Source Characterization of Sedimentary Organic Matter in a Tropical Estuary, Southwest Coast of India: A Biomarker Approach

The source, fate and diagentic pathway of sedimentary organic matter in estuaries are difficult to delineate due to the complexity of organic matter sources, intensive physical mixing and biological processes. A combination of bulk organic matter techniques and molecular biomarkers are found to be successful in explaining organic matter dynamics in estuaries. The basic requirement for these multi-proxy approaches are (i) sources have significantly differing characteristics, (ii) there are a sufficient number of tracers to delineate all sources and (iii) organic matter degradation and processing have little, similar or predictable effects on end member characteristics. Although there have been abundant researches that have attempted to tackle difficulties related to the source and fate of organic matter in estuarine systems, our understanding remains limited or rather inconsistent regarding the Indian estuaries. Cochin estuary is the largest among many extensive estuarine systems along the southwest coast of India. It supports as much biological productivity and diversity as tropical rain forests. In this study, we have used a combination of bulk geochemical parameters and different group of molecular biomarkers to define organic matter sources and thereby identifying various biogeochemical processes acting along the salinity gradient of the Cochin estuary

In vivo screening of mangrove plants for anti WSSV activity in Penaeus monodon, and evaluation of Ceriops tagal as a potential source of antiviral molecules

The objective of the study was to find out a natural way to fight white spot syndrome virus (WSSV) in cultured shrimps, as the present scenario necessitated an organic remedy for the devastating pathogen in crustaceans. Under this research programme seven mangrove plants were collected, identified and aqueous extracts screened for their protective effect on the giant tiger shrimp Penaeus monodon against WSSV. The experimental design consisted two modes of application, such as exposure of the virus to the extract and injection challenge, and oral administration of the extract coated feed followed by oral challenge. All experimental animals were monitored through a nested diagnostic PCR analysis. Of the seven mangrove extracts screened aqueous extract from Ceriops tagal imparted total protection to shrimp from WSSV when challenged by both methods. Shrimps administered with the aqueous extract from C. tagal were devoid of virions. The HPLC fingerprint of the aqueous extracts from C. tagal showed more than 25 peaks and 7 of them were larger and well separated. Preliminary phytochemical analysis revealed the presence of alkaloids, flavonoids, polyphenolics, cardiac glycosides, saponins and sterols. The study indicated suitability of the aqueous extract of C. tagal as a possible prophylaxis for WSSV infection in shrimp. This is the first report on the anti WSSV property of the mangrove plant C. tagal

MicroRNA–mRNA interaction network using TSK-type recurrent neural fuzzy network

MicroRNAs are short non-coding RNAs that can regulate gene expression during various crucial cell processes such as differentiation, proliferation and apoptosis. Changes in expression profiles of miRNA play an important role in the development of many cancers, including CRC. Therefore, the identification of cancer related miRNAs and their target genes are important for cancer biology research. In this paper, we applied TSK-type recurrent neural fuzzy network (TRNFN) to infer miRNA–mRNA association network from paired miRNA, mRNA expression profiles of CRC patients. We demonstrated that the method we proposed achieved good performance in recovering known experimentally verified miRNA–mRNA associations. Moreover, our approach proved successful in identifying 17 validated cancer miRNAs which are directly involved in the CRC related pathways. Targeting such miRNAs may help not only to prevent the recurrence of disease but also to control the growth of advanced metastatic tumors. Our regulatory modules provide valuable insights into the pathogenesis of cancer

Face Recognition using Probabilistic Neural Networks

In this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results

Using open source software for digital libraries A case study of CUSAT

The purpose of this paper is to describe the design and development of a digital library at Cochin University of Science and Technology (CUSAT), India, using DSpace open source software. The study covers the structure, contents and usage of CUSAT digital library. Design/methodology/approach – This paper examines the possibilities of applying open source in libraries. An evaluative approach is carried out to explore the features of the CUSAT digital library. The Google Analytics service is employed to measure the amount of use of digital library by users across the world. Findings – CUSAT has successfully applied DSpace open source software for building a digital library. The digital library has had visits from 78 countries, with the major share from India. The distribution of documents in the digital library is uneven. Past exam question papers share the major part of the collection. The number of research papers, articles and rare documents is less. Originality/value – The study is the first of its type that tries to understand digital library design and development using DSpace open source software in a university environment with a focus on the analysis of distribution of items and measuring the value by usage statistics employing the Google Analytics service. The digital library model can be useful for designing similar systems

Free/Open Source Software in CUSAT: A Success Story

Free/Open Source Software (FOSS) concept is very important in the academic community. The open philosophy of FOSS is consistent with academic freedom and the open dissemination of knowledge and information in academia. FOSS can lower the barriers to access of ICTs by reducing the cost of the software. This article discusses the success story of CUSAT's adoption of Free/Open Source Software

Face Recognition using Probabilistic Neural Networks

n this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results.

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al‐SiCp metal matrix composites and ANOVA analysis

Metal matrix composites (MMC) having aluminium (Al) in the matrix phase and silicon carbide particles (SiCp) in reinforcement phase, ie Al‐SiCp type MMC, have gained popularity in the re‐cent past. In this competitive age, manufacturing industries strive to produce superior quality products at reasonable price. This is possible by achieving higher productivity while performing machining at optimum combinations of process variables. The low weight and high strength MMC are found suitable for variety of components

Marine Bacteria As Source Of Pigment For Application As Dye In Textile Industry

The textile industry is one amongst the rapidly growing industries world wide, which utilizes enormous amounts of synthetic dyes. Consequently, the effluent from these textile industries poses serious threat to the environment which is often very difficult to treat and dispose. This has become a very grave problem in environment conservation and hence natural pigments have drawn the attention of industry as safe alternative. In this context, in the present study an attempt was made to bioprospect marine bacteria towards isolation of a suitable and ideal pigment that could be used as a natural dye. A marine Serratia sp. BTWJ8 was recognized to synthesize enormous amounts of a prodigiosin-like pigment. The pigment was isolated and characterized for various properties. The pigment was evaluated for application as a dye in the textile industry. Results of the studies indicated that this pigment could be used as a natural dye for imparting red-yellow colour to various grades of textile materials. The colour was observed to be stable after wash performance studies

In vivo screening of mangrove plants for anti WSSV activity in Penaeus monodon, and evaluation of Ceriops tagal as a potential source of antiviral molecules

The objective of the study was to find out a natural way to fight white spot syndrome virus (WSSV) in cultured shrimps, as the present scenario necessitated an organic remedy for the devastating pathogen in crustaceans. Under this research programme seven mangrove plants were collected, identified and aqueous extracts screened for their protective effect on the giant tiger shrimp Penaeus monodon against WSSV. The experimental design consisted two modes of application, such as exposure of the virus to the extract and injection challenge, and oral administration of the extract coated feed followed by oral challenge. All experimental animals were monitored through a nested diagnostic PCR analysis. Of the seven mangrove extracts screened aqueous extract from Ceriops tagal imparted total protection to shrimp from WSSV when challenged by both methods. Shrimps administered with the aqueous extract from C. tagal were devoid of virions. The HPLC fingerprint of the aqueous extracts from C. tagal showed more than 25 peaks and 7 of them were larger and well separated. Preliminary phytochemical analysis revealed the presence of alkaloids, flavonoids, polyphenolics, cardiac glycosides, saponins and sterols. The study indicated suitability of the aqueous extract of C. tagal as a possible prophylaxis for WSSV infection in shrimp. This is the first report on the anti WSSV property of the mangrove plant C. tagal

Archiving News In A University Using Open Source Software

Newspapers cover a large amount of information everyday on topics of varied interests. To a university, newspapers are essential components of communication as they cover various happenings in a university. These items of information are neither stored properly nor put in retrieval systems for future use. The news and views appeared in newspapers can effectively be organized in a digital library making use of open source software. The CUSAT digital library (http://dspace.cusat.ac.in/dspace/) has organized some news items that appeared in local newspapers about the university under a special community named “CUSAT-News”. This article describes the methods of collecting, selecting, organizing, providing access and preserving news items required by a university using DSpace open source software.

Adoption of Koha Open Source Software in Libraries: a Web Based Study

Since last few years open source integrated library systems gaining attention of library and information science professionals. This paper tries to identify the extent of adoption of Koha, an open source ILS in libraries around the world through a Web based study. The study found that Koha adoption in libraries is still at infancy

Catalogue Interfaces of Integrated Library Management Systems (ILMS): Experiences in a Proprietary and Open Source Software

The Central Library of Cochin University of Science and Technology (CUSAT) has been automated by proprietary software (Adlib Library) since 2000. After 11 years, in 2011, the university authorities decided to shift to an open source software (OSS), for integrated library management system (ILMS), Koha for automating the library housekeeping operations. In this context, this study attempts to share the experiences in cataloging with both type of software. The features of the cataloging modules of both the software are analysed on the badis of certain check points. It is found that the cataloging module of Koha is almost in par with that of proven proprietary software that has been in market for the past 25 years. Some suggestions made by this study may be incorporated for the further development and perfection of Koha.

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year