Universidad Nacional de Chimborazo

Andrade R.O., Yoo S.G., Tello-Oquendo L., Ortiz-Garces I.

Smart cities exploit emerging technologies such as Big Data, the Internet of Things (IoT), Cloud Computing, and Artificial Intelligence (AI) to enhance public services management. The use of IoT allows detecting and reporting specific parameters related to different domains of the city, such as health, waste management, agriculture, transportation, and energy. LoRa technologies, for instance, are used to develop IoT solutions for several smart city domains thanks to its available features, but sometimes people (i.e., citizens, information technology administrators, or city managers) might think that these available features involve cybersecurity risks. This study explores the cybersecurity aspects that define an assessment model of cybersecurity maturity of IoT solutions to develop smart city applications. In that sense, we perform a systematic literature review based on a top-down approach of cybersecurity incident response in IoT ecosystems. Besides, we propose and validate a model based on risk levels to evaluate the IoT cybersecurity maturity in a smart city.

Soria X., Sappa A., Humanante P., Akbarinia A.

Edge detection is the basis of many computer vision applications. State of the art predominantly relies on deep learning with two decisive factors: dataset content and network architecture. Most of the publicly available datasets are not curated for edge detection tasks. Here, we address this limitation. First, we argue that edges, contours and boundaries, despite their overlaps, are three distinct visual features requiring separate benchmark datasets. To this end, we present a new dataset of edges. Second, we propose a novel architecture, termed Dense Extreme Inception Network for Edge Detection (DexiNed), that can be trained from scratch without any pre-trained weights. DexiNed outperforms other algorithms in the presented dataset. It also generalizes well to other datasets without any fine-tuning. The higher quality of DexiNed is also perceptually evident thanks to the sharper and finer edges it outputs.

Pazzaglia J., Santillán-Sarmiento A., Helber S.B., Ruocco M., Terlizzi A., Marín-Guirao L., Procaccini G.

Seagrass meadows are disappearing at rates comparable to those reported for mangroves, coral reefs, and tropical rainforests. One of the main causes of their decline is the so-called cultural eutrophication, i.e., the input of abnormal amounts of nutrients derived from human activities. Besides the impact of eutrophication at a local scale, the occurrence of additional stress factors such as global sea warming may create synergisms in detriment of seagrass meadows’ health. In the present study, we aimed to evaluate if plants undergoing chronic cultural eutrophication and plants growing in relatively pristine waters are more (or less) sensitive to heat stress, nutrient load and the combination of both stressors. To address this question, a mesocosm experiment was conducted using Posidonia oceanica collected from two environments with different nutrients load history. Plants were exposed in controlled conditions to high nutrient concentrations, increased temperature and their combination for 5 weeks, to assess the effect of the single stressors and their interaction. Our results revealed that plants experiencing chronic cultural eutrophication (EU) are more sensitive to further exposure to multiple stressors than plants growing in oligotrophic habitats (OL). OL and EU plants showed different morphological traits and physiological performances, which corroborates the role of local pressures in activating different strategies in response to global environmental changes. EU-plants appeared to be weaker during the treatments, showing the greatest percentage of mortality, particularly under increased temperature. Temperature and nutrient treatments showed opposite effects when tested individually and an offset response when combined. The activation of physiological strategies with high energetic expenses to cope with excess of nutrients and other stressors, could affect plants present and future persistence, particularly under eutrophic conditions. Our results represent a step forward in understanding the complex interactions that occur in natural environments. Moreover, unraveling intraspecific strategies and the role of local acclimation/adaptation in response to multiple stressors could be crucial for seagrass conservation strategies under a climate change scenario.

Machado Sotomayor M.J., Arufe-Giráldez V., Ruíz-Rico G., Navarro-Patón R.

Parkinson’s disease can be approached from various points of view, one of which is music therapy—a complementary therapy to a pharmacological one. This work aims to compile the scientific evidence published in the last five years (2015–2020) on the effects of music therapy in patients with Parkinson’s disease. A systematic review has been performed using the Web of Science and Scopus databases with the descriptors “music therapy” and “Parkinson’s disease”. A total of 281 eligible articles were identified, which, after applying the inclusion and exclusion criteria, were reduced to 58 papers. The results display a great diversity of evidence, confirming positive effects on various spheres. All mentioned patients with Parkinson’s disease had experienced different music therapy programs. Some studies focused on the motor component, which can be addressed through listening, body rhythm, and rhythmic auditory stimulation. Other studies confirm effects on communication, swallowing, breathing, and the emotional aspect through programs that focus on singing, either individually or in groups, in order to improve the quality of life of people with PD. It was concluded that music therapy programs can achieve improvements in various areas of patients with Parkinson’s.

Pazzaglia J., Nguyen H.M., Santillán-Sarmiento A., Ruocco M., Dattolo E., Marín-Guirao L., Procaccini G.

Seagrasses are marine flowering plants providing key ecological services and functions in coasts and estuaries across the globe. Increased environmental changes fueled by human activities are affecting their existence, compromising natural habitats and ecosystems’ biodiversity and functioning. In this context, restoration of disturbed seagrass environments has become a worldwide priority to reverse ecosystem degradation and to recover ecosystem functionality and associated services. Despite the proven importance of genetic research to perform successful restoration projects, this aspect has often been overlooked in seagrass restoration. Here, we aimed to provide a comprehensive perspective of genetic aspects related to seagrass restoration. To this end, we first reviewed the importance of studying the genetic diversity and population structure of target seagrass populations; then, we discussed the pros and cons of different approaches used to restore and/or reinforce degraded populations. In general, the collection of genetic information and the development of connectivity maps are critical steps for any seagrass restoration activity. Traditionally, the selection of donor population preferred the use of local gene pools, thought to be the best adapted to current conditions. However, in the face of rapid ocean changes, alternative approaches such as the use of climate-adjusted or admixture genotypes might provide more sustainable options to secure the survival of restored meadows. Also, we discussed different transplantation strategies applied in seagrasses and emphasized the importance of long-term seagrass monitoring in restoration. The newly developed information on epigenetics as well as the application of assisted evolution strategies were also explored. Finally, a view of legal and ethical issues related to national and international restoration management is included, highlighting improvements and potential new directions to integrate with the genetic assessment. We concluded that a good restoration effort should incorporate: (1) a good understanding of the genetic structure of both donors and populations being restored; (2) the analysis of local environmental conditions and disturbances that affect the site to be restored; (3) the analysis of local adaptation constraints influencing the performances of donor populations and native plants; (4) the integration of distribution/connectivity maps with genetic information and environmental factors relative to the target seagrass populations; (5) the planning of long-term monitoring programs to assess the performance of the restored populations. The inclusion of epigenetic knowledge and the development of assisted evolution programs are strongly hoped for the future.

Zadeh Shirazi A., Fornaciari E., McDonnell M.D., Yaghoobi M., Cevallos Y., Tello-Oquendo L., Inca D., Gomez G.A.

In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images.

Soria X., Pomboza-Junez G., Sappa A.D.

This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection. The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 4% of parameters in comparison with these approaches. The proposed architecture generates thin edge maps and reaches the highest score (i.e., ODS) when compared with lightweight models (models with less than 1 million parameters), and reaches a similar performance when compare with heavy architectures (models with about 35 million parameters). Both quantitative and qualitative results and comparisons with state-of-the-art models, using different edge detection datasets, are provided. The proposed LDC does not use pre-trained weights and requires straightforward hyper-parameter settings. The source code is released at https://github.com/xavysp/LDC .

Baykara H., Cornejo M.H., Espinoza A., García E., Ulloa N.

The study of the fiber-matrix interface represents a crucial topic to determine the mechanical performance of geopolymer-based materials reinforced with polypropylene fibers (PPF). This research proposes the use of natural zeolite in the preparation geopolymers mortars through alkaline activation with NaOH, Ca(OH)2 and Na2SiO3, and with river sand as a fine aggregate. PPF were incorporated into the geopolymer-based mortar matrix in different proportions like 0, 0.5, and 1 wt.%. The mortars were cured for 24 h at 60 °C and then aged for six days more at room temperature. All samples analyzed through compressive strength were also characterized by X-ray diffraction, thermal analysis, Infrared Spectroscopy, and scanning electron microscopy techniques. The results indicated that the best mix design among the ones used: NaOH (10 M), Na2SiO3/NaOH = 3, Ca(OH)2 = 1.5 wt.% and PPF = 0.5 wt.%. The optimum mix design showed a compressive strength of 4.63 MPa on average. Besides, the fibers enhanced the compressive strength of those samples which the PP fibers probably have better dispersion inside the matrix of the geopolymer mortar.

Tenelanda-López D., Valdivia-Moral P., Castro-Sánchez M.

The objective of this research was to compare the healthy behaviors and caries index of young people in school to obtain an overview of their lifestyles, which would enable the development of educational programs for the promotion of oral health. The study design was carried out using a descriptive, cross-sectional, and observational methodology with a mixed approach. 380 twelve-year-old students participated in this research conducted in the city of Riobamba-Ecuador. The techniques used were observational and surveys with their respective instruments, the Dental Clinical History, and the Health Behavior in School-aged Children 2014-Spain questionnaire. The community index of the Decayed, Missing due to caries, and Filled Teeth (DMFT) reflected a high level (6.47) in the study subjects. A variety of foods such as fruits, chips, vegetables, candy, sugar-containing drinks, meat, fish, dairy, and cereals were consumed at least once a week by most students. Two statistically significant associations were demonstrated in this investigation. The first one was between fruit consumption and the DMFT index, the second one was between vegetable consumption and the DMFT index. Both associations showed significant values (p) of 0.049 and 0.028, respectively; these were not determining indicators since caries is a multifactorial pathology, which can develop not only as a product of poor eating habits.

Alda P., Lounnas M., Vázquez A.A., Ayaqui R., Calvopiña M., Celi-Erazo M., Dillon R.T., González Ramírez L.C., Loker E.S., Muzzio-Aroca J., Nárvaez A.O., Noya O., Pereira A.E., Robles L.M., Rodríguez-Hidalgo R., et. al.

Cryptic species can present a significant challenge to the application of systematic and biogeographic principles, especially if they are invasive or transmit parasites or pathogens. Detecting cryptic species requires a pluralistic approach in which molecular markers facilitate the detection of coherent taxonomic units that can then be analyzed using various traits (e.g., internal morphology) and crosses. In asexual or self-fertilizing species, the latter criteria are of limited use. We studied a group of cryptic freshwater snails (genus Galba) from the family Lymnaeidae that have invaded almost all continents, reproducing mainly by self-fertilization and transmitting liver flukes to humans and livestock. We aim to clarify the systematics, distribution, and phylogeny of these species with an integrative approach that includes morphology, molecular markers, wide-scale sampling across America, and data retrieved from GenBank (to include Old World samples). Our phylogenetic analysis suggests that the genus Galba originated ca. 22 Myr ago and today comprises six species or species complexes. Four of them show an elongated-shell cryptic phenotype and exhibit wide variation in their genetic diversity, geographic distribution, and invasiveness. The remaining two species have more geographically restricted distributions and exhibit a globose-shell cryptic phenotype, most likely phylogenetically derived from the elongated one. We emphasize that no Galba species should be identified without molecular markers. We also discuss several hypotheses that can explain the origin of cryptic species in Galba, such as convergence and morphological stasis.