Objective. This paper presents a novel domain adaptation (DA) framework to enhance the accuracy of electroencephalography (EEG)-based auditory attention classification, specifically for classifying the direction (left or right) of attended speech. The framework aims to improve the performances for subjects with initially low classification accuracy, overcoming challenges posed by instrumental and human factors. Limited dataset size, variations in EEG data quality due to factors such as noise, electrode misplacement or subjects, and the need for generalization across different trials, conditions and subjects necessitate the use of DA methods. By leveraging DA methods, the framework can learn from one EEG dataset and adapt to another, potentially resulting in more reliable and robust classification models. Approach. This paper focuses on investigating a DA method, based on parallel transport, for addressing the auditory attention classification problem. The EEG data utilized in this study originates from an experiment where subjects were instructed to selectively attend to one of the two spatially separated voices presented simultaneously. Main results. Significant improvement in classification accuracy was observed when poor data from one subject was transported to the domain of good data from different subjects, as compared to the baseline. The mean classification accuracy for subjects with poor data increased from 45.84% to 67.92%. Specifically, the highest achieved classification accuracy from one subject reached 83.33%, a substantial increase from the baseline accuracy of 43.33%. Significance. The findings of our study demonstrate the improved classification performances achieved through the implementation of DA methods. This brings us a step closer to leveraging EEG in neuro-steered hearing devices.

In this paper, we introduce and provide insight into the two innovative applications designed to enhance the lives of persons with Down syndrome, focusing on seamless integration between the two. The first is a mobile application that helps users manage their daily routines by monitoring and predicting activity durations, considering their unique challenges. The second is a web application for parents/teachers/other adults to streamline activity scheduling, progress tracking, and reminders.

Webpage layout presentation failures can negatively affect the usability of a web application as well as the end-to-end user experience. The need for automated methods of visual inspection becomes obvious in complex web applications. However, visual inspection still heavily relies on manual inspection because the tools currently available are not yet advanced enough. This paper compares the performance results of three visual testing tools: Galen, AyeSpy, and Percy, and focuses on opportunities for their enhancement.

The use of augmented reality (AR) allows for the integration of digital information onto our perception of the physical world. In this article, we present a comprehensive review of previously published literature on the implementation of AR in physics education, at the school and the university level. Our review includes an analysis of 96 papers from the Scopus and Eric databases, all of which were published between 1st January 2012 and 1st January 2023. We evaluated how AR has been used for facilitating learning about physics. Potential AR-based learning activities for different physics topics have been summarized and opportunities, as well as challenges associated with AR-based learning of physics have been reported. It has been shown that AR technologies may facilitate physics learning by providing complementary visualizations, optimizing cognitive load, allowing for haptic learning, reducing task completion time and promoting collaborative inquiry. The potential disadvantages of using AR in physics teaching are mainly related to the shortcomings of software and hardware technologies (e.g. camera freeze, visualization delay) and extraneous cognitive load (e.g. paying more attention to secondary details than to constructing target knowledge).

This paper explores the importance and role of soft skills in the context of education and career development. Education, as a key stage in the process of accumulation of knowledge and skills, constitutes a fundamental experience in the formation of individual competencies. In most of their lives, individuals devote considerable time and intellectual energy to the process of education, within which they acquire various forms of knowledge and skills. The acquired knowledge and skills then become key elements that shape their future professional career. But what separates highly successful individuals from the rest is often not the technical competencies acquired through formal education, but the so-called “soft skills”. Research on this topic becomes imperative in order to better understand the impact of soft skills on the performance of individuals in order to develop strategies to improve these skills. This paper recognizes the key role of soft skills in education and encourages further research to better understand their importance and to ensure that these skills are comprehensively incorporated into educational and professional programs. The research was conducted on a sample of students from the technical faculties of “Dzemal Bijedic” University of Mostar.

Circular RNAs (circRNAs) are single-stranded molecules that have attracted increasing attention in recent years due to their covalently closed structure and their diverse functional roles in mammalian cells, where they are involved in the regulation of gene expression and protein function. Increasing evidence suggests that circRNAs have similar functions in plants, where they play a role in plant development, resistance to biotic stress, and abiotic stress tolerance. Here, we investigated the agronomically relevant question of whether synthetic designer circRNAs can be used to modulate in a sequence-specific manner gene expression in plants. We show that treatment of GFP-expressing Arabidopsis protoplasts with designer 50 nt GFP antisense circRNA (circRNAGFP) reduces the cellular accumulation of the reporter protein in a sequence-specific and dose-dependent manner. This inhibitory activity of circRNAGFP was not abolished in various Arabidopsis ago and dcl mutants with defective RNAi pathways. Moreover, and in contrast to other types of RNA such as double-stranded (ds)RNA, circRNAs did not induce a PTI response in plant leaves. We discuss the possibility that circRNA may be applied to regulate endogenous plant genes and thus may have future potential as a novel bioherbicide.

Objectives This study aims to present an overview of the formal recognition of COVID-19 as occupational disease (OD) or injury (OI) across Europe. Methods A COVID-19 questionnaire was designed by a task group within COST-funded OMEGA-NET and sent to occupational health experts of 37 countries in WHO European region, with a last update in April 2022. Results The questionnaire was filled out by experts from 35 countries. There are large differences between national systems regarding the recognition of OD and OI: 40% of countries have a list system, 57% a mixed system and one country an open system. In most countries, COVID-19 can be recognised as an OD (57%). In four countries, COVID-19 can be recognised as OI (11%) and in seven countries as either OD or OI (20%). In two countries, there is no recognition possible to date. Thirty-two countries (91%) recognise COVID-19 as OD/OI among healthcare workers. Working in certain jobs is considered proof of occupational exposure in 25 countries, contact with a colleague with confirmed infection in 19 countries, and contact with clients with confirmed infection in 21 countries. In most countries (57%), a positive PCR test is considered proof of disease. The three most common compensation benefits for COVID-19 as OI/OD are disability pension, treatment and rehabilitation. Long COVID is included in 26 countries. Conclusions COVID-19 can be recognised as OD or OI in 94% of the European countries completing this survey, across different social security and embedded occupational health systems.

Renal cell carcinoma (RCC) represents around 3% of all cancers, with the most frequent histological types being clear-cell RCC (ccRCC), followed by papillary (pRCC) and chromophobe (chRCC). Hypoxia-inducible factors (HIFs), which promote the expression of various target genes, including vascular endothelial growth factor (VEGF) and the high- affinity glucose transporter 1, have an important role in the pathogenesis of RCC. This study investigated the immunohistochemical expression of HIF-1α and VEGF-A, showing significantly higher HIF-1α nuclear expression in pRCC compared to ccRCC, while there was no significant difference in VEGF-A protein expression between the analyzed histological RCC subtypes. The quantitative reverse transcription polymerase chain reaction for HIF1A showed no statistical difference between histological types. Data from publicly available RNA sequencing databases were analyzed and showed that, compared to healthy kidney tissue, VEGFA was significantly up-regulated in ccRCC and significantly down-regulated in pRCC. The comparison between histological subtypes of RCC revealed that VEGFA was significantly up-regulated in ccRCC compared to both pRCC and chRCC. There was no statistically significant difference in survival time between HIF1A high- and low-expression groups of patients. As for VEGFA expression, pRCC patients with low expression had a significantly higher survival rate compared to patients with high VEGFA expression.

This article is linked to Janssen et al papers. To view these articles, visit https://doi.org/10.1111/apt.17718 and https://doi.org/10.1111/apt.17745

The deep operator network (DeepONet) structure has shown great potential in approximating complex solution operators with low generalization errors. Recently, a sequential DeepONet (S-DeepONet) was proposed to use sequential learning models in the branch of DeepONet to predict final solutions given time-dependent inputs. In the current work, the S-DeepONet architecture is extended by modifying the information combination mechanism between the branch and trunk networks to simultaneously predict vector solutions with multiple components at multiple time steps of the evolution history, which is the first in the literature using DeepONets. Two example problems, one on transient fluid flow and the other on path-dependent plastic loading, were shown to demonstrate the capabilities of the model to handle different physics problems. The use of a trained S-DeepONet model in inverse parameter identification via the genetic algorithm is shown to demonstrate the application of the model. In almost all cases, the trained model achieved an $$R^2$$ R 2 value of above 0.99 and a relative $$L_2$$ L 2 error of less than 10% with only 3200 training data points, indicating superior accuracy. The vector S-DeepONet model, having only 0.4% more parameters than a scalar model, can predict two output components simultaneously at an accuracy similar to the two independently trained scalar models with a 20.8% faster training time. The S-DeepONet inference is at least three orders of magnitude faster than direct numerical simulations, and inverse parameter identifications using the trained model are highly efficient and accurate.

ABSTRACT Introduction Social knowledge is an important aspect of social cognition that pertains to broader knowledge of social concepts and norms. People with intellectual disabilities are more likely to experience mental health challenges, and it’s important to pay special attention to how comorbid conditions can affect their social cognition skills, potentially weakening these skills. Consequently, the present study seeks to compare social knowledge between two groups of adults in Bosnia and Herzegovina: those with intellectual disabilities and those diagnosed with a dual diagnosis encompassing intellectual disability and psychiatric conditions. An additional goal was to identify the factors contributing to social knowledge in these groups. Methods The study sample included 62 adults with mild intellectual disability, divided into two groups based on their comorbid psychiatric condition. We used a demographic questionnaire, Raven’s Progressive Matrices, Peabody Picture Vocabulary Test (PPVT), MINI PAS – ADD scale, and The Social Knowledge Test to assess social knowledge and intellectual functioning. We compared the social knowledge scores between the two groups and identified the predictors of social knowledge in each group. Results The results indicated that adult participants with dual diagnoses had lower social knowledge scores than those with intellectual disabilities only, even after controlling for intellectual functioning and verbal abilities. The predictors of social knowledge differed between the two groups, with age being the only statistically significant predictor in both groups. In individuals with intellectual disabilities, age and the presence of obsessive-compulsive disorder symptoms were important predictors of social knowledge, while in the group of participants with dual diagnoses, age and PPVT were significant predictors of social knowledge. Conclusion This study highlights the importance of social knowledge in individuals with intellectual disabilities and dual diagnoses. The findings suggest that individuals with dual diagnoses may have a specific deficit in social knowledge that is not fully explained by their intellectual functioning or verbal abilities. Clinicians and educators should focus on identifying and addressing social knowledge deficits in individuals with dual diagnoses to improve their overall social functioning.

The optimal reperfusion technique in patients with isolated posterior cerebral artery (PCA) occlusion is uncertain. We compared clinical and technical outcomes with first‐line stent retriever (SR), contact aspiration (CA), or combined techniques in patients with isolated PCA occlusion. This international case–control study was conducted at 30 sites in Europe and North America and included consecutive patients with isolated PCA occlusion presenting within 24 hours of time last seen well from January 2015 to August 2022. The primary outcome was the first‐pass effect (FPE), defined as expanded Treatment in Cerebral Infarction (TICI) 2c/3 on the first pass. Patients treated with SR, CA, or combined technique were compared with multivariable logistic regression. There were 326 patients who met inclusion criteria, 56.1% male, median age 75 (interquartile range 65–82) years, and median National Institutes of Health Stroke Scale score 8 (5–12). Occlusion segments were PCA‐P1 (53.1%), P2 (40.5%), and other (6.4%). Intravenous thrombolysis was administered in 39.6%. First‐line technique was SR, CA, and combined technique in 43 (13.2%), 106 (32.5%), and 177 (54.3%) patients, respectively; FPE was achieved in 62.8%, 42.5%, and 39.6%, respectively. FPE was lower in patients treated with first‐line CA or combined technique compared with SR (CA versus SR: adjusted odds ratio 0.45 [0.19–1.06]; P =0.07; combined versus SR: adjusted odds ratio 0.35 [0.016–0.80]; P =0.01). There were lower odds of functional independence (modified Rankin scale score 0–2) in the first‐line CA versus SR alone group (adjusted odds ratio 0.52 [0.28–0.95]; P =0.04). FPE was associated with higher rates of favorable outcomes (modified Rankin scale score 0–2: 58% versus 43.4%; P =0.01; modified Rankin scale score 0–1: 36.6% versus 25.8%; P =0.05). Overall, symptomatic intracranial hemorrhage was present in 5.6% (18/326) and mortality in 10.9% (35/326) without difference between first‐line technique. In patients with isolated PCA occlusion, SR was associated with a higher rate of FPE compared with CA or combined techniques with no difference in final successful reperfusion. Functional independence at 90 days was more likely with first‐line SR compared with CA. FPE was associated with better 90‐day clinical outcomes.

Early diagnosis and treatment of brain cancer depend on the detection and categorization of brain tumors. Deep learning algorithms have produced amazing results in medical imaging applications including tumor identification. Most of this field's research has concentrated on applying CNN algorithms like VGG16, DNN, and ANN to this problem. This work describes the identification and classification of brain tumors using the Python Imaging Library (PIL) and the VGG16 deep learning algorithm. A dataset of 7000 MRI pictures categorized by tumor type served as the foundation for the research. The main objective of this study was to develop a high-efficiency, high-accuracy model. We suggested utilizing the VGG16 architecture and preprocessing images with PIL to ensure consistent images for training on a sizable dataset of brain magnetic resonance imaging (MRI) images. A novel technique we have used in our work is one that can analyze a single image and predict the presence of a tumor from the results. The research's methods produced robust tumor detection across the dataset with 96, 9% accuracy, indicating the value of the method in helping medical professionals make informed decisions when diagnosing the presence of tumors.

Introduction The management of long-term physical conditions is a challenge worldwide, absorbing a majority resources despite the importance of acute care. The management of these conditions is done largely in primary care and so interventions to improve primary care could have an enormous impact. However, very little data exist on how to do this. Mental distress is frequently comorbid with long term physical conditions, and can impact on health behaviour and adherence, leading to poorer outcomes. DIALOG+ is a low-cost, patient-centred and solution-focused intervention, which is used in routine patient-clinician meetings and has been shown to improve outcomes in mental health care. The question arises as to whether it could also be used in primary care to improve the quality of life and mental health of patients with long-term physical conditions. This is particularly important for low- and middle-income countries with limited health care resources. Methods An exploratory non-controlled multi-site trial was conducted in Bosnia and Herzegovina, Colombia, and Uganda. Feasibility was determined by recruitment, retention, and session completion. Patient outcomes (quality of life, anxiety and depression symptoms, objective social situation) were assessed at baseline and after three approximately monthly DIALOG+ sessions. Results A total of 117 patients were enrolled in the study, 25 in Bosnia and Herzegovina, 32 in Colombia, and 60 in Uganda. In each country, more than 75% of anticipated participants were recruited, with retention rates over 90% and completion of the intervention exceeding 92%. Patients had significantly higher quality of life and fewer anxiety and depression symptoms at post-intervention follow-up, with moderate to large effect sizes. There were no significant improvements in objective social situation. Conclusion The findings from this exploratory trial suggest that DIALOG+ is feasible in primary care settings for patients with long-term physical conditions and may substantially improve patient outcomes. Future research may test implementation and effectiveness of DIALOG+ in randomized controlled trials in wider primary care settings in low- and middle-income countries. Trial registration All studies were registered prospectively within the ISRCTN Registry. ISRCTN17003451, 02/12/2020 (Bosnia and Herzegovina), ISRCTN14018729, 01/12/2020 (Colombia) and ISRCTN50335796, 02/12/2020 (Uganda).