Yoxas/statistical_literacy huggingface dataset:
Modified invasion games promote the development of real and perceived motor competence. Children with higher motor competence are more likely to participate in physical activity practice and to remain in it, both in adolescence and adulthood. (1) Background: The purpose of this study is to determine the effect of modified invasion games on the real motor competence and self-assessment of the physical condition fifth-grade students from a private school in Viña del Mar, Chile. (2) Methods: 40 girls and boys with an average age of 11.47 years (SD = 0.554) participated in this study during a 12-week intervention. The MOBAK 5-6 battery was used to assess actual motor competence, the SEMOK questionnaire was used to determine perceived motor competence, the International Fitness Scale (IFIS) self-assessment questionnaire was used to assess perceived physical fitness, and the weight/size ratio was used to determine BMI. A Friedman's nonparametric ANOVA analysis was applied to determine the effect of the intervention, in addition to an analysis of covariance (ANCOVA) to identify the influence of the covariates on motor competence. (3) Results: No statistically significant differences were established between weight, BMI, and waist circumference. There was a statistically significant difference after the intervention in the actual motor competence of object control (p = 0.005) and perceived motor competence of object control (p ≤ 0.001) (4) Conclusions: An intervention based on modified invasion games is effective for the improvement of actual and perceived motor competence of object control. It was not possible to identify a positive effect on the self-assessment of muscle strength after the intervention.
Modifying risk perceptions related to driving after cannabis use (DACU) could deter individuals from enacting this behavior, as low-risk perception is associated with DACU engagement. This study identified sociodemographic characteristics, substance use, other driving behaviors, peer norms, and psychological characteristics that are associated with lower risk perception regarding DACU. Canadian drivers aged 17-35 who have used cannabis in the past year (n = 1,467) completed an online questionnaire. A multivariate linear regression model allowed for identifying variables associated with the low-risk perception of DACU (i.e. believing it to be safe as one's driving ability is not impaired by cannabis or by being high). Lower risk perception of DACU was associated with identifying as male, weekly to daily cannabis use, engagement in DACU, general risky driving behaviors, being a passenger of a driver who engages in DACU, number of friends who engage in DACU, and peer approval of DACU. Having driven under the influence of alcohol, living in urban areas, having received traffic tickets in the past three years, and declaring past-week irritability and cognitive problems were associated with holding a higher risk perception related to DACU. Road education and prevention programs should target attitudes and perceptions regarding risks shaped by sociocultural norms and past risky driving experiences. They need to reach out more specifically to drivers with the identified characteristics associated with the low-risk perception of DACU. These interventions can potentially help reduce the rate of individuals who engage in this behavior.
Modulating the stress response and perioperative factors can have a paramount impact on the neurodevelopmental outcome of infants who undergo cardiac surgery utilizing cardiopulmonary bypass. In this single center prospective follow-up study, we evaluated the impact of three different anesthetic techniques on the neurodevelopmental outcomes of 19 children who previously underwent congenital cardiac surgery within their 1(st) year of life. Cases were done from May 2011 to December 2013. Children were assessed using the Stanford-Binet Intelligence Scales (5(th) edition). Multiple regression analysis was used to test different parental and perioperative factors that could significantly predict the different neurodevelopmental outcomes in the entire cohort of patients. When comparing the three groups regarding the major cognitive scores, a high-dose fentanyl (HDF) patients scored significantly higher than the low-dose fentanyl (LDF) + dexmedetomidine (DEX) (LDF + DEX) group in the quantitative reasoning scores (106 ± 22 vs. 82 ± 15 P = 0.046). The bispectral index (BIS) value at the end of surgery for the -LDF group was significantly higher than that in LDF + DEX group (P = 0.011). For the entire cohort, a strong correlation was seen between the standard verbal intelligence quotient (IQ) score and the baseline adrenocorticotropic hormone level, the interleukin-6 level at the end of surgery and the BIS value at the end of the procedure with an R(2) value of 0.67 and P < 0.04. There was an inverse correlation between the cardiac Intensive Care Unit length of stay and the full-scale IQ score (R = 0.4675 and P 0.027). Patients in the HDF group demonstrated overall higher neurodevelopmental scores, although it did not reach statistical significance except in fluid reasoning scores. Our results may point to a possible correlation between blunting the stress response and improvement of the neurodevelopmental outcome.
Molar pregnancies, encompassing complete and partial moles, represent a rare and enigmatic gestational disorder with potential ethnic variations in incidence. This study aimed to investigate relations of ethnicity with risks of complete and partial molar pregnancies within an Israeli population while accounting for age differences. A retrospective study was conducted of data recorded during 2007-2021 in an academic medical center in Israel. The study population comprised 167 women diagnosed with complete or partial moles, for whom data were obtained through histological examination and P57 immunostaining. Maternal age and ethnicity were extracted from electronic medical records. Incidence rates were calculated per 10,000 live births, and a nested case-control study compared demographic characteristics and molar pregnancy incidences between Arab and Jewish women. Statistical analyses included age-adjusted comparisons, relative risk calculations and multivariate logistic regression. The overall risk of molar pregnancy was 22 per 10,000 live births (95% confidence interval [CI] 18-25). Among Arab women, the overall risk was 21 (95% CI 17-25), and for PM and CM: 14 (95% CI 11-17) and 7 (95% CI 5-10), respectively. Among Jewish women, the overall risk was 23 (95% CI 18-29), and for PM and CM: 12 (95% CI 8-17) and 11 (95% CI 7-16), respectively. Among Arab women compared to Jewish women, the proportion of all the partial moles was higher: (65.3% vs. 51.6%, p = 0.05). The incidence of partial mole was higher among Arab than Jewish women, aged 35-39 years (26 vs. 8 per 10,000, p = 0.041), and did not differ in other age groups. After adjusting for age, the relative risk of partial moles was lower among Jews than Arabs (0.7, 95% CI 0.4-1.0, p = 0.053). For Arab compared to Jewish women, the mean age at molar pregnancies was younger: 31.0 vs. 35.1 years. However, other factors did not differ significantly between Arab and Jewish women with molar pregnancies. In multivariate analysis, Jewish ethnicity was significantly associated with a higher risk of complete molar pregnancies (OR = 2.19, 95% CI 1.09-4.41, p = 0.028). This study highlights ethnic differences in molar pregnancy risk within the Israeli population. Jewish ethnicity was associated with a higher risk of complete molar pregnancies, while Arab women had a significantly higher risk of partial moles. These findings underscore the need to consider ethnicity when studying gestational disorders. Further research should seek to elucidate the underlying factors contributing to these differences.
Molecular and cellular biology methodology is traditionally based on the reasoning called "the mechanistic explanation". In practice, this means identifying and selecting correlations between biological processes which result from our manipulation of a biological system. In theory, a successful application of this approach requires precise knowledge about all parameters of a studied system. However, in practice, due to the systems' complexity, this requirement is rarely, if ever, accomplished. Typically, it is limited to a quantitative or semi-quantitative measurements of selected parameters (e.g., concentrations of some metabolites), and a qualitative or semi-quantitative description of expression/post-translational modifications changes within selected proteins. A quantitative proteomics approach gives a possibility of quantitative characterization of the entire proteome of a biological system, in the context of the titer of proteins as well as their post-translational modifications. This enables not only more accurate testing of novel hypotheses but also provides tools that can be used to verify some of the most fundamental dogmas of modern biology. In this short review, we discuss some of the consequences of using quantitative proteomics to verify several key concepts in skeletal muscle physiology.
Molecular and cellular studies reveal that the resistance of hosts to parasites and pathogens is a cascade-like process with multiple steps required to be passed for successful infection. By contrast, much of evolutionary reasoning is based on strongly simplified, one- or two-step infection processes with simple genetics or on resistance being a quantitative trait. Here we attempt a conceptual unification of these two perspectives with the aim of cross-fostering research and filling some of the gaps in our concepts of the ecology and evolution of disease. This conceptual unification has a profound impact on the way we understand the genetics and evolution of host resistance, ecological immunity, evolution of virulence, defence portfolios, and host-pathogen coevolution.
Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding.
Molecular recognition features (MoRFs) are particular functional segments of disordered proteins, which play crucial roles in regulating the phase transition of membrane-less organelles and frequently serve as central sites in cellular interaction networks. As the association between disordered proteins and severe diseases continues to be discovered, identifying MoRFs has gained growing significance. Due to the limited number of experimentally validated MoRFs, the performance of existing MoRF's prediction algorithms is not good enough and still needs to be improved. In this research, we present a model named MoRF_ESM, which utilizes deep-learning protein representations to predict MoRFs in disordered proteins. This approach employs a pretrained ESM-2 protein language model to generate embedding representations of residues in the form of attention map matrices. These representations are combined with a self-learned TextCNN model for feature extraction and prediction. In addition, an averaging step was incorporated at the end of the MoRF_ESM model to refine the output and generate final prediction results. In comparison to other impressive methods on benchmark datasets, the MoRF_ESM approach demonstrates state-of-the-art performance, achieving [Formula: see text] higher AUC than other methods when tested on TEST1 and achieving [Formula: see text] higher AUC than other methods when tested on TEST2. These results imply that the combination of ESM-2 and TextCNN can effectively extract deep evolutionary features related to protein structure and function, along with capturing shallow pattern features located in protein sequences, and is well qualified for the prediction task of MoRFs. Given that ESM-2 is a highly versatile protein language model, the methodology proposed in this study can be readily applied to other tasks involving the classification of protein sequences.
Molecular structure-based predictive models provide a proven alternative to costly and inefficient animal testing. However, due to a lack of interpretability of predictive models built with abstract molecular descriptors they have earned the notoriety of being black boxes. Interpretable models require interpretable descriptors to provide chemistry-backed predictive reasoning and facilitate intelligent molecular design. We developed a novel set of extensible chemistry-aware substructures, Saagar, to support interpretable predictive models and read-across protocols. Performance of Saagar in chemical characterization and search for structurally similar actives for read-across applications was compared with four publicly available fingerprint sets (MACCS (166), PubChem (881), ECFP4 (1024), ToxPrint (729)) in three benchmark sets (MUV, ULS, and Tox21) spanning ∼145 000 compounds and 78 molecular targets at 1%, 2%, 5%, and 10% false discovery rates. In 18 of the 20 comparisons, interpretable Saagar features performed better than the publicly available, but less interpretable and fixed-bit length, fingerprints. Examples are provided to show the enhanced capability of Saagar in extracting compounds with higher scaffold similarity. Saagar features are interpretable and efficiently characterize diverse chemical collections, thus making them a better choice for building interpretable predictive in silico models and read-across protocols.
Monitoring childhood immunization programs is essential for health systems. Despite the introduction of an electronic immunization registry called e-Tracker in Rwanda, challenges such as lacking population denominators persist, leading to implausible reports of coverage rates of more than 100%. This study aimed to assess the extent to which the immunization e-Tracker responds to stakeholders' needs and identify key areas for improvement. In-depth interviews were conducted with all levels of e-Tracker users including immunization nurses, data managers, and supervisors from health facilities in 5 districts of Rwanda. We used an interview guide based on the constructs of the Human, Organization, and Technology-Fit (HOT-Fit) framework, and we analyzed and summarized our findings using the framework. Immunization nurses reported using the e-Tracker as a secondary data entry tool in addition to paper-based forms, which resulted in considerable dissatisfaction among nurses. While users acknowledged the potential of a digital tool compared to paper-based systems, they also reported the need for improvement of functionalities to support their work, such as digital client appointment lists, lists of defaulters, search and register functions, automated monthly reports, and linkages to birth notifications and the national identity system. Reducing dual documentation for users can improve e-Tracker use and user satisfaction. Our findings can help identify additional digital health interventions to support and strengthen the health information system for the immunization program.
Monitoring on the basis of sound recordings, or passive acoustic monitoring, can complement or serve as an alternative to real-time visual or aural monitoring of marine mammals and other animals by human observers. Passive acoustic data can support the estimation of common, individual-level ecological metrics, such as presence, detection-weighted occupancy, abundance and density, population viability and structure, and behaviour. Passive acoustic data also can support estimation of some community-level metrics, such as species richness and composition. The feasibility of estimation and certainty of estimates is highly context dependent, and understanding the factors that affect the reliability of measurements is useful for those considering whether to use passive acoustic data. Here, we review basic concepts and methods of passive acoustic sampling in marine systems that often are applicable to marine mammal research and conservation. Our ultimate aim is to facilitate collaboration among ecologists, bioacousticians, and data analysts. Ecological applications of passive acoustics require one to make decisions about sampling design, which in turn requires consideration of sound propagation, sampling of signals, and data storage. One also must make decisions about signal detection and classification and evaluation of the performance of algorithms for these tasks. Investment in the research and development of systems that automate detection and classification, including machine learning, are increasing. Passive acoustic monitoring is more reliable for detection of species presence than for estimation of other species-level metrics. Use of passive acoustic monitoring to distinguish among individual animals remains difficult. However, information about detection probability, vocalisation or cue rate, and relations between vocalisations and the number and behaviour of animals increases the feasibility of estimating abundance or density. Most sensor deployments are fixed in space or are sporadic, making temporal turnover in species composition more tractable to estimate than spatial turnover. Collaborations between acousticians and ecologists are most likely to be successful and rewarding when all partners critically examine and share a fundamental understanding of the target variables, sampling process, and analytical methods.
Monitoring water quality and quantity is crucial to be sure that water resources are sustainably used. However, there is no monitoring system of water quantity and quality in southwestern Ethiopia, despite expansion of agricultural activities demanding water resources. The objective of this study was to investigate the effect of agriculture on water quantity and quality with special emphasis on irrigation in southwestern Ethiopia. Data of water quantity was collected from four rivers and four irrigation canals during dry season of 2023. Physico-chemical water quality data was collected from 35 sites. Water quantity was calculated by estimating the water discharge of the rivers and irrigation canals. Weighted arithmetic water quality index was calculated to assess the status of the studied rivers. Principal component analysis was used to identify the relation of the sites with water quality parameters. This study revealed that the average amount of abstracted water for irrigation from the four studied rivers was 22,399 m3/day during the studied period, and the average percentage of abstracted water was 17%. Sites downstream of the irrigation site were characterized by poor water quality compared with the upstream sites. Sites surrounded by agricultural land use were correlated with chemical oxygen demand, electric conductivity, nitrate, orthophosphate, water temperature, and pH, whereas all sites surrounded by forest were positively correlated with dissolved oxygen. This study indicates that agricultural activities have a negative impact on surface water quality and quantity if not managed properly. Hence, we recommend sustainable use of water resources for the planned irrigation expansion.
Moral sensitivity is known to be the starting point for moral competence and even is a core concept in the curricula for bachelor's-level nursing students in the Netherlands. While the development of moral sensitivity in nursing is commonly agreed to be important, there is no clear understanding of how to develop moral sensitivity through nursing education and what components of nursing education contribute to moral sensitivity. Studies on educational interventions could build knowledge about what works in developing moral sensitivity and how to achieve this outcome. Therefore, the aim of this study is to explore if and how educational interventions contribute to the development of moral sensitivity in nursing students. A scoping review was conducted. Four electronic databases were searched: CINAHL, PubMed, MEDLINE and SpringerLink. Articles that were not about formal or initial nursing education and that had no link to moral development or moral sensitivity were excluded. After the final selection on educational interventions, 10 articles out of the initial 964 resources were included in the review. Three different but related dimensions of moral sensitivity emerged from the literature: (1) raising moral awareness, (2) providing the ability to frame and name ethical issues and (3) improving moral reasoning ability. Half of the studies used quantitative measures to evaluate the educational intervention, in particular the Moral Sensitivity Questionnaire; the other half used diverse qualitative evaluation methods. None of the studies presented teaching methods that included all three dimensions of moral sensitivity. Moral awareness of self appears to be more loosely connected to the other two dimensions, which raises the question of whether it can be seen as a prerequisite for them. To encompass all dimensions of moral sensitivity, a mix of quantitative and qualitative measures seems most appropriate to study that topic.
Moran's index is an important spatial statistical measure used to determine the presence or absence of spatial autocorrelation, thereby determining the selection orientation of spatial statistical methods. However, Moran's index is chiefly a statistical measurement rather than a mathematical model. This paper is devoted to establishing spatial autocorrelation models by means of linear regression analysis. Using standardized vector as independent variable, and spatial weighted vector as dependent variable, we can obtain a set of normalized linear autocorrelation equations based on quadratic form and vector inner product. The inherent structure of the models' parameters are revealed by mathematical derivation. The slope of the equation gives Moran's index, while the intercept indicates the average value of standardized spatial weight variable. The square of the intercept is negatively correlated with the square of Moran's index, but omitting the intercept does not affect the estimation of the slope value. The datasets of a real urban system are taken as an example to verify the reasoning results. A conclusion can be reached that the inner product equation of spatial autocorrelation based on Moran's index is effective. The models extend the function of spatial analysis, and help to understand the boundary values of Moran's index.