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Tehran, the most crowded city in Iran, suffers from severe air pollution, particularly during the cold months. This research endeavored to examine the statistical relationships between criteria air pollutants (CO, NO2, SO2, O3, PM10, and PM2.5) and meteorological elements (temperature, rainfall, wind speed, relative humidity, air pressure, sunshine hours, solar radiation, and cloudiness), as well as assess and compare the efficacy of six different algorithms (multiple linear regression (MLR), generalized additive model (GAM), classification and regression trees (CART), random forest (RF), gradient boosting machine (GBM), and deep learning (DL)) in modeling pollutants and climatic factors responsible for variations in Tehran's air pollution levels from 2001 to 2021 using R 4.3.2 software. The results of this study showed that O3 was strongly affected by weather conditions, while other pollutants were mainly influenced by each other than by meteorological parameters and more extensive research is required to pinpoint the precise impact of human activity on these pollutant levels in Tehran. Also based on the predictive model performance evaluation and concerning the principle of parsimony, in half of the cases, the MLR outperformed other models, despite its seeming simplicity and principal assumptions dependence. In other situations, the GAM was a good substitute. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the impact of the reduction in the number of test-day records per lactation on genetic parameters of test-day milk yield (TDMY) and the reliability of estimated breeding values for 305-day milk yield in Holstein cattle. Estimates of genetic parameters and breeding values were performed using the animal model of random regression and adjustment of the Legendre polynomial (fourth order). When comparing sires with the same number of daughters, greater reliability was found in the subpopulations with the highest number of milk test-day records per lactation per cow. It was also found that the elimination of at least one test-day record affected the reliability of estimated breeding value for 305-day milk yield in the sires, regardless of the class of number of daughters per sire. When selecting the 5% best sires and 20% best cows, the lowest order correlations were observed between the population with 10 test-day records per lactation (complete lactation) and the other subpopulations with incomplete lactations (4, 5, 6, 7, 8, 9 test-day records per lactation). The reduction in the number of test-day milk records per lactation interferes in the reliability of the estimated breeding value for sires and cows, negatively impacting the precision of selecting genetically superior animals. [ABSTRACT FROM AUTHOR]

The distribution of phytoplankton assemblages in response to physicochemical variables was assessed using Spearman's correlation and canonical correspondence analysis in four highly turbid estuaries of the Mekong River (MKR) system in Ben Tre Province, Vietnam. During two surveys (September 2017 and April 2018) at 20 sampling sites, a total of 162 species were recorded, with a dominance of diatoms (> 50%). Phytoplankton abundance varied from 3.01 × 105 to 11.85 × 105 cells/L, with the highest cell densities found at the BL2 station in the rainy season, mainly attributed to cyanobacteria during the rainy period, whereas diatoms were dominant in the dry season. Chlorophyll-a concentration in this area was low and decreased from the mouth stations. Similarity analysis distinguished the freshwater and oligohaline regions (characterized by lower phytoplankton abundance in the oligohaline areas) from the mesohaline and polyhaline zones (characterized by higher phytoplankton density in the polyhaline sections), based on the salinity gradient, which mostly explained the spatio-temporal distribution of phytoplankton. In which, freshwater and oligohaline sites were dominated by the diatom Coscinodiscus, the euglenoids Lepocinclis, and the green alga Scenedesmus in the rainy season, while mesohaline and polyhaline stations were dominated only by diatom species (Thalassionema and Skeletonema) in the dry season. High salinity and total dissolved solutes, and low nutrients are the major factors affecting phytoplankton growth and lead to low density and low primary biomass in turbid estuarine systems like the MKR estuaries. Our results further emphasize the importance of taking into account of salinity in understanding seasonal changes of phytoplankton communities, which can provide a valuable baseline data for ecological management strategies in tropical estuarine ecosystems. [ABSTRACT FROM AUTHOR]

Background: Over the past few decades, DNA microarray technology has emerged as a prevailing process for early identification of cancer subtypes. Several feature selection (FS) techniques have been widely applied for identifying cancer from microarray gene data but only very few studies have been conducted on distributing the feature selection process for detecting cancer subtypes. Objective: Not all the gene expressions are needed in prediction, this research article objective is to select discriminative biomarkers by using distributed FS method which helps in accurately diagnosis of cancer subtype. Traditional feature selection techniques have several drawbacks like unrelated features that could perform well in terms of classification accuracy with a suitable subset of genes will be left out of the selection. Method: To overcome the issue, in this paper a new filter-based method for gene selection is introduced which can select the highly relevant genes for distinguishing tissues from the gene expression dataset. In addition, it is used to compute the relation between gene–gene and gene–class and simultaneously identify subset of essential genes. Our method is tested on Diffuse Large B cell Lymphoma (DLBCL) dataset by using well-known classification techniques such as support vector machine, naïve Bayes, k-nearest neighbor, and decision tree. Results: Results on biological DLBCL dataset demonstrate that the proposed method provides promising tools for the prediction of cancer type, with the prediction accuracy of 97.62%, precision of 94.23%, sensitivity of 94.12%, F-measure of 90.12%, and ROC value of 99.75%. Conclusion: The experimental results reveal the fact that the proposed method is significantly improved classification accuracy and execution time, compared to existing standard algorithms when applied to the non-partitioned dataset. Furthermore, the extracted genes are biologically sound and agree with the outcome of relevant biomedical studies. [ABSTRACT FROM AUTHOR]

The present study was undertaken to estimate effect of various genetic and non-genetic factors on persistency of milk production and to identify the most appropriate persistency method that fits best in our environment. In the present study, effects of different non-genetic factors, viz. year, season, days to attain peak yield, and genetic group based on the level of exotic inheritance on persistency of milk yield in crossbred cattle were studied. Data comprised of 686 first lactation daily milk yield records of crossbred cattle that were maintained at GADVASU dairy farm over a period of 25 years from 1991 to 2015 were utilized to calculate persistency coefficients by four methods, viz., Ludwick and Peterson method (P1), Mahadevan method (P2), ratio method (P3), and Prasad et al. method (P4). Overall least squares means for persistency by Ludwick and Peterson method (P1), Mahadevan method (P2), ratio method (P3), and Prasad et al. method (P4) were 0.896 ± 0.096, 1.385 ± 0.224, 187.207 ± 26.398, and 0.621 ± 0.098, respectively. Effect of sires was significant (P < 0.05) on P2 and P4 methods. Effect of genetic group on all four methods was non-significant. Period of calving had significant (P < 0.01) effect on persistency of milk yield (P2, P3, and P4 methods). Effect of season of calving on persistency of milk yield was found to be significant in all estimates obtained by the four methods. Summer and autumn calvers were most persistent whereas spring and winter calvers were least persistent for (P2, P3, and P4 methods). Persistency of milk yield was significantly (P < 0.05) affected by days to attain peak yield in P1 and P2 methods. Maximum persistency was obtained in animals attaining peak at 41-57 days of lactation and minimum in < 41 days for Mahadevan method and ratio method. The highest heritability of persistency and minimum value of standard error was estimated as 0.275 ± 0.11 for the Mahadevan method followed by the Prasad method (0.197 ± 0.10) by half sib correlation method. The maximum coefficient of variation which indicates available variability was estimated as 20.788% for persistency by the Mahadevan method followed by 18.969% for the Prasad method. The highest correlation was also observed between P1 and P3 methods by Spearman’s and Pearson’s correlation for least squares breeding value of the sires. On the basis of heritability, standard error of heritability, and coefficient of variation, it can be concluded that the Mahadevan method followed by the Prasad method suits best to our environment for animals in first lactation as well as they can be utilized for effective selection for higher persistency in crossbred animals of Punjab. [ABSTRACT FROM AUTHOR]

In this paper, a new measure of image focus based on the statistical properties of polynomial coefficients and spectral radius is proposed. Spectral radius captures the dominant features and represents the important dynamics of an image. It is shown that the proposed focus measure is monotonic and unimodal with respect to the degree of defocusation, noise and blurring effects. Moreover, it is sufficiently invariant to contrast changes occur due to the variations in intensities of illumination. The noise studies show that the proposed focus measure is robust under the different noisy and blurring conditions. The performance of proposed focus measure is gauged by comparing with the existing image focus measures. Experimental results using synthetic as well as real-time images with known and unknown distortion conditions show the wider working capability and higher prediction consistency of the proposed focus measure. Moreover, the performance of the proposed approach is validated with most popular five image quality databases: TID2008, LIVE, CSIQ, IVC and Cornell-A57. Experimentation on the databases shows that the proposed metric provides the comparatively higher correlation with ideal mean observer score than the existing metrics. [ABSTRACT FROM AUTHOR]

In the present study, we examined the relationships between (1) N, P, total organic carbon (TOC), and total suspended sediment (TSS) each and stream flow and water table elevation, individually (2) N, P, and TOC, each and TSS, and (3) stream water C/N ratios and stream flow in managed pine forests with various switchgrass treatments implemented on four watersheds in coastal North Carolina plain. The treatments included a young pine forest–natural understorey (27.5 ha), a young pine forest with switchgrass intercropped between pine rows replacing natural understorey (IC) (26.3 ha), a mature thinned pine forest (25.9 ha), and pure switchgrass (27.1 ha). Precipitation, flow, water table elevation, N, phosphate, TOC, and TSS concentrations were measured from November 2009 to June 2014 (switchgrass growth from May 2012 after site preparation (SP) that ended in April 2012). Relationships (α = 0.05) among water quality and hydrologic variables were examined using a Spearman rank correlation coefficient and the principal component analysis (PCA). Nitrogen concentrations on IC were positively correlated with flow during SP. The export of nutrients and sediment from this drained pine plantation forest intercropped with switchgrass was affected by changes in hydrological and biochemical processes regulating the formation and transport of different water quality constituents during both site preparation and pine and switch growth periods. The PCA showed strong interaction between the hydrological and biochemical processes. [ABSTRACT FROM AUTHOR]