Current Issue : October - December Volume : 2014 Issue Number : 4 Articles : 5 Articles
Background: Visualisation of neurons labeled with fluorescent proteins or\ncompounds generally require exposure to intense light for a relatively long\nperiod of time, often leading to bleaching of the fluorescent probe and\nphotodamage of the tissue. Here we created a technique to drastically shorten\nlight exposure and improve the targeting of fluorescent labeled cells that is specially\nuseful for patch-clamp recordings. We applied image tracking and mask overlay to\nreduce the time of fluorescence exposure and minimise mistakes when identifying\nneurons.\nMethods: Neurons are first identified according to visual criteria (e.g. fluorescence\nprotein expression, shape, viability etc.) and a transmission microscopy image\nDifferential Interference Contrast (DIC) or Dodt contrast containing the cell used as a\nreference for the tracking algorithm. A fluorescence image can also be acquired later to\nbe used as a mask (that can be overlaid on the target during live transmission video). As\npatch-clamp experiments require translating the microscope stage, we used pattern\nmatching to track reference neurons in order to move the fluorescence mask to match\nthe new position of the objective in relation to the sample. For the image processing\nwe used the Open Source Computer Vision (OpenCV) library, including the\nSpeeded-Up Robust Features (SURF) for tracking cells. The dataset of images (n = 720)\nwas analyzed under normal conditions of acquisition and with influence of noise\n(defocusing and brightness).\nResults: We validated the method in dissociated neuronal cultures and fresh brain\nslices expressing Enhanced Yellow Fluorescent Protein (eYFP) or Tandem Dimer\nTomato (tdTomato) proteins, which considerably decreased the exposure to\nfluorescence excitation, thereby minimising photodamage. We also show that the\nneuron tracking can be used in differential interference contrast or Dodt contrast\nmicroscopy.\nConclusion: The techniques of digital image processing used in this work are an\nimportant addition to the set of microscopy tools used in modern electrophysiology,\nspecially in experiments with neuron cultures and brain slices....
Background: Respiratory disease accounts for three of the ten leading causes of\ndeath worldwide. Many of these diseases can be treated and diagnosed using a\nnebulizer. Nebulizers can also be used to safely and efficiently deliver vaccines.\nUnfortunately, commercially available nebulizers are not designed for use in regions\nof the world where lung disease is most prevalent: they are electricity-dependent,\ncost-prohibitive, and not built to be reliable in harsh operating conditions or under\nfrequent use.\nTo overcome these limitations, the Human Powered Nebulizer compressor (HPN)\nwas developed. The HPN does not require electricity; instead airflow is generated\nmanually through a hand-crank or bicycle-style pedal system. A health care worker\nor other trained individual operates the device while the patient receives treatment.\nThis study demonstrates functional specifications of the HPN in comparison with a\nstandard commercially available electric jet nebulizer compressor, the DeVilbiss\nPulmo-Aide 5650D (Pulmo-Aide).\nMethods: Pressure and flow characteristics were measured with a rotameter and\npressure transducer, respectively. Volume nebulized by each compressor was\ndetermined by mass, and particle size distribution was determined via laser\ndiffraction. The Hudson RCI Micro Mist nebulizer mouthpiece was used with\nboth compressors.\nResults: The pressure and flow generated by the HPN and Pulmo-Aide were:\n15.17 psi and 10.5 L/min; and 14.65 psi and 11.2 L/min, respectively. The volume\nof liquid delivered by each was equivalent, 1.097 �± 0.107 mL (mean �± s.e.m., n = 13)\nfor the HPN and 1.092 �± 0.116 mL for the Pulmo-Aide. The average particle size was\nalso equivalent, 5.38 �± 0.040 micrometers (mean �± s.e.m., n = 7) and 5.40 �± 0.025\nmicrometers, respectively.\nConclusions: Based on these characteristics, the HPNâ��s performance is equivalent to\na popular commercially available electric nebulizer compressor. The findings\npresented in this paper, combined with the results of two published clinical studies,\nsuggest that the HPN could serve as an important diagnostic and therapeutic tool in\nthe fight against global respiratory health challenges including: tuberculosis, chronic\nobstructive pulmonary disease, asthma, and lower respiratory infections....
Background: Chronic obstructive pulmonary disease (COPD) is one of the leading\ncauses of morbidity and mortality worldwide, and emphysema is a common\ncomponent of COPD. Currently, it is very difficult to detect early stage emphysema\nusing conventional radiographic imaging without contrast agents, because the change\nin X-ray attenuation is not detectable with absorption-based radiography. Compared\nwith the absorption-based CT, phase contrast imaging has more advantages in soft\ntissue imaging, because of its high spatial resolution and contrast.\nMethods: In this article, we used diffraction enhanced imaging (DEI) method to get\nthe images of early stage emphysematous and healthy samples, then extract X-ray\nabsorption, refraction, and ultra-small-angle X-ray scattering (USAXS) information from\nDEI images using multiple image radiography (MIR). We combined the absorption\nimage with the USAXS image by a scatter plot. The critical threshold in the scatter plot\nwas calibrated using the linear discriminant function in the pattern recognition.\nResults: USAXS image was sensitive to the change of tissue micro-structure, it could\nshow the lesions which were invisible in the absorption image. Combined with the\nabsorption-based image, the USAXS information enabled better discrimination between\nhealthy and emphysematous lung tissue in a mouse model. The false-color images\ndemonstrated that our method was capable of classifying healthy and emphysematous\ntissues.\nConclusion: Here we present USAXS images of early stage emphysematous and\nhealthy samples, where the dependence of the USAXS signal on micro-structures of\nbiomedical samples leads to improved diagnosis of emphysema in lung radiographs....
Background: We propose a mathematical model for multichannel assessment of the\ntrial-to-trial variability of auditory evoked brain responses in magnetoencephalography\n(MEG).\nMethods: Following the work of de Munck et al., our approach is based on the\nmaximum likelihood estimation and involves an approximation of the spatio-temporal\ncovariance of the contaminating background noise by means of the Kronecker product\nof its spatial and temporal covariance matrices. Extending the work of de Munck et al.,\nwhere the trial-to-trial variability of the responses was considered identical to all\nchannels, we evaluate it for each individual channel.\nResults: Simulations with two equivalent current dipoles (ECDs) with different\ntrial-to-trial variability, one seeded in each of the auditory cortices, were used to study\nthe applicability of the proposed methodology on the sensor level and revealed spatial\nselectivity of the trial-to-trial estimates. In addition, we simulated a scenario with\nneighboring ECDs, to show limitations of the method. We also present an illustrative\nexample of the application of this methodology to real MEG data taken from an\nauditory experimental paradigm, where we found hemispheric lateralization of the\nhabituation effect to multiple stimulus presentation.\nConclusions: The proposed algorithm is capable of reconstructing lateralization\neffects of the trial-to-trial variability of evoked responses, i.e. when an ECD of only one\nhemisphere habituates, whereas the activity of the other hemisphere is not subject to\nhabituation. Hence, it may be a useful tool in paradigms...
Background: Emotion recognition technology plays the essential role of enhancement\nin Human-Computer Interaction (HCI). In recent years, a novel approach for emotion\nrecognition has been reported, which is by keystroke dynamics. This approach can be\nconsidered to be rather desirable in HCI because the data used is rather non-intrusive\nand easy to obtain. However, there were only limited investigations about the\nphenomenon itself in previous studies. This study aims to examine the source of\nvariance in keystroke typing patterns caused by emotions.\nMethods: A controlled experiment to collect subjectsââ?¬â?¢ keystroke data in different\nemotional states induced by International Affective Picture System (IAPS) was\nconducted. Two-way Valence (3) Ã?â?? Arousal (3) ANOVAs were used to examine the\ncollected dataset.\nResults: The results of the experiment indicate that the effect of emotion is\nsignificant (p < .001) in the keystroke duration, keystroke latency, and accuracy rate\nof the keyboard typing. However, the size of the emotional effect is small, compare\nto the individual variability.\nConclusions: Our findings support the conclusion that the keystroke duration,\nkeystroke latency, and also the accuracy rate of typing, are influenced by emotional\nstates. Notably, the finding about the size of effect suggests that the accuracy rate\nof the emotion recognition could be further improved if personalized models are\nutilized. On the other hand, the finding also provides an explanation of why real-world\napplications which authenticate the identity of users by monitoring keystrokes may not\nbe interfered by the emotional states of users. The experiment was conducted using\nstandard instruments and hence is expected to be highly reproducible....
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