Predicting novel candidate human obesity genes and their site of action by systematic functional screening in Drosophila.

This is the final version. Available from This is the final version. Available from [publisher] via the DOI in this record.  via the DOI in this record. SCOOP and INTERVAL WES data are accessible from EGA (EGAS00001000124 and EGAS00001000825). All other data are available in the manuscript or the supplementary materialsThe discovery of human obesity-associated genes can reveal new mechanisms to target for weight loss therapy. Genetic studies of obese individuals and the analysis of rare genetic variants can identify novel obesity-associated genes. However, establishing a functional relationship between these candidate genes and adiposity remains a significant challenge. We uncovered a large number of rare homozygous gene variants by exome sequencing of severely obese children, including those from consanguineous families. By assessing the function of these genes in vivo in Drosophila, we identified 4 genes, not previously linked to human obesity, that regulate adiposity (itpr, dachsous, calpA, and sdk). Dachsous is a transmembrane protein upstream of the Hippo signalling pathway. We found that 3 further members of the Hippo pathway, fat, four-jointed, and hippo, also regulate adiposity and that they act in neurons, rather than in adipose tissue (fat body). Screening Hippo pathway genes in larger human cohorts revealed rare variants in TAOK2 associated with human obesity. Knockdown of Drosophila tao increased adiposity in vivo demonstrating the strength of our approach in predicting novel human obesity genes and signalling pathways and their site of action.Wellcome Trust Senior Investigator AwardWellcome TrustCRU

Detecting Distributed Denial of Service Attacks in Neighbour Discovery Protocol Using Machine Learning Algorithm Based on Streams Representation

© 2018, Springer International Publishing AG, part of Springer Nature. The main protocol of the Internet protocol version 6 suites is the neighbour discovery protocol, which is geared towards substitution of address resolution protocol, router discovery, and function redirection in Internet protocol version 4. Internet protocol version 6 nodes employ neighbour discovery protocol to detect linked hosts and routers in Internet protocol version 6 network without the dependence on dynamic host configuration protocol server, which has earned the neighbour discovery protocol the title of the stateless protocol. The authentication process of the neighbour discovery protocol exhibits weaknesses that make this protocol vulnerable to attacks. Denial of service attacks can be triggered by a malicious host through the introduction of spoofed addresses in neighbour discovery protocol messages. Internet version 6 protocols are not well supported by Network Intrusion Detection System as is the case with Internet Protocol version 4 protocols. Several data mining techniques have been introduced to improve the classification mechanism of Intrusion detection system. In addition, extensive researches indicated that there is no Intrusion Detection system for Internet Protocol version 6 using advanced machine-learning techniques toward distributed denial of service attacks. This paper aims to detect Distributed Denial of Service attacks of the Neighbour Discovery protocol using machine-learning techniques, due to the severity of the attacks and the importance of Neighbour Discovery protocol in Internet Protocol version 6. Decision tree algorithm and Random Forest Algorithm showed high accuracy results in comparison to the other benchmarked algorithms

A causal relationship between right paraduodenal hernia and superior mesenteric artery syndrome: a case report

<p>Abstract</p> <p>Introduction</p> <p>Cases of right paraduodenal hernia and superior mesenteric artery syndrome have been reported separately, but their occurrence in combination has not been reported.</p> <p>Case presentation</p> <p>A 46-year-old Japanese man who had never undergone laparotomy was admitted to our hospital due to an acute abdomen. An enhanced multidetector-row computed tomography scan of our patient showed a cluster of small intestines with ischemic change in his right lateral abdominal cavity. Emergency surgery was subsequently performed, and strangulation of the distal jejunum along with incidental right paraduodenal hernia was found. His necrotic ileum was resected, and the jejunum encapsulated by the sac was repaired manually without reduction.</p> <p>Three days after the operation, however, our patient developed vomiting. An upper gastrointestinal series revealed a straight line cut-off sign on the third portion of his duodenum. A second enhanced multidetector-row computed tomography scan showed that he had a lower aortomesenteric angle and a shorter aortomesenteric distance compared to his condition before his right paraduodenal hernia was surgically repaired. We strongly suspected that the right paraduodenal hernia repair may have induced superior mesenteric artery syndrome. On the 21st post-operative day, duodenojejunostomy was performed because conservative management had failed.</p> <p>Conclusions</p> <p>In this case, enhanced multidetector-row computed tomography, which permits reconstructed multiplanar imaging, helped us to visually identify these diseases easily. It is important to recognize that surgical repair of a right paraduodenal hernia may cause superior mesenteric artery syndrome.</p

Understanding the limits to generalizability of experimental evolutionary models.

Post print version of article deposited in accordance with SHERPA RoMEO guidelines. The final definitive version is available online at: http://www.nature.com/nature/journal/v455/n7210/abs/nature07152.htmlGiven the difficulty of testing evolutionary and ecological theory in situ, in vitro model systems are attractive alternatives; however, can we appraise whether an experimental result is particular to the in vitro model, and, if so, characterize the systems likely to behave differently and understand why? Here we examine these issues using the relationship between phenotypic diversity and resource input in the T7-Escherichia coli co-evolving system as a case history. We establish a mathematical model of this interaction, framed as one instance of a super-class of host-parasite co-evolutionary models, and show that it captures experimental results. By tuning this model, we then ask how diversity as a function of resource input could behave for alternative co-evolving partners (for example, E. coli with lambda bacteriophages). In contrast to populations lacking bacteriophages, variation in diversity with differences in resources is always found for co-evolving populations, supporting the geographic mosaic theory of co-evolution. The form of this variation is not, however, universal. Details of infectivity are pivotal: in T7-E. coli with a modified gene-for-gene interaction, diversity is low at high resource input, whereas, for matching-allele interactions, maximal diversity is found at high resource input. A combination of in vitro systems and appropriately configured mathematical models is an effective means to isolate results particular to the in vitro system, to characterize systems likely to behave differently and to understand the biology underpinning those alternatives

Screening the medicines for Malaria Venture "Malaria Box" against the Plasmodium falciparum aminopeptidases, M1, M17 and M18

Malaria is a parasitic disease that remains a global health burden. The ability of the parasite to rapidly develop resistance to therapeutics drives an urgent need for the delivery of new drugs. The Medicines for Malaria Venture have compounds known for their antimalarial ac- tivity, but not necessarily the molecular targets. In this study, we assess the ability of the “MMV 400” compounds to inhibit the activity of three metalloaminopeptidases from Plasmo- dium falciparum, PfA-M1, PfA-M17 and PfM18 AAP. We have developed a multiplex assay system to allow rapid primary screening of compounds against all three metalloaminopepti- dases, followed by detailed analysis of promising compounds. Our results show that there were no PfM18AAP inhibitors, whereas two moderate inhibitors of the neutral aminopepti- dases PfA-M1 and PfA-M17 were identified. Further investigation through structure-activity relationship studies and molecular docking suggest that these compounds are competitive inhibitors with novel binding mechanisms, acting through either non-classical zinc coordina- tion or independently of zinc binding altogether. Although it is unlikely that inhibition of PfA- M1 and/or PfA-M17 is the primary mechanism responsible for the antiplasmodial activity re- ported for these compounds, their detailed characterization, as presented in this work, pave the way for their further optimization as a novel class of dual PfA-M1/PfA-M17 inhibitors uti- lising non-classical zinc binding groups

DLDDO: Deep Learning to Detect Dummy Operations

Recently, research on deep learning based side-channel analysis (DLSCA) has received a lot of attention. Deep learning-based profiling methods similar to template attacks as well as non-profiling-based methods similar to differential power analysis have been proposed. DLSCA methods have been proposed for targets to which masking schemes or jitter-based hiding schemes are applied. However, most of them are methods for finding the secret key, except for methods for preprocessing, and there are no studies on the target to which the dummy-based hiding schemes or shuffling schemes are applied. In this paper, we propose a DLSCA for detecting dummy operations. In the previous study, dummy operations were detected using the method called BCDC, but there is a disadvantage in that it is impossible to detect dummy operations for commercial devices such as an IC card. We consider the detection of dummy operations as a multi-label classification problem and propose a deep learning method based on CNN to solve it. As a result, it is possible to successfully perform detection of dummy operations on an IC card, which was not possible in the previous study

Herbivore benefits from vectoring plant virus through reduction of period of vulnerability to predation

Herbivores can profit from vectoring plant pathogens because the induced defence of plants against pathogens sometimes interferes with the induced defence of plants against herbivores. Plants can also defend themselves indirectly by the action of the natural enemies of the herbivores. It is unknown whether the defence against pathogens induced in the plant also interferes with the indirect defence against herbivores mediated via the third trophic level. We previously showed that infection of plants with Tomato spotted wilt virus (TSWV) increased the developmental rate of and juvenile survival of its vector, the thrips Frankliniella occidentalis. Here, we present the results of a study on the effects of TSWV infections of plants on the effectiveness of three species of natural enemies of F. occidentalis: the predatory mites Neoseiulus cucumeris and Iphiseius degenerans, and the predatory bug Orius laevigatus. The growth rate of thrips larvae was positively affected by the presence of virus in the host plant. Because large larvae are invulnerable to predation by the two species of predatory mites, this resulted in a shorter period of vulnerability to predation for thrips that developed on plants with virus than thrips developing on uninfected plants (4.4 vs. 7.9 days, respectively). Because large thrips larvae are not invulnerable to predation by the predatory bug Orius laevigatus, infection of the plant did not affect the predation risk of thrips larvae from this predator. This is the first demonstration of a negative effect of a plant pathogen on the predation risk of its vector