Design, installation and commissioning of new read-out electronics for HADES ECAL and diamond detectors for T0-reconstruction and beam diagnostics

This work deals with the design, installation and commissioning of the front-end electronics for the newly installed HADES electromagnetic calorimeter (ECAL) detector at GSI Helmholtzzentrum für Schwerionenforschung GmbH in Darmstadt. A Charge-to-Digital-Converter (QDC) and Time-to-Digital-Converter (TDC) based on a commercial FPGA (Field Programmable Gate Array) technology is used to read out 978 Photomultiplier tubes (PMT) of the ECAL. The charge measurement of the detector signals is based on a modified time-over-threshold (TOT) measuring method. In the context of this work the second generation of the PaDiWa-AMPS front-end board for the TRB3 (General Purpose Trigger and Readout Board - generation 3) was designed, tested in the laboratory and integrated into the HADES data acquisition infrastructure. The front-end achieves a time measurement precision of σₜ = 16 ps. The relative charge measurement precision for signal amplitudes above 1 V is below 0.5 %. A successful operation of the read-out system was shown during a four week physics production beam time with an 1.58A GeV Ag beam. A similar read-out concept is used to read out diamond based beam detectors in the HADES experiment. Those detectors are used as a trigger and for the T0 determination in the HADES time-of-flight measuring system, which is important for the particle identification. Beside this, they are used for online beam monitoring purposes. The requirement for the time precision of the sensors is about 50 ps. Currently the read-out system is adapted to new Ultra-Fast Silicon Detector (UFSD) technology which might replace the diamond detectors in the HADES experiment in the future. A UFSD prototype detector has been tested successfully with a proton beam. Furthermore, it is planned to use this technology as a diagnostic instrument for Energy Recovery Linac (ERL) operations of the electron accelerator S-DALINAC at TU Darmstadt in future. For further research and development of beam detectors a permanent multi-purpose detector test set-up was installed at the S-DALINAC. It allows tests of detectors with an electron beam with an energy up to 130 MeV and beam currents up to 20 µA. The set-up has been successfully commissioned and offers optimal conditions for future tests for research and development of beam detectors with a beam of minimum ionizing particles

Medical Image Imputation from Image Collections

We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large data sets of clinical images contain a wealth of information, time constraints during acquisition result in sparse scans that fail to capture much of the anatomy. These characteristics often render computational analysis impractical as many image analysis algorithms tend to fail when applied to such images. Highly specialized algorithms that explicitly handle sparse slice spacing do not generalize well across problem domains. In contrast, we aim to enable application of existing algorithms that were originally developed for high resolution research scans to significantly undersampled scans. We introduce a generative model that captures fine-scale anatomical structure across subjects in clinical image collections and derive an algorithm for filling in the missing data in scans with large inter-slice spacing. Our experimental results demonstrate that the resulting method outperforms state-of-the-art upsampling super-resolution techniques, and promises to facilitate subsequent analysis not previously possible with scans of this quality. Our implementation is freely available at https://github.com/adalca/papago .Comment: Accepted at IEEE Transactions on Medical Imaging (\c{opyright} 2018 IEEE

Medical Image Imputation From Image Collections

We present an algorithm for creating high-resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large data sets of clinical images contain a wealth of information, time constraints during acquisition result in sparse scans that fail to capture much of the anatomy. These characteristics often render computational analysis impractical as many image analysis algorithms tend to fail when applied to such images. Highly specialized algorithms that explicitly handle sparse slice spacing do not generalize well across problem domains. In contrast, we aim to enable the application of existing algorithms that were originally developed for high-resolution research scans to significantly undersampled scans. We introduce a generative model that captures a fine-scale anatomical structure across subjects in clinical image collections and derives an algorithm for filling in the missing data in scans with large inter-slice spacing. Our experimental results demonstrate that the resulting method outperforms the state-of-the-art upsampling super-resolution techniques, and promises to facilitate subsequent analysis not previously possible with scans of this quality. Our implementation is freely available at https://github.com/adalca/papago

Optogenetic Control of Subcellular Protein Location and Signaling in Vertebrate Embryos.

This chapter describes the use of optogenetic heterodimerization in single cells within whole-vertebrate embryos. This method allows the use of light to reversibly bind together an "anchor" protein and a "bait" protein. Proteins can therefore be directed to specific subcellular compartments, altering biological processes such as cell polarity and signaling. I detail methods for achieving transient expression of fusion proteins encoding the phytochrome heterodimerization system in early zebrafish embryos (Buckley et al., Dev Cell 36(1):117-126, 2016) and describe the imaging parameters used to achieve subcellular light patterning

Array analysis of electromagnetic radiation from radio transmitters for submarine communication

International audienceThe array analyses used for seismic and infrasound research are adapted and applied here to the electromagnetic radiation from radio transmitters for submarine communication. It is found that the array analysis enables a determination of the slowness and the arrival azimuth of the wave number vectors associated with the electromagnetic radiation. The array analysis is applied to measurements of ∼20–24 kHz radio waves from transmitters for submarine communication with an array of 10 radio receivers distributed over an area of ∼1 km ×1 km. The observed slowness of the observed wave number vectors range from ∼2.7 ns/m to ∼4.1 ns/m, and the deviations between the expected arrival azimuths and the observed arrival azimuths range from ∼−9.7° to ∼14.5°. The experimental results suggest that it is possible to determine the locations of radio sources from transient luminous events above thunderclouds with an array of radio receivers toward detailed investigations of the electromagnetic radiation from sprites

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes

The relevance of rich club regions for functional outcome post-stroke is enhanced in women

This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich-club) brain regions on functional outcome post-stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and similar to 3-months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI-GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas-defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non-rich club regions. In spatial specificity analyses, we randomized the split into "rich club" and "non-rich club" regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex-specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female-specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club-combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long-term outcome in women than in men. All in all, lesions in rich dub regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.Peer reviewe

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent. Keywords: Protein function prediction, Disease gene prioritizationpublishedVersio