口语化疾病名称向国际规范疾病术语集的映射研究

目的：研究患者口语化疾病名称到术语疾病名称的映射，解决在线口语化疾病名称难以有效利用的问题。方法：抓取“微医网”上患者口语中的疾病名，将PubMed摘要、百度搜索页摘要与百科内容作为语料，训练词向量，用余弦距离计算相似度，由2名具有临床知识和从业经验的编码人员对结果的准确性进行检验。结果：将最相似词条的参数TopN设置为>= 30的时候效果比较好，映射到大类和细类的准确性分别稳定在60%和70%左右。结论：利用词向量将口语化疾病名称映射到国际疾病术语集，具有较强的通用性和可行性，对于在线问诊、智能分诊甚至自动化编码具有明显的参考借鉴价值。     

Easy Words: Reference Resolution in a Malevolent Referent World

This article describes early stages in the acquisition of a first vocabulary by infants and young children. It distinguishes two major stages, the first of which operates by a stand‐alone word‐to‐world pairing procedure and the second of which, using the evidence so acquired, builds a domain‐specific syntax‐sensitive structure‐to‐world pairing procedure. As we show, the first stage of learning is slow, restricted in character, and to some extent errorful, whereas the second procedure is determinative, rapid, and essentially errorless. Our central claim here is that the early, referentially based learning procedure succeeds at all because it is reined in by attention‐focusing properties of word‐to‐world timing and related indicants of referential intent.     

A Rational Model of Word Skipping in Reading: Ideal Integration of Visual and Linguistic Information

Readers intentionally do not fixate some words, thought to be those they have already identified. In a rational model of reading, these word skipping decisions should be complex functions of the particular word, linguistic context, and visual information available. In contrast, heuristic models of reading only predict additive effects of word and context features. Here we test these predictions by implementing a rational model with Bayesian inference and predicting human skipping with the entropy of this model's posterior distribution. Results showed a significant effect of the entropy in predicting skipping above a strong baseline model including word and context features. This pattern held for entropy measures from rational models with a frequency prior but not from models with a 5‐gram prior. These results suggest complex interactions between visual input and linguistic knowledge as predicted by the rational model of reading, and a dominant role of frequency in making skipping decisions.     

The Developmental Origins of Syntactic Bootstrapping

Children use syntax to learn verbs, in a process known as syntactic bootstrapping. The structure‐mapping account proposes that syntactic bootstrapping begins with a universal bias to map each noun phrase in a sentence onto a participant role in a structured conceptual representation of an event. Equipped with this bias, children interpret the number of noun phrases accompanying a new verb as evidence about the semantic predicate–argument structure of the sentence, and therefore about the meaning of the verb. In this paper, we first review evidence for the structure–mapping account, and then discuss challenges to the account arising from the existence of languages that allow verbs' arguments to be omitted, such as Korean. These challenges prompt us to (a) refine our notion of the distributional learning mechanisms that create representations of sentence structure, and (b) propose that an expectation of discourse continuity allows children to gather linguistic evidence for each verb’s arguments across sentences in a coherent discourse. Taken together, the proposed learning mechanisms and biases sketch a route whereby simple aspects of sentence structure guide verb learning from the start of multi‐word sentence comprehension, and do so even if some of the new verb’s arguments are omitted due to discourse redundancy.     

Determining the difficulty of Word Sense Disambiguation

Automatic processing of biomedical documents is made difficult by the fact that many of the terms they contain are ambiguous. Word Sense Disambiguation (WSD) systems attempt to resolve these ambiguities and identify the correct meaning. However, the published literature on WSD systems for biomedical documents report considerable differences in performance for different terms. The development of WSD systems is often expensive with respect to acquiring the necessary training data. It would therefore be useful to be able to predict in advance which terms WSD systems are likely to perform well or badly on.This paper explores various methods for estimating the performance of WSD systems on a wide range of ambiguous biomedical terms (including ambiguous words/phrases and abbreviations). The methods include both supervised and unsupervised approaches. The supervised approaches make use of information from labeled training data while the unsupervised ones rely on the UMLS Metathesaurus. The approaches are evaluated by comparing their predictions about how difficult disambiguation will be for ambiguous terms against the output of two WSD systems. We find the supervised methods are the best predictors of WSD difficulty, but are limited by their dependence on labeled training data. The unsupervised methods all perform well in some situations and can be applied more widely.     

心理因素对体外受精胚胎移植治疗结果的影响

目的评估在体外受精胚胎移植（IVF-ET）治疗过程中心理压力对治疗结果的影响。方法对52例不孕妇女在取卵前采用字、色、色字数（Stoop Color and Word Test，SCWT）测试表，来测量个体对应激的承受能力。测量测试前、测试中和测试结束10min后的血压和心率，并在治疗后14d按血绒毛膜促性腺激素（HCG）值分为成功组和失败组，以比较两组妇女对应激的反应能力与IVF—ET治疗结局的相关性。结果21例妊娠，其中2例流产，1例宫外孕，18例继续妊娠。成功组和失败组在年龄、文化程度、不孕年限、就业与否及IVF—ET中获卵数、胚胎移植数均基本相似（P〉0．05），在测量心血管系统对SCWT的反应中，与基础值比较，每项都有增加，而失败组妇女的收缩压和心率增加明显高于成功组。结论心理素质差、对压力承受能力弱的妇女在辅助生育治疗过程中常常伴有较低的成功率。     

基于小波分析和灰度纹理特征的乳腺X线图像微钙化点区域的提取

目的乳腺癌的早期发现对患者意义重大。为帮助医生进行乳腺癌的早期检查和诊断,本文提出利用小波分析与图像纹理特征提取相结合的方法来提取乳腺X线图像微钙化点区域,在提高检查准确性的同时避免漏检误检。方法首先利用灰度共生矩阵所提取的能量、熵、对比度、相关性以及小波分解后得到的各层高频系数的方差、能量作为图像的特征向量,然后利用支持向量机进行训练建立最优分类模型。最后利用建立的最优分类模型实现乳腺X线图像微钙化点区域的提取并利用检出率和误检率对结果进行评估。结果使用临床数据进行验证,结果表明利用小波分析与图像纹理特征提取相结合的方法能有效提取乳腺图像中的微钙化点区域。结论基于小波分析和灰度纹理特征的乳腺X线图像微钙化点区域的提取方法比单一的图像纹理特征提取或小波分析等方法,提取的效果更好。另外,该方法设计简单,更易于实现乳腺癌的自动化诊断。     

HPLC analysis of some allowable-antibiotic multiresidues in farmed rainbow trout in Iran

This study aimed to investigate the occurrence of antibiotic residues in rainbow trout muscle samples (n?=?74) obtained from Iranian trout farms. The levels of the antibiotics were determined by in-house validated HPLC methods. Oxytetracycline, tetracycline, enrofloxacin, ciprofloxacin, and florfenicol residues were detected in 6.76%, 37.8%, 31.1%, 10.8%, and 14.9% of the samples, respectively; while chlortetracycline was not detected. The detected range of concentrations for positive samples was 8.94–81.2 μg/kg for oxytetracycline, 7.99–98.4 μg/kg for tetracycline, 6.75–87.4 μg/kg for enrofloxacin, 5.83–11.5 μg/kg for ciprofloxacin, and 49.9–313.6 μg/kg for florfenicol. None of the samples exceeded the maximum residue levels (MRLs) established by the Institute of Standards and Industrial Research of Iran. The co-occurrence of antibiotics was detected in 40.5% of the analyzed samples. The frequency of co-occurrence of enrofloxacin-tetracycline was the highest (16.2%). Despite the fact that concentrations of the analyzed antibiotics in trout samples did not exceed the MRLs, their co-occurrence in such a level could be a serious public health problem.     

Atypical performance patterns on Delis–Kaplan Executive Functioning System Color–Word Interference Test: Cognitive switching and learning ability in older adults

Introduction: Cognitive set shifting requires flexible application of lower level processes. The Delis–Kaplan Executive Functioning System (DKEFS) Color–Word Interference Test (CWIT) is commonly used to clinically assess cognitive set shifting. An atypical pattern of performance has been observed on the CWIT; a subset of individuals perform faster, with equal or fewer errors, on the more difficult inhibition/switching than the inhibition trial. This study seeks to explore the cognitive underpinnings of this atypical pattern. It is hypothesized that atypical patterns on CWIT will be associated with better performance on underlying cognitive measures of attention, working memory, and learning when compared to typical CWIT patterns. Method: Records from 239 clinical referrals (age: M = 68.09 years, SD = 10.62; education: M = 14.87 years, SD = 2.73) seen for a neuropsychological evaluation as part of diagnostic work up in an outpatient dementia and movement disorders clinic were sampled. The standard battery of tests included measures of attention, learning, fluency, executive functioning, and working memory. Analyses of variance (ANOVAs) were conducted to compare the cognitive performance of those with typical versus atypical CWIT patterns. Results: An atypical pattern of performance was confirmed in 23% of our sample. Analyses revealed a significant group difference in acquisition of information on both nonverbal (Brief Visuospatial Memory Test–Revised, BVMT–R total recall), F(1, 213) = 16.61, p < .001, and verbal (Hopkins Verbal Learning Test–Revised, HVLT–R total recall) learning tasks, F(1, 181) = 6.43, p < .01, and semantic fluency (Animal Naming), F(1, 232) = 7.57, p = .006, with the atypical group performing better on each task. Effect sizes were larger for nonverbal (Cohen’s d = 0.66) than verbal learning (Cohen’s d = 0.47) and semantic fluency (Cohen’s d = 0.43). Conclusions: Individuals demonstrating an atypical pattern of performance on the CWIT inhibition/switching trial also demonstrated relative strengths in semantic fluency and learning.     

Identifying synonymy between relational phrases using word embeddings

Many text mining applications in the biomedical domain benefit from automatic clustering of relational phrases into synonymous groups, since it alleviates the problem of spurious mismatches caused by the diversity of natural language expressions. Most of the previous work that has addressed this task of synonymy resolution uses similarity metrics between relational phrases based on textual strings or dependency paths, which, for the most part, ignore the context around the relations. To overcome this shortcoming, we employ a word embedding technique to encode relational phrases. We then apply the k-means algorithm on top of the distributional representations to cluster the phrases. Our experimental results show that this approach outperforms state-of-the-art statistical models including latent Dirichlet allocation and Markov logic networks.