Preliminary characterization of a Moroccan honey with a predominance of Bupleurum spinosum pollen

Honey with Bupleurum spinosum (zandaz) as a main pollen source has not been the subject of previous detailed study. Therefore, twelve Moroccan samples of this honey were subjected to melissopalynological, physicochemical and microbiological quality characterization, as well as antioxidant activity assessment. From a quality point of view, almost all samples were within the limits established by Codex Alimentarius, and/or the European legislation. All samples presented predominance of B. spinosum pollen (more than 48%). Relatively high levels of trehalose (1.3-4.0 g/100 g) and melezitose (1.5-2.8 g/100 g) were detected. Those sugars, not common in monofloral honeys, could be used as an important factor to discriminate zandaz honey. Flavonoid content correlated positively with the honey color, melanoidin and polyphenol content, and negatively with the IC50 values of scavenging ABTS (2,2' - azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) free radicals, while proline amount correlated negatively with IC50 values of nitric oxide scavenging activity and chelating power. This correlation supports the use of anti-oxidant activities as important variables for PCA (principal component analysis). Both components explained 70% from the given data, and showed certain homogeneity upon analyzed samples independent of the region, suggesting the importance of B. spinosum nectar in the resulting honey characteristics.Fundacao para a Ciencia e Tecnologia for Research Center [UID/BIM/04773/2013 CBMR 1334, UID/AGR/00239/2013, UID/BIA/04050/2013 (POCI-01-0145-FEDER-007569)]; ERDF through the COMPETE - Programa Operacional Competitividade e Internacionalizacao (POCI

Bright blue emissions on UV-excitation of LaBO3 (B=In, Ga, Al) perovskite structured phosphors for commercial solid-state lighting applications

Bright blue photoluminescence (PL) was obtained from Bi3+-activated LaBO3 (B = In, Ga, Al) perovskite nanophosphors. A cost-effective and low-temperature chemical route was employed for preparing Bi3+ doped LaBO3 (B=In, Ga, Al) which were then annealed at 1000 °C. The phase formation, morphological studies and luminescent properties of the as-prepared samples were performed by X-ray diffraction (XRD), scanning electron microscopy (SEM), photoluminescence and optical absorption spectroscopy. Comparison of emission intensities, lifetime studies, energy band gaps and color purity of all samples (pure and Bi3+ doped) were investigated for promising applications in UV light-emitting diodes, variable frequency drive (VFD), field emission display (FED), and other photoelectric fields

Preliminary phytochemical screening from leaf and seed extracts of Senna alata L. Roxb-an Ethnomedicinalplant

ABSTRACT: Aim: The bioactive compounds present in the plant are responsible for the medicinal properties of the plant. The present investigation is aimed in screening the bioactive compounds present in the seed and fresh, dry leaves of S.alata -an important ethnomedicinal plant. The objectives of the work includes i) the screening of various bioactive compounds present in the seed, fresh and dry leaves of S.alata. ii) To identify the bio active compounds present in the fresh leaf extracts by Thin layer chromatography. Methodology: Qualitative analysis for the presence of phytochemicals was performed using methanol, chloroform, petroleum ether and aqueous extracts of seed and both fresh and dry leaves of Senna alata by various standard techniques availabl

HANDWRITTEN CHARACTER RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS

Handwritten character Recognition is one of the active area of research where deep neural networks are been utilized. Handwritten character Recognition is a challenging task because of many reasons. The Primary reason is different people have different styles of handwriting. The secondary reason is there are lot of characters like capital letters, small letters &amp; special symbols. In existing were immense research going on the field of handwritten character recognition system has been design using fuzzy logic and created on VLSI(very large scale integrated)structure. To Recognize the tamil characters they have use neural networks with the Kohonen self-organizing map(SOM) which is an unsupervised neural networks. In proposed system this project design a image segmentation based hand written character recognition system. The convolutional neural network is the current state of neural network which has wide application in fields like image, video recognition. The system easily identify or easily recognize text in English languages and letters, digits. By using Open cv for performing image processing and having tensor flow for training the neural network. To develop this concept proposing the innovative method for offline handwritten characters. detection using deep neural networks using python programming language.</jats:p

Online) An Open Access

ABSTRACT A total of 68 samples were collected, out of which 31 (45.59%) were benign and 37 (54.41%) were malignant tumours. Tumours were classified into epithelial 40(58.83%), mesenchymal 20 (29.40%), round cell 5 (7.36%) and mixed tumours 3 (4.41%). The highest risk of development of various tumours was found in the age group of 7-9 years, followed by 4-6 years, above 9 years and below 3 years and the incidence was 29 (42.65%), 20(29.41%),19 (27.94%) and 1 (1.47% ) respectively. The frequency of occurrence of neoplasms was slightly higher in females 38(55.88%) compared to the males 30 (44.12%). Among the breeds affected Pomeranian breed represented more with 18 (26.50%) followed by non descriptive 16 (23.52%), German Shepherd 16 (23.52%), Labrador 6 (8.82%), Doberman 6 (1.47%), Dachshund and Rottweiler 2 (2.94%), each one of Collie and Great Dane (1.47%). The organ wise incidence of tumours included skin and mesenchymal tumours 29 (42.65%), mammary tumours 17 (25%), joint and bone tumours 9 (13.23%), vaginal tumours 5 (7.53%), testicular tumours 4 (5.88%), vulva and penis 3 (4.41%), ovarian tumours 2 (2.94%) and each one of transitional cell carcinoma and prostate carcinoma (1.47%)