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%).

Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.

HER2 positive gastric and gastroesophageal adenocarcinoma; An Irish tertiary center experience

Background: Trastuzumab has been approved for patients with human epidermal growth factor receptor 2 (HER2) over expression and gene amplification metastatic gastric cancer. Here we present the prevalence of HER2 positive gastric cancer in an Irish population, the use of Trastuzumab in first line and beyond progression. Methods: The study was conducted in St James's Hospital, Dublin. A retrospective analysis of the date of patients with HER2 positive gastric cancer over a period of 3 years was carried out. Her2 positive was defined as immunohistochemistry (IHC) score of +3, of IHC score of +2 and increased gene copy number by fluorescence in situ hybridization (FISH). Overall survival was calculated from the day of initiation of treatment with Trastuzumab until death. Results: During the study period 140 patients with gastric and gastro-esophageal junction adenocarcinoma were treated. Out of those, 30 (21.4%) had HER2 positive disease. Among HER2 positive disease patients 18 (12.8%) were treated with first line Trastuzumab containing regimen with a median overall survival of 13 months. Nine (50%) developed progressive disease while on Trastuzumab and of those, 4 (22.2%) patients continued on Trastuzumab beyond progression, two (11.1%) of whom achieved stable disease and a prolonged survival. Conclusion: HER2 positivity rate in an Irish population with advanced gastric and gastro-esophageal junction adenocarcinoma is 21.4%. Treatment with Trastuzumab in the first line in combination with chemotherapy is a reasonable approach. Continuation of Trastuzumab beyond progression is a feasible strategy that requires further exploration.

Editorial: Are adolescent and young adult cancer patients affected by ‘chemobrain’?: A call for evidence

An adolescent and young adult (AYA) cancer patient is defined as an individual of 15 to 39 years of age at the time of initial cancer diagnosis.1,2 The number of AYA cancer survivors has dramatically increased over the past decades due to availability of novel therapeutics, with the 5-year overall survival rate among adolescents aged 15 to 19 years old exceeds 80% for most cancers.3 AYA cancer survivors, however, often experience a myriad of treatment-related chronic and late toxicities that can lead to functional impairment at great economic, emotional and social cost.4 As the cure rates of AYA cancers continue to improve and survivors live longer, post-treatment health issues in these survivors are becoming increasingly relevant, and more in-depth research is needed in this group of patients...