Tag: statistics

Sci Rep. 2023 Jan 31;13(1):1767. doi: 10.1038/s41598-022-24745-x.

ABSTRACT

Dust is an inherent byproduct of mining activities that raises notable health and safety concerns. Cumulative inhalation of respirable coal mine dust (RCMD) and respirable crystalline silica (RCS) can lead to obstructive lung diseases. Despite considerable efforts to reduce dust exposure by decreasing the permissible exposure limits (PEL) and improving the monitoring techniques, the rate of mine workers with respiratory diseases is still high. The root causes of the high prevalence of respiratory diseases remain unknown. This study aimed to investigate contributing factors in RCMD and RCS dust concentrations in both surface and underground mines. To this end, a data management approach is performed on MSHA’s database between 1989 and 2018 using SQL data management. In this process, all data were grouped by mine ID, and then, categories of interests were defined to conduct statistical analysis using the generalized estimating equation (GEE) model. The total number of 12,537 and 9050 observations for respirable dust concentration are included, respectively, in the U.S. underground and surface mines. Several variables were defined in four categories of interest including mine type, geographic location, mine size, and coal seam height. Hypotheses were developed for each category based on the research model and were tested using multiple linear regression analysis. The results of the analysis indicate higher RCMD concentration in underground compared to RCS concentration which is found to be relatively higher in surface coal mines. In addition, RCMD concentration is seen to be higher in the Interior region while RCS is higher in the Appalachia region. Moreover, mines of small sizes show lower RCMD and higher RCS concentrations. Finally, thin-seam coal has greater RCMD and RCS concentrations compared to thicker seams in both underground and surface mines. In the end, it is demonstrated that RCMD and RCS concentrations in both surface and underground mines have decreased. Therefore, further research is needed to investigate the efficacy of the current mass-concentration-based monitoring system.

PMID:36720966 | DOI:10.1038/s41598-022-24745-x

Sci Rep. 2023 Jan 31;13(1):1740. doi: 10.1038/s41598-023-28991-5.

ABSTRACT

The accuracy and quality of the landslide susceptibility map depend on the available landslide locations and the sampling strategy for absence data (non-landslide locations). In this study, we propose an objective method to determine the critical value for sampling absence data based on Mahalanobis distances (MD). We demonstrate this method on landslide susceptibility mapping of three subdistricts (Upazilas) of the Rangamati district, Bangladesh, and compare the results with the landslide susceptibility map produced based on the slope-based absence data sampling method. Using the 15 landslide causal factors, including slope, aspect, and plan curvature, we first determine the critical value of 23.69 based on the Chi-square distribution with 14 degrees of freedom. This critical value was then used to determine the sampling space for 261 random absence data. In comparison, we chose another set of the absence data based on a slope threshold of < 3°. The landslide susceptibility maps were then generated using the random forest model. The Receiver Operating Characteristic (ROC) curves and the Kappa index were used for accuracy assessment, while the Seed Cell Area Index (SCAI) was used for consistency assessment. The landslide susceptibility map produced using our proposed method has relatively high model fitting (0.87), prediction (0.85), and Kappa values (0.77). Even though the landslide susceptibility map produced by the slope-based sampling also has relatively high accuracy, the SCAI values suggest lower consistency. Furthermore, slope-based sampling is highly subjective; therefore, we recommend using MD -based absence data sampling for landslide susceptibility mapping.

PMID:36720965 | DOI:10.1038/s41598-023-28991-5

Sci Rep. 2023 Jan 31;13(1):1723. doi: 10.1038/s41598-023-27613-4.

ABSTRACT

Fiber reinforced polymer (FPR) bars have been widely used as a substitutional material of steel reinforcement in reinforced concrete elements in corrosion areas. Shear resistance of FRP reinforced concrete element can be affected by concrete properties and transverse FRP stirrups. Hence, studying the shear strength (V_s) mechanism is one of the highly essential for pre-design procedure for reinforced concrete elements. This research examines the ability of three machine learning (ML) models called M5-Tree (M5), extreme learning machine (ELM), and random forest (RF) in predicting V_s of 112 shear tests of FRP reinforced concrete beam with transverse reinforcement. For generating the prediction matrix of the developed ML models, statistical correlation analysis was conducted to generate the suitable inputs models for V_s prediction. Statistical evaluation and graphical approaches were used to evaluate the efficiency of the proposed models. The results revealed that all the proposed models performed in general well for all the input combinations. However, ELM-M1 and M5-Tree-M5 models exhibited less accuracy performance in comparison with the other developed models. The study showed that the best prediction performance was revealed by M5 tree model using nine input parameters, with coefficient of determination (R²) and root mean square error (RMSE) equal to 0.9313 and 35.5083 KN, respectively. The comparison results also indicated that ELM and RF were performed significant results with a less slight performance than M5 model. The study outcome contributes to basic knowledge of investigating the impact of stirrups on V_s of FRP reinforced concrete beam with the potential of applying different computer aid models.

PMID:36720939 | DOI:10.1038/s41598-023-27613-4

Nat Hum Behav. 2023 Jan 31. doi: 10.1038/s41562-023-01523-x. Online ahead of print.

NO ABSTRACT

PMID:36720938 | DOI:10.1038/s41562-023-01523-x

Sci Rep. 2023 Jan 31;13(1):1802. doi: 10.1038/s41598-023-28816-5.

ABSTRACT

Three years after the pandemic, we still have an imprecise comprehension of the pathogen landscape and we are left with an urgent need for early detection methods and effective therapy for severe COVID-19 patients. The implications of infection go beyond pulmonary damage since the virus hijacks the host’s cellular machinery and consumes its resources. Here, we profiled the plasma proteome and metabolome of a cohort of 57 control and severe COVID-19 cases using high-resolution mass spectrometry. We analyzed their proteome and metabolome profiles with multiple depths and methodologies as conventional single omics analysis and other multi-omics integrative methods to obtain the most comprehensive method that portrays an in-depth molecular landscape of the disease. Our findings revealed that integrating the knowledge-based and statistical-based techniques (knowledge-statistical network) outperformed other methods not only on the pathway detection level but even on the number of features detected within pathways. The versatile usage of this approach could provide us with a better understanding of the molecular mechanisms behind any biological system and provide multi-dimensional therapeutic solutions by simultaneously targeting more than one pathogenic factor.

PMID:36720931 | DOI:10.1038/s41598-023-28816-5

Sci Rep. 2023 Jan 31;13(1):1752. doi: 10.1038/s41598-023-28466-7.

ABSTRACT

Current study aimed to evaluate presence & concentration of salivary molecular pain biomarkers Calcitonin Gene Related Peptide (CGRP) and Brain-Derived Neurotrophic Factor (BDNF) during initial stages of orthodontic treatment and correlation with subjective pain scales, Numerical Rating Scale (NRS), Visual Analogue Scale (VAS), Verbal Rating Scale (VRS) and McGill Pain Questionnaire (MPQ). Consented, healthy-pain free patients (n = 40) undergoing orthodontic therapy, having moderate crowding with pre-molar extraction were recruited. Unstimulated whole saliva was collected and stored at -80 °C in cryotubes. Levels of CGRP & BDNF in salivary samples was assessed by enzyme-linked immunosorbent assay. Samples were collected under stipulated 5 time periods using saliva collection tube by passive drooling method: immediately after bonding but before wire placement (T0-baseline), after 24 h (T1), 48 h (T2), 72 h (T3) & 168 h (T4) after wire placement. Consolidated subjective pain scales were administered concurrently. Regression value (R² > 0.9) confirmed BDNF & CGRP in saliva. Significant change was observed from baseline to 168 h in all subjective parameters (p < 0.05). CGRP did not correlate with subjective pain scales statistically (p > 0.05). BDNF levels correlated with all the subjective pain scales, NRS (T3-p = 0.0092&T4-p = 0.0064), VRS (T3-p = 0.0112&T4-p = 0.0500), VAS (T3-p = 0.0092 &T4-p = 0.0064) &MPQ (T1-p = 0.0255). Mean BDNF & median subjective pain scale graphs were similar. BDNF correlated with all the subjective pain scales warranting further investigation.Trial registration; Clinical Trial Registry-India (CTRI) Reg No: CTRI/2018/12/016571; Registered 10th December, 2018 (10/12/2018) prospectively; http://ctri.nic.in/Clinicaltrials/pmaindet2.php?trialid=29640&EncHid=&userName=Dr%20Sagar%20S%20Bhat .

PMID:36720924 | DOI:10.1038/s41598-023-28466-7

Nat Commun. 2023 Jan 31;14(1):514. doi: 10.1038/s41467-023-36080-4.

ABSTRACT

The scattering of exotic quasiparticles may follow different rules than electrons. In the fractional quantum Hall regime, a quantum point contact (QPC) provides a source of quasiparticles with field effect selectable charges and statistics, which can be scattered on an ‘analyzer’ QPC to investigate these rules. Remarkably, for incident quasiparticles dissimilar to those naturally transmitted across the analyzer, electrical conduction conserves neither the nature nor the number of the quasiparticles. In contrast with standard elastic scattering, theory predicts the emergence of a mechanism akin to the Andreev reflection at a normal-superconductor interface. Here, we observe the predicted Andreev-like reflection of an e/3 quasiparticle into a – 2e/3 hole accompanied by the transmission of an e quasielectron. Combining shot noise and cross-correlation measurements, we independently determine the charge of the different particles and ascertain the coincidence of quasielectron and fractional hole. The present work advances our understanding on the unconventional behavior of fractional quasiparticles, with implications toward the generation of novel quasi-particles/holes and non-local entanglements.

PMID:36720855 | DOI:10.1038/s41467-023-36080-4

Transl Psychiatry. 2023 Feb 1;13(1):31. doi: 10.1038/s41398-023-02320-w.

ABSTRACT

The experience of adversity in childhood has been associated with poor health outcomes in adulthood. In search of the biological mechanisms underlying these effects, research so far focused on alterations of DNA methylation or shifts in transcriptomic profiles. The level of protein, however, has been largely neglected. We utilized mass spectrometry to investigate the proteome of CD14⁺ monocytes in healthy adults reporting childhood adversity and a control group before and after psychosocial stress exposure. Particular proteins involved in (i) immune processes, such as neutrophil-related proteins, (ii) protein metabolism, or (iii) proteins related to mitochondrial biology, such as those involved in energy production processes, were upregulated in participants reporting exposure to adversity in childhood. This functional triad was further corroborated by protein interaction- and co-expression analyses, was independent of stress exposure, i.e. observed at both pre- and post-stress time points, and became evident especially in females. In line with the mitochondrial allostatic load model, our findings provide evidence for the long-term effects of childhood adversity on mitochondrial biology.

PMID:36720844 | DOI:10.1038/s41398-023-02320-w

Methods Mol Biol. 2023;2545:325-348. doi: 10.1007/978-1-0716-2561-3_17.

ABSTRACT

Genomic patterns of diversity and divergence are impacted by certain life history traits, reproductive systems, and demographic history. The latter is characterized by fluctuations in population sizes over time, as well as by temporal patterns of introgression. For a given organism, identifying a demographic history that deviates from the standard neutral model allows a better understanding of its evolution but also helps to reduce the risk of false positives when screening for molecular targets of natural selection. Tetraploid organisms and beyond have demographic histories that are complicated by the mode of polyploidization, the mode of inheritance, and different scenarios of gene flow between sub-genomes and diploid parental species. Here we provide guidelines for experimenters wishing to address these issues through a flexible statistical framework: approximate Bayesian computation (ABC). The emphasis is on the general philosophy of the approach to encourage future users to exploit the enormous flexibility of ABC beyond the limitations imposed by generalist data analysis pipelines.

PMID:36720821 | DOI:10.1007/978-1-0716-2561-3_17

Methods Mol Biol. 2023;2545:209-225. doi: 10.1007/978-1-0716-2561-3_11.

ABSTRACT

Whole-genome duplications (WGDs) are important in shaping the evolution of complex genomes, including rewiring of genome regulation. To address key questions about how WGDs impact the evolution of genome regulation, we need to understand the relative importance of selection versus drift and temporal evolutionary dynamics. One promising class of statistical models that can help address such questions are phylogenetic Ornstein-Uhlenbeck (OU) models.Here we present a computational pipeline for the comparative phylogenetic analyses of genome regulation using an OU model. We have implemented this model in R and provide a step-by-step protocol for the use of this model, including example scripts and simulated test data. We provide the nonspecialist a brief overview of how this model works and how to perform tests for signatures of selection on genome regulation as well as power simulations to aid in experimental design and interpretation of results. We believe that these resources could help polyploidy research move forward in an era of rapidly increasing functional genomics data across the tree of life.

PMID:36720815 | DOI:10.1007/978-1-0716-2561-3_11