Foram submetidos à análise molecular, 15 isolados do vírus da bronquite infecciosa (VBI) obtidos durante o período de 1988 a 2000, de surtos à campo da Bronquite Infecciosa (BI), em aves de corte ou de postura das regiões Sul e Sudeste do Brasil. Os resultados obtidos da análise filogenética das sequências parciais dos genes da glicoproteína de espícula (S1) e da nucleoproteína (N) evidenciaram que a maior parte dos isolados estão distribuídos em dois grandes grupos; o primeiro deles mais estreitamente relacionado às estirpes do genótipo Massachusetts e o segundo constituído apenas por isolados brasileiros autóctones com uma grande diversidade em relação às estirpes ou isolados do grupo Massachusetts e de outros países ou continentes. Os sítios polimórficos mais importantes formaram-se em locais específicos e de maneira agrupada nas sequências dos genes S1 ou N e predominam em regiões codificadoras das cadeias polipeptídicas S1 e N que configuram sítios estruturais e antigênicos importantes envolvidos, na expressão de propriedades biológicas relevantes. / Fifteen Brazilian field isolates of infectious bronchitis virus (IBV); were recovered, between 1988 and 2000, from commercial broiler or layer flocks located in South and Southeast Brazilian regions. Molecular and phylogenetic analysis of partial sequences of 5\'-proximal of S1 gene and 3\'-terminus of N gene from these IBV isolates, identified two main groups; the Massachusetts group and a Brazilian indigenous group, which presenting a high diversity regarding the first group or other IBV strains from different countries and continents. The major polymorphic sites are arranged in clusters and predominate in the regions of S1 and N genes which code for relevant structural and antigenic sites responsible for the expression of important biological properties.

Infectious bronchitis virus (IBV) is the causative agent of an economically important disease of poultry. In Brazil this disease causes respiratory, renal and reproductive problems in birds of all ages, despite constant vaccination with the Massachusetts strain H120. This lack of immunological protection is known to be due the genetic variation in the spike glycoprotein of IBV, which is involved in host cell attachment, neutralization and the induction of protective immunity. Brazilian IBV variants resulting of this genetic variation are present since the 80s and this study aimed to epidemiologicaly analyze and molecularly characterize the existing variants during 2010-2015 and perform a bioinformatics analysis of the available sequences of IBV variants in a 40 year period. Of the 453 samples tested, 61.4% were positive for IBV and 75.9% of them were considered variants and were detected in birds of all ages, distributed in all five Brazilian regions. A fragment of 559-566 bp was obtained from 12 isolates, where BR-I was the predominant variant while only one isolate belonged to the BR-II genotype. Bioinformatics analysis of the sequences of 40 years of Brazilian IBV variants was performed and the ratio of non-synonymous substitutions per non-synonymous site (dn) to synonymous substitutions per synonymous site (ds) dN/dS was calculated. It revealed a predominance of codons with non-synonymous substitutions in the first third of the S1 gene and a dN/dS ratio of 0.6757, indicating that this portion of the gene was under negative selection. Additionally prediction of N-glycosilation sites showed that most of the BR-I variants (from 2003 to early 2014) present an extra site at animoacid position 20, while the newest ones lack this feature.Together these results suggest that IBV Brazilian variants had probably suffered drastic mutations in some points between the years 1983 to 2003 and after achieving an antigenic structure effective enough for invasion and replication in their hosts, the selection processes became silent. / O vírus da bronquite infecciosa das galinhas (IBV) é o agente causador de uma doença aviária economicamente importante. No Brasil, esta doença ocasiona problemas respiratórios, renais e reprodutivos em aves de todas as idades, apesar da vacinação constante com a cepa Massachusetts H120. Esta falha na proteção conferida pela vacina é ocasionada por mutações nos nucleotídeos do gene da glicoproteína da espícula, a qual está envolvida no processo de interação comas células do hospedeiro, a neutralização e a indução de imunidade protetora. As variantes brasileiras resultantes dessa mutação genética estão presentes desde os anos 80 e este estudo teve como objetivo analisar epidemiologicamente e caracterizar molecularmente os vírus variantes existentes durante 2010-2015 e realizar uma análise bioinformática das sequências disponíveis no GenBank em um período de 40 anos. Das 453 amostras analisadas, 61,4% foram positivas para IBV e 75,9% delas foram consideradas variantes e foram detectados em aves de todas as idades, distribuídos em todas as 5 regiões do Brasil. Um fragmento de 559-566 pb foi obtido a partir de 12 isolados, onde BR-I foi a variante predominante ao contrario que apenas um isolado pertencia ao genótipo BR-II. Análise bioinformática de 40 anos de variantes do IBV brasileiros revelou uma predominância de codões com as substituições não sinónimos no primeiro terço do gene S1 e uma relação dN / dS de 0,6757, indicando que esta porção do gene estava sob selecção negativa. Além disso a previsão de pontos de de N-glicosilação mostrou que a maioria das amostras variantes BR-I (entre o 2003 e início de 2014) apresentam um ponto adicional na posição 20, enquanto as variantes mais novas não apresentam esse ponto de nglicosilação. Estes resultados sugerem que as variantes brasileiras teriam sofrido mutações provavelmente drásticas em alguns pontos do genoma, entre os anos de 1983 a 2003 e depois de atingir uma estrutura antigênica eficaz o suficiente para a invasão e replicação em seus hospedeiros, o processo de seleção mudou para seleção negativa.

Light entering the eye is transformed by the retina into electrical signals. Extensive processing takes place in the retina before these signals are transmitted to the brain. Beginning in the outer retina, light-evoked electrical signals are distributed into parallel pathways specialized for different visual tasks, such as the detection of dark vs. bright ambient light, the onset or offset of light, and the direction of stimulus motion. Pathway diversity is a consequence of cell type diversity, differential cell connectivity, synapse organization, receptor expression, or any combination thereof. Cell connectivity itself can be accomplished through excitatory or inhibitory chemical synapses, or electrical coupling via gap junctions. Gap junctions are further specialized based on the expression of different connexin subunit isoforms. In aggregate, this diversity gives rise to ganglion cells with highly specialized functions, including ON and/or OFF responses, contrast-tuning and direction-selectivity (DS). The directionally-selective circuit, a circuit specialized for the encoding of stimulus motion, makes use of many of these circuit specializations. Bipolar cells, in response to glutamate release from cone photoreceptors, provide highly-sensitive glutamatergic input to amacrine cells and DS ganglion cells (DSGCs) in this circuit, while amacrine cells provide cholinergic and directionally-tuned GABAergic input to DSGCs. One population of DSGCs also transmit signals laterally to one another via gap junctions. Thus numerous specializations in bipolar cells, amacrine cells and ganglion cells endow DSGCs with their unique encoding abilities. In Chapters 2 and 3 of this dissertation I focus on synchronized firing between gap junction-coupled DSGCs. sDSGCs exhibit fine-scale correlations, with action potentials in an sDSGC more likely within ~2ms of action potential firing in a coupled neighbour. I first characterize electrical coupling of DSGCs through the identification of the molecular composition of DSGC gap junctions (Chapter 2). Physiological and immunohistochemical methods allowed me to demonstrate an important role for connexin 36 subunits in DSGC electrical coupling. Next (Chapter 3) I investigate the sub-cellular mechanisms underlying neuronal correlations between electrically coupled DSGCs. Using paired recordings, I show that chemical input (from bipolar cells and amacrine cells), electrical input (from gap junctions), and Na+ channel activity in DSGC dendrites underlie the generation of correlated spiking activity. While a common feature of electrically coupled networks, the mechanisms underlying correlations were previously unclear. In Chapter 4 I focus on the mechanisms within the DS circuit that endow these neurons with impressive sensitivity to stimulus contrast. Using physiological and pharmacological methods I first assess the relative contrast sensitivity of ganglion cells and starburst amacrine cells (SACs) in the DS circuit. The sensitivity of DSGC and SAC excitatory currents to antagonists of Na+ channels suggests an important role for these channels in amplifying low contrast responses and other weak inputs to the circuit. This role is later attributed to the differential expression of voltage-gated Na+ channels in specific bipolar cell populations. In aggregate, this dissertation describes several novel circuit mechanisms within the well-studied DS circuit. I also provide specific roles for such specializations in visual coding. / Graduate

Low intensity and low frequency ultrasound has been shown to modulate ion channel currents, membrane capacitive currents, and as a result, neuronal activity. Ultrasound has been used as a non-invasive way to modulate neuronal activity in vivo using mice as well as human subjects. Ultrasound with acoustic frequency as low as 0.35 MHz can be focussed on a region as small as 2 mm with reversible effects and no increase in temperature. In this study, two ultrasound transducers with different resonant frequency have been used to excite neuronal cultures. The resulting changes in the network properties such as synchronised network burst frequency, density, clustering and path length have been analysed. The study shows that ultrasound stimulation at acoustic frequency 450 kHz (ISPPA =11.3 mW/cm2) significantly modulates the above mentioned parameters and causes deviations from small world network properties of the control network.

A stochastic leaky integrate-and-fire neuron model was implemented in this study to simulate the spiking activity of the electrosensory "P-unit" receptor neurons of the weakly electric fish Apteronotus leptorhynchus. In the context of sensory coding, these cells have been previously shown to respond in experiment to natural random narrowband signals with either a linear or nonlinear coding scheme, depending on the intrinsic firing rate of the cell in the absence of external stimulation. It was hypothesised in this study that this duality is due to the relation of the stimulus to the neuron's excitation threshold. This hypothesis was validated with the model by lowering the threshold of the neuron or increasing its intrinsic noise, or randomness, either of which made the relation between firing rate and input strength more linear. Furthermore, synchronous P-unit firing to a common input also plays a role in decoding the stimulus at deeper levels of the neural pathways. Synchronisation and desynchronisation between multiple model responses for different types of natural communication signals were shown to agree with experimental observations. A novel result of resonance-induced synchrony enhancement of P-units to certain communication frequencies was also found.

<p>Spiking Neural Networks (SNNs), widely known as the third generation of artificial neural networks, offer a promising solution to approaching the brains' processing capability for cognitive tasks. With more biologically realistic perspective on input processing, SNN performs neural computations using spikes in an event-driven manner. The asynchronous spike-based computing capability can be exploited to achieve improved energy efficiency in neuromorphic hardware. Furthermore, SNN, on account of spike-based processing, can be trained in an unsupervised manner using Spike Timing Dependent Plasticity (STDP). STDP-based learning rules modulate the strength of a multi-bit synapse based on the correlation between the spike times of the input and output neurons. In order to achieve plasticity with compressed synaptic memory, stochastic binary synapse is proposed where spike timing information is embedded in the synaptic switching probability. A bio-plausible probabilistic-STDP learning rule consistent with Hebbian learning theory is proposed to train a network of binary as well as quaternary synapses. In addition, hybrid probabilistic-STDP learning rule incorporating Hebbian and anti-Hebbian mechanisms is proposed to enhance the learnt representations of the stochastic SNN. The efficacy of the presented learning rules are demonstrated for feed-forward fully-connected and residual convolutional SNNs on the MNIST and the CIFAR-10 datasets.<br></p><p>STDP-based learning is limited to shallow SNNs (<5 layers) yielding lower than acceptable accuracy on complex datasets. This thesis proposes block-wise complexity-aware training algorithm, referred to as BlocTrain, for incrementally training deep SNNs with reduced memory requirements using spike-based backpropagation through time. The deep network is divided into blocks, where each block consists of few convolutional layers followed by an auxiliary classifier. The blocks are trained sequentially using local errors from the respective auxiliary classifiers. Also, the deeper blocks are trained only on the hard classes determined using the class-wise accuracy obtained from the classifier of previously trained blocks. Thus, BlocTrain improves the training time and computational efficiency with increasing block depth. In addition, higher computational efficiency is obtained during inference by exiting early for easy class instances and activating the deeper blocks only for hard class instances. The ability of BlocTrain to provide improved accuracy as well as higher training and inference efficiency compared to end-to-end approaches is demonstrated for deep SNNs (up to 11 layers) on the CIFAR-10 and the CIFAR-100 datasets.<br></p><p>Feed-forward SNNs are typically used for static image recognition while recurrent Liquid State Machines (LSMs) have been shown to encode time-varying speech data. Liquid-SNN, consisting of input neurons sparsely connected by plastic synapses to randomly interlinked reservoir of spiking neurons (or liquid), is proposed for unsupervised speech and image recognition. The strength of the synapses interconnecting the input and liquid are trained using STDP, which makes it possible to infer the class of a test pattern without a readout layer typical in standard LSMs. The Liquid-SNN suffers from scalability challenges due to the need to primarily increase the number of neurons to enhance the accuracy. SpiLinC, composed of an ensemble of multiple liquids, where each liquid is trained on a unique input segment, is proposed as a scalable model to achieve improved accuracy. SpiLinC recognizes a test pattern by combining the spiking activity of the individual liquids, each of which identifies unique input features. As a result, SpiLinC offers comparable accuracy to Liquid-SNN with added synaptic sparsity and faster training convergence, which is validated on the digit subset of TI46 speech corpus and the MNIST dataset.</p>

Introduction: The coronavirus is an RNA virus with a lipid envelope. The initially known coronaviruses are (MERS-CoV and SARS-CoV) which caused fatal endemics in 2002 and 2012. At the end of 2019, a new variant of coronavirus called SARS-CoV-2 (severe acute respiratory syndrome coronavirus-2) was discovered in Wuhan in China. SARS-CoV-2 has caused serious diseases especially in the older groups with millions of infections and deaths. The World Health Organization (WHO) identified Covid-19 as a global health emergency on 30 January 2020 and classified it as a pandemic on March 11, 2020. Several companies started developing a vaccine to stop the spread of SARS-CoV-2. Several vaccination programs have been approved and are currently used against Covid-19. Objectives: The work aimed to investigate the safety and efficacy of the approved mRNA-based vaccines against SARS-CoV-2. Method: The work was based on reviewing published, scientific articles that examined safety and efficacy of mRNA-based vaccines (Pfizer BionTech BNT162b and mRNA-1273 Modern). In total there were five clinical trials selected from Pubmed. Two studies examined the safety and efficacy of mRNA-1273 Moderna and three other studies examined the safety and effectiveness of the Pfizer BionTech BNT162b. Results: Both vaccines have shown good safety and efficacy and were well tolerated in patients in different ages. mRNA-based vaccines have shown mild to moderate symptoms that were higher after dose 2 and disappeared after a few days. Both Pfizer BionTech and Modern mRNA-1273 have shown efficiencies over 90%.

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One of the most important objectives for transportation engineers is to understand pavement surface properties and their positive and negative effects on the user. This can improve the design of the infrastructure, adequacy of tools, and consistency of methodologies that are essential for transportation practitioners regarding macrotexture characterization. Important pavement surface characteristics, or tire-pavement interactions, such as friction, tire-pavement noise, splash and spray, and rolling resistance, are significantly influenced by pavement macrotexture. This dissertation compares static and dynamic macrotexture measurements and proposes and enhanced method to quantify the macrotexture. Dynamic measurements performed with vehicle-mounted lasers have the advantage of measuring macrotexture at traffic speed. One drawback of these laser devices is the presence of 'spikes' in the collected data, which impact the texture measurements. The dissertation proposes two robust and innovative methods to overcome this limitation. The first method is a data-driven adaptive method that detects and removes the spikes from high-speed laser texture measurements. The method first calculates the discrete wavelet transform of the texture measurements. It then detects (at all levels) and removes the spikes from the obtained wavelet coefficients (or differences). Finally, it calculates the inverse discrete wavelet transform with the processed wavelet coefficients (without outliers) to obtain the Mean Profile Depth (MPD) from the measurements with the spikes removed. The method was validated by comparing the results with MPD measurements obtained with a Circular Texture Meter (CTMeter) that was chosen as the control device. Although this first method was able to successfully remove the spikes, it has the drawback that it depends on manual modeling of the distribution of the wavelet coefficients to correctly define an appropriate threshold. The next step of this dissertation proposes an enhanced to the spike removal methodology for macrotexture measurements taken with high-speed laser devices. This denoising methodology uses an algorithm that defines the distribution of texture measurements by using the family of Generalized Gaussian Distributions (GGD), along with the False Discovery Rate (FDR) method that controls the proportion of wrongly identified spikes among all identified spikes. The FDR control allows for an adaptive threshold selection that differentiates between valid measurements and spikes. The validation of the method showed that the MPD results obtained with denoised dynamic measurements are comparable to MPD results from the control devices. This second method is included as a crucial step in the last stage of this dissertation as explained following. The last part of the dissertation presents an enhanced macrotexture characterization index based on the Effective Area for Water Evacuation (EAWE), which: (1) Estimates the potential of the pavement to drain water and (2) Correlates better with two pavement surface properties affected by macrotexture (friction and noise) that the current MPD method. The proposed index is defined by a three-step process that: (1) removes the spikes, assuring the reliability of the texture profile data, (2) finds the enveloping profile that is necessary to delimit the area between the tire and the pavement when contact occurs, and (3) computes the EAWE. Comparisons of current (MPD) and proposed (EAWE) macrotexture characterization indices showed that the MPD overestimates the ability of the pavement for draining the surface water under a tire. / Ph. D.