The mediating effects of depression, anxiety, and rapid eye movement sleep behavior disorder on the association between dopaminergic replacement therapy and impulse control disorders in Parkinson's disease.

OBJECTIVES: This study aims to longitudinally explore whether and how rapid eye movement sleep behavior disorder (RBD), depression, and anxiety mediate the association between dopaminergic replacement therapy (DRT) and impulse control disorders (ICDs) in patients with Parkinson's disease (PD). METHODS: Subjects were selected from the Parkinson's Progression Markers Initiative. After excluding missing data, 268, 223, 218, 238, and 219 patients with PD diagnosed at 12, 24, 36, 48, and 60 months prior, respectively, were included. We used the Questionnaire for Impulsive-Compulsive Disorders, RBD Screening Questionnaire, Geriatric Depression Scale, and State-Trait-Anxiety Inventory to assess ICBs, RBD, depression, and anxiety, respectively. We constructed three causal mediation analysis models to infer potential contingent pathways from DRT to ICD mediated by depression, anxiety, and RBD separately. RESULTS: DRT was associated with an increased risk of PD incidence. Aggravation of ICDs was partly explained by improvements in depression (the average causal mediation effect accounted for 8.0% of the total effect) and RBD (the average causal mediation effect of RBD accounted for 16.4% of the total effect). This suggested that anxiety (the average causal mediation effect accounted for 12.7% of the total effect) plays a mediating role. CONCLUSIONS: Focusing on changes in RBD, depression, and anxiety associated with hyperdopaminergic status should be an essential part of strategies to prevent ICDs in patients with Parkinson's disease.

The path linking excessive daytime sleepiness and activity of daily living in Parkinson's disease: the longitudinal mediation effect of autonomic dysfunction.

BACKGROUND: Excessive daytime sleepiness (EDS) and autonomic dysfunction have been verified to impair activity of daily living (ADL) in patients with Parkinson's disease (PD). Whether EDS can affect ADL in PD patients through autonomic dysfunction is still unclear. The purpose of this study is to explore the longitudinal mediation effect of autonomic dysfunction between EDS and ADL. METHODS: Data used in this study were from six-follow-up visits of 413 patients with newly diagnosed PD from the Parkinson's Progression Markers Initiative (PPMI). We used latent growth mediation modeling (LGMM) to explore whether the autonomic dysfunction is a longitudinal mediator between EDS and ADL. RESULTS: The results showed that as the disease progresses, EDS (P < 0.001) and autonomic dysfunction (P < 0.001) gradually worsened and ADL (P < 0.001) gradually decreased in PD patients. In addition, the more severe the patients' EDS symptom, the more worsened the symptoms of autonomic dysfunction, which result in a decrease in ADL. Both the intercept (95% CI: 0.142, 0.308) and the slope (95% CI: 0.083, 0.331) of autonomic dysfunction showed a partial mediating effect, and a longitudinal mediation effect was presented. CONCLUSION: Longitudinal changes in EDS affect the ADL of PD patients directly or indirectly by affecting the symptoms of autonomic dysfunction. Controlling the symptoms of autonomic dysfunction may improve the ADL of PD patients with EDS.

Highlight Removal of Multi-View Facial Images.

Highlight removal is a fundamental and challenging task that has been an active field for decades. Although several methods have recently been improved for facial images, they are typically designed for a single image. This paper presents a lightweight optimization method for removing the specular highlight reflections of multi-view facial images. This is achieved by taking full advantage of the Lambertian consistency, which states that the diffuse component does not vary with the change in the viewing angle, while the specular component changes the behavior. We provide non-negative constraints on light and shading in all directions, rather than normal directions contained in the face, to obtain physically reliable properties. The removal of highlights is further facilitated through the estimation of illumination chromaticity, which is done by employing orthogonal subspace projection. An important practical feature of the proposed method does not require face reflectance priors. A dataset with ground truth for highlight removal of multi-view facial images is captured to quantitatively evaluate the performance of our method. We demonstrate the robustness and accuracy of our method through comparisons to existing methods for removing specular highlights and improvement in applications such as reconstruction.

Longitudinal Mediating Effect of Depression on the Relationship between Excessive Daytime Sleepiness and Activities of Daily Living in Parkinson's Disease.

OBJECTIVES: Whether depression affects activities of daily living (ADLs) in patients with Parkinson's disease (PD) via excessive daytime sleepiness (EDS) remains unclear; moreover, few longitudinal studies have been conducted. METHODS: We recruited 421 patients from the Parkinson's Progression Markers Initiative. We constructed a latent growth mediation model to explore the longitudinal mediating effect of depression on the relationship between EDS and ADLs. RESULTS: EDS (p < .001) and depression scores (p < .001) both increased, and ADL scores (p < .001) decreased. Moreover, EDS was positively correlated with depression, whereas an increase in EDS significantly reduced ADLs. The initial value (95% confidence interval [CI]: 0.026, 0.154) and the rate of change (95% CI: 0.138, 0.514) of self-reported depression measured using the Geriatric Depression Scale(GDS) partially mediated the association between EDS and ADL score. CONCLUSIONS: The indirect effect of the longitudinal changes of depression on the relationship between EDS and ADLs highlights the importance of depression changes in PD patients with EDS. CLINICAL IMPLICATIONS: Depression should be considered a mediator by clinicians; preventing the worsening of depression is essential for improving ADLs in patients with PD, especially those with EDS.

Depth-based removal of thermal reflection with the light-field theory.

Thermal imaging is a useful imaging technique in many scenarios. It can capture the temperature distribution of scenes in the dark and see through sparse smoke and dust. However, some surfaces such as steel and glass with high reflectivity lead to a reflection problem in thermal imaging, while heavy mist and gases lead to the occlusion problem. We proposed an efficient algorithm to solve the occlusion problem in our earlier work. The reflection in thermal images causes errors in detection and temperature measurement. Therefore, the precise model and efficient algorithms to solve this problem are in high demand. In this paper, we mainly model the reflection problem in thermal imaging and propose an algorithm to deal with it. In our experiments, a thermal camera array is built to capture the thermal light-field images. We first separate a part of the reflection pixels from thermal images based on the depth information. After that, the thermal reflection is removed by optimizing a designed cost function. The experiment results show that our reflection removal method can separate the thermal reflection with high precision, retain the objects in the scene, and get better performance than existing methods.

Reconstruction of High-Precision Semantic Map.

We present a real-time Truncated Signed Distance Field (TSDF)-based three-dimensional (3D) semantic reconstruction for LiDAR point cloud, which achieves incremental surface reconstruction and highly accurate semantic segmentation. The high-precise 3D semantic reconstruction in real time on LiDAR data is important but challenging. Lighting Detection and Ranging (LiDAR) data with high accuracy is massive for 3D reconstruction. We so propose a line-of-sight algorithm to update implicit surface incrementally. Meanwhile, in order to use more semantic information effectively, an online attention-based spatial and temporal feature fusion method is proposed, which is well integrated into the reconstruction system. We implement parallel computation in the reconstruction and semantic fusion process, which achieves real-time performance. We demonstrate our approach on the CARLA dataset, Apollo dataset, and our dataset. When compared with the state-of-art mapping methods, our method has a great advantage in terms of both quality and speed, which meets the needs of robotic mapping and navigation.

Towards Clinical Translation of LED-Based Photoacoustic Imaging: A Review.

Photoacoustic imaging, with the capability to provide simultaneous structural, functional, and molecular information, is one of the fastest growing biomedical imaging modalities of recent times. As a hybrid modality, it not only provides greater penetration depth than the purely optical imaging techniques, but also provides optical contrast of molecular components in the living tissue. Conventionally, photoacoustic imaging systems utilize bulky and expensive class IV lasers, which is one of the key factors hindering the clinical translation of this promising modality. Use of LEDs which are portable and affordable offers a unique opportunity to accelerate the clinical translation of photoacoustics. In this paper, we first review the development history of LED as an illumination source in biomedical photoacoustic imaging. Key developments in this area, from point-source measurements to development of high-power LED arrays, are briefly discussed. Finally, we thoroughly review multiple phantom, ex-vivo, animal in-vivo, human in-vivo, and clinical pilot studies and demonstrate the unprecedented preclinical and clinical potential of LED-based photoacoustic imaging.

Modeling the occlusion problem in thermal imaging to allow seeing through mist and foliage.

Thermal imaging can easily see through smoke and dust. It is a useful technique in the military and industrial fields. However, thermal imaging can also be blocked by heavy mist or gases with high emissivity such as CO2. Allowing a thermal camera to see through these obstacles is in high demand. In this paper, we modeled the occlusion problem in thermal imaging and proposed an algorithm to image the objects through mist and foliage. We built a system to capture the thermal light field camera. We took thermal reflection and absorption of the obstacles into consideration. We removed the obstacle part in thermal images by estimating the intensity of infrared radiation. Then, we refocused the thermal images on the specific depth of the object for reconstruction. The experiment's results show that a proposed algorithm can reconstruct the occluded objects in a clear shape while blurring the obstacles. Based on the thermal occlusion model and refocusing, the thermal camera can image a human through mist and foliage.

Illumination separation of non-Lambertian scenes from a single hyperspectral image.

In this paper, we propose a general framework to estimate the spectrum of the illumination from global specular information in a single hyperspectral image. By utilizing the specular independent subspace, we iteratively separate the reflectance components and shape a weight scheme in order to find specular-contaminated pixels. After that, the illumination can be directly estimated by factorizing the weighted specular-contaminated pixels. The proposed method enables a direct and effective decomposition of the illumination and reflectance components from a single hyperspectral image. We demonstrate the robustness and accuracy of our method on simulation and real experiments. Moreover, we capture a hyperspectral image dataset with ground-truth illumination to quantitative compare the performance.

Measure and model a 3-D space-variant PSF for fluorescence microscopy image deblurring.

Conventional deconvolution methods assume that the microscopy system is spatially invariant, introducing considerable errors. We developed a method to more precisely estimate space-variant point-spread functions from sparse measurements. To this end, a space-variant version of deblurring algorithm was developed and combined with a total-variation regularization. Validation with both simulation and real data showed that our PSF model is more accurate than the piecewise-invariant model and the blending model. Comparing with the orthogonal basis decomposition based PSF model, our proposed model also performed with a considerable improvement. We also evaluated the proposed deblurring algorithm. Our new deblurring algorithm showed a significantly better signal-to-noise ratio and higher image quality than those of the conventional space-invariant algorithm.

FUS Interacts with HSP60 to Promote Mitochondrial Damage.

FUS-proteinopathies, a group of heterogeneous disorders including ALS-FUS and FTLD-FUS, are characterized by the formation of inclusion bodies containing the nuclear protein FUS in the affected patients. However, the underlying molecular and cellular defects remain unclear. Here we provide evidence for mitochondrial localization of FUS and its induction of mitochondrial damage. Remarkably, FTLD-FUS brain samples show increased FUS expression and mitochondrial defects. Biochemical and genetic data demonstrate that FUS interacts with a mitochondrial chaperonin, HSP60, and that FUS translocation to mitochondria is, at least in part, mediated by HSP60. Down-regulating HSP60 reduces mitochondrially localized FUS and partially rescues mitochondrial defects and neurodegenerative phenotypes caused by FUS expression in transgenic flies. This is the first report of direct mitochondrial targeting by a nuclear protein associated with neurodegeneration, suggesting that mitochondrial impairment may represent a critical event in different forms of FUS-proteinopathies and a common pathological feature for both ALS-FUS and FTLD-FUS. Our study offers a potential explanation for the highly heterogeneous nature and complex genetic presentation of different forms of FUS-proteinopathies. Our data also suggest that mitochondrial damage may be a target in future development of diagnostic and therapeutic tools for FUS-proteinopathies, a group of devastating neurodegenerative diseases.

Graph-regularized tensor robust principal component analysis for hyperspectral image denoising.

In this paper, we have developed a novel model that is named graph-regularized tensor robust principal component analysis (GTRPCA) for denoising hyperspectral images (HSIs). Incorporating spectral graph regularization into TRPCA makes the model more accurate by preserving local geometric structures embedded in a high-dimensional space. Based on tensor singular value decomposition (t-SVD), we introduce a general tensor-based altering direction method of multipliers (ADMM) algorithm which can solve the proposed model for denoising HSIs. Experiments on both the synthetic and real captured datasets have demonstrated the effectiveness of the proposed method.

Intrinsic decomposition from a single spectral image.

In this paper, we present a spectral intrinsic image decomposition (SIID) model, which is dedicated to resolve a natural scene into its purely independent intrinsic components: illumination, shading, and reflectance. By introducing spectral information, our work can solve many challenging cases, such as scenes with metameric effects, which are hard to tackle for trichromatic intrinsic image decomposition (IID), and thus offers potential benefits to many higher-level vision tasks, e.g., materials classification and recognition, shape-from-shading, and spectral image relighting. A both effective and efficient algorithm is presented to decompose a spectral image into its independent intrinsic components. To facilitate future SIID research, we present a public dataset with ground-truth illumination, shading, reflectance and specularity, and a meaningful error metric, so that the quantitative comparison becomes achievable. The experiments on this dataset and other images demonstrate the accuracy and robustness of the proposed method on diverse scenes, and reveal that more spectral channels indeed facilitate the vision task (i.e., segmentation and recognition).

An ALS-mutant TDP-43 neurotoxic peptide adopts an anti-parallel ß-structure and induces TDP-43 redistribution.

TDP-43 proteinopathies are clinically and genetically heterogeneous diseases that had been considered distinct from classical amyloid diseases. Here, we provide evidence for the structural similarity between TDP-43 peptides and other amyloid proteins. Atomic force microscopy and electron microscopy examination of peptides spanning a previously defined amyloidogenic fragment revealed a minimal core region that forms amyloid fibrils similar to the TDP-43 fibrils detected in FTLD-TDP brain tissues. An ALS-mutant A315E amyloidogenic TDP-43 peptide is capable of cross-seeding other TDP-43 peptides and an amyloid-ß peptide. Sequential Nuclear Overhauser Effects and double-quantum-filtered correlation spectroscopy in nuclear magnetic resonance (NMR) analyses of the A315E-mutant TDP-43 peptide indicate that it adopts an anti-parallel ß conformation. When added to cell cultures, the amyloidogenic TDP-43 peptides induce TDP-43 redistribution from the nucleus to the cytoplasm. Neuronal cultures in compartmentalized microfluidic-chambers demonstrate that the TDP-43 peptides can be taken up by axons and induce axonotoxicity and neuronal death, thus recapitulating key neuropathological features of TDP-43 proteinopathies. Importantly, a single amino acid change in the amyloidogenic TDP-43 peptide that disrupts fibril formation also eliminates neurotoxicity, supporting that amyloidogenesis is critical for TDP-43 neurotoxicity.

Dynamic Human Body Modeling Using a Single RGB Camera.

In this paper, we present a novel automatic pipeline to build personalized parametric models of dynamic people using a single RGB camera. Compared to previous approaches that use monocular RGB images, our system can model a 3D human body automatically and incrementally, taking advantage of human motion. Based on coarse 2D and 3D poses estimated from image sequences, we first perform a kinematic classification of human body parts to refine the poses and obtain reconstructed body parts. Next, a personalized parametric human model is generated by driving a general template to fit the body parts and calculating the non-rigid deformation. Experimental results show that our shape estimation method achieves comparable accuracy with reconstructed models using depth cameras, yet requires neither user interaction nor any dedicated devices, leading to the feasibility of using this method on widely available smart phones.

Characterization of bone microstructure using photoacoustic spectrum analysis.

Osteoporosis is a progressive bone disease that is characterized by a decrease in bone mass and the deterioration in bone microarchitecture. This study investigates the feasibility of characterizing bone microstructure by analyzing the frequency spectrum of the photoacoustic (PA) signal from the bone. Modeling and numerical simulation of PA signal were performed on trabecular bone simulations and CT scans with different trabecular thicknesses. The resulting quasi-linear photoacoustic spectra were fittted by linear regression, from which the spectral parameter slope was quantified. The simulation based on two different models both demonstrate that bone specimens with thinner trabecular thicknesses have higher slope. Experiment on osteoporotic rat femoral heads with different mineral content was conducted. The finding from the experiment was in good agreement with the simulation, demonstrating that the frequency-domain analysis of PA signals can provide an objective assessment of bone microstructure and deterioration. Considering that PA measurement is non-ionizing, non-invasive, and has sufficient penetration in both calcified and non-calcified tissues, this new bone evaluation method based on photoacoustic spectral analysis holds potential for clinical management of osteoporosis and other bone diseases.

Bone assessment via thermal photo-acoustic measurements.

The feasibility of an innovative biomedical diagnostic technique, thermal photo-acoustic (TPA) measurement, for non-ionizing and non-invasive assessment of bone health is investigated. Unlike conventional photo-acoustic PA methods that are mostly focused on the measurement of absolute signal intensity, TPA targets the change in PA signal intensity as a function of the sample temperature, i.e., the temperature-dependent Grueneisen parameter that is closely relevant to the chemical and molecular properties in the sample. Based on the differentiation measurement, the results from TPA technique are less susceptible to the variations associated with sample and system, and could be quantified with improved accurately. Due to the fact that the PA signal intensity from organic components such as blood changes faster than that from non-organic mineral under the same modulation of temperature, TPA measurement is able to objectively evaluate bone mineral density (BMD) and its loss as a result of osteoporosis. In an experiment on well-established rat models of bone loss and preservation, PA measurements of rat tibia bones were conducted over a temperature range from 37°C to 44°C. The slope of PA signal intensity verses temperature was quantified for each specimen. The comparison among three groups of specimens with different BMD shows that bones with lower BMD have higher slopes, demonstrating the potential of the proposed TPA technique in future clinical management of osteoporosis.

Leveraging Two Kinect Sensors for Accurate Full-Body Motion Capture.

Accurate motion capture plays an important role in sports analysis, the medical field and virtual reality. Current methods for motion capture often suffer from occlusions, which limits the accuracy of their pose estimation. In this paper, we propose a complete system to measure the pose parameters of the human body accurately. Different from previous monocular depth camera systems, we leverage two Kinect sensors to acquire more information about human movements, which ensures that we can still get an accurate estimation even when significant occlusion occurs. Because human motion is temporally constant, we adopt a learning analysis to mine the temporal information across the posture variations. Using this information, we estimate human pose parameters accurately, regardless of rapid movement. Our experimental results show that our system can perform an accurate pose estimation of the human body with the constraint of information from the temporal domain.

AxonQuant: A Microfluidic Chamber Culture-Coupled Algorithm That Allows High-Throughput Quantification of Axonal Damage.

Published methods for imaging and quantitatively analyzing morphological changes in neuronal axons have serious limitations because of their small sample sizes, and their time-consuming and nonobjective nature. Here we present an improved microfluidic chamber design suitable for fast and high-throughput imaging of neuronal axons. We developed the AxonQuant algorithm, which is suitable for automatic processing of axonal imaging data. This microfluidic chamber-coupled algorithm allows calculation of an 'axonal continuity index' that quantitatively measures axonal health status in a manner independent of neuronal or axonal density. This method allows quantitative analysis of axonal morphology in an automatic and nonbiased manner. Our method will facilitate large-scale high-throughput screening for genes or therapeutic compounds for neurodegenerative diseases involving axonal damage. When combined with imaging technologies utilizing different gene markers, this method will provide new insights into the mechanistic basis for axon degeneration. Our microfluidic chamber culture-coupled AxonQuant algorithm will be widely useful for studying axonal biology and neurodegenerative disorders.

3D Spine Model Reconstruction Based on RGBD Images of Unclothed Back Surface.

Most 3D spine reconstruction methods require X-ray images as input, which usually leads to high cost and radiation damage. Therefore, these methods are hardly ever applied to large scale scoliosis screening or spine pose monitoring during treatment. We propose a novel, low-cost, easy-to-operate and none-radioactive 3D spine model reconstruction method, which is based on human back surface information without requiring X-ray images as input. Our method fits a pre-built Spine Priors Model (SPrM) to human back surface information and reconstructs the main part of spine with 17 vertebrae: lumbar vertebrae L1-L5 and thoracic vertebrae T1-T12. The Spine Priors Model is constructed according to human spine priors, including Statistical Spine Shape Model (SSSM), Spine Pose Model (SPM) and Spine Biomechanical Simplified Model (SBSM). The spine-related information on back surface, including back surface spinous curve and local symmetry nearby spinous curve is extracted from the RGBD images of human back surface. We formulate the spine optimization constraints from spine-related feature on back surface and spine priors, then optimize the spine model by gradient descent to get the optimal personalized shape parameters and pose parameters of the Spine Priors Model (SPrM). We assess our reconstruction by scoliosis Cobb angle error, and the result is comparable to current X-ray based methods.