Time dependent numerical model for the emission of radiation from relativistic plasma

We describe a numerical model constructed for the study of the emission of radiation from relativistic plasma under conditions characteristic, e.g., to gamma-ray bursts (GRB's) and active galactic nuclei (AGN's). The model solves self consistently the kinetic equations for e^\pm and photons, describing cyclo-synchrotron emission, direct Compton and inverse Compton scattering, pair production and annihilation, including the evolution of high energy electromagnetic cascades. The code allows calculations over a wide range of particle energies, spanning more than 15 orders of magnitude in energy and time scales. Our unique algorithm, which enables to follow the particle distributions over a wide energy range, allows to accurately derive spectra at high energies, >100 \TeV. We present the kinetic equations that are being solved, detailed description of the equations describing the various physical processes, the solution method, and several examples of numerical results. Excellent agreement with analytical results of the synchrotron-SSC model is found for parameter space regions in which this approximation is valid, and several examples are presented of calculations for parameter space regions where analytic results are not available.Comment: Minor changes; References added, discussion on observational status added. Accepted for publication in Ap.

NiO gas sensing element prepared on needle-shaped silicon substrate

Abstract This study presents a new approach to enhancing the gas sensor properties based on increasing the sensing area by a structured substrate. Two types of needle-shaped silicon substrates with surface areas of 40 and 14 μm 2 were used as substrate for the preparation of NiO gas sensing element with a thickness of 25 nm. The surface morphology and composition of the prepared samples were examined by SEM, FIB-SEM, and GD OES methods. Deposited NiO films were continuous consisting of an agglomeration of small nanosized grains with arbitrary forms created on each Si needle. It was found that NiO had a polycrystalline nature. The gas sensing measurements revealed that hydrogen responses were better for NiO sensing elements prepared on needle-shape Si substrates with 40 μm 2 surface area than those with 14 μm 2 for all investigated concentrations and temperatures. The maximum relative sensitivity of 26% was measured at 250 ppm of hydrogen

Diagnostic sensitivity of abdominal fat aspiration in cardiac amyloidosis

Aims: Congo red staining of an endomyocardial biopsy is the diagnostic gold-standard in suspected cardiac amyloidosis (CA), but the procedure is associated with the risk, albeit small, of serious complications, and delay in diagnosis due to the requirement for technical expertise. In contrast, abdominal fat pad fine needle aspiration (FPFNA) is a simple, safe and well-established procedure in systemic amyloidosis, but its diagnostic sensitivity in patients with suspected CA remains unclear. Methods and results: We assessed the diagnostic sensitivity of FPFNA in 600 consecutive patients diagnosed with CA [216 AL amyloidosis, 113 hereditary transthyretin (ATTRm), and 271 wild-type transthyretin (ATTRwt) amyloidosis] at our Centre. Amyloid was detected on Congo red staining of FPFNAs in 181/216 (84%) patients with cardiac AL amyloidosis, including 100, 97, and 78% of those with a large, moderate, and small whole-body amyloid burden, respectively, as assessed by serum amyloid P (SAP) component scintigraphy (P < 0.001); the deposits were successfully typed as AL by immunohistochemistry in 102/216 (47%) cases. Amyloid was detected in FPFNAs of 51/113 (45%) patients with ATTRm CA, and only 42/271 (15%) cases with ATTRwt CA. Conclusions: FPFNA has reasonable diagnostic sensitivity in cardiac AL amyloidosis, particularly in patients with a large whole-body amyloid burden. Although the diagnostic sensitivity of FPFNA is substantially lower in transthyretin CA, particularly ATTRwt, it may nevertheless sometimes obviate the need for endomyocardial biopsy

Automated interpretation of systolic and diastolic function on the echocardiogram:a multicohort study

Background: Echocardiography is the diagnostic modality for assessing cardiac systolic and diastolic function to diagnose and manage heart failure. However, manual interpretation of echocardiograms can be time consuming and subject to human error. Therefore, we developed a fully automated deep learning workflow to classify, segment, and annotate two-dimensional (2D) videos and Doppler modalities in echocardiograms. Methods: We developed the workflow using a training dataset of 1145 echocardiograms and an internal test set of 406 echocardiograms from the prospective heart failure research platform (Asian Network for Translational Research and Cardiovascular Trials; ATTRaCT) in Asia, with previous manual tracings by expert sonographers. We validated the workflow against manual measurements in a curated dataset from Canada (Alberta Heart Failure Etiology and Analysis Research Team; HEART; n=1029 echocardiograms), a real-world dataset from Taiwan (n=31 241), the US-based EchoNet-Dynamic dataset (n=10 030), and in an independent prospective assessment of the Asian (ATTRaCT) and Canadian (Alberta HEART) datasets (n=142) with repeated independent measurements by two expert sonographers. Findings: In the ATTRaCT test set, the automated workflow classified 2D videos and Doppler modalities with accuracies (number of correct predictions divided by the total number of predictions) ranging from 0·91 to 0·99. Segmentations of the left ventricle and left atrium were accurate, with a mean Dice similarity coefficient greater than 93% for all. In the external datasets (n=1029 to 10 030 echocardiograms used as input), automated measurements showed good agreement with locally measured values, with a mean absolute error range of 9–25 mL for left ventricular volumes, 6–10% for left ventricular ejection fraction (LVEF), and 1·8–2·2 for the ratio of the mitral inflow E wave to the tissue Doppler e' wave (E/e' ratio); and reliably classified systolic dysfunction (LVEF <40%, area under the receiver operating characteristic curve [AUC] range 0·90–0·92) and diastolic dysfunction (E/e' ratio ≥13, AUC range 0·91–0·91), with narrow 95% CIs for AUC values. Independent prospective evaluation confirmed less variance of automated compared with human expert measurements, with all individual equivalence coefficients being less than 0 for all measurements. Interpretation: Deep learning algorithms can automatically annotate 2D videos and Doppler modalities with similar accuracy to manual measurements by expert sonographers. Use of an automated workflow might accelerate access, improve quality, and reduce costs in diagnosing and managing heart failure globally. Funding: A*STAR Biomedical Research Council and A*STAR Exploit Technologies