Abstract: The use of deep learning for species identification in camera trap images has revolutionised our ability to study, conserve and monitor species in a highly efficient and unobtrusive manner, with state-of-the-art models achieving accuracies surpassing the accuracy of manual human classification. The high imbalance of camera trap datasets, however, results in poor accuracies for minority (rare or endangered) species due to their relative insignificance to the overall model accuracy. This paper investigates the use of Focal Loss, in comparison to the traditional Cross Entropy Loss function, to improve the identification of minority species in the “255 Bird Species” dataset from Kaggle. The results show that, although Focal Loss slightly decreased the accuracy of the majority species, it was able to increase the F1-score by 0.06 and improve the identification of the bottom two, five and ten (minority) species by 37.5%, 15.7% and 10.8%, respectively, as well as resulting in an improved overall accuracy of 2.96%.
Abstract: Genome profiling (GP), a genotype based technology, which exploits random PCR and temperature gradient gel electrophoresis, has been successful in identification/classification of organisms. In this technology, spiddos (Species identification dots) and PaSS (Pattern similarity score) were employed for measuring the closeness (or distance) between genomes. Based on the closeness (PaSS), we can buildup phylogenetic trees of the organisms. We noticed that the topology of the tree is rather robust against the experimental fluctuation conveyed by spiddos. This fact was confirmed quantitatively in this study by computer-simulation, providing the limit of the reliability of this highly powerful methodology. As a result, we could demonstrate the effectiveness of the GP approach for identification/classification of organisms.
Abstract: The full length mitochondrial small subunit ribosomal
(mt-rns) gene has been characterized for Ophiostoma novo-ulmi
subspecies americana. The gene was also characterized for
Ophiostoma ulmi and a group II intron was noted in the mt-rns gene
of O. ulmi. The insertion in the mt-rns gene is at position S952 and it
is a group IIB1 intron that encodes a double motif LAGLIDADG
homing endonuclease from an open reading frame located within a
loop of domain III. Secondary structure models for the mt-rns RNA
of O. novo-ulmi subsp. americana and O. ulmi were generated to
place the intron within the context of the ribosomal RNA. The in vivo
splicing of the O.ul-mS952 group II intron was confirmed with
reverse transcription-PCR. A survey of 182 strains of Dutch Elm
Diseases causing agents showed that the mS952 intron was absent in
what is considered to be the more aggressive species O. novo-ulmi
but present in strains of the less aggressive O. ulmi. This observation
suggests that the O.ul-mS952 intron can be used as a PCR-based
molecular marker to discriminate between O. ulmi and O. novo-ulmi
subsp. americana.