Browse Articles

Elevated POSTN expression predicts poor prognosis and is associated with radioresistance in cervical cancer patients treated with radical radiotherapy

Electricity- and hydrogen-driven energy system sector-coupling in net-zero CO2 emission pathways

The impact of glucocorticoid receptor transactivation on context-dependent cell migration dynamics

Abstract The glucocorticoid receptor (GR) plays a significant role in breast cancer cell behaviour, although data on its effects are conflicting. The impact of GR agonist dexamethasone (dex) and antagonist mifepristone (mif) on oestrogen-positive (ER+) and triple-negative (TN) breast cancer cell lines in both 2D and 3D cultures was studied using multiple in vitro functional assays and transcriptome sequencing. GR activation increased cell motility in TN but not in ER + tumour cells, as observed in both collective and single-cell migration tests. Time-lapse analysis showed enhanced motility after 4–6 h in wound healing, despite dex inhibiting migration initially. This inhibition was observed at 2 h in single-cell tracking migration assays. Cell proliferation increased in TN and decreased in ER + cells upon GR activation, reversed by GR antagonist. RNA sequencing revealed dex’s impact on cell adhesion and extracellular matrix signalling in TN cells and on DNA replication in ER + cells. Based on data from 1085 human breast cancer specimens, GR pathway expression correlated with migratory, extracellular matrix, and angiogenesis gene signatures. Additionally, higher expression of GR and increased GR signature were observed in fast-migrating cells compared to slow-migrating ones. Positive correlation between the GR signature and migration signature at the single-cell level indicated an association between GR activity and cell migration. For the first time, we assessed altered time-lapse migration dynamics in TN breast cancer cells, potentially contributing to cancer progression and prognosis, highlighting that the effects of dexamethasone on breast cancer cell migration are influenced by ER status and treatment duration.

Human mobility is well described by closed-form gravity-like models learned automatically from data

Differential expression of CD175 and CA19-9 in pancreatic adenocarcinoma

Targeting ceramide transfer protein sensitizes AML to FLT3 inhibitors via a GRP78-ATF6-CHOP axis

Clinical benefits of the 5th intercostal incision in uniportal VATS for female patients

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.