Image_2_Metabolomic Profiling of Dongxiang Wild Rice Under Salinity Demonstrates the Significant Role of Amino Acids in Rice Salt Stress.pdf by Yaling Chen (713903)

“…A total of 90 metabolites were significantly changed (49 upregulated and 41 downregulated) under salinity, of which the largest increase was in DL-2-Aminoadipic acid (27.08-fold) and the largest decrease was in L-Carnitine (0.014-fold). …”

Table_1_Metabolomic Profiling of Dongxiang Wild Rice Under Salinity Demonstrates the Significant Role of Amino Acids in Rice Salt Stress.xlsx by Yaling Chen (713903)

“…A total of 90 metabolites were significantly changed (49 upregulated and 41 downregulated) under salinity, of which the largest increase was in DL-2-Aminoadipic acid (27.08-fold) and the largest decrease was in L-Carnitine (0.014-fold). …”

Table_3_Metabolomic Profiling of Dongxiang Wild Rice Under Salinity Demonstrates the Significant Role of Amino Acids in Rice Salt Stress.docx by Yaling Chen (713903)

“…A total of 90 metabolites were significantly changed (49 upregulated and 41 downregulated) under salinity, of which the largest increase was in DL-2-Aminoadipic acid (27.08-fold) and the largest decrease was in L-Carnitine (0.014-fold). …”

Image_1_Metabolomic Profiling of Dongxiang Wild Rice Under Salinity Demonstrates the Significant Role of Amino Acids in Rice Salt Stress.pdf by Yaling Chen (713903)

“…A total of 90 metabolites were significantly changed (49 upregulated and 41 downregulated) under salinity, of which the largest increase was in DL-2-Aminoadipic acid (27.08-fold) and the largest decrease was in L-Carnitine (0.014-fold). …”

Table_2_Metabolomic Profiling of Dongxiang Wild Rice Under Salinity Demonstrates the Significant Role of Amino Acids in Rice Salt Stress.xlsx by Yaling Chen (713903)

“…A total of 90 metabolites were significantly changed (49 upregulated and 41 downregulated) under salinity, of which the largest increase was in DL-2-Aminoadipic acid (27.08-fold) and the largest decrease was in L-Carnitine (0.014-fold). …”

Phenotype and molecular identification of <i>GmNN1/FT2a</i> in stable transgenic soybean plants. by Xinxin Li (281237)

Domestication of rice may have changed its arbuscular mycorrhizal properties by modifying phosphorus nutrition-related traits and decreasing symbiotic compatibility by Yingwei Li (18623035)

“…</p><p dir="ltr">· The results revealed significantly lower mycorrhizal growth responses and colonization intensity in domesticated rice compared to wild rice, and this change of AM properties may be associated with the domestication modifications of plant phosphorus utilization efficiency at physiological and genomic levels. …”

Rice correlation analysis results of the bioclimatic environmental variables. by Shahzad Ali (3700111)

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Changes in biomass volumes of selected rice cultivars during rice cropping season. by Ji Yeon Lim (11457494)

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Rice Defense against Brown Planthopper Partially by Suppressing the Expression of Transferrin Family Genes of Brown Planthopper by Jiangen Wu (12161066)

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Ingredient and chemical composition of concentrate diet, rice straw, and microencapsulated of lemongrass and mangosteen peel (MELM) (%DM). by Rittikeard Prachumchai (18771149)

The GmNN1/FT2a-GmNFYA-C-GmENOD40 module regulates soybean nodulation. by Xinxin Li (281237)

Application of Machine Learning-Based Models to Understand and Predict Critical Flux of Oil-in-Water Emulsion in Crossflow Microfiltration by Henry J. Tanudjaja (12106715)

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Occurrence points of wheat, rice and soybean in East Asia region. by Shahzad Ali (3700111)

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Temporal profiles of the key BO-NN features. by Julia Berezutskaya (9080269)

“…The shifts associated with non-significant prediction accuracies are greyed-out. <b>(c)</b> The results of the MDS for the key BO-NN features. …”

Global Land Use Change Impacts on Soil Nitrogen Availability and Environmental Losses by Jing Wang (6206297)

“…In contrast, reversing managed to natural ecosystems significantly increased NNM by 20% (9.7, 25.4%) and decreased NN by 89% (−125, −46%), indicating increasing N availability while decreasing potential N loss. …”

<i>Boro</i> rice yield (2002–2021) estimation utilizing MODIS data and a trained random forest model with 30 input variables having high importance. by Varun Tiwari (9258130)

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Accuracy of the random forest regression model with various input variables (all inputs vs. top 30 inputs). by Varun Tiwari (9258130)

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Mean uncertainty of <i>boro</i> rice yields estimated by random forest model from 2002–2021. by Varun Tiwari (9258130)

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Random forest top 30 variable importance utilizing all input variables, including the MODIS bands, spectral indices, and land surface temperature. by Varun Tiwari (9258130)

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