The emergence of Middle East respiratory syndrome-coronavirus (MERS-CoV) in the Middle East since 2012 has caused more than 900 human infections with ∼40% mortality to date. Animal models are needed ...for studying pathogenesis and for development of preventive and therapeutic agents against MERS-CoV infection. Nonhuman primates (rhesus macaques and marmosets) are expensive models of limited availability. Although a mouse lung infection model has been described using adenovirus vectors expressing human CD26/dipeptidyl peptidase 4 (DPP4), it is believed that a transgenic mouse model is needed for MERS-CoV research. We have developed this transgenic mouse model as indicated in this study. We show that transgenic mice globally expressing hCD26/DPP4 were fully permissive to MERS-CoV infection, resulting in relentless weight loss and death within days postinfection. High infectious virus titers were recovered primarily from the lungs and brains of mice at 2 and 4 days postinfection, respectively, whereas viral RNAs were also detected in the heart, spleen, and intestine, indicating a disseminating viral infection. Infected Tg(+) mice developed a progressive pneumonia, characterized by extensive inflammatory infiltration. In contrast, an inconsistent mild perivascular cuffing was the only pathological change associated with the infected brains. Moreover, infected Tg(+) mice were able to activate genes encoding for many antiviral and inflammatory mediators within the lungs and brains, coinciding with the high levels of viral replication. This new and unique transgenic mouse model will be useful for furthering knowledge of MERS pathogenesis and for the development of vaccine and treatments against MERS-CoV infection.
Small and economical animal models are required for the controlled and extensive studies needed for elucidating pathogenesis and development of vaccines and antivirals against MERS. Mice are the most desirable small-animal species for this purpose because of availability and the existence of a thorough knowledge base, particularly of genetics and immunology. The standard small animals, mice, hamsters, and ferrets, all lack the functional MERS-CoV receptor and are not susceptible to infection. So, initial studies were done with nonhuman primates, expensive models of limited availability. A mouse lung infection model was described where a mouse adenovirus was used to transfect lung cells for receptor expression. Nevertheless, all generally agree that a transgenic mouse model expressing the DPP4 receptor is needed for MERS-CoV research. We have developed this transgenic mouse model as indicated in this study. This new and unique transgenic mouse model will be useful for furthering MERS research.
Our understanding and quantification of global soil nitrous oxide (N2O) emissions and the underlying processes remain largely uncertain. Here, we assessed the effects of multiple anthropogenic and ...natural factors, including nitrogen fertilizer (N) application, atmospheric N deposition, manure N application, land cover change, climate change, and rising atmospheric CO2 concentration, on global soil N2O emissions for the period 1861–2016 using a standard simulation protocol with seven process‐based terrestrial biosphere models. Results suggest global soil N2O emissions have increased from 6.3 ± 1.1 Tg N2O‐N/year in the preindustrial period (the 1860s) to 10.0 ± 2.0 Tg N2O‐N/year in the recent decade (2007–2016). Cropland soil emissions increased from 0.3 Tg N2O‐N/year to 3.3 Tg N2O‐N/year over the same period, accounting for 82% of the total increase. Regionally, China, South Asia, and Southeast Asia underwent rapid increases in cropland N2O emissions since the 1970s. However, US cropland N2O emissions had been relatively flat in magnitude since the 1980s, and EU cropland N2O emissions appear to have decreased by 14%. Soil N2O emissions from predominantly natural ecosystems accounted for 67% of the global soil emissions in the recent decade but showed only a relatively small increase of 0.7 ± 0.5 Tg N2O‐N/year (11%) since the 1860s. In the recent decade, N fertilizer application, N deposition, manure N application, and climate change contributed 54%, 26%, 15%, and 24%, respectively, to the total increase. Rising atmospheric CO2 concentration reduced soil N2O emissions by 10% through the enhanced plant N uptake, while land cover change played a minor role. Our estimation here does not account for indirect emissions from soils and the directed emissions from excreta of grazing livestock. To address uncertainties in estimating regional and global soil N2O emissions, this study recommends several critical strategies for improving the process‐based simulations.
The ensemble of terrestrial biosphere models indicates that global soil N2O emissions have increased from 6.3 ± 1.1 Tg N2O‐N/year in the preindustrial period (the 1860s) to 10.0 ± 2.0 Tg N2O‐N/year in the recent decade (2007–2016). Cropland soil emissions increased from 0.3 Tg N2O‐N/year to 3.3 Tg N2O‐N/year over the same period, accounting for 82% of the total increase, among which 54% attributes to nitrogen fertilizer application. Regionally, China, South Asia, and Southeast Asia underwent rapid increases in cropland N2O emissions since the 1970s. However, European cropland N2O emissions appear to have decreased by 14%.
•We propose a novel vibration-based approach for monitoring and predicting gear wear.•Approach suitable for two main wear phenomena, gear profile change and surface pitting.•An effective and ...efficient surface pitting model is developed in the proposed methodology.•Effectiveness of approach validated using vibration data from lubricated and dry tests.
Gear wear often results in both tooth profile changes caused by abrasive wear, and fatigue pitting. Being able to accurately monitor and predict the profile change (i.e., the wear depth in the direction normal to the gear surface) and surface pitting propagation can bring enormous benefits to industrial practice. However, there is a lack of efficient, reliable, and effective tools to do so. To address this, this paper proposes a gear wear monitoring and prediction approach through the integration of: (i) a dynamic model, to simulate the dynamic responses of the gear system; (ii) two tribological models, to estimate wear depth (in the direction normal to the gear surface) and pitting density (on the gear surface); and (iii), model updating, by comparing simulated and measured vibration signals.
More specifically, a 21-degree-of-freedom dynamic model is used to simulate a spur gearbox setup and produce simulated vibrations and contact forces between the meshing gear teeth. Using the contact pressure (calculated from the force) as an input, the wear depth and pitting density are then predicted by the tribological models and used to modify the gear geometry profile and contact area in the dynamic model. The developed approach allows the dynamic model and the wear models to communicate so that both the gear tooth profile change and pitting density can be simulated continuously. To guarantee accurate prediction results from the models, novel approaches are developed to update the wear coefficients in the tribological models by comparing simulated and measured vibrations. The paper demonstrates the ability and effectiveness of the proposed vibration-based methodology in monitoring and predicting gear wear, specifically the tooth profile change and surface pitting propagation, using measurements from both a lubricated test, dominated by surface pitting propagation with mild tooth profile change, and a dry test dominated by tooth profile change.
•A vibration-based wear mechanism identification procedure is proposed.•Wear evolution is tracked using an indicator of vibration cyclostationarity (CS).•The correlation between surface features and ...vibration characteristics is investigated.•Methods validated using lubricated and dry gear wear tests.
Fatigue pitting and abrasive wear are the most common wear mechanisms in lubricated gears, and they have different effects on the gear transmission system. To develop effective methods for online gear wear monitoring, in this paper, a vibration-based wear mechanism identification procedure is proposed, and then the wear evolution is tracked using an indicator of vibration cyclostationarity (CS). More specifically, with consideration of the underlying physics of the gear meshing process, and the unique surface features induced by fatigue pitting and abrasive wear, the correlation between tribological features of the two wear phenomena and gearmesh-modulated second-order cyclostationary (CS2) properties of the vibration signal is investigated. Differently from previous works, the carrier frequencies (spectral content) of the gearmesh-cyclic CS2 components are analysed and used to distinguish and track the two wear phenomena. The effectiveness of the developed methods in wear mechanism identification and degradation tracking is validated using vibration data collected in two tests: a lubricated test dominated by fatigue pitting and a dry test dominated by abrasive wear. This development enables vibration-based techniques to be used for identifying and tracking fatigue pitting and abrasive wear.
•Tooth spalls and cracks change transmission error in different ways.•Vibration features of spalls and cracks are simulated and measured.•Timing of fault-related events can be used to distinguish ...different fault types.•Mesh phasing can be used to identify which planet is faulty.•A comprehensive diagnostic procedure for planet gears is presented.
Planetary gearboxes have a wide range of applications, but due to their complex layout, the diagnostics of planetary gearboxes is more challenging than that of fixed-axis gearboxes. This is especially so for planet gears, as multiple planets mesh simultaneously, share the same rotating speed and are in mesh with both the sun and ring gears. To enable the development of reliable diagnostic and, even more critically, prognostic algorithms, the detection of a fault needs to be complemented with information about its location and the dominant failure mode. In this study, we present a comprehensive procedure to diagnose localised planet gear faults, adding to fault detection the differential diagnosis of cracks and spalls and the identification of the faulty planet.
To do this, first the transmission error (TE) of planet cracks and spalls is investigated using a finite element model. This is aimed at providing a thorough understanding of how these two types of faults result in different excitations of the system. A lumped parameter model (LPM) is then used to link the changes in TE to specific vibration patterns, which are identified as characteristic of either tooth cracks or spalls. These observations are then combined with a recently proposed concept that uses mesh phasing to identify which planet gear carries a fault, resulting in a comprehensive framework that can both diagnose the type of fault and determine which planet gear it is on. The methodology is verified using experimental data obtained from a planetary gearbox test rig.
Gear tooth wear is an inevitable phenomenon and has a significant influence on gear dynamic features. Although vibration analysis has been widely used to diagnose localised gear tooth faults, its ...techniques for gear wear monitoring have not been well-established. This paper aims at developing a vibration indicator to evaluate the effects of wear on gear performance. For this purpose, a gear state vector is extracted from time synchronous averaged gear signals to describe the gear state. This gear state vector consists of the sideband ratios obtained from a number of tooth meshing harmonics and their sidebands. Then, two averaged logarithmic ratios, ALR and mALR, are defined with fixed and moving references, respectively, to provide complementary information for gear wear monitoring. Since a fixed reference is utilised in the definition of ALR, it reflects the cumulated wear effects on the gear state. An increase in the ALR value indicates that the gear state deviates further from its reference condition. With the use of a moving reference, the indicator mALR shows changes in the gear state within short time intervals, making it suitable for wear process monitoring. The efficiency of these vibration indicators is demonstrated using experimental results from two sets of tests, in which the gears experienced different wear processes. In addition to gear wear monitoring, the proposed indicators can be used as general parameters to detect the occurrence of other faults, such as a tooth crack or shaft misalignment, because these faults would also change the gear vibrations.
•The effects of tooth wear on gear transmission error and gear vibrations are clearly explained.•An averaged logarithmic ratio (ALR) is introduced to represent the changes in the gear conditions.•The efficiency of ALR has been demonstrated using the results of wear particle analysis.•ALR can also be used as a general parameter for gear condition monitoring.
Gear wear introduces geometric deviations in gear teeth and alters the load distribution across the tooth surface. Wear also increases the gear transmission error, generally resulting in increased ...vibration, noise and dynamic loads. This altered loading in turn promotes wear, forming a feedback loop between gear surface wear and vibration. Having the capability to monitor and predict the gear wear process would bring enormous benefits in cost and safety to a wide range of industries, but there are currently no reliable, effective and efficient tools to do so. This paper develops such tools using vibration-based methods.
For this purpose, a vibration-based scheme for updating a wear prediction model is proposed. In the proposed scheme, a dynamic model of a spur gear system is firstly developed to generate realistic vibrations, which allows a quantitative study of the effects of gear tooth surface wear on gearbox vibration responses. The sliding velocity and contact forces from the model are used in combination with the well-known Archard wear model to calculate the wear depth at each contact point in mesh. The worn gear tooth profile is then fed back into the dynamic model as a new geometric transmission error, which represents the deviation of the profile from an ideal involute curve and is thus zero for perfect gears. New vibration responses and tooth contact forces are then obtained from the model, and the process repeated to generate realistic gear wear profiles of varying severities. Since the wear coefficient in the model is not constant during the wear process (and in any case is difficult to estimate initially), measured vibrations are compared with those generated by the model, so as to update the coefficient when a deviation from predictions is detected. With the continually updated dynamic wear model, the wear process can be well monitored, and at any particular time the best possible prediction of remaining useful life can be achieved. The paper illustrates the ability and effectiveness of the proposed scheme using measurements from a laboratory gear rig.
•We propose a vibration-based scheme for updating gear wear prediction.•Prediction based on models of gearbox dynamics and abrasive wear.•Updating of wear constant based on comparing simulated and measured vibration.•Scheme allows reliable wear prediction using simple modelling tools.•Scheme experimentally validated on run-to-failure dry test with spur gears.
We present a microkinetic model for CO
reduction (CO
R) on Cu(211) towards C
products, based on energetics estimated from an explicit solvent model. We show that the differences in both Tafel slopes ...and pH dependence for C
vs C
activity arise from differences in their multi-step mechanisms. We find the depletion in C
products observed at high overpotential and high pH to arise from the 2
order dependence of C-C coupling on CO coverage, which decreases due to competition from the C
pathway. We further demonstrate that CO
reduction at a fixed pH yield similar activities, due to the facile kinetics for CO
reduction to CO on Cu, which suggests C
products to be favored for CO
R under alkaline conditions. The mechanistic insights of this work elucidate how reaction conditions can lead to significant enhancements in selectivity and activity towards higher value C
products.
To facilitate scientific collaboration on polygenic risk scores (PRSs) research, we created an extensive PRS online repository for 35 common cancer traits integrating freely available genome-wide ...association studies (GWASs) summary statistics from three sources: published GWASs, the NHGRI-EBI GWAS Catalog, and UK Biobank-based GWASs. Our framework condenses these summary statistics into PRSs using various approaches such as linkage disequilibrium pruning/p value thresholding (fixed or data-adaptively optimized thresholds) and penalized, genome-wide effect size weighting. We evaluated the PRSs in two biobanks: the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and the population-based UK Biobank (UKB). For each PRS construct, we provide measures on predictive performance and discrimination. Besides PRS evaluation, the Cancer-PRSweb platform features construct downloads and phenome-wide PRS association study results (PRS-PheWAS) for predictive PRSs. We expect this integrated platform to accelerate PRS-related cancer research.
Polygenic risk scores (PRS) are designed to serve as single summary measures that are easy to construct, condensing information from a large number of genetic variants associated with a disease. They ...have been used for stratification and prediction of disease risk. The primary focus of this paper is to demonstrate how we can combine PRS and electronic health records data to better understand the shared and unique genetic architecture and etiology of disease subtypes that may be both related and heterogeneous. PRS construction strategies often depend on the purpose of the study, the available data/summary estimates, and the underlying genetic architecture of a disease. We consider several choices for constructing a PRS using data obtained from various publicly-available sources including the UK Biobank and evaluate their abilities to predict not just the primary phenotype but also secondary phenotypes derived from electronic health records (EHR). This study was conducted using data from 30,702 unrelated, genotyped patients of recent European descent from the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. We examine the three most common skin cancer subtypes in the USA: basal cell carcinoma, cutaneous squamous cell carcinoma, and melanoma. Using these PRS for various skin cancer subtypes, we conduct a phenome-wide association study (PheWAS) within the MGI data to evaluate PRS associations with secondary traits. PheWAS results are then replicated using population-based UK Biobank data and compared across various PRS construction methods. We develop an accompanying visual catalog called PRSweb that provides detailed PheWAS results and allows users to directly compare different PRS construction methods.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
PDF
