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. …”

Spatial information is significantly decreased in dCA1 and vCA1 in APP/PS1 mice. by Udaysankar Chockanathan (18510288)

“…The spatial information in dCA1 was significantly larger than circularly shuffled spike trains with similar mean firing rates for C57BL/6 mice (mean ± std: empirical = 0.132 ± 0.048, shuffled = 0.124 ± 0.035, p < 0.001, two-sided Wilcoxon rank-sum test, n_empirical = 305 units from 5 recording sessions, n_shuffled = 30500 simulated units from 5 recording sessions), but not for APP/PS1 mice (mean ± std: empirical = 0.128 ± 0.051, shuffled = 0.123 ± .047, p = 0.39, two-sided Wilcoxon rank-sum test, n_empirical = 180 units from 4 recording sessions, n_shuffled = 18000 simulated units from 4 recording sessions). …”

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. …”

Example of predicted effects of choice bundling on preference for larger, later rewards (LLRs) over smaller, sooner rewards (SSRs). by Jeffrey S. Stein (11689918)

<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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Fig 1 - by Varun Tiwari (9258130)

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Fig 2 - by Varun Tiwari (9258130)

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Indices calculated using MODIS bands (B1-B7) which were used to estimate <i>boro</i> rice yields, where MODIS bands relate to indices as follows: B1 = RED, B2 = NIR1, B3 = BLUE, B4... by Varun Tiwari (9258130)

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Fig 7 - by Varun Tiwari (9258130)

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A flow chart of the methodology applied to estimate <i>boro</i> rice yield in Bangladesh. by Varun Tiwari (9258130)

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Fig 5 - by Varun Tiwari (9258130)

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