Active Multi-Field Learning for Spam Filtering

Ubiquitous spam messages cause a serious waste of time and resources. This paper addresses the practical spam filtering problem, and proposes a universal approach to fight with various spam messages. The proposed active multi-field learning approach is based on: 1) It is cost-sensitive to obtain a label for a real-world spam filter, which suggests an active learning idea; and 2) Different messages often have a similar multi-field text structure, which suggests a multi-field learning idea. The multi-field learning framework combines multiple results predicted from field classifiers by a novel compound weight, and each field classifier calculates the arithmetical average of multiple conditional probabilities predicted from feature strings according to a data structure of string-frequency index. Comparing the current variance of field classifying results with the historical variance, the active learner evaluates the classifying confidence and regards the more uncertain message as the more informative sample for which to request a label. The experimental results show that the proposed approach can achieve the state-of-the-art performance at greatly reduced label requirements both in email spam filtering and short text spam filtering. Our active multi-field learning performance, the standard (1-ROCA) % measurement, even exceeds the full feedback performance of some advanced individual classifying algorithm

Microwave plasma-assisted photoluminescence enhancement in nitrogendoped ultrananocrystalline diamond film

[[booktype]]電子

Improving protein secondary structure prediction based on short subsequences with local structure similarity

<p>Abstract</p> <p>Background</p> <p>When characterizing the structural topology of proteins, protein secondary structure (PSS) plays an important role in analyzing and modeling protein structures because it represents the local conformation of amino acids into regular structures. Although PSS prediction has been studied for decades, the prediction accuracy reaches a bottleneck at around 80%, and further improvement is very difficult.</p> <p>Results</p> <p>In this paper, we present an improved dictionary-based PSS prediction method called SymPred, and a meta-predictor called SymPsiPred. We adopt the concept behind natural language processing techniques and propose synonymous words to capture local sequence similarities in a group of similar proteins. A synonymous word is an <it>n-</it>gram pattern of amino acids that reflects the sequence variation in a protein’s evolution. We generate a protein-dependent synonymous dictionary from a set of protein sequences for PSS prediction.</p> <p>On a large non-redundant dataset of 8,297 protein chains (<it>DsspNr-25</it>), the average <it>Q</it>₃ of SymPred and SymPsiPred are 81.0% and 83.9% respectively. On the two latest independent test sets (<it>EVA Set_1</it> and <it>EVA_Set2</it>), the average <it>Q</it>₃ of SymPred is 78.8% and 79.2% respectively. SymPred outperforms other existing methods by 1.4% to 5.4%. We study two factors that may affect the performance of SymPred and find that it is very sensitive to the number of proteins of both known and unknown structures. This finding implies that SymPred and SymPsiPred have the potential to achieve higher accuracy as the number of protein sequences in the NCBInr and PDB databases increases.</p> <p>Conclusions</p> <p>Our experiment results show that local similarities in protein sequences typically exhibit conserved structures, which can be used to improve the accuracy of secondary structure prediction. For the application of synonymous words, we demonstrate an example of a sequence alignment which is generated by the distribution of shared synonymous words of a pair of protein sequences. We can align the two sequences nearly perfectly which are very dissimilar at the sequence level but very similar at the structural level. The SymPred and SymPsiPred prediction servers are available at <url>http://bio-cluster.iis.sinica.edu.tw/SymPred/</url>.</p

Long-term fenofibrate treatment impaired glucose-stimulated insulin secretion and up-regulated pancreatic NF-kappa B and iNOS expression in monosodium glutamate-induced obese rats: Is that a latent disadvantage?

<p>Abstract</p> <p>Background</p> <p>Fenofibrate, a PPAR alpha agonist, has been widely used in clinics as lipid-regulating agent. PPAR alpha is known to be expressed in many organs including pancreatic beta cells and regulate genes involved in fatty acid metabolism. Some reports based on cell lines or animals have provided evidences that PPAR alpha agonists may affect (increased or suppressed) beta cell insulin secretion, and several studies are producing interesting but still debated results.</p> <p>Methods</p> <p>In this research, we investigated the long term effects of fenofibrate on beta cell function in a metabolic syndrome animal model, monosodium glutamate (MSG) induced obese rats. Obese MSG rats were administered by gavage with fenofibrate at a dose of 100 mg/kg for 12 weeks. Oral glucose tolerance and insulin tolerance tests were performed to evaluate glucose metabolism and insulin sensitivity. We have used the hyperglycemic clamp technique to evaluate the capacity of beta cell insulin secretion. This technique provides an unbiased approach to understand the beta cell function in vivo. The changes of gene and protein expression in the pancreas and islets were also analyzed by Real-Time-PCR, Western blot and immunostaining.</p> <p>Results</p> <p>Fenofibrate reduced the plasma lipid levels within a few days, and showed no beneficial effects on glucose homeostasis or insulin sensitivity in obese MSG rats. But the animals treated with fenofibrate exhibited significantly decreased fasting plasma insulin and impaired insulin secretory response to glucose stimulation. Further studies confirmed that fenofibrate increased MDA level and decreased total ATPase activity in pancreatic mitochondrion, accompanied by the upregulation of iNOS and NF-kappa B and TNF alpha expression in pancreatic islets of obese MSG rats.</p> <p>Conclusions</p> <p>Long-term fenofibrate treatment disrupted beta cell function, and impaired glucose-stimulated insulin secretion in obese MSG rats, perhaps to some extent associated with the activated inflammatory pathway and increased formation of oxidative products, especially the up-regulation of NF-kappa B and iNOS expression in islets.</p

SUMOylation of the MAGUK protein CASK regulates dendritic spinogenesis

Membrane-associated guanylate kinase (MAGUK) proteins interact with several synaptogenesis-triggering adhesion molecules. However, direct evidence for the involvement of MAGUK proteins in synapse formation is lacking. In this study, we investigate the function of calcium/calmodulin-dependent serine protein kinase (CASK), a MAGUK protein, in dendritic spine formation by RNA interference. Knockdown of CASK in cultured hippocampal neurons reduces spine density and shrinks dendritic spines. Our analysis of the time course of RNA interference and CASK overexpression experiments further suggests that CASK stabilizes or maintains spine morphology. Experiments using only the CASK PDZ domain or a mutant lacking the protein 4.1–binding site indicate an involvement of CASK in linking transmembrane adhesion molecules and the actin cytoskeleton. We also find that CASK is SUMOylated. Conjugation of small ubiquitin-like modifier 1 (SUMO1) to CASK reduces the interaction between CASK and protein 4.1. Overexpression of a CASK–SUMO1 fusion construct, which mimicks CASK SUMOylation, impairs spine formation. Our study suggests that CASK contributes to spinogenesis and that this is controlled by SUMOylation

Testing Models of Magnetic Field Evolution of Neutron Stars with the Statistical Properties of Their Spin Evolutions

We test models for the evolution of neutron star (NS) magnetic fields (B). Our model for the evolution of the NS spin is taken from an analysis of pulsar timing noise presented by Hobbs et al. (2010). We first test the standard model of a pulsar's magnetosphere in which B does not change with time and magnetic dipole radiation is assumed to dominate the pulsar's spin-down. We find this model fails to predict both the magnitudes and signs of the second derivatives of the spin frequencies ($\ddot{\nu}$). We then construct a phenomenological model of the evolution of $B$, which contains a long term decay (LTD) modulated by short term oscillations (STO); a pulsar's spin is thus modified by its B-evolution. We find that an exponential LTD is not favored by the observed statistical properties of $\ddot{\nu}$ for young pulsars and fails to explain the fact that $\ddot{\nu}$ is negative for roughly half of the old pulsars. A simple power-law LTD can explain all the observed statistical properties of $\ddot{\nu}$. Finally we discuss some physical implications of our results to models of the B-decay of NSs and suggest reliable determination of the true ages of many young NSs is needed, in order to constrain further the physical mechanisms of their B-decay. Our model can be further tested with the measured evolutions of $\dot{\nu}$ and $\ddot{\nu}$ for an individual pulsar; the decay index, oscillation amplitude and period can also be determined this way for the pulsar.Comment: To appear in ApJ. 20 pages, 10 figures, first submitted to ApJ on May 14, 2012; referee comments incorporated and re-submitted; typos corrected and one reference added; additional minor comments from the referee incorporate