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Hashing has been proved an attractive technique for fast nearest neighbor search over big data. Compared with the projection based hashing methods, prototype-based ones own stronger power to generate discriminative binary codes for the data with complex intrinsic structure. However, existing prototype-based methods, such as spherical hashing and K-means hashing, still suffer from the ineffective coding that utilizes the complete binary codes in a hypercube. To address this problem, we propose an adaptive binary quantization (ABQ) method that learns a discriminative hash function with prototypes associated with small unique binary codes. Our alternating optimization adaptively discovers the prototype set and the code set of a varying size in an efficient way, which together robustly approximate the data relations. Our method can be naturally generalized to the product space for long hash codes, and enjoys the fast training linear to the number of the training data. We further devise a distributed framework for the large-scale learning, which can significantly speed up the training of ABQ in the distributed environment that has been widely deployed in many areas nowadays. The extensive experiments on four large-scale (up to 80 million) data sets demonstrate that our method significantly outperforms state-of-the-art hashing methods, with up to 58.84% performance gains relatively.
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To overcome the barrier of storage and computation when dealing with gigantic-scale data sets, compact hashing has been studied extensively to approximate the nearest neighbor search. Despite the recent advances, critical design issues remain open in how to select the right features, hashing algorithms, and/or parameter settings. In this paper, we address these by posing an optimal hash bit selection problem, in which an optimal subset of hash bits are selected from a pool of candidate bits generated by different features, algorithms, or parameters. Inspired by the optimization criteria used in existing hashing algorithms, we adopt the bit reliability and their complementarity as the selection criteria that can be carefully tailored for hashing performance in different tasks. Then, the bit selection solution is discovered by finding the best tradeoff between search accuracy and time using a modified dynamic programming method. To further reduce the computational complexity, we employ the pairwise relationship among hash bits to approximate the high-order independence property, and formulate it as an efficient quadratic programming method that is theoretically equivalent to the normalized dominant set problem in a vertex- and edge-weighted graph. Extensive large-scale experiments have been conducted under several important application scenarios of hash techniques, where our bit selection framework can achieve superior performance over both the naive selection methods and the state-of-the-art hashing algorithms, with significant accuracy gains ranging from 10% to 50%, relatively.
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Objective To explore the application of diffusion tensor imaging(DTI) in neonates with hypoxic ischemic encephalopathy(HIE).Methods 37 cases of full-term neonates diagnosed as HIE by pediatricians and 12 normal neonates were selected.All subjects underwent MRI and DTI at 3.0 Tesla.The correlation of FA value in posterior limb of internal capsule and neonatal NBNA score was examined.Results The FA values in posterior limb of internal capsule, splenium had significant difference between the HIE group (mild group, moderate group, severe group) and control group (P0.05).The FA in posterior limb of internal capsule and NBNA score(12-14 d;26-28 d)showed significant correlation (r=0.96,P<0.05),as well as the sequelae shape (r=0.87,P<0.05).Conclusion DTI is promising for early diagnosis of anisotropic index changes to evaluate HIE brain damage and prognosis.
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Objective To explore the changes of MR diffusion imaging (DWI) appearance in newborn rats with hypoxic‐ischemic brain damage(HIBD) ,and its relationship with the changes of Triphenyl tetrazolium chloride (TTC) staining .Methods Using liga‐tion of the left carotid artery method to establish three different degrees of HIBD animal models ;DWI was performed at each time point(6-12 h ,12-24 h ,3 d ,7 d);Fresh brain tissue taken from another model groups of newborn rats in 12 h ,24 h ,3 d ,7 d were staining in TTC ,then we observed its relationship with DWI .Results The lesion location of three model groups mainly distributed in the left side of cortex and subcortical region ,with prolonged hypoxia time ,hippocampus ,lateral side white matter ,thalamus were also have varying degrees of involvement .The right side of the cortex and subcortical in some cases involved .TTC staining showed posi‐tive results in 3 d ,its loss stained area were consistent with DWI abnormal signal area .Conclusion DWI can be evaluation of HIBD lesions early .The early lesions of HIBD mainly distributed in the left side of cortex and subcortical region .
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<p><b>OBJECTIVE</b>To investigate the effects of β-elemene in suppressing the proliferation and apoptosis of SGC7901 gastric cancer cells in vitro and explore the underlying mechanisms.</p><p><b>METHODS</b>Using MTT assay, flow cytometry, and clonogenic survival assay, we assessed the effects of β-elemene on the viability, apoptosis, cell cycle distribution, and clonogenic survival of gastric cancer SGC7901 cells and gastric mucosal epithelial GES-1 cells. Western blotting was employed to determine the changes in the protein expression profiles in SGC7901 cells in response to β-elemene treatment.</p><p><b>RESULTS</b>β-elemene significantly suppressed the cell viability and increased the apoptosis of SGC7901 cells, and these effects were less obvious in GES-1 cells. β-elemene decreased clonogenic survival of SGC7901 cells, increased the proportion of G2/M phase cells, decreased the expression of Bcl-2, and increased the expression of Bax and cleaved caspase-3. β-elemene did not obviously affect the expression of total p21-activated protein kinase 1 (Pak1) but decreased the level of phospho-Pak1 (Thr423) and phospho-ERK1/2 (Thr202/Tyr204) in SGC7901 cells.</p><p><b>CONCLUSION</b>β-elemene inhibits the proliferation and induces apoptosis of gastric cancer cells possibly by inhibiting Pak1/ERK signaling and regulating apoptosis-associated proteins such as Bcl-2 and Bax.</p>