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.

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.

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.

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.

Knockdown of the prolyl isomerase Pin1 inhibits Hep-2 cell growth, migration, and invasion by targeting the ß-catenin signaling pathway.

There is increasing evidence indicating that peptidylprolyl cis/trans isomerase, NIMA-interacting 1 (Pin1) plays a decisive role in a variety of cancers. Nevertheless, its function in laryngeal squamous cell carcinoma (LSCC) has not been elaborated. The aim of this study is to determine the role of Pin1 in LSCC. Here, we established stably transfected Hep-2 cells with low expression of Pin1. Intriguingly, cell proliferation, migration, and invasion was significantly inhibited in Pin1-silenced Hep-2 cells. Similarly, knockdown of Pin1 induced apoptosis of Hep-2 cells, as evidenced by increased expression of cleaved-caspase-3, cleaved-PARP, and bax, and decreased expression of bcl2. We also demonstrated that silencing of Pin1 down-regulated ß-catenin and cyclin D1 expression. Inversely, over-expression of ß-catenin reversed the inhibiting effect of Pin1 silencing on Hep-2 cells. Moreover, we proved that knockdown of Pin1 inhibited tumorigenesis of Hep-2 cells in vivo. Taken together, we demonstrate that silencing of Pin1 effectively suppresses the growth of Hep-2 cells through ß-catenin, indicating that Pin1 possess the potential to serve as a therapeutic target for the treatment of LSCC.

Sodium glycididazole enhances the radiosensitivity of laryngeal cancer cells through downregulation of ATM signaling pathway.

The purpose of this study was to evaluate the radiation-enhancing effect of sodium glycididazole, and the corresponding mechanisms of action on laryngeal cancer cells. Two laryngeal cancer cell lines (Hep-2 and UT-SCC-19A) were irradiated with X-rays in the presence or absence of sodium glycididazole. Cell survival, DNA damage and repair, cell apoptosis, cell cycle distribution, expression of proteins related to cell cycle checkpoint, and apoptosis were measured. Significantly increased DNA damages, decreased cells in the G1 phase, arrested cells at G2/M phase, decreased DNA repair protein XRCC1 foci formation, and enhanced cell apoptosis were observed in laryngeal cell lines treated by sodium glycididazole combined with irradiation compared with the irradiation alone. The combined treatment downregulated the protein expressions of ataxia-telangiectasia mutated (ATM), p-ATM, CHK2, and P53 but upregulated the protein expressions of MDM2 and Cdk2. This study indicates that sodium glycididazole enhances the radiosensitivity of laryngeal cancer cells through downregulation of ATM signaling pathway in vitro and in vivo.

Identification of thermophilic proteins by incorporating evolutionary and acid dissociation information into Chou's general pseudo amino acid composition.

Thermophilic proteins can thrive stalely at the high temperatures. Identification of thermophilic protein could be helpful to learn the function of protein. Automated prediction of thermophilic protein is an important tool for genome annotation. In this work, a powerful predictor is proposed by combining amino acid composition, evolutionary information, and acid dissociation constant. The overall prediction accuracy of 93.53% was obtained for using the algorithm of support vector machine. In order to check the performance of our method, two low-similarity independent testing datasets are used to test the proposed method. Comparisons with other methods show that the prediction results were better than other existing methods in literature. This indicates that our approach was effective to predict thermophilic proteins.

DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou's pseudo amino acid patterns.

Identification of the proteins secreted by the malaria parasite is important for developing effective drugs and vaccines against infection. Therefore, we developed an improved predictor called "DSPMP" (Discriminating Secretory Proteins of Malaria Parasite) to identify the secretory proteins of the malaria parasite by integrating several vector features using support vector machine-based methods. DSPMP achieved an overall predictive accuracy of 98.61%, which is superior to that of the existing predictors in this field. We show that our method is capable of identifying the secretory proteins of the malaria parasite and found that the amino acid composition for buried and exposed sequences, denoted by AAC(b/e), was the most important feature for constructing the predictor. This article not only introduces a novel method for detecting the important features of sample proteins related to the malaria parasite but also provides a useful tool for tackling general protein-related problems. The DSPMP webserver is freely available at http://202.207.14.87:8032/fuwu/DSPMP/index.asp.

Simultaneous Determination of 27 Pesticides in Ginseng by UPLC/MS/MS and Modified QuEChERS Procedure.

A novel, simple, and rapid method for the quantification of 27 commonly used pesticides in ginseng incorporating a modified QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) procedure was developed and validated. The pesticides were extracted with 1% acetic acid in acetonitrile and extracts purified by use of the mixed sorbents primary-secondary amine, C18, and graphitized carbon black. Quantitative analysis of all pesticides was performed by ultra-performance LC/MS/MS in the positive ionization mode in a single run due to the fast polarity switching capability of the mass spectrometer. Two precursor-product ion transitions were monitored for each compound in the multiple reaction monitoring mode. Quantification was carried out using matrix-matched standards for calibration. Recoveries of the proposed method from the spiked samples were achieved in the range 62.8-108.5%, and RSD ranged from 1.5 to 11.8%.

Articulated Non-Rigid Point Set Registration for Human Pose Estimation from 3D Sensors.

We propose a generative framework for 3D human pose estimation that is able to operate on both individual point sets and sequential depth data. We formulate human pose estimation as a point set registration problem, where we propose three new approaches to address several major technical challenges in this research. First, we integrate two registration techniques that have a complementary nature to cope with non-rigid and articulated deformations of the human body under a variety of poses. This unique combination allows us to handle point sets of complex body motion and large pose variation without any initial conditions, as required by most existing approaches. Second, we introduce an efficient pose tracking strategy to deal with sequential depth data, where the major challenge is the incomplete data due to self-occlusions and view changes. We introduce a visible point extraction method to initialize a new template for the current frame from the previous frame, which effectively reduces the ambiguity and uncertainty during registration. Third, to support robust and stable pose tracking, we develop a segment volume validation technique to detect tracking failures and to re-initialize pose registration if needed. The experimental results on both benchmark 3D laser scan and depth datasets demonstrate the effectiveness of the proposed framework when compared with state-of-the-art algorithms.

Joint infrared target recognition and segmentation using a shape manifold-aware level set.

We propose new techniques for joint recognition, segmentation and pose estimation of infrared (IR) targets. The problem is formulated in a probabilistic level set framework where a shape constrained generative model is used to provide a multi-class and multi-view shape prior and where the shape model involves a couplet of view and identity manifolds (CVIM). A level set energy function is then iteratively optimized under the shape constraints provided by the CVIM. Since both the view and identity variables are expressed explicitly in the objective function, this approach naturally accomplishes recognition, segmentation and pose estimation as joint products of the optimization process. For realistic target chips, we solve the resulting multi-modal optimization problem by adopting a particle swarm optimization (PSO) algorithm and then improve the computational efficiency by implementing a gradient-boosted PSO (GB-PSO). Evaluation was performed using the Military Sensing Information Analysis Center (SENSIAC) ATR database, and experimental results show that both of the PSO algorithms reduce the cost of shape matching during CVIM-based shape inference. Particularly, GB-PSO outperforms other recent ATR algorithms, which require intensive shape matching, either explicitly (with pre-segmentation) or implicitly (without pre-segmentation).

Improved local ternary patterns for automatic target recognition in infrared imagery.

This paper presents an improved local ternary pattern (LTP) for automatic target recognition (ATR) in infrared imagery. Firstly, a robust LTP (RLTP) scheme is proposed to overcome the limitation of the original LTP for achieving the invariance with respect to the illumination transformation. Then, a soft concave-convex partition (SCCP) is introduced to add some flexibility to the original concave-convex partition (CCP) scheme. Referring to the orthogonal combination of local binary patterns (OC_LBP), the orthogonal combination of LTP (OC_LTP) is adopted to reduce the dimensionality of the LTP histogram. Further, a novel operator, called the soft concave-convex orthogonal combination of robust LTP (SCC_OC_RLTP), is proposed by combing RLTP, SCCP and OC_LTP. Finally, the new operator is used for ATR along with a blocking schedule to improve its discriminability and a feature selection technique to enhance its efficiency. Experimental results on infrared imagery show that the proposed features can achieve competitive ATR results compared with the state-of-the-art methods.

PreDNA: accurate prediction of DNA-binding sites in proteins by integrating sequence and geometric structure information.

MOTIVATION: Protein-DNA interactions often take part in various crucial processes, which are essential for cellular function. The identification of DNA-binding sites in proteins is important for understanding the molecular mechanisms of protein-DNA interaction. Thus, we have developed an improved method to predict DNA-binding sites by integrating structural alignment algorithm and support vector machine-based methods. RESULTS: Evaluated on a new non-redundant protein set with 224 chains, the method has 80.7% sensitivity and 82.9% specificity in the 5-fold cross-validation test. In addition, it predicts DNA-binding sites with 85.1% sensitivity and 85.3% specificity when tested on a dataset with 62 protein-DNA complexes. Compared with a recently published method, BindN+, our method predicts DNA-binding sites with a 7% better area under the receiver operating characteristic curve value when tested on the same dataset. Many important problems in cell biology require the dense non-linear interactions between functional modules be considered. Thus, our prediction method will be useful in detecting such complex interactions.

Predicting peroxidase subcellular location by hybridizing different descriptors of Chou' pseudo amino acid patterns.

Peroxidases as universal enzymes are essential for the regulation of reactive oxygen species levels and play major roles in both disease prevention and human pathologies. Automated prediction of functional protein localization is rarely reported and also is important for designing new drugs and drug targets. In this study, we first propose a support vector machine (SVM)-based method to predict peroxidase subcellular localization. Various Chou' pseudo amino acid descriptors and gene ontology (GO)-homology patterns were selected as input features to multiclass SVM. Prediction results showed that the smoothed PSSM encoding pattern performed better than the other approaches. The best overall prediction accuracy was 87.0% in a jackknife test using a PSSM profile of pattern with width=5. We also demonstrate that the present GO annotation is far from complete or deep enough for annotating proteins with a specific function.

acACS: improving the prediction accuracy of protein subcellular locations and protein classification by incorporating the average chemical shifts composition.

The chemical shift is sensitive to changes in the local environments and can report the structural changes. The structure information of a protein can be represented by the average chemical shifts (ACS) composition, which has been broadly applied for enhancing the prediction accuracy in protein subcellular locations and protein classification. However, different kinds of ACS composition can solve different problems. We established an online web server named acACS, which can convert secondary structure into average chemical shift and then compose the vector for representing a protein by using the algorithm of auto covariance. Our solution is easy to use and can meet the needs of users.

Joint target tracking, recognition and segmentation for infrared imagery using a shape manifold-based level set.

We propose a new integrated target tracking, recognition and segmentation algorithm, called ATR-Seg, for infrared imagery. ATR-Seg is formulated in a probabilistic shape-aware level set framework that incorporates a joint view-identity manifold (JVIM) for target shape modeling. As a shape generative model, JVIM features a unified manifold structure in the latent space that is embedded with one view-independent identity manifold and infinite identity-dependent view manifolds. In the ATR-Seg algorithm, the ATR problem formulated as a sequential level-set optimization process over the latent space of JVIM, so that tracking and recognition can be jointly optimized via implicit shape matching where target segmentation is achieved as a by-product without any pre-processing or feature extraction. Experimental results on the recently released SENSIAC ATR database demonstrate the advantages and effectiveness of ATR-Seg over two recent ATR algorithms that involve explicit shape matching.

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.

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.

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: Two hundred forty-six 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 similarity distance of diversity measure for discriminating mesophilic and thermophilic proteins.

The successful prediction of thermophilic proteins is useful for designing stable enzymes that are functional at high temperature. We have used the increment of diversity (ID), a novel amino acid composition-based similarity distance, in a 2-class K-nearest neighbor classifier to classify thermophilic and mesophilic proteins. And the KNN-ID classifier was successfully developed to predict the thermophilic proteins. Instead of extracting features from protein sequences as done previously, our approach was based on a diversity measure of symbol sequences. The similarity distance between each pair of protein sequences was first calculated to quantitatively measure the similarity level of one given sequence and the other. The query protein is then determined using the K-nearest neighbor algorithm. Comparisons with multiple recently published methods showed that the KNN-ID proposed in this study outperforms the other methods. The improved predictive performance indicated it is a simple and effective classifier for discriminating thermophilic and mesophilic proteins. At last, the influence of protein length and protein identity on prediction accuracy was discussed further. The prediction model and dataset used in this article can be freely downloaded from http://wlxy.imu.edu.cn/college/biostation/fuwu/KNN-ID/index.htm .