Biomechanical Modelling of Musical Performance: A Case Study of the Guitar

Merged with duplicate record 10026.1/2517 on 07.20.2017 by CS (TIS)Computer-generated musical performances are often criticised for being unable to match the expressivity found in performances by humans. Much research has been conducted in the past two decades in order to create computer technology able to perform a given piece music as expressively as humans, largely without success. Two approaches have been often adopted to research into modelling expressive music performance on computers. The first focuses on sound; that is, on modelling patterns of deviations between a recorded human performance and the music score. The second focuses on modelling the cognitive processes involved in a musical performance. Both approaches are valid and can complement each other. In this thesis we propose a third complementary approach, focusing on the guitar, which concerns the physical manipulation of the instrument by the performer: a biomechanical approach. The essence of this thesis is a study on capturing, analyzing and modelling information about motor and biomechanical processes of guitar performance. The focus is on speed, precision, and force of a guitarist's left-hand. The overarching questions behind our study are: 1) Do unintentional actions originating from motor and biomechanical functions during musical performance contribute a material "human feel" to the performance? 2) Would it be possible determine and quantify such unintentional actions? 3) Would it be possible to model and embed such information in a computer system? The contributionst o knowledgep ursued in this thesis include: a) An unprecedented study of guitar mechanics, ergonomics, and playability; b) A detailed study of how the human body performs actions when playing the guitar; c) A methodologyt o formally record quantifiable data about such actionsin performance; d) An approach to model such information, and e) A demonstration of how the above knowledge can be embeddedin a system for music performance

Development of a Metric to Assess Completeness of Lesions Produced by Radiofrequency Ablation in the Heart

For approximately 25 years, ablation of cardiac tissue by radiofrequency energy has been used to halt arrhythmic conduction in the heart. Efficacy of cardiac ablation for the treatment of atrial fibrillation and other arrhythmias depends on the completeness of lesions and their ability to block arrhythmic conduction without disturbing sinus rhythm. A rapid index of lesion quality measured during surgical ablation procedures may improve the probability of long-term success in patients. Trans-lesion stimulus-excitation time delay was evaluated as an intraoperative indicator of lesion completeness. Time delay was shown to be significantly different for complete vs. incomplete lesions. Further, it was shown that activation path length corresponds with time delay, providing an explanation for post-ablation increases in time delay. Time delay of excitation measured on either side of a lesion having a small gap may increase after ablation, and then recover over time. This recovery would be important since the lesion may then be insufficient to suppress conduction. Although the Langendorff rabbit model used in this project is insufficient to observe long-term cardiac remodeling phenomena, short term recovery over tens of minutes post-ablation is practical with our apparatus. This would be useful for clinicians who may monitor time delay acutely in their patients before withdrawing the ablation probe. Complete recovery following initial trans-lesion block occurred within 5 minutes after ablation, while partial recovery occurred at 14.4 ± 1.0 minutes after ablation. Recovery of the activation complex occurred at 23.7 ± 7.9 minutes after ablation. Hyperthermia affects the myocardium in a wide variety of ways, including denaturation of myofilaments, disruption of the sarcolemmal membrane and/or denaturation of membrane ion channels, alterations in cellular metabolism, and alterations in intracellular and transmembrane calcium handling. Depending on the extent of the hyperthermic damage sustained during ablation, gaps within lesions may recover following a return to physiological temperature. Conduction recovery may occur following initially successful lesion formation, and it may allow arrhythmic conduction to resume or may result in the formation of one or more new arrhythmias. Further, the temperature of tissue within a small gap of an ablation lesion may correlate with the recovery of myocytes within these gaps. Thus temperature monitoring of ablated tissue may be a useful supplement for monitoring block by measuring time delay. Temperatures within lesion gaps during complete recovery reached a maximum value of 63.44° C, while temperatures within lesions gaps during partial recovery reached a value of 52.7 ± 3.0° C. In lesions which did not show initial block, supernormal conduction was observed following ablation. Decreases in time delay of 17.8 ± 2.6% were observed, and minima of time delay occurred 28.0 ± 4.0 seconds after ablation. A relationship between temperature and time delay was observed in cases where supernormal conduction occurred, but a relationship was not observed in cases where post-ablation block occurred

Design and training of deep reinforcement learning agents

Deep reinforcement learning is a field of research at the intersection of reinforcement learning and deep learning. On one side, the problem that researchers address is the one of reinforcement learning: to act efficiently. A large number of algorithms were developed decades ago in this field to update value functions and policies, explore, and plan. On the other side, deep learning methods provide powerful function approximators to address the problem of representing functions such as policies, value functions, and models. The combination of ideas from these two fields offers exciting new perspectives. However, building successful deep reinforcement learning experiments is particularly difficult due to the large number of elements that must be combined and adjusted appropriately. This thesis proposes a broad overview of the organization of these elements around three main axes: agent design, environment design, and infrastructure design. Arguably, the success of deep reinforcement learning research is due to the tremendous amount of effort that went into each of them, both from a scientific and engineering perspective, and their diffusion via open source repositories. For each of these three axes, a dedicated part of the thesis describes a number of related works that were carried out during the doctoral research. The first part, devoted to the design of agents, presents two works. The first one addresses the problem of applying discrete action methods to large multidimensional action spaces. A general method called action branching is proposed, and its effectiveness is demonstrated with a novel agent, named BDQ, applied to discretized continuous action spaces. The second work deals with the problem of maximizing the utility of a single transition when learning to achieve a large number of goals. In particular, it focuses on learning to reach spatial locations in games and proposes a new method called Q-map to do so efficiently. An exploration mechanism based on this method is then used to demonstrate the effectiveness of goal-directed exploration. Elements of these works cover some of the main building blocks of agents: update methods, neural architectures, exploration strategies, replays, and hierarchy. The second part, devoted to the design of environments, also presents two works. The first one shows how various tasks and demonstrations can be combined to learn complex skill spaces that can then be reused to solve even more challenging tasks. The proposed method, called CoMic, extends previous work on motor primitives by using a single multi-clip motion capture tracking task in conjunction with complementary tasks targeting out-of-distribution movements. The second work addresses a particular type of control method vastly neglected in traditional environments but essential for animals: muscle control. An open source codebase called OstrichRL is proposed, containing a musculoskeletal model of an ostrich, an ensemble of tasks, and motion capture data. The results obtained by training a state-of-the-art agent on the proposed tasks show that controlling such a complex system is very difficult and illustrate the importance of using motion capture data. Elements of these works demonstrate the meticulous work that must go into designing environment parts such as: models, observations, rewards, terminations, resets, steps, and demonstrations. The third part, on the design of infrastructures, presents three works. The first one explains the difference between the types of time limits commonly used in reinforcement learning and why they are often treated inappropriately. In one case, tasks are time-limited by nature and a notion of time should be available to agents to maintain the Markov property of the underlying decision process. In the other case, tasks are not time-limited by nature, but time limits are used for convenience to diversify experiences. This is the most common case. It requires a distinction between time limits and environmental terminations, and bootstrapping should be performed at the end of partial episodes. The second work proposes to unify the most popular deep learning frameworks using a single library called Ivy, and provides new differentiable and framework-agnostic libraries built with it. Four such code bases are provided for gradient-based robot motion planning, mechanics, 3D vision, and differentiable continuous control environments. Finally, the third paper proposes a novel deep reinforcement learning library, called Tonic, built with simplicity and modularity in mind, to accelerate prototyping and evaluation. In particular, it contains implementations of several continuous control agents and a large-scale benchmark. Elements of these works illustrate the different components to consider when building the infrastructure for an experiment: deep learning framework, schedules, and distributed training. Added to these are the various ways to perform evaluations and analyze results for meaningful, interpretable, and reproducible deep reinforcement learning research.Open Acces

Growth Differentiation Factor 11 and Myostatin: Mechanism and Therapeutic Role in Cardiovascular Diseases

During life, cardiac muscle is capable of remodeling in response to an increased hemodynamic demand through cardiac hypertrophy. However, in most cases, if the stress stimuli become chronic the initially compensatory hypertrophic response evolves towards a pathological condition and heart failure. In this scenario, a therapeutic approach capable of reducing pathological cardiac hypertrophy could be beneficial. Growth differentiation factor 11 (GDF11) is circulating factor able to reduce cardiac hypertrophy in mice. It is a member of TGF-β family, and it shares a high level of homology with myostatin (MSTN) a well-studied protein that regulates skeletal muscle mass and apparently minimal activity on cardiac mass. Our data showed different protein levels between cardiac and skeletal muscle tissues revealed a higher abundancy of type I TGF-β receptors (ALK4/5/7) in the heart samples. Moreover, ALK7 receptor knockout induced a significant reduction in SMAD3/4 signaling only after GDF11 treatment. These results showed a differential quantity and use of ALKs receptors, possibly explaining the higher GDF11 sensitivity of cardiomyocytes compared to skeletal myocytes. Using a model of pressure overload-induced cardiac hypertrophy it was possible to further confirm the anti-hypertrophic activity of GDF11. Interestingly, even if with lower potency, this effect was recapitulated also by MSTN, demonstrating that both peptides have overlapping effects on cardiac tissue. Furthermore, aiming to reduce controversies regarding GDF11 and MSTN serum quantifications, we contributed to develop a novel assay based on mass spectrometry that can discriminate and quantify reliably both proteins. Using this method, it was also possible to identify an age-dependent reduction of both GDF11 and MSTN ligand in mice. In conclusion, GDF11 and MSTN share a common cardiac anti-hypertrophic activity that was not previously expected. Modulation of GDF11/MSTN signaling pathway can be considered for development of novel therapeutic strategies and new biotherapeutics

Self-propelled rod-like swimmers near surfaces

Self-propelled microswimmers are biological organisms or synthetic objects that propel themselves through the surrounding fluid. Examples are sperm, various swimming bacteria such as Escherichia coli, the green alga Chlamydomonas reinhardtii and artificial bimetallic rods that catalyze chemical reactions in the surrounding hydrogen peroxide. Even though these swimmers differ in their size and driving mechanism, they can be classified as having pusher or puller polarity, which means that they are driven from the rear or the front, respectively. To study the differences in the dynamics of swimmers of different polarity, we develop a general model of rod-like swimmers and perform simulations in three dimensions, employing a particle-based mesoscopic simulation technique (multi-particle collision dynamics) for the hydrodynamic interactions. In the center of our interest are the interactions of swimmers with walls and with each other at higher densities. In the dilute case, we find that all polarities (pusher, puller and neutral) show surface adhesion, the strongest in the pusher case. For pushers, this adhesion originates from sterical alignment with the wall and hydrodynamic attraction towards the wall, making them swim closest to the wall. For pullers, we show that they swim at a slightly larger distance from the wall than pushers, and that they are inclined towards the wall by a hydrodynamic repulsion of their middle part, which also leads to strong surface adhesion. We also measure the attractive force between pusher and wall and compare it to the dipole model, which is a commonly used far-field approximation for the flow surrounding polar swimmers. Previous studies of self-propelled swimmers at high density were mostly performed in two dimensions or neglected either hydrodynamics or excluded-volume interactions. Using an efficient parallelization on GPU hardware, we are able to study the collective behavior of rods in three dimensions at various densities and driving forces, taking into account hydrodynamics and excluded-volume interactions. Our findings emphasize the importance of the polarity of swimmers: Neutrally propelled rods interact weakly via hydrodynamics, but display an isotropic-nematic phase transition at lower critical densities than passive rods. Pusher rods align parallel with each other and form medium sized motile clusters that can develop into flow defects such as jets and swirls. The clusters primarily swim close to the surfaces, where the rod concentration is highest. The surface aggregation decreases with increasing rod density. While polar order is apparent at short distances within the clusters, at longer scales the flow defects destroy the order. However, nematic order is found to be slightly positive at a system-wide scale for high-density systems, indicating that clusters can align with each other. The clusters in puller systems are radically different. At low rod densities several small non-motile hedgehog-like clusters are formed at the walls, merging into one giant, system-spanning cluster at high rod densities. These giant clusters usually include a large fraction of all rods in the system. While these are jamming clusters, they are not static but deform slowly. We conclude that the puller clusters are due to aster-like defects, which have been predicted for puller fluids, combined with excluded-volume interactions. A more specific model for sperm swimming is also being investigated. This model has been shown to display surface adhesion in the dilute solution and the capability to cluster and synchronize motion between two sperm. In multi-sperm simulations, we demonstrate the formation of small clusters by straight swimming sperm, but we find the interactions to be too weak for cluster formation among bent sperm. In order to strengthen interactions, we modify the sinusoidal beat pattern such that it displays an increasing amplitude towards the end of the tail. This indeed extends the time of two synchronized sperm swimming together, compared to the previous model

Elucidation of the Molecular Mechanisms of Electrically-Induced Cardioprotection

Cardiovascular disease is the leading cause of death worldwide. A myocardial infarction (MI), commonly known as a heart attack, is a major event in cardiovascular disease characterized by reduced blood flow to the heart. The ischemia and reperfusion (I/R) injury associated with an MI results in a region of dead tissue in the heart called an infarct, the size of which influences patient prognosis. In the 1980s, it was discovered that short, non-lethal episodes of I/R, termed ischemic preconditioning (IPC), can protect the heart against a subsequent MI. Ischemic preconditioning demonstrated the phenomenon of endogenous cardioprotection. Cardioprotection has great potential to reduce myocardial cell death and improve patient outcomes after MI, and yet most cardioprotective strategies have had limited success in clinical scenarios. Remote nociceptor-induced cardioprotection (NIC) elicits the most powerful reduction of cell death ever reported. Electrical stimulation (ES) administered via cutaneous patches offers a clinically feasible way to induce cardioprotection via NIC. Our previous work demonstrates that the transcription factor nuclear factor k-light- chain-enhancer of activated B cells (NF-kB) regulates many cardioprotective genes in the heart following IPC, including the heat shock proteins (HSPs), which act in concert with each other and their cofactors to assist in protein folding, repair, and degradation following myocardial injury. Little is known about the molecular mechanisms of electrically-induced cardioprotection, which is a barrier to the therapy\u27s optimization and successful translation to the clinic. Based on our work in IPC, we hypothesized that electrical stimulation of the skin is cardioprotective and requires the synthesis of NF-kB-dependent distal mediators of cardioprotection. In the studies herein, a cutaneous, 5-volt electrical stimulus applied to the abdomen reduces infarct size in a mouse surgical model of MI. Genetic blockade of NF-kB activation demonstrates the requirement of NF-kB in electrically-induced cardioprotection, yet RT-qPCR revealed small, nonsignificant changes in HSP mRNA and protein. Next-generation sequencing on mRNA and microRNA identified a unique transcriptome associated with electrically-induced cardioprotection that includes both recognized mediators and novel transcripts. Confirmatory studies on select molecular candidates were performed by RT-qPCR and Western blotting, and the functional role of the NF-kB-dependent gene nitric oxide synthase 2 (NOS2) was demonstrated in vivo. Results support that an electrical stimulus is cardioprotective in multiple paradigms of cardioprotection. Cardioprotection occurs without a concurrent increase in HSPs, but NF-kB and the NF-kB-dependent gene nitric oxide synthase 2 (NOS2) are required for ES- induced cardioprotection. Electrical stimulation also reduced cardiac levels of miR-10b, a circulating microRNA with no previously known role in cardioprotection. These changes were validated by RT-qPCR. In conclusion, electrically-induced cardioprotection is a novel and translational strategy to reduce cell death following MI. The molecular mechanisms of ES are cardioprotective via unique transcriptomic changes involving NF-kB and the NF-kB-dependent gene NOS2. The effect might be regulated epigenetically by microRNA

Uncovering the Mechanisms Underpinning Melanoma Invasiveness at Single Cell Resolution

Metastatic cancer is responsible for 90% of cancer-related deaths, and a lack of effective therapies has seen survival rates remain bleak. Solid cancer cells escape the primary tumour mass and acquire invasive potential through an epithelial to mesenchymal transition (EMT), regulated by a set of master transcription factors (TFs) known as EMT-TFs. EMT-TF activity is in turn controlled by interacting signalling pathways, including the TGFß, Wnt and NF- κB pathways, which become dysregulated in cancer. Once escaped, metastatic cancer cells employ interchangeable modes of migration, transitioning between fibroblast-like mesenchymal migration and amoeboid migration, where cells display a rounded morphology and navigate the extracellular matrix in a protease independent manner. However, the key molecules that orchestrate the switch between mesenchymal and amoeboid migratory modes remain incompletely understood. This thesis describes a novel 3D spheroid invasion assay and single cell isolation technique that provides detailed 3D data on growth and invasion, and allows for the specific isolation of cells of a given phenotype. Via the expression of a photoconvertible fluorescent protein, compact epithelial cells at the edge of a tumour mass, elongated cells in the process of leaving the mass, and rounded amoeboid cells migrating away from the mass were tagged for isolation. Photoconverted cells were then single-cell sorted by flow cytometry and subjected to paired-end Illumina single cell RNA sequencing. 463 differentially expressed genes were identified via DESeq2 and enriched pathways determined by GSEA analysis. Delta opioid receptor and folate transporter SLC19A1 expression were upregulated in amoeboid migration and their functions investigated via pharmacological perturbation, while INKA1 expression was downregulated in amoeboid cells and its function investigated via inducible overexpression. This work describes a novel, adaptable and readily implementable method for the analysis of the earliest phases of cancer cell invasion, and its application to the identification of genes underpinning the invasiveness of malignant melanoma