Abstract: Endogenous retroviruses (ERVs) are abundant in mammalian genomes and contain sequences modulating transcription. The impact of ERV propagation on the evolution of gene regulation remains poorly understood. We found that ERVs have shaped the evolution of a transcriptional network underlying the interferon (IFN) response, a major branch of innate immunity, and that lineage-specific ERVs have dispersed numerous IFN-inducible enhancers independently in diverse mammalian genomes. CRISPR-Cas9 deletion of a subset of these ERV elements in the human genome impaired expression of adjacent IFN-induced genes and revealed their involvement in the regulation of essential immune functions, including activation of the AIM2 inflammasome. Although these regulatory sequences likely arose in ancient viruses, they now constitute a dynamic reservoir of IFN-inducible enhancers fueling genetic innovation in mammalian immune defenses.

Abstract: Tumor-derived exosomes (TDEs) participate in formation and progression of different cancer processes, including tumor microenvironment (TME) remodeling, angiogenesis, invasion, metastasis and drug-resistance. Exosomes initiate or suppress various signaling pathways in the recipient cells via transmitting heterogeneous cargoes. In this review we discuss exosome biogenesis, exosome mediated metastasis and chemoresistance. Furthermore, tumor derived exosomes role in tumor microenvironment remodeling, and angiogenesis is reviewed. Also, exosome induction of epithelial mesenchymal transition (EMT) is highlighted. More importantly, we discuss extensively how exosomes regulate drug resistance in several cancers. Thus, understanding exosome biogenesis, their contents and the molecular mechanisms and signaling pathways that are responsible for metastasis and drug-resistance mediated by TDEs may help to devise novel therapeutic approaches for cancer progression particularly to overcome therapy-resistance and preventing metastasis as major factors of cancer mortality.

Abstract: Fear appeals are a polarizing issue, with proponents confident in their efficacy and opponents confident that they backfire. We present the results of a comprehensive meta-analysis investigating fear appeals' effectiveness for influencing attitudes, intentions, and behaviors. We tested predictions from a large number of theories, the majority of which have never been tested meta-analytically until now. Studies were included if they contained a treatment group exposed to a fear appeal, a valid comparison group, a manipulation of depicted fear, a measure of attitudes, intentions, or behaviors concerning the targeted risk or recommended solution, and adequate statistics to calculate effect sizes. The meta-analysis included 127 articles (9% unpublished) yielding 248 independent samples (NTotal = 27,372) collected from diverse populations. Results showed a positive effect of fear appeals on attitudes, intentions, and behaviors, with the average effect on a composite index being random-effects d = 0.29. Moderation analyses based on prominent fear appeal theories showed that the effectiveness of fear appeals increased when the message included efficacy statements, depicted high susceptibility and severity, recommended one-time only (vs. repeated) behaviors, and targeted audiences that included a larger percentage of female message recipients. Overall, we conclude that (a) fear appeals are effective at positively influencing attitude, intentions, and behaviors; (b) there are very few circumstances under which they are not effective; and (c) there are no identified circumstances under which they backfire and lead to undesirable outcomes.

Abstract: Background Patients with relapsed or refractory acute myeloid leukemia (AML) with mutations in the FMS-like tyrosine kinase 3 gene (FLT3) infrequently have a response to salvage chemothera...

Abstract: Human motion is fundamental to understanding behavior. Despite progress on single-image 3D pose and shape estimation, existing video-based state-of-the-art methods fail to produce accurate and natural motion sequences due to a lack of ground-truth 3D motion data for training. To address this problem, we propose "Video Inference for Body Pose and Shape Estimation'' (VIBE), which makes use of an existing large-scale motion capture dataset (AMASS) together with unpaired, in-the-wild, 2D keypoint annotations. Our key novelty is an adversarial learning framework that leverages AMASS to discriminate between real human motions and those produced by our temporal pose and shape regression networks. We define a novel temporal network architecture with a self-attention mechanism and show that adversarial training, at the sequence level, produces kinematically plausible motion sequences without in-the-wild ground-truth 3D labels. We perform extensive experimentation to analyze the importance of motion and demonstrate the effectiveness of VIBE on challenging 3D pose estimation datasets, achieving state-of-the-art performance. Code and pretrained models are available at https://github.com/mkocabas/VIBE

Abstract: Models that learn to represent textual and knowledge base relations in the same continuous latent space are able to perform joint inferences among the two kinds of relations and obtain high accuracy on knowledge base completion (Riedel et al., 2013). In this paper we propose a model that captures the compositional structure of textual relations, and jointly optimizes entity, knowledge base, and textual relation representations. The proposed model significantly improves performance over a model that does not share parameters among textual relations with common sub-structure.

Abstract: Spatial and temporal contextual information plays a key role for analyzing user behaviors, and is helpful for predicting where he or she will go next. With the growing ability of collecting information, more and more temporal and spatial contextual information is collected in systems, and the location prediction problem becomes crucial and feasible. Some works have been proposed to address this problem, but they all have their limitations. Factorizing Personalized Markov Chain (FPMC) is constructed based on a strong independence assumption among different factors, which limits its performance. Tensor Factorization (TF) faces the cold start problem in predicting future actions. Recurrent Neural Networks (RNN) model shows promising performance comparing with PFMC and TF, but all these methods have problem in modeling continuous time interval and geographical distance. In this paper, we extend RNN and propose a novel method called Spatial Temporal Recurrent Neural Networks (ST-RNN). ST-RNN can model local temporal and spatial contexts in each layer with time-specific transition matrices for different time intervals and distance-specific transition matrices for different geographical distances. Experimental results show that the proposed ST-RNN model yields significant improvements over the competitive compared methods on two typical datasets, i.e., Global Terrorism Database (GTD) and Gowalla dataset.

Abstract: G Du Toit, G Roberts, PH Sayre N Engl J Med 2015;372:803–813 To evaluate strategies of early peanut consumption or avoidance for prevention of peanut allergy in patients at risk The participants were between 4 and 11 months of age at randomization They suffered from severe eczema, egg allergy, or both A total of 640 patients were evaluated over a 60-month period They were stratified according to their sensitivity to skin testing to peanut extract Those with no measureable wheal were evaluated as not sensitized, those with wheal diameters 1 to 4 mm were considered sensitized, and participants with >4-mm wheal were excluded Participants were randomized to receive an initial supervised feeding …

Abstract: Objective Although COVID-19 is primarily a respiratory illness, there is mounting evidence suggesting that the GI tract is involved in this disease. We investigated whether the gut microbiome is linked to disease severity in patients with COVID-19, and whether perturbations in microbiome composition, if any, resolve with clearance of the SARS-CoV-2 virus. Methods In this two-hospital cohort study, we obtained blood, stool and patient records from 100 patients with laboratory-confirmed SARS-CoV-2 infection. Serial stool samples were collected from 27 of the 100 patients up to 30 days after clearance of SARS-CoV-2. Gut microbiome compositions were characterised by shotgun sequencing total DNA extracted from stools. Concentrations of inflammatory cytokines and blood markers were measured from plasma. Results Gut microbiome composition was significantly altered in patients with COVID-19 compared with non-COVID-19 individuals irrespective of whether patients had received medication (p Conclusion Associations between gut microbiota composition, levels of cytokines and inflammatory markers in patients with COVID-19 suggest that the gut microbiome is involved in the magnitude of COVID-19 severity possibly via modulating host immune responses. Furthermore, the gut microbiota dysbiosis after disease resolution could contribute to persistent symptoms, highlighting a need to understand how gut microorganisms are involved in inflammation and COVID-19.

Abstract: New sets of parameters ("tunes") for the underlying-event (UE) modeling of the PYTHIA8, PYTHIA6 and HERWIG++ Monte Carlo event generators are constructed using different parton distribution functions. Combined fits to CMS UE data at sqrt(s) = 7 TeV and to UE data from the CDF experiment at lower sqrt(s), are used to study the UE models and constrain their parameters, providing thereby improved predictions for proton-proton collisions at 13 TeV. In addition, it is investigated whether the values of the parameters obtained from fits to UE observables are consistent with the values determined from fitting observables sensitive to double-parton scattering processes. Finally, comparisons of the UE tunes to "minimum bias" (MB) events, multijet, and Drell-Yan (q q-bar to Z / gamma* to lepton-antilepton + jets) observables at 7 and 8 TeV are presented, as well as predictions of MB and UE observables at 13 TeV.

Abstract: The Asian vinegar fly Drosophila suzukii (spotted wing Drosophila (SWD)) has emerged as a major invasive insect pest of small and stone fruits in both the Americas and Europe since the late 2000s. While research efforts have rapidly progressed in Asia, North America, and Europe over the past 5 years, important new insights may be gained in comparing and contrasting findings across the regions affected by SWD. In this review, we explore common themes in the invasion biology of SWD by examining (1) its biology and current pest status in endemic and recently invaded regions; (2) current efforts and future research needs for the development of predictive models for its geographic expansion; and (3) prospects for both natural and classical (=importation) biological control of SWD in invaded habitats, with emphasis on the role of hymenopteran parasitoids. We conclude that particularly fruitful areas of research should include fundamental studies of its overwintering, host-use, and dispersal capa- bilities; as well as applied studies of alternative, cost-ef- fective management techniques to complement insecticide use within the integrated pest management framework. Finally, we emphasize that outreach efforts are critical to effective SWD management by highlighting successful

Abstract: Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. We demonstrate that adversarial examples can be directly attributed to the presence of non-robust features: features (derived from patterns in the data distribution) that are highly predictive, yet brittle and (thus) incomprehensible to humans. After capturing these features within a theoretical framework, we establish their widespread existence in standard datasets. Finally, we present a simple setting where we can rigorously tie the phenomena we observe in practice to a {\em misalignment} between the (human-specified) notion of robustness and the inherent geometry of the data.

Abstract: We develop models of 1+1 dimensional dilaton gravity describing flows to AdS2 from higher dimensional AdS and other spaces. We use these to study the effects of backreaction on holographic correlators. We show that this scales as a relevant effect at low energies, for compact transverse spaces. We also discuss effects of matter loops, as in the CGHS model.

Abstract: Despite being one of the oldest materials described in the chemical literature, graphitic carbon nitride (g-C3N4) has just recently experienced a renaissance as a highly active photocatalyst, and the metal-free polymer was shown to be able to generate hydrogen under visible light. The semiconductor nature of g-C3N4 has triggered tremendous endeavors on its structural manipulation for enhanced photo(electro)chemical performance, aiming at an affordable clean energy future. While pursuing the stem of g-C3N4 related catalysis (photocatalysis, electrocatalysis and photoelectrocatalysis), a number of emerging intrinsic properties of g-C3N4 are certainly interesting, but less well covered, and we believe that these novel applications outside of conventional catalysis can be favorably exploited as well. Thanks to the general efforts devoted to the exploration and enrichment of g-C3N4 based chemistry, the boundaries of this area have been possibly pushed far beyond what people could imagine in the beginning. This review strives to cover the achievements of g-C3N4 related materials in these unconventional application fields for depicting the broader future of these metal-free and fully stable semiconductors. This review starts with the general protocols to engineer g-C3N4 micro/nanostructures for practical use, and then discusses the newly disclosed applications in sensing, bioimaging, novel solar energy exploitation including photocatalytic coenzyme regeneration, templating, and carbon nitride based devices. Finally, we attempt an outlook on possible further developments in g-C3N4 based research.

Abstract: We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELE (DEep Local Feature). The new feature is based on convolutional neural networks, which are trained only with image-level annotations on a landmark image dataset. To identify semantically useful local features for image retrieval, we also propose an attention mechanism for key point selection, which shares most network layers with the descriptor. This frame-work can be used for image retrieval as a drop-in replacement for other keypoint detectors and descriptors, enabling more accurate feature matching and geometric verification. Our system produces reliable confidence scores to reject false positives–in particular, it is robust against queries that have no correct match in the database. To evaluate the proposed descriptor, we introduce a new large-scale dataset, referred to as Google-Landmarks dataset, which involves challenges in both database and query such as background clutter, partial occlusion, multiple landmarks, objects in variable scales, etc. We show that DELE outperforms the state-of-the-art global and local descriptors in the large-scale setting by significant margins.

Abstract: For artificial general intelligence (AGI) it would be efficient if multiple users trained the same giant neural network, permitting parameter reuse, without catastrophic forgetting. PathNet is a first step in this direction. It is a neural network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the network to re-use for new tasks. Agents are pathways (views) through the network which determine the subset of parameters that are used and updated by the forwards and backwards passes of the backpropogation algorithm. During learning, a tournament selection genetic algorithm is used to select pathways through the neural network for replication and mutation. Pathway fitness is the performance of that pathway measured according to a cost function. We demonstrate successful transfer learning; fixing the parameters along a path learned on task A and re-evolving a new population of paths for task B, allows task B to be learned faster than it could be learned from scratch or after fine-tuning. Paths evolved on task B re-use parts of the optimal path evolved on task A. Positive transfer was demonstrated for binary MNIST, CIFAR, and SVHN supervised learning classification tasks, and a set of Atari and Labyrinth reinforcement learning tasks, suggesting PathNets have general applicability for neural network training. Finally, PathNet also significantly improves the robustness to hyperparameter choices of a parallel asynchronous reinforcement learning algorithm (A3C).

Abstract: The one-pot synthesis of a target molecule in the same reaction vessel is widely considered to be an efficient approach in synthetic organic chemistry. In this review, the characteristics and limitations of various one-pot syntheses of biologically active molecules are explained, primarily involving organocatalytic methods as key tactics. Besides catalysis, the pot-economy concepts presented herein are also applicable to organometallic and organic reaction methods in general.

Abstract: In animals, PIWI-interacting RNAs (piRNAs) of 21–35 nucleotides in length silence transposable elements, regulate gene expression and fight viral infection. piRNAs guide PIWI proteins to cleave target RNA, promote heterochromatin assembly and methylate DNA. The architecture of the piRNA pathway allows it both to provide adaptive, sequence-based immunity to rapidly evolving viruses and transposons and to regulate conserved host genes. piRNAs silence transposons in the germ line of most animals, whereas somatic piRNA functions have been lost, gained and lost again across evolution. Moreover, most piRNA pathway proteins are deeply conserved, but different animals employ remarkably divergent strategies to produce piRNA precursor transcripts. Here, we discuss how a common piRNA pathway allows animals to recognize diverse targets, ranging from selfish genetic elements to genes essential for gametogenesis. PIWI-interacting RNAs (piRNAs) have numerous crucial biological roles, particularly transposon silencing in the germ line. In this Review, the authors describe our latest understanding of piRNA biogenesis and functions across diverse species, highlighting how, despite the universal importance of transposon control, different species have evolved intriguingly distinct mechanistic routes to achieve this.

Abstract: Objective:While the increased risk of psychopathology in the biological offspring of depressed parents has been widely replicated, the long-term outcome through their full age of risk is less known. The authors present a 30-year follow-up of biological offspring (mean age=47 years) of depressed (high-risk) and nondepressed (low-risk) parents.Method:One hundred forty-seven offspring of moderately to severely depressed or nondepressed parents selected from the same community were followed for up to 30 years. Diagnostic assessments were conducted blind to parents’ clinical status. Final diagnoses were made by a blinded M.D. or Ph.D. evaluator.Results:The risk for major depression was approximately three times as high in the high-risk offspring. The period of highest risk for first onset was between ages 15 and 25 in both groups. Prepubertal onsets were uncommon, but high-risk offspring had over 10-fold increased risk. The early onset of major depression seen in the offspring of depressed parents was not offs...

Abstract: The introduction of invasive species, which often differ functionally from the components of the recipient community, generates ecological impacts that propagate along the food web. This review aims to determine how consistent the impacts of aquatic invasions are across taxa and habitats. To that end, we present a global meta-analysis from 151 publications (733 cases), covering a wide range of invaders (primary producers, filter collectors, omnivores and predators), resident aquatic community components (macrophytes, phytoplankton, zooplankton, benthic invertebrates and fish) and habitats (rivers, lakes and estuaries). Our synthesis suggests a strong negative influence of invasive species on the abundance of aquatic communities, particularly macrophytes, zooplankton and fish. In contrast, there was no general evidence for a decrease in species diversity in invaded habitats, suggesting a time lag between rapid abundance changes and local extinctions. Invaded habitats showed increased water turbidity, nitrogen and organic matter concentration, which are related to the capacity of invaders to transform habitats and increase eutrophication. The expansion of invasive macrophytes caused the largest decrease in fish abundance, the filtering activity of filter collectors depleted planktonic communities, omnivores (including both facultative and obligate herbivores) were responsible for the greatest decline in macrophyte abundance, and benthic invertebrates were most negatively affected by the introduction of new predators. These impacts were relatively consistent across habitats and experimental approaches. Based on our results, we propose a framework of positive and negative links between invasive species at four trophic positions and the five different components of recipient communities. This framework incorporates both direct biotic interactions (predation, competition, grazing) and indirect changes to the water physicochemical conditions mediated by invaders (habitat alteration). Considering the strong trophic links that characterize aquatic ecosystems, this framework is relevant to anticipate the far-reaching consequences of biological invasions on the structure and functionality of aquatic ecosystems.

Abstract: Chikungunya virus infection is a rapid-onset, febrile disease with intense asthenia, arthralgia, myalgia, headache, and rash. This mosquito-borne alphavirus has spread throughout the Caribbean and into much of Central America. Further spread in the Americas seems likely.

Abstract: Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. We argue that both are suboptimal, since gold evidence is not always available, and QA is fundamentally different from IR. We show for the first time that it is possible to jointly learn the retriever and reader from question-answer string pairs and without any IR system. In this setting, evidence retrieval from all of Wikipedia is treated as a latent variable. Since this is impractical to learn from scratch, we pre-train the retriever with an Inverse Cloze Task. We evaluate on open versions of five QA datasets. On datasets where the questioner already knows the answer, a traditional IR system such as BM25 is sufficient. On datasets where a user is genuinely seeking an answer, we show that learned retrieval is crucial, outperforming BM25 by up to 19 points in exact match.

Abstract: Aging is the major risk factor for cancer, cardiovascular disease, diabetes, and neurodegenerative disorders. Although we are far from understanding the biological basis of aging, research suggests that targeting the aging process itself could ameliorate many age-related pathologies. Senescence is a cellular response characterized by a stable growth arrest and other phenotypic alterations that include a proinflammatory secretome. Senescence plays roles in normal development, maintains tissue homeostasis, and limits tumor progression. However, senescence has also been implicated as a major cause of age-related disease. In this regard, recent experimental evidence has shown that the genetic or pharmacological ablation of senescent cells extends life span and improves health span. Here, we review the cellular and molecular links between cellular senescence and aging and discuss the novel therapeutic avenues that this connection opens.

Abstract: Preface Part I. Explanation and Mechanisms: 1. Explanation 2. Mechanisms 3. Interpretation Part II. The Mind: 4. Motivations 5. Self-interest and altruism 6. Myopia and foresight 7. Beliefs 8. Emotions 9. Transmutations Part III. Action: 10. Constraints: opportunities and abilities 11. Reinforcement and selection 12. Persons and situations 13. Rational choice 14. Rationality and behavior 15. Responding to irrationality 16. Implications for textual interpretation Part IV. Interaction: 17. Unintended consequences 18. Strategic interaction 19. Games and behavior 20. Trust 21. Social norms 22. Collective belief formation 23. Collective action 24. Collective decision making 25. Institutions and constitutions Conclusion: is social science possible? Index.

Abstract: Background The Hu5F9-G4 (hereafter, 5F9) antibody is a macrophage immune checkpoint inhibitor blocking CD47 that induces tumor-cell phagocytosis. 5F9 synergizes with rituximab to eliminate...

Abstract: It is common in regression discontinuity analysis to control for third, fourth, or higher-degree polynomials of the forcing variable. There appears to be a perception that such methods are theoreti...

Abstract: The work of A.E.M. was supported by the Australian Research Council via Future Fellowship program (FT110100037). The authors at DSI were supported by DSI core funds. Fabrication, Scanning Electron Microscope Imaging and NSOM works were carried out in facilities provided by SnFPC@DSI (SERC Grant 092 160 0139). Zhen Ying Pan (DSI) is acknowledged for SEM imaging. Yi Zhou (DSI) is acknowledged for silicon film growth. Leonard Gonzaga (DSI), Yeow Teck Toh (DSI) and Doris Ng (DSI) are acknowledged for development of the silicon nanofabrication procedure. B.N.C. acknowledges support from the Government of Russian Federation, Megagrant No. 14.B25.31.0019.