This paper presents an intensive case study of 10 participants in the US and South Korea interacting with a robotic companion pet in their own homes over the course of several weeks. Participants were tracked every second of every day during that period of time. The fundamental goal was to determine whether there were significant differences in the types of interactions that occurred across those cultural settings, and how those differences affected modeling of the human-robot interactions. We collected a mix of quantitative and qualitative data through sensors onboard the robot, ecological momentary assessment (EMA), and participant interviews. Results showed that there were significant differences in how participants in Korea interacted with the robotic pet relative to participants in the US, which impacted machine learning and deep learning models of the interactions. Moreover, those differences were connected to differences in participant perceptions of the robot based on the qualitative interviews. The work here suggests that it may be necessary to develop culturally-specific models and/or sensor suites for human-robot interaction (HRI) in the future, and that simply adapting the same robot's behavior through cultural homophily may be insufficient.

INTRODUCTION Bosnia and Herzegovina (B&H) belongs to the countries of the Western Balkan located in Southeast Europe with an area of 51 129 km. The country is located in the continental part of Europe and extends to the Adriatic Sea. B&H is bordering Croatia in the north, west, and south; in the east, the country is bordered by Serbia, in the southeast by Montenegro, and in the southwest by the Adriatic Sea (Figure 1). According to the 2013 population census, B&H has 3 531 159 inhabitants, which is significantly less than in the 1991 census when the country counted 4 377 033. There are 2 219 220 people living in the Federation of B&H and 1 228 423 in the Republic of Srpska, and 83 880 people live in the Brčko District.

ABSTRACT Child sexual abuse (CSA) affects all cultures and sociodemographic sections. As health professionals, pediatricians are often the first line of defense when it comes to CSA identification and reporting. Our previous study examined encounters, competence, beliefs, and reporting regarding CSA among pediatricians in countries of the former Yugoslavia, Serbia, and Montenegro. Following up on this work, we expanded our research to another neighboring country, the Republic of Srpska (RS). Sixty pediatricians were surveyed in 2020, comprising 30% of all practicing pediatricians in the Republic of Srpska, Bosnia and Hercegovina. Compared to their colleagues from Serbia and Montenegro, RS pediatricians revealed an increased perception that children are often abused by someone they do know well, were more confident in their abilities to detect CSA, reported a higher number of CSA cases, and were more interested in further trainings on the subject of CSA. Despite this, the level of underreporting remained high (40%). To our knowledge, this is the first study to both assess and compare CSA-related attitudes in this part of the world. Key differences between RS, other two neighboring countries as well as from other countries worldwide are likely to stem from the level of pediatricians’ training and education, as well as from the socio-cultural and legislation context. Although encouraging, our data indicate a need for further research in the countries of the former Yugoslavia.

INTRODUCTION: Early detection of colorectal cancer (CRC) by screening programs is crucial because survival rates worsen at advanced stages. However, the currently used screening method, the fecal immunochemical test (FIT), suffers from a high number of false-positives and is insensitive for detecting advanced adenomas (AAs), resulting in false-negatives for these premalignant lesions. Therefore, more accurate, noninvasive screening tools are needed. In this study, the utility of analyzing volatile organic compounds (VOCs) in exhaled breath in a FIT-positive population to detect the presence of colorectal neoplasia was studied. METHODS: In this multicenter prospective study, breath samples were collected from 382 FIT-positive patients with subsequent colonoscopy participating in the national Dutch bowel screening program (n = 84 negative controls, n = 130 non-AAs, n = 138 AAs, and n = 30 CRCs). Precolonoscopy exhaled VOCs were analyzed using thermal desorption-gas chromatography-mass spectrometry, and the data were preprocessed and analyzed using machine learning techniques. RESULTS: Using 10 discriminatory VOCs, AAs could be distinguished from negative controls with a sensitivity and specificity of 79% and 70%, respectively. Based on this biomarker profile, CRC and AA combined could be discriminated from controls with a sensitivity and specificity of 77% and 70%, respectively, and CRC alone could be discriminated from controls with a sensitivity and specificity of 80% and 70%, respectively. Moreover, the feasibility to discriminate non-AAs from controls and AAs was shown. DISCUSSION: VOCs in exhaled breath can detect the presence of AAs and CRC in a CRC screening population and may improve CRC screening in the future.

Sleeping disease is a highly infectious viral disease caused by salmonid alphavirus subtype 2 (SAV2 FW), affecting mainly rainbow trout Oncorhynchus mykiss farmed in freshwater. During March to May 2014, disease episodes with clinical signs of sleeping disease in rainbow trout fingerlings occurred almost simultaneously in 2 trout farms located in Bosnia and Herzegovina (BiH) and Serbia. The infection of rainbow trout with SAV2 FW in 2 farms was confirmed by virus isolation and molecular methods. This is the first isolation and molecular characterization of SAV2 FW in BiH and Serbia.

Abstract Background Reproductive effects of ionizing radiation in organisms have been observed under laboratory and field conditions. Such assessments often rely on associations between exposure and effects, and thus lacking a detailed mechanistic understanding of causality between effects occurring at different levels of biological organization. The Adverse Outcome Pathway (AOP), a conceptual knowledge framework to capture, organize, evaluate and visualize the scientific knowledge of relevant toxicological effects, has the potential to evaluate the causal relationships between molecular, cellular, individual, and population effects. This paper presents the first development of a set of consensus AOPs for reproductive effects of ionizing radiation in wildlife. This work was performed by a group of experts formed during a workshop organized jointly by the Multidisciplinary European Low Dose Initiative (MELODI) and the European Radioecology Alliance (ALLIANCE) associations to present the AOP approach and tools. The work presents a series of taxon-specific case studies that were used to identify relevant empirical evidence, identify common AOP components and propose a set of consensus AOPs that could be organized into an AOP network with broader taxonomic applicability. Conclusion Expert consultation led to the identification of key biological events and description of causal linkages between ionizing radiation, reproductive impairment and reduction in population fitness. The study characterized the knowledge domain of taxon-specific AOPs, identified knowledge gaps pertinent to reproductive-relevant AOP development and reflected on how AOPs could assist applications in radiation (radioecological) research, environmental health assessment, and radiological protection. Future advancement and consolidation of the AOPs is planned to include structured weight of evidence considerations, formalized review and critical assessment of the empirical evidence prior to formal submission and review by the OECD sponsored AOP development program.

Circuit breakers (CBs) play an important role in modern society because they make the power transmission and distribution systems reliable and resilient. Therefore, it is important to maintain their reliability and to monitor their operation. A key to ensure a reliable operation of CBs is to monitor their condition. In this work, we performed an accelerated life testing for mechanical failures of a vacuum circuit breaker (VCB) by performing close-open operations continuously until failure. We recorded data for each operation and made the collected run-to-failure dataset publicly available. In our experiments, the VCB operated more than 26000 close-open operations without current load with the time span of five months. The run-to-failure long-term monitoring enables us to monitor the evolution of the VCB condition and the degradation over time. To monitor CB condition, closing time is one of the indicators, which is usually measured when the CB is taken out of operation and is completely disconnected from the network. We propose an algorithm that enables to infer the same information on the closing time from a non-intrusive sensor. By utilizing the short-time energy (STE) of the vibration signal, it is possible to identify the key moments when specific events happen including the time when the latch starts to move, and the closing time. The effectiveness of the proposed algorithm is evaluated on the VCB dataset and is also compared to the binary segmentation (BS) change point detection algorithm. This research highlights the potential for continuous online condition monitoring, which is the basis for applying future predictive maintenance strategies.

Leydig cells produce testosterone and insulin‐like 3, two hormones essential for male sex differentiation and reproductive function. The orphan nuclear receptor, chicken ovalbumin upstream promoter transcription factor type II (COUP‐TFII), and the zinc finger factor GATA4 are two transcription factors involved in Leydig cell differentiation, gene expression, and function.

A 61-year-old male presented to emergency department with symptoms of shortness of breath, palpitations, and night sweats. We performed bedside transthoracic echocardiography which showed shunt from the left ventricle to the right atrium in systole with color Doppler examination. Gerbode-type ventricular septal defect and an image of a fibrillar, mobile mass compatible with vegetation was observed just above the tricuspid valve. We performed transesophageal echocardiography which showed vegetations on the aortic valve noncoronary cusp. Two sets of blood cultures were positive for Streptococcus sanguinis. The patient was evaluated by the heart team and an operation decision was made for the patient. The patient underwent surgery after 2 weeks of antibiotic theraphy. In the surgery, the Gerbode-type ventricular septal defect was closed with a polytetrafluoroethylene patch. Tricuspid annuloplasty was performed with De Vega technique. Mechanical aortic valve was implanted. Postoperative transthoracic echocardiography showed no residual shunt.

High-throughput sequencing provides sufficient means for determining genotypes of clinically important pharmacogenes that can be used to tailor medical decisions to individual patients. However, pharmacogene genotyping, also known as star-allele calling, is a challenging problem that requires accurate copy number calling, structural variation discovery, variant calling and phasing within each pharmacogene copy present in the sample. Here we introduce Aldy 4, a fast and efficient tool for genotyping pharmacogenes that utilizes combinatorial optimization for accurate star-allele calling across different sequencing technologies. Aldy 4 adds support for long reads and ships with a novel phasing model and improved copy number and variant calling models. We compare Aldy 4 against the current state-of-the-art star-allele callers on a large and diverse set of samples and genes sequenced by various sequencing technologies, such as whole-genome and targeted Illumina sequencing, barcoded 10X Genomics and PacBio HiFi. We show that Aldy 4 is the most accurate star-allele caller with near-perfect accuracy in all evaluated contexts. We hope that Aldy remains an invaluable tool in the clinical toolbox even with the advent of long-read sequencing technologies. Availability Aldy 4 is available at https://github.com/0xTCG/aldy.

Background DIALOG+ is an evidence-based, generic, cost-saving and easily deliverable psychosocial intervention, adaptable to clinicians’ personal manner of interaction with patients. It was implemented in mental health services in five low- and middle-income countries in South-Eastern Europe during a 12-month randomised-controlled trial (IMPULSE) to improve the effectiveness of out-patient treatment for people with psychotic disorders. Aims To investigate barriers and facilitators to the perceived sustainability of DIALOG+ that has been successfully implemented as a part of the IMPULSE project. Method Three months after the IMPULSE trial's end, perceived sustainability of the DIALOG+ intervention was assessed via a short survey of clinicians and patients who took part in the trial. Quantitative data collected from the survey were analysed using descriptive statistics; content analysis assessed qualitative survey data. The views and experiences of key informants (patients, clinicians and healthcare policy influencers) regarding the sustainability and scale-up of DIALOG+ were further explored through semi-structured interviews. These data were explored using framework analysis. Results Clinicians mostly appreciated the comprehensiveness of DIALOG+, and patients described DIALOG+ meetings as empowering and motivating. The barrier most commonly identified by key informants was availability of financial resources; the most important facilitators were the clinically relevant structure and comprehensiveness of the DIALOG+ intervention. Conclusions Participants showed a willingness to sustain the implementation of DIALOG+. It is important to maintain collaboration with healthcare policy influencers to improve implementation of DIALOG+ across different levels of healthcare systems and ensure availability of resources for implementing psychosocial interventions such as DIALOG+.

Slušno procesiranje je sposobnost da slušamo, razumijemo i odgovaramo na informacije koje čujemo kroz svoje slušne kanale. Poremećaj slušnog procesiranja (PSP) je senzorni poremećaj koji najčešće pogađa razumijevanje govora i učenje, te stvara poteškoće u procesiranju auditivnih informacija. Istraživanje je provedeno sa ciljem utvrđivanja razlika u poremećajima slušnog procesiranja (PSP) kod djece različitih uzrasnih grupa. Ukupno je testirano 560 djece. Sa ciljem utvrđivanja postojanja razlika slušnog procesiranja između pet uzrasnih grupa korišten je statistički test analiza varijanse (ANOVA) za poređenje prosječnih vrijednosti postignutih na subtestovima. Na testu filtriranih riječi (TFR) registrovana je statistički značajna razlika ANOVA testom (F=13,09, p<0,001). Na testu govora u buci (TGB) takođe je registrovana statistički značajna razlika ANOVA testom (F=11,04, p<0,001). Na dihotičkom testu riječi (DTR), također je registrovana statistički značajna razlika ANOVA testom (F=12,30, p<0,001), kao i na dihotičkom testu rečenica (DTRE) (F=8,60, p<0,001). Analizom rezultata našeg istraživanja, mogu se konstatovati statistički značajne razlike među uzrasnim grupama, gdje je prisutan trend ostvarivanja boljih rezultata sa porastom hronološke dobi ispitanika. S obzirom da je prilikom ispitivanja normalne raspodjele Kolmogorov-Smirnov testom pokazano da neki od postignutih rezultata ne prate normalnu distribuciju, rezultati su analizirani i neparametrijskim Kruskal-Wallis testom, te je uočljiva saglasnost sa rezultatima ANOVA testa. Mjerni instrument koji je korišten u ovom istraživanju je Baterija testova za ispitivanje poremećaja slušnog procesiranja PSP 1 (Heđever, 2015) i isti je jako koristan standardizirani mjerni instrument.

Opći val useljenika, najviše sa prostora Bliskog istoka, ali i iz Afrike, posebice njenog sjevernog dijela, dolazi na granice evropskih i drugih zemalja. Uz opće loše fizičko stanje u kojem se imigranti nalaze, u velikom postotku idu i psihički problemi praćeni očajem i nasilnim ponašanjem, napadima na domaće stanovništvo i njihovu imovinu, itd., što još više produbljuje krizu njihovog statusa u društvu zemlje primateljice, stvarajući neku vrstu začaranog kruga. Cilj istraživanja jeste utvrditi prisustvo nasilničkog ponašanja kod imigranata sa Bliskog Istoka i Sjeveroistočne Afrike po ulasku u novu zemlju. U istraživanju se pošlo od pretpostavki da su imigranti izloženi nasilju i u većem broju su žrtve u odnosu na domaće stanovništvo, te da su imigranti koji dolaze u potrazi za boljim socioekonomskim prilikama više nasilni od onih koji bježe od rata. Uzorak studije obuhvaća 100 ispitanika, useljenika i domaćeg stanovništva, različitog spola, starosti, zemlje podrijetla i drugih demografskih faktora. Ispitivanje je provedeno u „St Andrew Church Refugee Service“ te online radi anonimnosti na području Egipta, Kairo. Podaci su prikupljeni ASEBA Youth Self-Report – YSR upitnikom. Rezultati istraživanja pokazuju da imigrantsko stanovništvo ima značajnije izražen osjećaj da drugi ljudi u društvi imaju želju da im naškode u usporedbi sa domicilnim stanovništvom. U rezultatima istraživanja su predstavljene i razlike između sjeverno-afričkih i blisko-istočnih useljenika u pogledu malicioznog ponašanja prema drugim osobama iz okoline (pošto blisko-istočni imigranti potječu iz regija sa aktualnim i većim ratnim razaranjima). Može se zaključiti da imigranti porijekla sa Bliskog istoka pokazuju veći stepen lošeg postupanja prema drugim ljudima, u poređenju sa sjeverno-afričkim imigrantima.

The potential advantages of intelligent wireless communications with millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) are based on the availability of instantaneous channel state information (CSI) at the base station (BS). However, no existence of channel reciprocity leads to the difficult acquisition of accurate CSI at the BS in frequency division duplex (FDD) systems. Many researchers explored effective architectures based on deep learning (DL) to solve this problem and proved the success of DL-based solutions. However, existing schemes focused on the acquisition of complete CSI while ignoring the beamforming and precoding operations. In this paper, we propose an intelligent channel feedback architecture using eigenmatrix and eigenvector feedback neural network (EMEVNet). With the help of the attention mechanism, the proposed EMEVNet can be considered as a dual channel auto-encoder, which is able to jointly encode the eigenmatrix and eigenvector into codewords. Simulation results show great performance improvement and robustness with extremely low overhead of the proposed EMEVNet method compared with the traditional DL-based CSI feedback methods.