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Mitochondrial DNA copy number (mtDNA-CN) is a critical marker of mitochondrial health and plays a key role in cellular bioenergetics. Alterations in mtDNA-CN have been associated with aging, metabolic disorders and neurodegenerative diseases. Recent studies have revealed that various plant-derived extracts, as well as the secondary metabolites they produce, known as phytochemicals, can modulate mtDNA-CN through mechanisms including the regulation of mitochondrial biogenesis, oxidative stress, and mtDNA repair. This review examines plant-derived extracts and phytochemical compounds from a wide range of plant species- including Ginkgo biloba, Crocus sativus, Curcumin and many others- able to modulate mtDNA dynamics, scavenging oxygen free radicals and improving antioxidant defense systems.
Water main breaks remain a persistent challenge to the reliability and sustainability of urban water distribution networks, yet accurate prediction is difficult because of interacting effects of infrastructure aging, environmental conditions, and operational factors. This review presents a structured synthesis of pipe break prediction models, including simplistic, physical-based, deterministic, probabilistic, and machine learning approaches. Unlike prior studies that emphasize individual techniques, the review compares these model families within a unified analytical framework, focusing on data requirements, interpretability, scalability, and practical utility for water utilities. Key predictive variables such as pipe age, diameter, and temperature are examined together with additional factors including soil properties, seasonal climate variability, and operational parameters such as hydraulic pressure. The review also discusses recurring challenges related to limited data availability, inconsistent records, and measurement uncertainty. Results highlight fundamental trade-offs among model transparency, data demand, and predictive performance. Machine learning approaches can capture complex nonlinear relationships when large datasets are available, but their implementation often requires substantial data infrastructure and careful validation. Physical and deterministic models provide clearer interpretability but may oversimplify system dynamics, while probabilistic models explicitly represent uncertainty yet require careful parameterization and calibration. Based on this synthesis, several research gaps are identified, including limited integration of environmental and operational datasets, inadequate treatment of censored or incomplete failure records, difficulties transferring models across utilities, and the absence of standardized benchmarking practices. Future research directions include hybrid physics–machine learning frameworks, improved uncertainty quantification, and standardized evaluation methods to support more reliable infrastructure maintenance planning and strategic asset management for water utilities worldwide and policymakers and practitioners.
3I/ATLAS is an interstellar object whose activity provides critical insights into its composition and origin. However, due to its orbital geometry, the object is too close to the Sun near perihelion to be observed from the ground, and space-based measurements are therefore required. Here we characterize the water production rate of 3I/ATLAS using Solar and Heliospheric Observatory/SWAN Ly alpha observations from 2025 November to December (heliocentric distances 1.4-2.2 au) with 3D Monte Carlo modeling. We report a peak postperihelion water production rate of QH2O approximate to 4x1028 molecules s-1, corresponding to a minimum active fraction of similar to 30% (assuming a maximum nucleus radius of 2.8 km). Comparison of our postperihelion measurements with published preperihelion results reveals a heliocentric asymmetry, with an r-5.9 +/- 0.8 scaling for the inbound rise, followed by a shallower r-3.3 +/- 0.3 scaling during the outbound decline, where r is heliocentric distance. The postperihelion behavior indicates that the water production of 3I/ATLAS was driven primarily by the varying solar insolation acting on a stable active area. Combined with other evidence, including comparison with the hyperactive comet 103P/Hartley 2, our findings suggest that its water production is likely dominated by a distributed source of icy grains. Furthermore, it displayed remarkable stability in the activity with no signs of outbursts or rapid depletion of water production.
Given the current context of climate change, new olive genotypes may offer valuable variability for enhancing extra virgin olive oil (EVOO) quality in the Mediterranean region. This study evaluated five novel genotypes coming from breeding program (I77, N × N, Fs17 × C, I79, N1 × N3) cultivated in Central Italy. Olive oils were analyzed at two harvest times (mid-October, mid-November) over two consecutive seasons (2023 and 2024), focusing on chemical composition and its relationship to pedoclimatic conditions. Results showed that both harvest time and genotype significantly influence key oil parameters, including peroxide value, free acidity, carotenoid and chlorophyll content, α-tocopherol, and total phenolic content. Non-metric multidimensional scaling (NMDS) revealed distinct fatty acid profile for most genotypes, except I79 and Fs17 × C, which showed similar profiles across years. As expected, oleacein, oleocanthal, and oleuropein predominated in I77 and Fs17 × C oils. Among all genotypes, I77 consistently exhibited higher total phenolics, carotenoids, and α-tocopherols, as well as greater MUFA/PUFA and OLP indicated superior nutritional and oxidative stability potential. Correlation analyses highlighted that these kays phenols appeared to be most closely associated with pedoclimatic variables, particularly temperature, solar radiation, and rainfall and that the response was genotype-dependent. Although the study spanned only two productive seasons, the consistent trends observed in phenolic response and their correlation with pedoclimatic variables suggest that genotype I77 holds strong potential for producing high-quality EVOO under variable climatic conditions. Moreover, the significant positive correlation between oleocanthal and oleacein with most pedoclimatic variables suggests environmental robustness and good nutraceutical potential of this genotype.
The effect of the microstructure of recycled IN738 (RIN738) on the hardness, machinability, and corrosion resistance was investigated. Three conditions were employed: one in the as-cast state, and two subjected to heat treatment involving a double solution treatment (DST) and aging. The solution treatment consisted of heating to 1180 °C for 120 min, followed by cooling, and then reheating to 1220 °C for 90 min, followed by aging. After treatments, samples that were cooled in air were named as AAC, while the ones quenched in water were labeled AWQ. The microstructure was analyzed using scanning electron microscopy combined with energy-dispersive x-ray (SEM EDX) and x-ray diffraction (XRD). Machining tests were performed using a conventional drill. The surface roughness, tool wear, and sub-machined surface features were investigated. The corrosion resistance at 40 °C in 10
