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Paralysis is a debilitating disorder that does not currently have safe and effective treatments. Implantable brain electrodes can be used to read brain waves and convert them into a corresponding motor function to restore movement in paralyzed patients. Tissue deformation induced around the implant site is believed to reduce their viability through the foreign body response. Developing electrodes that minimize deformation is challenging because the mechanical aspects of deformation are not fully understood and non-animal tissue models for testing electrodes are unavailable. Development of pre-clinical models for in vitro testing of the mechanical properties of electrodes can lead to a better understanding of this prevalent problem. The objective of this study was to construct a novel contrast-enhanced, brain mimicking hydrogel using photopolymerizable polyethylene glycol (PEG) polymer that contains alginate microspheres with enclosed gadolinium (Gd) contrast agent. 1.5% alginate microspheres were constructed with enclosed Gd-DTPA-BSA contrast agent and successively added into 10% PEG. Then, this mixture was photopolymerized using a 5 mW/cm2UV lamp to result in a successful brain mimicking hydrogel. Rheological testing showed that its elastic modulus was approximately 1.5 kPa, which is similar to that of a normal human brain. The model is valuable because the presence of the contrast agent in the hydrogel resulted in distinct bright spots on the MRI. This can facilitate the visualization of tissue deformation caused by electrode insertion via comparison of pre-insertion and post-insertion images. This brain-mimicking model has the potential to improve understanding of neural deformation from electrode implants in order to assist patients suffering from paralysis.

An orbital angular momentum (OAM) sorter concept was designed for high-speed data transfer. The OAM of a light beam known as an optical vortex can exist in one of an infinite number of states and may be used to carry information. The augmented alphabet of states carries the potential to increase date transfer speeds over conventional binary (0 and 1) methods. A vortex generator, or OAM encoder , was constructed from a slit cover slip functioning as an adjustable spiral phase plate, and a vortex analyzer, or OAM measurer, was created using a transparent print of a computer-generated hologram. The two components were then incorporated in an OAM sorter concept that that employs a novel combinatoric method for sorting data. The vortex generator and analyzer created were inexpensive simpli-fications of previous devices and have the potential to increase the alphabet of transmission states several thousand times over binary methods if implemented in the OAM sorter concept.

Every year more soluble and insoluble substances are added to the oceans by runoff. This process slowly increases the salinity of the oceans. As the salinity of the oceans increase, what effect will this have on our climate? The purpose of this investigation is to determine how an increase in salt concentration affects the evaporation of sea water. Four ten liter aquaria were filled with six liters of distilled water in each aquarium. One aquarium contained only water. The other three aquaria had different amounts of Kosher salt added as follows: 120. grams, 240.grams and 480.grams. Measurements of the level of water in each tank were taken over a period of two months. The tank with the highest concentration of salt had the smallest rate of evaporation. In the feature with less evaporation of sea water the atmosphere will receive less moisture. This could result in fewer clouds and less rain fall for the planet.

The Boolean Satisfiability problem, called SAT for short, is the problem of determining if a set of constraints involving Boolean (True/False) variables can be simultaneously satisfied. SAT solvers have become an integral part in many computations that involve making choices subject to constraints, such as scheduling software, chip design, decision making for robots (and even Sudoku!). Given their practical applications, one question is when SAT problems become hard to solve. The problem difficulty depends on the constrainedness of the SAT instance, which is defined as the ratio of the number of constraints to the number of variables. Research in the early 90’s showed that SAT problems are easy to solve both when the constrainedness is low and when it is high, abruptly transitioning (“boiling over” ) from easy to hard in a very narrow region in the middle. My project is aimed at verifying this surprising finding. I wrote a basic SAT solver in Python and used it to solve a large number of randomly generated 3SAT problems with given level of constrainedness. My experimental results showed that the percentage of problems with satisfying assignment transitions sharply from 100% to 0% as constrainedness varies between 4 and 5. Right at this point, the time taken to solve the problems peaks sharply. Similar behavior also holds for 2SAT and 4SAT. Thus, SAT problems do seem to exhibit phase transition behavior; my experimental data supported my hypothesis.

Deficiencies in clearance of apoptotic cells predispose to the development of autoimmune disease. This is evident in mice lacking the receptor tyrosine kinases Tyro3, Axl, and Mer that mediate uptake of apoptotic cells. Deficient mice exhibit an increased abundance of apoptotic cells in tissues and manifest diverse autoimmune conditions. To test these mice for the presence of autoantibodies to apoptotic cells, we generated spontaneous splenic B cell hybridomas and used microscopy to screen for clones reactive with apoptotic Jurkat cells. From hybridomas secreting IgG antibodies reactive with apoptotic cells, we selected one that recreated the major serum specificity for apoptotic cells. The antibody, LHC7.15, bound to an antigen that is differentially distributed between the nucleus and the cytoplasm in live and apoptotic cells. In late apoptotic cells, the antigen coalesces into aggregates that form blebs at the cell surface. Immunopurification of the antigen, followed by mass spectrometry, identifed a protein of 69kD whose partial sequence matched hnRNP P2. This multi-functional protein binds DNA, RNA, and several known RNP autoantigens. Our observations suggest that an RNP complex, formed and translocated to the cell surface in apoptosis, participates in the induction of linked sets of anti-RNP autoantibodies. Our results also implicate hnRNP P2 as a potential novel antigen involved in initiating and sustaining systemic autoimmune diseases.

Despite the multitude of biomedical scans conducted, there is still relatively low accuracy and standardization of diagnoses from these images. In both the fields of computer science and medicine there is very strong interest in developing personalized treatment policies for patients who have variable responses to treatments. The aim of my research was automatic segmentation of brain MRI scans to better analyze patients with tumors, multiple sclerosis, ALS, or Alzheimer’s. In particular, I aim to use this information, along with novel artificial intelligence algorithms, to find an optimal personalized treatment policy which is a non-deterministic function of the patient specific covariate data that maximizes the expected survival time or clinical outcome. The result of the research was IlluminaMed, a biomedical image analysis toolkit that relies on the development of new artificial neural networks and training algorithms and novel research in fuzzy logic. The networks can detect patterns more complex than humans can identify and create patterns over long periods of time. IlluminaMed was trained by a dataset of professionally and manually segmented MRI scans from several prestigious hospitals and universities. I then developed an algorithmic framework to solve multistage decision problem with a varying number of stages that are subject to censoring in which the “rewards” are expected survival times. In specific, I developed a novel Q-learning algorithm that dynamically adjusts for these parameters. Furthermore, I found finite upper bounds on the generalized error of the treatment paths constructed by this algorithm. I have also shown that when the optimal Q-function is an element of the approximation space, the anticipated survival times for the treatment regime constructed by the algorithm will converge to the optimal treatment path. I demonstrated the performance of the proposed algorithmic framework via simulation studies and through the analysis of chronic depression data and a hypothetical clinical trial. IlluminaMed can automatically segment the scans with 98% accuracy, find tumors with 96% accuracy and approximate their volume within a 2% margin of error. It can also find lesions in MS and ALS, distinguishing them from tumors with 94% accuracy. IlluminaMed can, in addition, determine the tendency of a patient to develop Alzheimer’s several months before patients develop symptoms correlating the brain structure and its fluctuations. Lastly, the censored Q-learning algorithm I developed is more effective than the state of the art clinical decision support systems and is able to operate in environments when many covariate parameters may be unobtainable or censored. IlluminaMed is the only fully automatic biomedical image analysis toolkit and personalized medicine engine. The personalized medicine engine runs at a level that is comparable to the best physicians. It is less computationally complex than similar software and is unique in the fact that it can find new patterns in the brain with possible future diagnoses. IlluminaMed’s implications are not only great in terms of the biomedical field, but also in the field of artificial intelligence with new findings in neural networks and the relationships of fuzzy extensional subsets.

Entamoeba histolytica is a protozoan parasite known to cause infectious colitis and amoebic dysentery in humans. Its life cycle consist of two parts: the infectious cyst stage and the multiplying trophozoite stage. Epinephrine, a neurotransmitter in vertebrates, is released by the trophozoites during the process of cyst formation. The addition of epinephrine to in vitro cultures of amoebas causes them to encyst, and addition of compounds that prevent epinephrine’s activity inhibits encystations. Therefore, epinephrine plays a critical role in encystation in vitro. An understanding of the molecular intricacies of epinephrine-induced encystations may allow for pharmacological manipulation of epinephrine metabolism to control cyst formation in vitro. Drugs that either prevent cyst formation or induce it before a large amoebic population is present would result in the release of fewer cyst forms of the parasite, reducing parasite transmission from person to person. Although trophozoites release epinephrine, it is no known if E.histolytica synthesizes epinephrine or extracts it from the growth medium. Phenylethanolamine N-methyltransferase(PNMT) is the enzyme that catalyzes production of epinephrine norepinephrine. This study aims to determine the source of epinephrine by determining if E.histolytica contains a PNMT-type enzyme. PNMT amino acid sequences from several higher organisms were compared to identify conserved regions of the enzyme. These conserved amino acid sequences were then used to search for similar sequences in a database containing the recently sequenced amoeba genome. A PNMT-like gene was found in the E.histolytica database and cloned in bacteria. Yeast cells containing the cloned E.histolytica PNMT gene expressed PMT enzyme activity. This suggests that E.histolytica produces its own epinephrine, and is the most evolutionarily ancient eukaryote shown to do so. The use of inhibitors against PNMT activity is under investigation.

The purpose of Computer Vision is to understand the methods by which humans\r process visual information and likewise to create computer algorithms similar to these\r processes. Through careful observation, a computer algorithm was developed to mimic\r how humans recognize logos in television commercials. After visual analysis of\r numerous commercial sequences, it was hypothesized that the key frames (frames in\r which the logo resides) could be found using the intersection of color histograms; the\r logo region could be found using the edge density within the key frames; and the logo\r could be identified utilizing a correlation method with a database of stored logos, scaled\r to different levels using Bilinear Interpolation.\r Color histograms were implemented using one-dimensional arrays with 24 bins;\r key frames were determined by calculating the intersection between consecutive frames’\r color histograms. The edge density was calculated by convolving the key frame with\r the number of edge pixels within a 21X21 area. The identification of the logo was\r determined by computing the Sum of Square Differences between the logo region and\r the database of logos on different scales; SSD values were normalized for different\r scales.\r The algorithm was tested on 14 different sequences and determined the key frame\r with 80% accuracy. By segmenting the sequence into two key frames, the algorithm\r generated 93% accuracy. The algorithm also identified the logo region with 93%\r accuracy. The identification of the logo yielded anomalous results. These data suggest\r that motion between consecutive frames in commercial segments decreases around the\r display of the logo. They also suggest that the logo region has the most visible edges\r within the key frame.\r Future study includes a complete overhaul of the logo recognition algorithm. The\r correlation algorithm (SSD) does not work accurately enough to be used. Therefore, the\r next step is possibly to look at the edge information about the key frames. As the Canny\r algorithm determines the edges of an image, it has to determine the direction (or\r orientation) of the edges. Therefore, a proposed study includes utilizing an edge\r orientation histogram of the database of the logos and the key frames. This would mean\r that the algorithm would identify the logo in the key frames by matching edge\r orientation histograms.

A study was conducted to improve the thermal stability, fire retardant (FR)\r properties and rheological properties of ethy-lene vinyl acetate because of\r its growing use in commercial applications. The approach employed\r was to modify an organo-clay, Closite 20A (C20A), with transition metal\r ions (TMI). In this study eight transition metal salts were acquired for\r modification. It was observed that all TMI modified organoclay\r nanocomposites improved thermal stability through thermo-gravimetric\r analysis (TGA). Rheological testing was done using a parallel plate\r measuring system (PP MS) to determine the dependence of storage\r modulus and loss modulus of copper and iron modified organoclay\r nanocomposites relative to pure EVA 350. The process of gelation was\r also tested for by calculating the ratio between the loss modulus and the\r storage modulus. It was found that copper modified organoclay\r nanocomposites promoted gelation and thus decreased the fluidity of\r EVA 350. The intercalation of the TMI modified organoclays with the\r polymer matrix was determined by the use of small angle X-ray\r scattering (SAXS). Testing revealed that the intercalation was\r successful, further proving that the TMIs had improved thermal stability,\r FR properties and rheological properties,