Twisted Sequences of Extensions

Gabber and Joseph introduced a ladder diagram between two natural sequences of extensions. Their diagram is used to produce a 'twisted' sequence that is applied to old and new results on extension groups in category $\mathcal{O}$.Comment: As accepted for publication in Communications in Algebr

Twisted Sequences of Extensions

Gabber and Joseph introduced a ladder diagram between two natural sequences of extensions. Their diagram is used to produce a `twisted\u27 sequence that is applied to old and new results on extension groups in category O

Serum profiling by MALDI-TOF mass spectrometry as a diagnostic tool for domoic acid toxicosis in California sea lions

<p>Abstract</p> <p>Background</p> <p>There are currently no reliable markers of acute domoic acid toxicosis (DAT) for California sea lions. We investigated whether patterns of serum peptides could diagnose acute DAT. Serum peptides were analyzed by MALDI-TOF mass spectrometry from 107 sea lions (acute DAT n = 34; non-DAT n = 73). Artificial neural networks (ANN) were trained using MALDI-TOF data. Individual peaks and neural networks were qualified using an independent test set (n = 20).</p> <p>Results</p> <p>No single peak was a good classifier of acute DAT, and ANN models were the best predictors of acute DAT. Performance measures for a single median ANN were: sensitivity, 100%; specificity, 60%; positive predictive value, 71%; negative predictive value, 100%. When 101 ANNs were combined and allowed to vote for the outcome, the performance measures were: sensitivity, 30%; specificity, 100%; positive predictive value, 100%; negative predictive value, 59%.</p> <p>Conclusions</p> <p>These results suggest that MALDI-TOF peptide profiling and neural networks can perform either as a highly sensitive (100% negative predictive value) or a highly specific (100% positive predictive value) diagnostic tool for acute DAT. This also suggests that machine learning directed by populations of predictive models offer the ability to modulate the predictive effort into a specific type of error.</p

Sensitivity of fluvial sediment source apportionment to mixing model assumptions: A Bayesian model comparison

Mixing models have become increasingly common tools for apportioning fluvial sediment load to various sediment sources across catchments using a wide variety of Bayesian and frequentist modeling approaches. In this study, we demonstrate how different model setups can impact upon resulting source apportionment estimates in a Bayesian framework via a one-factor-at-a-time (OFAT) sensitivity analysis. We formulate 13 versions of a mixing model, each with different error assumptions and model structural choices, and apply them to sediment geochemistry data from the River Blackwater, Norfolk, UK, to apportion suspended particulate matter (SPM) contributions from three sources (arable topsoils, road verges, and subsurface material) under base flow conditions between August 2012 and August 2013. Whilst all 13 models estimate subsurface sources to be the largest contributor of SPM (median ∼76%), comparison of apportionment estimates reveal varying degrees of sensitivity to changing priors, inclusion of covariance terms, incorporation of time-variant distributions, and methods of proportion characterization. We also demonstrate differences in apportionment results between a full and an empirical Bayesian setup, and between a Bayesian and a frequentist optimization approach. This OFAT sensitivity analysis reveals that mixing model structural choices and error assumptions can significantly impact upon sediment source apportionment results, with estimated median contributions in this study varying by up to 21% between model versions. Users of mixing models are therefore strongly advised to carefully consider and justify their choice of model structure prior to conducting sediment source apportionment investigations

Asbestos accelerates disease onset in a genetic model of malignant pleural mesothelioma

Hypothesis: Asbestos-driven inflammation contributes to malignant pleural mesothelioma beyond the acquisition of rate-limiting mutations.Methods: Genetically modified conditional allelic mice that were previously shown to develop mesothelioma in the absence of exposure to asbestos were induced with lentiviral vector expressing Cre recombinase with and without intrapleural injection of amosite asbestos and monitored until symptoms required euthanasia. Resulting tumours were examined histologically and by immunohistochemistry for expression of lineage markers and immune cell infiltration.Results: Injection of asbestos dramatically accelerated disease onset and end-stage tumour burden. Tumours developed in the presence of asbestos showed increased macrophage infiltration. Pharmacological suppression of macrophages in mice with established tumours failed to extend survival or to enhance response to chemotherapy.Conclusion: Asbestos-driven inflammation contributes to the severity of mesothelioma beyond the acquisition of rate-limiting mutations, however, targeted suppression of macrophages in established epithelioid mesothelioma showed no therapeutic benefit

Identifier mapping performance for integrating transcriptomics and proteomics experimental results

Background\ud Studies integrating transcriptomic data with proteomic data can illuminate the proteome more clearly than either separately. Integromic studies can deepen understanding of the dynamic complex regulatory relationship between the transcriptome and the proteome. Integrating these data dictates a reliable mapping between the identifier nomenclature resultant from the two high-throughput platforms. However, this kind of analysis is well known to be hampered by lack of standardization of identifier nomenclature among proteins, genes, and microarray probe sets. Therefore data integration may also play a role in critiquing the fallible gene identifications that both platforms emit.\ud \ud Results\ud We compared three freely available internet-based identifier mapping resources for mapping UniProt accessions (ACCs) to Affymetrix probesets identifications (IDs): DAVID, EnVision, and NetAffx. Liquid chromatography-tandem mass spectrometry analyses of 91 endometrial cancer and 7 noncancer samples generated 11,879 distinct ACCs. For each ACC, we compared the retrieval sets of probeset IDs from each mapping resource. We confirmed a high level of discrepancy among the mapping resources. On the same samples, mRNA expression was available. Therefore, to evaluate the quality of each ACC-to-probeset match, we calculated proteome-transcriptome correlations, and compared the resources presuming that better mapping of identifiers should generate a higher proportion of mapped pairs with strong inter-platform correlations. A mixture model for the correlations fitted well and supported regression analysis, providing a window into the performance of the mapping resources. The resources have added and dropped matches over two years, but their overall performance has not changed.\ud \ud Conclusions\ud The methods presented here serve to achieve concrete context-specific insight, to support well-informed decisions in choosing an ID mapping strategy for "omic" data merging

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie

Asbestos accelerates disease onset in a genetic model of malignant pleural mesothelioma

Hypothesis: Asbestos-driven inflammation contributes to malignant pleural mesothelioma beyond the acquisition of rate-limiting mutations. Methods: Genetically modified conditional allelic mice that were previously shown to develop mesothelioma in the absence of exposure to asbestos were induced with lentiviral vector expressing Cre recombinase with and without intrapleural injection of amosite asbestos and monitored until symptoms required euthanasia. Resulting tumours were examined histologically and by immunohistochemistry for expression of lineage markers and immune cell infiltration. Results: Injection of asbestos dramatically accelerated disease onset and end-stage tumour burden. Tumours developed in the presence of asbestos showed increased macrophage infiltration. Pharmacological suppression of macrophages in mice with established tumours failed to extend survival or to enhance response to chemotherapy. Conclusion: Asbestos-driven inflammation contributes to the severity of mesothelioma beyond the acquisition of rate-limiting mutations, however, targeted suppression of macrophages in established epithelioid mesothelioma showed no therapeutic benefit