Abstract: The gastrointestinal tract of mammals hosts a high and diverse number of different microorganisms, known as intestinal microbiota. Many probiotics were originally isolated from the gastrointestinal tract, and they were defined by the Food and Agriculture Organization of the United Nations (FAO)/WHO as "live microorganisms which when administered in adequate amounts confer a health benefit on the host." Probiotics exert their beneficial effects on the host through four main mechanisms: interference with potential pathogens, improvement of barrier function, immunomodulation and production of neurotransmitters, and their host targets vary from the resident microbiota to cellular components of the gut-brain axis. However, in spite of the wide array of beneficial mechanisms deployed by probiotic bacteria, relatively few effects have been supported by clinical data. In this regard, different probiotic strains have been effective in antibiotic-associated diarrhea or inflammatory bowel disease for instance. The aim of this review was to compile the molecular mechanisms underlying the beneficial effects of probiotics, mainly through their interaction with the intestinal microbiota and with the intestinal mucosa. The specific benefits discussed in this paper include among others those elicited directly through dietary modulation of the human gut microbiota.

Abstract: The paper provides a state of the art review of guided wave based structural health monitoring (SHM). First, the fundamental concepts of guided wave propagation and its implementation for SHM is explained. Following sections present the different modeling schemes adopted, developments in the area of transducers for generation, and sensing of wave, signal processing and imaging technique, statistical and machine learning schemes for feature extraction. Next, a section is presented on the recent advancements in nonlinear guided wave for SHM. This is followed by section on Rayleigh and SH waves. Next is a section on real-life implementation of guided wave for industrial problems. The paper, though briefly talks about the early development for completeness,. is primarily focussed on the recent progress made in the last decade. The paper ends by discussing and highlighting the future directions and open areas of research in guided wave based SHM.

Abstract: Recent studies have developed fundamental limitations on nanoscale thermodynamics, in terms of a set of independent free energy relations. Here we show that free energy relations cannot properly describe quantum coherence in thermodynamic processes. By casting time-asymmetry as a quantifiable, fundamental resource of a quantum state, we arrive at an additional, independent set of thermodynamic constraints that naturally extend the existing ones. These asymmetry relations reveal that the traditional Szilard engine argument does not extend automatically to quantum coherences, but instead only relational coherences in a multipartite scenario can contribute to thermodynamic work. We find that coherence transformations are always irreversible. Our results also reveal additional structural parallels between thermodynamics and the theory of entanglement. The statistical nature of standard thermodynamics provides an incomplete picture for individual processes at the nanoscale, and new relations have been developed to extend it. Here, the authors show that by quantifying time-asymmetry it is also possible to characterize how quantum coherence is modified in such processes.

Abstract: Individual astrophysical sources previously detected in neutrinos are limited to the Sun and the supernova 1987A, whereas the origins of the diffuse flux of high-energy cosmic neutrinos remain unidentified. On 22 September 2017 we detected a high-energy neutrino, IceCube-170922A, with an energy of approximately 290 TeV. Its arrival direction was consistent with the location of a known gamma-ray blazar TXS 0506+056, observed to be in a flaring state. An extensive multi-wavelength campaign followed, ranging from radio frequencies to gamma-rays. These observations characterize the variability and energetics of the blazar and include the first detection of TXS 0506+056 in very-high-energy gamma-rays. This observation of a neutrino in spatial coincidence with a gamma-ray emitting blazar during an active phase suggests that blazars may be a source of high-energy neutrinos.

Abstract: The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.

Abstract: Objectives To determine the relation between delirium in critically ill patients and their outcomes in the short term (in the intensive care unit and in hospital) and after discharge from hospital. Design Systematic review and meta-analysis of published studies. Data sources PubMed, Embase, CINAHL, Cochrane Library, and PsychINFO, with no language restrictions, up to 1 January 2015. Eligibility criteria for selection studies Reports were eligible for inclusion if they were prospective observational cohorts or clinical trials of adults in intensive care units who were assessed with a validated delirium screening or rating system, and if the association was measured between delirium and at least one of four clinical endpoints (death during admission, length of stay, duration of mechanical ventilation, and any outcome after hospital discharge). Studies were excluded if they primarily enrolled patients with a neurological disorder or patients admitted to intensive care after cardiac surgery or organ/tissue transplantation, or centered on sedation management or alcohol or substance withdrawal. Data were extracted on characteristics of studies, populations sampled, identification of delirium, and outcomes. Random effects models and meta-regression analyses were used to pool data from individual studies. Results Delirium was identified in 5280 of 16 595 (31.8%) critically ill patients reported in 42 studies. When compared with control patients without delirium, patients with delirium had significantly higher mortality during admission (risk ratio 2.19, 94% confidence interval 1.78 to 2.70; P Conclusions Nearly a third of patients admitted to an intensive care unit develop delirium, and these patients are at increased risk of dying during admission, longer stays in hospital, and cognitive impairment after discharge.

Abstract: The possibility to change the molecular assembled structures of organic and organometallic materials through mechanical stimulation is emerging as a general and powerful concept for the design of functional materials. In particular, the photophysical properties such as photoluminescence color, quantum yield, and emission lifetime of organic and organometallic fluorophores can significantly depend on the molecular packing, enabling the development of molecular materials with mechanoresponsive luminescence characteristics. Indeed, an increasing number of studies have shown in recent years that mechanical force can be utilized to change the molecular arrangement, and thereby the optical response, of luminescent molecular assemblies of π-conjugated organic or organometallic molecules. Here, the development of such mechanoresponsive luminescent (MRL) molecular assemblies consisting of organic or organometallic molecules is reviewed and emerging trends in this research field are summarized. After a brief introduction of mechanoresponsive luminescence observed in molecular assemblies, the concept of "luminescent molecular domino" is introduced, before molecular materials that show turn-on/off of photoluminescence in response to mechanical stimulation are reviewed. Mechanically stimulated multicolor changes and water-soluble MRL materials are also highlighted and approaches that combine the concept of MRL molecular assemblies with other materials types are presented in the last part of this progress report.

Abstract: We present a system for training deep neural networks for object detection using synthetic images. To handle the variability in real-world data, the system relies upon the technique of domain randomization, in which the parameters of the simulator-such as lighting, pose, object textures, etc.-are randomized in non-realistic ways to force the neural network to learn the essential features of the object of interest. We explore the importance of these parameters, showing that it is possible to produce a network with compelling performance using only non-artistically-generated synthetic data. With additional fine-tuning on real data, the network yields better performance than using real data alone. This result opens up the possibility of using inexpensive synthetic data for training neural networks while avoiding the need to collect large amounts of hand-annotated real-world data or to generate high-fidelity synthetic worlds-both of which remain bottlenecks for many applications. The approach is evaluated on bounding box detection of cars on the KITTI dataset.

Abstract: We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness. To approximate softmax attention-kernels, Performers use a novel Fast Attention Via positive Orthogonal Random features approach (FAVOR+), which may be of independent interest for scalable kernel methods. FAVOR+ can be also used to efficiently model kernelizable attention mechanisms beyond softmax. This representational power is crucial to accurately compare softmax with other kernels for the first time on large-scale tasks, beyond the reach of regular Transformers, and investigate optimal attention-kernels. Performers are linear architectures fully compatible with regular Transformers and with strong theoretical guarantees: unbiased or nearly-unbiased estimation of the attention matrix, uniform convergence and low estimation variance. We tested Performers on a rich set of tasks stretching from pixel-prediction through text models to protein sequence modeling. We demonstrate competitive results with other examined efficient sparse and dense attention methods, showcasing effectiveness of the novel attention-learning paradigm leveraged by Performers.

Abstract: Contemporary integrated circuits are designed and manufactured in a globalized environment leading to concerns of piracy, overproduction and counterfeiting. One class of techniques to combat these threats is logic encryption. Logic encryption modifies an IC design such that it operates correctly only when a set of newly introduced inputs, called key inputs, are set to the correct values. In this paper, we use algorithms based on satisfiability checking (SAT) to investigate the security of logic encryption. We present a SAT-based algorithm which allows an attacker to “decrypt” an encrypted netlist using a small number of carefully-selected input patterns and their corresponding output observations. We also present a “partial-break” algorithm that can reveal some of the key inputs even when the attack is not fully successful. We conduct a thorough evaluation of our attack by examining six proposals for logic encryption from the literature. We find that all of these are vulnerable to our attack. Among the 441 encrypted circuits we examined, we were able to decrypt 418 (95%). We discuss the strengths and limitations of our attack and suggest directions that may lead to improved logic encryption algorithms.

Abstract: In this paper we discuss recent developments in econometrics that we view as important for empirical researchers working on policy evaluation questions. We focus on three main areas, where in each case we highlight recommendations for applied work. First, we discuss new research on identification strategies in program evaluation, with particular focus on synthetic control methods, regression discontinuity, external validity, and the causal interpretation of regression methods. Second, we discuss various forms of supplementary analyses to make the identification strategies more credible. These include placebo analyses as well as sensitivity and robustness analyses. Third, we discuss recent advances in machine learning methods for causal effects. These advances include methods to adjust for differences between treated and control units in high-dimensional settings, and methods for identifying and estimating heterogeneous treatment effects.

Abstract: The mechanistic target of rapamycin complex 1 (mTORC1) protein kinase is a master growth regulator that responds to multiple environmental cues. Amino acids stimulate, in a Rag-, Ragulator-, and vacuolar adenosine triphosphatase-dependent fashion, the translocation of mTORC1 to the lysosomal surface, where it interacts with its activator Rheb. Here, we identify SLC38A9, an uncharacterized protein with sequence similarity to amino acid transporters, as a lysosomal transmembrane protein that interacts with the Rag guanosine triphosphatases (GTPases) and Ragulator in an amino acid-sensitive fashion. SLC38A9 transports arginine with a high Michaelis constant, and loss of SLC38A9 represses mTORC1 activation by amino acids, particularly arginine. Overexpression of SLC38A9 or just its Ragulator-binding domain makes mTORC1 signaling insensitive to amino acid starvation but not to Rag activity. Thus, SLC38A9 functions upstream of the Rag GTPases and is an excellent candidate for being an arginine sensor for the mTORC1 pathway.

Abstract: The microbiota plays a central role in human health and disease by shaping immune development, immune responses and metabolism, and by protecting from invading pathogens. Technical advances that allow comprehensive characterization of microbial communities by genetic sequencing have sparked the hunt for disease-modulating bacteria. Emerging studies in humans have linked the increased abundance of Prevotella species at mucosal sites to localized and systemic disease, including periodontitis, bacterial vaginosis, rheumatoid arthritis, metabolic disorders and low-grade systemic inflammation. Intriguingly, Prevotella abundance is reduced within the lung microbiota of patients with asthma and chronic obstructive pulmonary disease. Increased Prevotella abundance is associated with augmented T helper type 17 (Th17) -mediated mucosal inflammation, which is in line with the marked capacity of Prevotella in driving Th17 immune responses in vitro. Studies indicate that Prevotella predominantly activate Toll-like receptor 2, leading to production of Th17-polarizing cytokines by antigen-presenting cells, including interleukin-23 (IL-23) and IL-1. Furthermore, Prevotella stimulate epithelial cells to produce IL-8, IL-6 and CCL20, which can promote mucosal Th17 immune responses and neutrophil recruitment. Prevotella-mediated mucosal inflammation leads to systemic dissemination of inflammatory mediators, bacteria and bacterial products, which in turn may affect systemic disease outcomes. Studies in mice support a causal role of Prevotella as colonization experiments promote clinical and inflammatory features of human disease. When compared with strict commensal bacteria, Prevotella exhibit increased inflammatory properties, as demonstrated by augmented release of inflammatory mediators from immune cells and various stromal cells. These findings indicate that some Prevotella strains may be clinically important pathobionts that can participate in human disease by promoting chronic inflammation.

Abstract: Summary Metacommunity ecology addresses the situation where sets of local communities are connected by the dispersal of a number of potentially interacting species. Aquatic systems (e.g. lentic versus lotic versus marine) differ from each other in connectivity and environmental heterogeneity, suggesting that metacommunity organisation also differs between major aquatic systems. Here, we review findings from observational field studies on metacommunity organisation in aquatic systems. Species sorting (i.e. species are ‘filtered’ by environmental factors and occur only at environmentally suitable sites) prevails in aquatic systems, particularly in streams and lakes, but the degree to which dispersal limitation interacts with such environmental control varies among different systems and spatial scales. For example, mainstem rivers and marine coastal systems may be strongly affected by ‘mass effects’ (i.e. where high dispersal rates homogenise communities to some degree at neighbouring localities, irrespective of their abiotic and biotic environmental conditions), whereas isolated lakes and ponds may be structured by dispersal limitation (i.e. some species do not occur at otherwise-suitable localities simply because sites with potential colonists are too far away). Flow directionality in running waters also differs from water movements in other systems, and this difference may also have effects on the role of dispersal in different aquatic systems. Dispersal limitation typically increases with increasing spatial distance between sites, mass effects potentially increase in importance with decreasing distance between sites, and the dispersal ability of organisms may determine the spatial extents at which species sorting and dispersal processes are most important. A better understanding of the relative roles of species sorting, mass effects and dispersal limitation in affecting aquatic metacommunities requires the following: (i) characterising dispersal rates more directly or adopting better proxies than have been used previously; (ii) considering the nature of aquatic networks; (iii) combining correlative and experimental approaches; (iv) exploring temporal aspects of metacommunity organisation and (v) applying past approaches and statistical methods innovatively for increasing our understanding of metacommunity organisation.

Abstract: Heterogeneous nuclear ribonucleoproteins (hnRNPs) represent a large family of RNA-binding proteins (RBPs) that contribute to multiple aspects of nucleic acid metabolism including alternative splicing, mRNA stabilization, and transcriptional and translational regulation Many hnRNPs share general features, but differ in domain composition and functional properties This review will discuss the current knowledge about the different hnRNP family members, focusing on their structural and functional divergence Additionally, we will highlight their involvement in neurodegenerative diseases and cancer, and the potential to develop RNA-based therapies

Abstract: Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

Abstract: Macrophages and neutrophils play a decisive role in host responses to intracellular bacteria including the agent of tuberculosis (TB), Mycobacterium tuberculosis as they represent the forefront of innate immune defense against bacterial invaders. At the same time, these phagocytes are also primary targets of intracellular bacteria to be abused as host cells. Their efficacy to contain and eliminate intracellular M. tuberculosis decides whether a patient initially becomes infected or not. However, when the infection becomes chronic or even latent (as in the case of TB) despite development of specific immune activation, phagocytes have also important effector functions. Macrophages have evolved a myriad of defense strategies to combat infection with intracellular bacteria such as M. tuberculosis. These include induction of toxic anti-microbial effectors such as nitric oxide and reactive oxygen intermediates, the stimulation of microbe intoxication mechanisms via acidification or metal accumulation in the phagolysosome, the restriction of the microbe's access to essential nutrients such as iron, fatty acids, or amino acids, the production of anti-microbial peptides and cytokines, along with induction of autophagy and efferocytosis to eliminate the pathogen. On the other hand, M. tuberculosis, as a prime example of a well-adapted facultative intracellular bacterium, has learned during evolution to counter-balance the host's immune defense strategies to secure survival or multiplication within this otherwise hostile environment. This review provides an overview of innate immune defense of macrophages directed against intracellular bacteria with a focus on M. tuberculosis. Gaining more insights and knowledge into this complex network of host-pathogen interaction will identify novel target sites of intervention to successfully clear infection at a time of rapidly emerging multi-resistance of M. tuberculosis against conventional antibiotics.

Abstract: Cytokine dysregulation is believed to play a key role in the remodeling of the immune system at older age, with evidence pointing to an inability to fine-control systemic inflammation, which seems to be a marker of unsuccessful aging. This reshaping of cytokine expression pattern, with a progressive tendency toward a pro-inflammatory phenotype has been called "inflamm-aging." Despite research there is no clear understanding about the causes of "inflamm-aging" that underpin most major age-related diseases, including atherosclerosis, diabetes, Alzheimer's disease, rheumatoid arthritis, cancer, and aging itself. While inflammation is part of the normal repair response for healing, and essential in keeping us safe from bacterial and viral infections and noxious environmental agents, not all inflammation is good. When inflammation becomes prolonged and persists, it can become damaging and destructive. Several common molecular pathways have been identified that are associated with both aging and low-grade inflammation. The age-related change in redox balance, the increase in age-related senescent cells, the senescence-associated secretory phenotype (SASP) and the decline in effective autophagy that can trigger the inflammasome, suggest that it may be possible to delay age-related diseases and aging itself by suppressing pro-inflammatory molecular mechanisms or improving the timely resolution of inflammation. Conversely there may be learning from molecular or genetic pathways from long-lived cohorts who exemplify good quality aging. Here, we will discuss some of the current ideas and highlight molecular pathways that appear to contribute to the immune imbalance and the cytokine dysregulation, which is associated with "inflammageing" or parainflammation. Evidence of these findings will be drawn from research in cardiovascular disease, cancer, neurological inflammation and rheumatoid arthritis.

Abstract: In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation. In standard FCNs, only long skip connections are used to skip features from the contracting path to the expanding path in order to recover spatial information lost during downsampling. We extend FCNs by adding short skip connections, that are similar to the ones introduced in residual networks, in order to build very deep FCNs (of hundreds of layers). A review of the gradient flow confirms that for a very deep FCN it is beneficial to have both long and short skip connections. Finally, we show that a very deep FCN can achieve near-to-state-of-the-art results on the EM dataset without any further post-processing.

Abstract: What is a reconfigurable intelligent surface? What is a smart radio environment? What is a metasurface? How do metasurfaces work and how to model them? How to reconcile the mathematical theories of communication and electromagnetism? What are the most suitable uses and applications of reconfigurable intelligent surfaces in wireless networks? What are the most promising smart radio environments for wireless applications? What is the current state of research? What are the most important and challenging research issues to tackle? These are a few of the many questions that we investigate in this short opus, which has the threefold objective of introducing the emerging research field of smart radio environments empowered by reconfigurable intelligent surfaces, putting forth the need of reconciling and reuniting C. E. Shannon's mathematical theory of communication with G. Green's and J. C. Maxwell's mathematical theories of electromagnetism, and reporting pragmatic guidelines and recipes for employing appropriate physics-based models of metasurfaces in wireless communications.

Abstract: Purpose: Although clinical studies have shown promise for targeting programmed cell death protein-1 (PD-1) and ligand (PD-L1) signaling in non-small cell lung cancer (NSCLC), the factors that predict which subtype patients will be responsive to checkpoint blockade are not fully understood.Experimental Design: We performed an integrated analysis on the multiple-dimensional data types including genomic, transcriptomic, proteomic, and clinical data from cohorts of lung adenocarcinoma public (discovery set) and internal (validation set) database and immunotherapeutic patients. Gene set enrichment analysis (GSEA) was used to determine potentially relevant gene expression signatures between specific subgroups.Results: We observed that TP53 mutation significantly increased expression of immune checkpoints and activated T-effector and interferon-γ signature. More importantly, the TP53/KRAS comutated subgroup manifested exclusive increased expression of PD-L1 and a highest proportion of PD-L1+/CD8A+ Meanwhile, TP53- or KRAS-mutated tumors showed prominently increased mutation burden and specifically enriched in the transversion-high (TH) cohort. Further analysis focused on the potential molecular mechanism revealed that TP53 or KRAS mutation altered a group of genes involved in cell-cycle regulating, DNA replication and damage repair. Finally, immunotherapeutic analysis from public clinical trial and prospective observation in our center were further confirmed that TP53 or KRAS mutation patients, especially those with co-occurring TP53/KRAS mutations, showed remarkable clinical benefit to PD-1 inhibitors.Conclusions: This work provides evidence that TP53 and KRAS mutation in lung adenocarcinoma may be served as a pair of potential predictive factors in guiding anti-PD-1/PD-L1 immunotherapy. Clin Cancer Res; 23(12); 3012-24. ©2016 AACR.

Abstract: Free-standing monolayers of MoS2 exhibit piezoelectric behaviour due to inversion symmetry breaking. Piezoelectricity allows precise and robust conversion between electricity and mechanical force, and arises from the broken inversion symmetry in the atomic structure1,2,3. Reducing the dimensionality of bulk materials has been suggested to enhance piezoelectricity4. However, when the thickness of a material approaches a single molecular layer, the large surface energy can cause piezoelectric structures to be thermodynamically unstable5. Transition-metal dichalcogenides can retain their atomic structures down to the single-layer limit without lattice reconstruction, even under ambient conditions6. Recent calculations have predicted the existence of piezoelectricity in these two-dimensional crystals due to their broken inversion symmetry7. Here, we report experimental evidence of piezoelectricity in a free-standing single layer of molybdenum disulphide (MoS2) and a measured piezoelectric coefficient of e11 = 2.9 × 10–10 C m−1. The measurement of the intrinsic piezoelectricity in such free-standing crystals is free from substrate effects such as doping and parasitic charges. We observed a finite and zero piezoelectric response in MoS2 in odd and even number of layers, respectively, in sharp contrast to bulk piezoelectric materials. This oscillation is due to the breaking and recovery of the inversion symmetry of the two-dimensional crystal. Through the angular dependence of electromechanical coupling, we determined the two-dimensional crystal orientation. The piezoelectricity discovered in this single molecular membrane promises new applications in low-power logic switches for computing and ultrasensitive biological sensors scaled down to a single atomic unit cell8,9.

Abstract: Incremental learning (il) has received a lot of attention recently, however, the literature lacks a precise problem definition, proper evaluation settings, and metrics tailored specifically for the il problem. One of the main objectives of this work is to fill these gaps so as to provide a common ground for better understanding of il. The main challenge for an il algorithm is to update the classifier whilst preserving existing knowledge. We observe that, in addition to forgetting, a known issue while preserving knowledge, il also suffers from a problem we call intransigence, its inability to update knowledge. We introduce two metrics to quantify forgetting and intransigence that allow us to understand, analyse, and gain better insights into the behaviour of il algorithms. Furthermore, we present RWalk, a generalization of ewc++ (our efficient version of ewc [6]) and Path Integral [25] with a theoretically grounded KL-divergence based perspective. We provide a thorough analysis of various il algorithms on MNIST and CIFAR-100 datasets. In these experiments, RWalk obtains superior results in terms of accuracy, and also provides a better trade-off for forgetting and intransigence.

Abstract: Ovarian cancer is the second most common cause of gynecologic cancer death in women around the world. The outcomes are complicated, because the disease is often diagnosed late and composed of several subtypes with distinct biological and molecular properties (even within the same histological subtype), and there is inconsistency in availability of and access to treatment. Upfront treatment largely relies on debulking surgery to no residual disease and platinum-based chemotherapy, with the addition of antiangiogenic agents in patients who have suboptimally debulked and stage IV disease. Major improvement in maintenance therapy has been seen by incorporating inhibitors against poly (ADP-ribose) polymerase (PARP) molecules involved in the DNA damage-repair process, which have been approved in a recurrent setting and recently in a first-line setting among women with BRCA1/BRCA2 mutations. In recognizing the challenges facing the treatment of ovarian cancer, current investigations are enlaced with deep molecular and cellular profiling. To improve survival in this aggressive disease, access to appropriate evidence-based care is requisite. In concert, realizing individualized precision medicine will require prioritizing clinical trials of innovative treatments and refining predictive biomarkers that will enable selection of patients who would benefit from chemotherapy, targeted agents, or immunotherapy. Together, a coordinated and structured approach will accelerate significant clinical and academic advancements in ovarian cancer and meaningfully change the paradigm of care.

Abstract: Background Embolic strokes of undetermined source represent 20% of ischemic strokes and are associated with a high rate of recurrence. Anticoagulant treatment with rivaroxaban, an oral factor Xa inhibitor, may result in a lower risk of recurrent stroke than aspirin. Methods We compared the efficacy and safety of rivaroxaban (at a daily dose of 15 mg) with aspirin (at a daily dose of 100 mg) for the prevention of recurrent stroke in patients with recent ischemic stroke that was presumed to be from cerebral embolism but without arterial stenosis, lacune, or an identified cardioembolic source. The primary efficacy outcome was the first recurrence of ischemic or hemorrhagic stroke or systemic embolism in a time-to-event analysis; the primary safety outcome was the rate of major bleeding. Results A total of 7213 participants were enrolled at 459 sites; 3609 patients were randomly assigned to receive rivaroxaban and 3604 to receive aspirin. Patients had been followed for a median of 11 months when the ...

Abstract: Sepsis, severe sepsis, and septic shock represent increasingly severe systemic inflammatory responses to infection. Sepsis is common in the aging population, and it disproportionately affects patients with cancer and underlying immunosuppression. In its most severe form, sepsis causes multiple organ dysfunction that can produce a state of chronic critical illness characterized by severe immune dysfunction and catabolism. Much has been learnt about the pathogenesis of sepsis at the molecular, cell, and intact organ level. Despite uncertainties in hemodynamic management and several treatments that have failed in clinical trials, investigational therapies increasingly target sepsis induced organ and immune dysfunction. Outcomes in sepsis have greatly improved overall, probably because of an enhanced focus on early diagnosis and fluid resuscitation, the rapid delivery of effective antibiotics, and other improvements in supportive care for critically ill patients. These improvements include lung protective ventilation, more judicious use of blood products, and strategies to reduce nosocomial infections.

Abstract: Innate lymphoid cells (ILCs) are a growing family of immune cells that mirror the phenotypes and functions of T cells. However, in contrast to T cells, ILCs do not express acquired antigen receptors or undergo clonal selection and expansion when stimulated. Instead, ILCs react promptly to signals from infected or injured tissues and produce an array of secreted proteins termed cytokines that direct the developing immune response into one that is adapted to the original insult. The complex cross-talk between microenvironment, ILCs, and adaptive immunity remains to be fully deciphered. Only by understanding these complex regulatory networks can the power of ILCs be controlled or unleashed in order to regulate or enhance immune responses in disease prevention and therapy.

Abstract: In many robotics and VR/AR applications, 3D-videos are readily-available input sources (a sequence of depth images, or LIDAR scans). However, in many cases, the 3D-videos are processed frame-by-frame either through 2D convnets or 3D perception algorithms. In this work, we propose 4-dimensional convolutional neural networks for spatio-temporal perception that can directly process such 3D-videos using high-dimensional convolutions. For this, we adopt sparse tensors and propose generalized sparse convolutions that encompass all discrete convolutions. To implement the generalized sparse convolution, we create an open-source auto-differentiation library for sparse tensors that provides extensive functions for high-dimensional convolutional neural networks. We create 4D spatio-temporal convolutional neural networks using the library and validate them on various 3D semantic segmentation benchmarks and proposed 4D datasets for 3D-video perception. To overcome challenges in 4D space, we propose the hybrid kernel, a special case of the generalized sparse convolution, and trilateral-stationary conditional random fields that enforce spatio-temporal consistency in the 7D space-time-chroma space. Experimentally, we show that a convolutional neural network with only generalized 3D sparse convolutions can outperform 2D or 2D-3D hybrid methods by a large margin. Also, we show that on 3D-videos, 4D spatio-temporal convolutional neural networks are robust to noise and outperform the 3D convolutional neural network.

Abstract: Triple-negative breast cancer (TNBC) remains the most challenging breast cancer subtype to treat. To date, therapies directed to specific molecular targets have rarely achieved clinically meaningful improvements in outcomes of patients with TNBC, and chemotherapy remains the standard of care. Here, we seek to review the most recent efforts to classify TNBC based on the comprehensive profiling of tumors for cellular composition and molecular features. Technologic advances allow for tumor characterization at ever-increasing depth, generating data that, if integrated with clinical-pathologic features, may help improve risk stratification of patients, guide treatment decisions and surveillance, and help identify new targets for drug development. SIGNIFICANCE: TNBC is characterized by higher rates of relapse, greater metastatic potential, and shorter overall survival compared with other major breast cancer subtypes. The identification of biomarkers that can help guide treatment decisions in TNBC remains a clinically unmet need. Understanding the mechanisms that drive resistance is key to the design of novel therapeutic strategies to help prevent the development of metastatic disease and, ultimately, to improve survival in this patient population.

Abstract: Summary paragraph The Trans-Omics for Precision Medicine (TOPMed) program seeks to elucidate the genetic architecture and disease biology of heart, lung, blood, and sleep disorders, with the ultimate goal of improving diagnosis, treatment, and prevention. The initial phases of the program focus on whole genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here, we describe TOPMed goals and design as well as resources and early insights from the sequence data. The resources include a variant browser, a genotype imputation panel, and sharing of genomic and phenotypic data via dbGaP. In 53,581 TOPMed samples, >400 million single-nucleotide and insertion/deletion variants were detected by alignment with the reference genome. Additional novel variants are detectable through assembly of unmapped reads and customized analysis in highly variable loci. Among the >400 million variants detected, 97% have frequency