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• The delamination behavior of CNTs with different contents in PW and UD fabric composites is investigated. • The initial and propagation fracture toughness are calculated through the modified beam theory. • The acoustic emission signals of different damage mechanisms are distinguished. • The weakening and enhancing mechanisms are revealed by observing the surface photomicrograph. This paper investigates the delamination behavior of two composite structures with carbon nanotubes (CNTs). Double cantilever beam (DCB) samples with CNTs of different areal densities are adopted to analyze the toughening and damage mechanism via acoustic emission (AE) method. The result demonstrates that the CNT layer improves the fracture toughness of unidirectional (UD) sample interfaces effectively, but for the plain woven (PW) samples, only works on initial fracture toughness with low CNTs areal density. In addition, more AE events are recorded in the CNT modified samples than in the reference sample. Combined with the observation of the fracture surface and crack propagation path, the agglomeration with high content CNTs weakens the interlayer strength in PW samples, whereas the fracture toughness is significantly enhanced in UD composites due to the increase of the crack propagation path. [ABSTRACT FROM AUTHOR]

Ultrasonic testing (UT) is commonly used to inspect the geometric shape of internal damage in composite materials and the test results need to be interpreted by trained experts. In this work, an automatic signal classification method based on deep learning is proposed for depth estimation of the detects introduced by low-velocity impact (LVI) in carbon fiber reinforced plastics (CFRPs). Three kinds of neural networks, LSTM, CNN, and CNN-LSTM are used to analyze the attributes with different depths. Then, trained models are applied to identify the depth information of impact damage. The results show that the CNN-LSTM model is a more accurate in-depth classification for LVI defects in CFRP based on A-scan signals than the other two structures. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a leading cause of vision impairment and blindness among diabetics. We aimed to explore whether long-term exposure to residential greenness was beneficial to DR. We used data from a large-scale, cross-sectional screening survey conducted in 129 cities of 27 provincial regions of China from 2018 to 2021 among patients with diabetes. We measured residential greenness exposure as the 3-year average of annual maximum Normalized Difference Vegetation Index (NDVI) at a spatial resolution of 250 m. DR was assessed by ophthalmologists based on fundus photographs. The primary outcome was DR, and secondary outcome included DR severity status (i.e., nonproliferative and proliferative), hallmarks of retinal lesions and macular oedema. A total of 484,380 adult participants with diabetes were included in the current analysis, and 15.7% of them were diagnosed with DR. NDVI was inversely and linearly associated with DR prevalence, and an increment of 0.1 NDVI was associated with a 10% (9%–10%) decrease in DR prevalence. Significant and inverse associations were further found for nonproliferative and proliferative DR, hallmarks of lesions and macular oedema. The association between greenness and DR was stronger among participants who were older, obese, lived in the south, had longer duration of diabetes or did not take antidiabetic medications. This large-scale nationwide study provides the first-hand epidemiological evidence on the associations of residential greenness with DR. Our findings highlight the importance of residential greenness in alleviating DR risk especially in an era of aging and urbanization. • The first to examine the relationship between greenness and diabetic retinopathy (DR). • Greenness exposure was almost linearly associated with decreased odds of DR. • Greenness was inversely associated with nonproliferative and proliferative DR. • Greenness was inversely associated with hallmarks of DR and macular oedema. [ABSTRACT FROM AUTHOR]

• An method of multi-source data-driven unsaturated seepage parameter inversion. • NSGA-II is used to generate the pareto-optimal solutions of parameters. • Parameter decision based on a comprehensive index composed of multi-source data. • Inversion results are more in line with engineering practice and have higher accuracy. The inverse parameter's estimation of unsaturated seepage models is important to ensure the safety of earth dams, while most existing studies determine the hydraulic characteristics only based on seepage pressure monitoring data, which may lead to inversion results deviating from the intrinsic properties of dam materials. This study proposed an advanced methodology for unsaturated seepage parameter inversion of earth dams driven by multi-source data. The transient unsaturated seepage behavior of earth dams is simulated using the Van Genuchten (VG) model, and the extreme learning machine (ELM) is adopted as the surrogate model to estimate the approximate simulation results. Then the multi-objective function for parameter inversion is constructed by combining the observed transient pressure data and ELM models, and the Pareto-optimal solutions are generated using the elitist non-dominated sorting genetic algorithm (NSGA-II). Multi-source information, including parameter design values, in-situ testing results, and monitoring data, are fused to construct a comprehensive index to drive the parameter decision. The proposed method is applied to the PB core rockfill dam, which has a well-established seepage pressure monitoring system. In addition, the design values and in-situ testing results of the hydraulic conductivity of the dam material are also available. The results showed that the inversion parameters of the proposed method are closer to the physical characteristics of materials and have advantages in prediction accuracy. [ABSTRACT FROM AUTHOR]

Based on Doppler radar observation and reanalysis data, the statistical characteristics of mesovortices (MVs) during the first rainy season (April–June) in Guangdong, South China, from 2017 to 2019 are studied, including their spatiotemporal distributions, structural features and favorable environmental conditions. The results show that the MVs usually exhibit short lifetimes; about 70% last for less than 30 min. The intensity and horizontal scale of the MVs are proportional to their lifetime. Long-lived MVs have larger horizontal scales and stronger intensities than short-lived ones. The MVs are mainly observed over the Pearl River Delta region, followed by western Guangdong Province, but relatively fewer in both eastern and northern Guangdong Province. The uneven spatial distribution of the MVs is closely related to the differences in the topographical features and the environment conditions over South China. MVs are prone to form over flat regions. The Pearl River Delta and eastern Guangdong regions are relatively flat compared to the more mountainous western and northern Guangdong regions. Moreover, the monsoonal south-westerlies, water vapor flux, atmospheric instability and vertical wind shear over southwest Guangdong are significantly larger than those in other regions and are favorable for the formation of MVs. The occurrence frequencies of MVs in central and southern parts of Guangdong display similar diurnal variations, reaching the peak during the late afternoon and early evening while dropping to the minimum overnight. However, the situation is opposite in northern Guangdong, with the peak overnight and the minimum during the late afternoon and early evening. The regional differences in diurnal variation are likely related to the moving direction of mesoscale convective systems (MCSs) in Guangdong. [ABSTRACT FROM AUTHOR]

The low crystallization rate and long molding time of poly(ethylene terephthalate) (PET) restricts its application in the field of engineering plastics. PET/graphene oxide (GO) composites were synthesized through in situ polymerization method with dimethyl terephthalate (DMT), ethylene glycol (EG) as reactants and GO as filler in order to overcome the above shortcomings. Influence of GO on the crystallization performance of PET was investigated. It was revealed GO raised the crystallization temperature and crystallization rate of PET, demonstrating the excellent nucleation capability of GO. The mechanism of PET nucleation induced by GO was investigated by Avrami equation and Hoffman-Lauritzen theory. It was obvious the crystallization behavior of PET was improved by reducing the free energy of nucleation and the fold surface free energy of PET caused by GO. Moreover, the non-isothermal kinetics of thermal decomposition of PET and PET/GO composites were studied by Friedman method. It was obvious that all of PET and PET/GO composites exhibited a fine thermal stability with no significant mass loss observed up to 370 °C (< 0.5%). The addition of GO improved the thermal stability of PET through raising the apparent activation energy, inhibiting the escape of volatile products and absorbing the degradation products in PET thermal decomposition procedure. [ABSTRACT FROM AUTHOR]

To achieve a wide range and high accuracy detection of the vacuum level, for example, in an encapsulated vacuum microcavity, a composite-type MEMS Pirani gauge has been designed and fabricated. The Pirani gauge consists of two gauges of different sizes connected in series, with one gauge having a larger heat-sensitive area and a larger air gap for extending the lower measurable limit of pressure (i.e., the high vacuum end) and the other gauge having a smaller heat-sensitive area and a smaller air gap for extending the upper measurable limit. The high-resistivity titanium metal was chosen as the thermistor; SiNx was chosen as the dielectric layer, considering the factors relevant to simulation and manufacturing. By simulation using COMSOL Multiphysics and NI Multisim, a range of measurement of 2 × 10−2 to 2 × 105 Pa and a sensitivity of 52.4 mV/lgPa were obtained in an N2 environment. The performance of the fabricated Pirani gauge was evaluated by using an in-house made vacuum test system. In the test, the actual points of measurement range from 6.6 × 10−2 to 1.12 × 105 Pa, and the highest sensitivity is up to 457.6 mV/lgPa. The experimental results are better in the range of measurement, sensitivity, and accuracy than the simulation results. The Pirani gauge proposed in this study is simple in structure, easy to manufacture, and suitable for integration with other MEMS devices in a microcavity to monitor the vacuum level therein. [ABSTRACT FROM AUTHOR]

The effect of axial yarns on progressive bending damage of braided composite tubes is studied in this paper. Four specimens with different number and distribution of axial yarns were experimentally tested by quasi-static three-point bending. At the same time, infrared thermography was used to observe the damage in-situ. Combined with the cross-section morphology of Ultra-Depth 3D Microscope, the damage evolution and distribution characteristics of braided tubes were identified. It is found that axial yarns enhance the mechanical properties of braided composite tube. The detected changes of infrared maximum temperature have a good correlation with the damage initiation and failure mechanism of the specimen. The abrupt changes of the maximum temperature indicate the occurrence of local fiber breakage, especially the axial yarn breakage. If there are axial yarns on the compression side of the tube, the maximum temperature increases by 0.5 °C in the linear elastic stage, and two initial damages of debonding and fiber breakage will occur. The debonding started at the interface of the axial yarn. The axial yarns increased fiber crimp, resulting in obvious wavy delamination between layers of the specimen, and the axial damage range was expanded. In addition, the mixed structure with axial yarns only on the compression side can inhibit the damage from spreading down the braided tube wall. • The progressive bending damage of triaxial braided tube was studied. • The abrupt change of infrared maximum temperature indicates local fiber breakage. • The mixed structure with axial yarns only in the upper half can inhibit damage spreading downward. [ABSTRACT FROM AUTHOR]

We present a new magnetometry method integrating an ensemble of nitrogen-vacancy (NV) centers in a single-crystal diamond with an extended dynamic range for monitoring a fast changing magnetic-field. The NV-center spin resonance frequency is tracked using a closed-loop frequency locked technique with fast frequency hopping to achieve a 10 kHz measurement bandwidth, thus allowing for the detection of fast changing magnetic signals up to 0.723 T/s. This technique exhibits an extended dynamic range subjected to the working bandwidth of the microwave source. This extended dynamic range can reach up to 4.3 mT, which is 86 times broader than the intrinsic dynamic range. The essential components for NV spin control and signal processing, such as signal generation, microwave frequency control, data processing, and readout, are integrated in a board-level system. With this platform, we demonstrate a broadband magnetometry with an optimized sensitivity of 4.2 nT Hz−1/2. This magnetometry method has the potential to be implemented in a multichannel frequency locked vector magnetometer suitable for a wide range of practical applications, such as magnetocardiography and high-precision current sensors. [ABSTRACT FROM AUTHOR]

Introduction: Due to its progressive nature, early evaluation and timely prediction of legal blindness are important in patients with neovascular age-related macular degeneration (nAMD). We examined the association between early-stage variation in central retinal thickness (CRT) and long-term visual outcomes in patients with nAMD. Methods: We included 103 nAMD patients who were administered anti-vascular endothelial growth factor (anti-VEGF). Participants were considered qualified if they were (1) 50 years and older, (2) treatment-naïve, (3) received standard anti-VEGF treatment and had complete baseline information. We further excluded patients with less than 1-year follow-up data and those who experienced best corrected visual acuity ≤35. Early-stage variability in CRT was measured as the first-year coefficient of variability (CV) of CRT. Patients were then classified into the high-variability and low-variability groups according to the X-tile. A product-limit plot was used to demonstrate the differences and tested with the log-rank test. The association between first-year variability and visual outcomes was quantified using Cox regression models. Time-to-event primary endpoint was the overall visual preservation (OVP) rate, defined as the time from the first injection to legal blindness, i.e., best corrected visual acuity ≤35 Early Treatment Diabetic Retinopathy Study (ETDRS) letters. Results: A threshold of 20% of first-year CV in CRT was used to categorize 76 qualified patients into high variability (35, 46.1%) and low variability (41, 53.9%). The 5- and 10-year OVPs for patients with high versus low variability were 76% versus 48% and 59% versus 22%, respectively. High early-stage CRT variability showed a significantly higher risk of legal blindness. Even after adjusting for the demographic and clinical features, the variability remained significant (HR = 2.39, 95% CI: 1.20–4.78). Conclusion: First-year variability of CRT was predictive of long-term visual outcomes in patients with nAMD, and 20% of the variability could be used as a clinically convenient threshold to qualitatively classify patients into high- and low-variability groups. The current study is important for identifying high-risk populations and for long-term disease management. [ABSTRACT FROM AUTHOR]

Introduction: Previous studies have explored prediction value of serum metabolites in neoadjuvant chemoradiation therapy (NCRT) response for rectal cancer. To date, limited literature is available for serum metabolome changes dynamically through NCRT. Objectives: This study aimed to explore temporal change pattern of serum metabolites during NCRT, and potential metabolic biomarkers to predict the pathological response to NCRT in locally advanced rectal cancer (LARC) patients. Methods: Based on dynamic UHPLC-QTOF-MS untargeted metabolomics design, this study included 106 LARC patients treated with NCRT. Biological samples of the enrolled patients were collected in five consecutive time-points. Untargeted metabolomics was used to profile serum metabolic signatures from LARC patients. Then, we used fuzzy C-means clustering (FCM) to explore temporal change patterns in metabolites cluster and identify monotonously changing metabolites during NCRT. Repeated measure analysis of variance (RM-ANOVA) and multilevel partial least-squares discriminant analysis (ML-PLS-DA) were performed to select metabolic biomarkers. Finally, a panel of dynamic differential metabolites was used to build logistic regression prediction models. Results: Metabolite profiles showed a clearly tendency of separation between different follow-up panels. We identified two clusters of 155 serum metabolites with monotonously changing patterns during NCRT (74 decreased metabolites and 81 increased metabolites). Using RM-ANOVA and ML-PLS-DA, 8 metabolites (L-Norleucine, Betaine, Hypoxanthine, Acetylcholine, 1-Hexadecanoyl-sn-glycero-3-phosphocholine, Glycerophosphocholine, Alpha-ketoisovaleric acid, N-Acetyl-L-alanine) were further identified as dynamic differential biomarkers for predicting NCRT sensitivity. The area under the ROC curve (AUC) of prediction model combined with the baseline measurement was 0.54 (95%CI = 0.43 ~ 0.65). By incorporating the variability indexes of 8 dynamic differential metabolites, the prediction model showed better discrimination performance than baseline measurement, with AUC = 0.67 (95%CI 0.57 ~ 0.77), 0.64 (0.53 ~ 0.75), 0.60 (0.50 ~ 0.71), and 0.56 (0.45 ~ 0.67) for the variability index of difference, linear slope, ratio, and standard deviation, respectively. Conclusion: This study identified eight metabolites as dynamic differential biomarkers to discriminate NCRT-sensitive and resistant patients. The changes of metabolite level during NCRT show better performance in predicting NCRT sensitivity. These findings highlight the clinical significance of metabolites variabilities in metabolomics analysis. [ABSTRACT FROM AUTHOR]

Objective: To investigate whether peritoneal disease extent can predict the survival benefit of intraperitoneal/intravenous (IP/IV) chemotherapy in ovarian cancer. Design: A treatment‐free survival (TFS) analysis. Setting: Five‐centre trial. Population: An extended follow‐up of the Additional Intraperitoneal Cisplatin and Etoposide in ovarian cancer (AICE) trial (NCT01669226), with data cut‐off on 27 August 2020. Patients were categorised into subgroups with high tumour burden (HTB) and low tumour burden (LTB). Methods: Overall survival (OS) was divided into time on protocol treatment exposure (T), time free of subsequent treatment or death (TFS) and time after the first subsequent therapy (REL). TFS analyses and quality‐adjusted OS were calculated by multiplying the mean time in each health state by its assigned utility: quality‐adjusted OS = ut × T + TFS + urel × REL. Main outcome measures: The area under each Kaplan–Meier curve was estimated using the 96‐month restricted mean time, with threshold utility analyses used to illustrate quality‐adjusted OS comparisons. Results: In the HTB subgroup, the restricted mean TFS was 33.9 months and 18.7 months in the IP/IV and IV groups, respectively (p = 0.005), with a significant quality‐adjusted OS gain (13.2–16.0 months). In the LTB subgroup, IP/IV therapy yielded no survival benefit in either TFS (p = 0.268) or quality‐adjusted OS (range: 1.4–6.3 months). Conclusions: Both TFS and quality‐adjusted OS was longer across all utility weight values with IP/IV than with standard IV therapy in the HTB subgroup, whereas patients in the LTB subgroup did not benefit from the therapy. The tumour burden of ovarian cancer should be assessed before deciding on IP/IV versus IV treatment. [ABSTRACT FROM AUTHOR]

Triaxial braided composites employed as structural components need to be drilled for mechanical connections. This study is to investigate the thrust force, temperature elevation, and drilling-induced damages with varying machining parameters in the drilling of triaxial braided CFRP. A high-resolution infrared thermography was carried out to capture the temperature of the panel side surface and the drill-exit surface of CFRP. The damage characteristic of drilled hole was evaluated, and a relationship between force, temperature, and hole damage was established. The obtained results indicate that the temperature of triaxial braided CFRP was mainly transferred along the fiber direction. The temperature isothermal at the drill-exit surface developed from polygonal to roughly circular with the increase of drilling depth. The braided yarns tended to deflect, and induced poorly cut fibers due to the more heat generation with the low feed, making the matrix behaved from brittle to ductile and therefore resulting in more burrs. The increase of spindle speed could restrain the burrs. A larger thrust force with high feed will result in fiber-resin debonding and more tearing defects. The delamination occurred at the interface of fiber bundles at the outermost layer with high thrust force and cracks propagated along the yarn direction. [ABSTRACT FROM AUTHOR]

A constrained interval optimization model is proposed for the optimization of uncertain structures with their mechanical performance indices described as the objective and constraint functions of the design vector and interval uncertain parameters. Present indirect approaches for solving such interval optimization models by converting them into deterministic ones will result in the loss of uncertainty information and deviate from the original intention of realistically modeling engineering optimization problems. To overcome these shortcomings, a novel optimization algorithm is proposed for directly solving the nonlinear constrained interval optimization models based on a novel concept of the degree of interval constraint violation (DICV) and the DICV-based preferential guidelines. A nested genetic algorithm (GA) is developed to realize the direct interval ranking of various design vectors. The outer layer GA locates the optimal solution based on direct interval ranking. The inner layer GAs integrated with Kriging technique compute the intervals of the mechanical performance indices of every design vector in the current population of the outer layer GA. The validity and superiority of the proposed direct interval optimization algorithm was verified by three numerical examples. Finally, the proposed direct interval optimization method was applied to the optimization of the cone ring fixture with uncertain material properties in a large turbo generator aimed at moving its natural frequencies away from the exciting one. The results demonstrated its feasibility and effectiveness in optimizing practical engineering structures under uncertainties. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) has improved the prospects for many individuals with diseases affecting motor control, and recently it has shown promise for improving cognitive function as well. Several studies in individuals with Alzheimer disease and in amnesic rats have demonstrated that DBS targeted to the fimbria-fornix, the region that appears to regulate hippocampal activity, can mitigate defects in hippocampus-dependent memory. Despite these promising results, DBS has not been tested for its ability to improve cognition in any childhood intellectual disability disorder. Such disorders are a pressing concern: they affect as much as 3% of the population and involve hundreds of different genes. We proposed that stimulating the neural circuits that underlie learning and memory might provide a more promising route to treating these otherwise intractable disorders than seeking to adjust levels of one molecule at a time. We therefore studied the effects of forniceal DBS in a well-characterized mouse model of Rett syndrome (RTT), which is a leading cause of intellectual disability in females. Caused by mutations that impair the function of MeCP2 (ref. 6), RTT appears by the second year of life in humans, causing profound impairment in cognitive, motor and social skills, along with an array of neurological features. RTT mice, which reproduce the broad phenotype of this disorder, also show clear deficits in hippocampus-dependent learning and memory and hippocampal synaptic plasticity. Here we show that forniceal DBS in RTT mice rescues contextual fear memory as well as spatial learning and memory. In parallel, forniceal DBS restores in vivo hippocampal long-term potentiation and hippocampal neurogenesis. These results indicate that forniceal DBS might mitigate cognitive dysfunction in RTT. [ABSTRACT FROM AUTHOR]

Numerical simulation is a key means to evaluate the forming performance of 3D woven preforms (3DWPs). However, the macroscopic continuous model of 3DWPs cannot predict the orientation and deformation of yarns, while the microscopic discrete model is unsuitable for large-size samples forming simulation due to computational constraints. A novel mesoscopic hybrid element yarn model is thus proposed to establish a large-size 3D woven virtual yarn preform (VYP) model and the L-shaped virtual forming simulation model. The L-shaped forming experiment of the 3DWP is designed and executed, and the accuracy of the numerical simulation models is verified from three aspects: the mechanical curve, the sample's macroscopic morphology, and the local meso -structure features. Subsequently, the deformation behavior of the yarn structure inside the 3DWP during the L-shaped forming process is analyzed by combining experimental and simulation results. Furthermore, the effects of the L-shaped angle and chamfer radius on the deformation behavior of the 3DWP are studied, which helps guide the structural design of 3DWPs. [ABSTRACT FROM AUTHOR]

• GLNSYMnO 3 has a single-phase orthorhombic perovskite structure sintered at 1250 °C. • The high configurational entropy drives the structural stability of GLNSYMnO 3 even at temperatures below T C. • GLNSYMnO 3 has good magnetism due to the lattice distortion and double-exchange. • GLNSYMnO 3 is a second-order magnetic phase transition material with potential applications in magnetic storage. Rare-earth transition element high-entropy perovskite ceramics (HEPCs) (Gd 0.2 La 0.2 Nd 0.2 Sm 0.2 Y 0.2)MnO 3 (GLNSYMnO 3) were prepared by using a solid-state reaction method, and the structure, morphology and magnetic properties of the GLNSYMnO 3 were studied. The crystalline structure of GLNSYMnO 3 is a smooth surface and similar to "cashews" tubular particles, and a single-phase orthorhombic (Pbnm Space group) perovskite without impurities is confirmed when the sintered temperature reaches or exceeds 1250 °C by the analysis of phase composition and microstructures. Magnetic measurements indicate that GLNSYMnO 3 HEPCs have low Curie temperature (T C = 61 K), but they exhibit a good magnetism by comparison with other high-entropy ceramics, which is caused by the lattice distortion and exchange interactions between high concentrations of Mn3+ (92.4 %) and non-magnetic oxygen ions, promoting the movement of circulating electrons. GLNSYMnO 3 HEPCs did not undergo a phase transition below T C due to high configurational entropy driving structural stability. In addition, GLNSYMnO 3 HEPCs undergo a second-order magnetic phase transition from isothermal magnetization curves and Arrot curves, which have potential applications in magnetic storage. [ABSTRACT FROM AUTHOR]

I-section carbon fiber reinforced polymer (I-CFRP) laminates are widely used as hatch and cargo doors of wide-body airliners. However, estimating the loading capacity of I-CFRP laminates has always been empirically dependent since there is no uniform failure criterion for composite materials. This study attempts to reveal the failure characteristic points of I-CFRP laminates from a thermodynamic perspective. First, the test strain data can be modeled as state variables, thus equating the loaded engineering structure to a thermodynamic system. Based on the relation of state variables, matrices (Modes) and Hamiltonians (Characteristic parameters) that characterize the overall stressing state evolution of the specimen can be established. Integrating the Hamiltonians of each part into the whole is similar to the group representation and renormalization of Wilson's phase transition theory. Applying the clustering analysis (CA) criterion in combination with the bifurcation and transition of the mode and characteristic parameter curves reveals the phase transformation loads of the specimen. The accuracy and stability can be verified for the phase transition loads before and after the renormalization transformation. In conclusion, this study reveals the deformation-failure law of I-CFRP laminates from the thermodynamic perspective, which provides a new reference and method for the design of composite laminates. [Display omitted] • Eleven I-CFRP laminates axial compression tests were conducted. • Modeling the behavior of I-CFRP laminates from a thermodynamic perspective. • Propose a matrix representation (Mode) and Hamiltonian (Characteristic parameter) of the I-CFRP laminates' stressing state. • Reveal the phase transition point of I-CFRP laminates: EPB and FS points. [ABSTRACT FROM AUTHOR]

Systemic lupus erythematosus (SLE) is an autoimmune disorder characterized by complex clinical symptoms and multi-organ damage. One of the most prevalent complications of SLE is lupus nephritis (LN). Rutin, a natural flavonoid compound found in various plants used in traditional Chinese medicine, has shown promising anti-inflammatory, antioxidant, and renal protective effects. In our study, we treated MRL/lpr mice, a model known for spontaneously developing LN, with Rutin. Our findings reveal that Rutin markedly reduced serum cytokine and autoantibody levels and decreased inflammatory cell infiltration in renal tissues, thereby ameliorating kidney pathology. In vitro experiments indicated that Rutin's therapeutic effect on LN is linked to its significant reduction of oxidative stress in T cells. Further investigations suggest that Rutin enhances oxidative stress management through the modulation of Peroxisome proliferator-activated receptor gamma (PPARγ). We observed that Rutin modulates PPARγ activity, leading to reduced transcriptional activity of NF-κB and STAT3, which in turn inhibits the secretion of inflammatory cytokines such as IL-6, TNF-α, and IL-17. In summary, Rutin can exert an antioxidant effect by regulating PPARγ and shows therapeutic action against LN. [Display omitted] • Rutin ameliorates lupus-like symptoms in MRL/lpr mice by improving hematologic and renal histopathological conditions. • Rutin mitigates T cell oxidative stress through the PPARγ. • PPARγ interacts with STAT3 and NFκB, resulting in reduced STAT3 and NFκB activity. [ABSTRACT FROM AUTHOR]

CDs are applied in the enhancement of EL of Poly (9-vinyl carbazole) with the increased brightness by 54.3 %. [Display omitted] • CDs are applied in the enhancement of EL of PVK with increased brightness by 54.3 %. • The CDs enhanced PVK LEDs possess the EL wavelength of 432 nm. • The CIE coordinates of CDs enhanced PVK LEDs are at (0.17, 0.10) with the brightness of 176 cd m−2. • The mechanisms of charge transport between CDs and PVK are determined by TPV and method of EMD. Carbon dots (CDs) are one species of multifunctionally auxiliary materials with promising application in photoelectrocatalyst and photoelectrodevices. However, there are few reports on enhancing the electroluminescence (EL) of materials by CDs and the effects of CDs on the charge transport dynamics have not been revealed so far. Herein, CDs are firstly successful to be applied in the enhancement of EL of Poly (9-vinyl carbazole), which is set as model material for research, abbreviated as PVK with the EL wavelength of 432 nm. The brightness of CDs enhanced PVK LEDs reaches 176 cd m−2 that obviously 54.3 % higher than that of pure PVK based LEDs and other parameters are all improved. The Commission Internationale d'Eclairage coordinates of CDs enhanced PVK LEDs are situated at (0.17, 0.10). Notably, through the transient photovoltage technology and method of empirical mode decomposition, the mechanisms of charge transport dynamics between CDs and PVK molecules are determined. This work paves a new direction for the improvement of the luminescent properties of electroluminescent devices and a new mentality to understand the effects of CDs on charge transport in the EL process. [ABSTRACT FROM AUTHOR]

Miniaturization is a trend of development toward practical applications for diamond nitrogen-vacancy centers-based sensors. We demonstrate a compact diamond magnetic field sensor device using a standard microfabrication process. A single-crystal-diamond plate is embedded in a cavity formed with stacking of three silicon chips. Thermal compression bonding is implemented at silicon–silicon and diamond–silicon interfaces ensuring mechanical robustness. The specific construction volume for the essential sensor component is about 10 × 10 × 1.5 mm3. By integrating a gradient index lens pigtailed fiber to the sensor device, 532-nm laser light and emitted fluorescence share a common path for excitation and detection. An omega-shaped transmission line for applied microwave power is fabricated directly on the surface of diamond. The integrated sensor device exhibits an optimized sensitivity of 2.03 nT·Hz−1/2 and over twofold enhancement of fluorescence collection efficiency compared to bare diamond. Such a sensor is utilized to measure a magnetic field change caused by switching a household electrical appliance. [ABSTRACT FROM AUTHOR]

Student Dropout Prediction (SDP) is pivotal in mitigating withdrawals in Massive Open Online Courses. Previous studies generally modeled the SDP problem as a binary classification task, providing a single prediction outcome. Accordingly, some attempts introduce survival analysis methods to achieve continuous and consistent predictions over time. However, the volatility and sparsity of data always weaken the models' performance. Prevailing solutions rely heavily on data pre-processing independent of predictive models, which are labor-intensive and may contaminate authentic data. This paper proposes a Survival Analysis based Volatility and Sparsity Modeling Network (SAVSNet) to address these issues in an end-to-end deep learning framework. Specifically, SAVSNet smooths the volatile time series by convolution network while preserving the original data information using Long-Short Term Memory Network (LSTM). Furthermore, we propose a Time-Missing-Aware LSTM unit to mitigate the impact of data sparsity by integrating informative missingness patterns into the model. A survival analysis loss function is adopted for parameter estimation, and the model outputs monotonically decreasing survival probabilities. In the experiments, we compare the proposed method with state-of-the-art methods in two real-world MOOC datasets, and the experiment results show the effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the prevalence and characteristics of imipenem-resistant Acinetobacter baumanni isolated from surgical wards in a university hospital, China. A total of 143 non-duplicate A. baumannii were isolated from 517 inpatients in surgery intensive care units (ICUs), burn wards, and general surgery wards. Of these, 102 isolates of A. baumannii (71.3%) were resistant to imipenem. Among imipenem-resistant isolates, all isolates were resistant to almost all antimicrobial agents except polymyxin E, all isolates were positive for blaOXA-23 and bla OXA-51 in addition to IS Aba1, 52 (51%) were positive for blaOXA-58 , 8 (7.8%) contained blaVIM-2 , which co-harbored with blaOXA-58 . Molecular typing revealed the presence of three clones among imipenem-resistant isolates. This study confirmed that A. baumannii strains harboring OXA or VIM type β-lactamases are widely distributed throughout the surgery wards. The data demonstrate that there was a high prevalence of imipenem-resistant A. baumannii infection in the region. [ABSTRACT FROM AUTHOR]

Dynamic characteristics greatly influence the comprehensive performance of a structure. But they are rarely included as objectives in traditional robust optimization of structures. In this study, a robust optimization model including both means and standard deviations of dynamic characteristic indices in the objective and constraint functions is constructed for improving the structural dynamic characteristics and reducing their fluctuations under uncertainty. Adaptive Kriging models are employed for the efficient computation of dynamic characteristics. An intelligent resampling technology is proposed to save computational costs and accelerate convergence of Kriging models, which takes full advantage of the test points for precision verification, the sample points within the local region of the biggest relative maximum absolute error and the near-optimal point to improve the global and local precision of Krigings. The high efficiency of proposed intelligent resampling technology is demonstrated by a numerical example. Finally, an efficient algorithm integrating adaptive Kriging models, Monte Carlo (MC) method, constrained non-dominated sorting genetic algorithm (CNSGA) is proposed to solve the robust optimization model of structural dynamic characteristics. Kriging models are interfaced with MC method to efficiently compute the fitness of individuals during CNSGA. The implementation of proposed methodology is explained in detail and highlighted by the robust optimization of a cone ring fixture with lots of circumferentially distributed holes in a large turbo generator aimed at moving its natural frequencies away from the exciting one. The comparison of the optimized design with the initial one demonstrates that the proposed methodology is feasible and applicable in engineering practice. [ABSTRACT FROM AUTHOR]

Mesoporous hydroxyapatite (HA) was synthesized through gas–liquid chemical precipitation method at ambient temperature without any template. Structure, morphology and pore size distribution of HA were analyzed via X-ray powder diffraction, scanning electron microscopy, transmission electron microscopy, high-resolution electron microscopy and N 2 adsorption/desorption. The chemotherapeutic agent doxorubicin (DOX) was used to investigate the drug adsorption and release behavior of HA. The kinetics of DOX adsorption on HA followed the pseudo-second-order rate expression. Adsorption isotherms at various temperatures were obtained, and the equilibrium data fitted the Langmuir model. The values of thermodynamic parameters (Gibbs free energy, entropy, and enthalpy changes) demonstrated that the adsorption process was spontaneous and endothermic. In vitro pH-responsive (pH = 7.4, 5.8) controlled release was investigated. DOX-loaded HA showed a slow, long-term, and steady release rate. The release rate at pH5.8 was larger than that at pH7.4. Consequently, the as-prepared mesoporous HA has potential applications in controlled drug delivery systems. [ABSTRACT FROM AUTHOR]

In this contribution, we introduced a simple, efficient, and green method of preparing a mesoporous Fe 3 O 4 /hydroxyapatite (HA) composite. The as-prepared material had a large surface area, high pore volume, and good magnetic separability, which made it suitable for targeted drug delivery systems. The chemotherapeutic agent doxorubicin (DOX) was used to investigate the drug release behavior of Fe 3 O 4 /HA composite. The drug release profiles displayed a little burst effect and pH-dependent behavior. The release rate of DOX at pH 5.8 was larger than that at pH 7.4, which could be attributed to DOX protonation in acid medium. In addition, the released DOX concentrations remained at 0.83 and 1.39 μg/ml at pH 7.4 and 5.8, respectively, which indicated slow, steady, and safe release rates. Therefore, the as-prepared Fe 3 O 4 /hydroxyapatite composite could be an efficient platform for targeted anticancer drug delivery. [ABSTRACT FROM AUTHOR]

• A modified-YOLOv5s was proposed to detect defects of wire braided hoses. • K-means++ clustering algorithm is adopted to obtain more suitable anchor boxes. • Focal loss function was adopted to balance between negative and positive samples. • New tailored prediction heads and efficient channel attention (ECA) mechanism were used to improve the detection performance. High-pressure steel wire braided hoses have periodic braided steel wire structure and its productivity is too huge to refrain from making mistakes occasionally such as wire missed wire stacked and wire loosened. The major sticking points in detection are ultra-small, structural similarity and high reflectance of the steel wire braided hoses. Therefore, this paper proposed an automated method based on YOLOv5 to replace traditional naked eyes detection. The anchor boxes were optimized by K-means++ algorithm for defects dataset. Focal loss was used to mitigate the impact of samples imbalance. The predict heads were modified to increase the detection accuracy of ultra-small targets with guaranteeing speed. The effect of efficient channel attention mechanism (ECA) in different positions was analyzed to achieve a better network and increase detection performance. The experimental results showed that the accuracy and recognition efficiency of the proposed modified-YOLOv5s model could reach 92.2% and 23 frames per second. [ABSTRACT FROM AUTHOR]

Failure mechanism of complex profile component is always different from that of conventional plate counterpart due to the coupling effect of material and structure. In this work, the low-velocity impact (LVI) and compression after impact (CAI) behaviors of Ω-shape hybrid carbon/Kevlar 3 D orthogonal woven (3DOW) composite made for vehicle B-pillar were comprehensively studied by mechanical tests and mesoscale finite element (FE) analysis at component level, high-speed infrared (IR) thermal imaging, acoustic emission (AE) detection, and microscopic damage morphology characterization. It is found that a through-thickness stress concentration ring leads to high stress state and damage zone penetrating from the impact side to non-impact side along the ring path instead of at the lowest impactor position. The slope effect can not only help the stress conduction downward, but also inhibit the damage propagation from the impact side to the slope. Impact-induced cracks are concentrated around the R corners and extended along the axial direction of the specimen, forming the strip-shaped damage concentration zone along the upper eave of the slope. The Progressive Top-Down Crushing (PTDC) mode of compression after impact is due to the complex deformation process of each yarn such as squeezing, folding and eversion in the crushing process from the top of specimen. And the Middle Indentation Fracture (MIF) mode is the result of bending instability and abrupt fracture. This work presents a reference significance for the further development of composite strengthening components in vehicle bodywork. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including classroom use of clickers; impact of international trips on students to gain cross-cultural intelligence; and globalization.

To solve the problems of poor exploration ability and convergence speed of traditional deep reinforcement learning in the navigation task of the patrol robot under indoor specified routes, an improved deep reinforcement learning algorithm based on Pan/Tilt/Zoom(PTZ) image information was proposed in this paper. The obtained symmetric image information and target position information are taken as the input of the network, the speed of the robot is taken as the output of the next action, and the circular route with boundary is taken as the test. The improved reward and punishment function is designed to improve the convergence speed of the algorithm and optimize the path so that the robot can plan a safer path while avoiding obstacles first. Compared with Deep Q Network(DQN) algorithm, the convergence speed after improvement is shortened by about 40%, and the loss function is more stable. [ABSTRACT FROM AUTHOR]

• Advantages of asymmetric hybridization on impact resistance and resilience. • Rules of external damage area and internal crack volume distribution. • Damage mechanism of carbon/Kevlar hybridization laminates. The ply structure has a significant effect on the impact resistance and failure behavior of composite laminates. In this paper, carbon/Kevlar hybrid fabric reinforcements were prepared with different Kevlar ratio (0%, 25%, 50%, 75%, 100%) by replacing carbon fabric plies on the non-impact side by Kevlar plies. The low-velocity impact tests for all configurations were carried out under 15 J and 25 J energy respectively. The results show that the maximum deflection, impact duration, impact absorption energy and absorption energy efficiency of hybrid laminates increased with the Kevlar hybrid ratio, whereas the peak force presented a decreasing trend, which indicates that the adding of Kevlar layer makes the laminates more ductile than non-hybrid carbon structure. The damage mechanism of the carbon fiber ply was brittle fiber fracture, resin cracking and resin shedding, while the fibers pull-out and tows splitting was found in Kevlar ply. The critical structural damages start from the non-impact side and evolve to the impact side. And the rules of internal crack volume distribution with Kevlar plies are consistent with the trend of external damage area on the non-impact side. It is suggested that in order to take into account the peak strength and excellent structural resilience, Kevlar asymmetric-hybrid ratio of 25% is the most appropriate. The reason is that an appropriate small ratio of Kevlar cushion ply can not only effectively avoid the tensile fracture at non-impact side happened in the non-hybrid carbon structure, but also withstand enough impact load by maintaining a high carbon fiber content. [ABSTRACT FROM AUTHOR]

[Display omitted] • Self-assembly by electrostatic interaction between silane and MOF ligand. • The activation mechanism of electrochemical inert metal Co in SiO x materials was investigated. • The catalytic action of atomic Co site activates Si O bond and Li O bond to increase SiO x capacity and coulomb efficiency. • High mechanical strength and high conductivity Co nanoparticles enhance the conductivity and cycling stability of SiO x. The ultra-high theoretical capacity of silicon-based (Si) materials makes them promising anode materials for high energy density lithium-ion batteries. Unfortunately, the dramatic volume change (∼300%) and low electrical conductivity of silicon have severely hindered the commercial use of silicon anodes. Silicon suboxide (SiO x) is one of the ideal candidates for high energy density batteries due to its reduced swelling and lower cost as compared to silicon. However, it remains a huge challenge to address the low conductivity, low (initial) coulombic efficiency and apparent volume effects of SiO x. In this paper, SiO x /Co@C composite anode materials loaded with metal Co nanoparticles were uniformly and efficiently prepared by using 3-aminopropyl triethoxysilane (APTES) as silicon source and Co-MOF as Co source via molecular self-assembly strategy. The experimental results and density functional theory (DFT) calculation showed that the catalytic action of embedded Co nanoparticles had activated the silico-oxygen bond of SiO x and the irreversible product Li 2 O, thereby improving the initial coulombic efficiency (ICE) and enhancing the reversible capacity. At the same time, the metal Co with mechanical rigidity and electrical conductivity alleviated the volume fluctuation during alloying and improved the charge transfer capacity and ion transport rate of the composite. When compared with SiO x @C anodes, SiO x /Co@C-600 composites showed higher initial CE, superior cyclic stability and good rate performance. In particular, the successful matching of the anode with the lithium iron phosphate (LFP) cathode in full cell systems validated the possibility of its practical application. This work provides important insights into the application of Si-based materials in lithium-ion battery electrode materials and the development of high energy density batteries. [ABSTRACT FROM AUTHOR]

• A temperature component is given by theoretical inference and parameter optimization. • Separate modeling technique is proposed based on ICEEMDAN and clustering analysis. • The ET-SMT-DELM model is established by a multi-output deep extreme learning machine. • The proposed model has high accuracy, credibility and interpretability. The anatomy of service status through health monitoring models is essential to long-term structural safety. Since deformation is the intuitive representation of the dam's operating condition, it is crucial to investigate the deformation monitoring model with high accuracy and strong interpretability for the safety management of high arch dams. A novel deformation monitoring model is proposed by incorporating impulse response-based equivalent temperature (ET) and machine learning-aided separate modeling technique (SMT). This methodology has three main sources of novelty. First, the impulse response-based equivalent temperature and corresponding temperature component are derived from heat transfer and temperature convolution theories. Then, the model parameters are identified by an improved firefly algorithm. Second, the components in the monitored displacement are progressively stripped out by clustering analysis, separation under equal water level conditions, and a robust signal decomposition technique. Third, the separated deformation components and environmental factors are modeled by the multi-output deep extreme learning machine (DELM) with autoencoder, and the monitoring model ET-SMT-DELM is thus established. The world's highest arch dam is selected to illustrate the proposed model, and the prediction accuracy, early warning performance, component shares, and impulse response mechanism are comprehensively investigated by comparing with several typical baseline models. The results show that the overfitting of the proposed model is reduced, and the prediction and early warning performance is significantly improved. The resulting temperature impulse response function and component shares imply that the interpretability of the model is also enhanced. [ABSTRACT FROM AUTHOR]

Hydrothermal liquefaction (HTL) of microalgae for bio-oil production is a promising strategy to avoid extensive fossil fuel utilization, while tandem amidation and Maillard reactions during hydrolysis of cellular compounds unavoidably result in formation of nitrogen containing (N-containing) compounds, reducing quality of bio-oil. Accordingly, VO 4 3− intercalated Cu–Fe layered double oxides named as Cu 2 Fe-VO 4 -LDO is firstly synthesized to both catalyze hydrolysis of cellular compounds and block N-containing compounds formation. The results indicate Cu 2 Fe-VO 4 -LDO can improve bio-oil yield from 19.32 to 20.32 wt%, with decrease of N/C ratio from 0.70 of control to 0.67 of experimental group, and increase of H/C ratio from 0.129 to 0.132. The insert of VO 4 3− provides more strong-acid sites, which are beneficial to hydrolysis of cellular compounds. Meanwhile, more strong-base sites of Cu 2 Fe-VO 4 -LDO promote decarboxylation of fatty acids and removing amino-group of amino acids, subsequently improve production of alkanes. Furthermore, the reversible oxidation of VO 4 3− promotes oxidation of reducing sugars, blocking tandem amidation and Maillard reaction between intermedia of hydrolyzed lipids, carbohydrates, and proteins. This work reports a methodology for synthesis of Cu 2 Fe-VO 4 -LDO with strong acid-base sites and reversible oxidize interlayer sites, providing a strategy to simultaneously improve bio-oil yield and reduce its N/C ratio. [Display omitted] • Intercalated of VO 4 3− provides stronger basic sites and reversible redox property. • Enhanced de-amino and oxidation of glucose leads to decrease of N/C from 0.70 to 0.67. • VO 4 3− favors decarboxylation, leading to increase of H/C from 0.129 to 0.132. [ABSTRACT FROM AUTHOR]

Accurately identifying various meso-morphological features and sub-phases within the damaged braided composite fabric is crucial for assessing the mechanical properties of composites under complex loading conditions and different processing parameters. However, obtaining 3D reconstructions that can be quantitatively analyzed is still a challenge, primarily due to the low contrast of the meso-morphological features (yarn tow and matrix crack networks). In this work, a new data enhancement algorithm is proposed to generate a realistic-looking artificial training dataset for enriching the information of the meso-morphological features. Then, the effect of various training networks and training parameters (the size of real and hybrid training dataset, number of epochs) on segmentation performance were examined. Finally, the meso-morphological features were statistically analyzed to accurately evaluate the mechanical properties of the composite material. The work presented here provides an effective tool that enables the quantitative analysis of the dynamic crack evolution process under the axial load of the composite materials. [Display omitted] [ABSTRACT FROM AUTHOR]

Spatial variability of tensile strength, widely existing in concrete gravity dams due to the influence of long construction period and heterogeneous construction quality, has a profound impact on the nonlinear behaviour of structures. In this paper, a method to simulate the random field of the tensile strength of concrete gravity dams based on the random field theory is proposed. A typical non-overflow dam section of the Guandi gravity dam is taken as an example, artificial seismic waves with similar response spectra and natural seismic waves with different magnitudes are selected for the comparative analysis. Based on the concrete damage plasticity (CDP) model and incremental dynamic analysis (IDA) method, the damage characteristics, failure modes, probability density evolution processes, and fragility curves obtained from gravity dams with homogenous and random fields of tensile strength are compared with each other. The differences among waves and realisations of random fields are also investigated. It is seen that the spatial variability of tensile strength aggravates the damage degree, decreases the continuity of local damaged areas, and increased the failure probability of global dams. It reveals that the random characteristics affect the dams' nonlinear behaviour directly. [ABSTRACT FROM AUTHOR]

IR-drop is a fundamental constraint by almost all integrated circuits (ICs) physical designs, and many iterations of timing engineer change order (ECO), IR-drop ECO, or other ECO are needed before design signoff. However, IR-drop analysis usually takes a long time and wastes so many resources. In this work, we develop a fast dynamic IR-drop predictor based on a machine learning technique, XGBoost, and the prediction method can be applied to vector-based and vectorless IR-drop analysis simultaneously. Correlation coefficient is often used to characterize the symmetry of prediction data and golden data, and our experiments show that the prediction correlation coefficient is more than 0.96 and the average error is no more than 1.3 mV for two industry designs, which are of 2.4 million and 3.7 million instances, respectively, and that the analysis is speeded up over 4.3 times compared with the IR-drop analysis by commercial tool, Redhawk. [ABSTRACT FROM AUTHOR]

• Time to WBE results should be considered when evaluating early warning potential. • WBE early warning driven by differential in clinical and WBE reporting. • WBE early warning potential limited in situations with available and rapid clinical testing. Wastewater-based epidemiology (WBE) has emerged as a useful tool in the fight to track and contain COVID-19 spread within communities. One of the motives behind COVID-19 WBE efforts is the potential for 'early warning' of either the onset of disease in a new setting or changes in trends in communities where disease is endemic. Many initial reports of the early warning potential of WBE have relied upon retrospective sample analysis, and delays in WBE analysis and reporting should be considered when evaluating the early warning potential of WBE that enable public health action. Our purpose in this manuscript is to establish a framework to critique the potential of WBE to serve as an early warning system, with special attention to the onset of viral shedding and the differential between results reporting for WBE and clinical testing. While many uncertainties remain regarding both COVID-19 clinical presentation and technical factors influencing WBE results, our analysis suggests at most a modest lead time interval ranging from six days for clinical testing to four days for WBE during community-level wastewater surveillance where clinical testing is accessible on-demand with a rapid time to results. This potential lead time for WBE subsequently increases in settings with limited clinical testing capacity or utilization. Care should be taken when reporting 'early detection' of COVID-19 disease trends via WBE to consider underlying causes (e.g., clinical testing lag or delayed result reporting) to avoid misrepresenting WBE potential. [ABSTRACT FROM AUTHOR]

Background and Aims. Hepatic encephalopathy (HE) is characterized by recurrence and poor quality of life. Acute-on-chronic liver failure (ACLF) mainly occurs in patients with chronic liver diseases and often presents with HE. Several predictive models have been proposed to predict the outcomes of these patients. Our study is aimed at identifying associated risk factors and the prognostic accuracies of predictive models in HE patients with or without ACLF. Methods. Patients with liver cirrhosis were retrospectively enrolled. Risk factors were evaluated by multivariate regression analyses. The predictive capabilities of models were calculated using the receiver operating characteristic (ROC) curve analyses and compared by the DeLong tests. Outcomes were defined as in-hospital mortality, HE severity, and ACLF occurrence. Results. In multivariate regression analyses, serum biomarkers neutrophil and total bilirubin (TBIL) were independently correlated with in-hospital death. Alanine aminotransferase (ALT) and blood urea nitrogen (BUN) were independent serum biomarkers associated with HE severity. Hemoglobin, TBIL, BUN, and international normalized ratio (INR) were significant indicators associated with ACLF incidence. For prediction of in-hospital mortality, Child-Pugh was superior to the others in the whole patients, while NLR showed the best capability in the ACLF group. Conclusion. In cirrhotic patients present with HE, BUN is a risk factor associated with HE severity and ACLF incidence. Child-Pugh and NLR scores may be effective prognosticators in patients with HE. [ABSTRACT FROM AUTHOR]

Both Kaposi's sarcoma-associated herpesvirus (KSHV) and Epstein-Barr virus (EBV) establish the persistent, life-long infection primarily at the latent status, and associate with certain types of tumors, such as B cell lymphomas, especially in immuno-compromised individuals including people living with HIV (PLWH). Lytic reactivation of these viruses can be employed to kill tumor cells harboring latently infected viral episomes through the viral cytopathic effects and the subsequent antiviral immune responses. In this study, we identified that polo-like kinase 1 (PLK1) is induced by KSHV de novo infection as well as lytic switch from KSHV latency. We further demonstrated that PLK1 depletion or inhibition facilitates KSHV reactivation and promotes cell death of KSHV-infected lymphoma cells. Mechanistically, PLK1 regulates Myc that is critical to both maintenance of KSHV latency and support of cell survival, and preferentially affects the level of H3K27me3 inactive mark both globally and at certain loci of KSHV viral episomes. Furthremore, we recognized that PLK1 inhibition synergizes with STAT3 inhibition to efficiently induce KSHV reactivation. We also confirmed that PLK1 depletion or inhibition yields the similar effect on EBV lytic reactivation and cell death of EBV-infected lymphoma cells. Lastly, we noticed that PLK1 in B cells is elevated in the context of HIV infection and caused by HIV Nef protein to favor KSHV/EBV latency. Author summary: KSHV is a large double-stranded DNA virus belonging to human gamma-herpesviruses and capable of establishing the long-term persistent infection, which causes cancers under immunocompromised states, such as acquired immunodeficiency syndrome (AIDS). There are currently still no effective therapies to specifically treat KSHV-associated tumors, nor eliminate its persistent infection. In this study, we illustrated that PLK1 is upregulated by KSHV, which in return suppresses KSHV lytic infection. We further demonstrated that PLK1 is a novel host target that can be inhibited pharmacologically to benefit the viral oncolysis for promotion of KSHV lytic reactivation and elimination of KSHV-positive lymphoma cells, particularly for people living with HIV (PLWH). PLK1 inhibitors also exerted the similar effect on EBV that is the other member of human gamma-herpesvirus. [ABSTRACT FROM AUTHOR]

Here, we show the fabrication of the carbon dots (CDs) with green and orange emissions from PTCDA (perylene‐3,4,9,10‐tetracarboxylic dianhydride). Using these CDs as emitters, the orange (or green) CDs LEDs were fabricated, which show electroluminescence (EL) spectra centered at 560 nm (or 498 nm) with an external quantum efficiency (EQE) of 1.98 % (1.76 %) adhering a luminescence of 626 cd m−2 (or 519 cd m−2). The machine learning was successfully used to predict PL CCT value. With the model, the white photoluminescence (PL) emission with adjustable correlated color temperature (CCT) from 3093 to 11018 K via combining blue, green, and orange CDs was achieved. Then, we obtained the warm white CDs LEDs with CCT of 3107, 4071 and 4548 K, and cold white CDs LEDs with CCT of 5632 (CIE coordinates of (0.33, 0.33), EQE: 1.18 %, luminescence: 598 cd m−2) and 6034 K accurately. [ABSTRACT FROM AUTHOR]

Here, we show the fabrication of the carbon dots (CDs) with green and orange emissions from PTCDA (perylene‐3,4,9,10‐tetracarboxylic dianhydride). Using these CDs as emitters, the orange (or green) CDs LEDs were fabricated, which show electroluminescence (EL) spectra centered at 560 nm (or 498 nm) with an external quantum efficiency (EQE) of 1.98 % (1.76 %) adhering a luminescence of 626 cd m−2 (or 519 cd m−2). The machine learning was successfully used to predict PL CCT value. With the model, the white photoluminescence (PL) emission with adjustable correlated color temperature (CCT) from 3093 to 11018 K via combining blue, green, and orange CDs was achieved. Then, we obtained the warm white CDs LEDs with CCT of 3107, 4071 and 4548 K, and cold white CDs LEDs with CCT of 5632 (CIE coordinates of (0.33, 0.33), EQE: 1.18 %, luminescence: 598 cd m−2) and 6034 K accurately. [ABSTRACT FROM AUTHOR]

Objective: Sepsis causes millions of deaths on a global scale annually. Activation of peptidylarginine deiminase (PAD) enzymes in sepsis causes citrullination of histones, which results in neutrophil extracellular trap formation and sepsis progression. This study evaluates pan-PAD inhibitor, Cl-amidine, in a model of lipopolysaccharide (LPS)-induced endotoxic shock in rabbits. We hypothesized that Cl-amidine would improve survival and attenuate kidney injury. Methods: In the survival model, rabbits were injected injected intravenously with 1 mg/kg of LPS, and then randomly assigned either to receive dimethyl sulfoxide (DMSO; 1 mcL/g) or Cl-amidine (10 mg/kg diluted in 1 mcL/g DMSO). They were then monitored for 14 days to evaluate survival. In the non-survival experiment, the same insult and treatment were administered, however; the animals were euthanized 12 hours after LPS injection for kidney harvest. Acute kidney injury (AKI) scoring was performed by a histopathologist who was blinded to the group assignment. Serial blood samples were also collected and compared. Results: Rabbits that received Cl-amidine had a higher survival (72%) compared with the rabbits that received DMSO (14%; p < 0.05). Cl-amidine-treated rabbits had lower (p < 0.05) histopathologic AKI scores, as well as plasma creatinine and blood urea nitrogen (BUN) levels 12 hours after insult. Conclusions: Pan-PAD inhibitor Cl-amidine improves survival and attenuates kidney injury in LPS-induced endotoxic shock in rabbits. [ABSTRACT FROM AUTHOR]

In this article, five different cell placement structures of a vertical inverter chain were designed in a commercial bulk FinFET technology to investigate the effect of cell placement on a single-event transient (SET) pulse. 3-D TCAD mixed-mode simulation indicated that the cell placement has a significant effect on SET pulsewidths. Heavy-ion experiments at Cyclotron in the China Institute of Modern Physics in Lanzhou also indicated that the effect of cell placement on the SET pulse is obvious. The experiment also indicated that the effect of cell placement on P-hit SET is larger than that on N-hit SET, and charge sharing in vertical was becoming as important as that in horizontal. [ABSTRACT FROM AUTHOR]

The increasing global air traffic has placed higher demands on airport pavement design. However, the behavior of rigid concrete airport pavement (RCAP) under traffic loading is very complex, and the existing reliability theory and empirical concrete pavement analysis methods cannot detect the precise RCAP failure point. Based on this background, this paper attempts to study the failure of RCAP under traffic loading from a thermodynamic perspective. It selects the Federal Aviation Administration (FAA) test in Construction Cycle 8 (CC8) phase 3 as the research material. We apply the structural stressing state theory to equate the RCAP under traffic loading to a thermodynamic system with a phase transition, i.e., we model the whole process strain data as state variables. Then, analogous to the renormalization group method of Wilson's phase transition theory to reveal the critical point of the system, this study establishes matrixes (Modes) and Hamiltonians (Characteristic parameters) that can characterize the RCAP full-process stressing state evolution under traffic loading by accumulating the difference of state variables. We attempted to verify the effectiveness of this method under such complex working conditions, which can also be applied to many other high-speed time-variant loading conditions. By applying the clustering analysis criterion, we can reveal the phase transition points of the RCAP: elastoplastic branch (EPB), failure starting (FS), and progressive failure (PF) points, respectively. Taking the NW group as an example, the variances of EPB, FS, and PF points revealed by the mode and characteristic parameter curves conducted by the two homogeneous measured point selections, respectively, are 7.7,6.4, and 6.4, which are of exceptionally high stability. We also used thermodynamic modeling to reveal the phase transition points of the remaining three RCAPs and compared the effect of different joints on the phase transition points. Finally, this study also explores the feasibility of EPB-based concrete airport pavement design. [Display omitted] • Proposing to model the stressing state of RCAP from a thermodynamic perspective. • The stressing state matrixes and Hamiltonians of the RCAP are proposed. • Applying the clustering analysis criterion revealed the phase transition points of RCAP. • Compare the EPB and FS points for different joint types of RCAP. [ABSTRACT FROM AUTHOR]

In this work, an efficient and green glycerolysis reaction catalyzed by zinc glycerolate was developed to prepare biodiesel from oleic acid. The active phase of zinc glycerolate was investigated by XRD, FTIR, 1H NMR, and 13C NMR. An interesting result was obtained that zinc glycerolate was first dissolved to form zinc oleate and then regenerated to zinc glycerolate during the reaction, which was essential for the conversion of oleic acid. The relationship between dissolved zinc glycerolate and conversion of oleic acid was determined. The regeneration of zinc glycerolate was achieved when the molar ratio of glycerol to oleic acid was higher than 0.6:1. A plausible pathway for the glycerolysis reaction catalyzed by zinc glycerolate was proposed. Zinc glycerolate could be recycled 10 times with no obvious decrease in catalytic activity, and the oleic acid conversion of 98% was achieved. In addition, zinc glycerolate was applied to catalyze glycerolysis reaction of different feedstock and demonstrated excellent performance. The results show that zinc glycerolate is a promising catalyst in glycerolysis reaction for biodiesel production. [Display omitted] [ABSTRACT FROM AUTHOR]

Attracted due to their potentially high theoretical capacities, storage redox activities, and diverse shape, cobalt phosphides (Co 2 P, CoP etc.) are thought to be the promising anodes for lithium-ion batteries (LIBs). Nevertheless, there still remain challenging bottlenecks of their low intrinsic conductivity and severe volume expansion when subjected to repeated electrochemical cycles, which unavoidably bring about structural collapse, active particle aggregation and rapid capacity degradation, and thereby poor long-term lifespan. Herein, we present a distinctively facile route and adopt metal-organic frameworks (MOFs) as the self-sacrificing hosts to construct carbon-encapsulated CoP/Co 2 P@N C composites which are composed of monodispersed electroactive Co x P (CoP/Co 2 P) nanoparticles encapsulated in carbon nanotubes (CNTs) based on N-doped carbon substrates. Remarkably, one exemplary CoP/Co 2 P@N C-600 electrode among them, when serves as anode for LIBs, exhibits high reversible capacity (977.9 mAh g−1, 100 cycles at 0.1 A g−1) and superior rate capability (639.7 mAh g−1 at 4 A g−1). Furthermore, an assembled LFP//CoP/Co 2 P@N C-600 full cell displays remarkable cycling performance. The more advantageous synergistic interaction of distinctive carbon-encapsulated architectures, robust N-doped carbon frameworks and electroactive components provides the fundamental foundation for superior lithium storage capabilities, where the synergistic interaction contributes towards enhancing electronic conductivity, offering extra electrochemical active sites, shortening the Li+/e− transport channels and alleviating volumetric variation. This contribution emphasizes the value of rationally designing MOF-derived carbon-encapsulated metal phosphide-based anodes for advanced LIBs. [ABSTRACT FROM AUTHOR]

• This review summarizes Fe-MOFs and their derivatives as electrodes and electrocatalysts. • Various synthetic strategies of Fe-MOFs and their derivatives are discussed. • The prospects and application on EECS for Fe-MOFs and their derivatives have been highlighted. • The current challenges and possible solutions are proposed. With the increasing industrial activities, the surge of population, rising global climate change concerns, as well as the increasing energy consumption and demands, traditional rechargeable batteries have started losing the capacity to meet the needs of current and future markets. In order to rise up to this challenge, the development of advanced, flexible and controllable energy technology has become the need of the hour. Development of electrochemical energy conversion and storage (EECS) technology is a potential way forward because of its high energy efficiency and environmental friendliness. One way to improve the efficiency of EECS devices is to focus on the development and improvement of their components, such as electrode materials, separators and catalysts. Recently, metal–organic frameworks (MOFs) have become an emerging class of crystalline materials in materials science and coordination chemistry due to their unique activity, interesting properties and tunable structures. Among various transition-metal based MOFs, iron (Fe)-based metal organic frameworks (Fe-MOFs) have attracted special attention due to their excellent physicochemical properties and the abundance of iron. Herein, this article reviews the recent applications of Fe-MOFs and their derivatives in electrochemical energy conversion and storage. In this review, the synthesis methods of Fe-MOFs and their derivatives, energy storage applications of Fe-MOFs in lithium-ion batteries (LIBs), sodium-ion batteries (SIBs), lithium-sulfur batteries (LSBs), supercapacitors (SCs), electrocatalysis and other batteries are discussed in detail. In addition, the achievements and challenges of Fe-MOFs and their derivatives in the fields of rechargeable batteries, supercapacitors and electrocatalysis are summarized. Furthermore, the possible innovative design principles and future prospects of Fe-MOFs and their derivatives as EECS materials are presented. [ABSTRACT FROM AUTHOR]