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Sustainability is becoming essential and actively debated in the food sector, influencing companies and stakeholders globally. Sustainability practices have been developed and integrated into food industry actions and policies to meet present needs without compromising future needs. The aim of this study was to explore the current sustainability practices across the food industry in Europe and how initiatives are developed, implemented and evaluated to achieve food sustainability targets. This study formed part of a larger European project (2022-1-IE01-KA220-VET-000087508 Digitalisation of Sustainable Health Education). In-depth semi-structured interviews were conducted with 21 food industry employees with expertise in sustainability across Ireland, Poland, Lithuania, and Cyprus. Interviews were transcribed and thematically analysed. Three themes were identified: sustainable practices challenges, facilitators of green practice, and thinking to the future, Complying with the Science-Based Targets initiative, and setting emissions targets such net zero by 2050 and reducing waste output, drove sustainable activities. Participants identified barriers to initiative development and implementation including cost, monitoring time, product quality, and employee engagement. Employee and stakeholder understanding were crucial to sustainability initiative success. Gaps in research were identified as the proliferation of environmental labels and greater company collaboration to share sustainability data. A key consideration highlighted in discussions was the importance of collaboration and education for raising awareness and strengthening the implementation and long-term maintenance of food sustainability practices within industry. Greater collaboration between large food companies to share raw sustainability metric data could strengthen initiative outcomes and raise greater awareness among stakeholders, bridging the knowledge gap with producers or stakeholders who operate on a smaller scale. Full article

To address the recent increase in wildfire severity and incidence, as well as the subsequent financial and physical costs, forest managers and wildland firefighting agencies rely on remotely sensed products for better decision-making and mitigation efforts. To address the remote sensing needs of these agencies, which include high spatial resolution, immunity to atmospheric and solar illumination effects, and day/night capabilities, the use of synthetic aperture radar (SAR) is under investigation for application in current and upcoming systems for all phases of a wildfire. Focusing on the active phase, a method for monitoring wildfire activity is presented based on changes in the radar vegetation index (RVI). L-band backscatter measurements from NASA/JPL’s UAVSAR instrument are used to obtain RVI images on multiple dates during the 2020 Bobcat (located in Southern CA, USA) and Hennessey (located in Northern CA, USA) fires and the 2021 Caldor (located in the Sierra Nevada region of CA, USA) fire. Changes in the RVI between measurement dates of a single fire are then compared to indicators of fire activity such as ancillary GIS-based burn extent perimeters and the Landsat 8-based difference normalized burn ratio (dNBR). An RVI-based wildfire “burn” detector/index is then developed by thresholding the RVI change. A combination of the receiver operating characteristic (ROC) curves and F1 scores for this detector are used to derive change detection thresholds at varying spatial resolutions. Six repeat-track UAVSAR lines over the 2020 fires are used to determine appropriate threshold values, and the performance is subsequently investigated for the 2021 Caldor fire. The results show good performance for the Bobcat and Hennessey fires at 100 m resolution, with optimum probability of detections of 67.89% and 71.98%, F1 scores of 0.6865 and 0.7309, and Matthews correlation coefficients of 0.5863 and 0.6207, respectively, with an overall increase in performance for all metrics as spatial resolution becomes coarser. The results for pixels identified as “burned” compare well with other fire indicators such as soil burn severity, known progression maps, and post-fire agency publications. Good performance is also observed for the Caldor fire where the percentage of pixels identified as burned within the known fire perimeters ranges from 37.87% at ~5 m resolution to 88.02% at 500 m resolution, with a general increase in performance as spatial resolution increases. All detections for Caldor show dense collections of burned pixels within the known perimeters, while pixels identified as burned that lie outside of the know perimeters have a sparse spatial distribution similar to noise that decreases as spatial resolution is degraded. The Caldor results also align well with other fire indicators such as soil burn severity and vegetation disturbance. Full article

Colostrum intake is a crucial determinant of survival in newborn rabbits. Neonates rely entirely on passive immunity transfer from their mothers while suckling colostrum. The goal of this study was to explore the protein differences of rabbit milk during different lactation periods. Our findings showed that the daily milk yield exhibited an increasing trend from the 2nd to the 21st day of lactation. A data-independent acquisition proteomics approach identified a total of 2011 proteins. Significantly, different abundances were found for 525 proteins in the colostrum and the mature milk samples. Eleven differentially abundant proteins (DAPs) were examined using parallel reaction monitoring, which verified the reliability of the proteomic data. Gene Ontology analysis revealed that these DAPs were primarily associated with glycosyltransferase activity, macromolecule transmembrane transporter activity, and regulation of acute inflammatory response. The dominant metabolic pathways of the DAPs involve the complement and coagulation cascades. A protein–protein interaction analysis identified apolipoprotein B, apolipoprotein A1, triose phosphate isomerase 1, and albumin as the hub proteins responsible for distinguishing differences between biological properties in rabbit colostrum and mature milk. These findings enhance our comprehension of the rabbit milk proteome, particularly in expanding our knowledge regarding the requirements of neonatal rabbits. Full article

Volcanic eruptions stand as destructive threats to adjacent communities, unleashing multiple hazards such as earthquakes, tsunamis, pyroclastic flows, and toxic gases. The imperative for proactive management of volcanic risks and communities’ adaptation cannot be overstated, particularly in densely populated areas where the potential for widespread devastation looms large. Kolumbo, an active submarine volcano located approximately 7 km northeast of Santorini Island in Greece, serves as a pertinent case. Its historical record is characterised by an eruption in 1650 CE that produced a catastrophic tsunami. The aftermath witnessed havoc on neighbouring islands, coupled with casualties stemming from noxious gases in Santorini. Eyewitness accounts mention maximum water run-up heights of 20 m on the southern coast of Ios, inundation of an area of 240 m inland on Sikinos, and a flooding of up to 2 km² inland on the eastern coast of Santorini. Recent studies suggest that a potential future eruption of Kolumbo poses a substantial hazard to the northern and eastern coasts of Santorini. Unfortunately, the absence of a concrete management protocol leaves these areas vulnerable to an impending threat that demands immediate attention. Therefore, it is recommended that a comprehensive approach be adopted, involving scientific research (active monitoring, hazard maps), community engagement, preparedness planning with government agencies, and the development of timely response strategies to reduce the associated risks, prevent casualties, and mitigate the potential consequences on the region’s economy and infrastructure. Full article

Bridges are critical infrastructures that support our economic activities and daily lives. Aging bridges have been a major issue for decades, prompting researchers to improve resilience and performance through structural health monitoring. While most research focuses on superstructure damage, the majority of bridge failures are associated with support or joint damages, indicating the importance of bridge support. Indeed, bridge support affects the performance of both the substructure and superstructure by maintaining the load path and allowing certain movements to mitigate thermal and other stresses. The support deterioration leads to a change in fixity in the superstructure, compromising the bridge’s integrity and safety. Hence, a reliable method to determine support fixity level is essential to detecting bearing health and enhancing the accuracy of the bridge health monitoring system. However, such research is lacking because of its complexity. In this study, we developed a support fixity quantification method based on thermal responses using an Artificial Neural Network (ANN) model. A finite element (FE) model of a representative highway bridge is used to derive thermal displacement data under different bearing stiffnesses, superstructure damage, and thermal loading. The thermal displacement behavior of the bridge under different support fixity conditions is presented, and the model is trained on the simulated response. The performance of the developed FE model and ANN was validated with field monitoring data collected from two in-service bridges in Connecticut using a real-time Wireless Sensor Network (WSN). Finally, the support stiffnesses of both bridges were predicted using the ANN model for validation. Full article

Opioid-related deaths are attributed to overdoses, and fentanyl overdose has been on the rise in many parts of the world, including the USA. Glutamate transporter 1 (GLT-1) has been identified as a therapeutic target in several preclinical models of substance use disorders, and β-lactams effectively enhance its expression and function. In the current study, we characterized the metabolomic profile of the nucleus accumbens (NAc) in fentanyl-overdose mouse models, and we evaluated the protective effects of the functional enhancement of GLT-1 using β-lactams, ceftriaxone, and MC-100093. BALB/c mice were divided into four groups: control, fentanyl, fentanyl/ceftriaxone, and fentanyl/MC-100093. While the control group was intraperitoneally (i.p.) injected with normal saline simultaneously with other groups, all fentanyl groups were i.p. injected with 1 mg/kg of fentanyl as an overdose after habituation with four repetitive non-consecutive moderate doses (0.05 mg/kg) of fentanyl for a period of seven days. MC-100093 (50 mg/kg) and ceftriaxone (200 mg/kg) were i.p. injected from days 5 to 9. Gas chromatography–mass spectrometry (GC-MS) was used for metabolomics, and Western blotting was performed to determine the expression of target proteins. Y-maze spontaneous alternation performance and the open field activity monitoring system were used to measure behavioral manifestations. Fentanyl overdose altered the abundance of about 30 metabolites, reduced the expression of GLT-1, and induced the expression of inflammatory mediators IL-6 and TLR-4 in the NAc. MC-100093 and ceftriaxone attenuated the effects of fentanyl-induced downregulation of GLT-1 and upregulation of IL-6; however, only ceftriaxone attenuated fentanyl-induced upregulation of TRL4 expression. Both of the β-lactams attenuated the effects of fentanyl overdose on locomotor activities but did not induce significant changes in the overall metabolomic profile. Our findings revealed that the exposure to a high dose of fentanyl causes alterations in key metabolic pathways in the NAc. Pretreatment with ceftriaxone and MC-100093 normalized fentanyl-induced downregulation of GLT-1 expression with subsequent attenuation of neuroinflammation as well as the hyperactivity, indicating that β-lactams may be promising drugs for treating fentanyl use disorder. Full article

Pyocyanin is considered a maker of Pseudomonas aeruginosa (P. aeruginosa) infection. Pyocyanin is among the toxins released by the P. aeruginosa bacteria. Therefore, the development of a direct detection of PYO is crucial due to its importance. Among the different optical techniques, the Raman technique showed unique advantages because of its fingerprint data, no sample preparation, and high sensitivity besides its ease of use. Noble metal nanostructures were used to improve the Raman response based on the surface-enhanced Raman scattering (SERS) technique. Anodic metal oxide attracts much interest due to its unique morphology and applications. The porous metal structure provides a large surface area that could be used as a hard template for periodic nanostructure array fabrication. Porous shapes and sizes could be controlled by controlling the anodization parameters, including the anodization voltage, current, temperature, and time, besides the metal purity and the electrolyte type/concentration. The anodization of aluminum foil results in anodic aluminum oxide (AAO) formation with different roughness. Here, we will use the roughness as hotspot centers to enhance the Raman signals. Firstly, a thin film of gold was deposited to develop gold/alumina (Au/AAO) platforms and then applied as SERS-active surfaces. The morphology and roughness of the developed substrates were investigated using scanning electron microscopy (SEM) and atomic force microscopy (AFM) techniques. The Au/AAO substrates were used for monitoring pyocyanin secreted from Pseudomonas aeruginosa microorganisms based on the SERS technique. The results showed that the roughness degree affects the enhancement efficiency of this sensor. The high enhancement was obtained in the case of depositing a 30 nm layer of gold onto the second anodized substrates. The developed sensor showed high sensitivity toward pyocyanin with a limit of detection of 96 nM with a linear response over a dynamic range from 1 µM to 9 µM. Full article

This study examines the progress and challenges in the democratic landscape of Africa within the framework of the Africa Agenda 2063 (hereafter AA2063). Initiated in 2013, the Agenda signifies Africa’s commitment to an integrated, prosperous, and peaceful continent. Despite these aspirations, Africa faces persistent challenges, including political instability, socio-economic inequalities, and health crises. This study triangulates data from Afrobarometer public perception surveys, Varieties of Democracies expert insights, and the Ibrahim Index of African Governance to provide a broad understanding of the ten-year trend in democratic governance in Africa. The study found an increased demand for democracy, contrasted with a continuous decline in the supply of democracy. The study observed that, based on the average democratic performance over the decade, measured at 45% using V-Dem indices and 44% using IIAG percentage, Africa needs to score more than twice the current performance every year to align with the democratic aspirations of AA2063. This study underscores the need for targeted reforms to bridge the gap between current democratic performance and the envisioned goals of AA2063. It identifies key areas for improvement, including the separation of powers and checks and balances, citizens’ active participation, accessibility to state-owned media, and the independence of election monitoring bodies. Full article

Rapid urbanization and climate change exacerbate the urban heat island effect, increasing the vulnerability of urban residents to extreme heat. Although many studies have assessed urban heat vulnerability, there is a significant lack of standardized criteria and references for selecting indicators, building models, and validating those models. Many existing approaches do not adequately meet urban planning needs due to insufficient spatial resolution, temporal coverage, and accuracy. To address this gap, this paper introduces the U-HEAT framework, a conceptual model for analyzing urban heat vulnerability. The primary objective is to outline the theoretical foundations and potential applications of U-HEAT, emphasizing its conceptual nature. This framework integrates machine learning (ML) with remote sensing (RS) to identify urban heat vulnerability at both long-term and detailed levels. It combines retrospective and forward-looking mapping for continuous monitoring and assessment, providing essential data for developing comprehensive strategies. With its active learning capacity, U-HEAT enables model refinement and the evaluation of policy impacts. The framework presented in this paper offers a standardized and sustainable approach, aiming to enhance practical analysis tools. It highlights the importance of interdisciplinary research in bolstering urban resilience and stresses the need for sustainable urban ecosystems capable of addressing the complex challenges posed by climate change and increased urban heat. This study provides valuable insights for researchers, urban administrators, and planners to effectively combat urban heat challenges. Full article

Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) is a global economic pest that poses a serious threat to the fruit industry. In the southwest of China, Yunnan Province sustains a severe infestation of B. dorsalis. An automated monitoring system designed for B. dorsalis was employed in this study to elucidate the annual population dynamics of B. dorsalis in four counties: Yuanjiang, Huaping, Guangnan, and Ludian in Yunnan. The system utilizes sex parapheromone and image recognition technology. The data uploaded by the device are used to analyze the annual population dynamics of B. dorsalis in different regions. The results showed that the populations of adult B. dorsalis in all four counties peaked twice annually, with Yuanjiang experiencing the earliest peak periods, followed by Huaping, Guangnan, and Ludian. Adult B. dorsalis occurred in Yuanjiang throughout the year, and Yuanjiang had the highest number of B. dorsalis monitored. In Huaping, adult B. dorsalis occurred in March–December and was highly active, with a high population density in 2019. Bactrocera dorsalis did not occur in December in Guangnan but only in May–October in Ludian. Bactrocera dorsalis abundance was correlated with temperature in all four areas. The outcomes of this experiment provide a practical foundation for developing control strategies targeting B. dorsalis in various orchards across each county. Full article

Alpinae oxyphyllae fructus (AOF), the dried mature fruit of Alpinia oxyphylla Miquel of the Zingiberaceae family, shows many special pharmacological effects. In recent years, there has been an abundance of research results on AOF. In this paper, the new compounds isolated from AOF since 2018 are reviewed, including terpenes, flavonoids, diarylheptanoids, phenolic acid, sterols, alkanes, fats, etc. The isolation methods that were applied include the microwave-assisted method, response surface method, chiral high-performance liquid chromatography–multiple reaction monitoring–mass spectrometry (HPLC-MRM-MS) analytical method, ultra-high-performance liquid chromatography–quadrupole–electrostatic field orbitrap high-resolution mass spectrometry (UPLC-Orbitrap-HRMS) method, ultra-high-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) method, hot water leaching method, ethanol leaching method, and so on. Additionally, the pharmacological effects of AOF found from 2018 to 2024 are also summarized, including neuroprotection, regulation of metabolic disorders, antioxidant activity, antiapoptosis, antiinflammatory activity, antidiabetic activity, antihyperuricemia, antiaging, antidiuresis, immune regulation, anti-tumor activity, renal protection, hepatoprotection, and anti-asthma. This paper provides a reference for further research on AOF. Full article

Adenosine triphosphate (ATP) has a high intracellular millimolar concentration (ca. 2.4 mM) throughout the phylogenetic spectrum of eukaryotes, archaea, and prokaryotes. In addition, the function of ATP as a hydrotrope in the prevention of protein aggregation and maintenance of protein solubilization is essential to cellular, tissue, and organ homeostasis. The ³¹P spectral modulus (PSM) is a measure of the health status of cell, tissue, and organ systems, as well as of ATP, and it is based on in vivo ³¹P nuclear magnetic resonance (³¹P NMR) spectra. The PSM is calculated by dividing the area of the ³¹P NMR integral curve representing the high-energy phosphates by that of the low-energy phosphates. Unlike the difficulties encountered in measuring organophosphates such as ATP or any other phosphorylated metabolites in a conventional ³¹P NMR spectrum or in processed tissue samples, in vivo PSM measurements are possible with NMR surface-coil technology. The PSM does not rely on the resolution of individual metabolite signals but uses the total area derived from each of the NMR integral curves of the above-described spectral regions. Calculation is based on a simple ratio of the high- and low-energy phosphate bands, which are conveniently arranged in the high- and low-field portions of the ³¹P NMR spectrum. In practice, there is essentially no signal overlap between these two regions, with the dividing point being ca. −3 δ. ATP is the principal contributor to the maintenance of an elevated PSM that is typically observed in healthy systems. The purpose of this study is to demonstrate that (1) in general, the higher the metabolic activity, the higher the ³¹P spectral modulus, and (2) the modulus calculation does not require highly resolved ³¹P spectral signals and thus can even be used with reduced signal-to-noise spectra such as those detected as a result of in vivo analyses or those that may be obtained during a clinical MRI examination. With increasing metabolic stress or maturation of metabolic disease in cells, tissues, or organ systems, the PSM index declines; alternatively, with decreasing stress or resolution of disease states, the PSM increases. The PSM can serve to monitor normal homeostasis as a diagnostic tool and may be used to monitor disease processes with and without interventional treatment. Full article

Protein glutathionylation is a reversible post-translational modification that involves the attachment of glutathione to cysteine residues. It plays a role in the regulation of several cellular processes and protection against oxidative damage. Glutathionylation (GS-ylation) modulates protein function, inhibits or enhances enzymatic activity, maintains redox homeostasis, and shields several proteins from irreversible oxidative stress. Aberrant GS-ylation patterns are thus implicated in various diseases, particularly those associated with oxidative stress and inflammation, such as cardiovascular diseases, neurodegenerative disorders, cancer, and many others. Research in the recent years has highlighted the potential to manipulate protein GS-ylation for therapeutic purposes with strategies that imply both its enhancement and inhibition according to different cases. Moreover, it has become increasingly evident that monitoring the GS-ylation status of selected proteins offers diagnostic potential in different diseases. In this review, we try to summarize recent research in the field with a focus on our current understanding of the molecular mechanisms related to aberrant protein GS-ylation. Full article

Heat stress (HS) is one of the key factors affecting an animal’s immune system and productivity, as a result of a physiological reaction combined with environmental factors. This study examined the short-term effects of heat stress on cow behavior, as recorded by innovative technologies, and its impact on blood gas parameters, using 56 of the 1070 cows clinically evaluated during the second and subsequent lactations within the first 30 days postpartum. Throughout the experiment (from 4 June 2024 until 1 July 2024), cow behavior parameters (rumination time min/d. (RT), body temperature (°C), reticulorumen pH, water consumption (L/day), cow activity (h/day)) were monitored using specialized SmaXtec boluses and employing a blood gas analyzer (Siemens Healthineers, 1200 Courtneypark Dr E Mississauga, L5T 1P2, Canada). During the study period, the temperature–humidity index (THI), based on ambient temperature and humidity, was recorded and used to calculate THI and to categorize the data into four THI classes as follows: 1—THI 60–63 (4 June 2024–12 June 2024); 2—THI 65–69 (13 June 2024–18 June 2024); 3—THI 73–75 (19 June 2024–25 June 2024); and 4—THI 73–78 (26 June 2024–1 July 2024). The results showed that heat stress significantly reduced rumination time by up to 70% in cows within the highest THI class (73 to 78) and increased body temperature by 2%. It also caused a 12.6% decrease in partial carbon dioxide pressure (pCO2) and a 32% increase in partial oxygen pressure (pO2), also decreasing plasma sodium by 1.36% and potassium by 6%, while increasing chloride by 3%. The findings underscore the critical need for continuous monitoring, early detection, and proactive management to mitigate the adverse impacts of heat stress on dairy cow health and productivity. Recommendations include the use of advanced monitoring technologies and specific blood gas parameter tracking to detect the early signs of heat stress and implement more timely interventions. Full article

This paper explores and discusses the revolutionary applications of digital twin technology in space environments and its profound impact on future space exploration activities. Originating from a proposal by the National Aeronautics and Space Administration (NASA) in 2002, digital twin technology aims to enhance the safety and reliability of space missions by creating precise virtual models. As the technology has evolved, its applications have successfully expanded beyond aerospace to include Industry 4.0, healthcare, and urban management, demonstrating remarkable cross-industry adaptability and broad impact. In space applications, digital twin technology can not only improve spacecraft design and maintenance processes but also enhance the efficiency of mission planning and execution. It plays a crucial role in astronaut training and emergency response as well. Particularly in extreme space conditions, this technology provides real-time monitoring and fault prediction, significantly enhancing mission safety and success rates. However, despite its recognized potential, the implementation of digital twins in space environments faces numerous challenges, including data transmission delays, model accuracy, and the design of user–system interactions. In the future, as artificial intelligence (AI) and machine learning (ML) technologies become mature and integrated, the digital twin will play a more central role in space missions, especially in remote operations, complex system management, and deep space exploration. This article is to overview key technical features, application examples, and challenges of digital twin technology, aiming to provide a comprehensive reference framework for researchers and developers while inspiring further in-depth studies and innovative applications. Full article