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.

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.

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.

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 nano formulations 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.

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.

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.

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.

A Dynamics-Learning Multirate Estimation Approach for the Feeding Condition Perception of Complex Industry Processes.

In this study, we propose a dynamics-learning multirate estimation approach to perceive the quality-related indices (QRIs) of the feeding solution of a unit process. A quality-related index for estimation is an intermediate technical indicator between a unit process and a proceeding unit process; hence, the estimation problem is formulated as a two-stage estimation problem utilizing the production data of both unit processes. Dynamics-learning bidirectional long short-term memory (BiLSTM) with different inputs for the forward and backward layers is proposed to manage the input data from the different unit processes. In the dynamics-learning BiLSTM, a cycle control gate is added in the memory cell to learn the dynamics of the QRIs, thereby enabling a high-rate estimation under multirate conditions. A Bayesian estimation model is then combined with the dynamics-learning BiLSTM model to manage the process delay. Ablation and comparative experiments are conducted to evaluate the feasibility and effectiveness of the proposed estimation approach. The experimental results illustrate the performance and high-rate estimation ability of the proposed approach.

Error-Triggered Adaptive Sparse Identification for Predictive Control and Its Application to Multiple Operating Conditions Processes.

With the digital transformation of process manufacturing, identifying the system model from process data and then applying to predictive control has become the most dominant approach in process control. However, the controlled plant often operates under changing operating conditions. What is more, there are often unknown operating conditions such as first appearance operating conditions, which make traditional predictive control methods based on identified model difficult to adapt to changing operating conditions. Moreover, the control accuracy is low during operating condition switching. To solve these problems, this article proposes an error-triggered adaptive sparse identification for predictive control (ETASI4PC) method. Specifically, an initial model is established based on sparse identification. Then, a prediction error-triggered mechanism is proposed to monitor operating condition changes in real time. Next, the previously identified model is updated with the fewest modifications by identifying parameter change, structural change, and combination of changes in the dynamical equations, thus achieving precise control to multiple operating conditions. Considering the problem of low control accuracy during the operating condition switching, a novel elastic feedback correction strategy is proposed to significantly improve the control accuracy in the transition period and ensure accurate control under full operating conditions. To verify the superiority of the proposed method, a numerical simulation case and a continuous stirred tank reactor (CSTR) case are designed. Compared with some state-of-the-art methods, the proposed method can rapidly adapt to frequent changes in operating conditions, and it can achieve real-time control effects even for unknown operating conditions such as first appearance operating conditions.

Glucagon Enhances Chemotherapy Efficacy By Inhibition of Tumor Vessels in Colorectal Cancer.

Chemotherapy is widely used to treat colorectal cancer (CRC). Despite its substantial benefits, the development of drug resistance and adverse effects remain challenging. This study aimed to elucidate a novel role of glucagon in anti-cancer therapy. In a series of in vitro experiments, glucagon inhibited cell migration and tube formation in both endothelial and tumor cells. In vivo studies demonstrated decreased tumor blood vessels and fewer pseudo-vessels in mice treated with glucagon. The combination of glucagon and chemotherapy exhibited enhanced tumor inhibition. Mechanistic studies demonstrated that glucagon increased the permeability of blood vessels, leading to a pronounced disruption of vessel morphology. Signaling pathway analysis identified a VEGF/VEGFR-dependent mechanism whereby glucagon attenuated angiogenesis through its receptor. Clinical data analysis revealed a positive correlation between elevated glucagon expression and chemotherapy response. This is the first study to reveal a role for glucagon in inhibiting angiogenesis and vascular mimicry. Additionally, the delivery of glucagon-encapsulated PEGylated liposomes to tumor-bearing mice amplified the inhibition of angiogenesis and vascular mimicry, consequently reinforcing chemotherapy efficacy. Collectively, the findings demonstrate the role of glucagon in inhibiting tumor vessel network and suggest the potential utility of glucagon as a promising predictive marker for patients with CRC receiving chemotherapy.

ADP-Based Event-Triggered Constrained Optimal Control on Spatiotemporal Process: Application to Temperature Field in Roller Kiln.

The precise control of the spatiotemporal process in a roller kiln is crucial in the production of Ni-Co-Mn layered cathode material of lithium-ion batteries. Since the product is extremely sensitive to temperature distribution, temperature field control is of great significance. In this article, an event-triggered optimal control (ETOC) method with input constraints for the temperature field is proposed, which takes up an important position in reducing the communication and computation costs. A nonquadratic cost function is adopted to describe the system performance with input constraints. First, we present the problem description of the temperature field event-triggered control, where this field is described by a partial differential equation (PDE). Then, the event-triggered condition is designed according to the information of system states and control inputs. On this basis, a framework of the event-triggered adaptive dynamic programming (ETADP) method that is based on the model reduction technology is proposed for the PDE system. A critic network is used to approach the optimal performance index by a neural network (NN) together with that an actor network is used to optimize the control strategy. Furthermore, an upper bound of the performance index and a lower bound of interexecution times, as well as the stabilities of the impulsive dynamic system and the closed-loop PDE system, are also proved. Simulation verification demonstrates the effectiveness of the proposed method.

Multiscale Feature Fusion and Semi-Supervised Temporal-Spatial Learning for Performance Monitoring in the Flotation Industrial Process.

This article studies the performance monitoring problem for the potassium chloride flotation process, which is a critical component of potassium fertilizer processing. To address its froth image segmentation problem, this article proposes a multiscale feature extraction and fusion network (MsFEFNet) to overcome the multiscale and weak edge characteristics of potassium chloride flotation froth images. MsFEFNet performs simultaneous feature extraction at multiple image scales and automatically learns spatial information of interest at each scale to achieve efficient multiscale information fusion. In addition, the potassium chloride flotation process is a multistage dynamic process with massive unlabeled data. To overcome its dynamic time-varying and working condition spatial similarity characteristics, a semi-supervised froth-grade prediction model based on a temporal-spatial neighborhood learning network combined with Mean Teacher (MT-TSNLNet) is proposed. MT-TSNLNet designs a new objective function for learning the temporal-spatial neighborhood structure of data. The introduction of Mean Teacher can further utilize unlabeled data to promote the proposed prediction model to better track the concentrate grade. To verify the effectiveness of the proposed MsFEFNet and MT-TSNLNet, froth image segmentation and grade prediction experiments are performed on a real-world potassium chloride flotation process dataset.

Development of lactoferrin-coated multifunctional copolymer micelles to cross the blood-brain barrier.

The blood-brain barrier (BBB) prevents pathogens and toxins in the bloodstream from reaching the brain, but also inhibits the delivery of agents intended to treat central nervous system disorders, such as Alzheimer's disease (AD). In this study, we prepared and evaluated a novel nano-delivery vehicle system composed of lactoferrin-conjugated (Lf-PIC@Se) micelles. We used a COOH-PEG-PAsp-PV@Se synthesis-based method to prepare the micelles, which involved self-assembly followed by EDC-NHS coupling. Using glutaminyl cyclase inhibitor 8 as a model encapsulated chemical, Lf-PIC@Se micelles achieved a good loading capacity. In vitro analysis demonstrated that Lf-PIC@Se/8 micelles were stable in both neutral and acidic pH solutions in the presence or absence of H2O2, and confirmed their biosafety and compatibility in PC12 and bEND.3 cells. Notably, the cell uptake of Lf-PIC@Se/C6 micelles was much higher than that of PIC@Se micelles, and occurred through LfR-mediated endocytosis. The presence of Se meant that Lf-PIC@Se micelles acted as ROS scavengers in PC12 cells under H2O2-induced oxidative stress, which inhibited oxidative damage and increased mitochondrial membrane potential. Hemolysis assays further demonstrated that Lf-PIC@Se represent a biocompatible carrier. Finally, in vivo experiments in mice suggested that Lf-PIC@Se micelles successfully crossed the BBB, confirming their potential as vehicles for drug delivery when treating AD and other central nervous system disorders.

Multi-scale feature selection network for lightweight image super-resolution.

Recently, many super-resolution (SR) methods based on convolutional neural networks (CNNs) have achieved superior performance by utilizing deep and heavy models, which may not be suitable for real-world low-budget devices. To address this issue, we propose a novel lightweight SR network called a multi-scale feature selection network (MFSN). As the basic building block of MFSN, the multi-scale feature selection block (MFSB) is presented to extract the rich multi-scale features from a coarse-to-fine receptive field level. For a better representation ability, a wide-activated residual unit is adopted in each branch of MFSB except the last one. In MFSB, the scale selection module (SSM) is designed to effectively fuse the features from two adjacent branches by adjusting receptive field sizes adaptively. Further, a comprehensive channel attention mechanism (CCAM) is integrated into SSM to learn the dynamic selection weight by considering the local and global inter-channel dependencies. Extensive experimental results illustrate that the proposed MFSN is superior to other lightweight methods.

A Spatial-Temporal Variational Graph Attention Autoencoder Using Interactive Information for Fault Detection in Complex Industrial Processes.

Modern industry processes are typically composed of multiple operating units with reaction interaction and energy-mass coupling, which result in a mixed time-varying and spatial-temporal coupling of process variables. It is challenging to develop a comprehensive and precise fault detection model for the multiple interconnected units by simple superposition of the individual unit models. In this study, the fault detection problem is formulated as a spatial-temporal fault detection problem utilizing process data of multiple interconnected unit processes. A spatial-temporal variational graph attention autoencoder (STVGATE) using interactive information is proposed for fault detection, which aims to effectively capture the spatial and temporal features of the interconnected unit processes. First, slow feature analysis (SFA) is implemented to extract temporal information that reveals the dynamic relevance of the process data. Then, an integration method of metric learning and prior knowledge is proposed to construct coupled spatial relationships based on temporal information. In addition, a variational graph attention autoencoder (VGATE) is suggested to extract temporal and spatial information for fault detection, which incorporates the dominances of variational inference and graph attention mechanisms. The proposed method can automatically extract and deeply mine spatial-temporal interactive feature information to boost detection performance. Finally, three industrial process experiments are performed to verify the feasibility and effectiveness of the proposed method. The results demonstrate that the proposed method dramatically increases the fault detection rate (FDR) and reduces the false alarm rate (FAR).