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As an important basic industry of national economy, the iron and steel industry has provided an important raw material guarantee for a long time. However there are a large number of hazard sources which are prone to safety accidents in the production process. Then safety evaluation in the production system is highly needed to effectively prevent the occurrence of accidents in iron and steel enterprises. Hence we introduce a method based on deep learning model to evaluate safety of the enterprises. Firstly, the risk factors and casualties in production process are investigated, and a set of safety evaluation index system is constructed.Secondly, since deep neural network model has the characteristics of strong feature extraction ability and simple model structure, we design a safety evaluation model based on deep neural network. The 25-dimensional evaluation index value is the input of the network, and the network output corresponds to five risk levels. On this basis, the optimization algorithm of deep neural network model is designed to explore the mapping relationship between risk characteristics and safety level. Tensorflow deep learning framework is used to build the evaluation model, classification loss function and network optimization method are designed to train the model. Finally, through experiments, the optimal model structure is determined by comparing the influence of different parameter optimization strategies, different hidden layer structures, and different activation functions on the safety evaluation performance. A three hidden layer structure with the Adam back propagation algorithm and LeakyRelu activation function is adopted to obtain higher accuracy and faster convergence rate. The experiments show that our evaluation model provides an operational method for evaluating the safety management status of iron and steel enterprises.

Exogenous chemotaxis under the concentration gradient of growth factors (CGGF) is demonstrated as a mechanism of hematogenous metastasis. A bionic microfluidic device inspired by the leaky tumor vessels is proposed to study the mechanism.

Microfluidic devices have developed a wide range of applications in the fields of biomedicine, chemistry, and analytical science. But it is easy to form and accumulate bubbles in microfluidic devices. These bubbles could decrease the detection sensitivity, cause inaccurate analysis results, and even damage the functional region of the device. Inspired by the embolism repair mechanism of angiosperms and the permeability of gas permeable materials, this work proposes a bioinspired permeation-enhanced degassing method. Bionic redundant pits are used in this method to keep bubbles from spreading between microchannels and maintain the continuity of the flow. A hydrophobic gas permeable material is used to enhance the bubble capture capability and accelerate the degassing process. This method can eliminate bubbles automatically and continuously in real time without auxiliary equipment. Compared to the bubble removal only depending on solution in water, the degassing effect of the permeation-enhanced degassing method shows about 1.6 times improvement in the same conditions, and the capability of trapping bubbles is improved by 1.33 times. In this paper, this method was integrated into a concentration gradient generator and a cell culture device. The results show that the concentration gradient generator with degassing structures can dissolve bubbles in a rapid way and reach the stability of the concentration gradient within 5-15 min. The degassing method can run for a long time and improve the cell density and cell viability of HeLa cells up to 2.64 and 1.12 times, respectively. The method has a broad application prospect in microfluidic fields including biomedical fluid processing, virus detection, and microscale reactor operation.

Determining the mechanism that effects embolism repair in the xylem vessels of plants is of great significance in predicting plant distribution and the screening of drought-resistant plants. However, the mechanism of perforation plates of xylem vessels in the acceleration of embolism repair is still not clear by using conventional methods of anatomy and visualization technology. Microfluidic devices have shown their ability to simulate physiological environments and conduct quantitative experiments. This work proposes a biomimetic microfluidic device to study the mechanism of perforation plates of xylem vessels in the acceleration of embolism repair. The results proffered that the perforation plates increase the rate of embolism removal by increasing the pressure differential through the vessel, and the rate of embolism removal is related to the structural parameters of the perforation plate. A combination of the perforation size, the vessel diameter and the perforation plate angle can be optimised to generate higher pressure differentials, which can accelerate the process of embolism repair. This work provides a new method for studying the mechanism of microstructures of natural plants. Furthermore, the mechanism that perforation plates accelerate embolism repair was applied to an electrochemical flow sensor for online determination of heavy metal ions. Test results of this application indicate that the mechanism can be applied in the engineering field to solve the problems of reduced sensitivity of devices, inaccuracy of analysis results and poor reaction performance caused by bubbles that are generated or introduced easily in microdevices, which paves the way for applying the theory to engineering.

Objective To analyze the rates of blindness with the demographics and clinical characteristics of patients with primary angle-closure disease (PACD) to provide a comprehensive epidemiologic reference in China. Methods A retrospective analysis was conducted in the Chinese Glaucoma Study Consortium database, which is a national multicenter glaucoma research alliance of 111 hospitals participating between December 21, 2015 and September 9, 2018. The diagnosis of PACD was made by qualified physicians through examination. Comparison of sex, age, family history, subtypes of PACD, and blindness were analyzed. Results A total of 5762 glaucoma patients were included, of which 4588 (79.6%) had PACD. Of PACD patients, 72.1% were female with the sex ratio (F/M) of 2.6, and the average age of patients was 63.8±9.3 years with the majority between 60 and 70 years. Additionally, 30% of these patients had low vision in one eye, 8.8% had low vision in both eyes, 1.7% had blindness in one eye, and 0.3% had blindness in both eyes. There were statistical differences with regards to age between male and female patients with PACD, with male patients being older on average. Primary angle-closure glaucoma was more commonly diagnosed in males (60%) compared to females (35.9%), whereas acute primary angle closure (APAC) was more commonly diagnosed in females (54.3%) compared to males (37.7%). The visual acuity in APAC patients was lower and the rate of low vision and blindness was higher than other subtypes. Conclusion PACD was the major type of glaucoma in Chinese hospitals. There were more female patients with PACD, mostly between 60 and 70 years old, with higher rates of APAC in women. APAC resulted in the worst visual outcomes of all PACD subtypes.

World blindness is primarily caused by glaucoma. It has been predicted that by 2040, 118 million individuals will have glaucoma. Among Asians and Africans, primary angle-closure glaucoma (PACG) is the most prevalent type of glaucoma, for which treatment options are currently very limited. At present, lowering intraocular pressure (IOP) is the primary approach for PACG treatment. However, some PACG patients with decreased IOP measurements still advance. Additionally, because of the complicated pathophysiology, there are no biomarkers for diagnosis. Metabolomics is the study of the metabolites produced by all cellular processes in a biological sample, providing a method for identifying biomarkers and early diagnosis. Nevertheless, metabolomics has infrequently been applied to PACG. Previous research conducted by our lab on plasma metabolite fatty acids in PACG patients revealed reduced free fatty acid (FFA) levels, which may be connected to lipid peroxidation. To ascertain the relationship between other metabolites and PACG. We compared levels of amino acids and carnitine in patients with PACG (n = 147) and non-glaucoma (n = 340). Using metabolomics analysis, twenty-one amino acids and twenty-six carnitines (a total of ninety-six indicators) were examined. Odds ratios (OR) and 95% confidence intervals (CI) for these metabolites in relation to PACG were calculated. The relationship between ocular measures and metabolites was assessed by Spearman's rank correlation. Predictive performance was evaluated using the receiver operating characteristic (ROC). The C8/C2 level was comparable across patients with PACG and individuals without glaucoma based on the Wilcoxon rank-sum test. The PACG group had lower levels of Arginine (Arg), Ornithine (Orn), Arg/Orn, Orn/Cit, and C26/C20 than the nonglaucoma group, whereas Cit/Arg and C4/C2 ratios were greater. Both univariate and multivariate models showed a negative correlation between Orn and Orn/Cit and PACG. In the univariate model, palmitoylcarnitine (C16) had a negative correlation with PACG. According to our findings, metabolic profiles of plasma amino acids and carnitine between PACG patients and controls are different. The combination of amino acids and carnitine increased the predictive value of PACG. The Orn and Arg were negatively correlated with the local ocular neurodegenerative pathology. We speculate lipid peroxidation may explain the reduction in C16, and the decrease in Orn may be associated with hyperammonia neurotoxicity.

Long noncoding RNAs (lncRNAs) undergo extensive alternative splicing, but little is known about isoform functions. A prior investigation of lncRNA RP11-369C8.1 reported that its splice variant TRMP suppressed p27 translation through PTBP1. Here we characterize a second major splice variant, TRMP-S (short variant), whose enforced loss promotes cancer cell-cycle arrest and p27-dependent entry into cellular senescence. Remarkably, despite sharing a single common exon with TRMP, TRMP-S restrains p27 expression through distinct mechanisms. First, TRMP-S stabilizes UHRF1 protein levels, an epigenetic inhibitor of p27, by promoting interactions between UHRF1 and its deubiquitinating enzyme USP7. Alternatively, binding interactions between TRMP-S and FUBP3 prevent p53 mRNA interactions with RPL26 ribosomal protein, the latter essential for promoting p53 translation with ensuing suppression of p53 translation limiting p27 expression. Significantly, as TRMP-S is itself transactivated by p53, this identifies negative feedback regulation between p53 and TRMP-S. Different splicing variants of the RP11-369C8.1 gene thereby exert distinct roles that converge on the homeostatic control of p27 expression, providing an important precedent for understanding the actions of alternatively spliced lncRNAs., Graphical abstract, Akin to coding genes, lncRNAs undergo extensive splicing, but there are limited reports detailing functional outcomes. Shuai et al. reveal that TRMP-S, a variant transcript of the RP11-369C8.1 gene, functions through dual epigenetic and transcriptional mechanisms to inhibit p27 expression, restraining cancer cell-cycle arrest and entry into cellular senescence.

The artificial lateral line is a bionic sensor developed in recent years to detect physical parameters of gas and liquid flow field, which has great application prospects in underwater target detection, microfluidic velocity detection and smart skin. However, these sensors generally have only one kinocilium, resulting in inability to identify directions. Moreover, the kinocilium exposed directly in fluid shows performance of low sensitivity. This paper presents a flexible, lightweight, and sensitive piezoresistive sensor inspired by lateral line. The sensor presented comprises a cluster of micropillars arranged in stepwise distribution to detect fluid direction and a hydrogel cap to realize high-sensitive and low-speed threshold detection. The experiment of direction detection shows that the output signal in inhibitory direction of the sensor is smaller than the signal in excitatory direction and lagging behind, which can be used in orientation identification. The results show that the sensitivity of the sensor without hydrogel cap is 0.57 mV/(mm/s), while the sensor with hydrogel cap is 1.18 mV/(mm/s), which indicates that the hydrogel cap can increase the sensitivity by 107%. The sensor developed in this paper has a broad application prospect in biomedicine devices and micro-fluid equipment.

Most of in vivo tissue cells reside in 3D extracellular matrix (ECM) with fluid flow. To better study cell physiology and pathophysiology, there has been an increasing need in the development of methods for culturing cells in in vivo like microenvironments with a number of strategies currently being investigated including hydrogels, spheroids, tissue scaffolds and very promising microfluidic systems. In this paper, a ‘sandwich’ structure-liked microfluidic device integrated with a 3D printing scaffold is proposed for three-dimensional and dynamic cell culture. The device consists of three layers, i.e. upper layer, scaffold layer and bottom layer. The upper layer is used for introducing cells and fixing scaffold, the scaffold layer mimicking ECM is used for providing 3D attachment areas, and the bottom layer mimicking blood vessels is used for supplying dynamic medium for cells. Thermally assisted electrohydrodynamic jet (TAEJ) printing technology and microfabrication technology are combined to fabricate the device. The flow field in the chamber of device is evaluated by numerical simulation and particle tracking technology to investigate the effects of scaffold on fluid microenvironment. The cell culturing processes are presented by the flow behaviors of inks with different colors. The densities and viabilities of HeLa cells are evaluated and compared after 72 h of culturing in the microfluidic devices and 48-well plate. The dose-dependent cell responses to doxorubicin hydrochloride (DOX) are observed after 24 h treatment at different concentrations. These experimental results, including the evaluation of cell proliferation and in vitro cytotoxicity assessment of DOX in the devices and plate, demonstrate that the presented microfluidic device has good biocompatibility and feasibility, which have great potential in providing native microenvironments for in vitro cell studies, tissue engineering and drug screening for tumor therapy.

Symbionts participate in various physiological activities of their insect hosts, including detoxification metabolism. Emerging evidence has revealed that the bacterial symbiont Arsenophonus is involved in insecticide detoxification metabolism of Nilaparvata lugens, which harbors diverse symbionts. However, it is still unknown whether other bacterial symbionts have a functional role in this process. This study showed that pretreatment with antibiotics significantly increased N. lugens susceptibility to imidacloprid, chlorpyrifos, and clothianidin, and the detoxifying enzyme activities of the cytochrome P450 enzyme (P450) and glutathione S-transferase (GST) were significantly inhibited. Notably, the P450 genes NlCYP6ER1 and NlCYP4CE1, which are related to imidacloprid metabolism, were dramatically downregulated in ciprofloxacin- and tetracycline-pretreated N. lugens, respectively. Furthermore, the expression levels of various detoxifying genes (GSTs and P450s) were significantly positively correlated with Wolbachia, Arsenophonus, Acinetobacter, and Staphylococcus. These results indicated that bacterial symbionts may affect insecticide metabolism by regulating the expression of the insect host’s GST and P450 genes, and provide a foundation for further study on the mechanism of symbiont-mediated host detoxification metabolism in insect pests.

A sensitive and portable microfluidic electrochemical array device (μFED) was developed for the immunoassay of trace amounts of human cardiac troponin I (cTnI), which is an attractive biomarker for acute myocardial infarction (AMI). The classical "sandwich" method was adopted for the immunoassay. The capture antibody was immobilized using a self-assembled monolayer (SAM) technique, and the process was reorganized to be compatible with the bonding process. The detection antibody was labeled with alkaline phosphatase (AP) for signal amplification. The performance of the μFED was improved by eliminating the shielding effect of the microelectrode array (MEA) integrated in the μFED. The effects of the interstice and the width of the MEA on the response peak current were analyzed and simulated. The concentration gradient, about 3% of the gradient at the surface, was considered as the criterion for estimation of the optimal interstice between electrodes, and its effectiveness was proved. A stable and miniaturized reference electrode was integrated in the μFED, and its potential deviation was less than 5 mV in 15 min. These efforts resulted in the enhanced immunoassay performance of the μFED. A low limit of detection of about 5 pg/mL was obtained in serum samples, and the response current was proportional to the logarithm of concentration from 50 pg/mL to 1 μg/mL. The immunoassay process was accomplished in 15 min. The μFED was thus qualified and is a promising candidate for point-of-care immunoassay of cTnI. Graphical abstract.

In this study, we investigate the technical application of the regularized regression method Lasso for identifying systematic biases in decadal precipitation predictions from a high-resolution regional climate model (CCLM) for Europe. The Lasso approach is quite novel in climatological research. We apply Lasso to observed precipitation and a large number of predictors related to precipitation derived from a training simulation, and transfer the trained Lasso regression model to a virtual forecast simulation for testing. Derived predictors from the model include local predictors at a given grid box and EOF predictors that describe large-scale patterns of variability for the same simulated variables. A major added value of the Lasso function is the variation of the so-called shrinkage factor and its ability in eliminating irrelevant predictors and avoiding overfitting. Among 18 different settings, an optimal shrinkage factor is identified that indicates a robust relationship between predictand and predictors. It turned out that large-scale patterns as represented by the EOF predictors outperform local predictors. The bias adjustment using the Lasso approach mainly improves the seasonal cycle of the precipitation prediction and, hence, improves the phase relationship and reduces the root-mean-square error between model prediction and observations. Another goal of the study pertains to the comparison of the Lasso performance with classical model output statistics and with a bivariate bias correction approach. In fact, Lasso is characterized by a similar and regionally higher skill than classical approaches of model bias correction. In addition, it is computationally less expensive. Therefore, we see a large potential for the application of the Lasso algorithm in a wider range of climatological applications when it comes to regression-based statistical transfer functions in statistical downscaling and model bias adjustment.

Background Endometriosis (EMT) is a common occurrence in women of reproductive age. Since oxidative stress has been associated with the development and/or progression of the disease, the present study was conducted to detect the expression of peroxiredoxin (PRX) isoforms, including PRX1, PRX2, and PRX3. Methods Fifty-two patients with ovarian endometriosis cysts and 47 controls were included in the study. Serum levels of PRXs were detected by enzyme-linked immunosorbent assay, and the expression of PRX in the endometrium was examined by immunohistochemistry. Results Serum PRX1, PRX2, and PRX3 were significantly lower in EMT patients than in controls. The expression of the three isoforms was significantly decreased in ectopic endometrium compared to that in eutopic or control endometrium. There was no difference in PRX expression between eutopic endometrium of EMT patients and control endometrium, and no association was found between serum PRX levels and immunostaining scores. Conclusion Our results are the first report that PRXs are downregulated in EMT patients, which suggests that PRXs are involved in the oxidative state of the disease.

To cultivate correct labour values and good labour quality of college students and effectively promote the development of their labour concept education, this work explores the impact of wireless network mobile devices on college students’ labour concept education under the environment of artificial intelligence. Firstly, a questionnaire survey is used to investigate the labour concept of 400 college students. Secondly, the impact of wireless network mobile devices on college students' labour education is obtained by comparing group A (using artificial intelligence APPs for wireless network devices to learn about labour concepts) and group B (using traditional classroom teaching methods for to learn about labour concepts). According to the statistical survey results, about 20% of college students agree that “if they have enough money to live, they do not have to work”; while less than 50% agree that “they cannot be admitted to civil servants and senior managers in the company and are willing to engage in ordinary labour in the future”. Meanwhile, about 50%–60% of college students think that “housework has nothing to do with me, and it’s all the work of adults”. In the question “What would you do when you find that the public area is dirty and poor, but it’s not your turn to be on duty?”, only about 50% of college students are willing to clean actively. Comparing the data of group A and group B suggests that the labour view expressed in group A is more biased in the cognition of labour purpose, and students in group A is more negative and lazier in labour attitude and labour habits, which shows that wireless network mobile devices have a great negative impact on the overall labour view of college students. Therefore, it is revealed in this work that the correct use of artificial intelligence technology in the education of labour concept has extremely important value for the intelligent development of education methods.

With the advancement of science and technology, robotics has made considerable progress. Robots can free humans from heavy repetitive labor. From the industrial field to the lives of the general public, robots are playing an increasingly important role. Path planning is one of the core contents of industrial wheeled robotics and has very important significance. Based on the reinforcement Q learning and BP network, this work studies the path planning of industrial wheeled robots. According to task requirements of path planning, design learning strategies, and control rules, use wireless communication to transmit environmental perception information and propose corresponding control strategies. The main researches are as follows: (1) Based on grid map environment, a path planning algorithm with Q-CM learning is designed. The algorithm first designs robot states and actions based on reinforcement Q learning and grid map and establishes Q matrix. Secondly, a coordinate matching (CM) obstacle avoidance control rule is designed to improve the efficiency of robot avoidance. Then, a reward function is designed for the evaluation problem of action execution. (2) Based on the map environment of free space and the generalization ability of BP neural network, a robot path planning with Q-BP learning is designed. The algorithm first designs the sensor detection mechanism and action selection strategy according to the state of the robot in the map environment. Secondly, a dynamic reward function is designed. Then, according to the obstacle avoidance requirements of special obstacles, the obstacle avoidance rules after three shocks were designed. The experimental results show robots can perform better path planning in a discrete and continuous free space map, and the obstacle avoidance effect is good.

In order to make up for the shortcomings of traditional electronic information characteristics and classification recognition algorithms and improve the application ability of the technology, a special extraction algorithm based on the performance of deep multilayer autoencoders is required, and the training of deep learning network model is not supervised with pretraining and the supervision of the margin-based Fisher guidelines. The regularization means in the process of data generating pretraining and carving will prevent the prefining training. Experimental results on multiple data distributions further determine the efficiency of the algorithm. By analyzing the depth learning model, the image information in electronic information is used as an example, and its extraction technology is discussed, and the direction is indicated further. The experimental results show that the acceptance performance of the DMFA algorithm is improved in some cases compared with similar algorithms. And in Ionosphere, PIMA, IRIS Data Set this algorithm acquires the improvement effect, but the results obtained in other data sets are not ideal, thereby obtaining the most use of electronic information feature extraction and classification recognition.

The exploration expects to improve the teaching quality of the electronic technology distance course. The teaching management system of the electronic technology network course is constructed based on a flipped classroom. Firstly, the idea of the flipped classroom is analyzed, and the electronic technology course is designed. Secondly, the teaching management system of the electronic technology network course is established, and the remote experiment module is discussed. Finally, the questionnaire survey is made to investigate the use effect of the system. The results show that about 79% of the students are satisfied with the system. About 80% think it allows them to focus on the teaching process. Only 1.3% think that this system does not work for their learning. This shows that the system can help students improve their learning efficiency. However, the main defect is the lack of a real-time feedback module for users on the application effect of the system. In a word, the exploration provides a reference for developing and designing the follow-up network course teaching system.

Pursuing a higher response signal is the core challenge in improving the detection performance of a microelectrode-based amperometric electrochemical sensor. Band microelectrode arrays (bMEAs) are attractive due to their high response current and economical fabrication process. However, further amplifying the response current by arranging the array more compactly or increasing its size is generally hindered by the shielding effect or the restricted construction region. A novel array of sinuous band microelectrodes (sbMEA) is proposed, produced by deforming a bMEA by tilting and bending. Its response and diffusion characteristics are simulated and analyzed. The effects of structural parameters on its performance are revealed, and their optimal value is deduced. The simulation demonstrates that a sbMEA can generate a larger current than a MEA of traditional shape. Production of such a sbMEA does not involve any additional fabrication costs compared with a bMEA. Cyclic voltammetry (CV) tests with potassium ferrocyanide solution verify the theoretical performance of the sbMEA, and a 10% higher current was obtained compared to the corresponding bMEA.

Dysregulation of the dorsal raphe nucleus (DRN) has been revealed to contribute to cognitive and arousal impairments associated with post-traumatic stress disorder (PTSD) in an animal model. In our research an acute exposure to single prolonged stress (SPS) was used to establish PTSD rat model and the effects related to cell-cycle signaling pathway in DRN were examined. Apoptosis in DRN was detected by TUNEL staining, showing that DRN apoptosis number was sharply increased after SPS. SPS triggered cell-cycle CDK4/CyclinD1-pRB-E2F1 signal pathway. Treatment with CDK4 inhibitor Abemaciclib successfully attenuated the DRN apoptosis and rescued decreased spatial learning and memory abilities in SPS rats, indicating that activation of CDK4/CyclinD1-pRB-E2F1 pathway was involved in DRN apoptosis, which may be one of the pathogenesis for PTSD.