Dataset Information

Deep learning approaches for challenging species and gender identification of mosquito vectors.

ABSTRACT: Microscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm. This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes. The results indicated that the concatenated two YOLO v3 model exhibited the optimal performance in identifying the mosquitoes, as the mosquitoes were relatively small objects compared with the large proportional environment image. The robustness testing of the proposed model yielded a mean average precision and sensitivity of 99% and 92.4%, respectively. The model exhibited high performance in terms of the specificity and accuracy, with an extremely low rate of misclassification. The area under the receiver operating characteristic curve (AUC) was 0.958 ± 0.011, which further demonstrated the model accuracy. Thirteen classes were detected with an accuracy of 100% based on a confusion matrix. Nevertheless, the relatively low detection rates for the two species were likely a result of the limited number of wild-caught biological samples available. The proposed model can help establish the population densities of mosquito vectors in remote areas to predict disease outbreaks in advance.

SUBMITTER: Kittichai V

PROVIDER: S-EPMC7921658 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Deep learning approaches for challenging species and gender identification of mosquito vectors.

Kittichai Veerayuth V Pengsakul Theerakamol T Chumchuen Kemmapon K Samung Yudthana Y Sriwichai Patchara P Phatthamolrat Natthaphop N Tongloy Teerawat T Jaksukam Komgrit K Chuwongin Santhad S Boonsang Siridech S

Scientific reports 20210301 1

Microscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm. This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes. The results indicated that the concaten ...[more]

PMID: 33649429

Similar Datasets

Project description:BackgroundDespite the high commercial fisheries value and ecological importance as prey item for higher marine predators, very limited taxonomic work has been done on cephalopods in Malaysia. Due to the soft-bodied nature of cephalopods, the identification of cephalopod species based on the beak hard parts can be more reliable and useful than conventional body morphology. Since the traditional method for species classification was time-consuming, this study aimed to develop an automated identification model that can identify cephalopod species based on beak images.MethodsA total of 174 samples of seven cephalopod species were collected from the west coast of Peninsular Malaysia. Both upper and lower beaks were extracted from the samples and the left lateral views of upper and lower beak images were acquired. Three types of traditional morphometric features were extracted namely grey histogram of oriented gradient (HOG), colour HOG, and morphological shape descriptor (MSD). In addition, deep features were extracted by using three pre-trained convolutional neural networks (CNN) models which are VGG19, InceptionV3, and Resnet50. Eight machine learning approaches were used in the classification step and compared for model performance.ResultsThe results showed that the Artificial Neural Network (ANN) model achieved the best testing accuracy of 91.14%, using the deep features extracted from the VGG19 model from lower beak images. The results indicated that the deep features were more accurate than the traditional features in highlighting morphometric differences from the beak images of cephalopod species. In addition, the use of lower beaks of cephalopod species provided better results compared to the upper beaks, suggesting that the lower beaks possess more significant morphological differences between the studied cephalopod species. Future works should include more cephalopod species and sample size to enhance the identification accuracy and comprehensiveness of the developed model.

Project description:BackgroundThe accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies.ResultsHere we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary positional permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody. This approach supersedes the traditional method of defining epitopes as scores per amino acid on a protein sequence, where each score reflects each amino acids predicted probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as binary positional permutation vectors, the approach also uses the 3D macrostructure features of the unbound protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. This enables the algorithm to learn the key structural and physiochemical features of the unbound protein and embedded epitope that initiate the antibody binding process helping to eliminate "induced fit" biases in the training data. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools. Additionally, we show that this approach reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model during training and generalizes in a very robust manner on dissimilar data not seen by the model during training.ConclusionsWith the approach described herein, a primary protein sequence and a query positional permutation vector encoding a putative epitope is sufficient to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications.

Project description:BackgroundCanine dirofilariasis due to Dirofilaria immitis is known to be endemic in continental Portugal. However, information about the transmitting mosquito species is still scarce, with only Culex theileri identified to date, albeit with L1-2, through dissection. This study was carried out to investigate the potential vectors of Dirofilaria spp. in continental Portugal.MethodsMosquitoes were collected in three distinct seasons (Summer, Autumn and Spring), 2011-2013, in three districts. CDC traps and indoor resting collections were carried out in the vicinity of kennels. Mosquitoes were kept under controlled conditions for 7 days to allow the development of larval stages of Dirofilaria spp.. DNA extraction was performed separately for both head+thorax and abdomen in order to differentiate infective and infected specimens, respectively, in pools, grouped according to the species and collection site (1-40 specimen parts/pool), and examined by PCR using pan-filarial specific primers. Mosquito densities were compared using non-parametric tests. Dirofilaria development units (DDU) were estimated.ResultsIn total, 9156 female mosquitoes, from 11 different species, were captured. Mosquito densities varied among the 3 districts, according to capture method, and were generally higher in the second year of collections. From 5866 specimens screened by PCR, 23 head+thorax and 41 abdomens pools, corresponding to 54 mosquitoes were found positive for D. immitis DNA. These belonged to 5 species: Culex (Cux) theileri (estimated rate of infection (ERI)=0.71%), Cx. (Cux) pipiens f. pipiens and f. molestus (ERI=0.5%), Anopheles (Ano) maculipennis s.l. (ERI=3.12%), including An. (Ano) atroparvus, Aedes (Och) caspius (ERI=3.73%) and Ae. (Och) detritus s.l. (ERI=4.39%). All but Cx. pipiens, had at least one infective specimen. No D. repens infected specimens were found. Infection rates were: 3.21% in Coimbra, 1.22% in Setúbal and 0.54% in Santarém. DDU were at least 117/year in the study period.ConclusionsCulex theileri, Cx. pipiens, An. maculipennis s.l. An. atroparvus, Ae.caspius and Ae. detritus s.l. were identified as potential vectors of D. immitis in three districts of Portugal, from Spring to Autumn, in 5 of the 6 collection dates in 2011-2013. Implications for transmission, in the context of climate changes, and need for prophylactic measures, are discussed.

Dataset Information

Deep learning approaches for challenging species and gender identification of mosquito vectors.

Publications

Deep learning approaches for challenging species and gender identification of mosquito vectors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets