eMolTox: prediction of molecular toxicity with confidence.

PubMed

Ji, Changge; Svensson, Fredrik; Zoufir, Azedine; Bender, Andreas

2018-03-07

In this work we present eMolTox, a web server for the prediction of potential toxicity associated with a given molecule. 174 toxicology-related in vitro/vivo experimental datasets were used for model construction and Mondrian conformal prediction was used to estimate the confidence of the resulting predictions. Toxic substructure analysis is also implemented in eMolTox. eMolTox predicts and displays a wealth of information of potential molecular toxicities for safety analysis in drug development. The eMolTox Server is freely available for use on the web at http://xundrug.cn/moltox. chicago.ji@gmail.com or ab454@cam.ac.uk. Supplementary data are available at Bioinformatics online.

Magnetic Nanozyme-Linked Immunosorbent Assay for Ultrasensitive Influenza A Virus Detection.

PubMed

Oh, Sangjin; Kim, Jeonghyo; Tran, Van Tan; Lee, Dong Kyu; Ahmed, Syed Rahin; Hong, Jong Chul; Lee, Jaewook; Park, Enoch Y; Lee, Jaebeom

2018-04-18

Rapid and sensitive detection of influenza virus is of soaring importance to prevent further spread of infections and adequate clinical treatment. Herein, an ultrasensitive colorimetric assay called magnetic nano(e)zyme-linked immunosorbent assay (MagLISA) is suggested, in which silica-shelled magnetic nanobeads (MagNBs) and gold nanoparticles are combined to monitor influenza A virus up to femtogram per milliliter concentration. Two essential strategies for ultrasensitive sensing are designed, i.e., facile target separation by MagNBs and signal amplification by the enzymelike activity of gold nanozymes (AuNZs). The enzymelike activity was experimentally and computationally evaluated, where the catalyticity of AuNZ was tremendously stronger than that of normal biological enzymes. In the spiked test, a straightforward linearity was presented in the range of 5.0 × 10 -15 -5.0 × 10 -6 g·mL -1 in detecting the influenza virus A (New Caledonia/20/1999) (H1N1). The detection limit is up to 5.0 × 10 -12 g·mL -1 only by human eyes, as well as up to 44.2 × 10 -15 g·mL -1 by a microplate reader, which is the lowest record to monitor influenza virus using enzyme-linked immunosorbent assay-based technology as far as we know. Clinically isolated human serum samples were successfully observed at the detection limit of 2.6 PFU·mL -1 . This novel MagLISA demonstrates, therefore, a robust sensing platform possessing the advances of fathomable sample separation, enrichment, ultrasensitive readout, and anti-interference ability may reduce the spread of influenza virus and provide immediate clinical treatment.

Potential ecological risk assessment and prediction of soil heavy metal pollution around coal gangue dump

NASA Astrophysics Data System (ADS)

Jiang, X.; Lu, W. X.; Yang, Q. C.; Yang, Z. P.

2014-03-01

Aim of the present study is to evaluate the potential ecological risk and predict the trend of soil heavy metal pollution around a~coal gangue dump in Jilin Province (Northeast China). The concentrations of Cd, Pb, Cu, Cr and Zn were monitored by inductively coupled plasma mass spectrometry (ICP-MS). The potential ecological risk index method developed by Hakanson (1980) was employed to assess the potential risk of heavy metal pollution. The potential ecological risk in an order of E(Cd) > E(Pb) > E(Cu) > E(Cr) > E(Zn) have been obtained, which showed that Cd was the most important factor led to risk. Based on the Cd pollution history, the cumulative acceleration and cumulative rate of Cd were estimated, and the fixed number of years exceeding standard prediction model was established, which was used to predict the pollution trend of Cd under the accelerated accumulation mode and the uniform mode. Pearson correlation analysis and correspondence analysis are employed to identify the sources of heavy metal, and the relationship between sampling points and variables. These findings provide some useful insights for making appropriate management strategies to prevent and decrease heavy metal pollution around coal gangue dump in Yangcaogou coal mine and other similar areas elsewhere.

Phenology prediction component of GypsES

Treesearch

Jesse A. Logan; Lukas P. Schaub; F. William Ravlin

1991-01-01

Prediction of phenology is an important component of most pest management programs, and considerable research effort has been expended toward development of predictive tools for gypsy moth phenology. Although phenological prediction is potentially valuable for timing of spray applications (e.g. Bt, or Gypcheck) and other management activities (e.g. placement and...

Prime Contract Awards Alphabetically by Contractor, by State or Country, and Place, FY 88. Part 21. (VVKR Incorporated-Zymed, Inc.)

DTIC Science & Technology

1988-01-01

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Thermostability in endoglucanases is fold-specific

PubMed Central

2011-01-01

Background Endoglucanases are usually considered to be synergistically involved in the initial stages of cellulose breakdown-an essential step in the bioprocessing of lignocellulosic plant materials into bioethanol. Despite their economic importance, we currently lack a basic understanding of how some endoglucanases can sustain their ability to function at elevated temperatures required for bioprocessing, while others cannot. In this study, we present a detailed comparative analysis of both thermophilic and mesophilic endoglucanases in order to gain insights into origins of thermostability. We analyzed the sequences and structures for sets of endoglucanase proteins drawn from the Carbohydrate-Active enZymes (CAZy) database. Results Our results demonstrate that thermophilic endoglucanases and their mesophilic counterparts differ significantly in their amino acid compositions. Strikingly, these compositional differences are specific to protein folds and enzyme families, and lead to differences in intramolecular interactions in a fold-dependent fashion. Conclusions Here, we provide fold-specific guidelines to control thermostability in endoglucanases that will aid in making production of biofuels from plant biomass more efficient. PMID:21291533

Effect of Peppermint Oil on Serum Lipid Peroxidation and Hepatic Enzymes after Immobility Stress in Mice

PubMed Central

Marjani, Abdoljalal; Rahmati, Reza; Mansourian, Azad Reza; Veghary, Gholamreza

2012-01-01

This study was undertaken to determine the influences of various doses of peppermint oil on the hepatic en-zymes, alanine transaminase, apartate tranaminase, alkaline phosphotase and gamma glutamyl transferase and the level of malondialdehyde in the serum of mice with and without immobility stress. The mice exposed to drink water, 0.9, 27 and 60 mg/kg peppermint oil from the days 1 to 5 for a period of 4 h before and after immobility stress. Serum MDA in-creased in treatment group II, III and IV after immobility stress. There was a significant decrease in ALT in treatment group III and IV after immobility stress. There were also significant decreases in ALP and GGT in treatment group IV af-ter immobility stress. This result may suggest that, MDA level is higher in immobilization stress group than in the un-immobilized animals in serum and this results show that enzyme activities decreased after immobilization stress. PMID:22654997

eF-seek: prediction of the functional sites of proteins by searching for similar electrostatic potential and molecular surface shape.

PubMed

Kinoshita, Kengo; Murakami, Yoichi; Nakamura, Haruki

2007-07-01

We have developed a method to predict ligand-binding sites in a new protein structure by searching for similar binding sites in the Protein Data Bank (PDB). The similarities are measured according to the shapes of the molecular surfaces and their electrostatic potentials. A new web server, eF-seek, provides an interface to our search method. It simply requires a coordinate file in the PDB format, and generates a prediction result as a virtual complex structure, with the putative ligands in a PDB format file as the output. In addition, the predicted interacting interface is displayed to facilitate the examination of the virtual complex structure on our own applet viewer with the web browser (URL: http://eF-site.hgc.jp/eF-seek).

Remarkable evolutionary relatedness among the enzymes and proteins from the α-amylase family.

PubMed

Janeček, Štefan; Gabriško, Marek

2016-07-01

The α-amylase is a ubiquitous starch hydrolase catalyzing the cleavage of the α-1,4-glucosidic bonds in an endo-fashion. Various α-amylases originating from different taxonomic sources may differ from each other significantly in their exact substrate preference and product profile. Moreover, it also seems to be clear that at least two different amino acid sequences utilizing two different catalytic machineries have evolved to execute the same α-amylolytic specificity. The two have been classified in the Cabohydrate-Active enZyme database, the CAZy, in the glycoside hydrolase (GH) families GH13 and GH57. While the former and the larger α-amylase family GH13 evidently forms the clan GH-H with the families GH70 and GH77, the latter and the smaller α-amylase family GH57 has only been predicted to maybe define a future clan with the family GH119. Sequences and several tens of enzyme specificities found throughout all three kingdoms in many taxa provide an interesting material for evolutionarily oriented studies that have demonstrated remarkable observations. This review emphasizes just the three of them: (1) a close relatedness between the plant and archaeal α-amylases from the family GH13; (2) a common ancestry in the family GH13 of animal heavy chains of heteromeric amino acid transporter rBAT and 4F2 with the microbial α-glucosidases; and (3) the unique sequence features in the primary structures of amylomaltases from the genus Borrelia from the family GH77. Although the three examples cannot represent an exhaustive list of exceptional topics worth to be interested in, they may demonstrate the importance these enzymes possess in the overall scientific context.

Intermolecular potentials and the accurate prediction of the thermodynamic properties of water

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shvab, I.; Sadus, Richard J., E-mail: rsadus@swin.edu.au

2013-11-21

The ability of intermolecular potentials to correctly predict the thermodynamic properties of liquid water at a density of 0.998 g/cm{sup 3} for a wide range of temperatures (298–650 K) and pressures (0.1–700 MPa) is investigated. Molecular dynamics simulations are reported for the pressure, thermal pressure coefficient, thermal expansion coefficient, isothermal and adiabatic compressibilities, isobaric and isochoric heat capacities, and Joule-Thomson coefficient of liquid water using the non-polarizable SPC/E and TIP4P/2005 potentials. The results are compared with both experiment data and results obtained from the ab initio-based Matsuoka-Clementi-Yoshimine non-additive (MCYna) [J. Li, Z. Zhou, and R. J. Sadus, J. Chem. Phys.more » 127, 154509 (2007)] potential, which includes polarization contributions. The data clearly indicate that both the SPC/E and TIP4P/2005 potentials are only in qualitative agreement with experiment, whereas the polarizable MCYna potential predicts some properties within experimental uncertainty. This highlights the importance of polarizability for the accurate prediction of the thermodynamic properties of water, particularly at temperatures beyond 298 K.« less

Probing ionization potential, electron affinity and self-energy effect on the spectral shape and exciton binding energy of quantum liquid water with self-consistent many-body perturbation theory and the Bethe-Salpeter equation.

PubMed

Ziaei, Vafa; Bredow, Thomas

2018-05-31

An accurate theoretical prediction of ionization potential (IP) and electron affinity (EA) is key in understanding complex photochemical processes in aqueous environments. There have been numerous efforts in literature to accurately predict IP and EA of liquid water, however with often conflicting results depending on the level of theory and the underlying water structures. In a recent study based on hybrid-non-self-consistent many-body perturbation theory (MBPT) Gaiduk et al (2018 Nat. Commun. 9 247) predicted an IP of 10.2 eV and EA of 0.2 eV, resulting in an electronic band gap (i.e. electronic gap (IP-EA) as measured by photoelectron spectroscopy) of about 10 eV, redefining the widely cited experimental gap of 8.7 eV in literature. In the present work, we show that GW self-consistency and an implicit vertex correction in MBPT considerably affect recently reported EA values by Gaiduk et al (2018 Nat. Commun. 9 247) by about 1 eV. Furthermore, the choice of pseudo-potential is critical for an accurate determination of the absolute band positions. Consequently, the self-consistent GW approach with an implicit vertex correction based on projector augmented wave (PAW) method on top of quantum water structures predicts an IP of 10.2, an EA of 1.1, a fundamental gap of 9.1 eV and an exciton binding (Eb) energy of 0.9 eV for the first absorption band of liquid water via the Bethe-Salpeter equation (BSE). Only within such a self-consistent approach a simultanously accurate prediction of IP, EA, Eg, Eb is possible.

Probing ionization potential, electron affinity and self-energy effect on the spectral shape and exciton binding energy of quantum liquid water with self-consistent many-body perturbation theory and the Bethe–Salpeter equation

NASA Astrophysics Data System (ADS)

Ziaei, Vafa; Bredow, Thomas

2018-05-01

An accurate theoretical prediction of ionization potential (IP) and electron affinity (EA) is key in understanding complex photochemical processes in aqueous environments. There have been numerous efforts in literature to accurately predict IP and EA of liquid water, however with often conflicting results depending on the level of theory and the underlying water structures. In a recent study based on hybrid-non-self-consistent many-body perturbation theory (MBPT) Gaiduk et al (2018 Nat. Commun. 9 247) predicted an IP of 10.2 eV and EA of 0.2 eV, resulting in an electronic band gap (i.e. electronic gap (IP-EA) as measured by photoelectron spectroscopy) of about 10 eV, redefining the widely cited experimental gap of 8.7 eV in literature. In the present work, we show that GW self-consistency and an implicit vertex correction in MBPT considerably affect recently reported EA values by Gaiduk et al (2018 Nat. Commun. 9 247) by about 1 eV. Furthermore, the choice of pseudo-potential is critical for an accurate determination of the absolute band positions. Consequently, the self-consistent GW approach with an implicit vertex correction based on projector augmented wave (PAW) method on top of quantum water structures predicts an IP of 10.2, an EA of 1.1, a fundamental gap of 9.1 eV and an exciton binding (Eb) energy of 0.9 eV for the first absorption band of liquid water via the Bethe–Salpeter equation (BSE). Only within such a self-consistent approach a simultanously accurate prediction of IP, EA, Eg, Eb is possible.

Computational identification, characterization and validation of potential antigenic peptide vaccines from hrHPVs E6 proteins using immunoinformatics and computational systems biology approaches

PubMed Central

Junaid, Muhammad; Kaushik, Aman Chandra; Ali, Arif; Ali, Syed Shujait; Mehmood, Aamir; Wei, Dong-Qing

2018-01-01

High-risk human papillomaviruses (hrHPVs) are the most prevalent viruses in human diseases including cervical cancers. Expression of E6 protein has already been reported in cervical cancer cases, excluding normal tissues. Continuous expression of E6 protein is making it ideal to develop therapeutic vaccines against hrHPVs infection and cervical cancer. Therefore, we carried out a meta-analysis of multiple hrHPVs to predict the most potential prophylactic peptide vaccines. In this study, immunoinformatics approach was employed to predict antigenic epitopes of hrHPVs E6 proteins restricted to 12 Human HLAs to aid the development of peptide vaccines against hrHPVs. Conformational B-cell and CTL epitopes were predicted for hrHPVs E6 proteins using ElliPro and NetCTL. The potential of the predicted peptides were tested and validated by using systems biology approach considering experimental concentration. We also investigated the binding interactions of the antigenic CTL epitopes by using docking. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlighted the regions from 46–62 and 65–76 that could be the first choice for the development of prophylactic peptide vaccines against hrHPVs. To overcome the worldwide distribution, the predicted epitopes restricted to different HLAs could cover most of the vaccination and would help to explore the possibility of these epitopes for adaptive immunotherapy against HPVs infections. PMID:29715318

State of the art for ab initio vs empirical potentials for HeH+ (2e-), BeH+ (4e-), BeH (5e-), Li2 (6e-) and BH (6e-)

NASA Astrophysics Data System (ADS)

Dattani, Nike

For large internuclear distances, the potential energy between two atoms is known analytically, based on constants that are calculated from atomic ab initio rather than molecular ab initio. This analytic form can be built into models for molecular potentials that are fitted to spectroscopic data. Such empirical potentials constitute the most accurate molecular potentials known. For HeH+, and BeH+, the long-range form of the potential is based only on the polarizabilities for He and H respectively, for which we have included up to 4th order QED corrections. For BeH, the best ab initio potential matches all but one observed vibrational spacing to < 1 cm- accuracy, and for Li2 the discrepancy in the spacings is < 0.08 cm-1 for all vibrational levels. But experimental methods such as photoassociation require the absolute energies, not spacings, and these are still several in several cm-1 disagreement. So empirical potentials are still the only reliable way to predict energies for few-electron systems. We also give predictions for various unobserved ''halo nucleonic molecules'' containing the ''halo'' isotopes: 6,8He, 11Li, 11,14Be and 8 , 17 , 19B.

[Screen potential CYP450 2E1 inhibitors from Chinese herbal medicine based on support vector regression and molecular docking method].

PubMed

Chen, Xi; Lu, Fang; Jiang, Lu-di; Cai, Yi-Lian; Li, Gong-Yu; Zhang, Yan-Ling

2016-07-01

Inhibition of cytochrome P450 (CYP450) enzymes is the most common reasons for drug interactions, so the study on early prediction of CYPs inhibitors can help to decrease the incidence of adverse reactions caused by drug interactions.CYP450 2E1(CYP2E1), as a key role in drug metabolism process, has broad spectrum of drug metabolism substrate. In this study, 32 CYP2E1 inhibitors were collected for the construction of support vector regression (SVR) model. The test set data were used to verify CYP2E1 quantitative models and obtain the optimal prediction model of CYP2E1 inhibitor. Meanwhile, one molecular docking program, CDOCKER, was utilized to analyze the interaction pattern between positive compounds and active pocket to establish the optimal screening model of CYP2E1 inhibitors.SVR model and molecular docking prediction model were combined to screen traditional Chinese medicine database (TCMD), which could improve the calculation efficiency and prediction accuracy. 6 376 traditional Chinese medicine (TCM) compounds predicted by SVR model were obtained, and in further verification by using molecular docking model, 247 TCM compounds with potential inhibitory activities against CYP2E1 were finally retained. Some of them have been verified by experiments. The results demonstrated that this study could provide guidance for the virtual screening of CYP450 inhibitors and the prediction of CYPs-mediated DDIs, and also provide references for clinical rational drug use. Copyright© by the Chinese Pharmaceutical Association.

Human Papillomavirus DNA Methylation Predicts Response to Treatment Using Cidofovir and Imiquimod in Vulval Intraepithelial Neoplasia 3.

PubMed

Jones, Sadie E F; Hibbitts, Samantha; Hurt, Christopher N; Bryant, Dean; Fiander, Alison N; Powell, Ned; Tristram, Amanda J

2017-09-15

Purpose: Response rates to treatment of vulval intraepithelial neoplasia (VIN) with imiquimod and cidofovir are approximately 57% and 61%, respectively. Treatment is associated with significant side effects and, if ineffective, risk of malignant progression. Treatment response is not predicted by clinical factors. Identification of a biomarker that could predict response is an attractive prospect. This work investigated HPV DNA methylation as a potential predictive biomarker in this setting. Experimental Design: DNA from 167 cases of VIN 3 from the RT3 VIN clinical trial was assessed. HPV-positive cases were identified using Greiner PapilloCheck and HPV 16 type-specific PCR. HPV DNA methylation status was assessed in three viral regions: E2, L1/L2, and the promoter, using pyrosequencing. Results: Methylation of the HPV E2 region was associated with response to treatment. For cidofovir ( n = 30), median E2 methylation was significantly higher in patients who responded ( P ≤ 0.0001); E2 methylation >4% predicted response with 88.2% sensitivity and 84.6% specificity. For imiquimod ( n = 33), median E2 methylation was lower in patients who responded to treatment ( P = 0.03; not significant after Bonferroni correction); E2 methylation <4% predicted response with 70.6% sensitivity and 62.5% specificity. Conclusions: These data indicate that cidofovir and imiquimod may be effective in two biologically defined groups. HPV E2 DNA methylation demonstrated potential as a predictive biomarker for the treatment of VIN with cidofovir and may warrant investigation in a biomarker-guided clinical trial. Clin Cancer Res; 23(18); 5460-8. ©2017 AACR . ©2017 American Association for Cancer Research.

e-Cow: an animal model that predicts herbage intake, milk yield and live weight change in dairy cows grazing temperate pastures, with and without supplementary feeding.

PubMed

Baudracco, J; Lopez-Villalobos, N; Holmes, C W; Comeron, E A; Macdonald, K A; Barry, T N; Friggens, N C

2012-06-01

This animal simulation model, named e-Cow, represents a single dairy cow at grazing. The model integrates algorithms from three previously published models: a model that predicts herbage dry matter (DM) intake by grazing dairy cows, a mammary gland model that predicts potential milk yield and a body lipid model that predicts genetically driven live weight (LW) and body condition score (BCS). Both nutritional and genetic drives are accounted for in the prediction of energy intake and its partitioning. The main inputs are herbage allowance (HA; kg DM offered/cow per day), metabolisable energy and NDF concentrations in herbage and supplements, supplements offered (kg DM/cow per day), type of pasture (ryegrass or lucerne), days in milk, days pregnant, lactation number, BCS and LW at calving, breed or strain of cow and genetic merit, that is, potential yields of milk, fat and protein. Separate equations are used to predict herbage intake, depending on the cutting heights at which HA is expressed. The e-Cow model is written in Visual Basic programming language within Microsoft Excel®. The model predicts whole-lactation performance of dairy cows on a daily basis, and the main outputs are the daily and annual DM intake, milk yield and changes in BCS and LW. In the e-Cow model, neither herbage DM intake nor milk yield or LW change are needed as inputs; instead, they are predicted by the e-Cow model. The e-Cow model was validated against experimental data for Holstein-Friesian cows with both North American (NA) and New Zealand (NZ) genetics grazing ryegrass-based pastures, with or without supplementary feeding and for three complete lactations, divided into weekly periods. The model was able to predict animal performance with satisfactory accuracy, with concordance correlation coefficients of 0.81, 0.76 and 0.62 for herbage DM intake, milk yield and LW change, respectively. Simulations performed with the model showed that it is sensitive to genotype by feeding environment interactions. The e-Cow model tended to overestimate the milk yield of NA genotype cows at low milk yields, while it underestimated the milk yield of NZ genotype cows at high milk yields. The approach used to define the potential milk yield of the cow and equations used to predict herbage DM intake make the model applicable for predictions in countries with temperate pastures.

The B 1Πu potential energy curve and dissociation energy of 39K2

NASA Astrophysics Data System (ADS)

Heinze, Johannes; Engelke, Friedrich

1988-07-01

The 39K2 B 1Πu potential energy curve has been determined using laser spectroscopic techniques and quantum mechanical calculations. The dissociation energy is 2407.6±0.5 cm-1 (0.2985±0.0001 eV) including a potential barrier of 298±10 cm-1 (0.037±0.0013 eV) found with its maximum at 8.08±0.05 Å (15.3±0.1 bohr). The long-range behavior matches smoothly onto the form predicted from dispersion forces. The dissociation energy of the ground state X 1Σ+g, obtained by a long-range extrapolation of the vibrational separations, is De =4444±10 cm-1 (0.5506±0.0013 eV), in agreement with recent theoretical prediction.

CURRENT STATE OF PREDICTING THE RESPIRATORY ALLERGY POTENTIAL OF CHEMICALS: WHAT ARE THE ISSUES?

EPA Science Inventory

Current State of Predicting the Respiratory Allergy Potential of Chemicals: What Are the Issues? M I. Gilmour1 and S. E. Loveless2, 1USEPA, Research Triangle Park, NC and 2DuPont Haskell Laboratory, Newark, DE.

Many chemicals are clearly capable of eliciting immune respon...

Diversity of Microbial Carbohydrate-Active enZYmes (CAZYmes) Associated with Freshwater and Soil Samples from Caatinga Biome.

PubMed

Andrade, Ana Camila; Fróes, Adriana; Lopes, Fabyano Álvares Cardoso; Thompson, Fabiano L; Krüger, Ricardo Henrique; Dinsdale, Elizabeth; Bruce, Thiago

2017-07-01

Semi-arid and arid areas occupy about 33% of terrestrial ecosystems. However, little information is available about microbial diversity in the semi-arid Caatinga, which represents a unique biome that extends to about 11% of the Brazilian territory and is home to extraordinary diversity and high endemism level of species. In this study, we characterized the diversity of microbial genes associated with biomass conversion (carbohydrate-active enzymes, or so-called CAZYmes) in soil and freshwater of the Caatinga. Our results showed distinct CAZYme profiles in the soil and freshwater samples. Glycoside hydrolases and glycosyltransferases were the most abundant CAZYme families, with glycoside hydrolases more dominant in soil (∼44%) and glycosyltransferases more abundant in freshwater (∼50%). The abundances of individual glycoside hydrolase, glycosyltransferase, and carbohydrate-binding module subfamilies varied widely between soil and water samples. A predominance of glycoside hydrolases was observed in soil, and a higher contribution of enzymes involved in carbohydrate biosynthesis was observed in freshwater. The main taxa associated with the CAZYme sequences were Planctomycetia (relative abundance in soil, 29%) and Alphaproteobacteria (relative abundance in freshwater, 27%). Approximately 5-7% of CAZYme sequences showed low similarity with sequences deposited in non-redundant databases, suggesting putative homologues. Our findings represent a first attempt to describe specific microbial CAZYme profiles for environmental samples. Characterizing these enzyme groups associated with the conversion of carbohydrates in nature will improve our understanding of the significant roles of enzymes in the carbon cycle. We identified a CAZYme signature that can be used to discriminate between soil and freshwater samples, and this signature may be related to the microbial species adapted to the habitat. The data show the potential ecological roles of the CAZYme repertoire and associated biotechnological applications.

Can nutrient status of four woody plant species be predicted using field spectrometry?

NASA Astrophysics Data System (ADS)

Ferwerda, Jelle G.; Skidmore, Andrew K.

This paper demonstrates the potential of hyperspectral remote sensing to predict the chemical composition (i.e., nitrogen, phosphorous, calcium, potassium, sodium, and magnesium) of three tree species (i.e., willow, mopane and olive) and one shrub species (i.e., heather). Reflectance spectra, derivative spectra and continuum-removed spectra were compared in terms of predictive power. Results showed that the best predictions for nitrogen, phosphorous, and magnesium occur when using derivative spectra, and the best predictions for sodium, potassium, and calcium occur when using continuum-removed data. To test whether a general model for multiple species is also valid for individual species, a bootstrapping routine was applied. Prediction accuracies for the individual species were lower then prediction accuracies obtained for the combined dataset for all except one element/species combination, indicating that indices with high prediction accuracies at the landscape scale are less appropriate to detect the chemical content of individual species.

SWAT Model Prediction of Phosphorus Loading in a South Carolina Karst Watershed with a Downstream Embayment

Treesearch

Devendra M. Amatya; Manoj K. Jha; Thomas M. Williams; Amy E. Edwards; Daniel R. Hitchcock

2013-01-01

The SWAT model was used to predict total phosphorus (TP) loadings for a 1555-ha karst watershed—Chapel Branch Creek (CBC)—which drains to a lake via a reservoir-like embayment (R-E). The model was first tested for monthly streamflow predictions from tributaries draining three potential source areas as well as the downstream R-E, followed by TP loadings using data...

Active sites prediction and binding analysis E1-E2 protein human papillomavirus with biphenylsulfonacetic acid

NASA Astrophysics Data System (ADS)

Iryani, I.; Amelia, F.; Iswendi, I.

2018-04-01

Cervix cancer triggered by Human papillomavirus infection is the second cause to woman death in worldwide. The binding site of E1-E2 protein of HPV 16 is not known from a 3-D structure yet, so in this study we address this issue to study the structure of E1-E2 protein from Human papillomavirus type 16 and to find its potential binding sites using biphenylsulfonacetic acid as inhibitor. Swiss model was used for 3D structure prediction and PDB: 2V9P (E1 protein) and 2NNU (E2 protein) having 52.32% and 100% identity respectively was selected as a template. The 3D model structure developed of E1 and E2 in the core and allowed regions were 99.2% and 99.5%. The ligand binding sites were predicted using online server meta pocket 2.0 and MOE 2009.10 was used for docking. E1-and E2 protein of HPV-16 has three potential binding site that can interact with the inhibitors. The Docking biphenylsulfonacetic acid using these binding sites shows that ligand interact with the protein through hydrogen bonds on Lys 403, Arg 410, His 551 in the first pocket, on Tyr 32, Leu 99 in the second pocket, and Lys 558m Lys 517 in the third pocket.

Predicted facies, sedimentary structures and potential resources of Jurassic petroleum complex in S-E sWestern Siberia (based on well logging data)

NASA Astrophysics Data System (ADS)

Prakojo, F.; Lobova, G.; Abramova, R.

2015-11-01

This paper is devoted to the current problem in petroleum geology and geophysics- prediction of facies sediments for further evaluation of productive layers. Applying the acoustic method and the characterizing sedimentary structure for each coastal-marine-delta type was determined. The summary of sedimentary structure characteristics and reservoir properties (porosity and permeability) of typical facies were described. Logging models SP, EL and GR (configuration, curve range) in interpreting geophysical data for each litho-facies were identified. According to geophysical characteristics these sediments can be classified as coastal-marine-delta. Prediction models for potential Jurassic oil-gas bearing complexes (horizon J11) in one S-E Western Siberian deposit were conducted. Comparing forecasting to actual testing data of layer J11 showed that the prediction is about 85%.

A Lonely Search?: Risk for Depression When Spirituality Exceeds Religiosity.

PubMed

Vittengl, Jeffrey R

2018-05-01

This study clarified longitudinal relations of spirituality and religiosity with depression. Spirituality's potential emphasis on internal (e.g., intrapsychic search for meaning) versus religiosity's potential emphasis on external (e.g., engagement in socially-sanctioned belief systems) processes may parallel depression-linked cognitive-behavioral phenomena (e.g., rumination and loneliness) conceptually. Thus, this study tested the hypothesis that greater spirituality than religiosity, separate from the overall level of spirituality and religiosity, predicts longitudinal increases in depression. A national sample of midlife adults completed diagnostic interviews and questionnaires of spiritual and religious intensity up to three times over 18 years. In time-lagged multilevel models, overall spirituality plus religiosity did not predict depression. However, in support of the hypothesis, greater spirituality than religiosity significantly predicted subsequent increases in depressive symptoms and risk for major depressive disorder (odds ratio = 1.34). If replicated, the relative balance of spirituality and religiosity may inform depression assessment and prevention efforts.

Potential evapotranspiration and the likelihood of future drought

NASA Technical Reports Server (NTRS)

Rind, D.; Hansen, J.; Goldberg, R.; Rosenzweig, C.; Ruedy, R.

1990-01-01

The possibility that the greenhouse warming predicted by the GISS general-circulation model and other GCMs could lead to severe droughts is investigated by means of numerical simulations, with a focus on the role of potential evapotranspiration E(P). The relationships between precipitation (P), E(P), soil moisture, and vegetation changes in GCMs are discussed; the empirically derived Palmer drought-intensity index and a new supply-demand index (SDDI) based on changes in P - E(P) are described; and simulation results for the period 1960-2060 are presented in extensive tables, graphs, and computer-generated color maps. Simulations with both drought indices predict increasing drought frequency for the U.S., with effects already apparent in the 1990s and a 50-percent frequency of severe droughts by the 2050s. Analyses of arid periods during the Mesozoic and Cenozoic are shown to support the use of the SDDI in GCM drought prediction.

Predictable patterns of the May-June rainfall anomaly over East Asia

NASA Astrophysics Data System (ADS)

Xing, Wen; Wang, Bin; Yim, So-Young; Ha, Kyung-Ja

2017-02-01

During early summer (May-June, MJ), East Asia (EA) subtropical front is a defining feature of Asian monsoon, which produces the most prominent precipitation band in the global subtropics. Here we show that dynamical prediction of early summer EA (20°N-45°N, 100°E-130°E) rainfall made by four coupled climate models' ensemble hindcast (1979-2010) yields only a moderate skill and cannot be used to estimate predictability. The present study uses an alternative, empirical orthogonal function (EOF)-based physical-empirical (P-E) model approach to predict rainfall anomaly pattern and estimate its potential predictability. The first three leading modes are physically meaningful and can be, respectively, attributed to (a) the interaction between the anomalous western North Pacific subtropical high and underlying Indo-Pacific warm ocean, (b) the forcing associated with North Pacific sea surface temperature (SST) anomaly, and (c) the development of equatorial central Pacific SST anomalies. A suite of P-E models is established to forecast the first three leading principal components. All predictors are 0 month ahead of May, so the prediction here is named as a 0 month lead prediction. The cross-validated hindcast results demonstrate that these modes may be predicted with significant temporal correlation skills (0.48-0.72). Using the predicted principal components and the corresponding EOF patterns, the total MJ rainfall anomaly was hindcasted for the period of 1979-2015. The time-mean pattern correlation coefficient (PCC) score reaches 0.38, which is significantly higher than dynamical models' multimodel ensemble skill (0.21). The estimated potential maximum attainable PCC is around 0.65, suggesting that the dynamical prediction models may have large rooms to improve. Limitations and future work are discussed.

Predicting Potential Changes in Suitable Habitat and Distribution by 2100 for Tree Species of the Eastern United States

Treesearch

Louis R Iverson; Anantha M. Prasad; Mark W. Schwartz; Mark W. Schwartz

2005-01-01

We predict current distribution and abundance for tree species present in eastern North America, and subsequently estimate potential suitable habitat for those species under a changed climate with 2 x CO2. We used a series of statistical models (i.e., Regression Tree Analysis (RTA), Multivariate Adaptive Regression Splines (MARS), Bagging Trees (...

Effect of annealing on structural changes and oxygen diffusion in amorphous HfO2 using classical molecular dynamics

NASA Astrophysics Data System (ADS)

Shen, Wenqing; Kumari, Niru; Gibson, Gary; Jeon, Yoocharn; Henze, Dick; Silverthorn, Sarah; Bash, Cullen; Kumar, Satish

2018-02-01

Non-volatile memory is a promising alternative to present memory technologies. Oxygen vacancy diffusion has been widely accepted as one of the reasons for the resistive switching mechanism of transition-metal-oxide based resistive random access memory. In this study, molecular dynamics simulation is applied to investigate the diffusion coefficient and activation energy of oxygen in amorphous hafnia. Two sets of empirical potential, Charge-Optimized Many-Body (COMB) and Morse-BKS (MBKS), were considered to investigate the structural and diffusion properties at different temperatures. COMB predicts the activation energy of 0.53 eV for the temperature range of 1000-2000 K, while MBKS predicts 2.2 eV at high temperature (1600-2000 K) and 0.36 eV at low temperature (1000-1600 K). Structural changes and appearance of nano-crystalline phases with increasing temperature might affect the activation energy of oxygen diffusion predicted by MBKS, which is evident from the change in coordination number distribution and radial distribution function. None of the potentials make predictions that are fully consistent with density functional theory simulations of both the structure and diffusion properties of HfO2. This suggests the necessity of developing a better multi-body potential that considers charge exchange.

On Predicting Form and Meaning in a Second Language

ERIC Educational Resources Information Center

Ito, Aine; Martin, Andrea E.; Nieuwland, Mante S.

2017-01-01

We used event-related potentials (ERP) to investigate whether Spanish-English bilinguals preactivate form and meaning of predictable words. Participants read high-cloze sentence contexts (e.g., "The student is going to the library to borrow a..."), followed by the predictable word ("book"), a word that was form-related…

Biomarker Surrogates Do Not Accurately Predict Sputum Eosinophils and Neutrophils in Asthma

PubMed Central

Hastie, Annette T.; Moore, Wendy C.; Li, Huashi; Rector, Brian M.; Ortega, Victor E.; Pascual, Rodolfo M.; Peters, Stephen P.; Meyers, Deborah A.; Bleecker, Eugene R.

2013-01-01

Background Sputum eosinophils (Eos) are a strong predictor of airway inflammation, exacerbations, and aid asthma management, whereas sputum neutrophils (Neu) indicate a different severe asthma phenotype, potentially less responsive to TH2-targeted therapy. Variables such as blood Eos, total IgE, fractional exhaled nitric oxide (FeNO) or FEV1% predicted, may predict airway Eos, while age, FEV1%predicted, or blood Neu may predict sputum Neu. Availability and ease of measurement are useful characteristics, but accuracy in predicting airway Eos and Neu, individually or combined, is not established. Objectives To determine whether blood Eos, FeNO, and IgE accurately predict sputum eosinophils, and age, FEV1% predicted, and blood Neu accurately predict sputum neutrophils (Neu). Methods Subjects in the Wake Forest Severe Asthma Research Program (N=328) were characterized by blood and sputum cells, healthcare utilization, lung function, FeNO, and IgE. Multiple analytical techniques were utilized. Results Despite significant association with sputum Eos, blood Eos, FeNO and total IgE did not accurately predict sputum Eos, and combinations of these variables failed to improve prediction. Age, FEV1%predicted and blood Neu were similarly unsatisfactory for prediction of sputum Neu. Factor analysis and stepwise selection found FeNO, IgE and FEV1% predicted, but not blood Eos, correctly predicted 69% of sputum Eos

Predictive microbiology in food packaging applications

USDA-ARS?s Scientific Manuscript database

Predictive microbiology including growth, inactivation, surface transfer (or cross-contamination), and survival, plays important roles in understanding microbial food safety. Growth models may involve the growth potential of a specified pathogen under different stresses, e.g., temperature, pH, wate...

Bioaccumulation Assessment using Predictive Approaches

EPA Science Inventory

Mandated efforts to assess chemicals for their potential to bioaccumulate within the environment are increasingly moving into the realm of data inadequacy. Consequently, there is an increasing reliance on predictive tools to complete regulatory requirements in a timely and cost-e...

Influence of soil texture on hydraulic properties and water relations of a dominant warm-desert phreatophyte.

PubMed

Hultine, K R; Koepke, D F; Pockman, W T; Fravolini, A; Sperry, J S; Williams, D G

2006-03-01

We investigated hydraulic constraints on water uptake by velvet mesquite (Prosopis velutina Woot.) at a site with sandy-loam soil and at a site with loamy-clay soil in southeastern Arizona, USA. We predicted that trees on sandy-loam soil have less negative xylem and soil water potentials during drought and a lower resistance to xylem cavitation, and reach E(crit) (the maximum steady-state transpiration rate without hydraulic failure) at higher soil water potentials than trees on loamy-clay soil. However, minimum predawn leaf xylem water potentials measured during the height of summer drought were significantly lower at the sandy-loam site (-3.5 +/- 0.1 MPa; all errors are 95% confidence limits) than at the loamy-clay site (-2.9 +/- 0.1 MPa). Minimum midday xylem water potentials also were lower at the sandy-loam site (-4.5 +/- 0.1 MPa) than at the loamy-clay site (-4.0 +/- 0.1 MPa). Despite the differences in leaf water potentials, there were no significant differences in either root or stem xylem embolism, mean cavitation pressure or Psi(95) (xylem water potential causing 95% cavitation) between trees at the two sites. A soil-plant hydraulic model parameterized with the field data predicted that E(crit) approaches zero at a substantially higher bulk soil water potential (Psi(s)) on sandy-loam soil than on loamy-clay soil, because of limiting rhizosphere conductance. The model predicted that transpiration at the sandy-loam site is limited by E(crit) and is tightly coupled to Psi(s) over much of the growing season, suggesting that seasonal transpiration fluxes at the sandy-loam site are strongly linked to intra-annual precipitation pulses. Conversely, the model predicted that trees on loamy-clay soil operate below E(crit) throughout the growing season, suggesting that fluxes on fine-textured soils are closely coupled to inter-annual changes in precipitation. Information on the combined importance of xylem and rhizosphere constraints to leaf water supply across soil texture gradients provides insight into processes controlling plant water balance and larger scale hydrologic processes.

Coupling of Bayesian Networks with GIS for wildfire risk assessment on natural and agricultural areas of the Mediterranean

NASA Astrophysics Data System (ADS)

Scherb, Anke; Papakosta, Panagiota; Straub, Daniel

2014-05-01

Wildfires cause severe damages to ecosystems, socio-economic assets, and human lives in the Mediterranean. To facilitate coping with wildfire risks, an understanding of the factors influencing wildfire occurrence and behavior (e.g. human activity, weather conditions, topography, fuel loads) and their interaction is of importance, as is the implementation of this knowledge in improved wildfire hazard and risk prediction systems. In this project, a probabilistic wildfire risk prediction model is developed, with integrated fire occurrence and fire propagation probability and potential impact prediction on natural and cultivated areas. Bayesian Networks (BNs) are used to facilitate the probabilistic modeling. The final BN model is a spatial-temporal prediction system at the meso scale (1 km2 spatial and 1 day temporal resolution). The modeled consequences account for potential restoration costs and production losses referred to forests, agriculture, and (semi-) natural areas. BNs and a geographic information system (GIS) are coupled within this project to support a semi-automated BN model parameter learning and the spatial-temporal risk prediction. The coupling also enables the visualization of prediction results by means of daily maps. The BN parameters are learnt for Cyprus with data from 2006-2009. Data from 2010 is used as validation data set. A special focus is put on the performance evaluation of the BN for fire occurrence, which is modeled as binary classifier and thus, could be validated by means of Receiver Operator Characteristic (ROC) curves. With the final best models, AUC values of more than 70% for validation could be achieved, which indicates potential for reliable prediction performance via BN. Maps of selected days in 2010 are shown to illustrate final prediction results. The resulting system can be easily expanded to predict additional expected damages in the mesoscale (e.g. building and infrastructure damages). The system can support planning of preventive measures (e.g. state resources allocation for wildfire prevention and preparedness) and assist recuperation plans of damaged areas.

How predictable is the anomaly pattern of the Indian summer rainfall?

NASA Astrophysics Data System (ADS)

Li, Juan; Wang, Bin

2016-05-01

Century-long efforts have been devoted to seasonal forecast of Indian summer monsoon rainfall (ISMR). Most studies of seasonal forecast so far have focused on predicting the total amount of summer rainfall averaged over the entire India (i.e., all Indian rainfall index-AIRI). However, it is practically more useful to forecast anomalous seasonal rainfall distribution (anomaly pattern) across India. The unknown science question is to what extent the anomalous rainfall pattern is predictable. This study attempted to address this question. Assessment of the 46-year (1960-2005) hindcast made by the five state-of-the-art ENSEMBLE coupled dynamic models' multi-model ensemble (MME) prediction reveals that the temporal correlation coefficient (TCC) skill for prediction of AIRI is 0.43, while the area averaged TCC skill for prediction of anomalous rainfall pattern is only 0.16. The present study aims to estimate the predictability of ISMR on regional scales by using Predictable Mode Analysis method and to develop a set of physics-based empirical (P-E) models for prediction of ISMR anomaly pattern. We show that the first three observed empirical orthogonal function (EOF) patterns of the ISMR have their distinct dynamical origins rooted in an eastern Pacific-type La Nina, a central Pacific-type La Nina, and a cooling center near dateline, respectively. These equatorial Pacific sea surface temperature anomalies, while located in different longitudes, can all set up a specific teleconnection pattern that affects Indian monsoon and results in different rainfall EOF patterns. Furthermore, the dynamical models' skill for predicting ISMR distribution primarily comes primarily from these three modes. Therefore, these modes can be regarded as potentially predictable modes. If these modes are perfectly predicted, about 51 % of the total observed variability is potentially predictable. Based on understanding the lead-lag relationships between the lower boundary anomalies and the predictable modes, a set of P-E models is established to predict the principal component of each predictable mode, so that the ISMR anomaly pattern can be predicted by using the sum of the predictable modes. Three validation schemes are used to assess the performance of the P-E models' hindcast and independent forecast. The validated TCC skills of the P-E model here are more than doubled that of dynamical models' MME hindcast, suggesting a large room for improvement of the current dynamical prediction. The methodology proposed here can be applied to a wide range of climate prediction and predictability studies. The limitation and future improvement are also discussed.

Wave Energy Potential in the Eastern Mediterranean Levantine Basin. An Integrated 10-year Study

DTIC Science & Technology

2014-01-01

SUBTITLE Wave energy potential in the Eastern Mediterranean Levantine Basin. An integrated 10-year study 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c... Cardone CV, Ewing JA, et al. The WAM model e a third generation ocean wave prediction model. J Phys Oceanogr 1988;18(12):1775e810. [70] Varinou M

Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil.

PubMed

Olawoyin, Richard

2016-10-01

The backpropagation (BP) artificial neural network (ANN) is a renowned and extensively functional mathematical tool used for time-series predictions and approximations; which also define results for non-linear functions. ANNs are vital tools in the predictions of toxicant levels, such as polycyclic aromatic hydrocarbons (PAH) potentially derived from anthropogenic activities in the microenvironment. In the present work, BP ANN was used as a prediction tool to study the potential toxicity of PAH carcinogens (PAHcarc) in soils. Soil samples (16 × 4 = 64) were collected from locations in South-southern Nigeria. The concentration of PAHcarc in laboratory cultivated white melilot, Melilotus alba roots grown on treated soils was predicted using ANN model training. Results indicated the Levenberg-Marquardt back-propagation training algorithm converged in 2.5E+04 epochs at an average RMSE value of 1.06E-06. The averagedR(2) comparison between the measured and predicted outputs was 0.9994. It may be deduced from this study that, analytical processes involving environmental risk assessment as used in this study can successfully provide prompt prediction and source identification of major soil toxicants. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Impact Hazard in the Context of Other Natural Hazards and Predictive Science

NASA Astrophysics Data System (ADS)

Chapman, C. R.

1998-09-01

The hazard due to impact of asteroids and comets has been recognized as analogous, in some ways, to other infrequent but consequential natural hazards (e.g. floods and earthquakes). Yet, until recently, astronomers and space agencies have felt no need to do what their colleagues and analogous agencies must do in order the assess, quantify, and communicate predictions to those with a practical interest in the predictions (e.g. public officials who must assess the threats, prepare for mitigation, etc.). Recent heightened public interest in the impact hazard, combined with increasing numbers of "near misses" (certain to increase as Spaceguard is implemented) requires that astronomers accept the responsibility to place their predictions and assessments in terms that may be appropriately considered. I will report on preliminary results of a multi-year GSA/NCAR study of "Prediction in the Earth Sciences: Use and Misuse in Policy Making" in which I have represented the impact hazard, while others have treated earthquakes, floods, weather, global climate change, nuclear waste disposal, acid rain, etc. The impact hazard presents an end-member example of a natural hazard, helping those dealing with more prosaic issues to learn from an extreme. On the other hand, I bring to the astronomical community some lessons long adopted in other cases: the need to understand the policy purposes of impact predictions, the need to assess potential societal impacts, the requirements to very carefully assess prediction uncertainties, considerations of potential public uses of the predictions, awareness of ethical considerations (e.g. conflicts of interest) that affect predictions and acceptance of predictions, awareness of appropriate means for publicly communicating predictions, and considerations of the international context (especially for a hazard that knows no national boundaries).

gCUP: rapid GPU-based HIV-1 co-receptor usage prediction for next-generation sequencing.

PubMed

Olejnik, Michael; Steuwer, Michel; Gorlatch, Sergei; Heider, Dominik

2014-11-15

Next-generation sequencing (NGS) has a large potential in HIV diagnostics, and genotypic prediction models have been developed and successfully tested in the recent years. However, albeit being highly accurate, these computational models lack computational efficiency to reach their full potential. In this study, we demonstrate the use of graphics processing units (GPUs) in combination with a computational prediction model for HIV tropism. Our new model named gCUP, parallelized and optimized for GPU, is highly accurate and can classify >175 000 sequences per second on an NVIDIA GeForce GTX 460. The computational efficiency of our new model is the next step to enable NGS technologies to reach clinical significance in HIV diagnostics. Moreover, our approach is not limited to HIV tropism prediction, but can also be easily adapted to other settings, e.g. drug resistance prediction. The source code can be downloaded at http://www.heiderlab.de d.heider@wz-straubing.de. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A data mining approach to predict in situ chlorinated ethene detoxification potential

NASA Astrophysics Data System (ADS)

Lee, J.; Im, J.; Kim, U.; Loeffler, F. E.

2015-12-01

Despite major advances in physicochemical remediation technologies, in situ biostimulation and bioaugmentation treatment aimed at stimulating Dehalococcoides mccartyi (Dhc) reductive dechlorination activity remains a cornerstone approach to remedy sites impacted with chlorinated ethenes. In practice, selecting the best remedial strategy is challenging due to uncertainties associated with the microbiology (e.g., presence and activity of Dhc) and geochemical factors influencing Dhc activity. Extensive groundwater datasets collected over decades of monitoring exist, but have not been systematically analyzed. In the present study, geochemical and microbial data sets collected from 35 wells at 5 contaminated sites were used to develop a predictive empirical model using a machine learning algorithm (i) to rank the relative importance of parameters that affect in situ reductive dechlorination potential, and (ii) to provide recommendations for selecting the optimal remediation strategy at a specific site. Classification and regression tree (CART) analysis was applied, and a representative classification tree model was developed that allowed short-term prediction of dechlorination potential. Indirect indicators for low dissolved oxygen (e.g., low NO3-and NO2-, high Fe2+ and CH4) were the most influential factors for predicting dechlorination potential, followed by total organic carbon content (TOC) and Dhc cell abundance. These findings indicate that machine learning-based data mining techniques applied to groundwater monitoring data can lead to the development of predictive groundwater remediation models. A major need for improving the predictive capabilities of the data mining approach is a curated, up-to-date and comprehensive collection of groundwater monitoring data.

The adiabatic energy change of plasma electrons and the frame dependence of the cross-shock potential at collisionless magnetosonic shock waves

NASA Technical Reports Server (NTRS)

Goodrich, C. C.; Scudder, J. D.

1984-01-01

The adiabatic energy gain of electrons in the stationary electric and magnetic field structure of collisionless shock waves was examined analytically in reference to conditions of the earth's bow shock. The study was performed to characterize the behavior of electrons interacting with the cross-shock potential. A normal incidence frame (NIF) was adopted in order to calculate the reversible energy change across a time stationary shock, and comparisons were made with predictions made by the de Hoffman-Teller (HT) model (1950). The electron energy gain, about 20-50 eV, is demonstrated to be consistent with a 200-500 eV potential jump in the bow shock quasi-perpendicular geometry. The electrons lose energy working against the solar wind motional electric field. The reversible energy process is close to that modeled by HT, which predicts that the motional electric field vanishes and the electron energy gain from the electric potential is equated to the ion energy loss to the potential.

New equations for predicting postoperative risk in patients with hip fracture.

PubMed

Hirose, Jun; Ide, Junji; Irie, Hiroki; Kikukawa, Kenshi; Mizuta, Hiroshi

2009-12-01

Predicting the postoperative course of patients with hip fractures would be helpful for surgical planning and risk management. We therefore established equations to predict the morbidity and mortality rates in candidates for hip fracture surgery using the Estimation of Physiologic Ability and Surgical Stress (E-PASS) risk-scoring system. First we evaluated the correlation between the E-PASS scores and postoperative morbidity and mortality rates in all 722 patients surgically treated for hip fractures during the study period (Group A). Next we established equations to predict morbidity and mortality rates. We then applied these equations to all 633 patients with hip fractures treated at seven other hospitals (Group B) and compared the predicted and actual morbidity and mortality rates to assess the predictive ability of the E-PASS and Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) systems. The ratio of actual to predicted morbidity and mortality rates was closer to 1.0 with the E-PASS than the POSSUM system. Our data suggest the E-PASS scoring system is useful for defining postoperative risk and its underlying algorithm accurately predicts morbidity and mortality rates in patients with hip fractures before surgery. This information then can be used to manage their condition and potentially improve treatment outcomes. Level II, prognostic study. See the Guidelines for Authors for a complete description of levels of evidence.

The Efficacy of Intraoperative Neurophysiological Monitoring Using Transcranial Electrically Stimulated Muscle-evoked Potentials (TcE-MsEPs) for Predicting Postoperative Segmental Upper Extremity Motor Paresis After Cervical Laminoplasty.

PubMed

Fujiwara, Yasushi; Manabe, Hideki; Izumi, Bunichiro; Tanaka, Hiroyuki; Kawai, Kazumi; Tanaka, Nobuhiro

2016-05-01

Prospective study. To investigate the efficacy of transcranial electrically stimulated muscle-evoked potentials (TcE-MsEPs) for predicting postoperative segmental upper extremity palsy following cervical laminoplasty. Postoperative segmental upper extremity palsy, especially in the deltoid and biceps (so-called C5 palsy), is the most common complication following cervical laminoplasty. Some papers have reported that postoperative C5 palsy cannot be predicted by TcE-MsEPs, although others have reported that it can be predicted. This study included 160 consecutive cases that underwent open-door laminoplasty, and TcE-MsEP monitoring was performed in the biceps brachii, triceps brachii, abductor digiti minimi, tibialis anterior, and abductor hallucis. A >50% decrease in the wave amplitude was defined as an alarm point. According to the monitoring alarm, interventions were performed, which include steroid administration, foraminotomies, etc. Postoperative deltoid and biceps palsy occurred in 5 cases. Among the 155 cases without segmental upper extremity palsy, there were no monitoring alarms. Among the 5 deltoid and biceps palsy cases, 3 had significant wave amplitude decreases in the biceps during surgery, and palsy occurred when the patients awoke from anesthesia (acute type). In the other 2 cases in which the palsy occurred 2 days after the operation (delayed type), there were no significant wave decreases. In all of the cases, the palsy was completely resolved within 6 months. The majority of C5 palsies have been reported to occur several days after surgery, but some of them have been reported to occur immediately after surgery. Our results demonstrated that TcE-MsEPs can predict the acute type, whereas the delayed type cannot be predicted. A >50% wave amplitude decrease in the biceps is useful to predict acute-type segmental upper extremity palsy. Further examination about the interventions for monitoring alarm will be essential for preventing palsy.

Computational Approaches to Predict Indices of Cyanobacteria Toxicity.

EPA Science Inventory

As nutrient inputs increase, productivity increases and lakes transition from low trophic state (e.g., oligotrophic) to higher trophic states (e.g., eutrophic). These broad trophic state classifications are good predictors of ecosystem health and the potential for ecosystem serv...

Computational Approaches to Predict Indices of Cyanobacteria Toxicity

EPA Science Inventory

As nutrient inputs increase, productivity increases and lakes transition from low trophic state (e.g. oligotrophic) to higher trophic states (e.g. eutrophic). These broad trophic state classifications are good predictors of ecosystem health and the potential for ecosystem servic...

Psychophysiological prediction of choice: relevance to insight and drug addiction

PubMed Central

Moeller, Scott J.; Hajcak, Greg; Parvaz, Muhammad A.; Dunning, Jonathan P.; Volkow, Nora D.

2012-01-01

An important goal of addiction research and treatment is to predict behavioural responses to drug-related stimuli. This goal is especially important for patients with impaired insight, which can interfere with therapeutic interventions and potentially invalidate self-report questionnaires. This research tested (i) whether event-related potentials, specifically the late positive potential, predict choice to view cocaine images in cocaine addiction; and (ii) whether such behaviour prediction differs by insight (operationalized in this study as self-awareness of image choice). Fifty-nine cocaine abusers and 32 healthy controls provided data for the following laboratory components that were completed in a fixed-sequence (to establish prediction): (i) event-related potential recordings while passively viewing pleasant, unpleasant, neutral and cocaine images, during which early (400–1000 ms) and late (1000–2000 ms) window late positive potentials were collected; (ii) self-reported arousal ratings for each picture; and (iii) two previously validated tasks: one to assess choice for viewing these same images, and the other to group cocaine abusers by insight. Results showed that pleasant-related late positive potentials and arousal ratings predicted pleasant choice (the choice to view pleasant pictures) in all subjects, validating the method. In the cocaine abusers, the predictive ability of the late positive potentials and arousal ratings depended on insight. Cocaine-related late positive potentials better predicted cocaine image choice in cocaine abusers with impaired insight. Another emotion-relevant event-related potential component (the early posterior negativity) did not show these results, indicating specificity of the late positive potential. In contrast, arousal ratings better predicted respective cocaine image choice (and actual cocaine use severity) in cocaine abusers with intact insight. Taken together, the late positive potential could serve as a biomarker to help predict drug-related choice—and possibly associated behaviours (e.g. drug seeking in natural settings, relapse after treatment)—when insight (and self-report) is compromised. PMID:23148349

On the role of mid-infrared predicted phenotypes in fertility and health dairy breeding programs.

PubMed

Bastin, C; Théron, L; Lainé, A; Gengler, N

2016-05-01

Fertility and health traits are of prime importance in dairy breeding programs. However, these traits are generally complex, difficult to record, and lowly heritable (<0.10), thereby hampering genetic improvement in disease resistance and fertility. Hence, indicators are useful in the prediction of genetic merit for fertility and health traits as long as they are easier to measure than direct fitness traits, heritable, and genetically correlated. Considering that changes in (fine) milk composition over a lactation reflect the physiological status of the cow, mid-infrared (MIR) analysis of milk opens the door to a wide range of potential indicator traits of fertility and health. Previous studies investigated the phenotypic and genetic relationships between fertility and MIR-predicted phenotypes, most being related to negative postpartum energy balance and body fat mobilization (e.g., fat:protein ratio, urea, fatty acids profile). Results showed that a combination of various fatty acid traits (e.g., C18:1 cis-9 and C10:0) could be used to improve fertility. Furthermore, occurrence of (sub)clinical ketosis has been related to milk-based phenotypes such as fat:protein ratio, fatty acids, and ketone bodies. Hence, MIR-predicted acetone and β-hydroxybutyrate contents in milk could be useful for breeding cows less susceptible to ketosis. Although studies investigating the genetic association among mastitis and MIR-predicted phenotypes are scarce, a wide range of traits, potentially predicted by MIR spectrometry, are worthy of consideration. These include traits related to the disease response of the cow (e.g., lactoferrin), reduced secretory activity (e.g., casein), and the alteration of the blood-milk barrier (e.g., minerals). Moreover, direct MIR prediction of fertility and health traits should be further considered. To conclude, MIR-predicted phenotypes have a role to play in the improvement of dairy cow fertility and health. However, further studies are warranted to (1) grasp underlying associations among MIR-predicted indicator and fitness traits, (2) estimate the genetic parameters, and (3) include these traits in broader breeding strategies. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

New results on low energy exclusive hadronic final states from BABAR

NASA Astrophysics Data System (ADS)

Gary, J. William

2018-01-01

The 3.6 standard deviation discrepancy between the standard model (SM) prediction for the muon anomalous magnetic moment gμ - 2 and the corresponding experimental measurement is one of the most persistent and intriguing potential signals in particle physics for physics beyond the SM. The largest uncertainty in the SM prediction for gμ - 2 arises from the uncertainty in the measured low energy inclusive e+e- → hadrons cross section. New results from the BABAR experiment at SLAC for the e+e- → π+ π- π0 π0 and e+e- → KK ππ cross sections are presented that significantly reduce this uncertainty. New BABAR results for other low energy exclusive hadronic processes are also discussed.

Predicting Subsequent Service Potential for Former Army Prisoners

DTIC Science & Technology

1983-03-25

Esteem Scale, and Hudson’s (1974) Index of Self - Esteem , fou9d significant d_ e.re.e beteen-gr duates-and-non-gj The results of the two studies are...a greater need for autonomy, on the EPPS, and less self - esteem , on the Rosenberg (1965) scale. 400 Table 1 Predicting Graduation/Discharge from...Acceptance (CPI) .588 Education Completed .410 Socialization (CPI) -.445 Highest Pay Grade -.388 Social Presence (CPI) -.443 Marital Status .388 Self - Esteem (Rosenberg

Mining author relationship in scholarly networks based on tripartite citation analysis

PubMed Central

Wang, Xiaohan; Yang, Siluo

2017-01-01

Following scholars in Scientometrics as examples, we develop five author relationship networks, namely, co-authorship, author co-citation (AC), author bibliographic coupling (ABC), author direct citation (ADC), and author keyword coupling (AKC). The time frame of data sets is divided into two periods: before 2011 (i.e., T1) and after 2011 (i.e., T2). Through quadratic assignment procedure analysis, we found that some authors have ABC or AC relationships (i.e., potential communication relationship, PCR) but do not have actual collaborations or direct citations (i.e., actual communication relationship, ACR) among them. In addition, we noticed that PCR and AKC are highly correlated and that the old PCR and the new ACR are correlated and consistent. Such facts indicate that PCR tends to produce academic exchanges based on similar themes, and ABC bears more advantages in predicting potential relations. Based on tripartite citation analysis, including AC, ABC, and ADC, we also present an author-relation mining process. Such process can be used to detect deep and potential author relationships. We analyze the prediction capacity by comparing between the T1 and T2 periods, which demonstrate that relation mining can be complementary in identifying authors based on similar themes and discovering more potential collaborations and academic communities. PMID:29117198

Learning a Continuous-Time Streaming Video QoE Model.

PubMed

Ghadiyaram, Deepti; Pan, Janice; Bovik, Alan C

2018-05-01

Over-the-top adaptive video streaming services are frequently impacted by fluctuating network conditions that can lead to rebuffering events (stalling events) and sudden bitrate changes. These events visually impact video consumers' quality of experience (QoE) and can lead to consumer churn. The development of models that can accurately predict viewers' instantaneous subjective QoE under such volatile network conditions could potentially enable the more efficient design of quality-control protocols for media-driven services, such as YouTube, Amazon, Netflix, and so on. However, most existing models only predict a single overall QoE score on a given video and are based on simple global video features, without accounting for relevant aspects of human perception and behavior. We have created a QoE evaluator, called the time-varying QoE Indexer, that accounts for interactions between stalling events, analyzes the spatial and temporal content of a video, predicts the perceptual video quality, models the state of the client-side data buffer, and consequently predicts continuous-time quality scores that agree quite well with human opinion scores. The new QoE predictor also embeds the impact of relevant human cognitive factors, such as memory and recency, and their complex interactions with the video content being viewed. We evaluated the proposed model on three different video databases and attained standout QoE prediction performance.

Defensive motivation and attention in anticipation of different types of predictable and unpredictable threat: A startle and event-related potential investigation.

PubMed

Nelson, Brady D; Hajcak, Greg

2017-08-01

Predictability is an important characteristic of threat that impacts defensive motivation and attentional engagement. Supporting research has primarily focused on actual threat (e.g., shocks), and it is unclear whether the predictability of less intense threat (e.g., unpleasant pictures) similarly affects motivation and attention. The present study utilized a within-subject design and examined defensive motivation (startle reflex and self-reported anxiety) and attention (probe N100 and P300) in anticipation of shocks and unpleasant pictures during a no, predictable, and unpredictable threat task. This study also examined the impact of predictability on the P300 to shocks and late positive potential (LPP) to unpleasant pictures. The startle reflex and self-reported anxiety were increased in anticipation of both types of threat relative to no threat. Furthermore, startle potentiation in anticipation of unpredictable threat was greater for shocks compared to unpleasant pictures, but there was no difference for predictable threat. The probe N100 was enhanced in anticipation of unpredictable threat relative to predictable threat and no threat, and the probe P300 was suppressed in anticipation of predictable and unpredictable threat relative to no threat. These effects did not differ between the shock and unpleasant picture trials. Finally, the P300 and early LPP component were increased in response to unpredictable relative to predictable shocks and unpleasant pictures, respectively. The present study suggests that the unpredictability of unpleasant pictures increases defensive motivation, but to a lesser degree relative to actual threat. Moreover, unpredictability enhances attentional engagement in anticipation of, and in reaction to, both types of threat. © 2017 Society for Psychophysiological Research.

Molecular cloning, expression, IgE binding activities and in silico epitope prediction of Per a 9 allergens of the American cockroach

PubMed Central

Yang, Haiwei; Chen, Hao; Jin, Min; Xie, Hua; He, Shaoheng; Wei, Ji-Fu

2016-01-01

Per a 9 is a major allergen of the American cockroach (CR), which has been recognized as an important cause of imunoglobulin E-mediated type I hypersensitivity worldwide. However, it is not neasy to obtain a substantial quantity of this allergen for use in functional studies. In the present study, the Per a 9 gene was cloned and expressed in Escherichia coli (E. coli) systems. It was found that 13/16 (81.3%) of the sera from patients with allergies caused by the American CR reacted to Per a 9, as assessed by enzyme-linked immunosorbent assay, confirming that Per a 9 is a major allergen of CR. The induction of the expression of CD63 and CCR3 in passively sensitized basophils (from sera of patients with allergies caused by the American CR) by approximately 4.2-fold indicated that recombinant Per a 9 was functionally active. Three immunoinformatics tools, including the DNASTAR Protean system, Bioinformatics Predicted Antigenic Peptides (BPAP) system and the BepiPred 1.0 server were used to predict the potential B cell epitopes, while Net-MHCIIpan-2.0 and NetMHCII-2.2 were used to predict the T cell epitopes of Per a 9. As a result, we predicted 11 peptides (23–28, 39–46, 58–64, 91–118, 131–136, 145–154, 159–165, 176–183, 290–299, 309–320 and 338–344) as potential B cell linear epitopes. In T cell prediction, the Per a 9 allergen was predicted to have 5 potential T cell epitope sequences, 119–127, 194–202, 210–218, 239–250 and 279–290. The findings of our study may prove to be useful in the development of peptide-based vaccines to combat CR-induced allergies. PMID:27840974

Molecular cloning, expression, IgE binding activities and in silico epitope prediction of Per a 9 allergens of the American cockroach.

PubMed

Yang, Haiwei; Chen, Hao; Jin, Min; Xie, Hua; He, Shaoheng; Wei, Ji-Fu

2016-12-01

Per a 9 is a major allergen of the American cockroach (CR), which has been recognized as an important cause of imunoglobulin E-mediated type I hypersensitivity worldwide. However, it is not neasy to obtain a substantial quantity of this allergen for use in functional studies. In the present study, the Per a 9 gene was cloned and expressed in Escherichia coli (E. coli) systems. It was found that 13/16 (81.3%) of the sera from patients with allergies caused by the American CR reacted to Per a 9, as assessed by enzyme-linked immunosorbent assay, confirming that Per a 9 is a major allergen of CR. The induction of the expression of CD63 and CCR3 in passively sensitized basophils (from sera of patients with allergies caused by the American CR) by approximately 4.2-fold indicated that recombinant Per a 9 was functionally active. Three immunoinformatics tools, including the DNAStar Protean system, Bioinformatics Predicted Antigenic Peptides (BPAP) system and the BepiPred 1.0 server were used to predict the potential B cell epitopes, while Net-MHCIIpan-2.0 and NetMHCII-2.2 were used to predict the T cell epitopes of Per a 9. As a result, we predicted 11 peptides (23-28, 39-46, 58-64, 91-118, 131-136, 145-154, 159-165, 176-183, 290-299, 309-320 and 338-344) as potential B cell linear epitopes. In T cell prediction, the Per a 9 allergen was predicted to have 5 potential T cell epitope sequences, 119-127, 194-202, 210-218, 239-250 and 279-290. The findings of our study may prove to be useful in the development of peptide-based vaccines to combat CR-induced allergies.

The Potential for Predicting Precipitation on Seasonal-to-Interannual Timescales

NASA Technical Reports Server (NTRS)

Koster, R. D.

1999-01-01

The ability to predict precipitation several months in advance would have a significant impact on water resource management. This talk provides an overview of a project aimed at developing this prediction capability. NASA's Seasonal-to-Interannual Prediction Project (NSIPP) will generate seasonal-to-interannual sea surface temperature predictions through detailed ocean circulation modeling and will then translate these SST forecasts into forecasts of continental precipitation through the application of an atmospheric general circulation model and a "SVAT"-type land surface model. As part of the process, ocean variables (e.g., height) and land variables (e.g., soil moisture) will be updated regularly via data assimilation. The overview will include a discussion of the variability inherent in such a modeling system and will provide some quantitative estimates of the absolute upper limits of seasonal-to-interannual precipitation predictability.

Dolichol phosphate mannose synthase: a Glycosyltransferase with Unity in molecular diversities.

PubMed

Banerjee, Dipak K; Zhang, Zhenbo; Baksi, Krishna; Serrano-Negrón, Jesús E

2017-08-01

N-glycans provide structural and functional stability to asparagine-linked (N-linked) glycoproteins, and add flexibility. Glycan biosynthesis is elaborative, multi-compartmental and involves many glycosyltransferases. Failure to assemble N-glycans leads to phenotypic changes developing infection, cancer, congenital disorders of glycosylation (CDGs) among others. Biosynthesis of N-glycans begins at the endoplasmic reticulum (ER) with the assembly of dolichol-linked tetra-decasaccharide (Glc 3 Man 9 GlcNAc 2 -PP-Dol) where dolichol phosphate mannose synthase (DPMS) plays a central role. DPMS is also essential for GPI anchor biosynthesis as well as for O- and C-mannosylation of proteins in yeast and in mammalian cells. DPMS has been purified from several sources and its gene has been cloned from 39 species (e.g., from protozoan parasite to human). It is an inverting GT-A folded enzyme and classified as GT2 by CAZy (carbohydrate active enZyme; http://www.cazy.org ). The sequence alignment detects the presence of a metal binding DAD signature in DPMS from all 39 species but finds cAMP-dependent protein phosphorylation motif (PKA motif) in only 38 species. DPMS also has hydrophobic region(s). Hydropathy analysis of amino acid sequences from bovine, human, S. crevisiae and A. thaliana DPMS show PKA motif is present between the hydrophobic domains. The location of PKA motif as well as the hydrophobic domain(s) in the DPMS sequence vary from species to species. For example, the domain(s) could be located at the center or more towards the C-terminus. Irrespective of their catalytic similarity, the DNA sequence, the amino acid identity, and the lack of a stretch of hydrophobic amino acid residues at the C-terminus, DPMS is still classified as Type I and Type II enzyme. Because of an apparent bio-sensing ability, extracellular signaling and microenvironment regulate DPMS catalytic activity. In this review, we highlight some important features and the molecular diversities of DPMS.

Climatic niche conservatism and biogeographical non-equilibrium in Eschscholzia californica (Papaveraceae), an invasive plant in the Chilean Mediterranean region.

PubMed

Peña-Gómez, Francisco T; Guerrero, Pablo C; Bizama, Gustavo; Duarte, Milén; Bustamante, Ramiro O

2014-01-01

Species climate requirements are useful for predicting their geographic distribution. It is often assumed that the niche requirements for invasive plants are conserved during invasion, especially when the invaded regions share similar climate conditions. California and central Chile have a remarkable degree of convergence in their vegetation structure, and a similar Mediterranean climate. Such similarities make these geographic areas an interesting natural experiment for testing climatic niche dynamics and the equilibrium of invasive species in a new environment. We tested to see if the climatic niche of Eschscholzia californica is conserved in the invaded range (central Chile), and we assessed whether the invasion process has reached a biogeographical equilibrium, i.e., occupy all the suitable geographic locations that have suitable conditions under native niche requirements. We compared the climatic niche in the native and invaded ranges as well as the projected potential geographic distribution in the invaded range. In order to compare climatic niches, we conducted a Principal Component Analysis (PCA) and Species Distribution Models (SDMs), to estimate E. californica's potential geographic distribution. We also used SDMs to predict altitudinal distribution limits in central Chile. Our results indicated that the climatic niche occupied by E. californica in the invaded range is firmly conserved, occupying a subset of the native climatic niche but leaving a substantial fraction of it unfilled. Comparisons of projected SDMs for central Chile indicate a similarity, yet the projection from native range predicted a larger geographic distribution in central Chile compared to the prediction of the model constructed for central Chile. The projected niche occupancy profile from California predicted a higher mean elevation than that projected from central Chile. We concluded that the invasion process of E. californica in central Chile is consistent with climatic niche conservatism but there is potential for further expansion in Chile.

Predicting the influence of liposomal lipid composition on liposome size, zeta potential and liposome-induced dendritic cell maturation using a design of experiments approach.

PubMed

Soema, Peter C; Willems, Geert-Jan; Jiskoot, Wim; Amorij, Jean-Pierre; Kersten, Gideon F

2015-08-01

In this study, the effect of liposomal lipid composition on the physicochemical characteristics and adjuvanticity of liposomes was investigated. Using a design of experiments (DoE) approach, peptide-containing liposomes containing various lipids (EPC, DOPE, DOTAP and DC-Chol) and peptide concentrations were formulated. Liposome size and zeta potential were determined for each formulation. Moreover, the adjuvanticity of the liposomes was assessed in an in vitro dendritic cell (DC) model, by quantifying the expression of DC maturation markers CD40, CD80, CD83 and CD86. The acquired data of these liposome characteristics were successfully fitted with regression models, and response contour plots were generated for each response factor. These models were applied to predict a lipid composition that resulted in a liposome with a target zeta potential. Subsequently, the expression of the DC maturation factors for this lipid composition was predicted and tested in vitro; the acquired maturation responses corresponded well with the predicted ones. These results show that a DoE approach can be used to screen various lipids and lipid compositions, and to predict their impact on liposome size, charge and adjuvanticity. Using such an approach may accelerate the formulation development of liposomal vaccine adjuvants. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Phylogenomic Relationships between Amylolytic Enzymes from 85 Strains of Fungi

PubMed Central

Chen, Wanping; Xie, Ting; Shao, Yanchun; Chen, Fusheng

2012-01-01

Fungal amylolytic enzymes, including α-amylase, gluocoamylase and α-glucosidase, have been extensively exploited in diverse industrial applications such as high fructose syrup production, paper making, food processing and ethanol production. In this paper, amylolytic genes of 85 strains of fungi from the phyla Ascomycota, Basidiomycota, Chytridiomycota and Zygomycota were annotated on the genomic scale according to the classification of glycoside hydrolase (GH) from the Carbohydrate-Active enZymes (CAZy) Database. Comparisons of gene abundance in the fungi suggested that the repertoire of amylolytic genes adapted to their respective lifestyles. Amylolytic enzymes in family GH13 were divided into four distinct clades identified as heterologous α- amylases, eukaryotic α-amylases, bacterial and fungal α-amylases and GH13 α-glucosidases. Family GH15 had two branches, one for gluocoamylases, and the other with currently unknown function. GH31 α-glucosidases showed diverse branches consisting of neutral α-glucosidases, lysosomal acid α-glucosidases and a new clade phylogenetically related to the bacterial counterparts. Distribution of starch-binding domains in above fungal amylolytic enzymes was related to the enzyme source and phylogeny. Finally, likely scenarios for the evolution of amylolytic enzymes in fungi based on phylogenetic analyses were proposed. Our results provide new insights into evolutionary relationships among subgroups of fungal amylolytic enzymes and fungal evolutionary adaptation to ecological conditions. PMID:23166747

Thiopurine S-Methyltransferase as a Pharmacogenetic Biomarker: Significance of Testing and Review of Major Methods

PubMed Central

Asadov, Chingiz; Aliyeva, Gunay; Mustafayeva, Kamala

2017-01-01

Background: Thiopurine S-methyltransferase (TPMT) enzyme metabolizes thiopurine drugs which are widely used in various disciplines as well as in leukemias. Individual enzyme activity varies depending on the genetic polymorphisms of TPMT gene located at chromosome 6. Up to 14% of popu-lation is known to have a decreased enzyme activity, and if treated with standard doses of thiopurines, these individuals are at a high risk of severe Adverse Drug Reactions (ADR) as myelosuppression, gas-trointestinal intolerance, pancreatitis and hypersensitivity. However, TPMT-deficient patients can suc-cessfully be treated with decreased thiopurine doses if enzyme status is identified by a prior testing. TPMT status identification is a pioneering experience in application of pharmacogenetic testing in clini-cal settings. 4 TPMT (*2, *3A, *3B, *3C) alleles are known to account for 80-95% of a decreased en-zyme activity, and therefore, identifying the presence of these alleles supported by phenotypic measure-ment of the enzyme activity can reveal patient’s TPMT status. Evaluation of the levels of thiopurine me-tabolites further supports the practice of appropriate dose adjustment by providing the efficient monitor-ing of drug cytotoxicity. Conclusion: We hereby review the thiopurine pharmacogenetics and the methods applied in common practice to evaluate patient’s TPMT status. PMID:28552060

Experimental observation of ion beams in the Madison Helicon eXperiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wiebold, Matt; Sung, Yung-Ta; Scharer, John E.

2011-06-15

Argon ion beams up to E{sub b} = 165 eV at P{sub rf} = 500 W are observed in the Madison Helicon eXperiment (MadHeX) helicon source with a magnetic nozzle. A two-grid retarding potential analyzer (RPA) is used to measure the ion energy distribution, and emissive and rf-filtered Langmuir probes measure the plasma potential, electron density, and temperature. The supersonic ion beam (M = v{sub i}/c{sub s} up to 5) forms over tens of Debye lengths and extends spatially for a few ion-neutral charge-exchange mean free paths. The parametric variation of the ion beam energy is explored, including flow rate,more » rf power, and magnetic field dependence. The beam energy is equal to the difference in plasma potentials in the Pyrex chamber and the grounded expansion chamber. The plasma potential in the expansion chamber remains near the predicted eV{sub p} {approx} 5kT{sub e} for argon, but the upstream potential is much higher, likely due to wall charging, resulting in accelerated ion beam energies E{sub b} = e[V{sub beam} - V{sub plasma}] > 10kT{sub e}.« less

The Timing and Construction of Preference: A Quantitative Study

ERIC Educational Resources Information Center

Kendrick, Kobin H.; Torreira, Francisco

2015-01-01

Conversation-analytic research has argued that the timing and construction of preferred responding actions (e.g., acceptances) differ from that of dispreferred responding actions (e.g., rejections), potentially enabling early response prediction by recipients. We examined 195 preferred and dispreferred responding actions in telephone corpora and…

Anticipated Job Benefits, Career Aspiration, and Generalized Self-efficacy as Predictors for Migration Decision-Making

PubMed Central

Hoppe, Annekatrin; Fujishiro, Kaori

2015-01-01

This study aims to identify person-level factors, rather than economic situations, that influence migration decision-making and actual migration. Building on the theory of planned behavior, this study investigated potential migrants’ expectations and attitudes toward migration and career (i.e., anticipated job benefits of migration, career aspiration) as well as beliefs (i.e., generalized self-efficacy) as predictors of migration decision-making conceptualized in three phases: the pre-decisional, pre-actional, and actional phases. This was examined with cross-sectional pre-migration questionnaire data from 1163 potential migrants from Spain to Germany. We also examined whether the migration decision-making phases predicted actual migration with a subsample (n=249) which provided follow-up data within twelve months. For the cross-sectional sample, multinomial logistic regressions revealed that anticipated job benefits and career aspiration are predictive for all migration phases. Self-efficacy predicts the preactional (e.g., gathering information) and actional phases (e.g., making practical arrangements). Finally, for those with low self-efficacy, anticipated job benefits play a stronger role for taking action. For the longitudinal subsample, a logistic regression revealed that being in the preactional and actional phases at baseline is predictive of actual migration within twelve months. This study expands previous research on migration intentions and behaviors by focusing on expectations, values, and beliefs as person-level predictors for migration decision-making. With a longitudinal sample, it shows that international migration is a process that involves multiple phases. PMID:26379343

Metabolic pathways for the whole community.

PubMed

Hanson, Niels W; Konwar, Kishori M; Hawley, Alyse K; Altman, Tomer; Karp, Peter D; Hallam, Steven J

2014-07-22

A convergence of high-throughput sequencing and computational power is transforming biology into information science. Despite these technological advances, converting bits and bytes of sequence information into meaningful insights remains a challenging enterprise. Biological systems operate on multiple hierarchical levels from genomes to biomes. Holistic understanding of biological systems requires agile software tools that permit comparative analyses across multiple information levels (DNA, RNA, protein, and metabolites) to identify emergent properties, diagnose system states, or predict responses to environmental change. Here we adopt the MetaPathways annotation and analysis pipeline and Pathway Tools to construct environmental pathway/genome databases (ePGDBs) that describe microbial community metabolism using MetaCyc, a highly curated database of metabolic pathways and components covering all domains of life. We evaluate Pathway Tools' performance on three datasets with different complexity and coding potential, including simulated metagenomes, a symbiotic system, and the Hawaii Ocean Time-series. We define accuracy and sensitivity relationships between read length, coverage and pathway recovery and evaluate the impact of taxonomic pruning on ePGDB construction and interpretation. Resulting ePGDBs provide interactive metabolic maps, predict emergent metabolic pathways associated with biosynthesis and energy production and differentiate between genomic potential and phenotypic expression across defined environmental gradients. This multi-tiered analysis provides the user community with specific operating guidelines, performance metrics and prediction hazards for more reliable ePGDB construction and interpretation. Moreover, it demonstrates the power of Pathway Tools in predicting metabolic interactions in natural and engineered ecosystems.

Statistical Mining of Predictability of Seasonal Precipitation over the United States

NASA Technical Reports Server (NTRS)

Lau, William K. M.; Kim, Kyu-Myong; Shen, S. P.

2001-01-01

Results from a new ensemble canonical correlation (ECC) prediction model yield a remarkable (10-20%) increases in baseline prediction skills for seasonal precipitation over the US for all seasons, compared to traditional statistical predictions. While the tropical Pacific, i.e., El Nino, contributes to the largest share of potential predictability in the southern tier States during boreal winter, the North Pacific and the North Atlantic are responsible for enhanced predictability in the northern Great Plains, Midwest and the southwest US during boreal summer. Most importantly, ECC significantly reduces the spring predictability barrier over the conterminous US, thereby raising the skill bar for dynamical predictions.

Elevated carbon dioxide is predicted to promote coexistence among competing species in a trait-based model

DOE PAGES

Ali, Ashehad A.; Medlyn, Belinda E.; Aubier, Thomas G.; ...

2015-10-06

Differential species responses to atmospheric CO 2 concentration (C a) could lead to quantitative changes in competition among species and community composition, with flow-on effects for ecosystem function. However, there has been little theoretical analysis of how elevated C a (eC a) will affect plant competition, or how composition of plant communities might change. Such theoretical analysis is needed for developing testable hypotheses to frame experimental research. Here, we investigated theoretically how plant competition might change under eC a by implementing two alternative competition theories, resource use theory and resource capture theory, in a plant carbon and nitrogen cycling model.more » The model makes several novel predictions for the impact of eC a on plant community composition. Using resource use theory, the model predicts that eC a is unlikely to change species dominance in competition, but is likely to increase coexistence among species. Using resource capture theory, the model predicts that eC a may increase community evenness. Collectively, both theories suggest that eC a will favor coexistence and hence that species diversity should increase with eC a. Our theoretical analysis leads to a novel hypothesis for the impact of eC a on plant community composition. In this study, the hypothesis has potential to help guide the design and interpretation of eC a experiments.« less

Predicting therapy success for treatment as usual and blended treatment in the domain of depression.

PubMed

van Breda, Ward; Bremer, Vincent; Becker, Dennis; Hoogendoorn, Mark; Funk, Burkhardt; Ruwaard, Jeroen; Riper, Heleen

2018-06-01

In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression. We use this data, where 276 patients received treatment as usual and 227 received blended treatment, to investigate to what extent we are able to predict therapy success. We utilize different encoding strategies for preprocessing, varying feature selection techniques, and different statistical procedures for this purpose. Significant predictive power is found with average AUC values up to 0.7628 for treatment as usual and 0.7765 for blended treatment. Adding daily assessment data for blended treatment does currently not add predictive accuracy. Cost effectiveness analysis is needed to determine the added potential for real-world applications.

Suitability of different Escherichia coli enumeration techniques to assess the microbial quality of different irrigation water sources.

PubMed

Truchado, P; Lopez-Galvez, F; Gil, M I; Pedrero-Salcedo, F; Alarcón, J J; Allende, A

2016-09-01

The use of fecal indicators such as Escherichia coli has been proposed as a potential tool to characterize microbial contamination of irrigation water. Recently, not only the type of microbial indicator but also the methodologies used for enumeration have been called into question. The goal of this study was to assess the microbial quality of different water sources for irrigation of zucchini plants by using E. coli as an indicator of fecal contamination and the occurrence of foodborne pathogens. Three water sources were evaluated including reclaimed secondary treated water (RW-2), reclaimed tertiary UV-C treated water (RW-3) and surface water (SW). The suitability of two E. coli quantification techniques (plate count and qPCR) was examined for irrigation water and fresh produce. E. coli levels using qPCR assay were significantly higher than that obtained by plate count in all samples of irrigation water and fresh produce. The microbial quality of water samples from RW-2 was well predicted by qPCR, as the presence of foodborne pathogens were positively correlated with high E. coli levels. However, differences in the water characteristics influenced the suitability of qPCR as a tool to predict potential contamination in irrigation water. No significant differences were obtained between the number of cells of E. coli from RW-2 and RW-3, probably due to the fact that qPCR assay cannot distinguish between viable and dead cells. These results indicated that the selection of the most suitable technique for enumeration of indicator microorganisms able to predict potential presence of fecal contamination might be influenced by the water characteristics. Copyright © 2016 Elsevier Ltd. All rights reserved.

Classical and quantum simulations of warm dense carbon

NASA Astrophysics Data System (ADS)

Whitley, Heather; Sanchez, David; Hamel, Sebastien; Correa, Alfredo; Benedict, Lorin

We have applied classical and DFT-based molecular dynamics (MD) simulations to study the equation of state of carbon in the warm dense matter regime (ρ = 3.7 g/cc, 0.86 eV

Theoretical Prediction of the Heats of Formation, Densities, and Relative Sensitivities for 1,7-dinitro-3,4,5,8-tetra N-oxide-bis([1,2,3]triazolo)[4,5-b:5’,4’ e]pyrazine, 1,7-dinitro-3,5,8-tri N-oxide bis([1,2,3]triazolo)[4,5-b:5’,4’ e]pyrazine, 1,7-dinitro 3,5-bis N-oxide bis([1,2,3]triazolo)[4,5-b:5’,4’ e]pyrazine, 1,7-dinitro 1,7-dihydrobis([1,2,3]triazolo)[4,5-b:4’,5’-e]pyrazine

DTIC Science & Technology

2016-09-01

2 Fig. 2 Electrostatic potential map of 1, without a) and with b) molecule overlay...3 Fig. 3 Electrostatic potential map of 2, without a) and with b) molecule overlay...3 Fig. 4 Electrostatic potential map of 3, without a) and

Application of artificial neural networks to assess pesticide contamination in shallow groundwater

USGS Publications Warehouse

Sahoo, G.B.; Ray, C.; Mehnert, E.; Keefer, D.A.

2006-01-01

In this study, a feed-forward back-propagation neural network (BPNN) was developed and applied to predict pesticide concentrations in groundwater monitoring wells. Pesticide concentration data are challenging to analyze because they tend to be highly censored. Input data to the neural network included the categorical indices of depth to aquifer material, pesticide leaching class, aquifer sensitivity to pesticide contamination, time (month) of sample collection, well depth, depth to water from land surface, and additional travel distance in the saturated zone (i.e., distance from land surface to midpoint of well screen). The output of the neural network was the total pesticide concentration detected in the well. The model prediction results produced good agreements with observed data in terms of correlation coefficient (R = 0.87) and pesticide detection efficiency (E = 89%), as well as good match between the observed and predicted "class" groups. The relative importance of input parameters to pesticide occurrence in groundwater was examined in terms of R, E, mean error (ME), root mean square error (RMSE), and pesticide occurrence "class" groups by eliminating some key input parameters to the model. Well depth and time of sample collection were the most sensitive input parameters for predicting the pesticide contamination potential of a well. This infers that wells tapping shallow aquifers are more vulnerable to pesticide contamination than those wells tapping deeper aquifers. Pesticide occurrences during post-application months (June through October) were found to be 2.5 to 3 times higher than pesticide occurrences during other months (November through April). The BPNN was used to rank the input parameters with highest potential to contaminate groundwater, including two original and five ancillary parameters. The two original parameters are depth to aquifer material and pesticide leaching class. When these two parameters were the only input parameters for the BPNN, they were not able to predict contamination potential. However, when they were used with other parameters, the predictive performance efficiency of the BPNN in terms of R, E, ME, RMSE, and pesticide occurrence "class" groups increased. Ancillary data include data collected during the study such as well depth and time of sample collection. The BPNN indicated that the ancillary data had more predictive power than the original data. The BPNN results will help researchers identify parameters to improve maps of aquifer sensitivity to pesticide contamination. ?? 2006 Elsevier B.V. All rights reserved.

Prediction of the binding site of 1-benzyl-4-[(5,6-dimethoxy-1-indanon-2-yl)methyl]piperidine in acetylcholinesterase by docking studies with the SYSDOC program

NASA Astrophysics Data System (ADS)

Pang, Yuan-Ping; Kozikowski, Alan P.

1994-12-01

In the preceding paper we reported on a docking study with the SYSDOC program for predicting the binding sites of huperzine A in acetylcholinesterase (AChE) [Pang, Y.-P. and Kozikowski, A.P., J. Comput.-Aided Mol. Design, 8 (1994) 669]. Here we present a prediction of the binding sites of 1-benzyl-4-[(5,6-dimethoxy-1-indanon-2-yl)methyl]piperidine (E2020) in AChE by the same method. E2020 is one of the most potent and selective reversible inhibitors of AChE, and this molecule has puzzled researchers, partly due to its flexible structure, in understanding how it binds to AChE. Based on the results of docking 1320 different conformers of E2020 into 69 different conformers of AChE and on the pharmacological data reported for E2020 and its analogs, we predict that both the R- and the S-isomer of E2020 span the whole binding cavity of AChE, with the ammonium group interacting mainly with Trp84, Phe330 and Asp72, the phenyl group interacting mainly with Trp84 and Phe330, and the indanone moiety interacting mainly with Tyr70 and Trp279. The topography of the calculated E2020 binding sites provides insights into understanding the high potency of E2020 in the inhibition of AChE and provides hints as to possible structural modifications for identifying improved AChE inhibitors as potential therapeutics for the palliative treatment of Alzheimer's disease.

Science with the space-based interferometer eLISA. II: gravitational waves from cosmological phase transitions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caprini, Chiara, E-mail: chiara.caprini@cea.fr; Hindmarsh, Mark; Huber, Stephan

We investigate the potential for the eLISA space-based interferometer to detect the stochastic gravitational wave background produced by strong first-order cosmological phase transitions. We discuss the resulting contributions from bubble collisions, magnetohydrodynamic turbulence, and sound waves to the stochastic background, and estimate the total corresponding signal predicted in gravitational waves. The projected sensitivity of eLISA to cosmological phase transitions is computed in a model-independent way for various detector designs and configurations. By applying these results to several specific models, we demonstrate that eLISA is able to probe many well-motivated scenarios beyond the Standard Model of particle physics predicting strong first-ordermore » cosmological phase transitions in the early Universe.« less

Semimicroscopic, Lane-consistent nucleon-nucleus optical model potential up to 200 MeV

NASA Astrophysics Data System (ADS)

Bauge, Eric; Delaroche, Jean-Paul; Girod, Michel

2000-10-01

Our semimicroscopic optical model potential (E. Bauge et al., Phys. Rev. C 58), 1118 (1998). is re-evaluated in order to obtain a Lane-consistent description of (p,p), (n,n) and (p,n IAS) elastic scattering and reaction observables. The re-assessed nuclear matter interaction (which includes sizable renormalizations of the isovector potentials) is folded with microscopic HFB nuclear densities, producing OMPs that are free of adjustable parameters for nuclei with A >= 40. With Lane-consistency of the interaction, and the predictive nature of our HFB calculations, this scheme can be used to calculate observables for nuclei far from the stability line with good predictivity.

Neural Correlates of Encoding Predict Infants' Memory in the Paired-Comparison Procedure

ERIC Educational Resources Information Center

Snyder, Kelly A.

2010-01-01

The present study used event-related potentials (ERPs) to monitor infant brain activity during the initial encoding of a previously novel visual stimulus, and examined whether ERP measures of encoding predicted infants' subsequent performance on a visual memory task (i.e., the paired-comparison task). A late slow wave component of the ERP measured…

[Ecology suitability study of Ephedra intermedia].

PubMed

Ma, Xiao-Hui; Lu, You-Yuan; Huang, De-Dong; Zhu, Tian-Tian; Lv, Pei-Lin; Jin, Ling

2017-06-01

The study aims at predicting ecological suitability of Ephedra intermedia in China by using maximum entropy Maxent model combined with GIS, and finding the main ecological factors affecting the distribution of E. intermedia suitability in appropriate growth area. Thirty-eight collected samples of E. intermedia and E. intermedia and 116 distribution information from CVH information using ArcGIS technology were analyzed. MaxEnt model was applied to forecast the E. intermedia in our country's ecology. E. intermedia MaxEnt ROC curve model training data and testing data sets the AUC value was 0.986 and 0.958, respectively, which were greater than 0.9, tending to be 1.The calculated E. intermedia habitat suitability by the model showed a high accuracy and credibility, which indicated that MaxEnt model could well predict the potential distribution area of E. intermedia in China. Copyright© by the Chinese Pharmaceutical Association.

[Predictive factors of anxiety disorders].

PubMed

Domschke, K

2014-10-01

Anxiety disorders are among the most frequent mental disorders in Europe (12-month prevalence 14%) and impose a high socioeconomic burden. The pathogenesis of anxiety disorders is complex with an interaction of biological, environmental and psychosocial factors contributing to the overall disease risk (diathesis-stress model). In this article, risk factors for anxiety disorders will be presented on several levels, e.g. genetic factors, environmental factors, gene-environment interactions, epigenetic mechanisms, neuronal networks ("brain fear circuit"), psychophysiological factors (e.g. startle response and CO2 sensitivity) and dimensional/subclinical phenotypes of anxiety (e.g. anxiety sensitivity and behavioral inhibition), and critically discussed regarding their potential predictive value. The identification of factors predictive of anxiety disorders will possibly allow for effective preventive measures or early treatment interventions, respectively, and reduce the individual patient's suffering as well as the overall socioeconomic burden of anxiety disorders.

Investigation of the binding of a carbohydrate-mimetic peptide to its complementary anticarbohydrate antibody by STD-NMR spectroscopy and molecular-dynamics simulations.

PubMed

Szczepina, Monica G; Bleile, Dustin W; Pinto, B Mario

2011-10-04

Saturation transfer difference (STD)-NMR spectroscopy was used to probe experimentally the bioactive solution conformation of the carbohydrate mimic MDWNMHAA 1 of the O-polysaccharide of Shigella flexneri Y when bound to its complementary antibody, mAb SYA/J6. Molecular dynamics simulations using the ZymeCAD™ Molecular Dynamics platform were also undertaken to give a more accurate picture of the conformational flexibility and the possibilities for bound ligand conformations. The ligand topology, or the dynamic epitope, was mapped with the CORCEMA-ST (COmplete Relaxation and Conformational Exchange Matrix Analysis of Saturation Transfer) program that calculates a total matrix analysis of relaxation and exchange effects to generate predicted STD-NMR intensities from simulation. The comparison of these predicted STD enhancements with experimental data was used to select a representative binding mode. A protocol that employed theoretical STD effects calculated at snapshots during the entire course of a molecular dynamics (MD) trajectory of the peptide bound to the Fv portion of the antibody, and not the averaged atomic positions of receptor-ligand complexes, was also examined. In addition, the R factor was calculated on the basis of STD (fit) to avoid T1 bias, and an effective R factor, R(eff), was defined such that if the calculated STD (fit) for proton k was within error of the experimental STD (fit) for proton k, then that calculated STD (fit) for proton k was not included in the calculation of the R factor. This protocol was effective in deriving the antibody-bound solution conformation of the peptide which also differed from the bound conformation determined by X-ray crystallography; however, several discrepancies between experimental and calculated STD (fit) values were observed. The bound conformation was therefore further refined with a simulated annealing refinement protocol known as STD-NMR intensity-restrained CORCEMA optimization (SICO) to give a more accurate representation of the bound peptide epitope. Further optimization was required in this case, but a satisfactory correlation between experimental and calculated STD values was obtained. Attempts were also made to obtain STD enhancements with a synthetic pentasaccharide hapten, corresponding to the O-polysaccharide, while bound to the antibody. However, unfavorable kinetics of binding in this system prevented sufficient STD build-up, which, in turn, hindered a rigorous analysis via full STD build-up curves. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

MnNiO3 revisited with modern theoretical and experimental methods

NASA Astrophysics Data System (ADS)

Dzubak, Allison L.; Mitra, Chandrima; Chance, Michael; Kuhn, Stephen; Jellison, Gerald E.; Sefat, Athena S.; Krogel, Jaron T.; Reboredo, Fernando A.

2017-11-01

MnNiO3 is a strongly correlated transition metal oxide that has recently been investigated theoretically for its potential application as an oxygen-evolution photocatalyst. However, there is no experimental report on critical quantities such as the band gap or bulk modulus. Recent theoretical predictions with standard functionals such as LDA+U and HSE show large discrepancies in the band gaps (about 1.23 eV), depending on the nature of the functional used. Hence there is clearly a need for an accurate quantitative prediction of the band gap to gauge its utility as a photocatalyst. In this work, we present a diffusion quantum Monte Carlo study of the bulk properties of MnNiO3 and revisit the synthesis and experimental properties of the compound. We predict quasiparticle band gaps of 2.0(5) eV and 3.8(6) eV for the majority and minority spin channels, respectively, and an equilibrium volume of 92.8 Å3, which compares well to the experimental value of 94.4 Å3. A bulk modulus of 217 GPa is predicted for MnNiO3. We rationalize the difficulty for the formation of ordered ilmenite-type structure with specific sites for Ni and Mn to be potentially due to the formation of antisite defects that form during synthesis, which ultimately affects the physical properties of MnNiO3.

Effect of secondary electron emission on the plasma sheath

NASA Astrophysics Data System (ADS)

Langendorf, S.; Walker, M.

2015-03-01

In this experiment, plasma sheath potential profiles are measured over boron nitride walls in argon plasma and the effect of secondary electron emission is observed. Results are compared to a kinetic model. Plasmas are generated with a number density of 3 × 1012 m-3 at a pressure of 10-4 Torr-Ar, with a 1%-16% fraction of energetic primary electrons. The sheath potential profile at the surface of each sample is measured with emissive probes. The electron number densities and temperatures are measured in the bulk plasma with a planar Langmuir probe. The plasma is non-Maxwellian, with isotropic and directed energetic electron populations from 50 to 200 eV and hot and cold Maxwellian populations from 3.6 to 6.4 eV and 0.3 to 1.3 eV, respectively. Plasma Debye lengths range from 4 to 7 mm and the ion-neutral mean free path is 0.8 m. Sheath thicknesses range from 20 to 50 mm, with the smaller thickness occurring near the critical secondary electron emission yield of the wall material. Measured floating potentials are within 16% of model predictions. Measured sheath potential profiles agree with model predictions within 5 V (˜1 Te), and in four out of six cases deviate less than the measurement uncertainty of 1 V.

Environmental transport of endogenous dairy manure estrogens.

PubMed

Popova, Inna E; Morra, Matthew J

2017-11-02

Although estrogens originating from dairy manure applied to agricultural soils as a fertilizer can potentially contaminate surface water and groundwater, the variables that control transport are poorly understood. Our objective was to assess the potential for off-site movement of endogenous dairy cattle estrogens when manure is applied on fields at agronomically relevant fertilization rates. Estrone (E1), 17α-estradiol (α-E2), and 17β-estradiol (β-E2) were used in laboratory sorption, desorption, and transformation incubations with both manure and an agriculturally relevant soil. Sorption on manure containing 44% organic carbon exceeded sorption on soil containing 0.8% organic carbon by 20 to 150 times, following the pattern of β-E2 > α-E2 > E1. Approximately 20% of E1 and 17% of α-E2 were desorbed from manure, whereas only about 4% of β-E2 was desorbed. Thirty to seventy percent of α-E2 and β-E2 were converted to E1 in soil and manure, making it imperative that transformation reactions be considered when predicting transport and potential biological effects in the environment. Overall results indicate that high organic carbon concentrations and relatively low amounts of desorption inhibit the potential for off-site transport of endogenous dairy manure estrogens.

The Future of Medical Dosimetry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adams, Robert D., E-mail: robert_adams@med.unc.edu

2015-07-01

The world of health care delivery is becoming increasingly complex. The purpose of this manuscript is to analyze current metrics and analytically predict future practices and principles of medical dosimetry. The results indicate five potential areas precipitating change factors: a) evolutionary and revolutionary thinking processes, b) social factors, c) economic factors, d) political factors, and e) technological factors. Outcomes indicate that significant changes will occur in the job structure and content of being a practicing medical dosimetrist. Discussion indicates potential variables that can occur within each process and change factor and how the predicted outcomes can deviate from normative values.more » Finally, based on predicted outcomes, future opportunities for medical dosimetrists are given.« less

Implications of Marcus-Hush theory for steady-state heterogeneous electron transfer at an inlaid disk electrode.

PubMed

Feldberg, Stephen W

2010-06-15

For an outer-sphere heterogeneous electron transfer, Ox + e = Red, between an electrode and a redox couple, the Butler-Volmer formalism predicts that the operative heterogeneous rate constant, k(red) (cm s(-1)) for reduction (or k(ox) for oxidation) increases without limit as an exponential function of -alpha (E - E(0)) for reduction (or (1 - alpha)(E - E(0)) for oxidation), where E is the applied electrode potential, alpha (~1/2) is the transfer coefficient and E(0) is the formal potential. The Marcus-Hush formalism, as exposited by Chidsey (Chidsey, C. E. D. Science 1991, 215, 919), predicts that the value of k(red) or k(ox) limits at sufficiently large values of -(E - E(0)) or (E - E(0)). The steady-state currents at an inlaid disk electrode obtained for a redox species in solution were computed using both formalisms with the Oldham-Zoski approximation (Oldham, K. B.; Zoski, C. G. J. Electroanal. Chem. 1988, 256, 11). Significant differences are noted for the two formalisms. When k(0)r(0)/D is sufficiently small (k(0) is the standard rate constant, r(0) is the radius of the disk electrode, and D is the diffusion coefficient of the redox species), the Marcus-Hush formalism effects a limiting current that can be significantly smaller than the mass transport limited current. This is easily explained in terms of the limiting values of k(red) and k(ox) predicted by the Marcus-Hush formalism. The experimental conditions that must be met to effect significant differences in behavior are discussed; experimental conditions that effect virtually identical behavior are also discussed. As a caveat for experimentalists, applications of the Butler-Volmer formalism to systems that are more properly described using the Marcus-Hush formalism are shown to yield incorrect values of k(0) and meaningless values of alpha, which serves only as a fitting parameter.

New developments and concepts related to biomarker application to vaccines

PubMed Central

Ahmed, S. Sohail; Black, Steve; Ulmer, Jeffrey

2012-01-01

Summary This minireview will provide a perspective on new developments and concepts related to biomarker applications for vaccines. In the context of preventive vaccines, biomarkers have the potential to predict adverse events in select subjects due to differences in genetic make‐up/underlying medical conditions or to predict effectiveness (good versus poor response). When expanding them to therapeutic vaccines, their utility in identification of patients most likely to respond favourably (or avoid potentially negative effects of treatment) becomes self‐explanatory. Despite the progress made so far on dissection of various pathways of biological significance in humans, there is still plenty to unravel about the mysteries related to the quantitative and qualitative aspects of the human host response. This review will provide a focused overview of new concepts and developments in the field of vaccine biomarkers including (i) vaccine‐dependent signatures predicting subject response and safety, (ii) predicting therapeutic vaccine efficacy in chronic diseases, (iii) exploring the genetic make‐up of the host that may modulate subject‐specific adverse events or affect the quality of immune responses, and (iv) the topic of volunteer stratification as a result of biomarker screening (e.g. for therapeutic vaccines but also potentially for preventive vaccines) or as a reflection of an effort to compare select groups (e.g. vaccinated subjects versus patients recovering from infection) to enable the discovery of clinically relevant biomarkers for preventive vaccines. PMID:21895991

Using the Personality Assessment Inventory Antisocial and Borderline Features Scales to Predict Behavior Change.

PubMed

Penson, Brittany N; Ruchensky, Jared R; Morey, Leslie C; Edens, John F

2016-11-01

A substantial amount of research has examined the developmental trajectory of antisocial behavior and, in particular, the relationship between antisocial behavior and maladaptive personality traits. However, research typically has not controlled for previous behavior (e.g., past violence) when examining the utility of personality measures, such as self-report scales of antisocial and borderline traits, in predicting future behavior (e.g., subsequent violence). Examination of the potential interactive effects of measures of both antisocial and borderline traits also is relatively rare in longitudinal research predicting adverse outcomes. The current study utilizes a large sample of youthful offenders ( N = 1,354) from the Pathways to Desistance project to examine the separate effects of the Personality Assessment Inventory Antisocial Features (ANT) and Borderline Features (BOR) scales in predicting future offending behavior as well as trends in other negative outcomes (e.g., substance abuse, violence, employment difficulties) over a 1-year follow-up period. In addition, an ANT × BOR interaction term was created to explore the predictive effects of secondary psychopathy. ANT and BOR both explained unique variance in the prediction of various negative outcomes even after controlling for past indicators of those same behaviors during the preceding year.

Systematic review of compound action potentials as predictors for cochlear implant performance.

PubMed

van Eijl, Ruben H M; Buitenhuis, Patrick J; Stegeman, Inge; Klis, Sjaak F L; Grolman, Wilko

2017-02-01

The variability in speech perception between cochlear implant users is thought to result from the degeneration of the auditory nerve. Degeneration of the auditory nerve, histologically assessed, correlates with electrophysiologically acquired measures, such as electrically evoked compound action potentials (eCAPs) in experimental animals. To predict degeneration of the auditory nerve in humans, where histology is impossible, this paper reviews the correlation between speech perception and eCAP recordings in cochlear implant patients. PubMed and Embase. We performed a systematic search for articles containing the following major themes: cochlear implants, evoked potentials, and speech perception. Two investigators independently conducted title-abstract screening, full-text screening, and critical appraisal. Data were extracted from the remaining articles. Twenty-five of 1,429 identified articles described a correlation between speech perception and eCAP attributes. Due to study heterogeneity, a meta-analysis was not feasible, and studies were descriptively analyzed. Several studies investigating presence of the eCAP, recovery time constant, slope of the amplitude growth function, and spatial selectivity showed significant correlations with speech perception. In contrast, neural adaptation, eCAP threshold, and change with varying interphase gap did not significantly correlate with speech perception in any of the identified studies. Significant correlations between speech perception and parameters obtained through eCAP recordings have been documented in literature; however, reporting was ambiguous. There is insufficient evidence for eCAPs as a predictive factor for speech perception. More research is needed to further investigate this relation. Laryngoscope, 2016 127:476-487, 2017. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

Predictions of future ephemeral springtime waterbird stopover habitat availability under global change

USGS Publications Warehouse

Uden, Daniel R.; Allen, Craig R.; Bishop, Andrew A.; Grosse, Roger; Jorgensen, Christopher F.; LaGrange, Theodore G.; Stutheit, Randy G.; Vrtiska, Mark P.

2015-01-01

In the present period of rapid, worldwide change in climate and landuse (i.e., global change), successful biodiversity conservation warrants proactive management responses, especially for long-distance migratory species. However, the development and implementation of management strategies can be impeded by high levels of uncertainty and low levels of control over potentially impactful future events and their effects. Scenario planning and modeling are useful tools for expanding perspectives and informing decisions under these conditions. We coupled scenario planning and statistical modeling to explain and predict playa wetland inundation (i.e., presence/absence of water) and ponded area (i.e., extent of water) in the Rainwater Basin, an anthropogenically altered landscape that provides critical stopover habitat for migratory waterbirds. Inundation and ponded area models for total wetlands, those embedded in rowcrop fields, and those not embedded in rowcrop fields were trained and tested with wetland ponding data from 2004 and 2006–2009, and then used to make additional predictions under two alternative climate change scenarios for the year 2050, yielding a total of six predictive models and 18 prediction sets. Model performance ranged from moderate to good, with inundation models outperforming ponded area models, and models for non-rowcrop-embedded wetlands outperforming models for total wetlands and rowcrop-embedded wetlands. Model predictions indicate that if the temperature and precipitation changes assumed under our climate change scenarios occur, wetland stopover habitat availability in the Rainwater Basin could decrease in the future. The results of this and similar studies could be aggregated to increase knowledge about the potential spatial and temporal distributions of future stopover habitat along migration corridors, and to develop and prioritize multi-scale management actions aimed at mitigating the detrimental effects of global change on migratory waterbird populations.

Subjective Life Expectancy Among College Students.

PubMed

Rodemann, Alyssa E; Arigo, Danielle

2017-09-14

Establishing healthy habits in college is important for long-term health. Despite existing health promotion efforts, many college students fail to meet recommendations for behaviors such as healthy eating and exercise, which may be due to low perceived risk for health problems. The goals of this study were to examine: (1) the accuracy of life expectancy predictions, (2) potential individual differences in accuracy (i.e., gender and conscientiousness), and (3) potential change in accuracy after inducing awareness of current health behaviors. College students from a small northeastern university completed an electronic survey, including demographics, initial predictions of their life expectancy, and their recent health behaviors. At the end of the survey, participants were asked to predict their life expectancy a second time. Their health data were then submitted to a validated online algorithm to generate calculated life expectancy. Participants significantly overestimated their initial life expectancy, and neither gender nor conscientiousness was related to the accuracy of these predictions. Further, subjective life expectancy decreased from initial to final predictions. These findings suggest that life expectancy perceptions present a unique-and potentially modifiable-psychological process that could influence college students' self-care.

Geostatistical enhancement of european hydrological predictions

NASA Astrophysics Data System (ADS)

Pugliese, Alessio; Castellarin, Attilio; Parajka, Juraj; Arheimer, Berit; Bagli, Stefano; Mazzoli, Paolo; Montanari, Alberto; Blöschl, Günter

2016-04-01

Geostatistical Enhancement of European Hydrological Prediction (GEEHP) is a research experiment developed within the EU funded SWITCH-ON project, which proposes to conduct comparative experiments in a virtual laboratory in order to share water-related information and tackle changes in the hydrosphere for operational needs (http://www.water-switch-on.eu). The main objective of GEEHP deals with the prediction of streamflow indices and signatures in ungauged basins at different spatial scales. In particular, among several possible hydrological signatures we focus in our experiment on the prediction of flow-duration curves (FDCs) along the stream-network, which has attracted an increasing scientific attention in the last decades due to the large number of practical and technical applications of the curves (e.g. hydropower potential estimation, riverine habitat suitability and ecological assessments, etc.). We apply a geostatistical procedure based on Top-kriging, which has been recently shown to be particularly reliable and easy-to-use regionalization approach, employing two different type of streamflow data: pan-European E-HYPE simulations (http://hypeweb.smhi.se/europehype) and observed daily streamflow series collected in two pilot study regions, i.e. Tyrol (merging data from Austrian and Italian stream gauging networks) and Sweden. The merger of the two study regions results in a rather large area (~450000 km2) and might be considered as a proxy for a pan-European application of the approach. In a first phase, we implement a bidirectional validation, i.e. E-HYPE catchments are set as training sites to predict FDCs at the same sites where observed data are available, and vice-versa. Such a validation procedure reveals (1) the usability of the proposed approach for predicting the FDCs over the entire river network of interest using alternatively observed data and E-HYPE simulations and (2) the accuracy of E-HYPE-based predictions of FDCs in ungauged sites. In a second phase, we develop a module, to be added to the flow-duration curve prediction framework, capable of enhancing E-HYPE-based predictions of FDCs by modelling the residuals obtained from the first phase. Among all possible methods, we apply geostatistical modelling of residuals and, alternatively, regional regression, so that residuals between empirical and E-HYPE-base predicted FDCs are described in terms of geomorphological and climatic catchment descriptors.

Kinetic Isotope Effects as a Probe for the Protonolysis Mechanism of Alkylmetal Complexes: VTST/MT Calculations Based on DFT Potential Energy Surfaces.

PubMed

Mai, Binh Khanh; Kim, Yongho

2016-10-03

Protonolysis by platinum or palladium complexes has been extensively studied because it is the microscopic reverse of the C-H bond activation reaction. The protonolysis of (COD)Pt II Me 2 , which exhibits abnormally large kinetic isotope effects (KIEs), is proposed to occur via a concerted pathway (S E 2 mechanism) with large tunneling. However, further investigation of KIEs for the protonolysis of ZnMe 2 and others led to a conclusion that there is no noticeable correlation between the mechanism and magnitude of KIE. In this study, we demonstrated that variational transition state theory including multidimensional tunneling (VTST/MT) could accurately predict KIEs and Arrhenius parameters of the protonolysis of alkylmetal complexes based on the potential energy surfaces generated by density functional theory. The predicted KIEs, E a D - E a H values, and A H /A D ratios for the protonolysis of (COD)Pt II Me 2 and Zn II Me 2 by TFA agreed very well with experimental values. The protonolysis of ZnMe 2 with the concerted pathway has a very flat potential energy surface, which produces a very small tunneling effect and therefore a small KIE. The predicted KIE for the stepwise protonolysis (S E (ox) mechanism) of (COD)Pt II Me 2 was much smaller than that of the concerted pathway, but greater than the KIE of the concerted protonolysis of ZnMe 2 . A large KIE, which entails a significant tunneling effect, could be used as an experimental probe of the concerted pathway. However, a normal or small KIE should not be used as an indicator of the stepwise mechanism, and the interplay between experiments and reliable theory including tunneling would be essential to uncover the mechanism correctly.

Interlayer interactions in graphites.

PubMed

Chen, Xiaobin; Tian, Fuyang; Persson, Clas; Duan, Wenhui; Chen, Nan-xian

2013-11-06

Based on ab initio calculations of both the ABC- and AB-stacked graphites, interlayer potentials (i.e., graphene-graphene interaction) are obtained as a function of the interlayer spacing using a modified Möbius inversion method, and are used to calculate basic physical properties of graphite. Excellent consistency is observed between the calculated and experimental phonon dispersions of AB-stacked graphite, showing the validity of the interlayer potentials. More importantly, layer-related properties for nonideal structures (e.g., the exfoliation energy, cleave energy, stacking fault energy, surface energy, etc.) can be easily predicted from the interlayer potentials, which promise to be extremely efficient and helpful in studying van der Waals structures.

Petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy.

PubMed

Jung, Jinmyung; Kwon, Mijin; Bae, Sunghwa; Yim, Soorin; Lee, Doheon

2018-03-05

Muscle atrophy, an involuntary loss of muscle mass, is involved in various diseases and sometimes leads to mortality. However, therapeutics for muscle atrophy thus far have had limited effects. Here, we present a new approach for therapeutic target prediction using Petri net simulation of the status of phosphorylation, with a reasonable assumption that the recovery of abnormally phosphorylated proteins can be a treatment for muscle atrophy. The Petri net model was employed to simulate phosphorylation status in three states, i.e. reference, atrophic and each gene-inhibited state based on the myocyte-specific phosphorylation network. Here, we newly devised a phosphorylation specific Petri net that involves two types of transitions (phosphorylation or de-phosphorylation) and two types of places (activation with or without phosphorylation). Before predicting therapeutic targets, the simulation results in reference and atrophic states were validated by Western blotting experiments detecting five marker proteins, i.e. RELA, SMAD2, SMAD3, FOXO1 and FOXO3. Finally, we determined 37 potential therapeutic targets whose inhibition recovers the phosphorylation status from an atrophic state as indicated by the five validated marker proteins. In the evaluation, we confirmed that the 37 potential targets were enriched for muscle atrophy-related terms such as actin and muscle contraction processes, and they were also significantly overlapping with the genes associated with muscle atrophy reported in the Comparative Toxicogenomics Database (p-value < 0.05). Furthermore, we noticed that they included several proteins that could not be characterized by the shortest path analysis. The three potential targets, i.e. BMPR1B, ROCK, and LEPR, were manually validated with the literature. In this study, we suggest a new approach to predict potential therapeutic targets of muscle atrophy with an analysis of phosphorylation status simulated by Petri net. We generated a list of the potential therapeutic targets whose inhibition recovers abnormally phosphorylated proteins in an atrophic state. They were evaluated by various approaches, such as Western blotting, GO terms, literature, known muscle atrophy-related genes and shortest path analysis. We expect the new proposed strategy to provide an understanding of phosphorylation status in muscle atrophy and to provide assistance towards identifying new therapies.

Why inputs matter: Selection of climatic variables for species distribution modelling in the Himalayan region

NASA Astrophysics Data System (ADS)

Bobrowski, Maria; Schickhoff, Udo

2017-04-01

Betula utilis is a major constituent of alpine treeline ecotones in the western and central Himalayan region. The objective of this study is to provide first time analysis of the potential distribution of Betula utilis in the subalpine and alpine belts of the Himalayan region using species distribution modelling. Using Generalized Linear Models (GLM) we aim at examining climatic factors controlling the species distribution under current climate conditions. Furthermore we evaluate the prediction ability of climate data derived from different statistical methods. GLMs were created using least correlated bioclimatic variables derived from two different climate models: 1) interpolated climate data (i.e. Worldclim, Hijmans et al., 2005) and 2) quasi-mechanistical statistical downscaling (i.e. Chelsa; Karger et al., 2016). Model accuracy was evaluated by the ability to predict the potential species distribution range. We found that models based on variables of Chelsa climate data had higher predictive power, whereas models using Worldclim climate data consistently overpredicted the potential suitable habitat for Betula utilis. Although climatic variables of Worldclim are widely used in modelling species distribution, our results suggest to treat them with caution when remote regions like the Himalayan mountains are in focus. Unmindful usage of climatic variables for species distribution models potentially cause misleading projections and may lead to wrong implications and recommendations for nature conservation. References: Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965-1978. Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N., Linder, H.P. & Kessler, M. (2016) Climatologies at high resolution for the earth land surface areas. arXiv:1607.00217 [physics].

The Algicidal Fungus Trametes versicolor F21a Eliminating Blue Algae via Genes Encoding Degradation Enzymes and Metabolic Pathways Revealed by Transcriptomic Analysis.

PubMed

Dai, Wei; Chen, Xiaolin; Wang, Xuewen; Xu, Zimu; Gao, Xueyan; Jiang, Chaosheng; Deng, Ruining; Han, Guomin

2018-01-01

The molecular mechanism underlying the elimination of algal cells by fungal mycelia has not been fully understood. Here, we applied transcriptomic analysis to investigate the gene expression and regulation at time courses of Trametes versicolor F21a during the algicidal process. The obtained results showed that a total of 193, 332, 545, and 742 differentially expressed genes were identified at 0, 6, 12, and 30 h during the algicidal process, respectively. The gene ontology terms were enriched into glucan 1,4-α-glucosidase activity, hydrolase activity, lipase activity, and endopeptidase activity. The KEGG pathways were enriched in degradation and metabolism pathways including Glycolysis/Gluconeogenesis, Pyruvate metabolism, the Biosynthesis of amino acids, etc. The total expression levels of all Carbohydrate-Active enZYmes (CAZyme) genes for the saccharide metabolism were increased by two folds relative to the control. AA5, GH18, GH5, GH79, GH128, and PL8 were the top six significantly up-regulated modules among 43 detected CAZyme modules. Four available homologous decomposition enzymes of other species could partially inhibit the growth of algal cells. The facts suggest that the algicidal mode of T. versicolor F21a might be associated with decomposition enzymes and several metabolic pathways. The obtained results provide a new candidate way to control algal bloom by application of decomposition enzymes in the future.

How to make thermodynamic perturbation theory to be suitable for low temperature?

NASA Astrophysics Data System (ADS)

Zhou, Shiqi

2009-02-01

Low temperature unsuitability is a problem plaguing thermodynamic perturbation theory (TPT) for years. Present investigation indicates that the low temperature predicament can be overcome by employing as reference system a nonhard sphere potential which incorporates one part of the attractive ingredient in a potential function of interest. In combination with a recently proposed TPT [S. Zhou, J. Chem. Phys. 125, 144518 (2006)] based on a λ expansion (λ being coupling parameter), the new perturbation strategy is employed to predict for several model potentials. It is shown that the new perturbation strategy can very accurately predict various thermodynamic properties even if the potential range is extremely short and hence the temperature of interest is very low and current theoretical formalisms seriously deteriorate or critically fail to predict even the existence of the critical point. Extensive comparison with existing liquid state theories and available computer simulation data discloses a superiority of the present TPT to two Ornstein-Zernike-type integral equation theories, i.e., hierarchical reference theory and self-consistent Ornstein-Zernike approximation.

How to make thermodynamic perturbation theory to be suitable for low temperature?

PubMed

Zhou, Shiqi

2009-02-07

Low temperature unsuitability is a problem plaguing thermodynamic perturbation theory (TPT) for years. Present investigation indicates that the low temperature predicament can be overcome by employing as reference system a nonhard sphere potential which incorporates one part of the attractive ingredient in a potential function of interest. In combination with a recently proposed TPT [S. Zhou, J. Chem. Phys. 125, 144518 (2006)] based on a lambda expansion (lambda being coupling parameter), the new perturbation strategy is employed to predict for several model potentials. It is shown that the new perturbation strategy can very accurately predict various thermodynamic properties even if the potential range is extremely short and hence the temperature of interest is very low and current theoretical formalisms seriously deteriorate or critically fail to predict even the existence of the critical point. Extensive comparison with existing liquid state theories and available computer simulation data discloses a superiority of the present TPT to two Ornstein-Zernike-type integral equation theories, i.e., hierarchical reference theory and self-consistent Ornstein-Zernike approximation.

Potential Utility of the Real-Time TMPA-RT Precipitation Estimates in Streamflow Prediction

NASA Technical Reports Server (NTRS)

Su, Fengge; Gao, Huilin; Huffman, George J.; Lettenmaier, Dennis P.

2010-01-01

We investigate the potential utility of the real-time Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA-RT) data for streamflow prediction, both through direct comparisons of TMPA-RT estimates with a gridded gauge product, and through evaluation of streamflow simulations over four tributaries of La Plata Basin (LPB) in South America using the two precipitation products. Our assessments indicate that the relative accuracy and the hydrologic performance of TMPA-RT-based streamflow simulations generally improved after February 2005. The improvements in TMPA-RT since 2005 are closely related to upgrades in the TMPA-RT algorithm in early February, 2005 which include use of additional microwave sensors (AMSR-E and AMSU-B) and implementation of different calibration schemes. Our work suggests considerable potential for hydrologic prediction using purely satellite-derived precipitation estimates (no adjustments by in situ gauges) in parts of the globe where in situ observations are sparse.

Retrospective study of long-term outcomes of enzyme replacement therapy in Fabry disease: Analysis of prognostic factors

PubMed Central

Biegstraaten, Marieke; Hughes, Derralynn A.; Mehta, Atul; Elliott, Perry M.; Oder, Daniel; Watkinson, Oliver T.; Vaz, Frédéric M.; van Kuilenburg, André B. P.; Wanner, Christoph; Hollak, Carla E. M.

2017-01-01

Despite enzyme replacement therapy, disease progression is observed in patients with Fabry disease. Identification of factors that predict disease progression is needed to refine guidelines on initiation and cessation of enzyme replacement therapy. To study the association of potential biochemical and clinical prognostic factors with the disease course (clinical events, progression of cardiac and renal disease) we retrospectively evaluated 293 treated patients from three international centers of excellence. As expected, age, sex and phenotype were important predictors of event rate. Clinical events before enzyme replacement therapy, cardiac mass and eGFR at baseline predicted an increased event rate. eGFR was the most important predictor: hazard ratios increased from 2 at eGFR <90 ml/min/1.73m2 to 4 at eGFR <30, compared to patients with an eGFR >90. In addition, men with classical disease and a baseline eGFR <60 ml/min/1.73m2 had a faster yearly decline (-2.0 ml/min/1.73m2) than those with a baseline eGFR of >60. Proteinuria was a further independent risk factor for decline in eGFR. Increased cardiac mass at baseline was associated with the most robust decrease in cardiac mass during treatment, while presence of cardiac fibrosis predicted a stronger increase in cardiac mass (3.36 gram/m2/year). Of other cardiovascular risk factors, hypertension significantly predicted the risk for clinical events. In conclusion, besides increasing age, male sex and classical phenotype, faster disease progression while on enzyme replacement therapy is predicted by renal function, proteinuria and to a lesser extent cardiac fibrosis and hypertension. PMID:28763515

Discovery and Annotation of Plant Endogenous Target Mimicry Sequences from Public Transcriptome Libraries: A Case Study of Prunus persica.

PubMed

Karakülah, Gökhan

2017-06-28

Novel transcript discovery through RNA sequencing has substantially improved our understanding of the transcriptome dynamics of biological systems. Endogenous target mimicry (eTM) transcripts, a novel class of regulatory molecules, bind to their target microRNAs (miRNAs) by base pairing and block their biological activity. The objective of this study was to provide a computational analysis framework for the prediction of putative eTM sequences in plants, and as an example, to discover previously un-annotated eTMs in Prunus persica (peach) transcriptome. Therefore, two public peach transcriptome libraries downloaded from Sequence Read Archive (SRA) and a previously published set of long non-coding RNAs (lncRNAs) were investigated with multi-step analysis pipeline, and 44 putative eTMs were found. Additionally, an eTM-miRNA-mRNA regulatory network module associated with peach fruit organ development was built via integration of the miRNA target information and predicted eTM-miRNA interactions. My findings suggest that one of the most widely expressed miRNA families among diverse plant species, miR156, might be potentially sponged by seven putative eTMs. Besides, the study indicates eTMs potentially play roles in the regulation of development processes in peach fruit via targeting specific miRNAs. In conclusion, by following the step-by step instructions provided in this study, novel eTMs can be identified and annotated effectively in public plant transcriptome libraries.

IUS solid rocket motor contamination prediction methods

NASA Technical Reports Server (NTRS)

Mullen, C. R.; Kearnes, J. H.

1980-01-01

A series of computer codes were developed to predict solid rocket motor produced contamination to spacecraft sensitive surfaces. Subscale and flight test data have confirmed some of the analytical results. Application of the analysis tools to a typical spacecraft has provided early identification of potential spacecraft contamination problems and provided insight into their solution; e.g., flight plan modifications, plume or outgassing shields and/or contamination covers.

A Simplified Model to Predict the Effect of Increasing Atmospheric CO[subscript 2] on Carbonate Chemistry in the Ocean

ERIC Educational Resources Information Center

Bozlee, Brian J.; Janebo, Maria; Jahn, Ginger

2008-01-01

The chemistry of dissolved inorganic carbon in seawater is reviewed and used to predict the potential effect of rising levels of carbon dioxide in the atmosphere. In agreement with more detailed treatments, we find that calcium carbonate (aragonite) may become unsaturated in cold surface seawater by the year 2100 C.E., resulting in the destruction…

MnNiO 3 revisited with modern theoretical and experimental methods

DOE PAGES

Dzubak, Allison L.; Mitra, Chandrima; Chance, Michael; ...

2017-11-03

MnNiO 3 is a strongly correlated transition metal oxide that has recently been investigated theoretically for its potential application as an oxygen-evolution photocatalyst. However, there is no experimental report on critical quantities such as the band gap or bulk modulus. Recent theoretical predictions with standard functionals such as LDA+U and HSE show large discrepancies in the band gaps (about 1.23 eV), depending on the nature of the functional used. Hence there is clearly a need for an accurate quantitative prediction of the band gap to gauge its utility as a photocatalyst. In this work, we present a diffusion quantum Montemore » Carlo study of the bulk properties of MnNiO 3 and revisit the synthesis and experimental properties of the compound. We predict quasiparticle band gaps of 2.0(5) eV and 3.8(6) eV for the majority and minority spin channels, respectively, and an equilibrium volume of 92.8 Å 3, which compares well to the experimental value of 94.4 Å 3. A bulk modulus of 217 GPa is predicted for MnNiO 3. As a result, we rationalize the difficulty for the formation of ordered ilmenite-type structure with specific sites for Ni and Mn to be potentially due to the formation of antisite defects that form during synthesis, which ultimately affects the physical properties of MnNiO 3.« less

Predicting characteristics of rainfall driven estrogen runoff and transport from swine AFO spray fields.

PubMed

Lee, Boknam; Kullman, Seth W; Yost, Erin E; Meyer, Michael T; Worley-Davis, Lynn; Williams, C Michael; Reckhow, Kenneth H

2015-11-01

Animal feeding operations (AFOs) have been implicated as potentially major sources of estrogenic contaminants into the aquatic environment due to the relatively minimal treatment of waste and potential mobilization and transport of waste components from spray fields. In this study a Bayesian network (BN) model was developed to inform management decisions and better predict the transport and fate of natural steroidal estrogens from these sites. The developed BN model integrates processes of surface runoff and sediment loss with the modified universal soil loss equation (MUSLE) and the soil conservation service curve number (SCS-CN) runoff model. What-if scenario simulations of lagoon slurry wastes to the spray fields were conducted for the most abundant natural estrogen estrone (E1) observed in the system. It was found that E1 attenuated significantly after 2 months following waste slurry application in both spring and summer seasons, with the overall attenuation rate predicted to be higher in the summer compared to the spring. Using simulations of rainfall events in conjunction with waste slurry application rates, it was predicted that the magnitude of E1 runoff loss is significantly higher in the spring as compared to the summer months, primarily due to spray field crop management plans. Our what-if scenario analyses suggest that planting Bermuda grass in the spray fields is likely to reduce runoff losses of natural estrogens near the water bodies and ecosystems, as compared to planting of soybeans. Copyright © 2015 Elsevier B.V. All rights reserved.

MnNiO 3 revisited with modern theoretical and experimental methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dzubak, Allison L.; Mitra, Chandrima; Chance, Michael

MnNiO 3 is a strongly correlated transition metal oxide that has recently been investigated theoretically for its potential application as an oxygen-evolution photocatalyst. However, there is no experimental report on critical quantities such as the band gap or bulk modulus. Recent theoretical predictions with standard functionals such as LDA+U and HSE show large discrepancies in the band gaps (about 1.23 eV), depending on the nature of the functional used. Hence there is clearly a need for an accurate quantitative prediction of the band gap to gauge its utility as a photocatalyst. In this work, we present a diffusion quantum Montemore » Carlo study of the bulk properties of MnNiO 3 and revisit the synthesis and experimental properties of the compound. We predict quasiparticle band gaps of 2.0(5) eV and 3.8(6) eV for the majority and minority spin channels, respectively, and an equilibrium volume of 92.8 Å 3, which compares well to the experimental value of 94.4 Å 3. A bulk modulus of 217 GPa is predicted for MnNiO 3. As a result, we rationalize the difficulty for the formation of ordered ilmenite-type structure with specific sites for Ni and Mn to be potentially due to the formation of antisite defects that form during synthesis, which ultimately affects the physical properties of MnNiO 3.« less

Predicting Characteristics of Rainfall Driven Estrogen Runoff and Transport from Swine AFO Spray Fields

PubMed Central

Lee, Boknam; Kullman, Seth W.; Yost, Erin E.; Meyer, Michael T.; Worley-Davis, Lynn; Williams, C. Michael; Reckhow, Kenneth H.

2017-01-01

Animal feeding operations (AFOs) have been implicated as potentially major sources of estrogenic contaminants into the aquatic environment due to the relatively minimal treatment of waste and potential mobilization and transport of waste components from spray fields. In this study a Bayesian network (BN) model was developed to inform management decisions and better predict the transport and fate of natural steroidal estrogens from these sites. The developed BN model integrates processes of surface runoff and sediment loss with the modified universal soil loss equation (MUSLE) and the soil conservation service curve number (SCS-CN) runoff model. What-if scenario simulations of lagoon slurry wastes to the spray fields were conducted for the most abundant natural estrogen estrone (E1) observed in the system. It was found that E1 attenuated significantly after 2 months following waste slurry application in both spring and summer seasons, with the overall attenuation rate predicted to be higher in the summer compared to the spring. Using simulations of rainfall events in conjunction with waste slurry application rates, it was predicted that the magnitude of E1 runoff loss is significantly higher in the spring as compared to the summer months, primarily due to spray field crop management plans. Our what-if scenario analyses suggest that planting Bermuda grass in the spray fields is likely to reduce runoff losses of natural estrogens near the water bodies and ecosystems, as compared to planting of soybeans. PMID:26102057

Genomic Prediction of Gene Bank Wheat Landraces.

PubMed

Crossa, José; Jarquín, Diego; Franco, Jorge; Pérez-Rodríguez, Paulino; Burgueño, Juan; Saint-Pierre, Carolina; Vikram, Prashant; Sansaloni, Carolina; Petroli, Cesar; Akdemir, Deniz; Sneller, Clay; Reynolds, Matthew; Tattaris, Maria; Payne, Thomas; Guzman, Carlos; Peña, Roberto J; Wenzl, Peter; Singh, Sukhwinder

2016-07-07

This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH), and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, "diversity" and "prediction", including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15-20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite materials. Copyright © 2016 Crossa et al.

Theoretical Prediction of an Antimony-Silicon Monolayer (penta-Sb2Si): Band Gap Engineering by Strain Effect

NASA Astrophysics Data System (ADS)

Morshedi, Hosein; Naseri, Mosayeb; Hantehzadeh, Mohammad Reza; Elahi, Seyed Mohammad

2018-04-01

In this paper, using a first principles calculation, a two-dimensional structure of silicon-antimony named penta-Sb2Si is predicted. The structural, kinetic, and thermal stabilities of the predicted monolayer are confirmed by the cohesive energy calculation, phonon dispersion analysis, and first principles molecular dynamic simulation, respectively. The electronic properties investigation shows that the pentagonal Sb2Si monolayer is a semiconductor with an indirect band gap of about 1.53 eV (2.1 eV) from GGA-PBE (PBE0 hybrid functional) calculations which can be effectively engineered by employing external biaxial compressive and tensile strain. Furthermore, the optical characteristics calculation indicates that the predicted monolayer has considerable optical absorption and reflectivity in the ultraviolet region. The results suggest that a Sb2Si monolayer has very good potential applications in new nano-optoelectronic devices.

MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis.

PubMed

Wang, QuanQiu; Xu, Rong

2017-07-01

Human metabolomics has great potential in disease mechanism understanding, early diagnosis, and therapy. Existing metabolomics studies are often based on profiling patient biofluids and tissue samples and are difficult owing to the challenges of sample collection and data processing. Here, we report an alternative approach and developed a computation-based prediction system, MetabolitePredict, for disease metabolomics biomarker prediction. We applied MetabolitePredict to identify metabolite biomarkers and metabolite targeting therapies for rheumatoid arthritis (RA), a last-lasting complex disease with multiple genetic and environmental factors involved. MetabolitePredict is a de novo prediction system. It first constructs a disease-specific genetic profile using genes and pathways data associated with an input disease. It then constructs genetic profiles for a total of 259,170 chemicals/metabolites using known chemical genetics and human metabolomic data. MetabolitePredict prioritizes metabolites for a given disease based on the genetic profile similarities between disease and metabolites. We evaluated MetabolitePredict using 63 known RA-associated metabolites. MetabolitePredict found 24 of the 63 metabolites (recall: 0.38) and ranked them highly (mean ranking: top 4.13%, median ranking: top 1.10%, P-value: 5.08E-19). MetabolitePredict performed better than an existing metabolite prediction system, PROFANCY, in predicting RA-associated metabolites (PROFANCY: recall: 0.31, mean ranking: 20.91%, median ranking: 16.47%, P-value: 3.78E-7). Short-chain fatty acids (SCFAs), the abundant metabolites of gut microbiota in the fermentation of fiber, ranked highly (butyrate, 0.03%; acetate, 0.05%; propionate, 0.38%). Finally, we established MetabolitePredict's potential in novel metabolite targeting for disease treatment: MetabolitePredict ranked highly three known metabolite inhibitors for RA treatments (methotrexate:0.25%; leflunomide: 0.56%; sulfasalazine: 0.92%). MetabolitePredict is a generalizable disease metabolite prediction system. The only required input to the system is a disease name or a set of disease-associated genes. The web-based MetabolitePredict is available at:http://xulab. edu/MetabolitePredict. Copyright © 2017 Elsevier Inc. All rights reserved.

A test of the embodied simulation theory of object perception: potentiation of responses to artifacts and animals.

PubMed

Matheson, Heath E; White, Nicole C; McMullen, Patricia A

2014-07-01

Theories of embodied object representation predict a tight association between sensorimotor processes and visual processing of manipulable objects. Previous research has shown that object handles can 'potentiate' a manual response (i.e., button press) to a congruent location. This potentiation effect is taken as evidence that objects automatically evoke sensorimotor simulations in response to the visual presentation of manipulable objects. In the present series of experiments, we investigated a critical prediction of the theory of embodied object representations that potentiation effects should be observed with manipulable artifacts but not non-manipulable animals. In four experiments we show that (a) potentiation effects are observed with animals and artifacts; (b) potentiation effects depend on the absolute size of the objects and (c) task context influences the presence/absence of potentiation effects. We conclude that potentiation effects do not provide evidence for embodied object representations, but are suggestive of a more general stimulus-response compatibility effect that may depend on the distribution of attention to different object features.

Do Potential Past and Future Events Activate the Left-Right Mental Timeline?

ERIC Educational Resources Information Center

Aguirre, Roberto; Santiago, Julio

2017-01-01

Current evidence provides support for the idea that time is mentally represented by spatial means, i.e., a left-right mental timeline. However, available studies have tested only factual events, i.e., those which have occurred in the past or can be predicted to occur in the future. In the present study we tested whether past and future potential…

Exploring the associations between drug side-effects and therapeutic indications.

PubMed

Wang, Fei; Zhang, Ping; Cao, Nan; Hu, Jianying; Sorrentino, Robert

2014-10-01

Drug therapeutic indications and side-effects are both measurable patient phenotype changes in response to the treatment. Inferring potential drug therapeutic indications and identifying clinically interesting drug side-effects are both important and challenging tasks. Previous studies have utilized either chemical structures or protein targets to predict indications and side-effects. In this study, we compared drug therapeutic indication prediction using various information including chemical structures, protein targets and side-effects. We also compared drug side-effect prediction with various information sources including chemical structures, protein targets and therapeutic indication. Prediction performance based on 10-fold cross-validation demonstrates that drug side-effects and therapeutic indications are the most predictive information source for each other. In addition, we extracted 6706 statistically significant indication-side-effect associations from all known drug-disease and drug-side-effect relationships. We further developed a novel user interface that allows the user to interactively explore these associations in the form of a dynamic bipartitie graph. Many relationship pairs provide explicit repositioning hypotheses (e.g., drugs causing postural hypotension are potential candidates for hypertension) and clear adverse-reaction watch lists (e.g., drugs for heart failure possibly cause impotence). All data sets and highly correlated disease-side-effect relationships are available at http://astro.temple.edu/∼tua87106/druganalysis.html. Copyright © 2014 Elsevier Inc. All rights reserved.

Ionic electrodeposition of II-VI and III-V compounds. III. Computer simulation of quasi-rest potentials for M/sub 1/X/sub 1/ compounds analogous to CdTe

DOE Office of Scientific and Technical Information (OSTI.GOV)

Engelken, R.D.

1987-04-01

The quasi-rest potential (QRP) has been proposed as a key quantity in characterizing compound semiconductor (e.g. CdTe) electrodeposition. This article expands the modeling/simulation representative of Cd/sub x/Te in chemical equilibrium to calculate two ''QRP's'': E/sub M/1/sub /, the mixed potential occurring immediately after current interruption and before any relaxation in double layer ion concentration and significant ion exchange/surface stoichiometry change occur, and E/sub M/2/sub /, another mixed potential occurring after the double layer ion concentrations have relaxed to their bulk values but still before any significant surface composition change occurs. Significant predictions include existence of a dramatic negative transition inmore » QRP, with negative-going deposition potential, centered on the potential of perfect stoichiometry (PPS), inequality, in general, between the PPS and E/sub M/1/sub / unless the deposit remains in equilibrium with the electrolyte (no ion exchange at open circuit), negligible sensitivity of QRP-E curves to the activity coefficient parameter implying the importance of the PPS in characterizing compound deposition, and disappearance of the transition structure for sufficiently positive Gibbs free energies.« less

One wouldn't expect an expert bowler to hit only two pins: Hierarchical predictive processing of agent-caused events.

PubMed

Heil, Lieke; Kwisthout, Johan; van Pelt, Stan; van Rooij, Iris; Bekkering, Harold

2018-01-01

Evidence is accumulating that our brains process incoming information using top-down predictions. If lower level representations are correctly predicted by higher level representations, this enhances processing. However, if they are incorrectly predicted, additional processing is required at higher levels to "explain away" prediction errors. Here, we explored the potential nature of the models generating such predictions. More specifically, we investigated whether a predictive processing model with a hierarchical structure and causal relations between its levels is able to account for the processing of agent-caused events. In Experiment 1, participants watched animated movies of "experienced" and "novice" bowlers. The results are in line with the idea that prediction errors at a lower level of the hierarchy (i.e., the outcome of how many pins fell down) slow down reporting of information at a higher level (i.e., which agent was throwing the ball). Experiments 2 and 3 suggest that this effect is specific to situations in which the predictor is causally related to the outcome. Overall, the study supports the idea that a hierarchical predictive processing model can account for the processing of observed action outcomes and that the predictions involved are specific to cases where action outcomes can be predicted based on causal knowledge.

Genetic Influences Can Protect Against Unresponsive Parenting in the Prediction of Child Social Competence

PubMed Central

Van Ryzin, Mark J.; Leve, Leslie D.; Neiderhiser, Jenae M.; Shaw, Daniel S.; Natsuaki, Misaki N.; Reiss, David

2014-01-01

Although social competence in children has been linked to the quality of parenting, prior research has typically not accounted for genetic similarities between parents and children, or for interactions between environmental (i.e., parental) and genetic influences. In this paper, we evaluate the possibility of a gene-by-environment (GxE) interaction in the prediction of social competence in school-age children. Using a longitudinal, multi-method dataset from a sample of children adopted at birth (N = 361), we found a significant interaction between birth parent sociability and sensitive, responsive adoptive parenting when predicting child social competence at school entry (age 6), even when controlling for potential confounds. An analysis of the interaction revealed that genetic strengths can buffer the effects of unresponsive parenting. PMID:25581124

Academic performance, career potential, creativity, and job performance: can one construct predict them all?

PubMed

Kuncel, Nathan R; Hezlett, Sarah A; Ones, Deniz S

2004-01-01

This meta-analysis addresses the question of whether 1 general cognitive ability measure developed for predicting academic performance is valid for predicting performance in both educational and work domains. The validity of the Miller Analogies Test (MAT; W. S. Miller, 1960) for predicting 18 academic and work-related criteria was examined. MAT correlations with other cognitive tests (e.g., Raven's Matrices [J. C. Raven, 1965]; Graduate Record Examinations) also were meta-analyzed. The results indicate that the abilities measured by the MAT are shared with other cognitive ability instruments and that these abilities are generalizably valid predictors of academic and vocational criteria, as well as evaluations of career potential and creativity. These findings contradict the notion that intelligence at work is wholly different from intelligence at school, extending the voluminous literature that supports the broad importance of general cognitive ability (g).

Genomic Prediction of Gene Bank Wheat Landraces

PubMed Central

Crossa, José; Jarquín, Diego; Franco, Jorge; Pérez-Rodríguez, Paulino; Burgueño, Juan; Saint-Pierre, Carolina; Vikram, Prashant; Sansaloni, Carolina; Petroli, Cesar; Akdemir, Deniz; Sneller, Clay; Reynolds, Matthew; Tattaris, Maria; Payne, Thomas; Guzman, Carlos; Peña, Roberto J.; Wenzl, Peter; Singh, Sukhwinder

2016-01-01

This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH), and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15–20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite materials. PMID:27172218

Predicting Spike Occurrence and Neuronal Responsiveness from LFPs in Primary Somatosensory Cortex

PubMed Central

Storchi, Riccardo; Zippo, Antonio G.; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E. M.

2012-01-01

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role. PMID:22586452

Predicting spike occurrence and neuronal responsiveness from LFPs in primary somatosensory cortex.

PubMed

Storchi, Riccardo; Zippo, Antonio G; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E M

2012-01-01

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neuronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role.

Genome-wide analysis of gene expression and protein secretion of Babesia canis during virulent infection identifies potential pathogenicity factors.

PubMed

Eichenberger, Ramon M; Ramakrishnan, Chandra; Russo, Giancarlo; Deplazes, Peter; Hehl, Adrian B

2017-06-13

Infections of dogs with virulent strains of Babesia canis are characterized by rapid onset and high mortality, comparable to complicated human malaria. As in other apicomplexan parasites, most Babesia virulence factors responsible for survival and pathogenicity are secreted to the host cell surface and beyond where they remodel and biochemically modify the infected cell interacting with host proteins in a very specific manner. Here, we investigated factors secreted by B. canis during acute infections in dogs and report on in silico predictions and experimental analysis of the parasite's exportome. As a backdrop, we generated a fully annotated B. canis genome sequence of a virulent Hungarian field isolate (strain BcH-CHIPZ) underpinned by extensive genome-wide RNA-seq analysis. We find evidence for conserved factors in apicomplexan hemoparasites involved in immune-evasion (e.g. VESA-protein family), proteins secreted across the iRBC membrane into the host bloodstream (e.g. SA- and Bc28 protein families), potential moonlighting proteins (e.g. profilin and histones), and uncharacterized antigens present during acute crisis in dogs. The combined data provides a first predicted and partially validated set of potential virulence factors exported during fatal infections, which can be exploited for urgently needed innovative intervention strategies aimed at facilitating diagnosis and management of canine babesiosis.

Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.

PubMed

Chen, Jonathan H; Goldstein, Mary K; Asch, Steven M; Mackey, Lester; Altman, Russ B

2017-05-01

Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% ( P  < 10 -20 ) by using probabilistic topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., "critical care," "pneumonia," "neurologic evaluation"). Existing order sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets

PubMed Central

Goldstein, Mary K; Asch, Steven M; Mackey, Lester; Altman, Russ B

2017-01-01

Objective: Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. Materials and Methods: The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. Results: Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% (P < 10−20) by using probabilistic topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., “critical care,” “pneumonia,” “neurologic evaluation”). Discussion: Existing order sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. Conclusion: Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. PMID:27655861

A habitat overlap analysis derived from maxent for tamarisk and the south-western willow flycatcher

NASA Astrophysics Data System (ADS)

York, Patricia; Evangelista, Paul; Kumar, Sunil; Graham, James; Flather, Curtis; Stohlgren, Thomas

2011-06-01

Biologic control of the introduced and invasive, woody plant tamarisk ( Tamarix spp, saltcedar) in south-western states is controversial because it affects habitat of the federally endangered South-western Willow Flycatcher ( Empidonax traillii extimus). These songbirds sometimes nest in tamarisk where floodplain-level invasion replaces native habitats. Biologic control, with the saltcedar leaf beetle ( Diorhabda elongate), began along the Virgin River, Utah, in 2006, enhancing the need for comprehensive understanding of the tamarisk-flycatcher relationship. We used maximum entropy (Maxent) modeling to separately quantify the current extent of dense tamarisk habitat (>50% cover) and the potential extent of habitat available for E. traillii extimus within the studied watersheds. We used transformations of 2008 Landsat Thematic Mapper images and a digital elevation model as environmental input variables. Maxent models performed well for the flycatcher and tamarisk with Area Under the ROC Curve (AUC) values of 0.960 and 0.982, respectively. Classification of thresholds and comparison of the two Maxent outputs indicated moderate spatial overlap between predicted suitable habitat for E. traillii extimus and predicted locations with dense tamarisk stands, where flycatcher habitat will potentially change flycatcher habitats. Dense tamarisk habitat comprised 500 km2 within the study area, of which 11.4% was also modeled as potential habitat for E. traillii extimus. Potential habitat modeled for the flycatcher constituted 190 km2, of which 30.7% also contained dense tamarisk habitat. Results showed that both native vegetation and dense tamarisk habitats exist in the study area and that most tamarisk infestations do not contain characteristics that satisfy the habitat requirements of E. traillii extimus. Based on this study, effective biologic control of Tamarix spp. may, in the short term, reduce suitable habitat available to E. traillii extimus, but also has the potential in the long term to increase suitable habitat if appropriate mixes of native woody vegetation replace tamarisk in biocontrol areas.

An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential.

PubMed

Fitzpatrick, J M; Roberts, D W; Patlewicz, G

2018-06-01

Predictive testing to characterise substances for their skin sensitisation potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximisation Test (GPMT). In recent years, EU regulations, have provided a strong incentive to develop non-animal alternatives, such as expert systems software. Here we selected three different types of expert systems: VEGA (statistical), Derek Nexus (knowledge-based) and TIMES-SS (hybrid), and evaluated their performance using two large sets of animal data: one set of 1249 substances from eChemportal and a second set of 515 substances from NICEATM. A model was considered successful at predicting skin sensitisation potential if it had at least the same balanced accuracy as the LLNA and the GPMT had in predicting the other outcomes, which ranged from 79% to 86%. We found that the highest balanced accuracy of any of the expert systems evaluated was 65% when making global predictions. For substances within the domain of TIMES-SS, however, balanced accuracies for the two datasets were found to be 79% and 82%. In those cases where a chemical was within the TIMES-SS domain, the TIMES-SS skin sensitisation hazard prediction had the same confidence as the result from LLNA or GPMT.

Potential energy surface and vibrational band origins of the triatomic lithium cation

NASA Astrophysics Data System (ADS)

Searles, Debra J.; Dunne, Simon J.; von Nagy-Felsobuki, Ellak I.

The 104 point CISD Li +3 potential energy surface and its analytical representation is reported. The calculations predict the minimum energy geometry to be an equilateral triangle of side RLiLi = 3.0 Å and of energy - 22.20506 E h. A fifth-order Morse—Dunham type analytical force field is used in the Carney—Porter normal co-ordinate vibrational Hamiltonian, the corresponding eigenvalue problem being solved variationally using a 560 configurational finite-element basis set. The predicted assignment of the vibrational band origins is in accord with that reported for H +3. Moreover, for 6Li +3 and 7Li +3 the lowest i.r. accessible band origin is the overlineν0,1,±1 predicted to be at 243.6 and 226.0 cm -1 respectively.

eShadow: A tool for comparing closely related sequences

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ovcharenko, Ivan; Boffelli, Dario; Loots, Gabriela G.

2004-01-15

Primate sequence comparisons are difficult to interpret due to the high degree of sequence similarity shared between such closely related species. Recently, a novel method, phylogenetic shadowing, has been pioneered for predicting functional elements in the human genome through the analysis of multiple primate sequence alignments. We have expanded this theoretical approach to create a computational tool, eShadow, for the identification of elements under selective pressure in multiple sequence alignments of closely related genomes, such as in comparisons of human to primate or mouse to rat DNA. This tool integrates two different statistical methods and allows for the dynamic visualizationmore » of the resulting conservation profile. eShadow also includes a versatile optimization module capable of training the underlying Hidden Markov Model to differentially predict functional sequences. This module grants the tool high flexibility in the analysis of multiple sequence alignments and in comparing sequences with different divergence rates. Here, we describe the eShadow comparative tool and its potential uses for analyzing both multiple nucleotide and protein alignments to predict putative functional elements. The eShadow tool is publicly available at http://eshadow.dcode.org/« less

Environmental risk assessment of a genetically-engineered microorganism: Erwinia carotovora

DOE Office of Scientific and Technical Information (OSTI.GOV)

Orvos, D.R.

1989-01-01

Environmental use of genetically-engineered microorganisms (GEMs) has raised concerns over potential ecological impact. Development of microcosm systems useful in preliminary testing for risk assessment will provide useful information for predicting potential structural, functional, and genetic effects of GEM release. This study was executed to develop techniques that may be useful in risk assessment and microbial ecology, to ascertain which parameters are useful in determining risk and to predict risk from releasing an engineered strain of Erwinia carotovora. A terrestrial microcosm system for use in GEM risk assessment studies was developed for use in assessing alterations of microbial structure and functionmore » that may be caused by introducing the engineered strain of E. carotovora. This strain is being developed for use as a biological control agent for plant soft rot. Parameters that were monitored included survival and intraspecific competition of E. carotovora, structural effects upon both total bacterial populations and numbers of selected bacterial genera, effects upon activities of dehydrogenase and alkaline phosphatase, effects upon soil nutrients, and potential for gene transfer into or out of the engineered strain.« less

A Novel Application of Multiscale Entropy in Electroencephalography to Predict the Efficacy of Acetylcholinesterase Inhibitor in Alzheimer's Disease

PubMed Central

Tsai, Ping-Huang; Liu, Fang-Chun; Tsao, Jenho; Wang, Yung-Hung; Lo, Men-Tzung

2015-01-01

Alzheimer's disease (AD) is the most common form of dementia. According to one hypothesis, AD is caused by the reduced synthesis of the neurotransmitter acetylcholine. Therefore, acetylcholinesterase (AChE) inhibitors are considered to be an effective therapy. For clinicians, however, AChE inhibitors are not a predictable treatment for individual patients. We aimed to disclose the difference by biosignal processing. In this study, we used multiscale entropy (MSE) analysis, which can disclose the embedded information in different time scales, in electroencephalography (EEG), in an attempt to predict the efficacy of AChE inhibitors. Seventeen newly diagnosed AD patients were enrolled, with an initial minimental state examination (MMSE) score of 18.8 ± 4.5. After 12 months of AChE inhibitor therapy, 7 patients were responsive and 10 patients were nonresponsive. The major difference between these two groups is Slope 2 (MSE6 to 20). The area below the receiver operating characteristic (ROC) curve of Slope 2 is 0.871 (95% CI = 0.69–1). The sensitivity is 85.7% and the specificity is 60%, whereas the cut-off value of Slope 2 is −0.024. Therefore, MSE analysis of EEG signals, especially Slope 2, provides a potential tool for predicting the efficacy of AChE inhibitors prior to therapy. PMID:26120358

Adsorption of metal atoms at a buckled graphene grain boundary using model potentials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Helgee, Edit E.; Isacsson, Andreas

Two model potentials have been evaluated with regard to their ability to model adsorption of single metal atoms on a buckled graphene grain boundary. One of the potentials is a Lennard-Jones potential parametrized for gold and carbon, while the other is a bond-order potential parametrized for the interaction between carbon and platinum. Metals are expected to adsorb more strongly to grain boundaries than to pristine graphene due to their enhanced adsorption at point defects resembling those that constitute the grain boundary. Of the two potentials considered here, only the bond-order potential reproduces this behavior and predicts the energy of themore » adsorbate to be about 0.8 eV lower at the grain boundary than on pristine graphene. The Lennard-Jones potential predicts no significant difference in energy between adsorbates at the boundary and on pristine graphene. These results indicate that the Lennard-Jones potential is not suitable for studies of metal adsorption on defects in graphene, and that bond-order potentials are preferable.« less

Neural Correlates of Semantic Prediction and Resolution in Sentence Processing.

PubMed

Grisoni, Luigi; Miller, Tally McCormick; Pulvermüller, Friedemann

2017-05-03

Most brain-imaging studies of language comprehension focus on activity following meaningful stimuli. Testing adult human participants with high-density EEG, we show that, already before the presentation of a critical word, context-induced semantic predictions are reflected by a neurophysiological index, which we therefore call the semantic readiness potential (SRP). The SRP precedes critical words if a previous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in unpredictable (low-constraint) contexts. Specific semantic predictions were indexed by SRP sources within the motor system-in dorsolateral hand motor areas for expected hand-related words (e.g., "write"), but in ventral motor cortex for face-related words ("talk"). Compared with affirmative sentences, negated ones led to medial prefrontal and more widespread motor source activation, the latter being consistent with predictive semantic computation of alternatives to the negated expected concept. Predictive processing of semantic alternatives in negated sentences is further supported by a negative-going event-related potential at ∼400 ms (N400), which showed the typical enhancement to semantically incongruent sentence endings only in high-constraint affirmative contexts, but not to high-constraint negated ones. These brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) processing in sentence understanding. SIGNIFICANCE STATEMENT Most neuroscientists agree on the eminent importance of predictive mechanisms for understanding basic as well as higher brain functions. This contrasts with a sparseness of brain measures that directly reflects specific aspects of prediction, as they are relevant in the processing of language and thought. Here we show that when critical words are strongly expected in their sentence context, a predictive brain response reflects meaning features of these anticipated symbols already before they appear. The granularity of the semantic predictions was so fine grained that the cortical sources in sensorimotor and medial prefrontal cortex even distinguished between predicted face- or hand-related action words (e.g., the words "lick" or "pick") and between affirmative and negated sentence meanings. Copyright © 2017 Grisoni et al.

Neural Correlates of Semantic Prediction and Resolution in Sentence Processing

PubMed Central

2017-01-01

Most brain-imaging studies of language comprehension focus on activity following meaningful stimuli. Testing adult human participants with high-density EEG, we show that, already before the presentation of a critical word, context-induced semantic predictions are reflected by a neurophysiological index, which we therefore call the semantic readiness potential (SRP). The SRP precedes critical words if a previous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in unpredictable (low-constraint) contexts. Specific semantic predictions were indexed by SRP sources within the motor system—in dorsolateral hand motor areas for expected hand-related words (e.g., “write”), but in ventral motor cortex for face-related words (“talk”). Compared with affirmative sentences, negated ones led to medial prefrontal and more widespread motor source activation, the latter being consistent with predictive semantic computation of alternatives to the negated expected concept. Predictive processing of semantic alternatives in negated sentences is further supported by a negative-going event-related potential at ∼400 ms (N400), which showed the typical enhancement to semantically incongruent sentence endings only in high-constraint affirmative contexts, but not to high-constraint negated ones. These brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) processing in sentence understanding. SIGNIFICANCE STATEMENT Most neuroscientists agree on the eminent importance of predictive mechanisms for understanding basic as well as higher brain functions. This contrasts with a sparseness of brain measures that directly reflects specific aspects of prediction, as they are relevant in the processing of language and thought. Here we show that when critical words are strongly expected in their sentence context, a predictive brain response reflects meaning features of these anticipated symbols already before they appear. The granularity of the semantic predictions was so fine grained that the cortical sources in sensorimotor and medial prefrontal cortex even distinguished between predicted face- or hand-related action words (e.g., the words “lick” or “pick”) and between affirmative and negated sentence meanings. PMID:28411271

Health literacy and global cognitive function predict e-mail but not internet use in heart failure patients.

PubMed

Schprechman, Jared P; Gathright, Emily C; Goldstein, Carly M; Guerini, Kate A; Dolansky, Mary A; Redle, Joseph; Hughes, Joel W

2013-01-01

Background. The internet offers a potential for improving patient knowledge, and e-mail may be used in patient communication with providers. However, barriers to internet and e-mail use, such as low health literacy and cognitive impairment, may prevent patients from using technological resources. Purpose. We investigated whether health literacy, heart failure knowledge, and cognitive function were related to internet and e-mail use in older adults with heart failure (HF). Methods. Older adults (N = 119) with heart failure (69.84 ± 9.09 years) completed measures of health literacy, heart failure knowledge, cognitive functioning, and internet use in a cross-sectional study. Results. Internet and e-mail use were reported in 78.2% and 71.4% of this sample of patients with HF, respectively. Controlling for age and education, logistic regression analyses indicated that higher health literacy predicted e-mail (P < .05) but not internet use. Global cognitive function predicted e-mail (P < .05) but not internet use. Only 45% used the Internet to obtain information on HF and internet use was not associated with greater HF knowledge. Conclusions. The majority of HF patients use the internet and e-mail, but poor health literacy and cognitive impairment may prevent some patients from accessing these resources. Future studies that examine specific internet and email interventions to increase HF knowledge are needed.

Ion acceleration in a helicon source due to the self-bias effect

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wiebold, Matt; Sung, Yung-Ta; Scharer, John E.

2012-05-15

Time-averaged plasma potential differences up to 165 V over several hundred Debye lengths are observed in low pressure (p{sub n} < 1 mTorr) expanding argon plasmas in the Madison Helicon eXperiment (MadHeX). The potential gradient leads to ion acceleration greater than that predicted by ambipolar expansion, exceeding E{sub i} Almost-Equal-To 7 kT{sub e} in some cases. RF power up to 500 W at 13.56 MHz is supplied to a half-turn, double-helix antenna in the presence of a nozzle magnetic field, adjustable up to 1 kG. A retarding potential analyzer (RPA) measures the ion energy distribution function (IEDF) and a sweptmore » emissive probe measures the plasma potential. Single and double probes measure the electron density and temperature. Two distinct mode hops, the capacitive-inductive (E-H) and inductive-helicon (H-W) transitions, are identified by jumps in density as RF power is increased. In the capacitive (E) mode, large fluctuations of the plasma potential (V{sub p-p} Greater-Than-Or-Equivalent-To 140V, V{sub p-p}/V{sub p} Almost-Equal-To 150%) exist at the RF frequency and its harmonics. The more mobile electrons can easily respond to RF-timescale gradients in the plasma potential whereas the inertially constrained ions cannot, leading to an initial flux imbalance and formation of a self-bias voltage between the source and expansion chambers. In the capacitive mode, the ion acceleration is not well described by an ambipolar relation, while in the inductive and helicon modes the ion acceleration more closely follows an ambipolar relation. The scaling of the potential gradient with the argon flow rate and RF power are investigated, with the largest potential gradients observed for the lowest flow rates in the capacitive mode. The magnitude of the self-bias voltage agrees with that predicted for RF self-bias at a wall. Rapid fluctuations in the plasma potential result in a time-dependent axial electron flux that acts to 'neutralize' the accelerated ion population, resulting in a zero net time-averaged current through the acceleration region when an insulating upstream boundary condition is enforced. Grounding the upstream endplate increases the self-bias voltage compared to a floating endplate.« less

Coulomb-stable triply charged diatomic: HeY3+

NASA Astrophysics Data System (ADS)

Wesendrup, Ralf; Pernpointner, Markus; Schwerdtfeger, Peter

1999-11-01

Accurate relativistic coupled-cluster calculations show that the triply charged species HeY3+ is a stable molecule and represents the lightest diatomic trication that does not undergo a Coulomb fragmentation into charged fragments. The diatomic potential-energy curve is approximated by an extended Morse potential, and vibrational-rotational constants for HeY3+ are predicted (Re=224.3 pm, D0=0.394 eV, ωe=437 cm-1, ωexe=15.8 cm-1, Be=0.877 cm-1). It is further shown that the He-Y3+ bond can basically be described as a charge-induced dipole interaction.

Incorporation of absorption and metabolism into liver toxicity prediction for phytochemicals: A tiered in silico QSAR approach.

PubMed

Liu, Yitong

2018-05-18

An increased use of herbal dietary supplements has been associated with adverse liver effects such as elevated serum enzymes and liver failure. The safety assessment for herbal dietary supplements is challenging since they often contain complex mixtures of phytochemicals, most of which have unknown pharmacokinetic and toxicological properties. Rapid tools are needed to evaluate large numbers of phytochemicals for potential liver toxicity. The current study demonstrates a tiered approach combining identification of phytochemicals in liver toxic botanicals, followed by in silico quantitative structure-activity relationship (QSAR) evaluation of these phytochemicals for absorption (e.g. permeability), metabolism (cytochromes P450) and liver toxicity (e.g. elevated transaminases). First, 255 phytochemicals from 20 botanicals associated with clinical liver injury were identified, and the phytochemical structures were subsequently used for QSAR evaluation. Among these identified phytochemicals, 193 were predicted to be absorbed and then used to generate metabolites, which were both used to predict liver toxicity. Forty-eight phytochemicals were predicted as liver toxic, either due to parent phytochemicals or metabolites. Among them, nineteen phytochemicals have previous evidence of liver toxicity (e.g. pyrrolizidine alkaloids), while the majority were newly discovered (e.g. sesquiterpenoids). These findings help reveal new toxic phytochemicals in herbal dietary supplements and prioritize future toxicological testing. Published by Elsevier Ltd.

Eukaryotic Protein Kinases (ePKs) of the Helminth Parasite Schistosoma mansoni

PubMed Central

2011-01-01

Background Schistosomiasis remains an important parasitic disease and a major economic problem in many countries. The Schistosoma mansoni genome and predicted proteome sequences were recently published providing the opportunity to identify new drug candidates. Eukaryotic protein kinases (ePKs) play a central role in mediating signal transduction through complex networks and are considered druggable targets from the medical and chemical viewpoints. Our work aimed at analyzing the S. mansoni predicted proteome in order to identify and classify all ePKs of this parasite through combined computational approaches. Functional annotation was performed mainly to yield insights into the parasite signaling processes relevant to its complex lifestyle and to select some ePKs as potential drug targets. Results We have identified 252 ePKs, which corresponds to 1.9% of the S. mansoni predicted proteome, through sequence similarity searches using HMMs (Hidden Markov Models). Amino acid sequences corresponding to the conserved catalytic domain of ePKs were aligned by MAFFT and further used in distance-based phylogenetic analysis as implemented in PHYLIP. Our analysis also included the ePK homologs from six other eukaryotes. The results show that S. mansoni has proteins in all ePK groups. Most of them are clearly clustered with known ePKs in other eukaryotes according to the phylogenetic analysis. None of the ePKs are exclusively found in S. mansoni or belong to an expanded family in this parasite. Only 16 S. mansoni ePKs were experimentally studied, 12 proteins are predicted to be catalytically inactive and approximately 2% of the parasite ePKs remain unclassified. Some proteins were mentioned as good target for drug development since they have a predicted essential function for the parasite. Conclusions Our approach has improved the functional annotation of 40% of S. mansoni ePKs through combined similarity and phylogenetic-based approaches. As we continue this work, we will highlight the biochemical and physiological adaptations of S. mansoni in response to diverse environments during the parasite development, vector interaction, and host infection. PMID:21548963

Climate Change and a Global City: An Assessment of the Metropolitan East Coast Region

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia; Solecki, William

1999-01-01

The objective of the research is to derive an assessment of the potential climate change impacts on a global city - in this case the 31 county region that comprises the New York City metropolitan area. This study comprises one of the regional components that contribute to the ongoing U.S. National Assessment: The Potential Consequences of Climate Variability and Change and is an application of state-of-the-art climate change science to a set of linked sectoral assessment analyses for the Metro East Coast (MEC) region. We illustrate how three interacting elements of global cities react and respond to climate variability and change with a broad conceptual model. These elements include: people (e.g., socio- demographic conditions), place (e.g., physical systems), and pulse (e.g., decision-making and economic activities). The model assumes that a comprehensive assessment of potential climate change can be derived from examining the impacts within each of these elements and at their intersections. Thus, the assessment attempts to determine the within-element and the inter-element effects. Five interacting sector studies representing the three intersecting elements are evaluated. They include the Coastal Zone, Infrastructure, Water Supply, Public Health, and Institutional Decision-making. Each study assesses potential climate change impacts on the sector and on the intersecting elements, through the analysis of the following parts: 1. Current conditions of sector in the region; 2. Lessons and evidence derived from past climate variability; 3. Scenario predictions affecting sector; potential impacts of scenario predictions; 4. Knowledge/information gaps and critical issues including identification of additional research questions, effectiveness of modeling efforts, equity of impacts, potential non-local interactions, and policy recommendations; and 5. Identification of coping strategies - i.e., resilience building, mitigation strategies, new technologies, education that affects decision-making, and better preparedness for contingencies.

High-Throughput Gene Expression Profiles to Define Drug Similarity and Predict Compound Activity.

PubMed

De Wolf, Hans; Cougnaud, Laure; Van Hoorde, Kirsten; De Bondt, An; Wegner, Joerg K; Ceulemans, Hugo; Göhlmann, Hinrich

2018-04-01

By adding biological information, beyond the chemical properties and desired effect of a compound, uncharted compound areas and connections can be explored. In this study, we add transcriptional information for 31K compounds of Janssen's primary screening deck, using the HT L1000 platform and assess (a) the transcriptional connection score for generating compound similarities, (b) machine learning algorithms for generating target activity predictions, and (c) the scaffold hopping potential of the resulting hits. We demonstrate that the transcriptional connection score is best computed from the significant genes only and should be interpreted within its confidence interval for which we provide the stats. These guidelines help to reduce noise, increase reproducibility, and enable the separation of specific and promiscuous compounds. The added value of machine learning is demonstrated for the NR3C1 and HSP90 targets. Support Vector Machine models yielded balanced accuracy values ≥80% when the expression values from DDIT4 & SERPINE1 and TMEM97 & SPR were used to predict the NR3C1 and HSP90 activity, respectively. Combining both models resulted in 22 new and confirmed HSP90-independent NR3C1 inhibitors, providing two scaffolds (i.e., pyrimidine and pyrazolo-pyrimidine), which could potentially be of interest in the treatment of depression (i.e., inhibiting the glucocorticoid receptor (i.e., NR3C1), while leaving its chaperone, HSP90, unaffected). As such, the initial hit rate increased by a factor 300, as less, but more specific chemistry could be screened, based on the upfront computed activity predictions.

The 13Carbon footprint of B[e] supergiants

NASA Astrophysics Data System (ADS)

Liermann, A.; Kraus, M.; Schnurr, O.; Fernandes, M. Borges

2010-10-01

We report on the first detection of 13C enhancement in two B[e] supergiants (B[e]SGs) in the Large Magellanic Cloud. Stellar evolution models predict the surface abundance in 13C to strongly increase during main-sequence and post-main-sequence evolution of massive stars. However, direct identification of chemically processed material on the surface of B[e]SGs is hampered by their dense, disc-forming winds, hiding the stars. Recent theoretical computations predict the detectability of enhanced 13C via the molecular emission in 13CO arising in the circumstellar discs of B[e]SGs. To test this potential method and to unambiguously identify a post-main-sequence B[e] SG by its 13CO emission, we have obtained high-quality K-band spectra of two known B[e] SGs in the Large Magellanic Cloud, using the Very Large Telescope's Spectrograph for INtegral Field Observation in the Near-Infrared (VLT/SINFONI). Both stars clearly show the 13CO band emission, whose strength implies a strong enhancement of 13C, in agreement with theoretical predictions. This first ever direct confirmation of the evolved nature of B[e]SGs thus paves the way to the first identification of a Galactic B[e]SG. Based on observations collected with the ESO VLT Paranal Observatory under programme 384.D-1078(A). E-mail: liermann@mpifr-bonn.mpg.de (AL); kraus@sunstel.asu.cas.cz (MK); oschnurr@aip.de (OS); borges@on.br (MBF)

Prediction of Binding Energy of Keap1 Interaction Motifs in the Nrf2 Antioxidant Pathway and Design of Potential High-Affinity Peptides.

PubMed

Karttunen, Mikko; Choy, Wing-Yiu; Cino, Elio A

2018-06-07

Nuclear factor erythroid 2-related factor 2 (Nrf2) is a transcription factor and principal regulator of the antioxidant pathway. The Kelch domain of Kelch-like ECH-associated protein 1 (Keap1) binds to motifs in the N-terminal region of Nrf2, promoting its degradation. There is interest in developing ligands that can compete with Nrf2 for binding to Kelch, thereby activating its transcriptional activities and increasing antioxidant levels. Using experimental Δ G bind values of Kelch-binding motifs determined previously, a revised hydrophobicity-based model was developed for estimating Δ G bind from amino acid sequence and applied to rank potential uncharacterized Kelch-binding motifs identified from interaction databases and BLAST searches. Model predictions and molecular dynamics (MD) simulations suggested that full-length MAD2A binds Kelch more favorably than a high-affinity 20-mer Nrf2 E78P peptide, but that the motif in isolation is not a particularly strong binder. Endeavoring to develop shorter peptides for activating Nrf2, new designs were created based on the E78P peptide, some of which showed considerable propensity to form binding-competent structures in MD, and were predicted to interact with Kelch more favorably than the E78P peptide. The peptides could be promising new ligands for enhancing the oxidative stress response.

Thermoelectric properties of 2H-CuGaO2 for device applications: A first principle TB-mBJ potential study

NASA Astrophysics Data System (ADS)

Bhamu, K. C.; Praveen, C. S.

2017-12-01

Here we report the structural, electronic, optical, and thermoelectric properties of delafossite type 2H-CuGaO2 using first principles calculations. The present calculation predict an indirect band gap of 1.20 eV and a direct band gap of 3.48 eV. A detailed analysis of the electronic structure is provided based on atom and orbital projected density of states. Frequency dependent dielectric functions, refractive index, and absorption coefficient as a function of photon energy are discussed. The thermoelectric properties with power factor, and the figure of merit are reported as a function of chemical potential in the region ± 0.195 (μ -EF) eV at constant temperature of 300 and 800 K. The thermoelectric properties shows that 2H-CuGaO2 could be potential candidate for engineering devises operating at high temperature for the chemical potential in the range of ± 0.055 (μ -EF) eV and beyond this range the thermoelectric performance of 2H-CuGaO2 get reduced.

Modeling to Predict Escherichia coli at Presque Isle Beach 2, City of Erie, Erie County, Pennsylvania

USGS Publications Warehouse

Zimmerman, Tammy M.

2008-01-01

The Lake Erie beaches in Pennsylvania are a valuable recreational resource for Erie County. Concentrations of Escherichia coli (E. coli) at monitored beaches in Presque Isle State Park in Erie, Pa., occasionally exceed the single-sample bathing-water standard of 235 colonies per 100 milliliters resulting in potentially unsafe swimming conditions and prompting beach managers to post public advisories or to close beaches to recreation. To supplement the current method for assessing recreational water quality (E. coli concentrations from the previous day), a predictive regression model for E. coli concentrations at Presque Isle Beach 2 was developed from data collected during the 2004 and 2005 recreational seasons. Model output included predicted E. coli concentrations and exceedance probabilities--the probability that E. coli concentrations would exceed the standard. For this study, E. coli concentrations and other water-quality and environmental data were collected during the 2006 recreational season at Presque Isle Beach 2. The data from 2006, an independent year, were used to test (validate) the 2004-2005 predictive regression model and compare the model performance to the current method. Using 2006 data, the 2004-2005 model yielded more correct responses and better predicted exceedances of the standard than the use of E. coli concentrations from the previous day. The differences were not pronounced, however, and more data are needed. For example, the model correctly predicted exceedances of the standard 11 percent of the time (1 out of 9 exceedances that occurred in 2006) whereas using the E. coli concentrations from the previous day did not result in any correctly predicted exceedances. After validation, new models were developed by adding the 2006 data to the 2004-2005 dataset and by analyzing the data in 2- and 3-year combinations. Results showed that excluding the 2004 data (using 2005 and 2006 data only) yielded the best model. Explanatory variables in the 2005-2006 model were log10 turbidity, bird count, and wave height. The 2005-2006 model correctly predicted when the standard would not be exceeded (specificity) with a response of 95.2 percent (178 out of 187 nonexceedances) and correctly predicted when the standard would be exceeded (sensitivity) with a response of 64.3 percent (9 out of 14 exceedances). In all cases, the results from predictive modeling produced higher percentages of correct predictions than using E. coli concentrations from the previous day. Additional data collected each year can be used to test and possibly improve the model. The results of this study will aid beach managers in more rapidly determining when waters are not safe for recreational use and, subsequently, when to close a beach or post an advisory.

Context-sensitive network-based disease genetics prediction and its implications in drug discovery.

PubMed

Chen, Yang; Xu, Rong

2017-04-01

Disease phenotype networks play an important role in computational approaches to identifying new disease-gene associations. Current disease phenotype networks often model disease relationships based on pairwise similarities, therefore ignore the specific context on how two diseases are connected. In this study, we propose a new strategy to model disease associations using context-sensitive networks (CSNs). We developed a CSN-based phenome-driven approach for disease genetics prediction, and investigated the translational potential of the predicted genes in drug discovery. We constructed CSNs by directly connecting diseases with associated phenotypes. Here, we constructed two CSNs using different data sources; the two networks contain 26 790 and 13 822 nodes respectively. We integrated the CSNs with a genetic functional relationship network and predicted disease genes using a network-based ranking algorithm. For comparison, we built Similarity-Based disease Networks (SBN) using the same disease phenotype data. In a de novo cross validation for 3324 diseases, the CSN-based approach significantly increased the average rank from top 12.6 to top 8.8% for all tested genes comparing with the SBN-based approach ( p

Comparative analysis of remotely-sensed data products via ecological niche modeling of avian influenza case occurrences in Middle Eastern poultry.

PubMed

Bodbyl-Roels, Sarah; Peterson, A Townsend; Xiao, Xiangming

2011-03-28

Ecological niche modeling integrates known sites of occurrence of species or phenomena with data on environmental variation across landscapes to infer environmental spaces potentially inhabited (i.e., the ecological niche) to generate predictive maps of potential distributions in geographic space. Key inputs to this process include raster data layers characterizing spatial variation in environmental parameters, such as vegetation indices from remotely sensed satellite imagery. The extent to which ecological niche models reflect real-world distributions depends on a number of factors, but an obvious concern is the quality and content of the environmental data layers. We assessed ecological niche model predictions of H5N1 avian flu presence quantitatively within and among four geographic regions, based on models incorporating two means of summarizing three vegetation indices derived from the MODIS satellite. We evaluated our models for predictive ability using partial ROC analysis and GLM ANOVA to compare performance among indices and regions. We found correlations between vegetation indices to be high, such that they contain information that overlaps broadly. Neither the type of vegetation index used nor method of summary affected model performance significantly. However, the degree to which model predictions had to be transferred (i.e., projected onto landscapes and conditions not represented on the landscape of training) impacted predictive strength greatly (within-region model predictions far out-performed models projected among regions). Our results provide the first quantitative tests of most appropriate uses of different remotely sensed data sets in ecological niche modeling applications. While our testing did not result in a decisive "best" index product or means of summarizing indices, it emphasizes the need for careful evaluation of products used in modeling (e.g. matching temporal dimensions and spatial resolution) for optimum performance, instead of simple reliance on large numbers of data layers.

Protected Repository for the Defense of Infrastructure Against Cyber Threats (PREDICT) Dataset Development and Hosting

DTIC Science & Technology

2018-04-01

Creation, curation and hosting of datasets for cybersecurity researchers; • Serving as a “host of convenience” for datasets from PREDICT and IMPACT non...assumptions and preferences, as cybersecurity investigators and proxies for the broader current and potential user communities. No attempt was made to...understand what would have been of use, i.e., cybersecurity researcher needs were known more anecdotally than systematically.[1] We believe that

In silico prediction of escherichia coli proteins targeting the host cell nucleus, with special reference to their role in colon cancer etiology.

PubMed

Khan, Abdul Arif

2014-06-01

The potential role of Escherichia coli in the development of colorectal carcinoma (CRC) has been investigated in many studies. Although the exact mechanism is not clear, chronic inflammation caused by E. coli and other related events are suggested as possible causes behind E. coli-induced colon cancer. It has been found that CRC cells, but not normal cells, are colonized by an intracellular form of E. coli. We predicted nuclear targeting of bacterial proteins in the host cell through computational tools nuclear localization signal (NLS) mapper and balanced subcellular localization predictor (BaCeILo). During intracellular E. coli residence, such targeting is highly likely and may have a possible role in colon cancer etiology. We observed that several gene expression-associated proteins of E. coli can migrate to the host nucleus during intracellular infections. This situation provides an opportunity for competitive interaction of host and pathogen proteins with similar cellular substrates, thereby increasing the chances of development of colon cancer. Moreover, the results indicated that proteins localized in the membrane of E. coli mostly act as secretary proteins in host cells. No exact correlation was observed between NLS prediction and nuclear localization prediction by BaCeILo. This is partly because of a number of reasons, including that only 30% of nuclear proteins carry NLS and that proteins <40 kDa molecular weight can passively target the host nucleus. This study concludes that detection of gene expression-specific E. coli proteins and their targeting of the nucleus may have a profound impact on CRC etiology.

Ion concentrations and velocity profiles in nanochannel electroosmotic flows

NASA Astrophysics Data System (ADS)

Qiao, R.; Aluru, N. R.

2003-03-01

Ion distributions and velocity profiles for electroosmotic flow in nanochannels of different widths are studied in this paper using molecular dynamics and continuum theory. For the various channel widths studied in this paper, the ion distribution near the channel wall is strongly influenced by the finite size of the ions and the discreteness of the solvent molecules. The classical Poisson-Boltzmann equation fails to predict the ion distribution near the channel wall as it does not account for the molecular aspects of the ion-wall and ion-solvent interactions. A modified Poisson-Boltzmann equation based on electrochemical potential correction is introduced to account for ion-wall and ion-solvent interactions. The electrochemical potential correction term is extracted from the ion distribution in a smaller channel using molecular dynamics. Using the electrochemical potential correction term extracted from molecular dynamics (MD) simulation of electroosmotic flow in a 2.22 nm channel, the modified Poisson-Boltzmann equation predicts the ion distribution in larger channel widths (e.g., 3.49 and 10.00 nm) with good accuracy. Detailed studies on the velocity profile in electro-osmotic flow indicate that the continuum flow theory can be used to predict bulk fluid flow in channels as small as 2.22 nm provided that the viscosity variation near the channel wall is taken into account. We propose a technique to embed the velocity near the channel wall obtained from MD simulation of electroosmotic flow in a narrow channel (e.g., 2.22 nm wide channel) into simulation of electroosmotic flow in larger channels. Simulation results indicate that such an approach can predict the velocity profile in larger channels (e.g., 3.49 and 10.00 nm) very well. Finally, simulation of electroosmotic flow in a 0.95 nm channel indicates that viscosity cannot be described by a local, linear constitutive relationship that the continuum flow theory is built upon and thus the continuum flow theory is not applicable for electroosmotic flow in such small channels.

Intelligent Planning for Laser Refractive Surgeries

NASA Astrophysics Data System (ADS)

Wang, Wei; Yue, Yong; Elsheikh, Ahmed; Bao, Fangjun

2018-02-01

Refractive error is one of leading ophthalmic diseases for both genders all over the world. Laser refractive correction surgery, e.g., laser in-situ keratomileusis (LASIK), has been commonly used worldwide. The prediction of surgical parameters, e.g., corneal ablation depth, depends on the doctor’s experience, theoretical formula and surgery reference manual in the preoperative diagnosis. The error of prediction may present a potential surgical risk and complication. Being aware of the surgery parameters is important because these can be used to estimate a patient’s post-operative visual quality and help the surgeon plan a suitable treatment. Therefore, in this paper we discuss data mining techniques that can be utilized for the prediction of laser refractive correction surgery parameters. It can provide the surgeon with a reference for possible surgical parameters and outcomes of the patient before the laser refractive correction surgery.

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

PubMed Central

Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D.

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in “real-time”) and forecasting (predicting the future) ILI dynamics in the 2011 – 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets. PMID:29244814

Forecasting influenza-like illness dynamics for military populations using neural networks and social media.

PubMed

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in "real-time") and forecasting (predicting the future) ILI dynamics in the 2011 - 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets.

Peak-summer East Asian rainfall predictability and prediction part II: extratropical East Asia

NASA Astrophysics Data System (ADS)

Yim, So-Young; Wang, Bin; Xing, Wen

2016-07-01

The part II of the present study focuses on northern East Asia (NEA: 26°N-50°N, 100°-140°E), exploring the source and limit of the predictability of the peak summer (July-August) rainfall. Prediction of NEA peak summer rainfall is extremely challenging because of the exposure of the NEA to midlatitude influence. By examining four coupled climate models' multi-model ensemble (MME) hindcast during 1979-2010, we found that the domain-averaged MME temporal correlation coefficient (TCC) skill is only 0.13. It is unclear whether the dynamical models' poor skills are due to limited predictability of the peak-summer NEA rainfall. In the present study we attempted to address this issue by applying predictable mode analysis method using 35-year observations (1979-2013). Four empirical orthogonal modes of variability and associated major potential sources of variability are identified: (a) an equatorial western Pacific (EWP)-NEA teleconnection driven by EWP sea surface temperature (SST) anomalies, (b) a western Pacific subtropical high and Indo-Pacific dipole SST feedback mode, (c) a central Pacific-El Nino-Southern Oscillation mode, and (d) a Eurasian wave train pattern. Physically meaningful predictors for each principal component (PC) were selected based on analysis of the lead-lag correlations with the persistent and tendency fields of SST and sea-level pressure from March to June. A suite of physical-empirical (P-E) models is established to predict the four leading PCs. The peak summer rainfall anomaly pattern is then objectively predicted by using the predicted PCs and the corresponding observed spatial patterns. A 35-year cross-validated hindcast over the NEA yields a domain-averaged TCC skill of 0.36, which is significantly higher than the MME dynamical hindcast (0.13). The estimated maximum potential attainable TCC skill averaged over the entire domain is around 0.61, suggesting that the current dynamical prediction models may have large rooms to improve. Limitations and future work are also discussed.

Oral administration of drugs with hypersensitivity potential induces germinal center hyperplasia in secondary lymphoid organ/tissue in Brown Norway rats, and this histological lesion is a promising candidate as a predictive biomarker for drug hypersensitivity occurrence in humans

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tamura, Akitoshi, E-mail: akitoshi-tamura@ds-pharma.co.jp; Miyawaki, Izuru; Yamada, Toru

It is important to evaluate the potential of drug hypersensitivity as well as other adverse effects during the preclinical stage of the drug development process, but validated methods are not available yet. In the present study we examined whether it would be possible to develop a new predictive model of drug hypersensitivity using Brown Norway (BN) rats. As representative drugs with hypersensitivity potential in humans, phenytoin (PHT), carbamazepine (CBZ), amoxicillin (AMX), and sulfamethoxazole (SMX) were orally administered to BN rats for 28 days to investigate their effects on these animals by examinations including observation of clinical signs, hematology, determination ofmore » serum IgE levels, histology, and flow cytometric analysis. Skin rashes were not observed in any animals treated with these drugs. Increases in the number of circulating inflammatory cells and serum IgE level did not necessarily occur in the animals treated with these drugs. However, histological examination revealed that germinal center hyperplasia was commonly induced in secondary lymphoid organs/tissues in the animals treated with these drugs. In cytometric analysis, changes in proportions of lymphocyte subsets were noted in the spleen of the animals treated with PHT or CBZ during the early period of administration. The results indicated that the potential of drug hypersensitivity was identified in BN rat by performing histological examination of secondary lymphoid organs/tissues. Data obtained herein suggested that drugs with hypersensitivity potential in humans gained immune reactivity in BN rat, and the germinal center hyperplasia induced by administration of these drugs may serve as a predictive biomarker for drug hypersensitivity occurrence. - Highlights: • We tested Brown Norway rats as a candidate model for predicting drug hypersensitivity. • The allergic drugs did not induce skin rash, whereas D-penicillamine did so in the rats. • Some of allergic drugs increased inflammatory cells and IgE, but the others did not. • The allergic drugs commonly induced germinal center hyperplasia in lymphoid tissues. • Some of these allergic drugs transiently increased CD4{sup +}CD25{sup +} T cells in the spleen.« less

GPS-ARM: Computational Analysis of the APC/C Recognition Motif by Predicting D-Boxes and KEN-Boxes

PubMed Central

Ren, Jian; Cao, Jun; Zhou, Yanhong; Yang, Qing; Xue, Yu

2012-01-01

Anaphase-promoting complex/cyclosome (APC/C), an E3 ubiquitin ligase incorporated with Cdh1 and/or Cdc20 recognizes and interacts with specific substrates, and faithfully orchestrates the proper cell cycle events by targeting proteins for proteasomal degradation. Experimental identification of APC/C substrates is largely dependent on the discovery of APC/C recognition motifs, e.g., the D-box and KEN-box. Although a number of either stringent or loosely defined motifs proposed, these motif patterns are only of limited use due to their insufficient powers of prediction. We report the development of a novel GPS-ARM software package which is useful for the prediction of D-boxes and KEN-boxes in proteins. Using experimentally identified D-boxes and KEN-boxes as the training data sets, a previously developed GPS (Group-based Prediction System) algorithm was adopted. By extensive evaluation and comparison, the GPS-ARM performance was found to be much better than the one using simple motifs. With this powerful tool, we predicted 4,841 potential D-boxes in 3,832 proteins and 1,632 potential KEN-boxes in 1,403 proteins from H. sapiens, while further statistical analysis suggested that both the D-box and KEN-box proteins are involved in a broad spectrum of biological processes beyond the cell cycle. In addition, with the co-localization information, we predicted hundreds of mitosis-specific APC/C substrates with high confidence. As the first computational tool for the prediction of APC/C-mediated degradation, GPS-ARM is a useful tool for information to be used in further experimental investigations. The GPS-ARM is freely accessible for academic researchers at: http://arm.biocuckoo.org. PMID:22479614

The accuracy of new wheelchair users' predictions about their future wheelchair use.

PubMed

Hoenig, Helen; Griffiths, Patricia; Ganesh, Shanti; Caves, Kevin; Harris, Frances

2012-06-01

This study examined the accuracy of new wheelchair user predictions about their future wheelchair use. This was a prospective cohort study of 84 community-dwelling veterans provided a new manual wheelchair. The association between predicted and actual wheelchair use was strong at 3 mos (ϕ coefficient = 0.56), with 90% of those who anticipated using the wheelchair at 3 mos still using it (i.e., positive predictive value = 0.96) and 60% of those who anticipated not using it indeed no longer using the wheelchair (i.e., negative predictive value = 0.60, overall accuracy = 0.92). Predictive accuracy diminished over time, with overall accuracy declining from 0.92 at 3 mos to 0.66 at 6 mos. At all time points, and for all types of use, patients better predicted use as opposed to disuse, with correspondingly higher positive than negative predictive values. Accuracy of prediction of use in specific indoor and outdoor locations varied according to location. This study demonstrates the importance of better understanding the potential mismatch between the anticipated and actual patterns of wheelchair use. The findings suggest that users can be relied upon to accurately predict their basic wheelchair-related needs in the short-term. Further exploration is needed to identify characteristics that will aid users and their providers in more accurately predicting mobility needs for the long-term.

Computer simulation of the active site of human serum cholinesterase

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kefang Jiao; Song Li; Zhengzheng Lu

1996-12-31

The first 3D-structure of acetylchelinesterase from Torpedo California electric organ (T.AChE) was published by JL. Sussman in 1991. We have simulated 3D-structure of human serum cholinesterase (H.BuChE) and the active site of H.BuChE. It is discovered by experiment that the residue of H.BuChE is still active site after a part of H.BuChE is cut. For example, the part of 21KD + 20KD is active site of H.BuChE. The 20KD as it is. Studies on these peptides by Hemelogy indicate that two active peptides have same negative electrostatic potential maps diagram. These negative electrostatic areas attached by acetyl choline with positivemore » electrostatic potency. We predict that 147...236 peptide of AChE could be active site because it was as 20KD as with negative electrostatic potential maps. We look forward to proving from other ones.« less

A Game Theoretical Approach to Hacktivism: Is Attack Likelihood a Product of Risks and Payoffs?

PubMed

Bodford, Jessica E; Kwan, Virginia S Y

2018-02-01

The current study examines hacktivism (i.e., hacking to convey a moral, ethical, or social justice message) through a general game theoretic framework-that is, as a product of costs and benefits. Given the inherent risk of carrying out a hacktivist attack (e.g., legal action, imprisonment), it would be rational for the user to weigh these risks against perceived benefits of carrying out the attack. As such, we examined computer science students' estimations of risks, payoffs, and attack likelihood through a game theoretic design. Furthermore, this study aims at constructing a descriptive profile of potential hacktivists, exploring two predicted covariates of attack decision making, namely, peer prevalence of hacking and sex differences. Contrary to expectations, results suggest that participants' estimations of attack likelihood stemmed solely from expected payoffs, rather than subjective risks. Peer prevalence significantly predicted increased payoffs and attack likelihood, suggesting an underlying descriptive norm in social networks. Notably, we observed no sex differences in the decision to attack, nor in the factors predicting attack likelihood. Implications for policymakers and the understanding and prevention of hacktivism are discussed, as are the possible ramifications of widely communicated payoffs over potential risks in hacking communities.

Complexity versus certainty in understanding species’ declines

USGS Publications Warehouse

Sundstrom, Shana M.; Allen, Craig R.

2014-01-01

Traditional approaches to predict species declines (e.g. government processes or IUCN Red Lists), may be too simplistic and may therefore misguide management and conservation. Using complex systems approaches that account for scale-specific patterns and processes have the potential to overcome these limitations.

Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

NASA Astrophysics Data System (ADS)

Todisco, F.; Brocca, L.; Termite, L. F.; Wagner, W.

2015-09-01

The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008-2013. The results showed that including soil moisture observations in the event rainfall-runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ~ 0.35 and a root mean square error (RMSE) of ~ 2.8 Mg ha-1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

Use of satellite and modelled soil moisture data for predicting event soil loss at plot scale

NASA Astrophysics Data System (ADS)

Todisco, F.; Brocca, L.; Termite, L. F.; Wagner, W.

2015-03-01

The potential of coupling soil moisture and a~USLE-based model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in Central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e. the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008-2013. The results showed that including soil moisture observations in the event rainfall-runoff erosivity factor of the RUSLE/USLE, enhances the capability of the model to account for variations in event soil losses, being the soil moisture an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to of ~ 0.35 and a root-mean-square error (RMSE) of ~ 2.8 Mg ha-1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

State anxiety modulates the return of fear.

PubMed

Kuhn, Manuel; Mertens, Gaetan; Lonsdorf, Tina B

2016-12-01

Current treatments for anxiety disorders are effective but limited by the high frequency of clinical relapse. Processes underlying relapse are thought to be experimentally modeled in fear conditioning experiments with return fear (ROF) inductions. Thereby reinstatement-induced ROF might be considered a model to study mechanisms underlying adversity-induced relapse. Previous studies have reported differential ROF (i.e. specific for the danger stimulus) but also generalized ROF (i.e. for safe and danger stimuli), but reasons for these divergent findings are not clear yet. Hence, the response pattern (i.e. differential or generalized) following reinstatement may be of importance for the prediction of risk or resilience for ROF. The aim of this study was to investigate state anxiety as a potential individual difference factor contributing to differentiability or generalization of return of fear. Thirty-six participants underwent instructed fear expression, extinction and ROF induction through reinstatement while physiological (skin conductance response, fear potentiated startle) and subjective measures of fear and US expectancy were acquired. Our data show that, as expected, high state anxious individuals show deficits in SCR discrimination between dangerous and safe cues after reinstatement induced ROF (i.e. generalization) as compared to low state anxious individuals. The ability to maintain discrimination under aversive circumstances is negatively associated with pathological anxiety and predictive of resilient responding while excessive generalization is a hallmark of anxiety disorders. Therefore, we suggest that experimentally induced ROF might prove useful in predicting relapse risk in clinical settings and might have implications for possible interventions for relapse prevention. Copyright © 2016 Elsevier B.V. All rights reserved.

Identification of small molecule inhibitors of botulinum neurotoxin serotype E via footprint similarity

DOE PAGES

Zhou, Yuchen; McGillick, Brian E.; Teng, Yu-Han Gary; ...

2016-07-18

Botulinum neurotoxins (BoNT) are among the most poisonous substances known, and of the 7 serotypes (A–G) identified thus far at least 4 can cause death in humans. Here, the goal of this work was identification of inhibitors that specifically target the light chain catalytic site of the highly pathogenic but lesser-studied E serotype (BoNT/E). Large-scale computational screening, employing the program DOCK, was used to perform atomic-level docking of 1.4 million small molecules to prioritize those making favorable interactions with the BoNT/E site. In particular, ‘footprint similarity’ (FPS) scoring was used to identify compounds that could potentially mimic features on themore » known substrate tetrapeptide RIME. Among 92 compounds purchased and experimentally tested, compound C562-1101 emerged as the most promising hit with an apparent IC 50 value three-fold more potent than that of the first reported BoNT/E small molecule inhibitor NSC-77053. Additional analysis showed the predicted binding pose of C562-1101 was geometrically and energetically stable over an ensemble of structures generated by molecular dynamic simulations and that many of the intended interactions seen with RIME were maintained. Finally, several analogs were also computationally designed and predicted to have further molecular mimicry thereby demonstrating the potential utility of footprint-based scoring protocols to help guide hit refinement.« less

Identification of small molecule inhibitors of botulinum neurotoxin serotype E via footprint similarity.

PubMed

Zhou, Yuchen; McGillick, Brian E; Teng, Yu-Han Gary; Haranahalli, Krupanandan; Ojima, Iwao; Swaminathan, Subramanyam; Rizzo, Robert C

2016-10-15

Botulinum neurotoxins (BoNT) are among the most poisonous substances known, and of the 7 serotypes (A-G) identified thus far at least 4 can cause death in humans. The goal of this work was identification of inhibitors that specifically target the light chain catalytic site of the highly pathogenic but lesser-studied E serotype (BoNT/E). Large-scale computational screening, employing the program DOCK, was used to perform atomic-level docking of 1.4 million small molecules to prioritize those making favorable interactions with the BoNT/E site. In particular, 'footprint similarity' (FPS) scoring was used to identify compounds that could potentially mimic features on the known substrate tetrapeptide RIME. Among 92 compounds purchased and experimentally tested, compound C562-1101 emerged as the most promising hit with an apparent IC 50 value three-fold more potent than that of the first reported BoNT/E small molecule inhibitor NSC-77053. Additional analysis showed the predicted binding pose of C562-1101 was geometrically and energetically stable over an ensemble of structures generated by molecular dynamic simulations and that many of the intended interactions seen with RIME were maintained. Several analogs were also computationally designed and predicted to have further molecular mimicry thereby demonstrating the potential utility of footprint-based scoring protocols to help guide hit refinement. Copyright © 2016 Elsevier Ltd. All rights reserved.

Identification of small molecule inhibitors of botulinum neurotoxin serotype E via footprint similarity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Yuchen; McGillick, Brian E.; Teng, Yu-Han Gary

Botulinum neurotoxins (BoNT) are among the most poisonous substances known, and of the 7 serotypes (A–G) identified thus far at least 4 can cause death in humans. Here, the goal of this work was identification of inhibitors that specifically target the light chain catalytic site of the highly pathogenic but lesser-studied E serotype (BoNT/E). Large-scale computational screening, employing the program DOCK, was used to perform atomic-level docking of 1.4 million small molecules to prioritize those making favorable interactions with the BoNT/E site. In particular, ‘footprint similarity’ (FPS) scoring was used to identify compounds that could potentially mimic features on themore » known substrate tetrapeptide RIME. Among 92 compounds purchased and experimentally tested, compound C562-1101 emerged as the most promising hit with an apparent IC 50 value three-fold more potent than that of the first reported BoNT/E small molecule inhibitor NSC-77053. Additional analysis showed the predicted binding pose of C562-1101 was geometrically and energetically stable over an ensemble of structures generated by molecular dynamic simulations and that many of the intended interactions seen with RIME were maintained. Finally, several analogs were also computationally designed and predicted to have further molecular mimicry thereby demonstrating the potential utility of footprint-based scoring protocols to help guide hit refinement.« less

Ternary chalcogenides C s 2 Z n 3 S e 4 and C s 2 Z n 3 T e 4 : Potential p -type transparent conducting materials

DOE PAGES

Shi, Hongliang; Saparov, Bayrammurad; Singh, David J.; ...

2014-11-11

Here we report prediction of two new ternary chalcogenides that can potentially be used as p-type transparent conductors along with experimental synthesis and initial characterization of these previously unknown compounds, Cs 2Zn 3Ch 4 (Ch = Se, Te). In particular, the structures are predicted based on density functional calculations and confirmed by experiments. Phase diagrams, electronic structure, optical properties, and defect properties of Cs 2Zn 3Se 4 and Cs 2Zn 3Te 4 are calculated to assess the viability of these materials as p-type TCMs. Cs 2Zn 3Se 4 and Cs 2Zn 3Te 4, which are stable under ambient air, displaymore » large optical band gaps (calculated to be 3.61 and 2.83 eV, respectively) and have small hole effective masses (0.5-0.77 m e) that compare favorably with other proposed p-type TCMs. Defect calculations show that undoped Cs2Zn3Se4 and Cs2Zn3Te4 are p-type materials. However, the free hole concentration may be limited by low-energy native donor defects, e.g., Zn interstitials. Lastly, non-equilibrium growth techniques should be useful for suppressing the formation of native donor defects, thereby increasing the hole concentration.« less

An entropy and viscosity corrected potential method for rotor performance prediction

NASA Technical Reports Server (NTRS)

Bridgeman, John O.; Strawn, Roger C.; Caradonna, Francis X.

1988-01-01

An unsteady Full-Potential Rotor code (FPR) has been enhanced with modifications directed at improving its drag prediction capability. The shock generated entropy has been included to provide solutions comparable to the Euler equations. A weakly interacted integral boundary layer has also been coupled to FPR in order to estimate skin-friction drag. Pressure distributions, shock positions, and drag comparisons are made with various data sets derived from two-dimensional airfoil, hovering, and advancing high speed rotor tests. In all these comparisons, the effect of the nonisentropic modification improves (i.e., weakens) the shock strength and wave drag. In addition, the boundary layer method yields reasonable estimates of skin-friction drag. Airfoil drag and hover torque data comparisons are excellent, as are predicted shock strength and positions for a high speed advancing rotor.

On Predicting the Crystal Structure of Energetic Materials From Quantum Mechanics

DTIC Science & Technology

2008-12-01

DE ABSTRACT A quantum-mechanically-based potential energy function that describes interactions of dimers of the explosive ...method is capable of producing force fields for interactions of the molecular crystalline explosive RDX, and appears to be suitable to enable reliable...Ridge, TN. Byrd, E.F.C., Scuseria, G.E., Chabalowski, C.F., 2004: “An ab initio study of solid nitromethane , HMX, RDX and CL20: Successes and

Demonstration and Validation of GTS Long-Term Monitoring Optimization Software at Military and Government Sites

DTIC Science & Technology

2011-02-01

Defense DoE Department of Energy DPT Direct push technology EPA Environmental Protection Agency ERPIMS Enviromental Restoration Program...and 3) assessing whether new wells should be added and where (i.e., network adequacy). • Predict allows import and comparison of new sampling...data against previously estimated trends and maps. Two options include trend flagging and plume flagging to identify potentially anomalous new values

Using Predictive Analytics to Predict Power Outages from Severe Weather

NASA Astrophysics Data System (ADS)

Wanik, D. W.; Anagnostou, E. N.; Hartman, B.; Frediani, M. E.; Astitha, M.

2015-12-01

The distribution of reliable power is essential to businesses, public services, and our daily lives. With the growing abundance of data being collected and created by industry (i.e. outage data), government agencies (i.e. land cover), and academia (i.e. weather forecasts), we can begin to tackle problems that previously seemed too complex to solve. In this session, we will present newly developed tools to aid decision-support challenges at electric distribution utilities that must mitigate, prepare for, respond to and recover from severe weather. We will show a performance evaluation of outage predictive models built for Eversource Energy (formerly Connecticut Light & Power) for storms of all types (i.e. blizzards, thunderstorms and hurricanes) and magnitudes (from 20 to >15,000 outages). High resolution weather simulations (simulated with the Weather and Research Forecast Model) were joined with utility outage data to calibrate four types of models: a decision tree (DT), random forest (RF), boosted gradient tree (BT) and an ensemble (ENS) decision tree regression that combined predictions from DT, RF and BT. The study shows that the ENS model forced with weather, infrastructure and land cover data was superior to the other models we evaluated, especially in terms of predicting the spatial distribution of outages. This research has the potential to be used for other critical infrastructure systems (such as telecommunications, drinking water and gas distribution networks), and can be readily expanded to the entire New England region to facilitate better planning and coordination among decision-makers when severe weather strikes.

GP0.4 from bacteriophage T7: in silico characterisation of its structure and interaction with E. coli FtsZ.

PubMed

Simpkin, Adam J; Rigden, Daniel J

2016-07-13

Proteins produced by bacteriophages can have potent antimicrobial activity. The study of phage-host interactions can therefore inform small molecule drug discovery by revealing and characterising new drug targets. Here we characterise in silico the predicted interaction of gene protein 0.4 (GP0.4) from the Escherichia coli (E. coli) phage T7 with E. coli filamenting temperature-sensitive mutant Z division protein (FtsZ). FtsZ is a tubulin homolog which plays a key role in bacterial cell division and that has been proposed as a drug target. Using ab initio, fragment assembly structure modelling, we predicted the structure of GP0.4 with two programs. A structure similarity-based network was used to identify a U-shaped helix-turn-helix candidate fold as being favoured. ClusPro was used to dock this structure prediction to a homology model of E. coli FtsZ resulting in a favourable predicted interaction mode. Alternative docking methods supported the proposed mode which offered an immediate explanation for the anti-filamenting activity of GP0.4. Importantly, further strong support derived from a previously characterised insertion mutation, known to abolish GP0.4 activity, that is positioned in close proximity to the proposed GP0.4/FtsZ interface. The mode of interaction predicted by bioinformatics techniques strongly suggests a mechanism through which GP0.4 inhibits FtsZ and further establishes the latter's druggable intrafilament interface as a potential drug target.

Manipulating time and space: Collision prediction in peripersonal and extrapersonal space.

PubMed

Iachini, Tina; Ruotolo, Francesco; Vinciguerra, Michela; Ruggiero, Gennaro

2017-09-01

Being able to predict potential collisions is a necessary survival prerequisite for all moving species. Temporal and spatial information is fundamental for this purpose. However, it is not clear yet if the peripersonal (i.e. near) and extrapersonal (i.e. far) distance between our body and the moving objects affects the way in which we can predict possible collisions. In order to assess this, we manipulated independently velocity and path of two balls moving one towards the other in such a way as to collide or not in peripersonal and extrapersonal space. In two experiments, participants had to judge if these balls were to collide or not. The results consistently showed a lower discrimination capacity and a more liberal tendency to predict collisions when the moving balls were in peripersonal space and their velocity was different rather than equal. This did not happen in extrapersonal space. Therefore, peripersonal space was particularly affected by temporal information. The possible link between the motor and anticipatory adaptive function of peripersonal space and collision prediction mechanisms is discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

A structural model for the flexural mechanics of nonwoven tissue engineering scaffolds.

PubMed

Engelmayr, George C; Sacks, Michael S

2006-08-01

The development of methods to predict the strength and stiffness of biomaterials used in tissue engineering is critical for load-bearing applications in which the essential functional requirements are primarily mechanical. We previously quantified changes in the effective stiffness (E) of needled nonwoven polyglycolic acid (PGA) and poly-L-lactic acid (PLLA) scaffolds due to tissue formation and scaffold degradation under three-point bending. Toward predicting these changes, we present a structural model for E of a needled nonwoven scaffold in flexure. The model accounted for the number and orientation of fibers within a representative volume element of the scaffold demarcated by the needling process. The spring-like effective stiffness of the curved fibers was calculated using the sinusoidal fiber shapes. Structural and mechanical properties of PGA and PLLA fibers and PGA, PLLA, and 50:50 PGA/PLLA scaffolds were measured and compared with model predictions. To verify the general predictive capability, the predicted dependence of E on fiber diameter was compared with experimental measurements. Needled nonwoven scaffolds were found to exhibit distinct preferred (PD) and cross-preferred (XD) fiber directions, with an E ratio (PD/XD) of approximately 3:1. The good agreement between the predicted and experimental dependence of E on fiber diameter (R2 = 0.987) suggests that the structural model can be used to design scaffolds with E values more similar to native soft tissues. A comparison with previous results for cell-seeded scaffolds (Engelmayr, G. C., Jr., et al., 2005, Biomaterials, 26(2), pp. 175-187) suggests, for the first time, that the primary mechanical effect of collagen deposition is an increase in the number of fiber-fiber bond points yielding effectively stiffer scaffold fibers. This finding indicated that the effects of tissue deposition on needled nonwoven scaffold mechanics do not follow a rule-of-mixtures behavior. These important results underscore the need for structural approaches in modeling the effects of engineered tissue formation on nonwoven scaffolds, and their potential utility in scaffold design.

Water Relations Link Carbon and Oxygen Isotope Discrimination to Phloem Sap Sugar Concentration in Eucalyptus globulus

PubMed Central

Cernusak, Lucas A.; Arthur, David J.; Pate, John S.; Farquhar, Graham D.

2003-01-01

A strong correlation was previously observed between carbon isotope discrimination (Δ13C) of phloem sap sugars and phloem sap sugar concentration in the phloem-bleeding tree Eucalyptus globulus Labill. (J. Pate, E. Shedley, D. Arthur, M. Adams [1998] Oecologia 117: 312–322). We hypothesized that correspondence between these two parameters results from covarying responses to plant water potential. We expected Δ13C to decrease with decreasing plant water potential and phloem sap sugar concentration to increase, thereby maintaining turgor within sieve tubes. The hypothesis was tested with analyses of E. globulus trees growing on opposite ends of a rainfall gradient in southwestern Australia. The Δ13C of phloem sap sugars was closely related to phloem sap sugar concentration (r = −0.90, P < 0.0001, n = 40). As predicted, daytime shoot water potential was positively related to Δ13C (r = 0.70, P < 0.0001, n = 40) and negatively related to phloem sap sugar concentration (r = −0.86, P < 0.0001, n = 40). Additional measurements showed a strong correspondence between predawn shoot water potential and phloem sap sugar concentration measured at midday (r = −0.87, P < 0.0001, n = 30). The Δ13C of phloem sap sugars collected from the stem agreed well with that predicted from instantaneous measurements of the ratio of intercellular to ambient carbon dioxide concentrations on subtending donor leaves. In accordance, instantaneous ratio of intercellular to ambient carbon dioxide concentrations correlated negatively with phloem sap sugar concentration (r = −0.91, P < 0.0001, n = 27). Oxygen isotope enrichment (Δ18O) in phloem sap sugars also varied with phloem sap sugar concentration (r = 0.91, P < 0.0001, n = 39), consistent with predictions from a theoretical model of Δ18O. We conclude that drought induces correlated variation in the concentration of phloem sap sugars and their isotopic composition in E. globulus. PMID:12692314

Quantitative Microbial Risk Assessment for Escherichia coli O157:H7 in Fresh-Cut Lettuce.

PubMed

Pang, Hao; Lambertini, Elisabetta; Buchanan, Robert L; Schaffner, Donald W; Pradhan, Abani K

2017-02-01

Leafy green vegetables, including lettuce, are recognized as potential vehicles for foodborne pathogens such as Escherichia coli O157:H7. Fresh-cut lettuce is potentially at high risk of causing foodborne illnesses, as it is generally consumed without cooking. Quantitative microbial risk assessments (QMRAs) are gaining more attention as an effective tool to assess and control potential risks associated with foodborne pathogens. This study developed a QMRA model for E. coli O157:H7 in fresh-cut lettuce and evaluated the effects of different potential intervention strategies on the reduction of public health risks. The fresh-cut lettuce production and supply chain was modeled from field production, with both irrigation water and soil as initial contamination sources, to consumption at home. The baseline model (with no interventions) predicted a mean probability of 1 illness per 10 million servings and a mean of 2,160 illness cases per year in the United States. All intervention strategies evaluated (chlorine, ultrasound and organic acid, irradiation, bacteriophage, and consumer washing) significantly reduced the estimated mean number of illness cases when compared with the baseline model prediction (from 11.4- to 17.9-fold reduction). Sensitivity analyses indicated that retail and home storage temperature were the most important factors affecting the predicted number of illness cases. The developed QMRA model provided a framework for estimating risk associated with consumption of E. coli O157:H7-contaminated fresh-cut lettuce and can guide the evaluation and development of intervention strategies aimed at reducing such risk.

The Algicidal Fungus Trametes versicolor F21a Eliminating Blue Algae via Genes Encoding Degradation Enzymes and Metabolic Pathways Revealed by Transcriptomic Analysis

PubMed Central

Dai, Wei; Chen, Xiaolin; Wang, Xuewen; Xu, Zimu; Gao, Xueyan; Jiang, Chaosheng; Deng, Ruining; Han, Guomin

2018-01-01

The molecular mechanism underlying the elimination of algal cells by fungal mycelia has not been fully understood. Here, we applied transcriptomic analysis to investigate the gene expression and regulation at time courses of Trametes versicolor F21a during the algicidal process. The obtained results showed that a total of 193, 332, 545, and 742 differentially expressed genes were identified at 0, 6, 12, and 30 h during the algicidal process, respectively. The gene ontology terms were enriched into glucan 1,4-α-glucosidase activity, hydrolase activity, lipase activity, and endopeptidase activity. The KEGG pathways were enriched in degradation and metabolism pathways including Glycolysis/Gluconeogenesis, Pyruvate metabolism, the Biosynthesis of amino acids, etc. The total expression levels of all Carbohydrate-Active enZYmes (CAZyme) genes for the saccharide metabolism were increased by two folds relative to the control. AA5, GH18, GH5, GH79, GH128, and PL8 were the top six significantly up-regulated modules among 43 detected CAZyme modules. Four available homologous decomposition enzymes of other species could partially inhibit the growth of algal cells. The facts suggest that the algicidal mode of T. versicolor F21a might be associated with decomposition enzymes and several metabolic pathways. The obtained results provide a new candidate way to control algal bloom by application of decomposition enzymes in the future. PMID:29755442

Comprehensive functional characterization of the glycoside hydrolase family 3 enzymes from Cellvibrio japonicus reveals unique metabolic roles in biomass saccharification.

PubMed

Nelson, Cassandra E; Attia, Mohamed A; Rogowski, Artur; Morland, Carl; Brumer, Harry; Gardner, Jeffrey G

2017-12-01

Lignocellulose degradation is central to the carbon cycle and renewable biotechnologies. The xyloglucan (XyG), β(1→3)/β(1→4) mixed-linkage glucan (MLG) and β(1→3) glucan components of lignocellulose represent significant carbohydrate energy sources for saprophytic microorganisms. The bacterium Cellvibrio japonicus has a robust capacity for plant polysaccharide degradation, due to a genome encoding a large contingent of Carbohydrate-Active enZymes (CAZymes), many of whose specific functions remain unknown. Using a comprehensive genetic and biochemical approach, we have delineated the physiological roles of the four C. japonicus glycoside hydrolase family 3 (GH3) members on diverse β-glucans. Despite high protein sequence similarity and partially overlapping activity profiles on disaccharides, these β-glucosidases are not functionally equivalent. Bgl3A has a major role in MLG and sophorose utilization, and supports β(1→3) glucan utilization, while Bgl3B underpins cellulose utilization and supports MLG utilization. Bgl3C drives β(1→3) glucan utilization. Finally, Bgl3D is the crucial β-glucosidase for XyG utilization. This study not only sheds the light on the metabolic machinery of C. japonicus, but also expands the repertoire of characterized CAZymes for future deployment in biotechnological applications. In particular, the precise functional analysis provided here serves as a reference for informed bioinformatics on the genomes of other Cellvibrio and related species. © 2017 Society for Applied Microbiology and John Wiley & Sons Ltd.

High accuracy operon prediction method based on STRING database scores.

PubMed

Taboada, Blanca; Verde, Cristina; Merino, Enrique

2010-07-01

We present a simple and highly accurate computational method for operon prediction, based on intergenic distances and functional relationships between the protein products of contiguous genes, as defined by STRING database (Jensen,L.J., Kuhn,M., Stark,M., Chaffron,S., Creevey,C., Muller,J., Doerks,T., Julien,P., Roth,A., Simonovic,M. et al. (2009) STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res., 37, D412-D416). These two parameters were used to train a neural network on a subset of experimentally characterized Escherichia coli and Bacillus subtilis operons. Our predictive model was successfully tested on the set of experimentally defined operons in E. coli and B. subtilis, with accuracies of 94.6 and 93.3%, respectively. As far as we know, these are the highest accuracies ever obtained for predicting bacterial operons. Furthermore, in order to evaluate the predictable accuracy of our model when using an organism's data set for the training procedure, and a different organism's data set for testing, we repeated the E. coli operon prediction analysis using a neural network trained with B. subtilis data, and a B. subtilis analysis using a neural network trained with E. coli data. Even for these cases, the accuracies reached with our method were outstandingly high, 91.5 and 93%, respectively. These results show the potential use of our method for accurately predicting the operons of any other organism. Our operon predictions for fully-sequenced genomes are available at http://operons.ibt.unam.mx/OperonPredictor/.

Cortical Brain Activity Reflecting Attentional Biasing Toward Reward-Predicting Cues Covaries with Economic Decision-Making Performance.

PubMed

San Martín, René; Appelbaum, Lawrence G; Huettel, Scott A; Woldorff, Marty G

2016-01-01

Adaptive choice behavior depends critically on identifying and learning from outcome-predicting cues. We hypothesized that attention may be preferentially directed toward certain outcome-predicting cues. We studied this possibility by analyzing event-related potential (ERP) responses in humans during a probabilistic decision-making task. Participants viewed pairs of outcome-predicting visual cues and then chose to wager either a small (i.e., loss-minimizing) or large (i.e., gain-maximizing) amount of money. The cues were bilaterally presented, which allowed us to extract the relative neural responses to each cue by using a contralateral-versus-ipsilateral ERP contrast. We found an early lateralized ERP response, whose features matched the attention-shift-related N2pc component and whose amplitude scaled with the learned reward-predicting value of the cues as predicted by an attention-for-reward model. Consistently, we found a double dissociation involving the N2pc. Across participants, gain-maximization positively correlated with the N2pc amplitude to the most reliable gain-predicting cue, suggesting an attentional bias toward such cues. Conversely, loss-minimization was negatively correlated with the N2pc amplitude to the most reliable loss-predicting cue, suggesting an attentional avoidance toward such stimuli. These results indicate that learned stimulus-reward associations can influence rapid attention allocation, and that differences in this process are associated with individual differences in economic decision-making performance. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Intergenerational Transmission of Educational Attitudes in Chinese American Families: Interplay of Socioeconomic Status and Acculturation

PubMed Central

Shen, Yishan; Kim, Su Yeong; Wang, Yijie

2016-01-01

This longitudinal study examined the influence of parents’ educational attitudes on adolescents’ educational attitudes and identified antecedents (i.e., parent education, family income, and parent acculturation), consequences (i.e., academic achievement and engagement), and a potential moderator (i.e., adolescent acculturation) of the transmission process. The sample was 444 Chinese American mothers, fathers, and adolescents (12–15 at W1). Using path analysis, this study found significant two-way interactions among parent education, income, and acculturation in predicting parents’ concurrent positive educational attitudes, which, in turn, predicted adolescents’ attitudes at W2. The latter link was further moderated by W1 and W2 adolescent acculturation for mother-adolescent and father-adolescent dyads. Adolescents’ positive educational attitudes at W2, in turn, were positively associated with their concurrent academic achievement and engagement. PMID:27138812

Linear summation in the barn owl's brainstem underlies responses to interaural time differences.

PubMed

Kuokkanen, Paula T; Ashida, Go; Carr, Catherine E; Wagner, Hermann; Kempter, Richard

2013-07-01

The neurophonic potential is a synchronized frequency-following extracellular field potential that can be recorded in the nucleus laminaris (NL) in the brainstem of the barn owl. Putative generators of the neurophonic are the afferent axons from the nucleus magnocellularis, synapses onto NL neurons, and spikes of NL neurons. The outputs of NL, i.e., action potentials of NL neurons, are only weakly represented in the neurophonic. Instead, the inputs to NL, i.e., afferent axons and their synaptic potentials, are the predominant origin of the neurophonic (Kuokkanen PT, Wagner H, Ashida G, Carr CE, Kempter R. J Neurophysiol 104: 2274-2290, 2010). Thus in NL the monaural inputs from the two brain sides converge and create a binaural neurophonic. If these monaural inputs contribute independently to the extracellular field, the response to binaural stimulation can be predicted from the sum of the responses to ipsi- and contralateral stimulation. We found that a linear summation model explains the dependence of the responses on interaural time difference as measured experimentally with binaural stimulation. The fit between model predictions and data was excellent, even without taking into account the nonlinear responses of NL coincidence detector neurons, although their firing rate and synchrony strongly depend on the interaural time difference. These results are consistent with the view that the afferent axons and their synaptic potentials in NL are the primary origin of the neurophonic.

Repulsive nature of optical potentials for high-energy heavy-ion scattering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Furumoto, T.; Sakuragi, Y.; Yamamoto, Y.

2010-10-15

The recent works by the present authors predicted that the real part of heavy-ion optical potentials changes its character from attraction to repulsion around the incident energy per nucleon E/A=200-300 MeV on the basis of the complex G-matrix interaction and the double-folding model (DFM) and revealed that the three-body force plays an important role there. In the present paper, we have precisely analyzed the energy dependence of the calculated DFM potentials and its relation to the elastic-scattering angular distributions in detail in the case of the {sup 12}C+{sup 12}C system in the energy range of E/A=100-400 MeV. The tensor forcemore » contributes substantially to the energy dependence of the real part of the DFM potentials and plays an important role to lower the attractive-to-repulsive transition energy. The nearside and farside (N/F) decompositions of the elastic-scattering amplitudes clarify the close relation between the attractive-to-repulsive transition of the potentials and the characteristic evolution of the calculated angular distributions with the increase of the incident energy. Based on the present analysis, we propose experimental measurements for the predicted strong diffraction phenomena of the elastic-scattering angular distribution caused by the N/F interference around the attractive-to-repulsive transition energy together with the reduced diffractions below and above the transition energy.« less

Effects of consumption-oriented versus trophy-oriented fisheries on Muskellunge population size structure in northern Wisconsin

USGS Publications Warehouse

Faust, Matthew D.; Hansen, Michael J.

2016-01-01

To determine whether a consumption-oriented fishery was compatible with a trophy-oriented fishery for Muskellunge Esox masquinongy, we modeled effects of a spearing fishery and recreational angling fishery on population size structure (i.e., numbers of fish ≥ 102, 114, and 127 cm) in northern Wisconsin. An individual-based simulation model was used to quantify the effect of harvest mortality at currently observed levels of recreational angling and tribal spearing fishery exploitation, along with simulated increases in exploitation, for three typical growth potentials (i.e., low, moderate, and high) of Muskellunge in northern Wisconsin across a variety of minimum length limits (i.e., 71, 102, 114, and 127 cm). Populations with moderate to high growth potential and minimum length limits ≥ 114 cm were predicted to have lower declines in numbers of trophy Muskellunge when subjected to angling-only and mixed fisheries at observed and increased levels of exploitation, which suggested that fisheries with disparate motivations may be able to coexist under certain conditions such as restrictive length limits and low levels of exploitation. However, for most Muskellunge populations in northern Wisconsin regulated by a 102-cm minimum length limit, both angling and spearing fisheries may reduce numbers of trophy Muskellunge as larger declines were predicted across all growth potentials. Our results may be useful if Muskellunge management options in northern Wisconsin are re-examined in the future.

Exposure Space: Integrating Exposure Data and Modeling with Toxicity Information

EPA Science Inventory

Recent advances have been made in high-throughput (HTP) toxicity testing, e.g. from ToxCast, which will ultimately be combined with HTP predictions of exposure potential to support next-generation chemical safety assessment. Rapid exposure methods are essential in selecting chemi...

ExpoCast Framework for Rapid Exposure Forecasts (ISES ExpoDat symposium presentation)

EPA Science Inventory

The U.S. E.P.A. ExpoCast project uses high throughput exposure models (simulation) and any easily-obtained exposure heuristics to generate forward predictions of potential exposures from chemical properties. By comparison with exposures inferred via reverse pharmacokinetic modeli...

Social identity threat motivates science-discrediting online comments.

PubMed

Nauroth, Peter; Gollwitzer, Mario; Bender, Jens; Rothmund, Tobias

2015-01-01

Experiencing social identity threat from scientific findings can lead people to cognitively devalue the respective findings. Three studies examined whether potentially threatening scientific findings motivate group members to take action against the respective findings by publicly discrediting them on the Web. Results show that strongly (vs. weakly) identified group members (i.e., people who identified as "gamers") were particularly likely to discredit social identity threatening findings publicly (i.e., studies that found an effect of playing violent video games on aggression). A content analytical evaluation of online comments revealed that social identification specifically predicted critiques of the methodology employed in potentially threatening, but not in non-threatening research (Study 2). Furthermore, when participants were collectively (vs. self-) affirmed, identification did no longer predict discrediting posting behavior (Study 3). These findings contribute to the understanding of the formation of online collective action and add to the burgeoning literature on the question why certain scientific findings sometimes face a broad public opposition.

Social Identity Threat Motivates Science-Discrediting Online Comments

PubMed Central

Nauroth, Peter; Gollwitzer, Mario; Bender, Jens; Rothmund, Tobias

2015-01-01

Experiencing social identity threat from scientific findings can lead people to cognitively devalue the respective findings. Three studies examined whether potentially threatening scientific findings motivate group members to take action against the respective findings by publicly discrediting them on the Web. Results show that strongly (vs. weakly) identified group members (i.e., people who identified as “gamers”) were particularly likely to discredit social identity threatening findings publicly (i.e., studies that found an effect of playing violent video games on aggression). A content analytical evaluation of online comments revealed that social identification specifically predicted critiques of the methodology employed in potentially threatening, but not in non-threatening research (Study 2). Furthermore, when participants were collectively (vs. self-) affirmed, identification did no longer predict discrediting posting behavior (Study 3). These findings contribute to the understanding of the formation of online collective action and add to the burgeoning literature on the question why certain scientific findings sometimes face a broad public opposition. PMID:25646725

Evoked EMG-based torque prediction under muscle fatigue in implanted neural stimulation

NASA Astrophysics Data System (ADS)

Hayashibe, Mitsuhiro; Zhang, Qin; Guiraud, David; Fattal, Charles

2011-10-01

In patients with complete spinal cord injury, fatigue occurs rapidly and there is no proprioceptive feedback regarding the current muscle condition. Therefore, it is essential to monitor the muscle state and assess the expected muscle response to improve the current FES system toward adaptive force/torque control in the presence of muscle fatigue. Our team implanted neural and epimysial electrodes in a complete paraplegic patient in 1999. We carried out a case study, in the specific case of implanted stimulation, in order to verify the corresponding torque prediction based on stimulus evoked EMG (eEMG) when muscle fatigue is occurring during electrical stimulation. Indeed, in implanted stimulation, the relationship between stimulation parameters and output torques is more stable than external stimulation in which the electrode location strongly affects the quality of the recruitment. Thus, the assumption that changes in the stimulation-torque relationship would be mainly due to muscle fatigue can be made reasonably. The eEMG was proved to be correlated to the generated torque during the continuous stimulation while the frequency of eEMG also decreased during fatigue. The median frequency showed a similar variation trend to the mean absolute value of eEMG. Torque prediction during fatigue-inducing tests was performed based on eEMG in model cross-validation where the model was identified using recruitment test data. The torque prediction, apart from the potentiation period, showed acceptable tracking performances that would enable us to perform adaptive closed-loop control through implanted neural stimulation in the future.

Molecular structure and spectral properties of ethyl 3-quinolinecarboxylate (E3Q) and [Ag(E3Q)2(TCA)] complex (TCA = Trichloroacetate)

NASA Astrophysics Data System (ADS)

Soliman, Saied M.; Kassem, Taher S.; Badr, Ahmed M. A.; Abou Youssef, Morsy A.; Assem, Rania

2014-09-01

A new [Ag(E3Q)2(TCA)] complex; (E3Q = Ethyl 3-quinolinecarboxylate and TCA = Trichloroacetate) has been synthesized and characterized using elemental analysis, FTIR, NMR and mass spectroscopy. The molecular geometry and spectroscopic properties of the complex as well as the free ligand have been calculated using the hybrid B3LYP method. The calculations predicted a distorted tetrahedral arrangement around Ag(I) ion. The vibrational spectra of the studied compounds have been assigned using potential energy distribution (PED). TD-DFT method was used to predict the electronic absorption spectra. The most intense absorption band showed a bathochromic shift and lowering of intensity in case of the complex (233.7 nm, f = 0.5604) compared to E3Q (λmax = 228.0 nm, f = 0.9072). The calculated 1H NMR chemical shifts using GIAO method showed good correlations with the experimental data. The computed dipole moment, polarizability and HOMO-LUMO energy gap were used to predict the nonlinear optical (NLO) properties. It is found that Ag(I) enhances the NLO activity. The natural bond orbital (NBO) analyses were used to elucidate the intramolecular charge transfer interactions causing stabilization for the investigated systems.

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

DOE PAGES

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; ...

2017-12-15

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \\real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns,more » emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.« less

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \\real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns,more » emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.« less

Design of Metastable Tin Titanium Nitride Semiconductor Alloys

DOE PAGES

Bikowski, Andre; Siol, Sebastian; Gu, Jing; ...

2017-07-07

Here, we report on design of optoelectronic properties in previously unreported metastable tin titanium nitride alloys with spinel crystal structure. Theoretical calculations predict that Ti alloying in metastable Sn 3N 4 compound should improve hole effective mass by up to 1 order of magnitude, while other optical bandgaps remains in the 1–2 eV range up to x ~ 0.35 Ti composition. Experimental synthesis of these metastable alloys is predicted to be challenging due to high required nitrogen chemical potential (Δμ N ≥ +1.0 eV) but proven to be possible using combinatorial cosputtering from metal targets in the presence of nitrogenmore » plasma. Characterization experiments confirm that thin films of such (Sn 1–xTi x) 3N 4 alloys can be synthesized up to x = 0.45 composition, with suitable optical band gaps (1.5–2.0 eV), moderate electron densities (10 17 to 10 18 cm –3), and improved photogenerated hole transport (by 5×). Overall, this study shows that it is possible to design the metastable nitride materials with properties suitable for potential use in solar energy conversion applications.« less

Understanding the toxicological potential of aerosol organic compounds using informatics based screening

NASA Astrophysics Data System (ADS)

Topping, David; Decesari, Stefano; Bassan, Arianna; Pavan, Manuela; Ciacci, Andrea

2016-04-01

Exposure to atmospheric particulate matter is responsible for both short-term and long-term adverse health effects. So far, all efforts spent in achieving a systematic epidemiological evidence of specific aerosol compounds determining the overall aerosol toxicity were unsuccessful. The results of the epidemiological studies apparently conflict with the laboratory toxicological analyses which have highlighted very different chemical and toxicological potentials for speciated aerosol compounds. Speciation remains a problem, especially for organic compounds: it is impossible to conduct screening on all possible molecular species. At the same time, research on toxic compounds risks to be biased towards the already known compounds, such as PAHs and dioxins. In this study we present results from an initial assessment of the use of in silico methods (i.e. (Q)SAR, read-across) to predict toxicity of atmospheric organic compounds including evaluation of applicability of a variety of popular tools (e.g. OECD QSAR Toolbox) for selected endpoints (e.g. genotoxicity). Compounds are categorised based on the need of new experimental data for the development of in silico approaches for toxicity prediction covering this specific chemical space, namely the atmospheric aerosols. Whilst only an initial investigation, we present recommendations for continuation of this work.

e-Science on Earthquake Disaster Mitigation by EUAsiaGrid

NASA Astrophysics Data System (ADS)

Yen, Eric; Lin, Simon; Chen, Hsin-Yen; Chao, Li; Huang, Bor-Shoh; Liang, Wen-Tzong

2010-05-01

Although earthquake is not predictable at this moment, with the aid of accurate seismic wave propagation analysis, we could simulate the potential hazards at all distances from possible fault sources by understanding the source rupture process during large earthquakes. With the integration of strong ground-motion sensor network, earthquake data center and seismic wave propagation analysis over gLite e-Science Infrastructure, we could explore much better knowledge on the impact and vulnerability of potential earthquake hazards. On the other hand, this application also demonstrated the e-Science way to investigate unknown earth structure. Regional integration of earthquake sensor networks could aid in fast event reporting and accurate event data collection. Federation of earthquake data center entails consolidation and sharing of seismology and geology knowledge. Capability building of seismic wave propagation analysis implies the predictability of potential hazard impacts. With gLite infrastructure and EUAsiaGrid collaboration framework, earth scientists from Taiwan, Vietnam, Philippine, Thailand are working together to alleviate potential seismic threats by making use of Grid technologies and also to support seismology researches by e-Science. A cross continental e-infrastructure, based on EGEE and EUAsiaGrid, is established for seismic wave forward simulation and risk estimation. Both the computing challenge on seismic wave analysis among 5 European and Asian partners, and the data challenge for data center federation had been exercised and verified. Seismogram-on-Demand service is also developed for the automatic generation of seismogram on any sensor point to a specific epicenter. To ease the access to all the services based on users workflow and retain the maximal flexibility, a Seismology Science Gateway integating data, computation, workflow, services and user communities would be implemented based on typical use cases. In the future, extension of the earthquake wave propagation to tsunami mitigation would be feasible once the user community support is in place.

Genetic influences can protect against unresponsive parenting in the prediction of child social competence.

PubMed

Van Ryzin, Mark J; Leve, Leslie D; Neiderhiser, Jenae M; Shaw, Daniel S; Natsuaki, Misaki N; Reiss, David

2015-01-01

Although social competence in children has been linked to the quality of parenting, prior research has typically not accounted for genetic similarities between parents and children, or for interactions between environmental (i.e., parental) and genetic influences. In this article, the possibility of a Gene x Environment (G × E) interaction in the prediction of social competence in school-age children is evaluated. Using a longitudinal, multimethod data set from a sample of children adopted at birth (N = 361), a significant interaction was found between birth parent sociability and sensitive, responsive adoptive parenting when predicting child social competence at school entry (age 6), even when controlling for potential confounds. An analysis of the interaction revealed that genetic strengths can buffer the effects of unresponsive parenting. © 2015 The Authors. Child Development © 2015 Society for Research in Child Development, Inc.

Looking good or doing good? Motivations for organisational citizenship behaviour in Turkish versus South Korean collectivists.

PubMed

Alabak, Merve; Peker, Müjde; Booth, Robert W

2016-06-01

The purpose of this article is to explore potential motivations to perform organisational citizenship behaviour (OCB) in collectivistic Turkish and South Korean societies. Although collectivism has been proposed as a predictor of OCB, previous research has not fully explored the possibility that collectivistic individuals' OCB may result from their self-oriented motives (i.e. social desirability concerns) or their future-oriented motives (i.e. long-term orientation concerns). We predicted that OCB stems from social desirability concerns among Turkish collectivists, meaning it is used for maintaining a positive image within the organisation. However, for South Korean collectivists, we predicted that OCB stems from their long-term orientation concerns, meaning it is used to make the organisation better. The results were in line with our predictions, and the findings are discussed in terms of their implications for firms in collectivistic societies. © 2015 International Union of Psychological Science.

Predicting concentrations of trace organic compounds in municipal wastewater treatment plant sludge and biosolids using the PhATE™ model.

PubMed

Cunningham, Virginia L; D'Aco, Vincent J; Pfeiffer, Danielle; Anderson, Paul D; Buzby, Mary E; Hannah, Robert E; Jahnke, James; Parke, Neil J

2012-07-01

This article presents the capability expansion of the PhATE™ (pharmaceutical assessment and transport evaluation) model to predict concentrations of trace organics in sludges and biosolids from municipal wastewater treatment plants (WWTPs). PhATE was originally developed as an empirical model to estimate potential concentrations of active pharmaceutical ingredients (APIs) in US surface and drinking waters that could result from patient use of medicines. However, many compounds, including pharmaceuticals, are not completely transformed in WWTPs and remain in biosolids that may be applied to land as a soil amendment. This practice leads to concerns about potential exposures of people who may come into contact with amended soils and also about potential effects to plants and animals living in or contacting such soils. The model estimates the mass of API in WWTP influent based on the population served, the API per capita use, and the potential loss of the compound associated with human use (e.g., metabolism). The mass of API on the treated biosolids is then estimated based on partitioning to primary and secondary solids, potential loss due to biodegradation in secondary treatment (e.g., activated sludge), and potential loss during sludge treatment (e.g., aerobic digestion, anaerobic digestion, composting). Simulations using 2 surrogate compounds show that predicted environmental concentrations (PECs) generated by PhATE are in very good agreement with measured concentrations, i.e., well within 1 order of magnitude. Model simulations were then carried out for 18 APIs representing a broad range of chemical and use characteristics. These simulations yielded 4 categories of results: 1) PECs are in good agreement with measured data for 9 compounds with high analytical detection frequencies, 2) PECs are greater than measured data for 3 compounds with high analytical detection frequencies, possibly as a result of as yet unidentified depletion mechanisms, 3) PECs are less than analytical reporting limits for 5 compounds with low analytical detection frequencies, and 4) the PEC is greater than the analytical method reporting limit for 1 compound with a low analytical detection frequency, possibly again as a result of insufficient depletion data. Overall, these results demonstrate that PhATE has the potential to be a very useful tool in the evaluation of APIs in biosolids. Possible applications include: prioritizing APIs for assessment even in the absence of analytical methods; evaluating sludge processing scenarios to explore potential mitigation approaches; using in risk assessments; and developing realistic nationwide concentrations, because PECs can be represented as a cumulative probability distribution. Finally, comparison of PECs to measured concentrations can also be used to identify the need for fate studies of compounds of interest in biosolids. Copyright © 2011 SETAC.

Evaluating the pathogenic potential of environmental Escherichia coli by using the Caenorhabditis elegans infection model.

PubMed

Merkx-Jacques, Alexandra; Coors, Anja; Brousseau, Roland; Masson, Luke; Mazza, Alberto; Tien, Yuan-Ching; Topp, Edward

2013-04-01

The detection and abundance of Escherichia coli in water is used to monitor and mandate the quality of drinking and recreational water. Distinguishing commensal waterborne E. coli isolates from those that cause diarrhea or extraintestinal disease in humans is important for quantifying human health risk. A DNA microarray was used to evaluate the distribution of virulence genes in 148 E. coli environmental isolates from a watershed in eastern Ontario, Canada, and in eight clinical isolates. Their pathogenic potential was evaluated with Caenorhabditis elegans, and the concordance between the bioassay result and the pathotype deduced by genotyping was explored. Isolates identified as potentially pathogenic on the basis of their complement of virulence genes were significantly more likely to be pathogenic to C. elegans than those determined to be potentially nonpathogenic. A number of isolates that were identified as nonpathogenic on the basis of genotyping were pathogenic in the infection assay, suggesting that genotyping did not capture all potentially pathogenic types. The detection of the adhesin-encoding genes sfaD, focA, and focG, which encode adhesins; of iroN2, which encodes a siderophore receptor; of pic, which encodes an autotransporter protein; and of b1432, which encodes a putative transposase, was significantly associated with pathogenicity in the infection assay. Overall, E. coli isolates predicted to be pathogenic on the basis of genotyping were indeed so in the C. elegans infection assay. Furthermore, the detection of C. elegans-infective environmental isolates predicted to be nonpathogenic on the basis of genotyping suggests that there are hitherto-unrecognized virulence factors or combinations thereof that are important in the establishment of infection.

Impact of predictive scoring model and e-mail messages on African American blood donors.

PubMed

Bachegowda, Lohith S; Timm, Brad; Dasgupta, Pinaki; Hillyer, Christopher D; Kessler, Debra; Rebosa, Mark; France, Christopher R; Shaz, Beth H

2017-06-01

Expanding the African American (AA) donor pool is critical to sustain transfusion support for sickle cell disease patients. The aims were to: 1) apply cognitive computing on donation related metrics to develop a predictive model that effectively identifies repeat AA donors, 2) determine whether a single e-mail communication could improve AA donor retention and compare retention results on higher versus lower predictive score donors, and 3) evaluate the effect of e-mail marketing on AA donor retention with culturally versus nonculturally tailored message. Between 2011 and 2012, 30,786 AA donors donated blood at least once on whom predictive repeat donor scores (PRDSs) was generated from donor-related metrics (frequency of donations, duration between donations, age, blood type, and sex). In 2013, 28% (8657/30,786) of 2011 to 2012 donors returned to donate on whom PRDS was validated. Returning blood donors had a higher mean PRDS compared to nonreturning donors (0.649 vs. 0.268; p < 0.001). In the e-mail pilot, high PRDS (≥0.6) compared to low PRDS (<0.6) was associated with 89% higher donor presentation rate (p < 0.001), 20% higher e-mail opening rate (p < 0.001), and, specifically among those who opened the e-mail, 159% higher presentation rate (p < 0.001). Finally, blood donation rate did not differ (p = 0.79) as a function of generic (n = 9312, 1.4%) versus culturally tailored (n = 9326, 1.3%) message. Computational algorithms utilizing readily available donor metrics can identify highly committed AA donors and in conjunction with targeted e-mail communication has the potential to increase the efficiency of donor marketing. © 2017 AABB.

Intolerance of uncertainty and startle potentiation in relation to different threat reinforcement rates.

PubMed

Chin, Brian; Nelson, Brady D; Jackson, Felicia; Hajcak, Greg

2016-01-01

Fear conditioning research on threat predictability has primarily examined the impact of temporal (i.e., timing) predictability on the startle reflex. However, there are other key features of threat that can vary in predictability. For example, the reinforcement rate (i.e., frequency) of threat is a crucial factor underlying fear learning. The present study examined the impact of threat reinforcement rate on the startle reflex and self-reported anxiety during a fear conditioning paradigm. Forty-five participants completed a fear learning task in which the conditioned stimulus was reinforced with an electric shock to the forearm on 50% of trials in one block and 75% of trials in a second block, in counter-balanced order. The present study also examined whether intolerance of uncertainty (IU), the tendency to perceive or experience uncertainty as stressful or unpleasant, was associated with the startle reflex during conditions of low (50%) vs. high (75%) reinforcement. Results indicated that, across all participants, startle was greater during the 75% relative to the 50% reinforcement condition. IU was positively correlated with startle potentiation (i.e., increased startle response to the CS+ relative to the CS-) during the 50%, but not the 75%, reinforcement condition. Thus, despite receiving fewer electric shocks during the 50% reinforcement condition, individuals with high IU uniquely demonstrated greater defense system activation when impending threat was more uncertain. The association between IU and startle was independent of state anxiety. The present study adds to a growing literature on threat predictability and aversive responding, and suggests IU is associated with abnormal responding in the context of uncertain threat. Copyright © 2015 Elsevier B.V. All rights reserved.

Potential Consequences of E-Cigarette Use: Is Youth Health Going Up in Smoke

DTIC Science & Technology

2016-10-01

composed of adolescents and young adults. Using data from the National Youth Tobacco Survey (NYTS), we examine trends over time in the use of traditional...among NYTS respondents participating in the 2011-2014 survey waves. By comparing the accuracy of these predictions across e-cigarette users and...This study draws on existing literature and publicly available data from the National Youth Tobacco Survey (NYTS) to address three fundamental

THE FUTURE OF TOXICOLOGY-PREDICTIVE TOXICOLOGY ...

EPA Pesticide Factsheets

A chemistry approach to predictive toxicology relies on structure−activity relationship (SAR) modeling to predict biological activity from chemical structure. Such approaches have proven capabilities when applied to well-defined toxicity end points or regions of chemical space. These approaches are less well-suited, however, to the challenges of global toxicity prediction, i.e., to predicting the potential toxicity of structurally diverse chemicals across a wide range of end points of regulatory and pharmaceutical concern. New approaches that have the potential to significantly improve capabilities in predictive toxicology are elaborating the “activity” portion of the SAR paradigm. Recent advances in two areas of endeavor are particularly promising. Toxicity data informatics relies on standardized data schema, developed for particular areas of toxicological study, to facilitate data integration and enable relational exploration and mining of data across both historical and new areas of toxicological investigation. Bioassay profiling refers to large-scale high-throughput screening approaches that use chemicals as probes to broadly characterize biological response space, extending the concept of chemical “properties” to the biological activity domain. The effective capture and representation of legacy and new toxicity data into mineable form and the large-scale generation of new bioassay data in relation to chemical toxicity, both employing chemical stru

Low-energy elastic differential scattering of He/++/ by He.

NASA Technical Reports Server (NTRS)

Lam, S. K.; Doverspike, L. D.; Champion, R. L.

1973-01-01

Experimental results are developed for the relative elastic differential scattering of He(++) by He for collision energies in the range 4 equal to or less than E equal to or less than 75 eV. In the analysis of the data, semiclassical considerations are utilized, assuming that the dynamics of the scattering is governed solely by the B and E states of He2(++). It is shown that existing ab initio calculations for the intermolecular potentials predict differential cross sections which are not in particularly good agreement with the experimental data.

Potential Distribution of Two Species in the Medically Important Anopheles minimus Complex (Diptera: Culicidae)

DTIC Science & Technology

2008-09-01

605Ð608. Srivastava, A., B . N. Nagpal, R. Saxena, V. Dev, and S. K . Subbarao . 2005. Prediction of Anopheles minimus habi- tat in India Ð a tool...global risk of inva- sion by themosquitoAedes albopictus.Vector BorneZoo- notic Dis. 7: 76Ð85. Chen, B ., R. E. Harbach, and R. K . Butlin. 2002. Molecular...Grinnell, J. 1917. The niche-relationships of the California Thrasher. Auk 34: 427Ð433. Harbach, R. E., E. Parkin, B . Chen, and R. K . Butlin. 2006

Probing Higgs self-coupling of a classically scale invariant model in e+e- → Zhh: Evaluation at physical point

NASA Astrophysics Data System (ADS)

Fujitani, Y.; Sumino, Y.

2018-04-01

A classically scale invariant extension of the standard model predicts large anomalous Higgs self-interactions. We compute missing contributions in previous studies for probing the Higgs triple coupling of a minimal model using the process e+e- → Zhh. Employing a proper order counting, we compute the total and differential cross sections at the leading order, which incorporate the one-loop corrections between zero external momenta and their physical values. Discovery/exclusion potential of a future e+e- collider for this model is estimated. We also find a unique feature in the momentum dependence of the Higgs triple vertex for this class of models.

Predicting Reduction Rates of Energetic Nitroaromatic Compounds Using Calculated One-Electron Reduction Potentials

DOE PAGES

Salter-Blanc, Alexandra; Bylaska, Eric J.; Johnston, Hayley; ...

2015-02-11

The evaluation of new energetic nitroaromatic compounds (NACs) for use in green munitions formulations requires models that can predict their environmental fate. The susceptibility of energetic NACs to nitro reduction might be predicted from correlations between rate constants (k) for this reaction and one-electron reduction potentials (E1NAC) / 0.059 V, but the mechanistic implications of such correlations are inconsistent with evidence from other methods. To address this inconsistency, we have reevaluated existing kinetic data using a (non-linear) free-energy relationship (FER) based on the Marcus theory of outer-sphere electron transfer. For most reductants, the results are inconsistent with rate limitation bymore » an initial, outer-sphere electron transfer, suggesting that the strong correlation between k and E1NAC is justified only as an empirical model. This empirical correlation was used to calibrate a new quantitative structure-activity relationship (QSAR) using previously reported values of k for non-energetic NAC reduction by Fe(II) porphyrin and newly reported values of E1NAC determined using density functional theory at the B3LYP/6-311++G(2d,2p) level with the COSMO solvation model. The QSAR was then validated for energetic NACs using newly measured kinetic data for 2,4,6-trinitrotoluene (TNT), 2,4-dinitrotoluene (2,4-DNT), and 2,4-dinitroanisole (DNAN). The data show close agreement with the QSAR, supporting its applicability to energetic NACs.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Raugei, Simone; DuBois, Daniel L.; Rousseau, Roger J.

Rational design of molecular catalysts requires a systematic approach to designing ligands with specific functionality and precisely tailored electronic and steric properties. It then becomes possible to devise computer protocols to predict accurately the required properties and ultimately to design catalysts by computer. In this account we first review how thermodynamic properties such as oxidation-reduction potentials (E0), acidities (pKa), and hydride donor abilities (ΔGH-) form the basis for a systematic design of molecular catalysts for reactions that are critical for a secure energy future (hydrogen evolution and oxidation, oxygen and nitrogen reduction, and carbon dioxide reduction). We highlight how densitymore » functional theory allows us to determine and predict these properties within “chemical” accuracy (~ 0.06 eV for redox potentials, ~ 1 pKa unit for pKa values, and ~ 1.5 kcal/mol for hydricities). These quantities determine free energy maps and profiles associated with catalytic cycles, i.e. the relative energies of intermediates, and help us distinguish between desirable and high-energy pathways and mechanisms. Good catalysts have flat profiles that avoid high activation barriers due to low and high energy intermediates. We illustrate how the criterion of a flat energy profile lends itself to the prediction of design points by computer for optimum catalysts. This research was carried out in the Center for Molecular Electro-catalysis, an Energy Frontier Research Center funded by the U.S. Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences. Pacific Northwest National Laboratory (PNNL) is operated for the DOE by Battelle.« less

Designing optimal cell factories: integer programming couples elementary mode analysis with regulation

PubMed Central

2012-01-01

Background Elementary mode (EM) analysis is ideally suited for metabolic engineering as it allows for an unbiased decomposition of metabolic networks in biologically meaningful pathways. Recently, constrained minimal cut sets (cMCS) have been introduced to derive optimal design strategies for strain improvement by using the full potential of EM analysis. However, this approach does not allow for the inclusion of regulatory information. Results Here we present an alternative, novel and simple method for the prediction of cMCS, which allows to account for boolean transcriptional regulation. We use binary linear programming and show that the design of a regulated, optimal metabolic network of minimal functionality can be formulated as a standard optimization problem, where EM and regulation show up as constraints. We validated our tool by optimizing ethanol production in E. coli. Our study showed that up to 70% of the predicted cMCS contained non-enzymatic, non-annotated reactions, which are difficult to engineer. These cMCS are automatically excluded by our approach utilizing simple weight functions. Finally, due to efficient preprocessing, the binary program remains computationally feasible. Conclusions We used integer programming to predict efficient deletion strategies to metabolically engineer a production organism. Our formulation utilizes the full potential of cMCS but adds additional flexibility to the design process. In particular our method allows to integrate regulatory information into the metabolic design process and explicitly favors experimentally feasible deletions. Our method remains manageable even if millions or potentially billions of EM enter the analysis. We demonstrated that our approach is able to correctly predict the most efficient designs for ethanol production in E. coli. PMID:22898474

Using kinematic analysis of movement to predict the time occurrence of an evoked potential associated with a motor command.

PubMed

O'Reilly, Christian; Plamondon, Réjean; Landou, Mohamed K; Stemmer, Brigitte

2013-01-01

This article presents an exploratory study investigating the possibility of predicting the time occurrence of a motor event related potential (ERP) from a kinematic analysis of human movements. Although the response-locked motor potential may link the ERP components to the recorded response, to our knowledge no previous attempt has been made to predict a priori (i.e. before any contact with the electroencephalographic data) the time occurrence of an ERP component based only on the modeling of an overt response. The proposed analysis relies on the delta-lognormal modeling of velocity, as proposed by the kinematic theory of rapid human movement used in several studies of motor control. Although some methodological aspects of this technique still need to be fine-tuned, the initial results showed that the model-based kinematic analysis allowed the prediction of the time occurrence of a motor command ERP in most participants in the experiment. The average map of the motor command ERPs showed that this signal was stronger in electrodes close to the contra-lateral motor area (Fz, FCz, FC1, and FC3). These results seem to support the claims made by the kinematic theory that a motor command is emitted at time t(0), the time reference parameter of the model. This article proposes a new time marker directly associated with a cerebral event (i.e. the emission of a motor command) that can be used for the development of new data analysis methodologies and for the elaboration of new experimental protocols based on ERP. © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

A theoretical study of the adiabatic and vertical ionization potentials of water.

PubMed

Feller, David; Davidson, Ernest R

2018-06-21

Theoretical predictions of the three lowest adiabatic and vertical ionization potentials of water were obtained from the Feller-Peterson-Dixon approach. This approach combines multiple levels of coupled cluster theory with basis sets as large as aug-cc-pV8Z in some cases and various corrections up to and including full configuration interaction theory. While agreement with experiment for the adiabatic ionization potential of the lowest energy 2 B 1 state was excellent, differences for other states were much larger, sometimes exceeding 10 kcal/mol (0.43 eV). Errors of this magnitude are inconsistent with previous benchmark work on 52 adiabatic ionization potentials, where a root mean square of 0.20 kcal/mol (0.009 eV) was found. Difficulties in direct comparisons between theory and experiment for vertical ionization potentials are discussed. With regard to the differences found for the 2 A 1 / 2 Π u and 2 B 2 adiabatic ionization potentials, a reinterpretation of the experimental spectrum appears justified.

Anti-E1E2 antibodies status prior therapy favors direct-acting antiviral treatment efficacy.

PubMed

Virlogeux, Victor; Berthillon, Pascale; Bordes, Isabelle; Larrat, Sylvie; Crouy, Stéphanie; Scholtès, Caroline; Pradat, Pierre; Maynard, Marianne; Zoulim, Fabien; Leroy, Vincent; Chemin, Isabelle; Trépo, Christian; Petit, Marie-Anne

2018-03-15

Presence of anti-E1E2 antibodies was previously associated with spontaneous cure of hepatitis C virus (HCV) and predictive before treatment of a sustained virological response (SVR) to bi- or tri-therapy in naïve or experienced patients, regardless of HCV genotype. We investigated the impact of anti-E1E2 seroprevalence at baseline on treatment response in patients receiving direct-acting antiviral (DAA) therapy. We screened anti-E1E2 antibodies by ELISA in serum samples collected at treatment initiation for two groups of patients: 59 with SVR at the end of DAA treatment and 44 relapsers after DAA treatment. Nineteen patients received a combination of ribavirin (RBV) or PEG-interferon/ribavirin with sofosbuvir or daclatasvir and others received interferon-free treatment with DAA±RBV. HCV viral load was measured at different time points during treatment in a subgroup of patients. A significant association was observed between presence of anti-E1E2 and HCV viral load<6log10 prior treatment. Among patients with anti-E1E2 at baseline, 70% achieved SVR whereas among patients without anti-E1E2, only 45% achieved SVR. Conversely, 66% of patients experiencing DAA-failure were anti-E1E2 negative at baseline. In the multivariate analysis, presence of anti-E1E2 was significantly associated with SVR after adjustment on potential cofounders such as age, sex, fibrosis stage, prior HCV treatment and alanine aminotransferase (ALT) level. The presence of anti-E1E2 at treatment initiation is a predictive factor of SVR among patients treated with DAA and more likely among patients with low initial HCV viral load (<6log10). Absence of anti-E1E2 at baseline could predict DAA-treatment failure. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Analysis of yield and oil from a series of canola breeding trials. Part II. Exploring variety by environment interaction using factor analysis.

PubMed

Cullis, B R; Smith, A B; Beeck, C P; Cowling, W A

2010-11-01

Exploring and exploiting variety by environment (V × E) interaction is one of the major challenges facing plant breeders. In paper I of this series, we presented an approach to modelling V × E interaction in the analysis of complex multi-environment trials using factor analytic models. In this paper, we develop a range of statistical tools which explore V × E interaction in this context. These tools include graphical displays such as heat-maps of genetic correlation matrices as well as so-called E-scaled uniplots that are a more informative alternative to the classical biplot for large plant breeding multi-environment trials. We also present a new approach to prediction for multi-environment trials that include pedigree information. This approach allows meaningful selection indices to be formed either for potential new varieties or potential parents.

Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments.

PubMed

Windhausen, Vanessa S; Atlin, Gary N; Hickey, John M; Crossa, Jose; Jannink, Jean-Luc; Sorrells, Mark E; Raman, Babu; Cairns, Jill E; Tarekegne, Amsal; Semagn, Kassa; Beyene, Yoseph; Grudloyma, Pichet; Technow, Frank; Riedelsheimer, Christian; Melchinger, Albrecht E

2012-11-01

Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F(2)-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F(2)-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set.

Experimental Simulation of Solar Wind Interaction with MagneticDipole Fields above Insulating Surfaces

NASA Astrophysics Data System (ADS)

Yeo, L. H.; Han, J.; Wang, X.; Werner, G.; Deca, J.; Munsat, T.; Horanyi, M.

2017-12-01

Magnetic anomalies on the surfaces of airless bodies such as the Moon interact with the solar wind, resulting in both magnetic and electrostatic deflection/reflection of thecharged particles. Consequently, surface charging in these regions will be modified. Using the Colorado Solar Wind Experiment facility, this interaction is investigated with high-energy flowing plasmas (100-800 eV beam ions) that are incident upon a magnetic dipole (0.13 T) embedded under various insulating surfaces. The dipole moment is perpendicular to the surface. Using an emissive probe, 2D plasma potential profiles are obtained above the surface. In the dipole lobe regions, the surfaces are charged to significantly positive potentials due to the impingement of the unmagnetized ions while the electrons are magnetically shielded. At low ion beam energies, the results agree with the theoretical predictions, i.e., the surface potential follows the energy of the beam ions in eV. However, at high energies, the surface potentials in the electron-shielded regions are significantly lower than the beam energies. A series of investigations have been conducted and indicate that the surface properties (e.g., modified surface conductance, ion induced secondary electrons and electron-neutral collision at the surface) are likely to play a role in determining the surface potential.

Probabilistic measures of climate change vulnerability, adaptation action benefits, and related uncertainty from maximum temperature metric selection.

PubMed

DeWeber, Jefferson T; Wagner, Tyler

2018-06-01

Predictions of the projected changes in species distributions and potential adaptation action benefits can help guide conservation actions. There is substantial uncertainty in projecting species distributions into an unknown future, however, which can undermine confidence in predictions or misdirect conservation actions if not properly considered. Recent studies have shown that the selection of alternative climate metrics describing very different climatic aspects (e.g., mean air temperature vs. mean precipitation) can be a substantial source of projection uncertainty. It is unclear, however, how much projection uncertainty might stem from selecting among highly correlated, ecologically similar climate metrics (e.g., maximum temperature in July, maximum 30-day temperature) describing the same climatic aspect (e.g., maximum temperatures) known to limit a species' distribution. It is also unclear how projection uncertainty might propagate into predictions of the potential benefits of adaptation actions that might lessen climate change effects. We provide probabilistic measures of climate change vulnerability, adaptation action benefits, and related uncertainty stemming from the selection of four maximum temperature metrics for brook trout (Salvelinus fontinalis), a cold-water salmonid of conservation concern in the eastern United States. Projected losses in suitable stream length varied by as much as 20% among alternative maximum temperature metrics for mid-century climate projections, which was similar to variation among three climate models. Similarly, the regional average predicted increase in brook trout occurrence probability under an adaptation action scenario of full riparian forest restoration varied by as much as .2 among metrics. Our use of Bayesian inference provides probabilistic measures of vulnerability and adaptation action benefits for individual stream reaches that properly address statistical uncertainty and can help guide conservation actions. Our study demonstrates that even relatively small differences in the definitions of climate metrics can result in very different projections and reveal high uncertainty in predicted climate change effects. © 2018 John Wiley & Sons Ltd.

Probabilistic measures of climate change vulnerability, adaptation action benefits, and related uncertainty from maximum temperature metric selection

USGS Publications Warehouse

DeWeber, Jefferson T.; Wagner, Tyler

2018-01-01

Predictions of the projected changes in species distributions and potential adaptation action benefits can help guide conservation actions. There is substantial uncertainty in projecting species distributions into an unknown future, however, which can undermine confidence in predictions or misdirect conservation actions if not properly considered. Recent studies have shown that the selection of alternative climate metrics describing very different climatic aspects (e.g., mean air temperature vs. mean precipitation) can be a substantial source of projection uncertainty. It is unclear, however, how much projection uncertainty might stem from selecting among highly correlated, ecologically similar climate metrics (e.g., maximum temperature in July, maximum 30‐day temperature) describing the same climatic aspect (e.g., maximum temperatures) known to limit a species’ distribution. It is also unclear how projection uncertainty might propagate into predictions of the potential benefits of adaptation actions that might lessen climate change effects. We provide probabilistic measures of climate change vulnerability, adaptation action benefits, and related uncertainty stemming from the selection of four maximum temperature metrics for brook trout (Salvelinus fontinalis), a cold‐water salmonid of conservation concern in the eastern United States. Projected losses in suitable stream length varied by as much as 20% among alternative maximum temperature metrics for mid‐century climate projections, which was similar to variation among three climate models. Similarly, the regional average predicted increase in brook trout occurrence probability under an adaptation action scenario of full riparian forest restoration varied by as much as .2 among metrics. Our use of Bayesian inference provides probabilistic measures of vulnerability and adaptation action benefits for individual stream reaches that properly address statistical uncertainty and can help guide conservation actions. Our study demonstrates that even relatively small differences in the definitions of climate metrics can result in very different projections and reveal high uncertainty in predicted climate change effects.

The vegetation outlook (VegOut): a new method for predicting vegetation seasonal greenness

USGS Publications Warehouse

Tadesse, T.; Wardlow, B.; Hayes, M.; Svoboda, M.; Brown, J.

2010-01-01

The vegetation outlook (VegOut) is a geospatial tool for predicting general vegetation condition patterns across large areas. VegOut predicts a standardized seasonal greenness (SSG) measure, which represents a general indicator of relative vegetation health. VegOut predicts SSG values at multiple time steps (two to six weeks into the future) based on the analysis of "historical patterns" (i.e., patterns at each 1 km grid cell and time of the year) of satellite, climate, and oceanic data over an 18-year period (1989 to 2006). The model underlying VegOut capitalizes on historical climate-vegetation interactions and ocean-climate teleconnections (such as El Niño and the Southern Oscillation, ENSO) expressed over the 18-year data record and also considers several environmental characteristics (e.g., land use/cover type and soils) that influence vegetation's response to weather conditions to produce 1 km maps that depict future general vegetation conditions. VegOut provides regionallevel vegetation monitoring capabilities with local-scale information (e.g., county to sub-county level) that can complement more traditional remote sensing-based approaches that monitor "current" vegetation conditions. In this paper, the VegOut approach is discussed and a case study over the central United States for selected periods of the 2008 growing season is presented to demonstrate the potential of this new tool for assessing and predicting vegetation conditions.

Niche conservatism and the invasive potential of the wild boar.

PubMed

Sales, Lilian Patrícia; Ribeiro, Bruno R; Hayward, Matt Warrington; Paglia, Adriano; Passamani, Marcelo; Loyola, Rafael

2017-09-01

Niche conservatism, i.e. the retention of a species' fundamental niche through evolutionary time, is cornerstone for biological invasion assessments. The fact that species tend to maintain their original climate niche allows predictive maps of invasion risk to anticipate potential invadable areas. Unravelling the mechanisms driving niche shifts can shed light on the management of invasive species. Here, we assessed niche shifts in one of the world's worst invasive species: the wild boar Sus scrofa. We also predicted potential invadable areas based on an ensemble of three ecological niche modelling methods, and evaluated the performance of models calibrated with native vs. pooled (native plus invaded) species records. By disentangling the drivers of change on the exotic wild boar population's niches, we found strong evidence for niche conservatism during biological invasion. Ecological niche models calibrated with both native and pooled range records predicted convergent areas. Also, observed niche shifts are mostly explained by niche unfilling, i.e. there are unoccupied areas in the exotic range where climate is analogous to the native range. Niche unfilling is expected as result of recent colonization and ongoing dispersal, and was potentially stronger for the Neotropics, where a recent wave of introductions for pig-farming and game-hunting has led to high wild boar population growth rates. The invasive potential of wild boar in the Neotropics is probably higher than in other regions, which has profound management implications if we are to prevent their invasion into species-rich areas, such as Amazonia, coupled with expansion of African swine fever and possibly great economic losses. Although the originally Eurasian-wide distribution suggests a pre-adaptation to a wide array of climates, the wild boar world-wide invasion does not exhibit evidence of niche evolution. The invasive potential of the wild boar therefore probably lies on the reproductive, dietary and morphological characteristics of this species, coupled with behavioural thermoregulation. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

Estimating drought induced tree mortality in the Amazon rainforest: A simulation study with a focus on plant hydraulic processes

NASA Astrophysics Data System (ADS)

Papastefanou, P.; Fleischer, K.; Hickler, T.; Grams, T.; Lapola, D.; Quesada, C. A.; Zang, C.; Rammig, A.

2017-12-01

The Amazon basin was recently hit by severe drought events that were unprecedented in their severity and spatial extent, e.g. during 2005, 2010 and 2015/2016. Significant amounts of biomass were lost, turning large parts of the rainforest from a carbon sink into a carbon source. It is assumed that drought-induced tree mortality from hydraulic failure played an important role during these events and may become more frequent in the Amazon region in the future. Many state-of-the-art dynamic vegetation models do not include plant hydraulic processes and fail to reproduce observed rainforest responses to drought events, such as e.g. increased tree mortality. We address this research gap by developing a simple plant-hydraulic module for the dynamic vegetation model LPJ-GUESS. This plant-hydraulic module uses leaf water potential and cavitation as baseline processes to simulate tree mortality under drought stress. Furthermore, we introduce different plant strategies in the model, which describe e.g. differences in the stomatal regulation under drought stress. To parameterize and evaluate our hydraulic module, we use a set of available observational data from the Amazon region. We apply our model to the Amazon Basin and highlight similarities and differences across other measured and predicted drought responses, e.g. extrapolated observations and data derived from satellite measurements. Our results highlight the importance of including plant hydraulic processes in dynamic vegetation models to correctly predict vegetation dynamics under drought stress and show major differences on the vegetation dynamics depending on the selected plant strategies. We also identify gaps in process understanding of the triggering factors, the extent and the consequences of drought responses that hampers our ability to predict potential impact of future drought events on the Amazon rainforest.

Diagnosis of 25 genotypes of human papillomaviruses for their physical statuses in cervical precancerous/cancerous lesions: a comparison of E2/E6E7 ratio-based vs. multiple E1-L1/E6E7 ratio-based detection techniques.

PubMed

Zhang, Rong; He, Yi-feng; Chen, Mo; Chen, Chun-mei; Zhu, Qiu-jing; Lu, Huan; Wei, Zhen-hong; Li, Fang; Zhang, Xiao-xin; Xu, Cong-jian; Yu, Long

2014-10-02

Cervical lesions caused by integrated human papillomavirus (HPV) infection are highly dangerous because they can quickly develop into invasive cancers. However, clinicians are currently hampered by the lack of a quick, convenient and precise technique to detect integrated/mixed infections of various genotypes of HPVs in the cervix. This study aimed to develop a practical tool to determine the physical status of different HPVs and evaluate its clinical significance. The target population comprised 1162 women with an HPV infection history of > six months and an abnormal cervical cytological finding. The multiple E1-L1/E6E7 ratio analysis, a novel technique, was developed based on determining the ratios of E1/E6E7, E2/E6E7, E4E5/E6E7, L2/E6E7 and L1/E6E7 within the viral genome. Any imbalanced ratios indicate integration. Its diagnostic and predictive performances were compared with those of E2/E6E7 ratio analysis. The detection accuracy of both techniques was evaluated using the gold-standard technique "detection of integrated papillomavirus sequences" (DIPS). To realize a multigenotypic detection goal, a primer and probe library was established. The integration rate of a particular genotype of HPV was correlated with its tumorigenic potential and women with higher lesion grades often carried lower viral loads. The E1-L1/E6E7 ratio analysis achieved 92.7% sensitivity and 99.0% specificity in detecting HPV integration, while the E2/E6E7 ratio analysis showed a much lower sensitivity (75.6%) and a similar specificity (99.3%). Interference due to episomal copies was observed in both techniques, leading to false-negative results. However, some positive results of E1-L1/E6E7 ratio analysis were missed by DIPS due to its stochastic detection nature. The E1-L1/E6E7 ratio analysis is more efficient than E2/E6E7 ratio analysis and DIPS in predicting precancerous/cancerous lesions, in which both positive predictive values (36.7%-82.3%) and negative predictive values (75.9%-100%) were highest (based on the results of three rounds of biopsies). The multiple E1-L1/E6E7 ratio analysis is more sensitive and predictive than E2/E6E7 ratio analysis as a triage test for detecting HPV integration. It can effectively narrow the range of candidates for colposcopic examination and cervical biopsy, thereby lowering the expense of cervical cancer prevention.

Thermophysical properties of liquid UO2, ZrO2 and corium by molecular dynamics and predictive models

NASA Astrophysics Data System (ADS)

Kim, Woong Kee; Shim, Ji Hoon; Kaviany, Massoud

2017-08-01

Predicting the fate of accident-melted nuclear fuel-cladding requires the understanding of the thermophysical properties which are lacking or have large scatter due to high-temperature experimental challenges. Using equilibrium classical molecular dynamics (MD), we predict the properties of melted UO2 and ZrO2 and compare them with the available experimental data and the predictive models. The existing interatomic potential models have been developed mainly for the polymorphic solid phases of these oxides, so they cannot be used to predict all the properties accurately. We compare and decipher the distinctions of those MD predictions using the specific property-related autocorrelation decays. The predicted properties are density, specific heat, heat of fusion, compressibility, viscosity, surface tension, and the molecular and electronic thermal conductivities. After the comparisons, we provide readily usable temperature-dependent correlations (including UO2-ZrO2 compounds, i.e. corium melt).

Exploiting Social Media Sensor Networks through Novel Data Fusion Techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kouri, Tina

Unprecedented amounts of data are continuously being generated by sensors (“hard” data) and by humans (“soft” data), and this data needs to be exploited to its full potential. The first step in exploiting this data is determine how the hard and soft data are related to each other. In this project we fuse hard and soft data, using the attributes of each (e.g., time and space), to gain more information about interesting events. Next, we attempt to use social networking textual data to predict the present (i.e., predict that an interesting event is occurring and details about the event) usingmore » data mining, machine learning, natural language processing, and text analysis techniques.« less

Intergenerational Transmission of Educational Attitudes in Chinese American Families: Interplay of Socioeconomic Status and Acculturation.

PubMed

Shen, Yishan; Kim, Su Yeong; Wang, Yijie

2016-09-01

This longitudinal study examined the influence of parents' educational attitudes on adolescents' educational attitudes and identified antecedents (i.e., parent education, family income, and parent acculturation), consequences (i.e., academic achievement and engagement), and a potential moderator (i.e., adolescent acculturation) of the transmission process. The sample was 444 Chinese American mothers, fathers, and adolescents (12-15 at W1). Using path analysis, this study found significant two-way interactions among parent education, income, and acculturation in predicting parents' concurrent positive educational attitudes, which, in turn, predicted adolescents' attitudes at W2. The latter link was further moderated by W1 and W2 adolescent acculturation for mother-adolescent and father-adolescent dyads. Adolescents' positive educational attitudes at W2, in turn, were positively associated with their concurrent academic achievement and engagement. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.

The priority heuristic: making choices without trade-offs.

PubMed

Brandstätter, Eduard; Gigerenzer, Gerd; Hertwig, Ralph

2006-04-01

Bernoulli's framework of expected utility serves as a model for various psychological processes, including motivation, moral sense, attitudes, and decision making. To account for evidence at variance with expected utility, the authors generalize the framework of fast and frugal heuristics from inferences to preferences. The priority heuristic predicts (a) the Allais paradox, (b) risk aversion for gains if probabilities are high, (c) risk seeking for gains if probabilities are low (e.g., lottery tickets), (d) risk aversion for losses if probabilities are low (e.g., buying insurance), (e) risk seeking for losses if probabilities are high, (f) the certainty effect, (g) the possibility effect, and (h) intransitivities. The authors test how accurately the heuristic predicts people's choices, compared with previously proposed heuristics and 3 modifications of expected utility theory: security-potential/aspiration theory, transfer-of-attention-exchange model, and cumulative prospect theory. ((c) 2006 APA, all rights reserved).

Spatial and temporal variation of fecal indicator organisms in two creeks in Beltsville, Maryland

USDA-ARS?s Scientific Manuscript database

Evaluation of microbial water quality is commonly achieved by monitoring populations of indicator bacteria such as E. coli and enterococci. Monitoring data are utilized by water managers to predict potential fecal contaminations as well as a decision tool to improve microbial water quality. Both te...

Diversity Matters: Parent Input Predicts Toddler Verb Production

ERIC Educational Resources Information Center

Hsu, Ning; Hadley, Pamela A.; Rispoli, Matthew

2017-01-01

The contribution of parent input to children's subsequent expressive verb diversity was explored in twenty typically developing toddlers with small verb lexicons. Child developmental factors and parent input measures (i.e. verb quantity, verb diversity, and verb-related structural cues) at age 1;9 were examined as potential predictors of…

Application of Functional Use Predictions to Aid in Structure Identification of Chemicals in House Dust

EPA Science Inventory

Humans are potentially exposed to thousands of anthropogenic chemicals in commerce. Recent work has shown that the bulk of this exposure may occur in near-field indoor environments (e.g., home, school, work, etc.). Advances in suspect screening analyses (SSA) now allow an improve...

COMBINED AND INTERACTIVE EFFECTS OF GLOBAL CLIMATE CHANGE AND TOXICANTS ON POPULATIONS AND COMMUNITIES

PubMed Central

Moe, S Jannicke; De Schamphelaere, Karel; Clements, William H; Sorensen, Mary T; Van den Brink, Paul J; Liess, Matthias

2013-01-01

Increased temperature and other environmental effects of global climate change (GCC) have documented impacts on many species (e.g., polar bears, amphibians, coral reefs) as well as on ecosystem processes and species interactions (e.g., the timing of predator–prey interactions). A challenge for ecotoxicologists is to predict how joint effects of climatic stress and toxicants measured at the individual level (e.g., reduced survival and reproduction) will be manifested at the population level (e.g., population growth rate, extinction risk) and community level (e.g., species richness, food-web structure). The authors discuss how population- and community-level responses to toxicants under GCC are likely to be influenced by various ecological mechanisms. Stress due to GCC may reduce the potential for resistance to and recovery from toxicant exposure. Long-term toxicant exposure can result in acquired tolerance to this stressor at the population or community level, but an associated cost of tolerance may be the reduced potential for tolerance to subsequent climatic stress (or vice versa). Moreover, GCC can induce large-scale shifts in community composition, which may affect the vulnerability of communities to other stressors. Ecological modeling based on species traits (representing life-history traits, population vulnerability, sensitivity to toxicants, and sensitivity to climate change) can be a promising approach for predicting combined impacts of GCC and toxicants on populations and communities. Environ. Toxicol. Chem. 2013;32:49–61. © 2012 SETAC PMID:23147390

Computational Approaches to Predict Indices of ...

EPA Pesticide Factsheets

As nutrient inputs increase, productivity increases and lakes transition from low trophic state (e.g., oligotrophic) to higher trophic states (e.g., eutrophic). These broad trophic state classifications are good predictors of ecosystem health and the potential for ecosystem services (e.g., recreation, aesthetics, and fisheries). Additionally, some ecosystem disservices, such as cyanobacteria blooms, are also associated with increased nutrient inputs. Thus, trophic state can be used as a proxy for cyanobacteria bloom risk. To explore this idea, we construct two random forest models of trophic state (as determined by chlorophyll a concentration). First we define an “All Variable” model that estimates trophic state with both in situ and universally available data, and then we reduce this to a “GIS Only” model that uses only the universally available data. The “All Variables” model had a root mean square error (RMSE) of 0.09 and R2 of 0.8; whereas, the “GIS Only” model was 0.22 and 0.48 for RMSE and R2, respectively. Examining the “GIS Only” model (i.e., the model that has broadest applicability) we see that in spite of lower overall accuracy, it still has better than even odds (i.e., prediction probability is > 50%) of being correct in more than 1091 of the 1138 lakes included in this model. The “GIS Only” model has tremendous potential for exploring spatial trends at the national level since the datasets required to parameterize the

Biomarkers for monitoring clinical efficacy of allergen immunotherapy for allergic rhinoconjunctivitis and allergic asthma: an EAACI Position Paper.

PubMed

Shamji, M H; Kappen, J H; Akdis, M; Jensen-Jarolim, E; Knol, E F; Kleine-Tebbe, J; Bohle, B; Chaker, A M; Till, S J; Valenta, R; Poulsen, L K; Calderon, M A; Demoly, P; Pfaar, O; Jacobsen, L; Durham, S R; Schmidt-Weber, C B

2017-08-01

Allergen immunotherapy (AIT) is an effective treatment for allergic rhinoconjunctivitis (AR) with or without asthma. It is important to note that due to the complex interaction between patient, allergy triggers, symptomatology and vaccines used for AIT, some patients do not respond optimally to the treatment. Furthermore, there are no validated or generally accepted candidate biomarkers that are predictive of the clinical response to AIT. Clinical management of patients receiving AIT and efficacy in randomised controlled trials for drug development could be enhanced by predictive biomarkers. The EAACI taskforce reviewed all candidate biomarkers used in clinical trials of AR patients with/without asthma in a literature review. Biomarkers were grouped into seven domains: (i) IgE (total IgE, specific IgE and sIgE/Total IgE ratio), (ii) IgG-subclasses (sIgG1, sIgG4 including SIgE/IgG4 ratio), (iii) Serum inhibitory activity for IgE (IgE-FAB and IgE-BF), (iv) Basophil activation, (v) Cytokines and Chemokines, (vi) Cellular markers (T regulatory cells, B regulatory cells and dendritic cells) and (vii) In vivo biomarkers (including provocation tests?). All biomarkers were reviewed in the light of their potential advantages as well as their respective drawbacks. Unmet needs and specific recommendations on all seven domains were addressed. It is recommended to explore the use of allergen-specific IgG4 as a biomarker for compliance. sIgE/tIgE and IgE-FAB are considered as potential surrogate candidate biomarkers. Cytokine/chemokines and cellular reponses provided insight into the mechanisms of AIT. More studies for confirmation and interpretation of the possible association with the clinical response to AIT are needed. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Influence of surface conductivity on the apparent zeta potential of calcite.

PubMed

Li, Shuai; Leroy, Philippe; Heberling, Frank; Devau, Nicolas; Jougnot, Damien; Chiaberge, Christophe

2016-04-15

Zeta potential is a physicochemical parameter of particular importance in describing the surface electrical properties of charged porous media. However, the zeta potential of calcite is still poorly known because of the difficulty to interpret streaming potential experiments. The Helmholtz-Smoluchowski (HS) equation is widely used to estimate the apparent zeta potential from these experiments. However, this equation neglects the influence of surface conductivity on streaming potential. We present streaming potential and electrical conductivity measurements on a calcite powder in contact with an aqueous NaCl electrolyte. Our streaming potential model corrects the apparent zeta potential of calcite by accounting for the influence of surface conductivity and flow regime. We show that the HS equation seriously underestimates the zeta potential of calcite, particularly when the electrolyte is diluted (ionic strength ⩽ 0.01 M) because of calcite surface conductivity. The basic Stern model successfully predicted the corrected zeta potential by assuming that the zeta potential is located at the outer Helmholtz plane, i.e. without considering a stagnant diffuse layer at the calcite-water interface. The surface conductivity of calcite crystals was inferred from electrical conductivity measurements and computed using our basic Stern model. Surface conductivity was also successfully predicted by our surface complexation model. Copyright © 2016 Elsevier Inc. All rights reserved.

Modulation of microRNA-mRNA Target Pairs by Human Papillomavirus 16 Oncoproteins

PubMed Central

Harden, Mallory E.; Prasad, Nripesh; Griffiths, Anthony

2017-01-01

ABSTRACT The E6 and E7 proteins are the major oncogenic drivers encoded by high-risk human papillomaviruses (HPVs). While many aspects of the transforming activities of these proteins have been extensively studied, there are fewer studies that have investigated how HPV E6/E7 expression affects the expression of cellular noncoding RNAs. The goal of our study was to investigate HPV16 E6/E7 modulation of cellular microRNA (miR) levels and to determine the potential consequences for cellular gene expression. We performed deep sequencing of small and large cellular RNAs in primary undifferentiated cultures of human foreskin keratinocytes (HFKs) with stable expression of HPV16 E6/E7 or a control vector. After integration of the two data sets, we identified 51 differentially expressed cellular miRs associated with the modulation of 1,456 potential target mRNAs in HPV16 E6/E7-expressing HFKs. We discovered that the degree of differential miR expression in HFKs expressing HPV16 E6/E7 was not necessarily predictive of the number of corresponding mRNA targets or the potential impact on gene expression. Additional analyses of the identified miR-mRNA pairs suggest modulation of specific biological activities and biochemical pathways. Overall, our study supports the model that perturbation of cellular miR expression by HPV16 E6/E7 importantly contributes to the rewiring of cellular regulatory circuits by the high-risk HPV E6 and E7 proteins that contribute to oncogenic transformation. PMID:28049151

The validation and preference among different EAM potentials to describe the solid-liquid transition of aluminum

NASA Astrophysics Data System (ADS)

Jiang, Yewei; Luo, Jie; Wu, Yongquan

2017-06-01

Empirical potential is vital to the classic atomic simulation, especially for the study of phase transitions, as well as the solid-interface. In this paper, we attempt to set up a uniform procedure for the validation among different potentials before the formal simulation study of phase transitions of metals. Two main steps are involved: (1) the prediction of the structures of both solid and liquid phases and their mutual transitions, i.e. melting and crystallization; (2) the prediction of vital thermodynamic (the equilibrium melting point at ambient pressure) and dynamic properties (the degrees of superheating and undercooling). We applied this procedure to the testing of seven published embedded-atom potentials (MKBA (Mendelev et al 2008 Philos. Mag. 88 1723), MFMP (Mishin et al 1999 Phys. Rev. B 59 3393), MDSL (Sturgeon and Laird 2000 Phys. Rev. B 62 14720), ZM (Zope and Mishin 2003 Phys. Rev. B 68 024102), LEA (Liu et al 2004 Model. Simul. Mater. Sci. Eng. 12 665), WKG (Winey et al 2009 Model. Simul. Mater. Sci. Eng. 17 055004) and ZJW (Zhou et al 2004 Phys. Rev. B 69 144113)) for the description of the solid-liquid transition of Al. All the predictions of structure, melting point and superheating/undercooling degrees were compared with the experiments or theoretical calculations. Then, two of them, MKBA and MDSL, were proven suitable for the study of the solid-liquid transition of Al while the residuals were unqualified. However, potential MKBA is more accurate to predict the structures of solid and liquid, while MDSL works a little better in the thermodynamic and dynamic predictions of solid-liquid transitions.

High-Level ab Initio Predictions for the Ionization Energies, Bond Dissociation Energies, and Heats of Formation of Titanium Oxides and Their Cations (TiOn/TiOn+, n = 1 and 2).

PubMed

Pan, Yi; Luo, Zhihong; Chang, Yih-Chung; Lau, Kai-Chung; Ng, C Y

2017-01-26

The ionization energies (IEs) of TiO and TiO 2 and the 0 K bond dissociation energies (D 0 ) and the heats of formation at 0 K (ΔH° f0 ) and 298 K (ΔH° f298 ) for TiO/TiO + and TiO 2 /TiO 2 + are predicted by the wave-function-based CCSDTQ/CBS approach. The CCSDTQ/CBS calculations involve the approximation to the complete basis set (CBS) limit at the coupled cluster level up to full quadruple excitations along with the zero-point vibrational energy (ZPVE), high-order correlation (HOC), core-valence (CV) electronic, spin-orbit (SO) coupling, and scalar relativistic (SR) effect corrections. The present calculations yield IE(TiO) = 6.815 eV and are in good agreement with the experimental IE value of 6.819 80 ± 0.000 10 eV determined in a two-color laser-pulsed field ionization-photoelectron (PFI-PE) study. The CCSDT and MRCI+Q methods give the best predictions to the harmonic frequencies: ω e (ω e + ) = 1013 (1069) and 1027 (1059) cm -1 and the bond lengths r e (r e + ) = 1.625 (1.587) and 1.621 (1.588) Å, for TiO (TiO + ) compared with the experimental values. Two nearly degenerate, stable structures are found for TiO 2 cation: TiO 2 + (C 2v ) structure has two equivalent TiO bonds, while the TiO 2 + (C s ) structure features a long and a short TiO bond. The IEs for the TiO 2 + (C 2v )←TiO 2 and TiO 2 + (C s )←TiO 2 ionization transitions are calculated to be 9.515 and 9.525 eV, respectively, giving the theoretical adiabatic IE value in good agreement with the experiment IE(TiO 2 ) = 9.573 55 ± 0.000 15 eV obtained in the previous vacuum ultraviolet (VUV)-PFI-PE study of TiO 2 . The potential energy surface of TiO 2 + along the normal vibrational coordinates of asymmetric stretching mode (ω 3 + ) is nearly flat and exhibits a double-well potential with the well of TiO 2 + (C s ) situated around the central well of TiO 2 + (C 2v ). This makes the theoretical calculation of ω 3 + infeasible. For the symmetric stretching (ω 1 + ), the current theoretical predictions overestimate the experimental value of 829.1 ± 2.0 cm -1 by more than 100 cm -1 . This work together with the previous experimental and theoretical investigations supports the conclusion that the CCSDTQ/CBS approach is capable of providing reliable IE and D 0 predictions for TiO/TiO + and TiO 2 /TiO 2 + with error limits less than or equal to 60 meV. The CCSDTQ/CBS calculations give the predictions of D 0 (Ti + -O) - D 0 (Ti-O) = 0.004 eV and D 0 (O-TiO) - D 0 (O-TiO + ) = 2.699 eV, which are also consistent with the respective experimental determination of 0.008 32 ± 0.000 10 and 2.753 75 ± 0.000 18 eV.

Chemical Potentials, Activity Coefficients, and Solubility in Aqueous NaCl Solutions: Prediction by Polarizable Force Fields.

PubMed

Moučka, Filip; Nezbeda, Ivo; Smith, William R

2015-04-14

We describe a computationally efficient molecular simulation methodology for calculating the concentration dependence of the chemical potentials of both solute and solvent in aqueous electrolyte solutions, based on simulations of the salt chemical potential alone. We use our approach to study the predictions for aqueous NaCl solutions at ambient conditions of these properties by the recently developed polarizable force fields (FFs) AH/BK3 of Kiss and Baranyai (J. Chem. Phys. 2013, 138, 204507) and AH/SWM4-DP of Lamoureux and Roux (J. Phys. Chem. B 2006, 110, 3308 - 3322) and by the nonpolarizable JC FF of Joung and Cheatham tailored to SPC/E water (J. Phys. Chem. B 2008, 112, 9020 - 9041). We also consider their predictions of the concentration dependence of the electrolyte activity coefficient, the crystalline solid chemical potential, the electrolyte solubility, and the solution specific volume. We first highlight the disagreement in the literature concerning calculations of solubility by means of molecular simulation in the case of the JC FF and provide strong evidence of the correctness of our methodology based on recent independently obtained results for this important test case. We then compare the predictions of the three FFs with each other and with experiment and draw conclusions concerning their relative merits, with particular emphasis on the salt chemical potential and activity coefficient vs concentration curves and their derivatives. The latter curves have only previously been available from Kirkwood-Buff integrals, which require approximate numerical integrations over system pair correlation functions at each concentration. Unlike the case of the other FFs, the AH/BK3 curves are nearly parallel to the corresponding experimental curves at moderate and higher concentrations. This leads to an excellent prediction of the water chemical potential via the Gibbs-Duhem equation and enables the activity coefficient curve to be brought into excellent agreement with experiment by incorporating an appropriate value of the standard state chemical potential in the Henry Law convention.

Modelling in vivo action potential propagation along a giant axon.

PubMed

George, Stuart; Foster, Jamie M; Richardson, Giles

2015-01-01

A partial differential equation model for the three-dimensional current flow in an excitable, unmyelinated axon is considered. Where the axon radius is significantly below a critical value R(crit) (that depends upon intra- and extra-cellular conductivity and ion channel conductance) the resistance of the intracellular space is significantly higher than that of the extracellular space, such that the potential outside the axon is uniformly small whilst the intracellular potential is approximated by the transmembrane potential. In turn, since the current flow is predominantly axial, it can be shown that the transmembrane potential is approximated by a solution to the one-dimensional cable equation. It is noted that the radius of the squid giant axon, investigated by (Hodgkin and Huxley 1952e), lies close to R(crit). This motivates us to apply the three-dimensional model to the squid giant axon and compare the results thus found to those obtained using the cable equation. In the context of the in vitro experiments conducted in (Hodgkin and Huxley 1952e) we find only a small difference between the wave profiles determined using these two different approaches and little difference between the speeds of action potential propagation predicted. This suggests that the cable equation approximation is accurate in this scenario. However when applied to the it in vivo setting, in which the conductivity of the surrounding tissue is considerably lower than that of the axoplasm, there are marked differences in both wave profile and speed of action potential propagation calculated using the two approaches. In particular, the cable equation significantly over predicts the increase in the velocity of propagation as axon radius increases. The consequences of these results are discussed in terms of the evolutionary costs associated with increasing the speed of action potential propagation by increasing axon radius.

Revising the predictions of inflation for the cosmic microwave background anisotropies.

PubMed

Agulló, Iván; Navarro-Salas, José; Olmo, Gonzalo J; Parker, Leonard

2009-08-07

We point out that, if quantum field renormalization is taken into account and the counterterms are evaluated at the Hubble-radius crossing time or few e-foldings after it, the predictions of slow-roll inflation for both the scalar and the tensorial power spectrum change significantly. This leads to a change in the consistency condition that relates the tensor-to-scalar amplitude ratio with spectral indices. A reexamination of the potentials varphi;{2} and varphi;{4} shows that both are compatible with five-year WMAP data. Only when the counterterms are evaluated at much larger times beyond the end of inflation does one recover the standard predictions. The alternative predictions presented here may soon come within the range of measurement of near-future experiments.

Accurate prediction of cation-π interaction energy using substituent effects.

PubMed

Sayyed, Fareed Bhasha; Suresh, Cherumuttathu H

2012-06-14

Substituent effects on cation-π interactions have been quantified using a variety of Φ-X···M(+) complexes where Φ, X, and M(+) are the π-system, substituent, and cation, respectively. The cation-π interaction energy, E(M(+)), showed a strong linear correlation with the molecular electrostatic potential (MESP) based measure of the substituent effect, ΔV(min) (the difference between the MESP minimum (V(min)) on the π-region of a substituted system and the corresponding unsubstituted system). This linear relationship is E(M(+)) = C(M(+))(ΔV(min)) + E(M(+))' where C(M(+)) is the reaction constant and E(M(+))' is the cation-π interaction energy of the unsubstituted complex. This relationship is similar to the Hammett equation and its first term yields the substituent contribution of the cation-π interaction energy. Further, a linear correlation between C(M(+))() and E(M(+))()' has been established, which facilitates the prediction of C(M(+)) for unknown cations. Thus, a prediction of E(M(+)) for any Φ-X···M(+) complex is achieved by knowing the values of E(M(+))' and ΔV(min). The generality of the equation is tested for a variety of cations (Li(+), Na(+), K(+), Mg(+), BeCl(+), MgCl(+), CaCl(+), TiCl(3)(+), CrCl(2)(+), NiCl(+), Cu(+), ZnCl(+), NH(4)(+), CH(3)NH(3)(+), N(CH(3))(4)(+), C(NH(2))(3)(+)), substituents (N(CH(3))(2), NH(2), OCH(3), CH(3), OH, H, SCH(3), SH, CCH, F, Cl, COOH, CHO, CF(3), CN, NO(2)), and a large number of π-systems. The tested systems also include multiple substituted π-systems, viz. ethylene, acetylene, hexa-1,3,5-triene, benzene, naphthalene, indole, pyrrole, phenylalanine, tryptophan, tyrosine, azulene, pyrene, [6]-cyclacene, and corannulene and found that E(M)(+) follows the additivity of substituent effects. Further, the substituent effects on cationic sandwich complexes of the type C(6)H(6)···M(+)···C(6)H(5)X have been assessed and found that E(M(+)) can be predicted with 97.7% accuracy using the values of E(M(+))' and ΔV(min). All the Φ-X···M(+) systems showed good agreement between the calculated and predicted E(M(+))() values, suggesting that the ΔV(min) approach to substituent effect is accurate and useful for predicting the interactive behavior of substituted π-systems with cations.

Landscape models of brook trout abundance and distribution in lotic habitat with field validation

USGS Publications Warehouse

McKenna, James E.; Johnson, James H.

2011-01-01

Brook trout Salvelinus fontinalis are native fish in decline owing to environmental changes. Predictions of their potential distribution and a better understanding of their relationship to habitat conditions would enhance the management and conservation of this valuable species. We used over 7,800 brook trout observations throughout New York State and georeferenced, multiscale landscape condition data to develop four regionally specific artificial neural network models to predict brook trout abundance in rivers and streams. Land cover data provided a general signature of human activity, but other habitat variables were resistant to anthropogenic changes (i.e., changing on a geological time scale). The resulting models predict the potential for any stream to support brook trout. The models were validated by holding 20% of the data out as a test set and by comparison with additional field collections from a variety of habitat types. The models performed well, explaining more than 90% of data variability. Errors were often associated with small spatial displacements of predicted values. When compared with the additional field collections (39 sites), 92% of the predictions were off by only a single class from the field-observed abundances. Among “least-disturbed” field collection sites, all predictions were correct or off by a single abundance class, except for one where brown trout Salmo trutta were present. Other degrading factors were evident at most sites where brook trout were absent or less abundant than predicted. The most important habitat variables included landscape slope, stream and drainage network sizes, water temperature, and extent of forest cover. Predicted brook trout abundances were applied to all New York streams, providing a synoptic map of the distribution of brook trout habitat potential. These fish models set benchmarks of best potential for streams to support brook trout under broad-scale human influences and can assist with planning and identification of protection or rehabilitation sites.

Organic Pollutants in Shale Gas Flowback and Produced Waters: Identification, Potential Ecological Impact, and Implications for Treatment Strategies.

PubMed

Butkovskyi, Andrii; Bruning, Harry; Kools, Stefan A E; Rijnaarts, Huub H M; Van Wezel, Annemarie P

2017-05-02

Organic contaminants in shale gas flowback and produced water (FPW) are traditionally expressed as total organic carbon (TOC) or chemical oxygen demand (COD), though these parameters do not provide information on the toxicity and environmental fate of individual components. This review addresses identification of individual organic contaminants in FPW, and stresses the gaps in the knowledge on FPW composition that exist so far. Furthermore, the risk quotient approach was applied to predict the toxicity of the quantified organic compounds for fresh water organisms in recipient surface waters. This resulted in an identification of a number of FPW related organic compounds that are potentially harmful namely those compounds originating from shale formations (e.g., polycyclic aromatic hydrocarbons, phthalates), fracturing fluids (e.g., quaternary ammonium biocides, 2-butoxyethanol) and downhole transformations of organic compounds (e.g., carbon disulfide, halogenated organic compounds). Removal of these compounds by FPW treatment processes is reviewed and potential and efficient abatement strategies are defined.

Organic Pollutants in Shale Gas Flowback and Produced Waters: Identification, Potential Ecological Impact, and Implications for Treatment Strategies

PubMed Central

2017-01-01

Organic contaminants in shale gas flowback and produced water (FPW) are traditionally expressed as total organic carbon (TOC) or chemical oxygen demand (COD), though these parameters do not provide information on the toxicity and environmental fate of individual components. This review addresses identification of individual organic contaminants in FPW, and stresses the gaps in the knowledge on FPW composition that exist so far. Furthermore, the risk quotient approach was applied to predict the toxicity of the quantified organic compounds for fresh water organisms in recipient surface waters. This resulted in an identification of a number of FPW related organic compounds that are potentially harmful namely those compounds originating from shale formations (e.g., polycyclic aromatic hydrocarbons, phthalates), fracturing fluids (e.g., quaternary ammonium biocides, 2-butoxyethanol) and downhole transformations of organic compounds (e.g., carbon disulfide, halogenated organic compounds). Removal of these compounds by FPW treatment processes is reviewed and potential and efficient abatement strategies are defined. PMID:28376616

Competition-strength-dependent ground suppression in figure-ground perception.

PubMed

Salvagio, Elizabeth; Cacciamani, Laura; Peterson, Mary A

2012-07-01

Figure-ground segregation is modeled as inhibitory competition between objects that might be perceived on opposite sides of borders. The winner is the figure; the loser is suppressed, and its location is perceived as shapeless ground. Evidence of ground suppression would support inhibitory competition models and would contribute to explaining why grounds are shapeless near borders shared with figures, yet such evidence is scarce. We manipulated whether competition from potential objects on the ground side of figures was high (i.e., portions of familiar objects were potentially present there) or low (novel objects were potentially present). We predicted that greater competition would produce more ground suppression. The results of two experiments in which suppression was assessed via judgments of the orientation of target bars confirmed this prediction; a third experiment showed that ground suppression is short-lived. Our findings support inhibitory competition models of figure assignment, in particular, and models of visual perception entailing feedback, in general.

A Fully Associative, Non-Linear Kinematic, Unified Viscoplastic Model for Titanium Based Matrices

NASA Technical Reports Server (NTRS)

Arnold, S. M.; Saleeb, A. F.; Castelli, M. G.

1994-01-01

Specific forms for both the Gibb's and complementary dissipation potentials are chosen such that a complete (i.e., fully associative) potential based multiaxial unified viscoplastic model is obtained. This model possesses one tensorial internal state variable that is associated with dislocation substructure, with an evolutionary law that has nonlinear kinematic hardening and both thermal and strain induced recovery mechanisms. A unique aspect of the present model is the inclusion of non-linear hardening through the use of a compliance operator, derived from the Gibb's potential, in the evolution law for the back stress. This non-linear tensorial operator is significant in that it allows both the flow and evolutionary laws to be fully associative (and therefore easily integrated) and greatly influences the multiaxial response under non-proportional loading paths. In addition to this nonlinear compliance operator, a new consistent, potential preserving, internal strain unloading criterion has been introduced to prevent abnormalities in the predicted stress-strain curves, which are present with nonlinear hardening formulations, during unloading and reversed loading of the external variables. Specification of an experimental program for the complete determination of the material functions and parameters for characterizing a metallic matrix, e.g., TIMETAL 21S, is given. The experiments utilized are tensile, creep, and step creep tests. Finally, a comparison of this model and a commonly used Bodner-Partom model is made on the basis of predictive accuracy and numerical efficiency.

Using spatial-stream-network models and long-term data to understand and predict dynamics of faecal contamination in a mixed land-use catchment.

PubMed

Neill, Aaron James; Tetzlaff, Doerthe; Strachan, Norval James Colin; Hough, Rupert Lloyd; Avery, Lisa Marie; Watson, Helen; Soulsby, Chris

2018-01-15

An 11year dataset of concentrations of E. coli at 10 spatially-distributed sites in a mixed land-use catchment in NE Scotland (52km 2 ) revealed that concentrations were not clearly associated with flow or season. The lack of a clear flow-concentration relationship may have been due to greater water fluxes from less-contaminated headwaters during high flows diluting downstream concentrations, the importance of persistent point sources of E. coli both anthropogenic and agricultural, and possibly the temporal resolution of the dataset. Point sources and year-round grazing of livestock probably obscured clear seasonality in concentrations. Multiple linear regression models identified potential for contamination by anthropogenic point sources as a significant predictor of long-term spatial patterns of low, average and high concentrations of E. coli. Neither arable nor pasture land was significant, even when accounting for hydrological connectivity with a topographic-index method. However, this may have reflected coarse-scale land-cover data inadequately representing "point sources" of agricultural contamination (e.g. direct defecation of livestock into the stream) and temporal changes in availability of E. coli from diffuse sources. Spatial-stream-network models (SSNMs) were applied in a novel context, and had value in making more robust catchment-scale predictions of concentrations of E. coli with estimates of uncertainty, and in enabling identification of potential "hot spots" of faecal contamination. Successfully managing faecal contamination of surface waters is vital for safeguarding public health. Our finding that concentrations of E. coli could not clearly be associated with flow or season may suggest that management strategies should not necessarily target only high flow events or summer when faecal contamination risk is often assumed to be greatest. Furthermore, we identified SSNMs as valuable tools for identifying possible "hot spots" of contamination which could be targeted for management, and for highlighting areas where additional monitoring could help better constrain predictions relating to faecal contamination. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway

NASA Astrophysics Data System (ADS)

Huang, Lu; Jiang, Yuyang; Chen, Yuzong

2017-01-01

Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems.

Biometric assessment of prostate cancer's metastatic potential.

PubMed

Cooper, C R; Emmett, N; Harris-Hooker, S; Patterson, R; Cooke, D B

1994-01-01

Currently, no protocol exists that can assess the metastatic potential of prostate adenocarcinoma. The reason for this is partly due to the lack of information on cellular changes that result in a tumor cell's becoming metastatic. In this investigation, attempts were made to devise a method that correlated with the metastatic potential of AT-1, Mat-Lu, and Mat-LyLu cell lines of the Dunning R-3327 rat prostatic adenocarcinoma system. To accomplish this, we applied BioQuant biometric parameters, i.e., area, shape factor, and cell motility. AT-1 had a lower shape factor and a greater area as compared with the more highly metastatic Mat-Lu subline. No significant difference in area or shape factor was detected between the AT-1 cell line and the highly metastatic Mat-LyLu line. However, the lowly metastatic AT-1 line had less motility as compared with the Mat-Lu and Mat-LyLu lines. This study revealed that metastatic potential could be partially predicted via area and shape factor and accurately predicted via cell motility.

In silico screening of drug-membrane thermodynamics reveals linear relations between bulk partitioning and the potential of mean force

NASA Astrophysics Data System (ADS)

Menichetti, Roberto; Kanekal, Kiran H.; Kremer, Kurt; Bereau, Tristan

2017-09-01

The partitioning of small molecules in cell membranes—a key parameter for pharmaceutical applications—typically relies on experimentally available bulk partitioning coefficients. Computer simulations provide a structural resolution of the insertion thermodynamics via the potential of mean force but require significant sampling at the atomistic level. Here, we introduce high-throughput coarse-grained molecular dynamics simulations to screen thermodynamic properties. This application of physics-based models in a large-scale study of small molecules establishes linear relationships between partitioning coefficients and key features of the potential of mean force. This allows us to predict the structure of the insertion from bulk experimental measurements for more than 400 000 compounds. The potential of mean force hereby becomes an easily accessible quantity—already recognized for its high predictability of certain properties, e.g., passive permeation. Further, we demonstrate how coarse graining helps reduce the size of chemical space, enabling a hierarchical approach to screening small molecules.

A metabolomic study of low estimated GFR in non-proteinuric type 2 diabetes mellitus.

PubMed

Ng, D P K; Salim, A; Liu, Y; Zou, L; Xu, F G; Huang, S; Leong, H; Ong, C N

2012-02-01

We carried out a urinary metabolomic study to gain insight into low estimated GFR (eGFR) in patients with non-proteinuric type 2 diabetes. Patients were identified as being non-proteinuric using multiple urinalyses. Cases (n = 44) with low eGFR and controls (n = 46) had eGFR values <60 and ≥60 ml min(-1) 1.73 m(-2), respectively, as calculated using the Modification of Diet in Renal Disease formula. Urine samples were analysed by liquid chromatography/mass spectrometry (LC/MS) and GC/MS. False discovery rates were used to adjust for multiple hypotheses testing, and selection of metabolites that best predicted low eGFR status was achieved using least absolute shrinkage and selection operator logistic regression. Eleven GC/MS metabolites were strongly associated with low eGFR after correction for multiple hypotheses testing (smallest adjusted p value = 2.62 × 10(-14), largest adjusted p value = 3.84 × 10(-2)). In regression analysis, octanol, oxalic acid, phosphoric acid, benzamide, creatinine, 3,5-dimethoxymandelic amide and N-acetylglutamine were selected as the best subset for prediction and allowed excellent classification of low eGFR (AUC = 0.996). In LC/MS, 19 metabolites remained significant after multiple hypotheses testing had been taken into account (smallest adjusted p value = 2.04 × 10(-4), largest adjusted p value = 4.48 × 10(-2)), and several metabolites showed stronger evidence of association relative to the uraemic toxin, indoxyl sulphate (adjusted p value = 3.03 × 10(-2)). The potential effect of confounding on the association between metabolites was excluded. Our study has yielded substantial new insight into low eGFR and provided a collection of potential urinary biomarkers for its detection.

Transcriptomic profiling as a screening tool to detect trenbolone treatment in beef cattle.

PubMed

Pegolo, S; Cannizzo, F T; Biolatti, B; Castagnaro, M; Bargelloni, L

2014-06-01

The effects of steroid hormone implants containing trenbolone alone (Finaplix-H), combined with 17β-oestradiol (17β-E; Revalor-H), or with 17β-E and dexamethasone (Revalor-H plus dexamethasone per os) on the bovine muscle transcriptome were examined by DNA-microarray. Overall, large sets of genes were shown to be modulated by the different growth promoters (GPs) and the regulated pathways and biological processes were mostly shared among the treatment groups. Using the Prediction Analysis of Microarray program, GP-treated animals were accurately identified by a small number of predictive genes. A meta-analysis approach was also carried out for the Revalor group to potentially increase the robustness of class prediction analysis. After data pre-processing, a high level of accuracy (90%) was obtained in the classification of samples, using 105 predictive gene markers. Transcriptomics could thus help in the identification of indirect biomarkers for anabolic treatment in beef cattle to be applied for the screening of muscle samples collected after slaughtering. Copyright © 2014 Elsevier Ltd. All rights reserved.

Clinical responses to ERK inhibition in BRAFV600E-mutant colorectal cancer predicted using a computational model.

PubMed

Kirouac, Daniel C; Schaefer, Gabriele; Chan, Jocelyn; Merchant, Mark; Orr, Christine; Huang, Shih-Min A; Moffat, John; Liu, Lichuan; Gadkar, Kapil; Ramanujan, Saroja

2017-01-01

Approximately 10% of colorectal cancers harbor BRAF V600E mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth. Clinical predictions of anti-tumor activity were enabled by the use of tumor response data from three Phase 1 clinical trials testing combinations of EGFR, BRAF, and MEK inhibitors. Simulated responses to GDC-0994 monotherapy (overall response rate = 17%) accurately predicted results from a Phase 1 clinical trial regarding the number of responding patients (2/18) and the distribution of tumor size changes ("waterfall plot"). Prospective simulations were then used to evaluate potential drug combinations and predictive biomarkers for increasing responsiveness to MEK/ERK inhibitors in these patients.

Long-Term Temporal Trends of Polychlorinated Biphenyls and Their Controlling Sources in China.

PubMed

Zhao, Shizhen; Breivik, Knut; Liu, Guorui; Zheng, Minghui; Jones, Kevin C; Sweetman, Andrew J

2017-03-07

Polychlorinated biphenyls (PCBs) are industrial organic contaminants identified as persistent, bioaccumulative, toxic (PBT), and subject to long-range transport (LRT) with global scale significance. This study focuses on a reconstruction and prediction for China of long-term emission trends of intentionally and unintentionally produced (UP) ∑ 7 PCBs (UP-PCBs, from the manufacture of steel, cement and sinter iron) and their re-emissions from secondary sources (e.g., soils and vegetation) using a dynamic fate model (BETR-Global). Contemporary emission estimates combined with predictions from the multimedia fate model suggest that primary sources still dominate, although unintentional sources are predicted to become a main contributor from 2035 for PCB-28. Imported e-waste is predicted to play an increasing role until 2020-2030 on a national scale due to the decline of intentionally produced (IP) emissions. Hypothetical emission scenarios suggest that China could become a potential source to neighboring regions with a net output of ∼0.4 t year -1 by around 2050. However, future emission scenarios and hence model results will be dictated by the efficiency of control measures.

Test of Gravity on Large Scales with Weak Gravitational Lensing and Clustering Measurements of SDSS Luminous Red Galaxies

NASA Astrophysics Data System (ADS)

Reyes, Reinabelle; Mandelbaum, R.; Seljak, U.; Gunn, J.; Lombriser, L.

2009-01-01

We perform a test of gravity on large scales (5-50 Mpc/h) using 70,000 luminous red galaxies (LRGs) from the Sloan Digital Sky Survey (SDSS) DR7 with redshifts 0.16

Predictive Models for Carcinogenicity and Mutagenicity ...

EPA Pesticide Factsheets

Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t

Predicting the biological condition of streams: Use of geospatial indicators of natural and anthropogenic characteristics of watersheds

USGS Publications Warehouse

Carlisle, D.M.; Falcone, J.; Meador, M.R.

2009-01-01

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments. ?? Springer Science+Business Media B.V. 2008.

Confirmation of general relativity on large scales from weak lensing and galaxy velocities.

PubMed

Reyes, Reinabelle; Mandelbaum, Rachel; Seljak, Uros; Baldauf, Tobias; Gunn, James E; Lombriser, Lucas; Smith, Robert E

2010-03-11

Although general relativity underlies modern cosmology, its applicability on cosmological length scales has yet to be stringently tested. Such a test has recently been proposed, using a quantity, E(G), that combines measures of large-scale gravitational lensing, galaxy clustering and structure growth rate. The combination is insensitive to 'galaxy bias' (the difference between the clustering of visible galaxies and invisible dark matter) and is thus robust to the uncertainty in this parameter. Modified theories of gravity generally predict values of E(G) different from the general relativistic prediction because, in these theories, the 'gravitational slip' (the difference between the two potentials that describe perturbations in the gravitational metric) is non-zero, which leads to changes in the growth of structure and the strength of the gravitational lensing effect. Here we report that E(G) = 0.39 +/- 0.06 on length scales of tens of megaparsecs, in agreement with the general relativistic prediction of E(G) approximately 0.4. The measured value excludes a model within the tensor-vector-scalar gravity theory, which modifies both Newtonian and Einstein gravity. However, the relatively large uncertainty still permits models within f(R) theory, which is an extension of general relativity. A fivefold decrease in uncertainty is needed to rule out these models.

Toward automated e-cigarette surveillance: Spotting e-cigarette proponents on Twitter.

PubMed

Kavuluru, Ramakanth; Sabbir, A K M

2016-06-01

Electronic cigarettes (e-cigarettes or e-cigs) are a popular emerging tobacco product. Because e-cigs do not generate toxic tobacco combustion products that result from smoking regular cigarettes, they are sometimes perceived and promoted as a less harmful alternative to smoking and also as means to quit smoking. However, the safety of e-cigs and their efficacy in supporting smoking cessation is yet to be determined. Importantly, the federal drug administration (FDA) currently does not regulate e-cigs and as such their manufacturing, marketing, and sale is not subject to the rules that apply to traditional cigarettes. A number of manufacturers, advocates, and e-cig users are actively promoting e-cigs on Twitter. We develop a high accuracy supervised predictive model to automatically identify e-cig "proponents" on Twitter and analyze the quantitative variation of their tweeting behavior along popular themes when compared with other Twitter users (or tweeters). Using a dataset of 1000 independently annotated Twitter profiles by two different annotators, we employed a variety of textual features from latest tweet content and tweeter profile biography to build predictive models to automatically identify proponent tweeters. We used a set of manually curated key phrases to analyze e-cig proponent tweets from a corpus of over one million e-cig tweets along well known e-cig themes and compared the results with those generated by regular tweeters. Our model identifies e-cig proponents with 97% precision, 86% recall, 91% F-score, and 96% overall accuracy, with tight 95% confidence intervals. We find that as opposed to regular tweeters that form over 90% of the dataset, e-cig proponents are a much smaller subset but tweet two to five times more than regular tweeters. Proponents also disproportionately (one to two orders of magnitude more) highlight e-cig flavors, their smoke-free and potential harm reduction aspects, and their claimed use in smoking cessation. Given FDA is currently in the process of proposing meaningful regulation, we believe our work demonstrates the strong potential of informatics approaches, specifically machine learning, for automated e-cig surveillance on Twitter. Copyright © 2016 Elsevier Inc. All rights reserved.

Magnetic dipole transitions of Bc and Bc* mesons in the relativistic independent quark model

NASA Astrophysics Data System (ADS)

Patnaik, Sonali; Dash, P. C.; Kar, Susmita; Patra, Sweta P.; Barik, N.

2017-12-01

We study M1-transitions involving mesons: Bc(1 s ), Bc*(1 s ), Bc(2 s ), Bc*(2 s ), Bc(3 s ), and Bc*(3 s ) in the relativistic independent quark (RIQ) model based on a flavor independent average potential in the scalar-vector harmonic form. The transition form factor for Bc*→Bcγ is found to have analytical continuation from spacelike to physical timelike region. Our predicted coupling constant gBc*Bc=0.34 GeV-1 and decay width Γ (Bc*→Bcγ )=23 eV agree with other model predictions. In view of possible observation of Bc and Bc* s-wave states at LHC and Z-factory and potential use of theoretical estimate on M1-transitions, we investigate the allowed as well as hindered transitions of orbitally excited Bc-meson states and predict their decay widths in overall agreement with other model predictions. We consider the typical case of Bc*(1 s )→Bc(1 s )γ , where our predicted decay width which is found quite sensitive to the mass difference between Bc* and Bc mesons may help in determining the mass of Bc* experimentally.

Predicting Social Anxiety Treatment Outcome Based on Therapeutic Email Conversations.

PubMed

Hoogendoorn, Mark; Berger, Thomas; Schulz, Ava; Stolz, Timo; Szolovits, Peter

2017-09-01

Predicting therapeutic outcome in the mental health domain is of utmost importance to enable therapists to provide the most effective treatment to a patient. Using information from the writings of a patient can potentially be a valuable source of information, especially now that more and more treatments involve computer-based exercises or electronic conversations between patient and therapist. In this paper, we study predictive modeling using writings of patients under treatment for a social anxiety disorder. We extract a wealth of information from the text written by patients including their usage of words, the topics they talk about, the sentiment of the messages, and the style of writing. In addition, we study trends over time with respect to those measures. We then apply machine learning algorithms to generate the predictive models. Based on a dataset of 69 patients, we are able to show that we can predict therapy outcome with an area under the curve of 0.83 halfway through the therapy and with a precision of 0.78 when using the full data (i.e., the entire treatment period). Due to the limited number of participants, it is hard to generalize the results, but they do show great potential in this type of information.

Protein Loop Structure Prediction Using Conformational Space Annealing.

PubMed

Heo, Seungryong; Lee, Juyong; Joo, Keehyoung; Shin, Hang-Cheol; Lee, Jooyoung

2017-05-22

We have developed a protein loop structure prediction method by combining a new energy function, which we call E PLM (energy for protein loop modeling), with the conformational space annealing (CSA) global optimization algorithm. The energy function includes stereochemistry, dynamic fragment assembly, distance-scaled finite ideal gas reference (DFIRE), and generalized orientation- and distance-dependent terms. For the conformational search of loop structures, we used the CSA algorithm, which has been quite successful in dealing with various hard global optimization problems. We assessed the performance of E PLM with two widely used loop-decoy sets, Jacobson and RAPPER, and compared the results against the DFIRE potential. The accuracy of model selection from a pool of loop decoys as well as de novo loop modeling starting from randomly generated structures was examined separately. For the selection of a nativelike structure from a decoy set, E PLM was more accurate than DFIRE in the case of the Jacobson set and had similar accuracy in the case of the RAPPER set. In terms of sampling more nativelike loop structures, E PLM outperformed E DFIRE for both decoy sets. This new approach equipped with E PLM and CSA can serve as the state-of-the-art de novo loop modeling method.

Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks.

PubMed

Yi, Faliu; Yang, Lin; Wang, Shidan; Guo, Lei; Huang, Chenglong; Xie, Yang; Xiao, Guanghua

2018-02-27

Pathological angiogenesis has been identified in many malignancies as a potential prognostic factor and target for therapy. In most cases, angiogenic analysis is based on the measurement of microvessel density (MVD) detected by immunostaining of CD31 or CD34. However, most retrievable public data is generally composed of Hematoxylin and Eosin (H&E)-stained pathology images, for which is difficult to get the corresponding immunohistochemistry images. The role of microvessels in H&E stained images has not been widely studied due to their complexity and heterogeneity. Furthermore, identifying microvessels manually for study is a labor-intensive task for pathologists, with high inter- and intra-observer variation. Therefore, it is important to develop automated microvessel-detection algorithms in H&E stained pathology images for clinical association analysis. In this paper, we propose a microvessel prediction method using fully convolutional neural networks. The feasibility of our proposed algorithm is demonstrated through experimental results on H&E stained images. Furthermore, the identified microvessel features were significantly associated with the patient clinical outcomes. This is the first study to develop an algorithm for automated microvessel detection in H&E stained pathology images.

Endocannabinoids as biomarkers of human reproduction.

PubMed

Rapino, Cinzia; Battista, Natalia; Bari, Monica; Maccarrone, Mauro

2014-01-01

Infertility is a condition of the reproductive system that affects ∼10-15% of couples attempting to conceive a baby. More than half of all cases of infertility are a result of female conditions, while the remaining cases can be attributed to male factors, or to a combination of both. The search for suitable biomarkers of pregnancy outcome is a challenging issue in human reproduction, aimed at identifying molecules with predictive significance of the reproductive potential of male and female gametes. Among the various candidates, endocannabinoids (eCBs), and in particular anandamide (AEA), represent potential biomarkers of human fertility disturbances. Any perturbation of the balance between synthesis and degradation of eCBs will result in local changes of their tone in human female and male reproductive tracts, which in turn regulates various pathophysiological processes, oocyte and sperm maturation included. PubMed and Web of Science databases were searched for papers using relevant keywords like 'biomarker', 'endocannabinoid', 'infertility', 'pregnancy' and 'reproduction'. In this review, we discuss different studies on the measurements of AEA and related eCBs in human reproductive cells, tissues and fluids, where the local contribution of these bioactive lipids could be critical in ensuring normal sperm fertilizing ability and pregnancy. Based on the available data, we suggest that the AEA tone has the potential to be exploited as a novel diagnostic biomarker of infertility, to be used in association with assays of conventional hormones (e.g. progesterone, β-chorionic gonadotrophin) and semen analysis. However further quantitative research of its predictive capacity is required. © The Author 2014. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments

PubMed Central

Windhausen, Vanessa S.; Atlin, Gary N.; Hickey, John M.; Crossa, Jose; Jannink, Jean-Luc; Sorrells, Mark E.; Raman, Babu; Cairns, Jill E.; Tarekegne, Amsal; Semagn, Kassa; Beyene, Yoseph; Grudloyma, Pichet; Technow, Frank; Riedelsheimer, Christian; Melchinger, Albrecht E.

2012-01-01

Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F2-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F2-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set. PMID:23173094

Neural correlates of encoding processes predicting subsequent cued recall and source memory.

PubMed

Angel, Lucie; Isingrini, Michel; Bouazzaoui, Badiâa; Fay, Séverine

2013-03-06

In this experiment, event-related potentials were used to examine whether the neural correlates of encoding processes predicting subsequent successful recall differed from those predicting successful source memory retrieval. During encoding, participants studied lists of words and were instructed to memorize each word and the list in which it occurred. At test, they had to complete stems (the first four letters) with a studied word and then make a judgment of the initial temporal context (i.e. list). Event-related potentials recorded during encoding were segregated according to subsequent memory performance to examine subsequent memory effects (SMEs) reflecting successful cued recall (cued recall SME) and successful source retrieval (source memory SME). Data showed a cued recall SME on parietal electrode sites from 400 to 1200 ms and a late inversed cued recall SME on frontal sites in the 1200-1400 ms period. Moreover, a source memory SME was reported from 400 to 1400 ms on frontal areas. These findings indicate that patterns of encoding-related activity predicting successful recall and source memory are clearly dissociated.

Predicting the photoinduced electron transfer thermodynamics in polyfluorinated 1,3,5-triarylpyrazolines based on multiple linear free energy relationships†

PubMed Central

Verma, Manjusha; Chaudhry, Aneese F.; Fahrni, Christoph J.

2010-01-01

The photophysical properties of 1,3,5-triarylpyrazolines are strongly influenced by the nature and position of substituents attached to the aryl-rings, rendering this fluorophore platform well suited for the design of fluorescent probes utilizing a photoinduced electron transfer (PET) switching mechanism. To explore the tunability of two key parameters that govern the PET thermodynamics, the excited state energy ΔE00 and acceptor potential E(A/A−), a library of polyfluoro-substituted 1,3-diaryl-5-phenyl-pyrazolines was synthesized and characterized. The observed trends for the PET parameters were effectively captured through multiple Hammett linear free energy relationships (LFER) using a set of independent substituent constants for each of the two aryl rings. Given the lack of experimental Hammett constants for polyfluoro substituted aromatics, theoretically derived constants based on the electrostatic potential at the nucleus (EPN) of carbon atoms were employed as quantum chemical descriptors. The performance of the LFER was evaluated with a set of compounds that were not included in the training set, yielding a mean unsigned error of 0.05 eV for the prediction of the combined PET parameters. The outlined LFER approach should be well suited to design and optimize the performance of cation-responsive 1,3,5-triarylpyrazolines. PMID:19343239

To Regulate or Not to Regulate? Views on Electronic Cigarette Regulations and Beliefs about the Reasons for and against Regulation

PubMed Central

Sanders-Jackson, Ashley; Tan, Andy S. L.; Bigman, Cabral A.; Mello, Susan; Niederdeppe, Jeff

2016-01-01

Background Policies designed to restrict marketing, access to, and public use of electronic cigarettes (e-cigarettes) are increasingly under debate in various jurisdictions in the US. Little is known about public perceptions of these policies and factors that predict their support or opposition. Methods Using a sample of US adults from Amazon Mechanical Turk in May 2015, this paper identifies beliefs about the benefits and costs of regulating e-cigarettes and identifies which of these beliefs predict support for e-cigarette restricting policies. Results A higher proportion of respondents agreed with 8 different reasons to regulate e-cigarettes (48.5% to 83.3% agreement) versus 7 reasons not to regulate e-cigarettes (11.5% to 18.9%). The majority of participants agreed with 7 out of 8 reasons for regulation. When all reasons to regulate or not were included in a final multivariable model, beliefs about protecting people from secondhand vapor and protecting youth from trying e-cigarettes significantly predicted stronger support for e-cigarette restricting policies, whereas concern about government intrusion into individual choices was associated with reduced support. Discussion This research identifies key beliefs that may underlie public support or opposition to policies designed to regulate the marketing and use of e-cigarettes. Advocates on both sides of the issue may find this research valuable in developing strategic campaigns related to the issue. Implications Specific beliefs of potential benefits and costs of e-cigarette regulation (protecting youth, preventing exposure to secondhand vapor, and government intrusion into individual choices) may be effectively deployed by policy makers or health advocates in communicating with the public. PMID:27517716

To Regulate or Not to Regulate? Views on Electronic Cigarette Regulations and Beliefs about the Reasons for and against Regulation.

PubMed

Sanders-Jackson, Ashley; Tan, Andy S L; Bigman, Cabral A; Mello, Susan; Niederdeppe, Jeff

2016-01-01

Policies designed to restrict marketing, access to, and public use of electronic cigarettes (e-cigarettes) are increasingly under debate in various jurisdictions in the US. Little is known about public perceptions of these policies and factors that predict their support or opposition. Using a sample of US adults from Amazon Mechanical Turk in May 2015, this paper identifies beliefs about the benefits and costs of regulating e-cigarettes and identifies which of these beliefs predict support for e-cigarette restricting policies. A higher proportion of respondents agreed with 8 different reasons to regulate e-cigarettes (48.5% to 83.3% agreement) versus 7 reasons not to regulate e-cigarettes (11.5% to 18.9%). The majority of participants agreed with 7 out of 8 reasons for regulation. When all reasons to regulate or not were included in a final multivariable model, beliefs about protecting people from secondhand vapor and protecting youth from trying e-cigarettes significantly predicted stronger support for e-cigarette restricting policies, whereas concern about government intrusion into individual choices was associated with reduced support. This research identifies key beliefs that may underlie public support or opposition to policies designed to regulate the marketing and use of e-cigarettes. Advocates on both sides of the issue may find this research valuable in developing strategic campaigns related to the issue. Specific beliefs of potential benefits and costs of e-cigarette regulation (protecting youth, preventing exposure to secondhand vapor, and government intrusion into individual choices) may be effectively deployed by policy makers or health advocates in communicating with the public.

Chemical properties of forest soils

Treesearch

Charles H. Perry; Michael C. Amacher

2007-01-01

Why Is Soil Chemistry Important? The soil quality indicator was initially developed as a tool for assessing the current status of forest soil resources and predicting potential changes in soil properties. Soil chemistry data can be used to diagnose tree vigor and document the deposition of atmospheric pollutants (e.g., acid rain). This chapter focuses on two chemical...

New methods for sampling sparse populations

Treesearch

Anna Ringvall

2007-01-01

To improve surveys of sparse objects, methods that use auxiliary information have been suggested. Guided transect sampling uses prior information, e.g., from aerial photographs, for the layout of survey strips. Instead of being laid out straight, the strips will wind between potentially more interesting areas. 3P sampling (probability proportional to prediction) uses...

An Assessment of Policies Guiding School Emergency Disaster Management for Students with Disabilities in Australia

ERIC Educational Resources Information Center

Boon, Helen Joanna; Pagliano, Paul; Brown, Lawrence; Tsey, Komla

2012-01-01

Recent weather-related disasters (i.e., floods, fires) impacting Australia may potentially increase in frequency and severity as a result of predicted climate variability. The dearth of literature pertaining to school emergency response planning for vulnerable students with disabilities (including those with intellectual disabilities) when such…

Support vector machine applied to predict the zoonotic potential of E. coli O157 cattle isolates

USDA-ARS?s Scientific Manuscript database

Methods based on sequence data analysis facilitate the tracking of disease outbreaks, allow relationships between strains to be reconstructed and virulence factors to be identified. However, these methods are used postfactum after an outbreak has happened. Here, we show that support vector machine a...

Predicting Responsiveness to Treatment of Children with Autism: A Retrospective Study of the Importance of Physical Dysmorphology

ERIC Educational Resources Information Center

Stoelb, M.; Yarnal, R.; Miles, J.; Takahashi, T. N.; Farmer, J. E.; McCathren, R. B.

2004-01-01

This retrospective study examined predictors of outcome for children with autism following 6 and 12 months of early intensive behavioral intervention. Potential predictor variables included pretreatment functioning, age at onset of treatment, treatment intensity, family involvement, and physical characteristics (e.g., brain abnormalities,…

Validation of SWEEP for contrasting agricultural land use types in the Tarim Basin

USDA-ARS?s Scientific Manuscript database

In order to aid in identifying land management practices with the potential to control soil erosion, models such as the Wind Erosion Prediction System (WEPS) have been developed to assess soil erosion. The objective of this study was to test the performance of the WEPS erosion submodel (the Single-e...

The Relationship between Leisure and Life Satisfaction: Application of Activity and Need Theory

ERIC Educational Resources Information Center

Rodriguez, Ariel; Latkova, Pavlina; Sun, Ya-Yen

2008-01-01

The purpose of this study was to better understand the complex relationship between leisure and life satisfaction. Components of two distinct, but potentially integrative, theoretical frameworks (i.e., activity theory and need theory) predicting the relationship between leisure and life satisfaction were tested with a sample of residents from a…

Too many chemicals, too little time: Rapid in silico methods to characterize and predict ADME properties for chemical toxicity and exposure potential

EPA Science Inventory

Evaluating proposed alternative chemical structures to support the design of safer chemicals and products is an important component of EPA's Green Chemistry and Design for the Environment (DfE) Programs. As such, science-based alternatives assessment is essential to support EPA's...

Laboratory Evidence That Line-Tied Toroidal Magnetic Fields Can Suppress Loss-of-Equilibrium Flux Rope Eruptions in the Solar Corona

NASA Astrophysics Data System (ADS)

Myers, C. E.; Yamada, M.; Belova, E.; Ji, H.; Yoo, J.; Fox, W. R., II; Jara-Almonte, J.

2014-12-01

Loss-of-equilibrium mechanisms such as the ideal torus instability [Kliem & Török, Phys. Rev. Lett. 96, 255002 (2006)] are predicted to drive arched flux ropes in the solar corona to erupt. In recent line-tied flux rope experiments conducted in the Magnetic Reconnection Experiment (MRX), however, we find that quasi-statically driven flux ropes remain confined well beyond the predicted torus instability threshold. In order to understand this behavior, in situ measurements from a 300 channel 2D magnetic probe array are used to comprehensively analyze the force balance between the external (potential) and internal (plasma-generated) magnetic fields. We find that forces due to the line-tied toroidal magnetic field, which are not included in the basic torus instability theory, can play a major role in preventing eruptions. The dependence of these toroidal magnetic forces on various potential field and flux rope parameters will be discussed. This research is supported by DoE Contract Number DE-AC02-09CH11466 and by the NSF/DoE Center for Magnetic Self-Organization (CMSO).

Prediction of molecular mimicry candidates in human pathogenic bacteria.

PubMed

Doxey, Andrew C; McConkey, Brendan J

2013-08-15

Molecular mimicry of host proteins is a common strategy adopted by bacterial pathogens to interfere with and exploit host processes. Despite the availability of pathogen genomes, few studies have attempted to predict virulence-associated mimicry relationships directly from genomic sequences. Here, we analyzed the proteomes of 62 pathogenic and 66 non-pathogenic bacterial species, and screened for the top pathogen-specific or pathogen-enriched sequence similarities to human proteins. The screen identified approximately 100 potential mimicry relationships including well-characterized examples among the top-scoring hits (e.g., RalF, internalin, yopH, and others), with about 1/3 of predicted relationships supported by existing literature. Examination of homology to virulence factors, statistically enriched functions, and comparison with literature indicated that the detected mimics target key host structures (e.g., extracellular matrix, ECM) and pathways (e.g., cell adhesion, lipid metabolism, and immune signaling). The top-scoring and most widespread mimicry pattern detected among pathogens consisted of elevated sequence similarities to ECM proteins including collagens and leucine-rich repeat proteins. Unexpectedly, analysis of the pathogen counterparts of these proteins revealed that they have evolved independently in different species of bacterial pathogens from separate repeat amplifications. Thus, our analysis provides evidence for two classes of mimics: complex proteins such as enzymes that have been acquired by eukaryote-to-pathogen horizontal transfer, and simpler repeat proteins that have independently evolved to mimic the host ECM. Ultimately, computational detection of pathogen-specific and pathogen-enriched similarities to host proteins provides insights into potentially novel mimicry-mediated virulence mechanisms of pathogenic bacteria.

Prediction of molecular mimicry candidates in human pathogenic bacteria

PubMed Central

Doxey, Andrew C; McConkey, Brendan J

2013-01-01

Molecular mimicry of host proteins is a common strategy adopted by bacterial pathogens to interfere with and exploit host processes. Despite the availability of pathogen genomes, few studies have attempted to predict virulence-associated mimicry relationships directly from genomic sequences. Here, we analyzed the proteomes of 62 pathogenic and 66 non-pathogenic bacterial species, and screened for the top pathogen-specific or pathogen-enriched sequence similarities to human proteins. The screen identified approximately 100 potential mimicry relationships including well-characterized examples among the top-scoring hits (e.g., RalF, internalin, yopH, and others), with about 1/3 of predicted relationships supported by existing literature. Examination of homology to virulence factors, statistically enriched functions, and comparison with literature indicated that the detected mimics target key host structures (e.g., extracellular matrix, ECM) and pathways (e.g., cell adhesion, lipid metabolism, and immune signaling). The top-scoring and most widespread mimicry pattern detected among pathogens consisted of elevated sequence similarities to ECM proteins including collagens and leucine-rich repeat proteins. Unexpectedly, analysis of the pathogen counterparts of these proteins revealed that they have evolved independently in different species of bacterial pathogens from separate repeat amplifications. Thus, our analysis provides evidence for two classes of mimics: complex proteins such as enzymes that have been acquired by eukaryote-to-pathogen horizontal transfer, and simpler repeat proteins that have independently evolved to mimic the host ECM. Ultimately, computational detection of pathogen-specific and pathogen-enriched similarities to host proteins provides insights into potentially novel mimicry-mediated virulence mechanisms of pathogenic bacteria. PMID:23715053

At-edge minima in elastic photon scattering amplitudes for dilute aqueous ions

NASA Astrophysics Data System (ADS)

Bradley, D. A.; Hugtenburg, R. P.; Yusoff, A. L.

2006-11-01

Elastic photon scattering and absorption in the vicinity of core atomic orbital energies give rise to resonances in the elastic photon scattering cross-section. Of interest is whether a dilute-ion aqueous system provides an environment suitable for testing independent particle approximation (IPA) predictions. Predictions of the energy of these resonances have been determined for a Dirac-Slater exchange potential with a Latter tail. At BM28 (ESRF), tuneable X-rays were obtained at eV resolution using a 1 1 1 Si monochromator. From target systems including Cu 2+ and Zn 2+, the X-rays were scattered through high angle from an aqueous medium contained in a thin Perspex cell provided with 8 μm kaplan windows. An energy resolution of ˜500 eV from the HPGe detector was adequate to separate the elastic scattering signal from K α radiation but not from Compton or K β contributions. The Compton contribution from the medium was removed assuming validity of the relativistic impulse approximation. The contribution due to K β fluorescence and the resonant X-ray Raman scattering process were handled by assuming the branching ratio for K α and K β contributions to be constant and to be accurately described by fluorescent yields measured above edge. At ionic concentrations ranging from 0.01 to 0.1 mol/l, resonance structures accord with predictions of elastic scattering cross-sections calculated within IPA. Amplitudes calculated using modified form-factors and anomalous scatter factors computed from a Dirac-Slater exchange potential were convolved with a Lorentzian of several eV (FWHM).

Geographic relatedness and predictability of Escherichia coli along a peninsular beach complex of Lake Michigan

USGS Publications Warehouse

Nevers, M.B.; Shively, D.A.; Kleinheinz, G.T.; McDermott, C.M.; Schuster, W.; Chomeau, V.; Whitman, R.L.

2009-01-01

To determine more accurately the real-time concentration of fecal indicator bacteria (FIB) in beach water, predictive modeling has been applied in several locations around the Great Lakes to individual or small groups of similar beaches. Using 24 beaches in Door County, Wisconsin, we attempted to expand predictive models to multiple beaches of complex geography. We examined the importance of geographic location and independent variables and the consequential limitations for potential beach or beach group models. An analysis of Escherichia coli populations over 4 yr revealed a geographic gradient to the beaches, with mean E. coli concentrations decreasing with increasing distance from the city of Sturgeon Bay. Beaches grouped strongly by water type (lake, bay, Sturgeon Bay) and proximity to one another, followed by presence of a storm or creek outfall or amount of shoreline enclosure. Predictive models developed for beach groups commonly included wave height and cumulative 48-h rainfall but generally explained little E. coli variation (adj. R2 = 0.19-0.36). Generally low concentrations of E. coli at the beaches influenced the effectiveness of model results presumably because of low signal-to-noise ratios and the rarity of elevated concentrations. Our results highlight the importance of the sensitivity of regressors and the need for careful methods evaluation. Despite the attractiveness of predictive models as an alternative beach monitoring approach, it is likely that FIB fluctuations at some beaches defy simple prediction approaches. Regional, multi-beach, and individual beach predictive models should be explored alongside other techniques for improving monitoring reliability at Great Lakes beaches. Copyright ?? 2009 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.

From sequence to enzyme mechanism using multi-label machine learning.

PubMed

De Ferrari, Luna; Mitchell, John B O

2014-05-19

In this work we predict enzyme function at the level of chemical mechanism, providing a finer granularity of annotation than traditional Enzyme Commission (EC) classes. Hence we can predict not only whether a putative enzyme in a newly sequenced organism has the potential to perform a certain reaction, but how the reaction is performed, using which cofactors and with susceptibility to which drugs or inhibitors, details with important consequences for drug and enzyme design. Work that predicts enzyme catalytic activity based on 3D protein structure features limits the prediction of mechanism to proteins already having either a solved structure or a close relative suitable for homology modelling. In this study, we evaluate whether sequence identity, InterPro or Catalytic Site Atlas sequence signatures provide enough information for bulk prediction of enzyme mechanism. By splitting MACiE (Mechanism, Annotation and Classification in Enzymes database) mechanism labels to a finer granularity, which includes the role of the protein chain in the overall enzyme complex, the method can predict at 96% accuracy (and 96% micro-averaged precision, 99.9% macro-averaged recall) the MACiE mechanism definitions of 248 proteins available in the MACiE, EzCatDb (Database of Enzyme Catalytic Mechanisms) and SFLD (Structure Function Linkage Database) databases using an off-the-shelf K-Nearest Neighbours multi-label algorithm. We find that InterPro signatures are critical for accurate prediction of enzyme mechanism. We also find that incorporating Catalytic Site Atlas attributes does not seem to provide additional accuracy. The software code (ml2db), data and results are available online at http://sourceforge.net/projects/ml2db/ and as supplementary files.

Computational prediction of human salivary proteins from blood circulation and application to diagnostic biomarker identification.

PubMed

Wang, Jiaxin; Liang, Yanchun; Wang, Yan; Cui, Juan; Liu, Ming; Du, Wei; Xu, Ying

2013-01-01

Proteins can move from blood circulation into salivary glands through active transportation, passive diffusion or ultrafiltration, some of which are then released into saliva and hence can potentially serve as biomarkers for diseases if accurately identified. We present a novel computational method for predicting salivary proteins that come from circulation. The basis for the prediction is a set of physiochemical and sequence features we found to be discerning between human proteins known to be movable from circulation to saliva and proteins deemed to be not in saliva. A classifier was trained based on these features using a support-vector machine to predict protein secretion into saliva. The classifier achieved 88.56% average recall and 90.76% average precision in 10-fold cross-validation on the training data, indicating that the selected features are informative. Considering the possibility that our negative training data may not be highly reliable (i.e., proteins predicted to be not in saliva), we have also trained a ranking method, aiming to rank the known salivary proteins from circulation as the highest among the proteins in the general background, based on the same features. This prediction capability can be used to predict potential biomarker proteins for specific human diseases when coupled with the information of differentially expressed proteins in diseased versus healthy control tissues and a prediction capability for blood-secretory proteins. Using such integrated information, we predicted 31 candidate biomarker proteins in saliva for breast cancer.

Computational Prediction of Human Salivary Proteins from Blood Circulation and Application to Diagnostic Biomarker Identification

PubMed Central

Wang, Jiaxin; Liang, Yanchun; Wang, Yan; Cui, Juan; Liu, Ming; Du, Wei; Xu, Ying

2013-01-01

Proteins can move from blood circulation into salivary glands through active transportation, passive diffusion or ultrafiltration, some of which are then released into saliva and hence can potentially serve as biomarkers for diseases if accurately identified. We present a novel computational method for predicting salivary proteins that come from circulation. The basis for the prediction is a set of physiochemical and sequence features we found to be discerning between human proteins known to be movable from circulation to saliva and proteins deemed to be not in saliva. A classifier was trained based on these features using a support-vector machine to predict protein secretion into saliva. The classifier achieved 88.56% average recall and 90.76% average precision in 10-fold cross-validation on the training data, indicating that the selected features are informative. Considering the possibility that our negative training data may not be highly reliable (i.e., proteins predicted to be not in saliva), we have also trained a ranking method, aiming to rank the known salivary proteins from circulation as the highest among the proteins in the general background, based on the same features. This prediction capability can be used to predict potential biomarker proteins for specific human diseases when coupled with the information of differentially expressed proteins in diseased versus healthy control tissues and a prediction capability for blood-secretory proteins. Using such integrated information, we predicted 31 candidate biomarker proteins in saliva for breast cancer. PMID:24324552

Brain potentials measured during a Go/NoGo task predict completion of substance abuse treatment.

PubMed

Steele, Vaughn R; Fink, Brandi C; Maurer, J Michael; Arbabshirani, Mohammad R; Wilber, Charles H; Jaffe, Adam J; Sidz, Anna; Pearlson, Godfrey D; Calhoun, Vince D; Clark, Vincent P; Kiehl, Kent A

2014-07-01

U.S. nationwide estimates indicate that 50% to 80% of prisoners have a history of substance abuse or dependence. Tailoring substance abuse treatment to specific needs of incarcerated individuals could improve effectiveness of treating substance dependence and preventing drug abuse relapse. We tested whether pretreatment neural measures of a response inhibition (Go/NoGo) task would predict which individuals would or would not complete a 12-week cognitive behavioral substance abuse treatment program. Adult incarcerated participants (n = 89; women n = 55) who volunteered for substance abuse treatment performed a Go/NoGo task while event-related potentials (ERPs) were recorded. Stimulus- and response-locked ERPs were compared between participants who completed (n = 68; women = 45) and discontinued (n = 21; women = 10) treatment. As predicted, stimulus-locked P2, response-locked error-related negativity (ERN/Ne), and response-locked error positivity (Pe), measured with windowed time-domain and principal component analysis, differed between groups. Using logistic regression and support-vector machine (i.e., pattern classifiers) models, P2 and Pe predicted treatment completion above and beyond other measures (i.e., N2, P300, ERN/Ne, age, sex, IQ, impulsivity, depression, anxiety, motivation for change, and years of drug abuse). Participants who discontinued treatment exhibited deficiencies in sensory gating, as indexed by smaller P2; error-monitoring, as indexed by smaller ERN/Ne; and adjusting response strategy posterror, as indexed by larger Pe. The combination of P2 and Pe reliably predicted 83.33% of individuals who discontinued treatment. These results may help in the development of individualized therapies, which could lead to more favorable, long-term outcomes. © 2013 Society of Biological Psychiatry Published by Society of Biological Psychiatry All rights reserved.

Brain Potentials Measured During a Go/NoGo Task Predict Completion of Substance Abuse Treatment

PubMed Central

Steele, Vaughn R.; Fink, Brandi C.; Maurer, J. Michael; Arbabshirani, Mohammad R.; Wilber, Charles H.; Jaffe, Adam J.; Sidz, Anna; Pearlson, Godfrey D.; Calhoun, Vince D.; Clark, Vincent P.; Kiehl, Kent A.

2014-01-01

Background US nationwide estimates indicate 50–80% of prisoners have a history of substance abuse or dependence. Tailoring substance abuse treatment to specific needs of incarcerated individuals could improve effectiveness of treating substance dependence and preventing drug abuse relapse. The purpose of the present study was to test the hypothesis that pre-treatment neural measures of a Go/NoGo task would predict which individuals would or would not complete a 12-week cognitive behavioral substance abuse treatment program. Methods Adult incarcerated participants (N=89; Females=55) who volunteered for substance abuse treatment performed a response inhibition (Go/NoGo) task while event-related potentials (ERP) were recorded. Stimulus- and response-locked ERPs were compared between individuals who completed (N=68; Females=45) and discontinued (N=21; Females=10) treatment. Results As predicted, stimulus-locked P2, response-locked error-related negativity (ERN/Ne), and response-locked error positivity (Pe), measured with windowed time-domain and principal component analysis, differed between groups. Using logistic regression and support-vector machine (i.e., pattern classifiers) models, P2 and Pe predicted treatment completion above and beyond other measures (i.e., N2, P300, ERN/Ne, age, sex, IQ, impulsivity, and self-reported depression, anxiety, motivation for change, and years of drug abuse). Conclusions We conclude individuals who discontinue treatment exhibited deficiencies in sensory gating, as indexed by smaller P2, error-monitoring, as indexed by smaller ERN/Ne, and adjusting response strategy post-error, as indexed by larger Pe. However, the combination of P2 and Pe reliably predicted 83.33% of individuals who discontinued treatment. These results may help in the development of individualized therapies, which could lead to more favorable, long-term outcomes. PMID:24238783

Inroads to predict in vivo toxicology-an introduction to the eTOX Project.

PubMed

Briggs, Katharine; Cases, Montserrat; Heard, David J; Pastor, Manuel; Pognan, François; Sanz, Ferran; Schwab, Christof H; Steger-Hartmann, Thomas; Sutter, Andreas; Watson, David K; Wichard, Jörg D

2012-01-01

There is a widespread awareness that the wealth of preclinical toxicity data that the pharmaceutical industry has generated in recent decades is not exploited as efficiently as it could be. Enhanced data availability for compound comparison ("read-across"), or for data mining to build predictive tools, should lead to a more efficient drug development process and contribute to the reduction of animal use (3Rs principle). In order to achieve these goals, a consortium approach, grouping numbers of relevant partners, is required. The eTOX ("electronic toxicity") consortium represents such a project and is a public-private partnership within the framework of the European Innovative Medicines Initiative (IMI). The project aims at the development of in silico prediction systems for organ and in vivo toxicity. The backbone of the project will be a database consisting of preclinical toxicity data for drug compounds or candidates extracted from previously unpublished, legacy reports from thirteen European and European operation-based pharmaceutical companies. The database will be enhanced by incorporation of publically available, high quality toxicology data. Seven academic institutes and five small-to-medium size enterprises (SMEs) contribute with their expertise in data gathering, database curation, data mining, chemoinformatics and predictive systems development. The outcome of the project will be a predictive system contributing to early potential hazard identification and risk assessment during the drug development process. The concept and strategy of the eTOX project is described here, together with current achievements and future deliverables.

Real-time estimation of FES-induced joint torque with evoked EMG : Application to spinal cord injured patients.

PubMed

Li, Zhan; Guiraud, David; Andreu, David; Benoussaad, Mourad; Fattal, Charles; Hayashibe, Mitsuhiro

2016-06-22

Functional electrical stimulation (FES) is a neuroprosthetic technique for restoring lost motor function of spinal cord injured (SCI) patients and motor-impaired subjects by delivering short electrical pulses to their paralyzed muscles or motor nerves. FES induces action potentials respectively on muscles or nerves so that muscle activity can be characterized by the synchronous recruitment of motor units with its compound electromyography (EMG) signal is called M-wave. The recorded evoked EMG (eEMG) can be employed to predict the resultant joint torque, and modeling of FES-induced joint torque based on eEMG is an essential step to provide necessary prediction of the expected muscle response before achieving accurate joint torque control by FES. Previous works on FES-induced torque tracking issues were mainly based on offline analysis. However, toward personalized clinical rehabilitation applications, real-time FES systems are essentially required considering the subject-specific muscle responses against electrical stimulation. This paper proposes a wireless portable stimulator used for estimating/predicting joint torque based on real time processing of eEMG. Kalman filter and recurrent neural network (RNN) are embedded into the real-time FES system for identification and estimation. Prediction results on 3 able-bodied subjects and 3 SCI patients demonstrate promising performances. As estimators, both Kalman filter and RNN approaches show clinically feasible results on estimation/prediction of joint torque with eEMG signals only, moreover RNN requires less computational requirement. The proposed real-time FES system establishes a platform for estimating and assessing the mechanical output, the electromyographic recordings and associated models. It will contribute to open a new modality for personalized portable neuroprosthetic control toward consolidated personal healthcare for motor-impaired patients.

Microbial Survey of Pennsylvania Surface Water Used for Irrigating Produce Crops.

PubMed

Draper, Audrey D; Doores, Stephanie; Gourama, Hassan; LaBorde, Luke F

2016-06-01

Recent produce-associated foodborne illness outbreaks have been attributed to contaminated irrigation water. This study examined microbial levels in Pennsylvania surface waters used for irrigation, relationships between microbial indicator organisms and water physicochemical characteristics, and the potential use of indicators for predicting the presence of human pathogens. A total of 153 samples taken from surface water sources used for irrigation in southeastern Pennsylvania were collected from 39 farms over a 2-year period. Samples were analyzed for six microbial indicator organisms (aerobic plate count, Enterobacteriaceae, coliform, fecal coliforms, Escherichia coli, and enterococci), two human pathogens (Salmonella and E. coli O157), and seven physical and environmental characteristics (pH, conductivity, turbidity, air and water temperature, and sampling day and 3-day-accumulated precipitation levels). Indicator populations were highly variable and not predicted by water and environmental characteristics. Only five samples were confirmed positive for Salmonella, and no E. coli O157 was detected in any samples. Predictive relationships between microbial indicators and the occurrence of pathogens could therefore not be determined.

ESTree db: a Tool for Peach Functional Genomics

PubMed Central

Lazzari, Barbara; Caprera, Andrea; Vecchietti, Alberto; Stella, Alessandra; Milanesi, Luciano; Pozzi, Carlo

2005-01-01

Background The ESTree db represents a collection of Prunus persica expressed sequenced tags (ESTs) and is intended as a resource for peach functional genomics. A total of 6,155 successful EST sequences were obtained from four in-house prepared cDNA libraries from Prunus persica mesocarps at different developmental stages. Another 12,475 peach EST sequences were downloaded from public databases and added to the ESTree db. An automated pipeline was prepared to process EST sequences using public software integrated by in-house developed Perl scripts and data were collected in a MySQL database. A php-based web interface was developed to query the database. Results The ESTree db version as of April 2005 encompasses 18,630 sequences representing eight libraries. Contig assembly was performed with CAP3. Putative single nucleotide polymorphism (SNP) detection was performed with the AutoSNP program and a search engine was implemented to retrieve results. All the sequences and all the contig consensus sequences were annotated both with blastx against the GenBank nr db and with GOblet against the viridiplantae section of the Gene Ontology db. Links to NiceZyme (Expasy) and to the KEGG metabolic pathways were provided. A local BLAST utility is available. A text search utility allows querying and browsing the database. Statistics were provided on Gene Ontology occurrences to assign sequences to Gene Ontology categories. Conclusion The resulting database is a comprehensive resource of data and links related to peach EST sequences. The Sequence Report and Contig Report pages work as the web interface core structures, giving quick access to data related to each sequence/contig. PMID:16351742

Efficient searching and annotation of metabolic networks using chemical similarity

PubMed Central

Pertusi, Dante A.; Stine, Andrew E.; Broadbelt, Linda J.; Tyo, Keith E.J.

2015-01-01

Motivation: The urgent need for efficient and sustainable biological production of fuels and high-value chemicals has elicited a wave of in silico techniques for identifying promising novel pathways to these compounds in large putative metabolic networks. To date, these approaches have primarily used general graph search algorithms, which are prohibitively slow as putative metabolic networks may exceed 1 million compounds. To alleviate this limitation, we report two methods—SimIndex (SI) and SimZyme—which use chemical similarity of 2D chemical fingerprints to efficiently navigate large metabolic networks and propose enzymatic connections between the constituent nodes. We also report a Byers–Waterman type pathway search algorithm for further paring down pertinent networks. Results: Benchmarking tests run with SI show it can reduce the number of nodes visited in searching a putative network by 100-fold with a computational time improvement of up to 105-fold. Subsequent Byers–Waterman search application further reduces the number of nodes searched by up to 100-fold, while SimZyme demonstrates ∼90% accuracy in matching query substrates with enzymes. Using these modules, we have designed and annotated an alternative to the methylerythritol phosphate pathway to produce isopentenyl pyrophosphate with more favorable thermodynamics than the native pathway. These algorithms will have a significant impact on our ability to use large metabolic networks that lack annotation of promiscuous reactions. Availability and implementation: Python files will be available for download at http://tyolab.northwestern.edu/tools/. Contact: k-tyo@northwestern.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25417203

ESTree db: a tool for peach functional genomics.

PubMed

Lazzari, Barbara; Caprera, Andrea; Vecchietti, Alberto; Stella, Alessandra; Milanesi, Luciano; Pozzi, Carlo

2005-12-01

The ESTree db http://www.itb.cnr.it/estree/ represents a collection of Prunus persica expressed sequenced tags (ESTs) and is intended as a resource for peach functional genomics. A total of 6,155 successful EST sequences were obtained from four in-house prepared cDNA libraries from Prunus persica mesocarps at different developmental stages. Another 12,475 peach EST sequences were downloaded from public databases and added to the ESTree db. An automated pipeline was prepared to process EST sequences using public software integrated by in-house developed Perl scripts and data were collected in a MySQL database. A php-based web interface was developed to query the database. The ESTree db version as of April 2005 encompasses 18,630 sequences representing eight libraries. Contig assembly was performed with CAP3. Putative single nucleotide polymorphism (SNP) detection was performed with the AutoSNP program and a search engine was implemented to retrieve results. All the sequences and all the contig consensus sequences were annotated both with blastx against the GenBank nr db and with GOblet against the viridiplantae section of the Gene Ontology db. Links to NiceZyme (Expasy) and to the KEGG metabolic pathways were provided. A local BLAST utility is available. A text search utility allows querying and browsing the database. Statistics were provided on Gene Ontology occurrences to assign sequences to Gene Ontology categories. The resulting database is a comprehensive resource of data and links related to peach EST sequences. The Sequence Report and Contig Report pages work as the web interface core structures, giving quick access to data related to each sequence/contig.

Food allergy animal models: an overview.

PubMed

Helm, Ricki M

2002-05-01

Specific food allergy is characterized by sensitization to innocuous food proteins with production of allergen-specific IgE that binds to receptors on basophils and mast cells. Upon recurrent exposure to the same allergen, an allergic response is induced by mediator release following cross-linking of cell-bound allergen-specific IgE. The determination of what makes an innocuous food protein an allergen in predisposed individuals is unknown; however, mechanistic and protein allergen predictive models are being actively investigated in a number of animal models. Currently, there is no animal model that will actively profile known food allergens, predict the allergic potential of novel food proteins, or demonstrate clinically the human food allergic sensitization/allergic response. Animal models under investigation include mice, rats, the guinea pig, atopic dog, and neonatal swine. These models are being assessed for production of IgE, clinical responses to re-exposure, and a ranking of food allergens (based on potency) including a nonfood allergen protein source. A selection of animal models actively being investigated that will contribute to our understanding of what makes a protein an allergen and future predictive models for assessing the allergenicity of novel proteins is presented in this review.

The predictive value of skin prick testing for challenge-proven food allergy: a systematic review.

PubMed

Peters, Rachel L; Gurrin, Lyle C; Allen, Katrina J

2012-06-01

Immunoglobulin E-mediated (IgE) food allergy affects 6-8% of children, and the prevalence is believed to be increasing. The gold standard of food allergy diagnosis is oral food challenges (OFCs); however, they are resource-consuming and potentially dangerous. Skin prick tests (SPTs) are able to detect the presence of allergen-specific IgE antibodies (sensitization), but they have low specificity for clinically significant food allergy. To reduce the need for OFCs, it has been suggested that children forgo an OFC if their SPT wheal size exceeds a cutoff that has a high predictability for food allergy. Although data for these studies are almost always gathered from high-risk populations, the 95% positive predictive values (PPVs) vary substantially between studies. SPT thresholds with a high probability of food allergy generated from these studies may not be generalizable to other populations, because of highly selective samples and variability in participant's age, test allergens, and food challenge protocol. Standardization of SPT devices and allergens, OFC protocols including standardized cessation criteria, and population-based samples would all help to improve generalizability of PPVs of SPTs. © 2011 John Wiley & Sons A/S.

Prediction of force and acceleration control spectra for Space Shuttle orbiter sidewall-mounted payloads

NASA Technical Reports Server (NTRS)

Hipol, Philip J.

1990-01-01

The development of force and acceleration control spectra for vibration testing of Space Shuttle (STS) orbiter sidewall-mounted payloads requiresreliable estimates of the sidewall apparent weight and free (i.e. unloaded) vibration during lift-off. The feasibility of analytically predicting these quantities has been investigated through the development and analysis of a finite element model of the STS cargo bay. Analytical predictions of the sidewall apparent weight were compared with apparent weight measurements made on OV-101, and analytical predictions of the sidewall free vibration response during lift-off were compared with flight measurements obtained from STS-3 and STS-4. These analysis suggest that the cargo bay finite element model has potential application for the estimation of force and acceleration control spectra for STS sidewall-mounted payloads.

Synthesis and biological evaluation of novel tacrine derivatives and tacrine-coumarin hybrids as cholinesterase inhibitors.

PubMed

Hamulakova, Slavka; Janovec, Ladislav; Hrabinova, Martina; Spilovska, Katarina; Korabecny, Jan; Kristian, Pavol; Kuca, Kamil; Imrich, Jan

2014-08-28

A series of novel tacrine derivatives and tacrine-coumarin heterodimers were designed, synthesized, and biologically evaluated for their potential inhibitory effect on both acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE). Of these compounds, tacrine-coumarin heterodimer 7c and tacrine derivative 6b were found to be the most potent inhibitors of human AChE (hAChE), demonstrating IC50 values of 0.0154 and 0.0263 μM. Ligands 6b, 6c, and 7c exhibited the highest levels of inhibitory activity against human BuChE (hBuChE), demonstrating IC50 values that range from 0.228 to 0.328 μM. Docking studies were performed in order to predict the binding modes of compounds 6b and 7c with hAChE/hBuChE.

Interpersonal goals and change in anxiety and dysphoria in first-semester college students.

PubMed

Crocker, Jennifer; Canevello, Amy; Breines, Juliana G; Flynn, Heather

2010-06-01

Two longitudinal studies examined the associations between interpersonal goals (i.e., self-image and compassionate goals) and anxiety and dysphoria (i.e., distress). In Study 1, 199 college freshmen (122 women, 77 men) completed 12 surveys over 12 weeks. Compassionate goals predicted decreased distress, and self-image goals predicted increased distress from pretest to posttest when distress was assessed as anxiety, dysphoria, or a composite, and when the goals were worded as approach goals, avoidance goals, or a composite. In Study 2, 115 first-semester roommate pairs (86 female and 29 male pairs) completed 12 surveys over 12 weeks. Compassionate and self-image goals predicted distress in same-week, lagged-week, and pretest-to-posttest analyses; effects of compassionate goals remained significant when the authors controlled for several known risk factors. Having clear goals consistently explained the association between compassionate goals but not self-image goals and distress. Results supported a path model in which compassionate goals predict increased support given to roommates, which predicts decreased distress. Results also supported a reciprocal association; chronic distress predicted decreased compassionate and increased self-image goals from pretest to posttest, and weekly distress predicted decreased compassionate goals the subsequent week. The results suggest that compassionate goals contribute to decreased distress because they provide meaning and increase support given to others. Distress, in turn, predicts change in goals, creating the potential for upward and downward spirals of goals and distress. (c) 2010 APA, all rights reserved).

Cortical Brain Activity Reflecting Attentional Biasing Toward Reward-Predicting Cues Covaries with Economic Decision-Making Performance

PubMed Central

San Martín, René; Appelbaum, Lawrence G.; Huettel, Scott A.; Woldorff, Marty G.

2016-01-01

Adaptive choice behavior depends critically on identifying and learning from outcome-predicting cues. We hypothesized that attention may be preferentially directed toward certain outcome-predicting cues. We studied this possibility by analyzing event-related potential (ERP) responses in humans during a probabilistic decision-making task. Participants viewed pairs of outcome-predicting visual cues and then chose to wager either a small (i.e., loss-minimizing) or large (i.e., gain-maximizing) amount of money. The cues were bilaterally presented, which allowed us to extract the relative neural responses to each cue by using a contralateral-versus-ipsilateral ERP contrast. We found an early lateralized ERP response, whose features matched the attention-shift-related N2pc component and whose amplitude scaled with the learned reward-predicting value of the cues as predicted by an attention-for-reward model. Consistently, we found a double dissociation involving the N2pc. Across participants, gain-maximization positively correlated with the N2pc amplitude to the most reliable gain-predicting cue, suggesting an attentional bias toward such cues. Conversely, loss-minimization was negatively correlated with the N2pc amplitude to the most reliable loss-predicting cue, suggesting an attentional avoidance toward such stimuli. These results indicate that learned stimulus–reward associations can influence rapid attention allocation, and that differences in this process are associated with individual differences in economic decision-making performance. PMID:25139941

Coupled channel effects on resonance states of positronic alkali atom

NASA Astrophysics Data System (ADS)

Yamashita, Takuma; Kino, Yasushi

2018-01-01

S-wave Feshbach resonance states belonging to dipole series in positronic alkali atoms (e+Li, e+Na, e+K, e+Rb and e+Cs) are studied by coupled-channel calculations within a three-body model. Resonance energies and widths below a dissociation threshold of alkali-ion and positronium are calculated with a complex scaling method. Extended model potentials that provide positronic pseudo-alkali-atoms are introduced to investigate the relationship between the resonance states and dissociation thresholds based on a three-body dynamics. Resonances of the dipole series below a dissociation threshold of alkali-atom and positron would have some associations with atomic energy levels that results in longer resonance lifetimes than the prediction of the analytical law derived from the ion-dipole interaction.

The dynamics of learning about a climate threshold

NASA Astrophysics Data System (ADS)

Keller, Klaus; McInerney, David

2008-02-01

Anthropogenic greenhouse gas emissions may trigger threshold responses of the climate system. One relevant example of such a potential threshold response is a shutdown of the North Atlantic meridional overturning circulation (MOC). Numerous studies have analyzed the problem of early MOC change detection (i.e., detection before the forcing has committed the system to a threshold response). Here we analyze the early MOC prediction problem. To this end, we virtually deploy an MOC observation system into a simple model that mimics potential future MOC responses and analyze the timing of confident detection and prediction. Our analysis suggests that a confident prediction of a potential threshold response can require century time scales, considerably longer that the time required for confident detection. The signal enabling early prediction of an approaching MOC threshold in our model study is associated with the rate at which the MOC intensity decreases for a given forcing. A faster MOC weakening implies a higher MOC sensitivity to forcing. An MOC sensitivity exceeding a critical level results in a threshold response. Determining whether an observed MOC trend in our model differs in a statistically significant way from an unforced scenario (the detection problem) imposes lower requirements on an observation system than the determination whether the MOC will shut down in the future (the prediction problem). As a result, the virtual observation systems designed in our model for early detection of MOC changes might well fail at the task of early and confident prediction. Transferring this conclusion to the real world requires a considerably refined MOC model, as well as a more complete consideration of relevant observational constraints.

Competitive Inhibition Mechanism of Acetylcholinesterase without Catalytic Active Site Interaction: Study on Functionalized C60 Nanoparticles via in Vitro and in Silico Assays.

PubMed

Liu, Yanyan; Yan, Bing; Winkler, David A; Fu, Jianjie; Zhang, Aiqian

2017-06-07

Acetylcholinesterase (AChE) activity regulation by chemical agents or, potentially, nanomaterials is important for both toxicology and pharmacology. Competitive inhibition via direct catalytic active sites (CAS) binding or noncompetitive inhibition through interference with substrate and product entering and exiting has been recognized previously as an AChE-inhibition mechanism for bespoke nanomaterials. The competitive inhibition by peripheral anionic site (PAS) interaction without CAS binding remains unexplored. Here, we proposed and verified the occurrence of a presumed competitive inhibition of AChE without CAS binding for hydrophobically functionalized C 60 nanoparticles (NPs) by employing both experimental and computational methods. The kinetic inhibition analysis distinguished six competitive inhibitors, probably targeting the PAS, from the pristine and hydrophilically modified C 60 NPs. A simple quantitative nanostructure-activity relationship (QNAR) model relating the pocket accessible length of substituent to inhibition capacity was then established to reveal how the geometry of the surface group decides the NP difference in AChE inhibition. Molecular docking identified the PAS as the potential binding site interacting with the NPs via a T-shaped plug-in mode. Specifically, the fullerene core covered the enzyme gorge as a lid through π-π stacking with Tyr72 and Trp286 in the PAS, while the hydrophobic ligands on the fullerene surface inserted into the AChE active site to provide further stability for the complexes. The modeling predicted that inhibition would be severely compromised by Tyr72 and Trp286 deletions, and the subsequent site-directed mutagenesis experiments proved this prediction. Our results demonstrate AChE competitive inhibition of NPs without CAS participation to gain further understanding of both the neurotoxicity and the curative effect of NPs.

Probing 6D operators at future e - e + colliders

NASA Astrophysics Data System (ADS)

Chiu, Wen Han; Leung, Sze Ching; Liu, Tao; Lyu, Kun-Feng; Wang, Lian-Tao

2018-05-01

We explore the sensitivities at future e - e + colliders to probe a set of six-dimensional operators which can modify the SM predictions on Higgs physics and electroweak precision measurements. We consider the case in which the operators are turned on simultaneously. Such an analysis yields a "conservative" interpretation on the collider sensitivities, complementary to the "optimistic" scenario where the operators are individually probed. After a detail analysis at CEPC in both "conservative" and "optimistic" scenarios, we also considered the sensitivities for FCC-ee and ILC. As an illustration of the potential of constraining new physics models, we applied sensitivity analysis to two benchmarks: holographic composite Higgs model and littlest Higgs model.

Serum bilirubin: a simple routine surrogate marker of the progression of chronic kidney disease.

PubMed

Moolchandani, K; Priyadarssini, M; Rajappa, M; Parameswaran, S; Revathy, G

2016-10-01

Studies suggest that Chronic Kidney Disease (CKD) is a global burden health associated with significant comorbid conditions. Few biochemical parameters have gained significance in predicting the disease progression. The present work aimed to study the association of the simple biochemical parameter of serum bilirubin level with the estimated glomerular filtration rate (eGFR), and to assess their association with the co-morbid conditions in CKD. We recruited 188 patients with CKD who attended a Nephrology out-patient department. eGFR values were calculated based on the serum creatinine levels using CKD-EPI formula. Various biochemical parameters including glucose, creatinine, uric acid, total and direct bilirubin were assayed in all study subjects. Study subjects were categorized into subgroups based on their eGFR values and their diabetic status and the parameters were compared among the different subgroups. We observed a significantly decreased serum bilirubin levels (p < 0.001) in patients with lower eGFR values, compared to those with higher eGFR levels. There was a significant positive correlation between the eGFR levels and the total bilirubin levels (r = 0.92). We also observed a significant positive correlation between the eGFR levels and the direct bilirubin levels (r = 0.76). On multivariate linear regression analysis, we found that total and direct bilirubin independently predict eGFR, after adjusting for potential confounders (p < 0.001). Our results suggest that there is significant hypobilirubinemia in CKD, especially with increasing severity and co-existing diabetes mellitus. This finding has importance in the clinical setting, as assay of simple routine biochemical parameters such as serum bilirubin may help in predicting the early progression of CKD and more so in diabetic CKD.

Design, Synthesis and DFT/DNP Modeling Study of New 2-Amino-5-arylazothiazole Derivatives as Potential Antibacterial Agents.

PubMed

Abu-Melha, Sraa

2018-02-15

A new series of 2-amino-5-arylazothiazole derivatives has been designed and synthesized in 61-78% yields and screened as potential antibacterial drug candidates against the Gram negative bacterium Escherichia coli. The geometry of the title compounds were being studied using the Material Studio package and semi-core pseudopods calculations (dspp) were performed with the double numerica basis sets plus polarization functional (DNP) to predict the properties of materials using the hybrid FT/B3LYP method. Modeling calculations, especially the (E H -E L ) difference and the energetic parameters revealed that some of the title compounds may be promising tools for further research work and the activity is structure dependent.

Novel Applications of Metabolomics in Personalized Medicine: A Mini-Review.

PubMed

Li, Bingbing; He, Xuyun; Jia, Wei; Li, Houkai

2017-07-13

Interindividual variability in drug responses and disease susceptibility is common in the clinic. Currently, personalized medicine is highly valued, the idea being to prescribe the right medicine to the right patient. Metabolomics has been increasingly applied in evaluating the therapeutic outcomes of clinical drugs by correlating the baseline metabolic profiles of patients with their responses, i.e., pharmacometabonomics, as well as prediction of disease susceptibility among population in advance, i.e., patient stratification. The accelerated advance in metabolomics technology pinpoints the huge potential of its application in personalized medicine. In current review, we discussed the novel applications of metabolomics with typical examples in evaluating drug therapy and patient stratification, and underlined the potential of metabolomics in personalized medicine in the future.

Electric-field-induced modification in Dzyaloshinskii-Moriya interaction of Co monolayer on Pt(111)

NASA Astrophysics Data System (ADS)

Nakamura, Kohji; Akiyama, Toru; Ito, Tomonori; Ono, Teruo; Weinert, Michael

Magnetism induced by an external electric field (E-field) has received much attention as a potential approach for controlling magnetism at the nano-scale with the promise of ultra-low energy power consumption. Here, the E-field-induced modification of the Dzyaloshinskii-Moriya interaction (DMI) for a prototypical transition-metal thin layer of a Co monolayer on Pt(111) is investigated by first-principles calculations by using the full-potential linearized augmented plane wave method that treats spin-spiral structures in an E-field. With inclusion of the spin-orbit coupling (SOC) by the second variational method for commensurate spin-spiral structures, the DMI constants were estimated from an asymmetric contribution in the total energy with respect to the spin-spiral wavevector. The results predicted that the DMI is modified by the E-field, but the change is found to be small compared to that in the exchange interaction (a symmetric contribution in the total energy) by a factor of ten.

Ecological genomics predicts climate vulnerability in an endangered southwestern songbird.

PubMed

Ruegg, Kristen; Bay, Rachael A; Anderson, Eric C; Saracco, James F; Harrigan, Ryan J; Whitfield, Mary; Paxton, Eben H; Smith, Thomas B

2018-05-09

Few regions have been more severely impacted by climate change in the USA than the Desert Southwest. Here, we use ecological genomics to assess the potential for adaptation to rising global temperatures in a widespread songbird, the willow flycatcher (Empidonax traillii), and find the endangered desert southwestern subspecies (E. t. extimus) most vulnerable to future climate change. Highly significant correlations between present abundance and estimates of genomic vulnerability - the mismatch between current and predicted future genotype-environment relationships - indicate small, fragmented populations of the southwestern willow flycatcher will have to adapt most to keep pace with climate change. Links between climate-associated genotypes and genes important to thermal tolerance in birds provide a potential mechanism for adaptation to temperature extremes. Our results demonstrate that the incorporation of genotype-environment relationships into landscape-scale models of climate vulnerability can facilitate more precise predictions of climate impacts and help guide conservation in threatened and endangered groups. © 2018 John Wiley & Sons Ltd/CNRS.

Sociodemographic and psychosocial factors in childhood as predictors of adult mortality.

PubMed Central

Schwartz, J E; Friedman, H S; Tucker, J S; Tomlinson-Keasey, C; Wingard, D L; Criqui, M H

1995-01-01

OBJECTIVES: Childhood sociodemographic, psychosocial, and environmental factors are often assumed to affect adult health and longevity. These relationships were prospectively tested by using the 7-decade Terman Life Cycle Study of Children With High Ability (n = 1285). METHODS: Parental socioeconomic status, childhood health, objective childhood stressors (e.g., death or divorce of parents), and childhood personality were considered as potential predictors in hazard regression analyses of longevity through 1991. RESULTS: Parental divorce during childhood predicted decreased longevity, with sex controlled. Other potential social predictors failed to show significant associations with longevity. Three dimensions of childhood personality--conscientiousness, lack of cheerfulness, and permanency of mood (males only)--predicted increased longevity. The effects of parental divorce and childhood personality were largely independent and did not account for any of the gender difference in mortality. CONCLUSIONS: A small number of childhood factors significantly predicted mortality across the life span in this sample. Further research should focus on how these psychosocial factors influence longevity. PMID:7661231

Can postoperative deltoid weakness after cervical laminoplasty be prevented by using intraoperative neurophysiological monitoring?

PubMed

Ando, Muneharu; Tamaki, Tetsuya; Matsumoto, Takuji; Maio, Kazuhiro; Teraguchi, Masatoshi; Takiguchi, Noboru; Iwahashi, Hiroki; Onishi, Makiko; Nakagawa, Yukihiro; Iwasaki, Hiroshi; Tsutsui, Shunji; Takami, Masanari; Yamada, Hiroshi

2018-04-17

Laminoplasty, frequently performed in patients with cervical myelopathy, is safe and provides relatively good results. However, motor palsy of the upper extremities, which occurs after decompression surgery for cervical myelopathy, often reduces muscle strength of the deltoid muscle, mainly in the C5 myotome. The aim of this study was to investigate prospectively whether postoperative deltoid weakness (DW) can be predicted by performing intraoperative neurophysiological monitoring (IONM) during cervical laminoplasty and to clarify whether it is possible to prevent palsy using IONM. We evaluated the 278 consecutive patients (175 males and 103 females) who underwent French-door cervical laminoplasty for cervical myelopathy under IONM between November 2008 and December 2016 at our hospital. IONM was performed using muscle evoked potential after electrical stimulation to the brain [Br(E)-MsEP] from the deltoid muscle. Seven patients (2.5%) developed DW after surgery (2 with acute and 5 with delayed onset). In all patients, deltoid muscle strength recovered to ≥ 4 on manual muscle testing 3-6 months after surgery. Persistent IONM alerts occurred in 2 patients with acute-onset DW. To predict the acute onset of DW, Br(E)-MsEP alerts in the deltoid muscle had both a sensitivity and specificity of 100%. The PPV of persistent Br(E)-MsEP alerts had both a sensitivity and specificity of 100% for acute-onset DW. There was no change in Br(E)-MsEP in patients with delayed-onset palsy. The incidence of deltoid palsy was relatively low. Persistent Br(E)-MsEP alerts of the deltoid muscle had a 100% sensitivity and specificity for predicting a postoperative acute deficit. IONM was unable to predict delayed-onset DW. In only 1 patient were we able to prevent postoperative DW by performing a foraminotomy.

BetaTPred: prediction of beta-TURNS in a protein using statistical algorithms.

PubMed

Kaur, Harpreet; Raghava, G P S

2002-03-01

beta-turns play an important role from a structural and functional point of view. beta-turns are the most common type of non-repetitive structures in proteins and comprise on average, 25% of the residues. In the past numerous methods have been developed to predict beta-turns in a protein. Most of these prediction methods are based on statistical approaches. In order to utilize the full potential of these methods, there is a need to develop a web server. This paper describes a web server called BetaTPred, developed for predicting beta-TURNS in a protein from its amino acid sequence. BetaTPred allows the user to predict turns in a protein using existing statistical algorithms. It also allows to predict different types of beta-TURNS e.g. type I, I', II, II', VI, VIII and non-specific. This server assists the users in predicting the consensus beta-TURNS in a protein. The server is accessible from http://imtech.res.in/raghava/betatpred/

Application of Grey Model GM(1, 1) to Ultra Short-Term Predictions of Universal Time

NASA Astrophysics Data System (ADS)

Lei, Yu; Guo, Min; Zhao, Danning; Cai, Hongbing; Hu, Dandan

2016-03-01

A mathematical model known as one-order one-variable grey differential equation model GM(1, 1) has been herein employed successfully for the ultra short-term (<10days) predictions of universal time (UT1-UTC). The results of predictions are analyzed and compared with those obtained by other methods. It is shown that the accuracy of the predictions is comparable with that obtained by other prediction methods. The proposed method is able to yield an exact prediction even though only a few observations are provided. Hence it is very valuable in the case of a small size dataset since traditional methods, e.g., least-squares (LS) extrapolation, require longer data span to make a good forecast. In addition, these results can be obtained without making any assumption about an original dataset, and thus is of high reliability. Another advantage is that the developed method is easy to use. All these reveal a great potential of the GM(1, 1) model for UT1-UTC predictions.

Prediction of a two-dimensional S3N2 solid for optoelectronic applications

NASA Astrophysics Data System (ADS)

Xiao, Hang; Shi, Xiaoyang; Liao, Xiangbiao; Zhang, Yayun; Chen, Xi

2018-02-01

Two-dimensional materials have attracted tremendous attention for their fascinating electronic, optical, chemical, and mechanical properties. However, the band gaps of most reported two-dimensional (2D) materials are smaller than 2.0 eV, which has greatly restricted their optoelectronic applications in the blue and ultraviolet range of the spectrum. Here, we propose a stable trisulfur dinitride (S3N2 ) 2D crystal that is a covalent network composed solely of S-N σ bonds. The S3N2 crystal is dynamically, thermally, and chemically stable, as confirmed by the computed phonon spectrum and ab initio molecular dynamics simulations. GW calculations show that the S3N2 crystal is a wide, direct band-gap (3.92 eV) semiconductor with a small-hole effective mass. In addition, the band gap of S3N2 structures can be tuned by forming multilayer S3N2 crystals, S3N2 nanoribbons, and S3N2 nanotubes, expanding its potential applications. The anisotropic optical response of the 2D S3N2 crystal is revealed by GW-Bethe-Salpeter-equation calculations. The optical band gap of S3N2 is 2.73 eV and the exciton binding energy of S3N2 is 1.19 eV, showing a strong excitonic effect. Our result not only marks the prediction of a 2D crystal composed of nitrogen and sulfur, but also underpins potential innovations in 2D electronics and optoelectronics.

Chiral Anomaly from Strain-Induced Gauge Fields in Dirac and Weyl Semimetals

NASA Astrophysics Data System (ADS)

Pikulin, D. I.; Chen, Anffany; Franz, M.

2016-10-01

Dirac and Weyl semimetals form an ideal platform for testing ideas developed in high-energy physics to describe massless relativistic particles. One such quintessentially field-theoretic idea of the chiral anomaly already resulted in the prediction and subsequent observation of the pronounced negative magnetoresistance in these novel materials for parallel electric and magnetic fields. Here, we predict that the chiral anomaly occurs—and has experimentally observable consequences—when real electromagnetic fields E and B are replaced by strain-induced pseudo-electromagnetic fields e and b . For example, a uniform pseudomagnetic field b is generated when a Weyl semimetal nanowire is put under torsion. In accordance with the chiral anomaly equation, we predict a negative contribution to the wire resistance proportional to the square of the torsion strength. Remarkably, left- and right-moving chiral modes are then spatially segregated to the bulk and surface of the wire forming a "topological coaxial cable." This produces hydrodynamic flow with potentially very long relaxation time. Another effect we predict is the ultrasonic attenuation and electromagnetic emission due to a time-periodic mechanical deformation causing pseudoelectric field e . These novel manifestations of the chiral anomaly are most striking in the semimetals with a single pair of Weyl nodes but also occur in Dirac semimetals such as Cd3 As2 and Na3Bi and Weyl semimetals with unbroken time-reversal symmetry.

Prospective Predictors of Novel Tobacco and Nicotine Product Use in Emerging Adulthood

PubMed Central

Hampson, Sarah E.; Andrews, Judy A.; Severson, Herbert H.; Barckley, Maureen

2015-01-01

Objective To investigate whether risk factors for cigarette smoking assessed in adolescence predict the use of novel tobacco and nicotine products (hookah, little cigars, and e-cigarettes) in early emerging adulthood. Methods In a longitudinal study (N = 862), risk factors were measured in middle and high school and novel product use was measured in emerging adulthood (mean age 22.4 years). Structural equation modelling was used to test a model predicting lifetime use of any of hookah, little cigars, and e-cigarettes in early emerging adulthood from distal predictors (gender, maternal smoking through Grade 8, already tried alcohol, cigarettes, or marijuana by Grade 8, and sensation seeking at Grade 8), and potential mediators (intentions to smoke cigarettes, drink alcohol or smoke marijuana at Grade 9, and smoking trajectory across high school). Results The most prevalent novel tobacco product was hookah (21.7%), followed by little cigars (16.8%), and e-cigarettes (6.6%). Maternal smoking, having already tried substances, and sensation seeking each predicted the use of at least one of these products via an indirect path through intentions to use substances and membership in a high school smoking trajectory. Conclusions Risk factors for cigarette smoking were found to predict novel tobacco use, suggesting that interventions to prevent cigarette smoking could be extended to include common novel tobacco products. PMID:26206439

Drivers and seasonal predictability of extreme wind speeds in the ECMWF System 4 and a statistical model

NASA Astrophysics Data System (ADS)

Walz, M. A.; Donat, M.; Leckebusch, G. C.

2017-12-01

As extreme wind speeds are responsible for large socio-economic losses in Europe, a skillful prediction would be of great benefit for disaster prevention as well as for the actuarial community. Here we evaluate patterns of large-scale atmospheric variability and the seasonal predictability of extreme wind speeds (e.g. >95th percentile) in the European domain in the dynamical seasonal forecast system ECMWF System 4, and compare to the predictability based on a statistical prediction model. The dominant patterns of atmospheric variability show distinct differences between reanalysis and ECMWF System 4, with most patterns in System 4 extended downstream in comparison to ERA-Interim. The dissimilar manifestations of the patterns within the two models lead to substantially different drivers associated with the occurrence of extreme winds in the respective model. While the ECMWF System 4 is shown to provide some predictive power over Scandinavia and the eastern Atlantic, only very few grid cells in the European domain have significant correlations for extreme wind speeds in System 4 compared to ERA-Interim. In contrast, a statistical model predicts extreme wind speeds during boreal winter in better agreement with the observations. Our results suggest that System 4 does not seem to capture the potential predictability of extreme winds that exists in the real world, and therefore fails to provide reliable seasonal predictions for lead months 2-4. This is likely related to the unrealistic representation of large-scale patterns of atmospheric variability. Hence our study points to potential improvements of dynamical prediction skill by improving the simulation of large-scale atmospheric dynamics.

The Cassava Mealybug (Phenacoccus manihoti) in Asia: First Records, Potential Distribution, and an Identification Key

PubMed Central

Parsa, Soroush; Kondo, Takumasa; Winotai, Amporn

2012-01-01

Phenacoccus manihoti Matile-Ferrero (Hemiptera: Pseudococcidae), one of the most serious pests of cassava worldwide, has recently reached Asia, raising significant concern over its potential spread throughout the region. To support management decisions, this article reports recent distribution records, and estimates the climatic suitability for its regional spread using a CLIMEX distribution model. The article also presents a taxonomic key that separates P. manihoti from all other mealybug species associated with the genus Manihot. Model predictions suggest P. manihoti imposes an important, yet differential, threat to cassava production in Asia. Predicted risk is most acute in the southern end of Karnataka in India, the eastern end of the Ninh Thuan province in Vietnam, and in most of West Timor in Indonesia. The model also suggests P. manihoti is likely to be limited by cold stress across Vietnam's northern regions and in the entire Guangxi province in China, and by high rainfall across the wet tropics in Indonesia and the Philippines. Predictions should be particularly important to guide management decisions for high risk areas where P. manihoti is absent (e.g., India), or where it has established but populations remain small and localized (e.g., South Vietnam). Results from this article should help decision-makers assess site-specific risk of invasion, and develop proportional prevention and surveillance programs for early detection and rapid response. PMID:23077659

Accuracy of professional sports drafts in predicting career potential.

PubMed

Koz, D; Fraser-Thomas, J; Baker, J

2012-08-01

The forecasting of talented players is a crucial aspect of building a successful sports franchise and professional sports invest significant resources in making player choices in sport drafts. The current study examined the relationship between career performance (i.e. games played) and draft round for the National Football League, National Hockey League, National Basketball League, and Major League Baseball for players drafted from 1980 to 1989 (n = 4874) against the assumption of a linear relationship between performance and draft round (i.e. that players with the most potential will be selected before players of lower potential). A two-step analysis revealed significant differences in games played across draft rounds (step 1) and a significant negative relationship between draft round and games played (step 2); however, the amount of variance accounted for was relatively low (less than 17%). Results highlight the challenges of accurately evaluating amateur talent. © 2011 John Wiley & Sons A/S.

Allergic sensitization: screening methods

PubMed Central

2014-01-01

Experimental in silico, in vitro, and rodent models for screening and predicting protein sensitizing potential are discussed, including whether there is evidence of new sensitizations and allergies since the introduction of genetically modified crops in 1996, the importance of linear versus conformational epitopes, and protein families that become allergens. Some common challenges for predicting protein sensitization are addressed: (a) exposure routes; (b) frequency and dose of exposure; (c) dose-response relationships; (d) role of digestion, food processing, and the food matrix; (e) role of infection; (f) role of the gut microbiota; (g) influence of the structure and physicochemical properties of the protein; and (h) the genetic background and physiology of consumers. The consensus view is that sensitization screening models are not yet validated to definitively predict the de novo sensitizing potential of a novel protein. However, they would be extremely useful in the discovery and research phases of understanding the mechanisms of food allergy development, and may prove fruitful to provide information regarding potential allergenicity risk assessment of future products on a case by case basis. These data and findings were presented at a 2012 international symposium in Prague organized by the Protein Allergenicity Technical Committee of the International Life Sciences Institute’s Health and Environmental Sciences Institute. PMID:24739743

Factors Influencing the Adoption of a Health Promoting School Approach in the Province of Quebec, Canada

ERIC Educational Resources Information Center

Deschesnes, M.; Trudeau, F.; Kebe, M.

2010-01-01

This study examined a prediction model that integrated three categories of predictors likely to influence adoption of the Quebec Healthy Schools (HS) approach, i.e. attributes of the approach, individual and contextual characteristics. HS receptivity was considered as a potential mediator. For this study, 141 respondents representing 96 schools…

Predicting Adolescents' Organized Activity Involvement: The Role of Maternal Depression History, Family Relationship Quality, and Adolescent Cognitions

ERIC Educational Resources Information Center

Bohnert, Amy M.; Martin, Nina C.; Garber, Judy

2007-01-01

Although the potential benefits of organized activity involvement during high school have been documented, little is known about what familial and individual characteristics are associated with higher levels of participation. Using structural equation modeling, this longitudinal study examined the extent to which maternal depression history (i.e.,…

Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation.

PubMed

Zhang, Haiyuan; Ji, Zhaoxia; Xia, Tian; Meng, Huan; Low-Kam, Cecile; Liu, Rong; Pokhrel, Suman; Lin, Sijie; Wang, Xiang; Liao, Yu-Pei; Wang, Meiying; Li, Linjiang; Rallo, Robert; Damoiseaux, Robert; Telesca, Donatello; Mädler, Lutz; Cohen, Yoram; Zink, Jeffrey I; Nel, Andre E

2012-05-22

We demonstrate for 24 metal oxide (MOx) nanoparticles that it is possible to use conduction band energy levels to delineate their toxicological potential at cellular and whole animal levels. Among the materials, the overlap of conduction band energy (E(c)) levels with the cellular redox potential (-4.12 to -4.84 eV) was strongly correlated to the ability of Co(3)O(4), Cr(2)O(3), Ni(2)O(3), Mn(2)O(3), and CoO nanoparticles to induce oxygen radicals, oxidative stress, and inflammation. This outcome is premised on permissible electron transfers from the biological redox couples that maintain the cellular redox equilibrium to the conduction band of the semiconductor particles. Both single-parameter cytotoxic as well as multi-parameter oxidative stress assays in cells showed excellent correlation to the generation of acute neutrophilic inflammation and cytokine responses in the lungs of C57 BL/6 mice. Co(3)O(4), Ni(2)O(3), Mn(2)O(3), and CoO nanoparticles could also oxidize cytochrome c as a representative redox couple involved in redox homeostasis. While CuO and ZnO generated oxidative stress and acute pulmonary inflammation that is not predicted by E(c) levels, the adverse biological effects of these materials could be explained by their solubility, as demonstrated by ICP-MS analysis. These results demonstrate that it is possible to predict the toxicity of a large series of MOx nanoparticles in the lung premised on semiconductor properties and an integrated in vitro/in vivo hazard ranking model premised on oxidative stress. This establishes a robust platform for modeling of MOx structure-activity relationships based on band gap energy levels and particle dissolution. This predictive toxicological paradigm is also of considerable importance for regulatory decision-making about this important class of engineered nanomaterials.

Potential of electric bicycles to improve the health of people with Type 2 diabetes: a feasibility study.

PubMed

Cooper, A R; Tibbitts, B; England, C; Procter, D; Searle, A; Sebire, S J; Ranger, E; Page, A S

2018-05-08

To explore in a feasibility study whether 'e-cycling' was acceptable to, and could potentially improve the health of, people with Type 2 diabetes. Twenty people with Type 2 diabetes were recruited and provided with an electric bicycle for 20 weeks. Participants completed a submaximal fitness test at baseline and follow-up to measure predicted maximal aerobic power, and semi-structured interviews were conducted to assess the acceptability of using an electric bicycle. Participants wore a heart rate monitor and a Global Positioning System (GPS) receiver in the first week of electric bicycle use to measure their heart-rate during e-cycling. Eighteen participants completed the study, cycling a median (interquartile range) of 21.4 (5.5-37.7) km per week Predicted maximal aerobic power increased by 10.9%. Heart rate during electric bicycle journeys was 74.7% of maximum, compared with 64.3% of maximum when walking. Participants used the electric bicycles for commuting, shopping and recreation, and expressed how the electric bicycle helped them to overcome barriers to active travel/cycling, such as hills. Fourteen participants purchased an electric bicycle on study completion. There was evidence that e-cycling was acceptable, could increase fitness and elicited a heart rate that may lead to improvements in cardiometabolic risk factors in this population. Electric bicycles have potential as a health-improving intervention in people with Type 2 diabetes. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Quantitative analysis of microbial contamination in private drinking water supply systems.

PubMed

Allevi, Richard P; Krometis, Leigh-Anne H; Hagedorn, Charles; Benham, Brian; Lawrence, Annie H; Ling, Erin J; Ziegler, Peter E

2013-06-01

Over one million households rely on private water supplies (e.g. well, spring, cistern) in the Commonwealth of Virginia, USA. The present study tested 538 private wells and springs in 20 Virginia counties for total coliforms (TCs) and Escherichia coli along with a suite of chemical contaminants. A logistic regression analysis was used to investigate potential correlations between TC contamination and chemical parameters (e.g. NO3(-), turbidity), as well as homeowner-provided survey data describing system characteristics and perceived water quality. Of the 538 samples collected, 41% (n = 221) were positive for TCs and 10% (n = 53) for E. coli. Chemical parameters were not statistically predictive of microbial contamination. Well depth, water treatment, and farm location proximate to the water supply were factors in a regression model that predicted presence/absence of TCs with 74% accuracy. Microbial and chemical source tracking techniques (Bacteroides gene Bac32F and HF183 detection via polymerase chain reaction and optical brightener detection via fluorometry) identified four samples as likely contaminated with human wastewater.

A Holistic Approach to Climate and Health Research: Respiratory and Infectious Diseases

NASA Astrophysics Data System (ADS)

Asrar, G.; Alonoso, W.; McCormick, B.; Schuck-Paim, C.; Miller, M.

2014-12-01

The link between climate variability and change, especially extreme conditions, is well documented in both environmental and health literature. The focus of research in the recent past, and current studies, is to understand causal relationships between the disease agents and environmental conditions, based on post-hoc analysis of observed cases to develop predictive models for advance warning of public by health authorities. A combination of the isolated examination of individual diseases and routes of infection (e.g. respiratory system, skin, digestive tract, etc.) and reliance mostly on correlative evidence from past occurrences have restricted public health progress (e.g. compared to experimental evidence of the quantitative balance of different transmission routes) and the utility of knowledge gained from such studies (e.g. reliably predicting seasonal outbreaks is no longer an advance). We propose a shift from focusing on the prediction of individual disease pattern(s) to a more holistic identification and mitigation of broader vulnerabilities within the provision of public health. Such an approach has the potential to account for and reveal health vulnerabilities common to a broader range of health stresses, thus facilitating a more holistic response to health challenges. The human health fragilities associated with respiratory diseases caused by a combination of natural (i.e dust, pollen, etc.) and industrial particulates (i.e. soot, aerosols, etc.) and other infectious airborne agents, for example, and their adverse impact on human health such as respiratory, gastrointestinal, etc. is an ideal candidate for such a holistic approach to environment and health research.

Kinetics and efficiency of the hydrated electron-induced dehalogenation by the sulfite/UV process.

PubMed

Li, Xuchun; Fang, Jingyun; Liu, Guifang; Zhang, Shujuan; Pan, Bingcai; Ma, Jun

2014-10-01

Hydrated electron (e(aq)(-)), which is listed among the most reactive reducing species, has great potential for removal and detoxification of recalcitrant contaminants. Here we provided quantitative insight into the availability and conversion of e(aq)(-) in a newly developed sulfite/UV process. Using monochloroacetic acid as a simple e(aq)(-)-probe, the e(aq)(-)-induced dehalogenation kinetics in synthetic and surface water was well predicted by the developed models. The models interpreted the complex roles of pH and S(IV), and also revealed the positive effects of UV intensity and temperature quantitatively. Impacts of humic acid, ferrous ion, carbonate/bicarbonate, and surface water matrix were also examined. Despite the retardation of dehalogenation by electron scavengers, the process was effective even in surface water. Efficiency of the process was discussed, and the optimization approaches were proposed. This study is believed to better understand the e(aq)(-)-induced dehalogenation by the sulfite/UV process in a quantitative manner, which is very important for its potential application in water treatment. Copyright © 2014 Elsevier Ltd. All rights reserved.

Quantifying the role of woody debris in providing bioenergetically favorable habitat for juvenile salmon

NASA Astrophysics Data System (ADS)

Harrison, L.; Hafs, A. W.; Utz, R.; Dunne, T.

2013-12-01

The habitat complexity of a riverine ecosystem substantially influences aquatic communities, and especially the bioenergetics of drift feeding fish. We coupled hydrodynamic and bioenergetic models to assess the influence of habitat complexity, generated via large woody debris (LWD) additions, on juvenile Chinook salmon (Oncorhynchus tshawytscha) growth potential in a river that lacked large wood. Model simulations indicated that LWD diversified the flow field, creating pronounced velocity gradients, which enhanced fish feeding and resting activities at the micro-habitat (sub-meter) scale. Fluid drag created by individual wood structures was increased under higher wood loading rates, leading to a 5-19% reduction in the reach-averaged velocity. We found that wood loading was asymptotically related to the reach-scale growth potential, suggesting that the river became saturated with LWD and additional loading would produce minimal benefit. In our study reach, LWD additions could potentially quadruple the potential growth area available before that limit was reached. Wood depletion in the world's rivers has been widely documented, leading to widespread attempts by river managers to reverse this trend by adding wood to simplified aquatic habitats, though systematic prediction of the effects of wood on fish growth has not been previously accomplished. We offer a quantitative, theory-based approach for assessing the role of wood on habitat potential as it affects fish growth at the micro-habitat and reach-scales. Fig. 1. Predicted flow field and salmon growth potential maps produced from model simulations with no woody debris (Graphs A and D), a low density (Graphs B and E), and a high density (Graphs C and E) of woody debris.

QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants.

PubMed

Kovarich, Simona; Papa, Ester; Gramatica, Paola

2011-06-15

The identification of potential endocrine disrupting (ED) chemicals is an important task for the scientific community due to their diffusion in the environment; the production and use of such compounds will be strictly regulated through the authorization process of the REACH regulation. To overcome the problem of insufficient experimental data, the quantitative structure-activity relationship (QSAR) approach is applied to predict the ED activity of new chemicals. In the present study QSAR classification models are developed, according to the OECD principles, to predict the ED potency for a class of emerging ubiquitary pollutants, viz. brominated flame retardants (BFRs). Different endpoints related to ED activity (i.e. aryl hydrocarbon receptor agonism and antagonism, estrogen receptor agonism and antagonism, androgen and progesterone receptor antagonism, T4-TTR competition, E2SULT inhibition) are modeled using the k-NN classification method. The best models are selected by maximizing the sensitivity and external predictive ability. We propose simple QSARs (based on few descriptors) characterized by internal stability, good predictive power and with a verified applicability domain. These models are simple tools that are applicable to screen BFRs in relation to their ED activity, and also to design safer alternatives, in agreement with the requirements of REACH regulation at the authorization step. Copyright © 2011 Elsevier B.V. All rights reserved.

Uncertainty analysis of the nonideal competitive adsorption-donnan model: effects of dissolved organic matter variability on predicted metal speciation in soil solution.

PubMed

Groenenberg, Jan E; Koopmans, Gerwin F; Comans, Rob N J

2010-02-15

Ion binding models such as the nonideal competitive adsorption-Donnan model (NICA-Donnan) and model VI successfully describe laboratory data of proton and metal binding to purified humic substances (HS). In this study model performance was tested in more complex natural systems. The speciation predicted with the NICA-Donnan model and the associated uncertainty were compared with independent measurements in soil solution extracts, including the free metal ion activity and fulvic (FA) and humic acid (HA) fractions of dissolved organic matter (DOM). Potentially important sources of uncertainty are the DOM composition and the variation in binding properties of HS. HS fractions of DOM in soil solution extracts varied between 14 and 63% and consisted mainly of FA. Moreover, binding parameters optimized for individual FA samples show substantial variation. Monte Carlo simulations show that uncertainties in predicted metal speciation, for metals with a high affinity for FA (Cu, Pb), are largely due to the natural variation in binding properties (i.e., the affinity) of FA. Predictions for metals with a lower affinity (Cd) are more prone to uncertainties in the fraction FA in DOM and the maximum site density (i.e., the capacity) of the FA. Based on these findings, suggestions are provided to reduce uncertainties in model predictions.

Conformal Prediction Based on K-Nearest Neighbors for Discrimination of Ginsengs by a Home-Made Electronic Nose

PubMed Central

Sun, Xiyang; Miao, Jiacheng; Wang, You; Luo, Zhiyuan; Li, Guang

2017-01-01

An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called conformal prediction is introduced in this work for discriminating different kinds of ginsengs with a home-made electronic nose instrument. Nonconformity measure based on k-nearest neighbors (KNN) is implemented separately as underlying algorithm of conformal prediction. In offline mode, the conformal predictor achieves a classification rate of 84.44% based on 1NN and 80.63% based on 3NN, which is better than that of simple KNN. In addition, it provides an estimate of reliability for each prediction. In online mode, the validity of predictions is guaranteed, which means that the error rate of region predictions never exceeds the significance level set by a user. The potential of this framework for detecting borderline examples and outliers in the application of E-nose is also investigated. The result shows that conformal prediction is a promising framework for the application of electronic nose to make predictions with reliability and validity. PMID:28805721

Patterns and multi-scale drivers of phytoplankton species richness in temperate peri-urban lakes.

PubMed

Catherine, Arnaud; Selma, Maloufi; Mouillot, David; Troussellier, Marc; Bernard, Cécile

2016-07-15

Local species richness (SR) is a key characteristic affecting ecosystem functioning. Yet, the mechanisms regulating phytoplankton diversity in freshwater ecosystems are not fully understood, especially in peri-urban environments where anthropogenic pressures strongly impact the quality of aquatic ecosystems. To address this issue, we sampled the phytoplankton communities of 50 lakes in the Paris area (France) characterized by a large gradient of physico-chemical and catchment-scale characteristics. We used large phytoplankton datasets to describe phytoplankton diversity patterns and applied a machine-learning algorithm to test the degree to which species richness patterns are potentially controlled by environmental factors. Selected environmental factors were studied at two scales: the lake-scale (e.g. nutrients concentrations, water temperature, lake depth) and the catchment-scale (e.g. catchment, landscape and climate variables). Then, we used a variance partitioning approach to evaluate the interaction between lake-scale and catchment-scale variables in explaining local species richness. Finally, we analysed the residuals of predictive models to identify potential vectors of improvement of phytoplankton species richness predictive models. Lake-scale and catchment-scale drivers provided similar predictive accuracy of local species richness (R(2)=0.458 and 0.424, respectively). Both models suggested that seasonal temperature variations and nutrient supply strongly modulate local species richness. Integrating lake- and catchment-scale predictors in a single predictive model did not provide increased predictive accuracy; therefore suggesting that the catchment-scale model probably explains observed species richness variations through the impact of catchment-scale variables on in-lake water quality characteristics. Models based on catchment characteristics, which include simple and easy to obtain variables, provide a meaningful way of predicting phytoplankton species richness in temperate lakes. This approach may prove useful and cost-effective for the management and conservation of aquatic ecosystems. Copyright © 2016 Elsevier B.V. All rights reserved.

Development of an analytical solution for the Budyko watershed parameter in terms of catchment physical features

NASA Astrophysics Data System (ADS)

Reaver, N.; Kaplan, D. A.; Jawitz, J. W.

2017-12-01

The Budyko hypothesis states that a catchment's long-term water and energy balances are dependent on two relatively easy to measure quantities: rainfall depth and potential evaporation. This hypothesis is expressed as a simple function, the Budyko equation, which allows for the prediction of a catchment's actual evapotranspiration and discharge from measured rainfall depth and potential evaporation, data which are widely available. However, the two main analytically derived forms of the Budyko equation contain a single unknown watershed parameter, whose value varies across catchments; variation in this parameter has been used to explain the hydrological behavior of different catchments. The watershed parameter is generally thought of as a lumped quantity that represents the influence of all catchment biophysical features (e.g. soil type and depth, vegetation type, timing of rainfall, etc). Previous work has shown that the parameter is statistically correlated with catchment properties, but an explicit expression has been elusive. While the watershed parameter can be determined empirically by fitting the Budyko equation to measured data in gauged catchments where actual evapotranspiration can be estimated, this limits the utility of the framework for predicting impacts to catchment hydrology due to changing climate and land use. In this study, we developed an analytical solution for the lumped catchment parameter for both forms of the Budyko equation. We combined these solutions with a statistical soil moisture model to obtain analytical solutions for the Budyko equation parameter as a function of measurable catchment physical features, including rooting depth, soil porosity, and soil wilting point. We tested the predictive power of these solutions using the U.S. catchments in the MOPEX database. We also compared the Budyko equation parameter estimates generated from our analytical solutions (i.e. predicted parameters) with those obtained through the calibration of the Budyko equation to discharge data (i.e. empirical parameters), and found good agreement. These results suggest that it is possible to predict the Budyko equation watershed parameter directly from physical features, even for ungauged catchments.

The eTOX data-sharing project to advance in silico drug-induced toxicity prediction.

PubMed

Cases, Montserrat; Briggs, Katharine; Steger-Hartmann, Thomas; Pognan, François; Marc, Philippe; Kleinöder, Thomas; Schwab, Christof H; Pastor, Manuel; Wichard, Jörg; Sanz, Ferran

2014-11-14

The high-quality in vivo preclinical safety data produced by the pharmaceutical industry during drug development, which follows numerous strict guidelines, are mostly not available in the public domain. These safety data are sometimes published as a condensed summary for the few compounds that reach the market, but the majority of studies are never made public and are often difficult to access in an automated way, even sometimes within the owning company itself. It is evident from many academic and industrial examples, that useful data mining and model development requires large and representative data sets and careful curation of the collected data. In 2010, under the auspices of the Innovative Medicines Initiative, the eTOX project started with the objective of extracting and sharing preclinical study data from paper or pdf archives of toxicology departments of the 13 participating pharmaceutical companies and using such data for establishing a detailed, well-curated database, which could then serve as source for read-across approaches (early assessment of the potential toxicity of a drug candidate by comparison of similar structure and/or effects) and training of predictive models. The paper describes the efforts undertaken to allow effective data sharing intellectual property (IP) protection and set up of adequate controlled vocabularies) and to establish the database (currently with over 4000 studies contributed by the pharma companies corresponding to more than 1400 compounds). In addition, the status of predictive models building and some specific features of the eTOX predictive system (eTOXsys) are presented as decision support knowledge-based tools for drug development process at an early stage.

Target controlled infusion for kids: trials and simulations.

PubMed

Mehta, Disha; McCormack, Jon; Fung, Parry; Dumont, Guy; Ansermino, J

2008-01-01

Target controlled infusion (TCI) for Kids is a computer controlled system designed to administer propofol for general anesthesia. A controller establishes infusion rates required to achieve a specified concentration at the drug's effect site (C(e)) by implementing a continuously updated pharmacokinetic-pharmacodymanic model. This manuscript provides an overview of the system's design, preclinical tests, and a clinical pilot study. In pre-clinical tests, predicted infusion rates for 20 simulated procedures displayed complete convergent validity between two software implementations, Labview and Matlab, at computational intervals of 5, 10, and 15s, but diverged with 20s intervals due to system rounding errors. The volume of drug delivered by the TCI system also displayed convergent validity with Tivatrainer, a widely used TCI simulation software. Further tests, were conducted for 50 random procedures to evaluate discrepancies between volumes reported and those actually delivered by the system. Accuracies were within clinically acceptable ranges and normally distributed with a mean of 0.08 +/- 0.01 ml. In the clinical study, propofol pharmacokinetics were simulated for 30 surgical procedures involving children aged 3 months to 9 years. Predicted C(e) values during standard clinical practice, the accuracy of wake-up times predicted by the system, and potential correlations between patient wake-up times, C(e), and state entropy (SE) were assessed. Neither Ce nor SE was a reliable predictor of wake-up time in children, but the small sample size of this study does not fully accommodate the noted variation in children's response to propofol. A C(e) value of 1.9 mug/ml was found to best predict emergence from anesthesia in children.

Correlation of sensory bitterness in dairy protein hydrolysates: Comparison of prediction models built using sensory, chromatographic and electronic tongue data.

PubMed

Newman, J; Egan, T; Harbourne, N; O'Riordan, D; Jacquier, J C; O'Sullivan, M

2014-08-01

Sensory evaluation can be problematic for ingredients with a bitter taste during research and development phase of new food products. In this study, 19 dairy protein hydrolysates (DPH) were analysed by an electronic tongue and their physicochemical characteristics, the data obtained from these methods were correlated with their bitterness intensity as scored by a trained sensory panel and each model was also assessed by its predictive capabilities. The physiochemical characteristics of the DPHs investigated were degree of hydrolysis (DH%), and data relating to peptide size and relative hydrophobicity from size exclusion chromatography (SEC) and reverse phase (RP) HPLC. Partial least square regression (PLS) was used to construct the prediction models. All PLS regressions had good correlations (0.78 to 0.93) with the strongest being the combination of data obtained from SEC and RP HPLC. However, the PLS with the strongest predictive power was based on the e-tongue which had the PLS regression with the lowest root mean predicted residual error sum of squares (PRESS) in the study. The results show that the PLS models constructed with the e-tongue and the combination of SEC and RP-HPLC has potential to be used for prediction of bitterness and thus reducing the reliance on sensory analysis in DPHs for future food research. Copyright © 2014 Elsevier B.V. All rights reserved.

Microbial community structure and function in sediments from e-waste contaminated rivers at Guiyu area of China.

PubMed

Liu, Jun; Chen, Xi; Shu, Hao-Yue; Lin, Xue-Rui; Zhou, Qi-Xing; Bramryd, Torleif; Shu, Wen-Sheng; Huang, Li-Nan

2018-04-01

The release of toxic organic pollutants and heavy metals by primitive electronic waste (e-waste) processing to waterways has raised significant concerns, but little is known about their potential ecological effects on aquatic biota especially microorganisms. We characterized the microbial community composition and diversity in sediments sampled along two rivers consistently polluted by e-waste, and explored how community functions may respond to the complex combined pollution. High-throughput 16S rRNA gene sequencing showed that Proteobacteria (particularly Deltaproteobacteria) dominated the sediment microbial assemblages followed by Bacteroidetes, Acidobacteria, Chloroflexi and Firmicutes. PICRUSt metagenome inference provided an initial insight into the metabolic potentials of these e-waste affected communities, speculating that organic pollutants degradation in the sediment might be mainly performed by some of the dominant genera (such as Sulfuricurvum, Thiobacillus and Burkholderia) detected in situ. Statistical analyses revealed that toxic organic compounds contributed more to the observed variations in sediment microbial community structure and predicted functions (24.68% and 8.89%, respectively) than heavy metals (12.18% and 4.68%), and Benzo(a)pyrene, bioavailable lead and electrical conductivity were the key contributors. These results have shed light on the microbial assemblages in e-waste contaminated river sediments, indicating a potential influence of e-waste pollution on the microbial community structure and function in aquatic ecosystems. Copyright © 2017 Elsevier Ltd. All rights reserved.

An electronic health record based model predicts statin adherence, LDL cholesterol, and cardiovascular disease in the United States Military Health System

PubMed Central

Lucas, Joseph E.; Bazemore, Taylor C.; Alo, Celan; Monahan, Patrick B.

2017-01-01

HMG-CoA reductase inhibitors (or “statins”) are important and commonly used medications to lower cholesterol and prevent cardiovascular disease. Nearly half of patients stop taking statin medications one year after they are prescribed leading to higher cholesterol, increased cardiovascular risk, and costs due to excess hospitalizations. Identifying which patients are at highest risk for not adhering to long-term statin therapy is an important step towards individualizing interventions to improve adherence. Electronic health records (EHR) are an increasingly common source of data that are challenging to analyze but have potential for generating more accurate predictions of disease risk. The aim of this study was to build an EHR based model for statin adherence and link this model to biologic and clinical outcomes in patients receiving statin therapy. We gathered EHR data from the Military Health System which maintains administrative data for active duty, retirees, and dependents of the United States armed forces military that receive health care benefits. Data were gathered from patients prescribed their first statin prescription in 2005 and 2006. Baseline billing, laboratory, and pharmacy claims data were collected from the two years leading up to the first statin prescription and summarized using non-negative matrix factorization. Follow up statin prescription refill data was used to define the adherence outcome (> 80 percent days covered). The subsequent factors to emerge from this model were then used to build cross-validated, predictive models of 1) overall disease risk using coalescent regression and 2) statin adherence (using random forest regression). The predicted statin adherence for each patient was subsequently used to correlate with cholesterol lowering and hospitalizations for cardiovascular disease during the 5 year follow up period using Cox regression. The analytical dataset included 138 731 individuals and 1840 potential baseline predictors that were reduced to 30 independent EHR “factors”. A random forest predictive model taking patient, statin prescription, predicted disease risk, and the EHR factors as potential inputs produced a cross-validated c-statistic of 0.736 for classifying statin non-adherence. The addition of the first refill to the model increased the c-statistic to 0.81. The predicted statin adherence was independently associated with greater cholesterol lowering (correlation = 0.14, p < 1e-20) and lower hospitalization for myocardial infarction, coronary artery disease, and stroke (hazard ratio = 0.84, p = 1.87E-06). Electronic health records data can be used to build a predictive model of statin adherence that also correlates with statins’ cardiovascular benefits. PMID:29155848

Sociosexuality predicts women's preferences for symmetry in men's faces.

PubMed

Quist, Michelle C; Watkins, Christopher D; Smith, Finlay G; Little, Anthony C; Debruine, Lisa M; Jones, Benedict C

2012-12-01

Although men displaying cues of good physical condition possess traits that are desirable in a mate (e.g., good health), these men are also more likely to possess antisocial characteristics that are undesirable in a long-term partner (e.g., aggression and tendency to infidelity). How women resolve this trade-off between the costs and benefits associated with choosing a mate in good physical condition may lead to strategic variation in women's mate preferences. Because the costs of choosing a mate with antisocial personality characteristics are greater in long- than short-term relationships, women's sociosexuality (i.e., the extent to which they are interested in uncommitted sexual relationships) may predict individual differences in their mate preferences. Here we investigated variation in 99 heterosexual women's preferences for facial symmetry, a characteristic that is thought to be an important cue of physical condition. Symmetry preferences were assessed using pairs of symmetrized and original (i.e., relatively asymmetric) versions of 10 male and 10 female faces. Analyses showed that women's sociosexuality, and their sociosexual attitude in particular, predicted their preferences for symmetry in men's, but not women's, faces; women who reported being more interested in short-term, uncommitted relationships demonstrated stronger attraction to symmetric men. Our findings present new evidence for potentially adaptive variation in women's symmetry preferences that is consistent with trade-off theories of attraction.

Where does the carbon go? A model–data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest free-air CO2 enrichment sites

PubMed Central

De Kauwe, Martin G; Medlyn, Belinda E; Zaehle, Sönke; Walker, Anthony P; Dietze, Michael C; Wang, Ying-Ping; Luo, Yiqi; Jain, Atul K; El-Masri, Bassil; Hickler, Thomas; Wårlind, David; Weng, Ensheng; Parton, William J; Thornton, Peter E; Wang, Shusen; Prentice, I Colin; Asao, Shinichi; Smith, Benjamin; McCarthy, Heather R; Iversen, Colleen M; Hanson, Paul J; Warren, Jeffrey M; Oren, Ram; Norby, Richard J

2014-01-01

Elevated atmospheric CO2 concentration (eCO2) has the potential to increase vegetation carbon storage if increased net primary production causes increased long-lived biomass. Model predictions of eCO2 effects on vegetation carbon storage depend on how allocation and turnover processes are represented. We used data from two temperate forest free-air CO2 enrichment (FACE) experiments to evaluate representations of allocation and turnover in 11 ecosystem models. Observed eCO2 effects on allocation were dynamic. Allocation schemes based on functional relationships among biomass fractions that vary with resource availability were best able to capture the general features of the observations. Allocation schemes based on constant fractions or resource limitations performed less well, with some models having unintended outcomes. Few models represent turnover processes mechanistically and there was wide variation in predictions of tissue lifespan. Consequently, models did not perform well at predicting eCO2 effects on vegetation carbon storage. Our recommendations to reduce uncertainty include: use of allocation schemes constrained by biomass fractions; careful testing of allocation schemes; and synthesis of allocation and turnover data in terms of model parameters. Data from intensively studied ecosystem manipulation experiments are invaluable for constraining models and we recommend that such experiments should attempt to fully quantify carbon, water and nutrient budgets. PMID:24844873

Testing a hydraulic trait based model of stomatal control: results from a controlled drought experiment on aspen (Populus tremuloides, Michx.) and ponderosa pine (Pinus ponderosa, Douglas)

NASA Astrophysics Data System (ADS)

Love, D. M.; Venturas, M.; Sperry, J.; Wang, Y.; Anderegg, W.

2017-12-01

Modeling approaches for tree stomatal control often rely on empirical fitting to provide accurate estimates of whole tree transpiration (E) and assimilation (A), which are limited in their predictive power by the data envelope used to calibrate model parameters. Optimization based models hold promise as a means to predict stomatal behavior under novel climate conditions. We designed an experiment to test a hydraulic trait based optimization model, which predicts stomatal conductance from a gain/risk approach. Optimal stomatal conductance is expected to maximize the potential carbon gain by photosynthesis, and minimize the risk to hydraulic transport imposed by cavitation. The modeled risk to the hydraulic network is assessed from cavitation vulnerability curves, a commonly measured physiological trait in woody plant species. Over a growing season garden grown plots of aspen (Populus tremuloides, Michx.) and ponderosa pine (Pinus ponderosa, Douglas) were subjected to three distinct drought treatments (moderate, severe, severe with rehydration) relative to a control plot to test model predictions. Model outputs of predicted E, A, and xylem pressure can be directly compared to both continuous data (whole tree sapflux, soil moisture) and point measurements (leaf level E, A, xylem pressure). The model also predicts levels of whole tree hydraulic impairment expected to increase mortality risk. This threshold is used to estimate survivorship in the drought treatment plots. The model can be run at two scales, either entirely from climate (meteorological inputs, irrigation) or using the physiological measurements as a starting point. These data will be used to study model performance and utility, and aid in developing the model for larger scale applications.

Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks

PubMed Central

Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S.

2017-01-01

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a=(u,v) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages. PMID:28771201

Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

PubMed

Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S

2017-08-03

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.

Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals.

PubMed

Trisciuzzi, Daniela; Alberga, Domenico; Mansouri, Kamel; Judson, Richard; Novellino, Ettore; Mangiatordi, Giuseppe Felice; Nicolotti, Orazio

2017-11-27

We present a practical and easy-to-run in silico workflow exploiting a structure-based strategy making use of docking simulations to derive highly predictive classification models of the androgenic potential of chemicals. Models were trained on a high-quality chemical collection comprising 1689 curated compounds made available within the CoMPARA consortium from the US Environmental Protection Agency and were integrated with a two-step applicability domain whose implementation had the effect of improving both the confidence in prediction and statistics by reducing the number of false negatives. Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from the Protein Data Bank) was associated with the best performing structure-based classification model. Three validation sets comprising each 2590 compounds extracted by the DUD-E collection were used to challenge model performance and the effectiveness of Applicability Domain implementation. Next, the 2PNU model was applied to screen and prioritize two collections of chemicals. The first is a small pool of 12 representative androgenic compounds that were accurately classified based on outstanding rationale at the molecular level. The second is a large external blind set of 55450 chemicals with potential for human exposure. We show how the use of molecular docking provides highly interpretable models and can represent a real-life option as an alternative nontesting method for predictive toxicology.

ABodyBuilder: Automated antibody structure prediction with data–driven accuracy estimation

PubMed Central

Leem, Jinwoo; Dunbar, James; Georges, Guy; Shi, Jiye; Deane, Charlotte M.

2016-01-01

ABSTRACT Computational modeling of antibody structures plays a critical role in therapeutic antibody design. Several antibody modeling pipelines exist, but no freely available methods currently model nanobodies, provide estimates of expected model accuracy, or highlight potential issues with the antibody's experimental development. Here, we describe our automated antibody modeling pipeline, ABodyBuilder, designed to overcome these issues. The algorithm itself follows the standard 4 steps of template selection, orientation prediction, complementarity-determining region (CDR) loop modeling, and side chain prediction. ABodyBuilder then annotates the ‘confidence’ of the model as a probability that a component of the antibody (e.g., CDRL3 loop) will be modeled within a root–mean square deviation threshold. It also flags structural motifs on the model that are known to cause issues during in vitro development. ABodyBuilder was tested on 4 separate datasets, including the 11 antibodies from the Antibody Modeling Assessment–II competition. ABodyBuilder builds models that are of similar quality to other methodologies, with sub–Angstrom predictions for the ‘canonical’ CDR loops. Its ability to model nanobodies, and rapidly generate models (∼30 seconds per model) widens its potential usage. ABodyBuilder can also help users in decision–making for the development of novel antibodies because it provides model confidence and potential sequence liabilities. ABodyBuilder is freely available at http://opig.stats.ox.ac.uk/webapps/abodybuilder. PMID:27392298

Pharmacokinetic Interactions between Drugs and Botanical Dietary Supplements

PubMed Central

Sprouse, Alyssa A.

2016-01-01

The use of botanical dietary supplements has grown steadily over the last 20 years despite incomplete information regarding active constituents, mechanisms of action, efficacy, and safety. An important but underinvestigated safety concern is the potential for popular botanical dietary supplements to interfere with the absorption, transport, and/or metabolism of pharmaceutical agents. Clinical trials of drug–botanical interactions are the gold standard and are usually carried out only when indicated by unexpected consumer side effects or, preferably, by predictive preclinical studies. For example, phase 1 clinical trials have confirmed preclinical studies and clinical case reports that St. John’s wort (Hypericum perforatum) induces CYP3A4/CYP3A5. However, clinical studies of most botanicals that were predicted to interact with drugs have shown no clinically significant effects. For example, clinical trials did not substantiate preclinical predictions that milk thistle (Silybum marianum) would inhibit CYP1A2, CYP2C9, CYP2D6, CYP2E1, and/or CYP3A4. Here, we highlight discrepancies between preclinical and clinical data concerning drug–botanical interactions and critically evaluate why some preclinical models perform better than others in predicting the potential for drug–botanical interactions. Gaps in knowledge are also highlighted for the potential of some popular botanical dietary supplements to interact with therapeutic agents with respect to absorption, transport, and metabolism. PMID:26438626

Pharmacokinetic Interactions between Drugs and Botanical Dietary Supplements.

PubMed

Sprouse, Alyssa A; van Breemen, Richard B

2016-02-01

The use of botanical dietary supplements has grown steadily over the last 20 years despite incomplete information regarding active constituents, mechanisms of action, efficacy, and safety. An important but underinvestigated safety concern is the potential for popular botanical dietary supplements to interfere with the absorption, transport, and/or metabolism of pharmaceutical agents. Clinical trials of drug-botanical interactions are the gold standard and are usually carried out only when indicated by unexpected consumer side effects or, preferably, by predictive preclinical studies. For example, phase 1 clinical trials have confirmed preclinical studies and clinical case reports that St. John's wort (Hypericum perforatum) induces CYP3A4/CYP3A5. However, clinical studies of most botanicals that were predicted to interact with drugs have shown no clinically significant effects. For example, clinical trials did not substantiate preclinical predictions that milk thistle (Silybum marianum) would inhibit CYP1A2, CYP2C9, CYP2D6, CYP2E1, and/or CYP3A4. Here, we highlight discrepancies between preclinical and clinical data concerning drug-botanical interactions and critically evaluate why some preclinical models perform better than others in predicting the potential for drug-botanical interactions. Gaps in knowledge are also highlighted for the potential of some popular botanical dietary supplements to interact with therapeutic agents with respect to absorption, transport, and metabolism. Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics.

Benchmarking the Fundamental Electronic Properties of small TiO 2 Nanoclusters by GW and Coupled Cluster Theory Calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berardo, Enrico; Kaplan, Ferdinand; Bhaskaran-Nair, Kiran

We study the vertical ionisation potential, electron affinity, fundamental gap and exciton binding energy values of small bare and hydroxylated TiO 2 nanoclusters to understand how the excited state properties change as a function of size and hydroxylation. In addition, we have employed a range of many-body methods; including G 0 W 0, qs GW, EA/IP-EOM-CCSD and DFT (B3LYP, PBE), to compare the performance and predictions of the different classes of methods. We demonstrate that for bare (i.e. non-hydroxylated) clusters all many-body methods predict the same trend with cluster size. The highest occupied and lowest unoccupied DFT orbitals follow themore » same trends as the electron affinity and ionisation potentials predicted by the many-body methods but are generally far too shallow and deep respectively in absolute terms. In contrast, the ΔDFT method is found to yield values in the correct energy window. However, its predictions depend on the functional used and do not necessarily follow trends based on the many-body methods. The effect of hydroxylation of the clusters is to open up both the optical and fundamental gap. In conclusion, a simple microscopic explanation for the observed trends with cluster size and upon hydroxylation is proposed in terms of the Madelung onsite potential.« less

Benchmarking the Fundamental Electronic Properties of small TiO 2 Nanoclusters by GW and Coupled Cluster Theory Calculations

DOE PAGES

Berardo, Enrico; Kaplan, Ferdinand; Bhaskaran-Nair, Kiran; ...

2017-06-19

We study the vertical ionisation potential, electron affinity, fundamental gap and exciton binding energy values of small bare and hydroxylated TiO 2 nanoclusters to understand how the excited state properties change as a function of size and hydroxylation. In addition, we have employed a range of many-body methods; including G 0 W 0, qs GW, EA/IP-EOM-CCSD and DFT (B3LYP, PBE), to compare the performance and predictions of the different classes of methods. We demonstrate that for bare (i.e. non-hydroxylated) clusters all many-body methods predict the same trend with cluster size. The highest occupied and lowest unoccupied DFT orbitals follow themore » same trends as the electron affinity and ionisation potentials predicted by the many-body methods but are generally far too shallow and deep respectively in absolute terms. In contrast, the ΔDFT method is found to yield values in the correct energy window. However, its predictions depend on the functional used and do not necessarily follow trends based on the many-body methods. The effect of hydroxylation of the clusters is to open up both the optical and fundamental gap. In conclusion, a simple microscopic explanation for the observed trends with cluster size and upon hydroxylation is proposed in terms of the Madelung onsite potential.« less

Pathway index models for construction of patient-specific risk profiles.

PubMed

Eng, Kevin H; Wang, Sijian; Bradley, William H; Rader, Janet S; Kendziorski, Christina

2013-04-30

Statistical methods for variable selection, prediction, and classification have proven extremely useful in moving personalized genomics medicine forward, in particular, leading to a number of genomic-based assays now in clinical use for predicting cancer recurrence. Although invaluable in individual cases, the information provided by these assays is limited. Most often, a patient is classified into one of very few groups (e.g., recur or not), limiting the potential for truly personalized treatment. Furthermore, although these assays provide information on which individuals are at most risk (e.g., those for which recurrence is predicted), they provide no information on the aberrant biological pathways that give rise to the increased risk. We have developed an approach to address these limitations. The approach models a time-to-event outcome as a function of known biological pathways, identifies important genomic aberrations, and provides pathway-based patient-specific assessments of risk. As we demonstrate in a study of ovarian cancer from The Cancer Genome Atlas project, the patient-specific risk profiles are powerful and efficient characterizations useful in addressing a number of questions related to identifying informative patient subtypes and predicting survival. Copyright © 2012 John Wiley & Sons, Ltd.

Identifying cognitive predictors of reactive and proactive aggression.

PubMed

Brugman, Suzanne; Lobbestael, Jill; Arntz, Arnoud; Cima, Maaike; Schuhmann, Teresa; Dambacher, Franziska; Sack, Alexander T

2015-01-01

The aim of this study was to identify implicit cognitive predictors of aggressive behavior. Specifically, the predictive value of an attentional bias for aggressive stimuli and automatic association of the self and aggression was examined for reactive and proactive aggressive behavior in a non-clinical sample (N = 90). An Emotional Stroop Task was used to measure an attentional bias. With an idiographic Single-Target Implicit Association Test, automatic associations were assessed between words referring to the self (e.g., the participants' name) and words referring to aggression (e.g., fighting). The Taylor Aggression Paradigm (TAP) was used to measure reactive and proactive aggressive behavior. Furthermore, self-reported aggressiveness was assessed with the Reactive Proactive Aggression Questionnaire (RPQ). Results showed that heightened attentional interference for aggressive words significantly predicted more reactive aggression, while lower attentional bias towards aggressive words predicted higher levels of proactive aggression. A stronger self-aggression association resulted in more proactive aggression, but not reactive aggression. Self-reports on aggression did not additionally predict behavioral aggression. This implies that the cognitive tests employed in our study have the potential to discriminate between reactive and proactive aggression. Aggr. Behav. 41:51-64 2015. © 2014 Wiley Periodicals, Inc. © 2014 Wiley Periodicals, Inc.

Calibration and validation of a physiologically based model for soman intoxication in the rat, marmoset, guinea pig and pig.

PubMed

Chen, Kaizhen; Seng, Kok-Yong

2012-09-01

A physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model has been developed for low, medium and high levels of soman intoxication in the rat, marmoset, guinea pig and pig. The primary objective of this model was to describe the pharmacokinetics of soman after intravenous, intramuscular and subcutaneous administration in the rat, marmoset, guinea pig, and pig as well as its subsequent pharmacodynamic effects on blood acetylcholinesterase (AChE) levels, relating dosimetry to physiological response. The reactions modelled in each physiologically realistic compartment are: (1) partitioning of C(±)P(±) soman from the blood into the tissue; (2) inhibition of AChE and carboxylesterase (CaE) by soman; (3) elimination of soman by enzymatic hydrolysis; (4) de novo synthesis and degradation of AChE and CaE; and (5) aging of AChE-soman and CaE-soman complexes. The model was first calibrated for the rat, then extrapolated for validation in the marmoset, guinea pig and pig. Adequate fits to experimental data on the time course of soman pharmacokinetics and AChE inhibition were achieved in the mammalian models. In conclusion, the present model adequately predicts the dose-response relationship resulting from soman intoxication and can potentially be applied to predict soman pharmacokinetics and pharmacodynamics in other species, including human. Copyright © 2011 John Wiley & Sons, Ltd.

The effects of school closures on influenza outbreaks and pandemics: systematic review of simulation studies.

PubMed

Jackson, Charlotte; Mangtani, Punam; Hawker, Jeremy; Olowokure, Babatunde; Vynnycky, Emilia

2014-01-01

School closure is a potential intervention during an influenza pandemic and has been investigated in many modelling studies. To systematically review the effects of school closure on influenza outbreaks as predicted by simulation studies. We searched Medline and Embase for relevant modelling studies published by the end of October 2012, and handsearched key journals. We summarised the predicted effects of school closure on the peak and cumulative attack rates and the duration of the epidemic. We investigated how these predictions depended on the basic reproduction number, the timing and duration of closure and the assumed effects of school closures on contact patterns. School closures were usually predicted to be most effective if they caused large reductions in contact, if transmissibility was low (e.g. a basic reproduction number <2), and if attack rates were higher in children than in adults. The cumulative attack rate was expected to change less than the peak, but quantitative predictions varied (e.g. reductions in the peak were frequently 20-60% but some studies predicted >90% reductions or even increases under certain assumptions). This partly reflected differences in model assumptions, such as those regarding population contact patterns. Simulation studies suggest that school closure can be a useful control measure during an influenza pandemic, particularly for reducing peak demand on health services. However, it is difficult to accurately quantify the likely benefits. Further studies of the effects of reactive school closures on contact patterns are needed to improve the accuracy of model predictions.

Application of optical action potentials in human induced pluripotent stem cells-derived cardiomyocytes to predict drug-induced cardiac arrhythmias.

PubMed

Lu, H R; Hortigon-Vinagre, M P; Zamora, V; Kopljar, I; De Bondt, A; Gallacher, D J; Smith, G

2017-09-01

Human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) are emerging as new and human-relevant source in vitro model for cardiac safety assessment that allow us to investigate a set of 20 reference drugs for predicting cardiac arrhythmogenic liability using optical action potential (oAP) assay. Here, we describe our examination of the oAP measurement using a voltage sensitive dye (Di-4-ANEPPS) to predict adverse compound effects using hiPS-CMs and 20 cardioactive reference compounds. Fluorescence signals were digitized at 10kHz and the records subsequently analyzed off-line. Cells were exposed to 30min incubation to vehicle or compound (n=5/dose, 4 doses/compound) that were blinded to the investigating laboratory. Action potential parameters were measured, including rise time (T rise ) of the optical action potential duration (oAPD). Significant effects on oAPD were sensitively detected with 11 QT-prolonging drugs, while oAPD shortening was observed with I Ca -antagonists, I Kr -activator or ATP-sensitive K + channel (K ATP )-opener. Additionally, the assay detected varied effects induced by 6 different sodium channel blockers. The detection threshold for these drug effects was at or below the published values of free effective therapeutic plasma levels or effective concentrations by other studies. The results of this blinded study indicate that OAP is a sensitive method to accurately detect drug-induced effects (i.e., duration/QT-prolongation, shortening, beat rate, and incidence of early after depolarizations) in hiPS-CMs; therefore, this technique will potentially be useful in predicting drug-induced arrhythmogenic liabilities in early de-risking within the drug discovery phase. Copyright © 2017 Elsevier Inc. All rights reserved.

Electrochemically driven emulsion inversion

NASA Astrophysics Data System (ADS)

Johans, Christoffer; Kontturi, Kyösti

2007-09-01

It is shown that emulsions stabilized by ionic surfactants can be inverted by controlling the electrical potential across the oil-water interface. The potential dependent partitioning of sodium dodecyl sulfate (SDS) was studied by cyclic voltammetry at the 1,2-dichlorobenzene|water interface. In the emulsion the potential control was achieved by using a potential-determining salt. The inversion of a 1,2-dichlorobenzene-in-water (O/W) emulsion stabilized by SDS was followed by conductometry as a function of added tetrapropylammonium chloride. A sudden drop in conductivity was observed, indicating the change of the continuous phase from water to 1,2-dichlorobenzene, i.e. a water-in-1,2-dichlorobenzene emulsion was formed. The inversion potential is well in accordance with that predicted by the hydrophilic-lipophilic deviation if the interfacial potential is appropriately accounted for.

Breaking rural health care paradigms leads to collaboration. Interview by Donald E. Johnson.

PubMed

Knoble, J K

1993-05-01

Strategic planning in a rural community is a challenge. Trying to predict the impact of federal health care reforms while undertaking a 30 million dollar capital construction campaign to consolidate two deteriorating hospitals into one new medical center could have been a nightmare. Health Care Strategic Management publisher Donald E.L. Johnson and Eastern New Mexico Medical Center president and chief executive officer James K. Knoble discuss the challenges of federal health care reforms and rural health care administration, and explore potential opportunities for collaboration and integration.

Supercharging of the Lunar Surface by Solar Wind Halo Electrons

NASA Astrophysics Data System (ADS)

Stubbs, T. J.; Farrell, W. M.; Collier, M. R.; Halekas, J. S.; Delory, G. T.; Holland, M. P.; Vondrak, R. R.

2007-12-01

Lunar surface potentials can reach several kilovolts negative during Solar Energetic Particle (SEPs) events, as indicated by recent analysis of data from the Lunar Prospector Electron Reflectometer (LP/ER). The lunar surface- plasma interactions that result in such extreme surface potentials are poorly characterized and understood. Extreme lunar surface charging, and the associated electrostatic discharges and transport of charged dust, will likely present significant hazards to future human explorers. This is of particular concern near the terminator and polar regions, such as the South Pole/Aiken Basin site planned for NASA's manned outpost. It is the flux of electrons from the ambient plasma that charges the surface of the Moon to negative potentials. In the solar wind, the electron temperature is typically ~10 eV which tends to charge the lunar surface to ~100 V negative in shadow. However, during space weather events the solar wind electrons are often better described by the sum of two Maxwellian distributions, referred to as the "core" and "halo" components. The core electrons are relatively cool and dense (e.g., ~10 eV and ~10/cc), whereas the halo electrons are hot and tenuous (e.g., ~100 eV and ~0.1/cc). Despite, the tenuous nature of the halo electrons, our surface charging model - using core and halo electron data derived from the Solar Wind Experiment (SWE) aboard the Wind spacrcraft - predicts that they are capable of "supercharging" the lunar surface to kilovolt potentials during space weather events, which could explain the LP/ER observations.

Predicting skin sensitisation using a decision tree integrated testing strategy with an in silico model and in chemico/in vitro assays.

PubMed

Macmillan, Donna S; Canipa, Steven J; Chilton, Martyn L; Williams, Richard V; Barber, Christopher G

2016-04-01

There is a pressing need for non-animal methods to predict skin sensitisation potential and a number of in chemico and in vitro assays have been designed with this in mind. However, some compounds can fall outside the applicability domain of these in chemico/in vitro assays and may not be predicted accurately. Rule-based in silico models such as Derek Nexus are expert-derived from animal and/or human data and the mechanism-based alert domain can take a number of factors into account (e.g. abiotic/biotic activation). Therefore, Derek Nexus may be able to predict for compounds outside the applicability domain of in chemico/in vitro assays. To this end, an integrated testing strategy (ITS) decision tree using Derek Nexus and a maximum of two assays (from DPRA, KeratinoSens, LuSens, h-CLAT and U-SENS) was developed. Generally, the decision tree improved upon other ITS evaluated in this study with positive and negative predictivity calculated as 86% and 81%, respectively. Our results demonstrate that an ITS using an in silico model such as Derek Nexus with a maximum of two in chemico/in vitro assays can predict the sensitising potential of a number of chemicals, including those outside the applicability domain of existing non-animal assays. Copyright © 2016 Elsevier Inc. All rights reserved.

Seasonal differences of model predictability and the impact of SST in the Pacific

NASA Astrophysics Data System (ADS)

Lang, X. M.; Wang, H. J.

2005-01-01

Both seasonal potential predictability and the impact of SST in the Pacific on the forecast skill over China are investigated by using a 9-level global atmospheric general circulation model developed at the Institute of Atmospheric Physics under the Chinese Academy of Sciences (IAP9L-ACCM). For each year during 1970 to 1999, the ensemble consists of seven integrations started from consecutive observational daily atmospheric fields and forced by observational monthly SST. For boreal winter, spring and summer, the variance ratios of the SST-forced variability to the total variability and the differences in the spatial correlation coefficients of seasonal mean fields in special years versus normal years are computed respectively. It follows that there are slightly inter-seasonal differences in the model potential predictability in the Tropics. At northern middle and high latitudes, prediction skill is generally low in spring and relatively high either in summer for surface air temperature and middle and upper tropospheric geopotential height or in winter for wind and precipitation. In general, prediction skill rises notably in western China, especially in northwestern China, when SST anomalies (SSTA) in the Ni (n) over tildeo-3 region are significant. Moreover, particular attention should be paid to the SSTA in the North Pacific (NP) if one aims to predict summer climate over the eastern part of China, i.e., northeastern China, North China and southeastern China.

Comparative study of immunological and structural properties of two recombinant vaccine candidates against botulinum neurotoxin type E.

PubMed

Rostamian, Mosayeb; Mousavy, Seyed Jafar; Ebrahimi, Firouz; Ghadami, Seyyed Abolghasem; Sheibani, Nader; Minaei, Mohammad Ebrahim; Arefpour Torabi, Mohammad Ali

2012-01-01

Recently, botulinum neurotoxin (BoNT)-derived recombinant proteins have been suggested as potential botulism vaccines. Here, with concentrating on BoNT type E (BoNT/E), we studied two of these binding domain-based recombinant proteins: a multivalent chimer protein, which is composed of BoNT serotypes A, B and E binding subdomains, and a monovalent recombinant protein, which contains 93 amino acid residues from recombinant C-terminal heavy chain of BoNT/E (rBoNT/E-HCC). Both proteins have an identical region (48 aa) that contains one of the most important BoNT/E epitopes (YLTHMRD sequence). The recombinant protein efficiency in antibody production, their structural differences, and their BoNT/E-epitope location were compared by using ELISA, circular dichroism, computational modeling, and hydrophobicity predictions. Immunological studies indicated that the antibody yield against rBoNT/E-HCC was higher than chimer protein. Cross ELISA confirmed that the antibodies against the chimer protein recognized rBoNT/E-HCC more efficiently. However, both antibody groups (anti-chimer and anti-rBoNT/E-HCC antibodies) were able to recognize other proteins. Structural studies with circular dichroism showed that chimer proteins have slightly more secondary structures than rBoNT/E-HCC. The immunological results suggested that the above-mentioned identical region in rBoNT/E-HCC is more exposed. Circular dichroism, computational protein modeling and hydrophobicity predictions indicated a more exposed location for the identical region in rBoNT/E-HCC than the chimer protein, which is strongly in agreement with immunological results.

Rotational Parameters from Vibronic Eigenfunctions of Jahn-Teller Active Molecules

NASA Astrophysics Data System (ADS)

Garner, Scott M.; Miller, Terry A.

2017-06-01

The structure in rotational spectra of many free radical molecules is complicated by Jahn-Teller distortions. Understanding the magnitudes of these distortions is vital to determining the equilibrium geometric structure and details of potential energy surfaces predicted from electronic structure calculations. For example, in the recently studied {\\widetilde{A}^2E^{''} } state of the NO_3 radical, the magnitudes of distortions are yet to be well understood as results from experimental spectroscopic studies of its vibrational and rotational structure disagree with results from electronic structure calculations of the potential energy surface. By fitting either vibrationally resolved spectra or vibronic levels determined by a calculated potential energy surface, we obtain vibronic eigenfunctions for the system as linear combinations of basis functions from products of harmonic oscillators and the degenerate components of the electronic state. Using these vibronic eigenfunctions we are able to predict parameters in the rotational Hamiltonian such as the Watson Jahn-Teller distortion term, h_1, and compare with the results from the analysis of rotational experiments.

Nobody Is Perfect: ERP Effects Prior to Performance Errors in Musicians Indicate Fast Monitoring Processes

PubMed Central

Maidhof, Clemens; Rieger, Martina; Prinz, Wolfgang; Koelsch, Stefan

2009-01-01

Background One central question in the context of motor control and action monitoring is at what point in time errors can be detected. Previous electrophysiological studies investigating this issue focused on brain potentials elicited after erroneous responses, mainly in simple speeded response tasks. In the present study, we investigated brain potentials before the commission of errors in a natural and complex situation. Methodology/Principal Findings Expert pianists bimanually played scales and patterns while the electroencephalogram (EEG) was recorded. Event-related potentials (ERPs) were computed for correct and incorrect performances. Results revealed differences already 100 ms prior to the onset of a note (i.e., prior to auditory feedback). We further observed that erroneous keystrokes were delayed in time and pressed more slowly. Conclusions Our data reveal neural mechanisms in musicians that are able to detect errors prior to the execution of erroneous movements. The underlying mechanism probably relies on predictive control processes that compare the predicted outcome of an action with the action goal. PMID:19337379

Updates to the zoonotic niche map of Ebola virus disease in Africa

PubMed Central

Pigott, David M; Millear, Anoushka I; Earl, Lucas; Morozoff, Chloe; Han, Barbara A; Shearer, Freya M; Weiss, Daniel J; Brady, Oliver J; Kraemer, Moritz UG; Moyes, Catherine L; Bhatt, Samir; Gething, Peter W; Golding, Nick; Hay, Simon I

2016-01-01

As the outbreak of Ebola virus disease (EVD) in West Africa is now contained, attention is turning from control to future outbreak prediction and prevention. Building on a previously published zoonotic niche map (Pigott et al., 2014), this study incorporates new human and animal occurrence data and expands upon the way in which potential bat EVD reservoir species are incorporated. This update demonstrates the potential for incorporating and updating data used to generate the predicted suitability map. A new data portal for sharing such maps is discussed. This output represents the most up-to-date estimate of the extent of EVD zoonotic risk in Africa. These maps can assist in strengthening surveillance and response capacity to contain viral haemorrhagic fevers. DOI: http://dx.doi.org/10.7554/eLife.16412.001 PMID:27414263

Probing gravity at cosmological scales by measurements which test the relationship between gravitational lensing and matter overdensity.

PubMed

Zhang, Pengjie; Liguori, Michele; Bean, Rachel; Dodelson, Scott

2007-10-05

The standard cosmology is based on general relativity (GR) and includes dark matter and dark energy and predicts a fixed relationship between the gravitational potentials responsible for gravitational lensing and the matter overdensity. Alternative theories of gravity often make different predictions. We propose a set of measurements which can test this relationship, thereby distinguishing between dark energy or matter models and models in which gravity differs from GR. Planned surveys will be able to measure E(G), an observational quantity whose expectation value is equal to the ratio of the Laplacian of the Newtonian potentials to the peculiar velocity divergence, to percent accuracy. This will easily separate alternatives such as the cold dark matter model with a cosmological constant, Dvali-Gabadadze-Porrati, TeVeS, and f(R) gravity.

Temporal effects in trend prediction: identifying the most popular nodes in the future.

PubMed

Zhou, Yanbo; Zeng, An; Wang, Wei-Hong

2015-01-01

Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes' recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail.

Temporal Effects in Trend Prediction: Identifying the Most Popular Nodes in the Future

PubMed Central

Zhou, Yanbo; Zeng, An; Wang, Wei-Hong

2015-01-01

Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes’ recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail. PMID:25806810

A Signal to Noise Paradox in Climate Predictions

NASA Astrophysics Data System (ADS)

Eade, R.; Scaife, A. A.; Smith, D.; Dunstone, N. J.; MacLachlan, C.; Hermanson, L.; Ruth, C.

2017-12-01

Recent advances in climate modelling have resulted in the achievement of skilful long-range prediction, particular that associated with the winter circulation over the north Atlantic (e.g. Scaife et al 2014, Stockdale et al 2015, Dunstone et al 2016) including impacts over Europe and North America, and further afield. However, while highly significant and potentially useful skill exists, the signal-to-noise ratio of the ensemble mean to total variability in these ensemble predictions is anomalously small (Scaife et al 2014) and the correlation between the ensemble mean and historical observations exceeds the proportion of predictable variance in the ensemble (Eade et al 2014). This means the real world is more predictable than our climate models. Here we discuss a series of hypothesis tests that have been carried out to assess issues with model mechanisms compared to the observed world, and present the latest findings in our attempt to determine the cause of the anomalously weak predicted signals in our seasonal-to-decadal hindcasts.

Multi-Dimensional Shallow Landslide Stability Analysis Suitable for Application at the Watershed Scale

NASA Astrophysics Data System (ADS)

Milledge, D.; Bellugi, D.; McKean, J. A.; Dietrich, W.

2012-12-01

The infinite slope model is the basis for almost all watershed scale slope stability models. However, it assumes that a potential landslide is infinitely long and wide. As a result, it cannot represent resistance at the margins of a potential landslide (e.g. from lateral roots), and is unable to predict the size of a potential landslide. Existing three-dimensional models generally require computationally expensive numerical solutions and have previously been applied only at the hillslope scale. Here we derive an alternative analytical treatment that accounts for lateral resistance by representing the forces acting on each margin of an unstable block. We apply 'at rest' earth pressure on the lateral sides, and 'active' and 'passive' pressure using a log-spiral method on the upslope and downslope margins. We represent root reinforcement on each margin assuming that root cohesion is an exponential function of soil depth. We benchmark this treatment against other more complete approaches (Finite Element (FE) and closed form solutions) and find that our model: 1) converges on the infinite slope predictions as length / depth and width / depth ratios become large; 2) agrees with the predictions from state-of-the-art FE models to within +/- 30% error, for the specific cases in which these can be applied. We then test our model's ability to predict failure of an actual (mapped) landslide where the relevant parameters are relatively well constrained. We find that our model predicts failure at the observed location with a nearly identical shape and predicts that larger or smaller shapes conformal to the observed shape are indeed more stable. Finally, we perform a sensitivity analysis using our model to show that lateral reinforcement sets a minimum landslide size, while the additional strength at the downslope boundary means that the optimum shape for a given size is longer in a downslope direction. However, reinforcement effects cannot fully explain the size or shape distributions of observed landslides, highlighting the importance of spatial patterns of key parameters (e.g. pore water pressure) and motivating the model's watershed scale application. This watershed scale application requires an efficient method to find the least stable shapes among an almost infinite set. However, when applied in this context, it allows a more complete examination of the controls on landslide size, shape and location.

Effects of anticipated emotional category and temporal predictability on the startle reflex.

PubMed

Parisi, Elizabeth A; Hajcak, Greg; Aneziris, Eleni; Nelson, Brady D

2017-09-01

Anticipated emotional category and temporal predictability are key characteristics that have both been shown to impact psychophysiological indices of defensive motivation (e.g., the startle reflex). To date, research has primarily examined these features in isolation, and it is unclear whether they have additive or interactive effects on defensive motivation. In the present study, the startle reflex was measured in anticipation of low arousal neutral, moderate arousal pleasant, and high arousal unpleasant pictures that were presented with either predictable or unpredictable timing. Linear mixed-effects modeling was conducted to examine startle magnitude across time, and the intercept at the beginning and end of the task. Across the entire task, the anticipation of temporally unpredictable (relative to predictable) pictures and emotional (relative to neutral) pictures potentiated startle magnitude, but there was no interaction between the two features. However, examination of the intercept at the beginning of the task indicated a Predictability by Emotional Category interaction, such that temporal unpredictability enhanced startle potentiation in anticipation of unpleasant pictures only. Examination of the intercept at the end of the task indicated that the effects of predictability and emotional category on startle magnitude were largely diminished. The present study replicates previous reports demonstrating that emotional category and temporal predictability impact the startle reflex, and provides novel evidence suggesting an interactive effect on defensive motivation at the beginning of the task. This study also highlights the importance of examining the time course of the startle reflex. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantitative contamination assessment of Escherichia coli in baby spinach primary production in Spain: Effects of weather conditions and agricultural practices.

PubMed

Allende, Ana; Castro-Ibáñez, Irene; Lindqvist, Roland; Gil, María Isabel; Uyttendaele, Mieke; Jacxsens, Liesbeth

2017-09-18

A quantitative microbial contamination model of Escherichia coli during primary production of baby spinach was developed. The model included only systematic contamination routes (e.g. soil and irrigation water) and it was used to evaluate the potential impact of weather conditions, agricultural practices as well as bacterial fitness in soil on the E. coli levels present in the crop at harvest. The model can be used to estimate E. coli contamination of baby spinach via irrigation water, via soil splashing due to irrigation water or rain events, and also including the inactivation of E. coli on plants due to solar radiation during a variable time of culturing before harvest. Seasonality, solar radiation and rainfall were predicted to have an important impact on the E. coli contamination. Winter conditions increased E. coli prevalence and levels when compared to spring conditions. As regards agricultural practices, both water quality and irrigation system slightly influenced E. coli levels on baby spinach. The good microbiological quality of the irrigation water (average E. coli counts in positive water samples below 1 log/100mL) could have influenced the differences observed among the tested agricultural practices (water treatment and irrigation system). This quantitative microbial contamination model represents a preliminary framework that assesses the potential impact of different factors and intervention strategies affecting E. coli concentrations at field level. Taking into account that E. coli strains may serve as a surrogate organism for enteric bacterial pathogens, obtained results on E. coli levels on baby spinach may be indicative of the potential behaviour of these pathogens under defined conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

Perspectives on treatment options for mesial temporal lobe epilepsy with hippocampal sclerosis.

PubMed

Palleria, Caterina; Coppola, Antonietta; Citraro, Rita; Del Gaudio, Luigi; Striano, Salvatore; De Sarro, Giovambattista; Russo, Emilio

2015-01-01

Mesial temporal lobe epilepsy associated with hippocampal sclerosis (MTLE-HS) is a syndrome that is often refractory to drug treatment. The effects on specific syndromes are not currently available from the pre-marketing clinical development of new AEDs; this does not allow the prediction of whether new drugs will be more effective in the treatment of some patients. We have reviewed all the existing literature relevant to the understanding of a potential effectiveness in MTLE-HS patients for the latest AEDs, namely brivaracetam, eslicarbazepine, lacosamide, perampanel and retigabine also including the most relevant clinical data and a brief description of their pharmacological profile. Records were identified using predefined search criteria using electronic databases (e.g., PubMed, Cochrane Library Database of Systematic Reviews). Primary peer-reviewed articles published up to the 15 June 2015 were included. All the drugs considered have the potential to be effective in the treatment of MTLE-HS; in fact, they possess proven efficacy in animal models; currently considered valuable tools for predicting drug efficacy in TLE. Furthermore, for some of these (e.g., lacosamide and eslicarbazepine) data are already available from post-marketing studies while brivaracetam acting on SV2A like levetiracetam might have the same potential effectiveness with the possibility to be more efficacious considering its ability to inhibit voltage gated sodium channels; finally, perampanel and retigabine are very effective drugs in animal models of TLE.

Development of a novel in silico model of zeta potential for metal oxide nanoparticles: a nano-QSPR approach

NASA Astrophysics Data System (ADS)

Wyrzykowska, Ewelina; Mikolajczyk, Alicja; Sikorska, Celina; Puzyn, Tomasz

2016-11-01

Once released into the aquatic environment, nanoparticles (NPs) are expected to interact (e.g. dissolve, agglomerate/aggregate, settle), with important consequences for NP fate and toxicity. A clear understanding of how internal and environmental factors influence the NP toxicity and fate in the environment is still in its infancy. In this study, a quantitative structure-property relationship (QSPR) approach was employed to systematically explore factors that affect surface charge (zeta potential) under environmentally realistic conditions. The nano-QSPR model developed with multiple linear regression (MLR) was characterized by high robustness ({{{Q}}{{2}}}{{CV}}=0.90) and external predictivity ({{{Q}}{{2}}}{{EXT}}=0.93). The results clearly showed that zeta potential values varied markedly as functions of the ionic radius of the metal atom in the metal oxides, confirming that agglomeration and the extent of release of free MexOy largely depend on their intrinsic properties. A developed nano-QSPR model was successfully applied to predict zeta potential in an ionized solution of NPs for which experimentally determined values of response have been unavailable. Hence, the application of our model is possible when the values of zeta potential in the ionized solution for metal oxide nanoparticles are undetermined, without the necessity of performing more time consuming and expensive experiments. We believe that our studies will be helpful in predicting the conditions under which MexOy is likely to become problematic for the environment and human health.

GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran.

PubMed

Naghibi, Seyed Amir; Pourghasemi, Hamid Reza; Dixon, Barnali

2016-01-01

Groundwater is considered one of the most valuable fresh water resources. The main objective of this study was to produce groundwater spring potential maps in the Koohrang Watershed, Chaharmahal-e-Bakhtiari Province, Iran, using three machine learning models: boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF). Thirteen hydrological-geological-physiographical (HGP) factors that influence locations of springs were considered in this research. These factors include slope degree, slope aspect, altitude, topographic wetness index (TWI), slope length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, lithology, land use, drainage density, and fault density. Subsequently, groundwater spring potential was modeled and mapped using CART, RF, and BRT algorithms. The predicted results from the three models were validated using the receiver operating characteristics curve (ROC). From 864 springs identified, 605 (≈70 %) locations were used for the spring potential mapping, while the remaining 259 (≈30 %) springs were used for the model validation. The area under the curve (AUC) for the BRT model was calculated as 0.8103 and for CART and RF the AUC were 0.7870 and 0.7119, respectively. Therefore, it was concluded that the BRT model produced the best prediction results while predicting locations of springs followed by CART and RF models, respectively. Geospatially integrated BRT, CART, and RF methods proved to be useful in generating the spring potential map (SPM) with reasonable accuracy.

Embedded-atom-method interatomic potentials from lattice inversion.

PubMed

Yuan, Xiao-Jian; Chen, Nan-Xian; Shen, Jiang; Hu, Wangyu

2010-09-22

The present work develops a physically reliable procedure for building the embedded-atom-method (EAM) interatomic potentials for the metals with fcc, bcc and hcp structures. This is mainly based on Chen-Möbius lattice inversion (Chen et al 1997 Phys. Rev. E 55 R5) and first-principles calculations. Following Baskes (Baskes et al 2007 Phys. Rev. B 75 094113), this new version of the EAM eliminates all of the prior arbitrary choices in the determination of the atomic electron density and pair potential functions. Parameterizing the universal form deduced from the calculations within the density-functional scheme for homogeneous electron gas as the embedding function, the new-type EAM potentials for Cu, Fe and Ti metals have successfully been constructed by considering interatomic interactions up to the fifth neighbor, the third neighbor and the seventh neighbor, respectively. The predictions of elastic constants, structural energy difference, vacancy formation energy and migration energy, activation energy of vacancy diffusion, latent heat of melting and relative volume change on melting all satisfactorily agree with the experimental results available or first-principles calculations. The predicted surface energies for low-index crystal faces and the melting point are in agreement with the experimental data to the same extent as those calculated by other EAM-type potentials such as the FBD-EAM, 2NN MEAM and MS-EAM. In addition, the order among the predicted low-index surface energies is also consistent with the experimental information.

HLaffy: estimating peptide affinities for Class-1 HLA molecules by learning position-specific pair potentials.

PubMed

Mukherjee, Sumanta; Bhattacharyya, Chiranjib; Chandra, Nagasuma

2016-08-01

T-cell epitopes serve as molecular keys to initiate adaptive immune responses. Identification of T-cell epitopes is also a key step in rational vaccine design. Most available methods are driven by informatics and are critically dependent on experimentally obtained training data. Analysis of a training set from Immune Epitope Database (IEDB) for several alleles indicates that the sampling of the peptide space is extremely sparse covering a tiny fraction of the possible nonamer space, and also heavily skewed, thus restricting the range of epitope prediction. We present a new epitope prediction method that has four distinct computational modules: (i) structural modelling, estimating statistical pair-potentials and constraint derivation, (ii) implicit modelling and interaction profiling, (iii) feature representation and binding affinity prediction and (iv) use of graphical models to extract peptide sequence signatures to predict epitopes for HLA class I alleles. HLaffy is a novel and efficient epitope prediction method that predicts epitopes for any Class-1 HLA allele, by estimating the binding strengths of peptide-HLA complexes which is achieved through learning pair-potentials important for peptide binding. It relies on the strength of the mechanistic understanding of peptide-HLA recognition and provides an estimate of the total ligand space for each allele. The performance of HLaffy is seen to be superior to the currently available methods. The method is made accessible through a webserver http://proline.biochem.iisc.ernet.in/HLaffy : nchandra@biochem.iisc.ernet.in Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Survey of ocular irritation predictive capacity using Chorioallantoic Membrane Vascular Assay (CAMVA) and Bovine Corneal Opacity and Permeability (BCOP) test historical data for 319 personal care products over fourteen years.

PubMed

Donahue, D A; Kaufman, L E; Avalos, J; Simion, F A; Cerven, D R

2011-03-01

The Chorioallantoic Membrane Vascular Assay (CAMVA) and Bovine Corneal Opacity and Permeability (BCOP) test are widely used to predict ocular irritation potential for consumer-use products. These in vitro assays do not require live animals, produce reliable predictive data for defined applicability domains compared to the Draize rabbit eye test, and are rapid and inexpensive. Data from 304 CAMVA and/or BCOP studies (319 formulations) were surveyed to determine the feasibility of predicting ocular irritation potential for various formulations. Hair shampoos, skin cleansers, and ethanol-based hair styling sprays were repeatedly predicted to be ocular irritants (accuracy rate=0.90-1.00), with skin cleanser and hair shampoo irritation largely dependent on surfactant species and concentration. Conversely, skin lotions/moisturizers and hair styling gels/lotions were repeatedly predicted to be non-irritants (accuracy rate=0.92 and 0.82, respectively). For hair shampoos, ethanol-based hair stylers, skin cleansers, and skin lotions/moisturizers, future ocular irritation testing (i.e., CAMVA/BCOP) can be nearly eliminated if new formulations are systematically compared to those previously tested using a defined decision tree. For other tested product categories, new formulations should continue to be evaluated in CAMVA/BCOP for ocular irritation potential because either the historical data exhibit significant variability (hair conditioners and mousses) or the historical sample size is too small to permit definitive conclusions (deodorants, make-up removers, massage oils, facial masks, body sprays, and other hair styling products). All decision tree conclusions should be made within a conservative weight-of-evidence context, considering the reported limitations of the BCOP test for alcohols, ketones, and solids. Copyright © 2010 Elsevier Ltd. All rights reserved.

Prediction of preservative sensitization potential using surface marker CD86 and/or CD54 expression on human cell line, THP-1.

PubMed

Sakaguchi, Hitoshi; Miyazawa, Masaaki; Yoshida, Yukiko; Ito, Yuichi; Suzuki, Hiroyuki

2007-02-01

Preservatives are important components in many products, but have a history of purported allergy. Several assays [e.g., guinea pig maximization test (GPMT), local lymph node assay (LLNA)] are used to evaluate allergy potential of preservatives. We recently developed the human Cell Line Activation Test (h-CLAT), an in vitro skin sensitization test using human THP-1 cells. This test evaluates the augmentation of CD86 and CD54 expression, which are key events in the sensitization process, as an indicator of allergy following treatment with test chemical. Earlier, we found that a sub-toxic concentration was needed for the up-regulation of surface marker expression. In this study, we further evaluate the capability of h-CLAT to predict allergy potential using eight preservatives. Cytotoxicity was determined using propidium iodide with flow cytometry analysis and five doses that produce a 95, 85, 75, 65, and 50% cell viability were selected. If a material did not have any cytotoxicity at the highest technical dose (HTD), five doses are set using serial 1.3 dilutions of the HTD. The test materials used were six known allergic preservatives (e.g., methylchloroisothiazolinone/methylisothiazolinone, formaldehyde), and two non-allergic preservatives (methylparaben and 4-hydroxybenzoic acid). All allergic preservatives augmented CD86 and/or CD54 expression, indicating h-CLAT correctly identified the allergens. No augmentation was observed with the non-allergic preservatives; also correctly identified by h-CLAT. In addition, we report two threshold concentrations that may be used to categorize skin sensitization potency like the LLNA estimated concentration that yield a three-fold stimulation (EC3) value. These corresponding values are the estimated concentration which gives a relative fluorescence intensity (RFI) = 150 for CD86 and an RFI = 200 for CD54. These data suggest that h-CLAT, using THP-1 cells, may be able to predict the allergy potential of preservatives and possibility classify the potency of an allergen.

Families in a High-Tech Age: Technology Usage Patterns, Work and Family Correlates, and Gender

ERIC Educational Resources Information Center

Chesley, Noelle

2006-01-01

This study analyzes a couple-level ("N" = 581), longitudinal data set of employees to provide evidence about technology use over time, the factors that predict use, and the potential for a spouse to influence an individual's use. Although longitudinal usage patterns suggest a trend toward adoption and use of e-mail, the Internet, cell phones, and…

Examining Dosage Effects on Prevention Outcomes: Results from a Multi-Modal Longitudinal Preventive Intervention for Young Disruptive Boys

ERIC Educational Resources Information Center

Charlebois, Pierre; Brendgen, Mara; Vitaro, Frank; Normandeau, Sylvie; Boudreau, Jean-Francois

2004-01-01

The present study examined (a) the predictive effect of disruptive boys' attendance to a prevention program (i.e., dosage) on post-intervention academic achievement and behavior and (b) the potential moderating effects of child and family characteristics in this context. The 3-year intervention program included reading, self-regulation, and social…

Protein and Lipid Binding Parameters in Rainbow Trout (Oncorhynchus mykiss) Blood and Liver Fractions to Extrapolate from an in Vitro metabolic Degradation Assay to in Vivo Bioaccumulation Potential of Hydrophobic Organic Chemicals

EPA Science Inventory

Biotransformation reduces the extent to which environmental contaminants accumulate in fish and other aquatic biota. Unfortunately, the tendency for compounds to be metabolized is not easily predicted from physico-chemical properties (e.g., octanol:water partitioning) or an exam...

Review of nitrogen fate models applicable to forest landscapes in the Southern U.S.

Treesearch

D. M. Amatya; C. G. Rossi; A. Saleh; Z. Dai; M. A. Youssef; R. G. Williams; D. D. Bosch; G. M. Chescheir; G. Sun; R. W. Skaggs; C. C. Trettin; E. D. Vance; J. E. Nettles; S. Tian

2013-01-01

Assessing the environmental impacts of fertilizer nitrogen (N) used to increase productivity in managed forests is complex due to a wide range of abiotic and biotic factors affecting its forms and movement. Models developed to predict fertilizer N fate (e.g., cycling processes) and water quality impacts vary widely in their design, scope, and potential application. We...

Predicting the Optical Properties of the West Florida Shelf: Resolving the Potential Impacts of a Terrestrial Boundary Condition on the Distribution of Colored Dissolved and Particulate Matter

DTIC Science & Technology

2004-12-08

Emiliania huxleyi (Haptophyceae): 19’ hexanoylox- measurements of organic matter and chlorophyll fluorescence in yfucoxanthin as antenna pigment. Journal of...M.B., Miiller-Karger, F.E., and the prymnesiophyte Emiliania huxleyi. Marine Ecology. 1989. Nitrogen exchange at the continental margin: a numerical

Sida tuberculata (Malvaceae): a study based on development of extractive system and in silico and in vitro properties

PubMed Central

da Rosa, H.S.; Salgueiro, A.C.F.; Colpo, A.Z.C.; Paula, F.R.; Mendez, A.S.L.; Folmer, V.

2016-01-01

Sida tuberculata (Malvaceae) is a medicinal plant traditionally used in Brazil as an antimicrobial and anti-inflammatory agent. Here, we aimed to investigate the different extractive techniques on phytochemical parameters, as well as to evaluate the toxicity and antioxidant capacity of S. tuberculata extracts using in silico and in vitro models. Therefore, in order to determine the dry residue content and the main compound 20-hydroxyecdysone (20E) concentration, extracts from leaves and roots were prepared testing ethanol and water in different proportions. Extracts were then assessed by Artemia salina lethality test, and toxicity prediction of 20E was estimated. Antioxidant activity was performed by DPPH and ABTS radical scavenger assays, ferric reducing power assay, nitrogen derivative scavenger, deoxyribose degradation, and TBARS assays. HPLC evaluation detected 20E as main compound in leaves and roots. Percolation method showed the highest concentrations of 20E (0.134 and 0.096 mg/mL of extract for leaves and roots, respectively). All crude extracts presented low toxic potential on A. salina (LD50 >1000 µg/mL). The computational evaluation of 20E showed a low toxicity prediction. For in vitro antioxidant tests, hydroethanolic extracts of leaves were most effective compared to roots. In addition, hydroethanolic extracts presented a higher IC50 antioxidant than aqueous extracts. TBARS formation was prevented by leaves hydroethanolic extract from 0.015 and 0.03 mg/mL and for roots from 0.03 and 0.3 mg/mL on egg yolk and rat tissue, respectively (P<0.05). These findings suggest that S. tuberculata extracts are a considerable source of ecdysteroids and possesses a significant antioxidant property with low toxic potential. PMID:27409335

Sida tuberculata (Malvaceae): a study based on development of extractive system and in silico and in vitro properties.

PubMed

da Rosa, H S; Salgueiro, A C F; Colpo, A Z C; Paula, F R; Mendez, A S L; Folmer, V

2016-07-11

Sida tuberculata (Malvaceae) is a medicinal plant traditionally used in Brazil as an antimicrobial and anti-inflammatory agent. Here, we aimed to investigate the different extractive techniques on phytochemical parameters, as well as to evaluate the toxicity and antioxidant capacity of S. tuberculata extracts using in silico and in vitro models. Therefore, in order to determine the dry residue content and the main compound 20-hydroxyecdysone (20E) concentration, extracts from leaves and roots were prepared testing ethanol and water in different proportions. Extracts were then assessed by Artemia salina lethality test, and toxicity prediction of 20E was estimated. Antioxidant activity was performed by DPPH and ABTS radical scavenger assays, ferric reducing power assay, nitrogen derivative scavenger, deoxyribose degradation, and TBARS assays. HPLC evaluation detected 20E as main compound in leaves and roots. Percolation method showed the highest concentrations of 20E (0.134 and 0.096 mg/mL of extract for leaves and roots, respectively). All crude extracts presented low toxic potential on A. salina (LD50 >1000 µg/mL). The computational evaluation of 20E showed a low toxicity prediction. For in vitro antioxidant tests, hydroethanolic extracts of leaves were most effective compared to roots. In addition, hydroethanolic extracts presented a higher IC50 antioxidant than aqueous extracts. TBARS formation was prevented by leaves hydroethanolic extract from 0.015 and 0.03 mg/mL and for roots from 0.03 and 0.3 mg/mL on egg yolk and rat tissue, respectively (P<0.05). These findings suggest that S. tuberculata extracts are a considerable source of ecdysteroids and possesses a significant antioxidant property with low toxic potential.

Capitalizing on fine milk composition for breeding and management of dairy cows.

PubMed

Gengler, N; Soyeurt, H; Dehareng, F; Bastin, C; Colinet, F; Hammami, H; Vanrobays, M-L; Lainé, A; Vanderick, S; Grelet, C; Vanlierde, A; Froidmont, E; Dardenne, P

2016-05-01

The challenge of managing and breeding dairy cows is permanently adapting to changing production circumstances under socio-economic constraints. If managing and breeding address different timeframes of action, both need relevant phenotypes that allow for precise monitoring of the status of the cows, and their health, behavior, and well-being as well as their environmental impact and the quality of their products (i.e., milk and subsequently dairy products). Milk composition has been identified as an important source of information because it could reflect, at least partially, all these elements. Major conventional milk components such as fat, protein, urea, and lactose contents are routinely predicted by mid-infrared (MIR) spectrometry and have been widely used for these purposes. But, milk composition is much more complex and other nonconventional milk components, potentially predicted by MIR, might be informative. Such new milk-based phenotypes should be considered given that they are cheap, rapidly obtained, usable on a large scale, robust, and reliable. In a first approach, new phenotypes can be predicted from MIR spectra using techniques based on classical prediction equations. This method was used successfully for many novel traits (e.g., fatty acids, lactoferrin, minerals, milk technological properties, citrate) that can be then useful for management and breeding purposes. An innovation was to consider the longitudinal nature of the relationship between the trait of interest and the MIR spectra (e.g., to predict methane from MIR). By avoiding intermediate steps, prediction errors can be minimized when traits of interest (e.g., methane, energy balance, ketosis) are predicted directly from MIR spectra. In a second approach, research is ongoing to detect and exploit patterns in an innovative manner, by comparing observed with expected MIR spectra directly (e.g., pregnancy). All of these traits can then be used to define best practices, adjust feeding and health management, improve animal welfare, improve milk quality, and mitigate environmental impact. Under the condition that MIR data are available on a large scale, phenotypes for these traits will allow genetic and genomic evaluations. Introduction of novel traits into the breeding objectives will need additional research to clarify socio-economic weights and genetic correlations with other traits of interest. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation.

PubMed

Huey, Raymond B; Kearney, Michael R; Krockenberger, Andrew; Holtum, Joseph A M; Jess, Mellissa; Williams, Stephen E

2012-06-19

A recently developed integrative framework proposes that the vulnerability of a species to environmental change depends on the species' exposure and sensitivity to environmental change, its resilience to perturbations and its potential to adapt to change. These vulnerability criteria require behavioural, physiological and genetic data. With this information in hand, biologists can predict organisms most at risk from environmental change. Biologists and managers can then target organisms and habitats most at risk. Unfortunately, the required data (e.g. optimal physiological temperatures) are rarely available. Here, we evaluate the reliability of potential proxies (e.g. critical temperatures) that are often available for some groups. Several proxies for ectotherms are promising, but analogous ones for endotherms are lacking. We also develop a simple graphical model of how behavioural thermoregulation, acclimation and adaptation may interact to influence vulnerability over time. After considering this model together with the proxies available for physiological sensitivity to climate change, we conclude that ectotherms sharing vulnerability traits seem concentrated in lowland tropical forests. Their vulnerability may be exacerbated by negative biotic interactions. Whether tropical forest (or other) species can adapt to warming environments is unclear, as genetic and selective data are scant. Nevertheless, the prospects for tropical forest ectotherms appear grim.

Predicting a quaternary tungsten oxide for sustainable photovoltaic application by density functional theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sarker, Pranab; Huda, Muhammad N., E-mail: huda@uta.edu; Al-Jassim, Mowafak M.

2015-12-07

A quaternary oxide, CuSnW{sub 2}O{sub 8} (CTTO), has been predicted by density functional theory (DFT) to be a suitable material for sustainable photovoltaic applications. CTTO possesses band gaps of 1.25 eV (indirect) and 1.37 eV (direct), which were evaluated using the hybrid functional (HSE06) as a post-DFT method. The hole mobility of CTTO was higher than that of silicon. Further, optical absorption calculations demonstrate that CTTO is a better absorber of sunlight than Cu{sub 2}ZnSnS{sub 4} and CuIn{sub x}Ga{sub 1−x}Se{sub 2} (x = 0.5). In addition, CTTO exhibits rigorous thermodynamic stability comparable to WO{sub 3}, as investigated by different thermodynamic approaches such as bondingmore » cohesion, fragmentation tendency, and chemical potential analysis. Chemical potential analysis further revealed that CTTO can be synthesized at flexible experimental growth conditions, although the co-existence of at least one secondary phase is likely. Finally, like other Cu-based compounds, the formation of Cu vacancies is highly probable, even at Cu-rich growth condition, which could introduce p-type activity in CTTO.« less

Quantitative modeling of the reaction/diffusion kinetics of two-chemistry photopolymers

NASA Astrophysics Data System (ADS)

Kowalski, Benjamin Andrew

Optically driven diffusion in photopolymers is an appealing material platform for a broad range of applications, in which the recorded refractive index patterns serve either as images (e.g. data storage, display holography) or as optical elements (e.g. custom GRIN components, integrated optical devices). A quantitative understanding of the reaction/diffusion kinetics is difficult to obtain directly, but is nevertheless necessary in order to fully exploit the wide array of design freedoms in these materials. A general strategy for characterizing these kinetics is proposed, in which key processes are decoupled and independently measured. This strategy enables prediction of a material's potential refractive index change, solely on the basis of its chemical components. The degree to which a material does not reach this potential reveals the fraction of monomer that has participated in unwanted reactions, reducing spatial resolution and dynamic range. This approach is demonstrated for a model material similar to commercial media, achieving quantitative predictions of index response over three orders of exposure dose (~1 to ~103 mJ cm-2) and three orders of feature size (0.35 to 500 microns). The resulting insights enable guided, rational design of new material formulations with demonstrated performance improvement.

Validating computational predictions of night-time ventilation in Stanford's Y2E2 building

NASA Astrophysics Data System (ADS)

Chen, Chen; Lamberti, Giacomo; Gorle, Catherine

2017-11-01

Natural ventilation can significantly reduce building energy consumption, but robust design is a challenging task. We previously presented predictions of natural ventilation performance in Stanford's Y2E2 building using two models with different levels of fidelity, embedded in an uncertainty quantification framework to identify the dominant uncertain parameters and predict quantified confidence intervals. The results showed a slightly high cooling rate for the volume-averaged temperature, and the initial thermal mass temperature and window discharge coefficients were found to have an important influence on the results. To further investigate the potential role of these parameters on the observed discrepancies, the current study is performing additional measurements in the Y2E2 building. Wall temperatures are recorded throughout the nightflush using thermocouples; flow rates through windows are measured using hotwires; and spatial variability in the air temperature is explored. The measured wall temperatures are found the be within the range of our model assumptions, and the measured velocities agree reasonably well with our CFD predications. Considerable local variations in the indoor air temperature have been recorded, largely explaining the discrepancies in our earlier validation study. Future work will therefore focus on a local validation of the CFD results with the measurements. Center for Integrated Facility Engineering (CIFE).

Predicting Salmonella populations from biological, chemical, and physical indicators in Florida surface waters.

PubMed

McEgan, Rachel; Mootian, Gabriel; Goodridge, Lawrence D; Schaffner, Donald W; Danyluk, Michelle D

2013-07-01

Coliforms, Escherichia coli, and various physicochemical water characteristics have been suggested as indicators of microbial water quality or index organisms for pathogen populations. The relationship between the presence and/or concentration of Salmonella and biological, physical, or chemical indicators in Central Florida surface water samples over 12 consecutive months was explored. Samples were taken monthly for 12 months from 18 locations throughout Central Florida (n = 202). Air and water temperature, pH, oxidation-reduction potential (ORP), turbidity, and conductivity were measured. Weather data were obtained from nearby weather stations. Aerobic plate counts and most probable numbers (MPN) for Salmonella, E. coli, and coliforms were performed. Weak linear relationships existed between biological indicators (E. coli/coliforms) and Salmonella levels (R(2) < 0.1) and between physicochemical indicators and Salmonella levels (R(2) < 0.1). The average rainfall (previous day, week, and month) before sampling did not correlate well with bacterial levels. Logistic regression analysis showed that E. coli concentration can predict the probability of enumerating selected Salmonella levels. The lack of good correlations between biological indicators and Salmonella levels and between physicochemical indicators and Salmonella levels shows that the relationship between pathogens and indicators is complex. However, Escherichia coli provides a reasonable way to predict Salmonella levels in Central Florida surface water through logistic regression.

Predicting Salmonella Populations from Biological, Chemical, and Physical Indicators in Florida Surface Waters

PubMed Central

McEgan, Rachel; Mootian, Gabriel; Goodridge, Lawrence D.; Schaffner, Donald W.

2013-01-01

Coliforms, Escherichia coli, and various physicochemical water characteristics have been suggested as indicators of microbial water quality or index organisms for pathogen populations. The relationship between the presence and/or concentration of Salmonella and biological, physical, or chemical indicators in Central Florida surface water samples over 12 consecutive months was explored. Samples were taken monthly for 12 months from 18 locations throughout Central Florida (n = 202). Air and water temperature, pH, oxidation-reduction potential (ORP), turbidity, and conductivity were measured. Weather data were obtained from nearby weather stations. Aerobic plate counts and most probable numbers (MPN) for Salmonella, E. coli, and coliforms were performed. Weak linear relationships existed between biological indicators (E. coli/coliforms) and Salmonella levels (R2 < 0.1) and between physicochemical indicators and Salmonella levels (R2 < 0.1). The average rainfall (previous day, week, and month) before sampling did not correlate well with bacterial levels. Logistic regression analysis showed that E. coli concentration can predict the probability of enumerating selected Salmonella levels. The lack of good correlations between biological indicators and Salmonella levels and between physicochemical indicators and Salmonella levels shows that the relationship between pathogens and indicators is complex. However, Escherichia coli provides a reasonable way to predict Salmonella levels in Central Florida surface water through logistic regression. PMID:23624476

Estimating glomerular filtration rate in diabetes: a comparison of cystatin-C- and creatinine-based methods.

PubMed

Macisaac, R J; Tsalamandris, C; Thomas, M C; Premaratne, E; Panagiotopoulos, S; Smith, T J; Poon, A; Jenkins, M A; Ratnaike, S I; Power, D A; Jerums, G

2006-07-01

We compared the predictive performance of a GFR based on serum cystatin C levels with commonly used creatinine-based methods in subjects with diabetes. In a cross-sectional study of 251 consecutive clinic patients, the mean reference (plasma clearance of (99m)Tc-diethylene-triamine-penta-acetic acid) GFR (iGFR) was 88+/-2 ml min(-1) 1.73 m(-2). A regression equation describing the relationship between iGFR and 1/cystatin C levels was derived from a test population (n=125) to allow for the estimation of GFR by cystatin C (eGFR-cystatin C). The predictive performance of eGFR-cystatin C, the Modification of Diet in Renal Disease 4 variable formula (MDRD-4) and Cockcroft-Gault (C-G) formulas were then compared in a validation population (n=126). There was no difference in renal function (ml min(-1) 1.73 m(-2)) as measured by iGFR (89.2+/-3.0), eGFR-cystatin C (86.8+/-2.5), MDRD-4 (87.0+/-2.8) or C-G (92.3+/-3.5). All three estimates of renal function had similar precision and accuracy. Estimates of GFR based solely on serum cystatin C levels had the same predictive potential when compared with the MDRD-4 and C-G formulas.

Cytomics in predictive medicine

NASA Astrophysics Data System (ADS)

Tarnok, Attila; Valet, Guenther K.

2004-07-01

Predictive Medicine aims at the detection of changes in patient's disease state prior to the manifestation of deterioration or improvement of the current status. Patient-specific, disease-course predictions with >95% or >99% accuracy during therapy would be highly valuable for everyday medicine. If these predictors were available, disease aggravation or progression, frequently accompanied by irreversible tissue damage or therapeutic side effects, could then potentially be avoided by early preventive therapy. The molecular analysis of heterogeneous cellular systems (Cytomics) by cytometry in conjunction with pattern-oriented bioinformatic analysis of the multiparametric cytometric and other data provides a promising approach to individualized or personalized medical treatment or disease management. Predictive medicine is best implemented by cell oriented measurements e.g. by flow or image cytometry. Cell oriented gene or protein arrays as well as bead arrays for the capture of solute molecules form serum, plasma, urine or liquor are equally of high value. Clinical applications of predictive medicine by Cytomics will include multi organ failure in sepsis or non infectious posttraumatic shock in intensive care, or the pretherapeutic identification of high risk patients in cancer cytostatic. Early individualized therapy may provide better survival chances for individual patient at concomitant cost containment. Predictive medicine guided early reduction or stop of therapy may lower or abrogate potential therapeutic side effects. Further important aspects of predictive medicine concern the preoperative identification of patients with a tendency for postoperative complications or coronary artery disease patients with an increased tendency for restenosis. As a consequence, better patient care and new forms of inductive scientific hypothesis development based on the interpretation of predictive data patterns are at reach.

Extracolonic findings (ECF) on CT colonography (CTC) in patients presenting with colorectal symptoms.

PubMed

Badiani, Sarit; Tomas-Hernandez, Silvia; Karandikar, Sharad; Roy-Choudhury, Shuvro

2013-10-01

Computed tomographic colonography (CTC) is now an established method for imaging the colon and rectum in the screening and symptomatic setting. Additional benefit of CTC is the ability to assess for extracolonic findings especially in patients presenting with colorectal symptoms. To determine prevalence of extracolonic findings (ECF) in symptomatic patients undergoing CTC and determine accuracy of CTC for exclusion of significant abdominal disease and extracolonic malignancy (ECM). A total of 1359 unenhanced prone and postcontrast supine CTC studies were performed between March 2002 and December 2007. ECF were retrospectively classified according to C-RADS criteria into E1 to E4 findings. For ECM, a gold standard of clinical and/or radiological follow-up supplemented with data from the regional cancer registry with a median follow-up of 42 months was created. Sensitivity and negative predictive values for ECM was calculated. Following exclusions, 1177 CTCs were analyzed. Of 1423 extracolonic findings reported, 328/1423 (23%) E3 and 100/1423 (7%) E4 (including six eventual FP studies) findings were identified. Thirty-two ECMs were confirmed following further investigations. Seven further small ECMs were detected during the entire follow-up, of which two were potentially visible in retrospect (false-negative studies). Additional tests were generated from 55/1177 (4.7%) studies. Sensitivity and negative predictive value for ECM was 94.1% (95% CI 78.9-98.9%) and 99.8% (95% CI 99.3-99.9%), respectively. One in 37 patients were found to have an ECM. Two potentially detectable cancers were missed. Only a small proportion of patients underwent additional work-up.

Prospective Predictors of Novel Tobacco and Nicotine Product Use in Emerging Adulthood.

PubMed

Hampson, Sarah E; Andrews, Judy A; Severson, Herbert H; Barckley, Maureen

2015-08-01

The purpose of this study was to investigate whether risk factors for cigarette smoking assessed in adolescence predict the use of novel tobacco and nicotine products (hookah, little cigars, and e-cigarettes) in early emerging adulthood. In a longitudinal study (N = 862), risk factors were measured in middle and high school, and novel product use was measured in emerging adulthood (mean age 22.4 years). Structural equation modeling was used to test a model predicting lifetime use of any of hookah, little cigars, and e-cigarettes in early emerging adulthood from distal predictors (gender, maternal smoking through Grade 8; already tried alcohol, cigarettes, or marijuana by Grade 8; and sensation seeking at Grade 8) and potential mediators (intentions to smoke cigarettes, drink alcohol or smoke marijuana at Grade 9, and smoking trajectory across high school). The most prevalent novel tobacco product was hookah (21.7%), followed by little cigars (16.8%) and e-cigarettes (6.6%). Maternal smoking, having already tried substances, and sensation seeking each predicted the use of at least one of these products via an indirect path through intentions to use substances and membership in a high-school smoking trajectory. Risk factors for cigarette smoking were found to predict novel tobacco use, suggesting that interventions to prevent cigarette smoking could be extended to include common novel tobacco products. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Evaluating the Capabilities of Soil Enthalpy, Soil Moisture and Soil Temperature in Predicting Seasonal Precipitation

NASA Astrophysics Data System (ADS)

Zhao, Changyu; Chen, Haishan; Sun, Shanlei

2018-04-01

Soil enthalpy ( H) contains the combined effects of both soil moisture ( w) and soil temperature ( T) in the land surface hydrothermal process. In this study, the sensitivities of H to w and T are investigated using the multi-linear regression method. Results indicate that T generally makes positive contributions to H, while w exhibits different (positive or negative) impacts due to soil ice effects. For example, w negatively contributes to H if soil contains more ice; however, after soil ice melts, w exerts positive contributions. In particular, due to lower w interannual variabilities in the deep soil layer (i.e., the fifth layer), H is more sensitive to T than to w. Moreover, to compare the potential capabilities of H, w and T in precipitation ( P) prediction, the Huanghe-Huaihe Basin (HHB) and Southeast China (SEC), with similar sensitivities of H to w and T, are selected. Analyses show that, despite similar spatial distributions of H-P and T-P correlation coefficients, the former values are always higher than the latter ones. Furthermore, H provides the most effective signals for P prediction over HHB and SEC, i.e., a significant leading correlation between May H and early summer (June) P. In summary, H, which integrates the effects of T and w as an independent variable, has greater capabilities in monitoring land surface heating and improving seasonal P prediction relative to individual land surface factors (e.g., T and w).

Molecular interactions between photosystem I and ferredoxin: an integrated energy frustration and experimental model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cashman, Derek J.; Zhu, Tuo; Simmerman, Richard F.

2014-08-01

The stromal domain (PsaC, PsaD, and PsaE) of photosystem I (PSI) reduces transiently bound ferredoxin (Fd) or flavodoxin. Experimental structures exist for all of these protein partners individually, but no experimental structure of the PSI/Fd or PSI/flavodoxin complexes is presently available. Molecular models of Fd docked onto the stromal domain of the cyanobacterial PSI site are constructed here utilizing X-ray and NMR structures of PSI and Fd, respectively. Moreover, predictions of potential protein-protein interaction regions are based on experimental site-directed mutagenesis and cross-linking studies to guide rigid body docking calculations of Fd into PSI, complemented by energy landscape theory tomore » bring together regions of high energetic frustration on each of the interacting proteins. Results identify two regions of high localized frustration on the surface of Fd that contain negatively charged Asp and Glu residues. Our study predicts that these regions interact predominantly with regions of high localized frustration on the PsaC, PsaD, and PsaE chains of PSI, which include several residues predicted by previous experimental studies.« less

Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome

PubMed Central

Hu, Danqing; Huang, Zhengxing; Chan, Tak-Ming; Dong, Wei; Lu, Xudong; Duan, Huilong

2016-01-01

Background: Clinical major adverse cardiovascular event (MACE) prediction of acute coronary syndrome (ACS) is important for a number of applications including physician decision support, quality of care assessment, and efficient healthcare service delivery on ACS patients. Admission records, as typical media to contain clinical information of patients at the early stage of their hospitalizations, provide significant potential to be explored for MACE prediction in a proactive manner. Methods: We propose a hybrid approach for MACE prediction by utilizing a large volume of admission records. Firstly, both a rule-based medical language processing method and a machine learning method (i.e., Conditional Random Fields (CRFs)) are developed to extract essential patient features from unstructured admission records. After that, state-of-the-art supervised machine learning algorithms are applied to construct MACE prediction models from data. Results: We comparatively evaluate the performance of the proposed approach on a real clinical dataset consisting of 2930 ACS patient samples collected from a Chinese hospital. Our best model achieved 72% AUC in MACE prediction. In comparison of the performance between our models and two well-known ACS risk score tools, i.e., GRACE and TIMI, our learned models obtain better performances with a significant margin. Conclusions: Experimental results reveal that our approach can obtain competitive performance in MACE prediction. The comparison of classifiers indicates the proposed approach has a competitive generality with datasets extracted by different feature extraction methods. Furthermore, our MACE prediction model obtained a significant improvement by comparison with both GRACE and TIMI. It indicates that using admission records can effectively provide MACE prediction service for ACS patients at the early stage of their hospitalizations. PMID:27649220

Prediction of truly random future events using analysis of prestimulus electroencephalographic data

NASA Astrophysics Data System (ADS)

Baumgart, Stephen L.; Franklin, Michael S.; Jimbo, Hiroumi K.; Su, Sharon J.; Schooler, Jonathan

2017-05-01

Our hypothesis is that pre-stimulus physiological data can be used to predict truly random events tied to perceptual stimuli (e.g., lights and sounds). Our experiment presents light and sound stimuli to a passive human subject while recording electrocortical potentials using a 32-channel Electroencephalography (EEG) system. For every trial a quantum random number generator (qRNG) chooses from three possible selections with equal probability: a light stimulus, a sound stimulus, and no stimulus. Time epochs are defined preceding and post-ceding each stimulus for which mean average potentials were computed across all trials for the three possible stimulus types. Data from three regions of the brain are examined. In all three regions mean potential for light stimuli was generally enhanced relative to baseline during the period starting approximately 2 seconds before the stimulus. For sound stimuli, mean potential decreased relative to baseline during the period starting approximately 2 seconds before the stimulus. These changes from baseline may indicated the presence of evoked potentials arising from the stimulus. A P200 peak was observed in data recorded from frontal electrodes. The P200 is a well-known potential arising from the brain's processing of visual stimuli and its presence represents a replication of a known neurological phenomenon.

Action prediction based on anticipatory brain potentials during simulated driving.

PubMed

Khaliliardali, Zahra; Chavarriaga, Ricardo; Gheorghe, Lucian Andrei; Millán, José del R

2015-12-01

The ability of an automobile to infer the driver's upcoming actions directly from neural signals could enrich the interaction of the car with its driver. Intelligent vehicles fitted with an on-board brain-computer interface able to decode the driver's intentions can use this information to improve the driving experience. In this study we investigate the neural signatures of anticipation of specific actions, namely braking and accelerating. We investigated anticipatory slow cortical potentials in electroencephalogram recorded from 18 healthy participants in a driving simulator using a variant of the contingent negative variation (CNV) paradigm with Go and No-go conditions: count-down numbers followed by 'Start'/'Stop' cue. We report decoding performance before the action onset using a quadratic discriminant analysis classifier based on temporal features. (i) Despite the visual and driving related cognitive distractions, we show the presence of anticipatory event related potentials locked to the stimuli onset similar to the widely reported CNV signal (with an average peak value of -8 μV at electrode Cz). (ii) We demonstrate the discrimination between cases requiring to perform an action upon imperative subsequent stimulus (Go condition, e.g. a 'Red' traffic light) versus events that do not require such action (No-go condition; e.g. a 'Yellow' light); with an average single trial classification performance of 0.83 ± 0.13 for braking and 0.79 ± 0.12 for accelerating (area under the curve). (iii) We show that the centro-medial anticipatory potentials are observed as early as 320 ± 200 ms before the action with a detection rate of 0.77 ± 0.12 in offline analysis. We show for the first time the feasibility of predicting the driver's intention through decoding anticipatory related potentials during simulated car driving with high recognition rates.

Fit Point-Wise AB Initio Calculation Potential Energies to a Multi-Dimension Long-Range Model

NASA Astrophysics Data System (ADS)

Zhai, Yu; Li, Hui; Le Roy, Robert J.

2016-06-01

A potential energy surface (PES) is a fundamental tool and source of understanding for theoretical spectroscopy and for dynamical simulations. Making correct assignments for high-resolution rovibrational spectra of floppy polyatomic and van der Waals molecules often relies heavily on predictions generated from a high quality ab initio potential energy surface. Moreover, having an effective analytic model to represent such surfaces can be as important as the ab initio results themselves. For the one-dimensional potentials of diatomic molecules, the most successful such model to date is arguably the ``Morse/Long-Range'' (MLR) function developed by R. J. Le Roy and coworkers. It is very flexible, is everywhere differentiable to all orders. It incorporates correct predicted long-range behaviour, extrapolates sensibly at both large and small distances, and two of its defining parameters are always the physically meaningful well depth {D}_e and equilibrium distance r_e. Extensions of this model, called the Multi-Dimension Morse/Long-Range (MD-MLR) function, linear molecule-linear molecule systems and atom-non-linear molecule system. have been applied successfully to atom-plus-linear molecule, linear molecule-linear molecule and atom-non-linear molecule systems. However, there are several technical challenges faced in modelling the interactions of general molecule-molecule systems, such as the absence of radial minima for some relative alignments, difficulties in fitting short-range potential energies, and challenges in determining relative-orientation dependent long-range coefficients. This talk will illustrate some of these challenges and describe our ongoing work in addressing them. Mol. Phys. 105, 663 (2007); J. Chem. Phys. 131, 204309 (2009); Mol. Phys. 109, 435 (2011). Phys. Chem. Chem. Phys. 10, 4128 (2008); J. Chem. Phys. 130, 144305 (2009) J. Chem. Phys. 132, 214309 (2010) J. Chem. Phys. 140, 214309 (2010)

The Effectiveness of FES-Evoked EMG Potentials to Assess Muscle Force and Fatigue in Individuals with Spinal Cord Injury

PubMed Central

Ibitoye, Morufu Olusola; Estigoni, Eduardo H.; Hamzaid, Nur Azah; Wahab, Ahmad Khairi Abdul; Davis, Glen M.

2014-01-01

The evoked electromyographic signal (eEMG) potential is the standard index used to monitor both electrical changes within the motor unit during muscular activity and the electrical patterns during evoked contraction. However, technical and physiological limitations often preclude the acquisition and analysis of the signal especially during functional electrical stimulation (FES)-evoked contractions. Hence, an accurate quantification of the relationship between the eEMG potential and FES-evoked muscle response remains elusive and continues to attract the attention of researchers due to its potential application in the fields of biomechanics, muscle physiology, and rehabilitation science. We conducted a systematic review to examine the effectiveness of eEMG potentials to assess muscle force and fatigue, particularly as a biofeedback descriptor of FES-evoked contractions in individuals with spinal cord injury. At the outset, 2867 citations were identified and, finally, fifty-nine trials met the inclusion criteria. Four hypotheses were proposed and evaluated to inform this review. The results showed that eEMG is effective at quantifying muscle force and fatigue during isometric contraction, but may not be effective during dynamic contractions including cycling and stepping. Positive correlation of up to r = 0.90 (p < 0.05) between the decline in the peak-to-peak amplitude of the eEMG and the decline in the force output during fatiguing isometric contractions has been reported. In the available prediction models, the performance index of the eEMG signal to estimate the generated muscle force ranged from 3.8% to 34% for 18 s to 70 s ahead of the actual muscle force generation. The strength and inherent limitations of the eEMG signal to assess muscle force and fatigue were evident from our findings with implications in clinical management of spinal cord injury (SCI) population. PMID:25025551

Optical absorption edge of ZnO thin films: The effect of substrate

NASA Astrophysics Data System (ADS)

Srikant, V.; Clarke, D. R.

1997-05-01

The optical absorption edge and the near-absorption edge characteristics of undoped ZnO films grown by laser ablation on various substrates have been investigated. The band edge of films on C [(0001)] and R-plane [(1102)] sapphire, 3.29 and 3.32 eV, respectively, are found to be very close to the single crystal value of ZnO (3.3 eV) with the differences being accounted for in terms of the thermal mismatch strain using the known deformation potentials of ZnO. In contrast, films grown on fused silica consistently exhibit a band edge ˜0.1 eV lower than that predicted using the known deformation potential and the thermal mismatch strains. This behavior is attributed to the small grain size (50 nm) realized in these films and the effect of electrostatic potentials that exist at the grain boundaries. Additionally, the spread in the tail (E0) of the band edge for the different films is found to be very sensitive to the defect structure in the films. For films grown on sapphire substrates, values of E0 as low as 30 meV can be achieved on annealing in air, whereas films on fused silica always show a value >100 meV. We attribute this difference to the substantially higher density of high-angle grain boundaries in the films on fused silica.

Hydraulic limits on maximum plant transpiration and the emergence of the safety-efficiency trade-off.

PubMed

Manzoni, Stefano; Vico, Giulia; Katul, Gabriel; Palmroth, Sari; Jackson, Robert B; Porporato, Amilcare

2013-04-01

Soil and plant hydraulics constrain ecosystem productivity by setting physical limits to water transport and hence carbon uptake by leaves. While more negative xylem water potentials provide a larger driving force for water transport, they also cause cavitation that limits hydraulic conductivity. An optimum balance between driving force and cavitation occurs at intermediate water potentials, thus defining the maximum transpiration rate the xylem can sustain (denoted as E(max)). The presence of this maximum raises the question as to whether plants regulate transpiration through stomata to function near E(max). To address this question, we calculated E(max) across plant functional types and climates using a hydraulic model and a global database of plant hydraulic traits. The predicted E(max) compared well with measured peak transpiration across plant sizes and growth conditions (R = 0.86, P < 0.001) and was relatively conserved among plant types (for a given plant size), while increasing across climates following the atmospheric evaporative demand. The fact that E(max) was roughly conserved across plant types and scales with the product of xylem saturated conductivity and water potential at 50% cavitation was used here to explain the safety-efficiency trade-off in plant xylem. Stomatal conductance allows maximum transpiration rates despite partial cavitation in the xylem thereby suggesting coordination between stomatal regulation and xylem hydraulic characteristics. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

Drug-target interaction prediction via class imbalance-aware ensemble learning.

PubMed

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2016-12-22

Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was able to predict many of the interactions successfully. Our proposed method has improved the prediction performance over the existing work, thus proving the importance of addressing problems pertaining to class imbalance in the data.

Bridging the Nomothetic and Idiographic Approaches to the Analysis of Clinical Data.

PubMed

Beltz, Adriene M; Wright, Aidan G C; Sprague, Briana N; Molenaar, Peter C M

2016-08-01

The nomothetic approach (i.e., the study of interindividual variation) dominates analyses of clinical data, even though its assumption of homogeneity across people and time is often violated. The idiographic approach (i.e., the study of intraindividual variation) is best suited for analyses of heterogeneous clinical data, but its person-specific methods and results have been criticized as unwieldy. Group iterative multiple model estimation (GIMME) combines the assets of the nomothetic and idiographic approaches by creating person-specific maps that contain a group-level structure. The maps show how intensively measured variables predict and are predicted by each other at different time scales. In this article, GIMME is introduced conceptually and mathematically, and then applied to an empirical data set containing the negative affect, detachment, disinhibition, and hostility composite ratings from the daily diaries of 25 individuals with personality pathology. Results are discussed with the aim of elucidating GIMME's potential for clinical research and practice.

Gender identity outcomes in children with disorders/differences of sex development: Predictive factors.

PubMed

Bakula, Dana M; Mullins, Alexandria J; Sharkey, Christina M; Wolfe-Christensen, Cortney; Mullins, Larry L; Wisniewski, Amy B

2017-06-01

Disorders/differences of sex development (DSD) comprise multiple congenital conditions in which chromosomal, gonadal, and/or anatomical sex are discordant. The prediction of future gender identity (i.e., self-identifying as male, female, or other) in children with DSD can be imprecise, and current knowledge about the development of gender identity in people with, and without DSD, is limited. However, sex of rearing is the strongest predictor of gender identity for the majority of individuals with various DSD conditions. When making decisions regarding sex of rearing biological factors (e.g., possession of a Y chromosome, degree and duration of pre- and postnatal androgen exposure, phenotypic presentation of the external genitalia, and fertility potential), social and cultural factors, as well as quality of life should be considered. Information on gender identity outcomes across a range of DSD diagnoses is presented to aid in sex of rearing assignment. Copyright © 2017 Elsevier Inc. All rights reserved.

Interface engineering of quantum Hall effects in digital transition metal oxide heterostructures.

PubMed

Xiao, Di; Zhu, Wenguang; Ran, Ying; Nagaosa, Naoto; Okamoto, Satoshi

2011-12-20

Topological insulators are characterized by a non-trivial band topology driven by the spin-orbit coupling. To fully explore the fundamental science and application of topological insulators, material realization is indispensable. Here we predict, based on tight-binding modelling and first-principles calculations, that bilayers of perovskite-type transition-metal oxides grown along the [111] crystallographic axis are potential candidates for two-dimensional topological insulators. The topological band structure of these materials can be fine-tuned by changing dopant ions, substrates and external gate voltages. We predict that LaAuO(3) bilayers have a topologically non-trivial energy gap of about 0.15 eV, which is sufficiently large to realize the quantum spin Hall effect at room temperature. Intriguing phenomena, such as fractional quantum Hall effect, associated with the nearly flat topologically non-trivial bands found in e(g) systems are also discussed.

Tolerance of a high-protein baked-egg product in egg-allergic children.

PubMed

Saifi, Maryam; Swamy, Nithya; Crain, Maria; Brown, L Steven; Bird, John Andrew

2016-05-01

Egg allergy is one of the most common immunoglobulin E (IgE)-mediated food allergies. Extensively heating egg has been found to decrease its allergenicity and 64% to 84% of children allergic to egg have been found to tolerate baked-egg products. Because there is no reliable method for predicting baked-egg tolerance, oral food challenges remain the gold standard. Prior studies have reported on baked-egg challenges using up to 2.2 g of egg white (EW) protein. To establish whether children with egg allergy would pass a baked-egg challenge to a larger amount of egg protein and the potential criteria for predicting the likelihood of baked-egg tolerance. A chart review was conducted of all patients 6 months to 18 years of age with egg allergy who underwent oral baked-egg challenges at Children's Medical Center Dallas over a 2-year period. Challenges were conducted in the clinic with a 3.8-g baked-egg product. Fifty-nine of 94 patients (63%) tolerated the 3.8-g baked-egg product. The presence of asthma (P < .01), EW skin prick test (SPT; P < .01) reactive wheal, and EW-specific IgE level (P = .02) correlated with baked-egg reactivity, whereas ovomucoid-specific IgE level did not. The positive predictive value approached 66% at an EW SPT reactive wheal of 10 mm and 60% for an EW-specific IgE level of 8 kUA/L. Most subjects with egg allergy tolerated baked egg. This study is the first to use 3.8 g of EW protein for the challenges. The EW SPT wheal diameter and EW-specific IgE levels were the best predictors of baked-egg tolerance. Copyright © 2016 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Antimicrobial resistant Escherichia coli in the municipal wastewater system: effect of hospital effluent and environmental fate.

PubMed

Harris, Suvi; Morris, Carol; Morris, Dearbhaile; Cormican, Martin; Cummins, Enda

2014-01-15

The prevalence of antimicrobial resistant (AMR) bacteria is increasing worldwide and remains a significant medical challenge which may lead to antimicrobial redundancy. The contribution of hospital effluent to the prevalence of resistance in wastewater treatment plant (WWTP) effluents is not fully understood. AMR bacteria contained in hospital effluent may be released into the aquatic and soil environments after WWTP processing. Hence, the objective of this study is to identify the extent hospital effluent contributes to contamination of these environments by comparing two WWTPs, one which receives hospital effluent and one which does not. AMR Escherichia coli were monitored in the two WWTPs. A model was developed using these monitored values to predict the effect of hospital effluent within a WWTP. The model predicted levels of AMR E. coli in the aquatic environment and potential bather exposure to AMR E. coli. The model results were highly variable. WWTP influent containing hospital effluent had a higher mean percentage of AMR E. coli; although, there appeared to be no within treatment plant effect on the prevalence of AMR E. coli. Examination of WWTP sludge showed a similar variation. There appeared to be no consistent effect from the presence of hospital effluent. The human exposure assessment model predicted swimmer intake of AMR E. coli between 6 and 193CFU/100ml sea water. It appears that hospital effluent is not the main contributing factor behind the development and persistence of AMR E. coli within WWTPs, although resistance may be too well-developed to identify an influence from hospital effluent. Mitigation needs to focus on the removal of already present resistant bacteria but for new or hospital specific antimicrobials focus needs to be on their limited release within effluents or separate treatment. © 2013.

E6 and E7 Antibody Levels Are Potential Biomarkers of Recurrence in Patients with Advanced-Stage Human Papillomavirus-Positive Oropharyngeal Squamous Cell Carcinoma.

PubMed

Spector, Matthew E; Sacco, Assuntina G; Bellile, Emily; Taylor, Jeremy M G; Jones, Tamara; Sun, Kan; Brown, William C; Birkeland, Andrew C; Bradford, Carol R; Wolf, Gregory T; Prince, Mark E; Moyer, Jeffrey S; Malloy, Kelly; Swiecicki, Paul; Eisbruch, Avraham; McHugh, Jonathan B; Chepeha, Douglas B; Rozek, Laura; Worden, Francis P

2017-06-01

Purpose: There is a paucity of biomarkers to predict failure in human papillomavirus-positive (HPV + ) oropharyngeal squamous cell carcinoma (OPSCC) following curative therapy. E6/E7 viral oncoproteins are constitutively expressed in HPV + tumors and highly immunogenic, resulting in readily detected serum antibodies. The purpose of this study is to determine whether serum E6 and E7 antibody levels can potentially serve as a biomarker of recurrence in patients with HPV+OPSCC. Experimental Design: We evaluated E6/E7 antibody levels in patients with previously untreated, advanced stage (III, IVa-b), HPV+OPSCC receiving definitive chemoradiation under a uniform protocol from 2003 to 2010. Baseline and longitudinal serum samples were obtained from our archived repository. E6/E7 serum levels were measured using a glutathione- S -transferase capture ELISA and quantified by approximating the area under the dilution curve, and were analyzed using ANOVA and linear mixed model for longitudinal analysis. Results: We compared 22 HPV+OPSCC patients who developed recurrence with 30 patients who remained disease-free. There were no differences in T classification, N classification, disease subsite, or smoking status between the groups. In a longitudinal analysis, recurrent patients had significantly higher E6 and E7 serum antibody levels than the nonrecurrent patients over the follow-up period ( P = 0.02 and P = 0.002, respectively). Patients who recurred had a lower clearance of E7 antibody than patients who remained disease-free ( P = 0.0016). Conclusions: Patients with HPV+OPSCC whose disease recurs have a lower clearance of E6 and E7 antibodies than patients who do not have recurrence. The ratio of E7 antibody at disease recurrence compared with baseline is potentially a clinically significant measurement of disease status in HPV+OPSCC. Clin Cancer Res; 23(11); 2723-9. ©2016 AACR . ©2016 American Association for Cancer Research.

Predictive Performance Assessment: Trait and State Dimensions Should not be Confused

NASA Astrophysics Data System (ADS)

Pattyn, N.; Migeotte, P.-F.; Morais, J.; Cluydts, R.; Soetens, E.; Meeusen, R.; de Schutter, G.; Nederhof, E.; Kolinsky, R.

2008-06-01

One of the major aims of performance investigation is to obtain a measure predicting real-life performance, in order to prevent consequences of a potential decrement. Whereas the predictive validity of such assessment has been extensively described for long-term outcomes, as is the case for testing in selection context, equivalent evidence is lacking regarding the short-term predictive value of cognitive testing, i.e., whether these results reflect real-life performance on an immediately subsequent task. In this series of experiments, we investigated both medium-term and short-term predictive value of psychophysiological testing with regard to real-life performance in two operational settings: military student pilots with regard to their success on an evaluation flight, and special forces candidates with regard to their performance on their training course. Our results showed some relationships between test performance and medium-term outcomes. However, no short-term predictive value could be identified for cognitive testing, despite the fact physiological data showed interesting trends. We recommend a critical distinction between "state" and "trait" dimensions of performance with regard to the predictive value of testing.

Predictors of blood trihalomethane concentrations in NHANES 1999-2006.

PubMed

Riederer, Anne M; Dhingra, Radhika; Blount, Benjamin C; Steenland, Kyle

2014-07-01

Trihalomethanes (THMs) are water disinfection by-products that have been associated with bladder cancer and adverse birth outcomes. Four THMs (bromoform, chloroform, bromodichloromethane, dibromochloromethane) were measured in blood and tap water of U.S. adults in the National Health and Nutrition Examination Survey (NHANES) 1999-2006. THMs are metabolized to potentially toxic/mutagenic intermediates by cytochrome p450 (CYP) 2D6 and CYP2E1 enzymes. We conducted exploratory analyses of blood THMs, including factors affecting CYP2D6 and CYP2E1 activity. We used weighted multivariable regressions to evaluate associations between blood THMs and water concentrations, survey year, and other factors potentially affecting THM exposure or metabolism (e.g., prescription medications, cruciferous vegetables, diabetes, fasting, pregnancy, swimming). From 1999 to 2006, geometric mean blood and water THM levels dropped in parallel, with decreases of 32%-76% in blood and 38%-52% in water, likely resulting, in part, from the lowering of the total THM drinking water standard in 2002-2004. The strongest predictors of blood THM levels were survey year and water concentration (n = 4,232 total THM; n = 4,080 bromoform; n = 4,582 chloroform; n = 4,374 bromodichloromethane; n = 4,464 dibromochloromethane). We detected statistically significant inverse associations with diabetes and eating cruciferous vegetables in all but the bromoform model. Medications did not consistently predict blood levels. Afternoon/evening blood samples had lower THM concentrations than morning samples. In a subsample (n = 230), air chloroform better predicted blood chloroform than water chloroform, suggesting showering/bathing was a more important source than drinking. We identified several factors associated with blood THMs that may affect their metabolism. The potential health implications require further study.

Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data.

PubMed

Ribay, Kathryn; Kim, Marlene T; Wang, Wenyi; Pinolini, Daniel; Zhu, Hao

2016-03-01

Estrogen receptors (ERα) are a critical target for drug design as well as a potential source of toxicity when activated unintentionally. Thus, evaluating potential ERα binding agents is critical in both drug discovery and chemical toxicity areas. Using computational tools, e.g., Quantitative Structure-Activity Relationship (QSAR) models, can predict potential ERα binding agents before chemical synthesis. The purpose of this project was to develop enhanced predictive models of ERα binding agents by utilizing advanced cheminformatics tools that can integrate publicly available bioassay data. The initial ERα binding agent data set, consisting of 446 binders and 8307 non-binders, was obtained from the Tox21 Challenge project organized by the NIH Chemical Genomics Center (NCGC). After removing the duplicates and inorganic compounds, this data set was used to create a training set (259 binders and 259 non-binders). This training set was used to develop QSAR models using chemical descriptors. The resulting models were then used to predict the binding activity of 264 external compounds, which were available to us after the models were developed. The cross-validation results of training set [Correct Classification Rate (CCR) = 0.72] were much higher than the external predictivity of the unknown compounds (CCR = 0.59). To improve the conventional QSAR models, all compounds in the training set were used to search PubChem and generate a profile of their biological responses across thousands of bioassays. The most important bioassays were prioritized to generate a similarity index that was used to calculate the biosimilarity score between each two compounds. The nearest neighbors for each compound within the set were then identified and its ERα binding potential was predicted by its nearest neighbors in the training set. The hybrid model performance (CCR = 0.94 for cross validation; CCR = 0.68 for external prediction) showed significant improvement over the original QSAR models, particularly for the activity cliffs that induce prediction errors. The results of this study indicate that the response profile of chemicals from public data provides useful information for modeling and evaluation purposes. The public big data resources should be considered along with chemical structure information when predicting new compounds, such as unknown ERα binding agents.

Modeling the Inactivation of Intestinal Pathogenic Escherichia coli O157:H7 and Uropathogenic E. coli in Ground Chicken by High Pressure Processing and Thymol

PubMed Central

Chien, Shih-Yung; Sheen, Shiowshuh; Sommers, Christopher H.; Sheen, Lee-Yan

2016-01-01

Disease causing Escherichia coli commonly found in meat and poultry include intestinal pathogenic E. coli (iPEC) as well as extraintestinal types such as the Uropathogenic E. coli (UPEC). In this study we compared the resistance of iPEC (O157:H7) to UPEC in chicken meat using High Pressure Processing (HPP) in with (the hurdle concept) and without thymol essential oil as a sensitizer. UPEC was found slightly more resistant than E. coli O157:H7 (iPEC O157:H7) at 450 and 500 MPa. A central composite experimental design was used to evaluate the effect of pressure (300–400 MPa), thymol concentration (100–200 ppm), and pressure-holding time (10–20 min) on the inactivation of iPEC O157:H7 and UPEC in ground chicken. The hurdle approach reduced the high pressure levels and thymol doses imposed on the food matrices and potentially decreased food quality damaged after treatment. The quadratic equations were developed to predict the impact (lethality) on iPEC O157:H7 (R2 = 0.94) and UPEC (R2 = 0.98), as well as dimensionless non-linear models [Pr > F (<0.0001)]. Both linear and non-linear models were validated with data obtained from separated experiment points. All models may predict the inactivation/lethality within the same order of accuracy. However, the dimensionless non-linear models showed potential applications with parameters outside the central composite design ranges. The results provide useful information of both iPEC O157:H7 and UPEC in regard to how they may survive HPP in the presence or absence of thymol. The models may further assist regulatory agencies and food industry to assess the potential risk of iPEC O157:H7 and UPEC in ground chicken. PMID:27379050

Modulation of band gap by an applied electric field in BN-based heterostructures

NASA Astrophysics Data System (ADS)

Luo, M.; Xu, Y. E.; Zhang, Q. X.

2018-05-01

First-principles density functional theory (DFT) calculations are performed on the structural and electronic properties of the SiC/BN van der Waals (vdW) heterostructures under an external electric field (E-field). Our results reveal that the SiC/BN vdW heterostructure has a direct band gap of 2.41 eV in the raw. The results also imply that electrons are likely to transfer from BN to SiC monolayer due to the deeper potential of BN monolayer. It is also observed that, by applying an E-field, ranging from -0.50 to +0.65 V/Å, the band gap decreases from 2.41 eV to zero, which presents a parabola-like relationship around 0.0 V/Å. Through partial density of states (PDOS) plots, it is revealed that, p orbital of Si, C, B, and N atoms are responsible for the significant variations of band gap. These obtained results predict that, the electric field tunable band gap of the SiC/BN vdW heterostructures carries potential applications for nanoelectronics and spintronic device applications.

How does cognition evolve? Phylogenetic comparative psychology

PubMed Central

Matthews, Luke J.; Hare, Brian A.; Nunn, Charles L.; Anderson, Rindy C.; Aureli, Filippo; Brannon, Elizabeth M.; Call, Josep; Drea, Christine M.; Emery, Nathan J.; Haun, Daniel B. M.; Herrmann, Esther; Jacobs, Lucia F.; Platt, Michael L.; Rosati, Alexandra G.; Sandel, Aaron A.; Schroepfer, Kara K.; Seed, Amanda M.; Tan, Jingzhi; van Schaik, Carel P.; Wobber, Victoria

2014-01-01

Now more than ever animal studies have the potential to test hypotheses regarding how cognition evolves. Comparative psychologists have developed new techniques to probe the cognitive mechanisms underlying animal behavior, and they have become increasingly skillful at adapting methodologies to test multiple species. Meanwhile, evolutionary biologists have generated quantitative approaches to investigate the phylogenetic distribution and function of phenotypic traits, including cognition. In particular, phylogenetic methods can quantitatively (1) test whether specific cognitive abilities are correlated with life history (e.g., lifespan), morphology (e.g., brain size), or socio-ecological variables (e.g., social system), (2) measure how strongly phylogenetic relatedness predicts the distribution of cognitive skills across species, and (3) estimate the ancestral state of a given cognitive trait using measures of cognitive performance from extant species. Phylogenetic methods can also be used to guide the selection of species comparisons that offer the strongest tests of a priori predictions of cognitive evolutionary hypotheses (i.e., phylogenetic targeting). Here, we explain how an integration of comparative psychology and evolutionary biology will answer a host of questions regarding the phylogenetic distribution and history of cognitive traits, as well as the evolutionary processes that drove their evolution. PMID:21927850

How does cognition evolve? Phylogenetic comparative psychology.

PubMed

MacLean, Evan L; Matthews, Luke J; Hare, Brian A; Nunn, Charles L; Anderson, Rindy C; Aureli, Filippo; Brannon, Elizabeth M; Call, Josep; Drea, Christine M; Emery, Nathan J; Haun, Daniel B M; Herrmann, Esther; Jacobs, Lucia F; Platt, Michael L; Rosati, Alexandra G; Sandel, Aaron A; Schroepfer, Kara K; Seed, Amanda M; Tan, Jingzhi; van Schaik, Carel P; Wobber, Victoria

2012-03-01

Now more than ever animal studies have the potential to test hypotheses regarding how cognition evolves. Comparative psychologists have developed new techniques to probe the cognitive mechanisms underlying animal behavior, and they have become increasingly skillful at adapting methodologies to test multiple species. Meanwhile, evolutionary biologists have generated quantitative approaches to investigate the phylogenetic distribution and function of phenotypic traits, including cognition. In particular, phylogenetic methods can quantitatively (1) test whether specific cognitive abilities are correlated with life history (e.g., lifespan), morphology (e.g., brain size), or socio-ecological variables (e.g., social system), (2) measure how strongly phylogenetic relatedness predicts the distribution of cognitive skills across species, and (3) estimate the ancestral state of a given cognitive trait using measures of cognitive performance from extant species. Phylogenetic methods can also be used to guide the selection of species comparisons that offer the strongest tests of a priori predictions of cognitive evolutionary hypotheses (i.e., phylogenetic targeting). Here, we explain how an integration of comparative psychology and evolutionary biology will answer a host of questions regarding the phylogenetic distribution and history of cognitive traits, as well as the evolutionary processes that drove their evolution.

PEPSI-Dock: a detailed data-driven protein-protein interaction potential accelerated by polar Fourier correlation.

PubMed

Neveu, Emilie; Ritchie, David W; Popov, Petr; Grudinin, Sergei

2016-09-01

Docking prediction algorithms aim to find the native conformation of a complex of proteins from knowledge of their unbound structures. They rely on a combination of sampling and scoring methods, adapted to different scales. Polynomial Expansion of Protein Structures and Interactions for Docking (PEPSI-Dock) improves the accuracy of the first stage of the docking pipeline, which will sharpen up the final predictions. Indeed, PEPSI-Dock benefits from the precision of a very detailed data-driven model of the binding free energy used with a global and exhaustive rigid-body search space. As well as being accurate, our computations are among the fastest by virtue of the sparse representation of the pre-computed potentials and FFT-accelerated sampling techniques. Overall, this is the first demonstration of a FFT-accelerated docking method coupled with an arbitrary-shaped distance-dependent interaction potential. First, we present a novel learning process to compute data-driven distant-dependent pairwise potentials, adapted from our previous method used for rescoring of putative protein-protein binding poses. The potential coefficients are learned by combining machine-learning techniques with physically interpretable descriptors. Then, we describe the integration of the deduced potentials into a FFT-accelerated spherical sampling provided by the Hex library. Overall, on a training set of 163 heterodimers, PEPSI-Dock achieves a success rate of 91% mid-quality predictions in the top-10 solutions. On a subset of the protein docking benchmark v5, it achieves 44.4% mid-quality predictions in the top-10 solutions when starting from bound structures and 20.5% when starting from unbound structures. The method runs in 5-15 min on a modern laptop and can easily be extended to other types of interactions. https://team.inria.fr/nano-d/software/PEPSI-Dock sergei.grudinin@inria.fr. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Proton MRS in acute traumatic brain injury: role for glutamate/glutamine and choline for outcome prediction.

PubMed

Shutter, Lori; Tong, Karen A; Holshouser, Barbara A

2004-12-01

Proton magnetic resonance spectroscopy (MRS) is being used to evaluate individuals with acute traumatic brain injury and several studies have shown that changes in certain brain metabolites (N-acetylaspartate, choline) are associated with poor neurologic outcomes. The majority of previous MRS studies have been obtained relatively late after injury and none have examined the role of glutamate/ glutamine (Glx). We conducted a prospective MRS study of 42 severely injured adults to measure quantitative metabolite changes early (7 days) after injury in normal appearing brain. We used these findings to predict long-term neurologic outcome and to determine if MRS data alone or in combination with clinical outcome variables provided better prediction of long-term outcomes. We found that glutamate/glutamine (Glx) and choline (Cho) were significantly elevated in occipital gray and parietal white matter early after injury in patients with poor long-term (6-12-month) outcomes. Glx and Cho ratios predicted long-term outcome with 94% accuracy and when combined with the motor Glasgow Coma Scale score provided the highest predictive accuracy (97%). Somatosensory evoked potentials were not as accurate as MRS data in predicting outcome. Elevated Glx and Cho are more sensitive indicators of injury and predictors of poor outcome when spectroscopy is done early after injury. This may be a reflection of early excitotoxic injury (i.e., elevated Glx) and of injury associated with membrane disruption (i.e., increased Cho) secondary to diffuse axonal injury.

Radon potential mapping of the Tralee-Castleisland and Cavan areas (Ireland) based on airborne gamma-ray spectrometry and geology.

PubMed

Appleton, J D; Doyle, E; Fenton, D; Organo, C

2011-06-01

The probability of homes in Ireland having high indoor radon concentrations is estimated on the basis of known in-house radon measurements averaged over 10 km × 10 km grid squares. The scope for using airborne gamma-ray spectrometer data for the Tralee-Castleisland area of county Kerry and county Cavan to predict the radon potential (RP) in two distinct areas of Ireland is evaluated in this study. Airborne data are compared statistically with in-house radon measurements in conjunction with geological and ground permeability data to establish linear regression models and produce radon potential maps. The best agreement between the percentage of dwellings exceeding the reference level (RL) for radon concentrations in Ireland (% > RL), estimated from indoor radon data, and modelled RP in the Tralee-Castleisland area is produced using models based on airborne gamma-ray spectrometry equivalent uranium (eU) and ground permeability data. Good agreement was obtained between the % > RL from indoor radon data and RP estimated from eU data in the Cavan area using terrain specific models. In both areas, RP maps derived from eU data are spatially more detailed than the published 10 km grid map. The results show the potential for using airborne radiometric data for producing RP maps.

Drifting from slow to "D'oh!": working memory capacity and mind wandering predict extreme reaction times and executive control errors.

PubMed

McVay, Jennifer C; Kane, Michael J

2012-05-01

A combined experimental, individual-differences, and thought-sampling study tested the predictions of executive attention (e.g., Engle & Kane, 2004) and coordinative binding (e.g., Oberauer, Süβ, Wilhelm, & Sander, 2007) theories of working memory capacity (WMC). We assessed 288 subjects' WMC and their performance and mind-wandering rates during a sustained-attention task; subjects completed either a go/no-go version requiring executive control over habit or a vigilance version that did not. We further combined the data with those from McVay and Kane (2009) to (1) gauge the contributions of WMC and attentional lapses to the worst performance rule and the tail, or τ parameter, of reaction time (RT) distributions; (2) assess which parameters from a quantitative evidence-accumulation RT model were predicted by WMC and mind-wandering reports; and (3) consider intrasubject RT patterns--particularly, speeding--as potential objective markers of mind wandering. We found that WMC predicted action and thought control in only some conditions, that attentional lapses (indicated by task-unrelated-thought reports and drift-rate variability in evidence accumulation) contributed to τ, performance accuracy, and WMC's association with them and that mind-wandering experiences were not predicted by trial-to-trial RT changes, and so they cannot always be inferred from objective performance measures. (c) 2012 APA, all rights reserved.

Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques.

PubMed

Wang, Guanjin; Lam, Kin-Man; Deng, Zhaohong; Choi, Kup-Sze

2015-08-01

Bladder cancer is a common cancer in genitourinary malignancy. For muscle invasive bladder cancer, surgical removal of the bladder, i.e. radical cystectomy, is in general the definitive treatment which, unfortunately, carries significant morbidities and mortalities. Accurate prediction of the mortality of radical cystectomy is therefore needed. Statistical methods have conventionally been used for this purpose, despite the complex interactions of high-dimensional medical data. Machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support, e.g. cancer prediction and prognosis. Its ability to reveal the hidden nonlinear interactions and interpretable rules between dependent and independent variables is favorable for constructing models of effective generalization performance. In this paper, seven machine learning methods are utilized to predict the 5-year mortality of radical cystectomy, including back-propagation neural network (BPN), radial basis function (RBFN), extreme learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), naive Bayes (NB) classifier and k-nearest neighbour (KNN), on a clinicopathological dataset of 117 patients of the urology unit of a hospital in Hong Kong. The experimental results indicate that RELM achieved the highest average prediction accuracy of 0.8 at a fast learning speed. The research findings demonstrate the potential of applying machine learning techniques to support clinical decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

Drifting from Slow to “D’oh!” Working Memory Capacity and Mind Wandering Predict Extreme Reaction Times and Executive-Control Errors

PubMed Central

McVay, Jennifer C.; Kane, Michael J.

2012-01-01

A combined experimental, individual-differences, and thought-sampling study tested the predictions of executive attention (e.g., Engle & Kane, 2004) and coordinative binding (e.g., Oberauer, Süß, Wilhelm, & Sander, 2007) theories of working memory capacity (WMC). We assessed 288 subjects’ WMC and their performance and mind-wandering rates during a sustained-attention task; subjects completed either a go/no-go version requiring executive control over habit, or a vigilance version that did not. We further combined the data with those from McVay and Kane (2009) to: (1) gauge the contributions of WMC and attentional lapses to the worst-performance rule and the tail, or τ parameter, of response time (RT) distributions; (2) assess which parameters from a quantitative evidence-accumulation RT model were predicted by WMC and mind-wandering reports, and (3) consider intra-subject RT patterns – particularly, speeding – as potential objective markers of mind wandering. We found that WMC predicted action and thought control in only some conditions, that attentional lapses (indicated by TUT reports and drift-rate variability in evidence accumulation) contributed to τ, performance accuracy, and WMC’s association with them, and that mind-wandering experiences were not predicted by trial-to-trial RT changes, and so they cannot always be inferred from objective performance measures. PMID:22004270

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment.

PubMed

Marvin, Hans J P; Bouzembrak, Yamine; Janssen, Esmée M; van der Zande, Meike; Murphy, Finbarr; Sheehan, Barry; Mullins, Martin; Bouwmeester, Hans

2017-02-01

In this study, a Bayesian Network (BN) was developed for the prediction of the hazard potential and biological effects with the focus on metal- and metal-oxide nanomaterials to support human health risk assessment. The developed BN captures the (inter) relationships between the exposure route, the nanomaterials physicochemical properties and the ultimate biological effects in a holistic manner and was based on international expert consultation and the scientific literature (e.g., in vitro/in vivo data). The BN was validated with independent data extracted from published studies and the accuracy of the prediction of the nanomaterials hazard potential was 72% and for the biological effect 71%, respectively. The application of the BN is shown with scenario studies for TiO 2 , SiO 2 , Ag, CeO 2 , ZnO nanomaterials. It is demonstrated that the BN may be used by different stakeholders at several stages in the risk assessment to predict certain properties of a nanomaterials of which little information is available or to prioritize nanomaterials for further screening.

Modeling potential habitats for alien species Dreissena polymorpha in continental USA

USGS Publications Warehouse

Mingyang, Li; Yunwei, Ju; Kumar, Sunil; Stohlgren, Thomas J.

2008-01-01

The effective measure to minimize the damage of invasive species is to block the potential invasive species to enter into suitable areas. 1864 occurrence points with GPS coordinates and 34 environmental variables from Daymet datasets were gathered, and 4 modeling methods, i.e., Logistic Regression (LR), Classification and Regression Trees (CART), Genetic Algorithm for Rule-Set Prediction (GARP), and maximum entropy method (Maxent), were introduced to generate potential geographic distributions for invasive species Dreissena polymorpha in Continental USA. Then 3 statistical criteria of the area under the Receiver Operating Characteristic curve (AUC), Pearson correlation (COR) and Kappa value were calculated to evaluate the performance of the models, followed by analyses on major contribution variables. Results showed that in terms of the 3 statistical criteria, the prediction results of the 4 ecological niche models were either excellent or outstanding, in which Maxent outperformed the others in 3 aspects of predicting current distribution habitats, selecting major contribution factors, and quantifying the influence of environmental variables on habitats. Distance to water, elevation, frequency of precipitation and solar radiation were 4 environmental forcing factors. The method suggested in the paper can have some reference meaning for modeling habitats of alien species in China and provide a direction to prevent Mytilopsis sallei on the Chinese coast line.

Sub-seasonal precipitation during the South Asian summer monsoon onset period

NASA Astrophysics Data System (ADS)

Takaya, Y.; Yamaguchi, M.

2017-12-01

The South Asian summer monsoon (SASM) has a great impact on human activities (e.g., agriculture and health), thus skillful prediction of the SASM is highly anticipated. In particular, precipitation amount and timing of a rainy season onset are of great importance for crop planning. This study examines the performance of precipitation prediction during the onset period of the SASM using the WWRP/WCRP sub-seasonal to seasonal prediction project (S2S) dataset. Preliminary verification of ECMWF model reforecasts against the GSMaP precipitation analysis produced by Japan Aerospace Exploration Agency (JAXA) shows that a predictive skill of precipitation is reasonably high in a sub-seasonal time-range. It is also found that the predictive skill of precipitation in the South Asia is relatively higher around the onset period, consistent with our previous finding using the latest JMA seasonal prediction system (JMA/MRI-CPS2). The results suggest that state-of-the-art operational models have the capability to provide useful SASM onset predictions at a sub-seasonal time scale. In the presentation, we will also discuss the inherent potential predictability, feasibility of prediction of the monsoon onset and relevant processes.

Prelude and Fugue, predicting local protein structure, early folding regions and structural weaknesses.

PubMed

Kwasigroch, Jean Marc; Rooman, Marianne

2006-07-15

Prelude&Fugue are bioinformatics tools aiming at predicting the local 3D structure of a protein from its amino acid sequence in terms of seven backbone torsion angle domains, using database-derived potentials. Prelude(&Fugue) computes all lowest free energy conformations of a protein or protein region, ranked by increasing energy, and possibly satisfying some interresidue distance constraints specified by the user. (Prelude&)Fugue detects sequence regions whose predicted structure is significantly preferred relative to other conformations in the absence of tertiary interactions. These programs can be used for predicting secondary structure, tertiary structure of short peptides, flickering early folding sequences and peptides that adopt a preferred conformation in solution. They can also be used for detecting structural weaknesses, i.e. sequence regions that are not optimal with respect to the tertiary fold. http://babylone.ulb.ac.be/Prelude_and_Fugue.

Personality Assessment Inventory scores as predictors of misconduct among sex offenders civilly committed as sexually violent predators.

PubMed

Boccaccini, Marcus T; Rufino, Katrina A; Jackson, Rebecca L; Murrie, Daniel C

2013-12-01

We examined the usefulness of scores on the Personality Assessment Inventory (PAI; Morey, 1991) in predicting treatment program violations among 76 sexual offenders civilly committed as sexually violent predators. Scores on the Borderline Features scale (area under the curve [AUC] = .69, p = .005) and Negative Relationships subscale (BOR-N: AUC = .71, p < .001) were the strongest predictors of misconduct, outperforming scores on scales designed to predict poor treatment amenability and antisocial behavior. Incremental validity analyses indicated that BOR scores made a significant contribution to the prediction of misconduct after controlling for scores on measures of overall self-reported distress (e.g., Mean Clinical Elevation, Negative Impression), which were also predictive of program violations. Overall, our findings point to the potential utility of integrating components of treatment for borderline personality disorder into sex offender treatment. (c) 2013 APA, all rights reserved.

Ontology-based prediction of surgical events in laparoscopic surgery

NASA Astrophysics Data System (ADS)

Katić, Darko; Wekerle, Anna-Laura; Gärtner, Fabian; Kenngott, Hannes; Müller-Stich, Beat Peter; Dillmann, Rüdiger; Speidel, Stefanie

2013-03-01

Context-aware technologies have great potential to help surgeons during laparoscopic interventions. Their underlying idea is to create systems which can adapt their assistance functions automatically to the situation in the OR, thus relieving surgeons from the burden of managing computer assisted surgery devices manually. To this purpose, a certain kind of understanding of the current situation in the OR is essential. Beyond that, anticipatory knowledge of incoming events is beneficial, e.g. for early warnings of imminent risk situations. To achieve the goal of predicting surgical events based on previously observed ones, we developed a language to describe surgeries and surgical events using Description Logics and integrated it with methods from computational linguistics. Using n-Grams to compute probabilities of followup events, we are able to make sensible predictions of upcoming events in real-time. The system was evaluated on professionally recorded and labeled surgeries and showed an average prediction rate of 80%.

Romantic Partner Monitoring After Breakups: Attachment, Dependence, Distress, and Post-Dissolution Online Surveillance via Social Networking Sites.

PubMed

Fox, Jesse; Tokunaga, Robert S

2015-09-01

Romantic relationship dissolution can be stressful, and social networking sites make it difficult to separate from a romantic partner online as well as offline. An online survey (N = 431) tested a model synthesizing attachment, investment model variables, and post-dissolution emotional distress as predictors of interpersonal surveillance (i.e., "Facebook stalking") of one's ex-partner on Facebook after a breakup. Results indicated that anxious attachment predicted relational investment but also seeking relationship alternatives; avoidant attachment was negatively related to investment but positively related to seeking alternatives. Investment predicted commitment, whereas seeking alternatives was negatively related to commitment. Commitment predicted emotional distress after the breakup. Distress predicted partner monitoring immediately following the breakup, particularly for those who did not initiate the breakup, as well as current partner monitoring. Given their affordances, social media are discussed as potentially unhealthy enablers for online surveillance after relationship termination.

Sub-seasonal to Seasonal Prediction in the Midst of Uncertainties: Recognizing the Music in What May Seem Like Noises Across this scale

NASA Astrophysics Data System (ADS)

Tiwari, P.; Kar, S. C.; Dey, S.; Mohanty, U. C.

2016-12-01

Sub-seasonal to Seasonal (S2S) prediction has long been considered a predictability desert and forecasting across this scale has received much less attention than medium and seasonal scale. Hoskins (2013) has suggested that there is an urgent need to understand the phenomena and structures that provide the potential sources of predictability across this scale. Therefore, after a problem on this scale and its associated implications in various sectors (for e.g. agriculture and food security, water and health), the question arises whether strategies of S2S prediction that have proved useful elsewhere can they be adapted to the North Indian plains and complex terrain of Himalayas as well? The aim of the present study is in three-folds. Firstly, it attempts to assess the sub seasonal to seasonal predictive skill of six general circulation models (GCMs) for a period of 31 years (1982-2012) and identify forecast windows of opportunity. Secondly, an attempt has been made to reproduce the information of the GCMs at higher resolution using both dynamical and statistical downscaling approaches along with bias correction. Thirdly, an attempt has been also made to use the S2S prediction for water cycle studies as lives of millions of people in North Indian plains depends on water availability from rivers of western Himalayan origin. Finally, the plausible reasons of model failure, potential sources of predictability across this scale and how S2S framework has played a key role in addressing such issues is highlighted. Key words: S2S, predictability, downscaling, water cycle.

Electric Potential and Electric Field Imaging with Dynamic Applications & Extensions

NASA Technical Reports Server (NTRS)

Generazio, Ed

2017-01-01

The technology and methods for remote quantitative imaging of electrostatic potentials and electrostatic fields in and around objects and in free space is presented. Electric field imaging (EFI) technology may be applied to characterize intrinsic or existing electric potentials and electric fields, or an externally generated electrostatic field made be used for volumes to be inspected with EFI. The baseline sensor technology (e-Sensor) and its construction, optional electric field generation (quasi-static generator), and current e- Sensor enhancements (ephemeral e-Sensor) are discussed. Critical design elements of current linear and real-time two-dimensional (2D) measurement systems are highlighted, and the development of a three dimensional (3D) EFI system is presented. Demonstrations for structural, electronic, human, and memory applications are shown. Recent work demonstrates that phonons may be used to create and annihilate electric dipoles within structures. Phonon induced dipoles are ephemeral and their polarization, strength, and location may be quantitatively characterized by EFI providing a new subsurface Phonon-EFI imaging technology. Results from real-time imaging of combustion and ion flow, and their measurement complications, will be discussed. Extensions to environment, Space and subterranean applications will be presented, and initial results for quantitative characterizing material properties are shown. A wearable EFI system has been developed by using fundamental EFI concepts. These new EFI capabilities are demonstrated to characterize electric charge distribution creating a new field of study embracing areas of interest including electrostatic discharge (ESD) mitigation, manufacturing quality control, crime scene forensics, design and materials selection for advanced sensors, combustion science, on-orbit space potential, container inspection, remote characterization of electronic circuits and level of activation, dielectric morphology of structures, tether integrity, organic molecular memory, atmospheric science, weather prediction, earth quake prediction, and medical diagnostic and treatment efficacy applications such as cardiac polarization wave propagation and electromyography imaging.

Genetic regulation of gene expression in the lung identifies CST3 and CD22 as potential causal genes for airflow obstruction.

PubMed

Lamontagne, Maxime; Timens, Wim; Hao, Ke; Bossé, Yohan; Laviolette, Michel; Steiling, Katrina; Campbell, Joshua D; Couture, Christian; Conti, Massimo; Sherwood, Karen; Hogg, James C; Brandsma, Corry-Anke; van den Berge, Maarten; Sandford, Andrew; Lam, Stephen; Lenburg, Marc E; Spira, Avrum; Paré, Peter D; Nickle, David; Sin, Don D; Postma, Dirkje S

2014-11-01

COPD is a complex chronic disease with poorly understood pathogenesis. Integrative genomic approaches have the potential to elucidate the biological networks underlying COPD and lung function. We recently combined genome-wide genotyping and gene expression in 1111 human lung specimens to map expression quantitative trait loci (eQTL). To determine causal associations between COPD and lung function-associated single nucleotide polymorphisms (SNPs) and lung tissue gene expression changes in our lung eQTL dataset. We evaluated causality between SNPs and gene expression for three COPD phenotypes: FEV(1)% predicted, FEV(1)/FVC and COPD as a categorical variable. Different models were assessed in the three cohorts independently and in a meta-analysis. SNPs associated with a COPD phenotype and gene expression were subjected to causal pathway modelling and manual curation. In silico analyses evaluated functional enrichment of biological pathways among newly identified causal genes. Biologically relevant causal genes were validated in two separate gene expression datasets of lung tissues and bronchial airway brushings. High reliability causal relations were found in SNP-mRNA-phenotype triplets for FEV(1)% predicted (n=169) and FEV(1)/FVC (n=80). Several genes of potential biological relevance for COPD were revealed. eQTL-SNPs upregulating cystatin C (CST3) and CD22 were associated with worse lung function. Signalling pathways enriched with causal genes included xenobiotic metabolism, apoptosis, protease-antiprotease and oxidant-antioxidant balance. By using integrative genomics and analysing the relationships of COPD phenotypes with SNPs and gene expression in lung tissue, we identified CST3 and CD22 as potential causal genes for airflow obstruction. This study also augmented the understanding of previously described COPD pathways. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Genomic profiling is predictive of response to cisplatin treatment but not to PI3K inhibition in bladder cancer patient-derived xenografts

PubMed Central

Ramakrishnan, Swathi; Elbanna, May; Wang, Jianmin; Hu, Qiang; Glenn, Sean T.; Murakami, Mitsuko; Liu, Lu; Gomez, Eduardo Cortes; Sun, Yuchen; Conroy, Jacob; Miles, Kiersten Marie; Malathi, Kullappan; Ramaiah, Sudha; Anbarasu, Anand; Woloszynska-Read, Anna; Johnson, Candace S.; Conroy, Jeffrey; Liu, Song; Morrison, Carl D.; Pili, Roberto

2016-01-01

Purpose Effective systemic therapeutic options are limited for bladder cancer. In this preclinical study we tested whether bladder cancer gene alterations may be predictive of treatment response. Experimental design We performed genomic profiling of two bladder cancer patient derived tumor xenografts (PDX). We optimized the exome sequence analysis method to overcome the mouse genome interference. Results We identified a number of somatic mutations, mostly shared by the primary tumors and PDX. In particular, BLCAb001, which is less responsive to cisplatin than BLCAb002, carried non-sense mutations in several genes associated with cisplatin resistance, including MLH1, BRCA2, and CASP8. Furthermore, RNA-Seq analysis revealed the overexpression of cisplatin resistance associated genes such as SLC7A11, TLE4, and IL1A in BLCAb001. Two different PIK3CA mutations, E542K and E545K, were identified in BLCAb001 and BLCAb002, respectively. Thus, we tested whether the genomic profiling was predictive of response to a dual PI3K/mTOR targeting agent, LY3023414. Despite harboring similar PIK3CA mutations, BLCAb001 and BLCAb002 exhibited differential response, both in vitro and in vivo. Sustained target modulation was observed in the sensitive model BLCAb002 but not in BLCAb001, as well as decreased autophagy. Interestingly, computational modelling of mutant structures and affinity binding to PI3K revealed that E542K mutation was associated with weaker drug binding than E545K. Conclusions Our results suggest that the presence of activating PIK3CA mutations may not necessarily predict in vivo treatment response to PI3K targeted therapies, while specific gene alterations may be predictive for cisplatin response in bladder cancer models and, potentially, in patients as well. PMID:27823983

Characterization and identification of ubiquitin conjugation sites with E3 ligase recognition specificities.

PubMed

Nguyen, Van-Nui; Huang, Kai-Yao; Huang, Chien-Hsun; Chang, Tzu-Hao; Bretaña, Neil; Lai, K; Weng, Julia; Lee, Tzong-Yi

2015-01-01

In eukaryotes, ubiquitin-conjugation is an important mechanism underlying proteasome-mediated degradation of proteins, and as such, plays an essential role in the regulation of many cellular processes. In the ubiquitin-proteasome pathway, E3 ligases play important roles by recognizing a specific protein substrate and catalyzing the attachment of ubiquitin to a lysine (K) residue. As more and more experimental data on ubiquitin conjugation sites become available, it becomes possible to develop prediction models that can be scaled to big data. However, no development that focuses on the investigation of ubiquitinated substrate specificities has existed. Herein, we present an approach that exploits an iteratively statistical method to identify ubiquitin conjugation sites with substrate site specificities. In this investigation, totally 6259 experimentally validated ubiquitinated proteins were obtained from dbPTM. After having filtered out homologous fragments with 40% sequence identity, the training data set contained 2658 ubiquitination sites (positive data) and 5532 non-ubiquitinated sites (negative data). Due to the difficulty in characterizing the substrate site specificities of E3 ligases by conventional sequence logo analysis, a recursively statistical method has been applied to obtain significant conserved motifs. The profile hidden Markov model (profile HMM) was adopted to construct the predictive models learned from the identified substrate motifs. A five-fold cross validation was then used to evaluate the predictive model, achieving sensitivity, specificity, and accuracy of 73.07%, 65.46%, and 67.93%, respectively. Additionally, an independent testing set, completely blind to the training data of the predictive model, was used to demonstrate that the proposed method could provide a promising accuracy (76.13%) and outperform other ubiquitination site prediction tool. A case study demonstrated the effectiveness of the characterized substrate motifs for identifying ubiquitination sites. The proposed method presents a practical means of preliminary analysis and greatly diminishes the total number of potential targets required for further experimental confirmation. This method may help unravel their mechanisms and roles in E3 recognition and ubiquitin-mediated protein degradation.

Predicting emissions from oil and gas operations in the Uinta Basin, Utah.

PubMed

Wilkey, Jonathan; Kelly, Kerry; Jaramillo, Isabel Cristina; Spinti, Jennifer; Ring, Terry; Hogue, Michael; Pasqualini, Donatella

2016-05-01

In this study, emissions of ozone precursors from oil and gas operations in Utah's Uinta Basin are predicted (with uncertainty estimates) from 2015-2019 using a Monte-Carlo model of (a) drilling and production activity, and (b) emission factors. Cross-validation tests against actual drilling and production data from 2010-2014 show that the model can accurately predict both types of activities, returning median results that are within 5% of actual values for drilling, 0.1% for oil production, and 4% for gas production. A variety of one-time (drilling) and ongoing (oil and gas production) emission factors for greenhouse gases, methane, and volatile organic compounds (VOCs) are applied to the predicted oil and gas operations. Based on the range of emission factor values reported in the literature, emissions from well completions are the most significant source of emissions, followed by gas transmission and production. We estimate that the annual average VOC emissions rate for the oil and gas industry over the 2010-2015 time period was 44.2E+06 (mean) ± 12.8E+06 (standard deviation) kg VOCs per year (with all applicable emissions reductions). On the same basis, over the 2015-2019 period annual average VOC emissions from oil and gas operations are expected to drop 45% to 24.2E+06 ± 3.43E+06 kg VOCs per year, due to decreases in drilling activity and tighter emission standards. This study improves upon previous methods for estimating emissions of ozone precursors from oil and gas operations in Utah's Uinta Basin by tracking one-time and ongoing emission events on a well-by-well basis. The proposed method has proven highly accurate at predicting drilling and production activity and includes uncertainty estimates to describe the range of potential emissions inventory outcomes. If similar input data are available in other oil and gas producing regions, then the method developed here could be applied to those regions as well.

A paradigm shift for radical SAM reactions: The organometallic intermediate Ω is central to catalysis.

PubMed

Byer, Amanda S; Yang, Hao; McDaniel, Elizabeth C; Kathiresan, Venkatesan; Impano, Stella; Pagnier, Adrien; Watts, Hope; Denler, Carly; Vagstad, Anna; Piel, Jörn; Duschene, Kaitlin S; Shepard, Eric M; Shields, Thomas P; Scott, Lincoln G; Lilla, Edward A; Yokoyama, Kenichi; Broderick, William E; Hoffman, Brian M; Broderick, Joan B

2018-06-28

Radical S-adenosyl-L-methionine (SAM) en-zymes comprise a vast superfamily catalyzing diverse reactions essential to all life through ho-molytic SAM cleavage to liberate the highly-reactive 5-deoxyadenosyl radical (5-dAdo•). Our recent observation of a catalytically compe-tent organometallic intermediate Ω that forms dur-ing reaction of the radical SAM (RS) enzyme py-ruvate formate-lyase activating-enzyme (PFL-AE) was therefore quite surprising, and led to the question of its broad relevance in the superfamily. We now show that Ω in PFL-AE forms as an in-termediate under a variety of mixing order condi-tions, suggesting it is central to catalysis in this enzyme. We further demonstrate that Ω forms in a suite of RS enzymes chosen to span the totality of superfamily reaction types, implicating Ω as essential in catalysis across the RS superfamily. Finally, EPR and electron nuclear double reso-nance spectroscopy establish that Ω involves an Fe-C5 bond between 5-dAdo• and the [4Fe-4S] cluster. An analogous organometallic bond is found in the well-known adenosylcobalamin (co-enzyme B12) cofactor used to initiate radical reac-tions via a 5'-dAdo• intermediate. Generation of a 5'-dAdo• intermediate via homolytic metal-carbon bond cleavage thus appears to be similar for Ω and coenzyme B12. However coenzyme B12 is involved in enzymes catalyzing of only a small number (~12) of distinct reactions, while the RS superfamily has more than 100,000 distinct se-quences and over 80 reaction types character-ized to date. The appearance of Ω across the RS superfamily therefore dramatically enlarges the sphere of bio-organometallic chemistry in Nature.

Comparison of Three Enzyme-Linked Immunosorbent Assays for Detection of Immunoglobulin G Antibodies to Tetanus Toxoid with Reference Standards and the Impact on Clinical Practice▿

PubMed Central

van Hoeven, Karen H.; Dale, Connie; Foster, Phil; Body, Barbara

2008-01-01

Accurate determination of the concentrations of immunoglobulin G (IgG) antibody to tetanus toxoid is important in order to evaluate the immunogenicity of tetanus toxoid vaccines, determine immune competence in individual patients, and measure the prevalence of immunity in populations. The performance of three commercially available enzyme-linked immunosorbent assays (ELISAs) for IgG antibodies to tetanus toxoid were evaluated. Serially diluted NIBSC 76/589 and TE-3 human tetanus IgG immunoglobulin international reference standards were analyzed in quadruplicate using ELISAs manufactured by The Binding Site, Inc. (VaccZyme); Scimedx; and Euroimmun. In addition, IgG antibodies to tetanus toxoid were measured in 83 deidentified serum specimens using each manufacturer's ELISA. Each ELISA provided linear results when evaluated with the reference preparations. The Binding Site ELISA provided results that closely corresponded to the reference preparations (y = 1.09x − 0.08), whereas the Scimedx ELISA gave results that were consistently lower (y = 0.21x − 0.07) and the Euroimmun ELISA gave results that were consistently higher (y = 1.5x + 0.30) than the reference preparation concentrations. Using the recommended cutoff for each ELISA (<0.10 IU/ml), the overall agreement of all of the ELISA methods was 78%. Three of eighty-three (3.6%) human serum samples demonstrated inadequate immunity with all three assays. The Binding Site ELISA yielded nonprotective antibody concentrations in only these 3 samples, whereas 19 samples (22.9%) according to the Scimedx ELISA and 6 samples (7.2%) according to the Euroimmun ELISA demonstrated nonprotective concentrations. The performance characteristics of ELISAs for tetanus immunoglobulin titers were manufacturer dependent, and the differences translated into important disparities in reported results. PMID:18845832

Serum anti-tetanus and measles antibody titres in Ugandan children aged 4 months to 6 years: implications for vaccine programme.

PubMed

Warrener, Lenesha; Bwogi, Josephine; Andrews, Nick; Samuel, Dhanraj; Kabaliisa, Theopista; Bukenya, Henry; Brown, Kevin; Roper, Martha H; Featherstone, David A; Brown, David

2018-05-09

To study the antibody response to tetanus toxoid and measles by age following vaccination in children aged 4 months to 6 years in Entebbe, Uganda. Serum samples were obtained from 113 children aged 4-15 months, at the Mother-Child Health Clinic (MCHC), Entebbe Hospital and from 203 of the 206 children aged between 12 and 75 months recruited through the Outpatients Department (OPD). Antibodies to measles were quantified by plaque reduction neutralisation test (PRNT) and with Siemens IgG EIA. VaccZyme IgG EIA was used to quantify anti-tetanus antibodies. Sera from 96 of 113 (85.0%) children attending the MCHC contained Measles PRNT titres below the protective level (120 mIU/ml). Sera from 24 of 203 (11.8%) children attending the OPD contained PRNT titres 0.15 IU/ml by EIA, a level considered protective. The overall concentration of anti-tetanus antibody was sixfold higher in children under 12 months compared with the older children, with geometric mean concentrations of 3.15 IU/ml and 0.49 IU/ml, respectively. For each doubling in age between 4 and 64 months, the anti-tetanus antibody concentration declined by 50%. As time since the administration of the third DTP vaccination doubled, anti-tetanus antibody concentration declined by 39%. The low measles antibody prevalence in the children presenting at the MCHC is consistent with the current measles epidemiology in Uganda, where a significant number of measles cases occur in children under 1 year of age and earlier vaccination may be indicated. The consistent fall in anti-tetanus antibody titre over time following vaccination supports the need for further vaccine boosters at age 4-5 years as recommended by the WHO.

The structure-AChE inhibitory activity relationships study in a series of pyridazine analogues.

PubMed

Saracoglu, M; Kandemirli, F

2009-07-01

The structure-activity relationships (SAR) are investigated by means of the Electronic-Topological Method (ETM) followed by the Neural Networks application (ETM-NN) for a class of anti-cholinesterase inhibitors (AChE, 53 molecules) being pyridazine derivatives. AChE activities of the series were measured in IC(50) units, and relative to the activity levels, the series was partitioned into classes of active and inactive compounds. Based on pharmacophores and antipharmacophores calculated by the ETM-software as sub-matrices containing important spatial and electronic characteristics, a system for the activity prognostication is developed. Input data for the ETM were taken as the results of conformational and quantum-mechanics calculations. To predict the activity, we used one of the most well known neural networks, namely, the feed-forward neural networks (FFNNs) trained with the back propagation algorithm. The supervised learning was performed using a variant of FFNN known as the Associative Neural Networks (ASNN). The result of the testing revealed that the high ETM's ability of predicting both activity and inactivity of potential AChE inhibitors. Analysis of HOMOs for the compounds containing Ph1 and APh1 has shown that atoms with the highest values of the atomic orbital coefficients are mainly those atoms that enter into the pharmacophores. Thus, the set of pharmacophores and antipharmacophores found as the result of this study forms a basis for a system of the anti-cholinesterase activity prediction.

Additive Genetic Risk from Five Serotonin System Polymorphisms Interacts with Interpersonal Stress to Predict Depression

PubMed Central

Vrshek-Schallhorn, Suzanne; Stroud, Catherine B.; Mineka, Susan; Zinbarg, Richard E.; Adam, Emma K.; Redei, Eva E.; Hammen, Constance; Craske, Michelle G.

2016-01-01

Behavioral genetic research supports polygenic models of depression in which many genetic variations each contribute a small amount of risk, and prevailing diathesis-stress models suggest gene-environment interactions (GxE). Multilocus profile scores of additive risk offer an approach that is consistent with polygenic models of depression risk. In a first demonstration of this approach in a GxE predicting depression, we created an additive multilocus profile score from five serotonin system polymorphisms (one each in the genes HTR1A, HTR2A, HTR2C, and two in TPH2). Analyses focused on two forms of interpersonal stress as environmental risk factors. Using five years of longitudinal diagnostic and life stress interviews from 387 emerging young adults in the Youth Emotion Project, survival analyses show that this multilocus profile score interacts with major interpersonal stressful life events to predict major depressive episode onsets (HR = 1.815, p = .007). Simultaneously, there was a significant protective effect of the profile score without a recent event (HR = 0.83, p = .030). The GxE effect with interpersonal chronic stress was not significant (HR = 1.15, p = .165). Finally, effect sizes for genetic factors examined ignoring stress suggested such an approach could lead to overlooking or misinterpreting genetic effects. Both the GxE effect and the protective simple main effect were replicated in a sample of early adolescent girls (N = 105). We discuss potential benefits of the multilocus genetic profile score approach and caveats for future research. PMID:26595467

Cross-timescale Interference and Rainfall Extreme Events in South Eastern South America

NASA Astrophysics Data System (ADS)

Munoz, Angel G.

The physical mechanisms and predictability associated with extreme daily rainfall in South East South America (SESA) are investigated for the December-February season. Through a k-mean analysis, a robust set of daily circulation regimes is identified and then it is used to link the frequency of rainfall extreme events with large-scale potential predictors at subseasonal-to-seasonal scales. This basic set of daily circulation regimes is related to the continental and oceanic phases of the South Atlantic Convergence Zone (SACZ) and wave train patterns superimposed on the Southern Hemisphere Polar Jet. Some of these recurrent synoptic circulation types are conducive to extreme rainfall events in the region through synoptic control of different meso-scale physical features and, at the same time, are influenced by climate phenomena that could be used as sources of potential predictability. Extremely high rainfall (as measured by the 95th- and 99th-percentiles) is preferentially associated with two of these weather types, which are characterized by moisture advection intrusions from lower latitudes and the Pacific; another three weather types, characterized by above-normal moisture advection toward lower latitudes or the Andes, are preferentially associated with dry days (days with no rain). The analysis permits the identification of several subseasonal-to-seasonal scale potential predictors that modulate the occurrence of circulation regimes conducive to extreme rainfall events in SESA. It is conjectured that a cross-timescale interference between the different climate drivers improves the predictive skill of extreme precipitation in the region. The potential and real predictive skill of the frequency of extreme rainfall is then evaluated, finding evidence indicating that mechanisms of climate variability at one timescale contribute to the predictability at another scale, i.e., taking into account the interference of different potential sources of predictability at different timescales increases the predictive skill. This fact is in agreement with the Cross-timescale Interference Conjecture proposed in the first part of the thesis. At seasonal scale, a combination of those weather types tends to outperform all the other potential predictors explored, i.e., sea surface temperature patterns, phases of the Madden-Julian Oscillation, and combinations of both. Spatially averaged Kendall’s τ improvements of 43% for the potential predictability and 23% for realtime predictions are attained with respect to standard models considering sea-surface temperature fields alone. A new subseasonal-to-seasonal predictive methodology for extreme rainfall events is proposed, based on probability forecasts of seasonal sequences of these weather types. The cross-validated realtime skill of the new probabilistic approach, as measured by the Hit Score and the Heidke Skill Score, is on the order of twice that associated with climatological values. The approach is designed to offer useful subseasonal-to-seasonal climate information to decision-makers interested not only in how many extreme events will happen in the season, but also in how, when and where those events will probably occur. In order to gain further understanding about how the cross-timescale interference occurs, an externally-forced Lorenz model is used to explore the impact of different kind of forcings, at inter-annual and decadal scales, in the establishment of constructive interactions associated with the simulated “extreme events”. Using a wavelet analysis, it is shown that this simple model is capable of reproducing the same kind of cross-timescale structures observed in the wavelet power spectrum of the Nino3.4 index only when it is externally forced by both inter-annual and decadal signals: the annual cycle and a decadal forcing associated with the natural solar variability. The nature of this interaction is non-linear, and it impacts both mean and extreme values in the time series. No predictive power was found when using metrics like standard deviation and auto-correlation. Nonetheless, it was proposed that an early warning signal for occurrence of extreme rainfall in SESA may be possible via a continuous monitoring of relative phases between the cross-timescale leading components.

Using the Maxent program for species distribution modelling to assess invasion risk

USGS Publications Warehouse

Jarnevich, Catherine S.; Young, Nicholas E.; Venette, R.C

2015-01-01

MAXENT is a software package used to relate known species occurrences to information describing the environment, such as climate, topography, anthropogenic features or soil data, and forecast the presence or absence of a species at unsampled locations. This particular method is one of the most popular species distribution modelling techniques because of its consistent strong predictive performance and its ease to implement. This chapter discusses the decisions and techniques needed to prepare a correlative climate matching model for the native range of an invasive alien species and use this model to predict the potential distribution of this species in a potentially invaded range (i.e. a novel environment) by using MAXENT for the Burmese python (Python molurus bivittatus) as a case study. The chapter discusses and demonstrates the challenges that are associated with this approach and examines the inherent limitations that come with using MAXENT to forecast distributions of invasive alien species.

Data-driven identification of potential Zika virus vectors

PubMed Central

Evans, Michelle V; Dallas, Tad A; Han, Barbara A; Murdock, Courtney C; Drake, John M

2017-01-01

Zika is an emerging virus whose rapid spread is of great public health concern. Knowledge about transmission remains incomplete, especially concerning potential transmission in geographic areas in which it has not yet been introduced. To identify unknown vectors of Zika, we developed a data-driven model linking vector species and the Zika virus via vector-virus trait combinations that confer a propensity toward associations in an ecological network connecting flaviviruses and their mosquito vectors. Our model predicts that thirty-five species may be able to transmit the virus, seven of which are found in the continental United States, including Culex quinquefasciatus and Cx. pipiens. We suggest that empirical studies prioritize these species to confirm predictions of vector competence, enabling the correct identification of populations at risk for transmission within the United States. DOI: http://dx.doi.org/10.7554/eLife.22053.001 PMID:28244371

Soluble E-cadherin is an independent pretherapeutic factor for long-term survival in gastric cancer.

PubMed

Chan, Annie On-On; Chu, Kent-Man; Lam, Shiu-Kum; Wong, Benjamin Chun-Yu; Kwok, Ka-Fai; Law, Simon; Ko, Samuel; Hui, Wai-Mo; Yueng, Yui-Hung; Wong, John

2003-06-15

To evaluate whether pretherapeutic serum soluble E-cadherin is an independent factor predicting long-term survival in gastric cancer. Gastric cancer remains the second leading cause of cancer-related deaths in the world, but a satisfactory tumor marker is currently unavailable for gastric cancer. Soluble E-cadherin has recently been found to have prognostic value in gastric cancer. One hundred sixteen patients with histologically proven gastric adenocarcinoma were included in the trial. Pretherapeutic serum was collected, and soluble E-cadherin was assayed using a commercially available enzyme-linked immunosorbent assay kit. The patients were followed up prospectively at the outpatient clinic. There were 75 men and 41 women, with a mean (+/- SD) age of 66 +/- 14 years. Forty-eight percent of tumors were located in the gastric antrum. The median survival time was 11 months. The mean pretherapeutic value of soluble E-cadherin was 9,159 ng/mL (range, 6,002 to 10,025 ng/mL), and the mean pretherapeutic level of carcinoembryonic antigen was 11 ng/mL (range, 0.3 to 4,895 ng/mL). On multivariate analysis, soluble E-cadherin is an independent factor predicting long-term survival. Ninety percent of patients with a serum level of E-cadherin greater than 10,000 ng/mL had a survival time of less than 3 years (P =.009). Soluble E-cadherin is a potentially valuable pretherapeutic prognostic factor in patients with gastric cancer.

Response of urinary liver-type fatty acid-binding protein to contrast media administration has a potential to predict one-year renal outcome in patients with ischemic heart disease.

PubMed

Fujita, Daishi; Takahashi, Masao; Doi, Kent; Abe, Mitsuru; Tazaki, Junichi; Kiyosue, Arihiro; Myojo, Masahiro; Ando, Jiro; Fujita, Hideo; Noiri, Eisei; Sugaya, Takeshi; Hirata, Yasunobu; Komuro, Issei

2015-05-01

Urinary liver-type fatty acid-binding proteins (uL-FABP) have recently been recognized as a useful biomarker for predicting contrast-induced nephropathy. Although accumulating studies have evaluated short-term outcomes, its prognostic value for long-term renal prognosis in patients undergoing coronary angiography (CAG) has not been fully examined. This study aimed to evaluate the predictive value of uL-FABP for long-term renal outcome in patients with ischemic heart disease (IHD). Consecutive 24 patients with impaired renal function (serum creatinine >1.2 mg/dL) who underwent CAG were enrolled. uL-FABP was measured before CAG, 24 and 48 h after CAG. The changes in estimated glomerular filtration rate (eGFR) throughout CAG and at 1 year later were compared with the uL-FABP levels. The patients with a greater decrease in eGFR 1 year later had higher uL-FABP levels at all points, but only the value at 48 h after CAG reached statistical significance (lower vs. higher decreased eGFR group, 4.61 ± 3.87 vs. 17.71 ± 12.96; P < 0.01). Measurement of uL-FABP at 48 h after CAG (48h-uL-FABP) showed better correlation with the change in eGFR (pre-CAG uL-FABP vs. 48h-uL-FABP: R = 0.27, P = 0.20 vs. R = 0.65, P < 0.01). Moreover, the high-pre and high-48h-uL-FABP group showed a significantly larger decrease in eGFR compared with the high-pre and low-48h-uL-FABP group (change in eGFR; 8.12 ± 4.06 vs. 1.25 ± 2.23 mL/min/1.73 m2, P < 0.01), although the baseline eGFR levels were similar between these two groups. In this pilot study, measurement of uL-FABP levels at 48 h after CAG may be useful in detecting renal damage, and in predicting 1-year renal outcome in IHD patients undergoing CAG.

The identification of high potential archers based on relative psychological coping skills variables: A Support Vector Machine approach

NASA Astrophysics Data System (ADS)

Taha, Zahari; Muazu Musa, Rabiu; Majeed, A. P. P. Abdul; Razali Abdullah, Mohamad; Aizzat Zakaria, Muhammad; Muaz Alim, Muhammad; Arif Mat Jizat, Jessnor; Fauzi Ibrahim, Mohamad

2018-03-01

Support Vector Machine (SVM) has been revealed to be a powerful learning algorithm for classification and prediction. However, the use of SVM for prediction and classification in sport is at its inception. The present study classified and predicted high and low potential archers from a collection of psychological coping skills variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. Psychological coping skills inventory which evaluates the archers level of related coping skills were filled out by the archers prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed. SVM models, i.e. linear and fine radial basis function (RBF) kernel functions, were trained on the psychological variables. The k-means clustered the archers into high psychologically prepared archers (HPPA) and low psychologically prepared archers (LPPA), respectively. It was demonstrated that the linear SVM exhibited good accuracy and precision throughout the exercise with an accuracy of 92% and considerably fewer error rate for the prediction of the HPPA and the LPPA as compared to the fine RBF SVM. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected psychological coping skills variables examined which would consequently save time and energy during talent identification and development programme.

Developing a Predictive Capability for Bioluminescence Signatures

DTIC Science & Technology

2011-09-30

sources of bioluminescence are dinoflagellates, common unicellular plankton that are also known to form red tides. Dinoflagellate bioluminescence is...wakes to locate their prey. The bioluminescence signature of a moving object depends on the bioluminescence potential of the organisms (related to...AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) University of California, San

Reliable Prescreening of Candidate NerveAgent Prophylaxes via 3D QSAR

DTIC Science & Technology

2005-12-31

recognize and predict prospective toxicity among covalent -binding AChE inhibitors of potential application to nerve agent prophylaxis and...is below since many authors do not follow the 200 word limit 14. SUBJECT TERMS nerve agents , acetylcholinesterase, prophylaxis, QSAR, virtual...Report: Reliable Prescreening of Candidate NerveAgent Prophylaxes via 3D QSAR Report Title ABSTRACT Organophosphorus (OP) nerve agents are among the

Support for HIV-1 Intervention Therapy

DTIC Science & Technology

1993-10-01

I. Kiselev, and E. S. Severin. 1990. Amplification of DNA 46 sequences of Epstein - Barr and human immunodeficiency viruses using DNA-polymerase from... develop and validate assays that predict or demonstrate disease progression for use in interventional trials with an emphasis on molecular biologic...to stay on the leading edge of technology development . A potential problem in obtaining quality sequence information is the occurrence of template

Corrosion Monitors for Embedded Evaluation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robinson, Alex L.; Pfeifer, Kent B.; Casias, Adrian L.

2017-05-01

We have developed and characterized novel in-situ corrosion sensors to monitor and quantify the corrosive potential and history of localized environments. Embedded corrosion sensors can provide information to aid health assessments of internal electrical components including connectors, microelectronics, wires, and other susceptible parts. When combined with other data (e.g. temperature and humidity), theory, and computational simulation, the reliability of monitored systems can be predicted with higher fidelity.

Stimulus Onset Asynchrony and the Timeline of Word Recognition: Event-Related Potentials during Sentence Reading

ERIC Educational Resources Information Center

Dambacher, Michael; Dimigen, Olaf; Braun, Mario; Wille, Kristin; Jacobs, Arthur M.; Kliegl, Reinhold

2012-01-01

Three ERP experiments examined the effect of word presentation rate (i.e., stimulus onset asynchrony, SOA) on the time course of word frequency and predictability effects in sentence reading. In Experiments 1 and 2, sentences were presented word-by-word in the screen center at an SOA of 700 and 490ms, respectively. While these rates are typical…

Order of magnitude smaller limit on the electric dipole moment of the electron.

PubMed

Baron, J; Campbell, W C; DeMille, D; Doyle, J M; Gabrielse, G; Gurevich, Y V; Hess, P W; Hutzler, N R; Kirilov, E; Kozyryev, I; O'Leary, B R; Panda, C D; Parsons, M F; Petrik, E S; Spaun, B; Vutha, A C; West, A D

2014-01-17

The Standard Model of particle physics is known to be incomplete. Extensions to the Standard Model, such as weak-scale supersymmetry, posit the existence of new particles and interactions that are asymmetric under time reversal (T) and nearly always predict a small yet potentially measurable electron electric dipole moment (EDM), d(e), in the range of 10(-27) to 10(-30) e·cm. The EDM is an asymmetric charge distribution along the electron spin (S(→)) that is also asymmetric under T. Using the polar molecule thorium monoxide, we measured d(e) = (-2.1 ± 3.7stat ± 2.5syst) × 10(-29) e·cm. This corresponds to an upper limit of |d(e)| < 8.7 × 10(-29) e·cm with 90% confidence, an order of magnitude improvement in sensitivity relative to the previous best limit. Our result constrains T-violating physics at the TeV energy scale.

Risk factors for youth victimization: beyond a lifestyles/routine activities theory approach.

PubMed

Finkelhor, D; Asdigian, N L

1996-01-01

Past efforts to understand the risks for youth victimization have primarily utilized concepts from lifestyle or routine activity theory, such as the increased exposure and reduced guardianship that are entailed when youth engage in risky or delinquent behavior. In this article, we argue that other personal characteristics put youth at risk, not through any lifestyle or routine activity mechanism, but by making certain youth more "congruent" with the needs, motives, or reactivities of potential offenders. Three specific types of such characteristics are those that increase the potential victim's target vulnerability (e.g., physical weakness or psychological distress), target gratifiability (e.g., female gender for the crime of sexual assault), or target antagonism (e.g., behaviors or ethnic or group identities that may spark hostility or resentment). Using data from a national youth survey, we test variables measuring such aspects of target congruence and show that they make a significant contribution over and above lifestyle variables alone in predicting nonfamily, sexual, and parental assault.

Computational Prediction of the Heterodimeric and Higher-Order Structure of gpE1/gpE2 Envelope Glycoproteins Encoded by Hepatitis C Virus.

PubMed

Freedman, Holly; Logan, Michael R; Hockman, Darren; Koehler Leman, Julia; Law, John Lok Man; Houghton, Michael

2017-04-15

Despite the recent success of newly developed direct-acting antivirals against hepatitis C, the disease continues to be a global health threat due to the lack of diagnosis of most carriers and the high cost of treatment. The heterodimer formed by glycoproteins E1 and E2 within the hepatitis C virus (HCV) lipid envelope is a potential vaccine candidate and antiviral target. While the structure of E1/E2 has not yet been resolved, partial crystal structures of the E1 and E2 ectodomains have been determined. The unresolved parts of the structure are within the realm of what can be modeled with current computational modeling tools. Furthermore, a variety of additional experimental data is available to support computational predictions of E1/E2 structure, such as data from antibody binding studies, cryo-electron microscopy (cryo-EM), mutational analyses, peptide binding analysis, linker-scanning mutagenesis, and nuclear magnetic resonance (NMR) studies. In accordance with these rich experimental data, we have built an in silico model of the full-length E1/E2 heterodimer. Our model supports that E1/E2 assembles into a trimer, which was previously suggested from a study by Falson and coworkers (P. Falson, B. Bartosch, K. Alsaleh, B. A. Tews, A. Loquet, Y. Ciczora, L. Riva, C. Montigny, C. Montpellier, G. Duverlie, E. I. Pecheur, M. le Maire, F. L. Cosset, J. Dubuisson, and F. Penin, J. Virol. 89:10333-10346, 2015, https://doi.org/10.1128/JVI.00991-15). Size exclusion chromatography and Western blotting data obtained by using purified recombinant E1/E2 support our hypothesis. Our model suggests that during virus assembly, the trimer of E1/E2 may be further assembled into a pentamer, with 12 pentamers comprising a single HCV virion. We anticipate that this new model will provide a useful framework for HCV envelope structure and the development of antiviral strategies. IMPORTANCE One hundred fifty million people have been estimated to be infected with hepatitis C virus, and many more are at risk for infection. A better understanding of the structure of the HCV envelope, which is responsible for attachment and fusion, could aid in the development of a vaccine and/or new treatments for this disease. We draw upon computational techniques to predict a full-length model of the E1/E2 heterodimer based on the partial crystal structures of the envelope glycoproteins E1 and E2. E1/E2 has been widely studied experimentally, and this provides valuable data, which has assisted us in our modeling. Our proposed structure is used to suggest the organization of the HCV envelope. We also present new experimental data from size exclusion chromatography that support our computational prediction of a trimeric oligomeric state of E1/E2. Copyright © 2017 American Society for Microbiology.

Computational Prediction of the Heterodimeric and Higher-Order Structure of gpE1/gpE2 Envelope Glycoproteins Encoded by Hepatitis C Virus

PubMed Central

Logan, Michael R.; Hockman, Darren; Koehler Leman, Julia; Law, John Lok Man

2017-01-01

ABSTRACT Despite the recent success of newly developed direct-acting antivirals against hepatitis C, the disease continues to be a global health threat due to the lack of diagnosis of most carriers and the high cost of treatment. The heterodimer formed by glycoproteins E1 and E2 within the hepatitis C virus (HCV) lipid envelope is a potential vaccine candidate and antiviral target. While the structure of E1/E2 has not yet been resolved, partial crystal structures of the E1 and E2 ectodomains have been determined. The unresolved parts of the structure are within the realm of what can be modeled with current computational modeling tools. Furthermore, a variety of additional experimental data is available to support computational predictions of E1/E2 structure, such as data from antibody binding studies, cryo-electron microscopy (cryo-EM), mutational analyses, peptide binding analysis, linker-scanning mutagenesis, and nuclear magnetic resonance (NMR) studies. In accordance with these rich experimental data, we have built an in silico model of the full-length E1/E2 heterodimer. Our model supports that E1/E2 assembles into a trimer, which was previously suggested from a study by Falson and coworkers (P. Falson, B. Bartosch, K. Alsaleh, B. A. Tews, A. Loquet, Y. Ciczora, L. Riva, C. Montigny, C. Montpellier, G. Duverlie, E. I. Pecheur, M. le Maire, F. L. Cosset, J. Dubuisson, and F. Penin, J. Virol. 89:10333–10346, 2015, https://doi.org/10.1128/JVI.00991-15). Size exclusion chromatography and Western blotting data obtained by using purified recombinant E1/E2 support our hypothesis. Our model suggests that during virus assembly, the trimer of E1/E2 may be further assembled into a pentamer, with 12 pentamers comprising a single HCV virion. We anticipate that this new model will provide a useful framework for HCV envelope structure and the development of antiviral strategies. IMPORTANCE One hundred fifty million people have been estimated to be infected with hepatitis C virus, and many more are at risk for infection. A better understanding of the structure of the HCV envelope, which is responsible for attachment and fusion, could aid in the development of a vaccine and/or new treatments for this disease. We draw upon computational techniques to predict a full-length model of the E1/E2 heterodimer based on the partial crystal structures of the envelope glycoproteins E1 and E2. E1/E2 has been widely studied experimentally, and this provides valuable data, which has assisted us in our modeling. Our proposed structure is used to suggest the organization of the HCV envelope. We also present new experimental data from size exclusion chromatography that support our computational prediction of a trimeric oligomeric state of E1/E2. PMID:28148799

Segmental distribution of some common molecular markers for colorectal cancer (CRC): influencing factors and potential implications.

PubMed

Papagiorgis, Petros Christakis

2016-05-01

Proximal and distal colorectal cancers (CRCs) are regarded as distinct disease entities, evolving through different genetic pathways and showing multiple clinicopathological and molecular differences. Segmental distribution of some common markers (e.g., KRAS, EGFR, Ki-67, Bcl-2, COX-2) is clinically important, potentially affecting their prognostic or predictive value. However, this distribution is influenced by a variety of factors such as the anatomical overlap of tumorigenic molecular events, associations of some markers with other clinicopathological features (stage and/or grade), and wide methodological variability in markers' assessment. All these factors represent principal influences followed by intratumoral heterogeneity and geographic variation in the frequency of detection of particular markers, whereas the role of other potential influences (e.g., pre-adjuvant treatment, interaction between markers) remains rather unclear. Better understanding and elucidation of the various influences may provide a more accurate picture of the segmental distribution of molecular markers in CRC, potentially allowing the application of a novel patient stratification for treatment, based on particular molecular profiles in combination with tumor location.

How El Niño can be used to improve wind speed seasonal skill?

NASA Astrophysics Data System (ADS)

Gonzalez-Reviriego, Nube; Marcos, Raül; Doblas-Reyes, Francisco J.; Torralba, Verónica; Cortesi, Nicola; Lee, Doo Young; Soret, Albert

2017-04-01

The potential benefit of seasonal wind speed forecasts for the energy sector has been recently discussed (Torralba et al. 2016, Buontempo et al. 2016). Nevertheless, the lack of skill over several inland areas and especially at high lead times, can limit the application of these seasonal probabilistic forecasts. By using a simple methodology approach, this study aims to illustrate how the scientific user-driven research, conducted in a context of climate services, should play a role in the improvement of the wind speed seasonal forecast skill. In this framework the results obtained from the correlation coefficients between the ensemble mean prediction of the ECMWF System 4 and the observed wind speeds are compared with the results from the correlations between the wind speed constructed from the seasonal predicted El Niño index and the observations. An improvement of the skill at lead times ranging from 1 up to 5 months is measured over several regions such as Northern United States, Canada, Uruguay and Argentina. The added value of this constructed wind speed predictions is found in those areas over the world where the seasonal prediction system is not able to reproduce correctly the teleconnections of El Niño. Buontempo C, Hanlon H.M., Bruno Soares M., Christel I., Soubeyroux J-M., Viel C., Calmanti S, Bosi L., Falloon P., Palin E.J., Vanvyve E., Torralba V., Gonzalez-Reviriego N., Doblas-Reyes F.J., Pope E.C.D., Newton P. and Liggins F., 2016: What have we learnt from EUPORIAS climate service prototypes? Climate Services (Submitted) Torralba V., Doblas-Reyes F.J., Macleod D., Christel I. and Davis M., 2016: Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and Climatology (Submitted)

Evaluation and Application of Gridded Snow Water Equivalent Products for Improving Snowmelt Flood Predictions in the Red River Basin of the North

NASA Astrophysics Data System (ADS)

Schroeder, R.; Jacobs, J. M.; Vuyovich, C.; Cho, E.; Tuttle, S. E.

2017-12-01

Each spring the Red River basin (RRB) of the North, located between the states of Minnesota and North Dakota and southern Manitoba, is vulnerable to dangerous spring snowmelt floods. Flat terrain, low permeability soils and a lack of satisfactory ground observations of snow pack conditions make accurate predictions of the onset and magnitude of major spring flood events in the RRB very challenging. This study investigated the potential benefit of using gridded snow water equivalent (SWE) products from passive microwave satellite missions and model output simulations to improve snowmelt flood predictions in the RRB using NOAA's operational Community Hydrologic Prediction System (CHPS). Level-3 satellite SWE products from AMSR-E, AMSR2 and SSM/I, as well as SWE computed from Level-2 brightness temperatures (Tb) measurements, including model output simulations of SWE from SNODAS and GlobSnow-2 were chosen to support the snowmelt modeling exercises. SWE observations were aggregated spatially (i.e. to the NOAA North Central River Forecast Center forecast basins) and temporally (i.e. by obtaining daily screened and weekly unscreened maximum SWE composites) to assess the value of daily satellite SWE observations relative to weekly maximums. Data screening methods removed the impacts of snow melt and cloud contamination on SWE and consisted of diurnal SWE differences and a temperature-insensitive polarization difference ratio, respectively. We examined the ability of the satellite and model output simulations to capture peak SWE and investigated temporal accuracies of screened and unscreened satellite and model output SWE. The resulting SWE observations were employed to update the SNOW-17 snow accumulation and ablation model of CHPS to assess the benefit of using temporally and spatially consistent SWE observations for snow melt predictions in two test basins in the RRB.

Application of predictive modelling techniques in industry: from food design up to risk assessment.

PubMed

Membré, Jeanne-Marie; Lambert, Ronald J W

2008-11-30

In this communication, examples of applications of predictive microbiology in industrial contexts (i.e. Nestlé and Unilever) are presented which cover a range of applications in food safety from formulation and process design to consumer safety risk assessment. A tailor-made, private expert system, developed to support safe product/process design assessment is introduced as an example of how predictive models can be deployed for use by non-experts. Its use in conjunction with other tools and software available in the public domain is discussed. Specific applications of predictive microbiology techniques are presented relating to investigations of either growth or limits to growth with respect to product formulation or process conditions. An example of a probabilistic exposure assessment model for chilled food application is provided and its potential added value as a food safety management tool in an industrial context is weighed against its disadvantages. The role of predictive microbiology in the suite of tools available to food industry and some of its advantages and constraints are discussed.

A New NOAA Research Initiative on the Seasonal Prediction of U.S. Coastal High Water Levels

NASA Astrophysics Data System (ADS)

Mariotti, A.; Archambault, H. M.; Barrie, D.; Huang, J.

2017-12-01

A crucial part of NOAA's service mission is to make U.S. communities more resilient to rises in coastal sea level, which on a seasonal timescale may increase the threat for nuisance ("sunny day") flooding, as well as enhance the severity of storm surge events. Over a season, variability in climate or ocean dynamics, in combination with longer-term trends, can influence coastal sea level in a way that is potentially predictable. To leverage these emerging scientific findings, the Climate Program Office's Modeling, Analysis, Predictions, and Projections Program, in partnership with the National Marine Fisheries Service, has funded a set of three-year projects starting in FY 2017 to help develop NOAA's capability to produce skillful seasonal (i.e, 2-9 month) predictions of coastal high water levels as well as changing living marine resources. This presentation will describe the goals, scope and intended activities of this research initiative and its coordination via a new MAPP Ocean Prediction Task Force.

Potential of on-line visible and near infrared spectroscopy for measurement of pH for deriving variable rate lime recommendations.

PubMed

Tekin, Yücel; Kuang, Boyan; Mouazen, Abdul M

2013-08-08

This paper aims at exploring the potential of visible and near infrared (vis-NIR) spectroscopy for on-line measurement of soil pH, with the intention to produce variable rate lime recommendation maps. An on-line vis-NIR soil sensor set up to a frame was used in this study. Lime application maps, based on pH predicted by vis-NIR techniques, were compared with maps based on traditional lab-measured pH. The validation of the calibration model using off-line spectra provided excellent prediction accuracy of pH (R2 = 0.85, RMSEP = 0.18 and RPD = 2.52), as compared to very good accuracy obtained with the on-line measured spectra (R2 = 0.81, RMSEP = 0.20 and RPD = 2.14). On-line predicted pH of all points (e.g., 2,160) resulted in the largest overall field virtual lime requirement (1.404 t), as compared to those obtained with 16 validation points off-line prediction (0.28 t), on-line prediction (0.14 t) and laboratory reference measurement (0.48 t). The conclusion is that the vis-NIR spectroscopy can be successfully used for the prediction of soil pH and for deriving lime recommendations. The advantage of the on-line sensor over sampling with limited number of samples is that more detailed information about pH can be obtained, which is the reason for a higher but precise calculated lime recommendation rate.

Potential of On-Line Visible and Near Infrared Spectroscopy for Measurement of pH for Deriving Variable Rate Lime Recommendations

PubMed Central

Tekin, Yücel; Kuang, Boyan; Mouazen, Abdul M.

2013-01-01

This paper aims at exploring the potential of visible and near infrared (vis-NIR) spectroscopy for on-line measurement of soil pH, with the intention to produce variable rate lime recommendation maps. An on-line vis-NIR soil sensor set up to a frame was used in this study. Lime application maps, based on pH predicted by vis-NIR techniques, were compared with maps based on traditional lab-measured pH. The validation of the calibration model using off-line spectra provided excellent prediction accuracy of pH (R2 = 0.85, RMSEP = 0.18 and RPD = 2.52), as compared to very good accuracy obtained with the on-line measured spectra (R2 = 0.81, RMSEP = 0.20 and RPD = 2.14). On-line predicted pH of all points (e.g., 2,160) resulted in the largest overall field virtual lime requirement (1.404 t), as compared to those obtained with 16 validation points off-line prediction (0.28 t), on-line prediction (0.14 t) and laboratory reference measurement (0.48 t). The conclusion is that the vis-NIR spectroscopy can be successfully used for the prediction of soil pH and for deriving lime recommendations. The advantage of the on-line sensor over sampling with limited number of samples is that more detailed information about pH can be obtained, which is the reason for a higher but precise calculated lime recommendation rate. PMID:23966186

The Development of Storm Surge Ensemble Prediction System and Case Study of Typhoon Meranti in 2016

NASA Astrophysics Data System (ADS)

Tsai, Y. L.; Wu, T. R.; Terng, C. T.; Chu, C. H.

2017-12-01

Taiwan is under the threat of storm surge and associated inundation, which is located at a potentially severe storm generation zone. The use of ensemble prediction can help forecasters to know the characteristic of storm surge under the uncertainty of track and intensity. In addition, it can help the deterministic forecasting. In this study, the kernel of ensemble prediction system is based on COMCOT-SURGE (COrnell Multi-grid COupled Tsunami Model - Storm Surge). COMCOT-SURGE solves nonlinear shallow water equations in Open Ocean and coastal regions with the nested-grid scheme and adopts wet-dry-cell treatment to calculate potential inundation area. In order to consider tide-surge interaction, the global TPXO 7.1 tide model provides the tidal boundary conditions. After a series of validations and case studies, COMCOT-SURGE has become an official operating system of Central Weather Bureau (CWB) in Taiwan. In this study, the strongest typhoon in 2016, Typhoon Meranti, is chosen as a case study. We adopt twenty ensemble members from CWB WRF Ensemble Prediction System (CWB WEPS), which differs from parameters of microphysics, boundary layer, cumulus, and surface. From box-and-whisker results, maximum observed storm surges were located in the interval of the first and third quartile at more than 70 % gauge locations, e.g. Toucheng, Chengkung, and Jiangjyun. In conclusion, the ensemble prediction can effectively help forecasters to predict storm surge especially under the uncertainty of storm track and intensity

Projected effects of Climate-change-induced flow alterations on stream macroinvertebrate abundances.

PubMed

Kakouei, Karan; Kiesel, Jens; Domisch, Sami; Irving, Katie S; Jähnig, Sonja C; Kail, Jochem

2018-03-01

Global change has the potential to affect river flow conditions which are fundamental determinants of physical habitats. Predictions of the effects of flow alterations on aquatic biota have mostly been assessed based on species ecological traits (e.g., current preferences), which are difficult to link to quantitative discharge data. Alternatively, we used empirically derived predictive relationships for species' response to flow to assess the effect of flow alterations due to climate change in two contrasting central European river catchments. Predictive relationships were set up for 294 individual species based on (1) abundance data from 223 sampling sites in the Kinzig lower-mountainous catchment and 67 sites in the Treene lowland catchment, and (2) flow conditions at these sites described by five flow metrics quantifying the duration, frequency, magnitude, timing and rate of flow events using present-day gauging data. Species' abundances were predicted for three periods: (1) baseline (1998-2017), (2) horizon 2050 (2046-2065) and (3) horizon 2090 (2080-2099) based on these empirical relationships and using high-resolution modeled discharge data for the present and future climate conditions. We compared the differences in predicted abundances among periods for individual species at each site, where the percent change served as a proxy to assess the potential species responses to flow alterations. Climate change was predicted to most strongly affect the low-flow conditions, leading to decreased abundances of species up to -42%. Finally combining the response of all species over all metrics indicated increasing overall species assemblage responses in 98% of the studied river reaches in both projected horizons and were significantly larger in the lower-mountainous Kinzig compared to the lowland Treene catchment. Such quantitative analyses of freshwater taxa responses to flow alterations provide valuable tools for predicting potential climate-change impacts on species abundances and can be applied to any stressor, species, or region.

Towards prediction of heatwaves based on the complementary relationship between actual and potential evaporation - energy partitioning and hydrologic attributes

NASA Astrophysics Data System (ADS)

Or, D.; Aminzadeh, M.; Roderick, M. L.

2017-12-01

Prediction of extreme climate events such as heatwaves that are characterized by prolonged periods of high air temperatures (accompanied by low precipitation and high radiation) provides an opportunity to potentially mitigate the associated environmental, social and economic impacts. Vegetation may respond to these extreme conditions by reducing evaporative flux either due to soil water depletion or inability to meet the atmospheric evaporative demand (high canopy resistance). We implement a newly generalized Complementary Relationship (CR) for spatially heterogeneous land surfaces to predict the actual evaporation from drying landscapes covered by different vegetation types (i.e., grassland and forest). A strong correlation between air temperature and sensible heat flux anomalies identified from FLUXNET network data suggests that abrupt changes in sensible heat flux above climatological means can serve as indicators for predicting the onset of a heatwave. We thus capitalize on the inherent coupling between evaporative and sensible heat fluxes linked to moisture availability within the CR framework to predict rapid increase in regional sensible heat flux associated with soil drying (low precipitation) or with extreme evaporative demand (high radiation) while soil moisture is not limiting. The proposed approach evaluated using FLUXNET datasets provides an energy constraint framework based on the CR concept to obtain new insights into the onset of heatwaves and climate extremes such as regional droughts.

Use of qualitative and quantitative information in neural networks for assessing agricultural chemical contamination of domestic wells

USGS Publications Warehouse

Mishra, A.; Ray, C.; Kolpin, D.W.

2004-01-01

A neural network analysis of agrichemical occurrence in groundwater was conducted using data from a pilot study of 192 small-diameter drilled and driven wells and 115 dug and bored wells in Illinois, a regional reconnaissance network of 303 wells across 12 Midwestern states, and a study of 687 domestic wells across Iowa. Potential factors contributing to well contamination (e.g., depth to aquifer material, well depth, and distance to cropland) were investigated. These contributing factors were available in either numeric (actual or categorical) or descriptive (yes or no) format. A method was devised to use the numeric and descriptive values simultaneously. Training of the network was conducted using a standard backpropagation algorithm. Approximately 15% of the data was used for testing. Analysis indicated that training error was quite low for most data. Testing results indicated that it was possible to predict the contamination potential of a well with pesticides. However, predicting the actual level of contamination was more difficult. For pesticide occurrence in drilled and driven wells, the network predictions were good. The performance of the network was poorer for predicting nitrate occurrence in dug and bored wells. Although the data set for Iowa was large, the prediction ability of the trained network was poor, due to descriptive or categorical input parameters, compared with smaller data sets such as that for Illinois, which contained more numeric information.

Atomistic Design of Favored Compositions for Synthesizing the Al-Ni-Y Metallic Glasses

PubMed Central

Wang, Q.; Li, J. H.; Liu, J. B.; Liu, B. X.

2015-01-01

For a ternary alloy system promising for obtaining the so-called bulk metallic glasses (BMGs), the first priority issue is to predict the favored compositions, which could then serve as guidance for the appropriate alloy design. Taking the Al-Ni-Y system as an example, here we show an atomistic approach, which is developed based on a recently constructed and proven realistic interatomic potential of the system. Applying the Al-Ni-Y potential, series simulations not only clarify the glass formation mechanism, but also predict in the composition triangle, a hexagonal region, in which a disordered state, i.e., the glassy phase, is favored energetically. The predicted region is defined as glass formation region (GFR) for the ternary alloy system. Moreover, the approach is able to calculate an amorphization driving force (ADF) for each possible glassy alloy located within the GFR. The calculations predict an optimized sub-region nearby a stoichiometry of Al80Ni5Y15, implying that the Al-Ni-Y metallic glasses designed in the sub-region could be the most stable. Interestingly, the atomistic predictions are supported by experimental results observed in the Al-Ni-Y system. In addition, structural origin underlying the stability of the Al-Ni-Y metallic glasses is also discussed in terms of a hybrid packing mode in the medium-range scale. PMID:26592568

Atomistic Design of Favored Compositions for Synthesizing the Al-Ni-Y Metallic Glasses

NASA Astrophysics Data System (ADS)

Wang, Q.; Li, J. H.; Liu, J. B.; Liu, B. X.

2015-11-01

For a ternary alloy system promising for obtaining the so-called bulk metallic glasses (BMGs), the first priority issue is to predict the favored compositions, which could then serve as guidance for the appropriate alloy design. Taking the Al-Ni-Y system as an example, here we show an atomistic approach, which is developed based on a recently constructed and proven realistic interatomic potential of the system. Applying the Al-Ni-Y potential, series simulations not only clarify the glass formation mechanism, but also predict in the composition triangle, a hexagonal region, in which a disordered state, i.e., the glassy phase, is favored energetically. The predicted region is defined as glass formation region (GFR) for the ternary alloy system. Moreover, the approach is able to calculate an amorphization driving force (ADF) for each possible glassy alloy located within the GFR. The calculations predict an optimized sub-region nearby a stoichiometry of Al80Ni5Y15, implying that the Al-Ni-Y metallic glasses designed in the sub-region could be the most stable. Interestingly, the atomistic predictions are supported by experimental results observed in the Al-Ni-Y system. In addition, structural origin underlying the stability of the Al-Ni-Y metallic glasses is also discussed in terms of a hybrid packing mode in the medium-range scale.

Identification of potential herbal inhibitor of acetylcholinesterase associated Alzheimer's disorders using molecular docking and molecular dynamics simulation.

PubMed

Seniya, Chandrabhan; Khan, Ghulam Jilani; Uchadia, Kuldeep

2014-01-01

Cholinesterase inhibitors (ChE-Is) are the standard for the therapy of AD associated disorders and are the only class of approved drugs by the Food and Drug Administration (FDA). Additionally, acetylcholinesterase (AChE) is the target for many Alzheimer's dementia drugs which block the function of AChE but have some side effects. Therefore, in this paper, an attempt was made to elucidate cholinesterase inhibition potential of secondary metabolite from Cannabis plant which has negligible or no side effect. Molecular docking of 500 herbal compounds, against AChE, was performed using Autodock 4.2 as per the standard protocols. Molecular dynamics simulations have also been carried out to check stability of binding complex in water for 1000 ps. Our molecular docking and simulation have predicted high binding affinity of secondary metabolite (C28H34N2O6) to AChE. Further, molecular dynamics simulations for 1000 ps suggest that ligand interaction with the residues Asp72, Tyr70-121-334, and Phe288 of AChE, all of which fall under active site/subsite or binding pocket, might be critical for the inhibitory activity of AChE. This approach might be helpful to understand the selectivity of the given drug molecule in the treatment of Alzheimer's disease. The study provides evidence for consideration of C28H34N2O6 as a valuable small ligand molecule in treatment and prevention of AD associated disorders and further in vitro and in vivo investigations may prove its therapeutic potential.

Identification of Potential Herbal Inhibitor of Acetylcholinesterase Associated Alzheimer's Disorders Using Molecular Docking and Molecular Dynamics Simulation

PubMed Central

Seniya, Chandrabhan; Khan, Ghulam Jilani; Uchadia, Kuldeep

2014-01-01

Cholinesterase inhibitors (ChE-Is) are the standard for the therapy of AD associated disorders and are the only class of approved drugs by the Food and Drug Administration (FDA). Additionally, acetylcholinesterase (AChE) is the target for many Alzheimer's dementia drugs which block the function of AChE but have some side effects. Therefore, in this paper, an attempt was made to elucidate cholinesterase inhibition potential of secondary metabolite from Cannabis plant which has negligible or no side effect. Molecular docking of 500 herbal compounds, against AChE, was performed using Autodock 4.2 as per the standard protocols. Molecular dynamics simulations have also been carried out to check stability of binding complex in water for 1000 ps. Our molecular docking and simulation have predicted high binding affinity of secondary metabolite (C28H34N2O6) to AChE. Further, molecular dynamics simulations for 1000 ps suggest that ligand interaction with the residues Asp72, Tyr70-121-334, and Phe288 of AChE, all of which fall under active site/subsite or binding pocket, might be critical for the inhibitory activity of AChE. This approach might be helpful to understand the selectivity of the given drug molecule in the treatment of Alzheimer's disease. The study provides evidence for consideration of C28H34N2O6 as a valuable small ligand molecule in treatment and prevention of AD associated disorders and further in vitro and in vivo investigations may prove its therapeutic potential. PMID:25054066

Predicting extractives content of Eucalyptus bosistoana F. Muell. Heartwood from stem cores by near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Li, Yanjie; Altaner, Clemens

2018-06-01

Time and resource are the restricting factors for the wider use of chemical information of wood in tree breeding programs. NIR offers an advantage over wet-chemical analysis in these aspects and is starting to be used for tree breeding. This work describes the development of a NIR-based assessment of extractive content in heartwood of E. bosistoana, which does not require milling and conditioning of the samples. This was achieved by applying the signal processing algorithms (external parameter orthogonalisation (EPO) and significance multivariate correlation (sMC)) to spectra obtained from solid wood cores, which were able to correct for moisture content, grain direction and sample form. The accuracy of extractive content predictions was further improved by variable selection, resulting in a root mean square error of 1.27%. Considering the range of extractive content in E. bosistoana heartwood of 1.3 to 15.0%, the developed NIR calibration has the potential to be used in an E. bosistoana breeding program or to assess the special variation in extractive content throughout a stem.

Prosociality during the transition from late adolescence to young adulthood: the role of effortful control and ego-resiliency.

PubMed

Alessandri, Guido; Luengo Kanacri, Bernadette Paula; Eisenberg, Nancy; Zuffianò, Antonio; Milioni, Michela; Vecchione, Michele; Caprara, Gian Vittorio

2014-11-01

The present prospective study examined the prediction of prosociality from effortful control and ego-resiliency from late adolescence to emerging adulthood. Participants were 476 young adults (239 males and 237 females) with a mean age of 16 years (SD = .81) at T1, 18 years (SD = .83) at T2, 20 years (SD = .79) at T3, 22 years (SD = .81) at T4, and 26 years (SD = .81) at T5. Controlling for the stability of the examined variables and the effect of potential confounding variables (i.e., sex, socioeconomic status [SES], and age), results supported a model in which a temperamental dimension, effortful control, positively predicted a specific behavioral tendency (i.e., prosociality) indirectly through mediation by a personality factor (i.e., ego-resiliency). Practical implications of the results are discussed in terms of the importance of early prevention efforts designed to enhance the capacity to cope effectively with emotional reactions and difficult situations. © 2014 by the Society for Personality and Social Psychology, Inc.

Electron affinity of liquid water

DOE PAGES

Gaiduk, Alex P.; Pham, Tuan Anh; Govoni, Marco; ...

2018-01-16

Understanding redox and photochemical reactions in aqueous environments requires a precise knowledge of the ionization potential and electron affinity of liquid water. The former has been measured, but not the latter. We predict the electron affinity of liquid water and of its surface from first principles, coupling path-integral molecular dynamics with ab initio potentials, and many-body perturbation theory. Our results for the surface (0.8 eV) agree well with recent pump-probe spectroscopy measurements on amorphous ice. Those for the bulk (0.1-0.3 eV) differ from several estimates adopted in the literature, which we critically revisit. We show that the ionization potential ofmore » the bulk and surface are almost identical; instead their electron affinities differ substantially, with the conduction band edge of the surface much deeper in energy than that of the bulk. We also discuss the significant impact of nuclear quantum effects on the fundamental gap and band edges of the liquid.« less

Electron affinity of liquid water

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gaiduk, Alex P.; Pham, Tuan Anh; Govoni, Marco

Understanding redox and photochemical reactions in aqueous environments requires a precise knowledge of the ionization potential and electron affinity of liquid water. The former has been measured, but not the latter. We predict the electron affinity of liquid water and of its surface from first principles, coupling path-integral molecular dynamics with ab initio potentials, and many-body perturbation theory. Our results for the surface (0.8 eV) agree well with recent pump-probe spectroscopy measurements on amorphous ice. Those for the bulk (0.1-0.3 eV) differ from several estimates adopted in the literature, which we critically revisit. We show that the ionization potential ofmore » the bulk and surface are almost identical; instead their electron affinities differ substantially, with the conduction band edge of the surface much deeper in energy than that of the bulk. We also discuss the significant impact of nuclear quantum effects on the fundamental gap and band edges of the liquid.« less

Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series

PubMed Central

Liu, Datong; Peng, Yu; Peng, Xiyuan

2018-01-01

Effective anomaly detection of sensing data is essential for identifying potential system failures. Because they require no prior knowledge or accumulated labels, and provide uncertainty presentation, the probability prediction methods (e.g., Gaussian process regression (GPR) and relevance vector machine (RVM)) are especially adaptable to perform anomaly detection for sensing series. Generally, one key parameter of prediction models is coverage probability (CP), which controls the judging threshold of the testing sample and is generally set to a default value (e.g., 90% or 95%). There are few criteria to determine the optimal CP for anomaly detection. Therefore, this paper designs a graphic indicator of the receiver operating characteristic curve of prediction interval (ROC-PI) based on the definition of the ROC curve which can depict the trade-off between the PI width and PI coverage probability across a series of cut-off points. Furthermore, the Youden index is modified to assess the performance of different CPs, by the minimization of which the optimal CP is derived by the simulated annealing (SA) algorithm. Experiments conducted on two simulation datasets demonstrate the validity of the proposed method. Especially, an actual case study on sensing series from an on-orbit satellite illustrates its significant performance in practical application. PMID:29587372

Impacts of pesticide mixtures in European rivers as predicted by the Species Sensitivity Distribution (SSD) models and SPEAR bioindication

NASA Astrophysics Data System (ADS)

Jesenska, Sona; Liess, Mathias; Schäfer, Ralf; Beketov, Mikhail; Blaha, Ludek

2013-04-01

Species sensitivity distribution (SSD) is statistical method broadly used in the ecotoxicological risk assessment of chemicals. Originally it has been used for prospective risk assessment of single substances but nowadays it is becoming more important also in the retrospective risk assessment of mixtures, including the catchment scale. In the present work, SSD predictions (impacts of mixtures consisting of 25 pesticides; data from several catchments in Germany, France and Finland) were compared with SPEAR-pesticides, which a bioindicator index based on biological traits responsive to the effects of pesticides and post-contamination recovery. The results showed statistically significant correlations (Pearson's R, p<0.01) between SSD (predicted msPAF values) and values of SPEAR-pesticides (based on field biomonitoring observations). Comparisons of the thresholds established for the SSD and SPEAR approaches (SPEAR-pesticides=45%, i.e. LOEC level, and msPAF = 0.05 for SSD, i.e. HC5) showed that use of chronic toxicity data significantly improved the agreement between the two methods but the SPEAR-pesticides index was still more sensitive. Taken together, the validation study shows good potential of SSD models in predicting the real impacts of micropollutant mixtures on natural communities of aquatic biota.

Open Reading Frame E3-10.9K of Subspecies B1 Human Adenoviruses Encodes a Family of Late Orthologous Proteins That Vary in Their Predicted Structural Features and Subcellular Localization ▿

PubMed Central

Frietze, Kathryn M.; Campos, Samuel K.; Kajon, Adriana E.

2010-01-01

Subspecies B1 human adenoviruses (HAdV-B1s) are important causative agents of acute respiratory disease, but the molecular bases of their distinct pathobiology are still poorly understood. Marked differences in genetic content between HAdV-B1s and the well-characterized HAdV-Cs that may contribute to distinct pathogenic properties map to the E3 region. Between the highly conserved E3-19K and E3-10.4K/RIDα open reading frames (ORFs), and in the same location as the HAdV-C ADP/E3-11.6K ORF, HAdV-B1s carry ORFs E3-20.1K and E3-20.5K and a polymorphic third ORF, designated E3-10.9K, that varies in the size of its predicted product among HAdV-B1 serotypes and genomic variants. As an initial effort to define the function of the E3-10.9K ORF, we carried out a biochemical characterization of E3-10.9K-encoded orthologous proteins and investigated their expression in infected cells. Sequence-based predictions suggested that E3-10.9K orthologs with a hydrophobic domain are integral membrane proteins. Ectopically expressed, C-terminally tagged (with enhanced green fluorescent protein [EGFP]) E3-10.9K and E3-9K localized primarily to the plasma membrane, while E3-7.7K localized primarily to a juxtanuclear compartment that could not be identified. EGFP fusion proteins with a hydrophobic domain were N and O glycosylated. EGFP-tagged E3-4.8K, which lacked the hydrophobic domain, displayed diffuse cellular localization similar to that of the EGFP control. E3-10.9K transcripts from the major late promoter were detected at late time points postinfection. A C-terminally hemagglutinin-tagged version of E3-9K was detected by immunoprecipitation at late times postinfection in the membrane fraction of mutant virus-infected cells. These data suggest a role for ORF E3-10.9K-encoded proteins at late stages of HAdV-B1 replication, with potentially important functional implications for the documented ORF polymorphism. PMID:20739542

Nonlinear dielectric response and transient current: An effective potential for ferroelectric domain wall displacement

NASA Astrophysics Data System (ADS)

Placeres Jiménez, Rolando; Pedro Rino, José; Marino Gonçalves, André; Antonio Eiras, José

2013-09-01

Ferroelectric domain walls are modeled as rigid bodies moving under the action of a potential field in a dissipative medium. Assuming that the dielectric permittivity follows the dependence ɛ '∝1/(α+βE2), it obtained the exact expression for the effective potential. Simulations of polarization current correctly predict a power law. Such results could be valuable in the study of domain wall kinetic and ultrafast polarization processes. The model is extended to poled samples allowing the study of nonlinear dielectric permittivity under subswitching electric fields. Experimental nonlinear data from PZT 20/80 thin films and Fe+3 doped PZT 40/60 ceramic are reproduced.

Infrared spectrum, NBO, HOMO-LUMO, MEP and molecular docking studies (2E)-3-(3-nitrophenyl)-1-[4-piperidin-1-yl]prop-2-en-1-one.

PubMed

Panicker, C Yohannan; Varghese, Hema Tresa; Nayak, Prakash S; Narayana, B; Sarojini, B K; Fun, H K; War, Javeed Ahamad; Srivastava, S K; Van Alsenoy, C

2015-09-05

FT-IR spectrum of (2E)-3-(3-nitrophenyl)-1-[4-piperidin-1-yl]prop-2-en-1-one was recorded and analyzed. The vibrational wavenumbers were computed using HF and DFT quantum chemical calculations. The data obtained from wavenumber calculations are used to assign IR bands. Potential energy distribution was done using GAR2PED software. The geometrical parameters of the title compound are in agreement with the XRD results. NBO analysis, HOMO-LUMO, first and second hyperpolarizability and molecular electrostatic potential results are also reported. The possible electrophile attacking sites of the title molecule is identified using MEP surface plot study. Molecular docking results predicted the anti-leishmanic activity for the compound. Copyright © 2015. Published by Elsevier B.V.

Prediction of individual response to anticancer therapy: historical and future perspectives.

PubMed

Unger, Florian T; Witte, Irene; David, Kerstin A

2015-02-01

Since the introduction of chemotherapy for cancer treatment in the early 20th century considerable efforts have been made to maximize drug efficiency and at the same time minimize side effects. As there is a great interpatient variability in response to chemotherapy, the development of predictive biomarkers is an ambitious aim for the rapidly growing research area of personalized molecular medicine. The individual prediction of response will improve treatment and thus increase survival and life quality of patients. In the past, cell cultures were used as in vitro models to predict in vivo response to chemotherapy. Several in vitro chemosensitivity assays served as tools to measure miscellaneous endpoints such as DNA damage, apoptosis and cytotoxicity or growth inhibition. Twenty years ago, the development of high-throughput technologies, e.g. cDNA microarrays enabled a more detailed analysis of drug responses. Thousands of genes were screened and expression levels were correlated to drug responses. In addition, mutation analysis became more and more important for the prediction of therapeutic success. Today, as research enters the area of -omics technologies, identification of signaling pathways is a tool to understand molecular mechanism underlying drug resistance. Combining new tissue models, e.g. 3D organoid cultures with modern technologies for biomarker discovery will offer new opportunities to identify new drug targets and in parallel predict individual responses to anticancer therapy. In this review, we present different currently used chemosensitivity assays including 2D and 3D cell culture models and several -omics approaches for the discovery of predictive biomarkers. Furthermore, we discuss the potential of these assays and biomarkers to predict the clinical outcome of individual patients and future perspectives.

E-hail (Rideshare) Knowledge, Use, Reliance, and Future Expectations among Older Adults.

PubMed

Vivoda, Jonathon M; Harmon, Annie C; Babulal, Ganesh M; Zikmund-Fisher, Brian J

2018-05-01

The goals of this study were to explore e-hail (e.g., Uber/Lyft) knowledge, use, reliance, and future expectations among older adults. Specifically, we aimed to identify factors that were related to e-hail, and how older adults view this mode as a potential future transportation option. Data were collected from a sample of older adults using a pencil-and-paper mailed survey. Univariate, bivariate, and regression techniques were used to assess the relationships among e-hail and several demographic and other factors. Almost three-quarters of the sample (74%) reported no e-hail knowledge. Only 1.7% had used e-hail to arrange a ride,andonly 3.3% reported that they relied on e-hail for any of their transportation needs. Younger age, male gender, more education, higher transportation satisfaction, and discussing transportation options with others were all independently associated with greater e-hail knowledge. Male gender also predicted e-hail use. E-hail was the mode least relied upon by older adults. Current e-hail knowledge was the biggest predictor of anticipated future use. E-hail may be a viable future option for older adults who have limited or stopped driving. More exposure to e-hail and continued evolution of these services is required to overcome older adults' lower internet/smartphone use. Policies could be implemented at departments of motor vehicles to pair information or training on transportation alternatives (like e-hail) with elimination of driving privileges, or at doctors' offices, senior centers, or hospitals. Potential underlying reasons for the findings are also discussed.

Comparison of non-ideal solution theories for multi-solute solutions in cryobiology and tabulation of required coefficients.

PubMed

Zielinski, Michal W; McGann, Locksley E; Nychka, John A; Elliott, Janet A W

2014-10-01

Thermodynamic solution theories allow the prediction of chemical potentials in solutions of known composition. In cryobiology, such models are a critical component of many mathematical models that are used to simulate the biophysical processes occurring in cells and tissues during cryopreservation. A number of solution theories, both thermodynamically ideal and non-ideal, have been proposed for use with cryobiological solutions. In this work, we have evaluated two non-ideal solution theories for predicting water chemical potential (i.e. osmolality) in multi-solute solutions relevant to cryobiology: the Elliott et al. form of the multi-solute osmotic virial equation, and the Kleinhans and Mazur freezing point summation model. These two solution theories require fitting to only single-solute data, although they can make predictions in multi-solute solutions. The predictions of these non-ideal solution theories were compared to predictions made using ideal dilute assumptions and to available literature multi-solute experimental osmometric data. A single, consistent set of literature single-solute solution data was used to fit for the required solute-specific coefficients for each of the non-ideal models. Our results indicate that the two non-ideal solution theories have similar overall performance, and both give more accurate predictions than ideal models. These results can be used to select between the non-ideal models for a specific multi-solute solution, and the updated coefficients provided in this work can be used to make the desired predictions. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Use of geospatial technology for delineating groundwater potential zones with an emphasis on water-table analysis in Dwarka River basin, Birbhum, India

NASA Astrophysics Data System (ADS)

Thapa, Raju; Gupta, Srimanta; Gupta, Arindam; Reddy, D. V.; Kaur, Harjeet

2018-05-01

Dwarka River basin in Birbhum, West Bengal (India), is an agriculture-dominated area where groundwater plays a crucial role. The basin experiences seasonal water stress conditions with a scarcity of surface water. In the presented study, delineation of groundwater potential zones (GWPZs) is carried out using a geospatial multi-influencing factor technique. Geology, geomorphology, soil type, land use/land cover, rainfall, lineament and fault density, drainage density, slope, and elevation of the study area were considered for the delineation of GWPZs in the study area. About 9.3, 71.9 and 18.8% of the study area falls within good, moderate and poor groundwater potential zones, respectively. The potential groundwater yield data corroborate the outcome of the model, with maximum yield in the older floodplain and minimum yield in the hard-rock terrains in the western and south-western regions. Validation of the GWPZs using the yield of 148 wells shows very high accuracy of the model prediction, i.e., 89.1% on superimposition and 85.1 and 81.3% on success and prediction rates, respectively. Measurement of the seasonal water-table fluctuation with a multiplicative model of time series for predicting the short-term trend of the water table, followed by chi-square analysis between the predicted and observed water-table depth, indicates a trend of falling groundwater levels, with a 5% level of significance and a p-value of 0.233. The rainfall pattern for the last 3 years of the study shows a moderately positive correlation ( R 2 = 0.308) with the average water-table depth in the study area.

EMBEDDED LENSING TIME DELAYS, THE FERMAT POTENTIAL, AND THE INTEGRATED SACHS–WOLFE EFFECT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Bin; Kantowski, Ronald; Dai, Xinyu, E-mail: bchen3@fsu.edu

2015-05-01

We derive the Fermat potential for a spherically symmetric lens embedded in a Friedman–Lemaître–Robertson–Walker cosmology and use it to investigate the late-time integrated Sachs–Wolfe (ISW) effect, i.e., secondary temperature fluctuations in the cosmic microwave background (CMB) caused by individual large-scale clusters and voids. We present a simple analytical expression for the temperature fluctuation in the CMB across such a lens as a derivative of the lens’ Fermat potential. This formalism is applicable to both linear and nonlinear density evolution scenarios, to arbitrarily large density contrasts, and to all open and closed background cosmologies. It is much simpler to use andmore » makes the same predictions as conventional approaches. In this approach the total temperature fluctuation can be split into a time-delay part and an evolutionary part. Both parts must be included for cosmic structures that evolve and both can be equally important. We present very simple ISW models for cosmic voids and galaxy clusters to illustrate the ease of use of our formalism. We use the Fermat potentials of simple cosmic void models to compare predicted ISW effects with those recently extracted from WMAP and Planck data by stacking large cosmic voids using the aperture photometry method. If voids in the local universe with large density contrasts are no longer evolving we find that the time delay contribution alone predicts values consistent with the measurements. However, we find that for voids still evolving linearly, the evolutionary contribution cancels a significant part of the time delay contribution and results in predicted signals that are much smaller than recently observed.« less