Mental health problems are one of the various ills that afflict the world’s population. Early diagnosis and medical care are public health problems addressed from various perspectives. Among the mental illnesses that most afflict the population is depression; its early diagnosis is vitally important, as it can trigger more severe illnesses, such as suicidal ideation. Due to the lack of homogeneity in current diagnostic tools, the community has focused on using AI tools for opportune diagnosis. Unfortunately, there is a lack of data that allows the use of IA tools for the Spanish language. Our work has a cross-lingual scheme to address this issue, allowing us to identify Spanish and English texts. The experiments demonstrated the methodology’s effectiveness with an F1-score of 0.95. With this methodology, we propose a method to solve a classification problem for depression tweets (or short texts) by reusing English language databases with insufficient data to generate a classification model, such as in the Spanish language. We also validated the information obtained with public data to analyze the behavior of depression in Mexico during the COVID-19 pandemic. Our results show that the use of these methodologies can serve as support, not only in the diagnosis of depression, but also in the construction of different language databases that allow the creation of more efficient diagnostic tools.

Registration of remote sensing images has been approached using different strategies; one of the most popular is based on similarity measures. There are different measures of similarity in the literature: Normalized Cross-Correlation (NCC), Mutual Information (MI), etc. Normalized Mutual Information (NMI) has received the most attention in image processing; among the most important limitations are its high computational cost and lack of robustness to strong radiometric changes. For this reason, in this work, we introduce a co-registration approach based on the Histogram Kernel Predictability (HKP). This formulation reduces numerical errors and requires less computing time in comparison to NMI. To the best of our knowledge, this is the first work for registering any remote sensing images by using HKP. Additionally, we propose to use an algorithm based on meta-heuristics called Evolutionary Centers Algorithm (ECA), which allows having fewer iterations to solve the registration problem. In addition, we incorporate a parallelization scheme that permits reducing processing times. The results show that our proposal can solve co-registration problems that the NMI cannot solve while obtaining competitive computational times and registration errors comparable with other existing works in the literature. The HKP approach solves most of all the transformations of a set of simulated registration problems, while the NMI, in some cases, only solves half of the registration problems. Moreover, we compare our approach with feature-based methods in real datasets. This research presents an alternative to remote sensing problems where MI has traditionally been used.

With the objective to evaluate the prevalence of periodontal disease (PD) and monitor the most affected teeth in dogs under the subtropical conditions in Mexico, 184 randomly selected dogs underwent a periodontal examination. Of the evaluated dogs, 78.8% showed some degree of gingivitis most of them (76.6%) with a moderate index, and 30.4% presented some degree of PD being most of them of a mild degree. Old age and small size dogs were more prone to develop PD as reported elsewhere. Higher mobility index and furcation were seen in maxillary teeth 108 and 208 and mandibular teeth 308 and 408, but some other teeth were involved in less proportion. On probing, bleeding was observed in maxillary teeth 202, 11, 102, and 202 and mandibular teeth 309 and 409. Dental plaque and calculus were more frequent in maxillary teeth 202 and 102 and mandibular teeth 309, 310, and 409. Finally, dental loss was observed with more frequency in all maxillary incisors (102, 201, and 202) and molars 109 and 209; mandibular molars 411 and 311 were more prompt to be losed. This study demonstrates the high prevalence of gingivitis and development of PD in dogs in subtropics in Mexico and reveals the predisposition of some maxillary and mandibular teeth to develop PD and consequently their loss. The clinical implications of the study indicate that special attention should be paid to these teeth to be checked when the dogs come for consultation, during brushing and in dental prophylaxis.