Image_1_Overexpression of Tobacco GCN2 Stimulates Multiple Physiological Changes Associated With Stress Tolerance.TIF by Ning Li (45258)

“…<p>General control non-derepressible-2 (GCN2) is a ubiquitous protein kinase that phosphorylates the α subunit of the eukaryotic initiation factor, eIF2, preventing the initiation of a new cycle of protein synthesis, subsequently reducing the global protein biosynthesis. …”

DataSheet1_Cell spreading and viability on single-walled carbon nanotube/zinc oxide nanowire heterostructures synthesized by chemical vapor deposition.pdf by Emily Luc (18618094)

“…<p>Recent studies have shown the wide array of biomedical applications for nanomaterials such as single-walled carbon nanotubes (SWCNTs) and zinc oxide nanowires (ZnO NWs). SWCNTs are non-cytotoxic and have a varying range of mechanical, physical, and electrical properties useful to biomedical research. …”

Table 1_Soil properties and rhizosphere interactions affecting nitrous oxide emissions with mitigation by nitrification inhibitors in rice growth stages.docx by Haipeng Zhang (3413288)

“…Additionally, nitrification inhibitors (NIs) demonstrated a substantial reduction in N₂O emissions, with a decrease of 92.37% in JR-R and 91.93% in YC-R at selected growth stages, showing more pronounced effects compared to NR soils. …”

Image_1_CSF1R Inhibition Reduces Microglia Proliferation, Promotes Tissue Preservation and Improves Motor Recovery After Spinal Cord Injury.TIF by Yannick Nicolas Gerber (169700)

“…We first demonstrate that GW2580 treatment did not modify microglial response in non-injured spinal cords. Conversely, a strong decrease in proliferating microglia was observed following SCI. …”

CD43 deficiency does alter numbers of CD4⁺ or CD8⁺ T cells during sepsis. by Katherine T. Fay (2576998)

“…Data shown are cumulative from 2 separate experiments with a total of 9-11animals per group. Statistical comparisons were performed by one-way ANOVA followed by Kruskall-Wallis non-parametric post-tests.…”

Image_4_Overexpression of Tobacco GCN2 Stimulates Multiple Physiological Changes Associated With Stress Tolerance.TIF by Ning Li (45258)

“…<p>General control non-derepressible-2 (GCN2) is a ubiquitous protein kinase that phosphorylates the α subunit of the eukaryotic initiation factor, eIF2, preventing the initiation of a new cycle of protein synthesis, subsequently reducing the global protein biosynthesis. …”

Table_1_Unconscious and Distinctive Control of Vocal Pitch and Timbre During Altered Auditory Feedback.DOCX by Mingdi Xu (1531432)

“…For each type, three levels of shift (slight, medium, and severe) in both directions (increase or decrease) were used. After the experiment, participants were tested for whether they had perceived a change in the F0 and/or formants. …”

Lentiviral-mediated stable silencing of Abcd1 in human U87 astrocytoma and rat B12 oligodendrocytes. by Jaspreet Singh (533281)

“…Lentiviral-shRNAs induced gene silencing, indicated by a significant decrease in Abcd1 expression, analyzed by Western blot immunoassay detected with monoclonal antibody against Abcd1 protein levels (<b>B-i and F-i</b>) and qRT-PCR (<b>B-ii and F-ii</b>). …”

Role of Duplicate Genes in Robustness against Deleterious Human Mutations by Tzu-Lin Hsiao (42604)

“…The contribution of close sequence homologs to genetic robustness against null mutations has been previously demonstrated in simple organisms <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000014#pgen.1000014-Gu1">[4]</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000014#pgen.1000014-Conant1">[5]</a>. …”

Table_2_Differential effects on TDP-43, piezo-2, tight-junction proteins in various brain regions following repetitive low-intensity blast overpressure.DOCX by Lanier Heyburn (7055039)

“…</p>Methods<p>In this study, using a rodent model of relBOP and western blotting for protein expression we showed the differential expression of various neuropathological proteins like TDP-43, tight junction proteins (claudin-5, occludin, and glial fibrillary acidic protein (GFAP)) and a mechanosensitive protein (piezo-2) in different regions of the brain at different intensities and frequency of blast.…”

Overall model framework. by Ke Yan (331581)

“…On the other hand, taking English writing teaching as an example, the proposed method is further verified by designing a comparative experiment in groups. The results show that: (1) From the experimental data of word sense disambiguation, the accuracy of the SMOSS-LSTM model proposed in this paper is the lowest when the context range is "3+3", then it rises in turn at "5+5" and "7+7", reaches the highest at "7+7", and then begins to decrease at "10+10"; (2) Compared with the control group, the accuracy of syntactic analysis in the experimental group reached 89.5%, while that in the control group was only 73.2%. (3) In the aspect of English text error detection, the detection accuracy of the proposed model in the experimental group is as high as 94.8%, which is significantly better than the traditional SMOSS-based text error detection method, and its accuracy is only 68.3%. (4) Compared with other existing researches, although it is slightly inferior to Bidirectional Encoder Representations from Transformers (BERT) in word sense disambiguation, this proposed model performs well in syntactic analysis and English text error detection, and its comprehensive performance is excellent. …”

Table_3_Differential effects on TDP-43, piezo-2, tight-junction proteins in various brain regions following repetitive low-intensity blast overpressure.DOCX by Lanier Heyburn (7055039)

“…</p>Methods<p>In this study, using a rodent model of relBOP and western blotting for protein expression we showed the differential expression of various neuropathological proteins like TDP-43, tight junction proteins (claudin-5, occludin, and glial fibrillary acidic protein (GFAP)) and a mechanosensitive protein (piezo-2) in different regions of the brain at different intensities and frequency of blast.…”

Presentation4_Estimation of the P Fertilizer Demand of China Using the LePA Model.PPTX by Wenjia Yu (9422059)

“…We used the legacy P assessment model (LePA) to estimate P demand based on soil P management at the county, regional, and country scales according to six P application rate scenarios: (1) rate in 2012 maintained; (2) current rate maintained in low-P counties and P input stopped in high-P counties until critical Olsen-P level (CP) is reached, after which rate equals P-removal; (3) rate decreased to 1–1.5 kg ha⁻¹ year⁻¹ in low-P counties after CP is reached and in high-P counties; (4) rate in each county decreased to 1–8 kg ha⁻¹ year⁻¹ after soil Olsen-P reached CP in low P counties; (5) rate in each county was kept at P-removal rate after reduction; (6) P input was kept at the rate lower than P-offtake rate after reduction. …”

Presentation3_Estimation of the P Fertilizer Demand of China Using the LePA Model.PPTX by Wenjia Yu (9422059)

“…We used the legacy P assessment model (LePA) to estimate P demand based on soil P management at the county, regional, and country scales according to six P application rate scenarios: (1) rate in 2012 maintained; (2) current rate maintained in low-P counties and P input stopped in high-P counties until critical Olsen-P level (CP) is reached, after which rate equals P-removal; (3) rate decreased to 1–1.5 kg ha⁻¹ year⁻¹ in low-P counties after CP is reached and in high-P counties; (4) rate in each county decreased to 1–8 kg ha⁻¹ year⁻¹ after soil Olsen-P reached CP in low P counties; (5) rate in each county was kept at P-removal rate after reduction; (6) P input was kept at the rate lower than P-offtake rate after reduction. …”

Key parameters of LSTM training model. by Ke Yan (331581)

“…On the other hand, taking English writing teaching as an example, the proposed method is further verified by designing a comparative experiment in groups. The results show that: (1) From the experimental data of word sense disambiguation, the accuracy of the SMOSS-LSTM model proposed in this paper is the lowest when the context range is "3+3", then it rises in turn at "5+5" and "7+7", reaches the highest at "7+7", and then begins to decrease at "10+10"; (2) Compared with the control group, the accuracy of syntactic analysis in the experimental group reached 89.5%, while that in the control group was only 73.2%. (3) In the aspect of English text error detection, the detection accuracy of the proposed model in the experimental group is as high as 94.8%, which is significantly better than the traditional SMOSS-based text error detection method, and its accuracy is only 68.3%. (4) Compared with other existing researches, although it is slightly inferior to Bidirectional Encoder Representations from Transformers (BERT) in word sense disambiguation, this proposed model performs well in syntactic analysis and English text error detection, and its comprehensive performance is excellent. …”

DataSheet1_Estimation of the P Fertilizer Demand of China Using the LePA Model.xlsx by Wenjia Yu (9422059)

“…We used the legacy P assessment model (LePA) to estimate P demand based on soil P management at the county, regional, and country scales according to six P application rate scenarios: (1) rate in 2012 maintained; (2) current rate maintained in low-P counties and P input stopped in high-P counties until critical Olsen-P level (CP) is reached, after which rate equals P-removal; (3) rate decreased to 1–1.5 kg ha⁻¹ year⁻¹ in low-P counties after CP is reached and in high-P counties; (4) rate in each county decreased to 1–8 kg ha⁻¹ year⁻¹ after soil Olsen-P reached CP in low P counties; (5) rate in each county was kept at P-removal rate after reduction; (6) P input was kept at the rate lower than P-offtake rate after reduction. …”

Pdi1 is essential for fungal growth within the maize plant. by Miriam Marín-Menguiano (180236)

“…<i>maydis</i> hyphae with WGA-AF-488 (green), and visualized by fluorescence microscopy, showing a decrease in the growth and branching capability of the <i>pdi1</i> mutant. …”

Presentation2_Estimation of the P Fertilizer Demand of China Using the LePA Model.PPTX by Wenjia Yu (9422059)

“…We used the legacy P assessment model (LePA) to estimate P demand based on soil P management at the county, regional, and country scales according to six P application rate scenarios: (1) rate in 2012 maintained; (2) current rate maintained in low-P counties and P input stopped in high-P counties until critical Olsen-P level (CP) is reached, after which rate equals P-removal; (3) rate decreased to 1–1.5 kg ha⁻¹ year⁻¹ in low-P counties after CP is reached and in high-P counties; (4) rate in each county decreased to 1–8 kg ha⁻¹ year⁻¹ after soil Olsen-P reached CP in low P counties; (5) rate in each county was kept at P-removal rate after reduction; (6) P input was kept at the rate lower than P-offtake rate after reduction. …”

Comparison chart of model evaluation results. by Ke Yan (331581)

“…On the other hand, taking English writing teaching as an example, the proposed method is further verified by designing a comparative experiment in groups. The results show that: (1) From the experimental data of word sense disambiguation, the accuracy of the SMOSS-LSTM model proposed in this paper is the lowest when the context range is "3+3", then it rises in turn at "5+5" and "7+7", reaches the highest at "7+7", and then begins to decrease at "10+10"; (2) Compared with the control group, the accuracy of syntactic analysis in the experimental group reached 89.5%, while that in the control group was only 73.2%. (3) In the aspect of English text error detection, the detection accuracy of the proposed model in the experimental group is as high as 94.8%, which is significantly better than the traditional SMOSS-based text error detection method, and its accuracy is only 68.3%. (4) Compared with other existing researches, although it is slightly inferior to Bidirectional Encoder Representations from Transformers (BERT) in word sense disambiguation, this proposed model performs well in syntactic analysis and English text error detection, and its comprehensive performance is excellent. …”

Table_1_Differential effects on TDP-43, piezo-2, tight-junction proteins in various brain regions following repetitive low-intensity blast overpressure.XLSX by Lanier Heyburn (7055039)

“…</p>Methods<p>In this study, using a rodent model of relBOP and western blotting for protein expression we showed the differential expression of various neuropathological proteins like TDP-43, tight junction proteins (claudin-5, occludin, and glial fibrillary acidic protein (GFAP)) and a mechanosensitive protein (piezo-2) in different regions of the brain at different intensities and frequency of blast.…”