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The biological damage caused by the environmental factors such as radiation and its control methods are one of the frontiers of life science research that has received widespread attention. Ionizing radiation can directly interact with target molecules (such as DNA, proteins and lipids) or decomposed by radiation from water, leading to changes in oxidative events and biological activities in cells. Liver is a radiation-sensitive organ, and its radiosensitivity is second only to bone marrow, lymph, gastrointestinal tissue, gonads, embryos and kidneys. In addition, as a key organ of mammals, liver performs a series of functions, including the production of bile, the metabolism of nutrients, the elimination of waste, the storage of glycogen, and the synthesis of proteins. Therefore, liver is prone to various pathophysiological changes. In this review, the effects of radiation on liver injury, its pathogenesis, bystander effect and the natural traditional Chinese medicine to protect the radiation induced liver damage are discussed. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of how to estimate the remaining useful life (RUL) for partially observed linear stochastic degrading systems with survival measurements. The motivation of this paper arises from two engineering facts—the measured degradation signals are taken from a survival degradation path and the degradation progression of the system is inevitably affected by multisource variability. To do so, we first revisit the linear degradation modeling framework driven by the Wiener process to account for the above two facts, and a new state-space model with non-Gaussian state transitions is constructed based on the constraint of survival measurements. Then, the particle filtering algorithm is applied to real-time estimate the non-Gaussian degradation state and random-effect parameter from measurements. Furthermore, we derive the probabilistic distribution of the RUL which can be real-time updated based on the available measurements. To apply the presented method, a two-stage estimation procedure based on particle expectation maximization algorithm is presented to determine and update the model parameters. The novelty of the presented method is to exclude the probability of failure before the current monitoring time and account for both impacts of the multisource variability and surviving degradation path on the RUL estimation. Finally, we demonstrate the proposed approach by a case study on gyros in the inertial platform. [ABSTRACT FROM AUTHOR]

Background: Recent studies have shown that circular RNA (circRNA) is rich in microRNA (miRNA) binding sites. We have previously demonstrated that the antidepressant effect of ketamine is related to the abnormal expression of various miRNAs in the brain. This study determined the expression profile of circRNAs in the hippocampus of rats treated with ketamine. Methods: The aberrantly expressed circRNAs in rat hippocampus after ketamine injection were analyzed by microarray chip, and we further validated these circRNAs by quantitative reverse-transcription PCR (qRT-PCR). The target genes of the different circRNAs were predicted using bioinformatic analyses, and the functions and signal pathways of these target genes were investigated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Results: Microarray analysis showed that five circRNAs were aberrantly expressed in rat hippocampus after ketamine injection (fold change > 2.0, p < 0.05). The results from the qRT-PCR showed that one of the circRNAs was significantly increased (rno_circRNA_014900; fold change = 2.37; p = 0.03), while one was significantly reduced (rno_circRNA_005442; fold change = 0.37; p = 0.01). We discovered a significant enrichment in several GO terms and pathways associated with depression. Conclusion: Our findings showed the abnormal expression of ketamine-induced hippocampal circRNAs in rats. [ABSTRACT FROM AUTHOR]

This paper is concerned with estimating remaining useful life (RUL) for a class of stochastic degrading systems with surviving degradation paths and uncertain measurements. The motivation comes from two engineering facts: the system's degradation state is discretely monitored and the measured degradation signals are taken from a survival degradation path, i.e., its lifetime is greater than the latest observation time, and the underlying degradation state cannot be perfectly observed due to the noise, disturbance, nonideal instruments, etc. Thus, the measured degradation signals are uncertain, but related to the underlying degradation state. Toward this end, we first present a general stochastic degradation modeling approach to account for the abovementioned facts. Then, a non-Gaussian degradation state transition equation is derived considering the constraint of the survival path and thus the particle filtering algorithm is used to estimate the underlying degradation state in real time from uncertain measurements. Furthermore, we derive the RUL distribution based on the first-passage time concept which incorporates the uncertainty of the estimation for the degradation state and can be real-time updated based on the available surviving yet uncertain measurements. The novelty of this paper is to allow us to exclude the probability of failure between discrete monitoring times and to account for the impacts of surviving and uncertain measurements on estimating both the degradation state and the RUL. To apply the presented methodology, a maximum likelihood estimation framework is provided to determine the model parameters based on the expectation maximization algorithm together with particle filtering and smoothing methods. As a special case fallen into the presented framework, a linear degradation modeling and prognostic realization is discussed. Finally, we demonstrate the proposed approach by a case study. [ABSTRACT FROM AUTHOR]

• Prognosis for stochastic degrading systems with massive data is investigated. • A prognosis method is proposed from a data-model interactive perspective. • Deep health indicator extraction and model-driven prognosis are handled interactively. • The remaining life is predicted by modeling the progression of the extracted health indicator. • Advantages in the improvement of the prognosis accuracy are justified. Fast development of various advanced sensing techniques has provided the possibility acquiring massive monitoring data of stochastic degrading systems. In this circumstance, deep learning techniques have become the emerging topic in prognostics under massive data. However, the capability of quantifying the prognosis uncertainty is limited by such methods. In addition, existing studies have attempted to combine deep learning methods and stochastic degradation model based methods, but they are basically handled separately, which cannot guarantee that the extracted health indicator (HI) by deep learning method match the adopted degradation model. To address prognostic issues under massive data, this paper proposes a data-model interaction based prognostic method by constructing a closed-loop prediction mechanism between the HI extraction and degradation modeling. The main idea is to construct a novel optimization objective function related with the prognosis performance so as to establish an interactive linkage among the HI extraction, degradation modeling and prognosis. To do so, the extracted HI is hopefully matching the adopted degradation model. In the implementation process, the stacked denoising autoencoder is used to extract the HI from massive data while the Wiener process is used to model the extracted HI for prognosis. Based on the proposed interactive mechanism, these two parts are synchronously optimized. As such, the proposed method hold promise to integrate the advantages of the deep learning in handling the massive data and stochastic process method in quantifying the prognosis uncertainty. Finally, the effectiveness and superiority of the proposed method are demonstrated by a case study associated with turbofan engines. [ABSTRACT FROM AUTHOR]

Condition-based maintenance for a degrading system has been attached great importance in reducing unexpected failures and enabling the safe operation of the system. In this paper, we consider a condition-based replacement problem with observed degradation signals for the determination of the optimal replacement time of the system. The observed degradation signals contain some health related information of the system so that the system's health state may be more accurately estimated and the future degradation progression of the system can be predicted. Based on the predicted degradation state, the concerned replacement problem is formulated in the framework of the Markov decision process and then a new degradation-modeling framework based on maximum likelihood estimation is proposed to analyze the degradation signals and to predict the future degradation state of the system. To solve the proposed condition-based replacement decision problem, we analyze structural properties of the optimal replacement policy and an optimal monotonic control limit solution policy is developed for condition-based replacement. Finally, a case study is provided to illustrate the proposed optimal replacement method. [ABSTRACT FROM AUTHOR]

• An age- and state-dependent nonlinear degradation model is proposed for prognosis. • Nonlinearity between measurements and hidden degradation is characterized by a state space model. • Extended Kalman filtering and expectation-maximization algorithm are jointly applied to update the model. • Analytical distribution of remaining useful life is derived and updated for online prognosis. • Advantages in the prognosis accuracy are justified by numerical example and case study. To deal with age- and state-dependent degradation, unit-to-unit variability and partially observable or hidden degradation in remaining useful life prediction jointly, this paper proposes a condition-based prognostic approach for age- and state-dependent partially observable nonlinear degrading system. The dynamics and nonlinearity of the system degradation process are described by the age- and state-dependent nonlinear diffusion process. The nonlinear relationship between observations and the hidden degradation state is characterized by a state space model. To derive the distribution of the remaining useful life, we apply the extended Kalman filtering and expectation-maximization algorithm to adaptively estimate the degradation states and the unknown model parameters. Based on the estimated degradation states and model parameters, we derive the approximately analytical distribution of remaining useful life in the concept of the first hitting time. Furthermore, the distribution of remaining useful life can be updated according to the newly available data, thereby realizing real-time remaining useful life estimation. An illustrative example is given to explain the application of the proposed approach in the specific age- and state-dependent nonlinear degradation model. Finally, a numerical example and a case study for bearing degradation data are presented to verify the accuracy and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

• Prognosis for multi-sensor monitored stochastic degrading devices is investigated. • A data-model interactive prognosis method is proposed by a closed-loop feedback mechanism. • Data-driven health index construction and model-driven prognosis are handled interactively. • The model parameters and remaining useful life distribution are updated by the Bayesian method. • Advantages in the improvements of the signal quality and prognosis accuracy are justified. With advances in sensing and monitoring techniques, real time multi-sensor monitoring data of stochastic degrading devices has become the reality. How to effectively fuse these multi-sensor monitoring data to first construct the composite health index (CHI) and then model its degradation evolving process has become the emerging topic in the field of the remaining useful life (RUL) prediction. However, existing works treat the CHI construction and degradation modeling for prognostics under multi-sensor data as the disjoint problems rather than both though they are closely related in nature. To address this issue, this paper presents a novel data-model interactive RUL prediction method for multi-sensor monitored stochastic degrading devices. In the proposed method, based on the CHI extracted from multi-sensor historical data and the associated lifetime prediction via stochastic degradation modeling, an optimization objective function synthesizing the mean squared error between the predicted life and the actual life as well as the variance of the predicted life is constructed. As such, a closed-loop feedback mechanism is established for the CHI constructing and stochastic degradation modeling. Based on this feedback mechanism, the fusion coefficients for multi-sensor data and the failure threshold of the associated CHI are reversely optimized to realize the collaborative interaction between the CHI constructing and stochastic degradation modeling. To do so, the goal of making the constructed CHI automatically match the adopted stochastic degradation model can be achieved naturally. To make the degradation model accurately reflect the current reality of the in-service device, a sequential Bayesian method is proposed to update the degradation model parameters. Based on the updated model, the RUL distribution can be derived under the concept of the first passage time to achieve the prognosis. Finally, through multi-sensor data of aircraft gas turbine engines, we justify the necessity of applying the proposed method in prognosis and show its advantages in the improvements of the signal quality and prognosis accuracy. [ABSTRACT FROM AUTHOR]

• Box-Cox transformation is introduced in nonlinear degradation modeling. • Transformed degradation data are modeled by wiener process. • Two-stage estimation procedure is presented to identify the model parameters. • Closed-form remaining useful life distribution is derived for prognostics. • The proposed method is validated by degradation data of batteries and LCDs. Transforming nonlinear degradation paths into nearly linear ones has been widely used for nonlinear degradation modeling and prognostics. However, types of the current transformation functions are difficult to determine. This paper addresses issues in nonlinear stochastic degradation modeling and prognostics from a Box-Cox transformation (BCT) perspective. Specifically, the BCT is first used to transform the nonlinear degradation data into nearly linear data, and then the Wiener process with random drift is utilized to model the evolving process of the transformed data. To determine the model parameters, a two-stage estimation procedure is developed including offline stage and online stage. In the offline stage, the parameters are determined via maximum likelihood estimation method based on the historical degradation data and such estimated values are used to initialize the online stage. During the online stage, the Bayesian method is adopted to update the model parameters using the data of the degrading system in service, in which the hyperparameters are updated by the expectation maximization algorithm. A closed-form solution to remaining useful life with updated model parameters is further derived for prognostics. Finally, case studies for lithium-ion batteries and liquid coupling devices are provided to demonstrate the proposed approach. [ABSTRACT FROM AUTHOR]

• Physics-informed data-driven remaining useful life prediction methods are reviewed. • Physics-informed machine learning based prognosis methods are detailed. • Individual strengths and weaknesses of different kinds of methods are discussed. • Challenges and possible opportunities are provided to steer the future development. Remaining useful life (RUL) prediction, known as 'prognostics', has long been recognized as one of the key technologies in prognostics and health management (PHM) to maintain the safety and reliability of the system, and reduce the operating and management costs. Particularly, thanks to great advances in sensing and condition monitoring techniques, data-driven RUL prediction has attracted much attention and various data-driven RUL prediction methods have been reported. Despite the extensive studies on data-driven RUL prediction methods, the successful applications of such methods depend heavily on the volume and quality of the data, and purely data-driven methods possibly generate physically infeasible/inconsistent RUL prediction results and have the limited generalizability and interpretability. It is noted that there is an increasing consensus that embedding the physics or the domain knowledge into the data-driven methods and developing physics-informed data-driven methods will hold promise to improve the interpretability and efficiency of the RUL prediction results and lower the requirement of the volume and quality of the data. In this context, physics-informed data-driven RUL prediction has become an emerging topic in the prognostics field. However, there has not been a systematic review particularly focused on this emerging topic. To fill this gap, this paper reviews recent developments of physics-informed data-driven RUL prediction methods. In this review, current methods fallen into this type are broadly divided into three categories, i.e. physical model and data fusion methods, stochastic degradation model based methods, and physics-informed machine learning (PIML) based methods. Particularly, this review is centered on the PIML based methods since the fast development of such methods have been witnessed in the past five years. Through discussing the pros and cons of existing methods, we provide discussions on challenges and possible opportunities to steer the future development of physics-informed data-driven RUL prediction methods. [ABSTRACT FROM AUTHOR]

As one of the most important aspects in prognostics and system health management (PHM), spare parts play significant roles in enhancing reliability, shortening downtime, and reducing operating cost. However, the performance degradation of spare parts, though frequently encountered in industrial practice, has been rarely concerned during lifetime estimation and the according inventory optimization. Based on an amenable Wiener-process-based degradation model, this paper integrates the performance degradation of spare parts into lifetime estimation for stochastic multi-component degrading systems with congeneric key parts. Under two prevalent replacement policies, i.e., unit-by-unit replacement policy and batch-by-batch replacement policy, the lifetime distribution of the system considering the performance degradation of spare parts in storage, has been derived and the corresponding cost function for inventory management optimization has been constructed. Simulations and case study of an inertial navigation platform with three gyros are conducted to illustrate and validate the proposed method. The results show that the performance degradation of spare parts should be considered during inventory management, and the presented method can achieve an accurate lifetime estimation of the concerned system. • Lifetime PDF of multi-component systems with degrading spare parts is derived. • Influence of different replacement policies on the lifetime PDF is investigated. • The expected cost function for inventory management optimization is constructed. [ABSTRACT FROM AUTHOR]

Abstract: A convenient and efficient manganese‐catalyzed trifluoromethylazidation of alkenes has been developed by utilizing inexpensive and readily available CF3SO2Na under mild conditions. The various β‐trifluoromethylated alkyl azides were directly obtained from corresponding allyl, vinyl, chain terminal and internal alkenes, and easily further transformed into other CF3‐containing compounds. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Markov chain-based testability growth model (TGM) for the just in-time fix program. This model can help the system designers to manage the testability growth process during system maturation. We also derive a cost-benefit model for allocating test resources to optimize a specified testability metric subject to a constraint on cumulative test cost. Bayesian inference, coupled with a hybrid genetic and particle swarm optimization method, is used to estimate the parameters of the TGM from evolving data, and the resulting model is utilized to track and project the testability metric. A near-optimal Lagrangian relaxation-based algorithm is applied to solve the test resource allocation problem. The testability growth and resource allocation models are validated via simulation examples. Results show that the model and algorithms presented in this paper have the potential to efficiently manage the testability growth problem. [ABSTRACT FROM PUBLISHER]

The purpose of this study was to investigate the distribution and specify the transmission and cross-contamination of Clostridium perfringens (C. perfringens) in the beef slaughtering and butchering process. The prevalence of 21.2% (150/708) yielded 208 isolates of C. perfringens , including 80.8% type A and 19.2% type D, 0.4% (3/708) samples carried both type A and D strains, and 72.5% type D isolates carried both cpe and atyp. cpb2 genes. C. perfringens were identified through the whole slaughtering process but no type F (cpe and cpa isolates) was found. 69 isolates were further analyzed and classified into 28 PFGE genotypes and clade I contained 94.2% isolates and 24 PFGE genotypes, which showed the genetic diversity and epidemic correlation. Our study traced C. perfringens contamination along the handling processes and showed a gradually ascending contamination rate during the whole process, revealing widespread cross-contamination from the feces and hides of slaughtered cattle to the carcass in the slaughtering workshop, so as from tools and personnel to meat of the cutting workshops. Strains from different slaughterhouses (regions) have high homology, and type A is the predominant toxinotype. It is necessary to monitor and control several key points of cross-contamination during slaughtering process to reduce a risk of C. perfringens infection. • C. perfringens exists in the whole beef slaughtering and butchering process with high prevalence (21.2%). • Type A is the predominant toxinotype (80.8%) with PFGE genotype diversity, but type F is absent. • Widespread cross-contamination of C. perfringens occurs from feces/hides to carcass and tools/personnel to meat. • Transmission and contamination of C. perfringens was specified for the first time along the slaughtering process in China. [ABSTRACT FROM AUTHOR]

Endogenous and exogenous neurotoxins are important factors leading to neurodegenerative diseases. In the 1980s, the discovery that 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) contributes to Parkinson's disease (PD) symptoms led to new research investigations on neurotoxins. An abnormal metabolism of endogenous substances, such as condensation of bioamines with endogenous aldehydes, dopamine (DA) oxidation, and kynurenine pathway, can produce endogenous neurotoxins. Neurotoxins may damage the nervous system by inhibiting mitochondrial activity, increasing oxidative stress, increasing neuroinflammation, and up-regulating proteins related to cell death. This paper reviews the biological synthesis of various known endogenous neurotoxins and their toxic mechanisms. [ABSTRACT FROM AUTHOR]