Preface from the program chairs

Presents the introductory welcome message from the conference proceedings.

cudaMap: a GPU accelerated program for gene expression connectivity mapping

Background: Modern cancer research often involves large datasets and the use of sophisticated statistical techniques. Together these add a heavy computational load to the analysis, which is often coupled with issues surrounding data accessibility. Connectivity mapping is an advanced bioinformatic and computational technique dedicated to therapeutics discovery and drug re-purposing around differential gene expression analysis. On a normal desktop PC, it is common for the connectivity mapping task with a single gene signature to take >2h to complete using sscMap, a popular Java application that runs on standard CPUs (Central Processing Units). Here, we describe new software, cudaMap, which has been implemented using CUDA C/C++ to harness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing times for connectivity mapping.

Results: cudaMap can identify candidate therapeutics from the same signature in just over thirty seconds when using an NVIDIA Tesla C2050 GPU. Results from the analysis of multiple gene signatures, which would previously have taken several days, can now be obtained in as little as 10 minutes, greatly facilitating candidate therapeutics discovery with high throughput. We are able to demonstrate dramatic speed differentials between GPU assisted performance and CPU executions as the computational load increases for high accuracy evaluation of statistical significance.

Conclusion: Emerging 'omics' technologies are constantly increasing the volume of data and information to be processed in all areas of biomedical research. Embracing the multicore functionality of GPUs represents a major avenue of local accelerated computing. cudaMap will make a strong contribution in the discovery of candidate therapeutics by enabling speedy execution of heavy duty connectivity mapping tasks, which are increasingly required in modern cancer research. cudaMap is open source and can be freely downloaded from http://purl.oclc.org/NET/cudaMap.

Herschel key program HERITAGE:A far-infrared source catalog for the Magellanic Clouds

Observations from the HERschel Inventory of the Agents of Galaxy Evolution (HERITAGE ) have been used to identify dusty populations of sources in the Large and Small Magellanic Clouds (LMC and SMC). We conducted the study using the HERITAGE catalogs of point sources available from the Herschel Science Center from both the Photodetector Array Camera and Spectrometer (PACS; 100 and 160 μm) and Spectral and Photometric Imaging Receiver (SPIRE; 250, 350, and 500 μm) cameras. These catalogs are matched to each other to create a Herschel band-merged catalog and then further matched to archival Spitzer IRAC and MIPS catalogs from the Spitzer Surveying the Agents of Galaxy Evolution (SAGE) and SAGE-SMC surveys to create single mid- to far-infrared (far-IR) point source catalogs that span the wavelength range from 3.6 to 500 μm. There are 35,322 unique sources in the LMC and 7503 in the SMC. To be bright in the FIR, a source must be very dusty, and so the sources in the HERITAGE catalogs represent the dustiest populations of sources. The brightest HERITAGE sources are dominated by young stellar objects (YSOs), and the dimmest by background galaxies. We identify the sources most likely to be background galaxies by first considering their morphology (distant galaxies are point-like at the resolution of Herschel) and then comparing the flux distribution to that of the Herschel Astrophysical Terahertz Large Area Survey (ATLAS ) survey of galaxies. We find a total of 9745 background galaxy candidates in the LMC HERITAGE images and 5111 in the SMC images, in agreement with the number predicted by extrapolating from the ATLAS flux distribution. The majority of the Magellanic Cloud-residing sources are either very young, embedded forming stars or dusty clumps of the interstellar medium. Using the presence of 24 μm emission as a tracer of star formation, we identify 3518 YSO candidates in the LMC and 663 in the SMC. There are far fewer far-IR bright YSOs in the SMC than the LMC due to both the SMC's smaller size and its lower dust content. The YSO candidate lists may be contaminated at low flux levels by background galaxies, and so we differentiate between sources with a high ("probable") and moderate ("possible ") likelihood of being a YSO. There are 2493/425 probable YSO candidates in the LMC/SMC. Approximately 73% of the Herschel YSO candidates are newly identified in the LMC, and 35% in the SMC. We further identify a small population of dusty objects in the late stages of stellar evolution including extreme and post-asymptotic giant branch, planetary nebulae, and supernova remnants. These populations are identified by matching the HERITAGE catalogs to lists of previously identified objects in the literature. Approximately half of the LMC sources and one quarter of the SMC sources are too faint to obtain accurate ample FIR photometry and are unclassified.

Malware detection: program run length against detection rate

N-gram analysis is an approach that investigates the structure of a program using bytes, characters or text strings. This research uses dynamic analysis to investigate malware detection using a classification approach based on N-gram analysis. A key issue with dynamic analysis is the length of time a program has to be run to ensure a correct classification. The motivation for this research is to find the optimum subset of operational codes (opcodes) that make the best indicators of malware and to determine how long a program has to be monitored to ensure an accurate support vector machine (SVM) classification of benign and malicious software. The experiments within this study represent programs as opcode density histograms gained through dynamic analysis for different program run periods. A SVM is used as the program classifier to determine the ability of different program run lengths to correctly determine the presence of malicious software. The findings show that malware can be detected with different program run lengths using a small number of opcodes

Effect of a Web-Based Behavior Change Program on Weight Loss and Cardiovascular Risk Factors in Overweight and Obese Adults at High Risk of Developing Cardiovascular Disease:Randomized Controlled Trial

BACKGROUND: Web-based programs are a potential medium for supporting weight loss because of their accessibility and wide reach. Research is warranted to determine the shorter- and longer-term effects of these programs in relation to weight loss and other health outcomes.

OBJECTIVE: The aim was to evaluate the effects of a Web-based component of a weight loss service (Imperative Health) in an overweight/obese population at risk of cardiovascular disease (CVD) using a randomized controlled design and a true control group.

METHODS: A total of 65 overweight/obese adults at high risk of CVD were randomly allocated to 1 of 2 groups. Group 1 (n=32) was provided with the Web-based program, which supported positive dietary and physical activity changes and assisted in managing weight. Group 2 continued with their usual self-care (n=33). Assessments were conducted face-to-face. The primary outcome was between-group change in weight at 3 months. Secondary outcomes included between-group change in anthropometric measurements, blood pressure, lipid measurements, physical activity, and energy intake at 3, 6, and 12 months. Interviews were conducted to explore participants' views of the Web-based program.

RESULTS: Retention rates for the intervention and control groups at 3 months were 78% (25/32) vs 97% (32/33), at 6 months were 66% (21/32) vs 94% (31/33), and at 12 months were 53% (17/32) vs 88% (29/33). Intention-to-treat analysis, using baseline observation carried forward imputation method, revealed that the intervention group lost more weight relative to the control group at 3 months (mean -3.41, 95% CI -4.70 to -2.13 kg vs mean -0.52, 95% CI -1.55 to 0.52 kg, P<.001), at 6 months (mean -3.47, 95% CI -4.95 to -1.98 kg vs mean -0.81, 95% CI -2.23 to 0.61 kg, P=.02), but not at 12 months (mean -2.38, 95% CI -3.48 to -0.97 kg vs mean -1.80, 95% CI -3.15 to -0.44 kg, P=.77). More intervention group participants lost ≥5% of their baseline body weight at 3 months (34%, 11/32 vs 3%, 1/33, P<.001) and 6 months (41%, 13/32 vs 18%, 6/33, P=.047), but not at 12 months (22%, 7/32 vs 21%, 7/33, P=.95) versus control group. The intervention group showed improvements in total cholesterol, triglycerides, and adopted more positive dietary and physical activity behaviors for up to 3 months verus control; however, these improvements were not sustained.

CONCLUSIONS: Although the intervention group had high attrition levels, this study provides evidence that this Web-based program can be used to initiate clinically relevant weight loss and lower CVD risk up to 3-6 months based on the proportion of intervention group participants losing ≥5% of their body weight versus control group. It also highlights a need for augmenting Web-based programs with further interventions, such as in-person support to enhance engagement and maintain these changes.