All-cause mortality following a cancer diagnosis amongst multiple sclerosis patients: A Swedish population-based cohort study

Background and purpose: A reduced cancer risk amongst patients with multiple sclerosis (MS) has been reported. Theoretically, this could represent a genuine reduction in risk or, alternatively, 'diagnostic neglect', where cancer is undiagnosed when symptoms are misattributed to MS. Objective: Assess all-cause mortality risk following a cancer diagnosis in patients with MS compared with a cohort without MS. Patients: A cohort of MS patients (n = 19 364) and a cohort of the general population (n = 192 519) were extracted from national Swedish registers from 1969 to 2005. All-cause mortality after cancer in MS was compared with the general population. Poisson regression analysis was conducted in the MS and non-MS cohorts separately. The models were adjusted for follow-up duration, year at entry, sex, region and socioeconomic index. The two cohorts were combined and differences in mortality risk were assessed using interaction testing. Results: The adjusted relative risk (and 95 confidence interval) for all-cause mortality following a cancer diagnosis in MS patients (compared with MS patients without cancer) is 3.06 (2.86-3.27; n = 1768) and amongst those without MS 5.73 (5.62-5.85; n = 24 965). This lower magnitude mortality risk in the MS patients was confirmed by multiplicative interaction testing (P < 0.001). Conclusions: A consistent pattern of lower magnitude of all-cause mortality risk following cancer in MS patients for a range of organ-specific cancer types was found. It suggests that cancer diagnoses tend not to be delayed in MS and diagnostic neglect is unlikely to account for the reduced cancer risk associated with MS. The lower magnitude cancer risk in MS may be due to disease-associated characteristics or exposures. © 2015 EAN

Trace metals distribution and uptake in soil and rice grown on a 3-year vermicompost amended soil

This study was designed to investigate the influence of vermicompost (VC) on trace metals distribution and uptake in soil and rice plant in research field as split plot arrangement based on randomized complete block design with three replications in 2008. Main-plot was VC and chemical fertilizer (CF) that were added to soil in 6 levels (20 and 40 ton/ha VC, 20 and 40 ton/ha VC + 1/2 CF, CF and control). Application years considered as sub-plot comprised 1, 2 and 3 years. The results indicated thatfertilizers and application periods treatments influenced micronutrients in soil and rice. Available copper (Cu) had no significant difference under different treatments. The highest available iron (Fe) was found in the 40 ton treatment group. During the 3 years, application of 20 ton and enriched 40 ton gave the most available zinc (Zn) and manganese (Mn). In VC and enriched VC, treatments happened to give the highest Zn uptake by rice. Under the 3 years, application of 40 ton/ha VC, the highest Fe (91.19 ppm) and Cu (13.66 ppm) concentration was seen in flag leaf, while Fe (31.35 ppm) and Mn (27.56 ppm) was seen in grain. With the application of enriched 20 ton VC, the maximum uptake of Mn by flag leaf and Cu by grain was obtained

Appendicectomy and multiple sclerosis risk

Background: Appendicectomy for acute appendicitis, but not for other causes, is inversely associated with immune-mediated diseases such as ulcerative colitis, suggesting appendicitis is a marker of immune characteristics influencing immune-mediated disease risk. This study investigated the association of appendectomy and its underlying diagnosis with multiple sclerosis (MS). Methods: Swedish general population registers and the Swedish MS register provided information on 20542 cases with MS diagnosed between 1964-2006 and 204157 controls matched for age, sex, period and region. Appendicectomy prior to MS diagnosis was identified in 673 cases and 6518 controls. Conditional logistic regression, with adjustment for socio-economic index, assessed the association of diagnosis underlying appendicitis with MS risk. Results: A perforated appendix, the best indicator of acute appendicitis in this material, was inversely associated with MS, although not statistically significantly, with an odds ratio (and 95% confidence interval of 0.86 (0.70-1.04). The odds ratios are 1.04 (0.94-1.16) for appendicitis without perforation and 1.14 (0.98-1.33) for appendectomy without appendicitis. Conclusion: Although inconclusive in terms of assessing the hypothesis, these results may help to explain why earlier studies of appendicitis and MS risk have been inconsistent, as there may be variation in association by diagnosis underlying appendicectomy. © 2010 The Author(s). European Journal of Neurology © 2010 EFNS

The Photometric LSST Astronomical Time-series Classification Challenge PLAsTiCC: Selection of a Performance Metric for Classification Probabilities Balancing Diverse Science Goals

Classification of transient and variable light curves is an essential step in using astronomical observations to develop an understanding of the underlying physical processes from which they arise. However, upcoming deep photometric surveys, including the Large Synoptic Survey Telescope (LSST), will produce a deluge of low signal-to-noise data for which traditional type estimation procedures are inappropriate. Probabilistic classification is more appropriate for such data but is incompatible with the traditional metrics used on deterministic classifications. Furthermore, large survey collaborations like LSST intend to use the resulting classification probabilities for diverse science objectives, indicating a need for a metric that balances a variety of goals. We describe the process used to develop an optimal performance metric for an open classification challenge that seeks to identify probabilistic classifiers that can serve many scientific interests. The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) aims to identify promising techniques for obtaining classification probabilities of transient and variable objects by engaging a broader community beyond astronomy. Using mock classification probability submissions emulating realistically complex archetypes of those anticipated of PLAsTiCC, we compare the sensitivity of two metrics of classification probabilities under various weighting schemes, finding that both yield results that are qualitatively consistent with intuitive notions of classification performance. We thus choose as a metric for PLAsTiCC a weighted modification of the cross-entropy because it can be meaningfully interpreted in terms of information content. Finally, we propose extensions of our methodology to ever more complex challenge goals and suggest some guiding principles for approaching the choice of a metric of probabilistic data products

CosmoDC2: A Synthetic Sky Catalog for Dark Energy Science with LSST

This paper introduces cosmoDC2, a large synthetic galaxy catalog designed to support precision dark energy science with the Large Synoptic Survey Telescope (LSST). CosmoDC2 is the starting point for the second data challenge (DC2) carried out by the LSST Dark Energy Science Collaboration (LSST DESC). The catalog is based on a trillion-particle, 4.225 Gpc^3 box cosmological N-body simulation, the `Outer Rim' run. It covers 440 deg^2 of sky area to a redshift of z=3 and is complete to a magnitude depth of 28 in the r-band. Each galaxy is characterized by a multitude of properties including stellar mass, morphology, spectral energy distributions, broadband filter magnitudes, host halo information and weak lensing shear. The size and complexity of cosmoDC2 requires an efficient catalog generation methodology; our approach is based on a new hybrid technique that combines data-driven empirical approaches with semi-analytic galaxy modeling. A wide range of observation-based validation tests has been implemented to ensure that cosmoDC2 enables the science goals of the planned LSST DESC DC2 analyses. This paper also represents the official release of the cosmoDC2 data set, including an efficient reader that facilitates interaction with the data

Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)

Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a diverse set of machine-learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multilayer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state of the art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next-generation PLAsTiCC data set

Dietary glycemic load and gastric cancer risk in Italy

We investigated gastric cancer risk in relation to dietary glycemic index (GI) and glycemic load (GL), which represent indirect measures of carbohydrate absorption and consequently of dietary insulin demand, in a case-control study conducted in northern Italy between 1997 and 2007, including 230 patients with the incident, histologically confirmed gastric cancer and 547 frequency matched controls, admitted to the same hospitals as cases with acute non-neoplastic conditions. We used conditional logistic regression models, including terms for major recognised gastric cancer risk factors and non-carbohydrate energy intake. The odds ratios (ORs) in the highest vs lowest quintile were 1.9 (95% CI: 1.0–3.3) for GI and 2.5 (95% CI: 1.3–4.9) for GL. Compared with participants reporting low GL and high fruits/vegetables intake, the OR rose across strata of high GL and low fruits/vegetables, to reach 5.0 (95% CI: 2.2–11.5) for those reporting low fruits/vegetables intake and high GL. Our study may help to explain the direct relation observed in several studies between starchy foods and gastric cancer risk