Raw data for the paper, "Diffusion-guided 4D microprinting of soft microactuators". This dataset summarizes the stimulus-responsive behavior of the 3D LCN microstructures under both thermal and optical actuation, and also includes editable ChemDraw (.cdx) files and image files of the chemical structures of RM257 and the LC monomer. In addition, this dataset includes the original Blender source files and rendered images for the schematic figures in the manuscript and Supplementary Information, specifically Fig. 1b, Fig. 4(a), Fig. 4(d), Fig. 6(a), Fig. 6(c), Supplementary Fig. 9(a), and Supplementary Fig. 9(b).

Raw data for the paper, "Diffusion-guided 4D microprinting of soft microactuators". This dataset summarizes the stimulus-responsive behavior of the 3D LCN microstructures under both thermal and optical actuation, and also includes editable ChemDraw (.cdx) files and image files of the chemical structures of RM257 and the LC monomer. In addition, this dataset includes the original Blender source files and rendered images for the schematic figures in the manuscript and Supplementary Information, specifically Fig. 1b, Fig. 4(a), Fig. 4(d), Fig. 6(a), Fig. 6(c), Supplementary Fig. 9(a), and Supplementary Fig. 9(b).

Clinical trials are an essential aspect of the drug development process. Clinical endpoints and surrogate endpoints are two terms used in clinical trials to measure the effectiveness of a treatment. While clinical endpoints typically require higher costs and longer durations of observation to show direct clinical benefits, surrogate endpoints have been introduced as a cheaper and faster method that may be used to predict clinical effects. When there is a linear relationship between the surrogate and the clinical endpoint, the surrogate may still need to rule out a threshold that corresponds to no clinical benefit. The determination of such a threshold uses the knowledge of numerous parameters in the bivariate statistical distribution of the clinical response and the surrogate. In our work, we present a concept of “working” threshold to incorporate statistical uncertainties in determination of such a threshold.

Clinical trials are an essential aspect of the drug development process. Clinical endpoints and surrogate endpoints are two terms used in clinical trials to measure the effectiveness of a treatment. While clinical endpoints typically require higher costs and longer durations of observation to show direct clinical benefits, surrogate endpoints have been introduced as a cheaper and faster method that may be used to predict clinical effects. When there is a linear relationship between the surrogate and the clinical endpoint, the surrogate may still need to rule out a threshold that corresponds to no clinical benefit. The determination of such a threshold uses the knowledge of numerous parameters in the bivariate statistical distribution of the clinical response and the surrogate. In our work, we present a concept of “working” threshold to incorporate statistical uncertainties in determination of such a threshold.

Clinical trials are an essential aspect of the drug development process. Clinical endpoints and surrogate endpoints are two terms used in clinical trials to measure the effectiveness of a treatment. While clinical endpoints typically require higher costs and longer durations of observation to show direct clinical benefits, surrogate endpoints have been introduced as a cheaper and faster method that may be used to predict clinical effects. When there is a linear relationship between the surrogate and the clinical endpoint, the surrogate may still need to rule out a threshold that corresponds to no clinical benefit. The determination of such a threshold uses the knowledge of numerous parameters in the bivariate statistical distribution of the clinical response and the surrogate. In our work, we present a concept of “working” threshold to incorporate statistical uncertainties in determination of such a threshold.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.