Corrigendum: Effects of Caragana korshinskii tannin on fermentation, methane emission, community of methanogens, and metabolome of rumen in sheep.

[This corrects the article DOI: 10.3389/fmicb.2024.1334045.].

Polarization beam splitter based on 2D transmissive grating.

This paper introduces a two-dimensional transmissive grating polarization beam splitter (PBS) exhibiting exceptional polarization-sensitive properties with high diffraction efficiency. The optimized grating structure can concentrate the energy of TE-polarized light at the (0, ±1) orders and the energy of TM-polarized light at the (±1, 0) orders under normal incidence with a wavelength of 550nm. The polarization splitting diffraction efficiency (DE) of the grating can reach 40.17%, and the extinction ratio (ER) exceeds 18dB. This proposal marks the pioneering use of two-dimensional transmissive grating to achieve a polarization beam splitter in two perpendicular diffraction planes, presenting an innovative approach to the development of such devices. The proposed grating structure is simple, high-performing, tolerant, and applicable in a wide range of applications such as polarization imaging and high-precision two-dimensional displacement measurement.

Effects of Caragana korshinskii tannin on fermentation, methane emission, community of methanogens, and metabolome of rumen in sheep.

The purpose of this research was to investigate the impact of dietary supplementation of Caragana korshinskii tannin (CKT) on rumen fermentation, methane emission, methanogen community and metabolome in rumen of sheep. A total of 15 crossbred sheep of the Dumont breed with similar body conditions, were divided into three groups (n = 5), which were fed with CKT addition at 0, 2 and 4%/kg DM. The study spanned a total of 74 days, with a 14-day period dedicated to adaptation and a subsequent 60-day period for conducting treatments. The results indicated that the levels of ammonia nitrogen (NH3-N) and acetate were reduced (p < 0.05) in rumen sheep fed with 2 and 4% CKT; The crude protein (CP) digestibility of sheep in 2 and 4% CKT groups was decreased(p < 0.05); while the neutral detergent fiber (NDF) digestibility was increased (p < 0.05) in 4% CKT group. Furthermore, the supplementation of CKT resulted in a decrease (p < 0.05) in daily CH4 emissions from sheep by reducing the richness and diversity of ruminal methanogens community, meanwhile decreasing (p < 0.05) concentrations of tyramine that contribute to methane synthesis and increasing (p < 0.05) concentrations of N-methy-L-glutamic acid that do not contribute to CH4 synthesis. However, CH4 production of DMI, OMI, NDFI and metabolic weight did not differ significantly across the various treatments. To sum up, the addition of 4% CKT appeared to be a viable approach for reducing CH4 emissions from sheep without no negative effects. These findings suggest that CKT hold promise in mitigating methane emissions of ruminant. Further investigation is required to evaluate it effectiveness in practical feeding strategies for livestock.

Reinforcement Learning for Blast Furnace Ironmaking Operation With Safety and Partial Observation Considerations.

Making proper decision online in complex environment during the blast furnace (BF) operation is a key factor in achieving long-term success and profitability in the steel manufacturing industry. Regulatory lags, ore source uncertainty, and continuous decision requirement make it a challenging task. Recently, reinforcement learning (RL) has demonstrated state-of-the-art performance in various sequential decision-making problems. However, the strict safety requirements make it impossible to explore optimal decisions through online trial and error. Therefore, this article proposes a novel offline RL approach designed to ensure safety, maximize return, and address issues of partially observed states. Specifically, it utilizes an off-policy actor-critic framework to infer the optimal decision from expert operation trajectories. The "actor" in this framework is jointly trained by the supervision and evaluation signals to make decision with low risk and high return. Furthermore, we investigate a recurrent version of the actor and critic networks to better capture the complete observations, which solves the partially observed Markov decision process (POMDP) arising from sensor limitations. Verification within the BF smelting process demonstrates the improvements of the proposed algorithm in performance, i.e., safety and return.

Delicately Reinforced k-Nearest Neighbor Classifier Combined With Expert Knowledge Applied to Abnormity Forecast in Electrolytic Cell.

As the profit and safety requirements become higher and higher, it is more and more necessary to realize an advanced intelligent analysis for abnormity forecast of the synthetical balance of material and energy (AF-SBME) on aluminum reduction cells (ARCs). Without loss of generality, AF-SBME belongs to classification problems. Its advanced intelligent analysis can be realized by high-performance data-driven classifiers. However, AF-SBME has some difficulties, including a high requirement for interpretability of data-driven classifiers, a small number, and decreasing-over-time correctness of training samples. In this article, based on a preferable data-driven classifier, which is called a reinforced k -nearest neighbor (R-KNN) classifier, a delicately R-KNN combined with expert knowledge (DR-KNN/CE) is proposed. It improves R-KNN in two ways, including using expert knowledge as external assistance and enhancing self-ability to mine and synthesize data knowledge. The related experiments on AF-SBME, where the relevant data are directly sampled from practical production, have demonstrated that the proposed DR-KNN/CE not only makes an effective improvement for R-KNN, but also has a more advanced performance compared with other existing high-performance data-driven classifiers.

Memory Shapelet Learning for Early Classification of Streaming Time Series.

Early classification predicts the class of the incoming sequences before it is completely observed. How to quickly classify streaming time series without losing interpretability through early classification method is a challenging problem. A novel memory shapelet learning framework for early classification is proposed in this article. First, a memory distance matrix is introduced to store the historical characteristics of streaming time series, which can alleviate repetitive calculations caused by the growing length of time series. Second, early interpretable shapelets are extracted in the proposed method by optimizing both accuracy objective and earliness objective simultaneously. The proposed method employs end-to-end learning, which allows the model to directly learn early shapelets without the necessity of searching for numerous candidate shapelets. Third, an objective function of memory shapelet learning is proposed by overall considering accuracy and earliness, which can be optimized by gradient descent algorithm. Finally, experiments are conducted on benchmark dataset UCR, Tennessee Eastman process, and real-world aluminum electrolysis process in China. Comparable results with other state-of-the-art methods demonstrate the superior performance of the proposed method in interpretability, accuracy, earliness, and time complexity.

Antibiotic-Induced Dysbiosis of the Gut Microbiota Impairs Gene Expression in Gut-Liver Axis of Mice.

Antibiotics can be a double-edged sword. The application of broad-spectrum antibiotics leads to the suppression of microorganisms in the human body without selective targeting, including numerous non-pathogenic microorganisms within the gut. As a result, dysbiosis of the gut microbiota can occur. The gut microbiota is a vast and intricate ecosystem that has been connected with various illnesses. Significantly, the gut and liver function in a closely coupled anatomical and physiological relationship referred to as the "gut-liver axis". Consequently, metabolites stemming from the gut microbiota migrate via the portal vein to the liver, thereby influencing gene expression and proper physiological activity within the liver. This study aimed to investigate the dysbiosis of gut microbiota ecology and the disruption of gene expression resulting from oral antibiotics and their subsequent recovery. In the experiment, mice were tube-fed neomycin (0.5 mg/mL) and ampicillin (1 mg/mL) for 21 days (ABX group) to conduct 16s rRNA sequencing. By simultaneously analyzing public datasets PRJDB6615, which utilized the same antibiotics, it was found that nearly 50% of the total microbiota abundance was attributed to the f__Lactobacillaceae family. Additionally, datasets GSE154465 and GSE159761, using the same antibiotics, were used to screen for differentially expressed genes pre-and post-antibiotic treatment. Quantitative real-time PCR was employed to evaluate gene expression levels before and after antibiotic treatment. It was discovered that oral antibiotics significantly disrupted gene expression in the gut and liver, likely due to the dysregulation of the gut microbiota ecology. Fecal microbiota transplantation (FMT) was found to be an effective method for restoring gut microbiota dysbiosis. To further enhance the restoration of gut microbiota and gene expression, an antioxidant, vitamin C, was added to the FMT process to counteract the oxidative effect of antibiotics on microorganisms. The results showed that FMTs with vitamin C were more effective in restoring gut microbiota and gene expression to the level of the fecal transplant donor.

Optimization of aluminum fluoride addition in aluminum electrolysis process based on pruned sparse fuzzy neural network.

The aluminum fluoride (AF) addition in aluminum electrolysis process (AEP) can directly influence the current efficiency, energy consumption, and stability of the process. This paper proposes an optimization scheme for AF addition based on pruned sparse fuzzy neural network (PSFNN), aiming at providing an optimal AF addition for aluminum electrolysis cell under normal superheat degree (SD) condition. Firstly, a Gaussian mixture model (GMM) is introduced to identify SD conditions in which the operating modes of AEP are unknown. Then, PSFNN is proposed to establish the AF addition model under normal SD condition identified by GMM. Specifically, a sparse regularization term is designed in loss function of PSFNN to extract the sparse representation from nonlinear process data. A structure optimization strategy based on enhanced optimal brain surgeon (EOBS) algorithm is proposed to prune redundant neurons in the rule layer. Mini-batch gradient descent and AdaBound optimizer are then introduced to optimize the parameters of PSFNN. Finally, the performance is confirmed on the simulated Tennessee Eastman process (TEP) and real-world AEP. Experimental results demonstrate that the proposed scheme provides a satisfactory performance.

Toward Robust Fault Identification of Complex Industrial Processes Using Stacked Sparse-Denoising Autoencoder With Softmax Classifier.

This article proposes a robust end-to-end deep learning-induced fault recognition scheme by stacking multiple sparse-denoising autoencoders with a Softmax classifier, called stacked spare-denoising autoencoder (SSDAE)-Softmax, for the fault identification of complex industrial processes (CIPs). Specifically, sparse denoising autoencoder (SDAE) is established by integrating a sparse AE (SAE) with a denoising AE (DAE) for the low-dimensional but intrinsic feature representation of the CIP monitoring data (CIPMD) with possible noise contamination. SSDAE-Softmax is established by stacking multiple SDAEs with a layerwise pretraining procedure, and a Softmax classifier with a global fine-tuning strategy. Furthermore, SSDAE-Softmax hyperparameters are optimized by a relatively new global optimization algorithm, referred to as the state transition algorithm (STA). Benefiting from the deep learning-based feature representation scheme with the STA-based hyperparameter optimization, the underlying intrinsic characteristics of CIPMD can be learned automatically and adaptively for accurate fault identification. A numeric simulation system, the benchmark Tennessee Eastman process (TEP), and a real industrial process, that is, the continuous casting process (CCP) from a top steel plant of China, are used to validate the performance of the proposed method. Experimental results show that the proposed SSDAE-Softmax model can effectively identify various process faults, and has stronger robustness and adaptability against the noise interference in CIPMD for the process monitoring of CIPs.

Screening of Hub Genes in Hepatocellular Carcinoma Based on Network Analysis and Machine Learning.

Hepatocellular carcinoma (LIHC) is the fifth common cancer worldwide, and it requires effective diagnosis and treatment to prevent aggressive metastasis. The purpose of this study was to construct a machine learning-based diagnostic model for the diagnosis of liver cancer. Using weighted correlation network analysis (WGCNA), univariate analysis, and Lasso-Cox regression analysis, protein-protein interactions network analysis is used to construct gene networks from transcriptome data of hepatocellular carcinoma patients and find hub genes for machine learning. The five models, including gradient boosting, random forest, support vector machine, logistic regression, and integrated learning, were to identify a multigene prediction model of patients. Immunological assessment, TP53 gene mutation and promoter methylation level analysis, and KEGG pathway analysis were performed on these groups. Potential drug molecular targets for the corresponding hepatocellular carcinomas were obtained by molecular docking for analysis, resulting in the screening of 2 modules that may be relevant to the survival of hepatocellular carcinoma patients, and the construction of 5 diagnostic models and multiple interaction networks. The modes of action of drug-molecule interactions that may be effective against hepatocellular carcinoma core genes CCNA2, CCNB1, and CDK1 were investigated. This study is expected to provide research ideas for early diagnosis of hepatocellular carcinoma.

Reset control for leader-following consensus of multi-agent systems.

This paper studies the leader-following consensus problem of linear multi-agent systems over directed communication graphs. This paper proposes a novel distributed reset proportional-integral consensus controller with guaranteed Zeno-freeness property. With the proposed reset consensus controller, the closed-loop system becomes a hybrid system consisting of the flow dynamics and jump dynamics. By adopting a novel Lyapunov function and the hybrid system analysis approach, it obtains the conditions under which consensus is achieved. It further shows that the proposed reset consensus controller helps to improve the transient performance. Finally, a numerical example is presented to illustrate the theoretical results and effectiveness.

Advances in biomedical study of the myopia-related signaling pathways and mechanisms.

Myopia has become one of the most critical health problems in the world with the increasing time spent indoors and increasing close work. Pathological myopia may have multiple complications, such as myopic macular degeneration, retinal detachment, cataracts, open-angle glaucoma, and severe cases that can cause blindness. Mounting evidence suggests that the cause of myopia can be attributed to the complex interaction of environmental exposure and genetic susceptibility. An increasing number of researchers have focused on the genetic pathogenesis of myopia in recent years. Scleral remodeling and excessive axial elongating induced retina thinning and even retinal detachment are myopia's most important pathological manifestations. The related signaling pathways are indispensable in myopia occurrence and development, such as dopamine, nitric oxide, TGF-ß, HIF-1α, etc. We review the current major and recent progress of biomedicine on myopia-related signaling pathways and mechanisms.

Sliding-Mode-Based Admissible Consensus Tracking of Nonlinear Singular Multiagent Systems Under Jointly Connected Topologies.

The admissible consensus tracking problem of nonlinear singular multiagent systems (SMASs) with time-varying delay, uncertainties, and external disturbances under jointly connected topologies is investigated in this article. First, the sliding-mode control (SMC) is applied to effectively reduce the adverse effects of uncertainties and nonlinearities of systems. Then, by the combination of admissible analysis, the Cauchy convergence criterion, and SMC, the sufficient conditions for the admissible consensus tracking and disturbance rejection of SMASs under jointly connected topologies are provided. Furthermore, a distributed SMC law is designed such that the sliding-mode dynamics trajectories reach the sliding surface in finite time. Finally, the simulation results are utilized to indicate the effectiveness of the presented methods.

Polarization-independent 2×2 high diffraction efficiency beam splitter based on two-dimensional grating.

Better performances of two-dimensional (2D) grating are required recently, such as polarization independence, high efficiency, wide bandwidth and so forth. In this paper, we propose a 2×2 2D silver cylindrical array grating with excellent polarization-independent high diffraction efficiency (DE) over communication band for beam splitting. The grating was calculated by rigorous coupled wave analysis (RCWA) and can achieve over 24% DE of four first diffraction orders at 1550 nm with nonuniformity of 1.43% in both transverse electric (TE) and transverse magnetic (TM) polarizations, which is a significant improvement over previous reports. The holographic exposure technology, wet chemical development process and electron beam evaporation were used to fabricate the 2D grating. The correctness and accuracy of the calculation are fully verified with the measurement result of fabricated grating. Excellent performances of the 2D splitter we proposed will have great potential for applications in optical communication, semiconductor manufacturing and displacement measurement.

Automated cardiac segmentation of cross-modal medical images using unsupervised multi-domain adaptation and spatial neural attention structure.

Accurate cardiac segmentation of multimodal images, e.g., magnetic resonance (MR), computed tomography (CT) images, plays a pivot role in auxiliary diagnoses, treatments and postoperative assessments of cardiovascular diseases. However, training a well-behaved segmentation model for the cross-modal cardiac image analysis is challenging, due to their diverse appearances/distributions from different devices and acquisition conditions. For instance, a well-trained segmentation model based on the source domain of MR images is often failed in the segmentation of CT images. In this work, a cross-modal images-oriented cardiac segmentation scheme is proposed using a symmetric full convolutional neural network (SFCNN) with the unsupervised multi-domain adaptation (UMDA) and a spatial neural attention (SNA) structure, termed UMDA-SNA-SFCNN, having the merits of without the requirement of any annotation on the test domain. Specifically, UMDA-SNA-SFCNN incorporates SNA to the classic adversarial domain adaptation network to highlight the relevant regions, while restraining the irrelevant areas in the cross-modal images, so as to suppress the negative transfer in the process of unsupervised domain adaptation. In addition, the multi-layer feature discriminators and a predictive segmentation-mask discriminator are established to connect the multi-layer features and segmentation mask of the backbone network, SFCNN, to realize the fine-grained alignment of unsupervised cross-modal feature domains. Extensive confirmative and comparative experiments on the benchmark Multi-Modality Whole Heart Challenge dataset show that the proposed model is superior to the state-of-the-art cross-modal segmentation methods.

Illumination-Invariant Flotation Froth Color Measuring via Wasserstein Distance-Based CycleGAN With Structure-Preserving Constraint.

Froth color can be referred to as a direct and instant indicator to the key flotation production index, for example, concentrate grade. However, it is intractable to measure the froth color robustly due to the adverse interference of time-varying and uncontrollable multisource illuminations in the flotation process monitoring. In this article, we proposed an illumination-invariant froth color measuring method by solving a structure-preserved image-to-image color translation task via an introduced Wasserstein distance-based structure-preserving CycleGAN, called WDSPCGAN. WDSPCGAN is comprised of two generative adversarial networks (GANs), which have their own discriminators but share two generators, using an improved U-net-like full convolution network to conduct the spatial structure-preserved color translation. By an adversarial game training of the two GANs, WDSPCGAN can map the color domain of froth images under any illumination to that of the referencing illumination, while maintaining the structure and texture invariance. The proposed method is validated on two public benchmark color constancy datasets and applied to an industrial bauxite flotation process. The experimental results show that WDSPCGAN can achieve illumination-invariant color features of froth images under various unknown lighting conditions while keeping their structures and textures unchanged. In addition, WDSPCGAN can be updated online to ensure its adaptability to any operational conditions. Hence, it has the potential for being popularized to the online monitoring of the flotation concentrate grade.

Data-driven adaptive modeling method for industrial processes and its application in flotation reagent control.

In real industrial processes, new process "excitation" patterns that largely deviate from previously collected training data will appear due to disturbances caused by process inputs. To reduce model mismatch, it is important for a data-driven process model to adapt to new process "excitation" patterns. Although efforts have been devoted to developing adaptive process models to deal with this problem, few studies have attempted to develop an adaptive process model that can incrementally learn new process "excitation" patterns without performance degradation on old patterns. In this study, efforts are devoted to enabling data-driven process models with incremental learning ability. First, a novel incremental learning method is proposed for process model updating. Second, an adaptive neural network process model is developed based on the novel incremental learning method. Third, a nonlinear model predictive control based on the adaptive process model is implemented and applied for flotation reagent control. Experiments based on historical data provide evidence that the newly developed adaptive process model can accommodate new process "excitation" patterns and preserve its performance on old patterns. Furthermore, industry experiments carried out in a real-world lead-zinc froth flotation plant provide industrial evidence and show that the newly designed controller is promising for practical flotation reagent control.

Two-dimensional picometer comb for three-dimensional reconstruction.

A two-dimensional (2D) picometer comb, a novel optical element made by picometer-differential four times exposed in two perpendicular directions, is proposed to generate the dot array projection pattern for three-dimensional (3D) shape reconstruction and other applications. Not only does a 2D picometer comb generate a stable light field distribution with extremely long depth of field and small divergence angle as a one-dimensional picometer comb, it also has new properties, such as periodicity of diffraction field in two perpendicular directions and high concentration of energy of points, which is particularly suitable for providing dot array structured light. We demonstrate that the diffraction field of a 2D picometer comb provides a solution for non-defocusing 3D reconstruction with a dot array. In fabrication of a 2D picometer comb, we can modulate the holography by changing the angle of two beams slightly, so its period can be measured at picometer accuracy. A 2D picometer comb can be made to any scale, then it can be integrated to mobile devices, such as a mobile phone, for 3D shape reconstruction. Furthermore, the concept of a 2D picometer comb would be applied to generate a picometer light field for opening the door of pico-optics in the future.

Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping.

Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain. Most of the existing methods consider the rating information of users in different domains, the label information of users and items and the review information of users on items. However, they do not effectively use the latent sentiment information to find the accurate mapping of latent features in reviews between domains. User reviews usually include user's subjective views, which can reflect the user's preferences and sentiment tendencies to various attributes of the items. Therefore, in order to solve the cold-start problem in the recommendation process, this paper proposes a cross-domain recommendation algorithm (CDR-SAFM) based on sentiment analysis and latent feature mapping by combining the sentiment information implicit in user reviews in different domains. Different from previous sentiment research, this paper divides sentiment into three categories based on three-way decision ideas-namely, positive, negative and neutral-by conducting sentiment analysis on user review information. Furthermore, the Latent Dirichlet Allocation (LDA) is used to model the user's semantic orientation to generate the latent sentiment review features. Moreover, the Multilayer Perceptron (MLP) is used to obtain the cross domain non-linear mapping function to transfer the user's sentiment review features. Finally, this paper proves the effectiveness of the proposed CDR-SAFM framework by comparing it with existing recommendation algorithms in a cross-domain scenario on the Amazon dataset.

Whole transcriptome analysis on blue light-induced eye damage.

AIM: To analyze abnormal gene expressions of mice eyes exposed to blue light using RNA-seq and analyze the related signaling pathways. METHODS: Kunming mice were divided into an experimental group that was exposed to blue light and a control group that was exposed to natural light. After 14d, the mice were euthanized and their eyeballs were collected. Whole transcriptome analysis was attempted to analyze the gene expression of the eyeballs using RNA-seq to reconstruct genetic networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to reveal the related signaling pathways. RESULTS: The 737 differentially expressed genes were identified, including 430 up and 307 down regulated genes, by calculating the gene FPKM in each sample and conducting differential gene analysis. GO and KEGG pathway enrichment analysis showed that blue light damage may associated with the visual perception, sensory perception of light stimulus, phototransduction, and JAK-STAT signaling pathways. Differential lncRNA, circRNA and miRNA analysis showed that blue light exposure affected pathways for retinal cone cell development and phototransduction, among others. CONCLUSION: Exposure to blue light can cause a certain degree of abnormal gene expression and modulate signaling pathways in the eye.