Yoxas/statistical_literacy huggingface dataset:
Nerve conduction studies (NCS) require valid reference limits for meaningful interpretation. We aimed to further develop the extrapolated norms (e-norms) method for obtaining NCS reference limits from historical laboratory datasets for children and adults, and to validate it against traditionally derived reference limits. We compared reference limits obtained by applying a further developed e-norms with reference limits from healthy controls for the age strata's 9-18, 20-44 and 45-60 years old. The control data consisted of 65 healthy children and 578 healthy adults, matched with 1294 and 5628 patients respectively. Five commonly investigated nerves were chosen: The tibial and peroneal motor nerves (amplitudes, conduction velocities, F-waves), and the sural, superficial peroneal and medial plantar sensory nerves (amplitudes, conduction velocities). The datasets were matched by hospital to ensure identical equipment and protocols. The e-norms method was adapted, and reference limit calculation using both ±2 SD (original method) and ±2.5 SD (to compensate for predicted underestimation of population SD by the e-norms method) was compared to control data using ±2 SD. Percentage agreement between e-norms and the traditional method was calculated. On average, the e-norms method (mean ±2 SD) produced slightly stricter reference limits compared to the traditional method. Increasing the e-norms range to mean ±2.5 SD improved the results in children while slightly overcorrecting in the adult group. The average agreement between the two methods was 95 % (±2 SD) and 96 % (±2.5 SD). The e-norms method yielded slightly stricter reference limits overall than ones obtained through traditional methods; However, much of the difference can be attributed to a few outlying plots where the raters found it difficult to apply e-norms correctly. The two methods disagreed on classification of 4-5% of cases. Our e-norms software is suited to analyze large amounts of raw NCS data; it should further reduce bias and facilitate more accurate ratings. With small adaptations, the e-norms method adequately replicates traditionally derived reference limits, and is a viable method to produce reference limits from historical datasets.
Net escape velocity (NEV) and net escape probability (NEP) are concepts that describe that scalar quantity discharged from a source in an indoor air environment is expressed by the unique velocity scales of the returning and escaping populations. Despite the conceptual description and applications of several numerical simulations, the definitions were not precisely explained using a mathematical formula. Here, we derive rigorous mathematical formulations of the NEV and NEP. These formulations provide us with the physical interpretation of NEV, clarify the sufficient condition of perfect escape, and lead to a further formulation of the transfer probability of the scalar. To justify and apply the derived relationships, two simple problems were numerically solved: One was a diffusion equation, and the other was an advection-diffusion equation. The results of the diffusion problem clearly demonstrate that only the outgoing scalar flux exists on the surface of the control volume, covering the source at any location. In contrast, the advection-diffusion problem reveals that there is a returning population of the scalar in most locations, despite both diffusion and turbulent parts working to remove the scalar. This rigorous formulation contributes to apply NEV as an appropriate air quality index with the clear physical interpretation to determine the local scalar concentration.
Network meta-analysis is an extension of standard meta-analysis. It allows researchers to build a network of evidence to compare multiple interventions that may have not been compared directly in existing publications. With a Bayesian approach, network meta-analysis can be used to obtain a posterior probability distribution of all the relative treatment effects, which allows for the estimation of relative treatment effects to quantify the uncertainty of parameter estimates, and to rank all the treatments in the network. Ranking treatments using both direct and indirect evidence can provide guidance to policy makers and clinicians for making decisions. The purpose of this paper is to introduce fundamental concepts of Bayesian network meta-analysis (BNMA) to researchers in psychology and social sciences. We discuss several essential concepts of BNMA, including the assumptions of homogeneity and consistency, the fixed and random effects models, prior specification, and model fit evaluation strategies, while pointing out some issues and areas where researchers should use caution in the application of BNMA. Additionally, using an automated R package, we provide a step-by-step demonstration on how to conduct and report the findings of BNMA with a real dataset of psychological interventions extracted from PubMed.
Network modeling is now a widespread practice in systems biology, as well as in integrative genomics, and it constitutes a rich and diverse scientific research field. A conceptually clear understanding of the reasoning behind the main existing modeling approaches, and their associated technical terminologies, is required to avoid confusions and accelerate the transition towards an undeniable necessary more quantitative, multidisciplinary approach to biology. Herein, we focus on two main network-based modeling approaches that are commonly used depending on the information available and the intended goals: inference-based methods and system dynamics approaches. As far as data-based network inference methods are concerned, they enable the discovery of potential functional influences among molecular components. On the other hand, experimentally grounded network dynamical models have been shown to be perfectly suited for the mechanistic study of developmental processes. How do these two perspectives relate to each other? In this chapter, we describe and compare both approaches and then apply them to a given specific developmental module. Along with the step-by-step practical implementation of each approach, we also focus on discussing their respective goals, utility, assumptions, and associated limitations. We use the gene regulatory network (GRN) involved in Arabidopsis thaliana Root Stem Cell Niche patterning as our illustrative example. We show that descriptive models based on functional genomics data can provide important background information consistent with experimentally supported functional relationships integrated in mechanistic GRN models. The rationale of analysis and modeling can be applied to any other well-characterized functional developmental module in multicellular organisms, like plants and animals.
Network representation can give a holistic view of relationships for biomedical entities through network topology. Link prediction estimates the probability of link formation between the pair of unconnected nodes. In the drug discovery process, the link prediction method not only enables the detection of connectivity patterns but also predicts the effects of one biomedical entity to multiple entities simultaneously and vice versa, which is useful for many applications. The authors provide a comprehensive overview of network link prediction in drug discovery. Link prediction methodologies such as similarity-based approaches, embedding-based approaches, probabilistic model-based approaches, and preprocessing methods are summarized with examples. In addition to describing their properties and limitations, the authors discuss the applications of link prediction in drug discovery based on the relationship between biomedical concepts. Link prediction is a powerful method to infer the existence of novel relationships in drug discovery. However, link prediction has been hampered by the sparsity of data and the lack of negative links in biomedical networks. With preprocessing to balance positive and negative samples and the collection of more data, the authors believe it is possible to develop more reliable link prediction methods that can become invaluable tools for successful drug discovery.
Neural engineering is an emerging and multidisciplinary field in which engineering approaches are applied to neuroscience problems. Women are underrepresented in engineering fields, and indeed in science, technology, engineering, and mathematics (STEM) fields generally. Underrepresentation of women is particularly notable at later academic career stages, suggesting that even though women are interested in the field, barriers exist that ultimately cause them to leave. Here, we investigate many of the obstacles to women's success in the field of neural engineering and provide recommendations and materials to overcome them. We conducted a review of the literature from the past 15 years regarding the experiences of women in academic careers, as well as reports on the number of women in fields closely related to neural engineering from the National Science Foundation (NSF) and the American Society for Engineering Education (ASEE). Additionally, we interviewed six women in neural engineering who are involved in initiatives and outreach concerning the inclusion and experiences of women in engineering. Throughout the literature and interviews, we identified common themes spanning the role of identity and confidence, professional relationships, career-related hurdles, and personal and professional expectations. We explore each of these themes in detail and provide resources to support the growth of women as they climb within the field of neural engineering.
Neural oscillations reflect fluctuations in excitability, which biases the percept of ambiguous sensory input. Why this bias occurs is still not fully understood. We hypothesized that neural populations representing likely events are more sensitive, and thereby become active on earlier oscillatory phases, when the ensemble itself is less excitable. Perception of ambiguous input presented during less-excitable phases should therefore be biased toward frequent or predictable stimuli that have lower activation thresholds. Here, we show such a frequency bias in spoken word recognition using psychophysics, magnetoencephalography (MEG), and computational modelling. With MEG, we found a double dissociation, where the phase of oscillations in the superior temporal gyrus and medial temporal gyrus biased word-identification behavior based on phoneme and lexical frequencies, respectively. This finding was reproduced in a computational model. These results demonstrate that oscillations provide a temporal ordering of neural activity based on the sensitivity of separable neural populations.
Neural tube defects (NTDs) occur as a result of incomplete closure of the neural tube by the embryonic age of 26 to 28 days. Addressing factors associated with NTDs would help to identify and prioritize high-risk women, which in turn guides the preventive strategy. The study aimed to identify factors associated with NTDs among women who gave birth or had a second-trimester abortion at Adama Hospital Medical College, from January 1st to December 31st, 2019. Hospital based unmatched case-control study was conducted on 344 women. Data were analyzed with SPSS 20. Descriptive statistics were computed. Binary logistic regression analysis was performed to determine factors associations with NTDs. The odds of having a fetus with NTDs were 2.19 times higher among women who are not in a marital relationship (AOR = 2.19; 95% CI: 1.13, 4.25). Women with a previous history of Abortion or stillbirth had 3 fold increased risk of having a fetus with NTDs (AOR = 3.05; 95% CI: 1.58, 5.88). Inadequate housing condition nearly doubles the odds of having a fetus with NTDs (AOR = 1.91; 95% CI: 1.20, 3.04). Folic acid or multivitamin supplementation early in pregnancy reduced the odds of having a fetus with NTDs by 43% (AOR = 0.57; 95% CI: 0.35, 0.91). Being not in a marital relationship, previous history of abortion or stillbirth, and living in inadequate housing conditions were risk factors for NTDs, while multivitamins or folic acid supplementation was a protective factor.
Neuroanatomy comprehension is a keystone of understanding intracranial surgeries. Traditionally taught to students during ex cathedra courses, neuroanatomy is described as complex. Mixed reality (MxR) opens new perspectives in the learning process. This study aims to compare MxR-based courses with traditional ex cathedra lectures for neuroanatomy education. Two lectures describing the neuroanatomy of the anterior circulation arteries ("Vascular Lecture" [VS]) and important white matter fiber tracts ("White Fibers Lecture" [WF]) were designed and delivered in ex cathedra and MxR-based formats with the same audio content. Ninety-one medical students were randomly assigned to group A (ex cathedra WF/MxR VS) or group B (MxR WF/ex cathedra VS). The MxR content was delivered via MxR goggles. Prior to each lecture, students took a 10-item multiple choice question (MCQ) pretest. After the lectures, students took a 20-item MCQ posttest (75% neuroanatomy, 25% clinical correlation). The pretest scores showed no statistical difference between groups. Median posttest scores increased by 14.3% after using the MxR-based format compared to the ex cathedra format (16.00 [13.0, 18.0] vs 14.0 [11.0, 17.0], respectively, p < 0.01). Regarding the VS, students scored 21.7% better using the MxR format compared to the ex cathedra format (14.0 [12.0, 16.0] vs 11.5 [10.0, 14.0], p < 0.001). Concerning the WF, the median score using MxR was 18.0 (17.0, 19.0), and the median score using the ex cathedra format was 17.0 (16.0, 18.0; p < 0.01). Students showed high motivation to learn neuroanatomy in the future using MxR (74%) rather than ex cathedra format (25%; p < 0.001). Mild discomfort using the MxR goggles was reported by 48.3% of participants. Most participants (95.5%) preferred the MxR-based teaching. Students acquired a better knowledge of the anatomy of the anterior circulation arteries and white fiber tracts using MxR-based teaching as compared to the standard ex cathedra format. The perception of lecture quality and learning motivation was better using MxR-based teaching despite some mild discomfort. The development of MxR-based solutions is promising to improve neuroanatomy education.
Neurocognitive impairment (NCI) is one of the most common neurological complications in HIV-positive individuals, particularly in resource-limited countries. Neurocognitive impairments can occur at any stage of HIV infection, although the risk increases as the infection progresses. However, in Africa, there are few studies with highly variable and inconsistent results. Therefore, this study aimed to determine the prevalence and factors associated with NCI among HIV-positive people in Africa. For this systematic review and meta-analysis, we used PubMed/Medline, Scopus, Web of Science, the Cochrane Library, Embase, and PsycINFO to comprehensively search a number of papers. Studies reporting the prevalence of NCI and its factors were included in the estimation of the pooled prevalence. A consistent data extraction format was created in Microsoft Excel to extract the data, which was then imported into STATA 11 statistical software for analysis. The heterogeneity was evaluated using the I2 test, and a random effect meta-analysis model was employed to calculate the pooled prevalence of NCI because the included studies showed significant heterogeneity. In all, Africa had a pooled prevalence of NCI of 45.15% (95% CI: 36.86, 53.43). According to the subgroup analysis of this study, West Africa had the lowest frequency, at 42.40% (95% CI: 22.03, 62.77), whereas Central and South Africa had the highest prevalence, at 49.33% (95% CI: 10.72-87.95). In Africa, the cumulative prevalence of NCI was high. Being a woman, not having a formal education, those with only an elementary education, being older, having late-stage HIV, and abusing drugs were all often associated with NCI. The average burden of NCI in Africa is high and that would be a significant figure for interventional actions in the area.
Neurocomparative music and language research has seen major advances over the past two decades. The goal of this Special Issue "Advances in the Neurocognition of Music and Language" was to showcase the multiple neural analogies between musical and linguistic information processing, their entwined organization in human perception and cognition and to infer the applicability of the combined knowledge in pedagogy and therapy. Here, we summarize the main insights provided by the contributions and integrate them into current frameworks of rhythm processing, neuronal entrainment, predictive coding and cognitive control.
Neurodegenerative diseases such as Alzheimer's disease (AD) follow a slowly progressing dysfunctional trajectory, with a large presymptomatic component and many comorbidities. Using preclinical models and large-scale omics studies ranging from genetics to imaging, a large number of processes that might be involved in AD pathology at different stages and levels have been identified. The sheer number of putative hypotheses makes it almost impossible to estimate their contribution to the clinical outcome and to develop a comprehensive view on the pathological processes driving the clinical phenotype. Traditionally, bioinformatics approaches have provided correlations and associations between processes and phenotypes. Focusing on causality, a new breed of advanced and more quantitative modeling approaches that use formalized domain expertise offer new opportunities to integrate these different modalities and outline possible paths toward new therapeutic interventions. This article reviews three different computational approaches and their possible complementarities. Process algebras, implemented using declarative programming languages such as Maude, facilitate simulation and analysis of complicated biological processes on a comprehensive but coarse-grained level. A model-driven Integration of Data and Knowledge, based on the OpenBEL platform and using reverse causative reasoning and network jump analysis, can generate mechanistic knowledge and a new, mechanism-based taxonomy of disease. Finally, Quantitative Systems Pharmacology is based on formalized implementation of domain expertise in a more fine-grained, mechanism-driven, quantitative, and predictive humanized computer model. We propose a strategy to combine the strengths of these individual approaches for developing powerful modeling methodologies that can provide actionable knowledge for rational development of preventive and therapeutic interventions. Development of these computational approaches is likely to be required for further progress in understanding and treating AD.
Neuroeducation is characterized as a subarea of neuroscience that involves comprehending the teaching and learning processes and relating them to neuroanatomy, neurophysiology, and neuropsychology. The inclusion of some aspects of the neuroscience of learning in teachers' and students' formation, applying them in teaching-learning environments, contributes to the quality of education and impacts students' quality of life and health. Thus, the POPNEURO outreach program performs interventions with students and teachers of low-income schools to disseminate neuroscience concepts, relating them to the students' daily lives. This study reports the impact of these actions, assessed 1 yr after their conclusion. The results showed that the long-term impact of the activities carried out is, in general, positive. Even 1 yr after the activities end, students demonstrate knowledge about the neuroscience themes and satisfaction with participating.NEW & NOTEWORTHY This article reports on neuroscience disclosure activities performed with school students and describes their short- and long-term positive impact. Even 1 yr after the activities, students demonstrate knowledge about the themes worked on and satisfaction with the activities.
Neurofilament heavy-chain gene (NEFH) variants are associated with multiple neurodegenerative diseases, however, their relationship with ALS has not been robustly explored. Still, NEFH is commonly included in genetic screening panels worldwide. We therefore aimed to determine if NEFH variants modify ALS risk. Genetic data of 11,130 people with ALS and 7,416 controls from the literature and Project MinE were analysed. We performed meta-analyses of published case-control studies reporting NEFH variants, and variant analysis of NEFH in Project MinE whole-genome sequencing data. Fixed-effects meta-analysis found that rare (MAF <1%) missense variants in the tail domain of NEFH increase ALS risk (OR 4.55, 95% CI 2.13-9.71, p < 0.0001). In Project MinE, ultrarare NEFH variants increased ALS risk (OR 1.37 95% CI 1.14-1.63, p = 0.0007), with rod domain variants (mostly intronic) appearing to drive the association (OR 1.45 95% CI 1.18-1.77, pMadsen-Browning = 0.0007, pSKAT-O = 0.003). While in the tail domain, ultrarare (MAF <0.1%) pathogenic missense variants were also associated with higher risk of ALS (OR 1.94, 95% CI 0.86-4.37, pMadsen-Browning = 0.039), supporting the meta-analysis results. Finally, several tail in-frame deletions were also found to affect disease risk, however, both protective and pathogenic deletions were found in this domain, highlighting an intricated architecture that requires further investigation. We showed that NEFH tail missense and in-frame deletion variants, and intronic rod variants are risk factors for ALS. However, they are not variants of large effect, and their functional impact needs to be clarified in further studies. Therefore, their inclusion in routine genetic screening panels should be reconsidered.
NeuroHackademy ( https://neurohackademy.org ) is a two-week event designed to train early-career neuroscience researchers in data science methods and their application to neuroimaging. The event seeks to bridge the big data skills gap by introducing participants to data science methods and skills that are often ignored in traditional curricula. Such skills are needed for the analysis and interpretation of the kinds of large and complex datasets that have become increasingly important to neuroimaging research due to concerted data collection efforts. In 2020, the event rapidly pivoted from an in-person event to an online event that included hundreds of participants from all over the world. This experience and those of the participants substantially changed our valuation of large online-accessible events. In subsequent events held in 2022 and 2023, we have developed a "hybrid" format that includes both online and in-person participants. We discuss the technical and sociotechnical elements of hybrid events and discuss some of the lessons we have learned while organizing them. We emphasize in particular the role that these events can play in creating a global and inclusive community of practice in the intersection of neuroimaging and data science.