Browse Articles

Important role of H2 spillover in asymmetric hydrogenation of quinolines in hybrid systems

Multi scenario chaotic transient search optimization algorithm for global optimization technique

In-silico platform for the multifunctional design of 3D printed conductive components

Abstract The effective electric resistivity of conductive thermoplastics manufactured by filament extrusion methods is determined by both the material constituents and the printing parameters. The former determines the multifunctional nature of the composite, whereas the latter dictates the mesostructural characteristics such as filament adhesion and void distribution. This work provides a multi-scale computational framework to evaluate the thermo-electro-mechanical behaviour of printed conductive polymers. A full-field homogenisation model first provides the influence of material and mesostructural features (i.e., filament orientations, voids and adhesion between filaments). Then, a macroscopic continuum model elucidates the effects of thermo-electro-mechanical mixed boundary conditions. The in-silico multi-scale methodology is validated with extensive original multi-physical experiments and a functional application consisting of an electro-heatable printing cartridge. Overall, this work establishes the foundations to virtually break the gap between mesoscopic and macroscopic multifunctional responses in conductive components manufactured by additive manufacturing techniques.

Why PD-L1 expression varies between studies of lung cancer: results from a Bayesian meta-analysis

Al2O3/Al hybrid nanolaminates with superior toughness, strength and ductility

Investigation of multi-input convolutional neural networks for the prediction of particleboard mechanical properties

Phospho-seq: integrated, multi-modal profiling of intracellular protein dynamics in single cells

Abstract Cell signaling plays a critical role in neurodevelopment, regulating cellular behavior and fate. While multimodal single-cell sequencing technologies are rapidly advancing, scalable and flexible profiling of cell signaling states alongside other molecular modalities remains challenging. Here we present Phospho-seq, an integrated approach that aims to quantify cytoplasmic and nuclear proteins, including those with post-translational modifications, and to connect their activity with cis-regulatory elements and transcriptional targets. We utilize a simplified benchtop antibody conjugation method to create large custom neuro-focused antibody panels for simultaneous protein and scATAC-seq profiling on whole cells, alongside both experimental and computational strategies to incorporate transcriptomic measurements. We apply our workflow to cell lines, induced pluripotent stem cells, and months-old retinal and brain organoids to demonstrate its broad applicability. We show that Phospho-seq can provide insights into cellular states and trajectories, shed light on gene regulatory relationships, and help explore the causes and effects of diverse cell signaling in neurodevelopment.

Prediction of the axial compression capacity of ECC-CES columns using adaptive sampling and machine learning techniques

Abstract An innovative form of concrete-encased steel (CES) composite columns incorporating engineered cementitious composites (ECC) confinement (ECC-CES) has recently been introduced, displaying superior performance in failure behavior, ductility, and toughness compared to traditional CES columns. This study presents an innovative approach to predicting the axial capacity of ECC-CES columns using adaptive sampling and machine learning (ML) techniques. This study initially introduces a finite element (FE) modeling for ECC-CES columns, integrating material and geometric nonlinearities to accurately capture the inelastic behavior of ECC and steel through appropriate constitutive material laws. The FE model was validated against experimental data and demonstrated strong predictive accuracy. An adaptive sampling process is employed for efficient exploration of the design space to generate a database of 840 FE models. Subsequently, seven ML models are utilized to predict the axial compression capacity based on the FE database. These models were comprehensively evaluated, displaying a superior prediction performance compared to design standards such as EC4 and AISC360. From evolution metrics, the Gaussian process regression, CatBoost (CATB), and LightGBM (LGBM) models emerged as the most accurate and reliable model, with nearly more than 97% of FE samples within the 10% error range. Despite the robust performance of the ML models, their black-box nature limits practical applicability in design contexts. To address this, the study proposes a symbolic regression-derived design that offers interpretable, explicit design equations with competitive performance metrics.

Publisher Correction: Integrated electrocatalytic synthesis of ammonium nitrate from dilute NO gas on metal organic frameworks-modified gas diffusion electrodes

Using deep learning model integration to build a smart railway traffic safety monitoring system

Ion suppression correction and normalization for non-targeted metabolomics

Ensemble of feature augmented convolutional neural network and deep autoencoder for efficient detection of network attacks

Structural basis of SIRT7 nucleosome engagement and substrate specificity

Abstract Chromatin-modifying enzymes target distinct residues within histones to finetune gene expression profiles. SIRT7 is an NAD + -dependent deacylase often deregulated in cancer, which deacetylates either H3 lysine 36 (H3K36) or H3K18 with high specificity within nucleosomes. Here, we report structures of nucleosome-bound SIRT7, and uncover the structural basis of its specificity towards H3K36 and K18 deacylation, combining a mechanism-based cross-linking strategy, cryo-EM, and enzymatic and cellular assays. We show that the SIRT7 N-terminus represents a unique, extended nucleosome-binding domain, reaching across the nucleosomal surface to the acidic patch. The catalytic domain binds at the H3-tail exit site, engaging both DNA gyres of the nucleosome. Contacting H3K36 versus H3K18 requires a change in binding pose, and results in structural changes in both SIRT7 and the nucleosome. These structures reveal the basis of lysine specificity, allowing us to engineer SIRT7 towards enhanced H3K18ac selectivity, and provides a basis for small molecule modulator development.

Graphene oxide and its derivatives films for sustained-release trace element zinc based on cation-π interaction

Multi-omic spatial effects on high-resolution AI-derived retinal thickness

Abstract Retinal thickness is a marker of retinal health and more broadly, is seen as a promising biomarker for many systemic diseases. Retinal thickness measurements are procured from optical coherence tomography (OCT) as part of routine clinical eyecare. We processed the UK Biobank OCT images using a convolutional neural network to produce fine-scale retinal thickness measurements across > 29,000 points in the macula, the part of the retina responsible for human central vision. The macula is disproportionately affected by high disease burden retinal disorders such as age-related macular degeneration and diabetic retinopathy, which both involve metabolic dysregulation. Analysis of common genomic variants, metabolomic, blood and immune biomarkers, disease PheCodes and genetic scores across a fine-scale macular thickness grid, reveals multiple novel genetic loci including four on the X chromosome; retinal thinning associated with many systemic disorders including multiple sclerosis; and multiple associations to correlated metabolites that cluster spatially in the retina. We highlight parafoveal thickness to be particularly susceptible to systemic insults. These results demonstrate the gains in discovery power and resolution achievable with AI-leveraged analysis. Results are accessible using a bespoke web interface that gives full control to pursue findings.

Target miRNA identification for the LPL gene in large yellow croaker (Larimichthys crocea)

Visible light-responsive hydrogels for cellular dynamics and spatiotemporal viscoelastic regulation

Prediction of cold region dew volume based on an ECOA-BiTCN-BiLSTM hybrid model

Author Correction: Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning

CD4 expression controls epidermal stem cell balance

Abstract The balance of stem cell populations is essential for the maintenance, renewal, and repair of the mammalian epidermis. Here, we report that CD4, which is a typical marker of helper T cells, monocytes, macrophages, and dendritic cells, is also expressed on murine K5+ keratinocytes. Lineage tracing of CD4+ cells reveals that their epidermal progeny has self-renewal abilities and clonogenic potential. The progeny of CD4+ epidermal cells contributes to epidermal renewal and progressively colonizes the interfollicular epidermis and hair follicles with age, thereby developing to all epidermal lineages. Wound healing studies furthermore show that the progeny of CD4+ epidermal cells accumulates at wound sites. Finally, using CD4 knockout mice we demonstrate that CD4 expression is essential for maintaining fast-cycling epidermal stem cells during homeostasis and that CD4 loss mitigates the age-related decline in wound repair capacity. Collectively, our data support the conclusion that CD4 expression is required for long-term maintenance of the epidermal stem cell balance.