New and Exciting in PLoS Community Journals

Friday - time to take a look at the new articles in PLoS Computational Biology, Genetics and Pathogens - check them all out, but here are a couple of picks:

Exploration of Small RNAs:

There is substantial interest in noncoding RNAs (ncRNAs), which play an essential role in complex biological systems without encoding for proteins. Only a limited number of ncRNAs, such as ribosomal RNA (rRNA) and transfer RNA (tRNA), have previously been characterized in any depth. Recent studies revealed many novel ncRNAs, covering a wide range of sizes [1]. RNA molecules have several functions including catalytic activity and ability to act as a structural component. Of these functions, the ability to specify a nucleic acid sequence is superior compared to proteins. A common way in which ncRNA contributes to biological processes is through the ribonucleoprotein (RNP) complex, where its role is to guide recognition of nucleic acid target sequences relying upon sequence complementarity [2]. Small RNA molecules are widely utilized in this type of machinery, and are involved in important biological processes [3]. Exploration of novel small RNA species and their functions attracts substantial interest. The advent of recent technologies to profile cellular RNAs, such as high-throughput sequencing and microarray, coupled with computational analysis, has contributed to rapid progress in this field. Here, we review the recently discovered small RNA species and their pathways in a view of conservations and differences between higher eukaryotes. We also summarize recent exploration efforts of novel small RNAs based on devised technologies to provide a perspective for the future.

Why is Real-World Visual Object Recognition Hard?:

The ease with which we recognize visual objects belies the computational difficulty of this feat. At the core of this challenge is image variation--any given object can cast an infinite number of different images onto the retina, depending on the object's position, size, orientation, pose, lighting, etc. Recent computational models have sought to match humans' remarkable visual abilities, and, using large databases of "natural" images, have shown apparently impressive progress. Here we show that caution is warranted. In particular, we found that a very simple neuroscience "toy" model, capable only of extracting trivial regularities from a set of images, is able to outperform most state-of-the-art object recognition systems on a standard "natural" test of object recognition. At the same time, we found that this same toy model is easily defeated by a simple recognition test that we generated to better span the range of image variation observed in the real world. Together these results suggest that current "natural" tests are inadequate for judging success or driving forward progress. In addition to tempering claims of success in the machine vision literature, these results point the way forward and call for renewed focus on image variation as a central challenge in object recognition.

Getting Started in Text Mining:

Text mining is the use of automated methods for exploiting the enormous amount of knowledge available in the biomedical literature. There are at least as many motivations for doing text mining work as there are types of bioscientists. Model organism database curators have been heavy participants in the development of the field due to their need to process large numbers of publications in order to populate the many data fields for every gene in their species of interest. Bench scientists have built biomedical text mining applications to aid in the development of tools for interpreting the output of high-throughput assays and to improve searches of sequence databases (see [1] for a review). Bioscientists of every stripe have built applications to deal with the dual issues of the double-exponential growth in the scientific literature over the past few years and of the unique issues in searching PubMed/MEDLINE for genomics-related publications. A surprising phenomenon can be noted in the recent history of biomedical text mining: although several systems have been built and deployed in the past few years--Chilibot, Textpresso, and PreBIND (see Text S1 for these and most other citations), for example--the ones that are seeing high usage rates and are making productive contributions to the working lives of bioscientists have been built not by text mining specialists, but by bioscientists. We speculate on why this might be so below.