Journal: International Journal of Particle Therapy

Article Title: Phenomenological Study of Intra-Spill Break Spots in Dose-Driven Continuous Scanning Proton Therapy

doi: 10.1016/j.ijpt.2026.101315

Figure Lengend Snippet: Signal amplitude from the CROSSmini detector with signal fitting and zoomed in views. Figures A, B and C, on the left, shows the signal amplitude from the CROSSmini detector of a spill duration for energy level 70.2, 150.2, and 228.7 MeV, respectively, with nominal beam current 8 MU/s. The red line represents the original signal from CROSSmini and the black line represents the piecewise fitting for the edges. On the right is the zoomed in view of the fitting. The piecewise fitting quantifies the risetime ( t rise ) and measured nominal beam current ( I 0 , measured ).

Article Snippet: The beam current was measured using a CROSSmini 2D strip ionization chamber detector array (Liverage Biomedical Inc, Taiwan) positioned at the isocenter plane.

Techniques:

Journal: International Journal of Particle Therapy

Article Title: Phenomenological Study of Intra-Spill Break Spots in Dose-Driven Continuous Scanning Proton Therapy

doi: 10.1016/j.ijpt.2026.101315

Figure Lengend Snippet: Signal amplitude from the CROSSmini detector with signal fitting. shows the signal amplitude as a function of time measured by the CROSSmini detector and the piecewise fittings. Figures A, B, and C are measurements for energy level 70.2 MeV with nominal beam current 8, 14, and 20 MU/s, respectively. Figures D, E, and F are the measurements for energy level 228.7 MeV with nominal beam current 8, 14, and 20 MU/s, respectively.

Article Snippet: The beam current was measured using a CROSSmini 2D strip ionization chamber detector array (Liverage Biomedical Inc, Taiwan) positioned at the isocenter plane.

Techniques:

Journal: International Journal of Particle Therapy

Article Title: Phenomenological Study of Intra-Spill Break Spots in Dose-Driven Continuous Scanning Proton Therapy

doi: 10.1016/j.ijpt.2026.101315

Figure Lengend Snippet: t rise comparison between analytical and measured. The blue and red plots represent the analytically derived t rise and CROSSmini measured t rise , respectively, across the various nominal beam current settings.

Article Snippet: The beam current was measured using a CROSSmini 2D strip ionization chamber detector array (Liverage Biomedical Inc, Taiwan) positioned at the isocenter plane.

Techniques: Comparison, Derivative Assay

Journal: bioRxiv

Article Title: Deep Learning Structural Ensembles as Proxies for Protein Flexibility

doi: 10.64898/2026.05.16.725658

Figure Lengend Snippet: Ranksorted mean squared fluctuations (MSF) from NMR method and three deep learning structure prediction methods (AlphaFold3, AlphaFold2 and RosettaFold). A) Projections of ranksorted MSF onto protein structures for 2lah. Blue-White-Red color palette is used for the projections, where blue indicates low flexibility and red indicates high flexibility. B) 2D comparison of the experimental and the computed MSF for 2lah. Black line (with squares) is for the experimental data, blue line (with circles) is for AlphaFold3, orange line (with inverse triangles) is for AlphaFold2 and green line (with stars) is for RosettaFold. C) Cosine similarity of the experimental and the computed MSF for 70 proteins in the NMR dataset. AlphaFold3 bars are blue, AlphaFold2 bars are orange and RosettaFold bars are green. Averages of the cosine similarities over the entire dataset are also provided as horizontal lines for AlphaFold3 (blue dot-dashed line), AlphaFold2 (orange dashed line) and RosettaFold (green dotted line).

Article Snippet: An interactive 2D plot of the MSF is generated with Plotly Python library .

Techniques: Comparison

Journal: bioRxiv

Article Title: Deep Learning Structural Ensembles as Proxies for Protein Flexibility

doi: 10.64898/2026.05.16.725658

Figure Lengend Snippet: Ranksorted mean squared fluctuations (MSF) from dual conformations of X-Ray structures and three deep learning structure prediction methods (AlphaFold3, AlphaFold2 and RosettaFold). A) Projections of the ranksorted MSF onto protein structures for 3fweB and 4hzfA. Blue-White-Red color palette is used for the projections, where blue indicates low flexibility and red indicates high flexibility. B) 2D comparison of the experimental and the computed MSF for 3fweB and 4hzfA. Black line (with squares) is for the experimental data, blue line (with circles) is for AlphaFold3, orange line (with inverse triangles) is for AlphaFold2 and green line (with stars) is for RosettaFold. C) Cosine similarity of the experimental and the computed MSF for 43 proteins in the X-Ray dataset. AlphaFold3 bars are blue, AlphaFold2 bars are orange and RosettaFold bars are green. Averages of the cosine similarities over the entire dataset are also provided as horizontal lines for AlphaFold3 (blue dot-dashed line), AlphaFold2 (orange dashed line) and RosettaFold (green dotted line).

Article Snippet: An interactive 2D plot of the MSF is generated with Plotly Python library .

Techniques: Comparison

Journal: bioRxiv

Article Title: Deep Learning Structural Ensembles as Proxies for Protein Flexibility

doi: 10.64898/2026.05.16.725658

Figure Lengend Snippet: Ranksorted mean squared fluctuations (MSF) from all normal modes of cryo-EM structures and three deep learning structure prediction methods (AlphaFold3, AlphaFold2 and RosettaFold). A) Projections of the ranksorted MSF onto protein structures for 9yin. Blue-White-Red color palette is used for the projections, where blue indicates low flexibility and red indicates high flexibility. B) 2D comparison of the experimental and the computed MSF for 9yin. Black line (with squares) is for the experimental data, blue line (with circles) is for AlphaFold3, orange line (with inverse triangles) is for AlphaFold2 and green line (with stars) is for RosettaFold. C) Cosine similarity of the experimental and the computed MSF for 82 proteins in the cryo-EM dataset. AlphaFold3 bars are blue, AlphaFold2 bars are orange and RosettaFold bars are green. Averages of the cosine similarities over the entire dataset are also provided as horizontal lines for AlphaFold3 (blue dot-dashed line), AlphaFold2 (orange dashed line) and RosettaFold (green dotted line).

Article Snippet: An interactive 2D plot of the MSF is generated with Plotly Python library .

Techniques: Cryo-EM Sample Prep, Comparison

Journal: bioRxiv

Article Title: Deep Learning Structural Ensembles as Proxies for Protein Flexibility

doi: 10.64898/2026.05.16.725658

Figure Lengend Snippet: Ranksorted mean squared fluctuations (MSF) from molecular dynamics (MD) simulations and two deep learning structure prediction methods (AlphaFold3 and AlphaFold2). A) Projections of the ranksorted MSF onto protein structures for 6crk chain G. Blue-White-Red color palette is used for the projections, where blue indicates low flexibility and red indicates high flexibility. B) 2D comparison of the MD and the deep learning MSF for 6crk chain G. Blue (simulation 1), green (simulation 2) and orange (simulation 3) lines (with squares) are for the MD simulation data, black line (with circles) is for AlphaFold3, gray line (with inverse triangles) is for AlphaFold2. C) Cosine similarities of the first set of MD simulations and the deep learning ensemble MSF of 10 proteins in the MD dataset. D) Cosine similarities of the second set of MD simulations and the deep learning ensemble MSF of 10 proteins in the MD dataset. E) Cosine similarities of the third set of MD simulations and the deep learning ensemble MSF 10 proteins in the MD dataset. AlphaFold3 bars are black, AlphaFold2 bars are gray. Averages of the cosine similarities over the entire dataset are also provided as horizontal lines for AlphaFold3 (black dot-dashed line), AlphaFold2 (gray dashed line).

Article Snippet: An interactive 2D plot of the MSF is generated with Plotly Python library .

Techniques: Comparison