Reduction of L O-ag production and antigenic shift to O2-antigen in by Fermin E. Guerra (6084545)

The contribution of O-ag length and the Vi capsule to serum resistance is serovar dependent. by Fermin E. Guerra (6084545)

Table 1_Prognostic significance of early alpha fetoprotein and des-gamma carboxy prothrombin responses in unresectable hepatocellular carcinoma patients undergoing triple combinati... by Teng Zhang (457128)

“…</p>Conclusion<p>AFP or DCP response at 6-8 weeks post-therapy predicts better oncological outcomes in patients with uHCC treated with triple therapy.…”

Change in temperature from baseline. by Katharyn L. Flickinger (4759323)

Hemodynamic and respiratory changes over time. by Katharyn L. Flickinger (4759323)

Participants diagram of the study. by Nirmal Gautam (10031682)

“…Moreover, non-consumption of fatty foods and outdoor activities were found to be associated with a decrease in obesity by respectively. However, non-consumption of fruits and vegetables and maternal BMI were significantly correlated with an increased risk of obesity in children () and adolescents () respectively.…”

Study scheme. by Ayako Shoji (78037)

“…The incidence rate of suspected mild cognitive impairment was significantly lower in the post-CHAP group even after adjusted known factors associated with cognitive disorders. …”

Characteristics of the participants. by Ayako Shoji (78037)

“…The incidence rate of suspected mild cognitive impairment was significantly lower in the post-CHAP group even after adjusted known factors associated with cognitive disorders. …”

Incidence rates of sMCI and sSCI. by Ayako Shoji (78037)

“…The incidence rate of suspected mild cognitive impairment was significantly lower in the post-CHAP group even after adjusted known factors associated with cognitive disorders. …”

Flow Chart of Study Participant Selection. by Zhi Jin (3742471)

“…Notably, individuals with long sleep duration (>9 hours) had a significantly decreased risk of CVD (OR: 0.36, 95% CI: 0.15–0.85, P = 0.02) compared to those with shorter sleep durations.…”

Results of redundancy analysis. by Bianhua Zhang (22430652)

“…All the heavy metal contents in MLS were highest significantly except Cd among NL and LFA. Shannon and Chao1 indices in NC were significantly higher than those in MC (p < 0.05), In LFA, Shannon and Chao1 indices of MLX were the highest, while MLS was significantly lower than NL (p < 0.05). …”

Variation of the potential carbon mineralization (C–CO₂) at T₁₂₀ in the water samples incubations. by Daniel Cuevas-Lara (12188972)

DOC consumption at T₁₂₀ of the water samples incubations. by Daniel Cuevas-Lara (12188972)

Predicted chronic activation of the cardiomyocyte signaling network during VO. by Johane H. Bracamonte (12883844)

Supplementary file 1_Analysis of microbial composition in different dry skin areas of Beijing women.docx by Jingtao Wang (828021)

“…</p>Results<p>Analysis of physiological parameters showed that Hydration, TEWL and sebum secretion were significantly lower (P < 0.05) in the lower leg compared to the back of the hand. …”

Overall model framework. by Ke Yan (331581)

“…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. …”

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

“…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. …”

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

“…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. …”

Model performance evaluation results. by Ke Yan (331581)

“…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. …”

The result compared with other existing methods. by Ke Yan (331581)

“…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. …”