Anti-osteoporosis activity of Astragalus membranaceus Bunge extract in experimental rats

Purpose: To investigate the anti-osteoporosis effect of Astragalus membranaceus (Fisch.) Bunge. extract (AMBE) in experimental rats. Method: Female Sprague-Dawley rats were randomly divided into six groups: control group, ovariectomy (OVX) with vehicle group, OVX with 17β-estradiol (E2, 25 μg/kg/day) group, and OVX with AMBE doses (60, 120 and 240 mg/kg/day) groups. Daily oral administration of AMBE or E2 was started 4 weeks after OVX and lasted for 16 weeks. The bone mineral density (BMD) of L4 vertebrae and right femurs was evaluated. The length of each femur was measured with a micrometer, and the center of diaphysis was determined. Three representative L4 vertebrae were selected to evaluate trabecular microarchitecture. Serum alkaline phosphatase (ALP), urinary calcium (U-Ca), urinary phosphorus (UP), urinary creatinine (Cr) and osteocalcin (OC) levels were measured. Results: AMBE dose-dependently inhibited the bone mineral density (BMD) reduction of L4 vertebrae (0.27 ± 0.03 g/cm2, p < 0.05) and femurs (0.23 ± 0.03 g/cm2, p < 0.05) caused by OVX and prevented the deterioration of trabecular microarchitecture (p < 0.05), which were accompanied by a significant decrease in skeletal remodeling (p < 0.05) as evidenced by the lower levels of bone turnover markers. A higher dosage of AMBE treatment (240 mg/kg/day) increased U-Ca/Cr (0.27 ± 0.03 mmol/mmol), ALP (137.23 ± 16.72 U/L), U-P/Cr (4.18 ± 0.27 mmol/mmol) and OC (8.47 ± 0.26 mmol/L) levels (both p < 0.05). Conclusion: The findings of this study indicate that AMBE prevents OVX-induced osteoporosis in rats.

Anti-hyperprolactinemia mechanism of Radix bupleuri extract in rats

Purpose: To determine the mechanism underlying the anti-hyperprolactinemia effects of Radix bupleuri extract (RBE) in rats. Methods: Rats were divided into six groups (n=10 each group): healthy controls, untreated hyperprolactinemic rats, hyperprolactinemic rats treated with bromocriptine (0.6 mg/kg), and hyperprolactinemic rats treated with RBE (4.8, 9.6, or 19.2 g/kg). After 30 days, hypothalamic protein levels of dopamine D2 receptor, protein kinase A (PKA), and cyclic adenosine monophosphate (cAMP) were determined. Results: Dopamine D2 receptor levels were lower in untreated hyperprolactinemic rats than in healthy controls (p < 0.01), but this decrease was attenuated by RBE (p < 0.05). Elevated PKA levels in untreated hyperprolactinemic rats (0.61 ± 0.04 μg/ml, p < 0.01) were decreased by RBE (4.8 g/kg, 0.42 ± 0.03 μg/ml, p < 0.05; 9.6 g/kg, 0.33 ± 0.02 μg/ml, p < 0.01; 19.2 g/kg, 0.27 ± 0.03 μg/ml, p < 0.01). Similarly, elevated cAMP levels in hyperprolactinemic rats (2.4 ± 0.4 ng/ml) were decreased by RBE (4.8 g/kg, 1.8 ± 0.3 ng/ml, p < 0.05; 9.6 g/kg, 1.5 ± 0.3 ng/ml, p < 0.01; 19.2 g/kg, 1.2 ± 0.2 ng/ml, p < 0.01). Conclusions: RBE anti-hyperprolactinemia activity is mediated by dopamine D2 receptor signaling via the cAMP/PKA pathway.

Immunomodulatory activity of methanol extract of Adansonia digitata L

Purpose: To evaluate the immune-modulatory activities of various plant parts Adansonia digitata L. using delayed-type hypersensitivity rat model. Methods: Defatted leaf, root bark and fruit pulp of A. digitata were extracted with methanol. Immunomodulatory activity of the methanol extracts (250 and 500 mg/kg) were evaluated in sheep RBC (SRBC)-induced delayed-type hypersensitivity model, cell mediated immune re-sponse and phagocytic activity using carbon clearance test. Results: The extracts exhibited significant increase in delayed-type hypersensitivity reaction, indicating the ability of the extracts to stimulate T-cells. It also increased SRBC induced anti-body titer in immunesuppressed rats, and produced significant increase in phagocytic index by rapid removal of carbon particles from the blood stream. Conclusion: These results indicate that methanol extracts of the leaf, root bark and fruit pulp of A. digitata hold promise as immunemodulatory agents.

Anti-osteoporosis effect of Cistanche deserticola Ma extract in ovariectomized rats

Purpose: To investigate the therapeutic effects of Cistanche deserticola Ma. extract (CDME) on ovariectomy-induced osteoporosis in rats. Methods: Female Sprague-Dawley rats were randomly assigned to a control group and five ovariectomy (OVX) subgroups, that is, OVX with vehicle (OVX), OVX with 17ß-estradiol (E2, 25 μg/kg/day), and OVX with CDME doses (40, 80, or 160 mg/kg/day). Daily oral administration of E2 or CDME started 4 weeks after OVX and lasted for 16 weeks. Bone mineral density (BMD) of L4 vertebrae and right femur of rats was estimated, The length of each femur was measured, and biochemical analysis of serum and urine specimens were performed. Results: CDME dose-dependently inhibited the reduction in BMD of L4 vertebrae (0.23 ± 0.02 g/cm3, p < 0.05) and femurs (0.20 ± 0.03 g/cm3, p < 0.05) caused by OVX and prevented the deterioration of trabecular microarchitecture (p < 0.05), which were accompanied by a significant decrease in skeletal remodeling (p < 0.05) as evidenced by the lower levels of bone turnover markers. Conclusion: This study indicates that CDME prevents OVX-induced osteoporosis in rats, and could be used for treating osteoporosis in elderly women.

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

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.

Thermoregulatory response in hair sheep and shorn wool sheep

This study’s main goal was to evaluate the thermoregulatory responses velocity through the variation of rectal temperature (RT), related to the thermolytic pathways, respiratory rate (RR) and sweating rate (SR) among different sheep breeds. Ninety female sheep, eighteen of each breed: Santa Ines and Morada Nova (Brazilian hair breeds), Texel, Suffolk and Ile de France (wool breeds) were challenged during three non-consecutive summer days (22◦42′S, 47◦18′W, and 570m of altitude, maximum air temperature of 33.5◦C, average relative humidity of 52±6.9%). The physiological variables were registered at 0800h (T1), 1300 h (T2: after 2 h of shade rest), 1400 h (T3) (after one hour of sun exposure) and in the shade at 1415 h (T4), 1430 h (T5), 1445 h (T6) and 1500 h (T7) and a thermotolerance index (TCI) was calculated as (10-(T7 to T4)-T1). The statistical analysis was performed by a mathematical model including the fixed effects of breeds and time frames, and the interaction between these effects, besides random effects such as animal and day. The Santa Ines breed presented the lowest RT after sun exposure (39.3 ± 0.12 ◦ C; P < 0.05) and it was the only one to recover morning RT 60 min after heat stress (38.7 and 38.9 for 1300 h and 1500 h; P > 0.05). Hair breeds presented RR lower (P < 0.05) than wool breeds. Although thick wool or hair thickness differs among and within hair and wool breeds (P < 0.05), SR did not differ among breeds and time (227.7 ± 16.44 g m−2 h−1 ; P > 0.05). The thermotolerance index did not differ among breeds, but it showed similar response (P > 0.05) 45 min or 1 h of shade after sun exposure. One week post shearing is not enough to wool breeds present to show thermotolerance similar to hair breeds.

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

2011

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.

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.

Characterising anomalous events using change point correlation on unsolicited network traffic

Monitoring unused or dark IP addresses offers opportunities to extract useful information about both on-going and new attack patterns. In recent years, different techniques have been used to analyze such traffic including sequential analysis where a change in traffic behavior, for example change in mean, is used as an indication of malicious activity. Change points themselves say little about detected change; further data processing is necessary for the extraction of useful information and to identify the exact cause of the detected change which is limited due to the size and nature of observed traffic. In this paper, we address the problem of analyzing a large volume of such traffic by correlating change points identified in different traffic parameters. The significance of the proposed technique is two-fold. Firstly, automatic extraction of information related to change points by correlating change points detected across multiple traffic parameters. Secondly, validation of the detected change point by the simultaneous presence of another change point in a different parameter. Using a real network trace collected from unused IP addresses, we demonstrate that the proposed technique enables us to not only validate the change point but also extract useful information about the causes of change points.