Characterization of a mercury-reducing Bacillus cereus strain isolated from the Pulicat Lake sediments, south east coast of India

Pulicat Lake sediments are often severely polluted with the toxic heavy metal mercury. Several mercury-resistant strains of Bacillus species were isolated from the sediments and all the isolates exhibited broad spectrum resistance (resistance to both organic and inorganic mercuric compounds). Plasmid curing assay showed that all the isolated Bacillus strains carry chromosomally borne mercury resistance. Polymerase chain reaction and southern hybridization analyses using merA and merB3 gene primers/probes showed that five of the isolated Bacillus strains carry sequences similar to known merA and merB3 genes. Results of multiple sequence alignment revealed 99% similarity with merA and merB3 of TnMERI1 (class II transposons). Other mercury resistant Bacillus species lacking homology to these genes were not able to volatilize mercuric chloride, indicating the presence of other modes of resistance to mercuric compounds.

Characterization of a mercury-reducing Bacillus cereus strain isolated from the Pulicat Lake sediments, south east coast of India

Pulicat Lake sediments are often severely polluted with the toxic heavy metal mercury. Several mercury-resistant strains of Bacillus species were isolated from the sediments and all the isolates exhibited broad spectrum resistance (resistance to both organic and inorganic mercuric compounds). Plasmid curing assay showed that all the isolated Bacillus strains carry chromosomally borne mercury resistance. Polymerase chain reaction and southern hybridization analyses using merA and merB3 gene primers/probes showed that five of the isolated Bacillus strains carry sequences similar to known merA and merB3 genes. Results of multiple sequence alignment revealed 99% similarity with merA and merB3 of TnMERI1 (class II transposons). Other mercury resistant Bacillus species lacking homology to these genes were not able to volatilize mercuric chloride, indicating the presence of other modes of resistance to mercuric compounds.

Electrochemical reduction of Cu(II)-CyDTA complex at dropping mercury electrode. An impedance approach

The electrochemical reduction of Cu(II)-CyDTA (CyDTA — trans 1,2-cyclohexanediamine N, N, N′, N′ tetraacetic acid) by impedance method reveals the unusual behaviour of complex plane polarograms owing to potential dependence of double layer capacitance. The impedance plane plots by frequency variation method indicates the quasi-reversible nature of the system. From these plots the chargetransfer resistance at various potentials was evaluated. The standard rate constant was evaluated which complements the prediction of impedance plots for the quasireversible behaviour of the system.

Electrochemical reduction of Cu(II)-EDTA complex at dropping mercury electrode. An impedance approach

The study of electrochemical reduction of Cu(II)-EDTA system by phase sensitive a.c. impedance method at dropping mercury electrode reveals several interesting features. The complex plane polarograms exhibit loop like shape in contrast to the classical zinc ion reduction where crest like shape is found. Again, the relative placement of peaks of in-phase and quadrature components, and the relative placement of portions before and after the peaks of complex plane polarograms are different from that of zinc ion reduction. The complex plane plots suggest that electrochemical reduction of Cu-EDTA is charge transfer controlled.

Mining Land Cover Information Using Multilayer Perceptron and Decision Tree from MODIS Data

Land cover (LC) changes play a major role in global as well as at regional scale patterns of the climate and biogeochemistry of the Earth system. LC information presents critical insights in understanding of Earth surface phenomena, particularly useful when obtained synoptically from remote sensing data. However, for developing countries and those with large geographical extent, regular LC mapping is prohibitive with data from commercial sensors (high cost factor) of limited spatial coverage (low temporal resolution and band swath). In this context, free MODIS data with good spectro-temporal resolution meet the purpose. LC mapping from these data has continuously evolved with advances in classification algorithms. This paper presents a comparative study of two robust data mining techniques, the multilayer perceptron (MLP) and decision tree (DT) on different products of MODIS data corresponding to Kolar district, Karnataka, India. The MODIS classified images when compared at three different spatial scales (at district level, taluk level and pixel level) shows that MLP based classification on minimum noise fraction components on MODIS 36 bands provide the most accurate LC mapping with 86% accuracy, while DT on MODIS 36 bands principal components leads to less accurate classification (69%).

Tree structure for efficient data mining using rough sets

In data mining, an important goal is to generate an abstraction of the data. Such an abstraction helps in reducing the space and search time requirements of the overall decision making process. Further, it is important that the abstraction is generated from the data with a small number of disk scans. We propose a novel data structure, pattern count tree (PC-tree), that can be built by scanning the database only once. PC-tree is a minimal size complete representation of the data and it can be used to represent dynamic databases with the help of knowledge that is either static or changing. We show that further compactness can be achieved by constructing the PC-tree on segmented patterns. We exploit the flexibility offered by rough sets to realize a rough PC-tree and use it for efficient and effective rough classification. To be consistent with the sizes of the branches of the PC-tree, we use upper and lower approximations of feature sets in a manner different from the conventional rough set theory. We conducted experiments using the proposed classification scheme on a large-scale hand-written digit data set. We use the experimental results to establish the efficacy of the proposed approach. (C) 2002 Elsevier Science B.V. All rights reserved.

Stream-Close: Fast mining of Closed Frequent Itemsets in high speed data streams

With the emergence of large-volume and high-speed streaming data, the recent techniques for stream mining of CFIpsilas (closed frequent itemsets) will become inefficient. When concept drift occurs at a slow rate in high speed data streams, the rate of change of information across different sliding windows will be negligible. So, the user wonpsilat be devoid of change in information if we slide window by multiple transactions at a time. Therefore, we propose a novel approach for mining CFIpsilas cumulatively by making sliding width(ges1) over high speed data streams. However, it is nontrivial to mine CFIpsilas cumulatively over stream, because such growth may lead to the generation of exponential number of candidates for closure checking. In this study, we develop an efficient algorithm, stream-close, for mining CFIpsilas over stream by exploring some interesting properties. Our performance study reveals that stream-close achieves good scalability and has promising results.

Spatial Data Mining and Modeling for visualisation of Rapid Urbanisation

Rapid urbanisation in India has posed serious challenges to the decision makers in regional planning involving plethora of issues including provision of basic amenities (like electricity, water, sanitation, transport, etc.). Urban planning entails an understanding of landscape and urban dynamics with causal factors. Identifying, delineating and mapping landscapes on temporal scale provide an opportunity to monitor the changes, which is important for natural resource management and sustainable planning activities. Multi-source, multi-sensor, multi-temporal, multi-frequency or multi-polarization remote sensing data with efficient classification algorithms and pattern recognition techniques aid in capturing these dynamics. This paper analyses the landscape dynamics of Greater Bangalore by: (i) characterisation of direct impervious surface, (ii) computation of forest fragmentation indices and (iii) modeling to quantify and categorise urban changes. Linear unmixing is used for solving the mixed pixel problem of coarse resolution super spectral MODIS data for impervious surface characterisation. Fragmentation indices were used to classify forests – interior, perforated, edge, transitional, patch and undetermined. Based on this, urban growth model was developed to determine the type of urban growth – Infill, Expansion and Outlying growth. This helped in visualising urban growth poles and consequence of earlier policy decisions that can help in evolving strategies for effective land use policies.

Mercury Segregation and Diselenide Self-Assembly on Gold

We have investigated the self-assembly of didecyldiselenide on gold containing mercury using X-ray photoelectron spectroscopy, cyclic voltammetry and infrared spectroscopy. The analysis of intensity and chemical shift of selected Se, Hg, and Au photoelectron lines on samples with increasing Hg content, show that didecyldiselenide adsorption strongly contributed to segregation of bulk Hg to the surface. The voltammetry results support this conclusion and suggest the formation of Hg-Au surface amalgam. The Hg surface segregation effect must be related to the restructuring of the surface following initial adsorption, and to the strong selenophilicity of Hg. The reflectance absorbance infrared spectroscopy studies show that the molecular layer on Hg-Au substrates lacks good order.

Association rule mining activity : A general formalism

Mining association rules from a large collection of databases is based on two main tasks. One is generation of large itemsets; and the other is finding associations between the discovered large itemsets. Existing formalism for association rules are based on a single transaction database which is not sufficient to describe the association rules based on multiple database environment. In this paper, we give a general characterization of association rules and also give a framework for knowledge-based mining of multiple databases for association rules.

A survey of temporal data mining

Data mining is concerned with analysing large volumes of (often unstructured) data to automatically discover interesting regularities or relationships which in turn lead to better understanding of the underlying processes. The field of temporal data mining is concerned with such analysis in the case of ordered data streams with temporal interdependencies. Over the last decade many interesting techniques of temporal data mining were proposed and shown to be useful in many applications. Since temporal data mining brings together techniques from different fields such as statistics, machine learning and databases, the literature is scattered among many different sources. In this article, we present an overview of techniques of temporal data mining.We mainly concentrate on algorithms for pattern discovery in sequential data streams.We also describe some recent results regarding statistical analysis of pattern discovery methods.

Root cause diagnostics using temporal data mining

A method, system, and computer program product for fault data correlation in a diagnostic system are provided. The method includes receiving the fault data including a plurality of faults collected over a period of time, and identifying a plurality of episodes within the fault data, where each episode includes a sequence of the faults. The method further includes calculating a frequency of the episodes within the fault data, calculating a correlation confidence of the faults relative to the episodes as a function of the frequency of the episodes, and outputting a report of the faults with the correlation confidence.

System and Method for mining of Temporal Data

A system for temporal data mining includes a computer readable medium having an application configured to receive at an input module a temporal data series having events with start times and end times, a set of allowed dwelling times and a threshold frequency. The system is further configured to identify, using a candidate identification and tracking module, one or more occurrences in the temporal data series of a candidate episode and increment a count for each identified occurrence. The system is also configured to produce at an output module an output for those episodes whose count of occurrences results in a frequency exceeding the threshold frequency.

Suite of tools for statistical N-gram language modeling for pattern mining in whole genome sequences

Genome sequences contain a number of patterns that have biomedical significance. Repetitive sequences of various kinds are a primary component of most of the genomic sequence patterns. We extended the suffix-array based Biological Language Modeling Toolkit to compute n-gram frequencies as well as n-gram language-model based perplexity in windows over the whole genome sequence to find biologically relevant patterns. We present the suite of tools and their application for analysis on whole human genome sequence.