Use Of Computational Tools Research Articles

Formation of a Programming Languages Stack and a methodology of teaching to students specialized in Computer Science at Technical Universities in the context of interdisciplinarity

Rapid development of the computing hardware and software components has created many new technological directions for the use of computer tools. Thus, information, digital and end-to-end technologies for the processing of a variety of data exist and are developing, as well as many different types of platforms for their support, including digital platforms. At the same time, the active development of computing and its sub-disciplines directly impacts on the programming languages used in this area. This article attempts to assess the range of the most important and frequently used programming languages for the formation of a stack thereof and the methodology of their teaching in the context of the interdisciplinarity of modern computer specialties at technical Universities.

Shotgun Immunoproteomic Approach for the Discovery of Linear B-Cell Epitopes in Biothreat Agents Francisella tularensis and Burkholderia pseudomallei.

Peptide-based subunit vaccines are coming to the forefront of current vaccine approaches, with safety and cost-effective production among their top advantages. Peptide vaccine formulations consist of multiple synthetic linear epitopes that together trigger desired immune responses that can result in robust immune memory. The advantages of linear compared to conformational epitopes are their simple structure, ease of synthesis, and ability to stimulate immune responses by means that do not require complex 3D conformation. Prediction of linear epitopes through use of computational tools is fast and cost-effective, but typically of low accuracy, necessitating extensive experimentation to verify results. On the other hand, identification of linear epitopes through experimental screening has been an inefficient process that requires thorough characterization of previously identified full-length protein antigens, or laborious techniques involving genetic manipulation of organisms. In this study, we apply a newly developed generalizable screening method that enables efficient identification of B-cell epitopes in the proteomes of pathogenic bacteria. As a test case, we used this method to identify epitopes in the proteome of Francisella tularensis (Ft), a Select Agent with a well-characterized immunoproteome. Our screen identified many peptides that map to known antigens, including verified and predicted outer membrane proteins and extracellular proteins, validating the utility of this approach. We then used the method to identify seroreactive peptides in the less characterized immunoproteome of Select Agent Burkholderia pseudomallei (Bp). This screen revealed known Bp antigens as well as proteins that have not been previously identified as antigens. Although B-cell epitope prediction tools Bepipred 2.0 and iBCE-EL classified many of our seroreactive peptides as epitopes, they did not score them significantly higher than the non-reactive tryptic peptides in our study, nor did they assign higher scores to seroreactive peptides from known Ft or Bp antigens, highlighting the need for experimental data instead of relying on computational epitope predictions alone. The present workflow is easily adaptable to detecting peptide targets relevant to the immune systems of other mammalian species, including humans (depending upon the availability of convalescent sera from patients), and could aid in accelerating the discovery of B-cell epitopes and development of vaccines to counter emerging biological threats.

Effects of information and communication technology use in nursing and obstetric learning in low- and middle-income countries: A systematic review

Introduction and objective: Studies on the effects of information and communication technology (ICT) use in nursing and obstetric learning in low- and middle-income countries are limited despite growing scientific evidence that online learning has positive effects. This systematic review aims to identify and synthesize the effects of information and communication technologies utilization in nursing and obstetric learning in low- and middle-income countries.Methods: A search of articles published from 2016 to 2020 on the effects of ICT use in nursing and obstetric learning was conducted in PubMed, CINAHL, Epistemonikos and ERIC.Results: Of 483 articles identified, eleven were reviewed, and eight were found to be relevant. The included articles were synthesized into a narrative synthesis. The effects of using ICT in learning are related to student motivation, autonomy in learning, meaningful acquisition of knowledge and skills. Furthermore, students have a positive perception of the use of ICT in learning.Conclusions: The results of this study on the use of ICTs in nursing and obstetric sciences learning in low- and middle-income countries show that ICTs are used primarily as a medium for distance learning. In addition, it was found that e-learning has several advantages or positive effects. However, many students do not have a personal computer, and they have low or average skills in the use of computer tools, and access to the Internet is low. A limitation of this study is the lack of primary data on the effects of ICT use in obstetric sciences learning in low- and middle-income countries.

Sonic Tomograph as a Tool Supporting the Sustainable Management of Historical Greenery of the UMCS Botanical Garden in Lublin

The mission of botanical gardens is to work towards the conservation and sustainable use of plant diversity. In the case of gardens established on the premises of former manor estates, actions are also being taken to properly manage existing natural resources, e.g., historic tree stands. This article is based on the case study of the Botanical Garden in Lublin. To assess the health condition of the trees growing in the oldest part of the park—Kościuszko Redoubt—sonic tomography examinations were performed. The article presents the practical application of a newer form of digital imaging, put to the service of assessing the health of old trees in an important public space. The Redoubt is the only work of defense architecture in Poland related to the activity of General Tadeusz Kościuszko, the hero of Poland and the United States of America. It has survived in an excellent condition, preserved without significant changes, as an earth structure with ramparts; an interior, i.e., a yard; a cannon post located on an inaccessible cliff; and a brick basement serving as a shelter for soldiers and a warehouse. The entire surroundings also survived: ravines masked with a wild green, embankments with a safe hidden access road to the Redoubt, and a shaped defense line. The Botanical Garden area is located in the Sławinek district, in the valley of the Czechówka River, and includes a fragment of it and the slope adjacent to the west, cut by three loess ravines. The Botanical Garden was established in 1965, where there was a manor farm earlier, and then in the 19th century, a spa that was destroyed during World War I and II. Good soils and a varied microclimate provide the right ecological conditions for the high biodiversity of plants. With an area of 21.15 ha, the Garden area has the character of a landscape park with the theme of a manor garden from the turn of the 18th/19th century and a spa garden. The primary purpose of the work was to investigate the effectiveness of the use of computer tools in the sustainable management of historical greenery in the Botanical Garden. Research has shown that diagnostics with the use of sound waves allow for an accurate diagnosis and quick protective measures against the tree, improving the safety of visitors to the botanical garden. Moreover, the obtained results and a historical query were used to prepare an application to recognize Redoubt as a monument.

Fast and robust supervised machine learning approach for classification and prediction of Parkinson’s disease onset

ABSTRACT Parkinson’s disease (PD) is an incurable long-term neurodegenerative disorder that mainly influence the motor system and eventually results in significant morbidity. The use of computational tools for classification and prediction of PD is desirable for detecting the symptoms at the onset. In this paper, we developed a novel machine learning technique based on incremental support vector machine and modified Frank-Wolfe method (SVM-MFW) for classification and prediction of PD onset. The proposed model works on heterogeneous data gathered from the Parkinson’s progression markers initiative (PPMI) data repository. The work when compared to earlier state-of-the-art techniques outplays them in terms of both classification and prediction. It is shown that the model achieves an equivalent accuracy of 98.3% and a low cross-entropy value in less computational time.

A new approach for initial callus growth during fracture healing in long bones

The incidence of bone fracture has become a major clinical problem on a worldwide scale. In the past two decades there has been an increase in the use of computational tools to analyse the bone fracture problem. In several works, various study cases have been analysed to compare human and animal bone fracture healing. Unfortunately, there are not many publications about computational advances in this field and the existing approaches to the problem are usually similar.In this context, the objective of this work is the application of a diffusion problem in the model of the bone fragments resulting from fracture, working together with a mesh-growing algorithm that allows free growth of the callus depending on the established conditions, without a pre-meshed domain. The diffusion problem concerns the different biological magnitudes controlling the callus growth, among which Mesenchymal Stem Cells and chondrocytes concentrations were chosen, together with Tumour Necrosis Factor α and Bone Morphogenetic Protein as the factors influencing the velocity in the callus formation. A Finite Element approach was used to solve the corresponding diffusion problems, obtaining the concentration values along the entire domain and allowing detecting the zones in which biological magnitudes reach the necessary thresholds for callus growth. The callus growth is guided by a geometrical algorithm which performs an additional mesh generation process (self-added mesh) at each step of the iterative procedure until complete callus formation.The proposed approach was applied to different types of diaphyseal femoral fractures treated by means of intramedullary nailing. Axisymmetric models based on triangular quadratic elements were used, obtaining results in good agreement with clinical evidence of these kinds of fractures.The algorithm proposed has the advantage of a natural callus growth, without the existence of a previous mesh that may affect the conditions and direction of growth. The approach is intended for the initial phase of callus growth. Future work will address the implementation of the corresponding formulations for tissue transformation and bone remodelling in order to achieve complete fracture healing.

Geographical Information System (GIS) as a Tool for Determining Suitable Dam Sites in the Upper West Region of Ghana

Access to water continues to dwindle worldwide. There are global concerns over impending situations of this high-water stress, which invariably would limit domestic and industrial water usage. This development would affect the quality of livelihoods and lead to a decrease in agricultural output. Dams are one of the best options to store, use water efficiently and improve living conditions for rural and urban populations by providing water for industry, irrigation and drinking. Even though dams are designed to collect and store water for use, their best function lies in siting it appropriately. The Ghana Irrigation Development Authority (GIDA) is the government agency primarily tasked with constructing and managing dams in Ghana. The new attention of the Government in constructing dams under their flagship programme, 'one Village one Dam', necessitates research in areas where these programme objectives can be achieved. The traditional site selection process is manual, tedious and fails to accentuate certain terrain characteristics. However, using the Geographical Information system (GIS) can automate and scientifically solve this challenge with additional spatial capability. Multi-Criteria Decision Making makes use of computer tools to support the subjective assessment of performance criteria by policymakers. This study used Multi-Criteria Decision Making (MCDM) in a GIS environment in siting dams using five factors: slope, rainfall, soil type, protected zones, and Settlements. This resulted in a suitability map of sitting dam showing 4.3% suitable for dam construction, 33% of the area averagely suitable, 53.9% poorly suitable and 8.8% unsuitable for dam construction. The study recommends GIS and MCDM to effectively site dams primarily in semi-arid regions to solve the problem of dams not working efficiently because of the poor location of sites.

Machine learning-aided design optimization of a mechanical micromixer

In real-life mechanical engineering applications, it is often complex to achieve an optimal multi-objective design, because of the costs related to fabrication and test of different prototypes. For this reason, the use of computational tools is a recommended practice. In this work, the design of an efficient mixing mechanical device composed of a rectangular pillar confined in a microchannel is aided by machine learning techniques (addressed as machine learning-aided design optimization, MLADO, proposed in this work). A random forest classifier is trained to predict which geometric configuration may lead to vortex shedding. Later, a multi-objective optimization problem is investigated, which consists of minimizing the required pumping power and maximizing the mixing efficiency under some design constrains. If extra training data are desired for surrogates, the random forest classifier can be used to predict whether it is worthy or not to simulate the new configuration, avoiding to run irrelevant computational intensive cases and accelerating the data-driven process. The resulting optimal designs from using the NSGA-II genetic algorithm on the surrogates are simulated, and their performance is shown. The optimal geometric configurations, even for very unfavorable mixing conditions and a medium-low Reynolds number of 200, give a maximum mixing efficiency of around 50% at very low pumping power cost in a short channel, outperforming existing devices in the literature. The MLADO framework followed in this work can be easily extendable and automated for other similar design processes in mechanical engineering at any scale, by including shape parameterization strategies.

In search of RdRp and Mpro inhibitors against SARS CoV-2: Molecular docking, molecular dynamic simulations and ADMET analysis

Corona Virus Disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome coronavirus (SARS CoV-2) has been declared a worldwide pandemic by WHO recently. The complete understanding of the complex genomic structure of SARS CoV-2 has enabled the use of computational tools in search of SARS CoV-2 inhibitors against the multiple proteins responsible for its entry and multiplication in human cells. With this endeavor, 177 natural, anti-viral chemical entities and their derivatives, selected through the critical analysis of the literatures, were studied using pharmacophore screening followed by molecular docking against RNA dependent RNA polymerase and main protease. The identified hits have been subjected to molecular dynamic simulations to study the stability of ligand-protein complexes followed by ADMET analysis and Lipinski filters to confirm their drug likeliness. It has led to an important start point in the drug discovery and development of therapeutic agents against SARS CoV-2.

Allosteric Antagonist Modulation of TRPV2 by Piperlongumine Impairs Glioblastoma Progression.

The use of computational tools to identify biological targets of natural products with anticancer properties and unknown modes of action is gaining momentum. We employed self-organizing maps to deconvolute the phenotypic effects of piperlongumine (PL) and establish a link to modulation of the human transient receptor potential vanilloid 2 (hTRPV2) channel. The structure of the PL-bound full-length rat TRPV2 channel was determined by cryo-EM. PL binds to a transient allosteric pocket responsible for a new mode of anticancer activity against glioblastoma (GBM) in which hTRPV2 is overexpressed. Calcium imaging experiments revealed the importance of Arg539 and Thr522 residues on the antagonistic effect of PL and calcium influx modulation of the TRPV2 channel. Downregulation of hTRPV2 reduces sensitivity to PL and decreases ROS production. Analysis of GBM patient samples associates hTRPV2 overexpression with tumor grade, disease progression, and poor prognosis. Extensive tumor abrogation and long term survival was achieved in two murine models of orthotopic GBM by formulating PL in an implantable scaffold/hydrogel for sustained local therapy. Furthermore, in primary tumor samples derived from GBM patients, we observed a selective reduction of malignant cells in response to PL ex vivo. Our results establish a broadly applicable strategy, leveraging data-motivated research hypotheses for the discovery of novel means tackling cancer.

Abstract PO-030: Radiomics for head and neck cancer prognostication: results from the RADCURE machine learning challenge

Abstract Recently, there has been significant interest in using rich, multi-modal data routinely collected in the clinic, including imaging, demographic and clinical information for prognostic factor discovery in cancer. In particular, the emerging field of radiomics makes use of computational tools to extract quantitative features from radiological images, with the aim of capturing the morphological and biological characteristics of tumors. Previous studies have demonstrated the potential of computed tomography (CT) imaging features as independent prognostic factors for overall survival in multiple types of cancer, including head and neck (HNC). However, poor reproducibility and lack of large, rigorous validation studies have hindered widespread clinical use of radiomics so far. We conducted a HNC survival prediction challenge with the aim of 1) developing an accurate prognostic model for HNC survival using clinical, demographic and routinely collected CT imaging data and 2) evaluating the true added value of CT radiomics compared to other prognostic factors. Using a large, retrospective cohort of 2552 patients, we assessed prognostic performance of 12 different approaches developed by several research groups at University Health Network in Toronto, making use of engineered radiomics, deep learning, clinical information and combinations of those. To allow for unbiased comparison between different approaches, all participants had access to a public training dataset of 1802 patients, while 750 were held out for evaluation. The best challenge submission used a deep multi-task learning framework on clinical data and tumour volume, achieving area under the ROC curve (AUROC) of 0.812 [95% CI 0.763–0.858] for 2-year survival prediction and concordance (C) index for lifetime risk prediction of 0.795 [0.751–0.838], outperforming the best clinical-only model (AUROC=0.800 [0.749–0.848], C=0.708 [0.661–0.754]), best radiomics-only model (AUROC=0.766 [0.719–0.811], C=0.748 [0.704–0.790]), as well as the best model combining deep radiomics with clinical features (AUROC=0.786 [0.733–0.836], C=0.774 [0.726–0.820]). We also used a ‘wisdom of the crowds’ ensemble approach to combine the predictions of all challenge submissions to determine whether The ensemble achieved stronger performance than any individual model (AUROC=0.823 [0.778–0.864], C=0.810 [0.772–0.845]) indicating that there might be complementary information between the different data modalities. Our rigorous challenge framework allowed us to evaluate a diverse collection of prognostic models in a large multi-modal dataset, demonstrating the value of machine learning in HNC prognostication, as well as the advantages of simple imaging features over several hand-engineered and deep radiomics approaches. Furthemore, our ensemble approach achieves excellent performance for both 2-year and lifetime risk prediction, establishing new state-of-the-art in HNC prognostic modelling. Citation Format: Michal Kazmierski, Mattea Welch, Benjamin Haibe-Kains. Radiomics for head and neck cancer prognostication: results from the RADCURE machine learning challenge [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-030.

Ensemble machine learning predicts displacement of cantilevered fibers impacted by falling drops

In this study, we consider a simple system of water drops impacting cantilevered, circular fibers with velocity 1.0–2.4m∕s. The dynamics of the system are complicated because the motion of the drop and deflecting fiber at impact are highly coupled and the outcome is influenced by several continuous variables. The unpredictable dynamics of the system call for the use of computational tools that can reveal relationships between variables and make predictions about the physical outcomes for parameter values for which there is no experimental data. This study considers three fibers of contrasting properties, with hydrophilic and hydrophobic wetting conditions, exposed to a range of falling drop diameters and velocities. We predict maximal fiber deflection due to the impacting drop using an ensemble machine learning algorithm combining three base algorithms: a random forest regressor, a gradient boosting regressor, and a multi-layer perceptron. We train and test our algorithm with experimental datasets comprising 405 total trials using three different fibers and five input variables per fiber. The approach allows the determination of relative dominance of the input features in the prediction, reveals shortcomings of traditional scaling approaches for momentum transfer, and shows that drop momentum plays only a minor role in fiber deflection. Finally, our approach provides another example for the application of machine learning to characterize complex and coupled systems in fluid mechanics.

An Overview of Current Uses and Future Opportunities for Computer-Assisted Design of Vaccines for Neglected Tropical Diseases.

Neglected tropical diseases are infectious diseases that impose high morbidity and mortality rates over 1.5 billion people worldwide. Originally restricted to tropical and subtropical regions, changing climate conditions have increased their potential to emerge elsewhere. Control of their impact suffers from shortages like poor epidemiological surveillance or irregular drug distribution, and some NTDs still lack of appropriate diagnostics and/or efficient therapeutics. For these, availability of vaccines to prevent new infections, or the worsening of those already established, would mean a major breakthrough. However, only dengue and rabies count with approved vaccines at present. Herein, we review the state-of-the-art of vaccination strategies for NTDs, setting the focus on third generation vaccines and the concept of reverse vaccinology. Its capability to address pathogens´ biological complexity, likely contributing to save developmental costs is discussed. The use of computational tools is a fundamental aid to analyze increasingly large datasets aimed at designing vaccine candidates with the highest, possibly, opportunities to succeed. Ultimately, we identify and analyze those studies that took an in silico approach to find vaccine candidates, and experimentally assessed their immunogenicity and/or protection capabilities.

Adaptation d’un programme thérapeutique pendant le confinement du COVID-19

Since December 2019, many countries have enforced lockdowns against the evolving COVID-19 outbreak, which led to a new and unprecedented situation for the world and the Swiss population. The impact of restriction of movement on mental and physical health was quickly observed and documented. In an ambulatory mental health centre in Geneva, we quickly created therapeutic groups to reduce these consequences and to support vulnerable patients under psychiatric care. Therapeutic groups of adapted physical activities, emotional management and nutrition were offered to the patients for nine weeks via online videoconference. In 92.5% of cases, patients expressed a sense of well-being after a group session supervised by a professional. Limitations regarding the use of computer tools were highlighted, but prevention and health promotion provided by this means are undeniable. We honestly think that support measures must be anticipated in order to be able to react quickly on behalf of all vulnerable persons in the event of a COVID-19 rebound.

Study of Forest Cover Change Dynamics between 2000 and 2015 in the Ikongo District of Madagascar Using Multi-Temporal Landsat Satellite Images

Satellite images are considered reliable data that preserve land cover information. In the field of remote sensing, these images allow relevant analyses of changes in space over time through the use of computer tools. In this study, we have applied the “discriminant” change detection algorithm. In this, we have verified its effectiveness in multi-temporal studies. Also, we have determined the change in forest dynamics in the Ikongo district of Madagascar between 2000 and 2015. During the treatments, we have used the Landsat TM satellite images for the years 2000, 2005 and 2010 as well as ETM+ for 2015. Thus, analyses carried out have allowed us to note that between 2000-2005, 1.4% of natural forest disappeared. And, between 2005-2010, forests degradation was 1.8%. Also, between 2010-2015, about 0.5% of the natural forest conserved in 2010 disappeared. Furthermore, we have found that the discriminant algorithm is considerably efficient in terms of monitoring the dynamics of forest cover change.

Morphological parameters of image processing to characterize primary root emergence in evaluation of tomato seed vigor

Abstract: The use of computational tools has become notable in seed quality control to assist in confirming seed lots of higher potential. Thus, the aim of this study was to use a computational approach to investigate analysis of morphological parameters that characterize primary root emergence in evaluation of tomato seed vigor. For that purpose, five seed lots were analyzed for germination and vigor (seedling emergence and analysis of seedling images). A parallel analysis of primary root emergence was made using computer-assisted imaging; the images recorded as germination proceeded were processed with the ImageJ® software to obtain aspect ratio and seed area parameters. Vigor differences among tomato seed lots can be identified based on primary root emergence, and computer image processing based on aspect ratio proves to be an efficient procedure to characterize emergence of the primary root in evaluation of tomato seed vigor.

Funcionalidades del sistema de gestión de corpus, GECO, para la elaboración de diccionarios de especialidad

The use of computational tools for lexicographical work in the creation of specialty dictionaries is undeniable. The process starts from the design and constitution of the specialty corpus, through the extraction of terms, to the automated extraction of definitions, which is a work of computational lexicography. This paper sketches in first place the elements to be considered in specialty corpus constitution. Subsequently the GECO system is described, a manager to upload the corpus, as well as the system applications for dictionaries construction such as the automatic term extractor, the concordances viewer and the definitional contexts extractor. This approach shows an effective solution to the problems in the process of choosing terms and extracting definitions from a large corpus, which significantly reduces the processing time of the data obtained in order to improve the creation of a specialized dictionary.

Perspectives on data‐driven soil research

AbstractSoil is a complex system in which biological, chemical and physical interactions take place. The behaviour of these interactions changes in spatial scale from the atomic to the global, and in time. To understand how this system works, soil scientists usually rely on incremental improvements in the knowledge by refinement of theories through hypothesis testing and development using carefully designed experiments. In the last two decades, the primacy of this knowledge construction process has been challenged by the development of large soil databases and algorithms such as machine learning. The data‐driven research approach to soil science, the inference of soil knowledge directly from data by using computational tools and modelling techniques, is becoming more popular. Despite the wide adoption of a data‐driven research approach to soil science, there has been little discussion on how a research driven by data instead of hypotheses affects scientific progress. In this paper, we provide an introductory perspective on data‐driven soil research by discussing some of the issues and opportunities of knowledge discovery from soil data. We show that while data‐driven soil research may seem revolutionary for some, soil science has a long history of exploratory efforts to generate knowledge from data. Empirical and factual soil classifications, for example, were data driven. We further discuss, with examples, (i) data, databases and the logic of data storage for data‐driven soil research, (ii) the issues of extreme empiricist claims that arise corollary to the increase in the use of computational tools, and (iii) the challenge of formulating a scientific explanation based on patterns observed in the data and data analysis tools. By considering the epistemic challenges of the data‐driven scientific research in the light of the historical literature, we found that there is a continuity of practices, some being certainly amplified by recent technological changes, but that the core methods of scientific enquiry from data remain essentially unchanged.Highlights Historical account of data‐driven soil science research. Describe data to be used for data‐driven soil science. Discuss conceptual issues and opportunities for data‐driven soil science. Investigate the challenge of formulating an explanation from soil data.

Analyzing magnetic resonance imaging data from glioma patients using deep learning.

The quantitative analysis of images acquired in the diagnosis and treatment of patients with brain tumors has seen a significant rise in the clinical use of computational tools. The underlying technology to the vast majority of these tools are machine learning methods and, in particular, deep learning algorithms. This review offers clinical background information of key diagnostic biomarkers in the diagnosis of glioma, the most common primary brain tumor. It offers an overview of publicly available resources and datasets for developing new computational tools and image biomarkers, with emphasis on those related to the Multimodal Brain Tumor Segmentation (BraTS) Challenge. We further offer an overview of the state-of-the-art methods in glioma image segmentation, again with an emphasis on publicly available tools and deep learning algorithms that emerged in the context of the BraTS challenge.

Conocimientos, actitudes y disposición para el desarrollo sostenible en la universidad: Podcasts y concienciación ambiental

La investigación que da origen al presente artículo tuvo como objetivo estudiar las relaciones entre conocimiento, actitudes y disposición ambiental frente a los factores cargo y sexo de los miembros de una comunidad universitaria en Ecuador. La muestra estuvo conformada por 795 personas entre autoridades, profesorado, alumnado, personal administrativo y personal de servicios, quienes participaron en el proyecto durante el periodo 2018-2019. Los instrumentos administrados fueron: la escala de actitudes culturales hacia el desarrollo sostenible de estudiantes universitarios, el cuestionario conocimiento y actitudes hacia el calentamiento global y la escala de voluntad para mitigar los efectos del cambio climático por razones antropogénicas. El análisis estadístico de los datos colectados muestra que existen relaciones significativas entre conocimiento y actitudes ambientales con los factores cargo y sexo de los participantes; mientras que no existen relaciones significativas entre la voluntad para mitigar los efectos del cambio climático y los factores cargo y sexo. También se presenta una intervención educativa que hace uso de podcasts para fortalecer la conciencia ambiental de las personas. Los resultados permiten concluir que el uso creativo de herramientas informáticas tales como los podcasts, aportan al fortalecimiento de la conciencia ambiental en una comunidad universitaria.