Genome-wide association study identifies quantitative trait loci affecting hematological traits in an F2 intercross between Landrace and Korean native pigs.

Changes affecting the status of health and robustness can bring about physiological alterations including hematological parameters in swine. To identify quantitative trait loci (QTL) associated with eight hematological traits (one leukocyte trait, six erythrocyte traits and one platelet trait), we conducted a genome-wide association study using the PorcineSNP60K BeadChip in a resource population derived from an intercross between Landrace and Korean native pigs. A total of 36 740 SNPs from 816 F2 progeny were analyzed for each blood-related trait after filtering for quality control. Data were analyzed by the genome-wide rapid association using mixed model and regression (GRAMMAR) approach. A total of 257 significant SNPs (P < 1.36 × 10(-6) ) on SSC3, 6, 8, 13 and 17 were identified for blood-related traits in this study. Interestingly, the genomic region between 17.9 and 130 Mb on SSC8 was found to be significantly associated with red blood cell, mean corpuscular volume and mean corpuscular hemoglobin. Our results include the identification of five significant SNPs within five candidate genes (KIT, IL15, TXK, ARAP2 and ERG) for hematopoiesis. Further validation of these identified SNPs could give valuable information for understanding the variation of hematological traits in pigs.

Genome-wide association analysis identifies quantitative trait loci for growth in a Landrace purebred population.

Growth-related traits are complex and economically important in the livestock industry. The aim of this study was to identify quantitative trait loci (QTL) and the associated positional candidate genes affecting growth in pigs. A genome-wide association study (GWAS) was performed using the porcine single-nucleotide polymorphism (SNP) 60K bead chip. A mixed-effects model and linear regression approach were used for the GWAS. The data used in the study included 490 purebred Landrace pigs. All experimental animals were genotyped with 39 438 SNPs located throughout the pig autosomes. We identified a strong association between a SNP marker on chromosome 16 and body weight at 71 days of age (ALGA0092396, P = 5.35 × 10(-9) , Bonferroni adjusted P < 0.05). The SNP marker was located near the genomic region containing IRX4, which encodes iroquois homeobox 4. This SNP marker could be useful in the selective breeding program after validating its effect on other populations.

QTL analysis of body weight and carcass body length traits in an F2 intercross between Landrace and Korean native pigs.

Growth traits, such as body weight and carcass body length, directly affect productivity and economic efficiency in the livestock industry. We performed a genome-wide linkage analysis to detect the quantitative trait loci (QTL) that affect body weight, growth curve parameters and carcass body length in an F2 intercross between Landrace and Korean native pigs. Eight phenotypes related to growth were measured in approximately 1000 F2 progeny. All experimental animals were subjected to genotypic analysis using 173 microsatellite markers located throughout the pig genome. The least squares regression approach was used to conduct the QTL analysis. For body weight traits, we mapped 16 genome-wide significant QTL on SSC1, 3, 5, 6, 8, 9 and 12 as well as 22 suggestive QTL on SSC2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 16 and 17. On SSC12, we identified a major QTL affecting body weight at 140 days of age that accounted for 4.3% of the phenotypic variance, which was the highest test statistic (F-ratio = 45.6 under the additive model, nominal P = 2.4 × 10(-11) ) observed in this study. We also showed that there were significant QTL on SSC2, 5, 7, 8, 9 and 12 affecting carcass body length and growth curve parameters. Interestingly, the QTL on SSC2, 3, 5, 6, 8, 9, 10, 12 and 17 influencing the growth-related traits showed an obvious trend for co-localization. In conclusion, the identified QTL may play an important role in investigating the genetic structure underlying the phenotypic variation of growth in pigs.

The Possibility of TBC1D21 as a Candidate Gene for Teat Numbers in Pigs.

Based on a quantitative traits locus (QTL) study using a F2 intercross between Landrace and Korean native pigs, a significant QTL affecting teat numbers in SSC7 was identified. The strong positional candidate gene, TBC1D21, was selected due to its biological function for epithelial mesenchymal cell development. Sequence analysis revealed six single nucleotide polymorphisms (SNPs) in the TBC1D21 gene. Among these, two SNP markers, one silent mutation (SNP01) for g.13,050A>G and one missense mutation (SNP04) for c.829A>T (S277C), were genotyped and they showed significant associations with teat number traits (p value = 6.38E-05 for SNP01 and p value = 1.06E-07 for SNP04 with total teat numbers). Further functional validation of these SNPs could give valuable information for understanding the teat number variation in pigs.

QTL analysis of white blood cell, platelet and red blood cell-related traits in an F2 intercross between Landrace and Korean native pigs.

Haematological traits play important roles in disease resistance and defence functions. The objective of this study was to locate quantitative trait loci (QTL) and the associated positional candidate genes influencing haematological traits in an F(2) intercross between Landrace and Korean native pigs. Eight blood-related traits (six erythrocyte traits, one leucocyte trait and one platelet trait) were measured in 816 F(2) progeny. All experimental animals were genotyped with 173 informative microsatellite markers located throughout the pig genome. We report that nine chromosomes harboured QTL for the baseline blood parameters: genomic regions on SSC 1, 4, 5, 6, 8, 9, 11, 13 and 17. Eight of twenty identified QTL reached genome-wide significance. In addition, we evaluated the KIT locus, an obvious candidate gene locus affecting variation in blood-related traits. Using dense single nucleotide polymorphism marker data on SSC 8 and the marker-assisted association test, the strong association of the KIT locus with blood phenotypes was confirmed. In conclusion, our study identified both previously reported and novel QTL affecting baseline haematological parameters in pigs. Additionally, the positional candidate genes identified here could play an important role in elucidating the genetic architecture of haematological phenotype variation in swine and in humans.

Sewage sludge reduction and system optimization in a catalytic ozonation process.

The main objective of this study was to suggest a feasible, effective process for the reduction of sewage sludge using ozone oxidation catalysed by metal ion. A series of lab-scale experiments was conducted to select a suitable catalyst and its proper dose to achieve optimum sludge reduction. Using a central composite design under response surface methodology (RSM), system optimization with respect to sludge reduction and cost-effectiveness was performed by varying the independent parameters: dosages of ozone and ions. Five metal ions, Mn2+, Fe2+, Zn2+, CU2+, and Al3+1, were tested, and the manganese ion showed the highest sludge reduction, as measured by a decrease in total suspended solids. The ozone/Mn combination achieved approximately twice as much sludge reduction as the ozonation alone. Furthermore, the Mn dose of 10 mg/g-TS (total solids) resulted in the highest sludge reduction efficiency among the different doses, which ranged from 0 to 20 mg-Mn/g-TS. The predicted efficiency of sewage sludge reduction using the RSM was found to agree well with the experimental results, and the statistical analyses predicted optimum ranges for the doses of ozone and Mn ions, taking into account the overall cost for sewage sludge treatment.

A systematic approach to data-driven modeling and soft sensing in a full-scale plant.

The well-known mathematical modeling and neural networks (NNs) methods have limitations to incorporate the key process characteristics at the wastewater treatment plants (WWTPs) which are complex, non-stationary, temporal correlation, and nonlinear systems. In this study, a systematic methodology of NNs modeling which can be efficiently included in the key modeling information of the WWTPs is performed by selecting the temporal effect of the hydraulics based on multi-way principal components analysis (MPCA). The proposed method is applied for modeling wastewater quality of a full-scale plant, which is a Daewoo nutrient removal (DNR) process. Through the experimental results in a full-scale plant, the efficiency of the proposed method is evaluated and the prediction capability is highly improved by the inclusion of the hydraulics term due to the optimized structure of neural networks.

Sensor validation and reconciliation for a partial nitrification process.

Wastewater treatment plants (WWTP) are notorious for poor data quality and sensor reliability due to the hostile environment in which the measurement equipment has to function. In this paper, a structured residual approach with maximum sensitivity (SRAMS) based on the redundancy of the measurements is used to detect, identify and reconstruct single and multiple sensor faults in a single reactor for high activity ammonia removal over nitrite (SHARON) process. SRAMS is based on inferences, which are insensitive to the faults in the sensor of interest and sensitive to faults in the other sensors. It is used for four types of sensor failure detection: bias, drift, complete failure and precision degradation. The application of sensor validation shows that single and multiple sensor faults can be detected and that the fault magnitude and fault type can be estimated by the reconstruction scheme. This sensor validation method is not limited by the type or application of the considered sensors. The methodology can thus easily be applied for sensor surveillance of other continuously measuring sensors and analysers.

High-Level Ciprofloxacin-Resistant Campylobacter jejuni Isolates Circulating in Humans and Animals in Incheon, Republic of Korea.

Campylobacter jejuni is one of the major causative pathogens of outbreaks or sporadic cases of diarrhoeal diseases worldwide. In this study, we compared the phenotypic and genetic characteristics of C. jejuni isolates of human and food-producing animal origins in Korea and examined the genetic relatedness between these two groups of isolates. Regardless of isolation source, all C. jejuni isolates harboured four virulence genes, cadF, cdtB, ciaB and racR, whereas the wlaN and virB11 genes were more frequently observed in human isolates. Antimicrobial susceptibility testing showed that the majority of C. jejuni isolates displayed high-level resistance to fluoroquinolone (95.2%) or tetracycline (76.2%) antibiotics, and 12.4% of isolates exhibited multidrug resistance (more than three classes of antibiotics tested). Pulsed-field gel electrophoresis (PFGE) of all Campylobacter isolates revealed 51 different SmaI-PFGE patterns and six major clusters containing both human and animal isolates. These results indicate that genetically diverse strains of C. jejuni with antimicrobial drug-resistance and virulence properties have prevailed in Incheon. Nevertheless, some particular populations continue to circulate within the community, providing the evidence for an epidemiological link of C. jejuni infections between humans and food-producing animals. Therefore, the continued monitoring and surveillance of C. jejuni isolates of human and food-producing animal origins are required for public health and food safety.

Disturbance detection and isolation in the activated sludge process.

This paper proposes a new fault detection and isolation (FDI) method. This method monitors the distribution of process data and detects changes in this distribution, which reflect changes in the corresponding operating condition. A modified dissimilarity index and a FDI technique are defined to quantitatively evaluate the difference between data sets. This technique considers the importance of each transformed variable in the multivariate system. The FDI technique is applied to a benchmark simulation and to data from a real wastewater treatment plant. Simulation results show that it immediately detects disturbances and automatically distinguishes between serious and minor anomalies for various types of fault. The method not only detects the disturbances, but also isolates the scale of the disturbance, facilitating the interpretation of the disturbance source. The proposed monitoring technique is found to be appropriate for analyzing the biological wastewater treatment process, which is characterized by a variety of fault and disturbance sources and non-stationary characteristics.

New monitoring technique with an ICA algorithm in the wastewater treatment process.

A new monitoring method using independent component analysis (ICA) is suggested for the wastewater treatment process (WWTP). ICA is an extension of PCA (Principal Component Analysis). While PCA can only impose independence up to the second order (mean and variance) with constraint on the direction vectors to be orthogonal, ICA imposes statistical independence up to more than second order on the individual component and has no orthogonal condition. When the variables have the Gaussian distribution, PCA itself provides a satisfactory result in monitoring performance. However, the measured variables are not often normally distributed. In this case, ICA can provide better monitoring results than PCA since ICA is based on the assumption that the latent variables are not normally distributed. In this paper, the ICA monitoring algorithm with kernel density estimation was applied to fault detection and diagnosis of the wastewater simulation benchmark. ICA with kernel density estimation gives better results than PCA in disturbance detection in spite of severe periodic features of the wastewater plant.

Dynamic monitoring system for full-scale wastewater treatment plants.

This paper proposes a new process monitoring method using dynamic independent component analysis (ICA), ICA is a recently developed technique to extract the hidden factors that underlie sets of measurements, whereas principal component analysis (PCA) is a dimensionality reduction technique in terms of capturing the variance of the data. Its goal is to find a linear representation of non-Gaussian data so that the components are statistically independent. PCA aims at finding PCs that are uncorrelated and are linear combinations of the observed variables, while ICA is designed to separate the ICs that are independent and constitute the observed variables. The dynamic ICA monitoring method is applying ICA to the augmenting matrix with time-lagged variables. The dynamic monitoring method was applied to detect and monitor disturbances in a full-scale biological wastewater treatment (WWTP), which is characterized by a variety of dynamic and non-Gaussian characteristics. The dynamic ICA method showed more powerful monitoring performance on a WWTP application than the dynamic PCA method since it can extract source signals which are independent of time and cross-correlation of variables.

Closed-loop identification and control application for dissolved oxygen concentration in a full-scale coke wastewater treatment plant.

The objective of this paper is to apply a closed-loop identification to actual dissolved oxygen control system in the coke wastewater treatment plant. It approximates the dissolved oxygen dynamics to a high order model using the integral transform method and reduces it to the first-order plus time delay (FOPTD) or second-order plus time delay (SOPTD) for the PID controller tuning. To experiment the process identification on the real plant, a simple set-point change of the speed of surface aerator under the closed-loop control without any mode change was used as an activation signal of the identification. The full-scale experimental results show a good identification performance and a good tracking ability for set-point change. As a result of improved control performance, the fluctuation of dissolved oxygen concentration variation has been decreased and the electric power saving has been accomplished.

A new statistical framework for parameter subset selection and optimal parameter estimation in the activated sludge model.

A new model-calibration method has been proposed to solve the problems associated with parameter subset selection and parameter estimation of the activated sludge model (ASM). We propose the use of a statistical methodology for reasonable parameter selection and parameter estimation that consists of sensitivity analysis, similarity measures, hierarchical clustering and response surface methods (RSM). The introduction of effluent quality index (EQI) can reduce all of the outputs of the ASM model into one factor. The EQI was used to calculate a sensitivity matrix. Then, the hierarchical clustering algorithm was used for parameter subset selection. This selection was based on a similarity measure using the sensitivity matrix and was used to reduce the number of model parameters by selecting only one parameter per cluster group (parameter subset selection step). Lastly, a RSM analysis was conducted in order to determine the optimal parameter values. This study was conducted in order to develop a new statistical framework that can greatly reduce the computational effort required to find the optimal solution by reducing the number of parameters. The experimental results indicated that the calibrated model can improve the prediction quality of the ASM model and the efficiency of the modeling.

A new rehabilitation training system for postural balance control using virtual reality technology.

A new rehabilitation training system, designated as a virtual cycling system, was developed to improve postural balance control by combining virtual reality (VR) technology with a bicycle. Several parameters including path deviation, path deviation velocity, cycling time, and head movement were extracted and evaluated to quantify the extent of control. The system was effective as a training device and, in addition, the technology might have a wider applicability to the rehabilitation field.