Evidence of bovine immunodeficiency virus in cattle in Turkey

A seroepidemiological study of bovine immunodeficiency virus (BIV) and bovine leukemia virus (BLV) infections was conducted in four different cattle herds in Turkey. A total of 300 blood samples were analyzed and 12.3% were found to be positive for anti-BIV p26 antibodies by Western blot analysis and 1.6% positive for anti-BLV gp51 antibodies by an immunodiffusion test. BIV infection was confirmed with the detection of BIV-provirus DNA using the nested polymerase chain reaction. This is the first evidence for the presence of BIV in cattle in Turkey.     

Tarantula cubensis extract alters the degree of apoptosis and mitosis in canine mammary adenocarcinomas

In the present study, 13 clinical cases of canine mammary adenocarcinoma were evaluated in order to understand the effect of Tarantula cubensis extract (TCE) on tumor tissue. Punch biopsies were taken from the tumors before treatment with TCE. Subcutaneous injections of TCE were administered three times at weekly intervals (3 mL per dog). Between days 7 and 10 after the third injection, the tumor masses were extirpated by complete unilateral mastectomy. Pre- and post-treatment tumor tissues were immunohistochemically assessed. The expression of B-cell lymphoma 2 (Bcl-2) was found to be higher in pre-treatment compared to post-treatment tissues (p < 0.01) whereas Ki-67 expression was lower in post-treatment tissues (p < 0.01). No significant differences in fibroblast growth factor or vascular endothelial growth factor expression were observed between pre- and post-treatment tissues (p > 0.05). The apoptotic index was determined to be low before treatment and increased during treatment. These results suggest that TCE may be effective for controlling the local growth of canine mammary adenocarcinoma by regulating apoptosis.     

Using Artificial Neural Network and Multiple Linear Regression for Predicting the Chlorophyll Concentration Index of Saint John’s Wort Leaves

This research investigates and compares artificial neural network and multiple linear regression for predicting the chlorophyll concentration index of Saint John’s wort leaves (Hypericum perforatum L.). Plants were fertilized with 0, 30, 60, 90, and 120 kg ha^?1 nitrogen [34% nitrogen ammonium nitrate (NH₄NO₃)]. Chlorophyll concentration index of each leaf was measured using SPAD meter. Afterwards, rgb (red, green, and blue color) values of all leaf images were determined by image processing. Values obtained were modeled using both multiple regression analysis and artificial neural networks. Using multiple regression analysis R² values were between 0.61 and 0.97. Coefficient of determination values (R²) using artificial neutral network values were found to be 0.99. Artificial neutral network modeling successfully described the relationship between actual chlorophyll concentration index values and predicted chlorophyll concentration index values.     

Nutritional stimuli of gilthead seabream (Sparus aurata) larvae by dietary fatty acids: effects on larval performance,gene expression and neurogenesis

The concept of nutritional programming raises the interesting possibility of directing specific metabolic pathways or functions in juvenile fish, for example, to improve the use of substitutes to fishmeal and oil, and hence to promote sustainability in aquaculture. The aim of the study was to determine effects of early nutritional stimuli of gilthead seabream larvae and check if nutritional programming of gilthead sea bream is possible between 16 days post hatching (dph) and 26 dph. A trial was conducted to determine the effects of early nutritional stimuli of gilthead seabream larvae. Five experimental microdiets (pellet size <250 μm) were formulated containing five different proportions of a marine lipid source rich in long‐chain polyunsaturated fatty acids (LC‐PUFA) and two vegetable lipid sources rich in linolenic and linoleic acids. The results of this study demonstrate that dietary n‐3 LC‐PUFA levels increased larval growth and survival affecting Δ6 desaturase gen (fads2) expression and retinal neurons density. However, the high mortalities obtained along on‐growing in fish fed low n‐3 LC‐PUFA at 16 dph constrained the feasibility of nutritional programming of gilthead seabream during this late developmental window and needs to be further investigated.     

Dietary krill meal inclusion contributes to better growth performance of gilthead seabream juveniles

There is a need to find sustainable alternatives to fishmeal (FM) and fish oil (FO) in feed formulations to support the continued growth of aquaculture. FM is mostly produced from mass‐caught pelagic species, but the production has been relatively constant for several decades. The aim of this study was to investigate the potential of dietary krill meal (KM) inclusion as a sustainable alternative to FM. In view of that, a feeding trial with gilthead seabream juveniles was conducted to evaluate whether dietary KM at 3%, 6% and 9% inclusion improves growth performance in comparison with a control diet. At the end of the study, fish in the 9% KM group showed significantly higher body weight (32.76 g) compared with fish fed the control diet (30.30 g). Moreover, FM replacement by 9% KM indicated a reduction in the accumulation of lipid droplets in the hepatocytes and around the pancreatic islets. In summary, this study suggests that FM can be reduced in diets for seabream without negatively affecting growth performance, when KM is added. On the contrary, KM enhances gilthead seabream growth and reduces lipid accumulation and damage of hepatocytes, which will open an interesting innovation line to completely replace FM by alternative terrestrial protein sources and the partial inclusion of KM.     

Multilayer Perceptron Neural Network Approach to Estimate Chlorophyll Concentration Index of Lettuce (Lactuca sativa L.)

Nitrogen is an essential nutrient for greenhouse-grown lettuce (Lactuca sativa L.); however, excessive nutrient availability causes disease and detrimental effects on the leaf and root development. In this study, nitrogen content of the lettuce leaves was estimated by determining the chlorophyll concentrations of the leaves using image processing technique. The Hoagland solution was used as a fertilizer in five different doses (control, quarter of the solution, half of the solution, standard solution, and two times more of the solution). Multilayer perceptron neural network (MLPNN) model was developed based on the red, green, and blue components of the color image captured to estimate chlorophyll content and chlorophyll concentration index (SPAD values). According to the obtained results, the MLPNN model was capable of estimating the lettuce leaf chlorophyll content with a reasonable accuracy. The coefficient of determination was 0.98, and mean square error was 0.006 in validation process.