Narrow Intervals Research Articles

A novel hybrid forecasting system for crude oil futures prices: A dual perspective of deterministic forecasting and uncertainty analysis

The crude oil market occupies a crucial place in the composition of the global economy, however, the high level of uncertainty in crude oil prices makes it a challenging task to construct reliable forecasting techniques to obtain reliable expected results. Therefore, to effectively cope with the high uncertainty and the technical limitations of existing forecasting studies, this study constructs a hybrid forecasting system from the dual perspectives of deterministic forecasting and uncertainty analysis. With the concept of “denoising-integration” as the core, the system integrates several denoising techniques, econometric methods, and artificial intelligence methods, and establishes a hybrid forecasting model with high accuracy and robustness with the assistance of multi-objective optimization theory. Furthermore, by introducing fuzzy theory onto the basis of deterministic prediction results, an interval forecasting model with a narrower interval width and better comprehensive performance is constructed under the premise of ensuring that the coverage of prediction intervals meets the requirements of the confidence level. In this study, the forecasting system is applied to four global core crude oil futures markets, and the empirical study finds that the performance is improved by more than 50 % compared with the benchmark model, which has an impressive performance advantage.

43 Etiology of community-acquired bacterial meningitis in children seen in the emergency department in Canada

Abstract Background Bacterial meningitis poses a significant threat to paediatric health, resulting in substantial morbidity and mortality in children. Though previous studies have delineated the causative agents behind community-acquired meningitis, recent vaccination initiatives may have reshaped its underlying etiology. Objectives This study aimed to identify the bacteria responsible for community-acquired bacterial meningitis in children in Canada. Design/Methods This was a retrospective cohort study including children aged 1 day-18 years with confirmed bacterial meningitis, evaluated at a tertiary-care, Canadian emergency department between July 1st 2014 and December 31st 2022. The primary outcome was the pathogen identified. Other variables of interest were mortality, complications, age, sex, and diagnostic method. Medical charts were identified via the hospital’s archives and reviewed by a single co-author. A random sample of 20 (23%) medical charts were independently reviewed by a second co-author to assess inter-rater reliability. We set a target of evaluating at least 50 cases to allow narrow confidence intervals (+/- 13%) for proportions. Results All variables showed excellent inter-rater reliability with Kappa scores and intraclass correlation coefficients higher than 0.60. All 79 eligible cases were included. The main causal agents were Group B Streptococcus (GBS) (n=20; 25%), Streptococcus pneumoniae (n=16; 20%), Neisseria meningitidis (n=16; 20%) and Haemophilus influenzae (n=9; 11%). Etiology exhibited age-dependent variations, with 85% of GBS and 100% of E. coli infections identified in children under two months, while all pneumococcal and Group A Streptococcus cases were in children older than six months. All children infected by S. pneumoniae were vaccinated against pneumococcal infection but the serotype was not covered by vaccination in 7/8 infections. All children with N. meningitidis were either too young to be vaccinated (n=10) or infected by a serotype not covered by the vaccine. Six (7%) patients died and 29 (34%) suffered complications, including hearing impairment (n=5), cerebral ischemia (n=4), seizures (n=4), and subdural abscess (n=3). Fifty-nine bacterias were identified by cerebrospinal fluid culture, 13 by PCR and 7 by both methods. Conclusion GBS is the leading etiology of paediatric community-acquired bacterial meningitis in Canada. Causal agents of bacterial meningitis vary greatly by age. Future studies should work on improving the serotype spectrum of vaccines, prophylaxis in mothers who are GBS carriers, and use of PCR as a diagnostic tool for timely clinical interventions.

Evaluating the impact of multiple uncertainty shocks on China's airline stocks volatility: A novel joint quantile perspective

This study proposes a new joint quantile impulse response function (jQIRF) and applies that function to investigate the impact of multiple uncertainty shocks on the volatility of China's airline industry. The jQIRF not only allows one to examine the joint impact of multiple factors on the target variable; the impact of these factors on specific quantiles or states of the target variable can also be examined. The empirical results show that, compared to traditional IRF, the proposed jQIRF successfully reveals the positive impact of multiple uncertainties on the volatility of the airline industry and obtains a narrower confidence interval for IRF. The jQIRF also successfully corrects the overestimation bias caused by simple aggregation in generalized IRF. In addition, empirical results at different quantiles show the existence of a “leverage effect” in the impact of uncertainty on airline volatility. This means that, in more volatile market environments, the positive joint impact of uncertainties is stronger. However, research that has focused on individual airline stocks indicates that the airlines appear to be capable of implementing measures to stabilize stock volatility, thereby mitigating the negative impact of uncertainties on the airline industry. Overall, the proposed jQIRF and empirical conclusions in this paper help to more accurately assess the impact of multiple factors on the airline industry from a joint perspective. This ability is beneficial for both policymakers and investors.

Dynamics of the event structure of the life course in Russia

The article presents the results of a comparative analysis of the life-event structure of generations of Russians based on empirical data from the Russian Social Survey (RSS) wave 9, comparable with ESS. The analysis showed that the life calendar of currently living generations of Russians is quite strongly structured - events occur in certain and rather narrow time intervals and in a certain sequence. A feature of the life calendar in Russia is a high concentration of all the basic events of the first phase of adult life in a narrow time interval, a short period of entry into adulthood. Events of the late stage of life show greater chronological diversity and scenario variability. Significant differences in the life-event matrix of generations in Russia were found, including an increase in the average age of onset of almost all basic life events, an expansion of the boundaries of age-related life stages, an extension of the path of growing up and the transition from maturity to old age, as well as diversification of life scenarios in the professional and family-marital spheres. When comparing the indices of standardisation of the main events between generations, a trend towards destandardisation of life paths was revealed: in older generations, the level of similarity is significantly higher than among younger generations. The main changes occurred in the generation of 1970-1979 and accelerated in the next generation of 1980-1989. Macro-demographic factors, as well as changes in socio-economic and historical conditions, the transition to a post-industrial society might be named among possible reasons. The trend towards destandardisation of life paths in older generations primarily affected people with a higher level of education, while in younger generations it acquired a broader social and class character; gender differences began to appear in the generation of the 1980s. The authors suggest that the increase in destandardisation of life paths may have multidirectional social and individual consequences. On the one hand, new opportunities for development, building a life path taking into account individual characteristics, talents and desires, greater flexibility of life trajectories, a decrease in normativity and social pressure. On the other hand, this may lead to increased uncertainty, social tension, increased inequalities, and a deepening conflict between “fathers and children,” which will increase social risks in the foreseeable future.

New Thermodynamic Models for Anhydrous Alkaline-Silicate Magmatic Systems

Abstract A new thermodynamic model for silicate melt in the Na2O–CaO–K2O–FeO–MgO–Al2O3–SiO2–TiO2–Fe2O3–Cr2O3 model system is presented, building on the tholeiitic through to granitic melt model of Holland et al. (2018) [Journal of Petrology, 59, 881–900] but extending for the first time into anhydrous alkaline systems. The new melt model is accompanied by new thermodynamic models for nepheline, kalsilite, leucite, melilite and ilmenite. Collectively these models enable pseudosection modelling of alkaline-silicate magmatic systems, providing a new tool for investigating this geologically- and economically-important compositional space. The models are calibrated with respect to experimental data on phase relations among minerals and melt, and the fit is benchmarked here via detailed comparison with seven experimental datasets, which encompass a range of pressure (0–22 kbar), temperature (680–1350°C), oxygen fugacity (log fO2 ΔFMQ-3 to +1), total alkali (3–16 wt %) and silica (37–70 wt %) conditions. The calculated pseudosections successfully reproduce experimental crystallisation sequences and phase compositions, indicating that the thermodynamic models are well calibrated across this spectrum of conditions. Redox buffered experimental conditions are simulated using oxygen buffered pseudosections. Contouring of oxygen buffered pseudosections with XFe3+ (mol. Fe3+/Fetotal), or pseudosections of varying XFe3+ with ΔFMQ, reveals (i) often complex and non-intuitive relationships between these two representations of oxidation state, and (ii) substantial variation in ferric iron over narrow temperature intervals in some oxygen buffered sets of experiments. An implication is that simulating oxygen buffering is vital when benchmarking thermodynamic models using experimental results. Furthermore, because natural igneous systems likely feature a near-constant XFe3+, it is important to assess experimental results in this framework when making inferences about natural systems, recognising that oxygen fugacity is a consequence not a control of phase equilibria in nature. Overall, our new models provide a novel tool to explore the role of variables, such as pressure, fractional crystallisation and crustal assimilation in the petrogenesis of alkaline-silicate magmatic systems and their associated mineralisation.

Impact and learner perspectives of a spaced-education platform to assess hand hygiene auditor competency.

To ensure whether spaced education, which increases long-term knowledge retention, could be integrated into auditor competency assessment. Quality improvement project. Academic, freestanding children's hospital. Hand hygiene (HH) auditors. We enrolled trained HH auditors in an online spaced-education platform to assess mastery of knowledge, delivering 46 unique questions at spaced intervals followed by rationale; we retired questions after 3 correct answers. An e-mailed 10-item survey gauged participant satisfaction with the program. The Wilcoxon signed-rank test was used to compare change in median knowledge score from first to final attempt. A total of 12,120 questions were attempted by 126 auditors, and 49 (39%) completed the entire course. Median knowledge score increased significantly by 10.5 percentage points (IQR 4-15) between first and final attempts (P < 0.001). Thirty auditors (27%) responded to the survey. The majority agreed the number and complexity of questions were appropriate (57% and 67%, respectively). Eighty-seven percent reported the platform easy to navigate, and 77% agreed adequate time was provided for completion. Free-text suggestions included delivering fewer questions at a narrower spacing interval over a shorter time frame because of competing work demands. Auditor knowledge of HH indications and technique is critical to ensuring data validity. A spaced-education competency program improved HH auditor knowledge in the short term. Completion rate was low, and some participants expressed a desire for fewer questions over a shorter time frame. This study offers insight into ways to optimize spaced education as a potential tool for HH competency assessment.

Unleashing the power of Manta Rays Foraging Optimizer: A novel approach for hyper-parameter optimization in skin cancer classification

Optimizing hyperparameters is crucial for improving the performance of deep learning (DL) models, especially in complex applications like skin cancer classification from dermoscopic images. This study introduces a novel hyperparameter optimization strategy using the Manta Rays Foraging Optimizer (MRFO). A model tailored for skin cancer classification is created by fine-tuning a Convolutional Neural Network (CNN) with MRFO, coupled with in-depth image preprocessing. Empirical evaluations on diverse datasets (ISIC, PH2, HAM10000) showcase the significant superiority of the MRFO-based model over conventional optimization algorithms. The model achieves impressive accuracy and loss metrics (ISIC: 99.43 %, 0.0250; PH2: 99.96 %, 0.0033; HAM10000: 97.70 %, 0.0626), outperforming alternative optimization algorithms such as the Grey Wolf Optimizer (98.33 % accuracy, 0.17 loss), Whale Optimization Algorithm (96 % accuracy), Grasshopper Optimization Algorithm (97.2 % accuracy), Densnet121-MRFO (99.26 % accuracy), InceptionV3 with GA (99.9 % accuracy), and African Vulture Optimization Algorithm (92.7 % accuracy). The novel approach demonstrates superior accuracy and loss metrics, underscoring its potential for precise and efficient skin cancer detection. Additionally, narrow confidence intervals and balanced precision-recall confirm the model’s generalizability and effectiveness, paving the way for early and accurate skin cancer detection and potentially improving patient outcomes.

Tracking Urban Sprawl: A Systematic Review and Bibliometric Analysis of Spatio-Temporal Patterns Using Remote Sensing and GIS

The urban sprawl phenomenon refers to the expansion of urban areas driven by high population growth and migration. A spatio-temporal approach is indispensable in urban sprawl research. Monitoring and evaluating urban sprawl in a region is crucial for controlling drastic environmental changes. Integrated Remote Sensing (RS) and Geographic Information System (GIS) technologies can serve as essential tools for this purpose. The aim of this systematic literature review paper is to gather information on the latest data, methods, and findings to be considered in future urban sprawl research. The PRISMA method was employed, involving filtering from the Scopus database, resulting in 30 papers selected for an in-depth review to address the objectives of this paper. Landsat data remains the preferred choice for monitoring changes due to its extensive historical archive compared to other data sources. Landscape metrics represent a more advanced method com-pared to conventional change detection in quantifying urban sprawl. Other indices and quantifiers are also used to support the quantification of urban sprawl. Two perspectives exist in selecting the study's temporal intervals: consistent and inconsistent, which are adjusted based on the natural characteristics of "change," namely "abrupt" and "gradual." Suggestions for future research include using data with detailed spatial resolution and narrow study intervals while considering the patterns of urban sprawl formation.

Fabrication and Process Monitoring of 316L Stainless Steel by Laser Powder Bed Fusion with µ-Helix Scanning Strategy and Narrow Scanning Line Intervals

Fabrication and Process Monitoring of 316L Stainless Steel by Laser Powder Bed Fusion with µ-Helix Scanning Strategy and Narrow Scanning Line Intervals

An estimate of fitness reduction from mutation accumulation in a mammal allows assessment of the consequences of relaxed selection.

Each generation, spontaneous mutations introduce heritable changes that tend to reduce fitness in populations of highly adapted living organisms. This erosion of fitness is countered by natural selection, which keeps deleterious mutations at low frequencies and ultimately removes most of them from the population. The classical way of studying the impact of spontaneous mutations is via mutation accumulation (MA) experiments, where lines of small effective population size are bred for many generations in conditions where natural selection is largely removed. Such experiments in microbes, invertebrates, and plants have generally demonstrated that fitness decays as a result of MA. However, the phenotypic consequences of MA in vertebrates are largely unknown, because no replicated MA experiment has previously been carried out. This gap in our knowledge is relevant for human populations, where societal changes have reduced the strength of natural selection, potentially allowing deleterious mutations to accumulate. Here, we study the impact of spontaneous MA on the mean and genetic variation for quantitative and fitness-related traits in the house mouse using the MA experimental design, with a cryopreserved control to account for environmental influences. We show that variation for morphological and life history traits accumulates at a sufficiently high rate to maintain genetic variation and selection response. Weight and tail length measures decrease significantly between 0.04% and 0.3% per generation with narrow confidence intervals. Fitness proxy measures (litter size and surviving offspring) decrease on average by about 0.2% per generation, but with confidence intervals overlapping zero. When extrapolated to humans, our results imply that the rate of fitness loss should not be of concern in the foreseeable future.

Changes in the hematology and blood metabolites of guinea pigs (Cavia porcellus) under intensive rearing system in humid tropical conditions

Guinea pigs in intensive production generates an imperative necessity of physio-pathological bases for diagnosing their state of welfare, health, and production; those which are available for this species are referred to as laboratory or companion animal. The aim of this research is to determine changes in hematological and blood metabolites profiles in relation to age of Cavia porcellus reared in intensive system at 660 masl in humid tropic. For this purpose, forty 15-120 days old guinea pig in healthy conditions from the inti x Peru lines born in the humid tropic were used and the evaluation ages were 15-21, 22-35, 36-60 and 61-120 days. Blood samples were obtained by puncture of the cephalic vein; profiles of erythrocytes (RBC), total leucocytes (WBC), lymphocytes, granulocytes, hematocrit, hemoglobin, MCV, MCH and MCHC indices were determined. In serum, profiles of glucose, total protein (TP), albumin, total cholesterol (TC), triglycerides, total bilirubin (TB), and direct bilirubin (DB), alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were determined. Hemoglobin profile, MCH and MCHC indices increased as age increased (p &lt; 0,05) and the erythrocytes, leucocytes, hematocrit, hemoglobin profiles and MCV, MCH, MCHC indices showed the narrower interval ranges in the 15-35and 61-120-days old guinea pigs. Profiles of TP, DB and AST showed increase with age (p &lt; 0,05) and the DB, AST, ALT, and TC profiles showed the narrower interval range at 36-120 days old. The hematological and blood metabolites profiles of guinea pigs raised in intensive system in humid tropical conditions show significant changes with production age. .

Modeling the effects of ripening at refrigeration and ambient temperatures on physic-chemical and microbiological parameters of artisanal Minas cheese

This study aimed to assess the effects of ripening at refrigeration and ambient temperatures on physic-chemical and microbiological parameters of artisanal Minas cheeses (made from raw milk) and to model the kinetics of lactic acid bacteria – LAB, Escherichia coli – Ec, and coagulase-positive staphylococci – Sa along the ripening. Cheese pH, acidity, and moisture, fat, and protein contents were quantified, as well as LAB, Ec, and Sa concentrations. Richards and simplified Lotka-Volterra models were used to describe the microbial kinetic data. The fits were assessed by statistical indices, and the Monte-Carlo method was applied to identifiability analysis. Cheese moisture was reduced along the ripening, increasing protein concentration and acidity values. Cheese pH showed a reduction followed by an increase. LAB concentration increased to the stationary phase after a few days; Ec and Sa concentrations increased and then decreased due to interactions with LAB and low moisture content. Mathematical models successfully described the kinetics of LAB, Ec, and Sa in cheese throughout ripening. Most parameters were not correlated to each other and were estimated with narrow confidence intervals.

Comparing camera‐based ungulate density estimates: a case study using island populations of bighorn sheep and mule deer

AbstractCamera‐based abundance estimators are an alternative methodology of growing interest in both research and management applications. The statistical formulations of camera‐based abundance estimators using time‐lapse data should theoretically produce precise and unbiased estimates; however, production of unbiased results also requires meeting several important assumptions, and real‐world case studies evaluating such results remain relatively few. We applied instantaneous sampling (IS) and space‐to‐event (STE) estimators to remote camera data collected in April 2021 via time‐lapse sampling of closed populations of bighorn sheep (Ovis canadensis) and mule deer (Odocoileus hemionus) on Wild Horse Island in western Montana, USA, and compared results for bighorn sheep to aerial and ground‐based counts. Point estimates from camera‐based approaches underestimated bighorn sheep populations by 32–44% (IS estimator) and 62–69% (STE estimator) relative to aerial and ground counts. Patchy spatial distribution and group‐living behavior of sheep resulted in a high degree of noise surrounding the IS estimate. In comparison, a low point estimate with relatively narrow confidence intervals suggested potential sensitivity of the STE estimator to violating assumptions of independence among individual animals and sampling occasions. Estimates of mule deer had improved precision over sheep estimates, as indicated by lower estimated coefficients of variation of the mean (CVmean) derived from the analytic SE estimator. Using 15‐m viewsheds and the IS estimators, mule deer density estimates came with a 26% CVmean compared to 43% CVmean for bighorn sheep. This discrepancy may be a result of differences in distribution, behavior, and relative abundance between the 2 species. Accounting for group size and increasing time between sampling may improve accuracy of density estimates and adhere better to model assumptions when estimating precision. In addition, factors influencing viewshed and resulting density extrapolations must be considered carefully. While camera‐based methods theoretically provide an alternative way to estimate density when traditional methods are impractical, our results suggest that more work is needed to ensure density estimates are accurate and precise enough to inform population management.

Hyperspectral imaging combined with machine learning for high‐throughput phenotyping in winter wheat

AbstractUncrewed aerial vehicles (UAVs) are a highly successful and efficient method for phenotyping in plant breeding programs. This study explored using UAVs equipped with hyperspectral sensors to expedite breeders' decision‐making in selecting winter wheat (Triticum aestivum L.) genotypes with improved growth, biomass, leaf area, and canopy cover (CC). The hyperspectral image processing pipeline utilized for image analysis was described. The study was conducted with 2145 genotypes of winter wheat, and UAV‐based hyperspectral measurements were used to predict the phenotype. Reflectance measurements were taken at narrow wavelength intervals, spanning 400–2500 nm. For ground truthing, samples were collected from different parts of the field. These samples were used to evaluate various plant attributes, including CC, leaf area index, plant height, and dry biomass. The hyperspectral data were employed for computation of multiple vegetation indices (VIs), and to improve the prediction of plant traits, we employed partial least squares regression (PLSR) and random forest (RF) regression techniques on both the complete set of hyperspectral variables and the top 10 derived VIs. Our results show that using complete hyperspectral variables results in superior r, R2, and lower root mean square error for both models. We conclude that relying solely on linear regression models with VIs may not always result in accurate predictions of plant traits in winter wheat. However, combining these indices with the RF and PLSR algorithm significantly enhances the prediction accuracy. However, the performance of both PLSR and RF models shows minimal disparity, with only slight differences observed. This highlights the importance of utilizing appropriate machine learning algorithms for improved prediction of plant traits.

Theoretical framework and inference for fitting extreme data through the modified Weibull distribution in a first-failure censored progressive approach

The importance of biomedical physical data is underscored by its crucial role in advancing our comprehension of human health, unraveling the mechanisms underlying diseases, and facilitating the development of innovative medical treatments and interventions. This data serves as a fundamental resource, empowering researchers, healthcare professionals, and scientists to make informed decisions, pioneer research, and ultimately enhance global healthcare quality and individual well-being. It forms a cornerstone in the ongoing pursuit of medical progress and improved healthcare outcomes. This article aims to tackle challenges in estimating unknown parameters and reliability measures related to the modified Weibull distribution when applied to censored progressive biomedical data from the initial failure occurrence. In this context, the article proposes both classical and Bayesian techniques to derive estimates for unknown parameters, survival, and failure rate functions. Bayesian estimates are computed considering both asymmetric and symmetric loss functions. The Markov chain Monte Carlo method is employed to obtain these Bayesian estimates and their corresponding highest posterior density credible intervals. Due to the inherent complexity of these estimators, which cannot be theoretically compared, a simulation study is conducted to evaluate the performance of various estimation procedures. Additionally, a range of optimization criteria is utilized to identify the most effective progressive control strategies. Lastly, the article presents a medical application to illustrate the effectiveness of the proposed estimators. Numerical findings indicate that Bayesian estimates outperform other estimation methods by achieving minimal root mean square errors and narrower interval lengths.

EFFECT OF PRECIPITATION HARDENING ON MICROSTRUCTURE OF 17-7 PH STEEL WITH MODIFIED CHEMICAL COMPOSITION

&amp;lt;p&amp;gt;Steel 17-7PH is austenitic-martensitic steel with high strength, hardness, and resistance to creep, and&amp;lt;br /&amp;gt;corrosion. It is designed for aerospace components, but can also be used for other applications that require&amp;lt;br /&amp;gt;high strength and corrosion resistance, as well as leaf springs for operation at temperatures up to 316 &amp;amp;deg;C. It&amp;lt;br /&amp;gt;can be used in a solution-treated or heat-treated state to obtain a wide range of property values. This&amp;lt;br /&amp;gt;paperwork shows that modification of the contents of alloying elements with a narrower interval of Cr, Ni,&amp;lt;br /&amp;gt;and Al can be obtained from austenitic-martensitic steel 17-7PH which by, a subsequent heat treatment,&amp;lt;br /&amp;gt;can have values of mechanical and chemical properties required for components of an automotive engine.&amp;lt;br /&amp;gt;Chromium is an alphagenic alloying element that stabilizes the ferrite region, nickel is a gammagenic&amp;lt;br /&amp;gt;alloying element that stabilizes austenite and gives these steels good strength and toughness, even at low&amp;lt;br /&amp;gt;temperatures and aluminum increases corrosion resistance in low-carbon corrosion-resistant steels&amp;lt;br /&amp;gt;Research has determined the most suitable interval of Cr, Ni, and Al, which in combination with the&amp;lt;br /&amp;gt;cryogenic heat treatment RH950 at -50 &amp;amp;deg;C gives the mechanical and chemical properties that meet the&amp;lt;br /&amp;gt;requirements for steel with standard chemical composition.&amp;lt;/p&amp;gt;

Identification of novel genetic loci and candidate genes for progressive ethanol consumption in diversity outbred mice.

Mouse behavioral genetic mapping studies can identify genomic intervals modulating complex traits under well-controlled environmental conditions and have been used to study ethanol behaviors to aid in understanding genetic risk and the neurobiology of alcohol use disorder (AUD). However, historically such studies have produced large confidence intervals, thus complicating identification of potential causal candidate genes. Diversity Outbred (DO) mice offer the ability to perform high-resolution quantitative trait loci (QTL) mapping on a very genetically diverse background, thus facilitating identification of candidate genes. Here, we studied a population of 636 male DO mice with four weeks of intermittent ethanol access via a three-bottle choice procedure, producing a progressive ethanol consumption phenotype. QTL analysis identified 3 significant (Chrs 3, 4, and 12) and 13 suggestive loci for ethanol-drinking behaviors with narrow confidence intervals (1-4 Mbp for significant QTLs). Results suggested that genetic influences on initial versus progressive ethanol consumption were localized to different genomic intervals. A defined set of positional candidate genes were prioritized using haplotype analysis, identified coding polymorphisms, prefrontal cortex transcriptomics data, human GWAS data and prior rodent gene set data for ethanol or other misused substances. These candidates included Car8, the lone gene with a significant cis-eQTL within a Chr 4 QTL for week four ethanol consumption. These results represent the highest-resolution genetic mapping of ethanol consumption behaviors in mice to date, providing identification of novel loci and candidate genes for study in relation to the neurobiology of AUD.

The Impact of Chlorhexidine as an Endodontic Irrigant/Medicament on Post-Obturation Pain in Patients Following Endodontic Therapy: A Preliminary Bayesian Meta-Analysis.

To assess the overall impact of chlorhexidine, when used as an endodontic irrigant/medicament, on post-obturation pain following endodontic treatment by the application of Bayes methods of meta-analysis. This study aimed to combine recently documented evidence using rigorous and specific search criteria. The available data were analyzed using Bayesian methods, which have the advantage of accounting for uncertainties in estimates. This is in contrast to commonly used frequentist meta-analysis methods, which are not able to model uncertainties as effectively, particularly when there is heterogeneity present. A preliminary search was conducted in the Pubmed, Cochrane, and Scielo databases using appropriate keywords. The search aimed to identify human randomized clinical studies that evaluated post-operative endodontic pain and involved the use of chlorhexidine as an endodontic irrigant or medicament. The preliminary Bayesian meta-analysis produced a more accurate estimate compared to a frequentist random effect meta-analysis model, with a narrower credible interval. The frequentist model demonstrated a detrimental impact of chlorhexidine on post-obturation pain, while the Bayesian model indicated a somewhat favorable effect of chlorhexidine on post-obturation pain. Based on the scope of this investigation, it can be inferred that a Bayesian random effects meta-analysis model demonstrated a somewhat favorable impact of chlorhexidine on post-endodontic pain, which carries significant therapeutic significance.

Chronology and main stages of vegetation development in the central region of the East European Plain during the Mikulino interglacial

The chronology of the Mikulino Interglacial and its individual phases have been the subject of discussion. The goal of this study was to evaluate the time limits of the main stages of the Mikulino Interglacial on the Russian Plain according to ²³⁰Th/U dating and paleobotanical studies of lake and peat sediments from the known sections located within the Tver region on the Bolshaya Dubenka River, Malaya Kosha River, Granichnaya River, and Sizhina River (“Kileshino-2” section). An improved geochronological approach has been applied to identify layers suitable for the ²³⁰Th/U isochronous approximation. In combination with pollen and carpological studies of the deposits, this made it possible to date units corresponding to relatively narrow time intervals in the development of plant formations at different stages of the Last Interglacial. New paleobotanical studies of buried lake and peat sediments from the sections located on the Bolshaya Dubenka River, Malaya Kosha River, and Granichnaya River allowed us to restore the vegetation development during the Mikulino Interglacial in the interval of pollen zones M1–M7, i.e., more pollen zones have been analyzed and in greater detail than in 1960–1970. A chronological scheme of the main stages of vegetation development in the Mikulino Interglacial is proposed based on the results of ²³⁰Th/U dating and paleobotanical studies of organic-rich deposits from the Tver region sections in combination with previously published data obtained for the “Nizhnyaya Boyarshchina” section from the Smolensk region. The Mikulino Interglacial had begun about 130–126 kyr ago. Its first phase, corresponding to the M2 zone, ended ca. 118 kyr ago. The pre-optimal stages of vegetation development (M3 and M4 zones) fit into the time range of ca. 118–112 kyr ago. The climatic optimum of the interglacial (M5 and M6 zones) began ca. 112 kyr ago and ended ca. 100 kyr ago. The duration of the Mikulino Interglacial was probably at least 25 thousand years.

Multimodel Approaches Are Not the Best Way to Understand Multifactorial Systems.

Information-theoretic (IT) and multi-model averaging (MMA) statistical approaches are widely used but suboptimal tools for pursuing a multifactorial approach (also known as the method of multiple working hypotheses) in ecology. (1) Conceptually, IT encourages ecologists to perform tests on sets of artificially simplified models. (2) MMA improves on IT model selection by implementing a simple form of shrinkage estimation (a way to make accurate predictions from a model with many parameters relative to the amount of data, by "shrinking" parameter estimates toward zero). However, other shrinkage estimators such as penalized regression or Bayesian hierarchical models with regularizing priors are more computationally efficient and better supported theoretically. (3) In general, the procedures for extracting confidence intervals from MMA are overconfident, providing overly narrow intervals. If researchers want to use limited data sets to accurately estimate the strength of multiple competing ecological processes along with reliable confidence intervals, the current best approach is to use full (maximal) statistical models (possibly with Bayesian priors) after making principled, a priori decisions about model complexity.