Effect of Prunella vulgaris L extract on hyperprolactinemia in vitro and in vivo

Purpose: To investigate the anti-hyperprolactinemic activity of Prunella vulgaris L. extract (PVE) in vivo and in vitro. Methods: Rats were given intraperitoneal (i. p.) metoclopramide (MCP, 150 mg/kg daily) for 10 days to prepare hyperprolactinemia (hyperPRL) model. Bromocriptine was used as positive control drug. High (5.6 g/kg), medium (2.8 g/kg) and low (1.4 g/kg) doses of PVE were administered to hyperPRL rats. The effect of PVE on serum prolactin (PRL), estradiol (E2), progesterone (PGN), follicle stimulating hormone (FSH) and luteinizing hormone (LH) levels were investigated in the rats. MMQ cells derived from rat pituitary adenoma cells and GH3 cells from rat pituitary lactotropictumoral cells were used for in vitro experiments. The effect of PVE on PRL secretion were studied in MMQ cells and GH3 cells respectively. Results: Compared with the control group (446.21 ± 32.43 pg/mL), high (219.23 ± 10.62 pg/mL) and medium (245.47 ± 13.52 pg/mL) reduced PRL level of hyperPRL rats significantly (p 0.05). In MMQ cells, treatment with 5 mg/mL PVE or 10 mg/mL PVE) significantly suppressed PRL secretion and synthesis at 24h compared with controls (p < 0.01). Consistent with D2- action, PVE did not affect PRL in rat pituitary lactotropic tumor-derived GH3 cells that lack the D2 receptor expression, compared with controls. Conclusion: PVE showed anti-hyperPRL activity and can potentially be used for the treatment of hyperprolactinemi, but further studies are required to ascertain this

Transición del mini baloncesto al baloncesto en la selección de la escuela Fiscomisional “Sor Teresa Valsé” en la categoría intermedia comprendida en las edades 12 a 14 años

El presente trabajo se ha concebido bajo un carácter didáctico y con la intención de ser difundido y utilizado, dirigido a estudiantes, profesores y aquellos que sientan deseo de incursionar en este campo interesante. El baloncesto es un deporte practicado en edades tempranas, no está enfocado en la competitividad sino encausado a adquirir fundamentos: técnicos, tácticos, individuales y colectivos con una descripción detallada del proceso de aprendizaje y las destrezas básicas; iniciando de lo sencillo a lo complejo, a la iniciación del juego y sus reglas, acompañados de una disciplina deportiva de colaboración, compañerismo, responsabilidad, cooperación, alimentación, de higiene, de buenos hábitos, alejados del alcohol y las drogas, que afectan al normal desarrollo bio-psico-social-afectivo del individuo. Con la transición del minibaloncesto al baloncesto en la selección de la escuela Fiscomisional Sor Teresa Valsé en la categoría intermedia comprendida en las edades de 12 a 14 años realizado en los meses de agosto 2015 a mayo 2016, se pretendió poner al alcance de las niñas todas las ventajas y valores educativos del baloncesto, de tal manera que no se contraríe su naturaleza, posibilidades, intereses, ni exija esfuerzos incompatibles con la edad de las participantes. En consecuencia la transición del minibaloncesto al baloncesto repercute en forma positiva en el normal desarrollo de las niñas de estas edades, tanto en lo físico, técnico-táctico, psicológico y social, así como también este trabajo refleja el desarrollo y adquisición de habilidades, destrezas y el modelo sobre el cual se implementó cada uno de los mismos para alcanzar los resultados esperados de esta investigación.

Effects of aqueous extract from Asparagus officinalis L. roots on hypothalamic-pituitary-gonadal axis hormone levels and the number of ovarian follicles in adult rats

Background: Asparagus is a plant with high nutritional, pharmaceutical, and industrial values. Objective: The present study aimed to evaluate the effect of aqueous extract of asparagus roots on the hypothalamic-pituitary-gonadal axis hormones and oogenesis in female rats. Materials and Methods: In this experimental study, 40 adult female Wistar rats were divided into five groups, which consist 8 rats. Groups included control, sham and three experimental groups receiving different doses (100, 200, 400 mg/kg/bw) of aqueous extract of asparagus roots. All dosages were administered orally for 28 days. Blood samples were taken from rats to evaluate serum levels of Gonadotropin releasing hormone (GnRH), follicular stimulating hormone (FSH), Luteinal hormone (LH), estrogen, and progesterone hormones. The ovaries were removed, weighted, sectioned, and studied by light microscope. Results: Dose-dependent aqueous extract of asparagus roots significantly increased serum levels of GnRH, FSH, LH, estrogen, and progestin hormones compared to control and sham groups. Increase in number of ovarian follicles and corpus luteum in groups treated with asparagus root extract was also observed (p<0.05). Conclusion: Asparagus roots extract stimulates secretion of hypothalamic- pituitary- gonadal axis hormones. This also positively affects oogenesis in female rats.

Effect of Aloe vera extract on the improvement of the respiratory activity of leukocytes of matrinxã during the transport stress

This study evaluated the effect of extract of Aloe vera in the transport water of matrinxã (Brycon amazonicus) fish on stress response and leukocyte respiratory activity. Fish was transported for 4 h in water containing Aloe at levels 0; 0.02; 0.2 and 2 mg/L, and sampled before transport 2, 4, 24 and 96 h after for determination of plasma glucose and respiratory activity of leukocytes. An additional in vitro assay was conducted with another fish species, pacu (Piaractus mesopotamicus), to test the respiratory burst of leukocytes exposed to Aloe extract (0.0, phosphate-buffered saline (PBS) only) at 0.1, 0.2, 0.5 and 1 mg/L). Plasma glucose increased after 2 and 4 h of transport and returned to control levels within 24 h, but the addition of Aloe in the transport water did not affect the level of blood glucose. However, at 2 h of transport, Aloe enhanced the respiratory activity of leukocytes in a dose-dependent way. The highest value of respiratory burst activity of leukocytes was observed in the fish transported in water containing Aloe at 2 mg/L. The enhancing effect of the plant extract on the production of oxygen radicals was confirmed in vitro in leukocytes of pacu incubated in Aloe at concentrations 0.1 and 0.2 mg/L. The results suggest that Aloe vera is a modulator of the immune system in fish improving the innate immune response tested.

A black foot extract can delay downy mildew progress in grapevine under greenhouse conditions.

2011

O murucizeiro [Byrsonima crassifolia (L.) H.B.K.]: avanços no conhecimento e ações de pré-melhoramento.

Propagação do murucizeiro; Recursos genéticos e pré-melhoramento do murucizeiro; Aplicações de marcadores moleculares em Byrsonima crassifolia.

Aplicações de marcadores moleculares em Byrsonima crassifolia.

O uso de marcadores moleculares em espécies vegetais pode auxiliar no estudo da diversidade genética ao verificar a forma de distribuição da variação genética entre e dentro de populações naturais e os locais de maior ocorrência de variação. Também pode contribuir para inferir acerca da forma de reprodução das espécies. Os marcadores podem indicar locais no quais está ocorrendo maior incidência de cruzamentos entre aparentados, taxas de fluxo gênico entre as populações e relações entre os componentes da população. Além disso, auxiliam no melhoramento genético ao determinar a variação existente nas coleções/bancos de germoplasma, no direcionamento de cruzamentos, em testes de paternidade, verificação de métodos que geram variabilidade, seleção assistida por marcadores e obtenção de marcas que possam descrever clones/variedades/cultivares recomendadas. Dessa forma, o uso de marcadores moleculares em Byrsonima crassifolia pode contribuir em diversos aspectos.

Characterisation of indoor air organic pollutants in Brisbane

The occurrence and levels of airborne polycyclic aromatic hydrocarbons and volatile organic compounds in selected non-industrial environments in Brisbane have been investigated as part of an integrated indoor air quality assessment program. The most abundant and most frequently encountered compounds include, nonanal, decanal, texanol, phenol, 2-ethyl-1-hexanol, ethanal, naphthalene, 2,6-tert-butyl-4-methyl-phenol (BHT), salicylaldehyde, toluene, hexanal, benzaldehyde, styrene, ethyl benzene, o-, m- and pxylenes, benzene, n-butanol, 1,2-propandiol, and n-butylacetate. Many of the 64 compounds usually included in the European Collaborative Action method of TVOC analysis were below detection limits in the samples analysed. In order to extract maximum amount of information from the data collected, multivariate data projection methods have been employed. The implications of the information extracted on source identification and exposure control are discussed.

Particle number and mass emission factors from combustion of wood samples collected from Queensland forest

Knowledge of particle emission characteristics associated with forest fires and in general, biomass burning, is becoming increasingly important due to the impact of these emissions on human health. Of particular importance is developing a better understanding of the size distribution of particles generated from forest combustion under different environmental conditions, as well as provision of emission factors for different particle size ranges. This study was aimed at quantifying particle emission factors from four types of wood found in South East Queensland forests: Spotted Gum (Corymbia citriodora), Red Gum (Eucalypt tereticornis), Blood Gum (Eucalypt intermedia), and Iron bark (Eucalypt decorticans); under controlled laboratory conditions. The experimental set up included a modified commercial stove connected to a dilution system designed for the conditions of the study. Measurements of particle number size distribution and concentration resulting from the burning of woods with a relatively homogenous moisture content (in the range of 15 to 26 %) and for different rates of burning were performed using a TSI Scanning Mobility Particle Sizer (SMPS) in the size range from 10 to 600 nm and a TSI Dust Trak for PM2.5. The results of the study in terms of the relationship between particle number size distribution and different condition of burning for different species show that particle number emission factors and PM2.5 mass emission factors depend on the type of wood and the burning rate; fast burning or slow burning. The average particle number emission factors for fast burning conditions are in the range of 3.3 x 1015 to 5.7 x 1015 particles/kg, and for PM2.5 are in the range of 139 to 217 mg/kg.

Peer to peer English/Chinese cross-language information retrieval

Peer to peer systems have been widely used in the internet. However, most of the peer to peer information systems are still missing some of the important features, for example cross-language IR (Information Retrieval) and collection selection / fusion features. Cross-language IR is the state-of-art research area in IR research community. It has not been used in any real world IR systems yet. Cross-language IR has the ability to issue a query in one language and receive documents in other languages. In typical peer to peer environment, users are from multiple countries. Their collections are definitely in multiple languages. Cross-language IR can help users to find documents more easily. E.g. many Chinese researchers will search research papers in both Chinese and English. With Cross-language IR, they can do one query in Chinese and get documents in two languages. The Out Of Vocabulary (OOV) problem is one of the key research areas in crosslanguage information retrieval. In recent years, web mining was shown to be one of the effective approaches to solving this problem. However, how to extract Multiword Lexical Units (MLUs) from the web content and how to select the correct translations from the extracted candidate MLUs are still two difficult problems in web mining based automated translation approaches. Discovering resource descriptions and merging results obtained from remote search engines are two key issues in distributed information retrieval studies. In uncooperative environments, query-based sampling and normalized-score based merging strategies are well-known approaches to solve such problems. However, such approaches only consider the content of the remote database but do not consider the retrieval performance of the remote search engine. This thesis presents research on building a peer to peer IR system with crosslanguage IR and advance collection profiling technique for fusion features. Particularly, this thesis first presents a new Chinese term measurement and new Chinese MLU extraction process that works well on small corpora. An approach to selection of MLUs in a more accurate manner is also presented. After that, this thesis proposes a collection profiling strategy which can discover not only collection content but also retrieval performance of the remote search engine. Based on collection profiling, a web-based query classification method and two collection fusion approaches are developed and presented in this thesis. Our experiments show that the proposed strategies are effective in merging results in uncooperative peer to peer environments. Here, an uncooperative environment is defined as each peer in the system is autonomous. Peer like to share documents but they do not share collection statistics. This environment is a typical peer to peer IR environment. Finally, all those approaches are grouped together to build up a secure peer to peer multilingual IR system that cooperates through X.509 and email system.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

High-level concept annotation using ontology and probabilistic inference

Image annotation is a significant step towards semantic based image retrieval. Ontology is a popular approach for semantic representation and has been intensively studied for multimedia analysis. However, relations among concepts are seldom used to extract higher-level semantics. Moreover, the ontology inference is often crisp. This paper aims to enable sophisticated semantic querying of images, and thus contributes to 1) an ontology framework to contain both visual and contextual knowledge, and 2) a probabilistic inference approach to reason the high-level concepts based on different sources of information. The experiment on a natural scene database from LabelMe database shows encouraging results.