Absurdism and the absurd hero Meursault

Albert Camus was awarded the Nobel Prize for literature in 1957. He was born in Algeria, colony of France in 1913. His major contribution for philosophy is his views on the ‘absurd’. It means not at all logical or sensible. It is a nihilistic outlook on life which he explored in his novels, plays and essays. What is the meaning of life? For this profound question, three philosophies (Nihilism, Existentialism, Absurdism) have tackled in three ways. Although these three philosophical thoughts are united in that life has no meaning, there are some differences between them. This article explores that distinction. Mainly this article focuses the philosophy of Absurdism and the absurdist protagonist Meursault (In Albert Camus’s The Stranger’) who expresses that thought

The difference between Structuralism and Post Structuralism in the thinking of language

Structuralism and Post Structuralism are two different literary movements. Structuralism believes that the world should be understood through structures. According to structuralism, there is no reality outside the language. In language the individual words attained their meaning due to the existence of the structure.  Post-structuralism rejected this basic idea of Saussure. It says, there is no reality or absolute truth. There is no fixed connection between the signifier and signified. Meaning is always deferred. This article presents the difference between the structuralism and Post structuralism in the concept of language

The traces of Structural thoughts in Tholkappiyam

Structuralism focused how human behaviour is determined by cultural, social and psychological structures.  Structuralists believe that the underlying structures which organize rules and units into meaningful systems are generated by the human mind itself and not by sense perception.  Levi-Strauss applied this approach to anthropology.  The influence of this approach spread to other fields until the 1960s.  The gap between word (signifier) and the meaning (signified) that Saussure discovered in language does not reflect the world of language.  It led to a deep thought about what makes up the world.  The purpose of this article is to highlight the traces of structuralist thought such as signifier, signified, hidden meaning, multiple meaning can be found in the ancient grammar Tholkappiyam

Impact of drought on flowering, yield and quality parameters in diverse genotypes of tomato (Solanum lycopersicum L.)

The effect of drought stress on flowering, yield and quality of tomato (Solanum lycopersicum) genotypes was investigated under field conditions in rainout shelter. The drought condition was imposed on the first day after trans-planting based on field capacity of soil. Experimentation was undertaken with ten genotypes adopting Factorial Randomized Block Design with three replications and two treatments viz., 1.0 IW/CPE and 0.5 IW/CPE field capacity. As the stress increased from 100% field capacity to 50% field capacity, reductions in chlorophyll index, soluble protein con-tent, days to flower initiation, sucrose phosphate synthase (SPS) activity, fruit volume, fruit diameter, yield and increased flower abscission percentage were noted. Significant increases in TSS and lycopene were observed under drought. The genotypes LE 118, LE 57 and LE 114 showed significantly less reduction in soluble protein content; SPS activ-ity and fruit yield during drought were considered as drought tolerant. Genotypes LE 1 and LE 125, which gave the lowest soluble protein content, SPS activity and ultimately poor yield, were considered as drought susceptible

PROBING THE BROAD-SPECTRUM THERAPEUTIC POTENTIAL OF AIP II MIMICS TO COMBAT LYSOZYME MEDIATED STAPHYLOCOCCAL INVASION ON CONTACT LENS

Objective: Molecular recognition of AIP II mimics as a global inhibitor against the AgrC variants and to undertake a real-time clinical applications to treat the lysozyme mediated (tear protein) S. aureus adherence on contact lens.Methods: Structure activity relationship of the mimic peptides against the receptor AgrC variants were studied to score the global inhibitor. Further, the activity of the mimics as inhibitors was validated through in vitro and in vivo analysis.Results: Inhibition of agr expression of interstrains by the mimic compounds gained insight to recognize a global inhibitorâ€. Further, the in vitro data were designed in such a way to provide a natural eye environment (artificial tears) to see the effect of mAIP IIa (IC50) showed a greater significance of eradicating the clinical isolate, S. aureus biofilm and various other secreted toxins.Conclusion: The mimic peptide (mAIPII a) revealed to be a potential mimic of AIPII to show a broad range inhibition of all AgrC variants without any cytotoxic effects.Â

PHYTOCHEMICAL STUDIES AND QUALITATIVE ANALYSIS BY TLC OF MURRAYA KOENIGII BARK EXTRACT

Murraya koenigii is a medium size, ever green plant which has been utilized as a source of food, medicine, and other agricultural purposes in different communities. Thus, the preliminary phytochemical analysis and TLC separation was done using methanol, n-hexane, and ethyl acetate(1:3:1), as solvent system while iodine vapour as spotting agent. The phytochemical screening of diethyl ether extracts of bark revealed the presence of carbohydrates , anthraquinones glycosides ,saponins ,flavanoids, and alkaloids, while chloroform extracts of bark revealed carbohydrates, tannins, saponins, and alkaloids, while acetone extracts of bark revealed the presence of carbohydrates, anthraquinones glycosides, flavanoids and alkaloids,while ethanol extracts of bark revealed the presence of carbohydrates, tannins, anthraquinones glycosides,s aponins, flavanoids and alkaloids.TLC separation showed (3) spots each of Diethyl Ether, Chloroform, Acetone, Ethanol from bark extracts. From our findings, it can be concluded that Murraya Koenigii contains some significant phytochemicals that can exhibit desired therapeutic activities such as Antioxidant, Anti-Microbial, Anti-Fungal, Anti-Diabetic, Anti-Ulcer and Cosmetic use. However, there is a need to conduct further Pharmaceutical Analysis on test extracts in order to establish these biomedical applications. Keywords: Thin Layer Chromatography, Murraya koenigii Bark, Phytochemical screening

Identification of Prophages in Bacterial Genomes by Dinucleotide Relative Abundance Difference

BACKGROUND: Prophages are integrated viral forms in bacterial genomes that have been found to contribute to interstrain genetic variability. Many virulence-associated genes are reported to be prophage encoded. Present computational methods to detect prophages are either by identifying possible essential proteins such as integrases or by an extension of this technique, which involves identifying a region containing proteins similar to those occurring in prophages. These methods suffer due to the problem of low sequence similarity at the protein level, which suggests that a nucleotide based approach could be useful. METHODOLOGY: Earlier dinucleotide relative abundance (DRA) have been used to identify regions, which deviate from the neighborhood areas, in genomes. We have used the difference in the dinucleotide relative abundance (DRAD) between the bacterial and prophage DNA to aid location of DNA stretches that could be of prophage origin in bacterial genomes. Prophage sequences which deviate from bacterial regions in their dinucleotide frequencies are detected by scanning bacterial genome sequences. The method was validated using a subset of genomes with prophage data from literature reports. A web interface for prophage scan based on this method is available at http://bicmku.in:8082/prophagedb/dra.html. Two hundred bacterial genomes which do not have annotated prophages have been scanned for prophage regions using this method. CONCLUSIONS: The relative dinucleotide distribution difference helps detect prophage regions in genome sequences. The usefulness of this method is seen in the identification of 461 highly probable loci pertaining to prophages which have not been annotated so earlier. This work emphasizes the need to extend the efforts to detect and annotate prophage elements in genome sequences

Identifying Biological Network Structure, Predicting Network Behavior, and Classifying Network State With High Dimensional Model Representation (HDMR)

This work presents an adapted Random Sampling - High Dimensional Model Representation (RS-HDMR) algorithm for synergistically addressing three key problems in network biology: (1) identifying the structure of biological networks from multivariate data, (2) predicting network response under previously unsampled conditions, and (3) inferring experimental perturbations based on the observed network state. RS-HDMR is a multivariate regression method that decomposes network interactions into a hierarchy of non-linear component functions. Sensitivity analysis based on these functions provides a clear physical and statistical interpretation of the underlying network structure. The advantages of RS-HDMR include efficient extraction of nonlinear and cooperative network relationships without resorting to discretization, prediction of network behavior without mechanistic modeling, robustness to data noise, and favorable scalability of the sampling requirement with respect to network size. As a proof-of-principle study, RS-HDMR was applied to experimental data measuring the single-cell response of a protein-protein signaling network to various experimental perturbations. A comparison to network structure identified in the literature and through other inference methods, including Bayesian and mutual-information based algorithms, suggests that RS-HDMR can successfully reveal a network structure with a low false positive rate while still capturing non-linear and cooperative interactions. RS-HDMR identified several higher-order network interactions that correspond to known feedback regulations among multiple network species and that were unidentified by other network inference methods. Furthermore, RS-HDMR has a better ability to predict network response under unsampled conditions in this application than the best statistical inference algorithm presented in the recent DREAM3 signaling-prediction competition. RS-HDMR can discern and predict differences in network state that arise from sources ranging from intrinsic cell-cell variability to altered experimental conditions, such as when drug perturbations are introduced. This ability ultimately allows RS-HDMR to accurately classify the experimental conditions of a given sample based on its observed network state