Category: Statistics

J Speech Lang Hear Res. 2024 Dec 16:1-12. doi: 10.1044/2024_JSLHR-24-00360. Online ahead of print.

ABSTRACT

PURPOSE: When using the spatiotemporal index (STI) to measure variability across repetitions of the same stimulus, researchers will typically screen and remove productions that contain errors or disfluencies. However, this screening process is highly subjective, reduces the amount of data available, and may generate samples that are less representative of true speech difficulties. In this study, we quantify the degree to which the STI is skewed by the inclusion of highly deviating productions and whether alternative calculations could better facilitate their inclusion.

METHOD: First, we conducted a controlled simulation to quantify how highly deviating productions skew STI values. The traditional STI calculation was compared to three robust alternative measures proposed to reduce the influence of outlying productions. Next, using audio recordings from typically developing (TD) children and children with childhood apraxia of speech (CAS), we investigated how effectively each STI measure differentiated the two groups.

RESULTS: Simulation findings demonstrated that the STI can be heavily skewed (more than doubling in value) by the inclusion of a single outlying production. In contrast, the robust alternative measures were all able to incorporate multiple outlying productions before their value was significantly altered. The proposed best-5 STI measure produced larger group differences between TD children and children with CAS compared to the traditional STI in both “Mom pets the puppy” and “Buy Bobby a puppy” stimuli.

CONCLUSIONS: The STI is highly sensitive to outlying productions and requires careful consideration of the repetitions included in its calculation. However, conservative approaches to data removal may be problematic when studying populations that are prone to fluency errors. In these scenarios, more robust alternatives to the STI, such as the best-5 STI measure, may provide a more practical approach to measuring speech variability.

SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.27973236.

PMID:39680792 | DOI:10.1044/2024_JSLHR-24-00360

Int J Epidemiol. 2024 Dec 16;54(1):dyae164. doi: 10.1093/ije/dyae164.

ABSTRACT

BACKGROUND: There have been reports of sharp declines in acute coronary syndrome (ACS) during the COVID-19 pandemic. The study aims to assess nationwide ACS emergency department (ED) visit rates across age and sex subgroups and the general population, with a comparison before and throughout the pandemic’s various phases.

METHODS: A multiple interrupted time series analysis was used to assess 61 349 ACS nationwide hospital visits from January 2018 to December 2021 at monthly intervals. The study period was divided into three periods: January 2018-February 2020 (pre-pandemic period); March 2020-January 2021 (early-pandemic period); February 2021-December 2021 (late-pandemic period). Segmented regression with a seasonally adjusted autoregressive moving average structure was used to build predictive models with an estimated reference trendline (counterfactual).

RESULTS: Over 11 months of the early-pandemic period (lockdowns), the largest decrease in visits was seen in women aged 65 and above, of 18.4% [incidence rate ratio (IRR) 0.82; 95% confidence interval (CI) 0.77-0.86]. The lowest decrease was observed in men aged 25-64, of 7.2% (IRR 0.93; 0.91-0.94). During the late-pandemic period, which included high vaccination coverage and no lockdowns, the largest further decrease was in women aged 25-64 of 20.1% (IRR 0.80; 0.75-0.84) on average.

CONCLUSIONS: The pandemic influenced ACS ED visits variably, with substantial declines during phases of high COVID-19 morbidity and mortality. Older individuals, particularly women, demonstrated the largest decrease in ACS ED visits, highlighting the need for tailored public health strategies to maintain public confidence in access to critical care during future health emergencies.

PMID:39680786 | DOI:10.1093/ije/dyae164

Proc Natl Acad Sci U S A. 2024 Dec 24;121(52):e2321971121. doi: 10.1073/pnas.2321971121. Epub 2024 Dec 16.

ABSTRACT

Phase transitions are ubiquitous across life, yet hard to quantify and describe accurately. In this work, we develop an approach for characterizing generic attributes of phase transitions from very limited observations made deep within different phases’ domains of stability. Our approach is called thermodynamic maps (TM), which combines statistical mechanics and molecular simulations with score-based generative models. TM enable learning the temperature dependence of arbitrary thermodynamic observables across a wide range of temperatures. We show its usefulness by calculating phase transition attributes such as melting temperature, temperature-dependent heat capacities, and critical exponents. For instance, we demonstrate the ability of TM to infer the ferromagnetic phase transition of the Ising model, including temperature-dependent heat capacity and critical exponents, despite never having seen samples from the transition region. In addition, we efficiently characterize the temperature-dependent conformational ensemble and compute melting curves of the two RNA systems: a GCAA tetraloop and the HIV-TAR RNA, which are notoriously hard to sample due to glassy-like energy landscapes.

PMID:39680772 | DOI:10.1073/pnas.2321971121

Proc Natl Acad Sci U S A. 2024 Dec 24;121(52):e2419650121. doi: 10.1073/pnas.2419650121. Epub 2024 Dec 16.

ABSTRACT

For human infants, producing recognizable speech is more than a cognitive process. It is a motor skill that requires infants to learn to coordinate multiple muscles of varying functions across their body. This coordination is directly linked to ongoing fluctuations in heart rate; a physiological process that can scaffold behavior. We investigated whether ongoing fluctuations in heart rate coincide with vocal production and word formation in 24-mo-old infants. Infants were most likely to produce a vocalization when heart rate fluctuations reached a peak (local maximum) or trough (local minimum). Vocalizations produced at the peak were longer than expected by chance. Functionally, vocalizations produced just before the trough, while heart rate is decelerating, were more likely to be recognized as a word by naive listeners. Thus, for the developing infant, heart rate fluctuations align with the timing of vocal productions and are associated with their duration and the likelihood of producing recognizable speech. Our results have broad and immediate implications for understanding normative language development, the evolutionary basis and physiological process of vocal production, and potential early indicators of speech and communication disorders.

PMID:39680757 | DOI:10.1073/pnas.2419650121

Brief Bioinform. 2024 Nov 22;26(1):bbae649. doi: 10.1093/bib/bbae649.

ABSTRACT

Single-cell multi-omics refers to the various types of biological data at the single-cell level. These data have enabled insight and resolution to cellular phenotypes, biological processes, and developmental stages. Current advances hold high potential for breakthroughs by integrating multiple different omics layers. However, singlecell multi-omics data usually have different feature dimensions and direct or indirect relationships. How to keep the data structure of these different data and extract hidden relationships is a major challenge for omics data integration, and effective integration models are urgently needed. In this paper, we propose an irregular tensor decomposition model (GSTRPCA) based on tensor robust principal component analysis (TRPCA). We developed a weighted threshold model for the decomposition of irregular tensor data by combining low-rank and sparsity constraints, which requires that the low-dimensional embeddings of the data remain lowrank and sparse. The major advantage of the GSTRPCA algorithm is its ability to keep the original data structure and explore hidden related features among omics data. For GSTRPCA, we also designed an effective algorithm that theoretically guarantees global convergence for the tensor decomposition. The computational experiments on irregular tensor datasets demonstrate that GSTRPCA significantly outperformed the state-of-the-art methods and hence confirm the superiority of GSTRPCA in clustering single-cell multiomics data. To our knowledge, this is the first tensor decomposition method for irregular tensor data to keep the data structure and hence improve the clustering performance for single-cell multi-omics data. GSTRPCA is a Matlabbased algorithm, and the code is available from https://github.com/GGL-B/GSTRPCA.

PMID:39680741 | DOI:10.1093/bib/bbae649

J Bras Nefrol. 2025 Apr-Jun;47(2):e20240146. doi: 10.1590/2175-8239-JBN-2024-0146en.

ABSTRACT

INTRODUCTION: Visual Abstract is a visual summary of the most relevant information from a scientific article, presented as an infographic. Despite the growing use of Visual Abstracts by journals around the world, studies evaluating their components to guide their development remain scarce.

OBJECTIVE: The primary objective of this study is to identify the aesthetic perceptions of Visual Abstracts components by physicians and medical students.

METHODS: Cross-sectional study, using a virtual questionnaire sent via email to a convenience sample comprising physicians and medical students. Data were analyzed using descriptive statistics, with means and standard deviation or median and interquartile range, depending on the type of the variable distribution. Categorical variables are presented in absolute and relative numbers.

RESULT: The research sample consisted mainly of medical students (65%), who were female (57.2%), with a median age of 23.5 years (IQR 21-42.25). The majority of respondents declared no prior knowledge on Visual Abstracts (61.7%). Of the analyzed variables, preferences included icons (56.7%), in a monochrome style (36.7%), second-dimensional (81.1%), and moderately detailed layout (56.7%), using the “original” color (91.7%), and structured in IMRaD format (73.9%).

CONCLUSION: Several visual components influence the aesthetic perception of physicians and medical students regarding Visual Abstracts, with particular emphasis on textual objectivity, clarity of colors, and the use of icons.

PMID:39680739 | DOI:10.1590/2175-8239-JBN-2024-0146en

JMIR Med Educ. 2024 Dec 16;10:e56132. doi: 10.2196/56132.

ABSTRACT

BACKGROUND: Biochemistry is a cornerstone of medical education. Its knowledge is integral to the understanding of complex biological processes and how they are applied in several areas in health care. Also, its significance is reflected in the way it informs the practice of medicine, which can guide and help in both diagnosis and treatment. However, the retention of biochemistry knowledge over time remains a dilemma. Long-term retention of such crucial information is extremely important, as it forms the foundation upon which clinical skills are developed and refined. The effectiveness of biochemistry education, and consequently its long-term retention, is influenced by several factors. Educational methods play a critical role; interactional and integrative teaching approaches have been suggested to enhance retention compared with traditional didactic methods. The frequency and context in which biochemistry knowledge is applied in clinical settings can significantly impact its retention. Practical application reinforces theoretical understanding, making the knowledge more accessible in the long term. Prior knowledge (familiarity) of information suggests that it is stored in long-term memory, which makes its retention in the long term easier to recall.

OBJECTIVES: This investigation was conducted at King Saud bin Abdulaziz University for Health Sciences in Riyadh, Saudi Arabia. The aim of the study is to understand the dynamics of long-term retention of biochemistry among medical students. Specifically, it looks for the association between students’ familiarity with biochemistry content and actual knowledge retention levels.

METHODS: A cross-sectional correlational survey involving 240 students from King Saud bin Abdulaziz University for Health Sciences was conducted. Participants were recruited via nonprobability convenience sampling. A validated biochemistry assessment tool with 20 questions was used to gauge students’ retention in biomolecules, catalysis, bioenergetics, and metabolism. To assess students’ familiarity with the knowledge content of test questions, each question is accompanied by options that indicate students’ prior knowledge of the content of the question. Statistical analyses tests such as Mann-Whitney U test, Kruskal-Wallis test, and chi-square tests were used.

RESULTS: Our findings revealed a significant correlation between students’ familiarity of the content with their knowledge retention in the biomolecules (r=0.491; P<.001), catalysis (r=0.500; P<.001), bioenergetics (r=0.528; P<.001), and metabolism (r=0.564; P<.001) biochemistry knowledge domains.

CONCLUSIONS: This study highlights the significance of familiarity (prior knowledge) in evaluating the retention of biochemistry knowledge. Although limited in terms of generalizability and inherent biases, the research highlights the crucial significance of student’s familiarity in actual knowledge retention of several biochemistry domains. These results might be used by educators to customize instructional methods in order to improve students’ long-term retention of biochemistry information and boost their clinical performance.

PMID:39680441 | DOI:10.2196/56132

JMIR Public Health Surveill. 2024 Dec 16;10:e58391. doi: 10.2196/58391.

ABSTRACT

BACKGROUND: China is a country with a high burden of hepatitis B (Hep B) but a low treatment rate. One of the key reasons for the low treatment rate is the inadequate health literacy (HL) of the people, which may affect the awareness and knowledge of Hep B and its treatment, as well as the ability to actively and correctly seek medical resources.

OBJECTIVE: This study analyzed how HL contributed to the scale-up of antiviral treatment of Hep B in China. We expect that the findings of this study could be used to inform resource allocation for health education and other approaches intending to improve the HL of the Chinese population, thus facilitating the nationwide scale-up of Hep B treatment and contributing to the achievement of the 2030 goal of eliminating viral hepatitis as a major public health threat in China.

METHODS: We used the two-stage least squares regression method and adopted the mobile phone penetration rate as the instrumental variable to estimate the effect of improved HL on the number of 12-month standard Hep B antiviral treatments in China based on the panel data of 31 provinces from 2013 to 2020.

RESULTS: In the cross-sectional dimension, the higher the HL, the higher the number of treatments in the provinces in a specific year. In the time series dimension, the number of treatments in a specific province increased with the improvement of HL over time. After controlling the time-invariant inherent attributes of provinces, the instrumental variable estimation with two-stage least squares regression based on the province fixed effect model found that for every 1% increase of HL in each province, the number of treatments increased by 7.15% (0.0715 = e0.0691 – 1; P<.001). Such an increase turned to 5.19% (0.0519 = e0.0506 – 1; P<.001) for the analysis targeting the observation time from 2013 to 2019, as the data of 2020 were removed when the COVID-19 pandemic started. The study found no statistically significant effect of HL on the number of Hep B treatments in the provinces with higher newly reported Hep B incidence and lower gross domestic product per capita.

CONCLUSIONS: Our findings suggest that improved HL of the population is an important favorable facilitator for the scale-up of Hep B treatment in China. Building awareness and knowledge of Hep B and its treatment can help individuals understand their health status, ensuring a healthier lifestyle and appropriate health care-seeking behaviors and health care service utilization, so that people can be diagnosed and treated timely and appropriately. Enhancing resource allocation to improve the overall HL of the population and sending Hep B-specific messages to the infected people would be a feasible and effective approach to scale-up the treatment of Hep B in low- and middle-income settings with limited resources, and contribute to achieving the 2030 global goal of eliminating viral hepatitis as a major public health threat.

PMID:39680440 | DOI:10.2196/58391

Elife. 2024 Dec 16;13:RP96566. doi: 10.7554/eLife.96566.

ABSTRACT

Studying and understanding the code of large neural populations hinge on accurate statistical models of population activity. A novel class of models, based on learning to weigh sparse nonlinear Random Projections (RP) of the population, has demonstrated high accuracy, efficiency, and scalability. Importantly, these RP models have a clear and biologically plausible implementation as shallow neural networks. We present a new class of RP models that are learned by optimizing the randomly selected sparse projections themselves. This ‘reshaping’ of projections is akin to changing synaptic connections in just one layer of the corresponding neural circuit model. We show that Reshaped RP models are more accurate and efficient than the standard RP models in recapitulating the code of tens of cortical neurons from behaving monkeys. Incorporating more biological features and utilizing synaptic normalization in the learning process, results in accurate models that are more efficient. Remarkably, these models exhibit homeostasis in firing rates and total synaptic weights of projection neurons. We further show that these sparse homeostatic reshaped RP models outperform fully connected neural network models. Thus, our new scalable, efficient, and highly accurate population code models are not only biologically plausible but are actually optimized due to their biological features. These findings suggest a dual functional role of synaptic normalization in neural circuits: maintaining spiking and synaptic homeostasis while concurrently optimizing network performance and efficiency in encoding information and learning.

PMID:39680435 | DOI:10.7554/eLife.96566

JMIR Bioinform Biotechnol. 2024 Dec 16;5:e65506. doi: 10.2196/65506.

ABSTRACT

BACKGROUND: Health care students often experience high levels of stress, anxiety, and mental health issues, making it crucial to address these challenges. Variations in stress levels may be associated with changes in dehydroepiandrosterone sulfate (DHEA-S) and interleukin-6 (IL-6) levels and gene expression. Meditative practices have demonstrated effectiveness in reducing stress and improving mental well-being.

OBJECTIVE: This study aims to assess the effects of Heartfulness meditation on mental well-being, DHEA-S, IL-6, and gene expression profile.

METHODS: The 78 enrolled participants were randomly assigned to the Heartfulness meditation (n=42, 54%) and control (n=36, 46%) groups. The participants completed the Perceived Stress Scale (PSS) and Depression Anxiety Stress Scale (DASS-21) at baseline and after week 12. Gene expression with messenger RNA sequencing and DHEA-S and IL-6 levels were also measured at baseline and the completion of the 12 weeks. Statistical analysis included descriptive statistics, paired t test, and 1-way ANOVA with Bonferroni correction.

RESULTS: The Heartfulness group exhibited a significant 17.35% reduction in PSS score (from mean 19.71, SD 5.09 to mean 16.29, SD 4.83; P<.001) compared to a nonsignificant 6% reduction in the control group (P=.31). DASS-21 scores decreased significantly by 27.14% in the Heartfulness group (from mean 21.15, SD 9.56 to mean 15.41, SD 7.87; P<.001) while it increased nonsignificantly by 17% in the control group (P=.04). For the DASS-21 subcomponents-the Heartfulness group showed a statistically significant 28.53% reduction in anxiety (P=.006) and 27.38% reduction in stress (P=.002) versus an insignificant 22% increase in anxiety (P=.02) and 6% increase in stress (P=.47) in the control group. Further, DHEA-S levels showed a significant 20.27% increase in the Heartfulness group (from mean 251.71, SD 80.98 to mean 302.74, SD 123.56; P=.002) compared to an insignificant 9% increase in the control group (from mean 285.33, SD 112.14 to mean 309.90, SD 136.90; P=.10). IL-6 levels showed a statistically significant difference in both the groups (from mean 4.93, SD 1.35 to mean 3.67, SD 1.0; 28.6%; P<.001 [Heartfulness group] and from mean 4.52, SD 1.40 to mean 2.72, SD 1.74; 40%; P<.001 [control group]). Notably, group comparison at 12 weeks revealed a significant difference in perceived stress, DASS-21 and its subcomponents, and IL-6 (all P<.05/4). The gene expression profile with messenger RNA sequencing identified 875 upregulated genes and 1539 downregulated genes in the Heartfulness group compared to baseline, and there were 292 upregulated genes and 1180 downregulated genes in the Heartfulness group compared to the control group after the intervention.

CONCLUSIONS: Heartfulness practice was associated with decreased depression, anxiety, and stress scores and improved health measures in DHEA-S and IL-6 levels. The gene expression data point toward possible mechanisms of alleviation of symptoms of stress, anxiety and depression.

TRIAL REGISTRATION: ISRCTN Registry ISRCTN82860715; https://doi.org/10.1186/ISRCTN82860715.

PMID:39680432 | DOI:10.2196/65506