【正文】 ? The Lancet《 柳葉刀 》 Background背景 , Methods方法 , Findings發(fā)現(xiàn) , Interpretation解釋 ? JAMA Journal of the American Medical Association《 美國醫(yī)學(xué)會(huì)志 》 Context, Objective目的 , Design設(shè)計(jì) , Setting單位 , Patients對(duì)象 , Interventions解釋 , Main Oute Measures主要結(jié)果測定 , Results結(jié)果 , Conclusion結(jié)論 摘要：撰寫技巧 ? （ 1） 包括論文 IMRD 結(jié)構(gòu)；可適當(dāng)強(qiáng)調(diào)研究中的創(chuàng)新 、重要之處 （ 但不要使用評(píng)價(jià)性語言 ） 。 盡量包括論文的主要論點(diǎn)和重要細(xì)節(jié) (重要的論證或數(shù)據(jù) ) ? （ 2） 使用簡短的句子 , 用詞應(yīng)為潛在的讀者所熟悉；注意表述的邏輯性 , 盡量使用指示性的詞語來表達(dá)論文的不同部分 (層次 ) — 如使用 “ 研究表明 … ‖(We found that… )表示結(jié)果 。 使用 “ 通過對(duì) ...的分析 ， 認(rèn)為 … ‖(Based on… , we suggest that… )表示討論結(jié)果的含義等 摘要：撰寫技巧 ? （ 3） 確保摘要的 “ 獨(dú)立性 ” （ stand on its own) 或 “ 自明性 ” (selfcontained): 盡量避免引用文獻(xiàn) 、 圖表和縮寫；如果無法回避使用引文 ， 應(yīng)在引文出現(xiàn)位置將引文的書目信息標(biāo)注在方括號(hào)內(nèi) ? （ 4） 為了方便檢索系統(tǒng)檢索 ， 盡量避免使用化學(xué)結(jié)構(gòu)式 、數(shù)學(xué)表達(dá)式 、 角標(biāo)和希臘文等特殊符號(hào) ? （ 5） 查詢擬投稿期刊的作者指南 ， 了解其對(duì)摘要的字?jǐn)?shù)和形式的要求 。 如果是結(jié)構(gòu)式摘要應(yīng)分幾段 。 使用什么標(biāo)識(shí) 、 時(shí)態(tài) 、 是否使用縮寫或簡寫 。 SCI論文摘要中常用的表達(dá)方法 案例 要寫好摘要，需要建立一個(gè)適合自己需要的句型庫 （選擇的詞匯來源于 SCI高被引用論文） ? 引言部分 ? （ 1）回顧研究背景，常用詞匯有 review, summarize, present, outline, describe等 ? （ 2）說明寫作目的，常用詞匯有 purpose, attempt, aim等，另外還可以用動(dòng)詞不定式充當(dāng)目的壯語老表達(dá) ? （ 3）介紹論文的重點(diǎn)內(nèi)容或研究范圍，常用詞匯有 study, present, include, focus, emphasize, emphasis, attention等 ? 方法部分 ? （ 1）介紹研究或試驗(yàn)過程，常用詞匯有 test study, investigate, examine,experiment, discuss, consider, analyze, analysis等 ? （ 2）說明研究或試驗(yàn)方法，常用詞匯有 measure, estimate, calculate等 ? （ 3）介紹應(yīng)用、用途，常用詞匯有 use, apply, application等 SCI論文摘要中常用的表達(dá)方法 案例 ? 結(jié)果部分 ? （ 1）展示研究結(jié)果，常用詞匯有 show, result, present等 ? （ 2）介紹結(jié)論，常用詞匯有 summary, introduce,conclude等 ? 討論部分 ? （ 1）陳述論文的論點(diǎn)和作者的觀點(diǎn)，常用詞匯有 suggest, repot, present, expect, describe等 ? （ 2）說明論證，常用詞匯有 support, provide, indicate, identify, find, demonstrate, confirm, clarify等 ? （ 3）推薦和建議，常用詞匯有 suggest,suggestion, remend, remendation, propose,necessity,necessary,expect等。 摘要 引言部分 案例 review 引言部分 回顧研究背景 常用詞匯 review 摘要 引言部分 案例 詞匯 review ? Author(s): ROBINSON, TE。 BERRIDGE, KC ? Title: THE NEURAL BASIS OF DRUG CRAVING AN INCENTIVESENSITIZATION THEORY OF ADDICTION ? Source: BRAIN RESEARCH REVIEWS, 18 (3): 247291 SEPDEC 1993 《 腦研究評(píng)論 》 荷蘭 SCI被引用 1774 ? We review evidence for this view of addiction and discuss its implications for understanding the psychology and neurobiology of 摘要 引言部分 案例 summarize 引言部分 回顧研究背景 常用詞匯 summarize SCI高被引 摘要引言部分案例 詞匯 summarize ? Author(s): Bart, RM。 Carone, CD。 被引用 1571 ? Title: Particles and field .1. Review of particle physics ? Source: PHYSICAL REVIEW D, 54 (1): 1+ Part 1 JUL 1 1996:《 物理學(xué)評(píng)論， D輯 》 美國 引言部分 回顧研究背景常用詞匯 summarize Abstract: This biennial review summarizes much of Particle Physics. Using data from previous editions, plus 1900 new measurements from 700 papers, we list, evaluate, and average measured properties of gauge bosons, leptons, quarks, mesons, and baryons. We also summarize searches for hypothetical particles such as Higgs bosons, heavy neutrinos, and supersymmetric particles. All the particle properties and search limits are listed in Summary Tables. We also give numerous tables, figures, formulae, and reviews of topics such as the Standard Model, particle detectors, probability, and statistics. A booklet is available containing the Summary Tables and abbreviated versions of some of the other sections of this full Review. SCI摘要 引言部分 案例 attention 介紹論文的重點(diǎn)內(nèi)容或研究范圍，常用詞匯有 attention SCI摘要 方法部分 案例 consider 介紹研究或試驗(yàn)過程，常用詞匯有 consider SCI高被引 摘要引言部分案例 詞匯 outline ? Author(s): TIERNEY, L SCI引用 728次 ? Title: MARKOVCHAINS FOR EXPLORING POSTERIOR DISTRIBUTIONS 引言部分 回顧研究背景，常用詞匯 outline ? Source: ANNALS OF STATISTICS, 22 (4): 17011728 DEC 1994 ? 《 統(tǒng)計(jì)學(xué)紀(jì)事 》 美國 ? Abstract: Several Markov chain methods are available for sampling from a posterior distribution. Two important examples are the Gibbs sampler and the Metropolis algorithm. In addition, several strategies are available for constructing hybrid algorithms. This paper outlines some of the basic methods and strategies and discusses some related theoretical and practical issues. On the theoretical side, results from the theory of general state space Markov chains can be used to obtain convergence rates, laws of large numbers and central limit theorems for estimates obtained from Markov chain methods. These theoretical results can be used to guide the construction of more efficient algorithms. For the practical use of Markov chain methods, standard simulation methodology provides several Variance reduction techniques and also gives guidance on the choice of sample size and allocation. SCI高被引 摘要引言部分案例 回顧研究背景present ? Author(s): LYNCH, M。 MILLIGAN, BG SC I被 引用 661 ? Title: ANALYSIS OF POPULATION GENETICSTRUCTURE WITH RAPD MARKERS ? Source: MOLECULAR ECOLOGY, 3 (2): 9199 APR 1994《 分子生態(tài)學(xué) 》 英國 ? Abstract: Recent advances in the application of the polymerase chain reaction make it possible to score individuals at a large number of loci. The RAPD (random amplified polymorphic DNA) method is one such technique that has attracted widespread interest. The analysis of population structure with RAPD data is hampered by the lack of plete genotypic information resulting from dominance, since this enhances the sampling variance associated with single loci as well as induces bias in parameter estimation. We present estimators for several populationgeic parameters (gene and genotype frequencies, within and betweenpopulation heterozygosities, degree of inbreeding and population subdivision, and degree of individual relatedness) along with expressions for their sampling variances. Although pletely unbiased estimators do not appear to be possible with RAPDs, several steps are suggested that will insure that the bias in parameter estimates is negligible. To achieve the same degree of statistical power, on the order of 2