Effect of a stranded hole type on the performance of corn stover composite pipe.

To promote the comprehensive utilization of corn stover and the development of field water-saving irrigation technology, a method of returning corn stover to the field was prosed; in this method, the crop stalks were crushed, mixed with soil in different proportions of adulteration, and then extruded to form hollow round tubes. To compare the influence of the winch blade with or without a diameter change on the composite pipe molding performance, two composite pipe molding devices were theoretically designed, simulated, and analyzed using discrete element simulation software, and a composite pipe molding bench test was performed. The simulation test revealed that the composite pipe molding rate of the winch blade without the reducer molding device was 3.45 kg/s, the output power of the winch shaft was 20.7 kW, the composite pipe molding rate of the winch blade with the reducer molding device was 1.20 kg/s, and the output power of the winch shaft was 18.75 kW. By calculating the weighted average of two indices, the composite pipe forming rate and the winch shaft output power, the comprehensive performance index of the composite pipe forming device without a reducer was greater than that of the device with a reducer. The composite pipe forming bench test revealed two kinds of molding devices with an extrusion molding with an outer diameter of 100 mm and an inner diameter of 30 mm. The composite pipe density test average was greater than 1.30 g/cm3 and met the requirements of composite pipe molding; the winch blade without a reducer molding device had an average composite pipe molding rate of 3.23 kg/s, and the winch blade with an average reducer molding rate of 2.07 kg/s. The forming rate of the composite pipe without a reducer was faster. Therefore, a winch blade without a reducer composite pipe molding device is more conducive to improving the composite pipe molding performance.

Aurora kinase A-mediated phosphorylation triggers structural alteration of Rab1A to enhance ER complexity during mitosis.

Morphological rearrangement of the endoplasmic reticulum (ER) is critical for metazoan mitosis. Yet, how the ER is remodeled by the mitotic signaling remains unclear. Here, we report that mitotic Aurora kinase A (AURKA) employs a small GTPase, Rab1A, to direct ER remodeling. During mitosis, AURKA phosphorylates Rab1A at Thr75. Structural analysis demonstrates that Thr75 phosphorylation renders Rab1A in a constantly active state by preventing interaction with GDP-dissociation inhibitor (GDI). Activated Rab1A is retained on the ER and induces the oligomerization of ER-shaping protein RTNs and REEPs, eventually triggering an increase of ER complexity. In various models, from Caenorhabditis elegans and Drosophila to mammals, inhibition of Rab1AThr75 phosphorylation by genetic modifications disrupts ER remodeling. Thus, our study reveals an evolutionarily conserved mechanism explaining how mitotic kinase controls ER remodeling and uncovers a critical function of Rab GTPases in metaphase.

Effect of Non-Isothermal Aging on the Mechanical Properties and Corrosion Resistance of 2A12 Aluminum Alloy.

The effect of linear non-isothermal aging and composite non-isothermal aging on the mechanical properties and corrosion resistance of 2A12 aluminum alloy was investigated. Optical microscopy (OM) and scanning electron microscopy (SEM) equipped with energy-dispersive spectroscopy (EDS) were used to study the microstructure and intergranular corrosion morphology, and the precipitates were analyzed using X-ray diffraction (XRD) and transmission electron microscopy (TEM). The results showed that the mechanical properties of 2A12 aluminum alloy were improved by both non-isothermal aging techniques due to the formation of an S' phase and a point S″ phase in the alloy matrix. Linear non-isothermal aging resulted in better mechanical properties than composite non-isothermal aging. However, the corrosion resistance of the 2A12 aluminum alloy was reduced after non-isothermal aging due to the transformation of matrix precipitates and grain boundary precipitates. The corrosion resistance of the samples followed the order: annealed state > linear non-isothermal aging > composite non-isothermal aging.

An artificial-intelligence-based age-specific template construction framework for brain structural analysis using magnetic resonance images.

It is an essential task to construct brain templates and analyze their anatomical structures in neurological and cognitive science. Generally, templates constructed from magnetic resonance imaging (MRI) of a group of subjects can provide a standard reference space for analyzing the structural and functional characteristics of the group. With recent development of artificial intelligence (AI) techniques, it is desirable to explore AI registration methods for quantifying age-specific brain variations and tendencies across different ages. In this article, we present an AI-based age-specific template construction (called ASTC) framework for longitudinal structural brain analysis using T1-weighted MRIs of 646 subjects from 18 to 82 years old collected from four medical centers. Altogether, 13 longitudinal templates were constructed at a 5-year age interval using ASTC, and tissue segmentation and substructure parcellation were performed for analysis across different age groups. The results indicated consistent changes in brain structures along with aging and demonstrated the capability of ASTC for longitudinal neuroimaging study.

Evaluation of an automated intracranial aneurysm detection and rupture analysis approach using cascade detection and classification networks.

Intracranial aneurysm is commonly found in human brains especially for the elderly, and its rupture accounts for a high rate of subarachnoid hemorrhages. However, it is time-consuming and requires special expertise to pinpoint small aneurysms from computed tomography angiography (CTA) images. Deep learning-based detection has helped improve much efficiency but false-positives still render difficulty to be ruled out. To study the feasibility of deep learning algorithms for aneurysm analysis in clinical applications, this paper proposes a pipeline for aneurysm detection, segmentation, and rupture classification and validates its performance using CTA images of 1508 subjects. A cascade aneurysm detection model is employed by first using a fine-tuned feature pyramid network (FPN) for candidate detection and then applying a dual-channel ResNet aneurysm classifier to further reduce false positives. Detected aneurysms are then segmented by applying a traditional 3D V-Net to their image patches. Radiomics features of aneurysms are extracted after detection and segmentation. The machine-learning-based and deep learning-based rupture classification can be used to distinguish ruptured and un-ruptured ones. Experimental results show that the dual-channel ResNet aneurysm classifier utilizing image and vesselness information helps boost sensitivity of detection compared to single image channel input. Overall, the proposed pipeline can achieve a sensitivity of 90 % for 1 false positive per image, and 95 % for 2 false positives per image. For rupture classification the area under curve (AUC) of 0.906 can be achieved for the testing dataset. The results suggest feasibility of the pipeline for potential clinical use to assist radiologists in aneurysm detection and classification of ruptured and un-ruptured aneurysms.

Material-structure-performance integrated laser-metal additive manufacturing.

Laser-metal additive manufacturing capabilities have advanced from single-material printing to multimaterial/multifunctional design and manufacturing. Material-structure-performance integrated additive manufacturing (MSPI-AM) represents a path toward the integral manufacturing of end-use components with innovative structures and multimaterial layouts to meet the increasing demand from industries such as aviation, aerospace, automobile manufacturing, and energy production. We highlight two methodological ideas for MSPI-AM-"the right materials printed in the right positions" and "unique structures printed for unique functions"-to realize major improvements in performance and function. We establish how cross-scale mechanisms to coordinate nano/microscale material development, mesoscale process monitoring, and macroscale structure and performance control can be used proactively to achieve high performance with multifunctionality. MSPI-AM exemplifies the revolution of design and manufacturing strategies for AM and its technological enhancement and sustainable development.

A consistent deep registration network with group data modeling.

Medical image registration is a critical process for automated image computing, and ideally, the deformation field from one image to another should be smooth and inverse-consistent in order to bidirectionally align anatomical structures and to preserve their topology. Consistent registration can reduce bias caused by the order of input images, increase robustness, and improve reliability of subsequent quantitative analysis. Rigorous differential geometry constraints have been used in traditional methods to enforce the topological consistency but require comprehensive optimization and are time consuming. Recent studies show that deep learning-based registration methods can achieve comparable accuracy and are much faster than traditional registration. However, the estimated deformation fields do not necessarily possess inverse consistency when the order of two input images is swapped. To tackle this problem, we propose a new deep registration algorithm by employing the inverse consistency training strategy, so the forward and backward deformations of a pair of images can consistently align anatomical structures. In addition, since fine-tuned deformations among the training images reflect variability of shapes and appearances in a high-dimensional space, we formulate a group prior data modeling framework so that such statistics can be used to improve accuracy and consistency for registering new input image pairs. Specifically, we implement the wavelet principle component analysis (w-PCA) model of deformation fields and incorporate such prior constraints into the inverse-consistent deep registration network. We refer the proposed algorithm as consistent deep registration with group data modeling. Experiments on 3D brain magnetic resonance (MR) images showed that the unsupervised consistent deep registration and data modeling strategy yield consistent deformations after switching the input images and tolerated image variations well.

On the performance of lung nodule detection, segmentation and classification.

Computed tomography (CT) screening is an effective way for early detection of lung cancer in order to improve the survival rate of such a deadly disease. For more than two decades, image processing techniques such as nodule detection, segmentation, and classification have been extensively studied to assist physicians in identifying nodules from hundreds of CT slices to measure shapes and HU distributions of nodules automatically and to distinguish their malignancy. Thanks to new parallel computation, multi-layer convolution, nonlinear pooling operation, and the big data learning strategy, recent development of deep-learning algorithms has shown great progress in lung nodule screening and computer-assisted diagnosis (CADx) applications due to their high sensitivity and low false positive rates. This paper presents a survey of state-of-the-art deep-learning-based lung nodule screening and analysis techniques focusing on their performance and clinical applications, aiming to help better understand the current performance, the limitation, and the future trends of lung nodule analysis.

Computing infection distributions and longitudinal evolution patterns in lung CT images.

BACKGROUND: Spatial and temporal lung infection distributions of coronavirus disease 2019 (COVID-19) and their changes could reveal important patterns to better understand the disease and its time course. This paper presents a pipeline to analyze statistically these patterns by automatically segmenting the infection regions and registering them onto a common template. METHODS: A VB-Net is designed to automatically segment infection regions in CT images. After training and validating the model, we segmented all the CT images in the study. The segmentation results are then warped onto a pre-defined template CT image using deformable registration based on lung fields. Then, the spatial distributions of infection regions and those during the course of the disease are calculated at the voxel level. Visualization and quantitative comparison can be performed between different groups. We compared the distribution maps between COVID-19 and community acquired pneumonia (CAP), between severe and critical COVID-19, and across the time course of the disease. RESULTS: For the performance of infection segmentation, comparing the segmentation results with manually annotated ground-truth, the average Dice is 91.6% ± 10.0%, which is close to the inter-rater difference between two radiologists (the Dice is 96.1% ± 3.5%). The distribution map of infection regions shows that high probability regions are in the peripheral subpleural (up to 35.1% in probability). COVID-19 GGO lesions are more widely spread than consolidations, and the latter are located more peripherally. Onset images of severe COVID-19 (inpatients) show similar lesion distributions but with smaller areas of significant difference in the right lower lobe compared to critical COVID-19 (intensive care unit patients). About the disease course, critical COVID-19 patients showed four subsequent patterns (progression, absorption, enlargement, and further absorption) in our collected dataset, with remarkable concurrent HU patterns for GGO and consolidations. CONCLUSIONS: By segmenting the infection regions with a VB-Net and registering all the CT images and the segmentation results onto a template, spatial distribution patterns of infections can be computed automatically. The algorithm provides an effective tool to visualize and quantify the spatial patterns of lung infection diseases and their changes during the disease course. Our results demonstrate different patterns between COVID-19 and CAP, between severe and critical COVID-19, as well as four subsequent disease course patterns of the severe COVID-19 patients studied, with remarkable concurrent HU patterns for GGO and consolidations.

An artificial-intelligence lung imaging analysis system (ALIAS) for population-based nodule computing in CT scans.

Computed tomography (CT) screening is essential for early lung cancer detection. With the development of artificial intelligence techniques, it is particularly desirable to explore the ability of current state-of-the-art methods and to analyze nodule features in terms of a large population. In this paper, we present an artificial-intelligence lung image analysis system (ALIAS) for nodule detection and segmentation. And after segmenting the nodules, the locations, sizes, as well as imaging features are computed at the population level for studying the differences between benign and malignant nodules. The results provide better understanding of the underlying imaging features and their ability for early lung cancer diagnosis.

Carbon Nanotubes Enabled Laser 3D Printing of High-Performance Titanium with Highly Concentrated Reinforcement.

Zero- to two-dimensional nanomaterials have been incorporated into metal-matrices to improve the strength of metals, but challengingly, high-volume-fraction nanomaterials are difficult to disperse uniformly in metal matrices, severely degrading the ductility of conventionally processed metals. Here, a considerably dense uniform dispersion of in situ formed nanoscale lamellar TiC reinforcement (16.1 wt %) in Ti matrix is achieved through laser-tailored 3D printing and complete reaction of Ti powder with a small amount (1.0 wt %) of carbon nanotubes (CNTs). An enhanced tensile strength of 912 MPa and an outstanding fracture elongation of 16% are simultaneously achieved for laser-printed components, showing a maximum 350% improvement in "product of strength and elongation" compared with conventional Ti. In situ nanoscale TiC reinforcement favors the formation of ultrafine equiaxed Ti grains and metallurgically coherent interface with minimal lattice misfit between TiC lamellae and Ti matrix. Our approach hopefully provides a feasible way to broaden structural applications of CNTs in load-bearing Ti-based engineering components via laser-tailored reorganization with Ti.

Optimization of bio-inspired bi-directionally corrugated panel impact-resistance structures: Numerical simulation and selective laser melting process.

The telson (tail plate) of Stomatopoda (mantis shrimp) shows excellent impact resistance properties, and its special structure is an ideal prototype to mimic. In this paper, a series of bi-directionally corrugated panel (DCP) structures inspired by the telson of mantis shrimp was designed. The crush simulation of DCP structures with different structural parameters, namely wavelength (λ) and amplitude (A), was carried out using ANSYS LS-DYNA. In order to verify the simulation results, AlSi10Mg components with DCP structures were fabricated by selective laser melting and the out-of-plane compression tests were conducted to investigate the compression performance. The numerical simulation results indicated that the influence of wavelength of DCP structure on the energy absorption (EA) and specific energy absorption (SEA) capability was greater than that of the amplitude, and the DCP structure with A = 8 mm and λ = 6 mm possessed the best impact resistance performance. The SLM-processed AlSi10Mg components with DCP structures showed high surface quality and good forming accuracy, and the relation between experimental compression behavior and the DCP structure parameter was in good agreement with the numerical results.

[Pharmacokinetics of gypenoside XVâ ¡ in rats].

In this study, we established an HPLC-MS method to determine gypenoside XVⅡ in biosamples. The methodology results indicated that the linear range was 1-2 500 µg•L⁻¹ (r=0.996 3); intraday RSD values for high, medium and low concentrations were 9.9%, 3.0% 1.7%; interday RSD values were 16%, 14%, 2.5%; matrix effect ranged between 90.0%-100%, with RSD<15%. The recovery was more than 80.0%, with precision and accuracy in line with request. After the rats were orally and intravenously administered with gypenoside XVⅡ, the concentrations of gypenoside XVⅡ in plasma were determined, and pharmacokinetic parameter was calculated using pharmacokinetic software DAS 2.0. According to the main pharmacokinetic parameters of gypenoside XVⅡ, tmax was 0.17-0.20 h, t1/2 was 1.94-2.56 h, bioavailability of oral administration was 1.87%. The results indicated that the pharmacokinetic profiles of gypenoside XVⅡ were rapid absorption and distribution after oral administration, short time to peak and rapid elimination.

MFN1 structures reveal nucleotide-triggered dimerization critical for mitochondrial fusion.

Mitochondria are double-membraned organelles with variable shapes influenced by metabolic conditions, developmental stage, and environmental stimuli. Their dynamic morphology is a result of regulated and balanced fusion and fission processes. Fusion is crucial for the health and physiological functions of mitochondria, including complementation of damaged mitochondrial DNAs and the maintenance of membrane potential. Mitofusins are dynamin-related GTPases that are essential for mitochondrial fusion. They are embedded in the mitochondrial outer membrane and thought to fuse adjacent mitochondria via combined oligomerization and GTP hydrolysis. However, the molecular mechanisms of this process remain unknown. Here we present crystal structures of engineered human MFN1 containing the GTPase domain and a helical domain during different stages of GTP hydrolysis. The helical domain is composed of elements from widely dispersed sequence regions of MFN1 and resembles the 'neck' of the bacterial dynamin-like protein. The structures reveal unique features of its catalytic machinery and explain how GTP binding induces conformational changes to promote GTPase domain dimerization in the transition state. Disruption of GTPase domain dimerization abolishes the fusogenic activity of MFN1. Moreover, a conserved aspartate residue trigger was found to affect mitochondrial elongation in MFN1, probably through a GTP-loading-dependent domain rearrangement. Thus, we propose a mechanistic model for MFN1-mediated mitochondrial tethering, and our results shed light on the molecular basis of mitochondrial fusion and mitofusin-related human neuromuscular disorders.

Influence of additive multilayer feature on thermodynamics, stress and microstructure development during laser 3D printing of aluminum-based material.

A transient three dimensional model for describing the temperature behavior, thermo-capillary convection, microstructure evolution and the resultant mechanical properties during selective laser melting of AlN/AlSi10Mg composite is proposed. The powder-solid transformation, temperature dependent physical properties and the preservation of the heat are taken into account. The effect of the additive manufacturing multilayer feature on the molten pool dynamics, cooling rate, crystal size, microstructure morphology, micro-hardness and types of the residual stress has been investigated. It shows that the operating temperature and the thermo-capillary convection obtained within the molten pool generally increases as the processing multilayers are successively added, while the thermal effect depth is negatively reduced. The preferential direction of the heat diffusion generally changes from a downward pattern, then to the slightly strengthened horizontal direction and finally to a typically horizontal one for various deposited layers being processed. Therefore, the microstructure of the solidified part along the building direction (Region I to Region V) undergoes an interesting transformation: directional columnar cellular microstructure, crosswise-extended cellular microstructure, refined cellular microstructure, fragmentation microstructure and the coarse cellular microstructure. The tensile stress and the compressive stress are comprehensively obtained within the finally solidified layers, significantly influencing the micro-hardness.