New phenols and one aminobenzoic acid derivative isolated from the roots of Rhus chinensis Mill.

Four new phenols and one new aminobenzoic acid derivative, with five known phenols were isolated from the roots of Rhus chinensis Mill. Their structures were elucidated by UV, IR, HRESIMS, 1D and 2D NMR spectra, as well as optical rotations. Compound 4 significantly inhibited mouse ear inflammation (inhibitory rate of 44.03%), and significantly extended the time of pain response (extension rate of 48.55%), showing significant anti-inflammatory and analgesic effects in vivo.

A novel chitosan/cellulose phosphonate composite hydrogel for ultrafast and efficient removal of Pb(II) and Cu(II) from wastewater.

Developing green and high-performance adsorbents to separate heavy metals from wastewater is a challenging task. Biomass hydrogel has the advantages of low cost, renewability, and biodegradability, but it has the problem of low adsorption efficiency. Herein, a novel chitosan/cellulose phosphonate composite hydrogel(CS/MCCP) is fabricated by two steps of reactions including the Phosphorylation reaction and the Mannich reaction. As an excellent chelating group, the phosphonate group greatly enhances the adsorption efficiency of the biomass hydrogel. The CS/MCCP shows ultrafast adsorption rate and excellent adsorption capacity for Pb(II) and Cu(II). The saturated adsorption capacity of Pb(II) and Cu(II) is 211.42 and 74.29 mg·g-1, respectively. The adsorption equilibration time is only 10 min. The adsorption performance of the CS/MCCP is superior to that of the reported cellulose/chitosan hydrogels. Besides, an in-depth analysis of the adsorption mechanism is conducted using X-ray photoelectron spectroscopy(XPS) combined with Density Functional Theory(DFT) calculation. The results reveal that the adsorption mechanism is electrostatic attraction and surface complexation, and there is a synergistic coordination between the phosphonate groups and the amino groups.

Isolation and characterization of dihydrohomoisoflavonoids from Portulaca oleracea L.

Eight pairs of dihydrohomoisoflavonoids (1-8), including four pairs of enantiomeric aglycones [(R,S)-portulacanones B (1) and C (2) and (R,S)-oleracones C (3) and Q (4)] and four pairs of epimeric glycosides [portulacasides A-D and epiportulacasides A-D (5-8)], were obtained from Portulaca oleracea L. Among them, (R,S)-oleracone Q (4) and four pairs of epimeric glycosides (5-8) were reported for the first time. The 50% EtOH fraction from the 70% EtOH extract prevented HepG2 human liver cancer cell damage induced by N-acetyl-p-aminophenol (APAP), and the cell survival rate was 62.3%. Portulacaside B (6a), which was isolated from the 50% EtOH fraction, exhibited hepatoprotective and anti-inflammatory effects. The compound increased the survival rate of APAP-damaged HepG2 human liver cancer cells from 40.0% to 51.2% and reduced nitric oxide production in RAW 264.7 macrophages, resulting in an inhibitory rate of 46.8%.

A Phase Retrieval Method for 3D Shape Measurement of High-Reflectivity Surface Based on π Phase-Shifting Fringes.

Fringe projection profilometry (FPP) has been widely used for 3D reconstruction, surface measurement, and reverse engineering. However, if the surface of an object has a high reflectivity, overexposure can easily occur. Image saturation caused by overexposure can lead to an incorrect intensity of the captured pattern images, resulting in phase and measurement errors of FPP. To address this issue, we propose a phase retrieval method for the 3D shape measurement of high-reflectivity surfaces based on π phase-shifting fringes. Our method only requires eight images to be projected, including three single-frequency three-step phase-shifting patterns and one pattern used to provide phase unwrapping constraints, called conventional patterns, as well as the π phase-shifting patterns corresponding to the four conventional patterns, called supplemental patterns. Saturated pixels of conventional fringes are replaced by unsaturated pixels in supplemental fringes to suppress phase retrieval errors. We analyzed all 16 replacement cases of fringe patterns and provided calculation methods for unwrapped phases. The main advantages of our method are as follows: (1) By combining the advantages of the stereo phase unwrapping (SPU) algorithm, the number of projected fringes is reduced. (2) By utilizing the phase unwrapping constraint provided by the fourth fringe pattern, the accuracy of SPU is improved. For highly reflective surfaces, the experimental results demonstrate the performance of the proposed method.

Cellulose phosphonate/polyethyleneimine nano-porous composite remove toxic Pb(II) and Cu(II) from water in a short time.

Current cellulose-based adsorbents suffer from the drawbacks of low adsorption capacity or slow adsorption rate for heavy metal ions. It is imperative to prepare new cellulose-based materials to improve the adsorption ability. In this work, we aim to introduce phosphonate groups to improve the adsorption ability of cellulose and select polyethyleneimine (PEI) for synergistic adsorption. A novel cellulose phosphonate/polyethyleneimine composite (MCCP-PEI) is prepared via the Mannich reaction. The structure and composition of MCCP-PEI are characterized by various advanced microscopy and spectroscopy techniques, and the results show that MCCP-PEI possesses abundant nano-porous structure, strong chelating sites, and excellent hydrophilicity. Besides, the adsorption behavior of MCCP-PEI for heavy metals has been systematically investigated. The results show that the adsorbent can quickly remove toxic Cu(II) and Pb(II) from water within 15 min and 20 min, respectively. The saturated adsorption capacity for Cu(II) and Pb(II) is 250.0 and 534.7 mg·g-1, respectively. X-ray photoelectron spectroscopy analysis combined with Density Functional Theory calculations reveal that the adsorption mechanism is chemical complexation and electrostatic attraction, and the phosphonate group plays a key role in the adsorption process.

Recent Advances in Light-Conversion Phosphors for Plant Growth and Strategies for the Modulation of Photoluminescence Properties.

The advent of greenhouses greatly promoted the development of modern agriculture, which freed plants from regional and seasonal constraints. In plant growth, light plays a key role in plant photosynthesis. The photosynthesis of plants can selectively absorb light, and different light wavelengths result in different plant growth reactions. Currently, light-conversion films and plant-growth LEDs have become two effective ways to improve the efficiency of plant photosynthesis, among which phosphors are the most critical materials. This review begins with a brief introduction of the effects of light on plant growth and the various techniques for promoting plant growth. Next, we review the up-to-date development of phosphors for plant growth and discussed the luminescence centers commonly used in blue, red and far-red phosphors, as well as their photophysical properties. Then, we summarize the advantages of red and blue composite phosphors and their designing strategies. Finally, we describe several strategies for regulating the spectral position of phosphors, broadening the emission spectrum, and improving quantum efficiency and thermal stability. This review may offer a good reference for researchers improving phosphors to become more suitable for plant growth.

Robust lockwire segmentation with multiscale boundary-driven regional stability.

Lockwire segmentation plays a vital role in ensuring mechanical safety in industrial fields. Aiming at the missed detection problem encountered in blurred and low-contrast situations, we propose a robust lockwire segmentation method based on multiscale boundary-driven regional stability. We first design a novel multiscale boundary-driven stability criterion to generate a blur-robustness stability map. Then, the curvilinear structure enhancement metric and linearity measurement function are defined to compute the likeliness of stable regions to belong to lockwires. Finally, the closed boundaries of lockwires are determined to achieve accurate segmentation. Experimental results demonstrate that our proposed method outperforms state-of-the-art object segmentation methods.

From entrepreneurship education to entrepreneurial intention: Mindset, motivation, and prior exposure.

We studied the relationships between entrepreneurship education and entrepreneurial intention among college students, with a focus on the mediating role of an entrepreneurial mindset as well as the moderating roles of learning motivation and prior entrepreneurial exposure. More than 90,000 students from 100 colleges or universities participated in the investigation, the data were subjected to structural equation modeling with Mplus. The results indicated that entrepreneurship education (curriculum attendance and extracurricular activity) significantly enhanced the entrepreneurial mindset of students, which, in turn, strengthened their entrepreneurial intention. In terms of learning, intrinsic motivation positively moderated the relationships between curriculum attendance and entrepreneurial intention/mindset, whereas extrinsic motivation moderated it negatively. Entrepreneurial exposure positively moderated the correlation between extracurricular activity and academic performance. Implications concerning the adjustment of entrepreneurship education to the entrepreneurial climate are discussed.

Cost-Effectiveness Analysis in Performance Assessments: A Case Study of the Objective Structured Clinical Examination.

Medical education assessments are becoming more complex, resulting in the inappropriateness of traditional methods primarily consisting of direct observations, oral examinations, and multiple-choice tests. Advancements in research methods have led to the formation of new modalities, namely performance assessments, which are, on the other hand, always costly in development and implementation. Proposing using the Program Effectiveness and Cost Generalization flow within an assessment context (PRECOG-A), this brief report explores the real financial cost drivers associated with an assessment case in the context of medical education, presents the steps in bridging the effectiveness with its psychometric properties via cost-effectiveness analysis, and evaluates the two-side outcomes for further evaluation decision-making. Referentially providing a framework to investigators and researchers, the illustration of PRECOG-A in this study outlines instructional guidelines for conducting cost-effectiveness analysis in a performance assessment.

Optimization Model of Signal-to-Noise Ratio for a Typical Polarization Multispectral Imaging Remote Sensor.

The signal-to-noise ratio (SNR) is an important performance evaluation index of polarization spectral imaging remote sensors. The SNR-estimation method based on the existing remote sensor is not perfect. To improve the SNR of this model, a partial detector check slant direction is presented in this study, and a polarization extinction ratio related to the internal SNR model of a typical multispectral imaging remote sensor is combined with the vector radiative transfer model to construct the atmosphere 6SV-SNR coupling model. The new result is that the central wavelength of the detection spectrum, the observation zenith angle, and the extinction ratio all affect the SNR of the remote sensor, and the SNR increases with the increase in the central wavelength of the detection spectrum. It is proved that the model can comprehensively estimate the SNR of a typical polarization multispectral imaging remote sensor under different detection conditions, and it provides an important basis for the application evaluation of such remote sensors.

High Genetic Diversity of Porcine Sapovirus From Diarrheic Piglets in Yunnan Province, China.

As one of the most important enteric viruses, sapovirus (SaV) can infect humans and a variety of animals. Until now, 19 SaV genogroups have been identified, among which 4 from human (GI, GII, GIV, and GV) and 8 from swine (GIII, GV-GXI). Porcine sapovirus (PoSaV) GIII has been prevalent in China; however, the status of PoSaV infection in Yunnan province remains unknown. In this study, 202 fecal samples were collected from piglets associated with outbreaks of acute diarrhea in Yunnan between January and May 2020. PoSaV detection revealed that the total PoSaV infection rate in Yunnan was 35.2%, with 21 PoSaV strains determined and phylogenetically analyzed. The phylogenetic tree analyses demonstrated that twenty PoSaV strains belonged to GIII and fell into five genotypes, whereas one PoSaV strain (YNQB) belonged to GV. Sequence alignments revealed deletions in VP2 region in 10 of the 20 GIII strains, as well as deletions and insertions in VP1 region of the GV strain (YNQB). Furthermore, genomic recombination analyses showed that two GIII strains (YNAN and YNJD) were recombinants, closely related to reference sequences MK965898 and LC215880, MK965898 and FJ387164, respectively. In summary, PoSaV-GIII strains were identified in Yunnan in 2020, and for the first time, a PoSaV-GV strain was identified from China, whereas the comprehensive analyses illustrated high genetic diversity of Yunnan PoSaV strains. This study may shed new light on the current PoSaV infections in Yunnan and pave the way toward further control of the PoSaV infections in China.

A Monte Carlo Study of Confidence Interval Methods for Generalizability Coefficient.

Computing confidence intervals around generalizability coefficients has long been a challenging task in generalizability theory. This is a serious practical problem because generalizability coefficients are often computed from designs where some facets have small sample sizes, and researchers have little guide regarding the trustworthiness of the coefficients. As generalizability theory can be framed to a linear mixed-effect model (LMM), bootstrap and simulation techniques from LMM paradigm can be used to construct the confidence intervals. The purpose of this research is to examine four different LMM-based methods for computing the confidence intervals that have been proposed and to determine their accuracy under six simulated conditions based on the type of test scores (normal, dichotomous, and polytomous data) and data measurement design (p×i×r and p× [i:r]). A bootstrap technique called "parametric methods with spherical random effects" consistently produced more accurate confidence intervals than the three other LMM-based methods. Furthermore, the selected technique was compared with model-based approach to investigate the performance at the levels of variance components via the second simulation study, where the numbers of examines, raters, and items were varied. We conclude with the recommendation generalizability coefficients, the confidence interval should accompany the point estimate.

Left Atrial Appendage Depth and Tachycardia Bradycardia Syndrome as Important Predictors of Left Atrial Appendage Thrombus in Patients with Nonvalvular Atrial Fibrillation.

Background: Atrial fibrillation (AF) is the most common heart rhythm disorder that has been shown to be associated with a significant increase in stroke and systemic embolism risk. The left atrial appendage (LAA) is a finger-like extension originating from the left atrium; the formation of thrombus in LAA is the main reason of stroke and systemic embolism in patients with nonvalvular atrial fibrillation (NVAF). This study is aimed at finding out the risk of left atrial appendage thrombus (LAAT) in patients with nonvalvular atrial fibrillation (NVAF). Method: We retrospectively examined the clinic and left atrial computer tomography angiography (CTA) features of patients assessed in Zhengzhou No. 7 People's Hospital between January 2020 and January 2021 derivation. Student's t-test, chi-square test, receiver operating characteristics (ROC) curves, and logistic regression analysis were used to identify predictors of LAAT. Result: Of 480 patients included in the analysis, LAAT was found in approximately 9.2% of all patients. Univariate demographic predictors of LAAT included left atrium top and bottom diameter (LTD), left atrial appendage depth (LAAD), CHA2DS2-VASc, tachycardia bradycardia syndrome (TBS), and nonparoxysmal atrial fibrillation (PAF). In a multiple logistic regression analysis, the independent predictors of thrombus were LAAD > 23.45 mm (odds ratio: 4.216, 95% CI: 1.869-9.510, P = 0.001), TBS (odds ratio: 4.076, 95% CI: 1.655-10.038, P = 0.002), and non-PAF (odds ratio: 2.896, 95% CI: 1.183-7.094, P = 0.02). Conclusion: In NVAF patients with LAAT, evidence suggested that larger LAAD, non-PAF, and TBS present a high risk of LAAT. This is the first report demonstrating that the LAAD and TBS are associated with LAAT in patients with NVAF.

Single-Cell Quantitative Phenotyping via the Aptamer-Mounted Nest-PCR (Apt-nPCR).

Analyzing single-cell phenotypes is increasingly required in biomedical studies, for non-genetic understanding of cellular activities and the biological significance of rare cell subpopulations. However, as compared to the genotypic analysis, single-cell phenotype analysis is technically more challenging. Herein, a tractable method that allows quantitative phenotyping of single cell is developed in this work, termed as the aptamer-mounted nest-PCR (Apt-nPCR). In specific, only two rounds of PCR reactions are required to complete the analysis, where aptamers (short oligonucleotides that bind to specific target molecules) are used as the recognition elements to bind antigens and also as the templates of nPCR for multiplexed and quantitative detection. So, quantitative information of these target antigens can be revealed by quantitative PCR analysis of these aptamers, which can thus be used to interpret cell phenotypes in a quantitative-to-qualitative way. By addressing two technical issues that are involved in single-cell phenotype analysis─multiplexed detection plus high sensitivity, we have shown the availability of this method for single-cell phenotyping. Therefore, the Apt-nPCR method may represent a tractable method to facilitate the single-cell phenotype analysis, which can be used as a complementary method against these single-cell genotyping methods in our daily research.

A Fully Convolutional Network-Based Tube Contour Detection Method Using Multi-Exposure Images.

The tube contours in two-dimensional images are important cues for optical three-dimensional reconstruction. Aiming at the practical problems encountered in the application of tube contour detection under complex background, a fully convolutional network (FCN)-based tube contour detection method is proposed. Multi-exposure (ME) images are captured as the input of FCN in order to get information of tube contours in different dynamic ranges, and the U-Net type architecture is adopted by the FCN to achieve pixel-level dense classification. In addition, we propose a new loss function that can help eliminate the adverse effects caused by the positional deviation and jagged morphology of tube contour labels. Finally, we introduce a new dataset called multi-exposure tube contour dataset (METCD) and a new evaluation metric called dilate inaccuracy at optimal dataset scale (DIA-ODS) to reach an overall evaluation of our proposed method. The experimental results show that the proposed method can effectively improve the integrity and accuracy of tube contour detection in complex scenes.

Ultrafast and efficient removal of Pb(II) from acidic aqueous solution using a novel polyvinyl alcohol superabsorbent.

The direct removal of heavy metal ions from acidic wastewater is a hard problem. In this study, a novel superabsorbent, polyvinyl alcohol phosphate ester (PVAP), was designed and prepared to remove Pb(II) from acidic wastewater (pH = 3). The PVAP can absorb water and swell to reach equilibrium within 30 s, which provides the conditions for ultrafast kinetic adsorption. For 100 mg/L Pb(II) solution, the adsorption reaches equilibrium within 5 min, and the removal ratio is more than 99.9% over a wide pH range of 3-6. Adsorption kinetics and isotherm data are consistent with pseudo-second-order and Langmuir model, respectively. The calculated maximum adsorption capacity for Pb(II) is 558.66 mg/g. Thermodynamic results show that the adsorption is spontaneous and exothermic process. The removal ratio for Pb(II) of PVAP still maintains above 99% after ten recycles. The PVAP can also simultaneously remove more than 97% of other heavy metal ions (Cu(II), Cd(II), Zn(II), Co(II), and Ni(II)) from an acidic solution. Moreover, the PVAP can efficiently purify simulated acid mine heavy metal wastewater, and the results meet EPA drinking water standards. The studies of X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared (FT-IR) spectroscopy prove that the adsorption mechanism involves surface complexation. This new superabsorbent is a promising candidate for acidic heavy metal sewage disposal.

Knockdown of Long Non-Coding RNA HCP5 Increases Radiosensitivity Through Cellular Senescence by Regulating microRNA-128 in Gliomas.

INTRODUCTION: Glioma is the most common malignant brain tumor in adults. Radiation is a key therapy in glioma. However, the radioresistance of glioma was a big challenge. HLA complex P5 (HCP5) has been reported dysregulated in several types of malignant tumor, including glioma. The role of HCP5 in the radiosensitivity of glioma is so far unknown. The present study aimed to investigate the effect of HCP5 on radiosensitivity in gliomas. METHODS: The levels of HCP5 and microRNA (miR)-128 were detected using qRT-PCR. The cell growth curve was used to show the cell proliferation and evaluate the radiosensitivity of glioma cells following exposure to X-ray. Senescence-associated ß-galactosidase (SA-ß-Gal) staining was used to test the cellular senescence. Luciferase reporter and RNA immunoprecipitation (RIP) assays were performed to determine the correlation between HCP5 and miR-128. RESULTS: HCP5 level of glioma cells was significantly higher than human astrocytes, whereas miR-128 level was lower in glioma cells. Besides, the HCP5 expression was increased in glioma tissues compared to normal brain tissues (NBTs). Knockdown of HCP5 inhibited cell proliferation and increased radiosensitivity in glioma cells. MiR-128 was predicted to be a target of HCP5. It was demonstrated that HCP5 directly bound to miR-128 and regulated its expression in glioma cells. Furthermore, the effects of HCP5 knockdown on radiosensitivity of glioma cells were attenuated by the inhibitor of miR-128. CONCLUSION: These findings suggested that interaction between lncRNA HCP5 and microRNA-128 could regulate the radiosensitivity of glioma cells by intervening in cellular senescence. This might be used as the potential radio-sensitization targets for glioma therapy.

Impairment of Pol ß-related DNA base-excision repair leads to ovarian aging in mice.

The mechanism underlying the association between age and depletion of the human ovarian follicle reserves remains uncertain. Many identified that impaired DNA polymerase ß (Pol ß)-mediated DNA base-excision repair (BER) drives to mouse oocyte aging. With aging, DNA lesions accumulate in primordial follicles. However, the expression of most DNA BER genes, including APE1, OGG1, XRCC1, Ligase I, Ligase α, PCNA and FEN1, remains unchanged during aging in mouse oocytes. Also, the reproductive capacity of Pol ß+/- heterozygote mice was impaired, and the primordial follicle counts were lower than that of wild type (wt) mice. The DNA lesions of heterozygous mice increased. Moreover, the Pol ß knockdown leads to increased DNA damage in oocytes and decreased survival rate of oocytes. Oocytes over-expressing Pol ß showed that the vitality of senescent cells enhances significantly. Furthermore, serum concentrations of anti-Müllerian hormone (AMH) indicated that the ovarian reserves of young mice with Pol ß germline mutations were lower than those in wt. These data show that Pol ß-related DNA BER efficiency is a major factor governing oocyte aging in mice.

A Robust Laser Stripe Extraction Method for Structured-Light Vision Sensing.

Environmental sensing is a key technology for the development of unmanned cars, drones and robots. Many vision sensors cannot work normally in an environment with insufficient light, and the cost of using multiline LiDAR is relatively high. In this paper, a novel and inexpensive visual navigation sensor based on structured-light vision is proposed for environment sensing. The main research contents of this project include: First, we propose a laser-stripe-detection neural network (LSDNN) that can eliminate the interference of reflective noise and haze noise and realize the highly robust extraction of laser stripes region. Then we use a gray-gravity approach to extract the center of laser stripe and used structured-light model to reconstruct the point clouds of laser center. Then, we design a single-line structured-light sensor, select the optimal parameters for it and build a car-platform for experimental evaluation. This approach was shown to be effective in our experiments and the experimental results show that this method is more accurate and robust in complex environment.

Identifying multiple line-structured lights from images via a local-to-global graph representation.

Identifying multiple line-structured lights from an image is a fundamental yet challenging issue in the active 3D visual reconstruction. The existing approaches using complex coding schemes are typically time-consuming and inapplicable to real-time sparse 3D reconstruction. In this paper, we solve the multi-line ambiguity from a new viewpoint-distribution pattern of the light segments in the image. We construct a local-to-global graph framework to fully describe the hierarchical distribution of multiple line-structured lights in a 2D image. The lights are firstly grouped as several local graphs according to a light overlapping metric. Then, the hierarchies of the local graphs are unified via the depth of the node, leading to a global graph. The lights in the same level of the global graph come from the same laser plane. The experimental results show the applicability of the proposed algorithm to identify scattered light segments and the robustness to varying sensor poses. We further apply the proposed algorithm to a 3D reconstruction case, achieving a reconstruction precision of 0.025mm. The proposed approach avoids complex auxiliary laser coding and thus is more convenient to conduct.