NIF Blog

Cannabis in NIF’s Data Holdings

Anita — Thu, 13 Jun 2013 21:39:27 +0000

NIF was asked to give the National Institutes on Drug Abuse a report of the state of the data holdings for one abused substance: Cannabis. The report is included below. The data reflect the state of NIF in early 2013, following the links will potentially lead users to updated numbers.

Within NIF many sources have information about cannabis or the endocanabinoid system (we did not include an analysis of the literature). These results have been broken down by the number, below (for an interactive graph click here and then select the Graph Filters Box).

Genes

Looking at genes we find the two human genes CNR1 and CNR2, which are endocanabinoid receptors have these counterparts (CNRIP1, Cnr1, cnrip1, Cnr2, Cnrip1, and cnr1) in many other species including, mammals, birds, fish and tunicates. This indicates that the gene family is quite widespread.

Three clusters of genes have emerged based on the Homologene clustering algorithm. These gene families are for CNR1, CNR2 and the interacting protein called CNRIP1.

Drugs

The endocanabinoid receptors have several drugs (largely derived from THC) that interact with the receptors.

Drugbank, a leading source of drug information tells us that two small molecule drugs have been used to affect the endocannabinoid system.

Nabilone has been used largely as an anti-anxiety agent or an antiemetic. Nabilone is a cannabinoid with therapeutic uses. It is an analog of dronabinol (also known as tetrahydrocannabinol or THC), the psychoactive ingredient in cannabis. It is reserved for use in individuals who do not respond to the more commonly used anti-emetics.

Dronabinol has also been used as an antiemetic, but also analgesic, non-narcotic psychotropic drug and a hallucinogen. Marinol may have complex effects on the central nervous system (CNS), including cannabinoid receptors. Dronabinol may inhibit endorphins in the emetic center, suppress prostaglandin synthesis, and/or inhibit medullary activity through an unspecified cortical action.

The NIMH Chemical Synthesis and Drug Supply Program lists three more specific drugs including two specific antagonists and ChEBI lists 12 variants of THC, which have less associated data, but may be useful as highly experimental substances that may have some specificity as agonists or antagonists, see below.

ChEBI compounds can be found below.

Structure	ChEBI ID	Chemical Formula	Name
	CHEBI:219639	C21H27F3O2	6,6,9-Trimethyl-3-(5,5,5-trifluoro-pentyl)-6a,7,10,10a-tetrahydro-6H-benzo[c]chromen-1-ol (5′-F3-delta8-THC)
	CHEBI:219603	C21H29FO2	3-(5-Fluoro-pentyl)-6,6,9-trimethyl-6a,7,10,10a-tetrahydro-6H-benzo[c]chromen-1-ol (5′-F-delta8-THC)
	CHEBI:164237	C21H30O2	6,6,9-Trimethyl-3-pentyl-6a,7,8,10a-tetrahydro-6H-benzo[c]chromen-1-ol (delta9-THC)
	CHEBI:566631	C37H54O4	alpha-Cadinyl delta9-Tetrahydrocannabinolate
	CHEBI:566632	C37H54O4	gama-Eudesmyl delta9-Tetrahydrocannabinolate
	CHEBI:566609	C32H46O4	alpha-Terpenyl delta9-Tetrahydrocannabinolate
	CHEBI:566610	C32H46O4	beta-Fenchyl delta9-Tetrahydrocannabinolate
	CHEBI:566618	C32H46O4	alpha-Fenchyl delta9-Tetrahydrocannabinolate
	CHEBI:566619	C32H46O4	epi-Bornyl delta9-Tetrahydrocannabinolate
	CHEBI:566620	C32H46O4	Bornyl delta9-Tetrahydrocannabinolate
	CHEBI:566630	C32H46O4	4-Terpenyl delta9-Tetrahydrocannabinolate
	CHEBI:566636		delta9-tetrahydrocannabinolic acid A

Several of these compounds have been tested in various brain regions against the two known canabinoid receptors and from the Ki database we find that there is more data for CB1 (345), than the CB2 (173). Results are also organized by species and brain region with rat (296 results) and human (192) tested most frequently, followed by mouse (18), zebra finch (8) and newt (5) data.

Pathways

Wiki pathways, a collaborative platform from UCSF, lists the following pathways for CNR1 and CNR2:

Pathway Name

GPCRs, Class A Rhodopsin-like (11)
Small Ligand GPCRs (11)
GPCRs, Other (3)

Gene Symbol

CNR2 (7)
CNR1 (6)

Organism

Mus musculus (5)
Pan troglodytes (5)
Rattus norvegicus (5)
Homo sapiens (4)
Bos taurus (2)
Danio rerio (2)
Gallus gallus (2)

MultiMedia Information

Scientists studying endo- and exo-cannabinoids have written hundreds of blogs. Most of these focus on cannabis risks or the treatment of various disorders. A quick survey of these blogs indicates that there are links to Fragile X syndrome, multiple sclerosis, depression and decreases in intelligence on standardized tests. In addition, many scientists study the risk of motor vehicle accidents and possible interactions of mothers’ canabinoid exposure and the proclivity of the offspring toward opiates, but surprisingly some have also reported a Marijuana-Borne Salmonella Outbreak and poisoning of workers from herbicides used to kill the plants.

A few video talks and interviews with leading researchers are also available (see several examples below).

_____________________________________________________________________________

NIHVideo	Molecular Dissection of Cannabis Sensitivity in the Developing Brain	Tibor Harkany, PhD, Department of Medical Biochemistry and Biophysics, Karolinska Institute
NIHVideo	New Developments in Cannabinoid Research: The Path from Plant to Modern Prescription Medicine	Guy, Geoffrey W. National Institutes of Health (U.S.)
The Guardian: Science Videos	Cannabis ‘more harmful to under-18s than adults’ – video
NIHVideo	Brain Stress Systems and Addiction	Koob, George F. National Institutes of Health (U.S.)

Funding Sources

Not surprisingly, NIDA funds most research on cannabis, but a few current grants are also given by NIMH, Alcohol, and NINDS. The breakdown of the number of recent grants by institute follows: national institute on drug abuse (375), national institute of mental health (26), national institute on alcohol abuse and (26), national institute on aging (11), and many others.

From older grants (Research Crossroads dataset covering both federal and foundation grants) we find that many institutes and foundation have given out grants related to the cannabinoids, including:

National Institute on Drug Abuse(NIDA) (2937)
National Center for Research Resources(NCRR) (152)
National Institute of Neurological Disorders and Stroke(NINDS) (90)
National Institute on Alcohol Abuse and Alcoholism(NIAAA) (64)
National Institute of Mental Health(NIMH) (57)
National Institute of General Medical Sciences(NIGMS) (52)
National Cancer Institute(NCI) (33)
National Eye Institute(NEI) (33)
National Institute of Allergy and Infectious Diseases Extramural Activities(NIAID) (15)
National Institute of Child Health & Human Development(NICHD) (15)
Medical Research Council (UK) (12)
National Heart, Lung, and Blood Institute(NHLBI) (12)
CORDIS (11)
National Institute of Environmental Health Sciences(NIEHS) (9)
National Center for Complementary and Alternative Medicine(NCCAM) (7)
National Institute of Diabetes and Digestive and Kidney Diseases (7)
National Institute on Aging(NIA) (7)
Fogarty International Center(FIC) (5)

Our search also reveals that even private funders like the American Diabetes Association also gave out a grant focusing on the therapeutic effects of endogenous cannabinoids in diabetic retinopathy.

Diseases and Clinical Studies

There are few diseases directly associated with Marijuana, however Pubmed health mentions four, including:

Marijuana intoxication

Aspergillosis, which is an infection or allergic response due to the Aspergillus fungus. Aspergillosis is caused by a fungus (Aspergillus), which is commonly found growing on dead leaves, stored grain, compost piles, or in other decaying vegetation. It can also be found on marijuana leaves. Although most people are often exposed to aspergillus, infections caused by the fungus rarely occur in people who have a normal immune system. The rare infections caused by aspergillus include pneumonia and fungus ball (aspergilloma).

Lung cancer – non-small cell, which mentions that “research shows that smoking marijuana may help cancer cells grow, but there is no direct link between the drug and developing lung cancer.”

Paraquat poisoning, which describes paraquat (dipyridylium) as a highly toxic weed killer once promoted by the United States for use in Mexico to destroy marijuana plants. Research found that this herbicide was dangerous to workers who applied it to the plants. This article discusses the health problems that can occur from swallowing or breathing in Paraquat.

NIF searches across two sources of clinical data, and the US based ClinicalTrials.gov contains information about 294 clinical trials, but the European based EU Clinical Trials Register finds only 27 additional trials. Below you can find the main conditions, interventions and sponsors for the clinical trials, with numbers indicating the number of clinical trial results.

Conditions

Marijuana Dependence (15)
Marijuana Abuse (13)
Multiple Sclerosis (11)
Cannabis Dependence (10)
Healthy (8)
Marijuana Smoking (6)
Pain (5)
Schizophrenia (4)
Schizophrenia;Schizoaffective Disorder (4)
Substance-Related Disorders

Intervention

Behavioral: Behavior Therapy (6)
Drug: Dronabinol (6)
Drug: Cannabis (4)
Drug;Drug: Sativex®;Placebo (4)
Drug: GW-1000-02 (3)
Drug: Nabilone (3)
Drug: Smoked Cannabis (3)
Drug;Drug: GW-1000-02;Placebo (3)

How long does it take to get a resource into NIF? The case of the open source brain.

Anita — Wed, 05 Jun 2013 00:24:59 +0000

Believe it or not, there really is a project called open source brain, and it is a wonderful community of hackers that attempts to do very novel things with open source models, mainly in a format called NeuroML.

What is the open source brain?

Well, it takes models, converts them into cool visualizations and then allows users to manipulate them in their browser, with functionality similar to google body. The hope is to strap some significant computational power from the Neuroscience Gateway’s massive clusters so that the pretty pictures can be fully functional, but for now, this is a great way of exploring three-dimensional neurons and connectivity.

But the reason I am blogging about this project is not because of the “ooohh-aaaahhh” factor that nice graphics usually have on me, but also because this source came to NIF in an interesting way, by human flying from London on his way to another meeting. Unfortunately last week we did not know about the Open Source Brain, but Padraig knew about NIF and wanted to register the project, hoping to integrate his data or at least “get the process started”.

At 10:30 am we were sufficiently caffeinated to begin and created a registry entry, from which we obtained an identifier.

The identifier was then used to create a sitemap entry in the DISCO database (essentially anyone who has logged in to the NeuroLex can click a button at the bottom of a curated registry entry can actually do this).

Then we added an “interop” file, which instructs our crawler to put data the xml data output by open source brain into our local data warehouse making sure to specify appropriate tables and columns.

Then we went to lunch, came back after fighting much larger crowds at the indian place than were expected before finals, and created the “view” of the data (basically, wrote a sql statement and used our concept mapping tool to define what data would be displayed).

By 3:30 pm we had a view deployed. Well ok, we did have to import the data twice because we messed up the file once, and this deployment was the beta server and we had to wait to update to production until Friday night, but that is still pretty darn fast in my opinion.

The question for many people who have data has been how much effort will it take to make my data interoperable with community resources and for the first time ever, we can report …. it will only take a couple of hours (we should insert many caveats here).

The Tyranny of Formatting

jcachat — Wed, 22 May 2013 12:44:24 +0000

Scene: It is 4 am in the morning and the grant is due the next day. You have 12 pages to tell a story that will determine whether or not you can pay your salary and support your lab next year. You delete a sentence on page 5. The document swells to 13 pages. Oh no! The figures have all jumped around! The next paragraph is now in Courier 14 bold! You hit your head against the screen and spend the next hour trying to get everything back in place and the sentence deleted. The next day, you’re tired, irritable and have a broken computer screen.

Scene: My colleagues and I are writing a paper together. We spend weeks in Google Docs working on a collaborative document. We are all almost happy with the text. We then download it and import it into Word so we can work on the formatting and the figures. We then put it in Dropbox or email it around where all the versions get out of sync. My final edits don’t get copied into the final version. We get criticized for being sloppy.

Scene: You just got your rejection letter from the Nature. They liked your paper, but just not enough for this journal. They recommend Glia. You studiously look up the instructions for authors in Glia. You used a reference manager, but the references got screwed up anyway when you put it in Nature format. All the Endnote numbers are there, but it messed up your Word document, so you did some hand editing. You have to change “Methods” to “Materials and Methods” and other cosmetic changes. You spend a day reformatting the paper for Glia; you don’t prepare for your lecture, you don’t mentor a student, you don’t do your research; you don’t do anything but move around and rename sections, numbers and list the first 6 authors instead of the first 5. Science is stalled.

Scene: My child has a rare neurological disorder. A group of text miners offer to help mine the literature for a new druggable target. We ask a Foundation for access to all the literature on this disease. We are given 5000 abstracts and 600 pdfs from open access journals. We spend months working on agreements with the publishers, each of whom owns their own chunk of the field, for their content and go to open source repositories and authors. We get the other 4400 pdfs after a year. We spend 6 months cracking the pdf, trying to align the sections, and massaging the XML. My child can no longer walk.

Who is the common villain in all of this? You might be tempted to blame it on the publisher, and you wouldn’t be 100% wrong. But actually, you should blame it on formatting. At Beyond the PDF, Kaveh Bazargan put forth his proposal in the Vision session: “Why we should publish in XML and nothing else”. He didn’t win. But if you think about it, it’s actually a rather compelling vision. It’s not that he’s against formatting, it’s that formatting should be the “clothes that make the man” and not the man himself. The man should be born naked, as he is, and then dressed up to suit the occasion. And if all papers, as naked babies, are structured more or less the same, we can build all sorts of ways to add value to them.

So now let’s think about formatting. It is formatting that causes me to hit my head against the screen at 4 am. My number of words didn’t change in a significant way; the only thing that changed is how they were laid out on the page. Page numbers are a relic of the printing press. Look at your e-book on Kindle or the iPAD. There are no page numbers because there are no pages. If limits on grants and publications were consistently number of words and not pages, the problem would go away.

Why did I have to leave Google Docs? Because everyone knows that when it comes down to formatting and figures, Google Docs doesn’t cut it just yet. So we have to exit our workspace and go to a Word processor. Our paper didn’t change in a fundamental way; we were happy with the content. Anyone try to reconcile 3 Word documents on a Mac? There are probably great ways to do it, but I haven’t found them. Additional formatting is truly “an exercise in irrelevance”, to quote Philip Lord’s excellent blog.

Why , when when we have a reference ID, the DOI, that allows a publisher to retrieve all metadata for a piece of scholarly work, is the author being tasked with conforming to a reference style? Even if it requires a publisher to pay a company somewhere to format it, isn’t that what they do already? Why is the scholar doing this? Why do the publishers then have to pay someone else (CrossRef) to put back all the information that was stripped out of the reference in the first place?

And finally, as I learned from the School of Data, computers hate formatting. The first step of converting data into something that a machine can read is to strip out formatting. So if machines like to access XML or HTML5 or whatever, why are requiring all these transformations from Text to XML to PDF to XML to PDF to XML as the paper wends from Word processor to submission to editing to final. Who knows what comes out the other side and what happens to it along the way?

Kaveh points out that there needs to be a version of record. Isn’t it somewhat amazing now that we don’t have one? What is the version of record? The original document? I never go back and make the changes that the copy editor requests on my original text file, but that might be what I upload to my institutional repository or what I send to a colleague, if I don’t have the rights to the pdf. The pdf at the publisher? The version at Pub Med Central? Can we guarantee that they are all the same? It seems rather ridiculous that in 100 years, we might have people publishing doctoral dissertations on 21st century writings trying to determine the author’s original intent, much as current scholars try to reconstruct Euripides’ or Shakespeare’s intent from surviving versions.

So what is the alternative? Let’s envision a world where we have a standard XML or at least an interoperable XML for representing scholarly papers. Wouldn’t it make more sense, as Kaveh suggested, to have the XML version be the version of record and then have publishers, authors, whomever, dress it up whatever way they want? How many different ways are there of structuring a scientific paper? Why can’t the authors write their papers in generic format and then have the publishers present them as they wish? Why can’t the XML peer reviewed and typo-corrected version be deposited in a global ArXiv repository where it is accessible by text miners along with everything else? And why can’t this version appear as a PDF, HTML, DOC, or anything else it needs to be?

Isn’t it time that we as scholars throw our copy of Microsoft Word out the window and say “I’m mad as hell and I’m not going to take it anymore”. Has anyone tried to calculate the unproductive hours spent by scholars on formatting? Has anyone else been reduced to tears by jumping figures at 4 am? Has anyone then asked “Why?”

Kaveh may not have won because to the crowd at Beyond the PDF it seemed too obvious or unsophisticated. But isn’t it the obvious that we should be going after in FORCE11? We have on-line authoring tools available; we have the formats; we have institutional repositories and the arXiv model. If we have all the tools and technology and formats necessary to make content accessible to the web, text miners and publishers alike, then the obvious questions are “Why aren’t we?” and, “What would it take to make it happen?”

Scene (2014): I sit down to write my paper, because despite all advances, I still like to craft an argument in narrative, as my forbears have done for millenia. I log into (Annotum, Authorea, Word Press, Google, Microsoft, LaTeX-insert your tool here). These authoring tools are specially designed for scholarly publishing styles. My co-authors and I log in with our ORCID ID’s. We compose our paper with the appropriate sections. I insert my reference DOI’s from (Mendeley, EndNote, Zotero). I insert my figures reference ID from (my desktop, Cell Image Library, Fig Share). I reference my data sets DOI according to Amsterdam Manifesto standards from (Data Verse, NIF, CDL); my workflows from (WorkFlows 4 ever, MyExperiment); my code from (GitHub, Source Forge). All my coauthors hit “Acceptable” and I hit “Send”.

Send to whom? Why my preferred broker for peer review (Nature, Journal of Neuroscience, Peer J, Rubriq). My first choice doesn’t want me, so I go to my second. They will take it but want it reduced to 5000 words and 5 figures. OK, deal. I refine my argument and shorten. By doing that, some references disappear from the text. Bye. I send it off. The peer reviewers request refinements and catch some typos. I fix them. The Glia copy editor finds some additional mistakes. I correct them. When the mistakes are fixed, my coauthors say “Fine” and I say “Final”. We have gotten our permission slip and our work may now be admitted into the Body Scholarly for posterity. The (XML, HTML5, whatever) version is stamped “Version of Record” and it enters into the global ArXiv for scholarly communications.

Glia takes my XML and makes it look very pretty. They advertise it on their website and sell the pretty version back to institutions and individuals so they can read it. Some scholars have accused the publishers of existing only to sell us formatting. That’s OK. I pay for formatting all the time. I pay extra for the floral pattern on the Kleenex box because it goes with my color scheme. But my mother, who has no institutional subscription, can go to Pub Med Central and get my vanilla-formatted but still functional document. Sometimes, I buy generic.

Meanwhile, my text miner friends have a subscription to the Global ArXiv via their institution or a personal one, if they are not affiliated with an institution. They agree that they will not try to recreate an individual article for resale and then mine away. Perhaps a new target is discovered for my child’s disease or maybe there isn’t enough information available. But at least we have uniform access to the entire corpus in a form suitable for mining.

Now, with all my extra time saved by no formatting, I am largely done writing my grant proposal at 11 pm instead of 4 am. It needs to be 10,000 words with each figure counting for 500 words. At 11 pm, I am 10 words over. I delete a sentence and hit “Done”. At 11:15, I’m brushing my teeth. Grant reviewers get access to a nifty program that allows them to view/print/read in a variety of formats depending on their medium. One reviewer prefers to print things out on paper. Each figure is a full page and the font is 18 point so the grant is 40 pages long. But a younger reviewer with better vision who still likes paper chooses 12 point font and a half page for figures and her version is 12 pages. Another reviewer has an iPAD and likes to swipe and zoom but he still has my same 8, 000 words and 4 figures. Same content, same length-no extra burden on the reviewers-just different formatting.

There are many other current ways to enhance scholarly communication and many that haven’t yet been invented. But I think decoupling the front end human readable format from the back end machine readable format t is so fundamental to our current difficulties that we should tackle it collectively. And, I believe it is doable and just might be the carrot that induces our scholars to abandon their current models and move towards the future. From that, all else flows.

We have A LOT of neuroscience information, and would like to share….

jcachat — Tue, 14 May 2013 17:46:29 +0000

Over the past 4 years, the Neuroscience Information Framework systematically scanned the literature, internet and social buzz for all things neuroscience (& biomedical science). This tedious bookkeeping has resulted in the largest, most comprehensive catalog of neuroscience-relevant information ever amassed – with the added bonus of semantically enhanced search functions. And now, we would like to share it with you via myNIF…but before those details…

What do we mean “neuroscience information”?

Neuroscience information includes data, resources, literature, grants, multimedia, social buzz, a lexicon and more..

Data: Over 140 independent databases (i.e. CCDB, Grants.gov, GENSAT) are deeply indexed and semantically mapped by NIF – representing over 400 million pieces of data. These data are considered part of the “hidden web”, not indexed by major search engines because do so requires specialized database query statements for retrieving data within, rather than on the surfaces of pages surrounding the database. NIF has developed technologies to regularly re-crawl and update data content, index it, and provide search within the contents of these databases simultaneously. Moreover, data resulting from a search can be exported with a single click into standard data formats for desired, subsequent analysis. This can simply save you time – if you need to know what type of serotonin receptors have been classified in zebrafish (Danio rerio) – searching NIF for ‘zebrafish serotonin receptor’ provides results from authoritative data providers (HomoloGene, EntrezGene) which can be compared instantly, rather than visiting each site separately, and comparing through notes, multiple windows, or several downloads. In addition to this primary information , the results also include related, and sometimes very helpful information about zebrafish and serotonin – signaling pathways, antibodies, and grant information.

Resources: Need to find a software analysis package for microarray data? NIF can recommend 41 options, as well as 100+ unique organizations, centers, labs and websites that have similar interests. Looking for non-governmental funding of ALS research? Here are 7. What about a tissue bank with Alzheimer’s disease CNS tissue samples available for researchers? NIF is aware of around 88 worth a look. All of this to convey that a resource is object or entity, with a website, that provides potential value to neuroscience research or the researchers. Importantly, this catalog of resources indexed by NIF is maintained at NeuroLex, a semantic mediawiki website. Homologous to Wikipedia, in that any one can contribute their resource or favorite resources, but endowed with reasoning capabilities permitting logical reasoning on relationships between data (i.e. list all GABAergic Neurons).

Neuroinformatics summer school anyone?

Anita — Tue, 14 May 2013 17:15:51 +0000

*Stream Neuroscience, **VIII Summer School “Achievements and Applications
of Contemporary Informatics, Mathematics and Physics” (AACIMP-2013)*

The stream “Neuroscience” includes a broad range of topics from subcellular
level to cognition and social behaviour considered mainly from
computational point of view. It provides all required information to
understand the biological bases of modelled processes.

*Preliminary topics of the stream are:*

- Neurodynamic, types of excitability, bursting and bistability
- Dynamic clamp or Neuron-Computer Interface
- Spatial property of neurons, multicompartment models
- Local and global neural networks, synchronization and oscillations
- Brain networks, specific mediator systems and behavior
- Molecular and cellular mechanisms of pain
- Specific perceptions: Auditory, Visual, Somatosensory
- Brain-Computer Interface

*Prerequisites*: no prior knowledge in the field is expected.

*Dr. Romain Brette*, Assistant Professor, hearing lab at Ecole Normale
Supérieure, Paris, France; Member, Institut Universitaire de France.
*(confirmation
is expected)*
*Dr. Anton Chizhov, *Senior Researcher, Ioffe Physical Technical Institute
of the Russian Academy of Sciences, Saint Petersburg, Russia.
*Prof. Witali Dunin-Barkowski*, Department of Neuroinfomatics, Scientific
Research Institute for System Analysis, Russian Academy of Sciences,
Moscow, Russia.
*Dr. Gennady S. Cymbalyuk*, Assistant Professor, Dynamical Neuroscience
Laboratory, Neuroscience Institute, Georgia State University, Atlanta USA.
*Dr. Ruben Tikidji-Hamburyan*, Senior Researcher, A.B.Kogan Research
Institute for Neurocybernetics, Southern Federal University, Rostov-on-Don,
Russia; Visiting Researcher, Louisiana State University, Health Sciences
Center, New Orleans, USA.
*Dr. Sergiy Yakovenko*, Assistant Professor, Neural Engineering Laboratory,
West Virginia University School of Medicine, WV, USA.
*Ilya Kuzovkin*, MSc., researcher at the Department of Computer
Science, University
of Tartu, Estonia.
*Dr. Pavel Belan*, Bogomolets Institute of Physiology of the NASU, Kyiv,
Ukraine
*(confirmation is expected)*
*Dr. Evgenia Belova*, A.B. Kogan Research Institute for Neurocybernetics,
Southern Federal University, Rostov-on-Don, Russia * (confirmation is
expected)*
*Dr. Nana Voitenko*, Department of Molecular Physiology and Biophysics,
Physics and Technology Center of the NASU, Kyiv, Ukraine *(confirmation is
expected)*
*Dr. Carl van Vreeswijk*, Research Scientist, French National Centre for
Scientific Research, Paris, France. *(confirmation is expected)*

*Confirmation from a certain number of lecturers is expected.*

The stream “Neuroscience” is a part of the VIII Summer School “AACIMP-2013”
that will take place at the National Technical University of Ukraine “Kyiv
Polytechnic Institute” (Kyiv, Ukraine), August 1 – 17, 2013. It will
include lectures, seminars and workshops as well as social program.

In order to apply for participation in the stream, applicants need to
complete and submit the Summer School online application form -
http://summerschool.ssa.org.ua/. Programme committee will make the decision
on applicant’s admission within a month from the date of submitting the
application form.

*Early registration deadline is May 20, 2013*; late registration deadline
is June 20, 2013.
Detailed information and application form are available on the AACIMP-2013
website at http://summerschool.ssa.org.ua

*Organizing Committee:*
Student Science Association
37, Peremohy avenue, building 1, room 299(15)
03056, Ukraine, Kyiv,
Telephone/Fax: +38 044 454 9243
Web: http://ssa.org.ua
E-mail: summerschool@ssa.org.ua

What if scientists dropped the jargon?

Anita — Mon, 06 May 2013 04:06:41 +0000

This is a question asked by Alan Alda, a tireless advocate for clear communication of science to the public. He has been the host of the PBS series “Scientific American Frontiers” for many years and has worked since 2005 on a novel and amazing idea: scientists should drop the jargon and actually try to communicate clearly to the public.

Apparently if you drop the big words that are relatively inscrutable, most medical procedures begin to sound a little more comprehensible. Alan used the example of the end-to-end anastomosis, an inscrutable surgery that he performed on M.A.S.H. often, but when a doctor had to perform this same surgery on him, it was explained a little more clearly, in that a part of the colon that had gone bad needed to be removed and the healthy ends needed to be re-attached.

This does not take a genius to understand, but the terminology in medicine and science in general can be so full of jargon that scientists in adjacent fields have trouble. In fact, here is a sentence that I wrote, but would be hard pressed to ask anyone outside of a small field of neuroscientists to interpret correctly “In CNQX, ACPD only decreased EPSPs, but APV or bicuculline did not change the effect of ACPD.” This was not meant for a general audience, but I can see that we must do better in explaining our findings to each other, if not the general public. Why have we built a system that has become so difficult to understand?

If we think that this system is not a good system, like Alan Alda suggests, what can be done to change this?

Alan, for one is not sitting around asking questions, this year at Stony Brook University he will be starting a new chapter in his illustrious career as an academic. In 2009, he launched the “Alan Alda Center for Communicating Science” and has been touring universities, teaching workshops and campaigning since. This is a great effort and I for one, wish him the best of luck and hope that more scientists can think and talk clearly about science.

For more, see:

http://www.centerforcommunicatingscience.org/alan-alda/

How Do You Evaluate a Database

Anita — Fri, 03 May 2013 16:43:30 +0000

by Maryann E Martone

I was speaking with a colleague recently who, like many of us, had experienced the frustration of trying to support his on-line resources. He has assembled a comprehensive on-line resource, it is used by the community and was used by others to publish their studies. It is not Genbank or EBI; it is one of the thousands of on-line databases created by individuals or small groups that the Neuroscience Information Framework and others have catalogued. My colleague has spent years on this resource, pored over hundreds of references and entered close to a million statements in the database. By many means, it is a successful resource. But in the grant review, he was criticized for not having enough publications. I experienced the same thing in a failed grant for the resource that I had created, the Cell Centered Database. In fairness, that was not the most damning criticism, but it just seemed so very misplaced. I had succeeded in standing up and populating a resource, well before there was any thought of actually sharing data. People used the database and published papers on it, but apparently I should have been spending more time writing about it and less time working on it.

The problems of creating and maintaining these types of resources are well known and were discussed at Beyond the PDF2: to be funded, you have to be innovative. But you don’t have to be innovative to be useful. To quote or paraphrase Carole Goble at the recent conference, “Merely being useful is not enough.”

But presumably there is a threshold of perceived value where “merely being useful” is enough. I am thinking of the Protein Databank or Pub Med. These resources are well funded and also well used but hardly innovative. I am guessing that many of the resources like my colleague and I created were started with the hope that they would be as well supported and integral to people’s work as the PDB or Pub Med. But the truth is, they are not in the same class. But they are still valuable and represent works of scholarship. We are now allowed to list them on our biosketch for NSF. So my question to you is: how do we evaluate these thousands of smaller databases?

Ironically, our peers have no trouble evaluating an article about our databases, but they have much more trouble evaluating the resource itself. How does one weigh 30,000 curated statements against 1 article? What level of page views, visits, downloads and citations make a database worthwhile? If my colleague had published 10 papers, the reviewers wouldn’t have likely checked how often they were cited, particularly if they were recent. What is the equivalent of a citation classic for databases? If you don’t have the budget of NCBI, then what level of service can you reasonably expect from these databases? I thought that the gold standard was a published study that utilized your database to do something else, by a group unconnected to you. Grant reviewers found that unconvincing. Perhaps I didn’t have enough? But how many of these do you need, relative to the size of your community, and on what time frame should you expect them to appear? Sometimes studies take years to publish. Do they need to be from the community that you thought you were targeting (and whose institute may have funded your resource) or does evidence from other communities count?

So perhaps if we want to accept databases and other artefacts in lieu of the article, we should help define a reasonable set of criteria by which they can be evaluated. Anyone care to help here?

Lab Data Management Practices?

jcachat — Sun, 28 Apr 2013 16:20:09 +0000

A number of groups, from libraries and universities and academic projects are striving to implement flexible data management systems in order to harness the latest and greatest in semantic web technologies striving to integrate and facilitate breakthrough interdisciplinary analysis.

It is known that every lab, every individual research group (regardless of the discipline) has developed internal data management systems that “work” (i.e. literature & data collection > excel > stats > graphing > word processer) but what has your lab found useful and what are your biggest frustrations?

Please feel free to comment below, or join the discussion on ResearchGate.

Human Brain Mapping Hackathon in 2013

Anita — Wed, 17 Apr 2013 22:48:02 +0000

With support from the Allen Institute for Brain Science, Amazon Web Services, and numerous other contributors, the 19th Annual Meeting of the Organization for Human Brain Mapping (OHBM) will include an integrated hack room and associated cloud-computing contest called the HBM Hackathon. Participation is free, but to be eligible for the contest prizes and free cloud computing credits, at least one team member must be registered to attend the main OHBM 2013 Meeting in Seattle.

OHBM attendance is expected ~3,000 researchers focused on brain imaging methods like fMRI.

Top 25 for April

Anita — Mon, 15 Apr 2013 04:39:12 +0000

Its that time, we find out what have the users of NIF been searching for this month.
Looks like antibodies and money are still winners, but some interesting things climbing to the top. The gene ontology tools, which has joined the NIF registry is now in the top 10 search terms and the BAMS connectivity data seem to be a big winner this month.

Top databases of April are:
1. Grants.gov/Opportunity
2. ABCD/Brain Regions
3. BAMSCN/Con1
4. AllenInstitute/MouseBrainAtlas
5. BioNOT/Negation
6. AntibodyRegistry/ABs
7. NIF Integrated NSC/Connectivity
8. AmiGO/Genes
9. AddGene/Plasmids
10. BAMS/Brain Regions
11. NINDS/Disease 1
12. BrainInfo/Brain Region
13. Drug Related Gene Database/DRG
14. BREDE/Activation Foci
15. BrainMaps/Atlas
16. NIF Registry/Info
17. ClinicalTrials/ClinTr
18. CGC/Animals 3
19. CellImageLibrary/CIL
20. RePORTER/CurrentNIHGrants
21. ModelDB/Models
22. NIF Integrated Software/Info
23. SumsDB/Activation Foci
24. Allenbrain/Atlas
25. GENSAT/GENSAT

Top Search Terms of April:
1. antibodies
2. cerebellum
3. database
4. hippocampus
5. “Forebrain”
6. “Gene Ontology Tools”
7. “Frontal Lobe”
8. antibodyregistry
9. “Anterior Nuclear Group”
10. “Cerebellum”
11. NIDA
12. “Amygdala”
13. “Drug Related Gene Database”
14. “Hypothalamus”
15. “cerebellum”
16. “Flocculonodular Lobe”
17. brain
18. parkinson disease
19. parkinson’s disease
20. “Basal Ganglia”
21. “Medium Spiny Neuron”
22. “NIF Integrated Software”
23. “Oligodendrocyte Precursor Cell”
24. mouse development atlas
25. “Brainstem”