A.G.B. ter Meulen, Representing Time in Natural Language: The Dynamic Interpretation of Tense and Aspect, The MIT Press, 1997, ISBN: 978-0262200998 [Book review]

Abstract not available

Examining the Validity of the GRE General Test Scores and Undergraduate GPA for Predicting Success in Graduate School at a Large Racially and Ethnically Diverse Public University in Southeast Florida

The purpose of the study was to determine the degree of relationships among GRE scores, undergraduate GPA (UGPA), and success in graduate school, as measured by first year graduate GPA (FGPA), cumulative graduate GPA, and degree attainment status. A second aim of the study was to determine whether the relationships between the composite predictor (GRE scores and UGPA) and the three success measures differed by race/ethnicity and sex. A total of 7,367 graduate student records (masters, 5,990; doctoral: 1,377) from 2000 to 2010 were used to evaluate the relationships among GRE scores, UGPA and the three success measures. Pearson’s correlation, multiple linear and logistic regression, and hierarchical multiple linear and logistic regression analyses were performed to answer the research questions. The results of the correlational analyses differed by degree level. For master’s students, the ETS proposed prediction that GRE scores are valid predictors of first year graduate GPA was supported by the findings from the present study; however, for doctoral students, the proposed prediction was only partially supported. Regression and correlational analyses indicated that UGPA was the variable that consistently predicted all three success measures for both degree levels. The hierarchical multiple linear and logistic regression analyses indicated that at master’s degree level, White students with higher GRE Quantitative Reasoning Test scores were more likely to attain a degree than Asian Americans, while International students with higher UGPA were more likely to attain a degree than White students. The relationships between the three predictors and the three success measures were not significantly different between men and women for either degree level. Findings have implications both for practice and research. They will provide graduate school administrators with institution-specific validity data for UGPA and the GRE scores, which can be referenced in making admission decisions, while they will provide empirical and professionally defensible evidence to support the current practice of using UGPA and GRE scores for admission considerations. In addition, new evidence relating to differential predictions will be useful as a resource reference for future GRE validation researchers.

The role of xenobiotic metabolizing enzymes in arylamine toxicity and carcinogenesis: Functional and localization studies

In both animal models and humans, the first and obligatory step in the activation of arylamines is N-hydroxylation. This pathway is primarily mediated by the phase-I enzymes CYP1A1, CYP1A2 and CYP4B1. In the presence of flavonoids such as alpha-naphthoflavone and flavone, both CYP3A4 and CYP3A5 have also been shown to play a minor role in the activation of food-derived heterocyclic amines. The further activation of N-hydroxyarylamines by phase-II metabolism can involve both N,O-acetylation and N,O-sulfonation catalyzed by N-acetyltransferases (NAT1 and NAT2) and sulfotransferases, respectively. Using an array of techniques, we have been unable to detect constitutive CYP1A expression in any segments of the human gastrointestinal tract. This is in contrast to the rabbit where CYP1A1 protein was readily detectable on immunoblots in microsomes prepared from the small intestine. In humans, CYP3A3/3A4 expression was detectable in the esophagus and all segments of the small intestine. Northern blot analysis of eleven human colons showed considerable heterogeneity in CYP3A mRNA between individuals, with the presence of two mRNA species in same subjects. Employing the technique of hybridization histochemistry (also known as in situ hybridization), CYP4B1 expression was observed in some human colons but not in the liver or the small intestine. Hybridization histochemistry studies have also demonstrated variable NAT1 and NAT2 expression in the human gastrointestinal tract. NAT1 and NAT2 mRNA expression was detected in the human liver, small intestine, colon, esophagus, bladder, ureter, stomach and lung. Using a general aryl sulfotransferase riboprobe (HAST1), we have demonstrated marked sulfotransferase expression in the human colon, small intestine, lung, stomach and liver. These studies demonstrate that considerable variability exists in the expression of enzymes involved in the activation of aromatic amines in human tissues. The significance of these results in relation to a role for heterocyclic amines in colon cancer is discussed.

Stereological and allometric studies on neurons and axo-dendritic synapses in the superior cervical ganglia of rats, capybaras and horses

The superior cervical ganglion (SCG) in mammals varies in structure according to developmental age, body size, gender, lateral asymmetry, the size and nuclear content of neurons and the complexity and synaptic coverage of their dendritic trees. In small and medium-sized mammals, neuron number and size increase from birth to adulthood and, in phylogenetic studies, vary with body size. However, recent studies on larger animals suggest that body weight does not, in general, accurately predict neuron number. We have applied design-based stereological tools at the light-microscopic level to assess the volumetric composition of ganglia and to estimate the numbers and sizes of neurons in SCGs from rats, capybaras and horses. Using transmission electron microscopy, we have obtained design-based estimates of the surface coverage of dendrites by postsynaptic apposition zones and model-based estimates of the numbers and sizes of synaptophysin-labelled axo-dendritic synaptic disks. Linear regression analysis of log-transformed data has been undertaken in order to establish the nature of the relationships between numbers and SCG volume (V(scg)). For SCGs (five per species), the allometric relationship for neuron number (N) is N=35,067xV (scg) (0.781) and that for synapses is N=20,095,000xV (scg) (1.328) , the former being a good predictor and the latter a poor predictor of synapse number. Our findings thus reveal the nature of SCG growth in terms of its main ingredients (neurons, neuropil, blood vessels) and show that larger mammals have SCG neurons exhibiting more complex arborizations and greater numbers of axo-dendritic synapses.

Copper, nickel and zinc removal by peanut hulls: batch and column studies in mono, tri-component systems and with real effluent

The main goal of this research study was the removal of Cu(II), Ni(II) and Zn(II) from aqueous solutions using peanut hulls. This work was mainly focused on the following aspects: chemical characterization of the biosorbent, kinetic studies, study of the pH influence in mono-component systems, equilibrium isotherms and column studies, both in mono and tri-component systems, and with a real industrial effluent from the electroplating industry. The chemical characterization of peanut hulls showed a high cellulose (44.8%) and lignin (36.1%) content, which favours biosorption of metal cations. The kinetic studies performed indicate that most of the sorption occurs in the first 30 min for all systems. In general, a pseudo-second order kinetics was followed, both in mono and tri-component systems. The equilibrium isotherms were better described by Freundlich model in all systems. Peanut hulls showed higher affinity for copper than for nickel and zinc when they are both present. The pH value between 5 and 6 was the most favourable for all systems. The sorbent capacity in column was 0.028 and 0.025 mmol g-1 for copper, respectively in mono and tri-component systems. A decrease of capacity for copper (50%) was observed when dealing with the real effluent. The Yoon-Nelson, Thomas and Yan’s models were fitted to the experimental data, being the latter the best fit.

Transnational Migrants and Transnational Spirits: An African Religion in Lisbon

Journal of Ethnic and Migration Studies, Vol.34, n.2,pp. 253 — 269

Fractional pennes' bioheat equation: theoretical and numerical studies

In this work we provide a new mathematical model for the Pennes’ bioheat equation, assuming a fractional time derivative of single order. Alternative versions of the bioheat equation are studied and discussed, to take into account the temperature-dependent variability in the tissue perfusion, and both finite and infinite speed of heat propagation. The proposed bioheat model is solved numerically using an implicit finite difference scheme that we prove to be convergent and stable. The numerical method proposed can be applied to general reaction diffusion equations, with a variable diffusion coefficient. The results obtained with the single order fractional model, are compared with the original models that use classical derivatives.

Estudio ecofisiológico y agronómico del orégano en los sistemas de producción en Córdoba. EcophysiologicalL and agronomic studies of traditional and organic origanum production in Córdoba.

El presente proyecto da respuesta a la problemática agronómica de la Producción de Orégano en Córdoba siendo incluso extensible a la República Argentina. Existe una demanda del producto no satisfecha tanto a nivel nacional como internacional. La producción está en manos de pequeños productores los que necesitan de tecnologías para mejorar su capacidad productiva, e incluso exportar. En este contexto falta investigación básica y aplicada en relación a aspectos agronómicos de la Producción Primaria (Cultivo - Cosecha), de los ecotipos: Compacto Negrito, Criollo Mendocino, Chileno II, Verde Limón, Aromet Rosa 1, Aromet Rosa 2 y Orégano Invasor designado por la Cooperativa como Orégano Plaga. La hipótesis general del proyecto es: El Sistema de Producción Tradicional y Orgánico de los distintos ecotipos de orégano se los puede caracterizar agronómica y económicamente desde la perspectiva taxonómica, ecofisiológica, sanitaria y de la calidad del producto. Los objetivos son: a-Analizar los ecotipos de orégano en base a la taxonomía y el comportamiento ecofisiológico. b-Identificar las plagas y enfermedades. c-Evaluar la calidad del producto y el impacto económico de los Sistemas de Producción Tradicional y Orgánico. Las Metodologías incluyen evaluaciones respecto a variables: Taxonómicas, Anatómicas, Ecofisiológicas (Economía del Agua, del Carbono y de los Nutrientes), Fitosanitarias, Calidad del producto y evalución económica de los Sistemas Productivos en el contexto de la cadena de valor. De este proyecto se esperan obtener conocimientos básicos y aplicados sobre los Sistemas de Producción de orégano optimizando tecnologías económicamente sustentables.

Novel micelle carriers for cyclosporin A topical ocular delivery: in vivo cornea penetration, ocular distribution and efficacy studies.

Cornea transplantation is one of the most performed graft procedures worldwide with an impressive success rate of 90%. However, for "high-risk" patients with particular ocular diseases in addition to the required surgery, the success rate is drastically reduced to 50%. In these cases, cyclosporin A (CsA) is frequently used to prevent the cornea rejection by a systemic treatment with possible systemic side effects for the patients. To overcome these problems, it is a challenge to prepare well-tolerated topical CsA formulations. Normally high amounts of oils or surfactants are needed for the solubilization of the very hydrophobic CsA. Furthermore, it is in general difficult to obtain ocular therapeutic drug levels with topical instillations due to the corneal barriers that efficiently protect the intraocular structures from foreign substances thus also from drugs. The aim of this study was to investigate in vivo the effects of a novel CsA topical aqueous formulation. This formulation was based on nanosized polymeric micelles as drug carriers. An established rat model for the prevention of cornea graft rejection after a keratoplasty procedure was used. After instillation of the novel formulation with fluorescent labeled micelles, confocal analysis of flat-mounted corneas clearly showed that the nanosized carriers were able to penetrate into all corneal layers. The efficacy of a 0.5% CsA micelle formulation was tested and compared to a physiological saline solution and to a systemic administration of CsA. In our studies, the topical CsA treatment was carried out for 14 days, and the three parameters (a) cornea transparency, (b) edema, and (c) neovascularization were evaluated by clinical observation and scoring. Compared to the control group, the treated group showed a significant higher cornea transparency and significant lower edema after 7 and 13 days of the surgery. At the end point of the study, the neovascularization was reduced by 50% in the CsA-micelle treated animals. The success rate of cornea graft transplantation was 73% in treated animals against 25% for the control group. This result was as good as observed for a systemic CsA treatment in the same animal model. This new formulation has the same efficacy like a systemic treatment but without the serious CsA systemic side effects. Ocular drug levels of transplanted and healthy rat eyes were dosed by UPLC/MS and showed a high CsA value in the cornea (11710 ± 7530 ng(CsA)/g(tissue) and 6470 ± 1730 ng(CsA)/g(tissue), respectively). In conclusion, the applied formulation has the capacity to overcome the ocular surface barriers, the micelles formed a drug reservoir in the cornea from, where a sustained release of CsA can take place. This novel formulation for topical application of CsA is clearly an effective and well-tolerated alternative to the systemic treatment for the prevention of corneal graft rejection.

Role of dendritic cells in the lung: in vitro models, animal models and human studies

Dendritic cells (DCs) are the most potent antigen-presenting cells in the human lung and are now recognized as crucial initiators of immune responses in general. They are arranged as sentinels in a dense surveillance network inside and below the epithelium of the airways and alveoli, where thet are ideally situated to sample inhaled antigen. DCs are known to play a pivotal role in maintaining the balance between tolerance and active immune response in the respiratory system. It is no surprise that the lungs became a main focus of DC-related investigations as this organ provides a large interface for interactions of inhaled antigens with the human body. During recent years there has been a constantly growing body of lung DC-related publications that draw their data from in vitro models, animal models and human studies. This review focuses on the biology and functions of different DC populations in the lung and highlights the advantages and drawbacks of different models with which to study the role of lung DCs. Furthermore, we present a number of up-to-date visualization techniques to characterize DC-related cell interactions in vitro and/or in vivo.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Wet oxidation of recalcitrant lignin waters: Experimental and kinetic studies

The dissertation is based on four articles dealing with recalcitrant lignin water purification. Lignin, a complicated substance and recalcitrant to most treatment technologies, inhibits seriously pulp and paper industry waste management. Therefore, lignin is studied, using WO as a process method for its degradation. A special attention is paid to the improvement in biodegradability and the reduction of lignin content, since they have special importance for any following biological treatment. In most cases wet oxidation is not used as a complete ' mineralization method but as a pre treatment in order to eliminate toxic components and to reduce the high level of organics produced. The combination of wet oxidation with a biological treatment can be a good option due to its effectiveness and its relatively low technology cost. The literature part gives an overview of Advanced Oxidation Processes (AOPs). A hot oxidation process, wet oxidation (WO), is investigated in detail and is the AOP process used in the research. The background and main principles of wet oxidation, its industrial applications, the combination of wet oxidation with other water treatment technologies, principal reactions in WO, and key aspects of modelling and reaction kinetics are presented. There is also given a wood composition and lignin characterization (chemical composition, structure and origin), lignin containing waters, lignin degradation and reuse possibilities, and purification practices for lignin containing waters. The aim of the research was to investigate the effect of the operating conditions of WO, such as temperature, partial pressure of oxygen, pH and initial concentration of wastewater, on the efficiency, and to enhance the process and estimate optimal conditions for WO of recalcitrant lignin waters. Two different waters are studied (a lignin water model solution and debarking water from paper industry) to give as appropriate conditions as possible. Due to the great importance of re using and minimizing the residues of industries, further research is carried out using residual ash of an Estonian power plant as a catalyst in wet oxidation of lignin-containing water. Developing a kinetic model that includes in the prediction such parameters as TOC gives the opportunity to estimate the amount of emerging inorganic substances (degradation rate of waste) and not only the decrease of COD and BOD. The degradation target compound, lignin is included into the model through its COD value (CODligning). Such a kinetic model can be valuable in developing WO treatment processes for lignin containing waters, or other wastewaters containing one or more target compounds. In the first article, wet oxidation of "pure" lignin water was investigated as a model case with the aim of degrading lignin and enhancing water biodegradability. The experiments were performed at various temperatures (110 -190°C), partial oxygen pressures (0.5 -1.5 MPa) and pH (5, 9 and 12). The experiments showed that increasing the temperature notably improved the processes efficiency. 75% lignin reduction was detected at the lowest temperature tested and lignin removal improved to 100% at 190°C. The effect of temperature on the COD removal rate was lower, but clearly detectable. 53% of organics were oxidized at 190°C. The effect of pH occurred mostly on lignin removal. Increasing the pH enhanced the lignin removal efficiency from 60% to nearly 100%. A good biodegradability ratio (over 0.5) was generally achieved. The aim of the second article was to develop a mathematical model for "pure" lignin wet oxidation using lumped characteristics of water (COD, BOD, TOC) and lignin concentration. The model agreed well with the experimental data (R2 = 0.93 at pH 5 and 12) and concentration changes during wet oxidation followed adequately the experimental results. The model also showed correctly the trend of biodegradability (BOD/COD) changes. In the third article, the purpose of the research was to estimate optimal conditions for wet oxidation (WO) of debarking water from the paper industry. The WO experiments were' performed at various temperatures, partial oxygen pressures and pH. The experiments showed that lignin degradation and organics removal are affected remarkably by temperature and pH. 78-97% lignin reduction was detected at different WO conditions. Initial pH 12 caused faster removal of tannins/lignin content; but initial pH 5 was more effective for removal of total organics, represented by COD and TOC. Most of the decrease in organic substances concentrations occurred in the first 60 minutes. The aim of the fourth article was to compare the behaviour of two reaction kinetic models, based on experiments of wet oxidation of industrial debarking water under different conditions. The simpler model took into account only the changes in COD, BOD and TOC; the advanced model was similar to the model used in the second article. Comparing the results of the models, the second model was found to be more suitable for describing the kinetics of wet oxidation of debarking water. The significance of the reactions involved was compared on the basis of the model: for instance, lignin degraded first to other chemically oxidizable compounds rather than directly to biodegradable products. Catalytic wet oxidation of lignin containing waters is briefly presented at the end of the dissertation. Two completely different catalysts were used: a commercial Pt catalyst and waste power plant ash. CWO showed good performance using 1 g/L of residual ash gave lignin removal of 86% and COD removal of 39% at 150°C (a lower temperature and pressure than with WO). It was noted that the ash catalyst caused a remarkable removal rate for lignin degradation already during the pre heating for `zero' time, 58% of lignin was degraded. In general, wet oxidation is not recommended for use as a complete mineralization method, but as a pre treatment phase to eliminate toxic or difficultly biodegradable components and to reduce the high level of organics. Biological treatment is an appropriate post treatment method since easily biodegradable organic matter remains after the WO process. The combination of wet oxidation with subsequent biological treatment can be an effective option for the treatment of lignin containing waters.

Synthesis and magnetic studies of copper(II)-lanthanide(III) 5-bromosalicylideneglycylglycine

The physico-chemical properties of the new 3d-4f heteronuclear complexes with general formula LnCu3(C11H8N2 O4Br)3·13H2O (where Ln = Pr, Eu, Gd, Tb, Er, Yb and H3(C11H8N2 O4Br) - 5-bromosalicylideneglycylglycine) were studied. The compounds were characterized by elemental, spectral and thermal analyses and magnetic measurements. The formation of Schiff base is evidenced by a strong band at ca. 1646-1650 cm-1 attributable to C=N stretching mode. The presence of water molecules is confirmed by broad absorptions with maximum at 3360 - 3368 cm-1. The Cu(II)-Ln(III) complexes are stable up to ca. 318 K. During dehydration process the water molecules are lost probably in two stages. The magnetic susceptibility data for these complexes change with temperature according to the Curie-Weiss law.

Probiotics and oral health: In vitro and clinical studies

Aino Toiviainen Probiotics and oral health: in vitro and clinical studies University of Turku, Faculty of Medicine, Institute of Dentistry, Periodontology, Finnish Doctoral Program in Oral Sciences (FINDOS-Turku), Turku, Finland Annales Universitatis Turkuensis, Sarja – Ser. D, Medica-Odontologica. Painosalama Oy, Turku, Finland, 2015 Probiotics are used, for example, to prevent and treat diarrhea, allergies and respiratory infections, and there is an increasing interest to use probiotics also for oral health purposes. The most commonly used probiotic bacteria are lactobacilli and bifidobacteria, which are acidogenic and aciduric. From the oral point of view, use of these probiotics may, at least in theory, mean an increased risk of caries. In this thesis, the effects of probiotics on oral microbial composition, acid production of dental plaque and gingival health were studied through in vitro studies and two clinical studies. In a randomized, double-blind and crossover study, 13 healthy adults were allocated into two groups. Half of the subjects first consumed Lactobacillus rhamnosus GG tablets twice a day for two weeks, and after the washout period, L. reuteri tablets twice a day for two weeks. The other half of the subjects used the tablets in reverse order. In another controlled, randomized and double-blind study, 62 healthy adults were allocated into two groups. One group used the test tablets containing L. rhamnosus GG and B. lactis BB-12 and the other group used control tablets without probiotics. The recommendation for the use of the tablets was 4 per day for 4 weeks. Probiotic lactobacilli interfered with S. mutans biofilm formation and the adhesion of S. mutans to saliva-coated hydroxyapatite in vitro. No effect was found in S. mutans levels in the three-species biofilms. In clinical studies, the studied probiotics had no effect on the acid production of plaque. The counts of mutans streptococci and the oral microbial composition remained the same. Tablets containing L. rhamnosus GG and Bifidobacterium animalis subsp. lactis BB-12 did decrease the amount of plaque and gingival bleeding. According to our results, it seems that probiotics have beneficial effects on gingival health. The present results confirmed that probiotics are safe and have beneficial effects on oral health. Since the consumption of probiotics by the general population is steadily increasing, an understanding of the functions of probiotics in the oral cavity has become more important. Keywords: lactobacilli, bifidobacteria, caries, periodontal disease, mutans streptococci, probiotics