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Mutations that accrue through life set the stage for stomach cancer

Heat shock transcription factor-mediated thermal tolerance and cell size plasticity in marine diatoms

Characterization of the phyllosphere virome of fresh vegetables and potential transfer to the human gut

Understanding the influence of socioeconomic status on children physical disciplinary practices in Bangladeshi households

Background Despite progress in reducing violence against children, physical disciplinary practices remain prevalent in many parts of the world, including Bangladesh. Understanding the sociodemographic factors contributing to these practices is crucial for developing effective interventions to protect children from violence. This study aims to estimate the prevalence and impact of household economic status on the physical disciplinary practices experienced by children under five years old in various sociodemographic contexts. Methods Data from the Bangladesh Multiple Indicator Cluster Survey (MICS) 2019 was used to explore physical disciplinary practices against children under five, focusing on factors such as household wealth, region, type of residence, child’s gender, and mother’s education level. Logistic regression analysis was employed to examine the adjusted association between sociodemographic factors and physical disciplinary practices. Findings The study found that 64.88% of children under five experienced physical disciplinary practices at home. The odds of being subjected to PDP were higher for children from lower wealth index categories (poorest, poorer, middle, richer) compared to those in the richest category, with AORs ranging from 1.21 to 1.35 and all p-values <0.01. Urban residence was associated with increased odds of being physically punished (AOR: 1.09, 95% CI: 1.01–1.20). Boys had higher odds of being physically punished compared to girls (AOR: 1.15, 95% CI: 1.07–1.23). The odds of being physically punished were greater for children of less-educated mothers. Furthermore, mothers who believed that physical punishment was necessary for child-rearing had higher odds of using such practices (AOR: 2.10, 95% CI: 1.94–2.27). Conclusion Socioeconomic factors, especially household wealth, significantly influence the risk of experiencing physical disciplinary practices among children. The findings emphasize the need for targeted interventions to protect vulnerable children, particularly in urban and impoverished settings.

Universal photonic artificial intelligence acceleration

Author Correction: Digital automation of transdermal drug delivery with high spatiotemporal resolution

Fibre tracing in biomedical images: An objective comparison between seven algorithms

Obtaining the traces and the characteristics of elongated structures is an important task in computer vision pipelines. In biomedical applications, the analysis of traces of vasculature, nerves or fibres of the extracellular matrix can help characterise processes like angiogenesis or the effect of a certain treatment. This paper presents an objective comparison of six existing methodologies (Edge detection, CT Fire, Scale Space, Twombli, U-Net and Graph Based) and one novel approach called Trace Ridges to trace biomedical images with fibre-like structures. Trace Ridges is a fully automatic and fast algorithm that combines a series of image-processing algorithms including filtering, watershed transform and edge detection to obtain an accurate delineation of the fibre-like structures in a rapid time. To compare the algorithms, four biomedical data sets with vastly distinctive characteristics were selected. Ground truth was obtained by manual delineation of the fibre-like structures. Three pre-processing filtering options were used as a first step: no filtering, Gaussian low-pass and DnCnn, a deep-learning filtering. Three distance error metrics (total, average and maximum distance from the obtained traces to the ground truth) and processing time were calculated. It was observed that no single algorithm outperformed the others in all metrics. For the total distance error, which was considered the most significative, Trace Ridges ranked first, followed by Graph Based, U-Net, Twombli, Scale Space, CT Fire and Edge Detection. In terms of speed, Trace Ridges ranked second, only slightly slower than Edge Detection. Code is freely available at github.com/youssefarafat/Trace_Ridges .

Hyperinflammatory repolarisation of ovarian cancer patient macrophages by anti-tumour IgE antibody, MOv18, restricts an immunosuppressive macrophage:Treg cell interaction

Abstract Ovarian cancer is the most lethal gynaecological cancer and treatment options remain limited. In a recent first-in-class Phase I trial, the monoclonal IgE antibody MOv18, specific for the tumour-associated antigen Folate Receptor-α, was well-tolerated and preliminary anti-tumoural activity observed. Pre-clinical studies identified macrophages as mediators of tumour restriction and pro-inflammatory activation by IgE. However, the mechanisms of IgE-mediated modulation of macrophages and downstream tumour immunity in human cancer remain unclear. Here we study macrophages from patients with epithelial ovarian cancers naive to IgE therapy. High-dimensional flow cytometry and RNA-seq demonstrate immunosuppressive, FcεR-expressing macrophage phenotypes. Ex vivo co-cultures and RNA-seq interaction analyses reveal immunosuppressive associations between patient-derived macrophages and regulatory T (Treg) cells. MOv18 IgE-engaged patient-derived macrophages undergo pro-inflammatory repolarisation ex vivo and display induction of a hyperinflammatory, T cell-stimulatory subset. IgE reverses macrophage-promoted Treg cell induction to increase CD8+ T cell expansion, a signature associated with improved patient prognosis. On-treatment tumours from the MOv18 IgE Phase I trial show evidence of this IgE-driven immune signature, with increased CD68+ and CD3+ cell infiltration. We demonstrate that IgE induces hyperinflammatory repolarised states of patient-derived macrophages to inhibit Treg cell immunosuppression. These processes may collectively promote immune activation in ovarian cancer patients receiving IgE therapy.

Enhancing secondary school students’ science process skills through guided inquiry-based laboratory activities in biology

Science process skills (SPS) are vital for enhancing student engagement, critical thinking, and academic achievement in science education. However, Ethiopian secondary schools often rely on traditional, rote-based laboratory methods that hinder SPS development. This study examined the effect of Guided Inquiry-Based Laboratory Experiments enriched Instructional (GIBLEI) approach on improving students’ SPS in biology. GIBLEI promotes active, inquiry-based learning, encouraging students to investigate, hypothesize, experiment, and draw conclusions. By shifting from passive observation to hands-on exploration, GIBLEI addresses limitations of traditional methods, fostering deeper understanding, problem-solving skills, and reducing educational disparities in science classrooms. In this quasi-experimental study, two biology classes from selected schools were randomly assigned to experimental (EG, N = 46) and control groups (CG, N = 29). The EG received GIBLEI-based instruction for eight weeks, focusing on inquiry-based laboratory activities that require students to investigate, hypothesize, and draw conclusions. The CG, meanwhile, received traditional lab instruction with a focus on observation and confirmation of set procedures. Data on SPS development were gathered using essay tests scored with rubrics. Welch’s t-test revealed that post-test SPS scores in the EG were significantly higher than those in the CG, with a large effect size (82%), demonstrating GIBLEI’s effectiveness. ANCOVA further confirmed that the improvement was attributable to the GIBLEI approach rather than initial group differences. The Wilcoxon signed-rank test showed significant SPS improvement within the EG from pre-test to post-test, underscoring the approach’s effectiveness over time. Additionally, an independent samples t-test indicated no significant gender differences in SPS within the EG, suggesting that GIBLEI benefits both male and female students equally. The findings highlight GIBLEI as a promising tool to foster SPS, supporting its integration into biology curricula to enhance student engagement, skill acquisition, and equal learning outcomes across genders.

The Spatial Transcriptional Activity of Hepatic TCF7L2 Regulates Zonated Metabolic Pathways that Contribute to Liver Fibrosis

Unlocking renewable energy potential: Overcoming knowledge sharing hurdles in rural EU regions on example of poland, sweden and france

The optimal technological choice for sustainable development lies in renewable energy sources (RES). However, the potential offered by RES utilization poses significant challenges for mobile technologies and everyday living. Despite extensive research and information highlighting the benefits of renewable energy, there remains considerable debate, and limited awareness persists. The advantages of RES are not fully comprehended, raising concerns about its consistent application. Regrettably, lack of knowledge and a fundamental understanding hinders effective dissemination. To gauge the attitudes of residents in regions where RES is employed, this study employed a questionnaire authored by the researcher. The study was conducted between June 2022 and January 2023, with a total of 12,428 participants completing the survey. The sampling method utilized an online form distributed via various social media channels and among local contacts of the authors in Poland, Sweden, and France. Gender allocation: 58% male and 42% female. Respondents shared their perspectives on ecology and disclosed their familiarity with RES utilization. Results indicate public skepticism regarding the adequacy of RES security measures and the level of knowledge for its effective use. Insufficient experts, limited social advocacy, and reliance on online sources contribute to a low level of awareness. In several EU countries, the absence of widely accepted and easily accessible information on renewable energy sources (RES) hinders knowledge sharing and adoption. Despite the EU’s efforts to promote renewable energy through directives and subsidies, rural communities in these countries often lack adequate education and awareness about RES technologies. This gap in knowledge contributes to unfavorable perceptions, with some residents viewing renewables as unreliable or economically unfeasible options compared to traditional energy sources like coal or natural gas. Additionally, bureaucratic hurdles and inconsistent government policies further complicate the transition to renewable energy, discouraging investment and innovation in the sector. As a result, while the EU aims for a sustainable energy future, these barriers impede the widespread growth of RES and hinder progress towards climate targets. In Poland the study found that 76% of respondents expressed favorable perceptions of RES, indicating a general inclination towards adopting clean energy solutions. In Sweden, the analysis uncovered a high level of environmental awareness among participants, with 85% of respondents expressing concern about environmental degradation. Despite this awareness, 62% of participants reported reservations about the security and affordability of energy derived from renewable sources. Additionally, 48% of respondents expressed uncertainty or ambivalence regarding the environmental benefits of RES. In France, the research revealed similar concerns among respondents regarding the security and affordability of renewable energy. 59% of participants expressed reservations about the security of energy derived from renewable sources, while 53% cited perceived high costs as a barrier to adoption. Furthermore, 41% of respondents identified underdeveloped RES infrastructure as a hindrance to wider acceptance and utilization. The quantitative data highlights the complex landscape of renewable energy perceptions and attitudes in Poland, Sweden, and France. While there is a general awareness of environmental issues and a positive inclination towards clean energy solutions, concerns about security, affordability, and infrastructure remain significant barriers to widespread adoption. These findings underscore the importance of targeted interventions and educational efforts to address these challenges and promote sustainable energy practices across Europe. Renewable energy sources (RES) represent a critical avenue for sustainable development, offering a pathway to mitigate environmental degradation and reduce dependence on fossil fuels. This study investigates public attitudes, knowledge levels, and barriers to RES adoption in rural areas of Poland, Sweden, and France, highlighting the unique socio-economic and cultural factors influencing these regions. Conducted between June 2022 and January 2023, the research utilized an online survey, gathering responses from 12,428 participants across these countries. Respondents evaluated statements on environmental responsibility, RES knowledge and application, and perceived obstacles, using a five-point Likert scale. Key findings reveal that while environmental awareness is high, significant barriers persist in the form of limited knowledge, underdeveloped infrastructure, and perceptions of high costs associated with RES. In Poland, 76% of respondents expressed a positive view of RES but cited concerns about cost and security. Swedish participants demonstrated strong environmental awareness (85%), yet 62% voiced reservations about RES affordability and reliability. French respondents similarly highlighted concerns regarding infrastructure and costs, with 41% identifying underdeveloped RES systems as a primary hindrance. The study underscores the importance of targeted educational campaigns and policy interventions to bridge knowledge gaps and foster greater acceptance of RES. Tailored strategies addressing local barriers—such as financial incentives, community-based advocacy, and infrastructure investments—are essential to overcoming these challenges. By exploring diverse perspectives and barriers across the three countries, this research contributes valuable insights to the broader discourse on sustainable energy transitions in the EU.

Foundation model of neural activity predicts response to new stimulus types

Abstract The complexity of neural circuits makes it challenging to decipher the brain’s algorithms of intelligence. Recent breakthroughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brain’s computational objectives and neural coding. However, it is difficult for such models to generalize beyond their training distribution, limiting their utility. The emergence of foundation models 1 trained on vast datasets has introduced a new artificial intelligence paradigm with remarkable generalization capabilities. Here we collected large amounts of neural activity from visual cortices of multiple mice and trained a foundation model to accurately predict neuronal responses to arbitrary natural videos. This model generalized to new mice with minimal training and successfully predicted responses across various new stimulus domains, such as coherent motion and noise patterns. Beyond neural response prediction, the model also accurately predicted anatomical cell types, dendritic features and neuronal connectivity within the MICrONS functional connectomics dataset 2 . Our work is a crucial step towards building foundation models of the brain. As neuroscience accumulates larger, multimodal datasets, foundation models will reveal statistical regularities, enable rapid adaptation to new tasks and accelerate research.

Sandwiching of MOF nanoparticles between graphene oxide nanosheets among ice grains

Correction: Identification and quantification of glucose degradation products in heat-sterilized glucose solutions for parenteral use by thin-layer chromatography

A map of neural signals and circuits traces the logic of brain computation

Fluctuating magnetism and Pomeranchuk effect in multilayer graphene

MetaFlowTrain: a highly parallelized and modular fluidic system for studying exometabolite-mediated inter-organismal interactions

Abstract Metabolic fluxes between cells, organisms, or communities drive ecosystem assembly and functioning and explain higher-level biological organization. Exometabolite-mediated inter-organismal interactions, however, remain poorly described due to technical challenges in measuring these interactions. Here, we present MetaFlowTrain, an easy-to-assemble, cheap, semi-high-throughput, and modular fluidic system in which multiple media can be flushed at adjustable flow rates into gnotobiotic microchambers accommodating diverse micro-organisms, ranging from bacteria to small eukaryotes. These microchambers can be used alone or connected in series to create microchamber trains within which metabolites, but not organisms, directionally travel between microchambers to modulate organismal growth. Using MetaFlowTrain, we uncover soil conditioning effects on synthetic community structure and plant growth, and reveal microbial antagonism mediated by exometabolite production. Our study highlights MetaFlowTrain as a versatile system for investigating plant-microbe-microbe metabolic interactions. We also discuss the system´s potential to discover metabolites that function as signaling molecules, drugs, or antimicrobials across various systems.

Comparing MEG and EEG measurement set-ups for a brain–computer interface based on selective auditory attention

Auditory attention modulates auditory evoked responses to target vs. non-target sounds in electro- and magnetoencephalographic (EEG/MEG) recordings. Employing whole-scalp MEG recordings and offline classification algorithms has been shown to enable high accuracy in tracking the target of auditory attention. Here, we investigated the decrease in accuracy when moving from the whole-scalp MEG to lower channel count EEG recordings and when training the classifier only from the initial or middle part of the recording instead of extracting training trials throughout the recording. To this end, we recorded simultaneous MEG (306 channels) and EEG (64 channels) in 18 healthy volunteers while presented with concurrent streams of spoken “Yes”/“No” words and instructed to attend to one of them. We then trained support vector machine classifiers to predict the target of attention from unaveraged trials of MEG/EEG. Classifiers were trained on 204 MEG gradiometers or on EEG with 64, 30, nine or three channels with trials extracted randomly across or only from the beginning of the recording. The highest classification accuracy, 73.2% on average across the participants for one-second trials, was obtained with MEG when the training trials were randomly extracted throughout the recording. With EEG, the accuracy was 69%, 69%, 66%, and 61% when using 64, 30, nine, and three channels, respectively. When training the classifiers with the same amount of data but extracted only from the beginning of the recording, the accuracy dropped by 11%-units on average, causing the result from the three-channel EEG to fall below the chance level. The combination of five consecutive trials partially compensated for this drop such that it was one to 5%-units. Although moving from whole-scalp MEG to EEG reduces classification accuracy, usable auditory-attention-based brain-computer interfaces can be implemented with a small set of optimally placed EEG channels.

CoOx clusters-decorated IrO2 electrocatalyst activates NO3- mediator for benzylic C-H activation

Quantum-assisted trustworthiness for the Quantum Internet

Device redundancy is one of the most well-known mechanisms in distributed systems to increase the overall system fault tolerance and, consequently, trustworthiness. Existing algorithms in this regard aim to exchange a significant number of messages among nodes to identify and agree which communication links or nodes are faulty. This approach greatly degrades the performance of those wireless communication networks exposed to limited available bandwidth and/or energy consumption due to messages flooding. Lately, quantum-assisted mechanisms have been envisaged as an appealing alternative to improve the performance in this kind of communication networks and have been shown to obtain levels of performance close to the ones achieved in ideal conditions. The purpose of this paper is to further explore this approach by using super-additivity and superposed quantum trajectories in quantum Internet to obtain a higher system trustworthiness. More specifically, the wireless communication network that supports the permafrost telemetry service for the Antarctica together with five operational modes (three of them using classical techniques and two of them using quantum-assisted mechanisms) have been simulated. Obtained results show that the new quantum-assisted mechanisms can increase the system performance by up to a 28%.