Systematic transcriptome wide analysis of lncRNA-miRNA interactions

Long noncoding RNAs (lncRNAs) are a recently discovered class of non-protein coding RNAs which have now increasingly been shown to be involved in a wide variety of biological processes as regulatory molecules. Little is known regarding the regulatory interactions between noncoding RNA classes. Recent reports have suggested that lncRNAs could potentially interact with other noncoding RNAs including miroRNAs (miRNAs) and modulate their regulatory role through interactions. We hypothesized that long noncoding RNAs could participate as a layer of regulatory interactions with miRNAs. The availability of genome-scale datasets for argonaute targets across human transcriptome has prompted us to reconstruct a genome-scale network of interactions between miRNAs and lncRNAs. We used well characterized experimental Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) datasets and the recent genome-wide annotations for lncRNAs in public domain to construct a comprehensive transcriptome-wide map of miRNA regulatory elements. Comparative analysis revealed many of the miRNAs could target long noncoding RNAs, apart from the coding transcripts thus participating in a novel layer of regulatory interactions between noncoding RNA classes. We also find the miRNA regulatory elements have a positional preference, clustering towards the 3' and 5' ends of the long noncoding transcripts. We also further reconstruct a genome-wide map of miRNA interactions with lncRNAs as well as messenger RNAs. This analysis suggests widespread regulatory interactions between noncoding RNAs classes and suggests a novel functional role for lncRNAs. We also present the first transcriptome scale study on lncRNA-miRNA interactions and the first report of a genome-scale reconstruction of a noncoding RNA regulatory interactome involving lncRNAs

Translational groups as generators of gauge transformations

We examine the gauge generating nature of the translational subgroup of Wigner's little group for the case of massless tensor gauge theories and show that the gauge transformations generated by the translational group is only a subset of the complete set of gauge transformations. We also show that, just like the case of topologically massive gauge theories, translational groups act as generators of gauge transformations in gauge theories obtained by extending massive gauge noninvariant theories by a Stuckelberg mechanism. The representations of the translational groups that generate gauge transformations in such Stuckelberg extended theories can be obtained by the method of dimensional descent. We illustrate these with the examples of Stuckelberg extended first class versions of Proca, Einstein-Pauli-Fierz and massive Kalb-Ramond theories in 3+1 dimensions. A detailed analysis of the partial gauge generation in massive and massless 2nd rank symmetric gauge theories is provided. The gauge transformations generated by translational group in 2-form gauge theories are shown to explicitly manifest the reducibility of gauge transformations in these theories.Comment: Latex, 20 pages, no figures, Version to appear in Physical Review

Rotations associated with Lorentz boosts

It is possible to associate two angles with two successive non-collinear Lorentz boosts. If one boost is applied after the initial boost, the result is the final boost preceded by a rotation called the Wigner rotation. The other rotation is associated with Wigner's O(3)-like little group. These two angles are shown to be different. However, it is shown that the sum of these two rotation angles is equal to the angle between the initial and final boosts. This relation is studied for both low-speed and high-speed limits. Furthermore, it is noted that the two-by-two matrices which are under the responsibility of other branches of physics can be interpreted in terms of the transformations of the Lorentz group, or vice versa. Classical ray optics is mentioned as a case in point.Comment: LaTeX, 16 Pages, 4 epsfigure

Polarization Vectors, Doublet Structure and Wigner's Little Group in Planar Field Theory

We establish the equivalence of the Maxwell-Chern-Simons-Proca model to a doublet of Maxwell-Chern-Simons models at the level of polarization vectors of the basic fields using both Lagrangian and Hamiltonian formalisms. The analysis reveals a U(1) invariance of the polarization vectors in the momentum space. Its implications are discussed. We also study the role of Wigner's little group as a generator of gauge transformations in three space-time dimensions.Comment: LaTex, 30 pages, no figure

The Flavonoid Metabolite 2,4,6-Trihydroxybenzoic Acid Is a CDK Inhibitor and an Anti-Proliferative Agent: A Potential Role in Cancer Prevention

Flavonoids have emerged as promising compounds capable of preventing colorectal cancer (CRC) due to their anti-oxidant and anti-inflammatory properties. It is hypothesized that the metabolites of flavonoids are primarily responsible for the observed anti-cancer effects owing to the unstable nature of the parent compounds and their degradation by colonic microflora. In this study, we investigated the ability of one metabolite, 2,4,6-trihydroxybenzoic acid (2,4,6-THBA) to inhibit Cyclin Dependent Kinase (CDK) activity and cancer cell proliferation. Using in vitro kinase assays, we demonstrated that 2,4,6-THBA dose-dependently inhibited CDKs 1, 2 and 4 and in silico studies identified key amino acids involved in these interactions. Interestingly, no significant CDK inhibition was observed with the structurally related compounds 3,4,5-trihydroxybenzoic acid (3,4,5-THBA) and phloroglucinol, suggesting that orientation of the functional groups and specific amino acid interactions may play a role in inhibition. We showed that cellular uptake of 2,4,6-THBA required the expression of functional SLC5A8, a monocarboxylic acid transporter. Consistent with this, in cells expressing functional SLC5A8, 2,4,6-THBA induced CDK inhibitory proteins p21Cip1 and p27Kip1 and inhibited cell proliferation. These findings, for the first time, suggest that the flavonoid metabolite 2,4,6-THBA may mediate its effects through a CDK- and SLC5A8-dependent pathway contributing to the prevention of CRC

Barcoding of Asian seabass across its geographic range provides evidence for its bifurcation into two distinct species

Asian seabass or barramundi (Lates calcarifer) is an important food fish with commercial value and a wide geographic distribution. Though some reports based on molecular and/or morphological data exist, a comprehensive effort to establish species identity across its range is lacking. In order to address this issue and especially to ascertain whether the wide-spread distribution has resulted in bifurcation of the species, we collected Asian seabass samples from various locations representing the Western and Eastern Coastline of India, Andaman and Nicobar Islands, Bangladesh and Australia. Samples from Malaysia, Indonesia, Thailand and Singapore were collected as part of a previous study. DNA sequence variations, including cytochrome c oxidase subunit 1 (COI), 16S rDNA and the highly variable D-loop (or control region), were examined to establish species delineation. Data from all the sequences analyzed concordantly point to the existence of at least two distinct species—one representing the Indian subcontinent plus Myanmar, and a second, representing Southeast Asia (Singapore, Malaysia, Thailand and Indonesia) plus Northern Australia. These data are useful for conservation ecology, aquaculture management, for establishing the extent of genetic diversity in the Asian seabass and implementing selective breeding programs for members of this species complex

INFECTION PREVENTION AND CONTROL AWARENESS, ATTITUDES, AND PRACTICES AMONG HEALTHCARE PROFESSIONALS IN SOUTH INDIA

Introduction: Infection is a key challenge in healthcare settings around the world. Healthcare professionals (HCPs), including medical laboratory technologists (MLTs) and nurses, are at risk of infection because they are in close contact with infected patients. This investigation was conducted to evaluate the awareness, attitude, and practices of Infection Prevention Control (IPC) among HCPs working in private tertiary hospitals in two states in South India. Methodology: This quantitative study surveyed 571 HCPs in southern India. In September 2021, an online survey was used to collect data on the respondents’ demographic and IPC-related variables, as well as their awareness, attitudes, and practices of IPC. Results: The survey revealed high level of awareness, positive attitudes, and good IPC practices. Among the IPC practices, “changing gloves between contacts with different patients” was the most often practiced and “washing hands after removal of gloves” was the least practiced. Being a nurse, being older, finishing a graduate program, attending a risk assessment training, having sufficient Personal Protective Equipment (PPE) at work, and being aware of the safety guidelines were associated with better awareness. Being a nurse, being older, and holding a diploma were associated with more positive attitudes. Being MLT, attending risk assessment training, having sufficient PPE at work, and being aware of the safety guidelines were associated with better IPC practices. Conclusions: Measures to sustain the high awareness, positive attitudes, and good IPC practices by dealing with the factors associated with these variables identified in this study must be planned and implemented

Knowledge and attitudes about health research amongst a group of Pakistani medical students

Background Health research training is an important part of medical education. This study was conducted to assess the level of knowledge and attitudes regarding health research in a group of Pakistani medical students at Aga Khan University, Karachi. Methods It was a cross-sectional pilot study conducted among a group of Pakistani medical students. Through stratified random sampling, a pre-tested, structured and validated questionnaire was administered to 220 medical students. Knowledge and attitudes were recorded on a scale (graduated in percentages). Results Mean scores of students were 49.0% on knowledge scale and 53.7% on attitude scale. Both knowledge and attitudes improved significantly with increasing years of study in medical college [Regression coefficient 4.10 (p-value; 0.019) and 6.67 (p-value; \u3c 0.001) for knowledge and attitudes, respectively]. Conclusion Medical students demonstrate moderate level of knowledge and attitude towards health research. Intensive training in this regard is associated with significant improvement in knowledge and attitudes of students towards health research

Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets

<p>Abstract</p> <p>Background</p> <p>Tuberculosis is a contagious disease caused by <it>Mycobacterium tuberculosis </it>(Mtb), affecting more than two billion people around the globe and is one of the major causes of morbidity and mortality in the developing world. Recent reports suggest that Mtb has been developing resistance to the widely used anti-tubercular drugs resulting in the emergence and spread of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains throughout the world. In view of this global epidemic, there is an urgent need to facilitate fast and efficient lead identification methodologies. Target based screening of large compound libraries has been widely used as a fast and efficient approach for lead identification, but is restricted by the knowledge about the target structure. Whole organism screens on the other hand are target-agnostic and have been now widely employed as an alternative for lead identification but they are limited by the time and cost involved in running the screens for large compound libraries. This could be possibly be circumvented by using computational approaches to prioritize molecules for screening programmes.</p> <p>Results</p> <p>We utilized physicochemical properties of compounds to train four supervised classifiers (Naïve Bayes, Random Forest, J48 and SMO) on three publicly available bioassay screens of Mtb inhibitors and validated the robustness of the predictive models using various statistical measures.</p> <p>Conclusions</p> <p>This study is a comprehensive analysis of high-throughput bioassay data for anti-tubercular activity and the application of machine learning approaches to create target-agnostic predictive models for anti-tubercular agents.</p