623 research outputs found
Visible and near infrared spectroscopy in soil science
This chapter provides a review on the state of soil visible–near infrared (vis–NIR) spectroscopy. Our intention is for the review to serve as a source of up-to date information on the past and current role of vis–NIR spectroscopy in soil science. It should also provide critical discussion on issues surrounding the use of vis–NIR for soil analysis and on future directions. To this end, we describe the fundamentals of visible and infrared diffuse reflectance spectroscopy and spectroscopic multivariate calibrations. A review of the past and current role of vis–NIR spectroscopy in soil analysis is provided, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals. We then discuss the performance and generalization capacity of vis–NIR calibrations, with particular attention on sample pre-tratments, co-variations in data sets, and mathematical data preprocessing. Field analyses and strategies for the practical use of vis–NIR are considered. We conclude that the technique is useful to measure soil water and mineral composition and to derive robust calibrations for SOM and clay content. Many studies show that we also can predict properties such as pH and nutrients, although their robustness may be questioned. For future work we recommend that research should focus on: (i) moving forward with more theoretical calibrations, (ii) better understanding of the complexity of soil and the physical basis for soil reflection, and (iii) applications and the use of spectra for soil mapping and monitoring, and for making inferences about soils quality, fertility and function. To do this, research in soil spectroscopy needs to be more collaborative and strategic. The development of the Global Soil Spectral Library might be a step in the right direction
Computational Ligand Descriptors for Catalyst Design
Ligands,
especially phosphines and carbenes, can play a key role
in modifying and controlling homogeneous organometallic catalysts,
and they often provide a convenient approach to fine-tuning the performance
of known catalysts. The measurable outcomes of such catalyst modifications
(yields, rates, selectivity) can be set into context by establishing
their relationship to steric and electronic descriptors of ligand
properties, and such models can guide the discovery, optimization,
and design of catalysts. In this review we present a survey of calculated
ligand descriptors, with a particular focus on homogeneous organometallic
catalysis. A range of different approaches to calculating steric and
electronic parameters are set out and compared, and we have collected
descriptors for a range of representative ligand sets, including 30
monodentate phosphorusÂ(III) donor ligands, 23 bidentate P,P-donor
ligands, and 30 carbenes, with a view to providing a useful resource
for analysis to practitioners. In addition, several case studies of
applications of such descriptors, covering both maps and models, have
been reviewed, illustrating how descriptor-led studies of catalysis
can inform experiments and highlighting good practice for model comparison
and evaluation
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
Investigating summer thermal stratification in Lake Ontario
Summer thermal stratification in Lake Ontario is simulated using the 3D
hydrodynamic model Environmental Fluid Dynamics Code (EFDC). Summer temperature
differences establish strong vertical density gradients (thermocline) between the epilimnion
and hypolimnion. Capturing the stratification and thermocline formation has been a
challenge in modeling Great Lakes. Deviating from EFDC's original Mellor-Yamada (1982)
vertical mixing scheme, we have implemented an unidimensional vertical model that uses
different eddy diffusivity formulations above and below the thermocline (Vincon-Leite,
1991; Vincon-Leite et al., 2014). The model is forced with the hourly meteorological data
from weather stations around the lake, flow data for Niagara and St. Lawrence rivers; and
lake bathymetry is interpolated on a 2-km grid. The model has 20 vertical layers following
sigma vertical coordinates. Sensitivity of the model to vertical layers' spacing is thoroughly
investigated. The model has been calibrated for appropriate solar radiation coefficients and
horizontal mixing coefficients. Overall the new implemented diffusivity algorithm shows
some successes in capturing the thermal stratification with RMSE values between 2-3°C.
Calibration of vertical mixing coefficients is under investigation to capture the improved
thermal stratification
RMN na caracterização de correntes processuais de resĂduos pesados
Doutoramento em QuĂmicaThe main objective of this work was to monitor a set of physical-chemical properties of heavy oil procedural streams through nuclear magnetic resonance spectroscopy, in order to propose an analysis procedure and online data processing for process control. Different statistical methods which allow to relate the results obtained by nuclear magnetic resonance spectroscopy with the results obtained by the conventional standard methods during the characterization of the different streams, have been implemented in order to develop models for predicting these same properties. The real-time knowledge of these physical-chemical properties of petroleum fractions is very important for enhancing refinery operations, ensuring technically, economically and environmentally proper refinery operations.
The first part of this work involved the determination of many physical-chemical properties, at Matosinhos refinery, by following some standard methods important to evaluate and characterize light vacuum gas oil, heavy vacuum gas oil and fuel oil fractions. Kinematic viscosity, density, sulfur content, flash point, carbon residue, P-value and atmospheric and vacuum distillations were the properties analysed. Besides the analysis by using the standard methods, the same samples were analysed by nuclear magnetic resonance spectroscopy.
The second part of this work was related to the application of multivariate statistical methods, which correlate the physical-chemical properties with the quantitative information acquired by nuclear magnetic resonance spectroscopy. Several methods were applied, including principal component analysis, principal component regression, partial least squares and artificial neural networks. Principal component analysis was used to reduce the number of predictive variables and to transform them into new variables, the principal components. These principal components were used as inputs of the principal component regression and artificial neural networks models. For the partial least squares model, the original data was used as input. Taking into account the performance of the develop models, by analysing selected statistical performance indexes, it was possible to conclude that principal component regression lead to worse performances. When applying the partial least squares and artificial neural networks models better results were achieved. However, it was with the artificial neural networks model that better predictions were obtained for almost of the properties analysed.
With reference to the results obtained, it was possible to conclude that nuclear magnetic resonance spectroscopy combined with multivariate statistical methods can be used to predict physical-chemical properties of petroleum fractions. It has been shown that this technique can be considered a potential alternative to the conventional standard methods having obtained very promising results.O principal objetivo deste trabalho foi monitorizar um conjunto de propriedades fĂsico-quĂmicas de correntes processuais pesadas atravĂ©s da espectroscopia de ressonância magnĂ©tica nuclear, com o intuito de propor um procedimento de análise e processamento de dados em linha para o controlo processual. Vários mĂ©todos estatĂsticos que permitiram relacionar os resultados obtidos por espectroscopia de ressonância magnĂ©tica nuclear com os resultados obtidos pelos mĂ©todos convencionais, aquando da caracterização das diferentes correntes, foram implementados a fim de desenvolver modelos de previsĂŁo dessas mesmas propriedades. O conhecimento em tempo real das propriedades fĂsico-quĂmicas dos derivados de petrĂłleo Ă© essencial para otimizar as operações de refinação, garantindo assim operações tĂ©cnica, econĂłmica e ambientalmente adequadas.
A primeira parte deste trabalho envolveu a realização de um elevado nĂşmero de experiĂŞncias, efetuadas na refinaria de Matosinhos, seguindo mĂ©todos convencionais normalizados, importantes para avaliar e caracterizar as correntes de gasĂłleo de vácuo leve, gasĂłleo de vácuo pesado e fuel Ăłleo. As propriedades analisadas foram a massa volĂşmica, viscosidade cinemática, teor em enxofre, ponto de inflamação, resĂduo carbonoso, valor P e a destilação atmosfĂ©rica e de vácuo. Para alĂ©m da determinação de todas estas propriedades fĂsico-quĂmicas, usando os mĂ©todos convencionais, as mesmas amostras foram analisadas por espectroscopia de ressonância magnĂ©tica nuclear.
A segunda parte deste trabalho esteve relacionada com a aplicação de mĂ©todos estatĂsticos multivariáveis que relacionam as propriedades fĂsico-quĂmicas com a informação quantitativa adquirida por espectroscopia de ressonância magnĂ©tica nuclear. Vários mĂ©todos foram aplicados, incluindo a análise por componentes principais, a regressĂŁo mĂşltipla por componentes principais, as regressões mĂşltiplas parciais e as redes neuronais artificiais. A análise de componentes principais foi utilizada para reduzir o nĂşmero de variáveis preditivas e transformá-las em novas variáveis, os componentes principais. Estes componentes principais foram utilizados como variáveis de entrada da regressĂŁo mĂşltipla por componentes principais e das redes neuronais artificiais. Na regressĂŁo por mĂnimos quadrados parciais os dados originais foram usados como variáveis de entrada. Tomando em consideração o desempenho dos modelos desenvolvidos, com recurso a parâmetros estatĂsticos, foi possĂvel concluir que a regressĂŁo mĂşltipla por componentes principais contribuiu para piores desempenhos. Melhores desempenhos foram obtidos com a aplicação da regressĂŁo por mĂnimos quadrados parciais e das redes neuronais artificiais. No entanto, foi com os modelos de redes neuronais artificiais que melhores desempenhos foram obtidos em quase todas as propriedades analisadas.
Tendo em conta os resultados obtidos, foi possĂvel concluir que a espectroscopia de ressonância magnĂ©tica nuclear combinada com mĂ©todos estatĂsticos multivariáveis pode ser usada para prever as propriedades fĂsico-quĂmicas de derivados de petrĂłleo. Demonstrou-se que esta tĂ©cnica pode ser considerada como uma potencial alternativa aos mĂ©todos convencionais tendo-se obtido resultados bastantes promissores
A Multiscale Analysis of the Factors Controlling Nutrient Dynamics and Cyanobacteria Blooms in Lake Champlain
Cyanobacteria blooms have increased in Lake Champlain due to excessive nutrient loading, resulting in negative impacts on the local economy and environmental health. While climate warming is expected to promote increasingly severe cyanobacteria blooms globally, predicting the impacts of complex climate changes on individual lakes is complicated by the many physical, chemical, and biological processes which mediate nutrient dynamics and cyanobacteria growth across time and space. Furthermore, processes influencing bloom development operate on a variety of temporal scales (hourly, daily, seasonal, decadal, episodic), making it difficult to identify important factors controlling bloom development using traditional methods or coarse temporal resolution datasets. To resolve these inherent problems of scale, I use 4 years of high-frequency biological, hydrodynamic, and biogeochemical data from Missisquoi Bay, Lake Champlain; 23 years of lake-wide monitoring data; and integrated process-based climate-watershed-lake models driven by regional climate projections to answer the following research questions: 1) To what extent do external nutrient inputs or internal nutrient processing control nutrient concentrations and cyanobacteria blooms in Lake Champlain; 2) how do internal and external nutrient inputs interact with meteorological drivers to promote or suppress bloom development; and 3) how is climate change likely to impact these drivers and the risk of cyanobacteria blooms in the future? I find that cyanobacteria blooms are driven by specific combinations of meteorological and biogeochemical conditions in different areas of the lake, and that in the absence of strong management actions cyanobacteria blooms are likely to become more severe in the future due to climate change
Machine learning classification of microbial community compositions to predict anthropogenic pollutants in the Baltic Sea
Microbial communities react rapidly and specifically to changing environments, indicating distinct microbial fingerprints for a given environmental state. Machine learning with community data predicted the Baltic Sea-detected pollutants glyphosate and 2,4,6-trinitrotoluene, using the developed R package “phyloseq2ML”. Predictions by Random Forest and Artificial Neural Network were accurate. Relevant taxa were identified. The interpretability of machine learning models was found of particular importance. Microbial communities predicted even minor influencing factors in complex environments.Mikrobielle Gemeinschaften reagieren schnell und spezifisch auf sich ändernde Umgebungen und können somit bestimmte Umweltzustände anzeigen. Maschinelles Lernen mit Gemeinschaftsdaten sagte die Ostsee-präsenten Schadstoffe Glyphosat und 2,4,6-Trinitrotoluol voraus, wobei das entwickelte R-Paket "phyloseq2ML" verwendet wurde. Die Vorhersagen durch Random Forest und Artificial Neural Network waren genau. Relevante Taxa wurden identifiziert. Die Interpretierbarkeit der Modelle erwies sich als essentiell. Mikrobielle Gemeinschaften sagten selbst geringe Einflüsse in komplexen Umgebungen voraus
Aerospace medicine and biology: A cumulative index to a continuing bibliography (supplement 358)
This publication is a cumulative index to the abstracts contained in Supplements 346 through 357 of Aerospace Medicine and Biology: A Continuing Bibliography. It includes seven indexes: subject, personal author, corporate source, foreign technology, contract number, report number and accession number
The role of Fourier-transform mid-infrared spectroscopy in improving the prediction of new and existing traits in New Zealand dairy cattle : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Animal Science at Massey University, AL Rae Centre, Hamilton, New Zealand
Bovine milk is a rich source of dietary nutrients that are important to human health. Market demand for bovine milk is driven by its nutritional value, price, processability, and consumer expectations and perceptions about food production systems. The ability to quantify traits associated with milk quality, processability, animal health and environmental impact is critical for selective breeding and thus highly valuable to the dairy industry. However, obtaining direct measurements of such traits can be difficult and expensive. Estimation of major milk components using Fourier-transform mid-infrared (FT-MIR) spectroscopy is common practice, and spectral-based predictions of these traits are already widely used in milk payment and animal evaluation systems. Applications using FT-MIR spectra to predict other traits have increased in popularity over the last decade, and are attractive alternatives to directly measuring phenotypes because the FT-MIR spectra are readily available as a by-product of routine milk testing. The objectives of this thesis were to improve understanding of the phenotypic and genetic characteristics of FT-MIR spectra, and assess the role that such data can play in predicting new traits or improving the prediction of existing traits in New Zealand dairy cattle. We assessed different strategies for improving the quality of spectral data and demonstrated that there are limitations in predicting traits such as pregnancy status, due to confounding effects such as stage of lactation. From a genetics perspective, we reviewed the evolving role of spectral data in the improvement of dairy cattle by selection and discussed opportunities for consolidating spectral datasets with other genomic and molecular data sources. We conducted GWAS on individual FT-MIR wavenumbers and demonstrated that the individual wavenumbers provided stronger association effects and improved power for identifying candidate causal variants, compared to conducting GWAS on FT-MIR predicted traits. We also demonstrated the potential utility of spectral data for predicting and incorporating fatty acids and protein traits into breeding programs, but showed that even when genetic correlations between directly measured and FT-MIR predicted traits were high, the detectable QTL underpinning these traits were not always the same. Although there are many potential applications for FT-MIR spectral datasets, there are still challenges to developing robust prediction equations and understanding the genetic relationships between traits of interest and their FT-MIR predictions. Addressing these challenges will provide opportunities to improve the prediction of new and existing traits in dairy cattle milk production systems and breeding programs into the future
Challenges and Successes in Identifying the Transfer and Transformation of Phosphorus from Soils to Open Waters and Sediments
The anthropogenic loading of phosphorus (P) to water bodies continues to increase worldwide, in many cases leading to increased eutrophication and harmful algal blooms. Determining the sources of P and the biogeochemical processes responsible for this increase is often difficult because of the complexity of the inputs and pathways, which vary both in spatial and temporal scales. In order to effectively develop strategies to improve water quality, it is essential to develop a comprehensive understanding of the relationship of P pools with biological uptake and cycling under varied soil and water conditions. In this ebook, eight chapters cover the various aspects of basic-applied research on mineral–P interaction and how these reactions impact P mobilization, bioavailability, transfer, and speciation of P in different soil matrices using advanced analytical methods. Some of these methods include the application of XANES and field-based research related to stream bank legacy nutrients; natural and anthropogenic eutrophication and its relationship to climate change; and the evaluation of the impact of P due to (i) grazing systems, (ii) weathering and vegetation, and iii) soil and manure management practices. In addition, two review chapters take a holistic approach to cover an expansive area of P transformation processes along the cropland–riparian–stream continuum and the assessment of legacy P. Together, these contributions improve our current understanding of the reactions and processes that impact P concentration, speciation, cycling, loss, and transfer from agroecosystems
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