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In this paper, we tackle the problem of forecasting future pandemics by training models with a COVID-19 time series. We tested this approach by producing one model and using it to forecast a non-trained time series; however, we limited this paper to the eight states with the highest population density in Mexico. We propose a generalized pandemic forecasting framework that transforms the time series into a dataset via three different transformations using random forest and backward transformations. Additionally, we tested the impact of the horizon and dataset window sizes for the training phase. A Wilcoxon test showed that the best transformation technique statistically outperformed the other two transformations with 100% certainty. The best transformation included the accumulated efforts of the other two plus a normalization that helped rescale the non-trained time series, improving the sMAPE from the value of 25.48 attained for the second-best transformation to 13.53. The figures in the experimentation section show promising results regarding the possibility of forecasting the early stages of future pandemics with trained data from the COVID-19 time series. [ABSTRACT FROM AUTHOR]

In this paper, four probability functions are compared with the purpose of establishing a methodology to improve the accuracy of wind energy estimations in a desert city in Northwestern Mexico. Three time series of wind speed data corresponding to 2017, 2018, and 2019 were used for statistical modeling. These series were recorded with a sonic anemometer at a sampling frequency of 10 Hz. Analyses based on these data were performed at different stationarity periods (5, 30, 60, and 600 s). The estimation of the parameters characterizing the probability density functions (PDFs) was carried out using different methods; the statistical models were evaluated by the coefficient of determination and Nash–Sutcliffe efficiency coefficient, and their accuracy was estimated by the measured quadratic error, mean square error, mean absolute error, and mean absolute percentage error. Weibull, using the energy pattern factor method, and Gamma, using the Method of Moments, were the probability density functions that best described the statistical behavior of wind speed and were better at estimating the generated energy. We conclude that the proposed methodology will increase the confidence of both wind speed estimation and the energy supplied by small-scale wind installations. [ABSTRACT FROM AUTHOR]

Ecological Niche Models (ENM) are tools used to predict suitability, based on climatic variables selected and occurrence data of the target species, and characterize the environmental space. Sawflies (Hymenoptera: Diprionidae) are one of the main factors threatening forest health in Mexico, with cyclical population outbreaks and a wide range of hosts. In the present paper, we calculate the climatic niche in Mexico of three diprionids, Neodiprion abietis (Harris), N. omosus Smith, and Zadiprion rohweri (Middleton); the first and the latter with recent records in Mexico, and N. omosus with presence in Mexico and Guatemala; contrasting them against the distribution records of host species in the country. The climatic suitability of N. abietis was high in the Sierra Madre Occidental where its hosts, Pinus ponderosa, P. strobiformis, and P. menziesii are distributed. For N. omosus, the environmental suitability was projected towards the Southern Altiplano, where it coincides with a small presence of its hosts P. leiophylla and P. ayacahuite; however, it was possible to calculate its coincidence with more hosts in other biogeographic provinces. Pinaceae species considered under threat, Abies concolor, P. monophylla, and P. strobiformis, have populations within the environmental suitability of the sawflies. [ABSTRACT FROM AUTHOR]

Globally, there is growing concern over the presence of lead (Pb) in foods because it is a heavy metal with several toxic effects on human health. However, monitoring studies have not been conducted in Mexico. In this study, we estimated the concentrations of Pb in the most consumed foods and identified those that exceeded the maximum limits (MLs) for Pb in foods established by the International Standards. Based on the Mexican National Health and Nutrition Survey, 103 foods and beverages were selected and purchased in Mexico City retail stores and markets. Samples were analyzed twice using atomic absorption spectrophotometry. Values above the limit of quantification (0.0025 mg/kg) were considered to be detected. The percentage of detected values was 18%. The highest concentration was found in infant rice cereal (1.005 mg/kg), whole wheat bread (0.447 mg/kg), pre-cooked rice (0.276 mg/kg), black pepper (0.239 mg/kg), and turmeric (0.176 mg/kg). Among the foods with detected Pb, the levels in infant rice cereal, whole wheat bread, pre-cooked rice, and soy infant formula exceeded the MLs. The food groups with the highest percentages of exceeded MLs were baby foods (18%) and cereals (11%). Monitoring the concentration of contaminants in foods is essential for implementing food safety policies and protecting consumer health. [ABSTRACT FROM AUTHOR]

Leptospirosis is a neglected zoonotic disease that commonly affects cattle, pigs, horses, and dogs in many countries. Infection in dogs is usually subclinical, but acute cases of leptospirosis may occur along with systemic failure, which may become fatal. After recovery from an acute infection, dogs may become asymptomatic carriers and shed pathogenic leptospires through urine for long periods of time. Here, a study of ten different cases of leptospirosis is presented, showing the relevance of dogs as asymptomatic carriers of pathogenic Leptospira. The diagnosis was confirmed via isolation and further serological and genetic identification. Four Leptospira isolates (LOCaS28, 31, 34, and 46) were obtained from the kidneys and urine samples of 58 dogs destined for destruction (6.89%) at a Canine Control Center in Mexico City. No spirochetes were observed in the urine samples of those Leptospira-positive dogs examined under dark-field microscopy, and no clinical signs of disease were observed either. Six additional isolates were obtained: two came from asymptomatic carrier dogs (CEL60 and UADY22); another isolate came from an asymptomatic dog that was a pack companion of a clinically ill dog with fatal leptospirosis (AGFA24); and finally, three isolates were taken from dogs that died of leptospirosis (LOCaS59, Citlalli, and Nayar1). Nine out of the ten isolates were identified as being from the serogroup Canicola via cross-absorption MAT using reference strains and specific antisera, and their identity was genetically confirmed as Canicola ST34 via multi-locus sequencing typing (MLST). In contrast, the isolate Nayar1 was identified as serovar Copenhageni ST2. Interestingly, the asymptomatic dogs from which Leptospira isolates were recovered consistently showed high antibody titers in the microscopic agglutination test (MAT), revealing values of at least 1:3200 against serogroup Canicola and lower titer values against other serogroups. Isolates showed different virulence levels in the hamster model. Taken as a whole, all these findings confirmed that dogs may act as asymptomatic carriers of pathogenic leptospires and possibly spread them out to the environment, thus representing an active public health risk. The results also showed that the Canicola ST34 clone is the most prevalent Leptospira serovar in dogs in Mexico, and finally that the old-fashioned MAT is a good alternative for the detection of presumptive Leptospira asymptomatic carrier dogs. [ABSTRACT FROM AUTHOR]

Simple Summary: In Mexico, smallholder farmers produce locally adapted maize varieties, generally called landraces, for food. They participate in the conservation of landraces by selecting and storing their seeds from one production cycle to another, but are facing challenges such as storage losses caused by insects (mainly Sitophilus zeamais and Prostephanus truncatus) and climate change. In the current study, the effectiveness of the conventional storage practices of farmers in the highlands of Mexico (polypropylene woven bags) in minimizing storage losses and maintaining seed germination was compared with hermetic storage technologies. After six and three months of storage, the percentage of insect damage and weight loss was highest in samples stored in polypropylene woven bags, reaching 61.4% and 23.4% after six months of storage. On the contrary, with hermetic technologies, storage losses were minimal, with maximums of 4.1% and 2.2% for insect damage and weight loss, respectively. Overall, the germination rate of samples stored in these airtight containers was greater than 90%. The results of this study demonstrate the potential of hermetic technologies in preserving the biodiversity of maize seed landraces and strengthening smallholders' food security. Smallholder farmers who grow maize landraces face important challenges to preserve their seed biodiversity from one season to another. This study was carried out in the central highlands of Mexico to compare the effectiveness of two seed storage practices—specifically, polypropylene woven bags (farmers' conventional practice) vs. hermetic containers—for minimizing seed losses and maintaining germination. Four Mexican landraces were stored for three and six months. Data on moisture content and kernel damage were collected at the beginning and the end of the storage period. Pest-free samples collected were also analyzed for seed germination. Moisture content was below 13% overall and was not significantly affected by storage technology or storage time. Samples from the polypropylene woven bags suffered significant damage from Sitophilus zeamais and Prostephanus truncatus, with the percentages of insect damage and weight loss reaching 61.4% and 23.4%, respectively. Losses were minimal in seed stored in hermetic containers, with a maximum insect damage of 4.1% and weight loss of 2.2%. Overall, the germination rate of samples stored in these airtight containers was greater than 90%. This study provides additional evidence on the effectiveness of hermetic containers at maintaining Mexican landraces' seed quantity and quality during storage in smallholder conditions in central Mexico. [ABSTRACT FROM AUTHOR]

Some of the effects of climate change may be related to a change in patterns of rainfall intensity or scarcity. Therefore, humanity is facing environmental challenges due to an increase in the occurrence and intensity of droughts. The forecast of droughts can be of great help when trying to reduce the adverse effects that the scarcity of water brings, particularly in agriculture. When evaluating the conditions of water scarcity, as well as in the identification and characterization of droughts, the use of predictive models of drought indices could be a very useful tool. In this research, the utility of Artificial Neural Networks with exogenous inputs was tested, with the aim of predicting the monthly Standardized Precipitation Index in 4 regions (Semi-desert, Highlands, Canyons and Mountains) of north-central México using predictor data from 1979 to 2014. The best model was found using the scaled conjugate gradient backpropagation algorithm as the optimization method and was set to the following architecture: 6-25-1 network. The correlation coefficient of predicted and observed Standardized Precipitation Index values for the test dataset was between 0.84 and 0.95. As a result, the Artificial Neural Network models performed successfully in predicting Standardized Precipitation Index at the four analyzed regions. The developed and tested Artificial Neural Network models in this research suggest remarkable prediction abilities of the monthly Standardized Precipitation Index in the study region. [ABSTRACT FROM AUTHOR]

The Mexico City Metropolitan Area, located within the Mexico Basin, is the most important economic center in Mexico. An ever-growing population, currently at 22 million with increasing water demands, has resulted in the overexploitation of groundwater with associated impacts to hydrological conditions for a century. Land subsidence due to chronic groundwater level declines has damaged infrastructure and increased water delivery and flood control challenges, causing loss of aquifer storage. An additional associated problem is groundwater quality deterioration, which reduces potable supplies due to increasing anthropogenic pollution and salinization. A new integrated conceptual model of the Mexico Basin Aquifer has been constructed based on a comprehensive compilation of existing and new hydrogeological knowledge. As a result, this conceptual model updates and improves the understanding of the characteristics of the aquifer and current hydrodynamic behavior of groundwater. Four hydrogeological units were identified, their heads and related flow system interdependencies were evaluated and their hydraulic properties associated; this allowed identifying local, intermediate and regional flow systems, aquifer transition from confined to unconfined conditions, changes to land subsidence and groundwater quality deterioration. This conceptual model could be the basis in building a numerical model, and as a powerful tool to test different management scenarios for decision-making. [ABSTRACT FROM AUTHOR]

Automobile security became an essential theme over the last years, and some automakers invested much money for collision avoidance systems, but personalization of their driving systems based on the user's behavior was not explored in detail. Furthermore, efficiency gains could be had with tailored systems. In Mexico, 80% of automobile accidents are caused by human beings; the remaining 20% are related to other issues such as mechanical problems. Thus, 80% represents a significant opportunity to improve safety and explore driving efficiency gains. Moreover, when driving aggressively, it could be connected with mental health as a post-traumatic stress disorder. This paper proposes a Tailored Collision Mitigation Braking System, which evaluates the driver's personality driving treats through signal detection theory to create a cognitive map that understands the driving personality of the driver. In this way, aggressive driving can be detected; the system is then trained to recognize the personality trait of the driver and select the appropriate stimuli to achieve the optimal driving output. As a result, when aggressive driving is detected continuously, an automatic alert could be sent to the health specialists regarding particular risky behavior linked with mental problems or drug consumption. Thus, the driving profile test could also be used as a detector for health problems. [ABSTRACT FROM AUTHOR]

For 20 years, Plasmodium vivax has been the only prevalent malaria species in Mexico, and cases have declined significantly and continuously. Spatiotemporal genetic studies can be helpful for understanding parasite dynamics and developing strategies to weaken malaria transmission, thus facilitating the elimination of the parasite. The aim of the current contribution was to analyze P. vivax-infected blood samples from patients in southern Mexico during the control (1993–2007) and pre-elimination phases (2008–2011). Nucleotide and haplotype changes in the pvmsp142 fragment were evaluated over time. The majority of multiple genotype infections occurred in the 1990s, when the 198 single nucleotide sequences exhibited 57 segregating sites, 64 mutations, and 17 haplotypes. Nucleotide and genetic diversity parameters showed subtle fluctuations from across time, in contrast to the reduced haplotype diversity and the increase in the R2 index and Tajima's D value from 2008 to 2011. The haplotype network consisted of four haplogroups, the geographical distribution of which varied slightly over time. Haplogroup-specific B-cell epitopes were predicted. Since only high-frequency and divergent haplotypes persisted, there was a contraction of the parasite population. Given that 84% of haplotypes were exclusive to Mesoamerica, P. vivax flow is likely circumscribed to this region, representing important information for parasite surveillance. [ABSTRACT FROM AUTHOR]