K1 expression decreases in HA15 treated cells. by Guillermo Najarro (19964572)

Study 1_CFA1. by Chen-Yueh Chen (9740047)

“…Findings from Analysis of Covariance (ANCOVA) in Study II indicate that in Experiment I, positive electronic word of mouth does not help improve value co-creation among spectators while negative electronic word of mouth does decrease value co-creation among spectators. …”

Confirmatory factor analysis results for spectator value cocreation in sports events (N₁ = 300, N₂ = 400). by Chen-Yueh Chen (9740047)

Study 1_EFA. by Chen-Yueh Chen (9740047)

“…Findings from Analysis of Covariance (ANCOVA) in Study II indicate that in Experiment I, positive electronic word of mouth does not help improve value co-creation among spectators while negative electronic word of mouth does decrease value co-creation among spectators. …”

Study 2_Experiment 1. by Chen-Yueh Chen (9740047)

“…Findings from Analysis of Covariance (ANCOVA) in Study II indicate that in Experiment I, positive electronic word of mouth does not help improve value co-creation among spectators while negative electronic word of mouth does decrease value co-creation among spectators. …”

Study 1_CFA2. by Chen-Yueh Chen (9740047)

“…Findings from Analysis of Covariance (ANCOVA) in Study II indicate that in Experiment I, positive electronic word of mouth does not help improve value co-creation among spectators while negative electronic word of mouth does decrease value co-creation among spectators. …”

Exploratory factor analysis (N = 130) results for spectator value cocreation in sports events (N₁ = 300, N₂ = 400). by Chen-Yueh Chen (9740047)

Discriminant validity analysis (N₁ = 300, N₂ = 400). by Chen-Yueh Chen (9740047)

Table1_Leukocyte telomere length decreased the risk of mortality in patients with alcohol-associated liver disease.docx by Jiahong Yi (14481735)

“…</p>Results<p>LTL was a negative factor for all-cause mortality (all p-value < 0.05). The risk of cardiovascular disease (CVD)-related death was decreased in Q3 (p < 0.001) and Q4 levels of LTL (p < 0.001) compared with the Q1 group. …”

Study 2_Experiment 2. by Chen-Yueh Chen (9740047)

“…Findings from Analysis of Covariance (ANCOVA) in Study II indicate that in Experiment I, positive electronic word of mouth does not help improve value co-creation among spectators while negative electronic word of mouth does decrease value co-creation among spectators. …”

Study 2_Experiment 3. by Chen-Yueh Chen (9740047)

“…Findings from Analysis of Covariance (ANCOVA) in Study II indicate that in Experiment I, positive electronic word of mouth does not help improve value co-creation among spectators while negative electronic word of mouth does decrease value co-creation among spectators. …”

Study 2_Experiment 4. by Chen-Yueh Chen (9740047)

“…Findings from Analysis of Covariance (ANCOVA) in Study II indicate that in Experiment I, positive electronic word of mouth does not help improve value co-creation among spectators while negative electronic word of mouth does decrease value co-creation among spectators. …”

G1 cell cycle arrest and sub-G1 population induced by bortezomib/venetoclax were stronger in sLCL expressing BHRF-1, EBNA-3C, LMP-1 and NOXA. by Kam Pui Tam (19746865)

Bortezomib/venetoclax suppressed G1-S transition in sLCLs expressing EBNA-3C, LMP-1 and bortezomib-induced Noxa. by Kam Pui Tam (19746865)

Clinical baseline data. by Jie Zhao (49409)

“…Demographics, comorbidities, and lab indices were extracted; missing values were imputed using random forest. RPR’s dynamic changes and relation to prognosis were analyzed using latent category trajectory modeling. …”

Univariate analysis of hospitalized patients. by Jie Zhao (49409)

“…Demographics, comorbidities, and lab indices were extracted; missing values were imputed using random forest. RPR’s dynamic changes and relation to prognosis were analyzed using latent category trajectory modeling. …”

Univariate analysis of ICU patients. by Jie Zhao (49409)

“…Demographics, comorbidities, and lab indices were extracted; missing values were imputed using random forest. RPR’s dynamic changes and relation to prognosis were analyzed using latent category trajectory modeling. …”

Clinical baseline data after PSM. by Jie Zhao (49409)

“…Demographics, comorbidities, and lab indices were extracted; missing values were imputed using random forest. RPR’s dynamic changes and relation to prognosis were analyzed using latent category trajectory modeling. …”

International Classification of Diseases Codes. by Jie Zhao (49409)

“…Demographics, comorbidities, and lab indices were extracted; missing values were imputed using random forest. RPR’s dynamic changes and relation to prognosis were analyzed using latent category trajectory modeling. …”

Baseline data of clinical ICU patients. by Jie Zhao (49409)

“…Demographics, comorbidities, and lab indices were extracted; missing values were imputed using random forest. RPR’s dynamic changes and relation to prognosis were analyzed using latent category trajectory modeling. …”