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Objective: The aim of this study was to evaluate the potential effect of the methanolic extract of plant Glycyrrhiza glabra roots on bone mineral density and femoral bone strength of ovariectomized rats. Methods: Thirty 10-month-old Wistar rats were randomly separated into three groups of ten, Control, Ovariectomy and Ovariectomy-plus-Glycyrrhiza in their drinking water. Total and proximal tibial bone mineral density was measured in all groups before ovariectomy (baseline) and after 3 and 6 months post ovariectomy. Three-point-bending of the femurs and uterine weight and histology were examined at the end of the study. Results: No significant difference was noted in bone density percentage change of total tibia from baseline to 3 months between Control and Ovariectomy-plus-Glycyrrhiza groups (+5.31% ± 4.75 and +3.30% ± 6.31 respectively, P = non significant), and of proximal tibia accordingly (+5.58% ± 6.92 and +2.61% ± 13.62, P = non significant) demonstrating a strong osteoprotective effect. There was notable difference in percentage change of total tibia from baseline to 6 months between groups Ovariectomy and Ovariectomy-plus-Glycyrrhiza (−13.03% ± 5.11 and −0.84% ± 7.63 respectively, P

Fusarium head blight (FHB) continues to be a major problem in wheat production and is considered a disease complex caused by several fungal pathogens including Fusarium culmorum, F. graminearum and F. equiseti. With the objective of investigating diversity of mycoviruses in FHB-associated pathogens, we isolated Fusarium spp. from six wheat (Triticum aestivum) cultivars. In total, 56 Fusarium isolates (29 F. culmorum, 24 F. graminearum, one F. equiseti) were screened for mycoviruses by extracting and sequencing double-stranded RNA. We found that a large proportion of Fusarium isolates (46 %) were infected with mycoviruses. F. culmorum, previously described to harbor only one mycovirus, tended to host more viruses than F. graminearum, with a few isolates harboring seven mycoviruses simultaneously. Based on the RNA-dependent RNA polymerase domain analysis, ten were positive-sense single-stranded RNA viruses (related to viruses from families Mitoviridae, Botourmiaviridae, Narnaviridae, Tymoviridae, Gammaflexiviridae, as well as proposed Ambiguiviridae and ormycovirus viral group), one was double-stranded RNA virus (Partitiviridae), and five were negative-sense single-stranded RNA viruses (related to members in the families of Yueviridae, Phenuiviridae, Mymonaviridae, as well as proposed Mycoaspiviridae). Five mycoviruses were shared between F. graminearum and F. culmorum. These results increase our general understanding of mycovirology. To our knowledge, this is the first in-depth report of the mycovirome in F. culmorum and the first report on the diversity of mycoviruses from Danish isolates of FHB-causing fungi in general.

Monitoring food quality throughout the supply chain in a rapid and cost-effective way allows on-time decision−making, reducing food waste, and increasing sustainability. A portable multispectral imaging sensor was used for the rapid prediction of microbiological quality of fish fillets. Seabream fillets, packaged either in aerobic or vacuum conditions, were collected from both aquaculture and retail stores, while images were also acquired both from the skin and the flesh side of the fish fillets. In parallel to image acquisition, the microbial quality was also estimated for each fish fillet. The data were used for the training of predictive artificial neural network (ANN) models for the estimation of total aerobic counts (TACs). Models were built separately for fish parts (i.e., skin, flesh) and packaging conditions and were validated using two approaches (i.e., validation with data partitioning and external validation using samples from retail stores). The performance of the ANN models for the validation set with data partitioning was similar for the data collected from the flesh (RMSE = 0.402–0.547) and the skin side (RMSE = 0.500–0.533) of the fish fillets. Similar performance also was obtained from validation of the models of the different packaging conditions (i.e., aerobic, vacuum). The prediction capability of the models combining both air and vacuum packaged samples (RMSE = 0.531) was slightly lower compared to the models trained and validated per packaging condition, individually (RMSE = 0.510, 0.516 in air and vacuum, respectively). The models tested with unknown samples (i.e., fish fillets from retail stores-external validation) showed poorer performance (RMSE = 1.061–1.414) compared to the models validated with data partitioning (RMSE = 0.402–0.547). Multispectral imaging sensor appeared to be efficient for the rapid assessment of the microbiological quality of fish fillets for all the different cases evaluated. Hence, these outcomes could be beneficial not only for the industry and food operators but also for the authorities and consumers.

Abstract Background Dark pigmented snow and glacier ice algae on glaciers and ice sheets contribute to accelerating melt. The biological controls on these algae, particularly the role of viruses, remain poorly understood. Giant viruses, classified under the nucleocytoplasmic large DNA viruses (NCLDV) supergroup (phylum Nucleocytoviricota), are diverse and globally distributed. NCLDVs are known to infect eukaryotic cells in marine and freshwater environments, providing a biological control on the algal population in these ecosystems. However, there is very limited information on the diversity and ecosystem function of NCLDVs in terrestrial icy habitats. Results In this study, we investigate for the first time giant viruses and their host connections on ice and snow habitats, such as cryoconite, dark ice, ice core, red and green snow, and genomic assemblies of five cultivated Chlorophyta snow algae. Giant virus marker genes were present in almost all samples; the highest abundances were recovered from red snow and the snow algae genomic assemblies, followed by green snow and dark ice. The variety of active algae and protists in these GrIS habitats containing NCLDV marker genes suggests that infection can occur on a range of eukaryotic hosts. Metagenomic data from red and green snow contained evidence of giant virus metagenome-assembled genomes from the orders Imitervirales, Asfuvirales, and Algavirales. Conclusion Our study highlights NCLDV family signatures in snow and ice samples from the Greenland ice sheet. Giant virus metagenome-assembled genomes (GVMAGs) were found in red snow samples, and related NCLDV marker genes were identified for the first time in snow algal culture genomic assemblies; implying a relationship between the NCLDVs and snow algae. Metatranscriptomic viral genes also aligned with metagenomic sequences, suggesting that NCLDVs are an active component of the microbial community and are potential “top-down” controls of the eukaryotic algal and protistan members. This study reveals the unprecedented presence of a diverse community of NCLDVs in a variety of glacial habitats dominated by algae.

The Environmentally Sensitive Areas Index (ESAI) is a comprehensive tool for assessing the susceptibility of areas to desertification. This index analyzes various parameters that are vital for environmental health. Through this index, factors such as human activities, geology, soil quality, vegetation and climate patterns are scrutinized. The analysis assigns weights to each participating factor. Thus, the index is derived from the aggregation of four categories (vegetation, climate, soil quality and management practices), and each of them is independently assessed to understand ecological health. In this way, the level of vulnerability to desertification is effectively measured. The application of the index in Greece (for a period of 20 years, 1984–2004) showed signs of environmental degradation and identified many areas with a high risk of desertification. Notably, there was a substantial increase in cultivated land within rural areas, contributing to shifts in the environmental landscape. Furthermore, this period is distinguished as the driest in the last century, with a peak between 1988 and 1993. The consequential rise in irrigation demand, driven by the simultaneous growth of crops and the intensification of agricultural practices, underscores the intricate interplay between human activities and environmental vulnerability.

Abstract Background Two isoforms of Phosphoinositide 3-kinase (PI3K), p110γ and p110δ, are predominantly expressed in leukocytes and represent attractive therapeutic targets for the treatment of allergic asthma. The study aim was to assess the impact of administration of an inhaled PI3Kγδ inhibitor (AZD8154) in a rat model of asthma. Methods Firstly, we checked that the tool compound, AZD8154, inhibited rat PI3K γ & δ kinases using rat cell-based assays. Subsequently, a time-course study was conducted in a rat model of asthma to assess PI3K activity in the lung and how it is temporally associated with other key transcription pathways and asthma like features of the model. Finally, the impact on lung dosed AZD8154 on target engagement, pathway specificity, airway inflammation and lung function changes was assessed. Results Data showed that AZD8154 could inhibit rat PI3K γ & δ isoforms and, in a rat model of allergic asthma the PI3K pathway was activated in the lung. Intratracheal administration of AZD8154 caused a dose related suppression PI3K pathway activation (reduction in pAkt) and unlike after budesonide treatment, STAT and NF-κB pathways were not affected by AZD8154. The suppression of the PI3K pathway led to a marked inhibition of airway inflammation and reduction in changes in lung function. Conclusion These data show that a dual PI3Kγδ inhibitor suppress key features of disease in a rat model of asthma to a similar degree as budesonide and indicate that dual PI3Kγδ inhibition may be an effective treatment for people suffering from allergic asthma.

Abstract Coherent beam combination offers a solution to the challenges associated with the power handling capacity of individual fibres, however, the combined intensity profile strongly depends on the relative phase of each fibre. Optimal combination necessitates precise control over the phase of each fibre channel, however, determining the required phase compensations is challenging because phase information is typically not available. Additionally, the presence of continuously varying phase noise in fibre laser systems means that a single-step and high-speed correction process is required. In this work, we use a spatial light modulator to demonstrate coherent combination in a seven-beam system. Deep learning is used to identify the relative phase offsets for each beam directly from the combined intensity pattern, allowing real-time correction. Furthermore, we demonstrate that the deep learning agent can calculate the phase corrections needed to achieve user-specified target intensity profiles thus simultaneously achieving both beam combination and beam shaping.

Abstract Background Fear of childbirth is a frequent health issue for pregnant women. The Wijma Delivery Expectancy/Experience Questionnaire (W-DEQ) is a widely used instrument to measure the fear of childbirth during the antenatal period. The aim of the study was to assess the psychometric properties of the W-DEQ (version A) in a sample of Greek pregnant women. Methods Low-risk pregnant women in the second or third trimester of pregnancy (N = 201) were invited to participate in the study and to complete a booklet of questionnaires including the Greek versions of W-DEQ-A, State-Trait Anxiety Inventory (STAI), Coping Orientations to Problems Experienced (Brief COPE), Perceived Stress Scale (PSS-10) and Edinburgh Postnatal Depression Scale (EPDS). Exploratory (EFA) and confirmatory factor analysis (CFA) was performed. Results The mean age of participants was 34.2 years (SD = 4.3 years). EFA yielded six factors (“Lack of self-efficacy”, “Lack of positive anticipation”, “Lack of feeling lonely”, “Concerns about delivery and losing control”, “Calmness”, and “Concern for the child”) of 33 items of W-DEQ-A. CFA confirmed the multidimensionality of the instrument. All Cronbach’s alpha were over 0.7, indicating acceptable reliability of the factors. All factors were significantly correlated with each other, and convergent validity was demonstrated by a significant association with stress, anxiety, and depression among low-risk pregnant women. Conclusion The Greek version of W-DEQ-A proved to be a valid and reliable instrument of fear of childbirth among Greek low-risk pregnant women.

Abstract Microbial communities from extreme environments are largely understudied, but are essential as producers of metabolites, including enzymes, for industrial processes. As cultivation of most microorganisms remains a challenge, culture‐independent approaches for enzyme discovery in the form of metagenomics to analyse the genetic potential of a community are rapidly becoming the way forward. This study focused on analysing a metagenome from the cold and alkaline ikaite columns in Greenland, identifying 282 open reading frames (ORFs) that encoded putative carbohydrate‐modifying enzymes with potential applications in, for example detergents and other processes where activity at low temperature and high pH is desired. Seventeen selected ORFs, representing eight enzyme families were synthesized and expressed in two host organisms, Escherichia coli and Aliivibrio wodanis. Aliivibrio wodanis demonstrated expression of a more diverse range of enzyme classes compared to E. coli, emphasizing the importance of alternative expression systems for enzymes from extremophilic microorganisms. To demonstrate the validity of the screening strategy, we chose a recombinantly expressed cellulolytic enzyme from the metagenome for further characterization. The enzyme, Cel240, exhibited close to 40% of its relative activity at low temperatures (4°C) and demonstrated endoglucanase characteristics, with a preference for cellulose substrates. Despite low sequence similarity with known enzymes, computational analysis and structural modelling confirmed its cellulase‐family affiliation. Cel240 displayed activity at low temperatures and good stability at 25°C, activity at alkaline pH and increased activity in the presence of CaCl2, making it a promising candidate for detergent and washing industry applications.

Undoubtedly, one of the most common machine learning challenges is multiclass classification. In light of this, a novel bio-inspired neural network (NN) has been developed to address multiclass classification-related issues. Given that weights and structure determination (WASD) NNs have been acknowledged to alleviate the disadvantages of conventional back-propagation NNs, such as slow training pace and trapping in a local minimum, we developed a bio-inspired WASD algorithm for multiclass classification problems (BWASDC) by using the metaheuristic beetle antennae search (BAS) algorithm to enhance the WASD algorithm's learning process. The BWASDC's effectiveness is then evaluated through applications in occupational classification systems. It is important to mention that systems of occupational classification serve as a fundamental indicator of occupational exposure. For this reason, they are highly significant in social science research. According to the findings of four occupational classification experiments, the BWASDC model outperformed some of the most modern classification models obtainable through MATLAB's classification learner app on all fronts.

Business simulation games (BSG) have become a very popular pedagogical tool in university courses and programmes all around the world. Their immense popularity has been a subject of research and academic discourse, especially the past couple of decades when many experts have called for more student-centric and reality based pedagogical approaches. Their implementation in business schools has been particularly successful, leading to improved student outcomes, satisfaction and skills improvement. Their use in sport business courses, though, has been surprisingly neglected in the pertinent literature. In the present study, we attempt to close this gap in the literature by exploring the effectiveness of BSG implementation in sport business courses, as well as the conditions and factors that facilitate the teaching and learning experience in such cases. In doing so, this research followed a mixed methods case study approach. Our findings expand previous conclusions and arguments on the effectiveness of the use of BSG in universities, to the case of sport business courses. They also identify conditions under which the teaching and learning experience is improved, namely effective group work, student engagement, satisfaction from the game and others. Based on these findings we propose useful recommendations and suggestions for future research. We also establish BSG as a form of independent, guided learning activity which is facilitated merely by individual engagement and group work and less from the tutors.

The ikaite columns in the Ikka Fjord, SW Greenland, represent a permanently cold and alkaline environment known to contain a rich bacterial diversity. 16S and 18S rRNA gene amplicon and metagenomic sequencing was used to investigate the microbial diversity in the columns and for the first time, the eukaryotic and archaeal diversity in ikaite columns were analyzed. The results showed a rich prokaryotic diversity that varied across columns as well as within each column. Seven different archaeal phyla were documented in multiple locations inside the columns. The columns also contained a rich eukaryotic diversity with 27 phyla representing microalgae, protists, fungi, and small animals. Based on metagenomic sequencing, 25 high-quality MAGs were assembled and analyzed for the presence of genes involved in cycling of nitrogen, sulfur, and phosphorous as well as genes encoding carbohydrate-active enzymes (CAZymes), showing a potentially very bioactive microbial community.

In the continuously changing labor market, understanding the dynamics of online job postings is crucial for economic and workforce development. With the increasing reliance on Online Job Portals, analyzing online job postings has become an essential tool for capturing real-time labor-market trends. This paper presents a comprehensive methodology for processing online job postings to generate labor-market intelligence. The proposed methodology encompasses data source selection, data extraction, cleansing, normalization, and deduplication procedures. The final step involves information extraction based on employer industry, occupation, workplace, skills, and required experience. We address the key challenges that emerge at each step and discuss how they can be resolved. Our methodology is applied to two use cases: the first focuses on the analysis of the Greek labor market in the tourism industry during the COVID-19 pandemic, revealing shifts in job demands, skill requirements, and employment types. In the second use case, a data-driven ontology is employed to extract skills from job postings using machine learning. The findings highlight that the proposed methodology, utilizing NLP and machine-learning techniques instead of LLMs, can be applied to different labor market-analysis use cases and offer valuable insights for businesses, job seekers, and policymakers.

Nowadays, transactions carried out with digital currencies are increasing. Modern societies are asked to respond to growing challenges related to the management of digital currencies in their daily lives. However, due to the lack of digital skills of users, the management of digital currencies hides risks. To the best of our knowledge, the originality of the current research lies in the act of combining the concept of digital skills with the use of digital currencies. After all, the use of digital currencies is constantly increasing, which means that citizens should familiarize themselves with their use, an element that makes this study valuable for digital societies. Digital skills effectively contribute to the development of digital societies because they increase the employment of citizens, facilitate access to information, and contribute to the social inclusion of individuals through digital communication, while also increasing efficiency and productivity in the workplace. Also, the government and banking institutions can more effectively sensitize citizens to digital skills for more effective use of digital currencies. In this way, tax payments will be facilitated, the use of e-wallets will be safer, and e-governance will be greatly promoted, while the quality of banking services will be improved. The methodology of this study was based on the Digital Competence Framework for Modern Societies (DigComp) and was applied through a questionnaire completed by 443 respondents. The main objective was to evaluate their digital skills from the perspective of digital currency use. The analysis of the responses was carried out by using Structural Equation Modeling (SEM). The most important result from this research reveals that users of digital currencies are significantly capable of developing communication to solve everyday problems. At the same time, users of digital currencies mostly detect digital threats and effectively manage fake news without being affected by them. However, users of digital currencies consider that security issues are important, but only for transactions and not for their supporting functions. The study concludes with suggestions for improving the experience of digital currency users through individual actions, thus having a positive impact on the state and banking institutions.

Avocado (Persea americana) is a unique fruit with exceptional nutritional and technological characteristics, as well as proposed health benefits. Moreover, the commercial utilization of avocado to make guacamole and/or to extract its oil for several applications generates massive amounts of avocado bio-wastes, including peels and seeds by-products, which further impact the environment and waste management costs. Within this article, the proposed health benefits of moderate avocado consumption, as a functional component of a balanced diet against inflammation-related chronic disorders, and its potential applications are fully addressed. The numerous bioactive compounds present in avocado fruit and its by-products, such as its bioactive phenolics, dietary fiber, and lipid bioactives like unsaturated fatty acids and polar lipids, are also thoroughly outlined. The functional anti-inflammatory, antithrombotic, and antioxidant properties of each of these bioactives and avocado extracts, are then thoroughly reviewed. Emphasis is given to these avocado-derived bioactives and extracts that have the potential to be utilized in various industrial applications, such as in functional foods, supplements, nutraceuticals, and cosmetics related health-promoting applications. The limitations and future perspectives of these applications based on avocado bioactives are also discussed.

To pave the way towards buildings’ decarbonization in the context of the European Union’s (EU) policy, the methodology of cost-optimality assessment based on regulation 244/2012/EU is a useful tool to explore and foster the application of energy technologies in buildings. Meanwhile, the fostering of concentrated solar power is included in the EU solar energy strategy. In this study, the cost-optimal methodology is employed for the techno-economic assessment of the integration of a novel solar, multi-purpose energy technology, namely a parabolic trough collector-based trigeneration system, in two building types with different characteristics, namely an office and a hospital, in Greece, thus allowing the evaluation of the cost-optimal system design and the impact of the building type on the system’s techno-economic performance. Reference buildings are defined and their energy demand is calculated through dynamic energy simulations. The trigeneration system’s performance for different design scenarios is then parametrically investigated using a simulation model. For each scenario, energy, environmental and economic indicators are calculated and the cost-optimal designs are extracted. In the cost-optimal implementation, the system covered 18.19–36.39% and 3.58–15.71% of the heating and cooling demand, respectively, while the reduction of the primary energy consumption and emissions was estimated at 10–14% and 10–16%, respectively. However, differences between the buildings related to the operation schedule and the loads led to the implementation of the system being economically more attractive in the hospital, while for the office, financial support is necessary for a viable investment.