RESEARCH ON STRESS SPECTRUM GROUPING METHOD OF HIGH SPEED EMU BOGIE

The determination of structural dynamic stress spectrum is of great significance in the structural fatigue strength evaluation as well as reliability design.To evaluate structure of fatigue strength accurately,stress history on fatigue key parts of motor bogie framework was obtained through dynamic stress test of high-speed EMU on actual operation line.Fatigue damage values per kilometer of stress spectra with different grouping numbers were calculated using equidistant grouping method and Palmgren-Miner linear cumulative damage theory.Then,the effect of different group number on fatigue damage was analyzed.The results show that when the grouping number is small,it can lead to deviation of fatigue damage.As the grouping number increasing,damage decreasing,and tends to the theoretical value.This phenomenon can be interpreted by the EMU framework dynamic stress characteristic of small stress cycles high concentration.Meanwhile,an unequal distance grouping method was presented which is applicable to framework structure stress spectrum.The study can provide the basis for framework structure design

Application of machine learning on the modelling of diffusion Magnetic Resonance Imaging signal

International audienceAbstract The modelling of diffusion Magnetic Resonance Imaging (dMRI) signals is very important for medical clinical application. However, the traditional method is to use a fixed mathematical model to make assumptions about the diffusion-weighted (DW) signals of all regions of human organ, which is unreasonable. In this paper, Convolutional Neural Network (CNN), a machine learning based method is used for learning the different characteristics of the signals, and finally intelligently give multi-model predictions for different regions of human livers. The performance of the proposed method is verified on both simulation and real liver data. The results show that the multi-model predicted by CNN method has high performance in distinguishing normal liver from diseased liver, and has great clinical application prospect

Application of machine learning in optimizing b-value acquisition strategy of diffusion Magnetic Resonance Imaging

International audienceAbstract The b-value acquisition strategy of diffusion Magnetic Resonance Imaging (dMRI) is very important for medical clinical application, especially the low b-value strategy. However, the choice of b-values is affected by several factors: for example, different tissue, different regions of tissue, the dependence of dMRI signals on b-values are different. Specifically, dMRI signals in areas with faster blood circulation may be more sensitive to low b-values (b<50 s/mm 2 ); in addition, to obtain the diffusion or perfusion information from the diffusion-weighted (DW) signal, fitting methods are required, which also affected by low b-values. In this paper, Convolutional Neural Network (CNN), a machine learning based method is first used for learning the different characteristics of the DW signals in different regions of tissue and generated by different b-value acquisition strategy, and then analyse the dependence of DW signals on low b-values in different regions of the tissue. Finally, to study the dependence of the fitting methods on low b-values, which to determine the b-value acquisition strategy. The results show that the b-value acquisition strategy are different in different perfusion regions and using different fitting methods

Angiogenic and Osteogenic Coupling Effects of Deferoxamine-Loaded Poly(lactide-co-glycolide)-Poly(ethylene glycol)-Poly(lactide-co-glycolide) Nanoparticles

Angiogenesis and osteogenesis coupling processes are essential for bone regeneration, and human bone marrow stromal cells (hBMSCs) along with endothelial cells (ECs) are crucial participants. Deferoxamine (DFO), a hypoxia-mimetic agent, could activate the hypoxia-inducible factor (HIF)-1α signaling pathway and trigger angiogenic and osteogenic effects in these cells. However, the lifetime of DFO is very short, thus a suitable delivery system is urgently needed. In this study, we encapsulated DFO in Poly(lactide-co-glycolide)-Poly(ethylene glycol)-Poly(lactide-co-glycolide) (PLGA-PEG-PLGA) nanoparticles (DFO-loaded NPs) to realize its long-term angiogenic and osteogenic bioactivities. Surface morphology, size, size distribution of DFO-loaded NPs as well as DFO loading content (LC), encapsulation efficiency (EE) and release profile were systematically evaluated. When hBMSCs were exposed to the vehicle with DFO concentration of 100 μM, cells showed good viability, increased HIF-1α expression and enhanced vascular endothelial growth factor (VEGF) secretion. The transcriptional levels of the angiogenic and osteogenic genes were also upregulated. Moreover, promoted alkaline phosphatase (ALP) activity further confirmed better osteogenic differentiation. Similarly, angiogenic activity of human umbilical vein endothelial cells (HUVECs) were enhanced after the addition of DFO-loaded NPs, evidenced by increased angiogenic genes expressions and tube formation. Taken together, DFO-loaded NPs could provide a sustained supply of DFO, with its angiogenic and osteogenic coupling effects preserved, which extends the potential of this system for bone defect repair

Crack growth analysis in welded and non-welded T-joints based on lock-in digital image correlation and thermoelastic stress analysis

The method based on the digital image correlation (DIC) technique and thermoelastic stress analysis (TSA) is proposed to monitor the crack propagation process of T-joint specimens during the fatigue test. The lock-in amplifier is used to process DIC speckle pattern images while the specimen is dynamically loaded. The lock-in algorithm uses the fact that lock-in amplifier can be able to detect very small signal change within the measurement noise, which is often used in TSA. Using appropriate post-processing method, both the crack lengths and the stress intensity factors (SIF) can be evaluated in function of the number of fatigue cycles. The tests on non-welded T-joint specimens and welded T-joint specimens in two types of test rigs will be presented in the paper. All of the achieved results were validated by employing extended finite element method (XFEM) performed with ANSYS software.</p

Platelet-Rich Plasma-Loaded Poly(d,l-lactide)-Poly(ethylene glycol)-Poly(d,l-lactide) Hydrogel Dressing Promotes Full-Thickness Skin Wound Healing in a Rodent Model

Traditional therapeutic methods for skin wounds have many disadvantages, and new wound dressings that can facilitate the healing process are thus urgently needed. Platelet-rich plasma (PRP) contains multiple growth factors (GFs) and shows a significant capacity to heal soft tissue wounds. However, these GFs have a short half-life and deactivate rapidly; we therefore need a sustained delivery system to overcome this shortcoming. In this study, poly(d,l-lactide)-poly(ethylene glycol)-poly(d,l-lactide) (PDLLA-PEG-PDLLA: PLEL) hydrogel was successfully created as delivery vehicle for PRP GFs and was evaluated systematically. PLEL hydrogel was injectable at room temperature and exhibited a smart thermosensitive in situ gel-formation behavior at body temperature. In vitro cell culture showed PRP-loaded PLEL hydrogel (PRP/PLEL) had little cytotoxicity, and promoted EaHy926 proliferation, migration and tube formation; the factor release assay additionally indicated that PLEL realized the controlled release of PRP GFs for as long as 14 days. When employed to treat rodents’ full-thickness skin defects, PRP/PLEL showed a significantly better ability to raise the number of both newly formed and mature blood vessels compared to the control, PLEL and PRP groups. Furthermore, the PRP/PLEL-treated group displayed faster wound closure, better reepithelialization and collagen formation. Taken together, PRP/PLEL provides a promising strategy for promoting angiogenesis and skin wound healing, which extends the potential of this dressing for clinical application

Short-Term Evaluation of Bone–ACL–Bone Complex Allograft in ACL Reconstruction in a Rabbit Model

The study is to evaluate incorporation of a bone-anterior cruciate ligament-bone (B-ACL-B) allograft in anterior cruciate ligament (ACL) reconstruction in a rabbit model. A total of 61 New Zealand white rabbits were used, with 23 donor rabbits for harvesting B-ACL-B allografts and 38 recipient rabbits undergoing unilateral ACL reconstruction with B-ACL-B allograft. Animals were euthanized for biomechanical testing, micro-computed tomography examination, histological analysis, multi-photon microscopy and transmission electron microscopy testing at 2, 4 and 8 weeks after surgery. Gross inspection and radiographs confirmed the intact ACL allograft in the proper anatomic position. Progressive healing occurred between the bone block and the bone tunnel as demonstrated by a gradual increase in average bone volume fraction and total mineral density at 4 and 8 weeks. Histological analysis showed new bone formation at the bone block–tunnel interface, with maintenance of the native ACL enthesis. Ultrastructural analysis demonstrated the maintenance of overall collagen matrix alignment, while there was repopulation with smaller diameter collagen fibrils. There was no significant difference between 4 and 8 weeks in mean failure force (p = 0.39) or stiffness (p = 0.15) for the B-ACL-B allografts. This study demonstrates the restoration of the normal anatomy of the ACL and progressive graft incorporation and remodeling using a B-ACL-B allograft for ACL reconstruction in the rabbit knee

Accurate intravoxel incoherent motion parameter estimation using Bayesian fitting and reduced number of low b‐values

International audienc