Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering

Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. Results An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With 1H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. Conclusion Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. Results An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With 1H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. Conclusion The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of 1H MRSI data after cluster analysis

Analysis of characterizing phases on waveforms – an application to vertical jumps

The aim of this study is to propose a novel data analysis approach, ‘Analysis of Characterizing Phases’ (ACP), that detects and examines phases of variance within a sample of curves utilizing the time, magnitude and magnitude-time domain; and to compare the findings of ACP to discrete point analysis in identifying performance related factors in vertical jumps. Twenty five vertical jumps were analyzed. Discrete point analysis identified the initial-to-maximum rate of force development (p = .006) and the time from initial-to-maximum force (p = .047) as performance related factors. However, due to inter-subject variability in the shape of the force curves (i.e non-, uni- and bi-modal nature), these variables were judged to be functionally erroneous. In contrast, ACP identified the ability to: apply forces for longer (p < .038), generate higher forces (p < .027) and produce a greater rate of force development (p < .003) as performance related factors. Analysis of Characterizing Phases showed advantages over discrete point analysis in identifying performance related factors because it: (i) analyses only related phases, (ii) analyses the whole data set, (iii) can identify performance related factors that occur solely as a phase, (iv) identifies the specific phase over which differences occur, and (v) analyses the time, magnitude and combined magnitude-time domains

Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology

Non-negative matrix factorization is a useful tool for reducing the dimension of large datasets. This work considers simultaneous non-negative matrix factorization of multiple sources of data. In particular, we perform the first study that involves more than two datasets. We discuss the algorithmic issues required to convert the approach into a practical computational tool and apply the technique to new gene expression data quantifying the molecular changes in four tissue types due to different dosages of an experimental panPPAR agonist in mouse. This study is of interest in toxicology because, whilst PPARs form potential therapeutic targets for diabetes, it is known that they can induce serious side-effects. Our results show that the practical simultaneous non-negative matrix factorization developed here can add value to the data analysis. In particular, we find that factorizing the data as a single object allows us to distinguish between the four tissue types, but does not correctly reproduce the known dosage level groups. Applying our new approach, which treats the four tissue types as providing distinct, but related, datasets, we find that the dosage level groups are respected. The new algorithm then provides separate gene list orderings that can be studied for each tissue type, and compared with the ordering arising from the single factorization. We find that many of our conclusions can be corroborated with known biological behaviour, and others offer new insights into the toxicological effects. Overall, the algorithm shows promise for early detection of toxicity in the drug discovery process

Classification of continuous vertical ground reaction forces

The aim of this study is to assess and compare the performance of com- monly used hierarchical, partitional (k-means) and Gaussian model-based (Expectation-Maximization algorithm) clustering techniques to appropriately identify subgroup patterns within vertical ground reaction force data, using a continuous waveform analysis. In addition, we also compared the perfor- mance across each technique using normalized and non-normalization input scores. Both generated and real data (one hundred-and twenty two verti- cal jumps) were analyzed. The performance of each cluster technique was measured by assessing the ability to explain variances in jump height using a stepwise regression analysis. Only k-means (normalized scores; 82 %) and hierarchical clustering (normalized scores; 85 %) were able to extend the ability to describe variances in jump height beyond that achieved using the group analysis (i.e. one cluster; 78 %). Further, our findings strongly indicate the need to normalize the input data (similarity measure) when clustering. In contrast to the group analysis, the subgroup analysis was able to iden- tify cluster specific phases of variance, which improved the ability to explain variances in jump height, due to the identification of cluster specific predictor variables. Our findings therefore highlight the benefit of performing a subgroup analysis and may explain, at least in part, the contrasting findings between previous studies that used a single group level of analysis

Application of functional principal component analysis in race walking: an emerging methodology

This study considered the problem of identifying and evaluating the factors of individual performance during race walking. In particular, the study explored the use of functional principal component analysis (f-PCA), a multivariate data analysis, for assessing and classifying the kinematics and kinetics of the knee joint in competitive race walkers. Seven race walkers of international and national level participated to the study. An optoelectronic system and a force platform were used to capture three-dimensional kinematics and kinetics of lower limbs during the race walking cycle. Functional principal component analysis was applied bilaterally to the sagittal knee angle and net moment data, because knee joint motion is fundamental to race walking technique. Scatterplots of principal component scores provided evidence of athletes' technical differences and asymmetries even when traditional analysis (mean ± s curves) was not effective. Principal components provided indications for race walkers' classification and identified potentially important technical differences between higher and lower skilled athletes. Therefore, f-PCA might represent a future aid for the fine analysis of sports movements, if consistently applied to performance monitoring

A dynamic multi-organ-chip for long-term cultivation and substance testing proven by 3D human liver and skin tissue co-culture

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Current in vitro and animal tests for drug development are failing to emulate the systemic organ complexity of the human body and, therefore, to accurately predict drug toxicity. In this study, we present a multi-organ-chip capable of maintaining 3D tissues derived from cell lines, primary cells and biopsies of various human organs. We designed a multi-organ-chip with co-cultures of human artificial liver microtissues and skin biopsies, each a 1/100 000 of the biomass of their original human organ counterparts, and have successfully proven its long-term performance. The system supports two different culture modes: i) tissue exposed to the fluid flow, or ii) tissue shielded from the underlying fluid flow by standard Transwell® cultures. Crosstalk between the two tissues was observed in 14-day co-cultures exposed to fluid flow. Applying the same culture mode, liver microtissues showed sensitivity at different molecular levels to the toxic substance troglitazone during a 6-day exposure. Finally, an astonishingly stable long-term performance of the Transwell®-based co-cultures could be observed over a 28-day period. This mode facilitates exposure of skin at the air–liquid interface. Thus, we provide here a potential new tool for systemic substance testing.BMBF, 0315569, GO-Bio 3: Multi-Organ-Bioreaktoren für die prädiktive Substanztestung im Chipforma

Site, rate and extent of starch digestion in weaning infants

BACKGROUND The colon is believed to salvage energy from unabsorbed starch especially when the capacity of the small intestine to digest it is limited. The extent to which this occurs is not known.AIMS The aim of this thesis was to determine site and relative extent of starch digestion and fermentation in young children using the individual and combined approaches of stable isotope breath tests and in vitro stool fermentation models.STABLE ISOTOPE BREATH TEST METHODS Thirteen children (10m, 3f), median (range) age 11.8 mo (7.6 -22.7 mo), took a starchy breakfast containing ¹³C labelled wheat flour following an overnight fast. Duplicate breath samples were obtained before breakfast and every 30 min for 12 h. Breath ¹³CO₂ enrichment was measured by isotope ratio mass spectrometry and results were expressed as percentage dose recovered (PDR) for each 30 min. PDR data were analysed and mathematically curve fitted either assuming a constant estimate of CO₂ production rate or adjusted for physical activity.STABLE ISOTOPE BREATH TEST RESULTS Mean ± SD cumulative ¹³C PDR (cPDR) at 12 h was 21.3% ± 8.4% for unadjusted data and 26.5% ± 11.6% for adjusted data. A composite fit of two curves fitted significantly better than a single curve. Curve fitting allowed estimation of cPDRs of small intestine (17.5% ± 6.5% and 22.7% ± 9.3% for unadjusted and adjusted data respectively) and colon (4.6% ± 2.9% and 6.3% ± 5.4 %). From these results it is speculated that the colon may account for up to 20% of starch digestion in young children.IN VITRO COLONIC FERMENTATION METHODS A simulated colonic environment was used to account for the fate of raw and cooked starch that was fermented in the colon of young children. A slurry was prepared from faecal samples of 6 infants (7 - 10 mo), 6 toddlers (16 - 21 mo) and 7 adults (24 - 56 years). Each slurry was anaerobically incubated with raw or cooked maize starch in MacCartney bottles in a shaking water bath. Parallel incubations were stopped at 4 and 24 h. The headspace gas volume was analysed for CO₂ and methane. The culture supernatant was analyzed for the volatile short chain fatty acids acetate, propionate and butyrate (SCFA), lactate and residual starch.IN VITRO COLONIC FERMENTATION RESULTS There was a decreasing trend of SCFA production with age at 4 h which was not evident at 24 h. At 4 h, toddler stools produced the most CO₂ followed by infants and then adults, but this trend was not seen at 24 h. Methane was detected in 3 adults only. Lactate was detected mainly at 4 h in children only. The production of SCFA at 4 h generally declined with age but the differences at 24 h were less marked, suggesting fermentation is a more rapid process in young children than in adults. A highly efficient energy salvage process may take place in the colon of young children.CALCULATIONS USING BOTH DATA SETS AND CONCLUSIONS Using data from studies described in both parts of the dissertation, it has been possible to derive stoichiometric equations for the whole gut digestion of starch, and thereby calculate its potential energy. There are a number of limitations to the methodology and from assumptions that have been made, but this provides an attractive means to calculate relative roles of small intestine and colon to starch digestion in young children which in turn may form the scientific basis for nutritional advice given to mothers

Surf Biomechanics and Bioenergetics.

O surf moderno vem sendo descrito como uma atividade física intermitente, que varia em duração e intensidade, seguida de períodos de recuperação consideráveis. Atualmente, a análise e avaliação das sessões de surf são baseadas em conhecimento empírico, experiência e observação. Em outras palavras, procedimentos que envolvem grandes erros de medição. No entanto, é extremamente difícil obter informações analíticas sobre os parâmetros de desempenho. As primeiras investigações científicas no mundo do surf enfrentam uma dificuldade clássica do mundo da ciência, que é medir sem interferir. Além disso, o ambiente marítimo, particularmente devido à água salgada, é extremamente hostil aos componentes eletrônicos, que atualmente são a nossa maior fonte de informações quantitativas.O objetivo desta pesquisa foi investigar a fase horizontal do surf, especificamente a remada de potência, a remada de longa duração e a técnica de transição para ficar de pé na prancha de surf. Todo este pacote sob a perspetiva da biomecânica, associado a alguns parâmetros bioenergéticos. A abordagem geral foi apoiada por um processo de desconstrução dos movimentos e técnicas em partes didáticas, a fim de reconstruir um conhecimento global e uma melhor compreensão do surf. Olhando para o futuro, agregámos a este projeto o desenvolvimento de recursos tecnológicos que possibilitam explorar o surf diretamente no oceano. Tudo isso ganha ainda muito mais peso, desde que o Surf foi selecionado como novo desporto olímpico para os Jogos de Tóquio, em 2020. Os Jogos Olímpicos passam a ser uma excelente oportunidade, onde o surf se tornará mais profissionalizado e organizado. Neste contexto, as métricas para avaliação de desempenho são importantes para ajudar a validação de metodologias de ensino-aprendizagem, treinamento e julgamentos competitivos.Palavras-chave: biomecânica do surf, surfing, remadas do surf, ficar de pé, medições.Modern surfing has been described as an intermittent physical activity, which varies in duration and intensity, followed by considerable recovery periods. Currently, the analysis and judgment of surf sessions are based on empirical knowledge, experience, and observation. In other words, procedures that involve great measurement errors. However, it is extremely difficult to obtain analytical information on performance parameters. The first scientific investigations in the surf world faced a classic difficulty of the world of science, which is to measure without interfering. In addition, the maritime environment, particularly due to salt water, is extremely hostile to electronic components, which are currently our largest source of quantitative information.This research aimed to investigate the horizontal phase of surfing, specifically the sprint paddling, endurance paddling and the transition pop-up - standing technique. The whole pack under a biomechanics perspective, associated with bioenergetic parameters. The general approach was supported by process of deconstruction of movements and techniques in didactic parts, in order to reconstruct a global knowledge, and a better understanding of surfing. Looking to the future, we aggregate to this project the development of technological resources that make it possible to explore surf directly in the ocean. All this gained even more relevance since Surf has been selected as the new Olympic sport for the next Games of Tokyo 2020. The Olympic Games are an excellent opportunity where surfing will become more professional and organised. In this context, the metrics for performance evaluation are important to help validating teaching-learning methodologies, support training and competitive judgments

The application of autofluorescence lifetime metrology to the study of heart failure models and heart disease

Autofluorescence spectroscopy offers a promising label-free approach to characterise biological samples and has already shown diagnostic potential in a number of medical applications, although study of myocardium has been relatively limited. A number of myocardial molecules display autofluorescence, including those involved in energetics, e.g. NADH and flavoproteins, as well as structural molecules, e.g. collagen. This thesis discusses the application of a custom-built single point fibre-optic probe-based instrumentation for time-resolved spectrofluorometry utilising spectrally resolved time-correlated single photon counting detection (TCSPC) and white light reflectometry to the investigation of models of heart failure, both ex vivo and in vivo. Heart failure (HF) is a pathophysiological state in which an abnormality of cardiac function causes failure of the heart to pump blood at a rate commensurate with the requirements of the metabolising tissues. It affects 1-2% of the population rising to greater than 10% aged over 70 years. Despite recent therapeutic advances, annualized mortality can still approach 10%. HF results from a myocardial injury (e.g. myocardial infarction, chemotherapy) causing loss of myocytes, and maladaptive changes in surviving myocytes and extracellular matrix by ‘pathological remodelling’. That HF is characterized by structural and energetic changes was the principal motivation for the creation of an instrument to investigate changes in myocardial autofluorescence signature in disease states in vivo. If the signatures associated with known pathological diagnoses could be ascertained, such a technique could perform ‘virtual biopsy’ to aid diagnosis. This thesis describes the application of autofluorescence technique to an ex vivo Langendorff-heart to characterise the changes in autofluorescence signature with controlled insults of glucose deprivation and hypoxia. Additionally, it reports for the first-time the characterization of the autofluorescence lifetime signature in vivo at different time points in an established rat post-myocardial infarction heart failure. The thesis describes development of in vivo intravenous doxorubicin chemotherapy-cardiomyopathy heart failure model (DOX-HF) and subsequent characterization of in vivo autofluorescence signature. This investigation prompted development of a clinically viable instrument and the progress to date is described.Open Acces

On metabolic and phenotypic diversity in yeast

This thesis explores metabolic and phenotypic diversity in the two model yeasts Schizosaccharomyces pombe and Saccharomyces cerevisiae. Colony screens are a classical and powerful technique for investigating these topics, but there is a lack of modern, scalable bioinformatics tools. To address this need, I have developed pyphe which greatly facilitates colony screen data acquisition and statistical analysis. I explore optimal experimental designs, especially regarding the usefulness of timecourse imaging and colony viability analysis. Pyphe is used in a functional genomics screen, aiming to find functions for a set of largely uncharacterised lincRNAs. We identify hundreds of new lincRNA-associated phenotypes across numerous conditions and compare lincRNA phenotype profiles to those of codinggene mutants. Next, I have used pyphe to investigate the respiration/fermentation balance of wild S. pombe isolates. Contrary to the expectation that glucose completely represses respiration in this Crabtree-positive species, I find that strains generally strike a balance and that individual strains differ significantly in their residual respiration activity. This is associated with an unusual miss-sense variant in S. pombe’s sole pyruvate kinase gene. Its impact is dissected in detail, revealing a change in flux through pyruvate kinase and associated changes in gene expression, metabolism, growth and stress resistance. Finally, I explore how extracellular amino acids interact with cellular metabolism, with the aim of answering the important question whether or not clonal yeast cultures segregate into heterogeneous producer/consumer populations that exchange amino acids. I develop a novel proteomics-based method that characterises amino acid labelling patterns in peptides. I find that the supplementation of some, but not all amino acids completely suppresses selfsynthesis. However, I find no evidence for heterogeneous responses of our laboratory S. cerevisiae strain, but the functionality of the method is demonstrated clearly. Overall, this work represents several advancements to our understanding of yeast metabolism and physiology, as well as new experimental and computational methods