Artificial Intelligence Based Classification for Urban Surface Water Modelling

Estimations and predictions of surface water runoff can provide very useful insights, regarding flood risks in urban areas. To automatically predict the flow behaviour of the rainfall-runoff water, in real-world satellite images, it is important to precisely identify permeable and impermeable areas. This identification indicates and helps to calculate the amount of surface water, by taking into account the amount of water being absorbed in a permeable area and what remains on the impermeable area. In this research, a model of surface water has been established, to predict the behavioural flow of rainfall-runoff water. This study employs a combination of image processing, artificial intelligence and machine learning techniques, for automatic segmentation and classification of permeable and impermeable areas, in satellite images. These techniques investigate the image classification approaches for classifying three land-use categories (roofs, roads, and pervious areas), commonly found in satellite images of the earth’s surface. Three different classification scenarios are investigated, to select the best classification model. The first scenario involves pixel by pixel classification of images, using Classification Tree and Random Forest classification techniques, in 2 different settings of sequential and parallel execution of algorithms. In the second classification scenario, the image is divided into objects, by using Superpixels (SLIC) segmentation method, while three kinds of feature sets are extracted from the segmented objects. The performance of eight different supervised machine learning classifiers is probed, using 5-fold cross-validation, for multiple SLIC values, while detailed performance comparisons lead to conclusions about the classification into different classes, regarding Object-based and Pixel-based classification schemes. Pareto analysis and Knee point selection are used to select SLIC value and the suitable type of classification, among the aforementioned two. Furthermore, a new diversity and weighted sum-based ensemble classification model, called ParetoEnsemble, is proposed, in this classification scenario. The weights are applied to selected component classifiers of an ensemble, creating a strong classifier, where classification is done based on multiple votes from candidate classifiers of the ensemble, as opposed to individual classifiers, where classification is done based on a single vote, from only one classifier. Unbalanced and balanced data-based classification results are also evaluated, to determine the most suitable mode, for satellite image classifications, in this study. Convolutional Neural Networks, based on semantic segmentation, are also employed in the classification phase, as a third scenario, to evaluate the strength of deep learning model SegNet, in the classification of satellite imaging. The best results, from the three classification scenarios, are compared and the best classification method, among the three scenarios, is used in the next phase of water modelling, with the InfoWorks ICM software, to explore the potential of modelling process, regarding a partially automated surface water network. By using the parameter settings, with a specified amount of simulated rain falling, onto the imaged area, the amount of surface water flow is estimated, to get predictions about runoff situations in urban areas, since runoff, in such a situation, can be high enough to pose a dangerous flood risk. The area of Feock, in Cornwall, is used as a simulation area of study, in this research, where some promising results have been derived, regarding classification and modelling of runoff. The correlation coefficient estimation, between classification and runoff accuracy, provides useful insight, regarding the dependence of runoff performance on classification performance. The trained system was tested on some unknown area images as well, demonstrating a reasonable performance, considering the training and classification limitations and conditions. Furthermore, in these unknown area images, reasonable estimations were derived, regarding surface water runoff. An analysis of unbalanced and balanced data-based classification and runoff estimations, for multiple parameter configurations, provides aid to the selection of classification and modelling parameter values, to be used in future unknown data predictions. This research is founded on the incorporation of satellite imaging into water modelling, using selective images for analysis and assessment of results. This system can be further improved, and runoff predictions of high precision can be better achieved, by adding more high-resolution images to the classifiers training. The added variety, to the trained model, can lead to an even better classification of any unknown image, which could eventually provide better modelling and better insights into surface water modelling. Moreover, the modelling phase can be extended, in future research, to deal with real-time parameters, by calibrating the model, after the classification phase, in order to observe the impact of classification on the actual calibration

Semantic segmentation on small datasets of satellite images using convolutional neural networks

This is the final version. Available from SPIE via the DOI in this recordSemantic segmentation is one of the most popular and challenging applications of deep learning. It refers to the process of dividing a digital image into semantically homogeneous areas with similar properties. We employ the use of deep learning techniques to perform semantic segmentation on high-resolution satellite images representing urban scenes to identify roads, vegetation, and buildings. A SegNet-based neural network with an encoder–decoder architecture is employed. Despite the small size of the dataset, the results are promising. We show that the network is able to accurately distinguish between these groups for different test images, when using a network with four convolutional layers

Ethnic Variation in the Prevalence of Visual Impairment in People Attending Diabetic Retinopathy Screening in the United Kingdom (DRIVE UK)

PURPOSE: To provide estimates of visual impairment in people with diabetes attending screening in a multi-ethnic population in England (United Kingdom). METHODS: The Diabetic Retinopathy In Various Ethnic groups in UK (DRIVE UK) Study is a cross-sectional study on the ethnic variations of the prevalence of DR and visual impairment in two multi-racial cohorts in the UK. People on the diabetes register in West Yorkshire and South East London who were screened, treated or monitored between April 2008 to July 2009 (London) or August 2009 (West Yorkshire) were included in the study. Data on age, gender, ethnic group, visual acuity and diabetic retinopathy were collected. Ethnic group was defined according to the 2011 census classification. The two main ethnic minority groups represented here are Blacks ("Black/African/Caribbean/Black British") and South Asians ("Asians originating from the Indian subcontinent"). We examined the prevalence of visual impairment in the better eye using three cut-off points (a) loss of vision sufficient for driving (approximately <6/9) (b) visual impairment (<6/12) and (c) severe visual impairment (<6/60), standardising the prevalence of visual impairment in the minority ethnic groups to the age-structure of the white population. RESULTS: Data on visual acuity and were available on 50,331 individuals 3.4% of people diagnosed with diabetes and attending screening were visually impaired (95% confidence intervals (CI) 3.2% to 3.5%) and 0.39% severely visually impaired (0.33% to 0.44%). Blacks and South Asians had a higher prevalence of visual impairment (directly age standardised prevalence 4.6%, 95% CI 4.0% to 5.1% and 6.9%, 95% CI 5.8% to 8.0% respectively) compared to white people (3.3%, 95% CI 3.1% to 3.5%). Visual loss was also more prevalent with increasing age, type 1 diabetes and in people living in Yorkshire. CONCLUSIONS: Visual impairment remains an important public health problem in people with diabetes, and is more prevalent in the minority ethnic groups in the UK

Manipulation of the follicular phase: Uterodomes and pregnancy - is there a correlation?

BACKGROUND: Manipulation of the follicular phase uterine epithelium in women undergoing infertility treatment, has not generally shown differing morphological effects on uterine epithelial characteristics using Scanning Electron Microscopy (SEM) and resultant pregnancy rates have remained suboptimal utilising these manipulations. The present study observed manipulation of the proliferative epithelium, with either 7 or 14 days of sequential oestrogen (E) therapy followed by progesterone (P) and assessed the appearance of pinopods (now called uterodomes) for their usefulness as potential implantation markers in seven women who subsequently became pregnant. Three endometrial biopsies per patient were taken during consecutive cycles: day 19 of a natural cycle - (group 1), days 11/12 of a second cycle after 7 days E then P - (group 2), and days 19/22 of a third cycle after 14 days E then P - (group 3). Embryo transfer (ET) was performed in a subsequent long treatment cycle (as per Group 3). RESULTS: Seven pregnancies resulted in seven viable births including one twins and one miscarriage. Analysis of the individual regimes showed 5 days of P treatment to have a higher correlation for uterodomes in all 3 cycles observed individually. It was also observed that all 7 women demonstrated the appearance of uterodomes in at least one of their cycles. CONCLUSIONS: We conclude that manipulation of the follicular phase by shortening the period of E exposure to 7 days, does not compromise uterine epithelial morphology and we add weight to the conclusion that uterodomes indicate a receptive endometrium for implantation

Yeast : the soul of beer’s aroma—a review of flavour-active esters and higher alcohols produced by the brewing yeast

Among the most important factors influencing beer quality is the presence of well-adjusted amounts of higher alcohols and esters. Thus, a heavy body of literature focuses on these substances and on the parameters influencing their production by the brewing yeast. Additionally, the complex metabolic pathways involved in their synthesis require special attention. More than a century of data, mainly in genetic and proteomic fields, has built up enough information to describe in detail each step in the pathway for the synthesis of higher alcohols and their esters, but there is still place for more. Higher alcohols are formed either by anabolism or catabolism (Ehrlich pathway) of amino acids. Esters are formed by enzymatic condensation of organic acids and alcohols. The current paper reviews the up-to-date knowledge in the pathways involving the synthesis of higher alcohols and esters by brewing yeasts. Fermentation parameters affecting yeast response during biosynthesis of these aromatic substances are also fully reviewed.Eduardo Pires gratefully acknowledges the Fundacao para a Ciencia e a Tecnologia (FCT, Portugal) for the PhD fellowship support (SFRH/BD/61777/2009). The financial contributions of the EU FP7 project Ecoefficient Biodegradable Composite Advanced Packaging (EcoBioCAP, grant agreement no. 265669) as well as of the Grant Agency of the Czech Republic (project GACR P503/12/1424) are also gratefully acknowledged. The authors thank the Ministry of Education, Youth and Sports of the Czech Republic (MSM 6046137305) for their financial support

Down selecting adjuvanted vaccine formulations: a comparative method for harmonized evaluation.

The need for rapid and accurate comparison of panels of adjuvanted vaccine formulations and subsequent rational down selection, presents several challenges for modern vaccine development. Here we describe a method which may enable vaccine and adjuvant developers to compare antigen/adjuvant combinations in a harmonized fashion. Three reference antigens: Plasmodium falciparum apical membrane antigen 1 (AMA1), hepatitis B virus surface antigen (HBsAg), and Mycobacterium tuberculosis antigen 85A (Ag85A), were selected as model antigens and were each formulated with three adjuvants: aluminium oxyhydroxide, squalene-in-water emulsion, and a liposome formulation mixed with the purified saponin fraction QS21. The nine antigen/adjuvant formulations were assessed for stability and immunogenicity in mice in order to provide benchmarks against which other formulations could be compared, in order to assist subsequent down selection of adjuvanted vaccines. Furthermore, mouse cellular immune responses were analyzed by measuring IFN-γ and IL-5 production in splenocytes by ELISPOT, and humoral responses were determined by antigen-specific ELISA, where levels of total IgG, IgG1, IgG2b and IgG2c in serum samples were determined. The reference antigens and adjuvants described in this study, which span a spectrum of immune responses, are of potential use as tools to act as points of reference in vaccine development studies. The harmonized methodology described herein may be used as a tool for adjuvant/antigen comparison studies

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015

SummaryBackground The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding Bill & Melinda Gates Foundation

Risk of placental abruption in relation to migraines and headaches

<p>Abstract</p> <p>Background</p> <p>Migraine, a common chronic-intermittent disorder of idiopathic origin characterized by severe debilitating headaches and autonomic nervous system dysfunction, and placental abruption, the premature separation of the placenta, share many common pathophysiological characteristics. Moreover, endothelial dysfunction, platelet activation, hypercoagulation, and inflammation are common to both disorders. We assessed risk of placental abruption in relation to maternal history of migraine before and during pregnancy in Peruvian women.</p> <p>Methods</p> <p>Cases were 375 women with pregnancies complicated by placental abruption, and controls were 368 women without an abruption. During in-person interviews conducted following delivery, women were asked if they had physician-diagnosed migraine, and they were asked questions that allowed headaches and migraine to be classified according to criteria established by the International Headache Society. Logistic regression procedures were used to calculate odds ratios (aOR) and 95% confidence intervals (CI) adjusted for confounders.</p> <p>Results</p> <p>Overall, a lifetime history of any headaches or migraine was associated with an increased odds of placental abruption (aOR = 1.60; 95% CI 1.16-2.20). A lifetime history of migraine was associated with a 2.14-fold increased odds of placental abruption (aOR = 2.14; 95% CI 1.22-3.75). The odds of placental abruption was 2.11 (95% CI 1.00-4.45) for migraineurs without aura; and 1.59 (95% 0.70-3.62) for migraineurs with aura. A lifetime history of tension-type headache was also increased with placental abruption (aOR = 1.61; 95% CI 1.01-2.57).</p> <p>Conclusions</p> <p>This study adds placental abruption to a growing list of pregnancy complications associated with maternal headache/migraine disorders. Nevertheless, prospective cohort studies are needed to more rigorously evaluate the extent to which migraines and/or its treatments are associated with the occurrence of placental abruption.</p

Inflammatory pseudo-tumor of the liver: a rare pathological entity

Inflammatory pseudo-tumor (IPT) of the liver is a rare benign neoplasm and is often mistaken as a malignant entity. Few cases have been reported in the literature and the precise etiology of inflammatory pseudotumor remains unknown. Patients usually present with fever, abdominal pain and jaundice. The proliferation of spindled myofibroblast cells mixed with variable amounts of reactive inflammatory cells is characteristics of IPT. We reviewed the literature regarding possible etiology for IPT with a possible suggested etiology