STAFF SUPPORT FOR INTERNATIONALIZATION– A CASE OF LANGUAGE AND CONTENT TEACHERS COLLABORATION

In the context of growing efforts for becoming more international and, hence, more attractive for staff and students worldwide, higher education institutions implement and support English Medium Instruction (EMI) and try to enhance visibility of research results through publishing in English. The resources necessary for this successful enterprise include a teaching and research staff highly proficient in using English both for teaching subjects other than English and writing materials based on research. The general context of EMI can be further complicated by local factors which add to the complex puzzle of forces that shape higher education today. The present paper describes and analyzes the case of a Romanian higher education institution which, although offering English taught programs for over a decade in several engineering fields, has only recently decided to reconsider the needs of the EMI teaching and research staff and to provide ongoing support, with the view of increasing quality of EMI education and also of adding new programs taught in English. The recent support program consists of three components: language courses focused on speaking and listening skills and on grammar-discourse features of written texts, pedagogy-focused workshops and a one-to-one tutoring support for editing and improving the accuracy and readability of research-related texts to be published in English. The components were implemented as an integrated system which has fostered collaboration between language and content teachers involved in EMI. Informing each other in both practice and research, EMI and TESOL (here represented mostly by English for Specific Purposes) form a productive symbiosis when all stakeholders are involved. The implications of such cases can be consequential for the further development of support programs for EMI teaching and research staff, based on specific needs of local EMI communities of practice and on principles derived from the language and content teachers’ collaboratio

THE EFFECT OF INSULIN CONCENTRATION ON HUMAN NEUTROPHILS LUMINOL-DEPENDENT CHEMILUMINESCENCE

We investigated the effect of insulin on reactive oxygen species (ROS) production by human neutrophils stimulated with opsonized zymosan and incubated for 30 minutes with different insulin concentrations. For measuring ROS production, we used luminol-dependent chemiluminescence assay (CLLD). We observed that the CLLD depends on insulin concentration and this supports the hypothesis that insulin may play a role in ROS production by normal human neutrophils

MIANN models in medicinal, physical and organic chemistry

[Abstract] Reducing costs in terms of time, animal sacrifice, and material resources with computational methods has become a promising goal in Medicinal, Biological, Physical and Organic Chemistry. There are many computational techniques that can be used in this sense. In any case, almost all these methods focus on few fundamental aspects including: type (1) methods to quantify the molecular structure, type (2) methods to link the structure with the biological activity, and others. In particular, MARCH-INSIDE (MI), acronym for Markov Chain Invariants for Networks Simulation and Design, is a well-known method for QSAR analysis useful in step (1). In addition, the bio-inspired Artificial-Intelligence (AI) algorithms called Artificial Neural Networks (ANNs) are among the most powerful type (2) methods. We can combine MI with ANNs in order to seek QSAR models, a strategy which is called herein MIANN (MI & ANN models). One of the first applications of the MIANN strategy was in the development of new QSAR models for drug discovery. MIANN strategy has been expanded to the QSAR study of proteins, protein-drug interactions, and protein-protein interaction networks. In this paper, we review for the first time many interesting aspects of the MIANN strategy including theoretical basis, implementation in web servers, and examples of applications in Medicinal and Biological chemistry. We also report new applications of the MIANN strategy in Medicinal chemistry and the first examples in Physical and Organic Chemistry, as well. In so doing, we developed new MIANN models for several self-assembly physicochemical properties of surfactants and large reaction networks in organic synthesis. In some of the new examples we also present experimental results which were not published up to date.Ministerio de Ciencia e Innovación; CTQ2009-07733Universidad del Pais Vasco; UFI11/22Universidad del Pais Vasco; GIU 094

From Speaking Skills to Virtual Mobilities: Challenges of VR Technologies in Communication from the European University of Technology

Within the vision of the European University of Technology (EUt+), a University Alliance of 8 European partners, augmenting a Mobility Friendly Plan through a virtual worlds approach, helps prepare students to overcome communication, language and cultural barriers. Such virtual environments can allow students to be immersed in the academic environments of the destination location, creating spaces for team building, collaboration, and creative activities. In the context of effective social interaction, communication and language learning become key pillars. Technological means that develop key competencies and abilities in such immersive environments, should be tackled. The current paper describes three uses cases of VR environments from the European University of Technology Alliance, implemented with the purpose of facilitating communication skills to overcome language and cultural barriers. The application of various technology levels, from prototype-based to customization of existing platforms is analyzed, under a TAM adoption assessment, to identify common challenges that may accompany the development of a shared VR campus, intended for effective communication, while providing the students a feeling of comfort, safety and confidence

Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds

[Abstract] Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that measures the probability of a drug being active under certain conditions (protein, cell line, organism, etc.). In this paper, we used a linear discriminant analysis and neural network to train and compare PT and non-PT models. All the explored models have an accuracy of 89.19–95.25% for training and 89.22–95.46% in validation sets. PTML-based strategies have similar accuracy but generate simplest models. Therefore, they may become a versatile tool for predicting antisarcoma compounds.Ministerio de Economía y Competitividad; CTQ2016-74881-PMinisterio de Economía y Competitividad; UNLC08-1E-002Ministerio de Economía y Competitividad; UNLC13-13-3503Xunta de Galicia; ED431C 2018/49Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/23Gobierno Vasco; IT1045-16Instituto de Salud Carlos III; PI17/0182

Circular Pedagogy to Advance the Integration of Learning Technologies: Supporting Technological Universities Cultural Transformation

European countries need active and proactive educational systems assisted by models that can drive a cultural transformation that supports sustainable socio-economic and environmental development. In this paper, we reflect on the future of European education. We offer some insights on how the foundations of a new educational model (ANEM) could be cemented and solidly supported by pillars that acknowledge our societies\u27 rich and diverse cultures. Furthermore, the European University of Technology\u27s (EUt+) future educational model is taken as a case study to enable us to reflect and analyse the need for novel pedagogies that drive change for a more sustainable socio-economic and environmentally friendly European society. European education faces significant challenges from the need to enable learning environments guided by equity, diversity, and inclusive frameworks for all categories. To make progress, it is essential that we first learn how new inclusive learning environments can be articulated to help us address our contemporary society\u27s learning needs and demands. We are conscious that education worldwide faces a stark and unpleasant reality as the students/learners\u27 learning experience is significantly impacted by social status and economic disparities. Students are often confronted with difficult situations involving racism, discrimination and exclusion that materialise in students suffering mistreatment and microaggressions in learning environments still blind to the biases forwarded through teaching practices. The richness of our European cultures and languages and their significance in helping us to work together are paramount in our quest for high-quality education that cultivates, promotes, and cherishes European educational values while welcoming other cultures and languages. Within the complexities of our global societies, we argue that the future of our educational system must enable and foster mechanisms that nurture behaviours that will help us address cultural conflict, clashes, and potential detachment. Cultural clashes emerge as a major challenge for the development of our future European University, and we need to be able to minimise potential problems associated with multicultural, plurilingual and diverse working and learning environments. We are conscious of the need to develop appropriate educational programmes and curricula guided by our novel Circular Pedagogy , where we provide an initial and evolving framework for students, teachers, and researchers to interchange their roles. We propose a learner-centred, dynamic, and proactive pedagogy that helps us to manage and navigate the inevitable cultural conflict and supports us in understanding and identifying the triggers that might arise due to cultural clashes and increasing levels of detachment

Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models

Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository.This research and the APC were funded by Consolidation and Structuring of Competitive Research Units—Competitive Reference Groups (ED431C 2018/49) funded by the Ministry of Education, University and Vocational Training of Xunta de Galicia endowed with EU FEDER funds

MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products

The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for diferent enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difcult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R2=0.96 overall for training and validation series. It involved a Monte Carlo sampling of>100,000 pairs of query and reference reac‑ tions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived N-trifylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo. This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products.Ministerio de Ciencia e Innovación ( PID2019104148 GB-I00; PID2022-137365NB-I00), Gobierno Vasco IT1558-2

Enhancing Human Security by Transforming Education Through Science, Technology, and Innovations

In this paper, we provide in-depth critical analysis and reflections on how technology, innovation and digital literacy can help to bring awareness on the need for a new dimension and approach to foster a transformational attitude towards education. Learning drives change, and if we aim to make an impact, there is a need to enable collaboration between different disciplines so that new transformative educational models can emerge. At the centre of our analysis, we identify the role of pedagogy and how it can contribute to put forward humans as central and critical actors in using science, technology, and innovations (STIs) to foster human security. We explore the critical role of Higher Education Institutions (HEIs) and their engagement with science, technology, and innovation in the search for educational transformation that supports multicultural, diverse and inclusive learning environments within the tenets of social engagement and cohesion that guide us towards the principles of human security

Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning

The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different pairs of drugs and nanoparticles creating DDNP complexes with anti-glioblastoma activity. PTML models use the perturbations of molecular descriptors of drugs and nanoparticles as inputs in experimental conditions. The raw dataset was obtained by mixing the nanoparticle experimental data with drug assays from the ChEMBL database. Ten types of machine learning methods have been tested. Only 41 features have been selected for 855,129 drug-nanoparticle complexes. The best model was obtained with the Bagging classifier, an ensemble meta-estimator based on 20 decision trees, with an area under the receiver operating characteristic curve (AUROC) of 0.96, and an accuracy of 87% (test subset). This model could be useful for the virtual screening of nanoparticle-drug complexes in glioblastoma. All the calculations can be reproduced with the datasets and python scripts, which are freely available as a GitHub repository from authors. View Full-TextThe APC was funded by IKERDATA, S.L. under grant 3/12/DP/2021/00102—Area 1: Development of innovative business projects, from Provincial Council of Vizcaya (BEAZ for the Creation of Innovative Business Innovative business ventures)