CSNL: A cost-sensitive non-linear decision tree algorithm

This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision nodes and utilizes discriminant analysis to construct nonlinear decision trees that take account of costs of misclassification. The performance of the algorithm is evaluated by applying it to seventeen datasets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the datasets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using nonlinear decision nodes. The performance of the algorithm is evaluated by applying it to seventeen data sets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the data sets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using non-linear decision nodes

Alexandrium fundyense cysts in the Gulf of Maine : long-term time series of abundance and distribution, and linkages to past and future blooms

Author Posting. © The Author(s), 2013. This is the author's version of the work. It is posted here by permission of Elsevier for personal use, not for redistribution. The definitive version was published in Deep Sea Research Part II: Topical Studies in Oceanography 103 (2014): 6-26, doi:10.1016/j.dsr2.2013.10.002.Here we document Alexandrium fundyense cyst abundance and distribution patterns over nine years (1997 and 2004-2011) in the coastal waters of the Gulf of Maine (GOM) and identify linkages between those patterns and several metrics of the severity or magnitude of blooms occurring before and after each autumn cyst survey. We also explore the relative utility of two measures of cyst abundance and demonstrate that GOM cyst counts can be normalized to sediment volume, revealing meaningful patterns equivalent to those determined with dry weight normalization.Cyst concentrations were highly variable spatially. Two distinct 1 seedbeds (defined here as accumulation zones with > 300 cysts cm-3) are evident, one in the Bay of Fundy (BOF) and one in mid-coast Maine. Overall, seedbed locations remained relatively constant through time, but their area varied 3-4 fold, and total cyst abundance more than 10 fold among years. A major expansion of the mid-coast Maine seedbed occurred in 2009 following an unusually intense A. fundyense bloom with visible red-water conditions, but that feature disappeared by late 2010. The regional system thus has only two seedbeds with the bathymetry, sediment characteristics, currents, biology, and environmental conditions necessary to persist for decades or longer. Strong positive correlations were confirmed between the abundance of cysts in both the 0-1 and the 0-3 cm layers of sediments in autumn and geographic measures of the extent of the bloom that occurred the next year (i.e., cysts → blooms), such as the length of coastline closed due to shellfish toxicity or the southernmost latitude of shellfish closures. In general, these metrics of bloom geographic extent did not correlate with the number of cysts in sediments following the blooms (blooms → cysts). There are, however, significant positive correlations between 0-3 cm cyst abundances and metrics of the preceding bloom that are indicative of bloom intensity or vegetative cell abundance (e.g., cumulative shellfish toxicity, duration of detectable toxicity in shellfish, and bloom termination date). These data suggest that it may be possible to use cyst abundance to empirically forecast the geographic extent of the forthcoming bloom and, conversely, to use other metrics from bloom and toxicity events to forecast the size of the subsequent cyst population as the inoculum for the next year’s bloom. This is an important step towards understanding the excystment/encystment cycle in A. fundyense bloom dynamics while also augmenting our predictive capability for this HAB-forming species in the GOM.Research support provided by the ECOHAB Grant program through NOAA Grants NA06NOS4780245 and NA09NOS4780193, and through the Woods Hole Center for Oceans and Human Health, National 1 Science Foundation (NSF) Grants OCE-0430724, OCE-0911031, and OCE-1314642; and National Institute of Environmental Health Sciences (NIEHS) Grants 1-P50-ES012742-01 and 1-P01-ES021923-01, and funding through the states of ME, NH, and MA. We are also grateful for event response funding provided for many of the cruises. Funding for J.L. Martin was provided by Fisheries and Oceans Canada

BMQ

BMQ: Boston Medical Quarterly was published from 1950-1966 by the Boston University School of Medicine and the Massachusetts Memorial Hospitals

Particulate-Matter Emission Estimates from Agricultural Spring-Tillage Operations Using LIDAR and Inverse Modeling

Particulate-matter (PM) emissions from a typical spring agricultural tillage sequence and a strip–till conservation tillage sequence in California’s San Joaquin Valley were estimated to calculate the emissions control efficiency (η) of the strip–till conservation management practice (CMP). Filter-based PM samplers, PM-calibrated optical particle counters (OPCs), and a PM-calibrated light detection and ranging (LIDAR) system were used to monitored upwind and downwind PM concentrations during May and June 2008. Emission rates were estimated through inverse modeling coupled with the filter and OPC measurements and through applying a mass balance to the PM concentrations derived from LIDAR data. Sampling irregularities and errors prevented the estimation of emissions from 42% of the sample periods based on filter samples. OPC and LIDAR datasets were sufficiently complete to estimate emissions and the strip–till CMP η, which were ∼90% for all size fractions in both datasets. Tillage time was also reduced by 84%. Calculated emissions for some operations were within the range of values found in published studies, while other estimates were significantly higher than literature values. The results demonstrate that both PM emissions and tillage time may be reduced by an order of magnitude through the use of a strip–till conservation tillage CMP when compared to spring tillage activities

Economies of space and the school geography curriculum

This paper is about the images of economic space that are found in school curricula. It suggests the importance for educators of evaluating these representations in terms of the messages they contain about how social processes operate. The paper uses school geography texts in Britain since the 1970s to illustrate the different ways in which economic space has been represented to students, before exploring some alternative resources that could be used to provide a wider range of representations of economic space. The paper highlights the continued importance of understanding the politics of school knowledge

The Masses and Evolutionary State of the Stars in the Dwarf Nova SS Cygni

The dwarf nova SS Cygni is a close binary star consisting of a K star transferring mass to a white dwarf by way of an accretion disk. We have obtained new spectroscopic observations of SS Cyg with the Hobby-Eberly Telescope (HET). Fits of synthetic spectra for Roche-lobe-filling stars to the absorption-line spectrum of the K star yield the amplitude of the K star's radial velocity curve and the mass ratio: K_{K} = 162.5 +/- 1.0 km/s and q= M_{K} /M_{wd} = 0.685 +/- 0.015. The fits also show that the accretion disk and white dwarf contribute a fraction f = 0.535 +/- 0.075 of the total flux at 5500 angstroms. Taking the weighted average of our results with previously published results obtained using similar techniques, we find = 163.7 +/- 0.7 km/s and = 0.683 +/- 0.012. The orbital light curve of SS Cyg shows an ellipsoidal variation diluted by light from the disk and white dwarf. From an analysis of the ellipsoidal variations we limit the orbital inclination to the range 45 deg. <= i <= 56 deg. The derived masses of the K star and white dwarf are M_{K} = 0.55 +/- 0.13 M_sun and M_{wd} = 0.81 +/- 0.19 M_sun, where the uncertainties are dominated by systematic errors in the orbital inclination. The K star in SS Cyg is 10% to 50% larger than an unevolved star with the same mass and thus does not follow the mass-radius relation for Zero-Age Main-Sequence stars; nor does it follow the ZAMS mass/spectral-type relation. Its mass and spectral type are, however, consistent with models in which the core hydrogen has been significantly depleted

Stigma narratives: LGBT transitions and identities in Malta

This article is available open access through the publisher’s website at the link below. Copyright @ 2011 A B Academic Publishers.This article considers narratives of transition experiences of a group of Lesbian, Gay, Bisexual and Transgender (LGBT) young people in Malta. The article draws on Goffman's concept of stigma and uses this to explore transitions in a society that retains some traditional characteristics, particularly the code of honour and shame, although mediated by aspects of modernity. Interviews were undertaken with 15 young people with the goal of producing narratives. The article analyses the experience of stigma, its effects and how young people manage its consequences. It concludes by drawing attention to the pervasive nature of stigma and the importance of structure, agency and reflexivity in youth transitions. In particular stigma remains an important feature of societies in which hetero-normative sexuality remains dominant

Stratification of Patients With Sjögren’s Syndrome and Patients With Systemic Lupus Erythematosus According to Two Shared Immune Cell Signatures, With Potential Therapeutic Implications

OBJECTIVE: Similarities in the clinical and laboratory features of patients with primary Sjögren's syndrome (pSS) and systemic lupus erythematosus (SLE) have led to attempts to treat pSS and SLE patients with similar biologic therapeutics. However, the results of many clinical trials are disappointing, and no biologic treatments are licensed in pSS, while few are available for SLE patients with refractory disease. Identifying shared immunological features between pSS and SLE could lead to better treatment selection using a stratification approach. METHODS: Immune-phenotyping of 29 immune-cell subsets in peripheral blood from patients with pSS (n=45), SLE (n=29) and secondary SS associated with SLE (SLE/SS) (n=14) with low disease activity or in clinical remission, and sex-matched healthy controls (n=31), was performed using flow cytometry. Data were analysed using supervised machine learning (balanced random forest, sparse partial least squares discriminant analysis), logistic regression and multiple t-tests. Patients were stratified by k-means clustering, and clinical trajectory analysis. RESULTS: Patients with pSS and SLE had a similar immunological architecture despite having different clinical presentations and prognosis. K-means cluster analysis of the combined pSS, SLE and SLE/SS patient cohorts identified two endotypes characterized by distinct immune-cell profiles which spanned patient diagnoses. Logistic regression and machine learning models identified a signature of eight T-cell subsets that differentiated between the two endotypes with high accuracy (AUC=0.9979). Baseline and five-year clinical trajectory analysis identified differential damage scores and disease activity between the two endotypes. CONCLUSION: An immune-cell toolkit could differentiate patients across diseases with high accuracy for targeted therapeutic approaches

Evidence that the C-terminal PB2-binding region of the influenza A virus PB1 protein is a discrete alpha-helical domain

AbstractThe influenza A virus RNA-dependent RNA polymerase is a heterotrimer composed of PB1, PB2 and PA subunits and essential for viral replication. However, little detailed structural information is available for this important enzyme. We show by circular dichroism spectroscopy that polypeptides from the C-terminus of PB1 that are capable of binding efficiently to PB2 fold into stable α-helical structures. Structure prediction analysis of this region of PB1 indicates that it likely consists of a three-helical bundle. Deletion of any of the helices abrogated transcriptional function. Thus, PB1 contains a C-terminal α-helical PB2-binding domain that is essential for nucleotide polymerization activity