In this paper, we propose an analysis mechanism-based structured analysis discriminative dictionary learning analysis discriminative dictionary learning, framework. The ADDL seamlessly integrates ADDL, analysis representation, and analysis classifier training into a unified model. The applied analysis mechanism can make sure that the learned dictionaries, representations, and linear classifiers over different classes are independent and discriminating as much as possible. The dictionary is obtained by minimizing a reconstruction error and an analytical incoherence promoting term that encourages the subdictionaries associated with different classes to be independent. To obtain the representation coefficients, ADDL imposes a sparse -norm constraint on the coding coefficients instead of using or norm, since the - or -norm constraint applied in most existing DL criteria makes the training phase time consuming. The code-extraction projection that bridges data with the sparse codes by extracting special features from the given samples is calculated via minimizing a sparse code approximation term. Then we compute a linear classifier based on the approximated sparse codes by an analysis mechanism to simultaneously consider the classification and representation powers. Thus, the classification approach of our model is very efficient, because it can avoid the extra time-consuming sparse reconstruction process with trained dictionary for each new test data as most existing DL algorithms. Simulations on real image databases demonstrate that our ADDL model can obtain superior performance over other state of the arts.

The aim of this study was to establish reference ranges for lymphocyte subsets in Chinese adults. Venous blood specimens were obtained from 614 healthy, human immunodeficiency virus (HIV)-seronegative adults in Shanghai. Flow cytometry was used to determine percentages and absolute numbers of CD4 and CD8 T lymphocytes. Mean values for CD4 and CD8 lymphocytes were 727 and 540 cells/l, respectively, yielding a CD4/CD8 ratio of 1.49. While CD8 lymphocyte values varied with age and gender, no significant differences in CD4 lymphocyte values were observed. Shanghai adults had approximately 100 fewer CD4 lymphocytes/l on average than Caucasians, suggesting that lower CD4 lymphocyte cutoffs for classifying and monitoring HIV infection may be needed in China. Measurements of CD4ϩ lymphocytes are essential for assessing human immunodeficiency virus (HIV) disease course, clinical staging, epidemiological studies, and decisions regarding prophylactic therapies against opportunistic infections (2,3,8,9,21). Only in industrialized countries has it been feasible to routinely monitor CD4 lymphocyte subsets during routine HIV clinical care. In China, HIV has spread to all 31 provinces, regions, and municipalities and is currently moving into new segments of the populace (22). It is estimated that more than 1 million people are infected in China, and this number may reach 10 million by 2010 (11). Information is generally lacking on the normal range of lymphocyte subpopulations, including CD4 and CD8 lymphocytes, in China.To provide normal ranges for CD4 and CD8 lymphocyte subsets, and for CD4/CD8 ratios in normal Chinese adults, blood specimens were collected from healthy native adult residents of Shanghai who received routine annual health evaluations at Huashan Hospital between August 2000 and February 2001. Subjects were excluded if they were diagnosed with HIV type 1 (HIV-1) infection or other recent viral or bacterial infections or chronic organ diseases, were immunocompromised, or were recently exposed to toxic chemicals. Wholeblood samples were collected using sterile EDTA Vacutainer tubes. The Science and Research Bureau of Fudan University approved the study.Flow cytometry of lymphocyte subsets was carried out using a lamp-based flow cytometer (Bryte-HS, Bio-Rad, Hercules, Calif.) according to the manufacturer's instructions. Briefly, white blood cell counting and differentiation were performed using a Symex-SF3000 Coulter counter (Coulter Electronic, Luton, London). Blood samples were then stained using OptiClone CD4/CD8, immunoglobulin G1-fluorescein isothiocyanate, and immunoglobulin G1-phycoerythrin monoclonal antibodies (Coulter-Immunotech, Miami, Florida). The monoclonal antibodies, 13B8.2 and B9.11, were used to bind specifically to CD4 and CD8 subsets of peripheral blood T lymphocytes, respectively (7, 17). The determination of positive and negative cells for any combination of reagents was set with directly conjugated antibodies of irrelevant specificity as negative controls. Positive and negative controls were...

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Pure culture of magnetotactic bacteria with high magnetosome yield has been achieved for only a few strains. The major obstacles involve the nutritional requirements and culture conditions of the cells. To increase cell density and magnetosome production, it is necessary to elucidate the physiological characteristics of a particular strain during cell growth and develop an appropriate artificial control strategy. Large-scale culture of Magnetospirillum gryphiswaldense strain MSR-1 was successfully performed for 48 h in a 42-L autofermentor, and several key physiological parameters were measured in real time. Maximal values of cell density (OD565) (19.4) and cell yield (dry weight) (4.76 g/L) were attained at 40 h. The key time point for cell growth and magnetosome formation was found to be 18–20 h. At this point, cells entered the log phase of growth, the maximal values of Cmag (1.78), iron content (0.47%), and magnetosome number (26 ± 3 per cell) were observed, superoxide dismutase (SOD) activity began to decrease more rapidly, ATP content dropped to an extremely low level (0.17 fmol), and reducing power (NADH/NAD+ ratio) began to increase very rapidly. Excessive levels of dissolved oxygen (≥20 ppb) and lactic acid in the medium caused notable cytotoxic effects after 20 h. Artificial control measures for fermentation must be based on realistic cell physiological conditions. At the key time point (18–20 h), cell density is high and magnetosomes have matured. The process of magnetosome synthesis involves a high consumption of ATP and reducing power, and the cells require replenishment of nutrients prior to the 18–20 h time point. Culture conditions that effectively minimize dissolved oxygen accumulation, lactic acid content, and reducing power at this point will enhance magnetosome yield without obvious inhibition of cell growth.

Glioblastomas are the most aggressive forms of primary brain tumors due to their tendency to invade surrounding healthy brain tissues, rendering them largely incurable. The water channel protein, Aquaporin-4 (AQP4) is a key molecule for maintaining water and ion homeostasis in the central nervous system and has recently been reported with cell survival except for its well-known function in brain edema. An increased AQP4 expression has been demonstrated in glioblastoma multiforme (GBM), suggesting it is also involved in malignant brain tumors. In this study, we show that siRNA-mediated down regulation of AQP4 induced glioblastoma cell apoptosis in vitro and in vivo. We further show that several apoptotic key proteins, Cytochrome C, Bcl-2 and Bad are involved in AQP4 signaling pathways. Our results indicate that AQP4 may serve as an anti-apoptosis target for therapy of glioblastoma.

We propose a novel structured discriminative blockdiagonal dictionary learning method, referred to as scalable Locality-Constrained Projective Dictionary Learning (LC-PDL), for efficient representation and classification. To improve the scalability by saving both training and testing time, our LC-PDL aims at learning a structured discriminative dictionary and a block-diagonal representation without using costly l 0 /l 1 -norm. Besides, it avoids extra time-consuming sparse reconstruction process with the well-trained dictionary for new sample as many existing models. More importantly, LC-PDL avoids using the complementary data matrix to learn the sub-dictionary over each class. To enhance the performance, we incorporate a locality constraint of atoms into the DL procedures to keep local information and obtain the codes of samples over each class separately. A block-diagonal discriminative approximation term is also derived to learn a discriminative projection to bridge data with their codes by extracting the special block-diagonal features from data, which can ensure the approximate coefficients to associate with its label information clearly. Then, a robust multiclass classifier is trained over extracted block-diagonal codes for accurate label predictions. Experimental results verify the effectiveness of our algorithm.

This paper proposes an enhanced semi-supervised classification approach termed Nonnegative Sparse Neighborhood Propagation (SparseNP) that is an improvement to the existing neighborhood propagation due to the fact that the outputted soft labels of points cannot be ensured to be sufficiently sparse, discriminative, robust to noise and be probabilistic values. Note that the sparse property and strong discriminating ability of predicted labels is important, since ideally the soft label of each sample should have only one or few positive elements (i.e., less unfavorable mixed signs are included) deciding its class assignment. To reduce the negative effects of unfavorable mixed signs on the learning performance, we regularize the 2,1 l -norm on the soft labels during optimization for enhancing the prediction results. The non-negativity and sumto-one constraints are also included to ensure the outputted labels are probabilistic values. The proposed framework is solved in an alternative manner for delivering a more reliable solution so that the accuracy can be improved. Simulations show that satisfactory results can be obtained by the proposed SparseNP compared with other related approaches.

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