Reports coming out of Japan indicate that the fast food giant McDonalds is using the DS to train staff. Apparently a new programme called 'eSmart' is rolling out for staff and is expected to cut staff training by half compared to traditional approaches! Thanks

When it comes to deploying databases — or any infrastructural pieces, really — at web scale, many large sites opt to “go cheap, go custom or go home.” Given their unique needs, this credo makes sense, but I wonder if the companies following it aren’t making more work for themselves than is necessary. Might the resources spent developing open-source projects or building tools from scratch not become extraneous if companies could buy solutions that would work just fine?

Isn’t it plausible that a proprietary vendor –- Oracle, let’s say –- could launch a webscale database or analytics solution that would do the trick for a company like Facebook? If there’s one thing Larry Ellison knows better than relational databases, it’s how to make a buck. Hypothetically speaking, Oracle could offer database and data-analysis solutions that could save a company like Facebook from having to act like a software company itself. It certainly hasn’t hesitated to buy its way into alternative markets in the past.

Another consideration is where web companies draw the line regarding commercial solutions: Is an open-source but subscription-based vendor like Red Hat out of the question? What about any of the emerging startups tackling file systems, memcached and other issues?

I’m not suggesting that Facebook et al are heading down the garden path with their current approaches, or that there’s a glut of proprietary products on the market, only that it’s not inconceivable that commercial vendors could meet the needs of these companies. You can read my full column over at GigaOM Pro (subscription required). What do you think? Are open-source and DIY solutions really the best bet for webscale companies?

Publication Date: 2010 Apr PMID: 20300102Authors: Kotaleski, J. H. - Blackwell, K. T.Journal: Nat Rev NeurosciSynaptic plasticity is thought to underlie learning and memory, but the complexity of the interactions between the ion channels, enzymes and genes that are involved in synaptic plasticity impedes a deep understanding of this phenomenon. Computer modelling has been used to investigate the information processing that is performed by the signalling pathways involved in synaptic plasticity in principal neurons of the hippocampus, striatum and cerebellum. In the past few years, new software developments that combine computational neuroscience techniques with systems biology techniques have allowed large-scale, kinetic models of the molecular mechanisms underlying long-term potentiation and long-term depression. We highlight important advancements produced by these quantitative modelling efforts and introduce promising approaches that use advancements in live-cell imaging.post to: CiteULike

Publication Date: 2010 Mar 18 PMID: 20298601Authors: Hue, M. - Riffle, M. - Vert, J. P. - Noble, W. S.Journal: BMC BioinformaticsABSTRACT: BACKGROUND: The prediction of protein-protein interactions is an important step toward the elucidation of protein functions and the understanding of the molecular mechanisms inside the cell. While experimental methods for identifying these interactions remain costly and often noisy, the increasing quantity of solved 3D protein structures suggests that in silico methods to predict interactions between two protein structures will play an increasingly important role in screening candidate interacting pairs. Approaches using the knowledge of the structure are presumably more accurate than those based on sequence only. Approaches based on docking protein structures solve a variant of this problem, but these methods remain very computationally intensive and will not scale in the near future to the detection of interactions at the level of an interactome, involving millions of candidate pairs of proteins. RESULTS: Here, we describe a computational method to predict efficiently in silico whether two protein structures interact. This yes/no question is presumably easier to answer than the standard protein docking question, "How do these two protein structures interact?" Our approach is to discriminate between interacting and non-interacting protein pairs using a statistical pattern recognition method known as a support vector machine(SVM). We demonstrate that our structure-based method performs well on this task and scales well to the size 1 of an interactome. CONCLUSIONS: The use of structure information for the prediction of protein interaction yields significantly better performance than other sequence-based methods. Among structure-based classifiers, the SVM algorithm, combined with the metric learning pairwise kernel and the MAMMOTH kernel, performs best in our experiments.post to: CiteULike

Publication Date: 2010 Mar 17 PMID: 20299325Authors: Briesemeister, S. - Rahnenfuhrer, J. - Kohlbacher, O.Journal: BioinformaticsMOTIVATION: Protein subcellular localization is pivotal in understanding a protein's function. Computational prediction of subcellular localization has become a viable alternative to experimental approaches. while current machine learning-based methods yield good prediction accuracy, most of them suffer from two key problems: lack of interpretability and dealing with multiple locations. RESULTS: We present YLoc, a novel method for predicting protein subcellular localization that addresses these issues. Due to its simple architecture, YLoc can identify the relevant features of a protein sequence contributing to its subcellular localization, e.g., localization signals or motifs relevant to protein sorting. We present several example applications where YLoc identifies the sequence features responsible for protein localization and thus reveals not only to which location a protein is transported to, but also why it is transported there. YLoc also provides a confidence estimate for the prediction. The user can thus decide what level of error is acceptable for a prediction. Due to a probabilistic approach and the use of several thousands of dual-targeted proteins, YLoc is able to predict multiple locations per protein. YLoc was benchmarked using several independent datasets for protein subcellular localization and performs on a par with other state-of-the-art predictors. Disregarding low-confidence predictions, YLoc can achieve prediction accuracies of over 90%. Moreover, we show that YLoc is able to reliably predict multiple locations and outperforms the best predictors in this area. AVAILABILITY: www.multiloc.org/YLoc. CONTACT: briese@informatik.uni-tuebingen.de.post to: CiteULike

Publication Date: 2010 Mar 18 PMID: 20298584Authors: Kryukov, K. - Saitou, N.Journal: BMC BioinformaticsABSTRACT: BACKGROUND: Large nucleotide sequence datasets are becoming increasingly common objects of comparison. Complete bacterial genomes are reported almost everyday. This creates challenges for developing new multiple sequence alignment methods. Conventional multiple alignment methods are based on pairwise alignment and/or progressive alignment techniques. These approaches have performance problems when the number of sequences is large and when dealing with genome scale sequences. RESULTS: We present a new method of multiple sequence alignment, called MISHIMA (Method for Inferring Sequence History In terms of Multiple Alignment), that does not depend on pairwise sequence comparison. A new algorithm is used to quickly find rare oligonucleotide sequences shared by all sequences. Divide and conquer approach is then applied to break the sequences into fragments that can be aligned independently by an external alignment program. These partial alignments are assembled together to form a complete alignment of the original sequences. CONCLUSIONS: MISHIMA provides improved performance compared to the commonly used multiple alignment methods. As an example, six complete genome sequences of bacteria species Helicobacter pylori (about 1.7Mb each) were successfully aligned in about 6 hours using a single PC.post to: CiteULike

Nature reviews. Neuroscience, Vol. 11, No. 4. (April 2010), pp. 239-251.

Synaptic plasticity is thought to underlie learning and memory, but the complexity of the interactions between the ion channels, enzymes and genes that are involved in synaptic plasticity impedes a deep understanding of this phenomenon. Computer modelling has been used to investigate the information processing that is performed by the signalling pathways involved in synaptic plasticity in principal neurons of the hippocampus, striatum and cerebellum. In the past few years, new software developments that combine computational neuroscience techniques with systems biology techniques have allowed large-scale, kinetic models of the molecular mechanisms underlying long-term potentiation and long-term depression. We highlight important advancements produced by these quantitative modelling efforts and introduce promising approaches that use advancements in live-cell imaging.
Jeanette Hellgren Kotaleski, Kim Blackwell

Sensors and Actuators B: Chemical (03 December 2009)

A new drift compensation method based on common principal component analysis (CPCA) is proposed. The drift variance in data is found as the principal components computed by CPCA. This method finds components that are common for all gasses in feature space. The method is compared in classification task with respect to the other approaches published where the drift direction is estimated through a principal component analysis (PCA) of a reference gas. The proposed new method – employing no specific reference gas, but information from all gases – has shown the same performance as the traditional approach with the best-fitted reference gas. Results are shown with data lasting 7 months including three gases at different concentrations for an array of 17 polymeric sensors.
A Ziyatdinov, S Marco, A Chaudry, K Persaud, P Caminal, A Perera

Publication Date: 2010 Mar 18 PMID: 20237566Authors: Li, J. F. - Huang, Y. F. - Ding, Y. - Yang, Z. L. - Li, S. B. - Zhou, X. S. - Fan, F. R. - Zhang, W. - Zhou, Z. Y. - Wu de, Y. - Ren, B. - Wang, Z. L. - Tian, Z. Q.Journal: NatureSurface-enhanced Raman scattering (SERS) is a powerful spectroscopy technique that can provide non-destructive and ultra-sensitive characterization down to single molecular level, comparable to single-molecule fluorescence spectroscopy. However, generally substrates based on metals such as Ag, Au and Cu, either with roughened surfaces or in the form of nanoparticles, are required to realise a substantial SERS effect, and this has severely limited the breadth of practical applications of SERS. A number of approaches have extended the technique to non-traditional substrates, most notably tip-enhanced Raman spectroscopy (TERS) where the probed substance (molecule or material surface) can be on a generic substrate and where a nanoscale gold tip above the substrate acts as the Raman signal amplifier. The drawback is that the total Raman scattering signal from the tip area is rather weak, thus limiting TERS studies to molecules with large Raman cross-sections. Here, we report an approach, which we name shell-isolated nanoparticle-enhanced Raman spectroscopy, in which the Raman signal amplification is provided by gold nanoparticles with an ultrathin silica or alumina shell. A monolayer of such nanoparticles is spread as 'smart dust' over the surface that is to be probed. The ultrathin coating keeps the nanoparticles from agglomerating, separates them from direct contact with the probed material and allows the nanoparticles to conform to different contours of substrates. High-quality Raman spectra were obtained on various molecules adsorbed at Pt and Au single-crystal surfaces and from Si surfaces with hydrogen monolayers. These measurements and our studies on yeast cells and citrus fruits with pesticide residues illustrate that our method significantly expands the flexibility of SERS for useful applications in the materials and life sciences, as well as for the inspection of food safety, drugs, explosives and environment pollutants.post to: CiteULike