Detection of ice core particles via deep neural networks

Insoluble particles in ice cores record signatures of past climate parameters like vegetation, volcanic activity or aridity. Their analytical detection depends on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge and often restrict sampling strategies. To help overcome these limitations, we present a framework based on Flow Imaging Microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify 7 commonly found classes: mineral dust, felsic and basaltic volcanic ash (tephra), three species of pollen (Corylus avellana, Quercus robur, Quercus suber) and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8 % classification accuracy at test time. We present the system’s potentials and limitations with respect to the detection of mineral dust, pollen grains and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non destructive, fully reproducible and does not require any sample preparation step. The presented framework can bolster research in the field, by cutting down processing time, supporting human-operated microscopy and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented

Binary Black Hole Mergers in the first Advanced LIGO Observing Run

The first observational run of the Advanced LIGO detectors, from September 12, 2015 to January 19, 2016, saw the first detections of gravitational waves from binary black hole mergers. In this paper we present full results from a search for binary black hole merger signals with total masses up to $100 M_\odot$ and detailed implications from our observations of these systems. Our search, based on general-relativistic models of gravitational wave signals from binary black hole systems, unambiguously identified two signals, GW150914 and GW151226, with a significance of greater than $5\sigma$ over the observing period. It also identified a third possible signal, LVT151012, with substantially lower significance, which has a 87% probability of being of astrophysical origin. We provide detailed estimates of the parameters of the observed systems. Both GW150914 and GW151226 provide an unprecedented opportunity to study the two-body motion of a compact-object binary in the large velocity, highly nonlinear regime. We do not observe any deviations from general relativity, and place improved empirical bounds on several high-order post-Newtonian coefficients. From our observations we infer stellar-mass binary black hole merger rates lying in the range $9-240 \mathrm{Gpc}^{-3} \mathrm{yr}^{-1}$. These observations are beginning to inform astrophysical predictions of binary black hole formation rates, and indicate that future observing runs of the Advanced detector network will yield many more gravitational wave detections

A scoring system to predict the severity of appendicitis in children

BACKGROUND: It appears that two forms of appendicitis exist. Preoperative distinction between the two is essential to optimize treatment outcome. This study aimed to develop a scoring system to accurately determine the severity of appendicitis in children. MATERIALS AND METHODS: Historical cohort study of pediatric patients (aged 0-17 y old) with appendicitis treated between January 2010 and December 2012. Division into simple, complex appendicitis, or another condition based on preset criteria. Multiple logistic regression analysis was used to build the prediction model with subsequent validation. RESULTS: There were 64 patients with simple and 66 with complex appendicitis. Five variables explained 64% of the variation. Independent validation of the derived prediction model in a second cohort (55 simple and 10 complex appendicitis patients) demonstrated 90% sensitivity (54-99), 91% specificity (79-97), a positive predictive value of 64% (36-86), and an negative predictive value of 98% (88-100). The likelihood ratio+ was 10 (4.19-23.42), and likelihood ratio- was 0.11 (0.02-0.71). Diagnostic accuracy was 91% (84-98). CONCLUSIONS: Our scoring system consisting of five variables can be used to exclude complex appendicitis in clinical practice if the score is <4

Gene expression profiles of esophageal squamous cell cancers in Hodgkin lymphoma survivors versus sporadic cases.

Hodgkin lymphoma (HL) survivors are at increased risk of developing second primary esophageal squamous cell cancer (ESCC). We aimed to gain insight in the driving events of ESCC in HL survivors (hESCC) by using RNA sequencing and NanoString profiling. Objectives were to investigate differences in RNA signaling between hESCC and sporadic ESCC (sESCC), and to look for early malignant changes in non-neoplastic esophageal tissue of HL survivors (hNN-tissue). We analyzed material of 26 hESCC cases, identified via the Dutch pathology registry (PALGA) and 17 sESCC cases from one academic institute and RNA sequencing data of 44 sESCC cases from TCGA. Gene expression profiles for the NanoString panel PanCancer IO 360 were obtained from 16/26 hESCC and four hNN-tissue, while non-neoplastic squamous tissue of four sporadic cases (sNN-tissue) served as reference profile. Hierarchical clustering, differential expression and pathway analyses were performed. Overall, the molecular profiles of hESCC and sESCC were similar. There was increased immune, HMGB1 and ILK signaling compared to sNN-tissue. The profiles of hNN-tissue were distinct from sNN-tissue, indicating early field effects in the esophagus of HL survivors. The BRCA1 pathway was upregulated in hESCC tissue, compared to hNN tissue. Analysis of expression profiles reveals overlap between hESCC and sESCC, and differences between hESCC and its surrounding hNN-tissue. Further research is required to validate our results and to investigate whether the changes observed in hNN-tissue are already detectable before development of hESCC. In the future, our findings could be used to improve hESCC patient management

Applications of Evolutionary Computation

The application of genetic and evolutionary computation to problems in medicine has increased rapidly over the past five years, but there are specific issues and challenges that distinguish it from other real-world applications. Obtaining reliable and coherent patient data, establishing the clinical need and demonstrating value in the results obtained are all aspects that require careful and detailed consideration. This tutorial is based on research which uses genetic programming (a representation of Cartesian Genetic Programming) in the diagnosis and monitoring of Parkinson's disease, Alzheimer's disease and other neurodegenerative conditions, as well as in the early detection of breast cancer through automated assessment of mammograms. The work is supported by multiple clinical studies in progress in the UK (Leeds General Infirmary), USA (UCSF), UAE (Dubai Rashid Hospital), Australia (Monash Medical Center) and Singapore (National Neuroscience Institute). The technology is protected through three patent applications and a University spin-out company marketing four medical devices. The tutorial considers the following topics: Introduction to medical applications of genetic and evolutionary computation and how these differ from other real-world applications Overview of past work in the from a medical and evolutionary computation point of view Three case examples of medical applications: i. diagnosis and monitoring of Parkinson's disease ii. detection of beast cancer from mammograms iii. cancer screening using Raman spectroscopy Practical advice on how to get started working on medical applications, including existing medical databases and conducting new medical studies, commercialization and protecting intellectual property. Summary, further reading and link