Process and systematic study of gold recovery from flexible printed circuit boards (FPCBs) using a choline chloride-ethylene glycol system.

The traditional hydrometallurgy technology has been widely used to recover precious metals from electronic waste. However, such aqueous recycling systems often employ toxic/harsh chemicals, which may cause serious environmental problems. Herein, an efficient and environment-friendly method using a deep eutectic solvent (DES) mixed system of choline chloride-ethylene glycol-CuCl2·2H2O is developed for gold (Au) recovery from flexible printed circuit boards (FPCBs). The Au leaching and precipitation efficiency can reach approximately 100 % and 95.3 %, respectively, under optimized conditions. Kinetic results show that the Au leaching process follows a nucleation model, which is controlled by chemical surface reactions with an apparent activation energy of 80.29 kJ/mol. The present recycling system has a much higher selectivity for Au than for other base metals; the two-step recovery rate of Au can reach over 95 %, whereas those of copper and nickel are < 2 %. Hydrogen nuclear magnetic resonance spectroscopy (HNMR) and density functional theory (DFT) analyses confirm the formation of intermolecular hydrogen bonds in the DES mixed system, which increase the system melting and boiling points and facilitate the Au leaching process. The Au leaching system can be reused for several times, with the leaching efficiency remaining > 97 % after five cycles. Moreover, ethylene glycol (EG) and choline chloride (ChCl) act as aprotic solvents as well as coordinate with metals, decreasing the redox potential to shift the equilibrium to the leaching side. Overall, this research provides a theoretical and a practical basis for the recovery of metals from FPCBs.

Prognostic implication of residual inflammatory trajectories in acute type I aortic dissection: dual-center prospective cohort study.

BACKGROUND: Peripheral platelet-white blood cell ratio (PWR) integrating systemic inflammatory and coagulopathic pathways is a key residual inflammatory measurement in the management of acute DeBakey type I aortic dissection (AAD); however, trajectories of PWR in AAD is poorly defined. METHODS: Two AAD cohorts were included in two cardiovascular centers (2020-2022) if patients underwent emergency total arch replacement with frozen elephant trunk implantation. PWR data were collected over time at baseline and five consecutive days after surgery. Trajectory patterns of PWR were determined using the latent class mixed modelling (LCMM). Cox regression was used to determine independent risk factors. By adding PWR Trajectory, a user-friendly nomogram was developed for predicting mortality after surgery. RESULTS: 246 patients with AAD were included with a median follow-up of 26 (IRQ 20-37) months. Three trajectories of PWR were identified (cluster α 45[18.3%], ß105 [42.7%], and γ 96 [39.0%]). Cluster γ was associated with higher risk of mortality at follow-up (crude HR, 3.763; 95% CI, 1.126, 12.574; P=0.031) than cluster α. By the addition of PWR trajectories, an inflammatory nomogram, composed of age, hemoglobin, estimated glomerular filtration rate, and cardiopulmonary time was developed and internally validated, with adequate discrimination (the area under the receiver-operating characteristic curve 0.765, 95% CI [0.660-0.869]), calibration, and clinical utility. CONCLUSION: Based on PWR trajectories, three distinct clusters were identified with short-term outcomes, and longitudinal residual inflammatory shed some light to individualize treatment strategies for AAD.

A Review of Depth-Based Human Motion Enhancement: Past and Present.

In this article, we survey the current research trends of enhancement and denoising of depth-based motion capture data (D-Mocap) and also discuss possible future research issues. We first present the commonly used problem formulation for human motion enhancement. We then review related work and cover a broad set of methodologies including filtering based, learning based, and evolutionary based approaches. In addition, we present some important experiments-related issues, such as data creation or collection, reference data generation, and the metrics used for performance evaluation. It is our intent to provide a comprehensive tutorial and survey on the recent efforts on D-Mocap improvement, both methodologically and experimentally. By comparing the state-of-the-art methods, we also propose future research needs that could make D-Mocap more useful and relevant for real-world clinical applications.

Indoor Camera Pose Estimation from Room Layouts and Image Outer Corners.

To support indoor scene understanding, room layouts have been recently introduced that define a few typical space configurations according to junctions and boundary lines. In this paper, we study camera pose estimation from eight common room layouts with at least two boundary lines that is cast as a PnL (Perspective-n-Line) problem. Specifically, the intersecting points between image borders and room layout boundaries, named image outer corners (IOCs), are introduced to create additional auxiliary lines for PnL optimization. Therefore, a new PnL-IOC algorithm is proposed which has two implementations according to the room layout types. The first one considers six layouts with more than two boundary lines where 3D correspondence estimation of IOCs creates sufficient line correspondences for camera pose estimation. The second one is an extended version to handle two challenging layouts with only two coplanar boundaries where correspondence estimation of IOCs is ill-posed due to insufficient conditions. Thus the powerful NSGA-II algorithm is embedded in PnL-IOC to estimate the correspondences of IOCs. At the last step, the camera pose is jointly optimized with 3D correspondence refinement of IOCs in the iterative Gauss-Newton algorithm. Experiment results on both simulated and real images show the advantages of the proposed PnL-IOC method on the accuracy and robustness of camera pose estimation from eight different room layouts over the existing PnL methods. The code is available at https://github.com/XiaoweiChenOSU/PnL-IOC.

Diagnosis of Early Bacterial Pneumonia and Sepsis After Cardiovascular Surgery: A Diagnostic Prediction Model Based on LASSO Logistic Regression.

Background: Early postoperative bacterial pneumonia and sepsis (ePOPS), which occurs within the first 48 hours after cardiovascular surgery, is a serious life-threatening complication. Diagnosis of ePOPS is extremely challenging, and the existing diagnostic tools are insufficient. The purpose of this study was to construct a novel diagnostic prediction model for ePOPS. Methods: Least Absolute Shrinkage and Selection Operator (LASSO) with logistic regression was used to construct a model to diagnose ePOPS based on patients' comorbidities, medical history, and laboratory findings. The area under the receiver operating characteristic curve (AUC) was used to evaluate the model discrimination. Results: A total of 1203 patients were recruited and randomly split into a training and validation set in a 7:3 ratio. By early morning on the 3rd postoperative day (POD3), 103 patients had experienced 133 episodes of bacterial pneumonia or sepsis (15 patients had both). LASSO logistic regression model showed that duration of mechanical ventilation (P=0.015), NYHA class ≥ III (P=0.001), diabetes (P<0.001), exudation on chest radiograph (P=0.011) and IL-6 on POD3 (P<0.001) were independent risk factors. Based on these factors, we created a nomogram named DICS-I with an AUC of 0.787 in the training set and 0.739 in the validation set. Conclusion: The DICS-I model may be used to predict the risk of ePOPS after cardiovascular surgery, and is also especially suitable for predicting the risk of IRAO. The DICS-I model could help clinicians to adjust antibiotics on the POD3.

Inflammatory risk stratification individualizes anti-inflammatory pharmacotherapy for acute type A aortic dissection.

The systemic benefits of anti-inflammatory pharmacotherapy vary across cardiovascular diseases in clinical practice. We aimed to evaluate the application of artificial intelligence to acute type A aortic dissection (ATAAD) patients to determine the optimal target population who would benefit from urinary trypsin inhibitor use (ulinastatin). Patient characteristics at admission in the Chinese multicenter 5A study database (2016-2022) were used to develop an inflammatory risk model to predict multiple organ dysfunction syndrome (MODS). The population (5,126 patients from 15 hospitals) was divided into a 60% sample for model derivation, with the remaining 40% used for model validation. Next, we trained an extreme gradient-boosting algorithm (XGBoost) to develop a parsimonious patient-level inflammatory risk model for predicting MODS. Finally, a top-six-feature tool consisting of estimated glomerular filtration rate, leukocyte count, platelet count, De Ritis ratio, hemoglobin, and albumin was built and showed adequate predictive performance regarding its discrimination, calibration, and clinical utility in derivation and validation cohorts. By individual risk probability and treatment effect, our analysis identified individuals with differential benefit from ulinastatin use (risk ratio [RR] for MODS of RR 0.802 [95% confidence interval (CI) 0.656, 0.981] for the predicted risk of 23.5%-41.6%; RR 1.196 [0.698-2.049] for the predicted risk of <23.5%; RR 0.922 [95% CI 0.816-1.042] for the predicted risk of >41.6%). By using artificial intelligence to define an individual's benefit based on the risk probability and treatment effect prediction, we found that individual differences in risk probability likely have important effects on ulinastatin treatment and outcome, which highlights the need for individualizing the selection of optimal anti-inflammatory treatment goals for ATAAD patients.

A Novel Inflammation-Based Risk Score Predicts Mortality in Acute Type A Aortic Dissection Surgery: The Additive Anti-inflammatory Action for Aortopathy and Arteriopathy Score.

Objective: To develop an inflammation-based risk stratification tool for operative mortality in patients with acute type A aortic dissection. Methods: Between January 1, 2016 and December 31, 2021, 3124 patients from Beijing Anzhen Hospital were included for derivation, 571 patients from the same hospital were included for internal validation, and 1319 patients from other 12 hospitals were included for external validation. The primary outcome was operative mortality according to the Society of Thoracic Surgeons criteria. Least absolute shrinkage and selection operator regression were used to identify clinical risk factors. A model was developed using different machine learning algorithms. The performance of the model was determined using the area under the receiver operating characteristic curve (AUC) for discrimination, calibration curves, and Brier score for calibration. The final model (5A score) was tested with respect to the existing clinical scores. Results: Extreme gradient boosting was selected for model training (5A score) using 12 variables for prediction-the ratio of platelet to leukocyte count, creatinine level, age, hemoglobin level, prior cardiac surgery, extent of dissection extension, cerebral perfusion, aortic regurgitation, sex, pericardial effusion, shock, and coronary perfusion-which yields the highest AUC (0.873 [95% confidence interval (CI) 0.845-0.901]). The AUC of 5A score was 0.875 (95% CI 0.814-0.936), 0.845 (95% CI 0.811-0.878), and 0.852 (95% CI 0.821-0.883) in the internal, external, and total cohort, respectively, which outperformed the best existing risk score (German Registry for Acute Type A Aortic Dissection score AUC 0.709 [95% CI 0.669-0.749]). Conclusion: The 5A score is a novel, internally and externally validated inflammation-based tool for risk stratification of patients before surgical repair, potentially advancing individualized treatment. Trial Registration: clinicaltrials.gov Identifier: NCT04918108.

Human Motion Enhancement via Tobit Kalman Filter-Assisted Autoencoder.

We present a novel approach to enhance the quality of human motion data collected by low-cost depth sensors, namely D-Mocap, which suffers from low accuracy and poor stability due to occlusion, interference, and algorithmic limitations. Our approach takes advantage of a large set of high-quality and diverse Mocap data by learning a general motion manifold via the convolutional autoencoder. In addition, the Tobit Kalman filter (TKF) is used to capture the kinematics of each body joint and handle censored measurement distribution. The TKF is incorporated with the autoencoder via latent space optimization, maintaining adherence to the motion manifold while preserving the kinematic nature of the original motion data. Furthermore, due to the lack of an open source benchmark dataset for this research, we have developed an extension of the Berkeley Multimodal Human Action Database (MHAD) by generating D-Mocap data from RGB-D images. The newly extended MHAD dataset is skeleton-matched and time-synced to the corresponding Mocap data and is publicly available. Along with simulated D-Mocap data generated from the CMU Mocap dataset and our self-collected D-Mocap dataset, the proposed algorithm is thoroughly evaluated and compared with different settings. Experimental results show that our approach can improve the accuracy of joint positions and angles as well as skeletal bone lengths by over 50%.

Recovery of silver from crystal silicon solar panels in Self-Synthesized choline Chloride-Urea solvents system.

Traditional acid-base leching technology is the primary technology to recycle silver from crystal silicon solar panels, which is fussy and often employs poisonous/harmful chemicals. In the present study, silver was easily recycled from photovoltaic panels in self-synthesized. Deep-Eutectic Solvents System (DESs) without pretreatments and the reaction system could be cyclically utilized. The leaching and precipitation rate can reach 99% under the optimized conditions. In addition, the kinetic results showed that the leaching of silver followed the classical shrinkage core model, in which chemical reaction was the rate-controlling step and the apparent activation energy for leaching process is 172.36 kJ·mol-1. In the recycling process, Cu2+ acted as the oxidant, and the redox potential of Cu2+ in the DES system is much higher than that in aqueous system. Besides, the HNMR and FTIR analysis indicate that the intermolecular hydrogen bond formed in the DES mixed system, which would raise the melting and boiling point of the mixed system, and would be conducive to the following silver leaching process. Furthermore, the metal complex generation mechanisms were proposed in the present study, and urea plays not only an aprotic solvent which cannot solvate Cl-, but also the ligand which can complex with the metals as well as Cl- which can reduce the redox potentials and shift the equilibrium to the silver leaching side. In summary, this study can provide theoretical foundation and practical experience for recycling precious metals from waste crystal silicon solar panels environmentally efficient and cost-effective.

IDPsBind: a repository of binding sites for intrinsically disordered proteins complexes with known 3D structures.

BACKGROUND: Intrinsically disordered proteins (IDPs) lack a stable three-dimensional structure under physiological conditions but play crucial roles in many biological processes. Intrinsically disordered proteins perform various biological functions by interacting with other ligands. RESULTS: Here, we present a database, IDPsBind, which displays interacting sites between IDPs and interacting ligands by using the distance threshold method in known 3D structure IDPs complexes from the PDB database. IDPsBind contains 9626 IDPs complexes and 880 intrinsically disordered proteins verified by experiments. The current release of the IDPsBind database is defined as version 1.0. IDPsBind is freely accessible at http://www.s-bioinformatics.cn/idpsbind/home/ . CONCLUSIONS: IDPsBind provides more comprehensive interaction sites for IDPs complexes of known 3D structures. It can not only help the subsequent studies of the interaction mechanism of intrinsically disordered proteins but also provides a suitable background for developing the algorithms for predicting the interaction sites of intrinsically disordered proteins.

Joint Optimization of Kinematics and Anthropometrics for Human Motion Denoising.

In this paper, we propose a novel technique for human motion denoising by jointly optimizing kinematic and anthropometric constraints for a noisy skeleton data. Specifically, we are focused on depth-sensor-based motion capture (D-Mocap) data that are often prone to error, outliers and distortion. To capture human kinematics, we first propose a joint-level Tobit particle filter (TPF) that incorporates a unique observation model to characterize the censored measurement of D-Mocap data. A skeleton-level Differential Evolution (DE) algorithm is then integrated with the sequential Monte Carlo sampling in the TPF, allowing joint-level particles to be re-distributed and re-weighted according to the stability and consistency of skeletal bone lengths as well as the suitability of joint kinematics. This leads to an integrated TPF-DE algorithm that significantly improves the quality of D-Mocap data by making 3D joint trajectories more kinematically admissible and anthropometrically stable. Experimental results on both simulated and real-world D-Mocap show that the errors of joint positions and the bone lengths have been reduced by 30-60%, and the accuracy of joint angles has been improved by 40-60%. The proposed TPF-DE method outperforms the recent filtering-based and deep learning methods and demonstrate the synergy between the TPF and DE algorithms for effective human motion enhancement.

Circulating biomarker-based risk stratifications individualize arch repair strategy of acute Type A aortic dissection via the XGBoosting algorithm.

Aims: The incremental usefulness of circulating biomarkers from different pathological pathways for predicting mortality has not been evaluated in acute Type A aortic dissection (ATAAD) patients. We aim to develop a risk prediction model and investigate the impact of arch repair strategy on mortality based on distinct risk stratifications. Methods and results: A total of 3771 ATAAD patients who underwent aortic surgery retrospectively included were randomly divided into training and testing cohorts at a ratio of 7:3 for the development and validation of the risk model based on multiple circulating biomarkers and conventional clinical factors. Extreme gradient boosting was used to generate the risk models. Subgroup analyses were performed by risk stratifications (low vs. middle-high risk) and arch repair strategies (proximal vs. extensive arch repair). Addition of multiple biomarkers to a model with conventional factors fitted an ABC risk model consisting of platelet-leucocyte ratio, mean arterial pressure, albumin, age, creatinine, creatine kinase-MB, haemoglobin, lactate, left ventricular end-diastolic dimension, urea nitrogen, and aspartate aminotransferase, with adequate discrimination ability {area under the receiver operating characteristic curve (AUROC): 0.930 [95% confidence interval (CI) 0.906-0.954] and 0.954, 95% CI (0.930-0.977) in the derivation and validation cohort, respectively}. Compared with proximal arch repair, the extensive repair was associated with similar mortality risk among patients at low risk [odds ratio (OR) 1.838, 95% CI (0.559-6.038); P = 0.316], but associated with higher mortality risk among patients at middle-high risk [OR 2.007, 95% CI (1.460-2.757); P < 0.0001]. Conclusion: In ATAAD patients, the simultaneous addition of circulating biomarkers of inflammatory, cardiac, hepatic, renal, and metabolic abnormalities substantially improved risk stratification and individualized arch repair strategy.

ManhattanFusion: Online Dense Reconstruction of Indoor Scenes From Depth Sequences.

We present a new framework for online dense 3D reconstruction of indoor scenes by using only depth sequences. This research is particularly useful in cases with a poor light condition or in a nearly featureless indoor environment. The lack of RGB information makes long-range camera pose estimation difficult in a large indoor environment. The key idea of our research is to take advantage of the geometric prior of Manhattan scenes in each stage of the reconstruction pipeline with the specific aim to reduce the cumulative registration error and overall odometry drift in a long sequence. This idea is further boosted by local Manhattan frame growing and the local-to-global strategy that leads to implicit loop closure handling for a large indoor scene. Our proposed pipeline, namely ManhattanFusion, starts with planar alignment and local pose optimization where the Manhattan constraints are imposed to create detailed local segments. These segments preserve intrinsic scene geometry by minimizing the odometry drift even under complex and long trajectories. The final model is generated by integrating all local segments into a global volumetric representation under the constraint of Manhattan frame-based registration across segments. Our algorithm outperforms others that use depth data only in terms of both the mean distance error and the absolute trajectory error, and it is also very competitive compared with RGB-D based reconstruction algorithms. Moreover, our algorithm outperforms the state-of-the-art in terms of the surface area coverage by 10-40 percent, largely due to the usefulness and effectiveness of the Manhattan assumption through the reconstruction pipeline.

Hybrid repair of aberrant right subclavian artery with aortic dissection caused by Kommerell diverticulum.

BACKGROUND: Aberrant right subclavian artery (ARSA) with associated Kommerell diverticulum (KD) is a rare congenital aortic disease. KD patients have a high risk of rupture, dissection, and compression of adjacent structures. Although several treatment options have been proposed (traditional surgery, hybrid operation, and endovascular intervention), a consensus regarding optimal surgical management has not yet been established. CASE PRESENTATION: A case of successful hybrid repair of distal aortic arch dissection aneurysm by dissecting KD and ARSA with debranching of right and left common carotid arteries, left subclavian artery, and stent grafting was presented. CONCLUSIONS: The hybrid operation is suitable for elderly patients or those with high risks. Along with intervention, the hybrid operation needs to be developed as a minimally invasive method.

The impact of gene-body H3K36me3 patterns on gene expression level changes in chronic myelogenous leukemia.

Chronic myelogenous leukemia (CML) is a malignant clonal disease of hematopoietic stem cells. Researches have exhibited that the progression of CML is related to histone modifications. Here, we perform the systematic analyses of H3K36me3 patterns and gene expression level changes. We observe that the genes with higher gene-body H3K36me3 levels in normal cells show fewer expression changes during leukemogenesis, while the genes with lower gene-body H3K36me3 levels in normal cells yield obvious expression changes during leukemogenesis (ρ = -0.98, P = 9.30 × 10-8). These findings are conserved in human lung/breast cancers and mouse CML, regardless of gene expression levels and gene lengths. Regulatory element analysis and Random Forest regression display that Hoxd13, Rara, Scl, Smad3, Smad4 and Tgif1 induce the up-regulation of genes with lower H3K36me3 levels (ρ = 0.97, P = 2.35 × 10-56). Enrichment analysis shows that the differentially expressed genes with lower H3K36me3 levels are involved in leukemia-related pathways, such as leukocyte migration and regulation of leukocyte activation. Finally, six driver genes (Tp53, Wt1, Dnmt3a, Cacna1b, Phactr1 and Gbp4) with lower H3K36me3 levels are identified. Our analyses indicate that lower gene-body H3K36me3 levels may serve as a biomarker for the progression of CML.

DrsNet: Dual-resolution Semantic Segmentation with Rare Class-Oriented Superpixel Prior.

Rare-class objects in natural scene images that are usually small and less frequent often convey more important information for scene understanding than the common ones. However, they are often overlooked in scene labeling studies due to two main reasons, low occurrence frequency and limited spatial coverage. Many methods have been proposed to enhance overall semantic labeling performance, but only a few consider rare-class objects. In this work, we present a deep semantic labeling framework with special consideration of rare classes via three techniques. First, a novel dual-resolution coarse-to-fine superpixel representation is developed, where fine and coarse superpixels are applied to rare classes and background areas respectively. This unique dual representation allows seamless incorporation of shape features into integrated global and local convolutional neural network (CNN) models. Second, shape information is directly involved during the CNN feature learning for both frequent and rare classes from the re-balanced training data, and also explicitly involved in data inference. Third, the proposed framework incorporates both shape information and the CNN architecture into semantic labeling through a fusion of probabilistic multi-class likelihood. Experimental results demonstrate competitive semantic labeling performance on two standard datasets both qualitatively and quantitatively, especially for rare-class objects.

Preoperative use of furosemide may increase the incidence of acute kidney injury after coronary artery bypass grafting: a propensity score-matched study.

OBJECTIVES: Furosemide is usually administered before the Coronary artery bypass grafting (CABG) to improve water-sodium retention. However, no final conclusions are available on the postoperative renal outcome of furosemide. We evaluated the effect of preoperative furosemide on acute kidney injury (AKI) after CABG. METHODS: We recorded the use of furosemide 14 days before surgery in all patients who underwent CABG from 2016 to 2017. Patients were divided into furosemide (F) group and non-furosemide (NF) group according to preoperative use of furosemide. A 1:1 propensity score matching was performed. Multivariate analyses were conducted to determine risk factors for AKI after CABG. RESULTS: Overall, 974 patients were included in the study, of which 82 cases were complicated with postoperative AKI. The incidence of AKI was significantly increased in F group than NF group (28.9% vs. 7.4%, p = 0.000). After adjusting for risk factors, the incidence of AKI in the F group was 5.34 times more than the NF group (95% confidence interval [CI] 2.45-11.64; p = 0.000). The incidence of AKI increased significantly when the cumulative dosage of furosemide exceeded 110 mg (odds ratio [OR] 6.23; 95% CI 2.07-18.74, p = 0.001) and 250 mg (OR 8.31; 95% CI 2.87-24.02, p = 0.000). After the propensity-matching group analysis, same results were obtained. CONCLUSIONS: The incidence of AKI after CABG was related to the use of preoperative furosemide, and it increased exponentially with the increase of cumulative dose of furosemide. This provides guidance for the dose of preoperative furosemide.

Identification of Key Histone Modifications and Their Regulatory Regions on Gene Expression Level Changes in Chronic Myelogenous Leukemia.

Chronic myelogenous leukemia (CML) is a type of cancer with a series of characteristics that make it particularly suitable for observations on leukemogenesis. Research have exhibited that the occurrence and progression of CML are associated with the dynamic alterations of histone modification (HM) patterns. In this study, we analyze the distribution patterns of 11 HM signals and calculate the signal changes of these HMs in CML cell lines as compared with that in normal cell lines. Meanwhile, the impacts of HM signal changes on expression level changes of CML-related genes are investigated. Based on the alterations of HM signals between CML and normal cell lines, the up- and down-regulated genes are predicted by the random forest algorithm to identify the key HMs and their regulatory regions. Research show that H3K79me2, H3K36me3, and H3K27ac are key HMs to expression level changes of CML-related genes in leukemogenesis. Especially H3K79me2 and H3K36me3 perform their important functions in all 100 bins studied. Our research reveals that H3K79me2 and H3K36me3 may be the core HMs for the clinical treatment of CML.

Risk factors and clinical significance of acute kidney injury after on-pump or off-pump coronary artery bypass grafting: a propensity score-matched study.

OBJECTIVES: Our goal was to investigate risk factors for acute kidney injury (AKI) after coronary artery bypass grafting (CABG) and the impact of AKI on short-term outcomes. METHODS: Data on 1395 patients (1261 who had isolated CABG and 134 with other operations) who underwent non-emergent CABG from January 2013 to March 2016 were retrospectively collected from a single centre. Logistic regression was performed to analyse risk factors. Cox regression was used to analyse the impact of AKI on the postoperative 30-day death rate. A 1:1 propensity score matching was performed to balance the baseline characteristics. RESULTS: The incidence of AKI with on-pump and off-pump coronary artery bypass was 10.4% and 3.5%, respectively. With logistic regression, duration of surgery was a risk factor for AKI (stage ≥2); previous hypertension, preoperative renal function insufficiency and the presence of cardiopulmonary bypass (CPB) were risk factors for mild AKI (stage ≥1). CPB time >207.5 min could be used to predict AKI (sensitivity 79.2%, specificity 78.6%) in the combined group. After adjusting for the duration of the operation, postoperative AKI (stage ≥1) was a risk factor for 30-day death and there was no difference in the 30-day death rate between on-pump and off-pump CABG. CONCLUSIONS: The use of CPB was a risk factor for mild AKI that did not affect the 30-day death rate of CABG whereas moderate to severe AKI caused by prolonged CPB time associated with surgical complexity affected the 30-day death rate. AKI may indicate surgical injury. The decision to use the on- or off-pump technique does not affect the 30-day death rate of CABG.

Semantics-enhanced supervised deep autoencoder for depth image-based 3D model retrieval.

Increased accuracy and affordability of depth sensors such as Kinect has created a great depth-data source for various 3D oriented applications. Specifically, 3D model retrieval is attracting attention in the field of computer vision and pattern recognition due to its numerous applications. A cross-domain retrieval approach such as depth image based 3D model retrieval has the challenges of occlusion, noise and view variability present in both query and training data. In this paper, we propose a new supervised deep autoencoder approach followed by semantic modeling to retrieve 3D shapes based on depth images. The key novelty is the two-fold feature abstraction to cope with the incompleteness and ambiguity present in the depth images. First, we develop a supervised autoencoder to extract robust features from both real depth images and synthetic ones rendered from 3D models, which are intended to balance reconstruction and classification capabilities of mix-domain data. Then semantic modeling of the supervised autoencoder features offers the next level of abstraction to cope with the incompleteness and ambiguity of the depth data. It is interesting that unlike any other pairwise model structures, we argue that cross-domain retrieval is still possible using only one single deep network trained on real and synthetic data. The experimental results on the NYUD2 and ModelNet10 datasets demonstrate that the proposed supervised method outperforms the recent approaches for cross-modal 3D model retrieval.