Flow regime specific regulation shapes microbial-mediated nitrogen cycling of plain tidal river network.

Only in Halobacterium salinarum: Sugar modifications unique to an archaeal N-linked glycan.

Long-term behavioral and physiological consequences of developmental group size history in mice.

Evaluation of ERA5 reanalysis wave spectra with observations during seasonal and extreme conditions along the west coast of India

Coupled three-dimensional displacement response of the very large floating structure under extreme ocean conditions

Fabrication and performance of MAPbI3 perovskite solar cells under extreme humidity conditions: A spin-coating approach

Sustainable MXene-based wearable sensor reinforced with microcrystalline cellulose for human motion monitoring in subzero environments with integrated machine learning.

Differential response of taste perception to high-altitude exposure and ageing.

Since the 20th century, the extensive use of pesticides has played a crucial role in increasing agricultural productivity and addressing global food demand. However, their widespread application has led to significant environmental and human health concerns. Pesticide residues are frequently detected in soil, water and air, where they can persist for decades, accumulate in food chains, and interfere with natural environmental processes. Traditional remediation methods are often costly and inefficient, determining an increase of interest in more sustainable alternatives. In this context, bioremediation using microorganisms has gained attention, with yeasts, like Saccharomyces cerevisiae, Candida tropicalis and Trichosporon cutaneum, showing particular potential due to their metabolic versatility, ability to tolerate extreme environmental conditions, and ability to degrade a wide range of pesticides. This comprehensive review provides an overview of the current state of research on yeast-based pesticide bioremediation, highlighting the most effective ecotypes, the current known degradation mechanisms and the emerging research lines aimed at progressively guiding this knowledge toward future field applications.

Stomatal and leaf hydraulic conductivity responses to changing light and CO2 conditions in Phaseolus vulgaris.

Prediction of tension leg platform motion responses under extreme conditions based on physics-informed deep learning and uncertainty quantification

The limitations of ion transport kinetics in conventional electrolytes, particularly under extreme operating conditions, arise from suboptimal solvation structures and inefficient charge carrier utilization. Here, we present strategic electrolyte design that reconfigures Li⁺ coordination geometry by modulating intermolecular interactions and solvent molecule volume, fundamentally overcoming these transport constraints. By incorporating an optimized moderator with a low dipole moment and small molecular size, extensive anion aggregation is effectively disrupted into compact ion conduction domains, simultaneously increasing the number of free charge carriers and enhancing ion mobility. Guided by this principle, the designed electrolyte with dichloromethane (85.11 Å, 2.36 Debye) exhibits rapid Li+ hopping between adjacent coordination sites (152.3 ps for acetonitrile and 115.7 ps for FSI-). This electrolyte enables stable cycling of 1.0 Ah 4.5 V graphite (3.13 mAh cm-2)||LiNi0.8Mn0.1Co0.1O2 (2.85 mAh cm-2) pouch cells, delivering 0.87 Ah at -40 °C, surpassing commercial carbonate-based electrolytes, which fail to retain reversible capacity at this temperature. This study establishes fundamental principles for fast ion-transport electrolytes, paving the way for next-generation Li-ion batteries under extreme scenarios.

Abstract In this study, we investigated time- and space-resolved plasma emissions generated by reflected high-voltage (HV) pulses in deionised water to determine the electron density. HV pulses with a 3.5-ns rise time and 160-kV amplitude were applied in a point-to-plane electrode geometry under single-shot conditions. The primary pulse, delivered to the chamber through a coaxial cable, produced a series of reflected pulses owing to load mismatch. Optical emission characteristics were recorded and resolved spatio-temporally across visible and near-infrared spectral regions. High-speed images showed that the primary discharge was diffuse, whereas the reflected pulses produced filamentary plasma structures. Imaging spectrometry revealed that the primary pulse generated a continuous spectrum with higher intensity away from the anode apex, while the reflected pulses exhibited broadened hydrogen and oxygen atomic lines superimposed on the continuum. These reflected pulses were analysed to estimate electron density and temperature under both space- and time-resolved conditions. The continuum was first removed by fitting its profile, and the resulting residuals were used to extract plasma parameters. Electron density ($n_e$) was determined from the broadened spectral lines, and electron temperature ($T_e$) was obtained from Boltzmann plots. The density decayed over time (from $2.5$ to $0.5 \times 10^{19}$ cm$^{-3}$) while remaining roughly constant along the anode axis, whereas the electron temperature remained stable at 2--3 eV across space and time. The emergence of additional Balmer-series lines at higher reflections signalled ionisation threshold depression during the early reflections. Overall, this study provides the first consistent experimental evidence of the extreme conditions generated during nanosecond discharges in liquid water.

Abstract Reliable fault diagnosis of wind turbine planetary gearboxes is crucial for ensuring operational stability and minimizing maintenance costs. However, in practical industrial environments, vibration signals are often contaminated by noise, and the scarcity of fault samples severely constrains the effectiveness of conventional deep learning-based fault diagnosis methods. To address these challenges, this paper proposes a novel hybrid multi-scale and dilated convolutional attention network (MCAN) specifically designed for intelligent fault diagnosis of wind turbine planetary gearboxes under the combined conditions of noise and limited samples. The model incorporates multi-scale and dilated convolution branches to synergistically model both local transient features and long-range dependencies. Concurrently, a self-attention-based feature fusion module is designed to adaptively enhance discriminative key features while suppressing redundant noise. Comprehensive experiments conducted on the wind turbine planetary gearboxes dataset demonstrate that MCAN outperforms mainstream methods, including LiConvFormer, WDCNN, ResNet, TCN-BiLSTM, and MCNN-LSTM, across various noise levels and sample sizes. Ablation studies and attention visualization further validate the effectiveness of the model's architecture. Notably, even under extreme conditions (e.g., SNR = 2 dB and minimal samples), MCAN maintains a high diagnostic accuracy, showcasing exceptional robustness and generalization capability. This research provides an effective solution for the intelligent monitoring of wind power equipment under complex operating conditions, holding significant promise for broad engineering applications.

ABSTRACT Conformational changes in ionic liquids (ILs) under high pressure (HP) are closely related to pressure-induced phase transitions. The HP-induced conformational preferences (trans–gauche equilibrium for the N–C–C–C dihedral angle) of two 1-propyl-3-methylimidazolium-based ILs ([C3mim][X]; X= I and BF4) were investigated up to approximately 8 GPa using Raman spectroscopy. The trans conformer of [C3mim][I] increased monotonically with increasing pressure up to 8 GPa. Conversely, the trans conformer of [C3mim][BF4] decreased with increasing pressure up to 2.4 GPa and then increased up to 5.8 GPa. Subsequently, the conformational distribution remained constant up to approximately 8 GPa. The difference in the HP-induced conformational changes between these two [C3mim]+-based ILs were attributed to the anion position relative to the cation, unlike the [C4mim]+-based ILs. These findings provide insight into the role of cation–anion interactions in determining IL behavior under extreme conditions, which is crucial for their applications in HP environments.

Terrestrial hot springs are extreme environments shaped by geothermal heat, geogenic gases and extremes of pH and temperatures. Their gas fluxes, which include CO2, CO, H2S and SO2, mirror the chemical composition of CO2-rich waste streams. Microbial communities inhabiting these environments are typically thermotolerant or thermophilic and sustained by CO2 fixation and chemolithotrophic metabolism. Such communities may therefore provide a natural starting point for developing ex-situ, consortium-based biotechnologies capable of operating under elevated temperatures and chemically harsh conditions. Here, we assess the metabolic capabilities of hot spring microbiomes systematically through a biotechnological lens. We conducted comparative analysis of 73 worldwide hot spring metagenomes, spanning a wide range of environmental conditions (pH 1.5-10.0, temperatures 25-98°C). By taking a gene-centric approach to whole communities, we show that hot spring microbiomes ubiquitously encoded carbon fixation pathways and biosynthetic genes (and gene clusters) for the synthesis of value-added products, regardless of geographical location and pH-temperature conditions. Candidate value-added products include platform chemicals such as acetone, lactic acid, and 1,2-propanediol, as well as high-value biomolecules including B vitamins and alginate. This first biotechnology-focused assessment of hot spring microbiomes demonstrates that these communities encode the genomic potential to support novel, ex situ microbial platforms for upgrading CO2 and transforming chemically complex gas mixtures. Industrial CO2 waste streams pose both an environmental challenge and an unutilised resource. Harnessing microbial consortia to valorise CO2, through a circular bioeconomy, remains underexplored and could offer an alternative to energy-intensive chemical methods. By reanalysing predominantly publicly available metagenomic data, we demonstrate how hot spring microbiomes can be mined for traits pre-adapted to CO2-rich, high-temperature, and chemically extreme conditions. In doing so, we provide proof-of-concept for their future biotechnological application and establish a blueprint for other microbiome-scale bioprospecting surveys.

Introduction Quinoa (Chenopodium quinoa Willd.), a nutrient-dense “golden grain” from South America, is valued for its resilience to abiotic stresses, making it a strategic crop for arid regions. While its protein quality is well-established, the composition and bioactivity of oil from varieties grown under extreme hyper-arid conditions, such as those in the Algerian Sahara, remain unexplored. This study provides the first comprehensive characterization of the oil from Quinoa (Amarilla Sacaca) cultivated in this unique environment. Methods Quinoa seeds of the Amarilla Sacaca variety, harvested from the hyper-arid Ouargla region of Algeria, were processed to extract oil via Soxhlet method using n-hexane. The oil was characterized for its physicochemical properties (acid, peroxide, iodine, and saponification values, etc.) and bioactive phytochemical content (total phenolics, flavonoids, carotenoids) using standard AOAC and spectrophotometric methods. The fatty acid profile and minor lipophilic compounds were analyzed by GC-MS. Antioxidant activity was evaluated using three complementary in vitro assays: DPPH radical scavenging, β -carotene bleaching inhibition, and nitric oxide (NO) radical scavenging. Results The extracted oil yield was 3.98%. It was rich in bioactive compounds, with a total phenolic content of 467.78 ± 11.38 μg GAE/g, total flavonoids of 209.90 ± 8.83 μg RE/g, and total carotenoids of 7.88 ± 0.12 mg/kg. The fatty acid profile was dominated by unsaturated fatty acids (87.22%), with linoleic acid (C18:2 n-6, 51.33%) and an unusually high concentration of petroselaidic acid (C18:1 n-6t, 33.44%) as the major constituents. Squalene (1.01%) and 2,4-di-tert-butylphenol (0.83%) were identified as significant minor bioactive components. The oil demonstrated potent, multi-mechanistic antioxidant activity (IC50 values: DPPH = 45.67 μg/mL; β -carotene bleaching = 38.05 μg/mL; NO scavenging = 26.57 μg/mL), which was strongly correlated with its phytochemical content ( r > 0.85, p < 0.001). Discussion and conclusion The extreme arid conditions appear to induce a trade-off, suppressing oil yield while significantly enhancing the accumulation of bioactive phytochemicals. This results in a functionally superior oil with a unique fatty acid fingerprint and exceptional antioxidant capacity, surpassing that of pure α -tocopherol in some assays. The high squalene content positions it as a sustainable, plant-based alternative for nutraceutical and cosmetic applications. These findings highlight Saharan Quinoa oil as a high-value specialty product, supporting sustainable development goals (SDGs 2, 3, 9, 12) and offering a pathway for the integrated valorization of resilient crops within a circular bio-economy framework.

This study investigated the production and physicochemical characterization of a biosurfactant synthesized by Mucor circinelloides UCP 0017, isolated from mangrove sediment in Pernambuco, using corn steep liquor and waste frying soybean oil as alternative substrates. The microorganism exhibited high surfactant capacity, reducing surface tension from 41.8 to 31.0 mN/m over 96 h of cultivation. The biosurfactant maintained stability across a wide pH range (3–9), high saline concentrations (5–25% NaCl), and extreme temperatures (4–120 °C), demonstrating structural robustness. The cell-free metabolic liquid presented a critical micelle dilution (CMD) of 40% with a surface tension of 31 mN/m, while the isolated biosurfactant showed a Critical Micelle Concentration (CMC) of 900 mg/L. Extraction by precipitation with 70% ethanol in a 1:1 (v/v) ratio showed the highest yield (10.33 g/L). Biochemical characterization indicated a glycolipid with 74.22% lipids, 18.38% carbohydrates, and 7.40% proteins, with an anionic profile (Zeta potential of –23.13 mV). FTIR analysis indicated the presence of amide groups and saccharide functions, suggesting a glycolipid nature. The results demonstrate the biotechnological potential of the biosurfactant for biomedical, pharmaceutical, and industrial applications.

Catalytic cracking of endothermic hydrocarbon fuels offers a promising regenerative cooling strategy for hypersonic propulsion systems, yet conventional microporous zeolite catalysts suffer from severe diffusion limitations and rapid coke deposition under supercritical conditions. This study develops a hierarchical core–shell Beta@SBA-15 composite zeolite via a microwave synthesis method, integrating microporous nano zeolite Beta as the catalytic core with ordered mesoporous SBA-15 as the transport shell. Comprehensive characterization confirms the core–shell structure and the intimate integration of both components. Such core–shell catalyst exhibits 14.9% higher acidity-normalized activity than nano zeolite Beta. Its exceptional enhanced stability and reduced coke formation were also demonstrated, with only 25.39% conversion loss over 15 hour time-on-stream tests compared to 47.45% for nano zeolite Beta, while substantially reducing coke deposition from 6.3% to 3.8%. These results are attributed to the core–shell structure, where the mesoporous shell improves active site accessibility, facilitates rapid product and coke precursor removal, and effectively mitigates diffusion limitations in catalytic processes. These findings reveal the structure–performance correlation of microporous core-ordered mesoporous shell zeolite catalysts, offering insights for hydrocarbon conversion under extreme conditions.

Comprehensive Summary Lithium‐ion batteries subjected to extreme operating conditions—such as high temperature, high C‐rates, and deep overdischarge— exhibit rapid and coupled aging behaviors that are challenging to disentangle using conventional diagnostics. While purely data‐driven models often lack interpretability ("black‐box"), physics‐based methods typically require measurements unavailable in practical applications. To bridge this gap, we propose the SIX‐ICA framework, an interpretable machine learning approach that integrates Incremental Capacity Analysis (ICA) features with an XGBoost regressor and SHAP analysis. By extracting mechanism‐informed ICA peak features from routine cycling data, the framework achieves robust State‐of‐Health (SOH) estimation. Crucially, SHAP analysis provides transparent feature attribution, linking statistical inputs directly to degradation pathways. Validated on LiFePO 4 /graphite pouch cells cycled at 65 °C and 3 C (comparing 2.5 V vs. 1.0 V cutoffs), the framework identifies Loss of Lithium Inventory (LLI) as the primary driver of capacity fade, noting its significant intensification under deep over‐discharge, while Loss of Active Material (LAM) plays a secondary role. These findings are corroborated by OCV fitting and post‐mortem characterization. This workflow advances interpretable SOH diagnostics under extreme conditions and offers a scalable route for other battery chemistries.