Tracking 3DNA citations: Google scholar vs. Web of Science

Over the years, I have been following citations to 3DNA on a regular basis, using both Google Scholar and Web of Science. From a personal perspective, following the citations has turned out to be an excellent way to keep myself informed of progress related to nucleic acid structures.

Up to early this year, I had found 3DNA citations based on Google Scholar to be significantly larger than those from Web of Science. For example, for the 2003 Nucleic Acids Research (NAR) paper, the difference was well over one hundred. Over the past few months, however, I have noticed that citation numbers to the 2003 NAR paper fluctuating up and down in dozens. Moreover, I have been receiving significantly less email notifications to 3DNA citations from Google Scholar. Apparently, Google has been revising the algorithms for Google Scholar, leading to more conservative citation numbers that are now comparable to those from Web of Science.

Here are the current citation numbers to the three 3DNA publications, from both Google Scholar and Web of Science:

	Google Scholar	Web of Science
2003 NAR	544	473
2008 Nature Protocols	43	41
2009 NAR (w3DNA)	11	13

It is interesting to note that for the w3DNA paper, Web of Science gives a larger number (13) than Google Scholar. A careful check of the citation result from Web of Science shows that all of the 13 citations are legitimate journal articles. Clearly, Web of Science is missing something in this case.

Overall, I trust Web of Science more, which is why I have been using it to compile citations to the 2003 NAR paper. However, I use Google Scholar more frequently for quick reference simply because it is free and easy to access.

NAR's top ten articles

Recently, I noticed a new feature in the website of Nucleic Acids Research (NAR), i.e., its selection of top ten articles:

NAR’s Top Ten Articles are updated monthly and show recent articles that have been most often accessed in HTML and PDF formats in the specified month.

In the age of information explosion with flood of scientific journals and articles, it is easy get lost. NAR's pick of top ten and featured articles draws my attention to significant work I may otherwise overlook.

The current top ten articles (March 2011) are all selected from 2009/2010 publications in 'Database', 'Methods online', and 'Survey and Summary'. I am browsing the 2009 article by Thomas LaFramboise, "Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances", to get a better understanding of SNPs.

Fifty years of operon

In the latest issue of Science, there is a one-page editorial titled "The Birth of the Operon" by François Jacob, who won the Nobel Prize in Physiology or Medicine in 1965:

What is the operon, whose 50th anniversary is being celebrated this week? The word heralded the discovery of how genes are turned on and off, and it launched the now-immense field of gene regulation. ... we cannot presume to know how new ideas will arise and where scientific research will lead.

In the next three paragraphs, Jacob provides an insightful and vivid description of his research related to the discovery of the "operon" – a structural gene-regulatory gene ensemble. In consonant with his comment on scientific discovery, he concludes:

Our breakthrough was the result of “night science”: a stumbling, wandering exploration of the natural world that relies on intuition as much as it does on the cold, orderly logic of “day science.” In today’s vastly expanded scientific enterprise, obsessed with impact factors and competition, we will need much more night science to unveil the many mysteries that remain about the workings of organisms.

It is worth noting that the Journal of Molecular Biology (JMB) has recently published a special issue [Volume 409, Issue 1, Pages 1-88 (27 May 2011)], titled "The Operon Model and its Impact on Modern Molecular Biology" with historical accounts and reviews to celebrate operon's 50th anniversary. It is because of this event that motivated me to read the Jacob and Monod 1961 JMB review article "Genetic regulatory mechanisms in the synthesis of proteins" – I have come across this paper so many times before, and should have definitely read it long ago!

Scientific journals on nucleic acids

In my knowledge, Nucleic Acids Research (NAR) is a highly respected scientific journal with a broad impact in the field of nucleic acids. Over the years, I have been browsing NAR webpage on a regular basis to keep myself up to date to the latest development in this area. It is thus no surprise that the initial 3DNA paper was submitted to and published in NAR in 2003. Among the 500+ citations to that 3DNA paper, over 1/5 (100+) articles are from NAR itself (as an example, please see my January 22, 2011 blog post titled "Three structural biology papers in the latest issue of NAR cite 3DNA"). My latest contribution to NAR is the GpU story, which was actually selected as a featured article.

Another related journal I am quite familiar with is RNA, a publication of the RNA society. As the "About" section of its webpage succinctly summarizes,

RNA serves as an international forum for publishing original reports on RNA research in the broadest sense. The journal aims to unify this field by cutting across established disciplinary lines and focusing on "RNA-centered" science.

RNA currently has an impact factor (IF) of 5.198 (2009), slightly lower than NAR's 7.479. It is, nevertheless, a very decent journal in RNA-related research, and I frequently visit its website. As a side note, the GpU paper was initially submitted to RNA for its RNA-specific content and as a way to diversify my publication spectrum (as mentioned above, 3DNA was initially published in NAR). Unfortunately, the GpU paper was rejected by the RNA journal after two rounds of review, spanning over 6 months.

Another journal closely related to RNA (name wise) is called RNA Biology, which even has a slightly higher IF of 5.56. Admittedly, I was not familiar with this journal at all. Browsing through its website, I am interested in seeing the journal's explicit policy to reconsider papers "rejected by high impact journals [CNS] for reasons of novelty and impact, rather than the importance of the study or the integrity of the data." By enclosing "the reviewers’ and/or editorial comments" from these high impact journals, "it is possible the article might be accepted [by RNA Biology] based on its previous review. This will allow the urgent and competitive research to be published on the day of submission."

I became aware of the journal DNA Research quite recently through an email. From its website, "DNA Research is an internationally peer-reviewed journal which aims at publishing papers of highest quality in broad aspects of DNA and genome-related research." The journal currently has an IF of 4.917. Browsing a couple of its online issues, I sense that the journal is more on genome- than structure-related research.

While following up 3DNA citations recently, I noticed the paper titled "Insights into the Structures of DNA Damaged by Hydroxyl Radical: Crystal Structures of DNA Duplexes Containing 5-Formyluracil" by Tsunoda and Taknaka. It was published in the Journal of Nucleic Acids, which I have never (but probably should have) heard of before. From its website, "Journal of Nucleic Acids is a peer-reviewed, open access journal that publishes original research articles as well as review articles in all areas of nucleic acids." By virtue of this structure paper and its citation to 3DNA, I think the journal is surely of personal interest, and I have added it into my watch-list.

To sum up, there are currently four scientific journals (I know of) that are devoted to nucleic acids:

• Nucleic Acids Research
• RNA
• RNA Biology
• DNA Research
• Journal of Nucleic Acids

Do I still miss something? Please make your suggestion in the comment area.

[revised on May 17, 2011 by adding RNA Biology]

Ebook "Gregory Petsko in Genome Biology: The first 10 years"

Over the years, I have read some of Gregory Petsko's monthly columns in Genome Biology while browsing the journal online, and I like his sensible and entertaining columns quite a bit. Recently, I became aware of the ebook from BioMed Central, "Gregory Petsko in Genome Biology: The first 10 years":

Structural biologist Gregory Petsko has contributed a thought-provoking and entertaining monthly column to the scientific journal Genome Biology every month since its launch in 2000. To mark the 10th anniversary of Genome Biology this eBook brings together 10 years of Petsko's columns.

I downloaded the epub version of the book, and googled around, trying to find a corresponding ebook reader for my MacBook Pro (Snow Leopard) – even though I have some ebooks in the generic PDF format, I am not that familiar with epub or mobi. I finally settled with NOOK for Mac from B&N. It turns out reading ebooks with specifically-desinged apps such as NOOK is quite a different, yet more enjoyable, experience than through a PDF reader.

Now the ebook has become the top one in casual reading list. I am reading it from the very beginning, one column at a time, to have a historical perspective. So far I found the columns indeed very "thought-provoking and entertaining".

Tips and tricks from "The Geek Stuff"

As a devoted command line user, I am always interested in learning new tricks to make my life more enjoyable. Recently, I came across Ramesh Natarajan’s blog “The Geek Stuff” which is full of “instruction guides, how-to, troubleshooting tips and tricks on Linux, database, hardware, security and web” to help solve practical problems.

For example, in the section “Best of the Blog”, I recently benefitted quite a bit by reading the following posts:

• 15 Awesome Google Search Tips and Tricks
• 6 Awesome Linux cd command Hacks
• wget Tutorial: 15 Awesome Examples to Download Files from Internet

There are many other helpful tips/tricks as well; since I have bookmarked the site, I will surely come back!

Scripting in Ruby is fun

Over the years, I have played around with various scripting languages, including awk, bash, Perl, Python and Ruby. By far, I have enjoyed Ruby the most; nowadays, I write scripts nearly exclusively in Ruby.

Created by Yukihiro "Matz" Matsumoto in Japan during the mid-1990s, Ruby became popular worldwide in mid-2000s, with the Rails web application framework. Indeed, I first dug into Ruby through Rails, and by reading David Black's book "Ruby for Rails; Ruby techniques for Rails developers". As an exercise, I implemented the current 3DNA v2.0 website with Rails v1.x. Then I quickly realized that the rapidly evolving Rails framework was beyond my time and interest to follow. However, I did begin to appreciate Ruby's simplicity, consistency and expressiveness. Over the past few years, I have collected over a dozen Ruby-related (e)books, including "The Well-Grounded Rubyist" (David Black, covering v1.9), "The Ruby Programming Language" (David Flanagan and Yukihiro Matsumoto), and "Metaprogramming Ruby: Program Like the Ruby Pros" (Paolo Perrotta). Just as my experience with (ANSI) C, I feel Ruby "wears well as one's experience with it grows" (K&R, in the preface of "The C Programming Language"). The better I know Ruby, the more I enjoy using it.

I recently wrote two Ruby scripts for the analysis of molecular dynamics (MD) simulation trajectories using 3DNA. Honestly, I would not have bothered with Perl for the task (otherwise, it would have been done long time ago), given the sideline nature of my support of 3DNA. Yet, writing and refining the Ruby scripts (with help of git and rake) have turned out to be a pleasant experience. Another reason why scripting in Ruby is fun is due to its large, active and friendly user community; there are many user-contributed libraries (gems) that serve well of common programming needs. As an example, in the 3DNA-MD scripts, I took advantage of the elegant Trollop commandline option parser by William Morgan. I picked Trollop among many other choices because it is self-contained in a single file, simple to use, and "gets out of your way".

In the Ruby community, exciting new developments are happening all the time. Recently, I was drawn to thor, "a simple and efficient tool for building self-documenting command line utilities". Over the past couple of years, I have browsed Sinatra and Sequel – they also look brilliant! Of course, for bioinformatics, there is the BioRuby project.

Overall, in my experience, scripting in Ruby is fun and exciting. Are you a Rubist yet?

Retraction of scientific publications

Once in a while, I come across retraction notices of scientific publications in leading journals/magazines. Even for cases not directly related to my research areas, I normally browse through them.

In the March 3, 2011 issue of Nature, there is a retraction of the Letter "Mediation of pathogen resistance by exudation of antimicrobials from roots" [Nature 434, 217–221 (2005)]. I am intrigued by the first sentence of the note:

The authors wish to retract this Letter after a key reference by Walker et al. (ref. 9 in this Letter) was retracted from the scientific literature.

It turns out that the 2003 Walter et al. J. Agric. Food Chem. paper (withdrawn in October 2009) and the 2005 Nature Letter were from the same group. Overall, it took ~6 years each for the two papers to be retracted. As of today, they have been cited 76 and 84 times respectively accordingly to Google scholar.

Canned responses in gmail make it easy to send common messages

Through Gary Rosenzweig's MacMost Now video #509 "Gmail Labs" (January 28, 2011), I first heard of "Canned Responses" in Gmail Labs:

Email for the truly lazy. Save and then send your common messages using a button next to the compose form.

This is a truly handy feature that I have long been waiting for! Yet even though I am aware of Gmail Labs and enabled quite a few experimental features a while ago, I've not been searching Gmail Labs for new features ever since.

Over the past few weeks, I have found "Canned Responses" increasingly indispensable in my support of the 3DNA forum (as a sideline project):

1. When I activate a new 3DNA forum registration, I've always included a "standard" message to "make the forum policy upfront and explicit, in order to avoid misunderstandings or surprises." Previously, I had to copy-and-paste, e.g., from a specifically created text file or elsewhere. Surely, this worked, but I had felt intuitively that there must be a better way to get the job done. Well, that's exactly where "Canned Responses" fit in!
2. Over the past few months, I have been ever more bothered by spam registrations. So as a further filter, I have been sending the following enquiry message to each suspicious registration for activation:
   

   Thanks for your registration at the 3DNA forum. Please tell me a little bit about yourself and elaborate on how 3DNA could be useful to your project; we would like to make the forum spam-free.
   
   See also "Further notes on forum registration and posting" -- you may not need to register.

   Here once again, the "Canned Responses" feature makes my life much easier! Moreover, this step turns out to be extremely effective; a large percentage of registrations is filtered out at this final stage.
   

Are you using gmail? If so, you may also want to give "Canned Responses" a try.

Making data maximally available?

In the February 11, 2011 issue of Science (Vol. 331 no. 6018 p. 649), there is an editorial, titled "Making Data Maximally Available". Indeed, the issue contains a special section on "Dealing with Data".

Science is driven by data. New technologies have vastly increased the ease of data collection and consequently the amount of data collected, while also enabling data to be independently mined and reanalyzed by others ... It is obvious that making data widely available is an essential element of scientific research.

Especially, I like the following two (proposed) new policies:

1. To extended data access requirement "to include computer codes involved in the creation or analysis of data." If properly implemented/enforced, this policy could significantly increase the repeatability and assessment of published results. In my experience, I have observed too many times that secrets are hidden in the seemingly "little" subtle details.
2. "To produce a single list that combines references from the main paper and the SOM" (supporting online material) to "provide credit and reveal data sources more clearly". Potentially, this will also increase the citation of method papers.

Hopefully, other journals will follow Science's lead to make data maximally available, and to present data more transparently.

Trial of scientific papers by blogs and tweets?

In the January 20, 2011 issue of Nature, there is an interesting News Feature article by Apoorva Mandavilli, titled "Peer review: Trial by Twitter":

Blogs and tweets are ripping papers apart within days of publication, leaving researchers unsure how to react.

Specifically, two widely publicized papers in Science are singled out: one is about the longevity genes identified through genome-wide association study (GWAS), published last July; another is a more recent one on arsenic bacteria, published last December. In each case, while "the popular media was trumpeting the finding, other researchers were taking to the web to criticize the paper’s methodology." Yet, the authors failed to hold up their claims in the papers.

With great interest, I've been following the story on arsenic bacteria. I first noticed this work through Science Podcast, and found the topic of "arsenic" life intriguing. So for general knowledge, I read carefully the abstract and browses through the text. One week later came the Nature editorial "Response required", and from which, I followed the link to Rosie Redfield's blog post "Arsenic-associated bacteria (NASA's claims)". I read the post, and many of the comments therein; while I do not understand many of the technical details, I had no difficulty in following her argument. The lead author of the arsenic bacteria paper, Dr. Felisa Wolfe-Simon, did respond to comments on December 16, 2010. Science also published an interview with Wolfe-Simon, titled "Discoverer Asks for Time, Patience Over Arsenic Bacteria Controversy". On the same day, Redfield was quick to write another blog post "Comments on Dr. Wolfe-Simon's Response", which again has received many comments. So far, the story is still unfolding, and Science has promised to publish technical comments and responses in early 2011.

As a related topic, throughout the CCP4bb, I noticed the letter to editor titled "Is too ‘creative’ language acceptable in crystallography?" by Alexander Wlodawer et al. I agree fully with the authors that "While figures of speech are often useful and even educational, flashy titles combined with hyperbolae and imprecise language can mislead or deceive nonspecialist readers and should therefore be avoided."

In the Internet Age, bloggers and tweeters clearly have an important role to play in the assessment of research findings. In scientific publications, what counts is not how much one claims, but to what extent one can hold up such claims. Solid work holds up over time and the scrutiny of peers.

Open dictionary with command-control-d in Mac OS X

In a recent MacMost Newsletter, I came across the following handy trick in Mac OS X: while the cursor is over a word (not necessarily selected), one can press command-control-d to open dictionary which pops up a little window with the word's definition. This works in Safari and Mail, but not in Preview (unfortunately).

Previously, when I need to check the definition of a word, I right-click on it and then follow the link "Look Up in Dictionary". This will launch or pop up Dictionary with detailed information about the word (Dictionary/Thesaurus/wikipedia).

The right-click method seems to integrate better with other Mac OS X applications. For example, it works with Preview as well. However, now that I know it, I sometimes prefer the command-control-d approach better; it is quick and non-obstructive.

IUPAC nucleotide symbols and their complements

Recently, I was interested in knowing the complements of all the IUPAC nucleotide symbols. As blogged previously, I am quite familiar with the "meaning of nucleotide IUPAC codes" (namely A/C/G/T, and R/Y/N etc). However, when I first check the Gene Infinity website on nucleotide symbols, it still puzzled me for awhile to figure out the meaning of the DNA alphabet (with complements), as except below:

A  C  G  T    M  R  W  S  Y  K    B  D  H  V    N
|  |  |  |    |  |  |  |  |  |    |  |  |  |    |
T  G  C  A    K  Y  W  S  R  M    V  H  D  B    N

For example, some degenerated IUPAC symbols are complemented to themselves (e.g., W–W and S–S), while others are seemingly "hard" to apprehend (e.g., B–V and D–H).

After thinking it for a bit, things begin to become clear. They are based on the complementarity of Watson-Crick base-pairs (A–T and G–C) and the meaning of each degenerated IUPAC nucleotide symbol. For example,

• W represents A/T, meaning weak (with only two hydrogen-bonds). The complements of A/T are T/A respectively, which is W again.
• B (not A) represents C/G/T, and their complements are G/C/A respectively, which is V (not U/T).

It is easy to verify that all other complementary pairs follow exactly the same basic principle.

Extract images from PDF files using 'pdfimages'

Once in a while, I need to extract an image (or a portion thereof) from a PDF file. Usually, I open the PDF file using 'preview' or Adobe 'Acrobat Reader', and take a screenshot which is then cropped to the desired slice. This manual method "works", albeit a bit tedious.

Recently, I came across the handy command-line utility program 'pdfimages' which allows for automatic extraction of all images from a PDF file. The basic usage is simply:

pdfimages   yourfile.pdf   prefix

This will extract all images contained in yourfile.pdf to files prefix-001.ppm, prefix-002.ppm etc. With option "-j", images in DCT format are saved as JPEG files.

Nice blog on English writing by Lynn Gaertner-Johnston

A while back, I was somehow curious to figure out the differences between entitled vs. titled. A colleague referred me to a blog post on this topic by Lynn Gaertner-Johnston. After reading the post, it became clear that over the years I had made the same mistake numerous times. I then corrected nearly all occurrences of "entitled" to "titled" in my blog and 3DNA forum posts. Ever since, I have been following Lynn Gaertner-Johnston's blog, titled "Business Writing", on a regular basis, and found it helpful to improve my English writing skills.

Under-citation of method papers?

Recently in the CCP4BB, there is an interesting thread with extensive discussions on "Citations in supplementary material". The original poster refers to the recent Acta Cryst D editorial with the same title in which the authors highlights the issue of under-citation to papers published in the International Union of Crystallography (IUCr) journals.

The main point is that method papers are more likely to be cited in the supplementary materials only, which are not indexed by PubMed, Scopus, Web of Science or Google Scholar etc. As a result, they are statistically undercounted, and "Journals and scientists that focus on publishing methodologically oriented papers are particularly affected." Specifically, through a survey of articles on protein or nucleic acid structure determination published in Cell, Nature, Science, and PNAS in 2009, the authors found that "almost half of all references to publications in IUCr journals end up being published in the supplementary material only."

The findings of the editorial resonate with my observations, and I cannot agree more with the authors that "in the end, methods need to be continuously developed and refined in order to ensure progress."

On the other end of the spectrum, some highly influential method papers are heavily cited. As an extreme case, the large number of citations to the 2008 paper "A short history of SHELX" by George Sheldrick helps rocket up the impact factor of Acta Crystallographica A by 20-fold to 49.9 this year!

Transparency in the peer-review process of scientific papers

In the Nov. 4, 2010 issue of Nature, there is an interesting Comment, titled "Transparency showcases strength of peer review", by Bernd Pulverer, head of scientific publications at the European Molecular Biology Organization and chief editor of The EMBO Journal. In this article, Pulverer "reflects on his experience at The EMBO Journal of publishing referees’ reports, authors’ responses and editors’ comments alongside papers."

The peer-review process of scientific articles has traditionally been a "black box": (anonymous) reviewers' reports, editors' comments, and authors' responses – extremely valuable information in shaping the final form of published papers – are all hidden from public view. In the Internet era, technology (e.g., online space) is no longer an issue. Now The EMBO Journal has led the way, and "the experience has been overwhelmingly positive." Hopefully, other leading journals (e.g., Nature and Science) would follow the example. Afterall, making the peer-review process transparent is an excellent mean to increase the accountability of science and scientific publications.

Overall, this article is well-written, succinct and logical, and it touches an important topic in scientific publication. Over the past few days, I have read several "Review Process Files" accompanying papers I am interested in, e.g., "Recognition of the amber UAG stop codon by release factor RF1", and found them highly revealing.

Publication of scientific programming code

Recently on the Nature website, I read with great interest a news article, titled "Publish your computer code: it is good enough", by Nick Barnes, a professional software engineer:

Freely provided working code — whatever its quality — improves programming and enables others to engage with your research

Clearly the author knows the "trade secret" in scientific programming. He lists several common reasons why scientists are reluctant to share their source code, and then provides his responses:

1. The code is low quality — "software in all trades is written to be good enough for the job intended". All software has bugs. Sharing code would help improve the code itself and advance the research field.
2. Not a common practice — this is going to change or is already changing.
3. Demand for support — "Nobody is entitled to demand technical support for freely provided code."
4. Intellectual property issue — The most value part "lies in your expertise", code not backed by skilled experts is called abandonware. (I cannot agree more with this point.)
5. Polishing code takes time/effort — not need to, just supply, as supplementary materials in a website, the original code used in your publication.

As is evident from the many comments, this assay is well echoed by the community. As an active computational scientist for over a decade, I share mostly the same opinions. Essentially, the transparency of source code is to ensure repeatability of scientific publications. In the field of computational biology (bioinformatics), it is virtually impossible to reproduce exactly a published figure/table without direct access to details, including the source code.

Improving the design of existing code by refactoring

Another software engineering book I read recently is "Refactoring: Improving the Design of Existing Code" by Martin Fowler. According to the author,

Refactoring is the process of changing a software system in such a way that it does not alter the external behavior of the code yet improve its internal structure. It is a disciplined way to clean up code that minimize the chances of introducing bugs. In essence when you refractor you are improving the design of the code after it has been written.

The book is practical in nature; it not just explains the principles but provides a detailed account of over 70 commonly used refactorings. As vividly explained by the author in the first paragraph of Chapter 1, "Refactoring, a First Example", "it is with examples that I [the author] can see what is going on." This approach fits my style perfectly: to really understand a topic, I always find a worked example far more effective than general principles. While the examples in the book are illustrated in Java, the basic ideas can be applied well to other object-oriented or even procedural languages (such as C).

Over the years since I left Dr. Olson's laboratory at Rutgers, I have been maintaining and continuously refining 3DNA. I have taken each user's question as an opportunity to fix bugs and improve its design, thus making the code more robust and efficient. The majority of my efforts, as I now realize, is "refactoring" existing 3DNA code to make it easier to maintain and extend. Reading through this book gives me the chance to put my practices into a broader context. I will surely take advantage of some refactoring examples from the book for further refinements of 3DNA.

NSMB editorial: "Go figure"

In the October 2010 issue of Nature Structural & Molecular Biology (NSMB, Vol. 17, No. 10) there is another interesting one-page editorial, titled "Go figure", which provides tips on how to make a scientific figure that may worth 1000 words:

A picture may be worth a thousand words, but ensuring that those words make sense is important, especially in the context of a scientific figure. Here are some tips for making your figures count.

A recap of the tips is given below; by and large, they all follow conventional wisdom:

• General considerations: Each figure should make just one point and be self-explanatory.
• See guidelines. "At all stages, the figures should be clear and legible."
• How many figures? The figures should complement the Results section, and be included only necessary.
• How many panels? Better only one; multiple panels "should be logically connected."
• What’s in a label? Keep it succinct, but make the figure self-explanatory.
• Getting colorful. Use color wisely and constantly.
• A legendary figure. The figure legend should concise and informative.
• A model paper. Better have a figure (at the end) of the final model that conveys "the big picture". Honestly, I do not quite get this point.

As pointed out by the author, "These are just a few guidelines and suggestions for handling figures." Overall, "simplicity rules in scientific figures, as in life." I guess no one would argue with such general advices. However, it would be even more helpful to illustrate such points with concrete examples (I know that seems to be beyond the scope of a one-page editorial).