Investigating the efficacy and importance of mobile-based assessments for Parkinson's disease: uncovering the potential of novel digital tests.

This study introduces PDMotion, a mobile application comprising 11 digital tests, including those adapted from the MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III and novel assessments, for remote Parkinson's Disease (PD) motor symptoms evaluation. Employing machine learning techniques on data from 50 PD patients and 29 healthy controls, PDMotion achieves accuracies of 0.878 for PD status prediction and 0.715 for severity assessment. A post-hoc explanation model is employed to assess the importance of features and tasks in diagnosis and severity evaluation. Notably, novel tasks that are not adapted from MDS-UPDRS Part III like the circle drawing, coordination test, and alternative tapping test are found to be highly important, suggesting digital assessments for PD can go beyond digitizing existing tests. The alternative tapping test emerges as the most significant task. Using its features alone achieves prediction accuracies comparable to the full task set, underscoring its potential as an independent screening tool. This study addresses a notable research gap by digitalizing a wide array of tests, including novel ones, and conducting a comparative analysis of their feature and task importance. These insights provide guidance for task selection and future development in PD mobile assessments, a field previously lacking such comparative studies.

High efficient electrochemical biosensor based on exonuclease-â ¢-assisted dual-recycling amplification for ultrasensitive detection of kanamycin.

A target-triggered and exonuclease-Ⅲ-assisted strand displacement, dual-recycling amplification reaction-based biosensor was developed for the rapid, ultrasensitive and accurate detection of kanamycin. The robust profiling platform was constructed using high conductive MXene/VS2 for the electrode surface modification and high active CeCu2O4 bimetallic nanoparticles as nanozyme to improve the sensitivity as well as the catalytic signal amplification of the biosensor. Using the dual supplementary recycling of primer DNA and hairpin DNA, the electrochemical platform could accurately detect kanamycin to as low as 0.6 pM from the range of 5 pM to 5 µM. By profiling five other antibiotics, this platform exhibited high specificity, enhanced repeatability and reproducibility. Based on these intrinsic characteristics and by utilizing milk and water samples, the as-designed biosensor offers a remarkable strategy for antibiotic detection due to its favorable analytical accuracy and reliability, thereby demonstrating potential application prospect for various antibiotic biosensing in food quality control, water contamination detection and biological safety analysis.

Real-Time Shadow Detection From Live Outdoor Videos for Augmented Reality.

Simulating shadow interactions between real and virtual objects is important for augmented reality (AR), in which accurately and efficiently detecting real shadows from live videos is a crucial step. Most of the existing methods are capable of processing only scenes captured under a fixed viewpoint. In contrast, this article proposes a new framework for shadow detection in live outdoor videos captured under moving viewpoints. The framework splits each frame into a tracked region, which is the region tracked from the previous video frame through optical flow analysis, and an emerging region, which is newly introduced into the scene due to the moving viewpoint. The framework subsequently extracts features based on the intensity profiles surrounding the boundaries of candidate shadow regions. These features are then utilized to both correct erroneous shadow boundaries for the tracked region and to detect shadow boundaries for the emerging region by a Bayesian learning module. To remove spurious shadows, spatial layout constraints are further considered for emerging regions. The experimental results demonstrate that the proposed framework outperforms the state-of-the-art shadow tracking and detection algorithms on a variety of challenging cases in real time, including shadows on backgrounds with complex textures, nonplanar shadows, fast-moving shadows with changing typologies, and shadows cast by nonrigid objects. The quantitative experiments show that our method outperforms the best existing method, achieving a 33.3% increase in the average Fmeasure on a self-collected database. Coupled with an image-based shadow-casting method, the proposed framework generates realistic shadow interaction results. This capability will be particularly beneficial for supporting AR applications.

Multiple Slice k-space Deep Learning for Magnetic Resonance Imaging Reconstruction.

Magnetic resonance imaging (MRI) has been one of the most powerful and valuable imaging methods for medical diagnosis and staging of disease. Due to the long scan time of MRI acquisition, k-space under-samplings is required during the acquisition processing. Thus, MRI reconstruction, which transfers undersampled k-space data to high-quality magnetic resonance imaging, becomes an important and meaningful task. There have been many explorations on k-space interpolation for MRI reconstruction. However, most of these methods ignore the strong correlation between target slice and its adjacent slices. Inspired by this, we propose a fully data-driven deep learning algorithm for k-space interpolation, utilizing the correlation information between the target slice and its neighboring slices. A novel network is proposed, which models the inter-dependencies between different slices. In addition, the network is easily implemented and expended. Experiments show that our methods consistently surpass existing image-domain and k-space-domain magnetic resonance imaging reconstructing methods.

Compressed Nonlinear Equalizers for 112-Gbps Optical Interconnects: Efficiency and Stability.

Low-complexity nonlinear equalization is critical for reliable high-speed short-reach optical interconnects. In this paper, we compare the complexity, efficiency and stability performance of pruned Volterra series-based equalization (VE) and neural network-based equalization (NNE) for 112 Gbps vertical cavity surface emitting laser (VCSEL) enabled optical interconnects. The design space of nonlinear equalizers and their pruning algorithms are carefully investigated to reveal fundamental reasons of powerful nonlinear compensation capability and restriction factors of efficiency and stability. The experimental results show that NNE has more than one order of magnitude bit error rate (BER) advantage over VE at the same computation complexity and pruned NNE has around 50% lower computation complexity compared to VE at the same BER level. Moreover, VE shows serious performance instability due to its intricate structure when communication channel conditions become tough. Moreover, pruned VE presents more consistent equalization performance within varying bias values than NNE.

Automatic Spatially Varying Illumination Recovery of Indoor Scenes Based on a Single RGB-D Image.

We propose an automatic framework to recover the illumination of indoor scenes based on a single RGB-D image. Unlike previous works, our method can recover spatially varying illumination without using any lighting capturing devices or HDR information. The recovered illumination can produce realistic rendering results. To model the geometry of the visible and invisible parts of scenes corresponding to the input RGB-D image, we assume that all objects shown in the image are located in a box with six faces and build a planar-based geometry model based on the input depth map. We then present a confidence-scoring based strategy to separate the light sources from the highlight areas. The positions of light sources both in and out of the camera's view are calculated based on the classification result and the recovered geometry model. Finally, an iterative procedure is proposed to calculate the colors of light sources and the materials in the scene. In addition, a data-driven method is used to set constraints on the light source intensities. Using the estimated light sources and geometry model, environment maps at different points in the scene are generated that can model the spatial variance of illumination. The experimental results demonstrate the validity and flexibility of our approach.