Yoxas/statistical_literacy huggingface dataset:
Complex phenotypes, such as many common diseases and morphological traits, are controlled by multiple genetic factors, namely genetic mutations and genes, and are influenced by environmental conditions. Deciphering the genetics underlying such traits requires a systemic approach, where many different genetic factors and their interactions are considered simultaneously. Many association mapping techniques available nowadays follow this reasoning, but have some severe limitations. In particular, they require binary encodings for the genetic markers, forcing the user to decide beforehand whether to use, e.g. a recessive or a dominant encoding. Moreover, most methods cannot include any biological prior or are limited to testing only lower-order interactions among genes for association with the phenotype, potentially missing a large number of marker combinations. We propose HOGImine, a novel algorithm that expands the class of discoverable genetic meta-markers by considering higher-order interactions of genes and by allowing multiple encodings for the genetic variants. Our experimental evaluation shows that the algorithm has a substantially higher statistical power compared to previous methods, allowing it to discover genetic mutations statistically associated with the phenotype at hand that could not be found before. Our method can exploit prior biological knowledge on gene interactions, such as protein-protein interaction networks, genetic pathways, and protein complexes, to restrict its search space. Since computing higher-order gene interactions poses a high computational burden, we also develop a more efficient search strategy and support computation to make our approach applicable in practice, leading to substantial runtime improvements compared to state-of-the-art methods. Code and data are available at https://github.com/BorgwardtLab/HOGImine.
Complex scenarios, including miss detections, occlusions, false detections, and trajectory terminations, make the data association challenging. In this paper, we propose an online tracking-by-detection method to track multiple targets with unified handling of aforementioned complex scenarios, where current detection responses are linked to the previous trajectories. We introduce a dummy node to each trajectory to allow it to temporally disappear. If a trajectory fails to find its matching detection, it will be linked to its corresponding dummy node until the emergence of its matching detection. Source nodes are also incorporated to account for the entrance of new targets. The standard Hungarian algorithm, extended by the dummy nodes, can be exploited to solve the online data association implicitly in a global manner, although it is formulated between two consecutive frames. Moreover, as dummy nodes tend to accumulate in a fake or disappeared trajectory while they only occasionally appear in a real trajectory, we can deal with false detections and trajectory terminations by simply checking the number of consecutive dummy nodes. Our approach works on a single, uncalibrated camera, and requires neither scene prior knowledge nor explicit occlusion reasoning, running at 132 frames/s on the PETS09-S2L1 benchmark sequence. The experimental results validate the effectiveness of the dummy nodes in complex scenarios and show that our proposed approach is robust against false detections and miss detections. Quantitative comparisons with other methods on five benchmark sequences demonstrate that we can achieve comparable results with the most existing offline methods and better results than other online algorithms.
Complex social determinants of health may not be easily recognized by health care providers and pose a unique challenge in the vulnerable pediatric population where patients may not be able to advocate for themselves. The goal of this study was to examine the acceptability and feasibility of health care providers using an integrated brief pediatric screening tool in primary care and hospital settings. The framework of the Child and Adolescent Needs and Strengths (CANS) and Pediatric Intermed tools was used to inform the selection of items for the 9-item Child and Adolescent Needs and Strengths-Pediatric Complexity Indicator (CANS-PCI). The tool consisted of three domains: biological, psychological, and social. Semi-structured interviews were conducted with health care providers in pediatric medical facilities in Ottawa, Canada. A low inference and iterative thematic synthesis approach was used to analyze the qualitative interview data specific to acceptability and feasibility. Thirteen health care providers participated in interviews. Six overarching themes were identified: acceptability, logistics, feasibility, pros/cons, risk, and privacy. Overall, participants agreed that a routine, trained provider-led pediatric tool for the screening of social determinants of health is important (n = 10, 76.9%), acceptable (n = 11; 84.6%), and feasible (n = 7, 53.8%). Though the importance of social determinants of health are widely recognized, there are limited systematic methods of assessing, describing, and communicating amongst health care providers about the biomedical and psychosocial complexities of pediatric patients. Based on this study's findings, implementation of a brief provider-led screening tool into pediatric care practices may contribute to this gap.
Complex thinking is a cognitive skill that focuses on the integrated analysis and synthesis of information with a systemic and critical perspective that enables creative decision-making in the face of complex realities or challenges. At the educational level, it is valued as a transdisciplinary competency, meaning it is relevant for individuals regardless of their profession or field of study. This article presents the results of measuring the perceived achievement of complex thinking among 830 graduating students from a technological university in Mexico, aiming to identify possible significant differences based on their discipline or major. Methodologically, a multivariate descriptive statistical analysis was performed using R and RStudio software, including calculation of means and standard deviations, violin plots, boxplot and ANOVA significance analysis, and t-test. The results show that the differences were not statistically significant in all the disciplines, although it is possible to note significant differences, which reveals a differentiated behavior in the process of formation and development of complex thinking according to the discipline of study. In conclusion, the present study shows that the students' areas of training are associated with differences in perception of complex thinking and its associated sub-competencies, thus differentiating this ability in their graduation profile. This article contributes to the existing literature on the formation and development of complex thinking and its sub-competencies as relevant professional skills for lifelong learning.
Complicated mild traumatic brain injury (mTBI) or cmTBI is based on the presence of visibly identifiable brain pathology on the day-of-injury computed tomography (CT) scan. In a paediatric sample the relation of DOI CT to late MRI findings and neuropsychological outcome was examined. MRI (>12 months) was obtained in paediatric cmTBI patients and a sample of orthopaedically injured (OI) children. Those children with positive imaging findings (MRI+) were quantitatively compared to those without (MRI-) or with the OI sample. Groups were also compared in neurocognitive outcome from WASI sub-tests and the WISC-IV Processing Speed Index (PSI), along with the Test of Everyday Attention for Children (TEA-Ch) and a parent-rated behavioural functioning measure (ABAS-II). Despite the MRI+ group having significantly more DOI CT findings than the MRI- group, no quantitative differences were found. WASI Vocabulary and Matrix Reasoning scores were significantly lower, but not PSI, TEA-Ch or ABAS-II scores. MRI+ and MRI- groups did not differ on these measures. Heterogeneity in the occurrence of MRI-identified focal pathology was not associated with uniform changes in quantitative analyses of brain structure in cmTBI. Increased number of DOI CT abnormalities was associated with lowered neuropsychological performance.
Composite Index of Anthropometric Failure (CIAF) combines all three forms of anthropometric failures to assess undernutrition status of children. There is no study on CIAF to identify the real and severe form of under nutrition among Ethiopian children that addressed community level factors. So, this study determined CIAF and identified important factors which helps to design appropriate intervention strategies by using multi-level advanced statistical model. The study included 5,530 under five children and utilized a secondary data (EMDHS 2019) which was collected through community-based and cross-sectionally from March 21 to June 28, 2019. Composite index of anthropometric failure among under five children was assessed and a two-stage sampling technique was used to select the study participants. Descriptive summary statistics was computed. A multi-level binary logistic regression model was employed to identify important predictors of CIAF in under five children. Adjusted odds ratio with its 95% CI was estimated and level of significance 0.05 was used to determine significant predictors of CIAF. The prevalence of composite index of anthropometric failure (CIAF) was 40.69% (95% CI: 39.41, 42.00) in Ethiopia. Both individual and community level predictors were identified for CIAF in under five children. Among individual level predictors being male sex, older age, short birth interval, from mothers who have not formal education, and from poor household wealth quintile were associated with higher odds of CIAF among under five children. Low community women literacy and being from agriculturally based regions were the community level predictors that were associated with higher odds of CIAF in under five children in Ethiopia. The burden of composite index of anthropometric failure in under five children was high in Ethiopia. Age of child, sex of child, preceding birth interval, mother's education, household wealth index, community women literacy and administrative regions of Ethiopia were identified as significant predictors of CIAF. Therefore, emphasis should be given for those factors to decrease the prevalence of CIAF in under five children in Ethiopia.
Comprehensive abortion care is an emerging intervention being integrated into nursing and midwifery curricula. Yet, no studies have been conducted in Rwanda to determine whether faculty perceive themselves as capable of teaching comprehensive abortion care. This study aims to evaluate the perceived self-efficacy to teach comprehensive abortion care among nursing and midwifery faculty in higher learning institutions in Rwanda. The University of Rwanda College of Medicine and Health Sciences Institutional Review Board approved this study (UR-CMHS-IRB No 335/CMHSIRB/2022). In quantitative, a self-administered questionnaire was administered to 98 study participants. Data were entered into Statistical Package for the Social Sciences (SPSS) version 26 and analyzed using Chi-square test with a p-value of 0.05 set as the significance level. In the qualitative part, an interview guide was developed based on quantitative data to understand comprehensive abortion care teaching fully. Data were collected from four focus group discussions with eight participants in each group, entered in Dedoose, and analyzed thematically. Among the 98 study participants who were invited to participate in this study, only 85 filled out the questionnaires. This translates into 86.7% of the response rate. More than half 58.8% had adequate self-efficacy in teaching comprehensive abortion care. A Chi-square test has revealed that being a male, being a midwife, and having more years of working experience in nursing education were significantly associated with self-efficacy in teaching comprehensive abortion care (p value <0.05). In the qualitative phase, 32 study participants participated in four focus group discussions and four themes were identified: a) variability in confidence levels to teach comprehensive abortion care; b) readiness about teaching comprehensive abortion care; c) facilitators of teaching comprehensive abortion care; and d) contextual challenges to teach comprehensive abortion care. The findings revealed that faculty's self-efficacy in teaching comprehensive abortion care was not adequate. Personal and religious beliefs and institutional barriers were also reported to hinder self-efficacy in teaching comprehensive abortion care. Therefore, intensive comprehensive abortion care training for nursing and midwifery faculty in higher learning institutions should be provided, including values clarification and attitude transformation training for attitudes and beliefs. It is also critical for higher learning institutions to develop strategies for overcoming the challenges faculty face when teaching comprehensive abortion care.
Comprehensive descriptions of animal behavior require precise three-dimensional (3D) measurements of whole-body movements. Although two-dimensional approaches can track visible landmarks in restrictive environments, performance drops in freely moving animals, due to occlusions and appearance changes. Therefore, we designed DANNCE to robustly track anatomical landmarks in 3D across species and behaviors. DANNCE uses projective geometry to construct inputs to a convolutional neural network that leverages learned 3D geometric reasoning. We trained and benchmarked DANNCE using a dataset of nearly seven million frames that relates color videos and rodent 3D poses. In rats and mice, DANNCE robustly tracked dozens of landmarks on the head, trunk, and limbs of freely moving animals in naturalistic settings. We extended DANNCE to datasets from rat pups, marmosets, and chickadees, and demonstrate quantitative profiling of behavioral lineage during development.
Comprehensive high quality of care is critical in preventing diabetic complications and improving quality of life. This needs compliance with guidelines and focused therapy. There is no data in Ethiopia evaluating the quality of diabetes care using standard guidelines (American diabetic association and international diabetic federation) as a reference. A cross-sectional study was conducted at Yekatit 12 Hospital Medical College (YHMC) to assess the process and outcome quality indicators of diabetic patients. Data were collected from outpatient clinics between May and July 2022 over a period of 3 months. Diabetic patients with at least one year since diagnosis were selected using systematic random sampling. Both the process and outcome of diabetic quality care indicators were measured and compared with standard guidelines (ADA and IDF). Both descriptive statistics and logistic regression were used for data analysis. The P-value <0.05 was used as statistical significance. About 250 diabetic patients with a mean age of 53±15 were included. The majority were type 2 diabetes mellitus (83.2%). HbA1c was determined for 128 (51.2%) patients with the recent mean value of 8±1.6. Only 52 (40.6%) of patients achieved target HbA1c. Annual comprehensive feet examination, urine albuminuria test, and retinal examination were done for 54 (21.6%), 52 (20.8%), and 122 (48.8%), respectively. Single marital status (AOR = 5.76; 95% CI; 1.02-32.36) P = 0.047, determining HbA1c level at least twice a year (AOR = 6.27; 95% CI; 2.18-17.73) P = 0.001, and medication adherence (AOR = 7.1; 95% CI; 2.61-19.01)P = 0.001, were significantly associated with good glycemic control. The overall quality of diabetic care was found suboptimal both in process and outcome quality indicators. Thus, awareness creation about quality indicators for caregivers, compliance with guidelines, wise resource utilization, and cooperation with different stakeholders like hospital management teams, and government officials is needed.
Comprehensive knowledge of quantitative and qualitative research systematizes scholarly research and enhances the quality of research output. Scientific researchers must be familiar with them and skilled to conduct their investigation within the frames of their chosen research type. When conducting quantitative research, scientific researchers should describe an existing theory, generate a hypothesis from the theory, test their hypothesis in novel research, and re-evaluate the theory. Thereafter, they should take a deductive approach in writing the testing of the established theory based on experiments. When conducting qualitative research, scientific researchers raise a question, answer the question by performing a novel study, and propose a new theory to clarify and interpret the obtained results. After which, they should take an inductive approach to writing the formulation of concepts based on collected data. When scientific researchers combine the whole spectrum of inductive and deductive research approaches using both quantitative and qualitative research methodologies, they apply mixed-method research. Familiarity and proficiency with these research aspects facilitate the construction of novel hypotheses, development of theories, or refinement of concepts.
Computational and mathematical models are a must for the in silico analysis or design of Gene Regulatory Networks (GRN)as they offer a theoretical context to deeply address biological regulation. We have proposed a framework where models of network dynamics are expressed through a class of nonlinear and temporal multiscale Ordinary Differential Equations (ODE). To find out models that disclose network structures underlying an observed or desired network behavior, and parameter values that enable the candidate models to reproduce such behavior, we follow a reasoning cycle that alternates procedures for model selection and parameter refinement. Plausible network models are first selected via qualitative simulation, and next their parameters are given quantitative values such that the ODE model solution reproduces the specified behavior. This paper gives algorithms to tackle the parameter refinement problem formulated as an optimization problem. We search, within the parameter space symbolically expressed, for the largest hypersphere whose points correspond to parameter values such that the ODE solution gives an instance of the given qualitative trajectory. Our approach overcomes the limitation of a previously proposed stochastic approach, namely computational load and very reduced scalability. Its applicability and effectiveness are demonstrated through two benchmark synthetic networks with different complexity.
Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. Emphasising open-source availability, transparent methodologies, clear variant effect score interpretations, standardised scales, accessible predictions, and rigorous training data disclosure, we aim to improve the usability and interpretability of VEPs, and promote their integration into analysis and evaluation pipelines. We also provide a large, categorised list of currently available VEPs, aiming to facilitate the discovery and encourage the usage of novel methods within the scientific community.
Computational power and big data have created new opportunities to explore and understand the social world. A special synergy is possible when social scientists combine human attention to certain aspects of the problem with the power of algorithms to automate other aspects of the problem. We review selected exemplary applications where machine learning amplifies researcher coding, summarizes complex data, relaxes statistical assumptions, and targets researcher attention to further social science research. We aim to reduce perceived barriers to machine learning by summarizing several fundamental building blocks and their grounding in classical statistics. We present a few guiding principles and promising approaches where we see particular potential for machine learning to transform social science inquiry. We conclude that machine learning tools are increasingly accessible, worthy of attention, and ready to yield new discoveries for social research.
Computational techniques are widely used to solve complex optimization problems in different fields such as engineering, finance, biology, and so on. In this paper, the Human Conception Optimizer (HCO) is proposed as a novel metaheuristic algorithm to solve any optimization problems. The idea of this algorithm is based on some biological principles of the human conception process, such as the selective nature of cervical gel in the female reproductive system to allow only healthy sperm cells into the cervix, the guidance nature of mucus gel to help sperm track a genital tracking path towards the egg in the Fallopian tube, the asymmetric nature of flagellar movement which allows sperm cells to move in the reproductive system, the sperm hyperactivation process to make them able to fertilize an egg. Thus, the strategies pursued by the sperm in searching for the egg in the Fallopian tube are modeled mathematically. The best sperm which will meet the position of the egg will be the solution of the algorithm. The performance of the proposed HCO algorithm is examined with a set of basic benchmark test functions called IEEE CEC-2005 and IEEE CEC-2020. A comparative study is also performed between the HCO algorithm and other available algorithms. The significance of the results is verified with statistical test methods. To validate the proposed HCO algorithm, two real-world engineering optimization problems are examined. For this purpose, a complex 14 over-current relay based IEEE 8 bus distribution system is considered. With the proposed algorithm, an improvement of 50% to 60% in total relay operating times is observed comparing with some existing results for the same system. Another engineering problem of designing an optimal proportional integral derivative (PID) controller for a blower driven patient hose mechanical ventilator (MV) is examined. A significant improvement in terms of response time, settling time is observed in the MV system by comparing with existing results.
Computational thinking refers to the cognitive processes underpinning the application of computer science concepts and methodologies to the methodical approach and creation of a solution to a problem. The study aims to determine how students' cognitive, affective, and conative dispositions in using computational thinking are influenced by a gender. This study used a survey research design with quantitative approach. Five hundred thirty-five secondary school students were sampled using probability sampling with the Computational Thinking Disposition Instrument (CTDI). WINSTEPS version 3.71.0 software was subsequently employed to assess the Gender Differential item functioning (GDIF) including reliability and validity with descriptive statistics were employed to assess students' disposition toward practicing computational thinking. In addition to providing implications for the theory, the data give verifiable research that the CT disposition profile consists of three constructs. In addition, the demonstrated CTDI has good GDIF features, which may be employed to evaluate the efficacy of the application of CT in the Malaysian curriculum by measuring the level of CT in terms of the disposition profile of students.