Structure and recognition of polyubiquitin chains of different lengths and linkage

The polyubiquitin signal is post-translationally attached to a large number of proteins, often directing formation of macromolecular complexes resulting in the translocation, assembly or degradation of the attached protein. Recent structural and functional studies reveal general mechanisms by which different architectures and length of the signal are distinguished

SATURATION OF TUNNELING STATES IN GLASSES IN THE PRESENCE OF THE PHONON BOTTLENECK.

The author uses the continuous-time-random-walk theory as the basis of an investigation of tunneling two-level system behavior in the presence of both external field and phonon bottleneck

KINETIC PROPERTIES OF THE TWO-LEVEL SYSTEMS IN GLASSES WITH ACCOUNT OF TUNNELING.

The kinetics of two-level systems in glasses at low temperatures is studied with account of a distribution of their tunneling parameter values. Integral equations describing the process of spectral diffusion in a glass are obtained by the methods of the continuous time random walk theory. The long-time asymptotics of the process is a superposition of individual and 'collective' relaxations, the latter one is characterized by the single relaxation rate. Manifestation of the results in echo saturation recovery experiments in glasses is discussed

Quantum dots in photonic crystal cavities

During the past two decades, the development of micro- and nano-fabrication technologies has positively impacted multiple areas of science and engineering. In the photonics community, these technologies had numerous early adopters, which led to photonic devices that exhibit features at the nano-scale and operate at the most fundamental level of light–matter interaction [28, 39, 18, 29]. One of the leading platforms for these types of devices is based on gallium arsenide (GaAs) planar photonic crystals (PC) with embedded indium arsenide (InAs) quantum dots (QDs). The PC architecture is advantageous because it enables monolithic fabrication of photonic networks for efficient routing of light signals of the chip [26]. At the same time, PC devices have low loss and ultra-small optical mode volumes, which enable strong light–matter interactions. The InAs quantum dots are well suited for quantum photonic applications because they have excellent quantum efficiencies, large dipole moments, and a variety of quantum states that can be optically controlled [24, 3]

Answering PICO Clinical Questions: a Semantic Graph-Based Approach

International audienceIn this paper, we tackle the issue related to the retrieval of the best evidence that fits with a PICO (Population, Intervention, Comparison and Outcome) question. We propose a new document ranking algorithm that relies on semantic based query expansion bounded by the local search context to better discard irrelevant documents. Experiments using a standard dataset including 423 PICO questions and more than 1,2 million of documents, show that our aproach is promising

NLM at ImageCLEF 2017 caption task

This paper describes the participation of the U.S. National Library of Medicine (NLM) in the ImageCLEF 2017 caption task. We proposed different machine learning methods using training subsets that we selected from the provided data as well as retrieval methods using external data. For the concept detection subtask, we used Convolutional Neural Networks (CNNs) and Binary Relevance using decision trees for multi-label classification. We also proposed a retrieval-based approach using Open-i image search engine and MetaMapLite to recognize relevant terms and associated Concept Unique Identifiers (CUIs). For the caption prediction subtask, we used the recognized CUIs and the UMLS to generate the captions. We also applied Open-i to retrieve similar images and their captions. We submitted ten runs for the concept detection subtask and six runs for the caption prediction subtask. CNNs provided good results with regards to the size of the selected subsets and the limited number of CUIs used for training. Using the CUIs recognized by the CNNs, our UMLS-based method for caption prediction obtained good results with 0.2247 mean BLUE score. In both subtasks, the best results were achieved using retrieval-based approaches outperforming all submitted runs by all the participants with 0.1718 mean F1 score in the concept detection subtask and 0.5634 mean BLUE score in the caption prediction subtask

Cavity Quantum Electrodynamics with Anderson-localized Modes

A major challenge in quantum optics and quantum information technology is to enhance the interaction between single photons and single quantum emitters. Highly engineered optical cavities are generally implemented requiring nanoscale fabrication precision. We demonstrate a fundamentally different approach in which disorder is used as a resource rather than a nuisance. We generate strongly confined Anderson-localized cavity modes by deliberately adding disorder to photonic crystal waveguides. The emission rate of a semiconductor quantum dot embedded in the waveguide is enhanced by a factor of 15 on resonance with the Anderson-localized mode and 94 % of the emitted single-photons couple to the mode. Disordered photonic media thus provide an efficient platform for quantum electrodynamics offering an approach to inherently disorder-robust quantum information devices

Evaluating performance of biomedical image retrieval systems - an overview of the medical image retrieval task at ImageCLEF 2004-2013

Medical image retrieval and classification have been extremely active research topics over the past 15 years. Within the ImageCLEF benchmark in medical image retrieval and classification, a standard test bed was created that allows researchers to compare their approaches and ideas on increasingly large and varied data sets including generated ground truth. This article describes the lessons learned in ten evaluation campaigns. A detailed analysis of the data also highlights the value of the resources created

Combining classifiers for robust PICO element detection

<p>Abstract</p> <p>Background</p> <p>Formulating a clinical information need in terms of the four atomic parts which are Population/Problem, Intervention, Comparison and Outcome (known as PICO elements) facilitates searching for a precise answer within a large medical citation database. However, using PICO defined items in the information retrieval process requires a search engine to be able to detect and index PICO elements in the collection in order for the system to retrieve relevant documents.</p> <p>Methods</p> <p>In this study, we tested multiple supervised classification algorithms and their combinations for detecting PICO elements within medical abstracts. Using the structural descriptors that are embedded in some medical abstracts, we have automatically gathered large training/testing data sets for each PICO element.</p> <p>Results</p> <p>Combining multiple classifiers using a weighted linear combination of their prediction scores achieves promising results with an <it>f</it>-measure score of 86.3% for P, 67% for I and 56.6% for O.</p> <p>Conclusions</p> <p>Our experiments on the identification of PICO elements showed that the task is very challenging. Nevertheless, the performance achieved by our identification method is competitive with previously published results and shows that this task can be achieved with a high accuracy for the P element but lower ones for I and O elements.</p

Preparing a collection of radiology examinations for distribution and retrieval

OBJECTIVE: Clinical documents made available for secondary use play an increasingly important role in discovery of clinical knowledge, development of research methods, and education. An important step in facilitating secondary use of clinical document collections is easy access to descriptions and samples that represent the content of the collections. This paper presents an approach to developing a collection of radiology examinations, including both the images and radiologist narrative reports, and making them publicly available in a searchable database. MATERIALS AND METHODS: The authors collected 3996 radiology reports from the Indiana Network for Patient Care and 8121 associated images from the hospitals' picture archiving systems. The images and reports were de-identified automatically and then the automatic de-identification was manually verified. The authors coded the key findings of the reports and empirically assessed the benefits of manual coding on retrieval. RESULTS: The automatic de-identification of the narrative was aggressive and achieved 100% precision at the cost of rendering a few findings uninterpretable. Automatic de-identification of images was not quite as perfect. Images for two of 3996 patients (0.05%) showed protected health information. Manual encoding of findings improved retrieval precision. CONCLUSION: Stringent de-identification methods can remove all identifiers from text radiology reports. DICOM de-identification of images does not remove all identifying information and needs special attention to images scanned from film. Adding manual coding to the radiologist narrative reports significantly improved relevancy of the retrieved clinical documents. The de-identified Indiana chest X-ray collection is available for searching and downloading from the National Library of Medicine (http://openi.nlm.nih.gov/)