Model prediction error analysis index. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting curve parameter table. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Model prediction error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Schematic overview of the study workflow. by Yu Zhang (12946)

Mass spectrometric analyses for crystallins. by Akosua K. Boateng (21672931)

“…We also determined the changes in crystallin proteomic profiles in water-soluble, water-insoluble-urea-soluble, and water-insoluble-urea-insoluble fractions. …”

RNA-seq data showing top 15 downregulated genes. by Akosua K. Boateng (21672931)

“…We also determined the changes in crystallin proteomic profiles in water-soluble, water-insoluble-urea-soluble, and water-insoluble-urea-insoluble fractions. …”

RNA-seq data showing top 15 upregulated genes. by Akosua K. Boateng (21672931)

“…We also determined the changes in crystallin proteomic profiles in water-soluble, water-insoluble-urea-soluble, and water-insoluble-urea-insoluble fractions. …”

Overview of selected datasets. by Tejaswini Baral (14320736)

“…</p><p>Results</p><p>Our analysis revealed statistically significant alpha diversity differences in West Africa with decreased microbial diversity in pulmonary tuberculosis patients after two months of antitubercular therapy. …”

The sequences of si-RNAs used in this study. by Changji Li (4806930)

“…Our study observed a significant increase in CRNN expression in cSCC samples compared to healthy skin. …”

Primary antibodies used for immunoblot analysis. by Iratxe Zuazo-Gaztelu (21071939)

“…Known cancer dependency on IRE1 entails its enzymatic activation of the transcription factor XBP1s and of regulated RNA decay. We discovered surprisingly that some cancer cell lines require IRE1 but not its enzymatic activity. …”

Fuchioka dataset 251021. by Yusuke Fuchioka (22806178)

“…Recent advances in 3D-MRI analysis have enabled quantitative cartilage thickness measurement. We hypothesized that OWHTO would result in measurable decreases in the PF joint cartilage thickness, predominantly medially and detectable using quantitative 3D-MRI. …”

Quantitative Proteomics Unveils the Synergistic Effects of Combination Drugs on Cytoskeleton Composition and Autophagy-Mediated Cell Death in Neuroblastoma by Pei-Chen Yu (1860772)

“…Additionally, the research indicated that cell cycle arrest occurred under combination therapy. Furthermore, we confirmed that the extent of autophagy significantly increased after the combination treatment. …”

PPI network analysis. by Du Leng (20421711)

Primers used for RT-qPCR. by Yihua Wang (491766)

“…The results indicated that NS combination promoted autophagy by inhibiting the PI3K/Akt/mTOR pathway. This significantly alleviated inflammation, reduced apoptosis, and decreasing lipid accumulation, thereby improving the pathological progression of atherosclerosis. …”

Scheme of the SiTFarm tool–farm to sector level. by Jure Brečko (20314959)

“…Additionally, the economic situation of cattle fattening farms has been significantly impacted by two major shocks: the COVID-19 pandemic and the onset of the war in Ukraine. …”

S1 File - by Jure Brečko (20314959)

“…Additionally, the economic situation of cattle fattening farms has been significantly impacted by two major shocks: the COVID-19 pandemic and the onset of the war in Ukraine. …”

GHG emissions in TAHs. by Jure Brečko (20314959)

“…Additionally, the economic situation of cattle fattening farms has been significantly impacted by two major shocks: the COVID-19 pandemic and the onset of the war in Ukraine. …”

Characteristics of the included studies. by Si He (3236646)

“…The overall results revealed that NPWT significantly decreased the sternal wound reinfection (SWRI) rate (RR [95% CI] = 0.179 [0.099 to 0.323], 95% prediction interval [PI]: 0.082 to 0.442), in-hospital mortality (RR [95% CI] = 0.242 [0.149 to 0.394], 95% PI: 0.144 to 0.461), and shortened the length of intensive care unit (ICU) stay (SMD [95% CI] = −0.601 [−0.820 to −0.382], 95% PI: −1.317 to 0.128) compared with conventional wound care. …”

Extracted data and used for analysis. by Si He (3236646)

“…The overall results revealed that NPWT significantly decreased the sternal wound reinfection (SWRI) rate (RR [95% CI] = 0.179 [0.099 to 0.323], 95% prediction interval [PI]: 0.082 to 0.442), in-hospital mortality (RR [95% CI] = 0.242 [0.149 to 0.394], 95% PI: 0.144 to 0.461), and shortened the length of intensive care unit (ICU) stay (SMD [95% CI] = −0.601 [−0.820 to −0.382], 95% PI: −1.317 to 0.128) compared with conventional wound care. …”

Excluded studies with reasons for exclusion. by Si He (3236646)

“…The overall results revealed that NPWT significantly decreased the sternal wound reinfection (SWRI) rate (RR [95% CI] = 0.179 [0.099 to 0.323], 95% prediction interval [PI]: 0.082 to 0.442), in-hospital mortality (RR [95% CI] = 0.242 [0.149 to 0.394], 95% PI: 0.144 to 0.461), and shortened the length of intensive care unit (ICU) stay (SMD [95% CI] = −0.601 [−0.820 to −0.382], 95% PI: −1.317 to 0.128) compared with conventional wound care. …”