Yoxas/statistical_literacy huggingface dataset:
For people from asylum-seeking and refugee backgrounds, housing and the re-establishment of home are key social determinants of health. Research highlights the inequities faced by asylum seekers and refugees in the housing markets of high-income resettlement countries, resulting in their overrepresentation in precarious housing. There is also emerging evidence of the relationship between housing and health for this population relating to lack of affordability, insecurity of tenure, and poor suitability (physical and social). The mechanisms by which housing impacts health for this group within these housing contexts, is however, understudied - especially overtime. This qualitative longitudinal study aimed to address this gap. Semi-structured interviews were conducted with 25 people from asylum-seeking and refugee backgrounds in South Australia, recruited through a community survey. Thematic analysis of interview data across three time points over three years identified four material and psychosocial mechanisms through which housing contributed to health outcomes via psychological and physical stressors - physical environment; stability; safety; and social connections, support and services. The study also identified additional health promoting resources, particularly elements of ontological security. The dynamics of these indirect and direct mechanisms were further illuminated by considering the impact of international, national and local contexts and a range of intersecting social factors including gender, country/culture of origin, family circumstances, immigration status, language skills, income, and health status. Rebuilding a sense of home and ontological security is a key resettlement priority and crucial for wellbeing. More comprehensive strategies to facilitate this for refugees and asylum seekers are required.
For people with type 1 diabetes, extensive tasks involved in diabetes self-management have a significant impact on overall care and quality of life. Members of the working population with type 1 diabetes spend one third of their time at work, yet little attention has been paid to challenges specific to balancing diabetes management and work life. Diabetes is associated with increased absenteeism and presenteeism, as well as disability retirement and lower lifetime income. These quantitative outcomes are corroborated by qualitative studies highlighting some of the challenges of reconciling diabetes self-management with work life. This qualitative study expands on previous research by examining illness behavior in work life using Alonzo's concept of containment. Forty in-depth interviews with Danish people with type 1 diabetes were conducted from May to December 2016 and analyzed using abductive reasoning. We found that working people with type 1 diabetes live in tension between competing logics linked to diabetes and to work life. We illustrate how diabetes management can be articulated as a matter of containment, which refers to the assemblage of practices and mental and emotional work required to keep diabetes at the level of a side-involvement and maintain proper situational involvement in work life. The containment framework illuminates and characterizes diabetes management in the context of work life and reveals a hidden burden of disease carried by working people with type 1 diabetes. Further research is needed to advance the theory of containment.
For some students, school success is not a simple matter. A growing, innovative approach that supports students' functioning at school is programs in which animals are involved in education. The involvement of animals, especially dogs, in education is known as animal-assisted education (AAE). A literature review of AAE indicated a positive influence of AAE programs on the quality of learning and social emotional development in children. This study explored whether AAE positively impacts the social and emotional outcomes of elementary school students aged between 8 and 13 years through mixed methods. The methods used were a survey and an observational study. The survey section of the study showed that students participating in the program with the dogs rated themselves, after the intervention period, significantly higher in terms of self-confidence and had a more positive score for relationships with other students after the intervention. As rated by their teachers, after the intervention period, students scored significantly higher in relation to work attitude, pleasant behavior, emotional stability, and social behavior. In the observational study, we analyzed the video material of students who participated in an AAE program with dogs. We concluded that all verbal and non-verbal behaviors of the students increased, except eye contact. The current study indicates future directions for theoretical underpinnings, improved understanding, and the empirical measurement of the underlying variables and mechanisms.
For successful biomarker discovery, it is essential to develop computational frameworks that summarize high-dimensional neuroimaging data in terms of involved sub-systems of the brain, while also revealing underlying heterogeneous functional and structural changes covarying with specific cognitive and biological traits. However, unsupervised decompositions do not inculcate clinical assessment information, while supervised approaches extract only individual feature importance, thereby impeding qualitative interpretation at the level of subspaces. We present a novel framework to extract robust multimodal brain subspaces associated with changes in a given cognitive or biological trait. Our approach involves active subspace learning on the gradients of a trained machine learning model followed by clustering to extract and summarize the most salient and consistent subspaces associated with the target variable. Through a rigorous cross-validation procedure on an Alzheimer's disease (AD) dataset, our framework successfully extracts multimodal subspaces specific to a given clinical assessment (e.g., memory and other cognitive skills), and also retains predictive performance in standard machine learning algorithms. We also show that the salient active subspace directions occur consistently across randomly sub-sampled repetitions of the analysis. Compared to existing unsupervised decompositions based on principle component analysis, the subspace components in our framework retain higher predictive information. As an important step towards biomarker discovery, our framework not only uncovers AD-related brain regions in the associated brain subspaces, but also enables automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and proficiency in cognitive skills related to brain disorders like AD.
For the diagnosis and outcome prediction of gastric cancer (GC), machine learning methods based on whole slide pathological images (WSIs) have shown promising performance and reduced the cost of manual analysis. Nevertheless, accurate prediction of GC outcome may rely on multiple modalities with complementary information, particularly gene expression data. Thus, there is a need to develop multimodal learning methods to enhance prediction performance. In this paper, we collect a dataset from Ruijin Hospital and propose a multimodal learning method for GC diagnosis and outcome prediction, called GaCaMML, which is featured by a cross-modal attention mechanism and Per-Slide training scheme. Additionally, we perform feature attribution analysis via integrated gradient (IG) to identify important input features. The proposed method improves prediction accuracy over the single-modal learning method on three tasks, i.e., survival prediction (by 4.9% on C-index), pathological stage classification (by 11.6% on accuracy), and lymph node classification (by 12.0% on accuracy). Especially, the Per-Slide strategy addresses the issue of a high WSI-to-patient ratio and leads to much better results compared with the Per-Person training scheme. For the interpretable analysis, we find that although WSIs dominate the prediction for most samples, there is still a substantial portion of samples whose prediction highly relies on gene expression information. This study demonstrates the great potential of multimodal learning in GC-related prediction tasks and investigates the contribution of WSIs and gene expression, respectively, which not only shows how the model makes a decision but also provides insights into the association between macroscopic pathological phenotypes and microscopic molecular features.
For the first time in history, the COVID-19 pandemic required students at The University of Texas School of Dentistry (UTSD) to move to remote education. Based on a literature review, it was assumed that younger generations of students would prefer virtual teaching models over in-classroom formats. The purpose of this study is to assess students' perspectives of remote learning during COVID-19 in dental education relative to their generation and programme. An electronic survey was administered to all UTSD dental and dental hygiene students via Qualtrics. The survey collected information on demographics, pre-pandemic learning preferences, remote learning experiences during the pandemic, and a free response section. Descriptive statistics and Fisher's exact test were used to analyse data. The survey received a 60% response rate (n = 290). Over 95% of respondents belonged to Generations Y and Z. There were no significant differences in responses between generations. However, significant differences in responses between programmes arose with more dental hygiene students agreed that they liked learning in a classroom setting compared to dental students (p < 0.01). Significantly more dental students agreed that they stayed organized, were less stressed, and felt that the transition to online learning was easy compared to dental hygiene students (p < 0.01). Although the majority of students surveyed belong to Generations Y and Z, there are still preferences for traditional, in-person learning. Both cohorts recognized the benefits and challenges of distance learning, and the findings from this study shed light on how students are handling remote learning in dental education.
For the past ten years, falls have been the leading cause of nonfatal injuries for all age groups less than 15 years old. A significant rise in childhood sedentary behavior in schools and limited opportunities to be outside has led to motor coordination deficits which have contributed to fall injuries. A German assessment tool, the Körperkoordinationstest fur Kinder (KTK), which has been used for decades in Western European countries, allows researchers and physical education teachers to evaluate typical and atypical children's motor coordination competencies related to dynamic postural balance successfully. No research has been published on the use of this assessment tool in the United States. If its use were found to be feasible in this country for identifying motor coordination deficits in typical and atypical children, it would close the gap in determining motor coordination. Therefore, this study sought in Phase 1 to determine the feasibility of using the KTK assessment in U.S. children and Phase 2 sought to determine the adaptability of the scoring protocol from use in other countries to the United States. The Phase 1 results revealed the KTK assessment was feasible to administer in U.S. physical education class by addressing three challenges for U.S. schools: 1) KTK implementation, 2) time to assess each skill, and 3) the equipment availability and cost to implement the test in a physical education setting. In Phase 2, the researchers were able to determine the raw scores and motor quotient scores in this population and then were able to show similar scoring trends between U.S. children and Flemish children from a previous study. This assessment tool was deemed feasible and adaptable which is the first step to use the KTK in U.S. physical education elementary school settings.
For total hip arthroplasty (THA), a new technology in the evolution of computer-assisted surgery has emerged in the form of augmented reality (AR). We sought to determine the impact of AR on resident and fellow education after implementation at an academic teaching center. The senior author's intraoperative technique allows for the orthopedic trainee to use AR to correct the acetabular component's position after an attempt is made with standard instrumentation. One year after the implementation of this AR method, both resident and fellow trainees were issued an anonymous survey regarding their experience and descriptive statistics were calculated for the results. Sixteen trainees responded to the survey. One hundred percent felt the use of AR improved their understanding of acetabular component placement and improved their intraoperative experience. Sixty-nine percent reported feeling there was a small increase in operative time but 25% reported no increase in operative time when using AR. Seventy-five percent of trainees felt that patients benefited from the technology and would be in favor of AR if they were having a THA. The majority of those surveyed reported a desire to use AR in their practice if it is available. Computer-assisted surgery has demonstrated variable impacts on orthopedic education. After the implementation of AR at an academic teaching center, all trainees reported it improved their intraoperative experience and their understanding of acetabular component placement. Further studies are needed to determine if AR is able to improve a trainee's component placement. [Orthopedics. 202x;4x(x):xx-xx. ].
For well over 150 years, factors of safety (also known as safety factors) have been a fundamental engineering concept that expresses how much stronger a system is compared with the intended load. The pioneering work of Robert McNeill Alexander in the early 1980s applied this engineering concept to biomechanics. Over the next decade, evidence from comparative biomechanics supported the idea that safety factors are a fundamental principle of animal form and function. In terms of physiology, Jared Diamond related the maximal capacity of a physiological process to normal functional demands and incorporated evolutionary thinking into the concept of safety factors. It was proposed that evolutionary reasoning is required to understand the magnitudes of biological reserve capacities, an idea called 'quantitative evolutionary design'. However, the general idea of safety factors as related to organismal form and function is much older. In 1906, Samuel James Meltzer, a physiologist and physician, presented the 5th Harvey Lecture to the New York Academy of Medicine; a lecture entitled 'The Factors of Safety in Animal Structure and Animal Economy', which was later published in Science in 1907. The 1907 paper is rarely cited and has never been cited within comparative biomechanics or comparative physiology. The purpose of this Commentary is to highlight Meltzer's historical contribution to the concept of safety factors as a general principle of organismal 'design'.
For years, education has gone through transformation and incorporated innovation to improve the effectiveness of teaching and learning. The scholarship of teaching and learning (SoTL) is one such activity that transforms and innovates education to engineer effectiveness. SoTL is internationally accepted and applied in Education, Psychology, Management Science, Chemistry, History, English, Sociology, Communication Studies, Mathematics, Engineering, Medicine, and Interdisciplinary Programs with a growing interest in Emergency Management and Homeland Security (EMHS). In relation to the EMHS programs, the Federal Emergency Management Agency (FEMA) SoTL Special Interest Group members have participated in the American Educational Research Association annual meetings and then shared the relevance of the SoTL concept with the EM scholars, researchers, and practitioners at the FEMA Higher Education programs.1-4 Thus, the concept of SoTL cuts across academic barriers with methodological pluralism in educational practice. In addition, the success of SoTL has been driven with the publication of two edited volumes of Carnegie Scholars' works: (1) Opening Lines: Approaches to the Scholarship of Teaching and Learning5 and (2) Disciplinary Styles in the Scholarship of Teaching and Learning: Exploring Common Ground.6 However, even with these advances, there are some concerns about SoTL critiques. One such critique comes from Boshier who asserts that SoTL is a difficult academic concept to be accepted by the academic community.7 Criticisms -notwithstanding, there are many benefits due to SoTL. Its future is bright and encouraging as nations benefit from SoTL's educational transformative and innovative power.
Forecasting disease spread is a critical tool to help public health officials design and plan public health interventions. However, the expected future state of an epidemic is not necessarily well defined as disease spread is inherently stochastic, contact patterns within a population are heterogeneous, and behaviors change. In this work, we use time-dependent probability generating functions (PGFs) to capture these characteristics by modeling a stochastic branching process of the spread of a disease over a network of contacts in which public health interventions are introduced over time. To achieve this, we define a general transmissibility equation to account for varying transmission rates (e.g. masking), recovery rates (e.g. treatment), contact patterns (e.g. social distancing) and percentage of the population immunized (e.g. vaccination). The resulting framework allows for a temporal and probabilistic analysis of an intervention's impact on disease spread, which match continuous-time stochastic simulations that are much more computationally expensive. To aid policy making, we then define several metrics over which temporal and probabilistic intervention forecasts can be compared: Looking at the expected number of cases and the worst-case scenario over time, as well as the probability of reaching a critical level of cases and of not seeing any improvement following an intervention. Given that epidemics do not always follow their average expected trajectories and that the underlying dynamics can change over time, our work paves the way for more detailed short-term forecasts of disease spread and more informed comparison of intervention strategies.
Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. The use of technical analysis for financial forecasting has been successfully employed by many researchers. The existing qualitative based methods developed based on fuzzy reasoning techniques cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. Extended fuzzy sets (e.g., fuzzy probabilistic set) study the fuzziness of the membership grade to a concept. The cloud model, based on probability measure space, automatically produces random membership grades of a concept through a cloud generator. In this paper, a cloud model-based approach was proposed to confirm accurate stock based on Japanese candlestick. By incorporating probability statistics and fuzzy set theories, the cloud model can aid the required transformation between the qualitative concepts and quantitative data. The degree of certainty associated with candlestick patterns can be calculated through repeated assessments by employing the normal cloud model. The hybrid weighting method comprising the fuzzy time series, and Heikin-Ashi candlestick was employed for determining the weights of the indicators in the multi-criteria decision-making process. Fuzzy membership functions are constructed by the cloud model to deal effectively with uncertainty and vagueness of the stock historical data with the aim to predict the next open, high, low, and close prices for the stock. The experimental results prove the feasibility and high forecasting accuracy of the proposed model.
Forensic Investigative Genetic Genealogy, a recent sub discipline of forensic genomics, leverages the high throughput and sensitivity of detection of next generation sequencing and established genetic and genealogical approaches to support the identification of human remains from missing persons investigations and investigative lead generation in violent crimes. To facilitate forensic DNA evidence analysis, the ForenSeq® Kintelligence multiplex, consisting of 10,230 SNPs, was developed. Design of the ForenSeq Kintelligence Kit, the MiSeq FGx® Sequencing System and the ForenSeq Universal Analysis Software is described. Developmental validation in accordance with SWGDAM guidelines and forensic quality assurance standards, using single source samples, is reported for the end-to-end workflow from library preparation to data interpretation. Performance metrics support the conclusion that more genetic information can be obtained from challenging samples compared to other commercially available forensic targeted DNA assays developed for capillary electrophoresis (CE) or other current next generation sequencing (NGS) kits due to the higher number of markers, the overall shorter amplicon sizes (97.8% <150 bp), and kit design. Data indicate that the multiplex is robust and fit for purpose for a wide range of quantity and quality samples. The ForenSeq Kintelligence Kit and the Universal Analysis Software allow transfer of the genetic component of forensic investigative genetic genealogy to the operational forensic laboratory.
Formal deductive logic, used to express and reason over declarative, axiomatizable content, captures, we now know, essentially all of what is known in mathematics and physics, and captures as well the details of the proofs by which such knowledge has been secured. This is certainly impressive, but deductive logic alone cannot enable rational adjudication of arguments that are at variance (however much additional information is added). After affirming a fundamental directive, according to which argumentation should be the basis for human-centric AI, we introduce and employ both a deductive and-crucially-an inductive cognitive calculus. The former cognitive calculus, DCEC, is the deductive one and is used with our automated deductive reasoner ShadowProver; the latter, IDCEC, is inductive, is used with the automated inductive reasoner ShadowAdjudicator, and is based on human-used concepts of likelihood (and in some dialects of IDCEC, probability). We explain that ShadowAdjudicator centers around the concept of competing and nuanced arguments adjudicated non-monotonically through time. We make things clearer and more concrete by way of three case studies, in which our two automated reasoners are employed. Case Study 1 involves the famous Monty Hall Problem. Case Study 2 makes vivid the efficacy of our calculi and automated reasoners in simulations that involve a cognitive robot (PERI.2). In Case Study 3, as we explain, the simulation employs the cognitive architecture ARCADIA, which is designed to computationally model human-level cognition in ways that take perception and attention seriously. We also discuss a type of argument rarely analyzed in logic-based AI; arguments intended to persuade by leveraging human deficiencies. We end by sharing thoughts about the future of research and associated engineering of the type that we have displayed.