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No single technology can be rich enough to segment accurately due to the challenges of liver segmentation, which include low contrast with neighboring organs and the presence of pathology as well as highly varied shapes between subjects. This paper presents a Multi-stage framework for location and segmentation. First, Faster RCNN is employed to locate the liver region. Then, the Gaussian mixture model-based signed distance function is proposed to increase the flexibility of shape prior models. To reach the long and narrow ravine liver regions, the Gaussian pseudo variance level set is applied. Experimental results demonstrate the efficiency of the proposed method. More specifically, the proposed method is evaluated on 40 CT scan images, which are publicly available on three databases: SLIVER07, 3Dircadb, and LiTS. Our method has a slightly superior performance compared with other newly published methods.

Jinyu Zhu,1,2,&ast; Bufu Tang,1,3,&ast; Yang Gao,1,4 Suqin Xu,5 Jianfei Tu,1,4 Yajie Wang,4 Weibin Yang,1,4 Shiji Fang,4 Qiaoyou Weng,4 Zhongwei Zhao,1,4 Min Xu,1,4 Yang Yang,1,4 Minjiang Chen,1,4 Chenying Lu,1,4 Jiansong Ji1,4 1Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital, School of Medicine, Zhejiang University, Lishui, 323000, People’s Republic of China; 2Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, People’s Republic of China; 3Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People’s Republic of China; 4Department of Radiology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, People’s Republic of China; 5Clinical Laboratory, Fuyuan Hospital of Yiwu, Jinhua, 321000, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jiansong Ji; Chenying Lu Email jijiansong@zju.edu.cn; luchenying@zju.edu.cnIntroduction: Hepatocellular carcinoma (HCC) is a heterogeneous molecular disease with complex molecular pathogenesis that influences the efficacy of therapies. Exosomes play a crucial role in tumorigenesis and poor disease outcomes in HCC.Objective: The aim of this study was to identify the optimal gene set derived from exosomes in HCC with substantial predictive value to construct models for determining prognosis, recurrence risk and diagnosis and to identify candidates suitable for immunotherapy and chemotherapy, thereby providing new ideas for the individualized treatment of patients and for improving prognosis.Methods: Weighted correlation network analysis (WGCNA) and univariate and multivariate Cox PH regression analyses were applied to identify exosome-related signatures in the TCGA and exoRbase databases associated with clinical relevance, immunogenic features and tumor progression in HCC. Cell experiments were performed to further confirm the oncogenic effect of MYL6B and THOC2.Results: The models for prognosis and recurrence risk prediction were built based on two exosomal genes (MYL6B and THOC2) and were confirmed to be independent predictive factors with superior predictive performance. Patients with high prognostic risk had poorer prognosis than patients with low prognostic risk in all HCC datasets, namely, the TCGA cohort (HR=2.5, P< 0.001), the ICGC cohort (HR=3.15, P< 0.001) and the GSE14520 cohort (HR=1.85, P=0.004). A higher recurrence probability was found in HCC patients with high recurrence risk than in HCC patients with low recurrence risk in the TCGA cohort (HR=2.44, P< 0.001) and the GSE14520 cohort (HR=1.54, P=0.025). High prognostic risk patients had higher expression of immune checkpoint genes, such as PD1, B7H3, B7H5, CTLA4 and TIM3 (P< 0.05). Diagnostic models based on the same two genes were able to accurately distinguish HCC patients from normal individuals and HCC from dysplastic nodules.Conclusion: Our findings lay the foundation for identifying molecular markers to increase the early detection rate of HCC, improve disease outcomes, and determine more effective individualized treatment options for patients.Keywords: exosome, hepatocellular carcinoma, HCC, immune checkpoint, prognosis

The local spatio-temporal descriptor and feature encoding algorithm are two crucial key steps for human action recognition based on spatio-temporal interest points (STIP). Since the local descriptors for STIP are essentially a type of motion information based on the texture, the key point of local feature description is to extract invariable, robust and distinguishable local texture features and motion information in reference spatio-temporal volume. Scattering transform is an image transform method based on directional wavelet transform and scale convolution, which has local translation invariance, rotation invariance and elastic deformation stability for local texture features. A novel local descriptor for STIP based on spatio-temporal three-dimensional scattering transform is proposed in this paper, which extends the original scattering transform to spatio-temporal three-dimensional space. Compared to the traditional descriptors, such as HOG, HOF and so on, the proposed scattering transform coefficients based histogram of oriented gradients (STC-HOG) descriptor can capture more robust and distinguishable motion information of local texture for STIP. In order to incorporate the local descriptors into action video representation, the feature encoding algorithm is indispensable. For the problem that vector of locally aggregated descriptors (VLAD) loses feature distribution location information during feature encoding, a histogram of distribution vector of locally aggregated descriptors (HOD-VALD) based on Gaussian kernel is proposed. We validated the proposed algorithm for human action recognition on multiple public available datasets, such as KTH, UCF Sports, HMDB51 and so on. The evaluation experiment results indicate that the proposed descriptor and encoding method can improve the efficiency of human action recognition and the recognition accuracy.

The use of physical agent modalities dates back to the early days in the development of the field of physical and rehabilitation medicine. The term physiatrist is derived from the Greek words physis, pertaining to physical phenomena, and iatreia, referring to healer or physician. Thus a physiatrist is a physician who uses physical agents to relieve a patient's discomfort. Modalities are physical agents used to produce desired therapeutic effect. They include cold, heat, sound, electromagnetic waves, electricity, and mechanical forces. In this chapter, their physiologic effects, indications, techniques, and precautions are reviewed and discussed. Acupuncture and moxibustion, which use needling and heat to produce therapeutic effect, are also included. Physical agent modalities, although generally considered adjunctive rather than curative treatments, are widely used and important in the daily practices of most physiatrists.

Optimal healthcare quality can be defined as the provision of safe and effective care that improves outcomes, optimizes health, results in higher patient satisfaction, and furthers quality of life. The focus of healthcare worldwide has shifted from the fee-for-services model in which healthcare services rendered were rewarded in terms of volume to an emphasis on the best possible quality, safety, and outcomes as the top priorities. Thus the evaluation, measurement, and reporting of quality and outcomes have become the business of not only every healthcare institution and hospital but also of every individual healthcare provider. Physical medicine and rehabilitation practitioners have become increasingly aware of the importance of measuring healthcare quality and outcomes. This comes from increased scrutiny of healthcare quality, the cost of care, and quality reporting requirements from third-party payers, insurance companies, and reimbursing agencies. However, practicing physiatrists and professional member societies have also been realizing the importance of data on healthcare quality and outcomes. This has driven advancements in and availability of data repositories such as clinical registries. This chapter discusses healthcare quality, evidence-based medicine, clinical practice guidelines, outcome measures, performance measures, practice improvement, patient safety, and accreditation as it applies to healthcare in general and the practice of physical medicine and rehabilitation in particular.

Background Waste anesthetic gases (WAGs) leaked from new-type halogenated inhalational anesthetics such as sevoflurane have been were reported to pose a risk for the health of operating room personnel. The effects of WAGs on peripheral blood lymphocytes, however, remain yet controversial. The present study was undertaken to examine the effects of occupational sevoflurane exposure on the peripheral blood lymphocytes of medical personnel who work in the operating room. Methods A cohort of 56 medical residents were divided into exposed group (n = 28) and control group (non-exposed group) (n = 28). Gas chromatography was used to measure the concentration of sevoflurane in the medical resident’s breathing zone during surgeries under inhalation anesthesia in the exposure group. The gas collection lasted an hour. Peripheral blood lymphocytes were isolated from venous blood, and then apoptosis and cell cycle were analyzed by flow cytometry. EDTA-anticoagulated whole blood was harvested to analyze the lymphocyte subsets by flow cytometry. Immunoglobulins (IgA, IgM, IgG) were quantified by immunoturbidimetry. Results The average concentration of sevoflurane in the exposed group was 1.03 ppm with a range from 0.03 ppm to 2.24 ppm. No significant effects were found on the apoptosis rates or cell cycles of peripheral blood lymphocytes in the exposed group relative to the control group (P > 0.05). Similarly, there were no significant differences in the lymphocyte subsets or the levels of immunoglobulins (IgA, IgM, IgG) between the two groups (P > 0.05). Conclusions Occupational exposure to low-level sevoflurane has no significant effect on the peripheral blood lymphocytes of operating room staff, but this conclusion needs to be confirmed by multicenter and long-term follow-up studies with large samples. Trial registration number and date of registration ChiCTR2000040772, December 9, 2020 (Retrospective registration).

BACKGROUND Breast cancer, a common malignant tumor, has been considered as the leading cause of cancer-related death in women. Collagen type X alpha 1 (COL10A1) is overexpressed in breast cancer. The current study was designed to determine the functional involvement and regulatory mechanism of COL10A1 on the growth and metastasis of breast cancer. MATERIAL AND METHODS COL10A1 and Prolyl 4-hydroxylase beta polypeptide (P4HB) expressions in normal tissues and tumor tissues of breast cancer patients were obtained from the GEPIA dataset. COL10A1 and P4HB levels in breast cancer cell lines were detected by real-time quantitative polymerase chain reaction (RT-qPCR) and western blot analysis. Furthermore, the interaction between COL10A1 and P4HB was confirmed by co-immunoprecipitation (Co-IP) assay. Cell Counting Kit-8 (CCK-8) and colony formation assay were applied to evaluate cell proliferation and clone-forming abilities of breast cancer cells. In addition, wound healing assay and transwell assay were performed to measure cell migration and invasion capabilities, respectively, in breast cancer. RESULTS The GEPIA dataset presented overexpressed COL10A1 and P4HB in tumor tissues of breast cancer patients. COL10A1 and P4HB expression levels were greatly upregulated in breast cancer cell lines. In addition, COL10A1 could directly interact with P4HB. Functionally, overexpressed COL10A1 boosted the proliferation and metastasis of breast cancer cells and silenced COL10A1 impeded the progression of breast cancer. More importantly, knockdown of P4HB weakened the promoting effects of overexpressed COL10A1 on cell proliferation, migration, and invasion in breast cancer. CONCLUSIONS COL10A1 promotes the malignant progression of breast cancer by upregulating P4HB expression, indicating that COL10A1 functions as an oncogene in breast cancer.

The outbreak of novel coronavirus disease 2019 (COVID-19) has become the largest health threat worldwide, with more than 34.40 million positive cases and over 1.02 million deaths confirmed. In this study, we confirmed that significantly differentially expressed genes in COVID-19 patients were mainly involved in the regulation of immune and inflammation-related signaling pathways. It is worth noting that many infected COVID-19 patients have malignant tumors, and their prognosis is poor. To explore the susceptibility factors of cancer patients, we assessed the expression of ACE2, TMPRSS2, and the endocytic regulator AAK1 in lung adenocarcinoma (LUAD) patients and explored their effects on immune infiltration. We found that the expression of ACE2 and TMPRSS2 in LUAD patients was significantly increased, which may explain why LUAD patients are susceptible to SARS-CoV-2, and the patients with high-expression genes presented increased infiltration of immune cells such as B cells and CD4 T cells. In addition, we also identified miR-432-5p as a potential targeted molecule and bexarotene as a potential targeted drug of the three genes through bioinformatic analysis and further verified the anti-inflammatory effect of bexarotene, providing new ideas for the treatment of COVID-19.

Background: In this study, we performed a comprehensive analysis to identify the optimal gene set derived from exosomes in hepatocellular carcinoma (HCC) with great prognostic value to construct prognostic, recurrence and diagnostic models and predicted therapeutic response for hepatocellular carcinoma.Methods: Differential expression analysis and Weighted Gene Co-Expression Network Analysis (WGCNA) of RNA-sequencing were used to identify differentially expressed exosome-derived genes related to HCC. Univariate and multivariate Cox regression analyses were performed to determine exosome-derived genes associated with prognosis of HCC to construct a prognostic model, and recurrence and diagnostic models were built based on the same genes. Results: MYL6B and THOC2 were determined to be closely related to prognosis in HCC. The prognostic and recurrence models built based on these two genes were confirmed to be independent predictive factors with superior predictive performance. Patients with high prognostic risk had poorer prognosis than patients with low prognostic risk in all HCC sets, namely, the TCGA cohort (HR=2.5, PConclusions: Prognostic, recurrence, and diagnostic models based on the two exosome-derived genes (MYL6B and THOC2) were proven superior predictive performance. Our findings lay the foundation for identifying molecular markers to increase the early detection rate of HCC, improve disease outcomes, and determine more effective individualized treatment options for patients.

Fast and accurate visual scene understanding in autonomous vehicles is necessary but still very challenging. An autonomous vehicle must be taught to read the road like a human driver for better controlling the vehicle, so it is important to efficiently detect the road area and road markings. In this paper, we mainly focus on the vanishing point detection and its application in inverse perspective mapping (IPM) for road marking understanding. We first propose a fast and accurate vanishing point detection method for various types of roads, by adopting and improving Weber local descriptor to obtain salient representative texture and orientation information of the road area, and then voting for the dominant vanishing point with a simple line-voting scheme. Experimental results demonstrate that the proposed vanishing point detection approach gains a better performance than some state-of-the-art methods in terms of accuracy and computation time. Furthermore, we introduce the detected vanishing point into the IPM algorithm in the structured road environment, since some important calibration parameters can be automatically calculated by the vanishing point, especially on the rough road. Experiments also show that our proposed vanishing point-based IPM method is adaptive and accurate, which is conducive to the subsequent road marking detection and recognition.

Background: Early diagnostic of COVID-19 pneumonia remains a dilemma We aimed to investigate the importance of early stage chest CT imaging features in the ear

Background Recent evidence suggests early screening of preeclampsia and small-for-gestational-age (SGA) would benefit pregnancies followed by subsequent prophylactic use of aspirin. Multi-marker models have shown capability of predicting preeclampsia and SGA in first trimester. Yet the clinical feasibility of combined screening model for Chinese pregnancies has not been fully assessed. The aim of this study is to evaluate the applicability of a multi-marker screening model to the prediction of preeclampsia and SGA in first trimester particularly among Chinese population. Methods Three thousand two hundred seventy pregnancies meeting the inclusion criteria took first-trimester screening of preeclampsia and SGA. A prior risk based on maternal characteristics was evaluated, and a posterior risk was assessed by combining prior risk with multiple of median (MoM) values of mean arterial pressure (MAP), serum placental growth factor (PLGF) and pregnancy associated plasma protein A (PAPP-A). Both risks were calculated by Preeclampsia PREDICTOR™ software, Perkin Elmer. Screening performance of prior and posterior risks for early and late preeclampsia by using PREDICTOR software was shown by Receiver Operating Characteristics (ROC) curves. The estimation of detection rates and false positive rates of delivery with both preeclampsia and SGA was made. Results Eight cases developed early preeclampsia (0.24%) and 35 were diagnosed as late preeclampsia (1.07%). Five with early preeclampsia and ten with late preeclampsia later delivered SGA newborns (0.46%); 84 without preeclampsia gave birth to the SGAs (2.57%). According to ROC curves, posterior risks performed better than prior risks in terms of preeclampsia, especially in early preeclampsia. At 10% false positive rate, detection rates of early and late preeclampsia were 87.50 and 48.57%, detection rates of early and late SGA were 41.67 and 28.00%, respectively. For SGA, detection rates in cases with preeclampsia were much higher than those in absence of it. Conclusions This study demonstrates that combined screening model could be useful for predicting early preeclampsia in Chinese pregnancies. Furthermore, the performance of SGA screening by same protocol is strongly associated with preeclampsia. Electronic supplementary material The online version of this article (10.1186/s12884-019-2455-8) contains supplementary material, which is available to authorized users.

As a branch of data analysis, pattern alignment has received much attentions in recent years. More specifically, it learns to find intrinsic bridge between different domains and make data handling be transferrable for efficient recognition. In this work, an unsupervised feature learning method is proposed to meet demand on pattern alignment. Compared with existing methods, more efficiency can be reached owing to scalable learning, which is competent to tackle large-scale data for kernel alignment. Experimental results show proposed method can give comparable performance among the state-of-the-art methods.

Early forest fire detection is of great importance to avoid the huge damage of forests caused by fires. Early fire detection focuses on smoke detection. The proposed scheme of early fire detection is divided into two steps. Based on the slow spread of smoke, firstly a time delay parameter improves Gaussian mixture model for extracting candidate smoke regions. Then, two motion features of smoke, the rate of area change and motion style, are used to select smoke regions from the candidate regions. Our experiments verified the effectiveness of the proposed early fire detection scheme.

With more color images being widely used on the Internet, the research on embedding color watermark image into color host image has been receiving more attention. Recently, Su et al. have proposed a robust and blind watermarking scheme for dual color image, in which the main innovation is the using of two-level DCT. However, it has been demonstrated in this paper that the original scheme in Su’s study is not secure and can be attacked by our proposed method. In addition, some errors in the original scheme have been pointed out. Also, an improvement measure is presented to enhance the security of the original watermarking scheme. The proposed method has been confirmed by both theoretical analysis and experimental results.

The automatic analysis of microscopic images is an important subject of medical image processing, of which the cell detection is an important part. However, owing to the different size and shape, as also as the adhesion among cells, detecting and locating cells accurately seems to be a very challenging task. In this work, we investigate applying the Faster R-CNN, which has recently shown incredible performance on many public datasets, to cell detection. The Faster R-CNN contains both segmentation and classification. By training a Faster R-CNN model, a series of experiments are achieved. Experimental results show that the Faster R-CNN can detect almost all cells in a microscopic image. The proposed cell detector has improved detection performance, and it is easy-implemented and time-saving.

Images with low contrast, overlapped noise and intensity inhomogeneity of multiple objects make many existing level set methods disabled for image segmentation. To address the problem, an improved active contour model is proposed, driving by region-scalable and local Gaussian-distribution fitting energy for image segmentation. Firstly, we classify regions with similar intensity by utilizing the means and variances of local image intensities. Secondly, we define a new edge stopping functional to robustly capture the boundaries of multiple objects. Finally, we utilize LoG energy term to catch edge information and smooth the homogeneous regions, which can be optimized by an energy function. Experiments results on real and synthetic images validate that our method is faster, robuster and higher accuracy than other major region-based methods for images with multiple objects.

Road images taken from autonomous vehicles usually have perspective effects, which distort the shapes of road markings and hence make the subsequent road marking recognition difficult. A commonly used method to solve this problem is the Inverse Perspective Mapping (IPM), which could alleviate or even eliminate the perspective effect. However, it is time-consuming due to the interpolation operation. In this paper, we propose an efficient algorithm to reduce the perspective distortion of road markings. First, we reconstruct the road images by changing the viewpoint, as if they are captured by the camera right in front of the road markings in each lane. Second, Region of Interest (ROI) of each lane could be confirmed in its corresponding image. The proposed method could provide a fast and stable view of road markings and make them easy to detect and recognize. The experiments on a road image dataset demonstrate that our proposed algorithm is much faster than IPM while maintaining the comparable accuracy in road marking recognition.

In this paper, we consider the problem of recognizing human faces from frontal views with varying illumination, as well as occlusion and disguise. Motivated by the latest research on the recovery of low-rank matrix using robust principal component analysis (RPCA), we present a novel approach of robust face recognition by exploiting the sparse error component obtained by RPCA. Compared with low-rank component, it is revealed that the associated sparse error component exhibits more discriminating information which is of benefit to face identification. We define two descriptors (i.e., sparsity and smoothness) to represent characteristic of the sparse error component, and give two recognition protocols (i.e., the weighted based method and the ratio based method) to classify face images. The efficacy of the proposed approach is verified on publicly available databases (i.e., Extended Yale B and AR) with promising results. Meanwhile, the proposed algorithm manifests robustness since it does not assume any explicit prior knowledge about the illumination conditions, as well as the nature of corrupted and occluded regions. Furthermore, the proposed method is not limited to face recognition, also can be extended to other image-based object recognition. HighlightsThis paper is motivated by robust principal component analysis (RPCA).We exploit the sparse error component to perform face recognition.We define two descriptors (i.e., sparsity and smoothness) to represent the sparse error image.We present the weighted based method and ratio based method to classify face images.Our method shows good performance on public face databases with illumination and occlusion.

This paper proposes a robust inclinometer system using three monaxial microelectromechanical systems accelerometers and three monaxial fluxgate sensors. By formulating a basic three sensitive axes sensor model, we calibrate tilt and azimuth directly through a simple and effective linear model. To improve the accuracy of the proposed model, we present two different optimal solutions to minimize the systematic error, and adopt the interior-reflective Newton method and the sequential quadratic programming method to solve the problems, respectively. Experimental results demonstrate that our system performs excellently with the maximum error of tilt angle 0.09 $^{\circ}$ in our applied measurement range (0 $^{\circ}$ –120 $^{\circ}$ ), and the maximum error of azimuth angle 0.4 $^{\circ}$ in the measurement range (0 $^{\circ}$ –360 $^{\circ}$ ).

This paper proposes a novel method for visual saliency detection based on an universal probabilistic model, which measures the saliency by combining low level features and location prior. We view the task of estimating visual saliency as searching the most conspicuous parts in an image and extract the saliency map by computing the dissimilarity between different regions. We simulate the moving of the center of human visual field, and describe how the center shift process works on visual saliency. Furthermore, multiscale analysis is adopted for improving the robustness of our model. Experimental results on three public image datasets show that the proposed approach outperforms 18 state-of-the-art methods for both salient object detection and human eye fixation prediction.

Automatic human action recognition is a core functionality of systems for video surveillance and human object interaction. Sparse representation-based classification (SRC) has been widely used for face recognition (FR) and extended for video classification. Reconstruction error is an important scalar for object classification. If the reconstruction error of intra-class is small and the reconstruction error of inter-class is large, the classifier is robustness. In this paper, we propose an improved sparse representation-based classification method with l 1 -norm based re-construction error evaluation and local spatio-temporal features for human action recognition. The main idea in our method is to make the reconstruction error sparse and stable in order to improve the robustness of classifier. Because of L2-norm is sensitive to the singular samples, the reconstruction error with L2-norm scale is unstable for training and recognition. Against to L2-norm, the l 1 -norm makes the model more compact and sparse. So, we restrict the reconstruction error by a l 1 -norm constraint in sparse representation over-compete dictionary training and classifying. The performance of the proposed approach was evaluated on two public available datasets, KTH Action Dataset and UCF Sports Action Dataset, and obtained a higher recognition accuracy compared to other state-of-art methods in the literatures.

Since lane information is necessary for road security improvement in unmanned vehicle systems, the detection of the lane is an important task. Most existing approaches are particularly designed for specific road scenario (such as the highway, urban roads). However, the detection precision may be deteriorated if the lane markings are blurred and vestigial. Similarly, the reflection and smudges on the road surface also influence the detection result. In this paper, we introduce a novel robust lane detection algorithm based on the differential excitation. Firstly, we extract the region of interest (ROI) by considering human visual attention. Then we enhance salient texture information and remove the noise effectively through differential excitation. The binary image is obtained using Weber's law. Furthermore, we select the points that satisfy the proposed rules as voting points. Finally, the lane markings are detected and extracted by Hough transform accordingly. Experimental results on an open database indicate that the proposed method outperforms the classical Sebdani's approach and the Low's approach.

We aim to sense the tilt of a cylindrical object, which refers to the angle between the central axis of a given cylindrical object and the direction of gravitational acceleration. Since only the pitch angle is sensed, it is a specific case of attitude estimation. In our previous work to accurate tilt sensing, a numerical scheme with three linear numerical models based on a physical model has been proposed. In this paper, we introduce a simpler tilt measurement algorithm using only one linear numerical model based on the same physical model. This algorithm is able to determine accurate tilt angle with equivalent measurement error to our previous work. In the range (0°, 120°), the average error is below 0.1°. Moreover, it simplifies both software and hardware implementation.

Chronic pain can develop from numerous conditions and is one of the most widespread and disabling health problems today. Unfortunately, the pathophysiology of chronic pain in most of these conditions, along with consistently effective treatments, remain elusive. However, recent advances in neuroimaging and neurophysiology are rapidly expanding our understanding of these pain syndromes. It is now clear that substantial functional and structural changes, or plasticity, in the central nervous system (CNS) are associated with many chronic pain syndromes. A group of cortical and subcortical brain regions, often referred to as the "pain matrix," often show abnormalities on functional imaging studies in persons with chronic pain, even with different pain locations and etiologies. Changes in the motor and sensory homunculus also are seen. Some of these CNS changes return to a normal state with resolution of the pain. It is hoped that this knowledge will lead to more effective treatments or even new preventative measures. The purpose of this article is to review recent advances in the understanding of the CNS changes associated with chronic pain in a number of clinical entities encountered in the field of physical medicine and rehabilitation. These clinical entities include nonspecific low back pain, fibromyalgia, complex regional pain syndrome, postamputation phantom pain, and chronic pain after spinal cord injury.

Although the quartz accelerometer has made great advances, the performance, in some specific applications such as tilt sensing, needs to be well compensated in high temperature environment. Based on the high temperature testing of low cost quartz accelerometers, we found that the normalized positive and negative parts are asymmetrical at high temperature and the temperature curve of zero sensor output is related to the roll angle of the sensor. Traditional temperature compensation method only considers the temperature factor and ignores the roll sensitivity, which leads to deteriorated accuracy. To solve this problem, this paper proposes a novel and simple mathematical model to obtain a more accurate expression of zero sensor output, which makes the sensor output more robust at high temperature. Experimental results on two low cost quartz accelerometers demonstrate that the proposed model is feasible and effective, which could reduce the temperature drift error of the sensor output typically from 0.01 g to 0.001 g. Furthermore, we introduce the compensated sensors in the three-axis inclinometer system for tilt sensing, and the evaluation results show that the temperature drift error of the inclination in the range (20∘C,150∘C) is reduced typically from0.4∘to0.1∘compared to the traditional method.

Objective: To investigate whether the provision of power mobility would have an effect on body weight in adults who were first-time qualifiers for power mobility. Design: This was a retrospective observational study of consecutive subjects, who served as their own controls. The medical records of 468 subjects who were approved for power mobility during a 17-mo period were reviewed. Three weight groups (12 mos before, at, and 12 mos after the power mobility evaluation) were evaluated with repeated-measures analysis of variance (ANOVA). The weight changes on subjects in different age groups (45-54, 55-64, 65-74, and >74), in geriatric vs. nongeriatric groups, and in different body mass index (BMI) groups were analyzed. Results: Eighty-nine subjects met the inclusion criteria. They were obese (49.4%), and most of them were geriatric (64%). Congestive heart failure (30.34%) and chronic obstructive pulmonary disease (22.47%) were the two main presenting diagnoses. The repeated-measures ANOVA showed no significant weight change in the three studied weight groups. Similar results were seen in the age and BMI subgroups. Conclusions: There was no statistically significant weight change in adults who were first-time qualifiers and who used power mobility for 1 yr.

Most existing approaches detect the vanishing point by voting the local dominant texture orientation at each pixel. However, when it is hard to distinguish natural road clues (items for finding the vanishing point) from background noises (e.g. stones, grasses) in complex scenes, they may suffer deteriorated accuracy and efficiency. In this paper, the authors introduce a novel vanishing point detection algorithm with the proposed Weber Orientation Descriptor (WOD). The authors first employ the differential excitation component of WOD to extract reliable road clue regions from a complex background, and then adopt the orientation component of WOD and the authors proposed line-voting scheme (LVS) to locate the vanishing point. Experimental results on the benchmark dataset reveal a step forward in detection performance against the state-of-the-art vanishing point detection methods.

This paper proposes a novel method for estimating visual saliency based on a typical agreement that image saliency depends mainly on local and global contrast from various feature channels. We compute the contrast between image patches on different low-level feature maps which are generated by color space conversion and support value transform. To obtain the representative measurement effectively, we calculate the dissimilarity in a reduced dimensional principal component space. In addition, our method may be easily extended for more conspicuous feature channels in an efficient manner. Experimental results on two public available human eye fixation datasets demonstrate that our method outperforms other seven state-of-the-art saliency models.

Image saliency attempts to describe the most conspicuous part in an input image by mimicking human visual selective attention mechanism. Naturally, it could be adopted for improving object recognition. To demonstrate the effectiveness of saliency in object recognition, this paper proposes a salient hierarchical model. First, the traditional saliency model is modified for more robust saliency estimation. Second, the visual saliency detection method is combined with the Hierarchical Maximization model to provide more useful visual information for classification. Experimental results show that the improved saliency model extracts more accurate conspicuity, and the proposed salient hierarchical model outperforms Hierarchical Maximization model.

A novel first-detect-then-identify approach with SIFT features and discrete wavelet transform for tracking object is proposed in real surveillance scenarios. For accurate and fast moving object detection, discrete wavelet transform is adopted to eliminate the noises of the frames which may cause detection errors, and then objects are detected by applying the inter-frame difference method on the low frequency parts of two consecutive frames, and then SIFT feature is used for object representation and identification due to its invariant properties. Experimental results demonstrate that the proposed strategy improves the tracking performance by comparing with the classical mean shift method, and it is also shown that the proposed algorithm can be also applied in multiple objects tracking in real scenarios.

Shift schedule and torque converter lockup schedule are designed based on parameters of the vehicle. In addition, optimization of shift quality based on uniform design method, which can reduce test time, is put forward and the application method is specified. Furthermore, circuit diagram of transmission control unit (TCU) hardware including power supply module, signal input channels, signal output channels and controller area network (CAN) communication module are designed, and then TCU hardware is manufactured. Finally, dynamic performance, constant speed fuel economy and shift quality of vehicle equipped with the TCU are inspected, satisfactory results are obtained.

Background Transmetatarsal amputation (TMA) is an operation designed to remove a limited area of irremediable tissue ischemia and/or infection and preserve limb function. Patients are selected for TMA based on degree of tissue loss/infection, adequacy of tissue perfusion at the transmetatarsal level, current ambulatory status, and estimation of the likelihood of postprocedure ambulation. The purpose of this study was to assess the validity of these selection criteria. Methods An institutional review board–approved retrospective review was conducted of all patients undergoing TMA from January 1, 1997, until January 1, 2006. Information was collected on patient demographics, medical comorbidity, and clinical and surgical variables. Outcome measures included the proportion of patients requiring amputation revision to a more proximal level and ambulatory status at last follow-up. Results Fifty-two TMAs were performed. In 35 procedures, the skin was left open, and in 17 TMA was closed primarily. Primary indications for the procedure were vascular insufficiency or infection in 50 of 52 patients, whereas 2 patients required amputation for malignancy. The majority (46/52, 89%) of patients were diabetic. After the index TMA, 85 additional operations were required. Only 9 patients (18%) underwent a single operation. Revision of the TMA to a more proximal level was required in 29 of 52 (56%) patients, resulting in 4 Syme, 20 transtibial, and 5 transfemoral amputations. Non–insulin-dependent diabetes was associated with an increased likelihood of revision to a more proximal amputation (odds ratio [OR] = 5.4; 95% confidence interval [CI], 1.2–24). At the time of last follow-up (median 18 months), 37 of 50 (74%) patients were ambulatory (83% for TMAs and 67% for more proximal amputations, P = 0.18). Prior vascular procedures were associated with a significantly decreased likelihood of ambulation (OR = 14; 95% CI, 1.9–103). Conclusions Although most patients retain the ability to ambulate after TMA, multiple operations should be anticipated in the majority of patients and revision of a TMA to a more proximal level may be required. These data suggest that current selection criteria for TMA may be inadequate.