GRB 110205A: Anatomy of a long gamma-ray burst

The Swift burst GRB 110205A was a very bright burst visible in the Northern hemisphere. GRB 110205A was intrinsically long and very energetic and it occurred in a low-density interstellar medium environment, leading to delayed afterglow emission and a clear temporal separation of the main emitting components: prompt emission, reverse shock, and forward shock. Our observations show several remarkable features of GRB 110205A : the detection of prompt optical emission strongly correlated with the BAT light curve, with no temporal lag between the two ; the absence of correlation of the X-ray emission compared to the optical and high energy gamma-ray ones during the prompt phase ; and a large optical re-brightening after the end of the prompt phase, that we interpret as a signature of the reverse shock. Beyond the pedagogical value offered by the excellent multi-wavelength coverage of a GRB with temporally separated radiating components, we discuss several questions raised by our observations: the nature of the prompt optical emission and the spectral evolution of the prompt emission at high-energies (from 0.5 keV to 150 keV) ; the origin of an X-ray flare at the beginning of the forward shock; and the modeling of the afterglow, including the reverse shock, in the framework of the classical fireball model.Comment: 21 pages, 5 figure (all in colors), accepted for publication in Ap

Visual on-line learning in distributed camera networks

Automatic detection of persons is an important application in visual surveillance. In general, state-of-the-art systems have two main disadvantages: First, usually a general detector has to be learned that is applicable to a wide range of scenes. Thus, the training is time-consuming and requires a huge amount of labeled data. Second, the data is usually processed centralized, which leads to a huge network traffic. Thus, the goal of this paper is to overcome these problems, which is realized by a person detection system, that is based on distributed smart cameras (DSCs). Assuming that we have a large number of cameras with partly overlapping views, the main idea is to reduce the model complexity of the detector by training a specific detector for each camera. These detectors are initialized by a pre-trained classifier, that is then adapted for a specific camera by co-training. In particular, for co-training we apply an on-line learning method (i.e., boosting for feature selection), where the information exchange is realized via mapping the overlapping views onto each other by using a homography. Thus, we have a compact scenedependent representation, which allows to train and to evaluate the classifiers on an embedded device. Moreover, since the information transfer is reduced to exchanging positions the required network-traffic is minimal. The power of the approach is demonstrated in various experiments on different publicly available data sets. In fact, we show that on-line learning and applying DSCs can benefit from each other. Index Terms — visual on-line learning, object detection, multi-camera networks 1

The everyday lives of in- and outpatients when beginning therapy: The importance of values-consistent behavior

The manifestation of functional impairment in patients’ daily lives and interference with things they value is poorly understood. If values are compromised in patients, as theory suggests, social contexts (and the lack thereof) are especially important – though this is currently unexplored. We therefore examined whether daily values-consistent behavior was associated with the importance of a value and whether it involved social or non-social activity. Using Event Sampling Methodology, we examined daily values-consistent behavior in 57 transdiagnostic inpatients and 43 transdiagnostic outpatients at the beginning of treatment. Patients’ values-consistent behavior, its importance, and (social vs non-social) context was sampled six times per day during a one-week intensive longitudinal examination. Across both groups, the probability of subsequent values-consistent behavior increased if (1) it was judged as more important by the patient or (2) if it was embedded in a social context. The probability of reporting values-consistent behavior was higher for outpatients than inpatients. Clinicians are encouraged to examine the values of their patients more closely and to especially monitor important and/or social values. Incorporating these into clinical work might increase patients’ values-consistent behavior, which can play a role in reducing suffering

Modularity and hormone sensitivity of the Drosophila melanogaster insulin receptor/target of rapamycin interaction proteome

First systematic analysis of the evolutionary conserved InR/TOR pathway interaction proteome in Drosophila.Quantitative mass spectrometry revealed that 22% of identified protein interactions are regulated by the growth hormone insulin affecting membrane proximal as well as intracellular signaling complexes.Systematic RNA interference linked a significant fraction of network components to the control of dTOR kinase activity.Combined biochemical and genetic data suggest dTTT, a dTOR-containing complex required for cell growth control by dTORC1 and dTORC2 in vivo

Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge

Previous studies of artificial intelligence (AI) applied to dermatology have shown AI to have higher diagnostic classification accuracy than expert dermatologists; however, these studies did not adequately assess clinically realistic scenarios, such as how AI systems behave when presented with images of disease categories that are not included in the training dataset or images drawn from statistical distributions with significant shifts from training distributions. We aimed to simulate these real-world scenarios and evaluate the effects of image source institution, diagnoses outside of the training set, and other image artifacts on classification accuracy, with the goal of informing clinicians and regulatory agencies about safety and real-world accuracy.We designed a large dermoscopic image classification challenge to quantify the performance of machine learning algorithms for the task of skin cancer classification from dermoscopic images, and how this performance is affected by shifts in statistical distributions of data, disease categories not represented in training datasets, and imaging or lesion artifacts. Factors that might be beneficial to performance, such as clinical metadata and external training data collected by challenge participants, were also evaluated. 25?331 training images collected from two datasets (in Vienna [HAM10000] and Barcelona [BCN20000]) between Jan 1, 2000, and Dec 31, 2018, across eight skin diseases, were provided to challenge participants to design appropriate algorithms. The trained algorithms were then tested for balanced accuracy against the HAM10000 and BCN20000 test datasets and data from countries not included in the training dataset (Turkey, New Zealand, Sweden, and Argentina). Test datasets contained images of all diagnostic categories available in training plus other diagnoses not included in training data (not trained category). We compared the performance of the algorithms against that of 18 dermatologists in a simulated setting that reflected intended clinical use.64 teams submitted 129 state-of-the-art algorithm predictions on a test set of 8238 images. The best performing algorithm achieved 58·8% balanced accuracy on the BCN20000 data, which was designed to better reflect realistic clinical scenarios, compared with 82·0% balanced accuracy on HAM10000, which was used in a previously published benchmark. Shifted statistical distributions and disease categories not included in training data contributed to decreases in accuracy. Image artifacts, including hair, pen markings, ulceration, and imaging source institution, decreased accuracy in a complex manner that varied based on the underlying diagnosis. When comparing algorithms to expert dermatologists (2460 ratings on 1269 images), algorithms performed better than experts in most categories, except for actinic keratoses (similar accuracy on average) and images from categories not included in training data (26% correct for experts vs 6% correct for algorithms, p<0·0001). For the top 25 submitted algorithms, 47·1% of the images from categories not included in training data were misclassified as malignant diagnoses, which would lead to a substantial number of unnecessary biopsies if current state-of-the-art AI technologies were clinically deployed.We have identified specific deficiencies and safety issues in AI diagnostic systems for skin cancer that should be addressed in future diagnostic evaluation protocols to improve safety and reliability in clinical practice.Melanoma Research Alliance and La Marató de TV3.Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved

ICC-CLASS: isotopically-coded cleavable crosslinking analysis software suite

<p>Abstract</p> <p>Background</p> <p>Successful application of crosslinking combined with mass spectrometry for studying proteins and protein complexes requires specifically-designed crosslinking reagents, experimental techniques, and data analysis software. Using isotopically-coded ("heavy and light") versions of the crosslinker and cleavable crosslinking reagents is analytically advantageous for mass spectrometric applications and provides a "handle" that can be used to distinguish crosslinked peptides of different types, and to increase the confidence of the identification of the crosslinks.</p> <p>Results</p> <p>Here, we describe a program suite designed for the analysis of mass spectrometric data obtained with isotopically-coded <it>cleavable </it>crosslinkers. The suite contains three programs called: DX, DXDX, and DXMSMS. DX searches the mass spectra for the presence of ion signal doublets resulting from the light and heavy isotopic forms of the isotopically-coded crosslinking reagent used. DXDX searches for possible mass matches between cleaved and uncleaved isotopically-coded crosslinks based on the established chemistry of the cleavage reaction for a given crosslinking reagent. DXMSMS assigns the crosslinks to the known protein sequences, based on the isotopically-coded and un-coded MS/MS fragmentation data of uncleaved and cleaved peptide crosslinks.</p> <p>Conclusion</p> <p>The combination of these three programs, which are tailored to the analytical features of the specific isotopically-coded cleavable crosslinking reagents used, represents a powerful software tool for automated high-accuracy peptide crosslink identification. See: <url>http://www.creativemolecules.com/CM_Software.htm</url></p