Browse Articles

Bridging the performance gap: systematic optimization of local LLMs for Japanese medical PHI extraction

Multimodal evaluation of mannose engineered poly lactic glycolic acid nanoparticles with granulocyte colony stimulating factor focused delivery to bone marrow for neutropenia

Integrated assessment of environmental infrastructural and social risks for urban public safety

Water use efficiency regulated by ecosystem type and soil plant water interactions in cold arid regions

Dynamic rockfall risk assessment using multi-source data fusion and 3D simulation: a case study of Jiaohua rock

Author Correction: Exosomes derived from human adipose mensenchymal stem cells accelerates cutaneous wound healing via optimizing the characteristics of fibroblasts

Correction: The efficacy of adjuvant chemotherapy after total mesorectal excision without selective lateral lymph node dissection for locally advanced rectal cancer

ATF4 regulates mitochondrial dysfunction and mitophagy, contributing to corneal endothelial apoptosis

Predicting complications and mortality in myocardial infarction patients using a graph neural network model

Genome-wide screens identify core regulators of cell surface prion protein expression

Abstract Expression of the cellular prion protein, PrP C , on the surface of neurons plays an important role in the pathogenesis of prion disease. We performed genome-wide CRISPR/Cas9 knockout screens in prion-infectible cells of neuronal origin (CAD5) to identify regulators of cell surface PrP C expression. We identified and validated 46 positive and 21 negative regulators of cell surface PrP C expression in undifferentiated CAD5 cells. Pathway analysis of the screening dataset showed that genes involved in the glycophosphatidylinositol (GPI) anchor and N-glycosylation biosynthetic pathways were overrepresented as positive regulators of cell surface PrP C . We also sought to determine whether the same or different genes regulate cell surface PrP C in CAD5 cells that have been differentiated to a more neuronal state and validated 41 positive and 13 negative regulators of CAD5 cell surface PrP C expression in the differentiated state. We identified 23 core genes as shared between the undifferentiated and differentiated cell states, including many positive regulators involved in GPI anchor biosynthesis. Intriguingly, unique regulators were also identified in the undifferentiated and differentiated cell states, suggesting that some mechanisms regulating cell surface PrP C expression in CAD5 cells are dependent on cell state. This list of core genes involved in regulating cell surface PrP C expression in a prion-susceptible, neuron-like cell type offers a valuable guide for future research and may help identify potential therapeutic targets for prion disease and other neurodegenerative diseases.

Efficient coordination of hybrid energy system (fuel cell/photovoltaic/battery/supercapacitor) under the condition of fluctuated load using optimization based energy management strategy

Improved bayesian network with graph attention and prior algorithm for aircraft engine fault root cause analysis

Ligand-based machine learning models to classify active compounds for prostaglandin EP2 receptor

Hybrid deep learning framework for accurate classification of high dimensional genomic data

Performance enhancement of carbonyl iron-based magnetorheological elastomers through iron-doped multi-walled carbon nanotubes reinforcement

Abstract This paper aims to explore the potential of iron-doped multi-walled carbon nanotubes (Fe-MWCNTs) as additives for enhancing the performance of magnetorheological elastomers (MREs). We investigated carbonyl iron particles (CIPs)-based MREs reinforced with Fe-MWCNTs at doping contents of 10 wt% and 50 wt%. The fabricated samples were prepared using silicone rubber as the matrix and characterized using transmission electron microscopy (TEM), high-resolution field emission scanning electron microscopy (HR-FESEM), X-ray diffraction (XRD), vibrating sample magnetometer (VSM), and rheometer. The results showed that the addition of Fe-MWCNTs enhanced the stiffness and damping performance of MREs, as the increase in storage modulus and loss modulus, respectively, especially at a current of 3 A (0.472 Tesla). Furthermore, the MRE incorporating 50 wt% Fe-MWCNTs exhibited the highest MR effect (234%), followed by the 10 wt% Fe-MWCNTs sample (220%) and the conventional CIPs-based MRE (191%). Using the conventional CIPs-based MRE (191%) as the reference, the results indicate that Fe-MWCNT doping at 50 wt% enhances the MR effect by approximately 22.5%. Our work clarifies that Fe-MWCNTs have promising potential in improving the properties of MRE for future applications in vibration-damping systems in various fields, including automotive industries, earthquake resistance, and vibration isolation.

Correction: Habitual video gaming predicts multitasking performance while the role of cognitive capacity remains inconclusive

Scaling digital models

Abstract The development of accurate digital models (DMs) for physical systems requires virtual representations that faithfully capture the underlying physics of the system or equipment being represented. Physics-based DMs provide reliable predictions only when accurate mathematical models of physical systems exist. When such models are incomplete or uncertain, experimental calibration can significantly improve model fidelity. However, in industries where systems or equipment exist in multiple sizes or configurations, performing experimental calibration for each variant can be prohibitively expensive and time-consuming. To address this challenge, this paper introduces a novel methodology and modular computational framework that leverages machine learning (ML) and dimensional analysis (DA) to enable scaling of DMs. The proposed approach allows calibration to be performed on a single representative system, with results scaled to other system sizes, whether from full-scale to reduced-scale prototypes or vice versa. Traditional applications of DA in this context often encounter difficulties due to distorted scaling factors. This work resolves these challenges by developing a consistent scaling framework tailored for DMs. The methodology is demonstrated by a case study in which a calibrated DM of a wheel loader is scaled between an industrial-size system and a miniaturized laboratory system.

Effect and microscopic mechanism of nano-oxide modified cement solidified silty soft soil

A machine learning approach for predicting osmotic coefficients and deriving activity coefficients in alkyl ammonium salts

Plasma-activated solutions reverse chronic deltamethrin exposure induced intestinal injury via rebalancing redox homeostasis and counteracting cellular senescence