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High transmission in 120-degree sharp bends of inversion-symmetric and inversion-asymmetric photonic crystal waveguides
Investigating the role of the metabolic score for visceral Fat in assessing the prevalence of chronic kidney disease from the NHANES 1999–2018
Abstract This study investigates the association between the Metabolic Score for Visceral Fat (METS-VF) and chronic kidney disease (CKD), assessing METS-VF as a potential predictor of CKD risk. Utilizing data from the 1999–2018 National Health and Nutrition Examination Survey (NHANES), this cross-sectional study included 24,387 adult participants. Multivariable logistic regression, restricted cubic spline models, and threshold effect analyses were employed to explore the relationship. The results revealed a significant positive association, with multivariable logistic regression showing that each unit increase in METS-VF was associated with an 86% higher risk of CKD (OR: 1.86, 95% CI: 1.48–2.34). Critical METS-VF thresholds (6.10 and 7.55) were identified, at which CKD risk increased substantially. Subgroup analyses indicated that the association was particularly pronounced among older adults and males. These findings suggest that METS-VF is a reliable predictor for assessing CKD risk and that lifestyle interventions, including dietary modifications and exercise programs, may mitigate this risk.
Multiplexed transcriptomic analyzes of the plant embryonic hourglass
Multiscale wildfire and smoke detection in complex drone forest environments based on YOLOv8
Quantification and prediction of human fetal (-)-Δ9-tetrahydrocannabinol/(±)-11-OH-Δ9-tetrahydrocannabinol exposure during pregnancy to inform fetal cannabis toxicity
Impact of stain variation and color normalization for prognostic predictions in pathology
Abstract In recent years, deep neural networks (DNNs) have demonstrated remarkable performance in pathology applications, potentially even outperforming expert pathologists due to their ability to learn subtle features from large datasets. One complication in preparing digital pathology datasets for DNN tasks is the variation in tinctorial qualities. A common way to address this is to perform stain normalization on the images. In this study, we show that a well-trained DNN model trained on one batch of histological slides failed to generalize to another batch prepared at a different time from the same tissue blocks, even when stain normalization methods were applied. This study used sample data from a previously reported DNN that was able to identify patients with early-stage non-small cell lung cancer (NSCLC) whose tumors did and did not metastasize, with high accuracy, based on training and then testing of digital images from H&E stained primary tumor tissue sections processed at the same time. In this study, we obtained a new series of histologic slides from the adjacent recuts of the same tissue blocks processed in the same lab but at a different time. We found that the DNN trained on either batch of slides/images was unable to generalize and failed to predict progression in the other batch of slides/images (AUCcross-batch = 0.52 - 0.53 compared to AUCsame-batch = 0.74 - 0.81). The failure to generalize did not improve even when the tinctorial difference corrections were made through either traditional color-tuning or stain normalization with the help of a Cycle Generative Adversarial Network (CycleGAN) process. This highlights the need to develop an entirely new way to process and collect consistent microscopy images from histologic slides that can be used to both train and allow for the general application of predictive DNN algorithms.
Single-step retrosynthesis prediction via multitask graph representation learning
Benchmarking protein language models for protein crystallization
Abstract The problem of protein structure determination is usually solved by X-ray crystallography. Several in silico deep learning methods have been developed to overcome the high attrition rate, cost of experiments and extensive trial-and-error settings, for predicting the crystallization propensities of proteins based on their sequences. In this work, we benchmark the power of open protein language models (PLMs) through the TRILL platform, a be-spoke framework democratizing the usage of PLMs for the task of predicting crystallization propensities of proteins. By comparing LightGBM / XGBoost classifiers built on the average embedding representations of proteins learned by different PLMs, such as ESM2, Ankh, ProtT5-XL, ProstT5, xTrimoPGLM, SaProt with the performance of state-of-the-art sequence-based methods like DeepCrystal, ATTCrys and CLPred, we identify the most effective methods for predicting crystallization outcomes. The LightGBM classifiers utilizing embeddings from ESM2 model with 30 and 36 transformer layers and 150 and 3000 million parameters respectively have performance gains by 3- $$5\%$$ than all compared models for various evaluation metrics, including AUPR (Area Under Precision-Recall Curve), AUC (Area Under the Receiver Operating Characteristic Curve), and F1 on independent test sets. Furthermore, we fine-tune the ProtGPT2 model available via TRILL to generate crystallizable proteins. Starting with 3000 generated proteins and through a step of filtration processes including consensus of all open PLM-based classifiers, sequence identity through CD-HIT, secondary structure compatibility, aggregation screening, homology search and foldability evaluation, we identified a set of 5 novel proteins as potentially crystallizable.
Compositional data analysis enables statistical rigor in comparative glycomics
Abstract Comparative glycomics data are compositional data, where measured glycans are parts of a whole, indicated by relative abundances. Applying traditional statistical analyses to these data often results in misleading conclusions, such as spurious “decreases” of glycans when other structures increase in abundance, or high false-positive rates for differential abundance. Our work introduces a compositional data analysis framework, tailored to comparative glycomics, to account for these data dependencies. We employ center log-ratio and additive log-ratio transformations, augmented with a scale uncertainty/information model, to introduce a statistically robust and sensitive data analysis pipeline. Applied to comparative glycomics datasets, including known glycan concentrations in defined mixtures, this approach controls false-positive rates and results in reproducible biological findings. Additionally, we present specialized analysis modalities: alpha- and beta-diversity analyze glycan distributions within and between samples, while cross-class glycan correlations shed light on previously undetected interdependencies. These approaches reveal insights into glycome variations that are critical to understanding roles of glycans in health and disease.
Role of toxic and essential elements in sleep duration of patients with cardiovascular diseases
α-Synuclein fibrils enhance HIV-1 infection of human T cells, macrophages and microglia
Abstract HIV-associated neurocognitive disorders (HAND) and viral reservoirs in the brain remain a significant challenge. Despite their importance, the mechanisms allowing HIV-1 entry and replication in the central nervous system (CNS) are poorly understood. Here, we show that α-synuclein and (to a lesser extent) Aβ fibrils associated with neurological diseases enhance HIV-1 entry and replication in human T cells, macrophages, and microglia. Additionally, an HIV-1 Env-derived amyloidogenic peptide accelerated amyloid formation by α-synuclein and Aβ peptides. Mechanistic studies show that α-synuclein and Aβ fibrils interact with HIV-1 particles and promote virion attachment and fusion with target cells. Despite an overall negative surface charge, these fibrils facilitate interactions between viral and cellular membranes. The enhancing effects of human brain extracts on HIV-1 infection correlated with their binding to Thioflavin T, a dye commonly used to stain amyloids. Our results suggest a detrimental interplay between HIV-1 and brain amyloids that may contribute to the development of neurodegenerative diseases.
Presence of EGF ligand restricts the binding ability of EgB4 nanobody to EGFR extracellular domain
High-entropy assisted capacitive energy storage in relaxor ferroelectrics by chemical short-range order
Relationship between inherited genetic variation and survival from colorectal cancer stratified by tumour location
Abstract The location of a patient’s colorectal cancer (CRC) influences their outcome but inherited factors may also be involved. We studied 1899 patients with advanced CRC (514 had proximal colonic, 493 distal colonic and 892 rectal tumours) and carried out genome-wide association studies for survival. Single nucleotide polymorphisms (SNPs) suggestive of association (P < 1.0 × 10–5) were tested for replication in 5078 CRC patients from the UK Biobank. We investigated the relationship between Phosphatidylinositol 4-Kinase Type 2 Beta (PI4K2B) expression in colorectal tumours and survival in 597 patients from The Human Protein Atlas (THPA). We also analysed 3 SNPs previously associated with survival by anatomical site. We found that SNPs at 54 independent loci were suggestive of an association with survival when stratified by tumour location. rs76011559 replicated in patients with proximal tumours (COIN, COIN-B and UK Biobank combined Hazard Ratio [HR] = 1.53, 95% Confidence Intervals [CI] = 1.19–1.86, P = 7.5 × 10–7) and rs12273047 replicated in patients with rectal tumours (combined HR = 1.27, 95% CI = 1.09–1.46, P = 4.1 × 10–7). In gene analyses, PI4K2B associated with survival in patients with distal cancers (P = 2.1 × 10–6) and increased PI4K2B expression in colorectal tumours was associated with improved survival (P = 9.6 × 10–5). No previously associated SNPs were replicated. Our data identify novel loci associated with survival when stratified by tumour location.
Recombinant expression systems for production of stabilised virus-like particles as next-generation polio vaccines
Abstract Polioviruses have caused crippling disease in humans for centuries, prior to the successful development of vaccines in the mid-1900’s, which dramatically reduced disease prevalence. Continued use of these vaccines, however, threatens ultimate disease eradication and achievement of a polio-free world. Virus-like particles (VLPs) that lack a viral genome represent a safer potential vaccine, although they require particle stabilization. Using our previously established genetic techniques to stabilize the structural capsid proteins, we demonstrate production of poliovirus VLPs of all three serotypes, from four different recombinant expression systems. We compare the antigenicity, thermostability and immunogenicity of these stabilized VLPs against the current inactivated polio vaccine, demonstrating equivalent or superior immunogenicity in female Wistar rats. Structural analyses of these recombinant VLPs provide a rational understanding of the stabilizing mutations and the role of potential excipients. Collectively, we have established these poliovirus stabilized VLPs as viable next-generation vaccine candidates for the future.
A novel approach for target deconvolution from phenotype-based screening using knowledge graph
SAMHD1 shapes deoxynucleotide triphosphate homeostasis by interconnecting the depletion and biosynthesis of different dNTPs
Automated system for calving time prediction and cattle classification utilizing trajectory data and movement features
Structural basis for the interaction between the Drosophila RTK Sevenless (dROS1) and the GPCR BOSS
Abstract Sevenless, the Drosophila homologue of ROS1 (University of Rochester Sarcoma) (herein, dROS1) is a receptor tyrosine kinase (RTK) essential for the differentiation of Drosophila R7 photoreceptor cells. Activation of dROS1 is mediated by binding to the extracellular region (ECR) of the GPCR (G protein coupled receptor) BOSS (Bride Of Sevenless) on adjacent cells. Activation of dROS1 by BOSS leads to subsequent downstream signaling pathways including SOS (Son of Sevenless). However, the physical basis for how dROS1 interacts with BOSS has long remained unknown. Here we provide a cryo-EM structure of dROS1’s extracellular region, which mediates ligand binding. We show that the extracellular region of dROS1 adopts a folded-over conformation stabilized by an N-terminal domain comprised of two disulfide stapled helical hairpins. We further narrowed down the interacting binding epitopes on both dROS1 and BOSS using hydrogen-deuterium exchange mass spectrometry (HDX-MS). This includes beta-strands in dROS1’s third Fibronectin type III (FNIII) domain and a C-terminal peptide in BOSS’ ECR. Our mutagenesis studies, coupled with AlphaFold complex predictions, support a binding interaction mediated by a hydrophobic interaction and beta-strand augmentation between these regions. Our findings provide a fundamental understanding of the regulatory function of dROS1 and further provide mechanistic insight into the human ortholog and oncogene ROS1.