Effect of simultaneous injection of classical swine fever virus vaccine and Mycoplasma hyopneumoniae vaccine on immune response of swine

Objectives of this study were (1) to compare sero-conversion in pigs following simultaneous and separate vaccination against Classical Swine Fever (CSF) and Mycoplasma hyopneumoniae and (2) to determine safety of CSF and M. hyopneumoniae vaccines when given simultaneously. Twenty-four weaned pigs were divided into 3 groups of 8 heads. Groups were designated as non-simultaneous vaccinated group, simultaneous vaccinated group and negative control, respectively. Vaccines used in study were M.hyopneumoniae vaccine (SPRINTVAC®MH) and CSF vaccine (PESTIFFA®). IDEXX ELISA test kit (HerdChek M hyo) and LSIVET SUIS HC/PPC Blocking ELISA test kit were used to detect antibody titre on weekly basis. Sero-conversion rate of CSF antibody titre and M.hyo antibody titre were calculated. Result showed both simultaneous vaccination and non-simultaneous vaccination for CSF antibody titre reached 100% sero-conversion rate at 5 weeks post vaccination. Therefore, simultaneous vaccination was able to accomplish similar results as in non-simultaneous vaccination. Sero-conversion rate for CSF antibody titre in non-simultaneous group was slower before it reached 5 weeks post vaccination. 12.5% of animal from negative control group sero-converted at 5 weeks post vaccination due to false-positive result or field infections. M. hyopneumoniae antibody titre sero-conversion rate in both simultaneous vaccination and non-simultaneous vaccination reached 100% sero-conversion rate after 6 weeks post vaccination. Control group showed negative result for M. hyopneumoniae antibody titre throughout whole experiment. Vaccines used in trial did not cause any adverse effect after post vaccination when given simultaneously

Depression outcome expectancy in primary care in Singapore: symptom severity as a mediating determinant

Background: Depression has been identified as the most common mental illness in Singapore. To address this growing concern, the current study focused on the population within the primary care setting since depression has been demonstrated to be highly prevalent in these patients. This study examined the possible predictors of outcome expectancy based on illness perception and depression severity. Methods: One hundred and one adult patients with depressive symptoms in primary care were recruited for a cross-sectional study. Positive outcome expectancy was measured using the Depression Change Expectancy Scale, and illness perception was measured using the Illness Perception Questionnaire Mental Health. Depression severity was derived from the Patient Health Questionnaire-9 scores extracted from the participants' medical records. Regression and mediation analyses were applied to explore possible predictors of positive outcome expectancy. Results: Regression analysis demonstrated that symptom severity, and specific dimensions under illness perception (i.e., perception of chronicity, perception of personal control, and perception of treatment control) were the most significant predictors of positive outcome expectancy. Mediation analysis found that symptom severity partially mediated the relationship between perception of chronicity and positive outcome expectancy. Conclusions: Pharmacotherapy, interventions from allied health professionals, and psychotherapeutic interventions (e.g., strategies from positive psychology, solution-focused therapy, and strengths-based cognitive behavioral therapy) that aim to directly alleviate depressive symptoms as well as improve the perceptions of chronicity, personal control, and treatment control could potentially enhance treatment benefits in primary care patients with depression

Feature Selection Using Firefly Optimization for Classification and Regression Models

In this research, we propose a variant of the Firefly Algorithm (FA) for discriminative feature selection in classification and regression models for supporting decision making processes using data-based learning methods. The FA variant employs Simulated Annealing (SA)-enhanced local and global promising solutions, chaotic-accelerated attractiveness parameters and diversion mechanisms of weak solutions to escape from the local optimum trap and mitigate the premature convergence problem in the original FA algorithm. A total of 29 classification and 11 regression benchmark data sets have been used to evaluate the efficiency of the proposed FA model. It shows statistically significant improvements over other state-of-the-art FA variants and classical search methods for diverse feature selection problems. In short, the proposed FA variant offers an effective method to identify optimal feature subsets in classification and regression models for supporting data-based decision making processes

Intelligent facial emotion recognition using moth-firefly optimization

In this research, we propose a facial expression recognition system with a variant of evolutionary firefly algorithm for feature optimization. First of all, a modified Local Binary Pattern descriptor is proposed to produce an initial discriminative face representation. A variant of the firefly algorithm is proposed to perform feature optimization. The proposed evolutionary firefly algorithm exploits the spiral search behaviour of moths and attractiveness search actions of fireflies to mitigate premature convergence of the Levy-flight firefly algorithm (LFA) and the moth-flame optimization (MFO) algorithm. Specifically, it employs the logarithmic spiral search capability of the moths to increase local exploitation of the fireflies, whereas in comparison with the flames in MFO, the fireflies not only represent the best solutions identified by the moths but also act as the search agents guided by the attractiveness function to increase global exploration. Simulated Annealing embedded with Levy flights is also used to increase exploitation of the most promising solution. Diverse single and ensemble classifiers are implemented for the recognition of seven expressions. Evaluated with frontal-view images extracted from CK+, JAFFE, and MMI, and 45-degree multi-view and 90-degree side-view images from BU-3DFE and MMI, respectively, our system achieves a superior performance, and outperforms other state-of-the-art feature optimization methods and related facial expression recognition models by a significant margin

Intelligent Leukaemia Diagnosis with Bare-Bones PSO based Feature Optimization

In this research, we propose an intelligent decision support system for acute lymphoblastic leukaemia (ALL) diagnosis using microscopic images. Two Bare-bones Particle Swarm Optimization (BBPSO) algorithms are proposed to identify the most significant discriminative characteristics of healthy and blast cells to enable efficient ALL classification. The first BBPSO variant incorporates accelerated chaotic search mechanisms of food chasing and enemy avoidance to diversify the search and mitigate the premature convergence of the original BBPSO algorithm. The second BBPSO variant exhibits both of the abovementioned new search mechanisms in a subswarm-based search. Evaluated with the ALL-IDB2 database, both proposed algorithms achieve superior geometric mean performances of 94.94% and 96.25%, respectively, and outperform other metaheuristic search and related methods significantly for ALL classification

A New Sign Distance-Based Ranking Method for Fuzzy Numbers

In this paper, a new sign distance-based ranking method for fuzzy numbers is proposed. It is a synthesis of geometric centroid and sign distance. The use of centroid and sign distance in fuzzy ranking is not new. Most existing methods (e.g., distance-based method [9]) adopt the Euclidean distance from the origin to the centroid of a fuzzy number. In this paper, a fuzzy number is treated as a polygon, in which a new geometric centroid for the fuzzy number is proposed. Since a fuzzy number can be represented in different shapes with different spreads, a new dispersion coefficient pertaining to a fuzzy number is formulated. The dispersion coefficient is used to fine-tune the geometric centroid, and subsequently sign distance from the origin to the tuned geometric centroid is considered. As discussed in [5-9], an ideal fuzzy ranking method needs to satisfy seven reasonable fuzzy ordering properties. As a result, the capability of the proposed method in fulfilling these properties is analyzed and discussed. Positive experimental results are obtained

A new dempster-shafer theory-based method with fuzzy targets for fuzzy sets ranking

In this paper, a new Fuzzy Set (FS) ranking method (for type-1 and interval type-2 FSs), which is based on the Dempster-Shafer Theory (DST) of evidence with fuzzy targets, is investigated. Fuzzy targets are adopted to reflect human viewpoints on fuzzy ranking. Two important measures in DST, i.e., the belief and plausibility measures, are used to rank FSs. The proposed approach is evaluated with several benchmark examples. The use of the belief and plausibility measures in fuzzy ranking are discussed and compared. We further analyze the capability of the proposed approach in fulfilling six reasonable fuzzy ordering properties as discussed in [9]-[11]

A new fuzzy peer assessment methodology for cooperative learning of students

In this paper, a new fuzzy peer assessment methodology that considers vagueness and imprecision of words used throughout the evaluation process in a cooperative learning environment is proposed. Instead of numerals, words are used in the evaluation process, in order to provide greater flexibility. The proposed methodology is a synthesis of perceptual computing (Per-C) and a fuzzy ranking algorithm. Per-C is adopted because it allows uncertainties of words to be considered in the evaluation process. Meanwhile, the fuzzy ranking algorithm is deployed to obtain appropriate performance indices that reflect a student's contribution in a group, and subsequently rank the student accordingly. A case study to demonstrate the effectiveness of the proposed methodology is described. Implications of the results are analyzed and discussed. The outcomes clearly demonstrate that the proposed fuzzy peer assessment methodology can be deployed as an effective evaluation tool for cooperative learning of students

Monotone Data Samples Do Not Always Produce Monotone Fuzzy If- Then Rules: Learning with Ad hoc and System Identification Methods

In this paper, ad hoc and system identification methods are used to generate fuzzy If-Then rules for a zeroorder Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS) using a set of multi-attribute monotone data. Convex and normal trapezoidal fuzzy sets, with a strong fuzzy partition strategy, is employed. Our analysis shows that even with multi-attribute monotone data, non-monotone fuzzy If- Then rules can be produced using an ad hoc method. The same observation can be made, empirically, using a system identification method, e.g., a derivative–based optimization method and the genetic algorithm. This finding is important for modeling a monotone FIS model, as the result shows that even with a “clean” data set pertaining to a monotone system, the generated fuzzy If-Then rules may need to be preprocessed, before being used for FIS modeling. As such, monotone fuzzy rule relabeling is useful. Besides that, a constrained non-linear programming method for FIS modelling is suggested, as a variant of the system identification method