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Due to the complex radiative transfer process, the retrieval time of the physical retrieval algorithm is significantly increased compared with that of the statistical retrieval algorithm. The calculation of the Jacobian matrix is the most computationally intensive part of the physical retrieval algorithm. Further analysis showed that the changes in Jacobians had little effect on the performance of the physical retrieval algorithm. On the basis of the above findings, a fast physical-iterative retrieval algorithm was proposed by adaptively updating the Jacobian in keeping with the changes of the atmospheric state. The performance of the algorithm is evaluated using synthetic ground-based infrared spectra observations. The retrieval speed is significantly improved compared with the traditional physical retrieval algorithm under the condition that the parameters of the computing platform remain unchanged, with the average retrieval time reduced from 8.96 min to 3.69 min. The retrieval accuracy of the fast retrieval model is equivalent to that of the traditional algorithm, with maximum root-mean-square errors of less than 1.2 K and 1.0 g/kg for heights below 3 km for the temperature and water vapor mixing ratio (WVMR), respectively. The Jacobian updating strategy has a certain impact on the convergence of the retrieval algorithm, whose convergence rate is 98.7 %, which is lower than that of the traditional algorithm to some extent. However, reliable retrieval results can still be obtained by adjusting the convergence criteria.

The inhibitory effect of miR-200c on drug resistance against gastric cancer (GC) cells and proliferation may be related to expression of PTEN protein and Akt pathway. Nanoalbumin particles are expected as promising materials to prolong circulation of drugs and strengthen drugs’ efficacy. Herein, we explored the effect of miR-200c-modified nanoalbumin particles on cisplatin drug sensitivity of GC cells, to provide evidence for solving out the challenge of GC multidrug resistance. Cisplatin-resistant SGC7901/DDP GC cells were cultured to logarithmic phase, and transfected with empty vector, miR-200c-loaded nanoalbumin particles and miR-200c inhibitor. The cells were exposed to cisplatin at different concentrations followed by analysis of drug resistance against the GC cells by MTT method and PTEN and p-Akt level by western blot. The nanoalbumin particles carrying miR-200c effectively up-regulated the expression of miR-200c, suppressed proliferation of SGC7901/DDP cells, and increased GC cell sensitivity to cisplatin. Moreover, miR-200c-loaded nanoalbumin particles decreased p-Akt and increased PTEN. Reversely, silencing of miR-200c resulted in opposite outcomes. The miR-200c-loaded nanoalbumin particles exerted their effect on reversing multidrug resistance of GC cells through regulation of P13K/Akt/mTOR signaling pathway, thereby suppressing GC cell viability and improving development of GC.

Grain-oriented electric steel is hot rolled at different hot rolling temperature, and subsequent thermal maintained. Precipitation features of MnS and Cu2S were observed under different process conditions using thermal field emission scanning electron microscopy. The results show that Cu is precipitate out in the form of a composite precipitate with MnS during hot rolling, The hot rolling temperature and the postrolling maintain time had no obvious effect on the precipitation of MnS, The lower the hot rolling temperature, the faster the Cu2S precipitation. And the insulation process after high temperature hot rolling can make a large number of the Cu2S diffusion precipitation, There was no significant effect on the precipitation of Cu2S after low temperature hot rolling, still remain in the precipitate state after hot rolling.

The interannual variability of Tibetan Plateau (TP) summer climate has tremendous impacts on both regional hydrological cycles and global climate. In this study, we extract four dominant modes of the summertime large-scale circulation over the TP and surrounding areas from both the observation and simulations by a coupled general circulation model, CAS-FGOALS-f3-L. Based on the 10-member tropical Pacific pacemaker experiments, the ENSO-forced and ENSO-independent signals are isolated, each of which is represented by two dominant modes. The two ENSO-forced modes correspond to ENSO developing and decaying summer, respectively. The positive phase of the developing (decaying) ENSO-related mode is characterized by an anomalous baroclinic cyclone (anticyclone) over the western TP excited by the variations of the tropical summer monsoon rainfall. During the El Niño developing summer, the Indian monsoon rainfall variation is driven by an eastward shift of the Walker circulation due to warm anomalies in the equatorial central-eastern Pacific, whereas during the El Niño decaying summer it is caused by the basinwide warming in the tropical Indian Ocean. The two ENSO-independent modes are associated with the summer North Atlantic Oscillation (SNAO) and the circumglobal teleconnection (CGT) pattern, respectively. The positive phases of the SNAO- and CGT-related modes are characterized by an anomalous anticyclone over the western TP and zonal cyclone–anticyclone dipole pattern over the TP, respectively, both of which are associated with mid- and high-latitude stationary Rossby wave trains.

The problem of effectively exploiting the information multiple data sources has become a relevant but challenging research topic in remote sensing. In this paper, we propose a new approach to exploit the complementarity of two data sources: hyperspectral images (HSIs) and light detection and ranging (LiDAR) data. Specifically, we develop a new dual-channel spatial, spectral and multiscale attention convolutional long short-term memory neural network (called dual-channel A3CLNN) for feature extraction and classification of multisource remote sensing data. Spatial, spectral and multiscale attention mechanisms are first designed for HSI and LiDAR data in order to learn spectral- and spatial-enhanced feature representations, and to represent multiscale information for different classes. In the designed fusion network, a novel composite attention learning mechanism (combined with a three-level fusion strategy) is used to fully integrate the features in these two data sources. Finally, inspired by the idea of transfer learning, a novel stepwise training strategy is designed to yield a final classification result. Our experimental results, conducted on several multisource remote sensing data sets, demonstrate that the newly proposed dual-channel A3CLNN exhibits better feature representation ability (leading to more competitive classification performance) than other state-of-the-art methods., Comment: 16 pages, 10 figures

In the flame spray pyrolysis process, 2-ethylhexanoic acid manipulates morphology of Yb3+/Ho3+ co-doped Y2O3 upconversion nanoparticles towards homogeneous and ultrafine but causing upconversion luminescence quenching and thermal sensitivity lowering.

Deep neural networks have achieved promising performance for hyperspectral image (HSI) classification. However, due to the limitation of the available labeled samples, the traditional deeper and wider neural networks usually cause the overfitting problem and lose the detailed information. To solve this problem, a brain-like structure, namely spatial attention-driven recurrent feedback convolutional neural network (SARFNN), is proposed by utilizing the recurrent feedback and attention mechanism structures, from which two deep models are further developed for HSI classification. First, a 2-D SARFNN (SARF2DNN) model is developed to learn the spatial features from HSI data. After that, to better exploit the 3-D characteristic, the 3-D version is extended from SARF2DNN, thus constructing an SARF3DNN model to extract joint spatial-spectral features. Moreover, with the help of the idea of brain-likeness, the recurrent feedback module is designed to recover information loss caused by deeper structure and the dimension reduction operation. The experimental results conducted on two HSI data sets show that our SARFNN architecture can achieve more competitive performance than other state-of-the-art algorithms.

Accurately determining the height of the planetary boundary layer (PBL) is important since it can affect the climate, weather, and air quality. Ground-based infrared hyperspectral remote sensing is an effective way to obtain this parameter. Compared with radiosonde measurements, its temporal resolution is much higher. In this study, a method to retrieve the PBL height (PBLH) from the ground-based infrared hyperspectral radiance data is proposed based on machine learning. In this method, the channels that are sensitive to temperature and humidity profiles are selected as the feature vectors, and the PBLHs derived from radiosonde are taken as the true values. The support vector machine (SVM) is applied to train and test the data set, and the parameters are optimized in the process. The data set collected at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) from 2012 to 2015 is analyzed. The instruments used in this letter include Atmospheric Emitted Radiance Interferometer (AERI), Vaisala CL31 ceilometer, and radiosonde. It shows that the root mean square error (RMSE) between the PBLHs calculated by the proposed method using AERI data and those from radiosonde data can be within 370 m, and the square correlation coefficient (SCC) is greater than 0.7. Compared with the PBLHs derived from the ceilometer, it can be found that the new method is more stable and less affected by clouds.

In lanthanide-sensitized upconversion (UC) nanomaterials, the typical sensitizer Yb3+ can significantly modulate the codoped activator (such as Er3+ ions) to generate multiband transitions.

Here, we built a setup that can optically trap single microcrystals (MCs) in solution, and determine the time-resolved multiband upconversion luminescence at a nanosecond timescale.

Here, we successfully engineer microparticles through controlled interfacial self-assembly of polymers to harmonize ultrahigh drug loading with zero-order release of protein payloads. To address their poor miscibility with carrier materials, protein molecules are transformed into nanoparticles, whose surfaces are covered with polymer molecules. This polymer layer hinders the transfer of cargo nanoparticles from oil to water, achieving superior encapsulation efficiency (up to 99.9%). To control payload release, we enhance the polymer density at the oil-water interface, forming a compact shell for microparticles. The resultant microparticles could harvest up to 49.9% mass fraction of proteins with zero-order release kinetics in vivo, enabling an efficient glycemic control in type 1 diabetes. Moreover, the precise control of engineering process offered through continuous flow results in high batch-to-batch reproducibility and, ultimately, excellent scale-up feasibility.

Soil moisture plays an important role in ecology, hydrology, agriculture and climate change. This study proposes a soil moisture prediction model, based on the depth and water balance equation, which integrates the water balance equation with the seasonal ARIMA model, and introduces the depth parameter to consider the soil moisture at different depths. The experimental results showed that the model proposed in this study was able to provide a higher prediction accuracy for the soil moisture at 40 cm, 100 cm and 200 cm depths, compared to the seasonal ARIMA model. Different models were used for different depths. In this study, the seasonal ARIMA model was used at 10 cm, and the proposed model was used at 40 cm, 100 cm and 200 cm, from which more accurate prediction values could be obtained. The fluctuation of the predicted data has a certain seasonal trend, but the regularity decreases with the increasing depth until the soil moisture is almost independent of the external influence at a 200 cm depth. The accurate prediction of the soil moisture can contribute to the scientific management of the grasslands, thus promoting ecological stability and the sustainable development of the grasslands while rationalizing land use.

Cholesteryl ester (CE) accumulation is an essential signature of clear cell renal cell carcinoma (ccRCC), but its biological mechanism and pathological significance remain elusive. Here, enabled by label-free Raman spectromicroscopy, we confirmed aberrant CE accumulation in human ccRCC tissues but not in normal kidney tissues. To elucidate the underlying mechanism, commonly used ccRCC cell lines were quantitatively analyzed but unfortunately did not show detectable CEs. To overcome this problem, primary cancer cells were isolated from human tissues to retain the CE-rich phenotype. Our results further revealed that CE accumulation in ccRCC was induced by the mutation of tumor suppressor VHL and subsequent upregulation of the HIFα/PI3K/AKT/mTOR/SREBP pathway. Inspiringly, inhibition of cholesterol esterification remarkably suppressed ccRCC aggressiveness through the reduced membrane cholesterol-mediated downregulations of the integrin and MAPK signaling pathways. Collectively, our study improves current understanding of the role of CE accumulation in ccRCC and opens up new opportunities for treatment.SIGNIFICANCEEnabled by label-free Raman spectromicroscopy, our study elucidated the mechanism underlying CE accumulation, a key signature of ccRCC, which was induced by the mutation of tumor suppressor VHL and subsequent activation of the HIFα mediated PI3K/AKT/mTOR pathway. CE depletion remarkably impaired ccRCC aggressiveness, which opens a new avenue for treatment.

Water electrolysis is an efficient approach for high-purity hydrogen production. However, the anodic sluggish oxygen evolution reaction (OER) always needs high overpotential and thus brings about superfluous electricity cost of water electrolysis. Therefore, exploiting highly efficient OER electrocatalysts with small overpotential especially at high current density will undoubtedly boost the development of industrial water electrolysis. Herein, we used a simple hydrothermal method to prepare a novel FeOOH–CoS nanocomposite on nickel foam (NF). The as-prepared FeOOH–CoS/NF catalyst displays an excellent OER performance with extremely low overpotentials of 306 and 329 mV at 500 and 1000 mA cm−2 in 1.0 M KOH, respectively. In addition, the FeOOH–CoS/NF catalyst can maintain excellent catalytic stability for more than 50 h, and the OER catalytic activity shows almost no attenuation no matter after 1000 repeated CV cycles or 50 h of stability test. The high catalytic activity and stability have exceeded most non-noble metal electrocatalysts reported in literature, which makes the FeOOH–CoS/NF composite catalyst have promising applications in the industrial water electrolysis.

The control of cargo phase-transfer is of interest for many applications in science and technology. Herein, we report a simple, versatile and robust method to block the phase-transfer of cargo colloids by interfacial self-assembled amphiphilic polymer molecules. After simply increasing the concentration of amphiphilic polymers, the orientation of interfacial polymer molecules changed from flat to upright, forming a thick three-dimensional polymer layer at the oil-water interface. Even under fierce external force, this thick interfacial layer robustly prevented the phase-transfer of cargo colloids, resulting in an ultrahigh encapsulation efficiency (up to 97.1 %) for proteins and peptides. One single injection of high insulin-loaded microcomposites (58.3 wt%) kept the blood glucose level within the normoglycemic state for 10 days in type 1 diabetic rats. The mass of administrated amphiphilic polymers was 1889 times smaller than that of microcomposites prepared with non-amphiphilic ones.

dataset of extreme climate indices in Baoshan

The year-to-year variations of Tibetan Plateau (TP) summer rainfall have tremendous climate impacts on the adjoining and even global climate, attracting extensive research attention in recent decades to understand the underlying mechanism. In this study, we investigate an open question of how El Niño–Southern Oscillation (ENSO) influences the TP precipitation. We show that the developing ENSO has significant impacts on the summer rainfall over the southwestern TP (SWTP), which is the second EOF mode of the interannual variability of summer rainfall over the TP. The moisture budget indicates that both the suppressed vertical motion and the deficit of moisture contribute to the reduction of SWTP rainfall during El Niño’s developing summer, with the former contribution 4 times larger than the latter. Moist static energy analyses indicate that the anomalous advection of climatological moist enthalpy by anomalous zonal wind is responsible for the anomalous descending motions over the SWTP. The El Niño–related southward displacements of the South Asian high and the upper-level cyclonic anomalies over the west of TP stimulated by the suppressed Indian summer monsoon precipitation are two key processes dominating the anomalous zonal moist enthalpy advection over SWTP. Meanwhile, the India–Burma monsoon trough is strengthened during El Niño developing summer, which prevents the advection of water vapor into the SWTP, and thus contributes to the deficit of summer SWTP rainfall. Our results help to understand the complicated ENSO-related air–sea interaction responsible for the variability of TP precipitation and have implications for seasonal prediction of the TP climate.

Recurrent neural networks, especially the convolutional long short-term memory (ConvLSTM), have attracted plenty of attention and shown promising results due to their ability in modeling long-term dependencies in many research fields. In this paper, a lightweight tensor attention-driven ConvLSTM neural network (TACLNN) is proposed for hyperspectral image (HSI) classification. Firstly, to reduce the trainable parameters and memory requirements of ConvLSTM (specifically, the 2-D version of LSTM, i.e., ConvLSTM2D), a lightweight ConvLSTM2D cell is developed by utilizing tensor-train decomposition, resulting in a TT-ConvLSTM2D cell, with which a spatial-spectral TT-ConvLSTM 2-D neural network (SSTTCL2DNN) is built. However, it is inevitable for SSTTCL2DNN to obtain lower accuracies for HSI classification. To recover the accuracy loss caused by the TT-ConvLSTM2D cell in SSTTCL2DNN, a learnable tensor attention residual block (TARB) module is built to further enhance its geometrical structure. When applied to three widely used HSI benchmarks, the proposed TACLNN model outperforms several state-of-the-art methods for HSI classification. In addition, the proposed TACLNN can effectively reduce the number of parameters and storage requirements achieving higher classification accuracies as compared to other competitive baselines.

Recent studies indicate mammalian target of rapamycin (mTOR) may play an important role in PCa progression and drug resistance. Here, we investigated the effects of a novel mTORC1/C2 dual inhibitor, AZD2014, on naive and docetaxel (Doc)‐pre‐treated castration‐resistant PCa (CRPC) cells and explored its therapeutic potential in CRPCs. In the current study, AZD2014 has a greater inhibitory effect against 4EBP1 and AKT phosphorylation than rapamycin in CRPC cells and prevented the feedback activation of AKT signalling. Importantly, AZD2014 suppressed CRPC cell growth in vitro by suppressing proliferation, apoptosis, cell cycle arrest at G1 phase and autophagy to a greater extent than rapamycin. Moreover, AZD2014 was more efficacious than rapamycin in inhibiting migration, invasion and EMT progression in Doc‐sensitive and Doc‐resistant CRPC cells. Overall, AZD2014 showed significant antitumour effects. Thereby, the current study highlights a reliable theoretical basis for the clinical application of AZD2014 in both Doc‐sensitive and Doc‐resistant CRPCs.

Ferrimagnetic materials have recently been highly focused in the area of spintronics. In this work, epitaxial single-crystal ferrimagnetic Mn3Ga thin films with perpendicular magnetic anisotropy were fabricated by optimizing the sputtering growth temperature. The exchange coupling to a soft ferromagnetic CoFe layer, anisotropic magnetoresistance, the anomalous Hall effect, and the effect of measuring current on the zero-field anomalous Hall resistance were investigated. An anomalous anisotropic magnetoresistance was observed for this perpendicular magnetized material, which may provide evidence for the recently predicted Weyl points in ferrimagnetic Mn3Ga. In addition, this work may pave the way to current-induced magnetization switching assisted by Weyl-points-generated Berry curvature in a single ferrimagnetic layer under zero magnetic field.

An improved fuzzy method is proposed to derive a fuzzy number for characterizing uncertain wind power. The input measurement data are firstly converted into nested sets, and the fuzzy number is further obtained based on nested set transformation method. Numerical studies have demonstrated the effectiveness and advantages of the improved fuzzy method.

The development of low-cost, highly active and stable electrocatalysts for the oxygen evolution reaction (OER) is desirable and challenging. In this work, we report nickel foam (NF) supported low-crystalline NiCoFe nanosheet array electrocatalysts derived from NiCo layered double hydroxide via spontaneous cation exchange reaction. Driven by the redox properties of NiCo layered double hydroxide and iron salt precursors, the entire cation exchange reaction process is completed without extra energy input. The chemical composition and catalytic properties of the as-prepared NiCoFe/NF catalysts are strongly dependent on the valence state of the Fe cation in the iron salt precursors. The NiCoFe/NF–FeSO4 catalyst with FeSO4 as the iron salt precursor exhibits satisfactory OER activity with a much lower overpotential of 293 mV to reach a current density of 100 mA cm−2 as compared with NiCoFe/NF–Fe2(SO4)3 (η100 = 333 mV), NiCo/NF (η100 = 425 mV) and commercial RuO2 (η100 = 399 mV) catalysts. Moreover, NiCoFe/NF–FeSO4 also exhibits impressive long-term durability for the OER. The current density remains nearly unchanged during the 95 h of continuous OER test, demonstrating a promising application in actual industrial water electrolysis. These findings indicate that the iron ion valence states in the iron salt precursors have a significant impact on the composition of the catalysts and thus affect their catalytic performances. This work is instructive for the rational design and preparation of highly efficient Earth-abundant electrocatalysts for the OER.

The increasingly severe issue of antibiotic-induced pollution greatly stimulates the development of high-performance advanced adsorbents. In this contribution, a novel Fe-centered metal–organic aerogel (Fe-MOA) was synthesized through the use of bio-based ligand 2,5-furandicarboxylic acid (FDCA). Various characterization techniques including FT-IR, XPS, SEM, TEM, EDX and N2 adsorption–desorption analysis verified the successful preparation of the Fe-MOA with a hierarchically meso and macroporous network structure. The Fe-MOA was subsequently used as an adsorbent for the removal of tetracycline (TC) in aqueous solution. And the results suggested that the Fe-MOA exhibited an unprecedented adsorption capacity for TC with the record value of 1023 mg g−1. The adsorption equilibrium and isotherm data agreed well with the pseudo-second-order and Freundlich models, respectively. The mechanism investigations showed that the ultrahigh adsorption capability was synergistically driven by multiple affinities including electrostatic interaction, hydrogen bonding, surface complexation and π–π interactions between the conjugated groups in TC and the furan rings of the Fe-MOA. The recycling adsorption experiment results showed that the Fe-MOA retained 80% of the 1st-cycle adsorption capacity after being reused for 5 cycles, demonstrating the excellent reusability of Fe-MOA. Compared with other adsorbents, the ultrahigh TC adsorption capacity and excellent recycling performance make the Fe-MOA a favorable adsorbent candidate for TC pollution, highlighting the significant potential for solving practical TC-related pollution issues.

As a basic research topic in the field of remote sensing, semantic segmentation of high-resolution aerial images has broad application prospects. However, most existing semantic segmentation methods usually extract multiscale features of images in a hierarchical manner and fail to make full use of the surface information from high-resolution remote sensing images. To address the above problem, we propose a novel dual-channel scale-aware segmentation network with position and channel attentions (DSPCANet) for high-resolution aerial images, which contains an Xception branch and a digital surface model-based position and channel attention fusion (DSMPCF) branch to process the near-infrared, red, and green (IRRG) spectral images and DSM images, respectively. First, the inner residual block (R2_Block) module represents the multiscale features at the granularity level and increases the range of the receptive field of each network layer. Furthermore, channel attention module (CAM) and improved position attention module (IPAM) are developed to embed into the DSMPCF branch to learn the geographic feature representation from the DSM images, while the Xception branch is applied to process the IRRG spectral images. Finally, in the fusion part of the proposed model, IPAM and CAM are further utilized to effectively model the fusion features from the spatial and channel dimensions, obtain the class-based correlation, and recalibrate the class-level information. The proposed DSPCANet model is evaluated on the ISPRS Vaihingen and Potsdam datasets, and the extensive experiments demonstrate that it is more accurate and efficient than other state-of-the-art methods.

Phytohormones integrate external environmental and developmental signals with internal cellular responses for plant survival and multiplication in changing surroundings. Jasmonate (JA), which might originate from prokaryotes and benefit plant terrestrial adaptation, is a vital phytohormone that regulates diverse developmental processes and defense responses against various environmental stresses. In this review, we first provide an overview of ligand-receptor binding techniques used for the characterization of phytohormone-receptor interactions, then introduce the identification of the receptor COI1 and active JA molecules, and finally summarize recent advances on the regulation of JA perception and its evolution.

Vacuolar proteins play essential roles in plant physiology and development, but the factors and the machinery regulating their vesicle trafficking through the endomembrane compartments remain largely unknown. We and others have recently identified an evolutionarily conserved plant endosomal sorting complex required for transport (ESCRT)-associated protein apoptosis-linked gene-2 interacting protein X (ALIX), which plays canonical functions in the biogenesis of the multivesicular body/prevacuolar compartment (MVB/PVC) and in the sorting of ubiquitinated membrane proteins. In this study, we elucidate the roles and underlying mechanism of ALIX in regulating vacuolar transport of soluble proteins, beyond its conventional ESCRT function in eukaryotic cells. We show that ALIX colocalizes and physically interacts with the retromer core subunits Vps26 and Vps29 in planta. Moreover, double-mutant analysis reveals the genetic interaction of ALIX with Vps26 and Vps29 for regulating trafficking of soluble vacuolar proteins. Interestingly, depletion of ALIX perturbs membrane recruitment of Vps26 and Vps29 and alters the endosomal localization of vacuolar sorting receptors (VSRs). Taken together, ALIX functions as a unique retromer core subcomplex regulator by orchestrating receptor-mediated vacuolar sorting of soluble proteins.

Trifloxystrobin (TRI) and kresoxim-methyl (KRE), as quinone outside inhibitor fungicides (QoIs), have broad applications due to their effective activity against fungi. Excessive usages of agrochemicals trigger environmental risks, such as aquatic organisms (fish). Research performed in recent years has focused on the ecotoxicology of TRI and KRE in fish containing histologic morphology, enzyme activity, protein and gene expression under chronic toxicity conditions, whereas less is known about the underlying mechanisms of toxicity and differences between TRI and KRE in fish under acute toxicity conditions. In the present study, in comparison to different exposure routes [whole-body exposure (WBE), head exposure (HE), trunk exposure (TE), and Oral administration (OA)], the external substances TRI and KRE entered the fish body mainly via gill organs and led to fish toxicity. Furthermore, gill organs and gill cells were vulnerable to TRI and KRE exposure, which indicated that the gill is a vital impaired organ. The 96 h-LC

In view of the significance of food safety and the possible relationship between residual enrichment and acute toxicity for pesticides in different aquatic organisms, it is essential to establish a sensitive and reliable determination method for pesticides in different aquatic organisms to analyze the enrichment levels. Quinone outside inhibitor fungicides (QoIs) are lipophilic fungicides that pose environmental threats to aquatic organisms. Previous research has mainly focused on QoI residues in aquatic organisms under chronic toxicity, whereas less is known about how pesticide residues differ among aquatic organism under acutely toxic conditions. In the present study, the residues of QoIs in aquatic organisms (Danio rerio, Rana pipiens, Cherax quadricarinatus, Misgurnus anguillicaudatus, Corbicula fluminea, and Ampullaria gigas) were analyzed by ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) coupled with a proposed QuEChERS method. The proposed method was validated in terms of linearity (coefficients of determination of 0.9980-0.9999), the limits of quantification (0.01 μg·kg

A surface adsorption strategy is developed to enable the engineering of microcomposites featured with ultrahigh loading capacity and precise ratiometric control of co-encapsulated peptides. In this strategy, peptide molecules (insulin, exenatide, and bivalirudin) are formulated into nanoparticles and their surface is decorated with carrier polymers. This polymer layer blocks the phase transfer of peptide nanoparticles from oil to water and, consequently, realizes ultrahigh peptide loading degree (up to 78.9%). After surface decoration, all three nanoparticles are expected to exhibit the properties of adsorbed polymer materials, which enables the co-encapsulation of insulin, exenatide, and bivalirudin with a precise ratiometric control. After solidification of this adsorbed polymer layer, the release of peptides is synchronously prolonged. With the help of encapsulation, insulin achieves 8 days of glycemic control in type 1 diabetic rats with one single injection. The co-delivery of insulin and exenatide (1:1) efficiently controls the glycemic level in type 2 diabetic rats for 8 days. Weekly administration of insulin and exenatide co-encapsulated microcomposite effectively reduces the weight gain and glycosylated hemoglobin level in type 2 diabetic rats. The surface adsorption strategy sets a new paradigm to improve the pharmacokinetic and pharmacological performance of peptides, especially for the combination of peptides.