867 research outputs found

    Is the antisocial child father of the abusive man? : a 40-year prospective longitudinal study on the developmental antecedents of intimate partner violence

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    This prospective longitudinal study examined whether early childhood risk factors contributed to explaining and predicting intimate partner violence (IPV) in midadulthood. Participants included 202 men from the Cambridge longitudinal study who were in an intimate relationship in their mid-40s. Neuropsychological deficits and the presence of a criminogenic family environment were measured between ages 8 and 10. Antisocial behavior was measured between ages 8 and 18. IPV was measured at age 48 using a self-report instrument completed by the participants' female partners. Perpetration and victimization rates were relatively high; violence was mostly mutual, and men were more likely to be victims than perpetrators. Findings indicate that a criminogenic environment increases the risk of IPV by fostering the development of antisocial behavior and neuropsychological deficits. A link also exists between a high level of antisocial behavior during adolescence and the risk of IPV later in life. The results suggest the presence of both continuity and discontinuity of antisocial behavior as childhood risk factors that increase the likelihood of future involvement in IPV, but the role of these risk factors is modest

    Automated coding of under-studied medical concept domains: linking physical activity reports to the international classification of functioning, disability, and health

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    Linking clinical narratives to standardized vocabularies and coding systems is a key component of unlocking the information in medical text for analysis. However, many domains of medical concepts, such as functional outcomes and social determinants of health, lack well-developed terminologies that can support effective coding of medical text. We present a framework for developing natural language processing (NLP) technologies for automated coding of medical information in under-studied domains, and demonstrate its applicability through a case study on physical mobility function. Mobility function is a component of many health measures, from post-acute care and surgical outcomes to chronic frailty and disability, and is represented as one domain of human activity in the International Classification of Functioning, Disability, and Health (ICF). However, mobility and other types of functional activity remain under-studied in the medical informatics literature, and neither the ICF nor commonly-used medical terminologies capture functional status terminology in practice. We investigated two data-driven paradigms, classification and candidate selection, to link narrative observations of mobility status to standardized ICF codes, using a dataset of clinical narratives from physical therapy encounters. Recent advances in language modeling and word embedding were used as features for established machine learning models and a novel deep learning approach, achieving a macro-averaged F-1 score of 84% on linking mobility activity reports to ICF codes. Both classification and candidate selection approaches present distinct strengths for automated coding in under-studied domains, and we highlight that the combination of (i) a small annotated data set; (ii) expert definitions of codes of interest; and (iii) a representative text corpus is sufficient to produce high-performing automated coding systems. This research has implications for continued development of language technologies to analyze functional status information, and the ongoing growth of NLP tools for a variety of specialized applications in clinical care and research

    Writing habits and telltale neighbors: analyzing clinical concept usage patterns with sublanguage embeddings

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    Natural language processing techniques are being applied to increasingly diverse types of electronic health records, and can benefit from in-depth understanding of the distinguishing characteristics of medical document types. We present a method for characterizing the usage patterns of clinical concepts among different document types, in order to capture semantic differences beyond the lexical level. By training concept embeddings on clinical documents of different types and measuring the differences in their nearest neighborhood structures, we are able to measure divergences in concept usage while correcting for noise in embedding learning. Experiments on the MIMIC-III corpus demonstrate that our approach captures clinically-relevant differences in concept usage and provides an intuitive way to explore semantic characteristics of clinical document collections

    HARE: a flexible highlighting annotator for ranking and exploration

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    Exploration and analysis of potential data sources is a significant challenge in the application of NLP techniques to novel information domains. We describe HARE, a system for highlighting relevant information in document collections to support ranking and triage, which provides tools for post-processing and qualitative analysis for model development and tuning. We apply HARE to the use case of narrative descriptions of mobility information in clinical data, and demonstrate its utility in comparing candidate embedding features. We provide a web-based interface for annotation visualization and document ranking, with a modular backend to support interoperability with existing annotation tools. Our system is available online at https://github.com/OSU-slatelab/HARE

    A single chain analysis of doped quasi one dimensional spin 1 compounds: paramagnetic versus spin 1/2 doping

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    We present a numerical study of single chain models of doped spin 1 compounds. We use low energy effective one-dimensional models for both the cases of paramagnetic and spin-1/2 doping. In the case of paramagnetic doping, the effective model is equivalent to the bond disordered spin-1/2 chain model recently analyzed by means of real space renormalization group by Hyman and Yang. By means of exact diagonalizations in the XX limit, we confirm the stability of the Haldane phase for weak disorder. Above a critical amount of disorder, the effective model flows to the so called random singlet fixed point. In the case of spin-1/2 doping, we argue that the Haldane phase should be destabilized even for weak disorder. This picture is not in contradiction with existing experimental data. We also discuss the possible occurrence of (unobserved) antiferromagnetically ordered phases.Comment: 13 pages, 7 included figure

    The Coexpression of Reelin and Neuronal Nitric Oxide Synthase in a Subpopulation of Dentate Gyrus Neurons Is Downregulated in Heterozygous Reeler Mice

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    Reelin is an extracellular matrix protein expressed in several interneuron subtypes in the hippocampus and dentate gyrus. Neuronal nitric oxide synthase (nNOS) is also expressed by interneurons in these areas. We investigated whether reelin and nNOS are co-localized in the same population of hippocampal interneurons, and whether this colocalization is altered in the heterozygous reeler mouse. We found colocalization of nNOS in reelin-positive cells in the CA1 stratum radiatum and lacunosum moleculare, the CA3 stratum radiatum, and the dentate gyrus subgranular zone, molecular layer, and hilus. In heterozygous reeler mice, the colocalization of nNOS in reelin-positive cells was significantly decreased only in the subgranular zone and molecular layer. The coexpression of reelin and nNOS in several hippocampal regions suggests that reelin and nNOS may work synergistically to promote glutamatergic function, and the loss of this coexpression in heterozygous reeler mice may underlie some of the behavioral deficits observed in these animals

    Rapid Suppression of the Spin Gap in Zn-doped CuGeO_3 and SrCu_2O_3

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    The influence of non-magnetic impurities on the spectrum and dynamical spin structure factor of a model for CuGeO3_3 is studied. A simple extension to Zn-doped SrCu2O3{\rm Sr Cu_2 O_3} is also discussed. Using Exact Diagonalization techniques and intuitive arguments we show that Zn-doping introduces states in the Spin-Peierls gap of CuGeO3_3. This effect can beunderstood easily in the large dimerization limit where doping by Zn creates ``loose'' S=1/2 spins, which interact with each other through very weak effective antiferromagnetic couplings. When the dimerization is small, a similar effect is observed but now with the free S=1/2 spins being the resulting S=1/2 ground state of severed chains with an odd number of sites. Experimental consequences of these results are discussed. It is interesting to observe that the spin correlations along the chains are enhanced by Zn-doping according to the numerical data presented here. As recent numerical calculations have shown, similar arguments apply to ladders with non-magnetic impurities simply replacing the tendency to dimerization in CuGeO3_3 by the tendency to form spin-singlets along the rungs in SrCu2_2O3_3.Comment: 7 pages, 8 postscript figures, revtex, addition of figure 8 and a section with experimental predictions, submmited to Phys. Rev. B in May 199

    Jointly embedding entities and text with distant supervision

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    Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications. However, recent entity embedding methods have relied on structured resources that are expensive to create for new domains and corpora. We present a distantly-supervised method for jointly learning embeddings of entities and text from an unnanotated corpus, using only a list of mappings between entities and surface forms. We learn embeddings from open-domain and biomedical corpora, and compare against prior methods that rely on human-annotated text or large knowledge graph structure. Our embeddings capture entity similarity and relatedness better than prior work, both in existing biomedical datasets and a new Wikipedia-based dataset that we release to the community. Results on analogy completion and entity sense disambiguation indicate that entities and words capture complementary information that can be effectively combined for downstream use

    On the soliton width in the incommensurate phase of spin-Peierls systems

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    We study using bosonization techniques the effects of frustration due to competing interactions and of the interchain elastic couplings on the soliton width and soliton structure in spin-Peierls systems. We compare the predictions of this study with numerical results obtained by exact diagonalization of finite chains. We conclude that frustration produces in general a reduction of the soliton width while the interchain elastic coupling increases it. We discuss these results in connection with recent measurements of the soliton width in the incommensurate phase of CuGeO_3.Comment: 4 pages, latex, 2 figures embedded in the tex
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