RESUMO
The illegal trade in tigers (Panthera tigris) and their derivatives, such as bones, teeth and pelts, is a major threat to the species' long-term persistence. As wild tiger populations have dwindled, a large proportion of trafficked tiger products now derive from captive breeding facilities found throughout Asia. Moreover, wild tigers have been poached and laundered into captive facilities, then falsely designated as captive-bred. The establishment of a DNA registration system is recognized as a key tool to monitor compliance of captive facilities, support tiger trade investigations and improve prosecution outcomes. Here, we present a standardised wildlife forensic DNA profiling system for captive tigers called TigerBase. TigerBase has been developed in four South-East Asia countries with captive tiger facilities: Malaysia, Vietnam, Thailand and Lao PDR. TigerBase DNA profile data is based on 60 single nucleotide polymorphism (SNP) markers, genotyped using two different TaqMan®-based approaches: OpenArray® chip (capable of genotyping 60 SNPs for 48 samples in a single chip), and singleplex TaqMan® assays (capable of genotyping one SNP for one sample per reaction). Of the 60 SNPs, 53 are autosomal nuclear markers, suitable for individualisation and parentage applications, two are sex-linked markers, suitable for sexing, and five are mtDNA markers, suitable for maternal subspecies identification. We conducted a series of validation experiments to investigate the reliability and limitations of these SNP genotyping platforms. We found that the OpenArray® chip platform is more appropriate for generating reference data given its greater throughput, while the singleplex TaqMan® assays are more appropriate for genotyping lower quality casework samples, given their higher sensitivity and throughput flexibility. Only 19 autosomal nuclear markers were validated as singleplex TaqMan® assays, which generally provides ample power for individualisation analysis (probability of identity among siblings was <6.9 ×10-4), but may lack power for specific parentage questions, such as determining parentage of an offspring when one of the parent's genotypes is missing. Further, we have developed pipelines to support standardised SNP calling and decrease the chance of genotyping errors through the use of analytical workflows and synthetic positive controls. We expect the implementation of TigerBase will enhance enforcement of tiger trafficking cases and encourage compliance among captive tiger facilities, together contributing to combatting the illegal tiger trade.
RESUMO
Epileptic seizure is a neurological condition caused by short and unexpectedly occurring electrical disruptions in the brain. It is estimated that roughly 60 million individuals worldwide have had an epileptic seizure. Experiencing an epileptic seizure can have serious consequences for the patient. Automatic seizure detection on electroencephalogram (EEG) recordings is essential due to the irregular and unpredictable nature of seizures. By thoroughly analyzing EEG records, neurophysiologists can discover important information and patterns, and proper and timely treatments can be provided for the patients. This research presents a novel machine learning-based approach for detecting epileptic seizures in EEG signals. A public EEG dataset from the University of Bonn was used to validate the approach. Meaningful statistical features were extracted from the original data using discrete wavelet transform analysis, then the relevant features were selected using feature selection based on the binary particle swarm optimizer. This facilitated the reduction of 75% data dimensionality and 47% computational time, which eventually sped up the classification process. After having been selected, relevant features were used to train different machine learning models, then hyperparameter optimization was utilized to further enhance the models' performance. The results achieved up to 98.4% accuracy and showed that the proposed method was very effective and practical in detecting seizure presence in EEG signals. In clinical applications, this method could help relieve the suffering of epilepsy patients and alleviate the workload of neurologists.