Abstract Chronic inflammation and extensive osteoclast formation play critical roles in wear-debris-induced peri-implant osteolysis. We investigated the potential impact of dopamine on ...titanium-particle-induced inflammatory osteolysis in vivo and in vitro . Twenty-eight C57BL/6J mice were randomly assigned to four groups: sham control (PBS treatment), titanium (titanium/PBS treatment), low- (titanium/2 μg kg−1 day−1 dopamine) and high-dopamine (titanium/10 μg kg−1 day−1 dopamine). After 2 weeks, mouse calvariae were collected for micro-computed tomography (micro-CT) and histomorphometry analysis. Bone-marrow-derived macrophages (BMMs) were isolated to assess osteoclast differentiation. Dopamine significantly reduced titanium-particle-induced osteolysis compared with the titanium group as confirmed by micro-CT and histomorphometric data. Osteoclast numbers were 34.9% and 59.7% (both p < 0.01) lower in the low- and high-dopamine-treatment groups, respectively, than in the titanium group. Additionally, low RANKL, tumor necrosis factor-α, interleukin-1β and interleukin-6 immunochemistry staining were noted in dopamine-treatment groups. Dopamine markedly inhibited osteoclast formation, osteoclastogenesis-related gene expression and pro-inflammatory cytokine expression in BMMs in a dose-dependent manner. Moreover, the resorption area was decreased with 10−9 M and 10−8 M dopamine to 40.0% and 14.5% (both p < 0.01), respectively. Furthermore, the inhibitory effect of dopamine was reversed by the D2-like-receptor antagonist haloperidol but not by the D1-like-receptor antagonist SCH23390. These results suggest that dopamine therapy could be developed into an effective and safe method for osteolysis-related disease caused by chronic inflammation and excessive osteoclast formation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
The existing multi-satellite dynamic mission planning system hardly satisfies the requirements of fast response time and high mission benefit in highly dynamic situations. In the meantime, a ...reasonable decision-maker preference mechanism is an additional challenge for multi-satellite imaging dynamic mission planning based on user preferences (MSDMPUP). Therefore, this study proposes the hybrid preference interaction mechanism and knowledge transfer strategy for the multi-objective evolutionary algorithm (HPIM–KTSMOEA). Firstly, an MSDMPUP model based on a task rolling window is constructed to achieve timely updating of the target task importance degree through the simultaneous application of periodic triggering and event triggering methods. Secondly, the hybrid preference interaction mechanism is constructed to plan according to the satellite controller’s preference-based commands in different phases of the optimal search of the mission planning scheme to effectively respond to the dynamic changes in the environment. Finally, a knowledge transfer strategy for the multi-objective evolutionary algorithm is proposed to accelerate population convergence in new environments based on knowledge transfer according to environmental variability. Simulation experiments verify the effectiveness and stability of the method in processing MSDMPUP. This study found that the HPIM–KTSMOEA algorithm has high task benefit, short response time, and high task completion when processing MSDMPUP.
Abstract
Objective.
Asynchronous brain-computer interfaces (BCIs) show significant advantages in many practical application scenarios. Compared with the rapid development of synchronous BCIs ...technology, the progress of asynchronous BCI research, in terms of containing multiple targets and training-free detection, is still relatively slow. In order to improve the practicability of the BCI, a spatio-temporal equalization multi-window algorithm (STE-MW) was proposed for asynchronous detection of steady-state visual evoked potential (SSVEP) without the need for acquiring calibration data.
Approach.
The algorithm used SIE strategy to intercept EEG signals of different lengths through multiple stacked time windows and statistical decisions-making based on Bayesian risk decision-making. Different from the traditional asynchronous algorithms based on the ‘non-control state detection’ methods, this algorithm was based on the ‘statistical inspection-rejection decision’ mode and did not require a separate classification of non-control states, so it can be effectively applied to detections for large-scale candidates.
Main results.
Online experimental results involving 14 healthy subjects showed that, in the continuously input experiments of 40 targets, the algorithm achieved the average recognition accuracy of
97.2
±
2.6
%
and the average information transfer rate (ITR) of
106.3
±
32.0
bits
mi
n
−
1
. At the same time, the average false alarm rate in the 240 s resting state test was
0.607
±
0.602
mi
n
−
1
. In the free spelling experiments involving patients with severe amyotrophic lateral sclerosis, the subjects achieved an accuracy of 92.7% and an average ITR of 43.65 bits min
−1
in two free spelling experiments.
Significance.
This algorithm can achieve high-performance, high-precision, and asynchronous detection of SSVEP signals with low algorithm complexity and false alarm rate under multi-targets and training-free conditions, which is helpful for the development of asynchronous BCI systems.
With advancements in medical research, broader range of diseases may be curable, which indicates some patients may not die owing to the disease of interest. The mixture cure model, which can capture ...patients being cured, has received an increasing attention in practice. However, the existing mixture cure models only focus on major events with potential cures while ignoring the potential risks posed by other non-curable competing events, which are commonly observed in the real world. The main purpose of this article is to propose a new mixture cure model allowing non-curable competing risk. A semiparametric estimation method is developed via an EM algorithm, the asymptotic properties of parametric estimators are provided and its performance is demonstrated through comprehensive simulation studies. Finally, the proposed method is applied to a prostate cancer clinical trial dataset.
•Propose a new mixture cure model allowing non-curable competing risk.•Develop a semiparametric estimation method via an EM algorithm.•Derive the asymptotic properties of parametric estimators in the proposed model.
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Microbiomes can greatly affect the quality of fermented food and beverages, including tea. In this study, microbial populations were characterized during black and green tea manufacturing, revealing ...that tea processing steps can drive both the bacterial and fungal community structure. Tea leaves were found to mostly harbor Proteobacteria, Bacteriodetes, Firmicutes, and Actinobacteria among bacteria and Ascomycetes among fungi. During processing, tea microbial populations changed especially between sterilized and unsterilized samples. The surface sterilization of fresh leaves before processing can remove many microbes, especially the bacteria of the genera Sphingomonas and Methylobacteria, indicating that these are mostly phylloplane microbes on tea leaves. The surface sterilization removed most fungi, except the Debaryomyces. We also observed a fluctuation in the content of several tea quality-related metabolites during processing. Caffeine and theanine were found in the same quantities in green tea with or without leaf surface sterilization. However, the sterilization process dramatically decreased the content of total catechins and theanine in black tea, indicating that microbes on the surface of tea leaf may be involved in maintaining the formation of these important metabolites during black tea processing.
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Activating the redox chemistry of transition metal catalysts to dynamically construct adaptive heterojunctions while incorporating lattice mismatch-induced interfacial stress/lattice strain is ...critical for designing electrocatalysts with high water oxidation activity. Here, we perform electrochemically induced surface reconstruction of an adaptive hydrangea-like heterojunction (NS/NOOH), where crystalline NiOOH is epitaxially grown on the surface of NiSe nanorods (NS) to induce lattice mismatch and then generate interfacial stress. This lattice strain combined with the sufficient exposure of electrochemically active sites gifted by the hydrangea-like structure and the synergistic interfacial effect between the phases endows NS/NOOH-30 with a 25-fold and 30-fold improvement in OER performance compared with the NS/NOOH counterpart without stress during the initial cycle and original NS, respectively. Only 260 mV of overpotential is required even at high current densities (
j
= 500 mA cm
−2
), which meets the industrial requirements. This work demonstrates the importance of
in situ
surface phase transitions of electrocatalysts to generate interfacial stress and provide new insight into lattice engineering. These new insights also open up the possibility of developing highly active heterojunction catalysts through selected surface reconstruction processes.
Combined with the interface stress caused by lattice mismatch, an adaptive nanoflower heterostructure is dynamically constructed for highly efficient oxygen evolution reaction by activating the redox chemistry of NiSe.
Achieving accurate CFD prediction of turbulent combustion is challenging due to the multiscale nature of the dynamical system and the need to understand the effect of the small-scale physical ...features. Since direct numerical simulation (DNS) is still not feasible even for today’s computing power, Reynolds-averaged Navier-Stokes (RANS) or large-eddy simulation (LES) is commonly used as the practical approach for turbulent combustion modeling. Nevertheless, physical models employed by RANS or LES for describing the interactions between the turbulence, chemical kinetics, and thermodynamic properties of the fluid are often inadequate because of the uncertainties in the dynamical system, including those in the model parameters, initial and boundary conditions, and numerical methods. Understanding and reducing these uncertainties are critical to the CFD prediction of turbulence and chemical reactions. To achieve this, this dissertation is focused on the development of a Bayesian computational framework for the uncertainty estimation of the dynamical system. In the framework, a data assimilation (DA) algorithm is integrated to obtain a more accurate solution by combining the CFD model and available data. This research details the development, verification, and validation of a multi-algorithm system (referred to as DA+CFD system) that aims to increase the predictability of CFD modeling of turbulent and combusting flows. Specifically, in this research, we develop and apply a Bayesian computational framework by integrating our high-order CFD algorithm, Chord, with the maximum likelihood ensemble filter to improve the CFD prediction of turbulent combustion in complex geometry. The verified and validated system is applied to a time-evolving, reacting shear-layer mixing problem and turbulent flows in a bluff-body combustor with and without C$_3$H$_8$-air combustion. Results demonstrate the powerful capability of the DA+CFD system in improving our understanding of the uncertainties in model and data and the impact of data on the model. This research makes novel contributions, including (i) the development of a new alternative approach to improve the predictability of CFD modeling of turbulent combustion by applying data assimilation, (ii) the derivation of new insights on factors, such as where, what, and when data should be assimilated and thus providing potential guidance to experimental design, and (iii) the demonstration of data assimilation as a potentially powerful approach to improve CFD modeling of turbulent combustion in engineering applications and reduce the uncertainties with data. Future work will focus on a performance study of the present DA+CFD system for turbulent combustion of high Reynolds numbers and understanding the uncertainty in model parameters for developing and assessing physical models based on available information.
Jelinski Moranda (JM) model is frequently used in software reliability. The objective Bayesian inference was proposed to estimate the parameters of JM model. Jeffreys prior and reference priors have ...been derived. Besides, the properties of corresponding posteriors were deduced and some modifications were made which made the posterior distributions proper. Then Gibbs sampling was utilized to obtain the Bayesian estimators, credible intervals and coverage probabilities of the parameters. Comparisons in the efficiency of the maximum likelihood estimators and Bayesian estimators under different priors for various sample sizes have been done by simulations and a real data set was analyzed for illustrative purpose.
•Jeffreys prior and reference priors for JM (Jelinski Moranda) model parameters are derived.•Probability matching priors are obtained for JM model parameters.•Objective Bayesian estimates are better than MLE when sample size is small.
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