Neural Modulation of Leg Stiffness in Response to Neuromuscular Fatigue

The spring-mass model is able to accurately represent hopping spring-like behavior (leg and joint stiffness), and leg and joint stiffness changes can reveal overall motor control responses to neural and muscular contributors of neuromuscular fatigue. By understanding leg stiffness modulation, we can determine which variables the nervous system targets to maintain motor performance and stability. The purpose of this study was to determine how neuromuscular fatigue affects hopping behavior by examining leg and joint stiffness before and after a single-leg calf raise fatiguing protocol. Post-fatigue, leg stiffness decreased for the exercised leg, but not for the non-exercised leg. Ankle and knee joint stiffness did not significantly change for either leg. This indicates that leg stiffness decreases primarily from muscular fatigue, but was not explained by ankle and knee joint stiffness. The decrease in leg stiffness may be an attempt to soften landing impact, while at the same time maintaining performance.

Self-Organizing Map Neural Architectures Based on Limit Cycle Attractors

Recent efforts to develop large-scale neural architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason is that most conventional SOMs use a static encoding representation: Each input is typically represented by the fixed activation of a single node in the map layer. This not only carries information in an inefficient and unreliable way that impedes building robust multi-SOM neural architectures, but it is also inconsistent with rhythmic oscillations in biological neural networks. Here I develop and study an alternative encoding scheme that instead uses limit cycle attractors of multi-focal activity patterns to represent input patterns/sequences. Such a fundamental change in representation raises several questions: Can this be done effectively and reliably? If so, will map formation still occur? What properties would limit cycle SOMs exhibit? Could multiple such SOMs interact effectively? Could robust architectures based on such SOMs be built for practical applications? The principal results of examining these questions are as follows. First, conditions are established for limit cycle attractors to emerge in a SOM through self-organization when encoding both static and temporal sequence inputs. It is found that under appropriate conditions a set of learned limit cycles are stable, unique, and preserve input relationships. In spite of the continually changing activity in a limit cycle SOM, map formation continues to occur reliably. Next, associations between limit cycles in different SOMs are learned. It is shown that limit cycles in one SOM can be successfully retrieved by another SOM’s limit cycle activity. Control timings can be set quite arbitrarily during both training and activation. Importantly, the learned associations generalize to new inputs that have never been seen during training. Finally, a complete neural architecture based on multiple limit cycle SOMs is presented for robotic arm control. This architecture combines open-loop and closed-loop methods to achieve high accuracy and fast movements through smooth trajectories. The architecture is robust in that disrupting or damaging the system in a variety of ways does not completely destroy the system. I conclude that limit cycle SOMs have great potentials for use in constructing robust neural architectures.

Adaptation of motor control to training and disuse : contribution of muscle synergy analysis and movement variability

The well-known degrees of freedom problem originally introduced by Nikolai Bernstein (1967) results from the high abundance of degrees of freedom in the musculoskeletal system. Such abundance in motor control have two sides: i) because it is unlikely that the Central Nervous System controls each degree of freedom independently, the complexity of the control needs to be reduced, and ii) because there are many options to perform a movement, a repetition of a given movement is never the same. It leads to two main topics in motor control and biomechanics: motor coordination and motor variability. The present thesis aimed to understand how motor systems behave and adapt under specific conditions. This thesis comprises three studies that focused on three topics of major interest in the field of sports sciences and medicine: expertise, injury risk and fatigue. The first study (expertise) has focused on the muscle coordination topic to further investigate the effect of expertise on the muscle synergistic organization, which ultimately may represent the underlying neural strategies. Studies 2 (excessive medial knee displacement) and 3 (fatigue) both aimed to better understand its impact on the dynamic local stability. The main findings of the present thesis suggest: 1) there is a great robustness in muscle synergistic organization between swimmers at different levels of expertise (study 1, chapter II), which ultimately indicate that differences in muscle coordination is mainly explained by peripheral adaptations; 2) injury risk factors such as excessive medial knee displacement (study 2, chapter III) and fatigue (study 3, chapter IV) alter the dynamic local stability of the neuromuscular system towards a more unstable state. This change in dynamic local stability represents a loss of adaptability in the neuromuscular system reducing the flexibility to adapt to a perturbation.

Controle preditivo neural aplicado à processos petroquímicos

A pesquisa tem como objetivo desenvolver uma estrutura de controle preditivo neural, com o intuito de controlar um processo de pH, caracterizado por ser um sistema SISO (Single Input - Single Output). O controle de pH é um processo de grande importância na indústria petroquímica, onde se deseja manter constante o nível de acidez de um produto ou neutralizar o afluente de uma planta de tratamento de fluidos. O processo de controle de pH exige robustez do sistema de controle, pois este processo pode ter ganho estático e dinâmica nãolineares. O controlador preditivo neural envolve duas outras teorias para o seu desenvolvimento, a primeira referente ao controle preditivo e a outra a redes neurais artificiais (RNA s). Este controlador pode ser dividido em dois blocos, um responsável pela identificação e outro pelo o cálculo do sinal de controle. Para realizar a identificação neural é utilizada uma RNA com arquitetura feedforward multicamadas com aprendizagem baseada na metodologia da Propagação Retroativa do Erro (Error Back Propagation). A partir de dados de entrada e saída da planta é iniciado o treinamento offline da rede. Dessa forma, os pesos sinápticos são ajustados e a rede está apta para representar o sistema com a máxima precisão possível. O modelo neural gerado é usado para predizer as saídas futuras do sistema, com isso o otimizador calcula uma série de ações de controle, através da minimização de uma função objetivo quadrática, fazendo com que a saída do processo siga um sinal de referência desejado. Foram desenvolvidos dois aplicativos, ambos na plataforma Builder C++, o primeiro realiza a identificação, via redes neurais e o segundo é responsável pelo controle do processo. As ferramentas aqui implementadas e aplicadas são genéricas, ambas permitem a aplicação da estrutura de controle a qualquer novo processo

Fiber scaffolds of poly (glycerol-dodecanedioate) and its derivative via electrospinning for neural tissue engineering

Peripheral nerves have demonstrated the ability to bridge gaps of up to 6 mm. Peripheral Nerve System injury sites beyond this range need autograft or allograft surgery. Central Nerve System cells do not allow spontaneous regeneration due to the intrinsic environmental inhibition. Although stem cell therapy seems to be a promising approach towards nerve repair, it is essential to use the distinct three-dimensional architecture of a cell scaffold with proper biomolecule embedding in order to ensure that the local environment can be controlled well enough for growth and survival. Many approaches have been developed for the fabrication of 3D scaffolds, and more recently, fiber-based scaffolds produced via the electrospinning have been garnering increasing interest, as it offers the opportunity for control over fiber composition, as well as fiber mesh porosity using a relatively simple experimental setup. All these attributes make electrospun fibers a new class of promising scaffolds for neural tissue engineering. Therefore, the purpose of this doctoral study is to investigate the use of the novel material PGD and its derivative PGDF for obtaining fiber scaffolds using the electrospinning. The performance of these scaffolds, combined with neural lineage cells derived from ESCs, was evaluated by the dissolvability test, Raman spectroscopy, cell viability assay, real time PCR, Immunocytochemistry, extracellular electrophysiology, etc. The newly designed collector makes it possible to easily obtain fibers with adequate length and integrity. The utilization of a solvent like ethanol and water for electrospinning of fibrous scaffolds provides a potentially less toxic and more biocompatible fabrication method. Cell viability testing demonstrated that the addition of gelatin leads to significant improvement of cell proliferation on the scaffolds. Both real time PCR and Immunocytochemistry analysis indicated that motor neuron differentiation was achieved through the high motor neuron gene expression using the metabolites approach. The addition of Fumaric acid into fiber scaffolds further promoted the differentiation. Based on the results, this newly fabricated electrospun fiber scaffold, combined with neural lineage cells, provides a potential alternate strategy for nerve injury repair.^

Active Control of the Acoustic Field in a Vehicle Cabin

In this thesis, a thorough investigation on acoustic noise control systems for realistic automotive scenarios is presented. The thesis is organized in two parts dealing with the main topics treated: Active Noise Control (ANC) systems and Virtual Microphone Technique (VMT), respectively. The technology of ANC allows to increase the driver's/passenger's comfort and safety exploiting the principle of mitigating the disturbing acoustic noise by the superposition of a secondary sound wave of equal amplitude but opposite phase. Performance analyses of both FeedForwrd (FF) and FeedBack (FB) ANC systems, in experimental scenarios, are presented. Since, environmental vibration noises within a car cabin are time-varying, most of the ANC solutions are adaptive. However, in this work, an effective fixed FB ANC system is proposed. Various ANC schemes are considered and compared with each other. In order to find the best possible ANC configuration which optimizes the performance in terms of disturbing noise attenuation, a thorough research of \gls{KPI}, system parameters and experimental setups design, is carried out. In the second part of this thesis, VMT, based on the estimation of specific acoustic channels, is investigated with the aim of generating a quiet acoustic zone around a confined area, e.g., the driver's ears. Performance analysis and comparison of various estimation approaches is presented. Several measurement campaigns were performed in order to acquire a sufficient duration and number of microphone signals in a significant variety of driving scenarios and employed cars. To do this, different experimental setups were designed and their performance compared. Design guidelines are given to obtain good trade-off between accuracy performance and equipment costs. Finally, a preliminary analysis with an innovative approach based on Neural Networks (NNs) to improve the current state of the art in microphone virtualization is proposed.

1° level of automation: the effectiveness of adaptive cruise control on driving and visual behaviour

The research activities have allowed the analysis of the driver assistance systems, called Advanced Driver Assistance Systems (ADAS) in relation to road safety. The study is structured according to several evaluation steps, related to definite on-site tests that have been carried out with different samples of users, according to their driving experience with the ACC. The evaluation steps concern: •The testing mode and the choice of suitable instrumentation to detect the driver’s behaviour in relation to the ACC. •The analysis modes and outputs to be obtained, i.e.: - Distribution of attention and inattention; - Mental workload; - The Perception-Reaction Time (PRT), the Time To Collision (TTC) and the Time Headway (TH). The main purpose is to assess the interaction between vehicle drivers and ADAS, highlighting the inattention and variation of the workloads they induce regarding the driving task. The research project considered the use of a system for monitoring visual behavior (ASL Mobile Eye-XG - ME), a powerful GPS that allowed to record the kinematic data of the vehicle (Racelogic Video V-BOX) and a tool for reading brain activity (Electroencephalographic System - EEG). Just during the analytical phase, a second and important research objective was born: the creation of a graphical interface that would allow exceeding the frame count limit, making faster and more effective the labeling of the driver’s points of view. The results show a complete and exhaustive picture of the vehicle-driver interaction. It has been possible to highlight the main sources of criticalities related to the user and the vehicle, in order to concretely reduce the accident rate. In addition, the use of mathematical-computational methodologies for the analysis of experimental data has allowed the optimization and verification of analytical processes with neural networks that have made an effective comparison between the manual and automatic methodology.

Design and testing of innovative, artificial intelligence-based techniques to model and control the combustion process and to reduce the Co2 emissions in modern, high-performance, gasoline direct injection engines

This work deals with the development of calibration procedures and control systems to improve the performance and efficiency of modern spark ignition turbocharged engines. The algorithms developed are used to optimize and manage the spark advance and the air-to-fuel ratio to control the knock and the exhaust gas temperature at the turbine inlet. The described work falls within the activity that the research group started in the previous years with the industrial partner Ferrari S.p.a. . The first chapter deals with the development of a control-oriented engine simulator based on a neural network approach, with which the main combustion indexes can be simulated. The second chapter deals with the development of a procedure to calibrate offline the spark advance and the air-to-fuel ratio to run the engine under knock-limited conditions and with the maximum admissible exhaust gas temperature at the turbine inlet. This procedure is then converted into a model-based control system and validated with a Software in the Loop approach using the engine simulator developed in the first chapter. Finally, it is implemented in a rapid control prototyping hardware to manage the combustion in steady-state and transient operating conditions at the test bench. The third chapter deals with the study of an innovative and cheap sensor for the in-cylinder pressure measurement, which is a piezoelectric washer that can be installed between the spark plug and the engine head. The signal generated by this kind of sensor is studied, developing a specific algorithm to adjust the value of the knock index in real-time. Finally, with the engine simulator developed in the first chapter, it is demonstrated that the innovative sensor can be coupled with the control system described in the second chapter and that the performance obtained could be the same reachable with the standard in-cylinder pressure sensors.

Development and testing of methodologies for the control and diagnosis of innovative combustion systems

In pursuit of aligning with the European Union's ambitious target of achieving a carbon-neutral economy by 2050, researchers, vehicle manufacturers, and original equipment manufacturers have been at the forefront of exploring cutting-edge technologies for internal combustion engines. The introduction of these technologies has significantly increased the effort required to calibrate the models implemented in the engine control units. Consequently the development of tools that reduce costs and the time required during the experimental phases, has become imperative. Additionally, to comply with ever-stricter limits on 〖"CO" 〗_"2" emissions, it is crucial to develop advanced control systems that enhance traditional engine management systems in order to reduce fuel consumption. Furthermore, the introduction of new homologation cycles, such as the real driving emissions cycle, compels manufacturers to bridge the gap between engine operation in laboratory tests and real-world conditions. Within this context, this thesis showcases the performance and cost benefits achievable through the implementation of an auto-adaptive closed-loop control system, leveraging in-cylinder pressure sensors in a heavy-duty diesel engine designed for mining applications. Additionally, the thesis explores the promising prospect of real-time self-adaptive machine learning models, particularly neural networks, to develop an automatic system, using in-cylinder pressure sensors for the precise calibration of the target combustion phase and optimal spark advance in a spark-ignition engines. To facilitate the application of these combustion process feedback-based algorithms in production applications, the thesis discusses the results obtained from the development of a cost-effective sensor for indirect cylinder pressure measurement. Finally, to ensure the quality control of the proposed affordable sensor, the thesis provides a comprehensive account of the design and validation process for a piezoelectric washer test system.

Hypothalamic Inflammation And The Central Nervous System Control Of Energy Homeostasis.

The control of energy homeostasis relies on robust neuronal circuits that regulate food intake and energy expenditure. Although the physiology of these circuits is well understood, the molecular and cellular response of this program to chronic diseases is still largely unclear. Hypothalamic inflammation has emerged as a major driver of energy homeostasis dysfunction in both obesity and anorexia. Importantly, this inflammation disrupts the action of metabolic signals promoting anabolism or supporting catabolism. In this review, we address the evidence that favors hypothalamic inflammation as a factor that resets energy homeostasis in pathological states.

Chitosan/tripolyphosphate Nanoparticles Loaded With Paraquat Herbicide: An Environmentally Safer Alternative For Weed Control.

Paraquat is a fast acting nonselective contact herbicide that is extensively used worldwide. However, the aqueous solubility and soil sorption of this compound can cause problems of toxicity in nontarget organisms. This work investigates the preparation and characterization of nanoparticles composed of chitosan and sodium tripolyphosphate (TPP) to produce an efficient herbicidal formulation that was less toxic and could be used for safer control of weeds in agriculture. The toxicities of the formulations were evaluated using cell culture viability assays and the Allium cepa chromosome aberration test. The herbicidal activity was investigated in cultivations of maize (Zea mays) and mustard (Brassica sp.), and soil sorption of the nanoencapsulated herbicide was measured. The efficiency association of paraquat with the nanoparticles was 62.6 ± 0.7%. Encapsulation of the herbicide resulted in changes in its diffusion and release as well as its sorption by soil. Cytotoxicity and genotoxicity assays showed that the nanoencapsulated herbicide was less toxic than the pure compound, indicating its potential to control weeds while at the same time reducing environmental impacts. Measurements of herbicidal activity showed that the effectiveness of paraquat was preserved after encapsulation. It was concluded that the encapsulation of paraquat in nanoparticles can provide a useful means of reducing adverse impacts on human health and the environment, and that the formulation therefore has potential for use in agriculture.

Self-reported Diabetes In Older People: Comparison Of Prevalences And Control Measures.

The objective of this study was to analyze the prevalence of diabetes in older people and the adopted control measures. Data regarding older diabetic individuals who participated in the Health Surveys conducted in the Municipality of Sao Paulo, SP, ISA-Capital, in 2003 and 2008, which were cross-sectional studies, were analyzed. Prevalences and confidence intervals were compared between 2003 and 2008, according to sociodemographic variables. The combination of the databases was performed when the confidence intervals overlapped. The Chi-square (level of significance of 5%) and the Pearson's Chi-square (Rao-Scott) tests were performed. The variables without overlap between the confidence intervals were not tested. The age of the older adults was 60-69 years. The majority were women, Caucasian, with an income of between > 0.5 and 2.5 times the minimum salary and low levels of schooling. The prevalence of diabetes was 17.6% (95%CI 14.9;20.6) in 2003 and 20.1% (95%CI 17.3;23.1) in 2008, which indicates a growth over this period (p at the limit of significance). The most prevalent measure adopted by the older adults to control diabetes was hypoglycemic agents, followed by diet. Physical activity was not frequent, despite the significant differences observed between 2003 and 2008 results. The use of public health services to control diabetes was significantly higher in older individuals with lower income and lower levels of education. Diabetes is a complex and challenging disease for patients and the health systems. Measures that encourage health promotion practices are necessary because they presented a smaller proportion than the use of hypoglycemic agents. Public health policies should be implemented, and aimed mainly at older individuals with low income and schooling levels. These changes are essential to improve the health condition of older diabetic patients.

Incidence Of Basilar Invagination In Patients With Tonsillar Herniation? A Case Control Craniometrical Study.

A retrospective case-control study based on craniometrical evaluation was performed to evaluate the incidence of basilar invagination (BI). Patients with symptomatic tonsillar herniation treated surgically had craniometrical parameters evaluated based on CT scan reconstructions before surgery. BI was diagnosed when the tip of the odontoid trespassed the Chamberlain's line in three different thresholds found in the literature: 2, 5 or 6.6 mm. In the surgical group (SU), the mean distance of the tip of the odontoid process above the Chamberlain's line was 12 mm versus 1.2 mm in the control (CO) group (p<0.0001). The number of patients with BI according to the threshold used (2, 5 or 6.6 mm) in the SU group was respectively 19 (95%), 16 (80%) and 15 (75%) and in the CO group it was 15 (37%), 4 (10%) and 2 (5%).

Going Beyond Seizure Control: Can Pharmacotherapy Help To Restore Cognitive Network Function?

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Control Of The Properties Of Porous Chitosan-alginate Membranes Through The Addition Of Different Proportions Of Pluronic F68.

This work addresses the development and characterization of porous chitosan-alginate based polyelectrolyte complexes, obtained by using two different proportions of the biocompatible surfactant Pluronic F68. These biomaterials are proposed for applications as biodegradable and biocompatible wound dressing and/or scaffolds. The results indicate that thickness, roughness, porosity and liquid uptake of the membranes increase with the amount of surfactant used, while their mechanical properties and stability in aqueous media decrease. Other important properties such as color and surface hydrophilicity (water contact angle) are not significantly altered or did not present a clear tendency of variation with the increase of the amount of surfactant added to the polyelectrolyte complexes, such as real density, average pore diameter, total pore volume and surface area. The prepared biomaterials were not cytotoxic to L929 cells. In conclusion, it is possible to tune the physicochemical properties of chitosan-alginate polyelectrolyte complexes, through the variation of the proportion of surfactant (Pluronic F68) added to the mixture, so as to enable the desired application of these biomaterials.