A Numerical Investigation of the Sparkjet Actuator in Multiple-shot Mode

AbstractComputational simulations were performed in multiple-shot mode to investigate the effects of the boundary conditions and the deposition energy on the performance of the Sparkjet actuator. The user define function (UDF) was applied in the source term of the energy equation to imitate the very arc current discharges which produce the synthetic flow. The method of numerical simulation is verified by the existing experimental and analytical data. Two parameters including the integration mass and momentum are defined to evaluate the performance of the Sparkjet actuator. The simulation results show that Sparkjet flow is more affected by the boundary conditions of the external walls of the cavity and its deposition energy. The performance of Sparkjet actuator drops with the increase of operation cycle when the wall of cavity is adiabatic. When the temperature of wall of cavity is constant, the integration mass and momentum during the exhaling stage decrease with the increase of temperature. The performance of actuator decreases with the increase of heat transfer coefficient when the wall of cavity is set to be coupled with effect of radiation and convection. The performance of actuator increases with increase of deposition energy

Tanshinone IIA protects against dopaminergic neuron degeneration via regulation of DJ-1 and Nrf2/HO-1 pathways in a rodent model of Parkinson’s disease

Purpose: To study the potential neuroprotective effects of tanshinone IIA, a diterpene quinone, in an experimental model of 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP)-induced Parkinson disease (PD). Methods: Mice (C57BL/6) were administered freshly-prepared MPTP at a dose of 20 mg/kg body weight intraperitoneally, 4 times at 2-h intervals, to induce PD. Doses of 12.5, 25 and 50 mg/kg tanshinone IIA were administered to the mice as treatments for PD. Pole and Rota-rod tests were carried out to assess muscular coordination and bradykinesia. Protein expressions, reactive oxygen species (ROS) and malonaldehyde and other parameters were evaluated. Results: Tanshinone IIA at doses of 12.5, 25 and 50 mg/kg reduced deficits in muscular coordination and improved learning ability of MPTP-treated mice. It also reduced loss of tyrosine hydroxylase (TH)- positive neurons following MPTP-induction. Tanshinone IIA regulated apoptotic pathway proteins, i.e., Bax and Bcl-2, and inhibited the translocation of Cyt C to the mitochondria. Oxidative stress induced by MPTP was significantly inhibited by tanshinone IIA via up-regulation of DJ-1/Nrf2 /HO-1 expression and reduction of ROS and MDA levels. Brain tissue total glutathione content was increased by tanshinone IIA treatment. Conclusion: Tanshinone IIA effectively improves antioxidant status and reduces neuronal loss following MPTP treatment. These results indicate that tanshinone IIA exerts protective effects in MPTPinduced PD in mice. Thus, tanshinone IIA has a good potential for use as a therapy for PD

Influence of porosity on osteogenesis, bone growth and osteointegration in trabecular tantalum scaffolds fabricated by additive manufacturing

Porous tantalum implants are a class of materials commonly used in clinical practice to repair bone defects. However, the cumbersome and problematic preparation procedure have limited their widespread application. Additive manufacturing has revolutionized the design and process of orthopedic implants, but the pore architecture feature of porous tantalum scaffolds prepared from additive materials for optimal osseointegration are unclear, particularly the influence of porosity. We prepared trabecular bone-mimicking tantalum scaffolds with three different porosities (60%, 70% and 80%) using the laser powder bed fusing technique to examine and compare the effects of adhesion, proliferation and osteogenic differentiation capacity of rat mesenchymal stem cells on the scaffolds in vitro. The in vivo bone ingrowth and osseointegration effects of each scaffold were analyzed in a rat femoral bone defect model. Three porous tantalum scaffolds were successfully prepared and characterized. In vitro studies showed that scaffolds with 70% and 80% porosity had a better ability to osteogenic proliferation and differentiation than scaffolds with 60% porosity. In vivo studies further confirmed that tantalum scaffolds with the 70% and 80% porosity had a better ability for bone ingrowh than the scaffold with 60% porosity. As for osseointegration, more bone was bound to the material in the scaffold with 70% porosity, suggesting that the 3D printed trabecular tantalum scaffold with 70% porosity could be the optimal choice for subsequent implant design, which we will further confirm in a large animal preclinical model for better clinical use

Venous thromboembolism after oral and maxillofacial oncologic surgery : report and analysis of 14 cases in Chinese population

Venous thromboembolism (VTE) including deep vein thrombosis (DVT) and pulmonary embolism (PE) is a leading cause of death in cancer patients. The aim of this study was to explore the potential risk factor of VTE in oral and maxillofacial oncological surgery. The data of patients who received operation in our institution were gathered in this retrospective study. A diagnosis of VTE was screened and confirmed by computer tomography angiography (CTA) of pulmonary artery or ultrasonography examination of lower extremity. Medical history and all perioperative details were analyzed. 14 patients were diagnosed as VTE, including 6 cases of PE, 7 cases of DVT, 1case of DVT and PE. The mean age of these patients was 62.07 years. Reconstruction was performed in 12 patients of these cases, most of which were diagnosed as malignance. Mean length of surgery was 8.74 hours, and lower extremity deep venous cannula (DVC) was performed in all these patients. We analyzed several characters of oral and maxillofacial surgery and suggested pay attention to lower extremity DVC which had a high correlation with DVT according to our data

Ultrasound hyperthermia induces apoptosis in head and neck squamous cell carcinoma : an in vitro study

Hyperthermia is considered an efficient complement in the treatment of head and neck squamous cell carcinoma (HNSCC). Hyperthermia induces cell apoptosis in a temperature- and time-dependent manner. However, the molecular mechanism of hyperthermia remains unclear. The aim of this study was to investigate the mechanism of apoptosis induced by ultrasound hyperthermia in HNSCC cell lines HN-30 and HN-13. We examined the dynamic changes of early apoptosis and secondary necrosis in HN-30 and HN-13 cells treated by hyperthermia at 42°C for 10 min. We further examined mitochondrial membrane potential in vitro by ultrasound hyperthermia for different heating temperatures (38-44°C, 10 min) and heating times (42°C, 10-50 min). After heating by ultrasound at 42°C for 10 min, the apoptosis index achieved its highest level at 8 h after treatment, decreased rapidly and remained constant at a reduced level at 12 h. The level of secondary necrosis increased with the level of early apoptosis but remained at a higher level until 14 h. The level of secondary necrosis correlated with the level of early apoptosis (HN-13: r=0.7523, P=0.0030; HN-30: r=0.6510, P=0.016). The fractions of cells with low mitochondrial membrane potential (??) in the heating-temperature grads group and heating-time grads group decreased significantly over time. Therefore, HN-30 and HN-13 cells developed apoptosis after ultrasound hyperthermia treatment with decreases in the mitochondrial transmembrane potential level. Ultrasound hyperthermia induces apoptosis in HN-30 and HN-13 cells, possibly via the mitochondrial caspase pathway

Prevalence and risk factors of abnormal left ventricular geometrical patterns in untreated hypertensive patients

BACKGROUND: The various prevalence of LVH and abnormal LV geometry have been reported in different populations. So far, only a few reports are available on the prevalence of LV geometric patterns in a large Chinese untreated hypertensive population. METHODS: A total of 9,286 subjects (5167 men and 4119 women) completed the survey and 1641 untreated hypertensive patients (1044 males and 597 females) enrolled in the present study. The LV geometry was classified into four patterns: normal; abnormal,defined as concentric remodeling;concentric or eccentric hypertrophy based on the values of left ventricular mass index (LVMI) and relative wall thickness (RWT). Logistic regression model was applied to determine the odds ratio (OR) and 95% confidence intervals (CI) of the risk factors of left ventricular hypertrophy. RESULTS: The prevalence of LVH was 20.2% in untreated hypertensive patients, much higher in women (30.8%) than in men (14.2%) (P < 0.01). The prevalence of LV geometrical patterns was 34.9%, 11.1%, 9.1% for concentric remodeling, concentric and eccentric hypertrophy,respectively. After adjustment by using Logistic regression model, the risk factors for LVH and abnormal LV geometry were age, female, systolic blood pressure, and body mass index. And low high density lipoprotein maybe a positive factor. CONCLUSIONS: The prevalence of LVH and abnormal LV geometric patterns was higher in women than in men and increased with age. It is crucial to improve the awareness rate of hypertension and control the risk factors of CV complications in untreated hypertensive population

Two-Link Flexible Manipulator Modeling and Tip Trajectory Tracking Based on Absolute Nodal Coordinate Method

Abstract It has been demonstrated that the absolute nodal coordinate formulation (ANCF) proposed recently in literature can be used to exactly describe the flexible multibody system unlike traditional methods such as the floating coordinate method and assumed mode method. Therefore, in this paper a new dynamic modeling technique for a two-link flexible manipulator based on absolute nodal coordinate method is proposed. The link shear effect was taken into account by using the 2D ANCF shear beam element. The resulting state equation can be explicitly described by generalized coordinate since the system mass matrix is constant in the ANCF framework. The proposed method is validated through the two-link flexible manipulator tip circle and square trajectory tracking control simulations by using a simple PD controller. To improve computational efficiency, the invariant matrix method and the Broyden quasi-Newton method are introduced. To improve the tracking accuracy, different PD parameters in different simulation periods are used. The simulation results indicate that modeling and controlling the flexible manipulator based on the ANCF is effective

HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative

AI-based protein structure prediction pipelines, such as AlphaFold2, have achieved near-experimental accuracy. These advanced pipelines mainly rely on Multiple Sequence Alignments (MSAs) as inputs to learn the co-evolution information from the homologous sequences. Nonetheless, searching MSAs from protein databases is time-consuming, usually taking dozens of minutes. Consequently, we attempt to explore the limits of fast protein structure prediction by using only primary sequences of proteins. HelixFold-Single is proposed to combine a large-scale protein language model with the superior geometric learning capability of AlphaFold2. Our proposed method, HelixFold-Single, first pre-trains a large-scale protein language model (PLM) with thousands of millions of primary sequences utilizing the self-supervised learning paradigm, which will be used as an alternative to MSAs for learning the co-evolution information. Then, by combining the pre-trained PLM and the essential components of AlphaFold2, we obtain an end-to-end differentiable model to predict the 3D coordinates of atoms from only the primary sequence. HelixFold-Single is validated in datasets CASP14 and CAMEO, achieving competitive accuracy with the MSA-based methods on the targets with large homologous families. Furthermore, HelixFold-Single consumes much less time than the mainstream pipelines for protein structure prediction, demonstrating its potential in tasks requiring many predictions. The code of HelixFold-Single is available at https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/protein_folding/helixfold-single, and we also provide stable web services on https://paddlehelix.baidu.com/app/drug/protein-single/forecast

Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models

Protein-ligand structure prediction is an essential task in drug discovery, predicting the binding interactions between small molecules (ligands) and target proteins (receptors). Although conventional physics-based docking tools are widely utilized, their accuracy is compromised by limited conformational sampling and imprecise scoring functions. Recent advances have incorporated deep learning techniques to improve the accuracy of structure prediction. Nevertheless, the experimental validation of docking conformations remains costly, it raises concerns regarding the generalizability of these deep learning-based methods due to the limited training data. In this work, we show that by pre-training a geometry-aware SE(3)-Equivariant neural network on a large-scale docking conformation generated by traditional physics-based docking tools and then fine-tuning with a limited set of experimentally validated receptor-ligand complexes, we can achieve outstanding performance. This process involved the generation of 100 million docking conformations, consuming roughly 1 million CPU core days. The proposed model, HelixDock, aims to acquire the physical knowledge encapsulated by the physics-based docking tools during the pre-training phase. HelixDock has been benchmarked against both physics-based and deep learning-based baselines, showing that it outperforms its closest competitor by over 40% for RMSD. HelixDock also exhibits enhanced performance on a dataset that poses a greater challenge, thereby highlighting its robustness. Moreover, our investigation reveals the scaling laws governing pre-trained structure prediction models, indicating a consistent enhancement in performance with increases in model parameters and pre-training data. This study illuminates the strategic advantage of leveraging a vast and varied repository of generated data to advance the frontiers of AI-driven drug discovery

Research on Distribution of Flow Field and Simulation of Working Pulsation Based on Rotating-Sleeve Distributing-Flow System

To solve problems of leakage, vibration, and noise caused by disorders of flow field distribution and working pulsation in the rotating-sleeve distributing-flow system, governing equations of plunger and rotating sleeve and computational fluid dynamics (CFD) model are developed through sliding mesh and dynamic mesh technology to simulate flow field and working pulsation. Simulation results show that the following issues exist: obviously periodic fluctuation and sharp corner in flow pulsation, backward flow when fluid is transformed between discharge and suction, and serious turbulence and large loss in kinetic energy around the damping groove in transitional movements. Pressure in the pump chamber rapidly rises to 2.2 MPa involving over 10% more than nominal pressure when the plunger is at the Top Dead Center (TDC) considering changes about damping groove’s position and flow area in two transitional movements. Shortly pressure overshoot gradually decreases to a normal condition with increasing flow area. Similarly, pressure in the pump chamber instantaneously drops to a saturated vapor pressure −98.9 KPa when the plunger is at the Bottom Dead Center (BDC). With increasing flow area the overshoot gradually increases to the normal condition. This research provides foundations for investigating flow field characteristic and structure optimization of rotating-sleeve distributing-flow system