Cereal and nonfat milk support muscle recovery following exercise

All authors are with the Exercise Physiology and Metabolism Laboratory Department of Kinesiology and Health Education The University of Texas at Austin Austin, TX, USABackground: This study compared the effects of ingesting cereal and nonfat milk (Cereal) and a carbohydrate-electrolyte sports drink (Drink) immediately following endurance exercise on muscle glycogen synthesis and the phosphorylation state of proteins controlling protein synthesis: Akt, mTOR, rpS6 and eIF4E. -- Methods: Trained cyclists or triathletes (8 male: 28.0 ± 1.6 yrs, 1.8 ± 0.0 m, 75.4 ± 3.2 kg, 61.0 ± 1.6 ml O2•kg-1•min-1; 4 female: 25.3 ± 1.7 yrs, 1.7 ± 0.0 m, 66.9 ± 4.6 kg, 46.4 ± 1.2 mlO2•kg-1•min-1) completed two randomly-ordered trials serving as their own controls. After 2 hours of cycling at 60–65% VO2MAX, a biopsy from the vastus lateralis was obtained (Post0), then subjects consumed either Drink (78.5 g carbohydrate) or Cereal (77 g carbohydrate, 19.5 g protein and 2.7 g fat). Blood was drawn before and at the end of exercise, and at 15, 30 and 60 minutes after treatment. A second biopsy was taken 60 minutes after supplementation (Post60). Differences within and between treatments were tested using repeated measures ANOVA. -- Results: At Post60, blood glucose was similar between treatments (Drink 6.1 ± 0.3, Cereal 5.6 ± 0.2 mmol/L, p < .05), but after Cereal, plasma insulin was significantly higher (Drink 123.1 ± 11.8, Cereal 191.0 ± 12.3 pmol/L, p < .05), and plasma lactate significantly lower (Drink 1.4 ± 0.1, Cereal 1.00 ± 0.1 mmol/L, p < .05). Except for higher phosphorylation of mTOR after Cereal, glycogen and muscle proteins were not statistically different between treatments. Significant Post0 to Post60 changes occurred in glycogen (Drink 52.4 ± 7.0 to 58.6 ± 6.9, Cereal 58.7 ± 9.6 to 66.0 ± 10.0 μmol/g, p < .05) and rpS6 (Drink 17.9 ± 2.5 to 35.2 ± 4.9, Cereal 18.6 ± 2.2 to 35.4 ± 4.4 %Std, p < .05) for each treatment, but only Cereal significantly affected glycogen synthase (Drink 66.6 ± 6.9 to 64.9 ± 6.9, Cereal 61.1 ± 8.0 to 54.2 ± 7.2%Std, p < .05), Akt (Drink 57.9 ± 3.2 to 55.7 ± 3.1, Cereal 53.2 ± 4.1 to 60.5 ± 3.7 %Std, p < .05) and mTOR (Drink 28.7 ± 4.4 to 35.4 ± 4.5, Cereal 23.0 ± 3.1 to 42.2 ± 2.5 %Std, p < .05). eIF4E was unchanged after both treatments. -- Conclusion: These results suggest that Cereal is as good as a commercially-available sports drink in initiating post-exercise muscle recovery.Kinesiology and Health [email protected]

L dwarfs detection from SDSS images using improved Faster R-CNN

We present a data-driven approach to automatically detect L dwarfs from Sloan Digital Sky Survey(SDSS) images using an improved Faster R-CNN framework based on deep learning. The established L dwarf automatic detection (LDAD) model distinguishes L dwarfs from other celestial objects and backgrounds in SDSS field images by learning the features of 387 SDSS images containing L dwarfs. Applying the LDAD model to the SDSS images containing 93 labeled L dwarfs in the test set, we successfully detected 83 known L dwarfs with a recall rate of 89.25% for known L dwarfs. Several techniques are implemented in the LDAD model to improve its detection performance for L dwarfs,including the deep residual network and the feature pyramid network. As a result, the LDAD model outperforms the model of the original Faster R-CNN, whose recall rate of known L dwarfs is 80.65% for the same test set. The LDAD model was applied to detect L dwarfs from a larger validation set including 843 labeled L dwarfs, resulting in a recall rate of 94.42% for known L dwarfs. The newly identified candidates include L dwarfs, late M and T dwarfs, which were estimated from color (i-z) and spectral type relation. The contamination rates for the test candidates and validation candidates are 8.60% and 9.27%, respectively. The detection results indicate that our model is effective to search for L dwarfs from astronomical images.Comment: 12 pages, 10 figures, accepted to be published in A

Automatic detection of low surface brightness galaxies from SDSS images

Low surface brightness (LSB) galaxies are galaxies with central surface brightness fainter than the night sky. Due to the faint nature of LSB galaxies and the comparable sky background, it is difficult to search LSB galaxies automatically and efficiently from large sky survey. In this study, we established the Low Surface Brightness Galaxies Auto Detect model (LSBG-AD), which is a data-driven model for end-to-end detection of LSB galaxies from Sloan Digital Sky Survey (SDSS) images. Object detection techniques based on deep learning are applied to the SDSS field images to identify LSB galaxies and estimate their coordinates at the same time. Applying LSBG-AD to 1120 SDSS images, we detected 1197 LSB galaxy candidates, of which 1081 samples are already known and 116 samples are newly found candidates. The B-band central surface brightness of the candidates searched by the model ranges from 22 mag arcsec $^ {- 2}$ to 24 mag arcsec $^ {- 2}$, quite consistent with the surface brightness distribution of the standard sample. 96.46\% of LSB galaxy candidates have an axis ratio ($b/a$) greater than 0.3, and 92.04\% of them have $fracDev\_r$\textless 0.4, which is also consistent with the standard sample. The results show that the LSBG-AD model learns the features of LSB galaxies of the training samples well, and can be used to search LSB galaxies without using photometric parameters. Next, this method will be used to develop efficient algorithms to detect LSB galaxies from massive images of the next generation observatories.Comment: 11 pages, 9 figures,accepted to be published on MNRA

Analysis of cognitive function and its related factors after treatment in Meniere’s disease

A growing body of research recently suggested the association between vestibular dysfunction and cognitive impairment. Meniere’s disease (MD), a common clinical vestibular disorder, is usually accompanied by hearing loss and emotional stress, both of which may mediate the relationship between vestibule dysfunction and cognition. It is currently unknown whether the cognitive decline in MD patients could improve through treatment and how it relates to multiple clinical characteristics, particularly the severity of vertigo. Therefore, in the present study, the MD patients were followed up for 3, 6, and 12 months after treatment, and the cognitive functions, vertigo symptoms, and related physical, functional, and emotional effects of the patients were assessed using the Montreal Cognitive Assessment (MoCA) and Dizziness Handicap Inventory (DHI), aiming to explore the change in cognition before and after therapy and the correlation with various clinical features. It was found that cognitive decline in MD patients compared to healthy controls before therapy. Importantly, this cognitive impairment could improve after effective therapy, which was related to the severity of vertigo, especially in functional and physical impacts. Our results support the view that vestibular dysfunction is a potentially modifiable risk factor for cognitive decline

A Novel Framework for the Comparative Analysis of Biological Networks

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology. Our strategy includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in the current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which vastly increases its performance with respect to existing tools. Finally, we illustrate the biological significance of the results through the identification of novel complex components and potential cases of cross-talk between pathways and alternative signaling routes

Multi-Stage Hough Space Calculation for Lane Markings Detection via IMU and Vision Fusion

It is challenging to achieve robust lane detection based on a single frame, particularly when complicated driving scenarios are present. A novel approach based on multiple frames is proposed in this paper by taking advantage of the fusion of vision and Inertial Measurement Units (IMU). Hough space is employed as a storage medium where lane markings can be stored and visited conveniently. The detection of lane markings is achieved by the following steps. Firstly, primary line segments are extracted from a basic Hough space, which is calculated by Hough Transform. Secondly, a CNN-based classifier is introduced to measure the confidence probability of each line segment, and transforms the basic Hough space into a probabilistic Hough space. In the third step, pose information provided by the IMU is applied to align previous probabilistic Hough spaces to the current one and a filtered probabilistic Hough space is acquired by smoothing the primary probabilistic Hough space across frames. Finally, valid line segments with probability higher than 0.7 are extracted from the filtered probabilistic Hough space. The proposed approach is applied experimentally, and the results demonstrate a satisfying performance compared to various existing methods

Investigation of CO and formaldehyde oxidation over mesoporous Ag/Co3O4 catalysts

CO and formaldehyde (HCHO) oxidation reactions were investigated over mesoporous Ag/Co3O4 catalysts prepared by one-pot (OP) and impregnation (IM) methods. It was found that the one-pot method was superior to the impregnation method for synthesizing Ag/Co3O4 catalysts with high activity for both reactions. It was also found that the catalytic behavior of mesoporous Co3O4 and Ag/Co3O4 catalysts for the both reactions was different. And the addition of silver on mesoporous Co3O4 did not always enhance the catalytic activity of final catalyst for CO oxidation at room temperature (20 degrees C), but could significantly improve the catalytic activity of final catalyst for HCHO oxidation at low temperature (90 degrees C). The high surface area, uniform pore structure and the pretty good dispersion degree of the silver particle should be responsible for the excellent low-temperature CO oxidation activity. However, for HCHO oxidation, the addition of silver played an important role in the activity enhancement. And the silver particle size and the reducibility of Co3O4 should be indispensable for the high activity of HCHO oxidation at low temperature

Li-rich Giants Identified from LAMOST DR8 Low-resolution Survey

A small fraction of giants possess photospheric lithium (Li) abundance higher than the value predicted by the standard stellar evolution models, and the detailed mechanisms of Li enhancement are complicated and lack a definite conclusion. In order to better understand the Li enhancement behaviors, a large and homogeneous Li-rich giant sample is needed. In this study, we designed a modified convolutional neural network model called Coord-DenseNet to determine the A (Li) of Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution survey (LRS) giant spectra. The precision is good on the test set: MAE = 0.15 dex, and σ = 0.21 dex. We used this model to predict the Li abundance of more than 900,000 LAMOST DR8 LRS giant spectra and identified 7768 Li-rich giants with Li abundances ranging from 2.0 to 5.4 dex, accounting for about 1.02% of all giants. We compared the Li abundance estimated by our work with those derived from high-resolution spectra. We found that the consistency was good if the overall deviation of 0.27 dex between them was not considered. The analysis shows that the difference is mainly due to the high A (Li) from the medium-resolution spectra in the training set. This sample of Li-rich giants dramatically expands the existing sample size of Li-rich giants and provides us with more samples to further study the formation and evolution of Li-rich giants