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Welch Damsgaard posted an update 6 months, 1 week ago
Environmental factors such as the availability of oxygen are instructive cues that regulate stem cell maintenance and differentiation. We used a genetically encoded biosensor to monitor the hypoxic state of neural cells in the larval brain of Drosophila The biosensor reveals brain compartment and cell-type specific levels of hypoxia. The values correlate with differential tracheolation that is observed throughout development between the central brain and the optic lobe. Neural stem cells in both compartments show the strongest hypoxia response while intermediate progenitors, neurons and glial cells reveal weaker responses. We demonstrate that the distance between a cell and the next closest tracheole is a good predictor of the hypoxic state of that cell. Our study indicates that oxygen availability appears to be the major factor controlling the hypoxia response in the developing Drosophila brain and that cell intrinsic and cell-type specific factors contribute to modulate the response in an unexpected manner.This article has an associated First Person interview with the first author of the paper.
Anaphylaxis is a severe, potentially fatal allergic reaction best treated with intramuscular epinephrine via epinephrine auto-injectors (AAIs). Our published concerns over laceration injuries to young children associated with AAIs led to this service evaluation of the two administration methods swing and jab (S&J) and place and press (P&P), to determine potential laceration risk.
A trainer EpiPen was used with facepaint placed in the needle indentation which would record the length of movement of the AAI. The two different methods ‘administered’ were alternated. Children were asked to move their leg to simulate a withdrawal reaction. Age, whether they moved, and length of paint mark were recorded.
Outpatients waiting area in Noah’s Ark Children’s Hospital, Cardiff.
Children aged 5-11 with no prior knowledge of AAI use.
No intervention was implemented.
135 children (mean age 8 years; range 5-11 years) were asked to participate; measurements were taken from 100 children. 50 children moved for one or both methods. For those that moved, S&J mean paint length=8.3 mm (SD 17.4, 95% CI 3.4 to 13.3), P&P mean=3.5 mm (SD 11.0, 95% CI 0.4 to 6.6). Mean difference between methods was 4.8 mm (SD 10.1, 95% CI 1.9 to 7.7). Slightly more children moved for S&J (44) compared with 38 for P&P.
S&J produces more movement and longer paint marks than P&P. The risk of laceration when administering an EpiPen to young children may be lower by using the more controlled P&P. Selleck Triapine We feel it is advisable to teach P&P instead in children below 11 years of age.
S&J produces more movement and longer paint marks than P&P. The risk of laceration when administering an EpiPen to young children may be lower by using the more controlled P&P. We feel it is advisable to teach P&P instead in children below 11 years of age..The ChaLearn large-scale gesture recognition challenge has run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture
obreak recognition, respectively. It describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. In this article, we discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition and provide a detailed analysis of the current methods for large-scale isolated and continuous gesture recognition. In addition to the recognition rate and mean Jaccard index (MJI) as evaluation metrics used in previous challenges, we introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) method, determining video division points based on skeleton points. Experiments show that the proposed Bi-LSTM outperforms state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR.Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue level. Electroencephalography (EEG) has been demonstrated to be effective for monitoring changes in the human brain state and behavior. Thirty-seven subjects participated in this driving experiment and performed a perform lane-keeping task in a visual-reality environment. Three domains, namely, frequency, temporal, and 2-D spatial information, of the EEG channel location were comprehensively considered. A 4-D convolutional neural-network (4-D CNN) algorithm was then proposed to associate all information from the EEG signals and the changes in the human state and behavioral performance. A 4-D CNN achieves superior forecasting performance over 2-D CNN, 3-D CNN, and shallow networks. The results showed a 3.82% improvement in the root mean-square error, a 3.45% improvement in the error rate, and a 11.98% improvement in the correlation coefficient with 4-D CNN compared with 3-D CNN. The 4-D CNN algorithm extracts the significant θ and alpha activations in the frontal and posterior cingulate cortices under distinct fatigue levels. This work contributes to enhancing our understanding of deep learning methods in the analysis of EEG signals. We even envision that deep learning might serve as a bridge between translation neuroscience and further real-world applications.This article addresses an output-feedback flocking control problem for a swarm of autonomous surface vehicles (ASVs) to follow a leading ASV guided via a parameterized path. The leading and following ASVs are subject to completely unknown model parameters, external disturbances, and unmeasured velocities. A data-driven adaptive anti-disturbance control method is proposed for establishing a flocking behavior without any prior knowledge of model parameters. Specifically, a data-driven adaptive extended state observer (ESO) is proposed such that unknown input gains, unmeasured velocities, and total disturbance are simultaneously estimated. For the leading ASV, an output-feedback path-following control law is developed to follow a predefined parameterized path. For following ASVs, an output-feedback flocking control law is developed based on an artificial potential function for collision avoidance and connectivity preservation, in addition to a distributed ESO for estimating the velocity of the leading ASV through a cooperative estimation network.
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