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)

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

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Time distortions are suppressed for longer Standard events. by Franklenin Sierra (12257109)

“…<b>a)</b> With a 120-ms S (corresponding to an 8.33 Hz attentive rhythm) we observed a decrease of the CE as a function of ISI: ISIs longer than 800 ms decreased subjective distortions of the stimuli. …”

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). …”

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

This supplement contains a complete list of all terms that were significantly enriched using our transcriptomic data. by Amberleigh E. Henschen (3257235)

<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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Immunohistochemical analysis of p-Akt expression in various experimental groups showing negative expression in control group, marked increased expression along the extended fibrous... by Rehab F. Abdel-Rahman (11886404)