RSL-3 cancels the effect of NeuroD1 on promoting cell viability. by Ping Huang (30439)

Combined illusion score index across conditions (NI: Non-Illusion; NIT: Non-Illusion Tactile; MS: Multisensory; UV: Unimodal Visual). by Kirralise J. Hansford (20670869)

Spiking neuron network model with spike frequency adaptation (SFA) and synaptic depression (STD). by Brian Nils Lundstrom (14659973)

Structural Optimization and Pharmacological Evaluation of Inhibitors Targeting Dual-Specificity Tyrosine Phosphorylation-Regulated Kinases (DYRK) and CDC-like kinases (CLK) in Glio... by Qingqing Zhou (3801922)

“…The most potent inhibitors (IC₅₀ ≤ 50 nM) significantly decreased viability, clonogenic survival, migration, and invasion of glioblastoma cells. …”

Association between state-level COVID-19 mortality and change in cause-specific mortality, ages 25+. by Anneliese N. Luck (14725223)

Schematic figure of the promoter probe vectors. by Gábor Patyi (11846776)

The age distribution of the patients. by Ruijie Wang (5989841)

“…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”

Comparison experiments. by Ruijie Wang (5989841)

“…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”

External validation comparison results. by Ruijie Wang (5989841)

“…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”

Patient characteristics. by Ruijie Wang (5989841)

“…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”

Comparison with baseline methods. by Ruijie Wang (5989841)

“…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”

The details of the channel attention module. by Ruijie Wang (5989841)

“…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”

Ablation experiments. by Ruijie Wang (5989841)

“…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”

Pathological diagnosis. by Ruijie Wang (5989841)

“…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”

The details of ECRNet. by Ruijie Wang (5989841)

“…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”

Analysis of supernatant metabolites after <i>T</i>. <i>congolense</i> culture. by Pieter C. Steketee (10024552)

Secondary structure profile analysis for the SP domain of native PC and its mutant structures over 200 ns MD simulation times. by Mahvash Farajzadeh-Dehkordi (17468232)

The expression of tight junction proteins in intestinal wall. by Xueyan Lin (448667)

Domains and position of high-risk missense SNPs within the human PC. by Mahvash Farajzadeh-Dehkordi (17468232)

Amount of antimicrobial consumption in food producing animals by antimicrobial class (2017–2019) (mg/PCU_Thailand). by Angkana Lekagul (7180856)