Development of Safety and Quality Management System in Shrimp Farming

Shrimp Aquaculture has provided tremendous opportunity for the economic and social upliftment of rural communities in the coastal areas of our country Over a hundred thousand farmers, of whom about 90% belong to the small and marginal category, are engaged in shrimp farming. Penaeus monodon is the most predominant cultured species in India which is mainly exported to highly sophisticated, quality and safety conscious world markets. Food safety has been of concem to humankind since the dawn of history and the concern about food safety resulted in the evolution of a cost effective, food safety assurance method, the Hazard Analysis Critical Control Point (HACCP). Considering the major contribution of cultured Penaeus monodon to the total shrimp production and the economic losses encountered due to disease outbreak and also because traditional methods of quality control and end point inspection cannot guarantee the safety of our cultured seafood products, it is essential that science based preventive approaches like HACCP and Pre requisite Programmes (PRP) be implemented in our shrimp farming operations. PRP is considered as a support system which provides a solid foundation for HACCP. The safety of postlarvae (PL) supplied for brackish water shrimp farming has also become an issue of concern over the past few years. The quality and safety of hatchery produced seeds have been deteriorating and disease outbreaks have become very common in hatcheries. It is in this context that the necessity for following strict quarantine measures with standards and code of practices becomes significant. Though there were a lot of hue and cry on the need for extending the focus of seafood safety assurance from processing and exporting to the pre-harvest and hatchery rearing phases, an experimental move in this direction has been rare or nil. An integrated management system only can assure the effective control of the quality, hygiene and safety related issues. This study therefore aims at designing a safety and quality management system model for implementation in shrimp farming and hatchery operations by linking the concepts of HACCP and PRP.

Biochemical and molecular characterization of pathogenic vibrios from hatcheries and aquaculture farms

Vibrio are important during hatchery rearing. aquaculture phase and post-harvest quality of shrimps. Vibrio spp are of concern to shrimp farmers and hatchery operators because certain species can cause Vibriosis. Vibrio species are of concern to humans because certain species cause serious diseases.With the progress in aquaculture, intensive systems used for shrimp aquaculture create an artificial environment that increases bacterial growth. To maintain the productivity of such an intensive aquaculture, high inputs of fish protein have to be employed for feeding together with high levels of water exchange and the massive use of antibiotics/ probiotics / chemicals. It seems that the combination of these conditions favours the proliferation of vibrios and enhances their virulence and disease prevalence. The risk of a microbial infection is high, mainly at larval stages. The effect and severity are related to Vibrio species and dose, water, feed, shrimp quality and aquaculture management.Consumption of seafood can occasionally result in food-bome illnesses due to the proliferation of indigenous pathogens like Vibrio.Of the l2 pathogenic Vibrio species, 8 species are known to be directly food associated. Strict quality guidelines have been laid by the importing nations, for the food products that enter their markets. The microbiological quality requirement for export of frozen shrimp products is that V.cholerae, V.parahaemolyticus and V. vulnificus should be absent in 25g of the processed shrimp (Export Inspection Council of India, 1995). The mere presence of these pathogenic Vibrios is sufficient for the rejection of the exported product.The export rejections cause serious economic loss to the shrimp industry and might harm the brand image of the shrimp products from the countiy.There is a need for an independent study on the incidence of different pathogenic vibrios in shrimp aquaculture and investigate their biochemical characteristics to have a better understanding about the growth and survival of these organisms in the shrimp aquaculture niche. PCR based methods (conventional PCR, duplex PCR, multiplex-PCR and Real Time PCR) for the detection of the pathogenic Vibrios is important for rapid post-harvest quality assessment. Studies on the genetic heterogeneity among the specific pathogenic vibrio species isolated from shrimp aquaculture system provide; valuable information on the extent of genetic diversity of the pathogenic vibrios, the shrimp aquaculture system.So the present study was undertaken to study the incidence of pathogenic Vibrio spp. in Penaeus monodon shrimp hatcheries and aquaculture farms, to carry out biochemical investigations of the pathogenic Vibrio spp isolated from P. monodon hatchery and. aquaculture environments, to assess the effect of salt (NaCl) on the growth and enzymatic activities of pathogenic Vibrio spp., to study the effect of preservatives, and chemicals on the growth of pathogenic Vibrio spp. and to employ polymerase chain reaction (PCR) methods for the detection of pathogenic V ibrio spp.Samples of water (n=7) and post-larvae (n=7) were obtained from seven Penaeus monodon hatcheries and samples of water (n=5), sediment (n=5) and shrimp (n=5) were obtained from five P. monodon aquaculture farms located on the East Coast of lndia. The microbiological examination of water, sediment, post-larvae and shrimp samples was carried out employing standard methods and by using standard media.The higher bacterial loads were obtained in pond sediments which can be attributed to the accumulation of organic matter at the pond bottom which stimulated bacterial growth.Shrimp head. (4.78 x 105 +/- 3.0 x 104 cfu/g) had relatively higher bacterial load when compared to shrimp muscle 2.7 x 105 +/- 1.95 x 104 cfu/g). ln shrimp hatchery samples, the post-larvae (2.2 x 106 +/- 1.9 x 106 cfu/g) had higher bacterial load than water (5.6 x 103 +/- 3890 cfu/ml).The mean E.coli counts were higher in aquaculture pond sediment (204+/-13 cfu/g) and pond water (124+/-88 cfu/ml). Relatively lower Escherichia coli counts were obtained from shrimp samples (12+/-11 to 16+/-16.7 cfu/g). The presence of E.coli in aquaculture environment might have been from the source water. E.coli was not detected in hatchery waters and post-larvae.

Modeling Of Work-Related Musculoskeletal Disorders In Chemical Process Industry In India

In this modern complex world, stress at work is found to be increasingly a common feature in day to day life. For the same reason, job stress is one of the active areas in occupational health and safety research for over last four decades and is continuing to attract researchers in academia and industry. Job stress in process industries is of concern due to its influence on process safety, and worker‘s safety and health. Safety in process (chemical and nuclear material) industry is of paramount importance, especially in a thickly populated country like India. Stress at job is the main vector in inducing work related musculoskeletal disorders which in turn can affect the worker health and safety in process industries. In view of the above, the process industries should try to minimize the job stress in workers to ensure a safe and healthy working climate for the industry and the worker. This research is mainly aimed at assessing the influence of job stress in inducing work related musculoskeletal disorders in chemical process industries in India

Significance Of Classification Techniques In Prediction Of Learning Disabilities

The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified

Attribute reduction and missing value imputing with ANN: prediction of learning disabilities

Learning disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

Molecular Epidemiology of Escherichia coli isolates from Environmental, Food and Clinical Sources

In the present study diversity of E. coli in the water samples of Cochin estuary were studied for a period of 3 years ranging from January 2010- December 2012. The stations were selected based on the closeness to satellite townships and waste input. Two of the stations (Chitoor and Thevara) were fixed upstream, two in the central part of the estuary namely Bolgatty and Off Marine Science Jetty, and one at the Barmouth. Diversity was assessed in terms of serotypes, phylogenetic groups and genotypes. Two groups of seafood samples such as fish and shellfish collected from the Cochin estuary were used for isolation of E. coli. One hundred clinical E. coli isolates were collected from one public health centre, one hospital and five medical labs in and around Cochin City, Kerala. From our results it was clear that pathogen cycling is occurring through food, water and clinical sources. Pathogen cycling through food is very common and fish and shellfish that harbour these strains might pose potential health risk to consumer. Estuarine environment is a melting pot for various kinds of wastes, both organic and inorganic. Mixing up of waste water from various sources such as domestic, industries, hospitals and sewage released into these water bodies resulting in the co-existence of E. coli from various sources thus offering a conducive environment for horizontal gene transfer. Opportunistic pathogens might acquire genes for drug resistance and virulence turning them to potential pathogens. Prevalence of ExPEC in the Cochin estuary, pose threat to people who use this water for fishing and recreation. Food chain also plays an important role in the transit of virulence genes from the environments to the human. Antibiotic resistant E. coli are widespread in estuarine water, seafood and clinical samples, for reasons well known such as indiscriminate use of antibiotics in animal production systems, aquaculture and human medicine. Since the waste water from these sources entering the estuary provides selection pressure to drug resistant mutants in the environment. It is high time that the authorities concerned should put systems in place for monitoring and enforcement to curb such activities. Microbial contamination can limit people’s enjoyment of coastal waters for contact recreation or shellfish-gathering. E. coli can make people sick if they are present in high levels in water used for contact recreation or shellfish gathering. When feeding, shellfish can filter large volumes of seawater, so any microorganisms present in the water become accumulated and concentrated in the shellfish flesh. If E. coli contaminated shellfish are consumed the impact to human health includes gastroenteritis, urinary tract infections (UTIs), and bacteraemia. In conclusion, the high prevalence of various pathogenic serotypes and phylogenetic groups, multidrug-resistance, and virulence factor genes detected among E. coli isolates from stations close to Cochin city is a matter of concern, since there is a large reservoir of antibiotic resistance genes and virulence traits within the community, and that the resistance genes and plasmid-encoded genes for virulence were easily transferable to other strains. Given the severity of the clinical manifestations of the disease in humans and the inability and/or the potential risks of antibiotic administration for treatment, it appears that the most direct and effective measure towards prevention of STEC and ExPEC infections in humans and ensuring public health may be considered as a priority.

Strength Predictions of Hat Stiffened Plate

Hat Stiffened Plates are used in composite ships and are gaining popularity in metallic ship construction due to its high strength-to-weight ratio. Light weight structures will result in greater payload, higher speeds, reduced fuel consumption and environmental emissions. Numerical Investigations have been carried out using the commercial Finite Element software ANSYS 12 to substantiate the high strength-to-weight ratio of Hat Stiffened Plates over other open section stiffeners which are commonly used in ship building. Analysis of stiffened plate has always been a matter of concern for the structural engineers since it has been rather difficult to quantify the actual load sharing between stiffeners and plating. Finite Element Method has been accepted as an efficient tool for the analysis of stiffened plated structure. Best results using the Finite Element Method for the analysis of thin plated structures are obtained when both the stiffeners and the plate are modeled using thin plate elements having six degrees of freedom per node. However, one serious problem encountered with this design and analysis process is that the generation of the finite element models for a complex configuration is time consuming and laborious. In order to overcome these difficulties two different methods viz., Orthotropic Plate Model and Superelement for Hat Stiffened Plate have been suggested in the present work. In the Orthotropic Plate Model geometric orthotropy is converted to material orthotropy i.e., the stiffeners are smeared and they vanish from the field of analysis and the structure can be analysed using any commercial Finite Element software which has orthotropic elements in its element library. The Orthotropic Plate Model developed has predicted deflection, stress and linear buckling load with sufficiently good accuracy in the case of all four edges simply supported boundary condition. Whereas, in the case of two edges fixed and other two edges simply supported boundary condition even though the stress has been predicted with good accuracy there has been large variation in the deflection predicted. This variation in the deflection predicted is because, for the Orthotropic Plate Model the rigidity is uniform throughout the plate whereas in the actual Hat Stiffened Plate the rigidity along the line of attachment of the stiffeners to the plate is large as compared to the unsupported portion of the plate. The Superelement technique is a method of treating a portion of the structure as if it were a single element even though it is made up of many individual elements. The Superelement has predicted the deflection and in-plane stress of Hat Stiffened Plate with sufficiently good accuracy for different boundary conditions. Formulation of Superelement for composite Hat Stiffened Plate has also been presented in the thesis. The capability of Orthotropic Plate Model and Superelement to handle typical boundary conditions and characteristic loads in a ship structure has been demonstrated through numerical investigations.

Soft Computing Approach for Optimization of siRNA Efficiency Prediction for Post-transcriptional Gene Silencing

Post-transcriptional gene silencing by RNA interference is mediated by small interfering RNA called siRNA. This gene silencing mechanism can be exploited therapeutically to a wide variety of disease-associated targets, especially in AIDS, neurodegenerative diseases, cholesterol and cancer on mice with the hope of extending these approaches to treat humans. Over the recent past, a significant amount of work has been undertaken to understand the gene silencing mediated by exogenous siRNA. The design of efficient exogenous siRNA sequences is challenging because of many issues related to siRNA. While designing efficient siRNA, target mRNAs must be selected such that their corresponding siRNAs are likely to be efficient against that target and unlikely to accidentally silence other transcripts due to sequence similarity. So before doing gene silencing by siRNAs, it is essential to analyze their off-target effects in addition to their inhibition efficiency against a particular target. Hence designing exogenous siRNA with good knock-down efficiency and target specificity is an area of concern to be addressed. Some methods have been developed already by considering both inhibition efficiency and off-target possibility of siRNA against agene. Out of these methods, only a few have achieved good inhibition efficiency, specificity and sensitivity. The main focus of this thesis is to develop computational methods to optimize the efficiency of siRNA in terms of “inhibition capacity and off-target possibility” against target mRNAs with improved efficacy, which may be useful in the area of gene silencing and drug design for tumor development. This study aims to investigate the currently available siRNA prediction approaches and to devise a better computational approach to tackle the problem of siRNA efficacy by inhibition capacity and off-target possibility. The strength and limitations of the available approaches are investigated and taken into consideration for making improved solution. Thus the approaches proposed in this study extend some of the good scoring previous state of the art techniques by incorporating machine learning and statistical approaches and thermodynamic features like whole stacking energy to improve the prediction accuracy, inhibition efficiency, sensitivity and specificity. Here, we propose one Support Vector Machine (SVM) model, and two Artificial Neural Network (ANN) models for siRNA efficiency prediction. In SVM model, the classification property is used to classify whether the siRNA is efficient or inefficient in silencing a target gene. The first ANNmodel, named siRNA Designer, is used for optimizing the inhibition efficiency of siRNA against target genes. The second ANN model, named Optimized siRNA Designer, OpsiD, produces efficient siRNAs with high inhibition efficiency to degrade target genes with improved sensitivity-specificity, and identifies the off-target knockdown possibility of siRNA against non-target genes. The models are trained and tested against a large data set of siRNA sequences. The validations are conducted using Pearson Correlation Coefficient, Mathews Correlation Coefficient, Receiver Operating Characteristic analysis, Accuracy of prediction, Sensitivity and Specificity. It is found that the approach, OpsiD, is capable of predicting the inhibition capacity of siRNA against a target mRNA with improved results over the state of the art techniques. Also we are able to understand the influence of whole stacking energy on efficiency of siRNA. The model is further improved by including the ability to identify the “off-target possibility” of predicted siRNA on non-target genes. Thus the proposed model, OpsiD, can predict optimized siRNA by considering both “inhibition efficiency on target genes and off-target possibility on non-target genes”, with improved inhibition efficiency, specificity and sensitivity. Since we have taken efforts to optimize the siRNA efficacy in terms of “inhibition efficiency and offtarget possibility”, we hope that the risk of “off-target effect” while doing gene silencing in various bioinformatics fields can be overcome to a great extent. These findings may provide new insights into cancer diagnosis, prognosis and therapy by gene silencing. The approach may be found useful for designing exogenous siRNA for therapeutic applications and gene silencing techniques in different areas of bioinformatics.