ReadCube, the reference manager software from Nature Publishing Group, has recently released major update/re-development, which finally makes it a usable gadget and a legitimate competitor of Mendeley, my favorite one in this category.
Since my early days in science (2008 ish), I’ve always wanted a perfect file manager that organizes and displays my pdfs——-something like the “Papers” on Mac, but works on PC, has a cloud storage and is free.
After several months’ searching and comparing, I settled down on Mendeley and have used it ever since.
Mendeley has everything I need. Concise UI, automatic metadata fetching, great PDF display, notes, highlights and tags. The free version offers 1G (2G now) of cloud storage, which is a decent amount for PDF files, and sync between the desktop version and the web. It also has a web importer bookmarklet which allows instant paper import to the library when the metadata is able to be extracted from the current webpage. This works on PubMed and most of the journal websites. It has something beyond my expectation. It introduces the “watched folders”, in which every PDF stored are automatically listed and updated in the software library. Even better, you have the option to let Mendeley rename your files based on key metadata information such as “author”,”journal” and “year”. Last but not least, it’s also an Endnote. Easy reference import and bibliography generation in office word, open office and libre office.
The only two minor flaws in my opinion is: the font size of the library display is too small (~10.5 pt) and cannot be changed, which makes it hard to browse from a 15” laptop. This later becomes less of an issue when I more and more rely on searching with tags and authors and basically not use the main library display anymore. The other downside of Mendeley is the memory allocation. I’ve experienced, when the library contains over 1,000 PDF files, severe lag and poor performance of the software. Since then, I reduced the number of actual PDFs saved on my disk, instead saved most of them only in metadata format.
Back to ReadCube. ReadCube was first introduced, I believe, sometime in 2012. I gave it a try because of its stunning UI and the ability to fetch reference hyperlinks and supplemental information from the paper. The user interface is very Mac like: simplicity, nice button effects and awesome colors. For some of the papers that are “enhanceable”, ReadCube can automatically extract reference and supplemental information from the paper, which is great. But ReadCube at the time did not offer many other useful features like what Mendeley does: web importer, write-and-cite tool, cloud and file renaming. What was the worst and essentially prevented me using ReadCube was the display: it has three columns, the library info panel on the left, references/abstract/supplemental on the right, and the actual PDF in the middle. The two panels on either side cannot be closed/hidden and therefore the effective reading space is greatly reduced, and on my 15.5” laptop, too small a font to read. You can zoom in, but everything became a mess to navigate. Also, the addition of references and supplemental often times makes the software running slow and easy to crash.
But everything’s changed in this recent major re-make from last week. The two cumbersome panels can be removed during reading mode, which makes the actual size of reading space equivalent to that of Mendeley’s. Everything is smoother and quicker. I can finally enjoy reading a paper without having worry about the font size and potential crashes. It also introduces web importer and citation tool for Word. On the other hand, you have to pay for cloud space (though unlimited) and watched folder option for $5/mo. And it still doesn’t have tags and file-renaming. And for some reason (perhaps because of my customized font rendering), fonts in ReadCube do not look as sharp as in Mendeley.
So, Mendeley is still my go-to reference manager and paper organizer, although ReadCube has had some great features and improvement. Something I think could be useful is the online Mendeley community. You can invite friends and colleagues and share ideas and papers; you can see other people’s recommendation and comments on papers, sort of like F1000, but clearly less matured. I haven’t tested either of those because I have only one friend in my Mendeley account and we haven’t talked since February of 2011.
I was about to write something about this study led by Guri Giaever’s group from my beloved U of T MoGen department, when I saw the update from Derek Lowe’s blog on the same subject, where he and a few fellow chemists have spotted some silly chemistry mistakes.
First, they appear to recognize N-phenylbenzylamine and it’s biologically equivalent tautomer as two chemicals and apparently target two distinct cellular pathways.
Then there’s this issue with imidazole and protonated imidazole, which shouldn’t exist at all in a physiological buffer system.
And this one is particularly funny: they have this cyclohexa-2,4-dien-1-one
which should just be phenol indeed.
And there’s a few more examples of mis-nomenclature and small molecules invalid in a biological system like those.
What a shame on the authors! What a shame on the reviewers! and on Ron David, who is in the authorship list and whom I respect a lot.
PS. a note on Guri. Apparently she’s not at U of T anymore. She and her husband, another MoGen faculty, Corey Nislow have joined UBC since last year. Interesting move.
This paper is interesting. It’s not substantial.
I take it as a grain of salt. No, I don’t like it at all.
They were seeing reduced level of an metabolic enzyme Cystathionine γ-lyase (CSE), which makes cysteine and hydrogen sulfide in three models of huntington’ disease: cell lines, mouse models and post-mortem patient samples. And CSE KO mice exhibits certain behavior deficits resembling HD symptoms. Replenishing cysteine in diet alleviates these behavior deficits and increases survival.
First of all, it’s an interesting observation of a reduced level of CSE in HD models——perhaps among hundreds of other random proteins. There is no evidence suggesting CSE activity loss or global reduction in cysteine level, in any way, contributes to disease onset/progression.
Actually they can’t tell if cysteine is the culprit here. CSE makes both cysteine and hydrogen sulfide, and we know that H2S is an important second messenger in signaling in the brain and periphery. That cysteine rescue experiment does not rule out H2S function in the disease state.
The biggest problem I have with this study is all the behavior assay. They were trying to use a few of the standard motor tests to model HD, which is just not nearly satisfying at all. Who knows what causes these deficits when you deplete one of the nine essential amino acids ? It totally doesn’t have to be, in any way, Huntington-dependent, and these tests are not at all good means to monitor HD pathophysiology.
What should they do? Well, since it’s just too vague a connection between CSE expression and some random motor behavior tests, anything in between is essential and the more, the better. At least they should’ve looked at some cellular phenotypes including cell death, inclusion body aggregates and neurodegeneration. Some metabolic profiling and behavior are also welcomed.
I like this paper a lot because it’s yet another good example of decoding the functions of non-coding disease variants, and what’s more, it’s a megabase-long looping enhancer!
I’ll never forget, from my college biology class, that crazy drawing of how enhancer elements might work: a piece of DNA, anywhere from introns to intergenic deserts, is bound by some transcription factors, and somehow forms a weird loop like alligator’s jaws all the way to the proximal promoter, where the core transcription regulation occurs.
Given this possible long-range cis regulation, it is not uncommon to see DNA in one gene regulates not this gene, but another gene/genes miles away. And this causes a lot confusions in interpretation of the functions of each of these regulatory sequences, especially when you try to study a disease-associated variant. Continue reading “yet another megabase long enhancer”
同样的Arabidopsis thaliana, 同样的Pseudomonas syringae, 同样的host-pathogen interaction, 同样的PAMPs。我的工作那么简单，和这篇文章一样简单，直接。
This paper from Janelia Farm is fucking so cool and nearly flawless to me simply because I can’t understand most part of it.
They are studying transcription factor dynamics and mechanisms of chromatin binding and motif searching. Their raw data were acquired with extremely sophisticated (to me) and state-of-art microscopies. Their conclusions were derived from those raw data based on a series of complicated (to me) mathematical modeling and algorithms.
As my own research also involves molecule tracking and interaction in real time, I’d always dreamed of having one of those multi-focus microscopies they have and it is beyond a dream for me to have someone, applying rigorous computations, to figure out what’s going on in all the movies I’ve captured.
Looking at this work, I’ve never been so perplexed with the vortex of biomedical research that I and many others fall in. On one hand, we have these most careful nature observers who have the very detailed and amazing description of physiology, anatomy, development and disease; On the other hand, we also have these incredible mathematicians, physicists and engineers who take a completely different, very rational and theoretical route to uncover mysteries of biology.
And these two camps seem never be able to reconcile with each other, just like Catholics and Judeo-Christians can’t. I myself fall into the first camp, so I love the way I interact with biology. It’s like telling a story, fitting pieces and pieces together———in a logical manner, but not necessarily stringent, because I believe it’s just totally absurd to seek for absolute accuracy in biology. It loses all the beauty and drama of the nature we study.
But at the same time I’m fully aware of all the ugly bits from this angle of the approach: cherry-picking data, ascertainment bias, ill-designed statistics, and so on and so on. We are too into the little details and forget the general principles; and we are so obsessed with the nature in our eyes in a descriptive way that we couldn’t extract the skeletons and metaphysics of it. This is where the other camp come in. These abstract, cold-blooded mathematicians and computationists. They don’t see colors, they see patterns; they don’t see interactions, they see models; they don’t care about complexities and crosstalks, they care about generalizations and modularities.
That’s why I’m so afraid of them. They have incredible ability to develop new tools and techniques that revolutionize the field. Everything we do is absolutely dependent on their support. They have amazing mind to extract general principles out of messy, daunting experiments. I’m afraid of being useless because I’m not smarter than them and I can’t compete with the robots and software they devise.
As a matter of fact, I’m in a deep deep fear. I genuinely think biological research will be taken over by mathematicians and computer scientists, and the field of biology will become mere applications of physics and engineering.
I don’t know what to do. It’s too late for me to washout my brain to something I never used to and never liked.
My scientific belief has collapsed in front of codes and equations. I don’t see a future of myself.
剃刀是你， 三年后的melpo. 毫不客气的把我解的支离破碎。
everyone is the same.
im not indifferent anymore.
I’m happy to see the two teams that I’d been attached with over the years are finally growing into some prospect since I started watching NBA in 2000.
The Toronto Raptors and the Washinton Wizards.
Going to see a basketball game is such fun. I still remember the times I went to a Raptors’ game on a cozy cool summer night with friends and drinks. I enjoyed sitting upper rows and looking down at the shiny hardwood floor, the giant maple leaf flag and the glowing Raptors’ logo. And of course sometimes when I got lucky, I found myself having a slice of free pizza from pizza-pizza the next day because the Raptor’s won and scored over 100 pts.
I still remember once, after the game, at the entrance of Air Canada Center where the crowd was exiting, I looked up in the sky and saw an eclipse. When the beautiful pale moon was being shadowed, right over the lake, the stadium, the street lights and the people, I couldn’t help but thinking of J D Salinger and I depicted myself as the catcher in this land. I logged the scene and the emotion I had at the time, but from time to time, I can’t resist thinking back of that night over and over again and asked myself:
Where was I ? In the middle of the crowd, under the moon being shadowed, or I was nowhere, no one, no water, no moon ? Should I be there, or should I be somewhere else ? Was all these just my mere imagination ? The voices, the water, the echoes and the moonlight ——- all just illusions and hallucinations, and I was just by myself, totally detached from the scene, just one isolated piece of existence. I was nowhere.
The bottom line is, you don’t need a championship team to become a fan, to enjoy the games and a good time. Some good old days.
This is no doubt the most influential and magnificent sculpture in biomedical sciences. This monument should be erected at the front door of every single research building.
Look at the fine structure. Look at how delicate the paws are. Look at those innocent yet miserable eyes.
There is simply no other art work in biology quite near the level of this one.