Browse Articles

Development and optimization of a morphodiversity model for mountainous areas using supervised classification and artificial neural networks

Mechanism and optimization of hydrocyclone-based enrichment of calcium and magnesium in fine coal gangue

Coal gangue, an industrial solid waste generated during coal mining and processing, poses significant environmental challenges due to long-term stockpiling and landfilling. Its comprehensive utilization requires not only decarbonization but also the enrichment of valuable components such as calcium and magnesium through hydrocyclone separation. In this study, the multicomponent occurrence characteristics of coal gangue were examined at the microscale, and the flow-field behavior of hydrocyclones was investigated using computational fluid dynamics (CFD). Based on these insights, a hydrocyclone enrichment system for calcium and magnesium was developed by optimizing cyclone structure and operational parameters. The raw coal gangue exhibited a high ash content (81.66%), mainly composed of Al and Si, with 2.39% Ca and 0.46% Mg. After crushing to 0–1 mm, Ca was enriched in coarse, high-density fractions, while Mg was concentrated in fine, high-density fractions. In the conventional hydrocyclone, increasing feed velocity improved pressure and tangential velocity but caused instability in the locus of zero vertical velocity (LZVV) and air-core morphology, limiting separation accuracy. The bottom-impact hydrocyclone demonstrated superior performance at an impact-tube height of 80 mm and an impact velocity of 5 m/s, achieving improved pressure distribution, higher tangential velocity, and more stable air-core symmetry. Compared with the conventional design, the optimized structure enhanced classification efficiency from 92.47% to 96.13% and increased the Ca content in the underflow to 3.53%. However, Mg separation remained limited under all tested conditions.

Case 3-2026: A 58-Year-Old Woman with Diplopia and Fever

No meta-analytical effect of economic inequality on well-being or mental health

A nomogram prediction model incorporating noninvasive lens AGEs and conventional biochemical indicators for assessing and predicting diabetic kidney disease

The influence of generative artificial intelligence usage on employees’ innovative job performance

The rapid advancement of AI technology has accelerated the adoption of generative artificial intelligence (GenAI) tools in the workplace, eroding the boundaries between professional responsibilities and personal space, thus impacting employees’ innovative performance. This study empirically examines the link between innovative job performance and GenAI tool usage, framed through the Uses and Gratifications Theory. Analyzing survey data from 366 employees nationwide revealed that: (1) both cognitive and social uses of GenAI tools significantly enhance innovative performance; (2) cognitive use primarily facilitates knowledge transfer behaviors, while social use bolsters resource acquisition. Enhanced knowledge transfer and resource acquisition, in turn, improve job satisfaction, which is pivotal in driving innovative performance. This study introduces a novel framework for utilizing GenAI tools to optimize and manage employee performance within organizational settings.

Autonomy vs. Equity

An ATP-gated molecular switch orchestrates human mRNA export

Abstract The nuclear export of mRNA is an important step in eukaryotic gene expression 1 . Despite recent molecular insights into how newly transcribed mRNAs are packaged into ribonucleoprotein complexes (mRNPs) 2,3 , the subsequent events that govern mRNA export are poorly understood. Here we uncover the molecular basis underlying key events of human mRNA export, including the remodelling of mRNP-bound transcription–export complexes (TREX), the formation of export-competent mRNPs, the docking of mRNPs at the nuclear pore complex (NPC), and the release of mRNPs at the NPC to initiate their export. Our biochemical and structural data show that the ATPase UAP56 (also known as DDX39) acts as a central molecular switch that directs nucleoplasmic mRNPs from TREX to NPC-anchored TREX-2 complexes through its ATP-gated mRNA-binding cycle. Collectively, these findings establish a mechanistic framework for a general and evolutionarily conserved mRNA export pathway.

A comprehensive investigation of expired dextromethorphan HBr drug as a carbon steel corrosion inhibitor using gravimetric, electrochemical, and theoretical computational approaches

Machine learning-based prediction of diabetic retinopathy from pupillary abnormalities in a South Indian population

Diabetic retinopathy (DR) is a common complication of diabetes that can lead to vision loss. Early detection and prevention of DR is crucial to reduce the burden of this disease. The purpose of this study was to build a prediction model for DR using pupillary abnormalities as biomarkers. Pupillary parameters including Dark-adapted Baseline Pupillary Diameter (BPD), Amplitude of Pupillary Constriction (APC), Velocity of Pupillary Constriction (VPC), Amplitude of Pupil Re-dilatation after Maximum Constriction, and Velocity of Pupillary Dilatation (VPD) were collected and analyzed using machine learning algorithm including Support Vector Machine, Decision Trees, Artificial Neural Networks (ANN), Logistic Regressions, Random Forest, Naive Bayes Classifier. Utilizing ROC analysis and the Youden index, this study identified cut-off values for pupillary abnormalities to detect DR risk. The study found that ANN performed well with an accuracy of 0.807 (95% CI: 0.65–0.94) and AUC of 0.879 (95% CI: 0.71–0.98) in predicting DR using pupillary abnormalities as biomarkers. The findings of this research offer significant insights into the predictive value of pupillary abnormalities for DR, establishing a strong foundation for early intervention strategies. Particularly, the superior performance of ANN in detecting DR presents an opportunity to refine risk stratification and prevention approaches, potentially transforming the prognosis for individuals at elevated risk of this condition.

Sacituzumab Govitecan plus Pembrolizumab for Advanced Triple-Negative Breast Cancer

Nature vs. nurture: parental care cushions agricultural drought impacts on child health in South Africa

Abstract This study investigates the spatiotemporal trends of drought and its impact on child health in South Africa, focusing on low birth weight (LBW) and severe acute malnutrition in children under five. We collected data on child health indicators (LBW and malnutrition) and social determinants, including orphan status, child food poverty, proximity to clinics, water access, and sanitation access, from the Children’s Institute at the University of Cape Town. Environmental data, comprising the Normalised Difference Vegetation Index (NDVI), Standardised Precipitation Evapotranspiration Index (SPEI), and maximum temperatures, were retrieved from MODIS, Global SPEI, and TerraClimate datasets, covering 2002 to 2022. We then fitted a series of linear regressions and combined them into a structural equation model to explore relationships between socio-environmental factors and child health outcomes. Results indicate that the Northern Cape, Western Cape, and Free State are highly vulnerable to agricultural drought, with NDVI showing a strong negative association with LBW and malnutrition. Orphan status emerged as a stronger predictor of malnutrition than drought. The impact of orphan status on malnutrition level is mediated by limited access to basic services such as water and sanitation. Proximity to clinics significantly influenced access to basic services, highlighting a double burden of healthcare and environmental deprivations. These findings expose the need for targeted interventions to enhance food security, water, and sanitation access, particularly for orphaned children, and to integrate drought mitigation into child health policies in South Africa.

Feature recess-time sports activities as a school-based intervention to improve fitness in rural Chinese youth

Background China’s national school health policies face persistent implementation gaps, particularly in rural high schools prioritizing Gaokao. National surveys (2010–2019) documented alarming fitness declines: 50-m sprint speeds decreased (+0.3s boys; + 0.4s girls), pull-ups/sit-ups fell 29%/20%, and standing jumps shortened 6–7 cm. Methods A 16-week cluster quasi-experiment assigned intact rural high school classes (N = 98; age = 16.35 ± 0.48years) to: • Experimental (n = 50): Feature Recess-Time Sports Activities (FRTSA; 5x30-min/week). • Control (n = 48): Standard supervised running. Blinded assessors conducted the National Student Physical Health Standard tests. Results FRTSA elicited significant improvements versus control: • Speed: 50-m sprint ( p < 0.001, η 2  = 0.18, d  = 0.75). • Explosive Power: Standing jump ( p  = 0.022, η 2  = 0.05, d  = 0.41). • Flexibility: Sit-and-reach ( p  < 0.001, η 2  = 0.12, d  = 0.60). • Strength: Male pull-ups ( p  = 0.030, d = 0.41); female sit-ups ( p = 0.029, d = 0.45 ). No endurance benefits emerged (1000m/800m: all p  > 0.05, d  ≤ 0.18). Conclusion FRTSA is effective in enhancing speed, explosive power, flexibility, and strength, supporting policy integration of structured activity programs in rural schools.

Unraveling COPD pathogenesis: a multi-omics approach to identify metabolites and genetic links

Mechanistic insights into melanin-induced PCR inhibition and its NanoPCR-based mitigation

Relationship between lipoprotein(a) and PCSK9 in angiogram-proven premature coronary artery disease in an Asian cohort

Abstract Coronary artery disease (CAD) has been associated with elevated Lp(a) levels, yet the mechanism driving the pro-atherogenic and inflammatory effects remains unclear. Proprotein convertase subtilisin/kexin type 9 (PCSK9), a key regulator of lipid metabolism with emerging roles in vascular inflammation. This study explored the relationship between Lp (a) and PCSK9 in an Asian cohort with angiogram-proven premature CAD (AP-pCAD), with and without familial hypercholesterolemia (FH). Patients were recruited from Cardiology and Specialist Lipid Clinics; grouped into pCAD with FH ( n  = 70), pCAD without FH ( n  = 65), and normal controls (G3; n  = 69). FH was clinically diagnosed using the Dutch Lipid Clinic Network. Lp(a) and PCSK9 levels were measured using an automated chemistry analyser and ELISA. Lp(a) and PCSK9 levels were significantly higher in pCAD groups compared to controls. No significant correlation between Lp(a) and PCSK9 was observed in individual pCAD subgroups (G1 or G2); a weak positive correlation was found in the normal control group (G3; r  = 0.366, p  = 0.019). In multivariate analysis, Lp(a) emerged as a significant independent predictor of pCAD (adjusted OR: 5.036, p  = 0.015). In conclusion, Lp(a) independently predicts pCAD, while its association with PCSK9 appears modest and context-dependent, suggesting a more complex interplay possibly influenced by lipid-lowering therapy such as statin use.

Inhibitors supercharge kinase turnover through native proteolytic circuits

Abstract Targeted protein degradation is a pharmacological strategy that relies on small molecules such as proteolysis-targeting chimeras (PROTACs) or molecular glues, which induce proximity between a target protein and an E3 ubiquitin ligase to prompt target ubiquitination and proteasomal degradation 1 . Sporadic reports indicated that ligands designed to inhibit a target can also induce its destabilization 2–4 . Among others, this has repeatedly been observed for kinase inhibitors 5–7 . However, we lack an understanding of the frequency, generalizability and mechanistic underpinnings of these phenomena. Here, to address this knowledge gap, we generated dynamic abundance profiles of 98 kinases after cellular perturbations with 1,570 kinase inhibitors, revealing 160 selective instances of inhibitor-induced kinase destabilization. Kinases prone to degradation are frequently annotated as HSP90 clients, therefore affirming chaperone deprivation as an important route of destabilization. However, detailed investigation of inhibitor-induced degradation of LYN, BLK and RIPK2 revealed a differentiated, common mechanistic logic whereby inhibitors function by inducing a kinase state that is more efficiently cleared by endogenous degradation mechanisms. Mechanistically, effects can manifest by ligand-induced changes in cellular activity, localization or higher-order assemblies, which may be triggered by direct target engagement or network effects. Collectively, our data suggest that inhibitor-induced kinase degradation is a common event and positions supercharging of endogenous degradation circuits as an alternative to classical proximity-inducing degraders.

Plasma fatty acids reflect pain, disability, and psychological well-being in knee osteoarthritis in a longitudinal study with joint replacement surgery

Abstract We investigated the associations of pro- and anti-inflammatory fatty acids (FAs) with cartilage degradation, functional limitations, pain, and psychological well-being in knee osteoarthritis (KOA). Fasting plasma samples were obtained from controls ( n  = 12) and from end-stage KOA patients at baseline ( n  = 13), and 3 months ( n  = 11) and 12 months ( n  = 9) after knee replacement surgery. FA composition in total lipids was analyzed with gas chromatography and mass spectrometry. Cartilage loss was determined by magnetic resonance imaging, and knee pain and disability by physical performance and quantitative sensory testing, neuromuscular examination, and several questionnaires. The associations between variables were tested with the univariate analysis of variance adjusted for age and body mass index. KOA was characterized with elevated baseline 16:1n-7 percentages, while the proportions of 24:0 decreased 12 months after surgery and those of 24:1n-9 decreased 3 and 12 months after surgery. Several FA variables, such as 20:3n-6, 20:4n-6, long-chain saturated FAs, and 24:1n-9, were associated with pain, stiffness, disability, pain self-efficacy, or mental health. Circulating FAs can predict KOA symptoms, independent of age and body adiposity, and provide promising targets to design novel pain treatments.

Quantum effect observed for biggest objects yet

Binary classification of signal and background triggers of a transition edge sensor using convolutional neural networks

Abstract The Any Light Particle Search II (ALPS II) is a light shining through a wall experiment probing the existence of axions and axion-like particles using a 1064 nm laser source. While ALPS II is already taking data using a heterodyne based detection scheme, cryogenic transition edge sensor (TES) based single-photon detectors are planned to expand the detection system for cross-checking the potential signals, for which a sensitivity on the order of 10 -24  W is required. In order to reach this goal, we have investigated the use of convolutional neural networks (CNN) as binary classifiers to distinguish the experimentally measured 1064 nm photon triggered (light) pulses from background (dark) pulses. Despite rigorous hyperparameter optimization, the CNN based binary classifier did not outperform our previously optimized cut-based analysis in terms of detection significance. Our findings suggest that training confusion, introduced by near-1064 nm black-body photon triggers in the extrinsics background, is a significant factor limiting the CNNs performance for the associated dataset. The fiber coupled black-body radiation was identified as the limiting background source as concluded in our previous works. Given our results, we recommend that future studies explore regression-based CNNs, placing greater emphasis on the use of standardized and carefully structured training data rather than on extensive hyperparameter optimization. While the presented results and associated conclusions are obtained for a TES designed to be used in the ALPS II experiment, they should hold equivalently well for any device whose output signal can be considered as a univariate time trace.