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Function and structure are strongly coupled in obligated oligomers such as Triosephosphate isomerase (TIM). In animals and fungi, TIM monomers are inactive and unstable. Previously, we used ancestral sequence reconstruction to study TIM evolution and found that before these lineages diverged, the last opisthokonta common ancestor of TIM (LOCATIM) was an obligated oligomer that resembles those of extant TIMs. Notably, calorimetric evidence indicated that ancestral TIM monomers are more structured than extant ones. To further increase confidence about the function, structure, and stability of the LOCATIM, in this work, we applied two different inference methodologies and the worst plausible case scenario for both of them, to infer four sequences of this ancestor and test the robustness of their physicochemical properties. The extensive biophysical characterization of the four reconstructed sequences of LOCATIM showed very similar hydrodynamic and spectroscopic properties, as well as ligand-binding energetics and catalytic parameters. Their 3D structures were also conserved. Although differences were observed in melting temperature, all LOCATIMs showed reversible urea-induced unfolding transitions, and for those that reached equilibrium, high conformational stability was estimated (ΔGTot = 40.6-46.2 kcal/mol). The stability of the inactive monomeric intermediates was also high (ΔGunf = 12.6-18.4 kcal/mol), resembling some protozoan TIMs rather than the unstable monomer observed in extant opisthokonts. A comparative analysis of the 3D structure of ancestral and extant TIMs shows a correlation between the higher stability of the ancestral monomers with the presence of several hydrogen bonds located in the "bottom" part of the barrel.

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We introduce the bivariate unit-log-symmetric model based on the bivariate log-symmetric distribution (BLS) defined in Vila et al. [25] as a flexible family of bivariate distributions over the unit square. We then study its mathematical properties such as stochastic representations, quantiles, conditional distributions, independence of the marginal distributions and marginal moments. Maximum likelihood estimation method is discussed and examined through Monte Carlo simulation. Finally, the proposed model is used to analyze some soccer data sets.

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Cefepime and piperacillin/tazobactam are antimicrobials recommended by IDSA/ATS guidelines for the empirical management of patients admitted to the intensive care unit (ICU) with community-acquired pneumonia (CAP). Concerns have been raised about which should be used in clinical practice. This study aims to compare the effect of cefepime and piperacillin/tazobactam in critically ill CAP patients through a targeted maximum likelihood estimation (TMLE). A total of 2026 ICU-admitted patients with CAP were included. Among them, (47%) presented respiratory failure, and (27%) developed septic shock. A total of (68%) received cefepime and (32%) piperacillin/tazobactam-based treatment. After running the TMLE, we found that cefepime and piperacillin/tazobactam-based treatments have comparable 28-day, hospital, and ICU mortality. Additionally, age, PTT, serum potassium and temperature were associated with preferring cefepime over piperacillin/tazobactam (OR 1.14 95% CI [1.01-1.27], p = 0.03), (OR 1.14 95% CI [1.03-1.26], p = 0.009), (OR 1.1 95% CI [1.01-1.22], p = 0.039) and (OR 1.13 95% CI [1.03-1.24], p = 0.014)]. Our study found a similar mortality rate among ICU-admitted CAP patients treated with cefepime and piperacillin/tazobactam. Clinicians may consider factors such as availability and safety profiles when making treatment decisions.

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This paper aims to introduce a novel family of probability distributions by the well-known method of the T-X family of distributions. The proposed family is called a "Novel Generalized Exponent Power X Family" of distributions. A three-parameters special sub-model of the proposed method is derived and named a "Novel Generalized Exponent Power Weibull" distribution (NGEP-Wei for short). For the proposed family, some statistical properties are derived including the hazard rate function, moments, moment generating function, order statistics, residual life, and reverse residual life. The well-known method of estimation, the maximum likelihood estimation method is used for estimating the model parameters. Besides, a comprehensive Monte Carlo simulation study is conducted to assess the efficacy of this estimation method. Finally, the model selection criterion such as Akaike information criterion (AINC), the correct information criterion (CINC), the Bayesian information criterion (BINC), the Hannan-Quinn information criterion (HQINC), the Cramer-von-Misses (CRMI), and the ANDA (Anderson-Darling) are used for comparison purpose. The comparison of the NGEP-Wei with other rival distributions is made by Two COVID-19 data sets. In terms of performance, we show that the proposed method outperforms the other competing methods included in this study.

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The beta model is the most important distribution for fitting data with the unit interval. However, the beta distribution is not suitable to model bimodal unit interval data. In this paper, we propose a bimodal beta distribution constructed by using an approach based on the alpha-skew-normal model. We discuss several properties of this distribution, such as bimodality, real moments, entropies and identifiability. Furthermore, we propose a new regression model based on the proposed model and discuss residuals. Estimation is performed by maximum likelihood. A Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples with a discussion of the results. An application is provided to show the modelling competence of the proposed distribution when the data sets show bimodality.

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BACKGROUND: Cross-sectional studies are useful for the estimation of prevalence of a particular event with concerns in specific populations, as in the case of diseases or other public health interests. Most of these studies have been carried out with binary binomial logistic regression model which estimates OR values that could be overestimated due to the adjustment of the model. Thus, the selection of the best multivariate model for cross-sectional studies is a priority to control the overestimation of the associations. METHODS: We compared the precision of the estimates of the prevalence ratio (PR) of the negative Log-binomial model (NLB) with Mantel-Haenszel (MH) and the regression models Cox, Log-Poisson, Log-binomial, and the OR of the binary logistic regression in population-based cross-sectional studies. The prevalence from a previous cross-sectional study carried out in Colombia about the association of mental health disorders with the consumption of psychoactive substances (e.g., cocaine, marijuana, cigarette, alcohol and risk of consumption of psychoactive substances) were used. The precision of the point estimates of the PR was evaluated for the NLB model with robust variance estimates, controlled with confounding variables, and confidence interval of 95%. RESULTS: The NLB model adjusted with robust variance showed accuracy in the measurements of crude PRs, standard errors of estimate and its corresponding confidence intervals (95%CI) as well as a high precision of the PR estimate and standard errors of estimate after the adjustment of the model by grouped age compared with the MH PR estimate. Obtained PRs and 95%CI entre NLB y MH were: cocaine consumption (2.931,IC95%: 0.723-11.889 vs. 2.913, IC95%: 0.786-12.845), marijuana consumption (3.444, IC95%: 1.856-6.391 vs. 3.407, IC95%: 1.848, 6.281), cigarette smoking (2.175,IC95%: 1.493, 3.167 vs. 2.209, IC95%: 1.518-3.214), alcohol consumption (1.243,IC95%: 1.158-1.334 vs. 1.241, IC95%: 1.157-1.332), and risk of consumption of psychoactive substances (1.086, IC95%: 1.047-1.127 vs. 1.086, IC95%: 1.047, 1.126). The NLB model adjusted with robust variance showed mayor precision when increasing the prevalence, then the other models with robust variance with respect to MH. CONCLUSIONS: The NLB model with robust variance was shown as a powerful strategy for the estimation of PRs for cross-sectional population-based studies, as high precision levels were identified for point estimators, standard errors of estimate and its corresponding confidence intervals, after the adjustment of confounding variables. In addition, it does not represent convergence issues for high prevalence cases (as it occur with the Log-binomial model) and could be considered in cases of overdispersion and with greater precision and goodness of fit than the other models with robust variance, as it was shown with the data set of the cross-sectional study used in here.

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This article proposes a system for Content-Based Image Retrieval (CBIR) using stochastic distance for Synthetic-Aperture Radar (SAR) images. The methodology consists of three essential steps for image retrieval. First, it estimates the roughness (α^) and scale (γ^) parameters of the GI0 distribution that models SAR data in intensity. The parameters of the model were estimated using the Maximum Likelihood Estimation and the fast approach of the Log-Cumulants method. Second, using the triangular distance, CBIR-SAR evaluates the similarity between a query image and images in the database. The stochastic distance can identify the most similar regions according to the image features, which are the estimated parameters of the data model. Third, the performance of our proposal was evaluated by applying the Mean Average Precision (MAP) measure and considering clippings from three radar sensors, i.e., UAVSAR, OrbiSaR-2, and ALOS PALSAR. The CBIR-SAR results for synthetic images achieved the highest MAP value, retrieving extremely heterogeneous regions. Regarding the real SAR images, CBIR-SAR achieved MAP values above 0.833 for all polarization channels for image samples of forest (UAVSAR) and urban areas (ORBISAR). Our results confirmed that the proposed method is sensitive to the degree of texture, and hence, it relies on good estimates. They are inputs to the stochastic distance for effective image retrieval.

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Likelihood-based tests of phylogenetic trees are a foundation of modern systematics. Over the past decade, an enormous wealth and diversity of model-based approaches have been developed for phylogenetic inference of both gene trees and species trees. However, while many techniques exist for conducting formal likelihood-based tests of gene trees, such frameworks are comparatively underdeveloped and underutilized for testing species tree hypotheses. To date, widely used tests of tree topology are designed to assess the fit of classical models of molecular sequence data and individual gene trees and thus are not readily applicable to the problem of species tree inference. To address this issue, we derive several analogous likelihood-based approaches for testing topologies using modern species tree models and heuristic algorithms that use gene tree topologies as input for maximum likelihood estimation under the multispecies coalescent. For the purpose of comparing support for species trees, these tests leverage the statistical procedures of their original gene tree-based counterparts that have an extended history for testing phylogenetic hypotheses at a single locus. We discuss and demonstrate a number of applications, limitations, and important considerations of these tests using simulated and empirical phylogenomic data sets that include both bifurcating topologies and reticulate network models of species relationships. Finally, we introduce the open-source R package SpeciesTopoTestR (SpeciesTopology Tests in R) that includes a suite of functions for conducting formal likelihood-based tests of species topologies given a set of input gene tree topologies.

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The objective of this study was to estimate the genetic parameters for feed efficiency-related traits and their genetic correlations with growth, male fertility, and carcass traits using multi-trait analysis in Guzerat cattle. Further, it aimed to predict the direct and correlated responses for feed efficiency traits when selection was applied for growth, male fertility, and carcass traits. The evaluated traits were adjusted weight at 120 (W120), 210 (W210), 365 (W365), and 450 days of age (W450), adjusted scrotal circumference at 365 days of age (SC365) and at 450 days of age (SC450), scrotal circumference, ribeye area (REA), backfat thickness (BFT), rump fat thickness (RFT), residual feed intake (RFI), and dry matter intake (DMI). The genetic parameters were obtained by the restricted maximum likelihood method (REML), using an animal model in multi-trait analyses. The heritability estimates for W120, W210, W365, W450, SC365, and SC450 varied from low to high (0.17 to 0.39). The carcass traits, REA, BFT, and RFT, displayed low to moderate heritability estimates, 0.27, 0.10, and 0.31, respectively. The heritability estimates for RFI (0.15) and DMI (0.23) were low and moderate, respectively. The RFI showed low genetic correlations with growth traits, ranging from - 0.07 to 0.22, from 0.03 to 0.05 for scrotal circumference, and from - 0.35 to 0.16 for carcass, except for DMI, which ranged from 0.42 to 0.46. The RFI and DMI presented enough additive genetic variability to be used as selection criteria in Guzerat breed genetic improvement program. Additionally, the response to selection for RFI would be higher when selection is performed directly for this trait. The selection for residual feed intake would not promote unfavorable correlated responses for scrotal circumference, carcass (yield and finish), and growth traits. Therefore, the selection for more efficient animals would not compromise the productive, reproductive, and carcass performance, contributing to reduce the production costs, increasing the profitability and sustainability of beef cattle production in tropical areas.

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BACKGROUND: Accurate determination of the effective reproduction number (Rt) is a very important strategy in the epidemiology of contagious diseases, including coronavirus disease 2019 (COVID-19). This study compares different methods of estimating the Rt of susceptible population to identify the most accurate method for estimating Rt. STUDY DESIGN: A secondary study. METHODS: The value of Rt was estimated using attack rate (AR), exponential growth (EG), maximum likelihood (ML), time-dependent (TD), and sequential Bayesian (SB) methods, for Iran, the United States, the United Kingdom, India, and Brazil from June to October 2021. In order to accurately compare these methods, a simulation study was designed using forty scenarios. RESULTS: The lowest mean square error (MSE) was observed for TD and ML methods, with 15 and 12 cases, respectively. Therefore, considering the estimated values of Rt based on the TD method, it was found that Rt values in the United Kingdom (1.33; 95% CI: 1.14-1.52) and the United States (1.25; 95% CI: 1.12-1.38) substantially have been more than those in other countries, such as Iran (1.07; 95% CI: 0.95-1.19), India (0.99; 95% CI: 0.89-1.08), and Brazil (0.98; 95% CI: 0.84-1.14) from June to October 2021. CONCLUSION: The important result of this study is that TD and ML methods lead to a more accurate estimation of Rt of population than other methods. Therefore, in order to monitor and determine the epidemic situation and have a more accurate prediction of the incidence rate, as well as control COVID-19 and similar diseases, the use of these two methods is suggested to more accurately estimate Rt.

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This work presents an extension of the slash Lindley-Weibull distribution, of which it can be considered a modification. The new family is obtained by using the quotient of two independent random variables: a two-parameter Lindley-Weibull distribution divided by a power of the exponential distribution with parameter equal to 2. We present the pdf and cdf of the new distribution, analyzing their risk functions. Some statistical properties are studied and the moments and coefficients of asymmetry and kurtosis are shown. The parameter estimation problem is carried out by the maximum likelihood method. The method is assessed by a Monte Carlo simulation study. We use nutrition data, which are characterized by high kurtosis, to illustrate the usefulness of the proposed model.

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We propose a new continuous distribution in the interval ( 0 , 1 ) based on the generalized odd log-logistic-G family, whose density function can be symmetrical, asymmetric, unimodal and bimodal. The new model is implemented using the gamlss packages in R. We propose an extended regression based on this distribution which includes as sub-models some important regressions. We employ a frequentist and Bayesian analysis to estimate the parameters and adopt the non-parametric and parametric bootstrap methods to obtain better efficiency of the estimators. Some simulations are conducted to verify the empirical distribution of the maximum likelihood estimators. We compare the empirical distribution of the quantile residuals with the standard normal distribution. The extended regression can give more realistic fits than other regressions in the analysis of proportional data.

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Natural fibers used as reinforcements or fillers for materials development greatly affect properties and performance of end-use applications. As a consequence of conditioning processes such as grinding and sieving, average fiber length varies significantly. It is thus necessary to estimate the length as statistical data distribution rather than a single mean value. This approach implies length measurement of a significant number of fibers; however, a very high number of data points requires not only long-time frames but also significative amount of work. To address these issues, this article details a facile methodology to measure the length of a large number of natural fibers of oil palm empty fruit bunch (OPEFB) together with a statistical analysis to verify the correspondence between theoretical distributions and experimental data. Moreover, further information related to spectrophotometric, physico-chemical, mechanical, thermal, and morphological characteristics of OPEFB fibers coming from oil palm cultivation in Ecuador are presented. The data will contribute to comprehensively and rigorously describe the overall effects of natural fiber lengths on material properties.

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Dengue fever is a tropical disease transmitted mainly by the female Aedes aegypti mosquito that affects millions of people every year. As there is still no safe and effective vaccine, currently the best way to prevent the disease is to control the proliferation of the transmitting mosquito. Since the proliferation and life cycle of the mosquito depend on environmental variables such as temperature and water availability, among others, statistical models are needed to understand the existing relationships between environmental variables and the recorded number of dengue cases and predict the number of cases for some future time interval. This prediction is of paramount importance for the establishment of control policies. In general, dengue-fever datasets contain the number of cases recorded periodically (in days, weeks, months or years). Since many dengue-fever datasets tend to be of the overdispersed, long-tail type, some common models like the Poisson regression model or negative binomial regression model are not adequate to model it. For this reason, in this paper we propose modeling a dengue-fever dataset by using a Poisson-inverse-Gaussian regression model. The main advantage of this model is that it adequately models overdispersed long-tailed data because it has a wider skewness range than the negative binomial distribution. We illustrate the application of this model in a real dataset and compare its performance to that of a negative binomial regression model.

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DNA-barcoding is a species identification tool that uses a short section of the genome that provides a genetic signature of the species. The main advantage of this novel technique is that it requires a small sample of tissue from the tested organism. In most animal groups, this technique is very effective. However, in plants, the recommended standard markers, such as rbcLa, may not always work, and their efficacy remains to be tested in many plant groups, particularly from the Neotropical region. We examined the discriminating power of rbcLa in 55 tropical cloud forest vascular plant species from 38 families (Oaxaca, Mexico). We followed the CBOL criteria using BLASTn, genetic distance, and monophyly tree-based analyses (neighbor-joining, NJ, maximum likelihood, ML, and Bayesian inference, BI). rbcLa universal primers amplified 69.0% of the samples and yielded 91.3% bi-directional sequences. Sixty-three new rbcLa sequences were established. BLAST discriminates 80.8% of the genus but only 15.4% of the species. There was nil minimum interspecific genetic distances in Quercus, Oreopanax, and Daphnopsis. Contrastingly, Ericaceae (5.6%), Euphorbiaceae (4.6%), and Asteraceae (3.3%) species displayed the highest within-family genetic distances. According to the most recent angiosperm classification, NJ and ML trees successfully resolved (100%) monophyletic species. ML trees showed the highest mean branch support value (87.3%). Only NJ and ML trees could successfully discriminate Quercus species belonging to different subsections: Quercus martinezii (white oaks) from Q. callophylla and Q. laurina (red oaks). The ML topology could distinguish species in the Solanaceae clade with similar BLAST matches. Also, the BI topology showed a polytomy in this clade, and the NJ tree displayed low-support values. We do not recommend genetic-distance approaches for species discrimination. Severe shortages of rbcLa sequences in public databases of neotropical species hindered effective BLAST comparisons. Instead, ML tree-based analysis displays the highest species discrimination among the tree-based analyses. With the ML topology in selected genera, rbcLa helped distinguish infra-generic taxonomic categories, such as subsections, grouping affine species within the same genus, and discriminating species. Since the ML phylogenetic tree could discriminate 48 species out of our 55 studied species, we recommend this approach to resolve tropical montane cloud forest species using rbcLa, as an initial step and improve DNA amplification methods.

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In regression model applications, the errors may frequently present a symmetric shape. In such cases, the normal and Student t distributions are commonly used. In this paper, we shall be concerned only to model heavy-tailed, skewed errors and absence of variance homogeneity with two regression structures based on the skew t distribution. We consider a classic analysis for the parameters of the proposed model. We perform a diagnostic analysis based on global influence and quantile residuals. For different parameter settings and sample sizes, various simulation results are obtained and compared to evaluate the performance of the skew t regression. Further, we illustrate the usefulness of the new regression by means of a real data set (amount of potassium in different soil areas) from a study carried out at the Department of Soil Science of the Luiz de Queiroz School of Agriculture, University of São Paulo.

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Cucurbita moschata D. seed oil contains approximately 75% unsaturated fatty acids, with high levels of monounsaturated fatty acids and antioxidant compounds such as vitamin E and carotenoids, constituting a promising food in nutritional terms. In addition, the Brazilian germplasm of C. moschata exhibits remarkable variability, representing an important source for the genetic breeding of this vegetable and other cucurbits. The present study evaluated the productivity and profile of the seed oil of 91C. moschata accessions from different regions of Brazil maintained in the Vegetable Germplasm Bank of the Federal University of Viçosa (BGH-UFV). A field experiment was conducted between January and July 2016. The accessions showed high genetic variability in terms of characteristics related to seed oil productivity (SOP), such as the weight of seeds per fruit and productivity of seeds, providing predicted selection gains of 29.39 g and 0.26 t ha-1, respectively. Based on the phenotypic and genotypic correlations, a greater SOP can be achieved while maintaining a high oleic acid concentration and low linoleic acid concentration, providing oil of better nutritional and chemical quality. In the variability analysis, the accessions were clustered into five groups, which had different averages for SOP and fatty acid concentration of seed oil, an approach that will guide the use of appropriate germplasm in programs aimed at genetic breeding for SOP and seed oil profile. Per se analysis identified BGH-4610, BGH-5485A, BGH-6590, BGH-5556A, BGH-5472A, and BGH-5544A as the most promising accessions in terms of SOP, with an average (µ + g) of approximately 0.20 t ha-1. The most promising accessions for a higher oleic acid concentration of seed oil were BGH-5456A, BGH-3333A, BGH-5361A, BGH-5472A, BGH-5544A, BGH-5453A, and BGH-1749, with an average (µ + g) of approximately 30%, almost all of which were also the most promising in terms of a lower linoleic acid concentration of the seed oil, with an average (µ + g) of approximately 45%. Part of the C. moschata accessions evaluated in the present study can serve as a promising resource in genetic breeding programs for SOP and fatty acid profile, aiming at the production of oil with better nutritional and physicochemical quality.

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The taxonomy and systematics of the subfamily Allocosinae are poorly known, especially in South America. In the last century, several species have been described in genera from other subfamilies or transferred to them creating great confusion in the knowledge of Allocosinae. In this study we propose the new genus, Abaycosa gen. nov. to contain two species previously described, Orinocosa paraguensis (Gertsch & Wallace 1937) and Pardosa nanica Mello-Leitão 1941. Additionally, we propose two synonyms, Pardosa flammula Mello-Leitão 1945 as a junior synonym of Abaycosa nanica (Mello-Leitão 1941), comb. nov. and Alopecosa rosea Mello-Leitão 1945 as a junior synonym of Abaycosa paraguensis (Gertsch & Wallace 1937), comb. nov. The results of the phylogenetic analysis using molecular characters place Abaycosa in the subfamily Allocosinae, which is also supported by morphological data. Abaycosa can be distinguished from the remaining Allocosinae by the following characters: in males by the presence of only one distal macrosetae and a patch of flat setae on the tip of the cymbium, in females by the ventral position of the vulval chamber and by the short and stout stalk of the spermathecae.

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Environmental agencies are interested in relating mortality to pollutants and possible environmental contributors such as temperature. The Gaussianity assumption is often violated when modeling this relationship due to asymmetry and then other regression models should be considered. The class of Birnbaum-Saunders models, especially their regression formulations, has received considerable attention in the statistical literature. These models have been applied successfully in different areas with an emphasis on engineering, environment, and medicine. A common simplification of these models is that statistical dependence is often not considered. In this paper, we propose and derive a time-dependent model based on a reparameterized Birnbaum-Saunders (RBS) asymmetric distribution that allows us to analyze data in terms of a time-varying conditional mean. In particular, it is a dynamic class of autoregressive moving average (ARMA) models with regressors and a conditional RBS distribution (RBSARMAX). By means of a Monte Carlo simulation study, the statistical performance of the new methodology is assessed, showing good results. The asymmetric RBSARMAX structure is applied to the modeling of mortality as a function of pollution and temperature over time with sensor-related data. This modeling provides strong evidence that the new ARMA formulation is a good alternative for dealing with temporal data, particularly related to mortality with regressors of environmental temperature and pollution.

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OBJECTIVE: We study how life course objective socioeconomic position (SEP) predicts subjective social status (SSS) and the extent to which SSS mediates the association of objective SEP with nutritional status and mental health outcomes. METHODS: We use data from participants of the INCAP Longitudinal Study 1969-2018 (n = 1258) from Guatemala. We use the MacArthur ladder for two measures of SSS - perceived community respect and perceived economic status. We estimate the association of SSS with health outcomes after adjusting for early life characteristics and life course objective SEP (wealth, schooling, employment) using linear regression. We use path analysis to study the extent of mediation by SSS on the health outcomes of body mass index (BMI; kg/m2), psychological distress (using the WHO Self-Reported Questionnaire; SRQ-20) and happiness, using the Subjective Happiness Scale (SHS). RESULTS: Median participant rating was 5 [IQR: 3-8] for the perceived community respect and 3 [IQR: 1-5] for the perceived economic status, with no differences by sex. Objective SEP in early life and adulthood were predictive of both measures of SSS in middle adulthood as well as health outcomes (BMI, SRQ-20 and SHS). Perceived community respect (z-scores; 1 z = 3.1 units) was positively associated with happiness (0.13, 95 % CI: 0.07, 0.19). Perceived economic status (z-scores; 1 z = 2.3 units) was inversely associated with psychological distress (-0.28, 95 % CI: -0.47, -0.09). Neither measure of SSS was associated with BMI. Neither perceived community respect nor perceived economic status attenuated associations of objective SEP with health outcomes on inclusion as a mediator. CONCLUSIONS: Subjective social status was independently associated with happiness and psychological distress in middle adulthood after adjusting for objective SEP. Moreover, association of objective SEP with health was not mediated by SSS, suggesting potentially independent pathways.