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. …”

DataSheet1_The IRE1α Inhibitor KIRA6 Blocks Leukotriene Biosynthesis in Human Phagocytes.docx by Xiao Tang (801264)

“…However, we find that KIRA6 dose-dependently blocks phosphorylation of p38 and ERK, mitogen-activated protein kinases (MAPKs) that have established roles in activating cytosolic PLA₂α (cPLA₂α) and 5-LOX. The reduction of p38 and ERK phosphorylation is associated with a decrease in cPLA₂α phosphorylation and attenuated leukotriene production. …”

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.…”

Model performance 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. …”

Expression of Lin28B associated with stemness markers in HCC cells. by Shu-Wen Cheng (484650)

“…<p>Western blot analyses showed that Lin28B was overexpressed in captured EpCAM⁺ PLC/PRF/5 cells and Huh-7 cells but not in EpCAM⁺ HepG2 cells (A, upper panel). …”

Enteral fluid therapy administered in continuous flow by naso-ruminal route using three maintenance electrolyte solutions: effects on physiological biomarkers and the hemogram of b... by Pedro Ancelmo Nunes Ermita (5611484)

“…All solutions resulted in a significant decrease in erythrocyte concentration, hemoglobin concentration, and hematocrit, without affecting the leukogram. …”

The result compared with other existing methods. 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. …”

Data_Sheet_3_Differential effects on TDP-43, piezo-2, tight-junction proteins in various brain regions following repetitive low-intensity blast overpressure.CSV 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.…”

Data_Sheet_4_Differential effects on TDP-43, piezo-2, tight-junction proteins in various brain regions following repetitive low-intensity blast overpressure.CSV 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.…”

DataSheet2_Estimation of the P Fertilizer Demand of China Using the LePA Model.docx 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. …”

Presentation1_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. …”