Young novice drivers' road crash injuries and contributing factors: A crash data investigation.

OBJECTIVE: Collisions are a significant cause of injury and fatality among young novice drivers. Using real crash data, this study further explores the multifaceted and complex nature of young novice drivers' crash injury risk by synthesizing different driver attributes and crash scenarios in order to update and validate previous research findings and provide more feasible recommendations for preventive measures. METHODS: Detailed data on traffic crash of young novice drivers were extracted from the National Automobile Accident In-Depth Investigation System (NAIS) in China, and a mixed research methodology using a Random Forest and multinomial logit modeling framework was used in order to explore and study the important influences on traffic crash injuries of young novice drivers in Songjiang District, Shanghai, during the period from 2018 to 2022. RESULTS: The results of the study showed that human, vehicle, road and environmental characteristics contributed 36.83%, 22.65%, 17.07% and 23.45% respectively to the prediction of crash injury level of novice drivers. Among the various single factors, driver negligence was the most important factor affecting the crash injury level of novice drivers. Age of the vehicle, crash location, road signal condition and time of crash all had a significant effect on the crash injury level of young novice drivers (95% of the confidence level). CONCLUSIONS: The study comprehensively analyzed young novice driver crash data to reveal the crash injury risk and its severity faced by young novice drivers in different contexts, and suggested targeted safety improvements. There are similarities and differences with the results of previous studies, in which there are new contributions to understanding the driving risks of young novice drivers in daytime and nighttime.

Single-Subject Deep-Learning Image Reconstruction with a Neural Optimization Transfer Algorithm for PET-enabled Dual-Energy CT Imaging.

Combining dual-energy computed tomography (DECT) with positron emission tomography (PET) offers many potential clinical applications but typically requires expensive hardware upgrades or increases radiation doses on PET/CT scanners due to an extra X-ray CT scan. The recent PET-enabled DECT method allows DECT imaging on PET/CT without requiring a second X-ray CT scan. It combines the already existing X-ray CT image with a 511 keV γ-ray CT (gCT) image reconstructed from time-of-flight PET emission data. A kernelized framework has been developed for reconstructing gCT image but this method has not fully exploited the potential of prior knowledge. Use of deep neural networks may explore the power of deep learning in this application. However, common approaches require a large database for training, which is impractical for a new imaging method like PET-enabled DECT. Here, we propose a single-subject method by using neural-network representation as a deep coefficient prior to improving gCT image reconstruction without population-based pre-training. The resulting optimization problem becomes the tomographic estimation of nonlinear neural-network parameters from gCT projection data. This complicated problem can be efficiently solved by utilizing the optimization transfer strategy with quadratic surrogates. Each iteration of the proposed neural optimization transfer algorithm includes: PET activity image update; gCT image update; and least-square neural-network learning in the gCT image domain. This algorithm is guaranteed to monotonically increase the data likelihood. Results from computer simulation, real phantom data and real patient data have demonstrated that the proposed method can significantly improve gCT image quality and consequent multi-material decomposition as compared to other methods.

Optimization-derived blood input function using a kernel method and its evaluation with total-body PET for brain parametric imaging.

Dynamic PET allows quantification of physiological parameters through tracer kinetic modeling. For dynamic imaging of brain or head and neck cancer on conventional PET scanners with a short axial field of view, the image-derived input function (ID-IF) from intracranial blood vessels such as the carotid artery (CA) suffers from severe partial volume effects. Alternatively, optimization-derived input function (OD-IF) by the simultaneous estimation (SIME) method does not rely on an ID-IF but derives the input function directly from the data. However, the optimization problem is often highly ill-posed. We proposed a new method that combines the ideas of OD-IF and ID-IF together through a kernel framework. While evaluation of such a method is challenging in human subjects, we used the uEXPLORER total-body PET system that covers major blood pools to provide a reference for validation. METHODS: The conventional SIME approach estimates an input function using a joint estimation together with kinetic parameters by fitting time activity curves from multiple regions of interests (ROIs). The input function is commonly parameterized with a highly nonlinear model which is difficult to estimate. The proposed kernel SIME method exploits the CA ID-IF as a priori information via a kernel representation to stabilize the SIME approach. The unknown parameters are linear and thus easier to estimate. The proposed method was evaluated using 18F-fluorodeoxyglucose studies with both computer simulations and 20 human-subject scans acquired on the uEXPLORER scanner. The effect of the number of ROIs on kernel SIME was also explored. RESULTS: The estimated OD-IF by kernel SIME showed a good match with the reference input function and provided more accurate estimation of kinetic parameters for both simulation and human-subject data. The kernel SIME led to the highest correlation coefficient (R = 0.97) and the lowest mean absolute error (MAE = 10.5 %) compared to using the CA ID-IF (R = 0.86, MAE = 108.2 %) and conventional SIME (R = 0.57, MAE = 78.7 %) in the human-subject evaluation. Adding more ROIs improved the overall performance of the kernel SIME method. CONCLUSION: The proposed kernel SIME method shows promise to provide an accurate estimation of the blood input function and kinetic parameters for brain PET parametric imaging.

Super-resolution reconstruction of γ-ray CT images for PET-enabled dual-energy CT imaging.

Dual-energy computed tomography (DECT) enables material decomposition for tissues and produces additional information for PET/CT imaging to potentially improve the characterization of diseases. PET-enabled DECT (PDECT) allows the generation of PET and DECT images simultaneously with a conventional PET/CT scanner without the need for a second x-ray CT scan. In PDECT, high-energy γ-ray CT (GCT) images at 511 keV are obtained from time-of-flight (TOF) PET data and are combined with the existing x-ray CT images to form DECT imaging. We have developed a kernel-based maximum-likelihood attenuation and activity (MLAA) method that uses x-ray CT images as a priori information for noise suppression. However, our previous studies focused on GCT image reconstruction at the PET image resolution which is coarser than the image resolution of the x-ray CT. In this work, we explored the feasibility of generating super-resolution GCT images at the corresponding CT resolution. The study was conducted using both phantom and patient scans acquired with the uEXPLORER total-body PET/CT system. GCT images at the PET resolution with a pixel size of 4.0 mm × 4.0 mm and at the CT resolution with a pixel size of 1.2 mm × 1.2 mm were reconstructed using both the standard MLAA and kernel MLAA methods. The results indicated that the GCT images at the CT resolution had sharper edges and revealed more structural details compared to the images reconstructed at the PET resolution. Furthermore, images from the kernel MLAA method showed substantially improved image quality compared to those obtained with the standard MLAA method.

Feasibility of PET-enabled dual-energy CT imaging: First physical phantom and patient results.

X-ray computed tomography (CT) in PET/CT is commonly operated with a single energy, resulting in a limitation of lacking tissue composition information. Dual-energy (DE) spectral CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging, but would either require hardware upgrade or increase radiation dose due to the added second x-ray CT scan. Recently proposed PET-enabled DECT method allows dual-energy spectral imaging using a conventional PET/CT scanner without the need for a second x-ray CT scan. A gamma-ray CT (gCT) image at 511 keV can be generated from the existing time-of-flight PET data with the maximum-likelihood attenuation and activity (MLAA) approach and is then combined with the low-energy x-ray CT image to form dual-energy spectral imaging. To improve the image quality of gCT, a kernel MLAA method was further proposed by incorporating x-ray CT as a priori information. The concept of this PET-enabled DECT has been validated using simulation studies, but not yet with 3D real data. In this work, we developed a general open-source implementation for gCT reconstruction from PET data and use this implementation for the first real data validation with both a physical phantom study and a human subject study on a uEXPLORER total-body PET/CT system. These results have demonstrated the feasibility of this method for spectral imaging and material decomposition.

PET-Enabled Dual-Energy CT Using a Kernel Method with Neural Optimization Transfer.

Integrated use of dual-energy computed tomography (DECT) with positron emission tomography (PET) has many potential clinical applications. However, the integration would either require costly hardware upgrade or increase radiation dose on PET/CT scanners due to the need for an additional Xray CT scan. The recently proposed PET-enabled DECT method enables DECT imaging on PET/CT without requiring the second X-ray CT scan. It combines the already-existing low-energy X-ray CT image with a 511 keV {\gamma}-ray CT (gCT) image reconstructed from time-of-flight PET emission data. A kernelized attenuation and activity (KAA) estimation method has been developed for reconstructing the gCT image from PET but the method has not fully exploited the potential of image prior knowledge. In this work, we propose a neural KAA method by using neural network representation as a deep coefficient prior to improve the existing KAA method. The resulting maximum-likelihood neural network-based reconstruction problem can be efficiently solved by utilizing the theory of optimization transfer. Each iteration of the algorithm consists of three modular steps: PET activity image update, gCT image update, and neural-network learning in the image domain. This algorithm is guaranteed to monotonically increase the data likelihood. The results from computer simulation and real phantom data have demonstrated that the proposed neural KAA method can significantly improve gCT image quality and consequent multi-material decomposition as compared to other methods.

Invariant natural killer T cells drive hepatic homeostasis in nonalcoholic fatty liver disease via sustained IL-10 expression in CD170+ Kupffer cells.

Kupffer cells (KCs) are liver-resident macrophages involved in hepatic inflammatory responses, including nonalcoholic fatty liver disease (NAFLD) development. However, the contribution of KC subsets to liver inflammation remains unclear. Here, using high-dimensional single-cell RNA sequencing, we characterized murine embryo-derived KCs and identified two KC populations with different gene expression profiles: KC-1 and KC-2. KC-1 expressed CD170, exhibiting immunoreactivity and immune-regulatory abilities, while KC-2 highly expressed lipid metabolism-associated genes. In a high-fat diet-induced NAFLD model, KC-1 cells differentiated into pro-inflammatory phenotypes and initiated more frequent communications with invariant natural killer T (iNKT) cells. In KC-1, interleukin (IL)-10 expression was unaffected by the high-fat diet but impaired by iNKT cell ablation and upregulated by iNKT cell adoptive transfer in vivo. Moreover, in a cellular co-culture system, primary hepatic iNKT cells promoted IL-10 expression in RAW264.7 and primary KC-1 cells. CD206 signal blocking in KC-1 or CD206 knockdown in RAW264.7 cells significantly reduced IL-10 expression. In conclusion, we identified two embryo-derived KC subpopulations with distinct transcriptional profiles. The CD206-mediated crosstalk between iNKT and KC-1 cells maintains IL-10 expression in KC-1 cells, affecting hepatic immune balance. Therefore, KC-based therapeutic strategies must consider cellular heterogeneity and the local immune microenvironment for enhanced specificity and efficiency.

Transcriptome analysis reveals that PbMYB61 and PbMYB308 are involved in the regulation of lignin biosynthesis in pear fruit stone cells.

Pear fruit stone cells have thick walls and are formed by the secondary deposition of lignin in the primary cell wall of thin-walled cells. Their content and size seriously affect fruit characteristics related to edibility. To reveal the regulatory mechanism underlying stone cell formation during pear fruit development and to identify hub genes, we examined the stone cell and lignin contents of 30 'Shannongsu' pear flesh samples and analyzed the transcriptomes of 15 pear flesh samples collected at five developmental stages. On the basis of the RNA-seq data, 35 874 differentially expressed genes were detected. Additionally, two stone cell-related modules were identified according to a WGCNA. A total of 42 lignin-related structural genes were subsequently obtained. Furthermore, nine hub structural genes were identified in the lignin regulatory network. We also identified PbMYB61 and PbMYB308 as candidate transcriptional regulators of stone cell formation after analyzing co-expression networks and phylogenetic relationships. Finally, we experimentally validated and characterized the candidate transcription factors and revealed that PbMYB61 regulates stone cell lignin formation by binding to the AC element in the PbLAC1 promoter to upregulate expression. However, PbMYB308 negatively regulates stone cell lignin synthesis by binding to PbMYB61 to form a dimer that cannot activate PbLAC1 expression. In this study, we explored the lignin synthesis-related functions of MYB family members. The results presented herein are useful for elucidating the complex mechanisms underlying lignin biosynthesis during pear fruit stone cell development.

Interactions between MFAP5 + fibroblasts and tumor-infiltrating myeloid cells shape the malignant microenvironment of colorectal cancer.

BACKGROUND: The therapeutic targeting of the tumor microenvironment (TME) in colorectal cancer (CRC) has not yet been fully developed and utilized because of the complexity of the cell-cell interactions within the TME. The further exploration of these interactions among tumor-specific clusters would provide more detailed information about these communication networks with potential curative value. METHODS: Single-cell RNA sequencing, spatial transcriptomics, and bulk RNA sequencing datasets were integrated in this study to explore the biological properties of MFAP5 + fibroblasts and their interactions with tumor-infiltrating myeloid cells in colorectal cancer. Immunohistochemistry and multiplex immunohistochemistry were performed to confirm the results of these analyses. RESULTS: We profiled heterogeneous single-cell landscapes across 27,414 cells obtained from tumors and adjacent tissues. We mainly focused on the pro-tumorigenic functions of the identified MFAP5 + fibroblasts. We demonstrated that tumor-resident MFAP5 + fibroblasts and myeloid cells (particularly C1QC + macrophages) were positively correlated in both spatial transcriptomics and bulk RNA-seq public cohorts. These cells and their interactions might shape the malignant behavior of CRC. Intercellular interaction analysis suggested that MFAP5 + fibroblasts could reciprocally communicate with C1QC + macrophages and other myeloid cells to remodel unfavorable conditions via MIF/CD74, IL34/CSF1R, and other tumor-promoting signaling pathways. CONCLUSION: Our study has elucidated the underlying pro-tumor mechanisms of tumor-resident MFAP5 + fibroblasts and provided valuable targets for the disruption of their properties.

The regulatory module MdBZR1-MdCOL6 mediates brassinosteroid- and light-regulated anthocyanin synthesis in apple.

The anthocyanin content is an important indicator of the nutritional value of most fruits, including apple (Malus domestica). Anthocyanin synthesis is coordinately regulated by light and various phytohormones. In this study on apple, we revealed the antagonistic relationship between light and brassinosteroid (BR) signaling pathways, which is mediated by BRASSINAZOLE-RESISTANT 1 (MdBZR1) and the B-box protein MdCOL6. The exogenous application of brassinolide inhibited the high-light-induced anthocyanin accumulation in red-fleshed apple seedlings, whereas increases in the light intensity decreased the endogenous BR content. The overexpression of MdBZR1 inhibited the anthocyanin synthesis in apple plants. An exposure to a high-light intensity induced the degradation of dephosphorylated MdBZR1, resulting in functional impairment. MdBZR1 was identified as an upstream repressor of MdCOL6, which promotes anthocyanin synthesis in apple plants. Furthermore, MdBZR1 interacts with MdCOL6 to attenuate its ability to activate MdUFGT and MdANS transcription. Thus, MdBZR1 negatively regulates MdCOL6-mediated anthocyanin accumulation. Our study findings have clarified the molecular basis of the integration of light and BR signals during the regulation of anthocyanin biosynthesis, which is an important process influencing fruit quality.

Electrostatic Shielding Boosts Electrochemical Performance of Alloy-Type Anode Materials of Sodium-Ion Batteries.

The applications of alloy-type anode materials for Na-ion batteries are always obstructed by enormous volume variation upon cycles. Here, K+ ions are introduced as an electrolyte additive to improve the electrochemical performance via electrostatic shielding, using Sn microparticles (µ-Sn) as a model. Theoretical calculations and experimental results indicate that K+ ions are not incorporated in the electrode, but accumulate on some sites. This accumulation slows down the local sodiation at the "hot spots", promotes the uniform sodiation and enhances the electrode stability. Therefore, the electrode maintains a high specific capacity of 565 mAh g-1 after 3000 cycles at 2 A g-1 , much better than the case without K+ . The electrode also remains an areal capacity of ≈3.5 mAh cm-2 after 100 cycles. This method does not involve time-consuming preparation, sophisticated instruments and expensive reagents, exhibiting the promising potential for other anode materials.

Association between semen collection time and semen parameters: an observational study.

The process of semen collection plays a key role in the quality of semen specimens. However, the association between semen collection time and semen quality is still unclear. In this study, ejaculates by masturbation from 746 subfertile men or healthy men who underwent semen analysis were examined. The median (interquartile range) semen collection time for all participants was 7.0 (5.0-11.0) min, and the median time taken for semen collection was lower in healthy men than that in subfertile men (6.0 min vs 7.0 min). An increase in the time required to produce semen samples was associated with poorer semen quality. Among those undergoing assisted reproductive technology (ART), the miscarriage rate was positively correlated with the semen collection time. After adjusting for confounders, the highest quartile (Q4) of collection time was negatively associated with semen volume and sperm concentration. A longer time to produce semen samples (Q3 and Q4) was negatively correlated with progressive and total sperm motility. In addition, there was a significant negative linear association between the semen collection time and the sperm morphology. Higher risks of asthenozoospermia (adjusted odds ratio [OR] = 2.06, 95% confidence interval [CI]: 1.31-3.25, P = 0.002) and teratozoospermia (adjusted OR = 1.98, 95% CI: 1.10-3.55, P = 0.02) were observed in Q3 than those in Q1. Our results indicate that a higher risk of abnormal semen parameter values was associated with an increase in time for semen collection, which may be related to male fertility through its association with semen quality.

Microsized Gray Tin as a High-Rate and Long-Life Anode Material for Advanced Sodium-Ion Batteries.

Sodium-ion batteries (SIBs) are developed to address the serious concern about the limited resources of lithium. To achieve high energy density, anode materials with a large specific capacity and a low operation voltage are highly desirable. Herein, microsized particles of gray Sn (α-Sn) are explored as an anode material of SIBs for the first time. The distinct structure of α-Sn endows it the reduced volume change, the improved interaction with polymer binders and the in situ formation of amorphous Sn, as supported by in situ XRD, TEM and DFT calculations. Therefore, α-Sn exhibits an excellent electrochemical performance, much better than ß-Sn widely used before. Even microsized particles of α-Sn without any treatments deliver a capacity of ∼451 mAh g-1 after 3500 cycles at 2 A g-1 or ∼464 mAh g-1 at 4 A g-1 in a rate test. The results indicate the promising potential of α-Sn in SIBs.

Bimetallic Bi-Sn microspheres as high initial coulombic efficiency and long lifespan anodes for sodium-ion batteries.

Anode materials with a high initial coulombic efficiency and a long lifespan are highly desirable for sodium-ion batteries. Here, bimetallic µ-BiSn microspheres well combine the high capacity of Sn and the good stability of Bi together, exhibiting superior electrochemical performance, such as a high initial Coulombic efficiency (90.6%), a good cycling stability (541 mA h g-1 after 3000 cycles at 2 A g-1) and an excellent rate capability (393 mA h g-1 at 10 A g-1).

Mesocarbon Microbeads Boost the Electrochemical Performances of LiFePO4 ||Li4 Ti5 O12 through Anion Intercalation.

Li-ion batteries with LiFePO4 cathode and Li4 Ti5 O12 anode show promise for storing renewable energy. However, their low output voltage results in a low energy density. In contrast, dual-ion batteries with graphite cathode and Li4 Ti5 O12 anode can achieve a high output voltage of >3.0 V. In this study, mesocarbon microbeads (MCMB)@LiFePO4 ||Li4 Ti5 O12 dual-ion batteries are developed to address these issues. In the cathode, MCMB improves the conductivity of LiFePO4 and increases the output voltage by the intercalation of anions in the cell voltage range of 2.1-3.5 V. Moreover, the LiFePO4 shell sustains the structural integrity of MCMB and generates in situ a cathode-electrolyte interphase (CEI) with rich LiF. Owing to these unique compositional and structural features, MCMB@LiFePO4 ||Li4 Ti5 O12 manifests much better electrochemical performance than LiFePO4 ||Li4 Ti5 O12 and MCMB||Li4 Ti5 O12 . It sustains 89.6 % of the initial capacity after 1200 cycles at 0.2 A g-1 and achieves a specific energy up to 128 Wh kg-1 at 179 W kg-1 .

Nanoporous germanium prepared by a mechanochemical reaction with enhanced lithium storage properties.

The cost-effective and facile fabrication of nanostructured germanium for lithium-ion batteries (LIBs) remains a grand challenge. Herein, nanoporous Z-Ge was generated via a facile two-step mechanochemical-etching reaction with Mg2Ge and ZnCl2. The prepared nanoporous Ge nanoparticles, as the anode for Li-Ge half cells, showed superior LIB performance, in terms of a high capacity, good rate capability, and good long-term stability of 700 cycles. Significantly, the mechanochemical reaction was extended to produce other nanoporous Ge or Si materials such as A-Ge, Z-Si, and A-Si via the mechanochemical reaction between Mg2Ge and AlCl3, Mg2Si and ZnCl2, and Mg2Si and AlCl3.

Finite Element Analysis of Lightning Damage Factors Based on Carbon Fiber Reinforced Polymer.

While carbon-fiber-reinforced polymers (CFRPs) are widely used in the aerospace industry, they are not able to disperse current from lightning strikes because their conductivity is relatively low compared to metallic materials. As such, the undispersed current can cause the vaporization or delamination of the composites, threatening aircraft safety. In this paper, finite element models of lightning damage to CFRPs were established using commercial finite element analysis software, Abaqus, with the user-defined subroutines USDFLD and HEAVEL. The influences of factors such as the structural geometry, laminate sequence, and intrinsic properties of CFRPs on the degree of damage to the composites are further discussed. The results showed that when a current from lightning is applied to the CFRP surface, it mainly disperses along the fiber direction in the outermost layer. As the length of the CFRP increases, the injected current has a longer residence time in the material due to the increased current exporting distance. Consequently, larger amounts of current accumulate on the surface, eventually leading to more severe damage to the CFRP. This damage can be alleviated by increasing the thickness of the CFRP, as the greater overall resistance makes the CFRP a better insulator against the imposed current. This study also found that the damaged area increased as the angle between the first two layers increased, whereas the depth of the damage decreased due to the current dispersion between the first two layers. The analysis of the electrical conductivity of the composite suggested that damage in the fiber direction will be markedly reduced if the conductivity in the vertical fiber direction increases approximately up to the conductivity of the fiber direction. Moreover, increasing the thermal conductivity along the fiber direction will accelerate the heat dissipation process after the lightning strike, but the influence of the improved thermal conductivity on the extent of the lightning damage is less significant than that of the electrical conductivity.

Voxel-based partial volume correction of PET images via subtle MRI guided non-local means regularization.

PURPOSE: Positron emission tomography (PET) images tend to be significantly degraded by the partial volume effect (PVE) resulting from the limited spatial resolution of the reconstructed images. Our purpose is to propose a partial volume correction (PVC) method to tackle this issue. METHODS: In the present work, we explore a voxel-based PVC method under the least squares framework (LS) employing anatomical non-local means (NLMA) regularization. The well-known non-local means (NLM) filter utilizes the high degree of information redundancy that typically exists in images, and is typically used to directly reduce image noise by replacing each voxel intensity with a weighted average of its non-local neighbors. Here we explore NLM as a regularization term within iterative-deconvolution model to perform PVC. Further, an anatomical-guided version of NLM was proposed that incorporates MRI information into NLM to improve resolution and suppress image noise. The proposed approach makes subtle usage of the accompanying MRI information to define a more appropriate search space within the prior model. To optimize the regularized LS objective function, we used the Gauss-Seidel (GS) algorithm with the one-step-late (OSL) technique. RESULTS: After the import of NLMA, the visual and quality results are all improved. With a visual check, we notice that NLMA reduce the noise compared to other PVC methods. This is also validated in bias-noise curve compared to non-MRI-guided PVC framework. We can see NLMA gives better bias-noise trade-off compared to other PVC methods. CONCLUSIONS: Our efforts were evaluated in the base of amyloid brain PET imaging using the BrainWeb phantom and in vivo human data. We also compared our method with other PVC methods. Overall, we demonstrated the value of introducing subtle MRI-guidance in the regularization process, the proposed NLMA method resulting in promising visual as well as quantitative performance improvements.

Deconvolution-based partial volume correction of PET images with parallel level set regularization.

The partial volume effect (PVE), caused by the limited spatial resolution of positron emission tomography (PET), degrades images both qualitatively and quantitatively. Anatomical information provided by magnetic resonance (MR) images has the potential to play an important role in partial volume correction (PVC) methods. Post-reconstruction MR-guided PVC methods typically use segmented MR tissue maps, and further, assume that PET activity distribution is uniform in each region, imposing considerable constraints through anatomical guidance. In this work, we present a post-reconstruction PVC method based on deconvolution with parallel level set (PLS) regularization. We frame the problem as an iterative deconvolution task with PLS regularization that incorporates anatomical information without requiring MR segmentation or assuming uniformity of PET distributions within regions. An efficient algorithm for non-smooth optimization of the objective function (invoking split Bregman framework) is developed so that the proposed method can be feasibly applied to 3D images and produces sharper images compared to PLS method with smooth optimization. The proposed method was evaluated together with several other PVC methods using both realistic simulation experiments based on the BrainWeb phantom as well asin vivohuman data. Our proposed method showed enhanced quantitative performance when realistic MR guidance was provided. Further, the proposed method is able to reduce image noise while preserving structure details onin vivohuman data, and shows the potential to better differentiate amyloid positive and amyloid negative scans. Overall, our results demonstrate promise to provide superior performance in clinical imaging scenarios.

Deformation Resistance Performance of Carbon Fiber-Reinforced Plastic Machined by Controlling Drilling Area Temperature below the Glass Transition Temperature.

Drilling of carbon fiber-reinforced plastics (CFRPs) is a challenging task in aviation and aerospace field. Damages, which can reduce the strength of the structure, often occur during secondary machining operations due to the applied cutting force and generated heat. The main objective of this study was to investigate the drilling performance and the deformation resistance of CFRPs subjected to cryogenic treatment based on glass transition temperature (Tg). Therefore, a cryogenic machining approach was adopted by fixing the workpiece inside a cryogenic box to drill CFRPs. The machining performance was briefly evaluated. Moreover, a through-hole drilling method was promoted to analyze the mechanism of different deformation mechanical properties. The results showed that the cryogenic machining approach improved the machining performance of CFRPs. Nevertheless, the residual intensity of cryo-treated specimen decreased (about 7.14%) due to the Tg-based viscoelasticity. These results demonstrate the great potential of this approach in advanced industrial applications and further pave the way for efficient secondary machining operation of CFRP components.