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- Field evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sexPublication . González-Pérez, María I.; Faulhaber, Bastian; Aranda, Carles; Williams, Mark; Villalonga, Pancraç; Silva, Manuel; Costa Osório, Hugo; Encarnaçao, Joao; Talavera, Sandra; Busquets, NúriaBackground: Mosquito-borne diseases are a major concern for public and veterinary health authorities, highlighting the importance of effective vector surveillance and control programs. Traditional surveillance methods are labor-intensive and do not provide high temporal resolution, which may hinder a full assessment of the risk of mosquito-borne pathogen transmission. Emerging technologies for automated remote mosquito monitoring have the potential to address these limitations; however, few studies have tested the performance of such systems in the field. Methods: In the present work, an optical sensor coupled to the entrance of a standard mosquito suction trap was used to record 14,067 mosquito flights of Aedes and Culex genera at four temperature regimes in the laboratory, and the resulting dataset was used to train a machine learning (ML) model. The trap, sensor, and ML model, which form the core of an automated mosquito surveillance system, were tested in the field for two classification purposes: to discriminate Aedes and Culex mosquitoes from other insects that enter the trap and to classify the target mosquitoes by genus and sex. The field performance of the system was assessed using balanced accuracy and regression metrics by comparing the classifications made by the system with those made by the manual inspection of the trap. Results: The field system discriminated the target mosquitoes (Aedes and Culex genera) with a balanced accuracy of 95.5% and classified the genus and sex of those mosquitoes with a balanced accuracy of 88.8%. An analysis of the daily and seasonal temporal dynamics of Aedes and Culex mosquito populations was also performed using the time-stamped classifications from the system. Conclusions: This study reports results for automated mosquito genus and sex classification using an optical sensor coupled to a mosquito trap in the field with highly balanced accuracy. The compatibility of the sensor with commercial mosquito traps enables the sensor to be integrated into conventional mosquito surveillance methods to provide accurate automatic monitoring with high temporal resolution of Aedes and Culex mosquitoes, two of the most concerning genera in terms of arbovirus transmission.
- Changes in environmental exposures over decades may influence the genetic architecture of severe spermatogenic failurePublication . Cerván-Martín, Miriam; González-Muñoz, Sara; Guzmán-Jiménez, Andrea; Higueras-Serrano, Inmaculada; Castilla, José A.; Garrido, Nicolás; Luján, Saturnino; Bassas, Lluís; Seixas, Susana; Gonçalves, João; Lopes, Alexandra M; Larriba, Sara; Palomino-Morales, Rogelio J.; Bossini-Castillo, Lara; Carmona, F. DavidStudy question: Do the genetic determinants of idiopathic severe spermatogenic failure (SPGF) differ between generations? Summary answer: Our data support that the genetic component of idiopathic SPGF is impacted by dynamic changes in environmental exposures over decades. What is known already: The idiopathic form of SPGF has a multifactorial etiology wherein an interaction between genetic, epigenetic, and environmental factors leads to the disease onset and progression. At the genetic level, genome-wide association studies (GWASs) allow the analysis of millions of genetic variants across the genome in a hypothesis-free manner, as a valuable tool for identifying susceptibility risk loci. However, little is known about the specific role of non-genetic factors and their influence on the genetic determinants in this type of conditions. Study design, size, duration: Case-control genetic association analyses were performed including a total of 912 SPGF cases and 1360 unaffected controls. Participants/materials, setting, methods: All participants had European ancestry (Iberian and German). SPGF cases were diagnosed during the last decade either with idiopathic non-obstructive azoospermia (n = 547) or with idiopathic non-obstructive oligozoospermia (n = 365). Case-control genetic association analyses were performed by logistic regression models considering the generation as a covariate and by in silico functional characterization of the susceptibility genomic regions. Main results and the role of chance: This analysis revealed 13 novel genetic association signals with SPGF, with eight of them being independent. The observed associations were mostly explained by the interaction between each lead variant and the age-group. Additionally, we established links between these loci and diverse non-genetic factors, such as toxic or dietary habits, respiratory disorders, and autoimmune diseases, which might potentially influence the genetic architecture of idiopathic SPGF. Large scale data: GWAS data are available from the authors upon reasonable request. Limitations, reasons for caution: Additional independent studies involving large cohorts in ethnically diverse populations are warranted to confirm our findings. Wider implications of the findings: Overall, this study proposes an innovative strategy to achieve a more precise understanding of conditions such as SPGF by considering the interactions between a variable exposome through different generations and genetic predisposition to complex diseases.