Plantas medicinais: transmissão do conhecimento nas famílias de agricultores de base ecológica no Sul do RS

O objetivo deste estudo foi investigar o processo de transmissão do conhecimento relacionado às plantas medicinais entre as gerações familiares, no contexto de agricultores de base ecológica da região sul do Rio Grande do Sul. Trata-se de um estudo qualitativo realizado com oito famílias de agricultores, totalizando 19 entrevistados, residentes nos municípios de Pelotas, Morro Redondo, Canguçu e Arroio do Padre, entre janeiro e maio de 2009. A análise dos dados foi realizada através do método hermenêutico-dialético. A família foi referida como a principal fonte na transmissão do conhecimento em relação às plantas medicinais. A maioria dos sujeitos informou primeiro realizar o tratamento com as plantas medicinais para em seguida buscar o serviço formal de saúde. A construção do conhecimento relacionado às plantas medicinais pelas famílias é predominantemente oral, realizada através do convívio diário entre seus membros e compartilhada com os demais membros da comunidade na qual estão inseridos.

O olhar da enfermagem sobre as práticas de cuidado de famílias rurais à pessoa com câncer

O objetivo deste estudo foi identificar as práticas de cuidados das famílias rurais que vivenciam o cuidar da pessoa com câncer. Trata-se de estudo qualitativo, que utilizou como referencial teórico-metodológico o Modelo Bioecológico de Urie Bronfenbrenner e o método da inserção ecológica. Participaram três famílias da área rural, que tinham um de seus membros em tratamento quimioterápico no Serviço de Oncologia de um Hospital Escola da região Sul do Brasil. A coleta de dados ocorreu entre fevereiro e julho de 2009. Constatou-se que a família rural cuida a partir das práticas de cuidado que foram construídas com base nas interações entre as pessoas da família ao longo das gerações e em outras práticas da comunidade. O carinho, o amor, a proteção, a união familiar, a fé, o estar junto, a preocupação com a alimentação descrevem o cuidar e constituem-se como práticas de cuidado das famílias rurais à pessoa com câncer.

Mutations leading to X-linked hypohidrotic ectodermal dysplasia affect three major functional domains in the tumor necrosis factor family member ectodysplasin-A.

Mutations in the epithelial morphogen ectodysplasin-A (EDA), a member of the tumor necrosis factor (TNF) family, are responsible for the human disorder X-linked hypohidrotic ectodermal dysplasia (XLHED) characterized by impaired development of hair, eccrine sweat glands, and teeth. EDA-A1 and EDA-A2 are two splice variants of EDA, which bind distinct EDA-A1 and X-linked EDA-A2 receptors. We identified a series of novel EDA mutations in families with XLHED, allowing the identification of the following three functionally important regions in EDA: a C-terminal TNF homology domain, a collagen domain, and a furin protease recognition sequence. Mutations in the TNF homology domain impair binding of both splice variants to their receptors. Mutations in the collagen domain can inhibit multimerization of the TNF homology region, whereas those in the consensus furin recognition sequence prevent proteolytic cleavage of EDA. Finally, a mutation affecting an intron splice donor site is predicted to eliminate specifically the EDA-A1 but not the EDA-A2 splice variant. Thus a proteolytically processed, oligomeric form of EDA-A1 is required in vivo for proper morphogenesis.

Molecular and therapeutic characterization of anti-ectodysplasin A receptor (EDAR) agonist monoclonal antibodies.

The TNF family ligand ectodysplasin A (EDA) and its receptor EDAR are required for proper development of skin appendages such as hair, teeth, and eccrine sweat glands. Loss of function mutations in the Eda gene cause X-linked hypohidrotic ectodermal dysplasia (XLHED), a condition that can be ameliorated in mice and dogs by timely administration of recombinant EDA. In this study, several agonist anti-EDAR monoclonal antibodies were generated that cross-react with the extracellular domains of human, dog, rat, mouse, and chicken EDAR. Their half-life in adult mice was about 11 days. They induced tail hair and sweat gland formation when administered to newborn EDA-deficient Tabby mice, with an EC(50) of 0.1 to 0.7 mg/kg. Divalency was necessary and sufficient for this therapeutic activity. Only some antibodies were also agonists in an in vitro surrogate activity assay based on the activation of the apoptotic Fas pathway. Activity in this assay correlated with small dissociation constants. When administered in utero in mice or at birth in dogs, agonist antibodies reverted several ectodermal dysplasia features, including tooth morphology. These antibodies are therefore predicted to efficiently trigger EDAR signaling in many vertebrate species and will be particularly suited for long term treatments.

Biological activity of ectodysplasin A is conditioned by its collagen and heparan sulfate proteoglycan-binding domains.

Mutations in the TNF family ligand EDA1 cause X-linked hypohidrotic ectodermal dysplasia (XLHED), a condition characterized by defective development of skin appendages. The EDA1 protein displays a proteolytic processing site responsible for its conversion to a soluble form, a collagen domain, and a trimeric TNF homology domain (THD) that binds the receptor EDAR. In-frame deletions in the collagen domain reduced the thermal stability of EDA1. Removal of the collagen domain decreased its activity about 100-fold, as measured with natural and engineered EDA1-responsive cell lines. The collagen domain could be functionally replaced by multimerization domains or by cross-linking antibodies, suggesting that it functions as an oligomerization unit. Surprisingly, mature soluble EDA1 containing the collagen domain was poorly active when administered in newborn, EDA-deficient (Tabby) mice. This was due to a short stretch of basic amino acids located at the N terminus of the collagen domain that confers EDA1 with proteoglycan binding ability. In contrast to wild-type EDA1, EDA1 with mutations in this basic sequence was a potent inducer of tail hair development in vivo. Thus, the collagen domain activates EDA1 by multimerization, whereas the proteoglycan-binding domain may restrict the distribution of endogeneous EDA1 in vivo.

Aplicación de las técnicas de suavizado en la variación negativa contingente (CNV)

El presente trabajo recoge de forma breve laproblemática de la estimación de la serial en series temporales de datos obtenidos en registros ERP. Se centra en aquellos componentes de frecuencia mis baja, como es el caso de la CNV: Sepropone la utilización alternativa de las técnicas de suavizado del Análisis Exploratorio de Datos (EDA), para mejorar la estimación obtenida, en comparación con la técnica del promediado simple de diferentes ensayos.

Identification of ectodysplasin target genes reveals the involvement of chemokines in hair development.

Ectodysplasin (Eda), a member of the tumor necrosis factor (Tnf) family, regulates skin appendage morphogenesis via its receptor Edar and transcription factor NF-κB. In humans, inactivating mutations in the Eda pathway components lead to hypohidrotic ectodermal dysplasia (HED), a syndrome characterized by sparse hair, tooth abnormalities, and defects in several cutaneous glands. A corresponding phenotype is observed in Eda-null mice, where failure in the initiation of the first wave of hair follicle development is a hallmark of HED pathogenesis. In an attempt to discover immediate target genes of the Eda/NF-κB pathway, we performed microarray profiling of genes differentially expressed in embryonic skin explants after a short exposure to recombinant Fc-Eda protein. Upregulated genes included components of the Wnt, fibroblast growth factor, transforming growth factor-β, Tnf, and epidermal growth factor families, indicating that Eda modulates multiple signaling pathways implicated in skin appendage development. Surprisingly, we identified two ligands of the chemokine receptor cxcR3, cxcl10 and cxcl11, as new hair-specific transcriptional targets of Eda. Deficiency in cxcR3 resulted in decreased primary hair follicle density but otherwise normal hair development, indicating that chemokine signaling influences the patterning of primary hair placodes only.

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.

Impact of restrictive intravenous fluid replacement and combined epidural analgesia on perioperative volume balance and renal function within a Fast Track program.

BACKGROUND AND OBJECTIVE: Key factors of Fast Track (FT) programs are fluid restriction and epidural analgesia (EDA). We aimed to challenge the preconception that the combination of fluid restriction and EDA might induce hypotension and renal dysfunction. METHODS: A recent randomized trial (NCT00556790) showed reduced complications after colectomy in FT patients compared with standard care (SC). Patients with an effective EDA were compared with regard to hemodynamics and renal function. RESULTS: 61/76 FT patients and 59/75 patients in the SC group had an effective EDA. Both groups were comparable regarding demographics and surgery-related characteristics. FT patients received significantly less i.v. fluids intraoperatively (1900 mL [range 1100-4100] versus 2900 mL [1600-5900], P < 0.0001) and postoperatively (700 mL [400-1500] versus 2300 mL [1800-3800], P < 0.0001). Intraoperatively, 30 FT compared with 19 SC patients needed colloids or vasopressors, but this was statistically not significant (P = 0.066). Postoperative requirements were low in both groups (3 versus 5 patients; P = 0.487). Pre- and postoperative values for creatinine, hematocrit, sodium, and potassium were similar, and no patient developed renal dysfunction in either group. Only one of 82 patients having an EDA without a bladder catheter had urinary retention. Overall, FT patients had fewer postoperative complications (6 versus 20 patients; P = 0.002) and a shorter median hospital stay (5 [2-30] versus 9 d [6-30]; P< 0.0001) compared with the SC group. CONCLUSIONS: Fluid restriction and EDA in FT programs are not associated with clinically relevant hemodynamic instability or renal dysfunction.

Randomized clinical trial on epidural versus patient-controlled analgesia for laparoscopic colorectal surgery within an enhanced recovery pathway.

OBJECTIVE: To compare epidural analgesia (EDA) to patient-controlled opioid-based analgesia (PCA) in patients undergoing laparoscopic colorectal surgery. BACKGROUND: EDA is mainstay of multimodal pain management within enhanced recovery pathways [enhanced recovery after surgery (ERAS)]. For laparoscopic colorectal resections, the benefit of epidurals remains debated. Some consider EDA as useful, whereas others perceive epidurals as unnecessary or even deleterious. METHODS: A total of 128 patients undergoing elective laparoscopic colorectal resections were enrolled in a randomized clinical trial comparing EDA versus PCA. Primary end point was medical recovery. Overall complications, hospital stay, perioperative vasopressor requirements, and postoperative pain scores were secondary outcome measures. Analysis was performed according to the intention-to-treat principle. RESULTS: Final analysis included 65 EDA patients and 57 PCA patients. Both groups were similar regarding baseline characteristics. Medical recovery required a median of 5 days (interquartile range [IQR], 3-7.5 days) in EDA patients and 4 days (IQR, 3-6 days) in the PCA group (P = 0.082). PCA patients had significantly less overall complications [19 (33%) vs 35 (54%); P = 0.029] but a similar hospital stay [5 days (IQR, 4-8 days) vs 7 days (IQR, 4.5-12 days); P = 0.434]. Significantly more EDA patients needed vasopressor treatment perioperatively (90% vs 74%, P = 0.018), the day of surgery (27% vs 4%, P < 0.001), and on postoperative day 1 (29% vs 4%, P < 0.001), whereas no difference in postoperative pain scores was noted. CONCLUSIONS: Epidurals seem to slow down recovery after laparoscopic colorectal resections without adding obvious benefits. EDA can therefore not be recommended as part of ERAS pathways in laparoscopic colorectal surgery.

Ectodysplasin/NF-κB Promotes Mammary Cell Fate via Wnt/β-catenin Pathway.

Mammary gland development commences during embryogenesis with the establishment of a species typical number of mammary primordia on each flank of the embryo. It is thought that mammary cell fate can only be induced along the mammary line, a narrow region of the ventro-lateral skin running from the axilla to the groin. Ectodysplasin (Eda) is a tumor necrosis factor family ligand that regulates morphogenesis of several ectodermal appendages. We have previously shown that transgenic overexpression of Eda (K14-Eda mice) induces formation of supernumerary mammary placodes along the mammary line. Here, we investigate in more detail the role of Eda and its downstream mediator transcription factor NF-κB in mammary cell fate specification. We report that K14-Eda mice harbor accessory mammary glands also in the neck region indicating wider epidermal cell plasticity that previously appreciated. We show that even though NF-κB is not required for formation of endogenous mammary placodes, it is indispensable for the ability of Eda to induce supernumerary placodes. A genome-wide profiling of Eda-induced genes in mammary buds identified several Wnt pathway components as potential transcriptional targets of Eda. Using an ex vivo culture system, we show that suppression of canonical Wnt signalling leads to a dose-dependent inhibition of supernumerary placodes in K14-Eda tissue explants.

Enantioselective synthesis of aziridines using asymmetric transfer hydrogenation as a precursor for chiral derivatives used as bonding agent for rocket solid propellants

A rapid, expedient and enantioselective method for the synthesis of beta-hydroxy amines and monosubstituted aziridines in up to 99% e.e., via asymmetric transfer hydrogenation of a-amino ketones and cyclisation through treatment with tosyl chloride and base, is described. (1R,2R)-N-(para-toluenesulfonyl)-1,2-ethylenediamine with formic acid has been utilised as a ligand for the Ruthenium (II) catalysed enantioselective transfer hydrogenation of the ketones.The chiral 2-methyl aziridine, which is a potentially more efficient bonding agent for Rocket Solid Propellant has been successfully achieved.

Química de complexos de (etilenodiaminatetraacetato)rutenato(III/II)

This paper provides a survey of general aspects involved in the coordination chemistry of low-valent (mainly +III,+II), low-spin (d p5,d p6) ruthenium ions with ethylenediamine-N,N,N',N'-tetraacetate (edta) and their substituted derivatives. The topics covered herein include structure, reactivity, kinetics, thermodynamics, electrochemistry and spectroscopy. The contributions from either our research group or the literature over the last three decades are focused in this review.

Aplicação e modificação química da sílica gel obtida de areia

The silica gel was obtained from sand and its surface was modified with POCl3 to produce Si-Cl bonds on the silica surface. Ethylenediamine was covalently bonded onto the chlorinated silica surface. The adsorption of the chlorides of divalent cobalt, nickel and copper was qualitatively studied to show that the bonding of ethylenediamine onto the silica gel surface produces a solid base capable of chelating metal ions from solution. The experiments illustrate the extraction of silica gel, its reactivity, the development of modified surfaces and its application in removing metal ions from water and are deigned for undergraduate inorganic chemistry laboratories.