Yoxas/statistical_literacy huggingface dataset:
Real-world studies concerning different populations are valuable and bring new information regarding different regimens of Brolucizumab injections and their adverse reactions. The present study investigates the efficacy of a pro-re-nata regimen (PRN) for neovascular Age-related Macular Degeneration (nAMD). Separate from the main statistics we report the use of Brolucizumab in central serous chorioretinopathy (CSC). A retrospective observational single-center study was conducted on 82 eyes treated with Brolucizumab between 2021 and 2023, for nAMD. Patients were injected at intervals of at least 2 months after the loading phase. In 9 (3-20) months follow-up, only 0.26 % adverse reactions were noticed, with good resolution of retinal fluid (significant reduction of CST on SD-OCT, -72.50μ, p < 0.05), especially for subretinal fluid. 54 % of the eyes remained fluid-free. The interval of injection (INTOI, a parameter calculated by averaging the results of the division of the follow-up period to the number of injections received by each patient) was 2.68 (corresponding to an injection interval of 11 weeks). This could become an important parameter for the characterization of Brolucizumab and any other anti-VEGF therapy and could provide a more precise interval of injection in the future. Four patients also received Brolucizumab for the treatment of chronic CSC (3 doses each). All showed good response, 3 of them remaining fluid-free.
Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks. Graphs are also the underlying structures of interest in a wide range of more traditional fields ranging from logic-oriented knowledge representation and reasoning to graph kernels and statistical relational learning. In this review we outline a broad map and inventory of the field of learning and reasoning with graphs that spans the spectrum from reasoning in the form of logical deduction to learning node embeddings. To obtain a unified perspective on such a diverse landscape we introduce a simple and general semantic concept of a model that covers logic knowledge bases, graph neural networks, kernel support vector machines, and many other types of frameworks. Still at a high semantic level, we survey common strategies for model specification using probabilistic factorization and standard feature construction techniques. Based on this semantic foundation we introduce a taxonomy of reasoning tasks that casts problems ranging from transductive link prediction to asymptotic analysis of random graph models as queries of different complexities for a given model. Similarly, we express learning in different frameworks and settings in terms of a common statistical maximum likelihood principle. Overall, this review aims to provide a coherent conceptual framework that provides a basis for further theoretical analyses of respective strengths and limitations of different approaches to handling graph data, and that facilitates combination and integration of different modeling paradigms.
Reasoning and inference are well-studied aspects of basic cognition that have been explained as statistically optimal Bayesian inference. Using a simplified experimental design, we conducted quantitative comparisons between Bayesian inference and human inference at the level of individuals. In 3 experiments, with more than 13,000 participants, we asked people for prior and posterior inferences about the probability that 1 of 2 coins would generate certain outcomes. Most participants' inferences were inconsistent with Bayes' rule. Only in the simplest version of the task did the majority of participants adhere to Bayes' rule, but even in that case, there was a significant proportion that failed to do so. The current results highlight the importance of close quantitative comparisons between Bayesian inference and human data at the individual-subject level when evaluating models of cognition.
Reasoning the hidden relational structure from sequences of events is a crucial ability humans possess, which helps them to predict the future and make inferences. Besides simple statistical properties, humans also excel in learning more complex relational networks. Several brain regions are engaged in the process, yet the time-resolved neural implementation of relational structure learning and its contribution to behavior remains unknown. Here human subjects performed a probabilistic sequential prediction task on image sequences generated from a transition graph-like network, with their brain activities recorded using electroencephalography (EEG). We demonstrate the emergence of two key aspects of relational knowledge - lower-order transition probability and higher-order community structure, which arise around 540-930 ms after image onset and well predict behavioral performance. Furthermore, computational modeling suggests that the formed higher-order community structure, i.e., compressed clusters in the network, could be well characterized by a successor representation operation. Overall, human brains are computing the temporal statistical relationship among discrete inputs, based on which new abstract graph-like knowledge could be constructed.
Reasoning under uncertainty is the bread and butter of everyday life. Many areas of psychology, from cognitive, developmental, social, to clinical, are interested in how individuals make inferences and decisions with incomplete information. The ability to reason under uncertainty necessarily involves probability computations, be they exact calculations or estimations. What are the developmental origins of probabilistic reasoning? Recent work has begun to examine whether infants and toddlers can compute probabilities; however, previous experiments have confounded quantity and probability-in most cases young human learners could have relied on simple comparisons of absolute quantities, as opposed to proportions, to succeed in these tasks. We present four experiments providing evidence that infants younger than 12 months show sensitivity to probabilities based on proportions. Furthermore, infants use this sensitivity to make predictions and fulfill their own desires, providing the first demonstration that even preverbal learners use probabilistic information to navigate the world. These results provide strong evidence for a rich quantitative and statistical reasoning system in infants.
Recent advances in care and treatment have turned HIV into a "chronic but manageable condition". Despite this, some people living with HIV (PLHIV) continue to suffer from stigma and discrimination in accessing health care services. This study examined the experience of stigma and discrimination and access to health care services among PLHIV in Akwa Ibom State. The Center for Clinical Care and Clinical Research (CCCRN), implementing a USAID-funded Integrated Child Health and Social Services Award (ICHSSA 1) project, conducted a community-based cross-sectional survey in 12 randomly selected local government areas in Akwa Ibom State, Nigeria. A structured quantitative questionnaire was used for data collection. In total, 425 randomly selected PLHIV were interviewed after providing informed consent. Descriptive statistics and bivariate analyses were conducted using the data analytical application Stata 14. The study revealed that 215 PLHIV (50.4%) had been denied access to health care services, including dental care, because of their HIV status in Akwa Ibom State. Respondents reported being afraid of: gossip (78%), being verbally abused (17%), or being physically harassed or assaulted because of their positive status (13%). Self-stigmatization was also evident; respondents reported being ashamed because of their positive HIV status (29%), exhibiting self-guilt (16%), having low self-esteem (38%), and experiencing self-isolation (36%). Women, rural residents, PLHIV with no education, unemployed, single, young people aged between 19 and 29 years, and older adults were more likely to experience HIV-related stigmatization. Data from the study revealed that the percentage of PLHIV who experience health-related stigmatization because of their HIV status is high in Akwa Ibom State. This finding calls for the prioritization of interventions to reduce stigma, enhance self-esteem, and promote empathy and compassion for PLHIV. It also highlights the need for HIV education for family and community members and health care providers, to enhance the knowledge of HIV and improve acceptance of PLHIV within families, communities, and health care settings.
Recent breakthroughs in highly accurate protein structure prediction using deep neural networks have made considerable progress in solving the structure prediction component of the 'protein folding problem'. However, predicting detailed mechanisms of how proteins fold into specific native structures remains challenging, especially for multidomain proteins constituting most of the proteomes. Here, we develop a simple structure-based statistical mechanical model that introduces nonlocal interactions driving the folding of multidomain proteins. Our model successfully predicts protein folding processes consistent with experiments, without the limitations of protein size and shape. Furthermore, slight modifications of the model allow prediction of disulfide-oxidative and disulfide-intact protein folding. These predictions depict details of the folding processes beyond reproducing experimental results and provide a rationale for the folding mechanisms. Thus, our physics-based models enable accurate prediction of protein folding mechanisms with low computational complexity, paving the way for solving the folding process component of the 'protein folding problem'.
Recent calls for improving undergraduate biology education have emphasized the importance of students learning to apply quantitative skills to biological problems. Motivated by students' apparent inability to transfer their existing quantitative skills to biological contexts, we designed and taught an introductory molecular and cell biology course in which we integrated application of prerequisite mathematical skills with biology content and reasoning throughout all aspects of the course. In this paper, we describe the principles of our course design and present illustrative examples of course materials integrating mathematics and biology. We also designed an outcome assessment made up of items testing students' understanding of biology concepts and their ability to apply mathematical skills in biological contexts and administered it as a pre/postcourse test to students in the experimental section and other sections of the same course. Precourse results confirmed students' inability to spontaneously transfer their prerequisite mathematics skills to biological problems. Pre/postcourse outcome assessment comparisons showed that, compared with students in other sections, students in the experimental section made greater gains on integrated math/biology items. They also made comparable gains on biology items, indicating that integrating quantitative skills into an introductory biology course does not have a deleterious effect on students' biology learning.
Recent data reveal a marked rise in the detection and mortality rates of Desmoplastic Malignant Melanoma (DMM). This trend underscores the imperative for an in-depth analysis of DMM's epidemiology, which is crucial for the formulation of precise medical and public health strategies. This investigation seeks to elucidate the variations in the incidence and mortality of DMM over a 15-year period (2005-2019). Data on DMM patients was sourced from the Surveillance, Epidemiology, and End Results (SEER) database. Both incidence and incidence-based mortality rates (IBM) were directly extracted from the SEER database. Joinpoint regression was used to analyze and calculate the average annual percent change (AAPC) and its 95% confidence interval (CI). Between 2005 and 2019, 3,384 DMM cases were identified, boasting an age-adjusted incidence rate of 36.3 cases per 1000,000 person-years (95% CI 3.51-3.76) and an IBM of 1.65cases per 1000,000 person-years (95% CI 1.57-1.74). Of these, 2,353 were males (69.53%) and 1,031 were females (30.47%). There were 1894 patients (55.97%) who were over 70 years old. Predominantly, DMM lesions manifested in exposed areas: Limbs (955, 28.22%), Face (906, 26.77%), and Scalp and Neck (865, 25.56%). The incidence of DMM increased significantly at a rate of APC = 0.9% during 2005-2019, while the incidence-based mortality showed a significant upward trend (APC = 7%) during 2005-2012, and slowly increasing trend (APC = 0.6%) during 2012-2019. In contrast to the modest upward trajectory in female incidence and mortality, male incidence initially surged, later declining, while male mortality peaked and stabilized post-2012. The primary sites for incidence and mortality were chronically sun-exposed areas: Face, Scalp and Neck, and Limbs. In recent years, the incidence and incidence-based mortality of DMM have significantly increased. Each subgroup analysis has different trends, and these trends can provide better support for our exploration of DMM.
Recent decades have seen a rapid acceleration in global participation in formal education, due to worldwide initiatives aimed to provide school access to all children. Research in high income countries has shown that school quality indicators have a significant, positive impact on numeracy and literacy-skills required to participate in the increasingly globalized economy. Schools vary enormously in kind, resources, and teacher training around the world, however, and the validity of using diverse school quality measures in populations with diverse educational profiles remains unclear. First, we assessed whether children's numeracy and literacy performance across populations improves with age, as evidence of general school-related learning effects. Next, we examined whether several school quality measures related to classroom experience and composition, and to educational resources, were correlated with one another. Finally, we examined whether they were associated with children's (4-12-year-olds, N = 889) numeracy and literacy performance in 10 culturally and geographically diverse populations which vary in historical engagement with formal schooling. Across populations, age was a strong positive predictor of academic achievement. Measures related to classroom experience and composition were correlated with one another, as were measures of access to educational resources and classroom experience and composition. The number of teachers per class and access to writing materials were key predictors of numeracy and literacy, while the number of students per classroom, often linked to academic achievement, was not. We discuss these results in the context of maximising children's learning environments and highlight study limitations to motivate future research. RESEARCH HIGHLIGHTS: We examined the extent to which four measures of school quality were associated with one another, and whether they predicted children's academic achievement in 10 culturally and geographically diverse societies. Across populations, measures related to classroom experience and composition were correlated with one another as were measures of access to educational resources to classroom experience and composition. Age, the number of teachers per class, and access to writing materials were key predictors of academic achievement across populations. Our data have implications for designing efficacious educational initiatives to improve school quality globally.
Recent developments have enabled daily accumulated medical information to be converted into medical big data, and new evidence is expected to be created using databases and various open data sources. Database research using medical big data was actively conducted in the coronavirus disease 2019 (COVID-19) pandemic and created evidence for a new disease. Conversely, the new term "infodemic" has emerged and has become a social problem. Multiple posts on social networking services (SNS) overly stirred up safety concerns about the COVID-19 vaccines based on the analysis results of the Vaccine Adverse Event Reporting System (VAERS). Medical experts on SNS have attempted to correct these misunderstandings. Incidents where research papers about the COVID-19 treatment using medical big data were retracted due to the lack of reliability of the database also occurred. These topics of appropriate interpretation of results using spontaneous reporting databases and ensuring the reliability of databases are not new issues that emerged during the COVID-19 pandemic but issues that were present before. Thus, literacy regarding medical big data has become increasingly important. Research related to artificial intelligence (AI) is also progressing rapidly. Using medical big data is expected to accelerate AI development. However, as medical AI does not resolve all clinical setting problems, we also need to improve our medical AI literacy.
Recent evidence suggests that Alzheimer's disease (AD) genetic risk variants (rs1582763 and rs6591561) of the MS4A locus are genome-wide significant regulators of soluble TREM2 levels such that the minor allele of the protective variant (rs1582763) is associated with higher sTREM2 and lower AD risk while the minor allele of (rs6591561) relates to lower sTREM2 and higher AD risk. Our group previously found that higher sTREM2 relates to higher Aβ40, worse blood-brain barrier (BBB) integrity (measured with the CSF/plasma albumin ratio), and higher CSF tau, suggesting strong associations with amyloid abundance and both BBB and neurodegeneration complicate interpretation. We expand on this work by leveraging these common variants as genetic tools to tune the interpretation of high CSF sTREM2, and by exploring the potential modifying role of these variants on the well-established associations between CSF sTREM2 as well as TREM2 transcript levels in the brain with AD neuropathology. Biomarker analyses leveraged data from the Vanderbilt Memory & Aging Project (n = 127, age = 72 ± 6.43) and were replicated in the Alzheimer's Disease Neuroimaging Initiative (n = 399, age = 73 ± 7.39). Autopsy analyses were performed leveraging data from the Religious Orders Study and Rush Memory and Aging Project (n = 577, age = 89 ± 6.46). We found that the protective variant rs1582763 attenuated the association between CSF sTREM2 and Aβ40 (β = -0.44, p-value = 0.017) and replicated this interaction in ADNI (β = -0.27, p = 0.017). We did not observe this same interaction effect between TREM2 mRNA levels and Aβ peptides in brain (Aβ total β = -0.14, p = 0.629; Aβ1-38, β = 0.11, p = 0.200). In contrast to the effects on Aβ, the minor allele of this same variant seemed to enhance the association with blood-brain barrier dysfunction (β = 7.0e-4, p = 0.009), suggesting that elevated sTREM2 may carry a much different interpretation in carriers vs. non-carriers of this allele. When evaluating the risk variant (rs6591561) across datasets, we did not observe a statistically significant interaction against any outcome in VMAP and observed opposing directions of associations in ADNI and ROS/MAP on Aβ levels. Together, our results suggest that the protective effect of rs1582763 may act by decoupling the associations between sTREM2 and amyloid abundance, providing important mechanistic insight into sTREM2 changes and highlighting the need to incorporate genetic context into the analysis of sTREM2 levels, particularly if leveraged as a clinical biomarker of disease in the future.
Recent improvements in the accessibility of mapping tools and an increased recognition of the importance of leveraging data to inform public health operations has led to enthusiasm among public health departments to rapidly evolve their ability to analyze and apply data to programs. As the COVID-19 pandemic made evident, many health department data systems have been neglected for decades and data literacy among staff low. Significant federal dollars have been allocated to local health departments to modernize health systems. This case study recounts the effort to equip the Pima County Health Department with a highly sophisticated "COVID-19 Vaccines Solutions Dashboard" in 2021-2022, quantifying community vulnerability in the midst of the COVID-19 pandemic and shares key successes and challenges in process and outcomes that can guide other such dashboard initiatives. The experience informed the development of Pima' County Health Department's Data & Informatics Team as well as efforts to cultivate a more robust data culture throughout the department. Many health departments around the United States are in a similar position, and these lessons learned are widely applicable.
Recent literature has suggested that individuals with autism spectrum disorder (ASD) or autistic traits (ATs) would be more likely to encounter traumatic events in their lifetime and to develop post-traumatic stress disorder (PTSD). However, the nature of this relationship has not yet been fully elucidated. The aims of this study were to evaluate the relationship between AT and PTSD and to investigate which specific autistic dimension was more associated with trauma and stress-related symptoms. A total of 68 subjects with ASD and 64 healthy controls (HCs) were assessed with the Adult Autism Subthreshold Spectrum (AdAS Spectrum) and the Trauma and Loss Spectrum (TALS) questionnaires. Statistical analyses included Mann-Whitney U test, chi-square test, calculation of Spearman's coefficients, and logistic regression analysis. Patients with significant AT reported a 30% rate of PTSD and higher TALS total and domain scores than HCs, among whom no PTSD was found instead. Significant positive correlations were reported between AdAS Spectrum and TALS-SR scores in the whole sample. AdAS Spectrum total scores were statistically predictive of the presence of PTSD. High scores at AdAS Spectrum Inflexibility and adherence to routine and Restrictive interest and rumination domains were identified as positive predictors of a probable PTSD. Compared to HCs, subjects with significant AT are more likely to present symptoms of PTSD. In particular, AT related to ruminative thinking, narrow interests, and sensorial reactivity would seem to predict the presence of post-traumatic stress symptomatology.
Recent literature in evidence-based practice indicates that evidence is defined differently from practice and research perspectives. However, few published works address therapists' perspectives of the nature and use of evidence in everyday practice. This study describes the definition, types, and use of evidence from the perspective of six school-based occupational therapists. Data were collected through focus groups and participant-submitted documentation and analyzed using qualitative and quantitative content analysis. Two categories of evidence emerged: internalized evidence and evidence gathered during the intervention process. Clinical reasoning, identified as a key skill in evidence-based practice, supported the synthesis of therapist internalized evidence with "in-the-moment" evidence gathered from activity, contextual, and occupational analyses of the client. The findings support current literature that has suggested expanding the definition of evidence (i.e., more than research findings alone). Further investigation of evidence building in practice may help in constructing a more inclusive professional culture of evidence-based practice.