文章的摘要十分关键，因为很多研究人员只读摘要而不读全文。因此，摘要提供准确 而详尽的研究总结十分重要：它可以帮助研究人员了解你所开展的工作、你的研究目的和研究发现以及研究结果的益处和重要性。摘要必须能够独立成文，具备研究 概要的功能，使人不看全文就能读懂。在阅读摘要后对文章细节感兴趣的读者自然会继续阅读全文。因此摘要不必太面面俱到，例如，可不必列举方法细节。 尽 管摘要是论文的第一部分，但事实上应最后撰写。在完成其他部分后应尽快写摘要，因为这些内容依然清晰地印在你的脑海中，使你能够对自己的工作进行简明而全 面的总结，而不至于忽略任何重要的内容。不同期刊对摘要的撰写要求有所不同，因此应参照目标杂志的《稿约》了解具体要求。尽管杂志要求不同，但依然存在一 些普遍应遵守的惯例： • 应注意对字数的限制。通常来讲摘要的字数限制平均为250个词，但许多杂志要求更短些(如《Nature》和《BBRC》对摘要的篇幅限制为150个 字)，而许多杂志(如《BioMed Central》)允许摘要篇幅稍长些。这充分说明了为什么应在写文章之前确定目标杂志。 • 应避免使用技术行话，从而使摘要更易懂，更具可读性。不同目标期刊的“技术行话”取决于杂志的读者情况(可以通过期刊网站查询)。例如：“焦虑测试”一词 通常比“高架十字迷宫实验”更容易理解，除非该杂志专门针对行为研究人员。通常摘要因受篇幅所限不能对技术术语进行定义和解释。如果术语使用不可避免，应 在首次提到时用简单的措辞加以定义。 • 如同技术术语一样，应尽量不使用缩略语，其可用性也取决于不同的目标期刊。例如，大多数杂志接受HIV的使用。相比之下，RT-PCR对于分子生物学技术 的杂志是可以接受的，但绝大多数杂志要求在首次使用时给出完整拼写(reverse transcriptase polymerase chain reaction)。许多杂志在网页上列出可使用的缩略语。反复使用三次或以上的必要的缩略语应在首次使用时给出完整拼写。只使用一次或两次的缩略语应使 用全称，除非这样做超出了字数要求。摘要中已给出全称的缩略语在正文中首次使用时也应给出全称。 • 尽管一些杂志允许在摘要中引用文献，但绝大多数杂志不允许引用文献。因此，除非你要投稿的杂志允许这样做，否则不应在摘要中引用文献。 以下是BBRC杂志作者须知给出的指导性意见： • 摘要应放在第2页，即标题页之后 • 摘要应采用一段式，总结文章的主要发现，篇幅不超过150字 • 摘要后应列出10个用于收录和检索的关键词 一些杂志要求采用结构式摘要，分为背景、目的、方法、结果和结论。临床期刊可能要求额外或不同段落，如“patients”。因此，再次强调，在动笔之前应查阅目标杂志的《稿约》，确定杂志的具体版式或格式要求。 摘 要后经常需要列出由作者选择的关键词。《稿约》会指出要求列出多少个关键词，甚至提供可供参考的关键词清单。选择合适的关键词很重要，因为他们可作检索之 用。选择合适的关键词可以使你的文章更容易被发现和引用。因此，关键词越切合你的文章内容越好，应避免选择多数研究所适用的一般性术语。 实例：让为这个题目选择合适的关键词： “Region-specific neuronal degeneration after okadaic acid administration” 好的关键词：okadoic acid、hippocampus、neuronal degeneration、MAP kinase signaling以及mouse (或是rat或其他实验动物)。 差的关键词：neuron、brain、OA (简写)、regional-specific neuronal degeneration以及signaling。这些词过于笼统。 英文原文 The snapshot: abstract and keywords Your paper’s abstract is critical because many researchers will read that part only, rather than reading the entire paper. Therefore, it is critical that it provides an accurate and sufficiently detailed summary of your work so that those researchers can understand what you did, why you did it, what your findings are, and why your findings are useful and important. Your abstract must be able to stand alone, that is, to function as an overview of your study that can be understood without reading the entire text. Readers who become interested in learning more details than can be included in the abstract will inevitably proceed to the full text. Therefore, the abstract does not need to be overly detailed; for example, it does not need to include a detailed methods section. Even though the abstract is one of the first parts of your paper, it should actually be written last. You should write it soon after finishing the other sections, while the rest of the manuscript is fresh in your mind, enabling you to write a concise but comprehensive summary of your study without overlooking anything important. Requirements for abstracts differ among journals, so the target journal’s instructions for authors should be consulted for specific details. Despite differences among journals, there are a few general rules that should be obeyed when writing an abstract: • The word limit should be observed; 250 words is probably about average and commonly adopted as a word limit for the abstract, but many journals request shorter abstracts (for example, Nature Articles and BBRC both have a 150-word limit) while many others (for example, BioMed Central journals) allow longer ones. This is one good reason why the target journal should be identified before you write your paper. • Technical jargon should be avoided so that the abstract is understandable for a broad readership, although what is considered “technical” may vary depending on the target journal’s audience (check the journal’s website for details of their readership). For example, “a test of anxiety” would generally be clearer than “elevated plus-maze test” in an abstract unless the journal was specifically targeted to behavioral researchers. Usually, there simply isn’t enough space in the abstract to define and explain technical terminology. If such terminology is unavoidable, it should be defined in simple terms where it is first used. • Like technical jargon, abbreviations should be limited as much as possible, although their acceptability may again depend on the target journal. For example, HIV is likely to be acceptable in abbreviated form by most journals. By contrast, RT-PCR might be considered acceptable by a journal reporting molecular biology techniques, but would it need to be spelt in full (reverse transcriptase polymerase chain reaction) in most journals at first use. Many journals provide a list of acceptable abbreviations on their websites. Necessary abbreviations used three or more times should be defined at first use; however, abbreviations used only once or twice should be spelled out in full unless doing so causes the word limit to be exceeded. Abbreviations that are defined in the abstract will need to be defined again at first use in the main text. • Although some journals do allow references to be cited in the abstract, the vast majority do not. Therefore, unless you plan to submit to a journal that allows it, you should not cite references in your abstract. If we look at the instructions to authors for BBRC, we can see the following guidelines: • The Abstract should be on page 2, i.e., after the title page • The Abstract must be a single paragraph that summarizes the main findings of the paper in fewer than 150 words. • A list of up to 10 keywords useful for indexing or searching should be included after the Abstract. Some journals request structured abstracts divided into sections such as background, objectives, methods, results, and conclusions. Clinical journals may require additional or alternative sections, such as ‘patients’. Therefore, it is again necessary to check the target journal’s instructions for authors to determine the particular formatting/outline requirements prior to writing. Abstracts are frequently followed by a list of keywords selected by the authors. The instructions for authors will state how many keywords are required and may even provide a list of recommended keywords. Choosing appropriate keywords is important, because these are used for indexing purposes. Well chosen keywords enable your manuscript to be more easily identified and cited. Thus, the keywords should be as specific to your manuscript as possible, and general terms, which could apply to an enormous number of studies, should be avoided. Examples: Let’s consider some appropriate keywords for the following title: “Region-specific neuronal degeneration after okadaic acid administration”. Good keywords would be: okadaic acid, hippocampus, neuronal degeneration, MAP kinase signaling, and possibly mouse (or rat or whatever experimental animal was used). Poor keywords would be: neuron, brain, OA (as an abbreviation), regional-specific neuronal degeneration, and signaling. These terms are simply too general. Dr Daniel McGowan 分子神经学博士 理文编辑学术总监
今已没有几个行业可以完全不涉及统计学思维的，绝大多数学科都多少需要使用统计学….. 统计学已经从我们日常思维的一个方面发展为无处不在的系统性研究工具….统计学思维承认： 我们对世界的观察总存在某些不确定性，永不可能完全准确。 Rowntree D (1981). Statistics without tears. A primer for non-mathematicians. Penguin Books Ltd., London, England. 统计是指收集、处理和解释数据的方法。由于统计方法是科学探索的固有内容，因此我们的博客已经在研究设计、方法、结果、图表等数处提及统计。但考虑到统计在多数科学研究中的重要性，有必要专门讲解其使用和表达。 在开始研究之前，在初步的研究设计中就应该考虑统计。首先，要考虑你需要收集哪些信息来检验你的假设或解答你的研究问题。研究有个正确的开始非常重要；虽然数据检验错误相对容易弥补，要用另外的样本组重新收集数据或者从同一样本中追加获取变量可就费时费力得多。如果你想检验某种疗法对普通人群的效果，你的样本要能够代表这个群体。如果比较的是分别有两种疾病或行为的两个群体，那这两个群体的其他变量如年龄、性别、种族需要尽可能一致。这些涉及的都是数据收集；如果在这一步就犯了错，你就有可能遇到严重的问题，甚至可能会在数个月后在同行审稿阶段遭到严重质疑而推翻重来。 其次，你要考虑要采用何种统计检验才能从数据中提炼出有意义的结论。这取决于数据类型。是用来表达某种标志物存在与否的分类数据吗？还是有具体数值的定量数据？如果是定量数据，是连续数据(测量所得)还是离散数据(计数所得)？例如，年龄、体重、时间和温度都是连续数据因为他们的值是在连续，无限可分的尺度上测量出来的；相反，人和细胞的数目都是离散数据，他们不是无限可分的，他们的值是通过计数得到的。你也需要知道你数据的分布：是正态(高斯)分布还是偏态分布？这也关系到你该采取何种检验。你一定要知道你收集的是何种类型的数据，这样才能用适宜的统计检验来分析和恰当的方式来表示。下面这个网址提供了选择适宜检验方法的指南，可能会有所帮助：http://www.graphpad.com/www/Book/Choose.htm 最后，需要知道如何解读统计检验的结果。P值(或 t、 χ2 等)代表什么意思？这是统计检验的关键：确定结果到底意味着什么，你能下什么结论？统计能告诉我们某一数据集的集中趋势(如平均值和中位数)和离散趋势 (标准差、标准误和百分位间距)，从而明确该数据集的分布情况。统计学可以比较(如用t检验、方差分析和χ2检验)两个或多个样本组之间是否有非偶然的系统性差别。如果检验表明无效假设可能性很小，则差别具有显著性。一定要记住，用概率简化差别的“真实性”造成了两种风险，两种都取决于所选取显著性的阈值。第一个是第1类错误，是指本没有显著性差异之处检出了显著性差异。另一个是第2类错误，是指本有显著性差异但由于差别不够大而不能捡出。降低第1类错误的风险就会增加第2类错误的风险；不过这也比下不存在的结论要好。统计学也能给出关联的强度，从而允许从样本组中推断出适用于更广群体的结论。统计学赋予了本身价值有限的结果更多意义，并允许我们用概率下结论，虽然总是存在错误的可能。 实例 节选自《The Journal of Clinical Investigation》 (doi:10.1172/JCI38289; 经同意转载)。 清单 1. 在列举数据时，说明使用的是何种参数，如“均值±标准差”。 2. 说明数据分析所采用的统计检验方法。 3. 百分比给出分子分母，如“40% (100/250)”。 4. 正态分布数据用均值和标准差表示。 5. 非正态分布数据用中位数和 百分位数表示。 6. 给出具体的P 值, 如 写出 “P=0.0035”，而不要只写 “P0.05”。 7. “significant’ 这个词仅用于描述统计学上的显著差异。 英文原文 Statistics: what can we say about our findings? Today, few professional activities are untouched by statistical thinking, and most academic disciplines use it to a greater or lesser degree… Statistics has developed out of an aspect of our everyday thinking to be a ubiquitous tool of systematic research… Statistical thinking is a way of recognizing that our observations of the world can never be totally accurate; they are always somewhat uncertain. Rowntree D (1981). Statistics without tears. A primer for non-mathematicians. Penguin Books Ltd., London, England. The term ‘statistics’ refers to the methods used to collect, process and interpret data. Because these methods are so inherent in the process of scientific inquiry, there have been multiple references to statistics throughout our blog, namely, in the posts on study design, methods, results and display items. However, given the importance of statistics in most scientific studies, it is worthwhile having a separate post on how they should be used and presented. Statistics should first be considered long before the commencement of any research, during the initial study design. First, consider what information you need to collect in order to test your hypothesis or address your research question. It is important to get this right from the outset because, while data can be reanalyzed relatively easily if the wrong tests were used, it is far more difficult and time-consuming to repeat data collection with a different sample group or obtain additional variables from the same sample. If you wish to test the efficacy of a treatment for use in the general population, then your sample needs to be representative of the general population. If you wish to test its efficacy in a given ethnicity or age group, then your sample needs to be representative of that group. If comparing two groups of subjects separated on the basis of a particular disease or behavior, then other variables, such as age, sex and ethnicity, need to be matched as closely as possible between the two groups. This aspect of statistics relates to the collection of data; get it wrong and you could face major problems, potentially the need to start the research all over again, at the peer review stage many months later. Second, you need to consider what statistical tests should be applied so that you can make meaningful statements about your data. This depends on the type of data you have collected: do you have categorical data, perhaps describing the presence or absence of a particular marker, or quantitative data with numerical values? If your data is quantitative, is it continuous (that is, can it be measured) or discrete (counts)? For example, age, weight, time and temperature are all examples of continuous data because they are measured on continuous scales with units that are infinitely sub-divisible. By contrast, the number of people in a given group and the number of cells with apoptotic features are examples of discrete data that need to be counted and are not sub-divisible. You also need to know how your data is distributed: is it normally distributed (Gaussian) or skewed? This also affects the type of test that should be used. It is important that you know what type of data you are collecting so that you apply the appropriate statistical tests to analyze the data and so you present them in an appropriate manner. The following useful website provides a guide to choosing the appropriate statistical test: http://www.graphpad.com/www/Book/Choose.htm Finally, you need to know how to interpret the results of the statistical tests you have selected. What exactly does the p (or t or χ2 or other) value mean? That, after all is the point of statistical analysis: to determine what you can say about your findings; what they really mean. Statistics enable us to determine the central tendency (for example, mean and median) and dispersion (for example, standard deviation, standard error, and interpercentile range) of a dataset, giving us an idea of its distribution. Also using statistics, values from two or more different sample groups can be compared (for example, by t-test, analysis of variance, or χ2 test) to determine if a difference between or among groups could have arisen by chance. If this hypothesis, known as the null hypothesis, can be shown to be highly unlikely (usually less than 5% chance), then the difference is said to be significant. It is important to keep in mind that there are two risks associated with reducing a decision about the ‘reality’ of a difference to probabilities, and both depend on the threshold set to determine significance: the first, known as type I error, is the possibility that a difference is accepted as significant when it is not; the opposite risk, known as type II error, refers to the possibility that a significant difference is considered not to be significant because we demand a larger difference between groups to be certain. Reducing the risk of type I errors increases the risk of type II errors, but this is infinitely more preferable than reaching a conclusion that isn’t justified. Statistics also provides a measure of the strengths of correlations and enables inferences about a much larger population to be drawn on the basis of findings in a sample group. In this way, statistics puts meaning into findings that would otherwise be of limited value, and allows us to draw conclusions based on probabilities, even when the possibility of error remains. Example Extracts from The Journal of Clinical Investigation (doi:10.1172/JCI38289; reproduced with permission). Checklist 1. Indicate what parameters are described when listing data; for example, “means±S.D.” 2. Indicate the statistical tests used to analyze data 3. Give the numerator and denominator with percentages; for example “40% (100/250)” 4. Use means and standard deviations to report normally distributed data 5. Use medians and interpercentile ranges to report data with a skewed distribution 6. Report p values; for example, use “p=0.0035” rather than “p0.05” 7. Only use the word “significant’ when describing statistically significant differences. Choosing a statistical test FAQ# 1790 This is chapter 37 of the first edition of Intuitive Biostatistics by Harvey Motulsky. Copyright 1995 by Oxford University Press Inc. Chapter 45 of the second edition of Intuitive Biostatistics is an expanded version of this material. REVIEW OF AVAILABLE STATISTICAL TESTS This book has discussed many different statistical tests. To select the right test, ask yourself two questions: What kind of data have you collected? What is your goal? Then refer to Table 37.1. Type of Data Goal Measurement (from Gaussian Population) Rank, Score, or Measurement (from Non- Gaussian Population) Binomial (Two Possible Outcomes) Survival Time Describe one group Mean, SD Median, interquartile range Proportion Kaplan Meier survival curve Compare one group to a hypothetical value One-sample t test Wilcoxon test Chi-square or Binomial test ** Compare two unpaired groups Unpaired t test Mann-Whitney test Fisher's test (chi-square for large samples) Log-rank test or Mantel-Haenszel* Compare two paired groups Paired t test Wilcoxon test McNemar's test Conditional proportional hazards regression* Compare three or more unmatched groups One-way ANOVA Kruskal-Wallis test Chi-square test Cox proportional hazard regression** Compare three or more matched groups Repeated-measures ANOVA Friedman test Cochrane Q** Conditional proportional hazards regression** Quantify association between two variables Pearson correlation Spearman correlation Contingency coefficients** Predict value from another measured variable Simple linear regression or Nonlinear regression Nonparametric regression** Simple logistic regression* Cox proportional hazard regression* Predict value from several measured or binomial variables Multiple linear regression* or Multiple nonlinear regression** Multiple logistic regression* Cox proportional hazard regression* REVIEW OF NONPARAMETRIC TESTS Choosing the right test to compare measurements is a bit tricky, as you must choose between two families of tests: parametric and nonparametric. Many -statistical test are based upon the assumption that the data are sampled from a Gaussian distribution. These tests are referred to as parametric tests. Commonly used parametric tests are listed in the first column of the table and include the t test and analysis of variance. Tests that do not make assumptions about the population distribution are referred to as nonparametric- tests. You've already learned a bit about nonparametric tests in previous chapters. All commonly used nonparametric tests rank the outcome variable from low to high and then analyze the ranks. These tests are listed in the second column of the table and include the Wilcoxon, Mann-Whitney test, and Kruskal-Wallis tests. These tests are also called distribution-free tests. CHOOSING BETWEEN PARAMETRIC AND NONPARAMETRIC TESTS: THE EASY CASES Choosing between parametric and nonparametric tests is sometimes easy. You should definitely choose a parametric test if you are sure that your data are sampled from a population that follows a Gaussian distribution (at least approximately). You should definitely select a nonparametric test in three situations: The outcome is a rank or a score and the population is clearly not Gaussian. Examples include class ranking of students, the Apgar score for the health of newborn babies (measured on a scale of 0 to IO and where all scores are integers), the visual analogue score for pain (measured on a continuous scale where 0 is no pain and 10 is unbearable pain), and the star scale commonly used by movie and restaurant critics (* is OK, ***** is fantastic). Some values are "off the scale," that is, too high or too low to measure. Even if the population is Gaussian, it is impossible to analyze such data with a parametric test since you don't know all of the values. Using a nonparametric test with these data is simple. Assign values too low to measure an arbitrary very low value and assign values too high to measure an arbitrary very high value. Then perform a nonparametric test. Since the nonparametric test only knows about the relative ranks of the values, it won't matter that you didn't know all the values exactly. The data ire measurements, and you are sure that the population is not distributed in a Gaussian manner. If the data are not sampled from a Gaussian distribution, consider whether you can transformed the values to make the distribution become Gaussian. For example, you might take the logarithm or reciprocal of all values. There are often biological or chemical reasons (as well as statistical ones) for performing a particular transform. CHOOSING BETWEEN PARAMETRIC AND NONPARAMETRIC TESTS: THE HARD CASES It is not always easy to decide whether a sample comes from a Gaussian population. Consider these points: If you collect many data points (over a hundred or so), you can look at the distribution of data and it will be fairly obvious whether the distribution is approximately bell shaped. A formal statistical test (Kolmogorov-Smirnoff test, not explained in this book) can be used to test whether the distribution of the data differs significantly from a Gaussian distribution. With few data points, it is difficult to tell whether the data are Gaussian by inspection, and the formal test has little power to discriminate between Gaussian and non-Gaussian distributions. You should look at previous data as well. Remember, what matters is the distribution of the overall population, not the distribution of your sample. In deciding whether a population is Gaussian, look at all available data, not just data in the current experiment. Consider the source of scatter. When the scatter comes from the sum of numerous sources (with no one source contributing most of the scatter), you expect to find a roughly Gaussian distribution. When in doubt, some people choose a parametric test (because they aren't sure the Gaussian assumption is violated), and others choose a nonparametric test (because they aren't sure the Gaussian assumption is met). CHOOSING BETWEEN PARAMETRIC AND NONPARAMETRIC TESTS: DOES IT MATTER? Does it matter whether you choose a parametric or nonparametric test? The answer depends on sample size. There are four cases to think about: Large sample. What happens when you use a parametric test with data from a nongaussian population? The central limit theorem (discussed in Chapter 5) ensures that parametric tests work well with large samples even if the population is non-Gaussian. In other words, parametric tests are robust to deviations from Gaussian distributions, so long as the samples are large. The snag is that it is impossible to say how large is large enough, as it depends on the nature of the particular non-Gaussian distribution. Unless the population distribution is really weird, you are probably safe choosing a parametric test when there are at least two dozen data points in each group. Large sample. What happens when you use a nonparametric test with data from a Gaussian population? Nonparametric tests work well with large samples from Gaussian populations. The P values tend to be a bit too large, but the discrepancy is small. In other words, nonparametric tests are only slightly less powerful than parametric tests with large samples. Small samples. What happens when you use a parametric test with data from nongaussian populations? You can't rely on the central limit theorem, so the P value may be inaccurate. Small samples. When you use a nonparametric test with data from a Gaussian population, the P values tend to be too high. The nonparametric tests lack statistical power with small samples. Thus, large data sets present no problems. It is usually easy to tell if the data come from a Gaussian population, but it doesn't really matter because the nonparametric tests are so powerful and the parametric tests are so robust. Small data sets present a dilemma. It is difficult to tell if the data come from a Gaussian population, but it matters a lot. The nonparametric tests are not powerful and the parametric tests are not robust. ONE- OR TWO-SIDED P VALUE? With many tests, you must choose whether you wish to calculate a one- or two-sided P value (same as one- or two-tailed P value). The difference between one- and two-sided P values was discussed in Chapter 10. Let's review the difference in the context of a t test. The P value is calculated for the null hypothesis that the two population means are equal, and any discrepancy between the two sample means is due to chance. If this null hypothesis is true, the one-sided P value is the probability that two sample means would differ as much as was observed (or further) in the direction specified by the hypothesis just by chance, even though the means of the overall populations are actually equal. The two-sided P value also includes the probability that the sample means would differ that much in the opposite direction (i.e., the other group has the larger mean). The two-sided P value is twice the one-sided P value. A one-sided P value is appropriate when you can state with certainty (and before collecting any data) that there either will be no difference between the means or that the difference will go in a direction you can specify in advance (i.e., you have specified which group will have the larger mean). If you cannot specify the direction of any difference before collecting data, then a two-sided P value is more appropriate. If in doubt, select a two-sided P value. If you select a one-sided test, you should do so before collecting any data and you need to state the direction of your experimental hypothesis. If the data go the other way, you must be willing to attribute that difference (or association or correlation) to chance, no matter how striking the data. If you would be intrigued, even a little, by data that goes in the "wrong" direction, then you should use a two-sided P value. For reasons discussed in Chapter 10, I recommend that you always calculate a two-sided P value. PAIRED OR UNPAIRED TEST? When comparing two groups, you need to decide whether to use a paired test. When comparing three or more groups, the term paired is not apt and the term repeated measures is used instead. Use an unpaired test to compare groups when the individual values are not paired or matched with one another. Select a paired or repeated-measures test when values represent repeated measurements on one subject (before and after an intervention) or measurements on matched subjects. The paired or repeated-measures tests are also appropriate for repeated laboratory experiments run at different times, each with its own control. You should select a paired test when values in one group are more closely correlated with a specific value in the other group than with random values in the other group. It is only appropriate to select a paired test when the subjects were matched or paired before the data were collected. You cannot base the pairing on the data you are analyzing. FISHER'S TEST OR THE CHI-SQUARE TEST? When analyzing contingency tables with two rows and two columns, you can use either Fisher's exact test or the chi-square test. The Fisher's test is the best choice as it always gives the exact P value. The chi-square test is simpler to calculate but yields only an approximate P value. If a computer is doing the calculations, you should choose Fisher's test unless you prefer the familiarity of the chi-square test. You should definitely avoid the chi-square test when the numbers in the contingency table are very small (any number less than about six). When the numbers are larger, the P values reported by the chi-square and Fisher's test will he very similar. The chi-square test calculates approximate P values, and the Yates' continuity correction is designed to make the approximation better. Without the Yates' correction, the P values are too low. However, the correction goes too far, and the resulting P value is too high. Statisticians give different recommendations regarding Yates' correction. With large sample sizes, the Yates' correction makes little difference. If you select Fisher's test, the P value is exact and Yates' correction is not needed and is not available. REGRESSION OR CORRELATION? Linear regression and correlation are similar and easily confused. In some situations it makes sense to perform both calculations. Calculate linear correlation if you measured both X and Y in each subject and wish to quantity how well they are associated. Select the Pearson (parametric) correlation coefficient if you can assume that both X and Y are sampled from Gaussian populations. Otherwise choose the Spearman nonparametric correlation coefficient. Don't calculate the correlation coefficient (or its confidence interval) if you manipulated the X variable. Calculate linear regressions only if one of the variables (X) is likely to precede or cause the other variable (Y). Definitely choose linear regression if you manipulated the X variable. It makes a big difference which variable is called X and which is called Y, as linear regression calculations are not symmetrical with respect to X and Y. If you swap the two variables, you will obtain a different regression line. In contrast, linear correlation calculations are symmetrical with respect to X and Y. If you swap the labels X and Y, you will still get the same correlation coefficient.
文章的摘要十分关键，因为很多研究人员只读摘要而不读全文。因此，摘要提供准确 而详尽的研究总结十分重要：它可以帮助研究人员了解你所开展的工作、你的研究目的和研究发现以及研究结果的益处和重要性。摘要必须能够独立成文，具备研究 概要的功能，使人不看全文就能读懂。在阅读摘要后对文章细节感兴趣的读者自然会继续阅读全文。因此摘要不必太面面俱到，例如，可不必列举方法细节。 尽 管摘要是论文的第一部分，但事实上应最后撰写。在完成其他部分后应尽快写摘要，因为这些内容依然清晰地印在你的脑海中，使你能够对自己的工作进行简明而全 面的总结，而不至于忽略任何重要的内容。不同期刊对摘要的撰写要求有所不同，因此应参照目标杂志的《稿约》了解具体要求。尽管杂志要求不同，但依然存在一 些普遍应遵守的惯例： • 应注意对字数的限制。通常来讲摘要的字数限制平均为250个词，但许多杂志要求更短些(如《Nature》和《BBRC》对摘要的篇幅限制为150个 字)，而许多杂志(如《BioMed Central》)允许摘要篇幅稍长些。这充分说明了为什么应在写文章之前确定目标杂志。 • 应避免使用技术行话，从而使摘要更易懂，更具可读性。不同目标期刊的“技术行话”取决于杂志的读者情况(可以通过期刊网站查询)。例如：“焦虑测试”一词 通常比“高架十字迷宫实验”更容易理解，除非该杂志专门针对行为研究人员。通常摘要因受篇幅所限不能对技术术语进行定义和解释。如果术语使用不可避免，应 在首次提到时用简单的措辞加以定义。 • 如同技术术语一样，应尽量不使用缩略语，其可用性也取决于不同的目标期刊。例如，大多数杂志接受HIV的使用。相比之下，RT-PCR对于分子生物学技术 的杂志是可以接受的，但绝大多数杂志要求在首次使用时给出完整拼写(reverse transcriptase polymerase chain reaction)。许多杂志在网页上列出可使用的缩略语。反复使用三次或以上的必要的缩略语应在首次使用时给出完整拼写。只使用一次或两次的缩略语应使 用全称，除非这样做超出了字数要求。摘要中已给出全称的缩略语在正文中首次使用时也应给出全称。 • 尽管一些杂志允许在摘要中引用文献，但绝大多数杂志不允许引用文献。因此，除非你要投稿的杂志允许这样做，否则不应在摘要中引用文献。 以下是BBRC杂志作者须知给出的指导性意见： • 摘要应放在第2页，即标题页之后 • 摘要应采用一段式，总结文章的主要发现，篇幅不超过150字 • 摘要后应列出10个用于收录和检索的关键词 一些杂志要求采用结构式摘要，分为背景、目的、方法、结果和结论。临床期刊可能要求额外或不同段落，如“patients”。因此，再次强调，在动笔之前应查阅目标杂志的《稿约》，确定杂志的具体版式或格式要求。 摘 要后经常需要列出由作者选择的关键词。《稿约》会指出要求列出多少个关键词，甚至提供可供参考的关键词清单。选择合适的关键词很重要，因为他们可作检索之 用。选择合适的关键词可以使你的文章更容易被发现和引用。因此，关键词越切合你的文章内容越好，应避免选择多数研究所适用的一般性术语。 实例：让为这个题目选择合适的关键词： “Region-specific neuronal degeneration after okadaic acid administration” 好的关键词：okadoic acid、hippocampus、neuronal degeneration、MAP kinase signaling以及mouse (或是rat或其他实验动物)。 差的关键词：neuron、brain、OA (简写)、regional-specific neuronal degeneration以及signaling。这些词过于笼统。 英文原文 The snapshot: abstract and keywords Your paper’s abstract is critical because many researchers will read that part only, rather than reading the entire paper. Therefore, it is critical that it provides an accurate and sufficiently detailed summary of your work so that those researchers can understand what you did, why you did it, what your findings are, and why your findings are useful and important. Your abstract must be able to stand alone, that is, to function as an overview of your study that can be understood without reading the entire text. Readers who become interested in learning more details than can be included in the abstract will inevitably proceed to the full text. Therefore, the abstract does not need to be overly detailed; for example, it does not need to include a detailed methods section. Even though the abstract is one of the first parts of your paper, it should actually be written last. You should write it soon after finishing the other sections, while the rest of the manuscript is fresh in your mind, enabling you to write a concise but comprehensive summary of your study without overlooking anything important. Requirements for abstracts differ among journals, so the target journal’s instructions for authors should be consulted for specific details. Despite differences among journals, there are a few general rules that should be obeyed when writing an abstract: • The word limit should be observed; 250 words is probably about average and commonly adopted as a word limit for the abstract, but many journals request shorter abstracts (for example, Nature Articles and BBRC both have a 150-word limit) while many others (for example, BioMed Central journals) allow longer ones. This is one good reason why the target journal should be identified before you write your paper. • Technical jargon should be avoided so that the abstract is understandable for a broad readership, although what is considered “technical” may vary depending on the target journal’s audience (check the journal’s website for details of their readership). For example, “a test of anxiety” would generally be clearer than “elevated plus-maze test” in an abstract unless the journal was specifically targeted to behavioral researchers. Usually, there simply isn’t enough space in the abstract to define and explain technical terminology. If such terminology is unavoidable, it should be defined in simple terms where it is first used. • Like technical jargon, abbreviations should be limited as much as possible, although their acceptability may again depend on the target journal. For example, HIV is likely to be acceptable in abbreviated form by most journals. By contrast, RT-PCR might be considered acceptable by a journal reporting molecular biology techniques, but would it need to be spelt in full (reverse transcriptase polymerase chain reaction) in most journals at first use. Many journals provide a list of acceptable abbreviations on their websites. Necessary abbreviations used three or more times should be defined at first use; however, abbreviations used only once or twice should be spelled out in full unless doing so causes the word limit to be exceeded. Abbreviations that are defined in the abstract will need to be defined again at first use in the main text. • Although some journals do allow references to be cited in the abstract, the vast majority do not. Therefore, unless you plan to submit to a journal that allows it, you should not cite references in your abstract. If we look at the instructions to authors for BBRC, we can see the following guidelines: • The Abstract should be on page 2, i.e., after the title page • The Abstract must be a single paragraph that summarizes the main findings of the paper in fewer than 150 words. • A list of up to 10 keywords useful for indexing or searching should be included after the Abstract. Some journals request structured abstracts divided into sections such as background, objectives, methods, results, and conclusions. Clinical journals may require additional or alternative sections, such as ‘patients’. Therefore, it is again necessary to check the target journal’s instructions for authors to determine the particular formatting/outline requirements prior to writing. Abstracts are frequently followed by a list of keywords selected by the authors. The instructions for authors will state how many keywords are required and may even provide a list of recommended keywords. Choosing appropriate keywords is important, because these are used for indexing purposes. Well chosen keywords enable your manuscript to be more easily identified and cited. Thus, the keywords should be as specific to your manuscript as possible, and general terms, which could apply to an enormous number of studies, should be avoided. Examples: Let’s consider some appropriate keywords for the following title: “Region-specific neuronal degeneration after okadaic acid administration”. Good keywords would be: okadaic acid, hippocampus, neuronal degeneration, MAP kinase signaling, and possibly mouse (or rat or whatever experimental animal was used). Poor keywords would be: neuron, brain, OA (as an abbreviation), regional-specific neuronal degeneration, and signaling. These terms are simply too general. Dr Daniel McGowan 分子神经学博士 理文编辑学术总监
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