abstract: Ultra-concealable multi-threat body armor used by law-enforcement is a multi-purpose armor that protects against attacks from knife, spikes, and small caliber rounds. The design of this type of armor involves fiber-resin composite materials that are flexible, light, are not unduly affected by environmental conditions, and perform as required. The National Institute of Justice (NIJ) characterizes this type of armor as low-level protection armor. NIJ also specifies the geometry of the knife and spike as well as the strike energy levels required for this level of protection. The biggest challenges are to design a thin, lightweight and ultra-concealable armor that can be worn under street clothes. In this study, several fundamental tasks involved in the design of such armor are addressed. First, the roles of design of experiments and regression analysis in experimental testing and finite element analysis are presented. Second, off-the-shelf materials available from international material manufacturers are characterized via laboratory experiments. Third, the calibration process required for a constitutive model is explained through the use of experimental data and computer software. Various material models in LS-DYNA for use in the finite element model are discussed. Numerical results are generated via finite element simulations and are compared against experimental data thus establishing the foundation for optimizing the design. / Dissertation/Thesis / M.S. Civil and Environmental Engineering 2012

Made available in DSpace on 2016-08-29T15:33:23Z (GMT). No. of bitstreams: 1 tese_9295_Brenno Albino Lugon.pdf: 621313 bytes, checksum: fb6acacfa7bc1521bfdde981f176a251 (MD5) Previous issue date: 2015-11-06 / CAPES / Neste trabalho, utilizamos o algoritmo paralelo híbrido SPIKE um precondicionador para um método iterativo não estacionário combinando as arquiteturas de memoria distribuída e compartilhada,MPI e OpenMP. A fim de obter um bom precondicionador, resolvemos um conjunto de problemas combinatórios como reordenamentos e particionamento de grafos. Apresentamos os resultados avaliando a influencia de cada estrategia na  convergência e tempo de CPU do método iterativo.

Recently-developed technologies for monitoring activity in populations of neurons make it possible for the first time, in principle, to ask many basic questions in neuroscience. However, computational tools for analyzing newly available data need to be developed. The goal of this thesis is to contribute to this effort by focusing on two specific problems. First, we used a point-process regression framework to provide a methodology for statistical assessment of the link between neural spike synchrony and network-wide oscillations. In simulations, we showed that our method can recover ground-truth relationships, and in two types of spike train data we illustrated the kinds of results the method can produce. The approach improves on methods in the literature and may be adapted to many different experimental settings. Second, we considered the problem of source extraction in calcium imaging data, i.e., the detection of neurons within a field of view and the extraction of each neuron’s activity. The data we mainly focus on are recorded with a microendoscope, which has the unique advantage of imaging deep brain regions in freely behaving animals. These data suffer from high levels of background fluorescence, as well as the potential for overlapping neuronal signals. Based on the existing constrained nonnegative matrix factorization (CNMF) framework, we developed an efficient method to process microendoscopic data. Our method utilizes a novel algorithm to initialize the spatial shapes and temporal activity of the neurons from the raw video data independently from the strong fluctuating background. This step ensures the efficiency and accuracy of solving a nonconvex CNMF problem. Our method also models the complicated background by including its low-spatial frequency structure and the locally-low-rank feature to avoid absorbing cellular signals into the background term. We developed a tractable solution to estimate the background activity using this new model. After subtracting the approximated background, we followed the CNMF framework to demix neural signals and recover denoised and deconvolved temporal activity. We optimized several algorithms in solving the CNMF problems to get accurate results. In practice, our method outperforms all existing methods and has been adopted by many experimental labs.

Encoding behaviorally relevant stimuli in a noisy background is critical for animals to survive in their natural environment. We identify core biophysical and synaptic mechanisms that permit the encoding of low frequency signals in pyramidal neurons of the weakly electric fish Apteronotus leptorhynchus, an animal that can accurately encode miniscule (0.1%) amplitude modulations of its self-generated electric field. We demonstrate that slow NMDA-R mediated EPSPs are able to summate over many interspike intervals of the primary electrosensory afferents (EAs), effectively eliminating the EA spike train serial correlations from the pyramidal cell input. This permits stimulus-evoked changes in EA spiking to be transmitted efficiently to downstream ELL pyramidal cells, where a dynamic balance of NMDA-R and GABA-A-R currents is critical for encoding low frequency signals. Interestingly, AMPA-R activity is depressed and plays a negligible role in the generation of action potentials; instead, cell intrinsic membrane noise implements voltage-dependent stochastic resonance to amplify weak sensory input and appears to drive a significant proportion of pyramidal cell spikes. Together, these mechanisms may be sufficient for the ELL to encode signals near the threshold of behavioral detection.

Previous electrophysiological studies of the basal ganglia in Parkinson's disease (PD) patients have utilized linear analyses in time-or-frequency domains to characterize neuronal discharge patterns. However, these measures do not fully describe the non-linear features of discharge rates and oscillatory activities of basal ganglia neurons.In this original research, we investigate whether non-linear temporal organizations exist in the inter-spike interval series of neurons recorded in the globus pallidus or the subthalamic nucleus in PD patients undergoing surgery for the implantation of deep brain stimulating electrodes.Our data indicate that in approximately 80% of globus pallidus and subthalamic neurons, the raw inter-spike interval sequences have lower entropy values than those observed after shuffling of the original series. This is the first report establishing non-linear temporal organization as a common feature of neuronal discharge in the basal ganglia of PD patients.

No description available.

Understanding how the human brain works can lead to new discoveries and improved treatments for brain related diseases and disabilities such as Alzheimer's and autism. One method for studying brain activity is through electrophysiological recordings, particularly through the use of in vivo recording techniques. While these techniques have advanced significantly over the years, data analysis tools have not kept pace, making it difficult to isolate the activity of individual neurons from the recordings. In this thesis, we propose a unified protocol for isolating the spike activity of a neuron from an electrophysiology recording. Additionally, we conducted customized spike train analysis on the recorded cells in a pattern separation task. Preliminary results suggest that changes in the neural activity of mossy cells was not significant. However, for granule cells and interneurons, responses to punishment and reward were observed.

Thesis advisor: Jianman Gao / The interaction between the spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the human angiotensin-converting enzyme 2 (hACE2) receptor is an advantageous target for the development of therapies for COVID-19. We used an anti spike receptor binding domain (S RBD) antibody (AM122) to competitively elute phage binding to the S RBD in phage display screening to identify a novel peptide that binds the S protein and hACE2 interaction. We identified a peptide sequence (P1: CPLEYHTC) as a possible hit, and the KD was determined to be 2.667 μM, indicating the potential of this peptide sequence as a therapeutic agent. However, we found no inhibition of the spike protein and hACE2 receptor interaction, suggesting that the peptide may not directly bind to the hACE2 binding site on S RBD. Although further studies are needed, the competitive elution method in phage display screening appears to be an effective method for elucidating onsite peptide sequences that target protein-protein interactions (PPIs). / Thesis (BA) — Boston College, 2023. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Departmental Honors. / Discipline: Chemistry.

No description available.

Simultaneous recordings of single and multi-unit neural signals from multiple cortical areas in the brain are a vital tool for gaining more understanding of the operating mechanism of the brain as well as for developing Brain Machine Interfaces. Monitoring the activity levels of hundreds or even thousands of neurons can lead to reliable decoding of brain signals for controlling prosthesis of multiple degrees of freedom and different functionalities. With the advancement of high density microelectrode arrays, the craving of neuroscience research to record the activity of thousands of neurons is achievable. Recently CMOS-based Micro-electrode Arrays MEAs featuring high spatial and temporal resolution have been reported. The augmentation in the number of recording sites carries different challenges to the neural signal processing system. The primary challenge is the massive increase in the incoming data that needs to be transmitted and processed in real time. Data reduction based on the sparse nature of the neural signals with respect to time becomes essential. The dissertation presents the design of a neural spike-based data reduction platform that can handle a few thousands of channels on Field Programmable Gate Arrays (FPGAs), making use of their massive parallel processing capabilities and reconfigurability. For Standalone implementation the spike detector core uses Finite State Machines (FSMs) to control the interface with the data acquisition as well as sending the spike waveforms to a common output FIFO. The designed neural signal processing platform integrates the application of high-speed serial Multi-Gigabit transceivers on FPGAs to allow massive data transmission in real time. It also provides a design for autonomous threshold setting for each channel. / Electrical and Computer Engineering