Antibiotic resistance of Aeromonas hydrophila isolated from marketed fish and prawn of South India

A total of 319 strains of Aeromonas hydrophila were isolated from 536 fish and 278 prawns for a 2-year period. All the strains were tested for resistance to 15 antibiotics and 100% of the strains was resistant to methicillin and rifampicin followed by bacitracin and novobiocin (99%). Only 3% of the strains exhibited resistance against chloramphenicol. The multiple antibiotic resistance (MAR) indexing of A. hydrophila strains showed that all of them originated from high-risk sources

Prevalence of Aeromonas hydrophila in fish and prawns from the seafood market of Coimbatore, South India

Five hundred and thirty six samples offishes and 278 prawn samples from the major fish market ofCoimbatore, South India, were analysed for the prevalence of Aeromonas hydrophila over a period of2 years (June 1997–May 1999). The prevalence level of A. hydrophila varied from 17.62% in prawns to 33.58% in fishes. More than 30% of the popular table fishes such as Sardinella longiceps, Rastrelliger kanagurta, Mugil cephalus and Caranx sexfasciatus were tested positive for this organism. Among the different species of the prawns analysed, Penaeus semisulcatus showed higher incidence (23.52%). Seasonal variation in the prevalence levels of A. hydrophila in fish and prawns revealed a higher prevalence during the monsoon season during 1997–98 and 1998–99. Of the different body parts of the fishes analysed for A. hydrophila, the intestinal samples showed higher prevalence (38.43%), followed by body surface (32.46%) and gill (29.10%). Considering the psychrotrophic nature and role of A. hydrophila as a pathogen ofemerging importance, the considerably high levels ofthis organism in a popular food item such as fish and prawn raises serious concern

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.

Studies on Bio-Geochemistry, Bio-Optical Properties and Satellite Validation of Coastal Waters of South Eastern Arabian Sea

In situ methods used for water quality assessment have both physical and time constraints. Just a limited number of sampling points can be performed due to this, making it difficult to capture the range and variability of coastal processes and constituents. In addition, the mixing between fresh and oceanic water creates complex physical, chemical and biological environment that are difficult to understand, causing the existing measurement methodologies to have significant logistical, technical, and economic challenges and constraints. Remote sensing of ocean colour makes it possible to acquire information on the distribution of chlorophyll and other constituents over large areas of the oceans in short periods. There are many potential applications of ocean colour data. Satellite-derived products are a key data source to study the distribution pattern of organisms and nutrients (Guillaud et al. 2008) and fishery research (Pillai and Nair 2010; Solanki et al. 2001. Also, the study of spatial and temporal variability of phytoplankton blooms, red tide identification or harmful algal blooms monitoring (Sarangi et al. 2001; Sarangi et al. 2004; Sarangi et al. 2005; Bhagirathan et al., 2014), river plume or upwelling assessments (Doxaran et al. 2002; Sravanthi et al. 2013), global productivity analyses (Platt et al. 1988; Sathyendranath et al. 1995; IOCCG2006) and oil spill detection (Maianti et al. 2014). For remote sensing to be accurate in the complex coastal waters, it has to be validated with the in situ measured values. In this thesis an attempt to study, measure and validate the complex waters with the help of satellite data has been done. Monitoring of coastal ecosystem health of Arabian Sea in a synoptic way requires an intense, extensive and continuous monitoring of the water quality indicators. Phytoplankton determined from chl-a concentration, is considered as an indicator of the state of the coastal ecosystems. Currently, satellite sensors provide the most effective means for frequent, synoptic, water-quality observations over large areas and represent a potential tool to effectively assess chl-a concentration over coastal and oceanic waters; however, algorithms designed to estimate chl-a at global scales have been shown to be less accurate in Case 2 waters, due to the presence of water constituents other than phytoplankton which do not co-vary with the phytoplankton. The constituents of Arabian Sea coastal waters are region-specific because of the inherent variability of these optically-active substances affected by factors such as riverine input (e.g. suspended matter type and grain size, CDOM) and phytoplankton composition associated with seasonal changes.