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The process of mining and ore crushing generates a lot of dust. Bag filter is an effective equipment to remove particulate matter from dusty airflow. However, there is a problem of uneven distribution of air volume in the filter bag during operation. The existing way of adding deflectors in bag filters increases the complexity of its structure. In order to solve the problem of uneven distribution of mass flow of middle box lower inlet filter bag, a method of filter bag mass flow even distribution by adjusting selected bags’ permeability is proposed. Firstly, realizable k-ε equation model is selected to simulate the flow field of model bag filter. Then, the numerical simulation results are compared with model experimental results, which verifies the correctness of the simulation method. After that, a geometric model of filter bag with 160 filter bags installed in the middle box is established. Flow field characteristics, mass flow distribution and uniformity of the filter bag at the face velocity of 0.5～1.2 m/min are analyzed by numerical simulation. Finally, taking the uneven distribution of filter bag mass flow at 1 m/min face velocity as an example problem, a method to reduce the permeability of filter bag at the outlet side with large mass flow is designed. According to the Darcy formula and pressure drop calculation formula of filter material, relationship between permeability and filter material thickness, fiber diameter and filling ratio are given. The results show that under this research model, the increase of face velocity will make the mass flow distribution of the filter bag more uneven, and the mass flow of the filter bags on the outlet side of the bag filter will gradually increase. Reducing the permeability of 8−10 rows of filter bags in the area with high flow rates from 2.0×10−11 m2 to 1.7×10−11 m2 can reduce the root mean square deviation of mass flow distribution of filter bags from 0.158 to 0.099. The increase in the initial pressure drop of filter bag is 6.1%, proving this method is feasible. In the process of making filter material, the permeability of filter material can be adjusted, so it is feasible to make the mass flow distribution of filter bag uniform by adjusting the permeability of filter bag in selected area.

The emergence of advanced biotechnologies has intensified in recent years. The rapid development of these biotechnologies has had a wide and profound impact globally, with the majority on the frontier of biosecurity technologies. The global biosecurity situation is currently highly challenging, with the characteristics of internationalization, extremely serious harm, and complex development. The misuse and abuse of biotechnologies are common, which thereby endanger biosecurity. The international community and governments have attached great importance to cutting-edge biotechnologies and are implementing laws and regulations to control and prevent biosecurity-related influences. By tracking the frontier progress of the new technologies generated from gene editing, gene drives, synthetic biology, and related cross-disciplines as applied in the biosecurity field, we analyzed the development trends in biotechnologies and their potential influence. On the one hand, this paper proposes that there is an urgent need for global cooperation to formulate management, control strategies, and measures for cutting-edge technologies in biosecurity to jointly promote their sound development. On the other hand, the international community should jointly shoulder the responsibility for safeguarding biosecurity and restrict the biotechnologies to rational applications within legal and ethical frameworks. This paper aims to provide a reference for the prevention of misuse and abuse of biotechnologies and support global cooperation in biosecurity governance.

Recent years have seen increasing academic interest in exploring the correlation between temperature and crime. However, it is uncertain whether similar long-term trends or seasonality (rather than causal effect) of temperature and crime is the major reason for the observed correlation between them. To explore whether there is still a correlation between temperature and crime when long-term trends and seasonal cycles are filtered out, we use the Kalman filter to decompose the time series of temperature and crimes, and then the fast Fourier transform is used to calculate the exact circle of their seasonality separately. Based on that, the box-plot method and linear regression are used to explore the correlation between temperature residuals and crime residuals. The results show that more than half of the crime types have similar seasonal cycles (approximately 1 year) to that of temperature. Moreover, the daily residual analyses show that temperature residuals have a positive correlation with assault and robbery residuals in all cities, whose average slopes are more than 0.1. The other four types of crimes vary greatly from case to case. The temperature residuals show a weak correlation with the residuals of some crime types.

Abstract Background Bioconversion of plant biomass into biofuels and bio-products produces large amounts of lignin. The aromatic biopolymers need to be degraded before being converted into value-added bio-products. Microbes can be environment-friendly and efficiently degrade lignin. Compared to fungi, bacteria have some advantages in lignin degradation, including broad tolerance to pH, temperature, and oxygen and the toolkit for genetic manipulation. Results Our previous study isolated a novel ligninolytic bacterial strain Erwinia billingiae QL-Z3. Under optimized conditions, its rate of lignin degradation was 25.24% at 1.5 g/L lignin as the sole carbon source. Whole genome sequencing revealed 4556 genes in the genome of QL-Z3. Among 4428 protein-coding genes are 139 CAZyme genes, including 54 glycoside hydrolase (GH) and 16 auxiliary activity (AA) genes. In addition, 74 genes encoding extracellular enzymes are potentially involved in lignin degradation. Real-time PCR quantification demonstrated that the expression of potential ligninolytic genes were significantly induced by lignin. 8 knock-out mutants and complementary strains were constructed. Disruption of the gene for ELAC_205 (laccase) as well as EDYP_48 (Dyp-type peroxidase), ESOD_1236 (superoxide dismutase), EDIO_858 (dioxygenase), EMON_3330 (monooxygenase), or EMCAT_3587 (manganese catalase) significantly reduced the lignin-degrading activity of QL-Z3 by 47–69%. Heterologously expressed and purified enzymes further confirmed their role in lignin degradation. Fourier transform infrared spectroscopy (FTIR) results indicated that the lignin structure was damaged, the benzene ring structure and groups of macromolecules were opened, and the chemical bond was broken under the action of six enzymes encoded by genes. The abundant enzymatic metabolic products by EDYP_48, ELAC_205 and ESOD_1236 were systematically analyzed via liquid chromatography–mass spectrometry (LC–MS) analysis, and then provide a speculative pathway for lignin biodegradation. Finally, The activities of ligninolytic enzymes from fermentation supernatant, namely, LiP, MnP and Lac were 367.50 U/L, 839.50 U/L, and 219.00 U/L by orthogonal optimization. Conclusions Our findings provide that QL-Z3 and its enzymes have the potential for industrial application and hold great promise for the bioconversion of lignin into bioproducts in lignin valorization.

Aflatoxin B1 (AFB1) contamination poses a fatal risk to human beings and urgently needs highly sensitive detection for environmental monitoring and food safety. However, the existing challenges are the unsatisfied sensitivity of the immunoassay methods and the complex matrix effect. Rolling circle amplification (RCA) is a promising method for nucleic acid isothermal amplification due to its high specificity and sensitivity. Herein, we constructed a general RCA-based point-of-care test method (RCA−POCT). With biotinylated antibodies, streptavidin, and biotinylated RCA primers, we realized the signal transduction and preliminary signal amplification. In this way, the fluorescent signal of the immunocomplex on the microwells was greatly enhanced. Under optimal conditions, we recorded sensitive detection limits for aflatoxin B1 (AFB1) of 1.94, 16.3, and 37.7 fg/mL (femtogram per microliter), and wide linear ranges with 5 × 10−6 to 5, 5 × 10−5 to 5, and 5 × 10−5 to 5 ng/mL in the irrigation water, field soil, and peanut samples, respectively. Satisfactory recovery, specificity, repeatability, and reproducibility were observed. The RCA−POCT was validated by comparing it to the HPLC method. This work provides a general RCA-assisted detection method for AFB1 in the environment and food.

ObjectiveWhile bone metastases (BMs) are present in a minority of thyroid cancer (TC) patients at the time of initial diagnosis, there has been growing concern regarding their impact on life expectancy and quality of life. The aim of this study was to identify prognostic factors associated with overall survival (OS) and cancer-specific survival (CSS) in these patients and provide therapeutic recommendations based on the findings.MethodsIn this retrospective cohort study, we included 82 patients diagnosed as TC with BM received treatment in our department from 2011.03 to 2023.03 (average follow-up duration was 3.02 years). The retrospective study was performed according to the inclusion and exclusion criteria. Kaplan-Meier analysis was used to estimate the OS and CSS, while the univariate and multivariate Cox proportional hazard models were employed to determine prognostic factors associated with OS and CSS. Also, 287 patients’ data were collected from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015 to confirm the prognostic factors identified in the retrospective study.ResultsThe average survival time of the 82 patients was estimated to be 5.818 years (with a 95% confidence interval (CI) of 4.767 to 6.868 years). The cox regression analysis showed that older age (hazard ratio (HR) = 1.045, 95% CI: 1.001-1.092, P = 0.047), larger tumor size (>5cm, HR = 11.087, 95% CI: 3.728 - 32.976, P = 0.000), and the presence of extraosseous metastasis (HR = 3.247, 95% CI: 1.376 - 7.665, P = 0.007) were statistically significant factors associated with worse CSS. The results were furtherly confirmed in 287 SEER-sourced patients (age (HR = 1.020, 95% CI: 1.006 - 1.034, P = 0.006), tumor size (HR = 2.917, 95% CI: 2.044 - 4.161, P = 0.000), and extraosseous metastasis (HR = 3.726, 95% CI: 2.571 - 5.398, P = 0.000)).ConclusionsThese results offer a population-based assessment of prognostic factors for patients with TC and BMs, revealing that age, primary tumor size (>5cm), and presence of extraosseous metastases are independent prognostic factors that correlate with worse survival. Accordingly, treatment for such patients ought to concentrate on systemic integrative therapy instead of surgical intervention.

Telecom network fraud has become the most common and concerning type of crime and is an important public security incident that threatens urban resilience. Therefore, preventing a continuous rise in telecommunications and network fraud will help establish a resilient urban governance system. Undertaking the spatiotemporal forecasting of telecommunications-network fraud trends is of significant importance for aiding public security agencies in proactive crime prevention and implementing targeted anti-fraud campaigns. This study presents a telecommunication network fraudulent crime prediction method called TSE-Tran, which integrates temporal representation and transformer architecture. The time-series data of telecommunication-network fraud occurrences were input into the TimesNet module, which maps the sequence data to a more precise feature representation tensor that accounts for both intra- and inter-cycle features. Subsequently, the data are fed into the transformer-encoder module for further encoding, capturing long-range dependencies in the time-series data. Finally, occurrences of future telecommunication network frauds are predicted by a fully connected layer. The results of the study demonstrate that the proposed TSE-Tran method outperforms benchmark methods in terms of prediction accuracy. The results of this study are expected to aid in the prevention and control of telecommunications and network frauds effectively strengthen the resilience of urban development and the ability to respond to public security incidents.

Abstract Fracture-related infection (FRI) is one of the most common and intractable complications in orthopedic trauma surgery. This complication can impose severe psychological burdens and socio-economic impacts on patients. Although the definition of FRI has been proposed recently by an expert group, the diagnostic criteria for FRI are not yet standardized. A total of 4761 FRI patients and 4761 fracture patients (Non-FRI) were included in the study. The feature set of patients included imaging characteristics, demographic information, clinical symptoms, microbiological findings, and serum inflammatory markers, which were reduced by the Principal Component Analysis. To optimize the Support Vector Machine (SVM) model, the Traction Switching Delay Particle Swarm Optimization (TSDPSO) algorithm, a recognition method was proposed. Moreover, five machine learning models, including TSDPSO-SVM, were employed to distinguish FRI from Non-FRI. The Area under the Curve of TSDPSO-SVM was 0.91, at least 5% higher than that of other models. Compared with the Random Forest, Backpropagation Neural Network (BP), SVM and eXtreme Gradient Boosting (XGBoost), TSDPSO-SVM demonstrated remarkable accuracy in the test set ( $$\chi^{ 2} = 29.17, \,50.46, \,56.66,\,35.88, P < 0.01$$ χ 2 = 29.17 , 50.46 , 56.66 , 35.88 , P < 0.01 ). The recall of TSDPSO-SVM was 98.32%, indicating a significant improvement ( $$\chi^{2} = 91.78,\, 107.42, \,135.69, P < 0.01$$ χ 2 = 91.78 , 107.42 , 135.69 , P < 0.01 ). Compared with BP and SVM, TSDPSO-SVM exhibited significantly superior specificity, false positive rate and precision ( $$\chi^{2} > 3.84, P < 0.05)$$ χ 2 > 3.84 , P < 0.05 ) . The five models yielded consistent results in the training and testing of FRI patients across different age groups. TSDPSO-SVM is validated to have the maximum overall prediction ability and can effectively distinguish between FRI and Non-FRI. For the early diagnosis of FRI, TSDPSO-SVM may provide a reference basis for clinicians, especially those with insufficient experience. These results also lay a foundation for the intelligent diagnosis of FRI. Furthermore, these findings exhibit the application potential of this model in the diagnosis and classification of other diseases.

Abstract About half of osteosarcomas occur near the knee joint, but other sites such as the humerus, upper femur, fibula, spine, and ilium can also occur. However, rib osteosarcoma is rarely reported. Here, we report the case of a 17‐year‐old female who was found to have a left dorsal mass on physical examination. Computed tomography (CT) revealed bone destruction in the seventh rib, leading to surgery for mass excision. Pathological results suggested chondroblastic osteosarcoma. After surgery, the patient was treated with chemotherapy and is doing well.

In a previous study, rice bread was prepared using a combination of rice–wheat mixed flour. To investigate the impact of the partial adoption of extruded rice flour (ERF) on mixed flour (MF) and mixed dough (MD), the effects of adding ERF on the pasting, mixing characteristics, texture, and water retention of the MF and MD were examined by a rapid visco analyzer (RVA), Mixolab, texture profile analysis (TPA), and a low-field nuclear magnetic resonance analyzer (LF-NMR). The PV, TV, BD, FV, and SV of the MF declined as the incorporated amount of ERF increased. There was no significant difference in the PT at the 5–15% addition level (p < 0.05), but it showed an increasing trend at the 20–30% level (p < 0.05). The incorporation of ERF led to a significant increase in the water absorption (WA) of the MD, while the DT, ST, C2, C3, C4, and C5 exhibited a declining trend. The texture analysis revealed a significant decrease in the dough hardness with the addition of ERF, with a 55% reduction in the hardness of the 30% improved mixed dough (IMD), and the cohesiveness increased significantly (p < 0.05). The IMD was mainly composed of weakly bound water. The content of weakly bound water increased with the ERF amount.

IntroductionStaphylococcus aureus (S. aureus) is a prominent pathogen responsible for both hospital-acquired and community-acquired infections. Among its arsenal of virulence factors, Panton-Valentine Leucocidin (PVL) is closely associated with severe diseases such as profound skin infections and necrotizing pneumonia. Patients infected with pvl-positive S. aureus often exhibit more severe symptoms and carry a substantially higher mortality risk. Therefore, it is crucial to promptly and accurately detect pvl-positive S. aureus before initiating protective measures and providing effective antibacterial treatment.MethodsIn this study, we propose a precise identification and highly sensitive detection method for pvl-positive S. aureus based on recombinase-assisted amplification and the CRISPR-ERASE strip which we previously developed.ResultsThe results revealed that this method achieved a detection limit of 1 copy/μL for pvl-positive plasmids within 1 hour. The method successfully identified all 25 pvl-positive and 51 pvl-negative strains among the tested 76 isolated S. aureus samples, demonstrating its concordance with qPCR.DiscussionThese results show that the CRISPR-ERASE detection method for pvl-positive S. aureus has the advantages of high sensitivity and specificity, this method combines the characteristics of recombinase-assisted amplification at room temperature and the advantages of ERASE test strip visualization, which can greatly reduce the dependence on professional laboratories. It is more suitable for on-site detection than PCR and qPCR, thereby providing important value for rapid on-site detection of pvl.

Biosafety issues have become a major threat to the health of humans, animals, and ecosystems worldwide. As problems with food production and the food supply chain have become of greater concern to consumers, issues involving biosafety and food safety from a developmental perspective need to be urgently addressed. The term, food biosafety, is the combination of the core concepts of biosafety and food safety. It refers to the effective prevention of biological threats to food production and the food supply chain by controlling foodborne diseases arising from the consumption of edible plants and animal products, by preventing the establishment of invasive species, by strictly controlling the use of antibiotics, agricultural chemicals and veterinary drugs in the food supply chain, and by initiating food defense and anti-terrorism measures to protect the health of humans, animals, and ecosystems, thereby maintaining sustainable development in China. This article provides theoretical support for the extension of food biosafety to propose an innovative plan for the international co-governance of food safety.

The COVID-19 pandemic is strongly affecting many aspects of human life and society around the world. To investigate whether this pandemic also influences crime, the differences in crime incidents numbers before and during the pandemic in four large cities (namely Washington DC, Chicago, New York City and Los Angeles) are investigated. Moreover, the Granger causal relationships between crime incident numbers and new cases of COVID-19 are also examined. Based on that, new cases of COVID-19 with significant Granger causal correlations are used to improve the crime prediction performance. The results show that crime is generally impacted by the COVID-19 pandemic, but it varies in different cities and with different crime types. Most types of crimes have seen fewer incidents numbers during the pandemic than before. Several Granger causal correlations are found between the COVID-19 cases and crime incidents in these cities. More specifically, crime incidents numbers of theft in Washington DC, Chicago and New York City, fraud in Washington DC and Los Angeles, assault in Chicago and New York City, and robbery in Los Angeles and New York City, are significantly Granger caused by the new case of COVID-19. These results may be partially explained by the Routine Activity theory and Opportunity theory that people may prefer to stay at home to avoid being infected with COVID-19 during the pandemic, giving fewer chances for crimes. In addition, involving new cases of COVID-19 as a variable can slightly improve the performance of crime prediction in terms of some specific types of crime. This study is expected to obtain deeper insights into the relationships between the pandemic and crime in different cities, and to provide new attempts for crime prediction during the pandemic.

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection‐caused coronavirus disease 2019 (COVID‐19) is a global crisis with no satisfactory therapies. Vitamin D3 (VD3) is considered a potential candidate for COVID‐19 treatment; however, little information is available regarding the exact effects of VD3 on SARS‐CoV‐2 infection and the underlying mechanism. Herein, we confirmed that VD3 reduced SARS‐CoV‐2 nucleocapsid (N) protein‐caused hyperinflammation in human bronchial epithelial (HBE) cells. Meanwhile, VD3 inhibited the NOD‐like receptor family pyrin domain containing 3 (NLRP3) inflammasome activation in N protein‐overexpressed HBE (HBE‐N) cells. Notably, the inhibitors of caspase‐1, NLRP3, and NLRP3 or caspase‐1 small interference RNA (siRNA) enhanced VD3‐induced NLRP3 inflammasome inactivation, with subsequent suppression of interleukin‐6 (IL6) and IL1β release in HBE‐N cells, which were abolished by the NLRP3 agonist. Moreover, VD3 increased NLRP3 ubiquitination (Ub‐NLRP3) expression and the binding of the VDR with NLRP3, with decreased BRCA1/BRCA2‐containing complex subunit 3 (BRCC3) expression and NLRP3‐BRCC3 association. VD3‐induced Ub‐NLRP3 expression, NLRP3 inflammasome inactivation, and hyperinflammation inhibition were improved by the BRCC3 inhibitor or BRCC3 siRNA, which were attenuated by the vitamin D receptor (VDR) antagonist or VDR siRNA in HBE‐N cells. Finally, the results of the in vivo study in AAV‐Lung‐enhanced green fluorescent protein‐N‐infected lungs were consistent with the findings of the in vitro experiment. In conclusion, VD3 attenuated N protein‐caused hyperinflammation by inactivating the NLRP3 inflammasome partially through the VDR‐BRCC3 signaling pathway.

BackgroundThe purpose of this study was to evaluate the changes in fundus vascular density and micromorphological structure of all vascular plexuses during the different stages of diabetic retinopathy (DR), and the correlation between fundus blood flow and the DR severity.MethodsThis observational cross-sectional study was conducted of 50 eyes with different stages of DR, 25 diabetes mellitus (DM) patients without clinical signs of DR and 41 healthy eyes. The foveal avascular zone (FAZ), vessel density of superficial capillary plexus (SCP), and deep retinal capillary plexus (DCP) were acquired by RTVue XR Avanti OCTA device. The perfusion density (PD), skeleton vessel density (SVD) was manually calculated using ImageJ. The area under receiver operating characteristic (ROC) curve was used to determine the diagnostic value of OCTA parameters in distinguishing DR and healthy eyes.ResultsThe choroidal VD were significantly higher in the healthy group than in the DM without DR, NPDR, and PDR groups (p

Herein, we propose a simple and effective strategy for designing a zeolitic imidazolate frameworks (ZIFs) fluorescent probe with a two-dimensional leaf-like structure. By doping ZIF-L with phosphate, we developed a fluorescent probe for iron (Fe3+) in systems with high salinity. The fluorescence of P-ZIF-L was quenched effectively with the presence of Fe3+. The physicochemical structure, surface morphology, selectivity, stability and composition of the probe were investigated. Under optimized conditions, the fluorescent probe had a detection limit of 0.5 μM. Furthermore, the results that the probe exhibited desirable salt-tolerance and was suitable for determination of Fe3+ in brine water samples with satisfactory results.

To overcome the limitations of the sparrow search algorithm and the challenges of dynamic obstacle avoidance in mobile robots, an integrated method combining the enhanced sparrow search algorithm with the dynamic window approach is introduced. First, logistic–tent chaotic mapping is utilized for the initialization of the sparrow population, thereby achieving a uniform distribution of the sparrow population and simultaneously enhancing the exploratory capability of the algorithm. The implementation of the elite reverse learning strategy aims to diversify the sparrow population, thus improving the quality of initial solutions and the algorithm’s search accuracy. Additionally, the position update dynamic self-adaptive adjustment strategy is adopted to enhance the optimization capability of the algorithm by refining the position update formulas for both producers and scroungers. By combining the Lévy flight strategy and the optimal position perturbation strategy, the algorithm’s efficacy in escaping local optima can be improved. Second, an adaptive velocity adjustment strategy is presented for the dynamic window approach and optimized for its evaluation function to enhance the safety of the path. Third, the enhanced sparrow search algorithm is integrated with the dynamic window approach to tackle the problems of the non-smooth global path and inadequate dynamic obstacle avoidance capability. Both simulation and experimental results show the superiority of the enhanced sparrow search algorithm in comparison to other algorithms in terms of the path length, total rotation angle, and algorithm execution time. Notably, in comparison to the basic sparrow search algorithm, there is a decrease in average path lengths by 15.31% and 11.92% in the improved sparrow search algorithm. The integrated algorithm not only crafts local paths rooted in global paths but also adeptly facilitates real-time dynamic obstacle evasion, ensuring the robot’s safe arrival at its destination.

Casualty prediction is meaningful to the emergency management of natural hazards and human-induced disasters. In this study, a two-step machine learning method, including classification step and regression step, is proposed to predict the number of casualties under emergencies. In the classification step, whether there are casualties under an incident is firstly predicted, then in the regression step, samples predicted to have casualties are used to further predict the exact number of the casualties. Using an open-source dataset, this two-step method is validated. The results show that the two-step model performs better than the original regression models. Back propagation(BP) neural network combined with Random Forest performs the best in terms of the death toll and the number of injuries. Among all the two-step models, the lowest mean absolute error (MAE) for the death toll is 1.67 while that for the number of injuries is 4.13, which indicates that this method can accurately predict the number of casualties under emergencies. This study's results are expected to provide support for decision-making on rapid resource allocation and other emergency responses.

Accidents induced by natural disasters at sports sites may cause catastrophic loss of great concern. However, previous studies on risk assessments of sports sites have only focused on operational risk and equipment failure. With the frequent occurrence of extreme disasters, the risk of domino chains caused by natural disasters at large-scale events, such as large-scale winter sports sites, cannot be ignored. In this study, a natural disaster-induced accident-chain evolution analysis model (NAEA model) is proposed. Based on the results of the NAEA model, a fuzzy Bayesian network for domino accidents triggered by an earthquake at large-scale winter sports sites was established. Through sensitivity analysis and scenario analysis, it was found that fire and explosion accidents and crowded stampede accidents are the main causes of serious loss in domino disaster chains in large-scale sports sites. Simultaneously, improving the early warning capability, reliability of electrical equipment, and automatic sprinkler systems are the most effective ways to prevent and control major accidents. In addition, an optimal safety strategy improvement analysis was performed to facilitate the decision-making of safety managers to prevent serious accidents and reduce accident loss.

Sensitive, intelligent point-of-care test (iPOCT) methods for small molecules like aflatoxin B1 (AFB1) are urgently needed for food and the environment. The challenge remains of surface control in iPOCT. Herein, we developed an electrochemical sensor based on the DNA pyramid (DNP), combining a smartphone, app, and mobile electrochemical workstations to detect AFB1. The DNP’s structure can reduce local overcrowding and entanglement between neighboring probes, control the density and orientation of recognition probes (antibodies), produce uniform and orientational surface assemblies, and improve antigen–antibody-specific recognition and binding efficiency. Simultaneously, the hollow structure of the DNP enhances the electron transfer capacity and increases the sensitivity of electrochemical detection. In this work, the biosensor based on DNP was first combined with electrochemical (Ec) iPOCT to simultaneously achieve ordered interface modulation of recognition probes and intelligent detection of AFB1. Under optimal conditions, we found a detection limit of 3 pg/mL and a linear range of 0.006–30 ng/mL (R2 = 0.995). Further, using peanut, soybean, corn, and lake water as complex matrices, it recorded recoveries of 82.15–100.53%, excellent selectivity, acceptable stability, and good reproducibility. Finally, this Ec iPOCT provides consistent results compared to the high-performance liquid chromatography-tandem mass spectrometry method.

As an inverse problem, the parameter identification of manipulators is essential to in-situ calibration. Since the ill-posed inverse kinematic model is sensitive to the measurement value, even tiny errors will make the geometric model of the manipulator wrongly identified. To overcome this problem, a Regularized Parameter Identification Method (RPIM) is proposed to calibrate the geometric model of the manipulator. The inverse kinematic model of a 6 DOF manipulator is modified by a Tikhonov regularization to overcome its ill-posed problem. The regularization parameter is optimized by the improved L curve method to adjust the initial model to a well-posed one that approximates the real situation. A calibration system is designed to evaluate the effectiveness of the suggested method. The position of the selected targets is tested by using a laser tracker. The experimental result shows that the absolute position errors of the manipulator are 2.533mm without calibration, 0.472mm by RPIM, 1.445mm by Least Square Method (LSM), 1.353mm by Gradient Descent (GD), and 0.956mm by Gauss-Newton (GN). It shows that the absolute position error of RPIM is reduced by 81.331% after calibration, which is superior to other methods.

ST7 Staphylococcus aureus is highly prevalent in humans, pigs, as well as food in China; however, staphylococcal food poisoning (SFP) caused by this ST type has rarely been reported. On May 13, 2017, an SFP outbreak caused by ST7 S. aureus strains occurred in two campuses of a kindergarten in Hainan Province, China. We investigated the genomic characteristics and phylogenetic analysis of ST7 SFP strains combined with the 91 ST7 food-borne strains from 12 provinces in China by performing whole-genome sequencing (WGS). There was clear phylogenetic clustering of seven SFP isolates. Six antibiotic genes including blaZ, ANT (4′)-Ib, tetK, lnuA, norA, and lmrS were present in all SFP strains and also showed a higher prevalence rate in 91 food-borne strains. A multiple resistance plasmid pDC53285 was present in SFP strain DC53285. Among 27 enterotoxin genes, only sea and selx were found in all SFP strains. A ФSa3int prophage containing type A immune evasion cluster (sea, scn, sak, and chp) was identified in SFP strain. In conclusion, we concluded that this SFP event was caused by the contamination of cakes with ST7 S. aureus. This study indicated the potential risk of new emergencing ST7 clone for SFP.

The thermal decomposition pathways of the mineral wheatleyite (sodium bis(oxalato)cuprate(II) dihydrate, Na2Cu(C2O4)2⋅2H2O) have not been established with certainty and the enthalpy of formation of wheatleyite has not been reported. Thermogravimetric analysis (TG), attenuated total reflectance - Fourier transform infrared spectroscopy (ATR-FTIR) and powder X-ray diffraction (PXRD) were used to establish the chemical reactions that occur during the thermal decomposition of sodium bis(oxalato)cuprate(II) dihydrate under atmospheres of static air and flowing nitrogen. The decomposition occurred in three steps in either atmosphere, but the decomposition temperature and products produced depended on the atmosphere. The initial step was the loss of water to form Na2Cu(C2O4)2 in either atmosphere. The Na2Cu(C2O4)2 decomposed in the second step to form Na2C2O4 and a copper compound that depended on the carrier gas. Cu was the decomposition product formed in nitrogen while CuO was produced in air. During the final decomposition, Na2C2O4 decomposed to form Na2CO3. Once the decomposition reactions were established, differential scanning calorimetry (DSC) was used to measure the enthalpy of formation for Na2Cu(C2O4)2 and Na2Cu(C2O4)2⋅2H2O in an atmosphere of static air. The values determined for the enthalpies of formation were -2095 kJ mol−1 and -2670 kJ mol−1 for Na2Cu(C2O4)2 and Na2Cu(C2O4)2⋅2H2O respectively with a measured standard deviation of ±5 kJ mol−1 and estimated total uncertainty of ±10 kJ mol−1.

Because of the more emerging risks and stronger risk interactions, the risk of college campuses as well as students and staff received more and more attention. Current works on campus risk mostly focus on single-category factors, and few of them considered risk interactions. Therefore, an integrated model for assessing comprehensive risks on the campus is proposed to put forward risk reduction strategies. First, a comprehensive risk identification of the college campus is conducted by integrating the modified egg model and the fault tree. Then, DEMATEL (Decision-Making Trial and Evaluation Laboratory) is applied to quantify the complex risk interactions and determine the influential causes for further modelling. Finally, the Bayesian network is established for cause diagnosis, consequence prediction, and risk reduction. The identified most sensitive cause is alcohol use. In the case of the four sensitive causes simultaneously occurring, the probability of high campus risk will increase from 21.9% of the original to 39.4%. Moreover, an efficiency analysis of different risk reduction strategies is performed to determine the most efficient risk reduction strategy. The results indicate that the proposed methodology may of great significance for the risk reduction of the college campus in the changing age.

Deep learning evolves into a new form of machine learning technology that is classified under artificial intelligence (AI), which has substantial potential for large-scale healthcare screening and may allow the determination of the most appropriate specific treatment for individual patients. Recent developments in diagnostic technologies facilitated studies on retinal conditions and ocular disease in metabolism and endocrinology. Globally, diabetic retinopathy (DR) is regarded as a major cause of vision loss. Deep learning systems are effective and accurate in the detection of DR from digital fundus photographs or optical coherence tomography. Thus, using AI techniques, systems with high accuracy and efficiency can be developed for diagnosing and screening DR at an early stage and without the resources that are only accessible in special clinics. Deep learning enables early diagnosis with high specificity and sensitivity, which makes decisions based on minimally handcrafted features paving the way for personalized DR progression real-time monitoring and in-time ophthalmic or endocrine therapies. This review will discuss cutting-edge AI algorithms, the automated detecting systems of DR stage grading and feature segmentation, the prediction of DR outcomes and therapeutics, and the ophthalmic indications of other systemic diseases revealed by AI.

In the complex and ever-changing manufacturing environment, maintaining the long-term steady and efficient work of the assembly line is the ultimate goal pursued by relevant enterprises, the foundation of which is a balanced load. Therefore, this paper carries out research on the two-sided assembly line balance problem (TALBP) for load balancing. At first, a mathematical programming model is established with the objectives of optimizing the line efficiency, smoothness index, and completion time smoothness index of the two-sided assembly line (TAL). Secondly, a deep reinforcement learning algorithm combining distributed proximal policy optimization (DPPO) and the convolutional neural network (CNN) is proposed. Based on the distributed reinforcement learning agent structure assisted by the marker layer, the task assignment states of the two-sided assembly and decisions of selecting tasks are defined. Task assignment logic and reward function are designed according to the optimization objectives to guide task selection and assignment. Finally, the performance of the proposed algorithm is verified on the benchmark problem.

As an indispensable part of the automobile factory transmission system, the transmission assembly line has a high level of automation and can play as a pioneer in the digital transformation of production. However, for the transmission production process, especially the front and middle case, there are many problems, such as a low degree of informatization, low efficiency of information transmission, and lack of a platform for rapid information release and sharing. This leads to the difficulty of timely discovery and feedback of exceptional events, resulting in low assembly efficiency. Therefore, this paper constructs its cyber–physical system (CPS) with the help of the Internet of Things (IoT) technologies, such as physical identification, information collection, and transmission. It contributes to improving the intelligent level of transmission of front and middle case assembly, strengthening the close integration, interaction, and collaboration between a large amount of assembly data and the physical entity of the assembly process, realizing the real-time monitoring of the transmission front and middle case assembly process, timely discovering, provide feedback, and dealing with exceptional events. In addition, scientific and effective adjustment strategies based on the data can be provided. Ultimately, the assembly efficiency of the transmission front and middle case is improved, ensuring the stability of the assembly line.

Abstract Periodically ordered arrays of vertically aligned Si nanowires (Si NWs) are successfully fabricated with controllable diameters and lengths. Their photoconductive properties are investigated by photoconductive atomic force microscopy (PCAFM) on individual nanowires. The results show that the photocurrent of Si NWs increases significantly with the laser intensity, indicating that Si NWs have good photoconductance and photoresponse capability. This photoenhanced conductance can be attributed to the photoinduced Schottky barrier change, confirmed by I–V curve analyses. On the other hand, electrostatic force microscopy (EFM) results indicate that a large number of photogenerated charges are trapped in Si NWs under laser irradiation, leading to the lowering of barrier height. Moreover, the size dependence of photoconductive properties is studied on Si NWs with different diameters and lengths. It is found that the increasing magnitude of photocurrent with laser intensity is greatly relevant to the nanowires’ diameter and length. Si NWs with smaller diameters and shorter lengths display better photoconductive properties, which agrees well with the size-dependent barrier height variation induced by photogenerated charges. With optimized diameter and length, great photoelectrical properties are achieved on Si NWs. Overall, in this study the photoelectrical properties of individual Si NWs are systematically investigated by PCAFM and EFM, providing important information for the optimization of nanostructures for practical applications.

PurposeThe purpose of this study was to investigate pain and radiological features of different types of first-episode demyelinating optic neuritis (ON).MethodsEighty-three patients presenting with first-episode aquaporin-4 (AQP4) antibody-associated ON (AQP4-ON; n = 28), myelin oligodendrocyte glycoprotein (MOG) antibody-associated ON (MOG-ON; n = 26) and idiopathic demyelinating optic neuritis (IDON, n = 29) were included in this retrospective case-control study. We assessed optic nerve lesions on magnetic resonance imaging (MRI), acute pain associated with onset of optic neuritis and clinical characteristics of those ON patients with different serum autoantibody status.Results24 AQP4-ON patients (85.75%), 23 MOG-ON patients (88.5%) and 24 IDON patients (82.8%) suffered from ON-associated pain. MOG-ON had mostly retro-orbital pain; AQP4-ON and IDON had mostly neuropathic pain. In addition, pain was more severe in AQP4ON patients than in other ON patients. In MRI, bilateral involvement was more common in AQP4-ON than IDON (26.9 and 3.7%); radiological optic nerve head swelling was more common in MOG-ON than in AQP4-ON and IDON (68.0 vs. 23.1 vs. 25.9%). MRI lesion in peri-optic nerve sheath was more common in AQP4-ON (53.8 vs. 16.0 vs. 3.7%). In 70 patients with ON-associated pain, gadolinium enhancement of orbital optic nerve was most common in MOG-ON patients (82.4 vs. 55.0 vs. 33.3%, P = 0.018), and enhancement of optic chiasma was most common in AQP4-ON patients (40.0 vs. 5.9 vs. 6.7%, P = 0.015). Perineural and orbital enhancement was observed only in patients with MOG-ON (P < 0.001). The length of enhancement was longer in AQP4-ON patients than in MOG-ON and IDON patients.ConclusionPain is a common symptom in patients with all types of demyelinating ON. AQP4-ON is frequently associated with severe ON-associated pain and longitudinally extensive optic nerve inflammatory lesions. Intra-orbital and peri-optic inflammation were more frequently observed in patients with MOG-ON, which was closely related to optic disc swelling and retro-orbital pain provoked by eye movements.

Recent years have seen increasing school safety events along with growing numbers of students in China. In this paper, more than 400 serious school safety events between 2000 and 2018 in China were collected. The causes and characteristics of these events, taking into account the occurrence years, months, regions, education stages and types, were investigated. The results indicate that the number of school safety events has generally increased annually from 2000 to 2018 and can significantly vary each month, showing a higher frequency of occurrence during the “First Semester” (generally from September to December in China). Moreover, spatial distribution of school safety events is normally related to regional economic development; it was found that Guangdong, Jiangsu and Shandong is a first-level occurrence hotspots, followed by Zhejiang, Henan, Hebei and Sichuan, which are secondary occurrence hotspots. Furthermore, statistical analysis shows that the number of school safety events that occurred in kindergartens, primary schools and middle schools are approximately equal (around 1/3). Finally, it was found that the school safety events caused by “Accident” (such as school bus accidents, school fires, crowded stampedes and the collapses of school buildings) occupy a large proportion (57%).

Turbines are typical engineering-to-order products which can be highly customized. This paper considers the final assembly control under uncertainty in turbine assembly workstations. A cyber-physical framework based on Internet of Things (IoT) is proposed for the turbine assembly, which consists of physical components, cyber components and an IoT-based monitoring system. The IoT-based monitoring modules capture real-time data of the physical components, including the workers, tools, parts and the actual assembly process. These cyber modules can generate the original scheduling for the assembly tasks, re-sequence the assembly tasks, re-assign the workers and control the logistics when an unexpected event occurs. Physical activities are undertaken in a turbine assembly workstation based on assembly instructions and guidance from the cyber components. The proposed framework can be implemented in the large turbine assembly system, which can facilitate real-time information driven assembly process monitoring and collaborative control of the task sequence, the worker assignments and logistics in a closed-loop environment. The experimental results show that our method can significantly improve the quality and the efficiency of a turbine assembly system.

The chemical terrorist attack is a type of unconventional terrorism that threatens the safety of cities. This kind of terrorist attack is highly concealed and difficult to be detected. Once the attack is successful, the consequences will be severe and the scope of impact will be enormous. Therefore, public security and emergency departments need to perform risk analysis and dynamic knowledge update to reduce risk or mitigate the effects of accidents. In order to quickly and effectively analyze the risk of chemical terrorist attacks, this article proposed a hybrid approach (B-R model) to analyze the risk of chemical terrorist attacks. First, a modular and customizable Bayesian network (BN) model library was built, which can satisfy users to select multi-dimensional risk factors. Based on the personalized BN, a risk knowledge graph (RKG) is constructed with multi-source data to realize the combination of risk analysis and knowledge acquisition. Then the threat degree of terrorist organizations, the strength of defensive forces, and the risk value of targets is calculated and displayed. The BN-RKG method provides data and theoretical support for defenders' resource allocation and emergency decision-making. Finally, a case study was conducted for a hypothetical scenario analysis. The result shows that the hybrid method can help with risk control and have the potential to support practical policymaking.

This paper develops an innovative design method of a multi-coupled line (CL) topology of low-loss dual band negative group delay (NGD) circuit. The proposed is conceived using three transmission lines (TLs) with different length in which integrate two couplers. The three traveling paths are designed to generate the dual-band NGD effect, where the CLs allow lowering the signal attenuation. The NGD circuit S-parameter model as a function of the TLs physical lengths and coupling coefficient are established. To validate the NGD circuit proof-of-concept, it is fabricated and measured. The NGD values of approximately -4.06 ns and -3.83 ns are measured at center frequencies of 2.43 GHz and 3.02 GHz, respectively. The measured transmission coefficient is better than -2.9 dB and the measured reflection one is better than -12 dB into the NGD band. The measurement results of the NGD circuit are in good agreement with the simulations results.

Urbanization has been speeding up social and economic transformations in urban communities, the smallest social units in a city. However, urbanization brings challenges to urban management and security. Therefore, a system of risk prediction of crimes may be essential to crime prevention and control in urban communities and its system improvement. To tackle crime-related problems in urban communities, this paper proposes a model of daily crime prediction by combining Long Short-Term Memory Network (LSTM) and Spatial-Temporal Graph Convolutional Network (ST-GCN) to automatically and effectively detect the high-risk areas in a city. Topological maps of urban communities carry the dataset in the model, which mainly includes two modules — spatial-temporal features extraction module and temporal feature extraction module — to extract the factors of theft crimes collectively. We have performed the experimental evaluation of the existing crime data from Chicago, America. The results show that the integrated model demonstrates positive performance in predicting the number of crimes within the sliding time range.

Wet deposition, including both in- and below-cloud scavenging, is critical for the atmospheric transport modeling of 137Cs following the Fukushima Daiichi Nuclear power plant (FDNPP) accident. Although intensively investigated, wet deposition simulation is still subject to uncertainties of meteorological inputs and wet scavenging modeling, leading to biased 137Cs transport prediction. To reduce the dual uncertainties, in- and below-cloud wet scavenging schemes of 137Cs were simultaneously integrated into Weather Research and Forecasting-Chemistry (WRF-Chem), yielding online coupled modeling of meteorology and the two wet scavenging processes. The integration was performed using 25 combinations of different in- and below-cloud schemes, covering most schemes in the literature. Two microphysics schemes were also tested to better reproduce the precipitation. The 25 models and the ensemble mean of 9 representative models were systematically compared with the below-cloud-only WRF-Chem model, using the cumulative deposition and atmospheric concentrations of 137Cs measurements. The results reveal that, with the Morrison's double moment cloud microphysics scheme, the developed models could better reproduce the rainfall and substantially improve the cumulative deposition simulation. The in-cloud scheme is influential to the model behaviors and those schemes considering cloud parameters also improve the atmospheric concentration simulations, whereas the others solely dependent on the rain intensity are sensitive to meteorology. The ensemble mean achieves satisfactory performance except one plume event, but still outperforms most models.

With the rapid emergence of plasmid-mediated colistin resistance gene mcr-1, the increased resistance of Salmonella has attracted extensive attention. This study reports on 11 multidrug-resistant Salmonella enterica serovar Typhimurium strains harboring mcr-1 in China. They all presented resistance to colistin, and additionally, one that was isolated from a child’s stool sample was also resistant to ceftriaxone and azithromycin. We screened 1454 strains of Salmonella for mcr-1 gene through PCR, and these strains are all preserved in our laboratory. Antimicrobial sensitivity analysis was carried out for the screened mcr-1 positive strains. Genetic polymorphism analysis of S. Typhimurium was performed by using the Pulsed-Field Gel Electrophoresis (PFGE). The plasmids harboring mcr-1 were identified by S1-PFGE and southern blotting. Plasmid conjugation assays were used to analyze the transferability of colistin resistance. The plasmids harboring mcr-1 were characterized by sequencing and bioinformatic analysis. Eleven S. Typhimurium strains harboring mcr-1 with colistin resistance (MICs 4μg/ml) were detected, which were isolated from children and pig offal in China. All of them were multidrug-resistant strains. PFGE results revealed that the strains isolated from different samples or locations have identical genotypes. S1-PFGE and southern blotting experiments showed that three plasmids of different sizes (33, 60, and 250 kb) all carried the mcr-1 gene. The plasmid conjugation assays revealed that Salmonella acquired mcr-1 harboring plasmids by horizontal transfer. Sequencing and plasmid type analysis revealed that these plasmids were types IncX4, IncI2, and IncHI2. Among them, IncX4 and IncI2 plasmids had extremely similar backbones and contained one resistant gene mcr-1. IncHI2 plasmid contained multiple resistant genes including blaCTX–M, oqxB, sul, aph, aadA, and blaTEM. We identified 11 mcr-1 harboring S. Typhimurium strains in China and described their characteristics. Our findings indicate that the mcr-1 gene can effectively spread among intestinal bacteria by horizontal transfer of three types of plasmids. Moreover, the IncHI2 plasmid can also mediate the transfer of other drug resistance genes. These results reveal that constant surveillance of mcr-1 harboring S Typhimurium is imperative to prevent the spread of colistin resistance.

Directed cell migration arises from cells following a microenvironmental gradient (e.g. of a chemokine) or polarizing feature (e.g. a linear structure). However cells not only follow, but in many cases, also generate directionality cues by modifying their microenvironment. This bi-directional relationship is seen in the alignment of extracellular matrix (ECM) fibers ahead of invading cell masses. The forces generated by many migrating cells cause fiber alignment, which in turn promotes further migration in the direction of fiber alignment via contact guidance and durotaxis. While this positive-feedback relationship has been widely described for cells invading en masse, single cells are also able to align ECM fibers, as well as respond to contact guidance and durotaxis cues, and should therefore exhibit the same relationship. In this study, we directly tested this hypothesis by studying the migration persistence of individual HT-1080 fibrosarcoma cells migrating in photocrosslinked collagen matrices with limited remodeling potential. Our results demonstrate that this positive-feedback relationship is indeed a fundamental aspect of cell migration in fibrillar environments. We observed that the cells' inability to align and condense fibers resulted in a decrease in persistence relative to cells in native collagen matrices and even relative to isotropic (glass) substrates. Further experiments involving 2D collagen and electrospun polymer scaffolds suggest that substrates composed of rigid, randomly oriented fibers reduce cells' ability to follow another directionality cue by forcing them to meander to follow the available adhesive area (i.e. fibers). Finally, our results demonstrate that the bi-directional relationship between cell remodeling and migration is not a "dimensionality" effect, but a fundamental effect of fibrous substrate structure.

Ochratoxin A (OTA) poses a severe health risk to livestock along the food chain. Moreover, according to the International Agency for Research on Cancer, it is also categorized as being possibly carcinogenic to humans. The lack of intelligent point-of-care test (POCT) methods restricts its early detection and prevention. This work establishes a smartphone-enabled point-of-care test for OTA detection via a fluorescent lateral flow assay within 6 min. By using a smartphone and portable reader, the assay allows for the recording and sharing of the detection results in a cloud database. This intelligent POCT provided (iPOCT) a linearity range of 0.1–3.0 ng/mL and a limit of detection (LOD) of 0.02 ng/mL (0.32 µg/kg in wheat). By spiking OTA in blank wheat samples, the recoveries were 89.1–120.4%, with a relative standard deviation (RSD) between 3.9–9.1%. The repeatability and reproducibility were 94.2–101.7% and 94.6–103.4%, respectively. This work provides a promising intelligent POCT method for food safety.

Electromagnetic response of clustered charged particles is the foundation of electromagnetic wave interaction with various natural phenomena, such as sandstorm, cloud, and volcano eruption. Previous studies usually employed assumption of independent charged particles, without considering the coupling between them. Here, we build up a general numerical model considering the multiple scattering effect, and test it with a charged two- and four-particle system. The numerical results show that independence assumption fails, while the number density of clustered charged particles is getting larger. This work may pave the way for deeper understanding on the electromagnetic interaction of clustered charged particles.

Background: The coronavirus disease 2019 (COVID-19) pandemic has severely affected healthcare systems around the world. This study aimed to investigate the perceptions of cardiologists regarding how the COVID-19 pandemic has affected the clinical practice patterns for acute coronary syndrome (ACS). Methods: A multicenter clinician survey was sent to 300 cardiologists working in 22 provinces in China. The survey collected demographic information and inquired about their perceptions of how the COVID-19 pandemic has affected ACS clinical practice patterns. Results: The survey was completed by 211 (70.3%) cardiologists, 82.5% of whom were employed in tertiary hospitals, and 52.1% reported more than 10 years of clinical cardiology practice. Most respondents observed a reduction in ACS inpatients and outpatients in their hospitals during the pandemic. Only 29.9% of the respondents had access to a dedicated catheter room for the treatment of COVID-19-positive ACS patients. Most respondents stated that the COVID-19 pandemic had varying degrees of effect on the treatment of acute ST-segment elevation myocardial infarction (STEMI), acute non-ST-segment elevation myocardial infarction (NSTEMI), and unstable angina. Compared with the assumed non-pandemic period, in the designed clinical questions, the selection of coronary interventional therapy for STEMI, NSTEMI, and unstable angina during the COVID-19 pandemic was significantly decreased (all p < 0.05), and the selection of pharmacotherapy was increased (all p < 0.05). The selection of fibrinolytic therapy for STEMI during the pandemic was higher than in the assumed non-pandemic period (p < 0.05). Conclusions: The COVID-19 pandemic has profoundly affected ACS clinical practice patterns. The use of invasive therapies significantly decreased during the pandemic period, whereas pharmacotherapy was more often prescribed by the cardiologists.

Acute myocardial infarction (AMI) is the most severe cardiovascular event in the world. However, the molecular mechanisms underlying AMI remained largely unclear. Recently, long non-coding RNAs (lncRNAs) were reported to play important roles in human diseases. In the present work, we analyzed a public dataset GSE48060 to confirm key lncRNAs and mRNAs in AMI. We observed 4835 mRNAs and 442 lncRNAs were significantly differently expressed in AMI. Then, we for the first time constructed PPI networks and lncRNA co-expression networks in AMI. The protein-protein interaction (PPI) networks revealed several mRNAs such as RHOA, GNB1, GNG, RAC1, FBXO32, DET1, MEX3C and HECTD1 functioned as key regulators in AMI. LncRNA co-expression network analysis showed 8 lncRNAs (CA5BP1, LOC101927608, BZRAP1-AS1, EBLN3, FGD5-AS1, HNRNPU-AS1, LINC00342, and LOC101927204) played key roles in AMI. Gene ontology (GO) analysis demonstrated these differently expressed lncRNAs were associated with more signaling pathways, such as regulating transcription, protein amino acid phosphorylation, signal transduction, development. Taken together, our research unveiled a series of key lncRNAs and mRNAs in AMI. Several lncRNAs, including CA5BP1, LOC101927608, BZRAP1-AS1, EBLN3, FGD5-AS1, HNRNPU-AS1, LINC00342, and LOC101927204 were identified as key lncRNAs. PPI networks were constructed to reveal key mRNAs in AMI. These results provided useful information for exploring novel molecular target therapy for AMI.

Introduction Hypertrophic cardiomyopathy (HCM), a genetically heterogeneous disorder of cardiac myocytes, is one of the main causes of sudden cardiac death of young people. However, the molecular mechanism involved in HCM has remained largely unclear. Of note, non-coding RNAs were reported to play an important role in human diseases. In this study, we focused on identifying differentially expressed long non-coding RNA (lncRNAs) and mRNAs in HCM by analyzing a public dataset (GSE36961). Material and methods We performed bioinformatics analysis to explore key pathways underlying HCM progression. Gene Ontology (GO) analysis was first performed to evaluate the potential roles of differentially expressed genes and lncRNAs in HCM. Moreover, protein–protein interaction (PPI) networks were constructed to reveal interactions among differentially expressed proteins. Specifically, co-expression networks were also constructed to identify hub lncRNAs in HCM. Results A total of 6147 mRNAs (p < 0.001) and 126 lncRNAs (p < 0.001) were found to be dysregulated in HCM. Gene Ontology (GO) analysis showed that these differentially expressed genes and lncRNAs were associated with metabolism, energy pathways, signal transduction, and cell communication. Moreover, TSPYL3, LOC401431, LOC158376, LOC606724, PDIA3P and LOH3CR2A (p < 0.001) were identified as key lncRNAs in HCM progression. Conclusions Taken together, our analysis revealed a series of lncRNAs and mRNAs that were differentially expressed in HCM and which were involved in HCM progression by regulating pathways, such as metabolism, energy pathways, signal transduction, and cell communication. This study will provide useful information to explore the mechanisms underlying HCM progression and to provide potential candidate biomarkers for diagnosis in HCM.

An assembly line is an industrial arrangement of machines, equipment, and operators for continuous flow of workpieces in mass-production operations. In an assembly line balancing problem, tasks are allocated to workstations according to their processing times and precedence relationships amongst tasks. Nowadays, some research investigated the reliability of assembly production by taking account of task time uncertainties. Our research utilizes uncertainty theory to model task time uncertainties and introduces the belief reliability measure to the assembly line production for the first time. We proposed a multi-objective optimization model that aimed at maximizing the belief reliability and minimizing the cycle time. The problem is solved using a newly developed restart neighborhood search method. The numerical experiments are conducted to verify its efficiency. The methodology proposed in this paper is applicable to any industry (including the automotive industry) when the historical data on task processing times are very scarce.

In this paper, it has been shown that the data-driven reallocation of workers in a turbine valve assembly island is necessary because the occurrence of unexpected events delays the desired completion time defined in the schedule. The main characteristics of the problem are as follows: 1) the turbine valve is placed on a fixed site, and the workers and the required equipment are moved to the assembly site; 2) the sequence of assembly tasks is fixed, which reduces the number of significant disruptions to the upstream operations; and 3) the processing time of each task is dependent of the skill level and the number of the assigned workers. This paper proposes data-driven reallocation of workers in engineering-to-order assembly islands to meet the specified due dates and use human resources efficiently. The goal is to minimize the labor cost by reallocating the workers in order to meet the due dates when an unexpected event occurs. The problem was modeled as an integer program and optimally solved by means of the LINGO (Linear Interactive and General Optimizer) 11.0 optimizer. The experimental results showed that the solutions obtained were improvements to the solutions implemented by the foreman.

LncRNA SNHG1 (SNHG1) has been widely studied as the causative factor of angiogenesis and proliferative agent in gastric, lung, cervical, and hepatocellular carcinomas. However, its significance of angiogenesis and progression of corneal neovascularization (CRNV) is least understood. This study focuses on the molecular mechanisms followed by SNHG1 to establish CRNV and its angiogenesis. Bioinformatics analysis to identify potential miRNA targets of SNHG1 and vascular endothelial growth factor A (VEGF-A) was conducted using StarBase and was subsequently confirmed by the luciferase reporter assay. Relative quantitative expression of SNHG1 in human umbilical vein endothelial cells (HUVECs) was detected through qRT-PCR and western blot analysis. Cell proliferation was detected through CCK-8 assay, whereas migratory abilities of the cells were determined with transwell assay. A capillary-like tube formation assay was performed to detect the tube formation ability of the cells. Following this, relative expression of miR-195-5p and VEGF-A was determined through qRT-PCR and western blot analysis. Results from the experiments manifested upregulated levels of SNHG1 and VEGF-A in HUVECs and CRNV tissues as compared with the control group, whereas downregulated levels of miR-195-5p were measured in the CRNV tissues and HUVECs, suggesting the negative correlation between lncRNA and miRNA. Overexpressed vascular endothelial growth factor promoted cell proliferation and tube formation; however, its silencing leads to inhibition in angiogenesis and proliferation. Potential binding sites of SNHG1 showed miR-195-5p as its direct target and SNHG1 as a sponge for this miRNA. Knockdown and downregulated levels of SNHG1 showed a notable decrease and inhibition in angiogenesis and migration of CRNV cells. The study showed that SNHG1 inhibition significantly reduced cell proliferation, migration, and tube formation in HUVECs transfect with lncRNA SNHG1. Mechanistic insights into the SNHG1 showed that SNHG1 acts as a sponge for miR-195-5p and upregulates the levels of VEGF-A.

Crime issues have been attracting widespread attention from citizens and managers of cities due to their unexpected and massive consequences. As an effective technique to prevent and control urban crimes, the data-driven spatial–temporal crime prediction can provide reasonable estimations associated with the crime hotspot. It thus contributes to the decision making of relevant departments under limited resources, as well as promotes civilized urban development. However, the deficient performance in the aspect of the daily spatial–temporal crime prediction at the urban-district-scale needs to be further resolved, which serves as a critical role in police resource allocation. In order to establish a practical and effective daily crime prediction framework at an urban police-district-scale, an “online” integrated graph model is proposed. A residual neural network (ResNet), graph convolutional network (GCN), and long short-term memory (LSTM) are integrated with an attention mechanism in the proposed model to extract and fuse the spatial–temporal features, topological graphs, and external features. Then, the “online” integrated graph model is validated by daily theft and assault data within 22 police districts in the city of Chicago, US from 1 January 2015 to 7 January 2020. Additionally, several widely used baseline models, including autoregressive integrated moving average (ARIMA), ridge regression, support vector regression (SVR), random forest, extreme gradient boosting (XGBoost), LSTM, convolutional neural network (CNN), and Conv-LSTM models, are compared with the proposed model from a quantitative point of view by using the same dataset. The results show that the predicted spatial–temporal patterns by the proposed model are close to the observations. Moreover, the integrated graph model performs more accurately since it has lower average values of the mean absolute error (MAE) and root mean square error (RMSE) than the other eight models. Therefore, the proposed model has great potential in supporting the decision making for the police in the fields of patrolling and investigation, as well as resource allocation.