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.

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.

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.

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.

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.

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.

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.

Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion.

Infection caused by Fusarium head blight (FHB) has severely damaged the quality and yield of wheat in China and threatened the health of humans and livestock. Inaccurate disease detection increases the use cost of pesticide and pollutes farmland, highlighting the need for FHB detection in wheat fields. The combination of spectral and spatial information provided by image analysis facilitates the detection of infection-related damage in crops. In this study, an effective detection method for wheat FHB based on unmanned aerial vehicle (UAV) hyperspectral images was explored by fusing spectral features and image features. Spectral features mainly refer to band features, and image features mainly include texture and color features. Our aim was to explain all aspects of wheat infection through multi-class feature fusion and to find the best FHB detection method for field wheat combining current advanced algorithms. We first evaluated the quality of the two acquired UAV images and eliminated the excessively noisy bands in the images. Then, the spectral features, texture features, and color features in the images were extracted. The random forest (RF) algorithm was used to optimize features, and the importance value of the features determined whether the features were retained. Feature combinations included spectral features, spectral and texture features fusion, and the fusion of spectral, texture, and color features to combine support vector machine, RF, and back propagation neural network in constructing wheat FHB detection models. The results showed that the model based on the fusion of spectral, texture, and color features using the RF algorithm achieved the best performance, with a prediction accuracy of 85%. The method proposed in this study may provide an effective way of FHB detection in field wheat.

Laser-Generated Guided Waves for Damage Detection in Metal-Lined Composite-Overwrapped Pressure Vessels.

This paper characterizes laser-generated guided waves in a metal-lined composite-overwrapped pressure vessel (COPV) to assess typical damage, including interfacial debonding and low-velocity impact damage. First, an eigenfrequency approach that avoids additional coding is utilized to theoretically analyze the dispersion characteristics of a COPV. The theoretical results show that interfacial debonding significantly alters dispersion curves, and the wavenumber of the L(0, 1) mode is sensitive to impact damage. Experimental verifications were conducted based on the full wavefield acquired using a scanning laser-ultrasonic system with a repetition rate of 1 kHz. By comparing the experimental dispersion curves with the theoretical ones, it was found that the metal-composite interface was not bonded. In addition, a local wavenumber estimation method was established to detect the impact damage by obtaining the spatial distribution of the wavenumber of the L(0, 1) mode.

Intrinsically fluorescent polyureas toward conformation-assisted metamorphosis, discoloration and intracellular drug delivery.

Peptidomimetic polymers have attracted increasing interest because of the advantages of facile synthesis, high molecular tunability, resistance to degradation, and low immunogenicity. However, the presence of non-native linkages compromises their ability to form higher ordered structures and protein-inspired functions. Here we report a class of amino acid-constructed polyureas with molecular weight- and solvent-dependent helical and sheet-like conformations as well as green fluorescent protein-mimic autofluorescence with aggregation-induced emission characteristics. The copolymers self-assemble into vesicles and nanotubes and exhibit H-bonding-mediated metamorphosis and discoloration behaviors. We show that these polymeric vehicles with ultrahigh stability, superfast responsivity and conformation-assisted cell internalization efficiency could act as an "on-off" switchable nanocarrier for specific intracellular drug delivery and effective cancer theranosis in vitro and in vivo. This work provides insights into the folding and hierarchical assembly of biomacromolecules, and a new generation of bioresponsive polymers and nonconventional luminescent aliphatic materials for diverse applications.

The effects of topical iodine containing antiseptics on thyroidal status and early neurodevelopment of preterm infants.

OBJECTIVES: The goal of this study was to determine the quality of these disinfectants' effects on thyroid function and neurological scores in premature newborns aged 28 to 36 gestational weeks (GW). METHODS: This cohort study was conducted from October 2020 to September 2021 among 28-36 GW preterm infants at the neonatal care unit of Jiangnan University Hospital. We divided this 12 month period into two consecutive 6 month periods. Composite iodine disinfectants and alcohol are used for skin and umbilical cord disinfection of preterm infants, respectively. Urinary iodine concentration (UIC), thyroid hormone levels, and neonatal behavioral and neurological assessment (NBNA) scores were determined in both groups. RESULTS: A total of 126 patients were included in the study, 65 in the iodine exposed group and 61 in the alcohol group. The second UIC and the incidence of serum T4≤5 µg/dL and TSH≥10 mIU/L in the iodine exposed group were significantly higher than those in the alcohol group (p<0.05). The first NBNA score was lower in the iodine exposed group than in the alcohol group (p<0.05). However, whether it has clinical significance remains to be explored. There was a linear relationship between the two NBNA scores (iodine exposure group, R 2=0.344; alcohol group, R 2=0.227). No sepsis or other adverse outcomes occurred in the two groups of preterm infants after disinfection with different disinfectants. CONCLUSIONS: Iodine-containing disinfectants seem to have the potential to cause an increased rate of thyroid dysfunction and a decreased neurological score and should be evaluated in further studies.

Effectiveness of Recombinant Human Growth Hormone Therapy for Children With Phelan-McDermid Syndrome: An Open-Label, Cross-Over, Preliminary Study.

BACKGROUND: Phelan-McDermid syndrome (PMS), also known as the 22q13. 3 deletion syndrome, is a rare neurodevelopmental syndrome with approximately 2,800 patients reported worldwide. Previous pilot study demonstrated that IGF-1 could significantly improve in both social impairment and restrictive behaviors of the patients. However, most of the patients in the developing countries like China cannot afford the high cost of using IGF-1. Our research team speculated that rhGH might serve as a low-cost and more accessible treatment for PMS. Therefore, the purpose of this open-label, cross-over, pilot study was to further investigate the safety and efficiency of rhGH in patients with PMS. METHODS: A total of six children with PMS were enrolled in in this open-label, cross-over, pilot study. The children were randomly divided into two different groups. Group A received placebo followed by rhGH, while group B was treated with rhGH first. Neuropsychological and behavior assessments of the patients were performed before the stage I of study and 3 months after the intervention of stage I. After a 4-week period of washout, these assessments were conducted again before the stage II of study and 3 months after the intervention of stage II. Serum insulin-like growth factor-1 (IGF-1) and insulin-like growth factor binding-protein (IGFBP)-3 were also evaluated monthly during the intervention phases of the pilot study. RESULTS: Compared with the placebo, rhGH treatment significantly decreased subscale scores of GDS (P < 0.0085) and trended to improve the total scores of GDS (P < 0.05), while the total scores and subscale scores of SC-ABC significantly decreased (P < 0.0085) following 3-months rhGH treatment. The similar results were also observed in comparison with baseline. Compared with the baseline, the level of serum IGF-1 and IGFBP-3 increased significantly (P < 0.05) following 3-months rhGH treatment, while the placebo group had no significant impact on serum IGF-1 and IGFBP-3 (P > 0.05). One child developed skin allergy the day after the first rhGH treatment, which were resolved later. CONCLUSIONS: In summary, this pilot study involving six PMS children patients reveals that rhGH has a positive treatment effect on PMS. These results encourage the undertaking of a large, randomized placebo-controlled trial to conclusively prove rhGH efficacy and tolerability in PMS, thereby promoting it as a low-cost, more accessible treatment for PMS, as compared to IGF-1.

Determination of adulteration in wheat flour using multi-grained cascade forest-related models coupled with the fusion information of hyperspectral imaging.

Wheat flour (WF) is a common ingredient in staple foods. However, the presence of intentional or unintentional adulterants makes it difficult to guarantee WF quality. Multi-grained cascade forest (gcForest) model, a non-neural network deep learning structure, fused with image-spectra features from hyperspectral imaging (HSI) was employed for detecting adulterant type (peanut, walnut, or benzoyl peroxide) and the corresponding concentration (0.03%, 0.05%, 0.1%, 0.5%, 1%, and 2%). Based on the spectra of full wavelength and effective wavelength (EW) from hyperspectral images of WF samples, the gcForest-related models exhibited high performance (lowest ACCP = 92.45%) and stability (lowest area under the curve = 0.9986). Furthermore, the fusion of the EW and the image features extracted by the symmetric all convolutional neural network (SACNN) was used to establish the gcForest-related models. The maximum accuracy improvement of the fusion feature model relative to the single spectral model and the image model was 2.45% and 44.37%, respectively. The results indicate that the gcForest-related model, combined with the image-spectra fusion feature of HSI, provides an effective tool for detection in food and agriculture.

Fusion of hyperspectral imaging (HSI) and RGB for identification of soybean kernel damages using ShuffleNet with convolutional optimization and cross stage partial architecture.

Identification of soybean kernel damages is significant to prevent further disoperation. Hyperspectral imaging (HSI) has shown great potential in cereal kernel identification, but its low spatial resolution leads to external feature infidelity and limits the analysis accuracy. In this study, the fusion of HSI and RGB images and improved ShuffleNet were combined to develop an identification method for soybean kernel damages. First, the HSI-RGB fusion network (HRFN) was designed based on super-resolution and spectral modification modules to process the registered HSI and RGB image pairs and generate super-resolution HSI (SR-HSI) images. ShuffleNet improved with convolution optimization and cross-stage partial architecture (ShuffleNet_COCSP) was used to build classification models with the optimal image set of effective wavelengths (OISEW) of SR-HSI images obtained by support vector machine and ShuffleNet. High-quality fusion of HSI and RGB with the obvious spatial promotion and satisfactory spectral conservation was gained by HRFN. ShuffleNet_COCSP and OISEW obtained the optimal recognition performance of ACCp=98.36%, Params=0.805 M, and FLOPs=0.097 G, outperforming other classification methods and other types of images. Overall, the proposed method provides an accurate and reliable identification of soybean kernel damages and would be extended to analysis of other quality indicators of various crop kernels.

Industrial robot selection using a multiple criteria group decision making method with individual preferences.

This paper proposes a multiple criteria group decision making with individual preferences (MCGDM-IP) to address the robot selection problem (RSP). Four objective criteria elicitation approaches, namely, Shannon entropy approach, CRITIC approach, distance-based approach, and ideal-point approach, are proposed to indicate individual decision makers. A preliminary group decision matrix is therefore formulated. Both preferential differences representing the preference degrees among different robots, and preferential priorities representing the favorite ranking of robots for each individual decision maker, are analyzed to propose a revised group decision matrix. A satisfaction index is developed to manifest the merits of the proposed MCGDM-IP. An illustrative example using the data drawn from previous literature is conducted to indicate the effectiveness and validity of MCGDM-IP. The results demonstrate that the MCGDM-IP could generate a more satisfactory scheme to evaluate and select industrial robots, with an improvement of group satisfactory level as 2.12%.

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.