Acoustic features for multi-level classification of Australian frogs

Over past few decades, frog species have been experiencing dramatic decline around the world. The reason for this decline includes habitat loss, invasive species, climate change and so on. To better know the status of frog species, classifying frogs has become increasingly important. In this study, acoustic features are investigated for multi-level classification of Australian frogs: family, genus and species, including three families, eleven genera and eighty five species which are collected from Queensland, Australia. For each frog species, six instances are selected from which ten acoustic features are calculated. Then, the multicollinearity between ten features are studied for selecting non-correlated features for subsequent analysis. A decision tree (DT) classifier is used to visually and explicitly determine which acoustic features are relatively important for classifying family, which for genus, and which for species. Finally, a weighted support vector machines (SVMs) classifier is used for the multi- level classification with three most important acoustic features respectively. Our experiment results indicate that using different acoustic feature sets can successfully classify frogs at different levels and the average classification accuracy can be up to 85.6%, 86.1% and 56.2% for family, genus and species respectively.

The crude skin secretion of the pepper frog leptodactylus labyrinthicus is rich in metallo and serine peptidases

Peptidases are ubiquitous enzymes involved in diverse biological processes. Fragments from bioactive peptides have been found in skin secretions from frogs, and their presence suggests processing by peptidases. Thus, the aim of this work was to characterize the peptidase activity present in the skin secretion of Leptodactylus labyrinthicus. Zymography revealed the presence of three bands of gelatinase activity of approximately 60 kDa, 66 kDa, and 80 kDa, which the first two were calcium-dependent. These three bands were inhibited either by ethylenediaminetetraacetic acid (EDTA) and phenathroline; thus, they were characterized as metallopeptidases. Furthermore, the proteolytic enzymes identified were active only at pH 6.0–10.0, and their activity increased in the presence of CHAPS or NaCl. Experiments with fluorogenic substrates incubated with skin secretions identified aminopeptidase activity, with cleavage after leucine, proline, and alanine residues. This activity was directly proportional to the protein concentration, and it was inhibited in the presence of metallo and serine peptidase inhibitors. Besides, the optimal pH for substrate cleavage was determined to be 7.0–8.0. The results of the in gel activity assay showed that all substrates were hydrolyzed by a 45 kDa peptidase. Gly-Pro-AMC was also cleaved by a peptidase greater than 97 kDa. The data suggest the presence of dipeptidyl peptidases (DPPs) and metallopeptidases; however, further research is necessary. In conclusion, our work will help to elucidate the implication of these enzymatic activities in the processing of the bioactive peptides present in frog venom, expanding the knowledge of amphibian biology.

Effects of different surfaces on the perception of prey-generated noise by the Indian false vampire bat Megaderma lyra.

The low- and high-frequency components of a rustling sound, created when prey (freshly killed frog) was jerkily pulled on dry and wet sandy floors and asbestos, were recorded and played back to individual Indian false vampire bats (Megaderma lyra). Megaderma lyra responded with flight toward the speakers and captured dead frogs, that were kept as reward. The spectral peaks were at 8.6, 7.1 and 6.8 kHz for the low-frequency components of the sounds created at the dry, asbestos and wet floors, respectively. The spectral peaks for the high-frequency sounds created on the respective floors were at 36.8,27.2 and 23.3 kHz. The sound from the dry floor was more intense than that of from the other two substrata. Prey movements that generated sonic or ultrasonic sounds were both sufficient and necessary for the bats to detect and capture prey. The number of successful prey captures was significantly greater for the dry floor sound, especially to its high-frequency components. Bat-responses were low to the wet floor and moderate to the asbestos floor sounds. The bats did not respond to the sound of unrecorded parts of the tape. Even though the bats flew toward the speakers when the prey generated sounds were played back and captured the dead frogs we cannot rule out the possibility of M. lyra using echolocation to localize prey. However, the study indicates that prey that move on dry sandy floor are more vulnerable to predation by M. lyra.

Mauve: How one man invented the colour that changed the world

In an essay, "The Books of Last Things", Delia Falconer discusses the emergence of a new genre in publishing - microhistories. She cites a number of recent titles in non-fiction and fiction - Longitude, Cod, Tulips, Pushkin's Button, Nathaniel's Nutmeg, Zarafa, The Surgeon of Crowthorne, The Potato, The Perfect Storm. Delia Falconer observes of this tradition: "One has the sense, reading these books, of a surprising weight, of pleasant shock. In part, it is because we are looking at things which are generally present around us, but modestly out of sight and mind - historical nitty gritty like cod, potatoes, longitudinal clocks - which the authors have thrust suddenly, like a Biblical visitation of frogs or locusts, in our face. Things like spice and buttons and clocks are generally seen to enable history on the large scale, but are not often viewed as its worthy subjects. And by the same grand logic of history, more unusual phenomena like cabinets of curiosities or glass-making or farm lore or sailors' knots are simply odd blips on its radar screen, interesting footnotes. These new books, microhistories, reverse the usual order of history, which argues from the general to the particular, in order to prove its inevitable progress. They start from the footnotes. But by reversing the process, and walking through the back door of history, you don't necessarily end up at the front of the same house." Delia Falconer speculates about the reasons for the popularity of microhistories. She concludes: "I would like to think that reading them is not simply an exercise in nostalgia, but a challenge to the present". In Mauve, Simon Garfield provides a new way of thinking and writing about the history of intellectual property. Instead of providing a grand historical narrative of intellectual property, he tells the story of a particular invention, and its exploitation. Simon Garfield relates how English chemist William Perkin accidentally discovered a way to mass-produce colour mauve in a factory. Working on a treatment for malaria in his London home laboratory, Perkin failed to produce artificial quinine. Instead he created a dark oily sludge that turned silk a beautiful light purple. The colour was unique and became the most desirable shade in the fashion houses of Paris and London. ... The book Mauve will have a number of contemporary resonances for intellectual property lawyers and academics. Simon Garfield emphasizes the difficulties inherent in commercialising an invention and managing intellectual property. He investigates the uneasy collaboration between industry and science. Simon Garfield suggests that complaints about the efficacy of patent offices are perennial. He also highlights the problems faced by courts and law-makers in accommodating new technologies within the logic of patent law. In his elegant microhistory of the colour mauve, Simon Garfield confirms the conclusion of Brad Sherman and Lionel Bently that many aspects of modern intellectual property law can only be understood through an understanding of the past: "The image of intellectual property law that developed during the 19th century and the narrative of identity which this engendered played and continue to play an important role in the way we think about and understand intellectual property law".

Acoustic classification of Australian anurans using syllable features

Acoustic classification of anurans (frogs) has received increasing attention for its promising application in biological and environment studies. In this study, a novel feature extraction method for frog call classification is presented based on the analysis of spectrograms. The frog calls are first automatically segmented into syllables. Then, spectral peak tracks are extracted to separate desired signal (frog calls) from background noise. The spectral peak tracks are used to extract various syllable features, including: syllable duration, dominant frequency, oscillation rate, frequency modulation, and energy modulation. Finally, a k-nearest neighbor classifier is used for classifying frog calls based on the results of principal component analysis. The experiment results show that syllable features can achieve an average classification accuracy of 90.5% which outperforms Mel-frequency cepstral coefficients features (79.0%).

Application of image processing techniques for frog call classification

Frogs have received increasing attention due to their effectiveness for indicating the environment change. Therefore, it is important to monitor and assess frogs. With the development of sensor techniques, large volumes of audio data (including frog calls) have been collected and need to be analysed. After transforming the audio data into its spectrogram representation using short-time Fourier transform, the visual inspection of this representation motivates us to use image processing techniques for analysing audio data. Applying acoustic event detection (AED) method to spectrograms, acoustic events are firstly detected from which ridges are extracted. Three feature sets, Mel-frequency cepstral coefficients (MFCCs), AED feature set and ridge feature set, are then used for frog call classification with a support vector machine classifier. Fifteen frog species widely spread in Queensland, Australia, are selected to evaluate the proposed method. The experimental results show that ridge feature set can achieve an average classification accuracy of 74.73% which outperforms the MFCCs (38.99%) and AED feature set (67.78%).