Medicinal plants of the Argentine Yungas plants of the Las Yungas biosphere reserve, Northwest of Argentina, used in health care

We have compared the species richness of medicinal plants and the differential patterns of use amongst settlements in the Andean communities of Northwest Argentina which have differing levels of isolation. About 259 ethnoespecies, belonging to 74 plant families, were included, representing between 70 and 80% of the total estimate. The results indicate that Coronopus didymus is the most relevant and important species. The method of use of medicinal plants and the ailments treated by rural doctors compared to those of the layperson is different. Native and exotic plants are used differently according to the body system treated. There are some relationships between internal and external use and body systems and recipes. The greater medicinal species richness found in the less isolated locations is due to external enriching cultural influences.     

Using phylogeny to investigate the origins of the Cape flora: the importance of taxonomic, gene and genome sampling strategies

Phylogenetic methods hold great promise for the reconstruction of the transition from precursor to modern flora and the identification of underlying factors which drive the process. The phylogenetic methods presently used to address the question of the origin of the Cape flora of South Africa are considered here. The sampling requirements of each of these methods, which include dating of diversifications using calibrated molecular trees, sister pair comparisons, lineage through time plots and biogeographical optimizations, are reviewed. Sampling of genes, genomes and species are considered. Although increased higher-level studies and increased sampling are required for robust interpretation, it is clear that much progress is already made. It is argued that despite the remarkable richness of the flora, the Cape flora is a valuable model system to demonstrate the utility of phylogenetic methods in determining the history of a modern flora.     

Automated identification of diseases related to lymph system from lymphography data using artificial immune recognition system with fuzzy resource allocation mechanism (fuzzy-AIRS)

Artificial immune recognition system (AIRS) classification algorithm, which has an important place among classification algorithms in the field of artificial immune systems, has showed an effective and intriguing performance on the problems it was applied. AIRS was previously applied to some medical classification problems including breast cancer, Cleveland heart disease, diabetes and it obtained very satisfactory results. So, AIRS proved to be an efficient artificial intelligence technique in medical field. In this study, the resource allocation mechanism of AIRS was changed with a new one determined by fuzzy-logic. This system, named as fuzzy-AIRS was used as a classifier in the diagnosis of lymph diseases, which is of great importance in medicine. The classifications of lymph diseases dataset taken from University of California at Irvine (UCI) Machine Learning Repository were done using 10-fold cross-validation method. Reached classification accuracies were evaluated by comparing them with reported classifiers in UCI web site in addition to other systems that are applied to the related problems. Also, the obtained classification performances were compared with AIRS with regard to the classification accuracy, number of resources and classification time. While only AIRS algorithm obtained 83.138% classification accuracy, fuzzy-AIRS classified the lymph diseases dataset with 90.00% accuracy. For lymph diseases dataset, fuzzy-AIRS obtained the highest classification accuracy according to the UCI web site. Beside of this success, fuzzy-AIRS gained an important advantage over the AIRS by means of classification time. By reducing classification time as well as obtaining high classification accuracies in the applied datasets, fuzzy-AIRS classifier proved that it could be used as an effective classifier for medical problems.     

Cardiorenal fibrosis and dysfunction in aging: Imbalance in mediators and regulators of collagen

Cardiorenal fibrosis is a biological process that increases with age and contributes to dysfunction of the heart and kidney. While numerous circulating and tissue hormones, cytokines and enzymes have been identified in the development of cardiorenal fibrosis, several reports have suggested that the anti-fibrotic natriuretic peptide system (NPS), pro-fibrotic renin–angiotensin–aldosterone system (RAAS), transforming growth factor-beta 1 (TGF-β1), matrix metalloproteinases (MMPs) and tissue inhibitor of metalloproteinases (TIMPs) are fundamental regulators and mediators of this process. However, the simultaneous assessment of these components in the development of age-mediated cardiorenal fibrotic remodeling is not completely understood. Thus, we assessed cardiorenal structure and function, the circulating NPS and RAAS and the cardiorenal tissue gene expression of collagen (Col) I, Col III, TGF-β1, MMP-9 and TIMP-1 in 2 and 20 month old Fischer rats. Our studies determined that aging was characterized by an increase in cardiorenal fibrosis that was accompanied with cardiorenal dysfunction. These alterations were associated with lower circulating atrial and C-type natriuretic peptides and higher angiotensin II and aldosterone levels in the aged rats. Moreover, we observed a decrease in Col I and III and an increase in TIMP- mRNA expressions in the aged heart and kidney, while TGF-β1 expression increased and MMP-9 decreased only in the aged kidney. We conclude that the age-mediated alterations in these fibrotic regulator and mediator profiles favors collagen accumulation due to an imbalance between the NPS and RAAS as well as a decline in the degradative pathway, thus suggesting a therapeutic opportunity to target these components.     

Development of a multimetric plant-based index of biotic integrity for assessing the ecological state of forested,urban and agricultural natural wetlands of Jimma Highlands,Ethiopia

Wetlands are vital natural resources thereby providing ecological and socio-economic benefits to the people. However, anthropogenic activities have seriously changed the ecological conditions of wetlands worldwide, especially in developing nations like Ethiopia. Predominantly, the absence of biomonitoring tool greatly hampers the protection and management of wetlands. Therefore, the objective of this research is to develop a plant-based index of biological integrity for facilitating the management of wetlands. Accordingly, 122 plant species belonging to 37 families were collected and identified from forested, urban and agricultural wetland types and included in the analysis of the plant metrics. Initially, we reviewed and screened 35 potential metrics. Then, we selected four core metrics (% cover of native species, sensitive plant species richness, tolerant plant species richness and% of shrub species richness) using the decrease or increase responses to human disturbances, Mann-Whitney U test and redundant metric test. A trisect-quartile range system using box plots of the reference or impaired sites of wetlands was established to provide values for each core metrics. Then after, we combined the core metrics to develop the plant-based index of biological integrity. Finally, we validated the index by comparing the index response to different wetland types. Additionally, the index was validated based on the measured environmental variables that characterize the human disturbance gradient of wetlands. We found that the plant-based index is robust to discriminate the reference wetlands from impaired wetlands and can also be used as an effective tool for evaluating the long term natural wetland conditions of the Eastern African wetlands in the future.     

Identification of soil quality indicators for assessing the effect of different tillage practices through a soil quality index in a semi-arid environment

Tillage is known to potentially affect soil quality in various ways. In this study, a soil quality index (SQI) was developed by quantifying several soil attributes either sensitive or insensitive to physical disturbance, using factor analysis as a dimension reduction technique, in order to discriminate different tillage systems. Soil properties including physical (MWD), chemical (pH, organic C, total N, available P and POM contents) and microbial (MBC, MBN, PCM, PNM and three enzymes) parameters were measured to establish a minimum data set (MDS) for the assessment of overall SQI. The soil attributes were determined on samples (0–20 cm depth) collected under moldboard (MP) and disk (DP) plows as conventional tillage (CT), and rotary (RP) and chisel (CP) plows as reduced tillage (RT) systems with a similar plant C input rate and cover crop over a period of six years (2005–2011) in a semi-arid calcareous soil (Calcixerepts) from Central Iran. Results indicated a clear difference in soil quality among the tillage systems with a significant increase of SQI under RT over time, particularly under CP practices. Although RT improved most soil microbial attributes, not all attributes contributed to SQI because of their close interrelationship. The final SQI consisted only of geometric mean of microbial activity (GMA, the square root of the product of PCM and PNM) and geometric mean of enzyme activity (GME, the cube root of the product of enzyme activities). Soil GME and GMA were found to be as key indicators contributing 55% and 36% to SQI, respectively. Therefore, the GME and GMA were the most important indicators effectively discriminating tillage systems, and could be used to monitor the enhancement of soil quality under RT in this semiarid environment. The influence of tillage year on SQI was greater than that of tillage practices. In conclusion, RT systems were characterized by a higher value of SQI, suggesting a good recovery of soil capacity and functions after abandoning CT in the studied area. Smallholder farmers should therefore be aware of the potential for high soil quality in future as a result of continuing RT systems, especially with surface tillage using CP practices.     

Canopy closure estimation in a temperate forest using airborne LiDAR and LANDSAT ETM+ data

Aims　Forest canopy closure is one of the essential factors in forest survey, and plays an important role in forest ecosystem management. It is of great significance to study how to apply LiDAR (light detection and ranging) data efficiently in remote sensing estimation of forest canopy closure. LiDAR can be used to obtain data fast and accurately and therefore be used as training and validation data to estimate forest canopy closure in large spatial scale. It can compensate for the insufficiency (e.g. labor-intensive, time-consuming) of conventional ground survey, and provide foundations to forest inventory.Methods　In this study, we estimated canopy closure of a temperate forest in Genhe forest of Da Hinggan Ling area, Nei Mongol, China, using LiDAR and LANDSAT ETM+ data. Firstly, we calculated the canopy closure from ALS (Airborne Laser Scanning) high density point cloud data. Then, the estimated canopy closure from ALS data was used as training and validation data to modeling and inversion from eight vegetation indices computed from LANDSAT ETM+ data. Three approaches, multi-variable stepwise regression (MSR), random forest (RF) and Cubist, were developed and tested to estimate canopy closure from these vegetation indices, respectively.Important findings The validation results showed that the Cubist model yielded the highest accuracy compared to the other two models (determination coefficient (R²) = 0.722, root mean square error (RMSE) = 0.126, relative root mean square error (rRMSE) = 0.209, estimation accuracy (EA) = 79.883%). The combination of LiDAR data and LANDSAT ETM+ showed great potential to accurately estimate the canopy closure of the temperate forest. However, the model prediction capability needs to be further improved in order to be applied in larger spatial scale. More independent variables from other remotely sensed datasets, e.g. topographic data, texture information from high-resolution imagery, should be added into the model. These variables can help to reduce the influence of optical image, vegetation indices, terrain and shadow and so on. Moreover, the accuracy of the LiDAR-derived canopy closure needs to be further validated in future studies.