Hamiltonian monte carlo with energy conserving subsampling

© 2019 Khue-Dung Dang, Matias Quiroz, Robert Kohn, Minh-Ngoc Tran, Mattias Villani. Hamiltonian Monte Carlo (HMC) samples efficiently from high-dimensional posterior distributions with proposed parameter draws obtained by iterating on a discretized version of the Hamiltonian dynamics. The iterations make HMC computationally costly, especially in problems with large data sets, since it is necessary to compute posterior densities and their derivatives with respect to the parameters. Naively computing the Hamiltonian dynamics on a subset of the data causes HMC to lose its key ability to generate distant parameter proposals with high acceptance probability. The key insight in our article is that efficient subsampling HMC for the parameters is possible if both the dynamics and the acceptance probability are computed from the same data subsample in each complete HMC iteration. We show that this is possible to do in a principled way in a HMC-within-Gibbs framework where the subsample is updated using a pseudo marginal MH step and the parameters are then updated using an HMC step, based on the current subsample. We show that our subsampling methods are fast and compare favorably to two popular sampling algorithms that use gradient estimates from data subsampling. We also explore the current limitations of subsampling HMC algorithms by varying the quality of the variance reducing control variates used in the estimators of the posterior density and its gradients

Subsampling sequential Monte Carlo for static Bayesian models

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. We show how to speed up sequential Monte Carlo (SMC) for Bayesian inference in large data problems by data subsampling. SMC sequentially updates a cloud of particles through a sequence of distributions, beginning with a distribution that is easy to sample from such as the prior and ending with the posterior distribution. Each update of the particle cloud consists of three steps: reweighting, resampling, and moving. In the move step, each particle is moved using a Markov kernel; this is typically the most computationally expensive part, particularly when the dataset is large. It is crucial to have an efficient move step to ensure particle diversity. Our article makes two important contributions. First, in order to speed up the SMC computation, we use an approximately unbiased and efficient annealed likelihood estimator based on data subsampling. The subsampling approach is more memory efficient than the corresponding full data SMC, which is an advantage for parallel computation. Second, we use a Metropolis within Gibbs kernel with two conditional updates. A Hamiltonian Monte Carlo update makes distant moves for the model parameters, and a block pseudo-marginal proposal is used for the particles corresponding to the auxiliary variables for the data subsampling. We demonstrate both the usefulness and limitations of the methodology for estimating four generalized linear models and a generalized additive model with large datasets

Counter-current chromatography for the separation of terpenoids: A comprehensive review with respect to the solvent systems employed

Copyright @ 2014 The Authors.This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.Natural products extracts are commonly highly complex mixtures of active compounds and consequently their purification becomes a particularly challenging task. The development of a purification protocol to extract a single active component from the many hundreds that are often present in the mixture is something that can take months or even years to achieve, thus it is important for the natural product chemist to have, at their disposal, a broad range of diverse purification techniques. Counter-current chromatography (CCC) is one such separation technique utilising two immiscible phases, one as the stationary phase (retained in a spinning coil by centrifugal forces) and the second as the mobile phase. The method benefits from a number of advantages when compared with the more traditional liquid-solid separation methods, such as no irreversible adsorption, total recovery of the injected sample, minimal tailing of peaks, low risk of sample denaturation, the ability to accept particulates, and a low solvent consumption. The selection of an appropriate two-phase solvent system is critical to the running of CCC since this is both the mobile and the stationary phase of the system. However, this is also by far the most time consuming aspect of the technique and the one that most inhibits its general take-up. In recent years, numerous natural product purifications have been published using CCC from almost every country across the globe. Many of these papers are devoted to terpenoids-one of the most diverse groups. Naturally occurring terpenoids provide opportunities to discover new drugs but many of them are available at very low levels in nature and a huge number of them still remain unexplored. The collective knowledge on performing successful CCC separations of terpenoids has been gathered and reviewed by the authors, in order to create a comprehensive document that will be of great assistance in performing future purifications. © 2014 The Author(s)

How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism

Escaping local optima is one of the major obstacles to function optimisation. Using the metaphor of a fitness landscape, local optima correspond to hills separated by fitness valleys that have to be overcome. We define a class of fitness valleys of tunable difficulty by considering their length, representing the Hamming path between the two optima and their depth, the drop in fitness. For this function class we present a runtime comparison between stochastic search algorithms using different search strategies. The ((Formula presented.)) EA is a simple and well-studied evolutionary algorithm that has to jump across the valley to a point of higher fitness because it does not accept worsening moves (elitism). In contrast, the Metropolis algorithm and the Strong Selection Weak Mutation (SSWM) algorithm, a famous process in population genetics, are both able to cross the fitness valley by accepting worsening moves. We show that the runtime of the ((Formula presented.)) EA depends critically on the length of the valley while the runtimes of the non-elitist algorithms depend crucially on the depth of the valley. Moreover, we show that both SSWM and Metropolis can also efficiently optimise a rugged function consisting of consecutive valleys

Synchronized cycles of bacterial lysis for in vivo delivery

The pervasive view of bacteria as strictly pathogenic has given way to an ppreciation of the widespread prevalence of beneficial microbes within the human body. Given this milieu, it is perhaps inevitable that some bacteria would evolve to preferentially grow in environments that harbor disease and thus provide a natural platform for the development of engineered therapies. Such therapies could benefit from bacteria that are programmed to limit bacterial growth while continually producing and releasing cytotoxic agents in situ. Here, we engineer a clinically relevant bacterium to lyse synchronously at a threshold population density and to release genetically encoded cargo. Following quorum lysis, a small number of surviving bacteria reseed the growing population, thus leading to pulsatile delivery cycles. We use microfluidic devices to characterize the engineered lysis strain and we demonstrate its potential as a drug deliver platform via co-culture with human cancer cells in vitro. As a proof of principle, we track the bacterial population dynamics in ectopic syngeneic colorectal tumors in mice. The lysis strain exhibits pulsatile population dynamics in vivo, with mean bacterial luminescence that remained two orders of magnitude lower than an unmodified strain. Finally, guided by previous findings that certain bacteria can enhance the efficacy of standard therapies, we orally administer the lysis strain, alone or in combination with a clinical chemotherapeutic, to a syngeneic transplantation model of hepatic colorectal metastases. We find that the combination of both circuit-engineered bacteria and chemotherapy leads to a notable reduction of tumor activity along with a marked survival benefit over either therapy alone. Our approach establishes a methodology for leveraging the tools of synthetic biology to exploit the natural propensity for certain bacteria to colonize disease sites.National Institute of General Medical Sciences (U.S.) (GM069811)San Diego Center for Systems Biology (P50 GM085764)National Cancer Institute (U.S.). Swanson Biotechnology Center (Koch Institute Support Grant (P30-CA14051))National Institute of Environmental Health Sciences (Core Center Grant (P30- ES002109))National Institutes of Health (U.S.) (NIH Pathway to Independence Award NIH (K99 CA197649-01))Misrock Postdoctoral fellowshipNational Defense Science and Engineering Graduate (NDSEG) Fellowshi

Domain Analysis Reveals That a Deubiquitinating Enzyme USP13 Performs Non-Activating Catalysis for Lys63-Linked Polyubiquitin

Deubiquitination is a reverse process of cellular ubiquitination important for many biological events. Ubiquitin (Ub)-specific protease 13 (USP13) is an ortholog of USP5 implicated in catalyzing hydrolysis of various Ub chains, but its enzymatic properties and catalytic regulation remain to be explored. Here we report studies of the roles of the Ub-binding domains of USP13 in regulatory catalysis by biochemical and NMR structural approaches. Our data demonstrate that USP13, distinct from USP5, exhibits a weak deubiquitinating activity preferring to Lys63-linked polyubiquitin (K63-polyUb) in a non-activation manner. The zinc finger (ZnF) domain of USP13 shares a similar fold with that of USP5, but it cannot bind with Ub, so that USP13 has lost its ability to be activated by free Ub. Substitution of the ZnF domain with that of USP5 confers USP13 the property of catalytic activation. The tandem Ub-associated (UBA) domains of USP13 can bind with different types of diUb but preferentially with K63-linked, providing a possible explanation for the weak activity preferring to K63-polyUb. USP13 can also regulate the protein level of CD3δ in cells, probably depending on its weak deubiquitinating activity and the Ub-binding properties of the UBA domains. Thus, the non-activating catalysis of USP13 for K63-polyUb chains implies that it may function differently from USP5 in cellular deubiquitination processes

Polymorphisms in the SAA1/2 Gene Are Associated with Carotid Intima Media Thickness in Healthy Han Chinese Subjects: The Cardiovascular Risk Survey

BACKGROUND: Serum amyloid A protein (SAA) is not only an inflammatory factor, but also an apolipoprotein that can replace apolipoprotein A1 (apoA1) as the major apolipoprotein of high-density lipoprotein (HDL), which has been linked to atherosclerosis. However, the relationship between genetic polymorphisms of SAA and the intima-media thickness (IMT) of the common carotid artery in healthy subjects remains unclear. We investigated the role of SAA1 and SAA2 gene polymorphisms with IMT in a cohort of healthy subjects participating in the Cardiovascular Risk Survey (CRS) study. METHODOLOGY/PRINCIPAL FINDINGS: Anthropometric and B-mode ultrasound of the carotid IMT were measured in 1914 subjects (849 men; 1065 women) recruited from seven cities in Xinjiang province, (western China). Four SNPs (rs12218, rs2229338, rs1059559, and rs2468844) were genotyped by use of the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. The SNP rs12218 was associated with carotid IMT by analyses of a dominate model (P<0.001) and additive model (P = 0.003), and the difference remained significant after multivariate adjustment (P = 0.008, P<0.001, respectively). This relationship was also observed in rs2468844 after multivariate adjustment by recessive model analysis (P = 0.011) but this was not observed in rs2229338 and rs1059559 before and after multivariate adjustment. These associations were not modified by serum HDL concentration. Furthermore, there were significant interactions between rs2468844 and rs12218 (interaction P<0.001) and rs2229338 (interaction P = 0.001) on carotid IMT. CONCLUSION/SIGNIFICANCE: Both rs12218 of the SAA1 gene and rs2468844 of SAA2 gene are associated with carotid IMT in healthy Han Chinese subjects

Combined mutations of ASXL1, CBL, FLT3, IDH1, IDH2, JAK2, KRAS, NPM1, NRAS, RUNX1, TET2 and WT1 genes in myelodysplastic syndromes and acute myeloid leukemias

<p>Abstract</p> <p>Background</p> <p>Gene mutation is an important mechanism of myeloid leukemogenesis. However, the number and combination of gene mutated in myeloid malignancies is still a matter of investigation.</p> <p>Methods</p> <p>We searched for mutations in the <it>ASXL1, CBL, FLT3, IDH1, IDH2, JAK2, KRAS, NPM1, NRAS, RUNX1, TET2 </it>and <it>WT1 </it>genes in 65 myelodysplastic syndromes (MDSs) and 64 acute myeloid leukemias (AMLs) without balanced translocation or complex karyotype.</p> <p>Results</p> <p>Mutations in <it>ASXL1 </it>and <it>CBL </it>were frequent in refractory anemia with excess of blasts. Mutations in <it>TET2 </it>occurred with similar frequency in MDSs and AMLs and associated equally with either <it>ASXL1 </it>or <it>NPM1 </it>mutations. Mutations of <it>RUNX1 </it>were mutually exclusive with <it>TET2 </it>and combined with <it>ASXL1 </it>but not with <it>NPM1</it>. Mutations in <it>FLT3 (</it>mutation and internal tandem duplication), <it>IDH1</it>, <it>IDH2</it>, <it>NPM1 </it>and <it>WT1 </it>occurred primarily in AMLs.</p> <p>Conclusion</p> <p>Only 14% MDSs but half AMLs had at least two mutations in the genes studied. Based on the observed combinations and exclusions we classified the 12 genes into four classes and propose a highly speculative model that at least a mutation in one of each class is necessary for developing AML with simple or normal karyotype.</p

Defining the Molecular Basis of Tumor Metabolism: a Continuing Challenge Since Warburg's Discovery

Cancer cells are the product of genetic disorders that alter crucial intracellular signaling pathways associated with the regulation of cell survival, proliferation, differentiation and death mechanisms. the role of oncogene activation and tumor suppressor inhibition in the onset of cancer is well established. Traditional antitumor therapies target specific molecules, the action/expression of which is altered in cancer cells. However, since the physiology of normal cells involves the same signaling pathways that are disturbed in cancer cells, targeted therapies have to deal with side effects and multidrug resistance, the main causes of therapy failure. Since the pioneering work of Otto Warburg, over 80 years ago, the subversion of normal metabolism displayed by cancer cells has been highlighted by many studies. Recently, the study of tumor metabolism has received much attention because metabolic transformation is a crucial cancer hallmark and a direct consequence of disturbances in the activities of oncogenes and tumor suppressors. in this review we discuss tumor metabolism from the molecular perspective of oncogenes, tumor suppressors and protein signaling pathways relevant to metabolic transformation and tumorigenesis. We also identify the principal unanswered questions surrounding this issue and the attempts to relate these to their potential for future cancer treatment. As will be made clear, tumor metabolism is still only partly understood and the metabolic aspects of transformation constitute a major challenge for science. Nevertheless, cancer metabolism can be exploited to devise novel avenues for the rational treatment of this disease. Copyright (C) 2011 S. Karger AG, BaselFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Fed ABC UFABC, CCNH, Santo Andre, BrazilUniversidade Federal de São Paulo UNIFESP, Dept Ciencias Biol, São Paulo, BrazilUniversidade Federal de São Paulo UNIFESP, Dept Bioquim, São Paulo, BrazilUniv Fed Sao Carlos UFSCar, DFQM, Sorocaba, BrazilUniversidade Federal de São Paulo UNIFESP, Dept Ciencias Biol, São Paulo, BrazilUniversidade Federal de São Paulo UNIFESP, Dept Bioquim, São Paulo, BrazilFAPESP: 10/16050-9FAPESP: 10/11475-1FAPESP: 08/51116-0Web of Scienc