Synthesis of Novel Boronated Amino Acids for BNCT an Alternate Cancer Therapy and Use of Microwaves in Organic Synthesis

Boron neutron capture therapy (BNCT) is a binary form of cancer treatment wherein 10B nuclei, when irradiated with thermal neutrons, produce high energy transfer particles. These particles, due to their size and energy, are confined to a radius of 9-10μm, which is comparable to the size of single cell. Potential BNCT agents reported in the literature include boron-containing amino acids, nucleic acids, nucleosides, antibodies, and other biomolecules. In recent years, microwaves have gained importance in organic chemistry. Microwave induced reactions are energy efficient, often enhance reaction rates, and generally lead to enhanced product yields. Recent studies have shown that potassium organotrifluoroborates offer solutions to a number of problems that sometime occur in organoboron coupling reactions. This dissertation describes the synthesis of novel unnatural boronated amino acids as potential BNCT agents. The new microwave enhanced synthetic methodologies developed in this dissertation are important transformations in modern organic chemistry. Mild reaction conditions, short reaction times, and tolerance for various functional groups are advantages of these methodologies

Semi-supervised learning of order parameter in 2D Ising and XY models using Conditional Variational Autoencoders

We investigate the application of deep learning techniques employing the conditional variational autoencoders for semi-supervised learning of latent parameters to describe phase transition in the two-dimensional (2D) ferromagnetic Ising model and the two-dimensional XY model. For both models, we utilize spin configurations generated using the Wolff algorithms below and above the critical temperatures. For the 2D Ising model we find the latent parameter of conditional variational autoencoders is correlated to the known order parameter of magnetization more efficiently than their correspondence in variational autoencoders used previously. It can also clearly identify the restoration of the $\mathbb{Z}_2$ symmetry beyond the critical point. The critical temperature extracted from the latent parameter at larger lattices are found to be approaching its correct value. Similarly, for the 2D XY model, we find our chosen network with the latent representation of conditional variational autoencoders is equally capable of separating the two phases between the high and low temperatures, again at the correct critical temperature with reasonable accuracy. Together these results show that the latent representation of conditional variational autoencoders can be employed efficiently to identify the phases of condensed matter systems, without their prior knowledge.Comment: 9 pages, 8 figure

Harnessing Food Composition Data: Machine Learning models to predict taste and health outcomes of food processing

Food processing is a complex chemical process that transforms the chemical composition of the raw ingredients into their final food product, whose complexity is not yet deciphered. In our modern food system, understanding the impact of food processing on both taste and health outcomes is crucial. Traditional computational models offer some insight and utility in food production; these are essentially targeted approaches specific to foods, processes and nutritional and/or sensory outcomes. Their limitation is the inability to scale, which is necessary to address the current urgent demands of precision and personalized health and sustainable food production. These multi-variate challenges requires a deeper and more comprehensive understanding of the complexity of foods and processing methods and comprise of two main research efforts; to build food composition datasets that embody this information, and then to identify and apply the relationships as solutions. Machine Learning (ML) is widely hailed by research and industry as the technology best suited to address such an enormous multivariate problem. This shared vision has already led to efforts in building the necessary datasets. As relevant to this challenge, a common hypothesis is tested across two projects in this research - There a relationship between the chemical composition of a food and its nutritive and sensory properties in the processed state.The first project develops ML models to predict the content of seven vitamins (vitamin A, B1, B2, B3, B6, B9, C) and seven minerals (Calcium, Iron, Magnesium, Phosphorus, Potassium, Sodium, Zinc) in a processed food. The ML models are trained to learn the multi-parametric transformation patterns between the compositions of the raw and cooked foods. The focus was to be able address common dietary questions of consumers about choice of food and cooking method, and the selected training data included 425 plant and animal-based foods and 5 common cooking methods (steaming, boiling, roasting, grilling and broiling). The predictive model performed 43% and 18% better than using the standard USDA retention factor model for wet heat (steaming, boiling) and dry heat (roasting, grilling, broiling) processes, respectively. The breakdown of the predictive performance by food category revealed that legumes have the best among plant-based foods and beef the best in the animal-based foods. This suggests that nutrient loss is affected by the structural composition of foods, for future research. The second project explored structure-property models that aim to decipher the complex relation between the physical shape of a molecule and its physical properties and/or the functional role of the molecule in a product formulation. The focus was the modeling of glycans (i.e., carbohydrates), which are not only abundant in food, but essential to both food production and, more importantly, human health. In the study, regression methods were used to generalize the relationships between the structure of starch (e.g., chain length and composition of protein and amylose) and a range of its properties (e.g., gelatinization temperature, time series viscosity data, gel consistency, and sensory texture) for 301 samples of rice. The results indicated that the structure-composition data is a significantly better predictor (27% more predictive accuracy) of sensory mouthfeel than the physical properties, even though the latter is typically used in experimental research. The results of these projects demonstrate the ability of ML methods to learn a variety of complex multivariate relationships. However further progress is gated by the availability of high quality and high-resolution datasets and although the analytical methods exist, the challenge is knowing the relevant dataset for a specific prediction target. This challenge is addressed by both projects, where an assessment of what could improve prediction accuracy is the basis for future areas for data collection

Approaches to Retinal Detachment Prophylaxis among Patients with Stickler Syndrome

Stickler syndrome is the most common cause of pediatric rhegmatogenous retinal detachments. Given the dramatic long term visual impact and difficult surgical management of these detachments, there is increasing interest in determining whether prophylactic treatment can be used to prevent retinal detachments in this population. However, severity of ocular findings in Stickler syndrome can vary by subtype. Three commonly used modalities to provide prophylactic treatment against retinal detachments in patients with Stickler syndrome include scleral buckle, laser retinopexy, and cryotherapy. While laser retinopexy is the most common approach to prophylactic treatment, treatment settings can vary by specialist. In addition, the decision to treat and manage Stickler syndrome is nuanced and requires careful consideration of the individual patient. After reviewing the literature on prophylactic treatment approaches, this chapter will also over guidelines in management of this complex patient population

Machine learning models to predict micronutrient profile in food after processing.

The information on nutritional profile of cooked foods is important to both food manufacturers and consumers, and a major challenge to obtaining precise information is the inherent variation in composition across biological samples of any given raw ingredient. The ideal solution would address precision and generability, but the current solutions are limited in their capabilities; analytical methods are too costly to scale, retention-factor based methods are scalable but approximate, and kinetic models are bespoke to a food and nutrient. We provide an alternate solution that predicts the micronutrient profile in cooked food from the raw food composition, and for multiple foods. The prediction model is trained on an existing food composition dataset and has a 31% lower error on average (across all foods, processes and nutrients) than predictions obtained using the baseline method of retention-factors. Our results argue that data scaling and transformation prior to training the models is important to mitigate any yield bias. This study demonstrates the potential of machine learning methods over current solutions, and additionally provides guidance for the future generation of food composition data, specifically for sampling approach, data quality checks, and data representation standards

An improved representation of vehicle incompatibility in frontal NCAP tests using a modified rigid barrier

With the objective of better understanding the significance of New Car Assessment Program (NCAP) tests conducted by the National Highway Traffic Safety Administration (NHTSA), head-on collisions between two identical cars of different sizes and between cars and a pickup truck are studied in the present paper using LS-DYNA models. Available finite element models of a compact car (Dodge Neon), midsize car (Dodge Intrepid), and pickup truck (Chevrolet C1500) are first improved and validated by comparing theanalysis-based vehicle deceleration pulses against corresponding NCAP crash test histories reported by NHTSA. In confirmation of prevalent perception, simulation-bascd results indicate that an NCAP test against a rigid barrier is a good representation of a collision between two similar cars approaching each other at a speed of 56.3 kmph (35 mph) both in terms of peak deceleration and intrusions. However, analyses carried out for collisions between two incompatible vehicles, such as an Intrepid or Neon against a C1500, point to the inability of the NCAP tests in representing the substantially higher intrusions in the front upper regions experienced by the cars, although peak decelerations in cars arc comparable to those observed in NCAP tests. In an attempt to improve the capability of a front NCAP test to better represent real-world crashes between incompatible vehicles, i.e., ones with contrasting ride height and lower body stiffness, two modified rigid barriers are studied. One of these barriers, which is of stepped geometry with a curved front face, leads to significantly improved correlation of intrusions in the upper regions of cars with respect to those yielded in the simulation of collisions between incompatible vehicles, together with the yielding of similar vehicle peak decelerations obtained in NCAP tests