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With the growth of the transportation industry, large volumes of waste tires are being generated, which necessitates the development of effective solutions for recycling waste tires. In this study, expansive clay was mixed with rubber fibers obtained from waste tires. Triaxial tests were conducted on the rubber fiber-reinforced expansive clay after freeze–thaw cycles. The experimental results of the unreinforced expansive clay from previous studies were used to evaluate the effect of mixing rubber fibers on the mechanical properties of rubber fiber-reinforced expansive clay under freeze–thaw cycles. The results demonstrate that the mixing of rubber fibers significantly reduces the effect of freeze–thaw cycles on the shear strength and elastic modulus of expansive clay. The shear strength and elastic modulus of the unreinforced expansive clay decrease markedly as the number of freeze–thaw cycles increases, while the shear strength and elastic modulus of the rubber fiber-reinforced expansive clay do not exhibit any remarkable change. A calculation model of the deviatoric stress–axial strain curves after freeze–thaw cycles was established. The model describes the deviatoric stress–axial strain behavior of rubber fiber-reinforced expansive clay and unreinforced expansive clay under different confining pressures and different numbers of freeze–thaw cycles.

The crack detection of concrete bridges is an important link in the safety evaluation of bridge structures, and the rapid and accurate identification and detection of bridge cracks is a prerequisite for ensuring the safety and long-term stable use of bridges. To solve the incomplete crack detection and segmentation caused by the complex background and small proportion in the actual bridge crack images, this paper proposes a coarse–fine combined bridge crack detection method of “double detection + single segmentation” based on deep learning. To validate the effect and practicality of fine crack detection, images of old civil bridges and viaduct bridges against a complex background and images of a bridge crack against a simple background are used as datasets. You Only Look Once V5(x) (YOLOV5(x)) was preferred as the object detection network model (ODNM) to perform initial and fine detection of bridge cracks, respectively. Using U-Net as the optimal semantic segmentation network model (SSNM), the crack detection results are accurately segmented for fine crack detection. The test results showed that the initial crack detection using YOLOV5(x) was more comprehensive and preserved the original shape of bridge cracks. Second, based on the initial detection, YOLOV5(x) was adopted for fine crack detection, which can determine the location and shape of cracks more carefully and accurately. Finally, the U-Net model was used to segment the accurately detected cracks and achieved a maximum accuracy (AC) value of 98.37%. The experiment verifies the effectiveness and accuracy of this method, which not only provides a faster and more accurate method for fine detection of bridge cracks but also provides technical support for future automated detection and preventive maintenance of bridge structures and has practical value for bridge crack detection engineering.

Objective: This study aimed to investigate the infection status of Omicron in the population and the association between COVID-19 vaccination and infection with Omicron. Methods: We conducted a cross-sectional study to openly recruit participants for a survey of SARS-CoV-2 infection by convenience sampling from 1 January to 15 January 2023 after a COVID-19 pandemic swept across China. Additionally, the binary logistic regression model was adopted to evaluate the association between COVID-19 vaccination and the infection outcomes or symptom severity, respectively. Meanwhile, the relations between the vaccination and duration of the symptoms were estimated via ordinal logistic analysis. Results: Of the 2007 participants, the prevalence of infection with Omicron was 82.9%. Compared with unvaccinated individuals, inactivated COVID-19 vaccination could increase the risk of Omicron infection (OR = 1.942, 95% CI: 1.093–3.448), and the receipt of at least one dose of non-inactivated COVID-19 vaccines was a protective factor against infection (OR = 0.428, 95% CI: 0.226–0.812). By contrast, no relations were observed in COVID-19 vaccination with the symptoms of infection and duration of symptoms (p > 0.05). Conclusions: This cross-sectional study concluded that inactivated COVID-19 vaccination might increase the risk of Omicron infection, which should be a concern during COVID-19 vaccination and the treatment of variant infections in the future, and the receipt of at least one dose of non-inactivated COVID-19 vaccine was a protective factor against infection.

The separation of Li+ and [Bmim]+(1-Butyl-3-methylimidazolium) using a cation exchange membrane in an electric field is studied in this work. The effects of the type of cation exchange membrane, current density, total cation concentration, temperature, and anion types on the separation efficiency are investigated. The results indicate that it is feasible to achieve the efficient separation of Li+ and [Bmim]+ using the selectivity of cation exchange membranes under an electric field. The CIMS membrane (a type of cation exchange membrane produced by ASTOM Corporation) shows obvious selectivity of Li+ from [Bmim]+. When the current density is 5 mA/cm2 and the feed concentrations of Li+ and [Bmim]+ are 0.3 mol/L and 0.2 mol/L, respectively, the selective transport coefficient of CIMS can reach 5.9 in the first 120 min of the process. The separation efficiency can be effectively improved by reducing the current density and increasing the total cation concentration. Decreasing the feed temperature can slightly improve the separation efficiency. Changing the type of anion in the feed from chloride to acetate has no detectable effect on the separation.

Albizia julibrissin saponin active fraction (AJSAF), is a prospective adjuvant with dual Th1/Th2 and Tc1/Tc2 potentiating activity. Its adjuvant activity has previously been proven to be strictly dependent on its spatial co-localization with antigens, highlighting the role of local innate immunity in its mechanisms. However, its potential targets and pathways remain unclear. Here, its intracellular molecular mechanisms of innate immune response were explored using mouse C2C12 myoblast by integrative analysis of the in vivo and in vitro transcriptome in combination with experimental validations. AJSAF elicited a temporary cytotoxicity and inflammation towards C2C12 cells. Gene set enrichment analysis demonstrated that AJSAF regulated similar cell death- and inflammatory response-related genes in vitro and in vivo through activating second messenger–MAPK–CREB pathways. AJSAF markedly enhanced the Ca2+, cAMP, and reactive oxygen species levels and accelerated MAPK and CREB phosphorylation in C2C12 cells. Furthermore, Ca2+ chelator, CREB inhibitor, and MAPK inhibitors dramatically blocked the up-regulation of IL-6, CXCL1, and COX2 in AJSAF-treated C2C12 cells. Collectively, these results demonstrated that AJSAF induced innate immunity via Ca2+–MAPK–CREB pathways. This study is beneficial for insights into the molecular mechanisms of saponin adjuvants.

3β-hydroxy-12-oleanen-27-oic acid (ATA), a cytotoxic oleanane triterpenoid with C14-COOH isolated from the rhizome of Astilbe chinensis, has been previously proven to possess antitumor activity and may be a promising antitumor agent. However, its molecular mechanisms of antitumor action were still unclear. This study explored the underlying mechanisms of cytotoxicity and potential target of ATA against human colorectal cancer HCT116 cells via integrative analysis of transcriptomics and network pharmacology in combination with in vitro and in vivo experimental validations. ATA significantly inhibited the proliferation of HCT116 cells in a concentration- and time-dependent manner and induced the cell cycle arrest at the G0/G1 phase, apoptosis, autophagy, and ferroptosis. Transcriptomic analysis manifested that ATA regulated mRNA expression of the genes related to cell proliferation, cell cycle, and cell death in HCT116 cells. The integrated analysis of transcriptomics, network pharmacology, and molecular docking revealed that ATA exerted cytotoxic activity via interactions with FDFT1, PPARA, and PPARG. Furthermore, FDFT1 was verified to be an upstream key target mediating the antiproliferative effect of ATA against HCT116 cells. Of note, ATA remarkably suppressed the growth of HCT116 xenografts in nude mice and displayed an apparent attenuation of FDFT1 in tumor tissues accompanied by the alteration of the biomarkers of autophagy, cell cycle, apoptosis, and ferroptosis. These results demonstrate that ATA exerted in vitro and in vivo antiproliferative effects against HCT116 cells through inducing cell apoptosis, autophagy, and ferroptosis via targeting FDFT1.

This study aims to evaluate the effectiveness of maternal inactivated COVID-19 vaccination before delivery for infants against Omicron infection in Guangzhou, China. A test-negative case-control design was conducted. This study selected infants born from 1 November 2021 to 23 November 2022 and tested for SARS-CoV-2 between 13 April 2022 and 30 November 2022 during outbreaks in Guangzhou. Multivariable logistic regression was performed to compare the maternal vaccination status of inactivated COVID-19 vaccines before delivery in cases and controls to estimate vaccine effectiveness (VE) for infants within 12 months. According to eligibility criteria, we finally selected 205 test-positive and 114 test-negative infants, as well as their mothers. The effectiveness of inactivated COVID-19 vaccines among fully vaccinated mothers was 48.4% (7.3% to 71.7%) for infants within 12 months, with the effectiveness of partial and booster vaccination showing no significant difference. Effectiveness for full vaccination presented a slight increase according to infants’ age at testing, with 49.6% (−12.3% to 78.4%) for 0–6 months and 59.9% (−0.6% to 84.4%) for over 6 months. A greater protective effect of two-dose vaccination was manifested in infants whose mother had received the second dose during the first trimester (65.9%, 95% CI: 7.7% to 87.9%) of pregnancy rather than preconception (43.5%, 95% CI: −8.7% to 71.1%). Moreover, VE could be improved to 77.1% (11.1% to 95.3%) when mothers received two doses both during pregnancy and 91.8% (41.1% to 99.6%) with receipt of a booster dose during pregnancy. Maternal vaccination with two doses of inactivated COVID-19 vaccines before delivery was moderately effective against Omicron infection in infants during the first 12 months of life. Full vaccination or a booster dose during pregnancy could confer better protection against Omicron for infants, although it might be overestimated due to the insufficient sample size in subgroups.

Bridge crack detection based on deep learning is a research area of great interest and difficulty in the field of bridge health detection. This study aimed to investigate the effectiveness of coupling a deep learning framework (DLF) with a convolutional neural network (CNN) for bridge crack detection. A dataset consisting of 2068 bridge crack images was randomly split into training, verification, and testing sets with a ratio of 8:1:1, respectively. Several CNN models, including Faster R-CNN, Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO)-v5(x), U-Net, and Pyramid Scene Parsing Network (PSPNet), were used to conduct experiments using the PyTorch, TensorFlow2, and Keras frameworks. The experimental results show that the Harmonic Mean (F1) values of the detection results of the Faster R-CNN and SSD models under the Keras framework are relatively large (0.76 and 0.67, respectively, in the object detection model). The YOLO-v5(x) model of the TensorFlow2 framework achieved the highest F1 value of 0.67. In semantic segmentation models, the U-Net model achieved the highest detection result accuracy (AC) value of 98.37% under the PyTorch framework. The PSPNet model achieved the highest AC value of 97.86% under the TensorFlow2 framework. These experimental results provide optimal coupling efficiency parameters of a DLF and CNN for bridge crack detection. A more accurate and efficient DLF and CNN model for bridge crack detection has been obtained, which has significant practical application value.

Smooth state switching and accurate speed tracking are important for the stability and reactivity of bipedal robots when running. However, previous studies have rarely been able to synthesize these two capabilities online. In this paper, we present an online running-gait generator for bipedal robots that allows for smooth state switching and accurate speed tracking. Considering a fluctuating height nature and computational expediency, the robot is represented by a simplified variable-height inverted-pendulum (VHIP) model. In order to achieve smooth state switching at the beginning and end of running, a segmented zero moment point (ZMP) trajectory optimization is proposed to automatically provide a feasible and smooth center-of-mass (CoM) trajectory that enables the robot to stably start or stop running at the given speed. To accurately track online the desired speed during running, we propose an iterative algorithm to compute target footholds, which allows for the robot to follow the interactive desired speed after the next two steps. Lastly, a numerical experiment and the simulation of online variable speed running were performed with position-controlled bipedal robot BHR7P, and the results verified the effectiveness of the proposed methods.

Hyphantria cunea Drury (Lepidoptera: Arctiidae) is a worldwide quarantine pest that has a wide range of host plants. Quercetin is a secondary metabolite involved in chemical defense processes in plants. To understand how H. cunea adapt to quercetin in its host plants, we determined the effects of quercetin on larval mortality, growth, nutritional indices, and the activity or content of detoxification enzymes in H. cunea larvae by feeding them an artificial diet containing different concentrations of quercetin. Our results showed that 0.50% quercetin treatment significantly prolonged the development duration of H. cunea larvae and inhibited growth of H. cunea. Nutritional indices analysis indicated that quercetin significantly affected nutrient use, including effects on the approximate digestibility, consumption index, relative growth rate, and efficiency of conversion of ingested food to body substance. Furthermore, our results revealed that quercetin reduced the content of carboxylesterases, and increased the activity or content of glutathione S-transferases, UDP-glucuronosyltransferases, and ATP-binding cassette transporters in H. cunea larvae. These results provide a foundation for revealing the adaptation that H. cunea use to adapt to quercetin in host plants.

The Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) is the most widespread and devastating pest species in citrus orchards and is the natural vector of the phloem-limited bacterium that causes Huanglongbing (HLB) disease. Thus, reducing the population of D. citri is an important means to prevent the spread of HLB disease. Due to the long-term use of chemical control, biological control has become the most promising strategy. In this study, a novel highly pathogenic fungal strain was isolated from naturally infected cadavers of adult D. citri. The species was identified as Aspergillus fijiensis using morphological identification and phylogenetic analysis and assigned the strain name GDIZM-1. Tests to detect aflatoxin B1 demonstrated that A. fijiensis GDIZM-1 is a non-aflatoxin B1 producer. The pathogenicity of the strain against D. citri was determined under laboratory and greenhouse conditions. The results of the laboratory study indicated that nymphs from the 1st to 5th instar and adults of D. citri were infected by A. fijiensis GDIZM-1. The mortality of nymphs and adults of D. citri caused by infection with A. fijiensis increased with the concentration of the conidial suspension and exposure time, and the median lethal concentration (LC50) and median lethal time (LT50) values gradually decreased. The mortality of D. citri for all instars was higher than 70%, with high pathogenicity at the 7th day post treatment with 1 × 108 conidia/mL. The results of the greenhouse pathogenicity tests showed that the survival of D. citri adults was 3.33% on the 14th day post-treatment with 1 × 108 conidia/mL, which was significantly lower than that after treatment with the Metarhizium anisopliae GDIZMMa-3 strain and sterile water. The results of the present study revealed that the isolate of A. fijiensis GDIZM-1 was effective against D. citri and it provides a basis for the development of a new microbial pesticide against D. citri after validation of these results in the field.

Fractional vegetation cover (FVC) is an important indicator of ecosystem changes. Both satellite remote sensing and ground measurements are common methods for estimating FVC. However, desert vegetation grows sparsely and scantly and spreads widely in desert regions, making it challenging to accurately estimate its vegetation cover using satellite data. In this study, we used RGB images from two periods: images from 2006 captured with a small, light manned aircraft with a resolution of 0.1 m and images from 2019 captured with an unmanned aerial vehicle (UAV) with a resolution of 0.02 m. Three pixel-based machine learning algorithms, namely gradient enhancement decision tree (GBDT), k-nearest neighbor (KNN) and random forest (RF), were used to classify the main vegetation (woody and grass species) and calculate the coverage. An independent data set was used to evaluate the accuracy of the algorithms. Overall accuracies of GBDT, KNN and RF for 2006 image classification were 0.9140, 0.9190 and 0.9478, respectively, with RF achieving the best classification results. Overall accuracies of GBDT, KNN and RF for 2019 images were 0.8466, 0.8627 and 0.8569, respectively, with the KNN algorithm achieving the best results for vegetation cover classification. The vegetation coverage in the study area changed significantly from 2006 to 2019, with an increase in grass coverage from 15.47 ± 1.49% to 27.90 ± 2.79%. The results show that RGB images are suitable for mapping FVC. Determining the best spatial resolution for different vegetation features may make estimation of desert vegetation coverage more accurate. Vegetation cover changes are also important in terms of understanding the evolution of desert ecosystems.

From the perspective of treating wastes with wastes, bamboo sawdust was integrated with a hydroxyapatite (HAP) precursor to create engineered nano-HAP/micro-biochar composites (HBCs) by optimizing the co-precipitated precursor contents and co-pyrolysis temperature (300, 450, 600 °C). The physicochemical properties of HBCs, including morphologies, porosities, component ratios, crystalline structures, surface elemental chemical states, surface functional groups, and zeta potentials as a function of carbonization temperatures and components of precursors, were studied. Biochar matrix as an efficient carrier with enhanced specific surface area to prevent HAP from aggregation was desired. The sorption behavior of heavy metal (Cu(II), Cd(II), and Pb(II)) and pharmaceuticals (carbamazepine and tetracycline) on HBCs were analyzed given various geochemical conditions, including contact time, pH value, ionic strength, inferencing cations and anions, coexisting humic acid, and ambient temperature. HBCs could capture these pollutants efficiently from both simulated wastewaters and real waters. Combined with spectroscopic techniques, proper multiple dominant sorption mechanisms for each sorbate were elucidated separately. HBCs presented excellent reusability for the removal of these pollutants through six recycles, except for tetracycline. The results of this study provide meaningful insight into the proper integration of biochar–mineral composites for the management of aquatic heavy metals and pharmaceuticals.

The rapid settlement of tailings is an important technical guarantee for the continuous production of downhole filling. The selection of a reasonable flocculant is essential for accelerating the settlement speed of classified fine tailings. The present paper conducts indoor static sedimentation experiments, NMR observation, electron microscope scanning, and other methods to analyze the porosity and pore-size distribution characteristics of floc solution for classified fine tailing under four flocculants, namely, ZYZ, ZYD, JYC-1, and JYC-2. The dimension, spatial distribution characteristics, particle size characteristics, and morphological characteristics of the scanning electron microscope images of floc were studied. Results show that the unit consumption of flocculant at 30 g/t is the critical value for increasing the flocculation and sedimentation effect of the classified fine tailings solution. The highest distribution percentage of small-sized classified fine tailings and the lowest average pore size was observed under the ZYZ-type flocculant. This flocculant also obtained the lowest porosity, largest average floc size, largest area occupied by the floc, lowest pore percentage, and the densest floc structure. Thus, this flocculant showed the best flocculation effect. A negative correlation was observed between the equivalent diameter of floc with varying settlement heights. The dimension of floc increased with the decrease in bed settlement height, and the overall structure of the floc gradually transitioned from loose to dense from top to bottom. The present paper characterizes the microscopic morphology and spatial structure characteristics of floc under different flocculants from a microscopic point of view. The present paper also provides a scientific basis for the selection of the optimal flocculant.

The rapid development of modern society has increased the demand for high-performance geo-materials. As an advanced cementitious composite, fiber-reinforced concrete has attracted much attention and has been widely applied to various buildings and civil infrastructure. A basalt fiber-reinforced concrete is proposed as an advanced geo-material and the mechanical and thermal properties were investigated in this study. The basalt fiber-reinforced concrete was compared with ordinary concrete to confirm its superiority by determination of the physical parameters, static compressive test, and dynamic compressive test. The static compressive test was performed using the YAW-2000C constant stress pressure experimental machine under different heating temperatures and cooling methods, while the dynamic compressive test was performed using the 75-mm split-Hopkinson pressure bar under different loading rates, heating temperatures, and cooling methods. For the basic physical parameters, it was found that the mass loss and wave velocity of concrete decrease with the increase of the temperature. In the static compressive test, the static compressive strength for both the ordinary concrete and the fiber-reinforced concrete decreased with the increase of the temperature, and greater strength was observed with the air-cooled compared to the water-cooled method. It was found that the strength of basalt fiber-reinforced concrete is greater than that of ordinary concrete. In the dynamic compressive test, the strength increased with an increasing loading rate and descended with an increasing temperature, while for the same heating temperature and loading rate, water cooling produced more irregular and smaller fragments than air cooling. The dynamic compressive strength of basalt fiber-reinforced concrete was bigger than that of ordinary concrete.

A simple arylamine-catalyzed Mannich-cyclization cascade reaction was developed for facile synthesis of substituted 2H-benzo[h]chromenes. The notable feature of the process included the efficient generation of ortho-quinone methides (o-QMs) catalyzed by a simple aniline. The mild reaction conditions allowed for a broad spectrum of 1- and 2-naphthols and trans-cinnamaldehydes to engage in the cascade sequence with high efficiency.

Forest degradation has been considered as one of the main causes of climate change in recent years. The knowledge of estimating degraded forest areas without the application of remote sensing tools can be useful in finding solutions to resolve degradation problems through appropriate restoration methods. Using the existing knowledge through literature review and field-based primary information, we generated new knowledge by combining the information obtained from multi-criteria decision analyses with an analytic hierarchy process, and this was then used to estimate degraded forest area. Estimation involves determining forest degradation index (FDI) and degradation threshold. Continuous inventory data of permanent sample plots collected from degraded forests, consisting of various forest types divided by dominant tree species in the Guangdong province and Tibet autonomous region of China, were used for the purposes. We identified four different forest degradation levels through the determination and comprehensive evaluation of FDI. The degraded forest area with broad-leaved species as dominant tree species in the Guangdong province was estimated to be 83.3% of a total forest area of 24,037 km2. In the same province, the degraded forest area with eucalyptus as a dominant tree species was 59.5% of a total forest area of 18,665 km2. In the Tibet autonomous region, the degraded forest area with spruce as a dominant tree species was 99.1% of a total forest area of 17,614 km2, and with fir as a dominant tree species, the degraded area was 98.4% of a forest area of 12,103 km2. A sampling accuracy of forest areas with national forest inventory was about 95% in both provinces. Our study concludes that the FDI method used has a certain scientific rationality in estimating degraded forest area. The forest provides a variety of tangible and intangible goods and services for humans. Therefore, forest management should focus on the improvement of its overall productivity, which is only possible with improving forest site quality. One of the important steps to improve the quality of a forest site is to resolve its degradation issues. The presented method in this article will be useful in finding the solutions to forest degradation problems. This method, which does not need any remote sensing tool, is simple and can be easily applied for estimating any degraded forest area and developing effective forest restoration plans.

The Empoasca onukii (Hemiptera: Cicadellidae) female lays its eggs inside the epidermis of the tea plant shoots. This has led to speculation that shoot harvesting could represent a method of egg removal. To verify the validity of this hypothesis, we sought to determine which part of the shoot was used for the oviposition and how the value of the harvested shoot affects the cost of the egg removal. In this study, four tea cultivars were chosen to examine the preferences for the site of oviposition. In addition, a mathematical model was used to describe the correlation between the economic value of the selected shoot and eggs laid within the shoot. Our study revealed that the pest preferred the 3rd and 4th leaf order intervals of the shoot as the oviposition sites, and the oviposition preferences was dependent on the leaf order interval class across all tea cultivars. In addition, a significant negative exponential relationship was found between the economic value of the selected shoot and the percentage of the eggs laid within the shoot, indicating that egg removal through shoot harvesting was limited. The findings of this study could be used to better understand the role of shoot harvesting in egg removal and would provide new insights into the understanding of the incidence of this pest.

Relationship of total height and diameter at breast height (hereafter diameter) of the trees is generally nonlinear, and therefore has complex characteristics, which can be accurately described by the height-diameter model developed using the back propagation (BP) neural network approach. The multiple hidden layered-BP neural network has several hidden layers and neurons, and is therefore considered more appropriate modeling approach compared to the single hidden layered-BP neural network approach. However, the former approach is not widely applied for tree height prediction due to absence of the effective optimization method, but it can be done using the BP neural network modeling approach. The poplar (Populus spp. L.) plantation data acquired from Guangdong province of China were used for evaluating the BP neural network modeling approach and compared its results with those obtained from the traditional regression modeling and mixed-effects modeling approaches. We determined the best BP neural network structure with two hidden layers and five neurons in each layer, and logistic sigmoid transfer functions. Relative to the Mitscherlich height-diameter model that had the highest fitting precision among the six traditional height-diameter models evaluated, coefficient of determination (R2) of the neural network height-diameter model increased by 10.3%, root mean squares error (RMSE) and mean absolute error (MAE) decreased by 12% and 13.5%, respectively. The BP neural network height-diameter model also appeared more accurate than the mixed-effects height-diameter model. Our study proposes the method of determining the optimal numbers of hidden layers, neurons of each layer, and transfer functions in the BP neural network structure. This method can be useful for other modeling studies of similar or different types, such as tree crown modeling, height, and diameter increments modeling, and so on.

Rapidly advancing airborne laser scanning technology has become greatly useful to estimate tree- and stand-level variables at a large scale using high spatial resolution data. Compared with that of ground measurements, the accuracy of the inferred information of diameter at breast height (DBH) from a remotely sensed database and the models developed with traditional regression approaches (e.g., ordinary least square regression) may not be sufficient. Thus, this regression approach is no longer appropriate to develop accurate models and predict DBH from remotely sensed-related variables because DBH is subject to the random effects of forest stands. This study developed a generalized nonlinear mixed-effects DBH estimation model from remotely sensed imagery data. The light detection and ranging (LiDAR)-derived stand canopy density, crown projection area, and tree height were used as predictors in the DBH estimation model. These variables can be more readily measured over an extensive forest area with higher accuracy compared to the conventional field-based methods. The airborne LiDAR data for a total of 402 Picea crassifolia Kom trees on a sample plot that were divided into 16 sub-sample plots and located in the most important distribution region of western China were used. The leave-one sub-sample plot-out cross-validation method was applied to evaluate the model’s prediction accuracy. The results indicated that the random effects of the sub-sample plot on the prediction of DBH were large and their inclusion into the DBH model significantly improved the prediction accuracy. The prediction accuracy of the proposed model at the mean (M) response was also substantially improved relative to the accuracy obtained from the base model. Among several tree selection alternatives evaluated, a sample size of the two largest trees per sub-sample plot used in estimating the random effects showed a significantly higher accuracy compared to other sampling alternatives. This sample size would balance both the measurement cost and potential prediction errors. The nonlinear mixed-effects DBH estimation model at the M response can also be applied if obtaining the estimates of individual tree DBH with a relatively lower cost, and a lower prediction accuracy was the purpose of the study.

The rational design of high-performance flexible pressure sensors with both high sensitivity and wide linear range attracts great attention because of their potential applications in wearable electronics and human-machine interfaces. Here, polyaniline nanofiber wrapped nonwoven fabric was used as the active material to construct high performance, flexible, all fabric pressure sensors with a bottom interdigitated textile electrode. Due to the unique hierarchical structures, large surface roughness of the polyaniline coated fabric and high conductivity of the interdigitated textile electrodes, the obtained pressure sensor shows superior performance, including ultrahigh sensitivity of 46.48 kPa−1 in a wide linear range (

An isocratic, sensitive and stability-indicating high performance liquid chromatographic (HPLC) method for separation and determination of the related substances of micafungin sodium was developed. The chromatographic separation was achieved on Agilent Zorbax SB-C18 column (250 × 4.6 mm, 5 μm). Forced degradation study confirmed that the newly developed method was specific and selective to the degradation products. The performance of the method was validated according to the present ICH guidelines for specificity, linearity, accuracy, precision and robustness. Regression analysis showed correlation coefficient value greater than 0.999 for micafungin sodium and its six impurities. Limit of detection of impurities was in the range of 0.006%–0.013% indicating the high sensitivity of the newly developed method. Accuracy of the method was established based on the recovery obtained between 98.2% and 102.0% for all impurities. RSD obtained for the repeatability and intermediate precision experiments, was less than 1.0%. The method was successfully applied to quantify related substances of micafungin sodium in bulk drugs.

Background: 25-hydroxy vitamin D (Vit D)-deficiency is common among patients with chronic kidney disease (CKD) and contributes to cardiovascular disease (CVD). African Americans (AAs) suffer disproportionately from CKD and CVD, and 80% of AAs are Vit D-deficient. The impact of Vit D repletion on cardio-renal biomarkers in AAs is unknown. We examined Vit D repletion on full-length osteopontin (flOPN), c-terminal fibroblast growth factor-23 (FGF-23), and plasminogen activator inhibitor-1 (PAI-1), which are implicated in vascular and kidney pathology. Methods: We performed a randomized, placebo-controlled study of high-risk AAs with Vit D deficiency, treated with 100,000 IU Vit D3 (cholecalciferol; n = 65) or placebo (n = 65) every 4 weeks for 12 weeks. We measured kidney function (CKD-EPI eGFR), protein-to-creatinine ratio, vascular function (pulse wave velocity; PWV), augmentation index, waist circumference, sitting, and 24-h-ambulatory blood pressure (BP), intact parathyroid hormone (iPTH) and serum calcium at baseline and study end, and compared Vit D levels with laboratory variables. We quantified plasma FGF-23, PAI-1, and flOPN by enzyme-linked immunosorbent assay. Multiple regression analyzed the relationship between log flOPN, FGF-23, and PAI-1 with vascular and renal risk factors. Results: Compared to placebo, Vit D3 repletion increased Vit D3 2-fold (p < 0.0001), decreased iPTH by 12% (p < 0.01) and was significantly correlated with PWV (p < 0.009). Log flOPN decreased (p = 0.03), log FGF-23 increased (p = 0.04), but log PAI-1 did not change. Multiple regression indicated association between log flOPN and PWV (p = 0.04) and diastolic BP (p = 0.02), while log FGF-23 was associated with diastolic BP (p = 0.05), and a trend with eGFR (p = 0.06). Conclusion: Vit D3 repletion may reduce flOPN and improve vascular function in high risk AAs with Vit D deficiency.

The diffusion dynamics of fullerene (C 60 ) in unentangled linear atactic polystyrene (PS) and polypropylene (PP) melts and the structure and dynamic properties of polymers in interface area are investigated by performing all-atom molecular dynamics simulations. The comparison of the results in two systems emphasises the influence of local interactions exerted by polymer side group on the diffusion dynamics of the nanoparticle. In the normal diffusive regime at long time scales, the displacement distribution function (DDF) follows a Gaussian distribution in PP system, indicating a normal diffusion of C 60 . However, we observe multiple peaks in the DDF curve for C 60 diffusing in PS melt, which indicates a diffusion mechanism of hopping of C 60 . The attractive interaction between C 60 and phenyl ring side groups are found to be responsible for the observed hopping diffusion. In addition, we find that the C 60 is dynamically coupled with a subsection of a tetramer on PS chain, which has a similar size with C 60 . The phenyl ring on PS chain backbone tends to have a parallel configuration in the vicinity of C 60 surface, therefore neighbouring phenyl rings can form chelation effect on the C 60 surface. Consequently, the rotational dynamics of phenyl ring and the translational diffusion of styrene monomers are found to be slowed down in this interface area. We hope our results can be helpful for understanding of the influence of the local interactions on the nanoparticle diffusion dynamics and interfacial properties in polymer/nanoparticle composites.