UC San Diego Health System

We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online.

In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU threshold, e.g. 0.5, usually produces noisy detections. However, detection performance tends to degrade with increasing the IoU thresholds. Two main factors are responsible for this: 1) overfitting during training, due to exponentially vanishing positive samples, and 2) inference-time mismatch between the IoUs for which the detector is optimal and those of the input hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, is proposed to address these problems. It consists of a sequence of detectors trained with increasing IoU thresholds, to be sequentially more selective against close false positives. The detectors are trained stage by stage, leveraging the observation that the output of a detector is a good distribution for training the next higher quality detector. The resampling of progressively improved hypotheses guarantees that all detectors have a positive set of examples of equivalent size, reducing the overfitting problem. The same cascade procedure is applied at inference, enabling a closer match between the hypotheses and the detector quality of each stage. A simple implementation of the Cascade R-CNN is shown to surpass all single-model object detectors on the challenging COCO dataset. Experiments also show that the Cascade R-CNN is widely applicable across detector architectures, achieving consistent gains independently of the baseline detector strength. The code is available at https://github.com/zhaoweicai/cascade-rcnn.

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

Abstract Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature 1 . Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium 2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses 3–15 , enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated—but distinct—DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer.

Abstract Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale 1–3 . Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter 4 ; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation 5,6 ; analyses timings and patterns of tumour evolution 7 ; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity 8,9 ; and evaluates a range of more-specialized features of cancer genomes 8,10–18 .

Generation of Swine Flu As the newly emerged influenza virus starts its journey to infect the world's human population, the genetic secrets of the 2009 outbreak of swine influenza A(H1N1) are being revealed. In extensive phylogenetic analyses, Garten et al. (p. 197 , published online 22 May) confirm that of the eight elements of the virus, the basic components encoded by the hemagglutinin, nucleoprotein, and nonstructural genes originated in birds and transferred to pigs in 1918. Subsequently, these formed a triple reassortant with the RNA polymerase PB1 that transferred from birds in 1968 to humans and then to pigs in 1998, coupled with RNA polymerases PA and PB2 that transferred from birds to pigs in 1998. The neuraminidase and matrix protein genes that complete the virus came from birds and entered pigs in 1979. The analysis offers insights into drug susceptibility and virulence, as well as raising the possibility of hitherto unknown factors determining host specificity. A significant question is, what is the potential for the H1 component of the current seasonal flu vaccine to act as a booster? Apart from the need for ongoing sequencing to monitor for the emergence of new reassortants, future pig populations need to be closely monitored for emerging influenza viruses.

BACKGROUND: The incidence and severity of herpes zoster and postherpetic neuralgia increase with age in association with a progressive decline in cell-mediated immunity to varicella-zoster virus (VZV). We tested the hypothesis that vaccination against VZV would decrease the incidence, severity, or both of herpes zoster and postherpetic neuralgia among older adults. METHODS: We enrolled 38,546 adults 60 years of age or older in a randomized, double-blind, placebo-controlled trial of an investigational live attenuated Oka/Merck VZV vaccine ("zoster vaccine"). Herpes zoster was diagnosed according to clinical and laboratory criteria. The pain and discomfort associated with herpes zoster were measured repeatedly for six months. The primary end point was the burden of illness due to herpes zoster, a measure affected by the incidence, severity, and duration of the associated pain and discomfort. The secondary end point was the incidence of postherpetic neuralgia. RESULTS: More than 95 percent of the subjects continued in the study to its completion, with a median of 3.12 years of surveillance for herpes zoster. A total of 957 confirmed cases of herpes zoster (315 among vaccine recipients and 642 among placebo recipients) and 107 cases of postherpetic neuralgia (27 among vaccine recipients and 80 among placebo recipients) were included in the efficacy analysis. The use of the zoster vaccine reduced the burden of illness due to herpes zoster by 61.1 percent (P<0.001), reduced the incidence of postherpetic neuralgia by 66.5 percent (P<0.001), and reduced the incidence of herpes zoster by 51.3 percent (P<0.001). Reactions at the injection site were more frequent among vaccine recipients but were generally mild. CONCLUSIONS: The zoster vaccine markedly reduced morbidity from herpes zoster and postherpetic neuralgia among older adults.

Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities. We construct BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks. BDD100K opens the door for future studies in this important venue.

Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have led to significant improvement over previous semantic segmentation systems. Here we show how to improve pixel-wise semantic segmentation by manipulating convolution-related operations that are of both theoretical and practical value. First, we design dense upsampling convolution (DUC) to generate pixel-level prediction, which is able to capture and decode more detailed information that is generally missing in bilinear upsampling. Second, we propose a hybrid dilated convolution (HDC) framework in the encoding phase. This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue"caused by the standard dilated convolution operation. We evaluate our approaches thoroughly on the Cityscapes dataset, and achieve a state-of-art result of 80.1% mIOU in the test set at the time of submission. We also have achieved state-of-theart overall on the KITTI road estimation benchmark and the PASCAL VOC2012 segmentation task. Our source code can be found at https://github.com/TuSimple/TuSimple-DUC.

Randomized Controlled Trials (RCTs) are increasingly popular in the social sciences, not only in medicine. We argue that the lay public, and sometimes researchers, put too much trust in RCTs over other methods of investigation. Contrary to frequent claims in the applied literature, randomization does not equalize everything other than the treatment in the treatment and control groups, it does not automatically deliver a precise estimate of the average treatment effect (ATE), and it does not relieve us of the need to think about (observed or unobserved) covariates. Finding out whether an estimate was generated by chance is more difficult than commonly believed. At best, an RCT yields an unbiased estimate, but this property is of limited practical value. Even then, estimates apply only to the sample selected for the trial, often no more than a convenience sample, and justification is required to extend the results to other groups, including any population to which the trial sample belongs, or to any individual, including an individual in the trial. Demanding 'external validity' is unhelpful because it expects too much of an RCT while undervaluing its potential contribution. RCTs do indeed require minimal assumptions and can operate with little prior knowledge. This is an advantage when persuading distrustful audiences, but it is a disadvantage for cumulative scientific progress, where prior knowledge should be built upon, not discarded. RCTs can play a role in building scientific knowledge and useful predictions but they can only do so as part of a cumulative program, combining with other methods, including conceptual and theoretical development, to discover not 'what works', but 'why things work'.

BACKGROUND AND AIMS: Acute appendicitis (AA) is among the most common causes of acute abdominal pain. Diagnosis of AA is still challenging and some controversies on its management are still present among different settings and practice patterns worldwide. In July 2015, the World Society of Emergency Surgery (WSES) organized in Jerusalem the first consensus conference on the diagnosis and treatment of AA in adult patients with the intention of producing evidence-based guidelines. An updated consensus conference took place in Nijemegen in June 2019 and the guidelines have now been updated in order to provide evidence-based statements and recommendations in keeping with varying clinical practice: use of clinical scores and imaging in diagnosing AA, indications and timing for surgery, use of non-operative management and antibiotics, laparoscopy and surgical techniques, intra-operative scoring, and peri-operative antibiotic therapy. METHODS: This executive manuscript summarizes the WSES guidelines for the diagnosis and treatment of AA. Literature search has been updated up to 2019 and statements and recommendations have been developed according to the GRADE methodology. The statements were voted, eventually modified, and finally approved by the participants to the consensus conference and by the board of co-authors, using a Delphi methodology for voting whenever there was controversy on a statement or a recommendation. Several tables highlighting the research topics and questions, search syntaxes, and the statements and the WSES evidence-based recommendations are provided. Finally, two different practical clinical algorithms are provided in the form of a flow chart for both adults and pediatric (< 16 years old) patients. CONCLUSIONS: The 2020 WSES guidelines on AA aim to provide updated evidence-based statements and recommendations on each of the following topics: (1) diagnosis, (2) non-operative management for uncomplicated AA, (3) timing of appendectomy and in-hospital delay, (4) surgical treatment, (5) intra-operative grading of AA, (6) ,management of perforated AA with phlegmon or abscess, and (7) peri-operative antibiotic therapy.

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD-abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions.

Abstract Cancer develops through a process of somatic evolution 1,2 . Sequencing data from a single biopsy represent a snapshot of this process that can reveal the timing of specific genomic aberrations and the changing influence of mutational processes 3 . Here, by whole-genome sequencing analysis of 2,658 cancers as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) 4 , we reconstruct the life history and evolution of mutational processes and driver mutation sequences of 38 types of cancer. Early oncogenesis is characterized by mutations in a constrained set of driver genes, and specific copy number gains, such as trisomy 7 in glioblastoma and isochromosome 17q in medulloblastoma. The mutational spectrum changes significantly throughout tumour evolution in 40% of samples. A nearly fourfold diversification of driver genes and increased genomic instability are features of later stages. Copy number alterations often occur in mitotic crises, and lead to simultaneous gains of chromosomal segments. Timing analyses suggest that driver mutations often precede diagnosis by many years, if not decades. Together, these results determine the evolutionary trajectories of cancer, and highlight opportunities for early cancer detection.

PURPOSE: In the phase III CheckMate 067 trial, durable clinical benefit was demonstrated previously with nivolumab plus ipilimumab and nivolumab alone versus ipilimumab. Here, we report 6.5-year efficacy and safety outcomes. PATIENTS AND METHODS: Patients with previously untreated unresectable stage III or stage IV melanoma were randomly assigned 1:1:1 to receive nivolumab 1 mg/kg plus ipilimumab 3 mg/kg once every 3 weeks (four doses) followed by nivolumab 3 mg/kg once every 2 weeks (n = 314), nivolumab 3 mg/kg once every 2 weeks (n = 316), or ipilimumab 3 mg/kg once every 3 weeks (four doses; n = 315). Coprimary end points were progression-free survival and overall survival (OS) with nivolumab plus ipilimumab or nivolumab versus ipilimumab. Secondary end points included objective response rate, descriptive efficacy assessments of nivolumab plus ipilimumab versus nivolumab alone, and safety. Melanoma-specific survival (MSS; descriptive analysis), which excludes deaths unrelated to melanoma, was also evaluated. RESULTS: -wild-type tumors, respectively. In patients who discontinued treatment, the median treatment-free interval was 27.6, 2.3, and 1.9 months, respectively. Since the 5-year analysis, no new safety signals were observed. CONCLUSION: These 6.5-year CheckMate 067 results, which include the longest median OS in a phase III melanoma trial reported to date and the first report of MSS, showed durable, improved clinical outcomes with nivolumab plus ipilimumab or nivolumab versus ipilimumab in patients with advanced melanoma and, in descriptive analyses, with the combination over nivolumab monotherapy.

Cervical cancer is a malignant epithelial tumor that forms in the uterine cervix. Most cases of cervical cancer are preventable through human papilloma virus (HPV) vaccination, routine screening, and treatment of precancerous lesions. However, due to inadequate screening protocols in many regions of the world, cervical cancer remains the fourth-most common cancer in women globally. The complete NCCN Guidelines for Cervical Cancer provide recommendations for the diagnosis, evaluation, and treatment of cervical cancer. This manuscript discusses guiding principles for the workup, staging, and treatment of early stage and locally advanced cervical cancer, as well as evidence for these recommendations. For recommendations regarding treatment of recurrent or metastatic disease, please see the full guidelines on NCCN.org.

Ductal adenocarcinoma and its variants account for most pancreatic malignancies. High-quality multiphase imaging can help to preoperatively distinguish between patients eligible for resection with curative intent and those with unresectable disease. Systemic therapy is used in the neoadjuvant or adjuvant pancreatic cancer setting, as well as in the management of locally advanced unresectable and metastatic disease. Clinical trials are critical for making progress in treatment of pancreatic cancer. The NCCN Guidelines for Pancreatic Adenocarcinoma focus on diagnosis and treatment with systemic therapy, radiation therapy, and surgical resection.

The NCCN Guidelines for Colon Cancer provide recommendations regarding diagnosis, pathologic staging, surgical management, perioperative treatment, surveillance, management of recurrent and metastatic disease, and survivorship. These NCCN Guidelines Insights summarize the NCCN Colon Cancer Panel discussions for the 2018 update of the guidelines regarding risk stratification and adjuvant treatment for patients with stage III colon cancer, and treatment of BRAF V600E mutation–positive metastatic colorectal cancer with regimens containing vemurafenib.

Recent research has shown that adipose tissue is not simply an inert storage depot for lipids but is also an important endocrine organ that plays a key role in the integration of endocrine, metabolic, and inflammatory signals for the control of energy homeostasis. The adipocyte has been shown to secrete a variety of bioactive proteins into the circulation. These secretory proteins, which have been collectively named adipocytokines (1), include leptin (2), tumor necrosis factor (TNF)-α (3), plasminogen-activator inhibitor type 1 (PAI-1) (4), adipsin (5), resistin (6), and adiponectin (7). Adiponectin, the gene product of the adipose most abundant gene transcript 1 (apM1) (7), is a novel and important member of the adipocytokine family. Adiponectin cDNA was first isolated by large-scale random sequencing of the human adipose tissue cDNA library (7). It is a collagen-like protein that is exclusively synthesized in white adipose tissue, is induced during adipocyte differentiation, and circulates at relatively high (microgram/milliliter) concentrations in the serum. Both murine and human forms of adiponectin have been isolated independently by several groups, and various descriptive names have been given to the same compound by different investigators: adipocyte complement-related protein of 30 kilodalton (Acrp30) (8), Adipo Q (9), and gelatin binding protein of 28 kilodalton (GBP28) (10). The former two are murine analogs and the latter the human counterpart. Throughout this review, we will be referring to the protein by its most commonly used name, adiponectin. Adiponectin has been postulated to play an important role in the modulation of glucose and lipid metabolism in insulin-sensitive tissues in both humans and animals. Decreased circulating adiponectin levels have been demonstrated in genetic and diet-induced murine models of obesity (11), as well as in diet-induced forms of human obesity (12). Low adiponectin levels have also been strongly implicated in the development of insulin resistance …

Abstract A key mutational process in cancer is structural variation, in which rearrangements delete, amplify or reorder genomic segments that range in size from kilobases to whole chromosomes 1–7 . Here we develop methods to group, classify and describe somatic structural variants, using data from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), which aggregated whole-genome sequencing data from 2,658 cancers across 38 tumour types 8 . Sixteen signatures of structural variation emerged. Deletions have a multimodal size distribution, assort unevenly across tumour types and patients, are enriched in late-replicating regions and correlate with inversions. Tandem duplications also have a multimodal size distribution, but are enriched in early-replicating regions—as are unbalanced translocations. Replication-based mechanisms of rearrangement generate varied chromosomal structures with low-level copy-number gains and frequent inverted rearrangements. One prominent structure consists of 2–7 templates copied from distinct regions of the genome strung together within one locus. Such cycles of templated insertions correlate with tandem duplications, and—in liver cancer—frequently activate the telomerase gene TERT . A wide variety of rearrangement processes are active in cancer, which generate complex configurations of the genome upon which selection can act.

We present the first gesture recognition system implemented end-to-end on event-based hardware, using a TrueNorth neurosynaptic processor to recognize hand gestures in real-time at low power from events streamed live by a Dynamic Vision Sensor (DVS). The biologically inspired DVS transmits data only when a pixel detects a change, unlike traditional frame-based cameras which sample every pixel at a fixed frame rate. This sparse, asynchronous data representation lets event-based cameras operate at much lower power than frame-based cameras. However, much of the energy efficiency is lost if, as in previous work, the event stream is interpreted by conventional synchronous processors. Here, for the first time, we process a live DVS event stream using TrueNorth, a natively event-based processor with 1 million spiking neurons. Configured here as a convolutional neural network (CNN), the TrueNorth chip identifies the onset of a gesture with a latency of 105 ms while consuming less than 200 mW. The CNN achieves 96.5% out-of-sample accuracy on a newly collected DVS dataset (DvsGesture) comprising 11 hand gesture categories from 29 subjects under 3 illumination conditions.