Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems

The demand of probabilistic time series forecasting has been recently raised in various dynamic system scenarios, for example, system identification and prognostic and health management of machines. To this end, we combine the advances in both deep generative models and state space model (SSM) to come up with a novel, data-driven deep probabilistic sequence model. Specially, we follow the popular encoder-decoder generative structure to build the recurrent neural networks (RNN) assisted variational sequence model on an augmented recurrent input space, which could induce rich stochastic sequence dependency. Besides, in order to alleviate the issue of inconsistency between training and predicting as well as improving the mining of dynamic patterns, we (i) propose using a hybrid output as input at next time step, which brings training and predicting into alignment; and (ii) further devise a generalized auto-regressive strategy that encodes all the historical dependencies at current time step. Thereafter, we first investigate the methodological characteristics of the proposed deep probabilistic sequence model on toy cases, and then comprehensively demonstrate the superiority of our model against existing deep probabilistic SSM models through extensive numerical experiments on eight system identification benchmarks from various dynamic systems. Finally, we apply our sequence model to a real-world centrifugal compressor sensor data forecasting problem, and again verify its outstanding performance by quantifying the time series predictive distribution.Comment: 25 pages, 7 figures, 4 tables, preprint under revie

Bayesian Stochastic Neural Network Model for Turbomachinery Damage Prediction

Turbomachinery often suffers various defects such as impeller cracking, resulting in forced outage, increased maintenance costs, and reduced productivity. Condition monitoring and damage prognostics has been widely used as an increasingly important and powerful tool to improve the system availability, reliability, performance, and maintainability, but still very challenging due to multiple sources of data uncertainties and the complexity of analytics modeling. This paper presents an intelligent probabilistic methodology for anomaly prediction of high-fidelity turbomachine, considering multiple data imperfections and multivariate correlation. The proposed method adeptly integrates several advanced state-of-the-art signal processing and artificial intelligence techniques: wavelet multi-resolution decomposition, Bayesian hypothesis testing, probabilistic principal component analysis, and fuzzy stochastic neural network modeling. The advanced signal processing is employed to reduce dimensionality and to address multivariate correlation and data uncertainty for damage prediction. Instead of conventionally using raw time series data, principal components are utilized in the establishment of stochastic neural network model and anomaly prediction. Bayesian interval hypothesis testing metric is then presented to quantitatively compare the predicted and measured data for model validation and anomaly evaluation, thus providing a confidence indicator to judge the model quality and evaluate the equipment status. Bayesian method is developed in this study for denoising the raw data with multiresolution wavelet decomposition, quantifying the model accuracy, and assessing the equipment status. The dynamic stochastic neural network model is established via the nonlinear autoregressive moving average with exogenous inputs approach. It seamlessly integrates the fuzzy clustering and independent Bernoulli random function into radial basis function neural network. A natural gradient method based on Kullback-Leibler distance criterion is employed to maximize the log-likelihood loss function. The effectiveness of the proposed methodology and procedure is demonstrated with the 11-variable time series data and the forced outage event of a real-world centrifugal compressor

Discovery and excavation of lichen bioactive natural products

Lichen natural products are a tremendous source of new bioactive chemical entities for drug discovery. The ability to survive in harsh conditions can be directly correlated with the production of some unique lichen metabolites. Despite the potential applications, these unique metabolites have been underutilized by pharmaceutical and agrochemical industries due to their slow growth, low biomass availability, and technical challenges involved in their artificial cultivation. At the same time, DNA sequence data have revealed that the number of encoded biosynthetic gene clusters in a lichen is much higher than in natural products, and the majority of them are silent or poorly expressed. To meet these challenges, the one strain many compounds (OSMAC) strategy, as a comprehensive and powerful tool, has been developed to stimulate the activation of silent or cryptic biosynthetic gene clusters and exploit interesting lichen compounds for industrial applications. Furthermore, the development of molecular network techniques, modern bioinformatics, and genetic tools is opening up a new opportunity for the mining, modification, and production of lichen metabolites, rather than merely using traditional separation and purification techniques to obtain small amounts of chemical compounds. Heterologous expressed lichen-derived biosynthetic gene clusters in a cultivatable host offer a promising means for a sustainable supply of specialized metabolites. In this review, we summarized the known lichen bioactive metabolites and highlighted the application of OSMAC, molecular network, and genome mining-based strategies in lichen-forming fungi for the discovery of new cryptic lichen compounds

Study on the actual particle size, activity concentration, and migration process adsorption behavior of radioactive substances in liquid effluents from nuclear power plants

Radionuclides emitted by nuclear power plants may have effects on the environment and public health. At present, research on radioactive material effluent in the industry mainly focuses on the treatment of radioactive effluent and the particle size distribution of the primary circuit. There is little research on the particle size of radioactive material during the migration process outside the primary circuit system, as well as the flocculation precipitation and other enrichment phenomena during the collection process of effluent. Therefore, this study relies on the sampling of effluent from an in-service nuclear power plant to measure its radioactivity level by particle size range. At the same time, the mixing process of effluent is simulated in the laboratory to simulate the adsorption behavior of effluent during the migration process. It was found that in the activity concentration of detectable radioactive nuclides in the effluent samples, more than 95% of radioactive nuclides exist in the liquid with particle sizes less than 0.1μm, while particle sizes greater than 0.45 μm account for less than 5%. After the sample was filtered by the demineralizer, the radioactive activity decreased. The flocculation precipitation in the waste liquid of the waste water recovery system has a certain contribution to the enrichment of nuclides. With the extension of time, the enrichment of transition elements such as cobalt and manganese is particularly obvious, so that it is distributed in the liquid again with a large particle size. In addition, large particle size substances such as colloids in seawater have a certain adsorption effect on radionuclides, which will lead to its aggregation effect again

Progressive Thinning of Visual Motion Area in Lower Limb Amputees

Accumulating evidence has indicated that amputation or deafferentation of a limb induces functional or structural reorganization in the visual areas. However, the extent of the visual areas involved after lower limb amputation remains uncertain. In this investigation, we studied 48 adult patients with unilateral lower limb amputation and 48 matched healthy controls using T1-weighted magnetic resonance imaging. Template-based regions of interest analysis was implemented to detect the changes of cortical thickness in the specific visual areas. Compared with normal controls, amputees exhibited significantly lower thickness in the V5/middle temporal (V5/MT+) visual area, as well as a trend of cortical thinning in the V3d. There was no significant difference in the other visual areas between the two groups. In addition, no significant difference of cortical thickness was found between patients with amputation at different levels. Across all amputees, correlation analyses revealed that the cortical thickness of the V5/MT+ was negatively correlated to the time since amputation. In conclusion, our findings indicate that the amputation of unilateral lower limb could induce changes in the motor-related visual cortex, and provide an update on the plasticity of the human brain after limb injury

Lower limb arterial calcification and its clinical relevance with peripheral arterial disease

Lower limb arterial calcification (LLAC) is associated with an increased risk of mortality and it predicts poor outcomes after endovascular interventions in patients with peripheral artery disease (PAD). Detailed histological analysis of human lower artery specimens pinpointed the presence of LLAC in two distinct layers: the intima and the media. Intimal calcification has been assumed to be an atherosclerotic pathology and it is associated with smoking and obesity. It becomes instrumental in lumen stenosis, thereby playing a crucial role in disease progression. On the contrary, medial calcification is a separate process, systematically regulated and linked with age advancement, diabetes, and chronic kidney disease. It prominently interacts with vasodilation and arterial stiffness. Given that both types of calcifications frequently co-exist in PAD patients, it is vital to understand their respective mechanisms within the context of PAD. Calcification can be easily identifiable entity on imaging scans. Considering the highly improved abilities of novel imaging technologies in differentiating intimal and medial calcification within the lower limb arteries, this review aimed to describe the distinct histological and imaging features of the two types of LLAC. Additionally, it aims to provide in-depth insight into the risk factors, the effects on hemodynamics, and the clinical implications of LLAC, either occurring in the intimal or medial layers

N-Centered Chiral Self-Sorting and Supramolecular Helix of Tröger's Base-Based Dimeric Macrocycles in Crystalline State

Three stereoisomers of Tröger's Base-based dimeric macrocycles Trögerophane 1 (T1) including one pair of enantiomers (rac-T1) and one meso isomer (R2NS2N-T1) were obtained and fully characterized by X-ray analysis. In the crystalline stacking state R2NS2N-T1 showed heterochiral self-sorting behavior along a axis with cofacial π-π stacking interactions, while rac-T1 showed heterochiral self-sorting behavior along c axis with slipped π-π stacking interactions, respectively. Meanwhile both of them showed homochiral self-sorting behavior along b axis as well as one pair of supramolecular helixes were formed in both cases. All the self-sorting behaviors are controlled by two chiral Tröger's Base units from neighboring molecules. To the best of our knowledge, such chiral self-sorting and supramolecular helixes of N-centered chiral superstructures is a rare example. In addition, R2NS2N-T1 and rac-T1 demonstrated different adsorption capacities toward the vapor of dichloromethane and acetone, respectively

Single-cell RNA sequencing reveals cell type-specific immune regulation associated with human neuromyelitis optica spectrum disorder

IntroductionOne rare type of autoimmune disease is called neuromyelitis optica spectrum disorder (NMOSD) and the peripheral immune characteristics of NMOSD remain unclear.MethodsHere, single-cell RNA sequencing (scRNA-seq) is used to characterize peripheral blood mononuclear cells from individuals with NMOSD.ResultsThe differentiation and activation of lymphocytes, expansion of myeloid cells, and an excessive inflammatory response in innate immunity are observed. Flow cytometry analyses confirm a significant increase in the percentage of plasma cells among B cells in NMOSD. NMOSD patients exhibit an elevated percentage of CD8+ T cells within the T cell population. Oligoclonal expansions of B cell receptors are observed after therapy. Additionally, individuals with NMOSD exhibit elevated expression of CXCL8, IL7, IL18, TNFSF13, IFNG, and NLRP3.DiscussionPeripheral immune response high-dimensional single-cell profiling identifies immune cell subsets specific to a certain disease and identifies possible new targets for NMOSD