Tensorial Multiview Representation for Saliency Detection via Nonconvex Approach.

In the study of salient object detection, multiview features play an important role in identifying various underlying salient objects. As to current common patch-based methods, all different features are handled directly by stacking them into a high-dimensional vector to represent related image patches. These approaches ignore the correlations inhering in the original spatial structure, which may lead to the loss of certain underlying characterization such as view interaction. In this article, different from currently available approaches, a tensorial feature representation framework is developed for the salient object detection in order to better explore the complementary information of multiview features. Under the tensor framework, a tensor low-rank constraint is applied to the background to capture its intrinsic structure, a tensor group sparsity regularization is posed on the salient part, and a tensorial sliced Laplacian regularization is then introduced to enlarge the gap between the subspaces of the background and salient object. Moreover, a nonconvex tensor Log-determinant function, instead of the tensor nuclear norm, is adopted to approximate the tensor rank for effectively suppressing the confusing information resulted from underlying complex backgrounds. Further, we have deduced the closed-form solution of this nonconvex minimization problem and established a feasible algorithm whose convergence is mathematically proven. Experiments on five well-known public datasets are provided and the simulations demonstrate that our method outperforms the latest unsupervised handcrafted features-based methods in the literature. Furthermore, our model is flexible with various deep features and is competitive with the state-of-the-art approaches.

[Expression and Clinical Significance of MMSA-8 and MMSA-1 in Bone Marrow of Patients with M2 Acute Myeloid Leukemia].

OBJECTIVE: To detect the expression of multiple myeloma-associated antigen (MMSA)-8 and MMSA-1 in bone marrow mononuclear cells of patients with acute myeloid leukemia, and explore their roles in acute myeloid leukemia. METHODS: A total of 83 patients with M2 acute myeloid leukemia in our hospital from January 2019 to January 2020 were selected as research group, during the same period, 15 patients diagnosed iron deficiency anemia were selected as control group. Real-time fluorescence quantitative PCR was used to detect the levels of MMSA-8 and MMSA-1 in bone marrow mononuclear cells. Patients in the research group were divided into remission and non remission according to the clinical curative effect, and were divided into good prognosis, medium prognosis, and poor prognosis according to the prognosis. The relationship between MMSA-8, MMSA-1 and clinical efficacy, prognosis was analyzed. In addition, the general data of patients in the research group were collected, including white blood cell count (WBC), hemoglobin (Hb), platelet count (PLT), and percentage of bone marrow progenitor cells at admission. Pearson method was used to analyze the correlation between MMSA-8, MMSA-1 and clinical data, and MMSA-8 and MMSA-1. RESULTS: The analysis results about mRNA levels of MMSA-8 and MMSA-1 in bone marrow mononuclear cells of patients showed that patients in the research group were significantly higher than those in the control group (P<0.05); In the research group, patients without remission were also significantly higher than those with remission, as well as those with medium and poor prognosis than with good prognosis, while only mRNA level of MMSA-1 in patients with poor prognosis was significantly higher than those with medium prognosis (P<0.05). Pearson analysis showed that MMSA-8, MMSA-1 were positively correlated with WBC (r=0.468, r=0.516), and MMSA-8 was positively correlated with MMSA-1 (r=0.318). CONCLUSION: The levels of MMSA-8 and MMSA-1 in bone marrow mononuclear cells of patients with M2 acute myeloid leukemia are increased, which are closely related to the occurrence and development of the disease, and have certain value for the prognosis.

[Effect of Carvacrol on the Biological Behavior of Leukemia Cells and Its Mechanism].

OBJECTIVE: To explore the effect of carvacrol on the biological behavior of leukemia cells and its regulation to circ-0008717/miR-217 molecular axis. METHODS: Human acute lymphoblastic leukemia cells Molt-4 were cultured in vitro, and different concentrations of carvacrol were added to the cells. si-NC and si-circ-0008717 were transfected into Molt-4 cells (si-NC group, si-circ-0008717 group). pcDNA, pcDNA-circ-0008717, anti-miR-NC, anti-miR-217 were transfected into Molt-4 cells and then added to carvacrol-treated cells (carvacrol+pcDNA group, carvacrol+pcDNA-circ-0008717 group, carvacrol+anti-miR-NC group, carvacrol+anti-miR-217 group). MTT, plate clone formation experiment, and flow cytometry were used to detect the viability of the cell, colony formation number, and apoptosis rate of cells, respectively. The RT-qPCR method was used to detect the expression levels of circ-0008717 and miR-217. The dual luciferase reporter gene experiment was used to detect the targeting relationship between circ-0008717 and miR-217. RESULTS: After carvacrol treatment, the cell viability decreased significantly (r=-0.9405), expression level of circ-0008717 decreased (r=-0.9117), colonies formed number decreased (r=-0.9256), while the cell apoptosis rate increased (r= 0.8464), and the expression level of miR-217 increased (r=0.9468). Compared with the si-NC group, the expression level of miR-217 in si-circ-0008717 group increased (P＜0.001), the cell apoptosis rate increased (P＜0.001), while cell viability decreased (P＜0001), the number of colonies formed decreased (P＜0.001). Compared with the carvacrol+pcDNA group, the cell viability of the carvacrol+pcDNA-circ-0008717 group increased (P＜0.001), the number of colonies formed increased (P＜0.001), while the cell apoptosis rate decreased (P＜0.001). circ-0008717 could target miR-217. The cell viability of the carvacrol+anti-miR-217 group increased (P＜0.001), and the number of colonies formed increased (P＜0.001), while the cell apoptosis rate decreased (P＜0001) as compared with the carvacrol+anti-miR-NC group. CONCLUSION: Carvacrol can promote the expression of miR-217 by down-regulating the expression of circ-0008717, thereby reducing the proliferation and cloning ability of leukemia cells and promoting cell apoptosis.

Efficacy and safety of chimeric antigen receptor (CAR)-T cell therapy in the treatment of relapsed and refractory multiple myeloma: a systematic-review and meta-analysis of clinical trials.

Background: Multiple myeloma (MM) is a malignant tumor originating from plasma cells in the bone marrow. The existing treatment methods can prolong the survival time of patients, but they still face the problems of myeloma relapse and refractory disease. Chimeric antigen receptor (CAR)-T cell therapy is a new cellular immunotherapy that can target and recognize antigens and kill tumor cells but the efficacy and safety data varied in different studies. We performed this systematic review and meta-analysis to understand its efficacy and safety. Methods: Literature published from January 2015 to November 2021 was obtained by searching the keywords "CAR-T", "CAR-T Cell", and "Multiple Myeloma" by computer using the Embase, PubMed, Web of Science, and Cochrane library databases according to the PICOS (Participants, Interventions, Comparisons, Outcomes, Study type) criteria. The quality of the literature was assessed by the Joanna Briggs Institute (JBI) Critical Appraisal Tool for prevalence studies. The complete response rate, the incidence of cytokine release syndrome (CRS) above grade 3, and the overall incidence of adverse reactions were used as the outcome indicators. The pooled rates were performed and analyzed using the R language toolkit. Results: A total of 10 studies including 353 study cases were included. Meta-analysis showed that the pooled complete response rate of CAR-T therapy in the treatment of MM was 0.55, 95% confidence interval (CI): (0.50, 0.60), the pooled incidence of CRS was 0.55, 95% CI: (0.50, 0.60), and the pooled incidence of serious adverse reactions was 0.92, 95% CI: (0.88, 0.95). Subgroup analysis was performed based on antigen types or costimulatory molecules, and there was no significant difference in the efficacy of CAR-T and the incidence of CRS between the two subgroups (P>0.05). Conclusions: As a new immunotherapy strategy with great potential, CAR-T has a significant effect in the treatment of MM, but its safety needs to be further improved. The types of costimulatory molecules and CAR-T antigens can affect its efficacy and safety.

Deep residual contextual and subpixel convolution network for automated neuronal structure segmentation in micro-connectomics.

BACKGROUND AND OBJECTIVE: The goal of micro-connectomics research is to reconstruct the connectome and elucidate the mechanisms and functions of the nervous system via electron microscopy (EM). Due to the enormous variety of neuronal structures, neuron segmentation is among most difficult tasks in connectome reconstruction, and neuroanatomists desperately need a reliable neuronal structure segmentation method to reduce the burden of manual labeling and validation. METHODS: In this article, we proposed an effective deep learning method based on a deep residual contextual and subpixel convolution network to obtain the neuronal structure segmentation in anisotropic EM image stacks. Furthermore, lifted multicut is used for post-processing to optimize the prediction and obtain the reconstruction results. RESULTS: On the ISBI EM segmentation challenge, the proposed method ranks among the top of the leader board and yields a Rand score of 0.98788. On the public data set of mouse piriform cortex, it achieves a Rand score of 0.9562 and 0.9318 in the different testing stacks. The evaluation scores of our method are significantly improved when compared with those of state-of-the-art methods. CONCLUSIONS: The proposed automatic method contributes to the development of micro-connectomics, which improves the accuracy of neuronal structure segmentation and provides neuroanatomists with an effective approach to obtain the segmentation and reconstruction of neurons.

High efficacy and safety of CD38 and BCMA bispecific CAR-T in relapsed or refractory multiple myeloma.

BACKGROUND: B-cell maturation antigen (BCMA) chimeric antigen receptor T (CAR-T) cell therapy has obtained promising results in relapsed or refractory multiple myeloma (R/R MM), while some patients do not response, or relapse in short term after treatment. Combining with anti-CD38 might solve the problem of targeting BCMA alone. We aimed to assess the efficacy and safety of BCMA and CD38 (BCMA-CD38) bispecific CAR-T cells in R/R MM patients. METHODS: We did a single-center, single-arm clinical study at the Second Affiliated Hospital of Yangtze University in China. Patients meeting with the inclusion criteria were administered with fludarabine and cyclophosphamide before CAR-T cells infusion. Response and adverse events were assessed after infusion. This study was registered with the Chinese Clinical Trial Registration Center (ChiCTR1900026286). RESULTS: First, we found BCMA-CD38 CAR-T cells exhibited enhanced killing effect on BCMA+CD38+ cells in vitro, compared to BCMA CAR-T and CD38 CAR-T cells. We further demonstrated its anti-tumor activity in vivo. Then, we enrolled 16 R/R MM patients for safety and efficacy analyses. Of the 16 evaluable patients, 14 (87.5%) respond to the treatment, including 13 stringent complete response (sCR) and one partial response (PR), while two patients did not respond. At a median follow-up of 11.5 months, of the 13 patients who achieved sCR, 76.9% (10/13) did not relapse or progress during follow-up. Relapse occurred in 3 patients (Patient 2, 3 and 4) after achieving sCR. In sum, four patients died, of which one died of hemophagocytic lymphohistiocytosis syndrome secondary to severe cytokine release syndrome (CRS) and three died of disease progression or relapse. The 1-year progression-free survival rates was 68.8%. The 1-year overall survival rate was 75.0%. Extramedullary lesions were eliminated in 62.5% (5/8) patients. The most common symptoms after CAR-T infusion were cytopenia (16, 100%), fever (10, 62.5%), fatigue (8, 50.0%) and myalgias (8, 50.0%). Twelve patients (75.0%) were observed with various grades of CRS, of which five patients (31.3%) got serious CRS (Grade ≥ 3). The CAR+ cell expansion levels were associated with the severity of CRS. Transient clonal isotype switch was observed after CAR-T infusion. CONCLUSION: Our results confirm that BCMA-CD38 CAR-T cells therapy is feasible in treating R/R MM patients, with high response rate, low recurrence rate and manageable CRS, which will be a promising treatment option for R/R MM. TRIAL REGISTRATION: ChiCTR1900026286, registered on September 29, 2019, retrospectively registered, URL: https://www.chictr.org.cn/showproj.aspx?proj=43805.

Efficient Unsupervised Dimension Reduction for Streaming Multiview Data.

Multiview learning has received substantial attention over the past decade due to its powerful capacity in integrating various types of information. Conventional unsupervised multiview dimension reduction (UMDR) methods are usually conducted in an offline manner and may fail in many real-world applications, where data arrive sequentially and the data distribution changes periodically. Moreover, satisfying the requirements of high memory consumption and expensive retraining of the time cost in large-scale scenarios are difficult. To remedy these drawbacks, we propose an online UMDR (OUMDR) framework. OUMDR aims to seek a low-dimensional and informative consensus representation for streaming multiview data. View-specific weights are also learned in this article to reflect the contributions of different views to the final consensus presentation. A specific model called OUMDR-E is developed by introducing the exclusive group LASSO (EG-LASSO) to explore the intraview and interview correlations. Then, we develop an efficient iterative algorithm with limited memory and time cost requirements for optimization, where the convergence of each update is theoretically guaranteed. We evaluate the proposed approach in video-based expression recognition applications. The experimental results demonstrate the superiority of our approach in terms of both effectiveness and efficiency.

hsa­miR­429 targets CBX8 to promote cell apoptosis in diffuse large B­cell lymphoma.

Diffuse large B­cell lymphoma (DLBCL) is the most common type of non­Hodgkin lymphoma worldwide. Several studies have indicated that Homo sapiens (hsa)­microRNA (miR)­429 exerts a tumor­suppressive effect on a variety of malignant tumors. To the best of our knowledge, the molecular function and mechanism of action of hsa­miR­429 in DLBCL have not been evaluated to date. The present study demonstrated that the expression of hsa­miR­429 in DLBCL cells was significantly reduced. hsa­miR­429 inhibited the proliferation of the DLBCL cell lines, SUDHL­4 and DB, and promoted apoptosis. A dual luciferase reporter assay was used to demonstrate that chromobox 8 (CBX8) was the target gene of hsa­miR­429. Overexpression of CBX8 promoted the proliferation of SUDHL­4 and DB cells and inhibited apoptosis, thereby playing a cancer­promoting role. Transfection of hsa­miR­429 mimic into DB cells overexpressing CBX8 antagonized the effect of CBX8 on the proliferation of DB cells. Moreover, the apoptotic rate was increased in DB cells overexpressing CBX8 and transfected with hsa­miR­429 mimic, while the proportion of cells in the G2/M phase was significantly reduced. These results demonstrated the antagonistic effect of hsa­miR­429 on the oncogenic function of CBX8. Therefore, in DLBCL, the tumor suppressor effect of hsa­miR­429 may be achieved by targeted downregulation of CBX8, suggesting that hsa­miR­429 may be used as a diagnostic marker and a potential nucleic acid drug for DLBCL. CBX8 may also represent an effective therapeutic target for DLBCL.

A Contour Co-Tracking Method for Image Pairs.

We proposed a contour co-tracking method for co-segmentation of image pairs based on active contour model. Our method comprehensively re-models objects and backgrounds signified by level set functions, and leverages Hellinger distance to measure the similarity between image regions encoded by probability distributions. The main contribution are as follows. 1) The new energy functional, combining a rewarding and a penalty term, relaxes the assumptions of co-segmentation methods. 2) Hellinger distance, fulfilling the triangle inequality, ensures a coherence measurement between probability distributions in metric space, and contributes to finding a unique solution to the energy functional. The proposed contour co-tracking method was carefully verified against five representative methods on four popular datasets, i.e., the images pair dataset (105 pairs), MSRC dataset (30 pairs), iCoseg dataset (66 pairs) and Coseg-rep dataset (25 pairs). The comparison experiments suggest that our method achieves the competitive and even better performance compared to the state-of-the-art co-segmentation methods.

Clinical characteristics of patients with 2019 coronavirus disease in a non-Wuhan area of Hubei Province, China: a retrospective study.

BACKGROUND: Since December 2019, the 2019 coronavirus disease (COVID-19) has expanded to cause a worldwide outbreak that more than 600,000 people infected and tens of thousands died. To date, the clinical characteristics of COVID-19 patients in the non-Wuhan areas of Hubei Province in China have not been described. METHODS: We retrospectively analyzed the clinical characteristics and treatment progress of 91 patients diagnosed with COVID-19 in Jingzhou Central Hospital. RESULTS: Of the 91 patients diagnosed with COVID-19, 30 cases (33.0%) were severe and two patients (2.2%) died. The severe disease group tended to be older (50.5 vs. 42.0 years; p = 0.049) and have more chronic disease (40% vs. 14.8%; p = 0.009) relative to mild disease group. Only 73.6% of the patients were quantitative polymerase chain reaction (qPCR)-positive on their first tests, while typical chest computed tomography images were obtained for each patient. The most common complaints were cough (n = 75; 82.4%), fever (n = 59; 64.8%), fatigue (n = 35; 38.5%), and diarrhea (n = 14; 15.4%). Non-respiratory injury was identified by elevated levels of aspartate aminotransferase (n = 18; 19.8%), creatinine (n = 5; 5.5%), and creatine kinase (n = 14; 15.4%) in laboratory tests. Twenty-eight cases (30.8%) suffered non-respiratory injury, including 50% of the critically ill patients and 21.3% of the mild patients. CONCLUSIONS: Overall, the mortality rate of patients in Jingzhou was lower than that of Wuhan. Importantly, we found liver, kidney, digestive tract, and heart injuries in COVID-19 cases besides respiratory problems. Combining chest computed tomography images with the qPCR analysis of throat swab samples can improve the accuracy of COVID-19 diagnosis.

Recognition of Dorsal Hand Vein Based Bit Planes and Block Mutual Information.

The dorsal hand vein images captured by cross-device may have great differences in brightness, displacement, rotation angle and size. These deviations must influence greatly the results of dorsal hand vein recognition. To solve these problems, the method of dorsal hand vein recognition was put forward based on bit plane and block mutual information in this paper. Firstly, the input gray image of dorsal hand vein was converted to eight-bit planes to overcome the interference of brightness inside the higher bit planes and the interference of noise inside the lower bit planes. Secondly, the texture of each bit plane of dorsal hand vein was described by a block method and the mutual information between blocks was calculated as texture features by three kinds of modes to solve the problem of rotation and size. Finally, the experiments cross-device were carried out. One device was used to be registered, the other was used to recognize. Compared with the SIFT (Scale-invariant feature transform, SIFT) algorithm, the new algorithm can increase the recognition rate of dorsal hand vein from 86.60% to 93.33%.

Fusing multi-scale information in convolution network for MR image super-resolution reconstruction.

BACKGROUND: Magnetic resonance (MR) images are usually limited by low spatial resolution, which leads to errors in post-processing procedures. Recently, learning-based super-resolution methods, such as sparse coding and super-resolution convolution neural network, have achieved promising reconstruction results in scene images. However, these methods remain insufficient for recovering detailed information from low-resolution MR images due to the limited size of training dataset. METHODS: To investigate the different edge responses using different convolution kernel sizes, this study employs a multi-scale fusion convolution network (MFCN) to perform super-resolution for MRI images. Unlike traditional convolution networks that simply stack several convolution layers, the proposed network is stacked by multi-scale fusion units (MFUs). Each MFU consists of a main path and some sub-paths and finally fuses all paths within the fusion layer. RESULTS: We discussed our experimental network parameters setting using simulated data to achieve trade-offs between the reconstruction performance and computational efficiency. We also conducted super-resolution reconstruction experiments using real datasets of MR brain images and demonstrated that the proposed MFCN has achieved a remarkable improvement in recovering detailed information from MR images and outperforms state-of-the-art methods. CONCLUSIONS: We have proposed a multi-scale fusion convolution network based on MFUs which extracts different scales features to restore the detail information. The structure of the MFU is helpful for extracting multi-scale information and making full-use of prior knowledge from a few training samples to enhance the spatial resolution.

A Regularization Approach for Instance-Based Superset Label Learning.

Different from the traditional supervised learning in which each training example has only one explicit label, superset label learning (SLL) refers to the problem that a training example can be associated with a set of candidate labels, and only one of them is correct. Existing SLL methods are either regularization-based or instance-based, and the latter of which has achieved state-of-the-art performance. This is because the latest instance-based methods contain an explicit disambiguation operation that accurately picks up the groundtruth label of each training example from its ambiguous candidate labels. However, such disambiguation operation does not fully consider the mutually exclusive relationship among different candidate labels, so the disambiguated labels are usually generated in a nondiscriminative way, which is unfavorable for the instance-based methods to obtain satisfactory performance. To address this defect, we develop a novel regularization approach for instance-based superset label (RegISL) learning so that our instance-based method also inherits the good discriminative ability possessed by the regularization scheme. Specifically, we employ a graph to represent the training set, and require the examples that are adjacent on the graph to obtain similar labels. More importantly, a discrimination term is proposed to enlarge the gap of values between possible labels and unlikely labels for every training example. As a result, the intrinsic constraints among different candidate labels are deployed, and the disambiguated labels generated by RegISL are more discriminative and accurate than those output by existing instance-based algorithms. The experimental results on various tasks convincingly demonstrate the superiority of our RegISL to other typical SLL methods in terms of both training accuracy and test accuracy.

[Application of Real-time Quantitative PCR in Detecting Atypical BCR/ABL mRNA Transcripts in Chronic Myelocytic Leukemia].

OBJECTIVE: To detect atypical BCR/ABL mRNA transcript by real-time quantitative PCR in CML patients without e13a2/e14a2,e19a2 or e1a2 transcripts, and investigate its value of clinical application. METHODS: Twelve cases of CML with positive for t(9;22) translocation, but negative for common major and minor breakpoint cluster regions comfirmed by chromosome karyotyping or FISH analysis, were collected from July 2012 to December 2015. These 12 cases were then detected for b2a3(e13a3), b3a3(e14a3), e6a2, e8a2 and e1a3 fusion variants by real-time quantitative PCR. RESULTS: Among 12 cases 4 variant transcripts were detected, including e1a3 in 1 case (8.33%), e8a2 in 2 cases (16.67%), b2a3 in 5 cases (41.67%) and b3a3 in 4 cases (33.33%), with total positivity of 100%, moreover b2a3 and b3a3 were predominant. CONCLUSION: The detecting atypical BCR/ABL mRNA transcripts by real-time quantitative PCR is suitable for the diagnosis of CML negative for P210, P190 and P230 by standard real-time PCR test, and this detection is still the standard and economic method for monitoring minimal residual disease in CML patients with variants of BCR/ABL fusion gene.

Non-fragile sampled-data robust synchronization of uncertain delayed chaotic Lurie systems with randomly occurring controller gain fluctuation.

This paper proposes a new non-fragile stochastic control method to investigate the robust sampled-data synchronization problem for uncertain chaotic Lurie systems (CLSs) with time-varying delays. The controller gain fluctuation and time-varying uncertain parameters are supposed to be random and satisfy certain Bernoulli distributed white noise sequences. Moreover, by choosing an appropriate Lyapunov-Krasovskii functional (LKF), which takes full advantage of the available information about the actual sampling pattern and the nonlinear condition, a novel synchronization criterion is developed for analyzing the corresponding synchronization error system. Furthermore, based on the most powerful free-matrix-based integral inequality (FMBII), the desired non-fragile sampled-data estimator controller is obtained in terms of the solution of linear matrix inequalities. Finally, three numerical simulation examples of Chua's circuit and neural network are provided to show the effectiveness and superiorities of the proposed theoretical results.

Face Aging Effect Simulation Using Hidden Factor Analysis Joint Sparse Representation.

Face aging simulation has received rising investigations nowadays, whereas it still remains a challenge to generate convincing and natural age-progressed face images. In this paper, we present a novel approach to such an issue using hidden factor analysis joint sparse representation. In contrast to the majority of tasks in the literature that integrally handle the facial texture, the proposed aging approach separately models the person-specific facial properties that tend to be stable in a relatively long period and the age-specific clues that gradually change over time. It then transforms the age component to a target age group via sparse reconstruction, yielding aging effects, which is finally combined with the identity component to achieve the aged face. Experiments are carried out on three face aging databases, and the results achieved clearly demonstrate the effectiveness and robustness of the proposed method in rendering a face with aging effects. In addition, a series of evaluations prove its validity with respect to identity preservation and aging effect generation.

Robust Nonnegative Patch Alignment for Dimensionality Reduction.

Dimensionality reduction is an important method to analyze high-dimensional data and has many applications in pattern recognition and computer vision. In this paper, we propose a robust nonnegative patch alignment for dimensionality reduction, which includes a reconstruction error term and a whole alignment term. We use correntropy-induced metric to measure the reconstruction error, in which the weight is learned adaptively for each entry. For the whole alignment, we propose locality-preserving robust nonnegative patch alignment (LP-RNA) and sparsity-preserviing robust nonnegative patch alignment (SP-RNA), which are unsupervised and supervised, respectively. In the LP-RNA, we propose a locally sparse graph to encode the local geometric structure of the manifold embedded in high-dimensional space. In particular, we select large p -nearest neighbors for each sample, then obtain the sparse representation with respect to these neighbors. The sparse representation is used to build a graph, which simultaneously enjoys locality, sparseness, and robustness. In the SP-RNA, we simultaneously use local geometric structure and discriminative information, in which the sparse reconstruction coefficient is used to characterize the local geometric structure and weighted distance is used to measure the separability of different classes. For the induced nonconvex objective function, we formulate it into a weighted nonnegative matrix factorization based on half-quadratic optimization. We propose a multiplicative update rule to solve this function and show that the objective function converges to a local optimum. Several experimental results on synthetic and real data sets demonstrate that the learned representation is more discriminative and robust than most existing dimensionality reduction methods.

Error analysis of stochastic gradient descent ranking.

Ranking is always an important task in machine learning and information retrieval, e.g., collaborative filtering, recommender systems, drug discovery, etc. A kernel-based stochastic gradient descent algorithm with the least squares loss is proposed for ranking in this paper. The implementation of this algorithm is simple, and an expression of the solution is derived via a sampling operator and an integral operator. An explicit convergence rate for leaning a ranking function is given in terms of the suitable choices of the step size and the regularization parameter. The analysis technique used here is capacity independent and is novel in error analysis of ranking learning. Experimental results on real-world data have shown the effectiveness of the proposed algorithm in ranking tasks, which verifies the theoretical analysis in ranking error.

Error analysis of coefficient-based regularized algorithm for density-level detection.

In this letter, we consider a density-level detection (DLD) problem by a coefficient-based classification framework with [Formula: see text]-regularizer and data-dependent hypothesis spaces. Although the data-dependent characteristic of the algorithm provides flexibility and adaptivity for DLD, it leads to difficulty in generalization error analysis. To overcome this difficulty, an error decomposition is introduced from an established classification framework. On the basis of this decomposition, the estimate of the learning rate is obtained by using Rademacher average and stepping-stone techniques. In particular, the estimate is independent of the capacity assumption used in the previous literature.

[Regulatory effect of curcumin on p300 and HDAC1 in B-NHL cells].

The purpose of this study was to investigate the effect of curcumin on proliferation of B-NHL Raji cell line and explore the relationship between this effect and regulatory expression of p300 and HDAC1 transcription. The in vitro cultured Raji cells were treated with curcumin at various concentrations (6.25-50 micromol/L) and at different time points (0, 6, 12, 24 and 48 hours), the inhibitory ratio of cell growth was measured by MTT assay, the cell apoptosis rate was detected by flow cytometry with Annexin V-FITC/PI double staining, the changes of p300 and HDAC1 mRNA expression and protein level in Raji cells were determined by RT-PCR and Western blot. The results showed that the curcumin could inhibit Raji cell proliferation in significant time-and concentration-dependent manners, IC50 at 24 hours was 25 micromol/L; the curcumin could induce apoptosis of Raji cells in concentration-dependent manner, apoptosis rate was 14.38%-61.18%. The curcumin significantly inhibited activity and expression of p300 and HDAC1. At IC50 concentration, expression of p300 and HDAC1 mRNA and protein level decreased with time-dependent manner, difference between tested and control groups was significant (P < 0.05). It is concluded that the curcumin can inhibit proliferation of B-NHL Raji cells and promote apoptosis of those cells. Curcumin can inhibit the activity and expression of the transcriptional co-activator p300 and HDAC1, which may be involved in its pharmacological mechanisms on B lymphoma cells.