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Purpose: Tumor vessels influence the growth and response of tumors to therapy. Imaging vascular changes in vivo using dynamic contrast-enhanced MRI (DCE-MRI) has shown potential to guide clinical decision making for treatment. However, quantitative MR imaging biomarkers of vascular function have not been widely adopted, partly because their relationship to structural changes in vessels remains unclear. We aimed to elucidate the relationships between vessel function and morphology in vivo.Experimental Design: Untreated preclinical tumors with different levels of vascularization were imaged sequentially using DCE-MRI and CT. Relationships between functional parameters from MR (iAUC, Ktrans, and BATfrac) and structural parameters from CT (vessel volume, radius, and tortuosity) were assessed using linear models. Tumors treated with anti-VEGFR2 antibody were then imaged to determine whether antiangiogenic therapy altered these relationships. Finally, functional–structural relationships were measured in 10 patients with liver metastases from colorectal cancer.Results: Functional parameters iAUC and Ktrans primarily reflected vessel volume in untreated preclinical tumors. The relationships varied spatially and with tumor vascularity, and were altered by antiangiogenic treatment. In human liver metastases, all three structural parameters were linearly correlated with iAUC and Ktrans. For iAUC, structural parameters also modified each other's effect.Conclusions: Our findings suggest that MR imaging biomarkers of vascular function are linked to structural changes in tumor vessels and that antiangiogenic therapy can affect this link. Our work also demonstrates the feasibility of three-dimensional functional–structural validation of MR biomarkers in vivo to improve their biological interpretation and clinical utility. Clin Cancer Res; 24(19); 4694–704. ©2018 AACR.

Supplementary methods and data. Supplementary Table 1. Equations of chosen models fit using ordinary least squares regression with the best Bayesian Information Criterion for iAUC90, Ktrans, and BATfrac in untreated preclinical tumors (cohort 1). Goodness of fit for each model was assessed by two measures, residuals vs fitted and Q-Q plots, which are shown in Supplementary Figure 4. Supplementary Table 2. Evaluation of model fits for iAUC90, Ktrans, and BATfrac generated from cohort 1 applied to preclinical data from cohort 2 treated with control IgG or with anti-VEGFR2 antibody DC101. Goodness of fit for each model was assessed by two measures, residuals vs fitted and Q-Q plots, which are shown in Supplementary Figures 6-7. Supplementary Table 3. Equations of chosen linear models with best Bayesian Information Criterion for iAUC90 and Ktrans in clinical tumors. Supplementary Figure 1. Signal-to-noise ratio on DCE-MRI is affected > 5 mm away from surface coil. Supplementary Figure 2. Longitudinal, repeated imaging with CT curbs tumor growth. Supplementary Figure 3. Vascular parameters (vessel length (a), tortuosity (b), and radius (c)) correlate poorly with volume of MC38 and FaDu tumors. Supplementary Figure 4. For each MR parameter of untreated preclinical tumors of cohort 1, the chosen linear model has normal residuals, as assessed by residual vs fitted plots and Q-Q plots, demonstrating goodness of fit. Supplementary Figure 5. For each MR parameter of untreated preclinical tumors of cohort 2, the tested linear model has normal residuals, as assessed by residual vs fitted plots and Q-Q plots, demonstrating goodness of fit. Supplementary Figure 6. Goodness-of-fit, as assessed by residual vs fitted plots and by Q-Q plots, for linear models applied to DC101-treated preclinical tumors of cohort 2. The chosen linear models for iAUC and BAT have residuals that exhibit departures from normality, indicating that the treatment altered these parameters. Supplementary Figure 7. For each MR parameter of clinical tumors, the chosen linear models demonstrate normal residuals, indicating goodness-of-fit.

Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure1. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold2, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective., AlphaFold is used to predict the structures of almost all of the proteins in the human proteome—the availability of high-confidence predicted structures could enable new avenues of investigation from a structural perspective.

While the vast majority of well-structured single protein chains can now be predicted to high accuracy due to the recent AlphaFold [1] model, the prediction of multi-chain protein complexes remains a challenge in many cases. In this work, we demonstrate that an AlphaFold model trained specifically for multimeric inputs of known stoichiometry, which we call AlphaFold-Multimer, significantly increases accuracy of predicted multimeric interfaces over input-adapted single-chain AlphaFold while maintaining high intra-chain accuracy. On a benchmark dataset of 17 heterodimer proteins without templates (introduced in [2]) we achieve at least medium accuracy (DockQ [3] ≥ 0.49) on 13 targets and high accuracy (DockQ ≥ 0.8) on 7 targets, compared to 9 targets of at least medium accuracy and 4 of high accuracy for the previous state of the art system (an AlphaFold-based system from [2]). We also predict structures for a large dataset of 4,446 recent protein complexes, from which we score all non-redundant interfaces with low template identity. For heteromeric interfaces we successfully predict the interface (DockQ ≥ 0.23) in 70% of cases, and produce high accuracy predictions (DockQ ≥ 0.8) in 26% of cases, an improvement of +27 and +14 percentage points over the flexible linker modification of AlphaFold [4] respectively. For homomeric inter-faces we successfully predict the interface in 72% of cases, and produce high accuracy predictions in 36% of cases, an improvement of +8 and +7 percentage points respectively.

We describe the operation and improvement of AlphaFold* , the system that was entered by the team AlphaFold2 to the "human" category in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The AlphaFold system entered in CASP14 is entirely different to the one entered in CASP13. It used a novel end-to-end deep neural network trained to produce protein structures from amino acid sequence, multiple sequence alignments, and homologous proteins. In the assessors' ranking by summed z-scores (>2.0), AlphaFold scored 244.0 compared to 90.8 by the next best group. The predictions made by AlphaFold had a median domain GDT_TS of 92.4; this is the first time that this level of average accuracy has been achieved during CASP, especially on the more difficult Free Modelling targets, and represents a significant improvement in the state of the art in protein structure prediction. We report how AlphaFold was run as a human team during CASP14 and improved such that it now achieves an equivalent level of performance without intervention, opening the door to highly accurate large scale structure prediction. This article is protected by copyright. All rights reserved.

When multiple neutrons are emitted in a fission event they are correlated in both energy and their relative angle, which may impact the design of safeguards equipment and other instrumentation for non-proliferation applications. The most recent release of MCNP 6 . 2 contains the capability to simulate correlated fission neutrons using the event generators CGMF and FREYA . These radiation transport simulations will be post-processed by the detector response code, DRiFT , and compared directly to correlated fission measurements. DRiFT has been previously compared to single detector measurements, its capabilities have been recently expanded with correlated fission simulations in mind. This paper details updates to DRiFT specific to correlated fission measurements, including tracking source particle energy of all detector events (and non-events), expanded output formats, and digitizer waveform generation.

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm., AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

This work presents the organic scintillation simulation capabilities of DRiFT, a post-processing Detector Response Function Toolkit for MCNP ® output. DRiFT is used to create realistic scintillation detector response functions to incident neutron and gamma mixed-field radiation. As a post-processing tool, DRiFT leverages the extensively validated radiation transport capabilities of MCNP ® 6 , which also provides the ability to simulate complex sources and geometries. DRiFT is designed to be flexible, it allows the user to specify scintillator material, PMT type, applied PMT voltage, and quenching data used in simulations. The toolkit's capabilities, which include the generation of pulse shape discrimination plots and full-energy detector spectra, are demonstrated in a comparison of measured and simulated neutron contributions from 252Cf and PuBe, and photon spectra from 22Na and 228Th sources. DRiFT reproduced energy resolution effects observed in EJ-301 measurements through the inclusion of scintillation yield variances, photon transport noise, and PMT photocathode and multiplication noise.

Purpose: Tumor vessels influence the growth and response of tumors to therapy. Imaging vascular changes in vivo using dynamic contrast-enhanced MRI (DCE-MRI) has shown potential to guide clinical decision making for treatment. However, quantitative MR imaging biomarkers of vascular function have not been widely adopted, partly because their relationship to structural changes in vessels remains unclear. We aimed to elucidate the relationships between vessel function and morphology in vivo. Experimental Design: Untreated preclinical tumors with different levels of vascularization were imaged sequentially using DCE-MRI and CT. Relationships between functional parameters from MR (iAUC, Ktrans, and BATfrac) and structural parameters from CT (vessel volume, radius, and tortuosity) were assessed using linear models. Tumors treated with anti-VEGFR2 antibody were then imaged to determine whether antiangiogenic therapy altered these relationships. Finally, functional–structural relationships were measured in 10 patients with liver metastases from colorectal cancer. Results: Functional parameters iAUC and Ktrans primarily reflected vessel volume in untreated preclinical tumors. The relationships varied spatially and with tumor vascularity, and were altered by antiangiogenic treatment. In human liver metastases, all three structural parameters were linearly correlated with iAUC and Ktrans. For iAUC, structural parameters also modified each other's effect. Conclusions: Our findings suggest that MR imaging biomarkers of vascular function are linked to structural changes in tumor vessels and that antiangiogenic therapy can affect this link. Our work also demonstrates the feasibility of three-dimensional functional–structural validation of MR biomarkers in vivo to improve their biological interpretation and clinical utility. Clin Cancer Res; 24(19); 4694–704. ©2018 AACR.

The ability of MCNP Ⓡ to model radiation transport through complex geometries and materials is well established and validated. However MCNP lacks many of the capabilities required to reproduce observed detector response to mixed radiation fields. We have developed DRiFT, a Detector Response Function Toolkit for MCNP output, to address the need for realistic detector response capabilities. DRiFT retrieves pertinent particle information from MCNP particle tracking (PTRAC) files using MCNPTools. These files contain the position, timestamp, and energy of particles transported to (or created within) a detector volume. DRiFT's $\text{C}+ \quad +$ framework is modular, therefore once the function form of a component (for example, a PMT) has been developed, it can be used in multiple detector coupling configurations to calculate its contribution to observed detector spectra. DRiFT simulated spectra produced by a right circular cylinder coupled to a PMT have been shown to agree with neutron and photon measurements. This summary describes current and proposed/in progress DRiFT capabilities.

Vasculature is known to be of key biological significance, especially in the study of tumors. As such, considerable effort has been focused on the automated segmentation of vasculature in medical and pre-clinical images. The majority of vascular segmentation methods focus on bloodpool labeling methods; however, particularly, in the study of tumors, it is of particular interest to be able to visualize both the perfused and the non-perfused vasculature. Imaging vasculature by highlighting the endothelium provides a way to separate the morphology of vasculature from the potentially confounding factor of perfusion. Here, we present a method for the segmentation of tumor vasculature in 3D fluorescence microscopic images using signals from the endothelial and surrounding cells. We show that our method can provide complete and semantically meaningful segmentations of complex vasculature using a supervoxel-Markov random field approach. We show that in terms of extracting meaningful segmentations of the vasculature, our method outperforms both state-of-the-art method, specific to these data, as well as more classical vasculature segmentation methods.

This work revisits the concept of graph cuts as an efficient optimization technique in image registration. Previously, due to the computational burden involved, the use of graph cuts in this context has been mainly limited to 2D applications. Here we show how combining graph cuts with supervoxels, resulting in a sparse, yet meaningful graph-based image representation, can overcome previous limitations. Additionally, we show that a relaxed graph representation of the image allows for 'sliding' motion modeling and provides anatomically plausible estimation of the deformations. This is achieved by using image-guided filtering of the estimated sparse deformation field. We evaluate our method on a publicly available CT lung data set and show that our new approach compares very favourably with state-of-the-art in continuous and discrete image registration.

Regions of tissue which are well oxygenated respond better to radiotherapy than hypoxic regions by up to a factor of three. If these volumes could be accurately estimated, then it might be possible to selectively boost dose to radio-resistant regions, a concept known as dose-painting. While imaging modalities such as 18F-fluoromisonidazole positron emission tomography (PET) allow identification of hypoxic regions, they are intrinsically limited by the physics of such systems to the millimetre domain, whereas tumour oxygenation is known to vary over a micrometre scale. Mathematical modelling of microscopic tumour oxygen distribution therefore has the potential to complement and enhance macroscopic information derived from PET. In this work, we develop a general method of estimating oxygen distribution in three dimensions from a source vessel map. The method is applied analytically to line sources and quasi-linear idealized line source maps, and also applied to full three-dimensional vessel distributions through a kernel method and compared with oxygen distribution in tumour sections. The model outlined is flexible and stable, and can readily be applied to estimating likely microscopic oxygen distribution from any source geometry. We also investigate the problem of reconstructing three-dimensional oxygen maps from histological and confocal two-dimensional sections, concluding that two-dimensional histological sections are generally inadequate representations of the three-dimensional oxygen distribution.

IntroductionPreclinical in vivo CT is commonly used to visualise vessels at a macroscopic scale. However, it is prone to many artefacts which can degrade the quality of CT images significantly. Although some artefacts can be partially corrected for during image processing, they are best avoided during acquisition. Here, a novel imaging cradle and tumour holder was designed to maximise CT resolution. This approach was used to improve preclinical in vivo imaging of the tumour vasculature.ProceduresA custom built cradle containing a tumour holder was developed and fix-mounted to the CT system gantry to avoid artefacts arising from scanner vibrations and out-of-field sample positioning. The tumour holder separated the tumour from bones along the axis of rotation of the CT scanner to avoid bone-streaking. It also kept the tumour stationary and insensitive to respiratory motion. System performance was evaluated in terms of tumour immobilisation and reduction of motion and bone artefacts. Pre- and post-contrast CT followed by sequential DCE-MRI of the tumour vasculature in xenograft transplanted mice was performed to confirm vessel patency and demonstrate the multimodal capacity of the new cradle. Vessel characteristics such as diameter, and branching were quantified.ResultsImage artefacts originating from bones and out-of-field sample positioning were avoided whilst those resulting from motions were reduced significantly, thereby maximising the resolution that can be achieved with CT imaging in vivo. Tumour vessels >= 77 mu m could be resolved and blood flow to the tumour remained functional. The diameter of each tumour vessel was determined and plotted as histograms and vessel branching maps were created. Multimodal imaging using this cradle assembly was preserved and demonstrated.ConclusionsThe presented imaging workflow minimised image artefacts arising from scanner induced vibrations, respiratory motion and radiopaque structures and enabled in vivo CT imaging and quantitative analysis of the tumour vasculature at higher resolution than was possible before. Moreover, it can be applied in a multimodal setting, therefore combining anatomical and dynamic information.

Segmentation of vasculature is a common task in many areas of medical imaging, but complex morphology and weak signal often lead to incomplete segmentations. In this paper, we present a new gap filling strategy for 3D vascular networks. The novelty of our approach is to combine both skeleton- and intensity-based information to fill large discontinuities. Our approach also does not make any hypothesis on the network topology, which is particularly important for tumour vasculature due to the chaotic arrangement of vessels within tumours. Synthetic results show that using intensity-based information, in addition to skeleton-based information, can make the detection of large discontinuities more robust. Our strategy is also shown to outperform a classic gap filling strategy on 3D Micro-CT images of preclinical tumour models.

Intracellular genetic networks are more intelligent than was first assumed due to their ability to learn. One of the manifestations of this intelligence is the ability to learn associations of two stimuli within gene-regulating circuitry: Hebbian-type learning within the cellular life. However, gene expression is an intrinsically noisy process; hence, we investigate the effect of intrinsic and extrinsic noise on this kind of intracellular intelligence. We report a stochastic resonance in an intracellular associative genetic perceptron, a noise-induced phenomenon, which manifests itself in noise-induced increase of response in efficiency after the learning event under the conditions of optimal stochasticity.

Tesis (Educación Física) Diversas investigaciones [Stuessi, 2001] proponen metodologías para el entrenamiento de la musculatura respiratoria, a través de un dispositivo que aumenta el espacio muerto de las vías aéreas. Este dispositivo (DEM) ha sido utilizado en investigaciones ligadas al entrenamiento deportivo y también en estudios relacionados al ámbito de la salud. Los resultados indican que, a través del entrenamiento de la musculatura respiratoria, es posible aumentar el rendimiento, retrasando la fatiga de estos músculos. La musculatura respiratoria, de modo similar a la musculatura locomotora, responde adaptándose al entrenamiento específico y mejorando de igual modo ante la frecuencia, intensidad y tipo de estímulo. Toda actividad física, sea cual fuere su índole, conileva un gasto energético, el cual varía según la intensidad y duración del ejercicio. Por lo tanto, a un mayor trabajo de la musculatura respiratoria, producida por el aumento del espacio muerto, traería consigo un mayor gasto energético. Los objetivos de esta investigación son demostrar que, aumentando el espacio muerto anatómico, se obtiene un incremento significativo del gasto energético, usándolo tanto en reposo como durante la realización de ejercicios submáximos. Asimismo, se busca determinar si el uso de este dispositivo provoca alteraciones en el equilibrio ácido-base, debido a una posible acumulación de CO2 en el organismo (acidosis respiratoria).