Search

Subgraph enumeration is important for many applications such as network motif discovery, community detection, and frequent subgraph mining. To accelerate the execution, recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration. The performances of these parallel schemes are dominated by the set intersection operations which account for up to $95\%$ of the total processing time. (Un)surprisingly, a significant portion (as high as $99\%$) of these operations is actually redundant, i.e., the same set of vertices is repeatedly encountered and evaluated. Therefore, in this paper, we seek to salvage and recycle the results of such operations to avoid repeated computation. Our solution consists of two phases. In the first phase, we generate a reusable plan that determines the opportunity for reuse. The plan is based on a novel reuse discovery mechanism that can identify available results to prevent redundant computation. In the second phase, the plan is executed to produce the subgraph enumeration results. This processing is based on a newly designed reusable parallel search strategy that can efficiently maintain and retrieve the results of set intersection operations. Our implementation on GPUs shows that our approach can achieve up to $5$ times speedups compared with the state-of-the-art GPU solutions.

Supplementary Material B

Supplementary Material C

Recent trials of adoptive cell therapy (ACT), such as the chimeric antigen receptor (CAR) T-cell therapy, have demonstrated promising therapeutic effects for cancer patients. A main issue in the product development is to determine the appropriate dose of ACT. Traditional phase I trial designs for cytotoxic agents explicitly assume that toxicity increases monotonically with dose levels and implicitly assume the same for efficacy to justify dose escalation. ACT usually induces rapid responses, and the monotonic dose–response assumption is unlikely to hold due to its immunobiologic activities. We propose a toxicity and efficacy probability interval (TEPI) design for dose finding in ACT trials. This approach incorporates efficacy outcomes to inform dosing decisions to optimize efficacy and safety simultaneously. Rather than finding the maximum tolerated dose (MTD), the TEPI design is aimed at finding the dose with the most desirable outcome for safety and efficacy. The key features of TEPI are its simplicity, flexibility, and transparency, because all decision rules can be prespecified prior to trial initiation. We conduct simulation studies to investigate the operating characteristics of the TEPI design and compare it to existing methods. In summary, the TEPI design is a novel method for ACT dose finding, which possesses superior performance and is easy to use, simple, and transparent. Clin Cancer Res; 23(1); 13–20. ©2016 AACR.

In-memory data management systems have recently gained a lot of attraction due to cheaper and faster DRAM and other hardware advancement. However, these systems are either pure storage systems with online data query service, or just offline batch processing systems with data analytics functionality. Heavy data movement (e.g., data loading) occurs in order to analyze the data. In this paper, we propose an innovative in-memory data management system—MemepiC, which unifies both online data query and data analytics functionality, allowing low-latency storage service and efficient in-situ data analytics. We also explore the emerging RDMA technique in the context of in-memory data management systems, by designing an RDMA-based communication protocol for message delivery inside MemepiC, and proposing to overlap execution and RDMA communication. Extensive experiments are conducted to show the superior performance of MemepiC in terms of both the storage and the data analytics services, compared against other in-memory systems.

We assessed the safety and immunogenicity of an inactivated SARS-CoV-2 vaccine BBIBP-CorV, especially measured the resistance of four global variants of concern: Lineage B.1.1.7, Lineage B.1.351, Lineage P.1, and Lineage B.1.526 to neutralizing activity of vaccine-elicited sera. Among 1006 enrolled participants, no serious adverse event was reported within 28 days post-vaccination. Seroconversion of neutralizing antibodies was seen in 698 (91.84%) of 760 healthcare workers, and the geometric mean titres (GMTs) of neutralizing antibody titre was 62.68 (57.02–68.91) after the second immunization. We found that 57 (12.13%), 99 (20.97%), and 114 (24.26%) vaccine-elicited sera showed complete or partial loss of neutralizing activity against lineage B.1.1.7, lineage B.1.526, and lineage P.1, respectively, while 199 (42.34%) vaccine-elicited sera preserved neutralizing activity against lineage B.1.351, albeit at relatively low dilutions. These data indicated that humoral responses against SARS-CoV-2 could be effectively induced in vaccine recipients, although diminished neutralization potency against multiple variants was observed.

Background: Multiple system atrophy (MSA) and Parkinson’s disease (PD) have overlapping symptoms, making diagnosis challenging. Short-chain fatty acids (SCFAs) are produced exclusively by gut microbiota and were reduced in feces of MSA patients. However, plasma SCFA concentrations in MSA patients have not been investigated. Objective: We aimed to investigate the plasma SCFAs in MSA patients and to identify the potential differential diagnostic ability. Methods: Plasma SCFA were measured in 25 MSA patients, 46 healthy controls, and 46 PD patients using gas chromatography-mass spectrometry. Demographic and clinical characteristics of the participants were evaluated. Results: Acetic acid concentration was lower in MSA patients than in healthy controls. Acetic acid and propionic acid concentrations were lower in MSA and MSA with predominant parkinsonism (MSA-P) patients than in PD patients. A receiver operating characteristic curve (ROC) analysis revealed reduced acetic acid concentration discriminated MSA patients from healthy controls with 76% specificity but only 57% sensitivity and an area under the curve (AUC) of 0.68 (95% confidence interval (CI): 0.55–0.81). Combined acetic acid and propionic acid concentrations discriminated MSA patients from PD patients with an AUC of 0.82 (95% CI: 0.71–0.93), 84% specificity and 76% sensitivity. Especially, with combined acetic acid and propionic acid concentrations, MSA-P patients were separated from PD patients with an AUC of 0.89 (95% CI: 0.80–0.97), 91% specificity and 80% sensitivity. Conclusion: Plasma SCFAs were decreased in MSA patients. The combined acetic acid and propionic acid concentrations may be a potential biomarker for differentiating MSA patients from PD patients.

Subgraph enumeration is important for many applications such as network motif discovery and community detection. Recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration, but they can only handle graphs that fit into the GPU memory. In this paper, we propose a new approach for GPU-accelerated subgraph enumeration that can efficiently scale to large graphs beyond the GPU memory. Our approach divides the graph into partitions, each of which fits into the GPU memory. The GPU processes one partition at a time and searches the matched subgraphs of a given pattern (i.e., instances) within the partition as in the small graph. The key challenge is on enumerating the instances across different partitions, because this search would enumerate considerably redundant subgraphs and cause the expensive data transfer cost via the PCI-e bus. Therefore, we propose a novel shared execution approach to eliminate the redundant subgraph searches and correctly generate all the instances across different partitions. The experimental evaluation shows that our approach can scale to large graphs and achieve significantly better performance than the existing single-machine solutions.

We propose a multiple cohort expansion (MUCE) approach as a design or analysis method for phase 1b multiple expansion cohort trials, which are novel first-in-human studies conducted following phase 1a dose escalation. In a phase 1b expansion cohort trial, one or more doses of a new investigational drug identified from phase 1a are tested for initial antitumour activities in patients with different indications (cancer types and/or biomarker status). Each dose–indication combination defines an arm, and patients are enrolled in parallel cohorts to all the arms. The MUCE design is based on a class of Bayesian hierarchical models that adaptively borrow information across arms. Specifically, we employ a latent probit model that allows for different degrees of borrowing across doses and indications. Statistical inference is directly based on the posterior probability of each arm being efficacious, facilitating the decision making that decides which arm to select for further testing. The MUCE design also incorporates interim looks, based on which the nonpromising arms will be stopped early due to futility. Through simulation studies, we show that MUCE exhibits superior operating characteristics. We also compare the performance of MUCE with that of Simon’s two-stage design and some existing Bayesian designs for multiarm trials. To our knowledge, MUCE is the first Bayesian method for phase 1b expansion cohort trials with multiple doses and indications.

Recent advancements in high-performance networking interconnect significantly narrow the performance gap between intra-node and inter-node communications, and open up opportunities for distributed memory platforms to enforce cache coherency among distributed nodes. To this end, we propose GAM, an efficient distributed in-memory platform that provides a directory-based cache coherence protocol over remote direct memory access (RDMA). GAM manages the free memory distributed among multiple nodes to provide a unified memory model, and supports a set of user-friendly APIs for memory operations. To remove writes from critical execution paths, GAM allows a write to be reordered with the following reads and writes, and hence enforces partial store order (PSO) memory consistency. A light-weight logging scheme is designed to provide fault tolerance in GAM. We further build a transaction engine and a distributed hash table (DHT) atop GAM to show the ease-of-use and applicability of the provided APIs. Finally, we conduct an extensive micro benchmark to evaluate the read/write/lock performance of GAM under various workloads, and a macro benchmark against the transaction engine and DHT. The results show the superior performance of GAM over existing distributed memory platforms.

Personalized PageRank (PPR) is a well-known proximity measure in graphs. To meet the need for dynamic PPR maintenance, recent works have proposed a local update scheme to support incremental computation. Nevertheless, sequential execution of the scheme is still too slow for highspeed stream processing. Therefore, we are motivated to design a parallel approach for dynamic PPR computation. First, as updates always come in batches, we devise a batch processing method to reduce synchronization cost among every single update and enable more parallelism for iterative parallel execution. Our theoretical analysis shows that the parallel approach has the same asymptotic complexity as the sequential approach. Second, we devise novel optimization techniques to effectively reduce runtime overheads for parallel processes. Experimental evaluation shows that our parallel algorithm can achieve orders of magnitude speedups on GPUs and multi-core CPUs compared with the state-of-the-art sequential algorithm.

Recent trials of adoptive cell therapy (ACT), such as the chimeric antigen receptor (CAR) T-cell therapy, have demonstrated promising therapeutic effects for cancer patients. A main issue in the product development is to determine the appropriate dose of ACT. Traditional phase I trial designs for cytotoxic agents explicitly assume that toxicity increases monotonically with dose levels and implicitly assume the same for efficacy to justify dose escalation. ACT usually induces rapid responses, and the monotonic dose–response assumption is unlikely to hold due to its immunobiologic activities. We propose a toxicity and efficacy probability interval (TEPI) design for dose finding in ACT trials. This approach incorporates efficacy outcomes to inform dosing decisions to optimize efficacy and safety simultaneously. Rather than finding the maximum tolerated dose (MTD), the TEPI design is aimed at finding the dose with the most desirable outcome for safety and efficacy. The key features of TEPI are its simplicity, flexibility, and transparency, because all decision rules can be prespecified prior to trial initiation. We conduct simulation studies to investigate the operating characteristics of the TEPI design and compare it to existing methods. In summary, the TEPI design is a novel method for ACT dose finding, which possesses superior performance and is easy to use, simple, and transparent. Clin Cancer Res; 23(1); 13–20. ©2016 AACR.

Cohort-based enrollment can slow down dose-finding trials since the outcomes of the previous cohort must be fully evaluated before the next cohort can be enrolled. This results in frequent suspension of patient enrollment. The issue is exacerbated in recent immune-oncology trials where toxicity outcomes can take a long time to observe. We propose a novel phase I design, the probability-of-decision toxicity probability interval (PoD-TPI) design, to accelerate phase I trials. PoD-TPI enables dose assignment in real-time in the presence of pending toxicity outcomes. With uncertain outcomes, the dose assignment decisions are treated as a random variable, and we calculate the posterior distribution of the decisions. The posterior distribution reflects the variability in the pending outcomes and allows a direct and intuitive evaluation of the confidence of all possible decisions. Optimal decisions are calculated based on 0-1 loss, and extra safety rules are constructed to enforce sufficient protection from exposing patients to risky doses. A new and useful feature of PoD-TPI is that it allows investigators and regulators to balance the trade-off between enrollment speed and making risky decisions by tuning a pair of intuitive design parameters. Through numerical studies, we evaluate the operating characteristics of PoD-TPI and demonstrate that PoD-TPI shortens trial duration and maintains trial safety and efficiency compared to existing time-to-event designs.

We review Bayesian and Bayesian decision theoretic approaches to subgroup analysis and applications to subgroup-based adaptive clinical trial designs. Subgroup analysis refers to inference about subpopulations with significantly distinct treatment effects. The discussion mainly focuses on inference for a benefiting subpopulation, that is, a characterization of a group of patients who benefit from the treatment under consideration more than the overall population. We introduce alternative approaches and demonstrate them with a small simulation study. Then, we turn to clinical trial designs. When the selection of the interesting subpopulation is carried out as the trial proceeds, the design becomes an adaptive clinical trial design, using subgroup analysis to inform the randomization and assignment of treatments to patients. We briefly review some related designs. There are a variety of approaches to Bayesian subgroup analysis. Practitioners should consider the type of subpopulations in which they are interested and choose their methods accordingly. We demonstrate how subgroup analysis can be carried out by different Bayesian methods and discuss how they identify slightly different subpopulations.

To shorten trial duration and improve safety of Phase I trials, we propose R-TPI, a rolling enrollment design that combines the features in model-based designs such as mTPI-2 and rule-based designs such as rolling six. R-TPI employs a novel rolling enrollment scheme, which allows concurrent patient enrollment that is faster than cohort-based enrollment. Bench-marking against rolling six, we find that the R-TPI design is as fast in completing clinical trials but with fewer toxicity events and higher chance of finding the maximum tolerated dose (MTD) in the single scenario laid out in the 2008 rolling six publication. We also find that in a broad setting involving multiple scenarios, R-TPI is generally faster, safer, and more reliable than standard designs. R-TPI is a general design that can be applied to adult and pediatric Phase I trials. It reduces the length of trial duration, leads to safer trials with fewer toxicity events, and maintains relatively a high chance of identifying the MTD.

Social networks generate a massive amount of interaction data among users in the form of streams. To facilitate social network users to consume the continuously generated stream and identify preferred viral social contents, we present a real-time monitoring system called River to track a small set of influential social contents from high-speed streams in this demo. River has four novel features which distinguish itself from existing social monitoring systems: (1) River extracts a set of contents which collectively have the most significant influence coverage while reducing the influence overlaps; (2) River is topic-based and monitors the contents which are relevant to users' preferences; (3) River is location-aware, i.e., it enables user influence query on the contents falling into the region of interests; and (4) River employs a novel sparse influential checkpoint (SIC) index to support efficient updates against the streaming rates of real-world social networks in real-time.

In many oncology clinical trials it is necessary to insert new candidate doses when the prespecified doses are poorly elicited. Formal statistical designs with dose insertion are lacking. We propose a dose insertion design for phase I/II clinical trials in oncology based on both efficacy and toxicity outcomes. We also implement Bayesian model selection during the course of the trial so that better models can be adaptively chosen to achieve more accurate inference. The new design, TEAMS, achieves great operating characteristics in extensive simulation studies due to its ability to adaptively insert new doses as well as perform model selection. As a result, appropriate doses are inserted when necessary and desirable doses are selected with higher probabilities at the end of the trial.

There has been an increasing interest in using interval-based Bayesian designs for dose finding, one of which is the modified toxicity probability interval (mTPI) method. We show that the decision rules in mTPI correspond to an optimal rule under a formal Bayesian decision theoretic framework. However, the probability models in mTPI are overly sharpened by the Ockham's razor, which, while in general helps with parsimonious statistical inference, leads to undesirable decisions from safety perspective. We propose a new framework that blunts the Ockham's razor, and demonstrate the superior performance of the new method, called mTPI-2. An online web tool is provided for users who can generate the design, conduct clinical trials, and examine operating characteristics of the designs.

Main-memory database management systems (DBMS) can achieve excellent performance when processing massive volume of on-line transactions on modern multi-core machines. But existing durability schemes, namely, tuple-level and transaction-level logging-and-recovery mechanisms, either degrade the performance of transaction processing or slow down the process of failure recovery. In this paper, we show that, by exploiting application semantics, it is possible to achieve speedy failure recovery without introducing any costly logging overhead to the execution of concurrent transactions. We propose PACMAN, a parallel database recovery mechanism that is specifically designed for lightweight, coarse-grained transaction-level logging. PACMAN leverages a combination of static and dynamic analyses to parallelize the log recovery: at compile time, PACMAN decomposes stored procedures by carefully analyzing dependencies within and across programs; at recovery time, PACMAN exploits the availability of the runtime parameter values to attain an execution schedule with a high degree of parallelism. As such, recovery performance is remarkably increased. We evaluated PACMAN in a fully-fledged main-memory DBMS running on a 40-core machine. Compared to several state-of-the-art database recovery mechanisms, PACMAN can significantly reduce recovery time without compromising the efficiency of transaction processing., Comment: To appear in SIGMOD 2017

Magnetic Resonance Imaging (MRI) has been routinely used for the diagnosis and treatment of breast cancer. However, the relationship between the MRI tumor phenotypes and the underlying genetic mechanisms remains under-explored. We integrated multi-omics molecular data from The Cancer Genome Atlas (TCGA) with MRI data from The Cancer Imaging Archive (TCIA) for 91 breast invasive carcinomas. Quantitative MRI phenotypes of tumors (such as tumor size, shape, margin and blood flow kinetics) were associated with their corresponding molecular profiles (including DNA mutation, miRNA expression, protein expression, pathway gene expression and copy number variation). We found that transcriptional activities of various genetic pathways were positively associated with tumor size, blurred tumor margin and irregular tumor shape and that miRNA expressions were associated with the tumor size and enhancement texture, but not with other types of radiomic phenotypes. We provide all the association findings as a resource for the research community (available at http://compgenome.org/Radiogenomics/). These findings pave potential paths for the discovery of genetic mechanisms regulating specific tumor phenotypes and for improving MRI techniques as potential non-invasive approaches to probe the cancer molecular status.

Genomic and radiomic imaging profiles of invasive breast carcinomas from The Cancer Genome Atlas and The Cancer Imaging Archive were integrated and a comprehensive analysis was conducted to predict clinical outcomes using the radiogenomic features. Variable selection via LASSO and logistic regression were used to select the most-predictive radiogenomic features for the clinical phenotypes, including pathological stage, lymph node metastasis, and status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Higher AUCs were obtained in the prediction of pathological stage, ER, and PR status than for lymph node metastasis and HER2 status. Overall, the prediction performances by genomics alone, radiomics alone, and combined radiogenomics features showed statistically significant correlations with clinical outcomes; however, improvement on the prediction performance by combining genomics and radiomics data was not found to be statistically significant, most likely due to the small sample size of 91 cancer cases with 38 radiomic features and 144 genomic features.

Background: Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer. Methods: We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data. It is an evolution of existing knowledge by treating it as a prior graph, integrating it with a likelihood model derived by Bayesian graphical model based on TCGA data, and producing a posterior graph as updated and data-enhanced knowledge. In short, Zodiac realizes “Prior interaction map + TCGA data → Posterior interaction map.” Results: Zodiac provides molecular interactions for about 200 million pairs of genes. All the results are generated from a big-data analysis and organized into a comprehensive database allowing customized search. In addition, Zodiac provides data processing and analysis tools that allow users to customize the prior networks and update the genetic pathways of their interest. Zodiac is publicly available at www.compgenome.org/ZODIAC. Conclusions: Zodiac recapitulates and extends existingmore » knowledge of molecular interactions in cancer. It can be used to explore novel gene-gene interactions, transcriptional regulation, and other types of molecular interplays in cancer.« less