University of California, Irvine

About: University of California, Irvine is a education organization based out in Irvine, California, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 47031 authors who have published 113602 publications receiving 5521832 citations. The organization is also known as: UC Irvine & UCI.

Who are the crowdworkers?: shifting demographics in mechanical turk

Abstract: Amazon Mechanical Turk (MTurk) is a crowdsourcing system in which tasks are distributed to a population of thousands of anonymous workers for completion. This system is increasingly popular with researchers and developers. Here we extend previous studies of the demographics and usage behaviors of MTurk workers. We describe how the worker population has changed over time, shifting from a primarily moderate-income, U.S.-based workforce towards an increasingly international group with a significant population of young, well-educated Indian workers. This change in population points to how workers may treat Turking as a full-time job, which they rely on to make ends meet.

Activity and safety of crizotinib in patients with ALK-positive non-small-cell lung cancer: updated results from a phase 1 study.

Abstract: Summary Background ALK fusion genes occur in a subset of non-small-cell lung cancers (NSCLCs). We assessed the tolerability and activity of crizotinib in patients with NSCLC who were prospectively identified to have an ALK fusion within the first-in-man phase 1 crizotinib study. Methods In this phase 1 study, patients with ALK -positive stage III or IV NSCLC received oral crizotinib 250 mg twice daily in 28-day cycles. Endpoints included tumour responses, duration of response, time to tumour response, progression-free survival (PFS), overall survival at 6 and 12 months, and determination of the safety and tolerability and characterisation of the plasma pharmacokinetic profile of crizotinib after oral administration. Responses were analysed in evaluable patients and PFS and safety were analysed in all patients. This study is registered with ClinicalTrials.gov, number NCT00585195. Findings Between Aug 27, 2008, and June 1, 2011, 149 ALK -positive patients were enrolled, 143 of whom were included in the response-evaluable population. 87 of 143 patients had an objective response (60·8%, 95% CI 52·3–68·9), including three complete responses and 84 partial responses. Median time to first documented objective response was 7·9 weeks (range 2·1–39·6) and median duration of response was 49·1 weeks (95% CI 39·3–75·4). The response rate seemed to be largely independent of age, sex, performance status, or line of treatment. Median PFS was 9·7 months (95% CI 7·7–12·8). Median overall survival data are not yet mature, but estimated overall survival at 6 and 12 months was 87·9% (95% CI 81·3–92·3) and 74·8% (66·4–81·5), respectively. 39 patients continued to receive crizotinib for more than 2 weeks after progression because of perceived ongoing clinical benefit from the drug (12 for at least 6 months from the time of their initial investigator-defined disease progression). Overall, 144 (97%) of 149 patients experienced treatment-related adverse events, which were mostly grade 1 or 2. The most common adverse events were visual effects, nausea, diarrhoea, constipation, vomiting, and peripheral oedema. The most common treatment-related grade 3 or 4 adverse events were neutropenia (n=9), raised alanine aminotransferase (n=6), hypophosphataemia (n=6), and lymphopenia (n=6). Interpretation Crizotinib is well tolerated with rapid, durable responses in patients with ALK -positive NSCLC. There seems to be potential for ongoing benefit after initial disease progression in this population, but a more formal definition of ongoing benefit in this context is needed. Funding Pfizer.

Searching for dark matter annihilation from Milky Way dwarf spheroidal galaxies with six years of Fermi Large Area Telescope data

Abstract: The dwarf spheroidal satellite galaxies (dSphs) of the Milky Way are some of the most dark matter (DM) dominated objects known. We report on γ-ray observations of Milky Way dSphs based on six years of Fermi Large Area Telescope data processed with the new Pass8 event-level analysis. None of the dSphs are significantly detected in γ rays, and we present upper limits on the DM annihilation cross section from a combined analysis of 15 dSphs. These constraints are among the strongest and most robust to date and lie below the canonical thermal relic cross section for DM of mass ≲100 GeV annihilating via quark and τ-lepton channels.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.