Digital Simulator for Entrepreneurial Finance (FINANCEn_LAB)
Otro Materias > Ciencias Sociales Universidad Europea del Atlántico > Investigación > Proyectos I+D+I Cerrado Inglés The main proposal of this project is to create digital interactive tools that will help the end-beneficiaries (potential and current entrepreneurs) to develop skills and acquire necessary practical knowledge to effectively apply for funding and manage their financial situation. From the general perspective, our project is expected to contribute to the improvement in financial literacy, especially, among HE students, as potential entrepreneurs, through an effective method of learning by doing. The project aims at covering the gap of practical financial competences considered as a critical barrier for entrepreneurship. Possible solution goes through cooperation between financing actors and educational sector. Thus, common training will be complemented with real practice, which includes individual and collaborative work, very different from traditional school assignments, since it will connect funding agents (banking professionals, investors, mentors and similar) with entrepreneurs and students. This DIGITAL LEARNING ENVIRONMENT BASED ON COLLABORATIVE LEARNING with financial agents is the core of our innovative proposal. Besides that, the project will deploy actions to empower HE teachers and entrepreneur coaches. Thus, the project will reach the following segments of people: university students, entrepreneurs, teachers, start-up incubators, financial agents for an estimated total of 840 direct participants and 14600 additional reached online. The main project activities will be oriented to the production of four intellectual outputs: 1) Practical cases in entrepreneurial finance for training purposes. 2) Digital Simulator for entrepreneurial finance (trainer’s tool). 3) Digital Simulator for entrepreneurial finance (self-learning tool). 4) Report on recommendations for entrepreneurial finance stakeholders and policy makers. The project implies cooperation of different type of organizations. The HE institutions and Banking/Financial sector will collaborate closely to ensure that we use appropriate content. Representative institutions will incorporate the tools into their trainings and will disseminate the project results properly. Entrepreneurial institutions will support the outputs creation attracting the attention of practitioners and professionals in the entrepreneurial field. The project pursues long term impact by providing HE lecturers, VET/adult training providers and Entrepreneurial institutions with innovative tools in order to spread the practical knowledge on entrepreneurial funding. It is of special interest to raise awareness on the different alternative sources of funding besides the traditional bank loans, such as crowdfunding, business angels, capital venture and so on, and offer current and future entrepreneurs practical training since it will also generate significant impact measured over time. metadata , FUNIBER mail SIN ESPECIFICAR (2020) Digital Simulator for Entrepreneurial Finance (FINANCEn_LAB). Repositorio de la Universidad. (Inédito)
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The main proposal of this project is to create digital interactive tools that will help the end-beneficiaries (potential and current entrepreneurs) to develop skills and acquire necessary practical knowledge to effectively apply for funding and manage their financial situation. From the general perspective, our project is expected to contribute to the improvement in financial literacy, especially, among HE students, as potential entrepreneurs, through an effective method of learning by doing. The project aims at covering the gap of practical financial competences considered as a critical barrier for entrepreneurship. Possible solution goes through cooperation between financing actors and educational sector. Thus, common training will be complemented with real practice, which includes individual and collaborative work, very different from traditional school assignments, since it will connect funding agents (banking professionals, investors, mentors and similar) with entrepreneurs and students. This DIGITAL LEARNING ENVIRONMENT BASED ON COLLABORATIVE LEARNING with financial agents is the core of our innovative proposal. Besides that, the project will deploy actions to empower HE teachers and entrepreneur coaches. Thus, the project will reach the following segments of people: university students, entrepreneurs, teachers, start-up incubators, financial agents for an estimated total of 840 direct participants and 14600 additional reached online. The main project activities will be oriented to the production of four intellectual outputs: 1) Practical cases in entrepreneurial finance for training purposes. 2) Digital Simulator for entrepreneurial finance (trainer’s tool). 3) Digital Simulator for entrepreneurial finance (self-learning tool). 4) Report on recommendations for entrepreneurial finance stakeholders and policy makers. The project implies cooperation of different type of organizations. The HE institutions and Banking/Financial sector will collaborate closely to ensure that we use appropriate content. Representative institutions will incorporate the tools into their trainings and will disseminate the project results properly. Entrepreneurial institutions will support the outputs creation attracting the attention of practitioners and professionals in the entrepreneurial field. The project pursues long term impact by providing HE lecturers, VET/adult training providers and Entrepreneurial institutions with innovative tools in order to spread the practical knowledge on entrepreneurial funding. It is of special interest to raise awareness on the different alternative sources of funding besides the traditional bank loans, such as crowdfunding, business angels, capital venture and so on, and offer current and future entrepreneurs practical training since it will also generate significant impact measured over time.
| Tipo de Documento: | Otro |
|---|---|
| Palabras Clave: | emprendedores, simulador, financiación, finanzas, practicas, plataforma digital |
| Clasificación temática: | Materias > Ciencias Sociales |
| Divisiones: | Universidad Europea del Atlántico > Investigación > Proyectos I+D+I |
| Depositado: | 07 Sep 2022 23:30 |
| Ultima Modificación: | 17 Oct 2024 23:30 |
| URI: | https://repositorio.uneatlantico.es/id/eprint/3522 |
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Enzymatic treatment shapes in vitro digestion pattern of phenolic compounds in mulberry juice
The health benefits of mulberry fruit are closely associated with its phenolic compounds. However, the effects of enzymatic treatments on the digestion patterns of these compounds in mulberry juice remain largely unknown. This study investigated the impact of pectinase (PE), pectin lyase (PL), and cellulase (CE) on the release of phenolic compounds in whole mulberry juice. The digestion patterns were further evaluated using an in vitro simulated digestion model. The results revealed that PE significantly increased chlorogenic acid content by 77.8 %, PL enhanced cyanidin-3-O-glucoside by 20.5 %, and CE boosted quercetin by 44.5 %. Following in vitro digestion, the phenolic compound levels decreased differently depending on the treatment, while cyanidin-3-O-rutinoside content increased across all groups. In conclusion, the selected enzymes effectively promoted the release of phenolic compounds in mulberry juice. However, during gastrointestinal digestion, the degradation of phenolic compounds surpassed their enhanced release, with effects varying based on the compound's structure.
Peihuan Luo mail , Jian Ai mail , Qiongyao Wang mail , Yihang Lou mail , Zhiwei Liao mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Elwira Sieniawska mail , Weibin Bai mail , Lingmin Tian mail ,
Luo
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A novel machine learning-based proposal for early prediction of endometriosis disease
Background Endometriosis is one of the causes of female infertility, with some studies estimating its prevalence at around 10 % of reproductive-age women worldwide and between 30 and 50 % in symptomatic women. However, its diagnosis is complex and often delayed, highlighting the need for more accessible and accurate diagnostic methods. The difficulty lies in its diverse etiology and the variability of symptoms among those affected. Methods This study proposes a predictive model based on supervised machine learning for the early identification of endometriosis, providing support for decision-making by healthcare professionals. For this purpose, an anonymised dataset of 5,143 female patients diagnosed with endometriosis at the private fertility clinic Inebir was used. The model integrates clinical records and genetic analysis through supervised machine learning algorithms, focusing on clinical variables and pathogenic and potentially pathogenic genetic variants. Results The developed predictive model achieves high accuracy in identifying the presence of endometriosis, highlighting the importance of combining clinical and genetic data in diagnosis. The integration of this data into the DELFOS platform, a clinical decision support system, demonstrates the utility of machine learning in improving the diagnosis of endometriosis. Conclusions The findings underscore the potential of clinical and genetic factors in the early diagnosis of endometriosis using supervised machine learning algorithms. This study contributes to the classification of clinical variables that influence endometriosis, offering a valuable tool for clinicians in making therapeutic and management decisions for their female patients.
Elena Enamorado-Díaz mail , Leticia Morales-Trujillo mail , Julián-Alberto García-García mail , Ana Teresa Marcos Rodríguez mail anateresa.marcos@uneatlantico.es, José Manuel Navarro-Pando mail jose.navarro@uneatlantico.es, María-José Escalona-Cuaresma mail ,
Enamorado-Díaz
<a href="/17569/1/Food%20Frontiers%20-%202025%20-%20Romero%E2%80%90Marquez%20-%20Olive%20Leaf%20Extracts%20With%20High%20%20Medium%20%20or%20Low%20Bioactive%20Compounds%20Content.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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Alzheimer's disease (AD) involves β-amyloid plaques and tau hyperphosphorylation, driven by oxidative stress and neuroinflammation. Cyclooxygenase-2 (COX-2) and acetylcholinesterase (AChE) activities exacerbate AD pathology. Olive leaf (OL) extracts, rich in bioactive compounds, offer potential therapeutic benefits. This study aimed to assess the anti-inflammatory, anti-cholinergic, and antioxidant effects of three OL extracts (low, mid, and high bioactive content) in vitro and their protective effects against AD-related proteinopathies in Caenorhabditis elegans models. OL extracts were characterized for phenolic composition, AChE and COX-2 inhibition, as well as antioxidant capacity. Their effects on intracellular and mitochondrial reactive oxygen species (ROS) were tested in C. elegans models expressing human Aβ and tau proteins. Gene expression analyses examined transcription factors (DAF-16, skinhead [SKN]-1) and their targets (superoxide dismutase [SOD]-2, SOD-3, GST-4, and heat shock protein [HSP]-16.2). High-OL extract demonstrated superior AChE and COX-2 inhibition and antioxidant capacity. Low- and high-OL extracts reduced Aβ aggregation, ROS levels, and proteotoxicity via SKN-1/NRF-2 and DAF-16/FOXO pathways, whereas mid-OL showed moderate effects through proteostasis modulation. In tau models, low- and high-OL extracts mitigated mitochondrial ROS levels via SOD-2 but had limited effects on intracellular ROS levels. High-OL extract also increased GST-4 levels, whereas low and mid extracts enhanced GST-4 levels. OL extracts protect against AD-related proteinopathies by modulating oxidative stress, inflammation, and proteostasis. High-OL extract showed the most promise for nutraceutical development due to its robust phenolic profile and activation of key antioxidant pathways. Further research is needed to confirm long-term efficacy.
Jose M. Romero‐Marquez mail , María D. Navarro‐Hortal mail , Alfonso Varela‐López mail , Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Juan G. Puentes mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Cristina Sánchez‐González mail , Jianbo Xiao mail , Roberto García‐Ruiz mail , Sebastián Sánchez mail , Tamara Y. Forbes‐Hernández mail , José L. Quiles mail jose.quiles@uneatlantico.es,
Romero‐Marquez
<a href="/17570/1/eFood%20-%202025%20-%20Navarro%E2%80%90Hortal%20-%20Effects%20of%20a%20Garlic%20Hydrophilic%20Extract%20Rich%20in%20Sulfur%20Compounds%20on%20Redox%20Biology%20and.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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Garlic is a horticultural product highly valued for its culinary and medicinal attributes. The aim of this study was to evaluate the composition of a garlic hydrophilic extract as well as the influence on redox biology, Alzheimer's Disease (AD) markers and aging, using Caenorhabditis elegans as experimental model. The extract was rich in sulfur compounds, highlighting the presence of other compounds like phenolics, and the antioxidant property was corroborated. Regarding AD markers, the acetylcholinesterase inhibitory capacity was demonstrated in vitro. Although the extract did not modify the amyloid β-induced paralysis degree, it was able to improve, in a dose-dependent manner, some locomotive parameters affected by the hyperphosphorylated tau protein in C. elegans. It could be related to the effect found on GFP-transgenic stains, mainly regarding to the increase in the gene expression of HSP-16.2. Moreover, an initial investigation into the aging process revealed that the extract successfully inhibited the accumulation of intracellular and mitochondrial reactive oxygen species in aged worms. These results provide valuable insights into the multifaceted impact of garlic extract, particularly in the context of aging and neurodegenerative processes. This study lays a foundation for further research avenues exploring the intricate molecular mechanisms underlying garlic effects and its translation into potential therapeutic interventions for age-related neurodegenerative conditions.
María D. Navarro‐Hortal mail , Jose M. Romero‐Marquez mail , Johura Ansary mail , Cristina Montalbán‐Hernández mail , Alfonso Varela‐López mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Jianbo Xiao mail , Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Cristina Sánchez‐González mail , Tamara Y. Forbes‐Hernández mail , José L. Quiles mail jose.quiles@uneatlantico.es,
Navarro‐Hortal
<a href="/17573/1/s41598-025-96332-9.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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Novel hybrid transfer neural network for wheat crop growth stages recognition using field images
Wheat is one of the world’s most widely cultivated cereal crops and is a primary food source for a significant portion of the population. Wheat goes through several distinct developmental phases, and accurately identifying these stages is essential for precision farming. Determining wheat growth stages accurately is crucial for increasing the efficiency of agricultural yield in wheat farming. Preliminary research identified obstacles in distinguishing between these stages, negatively impacting crop yields. To address this, this study introduces an innovative approach, MobDenNet, based on data collection and real-time wheat crop stage recognition. The data collection utilized a diverse image dataset covering seven growth phases ‘Crown Root’, ‘Tillering’, ‘Mid Vegetative’, ‘Booting’, ‘Heading’, ‘Anthesis’, and ‘Milking’, comprising 4496 images. The collected image dataset underwent rigorous preprocessing and advanced data augmentation to refine and minimize biases. This study employed deep and transfer learning models, including MobileNetV2, DenseNet-121, NASNet-Large, InceptionV3, and a convolutional neural network (CNN) for performance comparison. Experimental evaluations demonstrated that the transfer model MobileNetV2 achieved 95% accuracy, DenseNet-121 achieved 94% accuracy, NASNet-Large achieved 76% accuracy, InceptionV3 achieved 74% accuracy, and the CNN achieved 68% accuracy. The proposed novel hybrid approach, MobDenNet, that synergistically merges the architectures of MobileNetV2 and DenseNet-121 neural networks, yields highly accurate results with precision, recall, and an F1 score of 99%. We validated the robustness of the proposed approach using the k-fold cross-validation. The proposed research ensures the detection of growth stages with great promise for boosting agricultural productivity and management practices, empowering farmers to optimize resource distribution and make informed decisions.
Aisha Naseer mail , Madiha Amjad mail , Ali Raza mail , Kashif Munir mail , Aseel Smerat mail , Henry Fabian Gongora mail henry.gongora@uneatlantico.es, Carlos Eduardo Uc Ríos mail carlos.uc@unini.edu.mx, Imran Ashraf mail ,
Naseer