Browse Articles

Cloud fraction response to aerosol driven by nighttime processes

Aerosol–cloud interactions remain one of the largest uncertainties in the anthropogenic forcing of the climate; a significant contribution to this is due to the aerosol effect on the development of cloud fraction and liquid water path in stratocumulus clouds. Stratocumulus are strongly modulated by the diurnal cycle, but many previous observational studies have primarily focused on the daytime behavior of these clouds. In this work, a Lagrangian framework is used to characterize the day-night variation in the cloud sensitivity to aerosol. It is shown that the cloud fraction response to aerosol is driven by nighttime processes, whereas aerosols play a lesser role in daytime cloud fraction breakup. The liquid water path response reveals that aerosols act to thin the cloud during the daytime; however, this effect is partially offset by other processes during the nighttime. These nighttime cloud processes play an important role in setting the cloud state at the start of the day and hence the daytime cloud evolution, during which stratocumulus clouds have the greatest radiative impact. Our findings are consistent with an aerosol induced suppression of precipitation that acts most effectively at night, when stratocumulus precipitation is strongest. These results highlight a requirement for nighttime observations of marine clouds and an improved representation of the diurnal cycle in model-observation comparisons, especially when assessing climate forcing and the viability of marine cloud brightening.

Solvent-induced partial cellular fixation decodes proteome-wide drug targets and downstream pathways in living cells

Detect pre-cancerous tongue lesions for early oral cancer diagnosis using deep learning algorithm

Abstract Precancerous tongue lesion is a prevalent, complex, and highly perilous kind of cancer. The tumour might be in the salivary glands, tonsils, neck, cheek, and mouth. Oral Cancer (OC) is commonly identified in advanced stages due to the limited accuracy of available screening methods for early detection despite their significant potential to reduce mortality rates. The study exclusively examines lesions that specifically manifest on the tongue. This work demonstrates that one of the deep learning (DL) such as convolutional neural networks (CNN) based models employed are novel in their capacity to effectively identify OC, primarily due to the limited research conducted in this field. The research utilizes a specifically created dataset due to the absence, to the best of our knowledge, of any existing information on tongue lesions occurring in the oral cavity. The research recommends using various methods, such as DenseNet121, DenseNet169, DenseNet201, MobileNet, MobileNetV2, VGG16, VGG19, ResNet50, EfficientNetV2B0, EfficientNetV2B1, EfficientNetV2B2, EfficientNetV2B3, Inception, AlexNet, and transfer learning, to enhance our ability to diagnose OC. The effectiveness of the enhanced technique is assessed based on customized data. The study’s input parameters consist of a portrait of the patient’s tongue. according to the assessed outcomes of training precision, validation precision, training loss, and validation loss. The outcome indicated that VGG16 achieved the highest performance based on the given parameters. It demonstrated a vital training accuracy of 97.66% and a commendable validation accuracy of 89.06%. The study’s Clinical trial number not applicable.

JAVEMACS-D: claims database analysis of avelumab maintenance therapy for advanced urothelial carcinoma in Japan

Abstract JAVEMACS-D was an observational, retrospective study describing real-world characteristics, treatment patterns, and outcomes in patients with advanced urothelial carcinoma (aUC) treated with avelumab maintenance after first-line platinum-based chemotherapy (PBC) in Japan using a large claims database (Medical Data Vision). Between February 2021 and April 2023, 773 patients received avelumab maintenance; median age was 74 years, primary tumor site was bladder in 59.9% and renal pelvis/ureter in 42.7%, and first-line PBC was cisplatin-gemcitabine in 61.3% and carboplatin-gemcitabine in 36.4%. Avelumab maintenance started in 2021 in 289 patients (37.4%) and in 2022 or later in 484 (62.6%). At data cutoff (October 2023), median follow-up was 19.3 months, and 22.0% of patients were still receiving avelumab. Among 394 patients (51.0%) who received second-line treatment, the most common was enfortumab vedotin (EV) in 47.0%. Median time to treatment failure (TTF; time from start of avelumab maintenance to discontinuation) was 4.5 months (95% CI 3.8–5.2). Median TTF2 (time from start of avelumab to discontinuation of subsequent treatment) was 10.1 months (95% CI 8.7–11.0). JAVEMACS-D represents the largest real-world dataset of avelumab maintenance therapy for aUC reported to date. Data provide insights about patient characteristics, treatment patterns, and outcomes within an evolving treatment landscape.

Prevalence of uremic neuropathy and the effect of dialysis in children with end-stage renal disease: A cohort study

Children with chronic kidney disease (CKD) face increased morbidity, mortality, and reduced quality of life. Uremic neuropathy (UN) is a common neurological complication, but data on its relationship with dialysis in pediatric populations are limited. This prospective study aimed to assess the prevalence of UN in children with end-stage renal disease (ESRD) in a Tunisian population and explore the association between dialysis and UN. Conducted between July and September 2023 in the nephrology and neurophysiology units of a Tunisian hospital, the study included 31 children with CKD G5. Clinical data, biological analyses, and nerve conduction studies via electroneuromyography (EMG) were performed at baseline and six months later. Participants were divided into pre-dialysis and dialysis groups for comparison. The mean age was 11 ± 3.5 years, and the average age at CKD diagnosis was 7.5 ± 4.2 years. UN was diagnosed in 45% of participants using EMG, including 13% with silent neuropathy. Axonal neuropathy was predominant, with no cases of demyelinating neuropathy identified. Initial comparisons between dialysis and pre-dialysis groups showed no significant differences in UN characteristics. However, clinical neuropathy, weight-for-age, and glomerular nephritis were significantly associated with UN. Follow-up revealed a significant improvement in UN in the dialysis group. From this study, we conclude the importance of screening for UN in pediatric ESRD care and recommend routine EMG evaluations, even in asymptomatic patients, to ensure early diagnosis and management.

Correction for Robinson et al., Dried fish provide widespread access to critical nutrients across Africa

Stable hydroxyl-anchored CuNi nanocatalysts from CuNiMgAl-LDH thermal reduction for efficient photothermal CO2 conversion

Spatiotemporal multimodal emotion recognition using Temporal video sequences and pose features for child emotion classification

Abstract Developmental psychology and affective computing have placed great emphasis on identifying children’s emotional cues in recent times. In this study, a novel Spatio-Temporal Multimodal Emotion Recognition Network (ST-MERN) for child emotion classification is proposed. Dense feature embeddings of the EmoReact dataset and temporal video sequences are utilized for the study. The proposed method uses 115 continuous frames per visual signal instance, e.g., rotational-translational vectors, facial keypoints, and pose predictions. With steady performance on each frame and a mean confidence of 0.967, this ensures the system maintains good detection fidelity. In order to track subtle emotional changes, our method captures dynamic data like scale variation and frame-to-frame variation (r x , r y , r z , t x , t y ). Latent features (p24–p33) provide a profound explanation of emotional states. The model is designed to preserve spatiotemporal consistency and improve emotion recognition by combining these features. Curiosity, uncertainty, excitement, happiness, surprise, disgust, fear, frustration, and valence are the nine categories on which the system categorizes children’s emotional states. Preliminary results show that our system effectively captures expressive nuances, with stable pose data and low feature variability across sequences. The model surpassed earlier models such as LSTM and TCN in generalization, with a high validation accuracy of 93.6% and test accuracy of 94.3% for the BiLSTM-based architecture. The BiLSTM model had enhanced classification capacity for different emotional states with an F1-score of 0.92. The TCN model is well-suited to real-time deployment since it recorded a competitive test accuracy of 91.7% with quick inference times of ~ 0.8 s per clip, even though it was slightly slower than the BiLSTM. With an F1-score of 0.89 and test accuracy of 90.2%, the LSTM model performed robustly; it trained faster than the BiLSTM and TCN, although its accuracy was slightly lower. By providing strong and interpretable classification that is sensitive to the dynamic nature of children’s emotional displays, this technique improves emotion detection in children. Our work provides the foundation for socially sensitive systems, therapy treatments, and affect-conscious education materials.

Early rising as an intervention to alleviate anxiety and enhance self-efficacy among college students

Correction: MT-MAG: Accurate and interpretable machine learning for complete or partial taxonomic assignments of metagenomeassembled genomes

Targeting orthotopic and metastatic pancreatic cancer with allogeneic stem cell–engineered mesothelin-redirected CAR-NKT cells

Pancreatic cancer (PC) remains one of the leading causes of cancer-related mortality worldwide. The majority of patients are diagnosed at advanced stages, with over 50% presenting with metastatic disease at the time of diagnosis. Although chimeric antigen receptor (CAR)-T cell therapy has shown promise in targeting PC, its clinical efficacy remains limited due to several critical challenges. These include tumor antigen heterogeneity, antigen loss or escape mechanisms, functional exhaustion of CAR-T cells within the tumor microenvironment, as well as inherent limitations of autologous approaches such as high manufacturing costs, prolonged production timelines, and restricted scalability. To address these challenges, we developed allogeneic IL-15–enhanced, mesothelin-specific CAR-engineered invariant natural killer T ( Allo15 MCAR-NKT) cells through gene engineering of human hematopoietic stem and progenitor cells (HSPCs) using a clinically guided culture method. These Allo15 MCAR-NKT cells exhibited robust and multifaceted antitumor activity against PC, driven by both CAR and NK receptor–mediated cytotoxic mechanisms. In orthotopic and metastatic human PC xenograft models, Allo15 MCAR-NKT cells demonstrated superior tumor control, enhanced trafficking and infiltration into tumor sites, sustained effector and cytotoxic phenotypes, and reduced expression of exhaustion markers. Importantly, Allo15 MCAR-NKT cells demonstrated a favorable safety profile, characterized by the absence of graft-versus-host disease and minimal cytokine release syndrome. Collectively, these findings validate Allo15 MCAR-NKT cells as a promising next-generation, off-the-shelf immunotherapeutic approach for PC, with the potential to overcome critical challenges including tumor heterogeneity, immune evasion, and therapeutic resistance, especially in the context of metastatic disease.

Achieving superior radiation tolerance in ceramics via in-situ defect recombination

Ophthalmology education and systemic disease integration in Syrian medical schools: a cross-sectional assessment of knowledge gaps

Investigation of binary blended cement mortar degradation driven by sulfate attack and thermal gradients in arid environments

University scientists’ willingness to participate in public engagement: A concept explication

Public engagement is increasingly recognized as a critical responsibility of the scientific community. Scientists in academic settings are well positioned to lead these efforts, but they are not always willing or able to participate in engagement. Public engagement can encompass a range of activities that may require different resources and skills, and which may have different outcomes for both scientists and non-scientists. Therefore, understanding which activities scientists are willing to participate in is critical for supporting their engagement efforts at the institutional level. Using survey data from a case study of science faculty at a large land-grant university in the United States, we conduct a systematic concept explication to better understand the dimensions of public engagement activities that scientists are willing to participate in. Based on thirteen different activities, we define and analyze the reliability of five dimensions of engagement: public scholarship, educational activities, direct engagement with public audiences, stakeholder-focused collaboration, and industry engagement. We also examine the validity of these five dimensions and how factors including institutional culture and norms, professional status, and attitudes towards engagement relate to scientists’ willingness to participate in engagement. Our results provide a robust categorization of willingness to engage as a blueprint for future research in this space.

Perception of own centrality in social networks

This study explores how individuals perceive their social networks, with a focus on their own positioning. Using experimental methods and network analysis, we show that people have a limited understanding of their social standing in terms of popularity (in-degree) and centrality. Few participants accurately estimate their popularity, and even fewer correctly identify their decile of centrality. A similar pattern emerges for their perceptions of the most popular and central individuals, but we find no correlation between the ability to assess one’s own position and the ability to detect key network members. Popular participants correctly perceive themselves as more popular, although they tend to misjudge their popularity more than less popular peers. They are nonetheless more accurate in estimating their centrality. Perceived centrality is only weakly correlated with actual centrality, but central individuals misperceive both their popularity and centrality to a greater extent. We further show that these misperceptions have real-world implications. Conditional on network positioning, students who see themselves as less popular and less central–and those with more accurate self-perceptions–tend to achieve higher grades, whereas individuals recognized by others as popular and central perform significantly better academically. These findings challenge theoretical models that assume accurate self-awareness of network positions and highlight the need to reconsider the implications for key-player interventions in public health, education, and organizational contexts.

Non-canonical roles of Keap1/Nrf2 in regulating quiescence and early activation in adult muscle stem cells

AutoXAI: a meta-learning approach for recommendation of explanation techniques

Protective role of bromelain’s antioxidant and anti-inflammatory effects in experimental lower limb ischemia-reperfusion injury

Assessing the feasibility of the Virtual Reality Education and Acceptance Protocol among baseball and softball players

Research has supported the use of virtual reality (VR) in sport to train skills such as decision-making and anticipation, as well as aid in injury rehabilitation. Despite this, VR is not commonly used as a training tool in sport. Barriers to its adoption include a lack of understanding, low awareness, risk of cybersickness, and cost. As such, there is a critical need to address these barriers and promote acceptance of VR in sport. The purpose of this single-arm, non-randomized, mixed-methods feasibility trial was to examine the feasibility of the Virtual Reality Education and Acceptance Protocol (VREAP), which was designed by the study’s authors to address barriers to VR adoption. While the VREAP is intended to be used in multiple domains, we assessed its feasibility among baseball and softball players. Specifically, we assessed pre- and post-training attitudes toward VR using the Attitudes toward Virtual Reality Technology Scale (AVRTS), which uses the Technology Acceptance Model (TAM) as a guiding framework. Participants ( n  = 18) completed the VREAP, which includes stages of education, acclimation, and application. Exit interviews provided further insights into participant experiences. Results from quantitative and reflexive content analyses demonstrated feasibility of the VREAP based on recruitment and adherence, acceptability, demand, implementation, and practicality. Statistical analyses from the AVRTS revealed significant pre- to post-training increases in overall attitudes toward VR as well as increases in enjoyment, perceived usefulness, and ease of use. Minimal cybersickness was reported. Our findings demonstrate the feasibility of the VREAP among baseball and softball players and show promise for its future research and application.