APOBEC3G levels predict rates of progression to AIDS

BACKGROUND: APOBEC3G (hA3G) is a newly discovered cellular factor of innate immunity that inhibits HIV replication in vitro. Whether hA3G conferrs protection against HIV in vivo is not known. To investigate the possible anti-HIV activity of hA3G in vivo, we examined hA3G mRNA abundance in primary human cells isolated from either HIV-infected or HIV-uninfected individuals, and found that hA3G mRNA levels follow a hierarchical order of long-term nonprogressors>HIV-uninfected>Progressors; and, hA3G mRNA abundance is correlated with surrogates of HIV disease progression: viral load and CD4 count. Another group later confirmed that HIV-infected subjects have lower hA3G mRNA levels than HIV-uninfected controls, but did not find correlations between hA3G mRNA levels and viral load or CD4 count. These conflicing results indicate that a more comprehensive, conclusive investigation of hA3G expression levels in various patient cohorts is urgently needed. PRESENTATION OF THE HYPOTHESIS: For exploring whether hA3G abundance might influence HIV disease progression, we have formulated a hypothesis that inlcudes two parts: a) in vivo, the basal hA3G mRNA expression level per PBMC is a constant – with minor physiologic fluctuations – determined by host genetic and epigenetic elements in a healthy individual; and that the basal hA3G mRNA expression levels in a population follow a Normal (or Gaussian) distribution; b) that although HIV infects randomly, it results in more rapid disease progression in those with lower hA3G mRNA levels, and slower disease progression in those with higher hA3G mRNA levels. TESTING THE HYPOTHESIS: This hypothesis could be tested by a straighforward set of experiments to compare the distribution of hA3G mRNA levels in HIV-uninfected healthy individuals and that in HIV-infected, antiretroviral therapy-naïve subjects who are at early and late stages of infection. IMPLICATION OF THE HYPOTHESIS: Testing this hypothesis will have significant implications for biomedical research. a) It will link hA3G to the mechanisms underlying slower disease progression in long-term nonprogressors. And, b) It may help to establiseh a new prognostic marker, the hA3G abundance measurement, for HIV-infected patients

Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

Due to its complexity, graph learning-based multi-modal integration and classification is one of the most challenging obstacles for disease prediction. To effectively offset the negative impact between modalities in the process of multi-modal integration and extract heterogeneous information from graphs, we propose a novel method called MMKGL (Multi-modal Multi-Kernel Graph Learning). For the problem of negative impact between modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology

Feluca : A two-stage graph coloring algorithm with color-centric paradigm on GPU

In this paper, we propose a two-stage high-performance graph coloring algorithm, called Feluca, aiming to address the above challenges. Feluca combines the recursion-based method with the sequential spread-based method. In the first stage, Feluca uses a recursive routine to color a majority of vertices in the graph. Then, it switches to the sequential spread method to color the remaining vertices in order to avoid the conflicts of the recursive algorithm. Moreover, the following techniques are proposed to further improve the graph coloring performance. i) A new method is proposed to eliminate the cycles in the graph; ii) a top-down scheme is developed to avoid the atomic operation originally required for color selection; and iii) a novel color-centric coloring paradigm is designed to improve the degree of parallelism for the sequential spread part. All these newly developed techniques, together with further GPU-specific optimizations such as coalesced memory access, comprise an efficient parallel graph coloring solution in Feluca. We have conducted extensive experiments on NVIDIA GPUs. The results show that Feluca can achieve 1.76 - 12.98x speedup over the state-of-the-art algorithms

MRI-based Multi-task Decoupling Learning for Alzheimer's Disease Detection and MMSE Score Prediction: A Multi-site Validation

Accurately detecting Alzheimer's disease (AD) and predicting mini-mental state examination (MMSE) score are important tasks in elderly health by magnetic resonance imaging (MRI). Most of the previous methods on these two tasks are based on single-task learning and rarely consider the correlation between them. Since the MMSE score, which is an important basis for AD diagnosis, can also reflect the progress of cognitive impairment, some studies have begun to apply multi-task learning methods to these two tasks. However, how to exploit feature correlation remains a challenging problem for these methods. To comprehensively address this challenge, we propose a MRI-based multi-task decoupled learning method for AD detection and MMSE score prediction. First, a multi-task learning network is proposed to implement AD detection and MMSE score prediction, which exploits feature correlation by adding three multi-task interaction layers between the backbones of the two tasks. Each multi-task interaction layer contains two feature decoupling modules and one feature interaction module. Furthermore, to enhance the generalization between tasks of the features selected by the feature decoupling module, we propose the feature consistency loss constrained feature decoupling module. Finally, in order to exploit the specific distribution information of MMSE score in different groups, a distribution loss is proposed to further enhance the model performance. We evaluate our proposed method on multi-site datasets. Experimental results show that our proposed multi-task decoupled representation learning method achieves good performance, outperforming single-task learning and other existing state-of-the-art methods.Comment: 15 page

2-(3,3,4,4-Tetra­fluoro­pyrrolidin-1-yl)aniline

In the title fluorinated pyrrolidine derivative, C10H10F4N2, the dihedral angle between the best planes of the benzene and pyrrolidine rings is 62.6 (1)°. The crystal packing features inter­molecular N—H⋯F hydrogen bonds

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements