Synergistic Effect of Trehalose and Saccharose Pretreatment on Maintenance of Lyophilized Human Red Blood Cell Quality

Purpose: To investigate the synergistic effect of trehalose and saccharose pretreatment on maintenance of lyophilized human red blood cell (RBC) quality.Methods: RBCs were pre-treated with trehalose and saccharose, and then lyophilized and re-hydrated. Prior to lyophilization and after re hydration, RBC parameters, RBC counts, total hemoglobin concentration, mean corpuscular hemoglobin (MCH), mean corpuscular volume (MCV), comprehensive deformation index, hemolysis ratio and phosphatidylserine (PS) expression, were determined using a hematology analyzer, an RBC deformation instrument, a spectrophotometer and a flow cytometer, respectively. Superoxide dismutase (SOD), glucose-6-phosphate dehydrogenase (G-6-PD), and adenosine triphosphatase (ATPase) activities were determined using kits for SOD, ATPase, and G-6- PD assay, respectively.Results: After lyophilization-rehydration, RBC counts and total hemoglobin recovery rates, deformability, and RBC SOD, ATPase, and G-6-PD activities were significantly decreased by 47.24 – 74.65 % (p < 0.01), compared with the normal group. RBC osmotic fragility and PS expression on the outer surface of the RBC membrane were significantly increased by 168.53 and 629.30 % (p < 0.01), respectively, compared with the normal group. RBC MCH and MCV values were not significantly affected by lyophilization rehydration (p > 0.05). Trehalose and saccharose pretreatment significantly reversed the effects of lyophilization-rehydration on these RBC parameters by approximately 13.16 – 211.11 % (p < 0.01), compared with the control group. The combined effects were synergistic.Conclusion: Trehalose and saccharose pretreatment synergistically enhances maintenance of lyophilized RBC quality.Keywords: Trehalose, Saccharose, Lyophilization, Red blood cell, Hematological parameter

Look, Listen and Learn - A Multimodal LSTM for Speaker Identification

Speaker identification refers to the task of localizing the face of a person who has the same identity as the ongoing voice in a video. This task not only requires collective perception over both visual and auditory signals, the robustness to handle severe quality degradations and unconstrained content variations are also indispensable. In this paper, we describe a novel multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies both visual and auditory modalities from the beginning of each sequence input. The key idea is to extend the conventional LSTM by not only sharing weights across time steps, but also sharing weights across modalities. We show that modeling the temporal dependency across face and voice can significantly improve the robustness to content quality degradations and variations. We also found that our multimodal LSTM is robustness to distractors, namely the non-speaking identities. We applied our multimodal LSTM to The Big Bang Theory dataset and showed that our system outperforms the state-of-the-art systems in speaker identification with lower false alarm rate and higher recognition accuracy.Comment: The 30th AAAI Conference on Artificial Intelligence (AAAI-16

6-Cyclo­hexyl­meth­yl-5-ethyl-2-[(2-oxo-2-phenyl­eth­yl)sulfan­yl]pyrimidin-4(3H)-one

In the title compound, C21H26N2O2S, the cyclo­hexane ring adopts a chair conformation. The angle at the methyl­ene bridge linking the pyrimidine and cyclo­hexane rings is 113.41 (13)°. This is in the range considered optimal for maximum activity of non-nucleoside reverse transcriptase inhibitors. In the crystal, mol­ecules are connected into centrosymmetric dimers via pairs of N—H⋯O hydrogen bonds

Accurate Single Stage Detector Using Recurrent Rolling Convolution

Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible regions were proposed in the first stage then followed by a second stage for decision refinement. Despite the simplicity of training and the efficiency in deployment, the single stage detection methods have not been as competitive when evaluated in benchmarks consider mAP for high IoU thresholds. In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation. We achieved this by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are "deep in context". We evaluated our method in the challenging KITTI dataset which measures methods under IoU threshold of 0.7. We showed that with RRC, a single reduced VGG-16 based model already significantly outperformed all the previously published results. At the time this paper was written our models ranked the first in KITTI car detection (the hard level), the first in cyclist detection and the second in pedestrian detection. These results were not reached by the previous single stage methods. The code is publicly available.Comment: CVPR 201