Multi-sensor classification of tennis strokes

In this work, we investigate tennis stroke recognition using a single inertial measuring unit attached to a player’s forearm during a competitive match. This paper evaluates the best approach for stroke detection using either accelerometers, gyroscopes or magnetometers, which are embedded into the inertial measuring unit. This work concludes what is the optimal training data set for stroke classification and proves that classifiers can perform well when tested on players who were not used to train the classifier. This work provides a significant step forward for our overall goal, which is to develop next generation sports coaching tools using both inertial and visual sensors in an instrumented indoor sporting environment.

Digital signal processing and artificial intelligence for the automated classification of food allergy

As a by-product of the ‘information revolution’ which is currently unfolding, lifetimes of man (and indeed computer) hours are being allocated for the automated and intelligent interpretation of data. This is particularly true in medical and clinical settings, where research into machine-assisted diagnosis of physiological conditions gains momentum daily. Of the conditions which have been addressed, however, automated classification of allergy has not been investigated, even though the numbers of allergic persons are rising, and undiagnosed allergies are most likely to elicit fatal consequences. On the basis of the observations of allergists who conduct oral food challenges (OFCs), activity-based analyses of allergy tests were performed. Algorithms were investigated and validated by a pilot study which verified that accelerometer-based inquiry of human movements is particularly well-suited for objective appraisal of activity. However, when these analyses were applied to OFCs, accelerometer-based investigations were found to provide very poor separation between allergic and non-allergic persons, and it was concluded that the avenues explored in this thesis are inadequate for the classification of allergy. Heart rate variability (HRV) analysis is known to provide very significant diagnostic information for many conditions. Owing to this, electrocardiograms (ECGs) were recorded during OFCs for the purpose of assessing the effect that allergy induces on HRV features. It was found that with appropriate analysis, excellent separation between allergic and nonallergic subjects can be obtained. These results were, however, obtained with manual QRS annotations, and these are not a viable methodology for real-time diagnostic applications. Even so, this was the first work which has categorically correlated changes in HRV features to the onset of allergic events, and manual annotations yield undeniable affirmation of this. Fostered by the successful results which were obtained with manual classifications, automatic QRS detection algorithms were investigated to facilitate the fully automated classification of allergy. The results which were obtained by this process are very promising. Most importantly, the work that is presented in this thesis did not obtain any false positive classifications. This is a most desirable result for OFC classification, as it allows complete confidence to be attributed to classifications of allergy. Furthermore, these results could be particularly advantageous in clinical settings, as machine-based classification can detect the onset of allergy which can allow for early termination of OFCs. Consequently, machine-based monitoring of OFCs has in this work been shown to possess the capacity to significantly and safely advance the current state of clinical art of allergy diagnosis

Neonatal EEG classification using atomic decomposition

The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.

Genetic Studies Identify Critical Biomarkers and Refine the Classification of Malignant Gliomas

Gliomagenesis is driven by a complex network of genetic alterations and while the glioma genome has been a focus of investigation for many years; critical gaps in our knowledge of this disease remain. The identification of novel molecular biomarkers remains a focus of the greater cancer community as a method to improve the consistency and accuracy of pathological diagnosis. In addition, novel molecular biomarkers are drastically needed for the identification of targets that may ultimately result in novel therapeutics aimed at improving glioma treatment. Through the identification of new biomarkers, laboratories will focus future studies on the molecular mechanisms that underlie glioma development. Here, we report a series of genomic analyses identifying novel molecular biomarkers in multiple histopathological subtypes of glioma and refine the classification of malignant gliomas. We have completed a large scale analysis of the WHO grade II-III astrocytoma exome and report frequent mutations in the chromatin modifier, alpha thalassemia mental retardation x-linked (ATRX), isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2), and mutations in tumor protein 53 (TP53) as the most frequent genetic mutations in low grade astrocytomas. Furthermore, by analyzing the status of recurrently mutated genes in 363 brain tumors, we establish that highly recurrent gene mutational signatures are an effective tool in stratifying homogeneous patient populations into distinct groups with varying outcomes, thereby capable of predicting prognosis. Next, we have established mutations in the promoter of telomerase reverse transcriptase (TERT) as a frequent genetic event in gliomas and in tissues with low rates of self renewal. We identify TERT promoter mutations as the most frequently mutated gene in primary glioblastoma. Additionally, we show that TERT promoter mutations in combination with IDH1 and IDH2 mutations are able to delineate distinct clinical tumor cohorts and are capable of predicting median overall survival more effectively than standard histopathological diagnosis alone. Taken together, these data advance our understanding of the genetic alterations that underlie the transformation of glial cells into neoplasms and we provide novel genetic biomarkers and multi – gene mutational signatures that can be utilized to refine the classification of malignant gliomas and provide opportunity for improved diagnosis.

NAFTA and the apparel commodity chain: Corporate strategies, interfirm networks, and industrial upgrading

The apparel industry is one of the oldest and largest export industries in the world, with global trade and production networks that connect firms and workers in countries at all levels of economic development. This chapter examines the impact of the North American Free Trade Agreement (NAFTA) as one of the most recent and significant developments to affect patterns of international trade and production in the apparel and textile industries. Tr ade policies are changing the institutional environment in which firms in this industry operate, and companies are responding to these changes with new strategies designed to increase their profitability and strengthen their control over the apparel commodity chain. Our hypothesis is that lead firms are establishing qualitatively different kinds of regional production networks in North America from those that existed prior to NAFTA, and that these networks have important consequences for industrial upgrading in the Mexican textile and apparel industries. Post-NAFTA crossborder production arrangements include full-package networks that link lead firms in the United States with apparel and textile manufacturers, contractors, and suppliers in Mexico. Full-package production is increasing the local value added provided by the apparel commodity chain in Mexico and creating new opportunities for Mexican firms and workers. The chapter is divided into four main sections. The first section uses trade and production data to analyze shifts in global apparel flows, highlighting the emergence and consolidation of a regional trade bloc in North America. The second section discusses the process of industrial upgrading in the apparel industry and introduces a distinction between assembly and full-package production networks. The third section includes case studies based on published industry sources and strategic interviews with several lead companies whose strategies are largely responsible for the shifting trade patterns and NAFTA-inspired cross-border production networks discussed in the previous section. The fourth section considers the implications of these changes for employment in the North American apparel industry. © 2009 by Temple University Press. All rights reserved.

Economic and Social Upgrading in Global Value Chains and Industrial Clusters: Why Governance Matters

© 2014, Springer Science+Business Media Dordrecht.The burgeoning literature on global value chains (GVCs) has recast our understanding of how industrial clusters are shaped by their ties to the international economy, but within this context, the role played by corporate social responsibility (CSR) continues to evolve. New research in the past decade allows us to better understand how CSR is linked to industrial clusters and GVCs. With geographic production and trade patterns in many industries becoming concentrated in the global South, lead firms in GVCs have been under growing pressure to link economic and social upgrading in more integrated forms of CSR. This is leading to a confluence of “private governance” (corporate codes of conduct and monitoring), “social governance” (civil society pressure on business from labor organizations and non-governmental organizations), and “public governance” (government policies to support gains by labor groups and environmental activists). This new form of “synergistic governance” is illustrated with evidence from recent studies of GVCs and industrial clusters, as well as advances in theorizing about new patterns of governance in GVCs and clusters.

Are the reference values of B cell subpopulations used in adults for classification of common variable immunodeficiencies appropriate for children?

Detailed phenotypic characterization of B cell subpopulations is of utmost importance for the diagnosis and management of humoral immunodeficiencies, as they are used for classification of common variable immunodeficiencies. Since age-specific reference values remain scarce in the literature, we analysed by flow cytometry the proportions and absolute values of total, memory, switched memory and CD21(-/low) B cells in blood samples from 168 healthy children (1 day to 18 years) with special attention to the different subpopulations of CD21(low) B cells. The percentages of total memory B cells and their subsets significantly increased up to 5-10 years. In contrast, the percentages of immature CD21(-) B cells and of immature transitional CD21(low)CD38(hi) B cells decreased progressively with age, whereas the percentage of CD21(low) CD38(low) B cells remained stable during childhood. Our data stress the importance of age-specific reference values for the correct interpretation of B cell subsets in children as a diagnostic tool in immunodeficiencies.

Interacción genotipo por ambiente sobre caracteres de calidad comercial e industrial en trigo pan (Triticum aestivum L.)

El genotipo (G), el ambiente (A) y la interacción G x A pueden influir de manera diferente sobre las característica que definen la calidad comercial e industrial de trigo pan. Los objetivos de esta tesis fueron: 1) Caracterizar el rendimiento y a sus componentes en cultivares de trigo pertenecientes a diferentes grupos de calidad, expuestos a ofertas de nitrógeno contrastantes. 2) Estudiar el impacto de distinta disponibilidad de nitrógeno sobre los componentes fisiológicos del llenado de los granos (i.e tasa y duración) en distintaas variedades de trigo pan y su posible efecto sobre los parámetros de calidad. 3) Caracterizar y cuantificar la interacción genotipo por ambiente sobre la expresión de los parámetros que determinan el rendimiento y la calidad comercial e industrial del trigo pan en ambientes con diferentes disponibilidades de nitrógeno. Se realizaron ensayos en dos localidades, durante dos años, utilizando seis variedades de distinta aptitud panadera (2 de cada grupo de clasificación por grupo de calidad -GC-), aplicando cuatro tratamientos de fertilización nitrogenadas. Se evaluó el efecto genético, ambiental y la interacción G x A, sobre el rendimiento y sus componentes, el peso de los granos y sus componentes y sobre los parámetros de calidad comercial e industrial. Los resultados mostraron que el rendimiento y sus componentes (número de granos, biomasa aérea, eficiencia de uso de la radiación interceptada acumulada) fueron afectados principalmente por el ambiente y el manejo nutricional dentro de de cada ambiente. Para el peso de los granos y sus componentes (tasa y duración) el efecto del manejo del nitrógeno no fue importante, aunque sí lo fue el efecto genotipo. Para los parámetros de calidad el efecto genotipo fue más importante solo para la tenacidad, mientras que el peso hectolítrico, gluten húmedo, fuerza panadera, la relación de equilibrio (P/L) y volumen de pan fueron modificados principalmente por el efecto ambiente no manejable como son el año y la localidad, en tanto la proteína fue afectada principalmente por el factor ambiental asociado al manejo nutricional. La interacción GxA fue el efecto que explicó en mayor medida las variaciones de rendimiento de harina, absorción de agua y tiempo de amasado. La fuerte interacción GxA observada para la mayoría de los parámetros de calidad determinó que variedades de un determinado GC cambien de grupo asociado principalmente a factores ambientales como la localidad y el año, mientras que el manejo nutricional tuvo un impacto menor

Sheep carcasse classification as a tool to improve the marketing system

p.103-111

Momento de ocurrencia, magnitud y reiteración de lluvias como determinante del lavado del grano de trigo y su efecto en la calidad comercial e industrial

p.45-53

Interacción genotipo por ambiente sobre caracteres de calidad comercial e industrial en trigo pan (Triticum aestivum L.)

El genotipo (G), el ambiente (A)y la interacción G x A pueden influir de manera diferente sobre las característica que definen la calidad comercial e industrial de trigo pan. Los objetivos de esta tesis fueron: 1)Caracterizar el rendimiento y a sus componentes en cultivares de trigo pertenecientes a diferentes grupos de calidad, expuestos a ofertas de nitrógeno contrastantes. 2)Estudiar el impacto de distinta disponibilidad de nitrógeno sobre los componentes fisiológicos del llenado de los granos (i.e tasa y duración)en distintaas variedades de trigo pan y su posible efecto sobre los parámetros de calidad. 3)Caracterizar y cuantificar la interacción genotipo por ambiente sobre la expresión de los parámetros que determinan el rendimiento y la calidad comercial e industrial del trigo pan en ambientes con diferentes disponibilidades de nitrógeno. Se realizaron ensayos en dos localidades, durante dos años, utilizando seis variedades de distinta aptitud panadera (2 de cada grupo de clasificación por grupo de calidad -GC-), aplicando cuatro tratamientos de fertilización nitrogenadas. Se evaluó el efecto genético, ambiental y la interacción G x A, sobre el rendimiento y sus componentes, el peso de los granos y sus componentes y sobre los parámetros de calidad comercial e industrial. Los resultados mostraron que el rendimiento y sus componentes (número de granos, biomasa aérea, eficiencia de uso de la radiación interceptada acumulada)fueron afectados principalmente por el ambiente y el manejo nutricional dentro de de cada ambiente. Para el peso de los granos y sus componentes (tasa y duración)el efecto del manejo del nitrógeno no fue importante, aunque sí lo fue el efecto genotipo. Para los parámetros de calidad el efecto genotipo fue más importante solo para la tenacidad, mientras que el peso hectolítrico, gluten húmedo, fuerza panadera, la relación de equilibrio (P/L)y volumen de pan fueron modificados principalmente por el efecto ambiente no manejable como son el año y la localidad, en tanto la proteína fue afectada principalmente por el factor ambiental asociado al manejo nutricional. La interacción GxA fue el efecto que explicó en mayor medida las variaciones de rendimiento de harina, absorción de agua y tiempo de amasado. La fuerte interacción GxA observada para la mayoría de los parámetros de calidad determinó que variedades de un determinado GC cambien de grupo asociado principalmente a factores ambientales como la localidad y el año, mientras que el manejo nutricional tuvo un impacto menor

Sheep carcasse classification as a tool to improve the marketing system

p.103-111

Efecto del pastoreo del trigo sobre el rendimiento y la calidad industrial del grano

p.93-102

Modelling of industrial multi-physics processes—a key role for computational mechanics.

In the analysis of industrial processes, there is an increasing emphasis on systems governed by interacting continuum phenomena. Mathematical models of such multi-physics processes can only be achieved for practical simulations through computational solution procedures—computational mechanics. Examples of such multi-physics systems in the context of metals processing are used to explore some of the key issues. Finite-volume methods on unstructured meshes are proposed as a means to achieve efficient rapid solutions to such systems. Issues associated with the software design, the exploitation of high performance computers, and the concept of the virtual computational-mechanics modelling laboratory are also addressed in this context.