Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Chemical reaction network of flavylium ions in heterogeneous media

Dissertação apresentada para a obtenção do Grau de Doutor em Química, especialidade em Química-Física, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

The strategic deployment of quality-improvement innovations

Journal of Business, Vol. 78 Issue 3, p1049-1072

New analytical methodologies for doping control – detection of anabolic androgenic steroids in human urine

Dissertation submitted to Faculdade de Ciências e Tecnologia - Universidade Nova de Lisboa in fulfilment of the requirements for the degree of Doctor of Philosophy (Biochemistry - Biotechnology)

Stochastic modeling of the thermal and catalytic degradation of polyethylene using simultaneous DSC/TG analysis

Dissertação para obtenção do Grau de Mestre em Engenharia Química e Bioquímica

Lime-metakaolin mortars for historical buildings repair: study of the hardening reaction

International Conference Durable Structures: from construction to rehabilitation. Lisbon, LNEC, 31 May-1 June 2012

Expressão e purificação da proteína E3 do vírus chikungunya (CHIKV)

RESUMO: O vírus chikungunya (CHIKV) é um vírus de RNA, com invólucro, da família Togaviridae, transmitido por mosquitos Aedes spp. Distribuído por largas regiões de África e Ásia, causa grandes epidemias de artrite grave. A semelhança de sintomas com outras doenças como a dengue e a malária e a persistência de IgM específicas, dificultam o diagnóstico da infeção por CHIKV. A deteção no sangue de E3, uma glicoproteína viral secretada, a incluir num ensaio imunoenzimático poderá melhorar o diagnóstico nos países onde as técnicas de biologia molecular são de difícil acesso. Para testar a utilidade de E3 num ensaio de diagnóstico, esta deverá ser expressa em quantidade, purificada e usada para produção de anticorpos específicos. Para expressar E3 numa forma solúvel, suscetível de ser purificada num único passo cromatográfico sem proteases, recorreu-se à estratégia da fusão com o domínio de ligação à quitina (CBD)-inteína (IMPACT™ System, NEB). A sequência codificadora de E3 foi amplificada a partir de RNA viral, clonada em pTYB21 e expressa em E. coli como uma proteína de fusão insolúvel de 64 kDa. A expressão a 12ºC induzida por IPTG 0,1 mM aumentou a solubilidade de CBD-inteína-E3. A aplicação de lisados celulares em colunas de quitina originou a retenção de CBD-inteína-E3 na matriz. Porém, a autoclivagem da inteína na coluna, induzida com reagentes tiol, foi pouco eficiente e mesmo a proteína E3 separada não eluiu da coluna. E3 foi ainda expressa em E. coli com uma cauda de seis histidinas (E3[His]6) por clonagem no vetor pET28b(+). Lisados celulares aplicados em colunas de níquel permitiram a eluição de uma proteína de 9 kDa, compatível com a massa molecular estimada para E3[His]6, ainda que com outros contaminantes proteicos. A identidade da proteína de 9 kDa será confirmada pela indução de anticorpos com esta preparação e reatividade daqueles com células infetadas com CHIKV.----------------ABSTRACT: Chikungunya virus (CHIKV) is an enveloped, positive strand RNA virus belonging to the family Togaviridae. Transmitted by Aedes spp mosquitoes, CHIKV causes large epidemics of severe arthritogenic disease in Africa and Asia and represents a serious threat in countries where vectors are present. Symptoms similarity with other diseases, e.g. dengue and malaria, along with CHIKV IgM persistence turns accurate CHIKV diagnosis a difficult task in low-income countries. Detection of E3, a small secreted viral glycoprotein, to be included in an immunoenzymatic test was envisaged as a possible improvement in CHIKV diagnosis. To test the diagnostic value of E3, recombinant E3 should be expressed and purified to generate antibodies. In order to express CHIKV E3 in a soluble form amenable to purification by a single step affinity chromatography, the chitin binding domain (CBD)-intein fusion strategy without proteases (IMPACT™ System, NEB) was employed. The E3 coding sequence was amplified from viral RNA, cloned in pTYB21 and expressed in E. coli ER2566 as an insoluble 64 kDa CBD-intein-E3 fusion protein. Solubility was partially achieved by lowering the expression temperature to 12ºC and the inducer (IPTG) concentration to 0.1 mM. Clarified cell lysate loaded onto a chitin column allowed ligation of the fusion protein but the intein-mediated cleavage efficiency was low and E3 failed to elute from the column as demonstrated by SDS-PAGE. E3 was further expressed with a six histidine tag, E3[His]6, employing the pET System (Novagen). E3[His]6 was expressed in E. coli Rosetta (30ºC, 0.4 mM IPTG) as a 9 kDa protein. Soluble cell extracts in 20-40 mM imidazole, applied onto a nickel column and eluted with 500 mM imidazole yielded a protein preparation enriched in the 9kDa protein. The 9 kDa will be used as antigen to generate antibodies that upon reaction with CHIKV infected cells will confirm its identity.

Influence of lime type and curing conditions on lime and lime-metakaolin mortars

Durability of Building Materials and Components (Vasco Peixoto de de Freitas, J.M.P.Q. Delgado, eds.), Building Pathology and Rehabilitation, vol. 3, VIII, 105-126. ISBN: 978-3-642-37474-6 (Print) 978-3-642-37475-3 (Online). Springer-Verlag Berlin Heidelberg. DOI: 10.1007/978-3-642-37475-3_5

Valorisation of vegetable oil deodorizer distillate by enzymatic reaction and membrane processing

Dissertação para obtenção do Grau de Doutora em Engenharia Química e Bioquímica

A SDK improvement towards gesture support

Human-Computer Interaction have been one of the main focus of the technological community, specially the Natural User Interfaces (NUI) field of research as, since the launch of the Kinect Sensor, the goal to achieve fully natural interfaces just got a lot closer to reality. Taking advantage of this conditions the following research work proposes to compute the hand skeleton in order to recognize Sign Language Shapes. The proposed solution uses the Kinect Sensor to achieve a good segmentation and image analysis algorithms to extend the skeleton from the extraction of high-level features. In order to recognize complex hand shapes the current research work proposes the redefinition of the hand contour making it immutable to translation, rotation and scaling operations, and a set of tools to achieve a good recognition. The validation of the proposed solution extended the Kinects Software Development Kit to allow the developer to access the new set of inferred points and created a template-matching based platform that uses the contour to define the hand shape, this prototype was tested in a set of predefined conditions and showed to have a good success ration and has proven to be eligible for real-time scenarios.

Information provision improvement with a geofencing event-bases system

Nowadays there is a big percentage of the population, specially young users, which are smartphone users and there is a lot of information to be provided within the applications, information provision should be done carefully and should be accurate, otherwise an overload of information will be produced, and the user will discard the app which is providing the information. Mobile devices are becoming smarter and provide many ways to filter information. However, there are alternatives to improve information provision from the side of the application. Some examples are, taking into account the local time, considering the battery level before doing an action and checking the user location to send personalized information attached to that location. SmartCampus and SmartCities are becoming a reality and they have more and more data integrated every day. With all this amount of data it is crucial to decide when and where is the user going to receive a notification with new information. Geofencing is a technique which allows applications to deliver information in a more useful way, in the right time and in the right place. It consists of geofences, physical regions delimited by boundaries, and devices that are eligible to receive the information assigned to the geofence. When devices cross one of these geofences an alert is pushed to the mobile device with the information.

Improvement of Lipton’s business model through wholesalers and traditional supermarkets channel

Field Lab in Entrepreneurial Innovative Ventures

Functional characterization and directed engineering of redox proteins for a rational improvement of Bioelectrochemical systems

In recent years, new methods of clean and environmentally friendly energy production have been the focus of intense research efforts. Microbial fuel cells (MFCs) are devices that utilize naturally occurring microorganisms that feed on organic matter, like waste water, while producing electrical energy. The natural habitats of bacteria thriving in microbial fuel cells are usually marine and freshwater sediments. These microorganisms are called dissimilatory metal reducing bacteria (DMRB), but in addition to metals like iron and manganese, they can use organic compounds like DMSO or TMAO, radionuclides and electrodes as terminal electron acceptors in their metabolic pathways.(...)

Continuous improvement logidtics project: A study on the increase of the finished goods warehouse of Unilever at Sta. Iria

This project is called Improvement Logistics Project and aims to study an opportunity of expansion of the output in 80% of the Unilever warehouse at Sta. Iria via an increase in exportations for the next two years. This has been done using the Distibuidora Luís Simões tariff rates as basis of comparison for the as-is and to-be situations. For this matter, an allocation of all the costs of the warehouse is prepared and described with the goal of comparing the differences with and without expansion. The results show that a better outcome is achieved with the investment, but the warehouse is yet to prove its efficiency against the distribution company.