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Deep reinforcement learning (DRL) targets to train an autonomous agent to interact with a pre-defined environment and strives to achieve specific goals through deep neural networks (DNN). Recurrent neural network (RNN) based DRL has demonstrated superior performance, as RNNs can effectively capture the temporal evolution of the environment and respond with proper agent actions. However, apart from the outstanding performance, little is known about how RNNs understand the environment internally and what has been memorized over time. Revealing these details is extremely important for deep learning experts to understand and improve DRLs, which in contrast, is also challenging due to the complicated data transformations inside these models. In this article, we propose Deep Reinforcement Learning Interactive Visual Explorer (DRLIVE), a visual analytics system to effectively explore, interpret, and diagnose RNN-based DRLs. Having focused on DRL agents trained for different Atari games, DRLIVE accomplishes three tasks: game episode exploration, RNN hidden/cell state examination, and interactive model perturbation. Using the system, one can flexibly explore a DRL agent through interactive visualizations, discover interpretable RNN cells by prioritizing RNN hidden/cell states with a set of metrics, and further diagnose the DRL model by interactively perturbing its inputs. Through concrete studies with multiple deep learning experts, we validated the efficacy of DRLIVE.

With the significant increase of e-commerce, freight transportation demand has surged significantly over the past decade. Most of the demand has been served by trucks in the United States. One major problem commonly identified across the country is the worsening truck parking availability because the increase of truck parking facilities has lagged behind the growth of trucking activities. The lack of parking spaces and real-time parking availability information greatly exacerbate the uncertainty of trips, and often results in illegal and potentially dangerous parking or overtime driving. This paper elaborates on pilot research on improving truck parking facilities cooperated with the Washington State Department of Transportation (WSDOT), building and testing the advanced Truck Parking Information and Management System (TPIMS) with the real-time user visualization and prediction function empowered by artificial intelligence. Furthermore, by analyzing the activities of truck drivers, the researchers aggregated the regularity of truck parking patterns by a customized sequential similarity methodology. A Truck Parking Occupancy Prediction (TPOP) neural network for time-variant occupancy prediction by deep learning and attributes embedding is proposed and integrated into the TPIMS. The TPOP achieves 5.82%, 5.07%, 4.84%, and 4.19% mean average percentage error (MAPE) for 16, 8, 4, and 2 minutes ahead of occupancy prediction respectively, significantly outperforms other state-of-the-art methods. Clearly, the proposed solutions can benefit both the truck drivers and government agencies by a more efficient and smart TPIMS.

Multi-label learning deals with training examples each represented by a single instance while associated with multiple class labels. Due to the exponential number of possible label sets to be considered by the predictive model, it is commonly assumed that label correlations should be well exploited to design an effective multi-label learning approach. On the other hand, class-imbalance stands as an intrinsic property of multi-label data which significantly affects the generalization performance of the multi-label predictive model. For each class label, the number of training examples with positive labeling assignment is generally much less than those with negative labeling assignment. To deal with the class-imbalance issue for multi-label learning, a simple yet effective class-imbalance aware learning strategy called cross-coupling aggregation (COCOA) is proposed in this article. Specifically, COCOA works by leveraging the exploitation of label correlations as well as the exploration of class-imbalance simultaneously. For each class label, a number of multiclass imbalance learners are induced by randomly coupling with other labels, whose predictions on the unseen instance are aggregated to determine the corresponding labeling relevancy. Extensive experiments on 18 benchmark datasets clearly validate the effectiveness of COCOA against state-of-the-art multi-label learning approaches especially in terms of imbalance-specific evaluation metrics.

Customized path-based speed prediction is an eventful tool for congestion avoidance, route optimization and travel time prediction for navigation apps, cab-hailing companies and autonomous vehicles. Traditionally, the speed prediction algorithms are based on road segments and can only support several main roads. Path-based speed prediction is very challenging since the speed is always changing in different path locations and is jointly affected by lots of complicated factors. This article presents a novel deep learning framework for customized path-based speed prediction. A Path-based Speed Prediction Neural Network (PSPNN) is designed to achieve speed predictions for a given path and attributes information. A hierarchical Convolutional Neural Network (CNN) and deep Bidirectional Long Short-Term Memory (Bi-LSTM) structure for different kinds of feature extraction are applied for multiple levels: the path cell, sub-path and the whole path. The method narrows down the prediction unit from road segments to customized path cells (mean length: 59.52m) and achieves a mean absolute error (MAE) of 1.94 m/s and Mean Absolute Percentage Error (MAPE) of 18.14%, showing the potential of serving rigorous data-driven applications. So far, PSPNN is the first made-to-order path-based speed prediction algorithm and can help both travelers and managers to obtain large-scale bespoke paths speed information in advance.

To investigate the presence and clinical relevance of hyperreflective foci (HRFs) in retinitis pigmentosa.Seventy seven retinitis pigmentosa cases were retrospectively reviewed. The 10-mm wide cross-line macular scans in optical coherence tomography were acquired. Hyperreflective foci were classified according to the location in optical coherence tomography: outer layers within the macula (HRF-outer-central), macular border beyond the central 3 mm (HRF-outer-perifoveal), and choroid (HRF-choroidal). The visual acuity at baseline, at 12 months, and other fundus characteristics were collected.The mean logMAR best-corrected visual acuity decreased from 0.59 ± 0.66 (20/78 in Snellen) to 0.74 ± 0.81 (20/106 in Snellen) in 1 year. Sixty-six (42.9%), 105 (68.2%), and 98 (63.6%) eyes were classified to HRF-outer-central, HRF-outer-perifoveal, and HRF-choroidal group, respectively. Hyperreflective foci were positively correlated with poorer vision, central macular thinning, and ellipsoid zone disruption (all P0.001). Worse vision was associated with older age, macular involvement, and the coexistence of two or three HRF groups (P = 0.014, 0.047, 0.019,0.001, respectively). Hyperreflective foci developed more frequently in patients with thick choroid than in those with thin choroid. The coexistence of three HRF groups was correlated with quicker visual deterioration (P = 0.034).Hyperreflective foci are common in retinitis pigmentosa and can be a negative prognostic indicator of macular thickness and visual preservation. Thick choroid was associated with all groups of HRFs, especially HRF-choroidal.

The immune checkpoint blockade therapy has profoundly revolutionized the field of cancer immunotherapy. However, despite great promise for a variety of cancers, the efficacy of immune checkpoint inhibitors is still low in colorectal cancer (CRC). This is mainly due to the immunosuppressive feature of the tumor microenvironment (TME). Emerging evidence reveals that certain chemotherapeutic drugs induce immunogenic cell death (ICD), demonstrating great potential for remodeling the immunosuppressive TME. In this study, the potential of ginsenoside Rg3 (Rg3) as an ICD inducer against CRC cells was confirmed using in vitro and in vivo experimental approaches. The ICD efficacy of Rg3 could be significantly enhanced by quercetin (QTN) that elicited reactive oxygen species (ROS). To ameliorate in vivo delivery barriers associated with chemotherapeutic drugs, a folate (FA)-targeted polyethylene glycol (PEG)-modified amphiphilic cyclodextrin nanoparticle (NP) was developed for co-encapsulation of Rg3 and QTN. The resultant nanoformulation (CD-PEG-FA.Rg3.QTN) significantly prolonged blood circulation and enhanced tumor targeting in an orthotopic CRC mouse model, resulting in the conversion of immunosuppressive TME. Furthermore, the CD-PEG-FA.Rg3.QTN achieved significantly longer survival of animals in combination with Anti-PD-L1. The study provides a promising strategy for the treatment of CRC., Graphical abstract A folate-targeted PEGylated cyclodextrin-based nanoparticle was developed for co-delivery of Rg3 and QTN. The combination of the resultant co-formulation and anti-PD-L1 antibody achieved chemo-immunotherapy for colorectal cancer.Image 1

Propionic acidemia (PA) is a severe autosomal recessive metabolic disease caused by deficiency of propionyl-CoA carboxylase (PCC). We studied PA transgenic (Pat) mice that lack endogenous PCC but express a hypoactive human PCCA cDNA, permitting their survival. Pat cohorts followed from 3 to 20 weeks of age showed growth failure and lethal crises of lethargy and hyperammonemia, commoner in males (27/50, 54%) than in females (11/52, 21%) and occurring mainly in Pat mice with the most severe growth deficiency. Groups of Pat mice were studied under basal conditions (P-Ba mice) and during acute crises (P-Ac). Plasma acylcarnitines in P-Ba mice, compared to controls, showed markedly elevated C3- and low C2-carnitine, with a further decrease in C2-carnitine in P-Ac mice. These clinical and biochemical findings resemble those of human PA patients. Liver acyl-CoA measurements showed that propionyl-CoA was a minor species in controls (propionyl-CoA/acetyl-CoA ratio, 0.09). In contrast, in P-Ba liver the ratio was 1.4 and in P-Ac liver, 13, with concurrent reductions of the levels of acetyl-CoA and other acyl-CoAs. Plasma ammonia levels in control, P-Ba and P-Ac mice were 109 ± 10, 311 ± 48 and 551 ± 61 μmol/L respectively. Four-week administration to Pat mice, of carglumate (N-carbamyl-L-glutamic acid), an analogue of N-carbamylglutamate, the product of the only acyl-CoA-requiring reaction directly related to the urea cycle, was associated with increased food consumption, improved growth and absence of fatal crises. Pat mice showed many similarities to human PA patients and provide a useful model for studying tissue pathophysiology and treatment outcomes.

Background: Current management guidelines for gallbladder polyps (GBPs) focus on a diameter more than 1 cm as an indication for cholecystectomy. Since most GBPs are not malignant, unnecessary cholecystectomies can lead to unnecessary complications and costs. We developed a score to identify true polyps focusing on their cross-sectional area (CSA). Methods: We retrospectively analyzed the demographic, clinical, laboratory, and sonographic characteristics of 522 patients with GBPs who had undergone cholecystectomy at our hospital between January 2010 and July 2020 (reference group). We used univariate analysis to compare these parameters between 88 true polyps and 434 pseudopolyps and multivariate logistic regression analysis to identify parameters to include in our scoring model. Receiver operating characteristics and area under the curve were used to identify cut-off values. The model was tested on a validation group of 98 patients. Results: In the multivariate analysis, a CSA >123 mm2, positive blood flow signal, age >55.5 years, alanine aminotransferase (ALT) levels > 50 U/L, and an ALT/aspartate aminotransferase ratio > 0.77 were significantly associated with true polyps (odds ratio 6.528, 2.377, 2.617, 2.445, and −0.372, respectively). A prediction model based on cut-off values was used to distinguish a low-risk and high-risk GBP group; true polyps accounted for 6.54% and 58.72%, respectively (p

Neovascular age-related macular degeneration (nAMD) is a leading cause of irreversible vision loss. The present consensus provides suggestions on diagnosis, evaluation, treatment, and follow-up strategies for nAMD from a panel of 11 practicing ophthalmologists. The experts suggest that the baseline visit for nAMD management should include a comprehensive ophthalmologic examination via a multimodal approach consisting of visual and anatomical evaluation. Patients diagnosed with nAMD should be subjected to treatment with the goal of maintaining visual function while diminishing anatomical disease activity and minimizing treatment burden. Currently, anti-VEGF therapy is the main treatment strategy for nAMD, and evaluation involving comprehensive ophthalmologic examination within 1 month of completion of the loading phase comprising three monthly injections is recommended to guide subsequent management. Either a treat-and-extend or pro re nata regimen can be considered for the maintenance phase of anti-VEGF therapy, and the regimen should be chosen and adjusted according to disease activity, reimbursement criteria, financial burden, and patient preferences. In the event of inactive nAMD or poor treatment outcomes, after thorough evaluation and patient education, anti-VEGF therapy may be stopped. The consensus provides practical nAMD management guidelines for ophthalmologists and fellow healthcare professionals.

In order to evaluate the stability of neonatal parenteral nutrition solution, in this paper, the prescription of neonatal parenteral nutrition solution was investigated and analyzed. The formula of neonatal parenteral nutrition solution used, particularly the one utilized in this study, is commonly used in clinical practice. All the neonatal parenteral nutrition solution required for the test was prepared on the purification workbench in a sterile environment. The time points of stability of parenteral nutrient solution were 0, 12, and 24 hours, respectively, and three parallel samples were taken at each time point. Likewise, to investigate the stability of two kinds of fat milk injection in parenteral nutrition solution of neonates and provide a reference for subsequent experiments and to investigate the influence of electrolyte, amino acid, temperature, pH value, mixing sequence, and the final concentration of glucose on the stability of neonatal parenteral nutrition solution, the stability indexes of neonatal parenteral nutrition liquid mainly include appearance, pH, insoluble particles, fat milk particle size, and particle size distribution. Neonatal parenteral nutrition solution prescriptions from the First Affiliated Hospital of Jinan University, specifically from January to June 2019, were collected and statistically processed. The experimental data were processed by SPSS 19.0 software and data mining technology. The results were expressed as mean ± standard deviation and statistically processed by ANOVA. P < 0.05 was considered statistically significant. The results showed that the stability of neonatal parenteral nutrient solution was influenced by many factors. The formula of neonatal parenteral nutrition solution is generally reasonable, but there are unreasonable phenomena which are needed to be improved further if feasible.

Wound management is a major global issue, and there is a growing challenge to develop more effective hemostatic dressings to control bleeding and prevent pathogen infections. In this study, a multifunctional wound dressing was developed to meet the clinical need. The hemostatic layer of wound dressing can quickly stop the bleeding. Meanwhile, the detection layer is used for real-time fluorescence monitoring of the bacterial colonization. When infection occurs, wound dressing is further subjected to illumination for in-situ photodynamic antibacterial treatment. In the rabbit ear artery hemostasis model, the hemostasis time of the wound dressing was 1 s. The detection limit of the wound dressing was 1.4 × 105 CFU/cm2 for Escherichia coli, 5.9 × 105 CFU/cm2 for Staphylococcus aureus, and 3.8 × 106 CFU/cm2 for Pseudomonas aeruginosa, respectively. Compared with the control group, an enhanced wound closure (up to 97.3%) were observed in mice treated with the wound dressing. In vitro and in vivo experiment results suggested that the wound dressing was effective in killing pathogenic bacterial and exhibited good biological compatibility, and induced no inflammatory reaction. The proposed design prevents massive bleeding and wound infection, and further promotes wound healing. Statement of significance In this work, we developed a multifunctional wound dressing, capable of rapid hemostasis, colorimetric monitoring of bacterial infection, and in situ photodynamic antibacterial. The hemostatic layer can quickly stop the bleeding due to its large specific surface area and adsorption pore size for platelet at bleeding site. Meanwhile, the detection layer can intelligently monitor the bacterial infection and respond to report bacterial infection by emitting fluorescence. When infection occurs, wound dressing can be used for in-situ photodynamic antibacterial treatment. In vitro and in vivo results showed that the wound dressing was biocompatible, prevented massive bleeding and wound infection, and further promoted wound healing.

As a selective estrogen receptor modulator (SERM), raloxifene is used in healthy postmenopausal women to prevent bone loss and reduce fractures. However, the benefit of raloxifene is uncertain in the treatment of osteoporosis among patients with end-stage renal disease (ESRD) or those who require maintenance dialysis. We assessed the safety and efficacy of raloxifene in this particular population. Studies were selected from PubMed, Springer, CNKI (Chinese National Knowledge Infrastructure) and Wanfang Database. Randomized controlled trials (RCTs) and prospective studies with control/placebo groups were included. Five studies were included with a total of 244 participants (121 patients in the raloxifene group and 123 patients in the placebo/control group). The median duration of treatment was 12 months. The incidence rate of side effects of raloxifene was 0/121 (0%). There was a significant improvement of lumbar spine bone mineral density (BMD) levels in the raloxifene group compared with the placebo group (MD: 33.88, 95% CI: 10.93, 56.84, p=0.004). There was no significant difference concerning the improvement of femoral neck BMD (MD: 8.42, 95% CI: –10.21, 27.04, p=0.38), intact parathyroid hormone (iPTH) (MD: –12.62, 95% CI: –35.36, 10.13, p=0.28), calcium (MD: -0.08, 95% CI: –0.61, 0.44, p=0.76), phosphorus (MD: 0.18, 95% CI: –0.12, 0.48, p=0.23) or bone alkaline phosphatase (BAP) (MD: –4.33, 95% CI: –14.44, 5.79, p=0.40). Raloxifene seems to be effective in improving the lumbar spine BMD in postmenopausal women with ESRD. More large RCTs are necessary to evaluate the long-term safety of raloxifene in uremic patients.

BACKGROUND Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Early diagnosis and timely treatment are essential for effective cancer control and have been widely analyzed in childhood cancer. However, few studies have described the time to diagnosis and treatment in children with ALL. This study investigated delays in diagnosis and treatment initiation and their impact on survival. METHODS This retrospective cohort study included 419 patients aged 0 to 14 years at a tertiary hospital between 2011 and 2015. The optimal cutoff values for delays were determined by X-tile software. The Kaplan-Meier method and Cox regression models were used to evaluate the impact of delays on survival. RESULTS The median diagnosis, treatment, and total delays were 21 (interquartile range [IQR]: 11-35), 4 (IQR: 2-7), and 26 (IQR: 16-43) days, respectively. The results of multivariate analyses showed that diagnosis delay, risk stratification, and minimal residual disease level were independent predictors for treatment outcome in childhood ALL. CONCLUSIONS These findings suggested that a longer time to diagnosis negatively affected the clinical outcome of childhood ALL. Reducing the time to diagnosis could help to improve survival in these patients.

Pull-up, as an important physical fitness test event of the “National Student Physical Health Standard”, is known as a difficult physical fitness test event. To improve the assessment criteria of pull-ups, this paper uses the functional near-infrared spectroscopy (fNIRS) to monitor the changes and activation of oxyhemoglobin (HbO) signals in the brain motor cortex of people with different body mass indexes (BMIs) during the pullup assessment. Then the relationship between BMIs and evaluation criteria is discussed. After collecting and analyzing experimental data of 18 recruited college students, it is found that the number of pull-ups performed by people with different BMIs is different when they reach the peak state of brain activation. The results of the study indicate that different assessment criteria should be adopted for different BMI groups. It is suggested that the BMI should be introduced as one of the test indexes in the examination of pull-ups event in “National Student Physical Health Standard”.

To provide real-world experiences of treating polypoidal choroidal vasculopathy (PCV) patients with photodynamic therapy (PDT) plus intravitreal injection of ranibizumab or intravitreal injection of aflibercept alone. Retrospective chart review of patients with PCV in a single tertiary referral center in Taiwan. Chart review of PCV patients treated with PDT and injection of ranibizumab or injection of aflibercept. A total of 101 eyes of 101 patients (38 females and 63 males) were reviewed. Of those, 48 and 53 eyes received primary/adjunctive PDT along with injections of ranibizumab or intravitreal injections of aflibercept only, respectively. Initial visual acuity (VA) and central subfield choroidal thickness were similar between the two groups (p > 0.05). In addition, changes in VA at 3, 6, and 12 months post treatment were similar. The central retinal thickness decreased with either treatment (p p > 0.05). In the subgroup analysis of pachychoroid and non-pachychoroid patients, better initial VA and post-treatment VA at 3 months and 6 months was noted in the latter group of patients treated with anti-vascular endothelial growth factor monotherapy (p

Aim This study aimed to develop an AI model for detecting a caller’s emotional state during out-of-hospital cardiac arrest calls by processing audio recordings of dispatch communications. Methods Audio recordings of 337 out-of-hospital cardiac arrest calls from March-April 2011 were retrieved. The callers’ emotional state was classified based on the emotional content and cooperative scores. Mel-frequency cepstral coefficients extracted essential information from the voice signals. A support vector machine was utilised for the automatic judgement, and repeated random sub-sampling cross validation (RRS-CV) was applied to evaluate robustness. The results from the artificial intelligence classifier were compared with the consensus of expert reviewers. Results The audio recordings were classified into five emotional content and cooperative score levels. The proposed model had an average positive predictive value of 72.97%, a negative predictive value of 93.47%, sensitivity of 38.76%, and specificity of 98.29%. If only the first 10 seconds of the recordings were considered, it had an average positive predictive value of 84.62%, a negative predictive value of 93.57%, sensitivity of 52.38%, and specificity of 98.64%. The artificial intelligence model’s performance maintained preferable results for emotionally stable cases. Conclusion Artificial intelligence models can possibly facilitate the judgement of callers’ emotional states during dispatch conversations. This model has the potential to be utilised in practice, by pre-screening emotionally stable callers, thus allowing dispatchers to focus on cases that are judged to be emotionally unstable. Further research and validation are required to improve the model's performance and make it suitable for the general population.

Background: We aimed to establish a novel diagnostic model for kidney diseases by combining artificial intelligence with complete mass spectrum information from urinary proteomics. Methods: We enrolled 134 patients (IgA nephropathy, membranous nephropathy, and diabetic kidney disease) and 68 healthy participants as controls, with a total of 610,102 mass spectra from their urinary proteomic profiles. The training data set (80%) was used to create a diagnostic model using XGBoost, random forest (RF), a support vector machine (SVM), and artificial neural networks (ANNs). The diagnostic accuracy was evaluated using a confusion matrix with a test dataset (20%). We also constructed receiver operating-characteristic, Lorenz, and gain curves to evaluate the diagnostic model. Results: Compared with the RF, SVM, and ANNs, the modified XGBoost model, called Kidney Disease Classifier (KDClassifier), showed the best performance. The accuracy of the XGBoost diagnostic model was 96.03%. The area under the curve of the extreme gradient boosting (XGBoost) model was 0.952 (95% confidence interval, 0.9307–0.9733). The Kolmogorov-Smirnov (KS) value of the Lorenz curve was 0.8514. The Lorenz and gain curves showed the strong robustness of the developed model. Conclusion: The KDClassifier achieved high accuracy and robustness and thus provides a potential tool for the classification of kidney diseases. Keywords: Kidney disease classification, urinary proteomics, machine learning algorithm, diagnosis, artificial intelligence

Repetitive transcranial magnetic stimulation (rTMS) of the brain is an effective clinical treatment for psychiatric disorders. Noninvasive neuroimaging during rTMS allows visualization of cortical brain activations and responses, and it is a potential tool for investigating the neurophysiological response occurring actively during stimulation. In this paper, we present a fast diffuse optical tomography (DOT) approach for three-dimensional brain mapping of hemodynamics during rTMS. Eight healthy subjects were enrolled in the study. These subjects received 10 Hz stimulation with 80%and 100%of resting motor threshold (rMT), respectively, for 4 seconds for each stimulation. Significant hemodynamic activation was observed in all cases with the strongest response when 100%rMT stimulation was applied. This work demonstrates that fast DOT has the potential to become a powerful tool for noninvasive three-dimensional imaging of the brain during rTMS.

Laser-based visual perception technology is an effective way to enhance the intelligent level of tunnel operation and maintenance, which could improve the efficiency of the automatic identification...

To investigate the effect of CT image acquisition parameters on the performance of radiomics in classifying benign and malignant pulmonary nodules (PNs) with respect to nodule size. We retrospectively collected CT images of 696 patients with PNs from March 2015 to March 2018. PNs were grouped by nodule diameter: T1a (diameter ≤ 1.0 cm), T1b (1.0 cm

AIM This study aimed to evaluate efficacy and safety of raltitrexed-based transcatheter arterial chemoembolization (TACE) for intermediate and advanced hepatocellular carcinoma (HCC) using real-world evidence. METHODS All eligible HCC cases were collected from multiple centers in Chongqing, China, from January 2013 to December 2018 and divided into the raltitrexed group (raltitrexed + lobaplatin + pirarubicin) and control group (lobaplatin + pirarubicin). Propensity score matching (PSM) with a 1:1 ratio was used to eliminate the imbalance of potential confounding factors between groups. The primary end-point was overall survival (OS) and the secondary end-points were progression-free survival (PFS) and disease control rate. RESULTS The median follow-up period for patients in the raltitrexed and control groups was 8.7 and 5.9 months, respectively. After PSM, median OS was 10.0 months in the raltitrexed group and 7.0 months in the control group (p = 0.002). The 6-month, 1-year, and 2-year OS rates of the raltitrexed group were significantly higher than those of the control group (78.2% vs. 60.9%, p = 0.010; 43.5% vs. 22.8%, p = 0.030; and 17.4% vs. 2.2% p = 0.001, respectively). Multivariate analysis of these propensity score-matched HCC patients revealed treatment, age, tumor size, lipiodol accumulation, and the number of TACE cycles as independent predictors of OS (all p

The monitoring and prevention of cracks in tunnel structures is an essential problem and the traditional methods are arduous to suffice the rapid increasing of tunnel mileage. Many researches, nowa...

This study presents a novel structure for electrochemical discharge machining (WECDM) of nonconductive quartz wafers with continuous electrolyte flow. A small and stable insulating gas film was formed in the gap between the wire electrode and workpiece to achieve improved WECDM in a small area. A pulsed power supply and ultrasonic-assisted processing were combined to machine the quartz workpiece. This approach can considerably reduce unstable discharge phenomena and discharge heat generation. Furthermore, it can avoid easy breakage and subsequent loss of the wire electrode. The machining accuracy and the machining speed can be improved using appropriate WECDM parameters. Experimental results revealed that a minimum slot width of 0.208 mm was obtained at a voltage of 44 V, duration time of 100 μs, duty factor of 40%, feed rate of 20 μm/s, and ultrasonic power level of 2. Accordingly, the proposed design can obtain a smaller slot width, which improves processing accuracy.

Metal wires have been used as an alternative to liquid junctions for the connection of solutions in microfabricated electrochemical devices. They exhibit similar performance to liquid junctions, provided that the interfacial potentials at both ends of the wires were appropriately canceled. Cyclic voltammograms of devices with liquid junctions and metal wires were very similar when no current or a low current flowed through the metal wire between the working and reference electrodes. Iridium wires with iridium oxide at both ends facilitated canceling of the interfacial potentials at either end of the junction particularly well, and were used effectively for voltammetry, amperometry, and potentiometry by adjusting the pH of the solutions in the working and reference electrode compartments to be equal. This approach was used to effectively integrate a reliable common reference electrode between multiple working electrodes and to conduct automated electrochemical control of solution transport in microfluidic systems.

Objective This research was performed to investigate the correlation between acute kidney injury (AKI) and systemic immune-inflammation index (SII) in severe acute pancreatitis (SAP) patients. Methods The study included 218 SAP patients from Chongqing Jiangjin Center Hospital during January 2016 to October 2020. The SII was defined as platelet × neutrophil/lymphocyte ratio. After univariate analysis, logistic regression analysis was used for analyzing independent risk factors of AKI in SAP patients. Receiver operating characteristic (ROC) curve was used for analyzing the prognostic value of the SII. Results AKI occurred in 74 cases and its incidence rate was 33.9%. The median SII value of AKI patients was higher than that of patients without AKI. After multivariate analysis, SII, age, triglyceride (TG), neutrophil ratio (NEU-R), C-reactive protein (CRP), aspartate aminotransferase (AST), and serum albumin (ALB) were independent predictors of AKI. Serum ALB was an independent protective factor. The optimum threshold truncation value of SII was 2880.1*10^9/L. Compared with other inflammatory factors, SII had a better prediction efficiency. Conclusion The SII, TG, NEU-R, CRP, and ALB were significant independent predictors of AKI in SAP patients. Serum TG, NEU-R, CRP, and SII were risk factors. Serum ALB was a protective factor. The SII may be a novel, simple, and strong marker for the accurate early prediction of AKI in SAP patients.

Background Out-of-hospital cardiac arrest (OHCA) is a critical condition with poor outcomes. Although the survival rate increases in those who undergo defibrillation, the utility of on-time defibrillation among bystanders remained low. An evaluation of the deployment strategy for public access defibrillators (PADs) is necessary to increase their use and accessibility. This study was to conduct a systematic review for deployment strategies of PADs. Methods Two authors independently searched for articles published before October 2019 from PubMed, Embase, Web of Science, and Cochrane Library. An independent librarian provided the search strategy and assisted the literature research. We included articles that were focused on the main topic, but excluded those which were missing results or that used an unclear definition. The qualitative outcomes were the utility and OHCA coverage of PADs. We performed a qualitative analysis across the studies, but a quantitative analysis was not available due to the studies' heterogeneity in design and variety of outcomes. Results We eventually included 15 studies. Three strategies were presented: guidelines-based, grid-based, and landmark-based. The guidelines-based deployment was common fit for OHCA events. The grid-based method increased the use of bystander defibrillation 3-fold, and 30-day survival doubled. The top 3 landmarks in the landmark-based strategy were offices (18.6%), schools (13.3%), and sports facilities (12.9%). Utility of PADs might increase if we optimize PAD location by mathematical modeling and evaluation feedback. Conclusion Three deployment strategies were presented. Although the optimal method could not be fully identified, a more efficient PAD deployment could benefit the population in terms of OHCA coverage and survival among patients with OHCA.

Environmental investment prediction has attracted much attention in the last few years. However, there are still great challenges in investment prediction modeling, e.g., 1) effective environmental indicators must be accurately selected to avoid the curse of dimensionality; 2) effective environmental data must be reasonably selected to downsize the scale of historical data; 3) the higher interpretability and lower complexity of prediction models must be considered. To address the above three challenges, a new environmental investment prediction model using fuzzy rule-based system (FRBS), evidential reasoning (ER) approach, and subtractive clustering (SC) algorithm is proposed in the present work, called FRBS-ERSC. In this new model, the FRBS is the core component for the modeling of environmental investment prediction and therefore provides good interpretability and complexity to environmental managers. Meanwhile, the ER approach is used as an improvement technique of the FRBS to combine the strengths of different feature selection methods for better indicator selection, and the SC algorithm is used as another improvement technique of the FRBS to select effective environmental data. An empirical case of environmental investment prediction is studied based on data on 31 provinces in China ranged from 2005 to 2018. The experimental results show that the proposed FRBS-ERSC not only provides interpretable and scalable environmental investment prediction based on effective indicator selection and data selection, but also produces satisfactory accuracy compared to some existing models.

A hybrid optical/electrical datacenter network (HOE-DCN) integrates the benefits of electrical packet switching (EPS) and optical circuit switching (OCS) for better architectural scalability. Meanwhile, as each network service usually involves multiple virtual machines (VMs) that form a virtual network (VNT), how to reconfigure the VNTs in an HOE-DCN to adapt to the dynamic network environment becomes an interesting and important problem to tackle. In this work, we optimize the procedure of VNT reconfigurations that remap VNTs to given virtual network embedding (VNE) schemes, i.e. , scheduling the required VM migrations and optical cross-connect (OXC) reconfiguration properly such that the overall VNT reconfiguration latency can be minimized. We first assume that all the VM migrations should be scheduled before the OXC reconfiguration to minimize service interruptions, and formulate a mixed integer linear programming (MILP) model to solve the scheduling problem exactly. Then, with the same assumption, we propose a time-efficient heuristic to schedule parallel VM migrations in batches such that the bandwidth competition can be significantly relieved. Finally, we divide the OXC reconfiguration into several progressive steps, and design a joint scheduling algorithm to arrange VM migrations together with the OXC reconfiguration steps.

This study highlights the clinical features and treatment response of polypoidal choroidal vasculopathy (PCV) among three different branching vascular network (BVN) morphologies in optical coherence tomography angiography (OCTA), and further correlates the BVN features with those under fluorescent angiography (FA) and indocyanine green angiography (ICGA). In total, we reviewed 70 eyes with PCV followed up for > 12 months. OCTA, ICGA and FA images were obtained at baseline and post-treatments. BVN was assessed using OCTA and divided into three types by a previously described BVN classification: type 1 (trunk), type 2 (glomeruli), and type 3 (stick). At baseline, type 1 BVN had the poorest vision and thinnest subfoveal choroidal thickness (SFCT), whereas type 3 had the best vision and thickest SFCT. The aforementioned trend sustained after treatments. Each BVN morphology in OCTA showed typical features in FA + ICGA and encompassed significant correlation (p = 0.004). In conclusion, OCTA is an innovative imaging tool for the detection and classification of BVN in PCV. Furthermore, OCTA has advantages of being noninvasive and free of systemic toxicities. The BVN can be divided into three types based on morphological characteristics in OCTA, which play crucial roles in clinical presentations and treatment outcomes.

Efficient visualization helps researchers obtain valuable mechanistic insights and present interesting results with regard to the functional enrichment analysis of omics data. However, the functions of existing published tools used to implement relevant visualization are neither sufficiently comprehensive nor easily accessible. Most of these tools require users to have professional programming skills. This study alleviates this issue by proposing EnrichVisBox, a web application developed for integrative and versatile data visualization, including bubble plots, UpSet plots, polar bar plots, rectangle plots, ridgeline plots, network plots, and variant chord plots. Specifically, scientists can use these insightful plots to conveniently present functional enrichment analysis results of omics data with a simple mouse click through a user-friendly interface.

Mesenchymal stromal/stem cells (MSCs) have a great potential to proliferate, undergo multi‐directional differentiation, and exert immunoregulatory effects. There is already much enthusiasm for their therapeutic potentials for respiratory inflammatory diseases. Although the mechanism of MSCs‐based therapy has been well explored, only a few articles have summarized the key advances in this field. We hereby provide a review over the latest progresses made on the MSCs‐based therapies for four types of inflammatory respiratory diseases, including idiopathic pulmonary fibrosis, acute respiratory distress syndrome, chronic obstructive pulmonary disease, and asthma, and the uncovery of their underlying mechanisms from the perspective of biological characteristics and functions. Furthermore, we have also discussed the advantages and disadvantages of the MSCs‐based therapies and prospects for their optimization., The dominant functions of MSCs in IPF, ARDS, COPD and Asthma.

An analysis of the correlation between polycyclic aromatic hydrocarbons (PAHs) and economic parameters demonstrates that the total population, gross domestic product, coal consumption, petroleum, temperature, and day consumption significantly affect PAH concentrations in Dianchi Lake, Yunnan province, China. An artificial neural network (ANN) model was developed to predict the trend in PAH concentrations in the sediments of Dianchi Lake over the next 10 years based on current indicators of economic development. The ANN model estimated the concentration of PAHs from 1980 to 2014. The model was evaluated using available observations for the historical trends; concentrations of PAHs in the sediments of Dianchi Lake are calculated to be at 2128.1 ng/g in 2025 and are expected to decline up to 1044.3 ng/g by 2030. These concentrations are considered relatively high because of their impacts on the health of people and aquatic organisms and the development of surrounding industries. We show the importance of the socioeconomic and climate factors in increasing the pollution levels. Our results could support the local government to formulate effective measures to reduce the pollution levels in the lake.

In downtown areas of large cities, it is very challenging for drivers to find available parking spots, even when they are provided with information on parking availability and location information. To overcome this challenge, this paper develops a dynamic vehicle routing system to search for the optimal routes for connected vehicles to find parking spots successfully and to minimize total trip time, including driving time and walking time. The system predicts the probability of each parking lot having available parking spots based on the existing available number of spots and the vehicle arrival and departure rates collected by connected vehicles. This probability is integrated in the search for vehicle routes to minimize total travel and walking times. Numerical experiments indicate that the proposed system can reduce the cruising time spent searching for available parking spots, and the total trip time can be reduced by up to 24%. In addition, the system can decrease the number of re-routing decisions, which reduces the stress of drivers on the road. A sensitivity analysis of the parking probability is also conducted. Some future work based on the proposed system is proposed in the conclusion to this paper.

Binocular stereo vision is a commonly applied computer vision technique with a wide range of applications in 3D scene perception. However, binocular stereo matching algorithms are computationally intensive and complicated. In addition, some traditional platforms are unable to meet the real-time and energy efficient dual requirements. In this paper, we proposed a hardware/software co-design FPGA (Field Programmable Gate Array) approach to overcome these limitations. Based on the characteristics of binocular stereo vision, we modularize the system functions to achieve the hardware/software partitioning. This accelerates the data processing on the FPGA, while simultaneously performing data control on the ARM (Advanced RISC Machine) cores. The parallelism of the FPGA allows for a full-pipeline design that is synchronized with an identical system clock for the simultaneous running of multiple stereo processing components, thus improving the processing speed. Furthermore, to minimize hardware costs, the collected images and data are compressed prior to matching, while the precision is subsequently enhanced during post-processing. The proposed system was evaluated on the PYNQ-Z2 development board, with experimental results revealing its high real-time performance and low power consumption for a 100M clock frequency. Compared with existing designs, the simple yet flexible system demonstrated a higher image processing speed and less hardware resource overhead (thus lower power consumption). The average error rate of the BM matching algorithm was also improved, particularly with the limited PYNQ-Z2 hardware resource. The proposed system has been opened on GitHub.

Background Few mortality-scoring models are available for solid tumor patients who are predisposed to develop Escherichia coli–caused bloodstream infection (ECBSI). We aimed to develop a mortality-scoring model by using information from blood culture time to positivity (TTP) and other clinical variables. Methods A cohort of solid tumor patients who were admitted to hospital with ECBSI and received empirical antimicrobial therapy was enrolled. Survivors and non-survivors were compared to identify the risk factors of in-hospital mortality. Univariable and multivariable regression analyses were adopted to identify the mortality-associated predictors. Risk scores were assigned by weighting the regression coefficients with corresponding natural logarithm of the odds ratio for each predictor. Results Solid tumor patients with ECBSI were distributed in the development and validation groups, respectively. Six mortality-associated predictors were identified and included in the scoring model: acute respiratory distress (ARDS), TTP ≤ 8 h, inappropriate antibiotic therapy, blood transfusion, fever ≥ 39 °C, and metastasis. Prognostic scores were categorized into three groups that predicted mortality: low risk ( 20% mortality, ≥ 3 points). The TTP-incorporated scoring model showed excellent discrimination and calibration for both groups, with AUC being 0.833 vs 0.844, respectively, and no significant difference in the Hosmer–Lemeshow test (6.709, P = 0.48) and the chi-square test (6.993, P = 0.46). Youden index showed the best cutoff value of ≥ 3 with 76.11% sensitivity and 79.29% specificity. TTP-incorporated scoring model had higher AUC than no TTP-incorporated model (0.837 vs 0.817, P Conclusions Our TTP-incorporated scoring model was associated with improving capability in predicting ECBSI-related mortality. It can be a practical tool for clinicians to identify and manage bacteremic solid tumor patients with high risk of mortality.

Background Even though immunotherapy has been an effective treatment for solid tumors, its efficacy in osteosarcoma remains sub‐optimal. It is therefore imperative to understand the complex tumor microenvironment (TME) of osteosarcoma to facilitate the development of immunotherapies against this cancer. Methods The mRNA expression profiles of osteosarcoma tissues were downloaded from The Cancer Genome Atlas (TCGA) database. Next, the ssGSEA, MCP‐counter, CIBERSORT, and Xcell algorithm analyses were performed to characterize the tumor microenvironment of osteosarcoma tissues. The tumor tissues were divided into inflammatory and non‐inflammatory. A comprehensive assessment of immune cell infiltration in osteosarcoma tissues was then performed. Sub‐group analysis of immune cell infiltration between men and women patients with osteosarcoma was also carried out. Results The results revealed that the infiltration of immune cells including activated B cell, activated CD8 T cell, CD56dim natural killer cell, and cytotoxic lymphocytes cells, in osteosarcoma tissues was higher in male than in female patients. Based on the infiltration profile of different immune cells, the osteosarcoma tissues were grouped into four clusters. The four clusters were further divided into hot and cold tumors. The differently expressed genes (DEGs) between cold and hot tumors were mainly associated with the activation and regulation of immune response. Additionally, a neuronal pentraxin (NPTX2) expression which was upregulated in cold tumors was found to be negatively correlated with the expression of CD8a Molecule (CD8A), Granzyme B (GZMB), and Interferon Gamma (IFNG). NPTX2 decreased CCL4 secretion. Knockdown of NPTX2 in osteosarcoma cells inhibited tumor growth and increased tumor cell apoptosis. Moreover, a prognosis prediction model of osteosarcoma was constructed and validated in patients receiving immunotherapy using external data. Conclusions To the best of our knowledge, this is the first study to characterize the infiltration of immune cells in osteosarcoma tissues from patients receiving immune infiltration therapy., We comprehensively analyzed the possible mechanism of the difference in the immune infiltration of the tumor microenvironment of osteosarcoma, screened out the hub gene set that affects immune infiltration, and verified the survival.

This paper describes how to explore gene expression data using a combination of graphical and numerical methods. We start from the general methodology for multivariate data visualization, describing heatmaps, par- allel coordinate plots and scatterplots. We propose new methods for gene expression data analysis using direct manipulation graphics. With linked scatterplots and parallel coordinate plots we explore gene expression data differently than many common practices. To check replicates in relation to treatments we introduce a new type of plot called a "replicate line" plot. There is a worked example, that focuses on an experimental study containing two two-level factors, genotype and cofactor presence, with two replicates.

The purpose of this study was to detect the presence of retinitis pigmentosa (RP) based on color fundus photographs using a deep learning model. A total of 1670 color fundus photographs from the Taiwan inherited retinal degeneration project and National Taiwan University Hospital were acquired and preprocessed. The fundus photographs were labeled RP or normal and divided into training and validation datasets (n = 1284) and a test dataset (n = 386). Three transfer learning models based on pre-trained Inception V3, Inception Resnet V2, and Xception deep learning architectures, respectively, were developed to classify the presence of RP on fundus images. The model sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve were compared. The results from the best transfer learning model were compared with the reading results of two general ophthalmologists, one retinal specialist, and one specialist in retina and inherited retinal degenerations. A total of 935 RP and 324 normal images were used to train the models. The test dataset consisted of 193 RP and 193 normal images. Among the three transfer learning models evaluated, the Xception model had the best performance, achieving an AUROC of 96.74%. Gradient-weighted class activation mapping indicated that the contrast between the periphery and the macula on fundus photographs was an important feature in detecting RP. False-positive results were mostly obtained in cases of high myopia with highly tessellated retina, and false-negative results were mostly obtained in cases of unclear media, such as cataract, that led to a decrease in the contrast between the peripheral retina and the macula. Our model demonstrated the highest accuracy of 96.00%, which was comparable with the average results of 81.50%, of the other four ophthalmologists. Moreover, the accuracy was obtained at the same level of sensitivity (95.71%), as compared to an inherited retinal disease specialist. RP is an important disease, but its early and precise diagnosis is challenging. We developed and evaluated a transfer-learning-based model to detect RP from color fundus photographs. The results of this study validate the utility of deep learning in automating the identification of RP from fundus photographs.

The effect of urolithiasis on pregnancy-related outcomes remains unknown. The aim of this study was to determine the risk of adverse maternal and neonatal outcomes. We searched PubMed, Embase, and the Cochrane Library through December 2020 for studies reporting on adverse maternal and neonatal outcomes in patients with urolithiasis. Risk ratios (ORs) with 95% confidence intervals (CIs) were calculated for these outcomes in pregnant mothers with urolithiasis and compared to healthy controls. Eight studies comprising 26,577 mothers with urolithiasis were included in our analysis. Preterm birth (OR = 1.63; 95% CI 1.37–1.95, p

PurposeDue to fierce competition in the mobile retailing market, it is desirable to identify the success factors driving consumers to reuse a mobile shopping application. This research intends to develop a model for mobile shopping app success by integrating an e-commerce systems success (ESS) model and sales promotion benefits and parasocial interaction literatures.Design/methodology/approach302 useable online questionnaires were obtained. The data analysis was conducted using the structural equation modelling.FindingsThe results indicate the validity of the ESS model in predicting consumers' reuse intention of mobile shopping apps where three quality dimensions of system, information and service facilitate both perceived value and user satisfaction, which in turn generates reuse intention. Furthermore, savings and entertainment denoting the utilitarian and hedonic sales promotion benefits have positive impact on perceived value but have no influence on satisfaction. Parasocial intention between consumers and sellers facilitates both value perception and satisfaction.Originality/valueThis research contributes to the mobile retailing literature by identifying the success factors driving consumers' continuance intention of mobile shopping apps. Theoretically, it validates and extends the ESS model in mobile shopping app context by identifying savings, entertainment and parasocial interaction as additional success factors. Based on the findings, two approaches are proposed to suggest mobile retailers design a successful mobile shopping app.

Multi-class and multi-attribute are two important features of classification problems and have different effects on the requirements and performance of the classifier. Decomposition strategy and overlap function are two effective ways to enhance the performance of classifiers, because the former decomposes a complex multi-class problem into multiple simple sub-problems; the latter uses various functions to specify the conjunctive relationship of input variables in a multi-attribute problem. Extended belief rule-based system (EBRBS) is an advanced rule-based system that has been widely used in classification problems. In order to apply decomposition strategies and overlap functions to enhance the performance of EBRBSs, the present work focuses on the investigative research and comparative evaluation of the commonly used one-versus-one (OVO) decomposition strategy and five common overlap functions to improve the performance of EBRBSs on multi-class and multi-attribute problems. More specifically, three typical kinds of EBRBSs, namely original EBRBS (O-EBRBS), EBRBS with dynamic rule activation (DRA-EBRBS), and a latest EBRBS for big data (Micro-EBRBS), are selected to conduct extensive experimental studies on twenty classification problems. To best of our knowledge, this present work is the first time to provide a meaningful and useful study in revealing the potential capability of the EBRBSs with decomposition strategy and overlap function for multi-class and multi-attribute problems. Experimental results demonstrate that the square product overlap function and the OVO strategy can enhance the performance of EBRBSs over others for twenty classification problems.

Yayun Lu,1,* Hao Yang,2,* Zhiyue Xu,1 Xuefeng Tang1 1Health Examination Center, Huadong Sanatorium, Wuxi, 214065, People’s Republic of China; 2Department of Stomatology, Huadong Sanatorium, Wuxi, 214065, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xuefeng Tang Tel +86 13961704961Email 978665164@qq.comPurpose: Obesity has become a public health challenge worldwide and can lead to the development of diabetes. However, studies examining the associations between different obesity patterns and the development of type 2 diabetes mellitus (T2DM) in China are limited. Therefore, this study aimed to explore the associations between three obesity patterns and the risk of T2DM development in Eastern China.Methods: A cross-sectional study was conducted at our health examination center, involving 5860 adults, from June to December 2019. Data, including sociodemographic information, lifestyle, and biochemical measurements, were collected, and obesity was classified into three patterns: overweight and general obesity, abdominal obesity, and compound obesity. Multivariate logistic regression was used to assess the associations between different obesity patterns and T2DM risk after adjustment for confounding factors. Subgroup analysis was used to further explore the associations between obesity patterns and T2DM risk.Results: A total of 5860 subjects were enrolled in this study. A significant difference in the T2DM incidence was observed between men and women with normal weight or overweight and general obesity (p < 0.05); however, no significant differences were observed between men and women with abdominal obesity and compound obesity. After multivariable adjustment, multivariate logistic regression analysis showed that the odds ratios (ORs) [95% confidence interval (CI)] for T2DM in individuals with abdominal and compound obesity were 1.55 [1.08– 2.24] and 1.85 [1.25– 2.73], respectively, compared with the normal-weight group. Subgroup analysis showed that different obesity patterns were not independent risk factors for T2DM development among adults aged ≥ 60 years, whereas abdominal and compound obesity were highly associated with the risk of T2DM development among individuals who report current smoking or alcohol drinking.Conclusion: Abdominal obesity and compound obesity are risk factors for T2DM. More attention should be paid to obesity prevention among individuals younger than 60 years and improving control of cigarette and alcohol abuse.Keywords: association, general obesity, abdominal obesity, type 2 diabetes

Sarcomatoid hepatocellular carcinoma (sHCC) is a rare type of liver malignancy. Currently, the tumor immune features of sHCC are poorly understood. We recruited 31 patients with resected sHCC for whom tissue samples and complete clinicopathologic and follow-up data were available. To understand the immune infiltration of sHCC, immunohistochemical staining was performed on the resected sHCC samples to compare the expressions of programmed death-1 (PD-1), programmed death-ligand 1 (PD-L1), B7-H3, indoleamine 2,3-dioxygenase (IDO), lymphocyte-activation gene 3 (LAG-3), CD8, FOXP3, and CD68 in tumor and peritumoral tissues. Kaplan-Meier and Cox regression analyses were used to assess the predictive value of immune markers. Sarcomatoid components were characterized with significantly higher expression of PD-L1 and B7-H3 in tumor cells than in conventional HCC components, as well as in peritumoral tissue. Additionally, sarcomatoid components had a higher density of FOXP3+ and LAG-3+ cells and a lower density of CD8+ cells than conventional HCC components or peritumoral tissue. Higher expression of PD-L1 in tumor cells significantly correlated with higher densities of CD8+, PD-1+, and LAG-3+ cells. Increased tumor PD-L1 expression and decreased CD8+ T-cell density were associated with poor overall survival (OS) and disease-free survival (DFS) in patients of sHCC. These findings suggest further characterization on relative mechanism of sHCC immune infiltration may identify therapeutic targets for immunotherapy.

Background Cervical cancer is the most common malignancy of the female reproductive system, for which radiotherapy is one of the main treatments. Gene methylation in cells is an important factor in tumorigenesis, and radiotherapy can change DNA methylation in cells. At the same time, combined with the clinical effect of radiotherapy, key genes of LIM homeobox 2 (LHX2) significantly related to cervical cancer. The LHX2 are LIM-homeobox genes that play important roles in signal transduction, cell differentiation, tissue-specific differentiation, and body formation. Methods In this study, bisulfite genomic sequencing (BSP-Seq) technology was used to analyze the methylation level of LHX2 in patients with cervical cancer before and after radiotherapy. In addition, combined with the clinical effect of radiotherapy, the function of LHX2 in siHA and C33A cells were analyzed with the help of overexpression, small interfering RNA (siRNA), cell invasion, and migration ability. The expression level of the migration- and apoptosis-related genes which were affected by LHX2 were tested with quantitative real time polymerase chain reaction (qRT-PCR). Results Combined with clinical treatment, methylation level difference, and correlation enrichment analysis, it was found that LHX2 genes were closely related to the occurrence and development of cervical cancer. After 5-aza-2’-deoxycytidine (5-Aza-dC) and radiotherapy, the methylation of LHX2 genes in siHA and C33A squamous cell carcinoma cells was decreased, and the messenger RNA (mRNA) and protein expression levels were relatively increased; meanwhile, the LHX2 could accelerate the ability for cell invasion and migration and inhibited the apoptosis of the cell after treatment with radiotherapy. Conclusions The methylation and expression levels of LHX2 genes are closely related to cervical cancer. The methylation level of LHX2 was reduced after radiation therapy. The LHX2 gene has a positive effect on cervical cancer through acceleration of the cell invasion and migration ability and inhibition of cell apoptosis after radiotherapy treatment.

Rubber mount is a type of vibration isolator that can absorb vibration and minimize external disturbance. This study proposes an optimization method to design the geometry of a rubber mount to achieve particular stiffness values in two different loading directions. The proposed objective function of the geometry optimization problem is to minimize the error ratios between calculated and target stiffness values in vertical and transverse directions; a weighted sum method is used to combine both error ratios with equal weighting factors. The design variables are the coordinates of some design points which form the cross-section geometry of the rubber mount. A three-dimensional design is then obtained by extrusion of the two-dimensional geometry. Cuckoo search algorithm is used to update the design variables until the objective function value is smaller than a tolerance. Nonlinear finite element analysis is used to estimate the stiffness values of the rubber mount. Both hyperelastic and viscoelastic rubber parameters are numerically identified by fitting to the experimental data. An optimal design of the rubber mount is obtained and prototyped. Experimental tests are performed to validate the design.