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Sicily (Italy) is among the regions most vulnerable to drought, with conditions expected to worsen under climate change, underscoring the need for sustainable water management and the exploration of unconventional water resources. This study investigates the Ragusa Oligo-Miocene Formation, a karstified and fractured carbonate aquifer with medium to high porosity, which outcrops across the Hyblean Plateau in southern Sicily. The primary objective was to assess and quantify the potential presence of fresh groundwater in the deeper and offshore extension of this carbonate aquifer along the southeastern coast of Sicily, where it is sealed beneath more recent deposits. To reconstruct its subsurface structure, data from 90 deep oil and gas wells, both onshore and offshore, were analysed. Geophysical logs were examined using advanced petrophysical methods, while hydrogeological data from onshore wells were integrated to refine the understanding of the regional aquifer system. The results provide clear evidence of freshened groundwater within the Ragusa regional aquifer, extending deeper than previously known, onshore and continuing offshore up to 10 km from the coastline. A preliminary, conservative volumetric estimate suggests approximately 3 km3 of fresh groundwater preserved in the offshore region of the study area, at depths between 500 and 1200 m below sea level. This discovery demonstrates the untapped potential of unconventional groundwater in both the deep onshore and offshore areas of southeastern Sicily, offering an additional solution to water shortage problems, and has significant implications for other countries along Mediterranean coastlines.

We present a deep learning pipeline to perform a model-independent, likelihood-free search for anomalous (i.e., non-background) events in the proposed next-generation multi-ton scale liquid xenon-based direct detection experiment, DARWIN. We train an anomaly detector comprising a variational autoencoder (VAE) and a classifier on high-dimensional simulated detector response data and construct a 1D anomaly score to reject the background-only hypothesis in the presence of an excess of non-background-like events. We use simulated validation data to determine the power of the method to reject the background-only hypothesis in the presence of a WIMP dark matter signal, without any model-dependent assumption about the nature of the signal. We show that our neural networks learn relevant features of the events from low-level, high-dimensional detector outputs, avoiding lossy and computationally expensive compression into lower-dimensional observables. Our approach is complementary to the usual likelihood-based analysis, in that it reduces the reliance on many of the corrections and cuts that are traditionally part of the analysis chain, with the potential of achieving higher accuracy and significant reduction of analysis time. We envisage the methodology presented in this work augmenting or complementing likelihood-based and other data-driven methods currently utilized in the DARWIN (and in the future, XLZD) analysis pipeline.

Biological aging varies between individuals and may be influenced by health behaviors. Using data from 45,438 UK Biobank participants, we found that a higher proteomic aging score (ProtAgeGap) was linked to lower physical activity and increased risk of type 2 diabetes. The UK Biobank cohort included both men and women. In a 12-week supervised exercise study (MyoGlu) in 26 men, ProtAgeGap decreased by the equivalent of 10 months. While most of the 204 proteins in the score remained stable, some, like CLEC14A, changed with exercise and were linked to improved insulin sensitivity. Transcriptomic data from muscle and fat tissue supported these protein-level changes, highlighting pathways, such as PI3K-Akt and MAPk signaling, involved in tissue remodeling and metabolism. Our findings suggest that while proteomic aging is mostly stable, it can be modestly reversed by exercise. Specific proteins within the signature may act as sensitive indicators of metabolic adaptation, supporting the idea that proteomic aging is a modifiable marker linked to lifestyle and disease risk. Clinical trial number: clinicaltrials.gov: NCT01803568 registered 2013-02-26.

Abstract Green computing plays a crucial role in addressing climate change by minimizing greenhouse gas emissions through enhanced energy efficiency in electronic devices. The challenge lies in lowering energy consumption without compromising performance. Building on previous work, this study explores the long-term impact of nano-roughened heat sinks (NRHSs), an advanced cooling technology designed to enhance both passive thermal management and energy efficiency in CPUs. By applying LEAP software, we model the adoption of NRHS up to the year 2050, highlighting its potential to drive substantial sustainability gains and reshape the design of future computing systems. A two-layer mathematical model was also developed to link these device-level improvements with large-scale adoption dynamics, enabling estimation of electricity and CO 2 savings over the 2026–2050 period. Our findings reveal that the NRHS can achieve a 26% decrease in PC energy consumption and a 40% improvement in processing speed, all while maintaining stable CPU temperatures. This innovative cooling solution offers significant future energy savings, with projections estimating 30.0 TWh saved by 2050. This reduction in energy demand is equivalent to preventing the release of 21.3 megatons of CO 2 emissions, comparable to the carbon sequestered by 352 million tree seedlings over a ten-year span or 25 million acres of U.S. forests annually. These results underscore NRHS’s vital role in supporting greener computing practices and advancing global efforts to reduce the carbon footprint of electronic devices, ultimately contributing to the achievement of carbon neutrality.

Label-free quantitative phase imaging is vital for optical microscopy and metrology applications. A multimode fiber stands out as a desirable platform for imaging. Here, we propose and experimentally demonstrate a non-interferometric non-iterative approach for high-speed high-resolution label-free quantitative phase imaging via a random light scattering in a multimode fiber.

Sensory perceptions significantly influence consumer acceptance of innovative food products, including alternative protein sources like house cricket powder. This study explored the impact of cricket protein powder concentrations on sensory preferences, acceptance, purchase intentions, and emotional responses to an instant congee product, integrating the Theory of Planned Behavior (TPB) and sensory preference theory. Four concentrations of cricket protein powder (0, 30, 50, and 70%) were tested using 100 consumer participants. Sensory attributes (color, taste, odor, texture, aftertaste, and overall preference), purchase intentions, and emotional responses were measured before and after tasting. Structural equation modeling (SEM) revealed that 50% and 70% cricket protein concentrations significantly influenced consumer acceptance and purchase intentions (p < 0.05), with 50% showing the strongest effects. Emotional responses showed positive shifts, with increased scores for “good,” “satisfied,” and “safe” and decreased scores for “worried” and “disgusted” after tasting. These findings suggest that cricket protein powder concentration affects consumer preferences, emotions, and purchase decisions, with 50% being the optimal level for balancing sensory and emotional appeal.

Lack of data on the aetiology of livestock diseases constrains effective interventions to improve livelihoods, food security and public health. Livestock abortion is an important disease syndrome affecting productivity and public health. Several pathogens are associated with livestock abortions but across Africa surveillance data rarely include information from abortions, little is known about aetiology and impacts, and data are not available to inform interventions. This paper describes outcomes from a surveillance platform established in Tanzania spanning pastoral, agropastoral and smallholder systems to investigate causes and impacts of livestock abortion. Abortion events were reported by farmers to livestock field officers (LFO) and on to investigation teams. Events were included if the research team or LFO could attend within 72 hr. If so, samples and questionnaire data were collected to investigate (a) determinants of attribution; (b) patterns of events, including species and breed, previous abortion history, and seasonality; (c) determinants of reporting, investigation and attribution; (d) cases involving zoonotic pathogens. Between 2017–2019, 215 events in cattle (n=71), sheep (n=44), and goats (n=100) were investigated. Attribution, achieved for 19.5% of cases, was significantly affected by delays in obtaining samples. Histopathology proved less useful than PCR due to rapid deterioration of samples. Vaginal swabs provided practical and sensitive material for pathogen detection. Livestock abortion surveillance, even at a small scale, can generate valuable information on causes of disease outbreaks, reproductive losses and can identify pathogens not easily captured through other forms of livestock disease surveillance. This study demonstrated the feasibility of establishing a surveillance system, achieved through engagement of community-based field officers, establishment of practical sample collection and application of molecular diagnostic platforms.

Metal–organic framework (MOF)-74 is known for\nits effectiveness\nin selectively capturing carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;). Especially\nthe Zn and Cu versions of MOF-74 show high efficiency of this material\nfor CO&lt;sub&gt;2&lt;/sub&gt;. However, the activation of this MOF, which is\na crucial step for its utilization, is so far not well understood.\nHere, we are closing the knowledge gap by examining the activation\nusing, for the first time in the MOF, three-dimensional electron diffraction\n(3DED) during &lt;i&gt;in situ&lt;/i&gt; heating. The use of state-of-the-art\ndirect electron detectors enables rapid acquisition and minimal exposure\ntimes, therefore minimizing beam damage to the very electron beam-sensitive\nMOF material. The activation process of Zn-MOF-74 and Cu-MOF-74 is\nsystematically studied &lt;i&gt;in situ&lt;/i&gt;, proving the creation\nof open metal sites. Differences in thermal stability between Zn-MOF-74\nand Cu-MOF-74 are attributed to the strength of the metal–oxygen\nbonds and Jahn–Teller distortions. In the case of Zn-MOF-74,\nwe observe previously unknown remaining electrostatic potentials inside\nthe MOF pores, which indicate the presence of remaining atoms that\nmight impede gas flow throughout the structure when using the MOF\nfor absorption purposes. We believe our study exemplifies the significance\nof employing advanced characterization techniques to enhance our material\nunderstanding, which is a crucial step for unlocking the full potential\nof MOFs in various applications.

Abstract Background Physical activity has been associated with preventing the development of type 2 diabetes and atherosclerotic cardiovascular disease. However, our understanding of the precise molecular mechanisms underlying these effects remains incomplete and good biomarkers to objectively assess physical activity are lacking. Methods We analyzed 3072 serum proteins in 26 men, normal weight or overweight, undergoing 12 weeks of a combined strength and endurance exercise intervention. We estimated insulin sensitivity with hyperinsulinemic euglycemic clamp, maximum oxygen uptake, muscle strength, and used MRI/MRS to evaluate body composition and organ fat depots. Muscle and subcutaneous adipose tissue biopsies were used for mRNA sequencing. Additional association analyses were performed in samples from up to 47,747 individuals in the UK Biobank, as well as using 2-sample Mendelian randomization and mice models. Results Following 12 weeks of exercise intervention, we observed significant changes in 283 serum proteins. Notably, 66 of these proteins were elevated in overweight men and positively associated with liver fat before the exercise regimen, but were normalized after exercise. Furthermore, for 19.7% and 12.1% of the exercise-responsive proteins, corresponding changes in mRNA expression levels in muscle and fat, respectively, were shown. The protein CD300LG displayed consistent alterations in blood, muscle, and fat. Serum CD300LG exhibited positive associations with insulin sensitivity, and to angiogenesis-related gene expression in both muscle and fat. Furthermore, serum CD300LG was positively associated with physical activity and negatively associated with glucose levels in the UK Biobank. In this sample, the association between serum CD300LG and physical activity was significantly stronger in men than in women. Mendelian randomization analysis suggested potential causal relationships between levels of serum CD300LG and fasting glucose, 2-hour glucose after an oral glucose tolerance test, and HbA1c. Additionally, Cd300lg responded to exercise in a mouse model, and we observed signs of impaired glucose tolerance in male, but not female, Cd300lg knockout mice. Conclusion Our study identified several novel proteins in serum whose levels change in response to prolonged exercise and were significantly associated with body composition, liver fat, and glucose homeostasis. Serum CD300LG increased with physical activity and is a potential causal link to improved glucose levels. CD300LG may be a promising exercise biomarker and a therapeutic target in type 2 diabetes.