Protective Effects of Walnut Extract Against Amyloid Beta Peptide-Induced Cell Death and Oxidative Stress in PC12 Cells

Amyloid beta-protein (Aβ) is the major component of senile plaques and cerebrovascular amyloid deposits in individuals with Alzheimer’s disease. Aβ is known to increase free radical production in neuronal cells, leading to oxidative stress and cell death. Recently, considerable attention has been focused on dietary antioxidants that are able to scavenge reactive oxygen species (ROS), thereby offering protection against oxidative stress. Walnuts are rich in components that have anti-oxidant and anti-inflammatory properties. The inhibition of in vitro fibrillization of synthetic Aβ, and solubilization of preformed fibrillar Aβ by walnut extract was previously reported. The present study was designed to investigate whether walnut extract can protect against Aβ-induced oxidative damage and cytotoxicity. The effect of walnut extract on Aβ-induced cellular damage, ROS generation and apoptosis in PC12 pheochromocytoma cells was studied. Walnut extract reduced Aβ-mediated cell death assessed by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) reduction, and release of lactate dehydrogenase (membrane damage), DNA damage (apoptosis) and generation of ROS in a concentration-dependent manner. These results suggest that walnut extract can counteract Aβ-induced oxidative stress and associated cell death

Multi-Criteria Decision-Making Model to Achieve Sustainable Developmental Goals in Industry 4.0 for Smart City Infrastructure

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME’s achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of pollution emissions are key driving forces for enhancing sustainability. Compared to other methodologies, Multi-Criteria Decision Making (MCDM) considers the interplay between conditions. This is why it is an excellent approach to assess the sustainability of any city. Our experimental findings highlight the impact of MCDM and sustainability towards achieving a city’s sustainable development goals. Compared to other methods, the SC-MCDM system is more successful rate of 89.7%, a more sustainable rate of 92.1%, a more precise ratio 93%), more accurate (95%), and a less mean absolute error, and mean squared error rate of 8.3% while trying to achieve sustainable city development goals.   Doi: 10.28991/HIJ-2024-05-04-018 Full Text: PD

Adapting Gloss Vector semantic relatedness measure for semantic similarity estimation: An evaluation in the biomedical domain

Automatic methods of ontology alignment are essential for establishing interoperability across web services. These methods are needed to measure semantic similarity between two ontologies’ entities to discover reliable correspondences. While existing similarity measures suffer from some difficulties, semantic relatedness measures tend to yield better results; even though they are not completely appropriate for the ‘equivalence’ relationship (e.g. “blood” and “bleeding” related but not similar). We attempt to adapt Gloss Vector relatedness measure for similarity estimation. Generally, Gloss Vector uses angles between entities’ gloss vectors for relatedness calculation. After employing Pearson’s chi-squared test for statistical elimination of insignificant features to optimize entities’ gloss vectors, by considering concepts’ taxonomy, we enrich them for better similarity measurement. Discussed measures get evaluated in the biomedical domain using MeSH, MEDLINE and dataset of 301 concept pairs. We conclude Adapted Gloss Vector similarity results are more correlated with human judgment of similarity compared to other measures

Reshaping teaching excellence through accounting and business students

The dynamics of higher education institutions are changing with the expectations of students for their educational experiences. In universities around the world, academics are encouraged to facilitate excellent education. Teaching excellence contributes to business students' optimal development to become professional and trusted value creators for business and society. However, there is an ongoing debate around the definition of excellence. Recognising an excellent teacher poses questions such as the characteristics and competencies of such candidates. Several studies have investigated different dimensions of teachers' effectiveness, but there is hardly a consensus on what makes an excellent teacher?. Moreover, various stakeholders have distinct perceptions of evaluating teaching excellence and the quality of teaching. This workshop is based on a large-scale international research project that examines how accounting and business students define teaching excellence, whether teaching excellence perceptions vary based on student demographics and between academic institutions in different countries, and whether teaching excellence perceptions vary between accounting and non-accounting students. The quantitative and qualitative empirical data is collected in ten countries across five continents to advance our understanding of teaching excellence from the perspective of business students. This workshop will benefit CAAA participants as understanding what teaching excellence means for students would enhance teaching practices and, as a result, improve student engagement, enhance the student learning experience, and develop employability skills.</p