Neural correlates linking trauma and physical symptoms

Highlights •Trauma patients showed greater physical health symptoms and decreased prefrontal but increased hippocampal responses to stress than controls.•More frequent physical symptoms were associated with an increased left hippocampal response to stress.•Trauma may increase physical health symptoms by compromising hippocampal function, which could also increase vulnerability to comorbid stress- and pain-related disorders.info:eu-repo/semantics/publishedVersio

Development of a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport

Monte Carlo simulation is the most accurate method for absorbed dose calculations in radiotherapy. Its efficiency still requires improvement for routine clinical applications, especially for online adaptive radiotherapy. In this paper, we report our recent development on a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport. We have implemented the Dose Planning Method (DPM) Monte Carlo dose calculation package (Sempau et al, Phys. Med. Biol., 45(2000)2263-2291) on GPU architecture under CUDA platform. The implementation has been tested with respect to the original sequential DPM code on CPU in phantoms with water-lung-water or water-bone-water slab geometry. A 20 MeV mono-energetic electron point source or a 6 MV photon point source is used in our validation. The results demonstrate adequate accuracy of our GPU implementation for both electron and photon beams in radiotherapy energy range. Speed up factors of about 5.0 ~ 6.6 times have been observed, using an NVIDIA Tesla C1060 GPU card against a 2.27GHz Intel Xeon CPU processor.Comment: 13 pages, 3 figures, and 1 table. Paper revised. Figures update

Genetic diagnosis of inborn errors of immunity using clinical exome sequencing

Inborn errors of immunity (IEI) include a variety of heterogeneous genetic disorders in which defects in the immune system lead to an increased susceptibility to infections and other complications. Accurate, prompt diagnosis of IEI is crucial for treatment plan and prognostication. In this study, clinical utility of clinical exome sequencing (CES) for diagnosis of IEI was evaluated. For 37 Korean patients with suspected symptoms, signs, or laboratory abnormalities associated with IEI, CES that covers 4,894 genes including genes related to IEI was performed. Their clinical diagnosis, clinical characteristics, family history of infection, and laboratory results, as well as detected variants, were reviewed. With CES, genetic diagnosis of IEI was made in 15 out of 37 patients (40.5%). Seventeen pathogenic variants were detected from IEI-related genes, BTK, UNC13D, STAT3, IL2RG, IL10RA, NRAS, SH2D1A, GATA2, TET2, PRF1, and UBA1, of which four variants were previously unreported. Among them, somatic causative variants were identified from GATA2, TET2, and UBA1. In addition, we identified two patients incidentally diagnosed IEI by CES, which was performed to diagnose other diseases of patients with unrecognized IEI. Taken together, these results demonstrate the utility of CES for the diagnosis of IEI, which contributes to accurate diagnosis and proper treatments

GPU-based ultra fast dose calculation using a finite pencil beam model

Online adaptive radiation therapy (ART) is an attractive concept that promises the ability to deliver an optimal treatment in response to the inter-fraction variability in patient anatomy. However, it has yet to be realized due to technical limitations. Fast dose deposit coefficient calculation is a critical component of the online planning process that is required for plan optimization of intensity modulated radiation therapy (IMRT). Computer graphics processing units (GPUs) are well-suited to provide the requisite fast performance for the data-parallel nature of dose calculation. In this work, we develop a dose calculation engine based on a finite-size pencil beam (FSPB) algorithm and a GPU parallel computing framework. The developed framework can accommodate any FSPB model. We test our implementation on a case of a water phantom and a case of a prostate cancer patient with varying beamlet and voxel sizes. All testing scenarios achieved speedup ranging from 200~400 times when using a NVIDIA Tesla C1060 card in comparison with a 2.27GHz Intel Xeon CPU. The computational time for calculating dose deposition coefficients for a 9-field prostate IMRT plan with this new framework is less than 1 second. This indicates that the GPU-based FSPB algorithm is well-suited for online re-planning for adaptive radiotherapy.Comment: submitted Physics in Medicine and Biolog

Implementation and evaluation of various demons deformable image registration algorithms on GPU

Online adaptive radiation therapy (ART) promises the ability to deliver an optimal treatment in response to daily patient anatomic variation. A major technical barrier for the clinical implementation of online ART is the requirement of rapid image segmentation. Deformable image registration (DIR) has been used as an automated segmentation method to transfer tumor/organ contours from the planning image to daily images. However, the current computational time of DIR is insufficient for online ART. In this work, this issue is addressed by using computer graphics processing units (GPUs). A grey-scale based DIR algorithm called demons and five of its variants were implemented on GPUs using the Compute Unified Device Architecture (CUDA) programming environment. The spatial accuracy of these algorithms was evaluated over five sets of pulmonary 4DCT images with an average size of 256x256x100 and more than 1,100 expert-determined landmark point pairs each. For all the testing scenarios presented in this paper, the GPU-based DIR computation required around 7 to 11 seconds to yield an average 3D error ranging from 1.5 to 1.8 mm. It is interesting to find out that the original passive force demons algorithms outperform subsequently proposed variants based on the combination of accuracy, efficiency, and ease of implementation.Comment: Submitted to Physics in Medicine and Biolog

Somatic mosaic truncating mutations of PPM1D in blood can result from expansion of a mutant clone under selective pressure of chemotherapy.

BackgroundPPM1D (Protein phosphatase magnesium-dependent 1δ) is known as a damage response regulator, a part of the p53 negative feedback loop. Truncating mutations of PPM1D, resulting in overexpression, are frequently found in the blood of patients with breast or ovarian cancer. To identify whether the PPM1D mutation predisposes patients to such cancers or if it results from the cancer and therapy, somatic PPM1D mutations in association with previous cancer and chemotherapy need to be explored.MethodsWe performed next-generation sequencing (NGS) analysis of blood samples from patients suspected to have hereditary cancer. We grouped the patients according to their diagnoses and history of chemotherapy. For the patients with PPM1D mutations in blood, tumor tissue specimens were examined for the PPM1D mutation using conventional sequencing.ResultsA total of 1,195 patients, including 719 patients with breast cancer and 240 with ovarian cancer, were tested, and four (~0.3%) had the truncating mutation in PPM1D. All truncating mutations were in exon 6, in mosaic form, with a mean allele fraction of 11.15%. While 395 out of the 1,195 patients had undergone chemotherapy, the four with the truncating mutation had a history of cisplatin-based chemotherapy. No corresponding mutations were identified in the tumor tissues.ConclusionsWe investigated the frequency of the somatic mosaic PPM1D mutation, in patients with breast or ovarian cancer, which is suggested to be low and related to a history of cisplatin-based chemotherapy. It may be a marker of previous exposure to selective pressure for cells with an impaired DNA damage response

A Single Multi-Task Deep Neural Network with Post-Processing for Object Detection with Reasoning and Robotic Grasp Detection

Applications of deep neural network (DNN) based object and grasp detections could be expanded significantly when the network output is processed by a high-level reasoning over relationship of objects. Recently, robotic grasp detection and object detection with reasoning have been investigated using DNNs. There have been efforts to combine these multitasks using separate networks so that robots can deal with situations of grasping specific target objects in the cluttered, stacked, complex piles of novel objects from a single RGB-D camera. We propose a single multi-task DNN that yields accurate detections of objects, grasp position and relationship reasoning among objects. Our proposed methods yield state-of-the-art performance with the accuracy of 98.6% and 74.2% with the computation speed of 33 and 62 frame per second on VMRD and Cornell datasets, respectively. Our methods also yielded 95.3% grasp success rate for novel object grasping tasks with a 4-axis robot arm and 86.7% grasp success rate in cluttered novel objects with a humanoid robot