The minimum bisection in the planted bisection model

In the planted bisection model a random graph $G(n,p_+,p_- )$ with $n$ vertices is created by partitioning the vertices randomly into two classes of equal size (up to $\pm1$). Any two vertices that belong to the same class are linked by an edge with probability $p_+$ and any two that belong to different classes with probability $p_- <p_+$ independently. The planted bisection model has been used extensively to benchmark graph partitioning algorithms. If $p_{\pm} =2d_{\pm} /n$ for numbers $0\leq d_- <d_+$ that remain fixed as $n\to\infty$, then w.h.p. the ``planted'' bisection (the one used to construct the graph) will not be a minimum bisection. In this paper we derive an asymptotic formula for the minimum bisection width under the assumption that $d_+ -d_- >c\sqrt{d_+ \ln d_+ }$ for a certain constant $c>0$

Investigating how enteropathogenic Escherichia coli (EPEC) subverts AKT signalling

PhD ThesisThe phosphoinositide 3-kinase (PI3K) signalling pathway is activated in macrophages in response to many bacterial pathogens, triggering phagocytic uptake mechanisms and phosphorylation-associated activation of the serine/threonine kinase AKT. Enteropathogenic E. coli (EPEC) inhibits both PI3K mediated phagocytosis and AKT phosphorylation; dependent on a type 3-secretion system (T3SS) critical for delivering up to 24 known effector proteins into target cells. The efficient translocation of most EPEC effectors is dependent on the T3SS effector chaperone CesT. Although the effectors and mechanisms for inhibiting phagocytosis are well described, little is known how EPEC inhibits AKT phosphorylation. AKT activation is a multi-step process involving its recruitment to the cell membrane and phosphorylation of Thr308 by PDK1 and Ser473 by mTORC2. This activation process is supressed by inositol (such as PTEN) and protein (such as PP1 & PP2A) phosphatases. Altered AKT signalling is associated with many cancers, diabetes, cardiovascular and infectious disease, thus identifying how EPEC inhibits AKT activity could provide insight into its complex regulatory process and/or new therapeutic strategies. Screening of bacterial strains, lacking or expressing subsets of EPEC effectors, by western blot analysis suggests that the inhibitory mechanism depends on the CesT chaperone but not the function of the 21 most studied effectors. The EPEC inhibitory mechanism was investigated through the development of a two-wave infection model, examining for T3SS dependent changes in AKT phosphorylation (Thr308 & Ser473), membrane localisation and activity of AKT associated signalling proteins (PDK1 & PTEN). This strategy revealed inhibition of AKT phosphorylation to be stable (up to 3 h) and linked to increased activity of serine/threonine protein phosphatase(s). This finding was supported by phosphatase inhibitor studies, suggesting the involvement of a host activated or bacterial delivered protein phosphatase. Thus, this study provides new insights into the requirement of the EPEC effector repertoire and suggests a novel mechanism by which EPEC inhibits AKT signalling.National Insitute for Health Research Newcastle Biomedical Research Centre (BRC)

Information-Theoretic and Algorithmic Thresholds for Group Testing

In the group testing problem we aim to identify a small number of infected individuals within a large population. We avail ourselves to a procedure that can test a group of multiple individuals, with the test result coming out positive iff at least one individual in the group is infected. With all tests conducted in parallel, what is the least number of tests required to identify the status of all individuals? In a recent test design [Aldridge et al. 2016] the individuals are assigned to test groups randomly, with every individual joining an equal number of groups. We pinpoint the sharp threshold for the number of tests required in this randomised design so that it is information-theoretically possible to infer the infection status of every individual. Moreover, we analyse two efficient inference algorithms. These results settle conjectures from [Aldridge et al. 2014, Johnson et al. 2019]

Versatile event correlation with algebraic effects

We present the first language design to uniformly express variants of n -way joins over asynchronous event streams from different domains, e.g., stream-relational algebra, event processing, reactive and concurrent programming. We model asynchronous reactive programs and joins in direct style, on top of algebraic effects and handlers. Effect handlers act as modular interpreters of event notifications, enabling fine-grained control abstractions and customizable event matching. Join variants can be considered as cartesian product computations with ”degenerate” control flow, such that unnecessary tuples are not materialized a priori. Based on this computational interpretation, we decompose joins into a generic, naive enumeration procedure of the cartesian product, plus variant-specific extensions, represented in terms of user-supplied effect handlers. Our microbenchmarks validate that this extensible design avoids needless materialization. Alongside a formal semantics for joining and prototypes in Koka and multicore OCaml, we contribute a systematic comparison of the covered domains and features. ERC, Advanced Grant No. 321217 ERC, Consolidator Grant No. 617805 DFG, SFB 1053 DFG, SA 2918/2-

The Use of ROC Analysis for the Qualitative Prediction of Human Oral Bioavailability from Animal Data

PURPOSE: To develop and evaluate a tool for the qualitative prediction of human oral bioavailability (F(human)) from animal oral bioavailability (F(animal)) data employing ROC analysis and to identify the optimal thresholds for such predictions. METHODS: A dataset of 184 compounds with known F(human) and F(animal) in at least one species (mouse, rat, dog and non-human primates (NHP)) was employed. A binary classification model for F(human) was built by setting a threshold for high/low F(human) at 50%. The thresholds for high/low F(animal) were varied from 0 to 100 to generate the ROC curves. Optimal thresholds were derived from ‘cost analysis’ and the outcomes with respect to false negative and false positive predictions were analyzed against the BDDCS class distributions. RESULTS: We successfully built ROC curves for the combined dataset and per individual species. Optimal F(animal) thresholds were found to be 67% (mouse), 22% (rat), 58% (dog), 35% (NHP) and 47% (combined dataset). No significant trends were observed when sub-categorizing the outcomes by the BDDCS. CONCLUSIONS: F(animal) can predict high/low F(human) with adequate sensitivity and specificity. This methodology and associated thresholds can be employed as part of decisions related to planning necessary studies during development of new drug candidates and lead selection. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11095-013-1193-2) contains supplementary material, which is available to authorized users