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Journal articles on the topic 'Quantitative Structure-Activity Relationship [MESH]'

Author: Grafiati

Published: 29 July 2024

Last updated: 30 July 2024

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1

Lin, Yu-Liang, Peng-Fei Fang, Xin Wang, Jie Wu, and Guo-Lin Yang. "Experimental and Numerical Study on Tensile Behavior of Double-Twisted Hexagonal Gabion Wire Mesh." Buildings 13, no. 7 (June 28, 2023): 1657. http://dx.doi.org/10.3390/buildings13071657.

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Double-twisted hexagonal gabion wire mesh is a type of reinforced soil material that is used in gabion retaining walls to stabilize the soil slope in geotechnical engineering. In this study, a series of tensile tests were conducted to investigate the tensile behavior of hexagonal gabion wire mesh. Meanwhile, numerical models of gabion wire mesh were built to investigate the whole tensile loading-strain process. The influence of wire diameter, mesh width, and mesh length on the tensile strength of hexagonal gabion wire mesh were evaluated based on laboratory tests and numerical simulation. The quantitative relationship of tensile strength versus wire diameter, mesh width, and mesh length was typically fitted by a quadratic function, linear function, and monotonically decreasing exponential function. The numerical result presents a good consistency with those obtained from the experiment. The result of the loading-strain curve obtained by both experiment and simulation exhibits an “S” shape with a distinct serrated characteristic. The loading-strain curve can be divided into the following four stages: mesh distortion stage, wire stretching stage, overall yield stage, and wire fracture stage, which well reflects the tensile behavior of double-twisted hexagonal wire mesh. The tensile behavior of gabion wire mesh is influenced by the structure pattern of wire mesh and the mechanical characteristic of steel wire.

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2

Ilyushchanka, Aliaksandr, Iryna Charniak, Aliaksei Kusin, Mihail Dechko, Ruslan Kusin, and Natalia Rutkovskaia. "Selection of factors and preparation of an experiment planning matrix for modeling a filter material with an orthotropic structure based on woven meshes." MATEC Web of Conferences 366 (2022): 05001. http://dx.doi.org/10.1051/matecconf/202236605001.

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The advantages of a filtering material with an orthotropic structure (FMTS) consisting of a package of woven meshes are described. Information is given on the first stage of FMTS modeling, which includes the choice of parameters and factors of the experiment and the compilation of a planning matrix to establish the relationship between technological characteristics and properties of FMTS. When constructing a stochastic mathematical model at this stage, two factors were chosen to describe the properties of the material – qualitative (mesh type), specified by the sigma constraint method, and quantitative (package thickness). The constructed matrix of experiment planning is given. The natural and coded values of FMTS samples were determined.

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3

Pereira Gomes, Dione, and Aníbal Danilo Farias. "Systematic review on the relationship between left heart failure and right ventricular dysfunction in the 2000s." SCT Proceedings in Interdisciplinary Insights and Innovations 1 (November 10, 2023): 143. http://dx.doi.org/10.56294/piii2023143.

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Background: Heart failure is a clinical syndrome characterized by symptoms such as dyspnea and fluid retention in the context of structural abnormalities of the heart. For many years, the importance of the right heart was ignored or forgotten, however, it has been revealed that the right ventricle is a key part in the prognosis of left heart failure. The right ventricle modulates the structure and function of the left ventricle. Interest arises in carrying out a bibliographic review on right heart failure as a cause of left heart failure. Material and methods: The study design was a systematic review of the literature available in PubMED, this review was of a quantitative-qualitative nature, through exhaustive and specific searches using MESH terminologies and boulean operators. Results: A total of 7 scientific articles were analyzed, with a total of 1134 patients, with a mean of 226. The most prevalent associated comorbidities were: arterial hypertension, diabetes, obesity and coronary disease. Conclusion: It is concluded that as a new therapeutic objective of HFpEF, something in which most of the authors of the analyzed articles agree, the focus should be placed on the treatment of right ventricular dysfunction.Right ventricular dysfunction is common and affects the normal function of the left ventricle, also impacting the symptoms and the chance of survival

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4

Liu, Jin Tao, Wei Shen, Qun Bo Fan, and H. N. Cai. "Modeling the Cracking Process of the YSZ Thermal Barrier Coating under the Thermal Shocking Loads." Key Engineering Materials 512-515 (June 2012): 463–68. http://dx.doi.org/10.4028/www.scientific.net/kem.512-515.463.

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For low thermal conductivity and high corrosion resistance, yttria stabilized zirconia (YSZ), as a top coat (TC), is widely used in thermal barrier coatings (TBCs), and the micro-structure of the TC has significant effects on it thermal shock resistance. Combining digital image processing technique with finite element mesh generation methods, finite element (EF) models based on actual microstructures of plasma sprayed YSZ thermal barrier coatings are built in this paper, so as to simulate the coating’s dynamic failure process when suffering thermal shocking loads. The cracking process is revealed by calculating both the stress and strain evolutions within the coating. Based on the proposed method, the effects of porosity and distribution are further studied. The simulation results agree well with the experimental observation, indicating that the cracks are mainly caused by pore connectivity, which promotes the growth of cracks. This work is expected to be helpful to establish the quantitative relationship between the TBCs porosity and the coating’s service performance.

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5

Huang, Shunjie, Xiangqian Wang, Yingming Li, Lei Wang, Gang Liu, Fukun Xiao, and O. V. Bashkov. "Analysis on Evolution Law of Small Structure Stress Arch and Composite Bearing Arch in Island Gob-Side Entry Driving." Geofluids 2022 (June 23, 2022): 1–12. http://dx.doi.org/10.1155/2022/4303681.

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At present, the theory of supporting the surrounding rock small structure of gob-side entry driving has been widely used, but there is no specific quantitative analytical formula for the bearing strength and bearing characteristics of the structure. Construct a small structural stress arch mechanical model based on the arch axis equation, and divide the width of coal pillars (fractured zone-plastic softening zone-plastic hardening zone) and small structural stress arch height. According to the relationship between the stress arch height and the size of the roadway, the anchor cable length is determined to be 7.3 m, and the “anchor mesh + ordinary long anchor cable + grouting anchor cable” coordinated support plan is proposed: anchor net support is used for the first support, and long anchor cable and grouting anchor cable are used for the second support. Combined with the supporting parameters, a mechanical model of the surrounding rock composite bearing stress arch is proposed, and the composite bearing stress arch structure is derived using elastoplastic mechanics to obtain the ultimate bearing strength relationship expression. The results show that the ultimate bearing capacity of the haulage gateway of 17236 island working-face in the north of Zhangji coal mine can reach 29.193 MPa after the composite bearing stress arch support. The feasibility of the supporting scheme is verified, and field monitoring showed that the deformation zone of the surrounding rock of the transportation haulage gateway is stable after being supported by the composite bearing stress arch structure, the maximum shrinkage of the top and bottom of the roadway is 287 mm, and the distance between the two sides is 640 mm.

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6

Golubenko, Daniil, Farah Ejaz Ahmed, and Nidal Hilal. "Novel Crosslinked Anion Exchange Membranes Based on Thermally Cured Epoxy Resin: Synthesis, Structure and Mechanical and Ion Transport Properties." Membranes 14, no. 6 (June 11, 2024): 138. http://dx.doi.org/10.3390/membranes14060138.

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Limitations in existing anion exchange membranes deter their use in the efficient treatment of industrial wastewater effluent. This work presents an approach to fabricating novel anion-conducting membranes using epoxy resin monomers like hydrophobic or hydrophilic diglycidyl ether and quaternized polyethyleneimine (PEI). Manipulating the diglycidyl ether nature, the quantitative composition of the copolymer and the conditions of quaternization allows control of the physicochemical properties of the membranes, including water uptake (20.0–330%), ion exchange capacity (1.5–3.7 mmol/g), ionic conductivity (0.2–17 mS/cm in the Cl form at 20 °C), potentiostatic transport numbers (75–97%), as well as mechanical properties. A relationship was established between copolymer structure and conductivity/selectivity trade-off. The higher the quaternized polyethyleneimine, diluent fraction, and hydrophilicity of diglycidyl ether, the higher the conductivity and the lower the permselectivity. Hydrophobic diglycidyl ether gives a much better conductivity/selectivity ratio since it provides a lower degree of hydration than hydrophilic diglycidyl ether. Different mesh and non-woven reinforcing materials were also examined. The developed membranes demonstrate good stability in both neutral and acidic environments, and their benchmark characteristics in laboratory electrodialysis cells and batch-mode dialysis experiments are similar to or superior to, commercial membranes such as Neosepta© AMX, FujiFilm© Type1, and Fumasep FAD-PET.

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7

NAKAGAWA, Yoshiaki. "Quantitative Structure-Activity Relationship." Japanese Journal of Pesticide Science 38, no. 1 (2013): 1. http://dx.doi.org/10.1584/jpestics.w12-39.

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8

FUJITA, Toshio. "Quantitative structure-activity relationship and drug design." Journal of the agricultural chemical society of Japan 64, no. 1 (1990): 1–11. http://dx.doi.org/10.1271/nogeikagaku1924.64.1.

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9

Düren, Reiner, and Horst A. Diehl. "Quantitative structure-activity relationship of coumarin derivatives." Journal of Chromatography A 445 (January 1988): 49–58. http://dx.doi.org/10.1016/s0021-9673(01)84507-6.

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10

De Benedetti, P. G. "Electrostatics in quantitative structure-activity relationship analysis." Journal of Molecular Structure: THEOCHEM 256 (April 1992): 231–48. http://dx.doi.org/10.1016/0166-1280(92)87169-z.

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11

Seregin, S. A. "Some peculiarities in vertical distribution of metazoan microzooplankton in the Black Sea in spring." Marine Biological Journal 5, no. 4 (December 30, 2020): 94–107. http://dx.doi.org/10.21072/mbj.2020.05.4.08.

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Based on material, received in the 84th and 93rd cruises of the RV “Professor Vodyanitsky”, vertical distribution of microplankton fraction of metazooplankton (MM) in the Black Sea in spring was analyzed. A total of 27 stations were examined both in the coastal zone and in the deep sea. The 10-L bottles of the CTD probes “Mark-III Neil Brown” and “Sea Bird 911” were used to collect 4–6 L of water from 4–11 horizons of the water column. The samples obtained were concentrated by the reverse filtration through the plankton net with the mesh size of 10 µm. Quantitative and systematic analysis of all samples was carried out totally in the Bogorov chamber using an MBS-9 stereo microscope. The main factors determining nature of the distribution are MM species composition, physical structure of the water column, and hydrodynamic processes affecting its stability/instability. Nauplii of Black Sea Copepoda and veligers of Bivalvia were the most numerous systematic groups in “spring” MM. Mollusc veligers determined abundance maxima in the lower layers of shallow water habitats, while copepods prevailed over large depths and determined total abundance peaks in the upper and middle water layers. Daily time series experiment showed that advective hydrodynamic processes can significantly affect MM vertical distribution, changing physical structure of the water column. For some species, in most cases, a correlation of their distribution with vertical profiles of temperature and salinity was revealed, which rarely manifested at total MM abundance level. A comparison of two spring seasons (2016 and 2017) showed the relationship between vertical distribution of MM abundance and temperature to be more pronounced in cases of low temperature. A change in the sign of correlation with temperature was detected during spring season for Oithona similis: an initially cold-loving species of Black Sea copepods. This revealed in a more superficial distribution of the maxima abundance of this species at lower seasonal temperatures, which could reflect a shift in temperature optimum for the species population and play the role of an adaptive reaction in conditions of seasonal changes in sea thermal characteristics.

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12

Bellera, Carolina L., and Alan Talevi. "Quantitative structure–activity relationship models for compounds with anticonvulsant activity." Expert Opinion on Drug Discovery 14, no. 7 (May 10, 2019): 653–65. http://dx.doi.org/10.1080/17460441.2019.1613368.

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13

Chang, Hyun-Joo, Hyun Jung Kim, and Hyang Sook Chun. "Quantitative structure−activity relationship (QSAR) for neuroprotective activity of terpenoids." Life Sciences 80, no. 9 (February 2007): 835–41. http://dx.doi.org/10.1016/j.lfs.2006.11.009.

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14

Wang, Hui, Thi Thanh Hien Nguyen, Shujun Li, Tao Liang, Yuanyuan Zhang, and Jian Li. "Quantitative structure–activity relationship of antifungal activity of rosin derivatives." Bioorganic & Medicinal Chemistry Letters 25, no. 2 (January 2015): 347–54. http://dx.doi.org/10.1016/j.bmcl.2014.11.034.

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15

Scotti, Marcus T., Mariane B. Fernandes, Marcelo J. P. Ferreira, and Vicente P. Emerenciano. "Quantitative structure–activity relationship of sesquiterpene lactones with cytotoxic activity." Bioorganic & Medicinal Chemistry 15, no. 8 (April 2007): 2927–34. http://dx.doi.org/10.1016/j.bmc.2007.02.005.

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16

Gupta, Satya. "Quantitative Structure-Activity Relationship Studies on Cholecystokinin Antagonists." Current Pharmaceutical Design 8, no. 2 (January 1, 2002): 111–24. http://dx.doi.org/10.2174/1381612023396500.

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17

Gupta, Satya, and Anantha Nagappa. "Quantitative Structure-Activity Relationship Studies on Cholecystokin Antagonists." Medicinal Chemistry Reviews - Online 1, no. 3 (July 1, 2004): 349–50. http://dx.doi.org/10.2174/1567203043401680.

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18

Kim, Hyun-Ock, and Eunice C. Y. Li-Chan. "Quantitative Structure−Activity Relationship Study of Bitter Peptides." Journal of Agricultural and Food Chemistry 54, no. 26 (December 2006): 10102–11. http://dx.doi.org/10.1021/jf062422j.

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19

Wang, Zongde, Jie Song, Zhaojiu Han, Zhikuan Jiang, Weiqing Zheng, Jinzhu Chen, Zhanqian Song, and Shibin Shang. "Quantitative Structure−Activity Relationship of Terpenoid Aphid Antifeedants." Journal of Agricultural and Food Chemistry 56, no. 23 (December 10, 2008): 11361–66. http://dx.doi.org/10.1021/jf802324v.

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20

Lien, Eric J., Shijun Ren, Huynh-Hoa Bui, and Rubin Wang. "Quantitative structure-activity relationship analysis of phenolic antioxidants." Free Radical Biology and Medicine 26, no. 3-4 (February 1999): 285–94. http://dx.doi.org/10.1016/s0891-5849(98)00190-7.

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21

Ungwitayatorn, J., M. Pickert, and A. W. Frahm. "Quantitative structure-activity relationship (QSAR) study of polyhydroxyxanthones." Pharmaceutica Acta Helvetiae 72, no. 1 (February 1997): 23–29. http://dx.doi.org/10.1016/s0031-6865(96)00043-x.

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22

Burden, Frank R. "Quantitative Structure−Activity Relationship Studies Using Gaussian Processes." Journal of Chemical Information and Computer Sciences 41, no. 3 (May 2001): 830–35. http://dx.doi.org/10.1021/ci000459c.

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23

Gupta, S. P. "Quantitative Structure-Activity Relationship Studies on Anticancer Drugs." Chemical Reviews 94, no. 6 (September 1994): 1507–51. http://dx.doi.org/10.1021/cr00030a003.

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24

Minovski, Nikola, Marjan Vračko, and Tom Šolmajer. "Quantitative structure–activity relationship study of antitubercular fluoroquinolones." Molecular Diversity 15, no. 2 (March 14, 2010): 417–26. http://dx.doi.org/10.1007/s11030-010-9238-5.

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25

Qi, Shaoying, K. James Hay, Mark J. Rood, and Mark P. Cal. "Carbon Fiber Adsorption Using Quantitative Structure-Activity Relationship." Journal of Environmental Engineering 126, no. 9 (September 2000): 865–68. http://dx.doi.org/10.1061/(asce)0733-9372(2000)126:9(865).

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26

Aizpuru, A., L. Malhautier, and J. L. Fanlo. "Quantitative Structure-Activity Relationship Modeling of Biofiltration Removal." Journal of Environmental Engineering 128, no. 10 (October 2002): 953–59. http://dx.doi.org/10.1061/(asce)0733-9372(2002)128:10(953).

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27

Klopman, Gilles, and Ju-Yun Li. "Quantitative structure-agonist activity relationship of capsaicin analogues." Journal of Computer-Aided Molecular Design 9, no. 3 (June 1995): 283–94. http://dx.doi.org/10.1007/bf00124458.

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28

Santos, Cleydson Breno Rodrigues dos, Cleison Carvalho Lobato, Marcos Alexandre Costa de Sousa, Williams Jorge da Cruz Macêdo, and José Carlos Tavares Carvalho. "Molecular Modeling: Origin, Fundamental Concepts and Applications Using Structure-Activity Relationship and Quantitative Structure-Activity Relationship." Reviews in Theoretical Science 2, no. 2 (June 1, 2014): 91–115. http://dx.doi.org/10.1166/rits.2014.1016.

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29

Montorsi, Monia, M. Cristina Menziani, Marina Cocchi, Francesca Fanelli, and Pier G. De Benedetti. "Computer Modeling of Size and Shape Descriptors of α1-Adrenergic Receptor Antagonists and Quantitative Structure–Affinity/Selectivity Relationships." Methods 14, no. 3 (March 1998): 239–54. http://dx.doi.org/10.1006/meth.1998.0581.

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30

Kamenska, Verginia, Lyubomir Dourmishev, Assen Dourmishev, Rusi Vasilev, and Ovanes Mekenyan. "Quantitative Structure-Activity Relationship Modeling of Dermatomyositis Activity of Drug Chemicals." Arzneimittelforschung 56, no. 12 (December 21, 2011): 856–65. http://dx.doi.org/10.1055/s-0031-1296798.

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31

Muranaka, Ken. "Anticancer Activity of Estradiol Derivatives: A Quantitative Structure-Activity Relationship Approach." Journal of Chemical Education 78, no. 10 (October 2001): 1390. http://dx.doi.org/10.1021/ed078p1390.

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32

Rajwade, R. P. "Quantitative structure–activity relationship (QSAR) studies on antitumor activity: glutamine analogues." New Biotechnology 27 (April 2010): S22—S23. http://dx.doi.org/10.1016/j.nbt.2010.01.015.

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33

Li, Zhiming, Kaiying Nie, Zhaojing Wang, and Dianhui Luo. "Quantitative Structure Activity Relationship Models for the Antioxidant Activity of Polysaccharides." PLOS ONE 11, no. 9 (September 29, 2016): e0163536. http://dx.doi.org/10.1371/journal.pone.0163536.

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34

Zhou, Xiao-Fei, Qingxiang Shao, Robert A. Coburn, and Marilyn E. Morris. "Quantitative Structure–Activity Relationship and Quantitative Structure–Pharmacokinetics Relationship of 1,4-Dihydropyridines and Pyridines as Multidrug Resistance Modulators." Pharmaceutical Research 22, no. 12 (September 20, 2005): 1989–96. http://dx.doi.org/10.1007/s11095-005-8112-0.

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35

Kothiwale, Sandeepkumar, Corina Borza, Ambra Pozzi, and Jens Meiler. "Quantitative Structure–Activity Relationship Modeling of Kinase Selectivity Profiles." Molecules 22, no. 9 (September 19, 2017): 1576. http://dx.doi.org/10.3390/molecules22091576.

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36

Jun-Jie, Ding, Ding Xiao-Qin, Zhao Li-Feng, and Chen Ji-Sheng. "Three Dimensional Quantitative Structure-activity Relationship of Dihydropyridine Derivatives." Acta Physico-Chimica Sinica 19, no. 12 (2003): 1108–13. http://dx.doi.org/10.3866/pku.whxb20031203.

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37

OKAZAKI, KIYO, YUKI MANABE, TAKUYA MAEDA, HIDEAKI NAGAMUNE, and HIROKI KOURAI. "Quantitative Structure-Activity Relationship of Antibacterial Dodecylpyridinium Iodide Derivatives." Biocontrol Science 1, no. 1 (1996): 51–59. http://dx.doi.org/10.4265/bio.1.51.

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38

Xie, Aihua, Chenzhong Liao, Zhibin Li, Zhiqiang Ning, Weiming Hu, Xianping Lu, Leming Shi, and Jiaju Zhou. "Quantitative Structure-Activity Relationship Study of Histone Deacetylase Inhibitors." Current Medicinal Chemistry-Anti-Cancer Agents 4, no. 3 (May 1, 2004): 273–99. http://dx.doi.org/10.2174/1568011043352948.

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39

Milojković-Opsenica, Dušanka, Filip Andrić, Sandra Šegan, Jelena Trifković, and Živoslav Tešić. "Thin-layer chromatography in quantitative structure-activity relationship studies." Journal of Liquid Chromatography & Related Technologies 41, no. 6 (April 3, 2018): 272–81. http://dx.doi.org/10.1080/10826076.2018.1447892.

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40

Roy, Kunal, and Probir Kumar Ojha. "Advances in quantitative structure–activity relationship models of antimalarials." Expert Opinion on Drug Discovery 5, no. 8 (June 17, 2010): 751–78. http://dx.doi.org/10.1517/17460441.2010.497812.

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41

Wang, P., X. Xu, S. Liao, J. Song, G. Fan, S. Chen, and Z. Wang. "Quantitative structure–activity relationship study of amide mosquito repellents." SAR and QSAR in Environmental Research 28, no. 4 (April 3, 2017): 341–53. http://dx.doi.org/10.1080/1062936x.2017.1320585.

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42

ZHAO, Jinsong. "3D-quantitative structure-activity relationship study of organophosphate compounds." Chinese Science Bulletin 49, no. 3 (2004): 240. http://dx.doi.org/10.1360/03wb0156.

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43

Kulkarni, Seema A., and Thirumurthy Madhavan. "Hologram Quantitative Structure Activity Relationship Analysis of JNK Antagonists." Journal of the Chosun Natural Science 8, no. 2 (June 30, 2015): 81–88. http://dx.doi.org/10.13160/ricns.2015.8.2.81.

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44

Fourches, Denis, and Jeremy Ash. "4D- quantitative structure–activity relationship modeling: making a comeback." Expert Opinion on Drug Discovery 14, no. 12 (September 12, 2019): 1227–35. http://dx.doi.org/10.1080/17460441.2019.1664467.

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45

Klopman, Gilles, Leming M. Shi, and Avner Ramu. "Quantitative Structure-Activity Relationship of Multidrug Resistance Reversal Agents." Molecular Pharmacology 52, no. 2 (August 1, 1997): 323–34. http://dx.doi.org/10.1124/mol.52.2.323.

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46

Gupta, S. P. "QSAR (quantitative structure-activity relationship) studies on local anesthetics." Chemical Reviews 91, no. 6 (September 1991): 1109–19. http://dx.doi.org/10.1021/cr00006a001.

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47

Udenigwe, Chibuike C., Huan Li, and Rotimi E. Aluko. "Quantitative structure–activity relationship modeling of renin-inhibiting dipeptides." Amino Acids 42, no. 4 (January 19, 2011): 1379–86. http://dx.doi.org/10.1007/s00726-011-0833-2.

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48

Misra, Milind, Qing Shi, Xiaocong Ye, Ewa Gruszecka-Kowalik, Wei Bu, Zhanzhu Liu, Margaret M. Schweri, Howard M. Deutsch, and Carol A. Venanzi. "Quantitative structure–activity relationship studies of threo-methylphenidate analogs." Bioorganic & Medicinal Chemistry 18, no. 20 (October 15, 2010): 7221–38. http://dx.doi.org/10.1016/j.bmc.2010.08.034.

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49

Zhao, Jinsong, Bin Wang, Zhaoxia Dai, Xiaodong Wang, Lingren Kong, and Liansheng Wang. "3D-quantitative structure-activity relationship study of organophosphate compounds." Chinese Science Bulletin 49, no. 3 (February 2004): 240–45. http://dx.doi.org/10.1007/bf03182805.

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50

Awad, Mohamed K., Saida A. El-Enien, and Mohammed H. Rizk. "Quantitative Structure-Trypanocidal Activity Relationship Analysis of Phenothiazine Derivatives." Indian Journal of Applied Research 3, no. 9 (October 1, 2011): 65–68. http://dx.doi.org/10.15373/2249555x/sept2013/20.

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