Observation of a Large Slip Effect in the Nanoscale Flow of Highly Viscous Supercooled Liquid Metals حسب Jun-Xiang Xiang (16751412)

"…Here, we report the observation of a large boundary slip in the nanoscale flow of highly viscous supercooled liquid metals (with viscosities of ≲10⁸ Pa s), enabled by the hydrophobic treatment of smooth nanochannels. …"

Comparison of the MLTD and MDD scores of text before and after hypernym substitution using the GPT-J-6B, Programmatic+AI, and T5 approaches. حسب 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>" حسب 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". The following acronyms included in the CSV file are defined as follows: Hbt is total hemoglobin, Art is artery region, DV is draining vein region, WV is whisker vein region, SEM is standard error mean, TS is time series, max peak is maximum peak, min peak is minima, AUC is area under the curve, WT is wild-type, AD is Alzheimer's disease, ATH is atherosclerosis and MIX is mixed AD/atherosclerosis. …"

Percentage decreases in scores of self-reported health, large muscle function, and mobility by chronic diseases per wave after disease incidence. حسب James Davis (2267599)

Comparison of physiological and perceptual measures of national-level rowing athletes taken during a 4-min time trial, before (Pre) and after (Post) a 10-session heat acclimation i... حسب Calvin P. Philp (12095878)

The weighted betweenness centrality increases and attains a peak about May 01, 2020, and steadily decreases ever since. حسب Satyam Mukherjee (2576785)

EU countries exporting a large share of their output to the UK experience highest production losses. حسب Leonie Wenz (9339107)

Summary of the number of species core areas projected to have moderate (> -0.1 SD change) or high (> -1 SD change) climate vulnerability due to decreasing large copepod biomass. حسب Melanie A. Smith (300377)

Table 1_Effect of decreased suspended sediment content on chlorophyll-a in Dongting Lake, China.docx حسب Le Zhang (88249)

"…The reduction in SSC may influence chlorophyll-a (Chl-a) concentrations in water, thereby further affecting the aquatic ecological environment. …"

Incorporating a social responsibility goal in the prompt causes overleverage in utilities (A) and decreases risk of low ESG rating (B) (Study 3). حسب Philipp Winder (21615570)

The weighted clustering coefficient increases with fluctuations attain a peak by about May 01, 2020, and decreases to a stable value from July 2020. حسب Satyam Mukherjee (2576785)

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

Schematic of training program during a 10-session heat acclimation intervention in national-level rowers. حسب Calvin P. Philp (12095878)

Table 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx حسب 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 حسب 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 حسب 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 حسب 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 حسب 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 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif حسب 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 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tiff حسب 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). …"