Synthesis and crystal structures of 2-acetylpyridine-N(4)-methyl-3-thiosemicarbazone (L) and its metal complexes: Anticancer activity of [Cu(L)(OAc)]

Synthesis and structural characterization of N(4)-substituted 2-acetylpyridine-N(4)-methyl-3-thiosemicarbazone (L) and its two metal complexes [Cu(L)(OAc)] (1) and [ZnL(OAc)]2 (2) are reported. Between the two complexes 1 is found to have good anticancer activity against Human lung cancer cell line (A549). Fluorescence microscopy finds the formation of reactive oxygen species on treatment of the cancer cells with 1. The IC50 value of the complex is measured as 0.72 mM which is lower than that of cisplatin against A549

Synthesis and crystal structures of 2-acetylpyridine-N(4)-methyl-3-thiosemicarbazone (L) and its metal complexes: Anticancer activity of [Cu(L)(OAc)]

531-537Synthesis and structural characterization of N(4)-substituted 2-acetylpyridine-N(4)-methyl-3-thiosemicarbazone (L) and its two metal complexes [Cu(L)(OAc)] (1) and [ZnL(OAc)]2 (2) are reported. Between the two complexes 1 is found to have good anticancer activity against Human lung cancer cell line (A549). Fluorescence microscopy finds the formation of reactive oxygen species on treatment of the cancer cells with 1. The IC50 value of the complex is measured as 0.72 mM which is lower than that of cisplatin against A549

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Not AvailableRecent trends in agriculture yields a large number of researches and articles related to it. It has become a daily routine to access these articles by modern day researchers. But due to the fact that these information are in an unstructured form, readers might face difficulty to access these information. Text Categorization, a branch of Text Mining, is a very useful technique to represent these unstructured text in a structured way. In this research, a number of research articles has been categorized using text categorization by applying some popular machine learning algorithms.Not Availabl

Building and Querying Microbial Ontology

AbstractThe microbial taxonomy is based on the characteristics of microorganisms that can be objectively observed and measured. There are many scheme of microbial classification, but the latest is the three domain system and is the most accepted. Ontologies are the new form of knowledge representation that acts in synergy with agents and Semantic Web Architecture. Ontologies define domain concepts and the relationships between them, and thus provide a domain language that is meaningful to both humans and machines. The relationships in Ontology are explicitly named and developed with specification of rules and constraints so that they reflect the context of domain for which the knowledge is modelled. Ontologies can be built by using various GUI based software tools, known as Ontology editors. Among all editors Protégé is widely supported by a huge research community. For effective use of Ontology, protégé provides a query interface known as SPARQL query panel. SPARQL is a syntactically-SQL-like language for querying RDF graphs. Microbial Taxonomy Ontology is developed for the three domain system of microbes for the domain Bacteria which will be helpful for the study of Agriculturally Important Microbes (Bacteria). This ontology is built in the Protégé OWL editor from Domain to Genus level. Using this ontology, a query interface can be developed that will help detailed study of microbial taxonomy, classification of microbes as well as exchange knowledge between software agents and systems

Building and Querying Microbial Ontology, Procedia Technology

Not AvailableThe microbial taxonomy is based on the characteristics of microorganisms that can be objectively observed and measured. There are many scheme of microbial classification, but the latest is the three domain system and is the most accepted. Ontologies are the new form of knowledge representation that acts in synergy with agents and Semantic Web Architecture. Ontologies define domain concepts and the relationships between them, and thus provide a domain language that is meaningful to both humans and machines. The relationships in Ontology are explicitly named and developed with specification of rules and constraints so that they reflect the context of domain for which the knowledge is modelled. Ontologies can be built by using various GUI based software tools, known as Ontology editors. Among all editors Protégé is widely supported by a huge research community. For effective use of Ontology, protégé provides a query interface known as SPARQL query panel. SPARQL is a syntactically-SQL-like language for querying RDF graphs. Microbial Taxonomy Ontology is developed for the three domain system of microbes for the domain Bacteria which will be helpful for the study of Agriculturally Important Microbes (Bacteria). This ontology is built in the Protégé OWL editor from Domain to Genus level. Using this ontology, a query interface can be developed that will help detailed study of microbial taxonomy, classification of microbes as well as exchange knowledge between software agents and systems.Not Availabl

Extreme learning machine framework for risk stratification of fatty liver disease using ultrasound tissue characterization

Fatty Liver Disease (FLD) is caused by the deposition of fat in liver cells and leads to deadly diseases such as liver cancer. Several FLD detection and characterization systems using machine learning (ML) based on Support Vector Machines (SVM) have been applied. These ML systems utilize large number of ultrasonic grayscale features, pooling strategy for selecting the best features and several combinations of training/testing. As result, they are computationally intensive, slow and do not guarantee high performance due to mismatch between grayscale features and classifier type. This study proposes a reliable and fast Extreme Learning Machine (ELM)-based tissue characterization system (a class of Symtosis) for risk stratification of ultrasound liver images. ELM is used to train single layer feed forward neural network (SLFFNN). The input-to-hidden layer weights are randomly generated reducing computational cost. The only weights to be trained are hidden-to-output layer which is done in a single pass (without any iteration) making ELM faster than conventional ML methods. Adapting four types of K-fold cross-validation (K = 2, 3, 5 and 10) protocols on three kinds of data sizes: S0-original, S4-four splits, S8-sixty four splits (a total of 12 cases) and 46 types of grayscale features, we stratify the FLD US images using ELM and benchmark against SVM. Using the US liver database of 63 patients (27 normal/36 abnormal), our results demonstrate superior performance of ELM compared to SVM, for all cross-validation protocols (K2, K3, K5 and K10) and all types of US data sets (S0, S4, and S8) in terms of sensitivity, specificity, accuracy and area under the curve (AUC). Using the K10 cross-validation protocol on S8 data set, ELM showed an accuracy of 96.75% compared to 89.01% for SVM, and correspondingly, the AUC: 0.97 and 0.91, respectively. Further experiments also showed the mean reliability of 99% for ELM classifier, along with the mean speed improvement of 40% using ELM against SVM. We validated the symtosis system using two class biometric facial public data demonstrating an accuracy of 100%