Squamous Cell dataset. Our pipeline was designed to distinguish cancerous from normal tissue and to predict both tumor stage and TNM classification from histological images, with the dataset split at the patient level to ensure independence between training and testing sets and performance evaluated using comprehensive metrics including receiver operating characteristic (ROC) analysis, precision, recall, F1-score, and confusion matrices. The HNSC-Classifier demonstrated exceptional performance with areas under the ROC curves (AUCs) of 0.998 for both cancer/normal classification and TNM system stage prediction at the tile level, while cross-validation showed high precision, recall, and F1 scores (>0.99) across all classification tasks. Patient-level classification achieved AUCs of 0.998 for tumor/normal discrimination and 0.992 for stage prediction, significantly outperforming existing approaches for cancer stage detection. Our deep learning approach provides pathologists with a powerful computational tool that can enhance diagnostic efficiency and accuracy in HNSCC detection and staging, with the HNSC-Classifier having potential to improve clinical workflow and patient outcomes through more timely and precise diagnoses, serving as an automated decision support system for histopathological analysis of HNSCC.

Background: Phenylketonuria (PKU) is caused by mutations in the phenylalanine hydroxylase (PAH) gene. Our study aimed to predict the phenotype using the allelic genotype. Methods: A total of 1291 PKU patients with 623 various variants were used as the training dataset for predicting allelic phenotypes. We designed a common machine learning framework to predict allelic genotypes associated with the phenotype. Results: We identified 235 different mutations and 623 various allelic genotypes. The features extracted from the structure of mutations and graph properties of the PKU network to predict the phenotype of PKU were named PPML (PKU phenotype predicted by machine learning). The phenotype of PKU was classified into three different categories: classical PKU (cPKU), mild PKU (mPKU) and mild hyperphenylalaninemia (MHP). Three hub nodes (c.728G>A for cPKU, c.721 for mPKU and c.158G>A for HPA) were used as each classification center, and 5 node attributes were extracted from the network graph for machine learning training features. The area under the ROC curve was AUC = 0.832 for cPKU, AUC = 0.678 for mPKU and AUC = 0.874 for MHP. This suggests that PPML is a powerful method to predict allelic phenotypes in PKU and can be used for genetic counseling of PKU families. Conclusions: The web version of PPML predicts PKU allele classification supported by applicable real cases and prediction results. It is an online database that can be used for PKU phenotype prediction http://www.bioinfogenetics.info/PPML/.

Background: Phylogenetic species trees are widely used in inferring evolutionary relationships. Existing software and algorithms mainly focus on phylogenetic inference. However, less attention has been paid to intermediate steps, such as processing extremely large sequences and preparing configure files to connect multiple software. When the species number is large, the intermediate steps become a bottleneck that may seriously affect the efficiency of tree building. Results: Here, we present an easy-to-use pipeline named PhySpeTree to facilitate the reconstruction of species trees across bacterial, archaeal, and eukaryotic organisms. Users need only to input the abbreviations of species names; PhySpeTree prepares complex configure files for different software, then automatically downloads genomic data, cleans sequences, and builds trees. PhySpeTree allows users to perform critical steps such as sequence alignment and tree construction by adjusting advanced options. PhySpeTree provides two parallel pipelines based on concatenated highly conserved proteins and small subunit ribosomal RNA sequences, respectively. Accessory modules, such as those for inserting new species, generating visualization configurations, and combining trees, are distributed along with PhySpeTree. Conclusions: Together with accessory modules, PhySpeTree significantly simplifies tree reconstruction. PhySpeTree is implemented in Python running on modern operating systems (Linux, macOS, and Windows). The source code is freely available with detailed documentation (https://github.com/yangfangs/physpetools).