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.

Study on the relationship between adolescent myopia and gut microbiota via 16S rRNA sequencing.

Myopia has become a global public health problem, with a high incidence among adolescents. In recent years, the correlation between gut microbiota and various diseases has become a research hotspot. This paper analyzes the relationship between myopia and gut microbiota in adolescents based on 16S rRNA sequencing, opening up a new avenue for the prevention and control of myopia. 80 adolescents aged 6-15 years were included; fecal samples were collected to compare their diversity and species differences. There was no significant difference in α diversity when considering richness and evenness at the same time (P > 0.05). While the group difference in ß diversity reached a significant level (R2 = 0.022, P < 0.05). The absolute quantification and relative abundance of phylum level Firmicutes and Actinobacteriota are different; among the top 30 genera, myopic group only one genus decreased in absolute quantification, while 13 genera decreased in relative quantification; so LEfSe analysis was performed, and the result showed that microbial community composition changed under Linear discriminant analysis (LDA) score, the top ten changes are shown in the figure; the Wilcoxon Rank sum test also found some significant changes in the absolute abundance of differential microbiota among different groups, at the phylum level, one bacterial phylum decreased and three bacterial phyla increased; at the genus level, 2 bacteria genera decreased and 29 bacteria genera increased. Functional pathways prediction found many myopic-related pathways were functionally enhanced in myopic patients (P < 0.05). Multivariate logistic regression analysis results showed that the area under the curve (AUC) of myopic patients predicted was close to or equal to 1. In conclusion, adolescent myopia is closely related to the gut microbiota, and the characteristic gut microbiota can distinguish myopia from healthy controls to a large extent. Therefore, it can be considered to regulate these characteristic gut microbiota to prevent and control myopia.

The AMPK-PP2A axis in insect fat body is activated by 20-hydroxyecdysone to antagonize insulin/IGF signaling and restrict growth rate.

In insects, 20-hydroxyecdysone (20E) limits the growth period by triggering developmental transitions; 20E also modulates the growth rate by antagonizing insulin/insulin-like growth factor signaling (IIS). Previous work has shown that 20E cross-talks with IIS, but the underlying molecular mechanisms are not fully understood. Here we found that, in both the silkworm Bombyx mori and the fruit fly Drosophila melanogaster, 20E antagonized IIS through the AMP-activated protein kinase (AMPK)-protein phosphatase 2A (PP2A) axis in the fat body and suppressed the growth rate. During Bombyx larval molt or Drosophila pupariation, high levels of 20E activate AMPK, a molecular sensor that maintains energy homeostasis in the insect fat body. In turn, AMPK activates PP2A, which further dephosphorylates insulin receptor and protein kinase B (AKT), thus inhibiting IIS. Activation of the AMPK-PP2A axis and inhibition of IIS in the Drosophila fat body reduced food consumption, resulting in the restriction of growth rate and body weight. Overall, our study revealed an important mechanism by which 20E antagonizes IIS in the insect fat body to restrict the larval growth rate, thereby expanding our understanding of the comprehensive regulatory mechanisms of final body size in animals.

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.

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.

Temperature Measurement Method for Blast Furnace Molten Iron Based on Infrared Thermography and Temperature Reduction Model.

The temperature measurement of blast furnace (BF) molten iron is a mandatory requirement in the ironmaking process, and the molten iron temperature is significant in estimating the molten iron quality and control blast furnace condition. However, it is not easy to realize real-time measurement of molten iron temperature because of the harsh environment in the blast furnace casthouse and the high-temperature characteristics of molten iron. To achieve continuous detection of the molten iron temperature of the blast furnace, this paper proposes a temperature measurement method based on infrared thermography and a temperature reduction model. Firstly, an infrared thermal imager is applied to capture the infrared thermal image of the molten iron flow after the skimmer. Then, based on the temperature distribution of the molten iron flow region, a temperature mapping model is established to measure the molten iron temperature after the skimmer. Finally, a temperature reduction model is developed to describe the relationship between the molten iron temperature at the taphole and skimmer, and the molten iron temperature at the taphole is calculated according to the temperature reduction model and the molten iron temperature after the skimmer. Industrial experiment results illustrate that the proposed method can achieve simultaneous measurement of molten iron temperature at the skimmer and taphole and provide reliable temperature data for regulating the blast furnace.

Transcriptomics Evidence for Common Pathways in Human Major Depressive Disorder and Glioblastoma.

Depression as a common complication of brain tumors. Is there a possible common pathogenesis for depression and glioma? The most serious major depressive disorder (MDD) and glioblastoma (GBM) in both diseases are studied, to explore the common pathogenesis between the two diseases. In this article, we first rely on transcriptome data to obtain reliable and useful differentially expressed genes (DEGs) by differential expression analysis. Then, we used the transcriptomics of DEGs to find out and analyze the common pathway of MDD and GBM from three directions. Finally, we determine the important biological pathways that are common to MDD and GBM by statistical knowledge. Our findings provide the first direct transcriptomic evidence that common pathway in two diseases for the common pathogenesis of the human MDD and GBM. Our results provide a new reference methods and values for the study of the pathogenesis of depression and glioblastoma.

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.

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.

Collagen is crucial target protein for scleral remodeling and biomechanical change in myopia progression and control.

In recent decades, the prevalence of myopia has been on the rise globally, attributed to changes in living environments and lifestyles. This increase in myopia has become a significant public health concern. High myopia can result in thinning of the sclera and localized ectasia of the posterior sclera, which is the primary risk factor for various eye diseases and significantly impacts patients' quality of life. Therefore, it is essential to explore effective prevention strategies and programs for individuals with myopia. Collagen serves as the principal molecule in the extracellular matrix (ECM) of scleral tissue, consisting of irregular collagen fibrils. Collagen plays a crucial role in myopia progression and control. During the development of myopia, the sclera undergoes a thinning process which is primarily influenced by collagen expression decreased and remodeled, thus leading to a decrease in its biomechanical properties. Improving collagen expression and promoting collagen crosslinking can slow down the progression of myopia. In light of the above, improving collagen expression or enhancing the mechanical properties of collagen fibers via medication or surgery represents a promising approach to control myopia.

Detecting cell types and densities in the tumor microenvironment improves prognostic risk assessment for breast cancer.

A comprehensive evaluation of the relationship between the densities of various cell types in the breast cancer tumor microenvironment and patient prognosis is currently lacking. Additionally, the absence of a large patch-level whole slide imaging (WSI) dataset of breast cancer with annotated cell types hinders the ability of artificial intelligence to evaluate cell density in breast cancer WSI. We first employed Lasso-Cox regression to build a breast cancer prognosis assessment model based on cell density in a population study. Pathology experts manually annotated a dataset containing over 70,000 patches and used transfer learning based on ResNet152 to develop an artificial intelligence model for identifying different cell types in these patches. The results showed that significant prognostic differences were observed among breast cancer patients stratified by cell density score (P = 0.0018), with the cell density score identified as an independent prognostic factor for breast cancer patients (P < 0.05). In the validation cohort, the predictive performance for overall survival (OS) was satisfactory, with area under the curve (AUC) values of 0.893 (OS) at 1-year, 0.823 (OS) at 3-year, and 0.861 (OS) at 5-year intervals. We trained a robust model based on ResNet152, achieving over 99% classification accuracy for different cell types in patches. These achievements offer new public resources and tools for personalized treatment and prognosis assessment.

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.].

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.

Molecular mechanism of hypoxia and alpha-ketoglutaric acid on collagen expression in scleral fibroblasts.

AIM: To investigate the molecular mechanisms underlying the influence of hypoxia and alpha-ketoglutaric acid (α-KG) on scleral collagen expression. METHODS: Meta-analysis and clinical statistics were used to prove the changes in choroidal thickness (ChT) during myopia. The establishment of a hypoxic myopia model (HYP) for rabbit scleral fibroblasts through hypoxic culture and the effects of hypoxia and α-KG on collagen expression were demonstrated by Sirius red staining. Transcriptome analysis was used to verify the genes and pathways that hypoxia and α-KG affect collagen expression. Finally, real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) was used for reverse verification. RESULTS: Meta-analysis results aligned with clinical statistics, revealing a thinning of ChT, leading to scleral hypoxia. Sirius red staining indicated lower collagen expression in the HYP group and higher collagen expression in the HYP+α-KG group, showed that hypoxia reduced collagen expression in scleral fibroblasts, while α-KG can elevated collagen expression under HYP conditions. Transcriptome analysis unveiled the related genes and signaling pathways of hypoxia and α-KG affect scleral collagen expression and the results were verified by RT-qPCR. CONCLUSION: The potential molecular mechanisms through which hypoxia and α-KG influencing myopia is unraveled and three novel genes TLCD4, TBC1D4, and EPHX3 are identified. These findings provide a new perspective on the prevention and treatment of myopia via regulating collagen expression.

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.

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.

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.

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.