Foot-and-mouth disease-associated myocarditis is age dependent in suckling calves.

Myocarditis is considered a fatal form of foot-and-mouth disease (FMD) in suckling calves. In the present study, a total of 17 calves under 4 months of age and suspected clinically for FMD were examined for clinical lesions, respiratory rate, heart rate, and heart rhythm. Lesion samples, saliva, nasal swabs, and whole blood were collected from suspected calves and subjected to Sandwich ELISA and reverse transcription multiplex polymerase chain reaction (RT-mPCR) for detection and serotyping of FMD virus (FMDV). The samples were found to be positive for FMDV serotype "O". Myocarditis was suspected in 6 calves based on tachypnoea, tachycardia, and gallop rhythm. Serum aspartate aminotransferase (AST), creatinine kinase myocardial band (CK-MB) and lactate dehydrogenase (LDH), and cardiac troponins (cTnI) were measured. Mean serum AST, cTn-I and LDH were significantly higher (P < 0.001) in < 2 months old FMD-infected calves showing clinical signs suggestive of myocarditis (264.833 ± 4.16; 11.650 ± 0.34 and 1213.33 ± 29.06) than those without myocarditis (< 2 months old: 110.00 ± 0.00, 0.06 ± 0.00, 1050.00 ± 0.00; > 2 months < 4 months: 83.00 ± 3.00, 0.05 ± 0.02, 1159.00 ± 27.63) and healthy control groups (< 2 months old: 67.50 ± 3.10, 0.047 ± 0.01, 1120.00 ± 31.62; > 2 months < 4 months: 72.83 ± 2.09, 0.47 ± 0.00, 1160.00 ± 18.44). However, mean serum CK-MB did not differ significantly amongst the groups. Four calves under 2 months old died and a necropsy revealed the presence of a pathognomic gross lesion of the myocardial form of FMD known as "tigroid heart". Histopathology confirmed myocarditis. This study also reports the relevance of clinical and histopathological findings and biochemical markers in diagnosing FMD-related myocarditis in suckling calves.

Comparative assessment of Pest damage identification of coconut plant using damage texture and color analysis.

Coconut cultivation is a promising agricultural activity. But to keep the coconut plants pest-free, the detection of various pest damage in coconut plants is of utmost importance for the cultivators. The processes that the cultivators use to detect pest damage in coconut plants are conventional methods, experts' views, or some laboratory techniques. But these procedures are not adequate in the detection of coconut damage identification. In this study, 16 different color and texture features are reported for 1265 coconut pest damage images by extracting the color and texture features of the damage images in the color and grey domain after the damage segmentation using the thresholding technique. The Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) techniques are applied to extract the texture features of the damages and two Artificial Neural Network (ANN) architectures are reported to classify the extracted data features of the damages into 5 different classes such as Eriophyid_Mite, Rhinoceros_Beetle, Red_Palm_Weevil, Rugose_Spiraling_White_fly, and Rugose_in_Mature with an average testing accuracy of almost 100% respectively. To compare the results with the other machine learning techniques, the Support Vector Machine(SVM), Decision Tree (DT), and Naïve Bayes (NB) are also introduced for damage identification where the SVM methods also report almost 100% accuracy on the fuse features of GLCM and GLRLM. The results of the ANN and SVM are compared by finding the confusion matrix, precision, recall, and f-1 score of the ANN model with the DT and NB classifier. The ANN and SVM outperform in all matrices and they can be used as the base model for further study of coconut pest damage identification using deep learning techniques.