Universidad EAFIT
  • Medellín, Antioquia, Colombia
Recent publications
Leigh syndrome (LS) is a rare, inherited neurometabolic disorder that presents with bilateral brain lesions caused by defects in the mitochondrial respiratory chain and associated nuclear-encoded proteins. We generated human induced pluripotent stem cells (iPSCs) from three LS patient-derived fibroblast lines. Using whole-exome and mitochondrial sequencing, we identified unreported mutations in pyruvate dehydrogenase (GM0372, PDH; GM13411, MT-ATP6/PDH) and dihydrolipoyl dehydrogenase (GM01503, DLD). These LS patient-derived iPSC lines were viable and capable of differentiating into progenitor populations, but we identified several abnormalities in three-dimensional differentiation models of brain development. LS patient-derived cerebral organoids showed defects in neural epithelial bud generation, size and cortical architecture at 100 days. The double mutant MT-ATP6/PDH line produced organoid neural precursor cells with abnormal mitochondrial morphology, characterized by fragmentation and disorganization, and showed an increased generation of astrocytes. These studies aim to provide a comprehensive phenotypic characterization of available patient-derived cell lines that can be used to study Leigh syndrome.
The Green's Functions Stiffness Method (GFSM) is a method to compute the analytic closed-form response (reactions, displacements, and internal forces fields) of structures. It merges the strengths of the stiffness method (SM) (an exact relation between forces and displacements at the ends of elements) with those of Green's functions (computation of closed-form analytic structural response due to any arbitrary load). Its formulation is based on the decomposition of the structural response into homogeneous and fixed parts. The former depends on the degrees of freedom (joints displacements and rotations) and yields the stiffness matrix, while the latter depends on the external loads and is related to the fixed end forces vector. The element response is computed directly from the nodal displacements. First, the displacement fields are computed, and from their derivatives, the internal forces fields are obtained. This paper presents the formulation of the GFSM for Euler-Bernoulli beams on elastic Winkler foundation with semi-rigid connections and, as a particular case, for Euler-Bernoulli beams with semi-rigid connections. Additionally, two examples, and conclusions are presented.
This article proposes a hybrid algorithm based on reinforcement learning and the inventory management methodology called DDMRP (Demand Driven Material Requirement Planning) to determine the optimal time to buy a certain product, and how much quantity should be requested. For this, the inventory management problem is formulated as a Markov Decision Process where the environment with which the system interacts is designed from the concepts raised in the DDMRP methodology, and through the reinforcement learning algorithm—specifically, Q-Learning. The optimal policy is determined for making decisions about when and how much to buy. To determine the optimal policy, three approaches are proposed for the reward function: the first one is based on inventory levels; the second is an optimization function based on the distance of the inventory to its optimal level, and the third is a shaping function based on levels and distances to the optimal inventory. The results show that the proposed algorithm has promising results in scenarios with different characteristics, performing adequately in difficult case studies, with a diversity of situations such as scenarios with discontinuous or continuous demand, seasonal and non-seasonal behavior, and with high demand peaks, among others.
Understanding learners’ behavior is the key to the success of any learning process. The more we know about them, the more likely we can personalize learning experiences and provide successful feedback. This paper presents a feedback model implemented in a ubiquitous microlearning environment based on contextual and behavioral information and evaluation results. The model uses SECA rules where the Scenario (S) represents the ubiquitous context variables reflecting the learner behavior during the learning process. The Event (E) identifies the probability that a learner fails or passes its evaluation. Condition (C) evaluates the results of the events. Moreover, Action (A) provides feedback to the learner. The proposal is developed through a controlled experiment whereby a microlearning environment can collect data from a ubiquitous context. The feedback model applies an analytics process to find the best context and behavior variables through different classification models. Those models predict whether a learner could fail, determine evaluation results’ causes, and provide feedback. The Random Forest was the model with the best performance. Thus, 94% accuracy, a 97% Recall, a 93% Precision, an F1 score of 95%, and a Jaccard of 91%. Hence, each scenario is defined from a branch of every tree obtained from the Random Forest model personalizing feedback actions applying clustering techniques. Finally, we presented an exemplified set of feedback rules, providing automatic recommendations and improving learner experiences. Thus, the experiment allows analyzing the learner behavior in a ubiquitous microlearning context from a feedback perspective.
The present study describes Oligogonotylus andinus sp. nov. and its life cycle from a rural fish farm in Sopetrán, Antioquia, Colombia. The endemic species of snail Aroapyrgus colombiensis and the fishes Poecilia caucana and Andinoacara latifrons are identified as the first intermediate host, the second intermediate host and the definitive host, respectively. The new species was defined through an integrative approach, combining the traditional morphology of its developmental stages with molecular analyses of the markers ITS2 from ribosomal DNA and COI from mitochondrial DNA. This new species can be distinguished from its congeners by genetic divergence, the position of the vitelline fields, and the number of gonotyls. This work represents the first report of a species of this genus in South America.
New and more complex methods of classification and monitoring of land cover change are developed daily. But simpler strategies could be sufficient for the tasks, more accessible to geographic analysts, and more interpretable to end-users at local-regional scales. This article uses a sequence of simple thresholds of spectral indices to obtain a coarse classification of four coarse coastal covers predominant in many tropical regions: water, mangrove, tidal, and sand. This semi-supervised method was used to (1) obtain current and historical classifications of Iscuandé and Guapi River Mouths, Colombia, and (2) measure the change in extension and distribution of coastal covers across 36 years (1984–2019). The overall accuracy of simple thresholding was high (85%), but lower than four machine learning algorithms (RF, CART, SVM, and GTB, ranging from 94 to 96%). Vegetation and water were classified with higher accuracy (97%), while tidal and sandy bare soils have lower accuracy (87 and 79% respectively). The simplicity of the current method allows the detection of temporal and spatial changes in coastal covers. Moreover, tidal flats and sandy bare soil increased an overall 42% and 83% respectively representing 743 ha of new bare soils during 36 years. Larger growth occurred in the early nineties and while changes were heterogeneous in magnitude, most of the localities studied show an overall increase in tidal and sand. Performance of simple thresholding could be improved using an alternative combination of indices, especially for bare soils, optimal adjustment of thresholds, object-oriented classification, or different strategies for building image mosaics. However, we believe this simple approach could aid the exploration of changes in coastal landscapes with sparse coverage of optical imagery and a lack of ground surveys.
In Colombia, the uptake rate of the HPV vaccine dropped from 96.7% after its introduction in 2013 to 9% in 2020. To identify the behavioural components of HPV-vaccine hesitancy in females aged 15 and under and their families, we conducted a convergent mixed-methods study in which 196 parents/caregivers responded to an online questionnaire and 10 focus groups were held with 13 of these parents/caregivers, and 50 age-eligible girls. The study is novel as it is the first to explore the factors influencing HPV-vaccine hesitancy alongside the COVID vaccine within an integrative model of behaviour change, the capability-opportunity-motivation-behaviour (COM-B) model. We found that COVID-19 has had an impact on the awareness of HPV and HPV vaccination. Lack of information about the vaccination programs, concerns about vaccine safety and the relationship between HPV and sexuality could be related to vaccine hesitancy. Trust in medical recommendations and campaigns focused on the idea that vaccination is a way of protecting daughters from cervical cancer could improve HPV vaccine uptake.
The alarming levels of carbon dioxide (CO2) are an environmental problem that affects the economic growth of the world. CO2 emissions represent penalties and restrictions due to the high carbon footprint. Therefore, sustainable strategies are required to reduce the negative impact that occurs. Among the potential systems for CO2 capture are microalgae. These are defined as photosynthetic microorganisms that use CO2 and sunlight to obtain oxygen (O2) and generate value-added products such as biofuels, among others. Despite the advantages that microalgae may present, there are still technical–economic challenges that limit industrial-scale commercialization and the use of biomass in the production of added-value compounds. Therefore, this study reviews the current state of research on CO2 capture with microalgae, for which bibliometric analysis was used to establish the trends of the subject in terms of scientometric parameters. Technological advances in the use of microalgal biomass were also identified. Additionally, it was possible to establish the different cooperation networks between countries, which showed interactions in the search to reduce CO2 concentrations through microalga
Detecting faults and anomalies in real-time industrial systems is a challenge due to the difficulty of sufficiently covering an industrial system’s complexity. Today, Industry 4.0 makes it possible to tackle these problems through emerging technologies such as the Internet of Things and Machine Learning. This paper proposes a hybrid machine-learning ensemble real-time anomaly-detection pipeline that combines three Machine Learning models–Local Outlier Factor, One-Class Support Vector Machine, and Autoencoder–, through a weighted average to improve anomaly detection. The ensemble model was tested with three air-blowing machines obtaining a ${F}_{{1}}$ -score value of 0.904, 0.890, and 0.887, respectively. The results of the ensemble model showed improved performance metrics concerning the individual metrics. A novelty of this model is that it consists of two stages inspired by a standard industrial system: i) a manufacturing stage and ii) an operation stage.
We investigate the transmission of natural gas shocks to electricity prices under different scenarios of electricity generation for 21 European markets, from January 1, 2015 to March 11, 2022, proposing indicators of market vulnerability based on the quantile slopes of the regressions of electricity on natural gas and the distance between the transmission effects at very high and low quantiles of the electricity price distribution. We determine that the level of market integration is the main factor underlying national differentiation. Denmark, Finland, Sweden, and Germany are the most vulnerable markets to natural gas price shocks under distress. Our results highlight a source of vulnerability that only emerges during market distress scenarios for countries with a small, but non-zero, proportion of natural gas in domestic generation mixes under marginal cost-based electricity pricing. Further market integration is proposed to increase resilience in European electricity markets, based on a different set of regressions.
One idea resonates after reading Human-like Computers: A Lesson in Absurdity. It is an idea that Manfred Velden reiterates to awaken the reader to the void to which so many assumptions lead. In the book, the author argues that there is a minority facing a majority of people who fervently believe that computers can be human (2022:11), who assume predictions as facts (2022:30), who participate in an idea put into circulation by people so influential that it is difficult to unmask its fallacy(2022:89). Manfred Velden is professor at the University of Mainz, Berlin (TU)and Osnabrück. He studied psychology at the Universities of Bonn and Berkeley (University of California). Within his publications resound direct criticisms of the way Psychological Biology approaches the study of the phenomena of consciousness or mental functions. The titles of His books: Biologism - the Consequence of an Illusion (Velden, 2010), Psychology - A Study of a Masquerade (Velden, 2016) and Brain Death of an Idea - the Heritability of Intelligence (Velden, 2014), besides being presented by provocative titles, are a clear indication of his position against the abuses to which the circulation and reception of certain ideas related to the scientific study of the mind and the brain have reached. For instance, the practice in which certain scientists and science communicators take research, to emphasize some ideas and not others or to remain in complicit silence in response to the propagation of information that distorts the results of the original research. Within the circulation of scientific ideas, Velden’s...
Epidemiological models have become powerful tools for studying and understanding the characteristics and impact of transmitted diseases in a population. However, these models usually require specifying several values of input parameters obtained from experimental data, characterized by high uncertainty levels due to biological variation. This situation is evident for models that simulate the transmission of vector-borne diseases such as dengue, our case study. Therefore, treating and modeling this uncertainty is essential to ensure the robustness of designed models. For this, we propose to model the uncertainty through interval analysis by representing the input parameters and initial conditions by real closed intervals in the forward problem. This approach has the advantage of making a minimal number of assumptions concerning uncertainties, unlike the traditional methods (probabilistic and fuzzy). To illustrate the performance of this methodology, we consider a coupled ODE system of seven state variables and nine parameters, representing the transmission of Dengue between host-vector populations. Additionally, to enhance the use of the numerical method utilized for solving the system, the uncertain quantities (parameters and initial conditions) are determined based on the results of (i) the sensitivity analysis of R0, (ii) the structural identifiability analysis of the model, (iii) the characteristics of the available information about mosquito population, and (iv) dengue incidence data in two municipalities in Colombia, Itagüí and Neiva, during the outbreaks in 2016. We believe that the methodology proposed here to select and incorporate uncertainty in epidemiological models through interval analysis is widely applicable to other phenomena and models in science and engineering.
Vitamin D is associated with the stimulation of innate immunity, inflammation, and host defense against pathogens. Macrophages express receptors of Vitamin D, regulating transcription of genes related to immune processes. However, the transcriptional and post-transcriptional strategies controlling gene expression in differentiated macrophages, and how they are influenced by Vitamin D are not well understood. We studied whether Vitamin D enhances immune response by regulating the expression of microRNAs and mRNAs. Analysis of the transcriptome showed differences in expression of 199 genes, of which 68% were up-regulated, revealing the cell state of monocyte-derived macrophages differentiated with Vitamin D (D3-MDMs) as compared to monocyte-derived macrophages (MDMs). The differentially expressed genes appear to be associated with pathophysiological processes, including inflammatory responses, and cellular stress. Transcriptional motifs in promoter regions of up- or down-regulated genes showed enrichment of VDR motifs, suggesting possible roles of transcriptional activator or repressor in gene expression. Further, microRNA-Seq analysis indicated that there were 17 differentially expressed miRNAs, of which, 7 were up-regulated and 10 down-regulated, suggesting that Vitamin D plays a critical role in the regulation of miRNA expression during macrophages differentiation. The miR-6501-3p, miR-1273h-5p, miR-665, miR-1972, miR-1183, miR-619-5p were down-regulated in D3-MDMs compared to MDMs. The integrative analysis of miRNA and mRNA expression profiles predict that miR-1972, miR-1273h-5p, and miR-665 regulate genes PDCD1LG2, IL-1B, and CD274, which are related to the inflammatory response. Results suggest an essential role of Vitamin D in macrophage differentiation that modulates host response against pathogens, inflammation, and cellular stress.
Few events have had an impact as the global crisis caused by COVID-19. However, prior to the pandemic, Latin American and Caribbean (LAC) countries already had severe problems in terms of inequality, environmental degradation, and dysfunctional political systems. Added to this are the growing challenges that climate change poses for this highly vulnerable region. This historic turning point represents a new call to consider future studies to re-imagine and reinvent alternative futures for the LAC region. For this paper, we conducted an in-depth qualitative futures study to identify how Latin American and Caribbean countries could build long-term resilience, focusing on adaptability to climate change risks, considering existing sustainable development challenges and the detrimental effects of the COVID-19 pandemic on the economic, environmental, and social aspects. This study's findings provide recommendations for policymakers and decision-makers to achieve sustainable futures for LAC. Finally, it reflects on the value of collective action for a future-proof region.
The adaptation of traditional systems to service-oriented architectures is very frequent, due to the increase in technologies for this type of architecture. This has led to the construction of frameworks or methodologies for adapting computational projects to service-oriented architecture (SOA) technology. In this work, a framework for adaptation to SOA in an educational organization is presented, through a specific case of adaptation of an autonomous recommendation system. The framework has a business model that extracts the specific needs of the organization and that will help to project the service architecture from an administrative perspective for a generation of value. The framework has components that conform to the organizational governance of Information Technology (IT) linked to the control mechanisms managed by any IT government of the organization. Finally, the framework has a self-management process that integrates intelligent mechanisms or paradigms for any autonomous process that manages the SOA. In general, the framework describes a methodological process of four general phases, allowing to identify requirements, design services, develop them, and deploy them in an organization, to be managed through control mechanisms through governance. The framework was tested in the adaptation of an autonomous recommendation system for virtual learning environments (VLE), which has two main processes, the creation of an academic course and the use of the course.
Calls to address diversity, equity and inclusion (DEI) have become common around the globe. This AIB Insights special issue examines dimensions and challenges associated with DEI in an international business environment. The introductory editorial to the issue first discusses some major challenges associated with implementing DEI in multinational organizations, including the multi-level factors influencing DEI policy adoption. It then overviews the issue’s seven articles and one practitioner interview, which address broader DEI issues such as refugees and migration and the influence of the institutional environment, as well as specific DEI dimensions related to gender, differently abledness and LGBT+ inclusion.
Enterprise risk management (ERM) is a discipline that is becoming increasingly necessary due to the changing environment in which companies operate. This paper is based on a research question that poses hypotheses questioning the impact of risk governance and associated practices and tools on ERM development. Hierarchical linear regression models were applied to test the hypotheses that suggest a relationship between predictor variables and the ERM criterion variable. A sample of 140 large private companies from different economic sectors in Colombia was used to evaluate their behaviour and/or organizational performance related to the analysis variables. The main results suggest that risk governance composed of senior management commitment and risk management structure has a positive correlation with ERM. Also, it is evidenced that the practices and tools integrated by risk maps and risk treatment measures have a positive correlation with the maturity of ERM. Finally, the study’s main findings and their implications are discussed, which serves as a basis for strengthening ERM in emerging markets.
Purpose Root inoculation of plants with beneficial microorganisms promotes plant growth and improves tolerance to biotic and abiotic stresses. In banana plants, microbial inoculation has shown promising effects on plant growth, but the effect on foliar diseases and on the resident native microbial community is yet unknown. We examined the effects of Bacillus subtilis EA-CB0575 introduced on roots of micropropagated banana plants on black Sigatoka disease and on the root microbiome. Methods In vitro banana seedlings were root-inoculated with strain EA-CB0575, and then treated with the foliar pathogen Pseudocercospora fijiensis. Root bacterial communities were characterized using 16S rRNA gene sequencing, before and after pathogen inoculation; and the severity of the disease was determined. Results Inoculation of strain EA-CB0575 on in vitro banana roots provided a reduction in the severity of black Sigatoka disease in greenhouse. This inoculation induced changes in the composition of the bacterial root microbiome, changes that arose from amplicon sequence variants (ASVs) of low abundances. Differential abundance analysis of ASV indicated that prior pathogen inoculation significantly more enriched sequences were identified in roots treated with EA-CB0575 (191-ASVs) compared to control plants (73-ASVs), but after pathogen inoculation more sequences were found in control (277-ASVs) than bacterial inoculated plants (60-ASVs). Furthermore, in vitro banana roots host a bacterial community that differ from that of greenhouse plants. Conclusion Results suggest that banana plants inoculated with B. subtilis EA-CB0575, reshape the composition of the bacterial community in a P. fijiensis dependent manner and induce systemic resistance protecting plants against black Sigatoka.
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4,417 members
Alejandro Peña
  • School of Management and Business
Nicolas Guarin-Zapata
  • Department of Civil Engineering
Laura Rojas de Francisco
  • Department of Marketing
Mario C. Velez-Gallego
  • Department of Production Engineering
Helmuth Trefftz
  • Department of Informatics and Systems
Carrera 49 # 7 Sur - 50, 050022, Medellín, Antioquia, Colombia
Head of institution
Claudia Patricia Restrepo Montoya
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