The "handedness" of language: Directional symmetry breaking of sign usage in words

Language, which allows complex ideas to be communicated through symbolic sequences, is a characteristic feature of our species and manifested in a multitude of forms. Using large written corpora for many different languages and scripts, we show that the occurrence probability distributions of signs at the left and right ends of words have a distinct heterogeneous nature. Characterizing this asymmetry using quantitative inequality measures, viz. information entropy and the Gini index, we show that the beginning of a word is less restrictive in sign usage than the end. This property is not simply attributable to the use of common affixes as it is seen even when only word roots are considered. We use the existence of this asymmetry to infer the direction of writing in undeciphered inscriptions that agrees with the archaeological evidence. Unlike traditional investigations of phonotactic constraints which focus on language-specific patterns, our study reveals a property valid across languages and writing systems. As both language and writing are unique aspects of our species, this universal signature may reflect an innate feature of the human cognitive phenomenon.Comment: 10 pages, 4 figures + Supplementary Information (15 pages, 8 figures), final corrected versio

Influential Quorum Sensing Proteins of multidrug resistant Proteus mirabilis causing urinary tract infections

Catheter-associated urinary tract infections (CAUTI) has become an alarming hospital based disease with the increase of multidrug resistance (MDR) strains of Proteus mirabilis. High prevalence of long-term hospital based CAUTI for patients along with moderate percentage of morbidity due to ignorance, failure and MDR, necessitates an immediate intervention strategy to combat the deadly disease. Several reports and reviews focus on revealing the important genes and proteins essential to tackle CAUTI caused by P. mirabilis. Despite longitudinal studies and methodical strategies to circumvent the issues, effective means of unearthing the most influential proteins to target for therapeutic uses have been meagre. Here we have reported a strategic approach for identifying the most influential proteins from the complete set of proteins of the whole genome of P. mirabilis, besides comparing the interactomes comprising the autoinducer-2 (AI-2) biosynthetic pathway along with other proteins involved in biofilm formation and responsible for virulence. Essentially, we have adopted a computational network model based approach to construct a set of small protein interaction networks (SPIN) along with the whole genome (GPIN) to identify, albeit theoretically, the most significant proteins. These might actually be responsible for the phenomenon of quorum sensing (QS) and biofilm formation and thus, could be therapeutically targeted to fight out the 188 MDR threats to antibiotics of P. mirabilis. Our approach signifies the eigenvector centrality coupled with k-core analyses to be a better measure in addressing the pressing issues

A side-effect free method for identifying cancer drug targets

Identifying efective drug targets, with little or no side efects, remains an ever challenging task. A potential pitfall of failing to uncover the correct drug targets, due to side efect of pleiotropic genes, might lead the potential drugs to be illicit and withdrawn. Simplifying disease complexity, for the investigation of the mechanistic aspects and identifcation of efective drug targets, have been done through several approaches of protein interactome analysis. Of these, centrality measures have always gained importance in identifying candidate drug targets. Here, we put forward an integrated method of analysing a complex network of cancer and depict the importance of k-core, functional connectivity and centrality (KFC) for identifying efective drug targets. Essentially, we have extracted the proteins involved in the pathways leading to cancer from the pathway databases which enlist real experimental datasets. The interactions between these proteins were mapped to build an interactome. Integrative analyses of the interactome enabled us to unearth plausible reasons for drugs being rendered withdrawn, thereby giving future scope to pharmaceutical industries to potentially avoid them (e.g. ESR1, HDAC2, F2, PLG, PPARA, RXRA, etc). Based upon our KFC criteria, we have shortlisted ten proteins (GRB2, FYN, PIK3R1, CBL, JAK2, LCK, LYN, SYK, JAK1 and SOCS3) as efective candidates for drug development

Network analysis of a corpus of undeciphered Indus civilization inscriptions indicates syntactic organization

Archaeological excavations in the sites of the Indus Valley civilization (2500-1900 BCE) in Pakistan and northwestern India have unearthed a large number of artifacts with inscriptions made up of hundreds of distinct signs. To date there is no generally accepted decipherment of these sign sequences and there have been suggestions that the signs could be non-linguistic. Here we apply complex network analysis techniques to a database of available Indus inscriptions, with the aim of detecting patterns indicative of syntactic organization. Our results show the presence of patterns, e.g., recursive structures in the segmentation trees of the sequences, that suggest the existence of a grammar underlying these inscriptions.Comment: 17 pages (includes 4 page appendix containing Indus sign list), 14 figure

Unequal representation of signs (1-grams) occurring at different positions in words in corpora written using different languages and writing systems.

<p>The Lorenz curves in the 24 panels (corresponding to all the scripts analyzed here except English, which is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190735#pone.0190735.g001" target="_blank">Fig 1</a>) show the differences in the cumulative distribution function of the occurrence probability of signs at left terminal position (blue, dash-dot curve), right terminal position (purple, dashed curve) and at any position (red, solid curve) of a word written in a particular script. The thin broken diagonal line corresponds to a perfectly uniform distribution, deviation from which indicates the extent of heterogeneity of sign occurrence distributions. This is measured in terms of the Gini index, the corresponding values at the left terminal (L), right terminal (R) and any position (A) for a script being indicated in each panel.</p

Asymmetry in the sign occurrence probability distributions at the left and right terminal positions of words in different languages correlate with the directions in which they are read.

<p>The normalized difference of the Gini indices Δ<i>G</i> = 2(<i>G</i>_<i>L</i> − <i>G</i>_<i>R</i>)/(<i>G</i>_<i>L</i> + <i>G</i>_<i>R</i>) (filled circles), which measures the relative heterogeneity between the occurrences of different signs in the terminal positions of words of a language, are shown for a number of different written languages (arranged in alphabetical order) that span a variety of possible writing systems—from alphabetic (e.g., English) and syllabic (e.g., Japanese kana) to logographic (Chinese) [see text for details]. All languages that are conventionally read from left to right (or rendered in that format in the databases used here) show a negative value for Δ<i>G</i>, while those read right to left exhibit positive values. The horizontal thick bars superposed on the circles represent the 95% bootstrap confidence interval for the estimated values of Δ<i>G</i>. To verify the significance of the empirical values, they are compared with corresponding Δ<i>G</i> (diamonds) calculated using an ensemble of 1000 randomized versions for each of the databases (obtained through multiple realizations of random permutations of the signs occurring in each word—see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190735#sec004" target="_blank">Materials and Methods</a> for details), the ranges of fluctuations being indicated by error bars. Along with the set of known languages, Δ<i>G</i> measured for a corpus of undeciphered inscriptions from the Indus Valley Civilization (2600–1900 BCE) is also shown (bottom row).</p

Interactome analyses of Salmonella pathogenicity islands reveal SicA indispensable for virulence

Background: Serovars of Salmonella enterica, namely Typhi and Typhimurium, reportedly, are the bacterial pathogens causing systemic infections like gastroenteritis and typhoid fever. To elucidate the role and importance in such infection, the proteins of the Type III secretion system of Salmonella pathogenicity islands and two component signal transduction systems, have been mainly focused. However, the most indispensable of these virulent ones and their hierarchical role has not yet been studied extensively. Results: We have adopted a theoretical approach to build an interactome comprising the proteins from the Salmonella pathogeneicity islands (SPI) and two component signal transduction systems. This interactome was then analyzed by using network parameters like centrality and k-core measures. An initial step to capture the fingerprint of the core network resulted in a set of proteins which are involved in the process of invasion and colonization, thereby becoming more important in the process of infection. These proteins pertained to the Inv, Org, Prg, Sip, Spa, Ssa and Sse operons along with chaperone protein SicA. Amongst them, SicA was figured out to be the most indispensable protein from different network parametric analyses. Subsequently, the gene expression levels of all these theoretically identified important proteins were confirmed by microarray data analysis. Finally, we have proposed a hierarchy of the proteins involved in the total infection process. This theoretical approach is the first of its kind to figure out potential virulence determinants encoded by SPI for therapeutic targets for enteric infection. Conclusions: A set of responsible virulent proteins was identified and the expression level of their genes was validated by using independent, published microarray data. The result was a targeted set of proteins that could serve as sensitive predictors and form the foundation for a series of trials in the wet-lab setting. Understanding these regulatory and virulent proteins would provide insight into conditions which are encountered by this intracellular enteric pathogen during the course of infection. This would further contribute in identifying novel targets for antimicrobial agents. (C) 2014 Elsevier Ltd. All rights reserved

Data_Sheet_1_In silico Identification of the Indispensable Quorum Sensing Proteins of Multidrug Resistant Proteus mirabilis.XLSX

<p>Catheter-associated urinary tract infections (CAUTI) is an alarming hospital based disease with the increase of multidrug resistance (MDR) strains of Proteus mirabilis. Cases of long term hospitalized patients with multiple episodes of antibiotic treatments along with urinary tract obstruction and/or undergoing catheterization have been reported to be associated with CAUTI. The cases are complicated due to the opportunist approach of the pathogen having robust swimming and swarming capability. The latter giving rise to biofilms and probably inducible through autoinducers make the scenario quite complex. High prevalence of long-term hospital based CAUTI for patients along with moderate percentage of morbidity, cropping from ignorance about drug usage and failure to cure due to MDR, necessitates an immediate intervention strategy effective enough to combat the deadly disease. Several reports and reviews focus on revealing the important genes and proteins, essential to tackle CAUTI caused by P. mirabilis. Despite longitudinal countrywide studies and methodical strategies to circumvent the issues, effective means of unearthing the most indispensable proteins to target for therapeutic uses have been meager. Here, we report a strategic approach for identifying the most indispensable proteins from the genome of P. mirabilis strain HI4320, besides comparing the interactomes comprising the autoinducer-2 (AI-2) biosynthetic pathway along with other proteins involved in biofilm formation and responsible for virulence. Essentially, we have adopted a theoretical network model based approach to construct a set of small protein interaction networks (SPINs) along with the whole genome (GPIN) to computationally identify the crucial proteins involved in the phenomenon of quorum sensing (QS) and biofilm formation and thus, could be therapeutically targeted to fight out the MDR threats to antibiotics of P. mirabilis. Our approach utilizes the functional modularity coupled with k-core analysis and centrality scores of eigenvector as a measure to address the pressing issues.</p