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Current Opinion in Cell Biology Jun 2024Phosphoinositides broadly impact membrane dynamics, signal transduction and cellular physiology. The orchestration of signaling complexity by this seemingly simple... (Review)
Review
Phosphoinositides broadly impact membrane dynamics, signal transduction and cellular physiology. The orchestration of signaling complexity by this seemingly simple metabolic pathway remains an open question. It is increasingly evident that comprehending the complexity of the phosphoinositides metabolic network requires a systems view based on nonlinear dynamics, where the products of metabolism can either positively or negatively modulate enzymatic function. These feedback and feedforward loops may be paradoxical, leading to counterintuitive effects. In this review, we introduce the framework of nonlinear dynamics, emphasizing distinct dynamical regimes such as the excitable state, oscillations, and mixed-mode oscillations-all of which have been experimentally observed in phosphoinositide metabolisms. We delve into how these dynamical behaviors arise from one or multiple network motifs, including positive and negative feedback loops, coherent and incoherent feedforward loops. We explore the current understanding of the molecular circuits responsible for these behaviors. While mapping these circuits presents both conceptual and experimental challenges, redefining cellular behavior based on dynamical state, lipid fluxes, time delay, and network topology is likely essential for a comprehensive understanding of this fundamental metabolic network.
Topics: Phosphatidylinositols; Humans; Animals; Nonlinear Dynamics; Signal Transduction; Metabolic Networks and Pathways; Models, Biological
PubMed: 38797149
DOI: 10.1016/j.ceb.2024.102373 -
Analytical Chemistry Oct 2023In this Perspective, we discuss the current status and advances in spatial transcriptomics technologies, which allow high-resolution mapping of gene expression in intact... (Review)
Review
In this Perspective, we discuss the current status and advances in spatial transcriptomics technologies, which allow high-resolution mapping of gene expression in intact cell and tissue samples. Spatial transcriptomics enables the creation of high-resolution maps of gene expression patterns within their native spatial context, adding an extra layer of information to the bulk sequencing data. Spatial transcriptomics has expanded significantly in recent years and is making a notable impact on a range of fields, including tissue architecture, developmental biology, cancer, and neurodegenerative and infectious diseases. The latest advancements in spatial transcriptomics have resulted in the development of highly multiplexed methods, transcriptomic-wide analysis, and single-cell resolution utilizing diverse technological approaches. In this Perspective, we provide a detailed analysis of the molecular foundations behind the main spatial transcriptomics technologies, including methods based on microdissection, sequencing, single-molecule FISH, spatial capturing, selection of regions of interest, and single-cell or nuclei dissociation. We contextualize the detection and capturing efficiency, strengths, limitations, tissue compatibility, and applications of these techniques as well as provide information on data analysis. In addition, this Perspective discusses future directions and potential applications of spatial transcriptomics, highlighting the importance of the continued development to promote widespread adoption of these techniques within the research community.
Topics: Transcriptome; Gene Expression Profiling; Tissue Array Analysis; Cell Nucleus; Data Analysis; Single-Cell Analysis
PubMed: 37814884
DOI: 10.1021/acs.analchem.3c02029 -
World Journal of Gastroenterology Oct 2023Nonalcoholic fatty liver disease (NAFLD) is chronic, with its progression leading to liver fibrosis and end-stage cirrhosis. Although NAFLD is increasingly common, no... (Review)
Review
BACKGROUND
Nonalcoholic fatty liver disease (NAFLD) is chronic, with its progression leading to liver fibrosis and end-stage cirrhosis. Although NAFLD is increasingly common, no treatment guideline has been established. Many mechanistic studies and drug trials have been conducted for new drug development to treat NAFLD. An up-to-date overview on the knowledge structure of NAFLD through bibliometrics, focusing on research hotspots, is necessary to reveal the rational and timely directions of development in this field.
AIM
To research the latest literature and determine the current trends in treatment for NAFLD.
METHODS
Publications related to treatment for NAFLD were searched on the Web of Science Core Collection database, from 2010 to 2023. VOSviewers, CiteSpace, and R package "bibliometrix" were used to conduct this bibliometric analysis. The key information was extracted, and the results of the cluster analysis were based on network data for generating and investigating maps for country, institution, journal, and author. Historiography analysis, bursts and cluster analysis, co-occurrence analysis, and trend topic revealed the knowledge structure and research hotspots in this field. GraphPad Prism 9.5.1.733 and Microsoft Office Excel 2019 were used for data analysis and visualization.
RESULTS
In total, 10829 articles from 120 countries (led by China and the United States) and 8785 institutions were included. The number of publications related to treatment for NAFLD increased annually. While China produced the most publications, the United States was the most cited country, and the United Kingdom collaborated the most from an international standpoint. The University of California-San Diego, Shanghai Jiao Tong University, and Shanghai University of Traditional Chinese Medicine produced the most publications of all the research institutions. The International Journal of Molecular Sciences was the most frequent journal out of the 1523 total journals, and Hepatology was the most cited and co-cited journal. Sanyal AJ was the most cited author, the most co-cited author was Younossi ZM, and the most influential author was Loomba R. The most studied topics included the epidemiology and mechanism of NAFLD, the development of accurate diagnosis, the precise management of patients with NAFLD, and the associated metabolic comorbidities. The major cluster topics were "emerging drug," "glucagon-like peptide-1 receptor agonist," "metabolic dysfunction-associated fatty liver disease," "gut microbiota," and "glucose metabolism."
CONCLUSION
The bibliometric study identified recent research frontiers and hot directions, which can provide a valuable reference for scholars researching treatments for NAFLD.
Topics: Humans; Bibliometrics; China; Cluster Analysis; Data Analysis; Liver Cirrhosis; Non-alcoholic Fatty Liver Disease
PubMed: 37899789
DOI: 10.3748/wjg.v29.i37.5339 -
JAMA Network Open Sep 2023Emerging studies have suggested that environmental factors are associated with fracture. However, little is known about the association of neighborhood walkability and...
IMPORTANCE
Emerging studies have suggested that environmental factors are associated with fracture. However, little is known about the association of neighborhood walkability and residential greenness with fracture.
OBJECTIVE
To investigate the association of long-term exposure to walkability and greenness with incident fracture and explore the potential interaction effect.
DESIGN, SETTING, AND PARTICIPANTS
This cohort study recruited participants aged 40 years or older in Ningbo, China from June 2015 to January 2018. Participants were observed for outcomes through February 2023, with data analysis conducted in March 2023.
EXPOSURES
Neighborhood walkability was measured by a modified walkability calculation method according to a walk score tool. Residential greenness was assessed by satellite-derived normalized difference vegetation index (NDVI) within a 1000-m buffer.
MAIN OUTCOMES AND MEASURES
Incident fracture was ascertained according to International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes via the Yinzhou Health Information System. Cox proportional hazards models were fit, with age as time scale to estimate the associations of walkability and greenness with fracture. Potential effect modification was explored by covariates, as well as the interactive effect of walkability and greenness.
RESULTS
A total of 23 940 participants were included in this study with 13 735 being female (57.4%). The mean (SD) age at baseline was 63.4 (9.4) years. During a follow-up period of 134 638 person-years, 3322 incident fractures were documented. In the full adjusted model, every IQR increment in neighborhood walkability and residential greenness was associated with a hazard ratio (HR) of 0.88 (95% CI, 0.83-0.92) and 0.84 (95% CI, 0.80-0.89), respectively, for fracture. Furthermore, the association of greenness and fracture was greater with an increase in walkability. The HR (Q4 vs Q1) for greenness was 0.62 (95% CI, 0.46-0.82) in neighborhoods with the highest quartile of walkability.
CONCLUSIONS AND RELEVANCE
This population cohort study suggested that long-term exposure to neighborhood walkability and residential greenness were both associated with lower risk of incident fracture. The benefits of greenness increased in more walkable areas.
Topics: Humans; Female; Male; Cohort Studies; China; Data Analysis; Fractures, Bone; Health Information Systems
PubMed: 37768665
DOI: 10.1001/jamanetworkopen.2023.35154 -
PloS One 2023Testing whether data are from a normal distribution is a traditional problem and is of great concern for data analyses. The normality is the premise of many statistical...
Testing whether data are from a normal distribution is a traditional problem and is of great concern for data analyses. The normality is the premise of many statistical methods, such as t-test, Hotelling T2 test and ANOVA. There are numerous tests in the literature and the commonly used ones are Anderson-Darling test, Shapiro-Wilk test and Jarque-Bera test. Each test has its own advantageous points since they are developed for specific patterns and there is no method that consistently performs optimally in all situations. Since the data distribution of practical problems can be complex and diverse, we propose a Cauchy Combination Omnibus Test (CCOT) that is robust and valid in most data cases. We also give some theoretical results to analyze the good properties of CCOT. Two obvious advantages of CCOT are that not only does CCOT have a display expression for calculating statistical significance, but extensive simulation results show its robustness regardless of the shape of distribution the data comes from. Applications to South African Heart Disease and Neonatal Hearing Impairment data further illustrate its practicability.
Topics: Computer Simulation; Normal Distribution; Sample Size; Data Analysis
PubMed: 37535617
DOI: 10.1371/journal.pone.0289498 -
PLoS Computational Biology Aug 2023Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics...
Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics studies typically begin with reduction to 2 or 3 dimensions to produce "all-in-one" visuals of the data that are amenable to the human eye, and these are subsequently used for qualitative and quantitative exploratory analysis. However, there is little theoretical support for this practice, and we show that extreme dimension reduction, from hundreds or thousands of dimensions to 2, inevitably induces significant distortion of high-dimensional datasets. We therefore examine the practical implications of low-dimensional embedding of single-cell data and find that extensive distortions and inconsistent practices make such embeddings counter-productive for exploratory, biological analyses. In lieu of this, we discuss alternative approaches for conducting targeted embedding and feature exploration to enable hypothesis-driven biological discovery.
Topics: Humans; Data Analysis; Genomics
PubMed: 37590228
DOI: 10.1371/journal.pcbi.1011288 -
Neuropsychology Review Jun 2024Mathematics incorporates a broad range of skills, which includes basic early numeracy skills, such as subitizing and basic counting to more advanced secondary skills... (Meta-Analysis)
Meta-Analysis Review
Mathematics incorporates a broad range of skills, which includes basic early numeracy skills, such as subitizing and basic counting to more advanced secondary skills including mathematics calculation and reasoning. The aim of this review was to undertake a detailed investigation of the severity and pattern of early numeracy and secondary mathematics skills in people with epilepsy. Searches were guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Twenty adult studies and 67 child studies were included in this review. Overall, meta-analyses revealed significant moderate impairments across all mathematics outcomes in both adults (g= -0.676), and children (g= -0.593) with epilepsy. Deficits were also observed for specific mathematics outcomes. For adults, impairments were found for mathematics reasoning (g= -0.736). However, two studies found that mathematics calculation was not significantly impaired, and an insufficient number of studies examined early numeracy skills in adults. In children with epilepsy, significant impairments were observed for each mathematics outcome: early numeracy (g= -0.383), calculation (g= -0.762), and reasoning (g= -0.572). The gravity of impairments also differed according to the site of seizure focus for children and adults, suggesting that mathematics outcomes were differentially vulnerable to the location of seizure focus.
Topics: Humans; Epilepsy; Mathematics; Child; Adult
PubMed: 37490196
DOI: 10.1007/s11065-023-09600-8 -
Quarterly Journal of Experimental... Sep 2023Mathematics skills are associated with future employment, well-being, and quality of life. However, many adults and children fail to learn the mathematics skills they...
Mathematics skills are associated with future employment, well-being, and quality of life. However, many adults and children fail to learn the mathematics skills they require. To improve this situation, we need to have a better understanding of the processes of learning and performing mathematics. Over the past two decades, there has been a substantial growth in psychological research focusing on mathematics. However, to make further progress, we need to pay greater attention to the nature of, and multiple elements involved in, mathematical cognition. Mathematics is not a single construct; rather, overall mathematics achievement is comprised of proficiency with specific components of mathematics (e.g., number fact knowledge, algebraic thinking), which in turn recruit basic mathematical processes (e.g., magnitude comparison, pattern recognition). General cognitive skills and different learning experiences influence the development of each component of mathematics as well as the links between them. Here, I propose and provide evidence for a framework that structures how these components of mathematics fit together. This framework allows us to make sense of the proliferation of empirical findings concerning influences on mathematical cognition and can guide the questions we ask, identifying where we are missing both research evidence and models of specific mechanisms.
Topics: Child; Adult; Humans; Quality of Life; Cognition; Learning; Mathematics; Achievement
PubMed: 37129432
DOI: 10.1177/17470218231175325 -
Trends in Ecology & Evolution Jul 2023Big Data science has significantly furthered our understanding of complex systems by harnessing large volumes of data, generated at high velocity and in great variety.... (Review)
Review
Big Data science has significantly furthered our understanding of complex systems by harnessing large volumes of data, generated at high velocity and in great variety. However, there is a risk that Big Data collection is prioritised to the detriment of 'Small Data' (data with few observations). This poses a particular risk to ecology where Small Data abounds. Machine learning experts are increasingly looking to Small Data to drive the next generation of innovation, leading to development in methods for Small Data such as transfer learning, knowledge graphs, and synthetic data. Meanwhile, meta-analysis and causal reasoning approaches are evolving to provide new insights from Small Data. These advances should add value to high-quality Small Data catalysing future insights for ecology.
Topics: Ecology; Data Analysis
PubMed: 36797167
DOI: 10.1016/j.tree.2023.01.015 -
Journal of Korean Medical Science Jan 2024Determining if the frequency distribution of a given data set follows a normal distribution or not is among the first steps of data analysis. Visual examination of the... (Review)
Review
Determining if the frequency distribution of a given data set follows a normal distribution or not is among the first steps of data analysis. Visual examination of the data, commonly by Q-Q plot, although is acceptable by many scientists, is considered subjective and not acceptable by other researchers. One-sample Kolmogorov-Smirnov test with Lilliefors correction (for a sample size ≥ 50) and Shapiro-Wilk test (for a sample size < 50) are common statistical tests for checking the normality of a data set quantitatively. As parametric tests, which assume that the data distribution is normal (Gaussian, bell-shaped), are more robust compared to their non-parametric counterparts, we commonly use transformations (e.g., log-transformation, Box-Cox transformation, etc.) to make the frequency distribution of non-normally distributed data close to a normal distribution. Herein, I wish to reflect on presenting how to practically work with these statistical methods through examining of real data sets.
Topics: Humans; Data Analysis; Physicians; Research Personnel; Statistics, Nonparametric
PubMed: 38258367
DOI: 10.3346/jkms.2024.39.e35