A method was proposed for the detection of outliers and influential observations in the framework of a mixed linear model, prior to the quantitative trait locus (QTL) mapping analysis. We investigated the impact of outliers on QTL mapping for complex traits in a mouse BXD population, and observed that the dropping of outliers could provide the evidence of additional QTL and epistatic loci affecting the 1stBrain-OB and the 2ndBrain-OB in a cross of the abovementioned population. The results could also reveal a remarkable increase in estimating heritabilities of QTL in the absence of outliers. In addition, simulations were conducted to investigate the detection powers and false discovery rates (FDRs) of QTLs in the presence and absence of outliers. The results suggested that the presence of a small proportion of outliers could increase the FDR and hence decrease the detection power of QTLs. A drastic increase could be obtained in the estimates of standard errors for position, additive and additivex environment interaction effects of QTLs in the presence of outliers.
Animals ; Chromosome Mapping ; methods ; Mice ; Mice, Inbred C57BL ; Mice, Inbred DBA ; Quantitative Trait Loci

Brix weight per stool (BW) of sugarcane is a complex trait, which is the final product of a combination of many components. Diallel cross experiments were conducted during a period of two years for BW and its five component traits, including stalk diameter (SD), stalk length (SL), stalk number (SN), stalk weight (SW), and brix scale (BS) of sugarcane. Phenotypic data of all the six traits were analyzed by mixed linear model and their phenotype variances were portioned into additive (A), dominance (D), additive x environment interaction (AE) and dominance x environment interaction (DE) effects, and the correlations of A, D, AE and DE effects between BW and its components were estimated. Conditional analysis was employed to investigate the contribution of the components traits to the variances of A, D, AE and DE effects of BW. It was observed that the heritabilities of BW were significantly attributed to A, D and DE by 23.9%, 30.9% and 28.5%, respectively. The variance of A effect for BW was significantly affected by SL, SN and BS by 25.3%, 93.7% and 17.4%, respectively. The variances of D and DE effects for BW were also significantly influenced by all the five components by 5.1%(85.5%. These determinants might be helpful in sugarcane breeding and provide valuable information for multiple-trait improvement of BW.
Alleles ; Body Weight ; Inheritance Patterns ; genetics ; Phenotype ; Saccharum ; anatomy & histology ; genetics ; growth & development

Water hyacinth (Eichhornia crassipes (Mart.) Solms) is a prolific free floating aquatic macrohpyte found in tropical and subtropical parts of the earth. The effects of pollutants from textile wastewater on the anatomy of the plant were studied. Water hyacinth exhibits hydrophytic adaptations which include reduced epidermis cells lacking cuticle in most cases, presence of large air spaces (7 approximately 50 microm), reduced vascular tissue and absorbing structures. Textile waste significantly affected the size of root cells. The presence of raphide crystals was noted in parenchyma cells of various organs in treated plants.
Eichhornia ; anatomy & histology ; growth & development ; Industrial Waste ; Plant Roots ; anatomy & histology ; Rhizome ; anatomy & histology ; Textile Industry ; Water Pollutants, Chemical

OBJECTIVEDuring present investigation the data of a laboratory-scale anoxic sulfide oxidizing (ASO) reactor were used in a neural network system to predict its performance.

METHODSFive uncorrelated components of the influent wastewater were used as the artificial neural network model input to predict the output of the effluent using back-propagation and general regression algorithms. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA) before they are fed to a back propagated neural network.

RESULTSWithin the range of experimental conditions tested, it was concluded that the ANN model gave predictable results for nitrite removal from wastewater through ASO process. The model did not predict the formation of sulfate to an acceptable manner.

CONCLUSIONApart from experimentation, ANN model can help to simulate the results of such experiments in finding the best optimal choice for ASObased denitrification. Together with wastewater collection and the use of improved treatment systems and new technologies, better control of wastewater treatment plant (WTP) can lead to more effective maneuvers by its operators and, as a consequence, better effluent quality.

Bioreactors ; Neural Networks (Computer) ; Oxidation-Reduction ; Sulfates ; chemistry ; Sulfides ; chemistry ; Time Factors ; Waste Disposal, Fluid ; methods