In everyday life, we make decisions in groups about a variety of issues. In group decision-making, group members discuss options, exchange preferences and opinions, and make a common decision. Decision support systems and group recommender systems facilitate this process by enabling preference elicitation, generating recommendations, and supporting the process. We are here interested in building a conversational system, namely, a chat app, enhanced with an AI agent supporting the group decision-making process. To design the system, rather than solely relying on our assumptions, we took one step back and conducted a comprehensive focus group study. This approach has allowed us to gain original insights into the specific needs and preferences of the future end-users, i.e., group members, ensuring that our system design aligns more closely with their requirements. The focus group study involved fourteen participants in three group compositions: friends, families, and couples. Our findings reveal that most of the group members define a good choice as one that maximizes overall satisfaction without leaving any member dissatisfied. Dealing with competing group members emerged as a primary concern, with study participants requesting specific help from the AI agent to address this challenge. Participants identified personality and group structure as crucial characteristics for the AI agent to properly operate, though some expressed privacy concerns. Lastly, participants expected an AI agent to provide private interactions with individual members, proactively guide discussions when necessary, continually analyze group interactions, and tailor support to those interactions.

Machine Learning-based heuristics have recently shown impressive performance in solving a variety of hard combinatorial optimization problems (COPs). However they generally rely on a separate neural model, specialized and trained for each single problem. Any variation of a problem requires adjustment of its model and re-training from scratch. In this paper, we propose GOAL (for Generalist combinatorial Optimization Agent Learning), a generalist model capable of efficiently solving multiple COPs and which can be fine-tuned to solve new COPs. GOAL consists of a single backbone plus light-weight problem-specific adapters for input and output processing. The backbone is based on a new form of mixed-attention blocks which allows to handle problems defined on graphs with arbitrary combinations of node, edge and instance-level features. Additionally, problems which involve heterogeneous types of nodes or edges are handled through a novel multi-type transformer architecture, where the attention blocks are duplicated to attend the meaningful combinations of types while relying on the same shared parameters. We train GOAL on a set of routing, scheduling and classic graph problems and show that it is only slightly inferior to the specialized baselines while being the first multi-task model that solves a wide range of COPs. Finally we showcase the strong transfer learning capacity of GOAL by fine-tuning it on several new problems. Our code is available at https://github.com/naver/goal-co/.

Machine Learning-based heuristics have recently shown impressive performance in solving a variety of hard combinatorial optimization problems (COPs). However, they generally rely on a separate neural model, specialized and trained for each single problem. Any variation of a problem requires adjustment of its model and re-training from scratch. In this paper, we propose GOAL (for Generalist combinatorial Optimization Agent Learner), a generalist model capable of efficiently solving multiple COPs and which can be fine-tuned to solve new COPs. GOAL consists of a single backbone plus light-weight problem-specific adapters for input and output processing. The backbone is based on a new form of mixed-attention blocks which allows to handle problems defined on graphs with arbitrary combinations of node, edge and instance-level features. Additionally, problems which involve heterogeneous types of nodes or edges are handled through a novel multi-type transformer architecture, where the attention blocks are duplicated to attend the meaningful combinations of types while relying on the same shared parameters. We train GOAL on a set of routing, scheduling and classic graph problems and show that it is only slightly inferior to the specialized baselines while being the first multi-task model that solves a wide range of COPs. Finally we showcase the strong transfer learning capacity of GOAL by fine-tuning it on several new problems. Our code is available at https://github.com/naver/goal-co/.

The purpose of the study was to evaluate and identifying the level of excess weight and obesity in older students between 15 and 18 years, as important benchmarks of the level of health in order to update the recommendations regarding the promotion of an active and healthy lifestyle. A cross-sectional study was conducted on a sample of 400 subjects, (186 boys and 214 girls), aged 15 to 18. Anthropometric data including: body height, body weight, Body Mass Index (BMI). Participants' BMI was estimated using the Percentile BMI calculator for children and teenagers aged 2 to 19. Study adolescents were defined as underweight, normal (healthy) weight, overweight, and obese according to the CDC child growth characteristics for age, sex, and BMI. 350 (85.5%) subjects were healthy weight; 26 respondents (6.5%) were overweight, 17 (4.25%), were obese, while 7 (1.75%) underweight. The analysis of the individual results of male and female subjects points to increased values of the body mass of males (18.81%), compared to female pupils (3%). Out of a total of 186 male students, 10.75% were in the overweight category, and 8.06% were categorized as obese, in constrast 2.80% of the girls were overweight and (1%<), in the obese category, which is an outstanding result, where obesity practically does not exist. According to the results of this study (for both sexes), in relation to gender, there were more malnourished girls (2.33%), compared to boys (1%<). Among high school students in Bosnia and Herzegovina, the number of children with overweight and obesity is relatively low compared to data from other countries. Based on the relevant results of this study, we consider it necessary to update strategies for promoting an active and healthy lifestyle regarding physical activity and eating habits for adolescents in relation to the specifics of the countries of residence and European trends. Keywords: BMI; students; overweight; obesity; weight status category; high school.

The purpose of this study was to evaluate the spatiotemporal immunoexpression pattern of microtubule-associated protein 1 light chain 3 beta (LC3B), glucose-regulated protein 78 (GRP78), heat shock protein 70 (HSP70), and lysosomal-associated membrane protein 2A (LAMP2A) in normal human fetal kidney development (CTRL) and kidneys affected with congenital anomalies of the kidney and urinary tract (CAKUT). Human fetal kidneys (control, horseshoe, dysplastic, duplex, and hypoplastic) from the 18th to the 38th developmental week underwent epifluorescence microscopy analysis after being stained with antibodies. Immunoreactivity was quantified in various kidney structures, and expression dynamics were examined using linear and nonlinear regression modeling. The punctate expression of LC3B was observed mainly in tubules and glomerular cells, with dysplastic kidneys displaying distinct staining patterns. In the control group’s glomeruli, LAMP2A showed a sporadic, punctate signal; in contrast to other phenotypes, duplex kidneys showed significantly stronger expression in convoluted tubules. GRP78 had a weaker expression in CAKUT kidneys, especially hypoplastic ones, while normal kidneys exhibited punctate staining of convoluted tubules and glomeruli. HSP70 staining varied among phenotypes, with dysplastic and hypoplastic kidneys exhibiting stronger staining compared to controls. Expression dynamics varied among observed autophagy markers and phenotypes, indicating their potential roles in normal and dysfunctional kidney development.

Machine Learning-based heuristics have recently shown impressive performance in solving a variety of hard combinatorial optimization problems (COPs). However they generally rely on a separate neural model, specialized and trained for each single problem. Any variation of a problem requires adjustment of its model and re-training from scratch. In this paper, we propose GOAL (for Generalist combinatorial Optimization Agent Learning), a generalist model capable of efficiently solving multiple COPs and which can be fine-tuned to solve new COPs. GOAL consists of a single backbone plus light-weight problem-specific adapters for input and output processing. The backbone is based on a new form of mixed-attention blocks which allows to handle problems defined on graphs with arbitrary combinations of node, edge and instance-level features. Additionally, problems which involve heterogeneous types of nodes or edges are handled through a novel multi-type transformer architecture, where the attention blocks are duplicated to attend the meaningful combinations of types while relying on the same shared parameters. We train GOAL on a set of routing, scheduling and classic graph problems and show that it is only slightly inferior to the specialized baselines while being the first multi-task model that solves a wide range of COPs. Finally we showcase the strong transfer learning capacity of GOAL by fine-tuning it on several new problems. Our code is available at https://github.com/naver/goal-co/.

Human mitochondrial genes MT-ATP6 and MT-ATP8 encode the subunits 6 and 8, respectively, of ATP synthase, a vital protein Complex V intricately involved in oxidative phosphorylation and ATP metabolism. This enzyme produces ATP from ADP in the mitochondrial matrix utilizing energy provided by the proton electrochemical gradient. Pathogenic mutations within these genes have been linked to various syndromes such as NARP syndrome, Leigh syndrome, mitochondrial myopathy with reversible cytochrome C oxidase deficiency, and progressive spastic paraparesis, among others. In our investigation, we sequenced 24 complete human mitochondrial genomes of healthy adult individuals from Bosnia and Herzegovina, each representing unique maternal lineage. Employing the Illumina MiSeq NGS platform and the Nextera XT DNA library preparation protocol, we obtained raw NGS reads. Subsequent analysis utilizing SAMtools enabled the identification of genetic variants within the MT-ATP6 and MT-ATP8 genes. We identified a total of 11 SNPs, including three in MT-ATP8 and eight in MT-ATP6, with none of them being associated with any mitochondrial diseases or conditions. Our results align well with previously reported genome variation data for European populations and set the groundwork for future mtDNA analysis for clinical purposes in Bosnia and Herzegovina.

Modern social movement imposes need for researching cultural policy, especially from their aspect of their creation and implementation at the local community level. Research conducted so far on development of local government and local policy in Bosnia and Herzegovina were primarily directed on economic and legally – the political aspect of local government. Goal of this research is advantage and possibility analysis, limiting the local growth of cultural politics on the level of territorial organisation of government in Bosnia and Herzegovina. What problems does the government face during creation of cultural policy? How are local and cultural identities formed? Do local governments have strategies for cultural development? In what measure is culture recognised as a developing chance especially in the time when Bosnia and Herzegovina becomes alternative tourist destination? How are cultural projects financed and in what measure do citizens show interest to participate in the growth of culture and local culture politics? By a strategic approach this research is contributing towards the definement of modern model culture policy, it is contributed to a total community growth of local communities.

Background and Objectives We recently developed a model (PROCEED) that predicts the occurrence of persistent perfusion deficit (PPD) at 24 hours in patients with incomplete angiographic reperfusion after thrombectomy. This study aims to externally validate the PROCEED model using prospectively acquired multicenter data. Methods Individual patient data for external validation were obtained from the Endovascular Therapy for Ischemic Stroke with Perfusion-Imaging Selection, Tenecteplase versus Alteplase Before Endovascular Therapy for Ischemic Stroke part 1 and 2 trials, and a prospective cohort of the Medical University of Graz. The model's primary outcome was the occurrence of PPD, defined as a focal, wedge-shaped perfusion delay on 24-hour follow-up perfusion imaging that corresponds to the capillary phase deficit on last angiographic series in patients with <Thrombolysis in Cerebral Infarction 3 reperfusion after thrombectomy. The model's performance was evaluated with discrimination, calibration accuracy, and clinical decision curves. Results We included 371 patients (38% with PPD). The externally validated model had good discrimination (C-statistic 0.81, 95% CI 0.77–0.86) and adequate calibration (intercept 0.25, 95% CI 0.21–0.29 and slope 0.98, 95% CI 0.90–1.12). Across a wide range of probability thresholds (i.e., depending on the physicians’ preferences on how the model should be used), the model shows net benefit on clinical decision curves, informing physicians on the likelihood of PPD. If a physician's attitude toward false-positive and false-negative ratings is equal, the model would reduce 13 in 100 unnecessary interventions by correctly predicting complete delayed reperfusion, without missing a patient with PPD. Discussion The externally validated model had adequate predictive accuracy and discrimination. Depending on the acceptable threshold probability, the model accurately predicts persistent incomplete reperfusion and may advise physicians whether additional reperfusion attempts should be performed.

Abstract Purpose Ultrasound is a highly effective imaging tool for assessing abnormalities within the breast. However, especially the identification of malignant tumors of the breast mimicking fibroadenomas (MTMF) by means of breast ultrasound can be challenging. This study aimed to identify reliable imaging characteristics of MTMF. Materials and Methods This retrospective study was approved by the local ethics review board. After screening 623 patients, 421 cases with histologically verified fibroadenomas and MTMF between 2011 and 2021 were included. Sonographic features were compared to histopathological results and an algorithm-based quantitative ranking of predictors contributing most to the correct classification of malignant tumors was conducted. Results A total of 363 benign, 18 intermediate, and 40 malignant lesions were analyzed. Algorithm-based quantitative ranking showed that the most predictive features indicating malignancy were a hyperechoic rim (gain ratio merit 0.135 ± 0.004), an irregular border (0.057 ± 0.002), perilesional stiffening (0.054 ± 0.002), pectoral contact (0.051 ± 0.003), an irregular shape (0.029 ± 0.001), and irregular vasculature (0.027 ± 0.002). Conclusion Ultrasound findings for fibroadenomas vary, making identification of MTMF challenging. Features such as indistinct margins and increased perilesional echogenicity are predictors for malignancy and should be considered during sonographic evaluation of fibroadenomas and MTMF.

Sport, particularly in the realm of professional competition, is a domain of human endeavor that is increasingly dependent on the use of analytical statistical information. Consequently, mathematics and statistics are becoming increasingly crucial elements in sports. Although experts recognize the importance of analytics in women’s basketball, the literature addressing this subject remains limited. The objective of this study is to employ quantitative methodologies to discover prevailing patterns in global women’s basketball representation. The entities examined in this article were the games contested during the 2021 Olympic Games, the 2022 World Cup, and the 2023 continental championships. Two regression models were created for the research, using thirteen standard variables observed in the game. The evaluation of the regression model was conducted using the stepwise regression method, incorporating dimensionality reduction based on the outcomes of factor analysis. Among the 14 models that were observed, 13 of them exhibited strong and moderate linkages, while only 1 displayed weak connections and lacked statistical significance. The primary factors that account for the disparity between winning and losing teams in games are primarily associated with shooting accuracy toward the basket. When examining individual championships, the percentage surpassed 50% in all cases except for AfroBasket. However, when considering the overall results, the significance of shooting rose to 86%. The variable representing offensive rebound efficiency had a significant influence on the outcome, being present in all individual competitions, whereas defensive rebound efficiency was only considered in the overall results.

Chickpea and lentils are one of the most important legumes not only as sources of food and nutrients but also for enrichment of soil as a nitrogen fixating crop. An early onset of higher temperatures and drought are affecting chickpea and lentil growth and flowering leading to reduction of yield. In search for a tolerant varieties presented study performed a large-scale screening of two legume varieties (chickpea and lentils) investigating phenotypical response to early onset of drought under heat stress. Under heat stress and two different irrigation conditions, 19 chickpea and 18 lentil accessions were examined. The evaluation focused on their growth, biomass production, and flowering rate in comparison to commercially available varieties. Six chickpea accessions showed tolerance to water stress while only two lentil accessions differed from the rest of tested accessions. Generally, lentils genotypes were less stressed by decreased water availability compared to chickpea. Large scale screening of legume accessions could be a valuable tool to identify new varieties that could show phenotypical traits more adaptable to climate related environmental stresses. To improve the reproductive efficiency in chickpeas and lentils under adverse conditions associated to climate change an extensive breeding effort should be focused on investigation of more tolerant genotypes and cultivation in crop systems.