Prevalence of Degnala disease in bovine along with screening of toxigenic fungi isolated from contaminated rice straw.

Toxigenic potential of different candidate fungi, isolated from rice straw feed of Degnala disease affected bovines was analyzed along with species, age, gender and seasonal prevalence. Of 1,536 cases, 104 (6.77%) showed positive signs with a significant association (p less than 0.05) between this disease and rice straw feeding, in buffaloes, and bovine aged over 1 year in the winter season. Complete blood count showed a marked increase in erythrocyte sedimentation rate and all white blood cells numbers, except lymphocytes in positive cases. There was a significant increase (p less than 0.05) in alanine amino transferase, aspirate amino transferase and alkaline phosphatase in the liver function test. At the same time, an increased value of creatinine was noted in the renal function test. For isolation and screening of toxigenic fungi, rice straw samples (n=40) being fed to the positive cases were processed further, and 85 fungal isolates were found, mainly of Aspergillus (57), Penicillium (10), Fusarium (04), Zygomycetes (03), Curvularia (01) and unidentified (10). All isolated fungi were subjected for mycotoxin production and only 11 showed mycotoxin-producing capability (including Aspergillus, Penicillium and Fusarium isolates) analyzed by thin layer chromatography and quantified through high performance liquid chromatography. It is concluded that all the fungi contaminating rice straw feed of Degnala affected animals were not toxigenic. This work will help in establishing major mycotoxin-producing fungi leading to the probable cause of Degnala disease in bovine.

Correcting for missing and irregular data in home-range estimation.

Home-range estimation is an important application of animal tracking data that is frequently complicated by autocorrelation, sampling irregularity, and small effective sample sizes. We introduce a novel, optimal weighting method that accounts for temporal sampling bias in autocorrelated tracking data. This method corrects for irregular and missing data, such that oversampled times are downweighted and undersampled times are upweighted to minimize error in the home-range estimate. We also introduce computationally efficient algorithms that make this method feasible with large data sets. Generally speaking, there are three situations where weight optimization improves the accuracy of home-range estimates: with marine data, where the sampling schedule is highly irregular, with duty cycled data, where the sampling schedule changes during the observation period, and when a small number of home-range crossings are observed, making the beginning and end times more independent and informative than the intermediate times. Using both simulated data and empirical examples including reef manta ray, Mongolian gazelle, and African buffalo, optimal weighting is shown to reduce the error and increase the spatial resolution of home-range estimates. With a conveniently packaged and computationally efficient software implementation, this method broadens the array of data sets with which accurate space-use assessments can be made.