The Unfolded Protein Response Sensor PERK Mediates Stiffness-Dependent Adaptation in Glioblastoma Cells

Glioblastoma multiforme (GBM) is the most aggressive brain tumor in adults. In addition to genetic causes, the tumor microenvironment (TME), including stiffening of the extracellular matrix (ECM), is a main driver of GBM progression. Mechano-transduction and the unfolded protein response (UPR) are essential for tumor-cell adaptation to harsh TME conditions. Here, we studied the effect of a variable stiff ECM on the morphology and malignant properties of GBM stem cells (GSCs) and, moreover, examined the possible involvement of the UPR sensor PERK herein. For this, stiffness-tunable human blood plasma (HBP)/alginate hydrogels were generated to mimic ECM stiffening. GSCs showed stiffness-dependent adaptation characterized by elongated morphology, increased proliferation, and motility which was accompanied by F-Actin cytoskeletal remodeling. Interestingly, in PERK-deficient GSCs, stiffness adaptation was severely impaired, which was evidenced by low F-Actin levels, the absence of F-Actin remodeling, and decreased cell proliferation and migration. This impairment could be linked with Filamin-A (FLN-A) expression, a known interactor of PERK, which was strongly reduced in PERK-deficient GSCs. In conclusion, we identified a novel PERK/FLNA/F-Actin mechano-adaptive mechanism and found a new function for PERK in the cellular adaptation to ECM stiffening

Load characterization of the main bearing of a large tunnel boring machine based on dynamic characteristic parameters

This research was carried out to solve the problem of the reasonable characterization of the working load of the main bearing of a large tunnel boring machine (TBM) under complex engineering geological conditions and equipment working statuses. A typical telescopic swing main drive system is considered, and a characterization approach based on acquired dynamic characteristic parameters is proposed. First, the axial load F a , radial load F r , overturning moment M k , and torque T are considered as the load indexes of the main bearing. The main drive load model is then developed, and the load indexes are expressed by exploring the relationships between each load index and dynamic characteristic parameters such as the pressures, displacements of the hydraulic cylinders, and torques of the driving motors. Finally, the load indexes are characterized based on a subsea tunnel shield project, representative engineering geologies, and characteristic load inputs. The results indicate that by taking into account the variable attitude of the main drive system, each load index can be expressed as functions of the pressures, displacements of the hydraulic cylinders, and torques of the driving motors. According to the variation of the dynamic characteristic parameters, the load condition of the main bearing during application is accurately characterized. Different geologies are found to correspond to different load levels; the load under the dolomitic limestone and filling karst cave strata is found to be almost 1.5–1.9 times greater than that under diabase, while the torque is almost five times greater. The proposed load characterization approach provides an accurate load input conforming to engineering practice for the design and selection of the main bearing

STRUCTURAL OPTIMIZATION FOR CONTINUOUS CHANGE RAIL LINE BASED ON IMPROVED ALGORITHMS CHAOS

Summed up six typical working conditions of the rail track replacement device. Its working load is calculated by three kinds of method. And the theoretical model of telescopic rail track replacement car for work equipment is established.Accelerating factor and inertia weight Improved PSO,and adding the chaos algorithm optimize five main parameters of hexagonal cross-section telescopic boom. The optimization results show,telescopic quality has reduced by 29. 6%; overall centroid position has down; counterweight with telescopic functionis smaller; the balance of front and rear axle bogie track replacement has achieved; In other railcars improved machine stability

Investigation into Multiaxial Character of Thermomechanical Fatigue Damage on High-Speed Railway Brake Disc

The multiaxial character of high-speed railway brake disc thermomechanical fatigue damage is studied in this work. Although the amplitudes and distributions of temperature, strain and stress are similar with uniform and rotating loading methods, the multiaxial behavior and out-of-phase failure status can only be revealed by the latter one. With the help of a multiaxial fatigue model, fatigue damage evaluation and fatigue life prediction are implemented, the contribution of a uniaxial fatigue parameter, multiaxial fatigue parameter and out-of-phase failure parameter to the total damage is discussed, and it is found that using the amplitude and distribution of temperature, stress and strain for fatigue evaluation will lead to an underestimation of brake disc thermomechanical fatigue damage. The results indicate that the brake disc thermomechanical fatigue damage belongs to a type of multiaxial fatigue. Using a uniaxial fatigue parameter causes around 14% underestimation of fatigue damage, while employing a multiaxial fatigue parameter without the consideration of out-of-phase failure will lead to an underestimation of about 5%. This work explains the importance of studying the thermomechanical fatigue damage of the brake disc from the perspective of multiaxial fatigue

Reading comprehension and metalinguistic knowledge in Chinese readers: a meta-analysis

Metalinguistic knowledge has a facilitative effect on reading comprehension. This meta-analysis examined the relationship between metalinguistic knowledge and reading comprehension among Chinese students. By focusing on both Chinese and English scripts’ reading comprehension performance, this study synthesized 46 studies with 73 independent samples that represented 10,793 Chinese students from primary school to university levels. We found that, in both Chinese and English scripts’ reading, morphological awareness had the strongest correlation with reading comprehension, whereas both phonological awareness and orthographical skill had a similar medium correlation with reading comprehension. All three metalinguistic knowledge, which was not significantly influenced by the selected moderators of grade group, area, language type, and assessment, had an independent correlation with reading comprehension. The results suggested that reading stages did not significantly impact the function of metalinguistic knowledge on both Chinese and English scripts’ reading comprehension for Chinese students’. In addition, for Chinese students, morphological awareness plays a more important role than phonological awareness and orthographical skill in both Chinese and English scripts’ reading comprehension

Energy-Efficient Data Transmission for Underwater Wireless Sensor Networks: A Novel Hierarchical Underwater Wireless Sensor Transmission Framework

The complexity of the underwater environment enables significant energy consumption of sensor nodes for communication with base stations in underwater wireless sensor networks (UWSNs), and the energy consumption of nodes in different water depths is unbalanced. How to improve the energy efficiency of sensor nodes and meanwhile balance the energy consumption of nodes in different water depths in UWSNs are thus urgent concerns. Therefore, in this paper, we first propose a novel hierarchical underwater wireless sensor transmission (HUWST) framework. We then propose a game-based, energy-efficient underwater communication mechanism in the presented HUWST. It improves the energy efficiency of the underwater sensors personalized according to the various water depth layers of sensor locations. In particular, we integrate the economic game theory in our mechanism to trade off variations in communication energy consumption due to sensors in different water depth layers. Mathematically, the optimal mechanism is formulated as a complex nonlinear integer programming (NIP) problem. A new energy-efficient distributed data transmission mode decision algorithm (E-DDTMD) based on the alternating direction method of multipliers (ADMM) is thus further proposed to tackle this sophisticated NIP problem. The systematic simulation results demonstrate the effectiveness of our mechanism in improving the energy efficiency of UWSNs. Moreover, our presented E-DDTMD algorithm achieves significantly superior performance to the baseline schemes

High-speed, sparse-sampling three-dimensional photoacoustic computed tomography in vivo based on principal component analysis

Photoacoustic computed tomography (PACT) has emerged as a unique and promising technology for multiscale biomedical imaging. To fully realize its potential for various preclinical and clinical applications, development of systems with high imaging speed, reasonable cost, and manageable data flow are needed. Sparse-sampling PACT with advanced reconstruction algorithms, such as compressed-sensing reconstruction, has shown potential as a solution to this challenge. However, most such algorithms require iterative reconstruction and thus intense computation, which may lead to excessively long image reconstruction times. Here, we developed a principal component analysis (PCA)-based PACT (PCA-PACT) that can rapidly reconstruct high-quality, three-dimensional (3-D) PACT images with sparsely sampled data without requiring an iterative process. In vivo images of the vasculature of a human hand were obtained, thus validating the PCA-PACT method. The results showed that, compared with the back-projection (BP) method, PCA-PACT required similar to 50% fewer measurements and similar to 40% less time for image reconstruction, and the imaging quality was almost the same as that for BP with full sampling. In addition, compared with compressed sensing-based PACT, PCA-PACT had approximately sevenfold faster imaging speed with higher imaging accuracy. This work suggests a promising approach for low-cost, 3-D, rapid PACT for various biomedical applications. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)11Nsciescopu

HER2-targeting two-dimensional black phosphorus as a nanoplatform for chemo-photothermal therapy in breast cancer

Trastuzumab (Tmab) targeted therapy or its combination with chemotherapy is normally insufficient to elicit a comprehensive therapeutic response owing to the inherent or acquired drug resistance and systemic toxicity observed in highly invasive HER2-positive breast cancer. In this study, we propose a novel approach that integrates photothermal therapy (PTT) with targeted therapy and chemotherapy, thereby achieving additive or synergistic therapeutic outcomes. We utilize PEGylated two-dimensional black phosphorus (2D BP) as a nanoplatform and photothermal agent to load chemotherapeutic drug mitoxantrone (MTO) and conjugate with Tmab (BP-PEG-MTO-Tmab). The in vitro and in vivo experiments demonstrated that the HER2-targeting BP-PEG-MTO-Tmab complexes exhibited desirable biocompatibility, safety and enhanced cancer cell uptake efficiency, resulting in increased accumulation and prolonged retention of BP and MTO within tumors. Consequently, the complex improved photothermal and chemotherapy treatment efficacy in HER2-positive cells in vitro and a subcutaneous tumor model in vivo, while minimized harm to normal cells and showed desirable organ compatibility. Collectively, our study provides compelling evidence for the remarkable efficacy of targeted and synergistic chemo-photothermal therapy utilizing all-in-one nanoparticles as a delivery system for BP and chemotherapeutic drug in HER2-positive breast cancer

Network Propagation with Dual Flow for Gene Prioritization

<div><p>Based on the hypothesis that the neighbors of disease genes trend to cause similar diseases, network-based methods for disease prediction have received increasing attention. Taking full advantage of network structure, the performance of global distance measurements is generally superior to local distance measurements. However, some problems exist in the global distance measurements. For example, global distance measurements may mistake non-disease hub proteins that have dense interactions with known disease proteins for potential disease proteins. To find a new method to avoid the aforementioned problem, we analyzed the differences between disease proteins and other proteins by using essential proteins (proteins encoded by essential genes) as references. We find that disease proteins are not well connected with essential proteins in the protein interaction networks. Based on this new finding, we proposed a novel strategy for gene prioritization based on protein interaction networks. We allocated positive flow to disease genes and negative flow to essential genes, and adopted network propagation for gene prioritization. Experimental results on 110 diseases verified the effectiveness and potential of the proposed method.</p></div