Meaningfulness and self-integrity at work amongst older, self-employed women entrepreneurs

Purpose This study identifies how self-employed older women experience and represent self-integrity - an element and source of meaningfulness - in their work, and how these experiences are intertwined with gendered ageing. Design/methodology/approach The authors used thematic analysis, influenced by an intersectional lens, to scrutinise qualitative data generated during a development project, with ten over 55-year-old self-employed women in Finland. Findings The study reveals three dominant practices of self-integrity at work: "Respecting one's self-knowledge", "Using one's professional abilities", and "Developing as a professional". Older age was mostly experienced and represented as a characteristic that deepened or strengthened the practices and experiences of self-integrity at work. However, being an older woman partly convoluted that. Self-integrity as a self-employed woman was repeatedly experienced and represented in contrast to the male norm of entrepreneurship. Originality/value The authors contribute to the literature on gender and entrepreneurship by highlighting the processual dimensions - how integrity with self is experienced, created and sustained, and how being an older woman relates to self-integrity in self-employment. The results show a nuanced interplay between gender and age: Age and gender both constrain and become assets for older women in self-employment through older women's experiences of self-integrity.Peer reviewe

Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies

Standard statistical approaches for prioritization of variants for functional testing in fine-mapping studies either use marginal association statistics or estimate posterior probabilities for variants to be causal under simplifying assumptions. Here, we present a probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation. A key feature of our approach is that it empirically estimates the contribution of each functional annotation to the trait of interest directly from summary association statistics while allowing for multiple causal variants at any risk locus. We devise efficient algorithms that estimate the parameters of our model across all risk loci to further increase performance. Using simulations starting from the 1000 Genomes data, we find that our framework consistently outperforms the current state-of-the-art fine-mapping methods, reducing the number of variants that need to be selected to capture 90% of the causal variants from an average of 13.3 to 10.4 SNPs per locus (as compared to the next-best performing strategy). Furthermore, we introduce a cost-to-benefit optimization framework for determining the number of variants to be followed up in functional assays and assess its performance using real and simulation data. We validate our findings using a large scale meta-analysis of four blood lipids traits and find that the relative probability for causality is increased for variants in exons and transcription start sites and decreased in repressed genomic regions at the risk loci of these traits. Using these highly predictive, trait-specific functional annotations, we estimate causality probabilities across all traits and variants, reducing the size of the 90% confidence set from an average of 17.5 to 13.5 variants per locus in this data

Invisible actors on a male dominated arena: female fish mongers’ roles, strategies and needs in the fisheries sector

In Zanzibar, an increasing number of women have entered fish markets acting as a link between fishers and consumers, i.e. a middleman. However, since fisheries are traditionally male dominated, women receive little recognition. Thus, gender analysis in the fisheries sector potentially can enhance management and development policies as well as environmental issues. In this study 23 women and 23 men from 12 fish markets in Zanzibar were interviewed to examine 1) women's and men's different strategies to succeed on the market, 2) how finance and social networks determine the access to different fish species, 3) knowledge about fish demand and markets, and 4) needs and challenges related to fish trade. Results show that even when women manage to penetrate typical male jobs, the execution of the activity differs greatly between the sexes. Men tend to dominate commercial channels and revenues of large valuable species such as tuna, kingfish and swordfish while women mainly have access to species of low economic value such as anchovies and small mackerels directed to local markets and consumption. The main reason for this was reported to be lack of access to economic and social resources. This, in combination with scarce knowledge about fish demand, market connections and limited mobility, exclude women from more profitable markets. The study suggests that a nuanced understanding of gender dynamics in fishing communities is needed and its relation to wider social, economic, cultural and political processes.This is part of the IIFET Special Session on Markets and Value Chains for Small Aquaculture & Fisheries Enterprises with a Focus on Gender that took place on 17 July 2012 in Dar Es Salaam, Tanzania in conjunction with 16th IIFET Conference. The complete proceedings of this special session are available ( http://aquafishcrsp.oregonstate.edu/Documents/Uploads/FileManager/IIFET%202012%20CRSP%20Session%20Proceedings%20Final_small.pdf) through the Aquaculture & Fisheries Collaborative Research Support Program gender web site, ( http://aquafishcrsp.oregonstate.edu/Gender/). Abstract only

Addition of a polygenic risk score, mammographic density, and endogenous hormones to existing breast cancer risk prediction models: A nested case–control study

Background No prior study to our knowledge has examined the joint contribution of a polygenic risk score (PRS), mammographic density (MD), and postmenopausal endogenous hormone levels D all well-confirmed risk factors for invasive breast cancer D to existing breast cancer risk prediction models. Methods and findings We conducted a nested case-control study within the prospective Nurses\u27 Health Study and Nurses\u27 Health Study II including 4,006 cases and 7,874 controls ages 34-70 years up to 1 June 2010. We added a breast cancer PRS using 67 single nucleotide polymorphisms, MD, and circulating testosterone, estrone sulfate, and prolactin levels to existing risk models. We calculated area under the curve (AUC), controlling for age and stratified by menopausal status, for the 5-year absolute risk of invasive breast cancer. We estimated the population distribution of 5-year predicted risks for models with and without biomarkers. For the Gail model, the AUC improved (p-values \u3c 0.001) from 55.9 to 64.1 (8.2 units) in premenopausal women (Gail + PRS + MD), from 55.5 to 66.0 (10.5 units) in postmenopausal women not using hormone therapy (HT) (Gail + PRS + MD + all hormones), and from 58.0 to 64.9 (6.9 units) in postmenopausal women using HT (Gail + PRS + MD + prolactin). For the Rosner-Colditz model, the corresponding AUCs improved (p-values \u3c 0.001) by 5.7, 6.2, and 6.5 units. For estrogen-receptor-positive tumors, among postmenopausal women not using HT, the AUCs improved (p-values \u3c 0.001) by 14.3 units for the Gail model and 7.3 units for the Rosner-Colditz model. Additionally, the percentage of 50-year-old women predicted to be at more than twice 5-year average risk (\u3e= 2.27%) was 0.2% for the Gail model alone and 6.6% for the Gail + PRS + MD + all hormones model. Limitations of our study included the limited racial/ethnic diversity of our cohort, and that general population exposure distributions were unavailable for some risk factors. Conclusions In this study, the addition of PRS, MD, and endogenous hormones substantially improved existing breast cancer risk prediction models. Further studies will be needed to confirm these findings and to determine whether improved risk prediction models have practical value in identifying women at higher risk who would most benefit from chemoprevention, screening, and other risk-reducing strategies

Fine-mapping identifies multiple prostate cancer risk loci at 5p15, one of which associates with TERT expression

Associations between single nucleotide polymorphisms (SNPs) at 5p15 and multiple cancer types have been reported. We have previously shown evidence for a strong association between prostate cancer (PrCa) risk and rs2242652 at 5p15, intronic in the telomerase reverse transcriptase (TERT) gene that encodes TERT. To comprehensively evaluate the association between genetic variation across this region and PrCa, we performed a fine-mapping analysis by genotyping 134 SNPs using a custom Illumina iSelect array or Sequenom MassArray iPlex, followed by imputation of 1094 SNPs in 22 301 PrCa cases and 22 320 controls in The PRACTICAL consortium. Multiple stepwise logistic regression analysis identified four signals in the promoter or intronic regions of TERT that independently associated with PrCa risk. Gene expression analysis of normal prostate tissue showed evidence that SNPs within one of these regions also associated with TERT expression, providing a potential mechanism for predisposition to disease

Resources for Teaching and Assessing the Vision and Change Biology Core Concepts

The Vision and Change report called for the biology community to mobilize around teaching the core concepts of biology. This essay describes a collection of resources developed by several different groups that can be used to respond to the report’s call to transform undergraduate education at both the individual course and departmental levels. First, we present two frameworks that help articulate the Vision and Change core concepts, the BioCore Guide and the Conceptual Elements (CE) Framework, which can be used in mapping the core concepts onto existing curricula and designing new curricula that teach the biology core concepts. Second, we describe how the BioCore Guide and the CE Framework can be used alongside the Partnership for Undergraduate Life Sciences Education curricular rubric as a way for departments to self-assess their teaching of the core concepts. Finally, we highlight three sets of instruments that can be used to directly assess student learning of the core concepts: the Biology Card Sorting Task, the Biology Core Concept Instruments, and the Biology—Measuring Achievement and Progression in Science instruments. Approaches to using these resources independently and synergistically are discussed

A qualitative analysis to identify the elements that support department level change in the life sciences: The PULSE Vision & Change Recognition Program

The 2011 report, Vision and Change in Undergraduate Biology Education: A Call to Action, provided the impetus to mobilize the undergraduate life sciences education community to affect change in order to enhance the educational experiences of life sciences majors. The work of the appointed Partnership for Undergraduate Life Sciences Education (PULSE) Vision and Change (V&C) Leadership Fellows has focused on the development of programs and resources to support departmental change. In this report, we present a qualitative assessment of several documents generated from the PULSE V&C Leadership Fellow Recognition Team. The Recognition Team developed two initiatives to provide departments with feedback on their change process. The first initiative, the validated PULSE V&C Rubrics, enables departments to collaboratively self-assess their progress in enacting change. The second initiative, the PULSE Recognition Program, involves completion of the aforementioned Rubrics and a site-visit by two Recognition Team members to provide external insights and suggestions to foster a department’s change process. Eight departments participated in the Recognition Program in 2014. An evaluation of the documents yielded from the Recognition Program review of seven of the eight departments and a comparison of Rubric scores from before and three years following the site-visits uncovered several common elements required for successful department level change. These elements include an institutional culture that values and supports excellence in teaching and learning with resources and infrastructure, a departmental emphasis on program and course level assessment, and, most importantly, a departmental champion who actively supports endeavors that enhance teaching excellence

Broadening Participation in Biology Education Research: Engaging Community College Students and Faculty

Nearly half of all undergraduates are enrolled at community colleges (CCs), including the majority of U.S. students who represent groups underserved in the sciences. Yet only a small minority of studies published in discipline-based education research journals address CC biology students, faculty, courses, or authors. This marked underrepresentation of CC biology education research (BER) limits the availability of evidence that could be used to increase CC student success in biology programs. To address this issue, a diverse group of stakeholders convened at the Building Capacity for Biology Education Research at Community Colleges meeting to discuss how to increase the prevalence of CC BER and foster participation of CC faculty as BER collaborators and authors. The group identified characteristics of CCs that make them excellent environments for studying biology teaching and learning, including student diversity and institutional cultures that prioritize teaching, learning, and assessment. The group also identified constraints likely to impede BER at CCs: limited time, resources, support, and incentives, as well as misalignment between doing research and CC faculty identities as teachers. The meeting culminated with proposing strategies for faculty, administrators, journal editors, scientific societies, and funding agencies to better support CC BER

Non-compartment model to compartment model pharmacokinetics transformation meta-analysis – a multivariate nonlinear mixed model

Background To fulfill the model based drug development, the very first step is usually a model establishment from published literatures. Pharmacokinetics model is the central piece of model based drug development. This paper proposed an important approach to transform published non-compartment model pharmacokinetics (PK) parameters into compartment model PK parameters. This meta-analysis was performed with a multivariate nonlinear mixed model. A conditional first-order linearization approach was developed for statistical estimation and inference. Results Using MDZ as an example, we showed that this approach successfully transformed 6 non-compartment model PK parameters from 10 publications into 5 compartment model PK parameters. In simulation studies, we showed that this multivariate nonlinear mixed model had little relative bias (<1%) in estimating compartment model PK parameters if all non-compartment PK parameters were reported in every study. If there missing non-compartment PK parameters existed in some published literatures, the relative bias of compartment model PK parameter was still small (<3%). The 95% coverage probabilities of these PK parameter estimates were above 85%. Conclusions This non-compartment model PK parameter transformation into compartment model meta-analysis approach possesses valid statistical inference. It can be routinely used for model based drug development