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The microstructural characteristics and morphological attributes of the target material, along with its intrinsic properties, can significantly influence the performance of the resultant film. Currently, commercially available nickel-vanadium targets employed in the industry have not been subjected to investigations aimed at controlling grain size and grain orientation. This study examines the effects of unidirectional rolling at room temperature, combined with substantial deformation, on the alterations in microstructure, texture evolution, and mechanical properties of the NiV7 alloy. The process of severe plastic cold rolling leads to a continuous elongation and refinement of the grains. The changes in microstructure and texture of the cold-rolled sheets were analyzed using electron backscatter diffraction (EBSD) technology. At an 80% reduction, a significant number of sub-grains are observed, which transform into deformed grains as the reduction reaches 95%. With a thickness reduction of 96.6%, the average grain size is approximately 1.10 μm. Further increases in reduction do not lead to significant alterations in the grain structure; instead, the grain orientation progressively becomes more uniform with higher levels of deformation. The combination of substantial deformation and lower rolling rates results in an increased number of high-angle grain boundaries. The alloy displays various textures throughout the rolling process, including S-type, copper-type, brass-type, fiber, and cubic textures. Notably, the prevalence of S-type and fiber textures diminishes as the degree of deformation increases, while the cubic texture becomes increasingly prominent. Upon achieving a reduction of 95%, the cubic texture emerges as the dominant texture. A transmission electron microscope (TEM) was utilized to examine the extensive array of dislocations, dislocation cells, dislocation walls, and shear bands generated during the cold rolling process. At a deformation level of 96.6%, the ultimate tensile strength of the alloy demonstrates a significant enhancement, attributed to mechanisms of dislocation strengthening and grain strengthening, reaching a value of 1231 ± 20 MPa, accompanied by a post-fracture elongation of approximately 2%. The ultimate tensile strength exhibited an increase of 781 ± 20 MPa compared to the sample prior to the rolling process; however, there was a notable reduction in plasticity.

Abstract New services, such as distributed photovoltaic regulation and control, pose new service requirements for communication networks in the new power system. These requirements include low latency, high reliability, and large bandwidth. Consequently, power heterogeneous communication networks face the challenge of maintaining quality of service (QoS) while enhancing network resource utilization. Therefore, this paper puts forward a highly efficient optimization algorithm for resource slicing and scheduling in power heterogeneous communication networks. Our first step involves establishing an architectural description model of heterogeneous wireless networks for electric power based on hypergraph. This model characterizes complex dynamic relationships among service requirements, virtual networks, and physical networks. The system congruence entropy characterizes the degree of matching between the service demand and resource supply. Then an optimization problem is formed to maximize the system congruence entropy through dynamic resource allocation. To solve this problem, a joint resource allocation and routing method based on Lagrangian dual decomposition is proposed. These methods provide the optimal solutions of the nodes and link mappings of service function chains. The simulation results demonstrate that the proposed algorithm in this paper can greatly enhance resource utilization and also meet the QoS requirements of various services.

A photovoltaic thermal panel (PV/T) is an integrated module that harnesses both photovoltaic and solar thermal technologies to convert solar energy into electricity and heat, thereby enhancing overall energy efficiency. This paper aims to explore the suitability of PV/T solar heat pump systems across various climate zones and assess their potential for widespread application. By analyzing the operating principles of an indirect expansion PV/T solar heat pump system in conjunction with the climate characteristics of different regions, MATLAB R2019b/Simulink software was employed to evaluate the photoelectric performance of PV and PV/T systems in representative cities across five distinct climate zones in China during typical winter days. Key metrics, such as power generation, hot water storage tank temperature, indoor temperature, and system COP, were chosen to assess the heating performance of the PV/T solar heat pump system. The findings indicate that the winter ambient temperature significantly affects the photoelectric efficiency of both the PV and PV/T systems. While higher latitudes with lower ambient temperatures yield greater photoelectric efficiency, the southern regions exhibit higher power generation during winter. The winter heating effectiveness of the PV/T solar heat pump system is mainly influenced by indoor and water tank temperatures, with Harbin’s system performing the poorest and failing to meet heating demands, whereas Nanjing’s system shows the best results.

Abstract Objective To investigate the infection status of high-risk human papillomavirus (HR-HPV) E6/E7 mRNA in patients with a cytological diagnosis of “atypical squamous cells of undetermined significance” (ASCUS) and to analyze the pathogenic rate of different high-risk HPV subtypes combined with biopsy pathological results to provide a more accurate basis for managing ASCUS patients. Methods A total of 1387 patients with ASCUS and HPV E6/E7 mRNA positivity who were referred for colposcopy were retrospectively analyzed. They were divided into HPV16+, 18/45 + and other HR-HPV + groups premenopausal and postmenopausal groups. The pathological results of the biopsy were divided into the LSIL- group (including normal and low-grade squamous intraepithelial lesions) and the HSIL + group (including high-grade squamous intraepithelial lesions and higher lesions). SPSS was used for the analysis. Results The age group 31–40 years had the highest level of HPV16+, and HPV18/45 + was the highest in the 41–50 years group. The detection rates of HSIL + in the HPV16+, HPV18/45+, HPV 16/18/45 + and Other HR-HPV + groups were 48.4%, 18.8%, 43.9% and 15.0%, respectively. The infection rates of HPV16/18/45 in postmenopausal and premenopausal women were 42.4% and 34.3%, respectively. In the HPV18/45 group, the incidence of HSIL + was 30.0% in postmenopausal women and 15.0% in premenopausal women (P

Abstract Purpose This study aimed to assess the value of an HPV E6/E7 mRNA assay and HPV 16 18/45 genotype assay combined with age stratification for triaging women negative for intraepithelial lesions or malignancy (NILM) cytology. Methods From January 2017 to December 2021, a total of 162,309 eligible women underwent cervical cancer screening at the Affiliated Hospital of Jining Medical University, China. Excluding those with negative HPV E6/E7 mRNA, abnormal and unsatisfactory cytology, and those who failed to undergo colposcopy, 6,845 women were ultimately included in our study. We analysed the triage guidance for different subtypes of HPV in the presence of NILM cytology. Results Among 162,309 women, 19,834 (12.2%) were positive for HPV E6/E7 mRNA. Of the 6,845 women included in the study, 1,941 (28.4%), 561 (8.2%), 55 (0.8%) and 4,288 (62.6%) tested positive for HPV 16, HPV 18/45, HPV16/18/45 or other HR-HPV genotypes, respectively. The proportions of LSIL+ (including LSIL, HSIL and ICC) and HSIL+ (including HSIL and ICC) pathological results in the HPV 16/18/45 + group were 57% and 34.1%, respectively, higher than 36.3% and 11% in the other HR-HPV + group (χ2 = 653.214, P 0.05). The above results were consistent after stratification according to age. Conclusion The rate of histopathological abnormalities was still high for the other HR-HPV subtypes with NILM cytology, although the rate of histopathological abnormalities was much higher for the HPV 16/18/45 positive subtypes. Therefore, colposcopy should be performed in women with HPV E6/E7 mRNA positivity and NILM cytology, regardless of age and HPV genotype.

Objective This prospective cohort study aimed to compare the clinical efficacy and safety of goserelin 10.8 mg administered trimonthly with goserelin 3.6 mg administered monthly in premenopausal females with symptomatic adenomyosis.Methods We recruited 139 premenopausal females with adenomyosis who complained of dysmenorrhea and/or menorrhagia. The first group (n = 70) received a single subcutaneous injection of goserelin 10.8 mg, and the second group (n = 69) received monthly subcutaneous goserelin 3.6 mg administered for 3 months. Follow-up was performed at the outpatient department after 12 weeks.Results Ultimately, 130 patients completed the study, including 68 and 62 patients in the goserelin 10.8 mg (n = 70) and 3.6 mg (n = 69) groups, respectively. We observed a significant decrease in the dysmenorrhea (NRS) score, uterine volume, and cancer antigen 125 (CA125) levels, and a significant increase in hemoglobin (HGB) levels in both treatment groups. There was no significant difference between the two groups. The sum of the adverse event scores was slightly higher in the goserelin 3.6 mg than in the 10.8 mg group.Conclusions The clinical efficacy of trimonthly administration of goserelin 10.8 mg was equivalent to monthly 3.6 mg dosing and was non-inferior regarding safety and tolerability. Hence, it can be a more cost-effective and convenient alternative treatment option in premenopausal females with symptomatic adenomyosis.Trial registration ChiCTR2200059548

Abstract Background In recent years, silver nanoparticles (AgNPs) have gradually been widely used, especially in the field of anticancer medicine. Ovarian cancer (OC) is the gynaecological malignancy with the highest mortality rate, and the current treatment is still based on surgery, chemotherapy and postoperative targeted therapy. Therefore, the development of safe and effective nanoparticles for targeted therapy of OC is very important. This study aimed to prepare a new type of triangular silver nanoparticles (tAgNPs) and evaluate the anticancer properties for OC in vitro and in vivo. Methods The tAgNPs were chemically synthesized and characterized using scanning electron microscopy (SEM), ultraviolet (UV) spectrophotometry and other techniques. By performing cell-based tests, such as cell counting kit-8 (CCK-8), plate colony formation, cell apoptosis, reactive oxygen species (ROS), and western blot (WB) assays, the inhibitory effects and related mechanisms of tAgNPs on OC cells were analysed.The anticancer effect of tAgNPs in vivo was verified by a SKOV3 tumor-bearing mouse model. Results Five types of tAgNPs with different colours were successfully synthesized, with a particle size of 25–50 nm and a good dispersion. The results of in vitro experiments showed that tAgNPs treatment reduced the viability and proliferation of SKOV3 cells, arrested the cell cycle in G0/G1 phase, inhibited the expression levels of proliferation-related factors and cyclins, and promoted cell apoptosis by producing ROS and increasing caspase-3 activity. Consistent with the results of in vitro experiments, in vivo animal experiments also showed that tAgNPs significantly inhibited the proliferation of ovarian cancer. More importantly, no obvious toxic and side effects were observed. Conclusions In this study, a novel triangular AgNPs was successfully prepared. tAgNPs are very stable, significantly inhibit the proliferation of OC cells and tumour growth in tumour-bearing mice, providing a promising nanotargeted therapy for OC.

The long-term settlement of calcareous sand foundations caused by daily periodic fluctuations has become a significant geological hazard, but effective monitoring tools to capture the deformation profiles are still rarely reported. In this study, a laboratory model test and an in situ monitoring test were conducted. An optical frequency domain reflectometer (OFDR) with high spatial resolution (1 mm) and high accuracy (±10-6) was used to record the soil strain responses to groundwater table and varied loads. The results indicated that the fiber-optic measurements can accurately locate the swelling and compressive zones. During the loading process, the interlock between calcareous sand particles was detected, which increased the internal friction angle of soil. The foundation deformation above the sliding surface was dominated by compression, and the soil was continuously compressed beneath the sliding surface. After 26–48 h, calcareous sand swelling occurred gradually above the water table, which was primarily dependent on capillary water. The swelling of the soil beneath the groundwater table was completed rapidly within less than 2 h. When the groundwater table and load remain constant, the compression creep behavior can be described by the Yasong-Wang model with R2 = 0.993. The daily periodically varying in situ deformation of calcareous sand primarily occurs between the highest and lowest groundwater tables, i.e. 4.2–6.2 m deep. The tuff interlayers with poor water absorption capacity do not swell or compress, but they produce compressive strain under the influence of deformed calcareous sand layers.

The phenological cycles of terrestrial ecosystems have shifted with the changing climate, and the altered timings of biogeochemical fluxes may also exert feedback on the climate system. As regulators of land carbon balance, relative shifts in photosynthetic and respiratory phenology under climate change are of great importance. However, the relative seasonal dynamics of these individual processes and their sensitivity to climate factors as well as the implications for carbon cycling are not well understood. In this study, we examined the relationship in the seasonality of gross primary production (GPP) and ecosystem respiration (RE) as well as their temperature sensitivities and the implications for carbon uptake with around 1500 site-years’ of data from FLUXNET 2015 and Boreal Ecosystem Productivity Simulator (BEPS) at 212 sites. The results showed that RE started earlier in the spring and ended later in the autumn than GPP over most biomes. Furthermore, the flux phenology metrics responded differently to temperature: GPP phenology was more sensitive to changes during the spring temperature than RE phenology, and less sensitive to autumn temperature than RE. We found large BEPS-observation discrepancies in seasonality metrics and their apparent temperature sensitivity. The site-based BEPS projections did not capture the observed seasonal metrics and temperature sensitivities in either GPP or RE seasonality metrics. Improved understanding of the asynchrony of GPP and RE as well as different sensitivity of environmental factors are of great significance for reliable future carbon balance projections.

Abstract Climate extremes are more frequent and affect the carbon cycle. On 10–20 February, an exceptional winter storm in Texas brought severe ecological and economic effects. However, its impacts on vegetation activities and the carbon cycle are unclear. This study evaluated the early 2021 ice storm effects on spring phenology and the carbon cycle. The results showed that the ice storm led to a significant later vegetation spring phenology and a reduction of 42.32 Tg C in gross primary production (GPP) and a reduction of 34. 47 Tg C net ecosystem production (NEP) in spring. In the 2021 spring, Texas became a carbon source from the carbon sink. More precipitation in late spring and summer precipitation recharged the rootzone soil moisture, which enhanced summer vegetation GPP and NEP and compensated for the effects of the ice storm on spring carbon uptake. The summer GPP enhancement offset all the spring GPP reduction, and thus GPP was increased by 4.97% during 2021. 91.47% of the spring NEP reduction was compensated by summer NEP enhancement due to higher root zone soil moisture. Due to the NEP enhancement during autumn, NEP did not decrease as well and increased by 10.88% during 2021. This observation‐based research highlights the seasonal compensation effects on the interactions between extreme climate events and the biosphere.

Considering the incomplete contact between the adjacent layers of crossanisotropic multilayered half-space media, an algorithm was derived based on the spectral element method (SEM) to determine the dynamic response at any point of the medium when an axisymmetric harmonic load is applied. The algorithm used two equivalent contact parameters to simulate the relationship between the discontinuity of the displacement and stress. The SEM was employed for the summation in the integral calculation to avoid the infinite integral in the integral transformation and greatly improve the efficiency of the solution to the dynamic response of the crossanisotropic layered half-space medium. The accuracy of the developed method is verified by the numerical examples as well as the efficiency of the developed method. Finally, an extensive parametric analysis of the contact parameters of the interface between the layers of the layered medium, namely, the point of the application of the external load, the excitation frequency, and the heterogeneous properties of the layered medium was conducted, providing a reliable numerical basis for engineering practice.

Based on the spectral element method (SEM) and a convolutional neural network (CNN), an inversion algorithm for the dynamic modulus of concrete pavement structures is proposed in this paper. In order to evaluate the service performance of pavement structures more systematically and accurately via the existing testing techniques using a falling weight deflectometer (FWD), it is necessary to obtain accurate dynamic modulus parameters of the structures. In this work, an inversion algorithm for predicting the dynamic modulus is established by using a CNN which is trained with the dynamic response samples of a multi-layered concrete pavement structure obtained through SEM. The gradient descent method is used to adjust the weight parameters in the network layer by layer in reverse. As a result, the accuracy of the CNN can be improved via iterative training. With the proposed algorithm, more accurate results of the dynamic modulus of pavement structures are obtained. The accuracy and numerical stability of the proposed algorithm are verified by several numerical examples. The dynamic modulus and thickness of concrete pavement structure layers can be accurately predicted by the CNN trained with a certain number of training samples based on the displacement curve of the deflection basin from the falling weight deflectometer. The proposed method can provide a reliable testing tool for the FWD technique of pavement structures.

Abstract Gene therapy is emerging as a valid method for the treatment of ovarian cancer, including small interfering RNA (siRNA). Although it is so powerful, few targeting efficient gene delivery systems seriously hindered the development of gene therapy. In this study, we synthesized a novel gene vector PEG-GO-PEI-FA by functionalized graphene oxide (GO), in which folic acid (FA) can specifically bind to the folate receptor (FR), which is overexpressed in ovarian cancer. Characterizations of the nanocomplexes were evaluated by dynamic light scattering (DLS), atomic force microscopy (AFM), and Fourier transform infrared spectroscopy (FTIR). The siRNA condensation ability and stability were assessed by agarose gel electrophoresis. Cellular uptake efficiency and lysosomal escape ability in ovarian cancer cells were investigated by confocal laser scanning microscopy. Furthermore, cellular biosafety of the system and inhibitory of the siRNA tolerability were evaluated by CCK-8 assay. The size of the PEG-GO-PEI-FA nanocomplexes was 216.1 ± 2.457 nm, exhibiting mild cytotoxicity in ovarian cancer cells. With high uptake efficiency, PEG-GO-PEI-FA can escape from the lysosome rapidly and release the gene. Moreover, PEG-GO-PEI-FA/siRNA can effectively inhibit the growth of ovarian cancer cells. By and large, the PEG-GO-PEI-FA/siRNA may offer a promising strategy for siRNA delivery in the treatment of FR-positive ovarian carcinoma or similar tumors.

Fire frequency and intensity are increasing due to higher temperatures and more droughts. The distributions of fuels (vegetation in natural conditions) are also changing in response to climate change. The vegetation in cold environments such as high latitudes and high altitudes is found to move upward or northward due to global warming. However, few studies have investigated the distribution changes of fire adaptive species in warm environments. This study estimated and compared the distributions of a typical fuelwood, the Eucalyptus globulus , under different climate scenarios. The species distribution modeling techniques were adopted to estimate the current distributions of the Eucalyptus globulus and the future distributions under scenarios of both SSP245 and SSP585 in 2060. Results show that the probability of the existence of the Eucalyptus globulus in the Northern Hemisphere increases significantly ( p < 0.001) under both SSP245 and SSP585, especially in North America and Europe. However, the probability in the Southern Hemisphere decreases. The distribution of the Eucalyptus globulus expands in the mid-latitude (40 N–60 N) of the Northern Hemisphere. High carbon emissions contribute to the boost of the establishment of the Eucalyptus globulus in the Northern Hemisphere. These findings demonstrate that the fire adaptive species shows the tendency of shifting northward in response to climate change, highlighting the challenge of northward expansion of fires in future warming.

Benzo(a)pyrene (BaP) is a polycyclic aromatic hydrocarbon that specifically causes cancer and is widely distributed in the environment. Poly (ADP-ribosylation), as a key post-translational modification in BaP-induced carcinogenesis, is mainly catalyzed by poly (ADP-ribose) glycohydrolase (PARG) in eukaryotic organisms. Previously, it is found that PARG silencing can counteract BaP-induced carcinogenesis in vitro, but the mechanism remained unclear. In this study, we further examined this process in vivo by using heterozygous PARG knockout mice (PARG+/−). Wild-type and PARG+/− mice were individually treated with 0 or 10 μg/m3 BaP for 90 or 180 days by dynamic inhalation exposure. Pathological analysis of lung tissues showed that, with extended exposure time, carcinogenesis and injury in the lungs of WT mice was progressively worse; however, the injury was minimal and carcinogenesis was not detected in the lungs of PARG+/− mice. These results indicate that PARG gene silencing protects mice against lung cancer induced by BaP inhalation exposure. Furthermore, as the exposure time was extended, the protein phosphorylation level was down-regulated in WT mice, but up-regulated in PARG+/− mice. The relative expression of Wnt2b and Wnt5b mRNA in WT mice were significantly higher than those in the control group, but there was no significant difference in PARG+/− mice. Meanwhile, the relative expression of Wnt2b and Wnt5b proteins, as assessed by immunohistochemistry and Western blot analysis, was significantly up-regulated by BaP in WT mice; while in PARG+/− mice it was not statistically affected. Our work provides initial evidence that PARG silencing suppresses BaP induced lung cancer and stabilizes the expression of Wnt ligands, PARG gene and Wnt ligands may provide new options for the diagnosis and treatment of lung cancer.

Anthropogenic carbon emissions from fires impact the global carbon budget and contribute to global warming. However, due to the lack of inventory data, little was known about how carbon emissions differed between human-caused and lightning-caused fires previously. In this study, the Fire Program Analysis fire-occurrence database (FPA FOD) and the Global Fire Emissions Database (GFED) were combined to analyze the influences of human-caused fires on carbon emissions. We found that the GFED burned area was larger than that of the FPA FOD since the FPA FOD did not cover human-caused fire usages like prescribed fires. Carbon emissions over the conterminous United States were increasing significantly from 1997 to 2015. Human-caused fires released 9.99 Tg C yr ^−1 over the conterminous United States, which were approximately twice those of carbon emissions from lightning-caused fires, 5.44 Tg C yr ^−1. Carbon emissions of lightning-caused fires were increasing while those of human-caused fires were decreasing significantly with rising temperatures. Emissions in ecoregions such as the Eastern Temperate Forests, the Great Plains, the Marine West Coast Forests, and the Northern Forests were dominated by human-caused fire emissions, whose proportions were over 86%. These results highlight the importance of human activities on carbon emissions, offering new insights into the role of humans in climate change mitigation.

Fractional vegetation cover (FVC) is an essential input parameter for many environmental and ecological models. Recently, several global FVC products have been generated using remote sensing data. The Global LAnd Surface Satellite (GLASS) FVC product, which is generated from Moderate Resolution Imaging Spectroradiometer (MODIS) data, has attained acceptable performance. However, the original MODIS operation design lifespan has been exceeded. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) satellite was designed to be the MODIS successor. Therefore, developing an FVC estimation algorithm for VIIRS data is important for maintaining continuous FVC estimates in case of MODIS failure. In this study, a global FVC estimation algorithm for VIIRS surface reflectance data was proposed based on machine learning methods, which investigated the performances of back propagating neural networks (BPNNs), general regression networks (GRNNs), multivariate adaptive regression splines (MARS), and Gaussian process regression (GPR). The training samples were extracted from the GLASS FVC product and corresponding reconstructed VIIRS surface reflectance in 2013 over the global sampling locations. The VIIRS reflectances of red and near infrared (NIR) bands were the input variables for these machine learning methods. The theoretical performances and independent validation results indicated that the four machine learning methods could achieve similar and reliable FVC estimates. Regarding the FVC estimation accuracy, the GPR method achieved the best performance (R2 = 0.9019, RMSE = 0.0887). The MARS method had the obvious advantage of computational efficiency. Furthermore, the FVC estimates achieved good spatial and temporal continuities. Therefore, the proposed FVC estimation algorithm for VIIRS data can potentially generate reliable global FVC data for related applications.

Northern China is one of the most sensitive and vulnerable regions in the country. To combat environmental degradation in northern China, a series of vegetation protection programs, such as the Three-North Shelter Forest Program (TNFSP), have been implemented. Whether the implementation of these programs in northern China has improved the vegetation conditions has merited global attention. Therefore, quantifying vegetation changes in northern China is essential for meteorological, hydrological, ecological, and societal implications. Fractional vegetation cover (FVC) is a crucial biophysical parameter which describes land surface vegetation conditions. In this study, four FVC data sets derived from remote sensing data over northern China are employed for a spatio-temporal analysis to determine the uncertainty of fractional vegetation cover change from 2001 to 2012. Trend analysis of these data sets (including an annually varying estimate of error) reveals that FVC has increased at the rate of 0.26 ± 0.13%, 0.30 ± 0.25%, 0.12 ± 0.03%, 0.49 ± 0.21% per year in northern China, Northeast China, Northwest China, and North China during the period 2001–2012, respectively. In all of northern China, only 33.03% of pixels showed a significant increase in vegetation cover whereas approximately 16.81% of pixels showed a significant decrease and 50.16% remained relatively stable.

Fractional vegetation cover (FVC) is an essential land surface parameter for Earth surface process simulations and global change studies. The currently existing FVC products are mostly obtained from low or medium resolution remotely sensed data, while many applications require the fine spatial resolution FVC product. The availability of well-calibrated coverage of Landsat imagery over large areas offers an opportunity for the production of FVC at fine spatial resolution. Therefore, the objective of this study is to develop a general and reliable land surface FVC estimation algorithm for Landsat surface reflectance data under various land surface conditions. Two machine learning methods multivariate adaptive regression splines (MARS) model and back-propagation neural networks (BPNNs) were trained using samples from PROSPECT leaf optical properties model and the scattering by arbitrarily inclined leaves (SAIL) model simulations, which included Landsat reflectance and corresponding FVC values, and evaluated to choose the method which had better performance. Thereafter, the MARS model, which had better performance in the independent validation, was evaluated using ground FVC measurements from two case study areas. The direct validation of the FVC estimated using the proposed algorithm (Heihe: R2 = 0.8825, RMSE = 0.097; Chengde using Landsat 7 ETM+: R2 = 0.8571, RMSE = 0.078, Chengde using Landsat 8 OLI: R2 = 0.8598, RMSE = 0.078) showed the proposed method had good performance. Spatial-temporal assessment of the estimated FVC from Landsat 7 ETM+ and Landsat 8 OLI data confirmed the robustness and consistency of the proposed method. All these results indicated that the proposed algorithm could obtain satisfactory accuracy and had the potential for the production of high-quality FVC estimates from Landsat surface reflectance data.

Hand, foot, and mouth disease (HFMD) affects more than one million children, is responsible for several hundred child deaths every year in China and is the cause of widespread concerns in society. Only a small fraction of HFMD cases will develop further into severe HFMD with neurologic complications. A timely and accurate diagnosis of severe HFMD is essential for assessing the risk of progression and planning the appropriate treatment. Human serum can reflect the physiological or pathological states, which is expected to be an excellent source of disease-specific biomarkers. In the present study, a comparative serological proteome analysis between severe HFMD patients and healthy controls was performed via a two-dimensional difference gel electrophoresis (2D-DIGE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) strategy. Fifteen proteins were identified as differentially expressed in the sera of the severe HFMD patients compared with the controls. The identified proteins were classified into different groups according to their molecular functions, biological processes, protein classes and physiological pathways by bioinformatics analysis. The up-regulations of two identified proteins, serum amyloid A (SAA) and clusterin (CLU), were confirmed in the sera of the HFMD patients by ELISA assay. This study not only increases our background knowledge about and scientific insight into the mechanisms of HFMD, but also reveals novel potential biomarkers for the clinical diagnosis of severe HFMD.

Long-term global land surface fractional vegetation cover (FVC) products are essential for various applications. Currently, several global FVC products have been generated from medium spatial resolution remote sensing data. However, validation results indicate that there are inconsistencies and spatial and temporal discontinuities in the current FVC products. Therefore, the Global LAnd Surface Satellite (GLASS) FVC product algorithm using general regression neural networks (GRNNs), which achieves an FVC estimation accuracy comparable to that of the GEOV1 FVC product with much improved spatial and temporal continuities, was developed. However, the computational efficiency of the GRNNs method is low and unsatisfactory for generating the long-term GLASS FVC product. Therefore, the objective of this study was to discover an alternative algorithm for generating the GLASS FVC product that has both an accuracy comparable to that of the GRNNs method and adequate computational efficiency. Four commonly used machine learning methods, back-propagation neural networks (BPNNs), GRNNs, support vector regression (SVR), and multivariate adaptive regression splines (MARS), were evaluated. After comparing its performance of training accuracy and computational efficiency with the other three methods, the MARS model was preliminarily selected as the most suitable algorithm for generating the GLASS FVC product. Direct validation results indicated that the performance of the MARS model (R2 = 0.836, RMSE = 0.1488) was comparable to that of the GRNNs method (R2 = 0.8353, RMSE = 0.1495), and the global land surface FVC generated from the MARS model had good spatial and temporal consistency with that generated from the GRNNs method. Furthermore, the computational efficiency of MARS was much higher than that of the GRNNs method. Therefore, the MARS model is a suitable algorithm for generating the GLASS FVC product from Moderate Resolution Imaging Spectroradiometer (MODIS) data.

Obstructive sleep apnea (OSA) is a prevalent sleep disorder that seriously affects patients' quality of life and health status. Traditional diagnostic methods are time-consuming and laborintensive, and apnea detection using deep learning algorithms also faces the problems of insufficient sample size and class imbalance. Therefore, in this paper, we propose a semisupervised apnea detection algorithm (Semi-DynaSeqNet) based on the oxygen saturation (SpO2) signal. In this study, we first extracted local features of the SpO2 signal using a onedimensional convolutional neural network, and then combined the gate recurrent unit for time series modeling to capture the signal's long-term dynamic features. On this basis, a self-attention mechanism is introduced to further enhance the recognition of key features. Considering the small-sample classification task of the OSA detection, we further proposed the semisupervised learning method with the adaptive adjustment of threshold. By iteratively training a model to generate pseudo-labeled samples of unlabeled pulse oximetry signals and incorporating them into the training set, while adaptively adjusting the semisupervised threshold to fully utilize the unlabeled sample information, we thereby improved the generalization ability of Semi-DynaSeqNet. The experimental results showed that the algorithm proposed in this paper achieves an F1-score of 90.94% for the model on the St. Vincent's University Hospital/University College Dublin Sleep Apnea Database with one-second detection, whereas the algorithm achieves an F1-score of 94.65% on the PhysioNet Apnea-ECG Database with one-minute detection, indicating that the algorithm can perform well in both sleep apnea detection tasks with different time scales, demonstrating its flexibility and scalability. [ABSTRACT FROM AUTHOR]

Fire carbon emissions contribute to the accumulation of atmospheric CO2 and affect climate change. It is crucial to accurately monitor the dynamics of global fire emissions for fire management and climate change mitigation. However, there are large uncertainties in the existing satellite-based global fire emission products. This study analyzed the uncertainties of six state-of-the-art fire emission products and merged them using the three-corner hat method (TCH), producing a new global fire emission dataset, FiTCH. Our results revealed that satellite-based products such as the Global Fire Assimilation System (GFAS), the Quick Fire Emissions Dataset (QFED), and the Global Fire Emissions Database (GFED) had low uncertainties in fire emissions, while the Fire INventory from National Center for Atmospheric Research (NCAR) (FINN), the Fire Energetics and Emissions Research (FEER), and Xu et al. (2021) data had high uncertainties. The proposed FiTCH dataset presented the lowest uncertainties with a mean annual fire emission of 1978.47 Tg C in 2001–2021. Among biomes, tropical forests and tundra showed higher uncertainties than other biomes such as temperate forests and Mediterranean forests. In drought years, forests showed increased fire emissions, especially in boreal forests, while nonforest regions like grasslands displayed decreased emissions. By integrating the FiTCH data and historical fire emissions in the late 20th century, 1994 was identified as a break year, before which global fire emissions increased significantly and after which the emissions decreased. Global land temperatures and fire emissions have decoupled in the past two decades. However, climate change still causes threats to forest carbon sequestration, especially for boreal forests. This study highlights the importance of forest fire monitoring and management for effective climate mitigation and ecosystem conservation. [ABSTRACT FROM AUTHOR]