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Objective: To elucidate the chemical profile of Xanthocerais lignum’s extracts of different polarities and their impact on rheumatoid arthritis (RA), we identified anti-RA markers and predicted their action mechanisms. Methods: A collagen-induced arthritis rat model was established, and UPLC-Q-Exactive Orbitrap MS technology was employed to analyze and identify the chemical constituents within the alcohol extract of Xanthocerais lignum and its various extraction fractions, as well as their translocation into the bloodstream. Serum spectrum–effect correlation analysis was utilized to elucidate the pharmacodynamic material basis of Xanthocerais lignum against RA and to screen for Q-Markers. Finally, the potential anti-RA mechanisms of the Q-Markers were predicted through compound-target interaction data and validated using molecular docking techniques. Results: We identified 71 compounds, with flavan-3-ols and flavanones as key components. Of these, 36 were detected in the bloodstream, including 17 original and 19 metabolized forms. Proanthocyanidin A2, dihydroquercetin, catechin, and epicatechin (plus glucuronides) showed potential anti-RA activity. These compounds, acting as Q-Markers, may modulate ERK, NF-κB, HIF-1α, and VEGF in the HIF-1 pathway. Conclusions: This research clarifies Xanthocerais lignum’s pharmacodynamic material basis against RA, identifies 4 Q-Markers, and offers insights into their mechanisms, aiding quality assessment and lead compound development for RA treatment.

In the study, natural deep eutectic solvents (NADESs) were used as alternatives to traditional chemical solvents for the extraction of polyphenols from Elaeagnus angustifolia L. Nine NADESs were tested for the first time and compared with ethanol and water (traditional solvents) regarding the extraction of phenolic compounds from E. angustifolia L. These solvents were particularly effective at extracting polyphenols, whose low water solubility usually requires high amounts of organic solvents. The solvent based on choline chloride and malonic acid provided optimal results and was selected for further optimization. The effects of material-to-liquid ratio, ultrasound time, and ultrasound temperature on the extraction efficiency were studied through single-factor experiments. These parameters were optimized by Box–Behnken design using response surface methodology. The optimal conditions identified were 49.86 g/mL of material-to-liquid ratio, 31.10 min of ultrasound time, and 62.35 °C of ultrasound temperature, resulting in a high yield of 140.30 ± 0.19 mg/g. The results indicated that the NADES extraction technique provided a higher yield than the conventional extraction process. The antioxidant activity of the extract of polyphenols from E. angustifolia L. was determined, and UPLC–IMS–QTOF–MS was used to analyze the phenolic compounds in it. The results revealed that the scavenging ability of 1,1-diphenyl-2-picryl-hydrazil and 2,2′-azinobis-(3-ethylbenzthiazoline-6-sulphonate) extracted by NADES was higher than that of polyphenols extracted by water and ethanol. Furthermore, a total of 24 phenolic compounds were identified in the extract. To the best of our knowledge, this is the first study in which a green and efficient NADES extraction method has been used to extract bioactive polyphenols from E. angustifolia L., which could provide potential value in pharmaceuticals, cosmetics, and food additives.

Background: Patient-based real-time quality control (PBRTQC) can be a valuable tool in clinical laboratories due to its cost-effectiveness and constant monitoring. More focus is placed on discovering and improving algorithms that compliment conventional internal control techniques. The practical implementation of PBRTQC with a biochemical instrument comparison is lacking. We aim to evaluate PBRTQC’s efficacy and practicality by comparing low-density lipoprotein cholesterol (LDL-C) test results to ensure consistent real-time monitoring across biochemical instrumentations in clinical laboratories. Method: From 1 September 2021 to 30 August 2022, the First Affiliated Hospital of Xi’an Jiaotong University collected data from 158,259 both healthy and diseased patients, including 84,187 male and 74,072 female patients, and examined their LDL-C results. This dataset encompassed a group comprising 50,556 individuals undergoing health examinations, a group comprising 42,472 inpatients (IP), and a group comprising 75,490 outpatients (OP) for the PBRTQC intelligent monitoring platform to conduct daily tests, parameter configuration, program development, real-time execution, and performance validation of the patients’ data. Moreover 40 patients’ LDL-C levels were assessed using two biochemical analyzers, designated as the reference and comparator instruments. A total of 160 LDL-C results were obtained from 40 both healthy and diseased patients, including 14 OP, 16 IP, and 10 health examination attendees, who were selected to represent LDL-C levels broadly. Two biochemical instruments measured LDL-C measurements from the same individuals to investigate consistency and reproducibility across patient statuses and settings. We employed exponentially weighted moving average (EWMA) and moving median (MM) methods to calculate inter-instrument bias and ensure analytical accuracy. Inter-instrument bias for LDL-C measurements was determined by analyzing fresh serum samples, different concentrations of quality control (QC), and commercialized calibrators, employing both EWMA and MM within two assay systems. The assessment of inter-instrumental bias with five different methods adhered to the external quality assessment standards of the Clinical Laboratory Center of the Health Planning Commission, which mandates a bias within ±15.0%. Result: We calculated inter-instrument comparison bias with each of the five methods based on patient big data. The comparison of fresh serum samples, different concentrations of QC, commercialized calibrators, and EWMA were all in the permissive range, except for MM. MM showed that the bias between two biochemical instruments in the concentration ranges of 1.5 mmoL/L–6.2 mmoL/L exceeded the permissible range. This was mainly due to the small number of specimens, affected by variations among individual patients, leading to increased false alarms and reduced effectiveness in monitoring the consistency of the inter-instrumental results. Moreover, the inter-comparison bias derived from EWMA was less than 3.01%, meeting the 15% range assessment criteria. The bias result for MM was lower than 24.66%, which was much higher than EWMA. Thus, EWMA is better than MM for monitoring inter-instrument comparability. PBRTQC can complement the use of inter-comparison bias between biochemical analyzers. EWMA has comparable inter-instrument comparability monitoring efficacy. Conclusions: The utilization of AI-based PBRTQC enables the automated real-time comparison of test results across different biochemical instruments, leading to a reduction in laboratory operating costs, enhanced work efficiency, and improved QC. This advanced technology facilitates seamless data integration and analysis, ultimately contributing to a more streamlined and efficient laboratory workflow in the biomedical field.

In order to overcome the interface emulsification problem of TBP-FeCl3 systems and the instability of β-diketone systems in high-concentration alkaline medium, it is necessary to design and synthesize some new extractants. By introducing amino groups into a phosphorus extractant, a new 2-ethylhexyl hydrogen {[bis(2-ethylhexyl)amino]methyl} phosphonate acid (HA) extractant was synthesized. In this study, an efficient method of recovering lithium from the effluent of spent lithium-ion batteries (LIBs) is proposed. Experiments were conducted to assess the influential factors in lithium recovery, including the solution pH, saponification degree, extractant concentration, and phase ratio. Over 95% of lithium in the effluent was extracted into the organic phase, and nearly all lithium in the organic phase could be stripped into the aqueous phase using a 3 mol/L HCl solution. There was no significant decrease in extraction capacity after 10 cycles. The experimental results indicated that the extraction mechanism was a cation exchange process, and the extractive complex was proposed as LiA. Importantly, after three months of stable operation, the process demonstrated excellent stability and extraction efficiency, with rapid phase separation and a clear interface. This study offers an efficient, cost-effective, and environmentally friendly method for lithium extraction from the effluent of spent LIBs.

It is challenging to investigate semantic change detection (SCD) in bi-temporal high-resolution (HR) remote sensing images. For the non-changing surfaces in the same location of bi-temporal images, existing SCD methods often obtain the results with frequent errors or incomplete change detection due to insufficient performance on overcoming the phenomenon of intraclass differences. To address the above-mentioned issues, we propose a novel multi-task consistency enhancement network (MCENet) for SCD. Specifically, a multi-task learning-based network is constructed by combining CNN and Transformer as the backbone. Moreover, a multi-task consistency enhancement module (MCEM) is introduced, and cross-task mapping connections are selected as auxiliary designs in the network to enhance the learning of semantic consistency in non-changing regions and the integrity of change features. Furthermore, we establish a novel joint loss function to alleviate the negative effect of class imbalances in quantity during network training optimization. We performed experiments on publicly available SCD datasets, including the SECOND and HRSCD datasets. MCENet achieved promising results, with a 22.06% Sek and a 37.41% Score on the SECOND dataset and a 14.87% Sek and a 30.61% Score on the HRSCD dataset. Moreover, we evaluated the applicability of MCENet on the NAFZ dataset that was employed for cropland change detection and non-agricultural identification, with a 21.67% Sek and a 37.28% Score. The relevant comparative and ablation experiments suggested that MCENet possesses superior performance and effectiveness in network design.

Background: Cerebral ischemia-reperfusion injury (CIRI) can cause irreversible brain damage and autophagy has been implicated in the pathophysiology. Increasing serum potassium (K+) levels reduces CIRI, but the relationship between its protective mechanism and autophagy is unclear. In this study, we aimed to find the optimal degree of raising serum (K+) and to investigate the relationship between high (K+) and autophagy and the underlying mechanisms in a cardiac arrest/cardiopulmonary resuscitation (CA/CPR) rat model. Methods: Sprague Dawley (SD) rats were divided into four groups: S group, N group, P group, and Q group. The rats S group and N group were administered saline. The rats P group and Q group were administered 640 mg/kg of potassium chloride (KCl) continuously pumped at 4 mL/h (21.3 mg/(kg·min) and divided according to the electrocardiogram (ECG) changes during the administration of KCl. After 24-h of resuscitation, neural damage was assessed by measuring neurological deficit score (NDS), oxidative stress markers, and pathological staining of the cerebral cortex. The level of autophagy and the expression of mTOR-ULK1-Beclin1 pathway-related proteins were evaluated using transmission electron microscopy (TEM), immunostaining, and western blotting. Results: Our results revealed that high (K+) improved NDS and decreased the oxidative stress markers. The autophagosomes, autolysosomes, and lysosomes were decreased following treatment KCl. Furthermore, the levels of micro-tubule-associated protein 1 light chain 3 (LC3) Ⅱ/Ⅰ, Unc-51-like kinase 1 (ULK1), and Beclin1 were decreased, whereas mTOR expression was increased in the cortex. Conclusion: The results demonstrated that moderate hyperkalemia could alleviate autophagy after CIRI via regulating the mTOR-ULK1-Beclin1 pathway.

Three mononuclear compounds formulated as {M[(2-1H-tetrazol-5-yl)pyridine]2(H2O)2} (M = FeII (1), CoII (2), CuII (3)) were reported and synthesized. Their space group is monoclinic, P21/c, revealed by single-crystal X-ray diffraction. Antiferromagnetic interactions exist in Compounds 1, 2 and 3, as evidenced by magnetic and low-temperature heat capacity measurements. In addition, their thermodynamic functions were determined by a relaxation calorimeter, indicating that Compound 1 exhibits a Schottky anomaly in low-temperature heat capacity. This work can provide an important fundamental basis for the research of the thermophysical properties of molecular-based magnetic materials.

Pichia kudriavzevii is one of the major non-Saccharomyces cerevisiae yeasts in Chinese baijiu brewing, which has shown a substantially higher tolerance to acid, heat, and ethanol. Exploring the mechanism of P. kudriavzevii could have a positive effect on the artificially controlled production of baijiu. In this study, an efficient acetic-acid-tolerant P. kudriavzevii strain, Y2, was isolated from the yellow water of strong-flavored baijiu brewing waste, and its molecular mechanism of acetic acid tolerance was investigated through a comparative transcriptomic analysis. The strain Y2 could tolerate 12 g/L of acetic acid. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, differentially expressed genes (DEGs) were mainly enriched in oxidative phosphorylation, the citrate cycle, glycolysis/gluconeogenesis, and carbon metabolism under low (AL group) and high (AH group) concentrations of acetic acid. However, the DEG enrichment was more profound in the AH group when compared to the control. Compared with the AL group, the expression of genes related to oxidative phosphorylation was more significantly upregulated, while in terms of the TCA cycle, phosphoenolpyruvate carboxykinase was significantly upregulated in both the AH and AL groups and was positively correlated with tolerance to acetic acid. This was followed by citrate synthase, isocitrate dehydrogenase, malate dehydrogenase, and succinate dehydrogenase. These results illustrated a possible mechanism of acid tolerance by regulating the metabolism-related pathways in P. kudriavzevii and provided a basis for the further investigation of the acid tolerance mechanism.

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, is a global health care emergency. Anti-SARS-CoV-2 serological profiling of critically ill COVID-19 patients was performed to determine their humoral response. Blood was collected from critically ill ICU patients, either COVID-19 positive (+) or COVID-19 negative (−), to measure anti-SARS-CoV-2 immunoglobulins: IgM; IgA; IgG; and Total Ig (combined IgM/IgA/IgG). Cohorts were similar, with the exception that COVID-19+ patients had a greater body mass indexes, developed bilateral pneumonias more frequently and suffered increased hypoxia when compared to COVID-19- patients (p < 0.05). The mortality rate for COVID-19+ patients was 50%. COVID-19 status could be determined by anti-SARS-CoV-2 serological responses with excellent classification accuracies on ICU day 1 (89%); ICU day 3 (96%); and ICU days 7 and 10 (100%). The importance of each Ig isotype for determining COVID-19 status on combined ICU days 1 and 3 was: Total Ig, 43%; IgM, 27%; IgA, 24% and IgG, 6%. Peak serological responses for each Ig isotype occurred on different ICU days (IgM day 13 > IgA day 17 > IgG persistently increased), with the Total Ig peaking at approximately ICU day 18. Those COVID-19+ patients who died had earlier or similar peaks in IgA and Total Ig in their ICU stay when compared to patients who survived (p < 0.005). Critically ill COVID-19 patients exhibit anti-SARS-CoV-2 serological responses, including those COVID-19 patients who ultimately died, suggesting that blunted serological responses did not contribute to mortality. Serological profiling of critically ill COVID-19 patients may aid disease surveillance, patient cohorting and help guide antibody therapies such as convalescent plasma.

Forest canopy height plays an important role in forest resource management and conservation. The accurate estimation of forest canopy height on a large scale is important for forest carbon stock, biodiversity, and the carbon cycle. With the technological development of satellite-based LiDAR, it is possible to determine forest canopy height over a large area. However, the forest canopy height that is acquired by this technology is influenced by topography and climate, and the canopy height that is acquired in complex subtropical mountainous regions has large errors. In this paper, we propose a method for estimating forest canopy height by combining long-time series Landsat images with GEDI satellite-based LiDAR data, with Fujian, China, as the study area. This approach optimizes the quality of GEDI canopy height data in topographically complex areas by combining stand age and tree height, while retaining the advantage of fast and effective forest canopy height measurements with satellite-based LiDAR. In this study, the growth curves of the main forest types in Fujian were first obtained by using a large amount of forest survey data, and the LandTrendr algorithm was used to obtain the forest age distribution in 2020. The obtained forest age was then combined with the growth curves of each forest type in order to determine the tree height distribution. Finally, the obtained average tree heights were merged with the GEDI_V27 canopy height product in order to create a modified forest canopy height model (MGEDI_V27) with a 30 m spatial resolution. The results showed that the estimated forest canopy height had a mean of 15.04 m, with a standard deviation of 4.98 m. In addition, we evaluated the accuracy of the GEDI_V27 and the MGEDI_V27 using the sample dataset. The MGEDI_V27 had a higher accuracy (R2 = 0.67, RMSE = 2.24 m, MAE = 1.85 m) than the GEDI_V27 (R2 = 0.39, RMSE = 3.35 m, MAE = 2.41 m). R2, RMSE, and MAE were improved by 71.79%, 33.13%, and 22.53%, respectively. We also produced a forest age distribution map of Fujian for the year 2020 and a forest disturbance map of Fujian for the past 32 years. The research results can provide decision support for forest ecological protection and management and for carbon sink analysis in Fujian.

Background: Screening cognitive impairment is complex and not an appliance for early screening. Gait performance is strongly associated with cognitive impairment. Objectives: We aimed to explore gait indicators that could potentially screen cognitive dysfunction. Methods: A total of 235 subjects were recruited from June 2021 to June 2022. Four gait tasks, including the walking test, the timed “Up & Go” test (TUG), foot pressure balance (FPB), and one-legged standing with eyes closed test (OLS-EC), were performed. Moreover, in the walking test, participants were instructed to walk at their usual pace for the single-gait test. For the dual-task tests, participants walked at their usual pace while counting backward from 100 by 1s. The data were analyzed by the independent sample t-test, univariate and multivariate logistic regression, a linear trend, stratified and interaction analysis, the receiver operating characteristic (ROC) curve, and Pearson’s correlations. Results: Among the 235 participants, 81 (34.5%) were men and 154 (65.5%) were women. The mean age of participants was 72 ± 7.836 years. The control, MCI, mild AD, and severe AD groups had means of 71, 63, 71, and 30, respectively. After adjusting for age, sex, education, and body mass index (BMI), the dual-task toe-off-ground angle (TOA) (odds ratio (OR) = 0.911, 95% confidence interval (CI): 0.847, 0.979), single-task TOA (OR = 0.904, 95% CI: 0.841–0.971), and the timed “Up & Go” time (TUGT) (OR = 1.515, 95% CI: 1.243–1.846) were significantly associated with an increased risk of cognitive impairment. In addition, the trend test and stratified analysis results had no significant differences (all p > 0.05). The area under the roc curve (AUC) values of TOA in the dual-task and TUGT were 0.812 and 0.847, respectively. Additionally, TOA < 36.75° in the dual-task, TOA < 38.90° in the single-task, and TUGT > 9.83 seconds (s) are likely to indicate cognitive impairment. The cognitive assessment scale scores were significantly correlated with TOA (all r > 0.3, p < 0.001) and TUGT (all r > 0.2), respectively. Conclusion: TOA and TUGT scores are, in some circumstances, associated with cognitive impairment; therefore, they can be used as simple initial screenings to identify patients at risk.

As one of the most important examinations in China, the National College Entrance Examination (commonly known as the Gaokao, hereafter denoted Gaokao) has a long history and has attracted attention from the Chinese educational community, among others. This study focused on mathematics test items of the Gaokao from the perspective of mathematical culture and examined the national papers (drafted by the Ministry of Education of the People’s Republic of China) of tests from 1978 to 2021 in order to investigate the content and time variation characteristics of mathematics test items reflecting mathematical culture since its restoration. A mathematical culture categorization conceptual framework was established based on previous studies and was applied to test item analysis. Mathematical culture in test items was classified using four categories: Historical Topics, Interdisciplinary Connections, Social Roles, and Aesthetics & Recreation. These were used for data coding and analysis. The results showed that mathematical culture in the mathematics test items of the Gaokao over a 44-year period, in terms of content categories, was diverse but uneven in distribution, with greater focus on demonstrating the social roles of mathematics and less on the historical development of mathematical knowledge. Moreover, the average number of items with mathematical culture during this period was small and fluctuated over time. The content of topics related to social context and technology also changed over time with the distinct characteristics of the times and changes in society. This study provides empirical evidence on how mathematical culture is integrated into test items and how they have changed over time.

Background and Objectives: This study aims to detect the prevalence of sarcopenia in community-dwelling older adults in Hunan Province, discuss factors related to lifestyle, and provide a reliable basis for the prevention and treatment of sarcopenia. Materials and Methods: In this study, a total of 1040 community-dwelling adults ≥ 60 years were examined for sarcopenia using a cluster stratified random sampling method, which was defined using the diagnostic criteria recommended by the Asian Working Group for Sarcopenia (AWGS) from September 2019 to March 2020. Multivariate logistic regression analysis was applied to determine the correlation between sarcopenia and smoking, drinking, nutritional status, physical activity, and sleep quality. Results: A total of 27.1% of the older adults were diagnosed with sarcopenia, with rates of 26.2% in men and 25.2% in women. Multiple logistic regression showed that advanced age (OR = 2.480, 95% CI: 1.730, 3.553), the risk of malnutrition (OR = 2.085, 95% CI: 1.440, 3.019), and malnutrition (OR = 1.212, 95% CI: 0.304, 4.834) were risk factors for sarcopenia. No falls in the previous year (OR = 0.616, 95% CI: 1.885, 1.209), normal weight (OR = 0.228, 95% CI: 0.109, 0.475), overweight (OR = 0.030, 95% CI: 0.013, 0.069), moderate physical activity (OR = 0.593, 95% CI: 0.377, 0.933), or high physical activity (OR = 0.417, 95% CI: 0.230, 0.755) were identified as protective factors for sarcopenia. Conclusions: The prevalence of sarcopenia was high among older adults in the community in Hunan Province. In addition, we found that lifestyle is an important factor in sarcopenia.

As a deep-processed product of peach, the aroma characteristics of peach spirit have not been systematically studied, and there has been no research on improving the aroma quality through process improvement. Pervaporation technology was used for the first time in the production of peach spirit instead of distillation, and its critical aroma compounds were analyzed compared with distilled peach spirit. Compared to the distilled peach spirit, pervaporation produced peach spirit presented stronger fruity, honey, and acidic aromas, and lighter cooked-apple aroma. Sixty-two and 65 aroma-active regions were identified in the distilled and pervaporation produced peach spirits, and 40 and 43 of them were quantified. The concentrations of esters, lactones, and acids were significantly higher in the pervaporation produced peach spirit than those in the distilled peach spirit, while terpenoids showed opposite tendency. Both of the overall aromas of distilled and pervaporation produced peach spirits were reconstituted successfully by the compounds with OAV ≥ 1. The omission tests identified 10 and 18 compounds as important aroma compounds for distilled and pervaporation-produced peach spirits, respectively. The differences in the key aroma compounds between the two types of peach spirits explained the differences in the aroma profiles.

Intestinal carriage of extended-spectrum β-lactamase-producing Enterobacteriaceae (ESBL-PE carriage) poses a health risk to the elderly. It was aimed to study the prevalence and the risk factors of intestinal ESBL-PE carriage in the elderly. An observational study of a 921-elderly cohort was examined at health checkup for intestinal ESBL-PE carriage at a tertiary medical center in Shanghai. The prevalence and risk factors of intestinal ESBL-PE carriage, especially antimicrobial use in the preceding 9 months, were studied. The prevalence of intestinal ESBL-PE carriage was 53.3% (491/921) in community-dwelling elderly people. A total of 542 ESBL-producing isolates, including E. coli (n = 484) and K. pneumoniae (n = 58), were obtained. On genotyping, the CTX-M-9 ESBL was the most prevalent for 66.0% (358/542) of all isolates. Multivariate analysis showed that antibiotic exposure, age (61–70 years), and nursing home residence were independent risk factors of the ESBL-PE carriage. The analysis on the monthly use of antimicrobials showed that antibiotic exposure during the 6 months prior to sample collection contributed to the high prevalence of ESBL-PE carriage. A single exposure to an antimicrobial increased the risk of the carriage significantly, and the risk increased with the frequency of antimicrobial exposure (RR, 1.825 to 5.255). Prior use of second or third generation cephalosporins, fluoroquinolones, and macrolides increased the risk of the carriage. The results of this study indicate the importance of using antimicrobials judiciously in clinical settings to reduce antimicrobial resistance. Further studies with multiple center surveillance and with comparison of ESBL-PE carriage in the elderly and in the general population simultaneously are needed.

The strong non-covalent interaction between biotin and streptavidin places streptavidin-based assays, used by many laboratories, at an increased risk of interference by biotin. At present, a few manufacturers have developed fully automated anti-biotin interference methods, although compared with many detection platforms, these remain insufficient. Additionally, there is a need for more methods that can achieve fully automated anti-biotin interference. We sought to develop and evaluate a new biotin interference-resisting method based on a biotin-streptavidin chemiluminescence immunoassay. Streptavidin-coated magnetic microparticles (M) of different concentrations were prepared and tested for their biotin-resistance capabilities in an automated setting (Cobas e 601). The precision, accuracy, and detection capability were also assessed. Higher concentrations of M were found to have a stronger ability to resist biotin interference. A 2.16 mg/mL concentration of M was able to resist 500 ng/mL of biotin in samples while simultaneously having a relatively weak shielding effect on the optical signals. Moreover, the total precision and accuracy of this method, designated as M3, met acceptable standards. M3 has an improved ability to resist biotin interference, can achieve full automation, and its detection performance can meet the general laboratory quality requirements.

The mountainous vegetation is important to regional sustainable development. However, the topographic effect is the main obstacle to the monitoring of mountainous vegetation using remote sensing. Aiming to retrieve the reflectance of frequently-used red–green–blue and near-infrared (NIR) wavebands of rugged mountains for vegetation mapping, we developed a new integrated topographic correction (ITC) using the SCS + C correction and the shadow-eliminated vegetation index. The ITC procedure consists of image processing, data training, and shadow correction and uses a random forest machine learning algorithm. Our study using the Landsat 8 Operational Land Imager (OLI) multi-spectral images in Fujian province, China, showed that the ITC achieved high performance in topographic correction of regional mountains and in transferability from the sunny area of a scene to the shadow area of three scenes. The ITC-corrected multi-spectral image with an NIR–red–green composite exhibited flat features with impressions of relief and topographic shadow removed. The linear regression of corrected waveband reflectance vs. the cosine of the solar incidence angle showed an inclination that nearly reached the horizontal, and the coefficient of determination decreased to 0.00~0.01. The absolute relative errors of the cast shadow and the self-shadow all dramatically decreased to the range of 0.30~6.37%. In addition, the achieved detection rate of regional vegetation coverage for the three cities of Fuzhou, Putian, and Xiamen using the ITC-corrected images was 0.92~6.14% higher than that using the surface reflectance images and showed a positive relationship with the regional topographic factors, e.g., the elevation and slope. The ITC-corrected multi-spectral images are beneficial for monitoring regional mountainous vegetation. Future improvements can focus on the use of the ITC in higher-resolution imaging.

Surface reflectance (SR) estimation is the most essential preprocessing step for multi-sensor remote sensing inversion of geophysical parameters. Therefore, accurate and stable atmospheric correction is particularly important, which is the premise and basis of the quantitative application of remote sensing. It can also be used to directly compare different images and sensors. The Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi-Spectral Instrument (MSI) surface reflectance products are publicly available and demonstrate high accuracy. However, there is not enough validation using synchronous spectral measurements over China’s land surface. In this study, we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products reconstructed by Categorical Boosting (CatBoost) and 30 m ASTER Global Digital Elevation Model (ASTER GDEM) data to adjust the relevant parameters to optimize the Second Simulation of Satellite Signal in the Solar Spectrum (6S) model. The accuracy of surface reflectance products obtained from the optimized 6S model was compared with that of the original 6S model and the most commonly used Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model. Surface reflectance products were validated and evaluated with synchronous in situ measurements from 16 sites located in five provinces of China: Fujian, Gansu, Jiangxi, Hunan, and Guangdong. Through the indirect and direct validation across two sensors and three methods, it provides evidence that the synchronous measurements have the higher and more reliable validation accuracy. The results of the validation indicated that, for Landsat-8 OLI and Sentinel-2 MSI SR products, the overall root mean square error (RMSE) calculated results of optimized 6S, original 6S and FLAASH across all spectral bands were 0.0295, 0.0378, 0.0345, and 0.0313, 0.0450, 0.0380, respectively. R2 values reached 0.9513, 0.9254, 0.9316 and 0.9377, 0.8822, 0.9122 respectively. Compared with the original 6S model and FLAASH model, the mean percent absolute error (MPAE) of the optimized 6S model was reduced by 32.20% and 15.86% for Landsat-8 OLI, respectively. On the other, for the Sentinel-2 MSI SR product, the MPAE value was reduced by 33.56% and 33.32%. For the two kinds of data, the accuracy of each band was improved to varying extents by the optimized 6S model with the auxiliary data. These findings support the hypothesis that reliable auxiliary data are helpful in reducing the influence of the atmosphere on images and restoring reality as much as is feasible.

Bletilla striata is an endangered medicinal plant the habitats of which are polluted by cadmium (Cd). Nutrient management is a potential way to mitigate Cd toxicity, providing reference for B. striata artificial cultivation. Pot experiments were undertaken to examine the effect of five levels of Cd concentration (control: 0.28 mg/kg; I: 0.37 mg/kg; II: 1.00 mg/kg; III: 7.39 mg/kg; IV: 54.6 mg/kg) on the temporal dynamics of nutrient allocation in B. striata components. The results showed that the content and accumulation of Cd increased significantly in all tissues of Bletilla striata as Cd concentration was increasing, and most accumulated in the roots and slightly transferred to the aboveground parts, while tubers were less likely to accumulate Cd. Excessive cadmium accumulation under long-term exposure reduced the biomass of all tissues of B. striata, and the yield was not significantly affected when cultivated in soil with a concentration of Cd ≤ 1 mg/kg. Potassium (K) was quickly transferred from the root to other tissues in response to the Cd stress. The absorption of zinc (Zn) increased at first and then decreased with increased Cd concentration. According to Pearson correlation analysis, a mechanism is suggested that Cd accumulation affects the absorption of nutrients but is positively and significantly correlated with translocation factor of K and Zn in the tuber. This may imply that B. striata tends to promote the absorption of nutrient elements to adapt to the Cd enrichment in tubers and to compensate for the interrupted mineral nutrition metabolism.

A gain slab configuration with a low thermally induced wavefront distortion, which is based on heating the edge by the cladding layer, is proposed. The gain slab will be applied to a helium-cooled Nd: glass multislab laser amplifier with an output of 100 J at a repetition rate of 10 Hz. Additionally, a 3D numerical simulation model is developed to analyze the thermo-optic effects in the gain slab. Some parameters, including the absorption coefficient (α) of the cladding layer, the shape of the pump beam, and the gap between the pump area and absorbing cladding layer, are optimized to eliminate the thermo-optic effects. The results indicate that the peak-to-valley (P-V) of the thermally induced wavefront distortion of the specific gain slab can be reduced by 61% if other parameters remain constant.

Tea is an important economic plant, which is widely cultivated in many countries, particularly in China. Accurately mapping tea plantations is crucial in the operations, management, and supervision of the growth and development of the tea industry. We propose an object-based convolutional neural network (CNN) to extract tea plantations from very high resolution remote sensing images. Image segmentation was performed to obtain image objects, while a fine-tuned CNN model was used to extract deep image features. We conducted feature selection based on the Gini index to reduce the dimensionality of deep features, and the selected features were then used for classifying tea objects via a random forest. The proposed method was first applied to Google Earth images and then transferred to GF-2 satellite images. We compared the proposed classification with existing methods: Object-based classification using random forest, Mask R-CNN, and object-based CNN without fine-tuning. The results show the proposed method achieved a higher classification accuracy than other methods and produced smaller over- and under-classification geometric errors than Mask R-CNN in terms of shape integrity and boundary consistency. The proposed approach, trained using Google Earth images, achieved comparable results when transferring to the classification of tea objects from GF-2 images. We conclude that the proposed method is effective for mapping tea plantations using very high-resolution remote sensing images even with limited training samples and has huge potential for mapping tea plantations in large areas.

An accurate estimation of evapotranspiration (ET) from crops is crucial in irrigation management, crop yield assessment, and optimal allocation of water resources, particularly in arid regions. This study explores the estimation of seasonal evapotranspiration for crops using multisource remote sensing images. The proposed estimation framework starts with estimating daily evapotranspiration (ETd) values, which are then used to calculate ET estimates during the crop growing season (ETs). We incorporated Landsat images into the surface energy balance algorithm over land (SEBAL) model, and we used the trapezoidal and sinusoidal methods to estimate the seasonal ET. The trapezoidal method used multitemporal ETd images, while the sinusoidal method employs time-series Moderate Resolution Imaging Spectroradiometer (MODIS) images and multitemporal ETd images. Experiments were implemented in the agricultural lands of the Kai-Kong River Basin, Xinjiang, China. The experimental results show that the obtained ETd estimates using the SEBAL model are comparable with those from the Penman–Monteith method. The ETs obtained using the trapezoidal and sinusoidal methods both have a relatively high spatial resolution of 30 m. The sinusoidal method performs better than the trapezoidal method when using low temporal resolution Landsat images. We observed that the omission of Landsat images during the middle stage of crop growth has the greatest impact on the estimation results of ETs using the sinusoidal method. Based on the results of the study, we conclude that the proposed sinusoidal method, with integrated multisource remote sensing images, offers a useful tool in estimating seasonal evapotranspiration for crops in arid regions.

Accurate and efficient extraction of cultivated land data is of great significance for agricultural resource monitoring and national food security. Deep-learning-based classification of remote-sensing images overcomes the two difficulties of traditional learning methods (e.g., support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF)) when extracting the cultivated land: (1) the limited performance when extracting the same land-cover type with the high intra-class spectral variation, such as cultivated land with both vegetation and non-vegetation cover, and (2) the limited generalization ability for handling a large dataset to apply the model to different locations. However, the “pooling” process in most deep convolutional networks, which attempts to enlarge the sensing field of the kernel by involving the upscale process, leads to significant detail loss in the output, including the edges, gradients, and image texture details. To solve this problem, in this study we proposed a new end-to-end extraction algorithm, a high-resolution U-Net (HRU-Net), to preserve the image details by improving the skip connection structure and the loss function of the original U-Net. The proposed HRU-Net was tested in Xinjiang Province, China to extract the cultivated land from Landsat Thematic Mapper (TM) images. The result showed that the HRU-Net achieved better performance (Acc: 92.81%; kappa: 0.81; F1-score: 0.90) than the U-Net++ (Acc: 91.74%; kappa: 0.79; F1-score: 0.89), the original U-Net (Acc: 89.83%; kappa: 0.74; F1-score: 0.86), and the Random Forest model (Acc: 76.13%; kappa: 0.48; F1-score: 0.69). The robustness of the proposed model for the intra-class spectral variation and the accuracy of the edge details were also compared, and this showed that the HRU-Net obtained more accurate edge details and had less influence from the intra-class spectral variation. The model proposed in this study can be further applied to other land cover types that have more spectral diversity and require more details of extraction.

Tree peonies (Paeonia ostii and Paeonia rockii) are popular ornamental plants. Moreover, these plants have become oil crops in recent years. However, there are limited compositional studies focused on fatty acids. Therefore, this work aims to reveal compositional characteristics, regarding fatty acids, sterols, γ-tocopherol and phenolic compounds, of tree peony seed oils from all major cultivation areas in China, and to compare with herbaceous peony seed oil. For that, an integrative analysis was performed by GC-FID, GC-MS and UHPLC-Q-TOF-MS technologies. The main fatty acid was α-linolenic acid (39.0–48.3%), while β-sitosterol (1802.5–2793.7 mg/kg) and fucosterol (682.2–1225.1 mg/kg) were the dominant phytosterols. Importantly, 34 phenolic compounds, including paeonol and “Paeonia glycosides” (36.62–103.17 μg/g), were characterized in vegetable oils for the first time. Conclusively, this work gives new insights into the phytochemical composition of peony seed oil and reveals the presence of bioactive compounds, including “Paeonia glycosides”.

We present a new vision based cooperative pose estimation scheme for systems of mobile robots equipped with RGB-D cameras. We first model a multi-robot system as an edge-weighted graph. Then, based on this model, and by using the real-time color and depth data, the robots with shared field-of-views estimate their relative poses in pairwise. The system does not need the existence of a single common view shared by all robots, and it works in 3D scenes without any specific calibration pattern or landmark. The proposed scheme distributes working loads evenly in the system, hence it is scalable and the computing power of the participating robots is efficiently used. The performance and robustness were analyzed both on synthetic and experimental data in different environments over a range of system configurations with varying number of robots and poses.

In this paper, the Relative Pose based Redundancy Removal (RPRR) scheme is presented, which has been designed for mobile RGB-D sensor networks operating under bandwidth-constrained operational scenarios. The scheme considers a multiview scenario in which pairs of sensors observe the same scene from different viewpoints, and detect the redundant visual and depth information to prevent their transmission leading to a significant improvement in wireless channel usage efficiency and power savings. We envisage applications in which the environment is static, and rapid 3D mapping of an enclosed area of interest is required, such as disaster recovery and support operations after earthquakes or industrial accidents. Experimental results show that wireless channel utilization is improved by 250% and battery consumption is halved when the RPRR scheme is used instead of sending the sensor images independently.

Chronotropic incompetence (CI) has been proven to be an important factor in the diagnosis and management of cardiovascular diseases. In this paper, we extend the existing CI parameters and propose chronotropic competence indices (CCI) to describe the exercise response of the cardiopulmonary system. A cardiac chronotropic competence Test (3CT), dedicated to CCI measurement using a wearable device, is also presented. Preliminary clinical trials are presented for the validation of 3CT measurement accuracy, and to show the potential of CCI in the prevention and rehabilitation of cardiovascular diseases.

Different kinds of deep eutectic solvents (DES) based on choline chloride (ChCl) and ionic liquids (ILs) based on 1-methylimidazole were used to modify Fe3O4/molecularly imprinted polymers (Fe3O4/MIPs), and the resulting materials were applied for the rapid purification of alkaloid isomers (theobromine and theophylline) from green tea with magnetic solid-phase extraction (M-SPE). The M-SPE procedure was optimized using the response surface methodology (RSM) to analyze the maximum conditions. The materials were characterized by Fourier transform infrared spectroscopy (FI-IR) and field emission scanning electron microscopy (FE-SEM). Compared to the ILs-Fe3O4/MIPs, the DESs-Fe3O4/MIPs were developed for the stronger recognition and higher recoveries of the isomers (theophylline and theobromine) from green tea, particularly DES-7-Fe3O4/MIPs. With RSM, the optimal recovery condition for theobromine and theophylline in the M-SPE were observed with ratio of methanol (80%) as the washing solution, methanol/acetic acid (HAc) (8:2) as the eluent at pH 3, and an eluent volume of 4 mL. The practical recoveries of theobromine and theophylline in green tea were 92.27% and 87.51%, respectively, with a corresponding actual extraction amount of 4.87 mg•g−1 and 5.07 mg•g−1. Overall, the proposed approach with the high affinity of Fe3O4/MIPs might offer a novel method for the purification of complex isomer samples.

A series of new 2-phenyl-quinoline-4-carboxylic acid derivatives was synthesized starting from aniline, 2-nitrobenzaldehyde, pyruvic acid followed by Doebner reaction, amidation, reduction, acylation and amination. All of the newly-synthesized compounds were characterized by 1H-NMR, 13C-NMR and HRMS. The antibacterial activities of these compounds against Gram-negative (Escherichia coli, Pseudomonas aeruginosa) and Gram-positive bacteria (Staphylococcus aureus, Bacillus subtilis), as well as one strain of methicillin-resistant Staphylococcus aureus (MRSA) bacteria were evaluated by the agar diffusion method (zone of inhibition) and a broth dilution method (minimum inhibitory concentration (MIC)), and their structure-activity relationships were obtained and discussed. The results revealed that some compounds displayed good antibacterial activity against Staphylococcus aureus, and Compounds 5a4 and 5a7 showed the best inhibition with an MIC value of 64 μg/mL against Staphylococcus aureus and with an MIC value of 128 μg/mL against Escherichia coli, respectively. The results of the MTT assay illustrated the low cytotoxicity of Compound 5a4.