Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression.

Purpose:To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT) images to identify novel, glaucoma-related structural features and improve detection of glaucoma and prediction of future glaucomatous progression. Methods:Wide-angle SS-OCT, OCT circumpapillary retinal nerve fiber layer (cpRNFL) circle scans spectral-domain (SD)-OCT, standard automated perimetry (SAP), and frequency doubling technology (FDT) visual field tests were completed every 3 months for 2 years from a cohort of 28 healthy participants (56 eyes) and 93 glaucoma participants (179 eyes). RNFL thickness maps were extracted from segmented SS-OCT images and an unsupervised machine learning approach based on principal component analysis (PCA) was used to identify novel structural features. Area under the receiver operating characteristic curve (AUC) was used to assess diagnostic accuracy of RNFL PCA for detecting glaucoma and progression compared to SAP, FDT, and cpRNFL measures. Results:The RNFL PCA features were significantly associated with mean deviation (MD) in both SAP (R2 = 0.49, P < 0.0001) and FDT visual field testing (R2 = 0.48, P < 0.0001), and with mean circumpapillary RNFL thickness (cpRNFLt) from SD-OCT (R2 = 0.58, P < 0.0001). The identified features outperformed each of these measures in detecting glaucoma with an AUC of 0.95 for RNFL PCA compared to an 0.90 for mean cpRNFLt (P = 0.09), 0.86 for SAP MD (P = 0.034), and 0.83 for FDT MD (P = 0.021). Accuracy in predicting progression was also significantly higher for RNFL PCA compared to SAP MD, FDT MD, and mean cpRNFLt (P = 0.046, P = 0.007, and P = 0.044, respectively). Conclusions:A computational approach can identify structural features that improve glaucoma detection and progression prediction

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

Book reviews

Teaching/Communication/Extension/Profession,

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

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

Management strategies to minimize the dredging impacts of coastal development on fish and fisheries

Accelerating coastal development and shipping activities dictate that dredging operations will intensify, increasing potential impacts to fishes. Coastal fishes have high economic, ecological, and conservation significance and there is a need for evidencebased, quantitative guidelines on how to mitigate the impacts of dredging activities. We assess the potential risk from dredging to coastal fish and fisheries on a global scale.We then develop quantitative guidelines for two management strategies: threshold reference values and seasonal restrictions. Globally, threatened species and nearshore fisheries occur within close proximity to ports. We find that maintaining suspended sediment concentrations below 44 mg/L (15–121 bootstrapped CI) and for less than 24 hours would protect 95% of fishes from dredging-induced mortality. Implementation of seasonal restrictions during peak periods of reproduction and recruitment could further protect species from dredging impacts. This study details the first evidence-based defensible approach to minimize impacts to coastal fishes from dredging activities

Dependently-Typed Formalisation of Typed Term Graphs

We employ the dependently-typed programming language Agda2 to explore formalisation of untyped and typed term graphs directly as set-based graph structures, via the gs-monoidal categories of Corradini and Gadducci, and as nested let-expressions using Pouillard and Pottier's NotSoFresh library of variable-binding abstractions.Comment: In Proceedings TERMGRAPH 2011, arXiv:1102.226

Multiple breath washout in bronchiectasis clinical trials:is it feasible?

Background: Evaluation of Multiple Breath Washout (MBW) set-up including staff training, certification and central “over-reading” for data quality control is essential to determine the feasibility of MBW in future bronchiectasis studies. Aims: To assess the outcomes of a MBW training, certification and central over-reading programme. Methods: MBW training and certification was conducted in European sites collecting LCI data in the BronchUK clinimetrics and/or i-BEST-1 studies. The blended training programme included the use of an eLearning tool and a 1-day face-to-face session. Sites submitted MBW data to trained central over-readers who determined validity and quality. Results: Thirteen training days were delivered to 56 participants from 22 sites. 18/22 (82%) were MBW naïve. Participant knowledge and confidence increased significantly (p<0.001). By the end of the study recruitment, 15/22 sites (68%) had completed certification with a mean (range) time since training of 6.2 (3-14) months. In the BronchUK clinimetrics study, 468/589 (79%) tests met45 the quality criteria following central over-reading, compared with 137/236 (58%) tests in the i-BEST-1 study. Conclusions: LCI is feasible in a bronchiectasis multicentre clinical trial setting however, consideration of site experience in terms of training as well as assessment of skill drift and the need for re-training may be important to reduce time to certification and optimise data quality. Longer times to certification, a higher percentage of naive sites and patients with worse lung function may have contributed to the lower success rate in the i-BEST-1 study