Acoustic feature extraction using perceptual wavelet packet decomposition for frog call classification

Frog protection has become increasingly essential due to the rapid decline of its biodiversity. Therefore, it is valuable to develop new methods for studying this biodiversity. In this paper, a novel feature extraction method is proposed based on perceptual wavelet packet decomposition for classifying frog calls in noisy environments. Pre-processing and syllable segmentation are first applied to the frog call. Then, a spectral peak track is extracted from each syllable if possible. Track duration, dominant frequency and oscillation rate are directly extracted from the track. With k-means clustering algorithm, the calculated dominant frequency of all frog species is clustered into k parts, which produce a frequency scale for wavelet packet decomposition. Based on the adaptive frequency scale, wavelet packet decomposition is applied to the frog calls. Using the wavelet packet decomposition coefficients, a new feature set named perceptual wavelet packet decomposition sub-band cepstral coefficients is extracted. Finally, a k-nearest neighbour (k-NN) classifier is used for the classification. The experiment results show that the proposed features can achieve an average classification accuracy of 97.45% which outperforms syllable features (86.87%) and Mel-frequency cepstral coefficients (MFCCs) feature (90.80%).

Analysis of word embeddings and sequence features for clinical information extraction

This study investigates the use of unsupervised features derived from word embedding approaches and novel sequence representation approaches for improving clinical information extraction systems. Our results corroborate previous findings that indicate that the use of word embeddings significantly improve the effectiveness of concept extraction models; however, we further determine the influence that the corpora used to generate such features have. We also demonstrate the promise of sequence-based unsupervised features for further improving concept extraction.

Optimisation of in-cell accelerated solvent extraction technique for the determination of organochlorine pesticides in river sediments

Organochlorine pesticides (OCPs) are ubiquitous environmental contaminants with adverse impacts on aquatic biota, wildlife and human health even at low concentrations. However, conventional methods for their determination in river sediments are resource intensive. This paper presents an approach that is rapid and also reliable for the detection of OCPs. Accelerated Solvent Extraction (ASE) with in-cell silica gel clean-up followed by Triple Quadrupole Gas Chromatograph Mass Spectrometry (GCMS/MS) was used to recover OCPs from sediment samples. Variables such as temperature, solvent ratio, adsorbent mass and extraction cycle were evaluated and optimised for the extraction. With the exception of Aldrin, which was unaffected by any of the variables evaluated, the recovery of OCPs from sediment samples was largely influenced by solvent ratio and adsorbent mass and, to some extent, the number of cycles and temperature. The optimised conditions for OCPs extraction in sediment with good recoveries were determined to be 4 cycles, 4.5 g of silica gel, 105 ᴼC, and 4:3 v/v DCM: hexane mixture. With the exception of two compounds (α-BHC and Aldrin) whose recoveries were low (59.73 and 47.66 % respectively), the recovery of the other pesticides were in the range 85.35 – 117.97% with precision < 10 % RSD. The method developed significantly reduces sample preparation time, the amount of solvent used, matrix interference, and is highly sensitive and selective.

vSpeak: Edge detection based feature extraction for sign to text conversion

This paper presents 'vSpeak', the first initiative taken in Pakistan for ICT enabled conversion of dynamic Sign Urdu gestures into natural language sentences. To realize this, vSpeak has adopted a novel approach for feature extraction using edge detection and image compression which gives input to the Artificial Neural Network that recognizes the gesture. This technique caters for the blurred images as well. The training and testing is currently being performed on a dataset of 200 patterns of 20 words from Sign Urdu with target accuracy of 90% and above.

Probiotics modify tight junction proteins in an animal model of non-alcoholic fatty liver disease

Objectives We have investigated the effects of a multi–species probiotic preparation containing a combination of probiotic bacterial genera that included Bifidobacteria, Lactobacilli and a Streptococcus in a mouse model of high fat diet/obesity induced liver steatosis. Methods Three groups of C57B1/6J mice were fed either a standard chow or a high fat diet for 20 weeks, while a third group was fed a high fat diet for 10 weeks and then concomitantly administered probiotics for a further 10 weeks. Serum, liver and large bowel samples were collected for analysis. Results The expression of the tight junction proteins ZO-1 and ZO-2 was reduced (p < 0.05) in high fat diet fed mice compared to chow fed mice. Probiotic supplementation helped to maintain tight ZO-1 and ZO-2 expression compared with the high fat diet group (p < 0.05), but did not restore ZO-1 or ZO-2 expression compared with chow fed mice. Mice fed a high fat diet ± probiotics had significant steatosis development compared to chow fed mice (p < 0.05); steatosis was less severe in the probiotics group compared to the high fat diet group. Hepatic triglycerides concentration was higher in mice fed a high fat diet ± probiotics compared to the chow group (p < 0.05), and was lower in the probiotics group compared to the high fat diet group (p < 0.05). Compared to chow fed mice, serum glucose and cholesterol concentrations, and the activity of alanine transaminase were higher (p < 0.05), whereas serum triglyceride concentration was lower (p < 0.05) in mice fed a high fat diet ± probiotics. Conclusions Supplementation with a multi-species probiotic formulation helped to maintain tight junction proteins ZO-1 and ZO-2, and reduced hepatic triglyceride concentrations compared with a HFD alone.

Comparison of partial extraction reagents for assessing potential bioavailability of heavy metals in sediments

Assessment of heavy metal bioavailability in sediments is complex because of the number of partial extraction methods available for the assessment and the general lack of certified reference materials. This study evaluates five different extraction methodologies to ascertain the relative strengths and weaknesses of each method. The results are then compared to previously published work to ascertain the most effective partial extraction technique, which was established to be dilute (0.75 – 1 M) nitric acid solutions. These results imply that single reagent; weak acid extractions provide a better assessment of potentially bioavailable metals than the chelating agents used in sequential extraction methods.