Overview of the ImageCLEFmed 2019 concept detection task

This paper describes the ImageCLEF 2019 Concept Detection Task. This is the 3rd edition of the medical caption task, after it was first proposed in ImageCLEF 2017. Concept detection from medical images remains a challenging task. In 2019, the format changed to a single subtask and it is part of the medical tasks, alongside the tuberculosis and visual question and answering tasks. To reduce noisy labels and limit variety, the data set focuses solely on radiology images rather than biomedical figures, extracted from the biomedical open access literature (PubMed Central). The development data consists of 56,629 training and 14,157 validation images, with corresponding Unified Medical Language System (UMLSR) concepts, extracted from the image captions. In 2019 the participation is higher, regarding the number of participating teams as well as the number of submitted runs. Several approaches were used by the teams, mostly deep learning techniques. Long short-term memory (LSTM) recurrent neural networks (RNN), adversarial auto-encoder, convolutional neural networks (CNN) image encoders and transfer learning-based multi-label classification models were the frequently used approaches. Evaluation uses F1-scores computed per image and averaged across all 10,000 test images

Nanometer-scale characterization of laser-driven compression, shocks, and phase transitions, by x-ray scattering using free electron lasers

We study the feasibility of using small angle X-ray scattering (SAXS) as a new experimental diagnostic for intense laser-solid interactions. By using X-ray pulses from a hard X-ray free electron laser, we can simultaneously achieve nanometer and femtosecond resolution of laser-driven samples. This is an important new capability for the Helmholtz international beamline for extreme fields at the high energy density endstation currently built at the European X-ray free electron laser. We review the relevant SAXS theory and its application to transient processes in solid density plasmas and report on first experimental results that confirm the feasibility of the method. We present results of two test experiments where the first experiment employs ultra-short laser pulses for studying relativistic laser plasma interactions, and the second one focuses on shock compression studies with a nanosecond laser system

Pharmacological Assessment of the Medicinal Potential of Acacia mearnsii De Wild.: Antimicrobial and Toxicity Activities

Acacia mearnsii De Wild. (Fabaceae) is a medicinal plant used in the treatment of microbial infections in South Africa without scientific validation of its bioactivity and toxicity. The antimicrobial activity of the crude acetone extract was evaluated by both agar diffusion and macrobroth dilution methods while its cytotoxicity effect was assessed with brine shrimp lethality assay. The study showed that both bacterial and fungal isolates were highly inhibited by the crude extract. The MIC values for the gram-positive bacteria (78.1–312.5) μg/mL, gram-negative bacteria (39.1–625) μg/mL and fungal isolates (625–5000) μg/mL differ significantly. The bacteria were more susceptible than the fungal strains tested. The antibiosis determination showed that the extract was more (75%) bactericidal than bacteriostatic (25%) and more fungicidal (66.67%) than fungistatic (33.33%). The cytotoxic activity of the extract was observed between 31.25 μg/mL and 500 μg/mL and the LC50 value (112.36 μg/mL) indicates that the extract was nontoxic in the brine shrimp lethality assay (LC50 > 100 μg/mL). These results support the use of A. mearnsii in traditional medicine for treatment of microbial infections. The extract exhibiting significant broad spectrum antimicrobial activity and nontoxic effects has potential to yield active antimicrobial compounds

Unconventional Transcriptional Response to Environmental Enrichment in a Mouse Model of Rett Syndrome

Background: Rett syndrome (RTT) is an X-linked postnatal neurodevelopmental disorder caused by mutations in the gene encoding methyl-CpG binding protein 2 (MeCP2) and one of the leading causes of mental retardation in females. RTT is characterized by psychomotor retardation, purposeless hand movements, autistic-like behavior and abnormal gait. We studied the effects of environmental enrichment (EE) on the phenotypic manifestations of a RTT mouse model that lacks MeCP2 (Mecp2 2/y). Principal Findings: We found that EE delayed and attenuated some neurological alterations presented by Mecp2 2/y mice and prevented the development of motor discoordination and anxiety-related abnormalities. To define the molecular correlate of this beneficial effect of EE, we analyzed the expression of several synaptic marker genes whose expression is increased by EE in several mouse models. Conclusions/Significance: We found that EE induced downregulation of several synaptic markers, suggesting that th

High-pressure chemistry of hydrocarbons relevant to planetary interiors and inertial confinement fusion

Diamond formation in polystyrene (C8H8)n, which is laser-compressed and heated to conditions around 150 GPa and 5000 K, has recently been demonstrated in the laboratory [Kraus et al., Nat. Astron. 1, 606–611 (2017)]. Here, we show an extended analysis and comparison to first-principles simulations of the acquired data and their implications for planetary physics and inertial confinement fusion. Moreover, we discuss the advanced diagnostic capabilities of adding high-quality small angle X-ray scattering and spectrally resolved X-ray scattering to the platform, which shows great prospects of precisely studying the kinetics of chemical reactions in dense plasma environments at pressures exceeding 100 GPa

Targets for high repetition rate laser facilities: Needs, challenges and perspectives

A number of laser facilities coming online all over the world promise the capability of high-power laser experiments with shot repetition rates between 1 and 10Ã\u82 Hz. Target availability and technical issues related to the interaction environment could become a bottleneck for the exploitation of such facilities. In this paper, we report on target needs for three different classes of experiments: Dynamic compression physics, electron transport and isochoric heating, and laser-driven particle and radiation sources. We also review some of the most challenging issues in target fabrication and high repetition rate operation. Finally, we discuss current target supply strategies and future perspectives to establish a sustainable target provision infrastructure for advanced laser facilities

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation

Electron-ion temperature relaxation in warm dense hydrogen observed with picosecond resolved X-Ray scattering

Angularly resolved X-ray scattering measurements from fs-laser heated hydrogen have been used to determine the equilibration of electron and ion temperatures in the warm dense matter regime. The relaxation of rapidly heated cryogenic hydrogen is visualized using 5.5 keV X-ray pulses from the Linac Coherent Light (LCLS) source in a 1 Hz repetition rate pump-probe setting. We demonstrate that the electron-ion energy transfer is faster than quasi-classical Landau-Spitzer models that use ad hoc cutoffs in the Coulomb logarithm

Towards a new image processing system at Wendelstein 7-X: From spatial calibration to characterization of thermal events

Wendelstein 7-X (W7-X) is the most advanced fusion experiment in the stellarator line and is aimed at proving that the stellarator concept is suitable for a fusion reactor. One of the most important issues for fusion reactors is the monitoring of plasma facing components when exposed to very high heat loads, through the use of visible and infrared (IR) cameras. In this paper, a new image processing system for the analysis of the strike lines on the inboard limiters from the first W7-X experimental campaign is presented. This system builds a model of the IR cameras through the use of spatial calibration techniques, helping to characterize the strike lines by using the information given by real spatial coordinates of each pixel. The characterization of the strike lines is made in terms of position, size, and shape, after projecting the camera image in a 2D grid which tries to preserve the curvilinear surface distances between points. The description of the strike-line shape is made by means of the Fourier Descriptors