Water Security for Climate Resilience: A synthesis of research from the Oxford University REACH programme (Summary report)

This Summary presents the main findings and recommendations of the Water Security for Climate Resilience Report. Through REACH research we demonstrate the unequal, and often hidden, impact of climate on people’s lives and livelihoods, which can be counter-intuitive to broad narratives around resilience and adaptation. We reveal the challenges to building anticipatory capacity to avoid the water security risks that result from shifting climate conditions, water use behaviours and policy decisions. We highlight the impact of seasonal fluctuations in weather on surface and groundwater quality. We present a deepened understanding of location- and contextspecific climate issues and dynamics, revealing a pressing need to consider and plan for different distributional impacts of climate and climate change

Is Clinical Decision Making Skills are Developed through Academic Nurturing? A Review Based on Available Literature

Introduction: Today’s nurses are having challenges, demanding their ability to the profession. Nursing education should concentrate on educating competent health care providers to handle complex health care technology with fundamental implications for latest generation of patients. This paper aims to identify the various strategies used to enhance the clinical decision making ability among nurses. Methods: A comprehensive systematic review of published literature and journal articles from PubMed and Cinhal databases was done. Search strategy specific to each database was used. During initial search 6808 titles were retrieved and after screening 12 articles were selected for full text screening. Finally 12 research articles were selected based on the inclusion criteria. Results: Out of 12 articles, 7 research studies supported that clinical decision making can be developed using different types of simulation (such as human patient simulators, simulated clinical experiences, simulation to create rubric assessment). Two of those studies propose clinical reasoning abilities can be acquired through Outcome-Present state Test (OPT) model. Individual studies used strategies like concept mapping, educational interventions, analogy guided learning experiences, structured reflection in education and workshops can develop clinical decision making. Computer based and multimedia computer simulation program did not showing any clear outcome. Conclusion: Clinical decision making is an abstract skill which can be developed by using different strategies in different specialities and different situations.  Since situational factors and time constraints are evident in practice, findings were supportive for clinical decision making(CDM) skill. The ideal setting for students to learn CDM skills is real clinical practice environment, especially when facilitated by opportunities for immediate feedback and reflection. CDM is necessary for providing quality patient care and favouring patient satisfaction. Keywords: Decision making, Nurses, Judgement, Clinical Competenc

Creating an enabling environment for research impact (REACH Discussion document)

Research should benefit society: that is widely accepted. There has been much written on how to design research to deliver impact through equitable partnerships, co-production, and more. However, there has been less reflection on the enabling environment that funders and universities create to support research to have impact. In this brief we explore the experiences of creating impact through research in international development, and the ways in which the enabling environment facilitated impact drawing on perspectives of researchers, research users from government and UN agencies, and funders. We highlight three areas for funders to focus on strengthening enabling environments: (1) foster science-practitioner networks, (2) enhance collaborative research environments based on equitable partnerships, and (3) shift financing and incentives to sustain partnerships for impact at scale

Calliandra macqueenii Barneby

Mexico, Guerrero. On the road from Acapulco to Mexico City, 15 km to the N of the Pemex station and crossroads to the north of Acapulco. LAT (17.01)N; LONG(99.47)W. ALT (130). 1-1.5 m bush with grey/brown smooth bark. Leaves are mid/dark green, though slightly paler beneath. Flowers are in heads close to the axis. The sepals and petals are pink being slightly darker towards their tips. Staminal filaments are cream in colour and form a tube at their base

A Comparison of the Tortuosity Phenomenon in Retinal Arteries and Veins Using Digital Image Processing and Statistical Methods

The tortuosity of retinal blood vessels is an important phenomenon, and it can act as a biomarker in the diagnosis of several eye diseases. The study of abnormalities in the tortuosity of retinal arteries and veins provides ophthalmologists with important information for disease diagnosis. Our study aims to compare the tortuosity relation between retinal arteries and veins by quantifying the vessels’ tortuosity in the retina using 14 tortuosity measures applied to the AV-classification retinal dataset. Two feature sets are created, one for arteries and the other for veins. The comparison between the tortuosity of arteries and veins is based on a two-sample T-test statistical method, a regression analysis between the quantified tortuosity features, principal component analysis at the dataset level, and the introduction of the arteriovenous length ratios concept to compare the variations in these new ratios to see the tortuosity behavior in each image. The methods’ results have shown that the tortuosity of retinal arteries and veins is similar. The result of the two-sample T-test supports the research hypothesis, as the P-value obtained was greater than 0.05. Furthermore, the regression analysis between arteries and veins features showed a high correlation (r2 = 89.39% and 89.11%) for arteries and veins, respectively. The study concludes that the retinal vessel type has no statistical significance in the tortuosity calculation results

Four Severity Levels for Grading the Tortuosity of a Retinal Fundus Image

Hypertensive retinopathy severity classification is proportionally related to tortuosity severity grading. No tortuosity severity scale enables a computer-aided system to classify the tortuosity severity of a retinal image. This work aimed to introduce a machine learning model that can identify the severity of a retinal image automatically and hence contribute to developing a hypertensive retinopathy or diabetic retinopathy automated grading system. First, the tortuosity is quantified using fourteen tortuosity measurement formulas for the retinal images of the AV-Classification dataset to create the tortuosity feature set. Secondly, a manual labeling is performed and reviewed by two ophthalmologists to construct a tortuosity severity ground truth grading for each image in the AV classification dataset. Finally, the feature set is used to train and validate the machine learning models (J48 decision tree, ensemble rotation forest, and distributed random forest). The best performance learned model is used as the tortuosity severity classifier to identify the tortuosity severity (normal, mild, moderate, and severe) for any given retinal image. The distributed random forest model has reported the highest accuracy (99.4%) compared to the J48 Decision tree model and the rotation forest model with minimal least root mean square error (0.0000192) and the least mean average error (0.0000182). The proposed tortuosity severity grading matched the ophthalmologist’s judgment. Moreover, detecting the tortuosity severity of the retinal vessels’, optimizing vessel segmentation, the vessel segment extraction, and the created feature set have increased the accuracy of the automatic tortuosity severity detection model