Automating Software Development for Mobile Computing Platforms

Mobile devices such as smartphones and tablets have become ubiquitous in today\u27s computing landscape. These devices have ushered in entirely new populations of users, and mobile operating systems are now outpacing more traditional desktop systems in terms of market share. The applications that run on these mobile devices (often referred to as apps ) have become a primary means of computing for millions of users and, as such, have garnered immense developer interest. These apps allow for unique, personal software experiences through touch-based UIs and a complex assortment of sensors. However, designing and implementing high quality mobile apps can be a difficult process. This is primarily due to challenges unique to mobile development including change-prone APIs and platform fragmentation, just to name a few. in this dissertation we develop techniques that aid developers in overcoming these challenges by automating and improving current software design and testing practices for mobile apps. More specifically, we first introduce a technique, called Gvt, that improves the quality of graphical user interfaces (GUIs) for mobile apps by automatically detecting instances where a GUI was not implemented to its intended specifications. Gvt does this by constructing hierarchal models of mobile GUIs from metadata associated with both graphical mock-ups (i.e., created by designers using photo-editing software) and running instances of the GUI from the corresponding implementation. Second, we develop an approach that completely automates prototyping of GUIs for mobile apps. This approach, called ReDraw, is able to transform an image of a mobile app GUI into runnable code by detecting discrete GUI-components using computer vision techniques, classifying these components into proper functional categories (e.g., button, dropdown menu) using a Convolutional Neural Network (CNN), and assembling these components into realistic code. Finally, we design a novel approach for automated testing of mobile apps, called CrashScope, that explores a given android app using systematic input generation with the intrinsic goal of triggering crashes. The GUI-based input generation engine is driven by a combination of static and dynamic analyses that create a model of an app\u27s GUI and targets common, empirically derived root causes of crashes in android apps. We illustrate that the techniques presented in this dissertation represent significant advancements in mobile development processes through a series of empirical investigations, user studies, and industrial case studies that demonstrate the effectiveness of these approaches and the benefit they provide developers

Facilitating smart HACCP strategies with Process Analytical Technology

The critical feature to any food management system, be it safety or quality focused, is timely and accurate product and process data. The HACCP framework is one of a systematic preventive approach that addresses hazards as a means of prevention rather than finished product inspection. The PAT initiative is a framework which also aims to change product assurance from end-product testing to preventive control through timely measurements of critical product and process parameters. The continued emergence of sensitive yet affordable sensors capable of direct measurement of biological, chemical and physical risks associated with foods during production, processing or over their shelf-life undoubtedly will support a higher temporal and spatial profiling of foods

Comparison of Outcomes of antibiotic Drugs and Appendectomy (CODA) trial: a protocol for the pragmatic randomised study of appendicitis treatment.

INTRODUCTION: Several European studies suggest that some patients with appendicitis can be treated safely with antibiotics. A portion of patients eventually undergo appendectomy within a year, with 10%-15% failing to respond in the initial period and a similar additional proportion with suspected recurrent episodes requiring appendectomy. Nearly all patients with appendicitis in the USA are still treated with surgery. A rigorous comparative effectiveness trial in the USA that is sufficiently large and pragmatic to incorporate usual variations in care and measures the patient experience is needed to determine whether antibiotics are as good as appendectomy. OBJECTIVES: The Comparing Outcomes of Antibiotic Drugs and Appendectomy (CODA) trial for acute appendicitis aims to determine whether the antibiotic treatment strategy is non-inferior to appendectomy. METHODS/ANALYSIS: CODA is a randomised, pragmatic non-inferiority trial that aims to recruit 1552 English-speaking and Spanish-speaking adults with imaging-confirmed appendicitis. Participants are randomised to appendectomy or 10 days of antibiotics (including an option for complete outpatient therapy). A total of 500 patients who decline randomisation but consent to follow-up will be included in a parallel observational cohort. The primary analytic outcome is quality of life (measured by the EuroQol five dimension index) at 4 weeks. Clinical adverse events, rate of eventual appendectomy, decisional regret, return to work/school, work productivity and healthcare utilisation will be compared. Planned exploratory analyses will identify subpopulations that may have a differential risk of eventual appendectomy in the antibiotic treatment arm. ETHICS AND DISSEMINATION: This trial was approved by the University of Washington\u27s Human Subjects Division. Results from this trial will be presented in international conferences and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT02800785

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Photochemically produced SO2 in the atmosphere of WASP-39b

Photochemistry is a fundamental process of planetary atmospheres that regulates the atmospheric composition and stability1. However, no unambiguous photochemical products have been detected in exoplanet atmospheres so far. Recent observations from the JWST Transiting Exoplanet Community Early Release Science Program2,3 found a spectral absorption feature at 4.05 μm arising from sulfur dioxide (SO2) in the atmosphere of WASP-39b. WASP-39b is a 1.27-Jupiter-radii, Saturn-mass (0.28 MJ) gas giant exoplanet orbiting a Sun-like star with an equilibrium temperature of around 1,100 K (ref. 4). The most plausible way of generating SO2 in such an atmosphere is through photochemical processes5,6. Here we show that the SO2 distribution computed by a suite of photochemical models robustly explains the 4.05-μm spectral feature identified by JWST transmission observations7 with NIRSpec PRISM (2.7σ)8 and G395H (4.5σ)9. SO2 is produced by successive oxidation of sulfur radicals freed when hydrogen sulfide (H2S) is destroyed. The sensitivity of the SO2 feature to the enrichment of the atmosphere by heavy elements (metallicity) suggests that it can be used as a tracer of atmospheric properties, with WASP-39b exhibiting an inferred metallicity of about 10× solar. We further point out that SO2 also shows observable features at ultraviolet and thermal infrared wavelengths not available from the existing observations