An epidemiological investigation was conducted to ascertain the prevalence of anaplasmosis in cattle in Sirajganj district of Bangladesh, during the period of December 2013 to November 2014. During one year study period a total of 395 cattle were examined, and where 102 were found to be infected with Anaplasma spp. On Geimsa stained blood smear examination, it was observed that the overall prevalence of anaplasmosis in cattle was recorded as 25.82%. The effect of area, age, sex, breed and season was observed in cattle during this study. The highest prevalence (34.19%) was reported in the older cattle (> 3 years of age) and the prevalence was higher in female (28.88%) than male (19.20%). All crossbred cattle was showed higher prevalence than local cattle. The prevalence of anaplasmosis infection was noticed as the highest in the rainy season (30.68%) in relation to summer (27.50%) and winter (15.15%) season. From the study it was evident that cattle were infected with the organism and caused a serious economic loss which will assist to take necessary preventive interventions. DOI: http://dx.doi.org/10.3329/ralf.v1i1.22370 Res. Agric., Livest. Fish.1(1): 97-103, Dec 2014

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The correlated random walk paradigm is the dominant conceptual framework for modeling animal movement patterns. Nonetheless, we do not know whether the randomness is apparent or actual. Apparent randomness could result from individuals reacting to environmental cues and their internal states in accordance with some set of behavioral rules. Here, we show how apparent randomness can result from one simple kind of algorithmic response to environmental cues. This results in an exponential step-length distribution in homogeneous environments and in generalized stretched exponential step-length distributions in more complex fractal environments. We find support for these predictions in the movement patterns of the Australian bull ant Myrmecia midas searching on natural surfaces and on artificial uniform and quasi-fractal surfaces. The bull ants spread their search significantly farther on the quasi-fractal surface than on the uniform surface, showing that search characteristics differed as a function of the substrate on which ants are searching. Further tentative support comes from a re-analysis of Australian desert ants Melophorus bagoti moving on smoothed-over sand and on a more strongly textured surface. Our findings call for more experimental studies on different surfaces to test the surprising predicted linkage between fractal dimension and the exponent in the step-length distribution.Significance statementAnimal search patterns often appear to be irregular and erratic. This behavior is captured by random walk models. Despite their considerable successes, extrapolation and prediction beyond observations remain questionable because the true nature and interpretation of the randomness in these models have until now been elusive. Here, we show how apparent randomness can result from simple algorithmic responses to environmental cues. Distinctive predictions from our theory find support in analyses of the search patterns of two species of Australian ants.Electronic supplementary materialThe online version of this article (10.1007/s00265-018-2527-1) contains supplementary material, which is available to authorized users.

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This article presents a hybrid wavelet-based algorithm to suppress the ocular artifacts from electroencephalography (EEG) signals. The hybrid wavelet transform (HWT) method is designed by the combination of discrete wavelet decomposition and wavelet packet transform. The artifact suppression is performed by the selection of sub-bands obtained by HWT. Fractional Gaussian noise (fGn) is used as the reference signal to select the sub-bands containing the artifacts. The multichannel EEG signal is decomposed HWT into a finite set of sub-bands. The energies of the sub-bands are compared to that of the fGn to the desired sub-band signals. The EEG signal is reconstructed by the selected sub-bands consisting of EEG. The experiments are conducted for both simulated and real EEG signals to study the performance of the proposed algorithm. The results are compared with recently developed algorithms of artifact suppression. It is found that the proposed method performs better than the methods compared in terms of performance metrics and computational cost.

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