Molecular characterization of the Indian Andigena potato core collection using microsatellite markers

Twenty-four (24) microsatellite (SSR) markers of a new PGI kit were used to validate the genetic diversity of the 77 Indian Andigena potato core collections. In SSR analysis, polymorphic information content (PIC), allelic richness per locus of microsatellite loci and cluster analysis showed the high diversity of core collection. In total, 214 SSR alleles were detected in the core collection, out of which 208 alleles were polymorphic with absolute frequencies between 2 to 58. The PIC values of SSR loci ranged from 0.61 to 0.90. SSR-based dendrogram revealed eight main groups (Clusters I to VIII) including 26 single accessions at Dice similarity coefficient value of 0.37. None of the accession showed full similarity with any other accession, except that the maximum similarity (0.83) was observed between the accessions JEX/A-316 and JEX/A-317. PCA revealed 47.31% variation in the first three components. Analysis of molecular variance (AMOVA) analysis which resulted into maximum variation was due to within country origins and yield types. The genetic diversity of the core collection based on the microsatellite data appeared to have quite distinct genotypes that were formed by the morph-agronomic traits. These findings not only demonstrate the diverse core collection but are also useful for selecting genetically distinct potato materials to widen the genetic background of the potato gene pool.Keywords: Core collection, genetic diversity, potato, Solanum tuberosum subsp. andigena, SSRAfrican Journal of Biotechnology Vol. 12(10), pp. 1025-1033, 6 March, 201

A CLINICAL STUDY ON THE EFFICACY OF SITAMANDOOR ON AMLAPITTA

Acharya Caraka & Kashyapa have explained that Amlapitta is mainly caused by dietetic pattern, mental stress and strain which are related to our digestion. The occurrence involves the GIT & chiefly Kapha & Pitta Dosha. The symptoms like Gastritis, peptic disorders, hyperacidity, hyperchlorhydria condition resemble that of Amlapitta. Aim & Objective: To evaluate the efficacy of Sitamandoor in Specific patients of Amlapitta Study Design: The study was conducted on total 18 patients of Amlapitta who completed the treatment all along the study period. The patients were selected from the OPD of DGMAMC&H, Gadag for respective clinical trial. Assessment criteria: Criteria of assessment are set aside on the basis of relief in the signs and symptoms of Amlapitta both subjective and objective parameters. Results: Among 18 patients 9 responded moderately, 3 markedly improved and 5 responded well to the treatment. Interpretation & Conclusion: Though the criteria of assessment & statistical analysis reveal the effect of the study but the fact that the mode of action of the drug is the base of the relief in signs & symptoms cannot be ignored. Conclusively the treatment was satisfactorily effective in the patients

Iatrogenic Periorbital Subcutaneous Emphysema after Endodontic Treatment – A Case Report.

BACKGROUND: Surgical emphysema is well known and many case reports have been published on this. Many authors have reported this as a complication post dentoalveolar treatment. Diffusion of air into facial planes and periorbital area during endodontic procedures has been rarely reported. The use of three-way air syringe and forceful irrigation of root canal can lead to surgical emphysema of subcutaneous tissue planes in and around the teeth which are involved. CASE REPORT: This case report highlights one such complication seen during endodontic treatment and conservative management of this dental office emergency. CONCLUSION: Endodontic treatment is not frequently associated with the presence of the SCE. However, it is very important to know how to recognize this situation when occurs, to treat the patient appropriately

Innovative Approach to Detect Mental Disorder Using Multimodal Technique

The human can display their emotions through facial expressions. To achieve more effective human- computer interaction, the emotion recognize from human face could prove to be an invaluable tool. In this work the automatic facial recognition system is described with the help of video. The main aim is to focus on detecting the human face from the video and classify the emotions on the basis of facial features .There have been extensive studies of human facial expressions. These facial expressions are representing happiness, sadness, anger, fear, surprise and disgust. It including preliterate ones, and found much commonality in the expression and recognition of emotions on the face. Emotion detection from speech has many important applications. In human-computer based systems, emotion recognition systems provide users with improved services as per their emotions criteria. It is quite limited on body of work on detecting emotion in speech. The developers are still debating what features effect the emotion identification in speech. There is no particularity for the best algorithm for classifying emotion, and which emotions to class together

Seronegative autoimmune hepatitis – A case report

Autoimmune hepatitis (AIH) is a complex liver disease. It is reported in children and adults of all age groups. An 11-year-old male child was admitted in our hospital for anorexia and yellowish discoloration of eyes, skin, and urine for 2 months. He had fever, itching was passing pale-colored stools. The child had hepatosplenomegaly and viral hepatitis markers as well as autoimmune markers were negative. Liver biopsy was suggestive of AIH. The child was started on prednisolone and showed drastic improvement clinically as well as biochemically. This was a case of seronegative AIH, which responded very well to corticosteroids. Hence, high index of suspicion is required to diagnose the case of seronegative AIH

Formulation and Evaluation of Lornoxicam Nanosuspension with Eudragit Rs100 Polymer

BCS Class II has a low aqueous solubility problem in pharmaceutical formulation. Hence to overcome this problem this research the was carried out for BCS Class II drug lornoxicam was selected to enhance the aqueous solubility by formulating nanosuspension with a polymer like Eudragit RL100, Eudragit RS100 and Poloxamber407 as a stabilizer. The present study focuses on the polymer Eudragit RS100. A total of four formulations LRS-F1, LRS-F2, LRS-F3, LRS-F4, and LRS-F4 were prepared with a ratio of drug: polymer: stabilizer (1:1:0.5 & 1:2:0.5, 1:1:1 & 1:2:1)LRS-F4 formulation was found to be optimized formulation with  90.00 – 100 nm particle size & -11.20 zeta potential and % drug release at the end of 10 hrs was found to be 96.88 % with the increase in the dissolution/saturation solubility of 70.36±0.09 ( µg /mL) of poorly water-soluble lornoxicam (reported solubility with 14.28 µg /mL µg /mL). The amount unincorporated was found to be 09.74 % with an optimized formulation. Moreover, the physical appearance of the nanosuspension was found to be up to the mark confirming that the nanosuspension is stable and has no crystal growth or crystal development with optimized formulation at a temperature of 4 °C for 3 months. Poloxamer 407 as a stabilizer

ICU MONITORING SYSTEM USING RASPBERRY PI

The aim of this project is to have a quantitative assessment of the important physiological variables of patient using “Raspberry Piâ€. This system is used for measuring continuously automatically the values of patient’s physiological parameters such as body temperature, blood pressure, heart activity and humidity content present in the ICU room. This unit may be inserted in a bedside display unit to display the physiological conditions of the patients. The device detects vital parameters of the patient by the use of sensors and displays result on an monitor screen mounted on the gadget. If this values are not within the safe limit, then corresponding LED indicator will glow and alarm will start ringing

Two-layer approach for Unsupervised and Semi-Supervised Learning for Satellite Images

In our galaxy, there are many advanced satellite. Large distance image can be captured with very high quality. The image provides sufficient information at global level and regional level. The field of satellite imagery is evaluated so much that it has created questions to human and environmental sustainability. It is still a challenge to scale those techniques to very high spatial resolutions. Satellite images are of greater spatial, spectral and high resolution creating large set of information about the image which makes it difficult to identify the features of images. This is because the images are unlabeled. Unsupervised method allows us to organize images into clusters. However, unsupervised method like machine learning uses features for clustering. Those images which are close, are kept in same group. The system uses satellite images datasets which provide aerial shots of different location. Images are grouped into sets of 5 where each image in a set was taken on a different day at a specific location but not necessarily at the same time each day. The images for each set cover the same area but not perfectly align. This dataset is provided as input dataset to proposed system. Feature extraction is done by sending a set of images through a network and extracting features at a certain layer which results in a feature set for a certain network. The process of transfer learning involves sending our own images through the network and extracting features at a certain layer. The process followed in this system is different from fine-tuning because images are not trained and the number of classes is not changed in the SoftMax layer of the network. Rather parameters learned by a pre-trained model to see if it can be used for an unlabeled dataset where fine tuning would not be possible are used