Piecewise Trend Approximation: A Ratio-Based Time Series Representation

A time series representation, piecewise trend approximation (PTA), is proposed to improve efficiency of time series data mining in high dimensional large databases. PTA represents time series in concise form while retaining main trends in original time series; the dimensionality of original data is therefore reduced, and the key features are maintained. Different from the representations that based on original data space, PTA transforms original data space into the feature space of ratio between any two consecutive data points in original time series, of which sign and magnitude indicate changing direction and degree of local trend, respectively. Based on the ratio-based feature space, segmentation is performed such that each two conjoint segments have different trends, and then the piecewise segments are approximated by the ratios between the first and last points within the segments. To validate the proposed PTA, it is compared with classical time series representations PAA and APCA on two classical datasets by applying the commonly used K-NN classification algorithm. For ControlChart dataset, PTA outperforms them by 3.55% and 2.33% higher classification accuracy and 8.94% and 7.07% higher for Mixed-BagShapes dataset, respectively. It is indicated that the proposed PTA is effective for high dimensional time series data mining

The Role of 7,8-Dihydroxyflavone in Preventing Dendrite Degeneration in Cortex After Moderate Traumatic Brain Injury

Our previous research showed that traumatic brain injury (TBI) induced by controlled cortical impact (CCI) not only causes massive cell death, but also results in extensive dendrite degeneration in those spared neurons in the cortex. Cell death and dendrite degeneration in the cortex may contribute to persistent cognitive, sensory, and motor dysfunction. There is still no approach available to prevent cells from death and dendrites from degeneration following TBI. When we treated the animals with a small molecule, 7,8-dihydroxyflavone (DHF) that mimics the function of brain-derived neurotrophic factor (BDNF) through provoking TrkB activation reduced dendrite swellings in the cortex. DHF treatment also prevented dendritic spine loss after TBI. Functional analysis showed that DHF improved rotarod performance on the third day after surgery. These results suggest that although DHF treatment did not significantly reduced neuron death, it prevented dendrites from degenerating and protected dendritic spines against TBI insult. Consequently, DHF can partially improve the behavior outcomes after TBI

A case of Timor-Leste: From independence to instability or prosperity?

This paper analyzes the economic growth problems of Timor-Leste since its independence. As for a typical country whose economic growth is mostly relying on the petroleum revenues, its monotonous source of income seems to be the biggest obstacle. Therefore, this article mainly analyzed the possible solutions for how to sustain and manage the economic growth for Timor-Leste. As the Strategic Development Plan was issued in 2011 by Timor-Leste government, a high-quality public spending strategy was set to be the main method to reach a rapid economic growth. Following this strategy, this paper indicates three plausible solutions consist of improvement of infrastructure, coffee production and trades, and development of tourism. After comparing these three alternatives according to the criterion of effectiveness and timing, the conclusion of this paper goes to the improvement of infrastructure being the best solution for Timor-Leste

A case of Timor-Leste: From independence to instability or prosperity?

This paper analyzes the economic growth problems of Timor-Leste since its independence. As for a typical country whose economic growth is mostly relying on the petroleum revenues, its monotonous source of income seems to be the biggest obstacle. Therefore, this article mainly analyzed the possible solutions for how to sustain and manage the economic growth for Timor-Leste. As the Strategic Development Plan was issued in 2011 by Timor-Leste government, a high-quality public spending strategy was set to be the main method to reach a rapid economic growth. Following this strategy, this paper indicates three plausible solutions consist of improvement of infrastructure, coffee production and trades, and development of tourism. After comparing these three alternatives according to the criterion of effectiveness and timing, the conclusion of this paper goes to the improvement of infrastructure being the best solution for Timor-Leste

Prediction of Pharmacological and Xenobiotic Responses to Drugs Based on Time Course Gene Expression Profiles

More and more people are concerned by the risk of unexpected side effects observed in the later steps of the development of new drugs, either in late clinical development or after marketing approval. In order to reduce the risk of the side effects, it is important to look out for the possible xenobiotic responses at an early stage. We attempt such an effort through a prediction by assuming that similarities in microarray profiles indicate shared mechanisms of action and/or toxicological responses among the chemicals being compared. A large time course microarray database derived from livers of compound-treated rats with thirty-four distinct pharmacological and toxicological responses were studied. The mRMR (Minimum-Redundancy-Maximum-Relevance) method and IFS (Incremental Feature Selection) were used to select a compact feature set (141 features) for the reduction of feature dimension and improvement of prediction performance. With these 141 features, the Leave-one-out cross-validation prediction accuracy of first order response using NNA (Nearest Neighbor Algorithm) was 63.9%. Our method can be used for pharmacological and xenobiotic responses prediction of new compounds and accelerate drug development

Extraction of Carbon from Fine Coal Gasification Slag by Hydrophobic-hydrophilic Separation

This is an article in the field of mining engineering. Coal gasification slag is a type of solid waste generated during the coal gasification process. The presence of residual carbon significantly limits its potential for reuse and recycling. Therefore, the extraction of residual carbon from coal gasification slag is a pressing concern. In this research, the separation of residual carbon and inorganic minerals from gasification fine slag was studied by hydrophobic-hydrophilic separation technology. The effects of stirring speed, stirring time, hydrophobic liquid dosage, pulp concentration, pulp temperature, and hydrophobic liquid type on the separation effect of carbon/ ash were investigated. The hydrophobic-hydrophilic separation technology has excellent carbon extraction and ash reduction effect on coal gasification slag, and the ash content of its carbon product can be up to 30% or less, while that of the ash product can be up to 95% or more. The separation mechanism was revealed by the characterisation analysis, which showed that the adsorption strength of residual carbon on paraffin was much higher than that of ash, which made the kerosene-treated residual carbon hydrophobicity greatly increased and easy to be captured by the oil phase. This study can provide important guidance for the efficient carbon extraction and ash reduction of coal gasification fine residue, which can help to achieve the comprehensive utilisation of coal gasification solid waste

Analysis and Identification of Aptamer-Compound Interactions with a Maximum Relevance Minimum Redundancy and Nearest Neighbor Algorithm

The development of biochemistry and molecular biology has revealed an increasingly important role of compounds in several biological processes. Like the aptamer-protein interaction, aptamer-compound interaction attracts increasing attention. However, it is time-consuming to select proper aptamers against compounds using traditional methods, such as exponential enrichment. Thus, there is an urgent need to design effective computational methods for searching effective aptamers against compounds. This study attempted to extract important features for aptamer-compound interactions using feature selection methods, such as Maximum Relevance Minimum Redundancy, as well as incremental feature selection. Each aptamer-compound pair was represented by properties derived from the aptamer and compound, including frequencies of single nucleotides and dinucleotides for the aptamer, as well as the constitutional, electrostatic, quantum-chemical, and space conformational descriptors of the compounds. As a result, some important features were obtained. To confirm the importance of the obtained features, we further discussed the associations between them and aptamer-compound interactions. Simultaneously, an optimal prediction model based on the nearest neighbor algorithm was built to identify aptamer-compound interactions, which has the potential to be a useful tool for the identification of novel aptamer-compound interactions. The program is available upon the request

Elevated expression of CDK4 in lung cancer

<p/> <p>Background</p> <p>The aim of the present study was to analyze the expression of Cyclin-dependent kinase 4 (<it>CDK4</it>) in lung cancer and its correlation with clinicopathologic features. Furthermore, the involvement of <it>CDK4</it>-mediated cell cycle progression and its molecular basis were investigated in the pathogenesis of lung cancer.</p> <p>Methods</p> <p>Using immunohistochemistry analysis, we analyzed <it>CDK4 </it>protein expression in 89 clinicopathologically characterized lung cancer patients (59 males and 30 females) with ages ranging from 36 to 78 years and compared them to 23 normal lung tissues. Cases with cytoplasmic and nuclear <it>CDK4 </it>immunostaining score values greater than or equal to 7 were regarded as high expression while scores less than 7 were considered low expression. The correlation between the expression level of <it>CDK4 </it>and clinical features was analyzed. Furthermore, we used lentiviral-mediated shRNA to suppress the expression of CDK4 and investigate its function and molecular mechanism for mediating cell cycle progression.</p> <p>Results</p> <p>The expression level of <it>CDK4 </it>protein was significantly increased in lung cancer tissues compared to normal tissues (<it>P </it>< 0.001). In addition, high levels of <it>CDK4 </it>protein were positively correlated with the status of pathology classification (<it>P </it>= 0.047), lymph node metastasis (<it>P </it>= 0.007), and clinical stage (<it>P </it>= 0.004) of lung cancer patients. Patients with higher <it>CDK4 </it>expression had a markedly shorter overall survival time than patients with low <it>CDK4 </it>expression. Multivariate analysis suggested the level of <it>CDK4 </it>expression was an independent prognostic indicator (<it>P </it>< 0.001) for the survival of patients with lung cancer. Use of lentiviral-mediated shRNA to inhibit the expression of <it>CDK4 </it>in lung cancer cell line A549 not only inhibited cell cycle progression, but also dramatically suppressed cell proliferation, colony formation, and migration. Furthermore, suppressing <it>CDK4 </it>expression also significantly elevated the expression of cell cycle regulator <it>p21</it></p> <p>Conclusion</p> <p>Overexpressed <it>CDK4 </it>is a potential unfavorable prognostic factor and mediates cell cycle progression by regulating the expression of <it>p21 </it>in lung cancer</p

Concept for a Future Super Proton-Proton Collider

Following the discovery of the Higgs boson at LHC, new large colliders are being studied by the international high-energy community to explore Higgs physics in detail and new physics beyond the Standard Model. In China, a two-stage circular collider project CEPC-SPPC is proposed, with the first stage CEPC (Circular Electron Positron Collier, a so-called Higgs factory) focused on Higgs physics, and the second stage SPPC (Super Proton-Proton Collider) focused on new physics beyond the Standard Model. This paper discusses this second stage.Comment: 34 pages, 8 figures, 5 table

Prediction of Deleterious Non-Synonymous SNPs Based on Protein Interaction Network and Hybrid Properties

Non-synonymous SNPs (nsSNPs), also known as Single Amino acid Polymorphisms (SAPs) account for the majority of human inherited diseases. It is important to distinguish the deleterious SAPs from neutral ones. Most traditional computational methods to classify SAPs are based on sequential or structural features. However, these features cannot fully explain the association between a SAP and the observed pathophysiological phenotype. We believe the better rationale for deleterious SAP prediction should be: If a SAP lies in the protein with important functions and it can change the protein sequence and structure severely, it is more likely related to disease. So we established a method to predict deleterious SAPs based on both protein interaction network and traditional hybrid properties. Each SAP is represented by 472 features that include sequential features, structural features and network features. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) were applied to obtain the optimal feature set and the prediction model was Nearest Neighbor Algorithm (NNA). In jackknife cross-validation, 83.27% of SAPs were correctly predicted when the optimized 263 features were used. The optimized predictor with 263 features was also tested in an independent dataset and the accuracy was still 80.00%. In contrast, SIFT, a widely used predictor of deleterious SAPs based on sequential features, has a prediction accuracy of 71.05% on the same dataset. In our study, network features were found to be most important for accurate prediction and can significantly improve the prediction performance. Our results suggest that the protein interaction context could provide important clues to help better illustrate SAP's functional association. This research will facilitate the post genome-wide association studies