Deep Self-Taught Hashing for Image Retrieval

Hashing algorithm has been widely used to speed up image retrieval due to its compact binary code and fast distance calculation. The combination with deep learning boosts the performance of hashing by learning accurate representations and complicated hashing functions. So far, the most striking success in deep hashing have mostly involved discriminative models, which require labels. To apply deep hashing on datasets without labels, we propose a deep self-taught hashing algorithm (DSTH), which generates a set of pseudo labels by analyzing the data itself, and then learns the hash functions for novel data using discriminative deep models. Furthermore, we generalize DSTH to support both supervised and unsupervised cases by adaptively incorporating label information. We use two different deep learning framework to train the hash functions to deal with out-of-sample problem and reduce the time complexity without loss of accuracy. We have conducted extensive experiments to investigate different settings of DSTH, and compared it with state-of-the-art counterparts in six publicly available datasets. The experimental results show that DSTH outperforms the others in all datasets

Investigation on risk prediction of pedestrian head injury by real-world accidents

Head injury is the most common and fatal injury in car-pedestrian accidents. Due to the lack of human test data, real-world accident data is useful for the research on the mechanism and tolerance of head injuries. The objective of the present work is to investigate pedestrian head-brain injuries through real car-pedestrian accidents and evaluate the existed injury criteria. Seven car-to-pedestrian accidents in China were selected from the IVAC (Investigation of Vehicle Accident in Changsha) database. Accident reconstructions using multi-body models were conducted to determine the kinematic parameters associated with the injury and were used to measure head injury criteria. Kinematic parameters were input into a finite element model to run simulations on the head-brain and car interface to determine levels of brain tissue stress, strain, and brain tissue injury criteria. A binary logistic regression model was used to determine the probability of head injury risk associated with AIS3+ injuries (Abbreviated Injury Scale). The results showed that head injury criteria using kinematic parameters can effectively predict injury risk of a pedestrians’ head skull. Regarding brain injuries, physical parameters like coup/countercoup pressure are more effective predictors. The results of this study can be used as the background knowledge for pedestrian friendly car design

Revealing the pathogenesis of gastric intestinal metaplasia based on the mucosoid air-liquid interface

Background: Gastric intestinal metaplasia (GIM) is an essential precancerous lesion. Although the reversal of GIM is challenging, it potentially brings a state-to-art strategy for gastric cancer therapeutics (GC). The lack of the appropriate in vitro model limits studies of GIM pathogenesis, which is the issue this work aims to address for further studies. Method: The air-liquid interface (ALI) model was adopted for the long-term culture of GIM cells in the present work. This study conducted Immunofluorescence (IF), quantitative real-time polymerase chain reaction (qRT-PCR), transcriptomic sequencing, and mucoproteomic sequencing (MS) techniques to identify the pathways for differential expressed genes (DEGs) enrichment among different groups, furthermore, to verify novel biomarkers of GIM cells. Result: Our study suggests that GIM-ALI model is analog to the innate GIM cells, which thus can be used for mucus collection and drug screening. We found genes MUC17, CDA, TRIM15, TBX3, FLVCR2, ONECUT2, ACY3, NMUR2, and MAL2 were highly expressed in GIM cells, while GLDN, SLC5A5, MAL, and MALAT1 showed down-regulated, which can be used as potential biomarkers for GIM cells. In parallel, these genes that highly expressed in GIM samples were mainly involved in cancer-related pathways, such as the MAPK signal pathway and oxidative phosphorylation signal pathway. Conclusion: The ALI model is validated for the first time for the in vitro study of GIM. GIM-ALI model is a novel in vitro model that can mimic the tissue micro-environment in GIM patients and further provide an avenue for studying the characteristics of GIM mucus. Our study identified new markers of GIM as well as pathways associated with GIM, which provides outstanding insight for exploring GIM pathogenesis and potentially other related conditions

Optimizing method of mechanism angle of upper limb rehabilitation robot at glenohumeral joint

At present, there are seventy million stroke patients in China and annual death toll of stroke is 1 million 650 thousand people. The survivors about 75% become disabled persons and lose the ability to move. To address this issue, a kind of rehabilitation exoskeleton robot called YANARM is proposed which for training of shoulder complex. This paper, a kind of method for optimizing the angle parameters of series dynamic axes under given workspace conditions is presented. The forward and inverse kinematics solutions of glenohumeral mechanism are solved based on the exponential product formula (POE) and the Paden-Kahan sub-problem. The range of joint rotation angle can be inversely solved according to the end of the arm position at the borderline of the workspace. A curve between the angle and the CP which equal mean variance of joint rotation range plus sum of all joint rotation ranges is solved. The mechanism angle of glenohumeral joint is optimized by this method

Proliferative Activity and Neuroprotective Effect of Ligustrazene Derivative by Irritation of Vascular Endothelial Growth Factor Expression in Middle Cerebral Artery Occlusion Rats

Purpose: To investigate the proliferative activity and neuroprotective effect of a newly identified ligustrazine derivative (4-((3,5,6-trimethylpyrazine-2 yl)methoxyl)-3-methox-ybenzoic acid-3,5,6- trimethylpyrazin- 2-methyl ester, T VA) and the possible mechanism related to vascular endothelial growth factor (VEGF) in cerebral ischemic injury.Methods: The pharmacological activity of T-VA was evaluated using MTT ((3 (4,5-dimethylthiazolyl2- yl)-2,5-diphenyltetrazolium bromide)) assay, while cellular morphology was observed with hematoxylin and eosin (HE) staining. Chick chorioallantoic membrane (CAM) model, immuno-histochemical analysis, and enzyme-linked immunosorbent assay (ELISA) were used to determine the expression of VEGF. Middle cerebral artery occlusion (MCAO) model was used to investigate both VEGF expression and the survival rate after treatment with T-VA.Results: T-VA promoted neuron activity, and the doses of 15 and 30 μM showed more significant effect (p < 0.05). The viability of PC12 cells increased significantly in T-VA (30 and 60 μM) groups (p < 0.05) and increased in a dose dependent manner. Immunohistochemical analysis showed stimulated VEGF expression, and CAM model results showed that T-VA (20 mg/egg) significantly promoted microangiogenesis (p < 0.01). Moreover, in MCAO model, the survival rate of T-VA (60 mg/kg) group reached 86.7 % while for the ischemia group it was 60.0 %. In addition, ELISA results showed that T-VA promoted the expression of VEGF (p < 0.05).Conclusion: These findings indicate that T-VA helps to prevent ischemic injury by increasing VEGF expression.Keywords: Ligustrazine, Neuron, PC12 cell, Chick Chorioallantoic Membrane, Middle Cerebral Artery Occlusion, Vascular Endothelial Growth Facto

Dual-stage structural response to quenching charge order in magnetite

The Verwey transition in magnetite (Fe_{3}O_{4} ) is the prototypical metal-insulator transition and has eluded a comprehensive explanation for decades. A major element of the challenge is the complex interplay between charge order and lattice distortions. Here we use ultrafast electron diffraction (UED) to disentangle the roles of charge order and lattice distortions by tracking the transient structural evolution after charge order is melted via ultrafast photoexcitation. A dual-stage response is observed in which X_{3}, X_{1}, and Delta5-type structural distortions occur on markedly different timescales of 0.7–3.2 ps and longer than 3.2 ps. We propose that these distinct timescales arise because X_{3}-type distortions strongly couple to the trimeron charge order, whereas the Delta5- distortions are more strongly associated with monoclinic to cubic distortions of the overall lattice. Our work aids in clarifying the charge-lattice interplay using UED method and illustrates the disentanglement of the complex phases in magnetite

Dual-stage structural response to quenching charge order in magnetite

The Verwey transition in magnetite (Fe3O4 ) is the prototypical metal-insulator transition and has eluded a comprehensive explanation for decades. A major element of the challenge is the complex interplay between charge order and lattice distortions. Here we use ultrafast electron diffraction (UED) to disentangle the roles of charge order and lattice distortions by tracking the transient structural evolution after charge order is melted via ultrafast photoexcitation. A dual stage response is observed in which X3, X1, and Delta5 type structural distortions occur on markedly different timescales of 0.7 to 3.2 ps and longer than 3.2 ps. We propose that these distinct timescales arise because X3 type distortions strongly couple to the trimeron charge order, whereas the Delta5-distortions are more strongly associated with monoclinic to cubic distortions of the overall lattice. Our work aids in clarifying the charge lattice interplay using UED method and illustrates the disentanglement of the complex phases in magnetite.Comment: 7 figures and 7 table