Approaches to decision-making among late-stage melanoma patients: a multifactorial investigation.

PurposeThe treatment decisions of melanoma patients are poorly understood. Most research on cancer patient decision-making focuses on limited components of specific treatment decisions. This study aimed to holistically characterize late-stage melanoma patients' approaches to treatment decision-making in order to advance understanding of patient influences and supports.Methods(1) Exploratory analysis of longitudinal qualitative data to identify themes that characterize patient decision-making. (2) Pattern analysis of decision-making themes using an innovative method for visualizing qualitative data: a hierarchically-clustered heatmap. Participants were 13 advanced melanoma patients at a large academic medical center.ResultsExploratory analysis revealed eight themes. Heatmap analysis indicated two broad types of patient decision-makers. "Reliant outsiders" relied on providers for medical information, demonstrated low involvement in decision-making, showed a low or later-in-care interest in clinical trials, and expressed altruistic motives. "Active insiders" accessed substantial medical information and expertise in their networks, consulted with other doctors, showed early and substantial interest in trials, demonstrated high involvement in decision-making, and employed multiple decision-making strategies.ConclusionWe identified and characterized two distinct approaches to decision-making among patients with late-stage melanoma. These differences spanned a wide range of factors (e.g., behaviors, resources, motivations). Enhanced understanding of patients as decision-makers and the factors that shape their decision-making may help providers to better support patient understanding, improve patient-provider communication, and support shared decision-making

Dynamic Matrix Factorization with Priors on Unknown Values

Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones (\emph{not missing at random}). In this work, we build on this assumption, and introduce a novel dynamic matrix factorization framework that allows to set an explicit prior on unknown values. When new ratings, users, or items enter the system, we can update the factorization in time independent of the size of data (number of users, items and ratings). Hence, we can quickly recommend items even to very recent users. We test our methods on three large datasets, including two very sparse ones, in static and dynamic conditions. In each case, we outrank state-of-the-art matrix factorization methods that do not use a prior on unknown ratings.Comment: in the Proceedings of 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining 201

Impact of magnetic activity on inferred stellar properties of main-sequence Sun-like stars

The oscillation frequencies observed in Sun-like stars are susceptible to being shifted by magnetic activity effects. The measured shifts depend on a complex relationship involving the mode type, the field strength, and spatial distribution of activity, as well as the inclination angle of the star. Evidence of these shifts is also present in frequency separation ratios that are often used when inferring global properties of stars in order to avoid surface effects. However, one assumption when using frequency ratios for this purpose is that there are no near-surface perturbations that are non-spherically symmetric. In this work, we studied the impact on inferred stellar properties when using frequency ratios that are influenced by non-homogeneous activity distributions. We generate several sets of artificial oscillation frequencies with various amounts of shift and determine stellar properties using two separate pipelines. We find that for asteroseismic observations of Sun-like targets we can expect magnetic activity to affect mode frequencies that will bias the results from stellar modelling analysis. Although for most stellar properties this offset should be small, typically less than 0.5 per cent in mass, estimates of age and central hydrogen content can have an error of up to 5 per cent and 3 per cent, respectively. We expect a larger frequency shift and therefore larger bias for more active stars. We also warn that for stars with very high or low inclination angles, the response of modes to activity is more easily observable in the separation ratios and hence will incur a larger bias

Neural Attentive Session-based Recommendation

Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main purpose in the current session is not emphasized. In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem. Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later. We then compute the recommendation scores for each candidate item with a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental results show that NARM outperforms state-of-the-art baselines on both datasets. Furthermore, we also find that NARM achieves a significant improvement on long sessions, which demonstrates its advantages in modeling the user's sequential behavior and main purpose simultaneously.Comment: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. arXiv admin note: text overlap with arXiv:1511.06939, arXiv:1606.08117 by other author

Beyond Exploratory: A Tailored Framework for Assessing Rigor in Qualitative Health Services Research

Objective: To propose a framework for assessing the rigor of qualitative research that identifies and distinguishes between the diverse objectives of qualitative studies currently used in patient-centered outcomes and health services research (PCOR and HSR). Study Design: Narrative review of published literature discussing qualitative guidelines and standards in peer-reviewed journals and national funding organizations that support PCOR and HSR. Principal Findings: We identify and distinguish three objectives of current qualitative studies in PCOR and HSR: exploratory, descriptive, and comparative. For each objective, we propose methodological standards that can be used to assess and improve rigor across all study phases—from design to reporting. Similar to quantitative studies, we argue that standards for qualitative rigor differ, appropriately, for studies with different objectives and should be evaluated as such. Conclusions: Distinguishing between different objectives of qualitative HSR improves the ability to appreciate variation in qualitative studies as well as appropriately evaluate the rigor and success of studies in meeting their own objectives. Researchers, funders, and journal editors should consider how adopting the criteria for assessing qualitative rigor outlined here may advance the rigor and potential impact of qualitative research in patient-centered outcomes and health services research

The K2 Galactic Caps Project - going beyond the Kepler field and ageing the Galactic disc

Analyses of data from spectroscopic and astrometric surveys have led to conflicting results concerning the vertical characteristics of the Milky Way. Ages are often used to provide clarity, but typical uncertainties of >40 per cent from photometry restrict the validity of the inferences made. Using the Kepler APOKASC sample for context, we explore the global population trends of two K2 campaign fields (3 and 6), which extend further vertically out of the Galactic plane than APOKASC. We analyse the properties of red giant stars utilizing three asteroseismic data analysis methods to cross-check and validate detections. The Bayesian inference tool PARAM is used to determine the stellar masses, radii, and ages. Evidence of a pronounced red giant branch bump and an [α/Fe] dependence on the position of the red clump is observed from the K2 fields radius distribution. Two peaks in the age distribution centred at ∼5 and ∼12 Gyr are found using a sample with σage 0.1) stars. As a function of vertical distance from the Galactic mid-plane (|Z|), the age distribution shows a transition from a young to old stellar population with increasing |Z| for the K2 fields. Further coverage of campaign targets with high-resolution spectroscopy is required to increase the yield of precise ages achievable with asteroseismology.We gratefully acknowledge the support of the UK Science and Technology Facilities Council (STFC). BMR, AM, GRD, BM, LG, and SK are grateful to the International Space Science Institute (ISSI) for support provided to the asteroSTEP ISSI International Team. AM acknowledges support from the ERC Consolidator Grant funding scheme (project ASTEROCHRONOMETRY, G.A. no. 772293). CC acknowledges support from DFG Grant CH1188/2- 1 and from the ChETEC COST Action (CA16117), supported by COST (European Cooperation in Science and Technology). BMR would like to thank the AIP for temporarily hosting him during the studies for this work. LC is the recipient of the ARC Future Fellowship FT160100402. SM acknowledges support from NASA grants NNX16AJ17G and NNX15AF13G, by the National Science Foundation grant AST-1411685 and the Ramon y Cajal fellowship number RYC-2015–17697. RAG acknowledges the funding received from the CNES through the PLATO grant. PJ acknowledges FONDECYT Iniciacion Grant Number 11170174. ´ TSR acknowledges financial support from Premiale 2015 MITiC (PI B. Garilli). AG acknowledges support from theSwedish National Space Board. Funding for the Stellar Astrophysics Centre is provided by the Danish National Research Foundation (Grant DNRF106). Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS acknowledges support and resources from the Center for HighPerformance Computing at the University of Utah. The SDSS web site is www.sdss.org

FliPer_Class:in search of solar-like pulsators among TESS targets

The NASA's Transiting Exoplanet Survey Satellite (TESS) is about to provide full-frame images of almost the entire sky. The amount of stellar data to be analysed represents hundreds of millions stars, which is several orders of magnitude above the amount of stars observed by CoRoT, Kepler, or K2 missions. We aim at automatically classifying the newly observed stars, with near real-time algorithms, to better guide their subsequent detailed studies. In this paper, we present a classification algorithm built to recognise solar-like pulsators among classical pulsators, which relies on the global amount of power contained in the PSD, also known as the FliPer (Flicker in spectral Power density). As each type of pulsating star has a characteristic background or pulsation pattern, the shape of the PSD at different frequencies can be used to characterise the type of pulsating star. The FliPer Classifier (FliPer$_{Class}$) uses different FliPer parameters along with the effective temperature as input parameters to feed a machine learning algorithm in order to automatically classify the pulsating stars observed by TESS. Using noisy TESS simulated data from the TESS Asteroseismic Science Consortium (TASC), we manage to classify pulsators with a 98% accuracy. Among them, solar-like pulsating stars are recognised with a 99% accuracy, which is of great interest for further seismic analysis of these stars like our Sun. Similar results are obtained when training our classifier and applying it to 27 days subsets of real Kepler data. FliPer$_{Class}$ is part of the large TASC classification pipeline developed by the TESS Data for Asteroseismology (T'DA) classification working group.Comment: 8 pages, 6 figures, accepted to A&

Chronologically dating the early assembly of the Milky Way

The standard cosmological model predicts that galaxies are built through hierarchical assembly on cosmological timescales1,2. The Milky Way, like other disk galaxies, underwent violent mergers and accretion of small satellite galaxies in its early history. Owing to Gaia Data Release 23 and spectroscopic surveys4, the stellar remnants of such mergers have been identified5–7. The chronological dating of such events is crucial to uncover the formation and evolution of the Galaxy at high redshift, but it has so far been challenging due to difficulties in obtaining precise ages for these oldest stars. Here we combine asteroseismology—the study of stellar oscillations—with kinematics and chemical abundances to estimate precise stellar ages (~11%) for a sample of stars observed by the Kepler space mission8. Crucially, this sample includes not only some of the oldest stars that were formed inside the Galaxy but also stars formed externally and subsequently accreted onto the Milky Way. Leveraging this resolution in age, we provide compelling evidence in favour of models in which the Galaxy had already formed a substantial population of its stars (which now reside mainly in its thick disk) before the infall of the satellite galaxy Gaia-Enceladus/Sausage5,6 around 10 billion years ago

New light on the Gaia DR2 parallax zero-point: Influence of the asteroseismic approach, in and beyond the Kepler field

The importance of studying the Gaia DR2 parallax zero-point by external means was underlined by the articles that accompanied the release, and initiated by several works making use of Cepheids, eclipsing binaries, and asteroseismology. Despite a very efficient elimination of basic-angle variations, a small fluctuation remains and shows up as a small offset in the Gaia DR2 parallaxes. By combining astrometric, asteroseismic, spectroscopic, and photometric constraints, we undertake a new analysis of the Gaia parallax offset for nearly 3000 red-giant branch (RGB) and 2200 red clump (RC) stars observed by Kepler, as well as about 500 and 700 red giants (all either in the RGB or RC phase) selected by the K2 Galactic Archaeology Program in campaigns 3 and 6. Engaging in a thorough comparison of the astrometric and asteroseismic parallaxes, we are able to highlight the influence of the asteroseismic method, and measure parallax offsets in the Kepler field that are compatible with independent estimates from literature and open clusters. Moreover, adding the K2 fields to our investigation allows us to retrieve a clear illustration of the positional dependence of the zero-point, in general agreement with the information provided by quasars. Lastly, we initiate a two-step methodology to make progress in the simultaneous calibration of the asteroseismic scaling relations and of the Gaia DR2 parallax offset, which will greatly benefit from the gain in precision with the third data release of Gaia.This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https: //www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. SK, AM, BM, GRD, BMR, DB, and LG are grateful to the International Space Science Institute (ISSI) for support provided to the asteroSTEP ISSI International Team. AM, WJC, GRD, BMR, YPE, and TSHN acknowledge the support of the UK Science and Technology Facilities Council (STFC). AM acknowledges support from the ERC Consolidator Grant funding scheme (project ASTEROCHRONOMETRY, G.A. n. 772293). LC is the recipient of the ARC Future Fellowship FT160100402. TSR acknowledges financial support from Premiale 2015 MITiC (PI B. Garilli). This work was supported by FCT/MCTES through national funds and by FEDER – Fundo Europeu de Desenvolvimento Regional through COMPETE2020 – Programa Operacional Competitividade e Internacionalização by grants: UID/FIS/04434/2019; PTDC/FIS-AST/30389/2017 & POCI-01-0145- FEDER-030389. DB is supported in the form of a work contract funded by national funds through Fundação para a Ciência e Tecnologia (FCT)