ALS-Associated E478G Mutation in Human OPTN (Optineurin) Promotes Inflammation and Induces Neuronal Cell Death

Amyotrophic Lateral Sclerosis (ALS) is a group of neurodegenerative disorders that featured with the death of motor neurons, which leads to loss of voluntary control on muscles. The etiologies vary among different subtypes of ALS, and no effective management or medication could be provided to the patients, with the underlying mechanisms incompletely understood yet. Mutations in human Optn (Optineurin), particularly E478G, have been found in many ALS patients. In this work, we report that NF-κB activity was increased in Optn knockout (Optn−/−) MEF (mouse embryonic fibroblast) cells expressing OPTN of different ALS-associated mutants especially E478G. Inflammation was significantly activated in mice infected with lenti-virus that allowed overexpression of OPTNE478G mutation in the motor cortex, with marked increase in the secretion of pro-inflammatory cytokines as well as neuronal cell death. Our work with both cell and animal models strongly suggested that anti-inflammation treatment could represent a powerful strategy to intervene into disease progression in ALS patients who possess the distinctive mutations in OPTN gene

Parallax correction in collocating CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) observations: Method and application to convection study

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95467/1/jgrd17318.pd

Analysis of Precursors Prior to Rock Burst in Granite Tunnel Using Acoustic Emission and Far Infrared Monitoring

To understand the physical mechanism of the anomalous behaviors observed prior to rock burst, the acoustic emission (AE) and far infrared (FIR) techniques were applied to monitor the progressive failure of a rock tunnel model subjected to biaxial stresses. Images of fracturing process, temperature changes of the tunnel, and spatiotemporal serials of acoustic emission were simultaneously recorded during deformation of the model. The b-value derived from the amplitude distribution data of AE was calculated to predict the tunnel rock burst. The results showed that the vertical stress enhanced the stability of the tunnel, and the tunnels with higher confining pressure demonstrated a more abrupt and strong rock burst. Abnormal temperature changes around the wall were observed prior to the rock burst of the tunnel. Analysis of the AE events showed that a sudden drop and then a quiet period could be considered as the precursors to forecast the rock burst hazard. Statistical analysis indicated that rock fragment spalling occurred earlier than the abnormal temperature changes, and the abnormal temperature occurred earlier than the descent of the AE b-value. The analysis indicated that the temperature changes were more sensitive than the AE b-value changes to predict the tunnel rock bursts

Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural Network

Different types of rocks generate acoustic emission (AE) signals with various frequencies and amplitudes. How to determine rock types by their AE characteristics in field monitoring is also useful to understand their mechanical behaviors. Different types of rock specimens (granulite, granite, limestone, and siltstone) were subjected to uniaxial compression until failure, and their AE signals were recorded during their fracturing process. The wavelet transform was used to decompose the AE signals, and the artificial neural network (ANN) was established to recognize the rock types and noise (artificial knock noise and electrical noise). The results show that different rocks had different rupture features and AE characteristics. The wavelet transform provided a powerful method to acquire the basic characteristics of the rock AE and the environmental noises, such as the energy spectrum and the peak frequency, and the ANN was proved to be a good method to recognize AE signals from different types of rocks and the environmental noises

Numerical Simulation Study of Brittle Rock Materials from Micro to Macro Scales Using Digital Image Processing and Parallel Computing

The multi-scale, high-resolution and accurate structural modeling of rocks is a powerful means to reveal the complex failure mechanisms of rocks and evaluate rock engineering safety. Due to the non-uniformity and opacity of rocks, describing their internal microstructure, mesostructure and macro joints accurately, and how to model their progressive fracture process, is a significant challenge. This paper aims to build a numerical method that can take into account real spatial structures of rocks and be applied to the study of crack propagation and failure in different scales of rocks. By combining the failure process analysis (RFPA) simulator with digital image processing technology, large-scale finite element models of multi-scale rocks, considering microstructure, mesostructure, and macro joints, were created to study mechanical and fracture behaviors on a cloud computing platform. The Windows-Linux interactive method was used for digital image processing and parallel computing. The simulation results show that the combination of a parallel RFPA solver and digital image modeling technology can achieve high-resolution structural modeling and high-efficiency calculation. In microscopic simulations, the influence of shale fractures and mineral spatial distribution on the fracture formation process can be revealed. In the mesostructure simulation, it can be seen that the spatial distribution of minerals has an impact on the splitting mode of the Brazilian splitting model. In the simulation of a joined rock mass, the progressive failure process can be effectively simulated. According to the results, it seems that the finite element parallel computing simulation method based on digital images can simulate the multi-scale failure process of brittle materials from micro to macro scales. Primarily, efficient parallel computing based on a cloud platform allows for the multi-scale, high-resolution and realistic modeling and analysis of rock materials

The Mechanical and Fracturing of Rockburst in Tunnel and Its Acoustic Emission Characteristics

The phenomenon of acoustic emission (AE) is associated with rock failure and rock fracturing. In order to investigate the influence of tectonic stress on rockburst in tunnel, a biaxial loading experiment system was used in this study. The excavation operation is undertaken at the center of samples to monitor the tunnel forming process in situ, and the different horizontal stresses can be studied by using the AE monitoring technique. The dynamical fracturing process of the tunnel model was summarized, and the timing parameters of AE signals in rockburst stages were obtained. The curves of AE energy and cumulative AE energy with time show a “step-like” rising trend before the occurrence of rockburst. The evolution of macro- and mesocracks is captured, and the mechanical conditions for a “V-shaped” rockburst pit are derived. As the horizontal stress increases, the effect of excavation unloading becomes more pronounced, and the damage caused by the rockburst intensifies. In the early stage of rockburst evolution, the fracturing type follows a model of tensile-shear mix model. A positive relationship between the ratio of shear fracturing type and the horizontal stress can be noted when the rock is about to burst, and the high intensity and the high energy released of from the rock-fracturing event have become evident. Thus, the results indicate that one should focus on monitoring both sides of the surrounding rock of the tunnel so as to extract the characteristics of the process of tunnel in tunnel. The applications of biaxial loading system and during an excavation operation provide a useful tool to simulate the rock burst in tunnel at an engineering site

Risk factors for infectious complications of ANCA-associated vasculitis: a cohort study

Abstract Background Severe infections are common complications of immunosuppressive treatment for antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) with renal involvement. We investigated the clinical characteristics and risk factors of severe infection in Chinese patients with AAV after immunosuppressive therapy. Methods A total of 248 patients with a new diagnosis of ANCA-associated vasculitis were included in this study. The incidence, time, site, and risk factors of severe infection by the induction therapies were analysed. Multivariate Cox proportional hazards models were used to calculate hazard ratios (HRs) with 95% confidence intervals (CI). Results A total of 103 episodes of severe infection were identified in 86 (34.7%, 86/248) patients during a median follow-up of 15 months. The incidence of infection during induction therapy was 38.5% for corticosteroids (CS), 39.0% for CS+ intravenous cyclophosphamide (IV-CYC), 33.8% for CS+ mycophenolate mofetil and 22.5% for CS + tripterygium glycosides, 76 (73.8%) infection episodes occurred within 6 months, while 66 (64.1%) occurred within 3 months. Pneumonia (71.8%, 74/103) was the most frequent type of infection, and the main pathogenic spectrum included bacteria (78.6%), fungi (12.6%), and viruses (8.7%). The risk factors associated with infection were age at the time of diagnosis (HR = 1.003, 95% CI = 1.000–1.006), smoking (HR = 2.338, 95% CI = 1.236–4.424), baseline secrum creatinine (SCr) ≥5.74 mg/dl (HR = 2.153, 95% CI = 1.323–3.502), CD4+ T cell< 281 μl (HR = 1.813, 95% CI = 1.133–2.900), and intravenous cyclophosphamide regimen (HR = 1.951, 95% CI =1.520–2.740). Twelve (13.9%) patients died of severe pneumonia. Conclusion The infection rate during induction therapy was high in patients with AAV. Bacterial pneumonia was the main type of infection encountered. Age at the time of diagnosis, smoking, baseline SCr ≥5.74 mg/dl, CD4+ T cell< 281 μl, and IV-CYC therapy were identified as risk factors for infection