The serologically defined colon cancer antigen-3 (SDCCAG3) is involved in the regulation of ciliogenesis

A primary cilium is present on most eukaryotic cells and represents a specialized organelle dedicated to signal transduction and mechanosensing. Defects in cilia function are the cause for several human diseases called ciliopathies. The serologically defined colon cancer antigen-3 (SDCCAG3) is a recently described novel endosomal protein mainly localized at early and recycling endosomes and interacting with several components of membrane trafficking pathways. Here we describe localization of SDCCAG3 to the basal body of primary cilia. Furthermore, we demonstrate that decreased expression levels of SDCCAG3 correlate with decreased ciliary length and a reduced percentage of ciliated cells. We show that SDCCAG3 interacts with the intraflagellar transport protein 88 (IFT88), a crucial component of ciliogenesis and intraciliary transport. Mapping experiments revealed that the N-terminus of SDCCAG3 mediates this interaction by binding to a region within IFT88 comprising several tetratricopeptide (TRP) repeats. Finally, we demonstrate that SDCCAG3 is important for ciliary localization of the membrane protein Polycystin-2, a protein playing an important role in the formation of polycystic kidney disease, but not for Rab8 another ciliary protein. Together these data suggest a novel role for SDCCAG3 in ciliogenesis and in localization of cargo to primary cilia

How Might Crime-Scripts Be Used to Support the Understanding and Policing of Cloud Crime?

Crime scripts are becoming an increasingly popular method for understanding crime by turning a crime from a static event into a process, whereby every phase of the crime is scripted. It is based on the work relating to cognitive scripts and rational-choice theory. With the exponential growth of cyber-crime, and more specifically cloud-crime, policing/law enforcement agencies are struggling with the amount of reported cyber-crime. This paper argues that crime scripts are the most effective way forward in terms of helping understand the behaviour of the criminal during the crime itself. They act as a common language between different stakeholders, focusing attention and resources on the key phases of a crime. More importantly, they shine a light on the psychological element of a crime over the more technical cyber-related elements. The paper concludes with an example of what a cloud-crime script might look like, asking future research to better understand: (i) cloud criminal fantasy development; (ii) the online cultures around cloud crime; (iii) how the idea of digital-drift affects crime scripts, and; (iv) to improve on the work by Ekblom and Gill in improving crime scripts

Epigenetic Regulation of Fatty Acid Amide Hydrolase in Alzheimer Disease

OBJECTIVE: Alzheimer disease (AD) is a progressive, degenerative and irreversible neurological disorder with few therapies available. In search for new potential targets, increasing evidence suggests a role for the endocannabinoid system (ECS) in the regulation of neurodegenerative processes. METHODS: We have studied the gene expression status and the epigenetic regulation of ECS components in peripheral blood mononuclear cells (PBMCs) of subjects with late-onset AD (LOAD) and age-matched controls (CT). RESULTS: We found an increase in fatty acid amide hydrolase (faah) gene expression in LOAD subjects (2.30 ± 0.48) when compared to CT (1.00 ± 0.14; *p<0.05) and no changes in the mRNA levels of any other gene of ECS elements. Consistently, we also observed in LOAD subjects an increase in FAAH protein levels (CT: 0.75 ± 0.04; LOAD: 1.11 ± 0.15; *p<0.05) and activity (pmol/min per mg protein CT: 103.80 ± 8.73; LOAD: 125.10 ± 4.00; *p<0.05), as well as a reduction in DNA methylation at faah gene promoter (CT: 55.90 ± 4.60%; LOAD: 41.20 ± 4.90%; *p<0.05). CONCLUSIONS: Present findings suggest the involvement of FAAH in the pathogenesis of AD, highlighting the importance of epigenetic mechanisms in enzyme regulation; they also point to FAAH as a new potential biomarker for AD in easily accessible peripheral cells

Biological detoxification of the mycotoxin deoxynivalenol and its use in genetically engineered crops and feed additives

Deoxynivalenol (DON) is the major mycotoxin produced by Fusarium fungi in grains. Food and feed contaminated with DON pose a health risk to humans and livestock. The risk can be reduced by enzymatic detoxification. Complete mineralization of DON by microbial cultures has rarely been observed and the activities turned out to be unstable. The detoxification of DON by reactions targeting its epoxide group or hydroxyl on carbon 3 is more feasible. Microbial strains that de-epoxidize DON under anaerobic conditions have been isolated from animal digestive system. Feed additives claimed to de-epoxidize trichothecenes enzymatically are on the market but their efficacy has been disputed. A new detoxification pathway leading to 3-oxo-DON and 3-epi-DON was discovered in taxonomically unrelated soil bacteria from three continents; the enzymes involved remain to be identified. Arabidopsis, tobacco, wheat, barley, and rice were engineered to acetylate DON on carbon 3. In wheat expressing DON acetylation activity, the increase in resistance against Fusarium head blight was only moderate. The Tri101 gene from Fusarium sporotrichioides was used; Fusarium graminearum enzyme which possesses higher activity towards DON would presumably be a better choice. Glycosylation of trichothecenes occurs in plants, contributing to the resistance of wheat to F. graminearum infection. Marker-assisted selection based on the trichothecene-3-O-glucosyltransferase gene can be used in breeding for resistance. Fungal acetyltransferases and plant glucosyltransferases targeting carbon 3 of trichothecenes remain promising candidates for engineering resistance against Fusarium head blight. Bacterial enzymes catalyzing oxidation, epimerization, and less likely de-epoxidation of DON may extend this list in future

Getting on with life: Accepting the permanency of an Implantable Cardioverter Defibrillator

Increasing numbers of people with cardiovascular disease are requiring the insertion of Implantable Cardioverter Defibrillators (ICDs). Although these devices are an effective life-saving treatment, psychological distress sometimes accompanies their insertion. A qualitative approach was used to explore the experiences, concerns and needs of recipients of the device in Western Australia. Twenty-two tape-recorded interviews were carried out and transcribed verbatim. This paper focuses on the physical and psychological adjustments following the insertion of the device. A central theme of ‘getting on with it’ used to cope with the permanency of the device was identified. This was an approach to life in which the presence of the device was accepted and then put aside while life was continued and optimized. This study provides directions for the identification of persons who might be experiencing difficulties adjusting, or who are taking extended amounts of time to accept the permanency of the device

Community detection in weighted directed networks using nature-inspired heuristics

Publisher Copyright: © Springer Nature Switzerland AG 2018.Finding groups from a set of interconnected nodes is a recurrent paradigm in a variety of practical problems that can be modeled as a graph, as those emerging from Social Networks. However, finding an optimal partition of a graph is a computationally complex task, calling for the development of approximative heuristics. In this regard, the work presented in this paper tackles the optimal partitioning of graph instances whose connections among nodes are directed and weighted, a scenario significantly less addressed in the literature than their unweighted, undirected counterparts. To efficiently solve this problem, we design several heuristic solvers inspired by different processes and phenomena observed in Nature (namely, Water Cycle Algorithm, Firefly Algorithm, an Evolutionary Simulated Annealing and a Population based Variable Neighborhood Search), all resorting to a reformulated expression for the well-known modularity function to account for the direction and weight of edges within the graph. Extensive simulations are run over a set of synthetically generated graph instances, aimed at elucidating the comparative performance of the aforementioned solvers under different graph sizes and levels of intra- and inter-connectivity among node groups. We statistically verify that the approach relying on the Water Cycle Algorithm outperforms the rest of heuristic methods in terms of Normalized Mutual Information with respect to the true partition of the graph.Acknowledgements. E. Osaba and J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK program. I. Fister Jr. and I. Fister acknowledge the financial support from the Slovenian Research Agency (Research Core Fundings No. P2-0041 and P2-0057). A. Iglesias and A. Galvez acknowledge the financial support from the projects TIN2017-89275-R (AEI/FEDER, UE), PDE-GIR (H2020, MSCA program, ref. 778035), and JU12 (SODERCAN/FEDER UE). E. Osaba and J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK program. I. Fister Jr. and I. Fister acknowledge the financial support from the Slovenian Research Agency (Research Core Fundings No. P2-0041 and P2-0057). A. Iglesias and A. Galvez acknowledge the financial support from the projects TIN2017-89275-R (AEI/FEDER, UE), PDE-GIR (H2020, MSCA program, ref. 778035), and JU12 (SODERCAN/FEDER UE).Peer reviewe

Dynamic Partitioning of Evolving Graph Streams Using Nature-Inspired Heuristics

Publisher Copyright: © 2019, Springer Nature Switzerland AG.Detecting communities of interconnected nodes is a frequently addressed problem in situation that be modeled as a graph. A common practical example is this arising from Social Networks. Anyway, detecting an optimal partition in a network is an extremely complex and highly time-consuming task. This way, the development and application of meta-heuristic solvers emerges as a promising alternative for dealing with these problems. The research presented in this paper deals with the optimal partitioning of graph instances, in the special cases in which connections among nodes change dynamically along the time horizon. This specific case of networks is less addressed in the literature than its counterparts. For efficiently solving such problem, we have modeled and implements a set of meta-heuristic solvers, all of them inspired by different processes and phenomena observed in Nature. Concretely, considered approaches are Water Cycle Algorithm, Bat Algorithm, Firefly Algorithm and Particle Swarm Optimization. All these methods have been adapted for properly dealing with this discrete and dynamic problem, using a reformulated expression for the well-known modularity formula as fitness function. A thorough experimentation has been carried out over a set of 12 synthetically generated dynamic graph instances, with the main goal of concluding which of the aforementioned solvers is the most appropriate one to deal with this challenging problem. Statistical tests have been conducted with the obtained results for rigorously concluding the Bat Algorithm and Firefly Algorithm outperform the rest of methods in terms of Normalized Mutual Information with respect to the true partition of the graph.Acknowledgements. E. Osaba and J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK program. A. Iglesias and A. Galvez acknowledge the financial support from the projects TIN2017-89275-R (AEI/FEDER, UE) and PDE-GIR (H2020, MSCA program, ref. 778035). Iztok Fister and Iztok Fister Jr. acknowledge the financial support from the Slovenian Research Agency (Research Core Founding No. P2-0041 and P2-0057).Peer reviewe