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Most existing large-scale multiview clustering algorithms attempt to capture data distribution in multiple views by selecting view-wise anchor representations beforehand with $k$k-means, or by direct matrix factorization on the original observations. Despite impressive performance, few of them have paid attention to the semantic correlations between anchor bases and cluster centroids, or even the underlying relations between clusters and data samples. In view of this, we propose a Concept Factorization based Multiview Clustering for Large-scale Data (CFMC) method with nearly linear complexity. The anchor bases learning, coefficient expression with clear semantic cues and partitioning are integrated together in this unified model. Meanwhile, explicit connections among multiview data, anchor bases and clusters are modeled via coefficient representations with semantic meanings. A four-step alternate minimizing algorithm is designed to handle the optimization problem, which is proved to have linear time complexity w.r.t. the sample size. Extensive experiments conducted on several challenging large-scale datasets confirm the superiority of the method compared with the state-of-the-art methods.

Deep contrastive clustering has recently gained significant attention due to its advantageous ability to leverage the contrastive learning paradigm for joint representation learning and clustering. However, previous deep contrastive clustering approaches mostly focus on instance discrimination or cluster discrimination, which often overlook the rich semantic information latent in the vast intermediate levels of granularity between instances and clusters. Moreover, they are typically prone to utilizing relationships only within the same level of granularity, e.g., instance-instance relationships and cluster-cluster relationships, but frequently neglect the interactions between different granularity-levels. To tackle these issues, this paper presents a novel end-to-end deep contrastive clustering approach termed Deep Clustering with Hybrid-Grained Contrastive and Discriminative Learning (DCHL). Particularly, the instance-level contrastive learning and cluster-level contrastive learning are first formulated, where the cluster-level contrastive learning is further split into fine-grained and coarse-grained branches. To capture global dependencies, the cluster-level contrastiveness is explored on the coarse-grained cluster branch. Meanwhile, to capture hybrid-grained relationships, the dual-level instance-group discrimination learning is enforced between the instance branch and the fine-grained cluster branch, where the self instance-group discrimination and the cross instance-group discrimination are simultaneously optimized for enhancing the deep clustering performance. Experiments on five challenging image datasets confirm the superiority of DCHL over the state-of-the-art. Code available: https://github.com/dengxiaozhi/DCHL.

Graph contrastive learning (GCL) has become the de-facto approach to conducting self-supervised learning on graphs for its superior performance. However, non-semantic graph augmentation methods prevent it from achieving better performance, and it suffers from vulnerability to graph attacks. To deal with these problems, we propose AEGCL to leverage graph AutoEncoder in Graph Contrastive Learning which directly targets graph property reconstruction to boost GCL effectiveness and robustness. Specifically, AEGCL has two distinctive characteristics, (1) a novel adaptive augmentation strategy based on motif centrality is proposed, which leverages semantic significant higher-order graph property; (2) the original attributed graph is decoupled into feature graph and topology graph to extract their dedicated information, and a simple AttnFuse is proposed to combine the two augmented graphs and the two decoupled graphs. Graph autoencoder can thus be applied to the topology domain and raw attribute domain. Empirically, extensive experiments on benchmark graph datasets show that AEGCL outperforms existing baseline methods in terms of classification accuracy and robustness.

As a challenging problem, incomplete multi-view clustering (MVC) has drawn much attention in recent years. Most of the existing methods contain the feature recovering step inevitably to obtain the clustering result of incomplete multi-view datasets. The extra target of recovering the missing feature in the original data space or common subspace is difficult for unsupervised clustering tasks and could accumulate mistakes during the optimization. Moreover, the biased error is not taken into consideration in the previous graph-based methods. The biased error represents the unexpected change of incomplete graph structure, such as the increase in the intra-class relation density and the missing local graph structure of boundary instances. It would mislead those graph-based methods and degrade their final performance. In order to overcome these drawbacks, we propose a new graph-based method named Graph Structure Refining for Incomplete MVC (GSRIMC). GSRIMC avoids recovering feature steps and just fully explores the existing subgraphs of each view to produce superior clustering results. To handle the biased error, the biased error separation is the core step of GSRIMC. In detail, GSRIMC first extracts basic information from the precomputed subgraph of each view and then separates refined graph structure from biased error with the help of tensor nuclear norm. Besides, cross-view graph learning is proposed to capture the missing local graph structure and complete the refined graph structure based on the complementary principle. Extensive experiments show that our method achieves better performance than other state-of-the-art baselines.

Next-basket recommendation methods focus on the inference of the next basket by considering the corresponding basket sequence. Although many methods have been developed for the task, they usually suffer from data sparsity. The number of interactions between entities is relatively small compared to their huge bases, so it is crucial to mine as much hidden information as possible from the limited historical interactions for prediction. However, the existing methods mainly just treat the next-basket recommendation task as a single-view sequential prediction problem, which leads to the inadequate mining of the information hidden in multiple views, and the mining of other patterns in the historical interactions is neglected, thus making it difficult to learn high-quality representations and limiting the recommendation effect. To alleviate the above issues, we propose a novel method named HapCL for next-basket recommendation, which mines information from multiple views and patterns with the help of polar contrastive learning. A hierarchical module is designed to mine multiple patterns of historical interactions from different views at two levels. In order to mine self-supervised signals, we design a polar contrastive learning module with a novel graph-based augmentation approach. Experiments on three real-world datasets validate the effectiveness of HapCL.

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendations under the condition that user profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each session individually and try to capture user interests within a session. Despite their encouraging results, these models can only perceive intra-session items and cannot draw upon the massive historical relational information. To solve this problem, we propose a novel method named global graph guided session-based recommendation (G3SR). G3SR decomposes the session-based recommendation workflow into two steps. First, a global graph is built upon all session data, from which the global item representations are learned in an unsupervised manner. Then, these representations are refined on session graphs under the graph networks, and a readout function is used to generate session representations for each session. Extensive experiments on two real-world benchmark datasets show remarkable and consistent improvements of the G3SR method over the state-of-the-art methods, especially for cold items.

Despite significant progress, there remain three limitations to the previous multi-view clustering algorithms. First, they often suffer from high computational complexity, restricting their feasibility for large-scale datasets. Second, they typically fuse multi-view information via one-stage fusion, neglecting the possibilities in multi-stage fusions. Third, dataset-specific hyperparameter-tuning is frequently required, further undermining their practicability. In light of this, we propose a fast multi-view clustering via ensembles (FastMICE) approach.Particularly, the concept of random view groups is presented to capture the versatile view-wise relationships, through which the hybrid early-late fusion strategy is designed to enable efficient multi-stage fusions. With multiple views extended to many view groups, three levels of diversity (w.r.t. features, anchors, and neighbors, respectively) are jointly leveraged for constructing the view-sharing bipartite graphs in the early-stage fusion. Then, a set of diversified base clusterings for different view groups are obtained via fast graph partitioning, which are further formulated into a unified bipartite graph for final clustering in the late-stage fusion. Notably, FastMICE has almost linear time and space complexity, and is free of dataset-specific tuning. Experiments on 22 multi-view datasets demonstrate its advantages in scalability (for extremely large datasets), superiority (in clustering performance), and simplicity (to be applied) over the state-of-the-art. Code available: https://github.com/huangdonghere/FastMICE.

Multiparty learning is an indispensable technique to improve the learning performance via integrating data from multiple parties. Unfortunately, directly integrating multiparty data could not meet the privacy-preserving requirements, which then induces the development of privacy-preserving machine learning (PPML), a key research task in multiparty learning. Despite this, the existing PPML methods generally cannot simultaneously meet multiple requirements, such as security, accuracy, efficiency, and application scope. To deal with the aforementioned problems, in this article, we present a new PPML method based on the secure multiparty interactive protocol, namely, the multiparty secure broad learning system (MSBLS) and derive its security analysis. To be specific, the proposed method employs the interactive protocol and random mapping to generate the mapped features of data, and then uses efficient broad learning to train the neural network classifier. To the best of our knowledge, this is the first attempt for privacy computing method that jointly combines secure multiparty computing and neural network. Theoretically, this method can ensure that the accuracy of the model will not be reduced due to encryption, and the calculation speed is very fast. Three classical datasets are adopted to verify our conclusion.

To improve the stress relaxation resistance of the Cu-5Ni-S8n alloy, the effects of Co or P addition on its stress relaxation resistance were investigated by scanning electron microscopy, transmission electron microscopy, electron backscatter diffraction), X-ray diffraction, and high-temperature creep durability tester. The results revealed that the relaxation rate increased dramatically as the temperature rose. The thermal stress relaxation resistance of Cu-15Ni-8Sn alloy was significantly improved with Co addition; After 100 h of stress relaxation tests at 200°C or 250°C, the residual stress value of the Cu-15Ni-8Sn alloy increased by 23 MPa or 22 MPa with Co addition, which was associated with the consumption of vacancies by Co atoms and the pinning effect of Co atoms on dislocation. However, at 250°C, the stress relaxation resistance of the Cu-15Ni-8Sn-0.2P alloy was lower than that of the Cu-15Ni-8Sn alloy. This difference was due to the inclusion of P, which promoted the recrystallization reaction.Article highlightsRecrystallization and the decrease of dislocation density are the major reasons for the stress relaxation of the samples.The stress relaxation resistance of Cu-15Ni-8Sn alloy was significantly improved with Co addition.The addition of the P element is not conducive to the improvement of thermal stress relaxation resistance.

Most existing multiview clustering methods are based on the original feature space. However, the feature redundancy and noise in the original feature space limit their clustering performance. Aiming at addressing this problem, some multiview clustering methods learn the latent data representation linearly, while performance may decline if the relation between the latent data representation and the original data is nonlinear. The other methods which nonlinearly learn the latent data representation usually conduct the latent representation learning and clustering separately, resulting in that the latent data representation might be not well adapted to clustering. Furthermore, none of them model the intercluster relation and intracluster correlation of data points, which limits the quality of the learned latent data representation and therefore influences the clustering performance. To solve these problems, this article proposes a novel multiview clustering method via proximity learning in latent representation space, named multiview latent proximity learning (MLPL). For one thing, MLPL learns the latent data representation in a nonlinear manner which takes the intercluster relation and intracluster correlation into consideration simultaneously. For another, through conducting the latent representation learning and consensus proximity learning simultaneously, MLPL learns a consensus proximity matrix with $k$ connected components to output the clustering result directly. Extensive experiments are conducted on seven real-world datasets to demonstrate the effectiveness and superiority of the MLPL method compared with the state-of-the-art multiview clustering methods.

Graph-oriented multi-view clustering methods have achieved impressive performances by employing relationships and complex structures hidden in multi-view data. However, most of them still suffer from the following two common problems. (1) They target at studying a common representation or pairwise correlations between views, neglecting the comprehensiveness and deeper higher-order correlations among multiple views. (2) The prior knowledge of view-specific representation can not be taken into account to obtain the consensus indicator graph in a unified graph construction and clustering framework. To deal with these problems, we propose a novel Low-rank Tensor Based Proximity Learning (LTBPL) approach for multi-view clustering, where multiple low-rank probability affinity matrices and consensus indicator graph reflecting the final performances are jointly studied in a unified framework. Specifically, multiple affinity representations are stacked in a low-rank constrained tensor to recover their comprehensiveness and higher-order correlations. Meanwhile, view-specific representation carrying different adaptive confidences is jointly linked with the consensus indicator graph. Extensive experiments on nine real-world datasets indicate the superiority of LTBPL compared with the state-of-the-art methods.

Egocentric videos, which record the daily activities of individuals from a first-person point of view, have attracted increasing attention during recent years because of their growing use in many popular applications, including life logging, health monitoring and virtual reality. As a fundamental problem in egocentric vision, one of the tasks of egocentric action recognition aims to recognize the actions of the camera wearers from egocentric videos. In egocentric action recognition, relation modeling is important, because the interactions between the camera wearer and the recorded persons or objects form complex relations in egocentric videos. However, only a few of existing methods model the relations between the camera wearer and the interacting persons for egocentric action recognition, and moreover they require prior knowledge or auxiliary data to localize the interacting persons. In this work, we consider modeling the relations in a weakly supervised manner, i.e., without using annotations or prior knowledge about the interacting persons or objects, for egocentric action recognition. We form a weakly supervised framework by unifying automatic interactor localization and explicit relation modeling for the purpose of automatic relation modeling. First, we learn to automatically localize the interactors, i.e., the body parts of the camera wearer and the persons or objects that the camera wearer interacts with, by learning a series of keypoints directly from video data to localize the action-relevant regions with only action labels and some constraints on these keypoints. Second, more importantly, to explicitly model the relations between the interactors, we develop an ego-relational LSTM (long short-term memory) network with several candidate connections to model the complex relations in egocentric videos, such as the temporal, interactive, and contextual relations. In particular, to reduce human efforts and manual interventions needed to construct an optimal ego-relational LSTM structure, we search for the optimal connections by employing a differentiable network architecture search mechanism, which automatically constructs the ego-relational LSTM network to explicitly model different relations for egocentric action recognition. We conduct extensive experiments on egocentric video datasets to illustrate the effectiveness of our method.

To alleviate the sparsity issue, many recommender systems have been proposed to consider the review text as the auxiliary information to improve the recommendation quality. Despite success, they only use the ratings as the ground truth for error backpropagation. However, the rating information can only indicate the users’ overall preference for the items, while the review text contains rich information about the users’ preferences and the attributes of the items. In real life, reviews with the same rating may have completely opposite semantic information. If only the ratings are used for error backpropagation, the latent factors of these reviews will tend to be consistent, resulting in the loss of a large amount of review information. In this article, we propose a novel deep model termed deep rating and review neural network (DRRNN) for recommendation. Specifically, compared with the existing models that adopt the review text as the auxiliary information, DRRNN additionally considers both the target rating and target review of the given user–item pair as ground truth for error backpropagation in the training stage. Therefore, we can keep more semantic information of the reviews while making rating predictions. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed DRRNN model in terms of rating prediction.

Multi-view clustering (MVC) has attracted more and more attention in the recent few years by making full use of complementary and consensus information between multiple views to cluster objects into different partitions. Although there have been two existing works for MVC survey, neither of them jointly takes the recent popular deep learning-based methods into consideration. Therefore, in this paper, we conduct a comprehensive survey of MVC from the perspective of representation learning. It covers a quantity of multi-view clustering methods including the deep learning-based models, providing a novel taxonomy of the MVC algorithms. Furthermore, the representation learning-based MVC methods can be mainly divided into two categories, i.e., shallow representation learning-based MVC and deep representation learning-based MVC, where the deep learning-based models are capable of handling more complex data structure as well as showing better expression. In the shallow category, according to the means of representation learning, we further split it into two groups, i.e., multi-view graph clustering and multi-view subspace clustering. To be more comprehensive, basic research materials of MVC are provided for readers, containing introductions of the commonly used multi-view datasets with the download link and the open source code library. In the end, some open problems are pointed out for further investigation and development.

Multiview subspace clustering has attracted an increasing amount of attention in recent years. However, most of the existing multiview subspace clustering methods assume linear relations between multiview data points when learning the affinity representation by means of the self-expression or fail to preserve the locality property of the original feature space in the learned affinity representation. To address the above issues, in this article, we propose a new multiview subspace clustering method termed smoothness regularized multiview subspace clustering with kernel learning (SMSCK). To capture the nonlinear relations between multiview data points, the proposed model maps the concatenated multiview observations into a high-dimensional kernel space, in which the linear relations reflect the nonlinear relations between multiview data points in the original space. In addition, to explicitly preserve the locality property of the original feature space in the learned affinity representation, the smoothness regularization is deployed in the subspace learning in the kernel space. Theoretical analysis has been provided to ensure that the optimal solution of the proposed model meets the grouping effect. The unique optimal solution of the proposed model can be obtained by an optimization strategy and the theoretical convergence analysis is also conducted. Extensive experiments are conducted on both image and document data sets, and the comparison results with state-of-the-art methods demonstrate the effectiveness of our method.

This paper presents a comprehensive study of geochronology, in situ clinopyroxene mineralogy, geochemistry and isotope geology for the Triassic mafic magmatism of the central Tibetan Plateau. Moreover, the results are used to understand the development of a continental back-arc basin and the generation of back-arc basin magmatism. Zircon U-Pb isotope analyses indicate that these rocks were formed during the Middle Triassic (c. 231 Ma). Both the mafic rocks and the trapped melt calculated by the clinopyroxene/melt partition coefficient (Dcpx/melt) exhibit distinct enrichments in light rare earth elements (LREE) and Th with pronounced depletions in Nb and Ta, which are similar to the geochemical composition of typical continental back-arc basin basalts. Geochemical features and isotopic variations indicate that the mafic melts originated from a depleted mantle modified by c. 10% crustal components. The ratios of Zn/Fet and REE further suggest a spinel-bearing peridotite source. Considering other regional geological data, we argue that the origin of Triassic mafic magmatism should be attributed to the extension of a continental back-arc basin. Slab rollback and mantle plume activity played vital roles in the formation and development of a continental back-arc basin in the central part of the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

The principle of justifiable granularity (PoJG) balances coverage and specificity to optimize information granularity. Although numerous studies have successfully formed information granules (IGs) using this principle, the stability of these IGs rarely receives attention. This paper analyzes the stability of such an IG's modal, performance and upper or lower bound. First, we define some concepts to quantify the stability. Then, by the use of the binomial distribution, the central limit theorem and the union bound, we prove some theorems, which rely on several reasonable hypotheses and reveal the relations between the data size and the stability of the IG's modal, performance and upper or lower bound. Furthermore, we put forward an algorithm built on the theorems to generate stable IGs by building data that have the proper size. Finally, we analyze its time complexity, applications and limitations. Experiments indicate the reliability of this algorithm when it is applied to several probability distributions and real datasets with a large scale of evidence. [ABSTRACT FROM AUTHOR]

Developing cost-effective electrocatalysts for oxygen evolution reaction (OER) in basic media is critical to hydrogen production from renewable energy. Herein, in-situelectrodeposited flower-like NiFeOxHyand NiFeOxHy/rGO composite electrocatalysts on Ni foam for OER are reported. The active sites of the flower-like electrocatalysts are increased significantly due to the enhanced NiFeOxHysurface areas and numerous exposed layered edges and edge defects. Reduced graphene oxide (rGO) has been introduced to fabricate NiFeOxHy/rGO composite film, further improving the conductivity and OER performance of the flower-like NiFeOxHy. The optimized NiFeOxHy/rGO exhibits superior OER performance with a Tafel slope of 29.11 mV/decade, an overpotential of 200 mV at 10 mA/cm2in 1 mol/L KOH and favorable long-term stability.

Methylation of cytosine to 5-methylcytosine (5mC) is a prevalent DNA modification found in many organisms. Sequential oxidation of 5mC by ten-eleven translocation (TET) dioxygenases results in a cascade of additional epigenetic marks and promotes demethylation of DNA in mammals1,2. However, the enzymatic activity and function of TET homologues in other eukaryotes remains largely unexplored. Here we show that the green alga Chlamydomonas reinhardtiicontains a 5mC-modifying enzyme (CMD1) that is a TET homologue and catalyses the conjugation of a glyceryl moiety to the methyl group of 5mC through a carbon–carbon bond, resulting in two stereoisomeric nucleobase products. The catalytic activity of CMD1 requires Fe(ii) and the integrity of its binding motif His-X-Asp, which is conserved in Fe-dependent dioxygenases3. However, unlike previously described TET enzymes, which use 2-oxoglutarate as a co-substrate4, CMD1 uses l-ascorbic acid (vitamin C) as an essential co-substrate. Vitamin C donates the glyceryl moiety to 5mC with concurrent formation of glyoxylic acid and CO2. The vitamin-C-derived DNA modification is present in the genome of wild-type C. reinhardtiibut at a substantially lower level in a CMD1mutant strain. The fitness of CMD1mutant cells during exposure to high light levels is reduced. LHCSR3, a gene that is critical for the protection of C. reinhardtiifrom photo-oxidative damage under high light conditions, is hypermethylated and downregulated in CMD1mutant cells compared to wild-type cells, causing a reduced capacity for photoprotective non-photochemical quenching. Our study thus identifies a eukaryotic DNA base modification that is catalysed by a divergent TET homologue and unexpectedly derived from vitamin C, and describes its role as a potential epigenetic mark that may counteract DNA methylation in the regulation of photosynthesis. An algal TET dioxygenase homologue, CMD1, uses vitamin C as a glycerol donor to modify 5-methylcytosine and helps to regulate gene transcription in response to high light levels.

Heat shock protein 90 (HSP90) is a member of genetically conserved heat shock protein family. As an important molecular chaperone in eukaryotic cells, HSP90 plays a key regulatory role in maintaining cellular protein homeostasis. HSP90 clients encompass a wide range of proteins, thus HSP90 is involved in diverse biological process. With the deeper study, it is found that HSP90 takes an important part in the development and metastasis of cancer, and has become a promising target for the study of anticancer biology. In this review, the progress of HSP90 as molecular chaperone and its relationship with cancer are discussed.

To assess toxicities of the air in Chinese medicine clinics polluted by moxa-burning smoke due to moxibustion-derived burning products (MBP).

The analysis of dissociation products of disulfide bonds in peptide sequences has been increasingly applied in various fields. Ion dissociation is typically performed by tandem mass spectrometry, in which the overall molecular weight is determined during the first stage and the protein sequence and structure are obtained by dissociation during the second stage. However, conventional dissociation methods have some disadvantages, such as insufficient dissociation efficiency, and low product signals and varieties. Ultraviolet photodissociation (UVPD) is a rapid, efficient, and practical dissociation method for protein analysis, which excites the biomolecule skeleton and then directly excites the molecules, forming the excited states within microseconds. Although the development of mass spectrometer platforms coupled with UVPD has been reported to date, most of these studies have focused on the application of UVPD in mass spectrometry (MS). Discussions on the experimental setup for UVPD lasers, including the trigger process of the ultraviolet (UV) laser, the timing sequence of the entire dissociation and detection process, and their influence on dissociation, are limited. Therefore, an instrument platform was developed with a linear ion-trap mass spectrometer and an excimer laser to achieve UVPD by adjusting the laser trigger signal. The platform dissociated the disulfide bond of the amino acid sequence ARACAKA-ECG into two characteristic valence states (m/z = 498 and 332), and their highest intensities were obtained by optimizing the experimental conditions of the platform. The dissociation results of UVPD showed the characteristic peaks of ARACAKA (m/z345) that were produced by disulfide bond cleavage, but these peaks were not detected by collision-induced dissociation (CID). Therefore, it was confirmed that the developed platform realized UVPD of the disulfide bond of the amino acid sequence of the polypeptide. Moreover, the developed platform provides a reference for the implementation of UVPD for peptide sequencing.

The combination of numerical simulation and orthogonal experimental design method can reduce the test number. Structural parametric modeling research on corrugated tube of heat exchangers in our work to establish the fluid flow and heat transfer numerical simulation model. A scheme of orthogonal test of a corrugated tube, which includes 4 factors and 3 levels and has taken heat exchange quantity, resistance of the internal tube and annular gap as evaluation indexes, was designed. A method combining with the difference and the variance analysis was taken to analysis the result of orthogonal test, and the effect of different structure parameter on corrugated tube was achieved. Finally the optimal structure parameter of the corrugated tube was obtained by a comprehensive balance method, and those methods above-mentioned provide a reference for the design or promotion of the corrugated tube of tube heat exchanger.

In the present work, investigations by high frequency resonance technique for diagnosis of defect frequencies of linear motion unit are reported. Raw vibration signature of the moving parts at different speeds of operation has been demodulated. Envelope detected spectrum is analyzed to evaluate various defect frequencies and their energy levels. Experimentally evaluated frequencies are compared with theoretically determined defect frequencies. These frequency values and their energy levels are used to monitor intrinsic condition of linear motion unit as well as to establish severity of existing/developed defects on the LM guide and inside the LM block. Relative comparisons of linear motion units of the same type are made at various operating speeds under identical conditions of operation on the basis of identified defect frequencies and severity of defects.

Abstract: Purpose: The subarachnoid space (SAS) lies between the arachnoid membrane and the pia mater of the human brain, normally filled with cerebrospinal fluid (CSF). Subarachnoid hemorrhage (SAH) is a serious complication of neurological disease that can have high mortality and high risk of disability. Computed tomography (CT) head scans are often used for diagnosing SAH which may be difficult when the hemorrhage is small or subtle. A computer-aided diagnosis system from CT images is thus developed to augment image interpretation. Methods: Supervised learning using the probability of distance features of several landmarks was employed to recognize SAS. For each CT image, the SAS was approximated in four steps: (1) Landmarks including brain boundary, midsagittal plane (MSP), anterior and posterior intersection points of brain boundary with the MSP, and superior point of the brain were extracted. (2) Distances to all the landmarks were calculated for every pixel in the CT image, and combined to construct a high-dimensional feature vector. (3) Using head CT images with manually delineated SAS as training dataset, the prior probabilities of distances for pixels within SAS and non-SAS were computed. (4) Any pixel of a head CT scan in the testing dataset was classified as an SAS or non-SAS pixel in a Bayesian decision framework based on its distance features. Results: The proposed method was validated on clinical head CT images by comparison with manual segmentation. The results showed that the automated method is consistent with the gold standard. Compared with elastic registration based on grayscale information, the proposed method was less affected by grayscale variation between normal controls and patients. Compared with manual delineation, the average spatial overlap, relative overlap, and similarity index were, respectively, 89, 63, and 76% for the automatic SAS approximation of the 69 head CT scans tested. The proposed method was tested for SAH detection and yielded a sensitivity of 100% and a specificity of 92%. Conclusion: Automated SAH detection with high sensitivity was shown feasible in a prototype computer-aided diagnosis system. The proposed method may be extended for computer-aided diagnosis of several CSF-related diseases relevant to SAS abnormalities.

In this paper, a new embedded numerical control (ENC) system for carton samplemaker based on Windows CE is presented. The hardware design of ENC system based on ARM9 processor S3C2440A and motion control chip MCX314As is discussed principally. Besides, the device driver of MCX314As is developed by using stream interface driver model under Windows CE operating system. Furthermore, the software structure of the ENC system is introduced, and the way how to utilize multi-thread technology to realize the real-time control of the carton samplemaker is also discussed in detail. The system is proved to be good at dealing with multiple tasks processing, real-time and reliability of motion control.

Abstract: Objective: To investigate the recurrent patterns and factors involved in node-negative advanced gastric cancer after curative resection.Patients and Methods: Clinicopathological characteristics and prognostic outcomes of 310 patients who had lymph node-negative advanced gastric adenocarcinoma and received curative resection between 2002 and 2006 were retrospectively evaluated. Results: Among the 300 patients, 15 (5.0%) had locoregional recurrence, 5 (1.7%) had lymph node recurrence, 27 (9.0%) had peritoneal seeding recurrence, and 21 (7.0%) had hematogenous metastasis. Using multivariate analysis, we found that the maximum tumor diameter (P=0.014), histological type (P=0.001) and Borrmann type (P=0.033) were independent factors predicting the locoregional recurrence. Lymph node recurrence was significantly affected by lymph node dissection (P=0.029) and lymphovascular invasion (P=0.004). Clinicopathological factors predicting the peritoneal seeding recurrence were the depth of invasion (P=0.001) and Borrmann type (P=0.002). In addition, lymphovascular invasion (P=0.013) and histological type (P=0.001) were significantly associated with hematogenous metastasis. Conclusion: Node-negative advanced gastric cancer has a high amount of peritoneal seeding and hematogenous metastasis.

Resistivity, thermal diffusivity, lattice and magnetic properties of double perovskite Sr_2−xLa_xMnMoO₆ are investigated with systematic change of La doping concentration x from 0.0 to 0.4. The insulator to metal phase transition is observed with increasing x above 0.3, suggesting that the extra electrons via substitution of La³⁺ for Sr²⁺ ions occupy mainly the conduction Mo-4d band. According to the insulator to metal phase transition, the thermal diffusivity of Sr_2−xLa_xMnMoO₆ enhances from 0.33 cm²/s at x = 0.0 to 0.49 cm²/s at x = 0.4. We further investigate the La doping effects on the lattice and magnetic properties.

AbstractThe preferences of Bemisia tabaciGennadius for five host plants: poinsettia, tomato, cabbage, sweet potato and flowering Chinese cabbage, was tested using a Y‐tube olfactometer and a desiccator in the laboratory. The results show that B. tabaciadults were attracted by the odors of the five plants. The order of preference was poinsettia > flowering Chinese cabbage > sweet potato > cabbage > tomato. Preference was extremely significant between poinsettia and the other four plants, and between flowering Chinese cabbage, cabbage and tomato. There was no significant difference in preference for flowering Chinese cabbage and sweet potato, sweet potato, cabbage and tomato or between cabbage and tomato.

A new approach using differential geometric theory to nonlinear control design for dual-excited synchronous generators is presented in this paper. As an example, a novel rotor angle and voltage stabilizing nonlinear optimal excitation control law of dual-excited synchronous generators is obtained, which is based on coordinate transformers and state feedback linearization combined with dynamic system. The computer simulation results on a single-machine infinite-bus power system show that dual-excited synchronous generator with proposed controller can improve dynamic stability and transient stability of power system to a great extent and has better dynamic characteristics than conventional synchronous generator.

Abstract: The density of dendritic cells (DC) and macrophages (Mϕ) in tissue specimens of gastric carcinoma (GC n=65) was investigated by ABC immunohistochemical method using anti-S100 protein and anti-lysozyme antibodies, and was compared with that in gastric ulcer (GU n=19), chronic atrophic gastritis (CAG n=28) and normal gastric mucosa (NGM n=15). The mean density if DC (cells/mm2) in GC (15.0 was significantly higher than that in NGM (3.8) and GU (8.3), but was remarkably lower when compared to that in CAG (29.5) (P<0.01). Statistically significant difference in the population density of DC was observed between well- and poorly-differentiated GC (P<0.01). With their unique dendritic processes, DC were mainly concentrated within dense lymphoid infiltrates or in the T-area of reactive lymphoid follicles and were interspersed among the tumor cells. In contrast, Mϕ were present around the necrotic foci and were rarely seen within the non-necrotic neoplastic tissues. These data suggest that DC, which differ in morphology, distribution, number and function form Mϕ may be more directly involved in the host immune reaction against tumor by acting as antigen presenting cells.

The aim of this study was to assess whether spectral turbulence analysis (STA) of the signal-averaged electrocardiogram (SAECG) is of prognostic use in patients with idiopathic dilated cardiomyopathy. SAECGs were recorded at presentation in 84 patients with idiopathic dilated cardiomyopathy and STA was performed using 183 Del Mar software. STA was abnormal (≥3 of the 4 standard parameters beyond the normal range) in 31 patients (37%). Patients were followed for a mean duration of 24 ± 18 months (range 1 to 59) during which time 24 (29%) developed progressive heart failure (14 underwent cardiac transplantation), 4 died suddenly or had aborted sudden death, and the others remained clinically stable. Progressive heart failure occurred more often in patients who had an abnormal versus a normal STA result (15 [48%] vs 9 [17%]; p < 0.002). Actuarial survival revealed a 1-year survival of 90% in patients with a normal STA result, and 63% in patients with an abnormal STA result (p < 0.01). The predictive ability of STA to identify patients with progressive heart failure was sensitivity 63%, specificity 77%, positive predictive value 54%, and negative predictive value 83%. Univariate analysis identified peak oxygen consumption as having the largest relative risk for the development of progressive heart failure (9.55,95% confidence interval [CI] 2.1 to 43.9). Left ventricular end-diastolic dimension (relative risk 4.18, 95% CI 1.5 to 11.4) and STA (relative risk 3.81, 95% CI 1.7 to 8.8) were also significantly associated with the development of progressive heart failure. Multivariate analysis showed that STA had a relative risk of 3.20 (95% CI 1.04 to 9.80). Abnormalities of the SAECG using STA are common in patients with idiopathic dilated cardiomyopathy and are a noninvasive marker of patients at increased risk of developing progressive heart failure.

This study sought to investigate the tribological properties of nitrile rubber (NBR)/ultrahigh molecular weight polyethylene (UHMWPE)/nano-molybdenum disulfide (nano-MoS2) nanocomposites containing various quantities of nano-MoS2. The apparatus used for these tests was a marine stern tube bearing testing apparatus SSB-100V that was water-lubricated and was run at low speed under heavy duty conditions. The coefficient of friction coefficient (COF), wear rate, and surface abrasion of the composite were obtained to determine the effect of the addition nano-MoS2 and to obtain the optimum nano-MoS2 content. The mechanical and physical properties of the rubber-plastic material met the requirements of the Chinese Ship standard CB/T769-2008 and U.S. military standard MIL-DTL-17901C(SH). The experimental results showed that the nanocomposites that contained 9 phr nano-MoS2 (parts by weight per hundred parts of rubber materials) exhibited good comprehensive friction and wear properties. It is believed that the experience achieved from this study can form a theoretical foundation for the improving the properties of the subject rubber-plastic material.

Primary myeofibrosis (PMF) is one of the Ph negative myeloproliferative neoplasms (MPN). The mainly clinical features are obviously physical symptoms and symptomatic splenomegaly. It may be converse to leukemia and has shortened life expectancy. Nowadays, the therapy of PMF is aimed at maintaining comfort and there was no effective treatments. PMF complicated with myelodysplastic syndrome (MDS), which is named as MDS/MPN-u, is a rare case, and the treatments are confused. In this study, we want to discuss an effective treatment in MDS/MPN via a case therapy and literature review.

The myelodysplastic syndromes (MDS) are a cluster of clonal hematopoietic disorders with diverse origins. MDS frequently progresses to secondary acute myeloid leukemia (sAML). sAML is unique from de novo AML and is associated with a poorer response to induction chemotherapy and decreased survival (Granfeldt Ostgard, et al 2015). To remedy this situation, we conducted a retrospective study of the National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) database. A logistic regression analysis was performed to determine the significant risk factors associated with transformation of MDS into sAML. In addition, a Cox proportional hazard regression model was used to analyze the prognostic factors for survival of the included patients. Leukemic transformation was fitted in the Cox model as a time-dependent covariate.

To discover novel biomarkers for early detection of human lung squamous cell cancer (LSCC) and explore possible mechanisms of LSCC carcinogenesis, iTRAQ-tagging combined with two dimensional liquid chromatography tandem MS analysis was used to identify differentially expressed proteins in human bronchial epithelial carcinogenic process using laser capture microdissection-purified normal bronchial epithelium (NBE), squamous metaplasia (SM), atypical hyperplasia (AH), carcinoma in situ(CIS) and invasive LSCC. As a result, 102 differentially expressed proteins were identified, and three differential proteins (GSTP1, HSPB1 and CKB) showing progressively expressional changes in the carcinogenic process were selectively validated by Western blotting. Immunohistochemistry was performed to detect the expression of the three proteins in an independent set of paraffin-embedded archival specimens including various stage tissues of bronchial epithelial carcinogenesis, and their ability for early detection of LSCC was evaluated by receiver operating characteristic analysis. The results showed that the combination of the three proteins could perfectly discriminate NBE from preneoplastic lesions (SM, AH and CIS) from invasive LSCC, achieving a sensitivity of 96% and a specificity of 92% in discriminating NBE from preneoplatic lesions, a sensitivity of 100% and a specificity of 98% in discriminating NBE from invasive LSCC, and a sensitivity of 92% and a specificity of 91% in discriminating preneoplatic lesions from invasive LSCC, respectively. Furthermore, we knocked down GSTP1 in immortalized human bronchial epithelial cell line 16HBE cells, and then measured their susceptibility to carcinogen benzo(a)pyrene-induced cell transformation. The results showed that GSTP1 knockdown significantly increased the efficiency of benzo(a)pyrene-induced 16HBE cell transformation. The present data first time show that GSTP1, HSPB1 and CKB are novel potential biomarkers for early detection of LSCC, and GSTP1 down-regulation is involved in human bronchial epithelial carcinogenesis.

Singular value decomposition (SVD) is a powerful linear algebra tool. It is widely used in many different signal processing methods, such principal component analysis (PCA), singular spectrum analysis (SSA), frequency domain decomposition (FDD), subspace identification and stochastic subspace identification method ( SI and SSI ). In each case, the data is arranged appropriately in matrix form and SVD is used to extract the feature of the data set. In this study three different algorithms on signal processing and system identification are proposed: SSA, SSI-COV and SSI-DATA. Based on the extracted subspace and null-space from SVD of data matrix, damage detection algorithms can be developed. The proposed algorithm is used to process the shaking table test data of the 6-story steel frame. Features contained in the vibration data are extracted by the proposed method. Damage detection can then be investigated from the test data of the frame structure through subspace-based and nullspace-based damage indices.

Many face recognition algorithms/systems have been developed in the last decade and excellent performances have also been reported when there is a sufficient number of representative training samples. In many real-life applications such as passport identification, only one well-controlled frontal sample image is available for training. Under this situation, the performance of existing algorithms will degrade dramatically or may not even be implemented. We propose a component-based linear discriminant analysis (LDA) method to solve the one training sample problem. The basic idea of the proposed method is to construct local facial feature component bunches by moving each local feature region in four directions. In this way, we not only generate more samples with lower dimension than the original image, but also consider the face detection localization error while training. After that, we propose a subspace LDA method, which is tailor-made for a small number of training samples, for the local feature projection to maximize the discrimination power. Theoretical analysis and experiment results show that our proposed subspace LDA is efficient and overcomes the limitations in existing LDA methods. Finally, we combine the contributions of each local component bunch with a weighted combination scheme to draw the recognition decision. A FERET database is used for evaluating the proposed method and results are encouraging. © 2005 Society of Photo-Optical Instrumentation Engineers.