Central University of Ecuador

Objectives This systematic review with network meta-analysis (NMA) evaluated whether three-dimensional (3D) printed resin-based composites (RBCs) intended for long-term dental restorations perform similarly or superiorly to conventional restorative materials. Data sources The review followed the PRISMA extension for NMA. Six electronic databases were searched up to November 2025. Study selection In vitro studies assessing at least one commercially available 3D-printed RBC for long-term dental restorations and at least one conventional restorative material were eligible. Methodological quality was assessed using the RoBDEMAT tool, and data were synthesized using Bayesian NMA. Results From 2,299 records, 50 studies met the inclusion criteria, of which 40 were included in the meta-analyses. Across mechanical, physical, reproducibility, and optical outcomes, 3D-printed RBCs generally performed similarly to or worse than most conventional materials. However, they demonstrated superior marginal and internal fit compared with glass ceramics, polymer-infiltrated ceramic networks, and polymeric restoratives, except when compared with direct RBCs, indicating enhanced dimensional accuracy. Conclusions Although 3D printing achieved superior trueness and internal/marginal fit relative to most conventional restorative materials, the inferior mechanical performance, suboptimal microstructural characteristics, and limited optical stability of 3D-printed resins underscore the need for improvements in material formulation, post-curing strategies, and finishing and polishing protocols. Clinical significance At present, 3D-printed resins for long-term single-unit restorations do not yet match the overall performance of established conventional restorative systems.

Understanding human emotions is of paramount importance in security, healthcare, and educational fields. Although nonverbal communication—making use of facial expressions, head gesture, and eye contact—comprises most of the interpersonal interaction, the rising technology of AI in the emotional recognition is still in its early days. In this work we examine the Human Emotion Recognition (HER) technology that has software tool for detection and classification of emotions by means of regression, classification SMO, J48, Multilayer Perceptron and rule based learning Apriori algorithms. This work may help to connect human expertise and computational resources in the service of affective analysis in naturalistic contexts. Experimental results, validated through expert annotation, show that interpretable classification models—particularly J48—produce decision structures that are logically consistent with expert reasoning. Rather than claiming generalizable accuracy, the proposed framework is positioned as an interpretability-driven proof-of-concept that demonstrates how symbolic reasoning and human validation can be integrated into emotion-aware AI systems.

The Sumaco volcano in Ecuador, which has a distinct geological origin from the Andes and is located in the Amazon basin, offers an opportunity to study untouched microbiomes. We explored comparative patterns of abundance from predicted functional profiling in soil samples collected along the elevation and sulfur gradients on its slopes. Using 16S rRNA gene metabarcoding, we inferred metagenome functional profiles, contrasting sample groups by altitude or soil sulfur concentration. We inferred that high-altitude communities may have higher predicted abundance for anaerobic metabolism (crotonate fermentation), coenzyme B12 synthesis, and degradation of diverse carbon sources (sugars and octane). High-sulfur soils were associated with an inferred enrichment of pathways for degrading complex organic compounds and nitrogen metabolism, reflecting adaptation to unique geochemical conditions. In contrast, low-sulfur soils are consistent with a higher predicted abundance of glycerol degradation. Within the limitation imposed by the potential weak associations of the applied predicted functional profiling to actual gene content, we propose that the inferred metabolic changes represent different ecological strategies for resource acquisition, energy generation, and stress tolerance, and they are optimized for varying conditions in this unique volcanic ecosystem. Our findings highlight how environmental gradients shape soil microbiome functional diversity and offer insights into microbial adaptation in Sumaco’s exceptional geochemistry within the Amazon. Further efforts linking functional predictions back to specific taxa will offer a complete ecological perspective of the microbiome exploration in the Sumaco volcano.

Phaseolus vulgaris (common bean) is nodulated by diverse Rhizobium species. Although Ecuador is recognized as one of the centers of bean diversification, its native rhizobial diversity and geographic distribution remains poorly characterized. We isolated 46 native Rhizobium strains from root nodules across four Andean provinces (Imbabura, Pichincha, Chimborazo, and Loja). Partial sequencing of the recA gene delineated nine strain clusters (R1-R9) within two major phylogenetic groups: (i) Rhizobium ecuadorense/Rhizobium leguminosarum/Rhizobium etli/Rhizobium phaseoli and (ii) R. tropici. Multilocus sequence analysis (MLSA) of the housekeeping genes recA, glnII, dnaK genes from 19 representative isolates showed four phylogenetic clusters (C1-C4). Cluster C1 (R. ecuadorense-related) predominated in northern Ecuador; C2 formed a distinct Chimborazo cluster; C3 appeared sporadically in Imbabura and Chimborazo; and C4 (R. tropici-related) was confined to Loja's Amotape-Huancabamba Zone and displayed unique phenotypes. In greenhouse assays on two local bean varieties, all isolates formed nodules in both varieties; several isolates induced significantly higher nodule counts than the commercial inoculant UMR1899 (Rhizobium tropici IIB CIAT 899T). These results suggest geographic variation among Ecuadorian Rhizobium populations and identify locally predominant groups for further evaluation as bioinoculants.

Complex landslides have characteristics and parameters that are difficult to analyze. The landslide on 16 June 2024 in the rural community of Quilloturo (Tungurahua, Ecuador) caused severe damage (14 deaths, 24 injuries, and hundreds of affected families) related to the area’s geological, social, and anthropogenic conditions. Its location in the eastern foothills of Ecuador’s Cordillera Real exacerbated the effects of a landslide involving various processes (mud and debris flows, landslides, and rock falls). This event was preceded by intense rainfall lasting more than 10 h, which accumulated and caused natural damming of the streams prior to the event. The lithology of the investigated area includes deformed metamorphic and intrusive rocks overlain by superficial clayey colluvial deposits. The relationship between the geological structures found, such as fractures, joints, schistosity, and shear, favored the formation of blocks within the flow, making mass movement more complex. Geomorphologically, the area features a relief with steep slopes, where ancient landslides or material movements, composed of these colluvial deposits, have already occurred. At the foot of these steep slopes, on plains less than 300 m wide and bordered by the Pastaza River, there are human settlements with less than 60 years of emplacement and a complex history of territorial occupation, characterized by a lack of planning and organization. The memory of the inhabitants identified mass movements that have occurred since the mid-20th century, with the highest frequency of occurrence recorded in the last decade of the present century (2018, 2022, and 2024). Furthermore, it was possible to identify several factors within the knowledge of the inhabitants that can be considered premonitory of a mass movement, specifically a flood, and that must be incorporated as critical elements in decision-making, both individual and collective, for the evacuation of the area.