Synchronization of Heteroclinic Circuits Through Learning in Chains of Neural Motifs

The synchronization of oscillatory activity in networks of neural networks is usually implemented through coupling the state variables describing neuronal dynamics. In this study we discuss another but complementary mechanism based on a learning process with memory. A driver network motif, acting as a teacher, exhibits winner-less competition (WLC) dynamics, while a driven motif, a learner, tunes its internal couplings according to the oscillations observed in the teacher. We show that under appropriate training the learner motif can dynamically copy the coupling pattern of the teacher and thus synchronize oscillations with the teacher. Then, we demonstrate that the replication of the WLC dynamics occurs for intermediate memory lengths only. In a unidirectional chain of N motifs coupled through teacher-learner paradigm the time interval required for pattern replication grows linearly with the chain size, hence the learning process does not blow up and at the end we observe phase synchronized oscillations along the chain. We also show that in a learning chain closed into a ring the network motifs come to a consensus, i.e. to a state with the same connectivity pattern corresponding to the mean initial pattern averaged over all network motifs.

Learning math through movement: physical education as another alternative

The purpose of this study was to determine the cognitive effects of applying physical recreational activities to two groups of pre-school students, related to mathematics to one of the groups and recreational games to the other.  A total of 27 subjects (13 girls and 14 boys) of 5 and a half and 6 and half years of age participated in the study.  The instrument used was a questionnaire including basic math concepts such as geometry, basic operations with concrete elements, and how to read the clock, based on the topics established by the Costa Rican Ministry of Public Education.  Once the instrument was developed, a plan of physical recreational activities related to math was prepared and applied to the experimental group (pre-school B) for one and a half months, while the other group played recreational games.  Data was analyzed using descriptive and inferential statistics.  Positive and significant effects were found in the physical recreational activity program regarding student performance in 10 of the 12 items that were applied to assess mastery of basic math concepts.  In conclusion, using physical education as another instrument to teach other disciplines represents an excellent alternative for pre-school teachers that try to satisfy the learning needs of children that will soon be attending school.  Using movement as part of guided and planned activities plays an indispensable role in children’s lives; therefore, learning academic subjects should be adapted to their needs to explore and know their environment.

Supporting Scientific Research Through Machine and Deep Learning: Fluorescence Microscopy and Operational Intelligence Use Cases

Although the debate of what data science is has a long history and has not reached a complete consensus yet, Data Science can be summarized as the process of learning from data. Guided by the above vision, this thesis presents two independent data science projects developed in the scope of multidisciplinary applied research. The first part analyzes fluorescence microscopy images typically produced in life science experiments, where the objective is to count how many marked neuronal cells are present in each image. Aiming to automate the task for supporting research in the area, we propose a neural network architecture tuned specifically for this use case, cell ResUnet (c-ResUnet), and discuss the impact of alternative training strategies in overcoming particular challenges of our data. The approach provides good results in terms of both detection and counting, showing performance comparable to the interpretation of human operators. As a meaningful addition, we release the pre-trained model and the Fluorescent Neuronal Cells dataset collecting pixel-level annotations of where neuronal cells are located. In this way, we hope to help future research in the area and foster innovative methodologies for tackling similar problems. The second part deals with the problem of distributed data management in the context of LHC experiments, with a focus on supporting ATLAS operations concerning data transfer failures. In particular, we analyze error messages produced by failed transfers and propose a Machine Learning pipeline that leverages the word2vec language model and K-means clustering. This provides groups of similar errors that are presented to human operators as suggestions of potential issues to investigate. The approach is demonstrated on one full day of data, showing promising ability in understanding the message content and providing meaningful groupings, in line with previously reported incidents by human operators.

Synthetic data generation for the assessment of antimicrobial resistance through machine learning

As a consequence of the diffusion of next generation sequencing techniques, metagenomics databases have become one of the most promising repositories of information about features and behavior of microorganisms. One of the subjects that can be studied from those data are bacteria populations. Next generation sequencing techniques allow to study the bacteria population within an environment by sampling genetic material directly from it, without the needing of culturing a similar population in vitro and observing its behavior. As a drawback, it is quite complex to extract information from those data and usually there is more than one way to do that; AMR is no exception. In this study we will discuss how the quantified AMR, which regards the genotype of the bacteria, can be related to the bacteria phenotype and its actual level of resistance against the specific substance. In order to have a quantitative information about bacteria genotype, we will evaluate the resistome from the read libraries, aligning them against CARD database. With those data, we will test various machine learning algorithms for predicting the bacteria phenotype. The samples that we exploit should resemble those that could be obtained from a natural context, but are actually produced by a read libraries simulation tool. In this way we are able to design the populations with bacteria of known genotype, so that we can relay on a secure ground truth for training and testing our algorithms.

ICE Emissions Estimation through Machine-learning techniques to support real-world R&D experiments

The emissions estimation, both during homologation and standard driving, is one of the new challenges that automotive industries have to face. The new European and American regulation will allow a lower and lower quantity of Carbon Monoxide emission and will require that all the vehicles have to be able to monitor their own pollutants production. Since numerical models are too computationally expensive and approximated, new solutions based on Machine Learning are replacing standard techniques. In this project we considered a real V12 Internal Combustion Engine to propose a novel approach pushing Random Forests to generate meaningful prediction also in extreme cases (extrapolation, very high frequency peaks, noisy instrumentation etc.). The present work proposes also a data preprocessing pipeline for strongly unbalanced datasets and a reinterpretation of the regression problem as a classification problem in a logarithmic quantized domain. Results have been evaluated for two different models representing a pure interpolation scenario (more standard) and an extrapolation scenario, to test the out of bounds robustness of the model. The employed metrics take into account different aspects which can affect the homologation procedure, so the final analysis will focus on combining all the specific performances together to obtain the overall conclusions.

Implantation Of Transcatheter Aortic Valve Prosthesis Through The Ascending Aorta Concomitant With Coronary Artery Bypass Grafting Without Cardiopulmonary Bypass.

Introdution: The transcatheter aortic valve implantation in the treatment of high-risk symptomatic aortic stenosis has increased the number of implants every year. The learning curve for transcatheter aortic valve implantation has improved since the last 12 years, allowing access alternatives. The aim of this study is to approach the implantation of transcatheter aortic valve through transaortic via associated with off-pump cardiopulmonary bypass surgery in a 67-year-old man, with chronic obstructive pulmonary disease, arterial hypertension and kidney transplant. Off-pump coronary artery bypass surgery was performed and the valve in the aortic position was released successfully. There were no complications in the intraoperative and postoperative period. Gradient reduction, effective orifice increasing of the prosthesis and absence of valvular regurgitation after implantation were observed by transesophageal echocardiography. Procedural success demonstrates that implantation of transcatheter aortic valve through the ascending aorta associated with coronary artery bypass surgery without CPB is a new option for these patients.

A dança na formação docente em educação física

Universidade Estadual de Campinas . Faculdade de Educação Física

Pedagogia do esporte : identificação, discussão e aplicação de procedimentos pedagogicos no processo de ensino-vivencia aprendizagem da modalidade basquetebol

Universidade Estadual de Campinas . Faculdade de Educação Física

Metodologia de ensino em educação fisica : contribuições de Vygotsky, para as reflexões sobre um modelo pedagogico

Universidade Estadual de Campinas. Faculdade de Educação Física

Trajetórias de vida : lembranças, caminhos e constituições dos saberes docentes de professores de Educação Física = Paths and trajectories of life : memories, paths and establishment of teaching knowledge of Physical Education Professors

Universidade Estadual de Campinas . Faculdade de Educação Física

Learning processes and the neural analysis of conditioning

Classical and operant conditioning principles, such as the behavioral discrepancy-derived assumption that reinforcement always selects antecedent stimulus and response relations, have been studied at the neural level, mainly by observing the strengthening of neuronal responses or synaptic connections. A review of the literature on the neural basis of behavior provided extensive scientific data that indicate a synthesis between the two conditioning processes based mainly on stimulus control in learning tasks. The resulting analysis revealed the following aspects. Dopamine acts as a behavioral discrepancy signal in the midbrain pathway of positive reinforcement, leading toward the nucleus accumbens. Dopamine modulates both types of conditioning in the Aplysia mollusk and in mammals. In vivo and in vitro mollusk preparations show convergence of both types of conditioning in the same motor neuron. Frontal cortical neurons are involved in behavioral discrimination in reversal and extinction procedures, and these neurons preferentially deliver glutamate through conditioned stimulus or discriminative stimulus pathways. Discriminative neural responses can reliably precede operant movements and can also be common to stimuli that share complex symbolic relations. The present article discusses convergent and divergent points between conditioning paradigms at the neural level of analysis to advance our knowledge on reinforcement.

Information Society, Teaching, and Learning: Education Reforms in Brazil

This article examines the subject matter of learning within the context of information society, through an inquiry concerning both the reforms in education adopted in Brazil in the last thirty years and their results. It provides a revision on the explanations of school failure based on assumptions of learning problems due to cognitive and linguistic deficits. From the guidelines related with written school forms as well as the constant cultural oppression accomplished inside the school, the article claims the necessity of changing the psychological and pedagogic views that, under the label of democratic practices, determine school institutions and its daily life, by means of instrumental relations with knowledge that disregard the reading practices which are congenial to popular culture.

Extending teaching and learning initiatives in the cross-disciplinary field of biotechnology

Our AUTC Biotechnology study (Phases 1 and 2) identified a range of areas that could benefit from a common approach by universities nationally. A national network of biotechnology educators needs to be solidified through more regular communication, biennial meetings, and development of methods for sharing effective teaching practices and industry placement strategies, for example. Our aims in this proposed study are to: a. Revisit the state of undergraduate biotechnology degree programs nationally to determine their rate of change in content, growth or shrinkage in student numbers (as the biotech industry has had its ups and downs in recent years), and sustainability within their institutions in light of career movements of key personnel, tightening budgets, and governmental funding priorities. b. Explore the feasibility of a range of initiatives to benefit university biotechnology education to determine factors such as how practical each one is, how much buy-in could be gained from potentially participating universities and industry counterparts, and how sustainable such efforts are. One of many such initiatives arising in our AUTC Biotech study was a national register of industry placements for final-year students. c. During scoping and feasibility study, to involve our colleagues who are teaching in biotechnology – and contributing disciplines. Their involvement is meant to yield not only meaningful insight into how to strengthen biotechnology teaching and learning but also to generate ‘buy-in’ on any initiatives that result from this effort.

Triple objective team mentoring: achieving learning objectives with chemical engineering students

A sophisticated style of mentoring has been found to be essential to support engineering student teams undertaking technically demanding, real-world problems as part of a Project-Centred Curriculum (PCC) at The University of Queensland. The term ‘triple-objective’ mentoring was coined to define mentoring that addresses not only the student’s technical goal achievement but also their time and team management. This is achieved through a number of formal mentor meetings that are informed by a confidential instrument which requires students to individually reflect on team processes prior to the meeting, and a checklist of technical requirements against which the interim student team progress and achievements are assessed. Triple-objective mentoring requires significant time input and coordination by the academic but has been shown to ensure effective student team work and learning undiminished by team dysfunction. Student feedback shows they value the process and agree that the tools developed to support the process are effective in developing and assessing team work and skills with average scores mostly above 3 on a four point scale.

Teaching Psychodynamics to Psychiatric Residents through Psychiatric Outpatient Interviews

Objective: This study evaluates whether a course that was designed for first-year psychiatric residents and that specifically addressed psychodynamic principles fostered residents` progress in knowledge, skills, and attitudes regarding these concepts. Methods: The course was given in the 2005 academic year to all residents (N = 18) in their first psychiatric postgraduate year at the Department and Institute of Psychiatry, University of Sao Paulo, Brazil. The residents were assessed in the first and last sessions of the course through a written test that was blindly rated by two independent judges. Residents were also interviewed to observe whether psychodynamic concepts had been integrated into actual practice. Their responses were subjected to content analysis. Significance was tested using analysis of variance or nonparametric tests when necessary. Agreement between the judges was tested using intraclass correlation coefficients. Results: The judges demonstrated a high level of agreement. The difference in mean scores before and after the course was such that the total score increased by a mean of 2.5 points (total test score was 10 points). Additionally, residents started to undergo personal psychotherapy after the course. They reported that this course had markedly improved their relationship with patients. They emphasized the opportunities for self-reflection and gaining insights into themselves and patient treatment issues. Conclusion: This initial study indicates that this educational method can effectively promote psychodynamic knowledge, skills, and appropriate attitudes for managing psychiatric outpatients among residents. The course was very well received by the residents, and a similar method can easily be instituted within other residency programs that pursue integrated teaching methods.