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Pan-sharpening aims to obtain high resolution multispectral (HRMS) images by integrating the information in the panchromatic and multispectral images. Existing pan-sharpening methods have demonstrated impressive sharpening performance. However, these methods inherently overlook the complementary characteristics and interaction between diverse source images, resulting in sharpened outcomes accompanied by distortion. To solve the above problems, we construct a novel cross fusion model with fine-grained detail reconstruction from the perspective of frequency-domain. The motivation of the model is twofold: (1) to reconstruct spatial detail representations from diverse source images, laying the foundation for the generation of fine details in the subsequent fused images; and (2) to enhance the interaction between diverse source features during the fusion process in order to attain high-fidelity fusion outcomes. Based on the theoretical model, we develop a frequency-spectral dual domain cross fusion network (CF2N) utilizing the deep learning technique. Consequently, the CF2N consist of two main stages, namely frequency-domain dominated detail reconstruction (FD2R) and frequency-spectral cross fusion (FSCF). Specifically, a more reasonable reconstruction of fine frequency details in HRMS can be achieved by performing adaptive weighted fusion of frequency details in the FD2R stage. Furthermore, the FSCF module, which seamlessly integrates frequency- and spectral-domain details in a highly interactive cross fusion manner. As a result, the CF2N possesses the capability to attain high frequency-spectral fidelity results with excellent interpretability. Extensive experiments show the superior performance of ours over state of the art, while maintaining high efficiency. All implementations of this work will be published at our website.

Abstract The continual large-scale urbanization and urban renewal in coastal cities of China have accumulated massive silty residue (SR) that may alter the coastal lines and always exerts high stresses to local environments. To meet China’s sustainable development policy, the need for green and high-efficient industrial treatments and reuse of SR is urgent. In this work, we may, for the first time, report a megaton-scale industrial project to upcycle low-quality SR and recycled aggregate (RA) to manufacture construction materials with hydrothermal mineralization (HM). In-situ pilot tests on five batches of SR-RA blocks were conducted. Results demonstrate that the produced blocks possess the compressive strengths of 11.4–15.8 MPa, densities of 1280–1430 kg/m3, porosities of 35–44%, CO2 emissions of 170.22–187.29 kg e-CO2/m3 and costs of 126.49–156.51 CNY/m3, comparable with or superior than the commercial blocks. The silica in SR could react with lime to produce tobermorite with stable pseudohexagonal plate under HM treatment, which improved the microstructure of the material. The findings validate the industrial practicability of upcycling low-quality SR and RA with HM for valuable construction block manufacture.

Abstract Cast iron alloys with low production cost and quite good mechanical properties are widely used in the automotive industry. To study the mechanical behavior of a typical ductile cast iron (GJS-450) with nodular graphite, uni-axial quasi-static and dynamic tensile tests at strain rates of 10−4, 1, 10, 100, and 250 s−1 were carried out. In order to investigate the influence of stress state on the deformation and fracture parameters, specimens with various geometries were used in the experiments. Stress strain curves and fracture strains of the GJS-450 alloy in the strain rate range of 10−4 to 250 s−1 were obtained. A strain rate-dependent plastic flow model was proposed to describe the mechanical behavior in the corresponding strain-rate range. The available damage model was extended to take the strain rate into account and calibrated based on the analysis of local fracture strains. Simulations with the proposed plastic flow model and the damage model were conducted to observe the deformation and fracture process. The results show that the strain rate has obviously nonlinear effects on the yield stress and fracture strain of GJS-450 alloys. The predictions with the proposed plastic flow and damage models at various strain rates agree well with the experimental results, which illustrates that the rate-dependent plastic flow and damage models can be used to describe the mechanical behavior of cast iron alloys at elevated strain rates. The proposed plastic flow and damage models can be used to describe the deformation and fracture analysis of materials with similar properties.

Abstract High-resolution oasis maps are imperative for understanding ecological and socio-economic development of arid regions. However, due to the late establishment and relatively niche nature of the oasis discipline, there are no high-precision datasets related to oases in the world to date. To fill this gap, detailed visual interpretation of remote sensing images on Google Earth Professional or Sentinel-2 was conducted in summer 2020, and for the first time, a high-precision dataset of China’s oases (abbreviation HDCO) with a resolution of 1 meter was constructed. HDCO comprises 1,466 oases with a total area of 277,375.56 km2. The kappa coefficient for this dataset validated by the field survey was 0.8686 and the AUC value for the ROC curve was 0.935. In addition, information on the geographic coordinates, climatic conditions, major landforms, and hydrological features of each oasis was added to the attribute table of the dataset. This dataset enables researchers to quantitatively monitor location and area of oases, fosters exploration of the relationship between oases and human under climate change and urbanization.

Food flavor plays an important role in people’s life. Traditional methods for flavor analysis and detection have limited ability to predict food flavor. In recent years, many researchers have used machine learning models to effectively process food flavor information and establish classification and prediction models, making flavor prediction more accurate and efficient. The principles of traditional and novel machine learning methods, such as support vector machine (SVM), random forest (RF), k-nearest neighbor (k-NN), and neural network, as well as recent progress on their combined application with flavor analysis instruments and molecular structure analysis for food flavor prediction are reviewed, aiming to provide new ideas for the application of machine learning models in food flavor analysis and prediction. It is found that machine learning models can be used to predict the impacts of different substance components on food flavor, identify the flavor characteristics of foods from different regions. The combination of multiple machine learning models can improve the accuracy and reliability of prediction, and promote in-depth research and development of food flavor.

Abstract Background For acute type A aortic dissection involving the aortic root with root diameter no more than 45 mm, there are various aortic root repair techniques. In this study, a novel surgical technique using a pericardial autograft for aortic root repair was introduced. We described its surgical steps in detail and compare its clinical outcomes with direct suture technique. Methods Between July 2017 and August 2022, 95 patients with acute type A aortic dissection who underwent aortic root repair were enrolled, including aortic root repair using pericardial autograft (group A, n = 49) or direct suture (group B, n = 46). The patient’s clinical data were retrospectively analyzed, and a 5-year follow-up was conducted. Results The 30-day mortality, re-exploration for bleeding, postoperative new-onset renal failure requiring continuous renal replacement therapy, stroke, and paraplegia occurred in 3%, 4%, 11%, 5%, and 2% of the overall patients, respectively. There was no significant difference in the 30-day mortality and complication rate between the two groups. The 30-day mortality and re-exploration for bleeding marked the primary endpoint events. Logistic regression analysis indicated that there was a significant correlation between the primary endpoint events and surgical technique (odds ratio, 0.002; 95% confidence interval, 0-0.159; P = 0.026). The aortic valve insufficiency of the two groups were significantly improved after operation (group A, P

Large-volume presses (LVPs) providing large volumes, liquid media, deformation capability, jump compression, and in situ measurements are in great demand for high-pressure research, particularly in the fields of geoscience, condensed matter physics, material science, chemistry, and biology. A high-pressure and high-temperature (HPHT) platform with different LVP subsystems, both solid-state and liquid environments, and nonequilibrium subsystems, has been constructed at the Synergetic Extreme Condition User Facility, Jilin University. This article describes the construction of the different subsystems and provides an overview of the capabilities and characteristics of the different HPHT subsystems. A large sample volume (1000 mm3) at 20 GPa is achieved through the use of a belt-type apparatus in the solid-state subsystem. HPHT conditions (1.8 GPa and 1000 K) are realized in the liquid subsystem through the use of a piston–cylinder-type LVP with optical diamond windows for in situ spectroscopic measurements. A maximum pressure jump to 10.2 GPa can be reached within 20 ms in the nonequilibrium subsystem with the use of an improved bladder-pressurization jump press. Some typical results obtained with different LVPs are briefly reviewed to illustrate the applications and advantages of these presses. In summary, the platform described here has the potential to contribute greatly to high-pressure research and to innovations in high-pressure technology.

IntroductionThe rapid evolution of online health services (OHS) has significantly improved healthcare accessibility. However, the impact of air pollution on individuals’ willingness to engage with OHS remains underexplored in the existing literature. This study addresses this gap by investigating how air pollution threats influence the intention to use OHS, grounded in Protection Motivation Theory (PMT) and Rational Choice Theory (RCT).MethodsUsing cross-sectional survey data, a theoretical model was developed and tested to examine the relationships between threat appraisal, coping appraisal, perceived benefits, perceived costs, and OHS usage intentions.ResultsThe findings indicate that threat appraisal, coping appraisal, and perceived benefits positively affect the intention to use OHS, while perceived costs negatively impact usage intention. Additionally, age significantly moderates the relationship between coping appraisal and the intention to use OHS.DiscussionThis study extends the application of PMT and RCT in the context of OHS. It provides valuable insights into the factors influencing users’ intentions to utilize these services, with implications for improving OHS adoption in environments affected by air pollution.

ABSTRACT: This study aimed to investigate the developmental change of body growth and gene expression related to fatty acid uptake and oxidation in the yolk sac membrane (YSM) and jejunum during embryogenesis in Muscovy ducks. The weights of embryos and yolk sac (YS) (5 embryos per replicate, n = 6) were recorded on embryonic days (E)16, E19, E22, E25, E28, E31, and the day of hatch (DOH). The fat and fatty acid contents in YSM, jejunal histology, and gene expression related to fatty acid metabolism in YSM and jejunum were determined in each sampling time. Among the nonlinear models, the maximum growth is estimated at 2.83 (E22.5), 2.67 (E22.1), and 2.60 (E21.3) g/d using logistic, Gompertz, and Von Bertalanffy models, respectively. The weight of YS, and ether extract-free YS as well as the amounts of fat and fatty acids in YS decreased (P < 0.05) linearly, whereas the villus height, crypt depth, villus height/crypt depth, and musculature thickness in jejunum increased (P < 0.05) linearly during embryogenesis. The mRNA expression of CD36, SLC27A4, and FABP1 related to fatty acid uptake as well as the mRNA and protein expressions of PPARα and CPT1 related to fatty acid oxidation increased in a quadratic manner (P < 0.05) in both YS and jejunum, and the maximum values were achieved during E25 to E28. In conclusion, the maximum growth rate of Muscovy duck embryos was estimated at 2.60 to 2.83 g/d on E21.3 to E23.5, while the accumulations of lipid and fatty acid in YS were decreased in association with the increased absorptive area of morphological structures in jejunum. The gene and protein expression involved in fatty acid metabolism displayed a similar enhancement pattern between YSM and jejunum during E25 to E28, suggesting that fatty acid utilization could be strengthened to meet the energy demand for embryonic development.

ABSTRACTIn the current study the metabolic response of peanut root exudates to Sclerotium rolfsii Sacc. infection was analyzed in plant-pathogen interaction systems. The analytical methodology employed in this study entailed the utilization of ultra-high-performance liquid chromatography coupled with tandem quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) in relation to metabolomics. The experimental findings have demonstrated that pathogen infection induces significant variations in both the composition and abundance of peanut root exudates. A total of 322 metabolites were identified in peanut root exudates, among which 19 metabolites showed significant differences under pathogen infection. The results of the orthogonal partial least squares score discriminant analysis (OPLS) and hierarchical cluster analysis (HCA) plots clearly demonstrated that the metabolic profiling effectively distinguished between peanut plants inoculated with S. rolfsii Sacc. and the healthy control plants that were not inoculated. Pathogen infection is associated with five metabolic pathways, the most correlated being arginine and proline metabolism and ABC transporters.

Microgrids comprising photovoltaic (PV), energy-storage systems and electric vehicles have received increasing attention. However, the increase in renewable energy sources, such as PVs, will introduce uncertainty factors to the frequency response, resulting in frequency fluctuations that can reduce power quality. Hence, we propose an outstanding learning-based control approach that combines a model of physical principles with a Gaussian Process (GP) learning approach. The learning model corrects the physical principles model and estimates its uncertainty. Simulations are performed by comparing the proposed controller with conventional model-predictive controllers. The results indicate that the learning-based controller proposed can adjust the frequency with less overshooting and a lower root mean square error compared to the conventional MPC control method.

Major depressive disorder (MDD) is currently the most common psychiatric disorder in the world. It characterized by a high incidence of disease with the symptoms like depressed mood, slowed thinking, and reduced cognitive function. Without timely intervention, there is a 20–30% risk of conversion to treatment-resistant depression (TRD) and a high burden for the patient, family and society. Numerous studies have shown that physical activity (PA) is a non-pharmacological treatment that can significantly improve the mental status of patients with MDD and has positive effects on cognitive function, sleep status, and brain plasticity. However, the physiological and psychological effects of different types of PA on individuals vary, and the dosage profile of PA in improving symptoms in patients with MDD has not been elucidated. In most current studies of MDD, PA can be categorized as continuous endurance training (ECT), explosive interval training (EIT), resistance strength training (RST), and mind–body training (MBT), and the effects on patients’ depressive symptoms, cognitive function, and sleep varied. Therefore, the present study was based on a narrative review and included a large number of existing studies to investigate the characteristics and differences in the effects of different PA interventions on MDD. The study also investigated the characteristics and differences of different PA interventions in MDD, and explained the neural mechanisms through the results of multimodal brain function monitoring, including the intracranial environment and brain structure. It aims to provide exercise prescription and theoretical reference for future research in neuroscience and clinical intervention in MDD.

Slit fin-and-tube heat exchangers are extensively utilized in industry. The design of fin structure is crucial for improving the performance of heat exchanger and reducing energy losses. In this study, a new type of fin-and-tube heat exchanger with trapezoidal slit fins was proposed in this study. To determine the performance of the fins, physical and mathematical models of heat exchanger with the fin spacing Fp of 2.65 mm were established. The effects of structural parameters on heat exchanger performance were investigated by numerical simulation, which includes the slit height Sh, slit width Sw, transverse tube spacing Pt, and longitudinal tube spacing Pl. The effects of the four structural parameters on the comprehensive performance were analyzed by the evaluation index. The results revealed that the comprehensive performance of trapezoidal slit fins had increased by 9.6 % compared to flat fins. Furthermore, the influence degree of structural parameters on heat transfer performance was obtained based through the orthogonal experiments. The order of influence degree was as follows: Pl > Pt > Sh > Sw. Additionally, the optimal parameter combination was also obtained as follows: Sh = 1.47 mm, Pt = 42 mm, Pl = 27.5 mm, and Sw = 4 mm.

BackgroundThe incidence of cardiac arrest (CA) during percutaneous coronary intervention (PCI) is relatively rare. However, when it does occur, the mortality rate is extremely high. Extracorporeal cardiopulmonary resuscitation (ECPR) has shown promising survival rates for in-hospital cardiac arrests (IHCA), with low-flow time being an independent prognostic factor for CA. However, there is no definitive answer on how to reduce low-flow time.MethodsThis retrospective study, conducted at a single center, included 39 patients who underwent ECPR during PCI between January 2016 and December 2022. The patients were divided into two cohorts based on whether standby extracorporeal membrane oxygenation (ECMO) was utilized during PCI: standby ECPR (SBE) (n = 13) and extemporaneous ECPR (EE) (n = 26). We compared the 30-day mortality rates between these two cohorts and investigated factors associated with survival.ResultsCompared to the EE cohort, the SBE cohort showed significantly lower low-flow time (P < 0.01), ECMO operation time (P < 0.01), and a lower incidence of acute kidney injury (AKI) (P = 0.017), as well as peak lactate (P < 0.01). Stand-by ECMO was associated with improved 30-day survival (p = 0.036), while prolonged low-flow time (p = 0.004) and a higher SYNTAX II score (p = 0.062) predicted death at 30 days.ConclusionsStandby ECMO can provide significant benefits for patients who undergo ECPR for CA during PCI. It is a viable option for high-risk PCI cases and may enhance the overall prognosis. The low-flow time remains a critical determinant of survival.

In the field of remote sensing, it is not feasible to obtain high spatial resolution multispectral (HRMS) images from a single satellite sensor. The existing methods use pansharpening techniques to obtain HRMS images by fusing panchromatic (PAN) and multispectral (MS) images. However, due to the scale difference between PAN and MS images, most pansharpening methods often use explicit sampling methods to integrate features at different scales. These explicit-based sampling techniques represent pixels as discrete points through predefined functions, rendering it challenging to fit the distribution among diverse modal data, this results in the loss of image texture details during the fusion process. Implicit neural networks can enhance the generative capability of images by incorporating pixel coordinate information, which is crucial for the fusion of remote sensing images with different spatial resolutions. Inspired by implicit neural representation, we propose a progressive implicit neural feature fusion network (PINFNet) for remote sensing images. A progressive implicit neural feature fusion is proposed; it establishes a coordinate modal relationship between the spatial and spectral information through the guidance of the high spatial features in PAN images. This enables the proposed PINFNet to progressively learn and integrate spatial and spectral information at different scales. Our method, as opposed to discrete sampling techniques, is capable of establishing a continuous representation between diverse modal data, which in turn preserves more texture detail information. Extensive experiments have shown that this approach outperforms state-of-the-art methods while maintaining high efficiency.

With the rising adoption of renewable energy sources worldwide, the uncertainty within electrical power systems is amplifying, leading to rising challenges in wind power assimilation and operational costs. In addressing these pressing issues, this research emphasizes a sophisticated modeling technique for the energy-intensive electro-fused magnesium loads on demand. Furthermore, we introduce a coordinated scheduling approach for power systems, harnessing the regulatory capabilities of these loads. Initially, an in-depth analysis of the characteristics of the electro-fused magnesium energy-intensive load is carried out, from which a refined model is developed. Subsequently, considering the flexible regulatory potential of the electro-fused magnesium furnace apparatus, a bi-level optimization scheduling model is crafted, prioritizing the maximization of wind power absorption at the upper level and minimizing system operational costs at the lower level. The efficacy and rationale of the proposed methodology are corroborated through computational analyses.

When traditional grid-forming converters directly participate in the grid-connected operation of the power grid, due to the lack of a pre-synchronization control system, the voltage amplitude and initial phase on both sides of the grid-connected point will deviate, resulting in voltage and current distortion during grid-connected mode. An active support phase-locked loop free pre-synchronization control strategy based on the third-order model of a synchronous generator is proposed to address the grid-connected problem of the grid-forming converter mentioned above. First, a model of active support control with frequency integral feedback at small signal levels was constructed. The root locus method was employed to examine how system parameters affect the stability of the active support control system. Second, by adding phase pre-synchronization controllers and amplitude pre-synchronization controllers to the active frequency loop and excitation voltage loop of the third-order model, it was ensured that the frequency, phase, and voltage amplitude of the unit are consistent with the power grid, achieving a fast and smooth grid-connected mode of the unit. Finally, by using a DC source to simulate all types of new energy power generation equipment, the active support pre-synchronization control system based on the three-order model of synchronous generator is built in the MATLAB/Simulink simulation environment, and the accuracy and effectiveness of the control strategy in this paper is verified.

AbstractThe GRAS transcription factor (TF) is a plant-specific regulator that plays a critical role in plant growth, development and response to various abiotic stresses. While the functions of many GRAS TFs have been extensively studied in numerous model plants with fully sequenced genomes, the GRAS TFs in white clover (Trifolium repens L.) remain elusive despite the sequencing of its genome. To bridge this knowledge gap, homologs of GRAS TFs from Arabidopsis were employed and subjected to a genome-wide blast against white clover proteins. This analysis identified and characterized 102 putative GRAS genes, designated as TrGRAS, based on the presence of GRAS domains in positive blast hits. Concurrently, an exploration of their gene structures, classification, evolutionary relationships and conservative motifs provided insights into the distinctive features of TrGRAS in white clover. Furthermore, gene duplication analysis revealed an expansion of TrGRAS genes in the PAT1 and LISCL subfamilies, indicating functional diversity within the white clover genome. Lastly, RNA-seq and quantitative reverse transcription-PCR (qRT-PCR) experiments confirmed their higher expression during the early or intermittent responses to cold stress, suggesting their potential and crucial roles in determining the cold tolerances of white clover. The analysis of TrGRAS genes, coupled with expression studies, contributes to the functional characterization of TrGRAS genes in white clover. This knowledge is pivotal for informing genetic improvement strategies in future breeding efforts aimed at enhancing cold tolerance in white clover.

This paper proposes an input-parallel output-series (IPOS) Si-SiC hybrid inverter with dual-frequency harmonic elimination modulation strategy. The proposed topology composed of two power conversion cells and a three-phase five-column medium-frequency step-down transformer, the low-frequency power conversion cell (LFPC-C, 1 kHz) leverages strong current-carrying capacity of silicon-based devices for dealing with the system main power, and the high-frequency power conversion cell (HFPC-C, 30 kHz) based on wide-bandgap semiconductor SiC devices is used to addressing the fractional harmonics compensation power. This topology combines the strong current carrying capability of Si devices with the low switching loss of SiC devices at high frequency and achieves high quality power conversion at low cost and low loss. Compared to existing “IPOP” Si-SiC hybrid inverters, this topology adopts a coupling step-down transformer on the output side of both LFPC-C and HFPC-C, which can effectively reduce current stress of HFPC-C SiC devices. Additionally, a dual-frequency harmonic elimination modulation strategy based on the topology is proposed to solve the fractional harmonics caused by the LFPC-C. The paper establishes a mathematical model according to the harmonic distribution characteristics of the LFPC-C and HFPC-C, and designs the system control schedules. Building upon the derivation of the voltage ripple model and the design of hardware parameters, Si IGBT and SiC MOSFET were selected for constructing a 7.5 kW prototype for testing, and the experimental results validate the feasibility of this topology and the accuracy of theoretical analysis.

A tremendous amount of research has witnessed the exploration of two-dimensional (2D) materials with intrinsic ferromagnetism and diverse physical properties. However, the low Curie temperature and deficient magnetic anisotropy hinder their practical applications in nanoscale spintronics. Based on first-principles calculations, we propose a new family of 2D ternary transition metal tellurides, CrX2Te4 (X = Al, Ga, and In), with both structural and magnetic stabilities at room temperature. Our calculations demonstrate that the 2D CrX2Te4 crystal exhibits the intrinsic 100% spin-polarized half-metallic feature with spin-up metallic and spin-down semi-conducting properties. With the remarkable magnetic moment of 4 μB per Cr atom, both 2D CrAl2Te4 and CrGa2Te4 crystals perform robust ferromagnetism with the out-of-plane magnetic anisotropy, while the 2D CrIn2Te4 crystal prefers the in-plane easy magnetization axis. The Monte Carlo simulation based on the 2D Heisenberg model shows that the critical Curie temperatures of the 2D CrAl2Te4, CrGa2Te4, and CrIn2Te4 crystals could reach 466, 431, and 536 K, respectively. Moreover, the magnetic exchange strength and magnetic anisotropy could be further enhanced by the in-plane biaxial strain. The novel electronic and magnetic features promote 2D CrX2Te4 (X = Al, Ga, and In) crystals as a new family of two-dimensional intrinsic ferromagnetic materials for next-generation advanced spintronics.

One-time application of blended controlled-release nitrogen fertilizer (CRN) has the potential to solve the difficulty of top-dressing fertilizer in the cultivation of rice and reduce the cost of CRN fertilizer application. However, its effects on rice dry matter and nitrogen (N) accumulation and translocation, yield and N-use efficiency (NUE) remain uncertain. Field experiments were carried out at three sites (Mingguang, Chaohu, and Guichi) in the Yangtze River Delta in China to compare the effects of the conventional split applications of urea and the blended CRN and on post-anthesis dry matter and N accumulation and translocation, yield, and NUE in rice at 0, 60, 120, 180, and 240 kg N ha-1. The results showed that at the equal N application rates, compared under the conventional N fertilizer treatment, the blended CRN application significantly increased the rice yield by an average of 0.9-6.9%, mainly due to increase the number of spikelets per panicle. The highest yield achieved with blended CRN treatment occurred at 200 kg N ha-1, with an NUE of 45.9%. Moreover, in comparison to the conventional N fertilizer, the blended CRN treatment increased pre-anthesis N translocation (Pre-NT) by 1.0-19.8%, and the contribution of pre-NT to grain N by 0.2-8.7%, and NUE by 3.2-28.4%. Meanwhile, the blended CRN treatment reduced labor costs by 1800 Yuan ha-1 and enhanced the economic gains by 21.5-68.8%. Therefore, one-time application of blended CRN ≤ 200 kg N ha-1 application rate improved rice yield, NUE, and economic profit compared to equivalent rates of split applied conventional N fertilizers.

Patient readmission for ischemic stroke significantly strains the healthcare and medical insurance systems. Current understanding of the risk factors associated with these readmissions, as well as their subsequent impact on mortality within China, remains insufficient. This is particularly evident in the context of comprehensive, contemporary population studies. This 4-year retrospective cohort study included 125 397 hospital admissions for ischemic stroke from 838 hospitals located in 22 regions (13 urban and 9 rural) of a major city in western China, between January 1, 2015 and December 31, 2018. The Chi-square tests were used in univariate analysis. Accounting for intra-subject correlations of patients’ readmissions, accelerated failure time (AFT) shared frailty models were used to examine readmission events and pure AFT models for mortality. Risk factors for patient readmission after ischemic stroke include frequent admission history, male gender, employee’s insurance, advanced age, residence in urban areas, index hospitalization in low-level hospitals, extended length of stay (LOS) during index hospitalization, specific comorbidities and subtypes of ischemic stroke. Furthermore, our findings indicated that an additional admission for ischemic stroke increased patient mortality by 16.4% ( P

Abstract The dense stroma of desmoplastic tumor limits nanotherapeutic penetration and hampers the antitumor immune response. Here, we report a denaturation-and-penetration strategy and the use of tin monosulfide nanoparticles (SnSNPs) as nano-sonosensitizers that can overcome the stromal barrier for the management of desmoplastic triple-negative breast cancer (TNBC). SnSNPs possess a narrow bandgap (1.18 eV), allowing for efficient electron (e−)-hole (h+) pair separation to generate reactive oxygen species under US activation. More importantly, SnSNPs display mild photothermal properties that can in situ denature tumor collagen and facilitate deep penetration into the tumor mass upon near-infrared irradiation. This approach significantly enhances sonodynamic therapy (SDT) by SnSNPs and boosts antitumor immunity. In mouse models of malignant TNBC and hepatocellular carcinoma (HCC), the combination of robust SDT and enhanced cytotoxic T lymphocyte infiltration achieves remarkable anti-tumor efficacy. This study presents an innovative approach to enhance SDT and antitumor immunity using the denaturation-and-penetration strategy, offering a potential combined sono-immunotherapy approach for the cancer nanomedicine field.

Abstract In terms of pig farming, pig gut microbes have a significant effect on farmers and the farm environment. However, it is still unclear which microbial composition is more likely to contribute to this effect. This study collected a total of 136 samples, including pigs’ faeces samples, farmers’ faeces samples, samples from individuals who had no contact with any type of farm animal (referred to as ‘non-exposed’ persons), and environmental dust samples (collected from inside and outside pig houses and the farm) from two pig farms, pig farm A and pig farm B. Whereafter, 16S rRNA sequencing and taxonomic composition analysis were performed. According to the study, compared to non-exposed persons, pig farmers had a significantly higher abundance of 7 genera. In addition, the farmers were grouped according to the duration of their occupational exposure, and it was shown that 4 genera, including Turicibacter, Terrisporobacter, and Clostridium_sensu_stricto_1, exhibited a rise in more frequent contact with pigs. As compared to outside the pig house, the environmental dust has a greater concentration of the 3 bacteria mentioned before. Therefore, these 3 microbes can be considered as co-occurring microbes that may exist both in humans and the environment. Also, the 3 co-occurring microbes are involved in the fermentation and production of short-chain fatty acids and their effectiveness decreased as distance from the farm increased. This study shows that the 3 microbes where pig farmers co-occur with the environment come from pig farms, which provides fresh ideas for preventing the spread of microbial aerosols in pig farms and reducing pollution.

Urban water dissipation is a significant part of the urban hydrologic cycle and has a typical natural–social dualistic attribute. Besides natural evaporation, the water dissipation in people's daily life and production process cannot be ignored. This study developed an urban water dissipation model based on different land uses and applied it in urban-built areas in Beijing. The results showed that the water dissipation of buildings and green spaces occupied the dominant position, and the water dissipation intensity of each district exceeded 500 mm, among which the six core districts were 700–1,100 mm. Comparing the water dissipation contribution rate and area rate of each underlying surface, it showed that the water dissipation intensity from strong to weak was building, water surface, green spaces, and hardened ground. According to the dualistic analysis of urban water dissipation, the contribution rates of social water dissipation in the six core districts were 45.3–69.1%, which was higher than the 17.8–36.1% of other suburbs obviously. This study reflected that the higher the degree of regional urbanization, the greater the water dissipation intensity, and artificial water dissipation was the main influencing factor. HIGHLIGHTS Developed a city-scale water dissipation calculation model based on urban land types.; Urban water dissipation includes traditional natural evapotranspiration and artificial water dissipation.; Buildings and green spaces are the major part of water dissipation in urban areas.; Regions with a high degree of urbanization in urban areas have greater water dissipation intensity.;

The growth status of winter wheat seedlings during the greening period is called the seedling situation. Timely and accurate determinations of the seedling situation type are important for subsequent field management measures and yield estimation. To solve the problems of low-efficiency artificial classification, subjective doping, inaccurate classification, and overfitting in transfer learning in classifying the seedling condition of winter wheat seedlings during the greening period, we propose an improved ConvNeXt winter wheat seedling status classification and identification network based on the pre-training–fine-tuning model addressing over-fitting in transfer learning. Based on ConvNeXt, a SETFL-ConvNeXt network (Squeeze and Excitation attention-tanh ConvNeXt using focal loss), a winter wheat seedling identification and grading network was designed by adding an improved SET attention module (Squeeze and Excitation attention-tanh) and replacing the Focal Loss function. The accuracy of the SETFL-ConvNeXt reached 96.68%. Compared with the classic ConvNeXt model, the accuracy of the Strong class, First class, and Third class increased by 1.188%, 2.199%, and 0.132%, respectively. With the model, we also compared the effects of different optimization strategies, five pre-training-fine-tuning models, and the degree of change in the pre-trained model. The accuracy of the fine-tuning models trained in the remaining layers increased by 0.19–6.19% using the last three frozen blocks, and the accuracy of the pre-trained model increased by 3.1–8.56% with the least degree of change method compared with the other methods. The SETFL-ConvNeXt network proposed in this study has high accuracy and can effectively address overfitting, providing theoretical and technical support for classifying winter wheat seedlings during the greening period. It also provides solutions and ideas for researchers who encounter overfitting.

Abstract Malignant tumors have been one of the major reasons for deaths worldwide. Timely and accurate diagnosis as well as effective intervention of tumors play an essential role in the survival of patients. Genomic instability is the important foundation and feature of cancer, hence, in vivo oncogene imaging based on novel probes provides a valuable tool for the diagnosis of cancer at early-stage. However, the in vivo oncogene imaging is confronted with great challenge, due to the extremely low copies of oncogene in tumor cells. By combining with various novel activatable probes, the molecular imaging technologies provide a feasible approach to visualize oncogene in situ, and realize accurate treatment of tumor. This review aims to declare the design of nanoprobes responded to tumor associated DNA or RNA, and summarize their applications in detection and bioimaging for tumors. The significant challenges and prospective of oncogene-targeting nanoprobes towards tumors diagnosis are revealed as well.

Abstract Babesia spp. are intraerythrocytic apicomplexans that digest and utilize red blood cells in a similar way to intraerythrocytic Plasmodium spp., but unlike the latter, are not sensitive to artemisinin. A comparison of Babesia and Plasmodium genomes revealed that Babesia genomes, which are smaller than those of Plasmodium, lack numerous genes, and especially haem synthesis-related genes, that are found in the latter. Single-cell sequencing analysis showed that the different treatment groups of Babesia microti with expressed pentose phosphate pathway-related, DNA replication-related, antioxidation-related, glycolysis-related, and glutathione-related genes were not as sensitive to artemether as Plasmodium yoelii 17XNL. In particular, pentose phosphate pathway-related, DNA replication-related, and glutathione-related genes, which were actively expressed in P. yoelii 17XNL, were not actively expressed in B. microti. Supplying iron in vivo can promote the reproduction of B. microti. These results suggest that Babesia spp. lack a similar mechanism to that of malaria parasites through which the haem or iron in hemoglobin is utilized, and that this likely leads to their insensitivity to artemisinin. Graphical Abstract

Agarwood is a resinous heartwood of Aquilaria sinensis that is formed in response to mechanical wounding. In the present study pre-treatment of Aquilaria sinensis was carried out, and then the dominant fungi were isolated and purified from the surface and electroshock holes of trees. The isolated Trichoderma sp. and Neurospora sp. were then screened for resistance against benzyl acetone and then inoculated into healthy Aquilaria sinensis trees. After six months, the agarwood was collected for analysis. The chemical composition of incense was analyzed using gas chromatography-mass spectroscopy, and 82 chemical constituents were identified. Agarwood products formed by using Trichoderma sp. and Neurospora sp. consisted of 50.22% and 48.71% ether extracts, respectively, which surpassed the 10% threshold specified by the Chinese Pharmacopoeia. Similarly, relative aromatic contents in the two agarwood products were 30.1% and 32.86%, while proportions of sesquiterpene constituents were 10.21% and 11.19%, respectively. These two agarwood-specific chemical constituents accounted for a large proportion of the total chemical composition, which showed that the generated agarwood was of good quality. The results of the study revealed that both Trichoderma sp. and Neurospora sp. were able to effectively induce agarwood production in Aquilaria sinensis trees in 6 months. This study expands the library of fungi that promote the production of agarwood from Aquilaria sinensis trees.

IntroductionCitrus chlorotic dwarf-associated virus (CCDaV) is an economically important citrus virus associated with leaf curling, deformation, and chlorosis found in China. Plants have evolved RNA silencing to defend against viral infections; however, the mechanism by which CCDaV suppresses RNA silencing in citrus remains unknown.MethodsSix proteins encoded by CCDaV were ectopically expressed in Nicotiana benthamiana 16c using the pCHF3 vector to identify RNA-silencing suppression activities.ResultsV2 protein encoded by CCDaV suppressed local RNA silencing and systemic RNA silencing triggered by GFP RNA, but did not impede short-distance movement of the RNA silencing signal in N. benthamiana 16c. GFP fluorescence observations showed that the ability of V2 protein to suppress RNA silencing was weaker than tomato bushy stunt virus P19. Deletion analysis showed that the putative nuclear localization signal (NLS, 25–54 aa) was involved in the RNA silencing suppression activity of V2 protein. Furthermore, V2 protein cannot block dsRNA-triggered RNA silencing. The subcellular localization assay suggested that V2 protein was localized to nucleus of N. benthamiana.ConclusionOverall, the results of this study demonstrate that CCDaV-V2 acts as an activity of silencing suppression. This is the first reported RNA-silencing suppressor encoded by Citlodavirus and will be valuable in revealing the molecular mechanism of CCDaV infection.

Abstract Although the extraordinary progress has been made in molecular biology, the prevention of cancer remains arduous. Most solid tumours exhibit both spatial and temporal heterogeneity, which is difficult to be mimicked in vitro. Additionally, the complex biochemical and immune features of tumour microenvironment significantly affect the tumour development. Molecular imaging aims at the exploitation of tumour‐associated molecules as specific targets of customized molecular probe, thereby generating image contrast of tumour markers, and offering opportunities to non‐invasively evaluate the pathological characteristics of tumours in vivo. Particularly, there are no “standard markers” as control in clinical imaging diagnosis of individuals, so the tumour pathological characteristics‐responsive nanoprobe‐based quantitative molecular imaging, which is able to visualize and determine the accurate content values of heterogeneous distribution of pathological molecules in solid tumours, can provide criteria for cancer diagnosis. In this context, a variety of “smart” quantitative molecular imaging nanoprobes have been designed, in order to provide feasible approaches to quantitatively visualize the tumour‐associated pathological molecules in vivo. This review summarizes the recent achievements in the designs of these nanoprobes, and highlights the state‐of‐the‐art technologies in quantitative imaging of tumour‐associated pathological molecules.