H-1 NMR study of the states of water in equilibrium Poly(HEMA-co-THFMA) hydrogels

The spin-spin relaxation times, T-2, of hydrated samples of poly(hydroxymethyl methacrylate), PHEMA, poly(tetrahydrofurfuryl methacrylate),PTHFMA, and the,corresponding HEMA-THFMA copolymers have been examined to probe the states of,the imbibed water in these polymers. The decay in the transverse magnetization of water. in fully hydrated samples of PHEMA, PTHFMA, and copolymers of HEMA and THFMA was described by a multiexponential function. The short component of T-2 was interpreted as water molecules that were strongly interacting with the polymer chains. The intermediate component of T-2 was assigned to water residing in the porous structure of the samples. The long component of T-2 was believed to arise from water residing in the remnants of cracks formed in the polymer network during water sorption.

PFG-NMR measurements of the self-diffusion coefficients of water in equilibrium poly(HEMA-co-THFMA) hydrogels

The self-diffusion coefficients for water in a series of copolymers of 2-hydroxyethyl methacrylate, HEMA, and tetrahydrofurfuryl methacrylate, THFMA, swollen with water to their equilibrium states have been studied at 310 K using PFG-NMR. The self-diffusion coefficients calculated from the Stejskal-Tanner equation, D-obs, for all of the hydrated polymers were found to be dependent on the NMR storage time, as a result of spin exchange between the proton reservoirs of the water and the polymers, reaching an equilibrium plateau value at long storage times. The true values of the diffusion coefficients were calculated from the values of D-obs, in the plateau regions by applying a correction for the fraction of water protons present, obtained from the equilibrium water contents of the gels. The true self-diffusion coefficient for water in polyHEMA obtained at 310 K by this method was 5.5 x 10(-10) m(2) s(-1). For the copolymers containing 20% HEMA or more a single value of the self-diffusion coefficient was found, which was somewhat larger than the corresponding values obtained for the macroscopic diffusion coefficient from sorption measurements. For polyTHFMA and copolymers containing less than 20% HEMA, the PFG-NMR stimulated echo attenuation decay curves and the log-attenuation plots were characteristic of the presence of two diffusing water species. The self-diffusion coefficients of water in the equilibrium-hydrated copolymers were found to be dependent on the copolymer composition, decreasing with increasing THFMA content.

High-speed MAS F-19 NMR analysis of an irradiated fluoropolymer

The effect of gamma-radiation on a perfluoroalkoxy (PFA) resin was examined using solid-state high-speed magic angle spinning (MAS) F-19 NMR spectroscopy. Samples were prepared for analysis by subjecting them to gamma-radiation in the dose range 0.5-3 MGy at either 303, 473, or 573 K. New structures identified include new saturated chain ends, short and long branches, and unsaturated groups. The formation of branched structures was found to increase with increasing irradiation temperature; however, at all temperatures the radiation chemical yield (G value) of new chain ends was greater than the G value of long branch points, suggesting that chain scission is the net process.

NMR analysis of covalent intermediates in thiamin diphosphate enzymes

Enzymic catalysis proceeds via intermediates formed in the course of substrate conversion. Here, we directly detect key intermediates in thiamin diphosphate (ThDP)-dependent enzymes during catalysis using H-1 NMR spectroscopy. The quantitative analysis of the relative intermediate concentrations allows the determination of the microscopic rate constants of individual catalytic steps. As demonstrated for pyruvate decarboxylase (PDC), this method, in combination with site-directed mutagenesis, enables the assignment of individual side chains to single steps in catalysis. In PDC, two independent proton relay systems and the stereochemical control of the enzymic environment account for proficient catalysis proceeding via intermediates at carbon 2 of the enzyme-bound cofactor. The application of this method to other ThDP-dependent enzymes provides insight into their specific chemical pathways.

Redox active macrocyclic receptors for neutral guests

A novel series of triazine-appended macrocyclic complexes has been investigated as potential hydrogen bonding receptors for complementarily disposed heterocycles. Cocrystallization of a melamine-appended azacyclam complex of Cull has been achieved with barbitone, the barbiturate anion and thymine. In each case, a complementary DAD/ADA hydrogen bonding motif between the melamine group and the heterocycle has been identified by X-ray crystallography. Electrochemical studies of the copper macrocycles in both nonaqueous and aqueous solution show anodic shifts of the CuII/I redox couple of more than 60 mV upon addition of guest molecules with matching H-bonding motifs. The Zn-II analogues have been synthesized via transmetalation of the Cull complex, and their guest binding properties investigated by NMR spectroscopy. H-1 NMR shifts of up to 0.8 ppm were observed upon addition of guest, and stability constants are similar to those obtained electrochemically.

Deuterium NMR Study of Orientational Order in Cellulosic Network Microfibers

Deuterium NMR was used to investigate the orientational order in a composite cellulosic formed by liquid crystalline acetoxypropylcellulose (A PC) and demented nematic 4'-penty1-4-cyanobiphenyl (5CB-4 alpha d(2)) with the per centage of 85% A PC by weight Three forms of the composite including electro spun microfibers, thin film and bulk samples were analyzed The NMR results initially suggest two distinct scenarios, one whet e the 503-alpha d(2), is confined to small droplets with dimensions smaller than the magnetic coherence length and the other where the 503-alpha d(2) molecules arc aligned with the A PC network chains Polarized optical microscopy (POW from thin film samples along with all the NMR results show the presence of 5CB-alpha d(2) droplets in the composite systems with a nematic wetting layer at the APC-5CB-alpha d(2) interface that experiences and order disorder transition driven by the polymer network N-I transition The characterization of the APC network I-N transition shows a pronounced subcritical behavior within a heterogeneity scenario.

Chemical and biological studies from an Azorean macroalga: Ulva rigida

3rd Portuguese Meeting on Medicinal Chemistry and 1st Portuguese-Spanish-Brazilian Meeting on Medicinal Chemistry, Aveiro, 28-30 Novembro 2012.

Water-Based Cellulose Liquid Crystal System Investigated by Rheo-NMR

Water-based cellulose cholesteric liquid crystalline phases at rest can undergo structural changes induced by shear flow. This reflects on the deuterium spectra recorded when the system is investigated by rheo-nuclear magnetic resonance (rheo-NMR) techniques. In this work, the model system hydroxypropylcellulose (HPC)+water is revisited using rheo-NMR to clarify unsettled points regarding its behavior under shear and in relaxation. The NMR spectra allow the identification of five different stable ordering states, within shear and relaxation, which are well integrated in a mesoscopic picture of the system's structural evolution under shear and relaxation. This picture emerging from the large body of studies available for this system by other experimental techniques, accounts well for the NMR data and is in good agreement with the three distinct regions of steady shear flow recognized for some lyotropic LC polymers. Shear rates in between 0.1 and 1.0 s(-1) where investigated using a Taylor-Couette flow and deuterated water was used as solvent for the deuterium NMR (DNMR) analysis.

Structural, physical, and chemical modifications induced by microwave heating on native agar-like galactans

Native agars from Gracilaria vermiculophylla produced in sustainable aquaculture systems (IMTA) were extracted under conventional (TWE) and microwave (MAE) heating. The optimal extracts from both processes were compared in terms of their properties. The agars’ structure was further investigated through Fourier transform infrared and NMR spectroscopy. Both samples showed a regular structure with an identical backbone, β-D-galactose (G) and 3,6-anhydro-α-L-galactose (LA) units; a considerable degree of methylation was found at C6 of the G units and, to a lesser extent, at C2 of the LA residues. The methylation degree in the G units was lower for MAEopt agar; the sulfate content was also reduced. MAE led to higher agar recoveries with drastic extraction time and solvent volume reductions. Two times lower values of [η] and Mv obtained for the MAEopt sample indicate substantial depolymerization of the polysaccharide backbone; this was reflected in its gelling properties; yet it was clearly appropriate for commercial application in soft-texture food products.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Gd(III) chelates as NMR probes of protein-protein interactions. Case study: rubredoxin and cytochrome c3

Inorganic Chemistry 50(21):10600-7

Chemical constituents from the stem of Brosimum potabile (Moraceae)

Three coumarins, 5-methoxypsoralene, xanthyletin, and (-)-marmesin, have been isolated from the ethanolic extract of the stem of the Amazonian plant Brosimum potabile. The structures were determined on the basis of NMR analyses and by comparison with spectroscopic data in the literature. The analysis of the hexane fractions by GC-MS in EIMS mode suggested the presence of (1-methylpentyl)-benzene; α,α-dimethyl-4-(1-methylethyl)-benzenemethanol; 1-methyl-3,5-bis(1-methylethyl)-benzene; urs-12-ene; chola-5,22-dien-3β-ol; cholesta-4,6-dien-3β-ol; sitosteryl 9(Z)-octadecenoate; cholesta-5,22-dien-3β-ol; cholesta-4,6,22-trien-3-one; and cholesta-4,22-dien-3-one. NMR data of other hexane fractions indicated the presence of 3β-acetoxy-lup-12,20(29)-diene; 3β-acetoxy-olean-12-ene; 3β-acetoxy-urs-12-ene; and adian-5-ene. All these compounds are first described in B. potabile.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

A comparative study of the physical, chemical and microbiological status of soils, as influenced by conventional and reduced tillage

Historically, shifts to reduced and no-tillage management for production of crops were fostered by needs to decrease soil erosion and loss of organic matter, reduce fuel and labour costs and conserve soil water, as compared with conventional fallow tillage management. Recent interest in maintaining soil quality has been stimulated by a renewed awareness of the importance of soil condition to both the sustainability of agricultural production systems and environmental quality (Doran and Parkin, 1996). The aim of this project was to determine the impact on the physical, chemical and microbiological status of the soil of conventional and reduced tillage. It has been suggested that the reduced soil disturbance associated with the tine cultivator improves soil structure, increases nutrient content in the top 10cm of soil, increases microbial activity and improves physical characteristics. From this study it was determined that the environmental benefits linked to reduced tillage in literature, did not develop in the first two years of this programmes implementation. The results of this study determined that soil nutrients did not increase in concentration in the top 10 cm of soil under reduced cultivation. The only exception was exchangeable potassium. As potassium is not a mobile nutrient its movement is dependent on soil disturbance, therefore under reduced cultivation its concentration was allowed to accumulate in the upper horizon of the soil profile. Microbial activity was greater in the conventionally tilled treatments, as determined by total aerobic bacterial numbers. This could be due to the increased rates of soil aeration in this treatment. Numbers of aerobic bacteria were greater in the conventional tillage treatments at both incubation temperatures of 22 and 32° C. The physical characteristics of the soil determined, indicate that below the depth of soil cultivation, cone penetration resistance increases. Therefore the reduced cultivation treatments would be more prone to soil compaction, higher in the soil profile.