Effective pattern discovery for text mining

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase) based approaches should perform better than the term-based ones, but many experiments did not support this hypothesis. This paper presents an innovative technique, effective pattern discovery which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance

Automatic pattern-taxonomy extraction for web mining

In this paper, we propose a model for discovering frequent sequential patterns, phrases, which can be used as profile descriptors of documents. It is indubitable that we can obtain numerous phrases using data mining algorithms. However, it is difficult to use these phrases effectively for answering what users want. Therefore, we present a pattern taxonomy extraction model which performs the task of extracting descriptive frequent sequential patterns by pruning the meaningless ones. The model then is extended and tested by applying it to the information filtering system. The results of the experiment show that pattern-based methods outperform the keyword-based methods. The results also indicate that removal of meaningless patterns not only reduces the cost of computation but also improves the effectiveness of the system. <br /

mTORC2/Akt activation in adipocytes is required for adipose tissue inflammation in tuberculosis

BACKGROUND: Mycobacterium tuberculosis has co-evolved with the human host, adapting to exploit the immune system for persistence and transmission. While immunity to tuberculosis (TB) has been intensively studied in the lung and lymphoid system, little is known about the participation of adipose tissues and non-immune cells in the host-pathogen interaction during this systemic disease. METHODS: C57BL/6J mice were aerosol infected with M. tuberculosis Erdman and presence of the bacteria and the fitness of the white and brown adipose tissues, liver and skeletal muscle were studied compared to uninfected mice. FINDINGS: M. tuberculosis infection in mice stimulated immune cell infiltration in visceral, and brown adipose tissue. Despite the absence of detectable bacterial dissemination to fat tissues, adipocytes produced localized pro-inflammatory signals that disrupted adipocyte lipid metabolism, resulting in adipocyte hypertrophy. Paradoxically, this resulted in increased insulin sensitivity and systemic glucose tolerance. Adipose tissue inflammation and enhanced glucose tolerance also developed in obese mice after aerosol M. tuberculosis infection. We found that infection induced adipose tissue Akt signaling, while inhibition of the Akt activator mTORC2 in adipocytes reversed TB-associated adipose tissue inflammation and cell hypertrophy. INTERPRETATION: Our study reveals a systemic response to aerosol M. tuberculosis infection that regulates adipose tissue lipid homeostasis through mTORC2/Akt signaling in adipocytes. Adipose tissue inflammation in TB is not simply a passive infiltration with leukocytes but requires the mechanistic participation of adipocyte signals

Adipose tissue mTORC2 regulates ChREBP-driven de novo lipogenesis and hepatic glucose metabolism

Adipose tissue de novo lipogenesis (DNL) positively influences insulin sensitivity, is reduced in obesity, and predicts insulin resistance. Therefore, elucidating mechanisms controlling adipose tissue DNL could lead to therapies for type 2 diabetes. Here, we report that mechanistic target of rapamycin complex 2 (mTORC2) functions in white adipose tissue (WAT) to control expression of the lipogenic transcription factor ChREBPbeta. Conditionally deleting the essential mTORC2 subunit Rictor in mature adipocytes decreases ChREBPbeta expression, which reduces DNL in WAT, and impairs hepatic insulin sensitivity. Mechanistically, Rictor/mTORC2 promotes ChREBPbeta expression in part by controlling glucose uptake, but without impairing pan-AKT signalling. High-fat diet also rapidly decreases adipose tissue ChREBPbeta expression and insulin sensitivity in wild-type mice, and does not further exacerbate insulin resistance in adipose tissue Rictor knockout mice, implicating adipose tissue DNL as an early target in diet-induced insulin resistance. These data suggest mTORC2 functions in WAT as part of an extra-hepatic nutrient-sensing mechanism to control glucose homeostasis

Electronic bandstructure of in-plane ferroelectric van der Waals β′−In2Se3\beta '-In_{2}Se_{3}

Layered indium selenides ($In_{2}Se_{3}$) have recently been discovered to host robust out-of-plane and in-plane ferroelectricity in the $\alpha$ and $\beta$' phases, respectively. In this work, we utilise angle-resolved photoelectron spectroscopy to directly measure the electronic bandstructure of $\beta '-In_{2}Se_{3}$, and compare to hybrid density functional theory (DFT) calculations. In agreement with DFT, we find the band structure is highly two-dimensional, with negligible dispersion along the c-axis. Due to n-type doping we are able to observe the conduction band minima, and directly measure the minimum indirect (0.97 eV) and direct (1.46 eV) bandgaps. We find the Fermi surface in the conduction band is characterized by anisotropic electron pockets with sharp in-plane dispersion about the $\overline{M}$ points, yielding effective masses of 0.21 $m_{0}$ along $\overline{KM}$ and 0.33 $m_{0}$ along $\overline{\Gamma M}$. The measured band structure is well supported by hybrid density functional theory calculations. The highly two-dimensional (2D) bandstructure with moderate bandgap and small effective mass suggest that $\beta'-In_{2}Se_{3}$ is a potentially useful new van der Waals semiconductor. This together with its ferroelectricity makes it a viable material for high-mobility ferroelectric-photovoltaic devices, with applications in non-volatile memory switching and renewable energy technologies.Comment: 19 pages, 4 + 1 figures; typos corrected;added references; updated figures & discussion to reflect changes in mode

The Lipid Handling Capacity of Subcutaneous Fat Is Programmed by mTORC2 during Development

Overweight and obesity are associated with type 2 diabetes, non-alcoholic fatty liver disease, cardiovascular disease and cancer, but all fat is not equal, as storing excess lipid in subcutaneous white adipose tissue (SWAT) is more metabolically favorable than in visceral fat. Here, we uncover a critical role for mTORC2 in setting SWAT lipid handling capacity. We find that subcutaneous white preadipocytes differentiating without the essential mTORC2 subunit Rictor upregulate mature adipocyte markers but develop a striking lipid storage defect resulting in smaller adipocytes, reduced tissue size, lipid re-distribution to visceral and brown fat, and sex-distinct effects on systemic metabolic fitness. Mechanistically, mTORC2 promotes transcriptional upregulation of select lipid metabolism genes controlled by PPARγ and ChREBP, including genes that control lipid uptake, synthesis, and degradation pathways as well as Akt2, which encodes a major mTORC2 substrate and insulin effector. Further exploring this pathway may uncover new strategies to improve insulin sensitivity.Fil: Hsiao, Wen Yu. University Of Massachussets. Medical School; Estados UnidosFil: Jung, Su Myung. University Of Massachussets. Medical School; Estados UnidosFil: Tang, Yuefeng. University Of Massachussets. Medical School; Estados UnidosFil: Haley, John A.. University Of Massachussets. Medical School; Estados UnidosFil: Li, Rui. University Of Massachussets. Medical School; Estados UnidosFil: Li, Huawei. University Of Massachussets. Medical School; Estados UnidosFil: Martinez Calejman, Camila. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Centro de Estudios Farmacológicos y Botánicos. Universidad de Buenos Aires. Facultad de Medicina. Centro de Estudios Farmacológicos y Botánicos; Argentina. University Of Massachussets. Medical School; Estados UnidosFil: Sanchez Gurmaches, Joan. University Of Massachussets. Medical School; Estados Unidos. University of Cincinnati; Estados UnidosFil: Hung, Chien-Min. University Of Massachussets. Medical School; Estados UnidosFil: Luciano, Amelia K.. University Of Massachussets. Medical School; Estados UnidosFil: DeMambro, Victoria. University of Maine; Estados UnidosFil: Wellen, Kathryn E.. University of Pennsylvania; Estados UnidosFil: Rosen, Clifford J.. University of Maine; Estados UnidosFil: Zhu, Lihua Julie. University Of Massachussets. Medical School; Estados UnidosFil: Guertin, David A.. University Of Massachussets. Medical School; Estados Unido

The Lipid Handling Capacity of Subcutaneous Fat Is Programmed by mTORC2 during Development

Overweight and obesity are associated with type 2 diabetes, non-alcoholic fatty liver disease, cardiovascular disease and cancer, but all fat is not equal, as storing excess lipid in subcutaneous white adipose tissue (SWAT) is more metabolically favorable than in visceral fat. Here, we uncover a critical role for mTORC2 in setting SWAT lipid handling capacity. We find that subcutaneous white preadipocytes differentiating without the essential mTORC2 subunit Rictor upregulate mature adipocyte markers but develop a striking lipid storage defect resulting in smaller adipocytes, reduced tissue size, lipid re-distribution to visceral and brown fat, and sex-distinct effects on systemic metabolic fitness. Mechanistically, mTORC2 promotes transcriptional upregulation of select lipid metabolism genes controlled by PPARgamma and ChREBP, including genes that control lipid uptake, synthesis, and degradation pathways as well as Akt2, which encodes a major mTORC2 substrate and insulin effector. Further exploring this pathway may uncover new strategies to improve insulin sensitivity

Long Non-Coding RNA XLOC_006753 Promotes the Development of Multidrug Resistance in Gastric Cancer Cells Through the PI3K/AKT/mTOR Signaling Pathway

Background/Aims: The development of multidrug resistance (MDR), which results in disease recurrence and metastasis, is a crucial obstacle to successful chemotherapy for patients with gastric cancer (GC). Long non-coding RNAs (lncRNAs) have been found to play various roles in cancer. This study aimed to investigate the effect of XLOC_006753 on the development of MDR in GC cells. Methods: The expression levels of XLOC_006753 in GC patients and MDR GC cell lines (SGC-7901/5-FU and SGC-7901/DDP cell line) were assessed by qRT-PCR. Statistical analyses were conducted to determine the relationship between XLOC_006753 expression and clinical features and to assess the prognostic value of XLOC_006753 for overall survival and progression-free survival. Then, a CCK-8 assay was used to detect cell proliferation ability and chemosensitivity. Flow cytometry was used to detect cell cycle and cell apoptosis. A wound-healing assay and transwell assay were used to detect cell migration. The expression of markers for MDR, G1/S transition, epithelial–mesenchymal transition (EMT) and PI3K/ AKT/mTOR signaling pathway were examined by western blot. Results: XLOC_006753 was highly expressed in GC patients and MDR GC cell lines (SGC-7901/5-FU and SGC-7901/DDP cell lines), and its high expression was positively associated with metastasis, TNM stage, tumor size, and poor survival in GC patients. Moreover, XLOC_006753 was an independent prognostic biomarker of overall survival and progression-free survival for gastric cancer patients. Knocking down XLOC_006753 in the two MDR GC cell lines significantly inhibited cell proliferation, cell viability, cell cycle G1/S transition, and migration. XLOC_006753 knockdown also promoted apoptosis. Furthermore, western blots showed that XLOC_006753 knockdown decreased some markers of MDR, G1/S transition, and EMT expression, while increasing caspase9 expression and inhibiting the PI3K/AKT/mTOR signaling pathway in SGC-7901/5-FU and SGC-7901/DDP cells. Conclusion: High expression of XLOC_006753 promoted the development of MDR, which was activated by the PI3K/AKT/mTOR pathway in GC cells

Fully automated high-quality NMR structure determination of small 2H-enriched proteins

Determination of high-quality small protein structures by nuclear magnetic resonance (NMR) methods generally requires acquisition and analysis of an extensive set of structural constraints. The process generally demands extensive backbone and sidechain resonance assignments, and weeks or even months of data collection and interpretation. Here we demonstrate rapid and high-quality protein NMR structure generation using CS-Rosetta with a perdeuterated protein sample made at a significantly reduced cost using new bacterial culture condensation methods. Our strategy provides the basis for a high-throughput approach for routine, rapid, high-quality structure determination of small proteins. As an example, we demonstrate the determination of a high-quality 3D structure of a small 8 kDa protein, E. coli cold shock protein A (CspA), using <4 days of data collection and fully automated data analysis methods together with CS-Rosetta. The resulting CspA structure is highly converged and in excellent agreement with the published crystal structure, with a backbone RMSD value of 0.5 Å, an all atom RMSD value of 1.2 Å to the crystal structure for well-defined regions, and RMSD value of 1.1 Å to crystal structure for core, non-solvent exposed sidechain atoms. Cross validation of the structure with 15N- and 13C-edited NOESY data obtained with a perdeuterated 15N, 13C-enriched 13CH3 methyl protonated CspA sample confirms that essentially all of these independently-interpreted NOE-based constraints are already satisfied in each of the 10 CS-Rosetta structures. By these criteria, the CS-Rosetta structure generated by fully automated analysis of data for a perdeuterated sample provides an accurate structure of CspA. This represents a general approach for rapid, automated structure determination of small proteins by NMR