peak picking und map alignment

We study two fundamental processing steps in mass spectrometric data analysis from a theoretical and practical point of view. For the detection and extraction of mass spectral peaks we developed an efficient peak picking algorithm that is independent of the underlying machine or ionization method, and is able to resolve highly convoluted and asymmetric signals. The method uses the multiscale nature of spectrometric data by first detecting the mass peaks in the wavelet-transformed signal before a given asymmetric peak function is fitted to the raw data. In two optional stages, highly overlapping peaks can be separated or all peak parameters can be further improved using techniques from nonlinear optimization. In contrast to currently established techniques, our algorithm is able to separate overlapping peaks of multiply charged peptides in LC-ESI-MS data of low resolution. Furthermore, applied to high-quality MALDI-TOF spectra it yields a high degree of accuracy and precision and compares very favorably with the algorithms supplied by the vendor of the mass spectrometers. On the high-resolution MALDI spectra as well as on the low-resolution LC-MS data set, our algorithm achieves a fast runtime of only a few seconds. Another important processing step that can be found in every typical protocol for labelfree quantification is the combination of results from multiple LC-MS experiments to improve confidence in the obtained measurements or to compare results from different samples. To do so, a multiple alignment of the LC-MS maps needs to be estimated. The alignment has to correct for variations in mass and elution time which are present in all mass spectrometry experiments. For the first time we formally define the multiple LC-MS raw and feature map alignment problem using our own distance function for LC-MS maps. Furthermore, we present a solution to this problem. Our novel algorithm aligns LC-MS samples and matches corresponding ion species across samples. In a first step, it uses an adapted pose clustering approach to efficiently superimpose raw maps as well as feature maps. This is done in a star-wise manner, where the elements of all maps are transformed onto the coordinate system of a reference map. To detect and combine corresponding features in multiple feature maps into a so-called consensus map, we developed an additional step based on techniques from computational geometry. We show that our alignment approach is fast and reliable as compared to five other alignment approaches. Furthermore, we prove its robustness in the presence of noise and its ability to accurately align samples with only few common ion species.Im Rahmen dieser Arbeit beschäftigen wir uns mit peak picking und map alignment; zwei fundamentalen Prozessierungsschritten bei der Analyse massenspektrometrischer Signale. Im Gegensatz zu vielen anderen peak picking Ansätzen haben wir einen Algorithmus entwickelt, der alle relevanten Informationen aus den massenspektrometrischen Peaks extrahiert und unabhängig von der analytischen Fragestellung und dem MS Instrument ist. Im ersten Teil dieser Arbeit stellen wir diesen generischen peak picking Algorithmus vor. Für die Detektion der Peaks nutzen wir die Multiskalen-Natur von MS Messungen und erlauben mit einem Wavelet-basierten Ansatz auch das Prozessieren von stark verrauschten und Baseline-behafteten Massenspektren. Neben der exakten m/z Position und dem FWHM Wert eines Peaks werden seine maximale Intensität sowie seine Gesamtintensität bestimmt. Mithilfe des Fits einer analytischen Peakfunktion extrahieren wir außerdem zusätzliche Informationen über die Peakform. Zwei weiterere optionale Schritte ermöglichen zum einen die Trennung stark überlappender Peaks sowie die Optimierung der berechneten Peakparameter. Anhand eines niedrig aufgelösten LC-ESI-MS Datensatzes sowie eines hoch aufgelösten MALDI-MS Datensatzes zeigen wir die Effizienz unseres generischen Algorithmus sowie seine schnelle Laufzeit im Vergleich mit kommerziellen peak picking Algorithmen. Ein direkter quantitativer Vergleich mehrer LC-MS Messungen setzt voraus, dass Signale des gleichen Peptids innerhalb unterschiedlicher Maps die gleichen RT und m/z Positionen besitzen. Aufgrund experimenteller Unsicherheiten sind beide Dimension verzerrt. Unabhängig vom Prozessierungsstand der LC-MS Maps müssen die Verzerrungen vor einem Vergleich der Maps korrigiert werden. Mithilfe eines eigens entwickelten Ähnlichkeitsmaßes für LC-MS Maps entwickeln wir die erste formale Definition des multiplen LC-MS Roh- und Featuremap Alignment Problems. Weiterhin stellen wir unseren geometrischen Ansatz zur Lösung des Problems vor. Durch die Betrachtung der LC-MS Maps als zwei-dimensionale Punktmengen ist unser Algorithmus unabhängig vom Prozessierungsgrad der Maps. Wir verfolgen einen sternförmigen Alignmentansatz, bei dem alle Maps auf eine Referenzmap abgebildet werden. Die Überlagerung der Maps erfolgt hierbei mithilfe eines pose clustering basierten Algorithmus. Diese Überlagerung der Maps löst bereits das Rohmap Alignment Problem. Zur Lösung des multiplen Featuremap Alignment Problems implementieren wir einen zusätzlichen, effizienten Gruppierungsschritt, der zusammengehörige Peptidsignale in unterschiedlichen Maps einander zuordnet. Wir zeigen die Effizienz und Robustheit unseres Ansatzes auf zwei realen sowie auf drei künstlichen Datensätzen. Wir vergleichen hierbei die Güte sowie die Laufzeit unseres Algorithmus mit fünf weiteren frei verfügbaren Featuremap-Alignmentmethoden. In allen Experimenten überzeugte unser Algorithmus mit einer schnellen Laufzeit und den besten recall Werten

Extraction of protein profiles from primary neurons using active contour models and wavelets

AbstractThe function of complex networks in the nervous system relies on the proper formation of neuronal contacts and their remodeling. To decipher the molecular mechanisms underlying these processes, it is essential to establish unbiased automated tools allowing the correlation of neurite morphology and the subcellular distribution of molecules by quantitative means.We developed NeuronAnalyzer2D, a plugin for ImageJ, which allows the extraction of neuronal cell morphologies from two dimensional high resolution images, and in particular their correlation with protein profiles determined by indirect immunostaining of primary neurons. The prominent feature of our approach is the ability to extract subcellular distributions of distinct biomolecules along neurites. To extract the complete areas of neurons, required for this analysis, we employ active contours with a new distance based energy. For locating the structural parts of neurons and various morphological parameters we adopt a wavelet based approach. The presented approach is able to extract distinctive profiles of several proteins and reports detailed morphology measurements on neurites.We compare the detected neurons from NeuronAnalyzer2D with those obtained by NeuriteTracer and Vaa3D-Neuron, two popular tools for automatic neurite tracing. The distinctive profiles extracted for several proteins, for example, of the mRNA binding protein ZBP1, and a comparative evaluation of the neuron segmentation results proves the high quality of the quantitative data and proves its practical utility for biomedical analyses

Computational methods and tools for protein phosphorylation analysis

Signaling pathways represent a central regulatory mechanism of biological systems where a key event in their correct functioning is the reversible phosphorylation of proteins. Protein phosphorylation affects at least one-third of all proteins and is the most widely studied posttranslational modification. Phosphorylation analysis is still perceived, in general, as difficult or cumbersome and not readily attempted by many, despite the high value of such information. Specifically, determining the exact location of a phosphorylation site is currently considered a major hurdle, thus reliable approaches are necessary for the detection and localization of protein phosphorylation. The goal of this PhD thesis was to develop computation methods and tools for mass spectrometry-based protein phosphorylation analysis, particularly validation of phosphorylation sites. In the first two studies, we developed methods for improved identification of phosphorylation sites in MALDI-MS. In the first study it was achieved through the automatic combination of spectra from multiple matrices, while in the second study, an optimized protocol for sample loading and washing conditions was suggested. In the third study, we proposed and evaluated the hypothesis that in ESI-MS, tandem CID and HCD spectra of phosphopeptides can be accurately predicted and used in spectral library searching. This novel strategy for phosphosite validation and identification offered accuracy that outperformed the other currently existing popular methods and proved applicable to complex biological samples. And finally, we significantly improved the performance of our command-line prototype tool, added graphical user interface, and options for customizable simulation parameters and filtering of selected spectra, peptides or proteins. The new software, SimPhospho, is open-source and can be easily integrated in a phosphoproteomics data analysis workflow. Together, these bioinformatics methods and tools enable confident phosphosite assignment and improve reliable phosphoproteome identification and reportin

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]

Mass spectrometry data mining for cancer detection

Early detection of cancer is crucial for successful intervention strategies. Mass spectrometry-based high throughput proteomics is recognized as a major breakthrough in cancer detection. Many machine learning methods have been used to construct classifiers based on mass spectrometry data for discriminating between cancer stages, yet, the classifiers so constructed generally lack biological interpretability. To better assist clinical uses, a key step is to discover ”biomarker signature profiles”, i.e. combinations of a small number of protein biomarkers strongly discriminating between cancer states. This dissertation introduces two innovative algorithms to automatically search for a signature and to construct a high-performance signature-based classifier for cancer discrimination tasks based on mass spectrometry data, such as data acquired by MALDI or SELDI techniques. Our first algorithm assumes that homogeneous groups of mass spectra can be modeled by (unknown) Gibbs distributions to generate an optimal signature and an associated signature-based classifier by robust log-likelihood analysis; our second algorithm uses a stochastic optimization algorithm to search for two lists of biomarkers, and then constructs a signature-based classifier. To support these two algorithms theoretically, this dissertation also studies the empirical probability distributions of mass spectrometry data and implements the actual fitting of Markov random fields to these high-dimensional distributions. We have validated our two signature discovery algorithms on several mass spectrometry datasets related to ovarian cancer and to colorectal cancer patients groups. For these cancer discrimination tasks, our algorithms have yielded better classification performances than existing machine learning algorithms and in addition,have generated more interpretable explicit signatures.Mathematics, Department o

Development of a novel high resolution and high throughput biosensing technology based on a Monolithic High Fundamental Frequency Quartz Crystal Microbalance (MHFF-QCM). Validation in food control

Tesis por compendio[ES] La sociedad actual demanda un mayor control en la seguridad y calidad de los alimentos que se consumen. Esta preocupación se ve reflejada en los diferentes planes estatales y europeos de investigación científica, los cuales, plantean la necesidad de innovar y desarrollar nuevas técnicas analíticas que cubran los requerimientos actuales. En el presente documento se aborda el problema de la presencia de residuos químicos en la miel. El origen de los mismos se debe, fundamentalmente, a los tramientos veterinarios con los que se tratan enfermedades y parásitos en las abejas, y a los tratamientos agrícolas con los que las abejas se ponen en contacto cuando recolectan el néctar en cultivos próximos a las colmenas. La Agencia Europea de Seguridad Alimentaria (EFSA) confirma esta realidad al notificar numerosas alertas sanitarias en la miel. En los últimos años, los métodos de análisis basados en inmunosensores piezoeléctricos se han posicionado como la base de una técnica de cribado muy prometedora, la cual puede ser empleada como técnica complementaria a las clásicas de cromatografía, gracias a su sencillez, rapidez y bajo coste. La tecnología de resonadores High-Fundamental Frequency Quartz Crystal Microbalance with Dissipation (HFF-QCMD) combina la detección directa en tiempo real, alta sensibilidad y selectividad con un fácil manejo y coste reducido en comparación con otras técnicas. Además, está tecnología permite aumentar el rendimiento del análisis mediante el diseño de arrays de resonadores en un mismo sustrato (Monolithic HFF-QCMD). En este documento se presenta el diseño de un array de 24 sensores HFF-QCMD, junto con un cartucho de micro-fluídica que traza diversos microcanales sobre los diferentes elementos sensores, a los que hace llegar la muestra de miel diluida a analizar. El cartucho actúa también como interfaz para realizar la conexión entre el array de resonadores y el instrumento de caracterización de los mismos. Para obtener el máximo partido del array diseñado, se desarrolla un método de medida robusto y fiable que permite elevar la tasa de adquisición de datos para facilitar la toma de registros eléctricos de un elevado número de resonadores de forma simultánea, e incluso en varios armónicos del modo fundamental de resonancia. La gran sensibilidad de la tecnología HFF-QCMD a los eventos bioquímicos a caracterizar se extiende también a otro tipo eventos externos, como son los cambios de temperatura o presión, lo que es necesario minimizar con el fin de reducir el impacto que estas perturbaciones no deseadas provocan en la estabilidad y fiabilidad de la medida. Con este fin, se desarrolla un algoritmo de procesado de señal basado en la Discrete Transform Wavelet (DTW). Finalmente, todos los desarrollos tecnológicos realizados se validan mediante la implementación de un inmunoensayo para la detección simultánea, en muestras de mieles reales, de residuos químicos de naturaleza química muy diferente, a saber, el fungicida tiabendazol y el antibiótico sulfatiazol.[CA] La societat actual demanda un major control en la seguretat i qualitat dels aliments que es consumeixen. Aquesta preocupació es veu reflectida en els diferents plans estatals i europeus d'investigació científica, els quals, plantegen la necessitat d'innovar i desenvolupar noves tècniques analítiques que cobrisquen els requeriments actuals. En el present document s'aborda el problema de la presència de residus químics en la mel. L'origen dels mateixos es deu, fonamentalment, als tractaments veterinaris amb els quals es tracten malalties i paràsits en les abelles, i als tractaments agrícoles amb els quals les abelles es posen en contacte quan recol·lecten el nèctar en cultius pròxims als ruscos. L'Agència Europea de Seguretat Alimentària (EFSA) confirma aquesta realitat notificant nombroses alertes sanitàries en la mel. En els últims anys, els mètodes d'anàlisis basades en immunosensors piezoelèctrics s'han posicionat com la base d'una tècnica de garbellat molt prometedora, la qual pot ser emprada com a tècnica complementària a les clàssiques de cromatografia, gràcies a la seua senzillesa, rapidesa i baix cost. La tecnologia de ressonadors High-Fundamental Frequency Quartz Crystal Microbalance with Dissipation (HFF-QCMD) combina la detecció directa en temps real, alta sensibilitat i selectivitat amb un fàcil maneig i cost reduït en comparació amb altres tècniques. A més, està tecnologia permet augmentar el rendiment del anàlisi mitjançant el disseny d'arrays de ressonadors en un mateix substrat (Monolithic HFF-QCMD). En aquest document es presenta el disseny d'un array de 24 sensors HFF-QCMD, juntament amb un cartutx de microfluídica que estableix diversos microcanals sobre els diferents elements sensors, als quals fa arribar la mostra de mel diluïda a analitzar. El cartutx actua també com a interfície per a realitzar la connexió entre l'array de ressonadors i l'instrument de caracterització d'aquests. Per a traure el màxim partit a l'array dissenyat, es desenvolupa un mètode de mesura robust i fiable que permet elevar la taxa d'adquisició de dades per a facilitar la presa de registres elèctrics d'un elevat nombre de ressonadors de manera simultània, i fins i tot en diversos harmònics del mode fonamental de ressonància. La gran sensibilitat de la tecnologia HFF-QCMD als esdeveniments bioquímics a caracteritzar s'estén també a un altre tipus esdeveniments externs, com són els canvis de temperatura o pressió, la qual cosa és necessari minimitzar amb la finalitat de reduir l'impacte que aquestes pertorbacions no desitjades provoquen en l'estabilitat i fiabilitat de la mesura. A aquest efecte, es desenvolupa un algorisme de processament de senyal basat en la Discrete Transform Wavelet (DTW). Finalment, tots els desenvolupaments tecnològics realitzats es validen mitjançant la implementació d'un immunoassaig per a la detecció simultània, en mostres de mel reals, de residus químics de naturalesa química molt diferent, a saber, el fungicida tiabendazol i l'antibiòtic sulfatiazol.[EN] Currently, society demands greater control over the safety and quality of the food consumed. This concern is reflected in the different states and European plans for scientific research, which establish the necessity to innovate and develop new analytical techniques that meet current requirements. This document addresses the problem of the presence of chemical residues in honey. Its origin is fundamentally due to the veterinary treatments against diseases and parasites in bees, and also to the agricultural treatments with which the bees come into contact when they collect the nectar in crops close to the hives. The European Food Safety Agency (EFSA) confirms this reality by notifying numerous health alerts in honey. In recent years, analysis methods based on piezoelectric immunosensors have been positioned as the basis of a very promising screening technique, which can be used as a complementary technique to the classic chromatography, thanks to its simplicity, speed and low cost. High-Fundamental Frequency Quartz Crystal Microbalance with Dissipation (HFF-QCMD) resonator technology combines direct real-time detection, high sensitivity and selectivity with easy handling and low cost compared to other techniques. In addition, this technology allows increasing the performance of the analysis through the design of resonator arrays on the same substrate (Monolithic HFF-QCMD). This document presents the design of an array of 24 HFF-QCMD sensors, together with a microfluidic cartridge that establish various microchannels on the different sensor elements, to provide them the diluted honey sample to be analyzed. The cartridge also acts as an interface to make the connection between the array of resonators and the characterization instrument. To get the most out of the designed array, a robust and reliable measurement method has been developed that allows increasing the data acquisition rate to facilitate electrical parameters readout from a high number of resonators simultaneously, and even in several harmonics of the fundamental resonance mode. The great sensitivity of the HFF-QCMD technology to the biochemical events to be characterized also is extended to other types of external events, such as changes in temperature or pressure, which must be minimized in order to reduce the impact that these unwanted disturbances cause in the stability and reliability of the measurement. To this end, a signal processing algorithm based on the Discrete Transform Wavelet (DTW) is developed. Finally, all the technological developments carried out are validated through the implementation of an immunoassay for the simultaneous detection, in real honey samples, of chemical residues of very different chemical nature, namely, the fungicide thiabendazole and the antibiotic sulfathiazole.The authors would also like to thank Jorge Martínez from the Laboratory of High Frequency Circuits (LCAF) of the Universitat Politècnica de València (UPV) for assistance with profilometry, and Manuel Planes, José Luis Moya, Mercedes Tabernero, Alicia Nuez and Joaquin Fayos from the Electron Microscopy Services of the UPV for helping with the AFM, and SEM measurements. M.Calero is the recipient of the doctoral fellowship BES-2017-080246 from the Spanish Ministry of Economy, Industry and Competitiveness (Madrid, Spain). This research was funded by Spanish Ministry of Economy and Competitiveness with FEDER funds (AGL 2016-77702-R) and European Commission Horizon 2020 Programme (Grant Agreement number H2020-FETOPEN-2016-2017/737212-CATCH-U-DNA - Capturing non-Amplified Tumor Circulating DNA with Ultrasound Hydrodynamics) for which the authors are grateful. Román Fernández is with the Center for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, València, Spain and with Advanced Wave Sensors S.L., Paterna, València, Spain. (e-mail: [email protected]); Yolanda Jiménez, Antonio Arnau and María Calero are with the Center for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, València, Spain; Ilya Reiviakine is with Advanced Wave Sensors S.L., Paterna, Valencia, Spain and with the Department of Bioengineering, University of Washington, Seattle, WA, 98150 USA; María Isabel Rocha-Gaso and José Vicente García are with Advanced Wave Sensors S.L., Paterna, València, Spain.Calero Alcarria, MDS. (2022). Development of a novel high resolution and high throughput biosensing technology based on a Monolithic High Fundamental Frequency Quartz Crystal Microbalance (MHFF-QCM). Validation in food control [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182652TESISCompendi

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

Computational Methods on Study of Differentially Expressed Proteins in Maize Proteomes Associated with Resistance to Aflatoxin Accumulation

Plant breeders have focused on improving maize resistance to Aspergillus flavus infection and aflatoxin accumulation by breeding with genotypes having the desirable traits. Various maize inbred lines have been developed for the breeding of resistance. Identification of differentially expressed proteins among such maize inbred lines will facilitate the development of gene markers and expedite the breeding process. Computational biology and proteomics approaches on the investigation of differentially expressed proteins were explored in this research. The major research objectives included 1) application of computational methods in homology and comparative modeling to study 3D protein structures and identify single nucleotide polymorphisms (SNPs) involved in changes of protein structures and functions, which can in turn increase the efficiency of the development of DNA markers; 2) investigation of methods on total protein profiling including purification, separation, visualization, and computational analysis at the proteome level. Special research goals were set on the development of open source computational methods using Matlab image processing tools to quantify and compare protein expression levels visualized by 2D protein electrophoresis gel techniques