First record of microplastic in the Brazilian sea hare Aplysia brasiliana Rang, 1828 (Mollusca: Aplysiidae).

The presence of plastic debris in the marine environment has reached massive levels in the past decades. In marine environments, microplastics can exist for hundreds of years and the presence of microplastics in this environment has been reported since 1970 and since then has been considered ubiquitous. Mollusks are being used as microplastic pollution indicators, especially in coastal areas and bivalves are more often used in microplastic-monitoring studies. On the other hand, gastropods are poorly used as indicators for microplastic pollution, even though they are the most diverse group of mollusks. The sea hares of the genus Aplysia are herbivorous gastropods, important model organisms commonly used in neuroscience studies, isolating the compounds in their defensive ink. Until today, there is no previous record of the presence of MPs in Aplysia gastropods. Therefore, this study aims to investigate the presence of microplastics in tissues of A. brasiliana found in southeastern Brazil. We collected seven individuals of A. brasiliana from a beach in southeastern Brazil, dissected them to isolate the digestive tract and the gills, and digested the tissues with a solution of 10 % NaOH. In the end, 1021 microplastic particles were found, 940 in the digestive tissue, and 81 in the gills. These results represent the first record of the presence of microplastics in the Brazilian sea hare A. brasiliana.

Abundance of microplastic in different coastal areas using Phragmatopoma caudata (Kroyer in Morch, 1863) (Polychaeta: Sabelariidae) as an indicator.

Plastic debris has been reported in the marine environment since the '70s. These plastic materials are introduced into the marine environment in several sizes, one of them microplastics (MP), and they have drawn great interest and concern in the past decades. Consumption of MP can cause weight loss, feeding rate decrease, reproductive activity decrease, and several other negative effects. Ingestion of MPs has already been reported for some species of polychaetes but the use of these annelids in MP studies is still poorly reported. Costa et al. (2021) was the first study to investigate the capability of the reef-building polychaete Phragmatopoma caudata to incorporate microplastic in its colony's structures. This makes the colonies a reservoir of MP and thus they reflect the environment's quality regarding MP presence. Consequently, this specie becomes an important asset to MP pollution investigation in coastal areas. Therefore, this work aims to investigate the abundance of MPs on the coastline of Espírito Santo using P. caudata as an indicator of MP presence. For this, we collected samples of P. caudata colonies in 12 sampling sites along the Espírito Santo coast (three replicates at each site). These colony samples were processed to extract the MPs particles from the colony surface, its inner structure, and tissues from the individuals. These MPs were counted using a stereomicroscope and sorted according to their color and type (filament, fragment, and other). Statistical analysis was performed using GraphPad Prism 9.3.0. Significant values followed p < 0.05. We found MP particles in all 12 sampled beaches, configuring a pollution rate of 100 %. The number of filaments was notably greater than the number of fragments and others. The most impacted beaches were found inside the metropolitan region of the state. Finally, P. caudata is an efficient and trustable indicator of microplastic in coastal areas.

Collaborative and Structured Network for Maintenance of Mechanical Ventilators during the SARS-CoV-2 Pandemic.

The SARS-CoV-2 pandemic in Brazil has grown rapidly since the first case was reported on 26 February 2020. As the pandemic has spread, the low availability of medical equipment has increased, especially mechanical ventilators. The Brazilian Unified Health System (SUS) claimed to have only 40,508 mechanical ventilators, which would be insufficient to support the Brazilian population at the pandemic peak. This lack of ventilators, especially in public hospitals, required quick, assertive, and effective actions to minimize the health crisis. This work provides an overview of the rapid deployment of a network for maintaining disused mechanical ventilators in public and private healthcare units in some regions of Brazil during the SARS-CoV-2 pandemic. Data referring to the processes of maintaining equipment, acquiring parts, and conducting national and international training were collected and analyzed. In total, 4047 ventilators were received by the maintenance sites, and 2516 ventilators were successfully repaired and returned to the healthcare units, which represents a success rate of 62.17%. The results show that the maintenance initiative directly impacted the availability and reliability of the equipment, allowing access to ventilators in the public and private health system and increasing the capacity of beds during the pandemic.

Optimization of convolutional neural network hyperparameters for automatic classification of adult mosquitoes.

The economic and social impacts due to diseases transmitted by mosquitoes in the latest years have been significant. Currently, no specific treatment or commercial vaccine exists for the control and prevention of arboviruses, thereby making entomological characterization fundamental in combating diseases such as dengue, chikungunya, and Zika. The morphological identification of mosquitos includes a visual exam of the samples. It is time consuming and requires adequately trained professionals. Accordingly, the development of a new automated method for realizing mosquito-perception and -classification is becoming increasingly essential. Therefore, in this study, a computational model based on a convolutional neural network (CNN) was developed to extract features from the images of mosquitoes and then classify the species Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus. In addition, the model was trained to detect the mosquitoes of the genus Aedes. To train CNNs to perform the automatic morphological classification of mosquitoes, a dataset, which included 7,561 images of the target mosquitoes and 1,187 images of other insects, was acquired. Various neural networks, such as Xception and DenseNet, were used for developing the automatic-classification model based on images. A structured optimization process of random search and grid search was developed to select the hyperparameters set and increase the accuracy of the model. In addition, strategies to eliminate overfitting were implemented to increase the generalization of the model. The optimized model, during the test phase, obtained the balanced accuracy (BA) of 93.5% in classifying the target mosquitoes and other insects and the BA of 97.3% in detecting the mosquitoes of the genus Aedes in comparison to Culex. The results provide fundamental information for performing the automatic morphological classification of mosquito species. Using a CNN-embedded entomological tool is a valuable and accessible resource for health workers and non-taxonomists for identifying insects that can transmit infectious diseases.

Application of convolutional neural networks for classification of adult mosquitoes in the field.

Dengue, chikungunya and Zika are arboviruses transmitted by mosquitos of the genus Aedes and have caused several outbreaks in world over the past ten years. Morphological identification of mosquitos is currently restricted due to the small number of adequately trained professionals. We implemented a computational model based on a convolutional neural network (CNN) to extract features from mosquito images to identify adult mosquitoes from the species Aedes aegypti, Aedes albopictus and Culex quinquefasciatus. To train the CNN to perform automatic morphological classification of mosquitoes, we used a dataset that included 4,056 mosquito images. Three neural networks, including LeNet, AlexNet and GoogleNet, were used. During the validation phase, the accuracy of the mosquito classification was 57.5% using LeNet, 74.7% using AlexNet and 83.9% using GoogleNet. During the testing phase, the best result (76.2%) was obtained using GoogleNet; results of 52.4% and 51.2% were obtained using LeNet and AlexNet, respectively. Significantly, accuracies of 100% and 90% were achieved for the classification of Aedes and Culex, respectively. A classification accuracy of 82% was achieved for Aedes females. Our results provide information that is fundamental for the automatic morphological classification of adult mosquito species in field. The use of CNN's is an important method for autonomous identification and is a valuable and accessible resource for health workers and taxonomists for the identification of some insects that can transmit infectious agents to humans.