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Metal-organic frameworks (MOFs) hold promise as theranostic carriers for atherosclerosis. However, to further advance their therapeutic effects with higher complexity and functionality, integrating multiple components with complex synthesis procedures are usually involved. Here, we reported a facile and general strategy to prepare multifunctional anti-atherosclerosis theranostic platform in a single-step manner. A custom-designed multifunctional polymer, poly(butyl methacrylate-co-methacrylic acid) branched phosphorylated β-glucan (PBMMA-PG), can effectively entrap different MOFs via coordination, simultaneously endow the MOF with enhanced stability, lesional macrophages selectivity and enhanced endosome escape. Sequential ex situ characterization and computational studies elaborated the potential mechanism. This facile post-synthetic modification granted the administered nanoparticles atherosclerotic tropism by targeting Dectin-1+ macrophages, enhancing in situ MR signal intensity by 72 %. Delivery of siNLRP3 effectively mitigated NLRP3 inflammasomes activation, resulting a 43 % reduction of plaque area. Overall, the current study highlights a simple and general approach for fabricating a MOF-based theranostic platform towards atherosclerosis conditioning, which may also expand to other indications targeting the lesional macrophages.

Abstract To address the design challenge of the rock-socketed piles posed by the void located below the pile tip, the physical laboratory model tests were designed and performed to simulate rock socketed piles using similar materials. The study investigates the behavior of the single pile under axial loading with the void located at varying distances from the pile tip. Through multi-level load tests, the variations of unit pile side friction, pile tip resistance, pile axial force and pile settlement are obtained for different positions of the void from the pile tip, as well as after grouting. Its comparison to the rock-socketed pile without void is performed as a reference to quantify the reduction in its bearing capacity. The results are presented in the form of graphs for different void positions and its grouting shows the influence on pile bearing capacity and emphasizes the importance of its detailed cautious investigation and introduction in the analysis. The 2D finite element modeling of the model pile-the void based on ABAQUS is performed to further investigate the influence of the void below pile tip on the bearing capacity of model pile, applying the Mohr Coulomb model as the constitutive model of rock mass behavior. The critical distance of the void below the pile tip is determined.

IntroductionMalignancy-related ascites (MRA) is a common serious complication of many advanced malignant tumors with high morbidity and mortality. The high hospital expenditures induced by unplanned readmission in patients with MRA have become an urgent issue to the public. We aimed to overall assess the unplanned early readmission rate of patients with MRA and explore the potential risk factors for such readmission.MethodsA retrospective cohort study based on 2018 Nationwide Readmissions Database was performed and patients with MRA were recruited into the analysis. The primary outcome was unplanned 30-day readmission rate and inpatient outcomes. The multivariate logistic regression analysis was performed to evaluate the potential risk factors for such early readmission.ResultsData obtained from 32,457 patients with MRA were analyzed, and of these 7,799 individuals (24.03%) were unplanned readmitted within 30-day follow-up. The mortality rate in the readmitted population was 15.15%. Patients at younger age were at a higher risk of readmission. The morbidities including hypertension (OR=1.117, 95%CI: 1.054-1.184), hyperlipemia (OR=1.075, 95%CI: 1.009-1.146) and diabetes (OR=1.118, 95%CI: 1.053-1.188), gastrointestinal malignancies and peritoneal procedure significantly increased the risk of 30-day readmission in patients with MRA.DiscussionMore than one in five patients with MRA was unplanned readmitted within 30-day follow-up. The above risk factors should be timely intervened and the corresponding medical care should be strengthened in patients with MRA to lessen the unplanned readmission and improve the readmission outcomes.

The advantages of field-assisted laser additive manufacturing in optimizing microstructure and performance are increasingly recognized. Recently, we investigated the effects of acoustic field (AF) assisted laser powder bed fusion (LPBF) on the mechanical properties of 316L stainless steel (SS) and 316L SS/WC composite materials. Due to the effects of acoustic streaming and cavitation, acoustic field-assisted LPBF achieved optimized mechanical properties. Building on our previous research on AF-assisted LPBF, we introduced magnetic field (MF) and compound field (MF+AF) to study their effects on the microstructure and mechanical properties of 316L SS, 1 wt% and 3 wt% WC/316L SS. Application of the MF induces induced currents in the melt pool due to the Thomson-Seebek effect. The interaction between the MF and induced currents generates thermoelectric magnetic force (TEMF) and thermoelectric magnetic convection (TEMC), which inhibit the growth of columnar grains and enhance mass and heat transfer in the melt pool. Compound field-assisted LPBF reduced the dislocation density of 316L SS, refined grain size, alleviated the concentration distribution of grain orientation, and the ultimate tensile strength of the samples reached 777 MPa and the elongation at break reached 57.5%. Interestingly, for WC/316L SS, both MF and compound field resulted in a decrease in tensile performance, with ultimate tensile strengths as low as 677 MPa and elongations at break as low as 25.1%. This is because the MF affects the uniform distribution of WC particles, causing significant agglomeration and severe unmelted defects in the composite material, leading to a decrease in tensile performance.

To enhance the computation efficiency and accuracy of three-dimensional steady temperature field of transformer windings, we propose a new non-invasive Reduced Order Model (ROM) based on a mechanism-embedded cascade network. Initially, a snapshot matrix is formed from the Full Order Model (FOM) and then combined with Proper Orthogonal Decomposition (POD) to extract key modal features that characterize the temperature field. Subsequently, a cascade network architecture, integrating Multilayer Perceptron (MLP) and Radial Basis Function Neural Network (RBFNN), is devised to swiftly map working condition parameters to modal coefficients. Additionally, the cascade network is embedded with condition sensitivity and modal contribution mechanisms to further enhance prediction accuracy. Finally, by linearly weighting the modes with predicted modal coefficients, a rapid reconstruction of the steady temperature field in transformer windings is achieved. Validation against Fluent software simulations and experimental measurements demonstrate a close agreement, with computational errors of less than 4K and an impressive single solution time of only 0.0087 s, which is 48760 times faster compared to Fluent software.

Abstract As the core component of solid-state batteries, neither current inorganic solid-state electrolytes nor solid polymer electrolytes can simultaneously possess satisfactory ionic conductivity, electrode compatibility and processability. By incorporating efficient Li+ diffusion channels found in inorganic solid-state electrolytes and polar functional groups present in solid polymer electrolytes, it is conceivable to design inorganic-organic hybrid solid-state electrolytes to achieve true fusion and synergy in performance. Herein, we demonstrate that traditional metal coordination compounds can serve as exceptional Li+ ion conductors at room temperature through rational structural design. Specifically, we synthesize copper maleate hydrate nanoflakes via bottom-up self-assembly featuring highly-ordered 1D channels that are interconnected by Cu2+/Cu+ nodes and maleic acid ligands, alongside rich COO− groups and structural water within the channels. Benefiting from the combination of ion-hopping and coupling-dissociation mechanisms, Li+ ions can preferably transport through these channels rapidly. Thus, the Li+-implanted copper maleate hydrate solid-state electrolytes shows remarkable ionic conductivity (1.17 × 10−4 S cm−1 at room temperature), high Li+ transference number (0.77), and a 4.7 V-wide operating window. More impressively, Li+-implanted copper maleate hydrate solid-state electrolytes are demonstrated to have exceptional compatibility with both cathode and Li anode, enabling long-term stability of more than 800 cycles. This work brings new insight on exploring superior room-temperature ionic conductors based on metal coordination compounds.

Oil-pressboard/paper insulation materials are essential in transformers for ensuring their safe and stable operation, primarily due to their roles in spatial electric field distribution and charge migration mechanisms. Current spatial distribution analyses rely on computational methods that lack empirical validation, particularly for oil-pressboard/paper composites. This study leverages the principles of the Kerr electro-optic effect to develop a rapid measurement platform for electric fields within oil-pressboard/paper insulation under impulse voltage conditions, which measures the spatial electric field characteristics using Cu-Cu and Al-Al electrodes under various scenarios: with asymmetric and symmetric pressboard coverage and different numbers of insulating paper layers. Findings indicated: (1) In asymmetric pressboard models, Cu-Cu electrodes exhibit a consistent peak electric field of approximately 16 kV/mm, while Al-Al electrodes show peak values of 18.13 kV/mm and −14.98 kV/mm. Charge density patterns are similar, with Cu-Cu at about 68 μC/m2 and Al-Al at 11.2 μC/m2 and −124.8 μC/m2. (2) Symmetric models present consistent peak electric fields and charge densities for both polarities. (3) Increasing insulating paper layers elevates electric field strengths. Both electrodes show the similar peak field of about 17 kV/mm with differing paper layers due to higher charge injection from the Al electrode. (4) Utilizing the Schottky effect and field emission principles, the study clarifies charge generation and migration mechanisms. These insights could provide a theoretical foundation for designing and verifying oil-pressboard/paper insulation structures in transformers.

The composition of the gut microbiota varies among end-stage renal disease (ESRD) patients on the basis of their mode of renal replacement therapy (RRT), with notably more pronounced dysbiosis occurring in those undergoing hemodialysis (HD). Interventions such as dialysis catheters, unstable hemodynamics, strict dietary restrictions, and pharmacotherapy significantly alter the intestinal microenvironment, thus disrupting the gut microbiota composition in HD patients. The gut microbiota may influence HD-related complications, including cardiovascular disease (CVD), infections, anemia, and malnutrition, through mechanisms such as bacterial translocation, immune regulation, and the production of gut microbial metabolites, thereby affecting both the quality of life and the prognosis of patients. This review focuses on alterations in the gut microbiota and its metabolites in HD patients. Additionally, understanding the impact of the gut microbiota on the complications of HD could provide insights into the development of novel treatment strategies to prevent or alleviate complications in HD patients.

Abstract We aimed to develop a prognostic model for primary pontine hemorrhage (PPH) patients and validate the predictive value of the model for a good prognosis at 90 days. A total of 254 PPH patients were included for screening of the independent predictors of prognosis, and data were analyzed by univariate and multivariable logistic regression tests. The cases were then divided into training cohort (n=219) and validation cohort (n=35) based on the two centers. A nomogram was developed using independent predictors from the training cohort to predict the 90-day good outcome and was validated from the validation cohort. Glasgow Coma Scale score, normalized pixels (used to describe bleeding volume), and mechanical ventilation were significant predictors of a good outcome of PPH at 90 days in the training cohort (all P

In recent years, with the accelerated development of technology towards automation and intelligence, autonomous decision-making capabilities in unmanned systems are poised to play a crucial role in contemporary aerial two-player zero-sum games (TZSGs). Deep reinforcement learning (DRL) methods enable agents to make autonomous manoeuvring decisions. This paper focuses on current mainstream DRL algorithms based on fundamental tactical manoeuvres, selecting a typical aerial TZSG scenario—within visual range (WVR) combat. We model the key elements influencing the game using a Markov decision process (MDP) and demonstrate the mathematical foundation for implementing DRL. Leveraging high-fidelity simulation software (Warsim v1.0), we design a prototypical close-range aerial combat scenario. Utilizing this environment, we train mainstream DRL algorithms and analyse the training outcomes. The effectiveness of these algorithms in enabling agents to manoeuvre in aerial TZSG autonomously is summarised, providing a foundational basis for further research.

BackgroundRadiation proctitis is a common complication that occurs as a result of radiation therapy used to treat pelvic malignancies. The most common and bothersome symptom resulting from radiation proctitis is rectal bleeding, which can be persistent or recurrent. This study aimed to review our experience and evaluate the efficacy and safety of transcolonoscopic spraying of formalin solution in patients with hemorrhagic radiation proctitis.MethodsA total of 37 patients with hemorrhagic radiation proctitis, aged between 48 and 79 years (mean age 62.56 ± 8.48 years), were divided into three cohorts based on the severity of radiation injury. Under direct endoscopic vision, a 4% formalin solution was applied directly to the rectal hemorrhagic mucosa. The patients were followed for a period of over 6 months after receiving treatment, during which the therapeutic effectiveness and occurrence of complications were observed.ResultsThe study resulted in an overall response rate of 89.2% among all patients. The response rates for patients with grades 1–3 were 100, 100, and 66.7%, respectively. Notably, the rate of response among patients with grade 3 radiation injury was significantly lower compared to those with grades 1–2 (p = 0.009). Mild adverse reactions, such as anal pain and tenesmus, were reported in a small number of patients but could be alleviated without any intervention.ConclusionThe endoscopic application of formalin solution for the treatment of hemorrhagic radiation proctitis has shown a significant effect, particularly in patients with grades 1–2 radiation injury. The observed effect is superior to that observed in patients with grade 3 radiation injury.

Background: The influence of white matter hyperintensity (WMH) on clinical outcomes in acute ischemic stroke (AIS) patients treated with mechanical thrombectomy (MT) remains controversial. We performed a systematic review and meta-analysis to examine whether WMH burden is associated with clinical outcomes in AIS patients after MT. Methods: PubMed, Embase, and Web of Science were searched from inception to Sep 03, 2023. The registration number for PROSPERO is CRD42022340568. Studies reporting an association between the burden of WMH in AIS patients and clinical outcomes after MT were included in the meta-analysis. A random-effects model was used for meta-analysis. The quality of the included studies was assessed using the Newcastle-Ottawa Scale. Additionally, the presence of imprecise-study effects was evaluated using Egger's test and funnel plot. Results: Fifteen studies with 3,456 patients were enrolled in this meta-analysis. Among AIS patients who underwent MT, moderate/severe WMH had higher odds of 90-day unfavorable functional outcomes (odds ratio [OR] 2.72, 95% confidence interval [CI] 2.14-3.44; I2 = 0.0%; 95% CI 0.0%-42.7%), 90-day mortality (OR 1.94, 95% CI 1.45-2.60; I2 = 19.5%; 95% CI 0.0%-65.2%) and futile recanalization (OR 2.99, 95% CI 1.42-6.28; I2 = 69.7%; 95% CI 0.0%-91.0%) compared with none/mild WMH. However, the two groups had no significant difference in successful recanalization, symptomatic hemorrhagic transformation, and hemorrhagic transformation. A subset analysis of patients from 3 articles showed that WMH volume was not significantly associated with these outcomes. A notable limitation is that this meta-analysis lacks direct adjustment for imbalances in important baseline covariates. Conclusions: Patients with moderate/severe WMH on baseline imaging are associated with substantially increased odds of 90-day unfavorable outcomes, futile recanalization, and 90-day mortality after MT. This association suggests that moderate/severe WMH may contribute to the prediction of clinical outcomes in AIS patients after MT.

Ultra-high molecular weight polyethylene (UHMWPE) fiber is a new kind of high-performance fiber. Due to its excellent physical and chemical characteristics, it is widely used in various fields. However, the surface UHMWPE fiber is smooth and demonstrates no-polar groups. The weak interfacial adhesion between fiber and resin seriously restricts the applications of UHMWPE fiber. Therefore, the surface modification treatments of UHMWPE fiber are used to improve the interfacial adhesion strength. The modified method by adding nanomaterials elucidates the easy fabrication, advanced equipment and proper technology. Thus, the progress of UHMWPE nanocomposite fibers prepared via adding various nanofillers are reviewed. Meanwhile, the effects of other various methods on surface modification are also reviewed. This work advances the various design strategies about nano technologies on improving interfacial adhesion performance via treatment methodologies.

Summary: Background: Glibenclamide alleviates brain edema and improves neurological outcomes in experimental models of stroke. We aimed to assess whether glibenclamide improves functional outcomes in patients with acute ischemic stroke treated with recombinant tissue plasminogen activator (rtPA). Methods: In this randomized, double-blind, placebo-controlled trial, patients with acute ischemic stroke were recruited to eight academic hospitals in China. Patients were eligible if they were aged 18–74 years, presented with a symptomatic anterior circulation occlusion with a deficit on the NIHSS of 4–25, and had been treated with rtPA within 4.5 h of symptom onset. We used web-based randomization (1:1) to allocate eligible participants to the glibenclamide or placebo group, stratified according to endovascular treatment and baseline stroke severity. Glibenclamide or placebo was taken orally or via tube feeding at a loading dose of 1.25 mg within 10 h after symptom onset, followed by 0.625 mg every 8 h for 5 days. The primary outcome was the proportion of patients with good outcomes (modified Rankin Scale of 0–2) at 90 days, assessed in all randomly assigned patients who had been correctly diagnosed and had begun study medication. The study is registered with ClinicalTrials.gov, NCT03284463, and is closed to new participants. Findings: Between January 1, 2018, and May 28, 2022, 305 patients were randomly assigned, of whom 272 (142 received glibenclamide and 130 received placebo) were included in the primary efficacy analysis. 103 (73%) patients in the glibenclamide group and 94 (72%) in the placebo group had a good outcome (adjusted risk difference 0.002, 95% CI −0.098 to 0.103; p = 0.96). 12 (8%) patients allocated to glibenclamide and seven (5%) patients allocated to placebo died from any cause at 90 days (p = 0.35). The number and type of adverse events were similar between the two groups. There were no drug-related adverse events and no drug-related deaths. Interpretation: The addition of glibenclamide to thrombolytic therapy did not increase the proportion of patients who achieved good outcomes after stroke compared with placebo, but it did not lead to any safety concerns. Funding: Southern Medical University and Nanfang Hospital.

Objectives Neutrophil-to-lymphocyte ratio (NLR) is a simple parameter implying the inflammatory status. We aimed to explore the association of brain-dead donor NLR change with delayed graft function (DGF) in kidney transplant recipients.Methods We retrospectively analyzed the data on 102 adult brain-dead donors and their corresponding 199 kidney transplant recipients (2018 − 2021). We calculated ΔNLR by subtracting the NLR before evaluating brain death from the preoperative NLR. Increasing donor NLR was defined as ΔNLR > 0.Results Forty-four (22%) recipients developed DGF after transplantation. Increasing donor NLR was significantly associated with the development of DGF in recipients (OR 2.8, 95% CI 1.2 − 6.6; p = .018), and remained significant (OR 2.6, 95% CI 1.0 − 6.4; p = .040) after adjustment of confounders including BMI, hypertension, diabetes, and the occurrence of cardiac arrest. When acute kidney injury (AKI) was included in the multivariable analysis, increasing donor NLR lost its independent correlation with DGF, while AKI remained an independent risk factor of recipient DGF (OR 4.5, 95% CI 2.7 − 7.6; p

Background Hypertension is a leading preventable risk factor for cardiovascular disease in hemodialysis patients. Pre-dialysis systolic blood pressure (SBP) more than 160 mmHg was thought to be associated with increased risk of cardiovascular events and all-cause mortality. The present study was performed to explore the clinical characteristics and management of hemodialysis patients with pre-dialysis SBP ≥ 160 mmHg.Methods A total of 1233 patients undergoing hemodialysis from nine hemodialysis centers were enrolled. Pre-dialysis and home BP were measured and clinical data were collected. The characteristics of patients with pre-dialysis SBP ≥ 160 mmHg were explored. Clinical parameters between hypertensive and non-hypertensive patients were compared. The partial correlation analyses performed to identify the associations between BP and clinical parameters.Results There were 24.6% of the hemodialysis patients had pre-dialysis SBP ≥ 160 mmHg and the average SBP was 173.8 ± 10.9 mmHg. Only 21.4% of the patients achieved dry weight after dialysis and up to 30.2% of patients were not given combination therapies of antihypertensive drugs. Compared to patients with pre-hemodialysis SBP < 160 mmHg, patients with pre-dialysis SBP ≥ 160 mmHg had lower target-reaching rate of Kt/v and higher incidences of intradialytic hypotension and muscle spasm. Most patients (96%) with pre-dialysis SBP ≥ 160 mmHg had home SBP≥ 135 mmHg. Patients with home SBP ≥ 160 mmHg had higher left ventricular weight index and lower hemoglobin levels when compared to their counterparts with home SBP

Abstract Although the oxygen reduction process to hydrogen peroxide (H2O2) is a green option for H2O2 generation, the low activity and selectivity hindered the industry's process. In recent years, the electrochemical synthesis of H2O2 through a 2e– transfer method of oxygen reduction reaction (ORR) has piqued the interest of both academics and industry. Metal oxide catalysts have emerged as a novel family of electrochemical catalysts due to their unusual physical, chemical, and electrical characteristics. In this work, we first developed a Ruddlesden–Popper perovskite oxide (Pr2NiO4+δ) as a highly selective and active catalyst for 2e– ORR to produce H2O2. Molybdenum was introduced here to adjust the oxidation states of these transition metals with successful substitution into Ni‐site to prepare Pr2Ni1‐xMoxO4+δ, and the molybdenum substitution improves the H2O2 selectivity during the ORR process, in 0.1 M KOH, from 60% of Pr2NiO4+δ to 79% of Pr2Ni0.8Mo0.2O4+δ at 0.55 V versus RHE. A limiting H2O2 concentration of 0.24 mM for Pr2NiO4+δ and 0.42 mM for Pr2Ni0.8Mo0.2O4+δ was obtained at a constant current of 10 mA/cm2 using a flow‐cell reactor using a gas‐diffusion electrode.

Objective To identify and propose blood transfusion suggestions for 3 children suspected to have red blood cell T polyagglutination. Methods According to the RBC reactions with phytohemagglutinin, adult serum and cord blood serum, aggregation test with polybrene reagent and MN antigen phenotype test were carried out on 3 children to confirm the presence of T polyagglutination. The donor serum with negative or weak reactions was selected by minor cross matching for the 3 children who needed therapeutic plasma exchange(TPE). Results Three cases of RBC T polyagglutination were caused by bacterial infection, with transient appearance of MN antigen; the samples were reactive to peanut agglutinin, soybean agglutinin, adult serum but nonreactive to cord blood serum, and didn′t aggregate after adding polybrene reagent. After receiving timely TPE, the T polyagglutination gradually disappeared. Conclusion Some bacteria, such as Streptococcus pneumoniae, may cause polyagglutination of red blood cells. The patients with suspected T polyagglutination should be diagnosed in time. For T polyagglutination patients, the minor matched plasma should be used for avoiding the random plasma with anti-T antibody transfusion.

Abstract With a wide range of applications in autonomous driving and robotics, semantic segmentation for large‐scale outdoor point clouds is a critical and challenging issue. Due to the large number and irregular arrangement of point clouds, it is difficult to balance the efficiency and effectiveness. In this paper, we propose LessNet, a lightweight and efficient voxel‐based method for LiDAR‐only semantic segmentation, taking advantage of cylindrical partition and intra‐voxel feature fusion. Specifically, we use a cylindrical partition method to distribute the outdoor point clouds more evenly in voxels. To better encode the voxel features, we adopt an intra‐voxel aggregation method without querying neighbours. The voxel features are further input into a lightweight and effective 3D U‐net to aggregate local features and dilate the receptive field. Extensive experiments prove the satisfied semantic segmentation performance and the improvement of each component in our proposed framework. Our method is capable of processing more than one million point clouds at a time while retaining low latency and few parameters. Moreover, our method achieves comparable performance with state‐of‐the‐art approaches and outperforms all projection‐based methods on the SemanticKITTI benchmark.

Infective endocarditis (IE) is a rare but life-threatening disease. Invasive dental treatment (IDT) can produce temporary bacteremia, so they are considered as potential risk factors for IE. The physicians should pay attention to the possibility of IE, when patients have fever of unknown origin after IDT. Streptococcus gordonii is one of the common colonization bacteria in periodontal environment. A case of infective endocarditis caused by Streptococcus gordon after tooth extraction was reported. This case suggests an important link between IE and IDT.

The strategy evolution process of game players is highly uncertain due to random emergent situations and other external disturbances. This paper investigates the issue of strategy interaction and behavioral decision-making among game players in simulated confrontation scenarios within a random interference environment. It considers the possible risks that random disturbances may pose to the autonomous decision-making of game players, as well as the impact of participants’ manipulative behaviors on the state changes of the players. A nonlinear mathematical model is established to describe the strategy decision-making process of the participants in this scenario. Subsequently, the strategy selection interaction relationship, strategy evolution stability, and dynamic decision-making process of the game players are investigated and verified by simulation experiments. The results show that maneuver-related parameters and random environmental interference factors have different effects on the selection and evolutionary speed of the agent’s strategies. Especially in a highly uncertain environment, even small information asymmetry or miscalculation may have a significant impact on decision-making. This also confirms the feasibility and effectiveness of the method proposed in the paper, which can better explain the behavioral decision-making process of the agent in the interaction process. This study provides feasibility analysis ideas and theoretical references for improving multi-agent interactive decision-making and the interpretability of the game system model. [ABSTRACT FROM AUTHOR]

Diabetic nephropathy (DN), as the one of most common complications of diabetes, is generally diagnosed based on a longstanding duration, albuminuria, and decreased kidney function. Some patients with the comorbidities of diabetes and other primary renal diseases have similar clinical features to DN, which is defined as non-diabetic renal disease (NDRD). It is necessary to distinguish between DN and NDRD, considering they differ in their pathological characteristics, treatment regimes, and prognosis. Renal biopsy provides a gold standard; however, it is difficult for this to be conducted in all patients. Therefore, it is necessary to discover non-invasive biomarkers that can distinguish between DN and NDRD. In this research, the urinary exosomes were isolated from the midstream morning urine based on ultracentrifugation combined with 0.22 μm membrane filtration. Data-independent acquisition-based quantitative proteomics were used to define the proteome profile of urinary exosomes from DN (n = 12) and NDRD (n = 15) patients diagnosed with renal biopsy and Type 2 diabetes mellitus (T2DM) patients without renal damage (n = 9), as well as healthy people (n = 12). In each sample, 3372 ± 722.1 proteins were identified on average. We isolated 371 urinary exosome proteins that were significantly and differentially expressed between DN and NDRD patients, and bioinformatic analysis revealed them to be mainly enriched in the immune and metabolic pathways. The use of least absolute shrinkage and selection operator (LASSO) logistic regression further identified phytanoyl-CoA dioxygenase domain containing 1 (PHYHD1) as the differential diagnostic biomarker, the efficacy of which was verified with another cohort including eight DN patients, five NDRD patients, seven T2DM patients, and nine healthy people. Additionally, a concentration above 1.203 μg/L was established for DN based on the ELISA method. Furthermore, of the 19 significantly different expressed urinary exosome proteins selected by using the protein–protein interaction network and LASSO logistic regression, 13 of them were significantly related to clinical indicators that could reflect the level of renal function and hyperglycemic management.

This paper presents a Multiple Unmanned Aerial Vehicles (UAVs) cooperative reconnaissance task allocation model based on heterogeneous target value and proposes an improved Multi-Verse Optimizer (MVO) algorithm. Firstly, according to the reconnaissance value of the target, the reconnaissance targets are divided into high-value targets, low-value targets and decoy targets. It improves the authenticity of the problem. The purpose of task allocation is to maximize the reconnaissance revenue of UAVs as much as possible under the condition of minimizing the reconnaissance time and fuel loss of UAVs to the targets. Then, to solve the model, this paper improves the traditional MVO algorithm. Adaptive compression factor is introduced to improve the convergence speed of the algorithm. In addition, the differential mutation operation is performed in the wormhole movement stage to enhance the global search ability of the algorithm. The simulation results show that the improved algorithm can successfully solve the reconnaissance task allocation problem under different target values, and has obvious advantages in reconnaissance revenue and calculation speed compared with other methods.

Ginsenoside 24-hydroxy-ginsengdiol (24-OH-PD), extracted from red ginseng, is a novel diol-type ginsenoside, strongly inhibits the growth of human T-cell acute lymphoblastic leukaemia (T-ALL) CCRF-CEM cells. Our research aimed at investigating the mechanism underlying this inhibition. Cell viability was determined using the cell counting kit-8 (CCK-8) assay, and NOD/SCID mice bearing CCRF-CEM cells were used to verify the therapeutic effect of 24-OH-PD on T-ALL in vivo. We equally analysed pathways related to 24-OH-PD in CCRF-CEM cells using RNA-Seq analysis. Cell apoptosis, reactive oxygen species (ROS), mitochondrial membrane potential (ΔΨm), and mitochondrial permeability transition pore (mPTP) levels were detected by flow cytometry. The activity of caspase3 and caspase9 was detected by enzyme activity detection kits. The expression levels of apoptosis-related proteins and mRNA were determined through western blotting and quantitative reverse-transcription PCR assays (qRT-PCR). CCK-8 assay and animal xenograft experiments confirmed that 24-OH-PD significantly inhibited T-ALL in a dose-dependent manner, both in vivo and in vitro. RNA-Seq results suggest that mitochondria-mediated apoptosis pathway plays an important role in this process. Furthermore, intracellular ROS levels increased, mPTP opened, and ΔΨm decreased following 24-OH-PD treatment. Pretreatment with the antioxidant, NAC, reversed the effects of 24-OH-PD on apoptosis and ROS generation. Moreover, 24-OH-PD treatment increased the expression of Bax and caspase family members, thereby releasing cytochrome c (Cytc) and inducing apoptosis. Our findings showed that, 24-OH-PD induces apoptosis in CCRF-CEM cells by activating the mitochondrial-dependent apoptosis pathway through ROS accumulation. This inhibitory effect implies that 24-OH-PD could be further developed as treatment of T-ALL.

Cell therapy has been a promising strategy for cardiac repair after myocardial infarction (MI), but a poor ischemic environment and low cell delivery efficiency remain significant challenges. The spleen serves as a hematopoietic stem cell niche and secretes cardioprotective factors after MI, but it is unclear whether it could be used for human pluripotent stem cell (hiPSC) cultivation and provide a proper microenvironment for cell grafts against the ischemic environment. Herein, we developed a splenic extracellular matrix derived thermoresponsive hydrogel (SpGel). Proteomics analysis indicated that SpGel is enriched with proteins known to modulate the Wnt signaling pathway, cell-substrate adhesion, cardiac muscle contraction and oxidation-reduction processes. In vitro studies demonstrated that hiPSCs could be efficiently induced into endothelial cells (iECs) and cardiomyocytes (iCMs) with enhanced function on SpGel. The cytoprotective effect of SpGel on iECs/iCMs against oxidative stress damage was also proven. Furthermore, in vivo studies revealed that iEC/iCM-laden SpGel improved cardiac function and inhibited cardiac fibrosis of infarcted hearts by improving cell survival, revascularization and remuscularization. In conclusion, we successfully established a novel platform for the efficient generation and delivery of autologous cell grafts, which could be a promising clinical therapeutic strategy for cardiac repair and regeneration after MI.

In the previous 10 years, researchers have suggested a critical role for the brain reward system in stress resilience. However, no study has provided an empirical link between activity in the mesostriatal reward regions during stress and the recovery of cortisol stress response. Moreover, although reward sensitivity as a trait has been demonstrated to promote stress resilience, it remains unclear whether it modulates the brain reward system in stress resilience and how this effect is achieved by the inherent neuroendocrine system. To investigate these uncertainties, 70 young adults were recruited to participate in a ScanSTRESS task, and their brain imaging data and saliva samples (for cortisol assay) were collected during the task. In addition, we assessed reward sensitivity, cortisol awakening response, and intrinsic functional connectivity of the brain in all the participants. We found that left putamen activation during stress exposure positively predicted cortisol recovery. In addition, reward sensitivity was positively linked with activation of the left putamen, and this relationship was serially mediated by the cortisol awakening response and right hippocampus-left inferior frontal gyrus intrinsic connectivity. These findings suggest that reward sensitivity modulates reward pathways in stress resilience through the interplay of the diurnal stress response system and network of the hippocampus-prefrontal circuitry. Summarily, the current study built a model to highlight the dynamic and multifaceted interaction between pertinent allostatic factors in the reward-resilience pathway and uncovered new insight into the resilience function of the mesostriatal reward system during stress.

Nowadays, a series of hyperbranched polymers have been developed as macroinitiators for hydrocarbon fuels. These macroinitiators are helpful to enhance the heat sink capability of endothermic hydrocarbon fuels to meet the great demand for thermal management when applied on a hypersonic or supersonic aircraft. An amphiphilic hyperbranched polyglycerol, DSHPG, has been studied when used as a novel macroinitiator. In this work, the thermal decomposition of DSHPG is investigated in detail to explore the pyrolysis mechanism of DSHPG based on the construction of mathematical models. The mathematical models are obtained from the thermal gravity analyses of DSHPG to predict the decomposition process of DSHPG and the kinetic parameters, such as activation energy and pre-exponential factor. Furthermore, the decomposition paths including possible radical reactions of DSHPG are proposed based on the qualitative investigation about the pyrolytic products detected from a pyrolysis-GC/MS apparatus. These results offer a detailed insight on the thermal decomposition of DSHPG to explore how the DSHPG functions as the macroinitiator for endothermic hydrocarbon fuels at high temperatures.

Diaporthe-Phomopsis disease complex causes considerable yield losses in soybean production worldwide. As one of the major pathogens, Phomopsis longicolla T. W. Hobbs (syn. Diaporthe longicolla) is not only the primary agent of Phomopsis seed decay but is also one of the agents of Phomopsis pod and stem blight and Phomopsis stem canker. We performed both PacBio long-read sequencing and Illumina short-read sequencing and obtained a genome assembly for the strain P. longicolla YC2-1, which was isolated from soybean stem with Phomopsis stem blight disease. The 63.1 Mb genome assembly contains 87 scaffolds, with a minimum, maximum, and N50 scaffold length of 20 kb, 4.6 Mb, and 1.5 Mb respectively, and a total of 17,407 protein-coding genes. The high-quality data expand the genomic resource of P. longicolla species and will provide a solid foundation for a better understanding of their genetic diversity and pathogenic mechanisms.[Graphic: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Crowdsensing high quality data relies on the efficient participation of users. However, the existing incentive mechanism is unable to take into account the dual requirements of both quantity and quality of users' participation. In this paper, we propose Crowdsensing Task Selection algorithm and rewards allocation incentive mechanism based on Reputation Evaluation model(CTSRE), which deploys the reputation weighted rewards allocation method to effectively encourage users to actively participate in the execution of tasks. In CTSRE, we adopt a game-theoretic approach and apply best response dynamics based algorithm to achieve the goal of maximizing users' utilities. We show that the task selection algorithm can converge in finite time and meet the fairness requirement. We also design a reputation conversion method and updating rule to improve incentive and fairness of the mechanism. Through numerical experiments and comparative analysis, we verify that the task selection algorithm meets the convergence requirements. The application of sigmoid function for reputation conversion improves the fairness of rewards allocation and motivate users to improve their reputation to obtain high rewards. Experimental results indicate that CTSRE can effectively ensure the quantity and the quality of users' participation.

With the rapid development of networks, network security is a serious problem. To evaluate a network accurately, this paper proposes a network risk evaluation method based on a differential manifold (DM) and research on traditional methods. The DM divides the network risk evaluation into network structure risk and network behavior risk evaluations. Network structure risk evaluates the network identity, and network behavior risk evaluates the attack and defense of the network. Network assets and asset vulnerabilities characterize a network, and the analytic hierarchy process (AHP) and the Common Vulnerability Scoring System (CVSS) are combined to evaluate the network identity. Network behavior causes high-dimensional indicator changes, and DMs are used to measure network behavior. To examine the effectiveness and accuracy of DMs, two experiments were performed. The experimental results show that the DM method is valid and accurate for evaluating network risk.

Prognosis of primary pontine hemorrhage (PPH) is important for treatment planning and patient management. However, only few clinical factors were reported to have prognostic value to PPH. Here, we propose a deep learning (DL) model that mines high-dimensional prognostic information from computed tomography (CT) images and combines clinical factors for predicting individualized prognosis of PPH. We proposed a multi-task DL model to learn high-dimensional CT features of hematoma and perihematomal areas for predicting the risk of 30-day mortality, 90-day mortality and 90-day functional outcome of PPH simultaneously. We further explored the combination of the DL model and clinical factors by building a combined model. All the models were trained in a training cohort (n = 219) and tested in an independent testing cohort (n = 35). The DL model achieved area under the curve (AUC) of 0.886, 0.886, and 0.759 in predicting 30-day mortality, 90-day mortality and 90-day functional outcome of PPH in the independent testing cohort, which improved over the previously reported new PPH score and the clinical model. When combining the DL model and clinical factors, the combined model achieved improved performance (AUC = 0.920, 0.941, and 0.894), indicating that DL model mines CT information that complements clinical factors. Through DL visualization technique, we found that the internal structure of hematoma and its expansion to perihematomal regions are important for predicting the prognosis of PPH. This DL model provides an easy-to-use way for predicting individualized prognosis of PPH by mining high-dimensional information from CT images, and showed improvement over clinical factors and present methods.

During the summer of 2016 and 1998 in the context of the decaying phase of the super El Niño event,the three persistent heavy rainfall events occurred in the middle and lower reaches of the Yangtze River from June 19 to July 7 in 2016 (hereinafter referred to as during the period of 2016),from June 12 to 27 (hereinafter referred to as during the period Ⅰ of 1998) in 1998, and from July 17 to August 2 (hereinafter referred to as during the period Ⅱ of 1998) in 1998, which brought great challenges to the national flood control business. Based on daily precipitation at 2407 meteorological observation stations in mainland China,daily reanalysis atmospheric data from NCEP/NCAR and other data,we have conducted a comparative analysis of the similarities and differences of summer persistent heavy rainfall and associated atmospheric circulation characteristics in the middle and lower reaches of the Yangtze River between 2016 and 1998. Results are as follows. (1) The persistent heavy rainfall belt is the northernmost during the period of 2016 and the southernmost during the period Ⅰ of 1998 among the three events,while the rainfall intensity during the period of 2016 is roughly the same as that during the period Ⅰ of 1998, and it for both periods is stronger than that for the period Ⅱ of 1998. (2) There are many similarities in the atmospheric circulation characteristics during the three periods of persistent heavy rainfall. For example,there exist low troughs near the eastern coast of China,the Northwest Pacific Subtropical High (NWPSH) and South Asia High (SAH) are larger in range and stronger in intensity than normal,and the intensity of the upper westerly jet (UWJ) and low southwest jet and the easterly water vapor transport along the south side of the NWPSH are also stronger than normal. (3) The significant differences in the atmospheric circulation during the three periods of persistent heavy rainfall events are that the low trough near the coastal of eastern China during the persistent heavy rainfall of 2006 (1998) is shallow (deep). When upper-level divergence zone between the north sides of NWPSH and SAH ridge lines and the south sides of westerly jet belt,low-level jet belt and shear line with convergence,and Australian High in the southern hemisphere are northerly (southerly) in position,the persistent heavy rainfall zones are also northerly (southerly). NWPSH,SAH,and upper-level westerly jet during the period of 2016 and the period Ⅰ of 1998 are significantly stronger than those during the period Ⅱ of 1998. The rainfall intensity in the former two periods is also greater than that in the later period.

Abstract Introduction Prenatal stress (PS) can cause depression in offspring. However, the underlying biological mechanism of these influences is still unclear. This work was implemented to investigate the molecular mechanisms of depressive‐like behavior of offspring rats insulted with PS. Methods Relative quantitative phosphoproteomics of the hippocampus of PS susceptibility (PS‐S) and control (CON) rat offspring was performed using liquid chromatography‐tandem mass spectrometry to confirm known pathways and to identify new mechanisms involved in depression. Results A total of 6790 phosphopeptides, 9817 phosphorylation sites, and 2978 phosphoproteins were detected. Among the 2978 phosphoproteins, 1760 (59.09%) had more than two phosphorylated sites, the ENSRNOP00000023460 protein had more than 117 phosphorylated sites, and the average distribution of modification sites per 100 amino acids was 2.97. There were 197 different phosphopeptides, including 140 increased phosphopeptides and 57 decreased phosphopeptides in the PS‐S offspring rats, compared to the CON offspring rats. These differential phosphopeptides corresponded to 100 upregulated and 44 downregulated phosphoproteins, respectively. Gene ontology enrichment analysis revealed that these different phosphoproteins in the top five enriched terms in the cellular component, molecular function, and biological proces categories were involved in a total of 35 different phosphoproteins, and these phosphoproteins were mainly related to myelin‐, microtubule‐ and synapse‐associated proteins. The enrichment of Kyoto Encyclopedia of Genes and Genome pathways was found to be involved in many essential biological pathways, and the top five pathways included amphetamine addiction, insulin secretion, Cushing syndrome, and the circadian entrainment signaling pathway. These first five pathways were related to nine phosphoproteins, including Adcy9, Apc, Cacna1c, Camk2a, Camk2b, Camk2g, Ctnnd2, Grin2a, and Stx1a. The full data are available via ProteomeXchange with identifier PXD019117. Conclusion We preliminarily identified 144 different phosphoproteins involved in myelin, microtubule, and synapse formation and plasticity in the hippocampus of susceptible offspring rats exposed to PS.

Software vulnerabilities are the root causes of various security risks. Once a vulnerability is exploited by malicious attacks, it will greatly compromise the safety of the system, and may even cause catastrophic losses. Hence automatic classification methods are desirable to effectively manage the vulnerability in software, improve the security performance of the system, and reduce the risk of the system being attacked and damaged. In this paper, a new automatic vulnerability classification model (TFI-DNN) has been proposed. The model is built upon term frequency-inverse document frequency (TF-IDF), information gain (IG), and deep neural network (DNN): the TF-IDF is used to calculate the frequency and weight of each word from vulnerability description; the IG is used for feature selection to obtain an optimal set of feature word, and; the DNN neural network model is used to construct an automatic vulnerability classifier to achieve effective vulnerability classification. The National Vulnerability Database of the United States has been used to validate the effectiveness of the proposed model. Compared to SVM, Naive Bayes, and KNN, the TFI-DNN model has achieved better performance in multi-dimensional evaluation indexes including accuracy, recall rate, precision, and F1-score.

Traditionally, network risk assessment uses a statistical computation method. This paper proposes Lie group kinematics to describe the feature space of the attack behavior. A matrix composed of indicators and topologies in a network system is mapped to a Lie group. The attack behavior path and the powers of the attack and defense are defined. The risk value of the attack and defense is calculated from the change in the indicators. Then, we evaluate the network security risk status using calculus. To examine the validity of the network risk assessment based on a Lie group, we conduct one experiment and utilize the CIC2017 dataset to show the applicability and efficiency of the proposed method. The experimental results show the effectiveness of the calculation method based on a Lie group, and the risk value of the attack and defense is valid compared to those of other machine learning algorithms. The calculation method based on a Lie group can quantitatively analyze network security risks..

In this paper, we propose a novel tracking method based on structured metric learning, which takes the advantages of both structured learning and distance metric learning. In our method, tracking is formulated as a structured metric learning problem, which not only considers the importance of different samples, but also improves the discriminability by learning a specific distance metric for matching. Specifically, a concrete structured metric learning method is realized by making use of the constraints from the target and its neighbour training samples under the above framework. Besides, a closed-form solution is derived for the structured metric learning problem. To improve the matching robustness, the K-nearest neighbours (KNN) distance is employed to determine the final tracking result. Experimental results in the benchmark dataset demonstrate that the proposed structured metric learning based tracking method can achieve desirable performance.

The electric field and space charge dynamics characteristics in oil under impulse voltage are key factors to the transformer insulation design, which are mainly obtained through simulation, falling short of experimental verifications. Applying the Kerr electro-optic method, the present paper carried out an actual measurement for the electric field characteristics in transformer oil under impulse voltage and managed to obtain their correlations with an electrode material, voltage amplitude, and wave-front time, respectively. First, it was found that, under impulse voltage and in pure transformer oil, the aluminum electrode had the largest amount of charge injection, followed by stainless steel and then copper, which was attributed to the different work functions of metal elements in electrodes. Second, with the increase of voltage amplitude, the effect of the space charge on the intermediate electric field of the oil gap was first enhanced, then weakened, and eventually enhanced. Third, as the wave-front time was prolonged, the peak of electric field in transformer oil showed a downward trend. For instance, the peak value of the electric field was reduced by 17.6 % when the wave-front time was increased from 0.5 μs to 40.0 μs. The reason is that when the wave-front time is longer, the amount of injected charge grows larger and the weakening effect becomes stronger. Fourth, a charge dynamic motion model in transformer oil under impulse voltage was established to demonstrate its spatial-temporal effects on the electric field. Besides, this paper has clarified the source of the negative and positive charges in the transformer oil under impulse voltage by using the theories of field emission and field ionization. Moreover, the variation of electron mobility over-voltage amplitude, wave-front time, and spatial distribution was worked out to present the quantitative results of its motion process.

Identifying defective software entities is essential to ensure software quality during software development. However, the high dimensionality and class distribution imbalance of software defect data seriously affect software defect prediction performance. In order to solve this problem, this paper proposes an Ensemble MultiBoost based on RIPPER classifier for prediction of imbalanced Software Defect data, called EMR_SD. Firstly, the algorithm uses principal component analysis (PCA) method to find out the most effective features from the original features of the data set, so as to achieve the purpose of dimensionality reduction and redundancy removal. Furthermore, the combined sampling method of adaptive synthetic sampling (ADASYN) and random sampling without replacement is performed to solve the problem of data class imbalance. This classifier establishes association rules based on attributes and classes, using MultiBoost to reduce deviation and variance, so as to achieve the purpose of reducing classification error. The proposed prediction model is evaluated experimentally on the NASA MDP public datasets and compared with existing similar algorithms. The results show that EMR_SD algorithm is superior to DNC, CEL and other defect prediction techniques in most evaluation indicators, which proves the effectiveness of the algorithm.