Yoxas/statistical_literacy huggingface dataset:
We describe a cognitive architecture for creating more robust intelligent systems. Our approach is to enable hybrids of algorithms based on different computational formalisms to be executed. The architecture is motivated by some features of human cognitive architecture and the following beliefs: 1) Most existing computational methods often exhibit some of the characteristics desired of intelligent systems at the cost of other desired characteristics and 2) a system exhibiting robust intelligence can be designed by implementing hybrids of these computational methods. The main obstacle to this approach is that the various relevant computational methods are based on data structures and algorithms that are difficult to integrate into one system. We describe a new method of executing hybrids of algorithms using the focus of attention of multiple modules. The key to this approach is the following two principles: 1) Algorithms based on very different computational frameworks (e.g., logical reasoning, probabilistic inference, and case-based reasoning) can be implemented using the same set of five common functions and 2) each of these common functions can be executed using multiple data structures and algorithms. This approach has been embodied in the Polyscheme cognitive architecture. Systems based on Polyscheme in planning, spatial reasoning, robotics, and information retrieval illustrate that this approach to hybridizing algorithms enables qualitative and measurable quantitative advances in the abilities of intelligent systems.
We describe a graduate course in quantitative biology that is based on original path-breaking papers in diverse areas of biology; each of these papers depends on quantitative reasoning and theory as well as experiment. Close reading and discussion of these papers allows students with backgrounds in physics, computational sciences or biology to learn essential ideas and to communicate in the languages of disciplines other than their own.
We describe an innovative bioinformatics course developed under grants from the National Science Foundation and the California State University Program in Research and Education in Biotechnology for undergraduate biology students. The project has been part of a continuing effort to offer students classroom experiences focused on principles and reasoning, using a computer-assisted, problem-based learning model. Here we report on the course design, methods of assessment for the course and materials, and analysis of results obtained in initial offerings of the course.
We describe and evaluate the introduction of a trauma family support service (TFSS) in an Australian tertiary paediatric hospital. A longitudinal mixed-methods cohort study evaluated the effectiveness of the TFSS on quality of life. PedsQL4.0 and EuroQol 5D-Y scores were collected at 6 and 12 months at intervention and non-intervention sites and outcomes were compared using a two-sample t-test. Qualitative data from field notes collected during the administration of the quality-of-life measures were analysed using inductive content analysis. Data were integrated during the interpretation of results to expand and strengthen findings. Data from 192 children were collected (intervention site: 104, control site: 88). Significant increases were seen in the PedsQL and EQ-5D-Y scores at the intervention site compared to the control site at both timepoints, indicating an increase in overall health related quality of life. Two main categories were generated from the qualitative analysis: "Psychosocial impact of trauma" and "Access to psychosocial services." The introduction of a dedicated family support service after paediatric injury improved well-being up to 12 months post injury. Healthcare providers should emphasise dedicated family support services for paediatric trauma patients, focusing on their psychosocial needs and ensuring access to suitable resources. Paediatric nurses are a major part of this service and should contribute to future research, co-designing and implementing these improved family support services to better serve families affected by paediatric trauma.
We describe optimal protocols for a class of mathematical models for tumor anti-angiogenesis for the problem of minimizing the tumor volume with an a priori given amount of vessel disruptive agents. The family of models is based on a biologically validated model by Hahnfeldt et al. and includes a modification by Ergun et al, but also provides two new variations that interpolate the dynamics for the vascular support between these existing models. The biological reasoning for the modifications of the models will be presented and we will show that despite quite different modeling assumptions, the qualitative structure of optimal controls is robust. For all the systems in the class of models considered here, an optimal singular arc is the defining element and all the syntheses of optimal controlled trajectories are qualitatively equivalent with quantitative differences easily computed.
We describe our institution's development and implementation of our Capstone course from a small elective course to the only required fourth-year course. The course's structure evolved from mostly didactic to one including various workshops and simulation sessions. Course content has become increasingly specialty-specific. Implementation requires high faculty and resident involvement. Evaluations indicate a positive impact of the course on participants' self-reported confidence and residency preparedness. Assessment remains pass/fail with more specialty-specific questions. As steadily increasing numbers of medical schools are developing transition to residency courses, we share our Capstone course's evolution and lessons learned over the past nine years. The online version contains supplementary material available at 10.1007/s40670-023-01880-2.
We describe the responsive feedback (RF) approach experience of a nongovernmental organization, Girls Health Champions (now known as Adolescent Health Champions [AHC]), that undertakes peer education interventions in Mumbai, India, schools to improve gender equality and health outcomes for adolescents aged 13-19 years. AHC used the RF approach at the onset of the COVID-19 pandemic in light of uncertainties stemming from school closures and the negative impact of the lockdown on adolescents' physical and mental health. Using an RF approach, AHC was able to: (1) understand pandemic-specific challenges faced by adolescents; (2) overhaul its theory of change; (3) pilot new modes of intervention delivery; (4) design a curriculum for parents/guardians and a COVID-19 module; (5) design an AHC mobile app; (6) develop a new, more gender-inclusive name and visual identity; (7) change the overall structure, adolescent-friendly nature, and agility of the organization; (8) and help clarify future directions taken by the organization. Overall, use of the RF approach had significant positive impacts on AHC as an organization, such as changes in organizational culture, deeper stakeholder engagement, and innovation, and was instrumental in AHC's growth, development, and pandemic response. This article outlines the steps of the process, from initial informal stakeholder consultations to the eventual formalization of the RF approach into the everyday working of AHC through the creation of a youth advisory board. We discuss challenges, such as time and resource constraints encountered; strategies for dealing with such challenges; and general key findings and learnings from this experience that could be beneficial to other youth- and community-serving organizations.
We develop a predictive oviposition model for a southern population of mountain pine beetle (MPB) using a previously developed rate curve, incorporating variation in both oviposition rate and fecundity. We also introduce a method for determining the time delay before oviposition. The model describes the probability of oviposition for a season of MPB attacks using hourly phloem temperature and adult MPB attack data. We also develop an asymptotic approximation of MPB oviposition that is much less computationally taxing. The detailed oviposition model and its asymptotic approximation are compared with other ovipositional models for MPB; the predictive capacity of each model is evaluated using previously published observations.
We developed a course-based undergraduate research experience (CURE) that gives students an opportunity to practice the process of science in a context that intersects with their everyday lives: purchasing grocery store chicken. Student mastery of concepts was assessed by pre- and postassessment questions and lab report worksheets that guided them through the process of writing a scientific paper. Learning to produce graphs from large data sets and comparing the results with published data emphasized quantitative reasoning, while working as a group and writing helped students practice scientific communication. Most students (>90%) met the learning objectives, and students in both groups reported feeling more confident producing graphs and figures; they also showed large gains in confidence and interest in bioinformatics. Lab protocols require biosafety level 2 safety guidelines; however, students in an online or dry lab setting can use the compiled data sets and whole-genome sequences to complete the objectives. Group discussions and essay prompts at the end encourage students to use evidence-based arguments to make decisions that impact the global issue of antimicrobial resistance.
We developed a curriculum for community health workers (CHWs) using an innovative, community-engaged focus group and Delphi process approach. Equipping CHWs with knowledge of hereditary breast and ovarian cancer syndrome (HBOC) and genetics could help enhance identification of women at risk for HBOC, referral, and navigation through genetic services. We conducted focus groups with five CHWs and a three-round Delphi process with eight experts. In the first round of the Delphi process, participants rated and commented on draft curriculum modules. The second round involved live video discussion to highlight points of confusion and concern in the modules. The curriculum was revised and refined based on quantitative and qualitative data and reassessed by the experts in Round 3. Ultimately, agreement was achieved on eight of 10 modules when assessing for clarity of learning objectives, seven out of 10 when assessing for adult learning theory, and nine out of 10 when assessing for participants' ability to learn desired knowledge. We plan to virtually deliver this curriculum to CHWs to enhance their HBOC and genomic competencies. By equipping CHWs to understand and participate in genomics education, we can enable more equitable participation in genomics-informed clinical care and research. Beyond this curriculum, the Delphi methodology can further be used to design content for new CHW curriculums.
We developed a novel four-dimensional spatial task called Shapebuilder and used it to predict performance on a wide variety of cognitive tasks. In six experiments, we illustrate that Shapebuilder: (1) Loads on a common factor with complex working memory (WM) span tasks and that it predicts performance on quantitative reasoning tasks and Ravens Progressive Matrices (Experiment 1), (2) Correlates well with traditional complex WM span tasks (Experiment 2), predicts performance on the conditional go/no go task (Experiment 3) and N-back (Experiment 4), and showed weak or nonsignificant correlations with the Attention Networks Task (Experiment 5), and task switching (Experiment 6). Shapebuilder shows that it exhibits minimal skew and kurtosis, and shows good reliability. We argue that Shapebuilder has many advantages over existing measures of WM, including the fact that it is largely language independent, is not prone to ceiling effects, and take less than 6 min to complete on average.
We developed an inherently interpretable multilevel Bayesian framework for representing variation in regression coefficients that mimics the piecewise linearity of ReLU-activated deep neural networks. We used the framework to formulate a survival model for using medical claims to predict hospital readmission and death that focuses on discharge placement, adjusting for confounding in estimating causal local average treatment effects. We trained the model on a 5% sample of Medicare beneficiaries from 2008 and 2011, based on their 2009-2011 inpatient episodes (approximately 1.2 million), and then tested the model on 2012 episodes (approximately 400 thousand). The model scored an out-of-sample AUROC of approximately 0.75 on predicting all-cause readmissions-defined using official Centers for Medicare and Medicaid Services (CMS) methodology-or death within 30-days of discharge, being competitive against XGBoost and a Bayesian deep neural network, demonstrating that one need-not sacrifice interpretability for accuracy. Crucially, as a regression model, it provides what blackboxes cannot-its exact gold-standard global interpretation, explicitly defining how the model performs its internal "reasoning" for mapping the input data features to predictions. In doing so, we identify relative risk factors and quantify the effect of discharge placement. We also show that the posthoc explainer SHAP provides explanations that are inconsistent with the ground truth model reasoning that our model readily admits.
We developed an online training module targeting nail salon workers' knowledge of chemical exposure and safety, responding directly to the workers' expressed needs in a Midwest State. Following a needs assessment, we designed and developed the module content. Implementation and evaluation approaches were rolled out into three phases. Seven workers completed the English version of the module and 24 workers completed the Vietnamese version. The average pre-test scores for those who completed the English and Vietnamese versions of the training were 81.43% and 58.33% respectively. The average post-test score was 98.57% for English and 91.67% for Vietnamese. Culturally appropriate educational resources are effective facets of enhancing nail salon workers' awareness, and their occupational health subsequently.
We developed labs on the evolution of antibiotic resistance to assess the costs and benefits of replacing traditional laboratory exercises in an introductory biology course for majors with a course-based undergraduate research experience (CURE). To assess whether participating in the CURE imposed a cost in terms of exam performance, we implemented a quasi-experiment in which four lab sections in the same term of the same course did the CURE labs, while all other students did traditional labs. To assess whether participating in the CURE impacted other aspects of student learning, we implemented a second quasi-experiment in which all students either did traditional labs over a two-quarter sequence or did CURE labs over a two-quarter sequence. Data from the first experiment showed minimal impact on CURE students' exam scores, while data from the second experiment showed that CURE students demonstrated a better understanding of the culture of scientific research and a more expert-like understanding of evolution by natural selection. We did not find disproportionate costs or benefits for CURE students from groups that are minoritized in science, technology, engineering, and mathematics.
We developed the Stem Cell Educator therapy among multiple clinical trials based on the immune modulations of multipotent cord blood-derived stem cells (CB-SCs) on different compartments of immune cells, such as T cells and monocytes/macrophages, in type 1 diabetes and other autoimmune diseases. However, the effects of CB-SCs on the B cells remained unclear. To better understand the molecular mechanisms underlying the immune education of CB-SCs, we explored the modulations of CB-SCs on human B cells. CB-SCs were isolated from human cord blood units and confirmed by flow cytometry with different markers for their purity. B cells were purified by using anti-CD19 immunomagnetic beads from human peripheral blood mononuclear cells (PBMCs). Next, the activated B cells were treated in the presence or absence of coculture with CB-SCs for 7 days before undergoing flow cytometry analysis of phenotypic changes with different markers. Reverse transcription-polymerase chain reaction (RT-PCR) was utilized to evaluate the levels of galectin expressions on CB-SCs with or without treatment of activated B cells in order to find the key galectin that was contributing to the B-cell modulation. Flow cytometry demonstrated that the proliferation of activated B cells was markedly suppressed in the presence of CB-SCs, leading to the downregulation of immunoglobulin production from the activated B cells. Phenotypic analysis revealed that treatment with CB-SCs increased the percentage of IgD+CD27- naïve B cells, but decreased the percentage of IgD-CD27+ switched B cells. The transwell assay showed that the immune suppression of CB-SCs on B cells was dependent on the galectin-9 molecule, as confirmed by the blocking experiment with the anti-galectin-9 monoclonal antibody. Mechanistic studies demonstrated that both calcium levels of cytoplasm and mitochondria were downregulated after the treatment with CB-SCs, causing the decline in mitochondrial membrane potential in the activated B cells. Western blot exhibited that the levels of phosphorylated Akt and Erk1/2 signaling proteins in the activated B cells were also markedly reduced in the presence of CB-SCs. CB-SCs displayed multiple immune modulations on B cells through the galectin-9-mediated mechanism and calcium flux/Akt/Erk1/2 signaling pathways. The data advance our current understanding of the molecular mechanisms underlying the Stem Cell Educator therapy to treat autoimmune diseases in clinics.
We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.
We discuss the feasibility of a brief, online mental health promotion programme for tertiary students and establish recommendations for future programmes. The programme 'Student Elevenses' was delivered at a tertiary education institution. 'Student Elevenses' aimed to promote student wellbeing during the coronavirus disease 2019 crisis, comprised of 10-15-min daily online micro-interventions targeting six lifestyle areas for wellbeing, and was delivered via video conference. Upon programme completion, all students were invited to complete barriers to engagement survey, irrespective of whether they had attended or heard of the programme. Descriptive statistics were calculated for demographics, as well as feasibility and acceptability outcomes including recruitment rates, attendance rates and reported barriers to attendance. Open-ended questions were coded for themes. Less than 1% of those who consented to participate actually attended the programme, with attendance ranging from 2 to 17 participants. Participants were predominantly female (68%), domestic students (81%) and had a mean age of 29.5 years. The barriers students reported included fixed time, online format, a belief programme would not be helpful, preference for existing supports and perceived impacts of coronavirus disease 2019. Students recommended embedding support within policies/teaching, offering a range of supports and involving students in design. Barriers to mental health promotion via telehealth should be considered to promote accessibility and acceptability for tertiary students. Future programmes should consider reaching students through mandatory activities (e.g. lectures, tutorials) and should include student consultation and co-design to support the development of programmes that meet student needs and preferences.
We discuss the interpretation of the detected signal by pulsar timing array (PTA) observations as a gravitational wave background of cosmological origin. We combine NANOGrav 15-years and EPTA-DR2new datasets and confront them against backgrounds from supermassive black hole binaries (SMBHBs), and cosmological signals from inflation, cosmic (super)strings, first-order phase transitions, Gaussian and non-Gaussian large scalar fluctuations, and audible axions. We find that scalar-induced, and to a lesser extent audible axion and cosmic superstring signals, provide a better fit than SMBHBs. These results depend, however, on modeling assumptions, so further data and analysis are needed to reach robust conclusions. Independently of the signal origin, the data strongly constrain the parameter space of cosmological signals, for example, setting an upper bound on primordial non-Gaussianity at PTA scales as |f_{nl}|≲2.34 at 95% C.L.
We employed a novel approach to identify the key loci that harbor genes influencing lipoprotein metabolism in approximately 2,000 pedigreed baboons fed various diets differing in levels of fat and cholesterol. In this study, 126 overlapping traits related to both LDL and HDL metabolism were normalized and subjected to genome-wide linkage screening. As was expected, the traits were highly, but not completely, correlated. We exploited the information in these correlated traits by focusing on those genomic regions harboring quantitative trait loci (QTL) for multiple traits, reasoning that the more influential genes would impact a larger number of traits. This study identified five major QTL clusters (each with at least two significant logarithm of the odds scores >4.7), two of which had not been previously reported in baboons. One of these mapped to the baboon ortholog of human chromosome 1p32-p34 and influenced concentrations of LDL-cholesterol on Basal and high-fat, low-cholesterol diets. The other novel QTL cluster mapped to the baboon ortholog of human chromosome 12q13.13-q14.1 and influenced LDL size properties on high-fat, low-cholesterol and high-fat, high-cholesterol, but not Basal, diets. Confirming the value of this approach, three of the QTL clusters replicated published linkage findings for the same or similar traits.