Mossbauer and magnetization studies of amorphous NdFeB compositionally modulated thin films

Several NdFeB compositionally modulated thin films are studied by using both conversion electron Mossbauer spectra and SQUID (superconducting quantum-interference-device) magnetometry. Both the hyperfine fields and the easy magnetization magnitude are not correlated with the modulation characteristic length (lambda) while the magnetization perpendicular to the thin-film plane decreases as lambda increases. The spectra were recorded at room temperature being the gamma rays perpendicular to the substrate plane. The magnetization measurements were recorded by using a SHE SQUID magnetometer in applied magnetic fields up to 5.5 T and in the temperature range between 1.8 and 30 K.

Gender and family firms: an interdisciplinary approach

This study contributes to developing our understanding of gender and family business, a topic so crucial to recent policies about competitive growth. It does so by providing an interdisciplinary synthesis of some major theoretical debates. It also contributes to this understanding by illuminating the role of women and their participation in the practices of the family and the business. Finally, it explores gender relations and the notion that leadership in family business may take complex forms crafted within constantly changing relationships. Leadership is introduced as a concept that captures the reality of women and men in family firms in a better way than other concepts used by historians or economists like ownership and management.

Parents' alcohol use: gender differences in the impact of household and family chores.

BACKGROUND: Social roles influence alcohol use. Nevertheless, little is known about how specific aspects of a given role, here parenthood, may influence alcohol use. The research questions for this study were the following: (i) are family-related indicators (FRI) linked to the alcohol use of mothers and fathers? and (ii) does the level of employment, i.e. full-time, part-time employment or unemployment, moderate the relationship between FRI and parental alcohol use? METHODS: Survey data of 3217 parents aged 25-50 living in Switzerland. Mean comparisons and multiple regression models of annual frequency of drinking and risky single occasion drinking, quantity per day on FRI (age of the youngest child, number of children in the household, majority of child-care/household duties). RESULTS: Protective relationships between FRI and alcohol use were observed among mothers. In contrast, among fathers, detrimental associations between FRI and alcohol use were observed. Whereas maternal responsibilities in general had a protective effect on alcohol use, the number of children had a detrimental impact on the quantity of alcohol consumed per day when mothers were in paid employment. Among fathers, the correlations between age of the youngest child, number of children and frequency of drinking was moderated by the level of paid employment. CONCLUSION: The study showed that in Switzerland, a systematic negative relationship was more often found between FRI and women's drinking than men's. Evidence was found that maternal responsibilities per se may protect from alcohol use but can turn into a detrimental triangle if mothers are additionally in paid employment.

Non-verbal behavioural patterns in trilogue play transitions and family alliances

This case study focuses on non-verbal behaviour in father-mother-infant triads. Analyses were done on transitional moments during which the partners exchanged an active role for a participant-observer role, or vice versa. Transitions are known to be crucial moments for revealing familial transactional mechanisms. Our sample was comprised of six non-clinical families, characterized by different types of functional or problematic alliances (which is the degree of coordination between the partners). Our methodology included micro-analysis of body and gaze formations, facial expressions, and so on. Data were analysed using the research package 'THEME' for the detection of hidden patterns. Different types of non-verbal patterns were found, which may be prototypes corresponding to the different types of alliance. The patterns of the families with high alliances had a more elaborate construction and were more efficient for the concluding of transitions than the patterns of families with low alliances, which were either elementary or laborious. (PsycINFO Database Record (c) 2006 APA, all rights reserved) (journal abstract)

Phytolith and spherulite studies of herbivores dung from the African Savannah as a tool for palaeocological reconstruction.

Los restos fecales están compuestos mayoritariamente por materia orgánica, la cual se degrada con el tiempo despareciendo finalmente del registro arqueológico. Sin embargo, estos restos fecales también contienen ciertos elementos resistentes al paso del tiempo y a los efectos postdeposicionales. Las esferulitas son cristales de carbonato cálcico formadas en los intestinos de ciertos animales herbívoros, principalmente rumiantes y que posteriormente son depositados en los restos fecales. Los fitolitos de sílice, aunque se forman en las plantas, son también comúnmente identificados en los restos fecales de animales herbívoros. Su número y morfología dependerá de la dieta vegetal de estos animales. El estudio que aquí se presenta se centra en el análisis microscópico de ambos elementos, fitolitos y esferulitas, identificados en restos fecales, de varios animales herbívoros, recolectados durante la estación seca en la Garganta de Olduvai en Tanzania. Los fitolitos y las esferulitas fueron identificados y analizados siguiendo un método morfológico y cuantitativo. Los fitolitos fueron luego comparados con una colección de referencia de plantas modernas de la misma zona geográfica con el propósito de estudiar la dieta de cada uno de los animales analizados. Finalmente los resultados fueron relacionados con los obtenidos del estudio de esferulitas, con el propósito de analizar la relación entre morfología y número de fitolitos y morfología y número de esferulitas para cada uno de los restos fecales analizados. El objetivo de este trabajo consiste en evaluar la utilidad de combinar ambas técnicas para identificar restos fecales en el registro arqueológico y, consecuentemente, responder a cuestiones relacionadas con el animal productor de estos restos, su dieta y movimientos migratorios y, paralelamente, la paleovegetación y el paleopaisaje en una región determinada.

Attitudes towards hastened death in ALS: A prospective study of patients and family caregivers.

Abstract Amyotrophic lateral sclerosis (ALS) may be associated with the wish to hasten death (WTHD). We aimed to determine the prevalence and stability of WTHD and end-of-life attitudes in ALS patients, identify predictive factors, and explore communication about WTHD. We conducted a prospective questionnaire study among patients and their primary caregivers attending ALS clinics in Germany and Switzerland. We enrolled 66 patients and 62 caregivers. Half of the patients could imagine asking for assisted suicide or euthanasia; 14% expressed a current WTHD at the baseline survey. While 75% were in favour of non-invasive ventilation, only 55% and 27% were in favour of percutaneous endoscopic gastrostomy and invasive ventilation, respectively. These attitudes were stable over 13 months. The WTHD was predicted by depression, anxiety, loneliness, perceiving to be a burden to others, and a low quality of life (all p < 0.05). Lower religiosity predicted whether patients could imagine assisted suicide or euthanasia. Two-thirds of patients had communicated their WTHD to relatives; no-one talked to the physician about it, yet half of them would like to do so. In conclusion, physicians should consider proactively asking for WTHD, and be sensitive towards neglected psychosocial problems and psychiatric comorbidity.

The family alliance assessment scales : steps toward validity and reliability of an observational assessment tool for early family interactions

We present the first steps in the validation of an observational tool for father-mother-infant interactions: the FAAS (Family Alliance Assessment Scales). Family-level variables are acknowledged as unique contributors to the understanding of the socio-affective development of the child, yet producing reliable assessments of family-level interactions poses a methodological challenge. There is, therefore, a clear need for a validated and clinically relevant tool. This validation study has been carried out on three samples: one non-referred sample, of families taking part in a study on the transition to parenthood (normative sample; n = 30), one referred for medically assisted procreation (infertility sample; n = 30) and one referred for a psychiatric condition in one parent (clinical sample; n = 15). Results show that the FAAS scales have (1) good inter-rater reliability and (2) good validity, as assessed through known-group validity by comparing the three samples and through concurrent validity by checking family interactions against parents' self-reported marital satisfaction.

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.

Polyhydroxyalknoate synthesis in plants as a tool for biotechnology and basic studies of lipid metabolism.

Polyhydroxyalkanoates (PHAs) are polyesters of hydroxyacids naturally synthesized in bacteria as a carbon reserve. PHAs have properties of biodegradable thermoplastics and elastomers and their synthesis in crop plants is seen as an attractive system for the sustained production of large amounts of polymers at low cost. A variety of PHAs having different physical properties have now been synthesized in a number of transgenic plants, including Arabidopsis thaliana, rape and corn. This has been accomplished through the creation of novel metabolic pathways either in the cytoplasm, plastid or peroxisome of plant cells. Beyond its impact in biotechnology, PHA production in plants can also be used to study some fundamental aspects of plant metabolism. Synthesis of PHA can be used both as an indicator and a modulator of the carbon flux to pathways competing for common substrates, such as acetyl-coenzyme A in fatty acid biosynthesis or 3-hydroxyacyl-coenzyme A in fatty acid degradation. Synthesis of PHAs in plant peroxisome has been used to demonstrate changes in the flux of fatty acids to the beta-oxidation cycle in transgenic plants and mutants affected in lipid biosynthesis, as well as to study the pathway of degradation of unusual fatty acids.

Les fentes labio-palatines: suivi psychologique et investissement de l'enfant dans sa famille [The cleft lip and palate: psychological following child and family investment].

Congrès de la Société Française de Pédiatrie et de l'Association des Pédiatres de Langue Française (APLF)

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.

Iowa's Maternal Health, Child Health and Family Planning Business Plan, January 2014

• Promotes access to regular preventive health care services for children through contracts with 22 agencies covering all of Iowa’s 99 counties • Fosters age appropriate growth and development by promoting early identification of children’s health concerns and referral for diagnosis and treatment • Assists families to establish medical and dental homes for their children • Targets low income families – children on Medicaid and those who are uninsured and under insured • Strives to meet family needs and remove barriers to accessing health care by linking families to community-based, culturally appropriate services