Process Manufacturing Intelligence Empowered by Industrial Metaverse: A Survey.

The intelligent goal of process manufacturing is to achieve high efficiency and greening of the entire production. Whereas the information system it used is functionally independent, resulting to knowledge gaps between each level. Decision-making still requires lots of knowledge workers making manually. The industrial metaverse is a necessary means to bridge the knowledge gaps by sharing and collaborative decision-making. Considering the safety and stability requirements of the process manufacturing, this article conducts a thorough survey on the process manufacturing intelligence empowered by industrial metaverse. First, it analyzes the current status and challenges of process manufacturing intelligence, and then summarizes the latest developments about key enabling technologies of industrial metaverse, such as interconnection technologies, artificial intelligence, cloud-edge computing, digital twin (DT), immersive interaction, and blockchain technology. On this basis, taking into account the characteristics of process manufacturing, a construction approach and architecture for the process industrial metaverse is proposed: a virtual-real fused industrial metaverse construction method that combines DTs with physical avatar, which can effectively ensure the safety of metaverse's application in industrial scenarios. Finally, we conducted preliminary exploration and research, to prove the feasibility of proposed method.

Learning a neural network-based soft sensor with double-errors parallel optimization towards effluent variable prediction in wastewater treatment plants.

With the development of machine learning and artificial intelligence (ML/AI) models, data-driven soft sensors, especially the neural network-based, have widespread utilization for the prediction of key water quality indicators in wastewater treatment plants (WWTPs). However, recent research indicates that the prediction performance and computational efficiency are greatly compromised due to the time-varying, nonlinear and high-dimensional nature of the wastewater treatment process. This paper proposes a neural network-based soft sensor with double-errors parallel optimization to achieve more accurate prediction for effluent variables timely. Firstly, relying on the Activity Based Classification (ABC) principle, an ensemble variable selection method that combines Pearson correlation coefficient (PCC) and mutual information (MI) is introduced to select the optimal process variables as auxiliary variables, thereby reducing the data dimensionality and simplifying the model complexity. Subsequently, a double-errors parallel optimization methodology with minimizing both point prediction error and distribution error simultaneously is proposed, aiming to enhancing the training efficiency and the fitting quality of neural networks. Finally, the effectiveness is quantitatively assessed in two datasets collected from the Benchmark Simulation Model no. 1 (BMS1) and an actual oxidation ditch WWTP. The experimental results illustrate that the proposed soft sensor achieves precise effluent variable prediction, with RMSE, MAE and R2 values being 0.0606, 0.0486, 0.99930, and 0.06939, 0.05381, 0.98040, respectively. Consequently, this soft sensor can expedite the convergence speed in the neural network training process and enhance the prediction performance, thereby contributing to the effective optimization management of WWTPs.

The value of myocardial contraction fraction and long-axis strain to predict late gadolinium enhancement in multiple myeloma patients with secondary cardiac amyloidosis.

The aim of this study is to assess the effectiveness of conventional and two additional functional markers derived from standard cardiac magnetic resonance (CMR) images in detecting the occurrence of late gadolinium enhancement (LGE) in patients with secondary cardiac amyloidosis (CA) related to multiple myeloma (MM). This study retrospectively included 32 patients with preserved ejection fraction (EF) who had MM-CA diagnosed consecutively. Conventional left ventricular (LV) function markers and two additional functional markers, namely myocardial contraction fraction (MCF) and LV long-axis strain (LAS), were obtained using commercial cardiac post-processing software. Logistic regression analyses and receiver operating characteristic (ROC) analysis were performed to evaluate the predictive performances. (1) There were no notable distinctions in clinical features between the LGE+ and LGE- groups, with the exception of a reduced systolic blood pressure in the former (105.60 ± 18.85 mmHg vs. 124.50 ± 20.95 mmHg, P = 0.022). (2) Patients with MM-CA presented with intractable heart failure with preserved ejection fraction (HFpEF). The LVEF in the LGE+ group exhibited a greater reduction (54.27%, IQR 51.59-58.39%) in comparison to the LGE- group (P < 0.05). And MM-CA patients with LGE+ had significantly higher LVMI (90.15 ± 23.69 g/m2), lower MCF (47.39%, IQR 34.28-54.90%), and the LV LAS were more severely damaged (- 9.94 ± 3.42%) than patients with LGE- (all P values < 0.05). (3) The study found that MCF exhibited a significant independent association with LGE, as indicated by an odds ratio of 0.89 (P < 0.05). The cut-off value for MCF was determined to be 64.25% with a 95% confidence interval ranging from 0.758 to 0.983. The sensitivity and specificity of this association were calculated to be 95% and 83%, respectively. MCF is a simple reproducible predict marker of LGE in MM-CA patients. It is a potentially CMR-based method that promise to reduce scan times and costs, and boost the accessibility of CMR.

A novel role for KIFC1-MYH9 interaction in triple-negative breast cancer aggressiveness and racial disparity.

African American (AA) women are twice as likely to develop triple-negative breast cancer (TNBC) as women of European descent. Additionally, AA women with TNBC present a much more aggressive disease course than their European American (EA) counterparts. Thus, there is an unmet clinical need to identify race-specific biomarkers and improve survival outcomes in AA patients with TNBC. The minus-end directed microtubule motor protein kinesin family member C1 (KIFC1) promotes centrosome clustering and chromosomal instability and is often overexpressed in TNBC. Previous findings suggest that KIFC1 plays a role in cell proliferation and migration in TNBC cells from AAs and that the levels of nuclear KIFC1 (nKIFC1) are particularly high in AA patients with TNBC. The nuclear localization of KIFC1 in interphase may underlie its previously unrecognized race-specific association. In this study, we found that in TNBC cells derived from AAs, nKIFC1 interacted with the tumor suppressor myosin heavy chain 9 (MYH9) over EA cells. Treatment of AA TNBC cells with commercial inhibitors of KIFC1 and MYH9 disrupted the interaction between KIFC1 and MYH9. To characterize the racial differences in the KIFC1-MYH9-MYC axis in TNBC, we established homozygous KIFC1 knockout (KO) TNBC cell lines. KIFC1 KO significantly inhibited proliferation, migration, and invasion in AA TNBC cells but not in EA TNBC cells. RNA sequencing analysis showed significant downregulation of genes involved in cell migration, invasion, and metastasis upon KIFC1 KO in TNBC cell lines from AAs compared to those from EAs. These data indicate that mechanistically, the role of nKIFC1 in driving TNBC progression and metastasis is stronger in AA patients than in EA patients, and that KIFC1 may be a critical therapeutic target for AA patients with TNBC.

Studies on the Increasing Saltiness and Antioxidant Effects of Peanut Protein Maillard Reaction Products.

The salt taste-enhancing and antioxidant effect of the Maillard reaction on peanut protein hydrolysates (PPH) was explored. The multi-spectroscopic and sensory analysis results showed that the Maillard reaction products (MRPs) of hexose (glucose and galactose) had slower reaction rates than those of pentose (xylose and arabinose), but stronger umami and increasing saltiness effects. The Maillard reaction can improve the flavor of PPH, and the galactose-Maillard reaction product (Ga-MRP) has the best umami and salinity-enhancing effects. The measured molecular weight of Ga-MRP were all below 3000 Da, among which the molecular weights between 500-3000 Da accounted for 46.7%. The products produced during the Maillard reaction process resulted in a decrease in brightness and an increase in red value of Ga-MRP. The amino acid analysis results revealed that compared with PPH, the content of salty and umami amino acids in Ga-MRPs decreased, but their proportion in total free amino acids increased, and the content of bitter amino acids decreased. In addition, the Maillard reaction enhances the reducing ability, DPPH radical scavenging ability, and Fe2+ chelating ability of PPH. Therefore, the Maillard reaction product of peanut protein can be expected to be used as a substitute for salt seasoning, with excellent antioxidant properties.

Sparse Wasserstein stationary subspace analysis for fault detection and diagnosis of nonstationary industrial processes.

Fault detection and diagnosis of nonstationary processes are crucial for ensuring the safety of industrial production systems. However, the nonstationarity of process data poses multifaceted challenges to them. First, conventional stationary fault detection methods encounter difficulties in discerning evolving trends within nonstationary data. Secondly, the majority of current nonstationary fault detection methods directly extract features from all variables, rendering them susceptible to redundant interference. Moreover, nonstationary trends possess the capacity to conceal and modify the correlations among variables. Coupled with the smearing effect of faults, it is challenging to achieve accurate fault diagnosis. To address these challenges, this paper proposes sparse Wasserstein stationary subspace analysis (SWSSA). Specifically, a ℓ2,p-norm constraint is introduced to endow the stationary subspace model with excellent sparse representation capability. Furthermore, recognizing that fault variables within the sparse stationary subspace influence only a limited subset of stationary sources, this paper proposes a novel contribution analysis method based on local dynamic preserving projection (LDPP), termed LDPPBC, which can effectively mitigate the smearing effect on nonstationary fault diagnosis. LDPPBC establishes a LDPP matrix by extracting the latent positional information of fault variables within the stationary subspace. This allows LDPPBC to selectively analyze the contributions of variables within the latent fault subspace to achieve precise fault diagnosis while avoiding the interference of variable contributions from the fault-free subspace. Finally, the superiority of the proposed method is thoroughly validated through a numerical simulation, a continuous stirred tank reactor, and a real industrial roaster.

Systems Genetics Analyses Reveals Key Genes Related to Behavioral Traits in the Striatum of CFW Mice.

The striatum plays a central role in directing many complex behaviors ranging from motor control to action choice and reward learning. In our study, we used 55 male CFW mice with rapid decay linkage disequilibrium to systematically mine the striatum-related behavioral functional genes by analyzing their striatal transcriptomes and 79 measured behavioral phenotypic data. By constructing a gene coexpression network, we clustered the genes into 13 modules, with most of them being positively correlated with motor traits. Based on functional annotations as well as Fisher's exact and hypergeometric distribution tests, brown and magenta modules were identified as core modules. They were significantly enriched for striatal-related functional genes. Subsequent Mendelian randomization analysis verified the causal relationship between the core modules and dyskinesia. Through the intramodular gene connectivity analysis, Adcy5 and Kcnma1 were identified as brown and magenta module hub genes, respectively. Knock outs of both Adcy5 and Kcnma1 lead to motor dysfunction in mice, and KCNMA1 acts as a risk gene for schizophrenia and smoking addiction in humans. We also evaluated the cellular composition of each module and identified oligodendrocytes in the striatum to have a positive role in motor regulation.

Classification and identification of tea diseases based on improved YOLOv7 model of MobileNeXt.

To address the issues of low accuracy and slow response speed in tea disease classification and identification, an improved YOLOv7 lightweight model was proposed in this study. The lightweight MobileNeXt was used as the backbone network to reduce computational load and enhance efficiency. Additionally, a dual-layer routing attention mechanism was introduced to enhance the model's ability to capture crucial details and textures in disease images, thereby improving accuracy. The SIoU loss function was employed to mitigate missed and erroneous judgments, resulting in improved recognition amidst complex image backgrounds.The revised model achieved precision, recall, and average precision of 93.5%, 89.9%, and 92.1%, respectively, representing increases of 4.5%, 1.9%, and 2.6% over the original model. Furthermore, the model's volum was reduced by 24.69M, the total param was reduced by 12.88M, while detection speed was increased by 24.41 frames per second. This enhanced model efficiently and accurately identifies tea disease types, offering the benefits of lower parameter count and faster detection, thereby establishing a robust foundation for tea disease monitoring and prevention efforts.

Fluorescent Labeling and Imaging of IL-22 mRNA-Loaded Lipid Nanoparticles.

Lipid nanoparticle (LNP)-based drug delivery systems (DDSs) are widely recognized for their ability to enhance efficient and precise delivery of therapeutic agents, including nucleic acids like DNA and mRNA. Despite this acknowledgment, there is a notable knowledge gap regarding the systemic biodistribution and organ accumulation of these nanoparticles. The ability to track LNPs in vivo is crucial for understanding their fate within biological systems. Fluorescent labeling of LNPs facilitates real-time tracking, quantification, and visualization of their behavior within biological systems, providing valuable insights into biodistribution, cellular uptake, and the optimization of drug delivery strategies. Our prior research established reversely engineered LNPs as an exceptional mRNA delivery platform for treating ulcerative colitis. This study presents a detailed protocol for labeling interleukin-22 (IL-22) mRNA-loaded LNPs, their oral administration to mice, and visualization of DiR-labeled LNPs biodistribution in the gastrointestinal tract using IVIS spectrum. This fluorescence-based approach will assist researchers in gaining a dynamic understanding of nanoparticle fate in other models of interest. Key features • This protocol is developed to assess the delivery of IL-22 mRNA to ulcerative colitis sites using lipid nanoparticles. • This protocol uses fluorescent DiR dye for imaging of IL-22 mRNA-loaded lipid nanoparticles in the gastrointestinal tract of mice. • This protocol employs the IVIS spectrum for imaging.

Analysis of DNA Barcodes Using DNA Subway.

DNA Subway makes bioinformatic analysis of DNA barcodes classroom friendly, eliminating the need for software installations or command line tools. Subway bundles research-grade bioinformatics software into workflows with an easy-to-use interface. This chapter covers DNA Subway's DNA barcoding analysis workflow (Blue Line) starting with one or more Sanger sequence reads. During analysis, users can view trace files and sequence quality, pair and align forward and reverse reads, create and trim consensus sequences, perform BLAST searches, select reference data, align multiple sequences, and compute phylogenetic trees. High-quality sequences with the required metadata can also be submitted as barcode sequences to NCBI GenBank.

Global, regional, and national burden of stroke from 1990 to 2019: A temporal trend analysis based on the Global Burden of Disease Study 2019.

BACKGROUND: Stroke is the second leading cause of death and the leading cause of disability worldwide. However, how the prevalence of stroke varies across the world is uncertain. AIMS: The aim of this study was to analyze temporal trends of prevalence for stroke, including ischemic stroke (IS), intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH) at the global, regional, and national levels. METHODS: The age-standardized prevalence rates (ASPR) of stroke, IS, ICH, and SAH, along with their corresponding 95% uncertainty intervals (UI), were derived from data in the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. This provides estimates for the burden of 369 diseases and injuries globally in 2019, as well as their temporal trends over the past 30 years. Joinpoint regression analysis was used to analyze the 1990-2019 temporal trends by calculating the annual percentage change (APC) and average annual percentage change (AAPC), as well as their 95% confidence interval (CI). RESULTS: In 2019, the global ASPR of stroke was 1240.263 per 100,000 population (95% UI: 1139.711 to 1352.987), with ASPRs generally lower in Europe compared to other regions. Over the period from 1990 to 2019, a significant global decrease in ASPR was observed for stroke (AAPC -0.200, 95% CI: -0.215 to -0.183), IS (AAPC -0.059%, 95% CI: -0.077 to -0.043), SAH (AAPC -0.476, 95% CI: -0.483 to -0.469), and ICH (AAPC -0.626, 95% CI: -0.642 to -0.611). The trends of ASPR of stroke, IS, SAH, and ICH varied significantly across 204 countries and territories. CONCLUSION: Our findings highlight significant global disparities in stroke prevalence, emphasizing the need for ongoing monitoring and intensified efforts in developing regions to reduce the global burden of stroke.

Hierarchical Self-Attention Network for Industrial Data Series Modeling With Different Sampling Rates Between the Input and Output Sequences.

For industrial processes, it is significant to carry out the dynamic modeling of data series for quality prediction. However, there are often different sampling rates between the input and output sequences. For the most traditional data series models, they have to carefully select the labeled sample sequence to build the dynamic prediction model, while the massive unlabeled input sequences between labeled samples are directly discarded. Moreover, the interactions of the variables and samples are usually not fully considered for quality prediction at each labeled step. To handle these problems, a hierarchical self-attention network (HSAN) is designed for adaptive dynamic modeling. In HSAN, a dynamic data augmentation is first designed for each labeled step to include the unlabeled input sequences. Then, a self-attention layer of variable level is proposed to learn the variable interactions and short-interval temporal dependencies. After that, a self-attention layer of sample level is further developed to model the long-interval temporal dependencies. Finally, a long short-term memory network (LSTM) network is constructed to model the new sequence that contains abundant interactions for quality prediction. The experiment on an industrial hydrocracking process shows the effectiveness of HSAN.

Adaptive Neural Consensus Observer Networks Design for a Class of Semilinear Parabolic PDE Systems.

This article concerns the investigation on the consensus problem for the joint state-uncertainty estimation of a class of parabolic partial differential equation (PDE) systems with parametric and nonparametric uncertainties. We propose a two-layer network consisting of informed and uninformed boundary observers where novel adaptation laws are developed for the identification of uncertainties. Particularly, all observer agents in the network transmit their information with each other across the entire network. The proposed adaptation laws include a penalty term of the mismatch between the parameter estimates generated by the other observer agents. Moreover, for the nonparametric uncertainties, radial basis function (RBF) neural networks are employed for the universal approximation of unknown nonlinear functions. Given the persistently exciting condition, it is shown that the proposed network of adaptive observers can achieve exponential joint state-uncertainty estimation in the presence of parametric uncertainties and ultimate bounded estimation in the presence of nonparametric uncertainties based on the Lyapunov stability theory. The effects of the proposed consensus method are demonstrated through a typical reaction-diffusion system example, which implies convincing numerical findings.

Unleashing the Potential of Oral Deliverable Nanomedicine in the Treatment of Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD), marked by chronic gastrointestinal tract inflammation, poses a significant global medical challenge. Current treatments for IBD, including corticosteroids, immunomodulators, and biologics, often require frequent systemic administration through parenteral delivery, leading to nonspecific drug distribution, suboptimal therapeutic outcomes, and adverse effects. There is a pressing need for a targeted drug delivery system to enhance drug efficacy and minimize its systemic impact. Nanotechnology emerges as a transformative solution, enabling precise oral drug delivery to inflamed intestinal tissues, reducing off-target effects, and enhancing therapeutic efficiency. The advantages include heightened bioavailability, sustained drug release, and improved cellular uptake. Additionally, the nano-based approach allows for the integration of theranostic elements, enabling simultaneous diagnosis and treatment. Recent preclinical advances in oral IBD treatments, particularly with nanoformulations such as functionalized polymeric and lipid nanoparticles, demonstrate remarkable cell-targeting ability and biosafety, promising to overcome the limitations of conventional therapies. These developments signify a paradigm shift toward personalized and effective oral IBD management. This review explores the potential of oral nanomedicine to enhance IBD treatment significantly, focusing specifically on cell-targeting oral drug delivery system for potential use in IBD management. We also examine emerging technologies such as theranostic nanoparticles and artificial intelligence, identifying avenues for the practical translation of nanomedicines into clinical applications.

Quality Prediction Modeling for Industrial Processes Using Multiscale Attention-Based Convolutional Neural Network.

Soft sensors have been increasingly applied for quality prediction in complex industrial processes, which often have different scales of topology and highly coupled spatiotemporal features. However, the existing soft sensing models usually face difficulties in extracting the multiscale local spatiotemporal features in multicoupled complex process data and harnessing them to their full potential to improve the prediction performance. Therefore, a multiscale attention-based CNN (MSACNN) is proposed in this article to alleviate such problems. In MSACNN, convolutional kernels of different sizes are first designed in parallel in the convolutional layers, which can generate feature maps containing local spatiotemporal features at different scales. Meanwhile, a channel-wise attention mechanism is designed on the feature maps in parallel to get their attention weights, representing the significance of the local spatiotemporal feature at different scales. The superiority of the proposed MSACNN over the other state-of-the-art methods is validated through the performance evaluation in two real industrial processes.

Treatment of Obesity Through Glial Cell-Derived Neurotrophic Factor Lipid Nanoparticle Delivery in Mice.

BACKGROUND AND AIMS: The overexpression of glial cell-derived neurotrophic factor (GDNF) in the liver and adipose tissues offers strong protection against high-fat diet (HFD)-induced obesity in mice. We hypothesize that sustainably enhancing GDNF expression in the liver may provide a therapeutic effect that can prevent the progression of HFD-induced obesity in mice. METHODS: Expression lentivector encoding mouse GDNF (GDNF(pDNA) or empty vector (pDNA, control) were encapsulated in lipid nanoparticles (LNPs) using the thin-film hydration method. Mice were fed with regular diet (RD) or HFD for 20 weeks prior to injection and the GDNF and control vector-loaded LNPs were administered by intravenous (IV) injection to mice once weekly for 5 weeks. Changes in body weight were monitored and mice tissues were collected and imaged for fluorescence using an IVIS in vivo imaging system. Post-treatment abdominal fat weight, colon length, and spleen weight were obtained. GDNF protein levels in the liver and serum were quantified by enzyme-linked immunosorbent assay, while liver AKT serine/threonine kinase and AMP-activated protein kinase phosphorylation levels were evaluated by Western blotting. RESULTS: IV-injected GDNF(pDNA)-loaded LNPs targeted the liver and remained in there for up to 15 days postinjection. A single injection of GDNF(pDNA)-loaded LNPs significantly increased GDNF expression for 7 days and consequently increased the levels of phosphorylated AKT serine/threonine kinase and AMP-activated protein kinase. Once weekly injections of GDNF(pDNA)-loaded LNPs for 5 weeks slowed increase in body weight, reduced abdominal fat, and modulated the gut microbiota toward a healthier composition in HFD-fed mice. CONCLUSION: GDNF(pDNA)-loaded LNPs could potentially be developed as a therapeutic strategy to reverse weight gain in obese patients.

Pest recognition in microstates state: an improvement of YOLOv7 based on Spatial and Channel Reconstruction Convolution for feature redundancy and vision transformer with Bi-Level Routing Attention.

Introduction: In order to solve the problem of precise identification and counting of tea pests, this study has proposed a novel tea pest identification method based on improved YOLOv7 network. Methods: This method used MPDIoU to optimize the original loss function, which improved the convergence speed of the model and simplifies the calculation process. Replace part of the network structure of the original model using Spatial and Channel reconstruction Convolution to reduce redundant features, lower the complexity of the model, and reduce computational costs. The Vision Transformer with Bi-Level Routing Attention has been incorporated to enhance the flexibility of model calculation allocation and content perception. Results: The experimental results revealed that the enhanced YOLOv7 model significantly boosted Precision, Recall, F1, and mAP by 5.68%, 5.14%, 5.41%, and 2.58% respectively, compared to the original YOLOv7. Furthermore, when compared to deep learning networks such as SSD, Faster Region-based Convolutional Neural Network (RCNN), and the original YOLOv7, this method proves to be superior while being externally validated. It exhibited a noticeable improvement in the FPS rates, with increments of 5.75 HZ, 34.42 HZ, and 25.44 HZ respectively. Moreover, the mAP for actual detection experiences significant enhancements, with respective increases of 2.49%, 12.26%, and 7.26%. Additionally, the parameter size is reduced by 1.39 G relative to the original model. Discussion: The improved model can not only identify and count tea pests efficiently and accurately, but also has the characteristics of high recognition rate, low parameters and high detection speed. It is of great significance to achieve realize the intelligent and precise prevention and control of tea pests.

TEAD1, MYO7A and NDUFC2 are novel functional genes associated with glucose metabolism in BXD recombinant inbred population.

AIM: The liver is an important metabolic organ that governs glucolipid metabolism, and its dysfunction may cause non-alcoholic fatty liver disease, type 2 diabetes mellitus, dyslipidaemia, etc. We aimed to systematic investigate the key factors related to hepatic glucose metabolism, which may be beneficial for understanding the underlying pathogenic mechanisms for obesity and diabetes mellitus. MATERIALS AND METHODS: Oral glucose tolerance test (OGTT) phenotypes and liver transcriptomes of BXD mice under chow and high-fat diet conditions were collected from GeneNetwork. QTL mapping was conducted to pinpoint genomic regions associated with glucose homeostasis. Candidate genes were further nominated using a multi-criteria approach and validated to confirm their functional relevance in vitro. RESULTS: Our results demonstrated that plasma glucose levels in OGTT were significantly affected by both diet and genetic background, with six genetic regulating loci were mapped on chromosomes 1, 4, and 7. Moreover, TEAD1, MYO7A and NDUFC2 were identified as the candidate genes. Functionally, siRNA-mediated TEAD1, MYO7A and NDUFC2 knockdown significantly decreased the glucose uptake and inhibited the transcription of genes related to insulin and glucose metabolism pathways. CONCLUSIONS: Our study contributes novel insights to the understanding of hepatic glucose metabolism, demonstrating the impact of TEAD1, MYO7A and NDUFC2 on mitochondrial function in the liver and their regulatory role in maintaining in glucose homeostasis.

A multi-subsystem collaborative Bi-LSTM-based adaptive soft sensor for global prediction of ammonia-nitrogen concentration in wastewater treatment processes.

Ammonia-nitrogen concentration is a key water quality indicator, which reflects changes in pollutant components during wastewater treatment processes. The timely and accurate detection results contribute to optimizing control and operational management of wastewater treatment plants (WWTPs), but current detection methods only focus on the effluent location. This paper proposes a multi-subsystem collaborative Bi-LSTM-based adaptive soft sensor to achieve the global prediction of ammonia-nitrogen concentration. Firstly, the wastewater treatment process is divided into several independent subsystems depending on the reaction mechanism, and the variable selection is performed using mutual information. Subsequently, the bidirectional long short-term memory network (Bi-LSTM) is employed to construct a model for predicting ammonia-nitrogen concentration within each subsystem, and the outputs between neighboring subsystems are incorporated as a set of new variables added into the training dataset to strengthen their connection. Finally, to address performance degradation caused by environmental factors, a probability density function (PDF)-based dynamic moving window method is proposed to enhance the robustness. The effectiveness and superiority of the proposed soft sensor are validated in the Benchmark Simulation Model no. 1 (BSM1). The experimental results demonstrate that the proposed soft sensor can accurately predict the global ammonia-nitrogen concentration in the face of different weather conditions including sunny, rainy, and stormy days. This study contributes to the stable operation of WWTPs with higher treatment efficiency and lower economic costs.

Unveiling Colitis: A Journey through the Dextran Sodium Sulfate-induced Model.

Animal models of inflammatory bowel disease (IBD) are valuable tools for investigating the factors involved in IBD pathogenesis and evaluating new therapeutic options. The dextran sodium sulfate (DSS)-induced model of colitis is arguably the most widely used animal model for studying the pathogenesis of and potential treatments for ulcerative colitis (UC), which is a primary form of IBD. This model offers several advantages as a research tool: it is highly reproducible, relatively easy to generate and maintain, and mimics many critical features of human IBD. Recently, it has also been used to study the role of gut microbiota in the development and progression of IBD and to investigate the effects of other factors, such as diet and genetics, on colitis severity. However, although DSS-induced colitis is the most popular and flexible model for preclinical IBD research, it is not an exact replica of human colitis, and some results obtained from this model cannot be directly applied to humans. This review aims to comprehensively discuss different factors that may be involved in the pathogenesis of DSS-induced colitis and the issues that should be considered when using this model for translational purposes.

This review discusses different factors that may be involved in the pathogenesis of DSS-induced colitis and the issues that should be considered when using this model for translational purposes.