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Showing papers by "Cornell University published in 2018"


Journal ArticleDOI
Gregory A. Roth1, Gregory A. Roth2, Degu Abate3, Kalkidan Hassen Abate4  +1025 moreInstitutions (333)
TL;DR: Non-communicable diseases comprised the greatest fraction of deaths, contributing to 73·4% (95% uncertainty interval [UI] 72·5–74·1) of total deaths in 2017, while communicable, maternal, neonatal, and nutritional causes accounted for 18·6% (17·9–19·6), and injuries 8·0% (7·7–8·2).

5,211 citations


Journal ArticleDOI
23 Mar 2018-Science
TL;DR: New-generation combinatorial therapies may overcome resistance mechanisms to immune checkpoint therapy, and evidence points to alterations that converge on the antigen presentation and interferon-γ signaling pathways.
Abstract: The release of negative regulators of immune activation (immune checkpoints) that limit antitumor responses has resulted in unprecedented rates of long-lasting tumor responses in patients with a variety of cancers. This can be achieved by antibodies blocking the cytotoxic T lymphocyte–associated protein 4 (CTLA-4) or the programmed cell death 1 (PD-1) pathway, either alone or in combination. The main premise for inducing an immune response is the preexistence of antitumor T cells that were limited by specific immune checkpoints. Most patients who have tumor responses maintain long-lasting disease control, yet one-third of patients relapse. Mechanisms of acquired resistance are currently poorly understood, but evidence points to alterations that converge on the antigen presentation and interferon-γ signaling pathways. New-generation combinatorial therapies may overcome resistance mechanisms to immune checkpoint therapy.

3,736 citations


Journal ArticleDOI
Lorenzo Galluzzi1, Lorenzo Galluzzi2, Ilio Vitale3, Stuart A. Aaronson4  +183 moreInstitutions (111)
TL;DR: The Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives.
Abstract: Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field.

3,301 citations


Journal ArticleDOI
05 Jan 2018-Science
TL;DR: It is found that primary resistance to ICIs can be attributed to abnormal gut microbiome composition, and Antibiotics inhibited the clinical benefit of ICIs in patients with advanced cancer.
Abstract: Immune checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 axis induce sustained clinical responses in a sizable minority of cancer patients. We found that primary resistance to ICIs can be attributed to abnormal gut microbiome composition. Antibiotics inhibited the clinical benefit of ICIs in patients with advanced cancer. Fecal microbiota transplantation (FMT) from cancer patients who responded to ICIs into germ-free or antibiotic-treated mice ameliorated the antitumor effects of PD-1 blockade, whereas FMT from nonresponding patients failed to do so. Metagenomics of patient stool samples at diagnosis revealed correlations between clinical responses to ICIs and the relative abundance of Akkermansia muciniphila Oral supplementation with A. muciniphila after FMT with nonresponder feces restored the efficacy of PD-1 blockade in an interleukin-12-dependent manner by increasing the recruitment of CCR9+CXCR3+CD4+ T lymphocytes into mouse tumor beds.

3,258 citations


Journal ArticleDOI
Jeffrey D. Stanaway1, Ashkan Afshin1, Emmanuela Gakidou1, Stephen S Lim1  +1050 moreInstitutions (346)
TL;DR: This study estimated levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs) by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017 and explored the relationship between development and risk exposure.

2,910 citations


Journal ArticleDOI
TL;DR: This AASLD 2018 Hepatitis B Guidance provides a data-supported approach to screening, prevention, diagnosis, and clinical management of patients with hepatitis B.

2,399 citations


Book ChapterDOI
08 Sep 2018
TL;DR: In this article, the authors propose a multimodal unsupervised image-to-image (MUNIT) framework, where the image representation can be decomposed into a content code that is domain-invariant and a style code that captures domain-specific properties.
Abstract: Unsupervised image-to-image translation is an important and challenging problem in computer vision. Given an image in the source domain, the goal is to learn the conditional distribution of corresponding images in the target domain, without seeing any examples of corresponding image pairs. While this conditional distribution is inherently multimodal, existing approaches make an overly simplified assumption, modeling it as a deterministic one-to-one mapping. As a result, they fail to generate diverse outputs from a given source domain image. To address this limitation, we propose a Multimodal Unsupervised Image-to-image \(\text{ Translation } \text{(MUNIT) }\) framework. We assume that the image representation can be decomposed into a content code that is domain-invariant, and a style code that captures domain-specific properties. To translate an image to another domain, we recombine its content code with a random style code sampled from the style space of the target domain. We analyze the proposed framework and establish several theoretical results. Extensive experiments with comparisons to state-of-the-art approaches further demonstrate the advantage of the proposed framework. Moreover, our framework allows users to control the style of translation outputs by providing an example style image. Code and pretrained models are available at https://github.com/nvlabs/MUNIT.

1,874 citations


Journal ArticleDOI
05 Apr 2018-Cell
TL;DR: This study reports a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations, identifying 299 driver genes with implications regarding their anatomical sites and cancer/cell types.

1,623 citations


Journal ArticleDOI
B. P. Abbott1, Richard J. Abbott1, T. D. Abbott2, Fausto Acernese3  +1235 moreInstitutions (132)
TL;DR: This analysis expands upon previous analyses by working under the hypothesis that both bodies were neutron stars that are described by the same equation of state and have spins within the range observed in Galactic binary neutron stars.
Abstract: On 17 August 2017, the LIGO and Virgo observatories made the first direct detection of gravitational waves from the coalescence of a neutron star binary system. The detection of this gravitational-wave signal, GW170817, offers a novel opportunity to directly probe the properties of matter at the extreme conditions found in the interior of these stars. The initial, minimal-assumption analysis of the LIGO and Virgo data placed constraints on the tidal effects of the coalescing bodies, which were then translated to constraints on neutron star radii. Here, we expand upon previous analyses by working under the hypothesis that both bodies were neutron stars that are described by the same equation of state and have spins within the range observed in Galactic binary neutron stars. Our analysis employs two methods: the use of equation-of-state-insensitive relations between various macroscopic properties of the neutron stars and the use of an efficient parametrization of the defining function pðρÞ of the equation of state itself. From the LIGO and Virgo data alone and the first method, we measure the two neutron star radii as R1 ¼ 10.8 þ2.0 −1.7 km for the heavier star and R2 ¼ 10.7 þ2.1 −1.5 km for the lighter star at the 90% credible level. If we additionally require that the equation of state supports neutron stars with masses larger than 1.97 M⊙ as required from electromagnetic observations and employ the equation-of-state parametrization, we further constrain R1 ¼ 11.9 þ1.4 −1.4 km and R2 ¼ 11.9 þ1.4 −1.4 km at the 90% credible level. Finally, we obtain constraints on pðρÞ at supranuclear densities, with pressure at twice nuclear saturation density measured at 3.5 þ2.7 −1.7 × 1034 dyn cm−2 at the 90% level.

1,595 citations


Journal ArticleDOI
TL;DR: It is suggested that deep learning approaches could be the vehicle for translating big biomedical data into improved human health and develop holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.
Abstract: Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.

1,573 citations


Journal ArticleDOI
22 Jun 2018-Science
TL;DR: It is demonstrated that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine, and it is shown that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures.
Abstract: Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.

Journal ArticleDOI
23 Aug 2018-Cell
TL;DR: The advances in ILC biology over the past decade are distill the advances to refine the nomenclature of ILCs and highlight the importance of I LCs in tissue homeostasis, morphogenesis, metabolism, repair, and regeneration.

Journal ArticleDOI
TL;DR: Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories and infers more accurate pseudotimes than other leading methods.
Abstract: Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve. We introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods. Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression.

Journal ArticleDOI
TL;DR: Analysis of genetic pathways suggested that MCD and BN2 DLBCLs rely on “chronic active” B‐cell receptor signaling that is amenable to therapeutic inhibition, and an algorithm was developed and implemented to discover genetic subtypes based on the co‐occurrence of genetic alterations.
Abstract: Background Diffuse large B-cell lymphomas (DLBCLs) are phenotypically and genetically heterogeneous. Gene-expression profiling has identified subgroups of DLBCL (activated B-cell–like [ABC], germinal-center B-cell–like [GCB], and unclassified) according to cell of origin that are associated with a differential response to chemotherapy and targeted agents. We sought to extend these findings by identifying genetic subtypes of DLBCL based on shared genomic abnormalities and to uncover therapeutic vulnerabilities based on tumor genetics. Methods We studied 574 DLBCL biopsy samples using exome and transcriptome sequencing, array-based DNA copy-number analysis, and targeted amplicon resequencing of 372 genes to identify genes with recurrent aberrations. We developed and implemented an algorithm to discover genetic subtypes based on the co-occurrence of genetic alterations. Results We identified four prominent genetic subtypes in DLBCL, termed MCD (based on the co-occurrence of MYD88L265P and CD79B muta...

Journal ArticleDOI
25 May 2018-Science
TL;DR: Research prospects for more sustainable routes to nitrogen commodity chemicals are reviewed, considering developments in enzymatic, homogeneous, and heterogeneous catalysis, as well as electrochemical, photochemical, and plasma-based approaches.
Abstract: BACKGROUND The invention of the Haber-Bosch (H-B) process in the early 1900s to produce ammonia industrially from nitrogen and hydrogen revolutionized the manufacture of fertilizer and led to fundamental changes in the way food is produced. Its impact is underscored by the fact that about 50% of the nitrogen atoms in humans today originate from this single industrial process. In the century after the H-B process was invented, the chemistry of carbon moved to center stage, resulting in remarkable discoveries and a vast array of products including plastics and pharmaceuticals. In contrast, little has changed in industrial nitrogen chemistry. This scenario reflects both the inherent efficiency of the H-B process and the particular challenge of breaking the strong dinitrogen bond. Nonetheless, the reliance of the H-B process on fossil fuels and its associated high CO 2 emissions have spurred recent interest in finding more sustainable and environmentally benign alternatives. Nitrogen in its more oxidized forms is also industrially, biologically, and environmentally important, and synergies in new combinations of oxidative and reductive transformations across the nitrogen cycle could lead to improved efficiencies. ADVANCES Major effort has been devoted to developing alternative and environmentally friendly processes that would allow NH 3 production at distributed sources under more benign conditions, rather than through the large-scale centralized H-B process. Hydrocarbons (particularly methane) and water are the only two sources of hydrogen atoms that can sustain long-term, large-scale NH 3 production. The use of water as the hydrogen source for NH 3 production requires substantially more energy than using methane, but it is also more environmentally benign, does not contribute to the accumulation of greenhouse gases, and does not compete for valuable and limited hydrocarbon resources. Microbes living in all major ecosystems are able to reduce N 2 to NH 3 by using the enzyme nitrogenase. A deeper understanding of this enzyme could lead to more efficient catalysts for nitrogen reduction under ambient conditions. Model molecular catalysts have been designed that mimic some of the functions of the active site of nitrogenase. Some modest success has also been achieved in designing electrocatalysts for dinitrogen reduction. Electrochemistry avoids the expense and environmental damage of steam reforming of methane (which accounts for most of the cost of the H-B process), and it may provide a means for distributed production of ammonia. On the oxidative side, nitric acid is the principal commodity chemical containing oxidized nitrogen. Nearly all nitric acid is manufactured by oxidation of NH 3 through the Ostwald process, but a more direct reaction of N 2 with O 2 might be practically feasible through further development of nonthermal plasma technology. Heterogeneous NH 3 oxidation with O 2 is at the heart of the Ostwald process and is practiced in a variety of environmental protection applications as well. Precious metals remain the workhorse catalysts, and opportunities therefore exist to develop lower-cost materials with equivalent or better activity and selectivity. Nitrogen oxides are also environmentally hazardous pollutants generated by industrial and transportation activities, and extensive research has gone into developing and applying reduction catalysts. Three-way catalytic converters are operating on hundreds of millions of vehicles worldwide. However, increasingly stringent emissions regulations, coupled with the low exhaust temperatures of high-efficiency engines, present challenges for future combustion emissions control. Bacterial denitrification is the natural analog of this chemistry and another source of study and inspiration for catalyst design. OUTLOOK Demands for greater energy efficiency, smaller-scale and more flexible processes, and environmental protection provide growing impetus for expanding the scope of nitrogen chemistry. Nitrogenase, as well as nitrifying and denitrifying enzymes, will eventually be understood in sufficient detail that robust molecular catalytic mimics will emerge. Electrochemical and photochemical methods also demand more study. Other intriguing areas of research that have provided tantalizing results include chemical looping and plasma-driven processes. The grand challenge in the field of nitrogen chemistry is the development of catalysts and processes that provide simple, low-energy routes to the manipulation of the redox states of nitrogen.

Journal ArticleDOI
TL;DR: An overview of the imaging procedures of the ABCD study is provided, the basis for their selection and preliminary quality assurance and results that provide evidence for the feasibility and age-appropriateness of procedures and generalizability of findings to the existent literature are provided.

Journal ArticleDOI
TL;DR: The international guideline for the assessment and management of PCOS provides clinicians with clear advice on best practice based on the best available evidence, expert multidisciplinary input and consumer preferences to promote consistent, evidence-based care and improve the experience and health outcomes of women with PCOS.
Abstract: Study Question What is the recommended assessment and management of women with polycystic ovary syndrome (PCOS), based on the best available evidence, clinical expertise, and consumer preference? Summary Answer International evidence-based guidelines including 166 recommendations and practice points, addressed prioritized questions to promote consistent, evidence-based care and improve the experience and health outcomes of women with PCOS. What Is Known Already Previous guidelines either lacked rigorous evidence-based processes, did not engage consumer and international multidisciplinary perspectives, or were outdated. Diagnosis of PCOS remains controversial and assessment and management are inconsistent. The needs of women with PCOS are not being adequately met and evidence practice gaps persist. Study Design, Size, Duration International evidence-based guideline development engaged professional societies and consumer organizations with multidisciplinary experts and women with PCOS directly involved at all stages. Appraisal of Guidelines for Research and Evaluation (AGREE) II-compliant processes were followed, with extensive evidence synthesis. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework was applied across evidence quality, feasibility, acceptability, cost, implementation and ultimately recommendation strength. Participants/Materials, Setting, Methods Governance included a six continent international advisory and a project board, five guideline development groups, and consumer and translation committees. Extensive health professional and consumer engagement informed guideline scope and priorities. Engaged international society-nominated panels included pediatrics, endocrinology, gynecology, primary care, reproductive endocrinology, obstetrics, psychiatry, psychology, dietetics, exercise physiology, public health and other experts, alongside consumers, project management, evidence synthesis, and translation experts. Thirty-seven societies and organizations covering 71 countries engaged in the process. Twenty face-to-face meetings over 15 months addressed 60 prioritized clinical questions involving 40 systematic and 20 narrative reviews. Evidence-based recommendations were developed and approved via consensus voting within the five guideline panels, modified based on international feedback and peer review, with final recommendations approved across all panels. Main Results and the Role of Chance The evidence in the assessment and management of PCOS is generally of low to moderate quality. The guideline provides 31 evidence based recommendations, 59 clinical consensus recommendations and 76 clinical practice points all related to assessment and management of PCOS. Key changes in this guideline include: i) considerable refinement of individual diagnostic criteria with a focus on improving accuracy of diagnosis; ii) reducing unnecessary testing; iii) increasing focus on education, lifestyle modification, emotional wellbeing and quality of life; and iv) emphasizing evidence based medical therapy and cheaper and safer fertility management. Limitations, Reasons for Caution Overall evidence is generally low to moderate quality, requiring significantly greater research in this neglected, yet common condition, especially around refining specific diagnostic features in PCOS. Regional health system variation is acknowledged and a process for guideline and translation resource adaptation is provided. Wider Implications of the Findings The international guideline for the assessment and management of PCOS provides clinicians with clear advice on best practice based on the best available evidence, expert multidisciplinary input and consumer preferences. Research recommendations have been generated and a comprehensive multifaceted dissemination and translation program supports the guideline with an integrated evaluation program. Study Funding/Competing Interest(S) The guideline was primarily funded by the Australian National Health and Medical Research Council of Australia (NHMRC) supported by a partnership with ESHRE and the American Society for Reproductive Medicine. Guideline development group members did not receive payment. Travel expenses were covered by the sponsoring organizations. Disclosures of conflicts of interest were declared at the outset and updated throughout the guideline process, aligned with NHMRC guideline processes. Full details of conflicts declared across the guideline development groups are available at https://www.monash.edu/medicine/sphpm/mchri/pcos/guideline in the Register of disclosures of interest. Of named authors, Dr Costello has declared shares in Virtus Health and past sponsorship from Merck Serono for conference presentations. Prof. Laven declared grants from Ferring, Euroscreen and personal fees from Ferring, Euroscreen, Danone and Titus Healthcare. Prof. Norman has declared a minor shareholder interest in an IVF unit. The remaining authors have no conflicts of interest to declare. The guideline was peer reviewed by special interest groups across our partner and collaborating societies and consumer organizations, was independently assessed against AGREEII criteria and underwent methodological review. This guideline was approved by all members of the guideline development groups and was submitted for final approval by the NHMRC.

Posted Content
TL;DR: Documentation to facilitate communication between dataset creators and consumers and consumers is presented.
Abstract: The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains. To address this gap, we propose datasheets for datasets. In the electronics industry, every component, no matter how simple or complex, is accompanied with a datasheet that describes its operating characteristics, test results, recommended uses, and other information. By analogy, we propose that every dataset be accompanied with a datasheet that documents its motivation, composition, collection process, recommended uses, and so on. Datasheets for datasets will facilitate better communication between dataset creators and dataset consumers, and encourage the machine learning community to prioritize transparency and accountability.

Journal ArticleDOI
TL;DR: In patients with advanced IDH1‐mutated relapsed or refractory AML, ivosidenib at a dose of 500 mg daily was associated with a low frequency of grade 3 or higher treatment‐related adverse events and with transfusion independence, durable remissions, and molecular remissions in some patients with complete remission.
Abstract: Background Mutations in the gene encoding isocitrate dehydrogenase 1 (IDH1) occur in 6 to 10% of patients with acute myeloid leukemia (AML). Ivosidenib (AG-120) is an oral, targeted, small-molecule inhibitor of mutant IDH1. Methods We conducted a phase 1 dose-escalation and dose-expansion study of ivosidenib monotherapy in IDH1-mutated AML. Safety and efficacy were assessed in all treated patients. The primary efficacy population included patients with relapsed or refractory AML receiving 500 mg of ivosidenib daily with at least 6 months of follow-up. Results Overall, 258 patients received ivosidenib and had safety outcomes assessed. Among patients with relapsed or refractory AML (179 patients), treatment-related adverse events of grade 3 or higher that occurred in at least 3 patients were prolongation of the QT interval (in 7.8% of the patients), the IDH differentiation syndrome (in 3.9%), anemia (in 2.2%), thrombocytopenia or a decrease in the platelet count (in 3.4%), and leukocytosis (in 1.7%...

Proceedings ArticleDOI
08 Oct 2018
TL;DR: TVM as discussed by the authors is a compiler that exposes graph-level and operator-level optimizations to provide performance portability to deep learning workloads across diverse hardware back-ends, such as mobile phones, embedded devices, and accelerators.
Abstract: There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narrow range of server-class GPUs. Deploying workloads to new platforms - such as mobile phones, embedded devices, and accelerators (e.g., FPGAs, ASICs) - requires significant manual effort. We propose TVM, a compiler that exposes graph-level and operator-level optimizations to provide performance portability to deep learning workloads across diverse hardware back-ends. TVM solves optimization challenges specific to deep learning, such as high-level operator fusion, mapping to arbitrary hardware primitives, and memory latency hiding. It also automates optimization of low-level programs to hardware characteristics by employing a novel, learning-based cost modeling method for rapid exploration of code optimizations. Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs. We also demonstrate TVM's ability to target new accelerator back-ends, such as the FPGA-based generic deep learning accelerator. The system is open sourced and in production use inside several major companies.

Journal ArticleDOI
TL;DR: It is shown that AF4 can serve as an improved analytical tool for isolating extracellular vesicles and addressing the complexities of heterogeneous nanoparticle subpopulations, and three nanoparticle subsets demonstrated diverse organ biodistribution patterns, suggesting distinct biological functions.
Abstract: The heterogeneity of exosomal populations has hindered our understanding of their biogenesis, molecular composition, biodistribution and functions. By employing asymmetric flow field-flow fractionation (AF4), we identified two exosome subpopulations (large exosome vesicles, Exo-L, 90–120 nm; small exosome vesicles, Exo-S, 60–80 nm) and discovered an abundant population of non-membranous nanoparticles termed ‘exomeres’ (~35 nm). Exomere proteomic profiling revealed an enrichment in metabolic enzymes and hypoxia, microtubule and coagulation proteins as well as specific pathways, such as glycolysis and mTOR signalling. Exo-S and Exo-L contained proteins involved in endosomal function and secretion pathways, and mitotic spindle and IL-2/STAT5 signalling pathways, respectively. Exo-S, Exo-L and exomeres each had unique N-glycosylation, protein, lipid, DNA and RNA profiles and biophysical properties. These three nanoparticle subsets demonstrated diverse organ biodistribution patterns, suggesting distinct biological functions. This study demonstrates that AF4 can serve as an improved analytical tool for isolating extracellular vesicles and addressing the complexities of heterogeneous nanoparticle subpopulations.

Journal ArticleDOI
TL;DR: Idiopathic Pulmonary Fibrosis IdiopATHic pulmonary fibrosis appears to be increasing in incidence, and it requires early recognition and intervention with supportive care and pharmacologic agents to forestall its progression.
Abstract: Idiopathic Pulmonary Fibrosis Idiopathic pulmonary fibrosis appears to be increasing in incidence. It requires early recognition and intervention with supportive care and pharmacologic agents to forestall its progression. Lung transplantation may be curative, but the disease may recur in transplanted lungs.

Journal ArticleDOI
06 Jun 2018-Nature
TL;DR: The genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study is characterized, and it is shown that protein quantitative trait loci overlap with gene expression quantitative traits, as well as with disease-associated loci, and evidence that protein biomarkers have causal roles in disease is found.
Abstract: Although plasma proteins have important roles in biological processes and are the direct targets of many drugs, the genetic factors that control inter-individual variation in plasma protein levels are not well understood. Here we characterize the genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study. We identify 1,927 genetic associations with 1,478 proteins, a fourfold increase on existing knowledge, including trans associations for 1,104 proteins. To understand the consequences of perturbations in plasma protein levels, we apply an integrated approach that links genetic variation with biological pathway, disease, and drug databases. We show that protein quantitative trait loci overlap with gene expression quantitative trait loci, as well as with disease-associated loci, and find evidence that protein biomarkers have causal roles in disease using Mendelian randomization analysis. By linking genetic factors to diseases via specific proteins, our analyses highlight potential therapeutic targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

Journal ArticleDOI
TL;DR: The results presented in this report reflect the 4-year update of the ongoing study with a database lock date of May 10, 2018.
Abstract: Summary Background Previously reported results from the phase 3 CheckMate 067 trial showed a significant improvement in objective responses, progression-free survival, and overall survival with nivolumab plus ipilimumab or nivolumab alone compared with ipilimumab alone in patients with advanced melanoma. The aim of this report is to provide 4-year updated efficacy and safety data from this study. Methods In this phase 3 trial, eligible patients were aged 18 years or older with previously untreated, unresectable, stage III or stage IV melanoma, known BRAFV600 mutation status, and an Eastern Cooperative Oncology Group performance status of 0 or 1. Patients were randomly assigned 1:1:1 to receive intravenous nivolumab 1 mg/kg plus ipilimumab 3 mg/kg every 3 weeks for four doses, followed by nivolumab 3 mg/kg every 2 weeks, or nivolumab 3 mg/kg every 2 weeks plus placebo, or ipilimumab 3 mg/kg every 3 weeks for four doses plus placebo. Randomisation was done via an interactive voice response system with a permuted block schedule (block size of six) and stratification by PD-L1 status, BRAF mutation status, and metastasis stage. The patients, investigators, study site staff, and study funder were masked to the study drug administered. The co-primary endpoints were progression-free survival and overall survival. Efficacy analyses were done on the intention-to-treat population, whereas safety was assessed in all patients who received at least one dose of study drug. The results presented in this report reflect the 4-year update of the ongoing study with a database lock date of May 10, 2018. This study is registered with ClinicalTrials.gov, number NCT01844505. Findings Between July 3, 2013, and March 31, 2014, 945 patients were enrolled and randomly assigned to nivolumab plus ipilimumab (n=314), nivolumab (n=316), or ipilimumab (n=315). Median follow-up was 46·9 months (IQR 10·9–51·8) in the nivolumab plus ipilimumab group, 36·0 months (10·5–51·4) in the nivolumab group, and 18·6 months (7·6–49·5) in the ipilimumab group. At a minimum follow-up of 48 months from the date that the final patient was enrolled and randomised, median overall survival was not reached (95% CI 38·2–not reached) in the nivolumab plus ipilimumab group, 36·9 months (28·3–not reached) in the nivolumab group, and 19·9 months (16·9–24·6) in the ipilimumab group. The hazard ratio for death for the combination versus ipilimumab was 0·54 (95% CI 0·44–0·67; p Interpretation The results of this analysis at 4 years of follow-up show that a durable, sustained survival benefit can be achieved with first-line nivolumab plus ipilimumab or nivolumab alone in patients with advanced melanoma. Funding Bristol-Myers Squibb.

Book
01 Dec 2018
TL;DR: This chapter presents a comprehensive review of the more recent developments in CMV biology and biochemistry that can be used as a reference work for general virologists and plant pathologists, as well as those specializing in the molecular biology of CMV and/or other multicomponent plant viruses.
Abstract: Publisher Summary Cucumber mosaic virus (CMV), the type member of the cucumovirus group, was first reported in 1916 as the causal agent of a disease of cucumber and muskmelon in Michigan and cucumber in New York. Since then, CMV has been found in most countries of the world, predominantly in the temperate zones, but increasingly more often in the tropical countries. CMV has the largest host range of any virus. The number of plant species identified as hosts for CMV has increased steadily over the past 60 years. The highlights of the more recent research include the following: (1) the complete nucleotide sequence of the genome of three strains of CMV has been determined, as well as nucleotide sequences of individual RNAs of eight other CMV strains, (2) the CMV replicase has been purified to homogeneity, and it functions in vitro to synthesize CMV RNA de novo , (3) infectious transcripts have been synthesized from full-length cDNA clones of the three strains of CMV, (4) these biologically active cDNAs are being used to map sequences involved in replication, movement, pathogenesis, resistance, and vector transmission. Biologically active cDNA clones of the satellite RNAs of CMV have been produced in seven laboratories and sequences involved in replication and pathogenicity have/are being identified, (5) finally, transgenic plants have been produced expressing either the CMV coat protein gene or satellite RNA sequences that show to protect such plants from infection by CMV. This chapter, while focusing on the more recent developments in CMV biology and biochemistry, also covers some of the same ground albeit in brief. The chapter presents a comprehensive review that can be used as a reference work for general virologists and plant pathologists, as well as those specializing in the molecular biology of CMV and/or other multicomponent plant viruses.

Posted Content
TL;DR: A Multimodal Unsupervised Image-to-image Translation (MUNIT) framework that assumes that the image representation can be decomposed into a content code that is domain-invariant, and a style code that captures domain-specific properties.
Abstract: Unsupervised image-to-image translation is an important and challenging problem in computer vision. Given an image in the source domain, the goal is to learn the conditional distribution of corresponding images in the target domain, without seeing any pairs of corresponding images. While this conditional distribution is inherently multimodal, existing approaches make an overly simplified assumption, modeling it as a deterministic one-to-one mapping. As a result, they fail to generate diverse outputs from a given source domain image. To address this limitation, we propose a Multimodal Unsupervised Image-to-image Translation (MUNIT) framework. We assume that the image representation can be decomposed into a content code that is domain-invariant, and a style code that captures domain-specific properties. To translate an image to another domain, we recombine its content code with a random style code sampled from the style space of the target domain. We analyze the proposed framework and establish several theoretical results. Extensive experiments with comparisons to the state-of-the-art approaches further demonstrates the advantage of the proposed framework. Moreover, our framework allows users to control the style of translation outputs by providing an example style image. Code and pretrained models are available at this https URL

Journal ArticleDOI
17 Jan 2018-Nature
TL;DR: It is shown that chromosomally unstable tumour cells co-opt chronic activation of innate immune pathways to spread to distant organs by sustaining a tumour cell-autonomous response to cytosolic DNA.
Abstract: Chromosomal instability is a hallmark of cancer that results from ongoing errors in chromosome segregation during mitosis. Although chromosomal instability is a major driver of tumour evolution, its role in metastasis has not been established. Here we show that chromosomal instability promotes metastasis by sustaining a tumour cell-autonomous response to cytosolic DNA. Errors in chromosome segregation create a preponderance of micronuclei whose rupture spills genomic DNA into the cytosol. This leads to the activation of the cGAS-STING (cyclic GMP-AMP synthase-stimulator of interferon genes) cytosolic DNA-sensing pathway and downstream noncanonical NF-κB signalling. Genetic suppression of chromosomal instability markedly delays metastasis even in highly aneuploid tumour models, whereas continuous chromosome segregation errors promote cellular invasion and metastasis in a STING-dependent manner. By subverting lethal epithelial responses to cytosolic DNA, chromosomally unstable tumour cells co-opt chronic activation of innate immune pathways to spread to distant organs.

Posted ContentDOI
Evan Bolyen1, Jai Ram Rideout1, Matthew R. Dillon1, Nicholas A. Bokulich1, Christian C. Abnet, Gabriel A. Al-Ghalith2, Harriet Alexander3, Harriet Alexander4, Eric J. Alm5, Manimozhiyan Arumugam6, Francesco Asnicar7, Yang Bai8, Jordan E. Bisanz9, Kyle Bittinger10, Asker Daniel Brejnrod6, Colin J. Brislawn11, C. Titus Brown3, Benjamin J. Callahan12, Andrés Mauricio Caraballo-Rodríguez13, John Chase1, Emily K. Cope1, Ricardo Silva13, Pieter C. Dorrestein13, Gavin M. Douglas14, Daniel M. Durall15, Claire Duvallet5, Christian F. Edwardson16, Madeleine Ernst13, Mehrbod Estaki15, Jennifer Fouquier17, Julia M. Gauglitz13, Deanna L. Gibson15, Antonio Gonzalez18, Kestrel Gorlick1, Jiarong Guo19, Benjamin Hillmann2, Susan Holmes20, Hannes Holste18, Curtis Huttenhower21, Curtis Huttenhower22, Gavin A. Huttley23, Stefan Janssen24, Alan K. Jarmusch13, Lingjing Jiang18, Benjamin D. Kaehler23, Kyo Bin Kang13, Kyo Bin Kang25, Christopher R. Keefe1, Paul Keim1, Scott T. Kelley26, Dan Knights2, Irina Koester13, Irina Koester18, Tomasz Kosciolek18, Jorden Kreps1, Morgan G. I. Langille14, Joslynn S. Lee27, Ruth E. Ley28, Ruth E. Ley29, Yong-Xin Liu8, Erikka Loftfield, Catherine A. Lozupone17, Massoud Maher18, Clarisse Marotz18, Bryan D Martin30, Daniel McDonald18, Lauren J. McIver21, Lauren J. McIver22, Alexey V. Melnik13, Jessica L. Metcalf31, Sydney C. Morgan15, Jamie Morton18, Ahmad Turan Naimey1, Jose A. Navas-Molina18, Jose A. Navas-Molina32, Louis-Félix Nothias13, Stephanie B. Orchanian18, Talima Pearson1, Samuel L. Peoples30, Samuel L. Peoples33, Daniel Petras13, Mary L. Preuss34, Elmar Pruesse17, Lasse Buur Rasmussen6, Adam R. Rivers35, Ii Michael S Robeson36, Patrick Rosenthal34, Nicola Segata7, Michael Shaffer17, Arron Shiffer1, Rashmi Sinha, Se Jin Song18, John R. Spear37, Austin D. Swafford18, Luke R. Thompson38, Luke R. Thompson39, Pedro J. Torres26, Pauline Trinh30, Anupriya Tripathi18, Anupriya Tripathi13, Peter J. Turnbaugh9, Sabah Ul-Hasan40, Justin J. J. van der Hooft41, Fernando Vargas18, Yoshiki Vázquez-Baeza18, Emily Vogtmann, Max von Hippel42, William A. Walters29, Yunhu Wan, Mingxun Wang13, Jonathan Warren43, Kyle C. Weber35, Kyle C. Weber44, Chase Hd Williamson1, Amy D. Willis30, Zhenjiang Zech Xu18, Jesse R. Zaneveld30, Yilong Zhang45, Rob Knight18, J. Gregory Caporaso1 
24 Oct 2018-PeerJ
TL;DR: QIIME 2 provides new features that will drive the next generation of microbiome research, including interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.
Abstract: We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

Journal ArticleDOI
TL;DR: VoxelMorph promises to speed up medical image analysis and processing pipelines while facilitating novel directions in learning-based registration and its applications and demonstrates that the unsupervised model’s accuracy is comparable to the state-of-the-art methods while operating orders of magnitude faster.
Abstract: We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich deformation models. In contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images. We parameterize the function via a convolutional neural network (CNN), and optimize the parameters of the neural network on a set of images. Given a new pair of scans, VoxelMorph rapidly computes a deformation field by directly evaluating the function. In this work, we explore two different training strategies. In the first (unsupervised) setting, we train the model to maximize standard image matching objective functions that are based on the image intensities. In the second setting, we leverage auxiliary segmentations available in the training data. We demonstrate that the unsupervised model's accuracy is comparable to state-of-the-art methods, while operating orders of magnitude faster. We also show that VoxelMorph trained with auxiliary data improves registration accuracy at test time, and evaluate the effect of training set size on registration. Our method promises to speed up medical image analysis and processing pipelines, while facilitating novel directions in learning-based registration and its applications. Our code is freely available at this http URL.

Proceedings Article
02 Jul 2018
TL;DR: In this article, a new model-poisoning methodology based on model replacement is proposed to poison a global model in federated learning, which can reach 100% accuracy on the backdoor task.
Abstract: Federated learning enables thousands of participants to construct a deep learning model without sharing their private training data with each other For example, multiple smartphones can jointly train a next-word predictor for keyboards without revealing what individual users type We demonstrate that any participant in federated learning can introduce hidden backdoor functionality into the joint global model, eg, to ensure that an image classifier assigns an attacker-chosen label to images with certain features, or that a word predictor completes certain sentences with an attacker-chosen word We design and evaluate a new model-poisoning methodology based on model replacement An attacker selected in a single round of federated learning can cause the global model to immediately reach 100% accuracy on the backdoor task We evaluate the attack under different assumptions for the standard federated-learning tasks and show that it greatly outperforms data poisoning Our generic constrain-and-scale technique also evades anomaly detection-based defenses by incorporating the evasion into the attacker's loss function during training