Biomolecules meet organic frameworks: from synthesis strategies to diverse applications.

Biomolecules are essential in pharmaceuticals, biocatalysts, biomaterials, etc., but unfortunately they are extremely susceptible to extraneous conditions. When biomolecules meet porous organic frameworks, significantly improved thermal, chemical, and mechanical stabilities are not only acquired for raw biomolecules, but also molecule sieving, substrate enrichment, chirality property, and other functionalities are additionally introduced for application expansions. In addition, the intriguing synergistic effect stemming from elaborate and concerted interactions between biomolecules and frameworks can further enhance application performances. In this paper, the synthesis strategies of the so-called bio-organic frameworks (BOFs) in recent years are systematically reviewed and classified. Additionally, their broad applications in biomedicine, catalysis, separation, sensing, and imaging are introduced and discussed. Before ending, the current challenges and prospects in the future for this infancy-stage but significant research field are also provided. We hope that this review will offer a concise but comprehensive vision of designing and constructing multifunctional BOF materials as well as their full explorations in various fields.

Gut microbiota and liver metabolomics reveal the potential mechanism of Lactobacillus rhamnosus GG modulating the liver toxicity caused by polystyrene microplastics in mice.

Microplastics (MPs) are known to cause liver toxicity as they can spread through the food chain. Most researches on their toxicity have focused on individual organs, neglecting the crucial "gut-liver axis"-a bidirectional communication pathway between the gut and liver. Probiotics have shown promise in modulating the effects of environmental pollutants. In this study, we exposed mice to Lactobacillus rhamnosus GG (LGG, 100 mg/kg b.w./d) and/or polystyrene microplastics (PS-MPs, 5 mg/kg b.w./d) for 28 d via gavage to investigate how probiotics influence live toxicity through the gut-liver axis. Our results demonstrated that PS-MPs induced liver inflammation (increased IL-6 and TNF-α) and disrupted lipid metabolism. However, when combined with LGG, these effects were alleviated. LGG also improved colon health, rectifying ciliary defects and abnormal mucus secretion caused by PS-MPs. Furthermore, LGG improved gut microbiota dysbiosis induced by PS-MPs. Metabolomics and gene expression analysis (Cyp7a1 and Cyp7b1) indicated that LGG modulated bile acid metabolism. In summary, LGG appears to protect the liver by maintaining gut homeostasis, enhancing gut barrier integrity, and reducing the liver inflammation. These findings confirm the potential of LGG to modulate liver toxicity caused by PS-MPs through the gut-liver axis, offering insights into probiotics' application for environmental pollutant detoxification.

Seasonal catchment memory of high mountain rivers in the Tibetan Plateau.

Rivers originating in the Tibetan Plateau are crucial to the population in Asia. However, research about quantifying seasonal catchment memory of these rivers is still limited. Here, we propose a model able to accurately estimate terrestrial water storage change (TWSC), and characterize catchment memory processes and durations using the memory curve and the influence/domination time, respectively. By investigating eight representative basins of the region, we find that the seasonal catchment memory in precipitation-dominated basins is mainly controlled by precipitation, and that in non-precipitation-dominated basins is strongly influenced by temperature. We further uncover that in precipitation-dominated basins, longer influence time corresponds to longer domination time, with the influence/domination time of approximately six/four months during monsoon season. In addition, the long-term catchment memory is observed in non-precipitation-dominated basins. Quantifying catchment memory can identify efficient lead times for seasonal streamflow forecasts and water resource management.

River ecological flow early warning forecasting using baseflow separation and machine learning in the Jiaojiang River Basin, Southeast China.

Ecological flow early warning is crucial for the rational management of watershed water resources. However, determining of accurate ecological flow threshold and choosing the appropriate forecasting model are challenging tasks. In this study, we initially developed a baseflow separation and Tennant method-based technique for calculating ecological river flow. Then an ecological flow early warning model was created using the machine learning technique based on distributed gradient enhancement framework (LightGBM). Finally, we utilized the framework of Shapley Additive Planning (SHAP) to explain how various hydrometeorological factors affect the variations in ecological flow conditions. The Jiaojiang River basin in southeast China is selected as the study area, and the hydrological stations in upstream of Baizhiao (BZA) and Shaduan (SD) are chosen for key analysis. The results of these applications show that the monthly baseflow frequency of the river ecological flow conditions of the two stations in the dry season is 20 % (7.49 m3/s) and 30 % (4.79 m3/s), respectively. The ecological flow level early warning forecasting accuracy is close to 90 % in the BZA and SD stations during dry and wet seasons. The variations of ecological flow are most affected by evaporation and base flow index. The results of this study can serve as a strong basis for the effective allocation and utilization of locally available water resources.

Scenario-based multi-objective optimization of reservoirs in silt-laden rivers: A case study in the Lower Yellow River.

Severe sedimentation often takes place in the river channel of silt-laden rivers, which is often mitigated through water-sediment regulation of the reservoirs. However, watersediment regulation is often competitive with other objectives of reservoirs, like water supply and hydropower generation; on the other hand, the reduction of channel sedimentation is often achieved at the expense of reservoir sedimentation, which reduces the service life of reservoirs. The Yellow River used to be the river with largest sediment transport over the world, but has experienced significant declination of runoff and sediment in recent years. This study presents a scenario-based multi-objective optimization operation model for the Xiaolangdi reservoir considering hydropower generation, reservoir sedimentation and channel sedimentation, with a generalized linear model coupled to calculate channel sedimentation based on runoff and sediment time series. A stochastic model that can reproduce both spatial correlations and low frequency attributes of the data series is adopted to generate two different scenarios based on different periods of observation and the performance of the multi-objective operation model under different scenarios is tested. The results indicate that: (1) the proposed optimization model can generate different schemes of reservoir operation and enhance operation performance; (2) the generalized linear model can well fit the relationship between daily channel sedimentation and runoff-sediment factors, but tends to overestimate the erosion efficiency after 2005; (3) the reservoir sedimentation and channel sedimentation show linear competitive relation, i.e., an average increase of 1 ton in reservoir sedimentation would result in declination of channel sedimentation from 0.455 to 0.488 tons, while the competitive relationship between hydropower generation and reservoir sedimentation is non-linear and weak; (4) the increase in the proportion of non-flood sediment load to the total sediment load makes it more difficult to prevent the reservoir from silting up.

Effects of Motor Imagery Tasks on Brain Functional Networks Based on EEG Mu/Beta Rhythm.

Motor imagery (MI) refers to the mental rehearsal of movement in the absence of overt motor action, which can activate or inhibit cortical excitability. EEG mu/beta oscillations recorded over the human motor cortex have been shown to be consistently suppressed during both the imagination and performance of movements, although the specific effect on brain function remains to be confirmed. In this study, Granger causality (GC) was used to construct the brain functional network of subjects during motor imagery and resting state based on EEG in order to explore the effects of motor imagery on brain function. Parameters of the brain functional network were compared and analyzed, including degree, clustering coefficient, characteristic path length and global efficiency of EEG mu/beta rhythm in different states. The results showed that the clustering coefficient and efficiency of EEG mu/beta rhythm decreased significantly during motor imagery (p < 0.05), while degree distribution and characteristic path length increased significantly (p < 0.05), mainly concentrated in the frontal lobe and sensorimotor area. For the resting state after motor imagery, the changes of brain functional characteristics were roughly similar to those of the task state. Therefore, it is concluded that motor imagery plays an important role in activation of cortical excitability.

Identifying how future climate and land use/cover changes impact streamflow in Xinanjiang Basin, East China.

Climate and land use/cover changes are the main factors altering hydrological regimes. To understand the impacts of climate and land use/cover changes on streamflow within a specific catchment, it is essential to accurately quantify their changes given many possibilities. We propose an integrated framework to assess how individual and combined climate and land use/cover changes impact the streamflow of Xinanjiang Basin, in East China, in the future. Five bias-corrected and downscaled General Circulation Model (GCM) projections are used to indicate the inter-model uncertainties under three Representative Concentration Pathways (RCPs). Additionally, three land use/cover change scenarios representing a range of tradeoffs between ecological protection (EP) and urban development (UD) are projected by Cellular Automata - Markov (CA-Markov). The streamflow in 2021-2050 is then assessed using the calibrated Soil and Water Assessment Tool (SWAT) with 15 scenarios and 75 possibilities. Finally, the uncertainty and attribution of streamflow changes to climate and land use/cover changes at monthly and annual scale are analyzed. Results show that while both land use/cover change alone and combined changes project an increase in streamflow, there is a disagreement on the direction of streamflow change under climate change alone. Future streamflow may undergo a more blurred boundary between the flood and non-flood seasons, potentially easing the operation stress of Xinanjiang Reservoir for water supply or hydropower generation. We find that the impacts of climate and land use/cover changes on monthly mean streamflow are sensitive to the impermeable area (IA). The impacts of climate change are stronger than those induced by land use/cover change under EP (i.e., lower IA); and land use/cover change has a greater impact in case of UD (i.e., higher IA). However, changes in annual mean streamflow are mainly driven by land use/cover change, and climate change may decrease the influence attributed to land use/cover change.