The influence of chlorine in indoor swimming pools on the composition of breathing phase of professional swimmers

Objectives: Swimming is one of the most popular forms of physical activity. Pool water is cleaned with chlorine, which - in combination with compounds contained in water - could form chloramines and trichloromethane in the swimmer’s lungs. The aim of the present study was to examine the effect of swimming training in an indoor pool on the composition of swimmers’ respiratory phase metabolomics, and develop a system to provide basic information about its impact on the swimmer’s airway mucosa metabolism, which could help to assess the risk of secondary respiratory tract diseases i.e. sport results, condition, and health including lung acute and chronic diseases). Design: A group of competitive swimmers participated in the study and samples of their respiratory phase before training, immediately after training, and 2 h after training were assessed. Methods: Sixteen male national and international-level competitive swimmers participated in this study. Respiratory phase analysis of the indoor swimming pool swimmers was performed. Gas chromatography combined with mass spectrometry (GCMS) was used in the measurements. All collected data were transferred to numerical analysis for trends of tracking and mapping. The breathing phase was collected on special porous material and analyzed using GCMS headspace. Results: The obtained samples of exhaled air were composed of significantly different metabolomics when compared before, during and after exercise training. This suggests that exposition to indoor chlorine causes changes in the airway mucosa Conclusion: This phenomenon may be explained by occurrence of a chlorine-initiated bio-reaction in the swimmers’ lungs. The obtained results indicate that chromatographic exhaled gas analysis is a sensitive method of pulmonary metabolomic changes assessment. Presented analysis of swimmers exhaled air indicates, that indoor swimming may be responsible for airway irritation caused by volatile chlorine compounds and their influence on lung metabolism

Applicability domains of neural networks for toxicity prediction

In this paper, the term "applicability domain" refers to the range of chemical compounds for which the statistical quantitative structure-activity relationship (QSAR) model can accurately predict their toxicity. This is a crucial concept in the development and practical use of these models. First, a multidisciplinary review is provided regarding the theory and practice of applicability domains in the context of toxicity problems using the classical QSAR model. Then, the advantages and improved performance of neural networks (NNs), which are the most promising machine learning algorithms, are reviewed. Within the domain of medicinal chemistry, nine different methods using NNs for toxicity prediction were compared utilizing 29 alternative artificial intelligence (AI) techniques. Similarly, seven NN-based toxicity prediction methodologies were compared to six other AI techniques within the realm of food safety, 11 NN-based methodologies were compared to 16 different AI approaches in the environmental sciences category and four specific NN-based toxicity prediction methodologies were compared to nine alternative AI techniques in the field of industrial hygiene. Within the reviewed approaches, given known toxic compound descriptors and behaviors, we observed a difficulty in being able to extrapolate and predict the effects with untested chemical compounds. Different methods can be used for unsupervised clustering, such as distance-based approaches and consensus-based decision methods. Additionally, the importance of model validation has been highlighted within a regulatory context according to the Organization for Economic Co-operation and Development (OECD) principles, to predict the toxicity of potential new drugs in medicinal chemistry, to determine the limits of detection for harmful substances in food to predict the toxicity limits of chemicals in the environment, and to predict the exposure limits to harmful substances in the workplace. Despite its importance, a thorough application of toxicity models is still restricted in the field of medicinal chemistry and is virtually overlooked in other scientific domains. Consequently, only a small proportion of the toxicity studies conducted in medicinal chemistry consider the applicability domain in their mathematical models, thereby limiting their predictive power to untested drugs. Conversely, the applicability of these models is crucial; however, this has not been sufficiently assessed in toxicity prediction or in other related areas such as food science, environmental science, and industrial hygiene. Thus, this review sheds light on the prevalent use of Neural Networks in toxicity prediction, thereby serving as a valuable resource for researchers and practitioners across these multifaceted domains that could be extended to other fields in future research

Steady-state detection, data reconciliation and machine learning for hybrid process modelling

In the process industry, it is possible to encounter systems whose behavior cannot be mapped through a first principles (white-box) model. Hybrid models aim at integrating data- driven (black-box) elements within white-box process models in order to fill the gap between the white-model model predictions and the actual system response. The goal of this Thesis is to propose and implement a hybrid modelling framework, and to assess its performance with respect to a white-box model

2021 Student Symposium Research and Creative Activity Book of Abstracts

The UMaine Student Symposium (UMSS) is an annual event that celebrates undergraduate and graduate student research and creative work. Students from a variety of disciplines present their achievements with video presentations. It’s the ideal occasion for the community to see how UMaine students’ work impacts locally – and beyond. The 2021 Student Symposium Research and Creative Activity Book of Abstracts includes a complete list of student presenters as well as abstracts related to their works

Estudio teórico y aplicado del potencial de la espectrometría de movilidad iónica

Ion mobility spectrometry (IMS) is an analytical technique based on the separation of gaseous ions under the influence of an electric field through an inert gas atmosphere. Some of the main limitations of IMS, depending on the context, may be the limited quantification capacity of compounds in real samples since narrow linear quantification ranges are normally obtained; the low selectivity due to the low resolution power of this type of equipment; and the difficulty of unequivocally identifying compounds in real samples since the existing databases are not as up-to-date as for other technologies such as mass spectrometry (MS). Therefore, it is evident that there is a demand for more selective methodologies and that provide greater analyte detection and quantification capacity. With these premises, it can be said that the greatest current challenge of the IMS is to maximize the detection capacity of the technique in order to achieve the unambiguous identification of a high number of analytes. This challenge is currently utopian when working with complex samples. For this reason, the main motivation of this Doctoral Thesis was to seek solutions for the different challenges that the IMS currently faces in a theoretical and applied context. The basic objective of the research was to explore the potential of IMS by using theoretical and applied strategies to improve the detection and identification coverage of the analysis carried out with this technology. These new strategies were applied throughout the main steps of the analytical process and allowed improving basic analytical features such as the selectivity and sensitivity of optimized analysis methods and their detection capacity. The achievement of this basic objective leaded to analysis methods of standards and real samples, such as explosives, drugs, soil, rosemary plant, olives and mainly different types of olive oils. This basic objective was divided into three general objectives according to the different research topics to address in this Doctoral Thesis: a) To take benefits derived from the study of theoretical aspects of IMS for improving the interpretation of IMS spectra and from the use of additional features such as structural information to enhance qualitative analysis; b) To develop approaches to improve the detection and identification capacity in IMS analysis; and c) To exploit the opportunities of gas chromatography (GC)-IMS and IMS devices for food analysis as an expanding application area in IMS based on untargeted analysis methods. In this context, the Thesis has included the following studies: (i) To study about the fundamentals of the formation of product ions through the modeling of ions stability using ab initio computations to math these results with the spectral patterns and structure of ions [1]. (ii) To explore the fragmentation of ions using an external electric field and the potential of the extra information of these fragments to enhance the rates of categorization by chemical class using neural networks [2]. (iii) To explore a thermal desorption (TD)-IMS device to obtain spectral fingerprints of Cannabis herbal samples, with and without pretreatment for rapid assignment to their different chemotypes by using principal component análisis (PCA) and linear discriminant analysis (LDA) [3]. (iv) To achieve the selectivity in response to trinitrotoluene (TNT) through reactive removal of interfering ions following mobility isolation using a tandem IMS with reactive stage as detection system [4]. (v) To develop a pioneer online coupling of supercritical fluid extraction (SFE) as sample introduction system (SIS) prior IMS using a column filled with Tenax TA material as sorbent trap to coupled both devices to improve analytical properties such as sensitivity and selectivity of future IMS methods [5]. (vi) To carry out a bibliographical study which gather and critically discuss recent publications related to analytical techniques to distinguish olive oils according to their quality as extra virgin (EVOO), virgin (VOO) or lampante (LOO) [6]. (vii) To investigate and compare different chemometric approaches for olive oil classification as EVOO, VOO or LOO using GC-IMS to get the most robust model over time [7]. (viii) To evaluate the combination of the results of orthogonal instrumental techniques to differentiate EVOO, VOO or LOO to imitate the expert panels [8]. (ix) To analyze olive and olive oil samples according with their production system to classify them as organic or conventional using ultraviolet (UV)-IMS, GC-IMS, GC-MS and/or capillary electrophoresis (CE)-UV [9].La espectrometría de movilidad iónica (IMS en inglés) es una técnica analítica que se basa en la separación de iones gaseosos bajo la influencia de un campo eléctrico a través de una atmósfera de gas inerte. Algunas de las principales limitaciones de la IMS, dependiendo del contexto, pueden ser la limitada capacidad de cuantificación de compuestos en muestras reales ya que se obtienen normalmente rangos lineales de cuantificación muy estrechos; la escasa selectividad debido al bajo poder de resolución de este tipo de equipos; y la dificultad de identificación de forma inequívoca de compuestos en muestras reales ya que las bases de datos existentes no están tan actualizadas como para otras tecnologías como la espectrometría de masas (MS en inglés). Por tanto, resulta evidente que existe una demanda de metodologías más selectivas y que proporcionen mayor capacidad de detección y cuantificación de analitos. Con estas premisas, se puede decir que el mayor reto actual de la IMS es maximizar la capacidad de detección de la técnica con el fin de conseguir la identificación inequívoca de un alto número de analitos. Este reto es actualmente utópico cuando se trabaja con muestras complejas. Por ello, la principal motivación de esta Tesis Doctoral fue buscar soluciones para los distintos retos a los que se enfrenta actualmente la IMS en un contexto teórico y aplicado. El objetivo básico de la investigación fue explorar el potencial de la IMS mediante el uso de estrategias teóricas y aplicadas para mejorar la capacidad de detección e identificación de los análisis realizados con esta tecnología. Estas nuevas estrategias se aplicaron a lo largo de las etapas principales del proceso analítico y permitieron mejorar características analíticas básicas, como la selectividad y la sensibilidad, de los métodos de análisis optimizados y su capacidad de detección. El logro de este objetivo básico condujo a métodos de análisis de estándares y muestras reales, como explosivos, drogas, suelo, plantas de romero, aceitunas y principalmente diferentes tipos de aceites de oliva. Este objetivo básico se dividió en tres objetivos generales de acuerdo con los diferentes temas de investigación para abordar en esta Tesis Doctoral: a) aprovechar los beneficios derivados del estudio de los aspectos teóricos de la IMS para mejorar la interpretación de los espectros de IMS y del uso de características adicionales como información estructural para mejorar el análisis cualitativo; b) desarrollar herramientas para mejorar la capacidad de detección e identificación en los análisis de IMS; y c) aprovechar las oportunidades de los instrumentos de cromatografía de gases (GC en inglés)-IMS e IMS para el análisis de alimentos como un área de aplicación en expansión en IMS basado en métodos de análisis no dirigidos. En este contexto, la Tesis ha incluido los siguientes estudios: (i) Estudiar los fundamentos de la formación de iones producto a través del modelado computacional de la estabilidad de los iones utilizando cálculos ab initio para combinarlos con los patrones espectrales y la estructura de los iones [1]. (ii) Explorar la fragmentación de iones utilizando un campo eléctrico externo y el potencial de la información adicional de estos fragmentos para mejorar las tasas de categorización por clase química utilizando redes neuronales [2]. (iii) Explorar un equipo de desorción térmica (TD en inglés)-IMS para obtener huellas espectrales de muestras de plantas de cannabis, con y sin pretratamiento, para la rápida asignación de los diferentes quimiotipos mediante análisis de componentes principales (PCA en inglés) y análisis discriminante lineal (LDA en inglés) [3]. (iv) Lograr la respuesta selectiva del trinitrotolueno (TNT en inglés) a través de la eliminación con etapa reactiva de iones interferentes usando el aislamiento de iones con un IMS en tándem con etapa reactiva como sistema de detección [4]. (v) Desarrollar un acoplamiento on-line pionero de la extracción con fluidos supercríticos (SFE en inglés) como sistema de introducción de muestra previo a la IMS utilizando una columna rellena con el material Tenax TA como trampa sorbente para acoplar ambos dispositivos para mejorar propiedades analíticas como la sensibilidad y la selectividad de futuros métodos IMS [5]. (vi) Realizar un estudio bibliográfico que reúna y discuta críticamente las publicaciones recientes relacionadas con técnicas analíticas para distinguir los aceites de oliva según su calidad como virgen extra (AOVE), virgen (AOV) o lampante (AOL) [6]. (vii) Investigar y comparar diferentes estrategias quimiométricas para la clasificación del aceite de oliva como AOVE, AOV o AOL utilizando la GC-IMS para obtener el modelo más robusto con el tiempo [7]. (viii) Evaluar la combinación de los resultados de técnicas instrumentales ortogonales para diferenciar AOVE, AOV o AOL para imitar los paneles de expertos [8]. (ix) Analizar muestras de aceitunas y aceite de oliva de acuerdo con su sistema de producción para clasificarlas como ecológicas o convencionales usando ultravioleta (UV)-IMS, GC-IMS, GC-MS y/o electroforesis capilar (CE en inglés)- UV [9]

Spectroscopic and process data fusion :enhanced monitoring of an industrial fermentation

PhD ThesisLarge scale manufacturing of pharmaceutical products is a highly competitive industry in which technological improvements can maintain fine business margins in the face of competition from those with lower manufacturing overheads. Processes in which pharmaceuticals are produced via fermentation are particularly susceptible to large variability and reduced productivity due to natural variation and limited monitoring and control options. The latest monitoring methods offer the potential to understand causes of variation, improve productivity and as a result maintain the competitive edge. Unfortunately the fermentation environment is not conducive to the implementation of instrumentation. This thesis shows how signals from spectral instruments can be enhanced by other process and spectroscopic measurements, to provide on-line measurements of critical broth concentrations traditionally only available from infrequent off-line analysis. Near infrared (NIR) and Mid infra red (MIR) spectral analysis of fermentation broth can provide measurements of key concentrations throughout a batch. The off-line analysis of broth samples is more straightforward but on-line implementation is possible. In the case of on-line implementation, the quality of information is compromised, placing greater demands on the signal interpretation methods. The objective of the thesis was to understand the causes of process variation and compensate for them during batch progression, consequently on-line implementation was essential. The construction of a robust calibration model for individual instruments is the first step in implementation. The traditional strategy is either to use multivariate techniques such as projection to latent structures (PLS) or wavelength selection through genetic algorithms followed by PLS. An alternative approach is developed where a search strategy identifies a limited number of spectral windows (SWS) that are most descriptive of the concentrations of interest. The benefit of using SWS is that problems associated with over-fitting the calibration model construction data are minimised. This is particularly important in a development environment where the number of batches is limited. The random nature of the search strategy of the SWS algorithm results in a range of calibration models. Multiple calibration models are `stacked' to provide improved accuracy and robustness. It is demonstrated that stacking provides an improved prediction capability compared to selecting the single `best' performing model. Finally, developing calibration models for sub-regions of fermentation operation is contrasted with a global model. The improvement in accuracy of measurements from SWS and stacking is significant but errors in the determination of the concentration of some compounds remained significant. To overcome these offsets, a model relating the calibration residuals to on-line process measurements was constructed using PLS. The model was then used to correct the spectral calibration prediction to result in improved determination of broth concentrations. The fermentation monitoring methodology is demonstrated by application to an industrial antibiotic production process. Corrected predictions of product concentration and broth nutrient levels demonstrate that combining multiple information sources is advantageous from a measurement perspective.Engineering and Physical Sciences Research Council(EPSRC): BatchPro: Centre for Process Analysis and Control Technology (CPACT)

Ab initio machine learning in chemical compound space

Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first principles based virtual sampling of this space, for example in search of novel molecules or materials which exhibit desirable properties, is therefore prohibitive for all but the smallest sub-sets and simplest properties. We review studies aimed at tackling this challenge using modern machine learning techniques based on (i) synthetic data, typically generated using quantum mechanics based methods, and (ii) model architectures inspired by quantum mechanics. Such Quantum mechanics based Machine Learning (QML) approaches combine the numerical efficiency of statistical surrogate models with an {\em ab initio} view on matter. They rigorously reflect the underlying physics in order to reach universality and transferability across CCS. While state-of-the-art approximations to quantum problems impose severe computational bottlenecks, recent QML based developments indicate the possibility of substantial acceleration without sacrificing the predictive power of quantum mechanics

Applications of Multiwavelets to Energies and Properties

The goal of quantum chemistry is to provide fast and accurate algorithms for the computation of molecular energies and properties. Over the years, a large number of methods have become available, and today computational chemists have a very rich toolbox of protocols that can be used to gain insight to chemistry. Gaussian type orbital (GTO) basis sets are at the core of most modern algorithms, and they have served the community well since they were first introduced. However, during the last 20 years, Multiwavelets (MWs) have emerged as a promising alternative to traditional GTO basis sets. Multiwavelets are built up from polynomial functions, and systematically approach completeness due to their robust mathematical foundation in multiresolution analysis.The in-house MW code MRChem has reached a level of maturity where it can be used to study chemical systems of 1000s of electrons. It provides functionality for the calculation of SCF energies, as well as electric and magnetic properties via density functional pertubation theory. This thesis presents the application of MWs in benchmark studies of static electric dipole polarizabilities (Paper I) and transition metal-ligand interaction energies (Paper II). Here we provide highly precise numerical results practically at the complete basis set limit, and with this reference we are able to quantify basis set incompleteness errors (BSIEs) in large GTO basis sets without ambiguity. The thesis also presents a prototype implementation of scalar relativistic effects via the zeroth order regular approximation into MRChem (Paper III, in preparation), and a preliminary quantification of BSIEs in several all-electron GTO basis sets for elements in the fifth row of the periodic table