洋教授实现我的算法给出评语 本人关于哈密尔顿环算法的论文放在 researchgate 网站上，一直也没有关注。昨天收到汪小龙老师的 QQ 信息，说我的研究有洋人认同，该洋教授在 researchgate 上说，他和他的学生编程实现了我的算法，运行结果非常好，并对我的研究给予了非常积极的评价。我才上到该网站看了。 可见，本人的英文论文并非不能看懂，只要肯花时间精力。我从未对该人就文中的英语表达做任何解释。他和他的学生却能将程序编出来，且运行很好。 从该留言评语可以看出：洋人的坦直、人品和胸怀！ 目前本人的论文在一国际知名期刊审稿，我对结果持有限乐观。为何是有限乐观呢？ 1 ，可以肯定，我的算法和证明都是对的， 2 ，论文书写和表达确实欠佳，思路表达上还有一些细小的 bug 。我也懒得修正小 bug 了，因为若评审者不花时间精力进入思路，一切都枉然，而若他进入了思路，发现了 bug ，我就会很快将 bug 问题解决。我相信遇到高实力能花时间精力进入思路和看懂的评审者机会很大。 说明：本博文不需要点击量，而只希望真关注我的和内行的朋友看到。 下面是该洋朋友在 researchgate 上的留言评论，以及他回复我的一封 email ： Me and my student Ivan Fedorov succeeded in implementing your wonderful algorithm! It was not an easy task regardless of the fact that you tried to make it very clear. I really think that you and your work deserve the world's recognition and the Clay Mathematics Institute Prize. Here is one of the shocking results. We compared Roberts Flores Backtracking algorithm and your algorithm (we now call it Lizhi Du algorithm) while solving Knight's tour problem ( https://en.wikipedia.org/wiki/Knight%27s_tour ). Roberts Flores Backtracking algorithm - over 780 millions steps (computer boiled for several hours). Lizhi Du algorithm - 234 steps !!! We could not believe our eyes! Our implementation uses C# and (controlled by the user) visual animation to clearly show what is going on (how the algorithm selects and deselects graph edges). If you want to see our results, please write me a private email . We are going to publish an article on codeproject.com and we will certainly praise your great work. Thank you very much. 12h ago · Thank you for the newest version of your article. I find it very explicit and clear. We managed to understand all the details of your paper! 以上文字写于2017年5月19日 和盘托出，详细的算法和详细的编程步骤，及证明 这一阵没上科学网，把这个表达修改一番，然后发表到 arxiv 。这次没有任何保留，我不仅将所有的算法细节都写出来了，甚至包括编程细节都说的很清楚。 证明也细化了，应该容易懂得多。 链接如下： http://arxiv.org/ftp/arxiv/papers/1004/1004.3702.pdf 各位，我真的欢迎有真高人批判它否定它。 我现在没有丝毫的理由怀疑它的正确性，按此编出的程序运行也棒极了！百分之百的计算正确，无任何例外。 说心里话，按我的性格，本来我不愿意公布详细的算法，尤其是细致到编程这一层面。世道有一些什么样的科研和 “科研人”各位不难看到，所以得谨慎一些。但前些时一顶级期刊说我算法没有说清楚，许多 options 没有说执行次序，所以我只好被迫将详细细节公布。 秦失其鹿，天下共逐之，疾足高才者先得。 下面是本人以前博文中一段对 NP 的形容： NP 是什么？ NP 是一只鹿，美丽无比，骄狂无比。自出现在地球上，出现在人世间，她就发足狂奔，快如闪电，张狂呼啸，睥睨万物。天下英才聚而逐之， NP 鹿落谁手？ 人群中算法功力超强的人的比例极其稀少，故能看懂算法和证明的人可能不那么多。但计算机专业或其他专业的编程高手则要多得多（相比之下），我这次是没有任何保留地和盘托出详细的算法和编程细节，当然是充分自信。编程高手们试一试就知道。 我希望内行们能提出宝贵意见，也希望善于发高档英文论文的牛哥们能给出经验指导。 今天是光棍节，祝各位节日愉快。可怜，今晚老杜只能去监考。 刚刚一位漂亮的女博士跟我打招呼，还有甜美的笑容，搞得我高兴万分。可惜不能与之 dating 。 著名洋大牛看不懂，怎么办？科研合作 直接将哈密尔顿环算法和证明最终版寄给著名洋大牛，也是著名期刊主编，几天后回信，意思还是看不懂，说 他无法 take on 我的 multiple revision ，并且说上一个版本他确实花了不少时间看。建议我先多跟自己学院的教授交流，因为沟通方便，成熟后再投稿。 估计我在 arxiv 上不断更新也给人不好的印象，认为我自己都没有把握没有成熟就投稿。 其实我在 arxiv 上的方式没有错。我的根本性方法包括程序早就是对的，不断地修改只是为了更容易让人懂，更容易表达证明。再说修改也并不意味前面的就是错的。 朱所长留言说：你不担心别人拿去发表？ 确实不排除有这样的下等人，要是有，那也不止一个。我不断更新，每次都有一些机关和 BUG ，这样的人不可能理解和解决机关和 BUG 。所以不断更新也可以起到防小人的作用。 我倒真的希望有高档次的人拿去改头换面发表在高档次的期刊上，那样省的我麻烦。 最新公布的确实是最终版，最多也就是小 BUG 或补充细节。我本人可以充分自信，肯定正确。 现在是考虑跟人合作的时候了。 第一作者和通信作者必须是我，我不介意再加第二作者和第三作者。 若是知名大牛加盟，署名还可以再商量。 当然，合作者必须有实质性贡献，简单的修改几个单词、句型、语法肯定没有意义。 大牛就是大牛。这次的洋大牛明确跟我说，他不介意我的英语表达，但我必须能让人看懂。这与以前档次不高的期刊审稿人一个劲挑剔我的英语表达大相径庭。 逻辑上，现在的版本对于算法实力真强大的大牛，没有理由看不懂。 汪小龙 2016-11-27 15:48 外国人和中国人的心理其实是一样的。 要合作肯定要你先提，难道要他提出和你合作？ 他就是想和你合作，也会不好意思先说的。 你如果相信他能帮你，就应该先提出合作。 博主回复(2016-11-27 17:32) ： 多谢汪老师提醒！ 那我可能找机会找找洋人大牛 删除 回复 蔡宁 2016-11-27 10:29 arXiv的作用，恰恰是为了防止别人拿去发表。私下流通的过程中，别人拿去发表了，没证据说是自己的。而arXiv可以提供确凿的著作权证据，别人发表了就是抄袭。 博主回复(2016-11-27 12:21) ： 多谢！ 说的是，上了arxiv就不应该再在意了 删除 回复 汪小龙 2016-11-27 10:14 杜老师，你应该直接和那个洋大牛合作。 博主回复(2016-11-27 12:20) ： 谢汪老师建议！我倒真的希望如此，不了解洋人文化，不知道主动跟他提出来会不会不好。
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 this tips series, namely, in the tips on study design, methods, results and display items. However, given the importance of statistics in most scientific studies, it is worthwhile having a separate tip 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 2 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 2 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 unlikely, 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 isnt 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, meansS.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. 在这里还需提请各位注意，Dr. McGowan 的母语是英语，无法阅读中文，因此请大家尽量使用英文回帖，如有任何需要与他沟通的学术和语言问题也请使用英语，Dr. McGowan 会及时回复大家的。 Dr. Daniel McGowan 曾任 Nature Reviews Neuroscience 副编辑，负责约稿，管理和撰写期刊内容。于2006年加入理文编辑（Edanz Group） 并从2008年起担任学术总监。Dr. Daniel McGowan 有超过十年的博士后和研究生阶段实验室研究经验，主要致力于神经退化疾病、分子及细胞生物学、蛋白质生物化学、蛋白质组学和基因组学。
Display items: a picture tells a thousand words In the tip on abstract I mentioned that many readers will only read the abstract of your paper, and so the abstract needed to be self-contained, describing all of the important findings and their significance. Some readers will go further than just the abstract and look at the display items to validate the findings described in the abstract, but still not read the entire paper. Thus, like the abstract, the display items in your paper (along with their associated legends) need to be able to stand alone and be understood without the need to refer to the text of the paper. Display items include figures and tables, which are essentially graphical representations of the results described in the text. Simply put, they are the most effective and efficient way to present your results. With good figures and tables you will be able to impart to the reader exactly what you found in your study in a relatively short period of time (that is, much faster than it would take the reader to read the entire paper). Researchers in rapidly moving fields or with limited time to keep up to date with advances in their field will appreciate the rapidity with which they can be informed of your findings; journals editors and peer reviewers will appreciate the clarity. Therefore, it is worthwhile devoting some thought and attention to developing good quality figures and tables that clearly convey your results. As with the other sections of scientific manuscripts, there are certain rules that should be followed when generating display items. First, if you have a target journal in mind already, consider how many display items they allow and ensure that you do not exceed that limit. If you have more results to describe than can be simply shown in the allowable number of display items, some may need to be included in a Supplementary Information section, or described in the text with the statement (data not shown). However, only the least important or peripheral findings should be described in this way, and all findings that support your hypothesis need to be shown. Thus, you may need to consider an alternative journal if your first choice will not allow you to present all of your important data. By contrast, if a journal allows more display items than is necessary to show your findings, do not add redundant or unnecessary display items simply because you can. All display items must have a clear and necessary purpose. Second, the data shown in figures and tables needs to be easy to interpret. Consider how much data you wish to show in a given display item and how it can be organized to convey the important message. Therefore, rather than combining multiple parameters or treatments into a single graphic, consider splitting the data across multiple simpler graphics that can be grouped together in a single figure. Remember to clearly label any graph axes, table columns and rows, and components of diagrams if appropriate. Trendlines, scale bars and the results of any statistical tests should be also shown, where relevant, for example by using an asterisk to indicate significance, or a variety of symbols to indicate different levels of significance. With large samples, report the % change or % difference as well as absolute values. Third, the legends accompanying display items need to be able to stand alone such that the display items are entirely understandable without the need to read the entire manuscript. That means abbreviations should not be used or need to be defined, and technical terms should be avoided. It should be clear exactly what was done and what was seen. Statistical tests should be briefly described in the legends, with p values given and any symbols used defined. Legends, including their headings, should be written in the present tense with the exception of any methods described within them. For example, use Western blot showing an increase in the levels of p53 after rather than Western blotting showed that the levels of p53 increased after. Finally, there should be no redundancies between the display items and the text. Therefore, do not produce a display item to show information that can easily and briefly be stated in the text, and do not duplicate information among tables and figures, for example, by making a table to show the same information already conveyed in a figure. Do not embed figures and their legends within the text of the manuscript you plan to submit. The publisher will put your text and display items into a template proof that will be specific to the target journal. What they usually require from authors is a separate file containing display items (occasionally these can be placed at the end of a manuscript files) and a text file that includes figure and table legends listed together at the end (usually following the references). Check the instructions for authors of your intended target journal for their specific requirements. By preparing good quality, clear display items before writing the results section, this section will practically write itself. The display items can be grouped in a logical order that progresses your argument or progressively strengthens your hypothesis. With one subsection and one display item for each of the major findings, the subsection headings will be similar to the relevant legend headings, and the text in each subsection will provide a brief description of the findings shown in each display item, complete with the results of statistical analyses, with the reader being referred to the display items for more detail. Examples Tables are a great way to present large amounts of necessary data with minimal description required. The table shown above is a truncated version of a table in a paper published in The Journal of Clinical Investigation (doi:10.1172/JCI37622; reproduced with permission). The data presented clearly and economically in this table would have required a considerable amount of word space to describe in the text, but the use of a table makes the information available without the need for a wordy description. All that was required to describe this in the main text was the following statement: Clinical characteristics of all patients and tumor samples are summarized in Table 1. The figure below, taken from the same paper, contains many of the elements of a successful display item described in this tip and listed in the checklist below. Checklist 1. Stand alone legends 2. Comply with the allowable number of display items 3. Avoid redundancy among display items or between display items and text 4. Divide data showing different effects or parameters among different panels within the same display item 5. Use scale bars, trendlines and clear labels, and show the results of statistical tests 6. Avoid or define all symbols and abbreviations 7. With large samples, show % changes/differences as well as absolute values 8. Submit figures in a separate file or at the end of the manuscript file rather than embedded in the main text 9. Check the instructions for authors for any specific requirements regarding format, size, color, number of items and any other parameters 在这里还需提请各位注意，Dr. McGowan 的母语是英语，无法阅读中文，因此请大家尽量使用英文回帖，如有任何需要与他沟通的学术和语言问题也请使用英语，Dr. McGowan 会及时回复大家的。 Dr. Daniel McGowan 曾任 Nature Reviews Neuroscience 副编辑，负责约稿，管理和撰写期刊内容。于2006年加入理文编辑（Edanz Group） 并从2008年起担任学术总监。Dr. Daniel McGowan 有超过十年的博士后和研究生阶段实验室研究经验，主要致力于神经退化疾病、分子及细胞生物学、蛋白质生物化学、蛋白质组学和基因组学。
Scientific enquiry can take a number of different forms. As a result, there is a variety of publication types, including papers describing original research, reviews, case studies, methodology papers and theoretical papers. By far the most common format for writing scientific papers describing original research is the IMRaD format. The letters in this acronym stand for introduction, methods, results and discussion, representing the sections lying between the abstract and references in such manuscripts (although in some journals, the methods section is presented at the end rather than after the introduction). The International Committee of Medical Journal Editors (ICMJE), in their Uniform Requirements for Manuscripts Submitted to Biomedical Journals: Writing and Editing for Biomedical Publication, section IV.A.1.a (General Principles), provide the following description of the IMRaD format and why it is used: The text of observational and experimental articles is usually (but not necessarily) divided into the following sections: Introduction, Methods, Results, and Discussion. This so-called IMRAD structure is not an arbitrary publication format but rather a direct reflection of the process of scientific discovery. Long articles may need subheadings within some sections (especially Results and Discussion) to clarify their content. The following four sections describe the important components of each of these sections as well as some common mistakes to avoid. However, it is worth mentioning that these sections should not be written in the order in which they appear (or in which they are described here); rather, there is a specific order in which the sections of a manuscript should be written to achieve maximum clarity and consistency throughout. The recommended order for writing these sections, with the addition of the abstract and title, is as follows: Methods Results Introduction Discussion Title Abstract The methods can be written while you are performing the research or, for certain standard protocols, before it has even begun. Doing this early in the course of your research could make you aware of any potential problems in your study design, or point to additional controls you might not previously have considered. The advantage of this is that the methods can be adjusted before performing experiments, preventing the need for time-consuming and costly repeats of experiments already performed. With the methods written up and the experiments performed, you will want to analyze your results to determine how they relate to your hypothesis, and what they actually show. It is pointless writing the introduction prior to this stage because the results you obtain will determine how the paper needs to be framed, that is, what context the results are described in. Therefore, the results should be analyzed and written up second. During this stage you will determine how your data should be presented (for example, in tables, graphs, schematics or photographs; see tip on graphics), how they need to be analyzed (see tip on statistics), and what they mean; once decided, you will then need to describe them. By now you will have a good idea of how your findings relate to your hypothesis and the existing literature in your field. It might be necessary at this point to ask a different research question or to change the focus of your research. Following such a change, re-analyses of your data and/or additional experiments might be necessary to make a complete story. Once these are done, the introduction can be written, to provide the context, and then the discussion can be written to describe the relevance of your findings within that context. Finally, with all of that fresh in your head, the abstract and title, the important components of which are described in previous tips, should be written last. 在这里还需提请各位注意，Dr. McGowan 的母语是英语，无法阅读中文，因此请大家尽量使用英文回帖，如有任何需要与他沟通的学术和语言问题也请使用英语，Dr. McGowan 会及时回复大家的。 Dr. Daniel McGowan 曾任 Nature Reviews Neuroscience 副编辑，负责约稿，管理和撰写期刊内容。于2006年加入理文编辑（Edanz Group） 并从2008年起担任学术总监。Dr. Daniel McGowan 有超过十年的博士后和研究生阶段实验室研究经验，主要致力于神经退化疾病、分子及细胞生物学、蛋白质生物化学、蛋白质组学和基因组学。
Choosing the right platform 在本帖中， 理文编辑 学术总监Dr. Daniel McGowan将向大家展示如何选择合适的目标期刊。 Selection of an appropriate journal and publication type is critical: get it right and you instantly increase your chances of successful publication and regular citation. By contrast, sending a manuscript to an inappropriate journal is a frequent cause of rejection. The aims and scope of the journal, the journals target audience and recent publication history, the significance and broadness of appeal of the findings described in your manuscript, and the type of study performed should all be considered before selecting your target journal. Start by considering what the main focus of your paper is, and therefore, who you would expect to want to read it. This should have become clear while writing the paper, particularly the discussion section. Is there a clinical focus or do you describe basic science findings? Are the findings of relevance to a broad cross-section of the scientific community or will they only appear to researchers in a specialist field? Are the findings preliminary, with more work required to make an irrefutable and comprehensive story, or do you have multiple types of complementary data to support your hypothesis? Indeed, do you need to publish right away, or can you delay publication while collecting more data to try for a journal with a higher impact factor? By asking yourself these questions, among others, you will be able to build up a picture of the type of journal you should be targeting. Then, you need to generate a short-list. An immediate source of potential target journals is in your own papers reference list. Any similar or related previous work should have been cited in your study; identify those studies and the journals they were published in. Some journals will appear more than once, and these are likely candidates. Another way to identify candidate journals is performing keyword searches in literature databases such as Medline and PubMed. Again, journals that appear repeatedly are potentially suitable. Of course, journals that havent previously published in the same area of research might equally be interested in your findings; the best way to identify these is to search or browse your librarys journal shelves, Thompson ISI databases, including the Science Citation Index, or the websites of major publishers (see below, but note that these are just a few of many publishers of academic journals). You should be able to recognise journals that might be appropriate based on your answers to the questions above. Now that you have a short-list of possible target journals and a clear picture of the type of journal your study would be suitable for, you need to merge the two to see where they correspond. Journal websites generally contain and aims and scope section and occasionally describe their target audience. They will usually also contain information on impact factor, publication types, publication frequency, time from acceptance to publication, rejection rates, and publication charges. All of these factors need to be weighed up. For example, if you require rapid publication, you should specifically look for journals that offer fast response times and short periods from acceptance to publication. If you are on a tight budget you may need to rule out open access journals or journals that have high publication charges. If you require publication in a journal with an impact factor above a certain level, you can instantly rule out any with impact factors lower than that. Study the journal websites closely and consider why the editors and readers of each would be interested in your findings; as well as giving you an angle for the approach in your cover letter ( see the previous post on journal cover letters and a free example letter here ), this will help you decide which of the remaining journals in your short-list is the most relevant platform from which to disseminate your findings. When your short-list has been reduced to two or three journals on the basis of the above criteria, you should rank them as first, second and third choices based on your particular requirements. Then you are ready to write your cover letter and submit your manuscript! To assist you in this process, Edanz has developed a Journal Selection Tool that is free to download. Helpful Links To search or browse Science Citation Index journals: http://science.thomsonreuters.com/cgi-bin/jrnlst/jloptions.cgi?PC=K Thompson ISI searchable databases: http://science.thomsonreuters.com/mjl/ US National Library of Medicine database PubMed: http://www.ncbi.nlm.nih.gov/pubmed/ Elsevier journal titles: http://www.elsevier.com/wps/find/journal_browse.cws_home Science Direct: http://www.sciencedirect.com/ Springer: http://www.springer.com/?SGWID=5-102-0-0-0 Wiley-Interscience journals: http://www3.interscience.wiley.com/browse/?type=JOURNAL Taylor Francis: http://www.tandf.co.uk/journals/sublist.asp Liwen Bianjis Select your field page (find journals from a wide variety of publishers for your field): http://www.liwenbianji.cn/selectyourfield Example The following manuscript title was used in the exercise accompanying the section on writing a good title: Region-specific neuronal degeneration after okadaic acid administration. This imaginary study showed degeneration of neurons in the CA3 and dentate gyrus regions of the hippocampus after administration of the toxin okadoic acid, let us assume in mice. It also showed involvement of a MAP kinase-dependent pathway in this neurodegeneration. Without a functional correlate of the neuronal cell loss, the study would be considered very preliminary and would be difficult to publish; thus, let us assume that behavioural studies were also performed and that these showed deficits in learning and memory in mice administered the toxin. Therefore, the data shown are histological, biochemical and behavioural. A keyword search of the PubMed database throws out very little in the way of similar studies (not surprisingly given that the example study is imagined), but does point to potential journals such as the Journal of Neuroscience, European Journal of Neuroscience, Neuroscience, Biochemical and Biophysical Research Communications, Neurobiology of Learning and Memory, Neuropharmacology and the Journal of the Neurological Sciences. Among these, the Journal of Neuroscience and the European Journal of Neuroscience are likely to require more data, perhaps showing relevance to a human disease or condition and/or an exhaustive analysis of the mechanisms involved in the cell death, although the latter journal is a possibility and might be worth an initial submission. The Journal of the Neurological Sciences has more of a clinical focus and should only be considered if administration of OA was known to provide a good model of a particular disease or condition. However, if such a link was shown, this journal would represent a good target. Biochemical and Biophysical Research Communications has a broad focus, publishing studies in diverse fields of biological research; however, neurobiology is one of their areas of interest and they claim to be devoted to rapid dissemination of results. For authors who want a quick answer or who need to publish soon, this could represent a good initial target journal. Depending on the novelty of the behavioural data, the journal Neurobiology of Learning and Memory could be a good target; with a rapid communications section, this journal could also suit those authors in need of immediate publication. Finally, Neuroscience represents a good target journal if the findings reveal aspects of how the nervous system works. Thus, depending on the focus of the final paper (eg. clinical vs neurobiological vs behavioural) and the authors requirements (impact factor and time to publication), the candidate journals selected can be ranked in terms of their suitability. Exercise Using the methods described above, and/or the Edanz Journal Selection Tool, generate a short-list of potential journals for your next (or any previous) paper and rank them in terms of their suitability. I will need a brief description of what your study showed, but will comment on the suitability and rankings of the journals you select, and offer alternative suggestions where appropriate. 练习 使用前面所讲的方法或者是理文编辑 目标期刊选定工具 为你即将发表的(或之前发表过的)文章列一个目标期刊清单，并按照优先性排序。请各位简单描述自己的研究内容，我会根据你的描述评价所选的目标期刊是否合适，排序是否科学；需要的话我还会帮助给出其他选择。欢迎大家踊跃参与。 在这里还需提请各位注意，Dr. McGowan 的母语是英语，无法阅读中文，因此请大家尽量使用英文回帖，如有任何需要与他沟通的学术和语言问题也请使用英语，Dr. McGowan 会及时回复大家的。 Dr. Daniel McGowan 曾任 Nature Reviews Neuroscience 副编辑，负责约稿，管理和撰写期刊内容。于2006年加入理文编辑（Edanz Group） 并从2008年起担任学术总监。Dr. Daniel McGowan 有超过十年的博士后和研究生阶段实验室研究经验，主要致力于神经退化疾病、分子及细胞生物学、蛋白质生物化学、蛋白质组学和基因组学。
Good study design and forward planning 在本帖中， 理文编辑 学术总监Dr. Daniel McGowan将向大家展示如何做好研究设计和预先规划工作。 Rejection following peer review can mean a considerable amount of additional work for many authors to get their studies published. In the worst cases, their studies may be simply un-publishable. Much heartbreak and hard work can be avoided by simply planning and designing your study properly in advance. In the long run, this will save you time, allowing you to get on with the research for your next big paper. No-one wants to have to repeat experiments because the controls were inappropriate or the case/sample numbers were insufficient to provide enough statistical power. Frequently though, researchers rush into experiments without making all the proper considerations, and this can result in delays when their manuscripts reach the peer review stage. Remembering a few basic principles of study design can help to reduce the risk of outright rejection and repeated experimentation. 1. Have a hypothesis or research question Having a hypothesis or appropriate research question enables you to frame your research within an appropriate context, which in turn will help you apply the appropriate controls. It will also help you describe the rationale for your study when it is time to write it up. Having a hypothesis also means that the objectives of the study are clearly defined, thus reducing the chance that your study will be open-ended and possibly criticised for being incomplete. You can then logically work through these objectives and, importantly, present your results in a logical manner rather than haphazardly. 2. Ensure that the appropriate methods are used Once you have a clear idea of the aims of your study, and the specific research question you are setting out to answer, you will need able to determine what methods would be appropriate to achieve these. Important considerations include deciding whether subjective, qualitative data will be sufficient to address your question, or whether there is a need for more quantitative methods. For basic science studies, such considerations might include the following questions. Will the combination of RT-PCR and in situ data be enough, or is there a need for qPCR? Is Western blotting alone sufficiently sensitive or do you need to also perform immunohistochemistry and cell counting experiments to show a difference between groups? For clinical studies, important considerations include the choice of controls, sample sizes, statistical tests and approach, all of which are described in more detail in the points below. 3. Ensure that the appropriate controls are used Controls are included in experiments to rule out alternative hypotheses. Theres an old saying that nothing can be proven, only disproved, and this is precisely why appropriate controls are necessary: to disprove any feasible alternative interpretations of the data you obtain and/or to eliminate or minimize the effects of extraneous variables. Consider what alternative hypotheses exist, and systematically rule them out by performing experiments that disprove them. There are generally two types of controls: positive and negative. Positive controls show that a negative result is not due to a failure of the experimental system. Negative controls provide an indication of the background noise or baseline value with which to compare values from your experimental sample. In quantitative studies, a relative control or housekeeping control is required to show that changes in the apparent levels of a target gene or protein are not caused by differences in the amounts of protein or DNA in the sample. These levels can be used as a baseline to measure changes in relative levels of a target gene or protein. Common housekeeping molecules include -actin and GAPDH. In clinical trials, subjects in a placebo group in intervention trials, and normal control subjects in observational trials, need to be matched as closely as possible to those in the treatment or disease group in terms of age, sex and numerous other potential confounding factors. In randomized controlled trials, accepted procedures for assignment to groups also need to be followed (see, for example, the ICH good clinical practice guidelines at: http://www.ich.org/LOB/media/MEDIA482.pdf). 4. Use sample sizes large enough to provide a definitive result Many studies fail to achieve the desired impact or to fully support a given hypothesis because the effect is too small or the variability too large to show statistical significance. Often this can be simply overcome by increasing the sample size. However, once a study has been performed and the data analyzed, it can be impossible to go back and increase the numbers without starting all over again. For this reason, pilot studies are often performed in advance of larger scale studies. Talk to a statistician. Determine the size of the effect of your treatment and/or the variability in your population before starting large-scale studies, and use this information to determine the sample size required to give you statistical power. Doing this can save you time, money and potential disappointment later. 5. Use appropriate statistical tests to analyze your data Statistical analysis of your data is essential to show that an effect is genuine and significant. Tests of significance demonstrate the robustness of your findings, essentially showing how unlikely it is that your findings were obtained by chance. Are your data continuous or discrete? Are they normally distributed or non-normally distributed? The nature of your data will determine how they should be analyzed and what tests are appropriate. If in doubt, consult a statistician who will be able to advise you on the most appropriate tests to use and what these tests indicate. Determining the right tests to use in advance will save you having to repeat your analyses if you got it wrong first time round, with the distinct possibility that no significant effect will be observed when the appropriate tests are used. For clinical trials, the following guidelines may be useful: http://www.ich.org/LOB/media/MEDIA485.pdf. 6. Remove investigator and patient bias Many experiments involve subjective measurements or assessments performed by the investigators, as opposed to objective results provided by the experimental system. If the investigator has prior knowledge of the groups to which individuals/samples belong, then investigator bias is a distinct possibility, and this can invalidate any of the findings obtained. In such cases, where the investigator is a factor inherent in the experimental system, it is essential that the investigator is blinded to the groups to which individuals or samples belong. Doing so ensures the objectivity of the findings and improves their reliability. Such blinding can refer to treatment in an intervention trial, or to assessment or interpretation of clinical findings in an observational trial. Similarly, the outcome of a treatment could be influenced if a patient knows if they are receiving a placebo or drug; such patient bias should be avoided, by blinding the patient to the nature of the treatment. Being aware of the potential for bias before commencing experimentation can again save the need for time- and resource-consuming repeats. 7. Comply with ethical requirements There are strict regulations regarding the use of human and animal subjects, and in many countries, regarding the use of stem cells, cell lines and genetically modified materials. Failure to comply with these regulations will prevent publication of your findings and could lead to legal issues; at best, it will limit the range of journals to which you can submit your findings. Make yourself aware of these regulations before you commence your study and ensure that all requirements are complied with so you dont encounter problems later on. As well as ethical requirements regarding experimentation, there are also strict guidelines provided by most journals regarding the requirements for authorship, and these also need to be complied with. Clinical trials should comply with the Declaration of Helsinki (http://www.wma.net/e/policy/b3.htm) in addition to any local requirements. Informed consent is essential for most trials involving human subjects. Animal studies should comply with local and national regulations, although many journals are now aligning themselves with standards such as the NIH Guidelines for the Care and Use of Animals (http://oacu.od.nih.gov/regs/guide/guide.pdf). Finally, many journals require a statement describing who gave ethical approval for the study. 8. Clinical study registration Many top-tier journals now request that prospective clinical trials involving human participants should be registered online in an accessible database. Many journals will instantly reject studies of this type that have not been registered. More information on this can be found at http://www.icmje.org/faq.pdf. International clinical trial registries include the Chinese Clinical Trials Register (http://www.chictr.org/), the Japanese Primary Registries Network (http://rctportal.niph.go.jp/), The International Standard Randomised Control Trial Number database (http://isrctn.org/) and Clinical Trials.gov (http://www.clinicaltrials.gov/). Registration should be done before the first participant is enrolled, but many of the databases do allow retrospective registration. However, by registering the trial once you receive ethical consent you will save time and overcome a major obstacle to publication. All studies are different and therefore have different requirements regarding appropriate study design. The points above are just a few of the important considerations that should be made prior to the commencement of experimentation, and the general principles apply to a variety of different study types. It is true that sometimes even peer review fails to detect flaws in study design, as shown, for example, in the following report on randomized controlled clinical trials published in Chinese journals: http://www.trialsjournal.com/content/10/1/46. However, if you want your study to stand the test of time, be published in a top-tier journal and to be widely accepted by the international research community, then planning ahead and designing your study to make it robust and reliable will only serve to save you time, money and heartbreak later on. 在这里还需提请各位注意，Dr. McGowan 的母语是英语，无法阅读中文，因此请大家尽量使用英文回帖，如有任何需要与他沟通的学术和语言问题也请使用英语，Dr. McGowan 会及时回复大家的。 Dr. Daniel McGowan 曾任 Nature Reviews Neuroscience 副编辑，负责约稿，管理和撰写期刊内容。于2006年加入理文编辑（Edanz Group） 并从2008年起担任学术总监。Dr. Daniel McGowan 有超过十年的博士后和研究生阶段实验室研究经验，主要致力于神经退化疾病、分子及细胞生物学、蛋白质生物化学、蛋白质组学和基因组学。
The snapshot: abstract and keywords 在本帖中， 理文编辑 学术总监Dr. Daniel McGowan将向大家展示如何写出吸引读者的摘要和关键词。 Your papers 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 interested in learning details than could not 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 head, 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 journals 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 abstracts, 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 very 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 journals audience. 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 behavioural researchers. Usually, there simply isnt 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 it would 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. Therefore, it is again necessary to check the target journals 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. Example: Lets consider some appropriate keywords for the example title from the previous post: Region-specific neuronal degeneration after okadaic acid administration (note that this title is and one of two suggested alternatives for the poor title in the example in the previous post). Good keywords would be: okadoic 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 练习： Suggest 35 suitable keywords to accompany the title in the exercise in the previous post: Carvedilol produces dose-related improvements in LV function and dose-related reductions in mortality and hospitalization rate in subjects with chronic heart failure from systolic dysfunction. They dont all have to relate to information contained in the title, so let your imagination run wild: it is the type of keyword rather than the content (the word itself) that is important to consider. 对于上述练习，希望各位可以例举3至5个合适的关键词；同时也欢迎发来您自己的英文摘要以获得 Dr. McGowan 的改进建议。 在这里还需提请各位注意，Dr. McGowan 的母语是英语，无法阅读中文，因此请大家尽量使用英文回帖，如有任何需要与他沟通的学术和语言问题也请使用英语，Dr. McGowan 会及时回复大家的。 Dr. Daniel McGowan 曾任 Nature Reviews Neuroscience 副编辑，负责约稿，管理和撰写期刊内容。于2006年加入理文编辑（Edanz Group） 并从2008年起担任学术总监。Dr. Daniel McGowan 有超过十年的博士后和研究生阶段实验室研究经验，主要致力于神经退化疾病、分子及细胞生物学、蛋白质生物化学、蛋白质组学和基因组学。
Dr. Daniel McGowan 论文写作系列第一讲：Making a good first impression: the importance of writing a good title 在本帖中， 理文编辑 学术总监Dr. Daniel McGowan 将向大家展示如何写出吸引读者的文章标题。 Making a good first impression: the importance of writing a good title The title of your paper is a hook that should be used to attract readersit is your opportunity to sell your paper to readers browsing a table of contents or search results. A poor title will cause potentially interested researchers to overlook your work and may attract the wrong audience. By contrast, a good title will attract the relevant researchers and increase the number of citations you receive. Therefore, it is important to get it right. A good title should be as brief as possible while still communicating the main finding(s) of the paper. Avoid excessive detail and unnecessary use of field-specific jargon and abbreviations. Your title must be understandable by a broad scientific audience, some of whom may not have a detailed knowledge of your particular field. How broad depends on the particular target journalconsider the readership of your target journal and write a title that can be easily understood by all, not only those in your immediate field. The target journals instructions for authors should also be consulted to ensure that character limits are complied with and to identify whether a running (short) title is also required. Good title checklist - Specific - Concise - Communicates the main findings - Will attract readers - Does not begin with the, a, or an - Avoids use of non-standard abbreviations Example of a poor title: The degeneration of neurons in the CA3 and DG following OA administration: involvement of a MAPK-dependent pathway in regional-specific neuronal degeneration This title is too long, contains non-standard abbreviations and a redundancy, and is too specific in parts. Further, many journals do not want titles that begin with the, a, or an. A better alternative would be: Region-specific neuronal degeneration after okadaic acid administration. MAP kinase-dependent neuronal degeneration after okadaic acid administration would probably also be acceptable, depending on the target journal, because the abbreviation MAP is widely used and understood. 欢迎各位访友踊跃参与下面的标题练习： Suggest a good alternative title for the following using what youve learned about writing good titles. Carvedilol produces dose-related improvements in LV function and dose-related reductions in mortality and hospitalization rate in subjects with chronic heart failure from systolic dysfunction 各位也可以贴出自己的文章标题，Dr. McGowan 将对您的标题做出评价并提出好的修改建议。 在这里还需提请各位注意，Dr. McGowan 的母语是英语，无法阅读中文，因此请大家尽量使用英文回帖，如有任何需要与他沟通的学术和语言问题也请使用英语，Dr. McGowan 会及时回复大家的。 Dr. Daniel McGowan 曾任 Nature Reviews Neuroscience 副编辑，负责约稿，管理和撰写期刊内容。于2006年加入理文编辑（Edanz Group） 并从2008年起担任学术总监。Dr. Daniel McGowan 有超过十年的博士后和研究生阶段实验室研究经验，主要致力于神经退化疾病、分子及细胞生物学、蛋白质生物化学、蛋白质组学和基因组学。
理文编辑 一直致力于通过各种途径，帮助中国作者提高英文写作水平。我们会陆续在博客中发布由理文编辑学术总监 Dr. Daneil McGowan 撰写的 How to Write a World Class Paper 系列英文讲义。Dr. McGowan 通过轻松诙谐的语言，以期刊审稿人的视角深入浅出地为大家解析期刊编辑对文章的评判标准，指出并解决母语非英语的作者在撰写英文学术论文过程中经常遇到的问题。希望能够对大家有所帮助。在每一讲的最后都会有互动练习，欢迎大家踊跃参与。在这里还需提请各位注意，Dr. McGowan 的母语是英语，无法阅读中文，因此请大家尽量使用英文回帖，如有任何需要与他沟通的学术和语言问题也请使用英语，Dr. McGowan 会及时回复大家的。 Dr. Daniel McGowan 曾任 Nature Reviews Neuroscience 副编辑，负责约稿，管理和撰写期刊内容。于2006年加入 理文编辑（Edanz Group） 并从2008年起担任学术总监。Dr. Daniel McGowan 有超过十年的博士后和研究生阶段实验室研究经验，主要致力于神经退化疾病、分子及细胞生物学、蛋白质生物化学、蛋白质组学和基因组学。
标签: 期刊审稿
