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Modifications to the compositional, topographical and morphological aspects of bone implants can lead to improved osseointegration, thus increasing the success of bone implant procedures. This study investigates the creation of dual-scale topography on Ti-5Al-5Mo-5V-3Cr (Ti5553), an alloy not presently used in the biomedical field, and compares it to Ti-6Al-4V (Ti64), the most used Ti alloy for bone implants. Dual-scale surface topography was obtained by combining selective laser melting (SLM) and electrochemical anodization, which resulted in micro- and nanoscale surface features, respectively. Ti5553 and Ti64 samples were manufactured by SLM and showed comparable surface topography. Subsequent electrochemical anodization succeeded in forming titania nanotubes (TNTs) on both alloys, with larger nanotubes obtained with Ti5553 at all investigated anodization voltages. At an anodization voltage of 40 V, a minimum time of 20 min was necessary to have nanotube formation on the surface of either alloy, while only nanopores were evident for shorter times. Seeded Saos-2 cells showed ideal interactions with surface-modified structures, with filopodia extending to both surface microparticles characteristic of SLM and to the interior of TNTs. Attractiveness of Ti5553 lies in its lower elastic modulus (E = 72 GPa) compared to Ti64, which should mitigate stress-shielding phenomena in vivo. This, combined with the analogous results obtained in terms of dual-scale surface topography and cell-substrate interaction, could indicate Ti5553 as a promising alternative to the widely-employed Ti64 for bone implant device manufacturing. © 2020 IOP Publishing Ltd.To improve respiratory-gated radiotherapy accuracy, we developed a machine learning approach for markerless tumor tracking and evaluated it using lung cancer patient data. Digitally reconstructed radiography (DRR) datasets were generated using planning 4DCT data. Tumor positions were selected on respective DRR images to place the GTV center of gravity in the center of each DRR. DRR subimages around the tumor regions were cropped so that the subimage size was defined by tumor size. Training data were then classified into two groups positive (including tumor) and negative (not including tumor) samples. Machine learning parameters were optimized by the extremely randomized tree method. For the tracking stage, a machine learning algorithm was generated to provide a tumor likelihood map using fluoroscopic images. find more Prior probability tumor positions were also calculated using the previous two frames. Tumor position was then estimated by calculating maximum probability on the tumor likelihood map and prior probability tumor positions. We acquired treatment planning 4DCT images in eight patients. Digital fluoroscopic imaging systems on either side of the vertical irradiation port allowed fluoroscopic image acquisition during treatment delivery. Each fluoroscopic dataset was acquired at 15 frames per second. We evaluated the tracking accuracy and computation times. Tracking positional accuracy averaged over all patients was 1.03 ± 0.34 mm (mean ± standard deviation, Euclidean distance) and 1.76 ± 0.71 mm (95th percentile). Computation time was 28.66 ± 1.89 ms/frame averaged over all frames. Our markerless algorithm successfully estimated tumor position in real time. © 2020 Institute of Physics and Engineering in Medicine.OBJECTIVE Recent works have shown that the flexible information processing is closely related to the reconfiguration of human brain networks underlying brain functions. However, the role of network switching for consciousness is poorly explored and whether such transition can indicate the behavioral performance of patients with disorder of consciousness (DOC) remains unknown. Here, we investigate the relationship between the switching of brain networks (states) over time and the consciousness levels. APPROACH By applying multilayer network methods, we calculated time-resolved functional connectivity from source-level EEG data in different frequency bands. At various time scales, we explored how the human brain change its community structure and traverses across defined network states (integrated and segregated states) in the subjects with different consciousness levels. MAIN RESULTS The network switching in human brain is decreased with increasing time scale opposite to that in random system. Transitions of community assignment (denoted by flexibility) are negatively correlated with the consciousness levels (particularly in alpha band) on short time scales. While on long time scales, an opposite trend is found. Compared to the healthy controls, the patients show a new balance between dynamic segregation and integration, with decreased proportion and mean duration of segregated state (contrary to those of integrated state) at small scales. SIGNIFICANCE These findings may contribute to the development of EEG-based network analysis and shed new light on the pathological mechanisms of neurological disorder like DOC. © 2020 IOP Publishing Ltd.A multi-functional Gd5Si1.3Ge2.7 thin film deposited by pulsed laser ablation in the form of an ensemble of nanoparticles was studied for 18 thermal cycles via electron transport measurements together with structural and magnetic characterization. A general negative thermal dependency of the resistivity (ρ) is observed, which contrasts with the metallic-like behavior observed in bulk Gd5SixGe4-x compounds. This general trend is interrupted by a two-step, positive-slope transition in ρ(T) throughout the [150,250]K interval, corresponding to two consecutive magnetic transitions a fully coupled magnetostructural followed by a purely magnetic order on heating. An avalanche-like behavior is unveiled by the ∂ρ/∂T(T) curves and is explained based on the severe strains induced cyclically by the magnetostructural transition, leading to a cycling evolution of the transition onset temperature (∂T''h/∂n ~ 1.6 K/cycle , n being the number of cycles). Such behavior is equivalent to the action of a pressure of 0.56 kBar being formed and building up at every thermal cycle due to the large volume induced change across the magnetostructural transition. Moreover the thermal hysteresis, detected in both ρ and magnetization versus temperature curves, evolves significantly along the cycles, decreasing as n increases. This picture corroborates the thermal activation energy enhancement - estimated via an exponential fitting of the ∂ρ/∂T(T) in the avalanche regime. This work demonstrates the importance of using a short-range order technique, to probe both pure magnetic and magnetostructural transitions and their evolution with thermal cycles. © 2020 IOP Publishing Ltd.
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