Category: Statistics
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Statistics of Insanity
Chaos. 2023 Dec 1;33(12):121101. doi: 10.1063/5.0176358.
ABSTRACT
Chaos is an important dynamic feature, which generally occurs in deterministic and stochastic nonlinear systems and is an inherent characteristic that is ubiquitous. Many difficulties have been solved and new research perspectives have been provided in many fields. The control of chaos is another problem that has been studied. In recent years, a recurrent neural network has emerged, which is widely used to solve many problems in nonlinear dynamics and has fast and accurate computational speed. In this paper, we employ reservoir computing to control chaos in dynamic systems. The results show that the reservoir calculation algorithm with a control term can control the chaotic phenomenon in a dynamic system. Meanwhile, the method is applicable to dynamic systems with random noise. In addition, we investigate the problem of different values for neurons and leakage rates in the algorithm. The findings indicate that the performance of machine learning techniques can be improved by appropriately constructing neural networks.
PMID:38079650 | DOI:10.1063/5.0176358
Chaos. 2023 Dec 1;33(12):123114. doi: 10.1063/5.0168400.
ABSTRACT
The correlation dimension (CD) is a nonlinear measure of the complexity of invariant sets. First introduced for describing low-dimensional chaotic attractors, it has been later extended to the analysis of experimental electroencephalographic (EEG), magnetoencephalographic (MEG), and local field potential (LFP) recordings. However, its direct application to high-dimensional (dozens of signals) and high-definition (kHz sampling rate) 2HD data revealed a controversy in the results. We show that the need for an exponentially long data sample is the main difficulty in dealing with 2HD data. Then, we provide a novel method for estimating CD that enables orders of magnitude reduction of the required sample size. The approach decomposes raw data into statistically independent components and estimates the CD for each of them separately. In addition, the method allows ongoing insights into the interplay between the complexity of the contributing components, which can be related to different anatomical pathways and brain regions. The latter opens new approaches to a deeper interpretation of experimental data. Finally, we illustrate the method with synthetic data and LFPs recorded in the hippocampus of a rat.
PMID:38079645 | DOI:10.1063/5.0168400
Ann Intern Med. 2023 Dec 12. doi: 10.7326/M23-1345. Online ahead of print.
ABSTRACT
BACKGROUND: Prediction of atherosclerotic cardiovascular disease (ASCVD) in primary prevention assessments exclusively with laboratory results may facilitate automated risk reporting and improve uptake of preventive therapies.
OBJECTIVE: To develop and validate sex-specific prediction models for ASCVD using age and routine laboratory tests and compare their performance with that of the pooled cohort equations (PCEs).
DESIGN: Derivation and validation of the CANHEART (Cardiovascular Health in Ambulatory Care Research Team) Lab Models.
SETTING: Population-based cohort study in Ontario, Canada.
PARTICIPANTS: A derivation and internal validation cohort of adults aged 40 to 75 years without cardiovascular disease from April 2009 to December 2015; an external validation cohort of primary care patients from January 2010 to December 2014.
MEASUREMENTS: Age and laboratory predictors measured in the outpatient setting included serum total cholesterol, high-density lipoprotein cholesterol, triglycerides, hemoglobin, mean corpuscular volume, platelets, leukocytes, estimated glomerular filtration rate, and glucose. The ASCVD outcomes were defined as myocardial infarction, stroke, and death from ischemic heart or cerebrovascular disease within 5 years.
RESULTS: Sex-specific models were developed and internally validated in 2 160 497 women and 1 833 147 men. They were well calibrated, with relative differences less than 1% between mean predicted and observed risk for both sexes. The c-statistic was 0.77 in women and 0.71 in men. External validation in 31 697 primary care patients showed a relative difference less than 14% and an absolute difference less than 0.3 percentage points in mean predicted and observed risks for both sexes. The c-statistics for the laboratory models were 0.72 for both sexes and were not statistically significantly different from those for the PCEs in women (change in c-statistic, -0.01 [95% CI, -0.03 to 0.01]) or men (change in c-statistic, -0.01 [CI, -0.04 to 0.02]).
LIMITATION: Medication use was not available at the population level.
CONCLUSION: The CANHEART Lab Models predict ASCVD with similar accuracy to more complex models, such as the PCEs.
PRIMARY FUNDING SOURCE: Canadian Institutes of Health Research.
PMID:38079638 | DOI:10.7326/M23-1345
Appl Physiol Nutr Metab. 2023 Dec 11. doi: 10.1139/apnm-2023-0387. Online ahead of print.
ABSTRACT
There is a significant effect of sex and muscle mass on the cardiorespiratory response to the skeletal muscle metaboreflex during isometric exercise. We therefore tested the hypothesis that sex differences would be present when isolated following dynamic exercise. We also tested the hypothesis that single and double leg post-exercise circulatory occlusion (PECO) following high-intensity exercise would elicit a cardiorespiratory response proportional to the absolute muscle mass. Healthy (24±4 y) males (n=10) and females (n=10) completed pulmonary function and an incremental cycle test to exhaustion. Participants completed two randomized, 6-min bouts of intense cycle exercise (84±7 % V̇O2max). One exercise bout was immediately followed by 3-min post-exercise circulatory occlusion (PECO; 220 mmHg) of the legs while the other exercise bout was followed by passive recovery. Males completed an additional session of testing with single leg PECO. The mean arterial pressure during PECO was statistically greater in males compared to females (p=0.004). The heart rate response to PECO was similar between males compared with females (p=0.118). PECO elicited a greater ventilatory response in males compared with females (p=0.026). In males we observed a dose-dependent cardiovascular, but not ventilatory, response to muscle mass occluded (all p<0.05). Our findings suggest the metaboreflex contribution to cardiorespiratory control during dynamic exercise is greater in males compared to females. The ventilatory response induced by double-leg occlusion but not single-leg, suggests that the ventilatory influence of the metaboreflex is less sensitive than the cardiovascular response and may be linked to the greater afferent activation induced by double-leg occlusion.
PMID:38079618 | DOI:10.1139/apnm-2023-0387
J Clin Oncol. 2023 Dec 11:JCO2301101. doi: 10.1200/JCO.23.01101. Online ahead of print.
ABSTRACT
PURPOSE: There is strong evidence that leisure-time physical activity is protective against postmenopausal breast cancer risk but the association with premenopausal breast cancer is less clear. The purpose of this study was to examine the association of physical activity with the risk of developing premenopausal breast cancer.
METHODS: We pooled individual-level data on self-reported leisure-time physical activity across 19 cohort studies comprising 547,601 premenopausal women, with 10,231 incident cases of breast cancer. Multivariable Cox regression was used to estimate hazard ratios (HRs) and 95% CIs for associations of leisure-time physical activity with breast cancer incidence. HRs for high versus low levels of activity were based on a comparison of risk at the 90th versus 10th percentiles of activity. We assessed the linearity of the relationship and examined subtype-specific associations and effect modification across strata of breast cancer risk factors, including adiposity.
RESULTS: Over a median 11.5 years of follow-up (IQR, 8.0-16.1 years), high versus low levels of leisure-time physical activity were associated with a 6% (HR, 0.94 [95% CI, 0.89 to 0.99]) and a 10% (HR, 0.90 [95% CI, 0.85 to 0.95]) reduction in breast cancer risk, before and after adjustment for BMI, respectively. Tests of nonlinearity suggested an approximately linear relationship (Pnonlinearity = .94). The inverse association was particularly strong for human epidermal growth factor receptor 2-enriched breast cancer (HR, 0.57 [95% CI, 0.39 to 0.84]; Phet = .07). Associations did not vary significantly across strata of breast cancer risk factors, including subgroups of adiposity.
CONCLUSION: This large, pooled analysis of cohort studies adds to evidence that engagement in higher levels of leisure-time physical activity may lead to reduced premenopausal breast cancer risk.
PMID:38079601 | DOI:10.1200/JCO.23.01101
Int J Surg. 2023 Dec 11. doi: 10.1097/JS9.0000000000000950. Online ahead of print.
ABSTRACT
BACKGROUND: Emerging three-dimensional digital visualization technology (DVT) provides more advantages than traditional microscopy in microsurgery; however, its impact on microsurgeons’ visual and nervous systems and delicate microsurgery is still unclear, which hinders the wider implementation of DVT in digital visualization for microsurgery.
METHODS AND MATERIAL: Forty-two microsurgeons from the *** were enrolled in this prospective self-controlled study. Each microsurgeon consecutively performed 30-minute conjunctival sutures using a three-dimensional digital display and a microscope, respectively. Visual function, autonomic nerve activity, and subjective symptoms were evaluated before and immediately after the operation. Visual functions, including accommodative lag, accommodative amplitude, near point of convergence and contrast sensitivity function (CSF), were measured by an expert optometrist. Heart rate variability (HRV) was recorded by a wearable device for monitoring autonomic nervous activity. Subjective symptoms were evaluated by questionnaires. Microsurgical performance was assessed by the video-based Objective Structured Assessment of Technical Skill (OSATS) tool.
RESULTS: Accommodative lag decreased from 0.63 [0.18] diopters (D) to 0.55 [0.16] D (P=0.014), area under the log CSF increased from 1.49 [0.15] to 1.52 [0.14] (P=0.037), and HRV decreased from 36.00 [13.54] milliseconds (ms) to 32.26 [12.35] ms (P=0.004) after using the DVT, but the changes showed no differences compared to traditional microscopy (P > 0.05). No statistical significance was observed for global OSATS scores between the two rounds of operations (mean difference, 0.05 [95% CI, -1.17 to 1.08] points; P=0.95). Subjective symptoms were quite mild after using both techniques.
CONCLUSIONS: The impact of DVT-based procedures on microsurgeons includes enhanced accommodation and sympathetic activity, but the changes and surgical performance are not significantly different from those of microscopy-based microsurgery. Our findings indicate that short-term use of DVT is reliable for microsurgery and the long-term effect of using DVT deserve more consideration.
PMID:38079600 | DOI:10.1097/JS9.0000000000000950
J Chem Inf Model. 2023 Dec 11. doi: 10.1021/acs.jcim.3c01525. Online ahead of print.
ABSTRACT
Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.
PMID:38079572 | DOI:10.1021/acs.jcim.3c01525