Extension of TSVM to Multi-Class and Hierarchical Text Classification Problems With General Losses

Transductive SVM (TSVM) is a well known semi-supervised large margin learning method for binary text classification. In this paper we extend this method to multi-class and hierarchical classification problems. We point out that the determination of labels of unlabeled examples with fixed classifier weights is a linear programming problem. We devise an efficient technique for solving it. The method is applicable to general loss functions. We demonstrate the value of the new method using large margin loss on a number of multi-class and hierarchical classification datasets. For maxent loss we show empirically that our method is better than expectation regularization/constraint and posterior regularization methods, and competitive with the version of entropy regularization method which uses label constraints

Mitochondrial Regulation of Yeast AMPK During Energy Stress

In eukaryotes, members of the conserved AMP-activated protein kinase (AMPK) family play a pivotal role in sensing and responding to energy stress. Mammalian AMPK becomes activated when the AMP:ATP ratio is too high, and functions to prevent unnecessary ATP spending and to increase ATP production. Due to their role in ATP production through aerobic respiration, mitochondria are known to play an indirect role in the negative control of AMPK. The conserved voltage-dependent anion channel (VDAC) proteins, also known as mitochondrial porins, mediate the passage of small metabolites between the mitochondria and cytoplasm, including the release of ATP. One would therefore expect VDACs to play a role in the negative regulation of AMPK. Contrary to this expectation, our results in budding yeast (Saccharomyces cerevisiae) provide evidence that mitochondria and VDACs play a role in the positive control of Snf1, the yeast homolog of AMPK. In yeast, Snf1 protein kinase stimulates the utilization of alternate carbon/energy sources when the preferred source – glucose – becomes limiting. Under carbon/energy stress conditions, Snf1 is activated and enriches in the nucleus to elicit various transcriptional responses. Our results indicate that Snf1 physically interacts with the yeast VDAC proteins Por1 and Por2. Interestingly, Por1 and Por2 contribute to the positive - rather than negative - control of Snf1. We present evidence indicating that Por1 and Por2 function redundantly to promote Snf1 catalytic activation, presumably as receptors of an intracellular glucose/energy signal. We also present evidence for novel mechanisms by which mitochondria positively regulate Snf1 nuclear localization. In summary, our experiments in yeast reveal an array of mechanisms by which mitochondria positively regulate Snf1/AMPK, i.e. in a way that would be entirely counter-intuitive to researchers in the mammalian field. Due to the evolutionary conservation of the players involved, further studies of these novel mechanisms in the yeast model could provide invaluable insights into the etiology and therapy of AMPK- ,VDAC-, and mitochondria-associated diseases including cancer, diabetes, obesity, and cardiac disorders

Difficulties Faced by II MBBS Students While Learning Microbiology

MBBS students enter the medical field with high ambitions and lot of dreams. They start learning new subjects. In IInd year MBBS, they have Microbiology as a subject in their curriculum. The subject Microbiology deals with the study of microorganisms. This article focuses on the difficulties faced by II MBBS students while learning microbiology

Semantic levels of domain-independent commonsense knowledgebase for visual indexing and retrieval applications

Building intelligent tools for searching, indexing and retrieval applications is needed to congregate the rapidly increasing amount of visual data. This raised the need for building and maintaining ontologies and knowledgebases to support textual semantic representation of visual contents, which is an important block in these applications. This paper proposes a commonsense knowledgebase that forms the link between the visual world and its semantic textual representation. This domain-independent knowledge is provided at different levels of semantics by a fully automated engine that analyses, fuses and integrates previous commonsense knowledgebases. This knowledgebase satisfies the levels of semantic by adding two new levels: temporal event scenarios and psycholinguistic understanding. Statistical properties and an experiment evaluation, show coherency and effectiveness of the proposed knowledgebase in providing the knowledge needed for wide-domain visual applications