Browse Articles

A streamlined base editor engineering strategy to reduce bystander editing

Research on the collapsing pattern of overburden rock and pore development characterization in the mining hollow area

Digital camouflage encompassing optical hyperspectra and thermal infrared-terahertz-microwave tri-bands

A multi-scale finite element method for investigating fiber remodeling in hypertrophic cardiomyopathy

Abstract A significant hallmark of hypertrophic cardiomyopathy (HCM) is fiber disarray, which is associated with various cardiac events such as heart failure. Quantifying fiber disarray remains critical for understanding the disease’s complex pathophysiology. This study investigates the role of heterogeneous HCM-induced cellular abnormalities in the development of fiber disarray and their subsequent impact on cardiac pumping function. Fiber disarray is predicted using a stress-based law to reorient myofibers and collagen within a multiscale finite element cardiac modeling framework, MyoFE. Specifically, the model is used to quantify the distinct impacts of heterogeneous distributions of hypercontractility, hypocontractility, and fibrosis on fiber disarray development and examines their effect on functional characteristics of the heart. Our results show that heterogenous cell level abnormalities highly disrupt the normal mechanics of myocardium and lead to significant fiber disarray. The pattern of disarray varies depending on the specific perturbation, offering valuable insights into the progression of HCM. Despite the random distribution of perturbed regions within the cardiac muscle, significantly higher fiber disarray is observed near the epicardium compared to the endocardium across all perturbed left ventricle (LV) models. This regional difference in fiber disarray, irrespective of perturbation severity, aligns with previous DT-MRI studies, highlighting the role of regional myocardial mechanics in the development of fiber disarray. Furthermore, cardiac performance declined in the remodeled LVs, particularly in those with fibrosis and hypocontractility. These findings provide important insights into the structural and functional consequences of HCM and offer a framework for future investigations into therapeutic interventions targeting cardiac remodeling.

Controlling nephron precursor differentiation to generate proximal-biased kidney organoids with emerging maturity

Autistic traits relate to speed/accuracy trade-off but not statistical learning and updating

Abstract Cognitive and social alterations characterize Autism Spectrum Disorder (ASD), yet comprehensive explanations are challenged by ASD’s heterogeneity. One candidate framework is predictive processing, which posits that predictive processes are altered in ASD (e.g., slower internal model updating). We tested this framework using the spectrum approach, which suggests that subclinical autistic traits are continuously distributed in the general population, with most diagnosed individuals above a threshold. We recruited neurotypical adults (N = 296) to examine the relationship between autistic traits and predictive processing. Using an implicit statistical learning task, we tested model updating in an unsupervised, ecologically valid manner, and assessed speed/accuracy trade-off to control for potential visuomotor performance confounds in ASD. We found no difference in model updating rate along autistic traits, suggesting no relationship between these traits and model updating in the general population, contrary to the slow updating hypothesis. However, our results reveal a difference in the evolution of speed/accuracy trade-off along the degree of autistic traits, potentially indicating a shift in the balance of goal-directed and habitual systems related to autistic traits. These findings set the stage for further research on the interaction between executive functions and predictive, habitual processes related to autistic symptoms.

Biomimetic microstructure design for ultrasensitive piezoionic mechanoreceptors in multimodal object recognition

Predicting postoperative imbalance in adult spinal deformity staged surgery using predictive thresholds

Temporal dynamics and microbial interactions shaping the gut resistome in early infancy

Abstract Despite the critical role of the gut resistome in spreading of antimicrobial resistance (AMR), strategies to reduce the abundance of antibiotic resistance genes (ARGs) during microbiota development in infancy remain underexplored. Using longitudinal quantitative metagenomic data, we here show that ARGs are present in the gut microbiota from the first week of life, with a peak in absolute ARG abundance and richness at 6 months. Delivery mode significantly affects early ARG dynamics, and vaginally delivered infants exhibit higher ARG abundance due to maternal transmission of Escherichia coli strains harbouring extensive resistance repertoires. The abundance of E. coli and other ARG-rich taxa inversely correlates with aromatic lactic acid-producing bifidobacteria, and aromatic lactic acids strongly inhibit the in vitro growth of E. coli and other opportunistic ARG-rich taxa. Our results highlight temporal and critical microbial interactions shaping the gut resistome in early infancy, pointing to potential interventions to curb AMR during this vulnerable developmental window by promoting colonization of aromatic lactic acid-producing bifidobacteria.

Segmentation-enhanced approach for emotion detection from EEG signals using the fuzzy C-mean and SVM

Evidence that mitochondria in macrophages are destroyed by microautophagy

Semi-supervised GAN with hybrid regularization and evolutionary hyperparameter tuning for accurate melanoma detection

Abstract Melanoma, influenced by changes in deoxyribonucleic acid (DNA), requires early detection for effective treatment. Traditional melanoma research often employs supervised learning methods, which necessitate large, labeled datasets and are sensitive to hyperparameter settings. This paper presents a diagnostic model for melanoma, utilizing a semi-supervised generative adversarial network (SS-GAN) to enhance the accuracy of the classifier. The model is further optimized through an enhanced artificial bee colony (ABC) algorithm for hyperparameter tuning. Conventional SS-GANs face challenges such as mode collapse, weak modeling of global dependencies, poor generalization to unlabeled data, and unreliable pseudo-labels. To address these issues, we propose four improvements. First, we add a reconstruction loss in the generator to minimize mode collapse and maintain structural integrity. Second, we introduce self-attention in both the generator and the discriminator to model long-range dependencies and enrich features. Third, we apply consistency regularization on the discriminator to stabilize predictions on augmented samples. Fourth, we use pseudo-labeling that leverages only confident predictions on unlabeled data for supervised training in the discriminator. To reduce dependence on hyperparameter choices, the Random Key method is applied, enhanced through a mutual learning-based ABC (ML-ABC) optimization. We evaluated the model on four datasets: International Skin Imaging Collaboration 2020 (ISIC-2020), Human Against Machine’s 10,000 images (HAM10000), Pedro Hispano Hospital (PH2), and DermNet datasets. The model demonstrated a strong ability to distinguish between melanoma and non-melanoma images, achieving F-measures of 92.769%, 93.376%, 90.629%, and 92.617%, respectively. This approach enhances melanoma image classification under limited labeled data, as validated on multiple benchmark datasets. Code is publicly available at https://github.com/AmirhoseinDolatabadi/Melanoma.

Deep learning-enabled ultra-broadband terahertz high-dimensional photodetector

Maternal mortality in Egypt during the COVID-19 pandemic using record-based data from January 2020 to December 2021

Abstract This study aimed to identify the characteristics and predictors of coronavirus disease (COVID-19)-related maternal deaths in 2020 and 2021, and to assess maternal mortality ratio (MMR) from 2018 to 2021 in Egypt. A record-based cross-sectional analytical study was conducted in four randomly selected governorates: Kafr El-Sheikh, El-Behira, (Lower Egypt) and Assiut, and Fayoum (Upper Egypt). Data from 541 maternal deaths were analyzed, revealing that 37.7% occurred in Assuit, 28.5% in El-Behira, 22.7% in Fayoum, and 11.1% in Kafr El-Sheik. The mean age of the studied population was 28.9 ± 6.4 years, with 39.0% having 1–2 children and 26.8% being nulliparous. Direct causes, including postpartum haemorrhage, preeclampsia, and embolism, accounted for 47.5% of deaths. As one of indirect causes of deaths, COVID-19 was diagnosed in 25.3%, it was the sole cause in 75.0% of them. Multivariable analysis identified the year 2021 (adjusted odds ratio (aOR) = 3.32; 95% CI, 1.9–5.81), residence in Lower Egypt (aOR = 5.15; 95% CI, 2.61–10.18), and hospital referral refusals (aOR = 8.72; 95% CI, 1.73–44.0) as key predictors of COVID-19-associated MM. The overall MMR increased between 2018 and 2021 with significant increases observed in Fayoum (from 39.25 to 71.65; p < 0.001) and Kafr El-Sheikh (from 36.44 to 56.6; p = 0.032). Yearly comparisons revealed significant inter-governorate differences in all years except 2021 (p = 0.15), with Assuit maintaining the highest MMR. Although the national MMR increased from 44.1 (pre-COVID-19) to 55.9 (post-COVID-19), this change was not statistically significant (p = 0.236). The findings highlight an alarming rise in maternal deaths and underscore the need for targeted interventions to address the direct causes of MM and improve healthcare access during crises like the COVID-19 pandemic.

GHz acousto-optic angular momentum with tunable topological charge

Abstract Controlling the symmetry of optical and mechanical waves is pivotal to their full exploitation in technological applications and topology-linked fundamental physics experiments. Leveraging on the control of orbital angular momentum, we introduce here a device forming acoustic vortices which can impart an orbital angular momentum modulation at super-high-frequency on reflected light beams. Originated by shape-engineering of a single-contact bulk acoustic wave resonator, acoustic vortices are generated in a wide band of frequencies around 4 GHz with topological charge ranging from 1 to beyond 13 tunable by the device geometry and/or excitation frequency. With all electrical control and on-chip integration our device offers compact solutions for angular-momentum-based light communication, three-dimensional particle manipulation, as well as alternative interaction schemes for optomechanical devices.

Mid-infrared ellipsometry enhanced by means of localized electromagnetic states of a one-dimensional photonic crystal

Modulating tumor collagen fiber alignment for enhanced lung cancer immunotherapy via inhaled RNA

Intrathoracic oxygen detects alveolar air leak following video-assisted thoracoscopic lung resection

Knowledge and data-driven two-layer networking for accurate metabolite annotation in untargeted metabolomics

Individual and combined effects of ischemic conditioning strategies on infarct size after myocardial ischemia reperfusion

Abstract Ischemic preconditioning (PreC), remote perconditioning (PerC), and postconditioning (PostC) are known to reduce myocardial infarct size, but their relative efficacy and potential additive effects remain unclear. This study compared the individual and combined effects of PreC, PerC, and PostC on infarct size and cardiac troponin I (cTnI) levels in a rat model of myocardial ischemia–reperfusion. Fifty-four male Sprague–Dawley rats underwent 40 min of coronary occlusion followed by 2 h of reperfusion. They were randomized into six groups: Control, PreC, PerC, PostC, PerC + PostC, or PreC + PerC + PostC. Infarct size was measured using Evans blue/TTC staining, and cTnI levels were assessed. All conditioning strategies significantly reduced infarct size and cTnI levels compared to control (p < 0.001). PreC and PreC + PerC + PostC were the most effective, while PostC was the least. No additive benefit was seen when combining PreC with other strategies (p = 0.9) or PerC with PostC (p = 0.9). These findings suggest that PreC provides the greatest cardioprotection, and combining conditioning strategies does not enhance outcomes, possibly due to overlapping protective mechanisms.