S2 File - by Young Chang (3666829)

Postmortem confirmation of tetrode placement. by Alice Berners-Lee (15354255)

Fig 3 - by Harshita Sharma (4836180)

Antimycin A has broad spectrum antiviral activity against RNA viruses. by Avi Raveh (493231)

“…The calculated IC₅₀ value for antimycin A in the HCV replicon system is shown on the graph. …”

No effects of trehalose treatment in mitochondria length in HT fibroblasts. by Collin M. MacLeod (17512385)

HuD and Msi2 double knockdown does not causes additive increase in the APAF1 levels and Bax/Bcl2 ratio. by Naina Gaikwad (20435889)

Trehalose treatment induces tubular and elongated mitochondria in NPC1 mutant fibroblasts. by Collin M. MacLeod (17512385)

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)

Schematic illustration of the possible steps of ‘stalk mechanism’ of the electrofusion of two ‘single GUVs’. by Md. Tariqul Islam Bhuiyan (18794375)

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. …”