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  • MDPI AG  (11)
  • Wang, Meng  (11)
  • English  (11)
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  • MDPI AG  (11)
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  • English  (11)
Years
  • 1
    In: Sustainability, MDPI AG, Vol. 15, No. 17 ( 2023-08-30), p. 13053-
    Abstract: Severe extrusion floor heave is the most common type of failure of floors in deep roadways, and it is also a major problem restricting the safe and efficient mining of deep coal resources. In deep roadways, reducing floor stress is an effective means to control floor heave. In this study, the method of creating directional stress-relief zones by constructing stress-relief boreholes is applied; while the stress is released, the path of stress from the ribs transferred to the floor and to the extrusion failure path is cut off, and floor heave control is achieved. Therefore, based on the stress-boundary and rock-mass parameters of the roadway, the control effects of the borehole angle, length, diameter, and row spacing on the extrusion floor heave were studied, and the reasonable thresholds of borehole parameters were shown to ensure the stress-relief effect on the roadway. In addition, the bolt-grouting technology was used to strengthen the floor of the roadway, the broken surrounding rock was modified via grouting consolidation, the support strength of the floor was increased using high-tension bolts (cable), and there was a good floor heave control effect in the field application. On the basis of traditional floor reinforcement, the control effect of stress regulation on floor heave is fully considered in this study, and stress-relief–anchor-grouting, a collaborative control technology for floor heave in deep roadways, is developed. Based on the three factors affecting the stability of deep roadways (stress, lithology, and support), the collaborative prevention and control of severe extrusion floor heave were realized, which provides a new method for deep roadway floor heave control and has good application value.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2518383-7
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  • 2
    In: Plants, MDPI AG, Vol. 12, No. 5 ( 2023-02-27), p. 1058-
    Abstract: Sea Island cotton is the best quality tetraploid cultivated cotton in the world, in terms of fiber quality. Glyphosate is a widely used herbicide in cotton production, and the improper use of herbicides has led to pollen abortion in sea island cotton and, consequently, to a dramatic decrease in yield; however, the mechanism remains unclear. In this study, different concentrations (0, 3.75, 7.5, 15, and 30 g/L) of glyphosate were applied to CP4-EPSPS transgenic sea island cotton Xinchang 5 in 2021 and 2022 at Korla, with 15 g/L glyphosate chosen as the suitable concentration. By comparing the paraffin sections of 2–24 mm anthers in the 15 g/L glyphosate treatment group with those in the water control group, we showed that the key period of anther abortion after glyphosate treatment was the formation and development of tetrads, which corresponded to 8–9 mm buds. Transcriptome sequencing analysis of the treated and control anthers revealed a significant enrichment of differentially expressed genes in phytohormone-related pathways, in particular abscisic acid response and regulation pathways. Additionally, after treatment with 15 g/L of glyphosate, there was a significant increase in the amount of abscisic acid in the anthers in the 8–9 mm buds. Further analysis of the differential expression of abscisic acid response and regulatory genes, an abscisic acid response gene GbTCP14 (Gbar_A11G003090) was identified, which was significantly upregulated in buds with 15 g/L glyphosate treatment than the control, and it could be a key candidate gene for the subsequent research involving male sterility induced by glyphosate in sea island cotton.
    Type of Medium: Online Resource
    ISSN: 2223-7747
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704341-1
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  • 3
    In: Coatings, MDPI AG, Vol. 12, No. 6 ( 2022-06-08), p. 795-
    Abstract: Non-metal doping introduces structural defects, which alter the metal oxide band gap, resulting in high photocatalytic performance. Herein, a F doped SnO2 was synthesized via a simple solvothermal method. Through adjusting the solvothermal time, surfactants and F doping ratio, the optimal sample was prepared. In addition, the as-prepared nano-powder was characterized and analyzed by X-Ray-Diffraction (XRD), Scanning Electron Microscope (SEM), Energy Disperse Spectroscopy (EDS) and Fourier Transform Infrared Spectrum (FT-IR). Interestingly, the results of photocatalytic degradation showed that the degradation rate of rhodamine B (Rh B) reached 92.9% in 25 min after a 5-hour solvent heat treatment with polyethylene glycol (PEG) surfactant and F doping ratio of n(F):n(Sn) = 1:15. Through the study of photocatalytic performance, we found that F-doped SnO2 has high photocatalytic activity during a short time and its development potential in the field of photocatalysis, which provides a strong support for our further study of its practical application.
    Type of Medium: Online Resource
    ISSN: 2079-6412
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662314-6
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  • 4
    In: Molecules, MDPI AG, Vol. 28, No. 5 ( 2023-03-06), p. 2398-
    Abstract: Due to the massive use and abuse of pesticides, practices which have led to serious threats to human health, the research community must develop on-site and rapid detection technology of pesticide residues to ensure food safety. Here, a paper-based fluorescent sensor, integrated with molecularly imprinted polymer (MIP) targeting glyphosate, was prepared by a surface-imprinting strategy. The MIP was synthesized by a catalyst-free imprinting polymerization technique and exhibited highly selective recognition capability for glyphosate. The MIP-coated paper sensor not only remained selective, but also displayed a limit of detection of 0.29 µmol and a linear detection range from 0.5 to 10 µmol. Moreover, the detection time only took about 5 min, which is beneficial for rapid detection of glyphosate in food samples. The detection accuracy of such paper sensor was good, with a spiked recovery rate of 92–117% in real samples. The fluorescent MIP-coated paper sensor not only has good specificity, which is helpful to reduce the food matrix interference and shorten the sample pretreatment time, but it also has the merits of high stability, low-cost and ease of operation and carrying, displaying great potential for application in the on-site and rapid detection of glyphosate for food safety.
    Type of Medium: Online Resource
    ISSN: 1420-3049
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2008644-1
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  • 5
    Online Resource
    Online Resource
    MDPI AG ; 2020
    In:  Polymers Vol. 12, No. 8 ( 2020-07-31), p. 1718-
    In: Polymers, MDPI AG, Vol. 12, No. 8 ( 2020-07-31), p. 1718-
    Abstract: This work examined the effect of ultrasound and microwave treatments, separate and in combination, on the physicochemical and functional properties of chestnut starch. The results revealed that the ultrasonic-microwave (UM) and microwave-ultrasonic (MU) dually modified samples exhibited more severe surface damage, weaker birefringence, and lower relative crystallinity and gelatinization enthalpy than the native and single-treated starches. The UM samples showed the highest oil absorption capacity, and the MU samples showed the highest water absorption capacity and the best freeze-thaw stability (five cycles) among all samples. The swelling power, peak, trough, final, and breakdown viscosities, and pasting temperature all decreased regardless of single or dual modification. This study provides a reference for potential industrial applications of ultrasound and microwave treatments for the modification of chestnut starch.
    Type of Medium: Online Resource
    ISSN: 2073-4360
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2527146-5
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  • 6
    In: Agronomy, MDPI AG, Vol. 14, No. 5 ( 2024-04-24), p. 891-
    Abstract: Sea Island cotton is renowned for its superior fiber quality. Although mechanical harvesting has the potential to significantly increase efficiency and reduce the production cost of Sea Island cotton, there is still little research in this area. In this study, we analyzed 240 Sea Island cotton germplasm resources and evaluated 19 traits related to mechanical harvesting. The coefficient of variation ranged from 5.42% to 66.96%, and the genetic diversity index spanned from 1.57 to 2.07. In most traits studied, there was a strong correlation between the height of the first fruiting branch and the defoliation rate. The 19 traits were categorized into 6 factorial groups by principal component analysis, in which the defoliation factor contributed the most (30.89%). The cluster analysis divided the 240 cotton accessions into four main groups, with the second group exhibiting favorable mechanical harvesting characteristics such as higher defoliation rate and first fruit branch height. Using stepwise regression, a model was constructed with the joint evaluation score F-value as the response variable and eight traits (X1: PH, X2: SNB, X3: SBN, X4: MBL, X5: AFBM, X7: MLIA, X8: NB, and X13: 15 d DR) as predictors: Y = −7.2 + 0.01X1 + 0.23X2 + 0.192X3 + 0.038X4 + 0.007X5 + 0.014X7 + 0.025X8 + 2.952X13. Selected materials suitable for machine harvesting, such as MoShi729, were identified. This study provides valuable theoretical insights into the mechanical harvesting of Sea Island cotton germplasm resources and identifies promising materials for targeted breeding and improvement programs.
    Type of Medium: Online Resource
    ISSN: 2073-4395
    Language: English
    Publisher: MDPI AG
    Publication Date: 2024
    detail.hit.zdb_id: 2607043-1
    SSG: 23
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  • 7
    Online Resource
    Online Resource
    MDPI AG ; 2023
    In:  Biomolecules Vol. 13, No. 2 ( 2023-02-06), p. 306-
    In: Biomolecules, MDPI AG, Vol. 13, No. 2 ( 2023-02-06), p. 306-
    Abstract: (1) Background: Ovarian cancer (OV) has the high mortality rate among gynecological cancers worldwide. Inefficient early diagnosis and prognostic prediction of OV leads to poor survival in most patients. OV is associated with ferroptosis, an iron-dependent form of cell death. Ferroptosis, believed to be regulated by long non-coding RNAs (lncRNAs), may have potential applications in anti-cancer treatments. In this study, we aimed to identify ferroptosis-related lncRNA signatures and develop a novel model for predicting OV prognosis. (2) Methods: We downloaded data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression, and Gene Expression Omnibus (GEO) databases. Prognostic lncRNAs were screened by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and a prognostic model was constructed. The model’s predictive ability was evaluated by Kaplan–Meier (KM) survival analysis and receiver operating characteristic (ROC) curves. The expression levels of these lncRNAs included in the model were examined in normal and OV cell lines using quantitative reverse transcriptase polymerase chain reaction. (3) Results: We constructed an 18 lncRNA prognostic prediction model for OV based on ferroptosis-related lncRNAs from TCGA patient samples. This model was validated using TCGA and GEO patient samples. KM analysis showed that the prognostic model was able to significantly distinguish between high- and low-risk groups, corresponding to worse and better prognoses. Based on the ROC curves, our model shows stronger prediction precision compared with other traditional clinical factors. Immune cell infiltration, immune checkpoint expression levels, and Tumor Immune Dysfunction and Exclusion analyses are also insightful for OV immunotherapy. (4) Conclusions: The prognostic model constructed in this study has potential for improving our understanding of ferroptosis-related lncRNAs and providing a new tool for prognosis and immune response prediction in patients with OV.
    Type of Medium: Online Resource
    ISSN: 2218-273X
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2701262-1
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  • 8
    In: International Journal of Environmental Research and Public Health, MDPI AG, Vol. 13, No. 8 ( 2016-07-27), p. 761-
    Type of Medium: Online Resource
    ISSN: 1660-4601
    Language: English
    Publisher: MDPI AG
    Publication Date: 2016
    detail.hit.zdb_id: 2175195-X
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  • 9
    In: Water, MDPI AG, Vol. 14, No. 12 ( 2022-06-10), p. 1868-
    Abstract: In order to improve the removal ability of nitrogen and phosphorus pollutants from sewage with low C/N ratio, this study prepared the glucose polymer-based pyrite tailings with core-shell structure through glucose polymerizing on the surface of pyrite tailings particles and constructed a heterotrophic-sulfur autotrophic mixed-denitrification system. The experimental results show that compared with ordinary pyrite tailings, pyrite tailings modified by glucose polymer can improve the water quality of pH, enhance the ability to remove NO3− in water, and prolong the ability of mineral to continuously treat sewage, which also has a good removal effect on PO43− in water. The results of this study are of great significance to solve the excessive nitrogen and phosphorus in the secondary effluent and alleviate the eutrophication of the natural water.
    Type of Medium: Online Resource
    ISSN: 2073-4441
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2521238-2
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  • 10
    In: Remote Sensing, MDPI AG, Vol. 12, No. 3 ( 2020-02-05), p. 508-
    Abstract: Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring of crop growth is instructive for the diagnosis of crop growth and prediction of grain yield. Unmanned aerial vehicle (UAV)-based remote sensing is widely used in precision agriculture due to its unique advantages in flexibility and resolution. This study was carried out on wheat trials treated with different nitrogen levels and seeding densities in three regions of Jiangsu Province in 2018–2019. Canopy spectral images were collected by the UAV equipped with a multi-spectral camera during key wheat growth stages. To verify the results of the UAV images, the LAI, LDM, and yield data were obtained by destructive sampling. We extracted the wheat canopy reflectance and selected the best vegetation index for monitoring growth and predicting yield. Simple linear regression (LR), multiple linear regression (MLR), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural network (ANN), and random forest (RF) modeling methods were used to construct a model for wheat yield estimation. The results show that the multi-spectral camera mounted on the multi-rotor UAV has a broad application prospect in crop growth index monitoring and yield estimation. The vegetation index combined with the red edge band and the near-infrared band was significantly correlated with LAI and LDM. Machine learning methods (i.e., PLSR, ANN, and RF) performed better for predicting wheat yield. The RF model constructed by normalized difference vegetation index (NDVI) at the jointing stage, heading stage, flowering stage, and filling stage was the optimal wheat yield estimation model in this study, with an R2 of 0.78 and relative root mean square error (RRMSE) of 0.1030. The results provide a theoretical basis for monitoring crop growth with a multi-rotor UAV platform and explore a technical method for improving the precision of yield estimation.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2513863-7
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