Range Of Sample Sizes Research Articles

Optimized 1D-CNN model for medicinal Psyllium Husk crop mapping with temporal optical satellite data

Deep Learning models are preferred for complex image analysis-based solutions to application-oriented problems. However, the architecture of such models largely influences the results which includes several hyperparameters that need to be tuned. This study aims at developing an optimized 1D-CNN model for medicinal Psyllium Husk crop mapping using open source temporal optical Sentinel-2A/2B satellite data. In this study, a sequential 1D-CNN model architecture was developed by optimizing hyperparameters which includes convolution layers, number of neurons, activation function, and batch size. Psyllium Husk crop fields were mapped in the Jalore district of Rajasthan using Sentinel 2A/ 2B (10 m) optical data. For spectral dimensionality reduction of the data, Modified Soil Adjusted Vegetation Index (MSAVI2) was used to maintain the data dimensionality since temporal data was utilized. The dataset was subsequently refined to include the target crop's specific phenological stages that distinguish it from other closely resembling species. The information corresponding to these specific crop stages was fed to the 1D-CNN model to carry out the classification. A range of training sample sizes were explored to determine the optimal number of training data points. As the output from the model, fractional images are obtained consisting of values proportional to the probability of a pixel lying in the target class. Accuracy assessment was carried out using fuzzy error matrix (FERM) by generating fractional output images from temporal optical PlanetScope data (3m) which was used as a reference. The best overall accuracy among the test cases came out to be 89.85% using conventional MSAVI2 with 1000 training samples.

Multi-objective optimization sampling based on Pareto optimality for soil mapping

Precise mapping based on sampling data is meaningful for efficient soil quality management. The distributions of sampling sites in feature space and geographical space, as well as the distribution of point pairs at different distances, all significantly impact mapping precision, and these three aspects should all be considered in a sampling design. Although optimizing the spread of sampling sites in the above three aspects has been realized and addressed in previous studies, their tradeoff relationship and influence on mapping accuracy have not been comprehensively investigated, partly due to the limitations of weighting-based optimization way. In this article, we proposed a sampling strategy based on Pareto optimality to examine the tradeoff relationship among the three aspects and their influence on interpolation precision. Based on soil organic matter data from Yi’an district, we applied this approach to generate sampling schemes and analyze their distributions and prediction errors. Single-objective optimization of these three aspects was also conducted for comparison. The results revealed that: (1) the sampling strategy that simultaneously optimizes the distributions of sampling sites in the three aspects was able to obtain sampling designs with higher mapping accuracy than the method that only optimizes one aspect. (2) There was an apparent synergistic relationship between the distributions of sampling sites in feature space and geographical space. However, these objectives had antagonistic relationships with the distribution of point pairs at different distances. (3) When considering the tradeoff among the three aspects, the more even the distribution in geographical space was, the higher the mapping accuracy they produced. Furthermore, more even distribution in feature space improved the mapping accuracy, but the benefit faded after a certain degree, whereas the mapping accuracy initially increased as the distance distribution of point pairs in different intervals became more even but subsequently declined beyond a certain point. Attention should be given to the tradeoff relationship and impact on interpolation when sampling for mapping. In similar scenarios like the study case, it is recommended that sampling sites should be distributed in feature space with more than 45% evenness of the optimal state and then distributed in geographical space as evenly as possible, while the distribution of point pairs should not exceed 65% evenness of its optimal state when the sample size range is approximately 50 to 150. This study provides helpful references for scientific sampling with the goal of precise mapping.

Statistical power in vitamin D randomized control trials investigating biomarkers as continuous outcomes

It has long been suspected that mean vitamin D level and differences among individuals in the population might deteriorate power in vitamin D randomised controlled trials (RCTs). However, standard statistical planning tools cannot accommodate these considerations. Here, to accommodate the large within-person and between-people heterogeneity in naturally fluctuating 25-hydroxyvitamin D (25OHD) concentration, a simulation based approach was used to investigate the power and sample size requirements in vitamin D supplementation RCTs looking at the proportion of regulatory T cells, %Tregs, as a continuous outcome. A range of sample sizes, mean increases in 25OHD in the intervention arm, and population 25OHD heterogeneity were tested. We found that in a population with a mean 25OHD of 50▒nmol/L and moderate heterogeneity in 25OHD (defined as inter-quartile range IQR = 20), sample size of approximately 1000 participants per arm is required to achieve 80% power if 25OHD increased by 10▒nmol/L in the intervention arm, compared to 250, < 100 and < 50 participants per arm if this increase was 20▒nmol/L, 40▒nmol/L or 60▒nmol/L, respectively. Thus we conclude that the increase in 25OHD in the intervention arm and population heterogeneity impact the power of vitamin D RCTs substantially. Sample size determination through simulation is a powerful approach for non-standard trials, and the work presented can easily be adopted to other intervention-outcome pairs.

Normative reference values and regression equations to predict the 6-minute walk distance in the Asian adult population aged 21-80 years.

The 6-min walk test (6MWT) is a widely used field walking test. This study reports the normative reference values (NRV) of distance walked during 6MWT (6MWD) in healthy Singaporeans (aged 21-80) and updates the 6MWD reference equations. This information may facilitate the interpretation of the 6MWD in clinical populations. The Singapore Institute of Technology-Institutional Review Board (SIT-IRB Project Number: 2019099) approved this study to be carried out from June 2019 to January 2021. All participants gave written informed consent before data collection began. The six-minute walk test (6MWT) is a widely adopted submaximal field-walking test to evaluate functional exercise capacity. This validated test is a reliable, safe, inexpensive, and straightforward assessment tool commonly used as an outcome measure, using the distance walked (6MWD) as the primary outcome. An earlier study has established the normative reference values (NRV) and equation in healthy Singaporeans - however, the small sample size and narrow age range curb adequate representation of the adult population profile. This study aims to update the NRV and reference equations to predict the distance walked during 6MWT (6MWD) for healthy Singaporeans aged 21-80. This cross-sectional study recruited community-dwelling healthy subjects aged 21-80 via convenience sampling. Each subject completed two trials of 6MWT according to the standard protocol. Primary outcome measures included 6MWD, pre-and post-test heart rate (HR), oxygen saturation, and blood pressure (BP). 172 healthy Singaporeans (, ) participated. The overall mean 6MWD was metres. The age-stratified mean 6MWD ranged from metres (aged 21-39) to metres (aged 60-80). Age, gender, and percentage maximum HR predicted (%PredHRmax) were the most significant variables (). 6MWD reference (HR reserve, bpm) (weight, kg), . Applying equations from other studies to the Singaporean population resulted in an overestimation of the 6MWD. This study updated the NRV and reference equations of 6MWD for healthy Singaporeans aged between 21-80 years. This update revises the local benchmarks of 6MWD in Singapore, a widely adopted outcome measure.

Health State Utility Values in People With Stroke: A Systematic Review and Meta-Analysis.

BackgroundHealth state utility values are commonly used to provide summary measures of health‐related quality of life in studies of stroke. Contemporaneous summaries are needed as a benchmark to contextualize future observational studies and inform the effectiveness of interventions aimed at improving post‐stroke quality of life.Methods and ResultsWe conducted a systematic search of the literature using Medline, EMBASE, and Web of Science from January 1995 until October 2020 using search terms for stroke, health‐related quality of life, and indirect health utility metrics. We calculated pooled estimates of health utility values for EQ‐5D‐3L, EQ‐5D‐5L, AQoL, HUI2, HUI3, 15D, and SF‐6D using random effects models. For the EQ‐5D‐3L we conducted stratified meta‐analyses and meta‐regression by key subgroups. We screened 14 251 abstracts and 111 studies met our inclusion criteria (sample size range 11 to 12 447). EQ‐5D‐3L was reported in 78% of studies (study n=87; patient n=56 976). The pooled estimate for EQ‐5D‐3L at ≥3 months following stroke was 0.65 (95% CI, 0.63–0.67), which was ≈20% below population norms. There was high heterogeneity (I2>90%) between studies, and estimates differed by study size, case definition of stroke, and country of study. Women, older individuals, those with hemorrhagic stroke, and patients prior to discharge had lower pooled EQ‐5D‐3L estimates.ConclusionsPooled estimates of health utility for stroke survivors were substantially below population averages. We provide reference values for health utility in stroke to support future clinical and economic studies and identify subgroups with lower healthy utility.RegistrationURL: https://www.crd.york.ac.uk/prospero/. Unique Identifier: CRD42020215942.

Relative exposure to microplastics and prey for a pelagic forage fish

In the global ocean, more than 380 species are known to ingest microplastics (plastic particles less than 5 mm in size), including mid-trophic forage fishes central to pelagic food webs. Trophic pathways that bioaccumulate microplastics in marine food webs remain unclear. We assess the potential for the trophic transfer of microplastics through forage fishes, which are prey for diverse predators including commercial and protected species. Here, we quantify Northern Anchovy (Engraulis mordax) exposure to microplastics relative to their natural zooplankton prey, across their vertical habitat. Microplastic and zooplankton samples were collected from the California Current Ecosystem in 2006 and 2007. We estimated the abundance of microplastics beyond the sampled size range but within anchovy feeding size ranges using global microplastic size distributions. Depth-integrated microplastics (0–30 m depth) were estimated using a depth decay model, accounting for the effects of wind-driven vertical mixing on buoyant microplastics. In this coastal upwelling biome, the median relative exposure for an anchovy that consumed prey 0.287–5 mm in size was 1 microplastic particle for every 3399 zooplankton individuals. Microplastic exposure varied, peaking within offshore habitats, during the winter, and during the day. Maximum exposure to microplastic particles relative to zooplankton prey was higher for juvenile (1:23) than adult (1:33) anchovy due to growth-associated differences in anchovy feeding. Overall, microplastic particles constituted fewer than 5% of prey-sized items available to anchovy. Microplastic exposure is likely to increase for forage fishes in the global ocean alongside declines in primary productivity, and with increased water column stratification and microplastic pollution.

Seasonality in the condition and variability in somatic relationships of megrim (Lepidorhombus whiffiagonis) in the northeast Atlantic

Somatic parameters' estimation of megrim (Lepidorhombus whiffiagonis), an economically important flatfish species and relevant one in the benthic community, is essential in fisheries biology. It helps us to deepen our understanding of the species life history traits and an adequate stock assessment and fishery management. Le Cren's condition factor (K), weight-length relationships and weight conversion factors were studied in megrim for more than two decades time-series (1998–2019) and wide area with a relevant fishing activity and the species is distributed into two stocks: northern Bay of Biscay-nBB and Celtic Sea-CS stock, and southern Bay of Biscay-sBB and Galician waters-Gw stock. Around 30,000 specimens, mostly collected from commercial landings by the research institutions IEO and AZTI, were sampled. Total fish length, total weight and commercial gutted weight were obtained, and five-year periods were defined throughout in the time-series. The influence of several factors, as seasonality, temporality, fish sex, maturity, abundance, and environmental conditions on the megrim condition were analyzed for each stock by GLM, being the season the most relevant factor. A clear seasonal pattern found for K, with the lowest values in spring, was found in both populations for the first time based on a multi-year period analysis. The pattern was more prominent in females and less marked in immature, related tothe reproduction process. The lowest condition value was observed in May in nBB-CS and a month earlier in sBB-Gw (both after the spawning period). A progressive increase (favorable feeding conditions) followed to highest values in November–February, showing the relevance of K as indicator of the nutritional/reproductive status of megrim. Temporality (five-year periods) also had a much smaller effect on condition than seasonality. Megrim condition was inversely related to its abundance, and to the NAO index in the nBB-CS stock. Total weight-length, gutted weight-length and total weight-gutted weight relationships were fitted for each megrim stock and their temporal variations were analyzed. The large sample size, size range and time-series available allowed obtaining robust somatic parameters for the total weight-length relationships (a = 0.0049, b = 3.1012; a = 0.0041, b = 3.1682, respectively in nBB-CS stock and sBB-Gw stock), gutted weight-length relationships (a = 0.0046, b = 3.1033; a = 0.0042, b = 3.1510, respectively in nBB-CS and sBB-Gw stock), and weight conversion factors (1.049; 1.056, respectively in nBB-CS and sBB-Gw stock). They were considered the most appropriate to be used in the stock assessment of both stocks. A historical analysis of the temporal variability of the weight-length relationships showed different trends between stocks.

Sensitivity of discrete symmetry metrics: Implications for metric choice.

Gait asymmetry is present in several pathological populations, including those with Parkinson's disease, Huntington's disease, and stroke survivors. Previous studies suggest that commonly used discrete symmetry metrics, which compare single bilateral variables, may not be equally sensitive to underlying effects of asymmetry, and the use of a metric with low sensitivity could result in unnecessarily low statistical power. The purpose of this study was to provide a comprehensive assessment of the sensitivity of commonly used discrete symmetry metrics to better inform design of future studies. Monte Carlo simulations were used to estimate the statistical power of each symmetry metric at a range of asymmetry magnitudes, group/condition variabilities, and sample sizes. Power was estimated by repeated comparison of simulated symmetric and asymmetric data with a paired t-test, where the proportion of significant results is equivalent to the power. Simulation results confirmed that not all common discrete symmetry metrics are equally sensitive to reference effects of asymmetry. Multiple symmetry metrics exhibit equivalent sensitivities, but the most sensitive discrete symmetry metric in all cases is a bilateral difference (e.g. left-right). A ratio (e.g. left/right) has poor sensitivity when group/condition variability is not small, but a log-transformation produces increased sensitivity. Additionally, two metrics which included an absolute value in their definitions showed increased sensitivity when the absolute value was removed. Future studies should consider metric sensitivity when designing analyses to reduce the possibility of underpowered research.

Selecting a Within- or Between-Subject Design for Mediation: Validity, Causality, and Statistical Power

Researchers with mediation hypotheses must consider which design to use: within-subject or between-subject? In this paper, I argue that three factors should influence design choice: validity, causality, and statistical power. Threats to validity include carry-over effects, participant awareness, measurement, and more. Causality is a core element of mediation, and the assumptions required for causal inference differ between the two designs. Between-subject designs require more restrictive no-confounder assumptions, but within-subject designs require the assumption of no carry-over effects. Statistical power should be higher in within-subject designs, but the degree and conditions of this advantage are unknown for mediation analysis. A Monte Carlo simulation compares designs under a broad range of sample sizes, effect sizes, and correlations among repeated measurements. The results show within-subject designs require about half the sample size of between-subject designs to detect indirect effects of the same size, but this difference can vary with population parameters. I provide an empirical example and R script for conducting power analysis for within-subject mediation analysis. Researchers interested in conducting mediation analysis should not select within-subject designs merely because of higher power, but they should also consider validity and causality in their decision, both of which can favor between-subject designs.

Selective flotation separation of magnesite from quartz by palmitoyl trimethylammonium chloride

To achieve efficient separation of magnesite from quartz, it is crucial to develop highly selective collector. In this work, for the size range of samples was −74 + 35 μm (d80 = 70.96 μm), the effect of palmitoyl trimethylammonium chloride (PTAC) on the flotation separation of magnesite and quartz was studied for the first time. Micro-flotation test results indicated that PTAC had a selective-collection influence on quartz and that magnesite separation from quartz could be accomplished at pH 7.0 and 30 mg/L PTAC. Zeta-potential and contact-angle tests indicated that PTAC could selectively increased the isoelectric point and hydrophobicity of quartz surface, but had no such effect on magnesite. Fourier-transform infrared (FTIR) spectroscopy and field emission scanning electron microscopy (FESEM) coupled with energy dispersion spectroscopy (EDS) measurements revealed that PTAC was mainly adsorbed on the surface of quartz, and selective adsorption of PTAC onto quartz surface could be attributed to electrostatic attraction and hydrogen bonds. Molecular dynamics simulation (MDS) further revealed the high selectivity of PTAC adsorption on quartz, through the peak position of PTAC molecular number density, water diffusion coefficient and the interaction energy between PTAC and the minerals. Finally, extended Derjaguin-Landau-Verwey-Overbeek theoretical calculation showed that the addition of 30 mg/L PTAC could change the total force VTED between magnesite and quartz from attraction to repulsion, which was conducive to the separation between them.

X-ray fluorescence microscopy methods for biological tissues.

Synchrotron-based X-ray fluorescence microscopy is a flexible tool for identifying the distribution of trace elements in biological specimens across a broad range of sample sizes. The technique is not particularly limited by sample type and can be performed on ancient fossils, fixed or fresh tissue specimens, and in some cases even live tissue and live cells can be studied. The technique can also be expanded to provide chemical specificity to elemental maps, either at individual points of interest in a map or across a large field of view. While virtually any sample type can be characterized with X-ray fluorescence microscopy, common biological sample preparation methods (often borrowed from other fields, such as histology) can lead to unforeseen pitfalls, resulting in altered element distributions and concentrations. A general overview of sample preparation and data-acquisition methods for X-ray fluorescence microscopy is presented, along with outlining the general approach for applying this technique to a new field of investigation for prospective new users. Considerations for improving data acquisition and quality are reviewed as well as the effects of sample preparation, with a particular focus on soft tissues. The effects of common sample pretreatment steps as well as the underlying factors that govern which, and to what extent, specific elements are likely to be altered are reviewed along with common artifacts observed in X-ray fluorescence microscopy data.

Structural validity of the Eating Disorder Examination-Questionnaire: A systematic review.

ObjectiveThe main aim was to perform a systematic literature review of studies investigating the factor structure of the Eating Disorder Examination‐Questionnaire (EDE‐Q), a widely used measure of eating pathology. Secondary aims were to summarize the quality of reporting of latent variable (factor) analyses in these studies and review support for different factor solutions.MethodLiterature was identified through Scopus, Medline, PsycInfo, and ProQuest databases published up to February 23, 2022 and outreach via an international listserv. All studies published in English reporting factor analysis of the EDE‐Q were included with few restrictions. Sixty studies including 63,389 participants met inclusion criteria.ResultsThe originally proposed four‐factor solution received little empirical support, although few alternative models have been robustly evaluated. Items assessing shape and weight concerns frequently coalesce in factor solutions, suggesting that these constructs are closely related. Investigations of brief versions of the EDE‐Q have produced more consistent findings, suggesting that these measures, particularly a seven‐item version, might be useful alternatives to the full version. Quality of studies was reasonable, with important methodological elements of factor analysis often reported.DiscussionThe findings are of relevance to practitioners and researchers, suggesting that the “original” factor structure of the EDE‐Q should be reconsidered and that use of a seven‐item version is to be encouraged.Public SignificanceSelf‐report questionnaires are widely used in the assessment of disordered eating. The current study found that there is little consensus about the structure of a common measure of eating psychopathology. There is more consistent support for a brief, seven‐item, version assessing dietary restraint, body dissatisfaction, and overvaluation of weight and shape.

Frequency importance analysis for chemical exchange saturation transfer magnetic resonance imaging using permuted random forest.

Chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) is a promising molecular imaging tool that allows sensitive detection of endogenous metabolic changes. However, because the CEST spectrum does not display a clear peak like MR spectroscopy, its signal interpretation is challenging, especially under 3-T field strength or with a large saturation B1 . Herein, as an alternative to conventional Z-spectral fitting approaches, a permuted random forest (PRF) method is developed to determine featured saturation frequencies for lesion identification, so-called CEST frequency importance analysis. Briefly, voxels in the CEST dataset were labeled as lesion and control according to multicontrast MR images. Then, by considering each voxel's saturation signal series as a sample, a permutation importance algorithm was employed to rank the contribution of saturation frequency offsets in the differentiation of lesion and normal tissue. Simulations demonstrated that PRF could correctly determine the frequency offsets (3.5 or -3.5ppm) for classifying two groups of Z-spectra, under a range of B0 , B1 conditions and sample sizes. For ischemic rat brains, PRF only displayed high feature importance around amide frequency at 2h postischemia, reflecting that the pH changes occurred at an early stage. By contrast, the data acquired at 24 h postischemia exhibited high feature importance at multiple frequencies (amide, water, and lipids), which suggested the complex tissue changes that occur during the later stages. Finally, PRF was assessed using 3-T CEST data from four brain tumor patients. By defining the tumor region on amide proton transfer-weighted images, PRF analysis identified different CEST frequency importance for two types of tumors (glioblastoma and metastatic tumor) (p < 0.05, with each image slice as a subject). In conclusion, the PRF method was able to rank and interpret the contribution of all acquired saturation offsets to lesion identification; this may facilitate CEST analysis in clinical applications, and open up new doors for comprehensive CEST analysis tools other than model-based approaches.

A new alternative unit- Lindley distribution with increasing failure rate

In this paper, a new one-parameter distribution is proposed by unitizing the Lindley distribution through the hyperbolic tangent transformation. The goal is to map the functionality of the Lindley distribution on the unit interval, with the perspective of offering a new modeling option for treating unit data. In the first part, we provide the motivations and some mathematical properties of the new distribution. Two truncated moments and hazard rate functions are used to characterize the distribution. The emphasis is then switched to its statistical characteristics. Several methods are used to discuss the point estimation of the parameter. The related bias and mean squared error behavior is tested using Monte Carlo simulations for a range of sample sizes. To demonstrate the ability of the model to fit real data, distributional analyses are given.

Inter- and intra-rater reliability of the modified modified ashworth scale in the assessment of muscle spasticity in cerebral palsy: A preliminary study.

The aim of the study was to investigate the inter- and intra-rater reliability of the Modified Modified Ashworth Scale (MMAS) in the assessment of lower extremity spasticity in children with spastic cerebral palsy (CP). Fifteen children (10 boys) with a mean age of 8.7±3.4 years participated. Two physiotherapists rated the spasticity of the hip adductors, knee extensors, and ankle plantar flexors for inter-rater reliability. Each child was examined again by one of the physiotherapists (same physiotherapist for all of the children) for intra-rater reliability (mean interval = 7 days). A random sequence of raters and muscles tested was applied. The reliability of the intraclass correlation coefficients (ICC) for individual muscle groups ranged between good to excellent (ICCagreement of 0.60-0.83). The ICC values for overall inter-rater (ICCagreement = 0.82) and intra-rater reliability (ICCagreement = 0.85) were excellent. The MMAS showed excellent reliability for the assessment of lower extremity muscle spasticity in children with cerebral palsy. However, an interpretation should be made with caution due to the small sample size and wide range of confidence interval values.

Families with children aged 3-7 during COVID-19: estimate of financial situation and benefit coverage

The paper analyzes the changes in the financial situation of families with children during the pandemic of COVID-19 and the introduction of new eligibility criteria for monthly payments to children aged from 3 to 7 years. The study is based on data from telephone monitoring surveys, representative for the Russian Federation, conducted by RANEPA in October and December 2020, February, May, October, and December 2021. The sample sizes range from 1606 to 2015 people. The results of the study show that the coronavirus epidemic has a negative impact on the financial situation of families with children. The share of poor families receiving social benefits to children aged 3 to 7 years has decreased following the introduction of additional eligibility criteria in 2021. Less than half of poor families with children of this age receive these payments. The benefit coverage decreased significantly among the families with unemployed people — by 2 times. The share of families with self-employed workers, receiving such benefits, has decreased by 2.3 times. As a result of the benefit increase, the share of families with children aged 3-7 years lifted out of poverty has grown by more than two times, but more than 60% of recipients remain monetary poor. This indicates that improvement of eligibility criteria and procedure for determining the level of social benefits are very important.

Development of a dose-response model for porcine cysticercosis.

Taenia solium is an important cause of acquired epilepsy worldwide and remains endemic in Asia, Africa, and Latin America. Transmission of this parasite is still poorly understood despite the design of infection experiments to improve our knowledge of the disease, with estimates for critical epidemiological parameters, such as the probability of human-to-pig infection after exposure to eggs, still lacking. In this paper, a systematic review was carried out and eight pig infection experiments were analyzed to describe the probability of developing cysts. These experiments included different pathways of inoculation: with ingestion of proglottids, eggs, and beetles that ingested eggs, and direct injection of activated oncospheres into the carotid artery. In these experiments, different infective doses were used, and the numbers of viable and degenerated cysts in the body and brain of each pig were registered. Five alternative dose-response models (exponential, logistic, log-logistic, and exact and approximate beta-Poisson) were assessed for their accuracy in describing the observed probabilities of cyst development as a function of the inoculation dose. Dose-response models were developed separately for the presence of three types of cysts (any, viable only, and cysts in the brain) and considered for each of the four inoculation methods ("Proglottids", "Eggs", "Beetles" and "Carotid"). The exact beta-Poisson model best fit the data for the three types of cysts and all relevant exposure pathways. However, observations for some exposure pathways were too scarce to reliably define a dose-response curve with any model. A wide enough range of doses and sufficient sample sizes was only found for the "Eggs" pathway and a merged "Oral" pathway combining the "Proglottids", "Eggs" and "Beetles" pathways. Estimated parameter values from this model suggest that a low infective dose is sufficient to result in a 50% probability for the development of any cyst or for viable cyst infections. Although this is a preliminary model reliant on a limited dataset, the parameters described in this manuscript should contribute to the design of future experimental infections related to T. solium transmission, as well as the parameterization of simulation models of transmission aimed at informing control.

High success rate of nocturnal enuresis treatment during the stay-home period for COVID-19.

ObjectivesTo clarify whether enuresis treatment was more effective during the stay‐home period for the coronavirus disease 2019 pandemic, when restrictions on activities enabled patients to concentrate on treatment.MethodsWe performed a retrospective, nonrandomized cohort study for monosymptomatic enuresis during the coronavirus disease 2019 pandemic (March–June 2020) and a 2‐year comparator period (March–June 2018 and March–June 2019). Primary outcome was treatment response, defined as a change in the number of wet nights per week within 6 months following enrollment. The time‐dependent occurrence of treatment response was evaluated with the Kaplan–Meier method and the log‐rank test. The Cox proportional hazards regression model was used to identify risk factors for treatment response. The range of appropriate sample sizes for this primary outcome was 39–48.ResultsOf our 41 enrolled patients, 28 (68%) were male and mean age was 8.8 years. The complete response rate was 73% during the coronavirus disease 2019 pandemic period and 27% during the comparator period. Log‐rank tests showed a higher cumulative incidence of complete response in the pandemic period (P = 0.020). Cox regression analysis identified treatment during the coronavirus disease 2019 pandemic (hazard ratio 2.533; 95% confidence interval 1.069–6.006) and dinner before 19:00 (hazard ratio 4.184; 95% confidence interval 1.56–11.252) as significantly associated with treatment response.ConclusionsThe rate of enuresis treatment response was uncommonly high during the stay‐home period for the coronavirus disease 2019 pandemic. Restrictions on daily life may provide opportunities to concentrate on treatments for chronic illnesses, leading to more success.

The uniform AUC: Dealing with the representativeness effect in presence–absence models

Abstract Species distribution models use occurrence data of the focus species together with environmental variables to estimate a habitat suitability score. Typically, the discrimination capacity of the models, that is, how good the instances of presence and absence are correctly classified, is assessed with statistics such as the area under the receiver operating characteristic curve (AUC). However, the value of any discrimination statistic depends on the distribution of the suitability scores in the evaluation dataset, the so‐called representativeness effect. For example, the same well‐calibrated model evaluated with two datasets, one set with mostly intermediate predicted values (around 0.5) and another set in which most cases have extreme predicted values (close to 0 and close to 1), will yield low (towards 0.5) and high (towards 1) AUC values, respectively. Thus, discrimination values are entirely context dependent and cannot be directly compare between datasets because they are not intrinsic measures of a model’s performance. In this contribution, I propose a methodology based on stratified bootstrapping with an inverse probability weighting that makes the distribution of the suitability values uniform and harmonizes the AUC so that the so‐called uAUC can now be compared between different datasets. I run simulations considering a range of sample sizes and different initial distributions of the suitability values to validate the method. I apply the method to an empirical exercise, and implement it in the R package vandalico. Although I mainly focused on the AUC, the harmonization procedure can be applied to any other discrimination measure that is prevalence independent in a strict mathematical sense, such as the sensitivity star (Se*). To provide accurate estimations, it is important to have a high‐quality evaluation dataset, that is, a dataset that covers the whole spectrum of suitability values and a minimum sample size of at least 150 cases. Further research is needed to develop a method to estimate reliable confidence intervals. I expect the harmonization methodology proposed here to be of interest in other research areas (besides ecology) that deal with discrimination/classification problems.

Arbitrary Path CT by Multi-Robot Imaging Platform (RadalyX)

Computed tomography (CT) has established itself as an important examination tool, not only in medicine but also in many scientific and industrial fields. It delivers volumetric information at a broad range of spatial resolutions and samples sizes. However, complex-shaped and large-sized parts impose a limitation on CT applicability due to the fact that the inspected parts must “fit” inside the CT machine. That applies even to large gantry-based scanners. Inspecting regions of interest of large structures, such as aircraft control surfaces, requires component disassembly, or even destructive cuts. Moreover, the wide range of sample shapes and sizes leads to a necessity to use a variety of scanning trajectories (cone-beam CT, helical scan, tomosynthesis, etc.). Each such trajectory requires a dedicated mechanics of the scanner. The robot’s movement flexibility allows using all these scanning approaches in one machine and even in one scan: an arbitrary path CT.