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The present study investigates molecular‐based PCR techniques to estimate the prevalence of fish pathogens in southwest Mexico where recurrent mortality in the tilapia cultures has been observed. Sample of internal organs and lesions of Nile tilapia were taken and analysed in 2018, 2019, 2020 and 2022 to detect bacterial pathogens using PCR. No samples were taken in 2021 due to the COVID‐19 pandemic. The real‐time PCR conditions were optimized to allow a qualitative reliable detection of the bacteria from fixed fish tissue. A total of 599 pond‐ and cage‐cultured tilapia from the southwestern Mexican Pacific (Guerrero, Oaxaca and Chiapas states) were analysed. In this tropical region, during 2018 and 2019 water temperatures of the tilapia cultures were generally with the optimal range to grow Nile tilapia, although extreme values were recorded on some farms. Most of the tilapia sampled were apparently healthy. No Francisella sp. was detected in any sample, and Staphylococcus sp. was the most prevalent (from 0% to 64%) bacteria from the three states over time. Low prevalence of Aeromonas sp. was found, from 0% to 4.3%, although the fish pathogen Aeromonas dhakensis was not detected. Sterptococcus iniae was only detected in Chiapas in 2019 at a low prevalence (1.4%), while the major tilapia pathogen S. agalactiae was detected at a high prevalence (from 0% to 59%) in the three Mexican states. This is the first detection of these pathogenic bacteria in rural farms using real‐time PCR and constitutes a great risk for tilapia aquaculture in Mexico, as well as a potential dispersion of these pathogens to other aquaculture areas. [ABSTRACT FROM AUTHOR]

A quick count seeks to estimate the voting trends of an election and communicate them to the population on the evening of the same day of the election. In quick counts, the sampling is based on a stratified design of polling stations. Voting information is gathered gradually, often with no guarantee of obtaining the complete sample or even information in all the strata. However, accurate interval estimates with partial information must be obtained. Furthermore, this becomes more challenging if the strata are additionally study domains. To produce partial estimates, two strategies are proposed: (1) a Bayesian model using a dynamic post-stratification strategy and a single imputation process defined after a thorough analysis of historic voting information; additionally, a credibility level correction is included to solve the underestimation of the variance and (2) a frequentist alternative that combines standard multiple imputation ideas with classic sampling techniques to obtain estimates under a missing information framework. Both solutions are illustrated and compared using information from the 2021 quick count. The aim was to estimate the composition of the Chamber of Deputies in Mexico. [ABSTRACT FROM AUTHOR]

Gravity anomaly inversion is a technique used to estimate underground density distribution using gravity data. This paper proposes a new three-dimensional (3D) gravity anomaly inversion method based on the LinkNet network. Compared with two-dimensional gravity anomaly inversion, 3D gravity anomaly inversion can determine the density distribution of the entire region below the observation surface. Additionally, compared with traditional methods, the neural network method does not require the selection of initial parameters, and several predictive models can be quickly sought during the prediction stage. The Tversky loss was used to improve the inversion accuracy of the boundary. By comparing the inversion of the fully convolutional network, UNet network, and LinkNet network proposed in this paper on simulated data, it was observed that the model reconstruction error obtained using the LinkNet network had the best fitting effect with gravity data, which were 0.3526 and 0.0521. The results reveal that this method can achieve accurate inversion. Using the San Nicolas deposit in Mexico as an example, the proposed method and the improved preconditioned conjugate gradient algorithm were compared to further illustrate the effectiveness of the algorithm. The results reveal that the position and shape trends of the geological body attained using the proposed approach are in good agreement with the drilling data. [ABSTRACT FROM AUTHOR]