Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea (Cicer arietinum L)
Salt stress is one of the most critical abiotic stresses having significant contribution in global agriculture production. Chickpea is sensitive to salt stress at various growth stages and a better knowledge of salt tolerance in chickpea would enable breeding of salt tolerant varieties. During prese...
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description | Salt stress is one of the most critical abiotic stresses having significant contribution in global agriculture production. Chickpea is sensitive to salt stress at various growth stages and a better knowledge of salt tolerance in chickpea would enable breeding of salt tolerant varieties. During present investigation, in vitro screening of desi chickpea by continuous exposure of seeds to NaCl-containing medium was performed. NaCl was applied in the MS medium at the rate of 6.25, 12.50, 25, 50, 75, 100, and 125 mM. Different germination indices and growth indices of roots and shoots were recorded. Mean germination (%) of roots and shoots ranged from 52.08 to 100%, and 41.67–100%, respectively. The mean germination time (MGT) of roots and shoots ranged from 2.40 to 4.78 d and 3.23–7.05 d. The coefficient of variation of the germination time (CVt) was recorded as 20.91–53.43% for roots, and 14.53–44.17% for shoots. The mean germination rate (MR) of roots was better than shoots. The uncertainty (U) values were tabulated as 0.43–1.59 (roots) and 0.92–2.33 (shoots). The synchronization index (Z) reflected the negative impact of elevated salinity levels on both root and shoot emergence. Application of NaCl exerted a negative impact on all growth indices compared to control and decreased gradually with elevated NaCl concentration. Results on salt tolerance index (STI) also revealed the reduced STI with elevated NaCl concentration and STI of roots was less than shoot. Elemental analysis revealed more Na and Cl accumulation with respective elevated NaCl concentrations. The In vitro growth parameters and STI values validated and predicted by multilayer perceptron (MLP) model revealed the relatively high
R
2
values of all growth indices and STI. Findings of this study will be helpful to broaden the understanding about the salinity tolerance level of desi chickpea seeds under in vitro conditions using various germination indices and seedling growth indices. |
doi_str_mv | 10.1007/s12298-023-01282-z |
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R
2
values of all growth indices and STI. Findings of this study will be helpful to broaden the understanding about the salinity tolerance level of desi chickpea seeds under in vitro conditions using various germination indices and seedling growth indices.</description><identifier>ISSN: 0971-5894</identifier><identifier>EISSN: 0974-0430</identifier><identifier>DOI: 10.1007/s12298-023-01282-z</identifier><identifier>PMID: 36875725</identifier><language>eng</language><publisher>New Delhi: Springer India</publisher><subject>Abiotic stress ; Agricultural production ; Artificial neural networks ; Biological and Medical Physics ; Biomedical and Life Sciences ; Biophysics ; Cell Biology ; Chickpeas ; Coefficient of variation ; Germination ; Legumes ; Life Sciences ; Multilayer perceptrons ; Neural networks ; Plant growth ; Plant Physiology ; Plant Sciences ; Research Article ; Roots ; Salinity ; Salinity effects ; Salinity tolerance ; Salt tolerance ; Seedlings ; Seeds ; Shoots ; Sodium chloride ; Synchronism ; Synchronization</subject><ispartof>Physiology and molecular biology of plants, 2023-02, Vol.29 (2), p.289-304</ispartof><rights>Prof. H.S. Srivastava Foundation for Science and Society 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>Prof. H.S. Srivastava Foundation for Science and Society 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c430t-aabf6ce2a1f89bf2cea0446444749c52bdf224ac5d4b8a7b96254227561538f33</citedby><cites>FETCH-LOGICAL-c430t-aabf6ce2a1f89bf2cea0446444749c52bdf224ac5d4b8a7b96254227561538f33</cites><orcidid>0000-0002-8524-9029</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9981858/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9981858/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,41464,42533,51294,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36875725$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Aasim, Muhammad</creatorcontrib><creatorcontrib>Akin, Fatma</creatorcontrib><creatorcontrib>Ali, Seyid Amjad</creatorcontrib><creatorcontrib>Taskin, Mehmet Burak</creatorcontrib><creatorcontrib>Colak, Muslume Sevba</creatorcontrib><creatorcontrib>Khawar, Khalid Mahmood</creatorcontrib><title>Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea (Cicer arietinum L)</title><title>Physiology and molecular biology of plants</title><addtitle>Physiol Mol Biol Plants</addtitle><addtitle>Physiol Mol Biol Plants</addtitle><description>Salt stress is one of the most critical abiotic stresses having significant contribution in global agriculture production. Chickpea is sensitive to salt stress at various growth stages and a better knowledge of salt tolerance in chickpea would enable breeding of salt tolerant varieties. During present investigation, in vitro screening of desi chickpea by continuous exposure of seeds to NaCl-containing medium was performed. NaCl was applied in the MS medium at the rate of 6.25, 12.50, 25, 50, 75, 100, and 125 mM. Different germination indices and growth indices of roots and shoots were recorded. Mean germination (%) of roots and shoots ranged from 52.08 to 100%, and 41.67–100%, respectively. The mean germination time (MGT) of roots and shoots ranged from 2.40 to 4.78 d and 3.23–7.05 d. The coefficient of variation of the germination time (CVt) was recorded as 20.91–53.43% for roots, and 14.53–44.17% for shoots. The mean germination rate (MR) of roots was better than shoots. The uncertainty (U) values were tabulated as 0.43–1.59 (roots) and 0.92–2.33 (shoots). The synchronization index (Z) reflected the negative impact of elevated salinity levels on both root and shoot emergence. Application of NaCl exerted a negative impact on all growth indices compared to control and decreased gradually with elevated NaCl concentration. Results on salt tolerance index (STI) also revealed the reduced STI with elevated NaCl concentration and STI of roots was less than shoot. Elemental analysis revealed more Na and Cl accumulation with respective elevated NaCl concentrations. The In vitro growth parameters and STI values validated and predicted by multilayer perceptron (MLP) model revealed the relatively high
R
2
values of all growth indices and STI. Findings of this study will be helpful to broaden the understanding about the salinity tolerance level of desi chickpea seeds under in vitro conditions using various germination indices and seedling growth indices.</description><subject>Abiotic stress</subject><subject>Agricultural production</subject><subject>Artificial neural networks</subject><subject>Biological and Medical Physics</subject><subject>Biomedical and Life Sciences</subject><subject>Biophysics</subject><subject>Cell Biology</subject><subject>Chickpeas</subject><subject>Coefficient of variation</subject><subject>Germination</subject><subject>Legumes</subject><subject>Life Sciences</subject><subject>Multilayer perceptrons</subject><subject>Neural networks</subject><subject>Plant growth</subject><subject>Plant Physiology</subject><subject>Plant Sciences</subject><subject>Research Article</subject><subject>Roots</subject><subject>Salinity</subject><subject>Salinity effects</subject><subject>Salinity tolerance</subject><subject>Salt tolerance</subject><subject>Seedlings</subject><subject>Seeds</subject><subject>Shoots</subject><subject>Sodium chloride</subject><subject>Synchronism</subject><subject>Synchronization</subject><issn>0971-5894</issn><issn>0974-0430</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kU9v1DAQxSMEoqXwBTggS1zaQ8D_7VyQqhUFpJV6KWfLcSZdt0kcbKcVlfjueHdLWzhwmhnNb5799KrqLcEfCMbqYyKUNrrGlNWYUE3ru2fVIW4UrzFn-PmuJ7XQDT-oXqV0hbFkXJGX1QGTWglFxWH16zRm33vn7YAmWOKu5NsQr9EYOhj8dIn6EFEHzs8biNs5bwD5Cd34HENpusVBh5IdMko5QkoohwGindwOcxvvrmew6HjlHURko4fsp2VE65PX1YveDgne3Nej6vvZ54vV13p9_uXb6nRdu2Ik19a2vXRALel10_bUgcWcS8654o0TtO16Srl1ouOttqptJBWcUiUkEUz3jB1Vn_a689KO0DmYcnFq5uhHG3-aYL35ezP5jbkMN6ZpNNFCF4Hje4EYfiyQshl9cjAMdoKwJEOVZkpLIWVB3_-DXoUlTsXeliKykUyJQtE95WJIKUL_8BmCzTZds0_XlHTNLl1zV47ePbXxcPInzgKwPZDmbVIQH9_-j-xvBLizGA</recordid><startdate>20230201</startdate><enddate>20230201</enddate><creator>Aasim, Muhammad</creator><creator>Akin, Fatma</creator><creator>Ali, Seyid Amjad</creator><creator>Taskin, Mehmet Burak</creator><creator>Colak, Muslume Sevba</creator><creator>Khawar, Khalid Mahmood</creator><general>Springer India</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-8524-9029</orcidid></search><sort><creationdate>20230201</creationdate><title>Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea (Cicer arietinum L)</title><author>Aasim, Muhammad ; Akin, Fatma ; Ali, Seyid Amjad ; Taskin, Mehmet Burak ; Colak, Muslume Sevba ; Khawar, Khalid Mahmood</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c430t-aabf6ce2a1f89bf2cea0446444749c52bdf224ac5d4b8a7b96254227561538f33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Abiotic stress</topic><topic>Agricultural production</topic><topic>Artificial neural networks</topic><topic>Biological and Medical Physics</topic><topic>Biomedical and Life Sciences</topic><topic>Biophysics</topic><topic>Cell Biology</topic><topic>Chickpeas</topic><topic>Coefficient of variation</topic><topic>Germination</topic><topic>Legumes</topic><topic>Life Sciences</topic><topic>Multilayer perceptrons</topic><topic>Neural networks</topic><topic>Plant growth</topic><topic>Plant Physiology</topic><topic>Plant Sciences</topic><topic>Research Article</topic><topic>Roots</topic><topic>Salinity</topic><topic>Salinity effects</topic><topic>Salinity tolerance</topic><topic>Salt tolerance</topic><topic>Seedlings</topic><topic>Seeds</topic><topic>Shoots</topic><topic>Sodium chloride</topic><topic>Synchronism</topic><topic>Synchronization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aasim, Muhammad</creatorcontrib><creatorcontrib>Akin, Fatma</creatorcontrib><creatorcontrib>Ali, Seyid Amjad</creatorcontrib><creatorcontrib>Taskin, Mehmet Burak</creatorcontrib><creatorcontrib>Colak, Muslume Sevba</creatorcontrib><creatorcontrib>Khawar, Khalid Mahmood</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Physiology and molecular biology of plants</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aasim, Muhammad</au><au>Akin, Fatma</au><au>Ali, Seyid Amjad</au><au>Taskin, Mehmet Burak</au><au>Colak, Muslume Sevba</au><au>Khawar, Khalid Mahmood</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea (Cicer arietinum L)</atitle><jtitle>Physiology and molecular biology of plants</jtitle><stitle>Physiol Mol Biol Plants</stitle><addtitle>Physiol Mol Biol Plants</addtitle><date>2023-02-01</date><risdate>2023</risdate><volume>29</volume><issue>2</issue><spage>289</spage><epage>304</epage><pages>289-304</pages><issn>0971-5894</issn><eissn>0974-0430</eissn><abstract>Salt stress is one of the most critical abiotic stresses having significant contribution in global agriculture production. Chickpea is sensitive to salt stress at various growth stages and a better knowledge of salt tolerance in chickpea would enable breeding of salt tolerant varieties. During present investigation, in vitro screening of desi chickpea by continuous exposure of seeds to NaCl-containing medium was performed. NaCl was applied in the MS medium at the rate of 6.25, 12.50, 25, 50, 75, 100, and 125 mM. Different germination indices and growth indices of roots and shoots were recorded. Mean germination (%) of roots and shoots ranged from 52.08 to 100%, and 41.67–100%, respectively. The mean germination time (MGT) of roots and shoots ranged from 2.40 to 4.78 d and 3.23–7.05 d. The coefficient of variation of the germination time (CVt) was recorded as 20.91–53.43% for roots, and 14.53–44.17% for shoots. The mean germination rate (MR) of roots was better than shoots. The uncertainty (U) values were tabulated as 0.43–1.59 (roots) and 0.92–2.33 (shoots). The synchronization index (Z) reflected the negative impact of elevated salinity levels on both root and shoot emergence. Application of NaCl exerted a negative impact on all growth indices compared to control and decreased gradually with elevated NaCl concentration. Results on salt tolerance index (STI) also revealed the reduced STI with elevated NaCl concentration and STI of roots was less than shoot. Elemental analysis revealed more Na and Cl accumulation with respective elevated NaCl concentrations. The In vitro growth parameters and STI values validated and predicted by multilayer perceptron (MLP) model revealed the relatively high
R
2
values of all growth indices and STI. Findings of this study will be helpful to broaden the understanding about the salinity tolerance level of desi chickpea seeds under in vitro conditions using various germination indices and seedling growth indices.</abstract><cop>New Delhi</cop><pub>Springer India</pub><pmid>36875725</pmid><doi>10.1007/s12298-023-01282-z</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-8524-9029</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Abiotic stress Agricultural production Artificial neural networks Biological and Medical Physics Biomedical and Life Sciences Biophysics Cell Biology Chickpeas Coefficient of variation Germination Legumes Life Sciences Multilayer perceptrons Neural networks Plant growth Plant Physiology Plant Sciences Research Article Roots Salinity Salinity effects Salinity tolerance Salt tolerance Seedlings Seeds Shoots Sodium chloride Synchronism Synchronization |
title | Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea (Cicer arietinum L) |
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