Construction and evaluation of classifiers for forensic document analysis

In this study we illustrate a statistical approach to questioned document examination. Specifically, we consider the construction of three classifiers that predict the writer of a sample document based on categorical data. To evaluate these classifiers, we use a data set with a large number of writers and a small number of writing samples per writer. Since the resulting classifiers were found to have near perfect accuracy using leave-one-out cross-validation, we propose a novel Bayesian-based cross-validation method for evaluating the classifiers.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS379 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Session 8: \u3cem\u3eStatistical Discrimination Methods for Forensic Source Interpretation of Aluminum Powders in Explosives\u3c/em\u3e

Aluminum (Al) powder is often used as a fuel in explosive devices; therefore, individuals attempting to make illegal improvised explosive devices often obtain it from legitimate commercial products or make it themselves using readily available Al starting materials. The characterization and differentiation between sources of Al powder for additional investigative and intelligence value has become increasingly important. Previous research modeled the distributions of micromorphometric features of Al powder particles within a subsample to support Al source discrimination. Since then, additional powder samples from a variety of different source types have been obtained and analyzed, providing a more comprehensive dataset for applying the two statistical methods for interpretation and discrimination of source. Here, we compare two different statistical techniques: one using linear discriminant analysis (LDA), and the other using a modification to the method used in ASTM E2927-16e1 and E2330-19. The LDA method results in an Al source classification for each questioned sample. Alternatively, our modification to the ASTM method uses an interval-based match criterion to associate or exclude each of the known sources as the actual source of a trace. Although the outcomes of these two statistical methods are fundamentally different, their performance with respect to the closed-set identification of source problem is compared. Additionally, the modified ASTM method will be adapted to provide a vector of scores in lieu of the binary decision as the first step towards a score-based likelihood ratio for interpreting Al powder micromorphometric measurement data

Interactions between global and local stressors of ecosystems determine management effectiveness in cumulative impact mapping

Ai

Gene expression patterns that predict sensitivity to epidermal growth factor receptor tyrosine kinase inhibitors in lung cancer cell lines and human lung tumors

BACKGROUND: Increased focus surrounds identifying patients with advanced non-small cell lung cancer (NSCLC) who will benefit from treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI). EGFR mutation, gene copy number, coexpression of ErbB proteins and ligands, and epithelial to mesenchymal transition markers all correlate with EGFR TKI sensitivity, and while prediction of sensitivity using any one of the markers does identify responders, individual markers do not encompass all potential responders due to high levels of inter-patient and inter-tumor variability. We hypothesized that a multivariate predictor of EGFR TKI sensitivity based on gene expression data would offer a clinically useful method of accounting for the increased variability inherent in predicting response to EGFR TKI and for elucidation of mechanisms of aberrant EGFR signalling. Furthermore, we anticipated that this methodology would result in improved predictions compared to single parameters alone both in vitro and in vivo. RESULTS: Gene expression data derived from cell lines that demonstrate differential sensitivity to EGFR TKI, such as erlotinib, were used to generate models for a priori prediction of response. The gene expression signature of EGFR TKI sensitivity displays significant biological relevance in lung cancer biology in that pertinent signalling molecules and downstream effector molecules are present in the signature. Diagonal linear discriminant analysis using this gene signature was highly effective in classifying out-of-sample cancer cell lines by sensitivity to EGFR inhibition, and was more accurate than classifying by mutational status alone. Using the same predictor, we classified human lung adenocarcinomas and captured the majority of tumors with high levels of EGFR activation as well as those harbouring activating mutations in the kinase domain. We have demonstrated that predictive models of EGFR TKI sensitivity can classify both out-of-sample cell lines and lung adenocarcinomas. CONCLUSION: These data suggest that multivariate predictors of response to EGFR TKI have potential for clinical use and likely provide a robust and accurate predictor of EGFR TKI sensitivity that is not achieved with single biomarkers or clinical characteristics in non-small cell lung cancers

An Overview of the A-3 Subscale Diffuser Test Project

The Subscale Diffuser Test (SDT) Project comprised a series of tests of a subscale model of SSC s A-3 Test Stand diffuser. SDT was conducted at NASA s Stennis Space Center (SSC) Apr 2007 - Jan 2008. Purpose of SDT was to mitigate design risk for the A-3 diffuser. Initial scope of the SDT project successfully completed in Jan 2008. Follow-on A-3 risk mitigation testing being planned/considered. This presentation presents an overview of the SDT project

Using threat maps for cost-effective prioritization of actions to conserve coastal habitats

Marine coastal habitats provide valuable ecosystem services, including food provision, carbon sequestration, and coastal protection, but they are highly threatened by human activities. The multitude of human stressors affecting coastal habitats renders their conservation a difficult task for environmental agencies with limited budgets. This study, using seagrass meadows - one of the world's most threatened coastal habitats - proposes a transparent framework for the conservation of coastal habitats that links information from habitat and threat maps to conservation actions, and their costs. The proposed framework and the use of a predictive model of seagrass loss allowed the selection of the most cost-effective actions to abate stoppable threats (trawling and anchoring), while avoiding areas affected by threats that are more difficult to manage, such as coastal development. The relative improvement in cost achieved by using the proposed approach was examined by comparing with other common prioritization criteria that do not consider cost, including choosing sites based on threat level or habitat cover alone. The establishment of anti-trawling reefs was found to be the most cost-effective action to achieve the European Union conservation target for the protection of seagrass Posidonia oceanica meadows. The number of anti-trawling reefs and their establishment location was sensitive to fine-scale information on the distribution of fishing activities. The proposed approach always conserved the same habitat for lower cost than prioritization schemes that focus actions in areas of highest seagrass coverage or highest threat level. The study results suggest that conservation actions should not be prioritized on the basis of habitat maps and/or threat maps alone. Impact assessment and habitat vulnerability at a local scale would greatly benefit from detailed knowledge of the spatial distribution of stressors. At the same time, methods of scaling up the quantitative impact of stressors are urgently needed to understand their relationship with seascape-wide habitat coverage and to inform conservation of coastal habitats. (C) 2015 Elsevier Ltd. All rights reserved

Computed cardiopulmonography and the idealized lung clearance index, iLCI2.5, in early-stage cystic fibrosis

This study explored the use of computed cardiopulmonography (CCP) to assess lung function in early-stage cystic fibrosis (CF). CCP has two components. The first is a particularly accurate technique for measuring gas exchange. The second is a computational cardiopulmonary model where patient-specific parameters can be estimated from the measurements of gas exchange. Twenty-five participants (14 healthy controls, 11 early-stage CF) were studied with CCP. They were also studied with a standard clinical protocol to measure the lung clearance index (LCI2.5). Ventilation inhomogeneity, as quantified through CCP parameter σlnCl, was significantly greater (P < 0.005) in CF than in controls, and anatomical deadspace relative to predicted functional residual capacity (DS/FRCpred) was significantly more variable (P < 0.002). Participant-specific parameters were used with the CCP model to calculate idealized values for LCI2.5 (iLCI2.5) where extrapulmonary influences on the LCI2.5, such as breathing pattern, had all been standardized. Both LCI2.5 and iLCI2.5 distinguished clearly between CF and control participants. LCI2.5 values were mostly higher than iLCI2.5 values in a manner dependent on the participant’s respiratory rate (r = 0.46, P < 0.05). The within-participant reproducibility for iLCI2.5 appeared better than for LCI2.5, but this did not reach statistical significance (F ratio = 2.2, P = 0.056). Both a sensitivity analysis on iLCI2.5 and a regression analysis on LCI2.5 revealed that these depended primarily on an interactive term between CCP parameters of the form σlnCL*(DS/FRC). In conclusion, the LCI2.5 (or iLCI2.5) probably reflects an amalgam of different underlying lung changes in early-stage CF that would require a multiparameter approach, such as potentially CCP, to resolve

Spitzer view on the evolution of star-forming galaxies from z=0 to z~3

We use a 24 micron selected sample containing more than 8,000 sources to study the evolution of star-forming galaxies in the redshift range from z=0 to z~3. We obtain photometric redshifts for most of the sources in our survey using a method based on empirically-built templates spanning from ultraviolet to mid-infrared wavelengths. The accuracy of these redshifts is better than 10% for 80% of the sample. The derived redshift distribution of the sources detected by our survey peaks at around z=0.6-1.0 (the location of the peak being affected by cosmic variance), and decays monotonically from z~1 to z~3. We have fitted infrared luminosity functions in several redshift bins in the range 0<z<~3. Our results constrain the density and/or luminosity evolution of infrared-bright star-forming galaxies. The typical infrared luminosity (L*) decreases by an order of magnitude from z~2 to the present. The cosmic star formation rate (SFR) density goes as (1+z)^{4.0\pm0.2} from z=0 to z=0.8. From z=0.8 to z~1.2, the SFR density continues rising with a smaller slope. At 1.2<z<3, the cosmic SFR density remains roughly constant. The SFR density is dominated at low redshift (z<0.5) by galaxies which are not very luminous in the infrared (L_TIR<1.e11 L_sun, where L_TIR is the total infrared luminosity, integrated from 8 to 1000 micron). The contribution from luminous and ultraluminous infrared galaxies (L_TIR>1.e11 L_sun) to the total SFR density increases steadily from z~0 up to z~2.5, forming at least half of the newly-born stars by z~1.5. Ultraluminous infrared galaxies (L_TIR>1.e12 L_sun) play a rapidly increasing role for z>~1.3.Comment: 28 pages, 17 figures, accepted for publication in Ap

An AICD-based functional screen to identify APP metabolism regulators

<p>Abstract</p> <p>Background</p> <p>A central event in Alzheimer's disease (AD) is the regulated intramembraneous proteolysis of the β-amyloid precursor protein (APP), to generate the β-amyloid (Aβ) peptide and the APP intracellular domain (AICD). Aβ is the major component of amyloid plaques and AICD displays transcriptional activation properties. We have taken advantage of AICD transactivation properties to develop a genetic screen to identify regulators of APP metabolism. This screen relies on an APP-Gal4 fusion protein, which upon normal proteolysis, produces AICD-Gal4. Production of AICD-Gal4 induces Gal4-UAS driven luciferase expression. Therefore, when regulators of APP metabolism are modulated, luciferase expression is altered.</p> <p>Results</p> <p>To validate this experimental approach we modulated α-, β-, and γ-secretase levels and activities. Changes in AICD-Gal4 levels as measured by Western blot analysis were strongly and significantly correlated to the observed changes in AICD-Gal4 mediated luciferase activity. To determine if a known regulator of APP trafficking/maturation and Presenilin1 endoproteolysis could be detected using the AICD-Gal4 mediated luciferase assay, we knocked-down Ubiquilin 1 and observed decreased luciferase activity. We confirmed that Ubiquilin 1 modulated AICD-Gal4 levels by Western blot analysis and also observed that Ubiquilin 1 modulated total APP levels, the ratio of mature to immature APP, as well as PS1 endoproteolysis.</p> <p>Conclusion</p> <p>Taken together, we have shown that this screen can identify known APP metabolism regulators that control proteolysis, intracellular trafficking, maturation and levels of APP and its proteolytic products. We demonstrate for the first time that Ubiquilin 1 regulates APP metabolism in the human neuroblastoma cell line, SH-SY5Y.</p