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The revolution of Industry 4.0 and Internet of Things facilitate large and cheap data acquisition from complex systems. However, the online processing and analytics of such multi-stream data still suffer difficulties. The key reason is that data from different sensors (multi-stream data) contain nonlinear within-and-cross sensor correlations, which poses challenges in the modeling and analysis of such data, especially for the online scenarios. To jointly model multi-stream data, multi-output Gaussian Process (MGP) has become popular and achieved great success. However, existing methods assume different outputs are linearly correlated, which significantly limits their applications. In this paper, we propose a nonlinear online MGP framework to simultaneously realize nonlinear correlation modeling and online processing of multi-stream data. Specifically, a tailored co-variance structure is developed based on the convolution process, which can capture nonlinear self-and-cross correlations. To realize online processing, Bayesian analysis and marginalized particle filter are applied to estimate function values and model parameters. Comparing to existing methods, the proposed method features superiorities in i) the non-linear characterization of self-and-cross correlations, and ii) the efficient modeling and prediction capability. These features make the proposed method especially appropriate for industrial informatics with complex relationship and large-scale data sets. The proposed method is validated with simulation studies and a real-case study of biophysical signals.

BACKGROUND Heart transplant recipients are at higher risk of developing skin cancer than the general population due to the long-term immunosuppression treatment. Cancer has been reported as one of the major causes of morbidity and mortality for patients after heart transplantation. Among different types of skin cancers, cutaneous squamous cell carcinoma (cSCC) is the most common one, which requires timely screening and better management. AIM To identify risk factors and predict the incidence of cSCC for heart transplant recipients. METHODS We retrospectively analyzed adult heart transplant recipients between 2000 and 2015 extracted from the United Network for Organ Sharing registry. The whole dataset was randomly divided into a derivation set (80%) and a validation set (20%). Uni- and multivariate Cox regression were done to identify significant risk factors associated with the development of cSCC. Receiver operating charac-teristics curves were generated and area under the curve (AUC) was calculated to assess the accuracy of the prediction model. Based on the selected risk factors, a risk scoring system was developed to stratify patients into different risk groups. A cumulative cSCC-free survival curve was generated using the Kaplan-Meier method for each group, and the log-rank test was done to compare the inter-group cSCC rates. RESULTS There were 23736 heart-transplant recipients during the study period, and 1827 of them have been reported with cSCC. Significant predictors of post-transplant cSCC were older age, male sex, white race, recipient and donor human leukocyte antigen (HLA) mismatch level, malignancy at listing, diagnosis with restrictive myopathy or hypertrophic myopathy, heart re-transplant, and induction therapy with OKT3 or daclizumab. The multivariate model was used to predict the 5-, 8- and 10-year incidence of cSCC and respectively provided AUC of 0.79, 0.78 and 0.77 in the derivation set and 0.80, 0.78 and 0.77 in the validation set. The risk scoring system assigned each patient with a risk score within the range of 0-11, based on which they were stratified into 4 different risk groups. The predicted and observed 5-year probability of developing cSCC match well among different risk groups. In addition, the log-rank test indicated significantly different cSCC-free survival across different groups. CONCLUSION A risk prediction model for cSCC among heart-transplant recipients has been generated for the first time. It offers a c-statistic of ≥ 0.77 in both derivation and validation sets.

Early fault diagnosis of rotating machinery is crucial in the industry. The network parameters of the traditional deep learning-based fault diagnosis method are optimized only by the single loss function, and the extracted features are usually not the most valuable features of the input signal. This article proposes a novel method, attention recurrent autoencoder (AE) hybrid model classification algorithm, for early fault diagnosis and severity detection of rotating machinery. The AE can learn the most valuable features in an unsupervised way. By adjusting the weight proportion of the two loss functions and optimizing the multibranch network simultaneously, the proposed method enables the network to extract the most valuable features of the input signals. Moreover, the proposed method can take the raw 1-D vibration signal as the input and does not need time–frequency conversion. By introducing long short-term memory networks in AE, the time-dependent features of the data can be extracted effectively. The proposed method was verified by five kinds of gears at different pitting degrees under six load conditions. The results indicate that the method can provide simultaneous accurate fault diagnosis and severity detection for different pitting degrees.

Pitting corrosion of rotating machinery is one of the most common faults in industrial engineering. The convolutional neural network (CNN) is increasingly applied to the fault diagnosis. However, the conventional CNN method will reduce the feature dimension of the collected signal and cause the loss of information during the pooling process. In this paper, a new method based on dilated CNN combined with spatial dropout (DCSD) is proposed to diagnose the early faults of rotating machinery. By filling the convolution kernel, the DCSD method can increase the receptive field of the CNN without increasing the number of parameters while retaining more features of the raw vibration signal of the rotating machine. To avoid the dropout method eliminates the adjacent elements with a strong correlation, the Spatial Dropout method is adopted to reduce the overfitting problem of deep networks. The early pitting gears experiment was designed to verify the DCSD method in this paper. The raw vibration signal data of 6 different healthy states were collected to verify the effectiveness of the method. The experimental results show that the DCSD method proposed can effectively distinguish the different early gears pitting, and the diagnostic accuracy is better than other popular deep learning methods.

The camera tracking systems based on visual image processing face a problem that they are completely ineffective in their blind zones. To address this problem, a design of acoustic enhanced tracking system combining visual and auditory target tracking methods is reported in this article. The system holds the abilities of performing sound direction estimation and target tracking in real-time. Estimating direction of arrival of the sound accompanied with the target helps the camera turn towards the target outside the field of view. This sound-triggered mode of camera operation makes a significant supplement to conventional cameras' working state. Considering the embedded system is necessary in consideration of the cost and size of the system in practical application, we designed a small aperture array with 7 digital omnidirectional MEMS microphones and built the overall system based on FPGA and ARM. The experiments were carried out in a normal indoor environment and the results confirmed that the system can perform auditory and visual tracking in real-time.

Although many cross-sectional studies that have examined the relationship of Serum 25-hydroxyvitamin D [25(OH)D] levels with incident type 2 diabetes (T2D), there are very few reports for cohort studies. Here, we performed an a prospective cohort study to investigate the effect modification of traditional risk factors on association of serum 25(OH)D with T2D and to determine if addition of serum 25(OH)D improve the risk prediction of T2D. A total of 1926 adults without diabetes were prospectively followed for 36 months in China. During a mean of 36 months of follow-up, 114 participants (5.9%) developed incident T2D. Although no significant relationship was found in total population, stratified analyses indicated that low 25(OH)D levels was associated with increased T2D risk among individuals who were male, overweight/obese, with insufficient physical activity, or normal fasting plasma glucose at baseline. Additive interaction analysis showed that low 25(OH)D levels and insufficient physical activity interacts to increase the risk of T2D (RERI = 0.875, 95% CI: 0.204–1.545). Addition of low serum 25(OH)D significantly improved the predictive performance of T2D beyond conventional risk factors (NRI = 0.205, 95% CI:0.019–0.391). Our results suggest that low serum 25(OH)D and insufficient physical activity interact synergistically to influence the risk of T2D in Chinese adult population. Adding serum 25(OH)D to conventional risk factors improves the risk prediction of T2D.

BackgroundVaricocele (VC) is present in 35 - 40% of men with infertility. However, current surgical and antioxidant treatments are not completely effective. In addition to oxidative stress, it is likely that other factors such as testicular immune microenvironment disorder contribute to irreversible testicular. Evidence suggests that VC is associated with anti-sperm antibodies (ASAs), spermatogenesis and testosterone secretion abnormalities, and testicular cytokine production. Moreover, inhibition of inflammation can alleviate VC-mediated pathogenesis. The normal function of the testis depends on its immune tolerance mechanism. Testicular immune regulation is complex, and many infectious or non-infectious diseases may damage this precision system.ResultsThe testicular immune microenvironment is composed of common immune cells and other cells involved in testicular immunity. The former includes testicular macrophages, T cells, dendritic cells (DCs), and mast cells, whereas the latter include Leydig cells and Sertoli cells (SCs). In animal models and in patients with VC, most studies have revealed an abnormal increase in the levels of ASAs and pro-inflammatory cytokines such as interleukin (IL)-1 and tumor necrosis factor (TNF)-alpha in the seminal plasma, testicular tissue, and even peripheral blood. It is also involved in the activation of potential inflammatory pathways, such as the nucleotide-binding oligomerization domain-like receptor family pyrin domain containing (NLRP)-3 pathway. Finally, the development of VC-mediated infertility (VMI) may be facilitated by abnormal permeability of proteins, such as claudin-11, that constitute the blood-testis barrier (BTB).ConclusionsThe testicular immune response, including the production of ASAs and inflammatory factors, activation of inflammatory pathways, and destruction of the BTB may be involved in the pathogenesis of VMI it is necessary to further explore how patient outcomes can be improved through immunotherapy.

Purpose: This study evaluated the efficacy of retrobulbar ropivacaine plus dexmedetomidine compared with systemic fentanyl in pediatric vitreoretinal (VR) surgery. Methods: This prospective double-blind, randomized controlled study was performed in 60 children undergoing VR surgery, age from 2 to 7 years. After general anesthesia, the following procedure was administrated: (1) retrobulbar block with 0.5% ropivacaine plus dexmedetomidine 1 μg/kg (group RD, n = 20), (2) retrobulbar block with 0.5% ropivacaine (group RB, n = 20), and (3) control group with general anesthesia only (group F, n = 20). Hemodynamics, postoperative pain scores, anesthetics consumption (remifentanil, propofol, fentanyl), and emergence agitation were recorded. Results: Respiratory depression was observed in 7 of the 20 patients in group F after the laryngeal mask airway was removed in the operating room, compared with none in groups RD and RB. All patients in group F required intraoperative rescue fentanyl (average intraoperative fentanyl consumption, 26.6 ± 12.6 μg per patient). Some rescue fentanyl was required in group RB (three patients required one dose of rescue fentanyl). Patients in group RD required none. Groups RD and RB reported lower pain scores than group F at 4 h postoperatively (RD group: P < 0.001; RB group: P =0.002); pain scores in group RD were lower than that in group F at 6 h postoperatively (P < 0.001). Conclusion: Retrobulbar dexmedetomidine as an adjuvant to ropivacaine is a safe and effective alternative to systemic fentanyl. This regimen provides better pain management, hemodynamic stability, and stress response suppression in pediatric VR surgery.

Fault diagnosis of rotating machinery is essential in the modern industry. Although fault diagnosis methods based on deep learning have achieved high accuracy, most of them only extract features from a single domain. Methods based on a single domain are difficult to apply to environments with noise. This paper presented a diagnosis method based on the attention mechanism and the fusion of time domain and frequency domain features to improve diagnosis accuracy. The presented method contains three modules. Firstly, two shallow convolutional neural networks are employed to extract the time domain and frequency domain features from the vibration signal. Then, the attention mechanism is adopted to extract important features and perform preliminary feature fusion. Finally, a deep convolutional network is used to fuse feature further and extract high-level features. The presented method can effectively fuse multi-domain features and improve diagnosis accuracy. This paper validates the effectiveness of the proposed method through a fault diagnosis experiment. A comparative experiment illustrates that the presented method has obvious advantages in noise resistance. When the signal to noise ratio equals 0dB, the diagnosis accuracy of the presented method is up to 6.4% higher than that of the single domain method.

Coronavirus disease 2019 (COVID-2019) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been an ongoing pandemic and worldwide public health emergency, having drawn a lot of attention around the world. The pathogenesis of COVID-19 is characterized by infecting angiotensin-converting enzyme 2 (ACE2)-expressing cells, including testis-specific cells, namely, Leydig, Sertoli, and spermatogenic cells, which are closely related to male reproduction. This leads to aberrant hyperactivation of the immune system generating damage to the infected organs. An impairment in testicular function through uncontrolled immune responses alerts more attention to male infertility. Meanwhile, the recent clinical data indicate that the infection of the human testis with SARS-CoV-2 may impair male germ cell development, leading to germ cell loss and higher immune cell infiltration. In this review, we investigated the evidence of male reproductive dysfunction associated with the infection with SARS-CoV-2 and its possible immunological explanations and clinical remedies.

Photon upconversion is a fascinating phenomenon that can convert low-energy photons to high-energy photons efficiently. However, most previous relevant research has been focused on upconversion systems with a sufficiently low lanthanide emitter concentration, such as 2 mol% for Er3+ in an Er–Yb coupled system. Realizing the upconversion from lanthanide heavily doped systems in particular, the emitter sublattice is still a challenge. Here, we report a mechanistic strategy to achieve the intense upconversion of the holmium sublattice in a core–shell-based nanostructure design through interfacial energy transfer channels. This design allowed a spatial separation of Ho3+ and sensitizers (e.g., Yb3+) into different regions and unwanted back energy transfers between them could then be minimized. By taking advantage of the dual roles of Yb3+ as both a migrator and energy trapper, a gradual color change from red to yellowish green was achievable upon 808 nm excitation, which could be further markedly enhanced by surface attaching indocyanine green dyes to facilitate the harvesting of the incident excitation energy. Moreover, emission colors could be tuned by applying non-steady state excitation. Such a fine-tunable color behavior holds great promise in anti-counterfeiting. Our results present a facile but effective conceptual model for the upconversion of the holmuim sublattice, which is helpful for the development of a new class of luminescent materials toward frontier applications.

The variety of sensors embedded in smartphones makes it possible to develop indoor navigation and localization systems on mobile terminals. However, these cheap sensors are plagued by bias and noise, leading to unbounded system drifts. Inspired by Expectation-Maximization algorithm, this paper proposes to combine zero-velocity detection with gated recurrent unit (GRU) neural networks, make full use of pedestrian motion characteristics, and naturally and accurately split the raw measurements into multiple weakly correlated windows step by step. The GRU is used to exploit dynamic context and predict the polar vector of each window. Several experiments were conducted to test the performance of proposed model, and IONet, a deep learning based inertial odometry model using fixed-size sliding window, was taken as a reference. The results show that the proposed model is able to generate smooth trajectories with high precision. Compared with IONet, the performance of proposed model in turning is better.

Most studies examining the effect of extended exposure to general anesthetic agents (GAAs) have demonstrated that extended exposure induces both structural and functional changes in the central nervous system. These changes are frequently accompanied by neurobehavioral changes that include impulse control disorders that are generally characterized by deficits in behavioral inhibition and executive function. In this review, we will.

Sevoflurane, in particular multiple exposures, has been reported to cause the abnormal neurological development including attention-deficit/hyperactivity disorder (ADHD). This study is to investigate ADHD-like impulsivity in adult mice after repeated sevoflurane exposures at the neonatal stage. Six-day-old pups were exposed to 60% oxygen in the presence or absence of 3% sevoflurane for 2 h and the treatment was administrated once daily for three consecutive days. To assess the impulsivity, the cliff avoidance reaction (CAR) was carried out at the 8th week. Our results showed that repeated sevoflurane treatment increased the number of jumps and shortened the jumping latency in the CAR test. The cortices were harvested for immunostaining to detect c-Fos and calmodulin-dependent protein kinase IIα (CaMKIIα) expression in the medial prefrontal cortex (mPFC). We found that mPFC neurons, especially excitatory neurons, were highly activated and related to impulsive behavior. The activation viruses (AAV-CaMKIIα-hM3Dq) were injected to evaluate the effects of specific activation of mPFC excitatory neurons on impulsive behavior in the presence of clozapine-N-oxide (CNO). Likewise, the inhibitory viruses (AAV-CaMKIIα-hM4Di) were injected in the sevoflurane group to explore whether the mPFC excitatory neuronal inhibition reduced the impulsivity. Our results revealed that chemogenetic activation of mPFC excitatory neurons induced impulsive behavior whereas inhibition of mPFC excitatory neurons partially rescued the deficit. These results indicate that repeated sevoflurane exposures at the critical time induce impulsive behavior accompanied with overactivation of mPFC excitatory neurons in adult stages. This work may further extend to understand the ADHD-like impulsive behavior of the anesthetic neurotoxicity.

The predicted wind power in coastal waters is an important factor when planning and developing offshore wind farms. The stochastic wind field challenges the accuracy of these predictions. Using single-point wind measurements, most previous studies have focused on the prediction of short-term wind power, ranging from minutes to several days. Longer-term wind power predictions would better support decision-making related to offshore wind power balance management and reserve capacities. In addition, larger-scale wind power predictions, based on gridded wind field data, would provide a more comprehensive understanding of the spatiotemporal variations of wind energy resources. In this study, a spatiotemporal ordinary kriging model was developed to predict the offshore wind power density on a monthly basis using the cross-calibrated multiplatform gridded wind field data. The spatiotemporal variations of wind power density were directly quantified through the development of spatiotemporal variograms that integrated spatial and temporal distances. The proposed model achieved a notable performance with an overall R2 of 0.94 and a relative prediction error of 16.35% in the validation experiment of predicting the monthly wind power density from 2013 in the coastal waters of China’s Guangdong Province. Using this model, the spatial distributions of wind power density along Guangdong’s coastal waters at monthly, seasonal, and annual time-scales from 2013 were accurately predicted. The experiment results demonstrated the remarkable potential of the spatiotemporal ordinary kriging model to provide reliable long-term prediction for offshore wind energy resources.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread widely and persistently over 100 countries. New challenges have occurred in the perioperative management of airway and anesthesia in children diagnosed with SARS-CoV-2 infection. According to current publications and to our own experiences in anesthesia management for cases with SARS-CoV-2 suspected, we reviewed concerns about the perioperative prevention of SARS-CoV-2 to medical staff and the anesthesia strategy to the patient.

The leakage aperture cannot be easily identified, when an oil pipeline has small leaks. To address this issue, a leak aperture recognition method based on wavelet packet analysis (WPA) and a deep belief network (DBN) with independent component regression (ICR) is proposed. WPA is used to remove the noise in the collected sound velocity of the ultrasonic signal. Next, the denoised sound velocity of the ultrasonic signal is input into the deep belief network with independent component regression (DBNICR) to recognize different leak apertures. Because the optimization of the weights of the DBN with the gradient leads to a local optimum and a slow learning rate, ICR is used to replace the gradient fine-tuning method in conventional DBN for improving the classification accuracy, and a Lyapunov function is constructed to prove the convergence of the DBNICR learning process. By analyzing the acquired ultrasonic sound velocity of different leak apertures, the results show that the proposed method can quickly and effectively identify different leakage apertures.

Background Previous research has suggested correlations between the end-tidal partial pressure of carbon dioxide (P ET CO 2 ) and the partial pressure of arterial carbon dioxide (PaCO 2 ) in mechanically ventilated patients, but both the relationship between P ET CO 2 and PaCO 2 and whether P ET CO 2 accurately reflects PaCO 2 in neonates and infants are still controversial. This study evaluated remote sampling of P ET CO 2 via an epidural catheter within an endotracheal tube to determine the procedure's clinical safety and efficacy in the perioperative management of neonates. Methods Abdominal surgery was performed under general anesthesia in 86 full-term newborns (age 1–30 days, weight 2.55–4.0 kg, American Society of Anesthesiologists class I or II). The infants were divided into 2 groups (n = 43 each), and carbon dioxide (CO 2 ) gas samples were collected either from the conventional position (the proximal end) or a modified position (the distal end) of the epidural catheter. Results The P ET CO 2 measured with the new method was significantly higher than that measured with the traditional method, and the difference between P ET CO 2 and PaCO 2 was also reduced. The accuracy of P ET CO 2 measured increased from 78.7% to 91.5% when the modified sampling method was used. The moderate correlation between P ET CO 2 and PaCO 2 by traditional measurement was 0.596, which significantly increased to 0.960 in the modified sampling group. Thus, the P ET CO 2 value was closer to that of PaCO 2 . Conclusion P ET CO 2 detected via modified carbon dioxide monitoring had a better accuracy and correlation with PaCO 2 in neonates.

In oil pipeline leak detection and location, noise in the pressure signal collected at the end of the pipeline affects the accuracy of leak detection and the error of leakage location. To reduce the noise interference, an improved local mean decomposition signal analysis method is proposed. The production functions (PFs) that are related to the leak signal can be exacted, and it is necessary to know the characteristics of leak signals or noise in advance. According to the cross-correlation function, there is a significant peak between the measured signals, which are decomposed into a number of PFs. These reconstructed principal PF components are obtained, and a wavelet analysis is used to remove the noise in the reconstructed signal. On this basis, the signal features are extracted according to the time-domain feature and the waveform feature, which are input into the least squares twin support vector machine (LSTSVM), to recognize pipeline leaks. According to the reconstructed signal after wavelet denoising, the time-delay estimate of the negative pressure signal at the end of the pipeline is obtained by the cross-correlation function, and the leak location is ultimately calculated by combining the time delay with the leak signal propagation velocity. A flow model for pipeline leakage is proposed based on the Flowmaster software, where the collected data of the different working conditions are processed. The experimental results show that the proposed method can effectively identify different working conditions and accurately locate the leakage point.

The aim of the present study was to examine the association between vascular endothelial growth factor receptor 2 (VEGFR2) rs11941492 C/T polymorphism and rheumatoid arthritis (RA) risk in an eastern Chinese Han population. We examined VEGFR2 rs11941492 C/T polymorphism in 615 RA patients and 839 controls in an East Chinese Han population. The power analysis was used for evaluating the reliability of the results. Genotyping was performed using a custom-by-design 48-Plex single nucleotide polymorphism scan Kit. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using logistic regression. Our results indicated that VEGFR2 rs11941492 C/T polymorphism (TT vs CC, P = .012, OR = 0.61, 95% CI = 0.41–0.89; TT vs CT + CC, P = .017, OR = 0.63, 95% CI = 0.43–0.92) was associated with a significantly decreased risk of RA. The power analysis showed that this study had a power of 98.5% to detect the effect of rs11941492 C/T polymorphism on RA susceptibility, assuming an OR of 0.61. After stratification analysis, a decreased risk of RA was associated with VEGFR2 rs11941492 TT genotype (TT vs CC) among female patients (TT vs CC, P = .007, OR = 0.53, 95% CI = 0.33–0.84), older patients (Yr ≥55) (TT vs CC, P = .039, OR = 0.58, 95% CI = 0.35–0.97), C-reactive protein-positive patients, anti-cyclic citrullinated peptide antibody-negative patients, rheumatoid factor-positive patients (TT vs CT + CC, P = .015, OR = 0.60, 95% CI = 0.39–0.90), functional class III + IV patients, patients with a DAS28 of ≥3.20, and those with an erythrocyte sedimentation rate of

Physical exercise has been proven to be beneficial for both healthy subjects and cardiac patients. It can improve cardiovascular health and promote recovery from various heart conditions. Heart Rate (HR) is a cardiovascular variable, which can be easily monitored and provides important insights about cardiac functions during and after physical exercise. This study presents a HR-based modeling and control framework to monitor physiological changes during exercise, from which the exercise intensity is optimized to capitalize exercise outcomes. HR models were previously developed to investigate exercise physiology, but efficient model identification has not been extensively discussed in the literature. Most existing HR models are nonlinear state-space models, and traditional optimization techniques may fail to provide accurate model identification results. In this work, we propose to use particle filter (PF) to identify HR model parameters and further optimize the intensity of exercise, e.g., walking or running speed, based on the calibrated model. Specifically, sequential importance sampling and resampling (SISR) and smoothing were chosen to estimate state variables, and particle marginal Metropolis-Hastings method was used to identify model parameters from HR observations. In addition, using predictions calculated from the HR model, treadmill speed was optimized by minimizing the difference between predictions and the target HR. The modeling and control framework is validated with different case studies. The results demonstrate that the proposed method is a useful tool for personalized HR modeling and exercise control, which can benefit both regular exercise training and cardiac rehabilitation.

Simple Summary The objective of this study was to determine the effects of intravenous infusions of L-glutamine (Gln) on the autophagy and apoptosis of duodenum cells in weaned calves. The results showed that the autophagy level of duodenal cells was increased with an increasing Gln infusion dose (0 to 20 g/d) and dropped when Gln was further increased to 40 g/d. We also found that the level of apoptosis was decreased with an increasing Gln infusion dose from 0 to 20 g/d, and then rose as the dose increased to 40 g/d. This knowledge will provide a reference for weaned calf health management. Abstract The objectives of this study were to determine the effects of intravenous infusions of L-glutamine (Gln) on the autophagy and apoptosis of duodenum cells in early-weaned calves. Holstein male calves were weaned at day 35 (20 male calves, birth weight 43 ± 1.8 kg; 35 ± 3 d of age) and randomly allocated to four treatments (5 calves/treatment). The treatments were: (1) infusion of NaCl, representing the control group (C); (2) infusion of 10 g/d of Gln solution (L); (3) infusion of 20 g/d of Gln solution (M); and (4) infusion of 40 g/d of Gln solution (H). The solutions were infused for 2 h daily for 3 consecutive days after weaning. All calves were killed on the third day post-weaning. The results showed that the autophagy level of the duodenal cells was increased as the Gln infusions increased from 0 to 20 g/d and dropped with a further increase in dose (40 g/d). We also found that the level of apoptosis was decreased with Gln infusion from 0 to 20 g/d and rose as the dose increased to 40 g/d. This knowledge provides a reference for weaned calf health management.

Extended finite element method (XFEM) is introduced in this study, and the normal direction of fracture surface under different failure modes is determined, which provides a new and efficient technology for failure prediction of composite laminates. According to the failure criterion, a user-defined FORTRAN subroutine (UDMGINI) has been written for Abaqus/Standard solver to model the damage initiation and crack propagation of G23 out-of-plane shear test. By comparing the load–displacement curve, strain field and failure mode of experiment, FEM and XFEM, XFEM can effectively predict the crack initiation and propagation. Meanwhile, the crack path of matrix failure simulated by XFEM is smooth without bifurcation, which is more consistent with experimental result. Moreover, by comparing damage factors of specimen before damage initiation in G23 test, it is found that the matrix tensile failure occurs more easily than matrix compressive failure at the root of V-notch, where cracks are the first to initiate.

Purpose Post-transplant patients are at increased risk for cancers of the skin. This study attempts to generate a risk prediction model using the UNOS database population. Methods Of the 24,735 adults (age 18 years and above) heart transplant recipients between 2000 and 2015 in the UNOS database 2,625 recipients developed skin cancer. Uni- and multi-variate Cox regression analysis were performed to assess the association of different risk factors with post-transplant skin cancer, and p-values, hazard ratios and their confidence intervals derived. . Variables in the univariate analysis were selected as inputs to the multivariate analysis. Stepwise forward selection was conducted to select the final multivariate model. The multivariate model was used to predict the probability of developing skin cancer in 5, 8, and 10 years after heart transplantation. The model accuracy was assessed using Receiver Operating Characteristics (ROC) curves and Area Under Curves (AUCs). All the analysis was performed using MATLAB software from the MathWorks, Inc. Results Multivariate analysis showed that whites had an OR of 35.5 as compared to blacks (p=0.001). Males had an OR of 2.79 (p=0.001). Malignancy at listing showed an OR of 1.73 (p=0.001) while malignancy at transplant showed an OR of 2.42 (p=0.001). OKT3 had an OR of 1.54 (p=0.001) as compared to other induction agents The ROC curves generated using these risk factors showed AUC at 5, 8 and 10 years post-transplant of 0.78, 0.78, 0.77 respectively in the training set and 0.77, 0.76, 0.75 respectively in the validation set (figure 1). Conclusion Male sex, white race, malignancy at the time of listing or at transplantation and OKT3 induction are major risk factors for skin cancers post transplantation. The risk prediction model generated for the first time for skin cancers in the post-cardiac transplant patients has a c-statistic of 0.75.

Purpose Prior literature shows that survival of waitlist patients is improved on axial continuous flow left ventricular assist devices (CF-LVAD). This study attempts to generate a risk prediction model for survival using the UNOS database population. Methods UNOS database was queried to extract patients (≥ 18 years of age) listed for heart transplantation between 2010 and 2015 with an axial CF-LVAD (Heartmate II(HMII)) while on the waiting list. The multivariate model was used to predict the probability of survival at 3,6 and 12 months after listing. Patients were divided into derivation (80%) and validation (20%) groups. Derivation group was used to develop the multivariate model and validation group was used for model validation. The model accuracy was assessed using Receiver Operating Characteristics (ROC) curves and Area Under Curves (AUCs). Kaplan Meier survival curves were generated to show the impact of different risk factors on waiting list survival. All the analysis was performed using MATLAB software from the MathWorks, Inc. Results Significant risk factors on multivariate analyses were diabetes type1 (HR = 2.5,p=0.018), presence of inotropes (HR = 1.6,p=0.005), creatinine at listing (HR = 1.2,p=0.00016). No significant differences were observed between the derivation and validation groups for all variables. The ROC curves generated using these risk factors showed AUC at 3, 6 and 12 months on the wait list of 0.7, 0.65, 0.63 respectively in the training set and 0.71, 0.65, 0.6 respectively in the validation set (figure 1). Survival analyses showed that patients implanted with HMII before listing had a better survival than those who did after being on the wait list (p-value Conclusion This is the first time a risk prediction model has been generated for wait list survival of HMII patients. A significant difference in survival was noted between patients who received their HMII prior to listing versus those who had it while waiting on the list.

Considering the frequent failure of aircraft caused by the multiaxial fatigue damage, it is very necessary to develop a reliable mathematical model to predict the multiaxial fatigue life for aircraft aluminum alloy. In our study, a new multiaxial fatigue life prediction model was proposed for both thin-walled tubular and notched specimens. In presented model, a new characteristic plane (subcritical plane) was defined to describe the particularity of additional cyclic hardening under non-proportional loading condition. On the new defined subcritical plane, a corresponding damage parameter containing the effect of additional hardening was also built, by which the dynamic path of stress spindle, combining material property and loading environment, was fully analysed. Finally, in order to verify the effectiveness of the new model for the multiaxial fatigue life prediction of aircraft aluminum alloy, multiaxial fatigue experiment for 2A12 aluminum alloy in four types of fatigue specimens was employed for the material fatigue failure simulation of the aircraft. Through the comparison result with three traditional models, it was evident the stability and accuracy of the new proposed model were much superior to those of the other three traditional models.

The bearing fault diagnosis is of vital significance in maintaining the safety of rotation machine. Among various fault detection techniques, the diagnosis based on vibration signal is widely applied in monitoring the condition of rotation machine. Variational mode decomposition (VMD) is a novel signal analysis method, which can decompose a multi-component signal into a certain number of band-limited intrinsic mode functions (BLIMFs) nonrecursively. VMD could overcome some problems such as mode mixing, the inference of noise, the determination of wavelet base, which exist in empirical mode decomposition, ensemble empirical mode decomposition, wavelet transform, respectively. However, the empirical selection of the parameters for VMD would affect the result of the decomposition. This paper presents an adaptive VMD method with parameter optimization for detecting the localized faults of rolling bearing. Kurtosis, sensitive to transient impulsive components, is employed as optimization index to evaluate the performance of the VMD. Two parameters in the VMD, namely the number of decomposition modes and data-fidelity constraint, are optimized synchronously based on the kurtosis index through artificial fish swarm algorithm. Executing VMD with the acquired parameters, the optimal BLIMF is obtained. The spectrum analysis of the optimal BLIMF could identify the characteristic frequency caused by the localized crack effectually. The validity of the proposed method is proved by means of a cyclic transient impulse response signal and two experiments with practical vibration signals of rolling bearings. Compared to several existing methods, the proposed method demonstrates reinforced results.

The extraction of repetitive impacts from vibration signals plays an essential role in bearing fault detection. Among different signal processing algorithms, morphological filter (MF) has attracted lots of attention because it could directly extract the geometric structure of the impulsive feature and only needs little computation. However, the conventional MF and some current improvements are based on the local optima of the raw signal to de-noise the noisy signal and its faulty feature extracting capability would be greatly affected by the noise. In this paper, a new improved MF algorithm is proposed to overcome such deficiency. Firstly, morphological gradient (MG) operator is selected in this paper due to its capability of picking up both positive and negative impulses. Then, based on the relationship between the defect induced impulse and a harmonic function with the resonant frequency, the harmonic waveform in a period is adopted to instruct the construction of structuring element (SE). The improved MF can obtain the fault feature from low SNR signals. The processing results of a simulation signal and two sets of experimental signals and a set of comparisons verify the effectiveness and robustness of the proposed method.

Purpose Post-transplant patients are at increased risk for squamous cell carcinoma (SCC) of the skin, secondary to long-term immunosuppression. This study was undertaken to identify risk factors using the UNOS database population. Methods The study population of 1766 patients derived from the UNOS database (2000-2015) was divided into two equal groups. One group was used for testing and the other for validation. Descriptive statistics were used to characterize the study cohort. Cox's proportional hazards regression was used to investigate the effect of different risk factors for SCC post-heart transplantation. Multivariate analysis was used to test the correlation between SCC and potential risk factors. Hazard ratio (OR) and the 95% confidence interval (CI) were calculated for each risk factor. Statistical significance was set at p Results Multivariate analysis showed that Black, Hispanic and other races as compared to whites had an OR of 0.07,0.35, 0.17 (p=0.0000). Recipient PRAs had an OR of 0.97 (p= 60 years had an OR of 2.5 (p=0.0000). Males had an OR of 2.05 (p=0.000). The ROC curves generated showed AUC at 5, 8 and 10 years post-transplant of 0.76, 0.76, 0.76 respectively in the testing set and 0.79, 0.77, 0.78 respectively in the validation set (figure 1). Conclusion Male sex, white race, age >60 years higher PRAs are major risk factors for SCC. The risk prediction model generated for the first time for SCC in post-cardiac transplant patients has a robust c-statistic of >0.75. This is important for risk stratification as SCC is the most common skin cancer seen in the post-cardiac transplant population.

Purpose Diabetes is an important factor in post-transplant survival. However its impact on post-transplant survival of Continuous Flow (CF) axial left ventricular assist device (LVAD) i.e. Heartmate II (HM II) bridge to transplant (BTT) population has not been studied. This study addresses the influence of Diabetes type II (DM type II) in this population. Methods The study consisted of two cohorts derived from the UNOS database (2005-2015). Cohort 1 consisted of Type II Diabetes patients with HM II (n=1172) and without HM II (n=1097). Cohort 2 consisted of HM II patients with DM type II (n=1172) and without DM type II (n=1171). Cox's proportional hazards regression was used to investigate the effect of diabetes on post-transplant survival in this population. Hazard ratio (OR) and the 95% confidence interval (CI) were calculated with statistical significance set at p Results The Kaplan Meir survival curve for cohort 1 showed that DM type II patients had better survival when they were bridged to transplant with HM II. The Hazard ratio (OR) for Cohort 1 was 0.717(p=0.00514)). In cohort 2 the Kaplan Meir survival curve showed that HMII without DM type II had better survival than those with type II DM. The hazard ratio (OR) for Cohort 2 was 1.31 (p=0.0209). The cohorts were propensity-matched. Conclusion This study shows for the first time that HM II implantation in DM type II patients have a better 2 year post-transplant survival as opposed to those without the device. This effect is probably best explained by improved hemodynamics on the endothelium in CF axial LVAD patients.

Background Basal cell carcinoma (BCC) is the second most common skin cancers in posttransplant patients. Long-term immunosuppression predisposes the patients to higher risk. This study was undertaken to develop a risk prediction model using the United Network for Organ Sharing (UNOS) database. Materials and methods Heart transplant recipients (2000~2015) from the UNOS database were analyzed. The Cox proportional hazards model was applied to screen the predictors associated with the development of BCC. Stepwise forward selection with Akaike information criterion was done to obtain the multivariate model. Area under the curve was derived from the receiver operating characteristics curve to assess the quality of the prediction model. A risk scoring system was developed to stratify patients into different risk groups, and the occurrence rates of posttransplant BCC among different groups were compared. Results There were 24,374 patients who received heart transplantation within this study period, and 1211 recipients have been reported with BCC. The multivariate model provides area under the curves at 5, 8, and 10 years posttransplant of 0.77, 0.76, and 0.76, respectively, in the derivation set and 0.75, 0.74, and 0.74, respectively, in the validation set. The predicted and observed probabilities of developing BCC in 5 years agree well across different risk groups. Kaplan-Meier survival curves were generated, which demonstrate significant differences between subjects in different risk groups. Conclusion A risk prediction model has been generated for the first time for BCC with a c-statistic of ≥ 0.74 in both derivation and validation sets, making it a good tool for risk stratification.

The existing studies on the association between polymorphisms of Calmodulin 1 (CALM1) gene and the risk of osteoarthritis (OA, a complex multifactorial disease and a major degenerative form of arthritis) in different populations have yielded conflicting findings. Therefore, we conducted a meta-analysis by systematically searching PubMed, Embase, Medline, Cochrane Library and Google Scholar, and assessing this association by calculating pooled odds ratios with 95% confidence intervals. Subgroup analyses stratified by ethnicity, OA type, and genotype were also conducted. Six studies (2752 cases and 3259 controls) involving six single nucleotide polymorphisms were included. Our data suggested that the T allele and genotype TT of the rs12885713 polymorphism, and the C allele of the rs2300496 polymorphism in the CALM1 gene all increased the risk of OA. The pooled results revealed no significant association between the CALM1 rs3213718 polymorphism and the risk of OA. Stratification analyses by ethnicity and OA type showed that the rs12885713 polymorphism increased the risk of OA among Asians and in knee OA, respectively. In conclusion, the rs12885713 and rs2300496 polymorphisms of the CALM1 gene may both increase the risk of OA. Owing to the limitations of the present study, this finding should be further confirmed in future well-designed studies.

Efficient and accurate people counting has become significant as it is essential in many applications such as smart guide and intelligent building. In this paper, a people counting approach based on fingerprinting localization is proposed for multiple regions in indoor environment. We obtain the number of people in each subarea by localizing each target in the environment. The approach is composed of the offline training phase and online counting phase. In offline phase, we construct the fingerprint database which is also called radiomap, and transform it through DIFF method to solve the problem of device diversity. Then we divide the whole region into different subareas utilizing Kernel Fuzzy C-Means (KFCM) Clustering algorithm to reduce the computational complexity. In online phase, we first select the nearest subarea of the target and then estimate its location. The location is compared with the boundaries of all subareas to determine the subarea it belongs to. When applied to multiple targets, the number of people in each region will be counted. Experimental results show that our proposed method is effective and the average people counting accuracy can achieve 90.24%.

BACKGROUND This study was designed to investigate the effects of leukocyte filtration of autologous salvaged blood on lung function, lung inflammatory reaction, and oxidative stress reaction in elderly patients undergoing lumbar spinal surgery. MATERIALS AND METHODS Sixty elderly patients undergoing lumbar spinal surgery were randomly divided into 2 groups: Leukocyte Filter group and Control group. Serum levels of inflammatory markers including white blood cell and polymorphonuclear count, neutrophil elastase, serum surfactant protein A, methane dicarboxylic aldehyde, superoxide dismutase, interleukin (IL)-6, IL-8, tumor necrosis factor-α, and respiratory function markers including dynamic respiratory system compliance, oxygenation index, and respiratory index were measured immediately before induction of anesthesia (T0), immediately before blood transfusion (T1), and 1 (T2), 6 (T3), and 12 hours (T4) after end of blood transfusion. RESULTS The Leukocyte Filter group had higher dynamic respiratory system compliance at T2, oxygenation index at T2 and T3, respiratory index and superoxide dismutase at T2, T3, and T4 than those in the Control group (P

DEFA1/DEFA3, genes encoding human neutrophil peptides (HNP) 1–3, display wide-ranging copy number variations (CNVs) and is functionally associated with innate immunity and infections. To identify potential associations between DEFA1/DEFA3 CNV and hospital-acquired infections (HAIs), we enrolled 106 patients with HAIs and 109 controls in the intensive care unit (ICU) and examined their DEFA1/DEFA3 CNVs. DEFA1/DEFA3 copy number ranged from 2 to 16 per diploid genome in all 215 critically ill patients, with a median of 7 copies. In HAIs, DEFA1/DEFA3 CNV varied from 2 to 12 with a median of 6, which was significantly lower than that in controls (2 to 16 with a median of 8, p=0.017). Patients with lower DEFA1/DEFA3 copy number (CNV p=0.014; OR, 2.010; 95% CI, 1.164–3.472). The area under the receiver operating characteristic (AUROC) of DEFA1/DEFA3 CNV combined with clinical characteristics to predict the incidence of HAIs was 0.763 (95% CI 0.700–0.827), showing strong predictive ability. Therefore, lower DEFA1/DEFA3 copy number contributes to higher susceptibility to HAIs in critically ill patients, and DEFA1/DEFA3 CNV is a significant hereditary factor for predicting HAIs.

Background: Dexmedetomidine (DEX) can prolong the duration of local anesthetics, but the use of retrobulbar DEX has not been fully elucidated. This study was designed to determine the effects of adding DEX to lidocaine-bupivacaine for retrobulbar block in orbital ball implants after enucleation surgery. Materials and Methods: A total of 200 patients of both sexes aged 30–60 years of American Society of Anesthesiologists I and II, scheduled for orbital ball implants after enucleation surgery, were enrolled for the study. Patients were randomly assigned into one of the two groups: Control (n = 100) received lidocaine-bupivacaine retrobulbar block, DEX (n = 100) received lidocaine-bupivacaine plus 1 ug/kg DEX retrobulbar block. Hemodynamic data, duration of motor and sensory blocks, pain by visual analog scale, bispectral index (BIS), side effects, consumption of dezocine as a rescue analgesic, patient and surgeon satisfaction were recorded. Results: Duration of analgesia was prolonged in the DEX, compared with the control group ([258.35 ± 66.82 min] as [130.75 ± 29.52 min], [P < 0.05]). The median number of postoperative analgesic requests per patient during the first 24 h was decreased in the DEX group (P < 0.05). In the first 24 postoperative hours, DEX group consumed significantly less dezocine (P < 0.05). BIS values and mean arterial pressure remained lower in the DEX group, but within the safe range (P < 0.05). The side effect profile was similar between the two groups. Patients and surgeon satisfaction were higher in the DEX group (P < 0.05). Demographic characteristics were comparable in both groups (P > 0.05). Conclusion: Retrobulbar DEX reduces consumption of rescue analgesic, prolonged the duration of retrobulbar block, improved postoperative pain, provided better sedation effects, and increased patient and surgeon satisfaction after orbital ball implants after enucleation surgery.

BACKGROUND This study was designed to evaluate the effectiveness and safety of ultrasound-guided transversus abdominis plane (TAP) and rectus sheath (RS) blocks with ropivacaine and dexmedetomidine in elderly, high-risk patients undergoing emergency abdominal surgery. METHODS Sixty elderly patients undergoing emergency abdominal surgery receiving both bilateral ultrasound-guided TAP and RS blocks were randomly divided into two groups: R+D (10 mL of 0.25% ropivacaine+0.5μg/kg dexmedetomidine) and R (10 mL of 0.25% ropivacaine). Pain scores (at rest and with movement) and overall analgesia satisfaction were assessed. The total amount of sufentanil administered postoperatively during the first 24 h, duration of sensory blockade, first time and total number of patient-controlled intravenous analgesia (PCIA) pump activations on the first postoperative day were recorded. RESULTS The duration of sensory blockade and the first time to PCIA pump activation in the R+D group were longer than that of the R group (P

Background Mivacurium is the shortest acting nondepolarizing muscle relaxant currently available; however, the effect of different dosages and injection times of intravenous mivacurium administration in children of different ages has rarely been reported. This study was aimed to evaluate the muscle relaxant effects and safety of different mivacurium dosages administered over different injection times in pediatric patients. Methods Six hundred forty cases of pediatric patients, aged 2 m-14 years, ASA I or II, were divided into four groups (Groups A, B, C, D) according to the age class (2–12 m, 13–35 m, 3–6 years and 7–14 years) respectively, also each group were divided into four subgroups by induction dose (0.15, 0.2 mg/kg in 2–12 m age class; 0.2, 0.25 mg/kg in other three age classes), and mivacurium injection time (20 s, 40 s), totally 16 subgroups. Neuromuscular transmission was monitored with supramaximal train-of-four stimulation of the ulnar nerve. Radial artery blood (1 ml) was sampled to quantify plasma histamine concentrations before and 1, 4, and 7 min after mivacurium injection (P0, P1, P2 and P3). Results Five hundred sixty-two cases completed the study. There were no demographic differences within the four groups. The onset time of 0.2 mg/kg groups in 2–12 m aged patients were shorter than those of 0.15 mg/kg groups (189 ± 64 s vs. 220 ± 73 s, 181 ± 60 s vs. 213 ± 71 s, P

This study was designed to evaluate the effects of testosterone supplementation (TS) on body composition in patients with HIV and the side effects of TS. A comprehensive literature search strategy was used to retrieve relevant randomized controlled trials (RCTs) examining the effects of TS on body composition. Atotal of 14 eligible studies were included, enrolling 388 and 349 randomized patients in TS and control groups, respectively. The quality of studies included was assessed, and data on total body weight (BW), lean body mass (LBM), fat mass (FM), serum total testosterone (TT), free testosterone (FT) levels, and adverse events were extracted and analyzed using Review Manager software 5.3. Meta-analysis results showed that TS was associated with a small but significant modification in total BW, serum TT, and FT levels in HIV-infected patients and in patients given various drug administrations. TS also significantly increased LBM in male patients, but no significant difference in LBM was observed between female counterparts treated with TS or not. Conversely, TS relative to placebo did not lead to a significant reduction in FM. No significant difference was observed between the two groups in terms of adverse effects. Our findings suggested that TS may be recommended to improve body composition in patients with HIV-related weight loss. However, owing to the high heterogeneity across included trials, further evaluations using large-scale, multi-center, blinded RCTs are needed.

This study was designed to determine the effects of dexmedetomidine on perioperative myocardial injury by observing peripheral circulatory changes in response to tracheal intubation and extubation, myocardial enzyme levels, myocardial ischaemia improvements, cardiovascular adverse events and cytokines in patients with coronary heart disease (CHD) undergoing non-cardiac surgery. This study was a prospective, randomized, double-blind trial. Eighty patients having CHD were scheduled for elective hip-replacement surgery and randomly allocated to receive a loading dose of 1 μg/kg dexmedetomidine followed by a 0.2 μg/kg per h infusion (Dex group; n = 40) or normal saline (control group; n = 40). Systolic blood pressure, diastolic blood pressure, mean arterial pressure, heart rate, rate-pressure product and changes in ST-T segment on the electrocardiogram were recorded every 5 min during surgery. Serum creatine kinase-MB (CK-MB), cardiac troponin I (cTnI), glycogen phosphorylase BB (GP-BB), interleukin (IL)-6 and tumour necrosis factor (TNF)-α protein levels were determined preoperatively, at the end of surgery and 12 and 24 h after surgery. The improvement rate of myocardial ischaemia was higher in the Dex than control group (87.5% vs 32.5%, respectively; P0.05). In addition, the Dex group had lower serum CK-MB, IL-6, cTnI and GP-BB concentrations than the control group (P0.05). There was no significance difference in TNF-α between the two groups (P0.05). Dexmedetomidine can reduce myocardial injury and cytokine levels in patients with CHD undergoing non-cardiac surgery.

With the development of wireless technology, indoor localization has gained wide attention. The fingerprint localization method is proposed in this paper, which is divided into two phases: offline training and online positioning. In offline training phase, the Improved Fuzzy C-means (IFCM) algorithm is proposed for regional division. The Between-Within Proportion (BWP) index is selected to divide fingerprint database, which can ensure the result of regional division consistent with the building plane structure. Moreover, the Agglomerative Nesting (AGNES) algorithm is applied to accomplish Access Point (AP) optimization. In the online positioning phase, sub-region selection is performed by nearest neighbor algorithm, then the Weighted K-nearest Neighbor (WKNN) algorithm based on Pearson Correlation Coefficient (PCC) is utilized to locate the target point. After the evaluation on the effect of regional division and AP optimization of location precision and time, the experiments show that the average positioning error is 2.53 m and the average computation time of the localization algorithm based on PCC reduced by 94.13%.

Accumulation of β-amyloid (Aβ) plaques is a pathological hallmark of Alzheimer disease. Aβ levels in animals and adults were reported to be associated with postoperative cognitive dysfunction (POCD). Our goal was to determine the plasma levels of Aβ in infants and young children after cardiac surgery with cardiopulmonary bypass (CPB). Forty-two infants and young children aged from 1 to 35 months undergoing cardiac surgery with general anesthetics were prospectively enrolled from January to June 2014 at a tertiary medical center. Perioperative plasma samples were obtained, and Aβ42 and Aβ40 levels were measured using ELISA. Other clinical characteristics of the patients were also recorded. Plasma levels of Aβ42 and Aβ40 decreased dramatically 2 hours after surgery and remained significantly lower 6 hours after operation. Baseline Aβ42 level correlated significantly with surgical intensive care unit (SICU) length of stay (LOS) and was an independent predictor for SICU LOS on multivariate analysis. Cardiac surgery with CPB decreases plasma Aβ levels. Plasma levels of Aβ42 and Aβ40 might be used as novel biomarkers for predicting outcomes in the patient population.

The objective of this study was to evaluate the effect of hypothermic cardiopulmonary bypass (CPB) on cerebral oxygen saturation (rSO2), internal jugular bulb venous oxygen saturation (SjvO2), mixed venous oxygen saturation (SvO2), and bispectral index (BIS) used to monitor cerebral oxygen balance in pediatric patients. Sixty American Society of Anesthesiologists Class II-III patients aged 1 to 4 years old with congenital heart disease scheduled for elective cardiac surgery were included in this study. Temperature, BIS, rSO2, mean arterial pressure, central venous pressure, cerebral perfusion pressure (CPP), and hematocrit were recorded. Internal jugular bulb venous oxygen saturation and SvO2 were obtained from blood gas analysis at the time points: after induction of anesthesia (T0), beginning of CPB (T1), ascending aortic occlusion (T2), 20 minutes after initiating CPB (T3), coronary reperfusion (T4), separation from CPB (T5), and at the end of operation (T6). The effect of hypothermia or changes in CPP on rSO2, SjvO2, SvO2, and BIS were analyzed. Compared with postinduction baseline values, rSO2 significantly decreased at all-time points: onset of extracorporeal circulation, ascending aortic occlusion, 20 minutes after CPB initiation, coronary reperfusion, and separation from CPB (P 0.05). Correlation analysis demonstrated that rSO2 was positively related to CPP (r = 0.687, P = 0.000), with a low linear correlation to temperature (r = 0.453, P = 0.000). Internal jugular bulb venous oxygen saturation was negatively related to temperature (r = −0.689, P = 0.000). Bispectral index was positively related to both temperature (r = 0.824, P = 0.000) and CPP (r = 0.782, P = 0.000). Cerebral oxygen saturation had a positive linear correlation with CPP and a low linear correlation to temperature. Internal jugular bulb venous oxygen saturation had a negative linear correlation to temperature. Pre-and and early postbypass periods are vulnerable times for adequate cerebral oxygenation. Anesthetic management must aim to optimize the supply and demand relationship.

In this paper the multiscale kernel principal component analysis (MSKPCA) based on sliding median filter (SFM) is proposed for fault detection in nonlinear system with outliers. The MSKPCA based on SFM (SFM-MSKPCA) algorithm is first proposed and applied to process monitoring. The advantages of SFM-MSKPCA are: (1) the dynamical multiscale monitoring method is proposed which combining the Kronecker production, the wavelet decomposition technique, the sliding median filter technique and KPCA. The Kronecker production is first used to build the dynamical model; (2) there are more disturbances and noises in dynamical processes compared to static processes. The sliding median filter technique is used to remove the disturbances and noises; (3) SFM-MSKPCA gives nonlinear dynamic interpretation compared to MSPCA; (4) by decomposing the original data into multiple scales, SFM-MSKPCA analyze the dynamical data at different scales, reconstruct scales contained important information by IDWT, eliminate the effects of the noises in the original data compared to kernel principal component analysis (KPCA). To demonstrate the feasibility of the SFM-MSKPCA method, its process monitoring abilities are tested by simulation examples, and compared with the monitoring abilities of the KPCA and MSPCA method on the quantitative basis. The fault detection results and the comparison show the superiority of SFM-MSKPCA in fault detection.

Mucinous cystadenocarcinoma (MCA) of the breast is extremely rare and was only recently described as a distinct variant of invasive ductal carcinoma of the breast. A case of MCA is reported in a 41-year-old woman. Mammographic and ultrasonographic examinations showed an irregularly shaped 10.0 × 8.0 × 5.5 cm lesion with patching calcification in the upper outer quadrant of the left breast. The gross examination revealed that the tumor has a well-circumscribed edge with a gelatinous cut surface and hemorrhage and necrosis were also noticed in the mass. Microscopically, the mass resembled mucinous cystic neoplasm of the ovary and pancreas closely, with cystic areas lined by columnar mucinous cells and associated with abundant extracellular and intracellular mucin, which is distinctively different from mucinous carcinoma with typically nests of low grade neoplastic cells floating in the mucin pool. The tumor cells were positive for CK7, CK20 and CDX2 were negative and displayed a typical immunophenotype of basal-like breast cancer (ER, PR, HER2 were negative, CK5/6 and EGFR were positive). Metastatic carcinoma was identified in three of 14 axillary lymph nodes. We describe here a very unusual case of breast MCA with basal-like immunophenotype.

A customer has to make transaction through a bank ATM (automated teller machine) if his withdrawal amount is less than the lower limit of withdrawal through a bank teller, or through a bank teller if his withdrawal amount is greater than the upper limit of withdrawal through an ATM. In order to decrease the long customer queue at counters, a bank increases the lower withdrawal limit and/or the upper one. Through a finite-source queue model, this paper analyzes the effects of the lower and/or upper limits on the length of customer queue at counters. Main conclusions are: (1) A bank may simultaneously shorten the queue at counters and improve satisfaction level of customers by increasing the withdrawal limit through an ATM and/or the one through a bank teller and ameliorating waiting environment; (2) The length of the queue at counters decreases with the number of the customers who are free in selecting service provider, bank teller or ATM.

As the centers of knowledge, discovery, and intellectual exploration, US universities provide appealing cybersecurity targets. Cyberattack origin patterns and relationships are not evident until data is visualized in maps and tested with statistical models. The current cybersecurity threat detection software utilized by University of North Florida's IT department records large amounts of attacks and attempted intrusions by the minute. This paper presents GIS mapping and spatial analysis of cybersecurity attacks on UNF. First, locations of cyberattack origins were detected by geographic Internet Protocol (GEO-IP) software. Second, GIS was used to map the cyberattack origin locations. Third, we used advanced spatial statistical analysis functions (exploratory spatial data analysis and spatial point pattern analysis) and R software to explore cyberattack patterns. The spatial perspective we promote is novel because there are few studies employing location analytics and spatial statistics in cyber-attack detection and prevention research.

PurposeDexmedetomidine can prolong the duration of local anesthetics, but the effect of retrobulbar dexmedetomidine on the potency of ropivacaine for retrobulbar block has not been investigated. Our study was designed to determine the effect of retrobulbar dexmedetomidine on ropivacaine for retrobulbar block in children.MethodsA group of 90 children aged 10-16 years scheduled for vitreoretinal surgery who received retrobulbar block were randomly assigned to 1 of 3 groups: group L (retrobulbar ropivacaine), group LD1 (ropivacaine plus 0.5 µg.kg−1dexmedetomidine), or group LD2 (ropivacaine plus 1 µg.kg−1dexmedetomidine). The minimum local anesthetic concentration (MLAC) was determined according to a Dixon-Massey protocol. The primary endpoint of the study was MLAC. Secondary outcomes were duration of postoperative analgesia, postoperative pain scores, dexmedetomidine side effects, and time to hospital discharge.ResultsThe MLAC values of retrobulbar ropivacaine were 0.314%, 0.259%, and 0.246% in groups L, LD1, and LD2, respectively. The median (interquartile range) durations of analgesia in the postoperative period were 66 (54-117), 89 (40-157), and 168 (120-194) minutes in groups L, LD1, and LD2, respectively (L vs LD1 or LD2, pConclusionsRetrobulbar dexmedetomidine reduces the MLAC values of ropivacaine and improves postoperative analgesia in children without any neurologic side effects.