bluuebunny/crossref_metadata_2025_split huggingface dataset:
FeCo nanoparticles as antibacterial agents with improved response in magnetic field: an insight into the associated toxicity mechanism
Authors: Moditma  , Singh Priyanka , Sharma Raksha , Verma Anita K , Annapoorni S 
Published: April 2021

Abstract
The emergence of multi-drug resistant bacterial infections has resulted in increased interest in the development of alternative systems which can sensitize bacteria to overcome resistance. In an attempt to contribute to the existing literature of potential antibacterial agents, we present here, a first report of the antibacterial potential of FeCo nanoparticles, both as stand-alone devices and in presence of magnetic field, against the bacterial strains of S. aureus and E. coli. A relatively simple polyol process was employed for nanoparticle synthesis. Formation of FeCo alloy in the desired BCC phase was confirmed by x-ray diffraction with a high saturation magnetization (M
s ∼ 180 Am2kg−1). Uniformly sized spherical structures with sharp edges were obtained. Solution stability was confirmed by the zeta potential value of −27.8 mV. Dose dependent bacterial growth inhibition was observed, the corresponding linear correlation coefficients being, R
2 = 0.74 for S. aureus and R
2 = 0.76 for E. coli. Minimum inhibitory concentration was accordingly ascertained to be >1024 μg ml−1 for both. Bacterial growth curves have been examined upon concomitant application of external magnetic field of varying intensities and revealed considerable enhancement in the antibacterial response upto 64% in a field of 100 mT. An effort has been made to understand the bacterial inhibitory mechanism by relating with the chemical and physical properties of the nanoparticles. The ease of field assisted targeting and retrieval of these highly magnetic, antibacterial nano-devices, with considerably improved response with magnetic fields, make them promising for several medical and environment remediation technologies.
Ultra-high-frequency (UHF) surface-acoustic-wave (SAW) microfluidics and biosensors
Authors: Agostini Matteo , Cecchini Marco 
Published: April 2021

Abstract
Surface acoustic waves (SAWs) have the potential to become the basis for a wide gamut of lab-on-a-chips (LoCs). These mechanical waves are among the most promising physics that can be exploited for fulfilling all the requirements of commercially appealing devices that aim to replace–or help–laboratory facilities. These requirements are low processing cost of the devices, scalable production, controllable physics, large flexibility of tasks to perform, easy device miniaturization. To date, SAWs are among the small set of technologies able to both manipulate and analyze biological liquids with high performance. Therefore, they address the main needs of microfluidics and biosensing. To this purpose, the use of high-frequency SAWs is key. In the ultra-high-frequency regime (UHF, 300 MHz—3 GHz) SAWs exhibit large sensitivities to molecule adsorption and unparalleled fluid manipulation capabilities, together with overall device miniaturization. The UHF-SAW technology is expected to be the realm for the development of complex, reliable, fully automated, high-performance LoCs. In this review, we present the most recent works on UHF-SAWs for microfluidics and biosensing, with a particular focus on the LoC application. We derive the relevant scale laws, useful formulas, fabrication guidelines, current limitations of the technology, and future developments.
Boosting photoresposive ability of WO3–Bi2O3 nanocomposite rods via annealing-induced intrinsic precipitation of nanosized Bi particles
Authors: Liang Yuan-Chang , Yang Ho-Chung 
Published: April 2021

Abstract
In this study, Bi-particle-functionalized tungsten trioxide–bismuth oxide (WO3–Bi2O3) composite nanorods were prepared by integrating sputtering and hydrothermal syntheses with an appropriate postannealing procedure to induce Bi particle precipitation. Unlike other routes in which metal particle decoration is achieved externally, in this study, photoresponsive one-dimensional WO3–Bi2O3 composite nanorods were decorated with Bi particles by using the internal precipitation method. Structural analysis revealed that the Bi-metal-particle-functionalized WO3–Bi2O3 composite nanorods with particle size ranging from 5 to 10 nm were formed through hydrogen gas annealing at an optimal annealing temperature of 350 °C. Compared with the pristine WO3 nanorod template, the Bi–WO3–Bi2O3 composite nanorods exhibited higher photoresponsive performance, substantial photogenerated charge transfer ability, and efficient separation of photogenerated electron–hole pairs. The study results indicated that the Bi–WO3–Bi2O3 composite nanorods had superior decontamination ability and excellent stability toward RhB dye as compared with pristine WO3. Moreover, the photogenerated charge separation and migration efficiencies of the WO3–Bi2O3 nanorods could be tuned through appropriate reduction of the surface oxide layer; this is a promising approach to designing WO3–Bi2O3 nanorods with high photoactive performance.
Mass segmentation for whole mammograms via attentive multi-task learning framework
Authors: Hou Xuan , Bai Yunpeng , Xie Yefan , Li Ying 
Published: April 2021

Abstract
Mass segmentation in the mammogram is a necessary and challenging task in the computer-aided diagnosis of breast cancer. Most of the existing methods tend to segment the mass by manually or automatically extracting mass-centered image patches. However, manual patch extraction is time-consuming, wheras automatic patch extraction can introduce errors that will affect the performance of subsequent segmentation. In order to improve the efficiency of mass segmentation and reduce segmentation errors, we proposed a novel mass segmentation method based on an attentive multi-task learning network (MTLNet), which is an end-to-end model to accurately segment mass in the whole mammogram directly, without the need for extraction in advance with the center of mass image patch. In MTLNet, we applied group convolution to the feature extraction network, which not only reduced the redundancy of the network but also improved the capacity of feature learning. Secondly, an attention mechanism is added to the backbone to highlight the feature channels that contain rich information. Eventually, the multi-task learning framework is employed in the model, which reduces the risk of model overfitting and enables the model not only to segment the mass but also to classify and locate the mass. We used five-fold cross validation to evaluate the performance of the proposed method under detection and segmentation tasks respectively on the two public mammographic datasets INbreast and CBIS-DDSM, and our method achieved a Dice index of 0.826 on INbreast and 0.863 on CBIS-DDSM.
PET-ABC: fully Bayesian likelihood-free inference for kinetic models
Authors: Fan Yanan , Emvalomenos Gaelle , Grazian Clara , Meikle Steven R 
Published: April 2021

Abstract

Aims. We describe an intuitive, easy to use method called PET-ABC that enables full Bayesian statistical inference from single subject dynamic PET data. The performance of PET-ABC was compared with weighted non-linear least squares (WNLS) in terms of reliability of kinetic parameter estimation and statistical power for model selection. Methods. Dynamic PET data based on 1-tissue and 2-tissue compartmental models were simulated with 2 noise models and 3 noise levels. PET-ABC was used to evaluate the reliability of parameter estimates under each condition. It was also used to perform model selection for a simulated noisy dataset composed of a mixture of 1- and 2-tissue compartment kinetics. Finally, PET-ABC was used to analyze a non-steady state dynamic [11C] raclopride study performed on a fully conscious rat administered either 2 mg.kg−1 amphetamine or saline 20 min after tracer injection. Results. PET-ABC yielded posterior point estimates for model parameters with smaller variance than WNLS, as well as probability density functions indicating confidence intervals for those estimates. It successfully identified the superiority of a 2-tissue compartment model to fit the simulated mixed model data. For the drug challenge study, the post observation probability of striatal displacement of the PET signal was 0.9 for amphetamine and approximately 0 for saline, indicating a high probability of amphetamine-induced endogenous dopamine release in the striatum. PET-ABC also demonstrated superior statistical power to WNLS (0.87 versus 0.09) for selecting the correct model in a simulated ligand displacement study. Conclusions. PET-ABC is a simple and intuitive method that provides complete Bayesian statistical analysis of single subject dynamic PET data, including the extent to which model parameter estimates and model choice are supported by the data. Software for PET-ABC is freely available as part of the PETabc package https://github.com/cgrazian/PETabc.
Axial-SpineGAN: simultaneous segmentation and diagnosis of multiple spinal structures on axial magnetic resonance imaging images
Authors: Gong Hao , Liu Jianhua , Li Shuo , Chen Bo 
Published: April 2021

Abstract
Providing a simultaneous segmentation and diagnosis of the spinal structures on axial magnetic resonance imaging (MRI) images has significant value for subsequent pathological analyses and clinical treatments. However, this task remains challenging, owing to the significant structural diversity, subtle differences between normal and abnormal structures, implicit borders, and insufficient training data. In this study, we propose an innovative network framework called ‘Axial-SpineGAN’ comprising a generator, discriminator, and diagnostor, aiming to address the above challenges, and to achieve simultaneous segmentation and disease diagnosis for discs, neural foramens, thecal sacs, and posterior arches on axial MRI images. The generator employs an enhancing feature fusion module to generate discriminative features, i.e. to address the challenges regarding the significant structural diversity and subtle differences between normal and abnormal structures. An enhancing border alignment module is employed to obtain an accurate pixel classification of the implicit borders. The discriminator employs an adversarial learning module to effectively strengthen the higher-order spatial consistency, and to avoid overfitting owing to insufficient training data. The diagnostor employs an automated diagnosis module to provide automated recognition of spinal diseases. Extensive experiments demonstrate that these modules have positive effects on improving the segmentation and diagnosis accuracies. Additionally, the results indicate that Axial-SpineGAN has the highest Dice similarity coefficient (94.9% ± 1.8%) in terms of the segmentation accuracy and highest accuracy rate (93.9% ± 2.6%) in terms of the diagnosis accuracy, thereby outperforming existing state-of-the-art methods. Therefore, our proposed Axial-SpineGAN is effective and potential as a clinical tool for providing an automated segmentation and disease diagnosis for multiple spinal structures on MRI images.
Validation of a 4D-MRI guided liver stereotactic body radiation therapy strategy for implementation on the MR-linac
Authors: van de Lindt Tessa N , Fast Martin F , van den Wollenberg Wouter , Kaas Jochem , Betgen Anja , Nowee Marlies E , Jansen Edwin PM , Schneider Christoph , van der Heide Uulke A , Sonke Jan-Jakob 
Published: April 2021

Abstract

Purpose. Accurate tumor localization for image-guided liver stereotactic body radiation therapy (SBRT) is challenging due to respiratory motion and poor tumor visibility on conventional x-ray based images. Novel integrated MRI and radiotherapy systems enable direct in-room tumor visualization, potentially increasing treatment accuracy. As these systems currently do not provide a 4D image-guided radiotherapy strategy, we developed a 4D-MRI guided liver SBRT workflow and validated all steps for implementation on the Unity MR-linac. Materials and Methods. The proposed workflow consists of five steps: (1) acquisition of a daily 4D-MRI scan, (2) 4D-MRI to mid-position planning-CT rigid tumor registration, (3) calculation of daily tumor midP misalignment, (4) plan adaptation using adapt-to-position (ATP) with segment-weights optimization and (5) adapted plan delivery. The workflow was first validated in a motion phantom, performing regular motion at different baselines (±5 to ±10 mm) and patient-derived respiratory signals with varying degrees of irregularity. 4D-MRI derived respiratory signals and 4D-MRI to planning CT registrations were compared to the phantom input, and gamma and dose-area-histogram analyses were performed on the delivered dose distributions on film. Additionally, 4D-MRI to CT registration performance was evaluated in patient images using the full-circle method (transitivity analysis). Plan adaption was further analyzed in-silico by creating adapted treatment plans for 15 patients with oligometastatic liver disease. Results. Phantom trajectories could be reliably extracted from 4D-MRI scans and 4D-MRI to CT registration showed submillimeter accuracy. The DAH-analysis demonstrated excellent coverage of the dose evaluation structures GTV and GTVTD. The median daily rigid 4D-MRI to midP-CT registration precision in patient images was <2 mm. The ATP strategy restored the target dose without increased exposure to the OARs and plan quality was independent from 3D shift distance in the range of 1–26 mm. Conclusions. The proposed 4D-MRI guided strategy showed excellent performance in all workflow tests in preparation of the clinical introduction on the Unity MR-linac.
Disruption prediction and model analysis using LightGBM on J-TEXT and HL-2A
Authors: Zhong Y , Zheng W , Chen Z Y , Xia F , Yu L M , Wu Q Q , Ai X K , Shen C S , Yang Z Y , Yan W , Ding Y H , Liang Y F , Chen Z P , Tong R H , Bai W , Fang J G , Li F 
Published: April 2021

Abstract
Using machine learning (ML) techniques to develop disruption predictors is an effective way to avoid or mitigate the disruption in a large-scale tokamak. The recent ML-based disruption predictors have made great progress regarding accuracy, but most of them have not achieved acceptable cross-machine performance. Before we develop a cross-machine predictor, it is very important to investigate the method of developing a cross-tokamak ML-based disruption prediction model. To ascertain the elements which impact the model’s performance and achieve a deep understanding of the predictor, multiple models are trained using data from two different tokamaks, J-TEXT and HL-2A, based on an implementation of the gradient-boosted decision trees algorithm called LightGBM, which can provide detailed information about the model and input features. The predictor models are not only built and tested for performance, but also analyzed from a feature importance perspective as well as for model performance variation. The relative feature importance ranking of two tokamaks is caused by differences in disruption types between different tokamaks. The result of two models with seven inputs showed that common diagnostics is very important in building a cross-machine predictor. This provided a strategy for selecting diagnostics and shots data for developing cross-machine predictors.
Quantum repeaters in space
Authors: Liorni Carlo , Kampermann Hermann , Bruß Dagmar 
Published: April 2021

Abstract
Long-distance entanglement is a very precious resource, but its distribution is very difficult due to the exponential losses of light in optical fibres. A possible solution consists in the use of quantum repeaters, based on entanglement swapping (ES) or quantum error correction. Alternatively, satellite-based free-space optical links can be exploited, achieving better loss-distance scaling. We propose to combine these two ingredients, quantum repeaters and satellite-based links, into a scheme that allows to achieve entanglement distribution over global distances with a small number of intermediate untrusted nodes. The entanglement sources, placed on satellites, send quantum states encoded in photons towards orbiting quantum repeater stations, where ES is performed. The performance of this repeater chain is assessed in terms of the secret key rate achievable by the BBM92 cryptographic protocol. We perform a comparison with other repeater chain architectures and show that our scheme, even though more technically demanding, is superior in many situations of interest. Finally, we analyse strengths and weaknesses of the proposed scheme and discuss exemplary orbital configurations. The integration of satellite-based links with ground repeater networks can be envisaged to represent the backbone of the future quantum internet.
Collisionless damping of nonplanar dust acoustic waves due to dust charge fluctuation in nonextensive polarized plasma
Authors: Gill Tarsem S , Bansal Sona 
Published: April 2021

Abstract
A rigorous theoretical investigation has been made of the cylindrical/spherical dust acoustic waves in collisionless dusty plasma with variable charges under the influence of polarization force by reductive perturbation method. It is shown that the collisionless damping due to dust charge fluctuation causes the dust acoustic wave propagation to be described by the nonplanar damped Korteweg–de Vries equation. By use of weighted residual method, a progressive wave solution to nonplanar damped KdV equation is presented for the first time. It is shown that dust charge fluctuation produce damping which is responsible for wave attenuation. The solution shows that as one goes towards the axis of the cylinder or centre of the sphere, the wave attenuates faster and the effects are shown in some 2D figures. Within a small amplitude limit, the influence of dust charge fluctuation, polarisation power, nonextensivity and ion temperature on the time evolution of dust acoustic solitons is also demonstrated. In laboratory experiments and space regions, findings obtained here may shed light on the salient characteristics of DA waves.
Simulating intergalactic gas for DESI-like small scale Lymanα forest observations
Authors: Walther Michael , Armengaud Eric , Ravoux Corentin , Palanque-Delabrouille Nathalie , Yèche Christophe , Lukić Zarija 
Published: April 2021

Abstract
Measurements of the Lyα forest based on large numbers of quasar spectra from sky surveys such as SDSS/eBOSS accurately probe the distribution of matter on small scales and thus provide important constraints on several ingredients of the cosmological model. A main summary statistic derived from those measurements is the one-dimensional power spectrum, P1D, of the Lyα absorption. However, model predictions for P1D rely on expensive hydrodynamical simulations of the intergalactic medium, which was the limiting factor in previous analyses. Datasets from upcoming surveys such as DESI will push observational accuracy near the 1%-level and probe even smaller scales. This observational push mandates even more accurate simulations as well as more careful exploration of parameter space. In this work we evaluate the robustness and accuracy of simulations and the statistical framework used to constrain cosmological parameters. We present a comparison between the grid-based simulation code Nyx and SPH-based code Gadget in the context of P1D. In addition, we perform resolution and box-size convergence tests using Nyx code.
We use a Gaussian process emulation scheme to reduce the number of simulations required for exploration of parameter space without sacrificing the model accuracy. We demonstrate the ability to produce unbiased parameter constraints in an end-to-end inference test using mock eBOSS- and DESI-like data, and we advocate for the usage of adaptive sampling schemes as opposed to using a fixed Latin hypercube design.
Numerical analysis of the pump’s spectral linewidth impact on single and multi-wavelength Brillouin fiber lasers
Authors: Silva Luís C B , Castellani Carlos E S 
Published: April 2021

Abstract
Brillouin fiber lasers (BFLs) are a promising optical source technology for industrial and academic purposes that have experienced continuous progress in recent years. Currently, part of this development is focused on delivering laser solutions with low resonator loss, relatively low pump threshold, high efficiency, very small linewidth, and multi-wavelength architecture. In this perspective, this study employs an accurate numerical model based on an analytical solution to investigate the effect of the pump’s spectral linewidth on BFLs working both in single and cascaded multi-wavelength configurations. Output powers, thresholds, and the Stokes lines number simulated are analyzed here using a standard single-mode optical fiber as the Brillouin gain medium. The numerical results obtained not only allow identifying the regions of the pump’s spectral linewidth in which there is greater, moderate, or even no suppression of the stimulated Brillouin scattering inside the laser cavity but also provides a deep understanding of the internal dynamics of the BFLs cavities.
Bounds on edge shear layer persistence while approaching the density limit
Authors: Singh Rameswar , Diamond P.H. 
Published: April 2021

Abstract
This paper details the theory of edge shear layer collapse as the density approaches the Greenwald density limit. It significantly extends earlier work, which was restricted in applicability. The zonal shear flow screening length is calculated for banana, plateau and Pfirsch–Schluter regimes. Poloidal field scaling persists in the plateau regime. Neoclassical screening and drift wave–zonal flow dynamics are combined in a theory, which is then reduced to a predator–prey model. Zonal noise, due to incoherent mode coupling, is retained. The threshold condition for edge shear layer collapse is computed, and linked to a critical value of the dimensionless parameter

ρ

s

/

ρ

s
c

L

n

. Here

ρ
s

is the ion sound radius,

ρ

s
c

is the zonal flow screening length and

L
n

is the equilibrium density scale length. The limiting initial edge density for shear layer collapse is derived and shown to scale favorably with the plasma current. The results are discussed in light of the density limit and Ohmic phenomenology.
Innovative recycling or extended use? Comparing the global warming potential of different ownership and end-of-life scenarios for textiles
Authors: Levänen Jarkko , Uusitalo Ville , Härri Anna , Kareinen Elisa , Linnanen Lassi 
Published: April 2021

Abstract
The implementation of circular economy (CE) thinking to reduce environmental impacts and resource use has led to the development of innovative recycling technologies and business models. The implications of these technologies and models, however, remain largely unclear. In many CE strategies, there is a high risk of rebound, meaning a situation in which activities aimed at environmental benefits are not realized because of external reasons. A similar risk relates to limited understanding about the behavioral changes required by extensive implementation of circular practices. Using life cycle assessment, we compare the global warming potential (GWP) of five ownership and end-of-life scenarios for creating and using a pair of jeans. The scenarios are as follows: (a) BASE, i.e. basic use with waste disposal; (b) REDUCE, i.e. extended use; (c) REUSE, i.e. re-selling; (d) RECYCLE, i.e. industrial processing into new raw materials; and (e) SHARE, i.e. a rental service. Our results show that the lowest global warming impacts are achieved in the REDUCE scenario, and the second lowest are achieved in the REUSE scenario. The RECYCLE scenario leads to relatively high overall emissions because the replaced emissions from cotton production are relatively low. The use of rental services is likely to increase customers’ mobility, and if that happens in a large scale, then the SHARE scenario has the highest GWP. It was found that many new CE innovations come with a high rebound risk, and existing practices carry similar, yet smaller risks.