Decomposing First Mover Advantages in the Mobile Telecommunications Industry

We study first-mover advantages and organizational pre-entry experience in a market with highly heterogeneous consumers – the global mobile telecoms industry. Specifically, we consider the fact that early consumers will be different from later ones. We suggest that early entrants will attract higher-value consumers, which results in first-mover advantages. This effect will be enhanced if these firms have acquired prior technical experience. Conversely, later, mass-market adopters are attracted by established (domestic) brand names. Our empirical results from the global telecommunications industry support our assertions and provide importation insight for the study of first-mover advantages in high-technology industries.First-mover advantage; Mobile telecommunications; Consumer-centric; Pre-entry experience; Firm pre-entry experience

Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques

Contribution of CgPDR1-Regulated Genes in Enhanced Virulence of Azole-Resistant Candida glabrata

In Candida glabrata, the transcription factor CgPdr1 is involved in resistance to azole antifungals via upregulation of ATP binding cassette (ABC)-transporter genes including at least CgCDR1, CgCDR2 and CgSNQ2. A high diversity of GOF (gain-of-function) mutations in CgPDR1 exists for the upregulation of ABC-transporters. These mutations enhance C. glabrata virulence in animal models, thus indicating that CgPDR1 might regulate the expression of yet unidentified virulence factors. We hypothesized that CgPdr1-dependent virulence factor(s) should be commonly regulated by all GOF mutations in CgPDR1. As deduced from transcript profiling with microarrays, a high number of genes (up to 385) were differentially regulated by a selected number (7) of GOF mutations expressed in the same genetic background. Surprisingly, the transcriptional profiles resulting from expression of GOF mutations showed minimal overlap in co-regulated genes. Only two genes, CgCDR1 and PUP1 (for PDR1 upregulated and encoding a mitochondrial protein), were commonly upregulated by all tested GOFs. While both genes mediated azole resistance, although to different extents, their deletions in an azole-resistant isolate led to a reduction of virulence and decreased tissue burden as compared to clinical parents. As expected from their role in C. glabrata virulence, the two genes were expressed as well in vitro and in vivo. The individual overexpression of these two genes in a CgPDR1-independent manner could partially restore phenotypes obtained in clinical isolates. These data therefore demonstrate that at least these two CgPDR1-dependent and -upregulated genes contribute to the enhanced virulence of C. glabrata that acquired azole resistance

Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

<p>Abstract</p> <p>Background</p> <p>RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM) approach was used to quantitatively model RNA interference activities.</p> <p>Results</p> <p>Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (<it>N</it>-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative.</p> <p>Conclusion</p> <p>The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall <it>t</it>-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid sequences can be found at the following site: <url>ftp://scitoolsftp.idtdna.com/SEQ2SVM/</url>.</p

Defining motility in the Staphylococci

The ability of bacteria to move is critical for their survival in diverse environments and multiple ways have evolved to achieve this. Two forms of motility have recently been described for Staphylococcus aureus, an organism previously considered to be non-motile. One form is called spreading, which is a type of sliding motility and the second form involves comet formation, which has many observable characteristics associated with gliding motility. Darting motility has also been observed in Staphylococcus epidermidis. This review describes how motility is defined and how we distinguish between passive and active motility. We discuss the characteristics of the various forms of Staphylococci motility, the molecular mechanisms involved and the potential future research directions

Dynamics of Co-Transcriptional Pre-mRNA Folding Influences the Induction of Dystrophin Exon Skipping by Antisense Oligonucleotides

Antisense oligonucleotides (AONs) mediated exon skipping offers potential therapy for Duchenne muscular dystrophy. However, the identification of effective AON target sites remains unsatisfactory for lack of a precise method to predict their binding accessibility. This study demonstrates the importance of co-transcriptional pre-mRNA folding in determining the accessibility of AON target sites for AON induction of selective exon skipping in DMD. Because transcription and splicing occur in tandem, AONs must bind to their target sites before splicing factors. Furthermore, co-transcriptional pre-mRNA folding forms transient secondary structures, which redistributes accessible binding sites. In our analysis, to approximate transcription elongation, a “window of analysis” that included the entire targeted exon was shifted one nucleotide at a time along the pre-mRNA. Possible co-transcriptional secondary structures were predicted using the sequence in each step of transcriptional analysis. A nucleotide was considered “engaged” if it formed a complementary base pairing in all predicted secondary structures of a particular step. Correlation of frequency and localisation of engaged nucleotides in AON target sites accounted for the performance (efficacy and efficiency) of 94% of 176 previously reported AONs. Four novel insights are inferred: (1) the lowest frequencies of engaged nucleotides are associated with the most efficient AONs; (2) engaged nucleotides at 3′ or 5′ ends of the target site attenuate AON performance more than at other sites; (3) the performance of longer AONs is less attenuated by engaged nucleotides at 3′ or 5′ ends of the target site compared to shorter AONs; (4) engaged nucleotides at 3′ end of a short target site attenuates AON efficiency more than at 5′ end

Impact of the TCR Signal on Regulatory T Cell Homeostasis, Function, and Trafficking

Signaling through the T cell antigen receptor (TCR) is important for the homeostasis of naïve and memory CD4+ T cells. The significance of TCR signaling in regulatory T (Treg) cells has not been systematically addressed. Using an Ox40-cre allele that is prominently expressed in Treg cells, and a conditional null allele of the gene encoding p56Lck, we have examined the importance of TCR signaling in Treg cells. Inactivation of p56Lck resulted in abnormal Treg homeostasis characterized by impaired turnover, preferential redistribution to the lymph nodes, loss of suppressive function, and striking changes in gene expression. Abnormal Treg cell homeostasis and function did not reflect the involvement of p56Lck in CD4 function because these effects were not observed when CD4 expression was inactivated by Ox40-cre.The results make clear multiple aspects of Treg cell homeostasis and phenotype that are dependent on a sustained capacity to signal through the TCR

A DNA methylation biomarker of alcohol consumption.

The lack of reliable measures of alcohol intake is a major obstacle to the diagnosis and treatment of alcohol-related diseases. Epigenetic modifications such as DNA methylation may provide novel biomarkers of alcohol use. To examine this possibility, we performed an epigenome-wide association study of methylation of cytosine-phosphate-guanine dinucleotide (CpG) sites in relation to alcohol intake in 13 population-based cohorts (ntotal=13 317; 54% women; mean age across cohorts 42-76 years) using whole blood (9643 European and 2423 African ancestries) or monocyte-derived DNA (588 European, 263 African and 400 Hispanic ancestry) samples. We performed meta-analysis and variable selection in whole-blood samples of people of European ancestry (n=6926) and identified 144 CpGs that provided substantial discrimination (area under the curve=0.90-0.99) for current heavy alcohol intake (⩾42 g per day in men and ⩾28 g per day in women) in four replication cohorts. The ancestry-stratified meta-analysis in whole blood identified 328 (9643 European ancestry samples) and 165 (2423 African ancestry samples) alcohol-related CpGs at Bonferroni-adjusted P<1 × 10-7. Analysis of the monocyte-derived DNA (n=1251) identified 62 alcohol-related CpGs at P<1 × 10-7. In whole-blood samples of people of European ancestry, we detected differential methylation in two neurotransmitter receptor genes, the γ-Aminobutyric acid-A receptor delta and γ-aminobutyric acid B receptor subunit 1; their differential methylation was associated with expression levels of a number of genes involved in immune function. In conclusion, we have identified a robust alcohol-related DNA methylation signature and shown the potential utility of DNA methylation as a clinically useful diagnostic test to detect current heavy alcohol consumption.Medical Research Council (Grant IDs: MC_UU_12015/1, MC_UU_12015/2), Wellcome Trust. Detailed acknowledgements are included in the Supplementary Information that accompanies the paper on the Molecular Psychiatry website

Central CD4+ T cell tolerance: deletion versus regulatory T cell differentiation

The diversion of MHC class II-restricted thymocytes into the regulatory T (Treg) cell lineage, similarly to clonal deletion, is driven by intrathymic encounter of agonist self-antigens. Somewhat paradoxically, it thus seems that the expression of an autoreactive T cell receptor is a shared characteristic of T cells that are subject to clonal deletion and those that are diverted into the Treg cell lineage. Here, we discuss how thymocyte-intrinsic and -extrinsic determinants may specify the choice between these two fundamentally different T cell fates