Rough set theory applied to pattern recognition of partial discharge in noise affected cable data

This paper presents an effective, Rough Set (RS) based, pattern recognition method for rejecting interference signals and recognising Partial Discharge (PD) signals from different sources. Firstly, RS theory is presented in terms of Information System, Lower and Upper Approximation, Signal Discretisation, Attribute Reduction and a flowchart of the RS based pattern recognition method. Secondly, PD testing of five types of artificial defect in ethylene-propylene rubber (EPR) cable is carried out and data pre-processing and feature extraction are employed to separate PD and interference signals. Thirdly, the RS based PD signal recognition method is applied to 4000 samples and is proven to have 99% accuracy. Fourthly, the RS based PD recognition method is applied to signals from five different sources and an accuracy of more than 93% is attained when a combination of signal discretisation and attribute reduction methods are applied. Finally, Back-propagation Neural Network (BPNN) and Support Vector Machine (SVM) methods are studied and compared with the developed method. The proposed RS method is proven to have higher accuracy than SVM and BPNN and can be applied for on-line PD monitoring of cable systems after training with valid sample data

Base-pair ambiguity and the kinetics of RNA folding

Background A pairings of nucleotide sequences. Given this forbidding free-energy landscape, mechanisms have evolved that contribute to a directed and efficient folding process, including catalytic proteins and error-detecting chaperones. Among structural RNA molecules we make a distinction between “bound” molecules, which are active as part of ribonucleoprotein (RNP) complexes, and “unbound,” with physiological functions performed without necessarily being bound in RNP complexes. We hypothesized that unbound molecules, lacking the partnering structure of a protein, would be more vulnerable than bound molecules to kinetic traps that compete with native stem structures. We defined an “ambiguity index”—a normalized function of the primary and secondary structure of an individual molecule that measures the number of kinetic traps available to nucleotide sequences that are paired in the native structure, presuming that unbound molecules would have lower indexes. The ambiguity index depends on the purported secondary structure, and was computed under both the comparative (“gold standard”) and an equilibrium-based prediction which approximates the minimum free energy (MFE) structure. Arguing that kinetically accessible metastable structures might be more biologically relevant than thermodynamic equilibrium structures, we also hypothesized that MFE-derived ambiguities would be less effective in separating bound and unbound molecules. Results We have introduced an intuitive and easily computed function of primary and secondary structures that measures the availability of complementary sequences that could disrupt the formation of native stems on a given molecule—an ambiguity index. Using comparative secondary structures, the ambiguity index is systematically smaller among unbound than bound molecules, as expected. Furthermore, the effect is lost when the presumably more accurate comparative structure is replaced instead by the MFE structure. Conclusions A statistical analysis of the relationship between the primary and secondary structures of non-coding RNA molecules suggests that stem-disrupting kinetic traps are substantially less prevalent in molecules not participating in RNP complexes. In that this distinction is apparent under the comparative but not the MFE secondary structure, the results highlight a possible deficiency in structure predictions when based upon assumptions of thermodynamic equilibrium

Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables

Probabilistic graphical models (PGMs) provide a compact representation of knowledge that can be queried in a flexible way: after learning the parameters of a graphical model once, new probabilistic queries can be answered at test time without retraining. However, when using undirected PGMS with hidden variables, two sources of error typically compound in all but the simplest models (a) learning error (both computing the partition function and integrating out the hidden variables is intractable); and (b) prediction error (exact inference is also intractable). Here we introduce query training (QT), a mechanism to learn a PGM that is optimized for the approximate inference algorithm that will be paired with it. The resulting PGM is a worse model of the data (as measured by the likelihood), but it is tuned to produce better marginals for a given inference algorithm. Unlike prior works, our approach preserves the querying flexibility of the original PGM: at test time, we can estimate the marginal of any variable given any partial evidence. We demonstrate experimentally that QT can be used to learn a challenging 8-connected grid Markov random field with hidden variables and that it consistently outperforms the state-of-the-art AdVIL when tested on three undirected models across multiple datasets

Model for the Vaporization of Mixed Organometallic Compounds in the Metalorganic Chemical Vapor Deposition of High Temperature Superconducting Films

A model of the vaporization and mass transport of mixed organometallics from a single source for thin film metalorganic chemical vapor deposition is presented. A stoichiometric gas phase can be obtained from a mixture of the organometallics in the desired mole ratios, in spite of differences in the volatilities of the individual compounds. Proper film composition and growth rates are obtained by controlling the velocity of a carriage containing the organometallics through the heating zone of a vaporizer

Vaporization of a mixed precursors in chemical vapor deposition for YBCO films

Single phase YBa2Cu3O7-delta thin films with T(c) values around 90 K are readily obtained by using a single source chemical vapor deposition technique with a normal precursor mass transport. The quality of the films is controlled by adjusting the carrier gas flow rate and the precursor feed rate

Downregulating Notch counteracts KrasG12D-induced ERK activation and oxidative phosphorylation in myeloproliferative neoplasm.

The Notch signaling pathway contributes to the pathogenesis of a wide spectrum of human cancers, including hematopoietic malignancies. Its functions are highly dependent on the specific cellular context. Gain-of-function NOTCH1 mutations are prevalent in human T-cell leukemia, while loss of Notch signaling is reported in myeloid leukemias. Here, we report a novel oncogenic function of Notch signaling in oncogenic Kras-induced myeloproliferative neoplasm (MPN). We find that downregulation of Notch signaling in hematopoietic cells via DNMAML expression or Pofut1 deletion significantly blocks MPN development in KrasG12D mice in a cell-autonomous manner. Further mechanistic studies indicate that inhibition of Notch signaling upregulates Dusp1, a dual phosphatase that inactivates p-ERK, and downregulates cytokine-evoked ERK activation in KrasG12D cells. Moreover, mitochondrial metabolism is greatly enhanced in KrasG12D cells but significantly reprogrammed by DNMAML close to that in control cells. Consequently, cell proliferation and expanded myeloid compartment in KrasG12D mice are significantly reduced. Consistent with these findings, combined inhibition of the MEK/ERK pathway and mitochondrial oxidative phosphorylation effectively inhibited the growth of human and mouse leukemia cells in vitro. Our study provides a strong rational to target both ERK signaling and aberrant metabolism in oncogenic Ras-driven myeloid leukemia

Design, synthesis and pharmacological evaluation of tricyclic derivatives as selective RXFP4 agonists

Relaxin family peptide receptors (RXFPs) are the potential therapeutic targets for neurological, cardiovascular, and metabolic indications. Among them, RXFP3 and RXFP4 (formerly known as GPR100 or GPCR142) are homologous class A G protein-coupled receptors with short N-terminal domain. Ligands of RXFP3 or RXFP4 are only limited to endogenous peptides and their analogues, and no natural product or synthetic agonists have been reported to date except for a scaffold of indole-containing derivatives as dual agonists of RXFP3 and RXFP4. In this study, a new scaffold of tricyclic derivatives represented by compound 7a was disclosed as a selective RXFP4 agonist after a high-throughput screening campaign against a diverse library of 52,000 synthetic and natural compounds. Two rounds of structural modification around this scaffold were performed focusing on three parts: 2-chlorophenyl group, 4-hydroxylphenyl group and its skeleton including cyclohexane-1,3-dione and 1,2,4-triazole group. Compound 14b with a new skeleton of 7,9-dihydro-4H-thiopyrano[3,4-d][1,2,4]triazolo[1,5-a]pyrimidin-8(5H)-one was thus obtained. The enantiomers of 7a and 14b were also resolved with their 9-(S)-conformer favoring RXFP4 agonism. Compared with 7a, compound 9-(S)-14b exhibited 2.3-fold higher efficacy and better selectivity for RXFP4 (selective ratio of RXFP4 vs. RXFP3 for 9-(S)-14b and 7a were 26.9 and 13.9, respectively)