History dependence disentangles the effects of input activation, reactivation and temporal depth of a binary autoregressive process. by Lucas Rudelt (4059505)

“…<p>(A) In the binary autoregressive process, the state of the next time step (grey box) is active (one) either because of an input activation with probability <i>h</i>, or because of an internal reactivation. …”

Supplementary information files for: Silica-silicon composites for near-infrared reflection: A comprehensive computational and experimental study by Kevin Conley (5986953)

“…The composites consolidated from nano- or micro-silica powder have a different porous microstructure which causes scattering at the air-matrix interface and larger reflectance primarily in the visible region. …”

Comparison of parsimonious 2004 to 2008 models of district-level JE incidence as a function of climate, agriculture and land-use. by Daniel E. Impoinvil (213005)

“…</p>†<p>All regression β coefficients represent a non-linear increase (or decrease when the coefficient value is negative) in JE incidence when there is a 1-unit increase in each respective predictor variable.…”

Table_6_Prediction of Soil Formation as a Function of Age Using the Percolation Theory Approach.XLSX by Markus Egli (3666178)

“…Nonetheless, the model is able to generate soil depth and confirms decreasing production rates with age. A steady state for soils is not reached before about 100 kyr to 1 Myr</p>…”

Table_1_Prediction of Soil Formation as a Function of Age Using the Percolation Theory Approach.XLSX by Markus Egli (3666178)

“…Nonetheless, the model is able to generate soil depth and confirms decreasing production rates with age. A steady state for soils is not reached before about 100 kyr to 1 Myr</p>…”

Table_5_Prediction of Soil Formation as a Function of Age Using the Percolation Theory Approach.XLSX by Markus Egli (3666178)

“…Nonetheless, the model is able to generate soil depth and confirms decreasing production rates with age. A steady state for soils is not reached before about 100 kyr to 1 Myr</p>…”

Table_2_Prediction of Soil Formation as a Function of Age Using the Percolation Theory Approach.XLSX by Markus Egli (3666178)

“…Nonetheless, the model is able to generate soil depth and confirms decreasing production rates with age. A steady state for soils is not reached before about 100 kyr to 1 Myr</p>…”

Table_1_Prediction of Soil Formation as a Function of Age Using the Percolation Theory Approach.XLSX by Markus Egli (3666178)

“…Nonetheless, the model is able to generate soil depth and confirms decreasing production rates with age. A steady state for soils is not reached before about 100 kyr to 1 Myr</p>…”

Table_4_Prediction of Soil Formation as a Function of Age Using the Percolation Theory Approach.XLSX by Markus Egli (3666178)

“…Nonetheless, the model is able to generate soil depth and confirms decreasing production rates with age. A steady state for soils is not reached before about 100 kyr to 1 Myr</p>…”

Table_5._Prediction of Soil Formation as a Function of Age Using the Percolation Theory Approach.XLSX by Markus Egli (3666178)

“…Nonetheless, the model is able to generate soil depth and confirms decreasing production rates with age. A steady state for soils is not reached before about 100 kyr to 1 Myr</p>…”

Table_3_Prediction of Soil Formation as a Function of Age Using the Percolation Theory Approach.XLSX by Markus Egli (3666178)

“…Nonetheless, the model is able to generate soil depth and confirms decreasing production rates with age. A steady state for soils is not reached before about 100 kyr to 1 Myr</p>…”

Table_6_Prediction of Soil Formation as a Function of Age Using the Percolation Theory Approach.XLSX by Markus Egli (3666178)

“…Nonetheless, the model is able to generate soil depth and confirms decreasing production rates with age. A steady state for soils is not reached before about 100 kyr to 1 Myr</p>…”

Table_4_Prediction of Soil Formation as a Function of Age Using the Percolation Theory Approach.XLSX by Markus Egli (3666178)

“…Nonetheless, the model is able to generate soil depth and confirms decreasing production rates with age. A steady state for soils is not reached before about 100 kyr to 1 Myr</p>…”

Table_3_Prediction of Soil Formation as a Function of Age Using the Percolation Theory Approach.XLSX by Markus Egli (3666178)

“…Nonetheless, the model is able to generate soil depth and confirms decreasing production rates with age. A steady state for soils is not reached before about 100 kyr to 1 Myr</p>…”

Table_2_Prediction of Soil Formation as a Function of Age Using the Percolation Theory Approach.XLSX by Markus Egli (3666178)

“…Nonetheless, the model is able to generate soil depth and confirms decreasing production rates with age. A steady state for soils is not reached before about 100 kyr to 1 Myr</p>…”

Purified recombinant WT-C100Flag dimer and trimer showed reduced Aβ production. by Joo In Jung (650746)

“…Aβ38, Aβ40 and Aβ42 levels decreased in dimer compared to monomer, and the Aβ levels for trimer were under detection limits. …”

Summary statistics for curated HLA-alleles. by Margaret Sunitha Selvaraj (13994016)

Table_1_Predictors of Mortality in Traumatic Intracranial Hemorrhage: A National Trauma Data Bank Study.docx by Esther Wu (9649016)

“…In the final model, high ISS, advanced age, subdural hemorrhage, and subarachnoid hemorrhage were associated with increased mortality, while high GCS verbal and motor subscores, current smoker, BAL beyond the legal limit, and level 1 trauma center were associated with decreased mortality.</p><p>Conclusions: A linear SVM model was developed for tICH, with nine features selected as predictors of mortality. …”

Image_1_Predictors of Mortality in Traumatic Intracranial Hemorrhage: A National Trauma Data Bank Study.TIFF by Esther Wu (9649016)

“…In the final model, high ISS, advanced age, subdural hemorrhage, and subarachnoid hemorrhage were associated with increased mortality, while high GCS verbal and motor subscores, current smoker, BAL beyond the legal limit, and level 1 trauma center were associated with decreased mortality.</p><p>Conclusions: A linear SVM model was developed for tICH, with nine features selected as predictors of mortality. …”

Supplementary Material for: A case of early occurrence of post-transplant lymphoproliferative disorders in the allograft after kidney transplantation by figshare admin karger (2628495)

“…Introduction: Post-transplant lymphoproliferative disorder (PTLD) is a serious complication after transplantation (Tx). …”