The blood-brain barrier BBB refers to the set of specialized endothelial cells that line the vasculature in the brain and effectively control movement of molecules into and out of the brain. While necessary for proper brain function, the BBB blocks 98% of drugs from entering the brain and is the most significant barrier to developing therapies for neurodegenerative diseases. Active transport allows some specific molecules to cross the BBB, but therapeutic development using this route has had limited success. A number of techniques have been used to bypass the BBB, but are often highly invasive and ineffective. Over the last two decades, a minimally invasive technique to transiently open the BBB has been under development that utilizes transcranial focused ultrasound (FUS) in combination with intravascular microbubble contrast agents. This method is often carried out in conjunction with magnetic resonance imaging (MRI) to guide and assess BBB opening and has been referred to as MRI guided FUS (MRgFUS). Because of the utility of mouse models of neurological disease and the exploratory nature of MRgFUS, systems that allow BBB opening in mice are a useful and necessary tool to develop and evaluate this method for clinical application. In this dissertation project, a custom built, cost-effective FUS system for opening the BBB in mice was developed, with the objective of using this device to deliver therapeutics to the brain. Being a custom device, it was necessary to evaluate the ultrasound output, verify in vivo safety, and anticipate the therapeutic effect. The scope of the work herein consists of the design, construction, and evaluation of system that fulfills these requirements. The final constructed system cost was an order of magnitude less than any commercially available MRgFUS system. At this low price point, the hardware could allow the implementation of the methodology in many more research areas than previously possible. Additionally, to anticipate the therapeutic effect, molecules of pharmacologically-relevant sizes were delivered to brain with a novel, multispectral approach. Results demonstrated that the device was able to safely open the BBB, and macromolecule delivery showed that both molecule size and FUS pressure both influence the amount and distribution of molecules in the brain. Using different ultrasound pressures, the threshold for BBB opening was found to be ≥ 180 kPa (0.13 MI). The threshold for damage was found to be ≥ 420 kPa (0.30 MI), and was minor at this pressure, but extensive for higher pressure (870 kPa, 0.62 MI), in which minor damage was caused by this pressure. Performing a novel implementation of a diffusion model on the fluorescence images of 500, 70, and 3 kDa dextran resulted in calculated diffusion coefficients of 0.032 ± 0.015, 12 ± 6.0, and 0.13 ± 0.094 square microns per second, respectively.

As part of the Bagnold Dune campaign conducted by Mars Science Laboratory rover Curiosity, visible/near-infrared reflectance spectra of dune sands were acquired using Mast Camera (Mastcam) multispectral imaging (445-1013nm) and Chemistry and Camera (ChemCam) passive point spectroscopy (400-840nm). By comparing spectra from pristine and rover-disturbed ripple crests and troughs within the dune field, and through analysis of sieved grain size fractions, constraints on mineral segregation from grain sorting could be determined. In general, the dune areas exhibited low relative reflectance, a weak similar to 530nm absorption band, an absorption band near 620nm, and a spectral downturn after similar to 685nm consistent with olivine-bearing sands. The finest grain size fractions occurred within ripple troughs and in the subsurface and typically exhibited the strongest similar to 530nm bands, highest relative reflectances, and weakest red/near-infrared ratios, consistent with a combination of crystalline and amorphous ferric materials. Coarser-grained samples were the darkest and bluest and exhibited weaker similar to 530nm bands, lower relative reflectances, and stronger downturns in the near-infrared, consistent with greater proportions of mafic minerals such as olivine and pyroxene. These grains were typically segregated along ripple crests and among the upper surfaces of grain flows in disturbed sands. Sieved dune sands exhibited progressive decreases in reflectance with increasing grain size, as observed in laboratory spectra of olivine size separates. The continuum of spectral features observed between the coarse- and fine-grained dune sands suggests that mafic grains, ferric materials, and air fall dust mix in variable proportions depending on aeolian activity and grain sorting.

Chromaticity analysis of LANDSAT Multispectral Scanner (MSS) imagery of Chilko Lake, B.C. reveals a. locus whose shape has not been previously reported. To investigate the cause of this and to come to a broader understanding of chromaticity analysis for MSS and Thematic Mapper (TM) data, an optical water quality model has been used. The model is composed of a four component reflectance model (R-model), an interface model and an atmospheric model. The R-model was calibrated for Chilko Lake by determining the specific absorption and backscattering spectra for suspended minerals (SM), chlorophyll-a uncorrected for phaeophytins (C) and yellow substance (YS). The fourth component is water. The model reproduces the observed locus shape and indicates that it is primarily a function of SM, with the unreported lower limb on MSS imagery caused by SM gradients with concentrations less than 1-2 mg/L. The effects of C, YS and SM cannot be separated on plots of chromaticity coordinates X and Y for either MSS or TM data. In addition, haze or wind gradients, if they occur over water with low levels of SM, would look similar to the lower limb on MSS XY plots. However, if brightness is used in combination with X, the model predicts that C and YS, though themselves inseparable, can be differentiated from SM at all but the lowest concentrations of SM. Furthermore, haze and wind gradients can be distinguished from the lower limb. Thus the addition of brightness to chromaticity analysis has the potential to significantly improve the technique. The model was tested by comparing simulated chromaticity results with results from actual images (one TM image and three MSS images) for which ground truth had been collected. Qualitative predictions regarding haze and water quality patchiness were confirmed. Correlation analysis with R² values from 0.81 to 0.95 also strongly confirmed predictions regarding SM, but showed that the model is systematically underestimating SM. Correlation tests for a combined C and YS factor (CYS) were inconclusive because of the systematic modeling error, but classification maps provide weak evidence that CYS is behaving qualitatively as predicted and that CYS can be differentiated from SM. The modeling error is thought to originate in atmospheric assumptions which are not met. The R-model which is fundamental to the study has been tested and is not a major source of error. The study concludes that the model is qualitatively correct and that the use of brightness improves chromaticity analysis by allowing separation of CYS and SM, though further work should be undertaken to verify these results. Maps of CYS and SM in Chilko Lake reveal that CYS tends to be higher along the western shore and where the hypolimnion is exposed. SM are highest near stream mouths. The distribution patterns are related to physical processes within the lake and provide a synoptic view of the connection between water quality parameters and circulation which would be difficult to achieve in any other way. / Forestry, Faculty of / Graduate

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第22075号 / 農博第2367号 / 新制||農||1072(附属図書館) / 学位論文||R1||N5229(農学部図書室) / 京都大学大学院農学研究科地域環境科学専攻 / (主査)教授 近藤 直, 准教授 小川 雄一, 教授 清水 浩 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

Vegetation monitoring plays a significant role in improving the quality of life above the earth's surface. However, vegetation resources management is challenging due to climate change, global warming, and urban development. The research aims to identify and extract vegetation communities for Jupiter Inlet Lighthouse Outstanding Natural Area (JILONA) using developed Unmanned Aerial Systems (UAS) deployed with five bands of RedEdge Micasence Multispectral Sensor. UAS has a lot of potential for various applications as it provides high-resolution imagery at lower altitudes. In this study, spectral reflectance values for each vegetation species were collected using a spectroradiometer instrument. Those values were correlated with five band UAS Image values to understand the sensor's performance, also added with reflectance’s similarities and divergence for vegetation species. Pixel and Object-based classification methods were performed using 0.15 ft Multispectral Imagery to identify the vegetation classes. Supervised Machine Learning Support Vector Machine (SVM) and Random Forest (RF) algorithms with topographical information were used to produce thematic vegetation maps. The Pixel-based procedure using the SVM algorithm generated an overall accuracy and kappa coefficient of above 90 percent. Both classification approaches have provided aesthetic vegetation thematic maps. According to statistical cross-validation findings and visual interpretation of vegetation communities, the pixel classification method outperformed object-based classification. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20694号 / 工博第4391号 / 新制||工||1682(附属図書館) / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 米田 稔, 教授 清水 芳久, 准教授 須崎 純一 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

American Cancer Society estimates that in 2021 nearly 300,000 women in the United States will be diagnosed with invasive breast cancer, and about 43,600 women will die from breast cancer. While many have access to health care and cancer screening, women from rural or underdeveloped communities often have limited access. Therefore, there is a need for an inexpensive and easy-to-use breast cancer identification device, which can be employed in small clinics to provide support to primary care physicians. This work aims to develop a method to characterize breast tumors and tissue using non-invasive imaging modalities. The proposed bimodal imaging system has tactile and multispectral imaging capabilities. Tactile imaging modality characterizes tumors by esti-mating their depth, size, and stiffness, along with the Tactile Index. Multispectral imaging modality identifies breast asymmetry, texture, and inflammation changes, together with the Spectral Index. These indices are combined with the BCRAT Index, the risk score devel¬oped by the National Institute of Health, to form the Multimodal Index for personalized breast cancer risk assessment. In this study, we will describe the development of the bimodal imaging system. We will present the algorithms for tactile and multispectral modalities. Tactile and Multispec¬tral Profile Diagrams are developed to capture broad imaging signals in a compact and application-specific way. A Tactile Profile Diagram is a pictorial representation of the rel¬ative depth, size, and stiffness of the imaged tumor. A Multispectral Profile Diagram is a representative pattern image for breast tissue superficial optical properties. To classify the profile diagrams, we employ the Convolutional Neural Network deep learning method. We will describe the results of the experiments conducted using tissue-mimicking phan¬toms and human in-vivo experiments. The results demonstrate the ability of the method to classify and quantify tumor and tissue characteristics. Finally, we describe the method to calculate Multimodal Index for the malignancy risk assessment via tactile and multispectral imaging modalities and the risk probability based on the health records. / Electrical and Computer Engineering

No description available.

Multispectral capabilities of nanowires (NWs) were explored for InAs and InAsSb NWs on Si(111) substrates. NWs were grown with the vapour-solid (VS) growth mode in a molecular beam epitaxy (MBE) system using an oxide template to control positions and diameters. Early attempts to integrate InSb NWs and silicon substrates proved unsuccessful. Instead studies of InAs NWs on silicon, and eventually InAsSb/InAs NWs on silicon were completed to achieve large-diameter, infrared (IR) sensitive photodetectors. InAs NWs were grown on silicon substrates to study their morphology characteristics and vertical NW yield. The five different growth modes explored were (1) Au-assisted vapour-liquid-solid (VLS), (2) positioned Au-assisted, (3) vapour solid, (4) positioned Au-assisted VLS using a patterned oxide mask (VLS-SAE), and (5) selective area epitaxy (SAE) using a patterned oxide mask. Optimal temperature and V/III flux ratios for achieving a high vertical yield were found for the SAE growth mode. Further understanding of the InAs SAE growth mode was gained through modeling of various oxide hole filling scenarios. Each scenario was defined by the arrival rates of the group III and group V materials to the holes. A parameter space is discussed for the growth of high yield InAs NWs, dependent on the V/III flux ratio and temperature of growth. Large diameter InAsSb NWs for IR absorptance were grown on silicon using a high yield InAs stem. Several NW array diameters were grown simultaneously on the same substrate to measure multispectral photodetection. Diameters were controlled by NW spacing. Fourier transform IR (FTIR) spectroscopy was used to measure absorptance in the NWs. NW diameters spanned 440 – 520 nm which resulted in enhanced absorptance in the short-wave IR region. Simulations of the HE11 resonances of the NW arrays were performed and compared with the FTIR measurements. Initial electrical measurements demonstrated a diameter-dependent photocurrent. / Thesis / Doctor of Philosophy (PhD)

Primary productivity is a fundamental ecological process and an important measure of ecosystem response to environmental change. Currently, there is a considerable lapse in our understanding of primary productivity in hot and cold deserts, due to the difficulty of measuring production in cryptogam vegetation. However, remote sensing can provide long-term, spatially-extensive estimates of primary production and are particularly well suited to remote environments, such as in the McMurdo Dry Valleys (MDV) of Antarctica, where cyanobacterial communities are the main drivers of primary production. These microbial communities form multi-layered sheets (i.e., microbial mats) on top of desert pavement. The cryptic nature of these communities, their often patchy spatial distribution, and their ability to survive desiccation make assessments of productivity challenging. I used field-based surveys of microbial mat biomass and pigment chemistry in conjunction with analyses of multispectral satellite data to examine the distribution and activity of microbial mats. This is the first satellite-derived estimate of microbial mat biomass for Antarctic microbial mat communities. I show strong correlations between multispectral satellite data (i.e., NDVI) and ground based measurements of microbial mats, including ground cover, biomass, and pigment chemistry. Elemental (C, N) and isotopic composition (15N, 13C) of microbial mats show that they have significant effects on biogeochemical cycling in the soil and sediment of this region where they occur. Using these relationships, I developed a statistical model that estimates biomass (kg of C) in selected wetlands in the Lake Fryxell Basin, Antarctica. Overall, this research demonstrates the importance of terrestrial microbial mats on C and N cycling in the McMurdo Dry Valleys, Antarctica. / Master of Science / Primary productivity is an essential ecological process and a useful measure of how ecosystems respond to climate change. Primary production is more difficult to measure in polar desert ecosystems where there is little to no vascular vegetation. Polar regions are also ecosystems where we expect to see significant responses to a changing climate. Remote sensing and image analysis can provide estimates of primary production and are particularly useful in remote environments. For example, in the McMurdo Dry Valleys (MDV) of Antarctica, cyanobacterial communities are the main primary producers. These microbial communities form multi-layered sheets (i.e., microbial mats) on top of rocks and soil. These communities are cryptic, do not cover large areas of ground continuously, and are able to survive desiccation and freezing. All of these characteristics make assessments of productivity especially challenging. For my master’s research, I collected microbial mat samples in conjunction with the acquisition of a satellite image of my study area in the MDV, and I determined biological parameters (e.g., percent ground cover, organic matter, and chlorophyll-a content) through laboratory analyses using these samples. I used this satellite image to extract spectral data and perform a vegetation analysis using the normalized difference vegetation index (i.e., NDVI), which determines areas in the image that contain vegetation (i.e., microbial mats). By linking the spectral data to the biological parameters, I developed a statistical model that estimates biomass (i.e., carbon content) of my study areas. These are the first microbial mat biomass estimates using satellite imagery for this region of Antarctica. Additionally, I researched the importance of microbial mats on nitrogen cycling in Taylor Valley. Using elemental and isotopic analyses, I determined microbial mats have significant effects on the underlying soil and nutrient cycling. Overall, this research demonstrates the importance of terrestrial microbial mats on C and N fixation in Antarctic soil environments.