Littérature scientifique sur le sujet « Analytical variability »

Les travaux de recherche présentés portent sur l’étude de la variabilité inter et intra spécifique des substances extractibles des écorces d’Abies alba, Picea abies, Pseudotsuga menziesii, Quercus robur et Fagus sylvatica. La connaissance de la variabilité de la composition chimique des écorces est un outil d’aide à la décision pour une valorisation industrielle future des écorces, qui apporterait une plus-value à la filière bois. Ainsi, les constituants des écorces sont séparés en deux catégories avec les polyphénols qui ont été analysés principalement à partir des extraits eau/éthanol et les biopolymères qui ont été extraits à partir des résidus d’écorces provenant des extractions successives. Pour les extractions eau/éthanol (1:1), 5 essences avec 8 arbres par essence et plus de 10 hauteurs par arbres ont été extraits. Une augmentation des taux d’extraits avec la hauteur a été observée pour les résineux et d’après ces résultats, il semble que les résineux contiennent davantage de matières extractibles que les feuillus et que la quantité d’extractibles augmente avec la hauteur. Pour les extractions successives avec le mélange toluène/éthanol (2:1), puis avec l’éthanol (100%), seulement 4 arbres par essence ont été extraits car il s’agissait essentiellement de récupérer des écorces dépourvues d’extractibles en vue d’analyser les biopolymères. Par la suite, les extraits d’écorces ont été étudiés grâce à des méthodes analytiques complémentaires que sont la LC-UV-MS, GC-MS, IR, RMN1H, MALDI-TOF, SEC ainsi que des tests spécifiques permettant de déterminer les taux de phénols totaux, holocellulose, α-cellulose, hémicellulose, lignine, subérine et cendres. Les résultats mettent en évidence l’existence d’une variabilité de la composition chimique des écorces de résineux en fonction de la hauteur : les taux de polyphénols, subérine, lignine et holocellulose diminuent avec la hauteur tandis que les taux de terpènes et de cendres augmentent avec la hauteur. A l’aide du même protocole analytique, une variabilité interarbres importante a été mise en évidence pour les écorces de feuillus. Certaines différences observées ont été expliquées par des paramètres biologiques tels que la hauteur, l’âge des tissus, les conditions de stockage des écorces ou encore une allométrie différente entre les arbres<br>This work focuses on the study of the inter and intra specific variability of the extractives substances of the barks of Abies alba, Picea abies, Pseudotsuga menziesii, Quercus robur and Fagus sylvatica. New knowledge about the variability in chemical composition of different bark types can be used as a support decision tool for a future industrial valorization, which would bring an added value to the wood industry. Thus, the constituents of the bark are separated into two categories with the polyphenols which were analyzed mainly from the water/ethanol extracts and the biopolymers which were extracted from the bark residues from the successive extractions. For the water/ethanol extractions (1:1), 5 species with 8 trees per species and more than 10 heights per tree were extracted. An increase of extractives rates with height was observed for softwoods and from these results it appears that softwoods contain more extractives than hardwoods and that the amount of extractive increases with height. For the successive extractions with the toluene / ethanol mixture (2:1), then with the ethanol (100%), only 4 trees by essence were extracted because it was essentially a question of recovering bark devoid of extractives in view of analyze biopolymers. Subsequently, the bark extracts were studied using complementary analytical methods such as LC-UV-MS, GC-MS, IR, 1H NMR, MALDI-TOF, SEC as well as specific tests to determine the levels. total phenols, holocellulose, α-cellulose, hemicellulose, lignin, suberin and ash. The results demonstrate the existence of variability in the chemical composition of softwood bark as a function of height: the levels of polyphenols, suberin, lignin and holocellulose decrease with height while the levels of terpenes and ash increase. with height. Using the same analytical protocol, significant inter-tree variability was demonstrated for hardwood bark. Some observed differences have been explained by biological parameters such as height, age of tissues, bark storage conditions or even different allometry between trees

Medidas para gerenciamento de saúde e segurança em laboratórios são de extrema importância em laboratórios químicos. As pessoas que realizam qualquer atividade em um ambiente de laboratório estão expostas a diversos perigos e, consequentemente, existe o risco de ocorrência de eventos adversos para a saúde e segurança. Este trabalho foi desenvolvido em um laboratório químico de uma universidade federal tem como principal objetivo o aperfeiçoamento de uma framework que permite a realização de uma análise sistemática qualitativa e quantitativa das folgas presentes em um sistema sócio-técnico complexo. Ferramentas da Engenharia de Resiliência foram utilizadas para estudar o laboratório, o qual foi considerado como um sistema sócio-técnico complexo. Uma das características de um sistema resiliente é a capacidade de lidar com a variabilidade, o que pode ser obtido por meio de recursos de folgas (slack) no sistema. O uso da framework permitiu obter dados importantes para a análise do sistema e sugestões de melhorias. Os aperfeiçoamentos propostos na framework mostraram-se eficazes, principalmente na quantificação das folgas e variabilidades, em função da utilização do método AHP (Analytical Hierarchy Process) para a análise de dados. O método AHP tornou possível substituir o uso de questionários para toda a equipe por uma avaliação direcionada a especialistas. Ao utilizar o AHP, os dados podem ser adquiridos com maior rapidez. Outro ganho obtido com o uso do método AHP foi a possibilidade de redução de uma etapa da framework, tornando-a mais concisa.<br>Measures for health and safety management are of paramount importance in chemical laboratories. People who perform any activity in a laboratory environment are exposed to a variety of hazards and consequently there is a risk of adverse health and safety events. This work was developed in a chemical laboratory of a federal university, and has as main objective the improvement of a framework that allows the accomplishment of a systematic qualitative and quantitative analysis of the slack present in a complex socio-technical system. Tools of Resilience Engineering were used for studying a chemical laboratory, which was considered as a complex socio-technical system. One of the characteristics of a resilient system is the ability to deal with variability, which can be obtained through slack resources in the system. This work was developed in a chemical laboratory of a federal university and consists in the improvement of a framework that allows the accomplishment of a systematic qualitative and quantitative analysis of the slack present in the system. The use of the framework allowed to obtain data important for the analysis of the system and suggestions for improvements. The improvements proposed in the framework proved to be effective, especially in the quantification of slack and variability, as a function of the AHP (Analytical Hierarchy Process) method for data collection. The AHP method made it possible to replace the use of questionnaires for the entire team by an expert team assessment. By using AHP, data can be acquired more quickly. Another gain obtained with the use of the AHP method was the possibility of reducing one stage of the framework, making it more concise.

Clinical applications for heart rate variability (HRV) have become increasingly popular, gaining momentum and value as societies increased understanding of physiology reveals their true potential to reflect health. An additional reason for the rising popularity of HRV analysis, along with many other algorithmic based medical processes, is the relatively recent exponential increase of computing power and capabilities. Despite this many medical standards lag behind this booming increase in scientific knowledge, as the risks and precautions involved with healthcare necessarily take priority. Resultantly, the standards which pertain to the acceptable tolerance for accurate R-peak detection have remain unchanged for decades. For similar reasons, medical software is also prone to lag behind state-of-the-art developments. Yet, society is currently on the precipice of an age of high computational abilities, mass data storage, and capabilities to apply deep learning algorithms to reveal patterns that were previously inconceivable. So, when considering the needs of the future in relation to the place of HRV in healthcare, there is a distinct need for its accurate and precise collection, storage, and processing. In the work presented in this dissertation, the overarching aim was to increase the reliability of electrocardiogram (ECG) based HRV for use in predictive health analytics. To ensure both clarity and attainability, this project-level aim was broken down and addressed in a series of several works. The first a im w ork w as t o address the problems associated with the precision specified f or a ccurate p eak d etection, and thereby increase the reliability of predictive health analytics generated using HRV metrics. The study conducted around this initial aim investigates the specifics of match window requirements, clarifies the difference between fiducial marker and QRS complex detection, and makes recommendations on the precision required for accurate HRV metric extraction. In the second work, the aim was to ensure that there is a reliable foundation for the conduction of HRV-related research. Here, a thorough investigation of relevant literature revealed the lack of a suitable software, particularly for research requiring the analysis of large databases. Consequently, an improved HRV analysis platform was developed. Through use of both user-feedback and quantitative comparison to highly regarded software, the proposed platform is shown to offer a similar standard in estimated HRV metrics but requires significantly l ess manual e ffort (batch-processing approach) than the traditional single patient focused approach. The third work also addressed this aim, providing the base peak detection algorithm implemented within the HRV analysis platform. Experimentation undertaken here ensured that the developed algorithm performed precise fiducial marker detection, thereby increasing the reliability of the generated HRV metrics (measured against the framework presented in the first work). In the fourth work, the aim was to address the lack of published literature on the relationship between ECG sampling frequency (fs) and extracted HRV, in order to further ensure the reliability of predictive health analytics generated using HRV metrics. Here, a quantitative experimental approach was taken to evaluate the impact of ECG fs on subsequent estimations of HRV. This experimentation resulted in a recommendation for the minimum required ECG fs for reliable HRV extraction. The aim of the final work was to further improve the foundation for future predicative health analytics, by developing a robust pre-processing algorithm capable of autonomous detection of regions of valid ECG signal. This type of algorithm should be considered of critical importance to furthering machine learning (ML) based applications in the medical field. ML algorithms are heavily reliant on access to vast amounts of data, and without an automated pre-processing stage would require an unrealistic amount of hand-processing for implementation.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Eng & Built Env<br>Science, Environment, Engineering and Technology<br>Full Text