Elementos de datos comunes para la artrogriposis múltiple congénita: Un marco internacional.

OBJETIVO: Para facilitar los estudios multicéntricos y la investigación clínica internacional, este estudio pretende identificar de forma consensuada los elementos de datos estandarizados para la artrogriposis múltiple congénita (AMC). MÉTODO: Estudio de métodos mixtos de grupos de discusión y tres rondas de encuestas Delphi modificadas para llegar a un consenso utilizando dos escalas de clasificación por niveles. RESULTADOS: En total, 45 expertos clínicos y adultos con experiencia vivida (incluidos 12 miembros de un consorcio de AMC) participaron en este estudio procedentes de 11 países: Norteamérica, Europa y Australia. Los CDEs incluyen 321 elementos de datos y 19 medidas estandarizadas en varios dominios desde el desarrollo fetal hasta la edad adulta. Los elementos de datos relativos a los rasgos fenotípicos del CDEs se mapearon de acuerdo con la Ontología de Fenotipos Humanos. Se identificaron como principales facilitadores la estructura de gobernanza universal, protocolos operados de forma local y los planes de sostenibilidad, mientras que los principales obstáculos observados son la capacidad limitada para compartir datos y la necesidad de una infraestructura informática federada. INTERPRETACIÓN: La recopilación de datos sistemáticos sobre la AMC mediante CDEs permitirá investigar las vías etiológicas, describir el perfil epidemiológico y establecer correlaciones genotipo­fenotipo de forma estandarizada. Los CDEs propuestos facilitarán las colaboraciones multidisciplinares internacionales mejorando los estudios a gran escala y las oportunidades para compartir datos, translación de conocimiento y difusión.

Éléments de données communs pour l'arthrogrypose multiple congénitale: Un cadre international.

OBJECTIF: Afin de faciliter les études multisites et la recherche clinique d'envergure internationale, cette étude a pour but d'identifier des éléments de données communs (EDCs) normalisés et fondés sur un consensus pour l'arthrogrypose multiple congénitale (AMC). MÉTHODE: Une étude à méthodes mixtes comprenant plusieurs groupes de discussion et trois séries d'enquêtes Delphi modifiées pour parvenir à un consensus ont été menées. RÉSULTATS: Dans l'ensemble, 45 experts cliniques ainsi qu'adultes ayant une expérience vécue (dont 12 membres d'un consortium d'AMC) ont participé à cette étude à travers 11 pays en Amérique du Nord, Europe et Australie. Les EDCs comprennent 321 éléments de données et 19 mesures standardisées dans divers domaines, du développement du fœtus à l'âge adulte. Les éléments de données relatifs aux traits phénotypiques de l'AMC ont été cartographiés conformément à l'ontologie du phénotype humain (HPO). Une structure de gouvernance universelle, des protocoles de fonctionnement et des plans de développement durable ont été identifiés comme les principaux facilitateurs considérant que la capacité limitée de partage des données et la nécessité d'une infrastructure informatique fédérée étaient les principaux obstacles. INTERPRÉTATION: Une collecte de données systématiques sur l'AMC à l'aide d'EDCs permettra d'étudier sur les voies étiologiques, décrire le profil épidémiologique, et établir des corrélations génotype­phénotype de manière standardisée. Les EDCs proposés faciliteront les collaborations internationales multidisciplinaires en améliorant à grande échelle les études multicentriques, les possibilités de partage des données, ainsi que le transfert et la diffusion des connaissances.

Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research.

A growing body of research supports stromal tumour-infiltrating lymphocyte (TIL) density in breast cancer to be a robust prognostic and predicive biomarker. The gold standard for stromal TIL density quantitation in breast cancer is pathologist visual assessment using haematoxylin and eosin-stained slides. Artificial intelligence/machine-learning algorithms are in development to automate the stromal TIL scoring process, and must be validated against a reference standard such as pathologist visual assessment. Visual TIL assessment may suffer from significant interobserver variability. To improve interobserver agreement, regulatory science experts at the US Food and Drug Administration partnered with academic pathologists internationally to create a freely available online continuing medical education (CME) course to train pathologists in assessing breast cancer stromal TILs using an interactive format with expert commentary. Here we describe and provide a user guide to this CME course, whose content was designed to improve pathologist accuracy in scoring breast cancer TILs. We also suggest subsequent steps to translate knowledge into clinical practice with proficiency testing.

Common data elements for arthrogryposis multiplex congenita: An international framework.

AIM: To facilitate multisite studies and international clinical research, this study aimed to identify consensus-based, standardized common data elements (CDEs) for arthrogryposis multiplex congenita (AMC). METHOD: A mixed-methods study comprising of several focus group discussions and three rounds of modified Delphi surveys to achieve consensus using two tiered-rating scales were conducted. RESULTS: Overall, 45 clinical experts and adults with lived experience (including 12 members of an AMC consortium) participated in this study from 11 countries in North America, Europe, and Australia. The CDEs include 321 data elements and 19 standardized measures across various domains from fetal development to adulthood. Data elements pertaining to AMC phenotypic traits were mapped according to the Human Phenotype Ontology. A universal governance structure, local operating protocols, and sustainability plans were identified as the main facilitators, whereas limited capacity for data sharing and the need for a federated informatics infrastructure were the main barriers. INTERPRETATION: Collection of systematic data on AMC using CDEs will allow investigations on etiological pathways, describe epidemiological profile, and establish genotype-phenotype correlations in a standardized manner. The proposed CDEs will facilitate international multidisciplinary collaborations by improving large-scale studies and opportunities for data sharing, knowledge translation, and dissemination.

2H-Thiopyran-2-thione sulfine, a compound for converting H2S to HSOH/H2S2 and increasing intracellular sulfane sulfur levels.

Reactive sulfane sulfur species such as persulfides (RSSH) and H2S2 are important redox regulators and closely linked to H2S signaling. However, the study of these species is still challenging due to their instability, high reactivity, and the lack of suitable donors to produce them. Herein we report a unique compound, 2H-thiopyran-2-thione sulfine (TTS), which can specifically convert H2S to HSOH, and then to H2S2 in the presence of excess H2S. Meanwhile, the reaction product 2H-thiopyran-2-thione (TT) can be oxidized to reform TTS by biological oxidants. The reaction mechanism of TTS is studied experimentally and computationally. TTS can be conjugated to proteins to achieve specific delivery, and the combination of TTS and H2S leads to highly efficient protein persulfidation. When TTS is applied in conjunction with established H2S donors, the corresponding donors of H2S2 (or its equivalents) are obtained. Cell-based studies reveal that TTS can effectively increase intracellular sulfane sulfur levels and compensate for certain aspects of sulfide:quinone oxidoreductase (SQR) deficiency. These properties make TTS a conceptually new strategy for the design of donors of reactive sulfane sulfur species.

Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models.

This work puts forth and demonstrates the utility of a reporting framework for collecting and evaluating annotations of medical images used for training and testing artificial intelligence (AI) models in assisting detection and diagnosis. AI has unique reporting requirements, as shown by the AI extensions to the Consolidated Standards of Reporting Trials (CONSORT) and Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklists and the proposed AI extensions to the Standards for Reporting Diagnostic Accuracy (STARD) and Transparent Reporting of a Multivariable Prediction model for Individual Prognosis or Diagnosis (TRIPOD) checklists. AI for detection and/or diagnostic image analysis requires complete, reproducible, and transparent reporting of the annotations and metadata used in training and testing data sets. In an earlier work by other researchers, an annotation workflow and quality checklist for computational pathology annotations were proposed. In this manuscript, we operationalize this workflow into an evaluable quality checklist that applies to any reader-interpreted medical images, and we demonstrate its use for an annotation effort in digital pathology. We refer to this quality framework as the Collection and Evaluation of Annotations for Reproducible Reporting of Artificial Intelligence (CLEARR-AI).

A cohort-based study of host gene expression: tumor suppressor and innate immune/inflammatory pathways associated with the HIV reservoir size.

The major barrier to an HIV cure is the HIV reservoir: latently-infected cells that persist despite effective antiretroviral therapy (ART). There have been few cohort-based studies evaluating host genomic or transcriptomic predictors of the HIV reservoir. We performed host RNA sequencing and HIV reservoir quantification (total DNA [tDNA], unspliced RNA [usRNA], intact DNA) from peripheral CD4+ T cells from 191 ART-suppressed people with HIV (PWH). After adjusting for nadir CD4+ count, timing of ART initiation, and genetic ancestry, we identified two host genes for which higher expression was significantly associated with smaller total DNA viral reservoir size, P3H3 and NBL1, both known tumor suppressor genes. We then identified 17 host genes for which lower expression was associated with higher residual transcription (HIV usRNA). These included novel associations with membrane channel (KCNJ2, GJB2), inflammasome (IL1A, CSF3, TNFAIP5, TNFAIP6, TNFAIP9, CXCL3, CXCL10), and innate immunity (TLR7) genes (FDR-adjusted q<0.05). Gene set enrichment analyses further identified significant associations of HIV usRNA with TLR4/microbial translocation (q = 0.006), IL-1/NRLP3 inflammasome (q = 0.008), and IL-10 (q = 0.037) signaling. Protein validation assays using ELISA and multiplex cytokine assays supported these observed inverse host gene correlations, with P3H3, IL-10, and TNF-α protein associations achieving statistical significance (p<0.05). Plasma IL-10 was also significantly inversely associated with HIV DNA (p = 0.016). HIV intact DNA was not associated with differential host gene expression, although this may have been due to a large number of undetectable values in our study. To our knowledge, this is the largest host transcriptomic study of the HIV reservoir. Our findings suggest that host gene expression may vary in response to the transcriptionally active reservoir and that changes in cellular proliferation genes may influence the size of the HIV reservoir. These findings add important data to the limited host genetic HIV reservoir studies to date.

MCPNet: a parallel maximum capacity-based genome-scale gene network construction framework.

MOTIVATION: Gene network reconstruction from gene expression profiles is a compute- and data-intensive problem. Numerous methods based on diverse approaches including mutual information, random forests, Bayesian networks, correlation measures, as well as their transforms and filters such as data processing inequality, have been proposed. However, an effective gene network reconstruction method that performs well in all three aspects of computational efficiency, data size scalability, and output quality remains elusive. Simple techniques such as Pearson correlation are fast to compute but ignore indirect interactions, while more robust methods such as Bayesian networks are prohibitively time consuming to apply to tens of thousands of genes. RESULTS: We developed maximum capacity path (MCP) score, a novel maximum-capacity-path-based metric to quantify the relative strengths of direct and indirect gene-gene interactions. We further present MCPNet, an efficient, parallelized gene network reconstruction software based on MCP score, to reverse engineer networks in unsupervised and ensemble manners. Using synthetic and real Saccharomyces cervisiae datasets as well as real Arabidopsis thaliana datasets, we demonstrate that MCPNet produces better quality networks as measured by AUPRC, is significantly faster than all other gene network reconstruction software, and also scales well to tens of thousands of genes and hundreds of CPU cores. Thus, MCPNet represents a new gene network reconstruction tool that simultaneously achieves quality, performance, and scalability requirements. AVAILABILITY AND IMPLEMENTATION: Source code freely available for download at https://doi.org/10.5281/zenodo.6499747 and https://github.com/AluruLab/MCPNet, implemented in C++ and supported on Linux.

A single-cell atlas reveals shared and distinct immune responses and metabolic profiles in SARS-CoV-2 and HIV-1 infections.

Introduction: Within the inflammatory immune response to viral infection, the distribution and cell type-specific profiles of immune cell populations and the immune-mediated viral clearance pathways vary according to the specific virus. Uncovering the immunological similarities and differences between viral infections is critical to understanding disease progression and developing effective vaccines and therapies. Insight into COVID-19 disease progression has been bolstered by the integration of single-cell (sc)RNA-seq data from COVID-19 patients with data from related viruses to compare immune responses. Expanding this concept, we propose that a high-resolution, systematic comparison between immune cells from SARS-CoV-2 infection and an inflammatory infectious disease with a different pathophysiology will provide a more comprehensive picture of the viral clearance pathways that underscore immunological and clinical differences between infections. Methods: Using a novel consensus single-cell annotation method, we integrate previously published scRNA-seq data from 111,566 single PBMCs from 7 COVID-19, 10 HIV-1+, and 3 healthy patients into a unified cellular atlas. We compare in detail the phenotypic features and regulatory pathways in the major immune cell clusters. Results: While immune cells in both COVID-19 and HIV-1+ cohorts show shared inflammation and disrupted mitochondrial function, COVID-19 patients exhibit stronger humoral immunity, broader IFN-I signaling, elevated Rho GTPase and mTOR pathway activity, and downregulated mitophagy. Discussion: Our results indicate that differential IFN-I signaling regulates the distinct immune responses in the two diseases, revealing insight into fundamental disease biology and potential therapeutic candidates.

Differences in expression of tumor suppressor, innate immune, inflammasome, and potassium/gap junction channel host genes significantly predict viral reservoir size during treated HIV infection.

The major barrier to an HIV cure is the persistence of infected cells that evade host immune surveillance despite effective antiretroviral therapy (ART). Most prior host genetic HIV studies have focused on identifying DNA polymorphisms (e.g., CCR5Δ32 , MHC class I alleles) associated with viral load among untreated "elite controllers" (~1% of HIV+ individuals who are able to control virus without ART). However, there have been few studies evaluating host genetic predictors of viral control for the majority of people living with HIV (PLWH) on ART. We performed host RNA sequencing and HIV reservoir quantification (total DNA, unspliced RNA, intact DNA) from peripheral CD4+ T cells from 191 HIV+ ART-suppressed non-controllers. Multivariate models included covariates for timing of ART initiation, nadir CD4+ count, age, sex, and ancestry. Lower HIV total DNA (an estimate of the total reservoir) was associated with upregulation of tumor suppressor genes NBL1 (q=0.012) and P3H3 (q=0.012). Higher HIV unspliced RNA (an estimate of residual HIV transcription) was associated with downregulation of several host genes involving inflammasome ( IL1A, CSF3, TNFAIP5, TNFAIP6, TNFAIP9 , CXCL3, CXCL10 ) and innate immune ( TLR7 ) signaling, as well as novel associations with potassium ( KCNJ2 ) and gap junction ( GJB2 ) channels, all q<0.05. Gene set enrichment analyses identified significant associations with TLR4/microbial translocation (q=0.006), IL-1ß/NRLP3 inflammasome (q=0.008), and IL-10 (q=0.037) signaling. HIV intact DNA (an estimate of the "replication-competent" reservoir) demonstrated trends with thrombin degradation ( PLGLB1 ) and glucose metabolism ( AGL ) genes, but data were (HIV intact DNA detected in only 42% of participants). Our findings demonstrate that among treated PLWH, that inflammation, innate immune responses, bacterial translocation, and tumor suppression/cell proliferation host signaling play a key role in the maintenance of the HIV reservoir during ART. Further data are needed to validate these findings, including functional genomic studies, and expanded epidemiologic studies in female, non-European cohorts. Author Summary: Although lifelong HIV antiretroviral therapy (ART) suppresses virus, the major barrier to an HIV cure is the persistence of infected cells that evade host immune surveillance despite effective ART, "the HIV reservoir." HIV eradication strategies have focused on eliminating residual virus to allow for HIV remission, but HIV cure trials to date have thus far failed to show a clinically meaningful reduction in the HIV reservoir. There is an urgent need for a better understanding of the host-viral dynamics during ART suppression to identify potential novel therapeutic targets for HIV cure. This is the first epidemiologic host gene expression study to demonstrate a significant link between HIV reservoir size and several well-known immunologic pathways (e.g., IL-1ß, TLR7, TNF-α signaling pathways), as well as novel associations with potassium and gap junction channels (Kir2.1, connexin 26). Further data are needed to validate these findings, including functional genomic studies and expanded epidemiologic studies in female, non-European cohorts.

Host variation in type I interferon signaling genes (MX1), C-C chemokine receptor type 5 gene, and major histocompatibility complex class I alleles in treated HIV+ noncontrollers predict viral reservoir size.

OBJECTIVE: Prior genomewide association studies have identified variation in major histocompatibility complex (MHC) class I alleles and C-C chemokine receptor type 5 gene (CCR5Δ32) as genetic predictors of viral control, especially in 'elite' controllers, individuals who remain virally suppressed in the absence of therapy. DESIGN: Cross-sectional genomewide association study. METHODS: We analyzed custom whole exome sequencing and direct human leukocyte antigen (HLA) typing from 202 antiretroviral therapy (ART)-suppressed HIV+ noncontrollers in relation to four measures of the peripheral CD4+ T-cell reservoir: HIV intact DNA, total (t)DNA, unspliced (us)RNA, and RNA/DNA. Linear mixed models were adjusted for potential covariates including age, sex, nadir CD4+ T-cell count, pre-ART HIV RNA, timing of ART initiation, and duration of ART suppression. RESULTS: Previously reported 'protective' host genetic mutations related to viral setpoint (e.g. among elite controllers) were found to predict smaller HIV reservoir size. The HLA 'protective' B∗57:01 was associated with significantly lower HIV usRNA (q = 3.3 × 10-3), and among the largest subgroup, European ancestry individuals, the CCR5Δ32 deletion was associated with smaller HIV tDNA (P = 4.3 × 10-3) and usRNA (P = 8.7 × 10-3). In addition, genomewide analysis identified several single nucleotide polymorphisms in MX1 (an interferon stimulated gene) that were significantly associated with HIV tDNA (q = 0.02), and the direction of these associations paralleled MX1 gene eQTL expression. CONCLUSIONS: We observed a significant association between previously reported 'protective' MHC class I alleles and CCR5Δ32 with the HIV reservoir size in noncontrollers. We also found a novel association between MX1 and HIV total DNA (in addition to other interferon signaling relevant genes, PPP1CB, DDX3X). These findings warrant further investigation in future validation studies.

Controllable Cycloadditions between 2H-(Thio)pyran-2-(thi)ones and Strained Alkynes: A Click-and-Release Strategy for COS/H2S Generation.

In this work, we carried out computational studies to predict the cycloaddition efficiency of strained alkynes with 2H-pyran-2-one and its three sulfur-containing analogues: 2H-pyran-2-thione, 2H-thiopyran-2-one, and 2H-thiopyran-2-thione. It was predicted that the decreased aromaticity of the substrate would yield higher reactivity. Experimental studies confirmed the calculation results, and 2H-pyan-2-thiones were found to be the most reactive substrates. This reaction proceeded effectively in aqueous buffers and in cellular environments. It also produced COS as the byproduct, which could be converted into hydrogen sulfide (H2S) in the presence of carbonate anhydrase. This click-and-release approach may serve as a unique way to deliver COS/H2S to specific locations.

First-in-human immunoPET imaging of HIV-1 infection using 89Zr-labeled VRC01 broadly neutralizing antibody.

A major obstacle to achieving long-term antiretroviral (ART) free remission or functional cure of HIV infection is the presence of persistently infected cells that establish a long-lived viral reservoir. HIV largely resides in anatomical regions that are inaccessible to routine sampling, however, and non-invasive methods to understand the longitudinal tissue-wide burden of HIV persistence are urgently needed. Positron emission tomography (PET) imaging is a promising strategy to identify and characterize the tissue-wide burden of HIV. Here, we assess the efficacy of using immunoPET imaging to characterize HIV reservoirs and identify anatomical foci of persistent viral transcriptional activity using a radiolabeled HIV Env-specific broadly neutralizing antibody, 89Zr-VRC01, in HIV-infected individuals with detectable viremia and on suppressive ART compared to uninfected controls (NCT03729752). We also assess the relationship between PET tracer uptake in tissues and timing of ART initiation and direct HIV protein expression in CD4 T cells obtained from lymph node biopsies. We observe significant increases in 89Zr-VRC01 uptake in various tissues (including lymph nodes and gut) in HIV-infected individuals with detectable viremia (N = 5) and on suppressive ART (N = 5) compared to uninfected controls (N = 5). Importantly, PET tracer uptake in inguinal lymph nodes in viremic and ART-suppressed participants significantly and positively correlates with HIV protein expression measured directly in tissue. Our strategy may allow non-invasive longitudinal characterization of residual HIV infection and lays the framework for the development of immunoPET imaging in a variety of other infectious diseases.

A single-cell atlas reveals shared and distinct immune responses and metabolism during SARS-CoV-2 and HIV-1 infections.

SARS-CoV-2 and HIV-1 are RNA viruses that have killed millions of people worldwide. Understanding the similarities and differences between these two infections is critical for understanding disease progression and for developing effective vaccines and therapies, particularly for 38 million HIV-1 + individuals who are vulnerable to SARS-CoV-2 co-infection. Here, we utilized single-cell transcriptomics to perform a systematic comparison of 94,442 PBMCs from 7 COVID-19 and 9 HIV-1 + patients in an integrated immune atlas, in which 27 different cell types were identified using an accurate consensus single-cell annotation method. While immune cells in both cohorts show shared inflammation and disrupted mitochondrial function, COVID-19 patients exhibit stronger humoral immunity, broader IFN-I signaling, elevated Rho GTPase and mTOR pathway activities, and downregulated mitophagy. Our results elucidate transcriptional signatures associated with COVID-19 and HIV-1 that may reveal insights into fundamental disease biology and potential therapeutic targets to treat these viral infections. HIGHLIGHTS: COVID-19 and HIV-1 + patients show disease-specific inflammatory immune signatures COVID-19 patients show more productive humoral responses than HIV-1 + patients SARS-CoV-2 elicits more enriched IFN-I signaling relative to HIV-IDivergent, impaired metabolic programs distinguish SARS-CoV-2 and HIV-1 infections.

Fast de Bruijn Graph Compaction in Distributed Memory Environments.

De Bruijn graph based genome assembly has gained popularity as short read sequencers become ubiquitous. A core assembly operation is the generation of unitigs, which are sequences corresponding to chains in the graph. Unitigs are used as building blocks for generating longer sequences in many assemblers, and can facilitate graph compression. Chain compaction, by which unitigs are generated, remains a critical computational task. In this paper, we present a distributed memory parallel algorithm for simultaneous compaction of all chains in bi-directed de Bruijn graphs. The key advantages of our algorithm include bounding the chain compaction run-time to logarithmic number of iterations in the length of the longest chain, and ability to differentiate cycles from chains within logarithmic number of iterations in the length of the longest cycle. Our algorithm scales to thousands of computational cores, and can compact a whole genome de Bruijn graph from a human sequence read set in 7.3 seconds using 7680 distributed memory cores, and in 12.9 minutes using 64 shared memory cores. It is 3.7× and 2.0× faster than equivalent steps in the state-of-the-art tools for distributed and shared memory environments, respectively. An implementation of the algorithm is available at https://github.com/ParBLiSS/bruno.

Circulating CD30+CD4+ T Cells Increase Before Human Immunodeficiency Virus Rebound After Analytical Antiretroviral Treatment Interruption.

BACKGROUND: Identification of nonviral markers of human immunodeficiency virus (HIV) infection that increase before viral rebound during analytical treatment interruption (ATI) may affect HIV persistence research. We previously showed that HIV ribonucleic acid (RNA) is enriched in CD30+CD4+ T cells in many individuals. Here, we studied CD30+CD4+ T-cell dynamics before ATI, during ATI (before detectable plasma RNA), and after HIV rebound. METHODS: Peripheral blood mononuclear cells from 23 participants collected longitudinally from 5 Adult AIDS Clinical Trials Group studies incorporating ATI were included in this study. Flow cytometric characterization of expression of CD30 and markers of T-cell activation and exhaustion were performed along with HIV-1 RNA and deoxyribonucleic acid quantification and measurement of soluble plasma CD30 and CD30 ligand. RESULTS: The percentage of CD4+ T cells expressing CD30 significantly increased from pre-ATI to postinterruption time points before detectible viremia (1.65 mean relative increase, P = .005). Seventy-seven percent of participants experienced an increase in CD30+ cells before viral rebound. In contrast, there were no significant differences between pre-ATI and postinterruption pre-rebound time points in percentages of lymphocytes expressing CD69, CD38/HLA-DR, or PD-1 until after HIV recrudescence. CONCLUSIONS: CD30 may be a surrogate marker of early replication or viral transcriptional activity before detection by routine peripheral blood sampling.

The use of a predictive statistical model to make a virtual control arm for a clinical trial.

BACKGROUND: Randomized clinical trials compare participants receiving an experimental intervention to participants receiving standard of care (SOC). If one could predict the outcome for participants receiving SOC, a trial could be designed where all participants received the experimental intervention, with the observed outcome of the experimental group compared to the prediction for those individuals. METHODS: We used the CancerMath calculator to predict outcomes for participants in two large clinical trials of adjuvant chemotherapy for breast cancer: NSABPB15 and CALGB9344. NSABPB15 was the training set, and we used the modified algorithm to predict outcomes for two groups from CALGB9344: one which received standard of care (SOC) chemotherapy and one which received paclitaxel in addition. We made a prediction for each individual CALGB9344 participant, assuming each received only SOC. RESULTS: The predicted outcome for the group which received only SOC matched what was observed in the CALGB9344 trial. In contrast, the predicted outcome for the group also receiving paclitaxel was significantly worse than what was observed for this group. This matches the conclusion of CALGB9344 that adding paclitaxel to SOC improves survival. CONCLUSION: This project proves that a statistical model can predict the outcome of clinical trial participants treated with SOC. In some circumstances, a predictive model could be used instead of a control arm, allowing all participants to receive experimental treatment. Predictive models for cancer and other diseases could be constructed using the vast amount of outcomes data available to the federal government, and made available for public use.

Kmerind: A Flexible Parallel Library for K-mer Indexing of Biological Sequences on Distributed Memory Systems.

Counting and indexing fixed length substrings, or $k$k-mers, in biological sequences is a key step in many bioinformatics tasks including genome alignment and mapping, genome assembly, and error correction. While advances in next generation sequencing technologies have dramatically reduced the cost and improved latency and throughput, few bioinformatics tools can efficiently process the datasets at the current generation rate of 1.8 terabases per 3-day experiment from a single sequencer. We present Kmerind, a high performance parallel $k$k-mer indexing library for distributed memory environments. The Kmerind library provides a set of simple and consistent APIs with sequential semantics and parallel implementations that are designed to be flexible and extensible. Kmerind's $k$k-mer counter performs similarly or better than the best existing $k$k-mer counting tools even on shared memory systems. In a distributed memory environment, Kmerind counts $k$k-mers in a 120 GB sequence read dataset in less than 13 seconds on 1024 Xeon CPU cores, and fully indexes their positions in approximately 17 seconds. Querying for 1 percent of the $k$k-mers in these indices can be completed in 0.23 seconds and 28 seconds, respectively. Kmerind is the first $k$k-mer indexing library for distributed memory environments, and the first extensible library for general $k$k-mer indexing and counting. Kmerind is available at https://github.com/ParBLiSS/kmerind.

Region Templates: Data Representation and Management for High-Throughput Image Analysis.

We introduce a region template abstraction and framework for the efficient storage, management and processing of common data types in analysis of large datasets of high resolution images on clusters of hybrid computing nodes. The region template abstraction provides a generic container template for common data structures, such as points, arrays, regions, and object sets, within a spatial and temporal bounding box. It allows for different data management strategies and I/O implementations, while providing a homogeneous, unified interface to applications for data storage and retrieval. A region template application is represented as a hierarchical dataflow in which each computing stage may be represented as another dataflow of finer-grain tasks. The execution of the application is coordinated by a runtime system that implements optimizations for hybrid machines, including performance-aware scheduling for maximizing the utilization of computing devices and techniques to reduce the impact of data transfers between CPUs and GPUs. An experimental evaluation on a state-of-the-art hybrid cluster using a microscopy imaging application shows that the abstraction adds negligible overhead (about 3%) and achieves good scalability and high data transfer rates. Optimizations in a high speed disk based storage implementation of the abstraction to support asynchronous data transfers and computation result in an application performance gain of about 1.13×. Finally, a processing rate of 11,730 4K×4K tiles per minute was achieved for the microscopy imaging application on a cluster with 100 nodes (300 GPUs and 1,200 CPU cores). This computation rate enables studies with very large datasets.

Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines.

We address the problem of efficient execution of a computation pattern, referred to here as the irregular wavefront propagation pattern (IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in several image processing operations. In the IWPP, data elements in the wavefront propagate waves to their neighboring elements on a grid if a propagation condition is satisfied. Elements receiving the propagated waves become part of the wavefront. This pattern results in irregular data accesses and computations. We develop and evaluate strategies for efficient computation and propagation of wavefronts using a multi-level queue structure. This queue structure improves the utilization of fast memories in a GPU and reduces synchronization overheads. We also develop a tile-based parallelization strategy to support execution on multiple CPUs and GPUs. We evaluate our approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs and 2 multicore CPUs) using the IWPP implementations of two widely used image processing operations: morphological reconstruction and euclidean distance transform. Our results show significant performance improvements on GPUs. The use of multiple CPUs and GPUs cooperatively attains speedups of 50× and 85× with respect to single core CPU executions for morphological reconstruction and euclidean distance transform, respectively.