A Low-Cost, Autonomous Gait Detection and Estimation System for Analyzing Gait Impairments in Mice.

With the advancement in imaging technology, many commercial systems have been developed for performing motion analysis in mice. However, available commercial systems are expensive and use proprietary software. In this paper, we describe a low-cost, camera-based design of an autonomous gait acquisition and analysis system for inspecting gait deficits in C57BL/6 mice. Our system includes video acquisition, autonomous gait-event detection, gait-parameter extraction, and result visualization. We provide a simple, user-friendly, step-by-step detailed methodology to apply well-known image processing techniques for detecting mice footfalls and calculating various gait parameters for analyzing gait abnormalities in healthy and neurotraumatic mice. The system was used in a live animal study for assessing recovery in a mouse model of Parkinson's disease. Using the videos acquired in the study, we validate the performance of our system with receiver operating characteristic (ROC) and Hit : Miss : False (H : M : F) detection analyses. Our system correctly detected the mice footfalls with an average H : M : F score of 92.1 : 2.3 : 5.6. The values for the area under an ROC curve for all the ROC plots are above 0.95, which indicates an almost perfect detection model. The ROC and H : M : F analyses show that our system produces accurate gait detection. The results observed from the gait assessment study are in agreement with the known literature. This demonstrates the practical viability of our system as a gait analysis tool.

Performance Study of Distance-Weighting Approach with Loopy Sum-Product Algorithm for Multi-Object Tracking in Clutter.

In this paper, we explore the performance of the distance-weighting probabilistic data association (DWPDA) approach in conjunction with the loopy sum-product algorithm (LSPA) for tracking multiple objects in clutter. First, we discuss the problem of data association (DA), which is to infer the correspondence between targets and measurements. DA plays an important role when tracking multiple targets using measurements of uncertain origin. Second, we describe three methods of data association: probabilistic data association (PDA), joint probabilistic data association (JPDA), and LSPA. We then apply these three DA methods for tracking multiple crossing targets in cluttered environments, e.g., radar detection with false alarms and missed detections. We are interested in two performance metrics: tracking accuracy and computation time. LSPA is known to be superior to PDA in terms of the former and to dominate JPDA in terms of the latter. Last, we consider an additional DA method that is a modification of PDA by incorporating a weighting scheme based on distances between position estimates and measurements. This distance-weighting approach, when combined with PDA, has been shown to enhance the tracking accuracy of PDA without significant change in the computation burden. Since PDA constitutes a crucial building block of LSPA, we hypothesize that DWPDA, when integrated with LSPA, would perform better under the two performance metrics above. Contrary to expectations, the distance-weighting approach does not enhance the performance of LSPA, whether in terms of tracking accuracy or computation time.

NF-κB Signaling in Astrocytes Modulates Brain Inflammation and Neuronal Injury Following Sequential Exposure to Manganese and MPTP During Development and Aging.

Chronic exposure to manganese (Mn) is associated with neuroinflammation and extrapyramidal motor deficits resembling features of Parkinson's disease. Activation of astrocytes and microglia is implicated in neuronal injury from Mn but it is not known whether early life exposure to Mn may predispose glia to more severe inflammatory responses during aging. We therefore examined astrocyte nuclear factor kappa B (NF-κB) signaling in mediating innate immune inflammatory responses during multiple neurotoxic exposures spanning juvenile development into adulthood. MnCl2 was given in drinking water for 30-day postweaning to both wildtype mice and astrocyte-specific knockout (KO) mice lacking I kappa B kinase 2, the central upstream activator of NF-κB. Following juvenile exposure to Mn, mice were subsequently administered 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) at 4 months of age. Animals were evaluated for behavioral alterations and brain tissue was analyzed for catecholamine neurotransmitters. Stereological analysis of neuronal and glial cell counts from multiple brain regions indicated that juvenile exposure to Mn amplified glial activation and neuronal loss from MPTP exposure in the caudate-putamen and globus pallidus, as well as increased the severity of neurobehavioral deficits in open field activity assays. These alterations were prevented in astrocyte-specific I kappa B kinase 2 KO mice. Juvenile exposure to Mn increased the number of neurotoxic A1 astrocytes expressing C3 as well as the number of activated microglia in adult mice following MPTP challenge, both of which were inhibited in KO mice. These results demonstrate that exposure to Mn during juvenile development heightens the innate immune inflammatory response in glia during a subsequent neurotoxic challenge through NF-κB signaling in astrocytes.

Stock price prediction using principal components.

The literature provides strong evidence that stock price values can be predicted from past price data. Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in a data set. This method is often used for dimensionality reduction and analysis of the data. In this paper, we develop a general method for stock price prediction using time-varying covariance information. To address the time-varying nature of financial time series, we assign exponential weights to the price data so that recent data points are weighted more heavily. Our proposed method involves a dimension-reduction operation constructed based on principle components. Projecting the noisy observation onto a principle subspace results in a well-conditioned problem. We illustrate our results based on historical daily price data for 150 companies from different market-capitalization categories. We compare the performance of our method to two other methods: Gauss-Bayes, which is numerically demanding, and moving average, a simple method often used by technical traders and researchers. We investigate the results based on mean squared error and directional change statistic of prediction, as measures of performance, and volatility of prediction as a measure of risk.

Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Network Functionalities.

Goal: The purpose of this article is to introduce a new strategy to identify areas with high human density and mobility, which are at risk for spreading COVID-19. Crowded regions with actively moving people (called at-risk regions) are susceptible to spreading the disease, especially if they contain asymptomatic infected people together with healthy people. Methods: Our scheme identifies at-risk regions using existing cellular network functionalities-handover and cell (re)selection-used to maintain seamless coverage for mobile end-user equipment (UE). The frequency of handover and cell (re)selection events is highly reflective of the density of mobile people in the area because virtually everyone carries UEs. Results: These measurements, which are accumulated over very many UEs, allow us to identify the at-risk regions without compromising the privacy and anonymity of individuals. Conclusions: The inferred at-risk regions can then be subjected to further monitoring and risk mitigation.

The Nurr1 Ligand,1,1-bis(3'-Indolyl)-1-(p-Chlorophenyl)Methane, Modulates Glial Reactivity and Is Neuroprotective in MPTP-Induced Parkinsonism.

The orphan nuclear receptor Nurr1 (also called nuclear receptor-4A2) regulates inflammatory gene expression in glial cells, as well as genes associated with homeostatic and trophic function in dopaminergic neurons. Despite these known functions of Nurr1, an endogenous ligand has not been discovered. We postulated that the activation of Nurr1 would suppress the activation of glia and thereby protect against loss of dopamine (DA) neurons after subacute lesioning with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Our previous studies have shown that a synthetic Nurr1 ligand, 1,1-bis(3'-indolyl)-1-(p-chlorophenyl)methane (C-DIM12), suppresses inflammatory gene expression in primary astrocytes and induces a dopaminergic phenotype in neurons. Pharmacokinetic analysis of C-DIM12 in mice by liquid chromatography-mass spectrometry demonstrated that approximately three times more compound concentrated in the brain than in plasma. Mice treated with four doses of MPTP + probenecid over 14 days were monitored for neurobehavioral function, loss of dopaminergic neurons, and glial activation. C-DIM12 protected against the loss of DA neurons in the substantia nigra pars compacta and DA terminals in the striatum, maintained a ramified phenotype in microglia, and suppressed activation of astrocytes. In vitro reporter assays demonstrated that C-DIM12 was an effective activator of Nurr1 transcription in neuronal cell lines. Computational modeling of C-DIM12 binding to the three-dimensional structure of human Nurr1 identified a high-affinity binding interaction with Nurr1 at the coactivator domain. Taken together, these data suggest that C-DIM12 is an activator of Nurr1 that suppresses glial activation and neuronal loss in vivo after treatment with MPTP, and that this receptor could be an efficacious target for disease modification in individuals with Parkinson's disease and related disorders.

Obstructed diffusion propagator analysis for single-particle tracking.

We describe a method for the analysis of the distribution of displacements, i.e., the propagators, of single-particle tracking measurements for the case of obstructed subdiffusion in two-dimensional membranes. The propagator for the percolation cluster is compared with a two-component mobility model against Monte Carlo simulations. To account for diffusion in the presence of obstacle concentrations below the percolation threshold, a propagator that includes the transient motion in finite percolation clusters and hopping between obstacle-induced compartments is derived. Finally, these models are shown to be effective in the analysis of Kv2.1 channel diffusive measurements in the membrane of living mammalian cells.