Nutrimetabolomics fingerprinting to identify biomarkers of bread exposure in a free-living population from the PREDIMED study cohort

Bread is one of the most widely consumed foods. Its impact on human health is currently of special interest for researchers. We aimed to identify biomarkers of bread consumption by applying a nutrimetabolomic approach to a free-living population. An untargeted HPLC q-TOF-MS and multivariate analysis was applied to human urine from 155 subjects stratified by habitual bread consumption in three groups: non-consumers of bread (n = 56), white-bread consumers (n = 48) and whole-grain bread consumers (n = 51). The most differential metabolites (variable importance for projection ≥1.5) included compounds originating from cereal plant phytochemicals such as benzoxazinoids and alkylresorcinol metabolites, and compounds produced by gut microbiota (such as enterolactones, hydroxybenzoic and dihydroferulic acid metabolites). Pyrraline, riboflavin, 3-indolecarboxylic acid glucuronide, 2,8-dihydroxyquinoline glucuronide and N-α-acetylcitrulline were also tentatively identified. In order to combine multiple metabolites in a model to predict bread consumption, a stepwise logistic regression analysis was used. Receiver operating curves were constructed to evaluate the global performance of individual metabolites and their combination. The area under the curve values [AUC (95 % CI)] of combined models ranged from 77.8 % (69.1 86.4 %) to 93.7 % (89.4 98.1 %), whereas the AUC for the metabolites included in the models had weak values when they were evaluated individually: from 58.1 % (46.6 69.7 %) to 78.4 % (69.8 87.1 %). Our study showed that a daily bread intake significantly impacted on the urinary metabolome, despite being examined under uncontrolled free-living conditions. We further concluded that a combination of several biomarkers of exposure is better than a single biomarker for the predictive ability of discriminative analysis.

Nutrimetabolomics fingerprinting to identify biomarkers of bread exposure in a free-living population from the PREDIMED study cohort

Bread is one of the most widely consumed foods. Its impact on human health is currently of special interest for researchers. We aimed to identify biomarkers of bread consumption by applying a nutrimetabolomic approach to a free-living population. An untargeted HPLC q-TOF-MS and multivariate analysis was applied to human urine from 155 subjects stratified by habitual bread consumption in three groups: non-consumers of bread (n = 56), white-bread consumers (n = 48) and whole-grain bread consumers (n = 51). The most differential metabolites (variable importance for projection ≥1.5) included compounds originating from cereal plant phytochemicals such as benzoxazinoids and alkylresorcinol metabolites, and compounds produced by gut microbiota (such as enterolactones, hydroxybenzoic and dihydroferulic acid metabolites). Pyrraline, riboflavin, 3-indolecarboxylic acid glucuronide, 2,8-dihydroxyquinoline glucuronide and N-α-acetylcitrulline were also tentatively identified. In order to combine multiple metabolites in a model to predict bread consumption, a stepwise logistic regression analysis was used. Receiver operating curves were constructed to evaluate the global performance of individual metabolites and their combination. The area under the curve values [AUC (95 % CI)] of combined models ranged from 77.8 % (69.1 86.4 %) to 93.7 % (89.4 98.1 %), whereas the AUC for the metabolites included in the models had weak values when they were evaluated individually: from 58.1 % (46.6 69.7 %) to 78.4 % (69.8 87.1 %). Our study showed that a daily bread intake significantly impacted on the urinary metabolome, despite being examined under uncontrolled free-living conditions. We further concluded that a combination of several biomarkers of exposure is better than a single biomarker for the predictive ability of discriminative analysis.

Condiciones de trabajo psicosociales y desgaste psíquico en trabajadores de diversos sectores empresariales de centro-oriente, suroccidente y región Caribe en Colombia

Objetivo: Determinar la percepción de trabajadores de distintos sectores empresariales de Colombia sobre los factores psicosociales presentes en su entorno laboral y la relación entre los factores psicosociales nocivos y los síntomas subjetivos y alteraciones de la salud. Materiales y métodos: Estudio no experimental, transversal y cuantitativo. Participaron 370 trabajadores, de diferentes sectores empresariales de Colombia (Centro-Oriente, Suroccidente y región Caribe). Instrumento: batería para el estudio de las condiciones de trabajo de carácter psicosocial (CTCPS-MAC), validada para población iberoamericana, permite evaluar cuatro dimensiones: Contexto de trabajo, Contenido de trabajo, Factores individuales y Desgaste psíquico e incluye catorce factores psicosociales. Los datos se analizaron con IBM SPSS statistics 21. Se realizó análisis bivariado y regresión logística multivariante de factores psicosociales nocivos y desgaste psíquico. Resultados: Los factores formación, baja médica, contexto de trabajo, contenido de trabajo y factores individuales están asociados en este estudio con desgaste psíquico. El contexto de trabajo es la variable que infiere mayor riesgo (p=0.000; Exp (B)= 5.355) para provocar desgaste psíquico, seguida de la formación técnica o superior y del contenido del trabajo. Conclusiones: Si bien aquellos trabajadores cuya percepción nociva del contexto de trabajo (interrelación trabajo-vida familiar/personal, cultura de la organización, gestión de la empresa, etc.), del contenido de trabajo (concepción tareas, carga y ritmo de trabajo, etc.) y los que tienen formación técnica o superior tienen mayor probabilidad de padecer desgaste psíquico, se observan aspectos positivos de las condiciones de trabajo psicosocial y su influencia en los trabajadores y en las organizaciones.

Analysis of patterns in mining time series using Qlucore

Raw measurement data does not always immediately convey useful information, but applying mathematical statistical analysis tools into measurement data can improve the situation. Data analysis can offer benefits like acquiring meaningful insight from the dataset, basing critical decisions on the findings, and ruling out human bias through proper statistical treatment. In this thesis we analyze data from an industrial mineral processing plant with the aim of studying the possibility of forecasting the quality of the final product, given by one variable, with a model based on the other variables. For the study mathematical tools like Qlucore Omics Explorer (QOE) and Sparse Bayesian regression (SB) are used. Later on, linear regression is used to build a model based on a subset of variables that seem to have most significant weights in the SB model. The results obtained from QOE show that the variable representing the desired final product does not correlate with other variables. For SB and linear regression, the results show that both SB and linear regression models built on 1-day averaged data seriously underestimate the variance of true data, whereas the two models built on 1-month averaged data are reliable and able to explain a larger proportion of variability in the available data, making them suitable for prediction purposes. However, it is concluded that no single model can fit well the whole available dataset and therefore, it is proposed for future work to make piecewise non linear regression models if the same available dataset is used, or the plant to provide another dataset that should be collected in a more systematic fashion than the present data for further analysis.

Optimization of wood flour acetylation by factorial design and partial least squares regression

Acetylation was performed to reduce the polarity of wood and increase its compatibility with polymer matrices for the production of composites. These reactions were performed first as a function of acetic acid and anhydride concentration in a mixture catalyzed by sulfuric acid. A concentration of 50%/50% (v/v) of acetic acid and anhydride was found to produced the highest conversion rate between the functional groups. After these reactions, the kinetics were investigated by varying times and temperatures using a 3² factorial design, and showed time was the most relevant parameter in determining the conversion of hydroxyl into carbonyl groups.

QSAR modeling: um novo pacote computacional open source para gerar e validar modelos QSAR

QSAR modeling is a novel computer program developed to generate and validate QSAR or QSPR (quantitative structure- activity or property relationships) models. With QSAR modeling, users can build partial least squares (PLS) regression models, perform variable selection with the ordered predictors selection (OPS) algorithm, and validate models by using y-randomization and leave-N-out cross validation. An additional new feature is outlier detection carried out by simultaneous comparison of sample leverage with the respective Studentized residuals. The program was developed using Java version 6, and runs on any operating system that supports Java Runtime Environment version 6. The use of the program is illustrated. This program is available for download at lqta.iqm.unicamp.br.

Country Risk and Market Reactions to Earnings Announcements: Evidence from Finnish Stock Markets

The objective of this thesis is to examine the market reaction around earnings announcements in Finnish stock markets. The aim is to find out whether the extreme market conditions during the financial crisis are reflected in stock prices as a stronger reaction. In addition to this, the purpose is to investigate how extensively Finnish listed companies report the country segmentation of revenues in their interim reports and whether the country risk is having a significant impact on perceived market reaction. The sample covers all companies listed in Helsinki stock exchange at 1.1.2010 and these companies’ interim reports from the first quarter of 2008 to last quarter of 2009. Final sample consists of 81 companies and 630 firm-quarter observations. The data sample has been divided in two parts, of which country risk sample contains 17 companies and 127 observations and comparison sample covers 66 companies and 503 observations. Research methodologies applied in this thesis are event study and cross-sectional regression analysis. Empirical results indicate that the market reaction occurs mainly during the announcement day and is slightly stronger in case of positive earnings surprises than the reactions observed in previous studies. In case of negative earnings surprises no significant differences can be observed. In case of country risk sample and negative earnings surprise market reaction is negative already in advance of the disclosure contrary to comparison sample. In case of positive surprise no differences can be observed. Country risk variable developed during this study seems to explain only minor part of the market reaction.

Accounting Beta Measuring Systematic Risk: Empirical Evidence from the Finnish Markets

The main purpose of this study is to examine whether accounting-based variables can be used to measure systematic risk of a company using Finnish data. When the fundamental sources of systematic risk are known, companies are able to manage these risks and increase company value. Accounting beta was formed based on OLS regression models. Theoretical background for the study was based on the findings of studies according to which business risk, financial risk, operating risk and growth risk can be theoretically regarded as determinants of the systematic risk. The results reveal that accounting variables describe systematic risk of a company. The accounting beta is found to be particularly sensitive to the changes in the risk components. The investigation is confidential until 15.10.2012.

Determination of copper at wide range concentrations using instrumental features of high-resolution continuum source flame atomic absorption spectrometry

This work describes a method to determine Cu at wide range concentrations in a single run without need of further dilutions employing high-resolution continuum source flame atomic absorption spectrometry. Different atomic lines for Cu at 324.754 nm, 327.396 nm, 222.570 nm, 249.215 nm and 224.426 nm were evaluated and main figures of merit established. Absorbance measurements at 324.754 nm, 249.215 nm and 224.426 nm allows the determination of Cu in the 0.07 - 5.0 mg L-1, 5.0 - 100 mg L-1 and 100 - 800 mg L-1 concentration intervals respectively with linear correlation coefficients better than 0.998. Limits of detection were 21 µg L-1, 310 µg L-1 and 1400 µg L-1 for 324.754 nm, 249.215 nm and 224.426 nm, respectively and relative standard deviations (n = 12) were £ 2.7%. The proposed method was applied to water samples spiked with Cu and the results were in agreement at a 95% of confidence level (paired t-test) with those obtained by line-source flame atomic absorption spectrometry.

Automated, adaptive methods for forest inventory

Forest inventories are used to estimate forest characteristics and the condition of forest for many different applications: operational tree logging for forest industry, forest health state estimation, carbon balance estimation, land-cover and land use analysis in order to avoid forest degradation etc. Recent inventory methods are strongly based on remote sensing data combined with field sample measurements, which are used to define estimates covering the whole area of interest. Remote sensing data from satellites, aerial photographs or aerial laser scannings are used, depending on the scale of inventory. To be applicable in operational use, forest inventory methods need to be easily adjusted to local conditions of the study area at hand. All the data handling and parameter tuning should be objective and automated as much as possible. The methods also need to be robust when applied to different forest types. Since there generally are no extensive direct physical models connecting the remote sensing data from different sources to the forest parameters that are estimated, mathematical estimation models are of "black-box" type, connecting the independent auxiliary data to dependent response data with linear or nonlinear arbitrary models. To avoid redundant complexity and over-fitting of the model, which is based on up to hundreds of possibly collinear variables extracted from the auxiliary data, variable selection is needed. To connect the auxiliary data to the inventory parameters that are estimated, field work must be performed. In larger study areas with dense forests, field work is expensive, and should therefore be minimized. To get cost-efficient inventories, field work could partly be replaced with information from formerly measured sites, databases. The work in this thesis is devoted to the development of automated, adaptive computation methods for aerial forest inventory. The mathematical model parameter definition steps are automated, and the cost-efficiency is improved by setting up a procedure that utilizes databases in the estimation of new area characteristics.

Patenttien vaikutus suomalaisten pörssiyritysten markkina-arvoon - reaalioptiosovellutus

Tutkielman tavoitteena oli tutkia onko patenteilla positiivinen vaikutus yrityksen markkina-arvoon. Aihetta tutkittiin reaalioptionäkökulmasta: miten patentit voidaan nähdä reaalioptioiden ilmentymänä ja millainen vaikutus niillä on yrityksen suoriutumiseen. Lisäksi tutkittiin onko patenttien vaikutuksessa toimialakohtaisia eroja. Tavoitteena oli myös selvittää onko patenttien vaikutus erilainen talouden eri suhdanteissa. Empiriana tutkimuksessa olivat suomalaiset pörssiyritykset ja niiden Suomeen myönnetyt patentit. Tutkittavana ajanjaksona oli 2001–2010. Tutkimusongelmista johdettuja hypoteeseja testattiin regressioanalyysien avulla. Selitettävänä muuttujana oli yrityksen Tobin’s q ja selittävänä muuttujana yrityksen voimassa olleiden patenttien ja tasearvon suhdeluku. Kontrollimuuttujina käytettiin vuotta ja toimialaa. Tulokseksi saatiin, että patenteilla on ollut positiivinen vaikutus suomalaisten pörssiyritysten markkina-arvoon 2000-luvulla. Varsinkin teknologian ja perusmateriaalien toimialoilla yhteys oli vahvempi kuin muilla toimialoilla. Saadut tulokset ovat yhdenmukaisia aiempien tutkimusten kanssa. Aineiston erityispiirteet toivat tutkimukseen omat haasteensa, jotka vaikuttivat muun muassa toimialaryhmien muodostamiseen sekä makroekonomisen ympäristön merkityksen tutkimiseen.

Non-Markovian Dynamics in Continuous Variable Quantum Systems

The present manuscript represents the completion of a research path carried forward during my doctoral studies in the University of Turku. It contains information regarding my scientific contribution to the field of open quantum systems, accomplished in collaboration with other scientists. The main subject investigated in the thesis is the non-Markovian dynamics of open quantum systems with focus on continuous variable quantum channels, e.g. quantum Brownian motion models. Non-Markovianity is here interpreted as a manifestation of the existence of a flow of information exchanged by the system and environment during the dynamical evolution. While in Markovian systems the flow is unidirectional, i.e. from the system to the environment, in non-Markovian systems there are time windows in which the flow is reversed and the quantum state of the system may regain coherence and correlations previously lost. Signatures of a non-Markovian behavior have been studied in connection with the dynamics of quantum correlations like entanglement or quantum discord. Moreover, in the attempt to recognisee non-Markovianity as a resource for quantum technologies, it is proposed, for the first time, to consider its effects in practical quantum key distribution protocols. It has been proven that security of coherent state protocols can be enhanced using non-Markovian properties of the transmission channels. The thesis is divided in two parts: in the first part I introduce the reader to the world of continuous variable open quantum systems and non-Markovian dynamics. The second part instead consists of a collection of five publications inherent to the topic.

Logistic regression to estimate the welfare of broiler breeders in relation to environmental and behavioral variables

The increasing demand of consumer markets for the welfare of birds in poultry house has motivated many scientific researches to monitor and classify the welfare according to the production environment. Given the complexity between the birds and the environment of the aviary, the correct interpretation of the conduct becomes an important way to estimate the welfare of these birds. This study obtained multiple logistic regression models with capacity of estimating the welfare of broiler breeders in relation to the environment of the aviaries and behaviors expressed by the birds. In the experiment, were observed several behaviors expressed by breeders housed in a climatic chamber under controlled temperatures and three different ammonia concentrations from the air monitored daily. From the analysis of the data it was obtained two logistic regression models, of which the first model uses a value of ammonia concentration measured by unit and the second model uses a binary value to classify the ammonia concentration that is assigned by a person through his olfactory perception. The analysis showed that both models classified the broiler breeder's welfare successfully.

Modeling and simulation of forced-air cooling of strawberries using variable convective coefficient

In the forced-air cooling process of fruits occurs, besides the convective heat transfer, the mass transfer by evaporation. The energy need in the evaporation is taken from fruit that has its temperature lowered. In this study it has been proposed the use of empirical correlations for calculating the convective heat transfer coefficient as a function of surface temperature of the strawberry during the cooling process. The aim of this variation of the convective coefficient is to compensate the effect of evaporation in the heat transfer process. Linear and exponential correlations are tested, both with two adjustable parameters. The simulations are performed using experimental conditions reported in the literature for the cooling of strawberries. The results confirm the suitability of the proposed methodology.