ABSTRACT Prior research has mainly focused on why individuals are engaging in sexting. However, little is known about sexting in intimate relationships, particularly sexting coercion. This study examined sexting coercion using social learning theory in a sample of young adults who had experience with a romantic relationship (N = 315, aged 14–28 years, 67.94% female). Individuals completed the sexting coercion scale and the social learning scale online. The results showed that over 33% of the respondents reported being victims of sexting coercion in an intimate relationship, while about 28% of them had perpetrated sexting coercion. The results suggest that sexting coercion in an intimate relationship is significantly predicted by differential association, differential reinforcement, and imitation. The findings of this study highlight the importance of exposing youth to evidence-based preventive educational interventions on sexting from the earliest age, based on the constructs of social learning theory. Practice Impact Statement The findings of this study of sexting coercion among young adults are relevant to educational programmes given recent evidence of the prevalence of sexting coercion victimisation and sexting coercion perpetration among youth in romantic relationships. The findings suggest that those who engage in sexting coercion use a social learning mechanism that should be considered when developing educational interventions to prevent sexting coercion.

Attending to the speech stream of interest in multi-talker environments can be a challenging task, particularly for listeners with hearing impairment. Research suggests that neural responses assessed with electroencephalography (EEG) are modulated by listener’s auditory attention, revealing selective neural tracking (NT) of the attended speech. NT methods mostly rely on hand-engineered acoustic and linguistic speech features to predict the neural response. Only recently, deep neural network (DNN) models without specific linguistic information have been used to extract speech features for NT, demonstrating that speech features in hierarchical DNN layers can predict neural responses throughout the auditory pathway. In this study, we go one step further to investigate the suitability of similar DNN models for speech to predict neural responses to competing speech observed in EEG. We recorded EEG data using a 64-channel acquisition system from 17 listeners with normal hearing instructed to attend to one of two competing talkers. Our data revealed that EEG responses are significantly better predicted by DNN-extracted speech features than by hand-engineered acoustic features. Furthermore, analysis of hierarchical DNN layers showed that early layers yielded the highest predictions. Moreover, we found a significant increase in auditory attention classification accuracies with the use of DNN-extracted speech features over the use of hand-engineered acoustic features. These findings open a new avenue for development of new NT measures to evaluate and further advance hearing technology.

This article explores a possibility to improve mathematical teaching by using 3D printing technology. The question is whether it is possible to use low cost additive manufacturing technology to develop and manufacture real physical prototypes of complex mathematical surfaces and volumes and on that way improve mathematics education. Five mathematical problems were chosen as case studies. Visualization of this problems was done using professor hand drawing, using computer visualization and using development and manufacturing of real physical prototypes. To find out how much better is understanding of these problems, survey with 57 students is carried out. Results showed significant improvements of understanding and better visualization of selected mathematical problems.

Type 2 diabetes mellitus (T2DM) increases the risk of cardiovascular disease, especially myocardial injury. Due to their hypoglycemic effects, glucagon-like peptide-1 receptor agonists (GLP-1RAs) are efficiently used for T2DM management. GLP-1RAs also have anti-inflammatory and antioxidative effects, and can improve cardiac function. The aim of this study was to investigate the cardioprotective effects of liraglutide, a GLP-1RA, on isoprenaline-induced myocardial injury in rats. The study included 4 groups of animals. They were pretreated with saline for 10 days + saline on days 9 and 10 (control), saline for 10 days + isoprenaline on days 9 and 10 (isoprenaline group), liraglutide for 10 days + saline on days 9 and 10 (liraglutide group), and liraglutide for 10 days and on days 9 and 10 they were administered isoprenaline. This study evaluated ECG, myocardial injury markers, oxidative stress markers, and pathohistological changes. The results showed that liraglutide mitigated the isoprenaline-induced cardiac dysfunction recorded by ECG. Liraglutide reduced serum markers of myocardial injury such as high-sensitive troponin I, aspartate aminotransferase, alanine aminotransferase, reduced TBARS, increased catalase and superoxide dismutase activity and increased reduced glutathione, and improved lipid profile. Liraglutide induced antioxidative protection and alleviated isoprenaline-induced myocardial injury.

Mycoplasma ovipneumoniae is an important pathogen in sheep, goats, and wild ruminants. We sequenced M. ovipneumoniae strains 150 and 274 from Bosnia and Herzegovina. Strain 150 has a circular genome of 1,053,380 bp with 29.15% GC content while strain 274 has 1,081,404 bp with 28.82% GC content. ABSTRACT Mycoplasma ovipneumoniae is an important pathogen in sheep, goats, and wild ruminants. We sequenced M. ovipneumoniae strains 150 and 274 from Bosnia and Herzegovina. Strain 150 has a circular genome of 1,053,380 bp with 29.15% GC content while strain 274 has 1,081,404 bp with 28.82% GC content.

Simple Summary The genus Sorbus (whitebeams, rowans, and service trees) encompasses forest trees and shrubs characterised by exceptional diversity resulting from the interplay of polyploidisation, hybridization, and apomixis. The spatiotemporal processes driving Sorbus diversification remain poorly understood. This research aims to provide insights into the evolution and diversification patterns of mountain whitebeam (S. austriaca) covering most of its range in the mountains of Central and South-eastern Europe. Our molecular and morphometric data revealed pronounced cryptic diversity within the S. austriaca complex; it is composed of different lineages, that likely originated via multiple allopolyploidisations accompanied by apomixes, and these lineages exhibit different distribution patterns. Our results are particularly valuable from a biodiversity conservation perspective due to the continuing generation of novel diversity in sympatric populations of the parental taxa. Such derived diversity requires process-oriented conservation plans and measures. Abstract The interplay of polyploidisation, hybridization, and apomixis contributed to the exceptional diversity of Sorbus (Rosaceae), giving rise to a mosaic of genetic and morphological entities. The Sorbus austriaca species complex from the mountains of Central and South-eastern Europe represents an allopolyploid apomictic system of populations that originated following hybridisation between S. aria and S. aucuparia. However, the mode and frequency of such allopolyploidisations and the relationships among different, morphologically more or less similar populations that have often been described as different taxa remain largely unexplored. We used amplified fragment length polymorphism (AFLP) fingerprinting, plastid DNA sequencing, and analyses of nuclear microsatellites, along with multivariate morphometrics and ploidy data, to disentangle the relationships among populations within this intricate complex. Our results revealed a mosaic of genetic lineages—many of which have not been taxonomically recognised—that originated via multiple allopolyploidisations. The clonal structure within and among populations was then maintained via apomixis. Our results thus support previous findings that hybridisation, polyploidization, and apomixis are the main drivers of Sorbus diversification in Europe.

Abstract This study aimed to investigate the effects of dietary supplementation with modified-hen-egg-yolk on plasma lipids and lipoprotein profiles in rats. During the four-week-experiment, 64 Wistar rats were divided into four groups of 16 (eight of both sexes), and fed commercial rat food (group C); food containing 70% commercial rat mixture and 30% freshly cooked egg yolk originating from laying hen eggs fed with 3% fish oil (group F); 3% palm olein (group P), or 3% lard (group L). The cooked egg yolk in the rat diet affected the concentrations of plasma total and LDL-cholesterol in males of the P and L groups. Cholesterol and total fat in the diet did not have a hypercholesterolemic effect on their own, but when in combination with fatty acid composition, they could contribute to an increase in plasma total and LDL cholesterol concentrations in rats. HDL-cholesterol was the most resilient plasma lipoprotein of rats to dietary treatments in our experiment. Compared to the control group, the addition of hen egg yolk to the rat diet regardless of its quality, adversely affected the values of HDL-C/TC and HDL-C/LDL-C in both males and females.

Protective relays are integral to the reliability of any electrical power system, and are fundamental to the decision-making of their protection systems. They support the detection and isolation of problems in the power system, so that the operation of unaffected parts can be maintained. ElectroMechanical Relays (EMRs) are still predominant around the globe in the high and extra high voltage transmission systems. Thus, ensuring the reliability and traceability of relays is of major importance. One way to achieve this is through parallel redundancy by implementing redundant sensor architectures, such as one-out-of-two (1oo2). In this paper, we propose a novel algorithm for the fault prognosis-i.e., detection and failure date prediction – and isolation prediction for redundant 1002 architectures. The algorithm predicts the failure ahead of time and provides an estimated date for the failure event. Our contribution in this work is on the fault isolation prediction, where we infer-ahead of time, before the occurrence of the fault-which relay of the pair will cause the failure. The fault isolation is achieved by means of Machine Learning (ML) based feature extraction and binary classification methods. We apply the algorithm on EMRs based solely on the discrepancy time signals of the opening and closing events of the relays. The algorithm has been tested on data from redundant EMRs from the publicly available SOReDD dataset. While relays are binary switches, our work could potentially not only be applied to other types of binary switches but also to binary sensors as they also produce a binary output signal.

In this paper we consider some metrical and topological properties of the river metric $d^*$ in the plane $\mathbb{R}^2^2$. We give the form of the metric segment and the set of all points that are equidistant from two points in $(\rR^2,d^*)$. We also give the characterization of a compact sets in this space.

This experimental study was conducted to determine the ability of a novel mycotoxins detoxification agent (MR) at a concentration of 0.2% to reduce the toxicity of aflatoxin B1 (AFB1) or T-2 toxin, alone or in combination, and to examine its effect on performance, pathohistological changes (PH) and the residue of these toxins in the tissues of broiler chicks. A total of 96 broiler chicks were divided into eight equal groups: group C, which served as control (without any additives); group MR, which received the novel detoxification agent (supplemented with 0.2%); group E-I (0.1 mg AFB1/kg of diet); group E-II (0.1 mg AFB1/kg of diet + MR 0.2%); group E-III (0.5 mg T-2 toxin/kg of diet); group E-IV (0.5 mg T-2 toxin/kg of diet + 0.2% MR); group E-V (combination of 0.1 mg AFB1/kg, 0.5 mg T-2 toxin/kg of diet); and group E-VI (combination of 0.1 mg AFB1/kg, 0.5 mg T-2 toxin + 0.2% MR). Results indicate that feeds containing AFB1 and T-2 toxin, alone or in combination, adversely affected the health and performance of poultry. However, the addition of MR to diets containing AFB1 and T-2 toxin singly and in combination exerted a positive effect on body weight, feed intake, weight gain, feed efficiency and microscopic lesions in visceral organs. Residual concentration of AFB1 in liver samples was significantly (p < 0.05) decreased when chicks were fed diets supplemented with 0.2% of MR.

Background COVID-19 remains a major public health challenge, requiring the development of tools to improve diagnosis and inform therapeutic decisions. As dysregulated inflammation and coagulation responses have been implicated in the pathophysiology of COVID-19 and sepsis, we studied their plasma proteome profiles to delineate similarities from specific features. Methods We measured 276 plasma proteins involved in Inflammation, organ damage, immune response and coagulation in healthy controls, COVID-19 patients during acute and convalescence phase, and sepsis patients; the latter included (i) community-acquired pneumonia (CAP) caused by Influenza, (ii) bacterial CAP, (iii) non-pneumonia sepsis, and (iv) septic shock patients. Results We identified a core response to infection consisting of 42 proteins altered in both COVID-19 and sepsis, although higher levels of cytokine storm-associated proteins were evident in sepsis. Furthermore, microbiologic etiology and clinical endotypes were linked to unique signatures. Finally, through machine learning, we identified biomarkers, such as TRIM21, PTN and CASP8, that accurately differentiated COVID-19 from CAP-sepsis with higher accuracy than standard clinical markers. Conclusions This study extends the understanding of host responses underlying sepsis and COVID-19, indicating varying disease mechanisms with unique signatures. These diagnostic and severity signatures are candidates for the development of personalized management of COVID-19 and sepsis.

This paper aims to show how business intelligence can be applied in the credit card approval process. More specifically, the paper investigates how information like an applicant’s age, credit score, debt, income, and prior default can be used in credit card approval prediction.The dataset used for analysis is a publicly available dataset from the UCI machine learning repository. Logistic regression is used to make a prediction model with a reasonable number of attributes for a comprehensible business model. The Chi-square test of independence is used to test the dependence of credit card approval results with attributes. Research uncovers that prior default is supposed to be the most important attribute in the approval process. Finally, the authors propose several visualizations that could help make smarter decisions with effective credit risk assessment.

Introduction: During the last two and a half years, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has spread around the world. Most of the SARS-CoV-2 vaccines are designed to produce anti-SARS-CoV-2 immunoglobulin G (IgG) against the viral S-glycoprotein. The aim of this study was to measure the anti-S antibody titres among the medical personnel who had been fully vaccinated with different types of vaccines, and to compare them with those who were COVID-19 convalescents. Material and methods: In this study serum was collected from 261 healthcare workers, of whom 227 were vaccinated, while 34 were recovered participants who were not immunised. Serum samples were collected 21 days after the first dose and 60 and 180 days after the second dose of the vaccines and tested with a commercial ELISA kit. Results: The highest antibody level (12 AU/ml) was measured in the Pfizer-BioNTech group, followed by Sinopharm (9.3 AU/ml), Sputnik V (5.9 AU/ml), Sinovac (4.6 AU/ml) and Oxford/Astra- Zeneca vaccine (2.5 AU/ml) 60 days after the second dose of the vaccines (90 days after the first dose). The seropositivity rate for mRNA vaccine was 88.5%, for vector vaccines 86.2% and for inactivated vaccines 71.4%. When comparing these antibody levels with COVID-19 convalescents, higher antibody titres were found in vaccinated participants (5.76 AU/ml vs 7.06 AU/ml), but the difference was not significant (p = 0.08). Conclusions: Individuals vaccinated with mRNA and vector vaccines had a higher seroconversion rate compared to the group vaccinated with inactivated vaccines, or convalescents.

Simple Summary MGMT-methylated glioblastomas have significantly lower ADC values, as compared to the glioblastomas with no MGMT methylation in peritumoral white matter. There were no differences in enhancing tumor areas. These findings could improve predictions of MGMT status in glioblastomas. Abstract Different results have been reported concerning the relationship of the apparent diffusion coefficient (ADC) values and the status of methylation as the promoter gene for the enzyme methylguanine-DNA methyltransferase (MGMT) in patients with glioblastomas (GBs). The aim of this study was to investigate if there were correlations between the ADC values of the enhancing tumor and peritumoral areas of GBs and the MGMT methylation status. In this retrospective study, we included 42 patients with newly diagnosed unilocular GB with one MRI study prior to any treatment and histopathological data. After co-registration of ADC maps with T1-weighted sequences after contrast administration and dynamic susceptibility contrast (DSC) perfusion, we manually selected one region-of-interest (ROI) in the enhancing and perfused tumor and one ROI in the peritumoral white matter. Both ROIs were mirrored in the healthy hemisphere for normalization. In the peritumoral white matter, absolute and normalized ADC values were significantly higher in patients with MGMT-unmethylated tumors, as compared to patients with MGMT-methylated tumors (absolute values p = 0.002, normalized p = 0.0007). There were no significant differences in the enhancing tumor parts. The ADC values in the peritumoral region correlated with MGMT methylation status, confirmed by normalized ADC values. In contrast to other studies, we could not find a correlation between the ADC values or the normalized ADC values and the MGMT methylation status in the enhancing tumor parts.