Volatomics in inflammatory bowel disease and irritable bowel syndrome

Volatile organic compounds (VOCs) are produced by the human metabolism, inflammation and gut microbiota and form the basis of innovative volatomics research. VOCs detected through breath and faecal analysis hence serve as attractive, non-invasive biomarkers for diagnosing and monitoring irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD). This review describes the clinical applicability of volatomics in discriminating between IBS, IBD and healthy volunteers with acceptable accuracy in breath (70%-100%) and faecal (58%-85%) samples. Promising compounds are propan-1-ol for diagnosing and monitoring of IBD patients, and 1-methyl-4-propan-2-ylcyclohexa-1,4-diene as biomarker for IBS diagnosis. However, these VOCs often seem to be related to inflammation and probably will need to be used in conjunction with other clinical evidence. Furthermore, three interventional studies underlined the potential of VOCs in predicting treatment outcome and patient follow-up. This shows great promise for future use of VOCs as non-invasive breath and faecal biomarkers in personalised medicine. However, properly designed studies that correlate VOCs to IBD/IBS pathogenesis, while taking microbial influences into account, are still key before clinical implementation can be expected. (C) 2020 The Author(s). Published by Elsevier B.V

Cumulative median estimation for sufficient dimension reduction

In this paper we present the Cumulative Median Estimation (CUMed) algorithm for robust sufficient dimension reduction. Compared with non-robust competitors, this algorithm performs better when there are outliers present in the data and comparably when outliers are not present. This is demonstrated in simulated and real data experiments

Exploring domestic rabbit (Oryctolagus cuniculus) personality utilising behaviour coding, behaviour testing and a novel behaviour rating tool

The purpose of the study was to attempt to identify personality traits in domestic rabbits (Oryctolagus cuniculus) and to evaluate a range of tools, suitable for use in a shelter setting, that can be used to measure personality traits. A literature review highlighted limited evaluation of reliability and validity in rabbit personality research published to date. Additionally, there is a lack of clarity on what is being measured by some behaviour tests that are currently employed in animal personality research and there are limited tools available to measure domestic rabbit responses to humans. Chapter three highlights several uses of rabbit behaviour and personality data in United Kingdom (UK) shelters. Shelter staff reported uses for understanding the behaviour of an individual rabbit to support the management of the individual while at the shelter and to match the rabbit to the most suitable future home. Challenges facing shelter staff to collect behavioural data for their rabbits centred around a lack of resources, specifically time available for collecting behavioural data. An additional challenge reported by shelter staff was inaccurate information being reported by the person handing the rabbit into the shelter. To ensure any personality assessment tool could be integrated into shelter routines, the tools would need to be relatively quick to complete and should ideally include a range of data collection methods so that a full picture can be available.  In Chapter four, the results of a behaviour rating survey that was distributed to a self-selected pool of rabbit owners or those that worked with rabbits, using social media are reported. The survey was also completed by animal care technicians for rabbits taking part in direct behavioural observations, including a suite of behaviour tests and observations within the home cage. The use of an online survey enabled a large number of participants to take part. Following examination of the reliability of the data (interrater) and dimension reduction statistics, three components were retained that included 15 of the initial 47 items and accounted for 60.6% of the variance in the data (n=1,234). However, sufficient thresholds for inter-rater reliability were not achieved. As intended in the selection of survey items, the retained components accounted for intraspecific social behaviour, human-rabbit interactions (avoidance of humans) and boldness in relation to the environment. However, only the human-rabbit interaction component had sufficient distribution of scores across the sample population to consider this a personality trait. Behavioural tests are commonly used as measures of an individual animal’s personality; however, several tests have conflicting interpretations of the underlying traits that may drive behaviour in these tests. In Chapter 5, a suite of tests were used, reflecting three commonly used test paradigms for domestic rabbits; the open field test, novel object test and a new human interaction test. Five human-interaction items measured were reliable between raters and between tests and two items, location during subtest 3 where the handler was sat inside the door of the enclosure and a combined outcome score for subtest 3, 4 (stroke rabbit) and 5 (pick up rabbit) were retained to create component 2 on the final solution of the principal component analysis. From two variations of both the open field and novel object tests, two components were also derived, reflecting exploration and curiosity in rabbits. These three components were reliable between raters and between tests and accounted for 75.2% of the cumulative variance in the data. The component labelled ‘exploration’ comprising variables of activity in the open field tests were found to negatively correlate with component 2 from the behaviour rating scale, reflecting avoidance of humans. This is similar to past research in young rabbits where resistance to handling was correlated with activity in the open field. The use of behavioural observations in the home cage environment is rarely performed for personality assessment in domestic animals due to how time consuming such observations can be. As a requirement for the tools was to be able to be utilised by shelter staff, where time constraints are an important factor, home cage behavioural observations were designed to be quick to complete. Following a pilot test including three hours of observations over the day, it was possible to determine the behaviours that could be observed using video cameras positioned adjacent to or above rabbit enclosures. Additionally, this pilot test revealed that within the times of day available for testing, none were preferable over any other in terms of the range of behaviours observed in 12 rabbits. The main study therefore utilised three five-minute sampling points across the day with the refined ethogram and 30 second focal sampling. It was not possible to complete dimension reductive statistics on the sample of 16 rabbits used for this part of the study, although the behaviours observed in the relatively short time frame did represent activity patterns observed in past research. Two tools, the behaviour rating survey and suite of behaviour tests, are proposed to be retained for future examination of the utility of these tests in a shelter setting to measure rabbit behaviour and personality. These retained tests would provide information on an individual rabbit’s social behaviour (intraspecific), response to humans, boldness in relation to the environment, exploration and curiosity. Future research is recommended to determine the suitability of these tests for use in shelters, and to understand the predictive validity of these tools. That is to understand the usefulness of rabbit personality assessments to identify aspects of behaviour that are stable between different environmental contexts, such as between a shelter setting and within a home following being rehomed

Using visual representations to demonstrate complexity in mixed emotional development across childhood

Previous studies have shown a developmental trend in mixed emotional understanding. As children develop throughout childhood, they begin to recognise simultaneity of positive and negative emotions. However, previous studies have limited ecological validity as they assessed emotion choice using only a single positive and single negative emotion. Therefore, the present study aims to broaden the understanding of mixed emotional development by allowing a wider emotion choice. Mixed emotions were measured using the Analogue Emotions Scale (AES) which allows both intensity of the emotional responses and time to be captured. In the present study 211 children aged 4-10 were divided into one of three protagonist conditions, (self, peer, and adult) and read a vignette about the protagonist moving house. Choosing from seven emotions (happy, calm, surprise, sad, worry, fear, anger) they plotted the intensity and duration of each emotion they thought was represented in the vignette. The present study replicated the developmental trend that younger children are more likely than older children to choose a single emotion, and older children are more likely to perceive more simultaneity of emotion than younger children. This trend was demonstrated in the number of emotions chosen, and also the complexity of the AES pattern plotted. Additionally, the present study extended previous research by demonstrating that by broadening the emotion choice, the emotion interaction is more complex than previous studies were able to show.Publisher PDFPeer reviewe

CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations

The long runtime of high-fidelity partial differential equation (PDE) solvers makes them unsuitable for time-critical applications. We propose to accelerate PDE solvers using reduced-order modeling (ROM). Whereas prior ROM approaches reduce the dimensionality of discretized vector fields, our continuous reduced-order modeling (CROM) approach builds a smooth, low-dimensional manifold of the continuous vector fields themselves, not their discretization. We represent this reduced manifold using continuously differentiable neural fields, which may train on any and all available numerical solutions of the continuous system, even when they are obtained using diverse methods or discretizations. We validate our approach on an extensive range of PDEs with training data from voxel grids, meshes, and point clouds. Compared to prior discretization-dependent ROM methods, such as linear subspace proper orthogonal decomposition (POD) and nonlinear manifold neural-network-based autoencoders, CROM features higher accuracy, lower memory consumption, dynamically adaptive resolutions, and applicability to any discretization. For equal latent space dimension, CROM exhibits 79$\times$ and 49$\times$ better accuracy, and 39$\times$ and 132$\times$ smaller memory footprint, than POD and autoencoder methods, respectively. Experiments demonstrate 109$\times$ and 89$\times$ wall-clock speedups over unreduced models on CPUs and GPUs, respectively

Índices espectrais baseados em programação genética para classificação de imagens de sensoriamento remoto

Orientador: Ricardo da Silva TorresDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Sensoriamento remoto é o conjunto de técnicas que permitem, por meio de sensores, analisar objetos a longas distâncias sem estabelecer contato físico com eles. Atualmente, sua contribuição em ciências naturais é enorme, dado que é possível adquirir imagens de alvos em mais regiões do espectro eletromagnético além do canal visível. Trabalhar com imagens compostas por múltiplas bandas espectrais requer tratar grandes quantidades de informação associada a uma única entidade, coisa que afeta negativamente o desempenho de algoritmos de predição, fazendo nacessário o uso de técnicas de redução da dimensionalidade. Este trabalho apresenta uma abordagem de extração de características baseada em índices espectrais aprendidos por Programação Genética (GP), que projetam os dados associados aos pixels em novos espaços de características, com o objetivo de aprimorar a acurácia de algoritmos de classificação. Índices espectrais são funções que relacionam a refletância, em canais específicos do espectro, com valores reais que podem ser interpretados como a abundância de características de interesse de objetos captados à distância. Com GP é possível aprender índices que maximizam a separabilidade de amostras de duas classes diferentes. Assim que os índices especializados para cada par possível de classes são obtidos, empregam-se duas abordagens diferentes para combiná-los e construir um sistema de classificação de pixels. Os resultados obtidos para os cenários binário e multi-classe mostram que o método proposto é competitivo com respeito a técnicas tradicionais de redução da dimensionalidade. Experimentos adicionais aplicando o método para análise sazonal de biomas tropicais mostram claramente a superioridade de índices aprendidos por GP para propósitos de discriminação, quando comparados a índices desenvolvidos por especialistas, independentemente da especificidade do problemaAbstract: Remote sensing is the set of techniques that allow, by means of sensor technologies, to analyze objects at long distances without making physical contact with them. Currently, its contribution for natural sciences is enormous, since it is possible to acquire images of target objects in more regions of the electromagnetic spectrum than the visible region only. Working with images composed of various spectral bands demands dealing with huge amounts of data associated with single entities, which affects negatively the performance in prediction tasks, and makes necessary the use of dimensionality reduction techniques. This work introduces a feature extraction approach, based on spectral indices learned by Genetic Programming (GP), to project data from pixel values into new feature spaces aiming to improve classification accuracy. Spectral indices are functions that map the reflectance of remotely sensed objects in specific wavelength intervals, into real scalars that can be interpreted as the abundance of features of interest. Through GP, it is possible to learn indices that maximize the separability of samples from two different classes. Once the indices specialized for all the pairs of classes are obtained, they are used in two different approaches to fuse them into a pixel classification system. Results for the binary and multi-class scenarios show that the proposed method is competitive with respect to traditional dimensionality reduction techniques. Additional experiments in tropical biomes seasonal analysis show clearly how superior GP-based spectral indices are for discrimination purposes, when compared to indices developed by experts, regardless the specificity of the problemMestradoCiência da ComputaçãoMestre em Ciência da Computação134089/2015-4CNP

A COMPARATIVE STUDY OF TWO METHODOLOGIES FOR BINARY DATASETS ANALYSIS

Abstract: Studied are differences of two approaches targeted to reveal latent variables in binary data. These approaches assume that the observed high dimensional data are driven by a small number of hidden binary sources combined due to Boolean superposition. The first approach is the Boolean matrix factorization (BMF) and the second one is the Boolean factor analysis (BFA). The two BMF methods are used for comparison. First is the M8 method from the BMDP statistical software package and the second one is the method suggested by Belohlavek & Vychodil. These two are compared to BFA, especially with the Expectationmaximization Boolean Factor Analysis we had developed earlier has, however, been extended with a binarization step developed here. The well-known bars problem and the mushroom dataset are used for revealing the methods' peculiarities. In particular, the reconstruction ability of the computed factors and the information gain as the measure of dimension reduction was under scrutiny. It was shown that BFA slightly loses to BMF in performance when noise-free signals are analyzed. Conversely, BMF loses considerably to BFA when input signals are noisy

Statistical methods for transcriptomics: From microarrays to RNA-seq

La transcriptómica estudia el nivel de expresión de los genes en distintas condiciones experimentales para tratar de identificar los genes asociados a un fenotipo dado así como las relaciones de regulación entre distintos genes. Los datos ómicos se caracterizan por contener información de miles de variables en una muestra con pocas observaciones. Las tecnologías de alto rendimiento más comunes para medir el nivel de expresión de miles de genes simultáneamente son los microarrays y, más recientemente, la secuenciación de RNA (RNA-seq). Este trabajo de tesis versará sobre la evaluación, adaptación y desarrollo de modelos estadísticos para el análisis de datos de expresión génica, tanto si ha sido estimada mediante microarrays o bien con RNA-seq. El estudio se abordará con herramientas univariantes y multivariantes, así como con métodos tanto univariantes como multivariantes.Tarazona Campos, S. (2014). Statistical methods for transcriptomics: From microarrays to RNA-seq [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48485TESISPremios Extraordinarios de tesis doctorale

The role of basic human values in determining teenagers' proenvironmental behaviour.

It is generally believed that basic human values provide the foundation for pro-environmental behaviours. Most research in this field focuses on adults, whereas values are believed to be formed during youth. This thesis aimed to determine the relationships between teenagers’ values and their environmental behaviours. Three studies were undertaken: The first study aimed to categorise pro-environmental behaviours (PEB) as ‘convenient private’, ‘inconvenient private’, or ‘public’. The second aimed to determine the relationship between basic human values and these categories of behaviour, and the ability of certain motivation orientations to mediate those relationships. The third study investigated the effect of a discursive education intervention that focussed on the impact of personal values for a sustainable future, and also the influence of parents and peers on teenagers’ markers of environmentalism. Values were measured using Bouman and colleagues’ (2018) Environmental Portrait Values Questionnaire (E-PVQ), and motivation orientation was measured using Pelletier and colleagues’ (1998) Motivation Toward the Environment Scale (MTES), which is based on Deci and Ryan’s (1985, 2000) Organismic Integration Theory. I found a positive relationship between Biospheric values and convenient private PEB that was explained predominantly by Identified regulation (beliefs about the worthiness of the behaviour), whereas the positive relationship found between Biospheric values and inconvenient private PEB was explained by Integrated regulation (environmental self-identity). The positive relationship found between Biospheric values and public PEB was explained by both Integrated regulation and Intrinsic regulation (enjoyment). Hedonic values were most negatively related to more effortful behaviours. The education intervention, designed to make teenagers more aware of the impact of their personal values for a sustainable future, appeared successful in developing Biospheric values for those whose Biospheric values were initially low. The intervention also increased the likelihood of the larger sample having environmental interactions with their peers in the longer term. Having environmental interactions with peers was found to be an important predictor of teenagers’ environmentalism, and merits further investigation