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lities in young children.Efficient, reliable and reproducible target volume delineation is a key step in the effective planning of breast radiotherapy. However, post-operative breast target delineation is challenging as the contrast between the tumor bed volume (TBV) and normal breast tissue is relatively low in CT images. In this study, we propose to mimic the marker-guidance procedure in manual target delineation. We developed a saliency-based deep learning segmentation (SDL-Seg) algorithm for accurate TBV segmentation in post-operative breast irradiation. The SDL-Seg algorithm incorporates saliency information in the form of markers' location cues into a U-Net model. The design forces the model to encode the location-related features, which underscores regions with high saliency levels and suppresses low saliency regions. The saliency maps were generated by identifying markers on CT images. Markers' location were then converted to probability maps using a distance transformation coupled with a Gaussian filter. Subsequently, the CTch as GammaPod based PBI.In comparison to synthetic hydrogels where ligand density and stiffness can be independently tuned, cell responses are expected to deviate on native biopolymer networks where ligand density and stiffness are coupled. Here we probe the tensional homeostasis of fibroblasts on methacrylated gelatin (GelMA) gels, which are widely used in tissue engineering applications. P22077 in vivo On 5%-15% GelMA gels which are very soft (10-100's of Pa's in stiffness), fibroblasts were found to spread extensively and assemble prominent stress fibers and focal adhesions. Probing of contractile mechanics using trypsin-induced detachment revealed adhesive drag, but not contractility, was sensitive to GelMA concentration. Contractility-altering drugs blebbistatin and nocodazole, which exhibited opposite effects on focal adhesion size, both led to reduction in adhesive drag and cell rounding. However, cell motility was impacted only in nocodazole-treated cells. Collectively, our experiments suggest that on soft GelMA gels, contractility-independent adhesion clustering mediated by high ligand density can drive cell spreading and motility.An innovative surface-enhanced Raman spectroscopy and lateral flow assay (SERS-LFA) biosensor combined with aptamer recognition had been developed for the convenient, rapid, sensitive and accurate detection of thrombin and platelet-derived growth factor-BB (PDGF-BB) associated with prostate cancer simultaneously. During the biosensor operation, thrombin and PDGF-BB in the sample were recognized and combined by thiol-modified aptamers immobilized on Au-Ag hollow nanoparticles (Au-Ag HNPs) surface and biotinylated aptamers immobilized on the test lines of the biosensor. Thus, thrombin and PDGF-BB were simultaneously captured between detection aptamers and capture aptamers in a sandwich structure. Finite difference time domain simulation confirmed that 'hot spots' appeared at the gaps of Au-Ag HNPs dimer in the enhanced electromagnetic field compared to that of a single Au-Ag HNP, indicating that the aggregated Au-Ag HNPs owned a good SERS signal amplification effect. The detection limits of thrombin and PDGF-BB in human plasma were as low as 4.837 pg ml-1and 3.802 pg ml-1, respectively. Moreover, the accuracy of the biosensor which was applied to detect thrombin and PDGF-BB in prostate cancer plasma had been verified. This designed biosensor had broad application prospects in the clinical diagnosis of prostate cancer.Several transition metal dichalcogenides (TMDs) can be exfoliated to produce nearly two-dimensional (2D) semiconductor layers supporting robust excitons with non-hydrogenic Rydberg series of states. Black phosphorus (BP) can also be layered to create a nearly 2D material with interesting properties including its pronounced in-plane anisotropy that influences, in particular, exciton states making them different from those in other 2D semiconductors. We apply the Rayleigh-Ritz variational method to evaluate the energies and approximate the wavefunctions of the ground and lowest excited states of the exciton in a 2D semiconductor with anisotropic effective masses of electrons and holes. The electron-hole interaction is described by the Rytova-Keldysh potential, which is considered beyond the standard zero-thickness approximation. The exciton binding energies calculated for BP and TMD (molybdenum disulphade and tungsten disulphade) monolayers are compared with previously published data.A kind of biomass-based hierarchical porous carbons (HPCs) is easily synthesized by the direct pyrolysis of castor oil/MgO solids, in which the solids can be prepared by mixing appropriate amounts of MgO into castor oil. The morphology, microstructure, phase structure, textural property, surface element composition and thermal stability properties are studied for the achieved HPCs. It is demonstrated that the HPCs belong to a type of high-graphitization, graphene-like and foamed carbon materials with high specific surface area and wide pore size distribution. The HPC obtained at 900 °C (HPC-900) displays the highest specific surface area of 1013.17 m2g and more reasonable pore size distribution. Without the need of conductive agents, the HPC-900 exhibits a maximum capacitance (340 F g-1at 0.5 A g-1), excellent rate performance (70.1% of capacitance retention at high current density of 10 A g-1) and a remarkable long-term cycling stability (about 99% capacitance retention after 9000 cycles) in the aqueous electrolyte of 6 M KOH. Meanwhile, assembled as-prepared sample into symmetrical supercapacitor, the HPC-900 provides a high energy and power density, with 8.6 Wh kg-1and 426.7 W kg-1in 1 M Na2SO4, respectively. The HPCs prepared based on castor oil show high potential for the electrode materials of supercapacitors.Magnetic particle images are currently most often reconstructed using classical Tikhonov regularization (i.e. anℓ2regularization term) combined with Kaczmarz method. Quality enhancing choices like sparsity promotingℓ1-regularization or TV regularization lead to problems that cannot be solved by standard Kaczmarz method. We propose to use stochastic primal-dual hybrid gradient method to gain more flexibility concerning the choice of data fitting term and regularization, respectively, and still obtain an algorithm which is at least as fast as Kaczmarz method. The proposed algorithm performs comparably to the current state-of-the-art method in terms of run time. The quality of reconstructions can be significantly improved as different regularization terms can be easily integrated. Moreover, in order to achieve further speed up of the method, we propose two new step size rules which lead to fast convergence and make the algorithm very easy to handle. We improve the performance of the algorithm further by applying a data-driven splitting scheme leading to a significant speed-up during the first iterations.
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