Variation In Estimates Research Articles

Genetic Analysis of F2 Populations for Quantitative Traits in Blackgram [Vigna mungo (L.) Hepper

Background: Blackgram is an important pulse crop grown in India. The creation of new variability through hybridization or mutation breeding will produce novel segregants for important yield attributing traits. The selection from the population will be effective if the traits have good genetic variability estimates. Methods: The present investigation was conducted on analysing genetic variability parameters in F2 populations of the blackgram crosses CO 5 × VBN 9 and CO 6 × VBN 11 which were evaluated during rabi, 2022. Results: Based on the coefficient of variations, it was found that sufficient amount of genetic variability and high to moderate heritability with high genetic advance were present in the populations for the traits viz., plant height, number of branches per plant, number of clusters per plant, number of pods per cluster, number of pods per plant and seed yield per plant indicates the presence of additive gene action which can be improved by simple selection in the earlier generations. The positive skewness was observed for the traits viz., number of branches per plant, number of pods per cluster, number of pods per plant, hundred seed weight and seed yield per plant in F2 population of CO 5 × VBN 9 and number of clusters per plant, number of pods per cluster, number of pods per plant and seed yield per plant in the F2 population of CO 6 × VBN 11. The above traits could be improved by intensive selection for rapid improvement. The leptokurtic nature of the distribution for the trait hundred seed weight in CO 5 × VBN 9 population and the traits viz., number of clusters per plant, number of pods per plant, pod length, number of seeds per pod and seed yield per plant in CO 6 × VBN 11 population indicates these traits are controlled by few minor genes. Hence, the good segregants could be selected from these two populations for yield improvement.

Estimates of Variability, Heritability, Genetic Advance for Yield and its Quality Traits in Onion (Allium cepa L.) Genotypes

The present investigation to estimate genetic variability was carried out with forty genotypes of onion which were collected from different locations of Karnataka and were evaluated for growth yield and quality traits. Among all the genotypes studied, Gadag Local exhibited highest plant height at 30 DAT (34.20 cm), 50 DAT (43.46) and 90 DAT (51.76 cm), highest number of leaves were produced by Nasik Red at 50 DAT (9.40 cm) and Savalagi Local at 90 DAT (13.53 cm), Sathara Local exhibited highest equatorial diameter (4.54 cm). highest total soluble solids (15.03%) was recorded by Gadag Local. Kalasakoppa Local produced highest dry matter (20.16%) and B-780 took less number of days to maturity (100days), Bilagi Local had highest polar diameter (4.93 cm). The high GCV and PCV was observed for ten bulb weight and leaf diameter respectively. High estimates of heritability coupled with high genetic advance as percent of mean was observed for ten bulb weight and Total yield(q/ha) Hence, for future onion improvement, these well performing genotypes can be subjected to selection and also used as parents in hybridization programme to obtain varieties/hybrids with desirable quality and higher yield.

On variance estimation of target population created by inverse probability weighting

ABSTRACT Inverse probability weighting (IPW) is frequently used to reduce or minimize the observed confounding in observational studies. IPW creates a pseudo-sample by weighting each individual by the inverse of the conditional probability of receiving the treatment level that he/she has actually received. In the pseudo-sample there is no variation among the multiple individuals generated by weighting the same individual in the original sample. This would reduce the variability of the data and therefore bias the variance estimate in the target population. Conventional variance estimation methods for IPW estimators generally ignore this underestimation and tend to produce biased estimates of variance. We here propose a more reasonable method that incorporates this source of variability by using parametric bootstrapping based on intra-stratum variability estimates. This approach firstly uses propensity score stratification and intra-stratum standard deviation to approximate the variability among multiple individuals generated based on a single individual whose propensity score falls within the corresponding stratum. The parametric bootstrapping is then used to incorporate the target variability by re-generating outcomes after adding a random error term to the original data. The performance of the proposed method is compared with three existing methods including the naïve model-based variance estimator, the nonparametric bootstrap variance estimator, and the robust variance estimator in the simulation section. An example of patients with sarcopenia is used to illustrate the implementation of the proposed approach. According to the results, the proposed approach has desirable statistical properties and can be easily implemented using the provided R code.

Genetic Diversity of Apple Clonal Rootstocks from the Collection of the Michurinsk State Agrarian University Based on SSR Markers.

The Michurinsk State Agrarian University (Michurinsk SAU) is one of the leading centers for breeding apple clonal rootstocks. A diverse collection of apple rootstocks, founded in 1930s by V.I. Budagovsky, is maintained at the Michurinsk SAU. In the present study, 87 rootstocks from this collection were analyzed using 18 SSR markers to assess their genetic diversity and relatedness. The detected polymorphism level was rather high compared to the previous estimates of apple rootstock genetic variability. A total of 199 alleles were detected with an average of 11.1 alleles per locus. Among the detected alleles, 67 (33.67%) were rare and 43 (21.61%) were unique. The average PIC value was 0.73, and the expected and observed heterozygosity averaged 0.76 and 0.69, respectively. All the studied accessions except two could be identified with the used marker set. Cluster analysis revealed several groups according to the rootstocks' pedigrees and genetic origin. Furthermore, Structure analysis revealed two main groups of the studied rootstock accessions. No significant differentiation of the studied sample according to dwarfing ability was detected, while weak differentiation was detected according to leaf color. SSR genotyping data can be used for rootstock fingerprinting and pedigree verification and will facilitate collection management. In addition, data on the genetic diversity and structure of the studied collection may be useful for further development of the Michurinsk SAU rootstock breeding program.

Comparable Minimally Important Differences and Responsiveness of Brief Pain Inventory and PEG Pain Scales across 6 Trials

The 3-item pain intensity (P), interference with the enjoyment of life (E), and interference with general activity (G), or PEG, has become one of the most widely used measures of pain severity and interference. The minimally important differences (MID) and responsiveness of the PEG are essential metrics for solidifying its role in research and clinical care. The current study aims to establish the MID and responsiveness of the PEG by synthesizing data from 1,710 participants across 6 controlled trials. MIDs were estimated using absolute score changes among individuals reporting their pain was “a little better” on a retrospective global change anchor as well as distribution-based estimates using standard deviation thresholds and 1 and 2 standard errors of measurement. Responsiveness was assessed using standardized response means, area under the curve, and treatment effect sizes. MID estimates for the PEG ranged from 0.60 to 1.1 when using 0.35 SD, and 0.78 to 1.22 using 1 standard error of measurement. MID estimates using the global anchor had somewhat more variability but most estimates ranged from 1.0 to 1.75. Responsiveness effect sizes were generally large (>.80) for standardized response means and moderate (>.50) for treatment effect. Similarly, the most area under the curve values demonstrated an acceptable level of scale responsiveness (≥.70). Importantly, MID estimates and responsiveness of the PEG and BPI scales were largely comparable when aggregating data across trials. Our synthesis indicates that 1 point is a reasonable MID estimate on these 0- to 10-point pain scales, with 2 points being an upper bound. PerspectiveThis article synthesizes data from 6 clinical trials to establish the minimally important difference (MID) and responsiveness of the 3-item PEG pain scale. The PEG demonstrated good responsiveness, and 1 to 2 points proved to be reasonable estimates for the lower and upper bounds of the MID.

Concurrent Measurement of Local and Global in-Plane Permeability of Reinforcement Fabrics through Real-Time Image Processing.

Properties of reinforcement fabrics, such as permeability, are typically characterized in a volume-averaging sense, whereas the fabric microstructure may vary spatially. This makes designing an effective resin infusion strategy for defect-free composite fabrication challenging. Our work presents a concurrent method for simultaneously measuring the local and global in-plane permeability and offers a handy technique for evaluating spatial variability. This experimental setup was similar to that of unidirectional in-plane permeability tests. The fabric, however, should be cut and tested along the angle bisector of warp and weft directions. The evolution of resin flow fronts was analyzed in real-time using in-house code through live video monitoring. The local and global in-plane permeability components were then obtained by applying Darcy's law regionally and globally. The results are in good agreement with those obtained by radial permeability experiments. Statistical analysis of local permeability reveals that the microstructure variability follows a normal distribution. A complete description of fabric microstructure provided by X-ray microcomputed tomography suggests that local permeability and microstructure variation are closely related, confirming the efficacy of the newly proposed method. This work enables the estimation of fabric structure variability and local and global in-plane permeability in a single test without resorting to expensive volume imaging techniques.

Personalized estimates of brain cortical structural variability in individuals with Autism spectrum disorder: the predictor of brain age and neurobiology relevance

BackgroundAutism spectrum disorder (ASD) is a heritable condition related to brain development that affects a person’s perception and socialization with others. Here, we examined variability in the brain morphology in ASD children and adolescent individuals at the level of brain cortical structural profiles and the level of each brain regional measure.MethodsWe selected brain structural MRI data in 600 ASDs and 729 normal controls (NCs) from Autism Brain Imaging Data Exchange (ABIDE). The personalized estimate of similarity between gray matter volume (GMV) profiles of an individual to that of others in the same group was assessed by using the person-based similarity index (PBSI). Regional contributions to PBSI score were utilized for brain age gap estimation (BrainAGE) prediction model establishment, including support vector regression (SVR), relevance vector regression (RVR), and Gaussian process regression (GPR). The association between BrainAGE prediction in ASD and clinical performance was investigated. We further explored the related inter‐regional profiles of gene expression from the Allen Human Brain Atlas with variability differences in the brain morphology between groups.ResultsThe PBSI score of GMV was negatively related to age regardless of the sample group, and the PBSI score was significantly lower in ASDs than in NCs. The regional contributions to the PBSI score of 126 brain regions in ASDs showed significant differences compared to NCs. RVR model achieved the best performance for predicting brain age. Higher inter-individual brain morphology variability was related to increased brain age, specific to communication symptoms. A total of 430 genes belonging to various pathways were identified as associated with brain cortical morphometric variation. The pathways, including short-term memory, regulation of system process, and regulation of nervous system process, were dominated mainly by gene sets for manno midbrain neurotypes.LimitationsThere is a sample mismatch between the gene expression data and brain imaging data from ABIDE. A larger sample size can contribute to the model training of BrainAGE and the validation of the results.ConclusionsASD has personalized heterogeneity brain morphology. The brain age gap estimation and transcription-neuroimaging associations derived from this trait are replenished in an additional direction to boost the understanding of the ASD brain.

Modelling the effects of ration on individual growth of Oncorhynchus tshawytscha under controlled conditions.

Fed aquaculture is one of the fastest growing and most valuable food production industries in the world. The efficiency with which farmed fish convert feed into biomass influences both environmental impact and economic revenue. Salmonid species, such as king salmon (Oncorhynchus tshawytscha), exhibit high levels of plasticity in vital rates such as feed intake and growth rates. Accurate estimations of individual variability in vital rates are important for production management. The use of mean trait values to evaluate feeding and growth performance can mask individual-level differences that potentially contribute to inefficiencies. Here, we apply a cohort integral projection model (IPM) framework to investigate individual variation in growth performance of 1625 individually tagged king salmon fed one of three distinct rations of 60%, 80% and 100% satiation and tracked over a duration of 276 days. To capture the observed sigmoidal growth of individuals, we compared a non-linear mixed-effects (logistic) model to a linear model used within the IPM framework. Ration significantly influenced several aspects of growth, both at the individual and at the cohort level. Mean final body mass and mean growth rate increased with ration, however, variance in body mass and feed intake also increased significantly over time. Trends in mean body mass and individual body mass variation were captured by both logistic and linear models, suggesting the linear model to be suitable for use in the IPM. We also observed that higher rations resulted in a decreasing proportion of individuals reaching the cohort's mean body mass or larger by the end of the experiment. This suggests that, in the present experiment, feeding to satiation did not produce the desired effects of efficient, fast, and uniform growth in juvenile king salmon. While monitoring individuals through time is challenging in commercial aquaculture settings, recent technological advances combined with an IPM approach could provide new scope for tracking growth performance in experimental and farmed populations. Using the IPM framework might allow the exploration of other size-dependent processes affecting vital rate functions, such as competition and mortality. This article is protected by copyright. All rights reserved.

Software Program for Analysis of HRV of Cardio Signals Registered through Electrocardiographic, Holter and Photopletysmographic Device

The article presents a demonstration software program for evaluating three types of cardiac signals. Mathematical methods for analyzing electrocardiographic, photoplethysmographic and Holter signals are considered. The different cardio signals are analyzed from the presented information system and conclusions are drawn regarding their practical use. The article describes software procedures for the analysis and estimation of heart rate variability extracted from the heart rate registration signals.

Estimation of the Extent of the Vulnerability of Agriculture to Climate Change Using Analytical and Deep-Learning Methods: A Case Study in Jammu, Kashmir, and Ladakh

Climate stress poses a threat to the agricultural sector, which is vital for both the economy and livelihoods in general. Quantifying its risk to food security, livelihoods, and sustainability is crucial. This study proposes a framework to estimate the impact climate stress on agriculture in terms of three objectives: assessing the regional vulnerability (exposure, sensitivity, and adaptive capacity), analysing the climate variability, and measuring agricultural performance under climatic stress. The vulnerability of twenty-two sub-regions in Jammu, Kashmir, and Ladakh is assessed using indicators to determine the collective susceptibility of the agricultural framework to climate change. An index-based approach with min–max normalization is employed, ranking the districts based on their relative performances across vulnerability indicators. This work assesses the impact of socio-economic and climatic indicators on the performance of agricultural growth using the benchmark Ricardian approach. The parameters of the agricultural growth function are estimated using a linear combination of socio-economic and exposure variables. Lastly, the forecasted trends of climatic variables are examined using a long short-term memory (LSTM)-based recurrent neural network, providing an annual estimate of climate variability. The results indicate a negative impact of annual minimum temperature and decreasing land holdings on agricultural GDP, while cropping intensity, rural literacy, and credit facilities have positive effects. Budgam, Ganderbal, and Bandipora districts exhibit higher vulnerability due to factors such as low literacy rates, high population density, and extensive rice cultivation. Conversely, Kargil, Rajouri, and Poonch districts show lower vulnerability due to the low population density and lower level of institutional development. We observe an increasing trend of minimum temperature across the region. The proposed LSTM synthesizes a predictive estimate across five essential climate variables with an average overall root mean squared error (RMSE) of 0.91, outperforming the benchmark ARIMA and exponential-smoothing models by 32–48%. These findings can guide policymakers and stakeholders in developing strategies to mitigate climate stress on agriculture and enhance resilience.

Estimation of Variability, Heritability, and Genetic Advance Among Released Varieties and Local Cultivar of Potato (<i>Solanum tuberosum</i> L.) in Eastern Ethiopia

Estimation of Variability, Heritability, and Genetic Advance Among Released Varieties and Local Cultivar of Potato (<i>Solanum tuberosum</i> L.) in Eastern Ethiopia

Agro-morphological traits-based genetic diversity assessment on oat (Avena sativa L.) genotypes in the central highlands of Ethiopia

The study aimed to estimate the extent of genetic variability, heritability, and expected genetic gain attributable to agro-morphological traits and to suggest an effective selection scheme and specify promising genotypes for future oat breeding programs. One hundred twenty oat (Avena sativa L.) genotypes were evaluated at two locations in the central highlands of Ethiopia. The analysis of variance indicated that the mean squares due to locations and genotype x location interaction varied significantly for some measured traits while that of genotypes varied significantly (P ≤ 0.001) among all studied traits. Moreover, the estimates of genetic variability, heritability, and expected genetic advance indicate the presence of remarkable genetic diversity in oat genotypes and the magnitude of selection response for these traits in the population to develop superior genotypes for oat improvement. The result revealed that very high to moderately high heritability in a broad sense (h2 b) coupled with high to moderate genetic advance as percent of mean (GAM) were recorded for forage dry matter yield (h2 b = 64.4%; GAM = 19.3%), number of leaves at forage harvest (h2 b = 86.4%; GAM = 19.9%), stem dry matter yield (h2 b = 62.9%; GAM = 18.3%), forage yield production rate (h2 b = 69.7%; GAM = 21.7%), grain yield (h2 b = 64.2%; GAM = 26.1%), panicle length at grain harvest (h2 b = 80.9%; GAM = 21.9%), number of spikes per panicle (h2 b = 68.8%; GAM = 16.0%), and harvest index (h2 b = 80.9%; GAM = 26.0%). The results suggested that these traits are governed more by additive gene action, and selection based on these traits could be effective to achieve the desired genetic gains for improvement. Therefore, high forage dry matter and grain yielding genotypes along with these desirable yield-related traits could be considered for selection in oat genotypes.

Diagnosis of intrapopulation adaptation genesis of bird cherry aphid <i>Rhopalosiphum padi</i> (L.) under nutrition conditions on various spring common wheat varieties

The widespread cultivation of highly productive varieties of agricultural crops often leads to the emergence and spread of new intraspecific forms of insects capable of mass reproduction. The control of these processes is of environmental importance and is possible based on monitoring the variability of the phenol appearance of pest populations. There has been carried out a vegetative and field estimation of phenogenetic variability of such pest of grain crops as the bird cherry-grass aphid Rhopalosiphum padi (L.) under nutrition on 15 spring common wheat varieties. There has been conducted an ecological phenotyping of the adaptability of the intrapopulation composition of aphids under vegetative conditions according to abundance and dispersal ability in the offspring of summer morphs (emigrants and wingless viviparas) in three different clones. There has been identified a differentiated interaction of clones and morphs of aphids with varieties of fodder plants for both indicators. There has been shown that the reproduction rates of emigrants and wingless viviparous aphids within clones (14, S and 85) differed (t14 = 3.9, p =.000; Fs = 8.8, p = 0.000; F85 = 7.9, p = 0.000). Clone S was more successful than others in reproducing emigrants and wingless vivipares (F14 = 17.8, p = 0.000; F85 = 3.4; p = 0.03; F'14 = 2.98, p = 0.05; F'85 = 5.3, p = 0.003, respectively). Favorability of individual varieties for clones did not match. When the aphid population was destabilized, compensatory mechanisms included a discrepancy in the reactivity of individual morphs to unfavorable conditions. There has been shown a correlation (r = 0.53; p = 0.04) between the degree of favorableness of varieties for feeding aphids in vegetation (antibiosis) and field conditions (antibiosis and antixenosis), which has confirmed the similarity of the genetic nature of these types of resistance.

Estimation of genetic variability, character association and path coefficient using sugarcane segregating population

Estimation of genetic variability, character association and path coefficient using sugarcane segregating population

Interobserver variability studies in diagnostic imaging: a methodological systematic review.

To review the methodology of interobserver variability studies; including current practice and quality of conducting and reporting studies. Interobserver variability studies between January 2019 and January 2020 were included; extracted data comprised of study characteristics, populations, variability measures, key results, and conclusions. Risk of bias was assessed using the COSMIN tool for assessing reliability and measurement error. Seventy-nine full-text studies were included covering various imaging tests and clinical areas. The median number of patients was 47 (IQR:23-88), and observers were 4 (IQR:2-7), with sample size justified in 12 (15%) studies. Most studies used static images (n = 75, 95%), where all observers interpreted images for all patients (n = 67, 85%). Intraclass correlation coefficients (ICC) (n = 41, 52%), Kappa (κ) statistics (n = 31, 39%) and percentage agreement (n = 15, 19%) were most commonly used. Interpretation of variability estimates often did not correspond with study conclusions. The COSMIN risk of bias tool gave a very good/adequate rating for 52 studies (66%) including any studies that used variability measures listed in the tool. For studies using static images, some study design standards were not applicable and did not contribute to the overall rating. Interobserver variability studies have diverse study designs and methods, the impact of which requires further evaluation. Sample size for patients and observers was often small without justification. Most studies report ICC and κ values, which did not always coincide with the study conclusion. High ratings were assigned to many studies using the COSMIN risk of bias tool, with certain standards scored 'not applicable' when static images were used. The sample size for both patients and observers was often small without justification. For most studies, observers interpreted static images and did not evaluate the process of acquiring the imaging test, meaning it was not possible to assess many COSMIN risk of bias standards for studies with this design. Most studies reported intraclass correlation coefficient and κ statistics; study conclusions often did not correspond with results.

Estimation of Heart Rate and Heart Rate Variability with Real-Time Images Based on Independent Component Analysis and Particle Swarm Optimization

With the rapid development of science and technology, the living habits of people have also changed from those in the past; the diet, living environment, various life pressures, etc., all overwhelm the body and mind, meaning that, nowadays, more people are suffering from mental illness and cardiovascular disease year on year. Therefore, a non-contact measurement of heart rate and heart rate variability (HRV) is proposed to assist physicians in diagnosing symptoms related to mental illness and cardiovascular disease. In this paper, continuous images are obtained by general network cameras with non-contact, facial feature points and regions of interest (ROI) employed to track faces and regional images; HRV parameters were analyzed with the green wavelength of RGB color space. The artifact signal is eliminated by a hybrid algorithm of independent component analysis (ICA) and particle swarming optimization (PSO). Finally, the values of heart rate and HRV are obtained with signal processes of using band-pass filter, fast Fourier transform (FFT), and power spectrum analysis in the time and frequency domains, respectively. The non-contact measurement performance of the proposed method can effectively not only avoid infection doubts and obtain heart rate and HRV quickly, but also provide better physiological parameters, root mean square error (RMSE), and mean absolute percentage error (MAPE), than those of recent published papers.

Six Hundred Years of Reconstructed Atmospheric River Activity Along the US West Coast

AbstractAtmospheric rivers (AR) are critically important to water resources management along the US west coast, driving variability in both droughts and floods across the region. Inter‐annual variability of ARs is well documented in the instrumental record back to the mid‐twentieth century, but long‐term variations in the frequency and landfall location of ARs along the US west coast are poorly understood due to limited records. This limitation impedes the ability to contextualize emerging trends and projections of AR activity. Here we use station‐based records of daily precipitation and tree‐ring records to present novel, spatially explicit estimates of daily AR occurrences in the first half of the twentieth century and annual AR counts over the last 600 years. First, we use neural networks and daily precipitation across Western North America to classify the daily occurrence of AR landfalls in three regions along the US west coast during the cold season back to 1916 CE. Then, we reconstruct the annual frequency of AR landfalls in those same regions back to 1400 CE using a gridded, tree‐ring based reconstruction of the standardized precipitation index and a Poisson regression framework. The skillful reconstruction of daily and annual AR occurrences provides previously unavailable estimates of AR landfall variability and highlights new peaks in AR activity and modes of low‐frequency variability prior to the instrumental record. Our reconstructions suggest that the average latitude of AR landfall has varied considerably on multi‐decadal scales over the last 600 years, but without any discernible trends beyond this quasi‐oscillatory behavior.

Comparison of Trihalomethane exposure assessment metrics in epidemiologic analyses of reproductive and developmental outcomes.

Researchers have developed exposure assessment metrics for disinfection by-products (DBPs) utilizing drinking water monitoring data and accounting for spatial and temporal variability, water consumption, and showering and bathing time with an expectation of decreasing exposure misclassification compared to the use of measured concentrations at public water supply (PWS) monitoring locations alone. We used exposure data collected for a previous study of DBPs to evaluate how different sources of information impact trihalomethane (THM) exposure estimates. We compared gestational exposure estimates to THMs based on water utility monitoring data alone, statistical imputation of daily concentrations to incorporate temporal variability, and personal water consumption and use (bathing and showering). We used Spearman correlation coefficients and ranked kappa statistics to compare exposure classifications. Exposure estimates based on measured or imputed daily THM concentrations, self-reported consumption, or bathing and showering differed substantially from estimates based solely on concentrations from PWS quarterly monitoring reports. Ranked exposure classifications, high to low quartiles or deciles, were generally consistent across each exposure metric (i.e., a subject with "high" exposure based on measured or imputed THM concentrations generally remained in the "high" category across exposure metrics.) The measured concentrations and imputed daily (i.e., spline regression) concentrations were highly correlated (r = 0.98). The weighted kappa statistics comparing exposure estimates using different exposure metrics ranged from 0.27 to 0.89, with the highest values for the ingestion + bathing/showering metrics compared to metrics for bathing/showering only (0.76 and 0.89). Bathing and showering contributed the most to "total" THM exposure estimates. We compare exposure metrics capturing temporal variability and multiple estimates of personal THM exposure with THM concentrations from PWS monitoring data. Our results show exposure estimates based on imputed daily concentrations accounting for temporal variability were very similar to the measured THM concentrations. We observed low agreement between imputed daily concentrations and ingestion-based estimates. Considering additional routes of exposure (e.g., inhalation and dermal) slightly increased agreement with the measured PWS exposure estimate in this population. Overall, the comparison of exposure assessment metrics allows researchers to understand the added value of additional data collection for future epidemiologic analyses of DBPs.

Estimates of Genetic Variability, Heritability and Genetic Advance in Rice (Oryza sativa L.) Under Sodic Soil

The experiment was carried out to evaluate the genetic variability, heritability and genetic advance in rice under the sodic soil condition. Results revealed highly significant variations within parents and hybrids. The five characters viz., number of spikelet’s per panicle, grain per panicle, amylose content, flag leaf area and 1000-grain weight showed high estimates of heritability coupled with high estimates of genetic advance as well as less than one average degree of dominance and predictability ratio nearly one in F1 ’s. This indicated that the inheritance of these five characters was governed by additive gene action and the direct selection for these characters would be rewarding

A validation of discrete-element model simulations for predicting tablet coating variability

Achieving an even coating distribution on tablets during the coating process can be challenging, not to mention the challenges of accurately measuring and quantifying inter-tablet coating variability. Computer simulations using the Discrete Element Method (DEM) provide a viable pathway towards model-predictive design of coating processes. The purpose of this study was to assess their predictivity accounting for both experimental and simulation input uncertainties. To this end, a comprehensive set of coating experiments covering various process scales, process conditions and tablet shapes were conducted. A water-soluble formulation was developed to enable rapid spectroscopic UV/VIS analysis of coating amounts on a large number of tablets. DEM predictions are found to lie within the experimentally inferred confidence intervals in all cases. A mean absolute comparison error of 0.54 % was found between model predictions of coating variability and respective sample point estimates. Among all simulation inputs the parameterization of spray area sizes is considered the most significant source for prediction errors. However, this error was found significantly smaller in magnitude compared to experimental uncertainties at larger process scales underlining the value of DEM in the design of industrial coating processes.