The Intensive Cognitive and Communication Rehabilitation program (ICCR), developed to advance young adults with acquired brain injury (ABI) to college, targets a range of cognitive domains (e.g., memory, writing, verbal expression) via classroom-style lectures, individual therapy, and technology- and computer-based interventions on an intensive schedule (i.e., six hours/day, four days/week, 12-week iterations). One of the driving hypotheses of this dissertation work is that cognitive rehabilitation programs that are embedded with principles of experience-dependent neuroplasticity (i.e., repetition, intensity, specificity, salience), like ICCR, should lead to changes in behavior and the brain. The initial two studies of this dissertation focused on the first aspect of this hypothesis (i.e., assessing the impact of ICCR on overall cognitive-linguistic function and specific cognitive domains important for academic success in young adults with ABI), while the final two studies addressed the second aspect (i.e., using fNIRS to measure brain activation during language and domain-general cognitive tasks in neurotypicals and individuals with ABI at a single timepoint and over time). In Study 1, young adults with ABI who participated in ICCR demonstrated significant gains in at least one standardized assessment of global cognitive-linguistic function, while control participants did not. Yet, the study did not reveal what specific cognitive domains important for academic success improved after the ICCR program—an essential intermediate step in evaluating the utility of these programs in preparing young adults with ABI for academic reentry. Study 2 addressed this unanswered question with a novel approach that aggregated items from standardized neuropsychological assessments into specific cognitive domains (e.g., attention, verbal expression, memory) and then, applied growth curve modeling to assess whether those domains improved significantly over time in young adults with ABI participating in the ICCR program. This study also directly compared whether the treatment group improved at a significantly faster rate in overall item accuracy and subdomain item accuracy than a deferred treatment/control usual care group, extending the findings from Study 1 with a larger participant sample. Study 3 was a pilot study using fNIRS to capture brain activation in expected regions during language and domain-general cognitive processing in neurotypicals and individuals with stroke-induced aphasia. Findings from the young healthy control group in this study would serve as a reference for interpreting brain activation patterns in the damaged brain in future work. This study also provided opportunities to determine the acceptability of the fNIRS behavioral tasks and acquisition procedures for individuals with stroke-induced aphasia and to assess the utility of a novel method for managing areas of lesion. Based on the robust findings of Study 1 and 2 (i.e., ICCR promoted gains in overall cognitive domains and specific cognitive processes important for college success) and the promising results of Study 3 (i.e., activation patterns during language and domain-general cognitive processing could be captured in neurotypicals and individuals with brain damage at a single timepoint using fNIRS), Study 4 was undertaken to assess pre- to post-treatment activation changes following ICCR participation via fNIRS. Five young adults with ABI underwent fNIRS measurement while performing the same behavioral task battery used in Study 3 (i.e. semantic feature, picture naming, arithmetic) before and after a 12-week semester of ICCR. This preliminary work provided opportunities 1) to apply fNIRS to measure treatment-related neuroplasticity in the ABI population; 2) to examine the extent to which treatment participants demonstrated changes in the brain following ICCR in conjunction with a positive treatment response and improved behavioral task accuracy; and 3) to identify methodological considerations for future studies in this area. In closing, this dissertation reviews key findings from each of these studies and discusses their implications for studying rehabilitation-induced recovery in adults with ABI in future work. / 2023-08-06T00:00:00Z

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Articular cartilage has limited intrinsic healing capacity due to its dense and avascular structure. Clinical approaches have been developed to address the limitations associated with the poor ability of articular cartilage to regenerate. Current clinically approved techniques, however, can result in repair tissue that lacks appropriate hyaline cartilage biochemical and biomechanical properties, which lead to uncertain long-term clinical outcomes. Using tissue engineering strategies and a range of scaffolding materials, cell types, growth factors, culture conditions, and culture times, engineered tissues have been produced with compositional and biomechanical properties that approximate that of native tissue. In these studies, a considerable number of samples are typically sacrificed to evaluate compositional and mechanical properties, such as the amount of deposited collagen and sulfated glycosaminoglycan (sGAG) in the constructs. The number of sacrificed samples, as well as the amount of time and resources spent to evaluate the sacrificed samples using current gold standards, motivates an alternative method for evaluation of compositional properties. Vibrational spectroscopy, including infrared, has been considered as an alternative technique for assessment of tissues over the last 15-20 years. Infrared spectroscopy is based on absorbance of infrared light by tissue functional groups at specific vibrational frequencies, and thus, no external contrast is required. Vibrational spectroscopy is typically performed in two frequency regions, the mid infrared region (750-4000 cm-1), where penetration depth is limited to approximately 10 microns, and the near infrared (NIR) region (4000-12000 cm-1). In the NIR region, penetration of light is on the order of millimeters or centimeters, which makes it ideal for obtaining data through the full depth of engineered constructs. Here we employ NIR spectroscopy to nondestructively monitor the development of tissue-engineered constructs over culture period. / Bioengineering

Large-scale optoelectronic integration is limited by the lack of efficient light sources and broadband photodetectors, which could be integrated with the silicon complementary metal-oxide-semiconductor (CMOS) technology. Persistent efforts continue to achieve efficient light emission as well as broadband photodetection from silicon in extending the silicon technology into fully integrated optoelectronic circuits. Recent breakthroughs, including the demonstration of high-speed optical modulators, photodetectors, and waveguides in silicon, have brought the concept of transition from electrical to optical interconnects closer to realization. The on-chip light sources based on silicon are still a key challenge due to the indirect bandgap of silicon that impedes coherent light sources. To overcome this issue, we have studied, fabricated, and characterized nanostructures including single semiconductor epilayers, multiple quantum wells, and graphene-semiconductor heterostructures to develop coherent light sources and photodetectors in silicon. To develop coherent light sources, we reported the demonstration of room-temperature lasing at the technologically crucial 1.5 m wavelength range from Er-doped GaN epilayers and Er-doped GaN multiple-quantum wells grown on silicon and sapphire. The realization of room-temperature lasing at the minimum loss window of optical fiber and in the eye-safe wavelength region of 1.5 m is highly sought-after for use in many applications in various fields including defense, industrial processing, communication, medicine, spectroscopy and imaging. The results laid the foundation for achieving hybrid GaN-Si lasers providing a new pathway towards full photonic integration for silicon optoelectronics. Silicon photodiodes contribute a large portion in the photodetector market. However, silicon photodetectors are sensitive in the UV to near infrared region. Photodetection in the mid-infrared is based on thermal radiation detectors, narrow bandgap materials (InGaAs, HgCdTe) semiconductors, photo-ionization of shallow impurities in semiconductors (Si:As, Ge:Ga), and quantum well structures. Such technology requires complicated fabrication processes or cryogenic operation, resulting in manufacturing costs and severe integration issues. To develop broadband photodetectors, we focus on graphene photodetectors on silicon. Graphene generates photocarriers by absorbing photons in a broadband spectrum from the deep-ultraviolet to the terahertz region. Graphene can be realized as the next generation broadband photodetection material, especially in the infrared to terahertz region. Here, we have demonstrated high-performance hybrid photodetectors operating from the deep-ultraviolet to the mid-infrared region with high sensitivity and ultrafast response by coupling graphene with a p-type semiconductor photosensitizer, nitrogen-doped Ta2O5 thin film. / Doctor of Philosophy / According to Moor's law, the number of transistors per die area doubles every 18 months with no increase in power consumption, which means that digital devices including smart phones and computers will become significantly faster and more energy-efficient than those of the previous generation. Photons (light) travel with the highest speed permitted by the known law of physics. The idea of optical interconnects, using photons instead of electrons, enables faster data transfer. Two important elements of the integrated circuits (ICs) based on photons are the coherent light source (laser) and the photodetector. We investigated the optical properties of erbium doped gallium nitride epilayers and multiple quantum wells grown on silicon and sapphire and demonstrated lasing from these materials at 1.5 µm. We also fabricated and characterized graphene photodetectors that can detect the light from the deep ultraviolet to the mid-infrared region. The results provided a new pathway towards full photonic integration for silicon optoelectronics. Besides, they are the heart of many important applications ranging from gas sensing, aerospace sensors and systems, thermal imaging, biomedical imaging, infrared spectroscopy, and lidar-to-optical telecommunications.

Emerging technologies in the healthcare industry encompass revolutionary devices or drugs that have the potential to change how healthcare will be practiced in the future. While there are several emerging healthcare technologies in the pipeline, a few key innovations are slated to be implemented clinically sooner based on their mass appeal and potential for healthcare breakthroughs. This thesis will focus on specific topics in the emerging technological fields of nanotechnology for photothermal cancer therapy, 3D printing for irreversible electroporation applications, and imageable biomaterials. While these general areas are receiving significant attention, we highlight the potential opportunities and limitations presented by our select efforts in these fields. First, in the realm of nanomedicine, we discuss the optimization and characterization of sodium thiosulfate facilitated gold nanoparticle synthesis. While many nanoparticles have been examined as agents for photothermal cancer therapy, we closely examine the structure and composition of these specific nanomaterials and discuss key findings that not only impact their future clinical use, but elucidate the importance of characterization prior to preclinical testing. Next, we examine the potential use of 3D printing to generate unprecedented multimodal medical devices for local pancreatic cancer therapy. This additive manufacturing technique offers exquisite design detail control, facilitating tools that would otherwise be difficult to fabricate by any other means. Lastly, in the field of imageable biomaterials, we demonstrate the development of composite catheters that can be visualized with near infrared imaging. This new biomaterial allows visualization with near infrared imaging, offering potentially new medical device opportunities that alleviate the use of ionizing radiation. This collective work emphasizes the need to thoroughly optimize and characterize emerging technologies prior to preclinical testing in order to facilitate rapid translation. / Master of Science

Unwanted and distressing visual imagery is a persistent and emotionally taxing symptom characteristic of several mental illnesses, including depression, schizophrenia, and posttraumatic stress disorder. Intrusive imagery symptoms have been linked to maladaptive memory formation, abnormal visual cortical activity during viewing, gaze pattern deficits, and trait characteristics of mental imagery. Emotional valence of visual stimuli has been shown to alter perceptual processes that influence the direction of attention to visual information, which may result in enhanced attention to suboptimal and generalizable visual properties. This study tested the hypothesis that aberrant gaze patterns to central and peripheral image regions influence the formation of decontextualized visual details which may facilitate involuntary and emotionally negative mental imagery experiences following a stressful or traumatic event. Gaze patterns and hemodynamic response from occipital cortical locations were recorded while healthy participants (N = 39) viewed and imagined scenes with negative or neutral emotional valence. Self-report behavioral assessments of baseline vividness of visual imagery and various cognitive factors were combined with these physiological measures to investigate the potential relationship between visual perception and mental recreation of negative scenes. Results revealed significant effects of task and valence conditions on specific fixation measures and hemodynamic response patterns in ventral visual areas, which interacted with cognitive factors such as imagery vividness and familiarity. Findings further suggest that behaviors observed during mental imagery reveal processes related to representational formation over and above perceptual performance and may be applied to the study of disorders such as PTSD. / Ph. D.

The Anthropocene epoch in which we are currently living, also known as the Holocene, has brought about unprecedented losses in planet Earth’s biodiversity. Numerous extirpations of floral and faunal species have been influenced by human encroachment and more specifically, the exploitation of such species and the respective habitats in which they reside. It is this notion that has propelled many scientists to take up intellectual arms in an effort to protect these invaluable resources. The purpose of this research was to develop technologies to measure and evaluate various variables that influence animal physiology, specifically in amphibians who represent the most threatened class of all animal taxa. Species-specific knowledge including life history and an understanding of evolutionary traits are often needed to effectively guide the management decisions surrounding any given animal population. Specific objectives of this project were to develop non-invasive methods, such as hormone monitoring, machine learning-aided ultrasonography, and near-infrared spectroscopy (NIRS), to assess vital physiological traits, such as biological sex, reproductive status, and chytrid fungus pathogen detection in threatened amphibian species. The novel technologies developed and applied in amphibians here may provide insights for addressing conservation related questions in other animal as well as plant species. Additionally, automation of physiological monitoring techniques through the use of machine learning methods reduces barrier to entry and enables these technologies to be operated by a larger practitioner base. This research also serves to advance methods surrounding chemometric analyses as it pertains to the discipline of wildlife spectroscopy, where large multivariate datasets require data manipulation strategies to produce robust prediction models for the physiological trait of interest for qualitative or quantitative assessment. To that end, a multi-model framework is provided for optimizing predictive outcomes to address questions relating to wildlife management and conservation initiatives.

Frequency Domain Diffuse Optical Spectroscopy (FD-DOS) is an emerging optical technique that uses near infrared light to probe the hemodynamics of biological tissue. Compared to more common Continuous Wave (CW) methods, FD-DOS uses light that is temporally modulated on the order of MHz to quantify the absorption and scattering of tissue. FD-DOS can also be used to obtain absolute concentration of tissue chromophores such as oxy- and deoxy-hemoglobin, which allow for quantitative measurements of tissue hemodynamics. This dissertation focuses on the evolution of our lab’s custom digital FD-DOS as a platform for taking optical measurement of biological tissue for respiratory and cardiovascular applications. Several important instrumentation improvements will be reviewed that have enhanced the performance of the system while making it more portable and clinic ready. Two translational applications will be described in detail: 1) the use of high-speed FD-DOS for the non-invasive extraction of venous oxygen saturation (SvO2) and 2) the use of FD-DOS to monitor the hemodynamics of the sternocleidomastoid (SCM) muscle towards the non-invasive monitoring of patients on mechanical ventilation. The custom FD-DOS system parameters were adjusted for each application, with a focus on high speed to extract the cardiac signal for the SvO2 project, and a focus on high SNR to measure the highly absorbing SCM. Measurements on healthy volunteers and rabbits were used to assess the feasibility of using FD-DOS for these applications. Finally, preliminary work was conducted to characterize a miniature FD-DOS source and detector with the goal of moving towards a wearable version of FD-DOS. / 2022-05-15T00:00:00Z

Made available in DSpace on 2017-07-21T14:19:39Z (GMT). No. of bitstreams: 1 Carlos Proenca.pdf: 1478553 bytes, checksum: 110e623f3d19df6239c0f3c3097ce444 (MD5) Previous issue date: 2012-08-01 / Determining the soil organic matter and clay are important to obtain indicators of soil quality. Such measurements are help the agronomic management providing support for the recommendation of lime and fertilizer. For this quantification, analysis of soil becomes a "tool" indispensable, being increasingly used, especially when associated the the precision farming technology, in which the producer performs a higher number of analyzes aiming to identify soil variability of the property. However, laboratory tests bring some disadvantages, such as the time required for the analyses, and also the generation of waste. An option to perform the analyzes of organic matter and clay soil, quickly and without chemical residues, is by the use of visible to infrared spectroscopy and near (vis-NIRS - Visible and Near Infrared Spectroscopy). The aim of this work was to propose a methodology for predicting the soil organic matter and clay, by combining the use of Regression Analysis and Artificial Neural Networks in order to develop models to estimate these attributes. A database with information about soil analysis obtained by the conventional method and the method vis-NIR was used. The first step was to select the spectral bands that presented a better correlation with the response variables (clay and organic matter) by means of multivariate regression model. In order to improve the estimation of soil organic matter and clay, the group that presented the highest coefficient of determination was used as input of the Artificial Neural Networks. The quantity of 111 soil samples were used for calibration the models of soil analysis, and their spectra were obtained on a spectrophotometer FOSS NIR model XDS. The results were evaluated by the coefficient of determination (R2), considering the significance level of 5%. Coeficients of 0,89 and 0,94 were obtained in the prediction of organic matter and clay respectively, with indices highly significant (P <0,001), indicating the proposed methodology could be useful to predict the attributes studied. / A determinação dos teores de matéria orgânica e argila consistem em importantes indicadores da qualidade do solo. Suas quantificações são fundamentais no auxílio do manejo agronômico podendo fornecer subsídios para as recomendações de corretivos e fertilizantes. Para esta quantificação, a análise de solo se torna uma “ferramenta” indispensável, sendo utilizada cada vez mais, principalmente após a chegada da tecnologia da agricultura de precisão, em que o produtor realiza um número muitas vezes superior de análises visando a identificação da variabilidade do solo da sua propriedade. Porém, as análises laboratoriais trazem alguns inconvenientes, como o tempo necessário para as determinações e a geração de resíduos. Uma opção para realizar as análises de matéria orgânica e argila do solo, de forma rápida e sem geração de resíduos químicos é a espectroscopia de infravermelho visível e próximo (vis-NIRS – visible and Near Infrared Spectroscopy). O objetivo deste trabalho foi propor uma metodologia para predição dos teores de matéria orgânica e argila, envolvendo a combinação do uso de Análise de Regressão e Redes Neurais Artificiais com o desenvolvimento de modelos de estimativa destes atributos, utilizando uma base de dados com informações de análises de solo obtidas pelo método convencional e pelo método vis-NIRS. Primeiramente foram selecionadas as bandas espectrais que melhor correlacionavam com as variáveis de resposta (matéria orgânica e argila), por meio de um modelo de regressão multivariada. O grupo que obteve o maior coeficiente de determinação foi utilizado como entrada das Redes Neurais Artificiais visando melhorar a estimativa dos teores de matéria orgânica e argila. Para isto, 111 amostras de solo foram utilizadas para a calibração de modelos de análises de solos, sendo seus espectros obtidos em um espectrofotômetro de infravermelho próximo modelo FOSS XDS. Os resultados dos modelos foram avaliados por meio do coeficiente de determinação (R2), e pelo grau de significância ao nível de 5%. Correlações de 0,89 e 0,94 foram obtidas na predição do teor de matéria orgânica e argila, respectivamente, com índices altamente significativos (P<0,001), o que indica que a metodologia proposta pode ser utilizada para a predição dos atributos estudados.