Crowdsourcing interface feature design with Bayesian optimization

Designing novel interfaces is challenging. Designers typically rely on experience or subjective judgment in the absence of analytical or objective means for selecting interface parameters. We demonstrate Bayesian optimization as an efficient tool for objective interface feature refinement. Specifically, we show that crowdsourcing paired with Bayesian optimization can rapidly and effectively assist interface design across diverse deployment environments. Experiment 1 evaluates the approach on a familiar 2D interface design problem: a map search and review use case. Adding a degree of complexity, Experiment 2 extends Experiment 1 by switching the deployment environment to mobile-based virtual reality. The approach is then demonstrated as a case study for a fundamentally new and unfamiliar interaction design problem: web-based augmented reality. Finally, we show how the model generated as an outcome of the refinement process can be used for user simulation and queried to deliver various design insights

Reproducible Software Appliances for Experimentation

International audienceExperiment reproducibility is a milestone of the scientific method. Reproducibility of experiments in computer science would bring several advantages such as code re-usability and technology transfer. The reproducibility problem in computer science has been solved partially, addressing particular class of applications or single machine setups. In this paper we present our approach oriented to setup complex environments for experimentation, environments that require a lot of configuration and the installation of several software packages. The main objective of our approach is to enable the exact and independent reconstruction of a given software environment and the reuse of code. We present a simple and small software appliance generator that helps an experimenter to construct a specific software stack that can be deployed on different available testbeds

Disease signatures are robust across tissues and experiments

Meta-analyses combining gene expression microarray experiments offer new insights into the molecular pathophysiology of disease not evident from individual experiments. Although the established technical reproducibility of microarrays serves as a basis for meta-analysis, pathophysiological reproducibility across experiments is not well established. In this study, we carried out a large-scale analysis of disease-associated experiments obtained from NCBI GEO, and evaluated their concordance across a broad range of diseases and tissue types. On evaluating 429 experiments, representing 238 diseases and 122 tissues from 8435 microarrays, we find evidence for a general, pathophysiological concordance between experiments measuring the same disease condition. Furthermore, we find that the molecular signature of disease across tissues is overall more prominent than the signature of tissue expression across diseases. The results offer new insight into the quality of public microarray data using pathophysiological metrics, and support new directions in meta-analysis that include characterization of the commonalities of disease irrespective of tissue, as well as the creation of multi-tissue systems models of disease pathology using public data

Stakeholder involvement in systematic reviews: a protocol for a systematic review of methods, outcomes and effects

Background There is an expectation for stakeholders (including patients, the public, health professionals, and others) to be involved in research. Researchers are increasingly recognising that it is good practice to involve stakeholders in systematic reviews. There is currently a lack of evidence about (A) how to do this and (B) the effects, or impact, of such involvement. We aim to create a map of the evidence relating to stakeholder involvement in systematic reviews, and use this evidence to address the two points above. Methods We will complete a mixed-method synthesis of the evidence, first completing a scoping review to create a broad map of evidence relating to stakeholder involvement in systematic reviews, and secondly completing two contingent syntheses. We will use a stepwise approach to searching; the initial step will include comprehensive searches of electronic databases, including CENTRAL, AMED, Embase, Medline, Cinahl and other databases, supplemented with pre-defined hand-searching and contacting authors. Two reviewers will undertake each review task (i.e., screening, data extraction) using standard systematic review processes. For the scoping review, we will include any paper, regardless of publication status or study design, which investigates, reports or discusses involvement in a systematic review. Included papers will be summarised within structured tables. Criteria for judging the focus and comprehensiveness of the description of methods of involvement will be applied, informing which papers are included within the two contingent syntheses. Synthesis A will detail the methods that have been used to involve stakeholders in systematic reviews. Papers from the scoping review that are judged to provide an adequate description of methods or approaches will be included. Details of the methods of involvement will be extracted from included papers using pre-defined headings, presented in tables and described narratively. Synthesis B will include studies that explore the effect of stakeholder involvement on the quality, relevance or impact of a systematic review, as identified from the scoping review. Study quality will be appraised, data extracted and synthesised within tables. Discussion This review should help researchers select, improve and evaluate methods of involving stakeholders in systematic reviews. Review findings will contribute to Cochrane training resources

Computational prediction and experimental validation associating FABP-1 and pancreatic adenocarcinoma with diabetes

<p/> <p>Background</p> <p>Pancreatic cancer, composed principally of pancreatic adenocarcinoma (PaC), is the fourth leading cause of cancer death in the United States. PaC-associated diabetes may be a marker of early disease. We sought to identify molecules associated with PaC and PaC with diabetes (PaC-DM) using a novel translational bioinformatics approach. We identified fatty acid binding protein-1 (FABP-1) as one of several candidates. The primary aim of this pilot study was to experimentally validate the predicted association between FABP-1 with PaC and PaC with diabetes.</p> <p>Methods</p> <p>We searched public microarray measurements for genes that were specifically highly expressed in PaC. We then filtered for proteins with known involvement in diabetes. Validation of FABP-1 was performed via antibody immunohistochemistry on formalin-fixed paraffin embedded pancreatic tissue microarrays (FFPE TMA). FFPE TMA were constructed using148 cores of pancreatic tissue from 134 patients collected between 1995 and 2002 from patients who underwent pancreatic surgery. Primary analysis was performed on 21 normal and 60 pancreatic adenocarcinoma samples, stratified for diabetes. Clinical data on samples was obtained via retrospective chart review. Serial sections were cut per standard protocol. Antibody staining was graded by an experienced pathologist on a scale of 0-3. Bivariate and multivariate analyses were conducted to assess FABP-1 staining and clinical characteristics.</p> <p>Results</p> <p>Normal samples were significantly more likely to come from younger patients. PaC samples were significantly more likely to stain for FABP-1, when FABP-1 staining was considered a binary variable. Compared to normals, there was significantly increased staining in diabetic PaC samples (p = 0.004) and there was a trend towards increased staining in the non-diabetic PaC group (p = 0.07). In logistic regression modeling, FABP-1 staining was significantly associated with diagnosis of PaC (OR 8.6 95% CI 1.1-68, p = 0.04), though age was a confounder.</p> <p>Conclusions</p> <p>Compared to normal controls, there was a significant positive association between FABP-1 staining and PaC on FFPE-TMA, strengthened by the presence of diabetes. Further studies with closely phenotyped patient samples are required to understand the true relationship between FABP-1, PaC and PaC-associated diabetes. A translational bioinformatics approach has potential to identify novel disease associations and potential biomarkers in gastroenterology.</p

Transduction of SIV-Specific TCR Genes into Rhesus Macaque CD8+ T Cells Conveys the Ability to Suppress SIV Replication

The SIV/rhesus macaque model for HIV/AIDS is a powerful system for examining the contribution of T cells in the control of AIDS viruses. To better our understanding of CD8(+) T-cell control of SIV replication in CD4(+) T cells, we asked whether TCRs isolated from rhesus macaque CD8(+) T-cell clones that exhibited varying abilities to suppress SIV replication could convey their suppressive properties to CD8(+) T cells obtained from an uninfected/unvaccinated animal.We transferred SIV-specific TCR genes isolated from rhesus macaque CD8(+) T-cell clones with varying abilities to suppress SIV replication in vitro into CD8(+) T cells obtained from an uninfected animal by retroviral transduction. After sorting and expansion, transduced CD8(+) T-cell lines were obtained that specifically bound their cognate SIV tetramer. These cell lines displayed appropriate effector function and specificity, expressing intracellular IFNγ upon peptide stimulation. Importantly, the SIV suppression properties of the transduced cell lines mirrored those of the original TCR donor clones: cell lines expressing TCRs transferred from highly suppressive clones effectively reduced wild-type SIV replication, while expression of a non-suppressing TCR failed to reduce the spread of virus. However, all TCRs were able to suppress the replication of an SIV mutant that did not downregulate MHC-I, recapitulating the properties of their donor clones.Our results show that antigen-specific SIV suppression can be transferred between allogenic T cells simply by TCR gene transfer. This advance provides a platform for examining the contributions of TCRs versus the intrinsic effector characteristics of T-cell clones in virus suppression. Additionally, this approach can be applied to develop non-human primate models to evaluate adoptive T-cell transfer therapy for AIDS and other diseases

Differentially Expressed RNA from Public Microarray Data Identifies Serum Protein Biomarkers for Cross-Organ Transplant Rejection and Other Conditions

Serum proteins are routinely used to diagnose diseases, but are hard to find due to low sensitivity in screening the serum proteome. Public repositories of microarray data, such as the Gene Expression Omnibus (GEO), contain RNA expression profiles for more than 16,000 biological conditions, covering more than 30% of United States mortality. We hypothesized that genes coding for serum- and urine-detectable proteins, and showing differential expression of RNA in disease-damaged tissues would make ideal diagnostic protein biomarkers for those diseases. We showed that predicted protein biomarkers are significantly enriched for known diagnostic protein biomarkers in 22 diseases, with enrichment significantly higher in diseases for which at least three datasets are available. We then used this strategy to search for new biomarkers indicating acute rejection (AR) across different types of transplanted solid organs. We integrated three biopsy-based microarray studies of AR from pediatric renal, adult renal and adult cardiac transplantation and identified 45 genes upregulated in all three. From this set, we chose 10 proteins for serum ELISA assays in 39 renal transplant patients, and discovered three that were significantly higher in AR. Interestingly, all three proteins were also significantly higher during AR in the 63 cardiac transplant recipients studied. Our best marker, serum PECAM1, identified renal AR with 89% sensitivity and 75% specificity, and also showed increased expression in AR by immunohistochemistry in renal, hepatic and cardiac transplant biopsies. Our results demonstrate that integrating gene expression microarray measurements from disease samples and even publicly-available data sets can be a powerful, fast, and cost-effective strategy for the discovery of new diagnostic serum protein biomarkers

Fludarabine Modulates Immune Response and Extends In Vivo Survival of Adoptively Transferred CD8 T Cells in Patients with Metastatic Melanoma

Adoptive T cell therapy involving the use of ex vivo generated antigen-specific cytotoxic T lymphocytes provides a promising approach to immunotherapy. It has become increasingly apparent that anti-tumor efficacy using adoptively transferred T cells is linked to their duration of in vivo persistence and can only be achieved when combined with some form of pre-infusion patient conditioning regimen. An optimal conditioning regimen that provides a positive benefit without serious toxicities has yet to be defined. We have established a unique clinical model that allows for evaluation of a given conditioning regimen on adoptively transferred T cells in humans. In this first-in-human study (FHCRC #1796), we evaluate the use of fludarabine, an FDA-approved reagent with predictable lymphodepleting kinetics and duration of action, as a conditioning regimen that promotes homeostatic upregulation of cytokines and growth signals contributing to in vivo T cell persistence.We conducted a phase I study in patients with refractory metastatic melanoma. Patients received two infusions of a single tumor-reactive antigen-specific CTL clone expanded to 10(10)/m(2); the first infusion was given without fludarabine conditioning, and the second CTL infusion was given after a course of fludarabine (25 mg/m(2)/dayx5 days). This design permits intra-patient comparison of in vivo T cell persistence pre- and post-fludarabine. Nineteen CTL infusions were administered to ten patients. No serious toxicities were observed. Three of nine evaluable patients experienced minor response or stable disease for periods of 5.8-11.0 months with two additional patients demonstrating delayed disease stabilization. The median overall survival in this heavily pre-treated population was 9.7 months. Fludarabine led to a 2.9 fold improvement in the in vivo persistence of transferred CTL clones from a median of 4.5 days (range 0-38+) to 13.0 days (range 2-63+) (p<0.05). Fludarabine lymphodepletion increased plasma levels of the homeostatic cytokines IL-7 and IL-15. Surprisingly, fludarabine also increased the relative percentage of CD4+ T cells expressing the regulatory protein Foxp3.Lymphodepletion with fludarabine enhances transferred T cell persistence but suggest that additional improvements to optimize T cell survival and address regulatory T cells are critical in providing anti-tumor efficacy.ClinicalTrials.gov NCT00317759

Effective knowledge management in translational medicine

<p>Abstract</p> <p>Background</p> <p>The growing consensus that most valuable data source for biomedical discoveries is derived from human samples is clearly reflected in the growing number of translational medicine and translational sciences departments across pharma as well as academic and government supported initiatives such as Clinical and Translational Science Awards (CTSA) in the US and the Seventh Framework Programme (FP7) of EU with emphasis on translating research for human health.</p> <p>Methods</p> <p>The pharmaceutical companies of Johnson and Johnson have established translational and biomarker departments and implemented an effective knowledge management framework including building a data warehouse and the associated data mining applications. The implemented resource is built from open source systems such as i2b2 and GenePattern.</p> <p>Results</p> <p>The system has been deployed across multiple therapeutic areas within the pharmaceutical companies of Johnson and Johnsons and being used actively to integrate and mine internal and public data to support drug discovery and development decisions such as indication selection and trial design in a translational medicine setting. Our results show that the established system allows scientist to quickly re-validate hypotheses or generate new ones with the use of an intuitive graphical interface.</p> <p>Conclusions</p> <p>The implemented resource can serve as the basis of precompetitive sharing and mining of studies involving samples from human subjects thus enhancing our understanding of human biology and pathophysiology and ultimately leading to more effective treatment of diseases which represent unmet medical needs.</p

Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets

Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state “signature”. These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities