Biochemical investigation of phosphodiesterase type IV post-translational modification, cellular localisation and interaction with associated binding proteins

cAMP is a secondary messenger that is involved in a variety of signalling pathways through its effectors including EPAC, PKA and ion channels. cAMP signalling regulates processes such as memory, muscle contraction and inflammatory responses. PDE enzymes offer a mechanism to negatively regulate elevated cAMP levels elicited by activators of adenylyl cyclase. Studies have shown that cAMP signalling is compartmentalised through binding of PDEs to A-kinase anchoring proteins (AKAPs) that scaffold PKA regulatory subunits. In this study post-translational-modification of PDE4 isoforms is investigated. SUMOylation is a relatively newly identified post-translational modification that is known to regulate the structure and function of its substrates. PDE4 isoforms of the PDE4A and 4D subfamilies are SUMOylated by an E3 ligase, PIASy. SUMOylation alters the rolipram sensitivity and potentiates the PKA mediated activation of the isoforms whilst it confers protection from ERK-mediated inhibition of PDE4 activity. SUMOylation alters the association of PDE4 isoforms with binding partners like β-Arrestin, AKAP18 δ and UBC9. Rolipram is an archetypal PDE4 specific inhibitor. In this study it is shown that in cells expressing a GFP tagged form of PDE4A4 undergoes redistribution into accretion foci upon chronic treatment with rolipram. Data suggests that foci formation requires protein turnover and is regulated by signalling pathways such as PI3 kinase pathway, p38 MAP kinase pathway and PKC pathways. Further, the Immunomodulatory drug Thalidomide® also inhibits foci formation. PDE4 isoforms have isoforms specific N-terminal regions, which play a crucial role in sub-cellular localisation and protein-protein interactions. It is shown here that PDE4D5 interacts with a novel RhoGAP called ARHGAP21 which has been previously reported to bind β-arrestins. This interaction is independent of GAP activity of ARHGAP as well as PDE4 activity. Previous reports have indicated a role of β-Arrestin, PDE4 and ARHGAP21 in regulation of actin cytoskeleton dynamics. Hence complex β-Arrestin-PDE4-ARHGAP21 may play a crucial role in regulating actin dynamics

Summary of the 2017 South Southeast Research Initiative (SARI) Agricultural Workshop

South/Southeast Asian countries are growing rapidly in terms of population, industrialization, andurbanization. As a result of this growth, one of the key policy challenges facing the region is foodsecuritythat is, those conditions when all people, at all times, have physical and economic access tosufficient, safe and nutritious food that meets their dietary needs and food preferences for an active andhealthy life.1 Although total food production has increased in the region since 1960 due to land areahaving been converted to agricultural use, more recently it has decreased, mostly due to loss ofproductive agricultural land due to urbanization and industrial development. Furthermore, the region isexperiencing variability in the timing of the monsoon and extreme weather events, resulting in droughtor flooding, which impact agricultural production. Monitoring crop production in a timely manner isessential to predict and prepare for disruptions in the food supply. To achieve such timely monitoringrequires improved and uptodate information on agricultural landuse practices.Although there has been significant progress in remote sensing and geospatial technologies over thepast few decades, there has been little emphasis placed on developing robust methods for operationalmapping and monitoring of areas devoted to crops. In South/Southeast Asia generally, most mappingefforts to date have focused on the broader classification of land cover types and generalized croplandareas into a single or limited number of thematic classes. Only a few countries have access to uptodatecrop type information. There is an urgent need to make this nearrealtime information morereadily available to stakeholders and to enhance national and regional operational systems formonitoring agricultural crops.

Modeling horizontal gene transfer (HGT) in the gut of the Chagas disease vector Rhodnius prolixus

<p>Abstract</p> <p>Background</p> <p>Paratransgenesis is an approach to reducing arthropod vector competence using genetically modified symbionts. When applied to control of Chagas disease, the symbiont bacterium <it>Rhodococcus rhodnii</it>, resident in the gut lumen of the triatomine vector <it>Rhodnius prolixus </it>(Hemiptera: Reduviidae), is transformed to export cecropin A, an insect immune peptide. Cecropin A is active against <it>Trypanosoma cruzi</it>, the causative agent of Chagas disease. While proof of concept has been achieved in laboratory studies, a rigorous and comprehensive risk assessment is required prior to consideration of field release. An important part of this assessment involves estimating probability of transgene horizontal transfer to environmental organisms (HGT). This article presents a two-part risk assessment methodology: a theoretical model predicting HGT in the gut of <it>R. prolixus </it>from the genetically transformed symbiont <it>R. rhodnii </it>to a closely related non-target bacterium, <it>Gordona rubropertinctus</it>, in the absence of selection pressure, and a series of laboratory trials designed to test the model.</p> <p>Results</p> <p>The model predicted an HGT frequency of less than 1.14 × 10^-16per 100,000 generations at the 99% certainty level. The model was iterated twenty times, with the mean of the ten highest outputs evaluated at the 99% certainty level. Laboratory trials indicated no horizontal gene transfer, supporting the conclusions of the model.</p> <p>Conclusions</p> <p>The model treats HGT as a composite event, the probability of which is determined by the joint probability of three independent events: gene transfer through the modalities of transformation, transduction, and conjugation. Genes are represented in matrices and Monte Carlo method and Markov chain analysis are used to simulate and evaluate environmental conditions. The model is intended as a risk assessment instrument and predicts HGT frequency of less than 1.14 × 10^-16per 100,000 generations. With laboratory studies that support the predictions of this model, it may be possible to argue that HGT is a negligible consideration in risk assessment of genetically modified <it>R. rhodnii </it>released for control of Chagas disease.</p

DEVELOPMENT AND VALIDATION OF RP-HPLC METHOD FOR SIMULTANEOUS ESTIMATION OF LAMIVUDINE AND ZIDOVUDINE IN BULK

Objective: The scope of the present work is to expand and optimization of the chromatographic conditions and to develop RP-HPLC method. Methods: The method was performed with various columns like C18 column, kromasil column, Hypersil BDS column. Among them, Symmetry C18 (250x4.6 mm, 5um) was found to be ideal as it gave good peak shape at 0.8 ml/min flow and validated for various parameters such as linearity, precision, accuracy, System suitability, Specificity, % Assay, Robustness, etc. Results: The system suitability parameters were evaluated from standard chromatograms by calculating the % RSD from five replicate injections for Lamivudine and Zidovudine retention times and peak areas. The % RSD for the retention times of principal peak from 5 replicate injections of each standard solution were less than 2.0 %. The Linearity and correlation coefficient of Lamivudine and zidovudine was found to be 0.999. Precision was performed and % RSD for Lamivudine and Zidovudine were found to be less than 2.0. The % Recovery for each level was found to be the range of 98.0 to 102%. In Robustness, the % RSD of the peak area of all peaks for five replicate injections should be not more than 2.0. Conclusion: Hence, the chromatographic method developed for Lamivudine and Zidovudine is said to be rapid, simple, specific, sensitive, precise, accurate and reliable that can be effectively applied for routine analysis in research institutions, quality control department in industries, approved testing laboratories, bio-pharmaceutics and bio-equivalence studies and in clinical pharmacokinetic studies

SENet: Visual Detection of Online Social Engineering Attack Campaigns

Social engineering (SE) aims at deceiving users into performing actions that may compromise their security and privacy. These threats exploit weaknesses in human's decision making processes by using tactics such as pretext, baiting, impersonation, etc. On the web, SE attacks include attack classes such as scareware, tech support scams, survey scams, sweepstakes, etc., which can result in sensitive data leaks, malware infections, and monetary loss. For instance, US consumers lose billions of dollars annually due to various SE attacks. Unfortunately, generic social engineering attacks remain understudied, compared to other important threats, such as software vulnerabilities and exploitation, network intrusions, malicious software, and phishing. The few existing technical studies that focus on social engineering are limited in scope and mostly focus on measurements rather than developing a generic defense. To fill this gap, we present SEShield, a framework for in-browser detection of social engineering attacks. SEShield consists of three main components: (i) a custom security crawler, called SECrawler, that is dedicated to scouting the web to collect examples of in-the-wild SE attacks; (ii) SENet, a deep learning-based image classifier trained on data collected by SECrawler that aims to detect the often glaring visual traits of SE attack pages; and (iii) SEGuard, a proof-of-concept extension that embeds SENet into the web browser and enables real-time SE attack detection. We perform an extensive evaluation of our system and show that SENet is able to detect new instances of SE attacks with a detection rate of up to 99.6% at 1% false positive, thus providing an effective first defense against SE attacks on the web

Managing peer relationships online - Investigating the use of Facebook by juvenile delinquents and youths-at-risk

10.1016/j.chb.2012.04.025Computers in Human Behavior2918-15CHBE