Learning environment

With the increasing problem of antimicrobial drug resistance, the search for new antimicrobial agents has become a crucial task in the field of medicine. Antimicrobial peptides, as a class of naturally occurring antimicrobial agents, possess broad-spectrum antimicrobial activity and lower risk of resistance development. However, traditional screening methods for antimicrobial peptides are inefficient, necessitating the development of an efficient screening model. In this study, we aimed to develop an ensemble learning model for the identification of antimicrobial peptides, named E-CLEAP, based on the Multilayer Perceptron Classifier (MLP Classifier)...

BACKGROUND: Environmental and behavioral factors are responsible for 12.6 million deaths annually and contribute to 25% of deaths and chronic diseases worldwide. Through the One Health initiative, the World Health Organization and other international health organizations plan to improve these indicators to create healthier environments by 2030. To meet this challenge, training primary care professionals should be the priority of national policies. General practitioners (GPs) are ready to become involved but need in-depth training to gain and apply environmental health (EH) knowledge to their practice...

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India...

Most medical schools have instituted undergraduate medical education (UME) well-being programs in recent years in response to high rates of medical student distress, but there is currently significant variability in the structure of UME well-being programs and limited guidance on how to best structure such programs to achieve success. In this article, the authors, all leaders of medical student well-being programs at their home institutions and members of the Association of American Medical Colleges Group on Student Affairs Committee on Student Affairs Working Group on Medical Student Well-Being between 2019 and 2023, offer guidance to the national community on how best to structure a UME well-being program...

PURPOSE: Educators lack tools to measure the workplace characteristics that learners perceive to affect learning. Without a tool that encompasses the social, organizational, and physical components of workplace learning environments (WLEs), it is challenging to identify and improve problematic workplace characteristics. Using echocardiography WLE, this study developed a tool to measure workplace characteristics that cardiology fellows perceive to affect learning. METHOD: The Workplace-Cognitive Load Tool (W-CLT) was developed, which encompasses 17 items to measure workplace characteristics that could affect perceived cognitive load and learning...

Despite the growing presence of women and historically underrepresented groups in academic medicine, significant disparities remain. This article examines a key aspect of these disparities: biases in assessment and learning environments. Reviewing current literature, including in OBGYN, reveals persistent gender and racial biases in subjective clinical narrative assessments. The paper then outlines a 2-pronged approach for change: first, enhancing the learning environment, and subsequently, providing targeted recommendations for individuals, academic leaders, and healthcare institutions to effectively address these biases...

Despite the explosion of soil metagenomic data, we lack a synthesized understanding of patterns in the distribution and functions of soil microorganisms. These patterns are critical to predictions of soil microbiome responses to climate change and resulting feedbacks that regulate greenhouse gas release from soils. To address this gap, we assay 1,512 manually curated soil metagenomes using complementary annotation databases, read-based taxonomy, and machine learning to extract multidimensional genomic fingerprints of global soil microbiomes...

BACKGROUND: Esophageal cancer (EC) remains a global health challenge, often diagnosed at advanced stages, leading to high mortality rates. Current diagnostic tools for EC are limited in their efficacy. This study aims to harness the potential of microRNAs (miRNAs) as novel, noninvasive diagnostic biomarkers for EC. Our objective was to determine the diagnostic accuracy of miRNAs, particularly in distinguishing miRNAs associated with EC from control miRNAs. METHODS: We applied machine learning (ML) techniques in WEKA (Waikato Environment for Knowledge Analysis) and TensorFlow Keras to a dataset of miRNA sequences and gene targets, assessing the predictive power of several classifiers: naïve Bayes, multilayer perceptron, Hoeffding tree, random forest, and random tree...

Classic research has shown a division in the neuroanatomical structures that support flexible (e.g., short-cutting) and habitual (e.g., familiar route following) navigational behavior, with hippocampal-caudate systems associated with the former and putamen systems with the latter. There is, however, disagreement about whether the neural structures involved in navigation process particular forms of spatial information, such as associations between constellations of cues forming a cognitive map, versus single landmark-action associations, or alternatively, perform particular reinforcement learning algorithms that allow the use of different spatial strategies, so-called model-based (flexible) or model-free (habitual) forms of learning...

Objective: Climate change is recognised as the biggest threat to global health of the 21st century and impacts on health and wellbeing through a range of factors. Due to this, the need to take action in order to protect population health and wellbeing is becoming ever more urgent. Methods: In 2019, Public Health Wales carried out a comprehensive mixed-method Health Impact Assessment (HIA) of climate change. Unlike other risk assessments, it appraised the potential impact of climate change on health and inequalities in Wales through participatory workshops, stakeholder consultations, systematic literature reviews and case studies...

Social-educational robotics, such as NAO humanoid robots with social, anthropomorphic, humanlike features, are tools for learning, education, and addressing developmental disorders (e.g., autism spectrum disorder or ASD) through social and collaborative robotic interactions and interventions. There are significant gaps at the intersection of social robotics and autism research dealing with how robotic technology helps ASD individuals with their social, emotional, and communication needs, and supports teachers who engage with ASD students...

The pressing issue of atmospheric pollution has prompted the exploration of affordable methods for measuring and monitoring air contaminants as complementary techniques to standard methods, able to produce high-density data in time and space. The main challenge of this low-cost approach regards the in-field accuracy and reliability of the sensors. This study presents the development of low-cost stations for high-time resolution measurements of CO2 and CH4 concentrations calibrated via an in-field machine learning-based method...

AIM: Investigate the influence of organizational intelligence on the development of soft skills and the overall thriving of nurses at Alexandria Main University Hospital in Egypt. DESIGN: A cross-sectional descriptive design following STROBE guidelines examined the relationship between organizational intelligence, soft skills, and nurses' thriving. METHODS AND TOOLS: Data were collected from 740 nurses working across critical care units using structured questionnaires...

Emerging spatial computing systems seamlessly superimpose digital information on the physical environment observed by a user, enabling transformative experiences across various domains, such as entertainment, education, communication and training1-3 . However, the widespread adoption of augmented-reality (AR) displays has been limited due to the bulky projection optics of their light engines and their inability to accurately portray three-dimensional (3D) depth cues for virtual content, among other factors4,5 ...

A popular and widely suggested measure for assessing unilateral hand motor skills in stroke patients is the box and block test (BBT). Our study aimed to create an augmented reality enhanced version of the BBT (AR-BBT) and evaluate its correlation to the original BBT for stroke patients. Following G-power analysis, clinical examination, and inclusion-exclusion criteria, 31 stroke patients were included in this study. AR-BBT was developed using the Open Source Computer Vision Library (OpenCV). The MediaPipe's hand tracking library uses a palm and a hand landmark machine learning model to detect and track hands...

We propose a novel framework that combines state-of-the-art deep learning approaches with pre- and post-processing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size analyzers and those based on dilution, image processing, or deep learning, typically excel with homogeneous backgrounds. Yet, they often fall short in accurately detecting particles against the intricate and varied backgrounds characteristic of heterogeneous particle-substrate (HPS) interfaces in manufacturing...

AIM: This study aims to identify the prospective skill requirements for future radiographers practising in the United Arab Emirates (UAE). Such information will inform educational institutions, healthcare organisations, and policymakers in developing effective strategies. METHODS: A cross-sectional study was conducted involving currently practising radiographers, nuclear medicine technologists, sonographers, and radiation therapists in the UAE (n =74). A comprehensive survey questionnaire was developed and validated through piloting and expert consultations...

Personalised drug delivery enables a tailored treatment plan for each patient compared to conventional drug delivery, where a generic strategy is commonly employed. It can not only achieve precise treatment to improve effectiveness but also reduce the risk of adverse effects to improve patients' quality of life. Drug delivery involves multiple interconnected physiological and physicochemical processes, which span a wide range of time and length scales. How to consider the impact of individual differences on these processes becomes critical...

(Objective). Vagus nerve stimulation (VNS) is being investigated as a potential therapy for cardiovascular diseases including heart failure, cardiac arrhythmia, and hypertension. The lack of a systematic approach for controlling and tuning the VNS parameters poses a significant challenge. Closed-loop VNS strategies combined with artificial intelligence (AI) approaches offer a framework for systematically learning and adapting the optimal stimulation parameters. In this study, we presented an interactive AI framework using reinforcement learning (RL) for automated data-driven design of closed-loop VNS control systems in a computational study...

A solid understanding of fractions is the cornerstone for acquiring proficiency with rational numbers and paves the way for learning advanced mathematical concepts such as algebra. Fraction difficulties limit not only students' educational and vocational opportunities but also their ability to solve everyday problems. Students who exit sixth grade with inadequate understanding of fractions may experience far-reaching repercussions that lead to lifelong avoidance of mathematics. This article presents the results of a randomized controlled trial focusing on the first two cohorts of a larger efficacy investigation aimed at building fraction sense in students with mathematics difficulties...