Intracellular Ca²⁺ signaling and IP3R2 expression are decreased in <i>Tcirg1</i>-knockdown osteoclasts. by Dongyan Zhang (640677)

Kinetics of growth of the Δ<i>corA</i>Δ<i>mgtA</i> small and large colony variants. by Selma Metaane (12329248)

Innovation and night light in large and small cities. by Saul Estrin (8629173)

Geographical distribution of large cities and small cities. by Saul Estrin (8629173)

“…The Figure reveals two patterns: 1) the maximum level of innovation is higher in large cities (2.53) than in small cities (2.02); 2) among large cities in <b>a</b>, innovation levels in general decrease with nightlight density. …”

Deletion of RecF or RecO decreases the filamentation of <i>ΔrarA ΔruvB</i> cells. by Kanika Jain (2884439)

Comparison of the MLTD and MDD scores of text before and after hypernym substitution using the GPT-J-6B, Programmatic+AI, and T5 approaches. by Karl Swanson (3580583)

<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>" by Kira Shaw (18796168)

“…<p dir="ltr">The CSV file 'Eyreetal_DrainingVein_SourceData' contains the averaged time series traces and extracted metrics from individual experiments used across Figures 1-5 in the manuscript "Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins". …”

Fibγ^Δ5 mice display decreased survival following <i>S. aureus</i> peritonitis. by Oscar Negrón (11965511)

The reduced virulence of Δalta1 mutants is not primarily due to a decrease in growth. by Shuhuan Zhang (748003)

Spatial information of excitatory neurons in APP/PS1 mice are decreased in dCA1 and vCA1. by Udaysankar Chockanathan (18510288)

Odds ratios for IVW MR estimates for each outcome with <i>cis</i>CRP exposures and are on the scale of lnCRP decrease. by Fergus W. Hamilton (10724401)

Rotation of a CLSM acquisition showing the large decrease in cathepsin D decoration of <i>L</i>.... by Sandrine Cojean (274005)

Terminal objective values for the Gilets Jaunes dataset as a function of <i>s</i>_{<i>network</i>} and <i>s</i>_<i>edge</i>. by Yen-Shao Chen (18264587)

Subjects:

Terminal objective values for the U.S. election dataset as a function of <i>s</i>_{<i>network</i>} and <i>s</i>_<i>edge</i>. by Yen-Shao Chen (18264587)

Subjects:

Pairwise correlations of excitatory neurons in APP/PS1 mice are decreased in dCA1, but increased in vCA1. by Udaysankar Chockanathan (18510288)

Table 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…Co-expression modules were then identified in the SD and stroke datasets by weighted gene co-expression network analysis (WGCNA), respectively, and machine learning algorithms (RandomForest, LASSO, and XGBoost) were performed to identify ARL2 as a key diagnostic biomarker with high predictive value (AUC = 0.91). …”

Table 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…Co-expression modules were then identified in the SD and stroke datasets by weighted gene co-expression network analysis (WGCNA), respectively, and machine learning algorithms (RandomForest, LASSO, and XGBoost) were performed to identify ARL2 as a key diagnostic biomarker with high predictive value (AUC = 0.91). …”

Table 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…Co-expression modules were then identified in the SD and stroke datasets by weighted gene co-expression network analysis (WGCNA), respectively, and machine learning algorithms (RandomForest, LASSO, and XGBoost) were performed to identify ARL2 as a key diagnostic biomarker with high predictive value (AUC = 0.91). …”

Table 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…Co-expression modules were then identified in the SD and stroke datasets by weighted gene co-expression network analysis (WGCNA), respectively, and machine learning algorithms (RandomForest, LASSO, and XGBoost) were performed to identify ARL2 as a key diagnostic biomarker with high predictive value (AUC = 0.91). …”

Image 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif by Hanlin Yu (17776399)

“…Co-expression modules were then identified in the SD and stroke datasets by weighted gene co-expression network analysis (WGCNA), respectively, and machine learning algorithms (RandomForest, LASSO, and XGBoost) were performed to identify ARL2 as a key diagnostic biomarker with high predictive value (AUC = 0.91). …”