Vertical cavity lasers for optical interconnects

Vertical-cavity surface-emitting lasers are generating much interest due to their geometric suitability for two-dimensional array fabrication and their potential for achieving ultra-low thresholds. Here we report on optically- and electrically-pumped microlaser devices. having transverse dimensions of a few microns and active material lengths of a few hundred A. The very small volumes are a key factor in achieving low thresholds. So far however surface recombination has prevented us from achieving thresholds much below 1 mA

Predicting functionality of protein–DNA interactions by integrating diverse evidence

Chromatin immunoprecipitation (ChIP-chip) experiments enable capturing physical interactions between regulatory proteins and DNA in vivo. However, measurement of chromatin binding alone is not sufficient to detect regulatory interactions. A detected binding event may not be biologically relevant, or a known regulatory interaction might not be observed under the growth conditions tested so far. To correctly identify physical interactions between transcription factors (TFs) and genes and to determine their regulatory implications under various experimental conditions, we integrated ChIP-chip data with motif binding sites, nucleosome occupancy and mRNA expression datasets within a probabilistic framework. This framework was specifically tailored for the identification of functional and non-functional DNA binding events. Using this, we estimate that only 50% of condition-specific protein–DNA binding in budding yeast is functional. We further investigated the molecular factors determining the functionality of protein–DNA interactions under diverse growth conditions. Our analysis suggests that the functionality of binding is highly condition-specific and highly dependent on the presence of specific cofactors. Hence, the joint analysis of both, functional and non-functional DNA binding, may lend important new insights into transcriptional regulation

Predator-Induced Vertical Behavior of a Ctenophore

Although many studies have focused on Mnemiopsis leidyi predation, little is known about the role of this ctenophore as prey when abundant in native and invaded pelagic systems. We examined the response of the ctenophore M. leidyi to the predatory ctenophore Beroe ovata in an experiment in which the two species could potentially sense each other while being physically separated. On average, M. leidyi responded to the predator’s presence by increasing variability in swimming speeds and by lowering their vertical distribution. Such behavior may help explain field records of vertical migration, as well as stratified and near-bottom distributions of M. leidyi

UNCLES: Method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

Background: Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Results: Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. Conclusions: The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.The National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0310-1004)

Mapping Genetically Compensatory Pathways from Synthetic Lethal Interactions in Yeast

Background: Synthetic lethal genetic interaction analysis has been successfully applied to predicting the functions of genes and their pathway identities. In the context of synthetic lethal interaction data alone, the global similarity of synthetic lethal interaction patterns between two genes is used to predict gene function. With physical interaction data, such as proteinprotein interactions, the enrichment of physical interactions within subsets of genes and the enrichment of synthetic lethal interactions between those subsets of genes are used as an indication of compensatory pathways. Result: In this paper, we propose a method of mapping genetically compensatory pathways from synthetic lethal interactions. Our method is designed to discover pairs of gene-sets in which synthetic lethal interactions are depleted among the genes in an individual set and where such gene-set pairs are connected by many synthetic lethal interactions. By its nature, our method could select compensatory pathway pairs that buffer the deleterious effect of the failure of either one, without the need of physical interaction data. By focusing on compensatory pathway pairs where genes in each individual pathway have a highly homogenous cellular function, we show that many cellular functions have genetically compensatory properties. Conclusion: We conclude that synthetic lethal interaction data are a powerful source to map genetically compensatory pathways, especially in systems lacking physical interaction information, and that the cellular function network contain

A Bayesian Search for Transcriptional Motifs

Identifying transcription factor (TF) binding sites (TFBSs) is an important step towards understanding transcriptional regulation. A common approach is to use gaplessly aligned, experimentally supported TFBSs for a particular TF, and algorithmically search for more occurrences of the same TFBSs. The largest publicly available databases of TF binding specificities contain models which are represented as position weight matrices (PWM). There are other methods using more sophisticated representations, but these have more limited databases, or aren't publicly available. Therefore, this paper focuses on methods that search using one PWM per TF. An algorithm, MATCHTM, for identifying TFBSs corresponding to a particular PWM is available, but is not based on a rigorous statistical model of TF binding, making it difficult to interpret or adjust the parameters and output of the algorithm. Furthermore, there is no public description of the algorithm sufficient to exactly reproduce it. Another algorithm, MAST, computes a p-value for the presence of a TFBS using true probabilities of finding each base at each offset from that position. We developed a statistical model, BaSeTraM, for the binding of TFs to TFBSs, taking into account random variation in the base present at each position within a TFBS. Treating the counts in the matrices and the sequences of sites as random variables, we combine this TFBS composition model with a background model to obtain a Bayesian classifier. We implemented our classifier in a package (SBaSeTraM). We tested SBaSeTraM against a MATCHTM implementation by searching all probes used in an experimental Saccharomyces cerevisiae TF binding dataset, and comparing our predictions to the data. We found no statistically significant differences in sensitivity between the algorithms (at fixed selectivity), indicating that SBaSeTraM's performance is at least comparable to the leading currently available algorithm. Our software is freely available at: http://wiki.github.com/A1kmm/sbasetram/building-the-tools

Increased platelet counts and platelet activation in early symptomatic versus asymptomatic carotid stenosis and relationship with microembolic status: Results from the Platelets And Carotid Stenosis (PACS) Study

Background: Cerebral microembolic signals (MES) may predict increased stroke risk in carotid stenosis. However, the relationship between platelet counts or platelet activation status and MES in symptomatic versus asymptomatic carotid stenosis has not been comprehensively assessed. Setting: University teaching hospitals. Methods: This prospective, pilot observational study assessed platelet counts and platelet activation status, and the relationship between platelet activation and MES in asymptomatic versus early (≤4 weeks after TIA/stroke) and late phase (≥3 months) symptomatic moderate or severe (≥50%) carotid stenosis patients. Full blood count measurements were performed, and whole blood flow cytometry was used to quantify platelet surface activation marker expression (CD62P and CD63) and circulating leucocyte-platelet complexes. Bilateral simultaneous transcranial Doppler ultrasound monitoring of the middle cerebral arteries was performed for 1 hour to classify patients as MES-positive or MES-negative. Results: Data from 31 asymptomatic patients were compared with 46 symptomatic patients in the early phase, and 35 of these patients followed up to the late phase after symptom onset. The median platelet count (211 vs. 200 x 109/L; p=0.03) and the median % lymphocyte-platelet complexes were higher in early symptomatic than asymptomatic patients (2.8 vs. 2.4%, p=0.001). The % lymphocyte-platelet complexes was higher in early symptomatic than asymptomatic patients with ≥70% carotid stenosis (p=0.0005), and in symptomatic patients recruited within 7 days of symptom onset (p=0.028). Complete TCD data were available in 25 asymptomatic and 31 early phase symptomatic, and 27 late phase symptomatic patients. 12% of asymptomatic versus 32% of early phase symptomatic (p=0.02) and 19% of late phase symptomatic patients (p=0.2) were MES-positive. Early symptomatic MES-negative patients had ahigher % lymphocyte-platelet complexes than asymptomatic MES-negative patients (2.8 vs. 2.3%; p=0.0085). Discussion: Recently symptomatic carotid stenosis patients have higher platelet counts (potentially reflecting increased platelet production, mobilisation or reduced clearance) and platelet activation status than asymptomatic patients. MES were more frequently detected in early symptomatic than asymptomatic patients, but the differences between late symptomatic and asymptomatic groups were not significant. Increased lymphocyte-platelet complex formation in recently symptomatic vs. asymptomatic MES-negative patients indicates enhanced platelet activation in this early symptomatic subgroup. Platelet biomarkers, in combination with TCD, have the potential to aid risk-stratification in asymptomatic and symptomatic carotid stenosis patients

Network-based functional enrichment

<p>Abstract</p> <p>Background</p> <p>Many methods have been developed to infer and reason about molecular interaction networks. These approaches often yield networks with hundreds or thousands of nodes and up to an order of magnitude more edges. It is often desirable to summarize the biological information in such networks. A very common approach is to use gene function enrichment analysis for this task. A major drawback of this method is that it ignores information about the edges in the network being analyzed, i.e., it treats the network simply as a set of genes. In this paper, we introduce a novel method for functional enrichment that explicitly takes network interactions into account.</p> <p>Results</p> <p>Our approach naturally generalizes Fisher’s exact test, a gene set-based technique. Given a function of interest, we compute the subgraph of the network induced by genes annotated to this function. We use the sequence of sizes of the connected components of this sub-network to estimate its connectivity. We estimate the statistical significance of the connectivity empirically by a permutation test. We present three applications of our method: i) determine which functions are enriched in a given network, ii) given a network and an interesting sub-network of genes within that network, determine which functions are enriched in the sub-network, and iii) given two networks, determine the functions for which the connectivity improves when we merge the second network into the first. Through these applications, we show that our approach is a natural alternative to network clustering algorithms.</p> <p>Conclusions</p> <p>We presented a novel approach to functional enrichment that takes into account the pairwise relationships among genes annotated by a particular function. Each of the three applications discovers highly relevant functions. We used our methods to study biological data from three different organisms. Our results demonstrate the wide applicability of our methods. Our algorithms are implemented in C++ and are freely available under the GNU General Public License at our supplementary website. Additionally, all our input data and results are available at <url>http://bioinformatics.cs.vt.edu/~murali/supplements/2011-incob-nbe/</url>.</p