Similarity Coefficient Index Research Articles

Contouring aid tools in radiotherapy. Smoothing: the false friend.

Contouring accuracy is critical in modern radiotherapy. Several tools are available to assist clinicians in this task. This study aims to evaluate the performance of the smoothing tool in the ARIA system to obtain more consistent volumes. Eleven different geometric shapes were delineated in ARIA v15.6 (Sphere, Cube, Square Prism, Six-Pointed Star Prism, Arrow Prism, And Cylinder and the respective volumes at 45° of axis deviation (_45)) in 1, 3, 5, 7, and 10cm side or diameter each. Post-processing drawing tools to smooth those first-generated volumes were applied in different options (2D-ALL vs 3D) and grades (1, 3, 5, 10, 15, and 20). These volumetric transformations were analyzed by comparing different parameters: volume changes, center of mass, and DICE similarity coefficient index. Then we studied how smoothing affected two different volumes in a head and neck cancer patient: a single rounded node and the volume delineating cervical nodal areas. No changes in data were found between 2D-ALL or 3D smoothing. Minimum deviations were found (range from 0 to 0.45cm) in the center of mass. Volumes and the DICE index decreased as the degree of smoothing increased. Some discrepancies were found, especially in figures with cleft and spikes that behave differently. In the clinical case, smoothing should be applied only once throughout the target delineation process, preferably in the largest volume (PTV) to minimize errors. Smoothing is a good tool to reduce artifacts due to the manual delineation of radiotherapy volumes. The resulting volumes must be always carefully reviewed.

Distinctive approach in brain tumor detection and feature extraction using biologically inspired DWT method and SVM

Brain tumors result from uncontrolled cell growth, potentially leading to fatal consequences if left untreated. While significant efforts have been made with some promising results, the segmentation and classification of brain tumors remain challenging due to their diverse locations, shapes, and sizes. In this study, we employ a combination of Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) to enhance performance and streamline the medical image segmentation process. Proposed method using Otsu's segmentation method followed by PCA to identify the most informative features. Leveraging the grey-level co-occurrence matrix, we extract numerous valuable texture features. Subsequently, we apply a Support Vector Machine (SVM) with various kernels for classification. We evaluate the proposed method's performance using metrics such as accuracy, sensitivity, specificity, and the Dice Similarity Index coefficient. The experimental results validate the effectiveness of our approach, with recall rates of 86.9%, precision of 95.2%, F-measure of 90.9%, and overall accuracy. Simulation of the results shows improvements in both quality and accuracy compared to existing techniques. In results section, experimental Dice Similarity Index coefficient of 0.82 indicates a strong overlap between the machine-extracted tumor region and the manually delineated tumor region.

Genetic Diversity Analysis by SSR Markers in Melia dubia Cav. -A Fast Growing Multipurpose Tree Species

The present work was conducted in the year 2019 at the department of Forestry and Environmental Science, GKVK, University of Agricultural Sciences, Bengaluru Karnataka, India aimed to estimate the genetic variation among the populations of Melia dubia. 12 SSR primers producing polymorphic and monomorphic bands were analysed. The fresh and matured leaf sample was collected from the South India germplasm bank of Melia dubia established and maintained by Karnataka Forest Department (KFD) in collaboration with Institute of Forest Genetics and Tree Breeding (IFGTB), Coimbatore at Hoskote near Bengaluru, is a part of rural Bengaluru district, Karnataka, India. Leaf surface was cleaned by wiping with alcohol and carried to the laboratory in the ice bucket. Samples were preserved at -86˚C in a deep freezer. Genetic distances were calculated for all the accession lines studied by SSR PCR. The polymorphic information content (PIC) varied from 0.18–0.35, with an overall mean of 0.28. Nei’s gene diversity (H) ranged from 0.22–0.45, with an overall mean of 0.34. Alleles maker-1 ranged between 1 and 4, with an average of 2.7 alleles marker-1. Genetic distances were used to generate a dendrogram . An UPGMA dendrogram based on similarity coefficient indices revealed high genetic diversity and segregate into thirteen distinct clusters. This can be attributed to highly cross-pollinating nature of the species. and small distributional range in the area. Based on the genetic variability found, tree improvement strategies could be developed for conservation and further improvement of the species.

A super-voxel-based method for generating surrogate lung ventilation images from CT.

Purpose: This study aimed to develop and evaluate , a super-voxel-based method for surrogate computed tomography ventilation imaging (CTVI). Methods and Materials: The study used four-dimensional CT (4DCT) and single-photon emission computed tomography (SPECT) images and corresponding lung masks from 21 patients with lung cancer obtained from the Ventilation And Medical Pulmonary Image Registration Evaluation dataset. The lung volume of the exhale CT for each patient was segmented into hundreds of super-voxels using the Simple Linear Iterative Clustering (SLIC) method. These super-voxel segments were applied to the CT and SPECT images to calculate the mean density values (D mean) and mean ventilation values (Vent mean), respectively. The final CT-derived ventilation images were generated by interpolation from the D mean values to yield . For the performance evaluation, the voxel- and region-wise differences between and SPECT were compared using Spearman's correlation and the Dice similarity coefficient index. Additionally, images were generated using two deformable image registration (DIR)-based methods, and , and compared with the SPECT images. Results: The correlation between the D mean and Vent mean of the super-voxel was 0.59 ± 0.09, representing a moderate-to-high correlation at the super-voxel level. In the voxel-wise evaluation, the method achieved a stronger average correlation (0.62 ± 0.10) with SPECT, which was significantly better than the correlations achieved with the (0.33 ± 0.14, p < 0.05) and (0.23 ± 0.11, p < 0.05) methods. For the region-wise evaluation, the Dice similarity coefficient of the high functional region for (0.63 ± 0.07) was significantly higher than the corresponding values for the (0.43 ± 0.08, p < 0.05) and (0.42 ± 0.05, p < 0.05) methods. Conclusion: The strong correlation between and SPECT demonstrates the potential usefulness of this novel method of ventilation estimation for surrogate ventilation imaging.

IoT-Based Bi-Cluster Forecasting Using Automated ML-Model Optimization for COVID-19

The current COVID-19 pandemic has raised huge concerns about outdoor air quality due to the expected lung deterioration. These concerns include the challenges associated with an increase of harmful gases like carbon dioxide, the iterative/repetitive inhalation due to mask usage, and harsh environmental temperatures. Even in the presence of air quality sensing devices, these challenges can hinder the prevention and treatment of respiratory diseases, epidemics, and pandemics in severe cases. In this research, a dual time series with a bi-cluster sensor data-stream-based novel optimized regression algorithm was proposed with optimization predictors and responses that use an automated iterative optimization of the model based on the similarity coefficient index. The algorithm was implemented over SeReNoV2 sensor nodes data, i.e., a multi-variate dual time-series sensor, of the environmental and US Environmental Protection Agency standard, which measures variables for the air quality index using air quality sensors with geospatial profiling. The SeReNoV2 systems were placed at four locations that were 3 km apart to monitor the air quality and their data was collected at Ubidots IoT platform over GSM. The results have shown that the proposed technique achieved a root mean square error (RMSE) of 1.0042 with a training time of 469.28 s for the control and an RMSE of 1.646 in a training time of 28.53 s when optimized. The estimated R-Squared error was 0.03, with the Mean-Square Error for temperature being 1.0084 °C, and 293.98 ppm for CO2. Furthermore, the Mean-Absolute Error (MAE) for temperature was 0.66226 °C and 10.252 ppm for the correlated-CO2 at a predicted speed of ~5100 observations/s. In the sample cluster for temperature, 45,000 observations/s for CO2 was achieved due to the iterative optimization of the training time (469.28 s). The correlated temperature and a time of 28.53 s for CO2 were very promising in forecasting COVID-19 countermeasures before time.

Genetic Analysis of Prunus salicina L. by Random Amplified Polymorphic DNA (RAPD) and Intersimple Sequence Repeat (ISSR).

Background Prunus salicina L. is an important fruit tree species of great economic value which is mainly distributed in the northern hemisphere. Methods 25 samples of Prunus salicina L. were collected from 8 provinces in China, Japan, USA, and New Zealand. The genetic variations of these samples were characterized by the random amplified polymorphic DNA (RAPD) and intersimple sequence repeat (ISSR) technique, respectively, and in combination. Results Totally, 257 RAPD bands ranging 200∼2300 bp was found, and 81.59% of these bands were polymorphic. ISSR analysis identified 179 bands ranging 300∼2500 bp, and 87.74% of the bands were polymorphic. ISSR results showed that the similarity coefficient index between samples P10 (Maihuangli in Anhui, Chin) and P13 (Longyuanqiuli in Heilongjiang, China) was lowest, while that between samples P10 (Maihuangli in Anhui, Chin) and P15 (Baili in Japan) was highest. Combined analysis of RAPD and ISSR demonstrated that the similarity coefficient index between samples P4 (Qiepili in Ningbo, Zhejiang, China) and P13 (Longyuanqiuli in Heilongjiang, China) was lowest, while that between samples P19 (Laroda in USA) and P20 (Red heart in USA) was highest. Conclusion RAPD combined with ISSR analysis can be used for genetic characterization of Prunus L. species.

Using Transfer Learning, a Mobile Application Detects Brain Tumors

The segmentation, identification, and extraction of contaminated tumour regions from magnetic resonance (MR) images is a serious problem, but it is a time-consuming and labor-intensive operation carried out by radiologists or clinical experts, whose accuracy is totally reliant on their knowledge. As a consequence, using computer-assisted technologies to circumvent these limits becomes more vital. In this study, we looked into Berkeley wavelet transformation (BWT) based brain tumour segmentation to improve performance and reduce the complexity of the medical image segmentation process. Furthermore, relevant properties are extracted from each segmented tissue to improve the support vector machine (SVM) based classifier's accuracy and quality rate. The experimental results of the recommended technique have been examined and validated for performance and quality analysis on magnetic resonance brain pictures based on accuracy, sensitivity, specificity, and dice similarity index coefficient. With 96.51 percent accuracy, 94.2 percent specificity, and 97.72 percent sensitivity, the recommended technique for discriminating normal and diseased tissues from brain MR images was shown to be effective. The results of the testing revealed an average dice similarity index coefficient of 0.82, showing that the automated (machine) extracted tumour area coincided with the manually determined tumour region by radiologists. The simulation results show the relevance of quality parameters and accuracy when compared to state-of-the-art approaches. The main objective is to develop a smartphone app for identifying brain tumours.

Impacts of Prosopis juliflora L. on Soil Seed Bank Flora in Amibara Rangeland of Afar Regional State, Ethiopia

Background: Prosopis juliflora L. is an evergreen exotic weedy species seriously devastating valuable rangeland resources in arid and semi-arid parts of Ethiopia. Its increasing alarming rate of invasiveness had affecting the livelihood of the pastoral communities in Amibara rangeland of Afar National Regional State. Therefore, the aim of this study was to investigate the effects of Prosopis juliflora L. on soil seed bank flora. Methods: The total of 20 soil samples were collected from five sites by measuring sample quadrats of (20 m × 20 m) area were laid out and the soil samples from each quadrant were taken from 5 sub-quadrats/m2, one from the center and four from the corners, at two depths (0-3 cm and 3-10 cm). The soil samples from the identical layers were mixed in plastic bags to form composite sample. Finally, the composite samples were transported to the Haramaya University to conduct seed germination test in the greenhouse. The soil samples were spread thinly (2 cm thickness) over sterilized soil in shallow trays (20 cm × 25 cm) placed on a bench and allowed for seed germination. Result: The total of 38 herbaceous plant species /m2 belongs to 16 families were emerged in the upper and lower layer (0-3 cm and 3-10 cm) of the soil seed bank samples. The highest values of species diversity were (3.17) in Awash-arba, (3.15) in Alaydagi, (3.04) in Kurkura within m2 area at the depth of 0-3 cm. While the least diversity values of 2.75 in Melka-Werer and 2.77 was recorded in Sarkamo. Jaccard’s coefficient similarity index was exhibited high species similarity of 55.6% in the upper soil seed bank layers (0-3 cm) in Melka-were and Sarkamo. Whereas, species similarities of 45.2% were obtained in the lower layer (3-10 cm) soil seed banks in Awash-arba and upper layer soil seed bank in Alaydagi respectively. While species similarity was decreases as 31.8% in the lower layer soil seed banks in Sarkamo and Kurkura. Increasing species richness and the diversity was also noticed in under open canopy upper layer soil seed bank samples, while decreasing richness and diversity observed in under closed canopy. This indicates that the Prosopis juliflora L. tree has high growth retarding potential on its under canopy soil seed bank flora and in the lower layer soil seed bank samples also.

Fractional R閚yi Entropy Image Enhancement for Deep Segmentation of Kidney MRI

Recently, many rapid developments in digital medical imaging have made further contributions to health care systems. The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses. Many factors like image contrast and quality affect the result of image segmentation. Due to that, image contrast remains a challenging problem for image segmentation. This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans. The proposed work consists of two stages: enhancement by fractional Rényi entropy, and MRI Kidney deep segmentation. The proposed enhancement model exploits the pixel’s probability representations for image enhancement. Since fractional Rényi entropy involves fractional calculus that has the ability to model the non-linear complexity problem to preserve the spatial relationship between pixels, yielding an overall better details of the kidney MRI scans. In the second stage, the deep learning kidney segmentation model is designed to segment kidney regions in MRI scans. The experimental results showed an average of 95.60% dice similarity index coefficient, which indicates best overlap between the segmented bodies with the ground truth. It is therefore concluded that the proposed enhancement model is suitable and effective for improving the kidney segmentation performance.

The Phenetic Relationship of Ferns (Polypodiaceae) at the Ascent of Cemoro Kandang, Mount Lawu

The fern is one of the highly diverse vascular plant species, which has a unique characteristic. The closely related at the family level because the genera within each family have some uniformity so that the relationship and groupings cannot be distinguished. One example of a diverse fern family is Polypodiaceae. Consequently, it is necessary to simplify the classification; one of them is through phenetic relationship analysis. This study aimed to analyze the phenetic relationship of the Polypodiaceae ferns in Climbing Cemoro, Mouth Lawu enclosure, Karangayar Regency, based on the similarity index on the dendogram. The data were collected using the exploration method and the sporophyte morphological characters were observed. The observations obtained 13 variations of characters. The relationship analysis was carried out with the hierarchical cluster program using SPSS 23. There were six species found from the Polypodiaceae family, there were including Belvisia mucronata, Goniophlebium sp, Lepisorus sp, Pyrrosia piloselloides, Crypsinus taeniatus and Drynaria sparsisora. The finding suggests that six species are divided into two clusters, the Group I consisting of Goniophlebium sp, Lepisorus sp, Lepisorus sp, and Pyrrosia pilloselloides with a similarity coefficient index of 53% and Group II consisting of Drynaria sparsisora, Goniophlebium sp, and Pyrrosia piloselloides with a similarity index of 46%.

Genetic characterization of Lactococcus garvieae isolated from farmed rainbow trout by random amplified polymorphic DNA-PCR in Iran

Lactococcosis is one of the main bacterial infections of fish around the world. Lactococcus garvieae has been a major cause of rainbow trout losses in freshwater farming. This study aimed to genotype and determine the variability of L. garvieae isolated from infected farmed rainbow trout in Iran by the RAPD-PCR method. Bacterial samples were collected from 12 farms located in the western part of Iran and suspected to carry Lactococcus infection. Two hundred bacterial cultures containing cocci shaped bacteria were cultured in Trypticase soy agar (TSA) and blood agar mediums. All bacterial cultures were tested by conventional microbiological and biochemical tests, and PCR assay to identify L. garvieae by 16S rDNA genes. The RAPD-PCR method was used to determine the genetic pattern of all isolates. The sample strain pattern of the isolates was analyzed in the NTSYS program. According to a similarity coefficient index of 70%, all L. garvieae isolates were separated into two groups with four RAPD profile types. The highest and the lowest genetic pairwise similarity among the isolates were 98% and 54%, respectively. The results of the present study revealed that RAPD-PCR is an applicable method to describe the genetic diversity of different strains of L .garvieae among farmed fish.

Genetic characterization of Lactococcus garvieae isolated from farmed rainbow trout by random amplified polymorphic DNA-PCR in Iran

Lactococcosis is one of the main bacterial infections of fish around the world. Lactococcus garvieae has been a major cause of rainbow trout losses in freshwater farming. This study aimed to evaluate the RAPD-PCR method for genotyping of L. garvieae isolates isolated from infected farmed rainbow trout in Iran. Bacterial specimens were collected from trout farms suspected to carry Lactococcosis around this country. 200 bacterial cultures containing cocci shaped bacteria were cultured in Tripticase soy agar (TSA) and blood agar mediums. All bacterial cultures were tested by conventional microbiological and biochemical tests, and PCR assay to identify L. gvarieae by 16S rDNA genes. The RAPD-PCR method was used to determine the genetic pattern of all isolates. The sample strain pattern of the isolates was analyzed in the NTSYS program. After genotyping, according to a similarity coefficient index of 70%, all L. garvieae isolates were separated into two groups with 4 RAPD types. The highest and lowest genetic pairwise similarity among the isolates was 98% and 54%, respectively. The results of the present study revealed that RAPD-PCR is an applicable method to describe the genetic diversity of different strains of L. garvieae among farmed fish.

Image analysis for brain tumour detection using GA-SVM with auto-report generation technique

In this study, we have presented image analysis for the brain tumour segmentation and detection based on Berkeley wavelet transformation, enabled by genetic algorithm and support vector machine. The proposed system uses double classification analysis to conclude tumour type. The proposed system also investigated auto-report generation technique using user-friendly graphical user interface in MATLAB. The experimental results of proposed technique is been evaluated and validated for performance and quality analysis on magnetic resonance (MR) medical images based on accuracy, sensitivity, specificity and dice similarity index coefficient. The experimental results achieved 97.77% accuracy, 98.98% sensitivity, 94.44% specificity and an average of 0.9849 dice similarity index coefficient, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from MR images. The experimental result is validated by extracting 89 features and selecting the relevant features appropriately using genetic algorithm optimise by support vector machine.

Highly-efficient technique for automatic segmentation of X-ray bone images based on fuzzy logic and an edge detection technique

Development of medical image segmentation techniques has become one of the most important challenges in many applications that employ computers-based medical image analysis techniques. However, most of current-existing medical image segmentation techniques still have poor efficiency and complexity in calculations. A new technique for X-ray bone image segmentation has been presented in this paper. The proposed technique is designed to generate a highly-efficient quality of bone extraction from X-ray bone image at low calculation burdens. The proposed approach begins with obtaining the inverse of the original X-ray bone image then applying bounded-product operator and fuzzy controller to control the contrast of the inverse image. At the same time, an adaptive thresholding of the gradient magnitude of the original X-ray bone image is achieved to detect the edges of the bone regions. Accordingly, the bone edges are integrated with the contrasted image to give the bone segmented image. To ensure the efficient quality of the proposed algorithm, more than sixty-one of X-ray bone images were tested using much vision and statistical investigations. Then, evaluations employed several measures such as Dice similarity coefficient index, Confusion Matrix, Accuracy, Precision, Sensitivity, Specificity, and processing speed. Furthermore, results obtained using the proposed were compared to those of conventional image segmentation techniques (such as Watershed segmentation, Otsu-thresholding, K-means, and fuzzy C-means). Comparison results demonstrated the superiority of the proposed technique over other conventional techniques in both of quality and processing speed. All obtained results were obtained using MATLAB R20014a over Windows XP with processing speed 2 GHz. The high efficiency and processing speed of the proposed technique makes it such a promising solution to be implemented in many real applications.

Urban Homegarden for Woody Species Conservation and Carbon Sequestration: The Case of Jimma City, Southwest Ethiopia

Homegardens in urban are found to be an important refuge for native biodiversity and provide huge contribution for climate change mitigation. The study was undertaken to investigate potential of homegarden in conservation of woody species and carbon sequestration in Jimma City. A complete listing of woody species within 138 homegardens and 39 sample plot size 10m×10m (100m 2 ) were surveyed. Diameter at breast) height and the height of the tree were measured. Shannon diversity and Jaccard coefficient of similarity index were used to determine the species composition. Allometric equation was used to estimate aboveground carbon stocks of woody species. A total of 40 woody species (36 in homegarden and 22 in government institution) belonging to 24 families were recorded. Very significantly higher (P&lt;0.001) species richness and diversity were observed in homegardens. But no significant difference was observed (p&gt;0.5) among homegardens in three kebeles. Jaccard coefficient of similarity index (47.5%) showed low similarity in woody composition between two systems. About 2,877.13Mgha -1 (884.2 Mgha -1 in homegarden &amp;1,992.95Mgha -1 in government institution) carbon of about 319.57Mg of CO 2 equivalents were measured. Very highly significant (p&lt;0.001) and non-significant difference (p&gt;0.05) in carbon storage were observed between systems and among homegardens respectively. The result reveled homegardens in Jimma city have good potential for biodiversity conservation and climate change mitigation. Hence, responsible bodies need to consider the role of homegarden in maintaining native biodiversity and climate change mitigation in urban development planning. Keywords : Biodiversity conservation, government institution compound, carbon stock, climate change mitigation. DOI : 10.7176/JNSR/9-13-02 Publication date :July 31 st 2019

A heart atlas for breast radiation therapy and the influence of delination education on both intra and interobserver variability

We developed a heart atlas for breast radiation therapy and evaluated the influence of education on intra and inter-observer similarity, and cardiac dose reporting. The data of 16 left breast cancer patients were analyzed. Eight observers delineated heart and cardiac subunits [left (LCA) and right (RCA) coronary arteries, left anterior descending artery (LAD), bilateral atrium and ventricles] before the education. A radiologist and radiation oncologist developed the atlas and delineated the gold standard (GS) volumes. Observers repeated the delineation after education. RT plans were made for pre/post-atlas contours. The similarity was assessed by Dice (DSC) and Jaccard (JSC) similarity coefficient indices. The absolute difference rate was calculated for the dose analysis. The inter-observer similarity increased in heart and all subunits. The intra-observer similarity showed a heterogeneous distribution. The absolute difference rate in dose reporting was statistically significant for the bilateral atrium, right ventricle, LAD, LCA+LAD, RCA's maximum doses (p < 0.05). The maximum dosereporting differences from the GS decreased from 16.9 to 8.9% for LAD (p = 0.011); from 14.8 to 9.3% for LCA+LAD (p = 0.010). The cardiac atlas reduces the intra-interobserver differences and improves dosereporting consistency. The first intra-observer similarity analysis was made in our study and revealed the need for repeated education to increase the consistency.

Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM.

The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.

Community profiling of culturable fluorescent pseudomonads in the rhizosphere of green gram (Vigna radiata L.).

Study on microbial diversity in the unexplored rhizosphere is important to understand their community structure, biology and ecological interaction with the host plant. This research assessed the genetic and functional diversity of fluorescent pseudomonads [FP] in the green gram rhizophere. One hundred and twenty types of morphologically distinct fluorescent pseudomonads were isolated during vegetative as well as reproductive growth phase of green gram. Rep PCR, ARDRA and RISA revealed two distinct clusters in each case at 75, 61 and 70% similarity coefficient index respectively. 16S rRNA partial sequencing analysis of 85 distantly related fluorescent pseudomonads depicted Pseudomonas aeruginosa as the dominant group. Out of 120 isolates, 23 (19%) showed antagonistic activity towards phytopathogenic fungi. These bacterial isolates showed varied production of salicylic acid, HCN and chitinase, 2, 4-diacetylphloroglucinol (DAPG), phenazine-1-carboxylic acid (PCA) and pyoluteorin (PLT). Production efficiency of inherent level of plant growth promoting (PGP) traits among the 120 isolates demonstrated that 10 (8%) solubilised inorganic phosphates, 25 (20%) produced indoles and 5 (4%) retained ACC deaminase activity. Pseudomonas aeruginosa GGRJ21 showed the highest production of all antagonistic and plant growth promoting (PGP) traits. In a greenhouse experiment, GGRJ21 suppressed root rot disease of green gram by 28–93% (p = 0.05). Consistent up regulation of three important stress responsive genes, i.e., acdS, KatA and gbsA and elevated production efficiency of different PGP traits could promote GGRJ21 as a potent plant growth regulator.

Genetic diversity of the Chinese traditional herb Blumea balsamifera (Asteraceae) based on AFLP markers.

Blumea balsamifera is a commercially important medicinal herb in China and other parts of Asia. It is used to produce borneol. This plant grows in the wild, but resources have diminished greatly in recent years. We examined the genetic diversity of this species to help develop conservation strategies; 35 plants from five provinces were analyzed using AFLPs. Eight AFLP primer combinations generated 1367 fragments, giving a mean of 172 fragments per primer combination. Polymorphism in the germplasm analysis was found for 1360 (99.48%) of the fragments, of which 264 (19.27%) fragments were unique (accession specific) and 423 (25.33%) of the fragments were rare (present in less than 10% of the accessions). The polymorphic fragments were used to group the accessions in a UPGMA phenogram. Most grouping was geographical. In general, accessions coming from Guizhou and Guangxi showed higher diversities as these accessions were scattered in different groups. The genetic distance estimated by Jaccard similarity coefficient index showed low variability among genotypes (coefficient value ranged from 0.60 to 0.95). More attention should be given to the study and conservation of the biodiversity of this economically important genus.

The Metabolism of Polysaccharide from Atractylodes macrocephala Koidz and Its Effect on Intestinal Microflora.

An active polysaccharide from the rhizome of Atractylodes macrocephala Koidz (PAM) was identified to improve and adjust disordered intestinal flora. High-performance gel permeation chromatography (HPGPC) and gas chromatography-mass spectrometry (GC-MS) were employed to identify the components of PAM as rhamnose, glucose, mannose, xylose, and galactose at a ratio of 0.03 : 0.25 : 0.15 : 0.41 : 0.15. PAM metabolized in gastrointestinal tract when incubated with artificial gastric and intestinal juices. Anaerobic incubation of PAM on intestinal flora confirmed that PAM promoted the ability of intestinal bacteria to digest reducing sugar. Based on the Shannon index and similarity coefficient index of enterobacterial repetitive intergenic consensus-PCR (ERIC-PCR) fingerprinting of the total intestinal bacteria DNA, we concluded that PAM can significantly improve disordered intestinal flora and may be used as an oral adjuvant to regulate intestinal flora.