Heterogeneous Condensation on Simplified Viral Envelope Protein Structures by Kawkab Ahasan (18784843)

“…The rapid initial condensation fills up the gap between the pillars, reducing the active surface area and leading to a gradual decrease and a plateau in the condensation rate. …”

Heterogeneous Condensation on Simplified Viral Envelope Protein Structures by Kawkab Ahasan (18784843)

“…The rapid initial condensation fills up the gap between the pillars, reducing the active surface area and leading to a gradual decrease and a plateau in the condensation rate. …”

Coverage of plant species in each plot. by Xuemei Xiang (20756894)

“…The plant functional richness index, functional diversity index, functional dispersion index, and Rao’s quadratic entropy index showed a decreasing trend. At the same time, with the increase in temperature and nitrogen deposition, the relationship between plant species diversity index and functional diversity index in the alpine meadow of Qinghai-Tibet Plateau gradually weakened. …”

Specifications and effects of heating devices. by Xuemei Xiang (20756894)

“…The plant functional richness index, functional diversity index, functional dispersion index, and Rao’s quadratic entropy index showed a decreasing trend. At the same time, with the increase in temperature and nitrogen deposition, the relationship between plant species diversity index and functional diversity index in the alpine meadow of Qinghai-Tibet Plateau gradually weakened. …”

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

“…Our results show that relative to WT lenses, the βA3ΔG91 lenses showed: (A) downregulation of genes associated with LECs proliferation and migration (B) abnormal suture line pattern, (C) significant reduction in proliferation and migration of LECs, (D) abnormal F-actin distribution, (E) increased high molecular weight (HMW) peak, and (F) insolubilization and degradation of crystallins and other lens proteins. …”

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

“…Our results show that relative to WT lenses, the βA3ΔG91 lenses showed: (A) downregulation of genes associated with LECs proliferation and migration (B) abnormal suture line pattern, (C) significant reduction in proliferation and migration of LECs, (D) abnormal F-actin distribution, (E) increased high molecular weight (HMW) peak, and (F) insolubilization and degradation of crystallins and other lens proteins. …”

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

“…Our results show that relative to WT lenses, the βA3ΔG91 lenses showed: (A) downregulation of genes associated with LECs proliferation and migration (B) abnormal suture line pattern, (C) significant reduction in proliferation and migration of LECs, (D) abnormal F-actin distribution, (E) increased high molecular weight (HMW) peak, and (F) insolubilization and degradation of crystallins and other lens proteins. …”

Causal relationships between allergic diseases and significant declines in lung function: a multivariable Mendelian randomization study by Jiannan Lin (6954983)

“…However, in multivariable MR analysis, the independent effects of atopic dermatitis and allergic conjunctivitis on lung function were no longer significant. Only allergic asthma continued to show a significant causal relationship with decreased lung function (OR [95%CI]: 1.019 [1.008–1.030], <i>p</i> < .001).…”

All-Atom Simulations Reveal the Effect of Membrane Composition on the Signaling of the NKG2A/CD94/HLA‑E Immune Receptor Complex by Martin Ljubič (15680161)

“…Additionally, we found that a large concentration of negative charge at the surface of the POPA-based membrane greatly increased the number of contacts with lipid molecules and significantly decreased the exposure of intracellular NKG2A ITIM regions to water molecules, thus likely halting the signal transduction process. …”

Structure diagram of ensemble model. 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 formula 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. …”

Test plan. 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 surface parameters. 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 generalisation validation 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. …”

Empirical 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. …”

Fitting curve parameters. 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. …”

Test instrument. 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. …”

Empirical model establishment process. 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 trend chart. 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. …”

Basic physical parameters of red clay. 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. …”