Measurements of poloidal rotation velocity using cross-correlation spectroscopy in the H-1 heliac

A correlation spectroscopy diagnostic [M.G. Shats and J. Howard, Fusion Eng. Des. 34–35, 271 (1997)] measures fluctuation spectra and local fluctuation intensities in a radiation-dominated plasma, such as the low-temperature plasma in the H-1 heliac (Te<50 eV, ne<2×10¹⁸ m⁻³). When the fluctuation coherence lengths in the poloidal and radial directions are shorter than the plasma radius, the cross-correlation function of the two crossed-sightline fluctuating intensities contains information about the fluctuations amplitude and their phase in the intersection volume. The optical setup on the H-1 heliac uses two nearly orthogonal views to image 20 optical fibers arranged into two linear arrays in the plasma poloidal cross section. A matrix of 10×10 cross-correlation functions is then analyzed to determine the poloidal phase velocity of the fluctuations, poloidal and radial correlation lengths, and the radial profiles of the fluctuations intensity. The results on the poloidal propagation velocity measured using the cross-correlation technique (time delay of the cross-correlation functions) are compared with the poloidal velocity measured using poloidally separated probes in the plasma. Both velocities are found to be in good agreement and also agree well with the E×B drift velocity in this plasma

[68Ga]-DOTATOC-PET/CT for meningioma IMRT treatment planning

<p>Abstract</p> <p>Purpose</p> <p>The observation that human meningioma cells strongly express somatostatin receptor (SSTR 2) was the rationale to analyze retrospectively in how far DOTATOC PET/CT is helpful to improve target volume delineation for intensity modulated radiotherapy (IMRT).</p> <p>Patients and Methods</p> <p>In 26 consecutive patients with preferentially skull base meningioma, diagnostic magnetic resonance imaging (MRI) and planning-computed tomography (CT) was complemented with data from [⁶⁸Ga]-DOTA-D Phe¹-Tyr³-Octreotide (DOTATOC)-PET/CT. Image fusion of PET/CT, diagnostic computed tomography, MRI and radiotherapy planning CT as well as target volume delineation was performed with OTP-Masterplan^®. Initial gross tumor volume (GTV) definition was based on MRI data only and was secondarily complemented with DOTATOC-PET information. Irradiation was performed as EUD based IMRT, using the Hyperion Software package.</p> <p>Results</p> <p>The integration of the DOTATOC data led to additional information concerning tumor extension in 17 of 26 patients (65%). There were major changes of the clinical target volume (CTV) which modify the PTV in 14 patients, minor changes were realized in 3 patients. Overall the GTV-MRI/CT was larger than the GTV-PET in 10 patients (38%), smaller in 13 patients (50%) and almost the same in 3 patients (12%). Most of the adaptations were performed in close vicinity to bony skull base structures or after complex surgery. Median GTV based on MRI was 18.1 cc, based on PET 25.3 cc and subsequently the CTV was 37.4 cc. Radiation planning and treatment of the DOTATOC-adapted volumes was feasible.</p> <p>Conclusion</p> <p>DOTATOC-PET/CT information may strongly complement patho-anatomical data from MRI and CT in cases with complex meningioma and is thus helpful for improved target volume delineation especially for skull base manifestations and recurrent disease after surgery.</p

SUBA: the Arabidopsis Subcellular Database

Knowledge of protein localisation contributes towards our understanding of protein function and of biological inter-relationships. A variety of experimental methods are currently being used to produce localisation data that need to be made accessible in an integrated manner. Chimeric fluorescent fusion proteins have been used to define subcellular localisations with at least 1100 related experiments completed in Arabidopsis. More recently, many studies have employed mass spectrometry to undertake proteomic surveys of subcellular components in Arabidopsis yielding localisation information for ∼2600 proteins. Further protein localisation information may be obtained from other literature references to analysis of locations (AmiGO: ∼900 proteins), location information from Swiss-Prot annotations (∼2000 proteins); and location inferred from gene descriptions (∼2700 proteins). Additionally, an increasing volume of available software provides location prediction information for proteins based on amino acid sequence. We have undertaken to bring these various data sources together to build SUBA, a SUBcellular location database for Arabidopsis proteins. The localisation data in SUBA encompasses 10 distinct subcellular locations, >6743 non-redundant proteins and represents the proteins encoded in the transcripts responsible for 51% of Arabidopsis expressed sequence tags. The SUBA database provides a powerful means by which to assess protein subcellular localisation in Arabidopsis ()

A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks

Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002 and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140

2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers

Enlarged lymph nodes (LNs) can provide important information for cancer diagnosis, staging, and measuring treatment reactions, making automated detection a highly sought goal. In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue. Our 2D detection can be effectively formulated as linear classification on a single image feature type of Histogram of Oriented Gradients (HOG), covering a moderate field-of-view of 45 by 45 voxels. We exploit both simple pooling and sparse linear fusion schemes to aggregate these 2D detection scores for the final 3D LN detection. In this manner, detection is more tractable and does not need to perform perfectly at instance level (as weak hypotheses) since our aggregation process will robustly harness collective information for LN detection. Two datasets (90 patients with 389 mediastinal LNs and 86 patients with 595 abdominal LNs) are used for validation. Cross-validation demonstrates 78.0% sensitivity at 6 false positives/volume (FP/vol.) (86.1% at 10 FP/vol.) and 73.1% sensitivity at 6 FP/vol. (87.2% at 10 FP/vol.), for the mediastinal and abdominal datasets respectively. Our results compare favorably to previous state-of-the-art methods.Comment: This article will be presented at MICCAI (Medical Image Computing and Computer-Assisted Intervention) 201

In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography

Among additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data integration are presented