A statistical and numerical modeling approach for spatiotemporal reconstruction of glaciations in the Central Asian mountains.
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ABSTRACT:
SUBMITTER: Saha S
PROVIDER: S-EPMC7110412 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe Central Asian flora plays a significant role in Eurasia and the Northern Hemisphere. Calophaca, a member of this flora, includes eight currently recognized species, and is centered in Central Asia, with some taxa extending into adjacent areas. A phylogenetic analysis of the genus utilizing nuclear ribosomal ITS and plastid trnS-trnG and rbcL sequences was carried out in order to confirm its taxonomic status and reconstruct its evolutionary history.Methodology/principal findingWe employed BEAST Bayesian inference for dating, and S-DIVA and BBM for ancestral area reconstruction, to study its spatiotemporal evolution. Our results show that Calophacais monophyletic and nested within Caragana. The divergence time of Calophaca is estimated at ca. 8.0 Ma, most likely driven by global cooling and aridification, influenced by rapid uplift of the Qinghai Tibet Plateau margins.Conclusions/significanceAccording to ancestral area reconstructions, the genus most likely originated in the Pamir Mountains, a global biodiversity hotspot and hypothesized Tertiary refugium of many Central Asian plant lineages. Dispersals from this location are inferred to the western Tianshan Mountains, then northward to the Tarbagatai Range, eastward to East Asia, and westward to the Caucasus, Russia, and Europe. The spatiotemporal evolution of Calophaca provides a case contributing to an understanding of the flora and biodiversity of the Central Asian mountains and adjacent regions.
| S-EPMC4388477 | biostudies-literature
Project description:Calcium is a ubiquitous second messenger that mediates vital physiological responses such as fertilization, secretion, gene expression, or apoptosis. Given this variety of processes mediated by Ca(2+), these signals are highly organized both in time and space to ensure reliability and specificity. This review deals with the spatiotemporal organization of the Ca(2+) signaling pathway in electrically nonexcitable cells in which InsP(3) receptors are by far the most important Ca(2+) channels. We focus on the aspects of this highly regulated dynamical system for which an interplay between experiments and modeling is particularly fruitful. In particular, the importance of the relative densities of the different InsP(3) receptor subtypes will be discussed on the basis of a modeling approach linking the steady-state behaviors of these channels in electrophysiological experiments with their behavior in a cellular environment. Also, the interplay between InsP(3) metabolism and Ca(2+) oscillations will be considered. Finally, we discuss the relationships between stochastic openings of the Ca(2+) releasing channels at the microscopic level and the coordinated, regular behavior observed at the whole cell level on the basis of a combined experimental and modeling approach.
| S-EPMC2931296 | biostudies-other
Project description:This paper is concerned with the modeling of infectious disease spread in a composite space-time domain under conditions of uncertainty. We focus on stochastic modeling that accounts for basic mechanisms of disease distribution and multi-sourced in situ uncertainties. Starting from the general formulation of population migration dynamics and the specification of transmission and recovery rates, the model studies the functional formulation of the evolution of the fractions of susceptible-infected-recovered individuals. The suggested approach is capable of: a) modeling population dynamics within and across localities, b) integrating the disease representation (i.e. susceptible-infected-recovered individuals) with observation time series at different geographical locations and other sources of information (e.g. hard and soft data, empirical relationships, secondary information), and c) generating predictions of disease spread and associated parameters in real time, while considering model and observation uncertainties. Key aspects of the proposed approach are illustrated by means of simulations (i.e. synthetic studies), and a real-world application using hand-foot-mouth disease (HFMD) data from China.
| S-EPMC3785461 | biostudies-literature
Project description:RNA plays key regulatory roles in diverse cellular processes, where its functionality often derives from folding into and converting between structures. Many RNAs further rely on co-existence of alternative structures, which govern their response to cellular signals. However, characterizing heterogeneous landscapes is difficult, both experimentally and computationally. Recently, structure profiling experiments have emerged as powerful and affordable structure characterization methods, which improve computational structure prediction. To date, efforts have centered on predicting one optimal structure, with much less progress made on multiple-structure prediction. Here, we report a probabilistic modeling approach that predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data. We demonstrate robust landscape reconstruction and quantitative insights into structural dynamics by analyzing numerous data sets. This work establishes a framework for data-directed characterization of structure landscapes to aid experimentalists in performing structure-function studies.
| S-EPMC5807309 | biostudies-literature
Project description:The Transantarctic Mountains (TAM) are the world's longest rift shoulder but the source of their high elevation is enigmatic. To discriminate the importance of mechanical vs. thermal sources of support, a 550 km-long transect of magnetotelluric geophysical soundings spanning the central TAM was acquired. These data reveal a lithosphere of high electrical resistivity to at least 150?km depth, implying a cold stable state well into the upper mantle. Here we find that the central TAM most likely are elevated by a non-thermal, flexural cantilever mechanism which is perhaps the most clearly expressed example anywhere. West Antarctica in this region exhibits a low resistivity, moderately hydrated asthenosphere, and concentrated extension (rift necking) near the central TAM range front but with negligible thermal encroachment into the TAM. Broader scale heat flow of east-central West Antarctica appears moderate, on the order of 60-70?mW?m-2, lower than that of the U.S. Great Basin.
| S-EPMC5693935 | biostudies-literature
Project description:BackgroundNon-invasive estimation of central blood pressure (BP) may have better prognostic value than brachial BP. The accuracy of central BP is limited in certain populations, such as in females and the elderly. This study aims to examine whether statistical modeling of central BP for clinical and hemodynamic parameters results in enhanced accuracy.MethodsThis study is a cross-sectional analysis of 500 patients who underwent cardiac catheterization. Non-invasive brachial cuff and central BP were measured simultaneously to invasive aortic systolic BP (AoSBP). Central BP was calibrated for brachial systolic (SBP) and diastolic BP (Type I calibration; C1SBP) or brachial mean and diastolic BP (Type II calibration; C2SBP). Differences between central SBP and the corresponding AoSBP were assessed with linear regression models using clinical and hemodynamic parameters. These parameters were then added to C1SBP and C2SBP in adjusted models to predict AoSBP. Accuracy and precision were computed in the overall population and per age or sex strata.ResultsC1SBP underestimated AoSBP by 11.2 mmHg (±13.5) and C2SBP overestimated it by 6.2 mmHg (±14.8). Estimated SBP amplification and heart rate were the greatest predictors of C1- and C2-AoSBP accuracies, respectively. Statistical modeling improved both accuracy (0.0 mmHg) and precision (±11.4) but more importantly, eliminated the differences of accuracy seen in different sex and age groups.ConclusionStatistical modeling greatly enhances the accuracy of central BP measurements and abolishes sex- and age-based differences. Such factors could easily be implemented in central BP devices to improve their accuracy.
| S-EPMC9728538 | biostudies-literature
Project description:BackgroundOpioid-related overdose mortality has remained at crisis levels across the United States, increasing 5-fold and worsened during the COVID-19 pandemic. The ability to provide forecasts of opioid-related mortality at granular geographical and temporal scales may help guide preemptive public health responses. Current forecasting models focus on prediction on a large geographical scale, such as states or counties, lacking the spatial granularity that local public health officials desire to guide policy decisions and resource allocation.ObjectiveThe overarching objective of our study was to develop Bayesian spatiotemporal dynamic models to predict opioid-related mortality counts and rates at temporally and geographically granular scales (ie, ZIP Code Tabulation Areas [ZCTAs]) for Massachusetts.MethodsWe obtained decedent data from the Massachusetts Registry of Vital Records and Statistics for 2005 through 2019. We developed Bayesian spatiotemporal dynamic models to predict opioid-related mortality across Massachusetts' 537 ZCTAs. We evaluated the prediction performance of our models using the one-year ahead approach. We investigated the potential improvement of prediction accuracy by incorporating ZCTA-level demographic and socioeconomic determinants. We identified ZCTAs with the highest predicted opioid-related mortality in terms of rates and counts and stratified them by rural and urban areas.ResultsBayesian dynamic models with the full spatial and temporal dependency performed best. Inclusion of the ZCTA-level demographic and socioeconomic variables as predictors improved the prediction accuracy, but only in the model that did not account for the neighborhood-level spatial dependency of the ZCTAs. Predictions were better for urban areas than for rural areas, which were more sparsely populated. Using the best performing model and the Massachusetts opioid-related mortality data from 2005 through 2019, our models suggested a stabilizing pattern in opioid-related overdose mortality in 2020 and 2021 if there were no disruptive changes to the trends observed for 2005-2019.ConclusionsOur Bayesian spatiotemporal models focused on opioid-related overdose mortality data facilitated prediction approaches that can inform preemptive public health decision-making and resource allocation. While sparse data from rural and less populated locales typically pose special challenges in small area predictions, our dynamic Bayesian models, which maximized information borrowing across geographic areas and time points, were used to provide more accurate predictions for small areas. Such approaches can be replicated in other jurisdictions and at varying temporal and geographical levels. We encourage the formation of a modeling consortium for fatal opioid-related overdose predictions, where different modeling techniques could be ensembled to inform public health policy.
| S-EPMC9960038 | biostudies-literature
Project description:PurposeTo develop a new approach for interobserver variability analysis.Methods and materialsEight radiation oncologists specializing in breast cancer radiation therapy delineated a patient's left breast "from scratch" and from a template that was generated using deformable image registration. Three of the radiation oncologists had previously received training in Radiation Therapy Oncology Group consensus contouring for breast cancer atlas. The simultaneous truth and performance level estimation algorithm was applied to the 8 contours delineated "from scratch" to produce a group consensus contour. Individual Jaccard scores were fitted to a beta distribution model. We also applied this analysis to 2 or more patients, which were contoured by 9 breast radiation oncologists from 8 institutions.ResultsThe beta distribution model had a mean of 86.2%, standard deviation (SD) of ±5.9%, a skewness of -0.7, and excess kurtosis of 0.55, exemplifying broad interobserver variability. The 3 RTOG-trained physicians had higher agreement scores than average, indicating that their contours were close to the group consensus contour. One physician had high sensitivity but lower specificity than the others, which implies that this physician tended to contour a structure larger than those of the others. Two other physicians had low sensitivity but specificity similar to the others, which implies that they tended to contour a structure smaller than the others. With this information, they could adjust their contouring practice to be more consistent with others if desired. When contouring from the template, the beta distribution model had a mean of 92.3%, SD ± 3.4%, skewness of -0.79, and excess kurtosis of 0.83, which indicated a much better consistency among individual contours. Similar results were obtained for the analysis of 2 additional patients.ConclusionsThe proposed statistical approach was able to measure interobserver variability quantitatively and to identify individuals who tended to contour differently from the others. The information could be useful as feedback to improve contouring consistency.
| S-EPMC3997068 | biostudies-literature
Project description:Coarticulation is a source of acoustic variability for vowels, but how large is this effect relative to other sources of variance? We investigate acoustic effects of anticipatory V-to-V coarticulation relative to variation due to the following C and individual speaker. We examine F1 and F2 from V1 in 48 V1-C#V2 contexts produced by 10 speakers of American English. ANOVA reveals significant effects of both V2 and C on F1 and F2 measures of V1. The influence of V2 and C on acoustic variability relative to that of speaker and target vowel identity is evaluated using hierarchical linear regression. Speaker and target vowel account for roughly 80% of the total variance in F1 and F2, but when this variance is partialed out C and V2 account for another 18% (F1) and 63% (F2) of the remaining target vowel variability. Multinomial logistic regression (MLR) models are constructed to test the power of target vowel F1 and F2 for predicting C and V2 of the upcoming context. Prediction accuracy is 58% for C-Place, 76% for C-Voicing and 54% for V2, but only when variance due to other sources is factored out. MLR is discussed as a model of the parsing mechanism in speech perception.
| S-EPMC3003261 | biostudies-literature
Project description:A river water quality spatial profile has a diverse pattern of variation over different climatic regions. To comprehend this phenomenon, our study evaluated the spatial scale variation of the Water Quality Index (WQI). The study was carried out over four main climatic classes in Asia based on the Koppen-Geiger climate classification system: tropical, temperate, cold, and arid. The one-dimensional surface water quality model, QUAL2Kw was selected and compared for water quality simulations. Calibration and validation were separately performed for the model predictions over different climate classes. The accuracy of the water quality model was assessed using different statistical analyses. The spatial profile of WQI was calculated using model predictions based on dissolved oxygen (DO), biological oxygen demand (BOD), nitrate (NO3), and pH. The results showed that there is a smaller longitudinal variation of WQI in the cold climatic regions than other regions, which does not change the status of WQI. Streams from arid, temperate, and tropical climatic regions show a decreasing trend of DO with respect to the longitudinal profiles of main river flows. Since this study found that each climate zone has the different impact on DO dynamics such as reaeration rate, reoxygenation, and oxygen solubility. The outcomes obtained in this study are expected to provide the impetus for developing a strategy for the viable improvement of the water environment.
| S-EPMC6209875 | biostudies-other