Approach to a Project Framework in the Environment of Sustainability and Corporate Social Responsibility (CSR): Case Study of a Training Proposal to a Group of Students in a Higher Education Institution

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Effects of enzymatic treatment on the in vitro digestion and fermentation patterns of mulberry fruit juice: A focus on carbohydrates

The aim of this study was to investigate the effects of enzymatic treatments (pectinase, pectin lyase, and cellulase) on the in vitro digestion and fermentation characteristics of whole mulberry fruit juice. The analysis focused on changes in carbohydrate properties within the black mulberry fruit matrix during simulated digestion and fermentation. Human fecal microbiota were collected and introduced to the fruit matrix to monitor the fate of both soluble and insoluble polysaccharides during fermentation. The results revealed that enzymatic treatments enhanced the solubilization of carbohydrates from mulberry fruits, with pectinase showing the most significant effect. Throughout the process of in vitro digestion, there was a gradual increase in the percentage of solubilized carbohydrates from the mulberry juice substrate. The digested suspensions underwent dialysis to remove degradation fragments, and a lower quantity of carbohydrate in the enzyme-treated groups compared to the control. Polysaccharide populations with varying molecular weights (Mw) were obtained from the soluble fractions of mulberry residues for subsequent fermentation. An increase in Mw of soluble polysaccharides was detected by HPSEC during fermentation in certain cases. The gut microbiota demonstrated the ability to convert specific insoluble fractions into soluble components, which were subsequently subjected to microbial utilization. Enzymatic treatments during mulberry juice preparation can potentially positively impact health by influencing gut microbiota and short-chain fatty acid (SCFA) modulations. Enzymes could serve as valuable tools for producing functional fruit and vegetable juices, with the need to specify processing conditions for specific raw materials remaining a subject of further investigation.

Producción Científica

Peihuan Luo mail , Jian Ai mail , Yuxin Wang mail , Songen Wang mail , Henk A. Schols mail , Hauke Smidt mail , Maurizio Battino mail maurizio.battino@uneatlantico.es, Weibin Bai mail , Lingmin Tian mail ,

Luo

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Can the phenolic compounds of Manuka honey chemosensitize colon cancer stem cells? A deep insight into the effect on chemoresistance and self-renewal

Manuka honey, which is rich in pinocembrin, quercetin, naringenin, salicylic, p-coumaric, ferulic, syringic and 3,4-dihydroxybenzoic acids, has been shown to have pleiotropic effects against colon cancer cells. In this study, potential chemosensitizing effects of Manuka honey against 5-Fluorouracil were investigated in colonspheres enriched with cancer stem cells (CSCs), which are responsible for chemoresistance. Results showed that 5-Fluorouracil increased when it was combined with Manuka honey by downregulating the gene expression of both ATP-binding cassette sub-family G member 2, an efflux pump and thymidylate synthase, the main target of 5-Fluorouracil which regulates the ex novo DNA synthesis. Manuka honey was associated with decreased self-renewal ability by CSCs, regulating expression of several genes in Wnt/β-catenin, Hedgehog and Notch pathways. This preliminary study opens new areas of research into the effects of natural compounds in combination with pharmaceuticals and, potentially, increase efficacy or reduce adverse effects.

Producción Científica

Danila Cianciosi mail , Yasmany Armas Diaz mail , José M. Alvarez-Suarez mail , Xiumin Chen mail , Di Zhang mail , Nohora Milena Martínez López mail nohora.martinez@uneatlantico.es, Mercedes Briones Urbano mail mercedes.briones@uneatlantico.es, José L. Quiles mail jose.quiles@uneatlantico.es, Adolfo Amici mail , Maurizio Battino mail maurizio.battino@uneatlantico.es, Francesca Giampieri mail francesca.giampieri@uneatlantico.es,

Cianciosi

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An Optimized Privacy Information Exchange Schema for Explainable AI Empowered WiMAX-based IoT networks

The Internet of Things (IoT) has recently surpassed wired communication. WiMAX is a wireless transmission technology that allows for faster internet access. Wireless network innovations, like some other communication networks, are not safe and secure. Security and authorization models are intended to prevent unauthorized use of network services. Numerous authorization and encrypted communication mechanisms have been introduced for WiMAX privacy, but the communication systems are still insecure and vulnerable to attacks such as zero-day attacks, rouge base station attacks, Man in the Middle (MITM) attacks, and Denial of Service (DoS) attacks. Wireless technologies have come a long way in the last few decades. Because most wireless transmission systems rely on radio signals, the system channel is essentially vulnerable to interception. As a result, data security is always critical in the presentation of a system. Because WiMAX is a wireless communication technology, it is particularly vulnerable to interception, so security is a top priority. Individuals must be protected from security breaches that occur across network interfaces, networking devices, and everything in between. Robust security management is required to protect WiMAX from attacks and vulnerabilities, despite the fact that emerging Artificial Intelligence (AI) technologies necessitate different security governance than existing technologies. We proposed an Optimized Privacy Information Exchange Schema for Explainable AI Empowered WiMAX-based IoT that addresses vulnerabilities and threats during the identification and authorization phases to improve the functionality and performance characteristics of the traditional system. The Scyther tool was used to validate the proposed privacy scheme, which is safer and more secure than existing systems.

Producción Científica

Premkumar Chithaluru mail , Aman Singh mail aman.singh@uneatlantico.es, Jagjit Singh Dhatterwal mail , Ali Hassan Sodhro mail , Marwan Ali Albahar mail , Anca Jurcut mail , Ahmed Alkhayyat mail ,

Chithaluru

<a class="ep_document_link" href="/8725/1/diagnostics-13-02871.pdf"><img class="ep_doc_icon" alt="[img]" src="/8725/1.hassmallThumbnailVersion/diagnostics-13-02871.pdf" border="0"/></a>

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Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer’s Disease Using Machine Learning Approaches

This study sought to investigate how different brain regions are affected by Alzheimer’s disease (AD) at various phases of the disease, using independent component analysis (ICA). The study examines six regions in the mild cognitive impairment (MCI) stage, four in the early stage of Alzheimer’s disease (AD), six in the moderate stage, and six in the severe stage. The precuneus, cuneus, middle frontal gyri, calcarine cortex, superior medial frontal gyri, and superior frontal gyri were the areas impacted at all phases. A general linear model (GLM) is used to extract the voxels of the previously mentioned regions. The resting fMRI data for 18 AD patients who had advanced from MCI to stage 3 of the disease were obtained from the ADNI public source database. The subjects include eight women and ten men. The voxel dataset is used to train and test ten machine learning algorithms to categorize the MCI, mild, moderate, and severe stages of Alzheimer’s disease. The accuracy, recall, precision, and F1 score were used as conventional scoring measures to evaluate the classification outcomes. AdaBoost fared better than the other algorithms and obtained a phenomenal accuracy of 98.61%, precision of 99.00%, and recall and F1 scores of 98.00% each.

Producción Científica

Samra Shahzadi mail , Naveed Anwer Butt mail , Muhammad Usman Sana mail , Iñaki Elío Pascual mail inaki.elio@uneatlantico.es, Mercedes Briones Urbano mail mercedes.briones@uneatlantico.es, Isabel de la Torre Díez mail , Imran Ashraf mail ,

Shahzadi

<a href="/8726/1/sensors-23-07710-v2.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/8726/1.hassmallThumbnailVersion/sensors-23-07710-v2.pdf" border="0"/></a>

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Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants

Adaptive equalization is crucial in mitigating distortions and compensating for frequency response variations in communication systems. It aims to enhance signal quality by adjusting the characteristics of the received signal. Particle swarm optimization (PSO) algorithms have shown promise in optimizing the tap weights of the equalizer. However, there is a need to enhance the optimization capabilities of PSO further to improve the equalization performance. This paper provides a comprehensive study of the issues and challenges of adaptive filtering by comparing different variants of PSO and analyzing the performance by combining PSO with other optimization algorithms to achieve better convergence, accuracy, and adaptability. Traditional PSO algorithms often suffer from high computational complexity and slow convergence rates, limiting their effectiveness in solving complex optimization problems. To address these limitations, this paper proposes a set of techniques aimed at reducing the complexity and accelerating the convergence of PSO.

Producción Científica

Arooj Khan mail , Imran Shafi mail , Sajid Gul Khawaja mail , Isabel de la Torre Díez mail , Miguel Ángel López Flores mail miguelangel.lopez@uneatlantico.es, Juan Castanedo Galán mail juan.castanedo@uneatlantico.es, Imran Ashraf mail ,

Khan