An annotated checklist of the bryophytes of Taita Hills region, Kenya

Based on previous literature and our own collections, we list 288 bryophyte species (145 liverworts, 143 mosses) from the Taita Hills region (including Mt. Kasigau and Maktau Hill) in SE Kenya. New records for Kenya include the liverworts Archilejeunea elobulata Steph., Bazzania nitida (F. Weber) Grolle, Cololejeunea grossepapillosa (Horik.) N. Kitag., Diplasiolejeunea kraussiana (Lindenb.) Steph., D. villaumei Steph., Lejeunea amaniensis E.W. Jones, L. cyathearum E.W. Jones, Lopholejeunea laciniata E.W. Jones, Metzgeria crassipilis (Lindb.) A. Evans, M. nudifrons Steph., Plagiochila boryana (F. Weber) Nees, and P. moenkemeyeri Steph., and the mosses Leucophanes hildebrandtii Müll. Hal. and Neckeromnion lepineanum (Mont.) S. Olsson, Enroth, Huttunen & D. Quandt. A further 22 liverworts and 13 mosses previously known from other parts of Kenya are reported for the first time from the Taita Hills region.Peer reviewe

Entrepreneurial experimentation : a key function in systems of innovation

The literature on innovation systems focuses on the supply side (the creation of technology) rather than on how innovations are converted into economic activity and growth via the market (the demand side, and the interface between supply and demand). One implication of this is that there is a dearth of research on the links between innovation systems and economic growth. The purpose of this paper is to begin to fill this gap in the literature. We articulate the function of entrepreneurial experimentation as an essential mechanism for translating new knowledge into economic activity and growth created in innovation systems. We argue that entrepreneurial experimentation comprises both “technical” and “market” experimentation. Spinoffs and acquisitions are proposed as micro-mechanisms that give rise to system-wide entrepreneurial experimentation. Our framework suggests that entrepreneurial experimentation is central in driving both the supply- and the demand-side dynamics of innovation systems, hence linking both innovation systems and entrepreneurship to economic growth

Genetic Risk Score for Intracranial Aneurysms: Prediction of Subarachnoid Hemorrhage and Role in Clinical Heterogeneity

Background: Recently, common genetic risk factors for intracranial aneurysm (IA) and aneurysmal subarachnoid hemorrhage (ASAH) were found to explain a large amount of disease heritability and therefore have potential to be used for genetic risk prediction. We constructed a genetic risk score to (1) predict ASAH incidence and IA presence (combined set of unruptured IA and ASAH) and (2) assess its association with patient characteristics. Methods: A genetic risk score incorporating genetic association data for IA and 17 traits related to IA (so-called metaGRS) was created using 1161 IA cases and 407 392 controls from the UK Biobank population study. The metaGRS was validated in combination with risk factors blood pressure, sex, and smoking in 828 IA cases and 68 568 controls from the Nordic HUNT population study. Furthermore, we assessed association between the metaGRS and patient characteristics in a cohort of 5560 IA patients. Results: Per SD increase of metaGRS, the hazard ratio for ASAH incidence was 1.34 (95% CI, 1.20-1.51) and the odds ratio for IA presence 1.09 (95% CI, 1.01-1.18). Upon including the metaGRS on top of clinical risk factors, the concordance index to predict ASAH hazard increased from 0.63 (95% CI, 0.59-0.67) to 0.65 (95% CI, 0.62-0.69), while prediction of IA presence did not improve. The metaGRS was statistically significantly associated with age at ASAH (β=-4.82×10-3per year [95% CI, -6.49×10-3to -3.14×10-3]; P=1.82×10-8), and location of IA at the internal carotid artery (odds ratio=0.92 [95% CI, 0.86-0.98]; P=0.0041). Conclusions: The metaGRS was predictive of ASAH incidence, although with limited added value over clinical risk factors. The metaGRS was not predictive of IA presence. Therefore, we do not recommend using this metaGRS in daily clinical care. Genetic risk does partly explain the clinical heterogeneity of IA warranting prioritization of clinical heterogeneity in future genetic prediction studies of IA and ASAH