Showing papers by "Korea University published in 2013"

Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis

Abstract: As discussed in the previous statistical notes, although many statistical methods have been proposed to test normality of data in various ways, there is no current gold standard method. The eyeball test may be useful for medium to large sized (e.g., n > 50) samples, however may not useful for small samples. The formal normality tests including Shapiro-Wilk test and Kolmogorov-Smirnov test may be used from small to medium sized samples (e.g., n < 300), but may be unreliable for large samples. Moreover we may be confused because ‘eyeball test’ and ‘formal normality test’ may show incompatible results for the same data. To resolve the problem, another method of assessing normality using skewness and kurtosis of the distribution may be used, which may be relatively correct in both small samples and large samples. 1) Skewness and kurtosis Skewness is a measure of the asymmetry and kurtosis is a measure of ’peakedness’ of a distribution. Most statistical packages give you values of skewness and kurtosis as well as their standard errors.

Activated Graphene-Based Carbons as Supercapacitor Electrodes with Macro- and Mesopores

Abstract: Electric double layer capacitors (or supercapacitors) store charges through the physisorption of electrolyte ions onto porous carbon electrodes. The control over structure and morphology of carbon electrode materials is therefore an effective strategy to render them high surface area and efficient paths for ion diffusion. Here we demonstrate the fabrication of highly porous graphene-derived carbons with hierarchical pore structures in which mesopores are integrated into macroporous scaffolds. The macropores were introduced by assembling graphene-based hollow spheres, and the mesopores were derived from the chemical activation with potassium hydroxide. The unique three-dimensional pore structures in the produced graphene-derived carbons give rise to a Brunauer–Emmett–Teller surface area value of up to 3290 m2 g–1 and provide an efficient pathway for electrolyte ions to diffuse into the interior surfaces of bulk electrode particles. These carbons exhibit both high gravimetric (174 F g–1) and volumetric (∼10...

Recent progress in luminescent and colorimetric chemosensors for detection of thiols

Abstract: In the past few decades, the development of optical probes for thiols has attracted great attention because of the biological importance of the thiol-containing molecules such as cysteine (Cys), homocysteine (Hcy), and glutathione (GSH). This tutorial review focuses on various thiol detection methods based on luminescent or colorimetric spectrophotometry published during the period 2010–2012. The discussion covers a diversity of sensing mechanisms such as Michael addition, cyclization with aldehydes, conjugate addition–cyclization, cleavage of sulfonamide and sulfonate esters, thiol–halogen nucleophilic substitution, disulfide exchange, native chemical ligation (NCL), metal complex-displace coordination, and nanomaterial-related and DNA-based chemosensors.

Capecitabine and cisplatin with or without cetuximab for patients with previously untreated advanced gastric cancer (EXPAND): a randomised, open-label phase 3 trial

Abstract: Summary Background Patients with advanced gastric cancer have a poor prognosis and few efficacious treatment options. We aimed to assess the addition of cetuximab to capecitabine-cisplatin chemotherapy in patients with advanced gastric or gastro-oesophageal junction cancer. Methods In our open-label, randomised phase 3 trial (EXPAND), we enrolled adults aged 18 years or older with histologically confirmed locally advanced unresectable (M0) or metastatic (M1) adenocarcinoma of the stomach or gastro-oesophageal junction. We enrolled patients at 164 sites (teaching hospitals and clinics) in 25 countries, and randomly assigned eligible participants (1:1) to receive first-line chemotherapy with or without cetuximab. Randomisation was done with a permuted block randomisation procedure (variable block size), stratified by disease stage (M0 vs M1), previous oesophagectomy or gastrectomy (yes vs no), and previous (neo)adjuvant (radio)chemotherapy (yes vs no). Treatment consisted of 3-week cycles of twice-daily capecitabine 1000 mg/m 2 (on days 1–14) and intravenous cisplatin 80 mg/m 2 (on day 1), with or without weekly cetuximab (400 mg/m 2 initial infusion on day 1 followed by 250 mg/m 2 per week thereafter). The primary endpoint was progression-free survival (PFS), assessed by a masked independent review committee in the intention-to-treat population. We assessed safety in all patients who received at least one dose of study drug. This study is registered at EudraCT, number 2007-004219-75. Findings Between June 30, 2008, and Dec 15, 2010, we enrolled 904 patients. Median PFS for 455 patients allocated capecitabine-cisplatin plus cetuximab was 4·4 months (95% CI 4·2–5·5) compared with 5·6 months (5·1–5·7) for 449 patients who were allocated to receive capecitabine-cisplatin alone (hazard ratio 1·09, 95% CI 0·92–1·29; p=0·32). 369 (83%) of 446 patients in the chemotherapy plus cetuximab group and 337 (77%) of 436 patients in the chemotherapy group had grade 3–4 adverse events, including grade 3–4 diarrhoea, hypokalaemia, hypomagnesaemia, rash, and hand-foot syndrome. Grade 3–4 neutropenia was more common in controls than in patients who received cetuximab. Incidence of grade 3–4 skin reactions and acne-like rash was substantially higher in the cetuximab-containing regimen than in the control regimen. 239 (54%) of 446 in the cetuximab group and 194 (44%) of 436 in the control group had any grade of serious adverse event. Interpretation Addition of cetuximab to capecitabine-cisplatin provided no additional benefit to chemotherapy alone in the first-line treatment of advanced gastric cancer in our trial. Funding Merck KGaA.

Stock Market Volatility and Macroeconomic Fundamentals

Abstract: We revisit the relation between stock market volatility and macroeconomic activity using a new class of component models that distinguish short-run from long-run movements. We formulate models with the long-term component driven by inflation and industrial production growth that are in terms of pseudo out-of-sample prediction for horizons of one quarter at par or outperform more traditional time series volatility models at longer horizons. Hence, imputing economic fundamentals into volatility models pays off in terms of long-horizon forecasting. We also find that macroeconomic fundamentals play a significant role even at short horizons.

Bacteria activate sensory neurons that modulate pain and inflammation

Abstract: Nociceptor sensory neurons are specialized to detect potentially damaging stimuli, protecting the organism by initiating the sensation of pain and eliciting defensive behaviours. Bacterial infections produce pain by unknown molecular mechanisms, although they are presumed to be secondary to immune activation. Here we demonstrate that bacteria directly activate nociceptors, and that the immune response mediated through TLR2, MyD88, T cells, B cells, and neutrophils and monocytes is not necessary for Staphylococcus aureus-induced pain in mice. Mechanical and thermal hyperalgesia in mice is correlated with live bacterial load rather than tissue swelling or immune activation. Bacteria induce calcium flux and action potentials in nociceptor neurons, in part via bacterial N-formylated peptides and the pore-forming toxin α-haemolysin, through distinct mechanisms. Specific ablation of Nav1.8-lineage neurons, which include nociceptors, abrogated pain during bacterial infection, but concurrently increased local immune infiltration and lymphadenopathy of the draining lymph node. Thus, bacterial pathogens produce pain by directly activating sensory neurons that modulate inflammation, an unsuspected role for the nervous system in host-pathogen interactions.

Observation of a new boson with mass near 125 GeV in pp collisions at $ \sqrt{s}=7 $ and 8 TeV

Abstract: A detailed description is reported of the analysis used by the CMS Collaboration in the search for the standard model Higgs boson in pp collisions at the LHC, which led to the observation of a new boson. The data sample corresponds to integrated luminosities up to 5.1 inverse femtobarns at sqrt(s) = 7 TeV, and up to 5.3 inverse femtobarns at sqrt(s) = 8 TeV. The results for five Higgs boson decay modes gamma gamma, ZZ, WW, tau tau, and bb, which show a combined local significance of 5 standard deviations near 125 GeV, are reviewed. A fit to the invariant mass of the two high resolution channels, gamma gamma and ZZ to 4 ell, gives a mass estimate of 125.3 +/- 0.4 (stat) +/- 0.5 (syst) GeV. The measurements are interpreted in the context of the standard model Lagrangian for the scalar Higgs field interacting with fermions and vector bosons. The measured values of the corresponding couplings are compared to the standard model predictions. The hypothesis of custodial symmetry is tested through the measurement of the ratio of the couplings to the W and Z bosons. All the results are consistent, within their uncertainties, with the expectations for a standard model Higgs boson.

Predicting Network Activity from High Throughput Metabolomics

Abstract: The functional interpretation of high throughput metabolomics by mass spectrometry is hindered by the identification of metabolites, a tedious and challenging task. We present a set of computational algorithms which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites. The algorithms were experimentally validated on the activation of innate immune cells.

Study of e + e - →π + π - J/ψ and Observation of a Charged Charmoniumlike State at Belle

Abstract: The cross section for ee+ e- → π+ π- J/ψ between 3.8 and 5.5 GeV is measured with a 967 fb(-1) data sample collected by the Belle detector at or near the Υ(nS) (n = 1,2,…,5) resonances. The Y(4260) state is observed, and its resonance parameters are determined. In addition, an excess of π+ π- J/ψ production around 4 GeV is observed. This feature can be described by a Breit-Wigner parametrization with properties that are consistent with the Y(4008) state that was previously reported by Belle. In a study of Y(4260) → π+ π- J/ψ decays, a structure is observed in the M(π(±)J/ψ) mass spectrum with 5.2σ significance, with mass M = (3894.5 ± 6.6 ± 4.5) MeV/c2 and width Γ = (63 ± 24 ± 26) MeV/c2, where the errors are statistical and systematic, respectively. This structure can be interpreted as a new charged charmoniumlike state.

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

Abstract: The accurate and reliable prediction of properties of molecules typically requires computationally intensive quantum-chemical calculations. Recently, machine learning techniques applied to ab initio calculations have been proposed as an efficient approach for describing the energies of molecules in their given ground-state structure throughout chemical compound space (Rupp et al. Phys. Rev. Lett. 2012, 108, 058301). In this paper we outline a number of established machine learning techniques and investigate the influence of the molecular representation on the methods performance. The best methods achieve prediction errors of 3 kcal/mol for the atomization energies of a wide variety of molecules. Rationales for this performance improvement are given together with pitfalls and challenges when applying machine learning approaches to the prediction of quantum-mechanical observables.

Renewable and metal-free carbon nanofibre catalysts for carbon dioxide reduction

Abstract: The efficient catalysis of the electrochemical reduction of carbon dioxide is an important industrial process, usually performed by noble metal catalysts. Here the authors report a metal-free carbon nanofibre-based catalyst operating with a negligible overpotential, high current density and long-term stability.

How students create motivationally supportive learning environments for themselves: The concept of agentic engagement.

Abstract: The present study introduced “agentic engagement” as a newly proposed student-initiated pathway to greater achievement and greater motivational support. Study 1 developed the brief, construct-congruent, and psychometrically strong Agentic Engagement Scale. Study 2 provided evidence for the scale’s construct and predictive validity, as scores correlated with measures of agentic motivation and explained independent variance in course-specific achievement not otherwise attributable to students’ behavioral, emotional, and cognitive engagement. Study 3 showed how agentically engaged students create motivationally supportive learning environments for themselves. Measures of agentic engagement and teacher-provided autonomy support were collected from 302 middle-school students in a 3-wave longitudinal research design. Multilevel structural equation modeling showed that (a) initial levels of students’ agentic engagement predicted longitudinal changes in midsemester perceived autonomy support and (b) early-semester changes in agentic engagement predicted longitudinal changes in late-semester autonomy support. Overall, these studies show how agentic engagement functions as a proactive, intentional, collaborative, and constructive student-initiated pathway to greater achievement (Study 2) and motivational support (Study 3).

Mesenchymal glioma stem cells are maintained by activated glycolytic metabolism involving aldehyde dehydrogenase 1A3.

Abstract: Tumor heterogeneity of high-grade glioma (HGG) is recognized by four clinically relevant subtypes based on core gene signatures. However, molecular signaling in glioma stem cells (GSCs) in individual HGG subtypes is poorly characterized. Here we identified and characterized two mutually exclusive GSC subtypes with distinct dysregulated signaling pathways. Analysis of mRNA profiles distinguished proneural (PN) from mesenchymal (Mes) GSCs and revealed a pronounced correlation with the corresponding PN or Mes HGGs. Mes GSCs displayed more aggressive phenotypes in vitro and as intracranial xenografts in mice. Further, Mes GSCs were markedly resistant to radiation compared with PN GSCs. The glycolytic pathway, comprising aldehyde dehydrogenase (ALDH) family genes and in particular ALDH1A3, were enriched in Mes GSCs. Glycolytic activity and ALDH activity were significantly elevated in Mes GSCs but not in PN GSCs. Expression of ALDH1A3 was also increased in clinical HGG compared with low-grade glioma or normal brain tissue. Moreover, inhibition of ALDH1A3 attenuated the growth of Mes but not PN GSCs. Last, radiation treatment of PN GSCs up-regulated Mes-associated markers and down-regulated PN-associated markers, whereas inhibition of ALDH1A3 attenuated an irradiation-induced gain of Mes identity in PN GSCs. Taken together, our data suggest that two subtypes of GSCs, harboring distinct metabolic signaling pathways, represent intertumoral glioma heterogeneity and highlight previously unidentified roles of ALDH1A3-associated signaling that promotes aberrant proliferation of Mes HGGs and GSCs. Inhibition of ALDH1A3-mediated pathways therefore might provide a promising therapeutic approach for a subset of HGGs with the Mes signature.

A tutorial on sensitivity analyses in clinical trials: the what, why, when and how

Abstract: Background Sensitivity analyses play a crucial role in assessing the robustness of the findings or conclusions based on primary analyses of data in clinical trials. They are a critical way to assess the impact, effect or influence of key assumptions or variations—such as different methods of analysis, definitions of outcomes, protocol deviations, missing data, and outliers—on the overall conclusions of a study. The current paper is the second in a series of tutorial-type manuscripts intended to discuss and clarify aspects related to key methodological issues in the design and analysis of clinical trials.

Machine Learning of Molecular Electronic Properties in Chemical Compound Space

Abstract: The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel and predictive structure?property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we present a machine learning model, trained on a database of ab initio calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity and excitation energies. The machine learning model is based on a deep multi-task artificial neural network, exploiting the underlying correlations between various molecular properties. The input is identical to ab initio methods, i.e.?nuclear charges and Cartesian coordinates of all atoms. For small organic molecules, the accuracy of such a ?quantum machine? is similar, and sometimes superior, to modern quantum-chemical methods?at negligible computational cost.

Machine Learning of Molecular Electronic Properties in Chemical Compound Space

Abstract: The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel, and predictive structure-property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we present a machine learning (ML) model, trained on a data base of \textit{ab initio} calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity, and excitation energies. The ML model is based on a deep multi-task artificial neural network, exploiting underlying correlations between various molecular properties. The input is identical to \emph{ab initio} methods, \emph{i.e.} nuclear charges and Cartesian coordinates of all atoms. For small organic molecules the accuracy of such a "Quantum Machine" is similar, and sometimes superior, to modern quantum-chemical methods---at negligible computational cost.

Contrast Enhancement Based on Layered Difference Representation of 2D Histograms

Abstract: A novel contrast enhancement algorithm based on the layered difference representation of 2D histograms is proposed in this paper. We attempt to enhance image contrast by amplifying the gray-level differences between adjacent pixels. To this end, we obtain the 2D histogram h(k, k+l) from an input image, which counts the pairs of adjacent pixels with gray-levels k and k+l, and represent the gray-level differences in a tree-like layered structure. Then, we formulate a constrained optimization problem based on the observation that the gray-level differences, occurring more frequently in the input image, should be more emphasized in the output image. We first solve the optimization problem to derive the transformation function at each layer. We then combine the transformation functions at all layers into the unified transformation function, which is used to map input gray-levels to output gray-levels. Experimental results demonstrate that the proposed algorithm enhances images efficiently in terms of both objective quality and subjective quality.

Layer-by-layer assembly of graphene oxide nanosheets on polyamide membranes for durable reverse-osmosis applications.

Abstract: Improving membrane durability associated with fouling and chlorine resistance remains one of the major challenges in desalination membrane technology. Here, we demonstrate that attractive features of graphene oxide (GO) nanosheets such as high hydrophilicity, chemical robustness, and ultrafast water permeation can be harnessed for a dual-action barrier coating layer that enhances resistance to both fouling and chlorine-induced degradation of polyamide (PA) thin-film composite (TFC) membranes while preserving their separation performance. GO multilayers were coated on the PA-TFC membrane surfaces via layer-by-layer (LbL) deposition of oppositely charged GO nanosheets. Consequently, it was shown that the conformal GO coating layer can increase the surface hydrophilicity and reduce the surface roughness, leading to the significantly improved antifouling performance against a protein foulant. It was also demonstrated that the chemically inert nature of GO nanosheets enables the GO coating layer to act as a ch...

Uniformly Interconnected Silver-Nanowire Networks for Transparent Film Heaters

Abstract: The fabrication and design principles for using silver-nanowire (AgNW) networks as transparent electrodes for flexible film heaters are described. For best practice, AgNWs are synthesized with a small diameter and network structures of the AgNW films are optimized, demonstrating a favorably low surface resistivity in transparent layouts with a high figure-of-merit value. To explore their potential in transparent electrodes, a transparent film heater is constructed based on uniformly interconnected AgNW networks, which yields an effective and rapid heating of the film at low input voltages. In addition, the AgNW-based film heater is capable of accommodating a large amount of compressive or tensile strains in a completely reversible fashion, thereby yielding an excellent mechanical flexibility. The AgNW networks demonstrated here possess attractive features for both conventional and emerging applications of transparent flexible electrodes.