Non-destructive method for estimating leaf area of Erythroxylum pauferrense (Erythroxylaceae) from linear dimensions of leaf blades

Background and Aims: Determining the leaf area is essential for studies on growth, propagation, and ecophysiology of forest species. Developing quick, practical, and accurate methods is needed to estimate leaf area without destroying leaves. Therefore, this research aimed to obtain an equation from regression models that meaningfully estimate the leaf area of Erythroxylum pauferrense using linear dimensions of its leaf blades.Methods: For this purpose, 1200 leaves were randomly collected from different plants in the Mata do Pau-Ferro, a state park located in Areia city, Paraíba state, Brazil. Equations were fitted from simple linear, linear without intercept, quadratic, cubic, power, and exponential regression models. Next, the best equation was selected by checking the following assumptions: higher determination coefficient (R²) and Willmott's index (d), lower Akaike information criterion (AIC) and root mean square error (RMSE), as well as the BIAS index closest to zero.Key results: Based on the criteria used, all equations fitted using the product of length by width (L.W) can estimate the leaf area of E. pauferrense.Conclusions: The equation ŷ=0.6740*LW from the linear model without intercept significantly estimates the leaf area of E. pauferrense in a quick and practical way (R²=0.9960; d=0.9953; AIC=1231.61; RMSE=0.4255; BIAS=-0.0130).

Theoretical Study of Negative Material Bias Index Symptoms

Theoretical research on the phenomenon of negative refractive index materialwas cunducted. The material is a known metamaterial that can manipulate electromagneticwaves through it. By changing the permeability and permittivity of a medium that is worthimaginary, then the value is included in the equation will produce a refractive index that isnegative. This value can be achieved through material forms are designed so that themagnetic field and electric field manipulation. The equation used on electromagnetic wavesthat Maxwell's equations, is revealed to be some similarities propagation magnetic field andelectric field. The Methods Finite Difference Time Domain (FDT D) as a modifier equationwas then equations analysis, simulated using MatlabR2014 program. Based on simulationresults, visible when the electromagnetic waves through a material metamaterial, the wavesare deflected, do not pass part of the material covered.

A NON-DESTRUCTIVE METHOD FOR ESTIMATING LEAF AREA OF Ceiba glaziovii (Kuntze) K. Schum.

The determination of leaf area is of fundamental importance in studies involving ecological and ecophysiological aspects of forest species. The objective of this research was to adjust an equation to determine the leaf area of Ceiba glaziovii as a function of linear measurements of leaves. Six hundred healthy leaf limbs were collected in different matrices, with different shapes and sizes, in the Mata do Pau-Ferro State Park, Areia, Paraíba state, Northeast Brazil. The maximum length (L), maximum width (W), product between length and width (L.W), and leaf area of the leaf limbs were calculated. The regression models used to construct equations were: linear, linear without intercept, quadratic, cubic, power and exponential. The criteria for choosing the best equation were based on the coefficient of determination (R²), Akaike information criterion (AIC), root mean square error (RMSE), Willmott concordance index (d) and BIAS index. All the proposed equations satisfactorily estimate the leaf area of C. glaziovii, due to their high determination coefficients (R² ≥ 0.851). The linear model without intercept, using the product between length and width (L.W), presented the best criteria to estimate the leaf area of the species, using the equation 0.4549*LW.

A comprehensive comparison and analysis of computational predictors for RNA N6-methyladenosine sites of Saccharomyces cerevisiae

N6-methyladenosine (m6A) modification, as one of the commonest post-transcription modifications in RNAs, has been reported to be highly related to many biological processes. Over the past decade, several tools for m6A sites prediction of Saccharomyces cerevisiae have been developed and are freely available online. However, the quality of predictions by these tools is difficult to quantify and compare. In this study, an independent dataset M6Atest6540 was compiled to systematically evaluate nine publicly available m6A prediction tools for S. cerevisiae. The experimental results indicate that RAM-ESVM achieved the best performance on M6Atest6540; however, most models performed substantially worse than their performances reported in the original papers. The benchmark dataset Met2614, which was used as the training dataset for the nine methods, were further analyzed by using a position bias index. The results demonstrated the significantly different bias of dataset Met2614 compared with the RNA segments around m6A sites recorded in RMBase. Moreover, newMet2614 was collected by randomly selecting RNA segments from non-redundant data recorded in RMBase, and three different kinds of features were extracted. The performances of the models built on Met2614 and newMet2614 with the features were compared, which shows the better generalization of models built on newMet2614. Our results also indicate the position-specific propensity-based features outperform other features, although they are also easily over-fitted on a biased dataset.

Perceived Subtle Gender Bias Index: Development and Validation for Use in Academia

In this article, we present the development and validation of the Perceived Subtle Gender Bias Index. Given the inherent difficulty in identifying and measuring the perceptions of subtle gender biases, this index provides researchers and interventionists with a tool that does not require participants to identify/label an event as a gender bias incident. We used a mixed method and constructivist approach that prioritized and privileged the voices and experiences of women in science, technology, engineering, and mathematics (STEM). The current article describes two studies: (1) index development and (2) index refinement and validation, using a national survey of women academics ( N = 882). Findings support a four-subscale structure, including perceived gender inequity, collegiality, mentorship, and institutional support. Methods and analyses support face, convergent, discriminant, and predictive validity for the use of the index among academic faculty women. Additional online materials for this article are available on PWQ’s website at http://journals.sagepub.com/doi/suppl/10.1177/0361684319877199

Estimation of leaf area of Erythroxylum citrifolium from linear leaf dimensions

Erythroxylum citrifolium is a neotropical plant species recorded in all regions of Brazil. Determining leaf area is of fundamental importance to studies related to plant propagation and growth. The objective was to obtain an equation to estimate the leaf area of E. citrifolium from linear dimensions of the leaf blade (length and width). A total of 200 leaf blades were collected in Parque Estadual Mata do Pau-Ferro in the municipality of Areia, state of Paraíba, Northeast Brazil. The models evaluated were: linear, linear without intercept, quadratic, cubic, power and exponential. The best model was determined by the criteria of: high coefficient of determination (R²), low root mean square error (RMSE), low Akaike information criterion (AIC), high Willmott concordance index (d) and a BIAS index close to zero. All of the models constructed satisfactorily estimated the leaf area of E. citrifolium, with coefficients of determination above 0.9050, but the power model using the product between length and width (L*W) ŷ = 0.5966 * LW1.0181 was the best, with the highest values of R² and d, low values of RMSE and AIC, and a BIAS index closest to zero.

Effect of right censoring bias on survival analysis.

e18188 Background: Survival Kaplan-Meier analysis represents the most objective measure of treatment efficacy in oncology, though subjected to potential bias which is worrisome in an era of precision medicine. Independent of the bias inherent to the design of clinical trials, bias may be the result of patient censoring, or incomplete observation. Unlike disease/progression free survival, overall survival is based on a well defined time point and thus avoids interval censoring, but it is our claim that right censoring, due to incomplete follow-up, may still be a source of bias. Methods: The R version 3.5.1 language and the integrated development environment RStudio were used for simulations and survival analysis with the survival package and their available datasets . Survival time was simulated according to a Weibull model with 2 parameters, shape and scale, that determine the event time for every case. Three types of right censoring mechanisms are considered and analyzed independently: 1) case censoring, in which a random number of cases are censored, and the resulting survival time is shortened by a random amount, 2) time censoring, in which a random censoring time variable is applied if and only if it is shorter than the event time, and 3) interim censoring, where a random time variable determines the case inclusion time since the start of trial, and a fixed cutt-off time determines if every case is censored (if the cutt-off time is shorter the the inclusion time plus the event time) or not. For every censoring mechanism, 100 trials was simulated with a 1000 uncensored cases arm and 1000 censored cases arm, in such a way that a censoring Cox hazard ratio (cHR) may be estimated for every trial. An interactive app showing the right censoring effect is presented. Results: A bias index (BI) was buit based on the survival time of event and censored cases. Case censoring was associated with higher BI (mean = 1.75, SD = 0.29) than time censoring (mean = 1.15, SD = 0.19, p = 2.02e-30) and interim censoring (mean = 0.72, SD = 0.21, p = 3.46e-34). It was found an inverse relationship between the censoring proportion and the cHR in case censoring (r = -0.86). Of all the available datasets, the Veterans' Administration Lung Cancer study showed a bias of 1.83, suggesting case censoring bias in both treatment arms. Conclusions: Based in the results of this study it is suggested that: 1) Final results should include all the events in the defined period of interest, 2) a bias index may help in detecting potential bias and correct estimated survival. Censoring bias analysis is planned in recent clinical trials.

Vědecké myšlení a metakognitivní monitorování studentů učitelství pro 1. stupeň základní školy

Výzkum popsaný v tomto článku je zaměřen na analýzu závislosti úrovně vědeckého myšlení a metakognitivního monitorováním studentů učitelství pro 1. stupeň ZŠ. Hlavní dikce je směřována k využití Lawsonova testu vědeckého myšlení rozšířeného o sebeposuzovací škály, které lze využít jako indikátor metakognitivního monitorování. Na základě výsledků daného testu v porovnání s odpověďmi na zmíněné škále byl vyhodnocen a analyzován index absolutní přesnosti (určuje míru přesnosti subjektivního odhadu výkonu v porovnání s objektivně prokázaným výkonem) a bias index (určuje míru jedincova sebepodceňování nebo sebenadhodnocování, tedy směr a velikost chyby v úsudku). Bylo analyzováno celkem 125 testů studentů učitelství s cílem zjistit vztah mezi výsledky v testu měřícího úroveň vědeckého myšlení a úrovní metakognitivního monitorování. Bylo zjištěno, že úroveň vědeckého myšlení studentů vykazuje v některých oblastech silnou korelaci se sebehodnocením respondenta (čím lepší byl výsledek jejich testu vědeckého myšlení, tím přesnější byli studenti v posouzení vlastního výkonu). V rámci studie byl také zaznamenán rozdíl v úrovni vědeckého myšlení mezi bývalými studenty gymnázia, kteří dosahovali v testu vyšších výsledků oproti studentům, kteří studovali jinou střední školu. U všech analyzovaných studentů se projevila tendence nadhodnocovat se, přičemž tento trend nesouvisí s druhem studované školy.