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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 seventy thousand 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.

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.

Abstract A PDE‐constrained model predictive control (MPC) algorithm for open‐channel systems based on the Saint‐Vevant(S‐V) equations is investigated in this paper. The S‐V equations, which precisely model the dynamics of open‐channel systems, are quasi‐linear hyperbolic partial differential equations (PDEs) without analytical solutions. Directly applying the S‐V equations to an MPC controller design becomes sophisticated. In this work, the calculus of variation is used to obtain the adjoint equations and the adjoint analysis method is utilized to deduce the gradients of the MPC optimization problem. Particularly, the physical constraints involving both the state and control variables are also considered. A gradient‐based optimization algorithm in combination with the numerical computation of Preissmann implicit scheme is proposed to solve the constrained MPC optimization problem. The control performances of the developed PDE‐constrained MPC algorithm with respect to the controlled water levels and gate openings are compared with those of the MPC controller designed for the linearized model. All the simulation tests are carried out on an aqueduct reach in Yehe Irrigation District in Hebei Province, China. The results show that the proposed PDE‐constrained MPC algorithm is a promising method in dealing with the constraints in terms of hyperbolic PDEs, control variables and state variables simultaneously.

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

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.

Cell voltage is a widely used signal that can be measured online from an industrial aluminum electrolysis cell. A variety of parameters for the analysis and control of industrial cells are calculated using the cell voltage. In this paper, the frequency segmentation of cell voltage is used as the basis for designing filters to obtain these parameters. Based on the qualitative analysis of the cell voltage, the sub-band instantaneous energy spectrum (SIEP) is first proposed, which is then used to quantitatively represent the characteristics of the designated frequency bands of the cell voltage under various cell conditions. Ultimately, a cell condition-sensitive frequency segmentation method is given. The proposed frequency segmentation method divides the effective frequency band into the [0, 0.001] Hz band of low-frequency signals and the [0.001, 0.050] Hz band of low-frequency noise, and subdivides the low-frequency noise into the [0.001, 0.010] Hz band of metal pad abnormal rolling and the [0.01, 0.05] Hz band of sub-low-frequency noise. Compared with the instantaneous energy spectrum based on empirical mode decomposition, the SIEP more finely represents the law of energy change with time in any designated frequency band within the effective frequency band of the cell voltage. The proposed frequency segmentation method is more sensitive to cell condition changes and can obtain more elaborate details of online cell condition information, thus providing a more reliable and accurate online basis for cell condition monitoring and control decisions.

Abstract It is a common fact that data (features, characteristics or variables) are collected at different sampling frequencies in some fields such as economic and industry. The existing methods usually either ignore the difference from the different sampling frequencies or hardly take notice of the inherent temporal characteristics in mixed frequency data. The authors propose an innovative dual attention‐based neural network for mixed frequency data (MID‐DualAtt), in order to utilize the inherent temporal characteristics and select the input characteristics reasonably without losing information. According to the authors’ knowledge, this is the first study to use the attention mechanism to process mixed frequency data. The MID‐DualAtt model uses the frequency alignment method to transform the high‐‐frequency variables into observation vectors at low frequency, and more critical input characteristics are selected for the current prediction index by attention mechanism. The temporal characteristics are explored by the encoder‐decoder with attention based on long‐ short‐term memory networks (LSTM). The proposed MID‐DualAtt has been tested in practical application, and the experimental results show that it has better prediction ability than the compared models.

The accurate and timely online estimation of the alumina concentration in an industrial aluminum electrolysis cell can improve its feed control performance. By using the proposed marginal kurtosis (MK), marginal kurtosis dominant frequency (MK-DF), integral feed frequency and sensitive frequency band (SFB), this study reveals that a normalized cell voltage with different current intensities and feed schemes has different alumina concentration SFBs and determines the general formula for calculating the SFB. On this basis, a general algorithm for acquiring the pseudo alumina concentration (PAC) using translation-invariant filtering is proposed in which the upper limit of the SFB provides the cutoff frequency of the low-pass filter. The PAC was used to estimate the absolute and relative concentrations of alumina. The absolute concentrations are validated using measured concentrations. Additionally, improved feed control performance of the industrial cells is presented using the relative concentration PAC-slope.

We proposed a slanted grating suitable for RGB bands which is polarization-independent and has a high diffraction efficiency of −1st order under normal incidence. We used Rigorous Coupled Wave Analysis (RCWA)and simulated annealing (SA) algorithm to optimize grating parameters and the simulation results showed that the -1st order diffraction efficiency of TE and TM polarizations in the RGB band under normal incidence both exceed 83%, which can be used for three-dimensional (3D) display. Then, by using rigorous modal theory, simplified modal method (SMM), and mode coupling theory, we analyzed the reason for the high-efficiency of −1st order diffracted light. Moreover, the numerical simulation of the simplified modal method is consistent with the rigorous coupled wave analysis of the small slanted angle grating. Therefore, the simplified modal method can be used for small slanted angle gratings as its physical analysis proved that small slanted angle gratings and rectangular gratings have equivalence to a certain extent. This work not only presents numerical solutions of slanted grating with high efficiencies over a wide range of RGB wavelength, but also enables us to have a deeper understanding of the diffraction of slanted gratings.

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-β, HIF-1α, etc. We review the current major and recent progress of biomedicine on myopia-related signaling pathways and mechanisms.

In complex industrial processes (CIPs), due to technical and economic limitations, key performance indicators (KPIs), especially the chemical content-related KPIs, are often difficult to measure in real time, which hinders the propagation of advanced process control technologies. This paper presents a soft sensor-based online KPI inference scheme by a state transition algorithm (STA)-optimized adaptive pre-sparse neuro-fuzzy inference system model, called STA-APSNFIS. It introduces a pre-sparse neural network to the traditional adaptive neuro-fuzzy inference system (ANFIS) model to establish an adaptive pre-sparse neuro-fuzzy inference system (APSNFIS) model to alleviate the adverse effects of data redundancy and noise interference in the detectable process monitoring data, which can effectively reduce the complexity of neuro-fuzzy inference system (NFIS) and speed up its convergence. Successively, to avoid being trapped at a local optimum, the STA-based optimization algorithm is adopted to replace the traditional gradient-based optimization approach to achieve an optimal APSNFIS model. Extensive validation and comparative experiments on nonlinear numeric simulation systems, benchmark Tenessee Eastman (TE) process and a real industrial bauxite flotation process demonstrated that the proposed STA-APSNFIS performed favorably against traditional ANFIS model as well as its variants, e.g., PSO-ANFIS, GA-ANFIS, and some other soft sensor-based KPI inference models.

In the aluminum reduction process, aluminum fluoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic efficiency. Making the decision on the amount of AlF3 addition (referred to in this work as MDAAA) is a complex and knowledge-based task that must take into consideration a variety of interrelated functions; in practice, this decision-making step is performed manually. Due to technician subjectivity and the complexity of the aluminum reduction cell, it is difficult to guarantee the accuracy of MDAAA based on knowledge-driven or data-driven methods alone. Existing strategies for MDAAA have difficulty covering these complex causalities. In this work, a data and knowledge collaboration strategy for MDAAA based on augmented fuzzy cognitive maps (FCMs) is proposed. In the proposed strategy, the fuzzy rules are extracted by extended fuzzy k-means (EFKM) and fuzzy decision trees, which are used to amend the initial structure provided by experts. The state transition algorithm (STA) is introduced to detect weight matrices that lead the FCMs to desired steady states. This study then experimentally compares the proposed strategy with some existing research. The results of the comparison show that the speed of FCMs convergence into a stable region based on the STA using the proposed strategy is faster than when using the differential Hebbian learning (DHL), particle swarm optimization (PSO), or genetic algorithm (GA) strategies. In addition, the accuracy of MDAAA based on the proposed method is better than those based on other methods. Accordingly, this paper provides a feasible and effective strategy for MDAAA. Keywords: AlF3 addition, Fuzzy cognitive maps, Learning algorithms, State transition algorithm, Fuzzy decision trees

The design and performance of a high-efficiency broadband and polarization-independent reflective grating is reported. The physical mechanism of the gratings can be described by the modal method. By using rigorous coupled wave analysis (RCWA) and simulated annealing (SA) algorithms, the parameters of grating were optimized. The calculated diffraction efficiencies of −1st order for TE and TM polarizations in Littrow mounting exceeded 95%, from 988 nm to 1122 nm, and by over 98% in the bandwidth ranging from 1015 nm to 1085 nm, with the value of polarization-dependent loss (PDL) lower than 0.06 dB. Moreover, the electric field distribution of the grating was simulated by the finite element method (FEM), which demonstrated that most of the energy of the incident light was diffracted to the −1st order and the electric field was distributed almost outside the grating. In addition, the great fabrication tolerances and incident angle tolerance ensured high performance of the designed grating in manufacture and application. With its properties of high efficiency, broadband, and polarization-independence, the designed grating should be of great interest for lots of practical applications, including chirped pulse amplification (CPA), interferometers, and spectrometers.

The fine control of endpoint temperatures of the dome and exhaust gas is the key to improving the heat storage efficiency and capacity of the pebble stove combustion process. However, owing to nonlinearity, time delay, uncertain endpoint time, and strong coupling issues of the combustion process, it is difficult to achieve the desired dome and exhaust gas endpoint temperatures, simultaneously. Therefore, a two-stage (fast heating stage and heat storage stage) control system for the endpoint temperature is developed for the combustion process of the pebble stove. According to the heat transfer theory and the principle of conservation of heat, a transient heat transfer model between the flue gas and the regenerator is developed for the combustion process of the pebble stove. Based on the transient heat transfer model, prediction models of the dome and exhaust gas endpoint temperatures are obtained by a finite-difference method. Moreover, to solve the strong coupling between the dome temperature in the fast heating period and the exhaust gas temperature in the thermal storage period, a two-stage intelligent proportion-integral-derivative decoupling control algorithm is developed based on a self-refreshing recurrent neural network to realize the decoupling control of the dome and exhaust gas endpoint temperatures. The performance of the developed system is demonstrated with simulation and industrial experiments. The results show that compared to the conventional control system, the developed system effectively shortens the adjusting time of the dome endpoint temperature by 13 min with a reduced temperature fluctuation from ±14 °C to ±4 °C and increases the exhaust gas endpoint temperature from 328.3 °C to 348.0 °C. The enhanced temperature control performance demonstrates the validity of the established system.

Semi-supervised extreme learning machine (SS-ELM) has been applied to many classification and regression assignments with high performance, in which both the labeled and unlabeled data are exploited to enhance accuracy and computation efficiency. The Laplacian manifold regularization method has been incorporated to explore the geometry of the underlying manifold structure. However, the Laplacian manifold regularization lacks the extrapolating ability and biases the solution to a real constant function. In this paper, we propose a novel algorithm, the Laplacian-Hessian regularization SS-ELM (LHRSS-ELM), to enhance the performance of conventional SS-ELM. The main advantages of LHRSS-ELM are as follows: 1) LHRSS-ELM exhibits the learning capability and computational efficiency of traditional SS-ELMs; 2) LHRSS-ELM algorithm combines both Laplacian and Hessian term to enhance the extrapolating power, accuracy, and robustness and also show significant performance in multiclass classification tasks; and 3) for the purpose of pursuing the best pair of hyperparameters to establish a comparable model, we dynamically update them from sequences. The proposed algorithm is evaluated on publicly available data sets and further applied for the state classification of superheating degree in the aluminum electrolysis process. The experimental results demonstrate that the proposed mechanism is superior to the existing state-of-the-art semi-supervised learning algorithms in the matter of accuracy and robustness.

The risk of blue light exposure to human health has attracted increased research attention. Blue light, with relatively high energy, can cause irreversible photochemical damage to eye tissue. Excessive exposure of the eye to blue light tends to cause a series of alterations, such as oxidative stress, mitochondrial apoptosis, inflammatory apoptosis, mitochondrial apoptosis and DNA damage, resulting in the development of dry eye disease, glaucoma, and keratitis. Accordingly, physical protection, chemical and pharmaceutical protective measures, gene therapy, and other methods are widely used in the clinical treatment of blue light hazard. We reviewed the studies on possible blue light-induced signaling pathways and mechanisms in the eye and summarized the therapeutic approaches to addressing blue light hazard.

We previously demonstrated that collagen metabolism affects scleral mechanical properties and scleral remodeling. Scleral remodeling changes the mechanical strain on sclera and scleral fibroblasts. We postulated that mechanical strain changes affect collagen metabolism in scleral fibroblasts. To understand the differences in collagen metabolism in scleral fibroblasts related to mechanical strain changes, scleral fibroblasts were isolated and cultured under different mechanical strains using the FX-4000 system or were treated with the TGF-β1 and TGFBR1 inhibitor LY364947. The collagen metabolism-related gene expression levels were detected. The results showed that the appropriate (lower) mechanical strain improved collagen synthesis and reduced collagen decomposition. In contrast, higher mechanical strain reduced collagen synthesis and enhanced collagen decomposition, especially a sustained higher strain. Furthermore, the effect of a transitory higher strain was recoverable, and collagen metabolism in scleral fibroblasts was regulated by TGF-β1. These results suggested that mechanical strain mediates TGF-β1 expression to regulate collagen metabolism in scleral fibroblasts, thereby affect scleral tissue remodeling.

Microcystin-leucine arginine (MC-LR) is a potent tumor initiator that can induce malignant cell transformation. Cellular mechanical characteristics are pivotal parameters that are closely related to cell invasion. The aim of this study is to determine the effect of MC-LR on mechanical parameters, microfilament, and cell invasion in DU145 and WPMY cells. Firstly, 10 μM MC-LR was selected as the appropriate concentration via cell viability assay. Subsequently, after MC-LR treatment, the cellular deformability and viscoelastic parameters were tested using the micropipette aspiration technique. The results showed that MC-LR increased the cellular deformability, reduced the cellular viscoelastic parameter values, and caused the cells to become softer. Furthermore, microfilament and microfilament-associated proteins were examined by immunofluorescence and Western blot, respectively. Our results showed that MC-LR induced microfilament reorganization and increased the expression of p-VASP and p-ezrin. Finally, the impact of MC-LR on cell invasion was evaluated. The results revealed that MC-LR promoted cell invasion. Taken together, our results suggested that mechanical changes and microfilament reorganization were involved in MC-LR-promoted cell invasion in DU145 and WPMY cells. Our data provide novel information to explain the toxicological mechanism of MC-LR.

Purpose: To increase the understanding of collagen metabolism as related to scleral remodeling in the process of emmetropization and myopization. Methods: An eyelid suture that lasted 60 days was performed on one-month-old rabbits to establish an experimental monocular visual deprivation myopia model and the axial length was recorded. HE staining and transmission electron microscope were used to observe the scleral structure. The Instron 5544 was used to measure scleral elastic modulus after measuring the scleral thickness. The content of scleral tissue collagen was determined by detecting the hydroxyproline quantity and the collagen I α1, MMP-2, and TIMP-2 mRNA expression were detected by RT-PCR Kit. Results: During regular emmetropization, the scleral collagen synthesis was higher than decomposition and the diameter of collagen fibrils increased, and then the sclera thickened and mechanical properties improved. During myopization, the scleral collagen decomposition was higher than synthesis and the diameter of collagen fibrils was smaller, and then the sclera thinner and mechanical properties lower, which resulted in a difference in scleral tissue strain. Conclusion: The results suggested that there were different scleral remodeling process between the emmetropization and myopization, and the collagen metabolism affected the scleral mechanical properties and scleral remodeling and then affected the scleral growth, axial elongation, and even myopization.

This paper investigates the problem of guaranteed cost control of an interval type-2 Takagi-Sugeno fuzzy descriptor systems with time-varying delays satisfying probabilistic characteristics. In order to fully consider the delay distributions, the delay segmentation approach is introduced. Then, an interval type-2 fuzzy controller is designed such that the closed-loop fuzzy descriptor system is regular, causal, mean square asymptotically stable, and the corresponding quadratic cost function is guaranteed which lower than a certain upper bound. Finally, one simulation example is given to demonstrate the effectiveness of the proposed method.

Mesenchymal stem cells and chondrocytes are an important source of the cells for cartilage tissue engineering. Therefore, the culture and expansion methods of these cells need to be improved to overcome the aging of chondrocytes and induced chondrogenic differentiation of mesenchymal stem cells. The aim of this study was to expand the cells for cartilage tissue engineering by combining the advantages of growing cells in co-culture and under a mechanically-stimulated environment. Rabbit chondrocytes and co-cultured cells (bone mesenchymal stem cells and chondrocytes) were subjected to cyclic sinusoidal dynamic tensile mechanical stimulationusing the FX-4000 tension system. Chondrocyte proliferation was assayed by flow cytometry and CFSE labeling. The cell cartilage phenotype was determined by detecting GAG, collagen II and TGF-β1 protein expression by ELISA and the Col2α1, TGF-β1 and Sox9 gene expression by RT-PCR. The results show that the co-culture improved both the proliferation ability of chondrocytes and the cartilage phenotype of co-cultured cells. A proper cyclic sinusoidal dynamic tensile mechanical stimulation improved the proliferation ability and cartilage phenotype of chondrocytes and co-cultured cells. These results suggest that the co-culture of mesenchymal stem cells with chondrocytes and proper mechanical stimulation may be an appropriate way to rapidly expand the cells that have an improved cartilage phenotype for cartilage tissue engineering.

Mesenchymal stem cells are ideal seed cell alternatives for articular cartilage tissue engineering, and the methods of the expansion of seed cells need to be improved. The mechanical factors play a significant role in the process of articular cartilage development and regeneration. The aim of this study was to improve the chondrogenic differentiation of mesenchymal stem cells and to expand seed cells for articular cartilage tissue engineering based on mechanical factors. Rabbit bone mesenchymal stem cells were subjected to cyclic dynamic square wave tensile mechanical loading using the FX-4000 tension system. The viscoelasticity of cells was investigated using the micropipette aspiration technique combined with the Kelvin standard linear viscoelastic solid model. The cell chondrogenic phenotype was assessed by detecting characteristic chondrocyte biomarkers; the expression of the GAG and TGF-β1 was analyzed by ELISA, and the expression of the Col2α1 and Sox9 gene was analyzed by RT-PCR. The results show that proper tensile mechanical stimulation improves the viscoelasticity and chondrogenic phenotype of mesenchymal stem cells such that it is similar to that of chondrocytes. These results suggest that viscoelasticity is a specific marker of chondrogenic differentiation and that proper mechanical stimulation culture methods can be used to expand seed cells and improve the chondrogenic phenotype for articular cartilage tissue engineering.

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.

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.

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.