Comparing second cancer risk for multiple radiotherapy modalities in survivors of hodgkin lymphoma

Objectives: To assess if Excess Absolute Risk (EAR) of radiation-induced solid cancer can be used to rank radiotherapy plans for treatment of Hodgkin Lymphoma (HL) in a statistically significant way. Methods: EAR models, calibrated with data from the Life Span Study and HL survivors, have been incorporated into a voxelised risk-calculation software, which is used to compare four treatment modalities planned for five virtual HL patients. Organ-specific parameters are generated repeatedly in a Monte Carlo fashion to model their uncertainties. This in turn enables a quantitative estimation of the EAR uncertainties. Results: Parameter driven uncertainties on total EAR are around 13%, decreasing to around 2–5% for relative EAR comparisons. Total EAR estimations indicate that Intensity Modulated Proton Therapy decreases the average risk by 40% compared to the Intensity Modulated Radiation Therapy plan, 28% compared to the Volumetric Modulated Arc Therapy plan whereas the 3D Conformal Radiation Therapy plan is equivalent within the uncertainty. Conclusions: Relative EAR is a useful metric for distinguishing between radiotherapy plans in terms of second cancer risk. Advances in knowledge: Relative EAR is not dominated by model or parameter uncertainties and can be used to guide the choice of radiotherapy for HL patients

Time Domain Reflectometry Waveform Interpretation With Convolutional Neural Networks

Interpreting time domain reflectometry (TDR) waveforms obtained in soils with non-uniform water content is an open question. We design a new TDR waveform interpretation model based on convolutional neural networks (CNNs) that can reveal the spatial variations of soil relative permittivity and water content along a TDR sensor. The proposed model, namely TDR-CNN, is constructed with three modules. First, the geometrical features of the TDR waveforms are extracted with a simplified version of VGG16 network. Second, the reflection positions in a TDR waveform are traced using a 1D version of the region proposal network. Finally, the soil relative permittivity values are estimated via a CNN regression network. The three modules are developed in Python using Google TensorFlow and Keras API, and then stacked together to formulate the TDR-CNN architecture. Each module is trained separately, and data transfer among the modules can be facilitated automatically. TDR-CNN is evaluated using simulated TDR waveforms with varying relative permittivity but under a relatively stable soil electrical conductivity, and the accuracy and stability of the TDR-CNN are shown. TDR measurements from a water infiltration study provide an application for TDR-CNN and a comparison between TDR-CNN and an inverse model. The proposed TDR-CNN model is simple to implement, and modules in TDR-CNN can be updated or fine-tuned individually with new data sets. In conclusion, TDR-CNN presents a model architecture that can be used to interpret TDR waveforms obtained in soil with a heterogeneous water content distribution

Modeling vapor transfer in soil water and heat simulations: A modularized, partially-coupled approach

Coupled water and heat transfer models are widely used to analyze soil water content and temperature dynamics, evaluate agricultural management systems, and support crop growth modelling. In relatively dry soils, vapor transfer, rather than liquid water flux, becomes the main pathway for water redistribution. However, in some modularized soil simulators, e.g., 2DSOIL (Timlin et al., 1996), vapor transfer is not included, which may induce errors in soil water and heat modelling. Directly embedding vapor transfer into existing water and heat transfer modules may violate the modularized architecture of those simulators. Therefore, the objectives of this study are to design a vapor transfer model, evaluate its performance, and implement it as a separate module in a coupled soil water and heat simulator, e.g., 2DSOIL. The efficacy of the vapor transfer model is evaluated by comparing the simulated soil water content and temperature before and after including the new vapor transfer model, and the soil water content and temperature simulated with the standard Philip and de Vries (1957) model. By implementing vapor transfer as a separate module in 2DSOIL, modifications to existing water and heat transfer modules can be minimized and the modularized model architecture can be maintained. Numerical examples of 2DSOIL with the new vapor transfer model are presented to illustrate the effects of vapor flux on soil water and temperature redistributions. In conclusion, the new vapor transfer model provides an effective and easy-to-use method to account for the effects of vapor transfer on coupled soil water and heat simulations

A piecewise analysis model for electrical conductivity calculation from time domain reflectometry waveforms

Electrical conductivity (EC) represents a material’s ability to conduct electric current. Soil EC has been used as a soil quality attribute related to soil pH, nutrient availability, crop suitability and soil microbial activity. Time domain reflectometry (TDR) estimates soil water content and EC based on the propagation/reflection and energy attenuation of voltage signals along a waveguide. To maximize the data use efficiency, waveform interpretations for simultaneous water content and EC determination are needed. A tangent line/bounded mean oscillation (TL-BMO) model is available to estimate soil water content from TDR waveforms, but an associated EC model is not yet available. The objectives of this study are (1) to introduce a piecewise analysis method for TDR waveform interpretation, and (2) to develop a model for EC computation along a TDR waveguide under homogeneous water content. The proposed model sequentially fits a TDR waveform for the coaxial cable, the connection, and the waveguide according to the transmission line equation. A TDR waveguide can be discretized into multiple successive pieces for the determination of EC variations along the waveguide. Simplifications of the fitting procedures via (1) existing models, e.g., TL-BMO and Topp et al. (1988) models, and (2) analysis of waveforms obtained from controlled conditions, e.g., in distilled water under room temperature (~20 °C) and air pressure (~101 kPa), are also applied. Accuracy and stability of the proposed model are tested via observed TDR waveforms obtained under uniform EC conditions but perturbated with a range of noise levels. EC values computed with only one discretized piece (i.e., no discretization along the waveguide) are consistent with the theoretical EC values, and the results are robust for all of the tested noise levels. As the number of discretized pieces and the noise levels increase, numerical oscillations in the results increase. The maximum relative errors are \u3c20%, occurring when the mean power of noise is as large as the mean power of waveforms (0 dB noise). Flexibility of the proposed model is tested using waveforms simulated under spatially varying EC, and the EC variations along a TDR waveguide can be detected by the proposed model. In summary, the proposed model provides reliable EC estimations, and it can evaluate uniform or varying EC distributions along a TDR waveguide under uniform moisture conditions. This model can be imbedded into the TL-BMO model for integrated water content and EC determination for commonly measured (251-scanning point) TDR waveforms

Coupled heat and water transfer in heterogeneous and deformable soils: Numerical model using mixed finite element method

We present a generic model framework for coupled heat and water transfer (CHWT) in deformable (non-rigid) soils with spatial variations of soil properties. The model backbone is a mixed finite element method (FEM), which solves the Philip and de Vries (1957) CHWT model and achieves conservation of mass and energy on both local and global scales. Spatial variations occur in soil hydraulic and thermal properties due to transient water content and temperature distributions. Based on the mixed FEM scheme, a gradient measure and a clustering model (“k-means”) are proposed to trace the regions with large spatial variations of soil properties, and an adaptive mesh refinement technique is developed to improve the spatial resolution and simulation accuracy. Deformation perturbates local soil topography and alters transient soil water and temperature regimes in the deformed regions. A quasi-static deformation model is presented, and the deformation effects are incorporated into the mixed FEM scheme. When external load exists, soil deformation is simulated with an updated Lagrangian formulation, and the local water content and temperature variations due to soil volume changes are also updated in the CHWT model. Numerical examples, including thermally induced soil water transfer and water infiltration, illustrate the model ability to provide plausible CHWT results, especially the refined solutions near the wetting fronts and the water content and temperature distributions when the soil is deformable. In conclusion, the proposed model framework provides an effective pipeline to incorporate and process the spatial variations of soil properties and soil deformation in CHWT simulations.This article is published as Wang, Zhuangji, Dennis Timlin, Gang Liu, David Fleisher, Wenguang Sun, Sahila Beegum, Joshua Heitman et al. "Coupled heat and water transfer in heterogeneous and deformable soils: Numerical model using mixed finite element method." Journal of Hydrology 634 (2024): 131068. doi:10.1016/j.jhydrol.2024.131068. Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted

Biocompatible self-assembly of nano-materials for Bio-MEMS and insect reconnaissance.

This report summarizes the development of new biocompatible self-assembly procedures enabling the immobilization of genetically engineered cells in a compact, self-sustaining, remotely addressable sensor platform. We used evaporation induced self-assembly (EISA) to immobilize cells within periodic silica nanostructures, characterized by unimodal pore sizes and pore connectivity, that can be patterned using ink-jet printing or photo patterning. We constructed cell lines for the expression of fluorescent proteins and induced reporter protein expression in immobilized cells. We investigated the role of the abiotic/biotic interface during cell-mediated self-assembly of synthetic materials

Energy Resolution Performance of the CMS Electromagnetic Calorimeter

The energy resolution performance of the CMS lead tungstate crystal electromagnetic calorimeter is presented. Measurements were made with an electron beam using a fully equipped supermodule of the calorimeter barrel. Results are given both for electrons incident on the centre of crystals and for electrons distributed uniformly over the calorimeter surface. The electron energy is reconstructed in matrices of 3 times 3 or 5 times 5 crystals centred on the crystal containing the maximum energy. Corrections for variations in the shower containment are applied in the case of uniform incidence. The resolution measured is consistent with the design goals

High throughput instruments, methods, and informatics for systems biology.

High throughput instruments and analysis techniques are required in order to make good use of the genomic sequences that have recently become available for many species, including humans. These instruments and methods must work with tens of thousands of genes simultaneously, and must be able to identify the small subsets of those genes that are implicated in the observed phenotypes, or, for instance, in responses to therapies. Microarrays represent one such high throughput method, which continue to find increasingly broad application. This project has improved microarray technology in several important areas. First, we developed the hyperspectral scanner, which has discovered and diagnosed numerous flaws in techniques broadly employed by microarray researchers. Second, we used a series of statistically designed experiments to identify and correct errors in our microarray data to dramatically improve the accuracy, precision, and repeatability of the microarray gene expression data. Third, our research developed new informatics techniques to identify genes with significantly different expression levels. Finally, natural language processing techniques were applied to improve our ability to make use of online literature annotating the important genes. In combination, this research has improved the reliability and precision of laboratory methods and instruments, while also enabling substantially faster analysis and discovery

Alignment of the CMS silicon tracker during commissioning with cosmic rays

This is the Pre-print version of the Article. The official published version of the Paper can be accessed from the link below - Copyright @ 2010 IOPThe CMS silicon tracker, consisting of 1440 silicon pixel and 15 148 silicon strip detector modules, has been aligned using more than three million cosmic ray charged particles, with additional information from optical surveys. The positions of the modules were determined with respect to cosmic ray trajectories to an average precision of 3–4 microns RMS in the barrel and 3–14 microns RMS in the endcap in the most sensitive coordinate. The results have been validated by several studies, including laser beam cross-checks, track fit self-consistency, track residuals in overlapping module regions, and track parameter resolution, and are compared with predictions obtained from simulation. Correlated systematic effects have been investigated. The track parameter resolutions obtained with this alignment are close to the design performance.This work is supported by FMSR (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES (Croatia); RPF (Cyprus); Academy of Sciences and NICPB (Estonia); Academy of Finland, ME, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NKTH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); NRF (Korea); LAS (Lithuania); CINVESTAV, CONACYT, SEP, and UASLP-FAI (Mexico); PAEC (Pakistan); SCSR (Poland); FCT (Portugal); JINR (Armenia, Belarus, Georgia, Ukraine, Uzbekistan); MST and MAE (Russia); MSTDS (Serbia); MICINN and CPAN (Spain); Swiss Funding Agencies (Switzerland); NSC (Taipei); TUBITAK and TAEK (Turkey); STFC (United Kingdom); DOE and NSF (USA)