Equilibrative nucleoside transporter 3 supports microglial functions and protects against the progression of Huntington's disease in the mouse model.

Huntington's disease (HD) is a hereditary neurodegenerative disorder characterized by involuntary movements, cognitive deficits, and psychiatric symptoms. Currently, there is no cure, and only limited treatments are available to manage the symptoms and to slow down the disease's progression. The molecular and cellular mechanisms of HD's pathogenesis are complex, involving immune cell activation, altered protein turnover, and disturbance in brain energy homeostasis. Microglia have been known to play a dual role in HD, contributing to neurodegeneration through inflammation but also enacting neuroprotective effects by clearing mHTT aggregates. However, little is known about the contribution of microglial metabolism to HD progression. This study explores the impact of a microglial metabolite transporter, equilibrative nucleoside transporter 3 (ENT3), in HD. Known as a lysosomal membrane transporter protein, ENT3 is highly enriched in microglia, with its expression correlated with HD severity. Using the R6/2 ENT3-/- mouse model, we found that the deletion of ENT3 increases microglia numbers yet worsens HD progression, leading to mHTT accumulation, cell death, and disturbed energy metabolism. These results suggest that the delicate balance between microglial metabolism and function is crucial for maintaining brain homeostasis and that ENT3 has a protective role in ameliorating neurodegenerative processes.

Thiazolidinediones Decrease the Recurrence of Intracerebral Hemorrhage in Type 2 Diabetes Mellitus Patients: A Nested Case-Control Study.

INTRODUCTION: Preclinical evidence demonstrated the therapeutic potential of thiazolidinediones (TZDs) for the treatment of intracerebral hemorrhage (ICH). The present study conducted an investigation of cerebrovascular and cardiovascular outcomes following ICH in patients with type 2 diabetes mellitus (T2DM) treated with or without TZDs. METHODS: This retrospective nested case-control study used data from the Taiwan National Health Insurance Research Database. A total of 62,515 T2DM patients who were hospitalized with a diagnosis of ICH were enrolled, including 7,603 TZD users. Data for TZD non-users were extracted using propensity score matching. Primary outcomes included death and major adverse cardiovascular events (MACEs), which were defined as a composite of ischemic stroke, hemorrhagic stroke (HS), acute myocardial infarction, and congestive heart failure. Patients aged <20 years with a history of traumatic brain injury or any prior history of MACEs were excluded. RESULTS: TZD users had significantly lower MACE risks compared with TZD non-users following ICH (adjusted hazard ratio [aHR]: 0.90, 95% confidence interval [CI]: 0.85-0.94, p < 0.001). The most significant MACE difference reported for TZD users was HS, which possessed lower incidence than in TZD non-users, especially for the events that happened within 3 months following ICH (aHR: 0.74, 95% CI: 0.62-0.89 within 1 month, p < 0.01; aHR: 0.68, 95% CI: 0.54-0.85 between 1 and 3 month). CONCLUSION: The use of TZD in patients with T2DM was associated with a lower risk of subsequent HS and mortality following ICH.

Emissions database development and dispersion model predictions of airborne particulate elements in the Canadian Athabasca oil sands region.

This study developed an emission inventory for 29 elements in PM2.5 and PM2.5-10 covering an area of approximately 300 by 420 km2 in the Athabasca Oil Sands Region in northern Alberta, Canada. Emission sources were aggregated into nine categories, of which the Oil Sands (OS) Sources emitted the most, followed by the Non-OS Dust sources for both fine and coarse elements over the study area. The top six fine particulate elements include Si, Ca, Al, Fe, S, and K (933, 442, 323, 269, 116, and 103 tonnes/year, respectively), the sum of which accounted for 20.5% of the total PM2.5 emissions. The top five coarse elements include Si, Ca, Al, Fe, and K (3713, 1815, 1198, 1073, and 404 tonnes/year), and their sum accounted for 29% of the total PM2.5-10 emissions. Using this emission inventory as input, the CALPUFF dispersion model simulated reasonable element concentrations in both PM2.5 and PM2.5-10 when compared to measurements collected at three sites during 2016-2017. Modeled PM10 concentrations of all elements were very close to the measurements at an industrial site with the highest ambient concentration, overestimated by 65% at another industrial site with moderate ambient concentration, and underestimated by 27% at a remote site with very low ambient concentration. Model-measurement differences of annual average concentrations were within 20% for Si, Ca, Al, Fe, Ti, Mn, and Cu in PM2.5, and were 20-50% for K, S, and Zn in PM2.5 at two sites located within the OS surface mineable area. Model-measurement differences were larger, but still within a factor of two for elements in PM2.5-10 at these two sites and for elements in both PM2.5 and PM2.5-10 at a background site.

Connectivity and synaptic features of hilar mossy cells and their effects on granule cell activity along the hippocampal longitudinal axis.

The hippocampus is an elongated brain structure which runs along a ventral-to-dorsal axis in rodents, corresponding to the anterior-to-posterior axis in humans. A glutamatergic cell type in the dentate gyrus (DG), the mossy cells (MCs), establishes extensive excitatory collateral connections with the DG principal cells, the granule cells (GCs), and inhibitory interneurons in both hippocampal hemispheres along the longitudinal axis. Although coupling of two physically separated GC populations via long-axis projecting MCs is instrumental for information processing, the connectivity and synaptic features of MCs along the longitudinal axis are poorly defined. Here, using channelrhodopsin-2 assisted circuit mapping, we showed that MC excitation results in a low synaptic excitation-inhibition (E/I) balance in the intralamellar (local) GCs, but a high synaptic E/I balance in the translamellar (distant) ones. In agreement with the differential E/I balance along the ventrodorsal axis, activation of MCs either enhances or suppresses the local GC response to the cortical input, but primarily promotes the distant GC activation. Moreover, activation of MCs enhances the spike timing precision of the local GCs, but not that of the distant ones. Collectively, these findings suggest that MCs differentially regulate the local and distant GC activity through distinct synaptic mechanisms. KEY POINTS: Hippocampal mossy cell (MC) pathways differentially regulate granule cell (GC) activity along the longitudinal axis. MCs mediate a low excitation-inhibition balance in intralamellar (local) GCs, but a high excitation-inhibition balance in translamellar (distant) GCs. MCs enhance the spiking precision of local GCs, but not distant GCs. MCs either promote or suppress local GC activity, but primarily promote distant GC activation.

Endoplasmic reticulum stress induces Alzheimer disease-like phenotypes in the neuron derived from the induced pluripotent stem cell with D678H mutation on amyloid precursor protein.

Alzheimer disease (AD), a progressive neurodegenerative disorder, is mainly caused by the interaction of genetic and environmental factors. The impact of environmental factors on the genetic mutation in the amyloid precursor protein (APP) is not well characterized. We hypothesized that endoplasmic reticulum (ER) stress would promote disease for the patient carrying the APP D678H mutation. Therefore, we analyzed the impact of a familial AD mutation on amyloid precursor protein (APP D678H) under ER stress. Induced pluripotent stem cells (iPSCs) from APP D678H mutant carrier was differentiated into neurons, which were then analyzed for AD-like changes. Immunocytochemistry and whole-cell patch-clamp recording revealed that the derived neurons on day 28 after differentiation showed neuronal markers and electrophysiological properties similar to those of mature neurons. However, the APP D678H mutant neurons did not have significant alterations in the levels of amyloid-ß (Aß) and phosphorylated tau (pTau) compared to its isogenic wild-type neurons. Only under ER stress, the neurons with the APP D678H mutation had more Aß and pTau via immune detection assays. The higher level of Aß in the APP D678H mutant neurons was probably due to the increased level of ß-site APP cleaving enzyme (BACE1) and decreased level of Aß-degrading enzymes under ER stress. Increased Aß and pTau under ER stress reduced the N-methyl-D-aspartate receptor (NMDAR) in Western blot analysis and altered electrophysiological properties in the mutant neurons. Our study provides evidence that the interaction between genetic mutation and ER stress would induce AD-like changes. Cover Image for this issue: https://doi.org/10.1111/jnc.15420.

The beneficial effect of synbiotics consumption on Alzheimer's disease mouse model via reducing local and systemic inflammation.

Alzheimer's disease (AD) is a neurodegenerative disease that impairs multiple memory domains without an effective prevention or treatment approach. Amyloid plaque-induced neuroinflammation exacerbates neurodegeneration and cognitive impairment in AD. To reduce neuroinflammation, we applied prebiotics or synbiotics to modulate the gut-brain axis in the AD mouse model. AD-like deficits were reduced in mice treated with synbiotics, suggesting that dietary modulation of the gut-brain axis is a potential approach to delay AD progression.

FAPNET: Feature Fusion with Adaptive Patch for Flood-Water Detection and Monitoring.

In satellite remote sensing applications, waterbody segmentation plays an essential role in mapping and monitoring the dynamics of surface water. Satellite image segmentation-examining a relevant sensor data spectrum and identifying the regions of interests to obtain improved performance-is a fundamental step in satellite data analytics. Satellite image segmentation is challenging for a number of reasons, which include cloud interference, inadequate label data, low lighting and the presence of terrain. In recent years, Convolutional Neural Networks (CNNs), combined with (satellite captured) multispectral image segmentation techniques, have led to promising advances in related research. However, ensuring sufficient image resolution, maintaining class balance to achieve prediction quality and reducing the computational overhead of the deep neural architecture are still open to research due to the sophisticated CNN hierarchical architectures. To address these issues, we propose a number of methods: a multi-channel Data-Fusion Module (DFM), Neural Adaptive Patch (NAP) augmentation algorithm and re-weight class balancing (implemented in our PHR-CB experimental setup). We integrated these techniques into our novel Fusion Adaptive Patch Network (FAPNET). Our dataset is the Sentinel-1 SAR microwave signal, used in the Microsoft Artificial Intelligence for Earth competition, so that we can compare our results with the top scores in the competition. In order to validate our approach, we designed four experimental setups and in each setup, we compared our results with the popular image segmentation models UNET, VNET, DNCNN, UNET++, U2NET, ATTUNET, FPN and LINKNET. The comparisons demonstrate that our PHR-CB setup, with class balance, generates the best performance for all models in general and our FAPNET approach outperforms relative works. FAPNET successfully detected the salient features from the satellite images. FAPNET with a MeanIoU score of 87.06% outperforms the state-of-the-art UNET, which has a score of 79.54%. In addition, FAPNET has a shorter training time than other models, comparable to that of UNET (6.77 min for 5 epochs). Qualitative analysis also reveals that our FAPNET model successfully distinguishes micro waterbodies better than existing models. FAPNET is more robust to low lighting, cloud and weather fluctuations and can also be used in RGB images. Our proposed method is lightweight, computationally inexpensive, robust and simple to deploy in industrial applications. Our research findings show that flood-water mapping is more accurate when using SAR signals than RGB images. Our FAPNET architecture, having less parameters than UNET, can distinguish micro waterbodies accurately with shorter training time.

RAVA: Region-Based Average Video Quality Assessment.

Video has become the most popular medium of communication over the past decade, with nearly 90 percent of the bandwidth on the Internet being used for video transmission. Thus, evaluating the quality of an acquired or compressed video has become increasingly important. The goal of video quality assessment (VQA) is to measure the quality of a video clip as perceived by a human observer. Since manually rating every video clip to evaluate quality is infeasible, researchers have attempted to develop various quantitative metrics that estimate the perceptual quality of video. In this paper, we propose a new region-based average video quality assessment (RAVA) technique extending image quality assessment (IQA) metrics. In our experiments, we extend two full-reference (FR) image quality metrics to measure the feasibility of the proposed RAVA technique. Results on three different datasets show that our RAVA method is practical in predicting objective video scores.

A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction.

Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson's Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used to help patients recover from FOG and resume normal gait. RAS might be ineffective due to the latency between the start of a FOG event, its detection and initialization of RAS. We propose a system capable of both FOG prediction and detection using signals from tri-axial accelerometer sensors that will be useful in initializing RAS with minimal latency. We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). We also propose three Ensemble Network Architectures that combine all the time frequency representations and DL architectures. Experimental results show that our ensemble architectures significantly improve the performance compared with existing techniques. We also present the results of applying our method trained on a publicly available dataset to data collected from patients using wearable sensors in collaboration with A.T. Still University.

Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring.

Pressure injury (PI) is a major problem for patients that are bound to a wheelchair or bed, such as seniors or people with spinal cord injuries. This condition can be life threatening in its later stages. It can be very costly to the healthcare system as well. Fortunately with proper monitoring and assessment, PI development can be prevented. The major factor that causes PI is prolonged interface pressure between the body and the support surface. A possible solution to reduce the chance of developing PI is changing the patient's in-bed pose at appropriate times. Monitoring in-bed pressure can help healthcare providers to locate high-pressure areas, and remove or minimize pressure on those regions. The current clinical method of interface pressure monitoring is limited by periodic snapshot assessments, without longitudinal measurements and analysis. In this paper we propose a pressure signal analysis pipeline to automatically eliminate external artefacts from pressure data, estimate a person's pose, and locate and track high-risk regions over time so that necessary attention can be provided.

Spatiotemporal trends of PM2.5 and its major chemical components at urban sites in Canada.

To evaluate the effectiveness of emission control regulations designed for reducing air pollution, chemically resolved PM2.5 data have been collected across Canada through the National Air Pollution Surveillance network in the past decade. 24-hr time integrated PM2.5 collected at seven urban and two rural sites during 2010-2016 were analyzed to characterize geographical and seasonal patterns and associated potential causes. Site-specific seven-year mean gravimetric PM2.5 mass concentrations ranged from 5.7 to 9.6 µg/m3. Seven-year mean concentrations of SO42-, NO3-, NH4+, organic carbon (OC), and elemental carbon (EC) were in the range of 0.68 to 1.6, 0.21 to 1.5, 0.27 to 0.71, 1.1 to 1.9, and 0.37 to 0.71 µg /m3, accounting for 10.8%-18.1%, 3.7%-16.7%, 4.7%-7.4%, 18.4%-21.0%, and 6.4%-10.6%, respectively, of gravimetric PM2.5 mass. PM2.5 and its five major chemical components showed higher concentrations in southeastern Canada and lower values in Atlantic Canada, with the seven-year mean ratios between the two regions being on the order of 1.7 for PM2.5 and 1.8-7.1 for its chemical components. When comparing the concentrations between urban and rural sites within the same region, those of SO42- and NH4+ were comparable, while those of NO3-, OC, and EC were around 20%, 40%-50%, and 70%-80%, respectively, higher at urban than rural sites, indicating the regional scale impacts of SO42- and NH4+ and effects of local sources on OC and EC. Monthly variations generally showed summertime peaks for SO42- and wintertime peaks for NO3-, but those of NH4+, OC, and EC exhibited different seasonality at different locations.

p75NTR and DR6 Regulate Distinct Phases of Axon Degeneration Demarcated by Spheroid Rupture.

The regressive events associated with trophic deprivation are critical for sculpting a functional nervous system. After nerve growth factor withdrawal, sympathetic axons derived from male and female neonatal mice maintain their structural integrity for ∼18 h (latent phase) followed by a rapid and near unison disassembly of axons over the next 3 h (catastrophic phase). Here we examine the molecular basis by which axons transition from latent to catastrophic phases of degeneration following trophic withdrawal. Before catastrophic degeneration, we observed an increase in intra-axonal calcium. This calcium flux is accompanied by p75 neurotrophic factor receptor-Rho-actin-dependent expansion of calcium-rich axonal spheroids that eventually rupture, releasing their contents to the extracellular space. Conditioned media derived from degenerating axons are capable of hastening transition into the catastrophic phase of degeneration. We also found that death receptor 6, but not p75 neurotrophic factor receptor, is required for transition into the catastrophic phase in response to conditioned media but not for the intra-axonal calcium flux, spheroid formation, or rupture that occur toward the end of latency. Our results support the existence of an interaxonal degenerative signal that promotes catastrophic degeneration among trophically deprived axons.SIGNIFICANCE STATEMENT Developmental pruning shares several morphological similarities to both disease- and injury-induced degeneration, including spheroid formation. The function and underlying mechanisms governing axonal spheroid formation, however, remain unclear. In this study, we report that axons coordinate each other's degeneration during development via axonal spheroid rupture. Before irreversible breakdown of the axon in response to trophic withdrawal, p75 neurotrophic factor receptor-RhoA signaling governs the formation and growth of spheroids. These spheroids then rupture, allowing exchange of contents ≤10 kDa between the intracellular and extracellular space to drive death receptor 6 and calpain-dependent catastrophic degeneration. This finding informs not only our understanding of regressive events during development but may also provide a rationale for designing new treatments toward myriad neurodegenerative disorders.

Marker-Less 3d Object Recognition and 6d Pose Estimation for Homogeneous Textureless Objects: An RGB-D Approach.

The task of recognising an object and estimating its 6d pose in a scene has received considerable attention in recent years. The accessibility and low-cost of consumer RGB-D cameras, make object recognition and pose estimation feasible even for small industrial businesses. An example is the industrial assembly line, where a robotic arm should pick a small, textureless and mostly homogeneous object and place it in a designated location. Despite all the recent advancements of object recognition and pose estimation techniques in natural scenes, the problem remains challenging for industrial parts. In this paper, we present a framework to simultaneously recognise the object's class and estimate its 6d pose from RGB-D data. The proposed model adapts a global approach, where an object and the Region of Interest (ROI) are first recognised from RGB images. The object's pose is then estimated from the corresponding depth information. We train various classifiers based on extracted Histogram of Oriented Gradient (HOG) features to detect and recognize the objects. We then perform template matching on the point cloud based on surface normal and Fast Point Feature Histograms (FPFH) to estimate the pose of the object. Experimental results show that our system is quite efficient, accurate and robust to illumination and background changes, even for the challenging objects of Tless dataset.

Changes in Plasma Amyloid and Tau in a Longitudinal Study of Normal Aging, Mild Cognitive Impairment, and Alzheimer's Disease.

BACKGROUND: Changes in cerebrospinal fluid, neuroimaging, and cognitive functions have been used as diagnostic biomarkers of Alzheimer's disease (AD). This study aimed to investigate the temporal trajectories of plasma biomarkers in subjects with mild cognitive impairment (MCI) and patients with AD relative to healthy controls (HCs). METHODS: In this longitudinal study, 82 participants (31 HCs, 33 MCI patients, and 18 AD patients) were enrolled. After 3 years, 7 HCs had transitioned to MCI and 10 subjects with MCI had converted to AD. We analyzed plasma amyloid beta (Aß) and tau proteins at baseline and annually to correlate with biochemical data and neuropsychological scores. RESULTS: Longitudinal data analysis showed an evolution of Aß-related biomarkers over time within patients, whereas tau-related biomarkers differed primarily across diagnostic classifications. An initial steady increase in Aß42 in the MCI stage was followed by a decrease just prior to clinical AD onset. Hyperphosphorylated tau protein levels correlated with cognitive decline in the MCI stage, but not in the AD stage. CONCLUSION: Plasma Aß and tau levels change in a dynamic, nonlinear, nonparallel manner over the AD continuum. Changes in plasma Aß concentration are time-dependent, whereas changes in hyperphosphorylated tau protein levels paralleled the clinical progression of MCI. It remains to be clarified whether diagnostic efficiency can be improved by combining multiple plasma markers or combining plasma markers with other diagnostic biomarkers.

Kin-FOG: Automatic Simulated Freezing of Gait (FOG) Assessment System for Parkinson's Disease.

Parkinson's disease (PD) is one of the leading neurological disorders in the world with an increasing incidence rate for the elderly. Freezing of Gait (FOG) is one of the most incapacitating symptoms for PD especially in the later stages of the disease. FOG is a short absence or reduction of ability to walk for PD patients which can cause fall, reduction in patients' quality of life, and even death. Existing FOG assessments by doctors are based on a patient's diaries and experts' manual video analysis which give subjective, inaccurate, and unreliable results. In the present research, an automatic FOG assessment system is designed for PD patients to provide objective information to neurologists about the FOG condition and the symptom's characteristics. The proposed FOG assessment system uses an RGB-D sensor based on Microsoft Kinect V2 for capturing data for 5 healthy subjects who are trained to imitate the FOG phenomenon. The proposed FOG assessment system is called "Kin-FOG". The analysis of foot joint trajectory of the motion captured by Kinect is used to find the FOG episodes. The evaluation of Kin-FOG is performed by two types of experiments, including: (1) simple walking (SW); and (2) walking with turning (WWT). Since the standing mode has features similar to a FOG episode, our Kin-FOG system proposes a method to distinguish between the FOG and standing episodes. Therefore, two general groups of experiments are conducted with standing state (WST) and without standing state (WOST). The gradient displacement of the angle between the foot and the ground is used as the feature for discriminating between FOG and standing modes. These experiments are conducted with different numbers of FOGs for getting reliable and general results. The Kin-FOG system reports the number of FOGs, their lengths, and the time slots when they occur. Experimental results demonstrate Kin-FOG has around 90% accuracy rate for FOG prediction in both experiments for different tasks (SW, WWT). The proposed Kin-FOG system can be used as a remote application at a patient's home or a rehabilitation clinic for sending a neurologist the required FOG information. The reliability and generality of the proposed system will be evaluated for bigger data sets of actual PD subjects.

Contrast and Homogeneity Feature Analysis for Classifying Tremor Levels in Parkinson's Disease Patients.

Early detection of different levels of tremors helps to obtain a more accurate diagnosis of Parkinson's disease and to increase the therapy options for a better quality of life for patients. This work proposes a non-invasive strategy to measure the severity of tremors with the aim of diagnosing one of the first three levels of Parkinson's disease by the Unified Parkinson's Disease Rating Scale (UPDRS). A tremor being an involuntary motion that mainly appears in the hands; the dataset is acquired using a leap motion controller that measures 3D coordinates of each finger and the palmar region. Texture features are computed using sum and difference of histograms (SDH) to characterize the dataset, varying the window size; however, only the most fundamental elements are used in the classification stage. A machine learning classifier provides the final classification results of the tremor level. The effectiveness of our approach is obtained by a set of performance metrics, which are also used to show a comparison between different proposed designs.

Deposition Mapping of Polycyclic Aromatic Compounds in the Oil Sands Region of Alberta, Canada and Linkages to Ecosystem Impacts.

This study produced gridded deposition estimates of polycyclic aromatic compounds (PACs), including 17 polycyclic aromatic hydrocarbons (PAHs), 21 alkylated PAHs (alk-PAHs), and 5 dibenzothiophenes (DBTs), over the oil sands region of Alberta, Canada and surrounding communities. Gridded annual total deposition of PACs in 2011 ranged from 55 to 175 000 µg m-2 yr-1 and the mean and median fluxes were 1700 and 760 µg m-2 yr-1, respectively. The domain-wide mean dry and wet deposition were 600 and 1100 µg m-2 yr-1. PAHs, alk-PAHs and DBTs contributed 19%, 74%, and 7% to the total dry deposition, and 42%, 49%, and 9% to the total wet deposition. Dominant chemical species contributing to total deposition were naphthalene, retene and phenanthrene for PAHs and C2-benz[a]anthracene/triphenylene/chrysene, C2-fluoranthene/pyrene and C2-fluorene for alk-PAHs. The highest PAC deposition was found over the surface mineable area, which received 9 times the deposition flux of outlying areas. Additional deposition hotspots were also observed south of the surface mineable area notably over in situ bitumen production sites. The deposition of alk-PAHs impacted a more extensive area than that of PAHs or DBTs. This result suggests that atmospheric deposition is a key process in wildlife exposure to PACs across the region.

Uncertainty Assessment of Gaseous Oxidized Mercury Measurements Collected by Atmospheric Mercury Network.

Gaseous oxidized mercury (GOM) measurement uncertainties undoubtedly impact the understanding of mercury biogeochemical cycling; however, there is a lack of consensus on the uncertainty magnitude. The numerical method presented in this study provides an alternative means of estimating the uncertainties of previous GOM measurements. Weekly GOM in ambient air was predicted from measured weekly mercury wet deposition using a scavenging ratio approach, and compared against field measurements of 2-4 hly GOM to estimate the measurement biases of the Tekran speciation instruments at 13 Atmospheric Mercury Network (AMNet) sites. Multiyear average GOM measurements were estimated to be biased low by more than a factor of 2 at six sites, between a factor of 1.5 and 1.8 at six other sites, and below a factor of 1.3 at one site. The differences between predicted and observed were significantly larger during summer than other seasons potentially because of higher ozone concentrations that may interfere with GOM sampling. The analysis data collected over six years at multiple sites suggests a systematic bias in GOM measurements, supporting the need for further investigation of measurement technologies and identifying the chemical composition of GOM.

Smart Sensor-Based Motion Detection System for Hand Movement Training in Open Surgery.

We introduce a smart sensor-based motion detection technique for objective measurement and assessment of surgical dexterity among users at different experience levels. The goal is to allow trainees to evaluate their performance based on a reference model shared through communication technology, e.g., the Internet, without the physical presence of an evaluating surgeon. While in the current implementation we used a Leap Motion Controller to obtain motion data for analysis, our technique can be applied to motion data captured by other smart sensors, e.g., OptiTrack. To differentiate motions captured from different participants, measurement and assessment in our approach are achieved using two strategies: (1) low level descriptive statistical analysis, and (2) Hidden Markov Model (HMM) classification. Based on our surgical knot tying task experiment, we can conclude that finger motions generated from users with different surgical dexterity, e.g., expert and novice performers, display differences in path length, number of movements and task completion time. In order to validate the discriminatory ability of HMM for classifying different movement patterns, a non-surgical task was included in our analysis. Experimental results demonstrate that our approach had 100 % accuracy in discriminating between expert and novice performances. Our proposed motion analysis technique applied to open surgical procedures is a promising step towards the development of objective computer-assisted assessment and training systems.