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.

Enhancing Multimodal Silent Speech Interfaces With Feature Selection

In research on Silent Speech Interfaces (SSI), different sources of information (modalities) have been combined, aiming at obtaining better performance than the individual modalities. However, when combining these modalities, the dimensionality of the feature space rapidly increases, yielding the well-known "curse of dimensionality". As a consequence, in order to extract useful information from this data, one has to resort to feature selection (FS) techniques to lower the dimensionality of the learning space. In this paper, we assess the impact of FS techniques for silent speech data, in a dataset with 4 non-invasive and promising modalities, namely: video, depth, ultrasonic Doppler sensing, and surface electromyography. We consider two supervised (mutual information and Fisher's ratio) and two unsupervised (meanmedian and arithmetic mean geometric mean) FS filters. The evaluation was made by assessing the classification accuracy (word recognition error) of three well-known classifiers (knearest neighbors, support vector machines, and dynamic time warping). The key results of this study show that both unsupervised and supervised FS techniques improve on the classification accuracy on both individual and combined modalities. For instance, on the video component, we attain relative performance gains of 36.2% in error rates. FS is also useful as pre-processing for feature fusion. Copyright © 2014 ISCA.

Lean learning academy: an innovative framework for lean manufacturing training

Worldwide competitiveness poses enormous challenges on managers, demanding a continuous quest to increase rationality in the use of resources. As a management philosophy, Lean Manufacturing focuses on the elimination of activities that do not create any type of value and therefore are considered waste. For companies to successfully implement the Lean Manufacturing philosophy it is crucial that the human resources of the organization have the necessary training, for which proper tools are required. At the same time, higher education institutions need innovative tools to increase the attractiveness of engineering curricula and develop a higher level of knowledge among students, improving their employability. This paper describes how Lean Learning Academy, an international collaboration project between five EU universities and five companies, from SME to Multinational/Global companies, developed and applied an innovative training programme for Engineers on Lean Manufacturing, a successful alternative to the traditional teaching methods in engineering courses.

Physiotherapy and Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a severe, progressive disease first described by Meryon in 1852 and later by Guillaume Duchene. It is the most common and severe form of childhood muscular dystrophy, affecting 1 in 3500 live male births. Is caused by an X—linked recessive genetic disorder resulting in a deficiency of the dystrophin protein, responsible for linking contractile proteins to the sarcolemma. Diagnosis is not always easy and the first symptoms are often related to weakness and difficulty or delay in acquiring the ability to perform simple activities. Progressive weakness leads to the use of compensatory strategies in order to maintain the ability to walk and perform other activities. Respiratory muscles are also affected and the complications resulting from its impairments are frequently the cause of early death of these patients. The advances in DMD management has increased life expectancy of these children with the need for adequate care in adulthood. DMD manifestations include muscle weakness, contractures, respiratory and cardiac complications. Some authors also refer that one-third of patients have difficulties with learning and delayed global development because the gene that encodes dystrophyn expresses various dystrophin isoforms that are found in Schwann and Purkinje celis in the brain. Body functions and structure impairments like muscle weakness, contractures and reduced range of motion lead to limitations in activities, i.e., impairments affect the performance of tasks by the individual. In a physiotherapist’s point of view analysing these limitations is mandatory because physiotherapy’s final purpose is to restore or preserve the ability to perform ADL and to improve quality of life.

Contextual and individual indicators associated with the presence of teeth in adults

OBJECTIVE The objective of this study was to analyze whether socioeconomic conditions and the period of availability of fluoridated water are associated with the number of teeth present.METHODSThis cross-sectional study analyzed data from 1,720 adults between 20 and 59 years of age who resided in Florianópolis, SC, Southern Brazil, in 2009. The outcome investigated was the self-reported number of teeth present. The individual independent variables included gender, age range, skin color, number of years of schooling, and per capita household income. The duration of residence was used as a control variable. The contextual exposures included the period of availability of fluoridated water to the households and the socioeconomic variable for the census tracts, which was created from factor analysis of the tract’s mean income, education level, and percentage of households with treated water. Multilevel logistic regression was performed and inter-level interactions were tested.RESULTS Residents in intermediate and poorer areas and those with fluoridated water available for less time exhibited the presence of fewer teeth compared with those in better socioeconomic conditions and who had fluoridated water available for a longer period (OR = 1.02; 95%CI 1.01;1.02). There was an association between the period of availability of fluoridated water, per capita household income and number of years of education. The proportion of individuals in the poorer and less-educated stratum, which had fewer teeth present, was higher in regions where fluoridated water had been available for less time.CONCLUSIONS Poor socioeconomic conditions and a shorter period of availability of fluoridated water were associated with the probability of having fewer teeth in adulthood. Public policies aimed at reducing socioeconomic inequalities and increasing access to health services such as fluoridation of the water supply may help to reduce tooth loss in the future.

Mobile and web tools for participative learning

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, para a obtenção do grau de Mestre em Engenharia Informática

Extended immersive learning environment: a hybrid remote/virtual laboratory

This paper presents a collaborative virtual learning environment, which includes technologies such as 3D virtual representations, learning and content management systems, remote experiments, and collaborative learning spaces, among others. It intends to facilitate the construction, management and sharing of knowledge among teachers and students, in a global perspective. The environment proposes the use of 3D social representations for accessing learning materials in a dynamic and interactive form, which is regarded to be closer to the physical reality experienced by teachers and students in a learning context. A first implementation of the proposed extended immersive learning environment, in the area of solid mechanics, is also described, including the access to theoretical contents and a remote experiment to determine the elastic modulus of a given object.These instructions give you basic guidelines for preparing camera-ready papers for conference proceedings. Use this document as a template if you are using Microsoft Word 6.0 or later. Otherwise, use this document as an instruction set. The electronic file of your paper will be formatted further. Define all symbols used in the abstract. Do not cite references in the abstract.

Formative assessment and motivation in higher education: a successful approach in mechanical engineer thermodynamics

Between 2000/01 and 2006/07, the approval rate of a Thermodynamics course in a Mechanical Engineer graduation was 25%. However, a careful analysis of the results showed that 41% of the students chosen not to attend or dropped out, missing the final examination. Thus, a continuous assessment methodology was developed, whose purpose was to reduce drop out, motivating students to attend this course, believing that what was observed was due, not to the incapacity to pass, but to the anticipation of the inevitability of failure by the students. If, on one hand, motivation is defined as a broad construct pertaining to the conditions and processes that account for the arousal, direction, magnitude, and maintenance of effort, on the other hand, assessment is one of the most powerful tools to change the will that students have to learn, motivating them to learn in a quicker and permanent way. Some of the practices that were implemented, included: promoting learning goal orientation rather than performance goal orientation; cultivating intrinsic interest in the subject and put less emphasis on grades but make grading criteria explicit; emphasizing teaching approaches that encourage collaboration among students and cater for a range of teaching styles; explaining the reasons for, and the implications of, tests; providing feedback to students about their performance in a form that is non-egoinvolving and non-judgemental and helping students to interpret it; broadening the range of information used in assessing the attainment of individual students. The continuous assessment methodology developed was applied in 2007/08 and 2008/09, having found an increase in the approval from 25% to 55% (30%), accompanied by a decrease of the drop out from 41% to 23,5% (17,5%). Flunking with a numerical grade lowered from 34,4% to 22,0% (12,4%). The perception by the students of the continuous assessment relevance was evaluated with a questionnaire. 70% of the students that failed the course respond that, nevertheless, didn’t repent having done the continuous assessment.

Reverse problem-based learning - a case study with a Braille machine

Engineering Education includes not only teaching theoretical fundamental concepts but also its verification during practical lessons in laboratories. The usual strategies to carry out this action are frequently based on Problem Based Learning, starting from a given state and proceeding forward to a target state. The possibility or the effectiveness of this procedure depends on previous states and if the present state was caused or resulted from earlier ones. This often happens in engineering education when the achieved results do not match the desired ones, e.g. when programming code is being developed or when the cause of the wrong behavior of an electronic circuit is being identified. It is thus important to also prepare students to proceed in the reverse way, i.e. given a start state generate the explanation or even the principles that underlie it. Later on, this sort of skills will be important. For instance, to a doctor making a patient?s story or to an engineer discovering the source of a malfunction. This learning methodology presents pedagogical advantages besides the enhanced preparation of students to their future work. The work presented on his document describes an automation project developed by a group of students in an engineering polytechnic school laboratory. The main objective was to improve the performance of a Braille machine. However, in a scenario of Reverse Problem-Based learning, students had first to discover and characterize the entire machine's function before being allowed (and being able) to propose a solution for the existing problem.