Estimation Of Distribution Research Articles

Estimation and comparison of Weibull-Normal distribution with some other probability models using Bayesian method of estimation

In statistical applications the Normal distribution is adjudged to be the best. Recent studies Terna (2017) using classical method indicated that Weibull-Normal distribution outperformed the Normal distribution. In this study we used the non-classical Bayesian method of estimation to estimate and compare the Weibull-Normal distribution with some other distributions including Normal and Gamma-Normal distributions. This study derived explicit expressions for basic statistical properties such as moments, moment generating function, the characteristic function, reliability analysis and the distribution of order statistics. It looks at estimation of confidence intervals for the parameters of the Weibull-Normal distribution and estimated the parameters of the new distribution using a non-classical approach for the purpose of theoretical comparisons. The two other distributions whose parameters were also estimated by using Bayesian estimation are the normal distribution and gamma distribution as well as the combination Gamma-Normal distribution. Based on the analyses and interpretations of the results obtained it was found that the parameters and other general properties of Normal distribution gives a better fit than other distributions. R-software was used; the models were written as an R code in R program using the rjags library, the distribution parameters were obtained from a Gibbs sampling of a Bayesian Fit for data set I and data set II. The criteria used in R for comparisons were the negative log-likelihood, AIC (Akaike information criterion), CAIC (Consistent Akaike Information Criterion) and BIC (Bayesian information Criterion).

Label-Aware Distribution Calibration for Long-Tailed Classification.

Real-world data usually present long-tailed distributions. Training on imbalanced data tends to render neural networks perform well on head classes while much worse on tail classes. The severe sparseness of training instances for the tail classes is the main challenge, which results in biased distribution estimation during training. Plenty of efforts have been devoted to ameliorating the challenge, including data resampling and synthesizing new training instances for tail classes. However, no prior research has exploited the transferable knowledge from head classes to tail classes for calibrating the distribution of tail classes. In this article, we suppose that tail classes can be enriched by similar head classes and propose a novel distribution calibration (DC) approach named as label-aware DC (LADC). LADC transfers the statistics from relevant head classes to infer the distribution of tail classes. Sampling from calibrated distribution further facilitates rebalancing the classifier. Experiments on both image and text long-tailed datasets demonstrate that LADC significantly outperforms existing methods. The visualization also shows that LADC provides a more accurate distribution estimation.

Exploring mm-Wavelength Radar Capabilities for Raindrop Size Distribution Retrieval: Estimating Mass-weighted Mean Diameter from the Differential Backscatter Phase

Abstract Accurate precipitation characterization relies on the estimation of raindrop size distribution (RDSD) from observations. While various techniques using cm-wavelength radars have been proposed for RDSD retrieval, the potential of mm-wavelength polarimetric radars, offering enhanced spatial and temporal resolution while capturing light to moderate rain, remains unexplored. This study focuses on retrieving the mass-weighted mean volume diameter (Dm) using a dual frequency cloud radar. Since the differential reflectivity (Zdr) is ineffective for Dm retrieval at 94 GHz, and simulations demonstrate a strong dependence of the differential backscatter phase (δco) on Dm, the estimation of δco takes precedence in this paper. Notably, δco remains unaffected by attenuation and polarimetric calibration. Addressing the initial requirement of disentangling backscattering and propagation effects at mm-wavelength, an automatic algorithm is proposed to detect Rayleigh plateaus in the spectral domain. Subsequently, a methodology for estimating δco and its associated error is presented. Leveraging simulation results, confidence intervals for Dm that align with δco confidence intervals are retrieved. The assessment of Dm and its confidence interval at 35 and 94 GHz is conducted employing disdrometer-derived Dm. The results demonstrate a comprehensive concordance within a margin of 0.2 mm, underscoring the cloud radar's efficacy in delineating nuanced variations in raindrop mean diameter versus altitude. The validation process encounters difficulties for Dm below 1 mm, as the disdrometer-derived Dm may exhibit an overestimation, while the cloud radar-derived Dm may exhibit an underestimation. The combination of 35 and 94 GHz serves to diminish the confidence interval associated with the retrieved Dm.

Updated range map of an endangered salamander and congeneric competitor reveals different niche preferences.

Estimating distributions for cryptic and highly range-restricted species induces unique challenges for species distribution modeling. In particular, bioclimatic covariates that are typically used to model species ranges at regional and continental scales may not show strong variation at scales of 100s and 10s of meters. This limits both the likelihood and usefulness of correlated occurrence to data typically used in distribution models. Here, we present analyses of species distributions, at 100 × 100 m resolution, for a highly range restricted salamander species (Shenandoah salamander, Plethodon shenandoah) and a closely related congener (red-backed salamander, Plethodon cinereus). We combined data across multiple survey types, account for seasonal variation in availability of our target species, and control for repeated surveys at locations- all typical challenges in range-scale monitoring datasets. We fit distribution models using generalized additive models that account for spatial covariates as well as unexplained spatial variation and spatial uncertainty. Our model accommodates different survey protocols using offsets and incorporates temporal variation in detection and availability resulting from survey-specific variation in temperature and precipitation. Our spatial random effect was crucial in identifying small-scale differences in the occurrence of each species and provides cell-specific estimates of uncertainty in the density of salamanders across the range. Counts of both species were seen to increase in the 3 days following a precipitation event. However, P. cinereus were observed even in extremely wet conditions, while surface activity of P. shenandoah was associated with a more narrow range. Our results demonstrate how a flexible analytical approach improves estimates of both distribution and uncertainty, and identify key abiotic relationships, even at small spatial scales and when scales of empirical data are mismatched. While our approach is especially valuable for species with small ranges, controlling for spatial autocorrelation, estimating spatial uncertainty, and incorporating survey-specific information in estimates can improve the reliability of distribution models in general.

Radiomics analysis of the tissue content of the esophageal hiatus of normal subjects without esophageal symptoms (establishing control values)

Introduction: The opening in the diaphragm through which the esophagus enters from the thorax into the abdomen, i.e., esophageal hiatus is formed by the right crus of the diaphragm. During esophageal peristalsis, the lower end of the esophagus, i.e., lower esophageal sphincter (LES), slides from the abdomen into chest, which we suspect is due to the presence of fat in the hiatus that serves as a lubricant. A recent study from our laboratory found that in patients with achalasia esophagus, the LES and crus of diaphragm are firmly anchored together, which we suspect is due to the loss of fat from the hiatus. Gray scale intensity analysis of the radiological images can be used to determine the nature of tissue, i.e., fat, connective tissue, muscle etc. Goal: To establish gray scale values of the esophageal hiatus from the CT scan images in controls. Methods: 42 patients who had undergone an abdominal CT scan for symptoms unrelated to upper gastrointestinal pathology were selected for this analysis. These patients had no esophageal symptoms and were not taking any medications related to the esophagus and stomach, which included acid inhibition medications. The CT scan was visualized in the Visage (San Diego, CA), a software program that allows rotation of images in X, Y and Z planes to visualize the esophageal hiatus in a 2D image. A software (Segcrus) was built to perform the grayscale intensity analysis of the 3 regions of equal size in the image, 1) subcutaneous fat, 2) muscle, and 3) esophageal hiatus. The 3 regions of interest were demarcated manually in the 2D image. The inner margin of the hiatus was selected as the region of interest for the hiatus image. The software generated a statistical non-parametric method approximation using kernel density estimation (KDE) of the grey scale intensity distributions of the 3 regions of interest. The median values were computed to represent the central tendencies of the distributions, providing an insight into the typical greyscale values of the tissue types. Results: distinct patterns in the region intensity distributions for hiatal region, muscle, and fat were seen. For the hiatus, the values were (Median = 105), interquartile range (IQR = 80.3), and range (Min = 61.5, Max = 137.95), providing a comprehensive description of central tendency, variability, and data spread. For muscle, (Median = 139.92), interquartile range (IQR = 71.1), and range (Min = 119.67, Max = 157.17), and for fat, (Median = 48.24), interquartile range (IQR = 52.9), and range (Min = 23.99, Max = 67.79. Hiatus to fat tissue intensity overlap was not related to BMI (r=0.17, p=0.29), or Age (r=-0.04, p=0.82). There was a notable correlation between the overlap of Hiatal-Muscle tissue intensity and Age (r=0.38, p=0.012). However, such correlation was not observed with BMI (r=-0.3, p=0.06). Additionally, there was no significant correlation between the overlap of Hiatal-Fat tissue intensity and gender (r=-0.26, p=0.09), and similarly, the overlap of Hiatal-Muscle intensity showed no correlation with sex (r=0.09, p=0.58). The results highlight the variability and central tendencies within these tissue types across the normal subjects. Conclusion: These analysis in subjects with no esophageal symptoms may be used as control values to determine if there are difference in the tissue content of the esophageal hiatus between normal and patients with various esophageal motility disorders. R01 DK109376. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Estimating distribution and abundance of wide-ranging species with integrated spatial models: Opportunities revealed by the first wolf assessment in south-central Italy.

Estimating demographic parameters for wide-ranging and elusive species living at low density is challenging, especially at the scale of an entire country. To produce wolf distribution and abundance estimates for the whole south-central portion of the Italian wolf population, we developed an integrated spatial model, based on the data collected during a 7-month sampling campaign in 2020-2021. Data collection comprised an extensive survey of wolf presence signs, and an intensive survey in 13 sampling areas, aimed at collecting non-invasive genetic samples (NGS). The model comprised (i) a single-season, multiple data-source, multi-event occupancy model and (ii) a spatially explicit capture-recapture model. The information about species' absence was used to inform local density estimates. We also performed a simulation-based assessment, to estimate the best conditions for optimizing sub-sampling and population modelling in the future. The integrated spatial model estimated that 74.2% of the study area in south-central Italy (95% CIs = 70.5% to 77.9%) was occupied by wolves, for a total extent of the wolf distribution of 108,534 km2 (95% CIs = 103,200 to 114,000). The estimate of total population size for the Apennine wolf population was of 2557 individuals (SD = 171.5; 95% CIs = 2127 to 2844). Simulations suggested that the integrated spatial model was associated with an average tendency to slightly underestimate population size. Also, the main contribution of the integrated approach was to increase precision in the abundance estimates, whereas it did not affect accuracy significantly. In the future, the area subject to NGS should be increased to at least 30%, while at least a similar proportion should be sampled for presence-absence data, to further improve the accuracy of population size estimates and avoid the risk of underestimation. This approach could be applied to other wide-ranging species and in other geographical areas, but specific a priori evaluations of model requirements and expected performance should be made.

Adaptive Sampling Offspring Generation Strategy for Multi-objective Optimization

A covariance adaptive sampling offspring generation strategy (CASS) based on fuzzy clustering is proposed, and a multi-objective distribution estimation algorithm (MEDCA) based on this strategy is introduced. The GK-FCM clustering partitioning strategy is designed to build a Gaussian model for each individual, collectively approximating the manifold of the Pareto solution set and generating offspring through sampling. The introduction of an individual’s survival generation adapts the individual’s preference for exploration and exploitation. This is achieved by incorporating it as a scaling factor of the covariance matrix in the sampling model, in order to satisfy the individual’s preferences for development and exploration in different evolutionary stages. This method significantly improves the performance of MEDCA in solving complex multi-objective optimization problems through covariance matrix adaptation sampling strategy and scaling factor adaptation strategy. The experimental results demonstrate the advantages of MEDCA in the application of offspring generation strategies during model sampling.

Maximum Likelihood Deconvolution of Beamforming Images with Signal-Dependent Speckle Fluctuations

Ocean Acoustic Waveguide Remote Sensing (OAWRS) typically utilizes large-aperture linear arrays combined with coherent beamforming to estimate the spatial distribution of acoustic scattering echoes. The conventional maximum likelihood deconvolution (DCV) method uses a likelihood model that is inaccurate in the presence of multiple adjacent targets with significant intensity differences. In this study, we propose a deconvolution algorithm based on a modified likelihood model of beamformed intensities (M-DCV) for estimation of the spatial intensity distribution. The simulated annealing iterative scheme is used to obtain the maximum likelihood estimation. An approximate expression based on the generalized negative binomial (GNB) distribution is introduced to calculate the conditional probability distribution of the beamformed intensity. The deconvolution algorithm is further simplified with an approximate likelihood model (AM-DCV) that can reduce the computational complexity for each iteration. We employ a direct deconvolution method based on the Fourier transform to enhance the initial solution, thereby reducing the number of iterations required for convergence. The M-DCV and AM-DCV algorithms are validated using synthetic and experimental data, demonstrating a maximum improvement of 73% in angular resolution and a sidelobe suppression of 15 dB. Experimental examples demonstrate that the imaging performance of the deconvolution algorithm based on a linear small-aperture array consisting of 16 array elements is comparable to that obtained through conventional beamforming using a linear large-aperture array consisting of 96 array elements. The proposed algorithm is applicable for Ocean Acoustic Waveguide Remote Sensing (OAWRS) and other sensing applications using linear arrays.

Bayesian modeling of quantiles of body mass index among under-five children in Ethiopia

BackgroundBody Mass Index (BMI) is a measurement of nutritional status, which is a vital pre-condition for good health. The prevalence of childhood malnutrition and the potential long-term health risks associated with obesity in Ethiopia have recently increased globally. The main objective of this study was to investigate the factors associated with the quantiles of under-five children’s BMI in Ethiopia.MethodsData on 5,323 children, aged between 0-59 months from March 21, 2019, to June 28, 2019, were obtained from the Ethiopian Mini Demographic Health Survey (EMDHS, 2019), based on the standards set by the World Health Organization. The study used a Bayesian quantile regression model to investigate the association of factors with the quantiles of under-five children’s body mass index. Markov Chain Monte Carlo (MCMC) with Gibbs sampling was used to estimate the country-specific marginal posterior distribution estimates of model parameters, using the Brq R package.ResultsOut of a total of 5323 children included in this study, 5.09% were underweight (less than 12.92 BMI), 10.05% were overweight (BMI: 17.06 – 18.27), and 5.02% were obese (greater than or equal to 18.27 BMI) children’s. The result of the Bayesian quantile regression model, including marginal posterior credible intervals (CIs), showed that for the prediction of the 0.05 quantile of BMI, the current age of children [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document}= -0.007, 95% CI :(-0.01, -0.004)], the region Afar [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document} = - 0.32, 95% CI: (-0.57, -0.08)] and Somalia[β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document} = -0.72, 95% CI: (-0.96, -0.49)] were negatively associated with body mass index while maternal age [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document} = 0.01, 95% CI: (0.005, 0.02)], mothers primary education [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document}= 0.19, 95% CI: (0.08, 0.29)], secondary and above [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document} = 0.44, 95% CI: (0.29, 0.58)], and family follows protestant [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document} = 0.22, 95% CI: (0.07, 0.37)] were positively associated with body mass index. In the prediction of the 0.95 (or 0.85?) quantile of BMI, in the upper quantile, still breastfeeding [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document} = -0.25, 95% CI: (-0.41, -0.10)], being female [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document} = -0.13, 95% CI: (-0.23, -0.03)] were negatively related while wealth index [β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document} = 0.436, 95% CI: (0.25, 0.62)] was positively associated with under-five children’s BMI.ConclusionsIn conclusion, the research findings indicate that the percentage of lower and higher BMI for under-five children in Ethiopia is high. Factors such as the current age of children, sex of children, maternal age, religion of the family, region and wealth index were found to have a significant impact on the BMI of under-five children both at lower and upper quantile levels. Thus, these findings highlight the need for administrators and policymakers to devise and implement strategies aimed at enhancing the normal or healthy weight status among under-five children in Ethiopia.

Estimation of Gumbel Distribution Based on Ordered Maximum Ranked Set Sampling with Unequal Samples

Sample selection is one of the most important factors in estimating the unknown parameters of distributions, as it saves time, saves effort, and gives the best results. One of the challenges is deciding on a suitable distribution estimate technique and adequate sample selection to provide the best results in comparison with earlier research. The method of moments (MOM) was decided on to estimate the unknown parameters of the Gumbel distribution, but with four changes in the sample selection, which were simple random sample (SRS), ranked set sampling (RSS), maximum ranked set sampling (MRSS), and ordered maximum ranked set sampling (OMRSS) techniques, due to small sample sizes. The MOM is a traditional method for estimation, but it is difficult to use when dealing with RSS modification. RSS modification techniques were used to improve the efficiency of the estimators based on a small sample size compared with the usual SRS estimator. A Monte Carlo simulation study was carried out to compare the estimates based on different sampling. Finally, two datasets were used to demonstrate the adaptability of the Gumbel distribution based on the different sampling techniques.

Meta-analysis of the global distribution of clinically relevant CYP2C8 alleles and their inferred functional consequences.

CYP2C8 is responsible for the metabolism of 5% of clinically prescribed drugs, including antimalarials, anti-cancer and anti-inflammatory drugs. Genetic variability is an important factor that influences CYP2C8 activity and modulates the pharmacokinetics, efficacy and safety of its substrates. We profiled the genetic landscape of CYP2C8 variability using data from 96 original studies and data repositories that included a total of 33,185 unrelated participants across 44 countries and 43 ethnic groups. The reduced function allele CYP2C8*2 was most common in West and Central Africa with frequencies of 16-36.9%, whereas it was rare in Europe and Asia (< 2%). In contrast, CYP2C8*3 and CYP2C8*4 were common throughout Europe and the Americas (6.9-19.8% for *3 and 2.3-7.5% for *4), but rare in African and East Asian populations. Importantly, we observe pronounced differences (> 2.3-fold) between neighboring countries and even between geographically overlapping populations. Overall, we found that 20-60% of individuals in Africa and Europe carry at least one CYP2C8 allele associated with reduced metabolism and increased adverse event risk of the anti-malarial amodiaquine. Furthermore, up to 60% of individuals of West African ancestry harbored variants that reduced the clearance of pioglitazone, repaglinide, paclitaxel and ibuprofen. In contrast, reduced function alleles are only found in < 2% of East Asian and 8.3-12.8% of South and West Asian individuals. Combined, the presented analyses mapped the genetic and inferred functional variability of CYP2C8 with high ethnogeographic resolution. These results can serve as a valuable resource for CYP2C8 allele frequencies and distribution estimates of CYP2C8 phenotypes that could help identify populations at risk upon treatment with CYP2C8 substrates. The high variability between ethnic groups incentivizes high-resolution pharmacogenetic profiling to guide precision medicine and maximize its socioeconomic benefits, particularly for understudied populations with distinct genetic profiles.

Does the approach used to measure sexual identity affect estimates of health disparities differently by race? A randomized experiment from the National Survey of Family Growth

The inclusion of “something else” as a response option for survey questions about sexual identity has been shown to significantly moderate estimated differences between sexual identity subgroups in the distributions of various health outcomes in national health surveys, specifically for substance use/misuse and reproductive health. This moderation effect may be larger depending on the respondent's race, possibly due to the intersectionality of multiple minority identities and compounding minority stressors. To test this hypothesis, we conducted secondary analyses of data from five consecutive years of the National Survey of Family Growth (2015–2019) [n = 18,903 (8510 males, 10,393 females)] in the United States, where respondents were randomly assigned to receive either a four-category version of the sexual identity question that included “something else” as a response option along with lesbian/gay, bisexual, and heterosexual, or a three-category version that omitted “something else.” We focused on model-based estimates of distributions of health outcomes across subgroups defined by both race and sexual identity and tested the race moderation hypothesis using three-way interactions involving sexual identity, question type received (four-category vs. three-category), and race (Black, White, and other). Based on this randomized experiment, we found evidence of significant differences between the four-category and three-category question versions of sexual identity, in terms of the associations between sexual identity and several health outcomes, along with moderation of these differences by the respondent's race. For some outcomes, such as wanting a child or another child, the magnitudes of the estimated sexual identity differences among racial minority individuals, specifically between bisexual and heterosexual women, were sensitive to the measurement of sexual identity. These results further suggest a need for improved sexual minority measurement, especially among racial minorities. These differences were also mostly found among women, suggesting further intersectionality of groups exposed to specific stressors.

Retrospective analysis of canine fecal flotation and coproantigen immunoassay hookworm positive results in Greyhounds and other dog breeds

Recent studies demonstrated that Greyhounds are commonly infected with Ancylostoma caninum and these infections have been shown to be resistant to anthelmintics. This study evaluated samples submitted to a commercial reference laboratory (IDEXX Laboratories) for canine fecal flotation zinc sulfate centrifugation and coproantigen immunoassay between January 1, 2019, and July 30, 2023 for evidence that Greyhounds were more often positive for Ancylostoma spp. (hookworms) compared to other breeds. The purpose of the study was to determine if Greyhounds were more likely to be hookworm-positive compared to other breeds, if Greyhounds on preventives with efficacy against hookworm infections are more likely to test positive than other breeds, if their infections take longer to resolve, to estimate how long this takes and to assess whether the proportion of hookworm positive tests for all breeds is increasing over time.Records of 25,440,055 fecal results were obtained representing 17,671,724 unique dogs. Of these, 49,795 (∼0.3%) were Greyhounds. The overall odds ratio (OR) of 15.3 (p < 0.001) suggests that Greyhounds are at significantly higher risk than other breeds for hookworm positive float findings, and the OR of 14.3 (p < 0.001) suggests significantly higher risk for hookworm antigen positive results. The median time to negative testing event from the Turnbull distribution estimate was in the interval of 1–2 days for other breeds and 71–72 days for Greyhounds. These results provide evidence that anthelmintic resistant A. caninum strains may be having population-level impacts on the frequency and duration of infections in Greyhounds. The findings have broader health implications beyond Greyhounds as MADR A. caninum strains could spread to other breeds and even pet owners.

Expert‐based range maps cannot be replicated using data‐driven methods but macroecological conclusions arising from them can

AbstractAimAnswering many fundamental and applied scientific questions relies on accurate geographic range maps for species, such as those compiled by experts working with the International Union for Conservation of Nature (IUCN). However, these maps are resource demanding to produce and only available for a limited number of organisms. Here, we test to what extent standardized, data‐driven methods based on publicly available occurrences can reproduce expert‐based IUCN range maps and the macroecological conclusions drawn from them.LocationGlobal.Time PeriodPresent.TaxaBirds.Materials and MethodsWe estimated the geographic ranges for 7385 non‐marine bird species which either were non‐migratory or had spatially connected breeding and wintering ranges from publicly available, georeferenced point occurrences. We then quantified the spatial overlap between these range estimates and the IUCN expert‐derived range estimates. Finally, we compared global species richness patterns and the environmental correlates that emerge from both approaches.ResultsWe find that range estimates based on point occurrence records overlap on average 52% with expert range estimates for the same species. The global species richness patterns estimated under both approaches are overall similar but show local and regional differences, for example, in the tropical Andes of northern South America and the Central Arc region of Africa. The estimated global drivers of richness are similar.Main ConclusionsExpert‐derived estimates of species distributions are not reproducible by data‐driven approaches relying on currently available public records, even for well‐documented taxa such as birds. However, these discrepancies do not substantially change our macroecological understanding of global drivers of bird diversity.

Estimating species distribution from camera trap by‐catch data, using jaguarundi (Herpailurus yagouaroundi) as an example

AbstractAimPlanning conservation action requires accurate estimates of abundance and distribution of the target species. For many mammals, particularly those inhabiting tropical forests, there are insufficient data to assess their conservation status. We present a framework for predicting species distribution using jaguarundi (Herpailurus yagouaroundi), a poorly known felid for which basic information on abundance and distribution is lacking.LocationMesoamerica and South America.Time PeriodFrom 2003 to 2021.Taxa Herpailurus yagouaroundi. MethodsWe combined camera‐trap data from multiple sites and used an occupancy modelling framework accounting for imperfect detection to identify habitat associations and predict the range‐wide distribution of jaguarundis.ResultsOur model predicted that the probability of jaguarundi occupancy is positively associated with rugged terrain, herbaceous cover, and human night‐time light intensity. Jaguarundi occupancy was predicted to be higher where precipitation was less seasonal, and at intermediate levels of diurnal temperature range. Our camera data also revealed additional detections of jaguarundis beyond the current International Union for Conservation of Nature (IUCN) range distribution, including the Andean foothills of Colombia and Bolivia.Main ConclusionOccupancy was predicted to be low throughout much of Amazonian lowlands, a vast area at the centre of jaguarundi known range. Further work is required to investigate whether this area represents sub‐optimal conditions for the species. Overall, we estimate a crude global jaguarundi population of 35,000 to 230,000 individuals, covering 4,453,406 km2 of Meso‐ and South America at the 0.5 probability level of occupancy. Our current framework allows for an initially detailed, well‐informed species distribution that should be challenged and refined with improved habitat layers and additional records of jaguarundi detection. We encourage similar studies of lesser‐known mammals, pooling existing by‐catch data from the growing bank of camera‐trap surveys around the world.

Estimation of microplastics distribution in soil sample from District Una, Himachal Pradesh, India

Plastics have become an indispensable part of our daily lives. Its production and usage are increasing day by day. Our lives have become dependent on plastic-based products and we are frequently exposed to plastics. The oxidation, fragmentation and leaching stimulate the formation of small size (1 µm–5000 µm) particles termed as microplastics. The current study facilitates the assessment and quantification of MPs in soil sample collected from 5 sites (Amb, Gagret, Mubarikpur, Una city and Tahliwal) in district Una, Himachal Pradesh, India. Soil samples were treated with NaCl for density separation and 30% H2O2 for digestion of organic matter. After sample treatment, obtained supernatant were visualized under stereomicroscope. In the current study, fragments (81.06%) were the dominant MPs type identified, followed by fibers (16.04%) and films (2.89%). Similarly, MPs obtained were of various colours such as purple (59%), greenish purple (5%), yellow (5%), blue (2%), green (1%) and transparent (28%). The highest microplastics concentration was detected in soil sample from Tahliwal due to the disposal from small scale industries and domestic waste while the lowest microplastics concentration was detected in soil sample from Una city. However, further research is needed to identify the polymer type and to check the possible source of microplastic examined.

Simulating Tree Root Water Uptake in the Frame of Sustainable Agriculture for Extreme Hyper-Arid Environments Using Modeling and Geophysical Techniques

In order to ensure sustainability in the agricultural sector and to meet global food needs, a particularly important challenge for our time is to investigate the possibility of increasing agricultural production in areas with extreme hyper-arid environments. Warming air temperatures and sandy soils significantly influence tree root water uptake (RWU) dynamics, making accurate estimation of RWU depth distribution and magnitude crucial for effective resource management, particularly in the context of precision irrigation within agroecosystems. This study employed two non-invasive techniques, namely HYDRUS 1D and electrical resistivity tomography (ERT), to simulate RWU under controlled experimental conditions and under an extreme hyper-arid environment. The results revealed that the highest RWU rates occurred during the morning (08:00–11:00). RWU activity predominantly concentrated in the upper soil profile (0–30 cm), and the soil water content in this area was notably lower compared to the deeper soil layers. With increasing temperature, there was a tendency for the RWU zone to shift to lower depths within the soil profile. The findings of this study could have important implications for farmers, providing valuable insights to implement irrigation water management strategies.

A parametric study on the effect of liquid water content and droplet median volume diameter on the ice distribution and anti-icing heat estimation of a wind turbine airfoil

This study uses an icing model, combining heat transfer with fluid flow and considering the roughness effect, to investigate the influence of liquid water content (LWC) and droplet median volume diameter (MVD) on the ice distribution and anti-icing heat estimation of a wind turbine blade airfoil through the numerical approach. The findings indicate that the simulated ice distribution can have excellent agreement with experimental data. Owing to variations of droplet collection efficiency and heat flux, the increase in LWC and MVD will amplify the fluctuations in ice accretion distribution, which will be prone to ice horns. Owing to high LWC increasing water film flow range, the jumping point of anti-icing heat flux is closer to the trailing edge. Owing to large MVD increasing droplet collection efficiency, the quantity of ice accumulation through solidification ascends to demand higher anti-icing heat flux. The peak anti-icing heat flux is more evidently influenced by LWC than that by MVD, due to the variations in heat flux, induced by water film evaporation and solidification. The findings offer valuable insights into the flow and heat transfer physics for wind turbine anti/de-icing design.

Characterization of stratospheric particle size distribution uncertainties using SAGE II and SAGE III/ISS extinction spectra

Abstract. A new algorithm was developed to infer particle size distribution parameters from the Stratospheric Aerosol and Gas Experiment II (SAGE II) and SAGE III on the International Space Station (SAGE III/ISS) extinction spectra using a lookup table (LUT) approach. Here, the SAGE-based extinction ratios were matched to LUT values, and, using these matches, weighted statistics were calculated to infer the median particle size distribution values and higher-moment parameters as well as quantify the uncertainty in these estimates. This was carried out by solving for both single-mode and bimodal lognormal distributions. The work presented herein falls under two general headings: (1) a theoretical study was carried out to determine the accuracy of this methodology, and (2) the solution algorithm was applied to the SAGE II and SAGE III/ISS records with a brief case study analysis of the 2022 Hunga Tonga eruption. This methodology was demonstrated to be ≈ 25 % accurate for mode radius and has a minor dependence on particle composition. While bimodal solutions were obtained from this algorithm, we provide a conclusive demonstration of how and why these estimates are inherently unstable using SAGE III/ISS extinction spectra alone. Finally, we demonstrated how the Hunga Tonga aerosol plume evolved in regard to both size and transport over 18 months after the 2022 Hunga Tonga eruption. The particle size distribution (PSD) estimates, higher-moment parameters, and uncertainties are new products within the SAGE III/ISS Level 2 (L2) products, are currently available for download, and will be merged into the main SAGE III/ISS release products in a subsequent L2 release.

Optimising Deep Neural Networks for Tumour Diagnosis Algorithms Based on Improved MRFO Algorithm

INTRODUCTION: Cancer has become one of the most prevalent diseases with the highest mortality rate in the world, and timely detection and early acceptance of medical therapeutic interventions are effective means of controlling the progression of cancer patients and improving their post-intervention outcomes.OBJECTIVES: To make the defects of incomplete features, low accuracy and low real-time performance of current tumour diagnosis methods.METHODS: This paper proposes a tumour diagnosis method based on the improved MRFO algorithm to improve the optimization process of DBN network parameters. Firstly, the diagnostic features are extracted by analysing the tumour diagnosis identification problem; then, the manta ray foraging optimization algorithm is improved by combining the good point set initialization strategy, the adaptive control parameter strategy and the distribution estimation strategy, and the tumour diagnostic model based on the improved manta ray foraging optimization algorithm to optimize the parameters of the depth confidence network is constructed; finally, the high accuracy and real-time performance of the proposed method are verified by the analysis of simulation experiments.RESULTS: The results show that the proposed method improves the accuracy of the diagnostic model.CONLUSION: Addresses the problem of poor accuracy and real-time availability of tumour diagnostic methods.