Immunomodulatory effects of seagrass Halophila ovalis polysaccharide mixed feed in adult black tiger shrimp Penaeus monodon and its protective efficacy against white spot syndrome virus infection

As white spot syndrome virus (WSSV) can be highly pathogenic in penaeid shrimp, various feed supplements have been tested to help to protect farmed shrimp against WSSV disease. Here a polysaccharide extract from Halophila ovalis (HO) seagrass was added to feeds at concentrations of 0.25, 0.5, and 1.0 g/kg to assess its ability to protect Black Tiger shrimp (Penaeus monodon) against WSSV challenge. Following feeding on these diets for 25 days, P. monodon were challenged by muscle injection and monitored for 21 days. On Day 0 and on Days 7 and 21 post-injection (pi), total haemocyte counts (THC), total protein concentrations, prophenoloxidase activity and respiratory burst activity were compared using haemolymph collected from 10 shrimp. All shrimp fed the basal diet died by Day 7 pi but survival times were extended among shrimp fed diets containing HO polysaccharide (HOP), and significantly at concentrations of 0.5 or 1 gkg^-1. Concomitantly with improved survival, all haemolymph immune parameters examined were enhanced significantly (p<0.05) among shrimp fed diets containing higher amounts of HOP. WSSV infection loads determined by real-time PCR were also lowered. The data suggest that if shrimp growth performance is not affected, inclusion of 0.5-1 gkg-1 HOP in commercial feeds might increase resilience of pond stocks of P. monodon against WSSV disease and when disease occurs, provide farmers with a longer management window to minimize economic losses

Bleed-Through Removal in Document Images

When documents are written on both sides, quite often ink bleeds through the paper. This is a common phenomenon with old documents and low quality paper. With the presence of increased bleed-through, reading and deciphering the text becomes tedious. This thesis implements algorithms for reducing bleed-through distortion using techniques in digital image processing. A comparative study of three methods has been performed with the first being basic enhancement through thresholding. The next two methods are founded on a registration process which aims at working on both the recto and verso sides simultaneously. Firstly, both sides of the documents are digitized. The verso is then flipped and corrected so as to correspond to the coordinates of one side exactly with the coordinates of the original writing on the other which is done using an affine transformation of six parameters. The parameters are found by optimizing the alignment process. A restoration algorithm is then applied to remove bleed though areas on the desired side page. The third method proposes the use of cross correlation to handle the registration process. The accuracy of bleed-through correction is found to be largely dependent on the accuracy of alignment of the documents

On the analysis of EEG power, frequency and asymmetry in Parkinson's disease during emotion processing

Objective: While Parkinson’s disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study was to investigate whether there are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal controls during emotion information processing. Method: EEG recordings from 14 scalp sites were collected from 20 PD patients and 30 age-matched normal controls. Multimodal (audio-visual) stimuli were presented to evoke specific targeted emotional states such as happiness, sadness, fear, anger, surprise and disgust. Absolute and relative power, frequency and asymmetry measures derived from spectrally analyzed EEGs were subjected to repeated ANOVA measures for group comparisons as well as to discriminate function analysis to examine their utility as classification indices. In addition, subjective ratings were obtained for the used emotional stimuli. Results: Behaviorally, PD patients showed no impairments in emotion recognition as measured by subjective ratings. Compared with normal controls, PD patients evidenced smaller overall relative delta, theta, alpha and beta power, and at bilateral anterior regions smaller absolute theta, alpha, and beta power and higher mean total spectrum frequency across different emotional states. Inter-hemispheric theta, alpha, and beta power asymmetry index differences were noted, with controls exhibiting greater right than left hemisphere activation. Whereas intra-hemispheric alpha power asymmetry reduction was exhibited in patients bilaterally at all regions. Discriminant analysis correctly classified 95.0% of the patients and controls during emotional stimuli. Conclusion: These distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients

Audit Exemption For Small And Medium Enterprises: Perceptions Of Malaysian Auditors

The objective of the study is to examine audit frm size, the provision of non-audit services (NAS) and audit tenure as factors that influence the likelihood that an auditor agrees with allowing audit exemption. This study employs a 2 × 2 × 2 within-subject experimental design. Respondent auditors were required to evaluate 8 case scenarios. A total of 79 questionnaires were returned and used for data analysis. General Linear Measurement (Repeat Measure) was used to analyse the data. The study found that an audit frm size has a signifcant impact on the likelihood that an auditor agrees with offering audit exemption. The larger the size of the frm, the greater is the likelihood of agreeing with allowing audit exemption. Therefore, it is suggested that small audit frms (with 5 or fewer employees) merge and focus on activities that contribute more added value such as consultancy. In so doing, auditors from these frms would be required to improve their knowledge and capacity by offering these services and not merely focusing on traditional audit work for SMEs. This is because SMEs are known to have limited resources and capacity and thus would be expected to have poor internal control. The requirement of a mandatory audit for such frms might lead auditors to compromise their independence. Thus, the governmen

Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: A comparative study

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders