PND-Net: Physics based Non-local Dual-domain Network for Metal Artifact Reduction

Metal artifacts caused by the presence of metallic implants tremendously degrade the reconstructed computed tomography (CT) image quality, affecting clinical diagnosis or reducing the accuracy of organ delineation and dose calculation in radiotherapy. Recently, deep learning methods in sinogram and image domains have been rapidly applied on metal artifact reduction (MAR) task. The supervised dual-domain methods perform well on synthesized data, while unsupervised methods with unpaired data are more generalized on clinical data. However, most existing methods intend to restore the corrupted sinogram within metal trace, which essentially remove beam hardening artifacts but ignore other components of metal artifacts, such as scatter, non-linear partial volume effect and noise. In this paper, we mathematically derive a physical property of metal artifacts which is verified via Monte Carlo (MC) simulation and propose a novel physics based non-local dual-domain network (PND-Net) for MAR in CT imaging. Specifically, we design a novel non-local sinogram decomposition network (NSD-Net) to acquire the weighted artifact component, and an image restoration network (IR-Net) is proposed to reduce the residual and secondary artifacts in the image domain. To facilitate the generalization and robustness of our method on clinical CT images, we employ a trainable fusion network (F-Net) in the artifact synthesis path to achieve unpaired learning. Furthermore, we design an internal consistency loss to ensure the integrity of anatomical structures in the image domain, and introduce the linear interpolation sinogram as prior knowledge to guide sinogram decomposition. Extensive experiments on simulation and clinical data demonstrate that our method outperforms the state-of-the-art MAR methods.Comment: 19 pages, 8 figure

Advantages of GaN Based Light-Emitting Diodes with a P-InGaN Hole Reservoir Layer

A p-type InGaN hole reservoir layer (HRL) was designed and incorporated in GaN based light-emitting diodes (LEDs) to enhance hole injection efficiency and alleviate efficiency droop. The fabricated LEDs with p-type HRL exhibited higher light output power, smaller emission energy shift and broadening as compared to its counterpart. Based on electrical and optical characteristics analysis and numerical simulation, these improvements are mainly attributed to the alleviated band bending in the last couple of quantum well and electron blocking layer, and thus better hole injection efficiency. Meanwhile, the efficiency droop can be effectively mitigated when the p-InGaN HRL was used

Understanding processes that control dust spatial distributions with global climate models and satellite observations

Dust aerosol is important in modulating the climate system at local and global scales, yet its spatiotemporal distributions simulated by global climate models (GCMs) are highly uncertain. In this study, we evaluate the spatiotemporal variations of dust extinction profiles and dust optical depth (DOD) simulated by the Community Earth System Model version 1 (CESM1) and version 2 (CESM2), the Energy Exascale Earth System Model version 1 (E3SMv1), and the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) against satellite retrievals from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer (MODIS), and Multi-angle Imaging SpectroRadiometer (MISR). We find that CESM1, CESM2, and E3SMv1 underestimate dust transport to remote regions. E3SMv1 performs better than CESM1 and CESM2 in simulating dust transport and the northern hemispheric DOD due to its higher mass fraction of fine dust. CESM2 performs the worst in the Northern Hemisphere due to its lower dust emission than in the other two models but has a better dust simulation over the Southern Ocean due to the overestimation of dust emission in the Southern Hemisphere. DOD from MERRA-2 agrees well with CALIOP DOD in remote regions due to its higher mass fraction of fine dust and the assimilation of aerosol optical depth. The large disagreements in the dust extinction profiles and DOD among CALIOP, MODIS, and MISR retrievals make the model evaluation of dust spatial distributions challenging. Our study indicates the importance of representing dust emission, dry/wet deposition, and size distribution in GCMs in correctly simulating dust spatiotemporal distributions. </div

Syntaxin of plants71 plays essential roles in plant development and stress response via regulating pH homeostasis

SYP71, a plant-specific Qc-SNARE with multiple subcellular localization, is essential for symbiotic nitrogen fixation in nodules in Lotus, and is implicated in plant resistance to pathogenesis in rice, wheat and soybean. Arabidopsis SYP71 is proposed to participate in multiple membrane fusion steps during secretion. To date, the molecular mechanism underlying SYP71 regulation on plant development remains elusive. In this study, we clarified that AtSYP71 is essential for plant development and stress response, using techniques of cell biology, molecular biology, biochemistry, genetics, and transcriptomics. AtSYP71-knockout mutant atsyp71-1 was lethal at early development stage due to the failure of root elongation and albinism of the leaves. AtSYP71-knockdown mutants, atsyp71-2 and atsyp71-3, had short roots, delayed early development, and altered stress response. The cell wall structure and components changed significantly in atsyp71-2 due to disrupted cell wall biosynthesis and dynamics. Reactive oxygen species homeostasis and pH homeostasis were also collapsed in atsyp71-2. All these defects were likely resulted from blocked secretion pathway in the mutants. Strikingly, change of pH value significantly affected ROS homeostasis in atsyp71-2, suggesting interconnection between ROS and pH homeostasis. Furthermore, we identified AtSYP71 partners and propose that AtSYP71 forms distinct SNARE complexes to mediate multiple membrane fusion steps in secretory pathway. Our findings suggest that AtSYP71 plays an essential role in plant development and stress response via regulating pH homeostasis through secretory pathway

Biochar modulates heavy metal toxicity and improves microbial carbon use efficiency in soil

Soil organic carbon is essential to improve soil fertility and ecosystem functioning. Soil microorganisms contribute significantly to the carbon transformation and immobilisation processes. However, microorganisms are sensitive to environmental stresses such as heavy metals. Applying amendments, such as biochar, to contaminated soils can alleviate the metal toxicity and add carbon inputs. In this study, Cd and Pb spiked soils treated with macadamia nutshell biochar (5% w/w) were monitored during a 49days incubation period. Microbial phospholipid fatty acids (PLFAs) were extracted and analysed as biomarkers in order to identify the microbial community composition. Soil properties, metal bioavailability, microbial respiration, and microbial biomass carbon were measured after the incubation period. Microbial carbon use efficiency (CUE) was calculated from the ratio of carbon incorporated into microbial biomass to the carbon mineralised. Total PLFA concentration decreased to a greater extent in metal contaminated soils than uncontaminated soils. Microbial CUE also decreased due to metal toxicity. However, biochar addition alleviated the metal toxicity, and increased total PLFA concentration. Both microbial respiration and biomass carbon increased due to biochar application, and CUE was significantly (p<0.01) higher in biochar treated soils than untreated soils. Heavy metals reduced the microbial carbon sequestration in contaminated soils by negatively influencing the CUE. The improvement of CUE through biochar addition in the contaminated soils could be attributed to the decrease in metal bioavailability, thereby mitigating the biotoxicity to soil microorganisms

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data