22,139 research outputs found

    Causally Interpretable Meta-Analysis of Multivariate Outcomes in Observational Studies

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    Integrating multiple observational studies to make unconfounded causal or descriptive comparisons of group potential outcomes in a large natural population is challenging. Moreover, retrospective cohorts, being convenience samples, are usually unrepresentative of the natural population of interest and have groups with unbalanced covariates. We propose a general covariate-balancing framework based on pseudo-populations that extends established weighting methods to the meta-analysis of multiple retrospective cohorts with multiple groups. Additionally, by maximizing the effective sample sizes of the cohorts, we propose a FLEXible, Optimized, and Realistic (FLEXOR) weighting method appropriate for integrative analyses. We develop new weighted estimators for unconfounded inferences on wide-ranging population-level features and estimands relevant to group comparisons of quantitative, categorical, or multivariate outcomes. The asymptotic properties of these estimators are examined, and accurate small-sample procedures are devised for quantifying estimation uncertainty. Through simulation studies and meta-analyses of TCGA datasets, we discover the differential biomarker patterns of the two major breast cancer subtypes in the United States and demonstrate the versatility and reliability of the proposed weighting strategy, especially for the FLEXOR pseudo-population.Comment: arXiv admin note: text overlap with arXiv:2212.0912

    Early warning signals for predicting cryptomarket vendor success using dark net forum networks

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    In this work we focus on identifying key players in dark net cryptomarkets. Law enforcement aims to disrupt criminal activity conducted through these markets by targeting key players vital to the market's existence and success. We particularly focus on detecting successful vendors responsible for the majority of illegal trade. Our methodology aims to uncover whether the task of key player identification should center around plainly measuring user and forum activity, or that it requires leveraging specific patterns of user communication. We focus on a large-scale dataset from the Evolution cryptomarket, which we model as an evolving communication network. While user and forum activity measures are useful for identifying the most successful vendors, we find that betweenness centrality additionally identifies those with lesser activity. But more importantly, analyzing the forum data over time, we find evidence that attaining a high betweenness score comes before vendor success. This suggests that the proposed network-driven approach of modelling user communication might prove useful as an early warning signal for key player identification

    Serial Biasing Technique for Rapid Single Flux Quantum Circuits

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    Superconductor electronics based on the Single Flux Quantum (SFQ) technology are considered a strong contender for the ‘beyond CMOS’ future of digital circuits because of the high speed and low power dissipation associated with them. In fact, digital operations beyond tens of GHz have been routinely demonstrated in the SFQ technology. These circuits have widespread applications such as high-speed analog-to-digital conversion, digital signal processing, high speed computing and in emerging topics such as control circuitry for superconducting quantum computing. Rapid Single Flux Quantum (RSFQ) circuits have emerged as a promising candidate within the SFQ technology, with information encoded in picosecond wide, milli-volt voltage pulses. As is the case with any integrated circuit technology, scalability of RSFQ circuits is essential to realizing their applications. These circuits, based on the Josephson junction, require a DC bias current for the correct operation. The DC bias current requirement increases with circuit complexity, and this has multiple implications on circuit operation. Large currents produce magnetic fields that can interfere with logic operation. Furthermore, the heat load delivered to the superconducting chip also increases with current which could result in the circuit becoming ‘normal’ and not superconducting. These problems make reduction of the bias current necessary. Serial Biasing (SB) is a bias current reduction technique, that has been proposed in the past. In this technique, a digital circuit is partitioned into multiple identical islands and bias current is provided to each island in a serial manner. While this scheme is promising, there are multiple challenges such as design of the driver-receiver pair circuit resulting in robust and wide operating bias margins, current management on the floating islands, etc. This thesis investigates SB in a systematic manner, focusing on the design and measurement of the fundamental components of this technique with an emphasis on reliability and scalability. It presents works on circuit techniques achieving high speed serially biased RSFQ circuits with robust operating margins and the experimental evidence to support the ideas. It develops a framework for serial biasing that could be used by electronic design tools to automate design and synthesis of complex RSFQ circuits. It also investigates Passive Transmission Lines (PTLs) for use as passive interconnects between library cells in a complex design, reducing the DC bias current required by the active circuitry

    NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors

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    Many real-world data are naturally represented as a sparse reorderable matrix, whose rows and columns can be arbitrarily ordered (e.g., the adjacency matrix of a bipartite graph). Storing a sparse matrix in conventional ways requires an amount of space linear in the number of non-zeros, and lossy compression of sparse matrices (e.g., Truncated SVD) typically requires an amount of space linear in the number of rows and columns. In this work, we propose NeuKron for compressing a sparse reorderable matrix into a constant-size space. NeuKron generalizes Kronecker products using a recurrent neural network with a constant number of parameters. NeuKron updates the parameters so that a given matrix is approximated by the product and reorders the rows and columns of the matrix to facilitate the approximation. The updates take time linear in the number of non-zeros in the input matrix, and the approximation of each entry can be retrieved in logarithmic time. We also extend NeuKron to compress sparse reorderable tensors (e.g. multi-layer graphs), which generalize matrices. Through experiments on ten real-world datasets, we show that NeuKron is (a) Compact: requiring up to five orders of magnitude less space than its best competitor with similar approximation errors, (b) Accurate: giving up to 10x smaller approximation error than its best competitors with similar size outputs, and (c) Scalable: successfully compressing a matrix with over 230 million non-zero entries.Comment: Accepted to WWW 2023 - The Web Conference 202

    How to avoid ordinal violations in incomplete pairwise comparisons

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    Assume that some ordinal preferences can be represented by a weakly connected directed acyclic graph. The data are collected into an incomplete pairwise comparison matrix, the missing entries are estimated, and the priorities are derived from the optimally filled pairwise comparison matrix. Our paper studies whether these weights are consistent with the partial order given by the underlying graph. According to previous results from the literature, two popular procedures, the incomplete eigenvector and the incomplete logarithmic least squares methods fail to satisfy the required property. Here, it is shown that the recently introduced lexicographically optimal completion combined with any of these weighting methods avoids ordinal violation in the above setting. This finding provides a powerful argument for using the lexicographically optimal completion to determine the missing elements in an incomplete pairwise comparison matrix.Comment: 11 pages, 2 figure

    Leveraging a machine learning based predictive framework to study brain-phenotype relationships

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    An immense collective effort has been put towards the development of methods forquantifying brain activity and structure. In parallel, a similar effort has focused on collecting experimental data, resulting in ever-growing data banks of complex human in vivo neuroimaging data. Machine learning, a broad set of powerful and effective tools for identifying multivariate relationships in high-dimensional problem spaces, has proven to be a promising approach toward better understanding the relationships between the brain and different phenotypes of interest. However, applied machine learning within a predictive framework for the study of neuroimaging data introduces several domain-specific problems and considerations, leaving the overarching question of how to best structure and run experiments ambiguous. In this work, I cover two explicit pieces of this larger question, the relationship between data representation and predictive performance and a case study on issues related to data collected from disparate sites and cohorts. I then present the Brain Predictability toolbox, a soft- ware package to explicitly codify and make more broadly accessible to researchers the recommended steps in performing a predictive experiment, everything from framing a question to reporting results. This unique perspective ultimately offers recommen- dations, explicit analytical strategies, and example applications for using machine learning to study the brain

    On Monte Carlo methods for the Dirichlet process mixture model, and the selection of its precision parameter prior

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    Two issues commonly faced by users of Dirichlet process mixture models are: 1) how to appropriately select a hyperprior for its precision parameter alpha, and 2) the typically slow mixing of the MCMC chain produced by conditional Gibbs samplers based on its stick-breaking representation, as opposed to marginal collapsed Gibbs samplers based on the Polya urn, which have smaller integrated autocorrelation times. In this thesis, we analyse the most common approaches to hyperprior selection for alpha, we identify their limitations, and we propose a new methodology to overcome them. To address slow mixing, we revisit three label-switching Metropolis moves from the literature (Hastie et al., 2015; Papaspiliopoulos and Roberts, 2008), improve them, and introduce a fourth move. Secondly, we revisit two i.i.d. sequential importance samplers which operate in the collapsed space (Liu, 1996; S. N. MacEachern et al., 1999), and we develop a new sequential importance sampler for the stick-breaking parameters of Dirichlet process mixtures, which operates in the stick-breaking space and which has minimal integrated autocorrelation time. Thirdly, we introduce the i.i.d. transcoding algorithm which, conditional to a partition of the data, can infer back which specific stick in the stick-breaking construction each observation originated from. We use it as a building block to develop the transcoding sampler, which removes the need for label-switching Metropolis moves in the conditional stick-breaking sampler, as it uses the better performing marginal sampler (or any other sampler) to drive the MCMC chain, and augments its exchangeable partition posterior with conditional i.i.d. stick-breaking parameter inferences after the fact, thereby inheriting its shorter autocorrelation times

    Multidrug-Resistant ESBL-Producing E. coli in Clinical Samples from the UK

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    Globally, cephalosporin therapy failure is a serious problem for infection control. One causative agent of cephalosporin-resistant infections is multidrug-resistant (MDR) E. coli producing extended-spectrum β-lactamases (ESBLs) and/or plasmid-encoded AmpC (pAmpC) β-lactamases. We evaluated the occurrence of ESBL/pAmpC genetic determinants in phenotypically MDR E. coli isolated from clinical samples of blood, faeces, ear effusion, urine and sputum from a UK hospital. Phenotypic resistance profiling for 18 antibiotics (from seven classes) showed that 32/35 isolates were MDR, with resistance to 4–16 of the tested antibiotics. Of the isolates, 97.1% showed resistance to ampicillin, 71.4% showed resistance to co-amoxiclav, cefotaxime, ceftazidime and ceftiofur, and 68.5% showed resistance to cefquinome. blaCTX-M, blaTEM and blaOXA-1 genes were detected in 23, 13 and 12 strains, respectively, and Intl1 was detected in 17 isolates. The most common subtypes among the definite sequence types were CTX-M-15 (40%) and TEM-1 (75%). No E. coli isolates carried pAmpC genes. Significant correlations were seen between CTX-M carriage and cefotaxime, ceftiofur, aztreonam, ceftazidime and cefquinome resistance; between blaCTX-M, blaTEM and blaOXA-1 carriage and ciprofloxacin resistance; and between Intl1 carriage and trimethoprim/sulfamethoxazole resistance. Thus, MDR phenotypes may be conferred by a relatively small number of genes. The level and pattern of antibiotic resistance highlight the need for better antibiotic therapy guidelines, including reduced use and improved surveillance

    Ramulus mori (Sangzhi) alkaloids regulates gut microbiota disorder and its metabolism profiles in obese mice induced by a high-fat diet

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    The imbalance of gut microbiota has been confirmed to have a close pathological and physiological correlation with obesity and metabolic syndrome. Ramulus Mori (Sangzhi) Alkaloids (SZ-A) derived from twigs of mulberry was approved by the National Medical Products Administration of China in 2020 for the treatment of type 2 diabetes mellitus. In addition to its hypoglycemic effect, previous studies have confirmed that SZ-A also alleviates high-fat diet-induced obesity and non-alcoholic fatty liver disease and ameliorates obesity-linked adipose tissue metabolism and inflammation, indicating the potential of SZ-A to regulate obesity and metabolic syndrome. However, whether SZ-A can improve obesity and metabolic syndrome by regulating gut microbiota and its metabolism profiles remains unclear. The purpose of this study was to assess the effect of SZ-A on gut microbiota in obese mice and to explore the association among changes in gut microbiota, obesity, and lipid metabolism. The results showed that oral administration of SZ-A could significantly reduce body weight, fat mass, and the level of total cholesterol and low-density lipoprotein in serum in obese mice induced by a high-fat diet. Interestingly, SZ-A also regulated gut microbiota and changed the fecal metabolite composition of obese mice. Compared with the high-fat diet group, the ratio of Firmicutes to Bacteroides changed at the phylum level and the abundance of Bifidobacterium and Akkermansia muciniphila significantly increased at the genus level in the SZ-A group. The gut microbiota of the SZ-A group was reshaped and the relative abundance of microbial genes in bile acid metabolism and fatty acid metabolism were altered, which was consistent with the metabolomics results. Additionally, SZ-A greatly enriched the number of goblet cells and reduced inflammatory colon injury and pro-inflammatory macrophage infiltration induced by a high-fat diet in obese mice. In conclusion, SZ-A can alleviate obesity and metabolic syndrome by improving the gut microbiota and its metabolism profiles of obese mice induced by a high-fat diet

    A pilot investigation of differential hydroxymethylation levels in patient-derived neural stem cells implicates altered cortical development in bipolar disorder

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    IntroductionBipolar disorder (BD) is a chronic mental illness characterized by recurrent episodes of mania and depression and associated with social and cognitive disturbances. Environmental factors, such as maternal smoking and childhood trauma, are believed to modulate risk genotypes and contribute to the pathogenesis of BD, suggesting a key role in epigenetic regulation during neurodevelopment. 5-hydroxymethylcytosine (5hmC) is an epigenetic variant of particular interest, as it is highly expressed in the brain and is implicated in neurodevelopment, and psychiatric and neurological disorders.MethodsInduced pluripotent stem cells (iPSCs) were generated from the white blood cells of two adolescent patients with bipolar disorder and their same-sex age-matched unaffected siblings (n = 4). Further, iPSCs were differentiated into neuronal stem cells (NSCs) and characterized for purity using immuno-fluorescence. We used reduced representation hydroxymethylation profiling (RRHP) to perform genome-wide 5hmC profiling of iPSCs and NSCs, to model 5hmC changes during neuronal differentiation and assess their impact on BD risk. Functional annotation and enrichment testing of genes harboring differentiated 5hmC loci were performed with the online tool DAVID.ResultsApproximately 2 million sites were mapped and quantified, with the majority (68.8%) located in genic regions, with elevated 5hmC levels per site observed for 3’ UTRs, exons, and 2-kb shorelines of CpG islands. Paired t-tests of normalized 5hmC counts between iPSC and NSC cell lines revealed global hypo-hydroxymethylation in NSCs and enrichment of differentially hydroxymethylated sites within genes associated with plasma membrane (FDR = 9.1 × 10−12) and axon guidance (FDR = 2.1 × 10−6), among other neuronal processes. The most significant difference was observed for a transcription factor binding site for the KCNK9 gene (p = 8.8 × 10−6), encoding a potassium channel protein involved in neuronal activity and migration. Protein–protein-interaction (PPI) networking showed significant connectivity (p = 3.2 × 10−10) between proteins encoded by genes harboring highly differentiated 5hmC sites, with genes involved in axon guidance and ion transmembrane transport forming distinct sub-clusters. Comparison of NSCs of BD cases and unaffected siblings revealed additional patterns of differentiation in hydroxymethylation levels, including sites in genes with functions related to synapse formation and regulation, such as CUX2 (p = 2.4 × 10−5) and DOK-7 (p = 3.6 × 10−3), as well as an enrichment of genes involved in the extracellular matrix (FDR = 1.0 × 10−8).DiscussionTogether, these preliminary results lend evidence toward a potential role for 5hmC in both early neuronal differentiation and BD risk, with validation and more comprehensive characterization to be achieved through follow-up study
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