Hidden in the Middle : Culture, Value and Reward in Bioinformatics

Bioinformatics - the so-called shotgun marriage between biology and computer science - is an interdiscipline. Despite interdisciplinarity being seen as a virtue, for having the capacity to solve complex problems and foster innovation, it has the potential to place projects and people in anomalous categories. For example, valorised 'outputs' in academia are often defined and rewarded by discipline. Bioinformatics, as an interdisciplinary bricolage, incorporates experts from various disciplinary cultures with their own distinct ways of working. Perceived problems of interdisciplinarity include difficulties of making explicit knowledge that is practical, theoretical, or cognitive. But successful interdisciplinary research also depends on an understanding of disciplinary cultures and value systems, often only tacitly understood by members of the communities in question. In bioinformatics, the 'parent' disciplines have different value systems; for example, what is considered worthwhile research by computer scientists can be thought of as trivial by biologists, and vice versa. This paper concentrates on the problems of reward and recognition described by scientists working in academic bioinformatics in the United Kingdom. We highlight problems that are a consequence of its cross-cultural make-up, recognising that the mismatches in knowledge in this borderland take place not just at the level of the practical, theoretical, or epistemological, but also at the cultural level too. The trend in big, interdisciplinary science is towards multiple authors on a single paper; in bioinformatics this has created hybrid or fractional scientists who find they are being positioned not just in-between established disciplines but also in-between as middle authors or, worse still, left off papers altogether

Dust Studies in DIII-D Tokamak

Studies of submicron dust using Mie scattering from Nd:YAG lasers and video data of micron to sub-millimeter sized dust on DIII-D tokamak have provided the first data of dust sources and transport during tokamak discharges. During normal operation on DIII-D dust observation rates are low, a few events per discharge or less. The net carbon content of the dust corresponds to a carbon atom density a few orders of magnitude below the core impurity density. Statistical analysis of Mie data collected over months of operation reveal correlation of increased dust rate with increased heating power and impulsive wall loading due to edge localized modes (ELMs) and disruptions. Generation of significant amounts of dust by disruptions is confirmed by the camera data. However, dust production by disruptions alone is insufficient to account for estimated in-vessel dust inventory in DIII-D. After an extended entry vent, thousands of dust particles are observed by cameras in the first 2-3 plasma discharges. Individual particles moving at velocities up to {approx}300 m/s, breakup of larger particles into pieces, and collisions of particles with walls are observed. After {approx}70 discharges, dust levels are reduced to a few events per discharge. In order to calibrate diagnostics and benchmark modeling, milligram amounts of micron-sized carbon dust have been injected into DIII-D discharges, leading to the core carbon density increase by a factor of 2-3. Following injection, dust trajectories in the divertor are mostly in the toroidal direction, consistent with the ion drag force. Dust from the injection is observed in the outboard midplane by a fast framing camera. The observed trajectories and velocities of the dust particles are in qualitative agreement with modeling by the 3D DustT code

Characterizing Emerging Canine H3 Influenza Viruses.

The continual emergence of novel influenza A strains from non-human hosts requires constant vigilance and the need for ongoing research to identify strains that may pose a human public health risk. Since 1999, canine H3 influenza A viruses (CIVs) have caused many thousands or millions of respiratory infections in dogs in the United States. While no human infections with CIVs have been reported to date, these viruses could pose a zoonotic risk. In these studies, the National Institutes of Allergy and Infectious Diseases (NIAID) Centers of Excellence for Influenza Research and Surveillance (CEIRS) network collaboratively demonstrated that CIVs replicated in some primary human cells and transmitted effectively in mammalian models. While people born after 1970 had little or no pre-existing humoral immunity against CIVs, the viruses were sensitive to existing antivirals and we identified a panel of H3 cross-reactive human monoclonal antibodies (hmAbs) that could have prophylactic and/or therapeutic value. Our data predict these CIVs posed a low risk to humans. Importantly, we showed that the CEIRS network could work together to provide basic research information important for characterizing emerging influenza viruses, although there were valuable lessons learned

A genome-wide association study identifies a new ovarian cancer susceptibility locus on 9p22.2

Epithelial ovarian cancer has a major heritable component, but the known susceptibility genes explain less than half the excess familial risk1. We performed a genome wide association study (GWAS) to identify common ovarian cancer susceptibility alleles. We evaluated 507,094 SNPs genotyped in 1,817 cases and 2,353 controls from the UK and ~2 million imputed SNPs. We genotyped the 22,790 top ranked SNPs in 4,274 cases and 4,809 controls of European ancestry from Europe, USA and Australia. We identified 12 SNPs at 9p22 associated with disease risk (P<10−8). The most significant SNP (rs3814113; P = 2.5 × 10−17) was genotyped in a further 2,670 ovarian cancer cases and 4,668 controls confirming its association (combined data odds ratio = 0.82 95% CI 0.79 – 0.86, P-trend = 5.1 × 10−19). The association differs by histological subtype, being strongest for serous ovarian cancers (OR 0.77 95% CI 0.73 – 0.81, Ptrend = 4.1 × 10−21)

Darius Milhaud: La Création du monde (Analytical Listening Guide)

170 screens (text: 138 full screens; glossary entries: 32 part screens), with manual. One of a series of analytical listening guides and other products created by the Teaching Learning Technology Programme (TLTP) Music Consortium, Lancaster University, 1996. Distribution: Bath Information and Data Services (BIDS), Computing Service, University of Bath; re-issued in downloadable form (University of Huddersfield: CALMA project, 2000)

Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome-0

<p><b>Copyright information:</b></p><p>Taken from "Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome"</p><p>http://www.biomedcentral.com/1471-2164/8/331</p><p>BMC Genomics 2007;8():331-331.</p><p>Published online 20 Sep 2007</p><p>PMCID:PMC2064937.</p><p></p>scores was plotted altogether. SEP was calculated with 231 genes correlated to 5-year recurrence outcome wit

Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome-4

<p><b>Copyright information:</b></p><p>Taken from "Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome"</p><p>http://www.biomedcentral.com/1471-2164/8/331</p><p>BMC Genomics 2007;8():331-331.</p><p>Published online 20 Sep 2007</p><p>PMCID:PMC2064937.</p><p></p>setta and Combined). The Kaplan-Meier survival curves corresponded to half of the patients who had SEP scores higher (green) or lower (red) than the median of all 286 scores. The contingency table, accuracy, and ROC curve area listed with plot were obtained after the Veridex patients were classified according to their 3-year prognosis as training patients

Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome-2

<p><b>Copyright information:</b></p><p>Taken from "Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome"</p><p>http://www.biomedcentral.com/1471-2164/8/331</p><p>BMC Genomics 2007;8():331-331.</p><p>Published online 20 Sep 2007</p><p>PMCID:PMC2064937.</p><p></p>ssion profiles was increased from 1 to 100 one by one. Median areas were summarized from 10,000 re-samplings. Size-weighted average areas achieved by the combined dataset (red line) were generally larger than the corresponding areas achieved by individual datasets (blue line)

Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome-5

<p><b>Copyright information:</b></p><p>Taken from "Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome"</p><p>http://www.biomedcentral.com/1471-2164/8/331</p><p>BMC Genomics 2007;8():331-331.</p><p>Published online 20 Sep 2007</p><p>PMCID:PMC2064937.</p><p></p>pwise reduction procedure was applied to randomly select three genes and remove them from the initial profile at each step. The consequent changing of permutation median and 90% CI of ROC curve area was presented