998 resultados para Post-dramatic
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Hindi
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HINDI
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HINDI
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HINDI
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Post-transcriptional gene silencing by RNA interference is mediated by small interfering RNA called siRNA. This gene silencing mechanism can be exploited therapeutically to a wide variety of disease-associated targets, especially in AIDS, neurodegenerative diseases, cholesterol and cancer on mice with the hope of extending these approaches to treat humans. Over the recent past, a significant amount of work has been undertaken to understand the gene silencing mediated by exogenous siRNA. The design of efficient exogenous siRNA sequences is challenging because of many issues related to siRNA. While designing efficient siRNA, target mRNAs must be selected such that their corresponding siRNAs are likely to be efficient against that target and unlikely to accidentally silence other transcripts due to sequence similarity. So before doing gene silencing by siRNAs, it is essential to analyze their off-target effects in addition to their inhibition efficiency against a particular target. Hence designing exogenous siRNA with good knock-down efficiency and target specificity is an area of concern to be addressed. Some methods have been developed already by considering both inhibition efficiency and off-target possibility of siRNA against agene. Out of these methods, only a few have achieved good inhibition efficiency, specificity and sensitivity. The main focus of this thesis is to develop computational methods to optimize the efficiency of siRNA in terms of “inhibition capacity and off-target possibility” against target mRNAs with improved efficacy, which may be useful in the area of gene silencing and drug design for tumor development. This study aims to investigate the currently available siRNA prediction approaches and to devise a better computational approach to tackle the problem of siRNA efficacy by inhibition capacity and off-target possibility. The strength and limitations of the available approaches are investigated and taken into consideration for making improved solution. Thus the approaches proposed in this study extend some of the good scoring previous state of the art techniques by incorporating machine learning and statistical approaches and thermodynamic features like whole stacking energy to improve the prediction accuracy, inhibition efficiency, sensitivity and specificity. Here, we propose one Support Vector Machine (SVM) model, and two Artificial Neural Network (ANN) models for siRNA efficiency prediction. In SVM model, the classification property is used to classify whether the siRNA is efficient or inefficient in silencing a target gene. The first ANNmodel, named siRNA Designer, is used for optimizing the inhibition efficiency of siRNA against target genes. The second ANN model, named Optimized siRNA Designer, OpsiD, produces efficient siRNAs with high inhibition efficiency to degrade target genes with improved sensitivity-specificity, and identifies the off-target knockdown possibility of siRNA against non-target genes. The models are trained and tested against a large data set of siRNA sequences. The validations are conducted using Pearson Correlation Coefficient, Mathews Correlation Coefficient, Receiver Operating Characteristic analysis, Accuracy of prediction, Sensitivity and Specificity. It is found that the approach, OpsiD, is capable of predicting the inhibition capacity of siRNA against a target mRNA with improved results over the state of the art techniques. Also we are able to understand the influence of whole stacking energy on efficiency of siRNA. The model is further improved by including the ability to identify the “off-target possibility” of predicted siRNA on non-target genes. Thus the proposed model, OpsiD, can predict optimized siRNA by considering both “inhibition efficiency on target genes and off-target possibility on non-target genes”, with improved inhibition efficiency, specificity and sensitivity. Since we have taken efforts to optimize the siRNA efficacy in terms of “inhibition efficiency and offtarget possibility”, we hope that the risk of “off-target effect” while doing gene silencing in various bioinformatics fields can be overcome to a great extent. These findings may provide new insights into cancer diagnosis, prognosis and therapy by gene silencing. The approach may be found useful for designing exogenous siRNA for therapeutic applications and gene silencing techniques in different areas of bioinformatics.
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Yacon (Smallanthus sonchifolius [Poepp. & Endl.] H. Robinson) is an under-exploited native root crop of the Andes, which stores oligofructans (fructo-oligosaccharides, FOS) as its main component of dry matter (DM). FOS are of increasing economic interest because of their low caloric value in human diets and bifidogenic benefits on colon health. Two on-farm experiments were conducted to: (i) determine the effect of shaded, short-term storage at 1990 and 2930 m a.s.l. in the Andean highlands; and (ii) address the effects of a traditional sunlight exposure (‘sunning’) on the carbohydrate composition in the DM of tuberous yacon roots. After a 6-day shade storage FOS concentrations were smaller at the lower (36–48% of DM) than at the higher altitude (39–58% of DM). After 12 days FOS concentrations were nearly equal at both sites (27–39% of DM). The concentration of free sugars (fructose, glucose, sucrose) increased accordingly from 29–34 to 48–52%. During the 6-day sunning experiment FOS concentrations decreased from 50–62 to 29–44% and free sugars increased from 29–34 to 45–51%. The results indicate that partial hydrolysis of oligofructans starts shortly after harvest. Storage in highland environments should wherever possible exploit the cooler temperatures at higher altitudes. Sunning of yacon’s tuberous roots effectively reduces much of the roots’ water content, in this experiment 40%, and thus allows energy to be saved if yacon is processed into dehydrated products.
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The following paper is a critical theorist analysis of post-structuralist philosophy. It examines the omission of an economic critique in post-structuralism and describes this omission as the result of a particular flaw in Nietzsche's epistemological work, an error which has persisted all the way down through deconstruction, post-colonialism, and cultural studies. The paper seeks to reintroduce an economic critique of capitalism back into the social critique of post-structuralism, with the promise that the combination of the two will prove stronger than either critical theory or post-structuralism alone. To achieve this it reinterprets Marx' concept of metabolism as a critical economic category that mirrors post-structuralism's concept of differance.
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Due to its transition from the socialist mode of production to the capitalist mode, workers in China have been exposed to the exploitative class relations that they hardly experienced before. The working class is now assuming a subordinate position in the relations of production while the capitalist class remains in the dominant position. As a consequence, workers’ protests are constantly emerging and class conflicts are exacerbating in the contemporary China. I have set out to study in this paper how the party-state in China contains labour unrest through the All China Federation of Trade Unions (the ACFTU), which I argue is a state apparatus that performs the ideological, political and economic functions in different situations. There has been an ongoing academic debate on if the ACFTU is defending workers’ interests. Some scholars have expressed optimism while some have taken a dim view. Drawing on Poulantzas’ theory of capitalist state, I hope to make contribution to this debate by demonstrating that the ACFTU is under some circumstances serving the short term interests of workers as individuals, but not the economic and political interests of workers as a class. Instead of organizing workers to overcome the effects of isolation or forming a class for itself, the ACFTU attempts to contain labour unrest and reproduce their subordination in the relations of production.