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In situ activation–graphitization method based on the atomically dispersed K and Fe in organic salts is developed to synthesize hierarchical porous graphitic carbon by directly pyrolysis potassium citrate and iron citrate. Moreover, (NH 4 ) 2 C 2 O 4 is also employed as both N dopant and porogen to open up internal structure and regulate pore structure. The inside-out activation leads to the homogeneous reaction and interconnected hierarchical porous structure with few dead pores. Accompanied by high specific surface area, appropriate pore distribution, good conductivity, and N/O functional groups, the sample exhibits high capacitance of 322.6 F g −1 at 0.5 A g −1 , good rate capability, and excellent cycling stability with 101.5% capacitance retention after 15,000 cycles. The supercapacitor shows an energy density of 21.3 W h kg −1 at 456.7 W kg −1 in 1 M Na 2 SO 4 . Easy synthesis, cost-effective, and environmentally benign, the work provides a promising strategy to produce hierarchical porous graphitic carbon applied in energy storage.

Cation intercalation is an effective method to optimize the electronic structures of metal oxides, but tuning intercalation structure and conductivity by manipulating ion movement is difficult. Here, we report a visual topochemical synthesis strategy to control intercalation pathways and structures and realize the rapid synthesis of flexible conductive metal oxide films in one minute at room temperature. Using flexible TiO 2 nanofiber films as the prototype, we design three charge-driven models to intercalate preset Li + -ions into the TiO 2 lattice slowly (µm/s), rapidly (mm/s), or ultrafast (cm/s). The Li + -intercalation causes real-time color changes of the TiO 2 films from white to blue and then black, corresponding to the structures of Li x TiO 2 and Li x TiO 2-δ , and the enhanced conductivity from 0 to 1 and 40 S/m. This work realizes large-scale and rapid synthesis of flexible TiO 2 nanofiber films with tunable conductivity and is expected to extend the synthesis to other conductive metal oxide films. Cation intercalation is an effective method to optimise the electronic structures of metal oxides. Here authors present a visual intercalation chemical synthesis strategy to control intercalated structures of metal oxides and synthesise flexible conductive metal oxide films in one minute at room temperature.

Conventional lithium-ion batteries embrace graphite anodes which operate at potential as low as metallic lithium, subjected to poor rate capability and safety issues. Among possible alternatives, oxides based on titanium redox couple, such as spinel Li 4 Ti 5 O 12 , have received renewed attention. Here we further expand the horizon to include a perovskite structured titanate La 0.5 Li 0.5 TiO 3 into this promising family of anode materials. With average potential of around 1.0 V vs. Li + /Li, this anode exhibits high specific capacity of 225 mA h g −1 and sustains 3000 cycles involving a reversible phase transition. Without decrease the particle size from micro to nano scale, its rate performance has exceeded the nanostructured Li 4 Ti 5 O 12 . Further characterizations and calculations reveal that pseudocapacitance dictates the lithium storage process and the favorable ion and electronic transport is responsible for the rate enhancement. Our findings provide fresh impetus to the identification and development of titanium-based anode materials with desired electrochemical properties. Exploration of high performance materials for lithium storage presents as a critical challenge. Here authors report micron-sized La 0.5 Li 0.5 TiO 3 as a promising anode material, which demonstrates improved capacity, rate capability and suitable voltage as anode for lithium ion batteries.

The glymphatic system (GS) is a novel defined brain-wide perivascular transit network between cerebrospinal fluid (CSF) and interstitial solutes that facilitates the clearance of brain metabolic wastes. The complicated network of the GS consists of the periarterial CSF influx pathway, astrocytes-mediated convective transport of fluid and solutes supported by AQP4 water channels, and perivenous efflux pathway. Recent researches indicate that the GS dysfunction is associated with various neurological disorders, including traumatic brain injury, hydrocephalus, epilepsy, migraine, and Alzheimer’s disease (AD). Meanwhile, the GS also plays a pivotal role in the pathophysiological process of stroke, including brain edema, blood–brain barrier (BBB) disruption, immune cell infiltration, neuroinflammation, and neuronal apoptosis. In this review, we illustrated the key anatomical structures of the GS, the relationship between the GS and the meningeal lymphatic system, the interaction between the GS and the BBB, and the crosstalk between astrocytes and other GS cellular components. In addition, we contributed to the current knowledge about the role of the GS in the pathology of stroke and the role of AQP4 in stroke. We further discussed the potential use of the GS in early risk assessment, diagnostics, prognostics, and therapeutics of stroke.

Background Predicting drug-drug interactions (DDIs) from social-media and drug descriptions is crucial for healthcare, drug regulation, and pharmaceutical research, yet remains a challenging task. Existing deep learning and Transformer-based approaches often struggle with modeling long-range dependencies within sentences or entail high computational costs, limiting their practical applicability in large-scale drug safety screening. Results To overcome these limitations, we propose a Fusion State Space Model (FSSM) for DDI prediction (DDIP). FSSM leverages a selective State Space Model (SSM) to efficiently capture long-range syntactic and semantic dependencies within sentences, while an Interaction-based Selective Filtering (ISF) module mitigates information redundancy from multimodal inputs. Experiments on the DDIExtraction-2013 corpus demonstrate that FSSM achieves an F1-score of 75.23%, matching state-of-the-art Transformer models while requiring significantly less training time. Ablation studies confirm that each architectural component contributes meaningfully, with social-media embedding providing the largest performance gain (8.93% drop upon removal) and the ISF module contributing a 4.80% improvement. Conclusions FSSM offers a promising balance of predictive performance and computational efficiency for drug-drug interaction prediction. The model’s linear complexity enables large-scale screening, real-time applications, and deployment in resource-constrained settings, making it a valuable tool for drug discovery and drug safety assessment.

The sediment delivery ratio is greatly affected by the water-sediment and the riverbed boundary, which represent the river’s capacity to transport sediment under specified conditions. This study examines the response of the sediment delivery ratio to water-sediment and riverbed boundary conditions in the Lower Yellow River (LYR) since the operation of the Xiaolangdi Reservoir began. It evaluates the spatial-temporal variations of water-sediment and riverbed boundaries based on hydrological data and topographic data from 2000 to 2023. Based on the sediment transport rate equation, a theoretical equation for the sediment delivery ratio during flood events has been developed, thoroughly considering the effects of riverbed boundary conditions, including median particle size of bed sediment, river gradient, and width-to-depth ratio. The results show that the sediment delivery ratio negatively correlates with the incoming sediment coefficient, the median particle size of bed sediment, and the width-to-depth ratio. In contrast, it positively correlates with the water load variation coefficient and river gradient. In comparison to solely accounting for water and sediment conditions, incorporating the riverbed boundary into the theoretical equation results in a more precise alignment with the measured sediment delivery ratio data. This indicates that the riverbed boundary is a crucial factor influencing the sediment delivery ratio. Under the current boundary conditions, the Aishan to Lijin reach has the highest sediment transport capacity. To improve the sediment transport capacity of the Tiexie to Lijin reach, it is recommended to narrow the river width upstream of Gaocun and increase the width-to-depth ratio. The findings of this study provide essential scientific insights into the sediment transport capacity of alluvial rivers subjected to variations in water-sediment and riverbed boundary conditions, thereby offering important references for hydrological engineering and river management practices.

The performance of three machine learning methods (support vector regression, random forests and artificial neural network) for estimating the LAI of paddy rice was evaluated in this study. Traditional univariate regression models involving narrowband NDVI with optimized band combinations as well as linear multivariate calibration partial least squares regression models were also evaluated for comparison. A four year field-collected dataset was used to test the robustness of LAI estimation models against temporal variation. The partial least squares regression and three machine learning methods were built on the raw hyperspectral reflectance and the first derivative separately. Two different rules were used to determine the models' key parameters. The results showed that the combination of the red edge and NIR bands (766 nm and 830 nm) as well as the combination of SWIR bands (1114 nm and 1190 nm) were optimal for producing the narrowband NDVI. The models built on the first derivative spectra yielded more accurate results than the corresponding models built on the raw spectra. Properly selected model parameters resulted in comparable accuracy and robustness with the empirical optimal parameter and significantly reduced the model complexity. The machine learning methods were more accurate and robust than the VI methods and partial least squares regression. When validating the calibrated models against the standalone validation dataset, the VI method yielded a validation RMSE value of 1.17 for NDVI(766,830) and 1.01 for NDVI(1114,1190), while the best models for the partial least squares, support vector machine and artificial neural network methods yielded validation RMSE values of 0.84, 0.82, 0.67 and 0.84, respectively. The RF models built on the first derivative spectra with mtry = 10 showed the highest potential for estimating the LAI of paddy rice.

Background: Spontaneous pregnancy loss (SPL) precedes an increased risk of reduced fertility, while its etiology mechanism remains largely unknown. Liver dysfunction presenting in early pregnancy may represent a pre-existing undiagnosed liver condition affecting fetal development. Here, we investigated whether maternal abnormal liver function in early pregnancy contributed to the incidence of SPL.Methods: Data on pregnant women were leveraged from the Maternal Health Care Information System in Shanghai City from 2017 to 2021. Liver dysfunction status was defined as having any elevated liver function biomarker levels (LFBs) at the first antenatal visit. SPL cases were defined as fetal death occurring before 28 weeks gestation. Generalized linear models were used to estimate crude and adjusted risk ratios (RRs and aRRs, respectively) and 95% confidence intervals (CIs).Results: Among 10,175 leveraged pregnant women, 918 (9.0%) SPL cases were recorded. Maternal liver dysfunction in early pregnancy was associated with a 49% increased risk of SPL (RR 1.49; 95% CI, 1.22–1.84). This positive association persisted after adjustment for covariates (aRR 1.55; 95% CI, 1.26–1.92). Higher γ-glutamyl transferase (GGT) and alkaline phosphatase (ALP) levels were also linked with increased risk of SPL in a linear fashion (aRRs per 1 standard deviation increase: 1.13; 95% CI, 1.08–1.17 and 1.13; 95% CI, 1.07–1.20, respectively). Similar magnitudes of associations were observed between normal weight and overweight pregnant women in subgroup analysis.Conclusion: We provide new evidence that maternal abnormal liver function in early pregnancy, as well as GGT and ALP, predisposes to an increased risk of SPL.

The exploration of unconventional oxidation states is pivotal for expanding fundamental bonding paradigms and accessing exotic matter. Achieving highly negative oxidation states in transition metals remains a significant challenge. Here, we propose a dual-driven strategy combining a strong reductant (Li) and oxidant (F) under high pressure, stabilizing the oxidation state −VII of gold (Au) in a ternary electride Li 10 AuF. This insulating phase hosts paired interstitial anionic electrons and features an Au center nominally isoelectronic with the noble gas radon, governed by F-enhanced charge polarization, pressure-induced orbital reshuffling, and p - d hybridization that lowers the energy of Au 6 p orbitals. Replacing F with I or P progressively reduces Au charge and interstitial electron localization, transforming semiconducting Li 10 AuF into semimetallic Li 10 AuI and ultimately superconducting Li 10 AuP. These results demonstrate how chemically polarized element combinations under compression can unlock unexpected oxidation states and charge distributions, guiding the design of quantum materials with emergent functionalities. The stabilization of an unconventional −VII oxidation state of gold is demonstrated in Li 10 AuF under high pressure. This electride phase features paired interstitial anionic electrons, expanding our understanding of charge distribution.

Rivers are the major carriers of dissolved black carbon (DBC) from land to ocean; the sources of DBC during its continuous transformation and cycling in the ocean, however, are not well characterized. Here, we present new carbon isotope data for DBC in four large and two small mountainous rivers, the Yangtze and Yellow river estuaries, the East China Sea and the North Pacific Ocean. We found that the carbon isotope signatures of DBC are relatively homogeneous, and the DBC 14 C ages in rivers are predominantly young and increase during continuous transport and cycling in the ocean. The results of charcoal leaching experiments indicate that DBC is released from charcoal and degraded by bacteria. Our study suggests that riverine DBC is labile and respired during transport and mixing into the ocean and that residual DBC is cycled and aged on the same time scales as bulk DOC in the ocean. Black carbon is a recalcitrant byproduct of biomass burning that ultimately accumulates in oceanic sinks. Here the authors assessed the sources and cycling of dissolved black carbon in rivers and oceans, finding that oceanic pools are cycled and aged on the same time scales as dissolved organic carbon.