Gender Equality in Academia

What is the state of gender equality in science and technology? Gender Equality in Academia – from Knowledge to Change presents the results of a comprehensive research project and program of initiatives at the University of Oslo’s Faculty of Mathematics and Natural Sciences. Researchers have examined gender equality within the department, looking at reasons for imbalance, and at what occurs when measures to promote equality are implemented. The book opens with an in-depth study of careers, gender issues and gender balance within academia. The study, based on questionnaires, interviews and follow-up evaluation, provides a new and updated understanding of the daily lives of academics in Norway. Among the topics covered are perceptions of equality and gender balance, effects of male dominance, sexual harassment, gender with respect to publishing, and the relationship between gender and diversity. In addition to presenting new empirical data, the book is also an innovative contribution to theoretical development within gender equality research. In the second part of the book, the authors present three working models that elucidate current mechanisms recreating gender imbalance, and challenges for gender equality. The book’s final part consists of analyses of measures taken to increase gender equality within the department and their effects, and what an organization can do to increase gender equality. The range of topics in this book make it relevant for everyone concerned with gender equality in research and higher education. Researchers, administrators, students, other practitioners, and politicians will all find this book of interest.Hvordan står det til med likestillingen innenfor realfag og teknologi? Likestilling i akademia – fra kunnskap til endring presenterer resultatene fra et omfattende forsknings- og tiltaksprosjekt ved Det matematisk-naturvitenskapelige fakultet ved Universitetet i Oslo. Forskerne har undersøkt likestillingssituasjonen ved fakultetet, sett på årsaker til ulikestilling, og på hva som skjer når man iverksetter tiltak for å fremme likestilling. Boka innleder med en dybdestudie av karriere, kjønn og likestilling i akademia. Studien, som er basert på både spørreundersøkelser, intervjuer og følgeforskning, gir ny og oppdatert kunnskap om forskerhverdagen i norsk akademia. Blant temaene som belyses er syn på likestilling og kjønnsbalanse, effekter av mannsdominans, seksuell trakassering, kjønn og publisering, og hvordan kjønn og mangfold henger sammen. I tillegg til å presentere ny empiri utgjør boka også et nyskapende bidrag til teoriutvikling innen likestillingsforskningen. I bokas del 2 presenterer forfatterne tre arbeidsmodeller som belyser aktuelle mekanismer og utfordringer i likestillingsarbeidet. Deretter trekkes linjen videre til arbeidet for å fremme likestilling. I siste del analyseres tiltakene som ble iverksatt for å øke likestillingen ved fakultetet, hvilke effekter de hadde, og hva en organisasjon kan gjøre for å øke likestillingen. Bredden i boka gjør den relevant for alle som er opptatt av likestilling i forskning og høyere utdanning. Både forskere, ansatte, studenter, praktikere og politikere vil ha stor nytte av boka. Øystein Gullvåg Holter er professor emeritus ved Senter for tverrfaglig kjønnsforskning, Universitetet i Oslo. Lotta Snickare er forsker ved Det matematisk-naturvitenskapelige fakultet, Universitetet i Oslo, og ved Kungliga tekniska högskolan, Stockholm

Reference gene alternatives to Gapdh in rodent and human heart failure gene expression studies

<p>Abstract</p> <p>Background</p> <p>Quantitative real-time RT-PCR (RT-qPCR) is a highly sensitive method for mRNA quantification, but requires invariant expression of the chosen reference gene(s). In pathological myocardium, there is limited information on suitable reference genes other than the commonly used <it>Gapdh </it>mRNA and <it>18S </it>ribosomal RNA. Our aim was to evaluate and identify suitable reference genes in human failing myocardium, in rat and mouse post-myocardial infarction (post-MI) heart failure and across developmental stages in fetal and neonatal rat myocardium.</p> <p>Results</p> <p>The abundance of <it>Arbp</it>, <it>Rpl32</it>, <it>Rpl4</it>, <it>Tbp</it>, <it>Polr2a</it>, <it>Hprt1</it>, <it>Pgk1</it>, <it>Ppia </it>and <it>Gapdh </it>mRNA and <it>18S </it>ribosomal RNA in myocardial samples was quantified by RT-qPCR. The expression variability of these transcripts was evaluated by the geNorm and Normfinder algorithms and by a variance component analysis method. Biological variability was a greater contributor to sample variability than either repeated reverse transcription or PCR reactions.</p> <p>Conclusions</p> <p>The most stable reference genes were <it>Rpl32</it>, <it>Gapdh </it>and <it>Polr2a </it>in mouse post-infarction heart failure, <it>Polr2a</it>, <it>Rpl32 </it>and <it>Tbp </it>in rat post-infarction heart failure and <it>Rpl32 </it>and <it>Pgk1 </it>in human heart failure (ischemic disease and cardiomyopathy). The overall most stable reference genes across all three species was <it>Rpl32 </it>and <it>Polr2a</it>. In rat myocardium, all reference genes tested showed substantial variation with developmental stage, with <it>Rpl4 </it>as was most stable among the tested genes.</p

Gender Equality in Academia

What is the state of gender equality in science and technology? Gender Equality in Academia – from Knowledge to Change presents the results of a comprehensive research project and program of initiatives at the University of Oslo’s Faculty of Mathematics and Natural Sciences. Researchers have examined gender equality within the department, looking at reasons for imbalance, and at what occurs when measures to promote equality are implemented. The book opens with an in-depth study of careers, gender issues and gender balance within academia. The study, based on questionnaires, interviews and follow-up evaluation, provides a new and updated understanding of the daily lives of academics in Norway. Among the topics covered are perceptions of equality and gender balance, effects of male dominance, sexual harassment, gender with respect to publishing, and the relationship between gender and diversity. In addition to presenting new empirical data, the book is also an innovative contribution to theoretical development within gender equality research. In the second part of the book, the authors present three working models that elucidate current mechanisms recreating gender imbalance, and challenges for gender equality. The book’s final part consists of analyses of measures taken to increase gender equality within the department and their effects, and what an organization can do to increase gender equality. The range of topics in this book make it relevant for everyone concerned with gender equality in research and higher education. Researchers, administrators, students, other practitioners, and politicians will all find this book of interest

The Genomic HyperBrowser: inferential genomics at the sequence level

The immense increase in the generation of genomic scale data poses an unmet analytical challenge, due to a lack of established methodology with the required flexibility and power. We propose a first principled approach to statistical analysis of sequence-level genomic information. We provide a growing collection of generic biological investigations that query pairwise relations between tracks, represented as mathematical objects, along the genome. The Genomic HyperBrowser implements the approach and is available at http://hyperbrowser.uio.no

Copynumber: Efficient algorithms for single- and multi-track copy number segmentation.

BACKGROUND: Cancer progression is associated with genomic instability and an accumulation of gains and losses of DNA. The growing variety of tools for measuring genomic copy numbers, including various types of array-CGH, SNP arrays and high-throughput sequencing, calls for a coherent framework offering unified and consistent handling of single- and multi-track segmentation problems. In addition, there is a demand for highly computationally efficient segmentation algorithms, due to the emergence of very high density scans of copy number. RESULTS: A comprehensive Bioconductor package for copy number analysis is presented. The package offers a unified framework for single sample, multi-sample and multi-track segmentation and is based on statistically sound penalized least squares principles. Conditional on the number of breakpoints, the estimates are optimal in the least squares sense. A novel and computationally highly efficient algorithm is proposed that utilizes vector-based operations in R. Three case studies are presented. CONCLUSIONS: The R package copynumber is a software suite for segmentation of single- and multi-track copy number data using algorithms based on coherent least squares principles.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

The Genomic HyperBrowser: an analysis web server for genome-scale data

The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome.publishedVersio

The Genomic HyperBrowser: an analysis web server for genome-scale data

The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome

DNA copy number motifs are strong and independent predictors of survival in breast cancer.

Somatic copy number alterations are a frequent sign of genome instability in cancer. A precise characterization of the genome architecture would reveal underlying instability mechanisms and provide an instrument for outcome prediction and treatment guidance. Here we show that the local spatial behavior of copy number profiles conveys important information about this architecture. Six filters were defined to characterize regional traits in copy number profiles, and the resulting Copy Aberration Regional Mapping Analysis (CARMA) algorithm was applied to tumors in four breast cancer cohorts (n = 2919). The derived motifs represent a layer of information that complements established molecular classifications of breast cancer. A score reflecting presence or absence of motifs provided a highly significant independent prognostic predictor. Results were consistent between cohorts. The nonsite-specific occurrence of the detected patterns suggests that CARMA captures underlying replication and repair defects and could have a future potential in treatment stratification

Deep learning for prediction of colorectal cancer outcome: a discovery and validation study

Background Improved markers of prognosis are needed to stratify patients with early-stage colorectal cancer to refine selection of adjuvant therapy. The aim of the present study was to develop a biomarker of patient outcome after primary colorectal cancer resection by directly analysing scanned conventional haematoxylin and eosin stained sections using deep learning. Methods More than 12 000 000 image tiles from patients with a distinctly good or poor disease outcome from four cohorts were used to train a total of ten convolutional neural networks, purpose-built for classifying supersized heterogeneous images. A prognostic biomarker integrating the ten networks was determined using patients with a non-distinct outcome. The marker was tested on 920 patients with slides prepared in the UK, and then independently validated according to a predefined protocol in 1122 patients treated with single-agent capecitabine using slides prepared in Norway. All cohorts included only patients with resectable tumours, and a formalin-fixed, paraffin-embedded tumour tissue block available for analysis. The primary outcome was cancer-specific survival. Findings 828 patients from four cohorts had a distinct outcome and were used as a training cohort to obtain clear ground truth. 1645 patients had a non-distinct outcome and were used for tuning. The biomarker provided a hazard ratio for poor versus good prognosis of 3·84 (95% CI 2·72–5·43; p<0·0001) in the primary analysis of the validation cohort, and 3·04 (2·07–4·47; p<0·0001) after adjusting for established prognostic markers significant in univariable analyses of the same cohort, which were pN stage, pT stage, lymphatic invasion, and venous vascular invasion. Interpretation A clinically useful prognostic marker was developed using deep learning allied to digital scanning of conventional haematoxylin and eosin stained tumour tissue sections. The assay has been extensively evaluated in large, independent patient populations, correlates with and outperforms established molecular and morphological prognostic markers, and gives consistent results across tumour and nodal stage. The biomarker stratified stage II and III patients into sufficiently distinct prognostic groups that potentially could be used to guide selection of adjuvant treatment by avoiding therapy in very low risk groups and identifying patients who would benefit from more intensive treatment regimes

Detection of Nonadherence to Antihypertensive Treatment by Measurements of Serum Drug Concentrations

Nonadherence to drugs is a challenge in hypertension treatment. We aimed to assess the prevalence of nonadherence by serum drug concentrations compared with 2 indirect methods and relate to the prescribed drug regimens in a nationwide multicenter study. Five hundred fifty patients with hypertension using ≥2 antihypertensive agents participated. We measured concentrations of 23 antihypertensive drugs using ultra high performance liquid chromatography tandem mass-spectrometry and compared with patients’ self-reports and investigators’ assessment based on structured interview. We identified 40 nonadherent patients (7.3%) using serum drug concentrations. They had higher office diastolic blood pressure (90 versus 83 mm Hg, P<0.01) and daytime diastolic blood pressure (85 versus 80 mm Hg, P<0.01) though systolic blood pressures did not differ significantly. They had more prescribed daily antihypertensive pills (2.5 versus 2.1 pills, P<0.01) and total daily pills (5.5 versus 4.4 pills, P=0.03). Prescription of fixed-dose combination pills were lower among the nonadherent patients identified by serum concentrations (45.0 versus 67.1%, P<0.01). Fifty-three patients self-reported nonadherence, while the investigators suspected 69 nonadherent patients. These groups showed no or few differences in drug regimens, respectively. In summary, we detected 7.3% prevalence of nonadherence by serum drug measurements in patients using ≥2 antihypertensive agents in a nationwide study; they had higher office and ambulatory diastolic blood pressures, higher number of prescribed daily pills, more daily antihypertensive pills, and less frequent prescriptions of fixed-dose combination pills. Indirect methods showed poor overlap with serum drugs concentrations and no or minimal medication differences. Thus, serum measurements of drugs were useful in detection and characterization of nonadherence to antihypertensive treatment.acceptedVersio