National electricity planning in settings with low pre-existing grid coverage: Development of a spatial model and case study of Kenya

We develop a spatial electricity planning model to guide grid expansion in countries with low pre-existing electricity coverage. The model can be used to rapidly estimate connection costs and compare different regions and communities. Inputs that are modeled include electricity demand, costs, and geographic characteristics. The spatial nature of the model permits accurate representation of the existing electricity network and population distribution, which form the basis for future expansion decisions. The methodology and model assumptions are illustrated using country-specific data from Kenya. Results show that under most geographic conditions, extension of the national grid is less costly than off-grid options. Based on realistic penetration rates for Kenya, we estimate an average connection cost of $1900 per household, with lower-cost connection opportunities around major cities and in denser rural regions. In areas with an adequate pre-existing medium-voltage backbone, we estimate that over 30$1000 per connection through infilling. The penetration rate, an exogenous factor chosen by electricity planners, is found to have a large effect on household connection costs, often outweighing socio-economic and spatial factors such as inter-household distance, per-household demand, and proximity to the national grid.Electricity planning Spatial model Sub-Saharan Africa

Modulation of Autophagy by a Small Molecule Inverse Agonist of ERRα Is Neuroprotective

Mechanistic insights into aggrephagy, a selective basal autophagy process to clear misfolded protein aggregates, are lacking. Here, we report and describe the role of Estrogen Related Receptor α (ERRα, HUGO Gene Nomenclature ESRRA), new molecular player of aggrephagy, in keeping autophagy flux in check by inhibiting autophagosome formation. A screen for small molecule modulators for aggrephagy identified ERRα inverse agonist XCT 790, that cleared α-synuclein aggregates in an autophagy dependent, but mammalian target of rapamycin (MTOR) independent manner. XCT 790 modulates autophagosome formation in an ERRα dependent manner as validated by siRNA mediated knockdown and over expression approaches. We show that, in a basal state, ERRα is localized on to the autophagosomes and upon autophagy induction by XCT 790, this localization is lost and is accompanied with an increase in autophagosome biogenesis. In a preclinical mouse model of Parkinson’s disease (PD), XCT 790 exerted neuroprotective effects in the dopaminergic neurons of nigra by inducing autophagy to clear toxic protein aggregates and, in addition, ameliorated motor co-ordination deficits. Using a chemical biology approach, we unrevealed the role of ERRα in regulating autophagy and can be therapeutic target for neurodegeneration

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<p>Mechanistic insights into aggrephagy, a selective basal autophagy process to clear misfolded protein aggregates, are lacking. Here, we report and describe the role of Estrogen Related Receptor α (ERRα, HUGO Gene Nomenclature ESRRA), new molecular player of aggrephagy, in keeping autophagy flux in check by inhibiting autophagosome formation. A screen for small molecule modulators for aggrephagy identified ERRα inverse agonist XCT 790, that cleared α-synuclein aggregates in an autophagy dependent, but mammalian target of rapamycin (MTOR) independent manner. XCT 790 modulates autophagosome formation in an ERRα dependent manner as validated by siRNA mediated knockdown and over expression approaches. We show that, in a basal state, ERRα is localized on to the autophagosomes and upon autophagy induction by XCT 790, this localization is lost and is accompanied with an increase in autophagosome biogenesis. In a preclinical mouse model of Parkinson’s disease (PD), XCT 790 exerted neuroprotective effects in the dopaminergic neurons of nigra by inducing autophagy to clear toxic protein aggregates and, in addition, ameliorated motor co-ordination deficits. Using a chemical biology approach, we unrevealed the role of ERRα in regulating autophagy and can be therapeutic target for neurodegeneration.</p

The Role of Precision Medicine in the Diagnosis and Treatment of Patients with Rare Cancers.

Rare cancers pose unique challenges for patients and their physicians arising from a lack of information regarding the best therapeutic options. Very often, a lack of clinical trial data leads physicians to choose treatments based on small case series or case reports. Precision medicine based on genomic analysis of tumors may allow for selection of better treatments with greater efficacy and less toxicity. Physicians are increasingly using genetics to identify patients at high risk for certain cancers to allow for early detection or prophylactic interventions. Genomics can be used to inform prognosis and more accurately establish a diagnosis. Genomic analysis may also expose therapeutic targets for which drugs are currently available and approved for use in other cancers. Notable successes in the treatment of previously refractory cancers have resulted. New more advanced sequencing technologies, tools for interpretation, and an increasing array of targeted drugs offer additional hope, but challenges remain

Weight-bearing in ankle fractures: An audit of UK practice.

INTRODUCTION: The purpose of this national study was to audit the weight-bearing practice of orthopaedic services in the National Health Service (NHS) in the treatment of operatively and non-operatively treated ankle fractures. METHODS: A multicentre prospective two-week audit of all adult ankle fractures was conducted between July 3rd 2017 and July 17th 2017. Fractures were classified using the AO/OTA classification. Fractures fixed with syndesmosis screws or unstable fractures (>1 malleolus fractured or talar shift present) treated conservatively were excluded. No outcome data were collected. In line with NICE (The National Institute for Health and Care Excellence) criteria, "early" weight-bearing was defined as unrestricted weight-bearing on the affected leg within 3 weeks of injury or surgery and "delayed" weight-bearing as unrestricted weight-bearing permitted after 3 weeks. RESULTS: 251 collaborators from 81 NHS hospitals collected data: 531 patients were managed non-operatively and 276 operatively. The mean age was 52.6 years and 50.5 respectively. 81% of non-operatively managed patients were instructed for early weight-bearing as recommended by NICE. In contrast, only 21% of operatively managed patients were instructed for early weight-bearing. DISCUSSION: The majority of patients with uni-malleolar ankle fractures which are managed non-operatively are treated in accordance with NICE guidance. There is notable variability amongst and within NHS hospitals in the weight-bearing instructions given to patients with operatively managed ankle fractures. CONCLUSION: This study demonstrates community equipoise and suggests that the randomized study to determine the most effective strategy for postoperative weight-bearing in ankle fractures described in the NICE research recommendation is feasible

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset