Intricate Ionic Behaviors in High-Performance Self-Powered Hydrothermal Chemical Generator Using Water and Iron (III) Gate.

Utilizing the ionic flux to generate voltage output has been confirmed as an effective way to meet the requirements of clean energy sources. Different from ionic thermoelectric (i-TE) and hydrovoltaic devices, a new hydrothermal chemical generator is designed by amorphous FeCl3 particles dispersing in MWCNT and unique ferric chloride or water gate. In the presence of gate, the special ion behaviors enable the cell to present a constant voltage of 0.60 V lasting for over 96 h without temperature difference. Combining the differences of cation concentration, humidity and temperature between the right and left side of sample, the maximum short-circuit current and power output can be obtained to 168.46 µA and 28.11 µW, respectively. The generator also can utilize the low-grade heat to produce electricity wherein Seebeck coefficient is 6.79 mV K-1. The emerged hydrothermal chemical generator offers a novel approach to utilize the low-grade heat, water and salt solution resources, which provides a simple, sustainable and low-cost strategy to realize energy supply.

Structure and Charge Regulation Strategy Enabling Superior Cyclability for Ni-Rich Layered Cathode Materials.

Ni-rich layered oxides are significantly promising cathode materials for commercial high-energy-density lithium-ion batteries. However, their major bottlenecks limiting their widespread applications are capacity fading and safety concerns caused by their inherently unstable crystal structure and highly reactive surface. Herein, surface structure and bulk charge regulation are concurrently achieved by introducing high-valence Ta5+ ions in Ni-rich cathodes, which exhibit superior electrochemical properties and thermal stability, especially a remarkable cyclic stability with a capacity retention of 80% for up to 768 cycles at a 1C rate versus Li/Li+ . Due to the partial Ta enrichment on surface, the regulated surface enables high reversibility of Li+ insertion/extraction by preventing surface Ni reduction in deep charging. Moreover, bulk charge regulation that boosts charge density and its localization on oxygen remarkably suppresses microcracks and oxygen loss, which in turn prevents the fragmentation of the regulated surface and structural degradation associated with oxygen skeleton. This study highlights the significance of an integrated optimization strategy for Ni-rich cathodes and, as a case study, provides a novel and deep insights into the underlying mechanisms of high-valence ions substitution of Ni-rich layered cathodes.

Novel compound heterozygous mutations of PCNT gene in MOPD type II with central precocious puberty.

We report on a 6-year and 11-month old girl with short stature, microcephaly, proboscis nose, small teeth, left breast Tanner stage II, and nasopharynx adenoid hypertrophy. Her gestational age was 37 weeks and birth weight was 800 g. Her growth hormone peak was higher than 35.2 ng/ml, luteinizing hormone peak 8.97 IU/l, and blood glucose of 120 min 7.82 mmol/l in oral glucose tolerance test. Genetic testing revealed two novel heterozygous mutations in the PCNT gene, an insertion mutation at c.1828dupT (p.S610Ffs*32), and a splice site mutation at c.1207 + 1G>A, which were inherited from healthy carrier patients. This case shows that MOPDII can be associated with central precocious puberty and impaired glucose tolerance in addition to intrauterine growth restriction, postpartum growth defect, and microcephaly.

Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) based on machine learning methods has been applied in material analysis, biological detection, food safety, and intelligent analysis. However, machine learning methods generally require extra preprocessing or feature engineering, and handling large-scale data using these methods is challenging. In this study, deep learning networks were used as fully connected networks, convolutional neural networks (CNN), fully convolutional networks (FCN), and principal component analysis networks (PCANet) to determine their abilities to recognise drugs in human urine and measure pirimiphos-methyl in wheat extract in the two input forms of a one-dimensional vector or a two-dimensional matrix. The best recognition result for drugs in urine with an accuracy of 98.05% in the prediction set was obtained using CNN with spectra as input in the matrix form. The optimal quantitation for pirimiphos-methyl was obtained using FCN with spectra in the matrix form, and the analysis was accomplished with a determination coefficient of 0.9997 and a root mean square error of 0.1574 in the prediction set. These networks performed better than the common machine learning methods. Overall, the deep learning networks provide feasible alternatives for the recognition and quantitation of SERS.

Diagnosis of the Severity of Fusarium Head Blight of Wheat Ears on the Basis of Image and Spectral Feature Fusion.

Fusarium head blight (FHB), one of the most prevalent and damaging infection diseases of wheat, affects quality and safety of associated food. In this study, to realize the early accurate monitoring of FHB, a diagnostic model of disease severity was proposed based on the fusion features of image and spectral features. First, the hyperspectral image of FHB infected in the range of the 400-1000 nm spectrum was collected, and the color parameters of wheat ear and spot region were segmented based on image features. Twelve sensitive bands were extracted using the successive projection algorithm, gray-scale co-occurrence matrix, and RGB color model. Four texture features were extracted from each feature band image as texture variables, and nine color feature variables were extracted from R, G, and B component images. Texture features with high correlation and color features were selected to participate in the final model building parameters via correlation analysis. Finally, the particle swarm optimization support vector machine (PSO-SVM) algorithm was used to build the model based on the diagnosis model of disease severity of FHB with different combinations of characteristic variables. The experimental results showed that the PSO-SVM model based on spectral and color feature fusion was optimal. Moreover, the accuracy of the training and prediction set was 95% and 92%, respectively. The method based on fusion features of image and spectral features can accurately and effectively diagnose the severity of FHB, thereby providing a technical basis for the timely and effective control of FHB and precise application of a pesticide.

Hyperspectral-based Estimation of Leaf Nitrogen Content in Corn Using Optimal Selection of Multiple Spectral Variables.

Accurate and dynamic monitoring of crop nitrogen status is the basis of scientific decisions regarding fertilization. In this study, we compared and analyzed three types of spectral variables: Sensitive spectral bands, the position of spectral features, and typical hyperspectral vegetation indices. First, the Savitzky-Golay technique was used to smooth the original spectrum, following which three types of spectral parameters describing crop spectral characteristics were extracted. Next, the successive projections algorithm (SPA) was adopted to screen out the sensitive variable set from each type of parameters. Finally, partial least squares (PLS) regression and random forest (RF) algorithms were used to comprehensively compare and analyze the performance of different types of spectral variables for estimating corn leaf nitrogen content (LNC). The results show that the integrated variable set composed of the optimal ones screened by SPA from three types of variables had the best performance for LNC estimation by the validation data set, with the values of R2, root means square error (RMSE), and normalized root mean square error (NRMSE) of 0.77, 0.31, and 17.1%, and 0.55, 0.43, and 23.9% from PLS and RF, respectively. It indicates that the PLS model with optimally multitype spectral variables can provide better fits and be a more effective tool for evaluating corn LNC.

Detection of Pirimiphos-Methyl in Wheat Using Surface-Enhanced Raman Spectroscopy and Chemometric Methods.

Pesticide residue detection is a hot issue in the quality and safety of agricultural grains. A novel method for accurate detection of pirimiphos-methyl residues in wheat was developed using surface-enhanced Raman spectroscopy (SERS) and chemometric methods. A simple pretreatment method was conducted to extract pirimiphos-methyl residue from wheat samples, and highly effective gold nanorods were prepared for SERS measurement. Raman peaks assignment was calculated using density functional theory. The Raman signal of pirimiphos-methyl can be detected when the concentrations of residue in wheat extraction solution and contaminated wheat is as low as 0.2 mg/L and 0.25 mg/L, respectively. Quantification of pirimiphos-methyl was performed by applying regression models developed by partial least squares regression, support vector machine regression and random forest with principal component analysis using different preprocessed methods. As for the contaminated wheat samples, the relative deviation between gas chromatography-mass spectrometry value and predicted value is in the range of 0.10%-6.63%, and predicted recovery is 94.12%-106.63%, ranging from 23.93 mg/L to 0.25 mg/L. Results demonstrated that the proposed SERS method is an effective and efficient analytical tool for detecting pirimiphos-methyl in wheat with high accuracy and excellent sensitivity.

The Dynamic Trends of Urinary LH and FSH Assayed by ICMA During Triptorelin Stimulation Tests in Girls - a Pilot Study.

BACKGROUND: Gonadotropin-releasing hormone stimulation test is a gold standard for evaluating the function of the hypothalamic-pituitary-gonadal axis (HPGA) in children. These tests are usually uncomfortable because of multi-venipunctures. A urine specimen is a good alternative because it is noninvasive and convenient. More studies have shown the correlation between sera and urine LH and FSH levels under different physiological and pathological conditions. METHODS: The study investigated the dynamic trends of urine LH (uLH) and FSH (uFSH) assayed by immunochemiluminometric assays (ICMA) during triptorelin stimulation tests in girls. The triptorelin stimulation tests were performed in 52 girls with disorders of puberty. The time 0 hour was regarded as the start time of the test (8:30 am). The day before the tests, urine samples were collected at 12 hours diurnal (-24 hours ~ -12 hours) and nocturnal (-12 hours ~ 0 hour) time points. On the day of the testing, the first 12 hours (0 hour ~ 12 hours), the second 12 hours (12 hours ~ 24 hours), the third 12 hours (24 hours ~ 36 hours), the fourth 12 hours (36 hours ~ 48 hours), the third and fourth overnight urine samples were also collected. The LH and FSH levels were assayed by ICMA, and uLH and uFSH were corrected for creatinine (Cr). RESULTS: The HPGA in 41 girls was activated but it was nonactivated in 11 girls. In girls with HPGA activated, uLH/Cr or uFSH/Cr was significantly elevated within 24 hours, and gradually dropped to baseline after 48 hours. When HPGA was nonactivated in girls, there were the same dynamic trends but much lower amplitude of uLH/Cr or uFSH/Cr, which dropped to baseline after 24 hours. CONCLUSIONS: The stimulated uLH and uFSH assayed by ICMA are valuable for evaluating the function of HPGA in girls, and the valuable time window is within 24 hours.

Fusion of Unmanned Aerial Vehicle Panchromatic and Hyperspectral Images Combining Joint Skewness-Kurtosis Figures and a Non-Subsampled Contourlet Transform.

To obtain fine and potential features, a highly informative fused image created by merging multiple images is usually required. In our study, a novel fusion algorithm called JSKF-NSCT is proposed for fusing panchromatic (PAN) and hyperspectral (HS) images by combining the joint skewness-kurtosis figure (JSKF) and the non-subsampled contourlet transform (NSCT). The JSKF model is used first to derive the three most sensitive bands from the original HS image according to the product of the skewness and the kurtosis coefficients of each band. Afterwards, an intensity-hue-saturation (IHS) transform is used to obtain the luminance component I of the produced false-colour image consisting of the above three bands. Then the NSCT method is used to decompose component I of the false-colour image and the PAN image. The weight-selection rule based on the regional energy is adopted to acquire the low-frequency coefficients and the correlation between the central pixel and its surrounding pixels is used to select the high-frequency coefficients. Finally, the fused image is obtained by applying an IHS inverse transform and an inverse NSCT transform. The unmanned aerial vehicle (UAV) HS and PAN images under low- and high-vegetation coverage of wheat at the flag leaf stage (Stage I) and the grain filling stage (Stage II) are used as the sample data sources. The fusion results are comparatively validated using spatial (entropy, standard deviation, average gradient and mean) and spectral (normalised difference vegetation, NDVI, and leaf area index, LAI) assessments. Additional comparative studies using anomaly detection and pixel clustering also demonstrate that the proposed method outperforms other methods. They show that the algorithm reported herein can better preserve the original spatial and spectral characteristics of the two types of images to be fused and is more stable than IHS, principal components analysis (PCA), non-negative matrix factorization (NMF) and Gram-Schmidt (GS).

Serum calcium and mortality in pediatric pneumonia patients admitted to the PICU: a retrospective cohort study.

This study investigates serum calcium's prognostic value in pediatric pneumonia, focusing on its correlation with PICU mortality, to enhance understanding and treatment approaches in this field. Data from 414 pediatric pneumonia patients (2010-2019) admitted to the intensive care units at the Children's Hospital, Zhejiang University School of Medicine were analyzed. The study utilized restricted cubic spline analysis, Cox proportional hazard regression, and Kaplan-Meier survival curve analysis to assess the relationship between serum calcium levels at admission and PICU mortality risk. After adjusting for multivariate factors, for each 1 mmol/dL increase in serum calcium, the risk of mortality decreased by 24% (HR: 0.76, 95% CI 0.67-0.87). Among the three levels of serum calcium groups, higher serum calcium levels were linked to a 63% reduction in the mortality rate compared to lower levels (HR: 0.37, 95% CI 0.16-0.84). The cumulative hazard estimates of mortality significantly differed across serum calcium groups (log-rank P = 0.032). This association was consistent across diverse subgroups (P for interaction > 0.05). Higher serum calcium levels are associated with decreased PICU mortality in pediatric pneumonia, highlighting its potential as a prognostic marker.

Necrotizing pneumonia secondary to Influenza A (H1N1) coinfection with Staphylococcus aureus: A case report.

Objective: This study aims to enhance understanding of necrotizing pneumonia and toxic shock syndrome by analyzing an adult case of community-acquired necrotizing pneumonia caused by co-infection of Influenza A (H1N1) and Staphylococcus aureus with LukS-PV and LukF-PV virulence factor genes. Method: The clinical data of one patient admitted to the intensive care unit (ICU) with co-infection of Influenza A (H1N1) and Staphylococcus aureus was retrospectively analyzed. Results: The patient exhibited typical clinical manifestations of viral and Staphylococcus aureus co-infection, including necrotizing pneumonia and toxic shock syndrome. The presence of LukS-PV and LukF-PV virulence factor genes of Staphylococcus aureus was detected in the patient's bronchoalveolar lavage fluid. Unfortunately，although antiviral agents (oseltamivir) and antibiotics (linezolid, imipenem-cilastatin) were timely administrated, as well as corticosteroids for anti-inflammatory purposes, the patient's condition was progressively deteriorated and eventually led to death. Conclusion: Clinical practitioners should be vigilant about the co-infection of Influenza virus and Staphylococcus aureus, particularly when the latter carries virulence factors. The presence of virulence factor genes of Staphylococcus aureus can lead to necrotizing pneumonia with a poor prognosis. This is a particular concern because both infections can be life threatening in young adults.

Relationship between serum plasminogen activator and D-dimer levels and the severity of Kawasaki disease in children as well as their predictive value for coronary artery lesion.

OBJECTIVE: To evaluate the relationships between serum plasminogen activator (PA) and D-dimer levels, the severity of Kawasaki disease (KD) in children, and their ability to predict coronary artery lesions (CAL). METHODS: This retrospective study analyzed the clinical data of 102 children diagnosed with KD at the Affiliated Hospital of Jiangnan University from January 2020 to September 2023. The cohort was divided into two groups: 31 children with CAL in the CAL group and 71 without it in the non-CAL group. The study assessed the incidence of CAL and investigated the correlations between serum PA and D-dimer levels and various inflammatory markers (white blood cell (WBC) count, platelet count, and erythrocyte sedimentation rate (ESR)). Receiver operating characteristic (ROC) curves were used to evaluate the predictive value of these biomarkers for CAL. RESULTS: CAL was present in 30.04% of the children. Pearson correlation analysis revealed that serum PA levels were inversely correlated with WBC count (P = 0.0187), platelet count (P = 0.0116), and ESR (P = 0.0041), while D-dimer levels were positively correlated with these markers (P < 0.001). A negative correlation between PA and D-dimer levels was also observed (P < 0.001). The combined use of PA and D-dimer levels to predict CAL achieved an area under the curve of 0.871. CONCLUSION: Serum PA levels were negatively associated with the severity of KD, whereas D-dimer levels were positively associated. Together, these markers showed significant predictive value for CAL, highlighting their utility in assessing disease severity and guiding management in children with KD.

Study on the Effect of Microwaved Brewer's Spent Grains on the Quality and Flavor Characteristics of Bread.

To enable a wider utilization of co-products from beer processing and minimize the negative effect of added grain on bread quality, flavor, and other attributes, brewer's spent grains (BSG) are processed through microwave pretreatment, and then the microwave-treated BSG (MW-BSG) is added to bread. So far, there has been no investigation on the effect of microwave-pretreated BSG on bread quality and flavor. In this study, we examined the effects of diverse microwave treatment variables on the physicochemical structure of BSG and explored the consequences of MW-BSG on the quality and flavor of bread. The results showed that soluble dietary fiber and water-soluble protein levels in MW-BSG increased significantly (144.88% and 23.35%) at a 540 W microwave power, 3 min processing time, and 1:5 material-liquid ratio of BSG to water. The proper addition of MW-BSG positively affected the bread texture properties and color, but excessive amounts led to an irregular size and distribution of the bread crumbs. The result of electronic nose and HS-SPME-GC-MS analyses showed that the addition of MW-BSG modified the odor profile of the bread. A sensory evaluation showed mean scores ranging from 6.81 to 4.41 for bread containing 0-10% MW-BSG. Consumers found a maximum level of 6% MW-BSG acceptable. This study endeavors to decrease environmental contamination caused by brewing waste by broadening the methods by which beer co-products can be utilized through an innovative approach.

Encapsulation of roast beef flavor by soy protein isolate/chitosan complex Pickering emulsions to improve its releasing properties during the processing of plant-based meat analogues.

During the production of plant-based meat analogues (PBMA), a significant loss of flavor characteristic compounds in meat-flavor essences could be observed. Pickering emulsion-based encapsulation is an effective method to improve their stability. Therefore, a soy protein isolate (SPI)/chitosan (CS) complex Pickering emulsion was fabricated to encapsulate roast beef flavor (RBF) and further applied in the processing of PBMA. Our results indicated that the network structure of emulsions was dominated by elasticity, while hydrogen and covalent bonding interactions played important roles in the encapsulation process. The release rate of flavor compounds gradually increased with the increase of pH value, glutamine transaminase, NaCl content, heating temperature or heating time, while encapsulation significantly reduced the loss of characteristic aroma compounds. In addition, the releasing characteristics of aroma compounds and textural properties of PBMA were greatly improved by treating with RBF-loaded emulsions. Consequently, the emulsions were promising to improve the flavor quality of PBMA.

The interstitial Ru dopant induces abundant Ni(Fe)Ru cooperative sites to promote ampere-level current density for overall water splitting.

Directionally induced interstitial Ru dopant rather than ordinary substitutional doping is a challenge. Furthermore, DFT calculations revealed that compared with the substituted Ru dopants, the interstitial Ru dopants induce abundant Ni(Fe)Ru cooperative sites, greatly expediting the reaction kinetics for HER and OER. Inspired by these, the interstitial Ru-doped NiFeP/NF electrode is constructed by the 'quenching doped Ru-phosphorization' strategy. Relevant physical characterizations confirmed that interstitial Ru dopants promote electron reset in the Ni(Fe)Ru synergistic sites, effectively avoiding metal atom dissolution and encouraging more Ni (Fe)OOH active species. As expected, the Ru-NiFeP/NF||Ru-NiFeP/NF electrolyzer only need as low as 1.54 V to yield a current density of 1 A cm-2. In summary, this work innovatively constructs the phosphide electrode with ampere-level current density from the perspective of regulating the doping position of Ru. This provides a new design idea for optimizing the Ru doping strategy.

Identification of surface-enhanced Raman spectroscopy using hybrid transformer network.

Surface-enhanced Raman Spectroscopy (SERS) is extensively implemented in drug detection due to its sensitivity and non-destructive nature. Deep learning methods, which are represented by convolutional neural network (CNN), have been widely applied in identifying the spectra from SERS for powerful learning ability. However, the local receptive field of CNN limits the feature extraction of sequential spectra for suppressing the analysis results. In this study, a hybrid Transformer network, TMNet, was developed to identify SERS spectra by integrating the Transformer encoder and the multi-layer perceptron. The Transformer encoder can obtain precise feature representations of sequential spectra with the aid of self-attention, and the multi-layer perceptron efficiently transforms the representations to the final identification results. TMNet performed excellently, with identification accuracies of 99.07% for the spectra of hair containing drugs and 97.12% for those of urine containing drugs. For the spectra with additive white Gaussian, baseline background, and mixed noises, TMNet still exhibited the best performance among all the methods. Overall, the proposed method can accurately identify SERS spectra with outstanding noise resistance and excellent generalization and holds great potential for the analysis of other spectroscopy data.

A novel strategy for estimating biomass of submerged aquatic vegetation in lake integrating UAV and Sentinel data.

Submerged aquatic vegetation (SAV) plays a fundamental ecological role in mediating carbon cycling within lakes, and its biomass is essential to assess the carbon sequestration potential of lake ecosystems. Remote sensing (RS) offers a powerful tool for large-scale SAV biomass retrieval. Given the underwater location of SAV, the spectral signal in RS data often exhibits weakness, capturing primarily horizontal structure rather than volumetric information crucial for biomass assessment. Fortunately, easily-measured SAV coverage can serve as an intermediary variable for difficultly-quantified SAV biomass inversion. Nevertheless, obtaining enough SAV coverage samples matching satellite image pixels for robust model development remains problematic. To overcome this challenge, we employed a UAV to acquire high-precision data, thereby replacing manual SAV coverage sample collection. In this study, we proposed an innovative strategy integrating unmanned aerial vehicle (UAV) and satellite data to invert large-scale SAV coverage, and subsequently estimate the biomass of the dominant SAV population (Potamogeton pectinatus) in Ulansuhai Lake. Firstly, a coverage-biomass model (R2 = 0.93, RMSE = 0.8 kg/m2) depicting the relationship between SAV coverage and biomass was developed. Secondly, in a designed experimental area, a high-precision multispectral image was captured by a UAV. Based on the Normalized Difference Water Index (NDWI), the UAV-based image was classified into non-vegetated and vegetated areas, thereby generating an SAV distribution map. Leveraging spatial correspondence between satellite pixels and the UAV-based SAV distribution map, the proportion of SAV within each satellite pixel, referred to as SAV coverage, was computed, and a coverage sample set matched with satellite pixels was obtained. Subsequently, based on the sample set, a satellite-scale SAV coverage estimation model (R2 = 0.78, RMSE = 14.05 %) was constructed with features from Sentinel-1 and Sentinel-2 data by XGBoost algorithm. Finally, integrating the coverage-biomass model with the obtained coverage inversion results, fresh biomass of SAV in Ulansuhai Lake was successfully estimated to be approximately 574,600 tons.

A group decision making method with preference analysis to re-build the Global Entrepreneurship Index.

This study proposes a group decision making (GDM) method with preference analysis to re-build the Global Entrepreneurship Index (GEI). Specifically, a single decision maker is firstly identified using a specified individual judgement about the importance order of three sub-indices of the GEI. A preliminary group decision matrix is constructed in terms of taking all possible individual judgments into account. Then the analysis of the preferential differences and preferential priorities with respect to the preliminary group decision matrix is conducted to obtain a revised group decision matrix, in which preferential differences calculate the weighted differences as the degrees of differences among different alternatives for each decision maker, preferential priorities describe the favorite ranking of alternatives for each decision maker. Finally, we employ the Stochastic Multicriteria Acceptability Analysis for group decision-making (SMAA-2) to create the holistic acceptability indices for measuring the entrepreneurship performance. In addition, a satisfaction index is developed to indicate the merits of proposed GDM method. A case study using the GEI-2019 of 19 G20 countries is carried out to validate our GDM method.

Drought stress identification of tomato plant using multi-features of hyperspectral imaging and subsample fusion.

Drought stress (DS) is one of the most frequently occurring stresses in tomato plants. Detecting tomato plant DS is vital for optimizing irrigation and improving fruit quality. In this study, a DS identification method using the multi-features of hyperspectral imaging (HSI) and subsample fusion was proposed. First, the HSI images were measured under imaging condition with supplemental blue lights, and the reflectance spectra were extracted from the HSI images of young and mature leaves at different DS levels (well-watered, reduced-watered, and deficient-watered treatment). The effective wavelengths (EWs) were screened by the genetic algorithm. Second, the reference image was determined by ReliefF, and the first four reflectance images of EWs that are weakly correlated with the reference image and mutually irrelevant were obtained using Pearson's correlation analysis. The reflectance image set (RIS) was determined by evaluating the superposition effect of reflectance images on identification. The spectra of EWs and the image features extracted from the RIS by LeNet-5 were adopted to construct DS identification models based on support vector machine (SVM), random forest, and dense convolutional network. Third, the subsample fusion integrating the spectra and image features of young and mature leaves was used to improve the identification further. The results showed that supplemental blue lights can effectively remove the high-frequency noise and obtain high-quality HSI images. The positive effect of the combination of spectra of EWs and image features for DS identification proved that RIS contains feature information pointing to DS. Global optimal classification performance was achieved by SVM and subsample fusion, with a classification accuracy of 95.90% and 95.78% for calibration and prediction sets, respectively. Overall, the proposed method can provide an accurate and reliable analysis for tomato plant DS and is hoped to be applied to other crop stresses.

Redox-dual-sensitive multiblock copolymer vesicles with disulfide-enabled sequential drug delivery.

Based on disulfide-enriched multiblock copolymer vesicles, we present a straightforward sequential drug delivery system with dual-redox response that releases hydrophilic doxorubicin hydrochloride (DOX·HCl) and hydrophobic paclitaxel (PTX) under oxidative and reductive conditions, respectively. When compared to concurrent therapeutic delivery, the spatiotemporal control of drug release allows for an improved combination antitumor effect. The simple and smart nanocarrier has promising applications in the field of cancer therapy.