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Diversity in leading and laggard regions: living standards, residual income and regional policy
No full textThis article develops a foundational economy contribution to debate about regional inequalities by presenting new calculations of household income after the costs of three essential items (housing, transport and utilities). These residual income measures illustrate the diversity of living standards within and between English and Welsh regions. The analysis shows a mosaic of variation in residual income and wealth accumulation driven by the variability of housing costs in four different tenure groups (outright owners, mortgage payers, private renters, social renters). The article argues that GVA averages and descriptors like ‘left behind’ are a poor guide to differences within and between regions; they also misdirect policy towards ‘levelling up’ the apparently unsuccessful places without directly addressing the quality of and access to essential services. From a foundational point of view, regional policy needs to focus on access to housing at reasonable cost for all income and tenure groups in every region
Towards a Holistic Supply Chain Model for Personalised Medicine
No full textPersonalised medicine (PM) refers to treatmentplans tailored for specific sub-populations and individuals byconsidering environmental, genetic, and social factors. The PMfield has led to the creation of breakthrough therapies capable totreat and even cure life-threatening diseases, such as rare geneticconditions or last stage cancers. These emerging biopharmaceuticalsare known as Advanced Therapies Medicinal Products(ATMPs) and they are created using advanced manufacturingprocedures. However, their mass delivery is problematic asadditional bottlenecks, such as low demand or short shelf-life, areadded to the already critical (bio)pharmaceutical supply chain.In this paper, we briefly introduce the problem and present someexistent research, while also discussing potential future directions
Review of data science trends and issues in porous media research with a focus on image-based techniques
No full textData science as a flourishing interdisciplinary domain of computer and mathematical sciences is playing an important role in guiding the porous material research streams. In the present narrative review, we have examined recent trends and issues in data-driven methods used in the image-based porous material research studies relevant to water resources researchers and scientists. Initially, the recent trends in porous material data-related issues have been investigated through search engine queries in terms of data source, data storage hub, programming languages, and software packages. Subsequent to a diligent analysis of the existing trends, a review of the common concepts of porous material research and data science are presented through six categories comprising big data, data regression, classication, image segmentation, geometry reconstruction, and image data resolution. We namely provide: (1) a focus on image-based and pore scale methods which has not been presented previously, (2) a detailed search engine research for trend investigation, and (3) practical examples and comparison of data storage in porous media image-based research. By reading this review article, an overall image of the active and popular interdisciplinary research domains can be obtained. Readers will also be informed of the latest data-driven efforts and recommended research directions for tackling the image-based porous material problems relevant to water resources research. We concluded that porous material image reconstruction and resolution improvement techniques are unique means to reveal unprecedented details of micro-structures that may have been missed in a medium quality tomography image
Hidden Markov models as recurrent neural networks: An application to Alzheimer’s disease
No full textHidden Markov models (HMMs) are commonlyused for disease progression modeling when the true patienthealth state is not fully known. Since HMMs typically have multiplelocal optima, incorporating additional patient covariatescan improve parameter estimation and predictive performance.To allow for this, we develop hidden Markov recurrent neuralnetworks (HMRNNs), a special case of recurrent neural networksthat combine neural networks’ flexibility with HMMs’interpretability. The HMRNN can be reduced to a standardHMM, with an identical likelihood function and parameterinterpretations, but can also be combined with other predictiveneural networks that take patient information as input. TheHMRNN estimates all parameters simultaneously via gradientdescent. Using a dataset of Alzheimer’s disease patients, wedemonstrate how the HMRNN can combine an HMM withother predictive neural networks to improve disease forecastingand to offer a novel clinical interpretation compared with astandard HMM trained via expectation-maximization
Assisted Counterexample-Guided Inductive Optimization for Robot Path Planning
No full textThis paper presents and evaluates a novel offlinemobile robot path planning algorithm based on the AssistedCounterexample-Guided Inductive Optimization (ACEGIO)technique. ACEGIO employs a technique to assist theCounterexample-Guided Inductive Optimization (CEGIO) techniqueby requesting counterexamples from Boolean Satisfiability(SAT) and Satisfiability Modulo Theories (SMT) solvers toimprove its efficiency and effectiveness. In particular, we implementedthe Gradient Descent (GD) technique as an auxiliarytechnique to CEGIO. GD-Assisted Counterexample-Guided Inductiveoptimization (ACEGIO-GD) has been successfully appliedto obtain two-dimensional paths for autonomous mobile robotsusing off-the-shelf SAT and SMT solvers. Experimental resultsdemonstrate that the ACEGIO-based path planning algorithmhas substantial improvements in efficiency and effectiveness comparedto the traditional CEGIO-based path planning algorithm,which allows generating the optimal paths for autonomous mobilerobots with much less execution time. If compared to othertraditional path planning optimization techniques (e.g., GA andPSO), the execution time of the proposed algorithm is relativelyhigh, whereas its performance is stable, reliable and robust
Markers of tumour inflammation as prognostic factors for overall survival in patients with advanced pancreatic cancer receiving first line FOLFIRINOX chemotherapy
No full textBackground: Identifying pre-treatment blood markers that distinguish prognostic groups of patients with advanced pancreatic ductal adenocarcinoma (PDAC) under first-line FOLFIRINOX chemotherapy has the potential to improve management of this condition. Aim of this study was to determine the prognostic utility of a range of pre-treatment, inflammation related, blood cell markers in this group of patients. Materials and Methods: Data from a training cohort were analyzed to identify potential pre-treatment blood markers correlating to survival outcomes. The most informative markers were further analyzed in a validation cohort comprised of patients from a geographically separate cancer center undergoing the same treatment.Results: 138 consecutive patients receiving FOLFIRINOX chemotherapy between 2010 & 2019, constituted the training cohort. Neutrophil/lymphocyte (NLR), monocyte/lymphocyte (MLR) and platelet/lymphocyte ratio (PLR) as well as the systemic inflammatory response index (SIRI) and CA19.9 showed prognostic significance in addition to tumour stage. A pre-treatment SIRI score cutoff of 2.35 differentiated between a poor prognostic group with median overall survival (mOS) 5.1 months and a better prognostic group, mOS 12.5 months. SIRI / > 2.35 was predictive of mOS in patients with locally advanced and metastatic PDAC. SIRI was confirmed as a prognostic marker in a validation cohort of 67 patients with mOS of 13.4 months and 6.3 months for those with SIRI 2.35 and >2.35 respectively. Additional analysis revealed baseline SIRI as being prognostic within additional subgroups of patients in both cohorts. Conclusion: This large, retrospective, analysis of real world patients receiving first-line FOLFIRINOX chemotherapy for advanced PDAC has identified the pre-treatment blood SIRI as a strong prognostic marker for survival. This will allow better counselling of patients with regards to the benefits of treatment, improved stratification within clinical trials and potentially identify groups of patients for novel therapy trials as first line treatment.<br/
Optimizing and Factorizing the Wilson Matrix
No full textThe Wilson matrix, W, is a 4 4 unimodular symmetric positive definite matrix ofintegers that has been used as a test matrix since the 1940s, owing to its mild ill-conditioning.We ask how close W is to being the most ill-conditioned matrix in its class, with or without therequirement of positive definiteness. By exploiting the matrix adjugate and applying variousmatrix norm bounds from the literature we derive bounds on the condition numbers for thetwo cases and we compare them with the optimal condition numbers found by exhaustivesearch. We also investigate the existence of factorizations W = ZTZ with Z having integeror rational entries. Drawing on recent research that links the existence of these factorizationsto number-theoretic considerations of quadratic forms, we show that W has an integer factorZ and two rational factors, up to signed permutations. This little 4 4 matrix continues to bea useful example on which to apply existing matrix theory as well as being capable of raisingchallenging questions that lead to new results
Pathways from the Early Language and Communication Environment to Literacy Outcomes at the End of Primary School:The Roles of Language Development and Social Development
No full textThe quality of a child’s early language and communication environment (ELCE) is an important predictor of later educational outcomes. However, less is known about the routes via which these early experiences influence the skills that support academic achievement. Using data from the Avon Longitudinal Study of Parents and Children (n=7,120) we investigated relations between ELCE (<2 years), literacy and social adjustment at school entry (5 years), structural language development and social development in mid-primary school (7-9 years), and, literacy outcomes (reading and writing) at the end of primary school (11 years) using structural equation modelling. ELCE was a significant, direct predictor of social adjustment and literacy skills at school entry and of linguistic and social competence at 7-9 years. ELCE did not directly explain variance in literacy outcomes at the end of primary school, instead the influence was exerted via indirect paths through literacy and social adjustment aged 5, and, language development and social development at 7-9 years. Linguistic and social skills were both predictors of literacy skills at the end of primary school. Findings are discussed with reference to their potential implications for the timing and targets of interventions designed to improve literacy outcomes
Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals
No full textGenetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequency > 0.05). In a meta-analysis of up to ~1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (minor allele frequency ≤ 0.01) variant BP associations (P < 5 × 10 −8), of which 32 were in new BP-associated loci and 55 were independent BP-associated single-nucleotide variants within known BP-associated regions. Average effects of rare variants (44% coding) were ~8 times larger than common variant effects and indicate potential candidate causal genes at new and known loci (for example, GATA5 and PLCB3). BP-associated variants (including rare and common) were enriched in regions of active chromatin in fetal tissues, potentially linking fetal development with BP regulation in later life. Multivariable Mendelian randomization suggested possible inverse effects of elevated systolic and diastolic BP on large artery stroke. Our study demonstrates the utility of rare-variant analyses for identifying candidate genes and the results highlight potential therapeutic targets. </p
