A feasibility study of enhanced prompt gamma imaging for range verification in proton therapy using deep learning.

Background and objective. Range uncertainty is a major concern affecting the delivery precision in proton therapy. The Compton camera (CC)-based prompt-gamma (PG) imaging is a promising technique to provide 3Din vivorange verification. However, the conventional back-projected PG images suffer from severe distortions due to the limited view of the CC, significantly limiting its clinical utility. Deep learning has demonstrated effectiveness in enhancing medical images from limited-view measurements. But different from other medical images with abundant anatomical structures, the PGs emitted along the path of a proton pencil beam take up an extremely low portion of the 3D image space, presenting both the attention and the imbalance challenge for deep learning. To solve these issues, we proposed a two-tier deep learning-based method with a novel weighted axis-projection loss to generate precise 3D PG images to achieve accurate proton range verification.Materials and methods: the proposed method consists of two models: first, a localization model is trained to define a region-of-interest (ROI) in the distorted back-projected PG image that contains the proton pencil beam; second, an enhancement model is trained to restore the true PG emissions with additional attention on the ROI. In this study, we simulated 54 proton pencil beams (energy range: 75-125 MeV, dose level: 1 × 109protons/beam and 3 × 108protons/beam) delivered at clinical dose rates (20 kMU min-1and 180 kMU min-1) in a tissue-equivalent phantom using Monte-Carlo (MC). PG detection with a CC was simulated using the MC-Plus-Detector-Effects model. Images were reconstructed using the kernel-weighted-back-projection algorithm, and were then enhanced by the proposed method.Results. The method effectively restored the 3D shape of the PG images with the proton pencil beam range clearly visible in all testing cases. Range errors were within 2 pixels (4 mm) in all directions in most cases at a higher dose level. The proposed method is fully automatic, and the enhancement takes only ∼0.26 s.Significance. Overall, this preliminary study demonstrated the feasibility of the proposed method to generate accurate 3D PG images using a deep learning framework, providing a powerful tool for high-precisionin vivorange verification of proton therapy.

Feedforward growth rate control mitigates gene activation burden.

Heterologous gene activation causes non-physiological burden on cellular resources that cells are unable to adjust to. Here, we introduce a feedforward controller that actuates growth rate upon activation of a gene of interest (GOI) to compensate for such a burden. The controller achieves this by activating a modified SpoT enzyme (SpoTH) with sole hydrolysis activity, which lowers ppGpp level and thus increases growth rate. An inducible RelA+ expression cassette further allows to precisely set the basal level of ppGpp, and thus nominal growth rate, in any bacterial strain. Without the controller, activation of the GOI decreased growth rate by more than 50%. With the controller, we could activate the GOI to the same level without growth rate defect. A cell strain armed with the controller in co-culture enabled persistent population-level activation of a GOI, which could not be achieved by a strain devoid of the controller. The feedforward controller is a tunable, modular, and portable tool that allows dynamic gene activation without growth rate defects for bacterial synthetic biology applications.

Synthetic biology by controller design.

Natural biological systems display complex regulation and synthetic biomolecular systems have been used to understand their natural counterparts and to parse sophisticated regulations into core design principles. At the same time, the engineering of biomolecular systems has unarguable potential to transform current and to enable new, yet-to-be-imagined, biotechnology applications. In this review, we discuss the progression of control systems design in synthetic biology, from the purpose of understanding the function of naturally occurring regulatory motifs to that of creating genetic circuits whose function is sufficiently robust for biotechnology applications.

Applications of Machine Learning to Improve the Clinical Viability of Compton Camera Based in vivo Range Verification in Proton Radiotherapy.

We studied the application of a deep, fully connected Neural Network (NN) to process prompt gamma (PG) data measured by a Compton camera (CC) during the delivery of clinical proton radiotherapy beams. The network identifies 1) recorded "bad" PG events arising from background noise during the measurement, and 2) the correct ordering of PG interactions in the CC to help improve the fidelity of "good" data used for image reconstruction. PG emission from a tissue-equivalent target during irradiation with a 150 MeV proton beam delivered at clinical dose rates was measured with a prototype CC. Images were reconstructed from both the raw measured data and the measured data that was further processed with a neural network (NN) trained to identify "good" and "bad" PG events and predict the ordering of individual interactions within the good PG events. We determine if NN processing of the CC data could improve the reconstructed PG images to a level in which they could provide clinically useful information about the in vivo range and range shifts of the proton beams delivered at full clinical dose rates. Results showed that a deep, fully connected NN improved the achievable contrast to noise ratio (CNR) in our images by more than a factor of 8x. This allowed the path, range, and lateral width of the clinical proton beam within a tissue equivalent target to easily be identified from the PG images, even at the highest dose rates of a 150 MeV proton beam used for clinical treatments. On average, shifts in the beam range as small as 3 mm could be identified. However, when limited by the amount of PG data measured with our prototype CC during the delivery of a single proton pencil beam (~1 × 109 protons), the uncertainty in the reconstructed PG images limited the identification of range shift to ~5 mm. Substantial improvements in CC images were obtained during clinical beam delivery through NN pre-processing of the measured PG data. We believe this shows the potential of NNs to help improve and push CC-based PG imaging toward eventual clinical application for proton RT treatment delivery verification.

Challenges and opportunities for the simulation of calcium waves on modern multi-core and many-core parallel computing platforms.

State-of-the-art distributed-memory computer clusters contain multicore CPUs with 16 and more cores. The second generation of the Intel Xeon Phi many-core processor has more than 60 cores with 16 GB of high-performance on-chip memory. We contrast the performance of the second-generation Intel Xeon Phi, code-named Knights Landing (KNL), with 68 computational cores to the latest multicore CPU Intel Skylake with 18 cores. A special-purpose code solving a system of nonlinear reaction-diffusion partial differential equations with several thousands of point sources modeled mathematically by Dirac delta distributions serves as realistic test bed. The system is discretized in space by the finite volume method and advanced by fully implicit time-stepping, with a matrix-free implementation that allows the complex model to have an extremely small memory footprint. The sample application is a seven variable model of calcium-induced calcium release (CICR) that models the interplay between electrical excitation, calcium signaling, and mechanical contraction in a heart cell. The results demonstrate that excellent parallel scalability is possible on both hardware platforms, but that modern multicore CPUs outperform the specialized many-core Intel Xeon Phi KNL architecture for a large class of problems such as systems of parabolic partial differential equations.

Effects of spatial heterogeneity on bacterial genetic circuits.

Intracellular spatial heterogeneity is frequently observed in bacteria, where the chromosome occupies part of the cell's volume and a circuit's DNA often localizes within the cell. How this heterogeneity affects core processes and genetic circuits is still poorly understood. In fact, commonly used ordinary differential equation (ODE) models of genetic circuits assume a well-mixed ensemble of molecules and, as such, do not capture spatial aspects. Reaction-diffusion partial differential equation (PDE) models have been only occasionally used since they are difficult to integrate and do not provide mechanistic understanding of the effects of spatial heterogeneity. In this paper, we derive a reduced ODE model that captures spatial effects, yet has the same dimension as commonly used well-mixed models. In particular, the only difference with respect to a well-mixed ODE model is that the association rate constant of binding reactions is multiplied by a coefficient, which we refer to as the binding correction factor (BCF). The BCF depends on the size of interacting molecules and on their location when fixed in space and it is equal to unity in a well-mixed ODE model. The BCF can be used to investigate how spatial heterogeneity affects the behavior of core processes and genetic circuits. Specifically, our reduced model indicates that transcription and its regulation are more effective for genes located at the cell poles than for genes located on the chromosome. The extent of these effects depends on the value of the BCF, which we found to be close to unity. For translation, the value of the BCF is always greater than unity, it increases with mRNA size, and, with biologically relevant parameters, is substantially larger than unity. Our model has broad validity, has the same dimension as a well-mixed model, yet it incorporates spatial heterogeneity. This simple-to-use model can be used to both analyze and design genetic circuits while accounting for spatial intracellular effects.

The effects of heat and mass diffusion on freely oscillating bubbles in a viscoelastic, tissue-like medium.

In certain cavitation-based ultrasound techniques, the relative importance of thermally vs mechanically induced damage is unclear. As a first step to investigate this matter, a numerical model for bubble dynamics in tissue-like, viscoelastic media is presented in which full thermal effects are included inside and outside the bubble, as well as interdiffusion of vapor and non-condensible gas inside the bubble. Soft tissue is assumed to behave according to a Kelvin-Voigt model in which viscous and elastic contributions are additive. A neo-Hookean formulation, appropriate for finite-strain elasticity, accounts for the large deformations produced by cavitation. Numerical solutions to problems of relevance to therapeutic ultrasound are examined, and linear analysis is used to explain the underlying mechanisms. The dependence between the surrounding medium's elasticity (shear modulus) and the extent to which the effects of heat and mass transfer influence bubble dynamics is quantified. In particular, the oscillation properties are related to the eigenvalues determined from linear theory. Regimes under which a polytropic relation describes the heat transfer to sufficient accuracy are identified, for which the complexity and computational expense associated with solving full partial differential equations can be avoided.

Emergence of fibroblasts with a proinflammatory epigenetically altered phenotype in severe hypoxic pulmonary hypertension.

Persistent accumulation of monocytes/macrophages in the pulmonary artery adventitial/perivascular areas of animals and humans with pulmonary hypertension has been documented. The cellular mechanisms contributing to chronic inflammatory responses remain unclear. We hypothesized that perivascular inflammation is perpetuated by activated adventitial fibroblasts, which, through sustained production of proinflammatory cytokines/chemokines and adhesion molecules, induce accumulation, retention, and activation of monocytes/macrophages. We further hypothesized that this proinflammatory phenotype is the result of the abnormal activity of histone-modifying enzymes, specifically, class I histone deacetylases (HDACs). Pulmonary adventitial fibroblasts from chronically hypoxic hypertensive calves (termed PH-Fibs) expressed a constitutive and persistent proinflammatory phenotype defined by high expression of IL-1ß, IL-6, CCL2(MCP-1), CXCL12(SDF-1), CCL5(RANTES), CCR7, CXCR4, GM-CSF, CD40, CD40L, and VCAM-1. The proinflammatory phenotype of PH-Fibs was associated with epigenetic alterations as demonstrated by increased activity of HDACs and the findings that class I HDAC inhibitors markedly decreased cytokine/chemokine mRNA expression levels in these cells. PH-Fibs induced increased adhesion of THP-1 monocytes and produced soluble factors that induced increased migration of THP-1 and murine bone marrow-derived macrophages as well as activated monocytes/macrophages to express proinflammatory cytokines and profibrogenic mediators (TIMP1 and type I collagen) at the transcriptional level. Class I HDAC inhibitors markedly reduced the ability of PH-Fibs to induce monocyte migration and proinflammatory activation. The emergence of a distinct adventitial fibroblast population with an epigenetically altered proinflammatory phenotype capable of recruiting, retaining, and activating monocytes/macrophages characterizes pulmonary hypertension-associated vascular remodeling and thus could contribute significantly to chronic inflammatory processes in the pulmonary artery wall.

Fracturas de clavícula. indicaciones y resultados funcionales del tratamiento quirúrgico / Clavicle fractures. Indications and functional results of the treatment with type Neer II prosthetic replacement

Las fracturas de clavícula son lesiones que afectan a personas jóvenes en edad productiva y ocupan el 35 por ciento de las fracturas de hombro. La deformidad producto del tratamiento inadecuado, interfiere en el buen funcionamiento de la articulación del hombro, dejando secuelas que van desde la subluxación recurrente de la articulación esternoclavicular hasta la compresión del plexo braquial por la formación exuberante de callo óseo. Tratamiento efectivo aunque no siempre los resultados son buenos por lo que la cirugía confiere además de una mejor solución funcional la estética postoperatoria. Por otro lado, la evolución de esta cirugía, es en un 69 por ciento, rápida y satisfactoria, y la clasificación del Dr. Iñárritu nos permite establecer los criterios para elegir este tratamiento del conservador