A new approach to trace the dynamic patterns of task-based functional connectivity, by combining signal segmentation, dynamic time warping (DTW), and Quality Threshold (QT) clustering techniques, is presented. Electroencephalography (EEG) signals of 5 healthy subjects were recorded as they performed an auditory oddball and a visual modified oddball tasks. To capture the dynamic patterns of functional connectivity during the execution of each task, EEG signals are segmented into durations that correspond to the temporal windows of previously well-studied event-related potentials (ERPs). For each temporal window, DTW is employed to measure the functional similarities among channels. Unlike commonly used temporal similarity measures, such as cross correlation, DTW compares time series by taking into consideration that their alignment properties may vary in time. QT clustering analysis is then used to automatically identify the functionally connected regions in each temporal window. For each task, the proposed approach was able to establish a unique sequence of dynamic pattern (observed in all 5 subjects) for brain functional connectivity.

BackgroundWe have explored the potential prefrontal hemodynamic biomarkers to characterize subjects with Traumatic Brain Injury (TBI) by employing the multivariate machine learning approach and introducing a novel task‐related hemodynamic response detection followed by a heuristic search for optimum set of hemodynamic features. To achieve this goal, the hemodynamic response from a group of 31 healthy controls and 30 chronic TBI subjects were recorded as they performed a complexity task.MethodsTo determine the optimum hemodynamic features, we considered 11 features and their combinations in characterizing TBI subjects. We investigated the significance of the features by utilizing a machine learning classification algorithm to score all the possible combinations of features according to their predictive power.Results and ConclusionsThe identified optimum feature elements resulted in classification accuracy, sensitivity, and specificity of 85%, 85%, and 84%, respectively. Classification improvement was achieved for TBI subject classification through feature combination. It signified the major advantage of the multivariate analysis over the commonly used univariate analysis suggesting that the features that are individually irrelevant in characterizing the data may become relevant when used in combination. We also conducted a spatio‐temporal classification to identify regions within the prefrontal cortex (PFC) that contribute in distinguishing between TBI and healthy subjects. As expected, Brodmann areas (BA) 10 within the PFC were isolated as the region that healthy subjects (unlike subjects with TBI), showed major hemodynamic activity in response to the High Complexity task. Overall, our results indicate that identified temporal and spatio‐temporal features from PFC's hemodynamic activity are promising biomarkers in classifying subjects with TBI.

[1] We present a semiautomated method to extract spectral end-members from hyperspectral images. This method employs superpixels, which are spectrally homogeneous regions of spatially contiguous pixels. The superpixel segmentation is combined with an unsupervised end-member extraction algorithm. Superpixel segmentation can complement per pixel classification techniques by reducing both scene-specific noise and computational complexity. The end-member extraction step explores the entire spectrum, recognizes target mineralogies within spectral mixtures, and enhances the discovery of unanticipated spectral classes. The method is applied to Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) images and compared to a manual expert classification and to state-of the-art image analysis techniques. The technique successfully recognizes all classes identified by the expert, producing spectral end-members that match well to target classes. Application of the technique to CRISM multispectral data and Moon Mineralogy Mapper (M 3 ) hyperspectral data demonstrates the flexibility of the method in the analysis of a range of data sets. The technique is then used to analyze CRISM data in Ariadnes Chaos, Mars, and recognizes both phyllosilicates and sulfates in the chaos mounds. These aqueous deposits likely reflect changing environmental conditions during the Late Noachian/Early Hesperian. This semiautomated focus-of-attention tool will facilitate the identification of materials of interest on planetary surfaces whose constituents are unknown.

BackgroundUnderstanding the neural basis of moral judgment (MJ) and human decision‐making has been the subject of numerous studies because of their impact on daily life activities and social norms. Here, we aimed to investigate the neural process of MJ using functional near‐infrared spectroscopy (fNIRS), a noninvasive, portable, and affordable neuroimaging modality.MethodsWe examined prefrontal cortex (PFC) activation in 33 healthy participants engaging in MJ exercises. We hypothesized that participants presented with personal (emotionally salient) and impersonal (less emotional) dilemmas would exhibit different brain activation observable through fNIRS. We also investigated the effects of utilitarian and nonutilitarian responses to MJ scenarios on PFC activation. Utilitarian responses are those that favor the greatest good while nonutilitarian responses favor moral actions. Mixed effect models were applied to model the cerebral hemodynamic changes that occurred during MJ dilemmas.Results and conclusionsOur analysis found significant differences in PFC activation during personal versus impersonal dilemmas. Specifically, the left dorsolateral PFC was highly activated during impersonal MJ when a nonutilitarian decision was made. This is consistent with the majority of relevant fMRI studies, and demonstrates the feasibility of using fNIRS, with its portable and motion tolerant capacities, to investigate the neural basis of MJ dilemmas.

Background: The raccoon, Procyon lotor Linn. (Procyonidae) is native to North and Central America but has been introduced in several European and Asian countries including Japan, Germany and Iran. Objective of this study was to determine frequency of gastrointestinal and tissue helminthes from feral raccoons in Iran. Methods: During 2015-2017, 30 feral raccoons including 12 males and 18 females were collected from Guilan Province, northern Iran (the only region in Iran where raccoons are found). The gastrointestinal tracts and tissues such as lung, liver and muscles were examined for presence of helminthes. Results: Twenty raccoons (66.7%) were found infected with five intestinal helminth species. The prevalence of infection with Strongyloides procyonis Little, 1966 (Nematoda) was 63.3%, Plagiorchis koreanus Ogata, 1938 (Trematoda) (13.3%), Centrorhynchus sp. Lühe, 1911 (Acanthocephala) (10.0%), Camerostrongylus didelphis Wolfgang, 1951 (Nematoda) (3.3%), and Spirocerca lupi Rudolphi, 1809 (Nematoda) (3.3%). No larvae or adult worms were found in other tissues of the examined raccoons. Conclusion: Most of the raccoons were infected with S. procyonis. The public health importance of zoonotic parasites transmittable through raccoons, the rapid control and decrease of raccoon populations and their distribution in Iran are also discussed.

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Summary We describe a compact, non-contact design for a Total Emission Detection (c-TED) system for intra-vital multi-photon imaging. To conform to a standard upright two-photon microscope design, this system uses a parabolic mirror surrounding a standard microscope objective in concert with an optical path that does not interfere with normal microscope operation. The non-contact design of this device allows for maximal light collection without disrupting the physiology of the specimen being examined. Tests were conducted on exposed tissues in live animals to examine the emission collection enhancement of the c-TED device compared to heavily optimized objective-based emission collection. The best light collection enhancement was seen from murine fat (5×-2× gains as a function of depth), while murine skeletal muscle and rat kidney showed gains of over two and just under two-fold near the surface, respectively. Gains decreased with imaging depth (particularly in the kidney). Zebrafish imaging on a reflective substrate showed close to a two-fold gain throughout the entire volume of an intact embryo (approximately 150 μm deep). Direct measurement of bleaching rates confirmed that the lower laser powers (enabled by greater light collection efficiency) yielded reduced photobleaching in vivo. The potential benefits of increased light collection in terms of speed of imaging and reduced photo-damage, as well as the applicability of this device to other multi-photon imaging methods is discussed.

Indoor video surveillance is now widely used in government, public, and private facilities. While the capacity to generate such video data is increasing, our ability to derive a coherent scene understanding of the structure of the scene and how it is being utilized, using only motion data, is still lagging behind. This paper proposes a framework for deriving indoor scene structure identifying abnormal motion behavior using only video tracking data, and without requiring a floor plan. The proposed framework, which is data-driven, is based on four sequential processing steps, namely detection of entrance and exit points, the analysis of the connectivity between entrance and exit points, the extraction of mean paths and motion corridors, and the statistical analysis of the length and velocity parameters of motion for the detection of abnormal motion behavior. The paper outlines the proposed framework and demonstrates its implementation using a realworld data set comprising 1138 trajectories.