Abstract: Many long non-coding RNAs (lncRNAs) affect gene expression, but the mechanisms by which they act are still largely unknown. One of the best-studied lncRNAs is Xist, which is required for transcriptional silencing of one X chromosome during development in female mammals. Despite extensive efforts to define the mechanism of Xist-mediated transcriptional silencing, we still do not know any proteins required for this role. The main challenge is that there are currently no methods to comprehensively define the proteins that directly interact with a lncRNA in the cell. Here we develop a method to purify a lncRNA from cells and identify proteins interacting with it directly using quantitative mass spectrometry. We identify ten proteins that specifically associate with Xist, three of these proteins--SHARP, SAF-A and LBR--are required for Xist-mediated transcriptional silencing. We show that SHARP, which interacts with the SMRT co-repressor that activates HDAC3, is not only essential for silencing, but is also required for the exclusion of RNA polymerase II (Pol II) from the inactive X. Both SMRT and HDAC3 are also required for silencing and Pol II exclusion. In addition to silencing transcription, SHARP and HDAC3 are required for Xist-mediated recruitment of the polycomb repressive complex 2 (PRC2) across the X chromosome. Our results suggest that Xist silences transcription by directly interacting with SHARP, recruiting SMRT, activating HDAC3, and deacetylating histones to exclude Pol II across the X chromosome.

Abstract: The stepped wedge cluster randomised trial is a novel research study design that is increasingly being used in the evaluation of service delivery type interventions. The design involves random and sequential crossover of clusters from control to intervention until all clusters are exposed. It is a pragmatic study design which can reconcile the need for robust evaluations with political or logistical constraints. While not exclusively for the evaluation of service delivery interventions, it is particularly suited to evaluations that do not rely on individual patient recruitment. As in all cluster trials, stepped wedge trials with individual recruitment and without concealment of allocation (or blinding of the intervention) are at risk of selection biases. In a stepped wedge design more clusters are exposed to the intervention towards the end of the study than in its early stages. This implies that the effect of the intervention might be confounded with any underlying temporal trend. A result that initially might seem suggestive of an effect of the intervention may therefore transpire to be the result of a positive underlying temporal trend. Sample size calculations and analysis must make allowance for both the clustered nature of the design and the confounding effect of time. The stepped wedge cluster randomised trial is an alternative to traditional parallel cluster studies, in which the intervention is delivered in only half the clusters with the remainder functioning as controls. When the clusters are relatively homogeneous (that is, the intra-cluster correlation is small), parallel studies tend to deliver better statistical performance than a stepped wedge trial. However, if substantial cluster-level effects are present (that is, larger intra-cluster correlations) or the clusters are large, the stepped wedge design will be more powerful than a parallel design, even one in which the intervention is preceded by a period of baseline control observations.

Abstract: Importance Catheter ablation is effective in restoring sinus rhythm in atrial fibrillation (AF), but its effects on long-term mortality and stroke risk are uncertain. Objective To determine whether catheter ablation is more effective than conventional medical therapy for improving outcomes in AF. Design, Setting, and Participants The Catheter Ablation vs Antiarrhythmic Drug Therapy for Atrial Fibrillation trial is an investigator-initiated, open-label, multicenter, randomized trial involving 126 centers in 10 countries. A total of 2204 symptomatic patients with AF aged 65 years and older or younger than 65 years with 1 or more risk factors for stroke were enrolled from November 2009 to April 2016, with follow-up through December 31, 2017. Interventions The catheter ablation group (n = 1108) underwent pulmonary vein isolation, with additional ablative procedures at the discretion of site investigators. The drug therapy group (n = 1096) received standard rhythm and/or rate control drugs guided by contemporaneous guidelines. Main Outcomes and Measures The primary end point was a composite of death, disabling stroke, serious bleeding, or cardiac arrest. Among 13 prespecified secondary end points, 3 are included in this report: all-cause mortality; total mortality or cardiovascular hospitalization; and AF recurrence. Results Of the 2204 patients randomized (median age, 68 years; 37.2% female; 42.9% had paroxysmal AF and 57.1% had persistent AF), 89.3% completed the trial. Of the patients assigned to catheter ablation, 1006 (90.8%) underwent the procedure. Of the patients assigned to drug therapy, 301 (27.5%) ultimately received catheter ablation. In the intention-to-treat analysis, over a median follow-up of 48.5 months, the primary end point occurred in 8.0% (n = 89) of patients in the ablation group vs 9.2% (n = 101) of patients in the drug therapy group (hazard ratio [HR], 0.86 [95% CI, 0.65-1.15];P = .30). Among the secondary end points, outcomes in the ablation group vs the drug therapy group, respectively, were 5.2% vs 6.1% for all-cause mortality (HR, 0.85 [95% CI, 0.60-1.21];P = .38), 51.7% vs 58.1% for death or cardiovascular hospitalization (HR, 0.83 [95% CI, 0.74-0.93];P = .001), and 49.9% vs 69.5% for AF recurrence (HR, 0.52 [95% CI, 0.45-0.60];P Conclusions and Relevance Among patients with AF, the strategy of catheter ablation, compared with medical therapy, did not significantly reduce the primary composite end point of death, disabling stroke, serious bleeding, or cardiac arrest. However, the estimated treatment effect of catheter ablation was affected by lower-than-expected event rates and treatment crossovers, which should be considered in interpreting the results of the trial. Trial Registration ClinicalTrials.gov Identifier:NCT00911508

Abstract: An intertwined supply network (ISN) is an entirety of interconnected supply chains (SC) which, in their integrity secure the provision of society and markets with goods and services. The ISNs are o...

Abstract: Viruses of microbes impact all ecosystems where microbes drive key energy and substrate transformations including the oceans, humans and industrial fermenters. However, despite this recognized importance, our understanding of viral diversity and impacts remains limited by too few model systems and reference genomes. One way to fill these gaps in our knowledge of viral diversity is through the detection of viral signal in microbial genomic data. While multiple approaches have been developed and applied for the detection of prophages (viral genomes integrated in a microbial genome), new types of microbial genomic data are emerging that are more fragmented and larger scale, such as Single-cell Amplified Genomes (SAGs) of uncultivated organisms or genomic fragments assembled from metagenomic sequencing. Here, we present VirSorter, a tool designed to detect viral signal in these different types of microbial sequence data in both a reference-dependent and reference-independent manner, leveraging probabilistic models and extensive virome data to maximize detection of novel viruses. Performance testing shows that VirSorter's prophage prediction capability compares to that of available prophage predictors for complete genomes, but is superior in predicting viral sequences outside of a host genome (i.e., from extrachromosomal prophages, lytic infections, or partially assembled prophages). Furthermore, VirSorter outperforms existing tools for fragmented genomic and metagenomic datasets, and can identify viral signal in assembled sequence (contigs) as short as 3kb, while providing near-perfect identification (>95% Recall and 100% Precision) on contigs of at least 10kb. Because VirSorter scales to large datasets, it can also be used in "reverse" to more confidently identify viral sequence in viral metagenomes by sorting away cellular DNA whether derived from gene transfer agents, generalized transduction or contamination. Finally, VirSorter is made available through the iPlant Cyberinfrastructure that provides a web-based user interface interconnected with the required computing resources. VirSorter thus complements existing prophage prediction softwares to better leverage fragmented, SAG and metagenomic datasets in a way that will scale to modern sequencing. Given these features, VirSorter should enable the discovery of new viruses in microbial datasets, and further our understanding of uncultivated viral communities across diverse ecosystems.

Abstract: Recently, Neural Architecture Search (NAS) has successfully identified neural network architectures that exceed human designed ones on large-scale image classification. In this paper, we study NAS for semantic image segmentation. Existing works often focus on searching the repeatable cell structure, while hand-designing the outer network structure that controls the spatial resolution changes. This choice simplifies the search space, but becomes increasingly problematic for dense image prediction which exhibits a lot more network level architectural variations. Therefore, we propose to search the network level structure in addition to the cell level structure, which forms a hierarchical architecture search space. We present a network level search space that includes many popular designs, and develop a formulation that allows efficient gradient-based architecture search (3 P100 GPU days on Cityscapes images). We demonstrate the effectiveness of the proposed method on the challenging Cityscapes, PASCAL VOC 2012, and ADE20K datasets. Auto-DeepLab, our architecture searched specifically for semantic image segmentation, attains state-of-the-art performance without any ImageNet pretraining.

Abstract: Background Apalutamide, a competitive inhibitor of the androgen receptor, is under development for the treatment of prostate cancer. We evaluated the efficacy of apalutamide in men with nonmetastatic castration-resistant prostate cancer who were at high risk for the development of metastasis. Methods We conducted a double-blind, placebo-controlled, phase 3 trial involving men with nonmetastatic castration-resistant prostate cancer and a prostate-specific antigen doubling time of 10 months or less. Patients were randomly assigned, in a 2:1 ratio, to receive apalutamide (240 mg per day) or placebo. All the patients continued to receive androgen-deprivation therapy. The primary end point was metastasis-free survival, which was defined as the time from randomization to the first detection of distant metastasis on imaging or death. Results A total of 1207 men underwent randomization (806 to the apalutamide group and 401 to the placebo group). In the planned primary analysis, which was performed after ...

Abstract: Interventions to change health-related behaviours typically have modest effects and may be more effective if grounded in appropriate theory. Most theories applied to public health interventions tend to emphasise individual capabilities and motivation, with limited reference to context and social factors. Intervention effectiveness may be increased by drawing on a wider range of theories incorporating social, cultural and economic factors that influence behaviour. The primary aim of this paper is to identify theories of behaviour and behaviour change of potential relevance to public health interventions across four scientific disciplines: psychology, sociology, anthropology and economics. We report in detail the methodology of our scoping review used to identify these theories including which involved a systematic search of electronic databases, consultation with a multidisciplinary advisory group, web searching, searching of reference lists and hand searching of key behavioural science journals. Of secondary interest we developed a list of agreed criteria for judging the quality of the theories. We identified 82 theories and 9 criteria for assessing theory quality. The potential relevance of this wide-ranging number of theories to public health interventions and the ease and usefulness of evaluating the theories in terms of the quality criteria are however yet to be determined.

Abstract: A M was supported by the King Abdullah University of Science and Technology (KAUST) T J acknowledges support from the EU FET Open RIA Grant No 766566, the Ministry of Education of the Czech Republic Grant No LM2015087 and LNSM-LNSpin, and the Grant Agency of the Czech Republic Grant No 19-28375X J S acknowledges the Alexander von Humboldt Foundation, EU FET Open Grant No 766566, EU ERC Synergy Grant No 610115, and the Transregional Collaborative Research Center (SFB/TRR) 173 SPIN+X K G and P G acknowledge stimulating discussions with C O Avci and financial support by the Swiss National Science Foundation (Grants No 200021-153404 and No 200020-172775) and the European Commission under the Seventh Framework Program (spOt project, Grant No 318144) A T acknowledges support by the Agence Nationale de la Recherche, Project No ANR-17-CE24-0025 (TopSky) J Ž acknowledges the Grant Agency of the Czech Republic Grant No 19-18623Y and support from the Institute of Physics of the Czech Academy of Sciences and the Max Planck Society through the Max Planck Partner Group programme

Abstract: Lymphodepletion chemotherapy followed by infusion of CD19-targeted chimeric antigen receptor-modified T (CAR-T) cells can be complicated by neurologic adverse events (AE) in patients with refractory B-cell malignancies. In 133 adults treated with CD19 CAR-T cells, we found that acute lymphoblastic leukemia, high CD19+ cells in bone marrow, high CAR-T cell dose, cytokine release syndrome, and preexisting neurologic comorbidities were associated with increased risk of neurologic AEs. Patients with severe neurotoxicity demonstrated evidence of endothelial activation, including disseminated intravascular coagulation, capillary leak, and increased blood-brain barrier (BBB) permeability. The permeable BBB failed to protect the cerebrospinal fluid from high concentrations of systemic cytokines, including IFNγ, which induced brain vascular pericyte stress and their secretion of endothelium-activating cytokines. Endothelial activation and multifocal vascular disruption were found in the brain of a patient with fatal neurotoxicity. Biomarkers of endothelial activation were higher before treatment in patients who subsequently developed grade ≥4 neurotoxicity.Significance: We provide a detailed clinical, radiologic, and pathologic characterization of neurotoxicity after CD19 CAR-T cells, and identify risk factors for neurotoxicity. We show endothelial dysfunction and increased BBB permeability in neurotoxicity and find that patients with evidence of endothelial activation before lymphodepletion may be at increased risk of neurotoxicity. Cancer Discov; 7(12); 1404-19. ©2017 AACR.See related commentary by Mackall and Miklos, p. 1371This article is highlighted in the In This Issue feature, p. 1355.

Abstract: In this letter, we propose a practical non-linear energy harvesting model and design a resource allocation algorithm for simultaneous wireless information and power transfer (SWIPT) systems. The algorithm design is formulated as a non-convex optimization problem for the maximization of the total harvested power at energy harvesting receivers subject to minimum required signal-to-interference-plus-noise ratios (SINRs) at multiple information receivers. We transform the considered non-convex objective function from sum-of-ratios form into an equivalent objective function in subtractive form, which enables the derivation of an efficient iterative resource allocation algorithm. In each iteration, a rank-constrained semidefinite program (SDP) is solved optimally by SDP relaxation. Numerical results unveil a substantial performance gain that can be achieved if the resource allocation design is based on the proposed non-linear energy harvesting model instead of the traditional linear model.

Abstract: Summary: CRISPRdirect is a simple and functional web server for selecting rational CRISPR/Cas targets from an input sequence. The CRISPR/Cas system is a promising technique for genome engineering which allows target-specific cleavage of genomic DNA guided by Cas9 nuclease in complex with a guide RNA (gRNA), that complementarily binds to a � 20 nt targeted sequence. The target sequence requirements are twofold. First, the 5 0 -NGG protospacer adjacent motif (PAM) sequence must be located adjacent to the target sequence. Second, the target sequence should be specific within the entire genome in order to avoid off-target editing. CRISPRdirect enables users to easily select rational target sequences with minimized off-target sites by performing exhaustive searches against genomic sequences. The server currently incorporates the genomic sequences of human, mouse, rat, marmoset, pig, chicken, frog, zebrafish, Ciona, fruit fly, silkworm, Caenorhabditis elegans, Arabidopsis, rice, Sorghum and budding yeast. Availability: Freely available at http://crispr.dbcls.jp/. Contact: y-naito@dbcls.rois.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract: Reliable data connectivity is vital for the ever increasingly intelligent, automated, and ubiquitous digital world. Mobile networks are the data highways and, in a fully connected, intelligent digital world, will need to connect everything, including people to vehicles, sensors, data, cloud resources, and even robotic agents. Fifth generation (5G) wireless networks, which are currently being deployed, offer significant advances beyond LTE, but may be unable to meet the full connectivity demands of the future digital society. Therefore, this article discusses technologies that will evolve wireless networks toward a sixth generation (6G) and which we consider as enablers for several potential 6G use cases. We provide a fullstack, system-level perspective on 6G scenarios and requirements, and select 6G technologies that can satisfy them either by improving the 5G design or by introducing completely new communication paradigms.

Abstract: Acquired drug resistance prevents cancer therapies from achieving stable and complete responses. Emerging evidence implicates a key role for non-mutational drug resistance mechanisms underlying the survival of residual cancer 'persister' cells. The persister cell pool constitutes a reservoir from which drug-resistant tumours may emerge. Targeting persister cells therefore presents a therapeutic opportunity to impede tumour relapse. We previously found that cancer cells in a high mesenchymal therapy-resistant cell state are dependent on the lipid hydroperoxidase GPX4 for survival. Here we show that a similar therapy-resistant cell state underlies the behaviour of persister cells derived from a wide range of cancers and drug treatments. Consequently, we demonstrate that persister cells acquire a dependency on GPX4. Loss of GPX4 function results in selective persister cell ferroptotic death in vitro and prevents tumour relapse in mice. These findings suggest that targeting of GPX4 may represent a therapeutic strategy to prevent acquired drug resistance.

Abstract: A host's microbiota may increase, diminish, or have no effect at all on cancer susceptibility. Assigning causal roles in cancer to specific microbes and microbiotas, unraveling host-microbiota interactions with environmental factors in carcinogenesis, and exploiting such knowledge for cancer diagnosis and treatment are areas of intensive interest. This Review considers how microbes and the microbiota may amplify or mitigate carcinogenesis, responsiveness to cancer therapeutics, and cancer-associated complications.

Abstract: This paper investigates how far a very deep neural network is from attaining close to saturating performance on existing 2D and 3D face alignment datasets. To this end, we make the following 5 contributions: (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and finally evaluate it on all other 2D facial landmark datasets. (b) We create a guided by 2D landmarks network which converts 2D landmark annotations to 3D and unifies all existing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date ~230,000 images. (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. (d) We further look into the effect of all "traditional" factors affecting face alignment performance like large pose, initialization and resolution, and introduce a "new" one, namely the size of the network. (e) We show that both 2D and 3D face alignment networks achieve performance of remarkable accuracy which is probably close to saturating the datasets used. Training and testing code as well as the dataset can be downloaded from this https URL

Abstract: Clinical medical data, especially in the intensive care unit (ICU), consist of multivariate time series of observations. For each patient visit (or episode), sensor data and lab test results are recorded in the patient's Electronic Health Record (EHR). While potentially containing a wealth of insights, the data is difficult to mine effectively, owing to varying length, irregular sampling and missing data. Recurrent Neural Networks (RNNs), particularly those using Long Short-Term Memory (LSTM) hidden units, are powerful and increasingly popular models for learning from sequence data. They effectively model varying length sequences and capture long range dependencies. We present the first study to empirically evaluate the ability of LSTMs to recognize patterns in multivariate time series of clinical measurements. Specifically, we consider multilabel classification of diagnoses, training a model to classify 128 diagnoses given 13 frequently but irregularly sampled clinical measurements. First, we establish the effectiveness of a simple LSTM network for modeling clinical data. Then we demonstrate a straightforward and effective training strategy in which we replicate targets at each sequence step. Trained only on raw time series, our models outperform several strong baselines, including a multilayer perceptron trained on hand-engineered features.

Abstract: It was a surprise for me when in 1995 I received a manuscript from the mathematician, experimental writer and innovative painter Florentin Smarandache, especially because the treated subject was of philosophy - revealing paradoxes - and logics. He had generalized the fuzzy logic, and introduced two new concepts: a) "neutrosophy" - study of neutralities as an extension of dialectics; b) and its derivative "neutrosophic", such as "neutrosophic logic", "neutrosophic set", "neutrosophic probability", and "neutrosophic statistics" and thus opening new ways of research in four fields: philosophy, logics, set theory, and probability/statistics.

Abstract: Background Despite improvements in the management of atrial fibrillation, patients with this condition remain at increased risk for cardiovascular complications. It is unclear whether earl...

Abstract: Polyurethanes (PUs) are a class of versatile materials with great potential for use in different applications, especially based on their structure–property relationships. Their specific mechanical, physical, biological, and chemical properties are attracting significant research attention to tailoring PUs for use in different applications. Enhancement of the properties and performance of PU-based materials may be achieved through changes to the production process or the raw materials used in their fabrication or via the use of advanced characterization techniques. Clearly, modification of the raw materials and production process through proper methods can produce PUs that are suitable for varied specific applications. The present study aims to shed light on the chemistry, types, and synthesis of different kinds of PUs. Some of the important research studies relating to PUs, including their synthesis method, characterization techniques, and research findings, are comprehensively discussed. Herein, recent advances in new types of PUs and their synthesis for various applications are also presented. Furthermore, information is provided on the environmental friendliness of the PUs, with a specific emphasis on their recyclability and recoverability.

Abstract: State-of-the-art visual perception models for a wide range of tasks rely on supervised pretraining. ImageNet classification is the de facto pretraining task for these models. Yet, ImageNet is now nearly ten years old and is by modern standards “small”. Even so, relatively little is known about the behavior of pretraining with datasets that are multiple orders of magnitude larger. The reasons are obvious: such datasets are difficult to collect and annotate. In this paper, we present a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images. Our experiments demonstrate that training for large-scale hashtag prediction leads to excellent results. We show improvements on several image classification and object detection tasks, and report the highest ImageNet-1k single-crop, top-1 accuracy to date: 85.4% (97.6% top-5). We also perform extensive experiments that provide novel empirical data on the relationship between large-scale pretraining and transfer learning performance.

Abstract: We conjecture a sharp bound on the rate of growth of chaos in thermal quantum systems with a large number of degrees of freedom. Chaos can be diagnosed using an out-of-time-order correlation function closely related to the commutator of operators separated in time. We conjecture that the influence of chaos on this correlator can develop no faster than exponentially, with Lyapunov exponent $\lambda_L \le 2 \pi k_B T/\hbar$. We give a precise mathematical argument, based on plausible physical assumptions, establishing this conjecture.

Abstract: We describe the design and implementation of Open vSwitch, a multi-layer, open source virtual switch for all major hypervisor platforms. Open vSwitch was designed de novo for networking in virtual environments, resulting in major design departures from traditional software switching architectures. We detail the advanced flow classification and caching techniques that Open vSwitch uses to optimize its operations and conserve hypervisor resources. We evaluate Open vSwitch performance, drawing from our deployment experiences over the past seven years of using and improving Open vSwitch.