High-resolution modeling of typhoon morakot (2009): Vortex rossby waves and their role in extreme precipitation over Taiwan

A high-resolution nonhydrostatic numerical model, the Advanced Regional Prediction System (ARPS), was used to simulate Typhoon Morakot (2009) as it made landfall over Taiwan, producing record rainfall totals. In particular, the mesoscale structure of the typhoon was investigated, emphasizing its associated deep convection, the development of inner rainbands near the center, and the resultant intense rainfall over western Taiwan. Simulations at 15- and 3-km grid spacing revealed that, following the decay of the initial inner eyewall, a new, much larger eyewall developed as the typhoon made landfall over Taiwan. Relatively large-amplitude wave structures developed in the outer eyewall and are identified as vortex Rossby waves (VRWs), based on the wave characteristics and their similarity to VRWs identified in previous studies. Moderate to strong vertical shear over the typhoon system produced a persistent wavenumber-1 (WN1) asymmetric structure during the landfall period, with upward motion and deep convection in the downshear and downshear-left sides, consistent with earlier studies. This strong asymmetry masks the effects of WN1 VRWs. WN2 and WN3 VRWs apparently are associated with the development of deep convective bands in Morakot's southwestern quadrant. This occurs as the waves move cyclonically into the downshear side of the cyclone. Although the typhoon track and topographic enhancement contribute most to the recordbreaking rainfall totals, the location of the convective bands, and their interaction with the mountainous terrain of Taiwan, also affect the rainfall distribution. Quantitatively, the 3-km ARPS rainfall forecasts are superior to those obtained from coarser-resolution models. © 2013 American Meteorological Society

Selecting electrical billing attributes: big data preprocessing improvements

The attribute selection is a very relevant activity of data preprocessing when discovering knowledge on databases. Its main objective is to eliminate irrelevant and/or redundant attributes to obtain computationally treatable issues, without affecting the quality of the solution. Various techniques are proposed, mainly from two approaches: wrapper and ranking. This article evaluates a novel approach proposed by Bradley and Mangasarian (Machine learning ICML. Morgan Kaufmann, Sn Fco, CA, pp. 82–90, 1998 [1]) which uses concave programming for minimizing the classification error and the number of attributes required to perform the task. The technique is evaluated using the electric service billing database in Colombia. The results are compared against traditional techniques for evaluating: attribute reduction, processing time, discovered knowledge size, and solution quality

Predicting short-term electricity demand through artificial neural network

Forecasting the consumption of electric power on a daily basis allows considerable money savings for the supplying companies, by reducing the expenses in generation and operation. Therefore, the cost of forecasting errors can be of such magnitude that many studies have focused on minimizing the forecasting error, which makes this topic as an integral part of planning in many companies of various kinds and sizes, ranging from generation, transmission, and distribution to consumption, by requiring reliable forecasting systems

STM Spectroscopy of ultra-flat graphene on hexagonal boron nitride

Graphene has demonstrated great promise for future electronics technology as well as fundamental physics applications because of its linear energy-momentum dispersion relations which cross at the Dirac point. However, accessing the physics of the low density region at the Dirac point has been difficult because of the presence of disorder which leaves the graphene with local microscopic electron and hole puddles, resulting in a finite density of carriers even at the charge neutrality point. Efforts have been made to reduce the disorder by suspending graphene, leading to fabrication challenges and delicate devices which make local spectroscopic measurements difficult. Recently, it has been shown that placing graphene on hexagonal boron nitride (hBN) yields improved device performance. In this letter, we use scanning tunneling microscopy to show that graphene conforms to hBN, as evidenced by the presence of Moire patterns in the topographic images. However, contrary to recent predictions, this conformation does not lead to a sizable band gap due to the misalignment of the lattices. Moreover, local spectroscopy measurements demonstrate that the electron-hole charge fluctuations are reduced by two orders of magnitude as compared to those on silicon oxide. This leads to charge fluctuations which are as small as in suspended graphene, opening up Dirac point physics to more diverse experiments than are possible on freestanding devices.Comment: Nature Materials advance online publication 13/02/201

Expression and DNA methylation of TNF, IFNG and FOXP3 in colorectal cancer and their prognostic significance.

BACKGROUND: Colorectal cancer (CRC) progression is associated with suppression of host cell-mediated immunity and local immune escape mechanisms. Our aim was to assess the immune function in terms of expression of TNF, IFNG and FOXP3 in CRC. METHODS: Sixty patients with CRC and 15 matched controls were recruited. TaqMan quantitative PCR and methylation-specific PCR was performed for expression and DNA methylation analysis of TNF, IFNG and FOXP3. Survival analysis was performed over a median follow-up of 48 months. RESULTS: TNF was suppressed in tumour and IFNG was suppressed in peripheral blood mononuclear cells (PBMCs) of patients with CRC. Tumours showed enhanced expression of FOXP3 and was significantly higher when tumour size was >38 mm (median tumour size; P=0.006, Mann-Whitney U-test). Peripheral blood mononuclear cell IFNG was suppressed in recurrent CRC (P=0.01). Methylated TNFpromoter (P=0.003) and TNFexon1 (P=0.001) were associated with significant suppression of TNF in tumours. Methylated FOXP3cpg was associated with significant suppression of FOXP3 in both PBMC (P=0.018) and tumours (P=0.010). Reduced PBMC FOXP3 expression was associated with significantly worse overall survival (HR=8.319, P=0.019). CONCLUSIONS: We have detected changes in the expression of immunomodulatory genes that could act as biomarkers for prognosis and future immunotherapeutic strategies

Discovering patterns in drug-protein interactions based on their fingerprints

<p>Abstract</p> <p>Background</p> <p>The discovering of interesting patterns in drug-protein interaction data at molecular level can reveal hidden relationship among drugs and proteins and can therefore be of paramount importance for such application as drug design. To discover such patterns, we propose here a computational approach to analyze the molecular data of drugs and proteins that are known to have interactions with each other. Specifically, we propose to use a data mining technique called <it>Drug-Protein Interaction Analysis </it>(<it>D-PIA</it>) to determine if there are any commonalities in the fingerprints of the substructures of interacting drug and protein molecules and if so, whether or not any patterns can be generalized from them.</p> <p>Method</p> <p>Given a database of drug-protein interactions, <it>D-PIA </it>performs its tasks in several steps. First, for each drug in the database, the fingerprints of its molecular substructures are first obtained. Second, for each protein in the database, the fingerprints of its protein domains are obtained. Third, based on known interactions between drugs and proteins, an interdependency measure between the fingerprint of each drug substructure and protein domain is then computed. Fourth, based on the interdependency measure, drug substructures and protein domains that are significantly interdependent are identified. Fifth, the existence of interaction relationship between a previously unknown drug-protein pairs is then predicted based on their constituent substructures that are significantly interdependent.</p> <p>Results</p> <p>To evaluate the effectiveness of <it>D-PIA</it>, we have tested it with real drug-protein interaction data. <it>D-PIA </it>has been tested with real drug-protein interaction data including enzymes, ion channels, and protein-coupled receptors. Experimental results show that there are indeed patterns that one can discover in the interdependency relationship between drug substructures and protein domains of interacting drugs and proteins. Based on these relationships, a testing set of drug-protein data are used to see if <it>D-PIA </it>can correctly predict the existence of interaction between drug-protein pairs. The results show that the prediction accuracy can be very high. An AUC score of a ROC plot could reach as high as 75% which shows the effectiveness of this classifier.</p> <p>Conclusions</p> <p><it>D-PIA </it>has the advantage that it is able to perform its tasks effectively based on the fingerprints of drug and protein molecules without requiring any 3D information about their structures and <it>D-PIA </it>is therefore very fast to compute. <it>D-PIA </it>has been tested with real drug-protein interaction data and experimental results show that it can be very useful for predicting previously unknown drug-protein as well as protein-ligand interactions. It can also be used to tackle problems such as ligand specificity which is related directly and indirectly to drug design and discovery.</p

Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites

Experimentally-determined or computationally-predicted protein phosphorylation sites for distinctive species are becoming increasingly common. In this paper, we compare the predictive performance of a novel classification algorithm with different encoding schemes to develop a rice-specific protein phosphorylation site predictor. Our results imply that the combination of Amino acid occurrence Frequency with Composition of K-Spaced Amino Acid Pairs (AF-CKSAAP) provides the best description of relevant sequence features that surround a phosphorylation site. A support vector machine (SVM) using AF-CKSAAP achieves the best performance in classifying rice protein phophorylation sites when compared to the other algorithms. We have used SVM with AF-CKSAAP to construct a rice-specific protein phosphorylation sites predictor, Rice-Phospho 1.0 (http://bioinformatics.fafu.edu.cn/rice-phospho1.0). We measure the Accuracy (ACC) and Matthews Correlation Coefficient (MCC) of Rice-Phospho 1.0 to be 82.0% and 0.64, significantly higher than those measures for other predictors such as Scansite, Musite, PlantPhos and PhosphoRice. Rice-Phospho 1.0 also successfully predicted the experimentally identified phosphorylation sites in LOC-Os03g51600.1, a protein sequence which did not appear in the training dataset. In summary, Rice-phospho 1.0 outputs reliable predictions of protein phosphorylation sites in rice, and will serve as a useful tool to the community

Identification of serum biomarkers of hepatocarcinoma through liquid chromatography/mass spectrometry-based metabonomic method

Late diagnosis of hepatocarcinoma (HCC) is one of the most primary factors for the poor survival of patients. Thereby, identification of sensitive and specific biomarkers for HCC early diagnosis is of great importance in biological medicine to date. In the present study, serum metabolites of the HCC patients and healthy controls were investigated using the improved liquid chromatography–mass spectrometry (LC/MS). A wavelet-based method was utilized to find and align peaks of LC–MS. The characteristic peaks were selected by performing a two-sample t test statistics (p value <0.05). Clustering analysis based on principal component analysis showed a clear separation between HCC patients and healthy individuals. The serum metabolite, namely 1-methyladenosine, was identified as the characteristic metabolite for HCC. Moreover, receiver–operator curves were calculated with 1-methyladenosine and/or alpha fetal protein (AFP). The higher area under curve value was achieved in 1-methyladenosine group than AFP group (0.802 vs. 0.592), and the diagnostic model combining 1-methyladenosine with AFP exhibited significant improved sensitivity, which could identify those patients who missed the diagnosis of HCC by determining serum AFP alone. Overall, these results suggested that LC/MS-based metabonomic study is a potent and promising strategy for identifying novel biomarkers of HCC