The burgeoning seafood processing industry generates nutrient-rich effluents that necessitate sustainable management to prevent environmental degradation. This study investigates, for the first time, the intensification of struvite fertilizer (MgNH4PO4·6H2O) crystallization from anaerobic granular sludge effluent of a seafood processing plant using ultrasonic irradiation. The process was systematically optimized to enhance nutrient recovery and crystal quality. Experimental results demonstrated that high-purity struvite with a well-defined prismatic morphology was successfully synthesized. Optimal precipitation conditions were identified at a Mg2+:NH4 +:PO4 3- molar ratio of 1:1:1, a stirring speed of 200 rpm, and a temperature of 25°C, with a combined reaction and settling time of 2.5 h. Notably, the integration of sonication-specifically in degas mode-significantly accelerated nucleation kinetics and facilitated uniform crystal growth. Compared to conventional mechanical agitation, the sonication-assisted approach yielded higher recovery efficiencies and improved crystal size distribution by mitigating localized supersaturation and enhancing mass transfer via acoustic cavitation. These findings underscore the potential of sonochemical intensification as a green, efficient strategy for transforming wastewater pollutants into high-value fertilizers, thereby advancing the circular economy in the seafood sector.

The examination of high-risk cells and regions in tissue samples from spatially resolved transcriptomics platforms offers meaningful insights into specific disease processes. For existing methods, while cell types or clusters can be identified and associated with disease attributes, individual cells are unable to be associated in the same manner.

Wax precipitation in crude oil poses a significant flow assurance challenge, leading to a reduced level of production and operational blockages. This study employs molecular dynamics (MD) simulations to investigate the molecular mechanisms of wax crystallization under varying wellbore conditions. A multicomponent crude oil model was constructed based on samples from the Mahu oilfield, and simulations were performed at different depths (0 to 3400 m) and CO2 concentrations (0% to 50%). The results reveal that wax crystallization, quantified by a decrease in the diffusion coefficient and an increase in cluster size, intensifies as the pressure and temperature decrease with wellbore depth. A complex, dual role of CO2 was identified, where it not only acts as a light component at high pressures, inhibiting wax formation, but also extracts light components from the oil, promoting aggregation of heavier molecules. This results in the maximum crystallization observed at 30% CO2 under flash separation conditions. To reconcile discrepancies with field observations, a differential separation ("degas") route was simulated, which demonstrated significantly enhanced crystallization, particularly at 50% CO2. These findings underscore the importance of the thermodynamic pathway in predicting wax deposition and suggest that maintaining high pressure and temperature and managing the CO2 concentration are key strategies for mitigation.

To enhance the portability of Lab-on-a-Chip technology, avoiding bulky electronic flow control systems is crucial. Self-powered microfluidics can significantly improve portability by eliminating the need for electronic components. Traditionally, self-powered microsystems handle small fluid volumes for up to one or two hours. However, many experiments, such as cell culture or real-time biomarker detection assays, require flow control for longer periods. In this study, we demonstrate that polymeric micropumps can provide self-powered flow control for intermediate durations, ranging from several to more than 10 hours. By monitoring the fluid front dynamics of a solution flowing through a microchannel over 1.5 meters long, we developed calibration curves for various micropump types. Our findings reveal that the pump's actuation time is influenced by degassing time, and effective surface area. Using these calibration curves, we compare mathematical models to predict flow rates and actuation times, facilitating the design of customized self-powered microsystems for both short and long-term applications. Epoxy-coated PDMS pumps represent a notable example of a long-operating self-powered microsystem, which holds significant potential for applications requiring controlled flow over extended periods.

Explainability is crucial for establishing user trust in Artificial Intelligence (AI), particularly within safety-critical domains such as Air Traffic Management (ATM) and Air Traffic Control (ATC). This study empirically investigates the effects of Explainable AI (XAI), specifically HeatMap-based visual explanations, on cognitive workload, user acceptance, and intention to use AI-driven decision-support systems among Air Traffic Control Officers (ATCOs). Despite significant theoretical advancements in the broader XAI domain, empirical evidence addressing the specific impact of visual explanations on human-AI interactions in safety-critical environments like ATC remains limited. To address these critical gaps, an experimental comparison was conducted between explainable (HeatMap) and non-explainable (BlackBox) AI conditions, involving two user groups: expert and student ATCOs. Both objective neurophysiological measures (Electroencephalography) and subjective questionnaires were employed to capture comprehensive user responses. Key findings revealed that the presence of visual explanations significantly reduced cognitive workload and enhanced users' willingness to adopt the AI system, regardless of participants' level of expertise. However, explicit perceptions of AI's impact on work performance were predominantly influenced by expertise, with less experienced controllers reporting a greater perceived impact than their expert counterparts. By combining objective neurometrics with subjective user assessments, this research advances methodological rigor in evaluating human-AI interactions and highlights the importance of tailored, user-centric explanations. These findings directly contribute to practical guidelines for designing cognitively compatible and trustworthy AI tools in ATC, providing nuanced insights for targeted training and deployment strategies based on user expertise.

The field effect describes the phenomena where environmental exposures, infection, and genetic predisposition result in molecular changes in cells that predispose them to developing cancer. Though this is a well-established concept in pathology, it remains underexplored in the context of high-resolution omics. We utilized the Diagnostic Evidence Gauge of Single Cells (DEGAS) deep transfer learning framework to analyze prostate cancer spatial transcriptomics to identify cells and tissues that are highly associated with cancer progression. DEGAS highlighted morphologically benign glands with reduced expression of microseminoprotein-beta (MSMB), a differentiation marker downregulated in aggressive tumors. These glands have upregulated genes associated with antigen presentation and aggressive neoplasms. Integration of single-cell transcriptomics and deep learning image analysis separately revealed altered immune-cell infiltration, suggesting a complex interplay in the tumor environment, facilitating aggressiveness. We used immunohistochemistry to quantify the MSMB protein (PSP-94) expression in morphologically normal and tumor tissues from patients with and without 5-year distant metastasis. Samples from patients who developed metastasis consistently showed lower fractions of positively stained cells, indicating a subtle yet significant "field effect" in seemingly benign regions. These proteomic results validate the transcriptomic findings and further underscore that inflammatory or immune-related changes in ostensibly normal tissue may contribute to aggressive disease progression.

β-elemene, a clinically approved antineoplastic agent, is isolated from Curcuma wenyujin at low abundance. Germacrene A is its direct precursor. To overcome the supply bottleneck, we systematically rewired the mevalonate pathway of Saccharomyces cerevisiae to expand the universal C15 precursor pool. Among 19 screened germacrene A synthases, DeGAS from Daldinia eschscholtzii yielded 399.8 mg/L. Fusion of DeGAS to farnesyl pyrophosphate synthase via the rigid (EAAAK)2 linker increased the titer to 492 mg/L, and triplication of the ERG20-DeGAS cassette further raised it to 985.5 mg/L. In a 5-L fed-batch bioreactor, germacrene A reached 19.82 g/L, a 20-fold gain over shake-flask cultures, and comparable to the highest titers previously reported in S. cerevisiae. This work provides a scalable microbial route to β-elemene and demonstrates a general strategy for sesquiterpene overproduction.

Chicken meat is highly consumed worldwide due to its nutritional value, but its high water content and abundance of polyunsaturated fatty acids make it particularly vulnerable to structural and oxidative damage during freezing and thawing. Slow freezing, in particular, generates large ice crystals that severely impair water-holding capacity (WHC), increase drip loss, promote color deterioration, and intensify protein and lipid oxidation. Innovative thawing strategies are therefore required to mitigate these quality losses. Ultrasound (US) has been successfully applied to accelerate thawing of fast-frozen meat; however, its potential for slowly frozen chicken breast remains poorly understood. This study aimed to evaluate the effects of US-assisted thawing at two frequencies (25 and 130 kHz), two amplitudes (100% and 60%), and three operating modes (normal, sweep, and degas) on the quality of slowly frozen chicken breast. Conventional thawing required 50 min, yielding WHC of 9.87%, drip loss of 4.65%, free sulfhydryls of 16.38 µmol/g, and ∆E of 3.91. In contrast, the optimized US condition (25 kHz, 100% amplitude, sweep mode) thawed samples in only 18 min, with markedly improved WHC (23.14%), reduced drip loss (3.25%), higher preservation of free sulfhydryls (24.69 µmol/g), and minimal color change (∆E = 3.72). Conversely, less effective parameters (e.g., 130 kHz, 60% amplitude, normal mode) prolonged thawing and compromised quality, with WHC dropping to 9.96% and drip loss increasing to 9.05%. Overall, US reduced thawing time under all conditions, but quality responses depended strongly on the applied parameters. The present findings demonstrate the novelty of optimizing US frequency, amplitude, and mode for thawing slowly frozen chicken breast, highlighting sweep mode at 25 kHz and 100% amplitude as the most effective strategy. Future research should explore its scalability and industrial applicability for poultry processing.

Lipids are essential for child development. Nutritional recommendations are numerous, evolving over time and are often based on expert opinions more than evidence-based medicine. The objective of this review is to critically analyse the evolution of current nutritional recommendations, identify existing knowledge gaps and propose avenues for improvement to optimise infant nutrition and development. A narrative literature review on Pubmed, EMBASE and Cochrane databases (2001-22) was conducted with the keywords: 'alpha-linolenic acid, arachidonic acid, children, cholesterol, docosahexaenoic acid, eicosapentaenoic acid, guidelines, infant, long-chain (LC) PUFA, linoleic acid, lipids and dietary intakes, newborn, palmitic acid and toddler'. Among 861 articles identified, 133 were selected. The main current recommendations are issued by the French Agency for Food Safety (AFSSA), French Agency for Food, Environmental and Occupational Health and Safety (ANSES) and the Food and Agriculture Organization and World Health Organization (FAO-WHO). In infants from 0 to 3 years of age the main challenge is to increase lipid intake while maintaining an optimal omega 6/omega 3 ratio. Current recommendations are focused on polyunsaturated fatty acids, emphasising the intake of linoleic, eicosapentaenoic and docosahexaenoic acids without any specific recommendation for arachidonic acid before the age of 6 months. Points of interest, but without any recommendation, are the incorporation of milk fat, cholesterol, monounsaturated fatty acids, and saturated fatty acids for infants under 6 months. In conclusion, this article identifies knowledge gaps regarding the structural aspect of lipids and the integration of new categories of lipids in future recommendations to promote the quality of infant formulas.

Diabetes affects >10% of adults worldwide and is caused by impaired production or response to insulin, resulting in chronic hyperglycemia. Pancreatic islet β-cells are the sole source of endogenous insulin, and our understanding of β-cell dysfunction and death in type 2 diabetes (T2D) is incomplete. Single-cell RNA-seq data supports heterogeneity as an important factor in β-cell function and survival. However, it is difficult to identify which β-cell phenotypes are critical for T2D etiology and progression. Our goal was to prioritize specific disease-related β-cell subpopulations to better understand T2D pathogenesis and identify relevant genes for targeted therapeutics. To address this, we applied a deep transfer learning tool, DEGAS, which maps disease associations onto single-cell RNA-seq data from bulk expression data. Independent runs of DEGAS using T2D or obesity status identified distinct β-cell subpopulations. A singular cluster of T2D-associated β-cells was identified; however, β-cells with high obese-DEGAS scores contained two subpopulations derived largely from either non-diabetic (ND) or T2D donors. The obesity-associated ND cells were enriched for translation and unfolded protein response genes compared to T2D cells. We selected CDKN1C and DLK1 for validation by immunostaining in human pancreas sections from healthy and T2D donors. Both CDKN1C and DLK1 were heterogeneously expressed among β-cells. CDKN1C was increased in β-cells from T2D donors, in agreement with the DEGAS predictions, while DLK1 appeared depleted from T2D islets of some donors. In conclusion, DEGAS has the potential to advance our holistic understanding of the β-cell transcriptomic phenotypes, including features that distinguish β-cells in obese ND or lean T2D states. Future work will expand this approach to additional human islet omics datasets to reveal the complex multicellular interactions driving T2D.

Sulfur and other volatiles could be transported from the martian interior to surface through magmatic processes, including mantle melting, magma differentiation, and degassing. However, these processes were not fully integrated in past sulfur cycling models because of complexity from the gas-melt interactions in chemically and dynamically evolving magmatic systems with multicomponent volatiles. Here, we incorporate these processes to simulate how sulfur, carbon, and hydrogen degas from martian melts. We find that reduced sulfur species, H2S and S2, are dominantly emitted through degassing at crustal to surficial pressures. These sulfur species could condense as sulfide and elemental sulfur, potentially yielding the sulfate deposits observed on the martian surface through secondary oxidation. Our models also show that evolved magmas reach graphite and sulfide saturation at crustal pressures and thus may establish sulfur and carbon reservoirs in the martian crust. The degassed H2S and S2 may form a hazy atmosphere with SF6, a potent greenhouse gas, to shape the paleoclimate of Mars.