Stimulated and spontaneous optical generation of electron spin coherence in charged GaAs quantum dots

We report on the coherent optical excitation of electron spin polarization in the ground state of charged GaAs quantum dots via an intermediate charged exciton (trion) state. Coherent optical fields are used for the creation and detection of the Raman spin coherence between the spin ground states of the charged quantum dot. The measured spin decoherence time, which is likely limited by the nature of the spin ensemble, approaches 10 ns at zero field. We also show that the Raman spin coherence in the quantum beats is caused not only by the usual stimulated Raman interaction but also by simultaneous spontaneous radiative decay of either excited trion state to a coherent combination of the two spin states.Comment: 4 pages, 3 figures. Minor modification

Normalizing biomedical terms by minimizing ambiguity and variability

<p>Abstract</p> <p>Background</p> <p>One of the difficulties in mapping biomedical named entities, e.g. genes, proteins, chemicals and diseases, to their concept identifiers stems from the potential variability of the terms. Soft string matching is a possible solution to the problem, but its inherent heavy computational cost discourages its use when the dictionaries are large or when real time processing is required. A less computationally demanding approach is to normalize the terms by using heuristic rules, which enables us to look up a dictionary in a constant time regardless of its size. The development of good heuristic rules, however, requires extensive knowledge of the terminology in question and thus is the bottleneck of the normalization approach.</p> <p>Results</p> <p>We present a novel framework for discovering a list of normalization rules from a dictionary in a fully automated manner. The rules are discovered in such a way that they minimize the ambiguity and variability of the terms in the dictionary. We evaluated our algorithm using two large dictionaries: a human gene/protein name dictionary built from BioThesaurus and a disease name dictionary built from UMLS.</p> <p>Conclusions</p> <p>The experimental results showed that automatically discovered rules can perform comparably to carefully crafted heuristic rules in term mapping tasks, and the computational overhead of rule application is small enough that a very fast implementation is possible. This work will help improve the performance of term-concept mapping tasks in biomedical information extraction especially when good normalization heuristics for the target terminology are not fully known.</p

A prospective study of symptoms, function, and medication use during acute illness in nursing home residents: design, rationale and cohort description

<p>Abstract</p> <p>Background</p> <p>Nursing home residents are at high risk for developing acute illnesses. Compared with community dwelling adults, nursing home residents are often more frail, prone to multiple medical problems and symptoms, and are at higher risk for adverse outcomes from acute illnesses. In addition, because of polypharmacy and the high burden of chronic disease, nursing home residents are particularly vulnerable to disruptions in transitions of care such as medication interruptions in the setting of acute illness. In order to better estimate the effect of acute illness on nursing home residents, we have initiated a prospective cohort which will allow us to observe patterns of acute illnesses and the consequence of acute illnesses, including symptoms and function, among nursing home residents. We also aim to examine the patterns of medication interruption, and identify patient, provider and environmental factors that influence continuity of medication prescribing at different points of care transition.</p> <p>Methods</p> <p>This is a prospective cohort of nursing home residents residing in two nursing homes in a metropolitan area. Baseline characteristics including age, gender, race, and comorbid conditions are recorded. Participants are followed longitudinally for a planned period of 3 years. We record acute illness incidence and characteristics, and measure symptoms including depression, pain, withdrawal symptoms, and function using standardized scales.</p> <p>Results</p> <p>76 nursing home residents have been followed for a median of 666 days to date. At baseline, mean age of residents was 74.4 (± 11.9); 32% were female; 59% were white. The most common chronic conditions were dementia (41%), depression (38%), congestive heart failure (25%) and chronic obstructive lung disease (27%). Mean pain score was 4.7 (± 3.6) on a scale of 0 to 10; Geriatric Depression Scale (GDS-15) score was 5.2 (± 4.4). During follow up, 138 acute illness episodes were identified, for an incidence of 1.5 (SD 2.0) episodes per resident per year; 74% were managed in the nursing home and 26% managed in the acute care setting.</p> <p>Conclusion</p> <p>In this report, we describe the conceptual model and methods of designing a longitudinal cohort to measure acute illness patterns and symptoms among nursing home residents, and describe the characteristics of our cohort at baseline. In our planned analysis, we will further estimate the effect of the use and interruption of medications on withdrawal and relapse symptoms and illness outcomes.</p

Scaling carbon fluxes from eddy covariance sites to globe: Synthesis and evaluation of the FLUXCOM approach

FLUXNET assembles globally-distributed eddy covariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary in order to obtain global-scale estimates of biosphere-atmosphere exchange from the flux tower network. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVM), we provide here a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing datasets, and sets of predictor variables were employed. Spatial patterns of mean GPP are consistent among FLUXCOM and DGVM ensembles (R2 > 0.94 at 1° spatial resolution) while the majority of DGVMs are outside the FLUXCOM range for 70 % of the land surface. Global mean GPP magnitudes for 2008–2010 from FLUXCOM members vary within 106 and 130 PgC yr−1 with the largest uncertainty in the tropics. Seasonal variations of independent SIF estimates agree better with FLUXCOM GPP (mean global pixel-wise R2 ~ 0.75) than with GPP from DGVMs (mean global pixel wise R2 ~ 0.6). Seasonal variations of FLUXCOM NEE show good consistency with atmospheric inversion-based net land carbon fluxes, particularly for temperate and boreal regions (R2 > 0.92). Interannual variability of global NEE in FLUXCOM is underestimated compared to inversions and DGVMs. The FLUXCOM version which uses also meteorological inputs shows a strong co-variation of interannual patterns with inversions (R2 = 0.88 for 2001–2010). Mean regional NEE from FLUXCOM shows larger uptake than inversion and DGVM-based estimates, particularly in the tropics with discrepancies of up to several hundred gC m2 yr−1. These discrepancies can only partly be reconciled by carbon loss pathways that are implicit in inversions but not captured by the flux tower measurements such as carbon emissions from fires and water bodies. We hypothesize that a combination of systematic biases in the underlying eddy covariance data, in particular in tall tropical forests, and a lack of site-history effects on NEE in FLUXCOM are likely responsible for the too strong tropical carbon sink estimated by FLUXCOM. Furthermore, as FLUXCOM does not account for CO2 fertilization effects carbon flux trends are not realistic. Overall, current FLUXCOM estimates of mean annual and seasonal cycles of GPP as well as seasonal NEE variations provide useful constraints of global carbon cycling, while interannual variability patterns from FLUXCOM are valuable but require cautious interpretation. Exploring the diversity of Earth Observation data and of machine learning concepts along with improved quality and quantity of flux tower measurements will facilitate further improvements of the FLUXCOM approach overall

Demonstrating a superconducting dual-rail cavity qubit with erasure-detected logical measurements

A critical challenge in developing scalable error-corrected quantum systems is the accumulation of errors while performing operations and measurements. One promising approach is to design a system where errors can be detected and converted into erasures. A recent proposal aims to do this using a dual-rail encoding with superconducting cavities. In this work, we implement such a dual-rail cavity qubit and use it to demonstrate a projective logical measurement with erasure detection. We measure logical state preparation and measurement errors at the $0.01\%$-level and detect over $99\%$ of cavity decay events as erasures. We use the precision of this new measurement protocol to distinguish different types of errors in this system, finding that while decay errors occur with probability $\sim 0.2\%$ per microsecond, phase errors occur 6 times less frequently and bit flips occur at least 170 times less frequently. These findings represent the first confirmation of the expected error hierarchy necessary to concatenate dual-rail erasure qubits into a highly efficient erasure code

Text Mining the History of Medicine

Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM) methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc.), synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.). TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while the processing pipeline and its modules may be used and configured within the Argo TM platform

Overview of the ID, EPI and REL tasks of BioNLP Shared Task 2011

We present the preparation, resources, results and analysis of three tasks of the BioNLP Shared Task 2011: the main tasks on Infectious Diseases (ID) and Epigenetics and Post-translational Modifications (EPI), and the supporting task on Entity Relations (REL). The two main tasks represent extensions of the event extraction model introduced in the BioNLP Shared Task 2009 (ST'09) to two new areas of biomedical scientific literature, each motivated by the needs of specific biocuration tasks. The ID task concerns the molecular mechanisms of infection, virulence and resistance, focusing in particular on the functions of a class of signaling systems that are ubiquitous in bacteria. The EPI task is dedicated to the extraction of statements regarding chemical modifications of DNA and proteins, with particular emphasis on changes relating to the epigenetic control of gene expression. By contrast to these two application-oriented main tasks, the REL task seeks to support extraction in general by separating challenges relating to part-of relations into a subproblem that can be addressed by independent systems. Seven groups participated in each of the two main tasks and four groups in the supporting task. The participating systems indicated advances in the capability of event extraction methods and demonstrated generalization in many aspects: from abstracts to full texts, from previously considered subdomains to new ones, and from the ST'09 extraction targets to other entities and events. The highest performance achieved in the supporting task REL, 58% F-score, is broadly comparable with levels reported for other relation extraction tasks. For the ID task, the highest-performing system achieved 56% F-score, comparable to the state-of-the-art performance at the established ST'09 task. In the EPI task, the best result was 53% F-score for the full set of extraction targets and 69% F-score for a reduced set of core extraction targets, approaching a level of performance sufficient for user-facing applications. In this study, we extend on previously reported results and perform further analyses of the outputs of the participating systems. We place specific emphasis on aspects of system performance relating to real-world applicability, considering alternate evaluation metrics and performing additional manual analysis of system outputs. We further demonstrate that the strengths of extraction systems can be combined to improve on the performance achieved by any system in isolation. The manually annotated corpora, supporting resources, and evaluation tools for all tasks are available from http://www.bionlp-st.org and the tasks continue as open challenges for all interested parties