Search alternatives:
significantly improving » significantly improved (Expand Search), significantly improve (Expand Search)
significant decrease » significant increase (Expand Search), significantly increased (Expand Search)
significantly improving » significantly improved (Expand Search), significantly improve (Expand Search)
significant decrease » significant increase (Expand Search), significantly increased (Expand Search)
-
2041
Source data for lung metabolomics.
Published 2025“…Importantly, when we knocked out the <i>Ido1</i> gene or inhibited IDO1 expression using a specific inhibitor 1-MT in mice, we observed a significant enhancement in T-cell mediated responses against hv<i>Kp</i>. …”
-
2042
Source data for Fig 5.
Published 2025“…Importantly, when we knocked out the <i>Ido1</i> gene or inhibited IDO1 expression using a specific inhibitor 1-MT in mice, we observed a significant enhancement in T-cell mediated responses against hv<i>Kp</i>. …”
-
2043
Source data for Fig 1.
Published 2025“…Importantly, when we knocked out the <i>Ido1</i> gene or inhibited IDO1 expression using a specific inhibitor 1-MT in mice, we observed a significant enhancement in T-cell mediated responses against hv<i>Kp</i>. …”
-
2044
Flow cytometry gating strategy for MDSC.
Published 2025“…Importantly, when we knocked out the <i>Ido1</i> gene or inhibited IDO1 expression using a specific inhibitor 1-MT in mice, we observed a significant enhancement in T-cell mediated responses against hv<i>Kp</i>. …”
-
2045
Source data for Fig 3.
Published 2025“…Importantly, when we knocked out the <i>Ido1</i> gene or inhibited IDO1 expression using a specific inhibitor 1-MT in mice, we observed a significant enhancement in T-cell mediated responses against hv<i>Kp</i>. …”
-
2046
Source data for Fig 4.
Published 2025“…Importantly, when we knocked out the <i>Ido1</i> gene or inhibited IDO1 expression using a specific inhibitor 1-MT in mice, we observed a significant enhancement in T-cell mediated responses against hv<i>Kp</i>. …”
-
2047
Investigating the Influence of Transition Metal Substitution in Lithium Argyrodites on Structure, Transport, and Solid-State Battery Performance
Published 2024“…Lithium argyrodites have gained significant attention as candidates for solid electrolytes in solid-state batteries due to their superior ionic conductivities and favorable mechanical properties. …”
-
2048
Accuracy test results.
Published 2025“…The research results show that after 600 rounds of training on the CIFAR-10 dataset, the final accuracy of the improved model reached 97.2%. The runtime memory usage on the CIFAR-100 dataset is only 44.52%, a decrease of 44.56% compared to the baseline model. …”
-
2049
Experiment environment and parameter.
Published 2025“…The research results show that after 600 rounds of training on the CIFAR-10 dataset, the final accuracy of the improved model reached 97.2%. The runtime memory usage on the CIFAR-100 dataset is only 44.52%, a decrease of 44.56% compared to the baseline model. …”
-
2050
Test results for NME and FR.
Published 2025“…The research results show that after 600 rounds of training on the CIFAR-10 dataset, the final accuracy of the improved model reached 97.2%. The runtime memory usage on the CIFAR-100 dataset is only 44.52%, a decrease of 44.56% compared to the baseline model. …”
-
2051
DARTS algorithm process.
Published 2025“…The research results show that after 600 rounds of training on the CIFAR-10 dataset, the final accuracy of the improved model reached 97.2%. The runtime memory usage on the CIFAR-100 dataset is only 44.52%, a decrease of 44.56% compared to the baseline model. …”
-
2052
Comparison result of memory usage.
Published 2025“…The research results show that after 600 rounds of training on the CIFAR-10 dataset, the final accuracy of the improved model reached 97.2%. The runtime memory usage on the CIFAR-100 dataset is only 44.52%, a decrease of 44.56% compared to the baseline model. …”
-
2053
LKA model structure.
Published 2025“…The research results show that after 600 rounds of training on the CIFAR-10 dataset, the final accuracy of the improved model reached 97.2%. The runtime memory usage on the CIFAR-100 dataset is only 44.52%, a decrease of 44.56% compared to the baseline model. …”
-
2054
Test results on different datasets.
Published 2025“…The research results show that after 600 rounds of training on the CIFAR-10 dataset, the final accuracy of the improved model reached 97.2%. The runtime memory usage on the CIFAR-100 dataset is only 44.52%, a decrease of 44.56% compared to the baseline model. …”
-
2055
Comparison result of memory usage.
Published 2025“…The research results show that after 600 rounds of training on the CIFAR-10 dataset, the final accuracy of the improved model reached 97.2%. The runtime memory usage on the CIFAR-100 dataset is only 44.52%, a decrease of 44.56% compared to the baseline model. …”
-
2056
Residual configuration.
Published 2025“…The research results show that after 600 rounds of training on the CIFAR-10 dataset, the final accuracy of the improved model reached 97.2%. The runtime memory usage on the CIFAR-100 dataset is only 44.52%, a decrease of 44.56% compared to the baseline model. …”
-
2057
Attitude towards NTDs in the study Area.
Published 2025“…Identification of disease symptoms improved, misconceptions regarding supernatural causation decreased, and preventive behaviors such as healthcare-seeking and participation in community health programs increased. …”
-
2058
Dataset of results.
Published 2025“…Identification of disease symptoms improved, misconceptions regarding supernatural causation decreased, and preventive behaviors such as healthcare-seeking and participation in community health programs increased. …”
-
2059
Respondents’ perception about the public artwork.
Published 2025“…Identification of disease symptoms improved, misconceptions regarding supernatural causation decreased, and preventive behaviors such as healthcare-seeking and participation in community health programs increased. …”
-
2060
Test results for P, R, F1, and OA.
Published 2025“…The research results show that after 600 rounds of training on the CIFAR-10 dataset, the final accuracy of the improved model reached 97.2%. The runtime memory usage on the CIFAR-100 dataset is only 44.52%, a decrease of 44.56% compared to the baseline model. …”