Distinct Domains Research Articles

Generating qualitative evidence of the validity of the Health Literacy Questionnaire Parent

Abstract Background Health literacy refers to the skills needed to access, understand, and navigate health information and services. The Health Literacy Questionnaire (HLQ) is a widely used validated measure of health literacy capturing nine distinct domains. The HLQ-Parent is a recent adaption of the HLQ which has been designed to capture the nuanced health literacy skills required by primary care givers of children. Generating qualitative evidence of validity of such tools is an important element of the validation process as it captures understanding and interpretation of items which is not achievable by quantitative methods alone. Therefore, the purpose of this study was to generate qualitative evidence of the validity of the HLQ-Parent. Methods A qualitative study implementing the HLQ-Parent during face-to-face cognitive interviews with concurrent probing was conducted. Primary carers of children aged 16 or under living in England were recruited via voluntary organisations supporting families. Qualitative data analysis was conducted on the audio recordings in the form of text summaries. Parents’ chosen HLQ-Parent responses were compared to their narrative responses and coded by the researcher as match, no match or unclear. Parents’ narrative responses were also compared to the HLQ-Parent item intent descriptions and coded as concordant, discordant, or unclear. Results Overall, participants (n = 9, mean age 40 years, 88.9% female) narrative responses matched to their chosen HLQ-Parent response, while their narrative responses were concordant with the HLQ-Parent item intent descriptions. Two participants narratives did not match their responses for the item:’ I have enough information to help me deal with my child’s health’. Conclusions The qualitative data provided evidence that participants understood and answered the HLQ-Parent items as intended. Study results contribute validity evidence for the use of HLQ-Parent to measure parental health literacy. Key messages • This study contributes evidence to the validity of the HLQ-Parent. • Qualitative methods provide valuable insights that enhance the overall understanding of health literacy measurement tools.

Cognitive patterns according to mini-mental state examination domains in patients with AF and association with all-cause mortality: a cluster analysis from a contemporary prospective study

Abstract Background Atrial fibrillation (AF) is associated with cognitive impairment even in the absence of previous stroke. However, data on specific patterns of cognitive impairment and their association with outcomes are limited. Purpose We aimed to identify different phenotypic groups of AF patients stratified according to specific cognitive domains using cluster analysis and evaluate the association between identified clusters and adverse outcomes. Methods We used data from a prospective observational study on AF patients excluding those with previous clinically overt stroke. We performed a hierarchical cluster analysis based on Ward’s Method using data from the Mini-Mental State Examination (MMSE) administered during the enrollment. The MMSE includes 6 distinct cognitive domains. We normalized each domain’s score from 0 to 100 to preserve the ranking and the interpretability of the test. Patients’ clusters were identified by examining the distance between cluster coefficients and by visual inspection of the dendrogram. All-cause mortality was the primary endpoint. Results A total of 872 stroke-free AF patients were included. Three clusters based on MMSE domains were identified: Cluster 1 (n=494, 56.7%); Cluster 2 (n=243, 27.9%) and Cluster 3 (n=135, 15.5%). The clusters greatly differed according to the cognitive domains affected and baseline characteristics (Table). The use of oral anticoagulants did not differ among clusters and it was substantially high (88.6%). Cluster 2 had a higher prevalence of cognitive impairment (37.9% vs 35.6% for Cluster 3 and 1.0% for Cluster 1, p<0.001) with a lower mean MMSE score compared to other clusters. The analysis of MMSE domains showed substantial deficits in the orientation, attention/calculation, and recall domains with peculiar differences among the clusters. Cluster 2 showed the most significant impairment in the copying domain (100%) while Cluster 3 had a higher impairment in attention/calculation and recall domains. After a median follow-up of 629 days, all-cause mortality was 15.9% with a significantly higher prevalence in Cluster 2 (26.9% vs 10.0% and 15.5% for Cluster 1 and Cluster 3 respectively, p<0.001). At the adjusted Cox regression analysis, Cluster 2 was independently associated with a higher risk of all-cause mortality compared with Cluster 1 (HR 1.83, 95% CI 1.18-2.82) while Cluster 3 was not independently associated with the outcome (Figure). Conclusions In a prospective observational cohort of stroke-free AF patients, we identified three clusters showing different patterns of cognitive impairment, affecting specific MMSE domains and resulting in a different risk of mortality. Our findings highlight the clinical implications of cognitive impairment and stress the need for a more holistic approach to AF patients including a comprehensive cognitive assessment, as well as the need for studies on interventions able to positively affect cognitive impairment.Figure

Bispecific antibody (ABL602 2 + 1) induced bistable acute myeloid leukemia kinetics

ABL602 2 + 1, a bispecific antibody with two distinct domains binding to CLL-1 on leukemias and CD3 on T cells, exhibits superior T cell activation and tumour lysing activity. Treatment outcomes of bispecific antibody rely on acute myeloid leukemia cell replication and antibody induced tumour lysing, but their quantitative relationship was unknown. Mathematical models are employed to quantitatively investigate HL-60 cell kinetics determined by bispecific antibody and tumour burden. First, we analysed cytotoxicity assay data testing HL-60 cell against bispecific antibody and T cells, and found efficiency of bispecific antibody induced tumour lysing increases but saturates with increase of HL-60 cell, T cell and bispecific antibody concentration. As a result, their interaction leads to bistable HL-60 cell kinetics; namely, at a given bispecific antibody and T cell concentration interval, HL-60 cell kinetics with small tumour burdens are inhibited but refractory to large tumour burdens. T cell concentration is strong negatively correlated with HL-60 cell concentration. With bispecific antibody clearance, observed bistable HL-60 cell kinetics still exists. Our finding explains observed phenomenon that bispecific antibody was less efficacious at high tumour burden even with enough activated cytotoxic CD8 + T cells. Maintaining high antibody concentration and preventing T-cell exhaustion are equivalently important to sustain long-term control.

The Problem of Scale Contamination and Construct Validity in Mental Health, Social Science and Spirituality-Related Research

This article explores the pervasive issue of scale contamination and its impact on construct validity in mental health, social science, and spirituality-related research. Scale contamination occurs when measurement tools designed to assess one specific domain inadvertently incorporate elements from unrelated constructs, leading to confusion regarding what the scale is actually measuring. The blending of distinct psychological domains, such as depression, anxiety and well-being, undermines measurement precision, leading to skewed research conclusions and misguided clinical practices. The failure to detect or address contamination can perpetuate errors in treatment efficacy, particularly in areas such as mental health and spirituality, where precise measurement is crucial. This article emphasizes the importance of ensuring construct validity, which refers to the accurate measurement of specific concepts such as moral injury or posttraumatic stress disorder, and it highlights the risk of conflating distinct constructs. The widespread use of contaminated scales, often adopted without critical evaluation, poses a significant threat to the integrity of research outcomes. In order to address these challenges, this study advocates for rigorous scale development and validation processes, encouraging researchers, peer reviewers and journal editors to scrutinize the psychometric properties of scales. By ensuring that scales measure only the intended constructs, future research can produce more accurate and meaningful results, leading to improved clinical interventions and policy decisions.

Redefining organizational digitality: a relational-ontological approach inspired by new materialism

Organizations, as central actors in societal structure, undergo significant transformations due to the impact of digitalization, often resulting in disruptive changes. Consequently, organizations increasingly view digitalization as an ongoing process of negotiation, which has led to the emergence of new operational modes and organizational norms. In this context, the interaction between organizations and digital technologies is characterized by recursive dynamics, which blur conventional boundaries. This presents a challenge in defining the distinct domains of the digital and the organizational within the framework of recursivity. This article draws upon new materialism and agential realism to propose an ontological-relational approach to understanding organizational digitality. This approach suggests a reconceptualization of organizational digitality as a mechanism that generates relational entities, thereby reshaping their inherent meanings. By transcending traditional boundaries between organizations and digital, this perspective provides a nuanced understanding of digital phenomena within organizational contexts.

Plasma membrane and cytoplasmic compartmentalization: a dynamic structural framework required for pollen tube tip growth.

Rapid, unidirectional pollen tube tip growth is essential for fertilization and is widely employed as a model of polar cell expansion, a process crucial for plant morphogenesis. Different proteins and lipids with key functions in the control of polar cell expansion are associated with distinct domains of the plasma membrane (PM) at the pollen tube tip. These domains need to be dynamically maintained during tip growth, which depends on massive secretory and endocytic membrane trafficking. Very little is currently known about the molecular and cellular mechanisms responsible for the compartmentalization of the pollen tube PM. To provide a reliable structural framework for the further characterization of these mechanisms, an integrated quantitative map was compiled of the relative positions in normally growing Nicotiana tabacum (tobacco) pollen tubes of PM domains 1) enriched in key signaling proteins or lipids, 2) displaying high membrane order, or 3) in contact with cytoplasmic structures playing important roles in apical membrane trafficking. Previously identified secretory and endocytic PM domains were also included into this map. Internalization of regulatory proteins or lipids associated with PM regions overlapping with the lateral endocytic domain was assessed based on brefeldin A (BFA) treatment. These analyses revealed remarkable aspects of the structural organization of tobacco pollen tube tips, which 1) enhance our understanding of cellular and regulatory processes underlying tip growth, and 2) highlight important areas of future research.

The Multifaceted Role of Epithelial Membrane Protein 2 in Cancer: from Biomarker to Therapeutic Target.

Tetraspanin superfamily proteins not only facilitate the trafficking of specific proteins to distinct plasma membrane domains but also influence cell-to-cell and cell-extracellular matrix interactions. Among these proteins, Epithelial Membrane Protein 2 (EMP2), a member of the growth arrest-specific gene 3/peripheral myelin protein 22 (GAS3/PMP22) family, is known to affect key cellular processes. Recent studies have revealed that EMP2 modulates critical signaling pathways and interacts with adhesion molecules and growth factor receptors, underscoring its potential as a biomarker for cancer diagnosis and prognosis. These findings suggest that EMP2 expression patterns could provide valuable insights into tumorigenesis and metastasis. Moreover, EMP2 has emerged as a promising therapeutic target, with approaches aimed at inhibiting or modulating its activity showing potential to disrupt tumor growth and metastasis. This review provides a comprehensive overview of recent advances in understanding the multifaceted roles of EMP2 in cancer, with a focus on its underlying mechanisms and clinical significance.

A decentralized authentication scheme for smart factory based on blockchain

The Industrial Internet of Things (IIoT) is recognized as one of the revolutionary technologies driving smart manufacturing and improving productivity. As manufacturing processes grow increasingly intricate, the entire manufacturing ecosystem encompasses multiple managed IoT domains. Within this highly interconnected environment, devices from diverse domains must collaborate, leading to considerable apprehensions about the security and privacy of device-to-device communications. Current authentication methods encounter several challenges. Traditional authentication schemes overly rely on trusted third parties, rendering them susceptible to external attacks or internal spoofing. This susceptibility gives rise to a range of security and privacy concerns. In response to these challenges, this paper aims to contribute to a more secure and efficient service scheme for smart factories by devising a blockchain-based distributed IoT architecture. The proposed scheme introduces a federated blockchain to establish trust among different domains, thereby enabling secure connections between devices in distinct domains. Through security analysis, it is proved that the proposed authentication scheme has integrity, mutual authentication, scalability, and resistance to four attacks. Furthermore, efficiency analysis experiments show that our scheme is feasible for smart factories, and as the number of peer nodes increases, the performance and efficiency of the blockchain network become better.

Specific Degradation of the Mucin Domain of Lubricin in Synovial Fluid Impairs Cartilage Lubrication.

Progressive cartilage degradation, synovial inflammation, and joint lubrication dysfunction are key markers of osteoarthritis. The composition of synovial fluid (SF) is altered in OA, with changes to both hyaluronic acid and lubricin, the primary lubricating molecules in SF. Lubricin's distinct bottlebrush mucin domain has been speculated to contribute to its lubricating ability, but the relationship between its structure and mechanical function in SF is not well understood. Here, we demonstrate the application of a novel mucinase (StcE) to selectively degrade lubricin's mucin domain in SF to measure its impact on joint lubrication and friction. Notably, StcE effectively degraded the lubricating ability of SF in a dose-dependent manner starting at nanogram concentrations (1-3.2 ng/mL). Further, the highest StcE doses effectively degraded lubrication to levels on par with trypsin, suggesting that cleavage at the mucin domain of lubricin is sufficient to completely inhibit the lubrication mechanism of the collective protein component in SF. These findings demonstrate the value of mucin-specific experimental approaches to characterize the lubricating properties of SF and reveal key trends in joint lubrication that help us better understand cartilage function in lubrication-deficient joints.

Research on the Knowledge Structure and Sustainable Development Pathways of Artificial Intelligence from the Perspective of Technological Science

Achieving significant breakthroughs in both the fundamental theories and technological applications of artificial intelligence is essential for fostering its long-term development. Under the guidance of Professor Qian Xuesen’s theory of technological science, exploring the internal mechanisms of knowledge evolution in artificial intelligence holds profound theoretical and practical significance for promoting sustainable technological advancement. This study draws on literature from the Web of Science (WOS) database and employs methods such as knowledge mapping, natural language processing, clustering analysis, and citation analysis to outline the knowledge structure of the field, clarify the trajectory of sustainable development, and trace the technological genealogy of VR/AR technologies.This study divides the knowledge structure within the field of technological science into “basic theoretical knowledge—applied basic knowledge—applied knowledge”, enriching Qian’s theory of technological science from within and providing strong intellectual support and technological pathways for sustainable technological development in practice. Artificial intelligence encompasses 10 distinct knowledge domains, among which machine learning and deep learning constitute the basic theoretical knowledge, data intelligence, computer vision, and swarm intelligence are the applied basic knowledge, and image processing and human-computer intelligence are the applied knowledge. The development of VR/AR technology has formed two main sustainable development paths: “machine learning—data intelligence—intelligent systems—human computer intelligence”, and “deep learning—computer vision—image processing”.

Revisiting the human umbilical cord epithelium. An atypical epithelial sheath with distinctive features.

The umbilical cord epithelium (UCE) is the surface tissue that covers the umbilical cord (UC). It is widely considered a single-layered epithelium composed of squamous or cuboidal cells, which are in constant contact with amniotic fluid. The objective of this study was to elucidate the distinctive structural characteristics and abundance of specific proteins in this unique epithelium, many of which have not been previously demonstrated. Samples of the UC were obtained from term pregnancies (n = 12) and processed for examination using stereo, light, electron, and 3D high-resolution confocal microscopy. Sections displayed a range of stratification, ranging from a single squamous layer to 4-5 layers of round/cuboid cells, challenging the notion of considering it as a single-layered structure. Cells are located on a well-developed basement membrane (BM), as evidenced by the expression of BM-specific proteins and PAS staining. The cells possess distinctive cytoplasmic domains that are tightly bound to each other by desmosomes and interdigitating anchoring surfaces. Desquamations and limited organelles suggest that the cells have reached the final stages of differentiation and are no longer actively synthesizing proteins, despite maintaining stratification-specific expression levels of cytoskeletal, junctional, receptor, and stem cell proteins. Although definitive keratinization was not observed, the distribution of proteins and the distinctive structural organization of the single/multi-layered cells suggest that they exhibit plasticity, likely due to adaptive mechanisms in response to chemical and/or mechanical stimuli during fetal development. These structural alterations may facilitate the active transportation of soluble ingredients between the amniotic fluid and cord blood through an intercellular route.

Unveiling the longitudinal reciprocal relationship between burnout and engagement among adolescent athletes in sport schools.

Burnout and engagement are pivotal for adolescents' well-being and have received extensive attention in the educational literature. However, less is known about how these factors develop and interact within and between school and sport when adolescent athletes follow dual (school and sport) careers. The aim of this study was to examine the reciprocal relationship between engagement and burnout in school and sport through a cross-lagged analysis of longitudinal associations. A sample of 165 Finnish adolescent athletes (aged 14.5 years, 48.9% girls) enrolled in lower secondary sport schools (i.e., junior high schools) participated in a survey in spring 2019 (i.e., in Grade 8) and 1 year later, in spring 2020 (i.e., in Grade 9). Engagement and burnout had significant autoregressive relationships within school and sport. Furthermore, low levels of sport engagement in Grade 8 predicted sport burnout in Grade 9, and low levels of school burnout in Grade 8 predicted school engagement in Grade 9. Regarding cross-domain effects, high levels of school engagement in Grade 8 predicted low levels of sport engagement in Grade 9. The study shows that school and sport are distinct domains with unique associations between engagement and burnout in each domain. It also shows that adolescent athletes may maintain high school engagement by reducing their sport engagement. These findings highlight the need for sport school practitioners to develop intervention programs to address adolescents' school and sport needs and to support their holistic well-being.

Machine learning classification of archaea and bacteria identifies novel predictive genomic features

BackgroundArchaea and Bacteria are distinct domains of life that are adapted to a variety of ecological niches. Several genome-based methods have been developed for their accurate classification, yet many aspects of the specific genomic features that determine these differences are not fully understood. In this study, we used publicly available whole-genome sequences from bacteria (N=2546\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=2546$$\\end{document}) and archaea (N=109\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=109$$\\end{document}). From these, a set of genomic features (nucleotide frequencies and proportions, coding sequences (CDS), non-coding, ribosomal and transfer RNA genes (ncRNA, rRNA, tRNA), Chargaff’s, topological entropy and Shannon’s entropy scores) was extracted and used as input data to develop machine learning models for the classification of archaea and bacteria.ResultsThe classification accuracy ranged from 0.993 (Random Forest) to 0.998 (Neural Networks). Over the four models, only 11 examples were misclassified, especially those belonging to the minority class (Archaea). From variable importance, tRNA topological and Shannon’s entropy, nucleotide frequencies in tRNA, rRNA and ncRNA, CDS, tRNA and rRNA Chargaff’s scores have emerged as the top discriminating factors. In particular, tRNA entropy (both topological and Shannon’s) was the most important genomic feature for classification, pointing at the complex interactions between the genetic code, tRNAs and the translational machinery.ConclusionstRNA, rRNA and ncRNA genes emerged as the key genomic elements that underpin the classification of archaea and bacteria. In particular, higher nucleotide diversity was found in tRNA from bacteria compared to archaea. The analysis of the few classification errors reflects the complex phylogenetic relationships between bacteria, archaea and eukaryotes.

Who Gets Paid (for) What? The Cultural Political Economy of News Content in Generative AI

One of the key controversies that generative artificial intelligence (AI) has recently stirred was whether compensation is due for the copyrighted materials used to train AI models. This article explores the logic, trajectories, and dynamics of content generation, including news, through generative AI in two distinctive yet intertwined domains. Guided by a cultural political economy approach, it examines how both the political context (validation/legitimation of AI-generated news content by established news media) and the economic context (use of unpaid and underpaid labor in the forms of freely scraped data and data annotation work) shape the deployment of news content on AI models. It further untangles how the space for serious, independent journalism may shrink, as big tech companies’ algorithmic technologies emerge as a solution to contemporary problems in journalism. A clear danger here is that AI companies’ proprietary algorithms, language training models, and value-laden parameters are incompatible with journalism's democratic obligations and responsibilities.

A deep-learning-based surrogate modeling method with application to plasma processing

Rapid and accurate system evolution predictions are crucial in scientific and engineering research. However, the complexity of processing systems, involving multiple physical field couplings and slow convergence of iterative numerical algorithms, leads to low computational efficiency. Hence, this paper introduces a systematic deep-learning-based surrogate modeling methodology for multi-physics-coupled process systems with limited data and long-range time evolution, accurately predicting physics dynamics and considerably improving computational efficiency and generalization. The methodology comprises three main components: (1) generating datasets using a sequential sampling strategy, (2) modeling multi-physics spatio-temporal dynamics by designing a heterogeneous Convolutional Autoencoder and Recurrent Neural Network, and (3) training high-precision models with limited data and long-range time evolution via a dual-phase training strategy. A holistic evaluation using a 2D low-temperature plasma processing example demonstrates the methodology’s superior computational efficiency, accuracy, and generalization capabilities. It predicts plasma dynamics approximately 105 times faster than traditional numerical solvers, with a consistent 2% relative error across different generalization tasks. Furthermore, the potential for transferability across various geometries is explored, and the model’s transfer capability is demonstrated with two distinct geometric domain examples.

System size effects on the free energy landscapes from molecular dynamics of phase-separating bilayers.

The "lipid raft" hypothesis proposes that cell membranes contain distinct domains of varying lipid compositions, where "rafts" of ordered lipids and cholesterol coexist with disordered lipid regions. Experimental and theoretical phase diagrams of model membranes have revealed multiple coexisting phases. Molecular dynamics (MD) simulations can also capture spontaneous phase separation of bilayers. However, these methods merely determine the signof the free energy change upon phase separation-whether or not it is favorable-but not the amplitude. Recently, we developed a workflow to compute the free energy of phase separation from MD simulations using the weighted ensemble method. However, while theoretical treatments generally focus on infinite systems and experimental measurements on mesoscopic to macroscopic systems, MD simulations are comparatively small. Therefore, if we are to put the results of these calculations into the appropriate context, we need to understand the effects the finite size of the simulation has on the computed free energy landscapes. In this study, we investigate this phenomenon by computing free energy profiles for a model phase-separating system as a function of system size, ranging from 324 to 10 110 lipids. The results suggest that, within the limits of statistical uncertainty, bulk-like behavior emerges once the systems contain roughly 4000 lipids.

Association of RDoC dimensions with post-mortem brain transcriptional profiles in Alzheimer's Disease.

Neuropsychiatric symptoms are common in people with Alzheimer's disease (AD) across all severity stages. Their heterogeneous presentation and variable temporal association with cognitive decline suggest shared and distinct biological mechanisms. We hypothesized that specific patterns of gene expression associate with distinct NIMH Research Domain Criteria (RDoC) domains in AD. Post-mortem bulk RNAseq on the insula and anterior cingulate cortex from 60 brain donors representing the spectrum of canonical AD neuropathology combined with natural language processing approaches based on the RDoC Clinical Domains. Distinct sets of >100 genes (p FDR <0.05) were specifically associated with at least one clinical domain (Cognitive, Social, Negative, Positive, Arousal). In addition, dysregulation of immune response pathways was shared across domains and brain regions. Our findings provide evidence for distinct transcriptional profiles associated with RDoC domains suggesting that each dimension is characterized by specific sets of genes providing insight into the underlying mechanisms.

Acoustic insights into the corn extrusion process for enhanced quality control

In industrial extrusion processes, a solid material is pressed through a die to obtain products of the desired shape and dimension. Fluctuations in the process parameters have a significant impact on the product quality. In food extrusion, the expansion noise at the die can serve as an indicator of the stability of the process. This study employs microphones to characterize the corn extrusion process, focusing on correlating acoustic emissions with predefined process parameters such as feed intake and water content. Experimental data from laboratory and industrial settings reveal distinct domain shifts, yet consistent findings confirm the distinguishability of various extrusion process parameters by analyzing acoustic emissions. Employing machine learning models, including support vector machines and convolutional neural networks, in conjunction with audio features such as log Mel spectrograms, yields promising accuracies above 90\% in discriminating between standard and non-standard process parameters. The proposed acoustic quality control approach has the potential to enhance the stability of extrusion processes and contributes to the development of automated monitoring systems in the field of food extrusion. This ensures consistent quality and reduced waste, ultimately leading to significant cost savings in production.

Structural basis of α-latrotoxin transition to a cation-selective pore

The potent neurotoxic venom of the black widow spider contains a cocktail of seven phylum-specific latrotoxins (LTXs), but only one, α-LTX, targets vertebrates. This 130 kDa toxin binds to receptors at presynaptic nerve terminals and triggers a massive release of neurotransmitters. It is widely accepted that LTXs tetramerize and insert into the presynaptic membrane, thereby forming Ca2+-conductive pores, but the underlying mechanism remains poorly understood. LTXs are homologous and consist of an N-terminal region with three distinct domains, along with a C-terminal domain containing up to 22 consecutive ankyrin repeats. Here we report cryoEM structures of the vertebrate-specific α-LTX tetramer in its prepore and pore state. Our structures, in combination with AlphaFold2-based structural modeling and molecular dynamics simulations, reveal dramatic conformational changes in the N-terminal region of the complex. Four distinct helical bundles rearrange and together form a highly stable, 15 nm long, cation-impermeable coiled-coil stalk. This stalk, in turn, positions an N-terminal pair of helices within the membrane, thereby enabling the assembly of a cation-permeable channel. Taken together, these data give insight into a unique mechanism for membrane insertion and channel formation, characteristic of the LTX family, and provide the necessary framework for advancing novel therapeutics and biotechnological applications.

Digital Drone Education in Sarawak: Enhancing STEM Learning through Hands-on Training for School Students

In Sarawak, concerns about low STEM enrolment, digital inclusivity gaps for rural children, and the transition from digital consumption to digital creation led to the introduction of the Drone Education Program. Participants began with foundational drone operation training, advancing to capturing images and videos. Subsequently, they were acquainted with elementary photo and video editing techniques, enabling them to enhance their drone-generated content. The aim of this research is to gauge the impact of the Drone Education Program on refining drone operations, bolstering digital editing competencies, and stimulating STEM engagement in students. In our methodology, we employed tailored pre- and post-surveys for evaluation, and younger students provided feedback via an emoji-based survey. Further analytical methods encompassed non-parametric tests, Exploratory Factor Analysis (EFA), and linear mixed-effect modelling. Results indicated significant post-intervention improvements in the targeted skills. The EFA grouped 21 Likert items into five distinct learning domains. Notable insights include district-wise variances in learning outcomes and the influential role of gender. The initiative showcases promise in confronting Sarawak's educational barriers, championing digital inclusivity, and preparing a digitally proficient generation.