Priority evaluation in transportation policies and programmes

This work is concerned with the development of techniques for the evaluation of large-scale highway schemes with particular reference to the assessment of their costs and benefits in the context of the current transport planning (T.P.P.) process. It has been carried out in close cooperation with West Midlands County Council, although its application and results are applicable elsewhere. The background to highway evaluation and its development in recent years has been described and the emergence of a number of deficiencies in current planning practise noted. One deficiency in particular stood out, that stemming from inadequate methods of scheme generation and the research has concentrated upon improving this stage of appraisal, to ensure that subsequent stages of design, assessment and implementation are based upon a consistent and responsive foundation. Deficiencies of scheme evaluation were found to stem from inadequate development of appraisal methodologies suffering from difficulties of valuation, measurement and aggregation of the disparate variables that characterise highway evaluation. A failure to respond to local policy priorities was also noted. A 'problem' rather than 'goals' based approach to scheme generation was taken, as it represented the current and foreseeable resource allocation context more realistically. A review of techniques with potential for highway problem based scheme generation, which would work within a series of practical and theoretical constraints were assessed and that of multivariate analysis, and classical factor analysis in particular, was selected, because it offerred considerable application to the difficulties of valuation, measurement and aggregation that existed. Computer programs were written to adapt classical factor analysis to the requirements of T.P.P. highway evaluation, using it to derive a limited number of factors which described the extensive quantity of highway problem data. From this, a series of composite problem scores for 1979 were derived for a case study area of south Birmingham, based upon the factorial solutions, and used to assess highway sites in terms of local policy issues. The methodology was assessed in the light of its ability to describe highway problems in both aggregate and disaggregate terms, to guide scheme design, coordinate with current scheme evaluation methods, and in general to improve upon current appraisal. Analysis of the results was both in subjective, 'common-sense' terms and using statistical methods to assess the changes in problem definition, distribution and priorities that emerged. Overall, the technique was found to improve upon current scheme generation methods in all respects and in particular in overcoming the problems of valuation, measurement and aggregation without recourse to unsubstantiated and questionable assumptions. A number of deficiencies which remained have been outlined and a series of research priorities described which need to be reviewed in the light of current and future evaluation needs.

Addressing missing data in geochemistry: a non-linear approach

Exploratory analysis of petroleum geochemical data seeks to find common patterns to help distinguish between different source rocks, oils and gases, and to explain their source, maturity and any intra-reservoir alteration. However, at the outset, one is typically faced with (a) a large matrix of samples, each with a range of molecular and isotopic properties, (b) a spatially and temporally unrepresentative sampling pattern, (c) noisy data and (d) often, a large number of missing values. This inhibits analysis using conventional statistical methods. Typically, visualisation methods like principal components analysis are used, but these methods are not easily able to deal with missing data nor can they capture non-linear structure in the data. One approach to discovering complex, non-linear structure in the data is through the use of linked plots, or brushing, while ignoring the missing data. In this paper we introduce a complementary approach based on a non-linear probabilistic model. Generative topographic mapping enables the visualisation of the effects of very many variables on a single plot, while also dealing with missing data. We show how using generative topographic mapping also provides an optimal method with which to replace missing values in two geochemical datasets, particularly where a large proportion of the data is missing.

The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods

Biological experiments often produce enormous amount of data, which are usually analyzed by data clustering. Cluster analysis refers to statistical methods that are used to assign data with similar properties into several smaller, more meaningful groups. Two commonly used clustering techniques are introduced in the following section: principal component analysis (PCA) and hierarchical clustering. PCA calculates the variance between variables and groups them into a few uncorrelated groups or principal components (PCs) that are orthogonal to each other. Hierarchical clustering is carried out by separating data into many clusters and merging similar clusters together. Here, we use an example of human leukocyte antigen (HLA) supertype classification to demonstrate the usage of the two methods. Two programs, Generating Optimal Linear Partial Least Square Estimations (GOLPE) and Sybyl, are used for PCA and hierarchical clustering, respectively. However, the reader should bear in mind that the methods have been incorporated into other software as well, such as SIMCA, statistiXL, and R.

Correlating liposomal adjuvant characteristics to in-vivo cell-mediated immunity using a novel Mycobacterium tuberculosis fusion protein:a multivariate analysis study

Objective In this study, we have used a chemometrics-based method to correlate key liposomal adjuvant attributes with in-vivo immune responses based on multivariate analysis. Methods The liposomal adjuvant composed of the cationic lipid dimethyldioctadecylammonium bromide (DDA) and trehalose 6,6-dibehenate (TDB) was modified with 1,2-distearoyl-sn-glycero-3-phosphocholine at a range of mol% ratios, and the main liposomal characteristics (liposome size and zeta potential) was measured along with their immunological performance as an adjuvant for the novel, postexposure fusion tuberculosis vaccine, Ag85B-ESAT-6-Rv2660c (H56 vaccine). Partial least square regression analysis was applied to correlate and cluster liposomal adjuvants particle characteristics with in-vivo derived immunological performances (IgG, IgG1, IgG2b, spleen proliferation, IL-2, IL-5, IL-6, IL-10, IFN-γ). Key findings While a range of factors varied in the formulations, decreasing the 1,2-distearoyl-sn-glycero-3-phosphocholine content (and subsequent zeta potential) together built the strongest variables in the model. Enhanced DDA and TDB content (and subsequent zeta potential) stimulated a response skewed towards a cell mediated immunity, with the model identifying correlations with IFN-γ, IL-2 and IL-6. Conclusion This study demonstrates the application of chemometrics-based correlations and clustering, which can inform liposomal adjuvant design.

Application of Statistical Experimental Design in Food Sciences

The development of new, health supporting food of high quality and the optimization of food technological processes today require the application of statistical methods of experimental design. The principles and steps of statistical planning and evaluation of experiments will be explained. By example of the development of a gluten-free rusk (zwieback), which is enriched by roughage compounds the application of a simplex-centroid mixture design will be shown. The results will be illustrated by different graphics.

Infecções por enterobacteriaceae resistente aos carbapenêmicos em hospital de ensino: epidemiologia e caracterização molecular

Introduction: The production of KPC (Klebsiella pneumoniae carbapenemase) has become an important mechanism of carbapenem-resistance among Enterobacteriaceae strains. In Brazil, KPC is already widespread and its incidence has increased significantly, reducing treatment options. The “perfect storm” combination of the absence of new drug developmentand the emergence of multidrug-resistant strains resulted in the need for the use of older drugs, with greater toxicity, such as polymyxins. Aims: To determine the occurrence of carbapenemase-producing strains in carbapenem-resistant Enterobacteriaceae isolated from patients with nosocomial infection/colonization during September/2014 to August/2015, to determine the risk factors associated with 30-day- mortality and the impact of inappropriate therapy. Materials and Methods: We performed a case control study to assess the risk factors (comorbidities, invasive procedures and inappropriate antimicrobial therapy) associated with 30-day-mortality, considering the first episode of infection in 111 patients. The resistance genes blaKPC, blaIMP, blaVIM and blaNDM-1 were detected by polymerase chain reaction technique. Molecular typing of the strains involved in the outbreak was performed by pulsed field gel electrophoresis technique. The polymyxin resistance was confirmed by the microdilution broth method. Results: 188 episodes of carbapenem-resistant Enterobacteriaceae infections/colonizations were detected; of these, 122 strains were recovered from the hospital laboratory. The presence of blaKPC gene were confirmed in the majority (74.59%) of these isolates. It was not found the presence of blaIMP , blaVIM and blaNDM-1 genes. K. pneumoniae was the most frequent microorganism (77,13%), primarily responsible for urinary tract infections (21,38%) and infections from patients of the Intensive Care Unit (ICU) (61,38%). Multivariate statistical analysis showed as predictors independently associated with mortality: dialysis and bloodstream infection. The Kaplan-Meier curve showed a lower probability of survival in the group of patients receiving antibiotic therapy inappropriately. Antimicrobial use in adult ICU varied during the study period, but positive correlation between increased incidence of strains and the consumption was not observed. In May and July 2015, the occurrence rates of carbapenem-resistant Enterobacteriaceae KPC-producing per 1000 patient-days were higher than the control limit established, confirming two outbreaks, the first caused by colistin-susceptible KPC-producing K. pneumoniae isolates, with a polyclonal profile and the second by a dominant clone of colistin-resistant (≥ 32 μg/mL) KPC-producing K. pneumoniae. The cross transmission between patients became clear by the temporal and spatial relationships observed in the second outbreak, since some patients occupied the same bed, showing problems in hand hygiene adherence among healthcare workers and inadequate terminal disinfection of environment. The outbreak was contained when the ICU was closed to new admissions. Conclusions: The study showed an endemicity of K. pneumoniae KPC-producing in adult ICU, progressing to an epidemic monoclonal expansion, resulted by a very high antibiotic consumption of carbapenems and polymyxins and facilitated by failures in control measures the unit.

Using Image Processing Methods to Improve the Detection of Buried Explosive Threats in GPR Data

Current state of the art techniques for landmine detection in ground penetrating radar (GPR) utilize statistical methods to identify characteristics of a landmine response. This research makes use of 2-D slices of data in which subsurface landmine responses have hyperbolic shapes. Various methods from the field of visual image processing are adapted to the 2-D GPR data, producing superior landmine detection results. This research goes on to develop a physics-based GPR augmentation method motivated by current advances in visual object detection. This GPR specific augmentation is used to mitigate issues caused by insufficient training sets. This work shows that augmentation improves detection performance under training conditions that are normally very difficult. Finally, this work introduces the use of convolutional neural networks as a method to learn feature extraction parameters. These learned convolutional features outperform hand-designed features in GPR detection tasks. This work presents a number of methods, both borrowed from and motivated by the substantial work in visual image processing. The methods developed and presented in this work show an improvement in overall detection performance and introduce a method to improve the robustness of statistical classification.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.

Exploring the joint compositional variability of major components and trace elements in the Tellus soil geochemistry survey (Northern Ireland)

The complexity of modern geochemical data sets is increasing in several aspects (number of available samples, number of elements measured, number of matrices analysed, geological-environmental variability covered, etc), hence it is becoming increasingly necessary to apply statistical methods to elucidate their structure. This paper presents an exploratory analysis of one such complex data set, the Tellus geochemical soil survey of Northern Ireland (NI). This exploratory analysis is based on one of the most fundamental exploratory tools, principal component analysis (PCA) and its graphical representation as a biplot, albeit in several variations: the set of elements included (only major oxides vs. all observed elements), the prior transformation applied to the data (none, a standardization or a logratio transformation) and the way the covariance matrix between components is estimated (classical estimation vs. robust estimation). Results show that a log-ratio PCA (robust or classical) of all available elements is the most powerful exploratory setting, providing the following insights: the first two processes controlling the whole geochemical variation in NI soils are peat coverage and a contrast between “mafic” and “felsic” background lithologies; peat covered areas are detected as outliers by a robust analysis, and can be then filtered out if required for further modelling; and peat coverage intensity can be quantified with the %Br in the subcomposition (Br, Rb, Ni).

Methods for Hypothesis Testing in Animal Carcinogenicity Experiments

Thesis (Ph.D.)--University of Washington, 2016-08

Ecomorfologia de dez espécies de peixes marinhos mais abundantes de Galinhos/RN

Ecomorphology is a science based on the idea that morphological differences among species could be associated with distinct biological and environmental pressures suffered by them. These differences can be studied employing morphological and biometric indexes denominated Ecomorphological attributes , representing standards that express characteristics of the individual in relation to its environment, and can be interpreted as indicators of life habits or adaptations suffered due its occupation of different habitats. This work aims to contribute for the knowledge of the ecomorphology of the Brazilian marine ichthyofauna, specifically from Galinhos, located at Rio Grande do Norte state. 10 different species of fish were studied, belonging the families Gerreidae (Eucinostomus argenteus), Haemulidae (Orthopristis ruber,Pomadasyscorvinaeformis,Haemulonaurolineatum,Haemulonplumieri,Haemulonsteindachneri), Lutjanidae (Lutjanus synagris), Paralichthyidae (Syaciummicrurum), Bothidae (Bothus ocellatus) and Tetraodontidae (Sphoeroidestestudineus), which were obtained during five collections, in the period time of September/2004 to April/2005, utilizing three special nets. The ecomorphological study was performed at the laboratory. Eight to ten samples of each fish specie were measured. Fifteen morphological aspects were considered to calculate twelve ecomorphological attributes. Multivariate statistical analysis methods such as Principal Component Analysis (PCA) and Cluster Analysis were done to identify ecmorphological patterns to describe the data set obtained. As results, H.aurolineatumwas the most abundant specie found (23,03%) and S.testudineusthe less one with 0,23%. The 1st Principal component showed variation of 60,03% with influence of the ecomorphological attribute related to body morphology, while the 2nd PC with 23,25% variation had influence of the ecomorphological attribute related to oral morphology. The Cluster Analiysis promoted the identification of three distinct groups Perciformes, Pleuronectiformes and Tetraodontiformes. Based on the obtained data, considering morphological characters differences among the species studied, we suggest that all of them live at the medium (E.argenteus,O.rubber, P.corvinaeformis,H.aurolineatum,H.plumieri,H.steindachneri,L.synagris) and bottom (S.micrurum,B.ocellatus,S.testudineus) region of column water.

Métabolomique et spectrométrie de masse : de nouvelles perspectives en analyse biomédicale

International audience

Preparation of environmental monitoring plan pattern for surface water quality in Iran (Case study: Gharehsou River)

Using water quality management programs is a necessary and inevitable way for preservation and sustainable use of water resources. One of the important issues in determining the quality of water in rivers is designing effective quality control networks, so that the measured quality variables in these stations are, as far as possible, indicative of overall changes in water quality. One of the methods to achieve this goal is increasing the number of quality monitoring stations and sampling instances. Since this will dramatically increase the annual cost of monitoring, deciding on which stations and parameters are the most important ones, along with increasing the instances of sampling, in a way that shows maximum change in the system under study can affect the future decision-making processes for optimizing the efficacy of extant monitoring network, removing or adding new stations or parameters and decreasing or increasing sampling instances. This end, the efficiency of multivariate statistical procedures was studied in this thesis. Multivariate statistical procedure, with regard to its features, can be used as a practical and useful method in recognizing and analyzing rivers’ pollution and consequently in understanding, reasoning, controlling, and correct decision-making in water quality management. This research was carried out using multivariate statistical techniques for analyzing the quality of water and monitoring the variables affecting its quality in Gharasou river, in Ardabil province in northwest of Iran. During a year, 28 physical and chemical parameters were sampled in 11 stations. The results of these measurements were analyzed by multivariate procedures such as: Cluster Analysis (CA), Principal Component Analysis (PCA), Factor Analysis (FA), and Discriminant Analysis (DA). Based on the findings from cluster analysis, principal component analysis, and factor analysis the stations were divided into three groups of highly polluted (HP), moderately polluted (MP), and less polluted (LP) stations Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for effective river water quality management. This study also shows the effectiveness of these techniques for getting better information about the water quality and design of monitoring network for effective management of water resources. Therefore, based on the results, Gharasou river water quality monitoring program was developed and presented.

The Effect of using online homework exercises on student achievement in a Quantitative methods course

Abstract: Quantitative Methods (QM) is a compulsory course in the Social Science program in CEGEP. Many QM instructors assign a number of homework exercises to give students the opportunity to practice the statistical methods, which enhances their learning. However, traditional written exercises have two significant disadvantages. The first is that the feedback process is often very slow. The second disadvantage is that written exercises can generate a large amount of correcting for the instructor. WeBWorK is an open-source system that allows instructors to write exercises which students answer online. Although originally designed to write exercises for math and science students, WeBWorK programming allows for the creation of a variety of questions which can be used in the Quantitative Methods course. Because many statistical exercises generate objective and quantitative answers, the system is able to instantly assess students’ responses and tell them whether they are right or wrong. This immediate feedback has been shown to be theoretically conducive to positive learning outcomes. In addition, the system can be set up to allow students to re-try the problem if they got it wrong. This has benefits both in terms of student motivation and reinforcing learning. Through the use of a quasi-experiment, this research project measured and analysed the effects of using WeBWorK exercises in the Quantitative Methods course at Vanier College. Three specific research questions were addressed. First, we looked at whether students who did the WeBWorK exercises got better grades than students who did written exercises. Second, we looked at whether students who completed more of the WeBWorK exercises got better grades than students who completed fewer of the WeBWorK exercises. Finally, we used a self-report survey to find out what students’ perceptions and opinions were of the WeBWorK and the written exercises. For the first research question, a crossover design was used in order to compare whether the group that did WeBWorK problems during one unit would score significantly higher on that unit test than the other group that did the written problems. We found no significant difference in grades between students who did the WeBWorK exercises and students who did the written exercises. The second research question looked at whether students who completed more of the WeBWorK exercises would get significantly higher grades than students who completed fewer of the WeBWorK exercises. The straight-line relationship between number of WeBWorK exercises completed and grades was positive in both groups. However, the correlation coefficients for these two variables showed no real pattern. Our third research question was investigated by using a survey to elicit students’ perceptions and opinions regarding the WeBWorK and written exercises. Students reported no difference in the amount of effort put into completing each type of exercise. Students were also asked to rate each type of exercise along six dimensions and a composite score was calculated. Overall, students gave a significantly higher score to the written exercises, and reported that they found the written exercises were better for understanding the basic statistical concepts and for learning the basic statistical methods. However, when presented with the choice of having only written or only WeBWorK exercises, slightly more students preferred or strongly preferred having only WeBWorK exercises. The results of this research suggest that the advantages of using WeBWorK to teach Quantitative Methods are variable. The WeBWorK system offers immediate feedback, which often seems to motivate students to try again if they do not have the correct answer. However, this does not necessarily translate into better performance on the written tests and on the final exam. What has been learned is that the WeBWorK system can be used by interested instructors to enhance student learning in the Quantitative Methods course. Further research may examine more specifically how this system can be used more effectively.

Degradação de Ocratoxina a: estudo de processo e toxicidade

A ocratoxina A (OTA), micotoxina encontrada em diferentes níveis e em diversas matrizes, apresenta efeitos carcinogênicos, nefrotóxicos e teratogênicos. O desenvolvimento de métodos capazes de diminuir esta contaminação a níveis permitidos pela legislação é incentivado e os processos biológicos utilizados envolvem o uso de enzimas e/ou microrganismos para degradação da OTA e são preferenciais pela especificidade, bem como pelas condições brandas para a detoxificação. O objetivo do trabalho foi estudar a ação de carboxipeptidase A nos níveis e na toxicidade de OTA, visando aplicar a técnica para detoxificar farinhas de trigo. Primeiramente foi estimado o risco de exposição à ocratoxina A pelo consumo de farinhas de trigo. Para isso foram estabelecidas condições de determinação de OTA em farinhas de trigo, empregando técnicas de estatística multivariada para definir os principais interferentes na extração de OTA pelo método de QuEChERS e detecção em CLAE-FL. O método validado permitiu a avaliação da ocorrência natural em 20 amostras de farinha de trigo, estando estas contaminadas na faixa de 0,22 a 0,85 µg.kg-1 , apresentando um valor de ingestão diária de 0,08 ngOTA.dia-1 .kgmassacorpórea -1 e uma disponibilidade de 94,4%. Em seguida foi realizada a padronização da extração de carboxipeptidase A em biomassa de Rhizopus oryzae que consistiu em agitação ultrassônica durante 30 minutos numa potencia fixa de 150 W e 40 kHz e a triagem de agentes biológicos para degradação de OTA. Para o estudo da degradação in vitro de OTA, método de extração e detecção de OTA e OTα em CLAEFL foi validado e o processo de degradação foi realizado com Rhizopus oryzae e Trichoderma reesei, obtendo-se uma redução máxima de 63,5% e 57,7%, respectivamente. A degradação apresentou uma correlação alta (R>0,9) e significativa (p<0,05) com a produção de Otα, indicando que ocorreu a produção de enzimas capazes de hidrolisar a micotoxina, por exemplo, a carboxipeptidase A. O estudo da toxicidade de OTA e seu metabólito OTα foi realizado em neutrófilos humanos, onde foi observado a ausência de efeito tóxico de OTα. Também foi determinado o mecanismo de toxicidade de OTA pelo aumento de Ca2+ intracelular pela liberação a partir das reservas internas. Esta liberação, subsequentemente, provoca uma cascata de eventos, nomeadamente: a produção de espécies reativas, depleção de ATP, perda de ΔΨm, levando à morte por necrose. Para reduzir o risco de exposição à micotoxina pela ingestão de matéria prima contaminada, carboxipeptidase A extraída de diferentes fontes foi aplicada na hidrólise de OTA em farinha de trigo para posterior determinação do conteúdo residual de OTA e OTα, empregando método validado. O estudo mostrou uma redução de OTA entre 16,8 e 78,5% e produção de OTα entre 2 a 8,2 ng.g-1 . As carboxipeptidases mais promissoras para degradação foram as provenientes de Rhizopus e Trichoderma e a carboxipeptidase comercial. Ficou demonstrado que se pode recomendar a aplicação de enzimas proteolíticas, tipo carboxipeptidase, para reduzir o risco de exposição à micotoxina quando utilizada matéria prima contaminada, por exemplo, farinha de trigo para diferentes processos. A transformação de OTA para OTα e seus efeitos na redução da toxicidade da micotoxina corroboram com esta afirmação.