Synthesis and Solution Chemistry of an Octabrominated Iron (III) Porphyrin for Oxygen-Activation Catalysis

The chemistry of water-soluble cationic porphyrins has been actively pursued in the interest of providing new materials with electronic and magnetic properties suitable for use in optical and memory devices as well as for the fabrication of metallocatalysts for the heterogeneous activation of oxygen. To this end, we have synthesized a new cationic iron(III) porphyrin bearing bromine substituents at the porphyrinic carbons. Electrochemical studies of the iron(III) complex indicated an increase in oxidation strength by 0.33 V shift to more positive potential for the Fe(IIVII) redox potential compared to the non-brominated iron complex. The synthesis and spectroscopic properties of the porphyrin will be presented

Poly(ethylene glycol) based biomaterial platforms for guiding cell behavior through control of presentation and release of bioactive, therapeutic proteins.

Poly(ethylene glycol) (PEG) based biomaterials offer a number of advantages for applications in biomedical technology, including drug delivery and tissue engineering. PEG is notable for both its hydrophilicity and bioinert properties, and is widely used to create hydrogels used for 3D cell culture. There is growing interest in strategies to introduce biological functionality into PEG-based materials, in order to develop platforms to study the complex biochemical and biomechanical cues that govern cell behavior in physiologically relevant context. This thesis explores photopolymerization conditions for hydrogel synthesis that maintain a high degree of bioactivity for proteins in-situ. Such reactions are then utilized to create hydrogels capable of directing cell fate and behavior. Photopolymerization conditions for the formation of hydrogels were characterized, as these reactions are widely used for protein encapsulation. First, the role of affinity peptides was studied during photoencapsulation of the cytokine transforming growth factor β (TGFβ). When peptides with affinity for TGFβ were included in monomer solutions, they increased the amount of soluble, bioactive protein released from PEG diacrylate hydrogels formed via a chain-growth polymerization. We then studied protein protection during photopolymerization of step-growth networks using a thiol-norbornene reaction. Thiol-ene reactions were shown to be milder than acrylate chain-growth, as they fully maintained the bioactivity of TGFβ and lysozyme. Protein deactivation was correlated to total photoinitiated radical concentrations in order to more fully characterize reaction conditions that maximized protein protection. We then applied this knowledge to create biomaterials that incorporated covalently linked, bioactive proteins capable of directing complex cellular functions. TGFβ was thiolated via reaction with 2-iminothiolane, a modification that had no impact on bioactivity. This thiolfunctionalized protein could then be readily linked into hydrogels using either thiol-acrylate or thiol-ene polymerizations. When human mesenchymal stem cells were encapsulated into tethered TGFβ hydrogels, the growth factor induced chondrogenic differentiation at levels similar to or exceeding that of soluble delivery. Further, tethered TGFβ hydrogels were used to culture valvular interstitial cells in order to study the combined roles of substrate elasticity and immobilized TGFβ play in activation of myofibroblast phenotype. The photopolymerization conditions and resulting functionalized hydrogels developed in this thesis demonstrate the use of PEG hydrogels to investigate complex cell-material interactions of interest in tissue engineering applications

Studies of Diffuse Interstellar Bands. V. Pairwise Correlations of Eight Strong DIBs and Neutral Hydrogen, Molecular Hydrogen, and Color Excess

We establish correlations between equivalent widths of eight diffuse interstellar bands (DIBs), and examine their correlations with atomic hydrogen, molecular hydrogen, and EB-V . The DIBs are centered at \lambda\lambda 5780.5, 6204.5, 6283.8, 6196.0, 6613.6, 5705.1, 5797.1, and 5487.7, in decreasing order of Pearson\^as correlation coefficient with N(H) (here defined as the column density of neutral hydrogen), ranging from 0.96 to 0.82. We find the equivalent width of \lambda 5780.5 is better correlated with column densities of H than with E(B-V) or H2, confirming earlier results based on smaller datasets. We show the same is true for six of the seven other DIBs presented here. Despite this similarity, the eight strong DIBs chosen are not well enough correlated with each other to suggest they come from the same carrier. We further conclude that these eight DIBs are more likely to be associated with H than with H2, and hence are not preferentially located in the densest, most UV shielded parts of interstellar clouds. We suggest they arise from different molecules found in diffuse H regions with very little H (molecular fraction f<0.01). Of the 133 stars with available data in our study, there are three with significantly weaker \lambda 5780.5 than our mean H-5780.5 relationship, all of which are in regions of high radiation fields, as previously noted by Herbig. The correlations will be useful in deriving interstellar parameters when direct methods are not available. For instance, with care, the value of N(H) can be derived from W{\lambda}(5780.5).Comment: Accepted for publication in The Astrophysical Journal; 37 pages, 11 figures, 6 table

Seismic analysis of the Roman Temple of Évora, Portugal

The Roman temple of Évora dates back to the 1st century AD and has undergone several changes throughout history, including various additions, which have been removed. Several archaeological studies have recently been carried out, but the structural safety of the temple is unknown. Of particular concern is the temple’s seismic resistance, as it is located in a region subjected to a moderate seismic hazard. The main purpose of this paper is to ascertain the temple’s behaviour under seismic excitation through limit analysis and discrete element analysis. Both analysis techniques will use the assumption that the structure is composed of rigid blocks connected with dry joints. Geometric information has been derived from a recent laser scanning surveying, while calibration undertaken using in-situ results from GPR and dynamic identification tests. The main results are presented and discussed in detail as well as the need for possible repair works within the framework of the ICARSAH guidelines

A weighted ensemble of regression methods for gross error identification problem.

In this study, we proposed a new ensemble method to predict the magnitude of gross errors (GEs) on measurement data obtained from the hydrocarbon and stream processing industries. Our proposed model consists of an ensemble of regressors (EoR) obtained by training different regression algorithms on the training data of measurements and their associated GEs. The predictions of the regressors are aggregated using a weighted combining method to obtain the final GE magnitude prediction. In order to search for optimal weights for combining, we modelled the search problem as an optimisation problem by minimising the difference between GE predictions and corresponding ground truths. We used Genetic Algorithm (GA) to search for the optimal weights associated with each regressor. The experiments were conducted on synthetic measurement data generated from 4 popular systems from the literature. We first conducted experiments in comparing the performances of the proposed ensemble using GA and Particle Swarm Optimisation (PSO), nature-based optimisation algorithms to search for combining weights to show the better performance of the proposed ensemble with GA. We then compared the performance of the proposed ensemble to those of two well-known weighted ensemble methods (Least Square and BEM) and two ensemble methods for regression problems (Random Forest and Gradient Boosting). The experimental results showed that although the proposed ensemble took higher computational time for the training process than those benchmark algorithms, it performed better than them on all experimental datasets

A comparative study of anomaly detection methods for gross error detection problems.

The chemical industry requires highly accurate and reliable measurements to ensure smooth operation and effective monitoring of processing facilities. However, measured data inevitably contains errors from various sources. Traditionally in flow systems, data reconciliation through mass balancing is applied to reduce error by estimating balanced flows. However, this approach can only handle random errors. For non-random errors (called gross errors, GEs) which are caused by measurement bias, instrument failures, or process leaks, among others, this approach would return incorrect results. In recent years, many gross error detection (GED) methods have been proposed by the research community. It is recognised that the basic principle of GED is a special case of the detection of outliers (or anomalies) in data analytics. With the developments of Machine Learning (ML) research, patterns in the data can be discovered to provide effective detection of anomalous instances. In this paper, we present a comprehensive study of the application of ML-based Anomaly Detection methods (ADMs) in the GED context on a number of synthetic datasets and compare the results with several established GED approaches. We also perform data transformation on the measurement data and compare its associated results to the original results, as well as investigate the effects of training size on the detection performance. One class Support Vector Machine outperformed other ADMs and five selected statistical tests for GED on Accuracy, F1 Score, and Overall Power while Interquartile Range (IQR) method obtained the best selectivity outcome among the top 6 AMDs and the five statistical tests. The results indicate that ADMs can potentially be applied to GED problems

Redefining noradrenergic neuromodulation of behavior : impacts of a modular locus coeruleus architecture

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