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Obtaining a burning plasma is a critical step towards self-sustaining fusion energy1. A burning plasma is one in which the fusion reactions themselves are the primary source of heating in the plasma, which is necessary to sustain and propagate the burn, enabling high energy gain. After decades of fusion research, here we achieve a burning-plasma state in the laboratory. These experiments were conducted at the US National Ignition Facility, a laser facility delivering up to 1.9 megajoules of energy in pulses with peak powers up to 500 terawatts. We use the lasers to generate X-rays in a radiation cavity to indirectly drive a fuel-containing capsule via the X-ray ablation pressure, which results in the implosion process compressing and heating the fuel via mechanical work. The burning-plasma state was created using a strategy to increase the spatial scale of the capsule2,3 through two different implosion concepts4–7. These experiments show fusion self-heating in excess of the mechanical work injected into the implosions, satisfying several burning-plasma metrics3,8. Additionally, we describe a subset of experiments that appear to have crossed the static self-heating boundary, where fusion heating surpasses the energy losses from radiation and conduction. These results provide an opportunity to study α-particle-dominated plasmas and burning-plasma physics in the laboratory.

In a burning plasma state1–7, alpha particles from deuterium–tritium fusion reactions redeposit their energy and are the dominant source of heating. This state has recently been achieved at the US National Ignition Facility8 using indirect-drive inertial-confinement fusion. Our experiments use a laser-generated radiation-filled cavity (a hohlraum) to spherically implode capsules containing deuterium and tritium fuel in a central hot spot where the fusion reactions occur. We have developed more efficient hohlraums to implode larger fusion targets compared with previous experiments9,10. This delivered more energy to the hot spot, whereas other parameters were optimized to maintain the high pressures required for inertial-confinement fusion. We also report improvements in implosion symmetry control by moving energy between the laser beams11–16 and designing advanced hohlraum geometry17 that allows for these larger implosions to be driven at the present laser energy and power capability of the National Ignition Facility. These design changes resulted in fusion powers of 1.5 petawatts, greater than the input power of the laser, and 170 kJ of fusion energy18,19. Radiation hydrodynamics simulations20,21 show energy deposition by alpha particles as the dominant term in the hot-spot energy balance, indicative of a burning plasma state.

Background A non-negligible proportion of myocardial infarction (MI) is not clinically recognized and unrecognized myocardial infarction (UMI) is associated with adverse outcomes. Purpose To determine the prevalence and prognostic significance of UMI by delayed-enhancement cardiac magnetic resonance (DE-CMR) before elective percutaneous coronary intervention (PCI). Methods In this prospective, single-center study, 236 patients with stable coronary artery disease undergoing elective and uncomplicated PCI were studied. All patients underwent DE-CMR before PCI. The prevalence of UMI was evaluated and the association of clinical and CMR-derived variables with primary MACE, defined as cardiovascular death, nonfatal MI, hospitalization for heart failure, unplanned late revascularization, and ischemic stroke was investigated. Results In the final analysis of 213 patients, 63 patients (29.6%) showed UMI. Target territory UMI was observed in 38 (17.8% of total, 60.3% of patients with UMI). UMI was significantly associated with sex, diabetes mellitus, left ventricular ejection fraction, SYNTAX score and fractional flow reserve in target vessels. During follow-up periods (median, 23 months), MACE was observed in 17 (27.0%) of patients with UMI, and 17 (11.3%) without (P=0.001). In a multivariable model, UMI (hazard ratio [HR] 2.18, 95% confidential interval, 1.10–4.33, P=0.001) remained as an independent predictor of MACE. Kaplan–Meier analysis indicated that the presence of UMI was significantly associated with higher incidence of MACE. Conclusions The prevalence of UMI in patients undergoing elective PCI was 29.6%. UMI was independently associated with an increased risk of MACE after successful PCI. Given the non-negligible prevalence and potential clinical significance of UMI, clinical studies comparing PCI and guideline directed medical therapy (GDMT) versus GDMT only strategy might have to take the presence of UMI into consideration. Funding Acknowledgement Type of funding sources: None.

In the DIMOSIC (DIfferent MOdels, Same Initial Conditions) project, forecasts from different global medium-range forecast models have been created based on the same initial conditions. The dataset consists of 10-day deterministic forecasts from seven models and includes 122 forecast dates spanning one calendar year. All forecasts are initialized from the same ECMWF operational analyses to minimize the differences due to initialization. The models are run at or near their respective operational resolutions to explore similarities and differences between operational global forecast models. The main aims of this study are: (1) evaluate the forecast skill and how it depends on model formulation, (2) assess systematic differences and errors at short lead times, (3) compare multi-model ensemble spread to model uncertainty schemes, and (4) identify models that generate similar solutions. Our results show that all models in this study are capable of producing high-quality forecasts given a high-quality analysis. But at the same time we find a large variety in model biases, both in terms of temperature errors and precipitation. We are able to identify models whose forecasts are more similar to each other than they are to those of other systems, due to the use of similar model physics packages. However, in terms of multi-model ensemble spread, our results also demonstrate that forecast sensitivities to different model formulations skill are substantial. We therefore believe that the diversity in model design that stems from parallel development efforts at global modeling centers around the world remains valuable for future progress in the numerical weather prediction community.

In both 5- and 15-min data, FI was significantly higher in malignant tissues than in benign tissues. The diagnostic accuracy was similar at 5 and 15 min. Therefore, the 5-min FI was enough applying in the further analyses.

Background and purpose:Oscillatory shear stress could not be directly measured in consideration of direction, although cerebrospinal fluid has repetitive movements synchronized with heartbeat. Our aim was to evaluate the important of oscillatory shear stress in the cerebral aqueduct and foramen magnum in idiopathic normal pressure hydrocephalus by comparing it with wall shear stress and the oscillatory shear index in patients with idiopathic normal pressure hydrocephalus., Materials and methods:By means of the 4D flow application, oscillatory shear stress, wall shear stress, and the oscillatory shear index were measured in 41 patients with idiopathic normal pressure hydrocephalus, 23 with co-occurrence of idiopathic normal pressure hydrocephalus and Alzheimer-type dementia, and 9 age-matched controls. These shear stress parameters at the cerebral aqueduct were compared with apertures and stroke volumes at the foramen of Magendie and cerebral aqueduct., Results:Two wall shear stress magnitude peaks during a heartbeat were changed to periodic oscillation by converting oscillatory shear stress. The mean oscillatory shear stress amplitude and time-averaged wall shear stress values at the dorsal and ventral regions of the cerebral aqueduct in the idiopathic normal pressure hydrocephalus groups were significantly higher than those in controls. Furthermore, those at the ventral region of the cerebral aqueduct in the idiopathic normal pressure hydrocephalus group were also significantly higher than those in the co-occurrence of idiopathic normal pressure hydrocephalus with Alzheimer-type dementia group. The oscillatory shear stress amplitude at the dorsal region of the cerebral aqueduct was significantly associated with foramen of Magendie diameters, whereas it was strongly associated with the stroke volume at the upper end of the cerebral aqueduct rather than that at the foramen of Magendie., Conclusions:Oscillatory shear stress, which reflects wall shear stress vector changes better than the conventional wall shear stress magnitude and the oscillatory shear index, can be directly measured on 4D flow MR imaging. Oscillatory shear stress at the cerebral aqueduct was considerably higher in patients with idiopathic normal pressure hydrocephalus.

Scientific analysis of plant growth helps in improving the efficiency of cultivation practices through optimization of their environmental conditions. The ultimate aim of this research was to derive an optimal policy for better growth of date palms by considering its dynamical response to environmental parameters such as solar radiation, soil moisture, and temperature. Field experiments were conducted at an irrigation scheme located in the Jordan Rift Valley. A drip irrigation system is installed to water ten trees of date palms either with fresh or saline water depending on the soil matric potential. The circumference of the trunk of a tree was measured using a dendrometer at 30 mins interval and recorded in a data logger. Environmental parameters including the soil matric potential, solar radiation, and soil temperature were also logged every 30 mins. This study focused on determining a nonlinear model representing the growth dynamics of the date palm tree responding to those environmental parameters. The linear regression was applied to estimate the kernel coefficients of discrete Volterra series modeling the time series. The non-linearity of the model is expected to explain diurnal shrinkage and swelling of the tree trunk under different environmental conditions.

The use of cisplatin may cause nephrotoxicity in patients. Hydration solutions supplemented with magnesium could reduce cisplatin-induced nephrotoxicity. In this study, we evaluated the preventive effect of magnesium pre-loading on cisplatin-induced nephrotoxicity in patients with esophageal cancer. We retrospectively evaluated the prevalence of, and risk factors for, nephrotoxicity in 160 patients with esophageal cancer treated with the 5-fluorouracil/cisplatin regimen from 2014 to 2016 with and without magnesium supplementation. Significant differences were observed between the magnesium and non-magnesium groups in terms of frequency of estimated creatinine clearance of grade 2 or higher that was at 4% (n = 3) and 13% (n = 10) (

Funding Acknowledgements Type of funding sources: None. Background At onset of acute heart failure (AHF), various clinical fundamental parameters including vital sign, laboratory data, or initial treatment were investigated, and we can roughly estimate the prognosis. However, machine learning method for prediction of the prognosis was not studied. Purpose To elucidate prognostic value of machine learning for AHF comparing conventional statistical model. Methods We enrolled consecutive 300 patients with AHF (79.5 ± 12.1 years, 158 Males). Patients with acute coronary syndrome, mechanical circulatory support cases, and cardio-pulmonary arrest cases were excluded. The patients were randomly divided into 80% (240 cases) and 20% (60 cases), and the former was used as train data, and the latter as validation data. Objective variable was set as cardiac death in one year. First, logistic regression analysis with Akaike’s information criterion (AIC) was performed, and extracted predictive parameters. The predictive model for the cardiac prognosis was constructed by cut-off value of ROC curve analysis of propensity score was calculated. Next, machine learning (random forest method and deep learning) to build predictive model was performed with the predictors. Finally, accuracy of each predictive model was compared. Results Thirty cases showed cardiac death in one year. Logistic regression with AIC extracted 8 predictors, and the cut off-value of propensity score with the 6 parameters was 0.110. The accuracy was 0.714 and area under ROC (AUROC) was 0.836. Conversely, random forest method demonstrated the accuracy as 0.927, AUROC 0.860. On deep learning, the accuracy was 0.937 and AUROC 0.901. The top 4 high feature importance of random forest were Cl/red blood cell count/pH/Anion Gap. However, accuracy of those predictors was lower than that of machine learning. Conclusion Machine learning was a powerful tool to predict cardiac prognosis of AHF, comparing with conventional statistical model. Abstract Figure. Statistical model

Non-invasive range measurement of particle beams is important to prevent deviation of the irradiated area in particle therapy. In this study, we made an experiment of imaging carbon-ion beams by setting the projection ranges into an acrylic target from 4.29 cm to 7.65 cm by a pitch of 0.21 cm. Secondary electron bremsstrahlung (SEB) generated on the beam trajectories were detected for the imaging by use of a pinhole camera system consisting of a pinhole collimator and a position-sensitive cadmium-telluride semiconductor detector (CdTe imager). Beam images were acquired, and their profiles along the longitudinal axis were numerically analyzed, to obtain a suitable parameter that was strongly correlated with the set range. Then we could propose a scheme to measure the particle beam range in the target during irradiation by use of the CdTe imager. As a result, the range shift can be measured in the case of the acrylic target with an accuracy of 0.1 cm.

In proton therapy, range verification is important to monitor the location of the Bragg peak within the patient’s body. Secondary-electron-bremsstrahlung (SEB) imaging is a promising method of proton range verification. Unfortunately, the SEB images measured by the dedicated gamma camera contain neutron-induced backgrounds as well as the SEB counts. To improve the accuracy of range verification in SEB imaging, we propose to apply a time-of-flight (TOF) method to reduce neutron background in measured images of SEB. The SEB and neutron generation and transport were calculated by a Monte Carlo simulation. Proton pencil beams were irradiated to a water phantom and then the time spectra and profiles of SEB and neutron were obtained by scoring on a detector. The total count of neutrons was approximately 10 times higher than that of SEB and increased with higher proton energy. The TOF method, selecting a time window for the TOF spectrum of SEB, reduced the neutron background by more than 97%. We concluded that the TOF methods can dramatically reduce neutron background and improve the accuracy of proton range verification in SEB imaging.

While single-junction solar cells may be capable of attaining AM1.5 efficiencies of up to 29%, multi-junction (MJ, Tandem) III–V compound solar cells appear capable of realistic efficiencies of up to 50% and are promising for space and terrestrial applications. In fact, the InGaP/GaAs/Ge 3-junction solar cells have been widely used for space applications since 1997. In addition, industrialization of concentrator solar cell modules using III–V compound MJ solar cells has been announced by some companies. This chapter presents principles and key issues for realizing high-efficiency MJ solar cells, issues relating to development and manufacturing, and applications for space and terrestrial uses.

The purpose of this study is to evaluate the effect of a lead radiation shield on the ability of a beam imaging device consist of an imaging plate (IP) and a collimator by Monte Carlo simulations. Simulations were performed using PHITS. A carbon-ion beam was injected to an acrylic target. A tungsten collimator having a pinhole was placed at the distance of 31.2 cm from the beam. A lead radiation shield was placed on the tungsten collimator. An IP was placed under the collimator. Beam images were acquired by recording the position distribution of energy deposition on the IP. We confirmed that therapeutic carbon-ion beam images could be acquired using the imaging device combining the IP and collimator. It was found that removal of the lead shield had no effect on the imaging results.

In nuclear medicine, the development of portable imaging devices that provide high imaging resolution and sensitivity, capable of imaging gamma rays with a wide energy range and multiple radioisotopes tracing capabilities, is so important. These goals have been possible thanks to developing a compact Compton camera, a collimatorless detector coupled to compact silicon photomultiplier(SiPM) array, using scintillator crystal. In this study, the portable segmented GAGG:Ce scintillator-based Compton camera (CC) is optimized with the GATE, a Monte Carlo simulation toolkit based on Geant4, to maximize its performance for a wide range of gamma-ray energy (364–1000 keV). The geometrical parameters are selected as optimization parameters to investigate their effects on CC's performance, including imaging resolution and absolute detection efficiency (DE a ). The geometry parameters of CC include the planner area of scatterer and absorber detectors, their thicknesses, and the distance between them. The results for the energy range of 364–1000 keV show that the most important contributions to the spatial resolution and DE a of the camera are SAD (scatterer to absorber distance) and the scatterer area while changing absorber area (AA ) showed the most negligible impact. In the short SADs, imaging resolution and DE a are significantly affected by the detector's size and thickness. On the other hand, in the long SADs (> 4 cm), both spatial resolution and DE a are significantly affected by the detector's area but less affected by the detector's thickness. Decreasing the scatterer's thickness and the absorber's size or thickness improves imaging resolution without significantly reducing DE a . The simulation study's findings presented here will provide valuable guidelines for researchers choosing a desired CC's design according to particular objectives, manufacturing limitations in scintillator growth, cost, etc.

Background Several patients with persistent atrial fibrillation (per-AF) suffer from recurrence after pulmonary vein isolation (PVI). Various methods to predict the recurrence were tried, but deep learning on 12-leads electrocardiography (ECG) after PVI was not studied. Purpose To elucidate diagnostic performance of deep learning on 12-leads ECG after PVI in patients with per-AF Methods We enrolled consecutive 109 patients with per-AF who underwent PVI (68.8±10.0 years, 83 males) excluding failure cases. We defined recurrence in 3–12 months after PVI. From the ECG just after PVI, five beats of each lead were sampled separately. Deep learning (convolutional neural network on bitmap ECG image) was performed by transfer learning of Inception-Resnet-V2 model. Gradient weighted class activation color mapping (GradCam) was performed to detect convolutional importance in the lead. Results Thirty-six patients showed recurrence in the period. Lead II (accuracy 0.701), aVR (0.690) were the top 2 leads of prediction, which showed larger accuracy than statistical accuracies of Non PV foci = SVC (accuracy = 0.541) and left atrial diameter >50mm (0.596). In lead II, GradCam spotlighted strong convolution of latter half of P wave in recurrent case, and former half of P wave and T wave in no-recurrent case. Conclusions Deep learning on ECG was a powerful tool to predict recurrence of per-AF after PVI. Funding Acknowledgement Type of funding sources: None. Results of deep learningResults of GradCam

To clarify the combined effects of O3 and soil salinity on net photosynthetic rate, stomatal conductance, and radical scavenging system of Bangladeshi spring wheat cv. BAW1059, plants were grown in two soil salinity levels (irrigated with 0 and 150 mM NaCl solutions) and exposed to three O3 concentrations [charcoal-filtered air (CF), 1.0-fold the ambient O3 concentration (1.0×O3), and 1.5-fold the ambient O3 concentration (1.5×O3)]. The soil salinity mitigated adverse effects of O3 on net photosynthesis in the 7th and flag leaves. The soil salinity did not induce stomatal closure, indicating no limitation of stomatal O3 uptake. The activities of ascorbate peroxidase and catalase were stimulated by the soil salinity in the 7th and flag leaves, especially under 1.5×O3 concentration. In the flag leaf, the soil salinity induced a significant increase in dehydroascorbate reductase activity and ascorbate concentration. These results suggest that the soil salinity activated detoxification capacity related to mitigation of O3 damage on net photosynthesis in both 7th and flag leaves, while the activated enzymes and antioxidants were different between the leaves.

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We hypothesized that suppression of peripheral circulation

Hematological toxicities induced by pemetrexed plus platinum therapy remain a critical issue in clinical practice. We hypothesized that inhibition of the renin-angiotensin system (RAS) can ameliorate pemetrexed-induced hematological toxicities through drug-drug interactions involving organic anion transporters. Thus, this study aimed to clarify whether RAS inhibitors (RASIs) could prevent pemetrexed plus platinum-induced hematological toxicities. We retrospectively analyzed data from 305 consecutive patients with non-small cell lung cancer or malignant pleural mesothelioma who received their first cycle of a pemetrexed plus platinum regimen and were treated with or without RASIs. The primary endpoint was the incidence of severe myelosuppression after the first cycle. Propensity score (PS)-matched, PS-adjusted, and inverse probability of treatment weighting (IPTW) analyses were used. The number of patients with grade ≥3 hematological toxicities was 27 (8.9%). PS-matched analyses revealed that the concomitant use of RASIs was slightly associated with a lower risk of grade ≥3 hematological toxicities (odds ratio [OR], 0.68; 95% confidence interval [CI], 0.20-2.32