General technical specification for GW154/6700. by Weidong Ji (129916)

“…On longer time scales (6–24 hours), the score and correlation between ERA5 and observations further increased, while the centered root-mean-square error (CRMSE) and standard deviation decrease. 4) Hourly wind data with a regular spatial distribution in ERA5 reanalysis provides valuable information for further detailed research on meteorology or renewable energy perspectives, but some inherent shortcomings should be considered.…”

Expression of proteins significantly correlated with stereotypy severity in CD1 Mice (n = 28). by Kendall M. Coden (18516237)

“…Expression of <i><i>Pdgfb</i></i>, <i><i>Riox2</i></i>, <i><i>Parp1</i></i>, <i><i>Snap29</i></i>, <i><i>Map2k6</i></i>, and <i><i>Cxcl9</i></i> increase with severity of stereotypy, whereas expression of <i><i>Vsig2</i></i> decreases with stereotypy severity. …”

The spatial pattern of dengue including hotspot and coldspot in Bangladesh in 2019–2023. by Kamal Hossain (331680)

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The spatial distribution of dengue incidence rate per million by month across districts of Bangladesh in 2023. by Kamal Hossain (331680)

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The spatial (districtwide) distribution of census population of Bangladesh in 2022. by Kamal Hossain (331680)

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The spatial distribution of dengue incidence rate per million by month across districts of Bangladesh in 2022. by Kamal Hossain (331680)

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Global Spatial Autocorrelation Analysis of dengue log incidence rate (DLIR) with higher order neighbor districts in the study periods. by Kamal Hossain (331680)

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The spatial distribution of districtwide dengue cases and dengue incidence rate (DIR) per million in Bangladesh in 2019–2023. by Kamal Hossain (331680)

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Monthly distribution of dengue cases across Bangladesh in 2022–2023. by Kamal Hossain (331680)

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Flowchart of Methodology. by Kamal Hossain (331680)

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Demographic and ocular features. by Mingxi Shao (10066570)

“…Pearson analysis revealed significant negative correlations: SOD with anterior chamber depth (r = −0.445, p = 0.012) and H₂O₂ with mean deviation values for the visual field (r = −0.412, p = 0.041). …”

Machine learning model to diagnose PCG. by Mingxi Shao (10066570)

“…Pearson analysis revealed significant negative correlations: SOD with anterior chamber depth (r = −0.445, p = 0.012) and H₂O₂ with mean deviation values for the visual field (r = −0.412, p = 0.041). …”

ROC curves of TAS + SOD + MDA to diagnose PCG. by Mingxi Shao (10066570)

“…Pearson analysis revealed significant negative correlations: SOD with anterior chamber depth (r = −0.445, p = 0.012) and H₂O₂ with mean deviation values for the visual field (r = −0.412, p = 0.041). …”

Multivariate associations of lifestyle-related parameters with TMAD. by Chul-young Bae (14709766)

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Correlation between age and telomere length (TL) according to sex. by Chul-young Bae (14709766)

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Association between biological age (BA) and telomere age (TMA). by Chul-young Bae (14709766)

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Association between biological age (BA) and telomere age (TMA). by Chul-young Bae (14709766)

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Correlation between age and standardized telomere length (standardized_TL) according to sex. by Chul-young Bae (14709766)

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Correlation between age and telomere age (TMA). by Chul-young Bae (14709766)

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Correlation between telomere age difference (TMAD) and lifestyle. by Chul-young Bae (14709766)

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