No / T-type Ca2+ channels are a distinct family of low voltage-activated Ca2+ channels which serve many roles in different tissues. Several studies have implicated them, for example, in the adaptive responses to chronic hypoxia in the cardiovascular and endocrine systems. Hydrogen sulfide (H2S) was more recently discovered as an important signalling molecule involved in many functions, including O2 sensing. Since ion channels are emerging as an important family of target proteins for modulation by H2S, and both T-type Ca2+ channels and H2S are involved in cellular responses to hypoxia, we have investigated whether recombinant and native T-type Ca2+ channels are a target for modulation by H2S. Using patch-clamp electrophysiology, we demonstrate that the H2S donor, NaHS, selectively inhibits Cav3.2 T-type Ca2+ channels heterologously expressed in HEK293 cells, whilst Cav3.1 and Cav3.3 channels were unaffected. Sensitivity of Cav3.2 channels to H2S required the presence of the redox-sensitive extracellular residue H191, which is also required for tonic binding of Zn2+ to this channel. Chelation of Zn2+ using TPEN prevented channel inhibition by H2S. H2S also selectively inhibited native T-type channels (primarily Cav3.2) in sensory dorsal root ganglion neurons. Our data demonstrate a novel target for H2S regulation, the T-type Ca2+ channel Cav3.2. Results have important implications for the proposed pro-nociceptive effects of this gasotransmitter. Implications for the control of cellular responses to hypoxia await further study.

Von Neumann Bottleneck, which refers to the limited throughput between the CPU and memory, has already become the major factor hindering the technical advances of computing systems. In recent years, neuromorphic systems started to gain increasing attention as compact and energy-efficient computing platforms. Spike based-neuromorphic computing systems require high performance and low power neural encoder and decoder to emulate the spiking behavior of neurons. These two spike-analog signals converting interface determine the whole spiking neuromorphic computing system's performance, especially the highest performance. Many state-of-the-art neuromorphic systems typically operate in the frequency range between 〖10〗^0KHz and 〖10〗^2KHz due to the limitation of encoding/decoding speed. In this dissertation, all these popular encoding and decoding schemes, i.e. rate encoding, latency encoding, ISI encoding, together with related hardware implementations have been discussed and analyzed. The contributions included in this dissertation can be classified into three main parts: neuron improvement, three kinds of ISI encoder design, two types of ISI decoder design. Two-path leakage LIF neuron has been fabricated and modular design methodology is invented. Three kinds of ISI encoding schemes including parallel signal encoding, full signal iteration encoding, and partial signal encoding are discussed. The first two types ISI encoders have been fabricated successfully and the last ISI encoder will be taped out by the end of 2019. Two types of ISI decoders adopted different techniques which are sample-and-hold based mixed-signal design and spike-timing-dependent-plasticity (STDP) based analog design respectively. Both these two ISI encoders have been evaluated through post-layout simulations successfully. The STDP based ISI encoder will be taped out by the end of 2019. A test bench based on correlation inspection has been built to evaluate the information recovery capability of the proposed spiking processing link. / Doctor of Philosophy / Neuromorphic computing is a kind of specific electronic system that could mimic biological bodies’ behavior. In most cases, neuromorphic computing system is built with analog circuits which have benefits in power efficient and low thermal radiation. Among neuromorphic computing system, one of the most important components is the signal processing interface, i.e. encoder/decoder. To increase the whole system’s performance, novel encoders and decoders have been proposed in this dissertation. In this dissertation, three kinds of temporal encoders, one rate encoder, one latency encoder, one temporal decoder, and one general spike decoder have been proposed. These designs could be combined together to build high efficient spike-based data link which guarantee the processing performance of whole neuromorphic computing system.

In recent years neuromorphic computing systems have achieved a lot of success due to its ability to process data much faster and using much less power compared to traditional Von Neumann computing architectures. There are two main types of Artificial Neural Networks (ANNs), Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN). In this thesis we first study the types of RNNs and then move on to Spiking Neural Networks (SNNs). SNNs are an improved version of ANNs that mimic biological neurons closely through the emission of spikes. This shows significant advantages in terms of power and energy when carrying out data intensive applications by allowing spatio-temporal information processing. On the other hand, emerging non-volatile memory (eNVM) technology is key to emulate neurons and synapses for in-memory computations for neuromorphic hardware. A particular eNVM technology, memristors, have received wide attention due to their scalability, compatibility with CMOS technology and low power consumption properties. In this work we develop a spiking neural network by incorporating an inter-spike interval encoding scheme to convert the incoming input signal to spikes and use a memristive crossbar to carry out in-memory computing operations. We develop a novel input and output processing engine for our network and demonstrate the spatio-temporal information processing capability. We demonstrate an accuracy of a 100% with our design through a small-scale hardware simulation for digit recognition and demonstrate an accuracy of 87% in software through MNIST simulations. / M.S. / In recent years neuromorphic computing systems have achieved a lot of success due to its ability to process data much faster and using much less power compared to traditional Von Neumann computing architectures. Artificial Neural Networks (ANNs) are models that mimic biological neurons where artificial neurons or neurodes are connected together via synapses, similar to the nervous system in the human body. here are two main types of Artificial Neural Networks (ANNs), Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN). In this thesis we first study the types of RNNs and then move on to Spiking Neural Networks (SNNs). SNNs are an improved version of ANNs that mimic biological neurons closely through the emission of spikes. This shows significant advantages in terms of power and energy when carrying out data intensive applications by allowing spatio-temporal information processing capability. On the other hand, emerging non-volatile memory (eNVM) technology is key to emulate neurons and synapses for in-memory computations for neuromorphic hardware. A particular eNVM technology, memristors, have received wide attention due to their scalability, compatibility with CMOS technology and low power consumption properties. In this work we develop a spiking neural network by incorporating an inter-spike interval encoding scheme to convert the incoming input signal to spikes and use a memristive crossbar to carry out in-memory computing operations. We demonstrate the accuracy of our design through a small-scale hardware simulation for digit recognition and demonstrate an accuracy of 87% in software through MNIST simulations.

Yes / Spinal muscular atrophy, the leading genetic cause of infant mortality, is a motor neuron disease caused by low levels of survival motor neuron (SMN) protein. SMN is a multifunctional protein that is implicated in numerous cytoplasmic and nuclear processes. Recently, increasing attention is being paid to the role of SMN in the maintenance of DNA integrity. DNA damage and genome instability have been linked to a range of neurodegenerative diseases. The ribosomal DNA (rDNA) represents a particularly unstable locus undergoing frequent breakage. Instability in rDNA has been associated with cancer, premature ageing syndromes, and a number of neurodegenerative disorders. Here, we report that SMN-deficient cells exhibit increased rDNA damage leading to impaired ribosomal RNA synthesis and translation. We also unravel an interaction between SMN and RNA polymerase I. Moreover, we uncover an spinal muscular atrophy motor neuron-specific deficiency of DDX21 protein, which is required for resolving R-loops in the nucleolus. Taken together, our findings suggest a new role of SMN in rDNA integrity.

Ultrasound waves are mechanical undulations above the threshold for human hearing, and have been used widely in both the human body and brain for diagnostic and therapeutic purposes. Ultrasound can be controlled using specially designed transducers into a focus of a few millimeters in diameter. Low intensity ultrasound, such as used in imaging applications, appears to be safe in adults. It is also known that ultrasound waves can penetrate through the skull and be focused within the brain for ablation purposes, employing the heat generation properties of high intensity focused ultrasound. High intensity focused ultrasound is thus used to irreversibly ablate brain tissue in localized areas without observable damage to intermediate tissue and vasculature. Ablation with high intensity focused ultrasound guided by magnetic resonance imaging is used for abolishing brain tumors, and experimentally for pain. Low intensity ultrasound can be utilized beyond imaging in neuroscience and neurology by focusing the ultrasound beam to investigate the structure and function of discrete brain circuits. In contrast to high intensity focused ultrasound, the effects of low intensity focused ultrasound on neurons are reversible. Considering the volume of work on high intensity focused ultrasound, low intensity focused ultrasound remains decidedly underdeveloped. Given the great potential for impact of low intensity focused ultrasound in both clinical and scientific neuromodulation applications, we sought to advance the use of low intensity focused ultrasound for noninvasive, transcranial neuromodulation of human cortex. This dissertation contains novel research on the use of low intensity transcranial focused ultrasound for noninvasive neuromodulation of human cortex. The importance of mechanical forces in the nervous system is highlighted throughout to expand beyond the stigma that nervous function is governed chiefly by electrical and chemical means. Methods of transcranial focused ultrasound are applied to significantly modulate human cortical function, shown using electroencephalographic recordings and behavioral investigations of sensory discrimination performance. This dissertation also describes computational models used to investigate the insertion behavior of ultrasound across various tissues in the context of transcranial neuromodulation, as ultrasound's application for neuromodulation is relatively new and crudely understood. These investigations are critical for the refinement of device design and the overall advancement of ultrasound methods for noninvasive neuromodulation. / Ph. D.

Yes / Despite intense attention from biomedical and chemical researchers, there are few approved treatments for amyotrophic lateral sclerosis (ALS), with only riluzole (Rilutek) and edaravone (Radicava) currently available to patients. Moreover, the mechanistic basis of the activity of these drugs is currently not well-defined, limiting the ability to design new medicines for ALS. This Letter describes the synthesis of triazole-containing riluzole analogues, and their testing in a novel neuroprotective assay. Seven compounds were identified as having neuroprotective activity, with two compounds having similar activity to riluzole.

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Submitted by Luciana Sebin (lusebin@ufscar.br) on 2016-10-05T18:08:34Z No. of bitstreams: 1 TeseERLR.pdf: 4461748 bytes, checksum: 2fe540767de5ff5f23af02775508026b (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-14T14:11:13Z (GMT) No. of bitstreams: 1 TeseERLR.pdf: 4461748 bytes, checksum: 2fe540767de5ff5f23af02775508026b (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-14T14:11:22Z (GMT) No. of bitstreams: 1 TeseERLR.pdf: 4461748 bytes, checksum: 2fe540767de5ff5f23af02775508026b (MD5) / Made available in DSpace on 2016-10-14T14:11:41Z (GMT). No. of bitstreams: 1 TeseERLR.pdf: 4461748 bytes, checksum: 2fe540767de5ff5f23af02775508026b (MD5) Previous issue date: 2015-12-15 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / This thesis combines image and signal processing to obtain virtual neuron distribution maps in a Microelectrode Array (MEA), which are devices designed for non-invasive electrophysiological signal recording for in vitro cultures of neuron cells. In the electrophysiological signal analysis, it is of interest the knowledge of the topological distribution of the cells along the MEA microelectrodes, but, usually the photographic images of the cell culture are not available. This doctoral work presents an approach to obtain the statistical topologic distribution of the neurons of an in vitro cell culture, denoted virtual distribution of neurons, from the electrophysiological signals. To certify that the statistical computation of the neuron counting is associated to each MEA microelectrode, it is used the ICA (Independent component Analysis) technique, for the separation of the neuron signals distributed throughout the MEA area, to obtain for each microelectrode, only the signals from its adjacent neurons. Assuming the hypothesis that the spontaneous neuron activities, spikes and bursts, are directly proportional to the neuron counting, it is realized the spike counting and burst counting, and it is assigned for each microelectrode, a number of neurons proportional to that numbers of activities. For the validation of the proposal, as well as for calibration of the system, to obtain the estimated number of neurons, it was used an experiment denoted 371, realized in Genoa University, Italy, in which it was recorded electrophysiological signals in 46 DIVs (Days In- Vitro), obtaining 20 minutes of recording in 25, 29, 32, 36, 39, 43, and 46 DIVs, and a set of photographic images in 38 DIV. Assuming that microelectrode neuron counting in the 38 DIV photographic image is proportional to the 39 DIV spontaneous electrophysiological activity signal recording, one day after the imaging, if was determined the neuron counting as function of the spontaneous electrophysiological activities recording, in a process denoted as calibration of the virtual number of neurons. The distance error from the neuron activities as function of the neuron counting in photographic image and in function of the recorded electrophysiological signals was calculated and compared for validation. In this way, it was possible to construct virtual topologic maps of neurons, proportional to the electrophysiological activities measured in 39 DIV, as a function of the spike and the burst countings. Comparing these two virtual maps, the spike counting virtual map was more close to the real neuron distributions viewed at the photographic image of 38 DIV. Also, the variance of the spike and burst counting along the 20 min of electrophysiological recording in a DIV, was calculated, and noted that the spike counting is more stable than burst counting. / Esta tese combina processamento de imagens e sinais, para a obtenção de uma distribuição virtual de neurônios em Matrizes de microeletrodos (Microelectrode Array, MEA), dispositivos projetados para o registro de sinais eletrofisiológicos de culturas de células neuronais, in-vitro, de forma não-invasiva. Na análise dos sinais eletrofisiológicos é de interesse o conhecimento da distribuição topológica das células ao longo dos microeletrodos, porém, nem sempre as imagens fotográficas das culturas são disponíveis. O presente trabalho apresenta uma metodologia de obtenção da distribuição topológica estatística dos neurônios numa cultura in-vitro, a partir dos sinais eletrofisiológicos. Para o cálculo estatístico do número de neurônios nessa distribuição topológica, é feito o uso da técnica de ICA (Independent Component Analysis), para obter os sinais relativos aos neurônios mais próximos para cada microeletrodo. Assumindo-se a hipótese de que as atividades eletrofisiológicas espontâneas dos neurônios, spikes e bursts, sejam diretamente proporcionais ao número de neurônios, realiza-se a contagem do número de spikes ou o número de bursts, e atribui-se o número de neurônios para cada microeletrodo, proporcionalmente à quantidade dessas atividades. Para a validação da proposta, foi utilizado um experimento, Experimento 371, realizado na Universidade de Gênova, Itália, em que foram registrados os sinais eletrofisiológicos ao longo de 46 DIVs (Dias In-Vitro), obtendo amostras de 20 minutos de registros para os 25, 29, 32, 36, 39, 43 e 46 DIVs, e um conjunto de imagens fotográficas da cultura no 38 DIV. Considerando-se que o número de neurônios associados a cada microeletrodo na imagem fotográfica no 38 DIV é proporcional à atividade eletrofisiológica espontânea dos neurônios, num registro realizado no 39 DIV, um dia após as fotos, foi feita uma regra de determinação do número virtual de neurônios em função das atividades eletrofisiológicas espontâneas medidas, denominada de calibração. O erro relativo à distância da atividade dos neurônios em relação à quantidade de neurônios na imagem fotográfica, e a atividade dos neurônios em função do registro de sinais eletrofisiológicos é calculado para comparação e validação. Dessa forma são construídos os mapas topológicos virtuais de neurônios proporcionais às atividades eletrofisiológicas medidas no 39 DIV, em função da quantidade de spikes e de bursts. O mapa obtido pela contagem de spikes se aproxima mais da distribuição real de neurônios vista na imagem fotográfica, do que o mapa obtido em função da contagem de bursts. No estudo de variância de atividades em função da contagem de spikes e bursts durante os 20 minutos de medidas num DIV, e constata-se que as atividades em contagem spikes é mais estável que em contagem de bursts.