Clonal analysis of meningococci during a 26 year period prior to the introduction of meningococcal serogroup C vaccines

Meningococcal disease remains a public health burden in the UK and elsewhere. Invasive Neisseria meningitidis, isolated in Scotland between 1972 and 1998, were characterised retrospectively to examine the serogroup and clonal structure of the circulating population. 2607 isolates causing invasive disease were available for serogroup and MLST analysis whilst 2517 were available for multilocus sequence typing (MLST) analysis only. Serogroup distribution changed from year to year but serogroups B and C were dominant throughout. Serogroup B was dominant throughout the 1970s and early 1980s until serogroup C became dominant during the mid-1980s. The increase in serogroup C was not associated with one particular sequence type (ST) but was associated with a number of STs, including ST-8, ST-11, ST-206 and ST-334. This is in contrast to the increase in serogroup C disease seen in the 1990s that was due to expansion of the ST-11 clonal complex. While there was considerable diversity among the isolates (309 different STs among the 2607 isolates), a large proportion of isolates (59.9%) were associated with only 10 STs. These data highlight meningococcal diversity over time and the need for ongoing surveillance during the introduction of new meningococcal vaccines

Epithelial stem cells in the mammary gland: casting light into dark corners

The epithelial structures of the human breast or the mouse mammary gland are derived from a relatively small number of multipotent, tissue-specific stem cells, of which we are surprisingly ignorant. We do not know how many are required to produce a complete mammary gland, how many times they divide during the process, where they are situated in the gland, or even what they look like. We want to know the answers to these questions, not just to satisfy intellectual curiosity, but also because the answers may shed light on the evolution of breast cancer. Now, studies carried out by Kordon and Smith at the National Cancer Institute have pointed the way toward a new understanding of mammary stem cells and their progeny

Identifying cancer biomarkers by network-constrained support vector machines

<p>Abstract</p> <p>Background</p> <p>One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets. Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers.</p> <p>Results</p> <p>We developed an integrated approach, namely network-constrained support vector machine (netSVM), for cancer biomarker identification with an improved prediction performance. The netSVM approach is specifically designed for network biomarker identification by integrating gene expression data and protein-protein interaction data. We first evaluated the effectiveness of netSVM using simulation studies, demonstrating its improved performance over state-of-the-art network-based methods and gene-based methods for network biomarker identification. We then applied the netSVM approach to two breast cancer data sets to identify prognostic signatures for prediction of breast cancer metastasis. The experimental results show that: (1) network biomarkers identified by netSVM are highly enriched in biological pathways associated with cancer progression; (2) prediction performance is much improved when tested across different data sets. Specifically, many genes related to apoptosis, cell cycle, and cell proliferation, which are hallmark signatures of breast cancer metastasis, were identified by the netSVM approach. More importantly, several novel hub genes, biologically important with many interactions in PPI network but often showing little change in expression as compared with their downstream genes, were also identified as network biomarkers; the genes were enriched in signaling pathways such as TGF-beta signaling pathway, MAPK signaling pathway, and JAK-STAT signaling pathway. These signaling pathways may provide new insight to the underlying mechanism of breast cancer metastasis.</p> <p>Conclusions</p> <p>We have developed a network-based approach for cancer biomarker identification, netSVM, resulting in an improved prediction performance with network biomarkers. We have applied the netSVM approach to breast cancer gene expression data to predict metastasis in patients. Network biomarkers identified by netSVM reveal potential signaling pathways associated with breast cancer metastasis, and help improve the prediction performance across independent data sets.</p

Evidence for Quantized Displacement in Macroscopic Nanomechanical Oscillators

We report the observation of discrete displacement of nanomechanical oscillators with gigahertz-range resonance frequencies at millikelvin temperatures. The oscillators are nanomachined single-crystal structures of silicon, designed to provide two distinct sets of coupled elements with very low and very high frequencies. With this novel design, femtometer-level displacement of the frequency-determining element is amplified into collective motion of the entire micron-sized structure. The observed discrete response possibly results from energy quantization at the onset of the quantum regime in these macroscopic nanomechanical oscillators.Comment: 4 pages, two-column format. Related papers available at http://nano.bu.edu

Challenges and Directions in Formalizing the Semantics of Modeling Languages

Developing software from models is a growing practice and there exist many model-based tools (e.g., editors, interpreters, debuggers, and simulators) for supporting model-driven engineering. Even though these tools facilitate the automation of software engineering tasks and activities, such tools are typically engineered manually. However, many of these tools have a common semantic foundation centered around an underlying modeling language, which would make it possible to automate their development if the modeling language specification were formalized. Even though there has been much work in formalizing programming languages, with many successful tools constructed using such formalisms, there has been little work in formalizing modeling languages for the purpose of automation. This paper discusses possible semantics-based approaches for the formalization of modeling languages and describes how this formalism may be used to automate the construction of modeling tools

Distributed Acoustic Sensing of Seismic Properties in a Borehole Drilled on a Fast‐Flowing Greenlandic Outlet Glacier

Abstract Distributed Acoustic Sensing (DAS) is a new technology in which seismic energy is detected, at high spatial and temporal resolution, using the propagation of laser pulses in a fiber‐optic cable. We show analyses from the first glaciological borehole DAS deployment to measure the englacial and subglacial seismic properties of Store Glacier, a fast‐flowing outlet of the Greenland Ice Sheet. We record compressional and shear waves in 1,043 m‐deep vertical seismic profiles, sampled at 10 m vertical resolution, and detect a transition from isotropic to anisotropic ice at 84% of ice thickness, consistent with the Holocene‐Wisconsin transition. We identify subglacial reflections originating from the base of a 20 m‐thick layer of consolidated sediment and, from attenuation measurements, interpret temperate ice in the lowermost 100 m of the glacier. Our findings highlight the promising potential of DAS technology to constrain the seismic properties of glaciers and ice sheets. Plain Language Summary Distributed Acoustic Sensing (DAS) is a new technology for seismic surveying in which the transmission of light through fiber‐optic cables is used to record seismic energy, with unprecedented spatial resolution compared to traditional techniques. Our paper presents data from the first borehole‐glaciological deployment of DAS, in which fiber‐optic cable was installed in a 1,043 m‐deep vertical borehole on Store Glacier, a fast‐flowing outlet of the Greenland Ice Sheet. The detailed seismic anatomy of the glacier that our survey provides—an independent measurement of the seismic response every 10 m—gives new insights about its internal flow regime and temperature and even allows us to detect layers of sediment underlying it. We predict that DAS surveying will play an increasingly large role in future glaciological investigations as the recognition of its promising potential grows

Motif-guided sparse decomposition of gene expression data for regulatory module identification

<p>Abstract</p> <p>Background</p> <p>Genes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated.</p> <p>Results</p> <p>We propose a novel approach, motif-guided sparse decomposition (mSD), to identify gene regulatory modules by integrating gene expression data and DNA sequence motif information. The mSD approach is implemented as a two-step algorithm comprising estimates of (1) transcription factor activity and (2) the strength of the predicted gene regulation event(s). Specifically, a motif-guided clustering method is first developed to estimate the transcription factor activity of a gene module; sparse component analysis is then applied to estimate the regulation strength, and so predict the target genes of the transcription factors. The mSD approach was first tested for its improved performance in finding regulatory modules using simulated and real yeast data, revealing functionally distinct gene modules enriched with biologically validated transcription factors. We then demonstrated the efficacy of the mSD approach on breast cancer cell line data and uncovered several important gene regulatory modules related to endocrine therapy of breast cancer.</p> <p>Conclusion</p> <p>We have developed a new integrated strategy, namely motif-guided sparse decomposition (mSD) of gene expression data, for regulatory module identification. The mSD method features a novel motif-guided clustering method for transcription factor activity estimation by finding a balance between co-regulation and co-expression. The mSD method further utilizes a sparse decomposition method for regulation strength estimation. The experimental results show that such a motif-guided strategy can provide context-specific regulatory modules in both yeast and breast cancer studies.</p