Lasagne : a static binary translator for weak memory model architectures

Funding: This work was supported by a UK RISE Grant.The emergence of new architectures create a recurring challenge to ensure that existing programs still work on them. Manually porting legacy code is often impractical. Static binary translation (SBT) is a process where a program’s binary is automatically translated from one architecture to another, while preserving their original semantics. However, these SBT tools have limited support to various advanced architectural features. Importantly, they are currently unable to translate concurrent binaries. The main challenge arises from the mismatches of the memory consistency model specified by the different architectures, especially when porting existing binaries to a weak memory model architecture. In this paper, we propose Lasagne, an end-to-end static binary translator with precise translation rules between x86 and Arm concurrency semantics. First, we propose a concurrency model for Lasagne’s intermediate representation (IR) and formally proved mappings between the IR and the two architectures. The memory ordering is preserved by introducing fences in the translated code. Finally, we propose optimizations focused on raising the level of abstraction of memory address calculations and reducing the number offences. Our evaluation shows that Lasagne reduces the number of fences by up to about 65%, with an average reduction of 45.5%, significantly reducing their runtime overhead.Postprin

miR-124 and miR-137 inhibit proliferation of glioblastoma multiforme cells and induce differentiation of brain tumor stem cells

Glioblastoma multiforme (GBM) is an invariably fatal central nervous system tumor despite treatment with surgery, radiation, and chemotherapy. Further insights into the molecular and cellular mechanisms that drive GBM formation are required to improve patient outcome. MicroRNAs are emerging as important regulators of cellular differentiation and proliferation, and have been implicated in the etiology of a variety of cancers, yet the role of microRNAs in GBM remains poorly understood. In this study, we investigated the role of microRNAs in regulating the differentiation and proliferation of neural stem cells and glioblastoma-multiforme tumor cells.status: publishe

The Mechanism of Excessive Intestinal Inflammation in Necrotizing Enterocolitis: An Immature Innate Immune Response

Necrotizing enterocolitis (NEC) is a devastating neonatal intestinal inflammatory disease, occurring primarily in premature infants, causing significant morbidity and mortality. The pathogenesis of NEC is associated with an excessive inflammatory IL-8 response. In this study, we hypothesized that this excessive inflammatory response is related to an immature expression of innate immune response genes. To address this hypothesis, intestinal RNA expression analysis of innate immune response genes was performed after laser capture microdissection of resected ileal epithelium from fetuses, NEC patients and children and confirmed in ex vivo human intestinal xenografts. Changes in mRNA levels of toll-like receptors (TLR)-2 and -4, their signaling molecules and transcription factors (MyD88, TRAF-6 and NFκB1) and negative regulators (SIGIRR, IRAK-M, A-20 and TOLLIP) and the effector IL-8 were characterized by qRT-PCR. The expression of TLR2, TLR4, MyD88, TRAF-6, NFκB1 and IL-8 mRNA was increased while SIGIRR, IRAK-M, A-20 and TOLLIP mRNA were decreased in fetal vs. mature human enterocytes and further altered in NEC enterocytes. Similar changes in mRNA expression were observed in immature, but not mature, human intestinal xenografts. Confirmation of gene expression was also validated with selective protein measurements and with suggested evidence that immature TRL4 enterocyte surface expression was internalized in mature enterocytes. Cortisone, an intestinal maturation factor, treatment corrected the mRNA differences only in the immature intestinal xenograft. Using specific siRNA to attenuate expression of primary fetal enterocyte cultures, both TOLLIP and A-20 were confirmed to be important when knocked down by exhibiting the same excessive inflammatory response seen in the NEC intestine. We conclude that the excessive inflammatory response of the immature intestine, a hallmark of NEC, is due to a developmental immaturity in innate immune response genes

Local Ranking Problem on the BrowseGraph

The "Local Ranking Problem" (LRP) is related to the computation of a centrality-like rank on a local graph, where the scores of the nodes could significantly differ from the ones computed on the global graph. Previous work has studied LRP on the hyperlink graph but never on the BrowseGraph, namely a graph where nodes are webpages and edges are browsing transitions. Recently, this graph has received more and more attention in many different tasks such as ranking, prediction and recommendation. However, a web-server has only the browsing traffic performed on its pages (local BrowseGraph) and, as a consequence, the local computation can lead to estimation errors, which hinders the increasing number of applications in the state of the art. Also, although the divergence between the local and global ranks has been measured, the possibility of estimating such divergence using only local knowledge has been mainly overlooked. These aspects are of great interest for online service providers who want to: (i) gauge their ability to correctly assess the importance of their resources only based on their local knowledge, and (ii) take into account real user browsing fluxes that better capture the actual user interest than the static hyperlink network. We study the LRP problem on a BrowseGraph from a large news provider, considering as subgraphs the aggregations of browsing traces of users coming from different domains. We show that the distance between rankings can be accurately predicted based only on structural information of the local graph, being able to achieve an average rank correlation as high as 0.8

Algorithm Instance Games

This paper introduces algorithm instance games (AIGs) as a conceptual classification applying to games in which outcomes are resolved from joint strategies algorithmically. For such games, a fundamental question asks: How do the details of the algorithm's description influence agents' strategic behavior? We analyze two versions of an AIG based on the set-cover optimization problem. In these games, joint strategies correspond to instances of the set-cover problem, with each subset (of a given universe of elements) representing the strategy of a single agent. Outcomes are covers computed from the joint strategies by a set-cover algorithm. In one variant of this game, outcomes are computed by a deterministic greedy algorithm, and the other variant utilizes a non-deterministic form of the greedy algorithm. We characterize Nash equilibrium strategies for both versions of the game, finding that agents' strategies can vary considerably between the two settings. In particular, we find that the version of the game based on the deterministic algorithm only admits Nash equilibrium in which agents choose strategies (i.e., subsets) containing at most one element, with no two agents picking the same element. On the other hand, in the version of the game based on the non-deterministic algorithm, Nash equilibrium strategies can include agents with zero, one, or every element, and the same element can appear in the strategies of multiple agents.Comment: 14 page

Genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: A multi-country population-based surveillance study

Background: In many countries, regular monitoring of the emergence of resistance to anti-tuberculosis drugs is hampered by the limitations of phenotypic testing for drug susceptibility. We therefore evaluated the use of genetic sequencing for surveillance of drugresistance in tuberculosis.Methods: Population-level surveys were done in hospitals and clinics in seven countries (Azerbaijan, Bangladesh, Belarus, Pakistan, Philippines, South Africa, and Ukraine) to evaluate the use of genetic sequencing to estimate the resistance of Mycobacterium tuberculosisisolates to rifampicin, isoniazid, ofloxacin, moxifloxacin, pyrazinamide, kanamycin, amikacin, and capreomycin. For each drug, we assessed the accuracy of genetic sequencing by a comparison of the adjusted prevalence of resistance, measured by genetic sequencing, with the true prevalence of resistance, determined by phenotypic testing.Findings: Isolates were taken from 7094 patients with tuberculosis who were enrolled in the study between November, 2009, and May, 2014. In all tuberculosis cases, the overall pooled sensitivity values for predicting resistance by genetic sequencing were 91% (95% CI 87-94) for rpoB (rifampicin resistance), 86% (74-93) for katG, inhA, and fabG promoter combined (isoniazid resistance), 54% (39-68) for pncA (pyrazinamide resistance), 85% (77-91) for gyrA and gyrB combined (ofloxacin resistance), and 88% (81-92) for gyrA and gyrB combined (moxifloxacin resistance). For nearly all drugs and in most settings, there was a large overlap in the estimated prevalence of drug resistanceby genetic sequencing and the estimated prevalence by phenotypic testing.Interpretation: Genetic sequencing can be a valuable tool for surveillance of drug resistance, providing new opportunities to monitor drug resistance in tuberculosis in resource-poor countries. Before its widespread adoption for surveillance purposes, there is a need to standardise DNA extraction methods, recording and reporting nomenclature, and data interpretation.Findings: Bill & Melinda Gates Foundation, United States Agency for International Development, Global Alliance for Tuberculosis DrugDevelopment

Nonsmooth Control Barrier Function design of continuous constraints for network connectivity maintenance

This paper considers the problem of maintaining global connectivity of a multi-robot system while executing a desired coordination task. Our approach builds on optimization-based feedback design formulations, where the nominal cost function and constraints encode desirable control objectives for the resulting input. We take advantage of the flexibility provided by control barrier functions to produce additional constraints that guarantee that the resulting optimization-based controller is continuous and maintains network connectivity. Our solution uses the algebraic connectivity of the multi-robot interconnection topology as a control barrier function and critically embraces its nonsmooth nature. The technical treatment combines elements from set-valued theory, nonsmooth analysis, and algebraic graph theory to imbue the proposed constraints with regularity properties so that they can be smoothly combined with other control constraints. We provide simulations and experimental results illustrating the effectiveness and continuity of the proposed approach in a resource gathering problem.Comment: submitted to Automatic