Internal transformation and damming regulate the longitudinal variation of DOM bioavailability in a large river.

Understanding the spatial patterns of dissolved organic matter (DOM) and factors that influence them is crucial for maintaining river ecosystem functions and riverine health, considering the significant role of DOM in water quality and aquatic ecosystems. Nevertheless, there is limited knowledge regarding the spatial variation of DOM bioavailability and the factors driving them in large river systems. This study involved 39 sampling locations along the main stem of the Changjiang River, spanning its entire length (> 5000 km) during a dry season. Spatial patterns of DOM were assessed by measurements of DOC concentrations and eight fluorescence DOM indices, namely fluorescence index (FI-A and FI-B), Trytophan/Tyrosine, Humic A, Humic C, humification indices (HIX-A and HIX-B), and Freshness index (ß/α). The results revealed that the water DOM in the main stem of the Changjiang River primarily originated from terrestrial sources. A decline in DOM bioavailability was observed from the upper to the lower basin, aligning with the carbon processing prediction rather than the river continuum concept (RCC). The pure effect of physicochemical factors (25.30%) was greater than that of geographic factors (9.40%). The internal transformation processes determined the significant longitudinal decreases of DOM bioavailability. While no significant difference in DOM bioavailability was observed between reaches before and after the dams, the construction of dams was found to improve DOM bioavailability at the subsection scale and reduce the spatial autocorrelation of DOM bioavailability across the entire basin.

Bacterial Diversity and Vertical Distribution Patterns in Sandy Sediments: A Study on the Bacterial Community Structure Based on Environmental Factors in Tributaries of the Yangtze River.

Bacterial diversity and its distribution characteristics in sediments are critical to understanding and revealing biogeochemical cycles in sediments. However, little is known about the relationship between biogeochemistry processes and vertical spatial distribution of bacterial communities in sandy sediments. In this study, we used fluorescence quantitative PCR, high-throughput sequencing technology and statistical analysis to explore the vertical distribution pattern of bacterial community diversity and its influencing factors in sandy sediments of the Yangtze River Basin. The aim is to enrich the understanding of the ecological characteristics and functions of bacteria in river ecosystems. The results showed that both sediment bacterial abundance and diversity showed a gradual decrease from surface to bottom in the vertical distribution. The main environmental factors that influenced the bacterial distribution pattern were pore water dissolved oxygen (DO), total nitrogen (TN) concentration and sediment nitrogen (N) content. The dominant bacterial species, Massilia and Flavobacterium, are suitable for growth and reproduction in high oxygen and nutrient-richer environments, while Limnobacter prefers low oxygen or anaerobic conditions. The vertical distribution pattern of bacteria and its influencing factors in river sandy sediment found in this study differ from the results in mud sediment, which may be related to the larger granular gap between sandy sediment and the lower content of organic matter. The findings of this study further our understanding of the distribution patterns and ecological preferences of microbial communities in river sediments, providing insights into how these communities may adapt to varying environmental conditions.

Perception-Aware Texture Similarity Prediction.

Texture similarity plays important roles in texture analysis and material recognition. However, perceptually-consistent fine-grained texture similarity prediction is still challenging. The discrepancy between the texture similarity data obtained using algorithms and human visual perception has been demonstrated. This dilemma is normally attributed to the texture representation and similarity metric utilised by the algorithms, which are inconsistent with human perception. To address this challenge, we introduce a Perception-Aware Texture Similarity Prediction Network (PATSP-Net). This network comprises a Bilinear Lateral Attention Transformer network (BiLAViT) and a novel loss function, namely, RSLoss. The BiLAViT contains a Siamese Feature Extraction Subnetwork (SFEN) and a Metric Learning Subnetwork (MLN), designed on top of the mechanisms of human perception. On the other hand, the RSLoss measures both the ranking and the scaling differences. To our knowledge, either the BiLAViT or the RSLoss has not been explored for texture similarity tasks. The PATSP-Net performs better than, or at least comparably to, its counterparts on three data sets for different fine-grained texture similarity prediction tasks. We believe that this promising result should be due to the joint utilization of the BiLAViT and RSLoss, which is able to learn the perception-aware texture representation and similarity metric.

Effect of pyrite particle size on the denitrification performance of autotrophic or split-mixotrophic bioreactors supported by pyrite/polycaprolactone.

In this study, a pyrite-based autotrophic denitrification (PAD) system, a polycaprolactone (PCL)-supported heterotrophic denitrification (PHD) system, and a pyrite+PCL-based split-mixotrophic denitrification (PPMD) system were constructed. The pyrite particle size was controlled in 1-3, 3-5, or 5-8 mm in both the PAD and PPMD systems to investigate the effect of pyrite particle size on the denitrification performance of autotrophic or split-mixotrophic bioreactors. It was found that the PAD system achieved the best denitrification efficiency with an average removal rate of 98.98% in the treatment of 1- to 3-mm particle size, whereas it was only 19.24% in the treatment of 5- to 8-mm particle size. At different phases of the whole experiment, the nitrate removal rates of both the PHD and PPMD systems remained stable at a high level (>94%). Compared with the PAD or PHD system, the PPMD system reduced the concentrations of sulfate and chemical oxygen demand in the final effluent efficiently. The interconnection network diagram explained the intrinsic metabolic pathways of nitrogen, sulfur, and carbon in the three denitrification systems at different phases. In addition, the microbial community analysis showed that the PPMD system was beneficial for the enrichment of Firmicutes. Finally, the impact mechanism of pyrite particle size on the performance of the PPMD system was proposed. PRACTITIONER POINTS: The reduction of pyrite particle size was beneficial for improving the efficiency of the PAD process. The change in particle size had an effect on NO2 --N accumulation in the PAD system. The accumulation of NH4 +-N in the PPMD system increased with the decrease in particle size. The reduction of pyrite particle size increased the production of SO4 2- in the PAD and PPMD systems. The correlations among the effluent indicators of the PAD and PPMD systems could be well explained.

Adaptive Weighted Error-Correction Method Based on the Error Distribution Characteristics of Multi-Channel Alignment.

As process nodes of advanced integrated circuits continue to decrease below 10 nm, the requirement for overlay accuracy is becoming stricter. The alignment sensor measures the position of the alignment mark relative to the wafer; thus, sub-nanometer alignment position accuracy is vital. The Phase Grating Alignment (PGA) method is widely used due to its high precision and stability. However, the alignment error caused by the mark asymmetry is the key obstacle preventing PGA technology from achieving sub-nanometer alignment accuracy. This error can be corrected using many methods, such as process verification and multi-channel weighted methods based on multi-diffraction, multi-wavelength and multi-polarization state alignment sensors. However, the mark asymmetry is unpredictable, complex and difficult to obtain in advance. In this case, the fixed-weight method cannot effectively reduce the alignment error. Therefore, an adaptive weighted method based on the error distribution characteristic of a multi-channel is proposed. Firstly, the simulation result proves that the error distribution characteristic of the multi-alignment result has a strong correlation with the mark asymmetry. Secondly, a concrete method of constructing weight values based on error distribution is described. We assume that the relationship between the weight value of each channel and the deviations of all channels' results is second-order linear. Finally, without other prior process correction in the simulation experiment, the residual error's Root Mean Square (RMS) of fixed weighted method is 14.0 nm, while the RMS of the adaptive weighted method is 0.01 nm, when dealing with five typical types of mark asymmetry. The adaptive weighted method exhibits a more stable error correction effect under unpredictable and complicated mark asymmetry.

Study on the Effect of Cations on the Surface Energy of Nano-SiO2 Particles for Oil/Gas Exploration and Development Based on the Density Functional Theory.

Although nano SiO2 exhibits excellent application potential in the field of oil and gas exploration and development, such as drilling fluid, enhanced oil/gas recovery, etc., it is prone to agglomeration and loses its effectiveness due to the action of cations in saline environments of oil and gas reservoirs. Therefore, it is crucial to study the mechanism of the change in energy between nano SiO2 and cations for its industrial application. In this paper, the effect of cations (Na+, K+, Ca2+, and Mg2+) on the surface energy of nano SiO2 particles is investigated from the perspective of molecular motion and electronic change by density functional theory. The results are as follows: Due to the electrostatic interactions, cations can migrate towards the surface of nano SiO2 particles. During the migration process, monovalent cations are almost unaffected by water molecules, and they can be directly adsorbed on the surface by nano SiO2 particles. However, when divalent cations migrate from a distance to the surface of nano SiO2 particles, they can combine with water molecules to create an energy barrier, which can prevent them from moving forward. When divalent cations break through the energy barrier, the electronic kinetic energy between them and nano SiO2 particles changes more strongly, and the electrons carried by them are more likely to break through the edge of the atomic nucleus and undergo charge exchange with nano SiO2 particles. The change in interaction energy is more intense, which can further disrupt the configuration stability of nano SiO2. The interaction energy between cations and nano SiO2 particles mainly comes from electrostatic energy, followed by Van der Waals energy. From the degree of influence of four cations on nano SiO2 particles, the order from small to large is as follows: K+ < Na+ < Mg2+ < Ca2+. The research results can provide a theoretical understanding of the interaction between nano SiO2 particles and cations during the application of nano SiO2 in the field of oil and gas exploration and development.

Deciphering the influencing mechanism of hydraulic retention time on purification performance of a mixotrophic system from the perspective of reaction kinetics.

At present, eutrophication is increasingly serious, so it is necessary to effectively reduce nitrogen and phosphorus in water bodies. In this study, a pyrite/polycaprolactone-based mixotrophic denitrification (PPMD) system using pyrite and polycaprolactone (PCL) as electron donors was developed and compared with pyrite-based autotrophic denitrification (PAD) system and PCL-based heterotrophic denitrification (PHD) system through continuous flow experiment. The removal efficiency of NO3--N (NRE) and PO43--P (PRE) and the contribution proportion of PAD in the PPMD system were significantly increased by prolonging hydraulic retention time (HRT, from 1 to 48 h). When HRT was equal to 24 h, the PPMD system conformed to the zero-order kinetic model, so NRE and PRE were mainly limited by the PAD process. When HRT was equal to 48 h, the PPMD system met the first-order kinetic model with NRE and PRE reaching 98.9 ± 1.1% and 91.8 ± 4.5%, respectively. When HRT = 48 h, the NRE and PRE by PAD system were 82.7 ± 9.1% and 88.5 ± 4.7%, respectively, but the effluent SO42- concentration was as high as 152.1 ± 13.7 mg/L (the influent SO42- concentration was 49.2 ± 3.3 mg/L); the NRE by PHD system was 98.5 ± 1.7%, but the PO43--P could not be removed ideally. The concentrations of NO3--N, total nitrogen, PO43--P, and SO42- in the PPMD system also showed distinct changes along the reactor column. In addition, the microbial diversity analysis showed that prolonging HRT (from 24 to 48 h) increased the abundance of autotrophic denitrifying microorganisms in the PPMD system, ultimately increasing the contribution proportion of PAD.

An optimized fluorescent biosensor for monitoring long-chain fatty acyl-CoAs metabolism in vivo.

Long-chain fatty acyl-CoAs (LCACoAs) are intermediates in lipid metabolism that exert a wide range of cellular functions. However, our knowledge about the subcellular distribution and regulatory impacts of LCACoAs is limited by a lack of methods for detecting LCACoAs in living cells and tissues. Here, we report our development of LACSerHR, a genetically encoded fluorescent biosensor that enables precise measurement of subtle fluctuations in the levels of endogenous LCACoAs in vivo. LACSerHR significantly improve the fluorescent brightness and analyte affinity, in vitro and in vivo testing showcased LACSerHR's large dynamic range. We demonstrate LACSerHR's capacity for real-time evaluation of LCACoA levels in specific subcellular compartments, for example in response to disruption of ACSL enzyme function in HEK293T cells. Moreover, we show the application of LACSerHR for sensitive measurement of elevated LCACoA levels in the livers of mouse models for two common metabolic diseases (NAFLD and type 2 diabetes). Thus, our LACSerHR sensor is a powerful, broadly applicable tool for studying LCACoAs metabolism and disease.

Monitoring Intracellular IP6 with a Genetically Encoded Fluorescence Biosensor.

Inositol hexakisphosphate (IP6), a naturally occurring metabolite of inositol with specific functions in different organelles or tissues, participates in numerous physiological processes and plays a key role in mammalian metabolic regulation. However, current IP6 detection methods, i.e., high-performance liquid chromatography and gel electrophoresis, require sample destruction and lack spatiotemporal resolution. Here, we construct and characterize a genetically encoded fluorescence biosensor named HIPSer that enables ratiometric quantitative IP6 detection in HEK293T cells and subcellular compartments. We demonstrate that HIPSer has a high sensitivity and relative selectivity for IP6 in vitro. We also provide proof-of-concept evidence that HIPSer can monitor IP6 levels in real time in HEK293T cells and can be targeted for IP6 detection in the nucleus of HEK293T cells. Moreover, HIPSer could also detect changes in IP6 content induced by chemical inhibition of IP6-metabolizing enzymes in HEK293T cells. Thus, HIPSer achieves spatiotemporally precise detection of fluctuations in endogenous IP6 in live cells and provides a versatile tool for mechanistic investigations of inositol phosphate functions in metabolism and signaling.

OIP5-AS1/CD147/TRPM7 axis promotes gastric cancer metastasis by regulating apoptosis related PI3K-Akt signaling.

Background: To explore the mechanism of OIP5-AS1/CD147/TRPM7 axis to gastric cancer (GC) metastasis. Methods: Bioinformatic analysis was performed to pick up the candidate genes associated with regulation GC metastasis. Using GC cell lines, AGS and MKN-45 as research objects, identify the effect of candidate genes on GC metastasis, judge cell proliferation status by MTT assay and cell clone number, and detect cell migration by Transwell and Wound-healing assay. The molecular mechanism of CD147/OIP5/TRPM7 axis regulating GC metastasis was further explored by RNA sequencing. The key signaling pathways were subsequently verified by flow cytometry and WB. Results: Bioinformatic analysis suggested OIP5-AS1/CD147/TRPM7 axis may be involving in GC metastasis. The RNA interference experiment proved that after gene interference, the proliferation ability of GC cells decreased significantly (P<0.05), which was manifested in the reduction of the number of cell clones. In addition, the migration ability of GC cells was also affected, which was based on the results of Wound Healing (P<0.05). CD147, OIP5-AS1 and TRPM7 all have harmful effects on GC cells. The relationship between OIP5-AS1 and CD147/TRPM7 was detected by RNA immunoprecipitation. Moreover, the RNA sequencing data indicated that CD147/OIP5-AS1/TRPM7 may coordinately regulate the PI3K-AKT pathway related to GC cell apoptosis, thereby affecting the proliferation and migration of GC cells. After RNA interference, the level of apoptosis increased both in AGS and MKN-45 cells. Meanwhile, the expression of pro-apoptotic proteins Caspase9 and BAX were up-regulated (P<0.05). In addition, the expression of PI3K and AKT proteins was reduced (P<0.05). The mouse tumorigenesis experiment corroborated the results of the in vitro study. Conclusion: OIP5-AS1/CD147/TRPM7 axis reduces GC cell proliferation by regulating apoptosis associated with PI3K-AKT signaling, further affecting cancer metastasis.

Latitudinal and meridional patterns of picophytoplankton variability are contrastingly associated with Ekman pumping and the warm pool in the tropical western Pacific.

Marine picophytoplankton plays a major role in marine cycling and energy conversion, and its effects on the carbon cycle and global climate change have been well documented. In this study, we investigated the response of picophytoplankton across a broad range of physicochemical conditions in two distinct regions of the tropical western Pacific. Our analysis considered the abundance, carbon biomass, size fraction, distribution, and regulatory factors of the picophytoplankton community, which included the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (picoeukaryotes). The first region was a latitudinal transect along the equator (142-163° E, 0° N), characterized by stratified oligotrophic conditions. The second region was a meridional transect (143° E, 0-22° N) known for its high-nutrient and low-chlorophyll (HNLC) conditions. Results showed that picophytoplankton contributed >80% of the chlorophyll a (Chl a), and was mainly distributed above 100 m. Prochlorococcus was the dominant organism in terms of cell abundance and estimated carbon biomass in both latitudinal and meridional transects, followed by Synechococcus and picoeukaryotes. In the warm pool, Prochlorococcus was primarily distributed below the isothermal layer, with the maximum subsurface abundance forming below it. The maximum Synechococcus abundance was restricted to the west-warm pool, due to the high temperature, and the second-highest Synechococcus abundance was associated with frontal interaction between the east-warm pool and the westward advance of Middle East Pacific water. In contrast, picoeukaryotes formed a maximum subsurface abundance corresponding to the subsurface Chl a maximum. In the mixed HNLC waters, the cell abundance and biomass of the three picophytoplankton groups were slightly lower than those in the warm pool. Due to a cyclonic eddy, the contours of the maximum subsurface Prochlorococcus abundance were uplifted, evidently with a lower value than the surrounding water. Synechococcus abundance varied greatly in patches, forming a weakly high subsurface peak when the isothermal layer rose to the near-surface (<50 m). The subsurface maximum picoeukaryote abundance was also highly consistent with that of the subsurface Chl a maximum. Correlation analysis and generalized additive models of environmental factors showed that nutrient availability had a two-faceted role in regulating the spatial patterns of picophytoplankton in diverse latitudinal and meridional environments. We concluded through regression that temperature and light irradiance were the key determinants of picophytoplankton variability in the tropical western Pacific. This study provides insights into the changing picophytoplankton community structure with potential future changing hydroclimatic force.

An effective hybrid feature selection using entropy weight method for automatic sleep staging.

Objective. Sleep staging is the basis for sleep quality assessment and diagnosis of sleep-related disorders. In response to the inadequacy of traditional manual judgement of sleep stages, using machine learning techniques for automatic sleep staging has become a hot topic. To improve the performance of sleep staging, numerous studies have extracted a large number of sleep-related characteristics. However, there are redundant and irrelevant features in the high-dimensional features that reduce the classification accuracy. To address this issue, an effective hybrid feature selection method based on the entropy weight method is proposed in this paper for automatic sleep staging.Approach. Firstly, we preprocess the four modal polysomnography (PSG) signals, including electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG). Secondly, the time domain, frequency domain and nonlinear features are extracted from the preprocessed signals, with a total of 185 features. Then, in order to acquire characteristics of the multi-modal signals that are highly correlated with the sleep stages, the proposed hybrid feature selection method is applied to choose effective features. This method is divided into two stages. In stage I, the entropy weight method is employed to combine two filter methods to build a subset of features. This stage evaluates features based on information theory and distance metrics, which can quickly obtain a subset of features and retain the relevant features. In stage II, Sequential Forward Selection is used to evaluate the subset of features and eliminate redundant features. Further more, to achieve better performance of classification, an ensemble model based on support vector machine, K-nearest neighbor, random forest and multilayer perceptron is finally constructed for classifying sleep stages.Main results. The experiment using the Cyclic Alternating Pattern (CAP) sleep database is performed to assess the performance of the method proposed in this paper. The proposed hybrid feature selection method chooses only 30 features highly correlated to sleep stages. The accuracy, F1 score and Kappa coefficient of 6 class sleep staging reach 88.86%, 83.15% and 0.8531%, respectively.Significance. Experimental results show the effectiveness of the proposed method compared to the existing state-of-the-art studies. It greatly reduces the number of features required while achieving outstanding auto-sleep staging results.

The enrichment of more functional microbes induced by the increasing hydraulic retention time accounts for the increment of autotrophic denitrification performance.

With pyrite (FeS2) and polycaprolactone (PCL) as electron donors, three denitrification systems, namely FeS2-based autotrophic denitrification (PAD) system, PCL-supported heterotrophic denitrification (PHD) system and split-mixotrophic denitrification (PPMD) system, were constructed and operated under varying hydraulic retention times (HRT, 1-48 h). Compared with PAD or PHD, the PPMD system could achieve higher removals of NO3--N and PO43--P, and the effluent SO42- concentration was greatly reduced to 7.28 mg/L. Similarly, the abundance of the dominant genera involved in the PAD (Thiobacillus, Sulfurimonas, and Ferritrophicum, etc.) or PHD (Syntrophomonas, Desulfomicrobium, and Desulfovibrio, etc.) process all increased in the PPMD system. Gene prediction completed by PICRUSt2 showed that the abundance of the functional genes involved in denitrification and sulfur oxidation all increased with the increase of HRT. This also accounted for the increased contribution of autotrophic denitrification to total nitrogen removal in the PPMD system. In addition, the analysis of metabolic pathways disclosed the specific conversion mechanisms of nitrogen and sulfur inside the reactor.

Research progress of colon-targeted oral hydrogel system based on natural polysaccharides.

The quality of life is significantly impacted by colon-related diseases. There have been a lot of interest in the oral colon-specific drug delivery system (OCDDS) as a potential carrier to decrease systemic side effects and protect drugs from degradation in the upper gastrointestinal tract (GIT). Hydrogels are effective oral colon-targeted drug delivery carriers due to their high biodegradability, substantial drug loading, and great biocompatibility. Natural polysaccharides give the hydrogel system unique structure and function to effectively respond to the complex environment of the GIT and deliver drugs to the colon. In this paper, the physiological factors of colonic drug delivery and the pathological characteristics of common colonic diseases are summarized, and the latest advances in the design, preparation and characterization of natural polysaccharide hydrogels are reviewed, which are expected to provide new references for colon-targeted oral hydrogel systems using natural polysaccharides as raw materials.

Depth-of-field expansion method based on multidimensional structure and edge-guided correction.

Multi-focus image fusion is a method to extend the depth of field to generate fully focused images. The effective detection of image focusing pixels and the optimization of image regions are the key to it. A method based on multidimensional structure and edge-guided correction (MSEGC) is proposed. The pixel-level focusing evaluation function is redesigned to preserve image details and non-texture regions. Edge-guided decision correction is used to suppress edge artifacts. With public data and semiconductor detection images for verification, the results show that compared with other methods, the objective evaluation is improved by 22-50%, providing better vision.

A multi-channel UNet framework based on SNMF-DCNN for robust heart-lung-sound separation.

Cardiopulmonary and cardiovascular diseases are fatal factors that threaten human health and cause many deaths worldwide each year, so it is essential to screen cardiopulmonary disease more accurately and efficiently. Auscultation is a non-invasive method for physicians' perception of the disease. The Heart Sounds (HS) and Lung Sounds (LS) recorded by an electronic stethoscope consist of acoustic information that is helpful in the diagnosis of pulmonary conditions. Still, inter-interference between HS and LS presented in both the time and frequency domains blocks diagnostic efficiency. This paper proposes a blind source separation （BSS）strategy that first classifies Heart-Lung-Sound (HLS) according to its LS features and then separates it into HS and LS. Sparse Non-negative Matrix Factorization (SNMF) is employed to extract the LS features in HLS, then proposed a network constructed by Dilated Convolutional Neural Network (DCNN) to classify HLS into five types by the magnitude features of LS. Finally, Multi-Channel UNet (MCUNet) separation model is utilized for each category of HLS. This paper is the first to propose the HLS classification method SNMF-DCNN and apply UNet to the cardiopulmonary sound separation domain. Compared with other state-of-the-art methods, the proposed framework in this paper has higher separation quality and robustness.

Deep learning-enhanced fluorescence microscopy via confocal physical imaging model.

Confocal microscopy is one of the most widely used tools for high-resolution cellular, tissue imaging and industrial inspection. Micrograph reconstruction based on deep learning has become an effective tool for modern microscopy imaging techniques. While most deep learning methods neglect the imaging process mechanism, which requires a lot of work to solve the multi-scale image pairs aliasing problem. We show that these limitations can be mitigated via an image degradation model based on Richards-Wolf vectorial diffraction integral and confocal imaging theory. The low-resolution images required for network training are generated by model degradation from their high-resolution counterparts, thereby eliminating the need for accurate image alignment. The image degradation model ensures the generalization and fidelity of the confocal images. By combining the residual neural network with a lightweight feature attention module with degradation model of confocal microscopy ensures high fidelity and generalization. Experiments on different measured data report that compared with the two deconvolution algorithms, non-negative least squares algorithm and Richardson-Lucy algorithm, the structural similarity index between the network output image and the real image reaches a high level above 0.82, and the peak signal-to-noise ratio can be improved by more than 0.6 dB. It also shows good applicability in different deep learning networks.

Coupling geochemical and microbial molecular techniques to reveal catchment-scale nitrate yield and fluvial export dynamics.

The disturbance of reactive nitrogen (N) on ecosystems and biogeochemical cycles is now one of the most severe environmental problems worldwide. Nitrate (NO3-) is usually a dominant reactive N species in river ecosystems. Excessive NO3- concentrations in rivers have led to eutrophication and consequent ecological and environmental damages. Quantifying catchment-scale NO3- yield and export dynamics is crucial for effective remediation of river NO3- pollution. Frequently, natural abundance isotopes of NO3- in a river (δ15N/δ18O-NO3-) are applied to identify sources and potential transformations of NO3- at a catchment scale, while microbial molecular techniques and 15N pairing experiments are employed to reveal the NO3- production and removal processes and their underlying mechanisms in microenvironments (e.g., sediments and soils). In this study, we developed a novel protocol that couples these complementary geochemical and molecular techniques to quantify catchment-scale NO3- yield and fluvial export dynamics. The protocol links microscopic processes with catchment-scale geochemical characteristics to explicitly describe the NO3- cycling processes and their underlying abiotic and biotic mechanisms within a catchment. We applied the protocol to the Dadu and Jiazela catchments on the Qinghai-Tibet Plateau, and demonstrated the effectiveness of the protocol in determining NO3- yield and export dynamics in the catchments.

Imaging changes in the polarity of lipid droplets during NAFLD-Induced ferroptosis via a red-emitting fluorescent probe with a large Stokes shift.

Cell death resulting from ferroptosis is a consequence of the accumulation of lipid peroxides that are produced when lipids and reactive oxygen species (ROS) interact. This process is dependent on iron and alters the structure and polarity of lipid droplets (LDs). Unlike reactive fluorescent probes, environment-sensitive fluorescent probes can accurately monitor metabolic activities by sensing the intracellular environment of living organisms. To this end, we developed a polarity-sensitive fluorescent probe LIP-Ser that anchors to LDs and can be used to monitor changes in the polarity of LDs during ferroptosis by in situ imaging. LIP-Ser has a red-emitting (λem = 634 nm) and a large Stokes shift (Δλ = 161 nm in 1,4-dioxane), which avoids it from autofluorescence interference and crosstalk between excitation and emission spectra, thereby preventing low signal-to-noise ratio and severe fluorescence self-quenching during imaging. Additionally, LIP-Ser is used in this study to demonstrate that non-alcoholic fatty liver disease (NAFLD) promotes ferroptosis at the cellular and in vivo levels, and that inhibition of cellular ferroptosis effectively reduces the damage caused by NAFLD to cells and mouse liver tissue.