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Research in Developmental Disabilities Aug 2023Studies focusing on math abilities in autism spectrum disorder (ASD) are limited and often provide inconsistent results. (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Studies focusing on math abilities in autism spectrum disorder (ASD) are limited and often provide inconsistent results.
AIM
This meta-analysis was conducted to investigate math abilities in people with autism spectrum disorder (ASD) compared to typically developing (TD) participants.
METHODS AND PROCEDURES
According with PRISMA guidelines, a systematic search strategy was adopted. First, 4405 records were identified through database searching; then, the title-abstract screening led to the identification of 58 potentially relevant studies and, finally, after the full-text screening, 13 studies were included.
OUTCOMES AND RESULTS
Results shows that the group with ASD (n = 533) performed lower than the TD group (n = 525) with a small-to-medium effect (g=0.49). The effect size was not moderated by task-related characteristics. Instead, sample-related characteristics, specifically age, verbal intellectual functioning, and working memory, were significant moderators.
CONCLUSIONS AND IMPLICATIONS
This meta-analysis shows that people with ASD have poorer math skills than their TD peers, suggesting the importance of investigating math abilities in autism, taking into account the role of moderating variables.
Topics: Humans; Autism Spectrum Disorder; Cognition; Memory, Short-Term; Autistic Disorder; Mathematics
PubMed: 37329855
DOI: 10.1016/j.ridd.2023.104559 -
Journal of Medical Internet Research Nov 2023ChatGPT is a 175-billion-parameter natural language processing model that is already involved in scientific content and publications. Its influence ranges from providing... (Review)
Review
BACKGROUND
ChatGPT is a 175-billion-parameter natural language processing model that is already involved in scientific content and publications. Its influence ranges from providing quick access to information on medical topics, assisting in generating medical and scientific articles and papers, performing medical data analyses, and even interpreting complex data sets.
OBJECTIVE
The future role of ChatGPT remains uncertain and a matter of debate already shortly after its release. This review aimed to analyze the role of ChatGPT in the medical literature during the first 3 months after its release.
METHODS
We performed a concise review of literature published in PubMed from December 1, 2022, to March 31, 2023. To find all publications related to ChatGPT or considering ChatGPT, the search term was kept simple ("ChatGPT" in AllFields). All publications available as full text in German or English were included. All accessible publications were evaluated according to specifications by the author team (eg, impact factor, publication modus, article type, publication speed, and type of ChatGPT integration or content). The conclusions of the articles were used for later SWOT (strengths, weaknesses, opportunities, and threats) analysis. All data were analyzed on a descriptive basis.
RESULTS
Of 178 studies in total, 160 met the inclusion criteria and were evaluated. The average impact factor was 4.423 (range 0-96.216), and the average publication speed was 16 (range 0-83) days. Among the articles, there were 77 editorials (48,1%), 43 essays (26.9%), 21 studies (13.1%), 6 reviews (3.8%), 6 case reports (3.8%), 6 news (3.8%), and 1 meta-analysis (0.6%). Of those, 54.4% (n=87) were published as open access, with 5% (n=8) provided on preprint servers. Over 400 quotes with information on strengths, weaknesses, opportunities, and threats were detected. By far, most (n=142, 34.8%) were related to weaknesses. ChatGPT excels in its ability to express ideas clearly and formulate general contexts comprehensibly. It performs so well that even experts in the field have difficulty identifying abstracts generated by ChatGPT. However, the time-limited scope and the need for corrections by experts were mentioned as weaknesses and threats of ChatGPT. Opportunities include assistance in formulating medical issues for nonnative English speakers, as well as the possibility of timely participation in the development of such artificial intelligence tools since it is in its early stages and can therefore still be influenced.
CONCLUSIONS
Artificial intelligence tools such as ChatGPT are already part of the medical publishing landscape. Despite their apparent opportunities, policies and guidelines must be implemented to ensure benefits in education, clinical practice, and research and protect against threats such as scientific misconduct, plagiarism, and inaccuracy.
Topics: Humans; Artificial Intelligence; Data Analysis; Educational Status; Language; Natural Language Processing
PubMed: 37865883
DOI: 10.2196/49368 -
Cell Reports Methods Aug 2023Single-cell-resolved systems biology methods, including omics- and imaging-based measurement modalities, generate a wealth of high-dimensional data characterizing the... (Review)
Review
Single-cell-resolved systems biology methods, including omics- and imaging-based measurement modalities, generate a wealth of high-dimensional data characterizing the heterogeneity of cell populations. Representation learning methods are routinely used to analyze these complex, high-dimensional data by projecting them into lower-dimensional embeddings. This facilitates the interpretation and interrogation of the structures, dynamics, and regulation of cell heterogeneity. Reflecting their central role in analyzing diverse single-cell data types, a myriad of representation learning methods exist, with new approaches continually emerging. Here, we contrast general features of representation learning methods spanning statistical, manifold learning, and neural network approaches. We consider key steps involved in representation learning with single-cell data, including data pre-processing, hyperparameter optimization, downstream analysis, and biological validation. Interdependencies and contingencies linking these steps are also highlighted. This overview is intended to guide researchers in the selection, application, and optimization of representation learning strategies for current and future single-cell research applications.
Topics: Humans; Learning; Law Enforcement; Neural Networks, Computer; Research Personnel; Data Analysis
PubMed: 37671013
DOI: 10.1016/j.crmeth.2023.100547 -
BMC Medical Research Methodology Sep 2023Healthcare, as with other sectors, has undergone progressive digitalization, generating an ever-increasing wealth of data that enables research and the analysis of... (Review)
Review
BACKGROUND
Healthcare, as with other sectors, has undergone progressive digitalization, generating an ever-increasing wealth of data that enables research and the analysis of patient movement. This can help to evaluate treatment processes and outcomes, and in turn improve the quality of care. This scoping review provides an overview of the algorithms and methods that have been used to identify care pathways from healthcare utilization data.
METHOD
This review was conducted according to the methodology of the Joanna Briggs Institute and the Preferred Reporting Items for Systematic Reviews Extension for Scoping Reviews (PRISMA-ScR) Checklist. The PubMed, Web of Science, Scopus, and EconLit databases were searched and studies published in English between 2000 and 2021 considered. The search strategy used keywords divided into three categories: the method of data analysis, the requirement profile for the data, and the intended presentation of results. Criteria for inclusion were that health data were analyzed, the methodology used was described and that the chronology of care events was considered. In a two-stage review process, records were reviewed by two researchers independently for inclusion. Results were synthesized narratively.
RESULTS
The literature search yielded 2,865 entries; 51 studies met the inclusion criteria. Health data from different countries ([Formula: see text]) and of different types of disease ([Formula: see text]) were analyzed with respect to different care events. Applied methods can be divided into those identifying subsequences of care and those describing full care trajectories. Variants of pattern mining or Markov models were mostly used to extract subsequences, with clustering often applied to find care trajectories. Statistical algorithms such as rule mining, probability-based machine learning algorithms or a combination of methods were also applied. Clustering methods were sometimes used for data preparation or result compression. Further characteristics of the included studies are presented.
CONCLUSION
Various data mining methods are already being applied to gain insight from health data. The great heterogeneity of the methods used shows the need for a scoping review. We performed a narrative review and found that clustering methods currently dominate the literature for identifying complete care trajectories, while variants of pattern mining dominate for identifying subsequences of limited length.
Topics: Humans; Algorithms; Checklist; Cluster Analysis; Data Analysis; Data Mining
PubMed: 37759162
DOI: 10.1186/s12874-023-02019-y -
PloS One 2024Cliodynamics is a still a relatively new research area with the purpose of investigating and modelling historical processes. One of its first important mathematical...
Cliodynamics is a still a relatively new research area with the purpose of investigating and modelling historical processes. One of its first important mathematical models was proposed by Turchin and called "Demographic-Fiscal Model" (DFM). This DFM was one of the first and is one of a few models that link population with state dynamics. In this work, we propose a possible alternative to the classical Turchin DFM, which contributes to further model development and comparison essential for the field of cliodynamics. Our "Demographic-Wealth Model" (DWM) aims to also model link between population and state dynamics but makes different modelling assumptions, particularly about the type of possible taxation. As an important contribution, we employ tools from nonlinear dynamics, e.g., existence theory for periodic orbits as well as analytical and numerical bifurcation analysis, to analyze the DWM. We believe that these tools can also be helpful for many other current and future models in cliodynamics. One particular focus of our analysis is the occurrence of Hopf bifurcations. Therefore, a detailed analysis is developed regarding equilibria and their possible bifurcations. Especially noticeable is the behavior of the so-called coexistence point. While changing different parameters, a variety of Hopf bifurcations occur. In addition, it is indicated, what role Hopf bifurcations may play in the interplay between population and state dynamics. There are critical values of different parameters that yield periodic behavior and limit cycles when exceeded, similar to the "paradox of enrichment" known in ecology. This means that the DWM provides one possible avenue setup to explain in a simple format the existence of secular cycles, which have been observed in historical data. In summary, our model aims to balance simplicity, linking to the underlying processes and the goal to represent secular cycles.
Topics: Models, Biological; Models, Theoretical; Ecology; Nonlinear Dynamics; Population Dynamics
PubMed: 38564574
DOI: 10.1371/journal.pone.0298318 -
PLoS Genetics Sep 2023Transcriptome-wide association studies (TWAS) aim to detect relationships between gene expression and a phenotype, and are commonly used for secondary analysis of...
Transcriptome-wide association studies (TWAS) aim to detect relationships between gene expression and a phenotype, and are commonly used for secondary analysis of genome-wide association study (GWAS) results. Results from TWAS analyses are often interpreted as indicating a genetic relationship between gene expression and a phenotype, but this interpretation is not consistent with the null hypothesis that is evaluated in the traditional TWAS framework. In this study we provide a mathematical outline of this TWAS framework, and elucidate what interpretations are warranted given the null hypothesis it actually tests. We then use both simulations and real data analysis to assess the implications of misinterpreting TWAS results as indicative of a genetic relationship between gene expression and the phenotype. Our simulation results show considerably inflated type 1 error rates for TWAS when interpreted this way, with 41% of significant TWAS associations detected in the real data analysis found to have insufficient statistical evidence to infer such a relationship. This demonstrates that in current implementations, TWAS cannot reliably be used to investigate genetic relationships between gene expression and a phenotype, but that local genetic correlation analysis can serve as a potential alternative.
Topics: Genome-Wide Association Study; Transcriptome; Chromosome Mapping; Computer Simulation; Data Analysis
PubMed: 37676898
DOI: 10.1371/journal.pgen.1010921 -
BMC Bioinformatics Nov 2023Galaxy is a web-based open-source platform for scientific analyses. Researchers use thousands of high-quality tools and workflows for their respective analyses in...
BACKGROUND
Galaxy is a web-based open-source platform for scientific analyses. Researchers use thousands of high-quality tools and workflows for their respective analyses in Galaxy. Tool recommender system predicts a collection of tools that can be used to extend an analysis. In this work, a tool recommender system is developed by training a transformer on workflows available on Galaxy Europe and its performance is compared to other neural networks such as recurrent, convolutional and dense neural networks.
RESULTS
The transformer neural network achieves two times faster convergence, has significantly lower model usage (model reconstruction and prediction) time and shows a better generalisation that goes beyond training workflows than the older tool recommender system created using RNN in Galaxy. In addition, the transformer also outperforms CNN and DNN on several key indicators. It achieves a faster convergence time, lower model usage time, and higher quality tool recommendations than CNN. Compared to DNN, it converges faster to a higher precision@k metric (approximately 0.98 by transformer compared to approximately 0.9 by DNN) and shows higher quality tool recommendations.
CONCLUSION
Our work shows a novel usage of transformers to recommend tools for extending scientific workflows. A more robust tool recommendation model, created using a transformer, having significantly lower usage time than RNN and CNN, higher precision@k than DNN, and higher quality tool recommendations than all three neural networks, will benefit researchers in creating scientifically significant workflows and exploratory data analysis in Galaxy. Additionally, the ability to train faster than all three neural networks imparts more scalability for training on larger datasets consisting of millions of tool sequences. Open-source scripts to create the recommendation model are available under MIT licence at https://github.com/anuprulez/galaxy_tool_recommendation_transformers.
Topics: Software; Neural Networks, Computer; Workflow; Data Analysis; Europe
PubMed: 38012574
DOI: 10.1186/s12859-023-05573-w -
Nutrients Sep 2023Choline plays many important roles, including the synthesis of acetylcholine, and may affect muscle responses to exercise. We previously observed correlations between... (Randomized Controlled Trial)
Randomized Controlled Trial
Choline plays many important roles, including the synthesis of acetylcholine, and may affect muscle responses to exercise. We previously observed correlations between low choline intake and reduced gains in strength and lean mass following a 12-week resistance exercise training (RET) program for older adults. To further explore these findings, we conducted a randomized controlled trial. Three groups of 50-to-69-year-old healthy adults underwent a 12-week RET program (3x/week, 3 sets, 8-12 reps, 70% of maximum strength (1RM)) and submitted >48 diet logs (>4x/week for 12 weeks). Participants' diets were supplemented with 0.7 mg/kg lean/d (low, n = 13), 2.8 mg/kg lean/d (med, n = 11), or 7.5 mg/kg lean/d (high, n = 13) of choline from egg yolk and protein powder. The ANCOVA tests showed that low choline intake, compared with med or high choline intakes, resulted in significantly diminished gains in composite strength (leg press + chest press 1RM; low, 19.4 ± 8.2%; med, 46.8 ± 8.9%; high, 47.4 ± 8.1%; = 0.034) and thigh-muscle quality (leg press 1RM/thigh lean mass; low, 12.3 ± 9.6%; med/high, 46.4 ± 7.0%; = 0.010) after controlling for lean mass, protein, betaine, and vitamin B. These data suggest that low choline intake may negatively affect strength gains with RET in older adults.
Topics: Humans; Aged; Middle Aged; Choline; Resistance Training; Acetylcholine; Betaine; Correlation of Data
PubMed: 37764658
DOI: 10.3390/nu15183874 -
BMC Infectious Diseases Jul 2023Pneumocystis jirovecii pneumonia (PJP) can be a life-threatening opportunistic infection. We aimed to evaluate the diagnostic accuracy of metagenomic next-generation... (Review)
Review
OBJECTIVE
Pneumocystis jirovecii pneumonia (PJP) can be a life-threatening opportunistic infection. We aimed to evaluate the diagnostic accuracy of metagenomic next-generation sequencing (mNGS) for PJP.
METHODS
A comprehensive electronic literature search of Web of Knowledge, PubMed, Cochrane Library, CNKI and Wanfang data was performed. Bivariate analysis was conducted to calculate the pooled sensitivity, specificity, diagnostic odds ratio (DOR), the area under the summary receiver operator characteristic (SROC) curve and the Q-point value (Q*).
RESULTS
The literature search resulted in 9 studies with a total of 1343 patients, including 418 cases diagnosed with PJP and 925 controls. The pooled sensitivity of mNGS for diagnosis of PJP was 0.974 [95% confidence interval (CI), 0.953-0.987]. The pooled specificity was 0.943 (95% CI, 0.926-0.957), the DOR was 431.58 (95% CI, 186.77-997.27), the area under the SROC curve was 0.987, and the Q* was 0.951. The I test indicated no heterogeneity between studies. The Deek funnel test suggested no potential publication bias. Subgroup analyses showed that the area under the SROC curve of mNGS for diagnosis of PJP in immunocompromised and non-HIV patients was 0.9852 and 0.979, respectively.
CONCLUSIONS
Current evidence indicates that mNGS exhibits excellent accuracy for the diagnosis of PJP. The mNGS is a promising tool for assessment of PJP in both immunocompromised and non-HIV patients.
Topics: Humans; Correlation of Data; High-Throughput Nucleotide Sequencing; Immunocompromised Host; Knowledge; Pneumonia, Pneumocystis
PubMed: 37430211
DOI: 10.1186/s12879-023-08440-4 -
The Journal of Pediatrics Jan 2024To test whether preschool academic skills were associated with educational attainment in adolescence and whether associations differed between individuals born preterm...
OBJECTIVES
To test whether preschool academic skills were associated with educational attainment in adolescence and whether associations differed between individuals born preterm and at full term.
STUDY DESIGN
This prospective cohort study comprised 6924 individuals, including n = 444 (6.4%) adolescents born preterm (<37 weeks of gestation) from the Avon Longitudinal Study of Parents and Children. Preschool academic (mathematics and literacy) skills were rated by teachers at 4-5 years. Educational attainment at 16 years was informed by attaining a General Certificate of Secondary Education (GCSE) in key subjects mathematics and English. Logistic regressions assessed the association between preterm birth, preschool mathematics, and GCSE Mathematics and between preterm birth, preschool literacy, and GCSE English.
RESULTS
Similar numbers of adolescents born preterm and at term achieved a GCSE in mathematics and English (53.6 % vs 57.4% and 59.5% vs 63.9%, respectively; P values > .05). Higher preschool academic skill scores in mathematics were associated with greater odds of attaining GCSE Mathematics and preschool literacy skills were associated with GCSE English. Adolescents born preterm with higher preschool mathematics (OR: 1.51, CI: 1.14, 2.00) and literacy skills (OR: 1.57, CI: 1.10, 2.25) were more likely to attain GCSEs in the respective subject than their term-born counterparts with equal levels of preschool skills.
CONCLUSIONS
Preschool academic skills in mathematics and literacy are associated with educational attainment of preterm and term-born individuals in adolescence. Children born prematurely may benefit more from preschool mathematics and literacy skills for academic and educational success into adolescence than term-born individuals.
Topics: Child; Female; Humans; Infant, Newborn; Child, Preschool; Adolescent; Literacy; Longitudinal Studies; Premature Birth; Prospective Studies; Educational Status; Mathematics
PubMed: 37722555
DOI: 10.1016/j.jpeds.2023.113731