Universidad Galileo
  • Guatemala City, Guatemala
Recent publications
Registration of dynamic CT image sequences is a crucial preprocessing step for clinical evaluation of multiple physiological determinants in the heart such as global and regional myocardial perfusion. In this work, we present a deformable deep learning-based image registration method for quantitative myocardial perfusion CT examinations, which in contrast to previous approaches, takes into account some unique challenges such as low image quality with less accurate anatomical landmarks, dynamic changes of contrast agent concentration in the heart chambers and tissue, and misalignment caused by cardiac stress, respiration, and patient motion. The introduced method uses a recursive cascade network with a ventricle segmentation module, and a novel loss function that accounts for local contrast changes over time. It was trained and validated on a dataset of n = 118 patients with known or suspected coronary artery disease and/or aortic valve insufficiency. Our results demonstrate that the proposed method is capable of registering dynamic cardiac perfusion sequences by reducing local tissue displacements of the left ventricle (LV), whereas contrast changes do not affect the registration and image quality, in particular the absolute CT (HU) values of the entire CT sequence. In addition, the deep learning-based approach presented reveals a short processing time of a few seconds compared to conventional image registration methods, demonstrating its application potential for quantitative CT myocardial perfusion measurements in daily clinical routine.
In Guatemala, neurocysticercosis (NCC) was first recognized in 1940; since then, cases of NCC have been reported in all Guatemalan departments. However, epidemiological studies on Taenia solium infections are scarce and most information remains unpublished. This study aims to provide evidence of T. solium infections as a public health problem in Guatemala. All information available, either published or unpublished, on T. solium infections in the country was compiled. Official data from the Ministry of Health for the period 2002-2019 were reviewed and analyzed, and all cases of T. solium infections were classified and counted. In total, 5246 cases of taeniasis and 454 cases of human cysticercosis were recorded. On the other hand, 44 studies were identified, mostly from local journals, which included 1591 cases of taeniasis, 543 cases of human cysticercosis, and 2590 cases of porcine cysticercosis. Cases were classified by geographic region, patient sex, and Taenia species in taeniasis cases when information was available, and the departments with the highest number of taeniasis and cysticercosis cases were identified. Meanwhile, in Zacapa, a northeastern department of Guatemala with one the highest number of taeniasis cases, a young man diagnosed with a severe form of NCC and two cases of porcine cysticercosis (both confirmed by necropsy) were identified. Taken together, the data herein reported indicate that T. solium infections are a major health problem in Guatemala that needs to be addressed.
Background and Objective Folate and vitamin B12 deficiencies can impair proper growth and brain development in children. Data on the folate and vitamin B12 status of children aged 6–59 months in Guatemala are scarce. Identification of factors associated with higher prevalence of these micronutrient deficiencies within the population is needed for national and regional policymakers. Objective To describe national and regional post-fortification folate and vitamin B12 status of children aged 6–59 months in Guatemala. Methods A multistage, cluster probability study was carried out with national and regional representation of children aged 6–59 months. Demographic and health information was collected for 1246 preschool children, but blood samples for red blood cell (RBC) folate and vitamin B12 were collected and analyzed for 1,245 and 1143 preschool children, respectively. We used the following deficiency criteria as cutoff points for the analyses: < 305 nmol/L for RBC folate, < 148 pmol/L for vitamin B12 deficiency, and 148–221 pmol/L for marginal vitamin B12 deficiency. Prevalence of RBC folate deficiency and vitamin B12 deficiency and marginal deficiency were estimated. Prevalence risk ratios of RBC folate and vitamin B12 deficiency were estimated comparing subpopulations of interest. Results The national prevalence estimates of RBC folate deficiency among children was 33.5% [95% CI 29.1, 38.3]. The prevalence of RBC folate deficiency showed wide variation by age (20.3–46.6%) and was significantly higher among children 6–11 months and 12–23 months (46.6 and 37.0%, respectively), compared to older children aged 48–59 months (20.3%). RBC folate deficiency also varied widely by household wealth index (22.6–42.0%) and geographic region (27.2–46.7%) though the differences were not statistically significant. The national geometric mean for RBC folate concentrations was 354.2 nmol/L. The national prevalences of vitamin B12 deficiency and marginal deficiency among children were 22.5% [95% CI 18.2, 27.5] and 27.5% [95% CI 23.7, 31.7], respectively. The prevalence of vitamin B12 deficiency was significantly higher among indigenous children than among non-indigenous children (34.5% vs. 13.1%, aPRR 2.1 95% CI 1.4, 3.0). The prevalence of vitamin B12 deficiency also significantly varied between the highest and lowest household wealth index (34.3 and 6.0%, respectively). The national geometric mean for vitamin B12 concentrations was 235.1 pmol/L. The geometric means of folate and B12 concentrations were significantly lower among children who were younger, had a lower household wealth index, and were indigenous (for vitamin B12 only). Folate and vitamin B12 concentrations showed wide variation by region (not statistically significant), and the Petén and Norte regions showed the lowest RBC folate and vitamin B12 concentrations, respectively. Conclusions In this study, a third of all children had RBC folate deficiency and half were vitamin B12 deficient. Folate deficiency was more common in younger children and vitamin B12 deficiency was more common in indigenous children and those from the poorest families. These findings suggest gaps in the coverage of fortification and the need for additional implementation strategies to address these gaps in coverage to help safeguard the health of Guatemalan children.
Biomechanical and clinical gait research observes muscles and tendons in limbs to study their functions and behaviour. Therefore, movements of distinct anatomical landmarks, such as muscle-tendon junctions, are frequently measured. We propose a reliable and time efficient machine-learning approach to track these junctions in ultrasound videos and support clinical biomechanists in gait analysis. In order to facilitate this process, a method based on deep-learning was introduced. We gathered an extensive data set, covering 3 functional movements, 2 muscles, collected on 123 healthy and 38 impaired subjects with 3 different ultrasound systems, and providing a total of 66864 annotated ultrasound images in our network training. Furthermore, we used data collected across independent laboratories and curated by researchers with varying levels of experience. For the evaluation of our method a diverse test-set was selected that is independently verified by four specialists. We show that our model achieves similar performance scores to the four human specialists in identifying the muscle-tendon junction position. Our method provides time-efficient tracking of muscle-tendon junctions, with prediction times of up to 0.078 seconds per frame (approx. 100 times faster than manual labeling). All our codes, trained models and test-set were made publicly available and our model is provided as a free-to-use online service on https://deepmtj.org/.
The complexity of the user interfaces and the operating modes present in numerous assistive devices, such as intelligent prostheses, influence patients to shed them from their daily living activities. A methodology to evaluate how diverse aspects impact the workload evoked when using an upper-limb bionic prosthesis for unilateral transradial amputees is proposed and thus able to determine how user-friendly an interface is. The evaluation process consists of adapting the same 3D-printed terminal device to the different user-prosthesis-interface schemes to facilitate running the tests and avoid any possible bias. Moreover, a study comparing the results gathered by both limb-impaired and healthy subjects was carried out to contrast the subjective opinions of both types of volunteers and determines if their reactions have a significant discrepancy, as done in several other studies.
Many higher education institutions (HEIs) around the world are involved in a variety of sustainability initiatives. These are acknowledged to be important elements in fostering the cause of sustainability in HEIs, in further developing the organizations’ culture and in acting as enablers in the institutional embedding of sustainability. But despite the relevance of sustainability initiatives, there is a lack of systematic international efforts in how best to map them, especially in Latin America. On the basis of the need to address this gap, this paper reports on the results of an empirical study, aimed at analyzing the current status of sustainability initiatives among Latin American HEIs. Apart from a review of the latest literature, an international survey was performed to design a model using principal component analysis to identify the main descriptors of sustainability initiatives among Latin American HEIs and also the major drivers and challenges. The study sheds some light on the ways universities perceive and handle sustainability-related initiatives. The results show that sustainability is being incorporated in more than 80% of the sampled universities, and that a special emphasis is being given to campus operations. The value of the paper resides on the fact that it one of the few papers that have holistically investigated trends in sustainable development across universities in Latin America. The implications of the study are twofold. It maps for the first time how sustainable development initiatives are being practiced in 157 universities in 13 countries, being one of the most comprehensive studies of its kind, and it also outlines some of the main challenges that universities in the region face. The central message of this paper is that the different levels of emphasis given to SD in Latin American universities need to be better understood in order to catalyze continued and long-term actions.
The Internet has proven to be able to connect billions of devices across the globe. In order to keep pace with today's high demands or even larger services and applications, traditional ways of networking will have to change or update. The future of computer networks needs to be agile without degrading efficiency, capacity, and availability. These new trends push forward the need for network programmability. Software‐defined networkings (SDNs) are the future of networking, but in order to deploy newer standards, they have to be tested. Furthermore, this calls for better testbeds that are close to a real environment as possible. Many tools have been proposed to provide a framework for testing newer approaches of networking, but they have come short in various characteristics, limiting the scenarios and their results. Our experience in containers (Docker) and the development of testbeds using this technology has now brought our attention to SDNs. DockSDN provides a tool that meets current and future needs and is our biggest contribution to the scientific community so far. DockSDN is presented in this work, which proposes various benefits and advantages over other tools. One of those benefits is that it uses dockers as building blocks for scenario deployment. Moreover, these scenarios can be done fairly quickly and is fully scalable through local PC hardware or elastic through cloud services. Now, the scientific community will be able to test a wide array of protocols in near real‐world conditions, saving financial resources and time.
Learning Analytics is a vast concept and a rapidly growing field in higher education used by professors to measure, collect and analyze digital learning records to improve learning, generate new pedagogies, and make decisions about technology-driven learning. The following article presents a mapping and systematic literature review on Learning Analytics and its link to the teaching skills carried out in university practice. The research process reviewed 7,886 articles during the period from 2016 to 2020. After applying the inclusion and exclusion criteria, 50 articles were analyzed in-depth under the dimensions of (1) purposes of Learning Analytics, (2) teaching competencies, and (3) teaching practice in higher education. This work provides a basis for identifying gaps and research opportunities related to the application of teaching competencies in the field of Learning Analytics and incorporating it into teaching practice in online tutoring.
Elucidation of human genome has increased understanding of human body response to drug administration (Nabirotchkin et al., 2020). Likewise, recent studies on the human genetic diversity have shown that it is still necessary to delve into individual genetic differences, adverse effects associated with drug metabolism and the drug response variability with the diet and even the human microbiome (Sharma et al., 2019). Access to biological samples in Latin America is essential to know the presence of genes that may be associated with adverse effects and pharmacological interactions due to their diversity of populations and to anticipate effects of new treatments. The Coronavirus disease 2019 (COVID-19) pandemic has commitment the urgent call to study the genetic differences amongst people with mild symptoms and those with complex responses to the disease (Ovsyannikova et al., 2020). During the pandemic, different drugs have been studied in search for therapeutic alternatives to combat it, mainly due to the "Solidarity Trial" and "Repurposing Drugs" initiatives of the World Health Organization (OMS) (Harrison, 2020). Similarly, new drugs for actual and future diseases can be designed by using pharmacogenetic information. Due to the diversity of drugs therapeutic action mechanisms, it is necessary to study in parallel the genetic world populations susceptibility to the entry of SARS-CoV-2 into the human cell. Pharmacogenetics studies of their allele variants is essential.
The use of different cardiac imaging modalities such as MRI, CT or ultrasound enables the visualization and interpretation of altered morphological structures and function of the heart. In recent years, there has been an increasing interest in AI and deep learning that take into account spatial and temporal information in medical image analysis. In particular, deep learning tools using temporal information in image processing has not yet found its way into daily clinical practice, despite its presumed high diagnostic and prognostic value. This review aims to synthesize the most relevant deep learning methods and discuss their clinical usability in dynamic cardiac imaging using for example the complete spatiotemporal image information of the heart cycle. Selected articles were categorized according to the following indicators: clinical applications, quality of datasets, preprocessing and annotation, learning methods and training strategy, and test performance. Clinical usability was evaluated based on these criteria by classifying the selected papers into (i) clinical level, (ii) robust candidate and (iii) proof of concept applications. Interestingly, not a single one of the reviewed papers was classified as a “clinical level” study. Almost 39% of the articles achieved a “robust candidate” and as many as 61% a “proof of concept” status. In summary, deep learning in spatiotemporal cardiac imaging is still strongly research-oriented and its implementation in clinical application still requires considerable efforts. Challenges that need to be addressed are the quality of datasets together with clinical verification and validation of the performance achieved by the used method.
La salud pública ha tenido un giro desde hace varios años mediante la articulación de acciones y funciones de las diferentes profesiones, a través de lineamientos dados por diversas instituciones internacionales tales como la Organización Mundial de Sanidad Animal (OIE), la Organización Mundial de la Salud (OMS) y la Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO). Esta articulación busca generar un equilibrio desde el punto de vista de la interrelación del hombre, el medioambiente y los animales, considerándolos como un todo y no como actores separados. En esta interfaz las poblaciones humanas tienen un papel imprescindible en el desarrollo de programas sanitarios que incluyan la prevención y el control de enfermedades transmisibles por alimentos y vectores, además de generar un equilibro que busque la producción eficiente y sostenible de proteína animal para la seguridad alimentaria del ser humano. En este documento se describen unos antecedentes del concepto de una salud, se profundiza en el concepto de zoonosis y se describe cómo es el abordaje desde Una Salud en este tipo de enfermedades. Así mismo, se aclara, profundiza y pone sobre la mesa la discusión y la importancia del abordaje de las zoonosis en el modelo descrito por diversas instituciones mundiales de lo que es Una Salud.
This workshop proposes specifically soliciting contributions and presentations from initiatives, programs, and platforms around the world. While many of these may already be pre-sented at the full conference, we are also interested in more casual experience reports, case studies, and background presentations from individuals more closely acquainted with how learning at scale initiatives—including MOOCs, for-credit degree programs, informal learning environments, government initiatives, and so on—have unique needs and opportunities based on their local context. We refer to this as Global Learning @ Scale.
The strict development processes of commercial upper-limb prostheses and the complexity of research projects required for their development makes them expensive for end users, both in terms of acquisition and maintenance. Moreover, many of them possess complex ways to operate and interact with the subjects, influencing patients to not favor these devices and shed them from their activities of daily living. The advent of 3D printers allows for distributed open-source research projects that follow new design principles; these consider simplicity without neglecting performance in terms of grasping capabilities, power consumption and controllability. In this work, a simple, yet functional design based on 3D printing is proposed, with the aim to reduce costs and manufacturing time. The operation process consists in interpreting the user intent with electromyography electrodes, while providing visual feedback through a μLCD screen. Its modular, parametric and self-contained design is intended to aid people with different transradial amputation levels, despite of the socket’s constitution. This approach allows for easy updates of the system and demands a low cognitive effort from the user, satisfying a trade-off between functionality and low cost. It also grants an easy customization of the amount and selection of available actions, as well as the sensors used for gathering the user intent, permitting alterations to fit the patients’ unique needs. Furthermore, experimental results showed an apt mechanical performance when interacting with everyday life objects, in addition to a highly accurate and responsive controller; this also applies for the user-prosthesis interface.
To stay competitive in the marketplace, manufacturing firms that rely on high product variety should involve the customer in the product design process. However, customer interaction with the product at the design level could compromise the product functionality and the operational arrangement of the manufacturing system. To address these challenges, we aim to align the preferences of the design and manufacturing teams within a manufacturing firm in the context of mass customization. The preference of the design team is in terms of product modularity, associated with product upgradeability and flexibility. In the manufacturing team, complexity is a preference associated with cost and quality. In our research, the motivation for selecting these attributes is that product modularity is used to deal with product variety, and complexity is related to the uncertainty of product manufacturing. These two perspectives could support the manufacturing firm decision of selecting which product architecture is more convenient for a given operational arrangement and vice versa. We use a Multi-Attribute Utility function to assess the preferences of the design and manufacturing teams in terms of modularity and manufacturing complexity. We analyze the functional requirements of the product and then their mapping into the product architecture. Finally, we study the relationship of the product components to the operational arrangement of the manufacturing system. In addition, we include a case study in which we analyze two products and their relationship to a single manufacturing system, where the highest utility produces the best relationship between product design and manufacturing configuration complexity. By using this integrated approach, a manufacturer can decide to update the product design, the related operational configuration, or both.
Semantic interoperability issues of international e-Government data exchanges have not been solved up until now. In the case of social security institutions, the data exchange operations have some particularities that make that the non-ambiguous definition of core concepts used in the institutions has a key impact on the success and quality of system interconnections. In this article, we present the result of a research to implement a new metadata specification based in Dublin Core elements for international social security exchanges, named Exchange Social Security Information Metadata (ESSIM). This proposal is based in a semantic approach using Linked Data for Interoperability, with technologies, such as RDF(S), SPARQL, Microdata and JSON-LD, in order to ensure interoperability between social security institutions from different countries. This will help to strengthen the protection of the social security rights of mobile workers by automating the application of international agreements on social security and to improve cross border communication between social security institutions of different countries. For the near future, the goal is to include this specification as part of information and communication technology Guidelines under development by International Social Security Association with the participation of authors of this article. This will facilitate a future adoption of the specification as an international standard.
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602 members
Ali Arafat Lemus
  • Computer Science
Eduardo J Kwiecien
  • Escuela de Enfermería Veterinaria. Facultad de Ciencias de la Salud
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Guatemala City, Guatemala
Website
http://www.galileo.edu/