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Conversational agents (CAs) have been widely used for many domains, such as healthcare, education, and business. One main category of CAs is task-oriented CAs, which aim to help users to complete a set of specific tasks. However, task-oriented CAs can fail to answer the user’s question, which can lead to a breakdown in the dialogue (when it is not possible to complete a conversation with a CA). Breakdown detection is an essential task for developing better CAs. Several related studies have focused on breakdown detection using different sets of features, for example, topic transition, word-based similarity and clustering; but, the existing studies develop features mainly from the system’s outputs or user’s inputs, whereas the features can be extracted from both sides, as well as from the interaction between them. Therefore, in this work, we developed a new supervised fusion machine learning (ML) model that combines the prediction from two machine learning algorithms for breakdown detection CAs services system. We developed features from different groups focusing on both the user input and the system response. Then we select the optimal combined features. The features are based on sentence similarity, sentiment features, and count-based features. The developed fusion model is mainly based on the two best performances of the single classifiers (SVM and RF). We explore several single ML algorithms using different sets of features and the combined features. To verify the effectiveness of the proposed fusion model, we compared the proposed models against baseline methods using four sets of data. We conclude that the proposed fusion model with the combined features outperforms the baselines and all other models in terms of prediction accuracy and f-score measures. [ABSTRACT FROM AUTHOR]

Attributed graph clustering, the task of grouping nodes into communities using both graph structure and node attributes, is a fundamental problem in graph analysis. Recent approaches have utilized deep learning for node embedding followed by conventional clustering methods. However, these methods often suffer from the limitations of relying on the original network structure, which may be inadequate for clustering due to sparsity and noise, and using separate approaches that yield suboptimal embeddings for clustering. To address these limitations, we propose a novel method called Deep Attributed Clustering with High-order Proximity Preserve (DAC-HPP) for attributed graph clustering. DAC-HPP leverages an end-to-end deep clustering framework that integrates high-order proximities and fosters structural cohesiveness and attribute homogeneity. We introduce a modified Random Walk with Restart that captures k-order structural and attribute information, enabling the modelling of interactions between network structure and high-order proximities. A consensus matrix representation is constructed by combining diverse proximity measures, and a deep joint clustering approach is employed to leverage the complementary strengths of embedding and clustering. In summary, DAC-HPP offers a unique solution for attributed graph clustering by incorporating high-order proximities and employing an end-to-end deep clustering framework. Extensive experiments demonstrate its effectiveness, showcasing its superiority over existing methods. Evaluation on synthetic and real networks demonstrates that DAC-HPP outperforms seven state-of-the-art approaches, confirming its potential for advancing attributed graph clustering research. [ABSTRACT FROM AUTHOR]

As an inherent property of the accumulation of elastic energy and the sudden instability failure of coal, coal bursting liability (CBL) is the basis of the research on the early warning and prevention of coal burst. To accurately classify the CBL level, the support-vector-machine (SVM) method was introduced in this paper, and the dynamic failure time (DT), elastic energy index (WET), impact energy index (KE) and uniaxial compressive strength (RC) were selected as the classification indexes. An imbalanced sample set, containing 95 groups of measured data of CBL, was established, and eight SVM classification models were constructed, based on different kernel functions and swarm-intelligence-optimization algorithms. Focusing on the problem of sample imbalance, the classification accuracy, A, F1-score and kappa coefficient were used to comprehensively evaluate the classification performance of SVM models, and the grey-wolf-optimizer SVM (GWO-SVM) model was selected as the best model in this paper, reaching the highest accuracy of 98.9%. The GWO-SVM was applied to identify the CBL level of the 4# coal seam in Xiaozhuang Coal Mine and the 1# coal seam in the Wanfeng Coal Mine. The results of the engineering application are consistent with those from the engineering field, and show that the proposed model is scientific and practical, and can be a new method for CBL classification. [ABSTRACT FROM AUTHOR]

Objective: The performance and stability of radiomics model caused by dimension reduction remain being confronted with major challenges. In this study, we aimed to propose a new scheme of global feature management independent of dimension reduction to improve it. Methods: The non-contrast computed tomography (NCCT) images of acute brainstem infarction (ABI) from two medical centers were used as test and validation sets. A new scheme was constructed based on global feature management, and the traditional scheme dependent on dimension reduction was used as control. The radiomic features of NCCT images were extracted in Matlab R2013a. The performance of prediction model was evaluated by the generalized linear model (GLM) and multivariate logistic regression. And, the stability of radiomics model was evaluated with the difference of area under curve (AUC) between the test and validation sets. Results: Compared with the traditional scheme, the new scheme presented a similar detection performance (AUC: 0.875 vs. 0.883), yet a better performance in predicting prognosis (AUC: 0.864, OR = 0.917, p = 0.021 vs. AUC:0.806, OR = 0.972, p = 0.007). All these results were well verified in an independent validation set. Moreover, the new scheme showed stronger stability in both the detection model (ΔAUC: 0.013 vs. 0.039) and prediction model (ΔAUC = 0.004 vs. 0.044). Conclusion: Although there might be several limitations, this study proved that the scheme of global feature management independent of dimension reduction could be a powerful supplement to the radiomics methodology. Key Points: • The new scheme (Swavelet) presented similar detection performances for ABI with the traditional scheme. • A better predictive performance for END was found in the new scheme (Swavelet) compared with the traditional scheme. • Stronger model stability was found in both the detection and prediction models based on the new scheme. [ABSTRACT FROM AUTHOR]

Blasting operations are widely and frequently used for rock excavation in Civil and Mining constructions. Flyrock is one of the most important issues induced by blasting operations in open pit mines, and therefore needs to be well predicted in order to identify the safety zone to prevent the potential injuries. For this purpose, 234 sets of blasting data were collected from Sungun Copper Mine site, and a stacked deep multi-kernel learning (SD-MKL) framework was proposed to estimate the blast induced flyrock with confidence accuracy. The proposed model uses the stacking-based representation learning framework (S-RL) to achieve deep learning on small-scale training sets. A multi-kernel learning model (MKL) is used as the base module of S-RL framework, which uses a multi-feature fusion strategy to generate multiple kernels with different kernel length in order to reduce the effort in tuning hyperparameters. In addition, this study further enhanced the predictive capability of SD-MKL by introducing the boosting method into the S-RL framework and hence proposed a boosted SD-MKL model. For comparison purpose, several existing machine learning models were implemented, i.e., kernel ridge regression (KRR), support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), ensemble deep random vector functional link (edRVFL), SD-KRR and SD-SVM. Our experimental results showed that the proposed boosted SD-MKL achieved the best overall performance, with the lowest RMSE of 0.21/1.73, MAE of 0.08/0.78, and the highest VAF of 99.98/99.24. • A stacked deep learning multi-kernel learning model (SD-MKL) for flyrock prediction. • Use stacked-representation learning (S-RL) framework to achieve deep learning. • Use the multi-kernel learning model with multi-feature fusion strategy to learn the relationship between generated feature and original feature. • Use boosting method to improve the prediction accuracy of SD-MKL. [ABSTRACT FROM AUTHOR]

Activated carbon (AC) was treated with nitric acid, hydrogen peroxide, and urea. The obtained products were then used as supports for the preparation of palladium/activated carbon (Pd/AC) catalysts. The characterization results revealed that the AC textural structure and surface properties were selectively modified via different treatments, and the surface functional groups of the supports could affect the particle size, dispersion, and chemical states of the Pd nanoparticles. The surface of the urea-treated AC (AC-U) contained more carbonylic groups and nitrogenous species but fewer hydroxylic and carboxylic groups than the surfaces of the ACs subjected to other treatments. Consequently, in the hydrogenation synthesis of meropenem, the Pd/AC-U catalyst provided a higher meropenem yield and lower amounts of impurities in the meropenem product than the other catalysts. The nitrogenous groups not only provided efficient anchoring sites for Pd nanoparticles, improving the reaction activity, but also reduced the side reactions, decreasing the impurity content in the product. [ABSTRACT FROM AUTHOR]

Neighborhood rough set theories are commonly used in global feature selection to achieve high performance in continuous data classification. However, selecting a single feature subset to represent the entire dataset may degrade the performance when there are intra-class dissimilarities among objects. Therefore, this paper proposes a novel feature-selection method, Granule-specific Feature Selection (GFS) to select local feature subsets for continuous data classification. The feature selection approach constitutes a novel feature selection algorithm and a novel feature evaluation function and uses existing approaches for granule identification and classification with some adjustments. The neighborhood rough set theories are used in granule (subclass) identification within each class when there are no subclass label information available in the training data, while an improved k-Nearest Neighbors approach is used in classification with granule-specific feature subsets. Experimental results show GFS outperforms most of the global, class-specific, and local feature selection baselines in terms of classification performance. • Granule-specific feature selection using neighborhood rough set theories. • Feature selection while managing intra-class dissimilarities of objects. • Handle data uncertainty of continuous data when selecting features. • Improve classification performance using granule-specific feature subsets. [ABSTRACT FROM AUTHOR]

Outlier detection is extensively adopted in machine learning applications to identify rare but significant objects in a data distribution that deviate from the majority of objects. However, none of the existing outlier definitions use the available contextual label information in a dataset to improve the identification of outliers, though it could improve the performance of data analysis. In this study, we propose a novel definition for outliers considering the available contextual label information along with a method to remove outliers based on the proposed definition to improve classification performance and granulation purity. The experimental results on eight public datasets show that the removal of outliers using the novel method highly improves the classification accuracy with Support Vector Machine, k-Nearest Neighbors, and Classification and Regression Tree algorithms while identifying 100% pure subclasses of the labeled major classes. This method is beneficial in identifying the outliers automatically when the data contains classification information about a specific application instead of outlierness information. • Outlier removal approach to improve the performance of the remaining data. • Define outliers using the available contextual label information. • Identify outliers which reduce the purity of data granules within a dataset. • Confirm 100% purity of the granules (clusters) after the outlier removal. [ABSTRACT FROM AUTHOR]

Aimed at investigating the differentiation of acoustic emission (AE) signals and fractal precursory characteristics between strong, weak, and no bursting liability coals under uniaxial compression, as well as improving the accuracy of rockburst monitoring and early warning by AE techniques, we experimentally studied the evolution law and differences of AE ring count rate, energy rate, and correlation dimension between different loaded bursting liability coals by the YAW4306 electric mechanical test system and CTA-1 AE monitor. Our experimental results indicated that the AE count and energy of coal samples with different bursting liabilities showed a similar evolution law of "sharp increase-calm-sharp increase" before their main rupture. The active points of AE signals emitted from coal with strong, weak, and no bursting liability appeared at about 85∼90%, 75∼78%, and 51∼55% of the peak stress, respectively. The stronger the bursting liability of coal, the shorter the duration of main rupture and postpeak failure stage, and the greater the AE energy rate in the main rupture. The AE counts of different coals had obvious fractal characteristics, and the AE correlation dimension values of strong and weak bursting liability coal samples presented the phenomenon of "fluctuating rise to a peak value-sharp drop-continuous decrease," which can be used as a precursory information of coal failure. [ABSTRACT FROM AUTHOR]

The progress of construction and safe production in mining, water conservancy, tunnels, and other types of deep underground engineering is seriously affected by rockburst disasters. This makes it essential to accurately predict rockburst intensity. In this paper, the ratio of maximum tangential stress of surrounding rock to rock uniaxial compressive strength (σθ/σc), the ratio of rock uniaxial compressive strength to rock uniaxial tensile strength (σc/σt), and the elastic energy index of rock (Wet) were chosen as input indices, and rockbursts were graded as level I (none rockburst), level II (light rockburst), level III (medium rockburst), and level IV (strong rockburst). A total of 104 groups of rockburst engineering samples, collected widely from around the world, were divided into a training set (84 groups of samples) and a test set (20 groups of samples). Based on the kernel principal component analysis (KPCA), the adaptive particle swarm optimization (APSO) algorithm, and the support vector machine (SVM), the KPCA-APSO-SVM model was established. The proposed model showed satisfactory classification performance: the prediction accuracies of the training set and test set were 98.81% and 95%, respectively. In addition, the trained prediction model was applied to five rockburst engineering cases and compared with the BP neural network model, SVM model, and APSO-SVM model. The comparative results show that the KPCA-APSO-SVM model has a higher prediction accuracy; as such, it provides a new reliable method for rockburst prediction. [ABSTRACT FROM AUTHOR]

• Hillslope topography is essential for local hydrology and peatland formation. • Microtopographic patterning enhances the positive water balance of the peatland. • Feedback between plant growth and pool development reinforces the stability of the peatland. Some sloping peatlands in northern regions often develop surface microtopographic patterns to maintain their water balance and ecosystem functioning. However, we do not know whether and how spatial patterning would influence the water balance and peat formation of permafrost-affected peatlands in relatively dry regions. Here we used data from the field observations and Unmanned Aerial Vehicle (UAV) survey of a slope peatland at an elevation of around 4800 m in the hinterland of the Qinghai-Tibetan Plateau (QTP) to document and understand the topographic controls of water balance and vegetation growth. Our terrain analysis result shows that the peatland—located on the middle section of a hillslope—has a gentle slope of 5.6° ± 2.5°, while the non-peatland upper section has a steep slope of 12° ± 4.5°. The great upstream catchment area and the presence of shallow impermeable permafrost likely create a saturated condition for peat formation. Our UAV results show obvious spatial patterning of abundant pools and ridges across this peatland, and pool sizes and ridge abundance increase with increasing slopes, suggesting that slope-controlled water flow gradient is the main driver of ridge formation and that ridges is to slow down the runoff. UAV-derived greenness values show a positive relationship with the total pool extent locally (R2 = 0.60) and decrease with increasing distance from the individual pools, suggesting sensitive responses of vegetation growth to surface moisture. Thus, enhanced vegetation growth and likely resultant great peat accumulation immediately around pools potentially further differentiate surface microtopography, strengthening the pool stability. We conclude that the local slope gradient, surface patterning (pools and ridges) and permafrost interact together to regulate water flow and maintain water balance, which in turn regulate the vegetation growth, peat accumulation and peatland stability. Our study implies that the delicate water balance maintained partly by microtopography is sensitive to climate change—especially potential extreme hydroclimate events—and natural and human-induced disturbances that may modify the surface patterning and weaken the peatland's stability, affecting the carbon sequestration ability of this type of peatlands. [ABSTRACT FROM AUTHOR]

Hedysari Radix Praeparata Cum Melle (HRPCM) and Astragali Radix Praeparata Cum Melle (ARPCM) are capable of improving spleen-qi deficiency (SQD) syndrome especially in the gastrointestinal dysfunction and decreased immunity in traditional Chinese medicine clinically. This study aims to compare and reveal the metabolic differences between HRPCM and ARPCM for SQD rats. Firstly, HRPCM (12.6 g/kg) and ARPCM (12.6 g/kg) were used to intervene SQD rats to further evaluate the effect. The results showed that HRPCM and ARPCM were able to improve the spleen pathology, increase the body weight, the rectal temperature, the spleen index, the thymus index, the levels of GAS and D -xylose in serum, and decrease the levels of IL-2, IL-6 and TNF-α in serum for SQD rats. Then, the studies of metabolic differences in serum and spleen were carried out using UPLC-Q-TOF-MS. The findings emphasized that HRPCM and ARPCM not only regulated metabolic profiling of serum and spleen in SQD rats, but also existed differences. HRPCM and ARPCM regulated metabolic pathways mainly including lipid metabolism, energy metabolism, amino acid metabolism, nucleotide metabolism, sugar metabolism and other types of metabolism for SQD rats. However, the metabolite profiles in SQD rats changed significantly, mainly involving abnormal glycine synthesis occurred in SQD rats. The expression trends of metabolites in HRPCM and ARPCM intervention for SQD rats were partly the same. Interestingly, there are similarities and differences in metabolic profiling between HRPCM and ARPCM for SQD rats. The differences were mainly in the synthesis of L -glutamine in amino acid metabolism. [Display omitted] • Hedysari Radix Praeparata Cum Melle (HRPCM) and Astragali Radix Praeparata Cum Melle (ARPCM) improve spleen-qi deficiency (SQD) syndrome including the gastrointestinal dysfunction and decreased immunity. • HRPCM and ARPCM treat SQD rats by regulating lipid metabolism, amino acid metabolism, nucleotide metabolism, sugar metabolism, and energy metabolism. • The differences in metabolic profiling between HRPCM and ARPCM for SQD rats are mainly in the synthesis of L -glutamine in amino acid metabolism. [ABSTRACT FROM AUTHOR]

The basis for the formation of spherical edge burrs during servo valve spool shoulder grinding and a method for eliminating these burrs were investigated. Based on the results of this investigation, a grinding and synchronous deburring device was designed. A geometric model of single abrasive grains was established and the formation mechanism of the valve spool edge burrs was evaluated. Subsequently, the deburring process was analyzed via dynamic simulations, and based on the results, the aforementioned device was designed for mounting on an S20 precision cylindrical grinder. The correctness of the simulation and the feasibility of the device were verified via experiments. The experimentally observed burr shape and size corresponded closely to the simulated features. Moreover, the test device was effective in removing the edge burrs of the valve spool, as evidenced by the lack of sharp edge burrs. Hence, this device could be helpful in grinding the valve spool shoulder for significant improvement in the processing quality and efficiency. [ABSTRACT FROM AUTHOR]

We simulate pulse shaping of bright–dark vector soliton pair in an optical fiber system. Through changing input pulse parameters (amplitude ratio, projection angle, time delay, and phase difference), different kinds of pulse shapes and spectra can be generated. For input bright–dark vector soliton pair with the same central wavelength, "2+1"- and "2+2"-type pseudo-high-order bright–dark vector soliton pairs are achieved. While for the case of different central wavelengths, bright–dark vector soliton pairs with multiple pulse peaks/dips are demonstrated with appropriate pulse parameter setting. [ABSTRACT FROM AUTHOR]

In the current information era over the internet, social media has become one of the essential information sources for users. While the text is the primary information representation, finding relevant information is a challenging mission for researchers due to its nature (e.g., short length, sparseness). Acquiring high-quality search results from massive data, such as social media needs a set of representative query terms that are not always available. In this paper, we propose a novel query-based unsupervised learning model to represent the implicit relationships in the short text from social media. This bridges the gap of the lack of word co-occurrences without requiring many parameters to be estimated and external evidence to be collected. To confirm the proposed model effectiveness, we compare the proposed model with state-of-the-art lexical, topic model and temporal models on the large-scale TREC microblog 2011-2014 collections. The experimental results show that the proposed model significantly improved overall state-of-the-art lexical, topic model and temporal models with the maximum percentage of increase reaching 33.97% based on MAP value and 21.38% based on Precision at top 30 documents. The proposed model can improve the social media search effectiveness in potential closely retrieval tasks, such as question answering and timeline summarisation. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this study was to evaluate whether baseline 18F-fluorodeoxyglucose (FDG) uptake is associated with carotid plaque progression.Methods: A total of 156 subjects with carotid plaque were enrolled and underwent carotid magnetic resonance imaging (MRI) (at baseline and the 12-month follow-up) and positron emission tomography-computed tomography (PET-CT) (baseline). Carotid plaque progression was evaluated by two indices (the incidence of plaque progression and percentage of plaque increase) with three-dimensional (3D) imaging, while the 18F-FDG uptake was evaluated by the 18F-FDG uptake levels and 18F-FDG uptake velocity. The association between plaque progression and 18F-FDG uptake was investigated by the trend test and multivariate logistic regression analysis.Results: Of the 156 subjects, 80 (51.3%) showed carotid plaque progression during the 12-month follow-up. Firstly, no association was found between 18F-FDG uptake levels and plaque progression. Secondly, significant differences in the incidence of plaque progression were observed among the groups with different uptake velocities, showing a significant decreasing trend ranging from high to intermediate to low (p = 0.002, trend test). After adjusting for covariates, an adequate prediction of the 18F-FDG uptake velocity for the incidence of plaque progression was revealed (OR = 0.682, p < 0.05). In addition, no association was found between the 18F-FDG uptake velocity and the percentage of plaque increase in the subjects with plaque progression (p = 0.757, trend test).Conclusions: Our findings suggest 18F-FDG uptake velocity is independently associated with the incidence of carotid plaque progression. Additionally, the 18F-FDG uptake velocity, as another important parameter of PET-CT, warrants further study in future clinical research.Key Points: • The18F-FDG uptake levels were not associated with the carotid plaque progression. • The18F-FDG uptake velocity could predict the incidence of carotid plaque progression. • The18F-FDG uptake velocity with related factors warrants more attention in future clinical research. [ABSTRACT FROM AUTHOR]

This study proposes a bi-level framework for real-time crash risk forecasting (RTCF) for signalised intersections, leveraging the temporal dependency among crash risks of contiguous time slices. At the first level of RTCF, a non-stationary generalised extreme value (GEV) model is developed to estimate the rear-end crash risk in real time (i.e., at a signal cycle level). Artificial intelligence techniques, like YOLO and DeepSort were used to extract traffic conflicts and time-varying covariates from traffic movement videos at three signalised intersections in Queensland, Australia. The estimated crash frequency from the non-stationary GEV model is compared against the historical crashes for the study locations (serving as ground truth), and the results indicate a close match between the estimated and observed crashes. Notably, the estimated mean crashes lie within the confidence intervals of observed crashes, further demonstrating the accuracy of the extreme value model. At the second level of RTCF, the estimated signal cycle crash risk is fed to a recurrent neural network to predict the crash risk of the subsequent signal cycles. Results reveal that the model can reasonably estimate crash risk for the next 20–25 min. The RTCF framework provides new pathways for proactive safety management at signalised intersections. [ABSTRACT FROM AUTHOR]

Currently, the methods for rolling bearing fault diagnosis using acquired signals still have certain deficiencies, such as mode mixing during signal decomposition and selection of the optimum fault feature. To address these problems, this paper used a method based on Hilbert vibration decomposition (HVD) and sample entropy to perform bearing fault diagnosis. This method firstly decomposed the acquired original signals of faulty bearings used the HVD algorithm, then extracted fault features from the processed signals, found the frequency and multiplied frequency of the bearing fault by substituting some experimental parameters and bearing parameters into the calculation formula. The results show that the method proposed in this paper can reduce other interfering signals during signal processing and achieve a fault diagnosis rate significantly higher than that of the original EMD algorithm. [ABSTRACT FROM AUTHOR]

Abstract To address the problem of query-focused multi-document summarisation, we present a novel unsupervised pattern-enhanced approach for representing coherent topics across documents, as well as the query relevance, in order to generate topically coherent summaries that meet the information needs of users. The proposed model employs not only a pattern-enhanced topic model to generate discriminative and semantic rich representations for topics and documents, but also a pattern-based relevance model for the query relevance of sentences. With these dual pattern-based representations for sentences, we are able to integrate various indicative metrics, such as rational coverage of document topics and sentence relevance, into a unified model. When evaluated on the datasets of the document understanding conferences of 2006 and 2007, the proposed approach shows a performance improvement as compared to a number of state-of-the-art methods and unsupervised baseline systems. [ABSTRACT FROM AUTHOR]

Abstract The work presented in this paper seeks to address the tracking problem for an Electric-motor-driven aerodynamic Load Simulator (ELS). The tracking performance of ELS is mainly affected by the actuator'sactive motion disturbance, friction nonlinear, and parametric uncertainties. Most of the previous studies on ELS focus on the actuator's active motion disturbance, while deemphasizing the other two factors. This paper concerns ELS as a motion loading system. A new high performancecontrol scheme is developed to deal with the three factors based on the established nonlinear mode of ELS. The control scheme is based on neural networks and the Linear Difference Inclusions (LDI) model. Aparallel distributed compensation (PDC) structure and H ∞ performance criterion are used to attenuate external disturbances. The stability of the whole closed-loop model is investigated using the well-known quadratic Lyapunovfunction. Both asimulation and an experiment are performed to validate the effectiveness of the developed algorithm. [ABSTRACT FROM AUTHOR]

Objectives Volume loss within the hippocampus is known as the most replicated finding of structural brain imaging studies of neuropsychiatric diseases. Although voxel-based auto or semi-auto volumetric measurements are widely used in the determination of the human hippocampus, the detection of hippocampal atrophy in rats is still a dilemma as it relies on a relatively primitive and complex approach. In this study, we aimed to develop a convenient way to measure the atrophy of the hippocampus in rats. Methods Twenty-four male Wistar rats were exposed to chronic unpredictable mild stress (CUMS) and a wheel running test (WRT) to simulate the conditions of hippocampal volume atrophy and improvement. The hippocampal volume and hippocampal fissure (HiF) width were dynamically measured using 7 T structural magnetic resonance imaging (MRI) with the grayscale method at week 0, 2, 4, and 8. The changes in the hippocampal volume and HiF width in rats were compared. In addition, hematoxylin-eosin (HE) staining of the HiF was used to verify the MRI findings. Results The hippocampal volume and the HiF width presented opposite trends based on the MRI findings and the histology data. The atrophy of the hippocampal subfields was closely related to the corresponding increase in the HiF width. Conclusion Determination of the HiF width may serve as a sensitive and convenient indicator of rat hippocampal atrophy. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM), a World Health Organization grade IV glioma, is the most common and aggressive primary brain tumor in humans. microRNAs (miRNAs) are aberrantly expressed in numerous cancer types, including GBM. Abnormally expressed miRNAs are commonly associated with malignant characteristics of GBM, including malignant growth, proliferation, apoptosis, invasion, metastasis and resistance to chemotherapy. miRNA (miR)-376a is abnormally expressed in multiple human cancers; however, the expression pattern and role of miR-376a in GBM, and the underlying molecular mechanisms by which miR-376a exerts its functions remain to be elucidated. Therefore, the aim of this study was to measure miR-376a expression and determine its biological roles in GBM as well as its associated molecular mechanism. In the present study, miR-376a expression was markedly downregulated in GBM tissues and cell lines. Overexpression of miR-376a markedly decreased the proliferation and invasion of GBM cells in vitro. In the present study, specificity protein 1 (SP1) was demonstrated to be a direct target of miR-376a. In addition, a negative association between SP1 mRNA and miR-376a expression was observed in GBM tissues. SP1 upregulation reduced the effects of miR-376a overexpression on GBM cell proliferation and invasion. miR-376a may be a therapeutic target for the treatment of patients with GBM. [ABSTRACT FROM AUTHOR]

Patterns are used as a fundamental means for analyzing data in many data mining applications. Many efficient techniques have been developed to discover patterns. However, the excessive number of discovered patterns and the lack of semantic information have made it difficult for a user to interpret and explore the patterns. A rough idea of the meanings of patterns can benefit the user in the process of exploring them. To address this issue, this paper presents a model for automatically annotating patterns with concepts. In addition, in a given context, the relative importance of each term that defines a concept is not the same. To define a context, there are a number of related information sources, such as documents, patterns, concepts, and an ontology. The question is which information sources are useful for estimating the relative importance of the terms? Should the most accurate one to be focused on or all of them be used to define the context? This research investigated these questions and defined an effective annotation context to estimate the relative importance of the terms, where the aim is to improve the performance of a machine that relies on the subject matter of a pattern set. The model is evaluated by comparing it with different baseline models on 2 standard datasets. The results show that the performance of the proposed model is significantly better. [ABSTRACT FROM AUTHOR]

Topic modelling methods such as Latent Dirichlet Allocation (LDA) have been successfully applied to various fields, since these methods can effectively characterize document collections by using a mixture of semantically rich topics. So far, many models have been proposed. However, the existing models typically outperform on full analysis on the whole collection to find all topics but difficult to capture coherent and specifically meaningful topic representations. Furthermore, it is very challenging to incorporate user preferences into existing topic modelling methods to extract relevant topics. To address these problems, we develop a novel personalized Association-based Topic Selection (ATS) model, which can identify semantically valid and relevant topics from a set of raw topics based on the semantical relatedness between users&#x02019; preferences and the structured patterns captured in topics. The advantage of the proposed ATS model is that it enables an interactive topic modelling process driven by users&#x02019; specific interests. Based on three benchmark datasets, namely, RCV1, R8, and WT10G under the context of information filtering (IF) and information retrieval (IR), our rigorous experiments show that the proposed ATS model can effectively identify relevant topics with respect to users&#x02019; specific interests, and hence to improve the performance of IF and IR. [ABSTRACT FROM AUTHOR]

Topic modelling methods such as Latent Dirichlet Allocation (LDA) have been successfully applied to various fields, since these methods can effectively characterize document collections by using a mixture of semantically rich topics. So far, many models have been proposed. However, the existing models typically outperform on full analysis on the whole collection to find all topics but difficult to capture coherent and specifically meaningful topic representations. Furthermore, it is very challenging to incorporate user preferences into existing topic modelling methods to extract relevant topics. To address these problems, we develop a novel personalized Association-based Topic Selection (ATS) model, which can identify semantically valid and relevant topics from a set of raw topics based on the semantical relatedness between users&#x02019; preferences and the structured patterns captured in topics. The advantage of the proposed ATS model is that it enables an interactive topic modelling process driven by users&#x02019; specific interests. Based on three benchmark datasets, namely, RCV1, R8, and WT10G under the context of information filtering (IF) and information retrieval (IR), our rigorous experiments show that the proposed ATS model can effectively identify relevant topics with respect to users&#x02019; specific interests, and hence to improve the performance of IF and IR. [ABSTRACT FROM AUTHOR]

The occurrences of dilated Virchow-Robin spaces (dVRSs) and inflammatory factors were closely related with development of depression and antidepressants. The present study indicated accumulation of inflammatory factors in dVRSs played a practical role in behaviors and the treatment-resistant in rats with depression. Eighty-one male Wistar rats were exposed to chronic unpredictable mild stress (CUMS) until the model was successfully established. According to the detection of dVRSs on 7 T structural magnetic resonance imaging (MRI), rats were divided into two groups (45 CUMSnon-dVRSs rats and 36 CUMSdVRSs rats). Subsequently, different antidepressant treatment (nontreat, aspirin, fluoxetine, fluoxetine+aspirin) was given to the two groups, respectively. Sucrose preference test (SPT), forced swim test (FST) and morris water maze (MWM) was used to test the behaviors of rats. ELISA and Western blot was used to determine the levels of inflammatory factors. Compared with the negative control (NC) group, there were obvious behavioral abnormalities and increased inflammatory factors in the CUMSnon-dVRSs rats. Interestingly, these changes were more significant in the CUMSdVRSs rats. In addition, the efficacy of fluoxetine was only found in CUMSnon-dVRSs rats rather than CUMSdVRSs rats. Meanwhile, behaviors and levels of inflammatory factor in hippocampus were ameliorated in the aspirin group of CUMSdVRSs rats, and these effects were significantly enhanced in the fluoxetine+aspirin group. The dVRSs in the hippocampus might exacerbate depression-like behaviors and the effects of antidepressant treatment in depressed rats by increasing the levels of inflammatory factors. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ni/Nb 2 O 5 catalysts with reducible NbO x species were synthesized. • An exceedingly high cyclopentanone yield was achieved by using Ni/Nb 2 O 5 in H 2 O. • Superior performance resulted from close cooperation between Ni-NbO x sites and H 2 O. • NbO x sites facilitated furfural adsorption via flat configuration. • H 2 O mediated furfural activation and transferred active hydrogen. Selective aqueous hydrogenation of furfurals to valuable cyclopentanones is attractive for biomass utilization. Herein, Ni/Nb 2 O 5 catalysts with different calcination temperatures were synthesized and employed to study the effect of reducible NbO x species and water solvent on the aqueous hydrogenation-rearrangement of furfural. A significantly higher cyclopentanone (CPO) yield (>92 %) was achieved in water on Ni/Nb 2 O 5 -500, while no CPO was detected in heptane. Catalysts characterization indicated that the calcination temperature of Nb 2 O 5 determined the crystallinity and modulated reducible NbO x. Kinetic and isotopic experiments elucidated that the excellent catalytic performance of Ni/Nb 2 O 5 remarkably correlated with surface reducible NbO x species and H 2 O-mediated effect. NbO x species closely interacted with Ni sites, in which Ni was in charge of H 2 activation and NbO x facilitated furfural adsorption via flat configuration. Water helped furfural activation and active hydrogen transfer between Ni sites and adsorbed furfural on NbO x sites, thus significantly boosted aqueous hydrogenation of furfural to CPO. [ABSTRACT FROM AUTHOR]

Many previous studies have demonstrated that both transverse trench and backward cooling injection are capable of improving jet lift-off problem of traditional cylindrical film hole at high blowing ratios. However, few studies have attempted to combine the above two cooling schemes to further alleviate the cooling deterioration phenomenon. This paper proposed two trenched film holes with different backward compound angles of β = 180° and 135°, and used highly-resolved Large Eddy Simulation method to explore in detail their unsteady flow fields including vortex structures, level of velocity fluctuation, turbulent shear stress, distributions of vorticity and adiabatic wall cooling effectiveness. The results are compared against those of the benchmark trenched film hole with β = 0°. The simulations were carried out under blowing ratio of 1.5, and the coolant to mainstream density ratio of 2.0. The detrimental symmetric counter-rotating vortex pair shown in the trench with β = 0° hole is replaced by an asymmetric vortex pair in the trench with β = 180° hole and only a single asymmetric vortex in the trench with β = 135° hole. Inside the trench, hairpin-like vortices are the dominant vortex structures for the trench with backward film holes. In the region downstream of the trench, the trench with β = 180° hole obtains the highest level of three velocity components fluctuations in the central region because of the direct intense collision between the coolant and mainstream, while the trench with β = 135° hole shows a highest level of lateral velocity component fluctuation in the lateral sides and a highest level of turbulent shear stress in the near wall region due to the significantly enhanced lateral motion of the coolant. The trenches with β = 180° and 135° holes significantly improve the lateral uniformity of the coolant, and respectively provide constantly high levels of 0.21 and 0.3 in laterally averaged cooling effectiveness on the wall downstream of the trench, compared to the trench with β = 0° hole with a low level of less than 0.2. • Transverse trench is combined with backward injection holes to improve cooling jet lift-off problem. • The trench with 135° hole provides the highest cooling effectiveness on the wall downstream of the trench. • Both the trenches with 135° and 180° hole enhance lateral coolant uniformity on the wall downstream of the trench. • An asymmetric vortex pair and one single vortex dominate the downstream of the trench with 180° and 135° hole respectively. [ABSTRACT FROM AUTHOR]

The work presented in this paper seeks to address the tracking problem for uncertain continuous nonlinear systems with external disturbances. The objective is to obtain a model that uses a reference-based output feedback tracking control law. The control scheme is based on neural networks and a linear difference inclusion (LDI) model, and a PDC structure andH∞performance criterion are used to attenuate external disturbances. The stability of the whole closed-loop model is investigated using the well-known quadratic Lyapunov function. The key principles of the proposed approach are as follows: neural networks are first used to approximate nonlinearities, to enable a nonlinear system to then be represented as a linearised LDI model. An LMI (linear matrix inequality) formula is obtained for uncertain and disturbed linear systems. This formula enables a solution to be obtained through an interior point optimisation method for some nonlinear output tracking control problems. Finally, simulations and comparisons are provided on two practical examples to illustrate the validity and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Northeast China receives most moisture through warm air masses from the western Pacific Ocean and some moisture from the Eurasian Continent predominantly through the westerlies. Changes in the contribution of these different moisture sources affect the amount and isotopic composition of precipitation, which would in turn influence peatland surface moisture and peat moss (Sphagnum) growth. Recently a dual δ13C and δ18O approach from peat-core archive has been used not only as a proxy for peatland moisture/wetness but also as a constraint for interpreting isotope in precipitation and moisture trajectories. Here we used the coupled δ13C and δ18O measurements of peat moss cellulose, along with macrofossil and other proxy data, from a well-dated peat core retrieved from a Sphagnum -dominated ombrotrophic (meteoric water-fed) bog in Northeast China to reconstruct regional climate and moisture sources of precipitation over the last millennium. Our results show no sign of extremely negative δ13C signals that would have been caused by the uptake of respired CO 2 by peat moss for photosynthesis, thus we argue that the cellulose δ13C signatures document the peatland moisture conditions, owing to water-film effect. We found a positive correlation between δ13C and δ18O, suggesting that δ18O primarily reflects the δ18O signal in precipitation rather than evaporative 18O-enrichment, because the evaporative enrichment would result in a negative correlation between these isotopes as documented in modern process studies along the hummock-hollow moisture gradients. We propose that the positive δ13C-δ18O correlation is a general feature in regions that are influenced by both summer monsoon and the westerlies, as warm Pacific moisture that has high δ18O values also brings abundant precipitation, resulting in wet conditions that induce high δ13C values, and vice versa. The δ13C results show a decrease of ca. 6‰ from −23 to −29‰, with large-magnitude fluctuations, over the last 1000 years, suggesting a long-term drying trend. An abrupt decrease at 1370 CE of 4‰ in δ18O from >21‰ to ca. 17‰ represents the transition from the warm and wet Medieval Climate Anomaly (MCA) at 815–1370 CE to the cold and dry Little Ice Age (LIA) at 1370–1940 CE, reaching the lowest δ18O value of ca. 15‰ at around 1850 CE. The increase in δ18O to 19‰ after 1900 CE reflects post-LIA climate warming. Enhanced moisture transport from the western Pacific might have contributed to the warm and wet MCA. Three short intervals with pronounced low δ13C and δ18O values occurred at 1370–1450 CE, the 19th Century, and the 1990s, suggesting extreme dry periods with reduced Pacific moisture inputs. The driest period with consistently low δ13C value of < -25‰ (Suess effect-corrected value of −23‰) occurred after 1990 CE, with a corresponding increase in dry-adapted moss Polytrichum. Variability in the regional atmospheric circulation—the western Pacific subtropical high (WPSH)—might have explained the change in moisture sources. The westward extension of the WPSH—often corresponding with the El Niño-like conditions—would block the Pacific moisture from transporting to Northeast China. Our record, together with other paleo-records over monsoon-influenced regions in East Asia, shows synchronous but opposite shifts of summer rainfall anomalies in North or Northeast China and South China on decadal to centennial scales, substantiating a coherent atmospheric circulation pattern associated with WPSH and ENSO variability. We argue that there were major shifts in synoptic-scale atmospheric circulation and associated hydroclimate change during the MCA, LIA, and Current Warming Period in Northeast China. [Display omitted] • Warm and wet Medieval Climate Anomaly (MCA) received abundant Pacific moisture transported from the Yellow Sea. • Dry episodes since the MCA suggest a reduction in Pacific moisture inputs, caused by the westward extension of the WPSH. • The driest period with consistently low δ13C value occurred after 1990 CE. • Dual isotopes provide critical information to track change in hydroclimate and synoptic-scale atmospheric circulations. [ABSTRACT FROM AUTHOR]

With the increase in web usage, opinion mining has become a new trend in analysing opinions. Nevertheless, opinion mining still faces huge challenges, such as uncertainty in opinions which can make opinions difficult to interpret using existing opinion mining models. These models have been developed to automate the opinion mining process of the user; however, extant models which use machine learning algorithms have limitations dealing with uncertainties in opinions such as online customer reviews. Fuzzy models were introduced to solve this problem. However, the fuzzy models have issues in regard to large uncertain boundaries. To address this serious issue, this research introduces a decision framework via which positive, negative and boundary regions are classified using fuzzy concepts. Then, a convolutional neural network (CNN) is used to further classify fuzzy concepts originally allocated to the boundary region. The framework uses formal concepts to represent uncertainties and the CNN classifies the boundary region concepts into either positive or negative opinions. A series of experiments was conducted, the outcomes of which suggest the uncertainties in opinions can be effectively handled using our three-way decision-making framework. Our work contributes to boosting the performance of opinion classification models. • Uncertainties in opinions are hard to deal with. • Fuzzy concepts are used to represent uncertainties. • Deep learning is proposed to classify uncertain boundaries. • A three-way decision Framework is effective for integrating advanced technologies. [ABSTRACT FROM AUTHOR]

Disposable mechanical elements, which are widely used in spacecrafts, undergo fracture primarily via shape-instability failure. A cyclic crystal plasticity model based on the probabilistic finite element method is developed with the aim of predicting the shape-instability lifespan. In the model, a two-dimensional (2D) finite element model is established, based on the Voronoi tessellation, to simulate the morphology of the microstructure measured during electron backscatter diffraction of 40Cr steel. A damage-coupled cyclic crystal plasticity model is used to describe the mechanical behavior and material degradation. In addition, the material properties described by a stochastic distribution are randomly generated and then assigned to the elements in the 2D model in order to simulate the heterogeneity of the material. The validity of the prediction model is revealed through a comparison of the simulation and experimental results. Furthermore, the sensitivities of the control parameters, such as the element size, variance of the stochastic distribution, and morphology of the microstructure, are determined. The presented model allows the determination of the shape-instability lifespan of disposable mechanical elements. This model accurately predicts, and hence negates the need for many measurements of, the lifespan and therefore has significant potential for use in an appropriate field. [ABSTRACT FROM AUTHOR]

Depression is a serious and potentially life-threatening mental disorder with unknown etiology. Emerging evidence shows that brain-derived neurotrophic factor (BDNF) and microRNAs (miRNAs) play critical roles in the etiology of depression. However, the molecular mechanisms are not fully understood. Expression of miR-182 and BDNF in the hippocampus were analyzed in a chronic unpredictable mild stress (CUMS) model. Male Wistar rats received bilateral intra-hippocampal infusions of BDNF- and miR-182-expressing (miR-182) or miR-182-silencers (si-miR-182) lentiviral vectors (LV). miR-182 upregulation was correlated with decreased BDNF expression in the hippocampus of a CUMS model. Accordingly, an anti-depressant like effect was observed when LV-BDNF or LV-si-miR-182 was injected into the hippocampus. Moreover, BDNF and its target gene cyclic AMP responsive element binding protein 1 (CREB1) decreased following LV-miR-182 injection and increased upon LV-si-miR-182 injection in rat hippocampus and cultured neuronal cells. In contrast, miR-182 overexpression exacerbated depression-like behaviors and decreased BDNF. Further, luciferase reporter evidence confirmed BDNF was a miR-182 target. Taken together, the current results reveal a potential molecular regulation of miR-182 on BDNF and the pronounced behavioral consequences of this regulation. [ABSTRACT FROM AUTHOR]

Accurate prediction of the unsteady flow mixing process and its effects on cooling performance in the turbine blade trailing edge (TE) is essential to improving TE cooling designs. This paper used high accuracy transient Scale Adaptive Simulation (SAS) method to investigate the unsteady flow and cooling characteristics of two novel turbine blades cutback TEs under blowing ratios BR s = 0.65 and 1.25. The two novel TEs have five rows of pin fins in the cooling slot and two rows of either cylindrical or fan-shaped film holes on the pressure side upstream of the cutback region. The results were compared against those of the baseline TE with only pin fins and the data obtained from Shear Stress Transport (SST) k- ω model. The results revealed that: 1) the coolant issued from upstream cylindrical or fan-shaped holes has significantly disturbed the Brown-Roshko vortices shedding off the lip of the top wall, but only largely reduced the magnitude of normal velocity fluctuation in the cutback region. The alternating vortices in the cutback region are stretched flat. The changes brought to the flow structures are more significant by the fan-shaped holes than the cylindrical holes; 2) Although increasing blowing ratio enhances flow unsteadiness in the cutback region of the three TEs, the differences in the magnitude of velocity fluctuations are much reduced among the three TEs; 3) The TE cutback with fan-shaped holes achieves a higher adiabatic wall cooling effectiveness η aw on the cutback wall at BR = 0.65 , and also a lower reduction in η aw at BR = 1.25 than the TE cutback with cylindrical holes; 4) Transient SAS method demonstrates a better agreement of η aw with the experimental data for the baseline TE on the cutback wall, and higher values of η aw than the results of the SST k- ω model on the top wall. [ABSTRACT FROM AUTHOR]

Pd-catalysed oxidative C–H/C–H coupling homopolymerization of thiazole derivatives with different numbers (n = 1–3) of thiophene as bridged units was described. It represents a facile and practical methodology to prepare thiazole-based conjugated polymers in excellent yields. Three conjugated polythiazole derivatives ( P1 – P3 ) were synthesized by utilization of ligand-free Pd(OAc) 2 as a catalyst in the presence of Ag 2 CO 3 and KOAc. Their chemical structure and molecular weights were established by 1 H and 13 C NMR, as well as size exclusion chromatography (SEC), respectively. Polymerization conditions including amounts of Pd(OAc) 2 and oxidant, solvent medium and other catalysts were screened and optimized. Furthermore, the optical and electrical properties of the resulting polymers P1 – P3 were investigated by UV-vis and fluorescent spectroscopy, as well as cyclic voltammetry, respectively, and the influence of the length of bridged units on the photoelectric properties of these polymers was also discussed. This synthetic strategy would be applied in direct oxidative C–H/C–H coupling polymerization of other heteroarenes to construct versatile π-conjugated polymers for optoelectronic applications. [ABSTRACT FROM AUTHOR]

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of large scale terms and data patterns. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, there has been often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences; yet, how to effectively use large scale patterns remains a hard problem in text mining. To make a breakthrough in this challenging issue, this paper presents an innovative model for relevance feature discovery. It discovers both positive and negative patterns in text documents as higher level features and deploys them over low-level features (terms). It also classifies terms into categories and updates term weights based on their specificity and their distributions in patterns. Substantial experiments using this model on RCV1, TREC topics and Reuters-21578 show that the proposed model significantly outperforms both the state-of-the-art term-based methods and the pattern based methods. [ABSTRACT FROM AUTHOR]

The antigenic variability of influenza viruses has always made influenza vaccine development challenging. The punctuated nature of antigenic drift of influenza virus suggests that a relatively small number of genetic changes or combinations of genetic changes may drive changes in antigenic phenotype. The present study aimed to identify antigenicity-associated sites in the hemagglutinin protein of A/H1N1 seasonal influenza virus using computational approaches. Random Forest Regression (RFR) and Support Vector Regression based on Recursive Feature Elimination (SVR-RFE) were applied to H1N1 seasonal influenza viruses and used to analyze the associations between amino acid changes in the HA1 polypeptide and antigenic variation based on hemagglutination-inhibition (HI) assay data. Twenty-three and twenty antigenicity-associated sites were identified by RFR and SVR-RFE, respectively, by considering the joint effects of amino acid residues on antigenic drift. Our proposed approaches were further validated with the H3N2 dataset. The prediction models developed in this study can quantitatively predict antigenic differences with high prediction accuracy based only on HA1 sequences. Application of the study results can increase understanding of H1N1 seasonal influenza virus antigenic evolution and accelerate the selection of vaccine strains. [ABSTRACT FROM AUTHOR]

Background: Our previous studies showed that increased levels of cyclophilin A (CyPA) may be a valuable marker for predicting the severity of acute coronary syndromes and that interruption of CD137-CD137L interactions diminished the formation and progression of atherosclerosis in apolipoprotein E-deficient (ApoE−/−) mice. Here, we sought to determine whether the proinflammatory factor CyPA is involved in atherosclerosis regulated by CD137-CD137L interactions. Methods and Results: A constrictive collar was placed around the right carotid arteries of ApoE−/− mice that were fed a high-fat diet to induce atherosclerotic plaque formation. After that, the mice were intraperitoneally injected with anti-CD137 or anti-CD137L in the presence or absence of the recombinant lentiviral vectors LVTHM-CyPA or pGC-FU-CyPA, respectively. Interestingly, activation of CD137-CD137L was negatively correlated with CyPA expression in vivo and in vitro. Stimulating CD137-CD137L interaction significantly increased CyPA, which was concurrent with the upregulation of proinflammatory cytokines, chemokines and matrix metalloproteinases and resulted in the promotion of atherosclerosis in ApoE-/- mice. Silencing CyPA could eliminate these effects, and restoration of CyPA effectively and consistently attenuated the atherosclerotic suppression phenotypes elicited by the blockade of CD137-CD137L. Conclusion: These observations suggest that CD137-CD137L interactions mediated via regulation of CyPA contribute to the progression of atherosclerosis. [ABSTRACT FROM AUTHOR]

Highlights: [•] We research low cycle fatigue and ratcheting behaviors of 40Cr under stress tests. [•] We define the shape instability life to describe the cyclic softening materials life. [•] We distinguish the shape instability life and the fatigue fracture life. [•] We found that the shape instability failures of disposable mechanical elements. [ABSTRACT FROM AUTHOR]

This study assesses the ability of the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations in capturing the interdecadal precipitation enhancement over the Yangtze River valley (YRV) and investigates the contributions of Arctic temperature and mid- to high-latitude warming to the interdecadal variability of the East Asian summer monsoon rainfall. Six CMIP5 historical simulations including models from the Canadian Centre for Climate Modeling and Analysis (CCCma), the Beijing Climate Center, the Max Planck Institute for Meteorology, the Meteorological Research Institute, the Met Office Hadley Centre, and NCAR are used. The NCEP-NCAR reanalysis and observed precipitation are also used for comparison. Among the six CMIP5 simulations, only CCCma can approximately simulate the enhancement of interdecadal summer precipitation over the YRV in 1990-2005 relative to 1960-75; the various relationships between the summer precipitation and surface temperature ( Ts), 850-hPa winds, and 500-hPa height field (H500); and the relationships between Ts and H500 determined using regression, correlation, and singular value decomposition (SVD) analyses. It is found that CCCma can reasonably simulate the interdecadal surface warming over the boreal mid- to high latitudes in winter, spring, and summer. The summer Baikal blocking anomaly is postulated to be the bridge that links the winter and spring surface warming over the mid- to high latitude and Arctic with the enhancement of summer precipitation over the YRV. Models that missed some or all of these relationships found in CCCma and the reanalysis failed to simulate the interdecadal enhancement of precipitation over the YRV. This points to the importance of Arctic and mid- to high-latitude processes on the interdecadal variability of the East Asian summer monsoon and the challenge for global climate models to correctly simulate the linkages. [ABSTRACT FROM AUTHOR]

Abstract: MicroRNAs are small non-coding RNA molecules that play important roles in the multistep process of colorectal carcinoma (CRC) development. However, the miRNA–mRNA regulatory network is far from being fully understood. The objective of this study was to investigate the expression and the biological roles of miR-330 in colorectal cancer cells. Cdc42, one of the best characterized members of the Rho GTPase family, was found to be up-regulated in several types of human tumors including CRC and has been implicated in cancer initiation and progression. In the present study, we identified miR-330, as a potential regulator of Cdc42, was found to be inversely correlated with Cdc42 expression in colorectal cancer cell lines. Ectopic expression of miR-330 down-regulated Cdc42 expression at both protein and mRNA level, mimicked the effect of Cdc42 knockdown in inhibiting proliferation, inducing G1 cell cycle arrest and apoptosis of the colorectal cancer cells, whereas restoration of Cdc42 could partially attenuate the effects of miR-330. In addition, elevated expression of miR-330 could suppress the immediate downstream effectors of Cdc42 and inhibit the growth of colorectal cancer cells in vivo. To sum up, our results establish a role of miR-330 in negatively regulating Cdc42 expression and colorectal cancer cell proliferation. They suggest that manipulating the expression level of Cdc42 by miR-330 has the potential to influence colorectal cancer progression. [Copyright &y& Elsevier]

After the end of the 1970s, there has been a tendency for enhanced summer precipitation over south China and the Yangtze River valley and drought over north China and northeastern China. Coincidentally, Arctic ice concentration has decreased since the late 1970s, with a larger reduction in summer than spring. However, the Arctic warming is more significant in spring than summer, suggesting that spring Arctic conditions could be more important in their remote impacts. This study investigates the potential impacts of the Arctic on summer precipitation in China. The leading spatial patterns and time coefficients of the unfiltered, interannual, and interdecadal precipitation (1960-2008) modes were analyzed and compared using empirical orthogonal function (EOF) analysis, which shows that the first three EOFs can capture the principal precipitation patterns (northern, central, and southern patterns) over eastern China. Regression of the Arctic spring and summer temperature onto the time coefficients of the leading interannual and interdecadal precipitation modes shows that interdecadal summer precipitation in China is related to the Arctic spring warming but that the relationship with Arctic summer temperature is weak. Moreover, no notable relationships were found between the first three modes of interannual precipitation and Arctic spring or summer temperatures. Finally, correlations between summer precipitation and the Arctic Oscillation (AO) index from January to August were investigated, which indicate that summer precipitation in China correlates with AO only to some extent. Overall, this study suggests important relationships between the Arctic spring temperature and summer precipitation over China at the interdecadal time scale. [ABSTRACT FROM AUTHOR]

Abstract: MicroRNAs (miRNA) have emerged as key players in carcinogenesis. Here, we investigated the role of miR-137 in the pathogenesis of lung cancer. The downregulation of miR-137 in lung cancer cells could be rescued following inhibition of DNA methylation. Ectopic expression of miR-137 in lung cancer cells significantly downregulated Cdc42, Cdk6 and induced G1 cell cycle arrest, leading to a significant decrease in cell growth in vivo and in vitro. Further, both Cdc42 and Cdk6 were confirmed as targets of miR-137. [Copyright &y& Elsevier]

Abstract: An analytical temperature model and an enthalpy difference function, based on lumped parameter method and assumption of rectangular effective specific heat, are presented in this study, which can measure the enthalpy change of inorganic salt high temperature. It is easy to carry out in practicable test, because an automated test equipment is designed to measure and record the T-history curves of the PCMs and “Valley, Bottom, and peak” and “Peak, Top, and Valley” criteria are proposed to select the starting temperature of phase-changing process (TS ), the ending temperature of phase-changing process (TE ) and the melting point (Tm ) of the PCMs. The binary eutectic LiNO3–NaNO3 (5.5:4.5, mass ratio), LiCl–NaCl (7:3, mass ratio) and Li2CO3–Na2CO3 (4:6, mass ratio) are tested, whose melting point and heat of fusion are (198.9°C, 255kJ/kg), (559°C, 460kJ/kg) and (499°C, 374kJ/kg). The maximum deviation between measurements and the values from DSC and references is less than 8%. It can be seen that with this method it is possible to obtain both melting point and heat of fusion in a simple way which will be of great help in selecting heat storage material in solar-energy and industrial waste heat utilization and in the subsequent design of a thermal energy storage system. [Copyright &y& Elsevier]

It is a big challenge to clearly identify the boundary between positive and negative streams. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on RCV1, and substantial experiments show that the proposed approach achieves encouraging performance. [ABSTRACT FROM AUTHOR]

Background: When an individual is ill or symptomatic, they have the options of seeking professional health care, self-treating or doing nothing. In China, some studies suggest that the number of individuals opting to self-treat has been rapidly increasing in recent years. Therefore, the aim of this study was to analyze the trends of and factors related to self-treatment in China. Methods: Self-treatment was measured based the concept and data of the China National Health Survey (CNHS), which covers 802,454 individuals. We used CNHS data from 1993, 1998, 2003, and 2008, and a Multinomial Logit Selection Model to estimate the factors influencing the decision to self-treat. Results: The prevalences of self-treatment with a recall period of two-weeks were significantly higher in urban compared with rural areas (31.2% vs 14.9% in 1993, 43.5% vs 21.4% in 1998, 47.2% vs 31.4% in 2003, 31.0% vs 25.3% in 2008) in China. Economic (per capita income, TV, sanitary water) and individual (education, profession, family members, exercise) factors, as well as accessibility to drugs had a positive association with the probability of self-treating. Different illness symptoms, severity, and duration show a negative association with the probability of self-treating, showing a degree of rationality in decision-making. Different insurance systems were also found to have an effect on self-treatment decision-making. Conclusions: Self-treatment and professional medical services have shared the incremental medical needs of residents in recent years in China. Self-perceived illness status, economic circumstances, and education play important roles in health care decision-making. [ABSTRACT FROM AUTHOR]

Abstract: Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we first propose a definition for redundancy, then propose a concise representation, called a Reliable basis, for representing non-redundant association rules. The Reliable basis contains a set of non-redundant rules which are derived using frequent closed itemsets and their generators instead of using frequent itemsets that are usually used by traditional association rule mining approaches. An important contribution of this paper is that we propose to use the certainty factor as the criterion to measure the strength of the discovered association rules. Using this criterion, we can ensure the elimination of as many redundant rules as possible without reducing the inference capacity of the remaining extracted non-redundant rules. We prove that the redundancy elimination, based on the proposed Reliable basis, does not reduce the strength of belief in the extracted rules. We also prove that all association rules, their supports and confidences, can be retrieved from the Reliable basis without accessing the dataset. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules. We also conduct experiments on the application of association rules to the area of product recommendation. The experimental results show that the non-redundant association rules extracted using the proposed method retain the same inference capacity as the entire rule set. This result indicates that using non-redundant rules only is sufficient to solve real problems needless using the entire rule set. [Copyright &y& Elsevier]