Predicting the timing and potential of the spring emergence of overwintered populations of Heliothis spp

NASA Technical Reports Server (NTRS)

Hartstack, A. W.; Witz, J. A.; Lopez, J. D. (Principal Investigator)

1981-01-01

The current state of knowledge dealing with the prediction of the overwintering population and spring emergence of Heliothis spp., a serious pest of numerous crops is surveyed. Current literature is reviewed in detail. Temperature and day length are the primary factors which program H. spp. larva for possible diapause. Although studies on the interaction of temperature and day length are reported, the complete diapause induction process is not identified sufficiently to allow accurate prediction of diapause timing. Mortality during diapause is reported as highly variable. The factors causing mortality are identified, but only a few are quantified. The spring emergence of overwintering H. spp. adults and mathematical models which predict the timing of emergence are reviewed. Timing predictions compare favorably to observed field data; however, prediction of actual numbers of emerging moths is not possible. The potential for use of spring emergence predictions in pest management applications, as an early warning of potential crop damage, are excellent. Research requirements to develop such an early warning system are discussed.

ACTP: A webserver for predicting potential targets and relevant pathways of autophagy-modulating compounds

PubMed Central

Ouyang, Liang; Cai, Haoyang; Liu, Bo

2016-01-01

Autophagy (macroautophagy) is well known as an evolutionarily conserved lysosomal degradation process for long-lived proteins and damaged organelles. Recently, accumulating evidence has revealed a series of small-molecule compounds that may activate or inhibit autophagy for therapeutic potential on human diseases. However, targeting autophagy for drug discovery still remains in its infancy. In this study, we developed a webserver called Autophagic Compound-Target Prediction (ACTP) (http://actp.liu-lab.com/) that could predict autophagic targets and relevant pathways for a given compound. The flexible docking of submitted small-molecule compound (s) to potential autophagic targets could be performed by backend reverse docking. The webpage would return structure-based scores and relevant pathways for each predicted target. Thus, these results provide a basis for the rapid prediction of potential targets/pathways of possible autophagy-activating or autophagy-inhibiting compounds without labor-intensive experiments. Moreover, ACTP will be helpful to shed light on identifying more novel autophagy-activating or autophagy-inhibiting compounds for future therapeutic implications. PMID:26824420

Complex mixtures of dissolved pesticides show potential aquatic toxicity in a synoptic study of Midwestern U.S. streams

USGS Publications Warehouse

Nowell, Lisa H.; Moran, Patrick W.; Schmidt, Travis S.; Norman, Julia E.; Nakagaki, Naomi; Shoda, Megan E.; Mahler, Barbara J.; Van Metre, Peter C.; Stone, Wesley W.; Sandstrom, Mark W.; Hladik, Michelle L.

2018-01-01

Aquatic organisms in streams are exposed to pesticide mixtures that vary in composition over time in response to changes in flow conditions, pesticide inputs to the stream, and pesticide fate and degradation within the stream. To characterize mixtures of dissolved-phase pesticides and degradates in Midwestern streams, a synoptic study was conducted at 100 streams during May–August 2013. In weekly water samples, 94 pesticides and 89 degradates were detected, with a median of 25 compounds detected per sample and 54 detected per site. In a screening-level assessment using aquatic-life benchmarks and the Pesticide Toxicity Index (PTI), potential effects on fish were unlikely in most streams. For invertebrates, potential chronic toxicity was predicted in 53% of streams, punctuated in 12% of streams by acutely toxic exposures. For aquatic plants, acute but likely reversible effects on biomass were predicted in 75% of streams, with potential longer-term effects on plant communities in 9% of streams. Relatively few pesticides in water—atrazine, acetochlor, metolachlor, imidacloprid, fipronil, organophosphate insecticides, and carbendazim—were predicted to be major contributors to potential toxicity. Agricultural streams had the highest potential for effects on plants, especially in May–June, corresponding to high spring-flush herbicide concentrations. Urban streams had higher detection frequencies and concentrations of insecticides and most fungicides than in agricultural streams, and higher potential for invertebrate toxicity, which peaked during July–August. Toxicity-screening predictions for invertebrates were supported by quantile regressions showing significant associations for the Benthic Invertebrate-PTI and imidacloprid concentrations with invertebrate community metrics for MSQA streams, and by mesocosm toxicity testing with imidacloprid showing effects on invertebrate communities at environmentally relevant concentrations. This study documents the most complex pesticide mixtures yet reported in discrete water samples in the U.S. and, using multiple lines of evidence, predicts that pesticides were potentially toxic to nontarget aquatic life in about half of the sampled streams.

Availability of human induced pluripotent stem cell-derived cardiomyocytes in assessment of drug potential for QT prolongation

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

Nozaki, Yumiko, E-mail: yumiko-nozaki@ds-pharma.co.jp; Honda, Yayoi, E-mail: yayoi-honda@ds-pharma.co.jp; Tsujimoto, Shinji, E-mail: shinji-tsujimoto@ds-pharma.co.jp

2014-07-01

Field potential duration (FPD) in human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs), which can express QT interval in an electrocardiogram, is reported to be a useful tool to predict K{sup +} channel and Ca{sup 2+} channel blocker effects on QT interval. However, there is no report showing that this technique can be used to predict multichannel blocker potential for QT prolongation. The aim of this study is to show that FPD from MEA (Multielectrode array) of hiPS-CMs can detect QT prolongation induced by multichannel blockers. hiPS-CMs were seeded onto MEA and FPD was measured for 2 min every 10 min formore » 30 min after drug exposure for the vehicle and each drug concentration. I{sub Kr} and I{sub Ks} blockers concentration-dependently prolonged corrected FPD (FPDc), whereas Ca{sup 2+} channel blockers concentration-dependently shortened FPDc. Also, the multichannel blockers Amiodarone, Paroxetine, Terfenadine and Citalopram prolonged FPDc in a concentration dependent manner. Finally, the I{sub Kr} blockers, Terfenadine and Citalopram, which are reported to cause Torsade de Pointes (TdP) in clinical practice, produced early afterdepolarization (EAD). hiPS-CMs using MEA system and FPDc can predict the effects of drug candidates on QT interval. This study also shows that this assay can help detect EAD for drugs with TdP potential. - Highlights: • We focused on hiPS-CMs to replace in vitro assays in preclinical screening studies. • hiPS-CMs FPD is useful as an indicator to predict drug potential for QT prolongation. • MEA assay can help detect EAD for drugs with TdP potentials. • MEA assay in hiPS-CMs is useful for accurately predicting drug TdP risk in humans.« less

Genomic Prediction of Single Crosses in the Early Stages of a Maize Hybrid Breeding Pipeline.

PubMed

Kadam, Dnyaneshwar C; Potts, Sarah M; Bohn, Martin O; Lipka, Alexander E; Lorenz, Aaron J

2016-09-19

Prediction of single-cross performance has been a major goal of plant breeders since the beginning of hybrid breeding. Recently, genomic prediction has shown to be a promising approach, but only limited studies have examined the accuracy of predicting single-cross performance. Moreover, no studies have examined the potential of predicting single crosses among random inbreds derived from a series of biparental families, which resembles the structure of germplasm comprising the initial stages of a hybrid maize breeding pipeline. The main objectives of this study were to evaluate the potential of genomic prediction for identifying superior single crosses early in the hybrid breeding pipeline and optimize its application. To accomplish these objectives, we designed and analyzed a novel population of single crosses representing the Iowa Stiff Stalk Synthetic/Non-Stiff Stalk heterotic pattern commonly used in the development of North American commercial maize hybrids. The performance of single crosses was predicted using parental combining ability and covariance among single crosses. Prediction accuracies were estimated using cross-validation and ranged from 0.28 to 0.77 for grain yield, 0.53 to 0.91 for plant height, and 0.49 to 0.94 for staygreen, depending on the number of tested parents of the single cross and genomic prediction method used. The genomic estimated general and specific combining abilities showed an advantage over genomic covariances among single crosses when one or both parents of the single cross were untested. Overall, our results suggest that genomic prediction of single crosses in the early stages of a hybrid breeding pipeline holds great potential to re-design hybrid breeding and increase its efficiency. Copyright © 2016 Author et al.

Geographic potential of disease caused by Ebola and Marburg viruses in Africa.

PubMed

Peterson, A Townsend; Samy, Abdallah M

2016-10-01

Filoviruses represent a significant public health threat worldwide. West Africa recently experienced the largest-scale and most complex filovirus outbreak yet known, which underlines the need for a predictive understanding of the geographic distribution and potential for transmission to humans of these viruses. Here, we used ecological niche modeling techniques to understand the relationship between known filovirus occurrences and environmental characteristics. Our study derived a picture of the potential transmission geography of Ebola virus species and Marburg, paired with views of the spatial uncertainty associated with model-to-model variation in our predictions. We found that filovirus species have diverged ecologically, but only three species are sufficiently well known that models could be developed with significant predictive power. We quantified uncertainty in predictions, assessed potential for outbreaks outside of known transmission areas, and highlighted the Ethiopian Highlands and scattered areas across East Africa as additional potentially unrecognized transmission areas. Copyright © 2016 Elsevier B.V. All rights reserved.

Personal contextual characteristics and cognitions: predicting child abuse potential and disciplinary style.

PubMed

Rodriguez, Christina M

2010-02-01

According to Social Information Processing theory, parents' cognitive processes influence their decisions to engage in physical maltreatment, although cognitions occur in the context of other aspects of the parents' life. The present study investigated whether cognitive processes (external locus of control, inappropriate developmental expectations) predicted child abuse potential and overreactive disciplinary style beyond personal contextual factors characteristic of the parent (hostility, stress, and coping). 363 parents were recruited online. Results highlight the relative importance of the contextual characteristics (particularly stress, avoidant coping, and irritability) relative to cognitive processes in predicting abuse potential and overreactive discipline strategies, although an external locus of control also significantly contributed. Findings do not support that parents' developmental expectations uniquely predict elevated abuse risk. Results indicate stressed parents who utilize avoidance coping strategies are more likely to use overreactive discipline and report increased abuse potential. Findings are discussed with regard to implications for prevention/intervention efforts.

Prediction of Thorough QT study results using action potential simulations based on ion channel screens.

PubMed

Mirams, Gary R; Davies, Mark R; Brough, Stephen J; Bridgland-Taylor, Matthew H; Cui, Yi; Gavaghan, David J; Abi-Gerges, Najah

2014-01-01

Detection of drug-induced pro-arrhythmic risk is a primary concern for pharmaceutical companies and regulators. Increased risk is linked to prolongation of the QT interval on the body surface ECG. Recent studies have shown that multiple ion channel interactions can be required to predict changes in ventricular repolarisation and therefore QT intervals. In this study we attempt to predict the result of the human clinical Thorough QT (TQT) study, using multiple ion channel screening which is available early in drug development. Ion current reduction was measured, in the presence of marketed drugs which have had a TQT study, for channels encoded by hERG, CaV1.2, NaV1.5, KCNQ1/MinK, and Kv4.3/KChIP2.2. The screen was performed on two platforms - IonWorks Quattro (all 5 channels, 34 compounds), and IonWorks Barracuda (hERG & CaV1.2, 26 compounds). Concentration-effect curves were fitted to the resulting data, and used to calculate a percentage reduction in each current at a given concentration. Action potential simulations were then performed using the ten Tusscher and Panfilov (2006), Grandi et al. (2010) and O'Hara et al. (2011) human ventricular action potential models, pacing at 1Hz and running to steady state, for a range of concentrations. We compared simulated action potential duration predictions with the QT prolongation observed in the TQT studies. At the estimated concentrations, simulations tended to underestimate any observed QT prolongation. When considering a wider range of concentrations, and conventional patch clamp rather than screening data for hERG, prolongation of ≥5ms was predicted with up to 79% sensitivity and 100% specificity. This study provides a proof-of-principle for the prediction of human TQT study results using data available early in drug development. We highlight a number of areas that need refinement to improve the method's predictive power, but the results suggest that such approaches will provide a useful tool in cardiac safety assessment. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction.

PubMed

Chen, Xing; Wu, Qiao-Feng; Yan, Gui-Ying

2017-07-03

Cumulative verified experimental studies have demonstrated that microRNAs (miRNAs) could be closely related with the development and progression of human complex diseases. Based on the assumption that functional similar miRNAs may have a strong correlation with phenotypically similar diseases and vice versa, researchers developed various effective computational models which combine heterogeneous biologic data sets including disease similarity network, miRNA similarity network, and known disease-miRNA association network to identify potential relationships between miRNAs and diseases in biomedical research. Considering the limitations in previous computational study, we introduced a novel computational method of Ranking-based KNN for miRNA-Disease Association prediction (RKNNMDA) to predict potential related miRNAs for diseases, and our method obtained an AUC of 0.8221 based on leave-one-out cross validation. In addition, RKNNMDA was applied to 3 kinds of important human cancers for further performance evaluation. The results showed that 96%, 80% and 94% of predicted top 50 potential related miRNAs for Colon Neoplasms, Esophageal Neoplasms, and Prostate Neoplasms have been confirmed by experimental literatures, respectively. Moreover, RKNNMDA could be used to predict potential miRNAs for diseases without any known miRNAs, and it is anticipated that RKNNMDA would be of great use for novel miRNA-disease association identification.

RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction

PubMed Central

Chen, Xing; Yan, Gui-Ying

2017-01-01

ABSTRACT Cumulative verified experimental studies have demonstrated that microRNAs (miRNAs) could be closely related with the development and progression of human complex diseases. Based on the assumption that functional similar miRNAs may have a strong correlation with phenotypically similar diseases and vice versa, researchers developed various effective computational models which combine heterogeneous biologic data sets including disease similarity network, miRNA similarity network, and known disease-miRNA association network to identify potential relationships between miRNAs and diseases in biomedical research. Considering the limitations in previous computational study, we introduced a novel computational method of Ranking-based KNN for miRNA-Disease Association prediction (RKNNMDA) to predict potential related miRNAs for diseases, and our method obtained an AUC of 0.8221 based on leave-one-out cross validation. In addition, RKNNMDA was applied to 3 kinds of important human cancers for further performance evaluation. The results showed that 96%, 80% and 94% of predicted top 50 potential related miRNAs for Colon Neoplasms, Esophageal Neoplasms, and Prostate Neoplasms have been confirmed by experimental literatures, respectively. Moreover, RKNNMDA could be used to predict potential miRNAs for diseases without any known miRNAs, and it is anticipated that RKNNMDA would be of great use for novel miRNA-disease association identification. PMID:28421868

The Impact of Team Identification on Biased Predictions of Player Performance

ERIC Educational Resources Information Center

Wann, Daniel L.; Koch, Katrina; Knoth, Tasha; Fox, David; Aljubaily, Hesham; Lantz, Christopher D.

2006-01-01

The current investigation examined sport fans' impressions of an athlete described as a potential member of their team or a potential member of a rival team. In Study 1, we predicted that individuals would exhibit an ingroup favoritism effect by reporting more positive evaluations of the player's performance when he was described as a…

Prediction Tables for Avionics Fundamentals Course, Class A.

ERIC Educational Resources Information Center

Baldwin, Robert O.; Johnson, Kirk A.

This study was conducted in 1966 to provide the avionics fundamentals course, class A, with a number of tables for predicting academic performance, either by precourse variables or by grades made early in the course. A means of identifying potential setbacks and potential failures was also desired. In September 1966 a 16 week course replaced the…

Predicting Child Abuse Potential: An Empirical Investigation of Two Theoretical Frameworks

ERIC Educational Resources Information Center

Begle, Angela Moreland; Dumas, Jean E.; Hanson, Rochelle F.

2010-01-01

This study investigated two theoretical risk models predicting child maltreatment potential: (a) Belsky's (1993) developmental-ecological model and (b) the cumulative risk model in a sample of 610 caregivers (49% African American, 46% European American; 53% single) with a child between 3 and 6 years old. Results extend the literature by using a…

Predicting the future trend of popularity by network diffusion.

PubMed

Zeng, An; Yeung, Chi Ho

2016-06-01

Conventional approaches to predict the future popularity of products are mainly based on extrapolation of their current popularity, which overlooks the hidden microscopic information under the macroscopic trend. Here, we study diffusion processes on consumer-product and citation networks to exploit the hidden microscopic information and connect consumers to their potential purchase, publications to their potential citers to obtain a prediction for future item popularity. By using the data obtained from the largest online retailers including Netflix and Amazon as well as the American Physical Society citation networks, we found that our method outperforms the accurate short-term extrapolation and identifies the potentially popular items long before they become prominent.

Predicting the future trend of popularity by network diffusion

NASA Astrophysics Data System (ADS)

Zeng, An; Yeung, Chi Ho

2016-06-01

Conventional approaches to predict the future popularity of products are mainly based on extrapolation of their current popularity, which overlooks the hidden microscopic information under the macroscopic trend. Here, we study diffusion processes on consumer-product and citation networks to exploit the hidden microscopic information and connect consumers to their potential purchase, publications to their potential citers to obtain a prediction for future item popularity. By using the data obtained from the largest online retailers including Netflix and Amazon as well as the American Physical Society citation networks, we found that our method outperforms the accurate short-term extrapolation and identifies the potentially popular items long before they become prominent.

Parenting stress and anger expression as predictors of child abuse potential.

PubMed

Rodriguez, C M; Green, A J

1997-04-01

To explore one potential pathway to physical child abuse, the present investigation used hierarchical regression analysis using measures of parenting stress and anger expression to jointly predict child abuse potential. The Parenting Stress Index, State-Trait Anger Expression Inventory, and the Child Abuse Potential Inventory were administered to two different samples of New Zealand parents. As expected, both studies revealed parenting stress and anger expression and were individually positively correlated with child abuse potential: the major finding involved the strong point contribution of parenting stress and anger expression in predicting Child Abuse Potential Inventory scores. Application of findings for intervention and prevention are discussed.

First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study.

PubMed

Austdal, Marie; Tangerås, Line H; Skråstad, Ragnhild B; Salvesen, Kjell; Austgulen, Rigmor; Iversen, Ann-Charlotte; Bathen, Tone F

2015-09-08

Hypertensive disorders of pregnancy, including preeclampsia, are major contributors to maternal morbidity. The goal of this study was to evaluate the potential of metabolomics to predict preeclampsia and gestational hypertension from urine and serum samples in early pregnancy, and elucidate the metabolic changes related to the diseases. Metabolic profiles were obtained by nuclear magnetic resonance spectroscopy of serum and urine samples from 599 women at medium to high risk of preeclampsia (nulliparous or previous preeclampsia/gestational hypertension). Preeclampsia developed in 26 (4.3%) and gestational hypertension in 21 (3.5%) women. Multivariate analyses of the metabolic profiles were performed to establish prediction models for the hypertensive disorders individually and combined. Urinary metabolomic profiles predicted preeclampsia and gestational hypertension at 51.3% and 40% sensitivity, respectively, at 10% false positive rate, with hippurate as the most important metabolite for the prediction. Serum metabolomic profiles predicted preeclampsia and gestational hypertension at 15% and 33% sensitivity, respectively, with increased lipid levels and an atherogenic lipid profile as most important for the prediction. Combining maternal characteristics with the urinary hippurate/creatinine level improved the prediction rates of preeclampsia in a logistic regression model. The study indicates a potential future role of clinical importance for metabolomic analysis of urine in prediction of preeclampsia.

Pharmacogenetics Biomarkers and Their Specific Role in Neoadjuvant Chemoradiotherapy Treatments: An Exploratory Study on Rectal Cancer Patients

PubMed Central

Dreussi, Eva; Cecchin, Erika; Polesel, Jerry; Canzonieri, Vincenzo; Agostini, Marco; Boso, Caterina; Belluco, Claudio; Buonadonna, Angela; Lonardi, Sara; Bergamo, Francesca; Gagno, Sara; De Mattia, Elena; Pucciarelli, Salvatore; De Paoli, Antonino; Toffoli, Giuseppe

2016-01-01

Background: Pathological complete response (pCR) to neoadjuvant chemoradiotherapy (CRT) in locally advanced rectal cancer (LARC) is still ascribed to a minority of patients. A pathway based-approach could highlight the predictive role of germline single nucleotide polymorphisms (SNPs). The primary aim of this study was to define new predictive biomarkers considering treatment specificities. Secondary aim was to determine new potential predictive biomarkers independent from radiotherapy (RT) dosage and cotreatment with oxaliplatin. Methods: Thirty germ-line SNPs in twenty-one genes were selected according to a pathway-based approach. Genetic analyses were performed on 280 LARC patients who underwent fluoropyrimidine-based CRT. The potential predictive role of these SNPs in determining pathological tumor response was tested in Group 1 (94 patients undergoing also oxaliplatin), Group 2 (73 patients treated with high RT dosage), Group 3 (113 patients treated with standard RT dosage), and in the pooled population (280 patients). Results: Nine new predictive biomarkers were identified in the three groups. The most promising one was rs3136228-MSH6 (p = 0.004) arising from Group 3. In the pooled population, rs1801133-MTHFR showed only a trend (p = 0.073). Conclusion: This exploratory study highlighted new potential predictive biomarkers of neoadjuvant CRT and underlined the importance to strictly define treatment peculiarities in pharmacogenetic analyses. PMID:27608007

Differential roles of two delayed rectifier potassium currents in regulation of ventricular action potential duration and arrhythmia susceptibility.

PubMed

Devenyi, Ryan A; Ortega, Francis A; Groenendaal, Willemijn; Krogh-Madsen, Trine; Christini, David J; Sobie, Eric A

2017-04-01

Arrhythmias result from disruptions to cardiac electrical activity, although the factors that control cellular action potentials are incompletely understood. We combined mathematical modelling with experiments in heart cells from guinea pigs to determine how cellular electrical activity is regulated. A mismatch between modelling predictions and the experimental results allowed us to construct an improved, more predictive mathematical model. The balance between two particular potassium currents dictates how heart cells respond to perturbations and their susceptibility to arrhythmias. Imbalances of ionic currents can destabilize the cardiac action potential and potentially trigger lethal cardiac arrhythmias. In the present study, we combined mathematical modelling with information-rich dynamic clamp experiments to determine the regulation of action potential morphology in guinea pig ventricular myocytes. Parameter sensitivity analysis was used to predict how changes in ionic currents alter action potential duration, and these were tested experimentally using dynamic clamp, a technique that allows for multiple perturbations to be tested in each cell. Surprisingly, we found that a leading mathematical model, developed with traditional approaches, systematically underestimated experimental responses to dynamic clamp perturbations. We then re-parameterized the model using a genetic algorithm, which allowed us to estimate ionic current levels in each of the cells studied. This unbiased model adjustment consistently predicted an increase in the rapid delayed rectifier K + current and a drastic decrease in the slow delayed rectifier K + current, and this prediction was validated experimentally. Subsequent simulations with the adjusted model generated the clinically relevant prediction that the slow delayed rectifier is better able to stabilize the action potential and suppress pro-arrhythmic events than the rapid delayed rectifier. In summary, iterative coupling of simulations and experiments enabled novel insight into how the balance between cardiac K + currents influences ventricular arrhythmia susceptibility. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

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.

Identification of informative features for predicting proinflammatory potentials of engine exhausts.

PubMed

Wang, Chia-Chi; Lin, Ying-Chi; Lin, Yuan-Chung; Jhang, Syu-Ruei; Tung, Chun-Wei

2017-08-18

The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment. To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm. A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively. The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.

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.

Predicting the potential distribution of the amphibian pathogen Batrachochytrium dendrobatidis in East and Southeast Asia.

PubMed

Moriguchi, Sachiko; Tominaga, Atsushi; Irwin, Kelly J; Freake, Michael J; Suzuki, Kazutaka; Goka, Koichi

2015-04-08

Batrachochytrium dendrobatidis (Bd) is the pathogen responsible for chytridiomycosis, a disease that is associated with a worldwide amphibian population decline. In this study, we predicted the potential distribution of Bd in East and Southeast Asia based on limited occurrence data. Our goal was to design an effective survey area where efforts to detect the pathogen can be focused. We generated ecological niche models using the maximum-entropy approach, with alleviation of multicollinearity and spatial autocorrelation. We applied eigenvector-based spatial filters as independent variables, in addition to environmental variables, to resolve spatial autocorrelation, and compared the model's accuracy and the degree of spatial autocorrelation with those of a model estimated using only environmental variables. We were able to identify areas of high suitability for Bd with accuracy. Among the environmental variables, factors related to temperature and precipitation were more effective in predicting the potential distribution of Bd than factors related to land use and cover type. Our study successfully predicted the potential distribution of Bd in East and Southeast Asia. This information should now be used to prioritize survey areas and generate a surveillance program to detect the pathogen.

Understanding the dynamics in distribution of invasive alien plant species under predicted climate change in Western Himalaya

PubMed Central

Chitale, Vishwas; Rijal, Srijana Joshi; Bisht, Neha; Shrestha, Bharat Babu

2018-01-01

Invasive alien plant species (IAPS) can pose severe threats to biodiversity and stability of native ecosystems, therefore, predicting the distribution of the IAPS plays a crucial role in effective planning and management of ecosystems. In the present study, we use Maximum Entropy (MaxEnt) modelling approach to predict the potential of distribution of eleven IAPS under future climatic conditions under RCP 2.6 and RCP 8.5 in part of Kailash sacred landscape region in Western Himalaya. Based on the model predictions, distribution of most of these invasive plants is expected to expand under future climatic scenarios, which might pose a serious threat to the native ecosystems through competition for resources in the study area. Native scrublands and subtropical needle-leaved forests will be the most affected ecosystems by the expansion of these IAPS. The present study is first of its kind in the Kailash Sacred Landscape in the field of invasive plants and the predictions of potential distribution under future climatic conditions from our study could help decision makers in planning and managing these forest ecosystems effectively. PMID:29664961

Understanding the dynamics in distribution of invasive alien plant species under predicted climate change in Western Himalaya.

PubMed

Thapa, Sunil; Chitale, Vishwas; Rijal, Srijana Joshi; Bisht, Neha; Shrestha, Bharat Babu

2018-01-01

Invasive alien plant species (IAPS) can pose severe threats to biodiversity and stability of native ecosystems, therefore, predicting the distribution of the IAPS plays a crucial role in effective planning and management of ecosystems. In the present study, we use Maximum Entropy (MaxEnt) modelling approach to predict the potential of distribution of eleven IAPS under future climatic conditions under RCP 2.6 and RCP 8.5 in part of Kailash sacred landscape region in Western Himalaya. Based on the model predictions, distribution of most of these invasive plants is expected to expand under future climatic scenarios, which might pose a serious threat to the native ecosystems through competition for resources in the study area. Native scrublands and subtropical needle-leaved forests will be the most affected ecosystems by the expansion of these IAPS. The present study is first of its kind in the Kailash Sacred Landscape in the field of invasive plants and the predictions of potential distribution under future climatic conditions from our study could help decision makers in planning and managing these forest ecosystems effectively.

Advancing alternatives analysis: The role of predictive toxicology in selecting safer chemical products and processes.

PubMed

Malloy, Timothy; Zaunbrecher, Virginia; Beryt, Elizabeth; Judson, Richard; Tice, Raymond; Allard, Patrick; Blake, Ann; Cote, Ila; Godwin, Hilary; Heine, Lauren; Kerzic, Patrick; Kostal, Jakub; Marchant, Gary; McPartland, Jennifer; Moran, Kelly; Nel, Andre; Ogunseitan, Oladele; Rossi, Mark; Thayer, Kristina; Tickner, Joel; Whittaker, Margaret; Zarker, Ken

2017-09-01

Alternatives analysis (AA) is a method used in regulation and product design to identify, assess, and evaluate the safety and viability of potential substitutes for hazardous chemicals. It requires toxicological data for the existing chemical and potential alternatives. Predictive toxicology uses in silico and in vitro approaches, computational models, and other tools to expedite toxicological data generation in a more cost-effective manner than traditional approaches. The present article briefly reviews the challenges associated with using predictive toxicology in regulatory AA, then presents 4 recommendations for its advancement. It recommends using case studies to advance the integration of predictive toxicology into AA, adopting a stepwise process to employing predictive toxicology in AA beginning with prioritization of chemicals of concern, leveraging existing resources to advance the integration of predictive toxicology into the practice of AA, and supporting transdisciplinary efforts. The further incorporation of predictive toxicology into AA would advance the ability of companies and regulators to select alternatives to harmful ingredients, and potentially increase the use of predictive toxicology in regulation more broadly. Integr Environ Assess Manag 2017;13:915-925. © 2017 SETAC. © 2017 SETAC.

Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

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

Valerio, Luis G.; Arvidson, Kirk B.; Chanderbhan, Ronald F.

2007-07-01

Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest ismore » MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200 chemicals, comprised primarily of pharmaceutical, industrial and some natural products developed under an FDA-MDL cooperative research and development agreement (CRADA). The predictive performance for this group of dietary natural products and the control group was 97% sensitivity and 80% concordance. Specificity was marginal at 53%. This study finds that the in silico QSAR analysis employing this software's rodent carcinogenicity database is capable of identifying the rodent carcinogenic potential of naturally occurring organic molecules found in the human diet with a high degree of sensitivity. It is the first study to demonstrate successful QSAR predictive modeling of naturally occurring carcinogens found in the human diet using an external validation test. Further test validation of this software and expansion of the training data set for dietary chemicals will help to support the future use of such QSAR methods for screening and prioritizing the risk of dietary chemicals when actual animal data are inadequate, equivocal, or absent.« less

Prediction of Suicide: A Replication Study

ERIC Educational Resources Information Center

Farberow, Norman L.; MacKinnon, Douglas

1975-01-01

A replication study was conducted to retest the validity of the Neuropsychiatric Hospital Suicide Potential Scale. Fifty four patients who had committed suicide and fifty who had not were the population studied to test, if possible, the limits of prediction the scale can attain by judging the behavior of these patients. (DEP)

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.

Computational prediction of protein-protein interactions in Leishmania predicted proteomes.

PubMed

Rezende, Antonio M; Folador, Edson L; Resende, Daniela de M; Ruiz, Jeronimo C

2012-01-01

The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI) study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping) and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks received some degree of functional annotation which represents an important contribution since approximately 60% of Leishmania predicted proteomes has no predicted function.

The Potential Utility of Urinary Biomarkers for Risk Prediction in Combat Casualties: A Prospective Observational Cohort Study

DTIC Science & Technology

2015-06-16

are associated with poor outcomes, including death and the need for renal replacement therapy. Methods : We conducted a prospective, observational study...penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE 16 JUN 2015...2. REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE The Potential Utility of Urinary Biomarkers for Risk Prediction in Combat

Multimethod prediction of child abuse risk in an at-risk sample of male intimate partner violence offenders.

PubMed

Rodriguez, Christina M; Gracia, Enrique; Lila, Marisol

2016-10-01

The vast majority of research on child abuse potential has concentrated on women demonstrating varying levels of risk of perpetrating physical child abuse. In contrast, the current study considered factors predictive of physical child abuse potential in a group of 70 male intimate partner violence offenders, a group that would represent a likely high risk group. Elements of Social Information Processing theory were evaluated, including pre-existing schemas of empathy, anger, and attitudes approving of parent-child aggression considered as potential moderators of negative attributions of child behavior. To lend methodological rigor, the study also utilized multiple measures and multiple methods, including analog tasks, to predict child abuse risk. Contrary to expectations, findings did not support the role of anger independently predicting child abuse risk in this sample of men. However, preexisting beliefs approving of parent-child aggression, lower empathy, and more negative child behavior attributions independently predicted abuse potential; in addition, greater anger, poorer empathy, and more favorable attitudes toward parent-child aggression also exacerbated men's negative child attributions to further elevate their child abuse risk. Future work is encouraged to consider how factors commonly considered in women parallel or diverge from those observed to elevate child abuse risk in men of varying levels of risk. Copyright © 2016 Elsevier Ltd. All rights reserved.

Theoretical study of solvent effects on the coil-globule transition

NASA Astrophysics Data System (ADS)

Polson, James M.; Opps, Sheldon B.; Abou Risk, Nicholas

2009-06-01

The coil-globule transition of a polymer in a solvent has been studied using Monte Carlo simulations of a single chain subject to intramolecular interactions as well as a solvent-mediated effective potential. This solvation potential was calculated using several different theoretical approaches for two simple polymer/solvent models, each employing hard-sphere chains and hard-sphere solvent particles as well as attractive square-well potentials between some interaction sites. For each model, collapse is driven by variation in a parameter which changes the energy mismatch between monomers and solvent particles. The solvation potentials were calculated using two fundamentally different methodologies, each designed to predict the conformational behavior of polymers in solution: (1) the polymer reference interaction site model (PRISM) theory and (2) a many-body solvation potential (MBSP) based on scaled particle theory introduced by Grayce [J. Chem. Phys. 106, 5171 (1997)]. For the PRISM calculations, two well-studied solvation monomer-monomer pair potentials were employed, each distinguished by the closure relation used in its derivation: (i) a hypernetted-chain (HNC)-type potential and (ii) a Percus-Yevick (PY)-type potential. The theoretical predictions were each compared to results obtained from explicit-solvent discontinuous molecular dynamics simulations on the same polymer/solvent model systems [J. Chem. Phys. 125, 194904 (2006)]. In each case, the variation in the coil-globule transition properties with solvent density is mostly qualitatively correct, though the quantitative agreement between the theory and prediction is typically poor. The HNC-type potential yields results that are more qualitatively consistent with simulation. The conformational behavior of the polymer upon collapse predicted by the MBSP approach is quantitatively correct for low and moderate solvent densities but is increasingly less accurate for higher densities. At high solvent densities, the PRISM-HNC and MBSP approaches tend to overestimate, while the PRISM-PY approach underestimates the tendency of the solvent to drive polymer collapse.

Prediction of anticancer property of bowsellic acid derivatives by quantitative structure activity relationship analysis and molecular docking study.

PubMed

Satpathy, Raghunath; Guru, R K; Behera, R; Nayak, B

2015-01-01

Boswellic acid consists of a series of pentacyclic triterpene molecules that are produced by the plant Boswellia serrata. The potential applications of Bowsellic acid for treatment of cancer have been focused here. To predict the property of the bowsellic acid derivatives as anticancer compounds by various computational approaches. In this work, all total 65 derivatives of bowsellic acids from the PubChem database were considered for the study. After energy minimization of the ligands various types of molecular descriptors were computed and corresponding two-dimensional quantitative structure activity relationship (QSAR) models were obtained by taking Andrews coefficient as the dependent variable. Different types of comparative analysis were used for QSAR study are multiple linear regression, partial least squares, support vector machines and artificial neural network. From the study geometrical descriptors shows the highest correlation coefficient, which indicates the binding factor of the compound. To evaluate the anticancer property molecular docking study of six selected ligands based on Andrews affinity were performed with nuclear factor-kappa protein kinase (Protein Data Bank ID 4G3D), which is an established therapeutic target for cancers. Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound. Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound.

Total energy expenditure in burned children using the doubly labeled water technique

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

Goran, M.I.; Peters, E.J.; Herndon, D.N.

Total energy expenditure (TEE) was measured in 15 burned children with the doubly labeled water technique. Application of the technique in burned children required evaluation of potential errors resulting from nutritional intake altering background enrichments during studies and from the high rate of water turnover relative to CO2 production. Five studies were discarded because of these potential problems. TEE was 1.33 +/- 0.27 times predicted basal energy expenditure (BEE), and in studies where resting energy expenditure (REE) was simultaneously measured, TEE was 1.18 +/- 0.17 times REE, which in turn was 1.16 +/- 0.10 times predicted BEE. TEE was significantlymore » correlated with measured REE (r2 = 0.92) but not with predicted BEE. These studies substantiate the advantage of measuring REE to predict TEE in severely burned patients as opposed to relying on standardized equations. Therefore we recommend that optimal nutritional support will be achieved in convalescent burned children by multiplying REE by an activity factor of 1.2.« less

Potential energy barriers to ion transport within lipid bilayers. Studies with tetraphenylborate.

PubMed Central

Andersen, P S; Fuchs, M

1975-01-01

Tetraphenylborate-induced current transients were studied in lipid bilayers formed from bacterial phosphatidylethanolamine in decane. This ion movement was essentially confined to the membrane in terior during the current transients. Charge movement through the interior of the membrane during the current transients was studied as a function of the applied potential. The transferred charge approached an upper limit with increasing potential, which is interpreted to be the amount of charge due to tetraphenylborate ions absorbed into the boundary regions of the bilayer. A further analysis of the charge transfer as a function of potential indicates that the movement of tetraphenylborate ions is only influenced by a certain farction of the applied potential. For bacterial phosphatidylethanolamine bilayers the effective potential is 77 +/- 4% of the applied potential. The initial conductance and the time constant of the current transients were studied as a function of the applied potential using a Nernst-Planck electrodiffusion regime. It was found that an image-force potential energy barrier gave a good prediction of the observed behavior, provided that the effective potential was used in the calculations. We could not get a satisfactory prediction of the observed behavior with an Eyring rate theory model or a trapezoidal potential energy barrier. PMID:1148364

Predicting maize phenology: Intercomparison of functions for developmental response to temperature

USDA-ARS?s Scientific Manuscript database

Accurate prediction of phenological development in maize is fundamental to determining crop adaptation and yield potential. A number of thermal functions are used in crop models, but their relative precision in predicting maize development has not been quantified. The objectives of this study were t...

Exploring the Potential of Direct-To-Consumer Genomic Test Data for Predicting Adverse Drug Events.

PubMed

Zhang, Patrick M; Sarkar, Indra Neil

2018-01-01

Recent technological advancements in genetic testing and the growing accessibility of public genomic data provide researchers with a unique avenue to approach personalized medicine. This feasibility study examined the potential of direct-to-consumer (DTC) genomic tests (focusing on 23andMe) in research and clinical applications. In particular, we combined population genetics information from the Personal Genome Project with adverse event reports from AEOLUS and pharmacogenetic information from PharmGKB. Primarily, associations between drugs based on co-occurring genetic variations and associations between variants and adverse events were used to assess the potential for leveraging single nucleotide polymorphism information from 23andMe. The results of this study suggest potential clinical uses of DTC tests in light of potential drug interactions. Furthermore, the results suggest great potential for analyzing associations at a population level to facilitate knowledge discovery in the realm of predicting adverse drug events.

The Road to Creative Achievement: A Latent Variable Model of Ability and Personality Predictors

PubMed Central

Jauk, Emanuel; Benedek, Mathias; Neubauer, Aljoscha C

2014-01-01

This study investigated the significance of different well-established psychometric indicators of creativity for real-life creative outcomes. Specifically, we tested the effects of creative potential, intelligence, and openness to experiences on everyday creative activities and actual creative achievement. Using a heterogeneous sample of 297 adults, we performed latent multiple regression analyses by means of structural equation modelling. We found openness to experiences and two independent indicators of creative potential, ideational originality and ideational fluency, to predict everyday creative activities. Creative activities, in turn, predicted actual creative achievement. Intelligence was found to predict creative achievement, but not creative activities. Moreover, intelligence moderated the effect of creative activities on creative achievement, suggesting that intelligence may play an important role in transforming creative activities into publically acknowledged creative achievements. This study supports the view of creativity as a multifaceted construct and provides an integrative model illustrating the potential interplay between its different facets. PMID:24532953

How climate change might influence the potential distribution of weed, bushmint (Hyptis suaveolens)?

PubMed

Padalia, Hitendra; Srivastava, Vivek; Kushwaha, S P S

2015-04-01

Invasive species and climate change are considered as the most serious global environmental threats. In this study, we investigated the influence of projected global climate change on the potential distribution of one of the world's most successful invader weed, bushmint (Hyptis suaveolens (L.) Poit.). We used spatial data on 20 environmental variables at a grid resolution of 5 km, and 564 presence records of bushmint from its native and introduced range. The climatic profiles of the native and invaded sites were analyzed in a multi-variate space in order to examine the differences in the position of climatic niches. Maximum Entropy (MaxEnt) model was used to predict the potential distribution of bushmint using presence records from entire range (invaded and native) along with 14 eco-physiologically relevant predictor variables. Subsequently, the trained MaxEnt model was fed with Hadley Centre Coupled Model (HadCM3) climate projections to predict potential distribution of bushmint by the year 2050 under A2a and B2a emission scenarios. MaxEnt predictions were very accurate with an Area Under Curve (AUC) value of 0.95. The results of Principal Component Analysis (PCA) indicated that climatic niche of bushmint on the invaded sites is not entirely similar to its climatic niche in the native range. A vast area spread between 34 ° 02' north and 28 ° 18' south latitudes in tropics was predicted climatically suitable for bushmint. West and middle Africa, tropical southeast Asia, and northern Australia were predicted at high invasion risk. Study indicates enlargement, retreat, or shift across bushmint's invasion range under the influence of climate change. Globally, bushmint's potential distribution might shrink in future with more shrinkage for A2a scenario than B2a. The study outcome has immense potential for undertaking effective preventive/control measures and long-term management strategies for regions/countries, which are at higher risk of bushmint's invasion.

Predicting disease risk, identifying stakeholders, and informing control strategies: A case study of anthrax in Montana

PubMed Central

Morris, Lillian R.; Blackburn, Jason K.

2018-01-01

Infectious diseases that affect wildlife and livestock are challenging to manage, and can lead to large scale die offs, economic losses, and threats to human health. The management of infectious diseases in wildlife and livestock is made easier with knowledge of disease risk across space and identifying stakeholders associated with high risk landscapes. This study focuses on anthrax, caused by the bacterium Bacillus anthracis, risk to wildlife and livestock in Montana. There is a history of anthrax in Montana, but the spatial extent of disease risk and subsequent wildlife species at risk are not known. Our objective was to predict the potential geographic distribution of anthrax risk across Montana, identify wildlife species at risk and their distributions, and define stakeholders. We used an ecological niche model to predict the potential distribution of anthrax risk. We overlaid susceptible wildlife species distributions and land ownership delineations on our risk map. We found that there was an extensive region across Montana predicted as potential anthrax risk. These potentially risky landscapes overlapped the ranges of all 6 ungulate species considered in the analysis and livestock grazing allotments, and this overlap was on public and private land for all species. Our findings suggest that there is the potential for a multi species anthrax outbreak on multiple landscapes across Montana. Our potential anthrax risk map can be used to prioritize landscapes for surveillance and for implementing livestock vaccination programs. PMID:27169560

Predicting Disease Risk, Identifying Stakeholders, and Informing Control Strategies: A Case Study of Anthrax in Montana.

PubMed

Morris, Lillian R; Blackburn, Jason K

2016-06-01

Infectious diseases that affect wildlife and livestock are challenging to manage and can lead to large-scale die-offs, economic losses, and threats to human health. The management of infectious diseases in wildlife and livestock is made easier with knowledge of disease risk across space and identifying stakeholders associated with high-risk landscapes. This study focuses on anthrax, caused by the bacterium Bacillus anthracis, risk to wildlife and livestock in Montana. There is a history of anthrax in Montana, but the spatial extent of disease risk and subsequent wildlife species at risk are not known. Our objective was to predict the potential geographic distribution of anthrax risk across Montana, identify wildlife species at risk and their distributions, and define stakeholders. We used an ecological niche model to predict the potential distribution of anthrax risk. We overlaid susceptible wildlife species distributions and land ownership delineations on our risk map. We found that there was an extensive region across Montana predicted as potential anthrax risk. These potentially risky landscapes overlapped the ranges of all 6 ungulate species considered in the analysis and livestock grazing allotments, and this overlap was on public and private land for all species. Our findings suggest that there is the potential for a multi-species anthrax outbreak on multiple landscapes across Montana. Our potential anthrax risk map can be used to prioritize landscapes for surveillance and for implementing livestock vaccination programs.

Detecting the influence of rare stressors on rare species in Yosemite National Park using a novel stratified permutation test

USGS Publications Warehouse

Matchett, John R.; Stark, Philip B.; Ostoja, Steven M.; Knapp, Roland A.; McKenny, Heather C.; Brooks, Matthew L.; Langford, William T.; Joppa, Lucas N.; Berlow, Eric L.

2015-01-01

Statistical models often use observational data to predict phenomena; however, interpreting model terms to understand their influence can be problematic. This issue poses a challenge in species conservation where setting priorities requires estimating influences of potential stressors using observational data. We present a novel approach for inferring influence of a rare stressor on a rare species by blending predictive models with nonparametric permutation tests. We illustrate the approach with two case studies involving rare amphibians in Yosemite National Park, USA. The endangered frog, Rana sierrae, is known to be negatively impacted by non-native fish, while the threatened toad, Anaxyrus canorus, is potentially affected by packstock. Both stressors and amphibians are rare, occurring in ~10% of potential habitat patches. We first predict amphibian occupancy with a statistical model that includes all predictors but the stressor to stratify potential habitat by predicted suitability. A stratified permutation test then evaluates the association between stressor and amphibian, all else equal. Our approach confirms the known negative relationship between fish and R. sierrae, but finds no evidence of a negative relationship between current packstock use and A. canorus breeding. Our statistical approach has potential broad application for deriving understanding (not just prediction) from observational data.

Detecting the influence of rare stressors on rare species in Yosemite National Park using a novel stratified permutation test

PubMed Central

Matchett, J. R.; Stark, Philip B.; Ostoja, Steven M.; Knapp, Roland A.; McKenny, Heather C.; Brooks, Matthew L.; Langford, William T.; Joppa, Lucas N.; Berlow, Eric L.

2015-01-01

Statistical models often use observational data to predict phenomena; however, interpreting model terms to understand their influence can be problematic. This issue poses a challenge in species conservation where setting priorities requires estimating influences of potential stressors using observational data. We present a novel approach for inferring influence of a rare stressor on a rare species by blending predictive models with nonparametric permutation tests. We illustrate the approach with two case studies involving rare amphibians in Yosemite National Park, USA. The endangered frog, Rana sierrae, is known to be negatively impacted by non-native fish, while the threatened toad, Anaxyrus canorus, is potentially affected by packstock. Both stressors and amphibians are rare, occurring in ~10% of potential habitat patches. We first predict amphibian occupancy with a statistical model that includes all predictors but the stressor to stratify potential habitat by predicted suitability. A stratified permutation test then evaluates the association between stressor and amphibian, all else equal. Our approach confirms the known negative relationship between fish and R. sierrae, but finds no evidence of a negative relationship between current packstock use and A. canorus breeding. Our statistical approach has potential broad application for deriving understanding (not just prediction) from observational data. PMID:26031755

The Study Skills Questionnaire (SSQUES): Preliminary Validation of a Measure for Assessing Students' Perceived Areas of Weakness.

ERIC Educational Resources Information Center

McCombs, Barbara L.; Dobrovolny, Jacqueline L.

The potential reliability and construct and predictive validity of a 30-item Study Skills Questionnaire (SSQUES) was evaluated for its ability to: (1) predict student performance in a self-paced, individualized, or computer-managed instructional environment, and (2) identify students needing some type of study skills remediation. The study was…

Diagnosis and early detection of CNS-SLE in MRL/lpr mice using peptide microarrays.

PubMed

Williams, Stephanie; Stafford, Phillip; Hoffman, Steven A

2014-06-07

An accurate method that can diagnose and predict lupus and its neuropsychiatric manifestations is essential since currently there are no reliable methods. Autoantibodies to a varied panel of antigens in the body are characteristic of lupus. In this study we investigated whether serum autoantibody binding patterns on random-sequence peptide microarrays (immunosignaturing) can be used for diagnosing and predicting the onset of lupus and its central nervous system (CNS) manifestations. We also tested the techniques for identifying potentially pathogenic autoantibodies in CNS-Lupus. We used the well-characterized MRL/lpr lupus animal model in two studies as a first step to develop and evaluate future studies in humans. In study one we identified possible diagnostic peptides for both lupus and altered behavior in the forced swim test. When comparing the results of study one to that of study two (carried out in a similar manner), we further identified potential peptides that may be diagnostic and predictive of both lupus and altered behavior in the forced swim test. We also characterized five potentially pathogenic brain-reactive autoantibodies, as well as suggested possible brain targets. These results indicate that immunosignaturing could predict and diagnose lupus and its CNS manifestations. It can also be used to characterize pathogenic autoantibodies, which may help to better understand the underlying mechanisms of CNS-Lupus.

Potential Predictability of the Monsoon Subclimate Systems

NASA Technical Reports Server (NTRS)

Yang, Song; Lau, K.-M.; Chang, Y.; Schubert, S.

1999-01-01

While El Nino/Southern Oscillation (ENSO) phenomenon can be predicted with some success using coupled oceanic-atmospheric models, the skill of predicting the tropical monsoons is low regardless of the methods applied. The low skill of monsoon prediction may be either because the monsoons are not defined appropriately or because they are not influenced significantly by boundary forcing. The latter characterizes the importance of internal dynamics in monsoon variability and leads to many eminent chaotic features of the monsoons. In this study, we analyze results from nine AMIP-type ensemble experiments with the NASA/GEOS-2 general circulation model to assess the potential predictability of the tropical climate system. We will focus on the variability and predictability of tropical monsoon rainfall on seasonal-to-interannual time scales. It is known that the tropical climate is more predictable than its extratropical counterpart. However, predictability is different from one climate subsystem to another within the tropics. It is important to understand the differences among these subsystems in order to increase our skill of seasonal-to-interannual prediction. We assess potential predictability by comparing the magnitude of internal and forced variances as defined by Harzallah and Sadourny (1995). The internal variance measures the spread among the various ensemble members. The forced part of rainfall variance is determined by the magnitude of the ensemble mean rainfall anomaly and by the degree of consistency of the results from the various experiments.

NEW PUBLIC DATA AND INTERNET RESOURCES IMPACTING PREDICTIVE TOXICOLOGY.

EPA Science Inventory

High-throughput screening (HTS) technologies, along with efforts to improve public access to chemical toxicity information resources and to systematize older toxicity studies, have the potential to significantly improve predictive capabilities in toxicology.

Methodological issues in current practice may lead to bias in the development of biomarker combinations for predicting acute kidney injury.

PubMed

Meisner, Allison; Kerr, Kathleen F; Thiessen-Philbrook, Heather; Coca, Steven G; Parikh, Chirag R

2016-02-01

Individual biomarkers of renal injury are only modestly predictive of acute kidney injury (AKI). Using multiple biomarkers has the potential to improve predictive capacity. In this systematic review, statistical methods of articles developing biomarker combinations to predict AKI were assessed. We identified and described three potential sources of bias (resubstitution bias, model selection bias, and bias due to center differences) that may compromise the development of biomarker combinations. Fifteen studies reported developing kidney injury biomarker combinations for the prediction of AKI after cardiac surgery (8 articles), in the intensive care unit (4 articles), or other settings (3 articles). All studies were susceptible to at least one source of bias and did not account for or acknowledge the bias. Inadequate reporting often hindered our assessment of the articles. We then evaluated, when possible (7 articles), the performance of published biomarker combinations in the TRIBE-AKI cardiac surgery cohort. Predictive performance was markedly attenuated in six out of seven cases. Thus, deficiencies in analysis and reporting are avoidable, and care should be taken to provide accurate estimates of risk prediction model performance. Hence, rigorous design, analysis, and reporting of biomarker combination studies are essential to realizing the promise of biomarkers in clinical practice.

Dread of uncertain pain: An event-related potential study

PubMed Central

Huang, Yujing; Shang, Qian; Dai, Shenyi; Ma, Qingguo

2017-01-01

Humans experience more stress about uncertain situations than certain situations. However, the neural mechanism underlying the uncertainty of a negative stimulus has not been determined. In the present study, event-related potential was recorded to examine neural responses during the dread of unpredictable pain. We used a cueing paradigm in which predictable cues were always followed by electric shocks, unpredictable cues by electric shocks at a 50/50 ratio and safe cues by no electric shock. Visual analogue scales following electric shocks were presented to quantify subjective anxiety levels. The behavioral results showed that unpredictable cues evoked high-level anxiety compared with predictable cues in both painful and unpainful stimulation conditions. More importantly, the ERPs results revealed that unpredictable cues elicited a larger P200 at parietal sites than predictable cues. In addition, unpredictable cues evoked larger P200 compared with safe cues at frontal electrodes and compared with predictable cues at parietal electrodes. In addition, larger P3b and LPP were observed during perception of safe cues compared with predictable cues at frontal and central electrodes. The similar P3b effect was also revealed in the left sites. The present study underlined that the uncertain dread of pain was associated with threat appraisal process in pain system. These findings on early event-related potentials were significant for a neural marker and development of therapeutic interventions. PMID:28832607

Predicting violence and recidivism in a large sample of males on probation or parole.

PubMed

Prell, Lettie; Vitacco, Michael J; Zavodny, Denis

This study evaluated the utility of items and scales from the Iowa Violence and Victimization Instrument in a sample of 1961 males from the state of Iowa who were on probation or released from prison to parole supervision. This is the first study to examine the potential of the Iowa Violence and Victimization Instrument to predict criminal offenses. The males were followed for 30months immediately following their admission to probation or parole. AUC analyses indicated fair to good predictive power for the Iowa Violence and Victimization Instrument for charges of violence and victimization, but chance predictive power for drug offenses. Notably, both scales of the instrument performed equally well at the 30-month follow-up. Items on the Iowa Violence and Victimization Instrument not only predicted violence, but are straightforward to score. Violence management strategies are discussed as they relate to the current findings, including the potential to expand the measure to other jurisdictions and populations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evaluating the applicability of using daily forecasts from seasonal prediction systems (SPSs) for agriculture: a case study of Nepal's Terai with the NCEP CFSv2

NASA Astrophysics Data System (ADS)

Jha, Prakash K.; Athanasiadis, Panos; Gualdi, Silvio; Trabucco, Antonio; Mereu, Valentina; Shelia, Vakhtang; Hoogenboom, Gerrit

2018-03-01

Ensemble forecasts from dynamic seasonal prediction systems (SPSs) have the potential to improve decision-making for crop management to help cope with interannual weather variability. Because the reliability of crop yield predictions based on seasonal weather forecasts depends on the quality of the forecasts, it is essential to evaluate forecasts prior to agricultural applications. This study analyses the potential of Climate Forecast System version 2 (CFSv2) in predicting the Indian summer monsoon (ISM) for producing meteorological variables relevant to crop modeling. The focus area was Nepal's Terai region, and the local hindcasts were compared with weather station and reanalysis data. The results showed that the CFSv2 model accurately predicts monthly anomalies of daily maximum and minimum air temperature (Tmax and Tmin) as well as incoming total surface solar radiation (Srad). However, the daily climatologies of the respective CFSv2 hindcasts exhibit significant systematic biases compared to weather station data. The CFSv2 is less capable of predicting monthly precipitation anomalies and simulating the respective intra-seasonal variability over the growing season. Nevertheless, the observed daily climatologies of precipitation fall within the ensemble spread of the respective daily climatologies of CFSv2 hindcasts. These limitations in the CFSv2 seasonal forecasts, primarily in precipitation, restrict the potential application for predicting the interannual variability of crop yield associated with weather variability. Despite these limitations, ensemble averaging of the simulated yield using all CFSv2 members after applying bias correction may lead to satisfactory yield predictions.

The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation.

PubMed

Al Sharif, Merilin; Tsakovska, Ivanka; Pajeva, Ilza; Alov, Petko; Fioravanzo, Elena; Bassan, Arianna; Kovarich, Simona; Yang, Chihae; Mostrag-Szlichtyng, Aleksandra; Vitcheva, Vessela; Worth, Andrew P; Richarz, Andrea-N; Cronin, Mark T D

2017-12-01

The aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework. To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC 50 ). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy=81%, sensitivity=85% and specificity=76%. The 3D QSAR model developed to predict EC 50 of PPARγ full agonists had the following statistical parameters: q 2 cv =0.610, N opt =7, SEP cv =0.505, r 2 pr =0.552. To support the linkage of PPARγ agonism predictions to prosteatotic potential, molecular modelling was combined with independently performed mechanistic mining of available in vivo toxicity data followed by ToxPrint chemotypes analysis. The approaches investigated demonstrated a potential to predict the MIE, to facilitate the process of MoA/AOP elaboration, to increase the scientific confidence in AOP, and to become a basis for 3D chemotype development. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A proteomic analysis identifies candidate early biomarkers to predict ovarian hyperstimulation syndrome in polycystic ovarian syndrome patients.

PubMed

Wu, Lan; Sun, Yazhou; Wan, Jun; Luan, Ting; Cheng, Qing; Tan, Yong

2017-07-01

Ovarian hyperstimulation syndrome (OHSS) is a potentially life‑threatening, iatrogenic complication that occurs during assisted reproduction. Polycystic ovarian syndrome (PCOS) significantly increases the risk of OHSS during controlled ovarian stimulation. Therefore, a more effective early prediction technique is required in PCOS patients. Quantitative proteomic analysis of serum proteins indicates the potential diagnostic value for disease. In the present study, the authors revealed the differentially expressed proteins in OHSS patients with PCOS as new diagnostic biomarkers. The promising proteins obtained from liquid chromatography‑mass spectrometry were subjected to ELISA and western blotting assay for further confirmation. A total of 57 proteins were identified with significant difference, of which 29 proteins were upregulated and 28 proteins were downregulated in OHSS patients. Haptoglobin, fibrinogen and lipoprotein lipase were selected as candidate biomarkers. Receiver operating characteristic curve analysis demonstrated all three proteins may have potential as biomarkers to discriminate OHSS in PCOS patients. Haptoglobin, fibrinogen and lipoprotein lipase have never been reported as a predictive marker of OHSS in PCOS patients, and their potential roles in OHSS occurrence deserve further studies. The proteomic results reported in the present study may gain deeper insights into the pathophysiology of OHSS.

Using remote sensing, ecological niche modeling, and Geographic Information Systems for Rift Valley fever risk assessment in the United States

NASA Astrophysics Data System (ADS)

Tedrow, Christine Atkins

The primary goal in this study was to explore remote sensing, ecological niche modeling, and Geographic Information Systems (GIS) as aids in predicting candidate Rift Valley fever (RVF) competent vector abundance and distribution in Virginia, and as means of estimating where risk of establishment in mosquitoes and risk of transmission to human populations would be greatest in Virginia. A second goal in this study was to determine whether the remotely-sensed Normalized Difference Vegetation Index (NDVI) can be used as a proxy variable of local conditions for the development of mosquitoes to predict mosquito species distribution and abundance in Virginia. As part of this study, a mosquito surveillance database was compiled to archive the historical patterns of mosquito species abundance in Virginia. In addition, linkages between mosquito density and local environmental and climatic patterns were spatially and temporally examined. The present study affirms the potential role of remote sensing imagery for species distribution prediction, and it demonstrates that ecological niche modeling is a valuable predictive tool to analyze the distributions of populations. The MaxEnt ecological niche modeling program was used to model predicted ranges for potential RVF competent vectors in Virginia. The MaxEnt model was shown to be robust, and the candidate RVF competent vector predicted distribution map is presented. The Normalized Difference Vegetation Index (NDVI) was found to be the most useful environmental-climatic variable to predict mosquito species distribution and abundance in Virginia. However, these results indicate that a more robust prediction is obtained by including other environmental-climatic factors correlated to mosquito densities (e.g., temperature, precipitation, elevation) with NDVI. The present study demonstrates that remote sensing and GIS can be used with ecological niche and risk modeling methods to estimate risk of virus establishment in mosquitoes and transmission to humans. Maps delineating the geographic areas in Virginia with highest risk for RVF establishment in mosquito populations and RVF disease transmission to human populations were generated in a GIS using human, domestic animal, and white-tailed deer population estimates and the MaxEnt potential RVF competent vector species distribution prediction. The candidate RVF competent vector predicted distribution and RVF risk maps presented in this study can help vector control agencies and public health officials focus Rift Valley fever surveillance efforts in geographic areas with large co-located populations of potential RVF competent vectors and human, domestic animal, and wildlife hosts. Keywords. Rift Valley fever, risk assessment, Ecological Niche Modeling, MaxEnt, Geographic Information System, remote sensing, Pearson's Product-Moment Correlation Coefficient, vectors, mosquito distribution, mosquito density, mosquito surveillance, United States, Virginia, domestic animals, white-tailed deer, ArcGIS

The potential for technology in brief interventions for substance use, and during-session prediction of computer-delivered brief intervention response.

PubMed

Ondersma, Steven J; Grekin, Emily R; Svikis, Dace

2011-01-01

We first provide an overview of the potential of technology in the area of brief interventions for substance use and describe recent projects from our lab that are illustrative of that potential. Second, we present data from a study of during-session predictors of brief intervention response. In a sample of postpartum women (N = 39), several variables showed promise as predictors of later drug use, and a brief index derived from them predicted abstinence with a sensitivity of .7 and a specificity of .89. This promising approach and initial study findings support the importance of future research in this area.

Predicting asthma exacerbations using artificial intelligence.

PubMed

Finkelstein, Joseph; Wood, Jeffrey

2013-01-01

Modern telemonitoring systems identify a serious patient deterioration when it already occurred. It would be much more beneficial if the upcoming clinical deterioration were identified ahead of time even before a patient actually experiences it. The goal of this study was to assess artificial intelligence approaches which potentially can be used in telemonitoring systems for advance prediction of changes in disease severity before they actually occur. The study dataset was based on daily self-reports submitted by 26 adult asthma patients during home telemonitoring consisting of 7001 records. Two classification algorithms were employed for building predictive models: naïve Bayesian classifier and support vector machines. Using a 7-day window, a support vector machine was able to predict asthma exacerbation to occur on the day 8 with the accuracy of 0.80, sensitivity of 0.84 and specificity of 0.80. Our study showed that methods of artificial intelligence have significant potential in developing individualized decision support for chronic disease telemonitoring systems.

Potential Seasonal Predictability of Water Cycle in Observations and Reanalysis

NASA Astrophysics Data System (ADS)

Feng, X.; Houser, P.

2012-12-01

Identification of predictability of water cycle variability is crucial for climate prediction, water resources availability, ecosystem management and hazard mitigation. An analysis that can assess the potential skill in seasonal prediction was proposed by the authors, named as analysis of covariance (ANOCOVA). This method tests whether interannual variability of seasonal means exceeds that due to weather noise under the null hypothesis that seasonal means are identical every year. It has the advantage of taking into account autocorrelation structure in the daily time series but also accounting for the uncertainty of the estimated parameters in the significance test. During the past several years, multiple reanalysis datasets have become available for studying climate variability and understanding climate system. We are motivated to compare the potential predictability of water cycle variation from different reanalysis datasets against observations using the newly proposed ANOCOVA method. The selected eight reanalyses include the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP/NCAR) 40-year Reanalysis Project (NNRP), the National Centers for Environmental Prediction-Department of Energy (NCEP/DOE) Reanalysis Project (NDRP), the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-year Reanalysis, The Japan Meteorological Agency 25-year Reanalysis Project (JRA25), the ECMWF) Interim Reanalysis (ERAINT), the NCEP Climate Forecast System Reanalysis (CFSR), the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA), and the National Oceanic and Atmospheric Administration-Cooperative Institute for Research in Environmental Sciences (NOAA/CIRES) 20th Century Reanalysis Version 2 (20CR). For key water cycle components, precipitation and evaporation, all reanalyses consistently show high fraction of predictable variance in the tropics, low predictability over the extratropics, more potential predictability over the ocean than land, and a stronger seasonal variation in potential predictability over land than ocean. The substantial differences are observed especially over the extropical areas where boundary-forced signal is not as significant as in tropics. We further evaluate the accuracy of reanalysis in estimating seasonal predictability over several selected regions, where rain gauge measurement or land surface data assimilation product is available and accurate, to gain insight on the strength and weakness of reanalysis products.

Characterization of the glass transition of water predicted by molecular dynamics simulations using nonpolarizable intermolecular potentials.

PubMed

Kreck, Cara A; Mancera, Ricardo L

2014-02-20

Molecular dynamics simulations allow detailed study of the experimentally inaccessible liquid state of supercooled water below its homogeneous nucleation temperature and the characterization of the glass transition. Simple, nonpolarizable intermolecular potentials are commonly used in classical molecular dynamics simulations of water and aqueous systems due to their lower computational cost and their ability to reproduce a wide range of properties. Because the quality of these predictions varies between the potentials, the predicted glass transition of water is likely to be influenced by the choice of potential. We have thus conducted an extensive comparative investigation of various three-, four-, five-, and six-point water potentials in both the NPT and NVT ensembles. The T(g) predicted from NPT simulations is strongly correlated with the temperature of minimum density, whereas the maximum in the heat capacity plot corresponds to the minimum in the thermal expansion coefficient. In the NVT ensemble, these points are instead related to the maximum in the internal pressure and the minimum of its derivative, respectively. A detailed analysis of the hydrogen-bonding properties at the glass transition reveals that the extent of hydrogen-bonds lost upon the melting of the glassy state is related to the height of the heat capacity peak and varies between water potentials.

GIMDA: Graphlet interaction-based MiRNA-disease association prediction.

PubMed

Chen, Xing; Guan, Na-Na; Li, Jian-Qiang; Yan, Gui-Ying

2018-03-01

MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA-Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA-disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave-one-out cross-validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five-fold cross-validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA-disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures. © 2017 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.

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.

Changes in event-related potential functional networks predict traumatic brain injury in piglets.

PubMed

Atlan, Lorre S; Lan, Ingrid S; Smith, Colin; Margulies, Susan S

2018-06-01

Traumatic brain injury is a leading cause of cognitive and behavioral deficits in children in the US each year. None of the current diagnostic tools, such as quantitative cognitive and balance tests, have been validated to identify mild traumatic brain injury in infants, adults and animals. In this preliminary study, we report a novel, quantitative tool that has the potential to quickly and reliably diagnose traumatic brain injury and which can track the state of the brain during recovery across multiple ages and species. Using 32 scalp electrodes, we recorded involuntary auditory event-related potentials from 22 awake four-week-old piglets one day before and one, four, and seven days after two different injury types (diffuse and focal) or sham. From these recordings, we generated event-related potential functional networks and assessed whether the patterns of the observed changes in these networks could distinguish brain-injured piglets from non-injured. Piglet brains exhibited significant changes after injury, as evaluated by five network metrics. The injury prediction algorithm developed from our analysis of the changes in the event-related potentials functional networks ultimately produced a tool with 82% predictive accuracy. This novel approach is the first application of auditory event-related potential functional networks to the prediction of traumatic brain injury. The resulting tool is a robust, objective and predictive method that offers promise for detecting mild traumatic brain injury, in particular because collecting event-related potentials data is noninvasive and inexpensive. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction

PubMed Central

Huang, Li

2017-01-01

Predicting novel microRNA (miRNA)-disease associations is clinically significant due to miRNAs’ potential roles of diagnostic biomarkers and therapeutic targets for various human diseases. Previous studies have demonstrated the viability of utilizing different types of biological data to computationally infer new disease-related miRNAs. Yet researchers face the challenge of how to effectively integrate diverse datasets and make reliable predictions. In this study, we presented a computational model named Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction (LRSSLMDA), which projected miRNAs/diseases’ statistical feature profile and graph theoretical feature profile to a common subspace. It used Laplacian regularization to preserve the local structures of the training data and a L1-norm constraint to select important miRNA/disease features for prediction. The strength of dimensionality reduction enabled the model to be easily extended to much higher dimensional datasets than those exploited in this study. Experimental results showed that LRSSLMDA outperformed ten previous models: the AUC of 0.9178 in global leave-one-out cross validation (LOOCV) and the AUC of 0.8418 in local LOOCV indicated the model’s superior prediction accuracy; and the average AUC of 0.9181+/-0.0004 in 5-fold cross validation justified its accuracy and stability. In addition, three types of case studies further demonstrated its predictive power. Potential miRNAs related to Colon Neoplasms, Lymphoma, Kidney Neoplasms, Esophageal Neoplasms and Breast Neoplasms were predicted by LRSSLMDA. Respectively, 98%, 88%, 96%, 98% and 98% out of the top 50 predictions were validated by experimental evidences. Therefore, we conclude that LRSSLMDA would be a valuable computational tool for miRNA-disease association prediction. PMID:29253885

Application of the KeratinoSens™ assay for assessing the skin sensitization potential of agrochemical active ingredients and formulations.

PubMed

Settivari, Raja S; Gehen, Sean C; Amado, Ricardo Acosta; Visconti, Nicolo R; Boverhof, Darrell R; Carney, Edward W

2015-07-01

Assessment of skin sensitization potential is an important component of the safety evaluation process for agrochemical products. Recently, non-animal approaches including the KeratinoSens™ assay have been developed for predicting skin sensitization potential. Assessing the utility of the KeratinoSens™ assay for use with multi-component mixtures such as agrochemical formulations has not been previously evaluated and is a significant need. This study was undertaken to evaluate the KeratinoSens™ assay prediction potential for agrochemical formulations. The assay was conducted for 8 agrochemical active ingredients (AIs) including 3 sensitizers (acetochlor, meptyldinocap, triclopyr), 5 non-sensitizers (aminopyralid, clopyralid, florasulam, methoxyfenozide, oxyfluorfen) and 10 formulations for which in vivo sensitization data were available. The KeratinoSens™ correctly predicted the sensitization potential of all the AIs. For agrochemical formulations it was necessary to modify the standard assay procedure whereby the formulation was assumed to have a common molecular weight. The resultant approach correctly predicted the sensitization potential for 3 of 4 sensitizing formulations and all 6 non-sensitizing formulations when compared to in vivo data. Only the meptyldinocap-containing formulation was misclassified, as a result of high cytotoxicity. These results demonstrate the promising utility of the KeratinoSens™ assay for evaluating the skin sensitization potential of agrochemical AIs and formulations. Copyright © 2015 Elsevier Inc. All rights reserved.

Toxico-Cheminformatics: A New Frontier for Predictive Toxicology

EPA Science Inventory

The DSSTox database network and efforts to improve public access to chemical toxicity information resources, coupled with high-throughput screening (HTS) data and efforts to systematize legacy toxicity studies, have the potential to significantly improve predictive capabilities i...

Application of Dempster-Shafer theory of evidence model to geoelectric and hydraulic parameters for groundwater potential zonation

NASA Astrophysics Data System (ADS)

Mogaji, Kehinde Anthony; Lim, Hwee San

2018-06-01

The application of a GIS - based Dempster - Shafer data driven model named as evidential belief function EBF- methodology to groundwater potential conditioning factors (GPCFs) derived from geophysical and hydrogeological data sets for assessing groundwater potentiality was presented in this study. The proposed method's efficacy in managing degree of uncertainty in spatial predictive models motivated this research. The method procedural approaches entail firstly, the database containing groundwater data records (bore wells location inventory, hydrogeological data record, etc.) and geophysical measurement data construction. From the database, different influencing groundwater occurrence factors, namely aquifer layer thickness, aquifer layer resistivity, overburden material resistivity, overburden material thickness, aquifer hydraulic conductivity and aquifer transmissivity were extracted and prepared. Further, the bore well location inventories were partitioned randomly into a ratio of 70% (19 wells) for model training and 30% (9 wells) for model testing. The synthesized of the GPCFs via applying the DS - EBF model algorithms produced the groundwater productivity potential index (GPPI) map which demarcated the area into low - medium, medium, medium - high and high potential zones. The analyzed percentage degree of uncertainty for the predicted lows potential zones classes and mediums/highs potential zones classes are >10% and <10%, respectively. The DS theory model-based GPPI map's validation through ROC approach established prediction rate accuracy of 88.8%. Successively, the determined transverse resistance (TR) values in the range of 1280 and 30,000 Ω my for the area geoelectrically delineated aquifer units of the predicted potential zones through Dar - Zarrouk Parameter analysis quantitatively confirm the DS theory modeling prediction results. This research results have expand the capability of DS - EBF model in predictive modeling by effective uncertainty management. Thus, the produced map could form part of decision support system reliable to be used by local authorities for groundwater exploitation and management in the area.

Assessment of in vivo organ-uptake and in silico prediction of CYP mediated metabolism of DA-Phen, a new dopaminergic agent.

PubMed

Sutera, Flavia Maria; Giannola, Libero Italo; Murgia, Denise; De Caro, Viviana

2017-12-01

The drug development process strives to predict metabolic fate of a drug candidate, together with its uptake in major organs, whether they act as target, deposit or metabolism sites, to the aim of establish a relationship between the pharmacodynamics and the pharmacokinetics and highlight the potential toxicity of the drug candidate. The present study was aimed at evaluating the in vivo uptake of 2-Amino-N-[2-(3,4-dihydroxy-phenyl)-ethyl]-3-phenyl-propionamide (DA-Phen) - a new dopaminergic neurotransmission modulator, in target and non-target organs of animal subjects and integrating these data with SMARTCyp results, an in silico method that predicts the sites of cytochrome P450-mediated metabolism of drug-like molecules. Wistar rats, subjected to two different behavioural studies in which DA-Phen was intraperitoneally administrated at a dose equal to 0.03mmol/kg, were sacrificed after the experimental protocols and their major organs were analysed to quantify the drug uptake. The data obtained were integrated with in silico prediction of potential metabolites of DA-Phen using the SmartCYP predictive tool. DA-Phen reached quantitatively the Central Nervous System and the results showed that the amide bond of the DA-Phen is scarcely hydrolysed as it was found intact in analyzed organs. As a consequence, it is possible to assume that DA-Phen acts as dopaminergic modulator per se and not as a Dopamine prodrug, thus avoiding peripheral release and toxic side effects due to the endogenous neurotransmitter. Furthermore the identification of potential metabolites related to biotransformation of the drug candidate leads to a more careful evaluation of the appropriate route of administration for future intended therapeutic aims and potential translation into clinical studies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting Reading Growth with Event-Related Potentials: Thinking Differently about Indexing “Responsiveness”

PubMed Central

Lemons, Christopher J.; Key, Alexandra P.F.; Fuchs, Douglas; Yoder, Paul J.; Fuchs, Lynn S.; Compton, Donald L.; Williams, Susan M.; Bouton, Bobette

2009-01-01

The purpose of this study was to determine if event-related potential (ERP) data collected during three reading-related tasks (Letter Sound Matching, Nonword Rhyming, and Nonword Reading) could be used to predict short-term reading growth on a curriculum-based measure of word identification fluency over 19 weeks in a sample of 29 first-grade children. Results indicate that ERP responses to the Letter Sound Matching task were predictive of reading change and remained so after controlling for two previously validated behavioral predictors of reading, Rapid Letter Naming and Segmenting. ERP data for the other tasks were not correlated with reading change. The potential for cognitive neuroscience to enhance current methods of indexing responsiveness in a response-to-intervention (RTI) model is discussed. PMID:20514353

Molecular simulation of the thermodynamic, structural, and vapor-liquid equilibrium properties of neon

NASA Astrophysics Data System (ADS)

Vlasiuk, Maryna; Frascoli, Federico; Sadus, Richard J.

2016-09-01

The thermodynamic, structural, and vapor-liquid equilibrium properties of neon are comprehensively studied using ab initio, empirical, and semi-classical intermolecular potentials and classical Monte Carlo simulations. Path integral Monte Carlo simulations for isochoric heat capacity and structural properties are also reported for two empirical potentials and one ab initio potential. The isobaric and isochoric heat capacities, thermal expansion coefficient, thermal pressure coefficient, isothermal and adiabatic compressibilities, Joule-Thomson coefficient, and the speed of sound are reported and compared with experimental data for the entire range of liquid densities from the triple point to the critical point. Lustig's thermodynamic approach is formally extended for temperature-dependent intermolecular potentials. Quantum effects are incorporated using the Feynman-Hibbs quantum correction, which results in significant improvement in the accuracy of predicted thermodynamic properties. The new Feynman-Hibbs version of the Hellmann-Bich-Vogel potential predicts the isochoric heat capacity to an accuracy of 1.4% over the entire range of liquid densities. It also predicts other thermodynamic properties more accurately than alternative intermolecular potentials.

A tungsten-rhenium interatomic potential for point defect studies

DOE PAGES

Setyawan, Wahyu; Gao, Ning; Kurtz, Richard J.

2018-05-28

A tungsten-rhenium (W-Re) classical interatomic potential is developed within the embedded atom method (EAM) interaction framework. A force-matching method is employed to fit the potential to ab initio forces, energies, and stresses. Simulated annealing is combined with the conjugate gradient technique to search for an optimum potential from over 1000 initial trial sets. The potential is designed for studying point defects in W-Re systems. It gives good predictions of the formation energies of Re defects in W and the binding energies of W self-interstitial clusters with Re. The potential is further evaluated for describing the formation energy of structures inmore » the σ and χ intermetallic phases. The predicted convex-hulls of formation energy are in excellent agreement with ab initio data. In pure Re, the potential can reproduce the formation energies of vacancy and self-interstitial defects sufficiently accurately, and gives the correct ground state self-interstitial configuration. Furthermore, by including liquid structures in the fit, the potential yields a Re melting temperature (3130 K) that is close to the experimental value (3459 K).« less

A tungsten-rhenium interatomic potential for point defect studies

NASA Astrophysics Data System (ADS)

Setyawan, Wahyu; Gao, Ning; Kurtz, Richard J.

2018-05-01

A tungsten-rhenium (W-Re) classical interatomic potential is developed within the embedded atom method interaction framework. A force-matching method is employed to fit the potential to ab initio forces, energies, and stresses. Simulated annealing is combined with the conjugate gradient technique to search for an optimum potential from over 1000 initial trial sets. The potential is designed for studying point defects in W-Re systems. It gives good predictions of the formation energies of Re defects in W and the binding energies of W self-interstitial clusters with Re. The potential is further evaluated for describing the formation energy of structures in the σ and χ intermetallic phases. The predicted convex-hulls of formation energy are in excellent agreement with ab initio data. In pure Re, the potential can reproduce the formation energies of vacancies and self-interstitial defects sufficiently accurately and gives the correct ground state self-interstitial configuration. Furthermore, by including liquid structures in the fit, the potential yields a Re melting temperature (3130 K) that is close to the experimental value (3459 K).

A tungsten-rhenium interatomic potential for point defect studies

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

Setyawan, Wahyu; Gao, Ning; Kurtz, Richard J.

A tungsten-rhenium (W-Re) classical interatomic potential is developed within the embedded atom method (EAM) interaction framework. A force-matching method is employed to fit the potential to ab initio forces, energies, and stresses. Simulated annealing is combined with the conjugate gradient technique to search for an optimum potential from over 1000 initial trial sets. The potential is designed for studying point defects in W-Re systems. It gives good predictions of the formation energies of Re defects in W and the binding energies of W self-interstitial clusters with Re. The potential is further evaluated for describing the formation energy of structures inmore » the σ and χ intermetallic phases. The predicted convex-hulls of formation energy are in excellent agreement with ab initio data. In pure Re, the potential can reproduce the formation energies of vacancy and self-interstitial defects sufficiently accurately, and gives the correct ground state self-interstitial configuration. Furthermore, by including liquid structures in the fit, the potential yields a Re melting temperature (3130 K) that is close to the experimental value (3459 K).« less

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.

Progastrin: a potential predictive marker of liver metastasis in colorectal cancer.

PubMed

Westwood, David A; Patel, Oneel; Christophi, Christopher; Shulkes, Arthur; Baldwin, Graham S

2017-07-01

Staging of colorectal cancer often fails to discriminate outcomes of patients with morphologically similar tumours that exhibit different clinical behaviours. Data from several studies suggest that the gastrin family of growth factors potentiates colorectal cancer tumourigenesis. The aim of this study was to investigate whether progastrin expression may predict clinical outcome in colorectal cancer. Patients with colorectal adenocarcinoma of identical depth of invasion who had not received neoadjuvant therapy were included. The patients either had stage IIa disease with greater than 3-year disease-free survival without adjuvant therapy or stage IV disease with liver metastases on staging CT. Progastrin expression in tumour sections was scored with reference to the intensity and area of immunohistochemical staining. Progastrin expression by stage IV tumours was significantly greater than stage IIa tumours with mean progastrin immunopositivity scores of 2.1 ± 0.2 versus 0.5 ± 0.2, respectively (P < 0.001). This is the first study to show that progastrin expression may be predictive of aggressive tumour behaviour in patients with colorectal cancer and supports its clinical relevance and potential use as a biomarker.

Study of cavitating inducer instabilities

NASA Technical Reports Server (NTRS)

Young, W. E.; Murphy, R.; Reddecliff, J. M.

1972-01-01

An analytic and experimental investigation into the causes and mechanisms of cavitating inducer instabilities was conducted. Hydrofoil cascade tests were performed, during which cavity sizes were measured. The measured data were used, along with inducer data and potential flow predictions, to refine an analysis for the prediction of inducer blade suction surface cavitation cavity volume. Cavity volume predictions were incorporated into a linearized system model, and instability predictions for an inducer water test loop were generated. Inducer tests were conducted and instability predictions correlated favorably with measured instability data.

Novel Design Strategy for Checkpoint Kinase 2 Inhibitors Using Pharmacophore Modeling, Combinatorial Fusion, and Virtual Screening

PubMed Central

Wang, Yen-Ling

2014-01-01

Checkpoint kinase 2 (Chk2) has a great effect on DNA-damage and plays an important role in response to DNA double-strand breaks and related lesions. In this study, we will concentrate on Chk2 and the purpose is to find the potential inhibitors by the pharmacophore hypotheses (PhModels), combinatorial fusion, and virtual screening techniques. Applying combinatorial fusion into PhModels and virtual screening techniques is a novel design strategy for drug design. We used combinatorial fusion to analyze the prediction results and then obtained the best correlation coefficient of the testing set (r test) with the value 0.816 by combining the BesttrainBesttest and FasttrainFasttest prediction results. The potential inhibitors were selected from NCI database by screening according to BesttrainBesttest + FasttrainFasttest prediction results and molecular docking with CDOCKER docking program. Finally, the selected compounds have high interaction energy between a ligand and a receptor. Through these approaches, 23 potential inhibitors for Chk2 are retrieved for further study. PMID:24864236

Continued Research into Characterizing the Preturbulence Environment for Sensor Development, New Hazard Algorithms and Experimental Flight Planning

NASA Technical Reports Server (NTRS)

Kaplan, Michael L.; Lin, Yuh-Lang

2005-01-01

The purpose of the research was to develop and test improved hazard algorithms that could result in the development of sensors that are better able to anticipate potentially severe atmospheric turbulence, which affects aircraft safety. The research focused on employing numerical simulation models to develop improved algorithms for the prediction of aviation turbulence. This involved producing both research simulations and real-time simulations of environments predisposed to moderate and severe aviation turbulence. The research resulted in the following fundamental advancements toward the aforementioned goal: 1) very high resolution simulations of turbulent environments indicated how predictive hazard indices could be improved resulting in a candidate hazard index that indicated the potential for improvement over existing operational indices, 2) a real-time turbulence hazard numerical modeling system was improved by correcting deficiencies in its simulation of moist convection and 3) the same real-time predictive system was tested by running the code twice daily and the hazard prediction indices updated and improved. Additionally, a simple validation study was undertaken to determine how well a real time hazard predictive index performed when compared to commercial pilot observations of aviation turbulence. Simple statistical analyses were performed in this validation study indicating potential skill in employing the hazard prediction index to predict regions of varying intensities of aviation turbulence. Data sets from a research numerical model where provided to NASA for use in a large eddy simulation numerical model. A NASA contractor report and several refereed journal articles where prepared and submitted for publication during the course of this research.

Seal carrion is a predictable resource for coastal ecosystems

NASA Astrophysics Data System (ADS)

Quaggiotto, Maria-Martina; Barton, Philip S.; Morris, Christopher D.; Moss, Simon E. W.; Pomeroy, Patrick P.; McCafferty, Dominic J.; Bailey, David M.

2018-04-01

The timing, magnitude, and spatial distribution of resource inputs can have large effects on dependent organisms. Few studies have examined the predictability of such resources and no standard ecological measure of predictability exists. We examined the potential predictability of carrion resources provided by one of the UK's largest grey seal (Halichoerus grypus) colonies, on the Isle of May, Scotland. We used aerial (11 years) and ground surveys (3 years) to quantify the variability in time, space, quantity (kg), and quality (MJ) of seal carrion during the seal pupping season. We then compared the potential predictability of seal carrion to other periodic changes in food availability in nature. An average of 6893 kg of carrion •yr-1 corresponding to 110.5 × 103 MJ yr-1 was released for potential scavengers as placentae and dead animals. A fifth of the total biomass from dead seals was consumed by the end of the pupping season, mostly by avian scavengers. The spatial distribution of carcasses was similar across years, and 28% of the area containing >10 carcasses ha-1 was shared among all years. Relative standard errors (RSE) in space, time, quantity, and quality of carrion were all below 34%. This is similar to other allochthonous-dependent ecosystems, such as those affected by migratory salmon, and indicates high predictability of seal carrion as a resource. Our study illustrates how to quantify predictability in carrion, which is of general relevance to ecosystems that are dependent on this resource. We also highlight the importance of carrion to marine coastal ecosystems, where it sustains avian scavengers thus affecting ecosystem structure and function.

Recent Developments in Toxico-Cheminformatics: A New Frontier for Predictive Toxicology

EPA Science Inventory

Efforts to improve public access to chemical toxicity information resources, coupled with new high-throughput screening (HTS) data and efforts to systematize legacy toxicity studies, have the potential to significantly improve predictive capabilities in toxicology. Important rec...

Predictive models reduce talent development costs in female gymnastics.

PubMed

Pion, Johan; Hohmann, Andreas; Liu, Tianbiao; Lenoir, Matthieu; Segers, Veerle

2017-04-01

This retrospective study focuses on the comparison of different predictive models based on the results of a talent identification test battery for female gymnasts. We studied to what extent these models have the potential to optimise selection procedures, and at the same time reduce talent development costs in female artistic gymnastics. The dropout rate of 243 female elite gymnasts was investigated, 5 years past talent selection, using linear (discriminant analysis) and non-linear predictive models (Kohonen feature maps and multilayer perceptron). The coaches classified 51.9% of the participants correct. Discriminant analysis improved the correct classification to 71.6% while the non-linear technique of Kohonen feature maps reached 73.7% correctness. Application of the multilayer perceptron even classified 79.8% of the gymnasts correctly. The combination of different predictive models for talent selection can avoid deselection of high-potential female gymnasts. The selection procedure based upon the different statistical analyses results in decrease of 33.3% of cost because the pool of selected athletes can be reduced to 92 instead of 138 gymnasts (as selected by the coaches). Reduction of the costs allows the limited resources to be fully invested in the high-potential athletes.

How potentially predictable are midlatitude ocean currents?

PubMed Central

Nonaka, Masami; Sasai, Yoshikazu; Sasaki, Hideharu; Taguchi, Bunmei; Nakamura, Hisashi

2016-01-01

Predictability of atmospheric variability is known to be limited owing to significant uncertainty that arises from intrinsic variability generated independently of external forcing and/or boundary conditions. Observed atmospheric variability is therefore regarded as just a single realization among different dynamical states that could occur. In contrast, subject to wind, thermal and fresh-water forcing at the surface, the ocean circulation has been considered to be rather deterministic under the prescribed atmospheric forcing, and it still remains unknown how uncertain the upper-ocean circulation variability is. This study evaluates how much uncertainty the oceanic interannual variability can potentially have, through multiple simulations with an eddy-resolving ocean general circulation model driven by the observed interannually-varying atmospheric forcing under slightly different conditions. These ensemble “hindcast” experiments have revealed substantial uncertainty due to intrinsic variability in the extratropical ocean circulation that limits potential predictability of its interannual variability, especially along the strong western boundary currents (WBCs) in mid-latitudes, including the Kuroshio and its eastward extention. The intrinsic variability also greatly limits potential predictability of meso-scale oceanic eddy activity. These findings suggest that multi-member ensemble simulations are essential for understanding and predicting variability in the WBCs, which are important for weather and climate variability and marine ecosystems. PMID:26831954

Prediction by data mining, of suicide attempts in Korean adolescents: a national study

PubMed Central

Bae, Sung Man; Lee, Seung A; Lee, Seung-Hwan

2015-01-01

Objective This study aimed to develop a prediction model for suicide attempts in Korean adolescents. Methods We conducted a decision tree analysis of 2,754 middle and high school students nationwide. We fixed suicide attempt as the dependent variable and eleven sociodemographic, intrapersonal, and extrapersonal variables as independent variables. Results The rate of suicide attempts of the total sample was 9.5%, and severity of depression was the strongest variable to predict suicide attempt. The rates of suicide attempts in the depression and potential depression groups were 5.4 and 2.8 times higher than that of the non-depression group. In the depression group, the most powerful factor to predict a suicide attempt was delinquency, and the rate of suicide attempts in those in the depression group with higher delinquency was two times higher than in those in the depression group with lower delinquency. Of special note, the rate of suicide attempts in the depressed females with higher delinquency was the highest. Interestingly, in the potential depression group, the most impactful factor to predict a suicide attempt was intimacy with family, and the rate of suicide attempts of those in the potential depression group with lower intimacy with family was 2.4 times higher than that of those in the potential depression group with higher intimacy with family. And, among the potential depression group, middle school students with lower intimacy with family had a 2.5-times higher rate of suicide attempts than high school students with lower intimacy with family. Finally, in the non-depression group, stress level was the most powerful factor to predict a suicide attempt. Among the non-depression group, students who reported high levels of stress showed an 8.3-times higher rate of suicide attempts than students who reported average levels of stress. Discussion Based on the results, we especially need to pay attention to depressed females with higher delinquency and those with potential depression with lower intimacy with family to prevent suicide attempts in teenagers. PMID:26396521

[Neuroimaging and Blood Biomarkers in Functional Prognosis after Stroke].

PubMed

Branco, João Paulo; Costa, Joana Santos; Sargento-Freitas, João; Oliveira, Sandra; Mendes, Bruno; Laíns, Jorge; Pinheiro, João

2016-11-01

Stroke remains one of the leading causes of morbidity and mortality around the world and it is associated with an important long-term functional disability. Some neuroimaging resources and certain peripheral blood or cerebrospinal fluid proteins can give important information about etiology, therapeutic approach, follow-up and functional prognosis in acute ischemic stroke patients. However, among the scientific community, there is currently more interest in the stroke vital prognosis over the functional prognosis. Predicting the functional prognosis during acute phase would allow more objective rehabilitation programs and better management of the available resources. The aim of this work is to review the potential role of acute phase neuroimaging and blood biomarkers as functional recovery predictors after ischemic stroke. Review of the literature published between 2005 and 2015, in English, using the terms "ischemic stroke", "neuroimaging" e "blood biomarkers". We included nine studies, based on abstract reading. Computerized tomography, transcranial doppler ultrasound and diffuse magnetic resonance imaging show potential predictive value, based on the blood flow study and the evaluation of stroke's volume and localization, especially when combined with the National Institutes of Health Stroke Scale. Several biomarkers have been studied as diagnostic, risk stratification and prognostic tools, namely the S100 calcium binding protein B, C-reactive protein, matrix metalloproteinases and cerebral natriuretic peptide. Although some biomarkers and neuroimaging techniques have potential predictive value, none of the studies were able to support its use, alone or in association, as a clinically useful functionality predictor model. All the evaluated markers were considered insufficient to predict functional prognosis at three months, when applied in the first hours after stroke. Additional studies are necessary to identify reliable predictive markers for functional prognosis after ischemic stroke.

Evaluation of biomarkers for the prediction of pre-eclampsia in women with type 1 diabetes mellitus: A systematic review.

PubMed

Wotherspoon, Amy C; Young, Ian S; McCance, David R; Holmes, Valerie A

2016-07-01

Pre-eclampsia is a leading cause of maternal and perinatal morbidity and mortality. Women with type 1 diabetes are considered a high-risk group for developing pre-eclampsia. Much research has focused on biomarkers as a means of screening for pre-eclampsia in the general maternal population; however, there is a lack of evidence for women with type 1 diabetes. To undertake a systematic review to identify potential biomarkers for the prediction of pre-eclampsia in women with type 1 diabetes. We searched Medline, EMBASE, Maternity and Infant Care, Scopus, Web of Science and CINAHL SELECTION CRITERIA: Studies were included if they measured biomarkers in blood or urine of women who developed pre-eclampsia and had pre-gestational type 1 diabetes mellitus Data collection and analysis A narrative synthesis was adopted as a meta-analysis could not be performed, due to high study heterogeneity. A total of 72 records were screened, with 21 eligible studies being included in the review. A wide range of biomarkers was investigated and study size varied from 34 to 1258 participants. No single biomarker appeared to be effective in predicting pre-eclampsia; however, glycaemic control was associated with an increased risk while a combination of angiogenic and anti-angiogenic factors seemed to be potentially useful. Limited evidence suggests that combinations of biomarkers may be more effective in predicting pre-eclampsia than single biomarkers. Further research is needed to verify the predictive potential of biomarkers that have been measured in the general maternal population, as many studies exclude women with diabetes preceding pregnancy. Copyright © 2016 Elsevier Inc. All rights reserved.

Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review.

PubMed

Senders, Joeky T; Staples, Patrick C; Karhade, Aditya V; Zaki, Mark M; Gormley, William B; Broekman, Marike L D; Smith, Timothy R; Arnaout, Omar

2018-01-01

Accurate measurement of surgical outcomes is highly desirable to optimize surgical decision-making. An important element of surgical decision making is identification of the patient cohort that will benefit from surgery before the intervention. Machine learning (ML) enables computers to learn from previous data to make accurate predictions on new data. In this systematic review, we evaluate the potential of ML for neurosurgical outcome prediction. A systematic search in the PubMed and Embase databases was performed to identify all potential relevant studies up to January 1, 2017. Thirty studies were identified that evaluated ML algorithms used as prediction models for survival, recurrence, symptom improvement, and adverse events in patients undergoing surgery for epilepsy, brain tumor, spinal lesions, neurovascular disease, movement disorders, traumatic brain injury, and hydrocephalus. Depending on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively. Compared with logistic regression, ML models performed significantly better and showed a median absolute improvement in accuracy and area under the receiver operating curve of 15% and 0.06, respectively. Some studies also demonstrated a better performance in ML models compared with established prognostic indices and clinical experts. In the research setting, ML has been studied extensively, demonstrating an excellent performance in outcome prediction for a wide range of neurosurgical conditions. However, future studies should investigate how ML can be implemented as a practical tool supporting neurosurgical care. Copyright © 2017 Elsevier Inc. All rights reserved.

In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network

PubMed Central

Isewon, Itunuoluwa; Aromolaran, Olufemi; Oladipupo, Olufunke

2018-01-01

Malaria is an infectious disease that affects close to half a million individuals every year and Plasmodium falciparum is a major cause of malaria. The treatment of this disease could be done effectively if the essential enzymes of this parasite are specifically targeted. Nevertheless, the development of the parasite in resisting existing drugs now makes discovering new drugs a core responsibility. In this study, a novel computational model that makes the prediction of new and validated antimalarial drug target cheaper, easier, and faster has been developed. We have identified new essential reactions as potential targets for drugs in the metabolic network of the parasite. Among the top seven (7) predicted essential reactions, four (4) have been previously identified in earlier studies with biological evidence and one (1) has been with computational evidence. The results from our study were compared with an extensive list of seventy-seven (77) essential reactions with biological evidence from a previous study. We present a list of thirty-one (31) potential candidates for drug targets in Plasmodium falciparum which includes twenty-four (24) new potential candidates for drug targets. PMID:29789805

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

Calculation of single chain cellulose elasticity using fully atomistic modeling

Treesearch

Xiawa Wu; Robert J. Moon; Ashlie Martini

2011-01-01

Cellulose nanocrystals, a potential base material for green nanocomposites, are ordered bundles of cellulose chains. The properties of these chains have been studied for many years using atomic-scale modeling. However, model predictions are difficult to interpret because of the significant dependence of predicted properties on model details. The goal of this study is...

The Role of Resilience, Delayed Gratification and Stress in Predicting Academic Performance

ERIC Educational Resources Information Center

Cheng, Vivienne; Catling, Jonathan

2015-01-01

Transition to university is an important and potentially stressful life event for students. Previous studies have shown that resilience, delay of gratification and stress can affect the academic performance of students. However, none have shown the effect of these factors in predicting academic performance, hence the current study aimed to look at…

A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data

DOE PAGES

Yue, Meng; Toto, Tami; Jensen, Michael P.; ...

2017-05-18

Severe weather events such as strong thunderstorms are some of the most significant and frequent threats to the electrical grid infrastructure. Outages resulting from storms can be very costly. While some tools are available to utilities to predict storm occurrences and damage, they are typically very crude and provide little means of facilitating restoration efforts. This study developed a methodology to use historical high-resolution (both temporal and spatial) radar observations of storm characteristics and outage information to develop weather condition dependent failure rate models (FRMs) for different grid components. Such models can provide an estimation or prediction of the outagemore » numbers in small areas of a utility’s service territory once the real-time measurement or forecasted data of weather conditions become available as the input to the models. Considering the potential value provided by real-time outages reported, a Bayesian outage prediction (BOP) algorithm is proposed to account for both strength and uncertainties of the reported outages and failure rate models. The potential benefit of this outage prediction scheme is illustrated in this study.« less

A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data

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

Yue, Meng; Toto, Tami; Jensen, Michael P.

Severe weather events such as strong thunderstorms are some of the most significant and frequent threats to the electrical grid infrastructure. Outages resulting from storms can be very costly. While some tools are available to utilities to predict storm occurrences and damage, they are typically very crude and provide little means of facilitating restoration efforts. This study developed a methodology to use historical high-resolution (both temporal and spatial) radar observations of storm characteristics and outage information to develop weather condition dependent failure rate models (FRMs) for different grid components. Such models can provide an estimation or prediction of the outagemore » numbers in small areas of a utility’s service territory once the real-time measurement or forecasted data of weather conditions become available as the input to the models. Considering the potential value provided by real-time outages reported, a Bayesian outage prediction (BOP) algorithm is proposed to account for both strength and uncertainties of the reported outages and failure rate models. The potential benefit of this outage prediction scheme is illustrated in this study.« less

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

Heavy particle decay studies using different versions of nuclear potentials

NASA Astrophysics Data System (ADS)

Santhosh, K. P.; Sukumaran, Indu

2017-10-01

The heavy particle decay from 212-240Pa , 219-245Np , 228-246Pu , 230-249Am , and 232-252Cm leading to doubly magic 208Pb and its neighboring nuclei have been studied using fourteen versions of nuclear potentials. The study has shown that the barrier penetrability as well as the decay half-lives are found to vary with the nuclear potential used. The investigated decay events of the emission of the clusters 22Ne , 24Ne , 26Mg , 28Mg , 32Si and 33Si are not experimentally detected yet but may be detectable in the future. As most of the half-lives predicted are found to lie within the experimental upper limit, T 1/2 < 1030 s, our predictions will be a guide to future experimental design. The GN plots studied are linear for different cluster emissions from different parents with varying slopes and intercepts. Also, it is to be noted that the linearity of the GN plots is unaltered using different nuclear potentials. The universal curve studied ( log10 T 1/2 vs. -ln P for various clusters emitted from various parents shows a linear behavior with the same slope and intercept irrespective of the nuclear potential used.

High-definition endoscopy with digital chromoendoscopy for histologic prediction of distal colorectal polyps.

PubMed

Rath, Timo; Tontini, Gian E; Nägel, Andreas; Vieth, Michael; Zopf, Steffen; Günther, Claudia; Hoffman, Arthur; Neurath, Markus F; Neumann, Helmut

2015-10-22

Distal diminutive colorectal polyps are common and accurate endoscopic prediction of hyperplastic or adenomatous polyp histology could reduce procedural time, costs and potential risks associated with the resection. Within this study we assessed whether digital chromoendoscopy can accurately predict the histology of distal diminutive colorectal polyps according to the ASGE PIVI statement. In this prospective cohort study, 224 consecutive patients undergoing screening or surveillance colonoscopy were included. Real time histology of 121 diminutive distal colorectal polyps was evaluated using high-definition endoscopy with digital chromoendoscopy and the accuracy of predicting histology with digital chromoendoscopy was assessed. The overall accuracy of digital chromoendoscopy for prediction of adenomatous polyp histology was 90.1 %. Sensitivity, specificity, positive and negative predictive values were 93.3, 88.7, 88.7, and 93.2 %, respectively. In high-confidence predictions, the accuracy increased to 96.3 % while sensitivity, specificity, positive and negative predictive values were calculated as 98.1, 94.4, 94.5, and 98.1 %, respectively. Surveillance intervals with digital chromoendoscopy were correctly predicted with >90 % accuracy. High-definition endoscopy in combination with digital chromoendoscopy allowed real-time in vivo prediction of distal colorectal polyp histology and is accurate enough to leave distal colorectal polyps in place without resection or to resect and discard them without pathologic assessment. This approach has the potential to reduce costs and risks associated with the redundant removal of diminutive colorectal polyps. ClinicalTrials NCT02217449.

New Methods for Estimating Seasonal Potential Climate Predictability

NASA Astrophysics Data System (ADS)

Feng, Xia

This study develops two new statistical approaches to assess the seasonal potential predictability of the observed climate variables. One is the univariate analysis of covariance (ANOCOVA) model, a combination of autoregressive (AR) model and analysis of variance (ANOVA). It has the advantage of taking into account the uncertainty of the estimated parameter due to sampling errors in statistical test, which is often neglected in AR based methods, and accounting for daily autocorrelation that is not considered in traditional ANOVA. In the ANOCOVA model, the seasonal signals arising from external forcing are determined to be identical or not to assess any interannual variability that may exist is potentially predictable. The bootstrap is an attractive alternative method that requires no hypothesis model and is available no matter how mathematically complicated the parameter estimator. This method builds up the empirical distribution of the interannual variance from the resamplings drawn with replacement from the given sample, in which the only predictability in seasonal means arises from the weather noise. These two methods are applied to temperature and water cycle components including precipitation and evaporation, to measure the extent to which the interannual variance of seasonal means exceeds the unpredictable weather noise compared with the previous methods, including Leith-Shukla-Gutzler (LSG), Madden, and Katz. The potential predictability of temperature from ANOCOVA model, bootstrap, LSG and Madden exhibits a pronounced tropical-extratropical contrast with much larger predictability in the tropics dominated by El Nino/Southern Oscillation (ENSO) than in higher latitudes where strong internal variability lowers predictability. Bootstrap tends to display highest predictability of the four methods, ANOCOVA lies in the middle, while LSG and Madden appear to generate lower predictability. Seasonal precipitation from ANOCOVA, bootstrap, and Katz, resembling that for temperature, is more predictable over the tropical regions, and less predictable in extropics. Bootstrap and ANOCOVA are in good agreement with each other, both methods generating larger predictability than Katz. The seasonal predictability of evaporation over land bears considerably similarity with that of temperature using ANOCOVA, bootstrap, LSG and Madden. The remote SST forcing and soil moisture reveal substantial seasonality in their relations with the potentially predictable seasonal signals. For selected regions, either SST or soil moisture or both shows significant relationships with predictable signals, hence providing indirect insight on slowly varying boundary processes involved to enable useful seasonal climate predication. A multivariate analysis of covariance (MANOCOVA) model is established to identify distinctive predictable patterns, which are uncorrelated with each other. Generally speaking, the seasonal predictability from multivariate model is consistent with that from ANOCOVA. Besides unveiling the spatial variability of predictability, MANOCOVA model also reveals the temporal variability of each predictable pattern, which could be linked to the periodic oscillations.

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.

Mapping Global Potential Risk of Mango Sudden Decline Disease Caused by Ceratocystis fimbriata.

PubMed

Galdino, Tarcísio Visintin da Silva; Kumar, Sunil; Oliveira, Leonardo S S; Alfenas, Acelino C; Neven, Lisa G; Al-Sadi, Abdullah M; Picanço, Marcelo C

2016-01-01

The Mango Sudden Decline (MSD), also referred to as Mango Wilt, is an important disease of mango in Brazil, Oman and Pakistan. This fungus is mainly disseminated by the mango bark beetle, Hypocryphalus mangiferae (Stebbing), by infected plant material, and the infested soils where it is able to survive for long periods. The best way to avoid losses due to MSD is to prevent its establishment in mango production areas. Our objectives in this study were to: (1) predict the global potential distribution of MSD, (2) identify the mango growing areas that are under potential risk of MSD establishment, and (3) identify climatic factors associated with MSD distribution. Occurrence records were collected from Brazil, Oman and Pakistan where the disease is currently known to occur in mango. We used the correlative maximum entropy based model (MaxEnt) algorithm to assess the global potential distribution of MSD. The MaxEnt model predicted suitable areas in countries where the disease does not already occur in mango, but where mango is grown. Among these areas are the largest mango producers in the world including India, China, Thailand, Indonesia, and Mexico. The mean annual temperature, precipitation of coldest quarter, precipitation seasonality, and precipitation of driest month variables contributed most to the potential distribution of MSD disease. The mango bark beetle vector is known to occur beyond the locations where MSD currently exists and where the model predicted suitable areas, thus showing a high likelihood for disease establishment in areas predicted by our model. Our study is the first to map the potential risk of MSD establishment on a global scale. This information can be used in designing strategies to prevent introduction and establishment of MSD disease, and in preparation of efficient pest risk assessments and monitoring programs.

Mapping Global Potential Risk of Mango Sudden Decline Disease Caused by Ceratocystis fimbriata

PubMed Central

Oliveira, Leonardo S. S.; Alfenas, Acelino C.; Neven, Lisa G.; Al-Sadi, Abdullah M.

2016-01-01

The Mango Sudden Decline (MSD), also referred to as Mango Wilt, is an important disease of mango in Brazil, Oman and Pakistan. This fungus is mainly disseminated by the mango bark beetle, Hypocryphalus mangiferae (Stebbing), by infected plant material, and the infested soils where it is able to survive for long periods. The best way to avoid losses due to MSD is to prevent its establishment in mango production areas. Our objectives in this study were to: (1) predict the global potential distribution of MSD, (2) identify the mango growing areas that are under potential risk of MSD establishment, and (3) identify climatic factors associated with MSD distribution. Occurrence records were collected from Brazil, Oman and Pakistan where the disease is currently known to occur in mango. We used the correlative maximum entropy based model (MaxEnt) algorithm to assess the global potential distribution of MSD. The MaxEnt model predicted suitable areas in countries where the disease does not already occur in mango, but where mango is grown. Among these areas are the largest mango producers in the world including India, China, Thailand, Indonesia, and Mexico. The mean annual temperature, precipitation of coldest quarter, precipitation seasonality, and precipitation of driest month variables contributed most to the potential distribution of MSD disease. The mango bark beetle vector is known to occur beyond the locations where MSD currently exists and where the model predicted suitable areas, thus showing a high likelihood for disease establishment in areas predicted by our model. Our study is the first to map the potential risk of MSD establishment on a global scale. This information can be used in designing strategies to prevent introduction and establishment of MSD disease, and in preparation of efficient pest risk assessments and monitoring programs. PMID:27415625

Pairwise contact energy statistical potentials can help to find probability of point mutations.

PubMed

Saravanan, K M; Suvaithenamudhan, S; Parthasarathy, S; Selvaraj, S

2017-01-01

To adopt a particular fold, a protein requires several interactions between its amino acid residues. The energetic contribution of these residue-residue interactions can be approximated by extracting statistical potentials from known high resolution structures. Several methods based on statistical potentials extracted from unrelated proteins are found to make a better prediction of probability of point mutations. We postulate that the statistical potentials extracted from known structures of similar folds with varying sequence identity can be a powerful tool to examine probability of point mutation. By keeping this in mind, we have derived pairwise residue and atomic contact energy potentials for the different functional families that adopt the (α/β) 8 TIM-Barrel fold. We carried out computational point mutations at various conserved residue positions in yeast Triose phosphate isomerase enzyme for which experimental results are already reported. We have also performed molecular dynamics simulations on a subset of point mutants to make a comparative study. The difference in pairwise residue and atomic contact energy of wildtype and various point mutations reveals probability of mutations at a particular position. Interestingly, we found that our computational prediction agrees with the experimental studies of Silverman et al. (Proc Natl Acad Sci 2001;98:3092-3097) and perform better prediction than i Mutant and Cologne University Protein Stability Analysis Tool. The present work thus suggests deriving pairwise contact energy potentials and molecular dynamics simulations of functionally important folds could help us to predict probability of point mutations which may ultimately reduce the time and cost of mutation experiments. Proteins 2016; 85:54-64. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

The practice of prediction: What can ecologists learn from applied, ecology-related fields?

USGS Publications Warehouse

Pennekamp, Frank; Adamson, Matthew; Petchey, Owen L; Poggiale, Jean-Christophe; Aguiar, Maira; Kooi, Bob W.; Botkin, Daniel B.; DeAngelis, Donald L.

2017-01-01

The pervasive influence of human induced global environmental change affects biodiversity across the globe, and there is great uncertainty as to how the biosphere will react on short and longer time scales. To adapt to what the future holds and to manage the impacts of global change, scientists need to predict the expected effects with some confidence and communicate these predictions to policy makers. However, recent reviews found that we currently lack a clear understanding of how predictable ecology is, with views seeing it as mostly unpredictable to potentially predictable, at least over short time frames. However, in applied, ecology-related fields predictions are more commonly formulated and reported, as well as evaluated in hindsight, potentially allowing one to define baselines of predictive proficiency in these fields. We searched the literature for representative case studies in these fields and collected information about modeling approaches, target variables of prediction, predictive proficiency achieved, as well as the availability of data to parameterize predictive models. We find that some fields such as epidemiology achieve high predictive proficiency, but even in the more predictive fields proficiency is evaluated in different ways. Both phenomenological and mechanistic approaches are used in most fields, but differences are often small, with no clear superiority of one approach over the other. Data availability is limiting in most fields, with long-term studies being rare and detailed data for parameterizing mechanistic models being in short supply. We suggest that ecologists adopt a more rigorous approach to report and assess predictive proficiency, and embrace the challenges of real world decision making to strengthen the practice of prediction in ecology.

Individual differences in the recognition of facial expressions: an event-related potentials study.

PubMed

Tamamiya, Yoshiyuki; Hiraki, Kazuo

2013-01-01

Previous studies have shown that early posterior components of event-related potentials (ERPs) are modulated by facial expressions. The goal of the current study was to investigate individual differences in the recognition of facial expressions by examining the relationship between ERP components and the discrimination of facial expressions. Pictures of 3 facial expressions (angry, happy, and neutral) were presented to 36 young adults during ERP recording. Participants were asked to respond with a button press as soon as they recognized the expression depicted. A multiple regression analysis, where ERP components were set as predictor variables, assessed hits and reaction times in response to the facial expressions as dependent variables. The N170 amplitudes significantly predicted for accuracy of angry and happy expressions, and the N170 latencies were predictive for accuracy of neutral expressions. The P2 amplitudes significantly predicted reaction time. The P2 latencies significantly predicted reaction times only for neutral faces. These results suggest that individual differences in the recognition of facial expressions emerge from early components in visual processing.

A study of microkinetic adjustments required to match shock wave experiments and Monte Carlo Direct Simulation for a wide Mach number range

NASA Technical Reports Server (NTRS)

Pham-Van-diep, Gerald C.; Muntz, E. Phillip; Erwin, Daniel A.

1990-01-01

Shock wave thickness predictions from Monte Carlo Direct Simulations, using differential scattering and the Maitland-Smith-Aziz interatomic potential, underpredict experiments as shock Mach numbers increase above about 4. Examination of several sources of data has indicated that at relatively high energies the repulsive portion of accepted potentials such as the Maitland-Smith-Aziz may be too steep. An Exponential-6 potential due to Ross, based on high energy molecular beam scattering data and shock velocity measurements in liquid argon, has been combined with the lower energy portion of the Maitland-Smith-Aziz potential. When this hybrid potential is used in Monte Carlo Direct Simulations, agreement with experiments is improved over the previous predictions using the pure Maitland-Smith-Aziz form.

Transient finite element analysis of electric double layer using Nernst-Planck-Poisson equations with a modified Stern layer.

PubMed

Lim, Jongil; Whitcomb, John; Boyd, James; Varghese, Julian

2007-01-01

A finite element implementation of the transient nonlinear Nernst-Planck-Poisson (NPP) and Nernst-Planck-Poisson-modified Stern (NPPMS) models is presented. The NPPMS model uses multipoint constraints to account for finite ion size, resulting in realistic ion concentrations even at high surface potential. The Poisson-Boltzmann equation is used to provide a limited check of the transient models for low surface potential and dilute bulk solutions. The effects of the surface potential and bulk molarity on the electric potential and ion concentrations as functions of space and time are studied. The ability of the models to predict realistic energy storage capacity is investigated. The predicted energy is much more sensitive to surface potential than to bulk solution molarity.

Investigating the importance of Delaunay-based definition of atomic interactions in scoring of protein-protein docking results.

PubMed

Jafari, Rahim; Sadeghi, Mehdi; Mirzaie, Mehdi

2016-05-01

The approaches taken to represent and describe structural features of the macromolecules are of major importance when developing computational methods for studying and predicting their structures and interactions. This study attempts to explore the significance of Delaunay tessellation for the definition of atomic interactions by evaluating its impact on the performance of scoring protein-protein docking prediction. Two sets of knowledge-based scoring potentials are extracted from a training dataset of native protein-protein complexes. The potential of the first set is derived using atomic interactions extracted from Delaunay tessellated structures. The potential of the second set is calculated conventionally, that is, using atom pairs whose interactions were determined by their separation distances. The scoring potentials were tested against two different docking decoy sets and their performances were compared. The results show that, if properly optimized, the Delaunay-based scoring potentials can achieve higher success rate than the usual scoring potentials. These results and the results of a previous study on the use of Delaunay-based potentials in protein fold recognition, all point to the fact that Delaunay tessellation of protein structure can provide a more realistic definition of atomic interaction, and therefore, if appropriately utilized, may be able to improve the accuracy of pair potentials. Copyright © 2016 Elsevier Inc. All rights reserved.

Predicting the ethanol potential of wheat straw using near-infrared spectroscopy and chemometrics: The challenge of inherently intercorrelated response functions

DOE PAGES

Rinnan, Asmund; Bruun, Sander; Lindedam, Jane; ...

2017-02-07

Here, the combination of NIR spectroscopy and chemometrics is a powerful correlation method for predicting the chemical constituents in biological matrices, such as the glucose and xylose content of straw. However, difficulties arise when it comes to predicting enzymatic glucose and xylose release potential, which is matrix dependent. Further complications are caused by xylose and glucose release potential being highly intercorrelated. This study emphasizes the importance of understanding the causal relationship between the model and the constituent of interest. It investigates the possibility of using near-infrared spectroscopy to evaluate the ethanol potential of wheat straw by analyzing more than 1000more » samples from different wheat varieties and growth conditions. During the calibration model development, the prime emphasis was to investigate the correlation structure between the two major quality traits for saccharification of wheat straw: glucose and xylose release. The large sample set enabled a versatile and robust calibration model to be developed, showing that the prediction model for xylose release is based on a causal relationship with the NIR spectral data. In contrast, the prediction of glucose release was found to be highly dependent on the intercorrelation with xylose release. If this correlation is broken, the model performance breaks down. A simple method was devised for avoiding this breakdown and can be applied to any large dataset for investigating the causality or lack of causality of a prediction model.« less

Predicting the ethanol potential of wheat straw using near-infrared spectroscopy and chemometrics: The challenge of inherently intercorrelated response functions

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

Rinnan, Asmund; Bruun, Sander; Lindedam, Jane

Here, the combination of NIR spectroscopy and chemometrics is a powerful correlation method for predicting the chemical constituents in biological matrices, such as the glucose and xylose content of straw. However, difficulties arise when it comes to predicting enzymatic glucose and xylose release potential, which is matrix dependent. Further complications are caused by xylose and glucose release potential being highly intercorrelated. This study emphasizes the importance of understanding the causal relationship between the model and the constituent of interest. It investigates the possibility of using near-infrared spectroscopy to evaluate the ethanol potential of wheat straw by analyzing more than 1000more » samples from different wheat varieties and growth conditions. During the calibration model development, the prime emphasis was to investigate the correlation structure between the two major quality traits for saccharification of wheat straw: glucose and xylose release. The large sample set enabled a versatile and robust calibration model to be developed, showing that the prediction model for xylose release is based on a causal relationship with the NIR spectral data. In contrast, the prediction of glucose release was found to be highly dependent on the intercorrelation with xylose release. If this correlation is broken, the model performance breaks down. A simple method was devised for avoiding this breakdown and can be applied to any large dataset for investigating the causality or lack of causality of a prediction model.« less

Prediction of genotoxic potential of cosmetic ingredients by an in silico battery system consisting of a combination of an expert rule-based system and a statistics-based system.

PubMed

Aiba née Kaneko, Maki; Hirota, Morihiko; Kouzuki, Hirokazu; Mori, Masaaki

2015-02-01

Genotoxicity is the most commonly used endpoint to predict the carcinogenicity of chemicals. The International Conference on Harmonization (ICH) M7 Guideline on Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk offers guidance on (quantitative) structure-activity relationship ((Q)SAR) methodologies that predict the outcome of bacterial mutagenicity assay for actual and potential impurities. We examined the effectiveness of the (Q)SAR approach with the combination of DEREK NEXUS as an expert rule-based system and ADMEWorks as a statistics-based system for the prediction of not only mutagenic potential in the Ames test, but also genotoxic potential in mutagenicity and clastogenicity tests, using a data set of 342 chemicals extracted from the literature. The prediction of mutagenic potential or genotoxic potential by DEREK NEXUS or ADMEWorks showed high values of sensitivity and concordance, while prediction by the combination of DEREK NEXUS and ADMEWorks (battery system) showed the highest values of sensitivity and concordance among the three methods, but the lowest value of specificity. The number of false negatives was reduced with the battery system. We also separately predicted the mutagenic potential and genotoxic potential of 41 cosmetic ingredients listed in the International Nomenclature of Cosmetic Ingredients (INCI) among the 342 chemicals. Although specificity was low with the battery system, sensitivity and concordance were high. These results suggest that the battery system consisting of DEREK NEXUS and ADMEWorks is useful for prediction of genotoxic potential of chemicals, including cosmetic ingredients.

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.

Identification of factors that affect the adoption of an ergonomic intervention among Emergency Medical Service workers.

PubMed

Weiler, Monica R; Lavender, Steven A; Crawford, J Mac; Reichelt, Paul A; Conrad, Karen M; Browne, Michael W

2012-01-01

This study explored factors contributing to intervention adoption decisions among Emergency Medical Service (EMS) workers. Emergency Medical Service workers (n = 190), from six different organisations, participated in a two-month longitudinal study following the introduction of a patient transfer-board (also known as slide-board) designed to ease lateral transfers of patients to and from ambulance cots. Surveys administered at baseline, after one month and after two months sampled factors potentially influencing the EMS providers' decision process. 'Ergonomics Advantage' and 'Patient Advantage' entered into a stepwise regression model predicting 'intention to use' at the end of month one (R (2 )= 0.78). After the second month, the stepwise regression indicated only two factors were predictive of intention to use: 'Ergonomics Advantage,' and 'Endorsed by Champions' (R (2 )= 0.58). Actual use was predicted by: 'Ergonomics Advantage' and 'Previous Tool Experience.' These results relate to key concepts identified in the diffusion of innovation literature and have the potential to further ergonomics intervention adoption efforts. Practitioner Summary. This study explored factors that potentially facilitate the adoption of voluntarily used ergonomics interventions. EMS workers were provided with foldable transfer-boards (slideboards) designed to reduce the physical demands when laterally transferring patients. Factors predictive of adoption measures included perceived ergonomics advantage, the endorsement by champions, and prior tool experience.

Molecular effective coverage surface area of optical clearing agents for predicting optical clearing potential

NASA Astrophysics Data System (ADS)

Feng, Wei; Ma, Ning; Zhu, Dan

2015-03-01

The improvement of methods for optical clearing agent prediction exerts an important impact on tissue optical clearing technique. The molecular dynamic simulation is one of the most convincing and simplest approaches to predict the optical clearing potential of agents by analyzing the hydrogen bonds, hydrogen bridges and hydrogen bridges type forming between agents and collagen. However, the above analysis methods still suffer from some problem such as analysis of cyclic molecule by reason of molecular conformation. In this study, a molecular effective coverage surface area based on the molecular dynamic simulation was proposed to predict the potential of optical clearing agents. Several typical cyclic molecules, fructose, glucose and chain molecules, sorbitol, xylitol were analyzed by calculating their molecular effective coverage surface area, hydrogen bonds, hydrogen bridges and hydrogen bridges type, respectively. In order to verify this analysis methods, in vitro skin samples optical clearing efficacy were measured after 25 min immersing in the solutions, fructose, glucose, sorbitol and xylitol at concentration of 3.5 M using 1951 USAF resolution test target. The experimental results show accordance with prediction of molecular effective coverage surface area. Further to compare molecular effective coverage surface area with other parameters, it can show that molecular effective coverage surface area has a better performance in predicting OCP of agents.

Adolescents' implicit theories predict desire for vengeance after peer conflicts: correlational and experimental evidence.

PubMed

Yeager, David S; Trzesniewski, Kali H; Tirri, Kirsi; Nokelainen, Petri; Dweck, Carol S

2011-07-01

Why do some adolescents respond to interpersonal conflicts vengefully, whereas others seek more positive solutions? Three studies investigated the role of implicit theories of personality in predicting violent or vengeful responses to peer conflicts among adolescents in Grades 9 and 10. They showed that a greater belief that traits are fixed (an entity theory) predicted a stronger desire for revenge after a variety of recalled peer conflicts (Study 1) and after a hypothetical conflict that specifically involved bullying (Study 2). Study 3 experimentally induced a belief in the potential for change (an incremental theory), which resulted in a reduced desire to seek revenge. This effect was mediated by changes in bad-person attributions about the perpetrators, feelings of shame and hatred, and the belief that vengeful ideation is an effective emotion-regulation strategy. Together, the findings illuminate the social-cognitive processes underlying reactions to conflict and suggest potential avenues for reducing violent retaliation in adolescents. PsycINFO Database Record (c) 2011 APA, all rights reserved

Predicting the safety and efficacy of buffer therapy to raise tumour pHe: an integrative modelling study.

PubMed

Martin, N K; Robey, I F; Gaffney, E A; Gillies, R J; Gatenby, R A; Maini, P K

2012-03-27

Clinical positron emission tomography imaging has demonstrated the vast majority of human cancers exhibit significantly increased glucose metabolism when compared with adjacent normal tissue, resulting in an acidic tumour microenvironment. Recent studies demonstrated reducing this acidity through systemic buffers significantly inhibits development and growth of metastases in mouse xenografts. We apply and extend a previously developed mathematical model of blood and tumour buffering to examine the impact of oral administration of bicarbonate buffer in mice, and the potential impact in humans. We recapitulate the experimentally observed tumour pHe effect of buffer therapy, testing a model prediction in vivo in mice. We parameterise the model to humans to determine the translational safety and efficacy, and predict patient subgroups who could have enhanced treatment response, and the most promising combination or alternative buffer therapies. The model predicts a previously unseen potentially dangerous elevation in blood pHe resulting from bicarbonate therapy in mice, which is confirmed by our in vivo experiments. Simulations predict limited efficacy of bicarbonate, especially in humans with more aggressive cancers. We predict buffer therapy would be most effectual: in elderly patients or individuals with renal impairments; in combination with proton production inhibitors (such as dichloroacetate), renal glomular filtration rate inhibitors (such as non-steroidal anti-inflammatory drugs and angiotensin-converting enzyme inhibitors), or with an alternative buffer reagent possessing an optimal pK of 7.1-7.2. Our mathematical model confirms bicarbonate acts as an effective agent to raise tumour pHe, but potentially induces metabolic alkalosis at the high doses necessary for tumour pHe normalisation. We predict use in elderly patients or in combination with proton production inhibitors or buffers with a pK of 7.1-7.2 is most promising.

How learning analytics can early predict under-achieving students in a blended medical education course.

PubMed

Saqr, Mohammed; Fors, Uno; Tedre, Matti

2017-07-01

Learning analytics (LA) is an emerging discipline that aims at analyzing students' online data in order to improve the learning process and optimize learning environments. It has yet un-explored potential in the field of medical education, which can be particularly helpful in the early prediction and identification of under-achieving students. The aim of this study was to identify quantitative markers collected from students' online activities that may correlate with students' final performance and to investigate the possibility of predicting the potential risk of a student failing or dropping out of a course. This study included 133 students enrolled in a blended medical course where they were free to use the learning management system at their will. We extracted their online activity data using database queries and Moodle plugins. Data included logins, views, forums, time, formative assessment, and communications at different points of time. Five engagement indicators were also calculated which would reflect self-regulation and engagement. Students who scored below 5% over the passing mark were considered to be potentially at risk of under-achieving. At the end of the course, we were able to predict the final grade with 63.5% accuracy, and identify 53.9% of at-risk students. Using a binary logistic model improved prediction to 80.8%. Using data recorded until the mid-course, prediction accuracy was 42.3%. The most important predictors were factors reflecting engagement of the students and the consistency of using the online resources. The analysis of students' online activities in a blended medical education course by means of LA techniques can help early predict underachieving students, and can be used as an early warning sign for timely intervention.

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.

Internal mechanisms underlying anticipatory language processing: Evidence from event-related-potentials and neural oscillations.

PubMed

Li, Xiaoqing; Zhang, Yuping; Xia, Jinyan; Swaab, Tamara Y

2017-07-28

Although numerous studies have demonstrated that the language processing system can predict upcoming content during comprehension, there is still no clear picture of the anticipatory stage of predictive processing. This electroencephalograph study examined the cognitive and neural oscillatory mechanisms underlying anticipatory processing during language comprehension, and the consequences of this prediction for bottom-up processing of predicted/unpredicted content. Participants read Mandarin Chinese sentences that were either strongly or weakly constraining and that contained critical nouns that were congruent or incongruent with the sentence contexts. We examined the effects of semantic predictability on anticipatory processing prior to the onset of the critical nouns and on integration of the critical nouns. The results revealed that, at the integration stage, the strong-constraint condition (compared to the weak-constraint condition) elicited a reduced N400 and reduced theta activity (4-7Hz) for the congruent nouns, but induced beta (13-18Hz) and theta (4-7Hz) power decreases for the incongruent nouns, indicating benefits of confirmed predictions and potential costs of disconfirmed predictions. More importantly, at the anticipatory stage, the strongly constraining context elicited an enhanced sustained anterior negativity and beta power decrease (19-25Hz), which indicates that strong prediction places a higher processing load on the anticipatory stage of processing. The differences (in the ease of processing and the underlying neural oscillatory activities) between anticipatory and integration stages of lexical processing were discussed with regard to predictive processing models. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predictors of Early Termination in a University Counseling Training Clinic

ERIC Educational Resources Information Center

Lampropoulos, Georgios K.; Schneider, Mercedes K.; Spengler, Paul M.

2009-01-01

Despite the existence of counseling dropout research, there are limited predictive data for counseling in training clinics. Potential predictor variables were investigated in this archival study of 380 client files in a university counseling training clinic. Multinomial logistic regression, predictive discriminant analysis, and classification and…

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 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.

Is love colorblind? Political orientation and interracial romantic desire.

PubMed

Eastwick, Paul W; Richeson, Jennifer A; Son, Deborah; Finkel, Eli J

2009-09-01

The present research examined the association of political orientation with ingroup favoritism in two live romantic contexts. In Study 1, White participants had sequential interactions with both a White and Black confederate and reported their romantic desire for each. In Study 2, both White and Black participants speed-dated multiple potential romantic partners and reported whether they would be interested in meeting each speed-dating partner again. In both studies, White participants' political conservatism positively predicted the strength of the ingroup-favoring bias: White conservatives were less likely than White liberals to desire Black (interracial) relative to White potential romantic partners. In contrast, Black participants' political conservatism negatively predicted the strength of the ingroup-favoring bias: Consistent with system-justification theory, Black conservatives were more likely than Black liberals to desire White (interracial) relative to Black potential romantic partners. Political orientation may be a key factor that influences the initiation of interracial romantic relationships.

A Comparison of the Predictive Capabilities of the Embedded-Atom Method and Modified Embedded-Atom Method Potentials for Lithium

DOE PAGES

Vella, Joseph R.; Stillinger, Frank H.; Panagiotopoulos, Athanassios Z.; ...

2015-07-23

Here, we compare six lithium potentials by examining their ability to predict coexistence properties and liquid structure using molecular dynamics. All potentials are of the embedded-atom-method (EAM) type. The coexistence properties we focus on are the melting curve, vapor pressure, saturated liquid density, and vapor-liquid surface tension. For each property studied, the simulation results are compared to available experimental data in order to properly assess the accuracy of each potential. We find that the Cui 2NN MEAM is the most robust potential, giving adequate agreement with most of the properties examined. For example, the zero-pressure melting point of this potentialmore » is shown to be around 443 K, while experimentally is it about 454 K. This potential also gives excellent agreement with saturated liquid densities, even though no liquid properties were used in the fitting procedure. Our study allows us to conclude that the Cui 2NN MEAM should be used for further simulations of lithiums.« less

Error-related negativities elicited by monetary loss and cues that predict loss.

PubMed

Dunning, Jonathan P; Hajcak, Greg

2007-11-19

Event-related potential studies have reported error-related negativity following both error commission and feedback indicating errors or monetary loss. The present study examined whether error-related negativities could be elicited by a predictive cue presented prior to both the decision and subsequent feedback in a gambling task. Participants were presented with a cue that indicated the probability of reward on the upcoming trial (0, 50, and 100%). Results showed a negative deflection in the event-related potential in response to loss cues compared with win cues; this waveform shared a similar latency and morphology with the traditional feedback error-related negativity.

Regression models for predicting peak and continuous three-dimensional spinal loads during symmetric and asymmetric lifting tasks.

PubMed

Fathallah, F A; Marras, W S; Parnianpour, M

1999-09-01

Most biomechanical assessments of spinal loading during industrial work have focused on estimating peak spinal compressive forces under static and sagittally symmetric conditions. The main objective of this study was to explore the potential of feasibly predicting three-dimensional (3D) spinal loading in industry from various combinations of trunk kinematics, kinetics, and subject-load characteristics. The study used spinal loading, predicted by a validated electromyography-assisted model, from 11 male participants who performed a series of symmetric and asymmetric lifts. Three classes of models were developed: (a) models using workplace, subject, and trunk motion parameters as independent variables (kinematic models); (b) models using workplace, subject, and measured moments variables (kinetic models); and (c) models incorporating workplace, subject, trunk motion, and measured moments variables (combined models). The results showed that peak 3D spinal loading during symmetric and asymmetric lifting were predicted equally well using all three types of regression models. Continuous 3D loading was predicted best using the combined models. When the use of such models is infeasible, the kinematic models can provide adequate predictions. Finally, lateral shear forces (peak and continuous) were consistently underestimated using all three types of models. The study demonstrated the feasibility of predicting 3D loads on the spine under specific symmetric and asymmetric lifting tasks without the need for collecting EMG information. However, further validation and development of the models should be conducted to assess and extend their applicability to lifting conditions other than those presented in this study. Actual or potential applications of this research include exposure assessment in epidemiological studies, ergonomic intervention, and laboratory task assessment.

Predicting the propagation of concentration and saturation fronts in fixed-bed filters.

PubMed

Callery, O; Healy, M G

2017-10-15

The phenomenon of adsorption is widely exploited across a range of industries to remove contaminants from gases and liquids. Much recent research has focused on identifying low-cost adsorbents which have the potential to be used as alternatives to expensive industry standards like activated carbons. Evaluating these emerging adsorbents entails a considerable amount of labor intensive and costly testing and analysis. This study proposes a simple, low-cost method to rapidly assess the potential of novel media for potential use in large-scale adsorption filters. The filter media investigated in this study were low-cost adsorbents which have been found to be capable of removing dissolved phosphorus from solution, namely: i) aluminum drinking water treatment residual, and ii) crushed concrete. Data collected from multiple small-scale column tests was used to construct a model capable of describing and predicting the progression of adsorbent saturation and the associated effluent concentration breakthrough curves. This model was used to predict the performance of long-term, large-scale filter columns packed with the same media. The approach proved highly successful, and just 24-36 h of experimental data from the small-scale column experiments were found to provide sufficient information to predict the performance of the large-scale filters for up to three months. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hexaquark states as possible candidates for di-baryonic molecular states with Yukawa potential in a semi-relativistic scheme

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

Patel, Smruti J., E-mail: fizix.smriti@gmail.com; Vinodkumar, P. C.

2016-05-06

We study the mass spectra of hexaquark states as di-hadronic molecules with Yukawa potential in a semi-relativistic scheme. We have solved numerically the relevant equation using mathematica notebook of Range-Kutta method including effective Yukawa like potential between two baryons to model the two-body interaction and have calculated their masses and binding energy. We have been able to assign the J{sup P} values for many of the exotic states according to their compositions. We have predicted some of the di-baryonic exotic states for which experimental as well as theoretical data are not available and we look forward to see the experimentalmore » support in favour of our predictions. So in the absence of such results our predictions can be used as guidelines for future experimental and theoretical analysis of exotic states.« less

Force versus fury: Sex differences in the relationships among physical and psychological threat potential, the facial width-to-height ratio, and judgements of aggressiveness.

PubMed

MacDonell, Elliott T; Geniole, Shawn N; McCormick, Cheryl M

2018-06-07

Individuals with larger facial width-to-height ratios (FWHRs) are judged as more threatening, and engage in more threat-related behavior, than do individuals with smaller FWHRs. Here we identified components of threat potential that are related to the FWHR. In Study 1, the FWHR was correlated positively with physical threat potential (bicep size) in women and with both physical and psychological (anger proneness) threat potential in men. Behavioral aggression was measured in a subset of these participants using the Point Subtraction Aggression Paradigm (costly aggression) and a Money Allocation Task (non-costly aggression). Psychological (but not physical) threat potential predicted non-costly aggression and physical (but not psychological) threat potential predicted costly aggression. In Study 2, a separate set of participants judged the anger proneness, strength, or aggressiveness of male participants photographed in Study 1. Participants' judgements of all three characteristics were associated with the FWHR, and there were sex differences in how aggressiveness was conceptualized (for women, aggressiveness was associated with anger proneness, for men, aggressiveness was associated with strength). These results are consistent with the hypothesis that the FWHR may be an adaptation to cue the threat potential of men. © 2018 Wiley Periodicals, Inc.

Novel design strategy for checkpoint kinase 2 inhibitors using pharmacophore modeling, combinatorial fusion, and virtual screening.

PubMed

Lin, Chun-Yuan; Wang, Yen-Ling

2014-01-01

Checkpoint kinase 2 (Chk2) has a great effect on DNA-damage and plays an important role in response to DNA double-strand breaks and related lesions. In this study, we will concentrate on Chk2 and the purpose is to find the potential inhibitors by the pharmacophore hypotheses (PhModels), combinatorial fusion, and virtual screening techniques. Applying combinatorial fusion into PhModels and virtual screening techniques is a novel design strategy for drug design. We used combinatorial fusion to analyze the prediction results and then obtained the best correlation coefficient of the testing set (r test) with the value 0.816 by combining the Best(train)Best(test) and Fast(train)Fast(test) prediction results. The potential inhibitors were selected from NCI database by screening according to Best(train)Best(test) + Fast(train)Fast(test) prediction results and molecular docking with CDOCKER docking program. Finally, the selected compounds have high interaction energy between a ligand and a receptor. Through these approaches, 23 potential inhibitors for Chk2 are retrieved for further study.

Predicting Choral Achievement Through Use of Musicality and Intelligence Scores

ERIC Educational Resources Information Center

Helwig, Carl; Thomas, Michael S.

1973-01-01

This study explored the possibility of using musicality and intelligence test scores to predict the potential success of pupils in choir instead of the usual methods of audition and observation. It also ivestigated the extent, if any, of teacher bias in the evaluation of pupil achievement. (Author/RK)

Health Literacy Predicts Cardiac Knowledge Gains in Cardiac Rehabilitation Participants

ERIC Educational Resources Information Center

Mattson, Colleen C.; Rawson, Katherine; Hughes, Joel W.; Waechter, Donna; Rosneck, James

2015-01-01

Objective: Health literacy is increasingly recognised as a potentially important patient characteristic related to patient education efforts. We evaluated whether health literacy would predict gains in knowledge after completion of patient education in cardiac rehabilitation. Method: This was a re-post observational analysis study design based on…

Can phylogeny predict chemical diversity and potential medicinal activity of plants? A case study of Amaryllidaceae

USDA-ARS?s Scientific Manuscript database

During evolution, plants and other organisms have developed a diversity of chemical defences, leading to the evolution of various groups of specialized metabolites selected for their endogenous biological function. A correlation between phylogeny and biosynthetic pathways could offer a predictive ap...

Developing Predictive Toxicity Signatures Using In Vitro Data from the EPA ToxCast Program

EPA Science Inventory

A major focus in toxicology research is the development of in vitro methods to predict in vivo chemical toxicity. Numerous studies have evaluated the use of targeted biochemical, cell-based and genomic assay approaches. Each of these techniques is potentially helpful, but provide...

Differential Prediction of College Performance between Gender.

ERIC Educational Resources Information Center

Patton, Timothy K.

Researchers in the past have found discrepancies in the prediction of college grade point average (GPA) between genders with the use of standardized tests such as the Scholastic Achievement Test (SAT) and the American College Test (ACT). These differences were studied to determine if the potential differences could be attributed to differential…

PREDICTING ABUSE POTENTIAL OF STIMULANTS AND OTHER DOPAMINERGIC DRUGS: OVERVIEW AND RECOMMENDATIONS

PubMed Central

Huskinson, Sally L.; Naylor, Jennifer E.; Rowlett, James K.; Freeman, Kevin B.

2014-01-01

Examination of a drug’s abuse potential at multiple levels of analysis (molecular/cellular action, whole-organism behavior, epidemiological data) is an essential component to regulating controlled substances under the Controlled Substances Act (CSA). We reviewed studies that examined several central nervous system (CNS) stimulants, focusing on those with primarily dopaminergic actions, in drug self-administration, drug discrimination, and physical dependence. For drug self-administration and drug discrimination, we distinguished between experiments conducted with rats and nonhuman primates (NHP) to highlight the common and unique attributes of each model in the assessment of abuse potential. Our review of drug self-administration studies suggests that this procedure is important in predicting abuse potential of dopaminergic compounds, but there were many false positives. We recommended that tests to determine how reinforcing a drug is relative to a known drug of abuse may be more predictive of abuse potential than tests that yield a binary, yes-or-no classification. Several false positives also occurred with drug discrimination. With this procedure, we recommended that future research follow a standard decision-tree approach that may require examining the drug being tested for abuse potential as the training stimulus. This approach would also allow several known drugs of abuse to be tested for substitution, and this may reduce false positives. Finally, we reviewed evidence of physical dependence with stimulants and discussed the feasibility of modeling these phenomena in nonhuman animals in a rational and practical fashion. PMID:24662599

A Review of Auditory Prediction and Its Potential Role in Tinnitus Perception.

PubMed

Durai, Mithila; O'Keeffe, Mary G; Searchfield, Grant D

2018-06-01

The precise mechanisms underlying tinnitus perception and distress are still not fully understood. A recent proposition is that auditory prediction errors and related memory representations may play a role in driving tinnitus perception. It is of interest to further explore this. To obtain a comprehensive narrative synthesis of current research in relation to auditory prediction and its potential role in tinnitus perception and severity. A narrative review methodological framework was followed. The key words Prediction Auditory, Memory Prediction Auditory, Tinnitus AND Memory, Tinnitus AND Prediction in Article Title, Abstract, and Keywords were extensively searched on four databases: PubMed, Scopus, SpringerLink, and PsychINFO. All study types were selected from 2000-2016 (end of 2016) and had the following exclusion criteria applied: minimum age of participants <18, nonhuman participants, and article not available in English. Reference lists of articles were reviewed to identify any further relevant studies. Articles were short listed based on title relevance. After reading the abstracts and with consensus made between coauthors, a total of 114 studies were selected for charting data. The hierarchical predictive coding model based on the Bayesian brain hypothesis, attentional modulation and top-down feedback serves as the fundamental framework in current literature for how auditory prediction may occur. Predictions are integral to speech and music processing, as well as in sequential processing and identification of auditory objects during auditory streaming. Although deviant responses are observable from middle latency time ranges, the mismatch negativity (MMN) waveform is the most commonly studied electrophysiological index of auditory irregularity detection. However, limitations may apply when interpreting findings because of the debatable origin of the MMN and its restricted ability to model real-life, more complex auditory phenomenon. Cortical oscillatory band activity may act as neurophysiological substrates for auditory prediction. Tinnitus has been modeled as an auditory object which may demonstrate incomplete processing during auditory scene analysis resulting in tinnitus salience and therefore difficulty in habituation. Within the electrophysiological domain, there is currently mixed evidence regarding oscillatory band changes in tinnitus. There are theoretical proposals for a relationship between prediction error and tinnitus but few published empirical studies. American Academy of Audiology.

The Influence of Viscous Effects on Ice Accretion Prediction and Airfoil Performance Predictions

NASA Technical Reports Server (NTRS)

Kreeger, Richard E.; Wright, William B.

2005-01-01

A computational study was conducted to evaluate the effectiveness of using a viscous flow solution in an ice accretion code and the resulting accuracy of aerodynamic performance prediction. Ice shapes were obtained for one single-element and one multi-element airfoil using both potential flow and Navier-Stokes flowfields in the LEWICE ice accretion code. Aerodynamics were then calculated using a Navier-Stokes flow solver.

In silico prediction of splice-altering single nucleotide variants in the human genome.

PubMed

Jian, Xueqiu; Boerwinkle, Eric; Liu, Xiaoming

2014-12-16

In silico tools have been developed to predict variants that may have an impact on pre-mRNA splicing. The major limitation of the application of these tools to basic research and clinical practice is the difficulty in interpreting the output. Most tools only predict potential splice sites given a DNA sequence without measuring splicing signal changes caused by a variant. Another limitation is the lack of large-scale evaluation studies of these tools. We compared eight in silico tools on 2959 single nucleotide variants within splicing consensus regions (scSNVs) using receiver operating characteristic analysis. The Position Weight Matrix model and MaxEntScan outperformed other methods. Two ensemble learning methods, adaptive boosting and random forests, were used to construct models that take advantage of individual methods. Both models further improved prediction, with outputs of directly interpretable prediction scores. We applied our ensemble scores to scSNVs from the Catalogue of Somatic Mutations in Cancer database. Analysis showed that predicted splice-altering scSNVs are enriched in recurrent scSNVs and known cancer genes. We pre-computed our ensemble scores for all potential scSNVs across the human genome, providing a whole genome level resource for identifying splice-altering scSNVs discovered from large-scale sequencing studies.

Potential predictability of a Colombian river flow

NASA Astrophysics Data System (ADS)

Córdoba-Machado, Samir; Palomino-Lemus, Reiner; Quishpe-Vásquez, César; García-Valdecasas-Ojeda, Matilde; Raquel Gámiz-Fortis, Sonia; Castro-Díez, Yolanda; Jesús Esteban-Parra, María

2017-04-01

In this study the predictability of an important Colombian river (Cauca) has been analysed based on the use of climatic variables as potential predictors. Cauca River is considered one of the most important rivers of Colombia because its basin supports important productive activities related with the agriculture, such as the production of coffee or sugar. Potential relationships between the Cauca River seasonal streamflow anomalies and different climatic variables such as sea surface temperature (SST), precipitation (Pt), temperature over land (Tm) and soil water (Sw) have been analysed for the period 1949-2009. For this end, moving correlation analysis of 30 years have been carried out for lags from one to four seasons for the global SST, and from one to two seasons for South America Pt, Tm and Sw. Also, the stability of the significant correlations have been also studied, identifying the regions used as potential predictors of streamflow. Finally, in order to establish a prediction scheme based on the previous stable correlations, a Principal Component Analysis (PCA) applied on the potential predictor regions has been carried out in order to obtain a representative time series for each predictor field. Significant and stable correlations between the seasonal streamflow and the tropical Pacific SST (El Niño region) are found for lags from one to four (one-year) season. Additionally, some regions in the Indian and Atlantic Oceans also show significant and stable correlations at different lags, highlighting the importance that exerts the Atlantic SST on the hydrology of Colombia. Also significant and stable correlations are found with the Pt, Tm and Sw for some regions over South America, at lags of one and two seasons. The prediction of Cauca seasonal streamflow based on this scheme shows an acceptable skill and represents a relative improvement compared with the predictability obtained using the teleconnection indices associated with El Niño. Keywords: Streamflow, predictability, Cauca, Colombia. Acknowledgements: This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

A 3-Year Study of Predictive Factors for Positive and Negative Appendicectomies.

PubMed

Chang, Dwayne T S; Maluda, Melissa; Lee, Lisa; Premaratne, Chandrasiri; Khamhing, Srisongham

2018-03-06

Early and accurate identification or exclusion of acute appendicitis is the key to avoid the morbidity of delayed treatment for true appendicitis or unnecessary appendicectomy, respectively. We aim (i) to identify potential predictive factors for positive and negative appendicectomies; and (ii) to analyse the use of ultrasound scans (US) and computed tomography (CT) scans for acute appendicitis. All appendicectomies that took place at our hospital from the 1st of January 2013 to the 31st of December 2015 were retrospectively recorded. Test results of potential predictive factors of acute appendicitis were recorded. Statistical analysis was performed using Fisher exact test, logistic regression analysis, sensitivity, specificity, and positive and negative predictive values calculation. 208 patients were included in this study. 184 patients had histologically proven acute appendicitis. The other 24 patients had either nonappendicitis pathology or normal appendix. Logistic regression analysis showed statistically significant associations between appendicitis and white cell count, neutrophil count, C-reactive protein, and bilirubin. Neutrophil count was the test with the highest sensitivity and negative predictive values, whereas bilirubin was the test with the highest specificity and positive predictive values (PPV). US and CT scans had high sensitivity and PPV for diagnosing appendicitis. No single test was sufficient to diagnose or exclude acute appendicitis by itself. Combining tests with high sensitivity (abnormal neutrophil count, and US and CT scans) and high specificity (raised bilirubin) may predict acute appendicitis more accurately.

Effects of Prediction and Contextual Support on Lexical Processing: Prediction takes Precedence

PubMed Central

Brothers, Trevor; Swaab, Tamara Y.; Traxler, Matthew J.

2014-01-01

Readers may use contextual information to anticipate and pre-activate specific lexical items during reading. However, prior studies have not clearly dissociated the effects of accurate lexical prediction from other forms of contextual facilitation such as plausibility or semantic priming. In this study, we measured electrophysiological responses to predicted and unpredicted target words in passages providing varying levels of contextual support. This method was used to isolate the neural effects of prediction from other potential contextual influences on lexical processing. While both prediction and discourse context influenced ERP amplitudes within the time range of the N400, the effects of prediction occurred much more rapidly, preceding contextual facilitation by approximately 100ms. In addition, a frontal, post-N400 positivity (PNP) was modulated by both prediction accuracy and the overall plausibility of the preceding passage. These results suggest a unique temporal primacy for prediction in facilitating lexical access. They also suggest that the frontal PNP may index the costs of revising discourse representations following an incorrect lexical prediction. PMID:25497522

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

A STUDY OF THE INFLUENCES OF THE FATHER'S JOB AND SOCIAL STATUS ON THE OCCUPATIONAL AND SOCIAL GOALS OF YOUTH. FINAL REPORT.

ERIC Educational Resources Information Center

GRINDER, ROBERT E.

THE MAJOR EMPHASIS OF THE STUDY WAS UPON THE HYPOTHESIS THAT, AMONG ADOLESCENT BOYS, STRONG ORIENTATION TOWARD THE FATHER AND DISINTEREST IN THE YOUTH CULTURE WILL PREDICT INVOLVEMENT IN THE COLLEGE-BOUND HIGH SCHOOL PROGRAM, AND CONVERSELY, WEAK ORIENTATION TOWARD THE FATHER AND HIGH INVOLVEMENT IN THE YOUTH CULTURE WILL PREDICT POTENTIAL DROPOUT…

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.

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.

Past speculations of the future: a review of the methods used for forecasting emerging health technologies

PubMed Central

Doos, Lucy; Packer, Claire; Ward, Derek; Simpson, Sue; Stevens, Andrew

2016-01-01

Objectives Forecasting can support rational decision-making around the introduction and use of emerging health technologies and prevent investment in technologies that have limited long-term potential. However, forecasting methods need to be credible. We performed a systematic search to identify the methods used in forecasting studies to predict future health technologies within a 3–20-year timeframe. Identification and retrospective assessment of such methods potentially offer a route to more reliable prediction. Design Systematic search of the literature to identify studies reported on methods of forecasting in healthcare. Participants People are not needed in this study. Data sources The authors searched MEDLINE, EMBASE, PsychINFO and grey literature sources, and included articles published in English that reported their methods and a list of identified technologies. Main outcome measure Studies reporting methods used to predict future health technologies within a 3–20-year timeframe with an identified list of individual healthcare technologies. Commercially sponsored reviews, long-term futurology studies (with over 20-year timeframes) and speculative editorials were excluded. Results 15 studies met our inclusion criteria. Our results showed that the majority of studies (13/15) consulted experts either alone or in combination with other methods such as literature searching. Only 2 studies used more complex forecasting tools such as scenario building. Conclusions The methodological fundamentals of formal 3–20-year prediction are consistent but vary in details. Further research needs to be conducted to ascertain if the predictions made were accurate and whether accuracy varies by the methods used or by the types of technologies identified. PMID:26966060

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.

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.

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.

Incorporating Psychological Predictors of Treatment Response into Health Economic Simulation Models: A Case Study in Type 1 Diabetes.

PubMed

Kruger, Jen; Pollard, Daniel; Basarir, Hasan; Thokala, Praveen; Cooke, Debbie; Clark, Marie; Bond, Rod; Heller, Simon; Brennan, Alan

2015-10-01

. Health economic modeling has paid limited attention to the effects that patients' psychological characteristics have on the effectiveness of treatments. This case study tests 1) the feasibility of incorporating psychological prediction models of treatment response within an economic model of type 1 diabetes, 2) the potential value of providing treatment to a subgroup of patients, and 3) the cost-effectiveness of providing treatment to a subgroup of responders defined using 5 different algorithms. . Multiple linear regressions were used to investigate relationships between patients' psychological characteristics and treatment effectiveness. Two psychological prediction models were integrated with a patient-level simulation model of type 1 diabetes. Expected value of individualized care analysis was undertaken. Five different algorithms were used to provide treatment to a subgroup of predicted responders. A cost-effectiveness analysis compared using the algorithms to providing treatment to all patients. . The psychological prediction models had low predictive power for treatment effectiveness. Expected value of individualized care results suggested that targeting education at responders could be of value. The cost-effectiveness analysis suggested, for all 5 algorithms, that providing structured education to a subgroup of predicted responders would not be cost-effective. . The psychological prediction models tested did not have sufficient predictive power to make targeting treatment cost-effective. The psychological prediction models are simple linear models of psychological behavior. Collection of data on additional covariates could potentially increase statistical power. . By collecting data on psychological variables before an intervention, we can construct predictive models of treatment response to interventions. These predictive models can be incorporated into health economic models to investigate more complex service delivery and reimbursement strategies. © The Author(s) 2015.

Can somatosensory and visual evoked potentials predict neurological outcome during targeted temperature management in post cardiac arrest patients?

PubMed

Choi, Seung Pill; Park, Kyu Nam; Wee, Jung Hee; Park, Jeong Ho; Youn, Chun Song; Kim, Han Joon; Oh, Sang Hoon; Oh, Yoon Sang; Kim, Soo Hyun; Oh, Joo Suk

2017-10-01

In cardiac arrest patients treated with targeted temperature management (TTM), it is not certain if somatosensory evoked potentials (SEPs) and visual evoked potentials (VEPs) can predict neurological outcomes during TTM. The aim of this study was to investigate the prognostic value of SEPs and VEPs during TTM and after rewarming. This retrospective cohort study included comatose patients resuscitated from cardiac arrest and treated with TTM between March 2007 and July 2015. SEPs and VEPs were recorded during TTM and after rewarming in these patients. Neurological outcome was assessed at discharge by the Cerebral Performance Category (CPC) Scale. In total, 115 patients were included. A total of 175 SEPs and 150 VEPs were performed. Five SEPs during treated with TTM and nine SEPs after rewarming were excluded from outcome prediction by SEPs due to an indeterminable N20 response because of technical error. Using 80 SEPs and 85 VEPs during treated with TTM, absent SEPs yielded a sensitivity of 58% and a specificity of 100% for poor outcome (CPC 3-5), and absent VEPs predicted poor neurological outcome with a sensitivity of 44% and a specificity of 96%. The AUC of combination of SEPs and VEPs was superior to either test alone (0.788 for absent SEPs and 0.713 for absent VEPs compared with 0.838 for the combination). After rewarming, absent SEPs and absent VEPs predicted poor neurological outcome with a specificity of 100%. When SEPs and VEPs were combined, VEPs slightly increased the prognostic accuracy of SEPs alone. Although one patient with absent VEP during treated with TTM had a good neurological outcome, none of the patients with good neurological outcome had an absent VEP after rewarming. Absent SEPs could predict poor neurological outcome during TTM as well as after rewarming. Absent VEPs may predict poor neurological outcome in both periods and VEPs may provide additional prognostic value in outcome prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

The business of palliative medicine--part 4: Potential impact of an acute-care palliative medicine inpatient unit in a tertiary care cancer center.

PubMed

Walsh, Declan

2004-01-01

In this study, a hematology/oncology computerized discharge database was qualitatively and quantitatively reviewed using an empirical methodology. The goal was to identify potential patients for admission to a planned acute-care, palliative medicine inpatient unit. Patients were identified by the International Classifications of Disease (ICD-9) codes. A large heterogenous population, comprising up to 40 percent of annual discharges from the Hematology/Oncology service, was identified. If management decided to add an acute-care, palliative medicine unit to the hospital, these are the patients who would benefit. The study predicted a significant change in patient profile, acuity, complexity, and resource utilization in current palliative care services. This study technique predicted the actual clinical load of the acute-care unit when it opened and was very helpful in program development. Our model predicted that 695 patients would be admitted to the acute-care palliative medicine unit in the first year of operation; 655 patients were actually admitted during this time.

Evidence-based selection of theories for designing behaviour change interventions: using methods based on theoretical construct domains to understand clinicians' blood transfusion behaviour.

PubMed

Francis, Jill J; Stockton, Charlotte; Eccles, Martin P; Johnston, Marie; Cuthbertson, Brian H; Grimshaw, Jeremy M; Hyde, Chris; Tinmouth, Alan; Stanworth, Simon J

2009-11-01

Many theories of behaviour are potentially relevant to predictive and intervention studies but most studies investigate a narrow range of theories. Michie et al. (2005) agreed 12 'theoretical domains' from 33 theories that explain behaviour change. They developed a 'Theoretical Domains Interview' (TDI) for identifying relevant domains for specific clinical behaviours, but the framework has not been used for selecting theories for predictive studies. It was used here to investigate clinicians' transfusion behaviour in intensive care units (ICU). Evidence suggests that red blood cells transfusion could be reduced for some patients without reducing quality of care. (1) To identify the domains relevant to transfusion practice in ICUs and neonatal intensive care units (NICUs), using the TDI. (2) To use the identified domains to select appropriate theories for a study predicting transfusion behaviour. An adapted TDI about managing a patient with borderline haemoglobin by watching and waiting instead of transfusing red blood cells was used to conduct semi-structured, one-to-one interviews with 18 intensive care consultants and neonatologists across the UK. Relevant theoretical domains were: knowledge, beliefs about capabilities, beliefs about consequences, social influences, behavioural regulation. Further analysis at the construct level resulted in selection of seven theoretical approaches relevant to this context: Knowledge-Attitude-Behaviour Model, Theory of Planned Behaviour, Social Cognitive Theory, Operant Learning Theory, Control Theory, Normative Model of Work Team Effectiveness and Action Planning Approaches. This study illustrated, the use of the TDI to identify relevant domains in a complex area of inpatient care. This approach is potentially valuable for selecting theories relevant to predictive studies and resulted in greater breadth of potential explanations than would be achieved if a single theoretical model had been adopted.

Fabrication and testing of non-graphitic superhybrid composites

NASA Technical Reports Server (NTRS)

Lark, R. F.; Sinclair, J. H.; Chamis, C. C.

1979-01-01

A study was conducted to determine the fabrication feasibility and the mechanical properties of adhesively-bonded boron aluminum/titanium and non-graphitic fiber/epoxy resin superhybrid (NGSH) composite laminates for potential aerospace applications. The major driver for this study was the elimination of a potential graphite fiber release problem in the event of a fire. The results of the study show that non-graphitic fibers, such as S-glass and Kevlar 49, may be substituted for the graphite fibers used in superhybrid (SH) composites for some applications. As is to be expected, however, the non-graphitic superhybrids have lower stiffness properties than the graphitic superhybrids. In-plane and flexural moduli of the laminates studied in this program can be predicted reasonably well using linear laminate theory while nonlinear laminate theory is required for strength predictions.

Biodiversity in environmental assessment-current practice and tools for prediction

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

Gontier, Mikael; Balfors, Berit; Moertberg, Ulla

Habitat loss and fragmentation are major threats to biodiversity. Environmental impact assessment and strategic environmental assessment are essential instruments used in physical planning to address such problems. Yet there are no well-developed methods for quantifying and predicting impacts of fragmentation on biodiversity. In this study, a literature review was conducted on GIS-based ecological models that have potential as prediction tools for biodiversity assessment. Further, a review of environmental impact statements for road and railway projects from four European countries was performed, to study how impact prediction concerning biodiversity issues was addressed. The results of the study showed the existing gapmore » between research in GIS-based ecological modelling and current practice in biodiversity assessment within environmental assessment.« less

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

Prediction of the contact sensitizing potential of chemicals using analysis of gene expression changes in human THP-1 monocytes.

PubMed

Arkusz, Joanna; Stępnik, Maciej; Sobala, Wojciech; Dastych, Jarosław

2010-11-10

The aim of this study was to find differentially regulated genes in THP-1 monocytic cells exposed to sensitizers and nonsensitizers and to investigate if such genes could be reliable markers for an in vitro predictive method for the identification of skin sensitizing chemicals. Changes in expression of 35 genes in the THP-1 cell line following treatment with chemicals of different sensitizing potential (from nonsensitizers to extreme sensitizers) were assessed using real-time PCR. Verification of 13 candidate genes by testing a large number of chemicals (an additional 22 sensitizers and 8 nonsensitizers) revealed that prediction of contact sensitization potential was possible based on evaluation of changes in three genes: IL8, HMOX1 and PAIMP1. In total, changes in expression of these genes allowed correct detection of sensitization potential of 21 out of 27 (78%) test sensitizers. The gene expression levels inside potency groups varied and did not allow estimation of sensitization potency of test chemicals. Results of this study indicate that evaluation of changes in expression of proposed biomarkers in THP-1 cells could be a valuable model for preliminary screening of chemicals to discriminate an appreciable majority of sensitizers from nonsensitizers. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower.

PubMed

Thorwarth, Patrick; Yousef, Eltohamy A A; Schmid, Karl J

2018-02-02

Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS) and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potential of GWAS and genomic prediction for improving curd-related traits in cauliflower ( Brassica oleracea var. botrytis ) by combining 174 randomly selected cauliflower gene bank accessions from two different gene banks. The collection was genotyped with genotyping-by-sequencing (GBS) and phenotyped for six curd-related traits at two locations and three growing seasons. A GWAS analysis based on 120,693 single-nucleotide polymorphisms identified a total of 24 significant associations for curd-related traits. The potential for genomic prediction was assessed with a genomic best linear unbiased prediction model and BayesB. Prediction abilities ranged from 0.10 to 0.66 for different traits and did not differ between prediction methods. Imputation of missing genotypes only slightly improved prediction ability. Our results demonstrate that GWAS and genomic prediction in combination with GBS and phenotyping of highly heritable traits can be used to identify useful quantitative trait loci and genotypes among genetically diverse gene bank material for subsequent utilization as genetic resources in cauliflower breeding. Copyright © 2018 Thorwarth et al.

Rapid biochemical methane potential prediction of urban organic waste with near-infrared reflectance spectroscopy.

PubMed

Fitamo, T; Triolo, J M; Boldrin, A; Scheutz, C

2017-08-01

The anaerobic digestibility of various biomass feedstocks in biogas plants is determined with biochemical methane potential (BMP) assays. However, experimental BMP analysis is time-consuming, costly and challenging to optimise stock management and feeding to achieve improved biogas production. The aim of the present study is to develop a fast and reliable model based on near-infrared reflectance spectroscopy (NIRS) for the BMP prediction of urban organic waste (UOW). The model comprised 87 UOW samples. Additionally, 88 plant biomass samples were included, to develop a combined model predicting BMP. The coefficient of determination (R 2 ) and root mean square error in prediction (RMSE P ) of the UOW model were 0.88 and 44 mL CH 4 /g VS, while the combined model was 0.89 and 50 mL CH 4 /g VS. Improved model performance was obtained for the two individual models compared to the combined version. The BMP prediction with NIRS was satisfactory and moderately successful. Copyright © 2017 Elsevier Ltd. All rights reserved.

Potentiality Prediction of Electric Power Replacement Based on Power Market Development Strategy

NASA Astrophysics Data System (ADS)

Miao, Bo; Yang, Shuo; Liu, Qiang; Lin, Jingyi; Zhao, Le; Liu, Chang; Li, Bin

2017-05-01

The application of electric power replacement plays an important role in promoting the development of energy conservation and emission reduction in our country. To exploit the potentiality of regional electric power replacement, the regional GDP (gross domestic product) and energy consumption are taken as potentiality evaluation indicators. The principal component factors are extracted with PCA (principal component analysis), and the integral potentiality analysis is made to the potentiality of electric power replacement in the national various regions; a region is taken as a research object, and the potentiality of electric power replacement is defined and quantified. The analytical model for the potentiality of multi-scenario electric power replacement is developed, and prediction is made to the energy consumption with the grey prediction model. The relevant theoretical research is utilized to realize prediction analysis on the potentiality amount of multi-scenario electric power replacement.

External validation of structure-biodegradation relationship (SBR) models for predicting the biodegradability of xenobiotics.

PubMed

Devillers, J; Pandard, P; Richard, B

2013-01-01

Biodegradation is an important mechanism for eliminating xenobiotics by biotransforming them into simple organic and inorganic products. Faced with the ever growing number of chemicals available on the market, structure-biodegradation relationship (SBR) and quantitative structure-biodegradation relationship (QSBR) models are increasingly used as surrogates of the biodegradation tests. Such models have great potential for a quick and cheap estimation of the biodegradation potential of chemicals. The Estimation Programs Interface (EPI) Suite™ includes different models for predicting the potential aerobic biodegradability of organic substances. They are based on different endpoints, methodologies and/or statistical approaches. Among them, Biowin 5 and 6 appeared the most robust, being derived from the largest biodegradation database with results obtained only from the Ministry of International Trade and Industry (MITI) test. The aim of this study was to assess the predictive performances of these two models from a set of 356 chemicals extracted from notification dossiers including compatible biodegradation data. Another set of molecules with no more than four carbon atoms and substituted by various heteroatoms and/or functional groups was also embodied in the validation exercise. Comparisons were made with the predictions obtained with START (Structural Alerts for Reactivity in Toxtree). Biowin 5 and Biowin 6 gave satisfactorily prediction results except for the prediction of readily degradable chemicals. A consensus model built with Biowin 1 allowed the diminution of this tendency.

The CRH1 antagonist GSK561679 increases human fear but not anxiety as assessed by startle.

PubMed

Grillon, Christian; Hale, Elizabeth; Lieberman, Lynne; Davis, Andrew; Pine, Daniel S; Ernst, Monique

2015-03-13

Fear to predictable threat and anxiety to unpredictable threat reflect distinct processes mediated by different brain structures, the central nucleus of the amygdala and the bed nucleus of the stria terminalis (BNST), respectively. This study tested the hypothesis that the corticotropin-releasing factor (CRF1) antagonist GSK561679 differentially reduces anxiety but increases fear in humans. A total of 31 healthy females received each of four treatments: placebo, 50 mg GSK561679 (low-GSK), 400 mg GSK561679 (high-GSK), and 1 mg alprazolam in a crossover design. Participants were exposed to three conditions during each of the four treatments. The three conditions included one in which predictable aversive shocks were signaled by a cue, a second during which shocks were administered unpredictably, and a third condition without shock. Fear and anxiety were assessed using the acoustic startle reflex. High-GSK had no effect on startle potentiation during unpredictable threat (anxiety) but increased startle potentiation during the predictable condition (fear). Low-GSK did not affect startle potentiation across conditions. Consistent with previous findings, alprazolam reduced startle potentiation during unpredictable threat but not during predictable threat. The increased fear by high-GSK replicates animal findings and suggests a lift of the inhibitory effect of the BNST on the amygdala by the CRF1 antagonist.

The potential of iRest in measuring the hand function performance of stroke patients.

PubMed

Abdul Rahman, Hisyam; Khor, Kang Xiang; Yeong, Che Fai; Su, Eileen Lee Ming; Narayanan, Aqilah Leela T

2017-01-01

Clinical scales such as Fugl-Meyer Assessment (FMA) and Motor Assessment Scale (MAS) are widely used to evaluate stroke patient's motor performance. However, there are several limitations with these assessment scales such as subjectivity, lack of repeatability, time-consuming and highly depend on the ability of the physiotherapy. In contrast, robot-based assessments are objective, repeatable, and could potentially reduce the assessment time. However, robot-based assessments are not as well established as conventional assessment scale and the correlation to conventional assessment scale is unclear. This study was carried out to identify important parameters in designing tasks that efficiently assess hand function of stroke patients and to quantify potential benefits of robotic assessment modules to predict the conventional assessment score with iRest. Twelve predictive variables were explored, relating to movement time, velocity, strategy, accuracy and smoothness from three robotic assessment modules which are Draw I, Draw Diamond and Draw Circle. Regression models using up to four predictors were developed to describe the MAS. Results show that the time given should be not too long and it would affect the trajectory error. Besides, result also shows that it is possible to use iRest in predicting MAS score. There is a potential of using iRest, a non-motorized device in predicting MAS score.

Potential-based and non-potential-based cohesive zone formulations under mixed-mode separation and over-closure-Part II: Finite element applications

NASA Astrophysics Data System (ADS)

Máirtín, Éamonn Ó.; Parry, Guillaume; Beltz, Glenn E.; McGarry, J. Patrick

2014-02-01

This paper, the second of two parts, presents three novel finite element case studies to demonstrate the importance of normal-tangential coupling in cohesive zone models (CZMs) for the prediction of mixed-mode interface debonding. Specifically, four new CZMs proposed in Part I of this study are implemented, namely the potential-based MP model and the non-potential-based NP1, NP2 and SMC models. For comparison, simulations are also performed for the well established potential-based Xu-Needleman (XN) model and the non-potential-based model of van den Bosch, Schreurs and Geers (BSG model). Case study 1: Debonding and rebonding of a biological cell from a cyclically deforming silicone substrate is simulated when the mode II work of separation is higher than the mode I work of separation at the cell-substrate interface. An active formulation for the contractility and remodelling of the cell cytoskeleton is implemented. It is demonstrated that when the XN potential function is used at the cell-substrate interface repulsive normal tractions are computed, preventing rebonding of significant regions of the cell to the substrate. In contrast, the proposed MP potential function at the cell-substrate interface results in negligible repulsive normal tractions, allowing for the prediction of experimentally observed patterns of cell cytoskeletal remodelling. Case study 2: Buckling of a coating from the compressive surface of a stent is simulated. It is demonstrated that during expansion of the stent the coating is initially compressed into the stent surface, while simultaneously undergoing tangential (shear) tractions at the coating-stent interface. It is demonstrated that when either the proposed NP1 or NP2 model is implemented at the stent-coating interface mixed-mode over-closure is correctly penalised. Further expansion of the stent results in the prediction of significant buckling of the coating from the stent surface, as observed experimentally. In contrast, the BSG model does not correctly penalise mixed-mode over-closure at the stent-coating interface, significantly altering the stress state in the coating and preventing the prediction of buckling. Case study 3: Application of a displacement to the base of a bi-layered composite arch results in a symmetric sinusoidal distribution of normal and tangential traction at the arch interface. The traction defined mode mixity at the interface ranges from pure mode II at the base of the arch to pure mode I at the top of the arch. It is demonstrated that predicted debonding patterns are highly sensitive to normal-tangential coupling terms in a CZM. The NP2, XN, and BSG models exhibit a strong bias towards mode I separation at the top of the arch, while the NP1 model exhibits a bias towards mode II debonding at the base of the arch. Only the SMC model provides mode-independent behaviour in the early stages of debonding. This case study provides a practical example of the importance of the behaviour of CZMs under conditions of traction controlled mode mixity, following from the theoretical analysis presented in Part I of this study.

Out-of-Position Rear Impact Tissue-Level Investigation Using Detailed Finite Element Neck Model.

PubMed

Shateri, Hamed; Cronin, Duane S

2015-01-01

Whiplash injuries can occur in automotive crashes and may cause long-term health issues such as neck pain, headache, and visual and auditory disturbance. Evidence suggests that nonneutral head posture can significantly increase the potential for injury in a given impact scenario, but epidemiological and experimental data are limited and do not provide a quantitative assessment of the increased potential for injury. Although there have been some attempts to evaluate this important issue using finite element models, none to date have successfully addressed this complex problem. An existing detailed finite element neck model was evaluated in nonneutral positions and limitations were identified, including musculature implementation and attachment, upper cervical spine kinematics in axial rotation, prediction of ligament failure, and the need for repositioning the model while incorporating initial tissue strains. The model was enhanced to address these issues and an iterative procedure was used to determine the upper cervical spine ligament laxities. The neck model was revalidated using neutral position impacts and compared to an out-of-position cadaver experiment in the literature. The effects of nonneutral position (axial head rotation) coupled with muscle activation were studied at varying impact levels. The laxities for the ligaments of the upper cervical spine were determined using 4 load cases and resulted in improved response and predicted failure loads relative to experimental data. The predicted head response from the model was similar to an experimental head-turned bench-top rear impact experiment. The parametric study identified specific ligaments with increased distractions due to an initial head-turned posture and the effect of active musculature leading to reduced ligament distractions. The incorporation of ligament laxity in the upper cervical spine was essential to predict range of motion and traumatic response, particularly for repositioning of the neck model prior to impact. The results of this study identify a higher potential for injury in out-of-position rear collisions and identified at-risk locations based on ligament distractions. The model predicted higher potential for injury by as much as 50% based on ligament distraction for the out-of-position posture and reduced potential for injury with muscle activation. Importantly, this study demonstrated that the location of injury or pain depends on the initial occupant posture, so that both the location of injury and kinematic threshold may vary when considering common head positions while driving.

A Resource-Based Modelling Framework to Assess Habitat Suitability for Steppe Birds in Semiarid Mediterranean Agricultural Systems

PubMed Central

Cardador, Laura; De Cáceres, Miquel; Bota, Gerard; Giralt, David; Casas, Fabián; Arroyo, Beatriz; Mougeot, François; Cantero-Martínez, Carlos; Moncunill, Judit; Butler, Simon J.; Brotons, Lluís

2014-01-01

European agriculture is undergoing widespread changes that are likely to have profound impacts on farmland biodiversity. The development of tools that allow an assessment of the potential biodiversity effects of different land-use alternatives before changes occur is fundamental to guiding management decisions. In this study, we develop a resource-based model framework to estimate habitat suitability for target species, according to simple information on species’ key resource requirements (diet, foraging habitat and nesting site), and examine whether it can be used to link land-use and local species’ distribution. We take as a study case four steppe bird species in a lowland area of the north-eastern Iberian Peninsula. We also compare the performance of our resource-based approach to that obtained through habitat-based models relating species’ occurrence and land-cover variables. Further, we use our resource-based approach to predict the effects that change in farming systems can have on farmland bird habitat suitability and compare these predictions with those obtained using the habitat-based models. Habitat suitability estimates generated by our resource-based models performed similarly (and better for one study species) than habitat based-models when predicting current species distribution. Moderate prediction success was achieved for three out of four species considered by resource-based models and for two of four by habitat-based models. Although, there is potential for improving the performance of resource-based models, they provide a structure for using available knowledge of the functional links between agricultural practices, provision of key resources and the response of organisms to predict potential effects of changing land-uses in a variety of context or the impacts of changes such as altered management practices that are not easily incorporated into habitat-based models. PMID:24667825

An EQT-based cDFT approach for thermodynamic properties of confined fluid mixtures

NASA Astrophysics Data System (ADS)

Motevaselian, M. H.; Aluru, N. R.

2017-04-01

We present an empirical potential-based quasi-continuum theory (EQT) to predict the structure and thermodynamic properties of confined fluid mixtures. The central idea in the EQT is to construct potential energies that integrate important atomistic details into a continuum-based model such as the Nernst-Planck equation. The EQT potentials can be also used to construct the excess free energy functional, which is required for the grand potential in the classical density functional theory (cDFT). In this work, we use the EQT-based grand potential to predict various thermodynamic properties of a confined binary mixture of hydrogen and methane molecules inside graphene slit channels of different widths. We show that the EQT-cDFT predictions for the structure, surface tension, solvation force, and local pressure tensor profiles are in good agreement with the molecular dynamics simulations. Moreover, we study the effect of different bulk compositions and channel widths on the thermodynamic properties. Our results reveal that the composition of methane in the mixture can significantly affect the ordering of molecules and thermodynamic properties under confinement. In addition, we find that graphene is selective to methane molecules.

Northern protected areas will become important refuges for biodiversity tracking suitable climates.

PubMed

Berteaux, Dominique; Ricard, Marylène; St-Laurent, Martin-Hugues; Casajus, Nicolas; Périé, Catherine; Beauregard, Frieda; de Blois, Sylvie

2018-03-15

The Northern Biodiversity Paradox predicts that, despite its globally negative effects on biodiversity, climate change will increase biodiversity in northern regions where many species are limited by low temperatures. We assessed the potential impacts of climate change on the biodiversity of a northern network of 1,749 protected areas spread over >600,000 km 2 in Quebec, Canada. Using ecological niche modeling, we calculated potential changes in the probability of occurrence of 529 species to evaluate the potential impacts of climate change on (1) species gain, loss, turnover, and richness in protected areas, (2) representativity of protected areas, and (3) extent of species ranges located in protected areas. We predict a major species turnover over time, with 49% of total protected land area potentially experiencing a species turnover >80%. We also predict increases in regional species richness, representativity of protected areas, and species protection provided by protected areas. Although we did not model the likelihood of species colonising habitats that become suitable as a result of climate change, northern protected areas should ultimately become important refuges for species tracking climate northward. This is the first study to examine in such details the potential effects of climate change on a northern protected area network.

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.

VLP Simulation: An Interactive Simple Virtual Model to Encourage Geoscience Skill about Volcano

NASA Astrophysics Data System (ADS)

Hariyono, E.; Liliasari; Tjasyono, B.; Rosdiana, D.

2017-09-01

The purpose of this study was to describe physics students predicting skills after following the geoscience learning using VLP (Volcano Learning Project) simulation. This research was conducted to 24 physics students at one of the state university in East Java-Indonesia. The method used is the descriptive analysis based on students’ answers related to predicting skills about volcanic activity. The results showed that the learning by using VLP simulation was very potential to develop physics students predicting skills. Students were able to explain logically about volcanic activity and they have been able to predict the potential eruption that will occur based on the real data visualization. It can be concluded that the VLP simulation is very suitable for physics student requirements in developing geosciences skill and recommended as an alternative media to educate the society in an understanding of volcanic phenomena.

Event-related potentials in impulsively aggressive juveniles: a retrospective chart-review study.

PubMed

Fisher, William; Ceballos, Natalie; Matthews, Dan; Fisher, Larry

2011-05-30

The assessment, treatment and management of aggressive youth represent a major clinical challenge facing pediatric mental health professionals today. Although a number of studies have examined physiological differences among aggressive patients vs. controls, the current literature lacks a comprehensive examination of the electroencephalographic activity of impulsively aggressive juveniles. The current study was designed to fill this void in the literature via a retrospective chart review of 80 male and female juveniles undergoing inpatient treatment for pathologically impulsive aggression. Clinical reports for mid- and late-latency event-related potentials (ERPs) were examined to determine their correlations with aggression characteristics, as well as any differential predictive utility of hemispheric differences and auditory vs. visual potentials. Results indicated that decrements of mid-latency potentials and ERPs evoked by auditory stimuli (vs. late-latency components and visual ERPs) were more highly predictive of aggressive behavior. No significant hemispheric differences were noted. Taken together, these results have theoretical significance for the etiology of impulsive aggression, and perhaps also clinical relevance for the treatment of this condition. Copyright © 2011 Elsevier Ltd. All rights reserved.

Mapping distribution of Rastrelliger kanagurta in the exclusive economic zone (EEZ) of Malaysia using maximum entropy modeling approach

NASA Astrophysics Data System (ADS)

Yusop, Syazwani Mohd; Mustapha, Muzzneena Ahmad

2018-04-01

The coupling of fishing locations for R. kanagurta obtained from SEAFDEC and multi-sensor satellite imageries of oceanographic variables; sea surface temperature (SST), sea surface height (SSH) and chl-a concentration (chl-a) were utilized to evaluate the performance of maximum entropy (MaxEnt) models for R. kanagurta fishing ground for prediction. Besides, this study was conducted to identify the relative percentage contribution of each environmental variable considered in order to describe the effects of the oceanographic factors on the species distribution in the study area. The potential fishing grounds during intermonsoon periods; April and October 2008-2009 were simulated separately and covered the near-coast of Kelantan, Terengganu, Pahang and Johor. The oceanographic conditions differed between regions by the inherent seasonal variability. The seasonal and spatial extents of potential fishing grounds were largely explained by chl-a concentration (0.21-0.99 mg/m3 in April and 0.28-1.00 mg/m3 in October), SSH (77.37-85.90 cm in April and 107.60-108.97 cm in October) and SST (30.43-33.70 °C in April and 30.48-30.97 °C in October). The constructed models were applicable and therefore they were suitable for predicting the potential fishing zones of R. kanagurta in EEZ. The results from this study revealed MaxEnt's potential for predicting the spatial distribution of R. kanagurta and highlighted the use of multispectral satellite images for describing the seasonal potential fishing grounds.

Estimating Subglottal Pressure from Neck-Surface Acceleration during Normal Voice Production

ERIC Educational Resources Information Center

Fryd, Amanda S.; Van Stan, Jarrad H.; Hillman, Robert E.; Mehta, Daryush D.

2016-01-01

Purpose: The purpose of this study was to evaluate the potential for estimating subglottal air pressure using a neck-surface accelerometer and to compare the accuracy of predicting subglottal air pressure relative to predicting acoustic sound pressure level (SPL). Method: Indirect estimates of subglottal pressure (P[subscript sg]') were obtained…

Psychiatric Outcomes at Age Seven for Very Preterm Children: Rates and Predictors

ERIC Educational Resources Information Center

Treyvaud, Karli; Ure, Alexandra; Doyle, Lex W.; Lee, Katherine J.; Rogers, Cynthia E.; Kidokoro, Hiroyuki; Inder, Terrie E.; Anderson, Peter J.

2013-01-01

Background: Uncertainty remains about the rate of specific psychiatric disorders and associated predictive factors for very preterm (VPT) children. The aims of this study were to document rates of psychiatric disorders in VPT children aged 7 years compared with term born children, and to examine potential predictive factors for psychiatric…

Emotional Intelligence as a Predictor for Success in Online Learning

ERIC Educational Resources Information Center

Berenson, Robin; Boyles, Gary; Weaver, Ann

2008-01-01

As students increasingly opt for online classes, it becomes more important for administrators to predict levels of potential academic success. This study examined the intrinsic factors of emotional intelligence (EI) and personality to determine the extent to which they predict grade point average (GPA), a measure of academic success, among…

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.

Predictive biomarkers of sorafenib efficacy in advanced hepatocellular carcinoma: Are we getting there?

PubMed Central

Shao, Yu-Yun; Hsu, Chih-Hung; Cheng, Ann-Lii

2015-01-01

Sorafenib is the current standard treatment for advanced hepatocellular carcinoma (HCC), but its efficacy is modest with low response rates and short response duration. Predictive biomarkers for sorafenib efficacy are necessary. However, efforts to determine biomarkers for sorafenib have led only to potential candidates rather than clinically useful predictors. Studies based on patient cohorts identified the potential of blood levels of angiopoietin-2, hepatocyte growth factor, insulin-like growth factor-1, and transforming growth factor-β1 for predicting sorafenib efficacy. Alpha-fetoprotein response, dynamic contrast-enhanced magnetic resonance imaging, and treatment-related side effects may serve as early surrogate markers. Novel approaches based on super-responders or experimental mouse models may provide new directions in biomarker research. These studies identified tumor amplification of FGF3/FGF4 or VEGFA and tumor expression of phospho-Mapk14 and phospho-Atf2 as possible predictive markers that await validation. A group effort that considers various prognostic factors and proper collection of tumor tissues before treatment is imperative for the success of future biomarker research in advanced HCC. PMID:26420960

Predictive biomarkers of sorafenib efficacy in advanced hepatocellular carcinoma: Are we getting there?

PubMed

Shao, Yu-Yun; Hsu, Chih-Hung; Cheng, Ann-Lii

2015-09-28

Sorafenib is the current standard treatment for advanced hepatocellular carcinoma (HCC), but its efficacy is modest with low response rates and short response duration. Predictive biomarkers for sorafenib efficacy are necessary. However, efforts to determine biomarkers for sorafenib have led only to potential candidates rather than clinically useful predictors. Studies based on patient cohorts identified the potential of blood levels of angiopoietin-2, hepatocyte growth factor, insulin-like growth factor-1, and transforming growth factor-β1 for predicting sorafenib efficacy. Alpha-fetoprotein response, dynamic contrast-enhanced magnetic resonance imaging, and treatment-related side effects may serve as early surrogate markers. Novel approaches based on super-responders or experimental mouse models may provide new directions in biomarker research. These studies identified tumor amplification of FGF3/FGF4 or VEGFA and tumor expression of phospho-Mapk14 and phospho-Atf2 as possible predictive markers that await validation. A group effort that considers various prognostic factors and proper collection of tumor tissues before treatment is imperative for the success of future biomarker research in advanced HCC.

Distinct plasma lipids profiles of recurrent ovarian cancer by liquid chromatography-mass spectrometry

PubMed Central

Li, Ang; Cheng, Jinlong; Yang, Kai; Wang, Jingtao; Wang, Wenjie; Zhang, Fan; Li, Zhenzi; Dhillon, Harman S.; Openkova, Margarita S; Zhou, Xiaohua; Li, Kang; Hou, Yan

2017-01-01

Epithelial ovarian cancer (EOC) is the most deadly gynecologic malignancy worldwide due to its high recurrence rate after surgery and chemotherapy. There is a critical need for discovery of novel biomarkers for EOC recurrence providing higher prediction power than that of the present ones. Lipids have been reported to associate with development and progression of cancer. In the current study, we aim to identify and validate the lipids which were relevant to the ovarian cancer recurrence based on plasma lipidomics performed by ultra-performance liquid chromatography coupled with mass spectrometry. In order to fulfill this objective, plasma from 70 EOC patients with follow up information was obtained. The results revealed that patients with and without recurrence could be clearly distinguished based on their lipid profiles. Thirty-one lipid metabolites were identified as potential biomarkers for EOC recurrence. The AUC value of these metabolite combinations for predicting EOC recurrence was 0.897. In terms of clinical applicability, LysoPG(20:5) arose as a potential EOC recurrence predictive biomarker to increase the predictive power of clinical predictors from AUC value 0.739 to 0.875. Additionally, we still found that individuals with early relapses (< 6 months) had a distinctive metabolomic pattern compared with late EOC and non-EOC recurrence subjects. Interestingly, decreased levels of triglycerides (TGs) were found to be a specific metabolic feature foreshadowing an early relapse. In conclusion, plasma lipidomics study could be used for predicting EOC recurrences, as well as early and late recurrent cases. The lipid biomarker research improves the predictive power of clinical predictors and the identified biomarkers are of great prognostic and therapeutic potential. PMID:27564116

Ferritin levels predict severe dengue.

PubMed

Soundravally, R; Agieshkumar, B; Daisy, M; Sherin, J; Cleetus, C C

2015-02-01

Currently, no tests are available to monitor and predict severity and outcome of dengue. To find potential markers that predict dengue severity, the present study validated the serum level of three acute-phase proteins α-1 antitrypsin, ceruloplasmin and ferritin in a pool of severe dengue cases compared to non-severe forms and other febrile illness controls. A total of 96 patients were divided into two equal groups with group 'A' comprising dengue-infected cases and group 'B' with other febrile illness cases negative for dengue. Out of 48 dengue-infected cases, 13 had severe dengue and the remaining 35 were classified as non-severe dengue. Immunoassays were performed to evaluate the serum levels of acute-phase proteins both on the day of admission and on the day of defervescence. The efficiency of individual proteins in predicting the disease severity was assessed using receiver operating characteristic curve. The study did not find any significant difference in the levels of α-1 antitrypsin between the clinical groups. A significant increase in the levels of ceruloplasmin around defervescence in severe cases compared to non-severe and other febrile controls was observed and this is the first report describing the potential association of ceruloplasmin and dengue severity. Interestingly, a steady increase in the level of serum ferritin was recorded throughout the course of illness. Among all the three proteins, the elevated ferritin level could predict the disease severity with highest sensitivity and specificity of 76.9 and 83.3 %, respectively, on the day of admission and the same was found to be 90 and 91.6 % around defervescence. On the basis of this diagnostic efficiency, we propose that ferritin may serve as a potential biomarker for an early prediction of disease severity.

Neural Reactivity to Emotional Stimuli Prospectively Predicts the Impact of a Natural Disaster on Psychiatric Symptoms in Children.

PubMed

Kujawa, Autumn; Hajcak, Greg; Danzig, Allison P; Black, Sarah R; Bromet, Evelyn J; Carlson, Gabrielle A; Kotov, Roman; Klein, Daniel N

2016-09-01

Natural disasters expose entire communities to stress and trauma, leading to increased risk for psychiatric symptoms. Yet, the majority of exposed individuals are resilient, highlighting the importance of identifying underlying factors that contribute to outcomes. The current study was part of a larger prospective study of children in Long Island, New York (n = 260). At age 9, children viewed unpleasant and pleasant images while the late positive potential (LPP), an event-related potential component that reflects sustained attention toward salient information, was measured. Following the event-related potential assessment, Hurricane Sandy, the second costliest hurricane in United States history, hit the region. Eight weeks after the hurricane, mothers reported on exposure to hurricane-related stress and children's internalizing and externalizing symptoms. Symptoms were reassessed 8 months after the hurricane. The LPP predicted both internalizing and externalizing symptoms after accounting for prehurricane symptomatology and interacted with stress to predict externalizing symptoms. Among children exposed to higher levels of hurricane-related stress, enhanced neural reactivity to unpleasant images predicted greater externalizing symptoms 8 weeks after the disaster, while greater neural reactivity to pleasant images predicted lower externalizing symptoms. Moreover, interactions between the LPP and stress continued to predict externalizing symptoms 8 months after the hurricane. Results indicate that heightened neural reactivity and attention toward unpleasant information, as measured by the LPP, predispose children to psychiatric symptoms when exposed to higher levels of stress related to natural disasters, while greater reactivity to and processing of pleasant information may be a protective factor. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

A stochastic approach for predicting the profitability of bioenergy grasses

USDA-ARS?s Scientific Manuscript database

Switchgrass (Panicum virgatum L.) and miscanthus (Miscanthus giganteus) have potential to meet a growing demand for renewable energy. Before producers will invest in planting these crops, they need credible estimations of the potential profits. The objective of this study was to examine profitabilit...

Blocking a Redundant Cue: What does it say about preschoolers’ causal competence?

PubMed Central

Kloos, Heidi; Sloutsky, Vladimir M.

2013-01-01

The current study investigates the degree to which preschoolers can engage in causal inferences in blocking paradigm, a paradigm in which a cue is consistently linked with a target, either alone (A-T) or paired with another cue (AB-T). Unlike previous blocking studies with preschoolers, we manipulated the causal structure of the events without changing the specific contingencies. In particular, cues were said to be either potential causes (prediction condition), or they were said to be potential effects (diagnosis condition). The causally appropriate inference is to block the redundant cue B when it is a potential cause of the target, but not when it is a potential effect. Findings show a stark difference in performance between preschoolers and adults: While adults blocked the redundant cue only in the prediction condition, children blocked the redundant cue indiscriminately across both conditions. Therefore, children, but not adults ignored the causal structure of the events. These findings challenge a developmental account that attributes sophisticated machinery of causal reasoning to young children. PMID:24033577

Radon soil gas measurements in a geological versatile region as basis to improve the prediction of areas with a high radon potential.

PubMed

Kabrt, Franz; Seidel, Claudia; Baumgartner, Andreas; Friedmann, Harry; Rechberger, Fabian; Schuff, Michael; Maringer, Franz Josef

2014-07-01

With the aim to predict the radon potential by geological data, radon soil gas measurements were made in a selected region in Styria, Austria. This region is characterised by mean indoor radon potentials of 130-280 Bq m(-3) and a high geological diversity. The distribution of the individual measuring sites was selected on the basis of geological aspects and the distribution of area settlements. In this work, the radon soil gas activity concentration and the soil permeability were measured at 100 sites, each with three single measurements. Furthermore, the local dose rate was determined and soil samples were taken at each site to determine the activity concentration of natural radionuclides. During two investigation periods, long-term soil gas radon measurements were made to study the time dependency of the radon activity concentration. All the results will be compared and investigated for correlation among each other to improve the prediction of areas with high radon potential. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning

PubMed Central

Chen, Ming; Wang, Quanxin; Zhang, Lixin; Yan, Guiying

2016-01-01

Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations. PMID:27415801

Inter-decadal change in potential predictability of the East Asian summer monsoon

NASA Astrophysics Data System (ADS)

Li, Jiao; Ding, Ruiqiang; Wu, Zhiwei; Zhong, Quanjia; Li, Baosheng; Li, Jianping

2018-05-01

The significant inter-decadal change in potential predictability of the East Asian summer monsoon (EASM) has been investigated using the signal-to-noise ratio method. The relatively low potential predictability appears from the early 1950s through the late 1970s and during the early 2000s, whereas the potential predictability is relatively high from the early 1980s through the late 1990s. The inter-decadal change in potential predictability of the EASM can be attributed mainly to variations in the external signal of the EASM. The latter is mostly caused by the El Niño-Southern Oscillation (ENSO) inter-decadal variability. As a major external signal of the EASM, the ENSO inter-decadal variability experiences phase transitions from negative to positive phases in the late 1970s, and to negative phases in the late 1990s. Additionally, ENSO is generally strong (weak) during a positive (negative) phase of the ENSO inter-decadal variability. The strong ENSO is expected to have a greater influence on the EASM, and vice versa. As a result, the potential predictability of the EASM tends to be high (low) during a positive (negative) phase of the ENSO inter-decadal variability. Furthermore, a suite of Pacific Pacemaker experiments suggests that the ENSO inter-decadal variability may be a key pacemaker of the inter-decadal change in potential predictability of the EASM.

Predicting Violent Behavior: What Can Neuroscience Add?

PubMed

Poldrack, Russell A; Monahan, John; Imrey, Peter B; Reyna, Valerie; Raichle, Marcus E; Faigman, David; Buckholtz, Joshua W

2018-02-01

The ability to accurately predict violence and other forms of serious antisocial behavior would provide important societal benefits, and there is substantial enthusiasm for the potential predictive accuracy of neuroimaging techniques. Here, we review the current status of violence prediction using actuarial and clinical methods, and assess the current state of neuroprediction. We then outline several questions that need to be addressed by future studies of neuroprediction if neuroimaging and other neuroscientific markers are to be successfully translated into public policy. Copyright © 2017 Elsevier Ltd. All rights reserved.

P09.62 Towards individualized survival prediction in glioblastoma patients using machine learning methods

PubMed Central

Vera, L.; Pérez-Beteta, J.; Molina, D.; Borrás, J. M.; Benavides, M.; Barcia, J. A.; Velásquez, C.; Albillo, D.; Lara, P.; Pérez-García, V. M.

2017-01-01

Abstract Introduction: Machine learning methods are integrated in clinical research studies due to their strong capability to discover parameters having a high information content and their predictive combined potential. Several studies have been developed using glioblastoma patient’s imaging data. Many of them have focused on including large numbers of variables, mostly two-dimensional textural features and/or genomic data, regardless of their meaning or potential clinical relevance. Materials and methods: 193 glioblastoma patients were included in the study. Preoperative 3D magnetic resonance images were collected and semi-automatically segmented using an in-house software. After segmentation, a database of 90 parameters including geometrical and textural image-based measures together with patients’ clinical data (including age, survival, type of treatment, etc.) was constructed. The criterion for including variables in the study was that they had either shown individual impact on survival in single or multivariate analyses or have a precise clinical or geometrical meaning. These variables were used to perform several machine learning experiments. In a first set of computational cross-validation experiments based on regression trees, those attributes showing the highest information measures were extracted. In the second phase, more sophisticated learning methods were employed in order to validate the potential of the previous variables predicting survival. Concretely support vector machines, neural networks and sparse grid methods were used. Results: Variables showing high information measure in the first phase provided the best prediction results in the second phase. Specifically, patient age, Stupp regimen and a geometrical measure related with the irregularity of contrast-enhancing areas were the variables showing the highest information measure in the first stage. For the second phase, the combinations of patient age and Stupp regimen together with one tumor geometrical measure and one tumor heterogeneity feature reached the best quality prediction. Conclusions: Advanced machine learning methods identified the parameters with the highest information measure and survival predictive potential. The uninformed machine learning methods identified a novel feature measure with direct impact on survival. Used in combination with other previously known variables multi-indexes can be defined that can help in tumor characterization and prognosis prediction. Recent advances on the definition of those multi-indexes will be reported in the conference. Funding: James S. Mc. Donnell Foundation (USA) 21st Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer [Collaborative award 220020450 and planning grant 220020420], MINECO/FEDER [MTM2015-71200-R], JCCM [PEII-2014-031-P].

Long-range empirical potential model: extension to hexagonal close-packed metals.

PubMed

Dai, Y; Li, J H; Liu, B X

2009-09-23

An n-body potential is developed and satisfactorily applied to hcp metals, Co, Hf, Mg, Re, Ti, and Zr, in the form of long-range empirical potential. The potential can well reproduce the lattice constants, c/a ratios, cohesive energies, and the bulk modulus for their stable structures (hcp) and metastable structures (bcc or fcc). Meanwhile, the potential can correctly predict the order of structural stability and distinguish the energy differences between their stable hcp structure and other structures. The energies and forces derived by the potential can smoothly go to zero at cutoff radius, thus completely avoiding the unphysical behaviors in the simulations. The developed potential is applied to study the vacancy, surface fault, stacking fault and self-interstitial atom in the hcp metals. The calculated formation energies of vacancy and divacancy and activation energies of self-diffusion by vacancies are in good agreement with the values in experiments and in other works. The calculated surface energies and stacking fault energies are also consistent with the experimental data and those obtained in other theoretical works. The calculated formation energies generally agree with the results in other works, although the stable configurations of self-interstitial atoms predicted in this work somewhat contrast with those predicted by other methods. The proposed potential is shown to be relevant for describing the interaction of bcc, fcc and hcp metal systems, bringing great convenience for researchers in constructing potentials for metal systems constituted by any combination of bcc, fcc and hcp metals.

Regional variations in the diversity and predicted metabolic potential of benthic prokaryotes in coastal northern Zhejiang, East China Sea

PubMed Central

Wang, Kai; Ye, Xiansen; Zhang, Huajun; Chen, Heping; Zhang, Demin; Liu, Lian

2016-01-01

Knowledge about the drivers of benthic prokaryotic diversity and metabolic potential in interconnected coastal sediments at regional scales is limited. We collected surface sediments across six zones covering ~200 km in coastal northern Zhejiang, East China Sea and combined 16 S rRNA gene sequencing, community-level metabolic prediction, and sediment physicochemical measurements to investigate variations in prokaryotic diversity and metabolic gene composition with geographic distance and under local environmental conditions. Geographic distance was the most influential factor in prokaryotic β-diversity compared with major environmental drivers, including temperature, sediment texture, acid-volatile sulfide, and water depth, but a large unexplained variation in community composition suggested the potential effects of unmeasured abiotic/biotic factors and stochastic processes. Moreover, prokaryotic assemblages showed a biogeographic provincialism across the zones. The predicted metabolic gene composition similarly shifted as taxonomic composition did. Acid-volatile sulfide was strongly correlated with variation in metabolic gene composition. The enrichments in the relative abundance of sulfate-reducing bacteria and genes relevant with dissimilatory sulfate reduction were observed and predicted, respectively, in the Yushan area. These results provide insights into the relative importance of geographic distance and environmental condition in driving benthic prokaryotic diversity in coastal areas and predict specific biogeochemically-relevant genes for future studies. PMID:27917954

Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

PubMed Central

Sharafoddini, Anis; Dubin, Joel A

2017-01-01

Background Physicians and health policy makers are required to make predictions during their decision making in various medical problems. Many advances have been made in predictive modeling toward outcome prediction, but these innovations target an average patient and are insufficiently adjustable for individual patients. One developing idea in this field is individualized predictive analytics based on patient similarity. The goal of this approach is to identify patients who are similar to an index patient and derive insights from the records of similar patients to provide personalized predictions.. Objective The aim is to summarize and review published studies describing computer-based approaches for predicting patients’ future health status based on health data and patient similarity, identify gaps, and provide a starting point for related future research. Methods The method involved (1) conducting the review by performing automated searches in Scopus, PubMed, and ISI Web of Science, selecting relevant studies by first screening titles and abstracts then analyzing full-texts, and (2) documenting by extracting publication details and information on context, predictors, missing data, modeling algorithm, outcome, and evaluation methods into a matrix table, synthesizing data, and reporting results. Results After duplicate removal, 1339 articles were screened in abstracts and titles and 67 were selected for full-text review. In total, 22 articles met the inclusion criteria. Within included articles, hospitals were the main source of data (n=10). Cardiovascular disease (n=7) and diabetes (n=4) were the dominant patient diseases. Most studies (n=18) used neighborhood-based approaches in devising prediction models. Two studies showed that patient similarity-based modeling outperformed population-based predictive methods. Conclusions Interest in patient similarity-based predictive modeling for diagnosis and prognosis has been growing. In addition to raw/coded health data, wavelet transform and term frequency-inverse document frequency methods were employed to extract predictors. Selecting predictors with potential to highlight special cases and defining new patient similarity metrics were among the gaps identified in the existing literature that provide starting points for future work. Patient status prediction models based on patient similarity and health data offer exciting potential for personalizing and ultimately improving health care, leading to better patient outcomes. PMID:28258046

Regional Arctic sea-ice prediction: potential versus operational seasonal forecast skill

NASA Astrophysics Data System (ADS)

Bushuk, Mitchell; Msadek, Rym; Winton, Michael; Vecchi, Gabriel; Yang, Xiaosong; Rosati, Anthony; Gudgel, Rich

2018-06-01

Seasonal predictions of Arctic sea ice on regional spatial scales are a pressing need for a broad group of stakeholders, however, most assessments of predictability and forecast skill to date have focused on pan-Arctic sea-ice extent (SIE). In this work, we present the first direct comparison of perfect model (PM) and operational (OP) seasonal prediction skill for regional Arctic SIE within a common dynamical prediction system. This assessment is based on two complementary suites of seasonal prediction ensemble experiments performed with a global coupled climate model. First, we present a suite of PM predictability experiments with start dates spanning the calendar year, which are used to quantify the potential regional SIE prediction skill of this system. Second, we assess the system's OP prediction skill for detrended regional SIE using a suite of retrospective initialized seasonal forecasts spanning 1981-2016. In nearly all Arctic regions and for all target months, we find a substantial skill gap between PM and OP predictions of regional SIE. The PM experiments reveal that regional winter SIE is potentially predictable at lead times beyond 12 months, substantially longer than the skill of their OP counterparts. Both the OP and PM predictions display a spring prediction skill barrier for regional summer SIE forecasts, indicating a fundamental predictability limit for summer regional predictions. We find that a similar barrier exists for pan-Arctic sea-ice volume predictions, but is not present for predictions of pan-Arctic SIE. The skill gap identified in this work indicates a promising potential for future improvements in regional SIE predictions.

Predicting groundwater recharge for varying land cover and climate conditions - a global meta-study

NASA Astrophysics Data System (ADS)

Mohan, Chinchu; Western, Andrew W.; Wei, Yongping; Saft, Margarita

2018-05-01

Groundwater recharge is one of the important factors determining the groundwater development potential of an area. Even though recharge plays a key role in controlling groundwater system dynamics, much uncertainty remains regarding the relationships between groundwater recharge and its governing factors at a large scale. Therefore, this study aims to identify the most influential factors of groundwater recharge, and to develop an empirical model to estimate diffuse rainfall recharge at a global scale. Recharge estimates reported in the literature from various parts of the world (715 sites) were compiled and used in model building and testing exercises. Unlike conventional recharge estimates from water balance, this study used a multimodel inference approach and information theory to explain the relationship between groundwater recharge and influential factors, and to predict groundwater recharge at 0.5° resolution. The results show that meteorological factors (precipitation and potential evapotranspiration) and vegetation factors (land use and land cover) had the most predictive power for recharge. According to the model, long-term global average annual recharge (1981-2014) was 134 mm yr-1 with a prediction error ranging from -8 to 10 mm yr-1 for 97.2 % of cases. The recharge estimates presented in this study are unique and more reliable than the existing global groundwater recharge estimates because of the extensive validation carried out using both independent local estimates collated from the literature and national statistics from the Food and Agriculture Organization (FAO). In a water-scarce future driven by increased anthropogenic development, the results from this study will aid in making informed decisions about groundwater potential at a large scale.

Visually impaired individuals, safety perceptions and traumatic events: a qualitative study of hazards, reactions and coping.

PubMed

Saur, Randi; Hansen, Marianne Bang; Jansen, Anne; Heir, Trond

2017-04-01

To explore the types of risks and hazards that visually impaired individuals face, how they manage potential threats and how reactions to traumatic events are manifested and coped with. Participants were 17 visually impaired individuals who had experienced some kind of potentially traumatic event. Two focus groups and 13 individual interviews were conducted. The participants experienced a variety of hazards and potential threats in their daily life. Fear of daily accidents was more pronounced than fear of disasters. Some participants reported avoiding help-seeking in unsafe situations due to shame at not being able to cope. The ability to be independent was highlighted. Traumatic events were re-experienced through a variety of sense modalities. Fear of labelling and avoidance of potential risks were recurring topics, and the risks of social withdrawal and isolation were addressed. Visual impairment causes a need for predictability and adequate information to increase and prepare for coping and self-efficacy. The results from this study call for greater emphasis on universal design in order to ensure safety and predictability. Fear of being labelled may inhibit people from using assistive devices and adequate coping strategies and seeking professional help in the aftermath of a trauma. Implications for Rehabilitation Visual impairment entails a greater susceptibility to a variety of hazards and potential threats in daily life. This calls for a greater emphasis on universal design in public spaces to ensure confidence and safety. Visual impairment implies a need for predictability and adequate information to prepare for coping and self-efficacy. Rehabilitation professionals should be aware of the need for independence and self-reliance, the possible fear of labelling, avoidance of help-seeking or reluctance to use assistive devices. In rehabilitation after accidents or potential traumatizing events, professionals' knowledge about the needs for information, training and predictability is crucial. The possibility of social withdrawal or isolation should be considered.

Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration

PubMed Central

Janssens, A Cecile JW; Ioannidis, John PA; Bedrosian, Sara; Boffetta, Paolo; Dolan, Siobhan M; Dowling, Nicole; Fortier, Isabel; Freedman, Andrew N; Grimshaw, Jeremy M; Gulcher, Jeffrey; Gwinn, Marta; Hlatky, Mark A; Janes, Holly; Kraft, Peter; Melillo, Stephanie; O'Donnell, Christopher J; Pencina, Michael J; Ransohoff, David; Schully, Sheri D; Seminara, Daniela; Winn, Deborah M; Wright, Caroline F; van Duijn, Cornelia M; Little, Julian; Khoury, Muin J

2011-01-01

The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis. PMID:21407270

Telluric currents: A meeting of theory and observation

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

Boteler, D.H.; Seager, W.H.

Pipe-to-soil (P/S) potential variations resulting from telluric currents have been observed on pipelines in many locations. However, it has never teen clear which parts of a pipeline will experience the worst effects. Two studies were conducted to answer this question. Distributed-source transmission line (DSTL) theory was applied to the problem of modeling geomagnetic induction in pipelines. This theory predicted that the largest P/S potential variations would occur at the ends of the pipeline. The theory also predicted that large P/S potential variations, of opposite sign, should occur on either side of an insulating flange. Independently, an observation program was conductedmore » to determine the change in telluric current P/S potential variations and to design counteractive measures along a pipeline in northern Canada. Observations showed that the amplitude of P/S potential fluctuations had maxima at the northern and southern ends of the pipeline. A further set of recordings around an insulating flange showed large P/S potential variations, of opposite sign, on either side of the flange. Agreement between the observations and theoretical predictions was remarkable. While the observations confirmed the theory, the theory explains how P/S potential variations are produced by telluric currents and provides the basis for design of cathodic protection systems for pipelines that can counteract any adverse telluric effects.« less

Wildfire potential evaluation during a drought event with a regional climate model and NDVI

Treesearch

Y. Liu; J. Stanturf; S. Goodrick

2010-01-01

Regional climate modeling is a technique for simulating high-resolution physical processes in the atmosphere, soil and vegetation. It can be used to evaluate wildfire potential by either providing meteorological conditions for computation of fire indices or predicting soil moisture as a direct measure of fire potential. This study examines these roles using a regional...

Hearing Screening of High-Risk Newborns with Brainstem Auditory Evoked Potentials: A Follow-Up Study.

ERIC Educational Resources Information Center

Shannon, Dorothy A.; And Others

1984-01-01

The brainstem auditory evoked potential (BAEP) was evaluated as a hearing screening test in 168 high-risk newborns. The BAEP was found to be a sensitive procedure for the early identification of hearing-impaired newborns. However, the yield of significant hearing abnormalities was less than predicted in other studies using BAEP. (Author/CL)

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

Progesterone at Encoding Predicts Subsequent Emotional Memory

ERIC Educational Resources Information Center

Ertman, Nicole; Andreano, Joseph M.; Cahill, Larry

2011-01-01

Significant sex differences in the well-documented relationship between stress hormones and memory have emerged in recent studies. The potentiating effects of glucocorticoids on memory vary across the menstrual cycle, suggesting a potential interaction between these stress hormones and endogenously cycling sex hormones. Here, we show that memory…

Predicting abuse potential of stimulants and other dopaminergic drugs: overview and recommendations.

PubMed

Huskinson, Sally L; Naylor, Jennifer E; Rowlett, James K; Freeman, Kevin B

2014-12-01

Examination of a drug's abuse potential at multiple levels of analysis (molecular/cellular action, whole-organism behavior, epidemiological data) is an essential component to regulating controlled substances under the Controlled Substances Act (CSA). We reviewed studies that examined several central nervous system (CNS) stimulants, focusing on those with primarily dopaminergic actions, in drug self-administration, drug discrimination, and physical dependence. For drug self-administration and drug discrimination, we distinguished between experiments conducted with rats and nonhuman primates (NHP) to highlight the common and unique attributes of each model in the assessment of abuse potential. Our review of drug self-administration studies suggests that this procedure is important in predicting abuse potential of dopaminergic compounds, but there were many false positives. We recommended that tests to determine how reinforcing a drug is relative to a known drug of abuse may be more predictive of abuse potential than tests that yield a binary, yes-or-no classification. Several false positives also occurred with drug discrimination. With this procedure, we recommended that future research follow a standard decision-tree approach that may require examining the drug being tested for abuse potential as the training stimulus. This approach would also allow several known drugs of abuse to be tested for substitution, and this may reduce false positives. Finally, we reviewed evidence of physical dependence with stimulants and discussed the feasibility of modeling these phenomena in nonhuman animals in a rational and practical fashion. This article is part of the Special Issue entitled 'CNS Stimulants'. Copyright © 2014 Elsevier Ltd. All rights reserved.

Neuroprediction, Violence, and the Law: Setting the Stage.

PubMed

Nadelhoffer, Thomas; Bibas, Stephanos; Grafton, Scott; Kiehl, Kent A; Mansfield, Andrew; Sinnott-Armstrong, Walter; Gazzaniga, Michael

2012-04-01

In this paper, our goal is to (a) survey some of the legal contexts within which violence risk assessment already plays a prominent role, (b) explore whether developments in neuroscience could potentially be used to improve our ability to predict violence, and (c) discuss whether neuropredictive models of violence create any unique legal or moral problems above and beyond the well worn problems already associated with prediction more generally. In "Violence Risk Assessment and the Law", we briefly examine the role currently played by predictions of violence in three high stakes legal contexts: capital sentencing ("Violence Risk Assessment and Capital Sentencing"), civil commitment hearings ("Violence Risk Assessment and Civil Commitment"), and "sexual predator" statutes ("Violence Risk Assessment and Sexual Predator Statutes"). In "Clinical vs. Actuarial Violence Risk Assessment", we briefly examine the distinction between traditional clinical methods of predicting violence and more recently developed actuarial methods, exemplified by the Classification of Violence Risk (COVR) software created by John Monahan and colleagues as part of the MacArthur Study of Mental Disorder and Violence [1]. In "The Neural Correlates of Psychopathy", we explore what neuroscience currently tells us about the neural correlates of violence, using the recent neuroscientific research on psychopathy as our focus. We also discuss some recent advances in both data collection ("Cutting-Edge Data Collection: Genetically Informed Neuroimaging") and data analysis ("Cutting-Edge Data Analysis: Pattern Classification") that we believe will play an important role when it comes to future neuroscientific research on violence. In "The Potential Promise of Neuroprediction", we discuss whether neuroscience could potentially be used to improve our ability to predict future violence. Finally, in "The Potential Perils of Neuroprediction", we explore some potential evidentiary ("Evidentiary Issues"), constitutional ("Constitutional Issues"), and moral ("Moral Issues") issues that may arise in the context of the neuroprediction of violence.

Behavior intentions of the public after bans on smoking in restaurants and bars.

PubMed Central

Biener, L; Siegel, M

1997-01-01

OBJECTIVES: This study assessed the potential effect of smoke-free policies on bar and restaurant patronage. METHODS: Random-digit dialing techniques were used in surveying a representative sample of Massachusetts adults (n = 2356) by telephone. RESULTS: Approximately 61% of the respondents predicted no change in their use of restaurants in response to smoke-free policies, 30% predicted increased use, and 8% predicted decreased use. In turn, 69% of the respondents predicted no change in their patronage of bars, while 20% predicted increased use and 11% predicted decreased use. CONCLUSIONS: These results suggest that smoke-free policies are likely to increase overall patronage of bars and restaurants. PMID:9431301

Genomics DNA Profiling in Elite Professional Soccer Players: A Pilot Study

PubMed Central

Kambouris, M; Del Buono, A; Maffulli, N

2014-01-01

Functional variants in exonic regions have been associated with development of cardiovascular disease, diabetes and cancer. Athletic performance can be considered a multi-factorial complex phenotype. Genomic DNA was extracted from buccal swabs of seven soccer players from the Fulham football team. Single nucleotide polymorphism (SNPs) genotyping was undertaken. To achieve optimal athletic performance, predictive genomics DNA profiling for sports performance can be used to aid in sport selection and elaboration of personalized training and nutrition programs. Predictive DNA profiling may be able to detect athletes with potential or frank injuries, or screening and selection of future athletes, and can help them to maximize utilization of their potential and improve performance in sports. The aim of this study is to provide a wide scenario of specific genomic variants that an athlete carries, to implement which measures should be taken to maximize the athlete’s potential. PMID:24809029

The development of a probabilistic approach to forecast coastal change

USGS Publications Warehouse

Lentz, Erika E.; Hapke, Cheryl J.; Rosati, Julie D.; Wang, Ping; Roberts, Tiffany M.

2011-01-01

This study demonstrates the applicability of a Bayesian probabilistic model as an effective tool in predicting post-storm beach changes along sandy coastlines. Volume change and net shoreline movement are modeled for two study sites at Fire Island, New York in response to two extratropical storms in 2007 and 2009. Both study areas include modified areas adjacent to unmodified areas in morphologically different segments of coast. Predicted outcomes are evaluated against observed changes to test model accuracy and uncertainty along 163 cross-shore transects. Results show strong agreement in the cross validation of predictions vs. observations, with 70-82% accuracies reported. Although no consistent spatial pattern in inaccurate predictions could be determined, the highest prediction uncertainties appeared in locations that had been recently replenished. Further testing and model refinement are needed; however, these initial results show that Bayesian networks have the potential to serve as important decision-support tools in forecasting coastal change.

A comparison between index of entropy and catastrophe theory methods for mapping groundwater potential in an arid region.

PubMed

Al-Abadi, Alaa M; Shahid, Shamsuddin

2015-09-01

In this study, index of entropy and catastrophe theory methods were used for demarcating groundwater potential in an arid region using weighted linear combination techniques in geographical information system (GIS) environment. A case study from Badra area in the eastern part of central of Iraq was analyzed and discussed. Six factors believed to have influence on groundwater occurrence namely elevation, slope, aquifer transmissivity and storativity, soil, and distance to fault were prepared as raster thematic layers to facility integration into GIS environment. The factors were chosen based on the availability of data and local conditions of the study area. Both techniques were used for computing weights and assigning ranks vital for applying weighted linear combination approach. The results of application of both modes indicated that the most influential groundwater occurrence factors were slope and elevation. The other factors have relatively smaller values of weights implying that these factors have a minor role in groundwater occurrence conditions. The groundwater potential index (GPI) values for both models were classified using natural break classification scheme into five categories: very low, low, moderate, high, and very high. For validation of generated GPI, the relative operating characteristic (ROC) curves were used. According to the obtained area under the curve, the catastrophe model with 78 % prediction accuracy was found to perform better than entropy model with 77 % prediction accuracy. The overall results indicated that both models have good capability for predicting groundwater potential zones.

Ghosts, UFOs, and magic: positive affect and the experiential system.

PubMed

King, Laura A; Burton, Chad M; Hicks, Joshua A; Drigotas, Stephen M

2007-05-01

Three studies examined the potential interactions of the experiential system and positive affect (PA) in predicting superstitious beliefs and sympathetic magic. In Study 1, experientiality and induced positive mood interacted to predict the emergence of belief in videos purporting to show unidentified flying objects or ghosts. In Study 2, naturally occurring PA interacted with experientiality to predict susceptibility to sympathetic magic, specifically difficulty in throwing darts at a picture of a baby (demonstrating the law of similarity). In Study 3, induced mood interacted with experientiality to predict sitting farther away from, and expressing less liking for, a partner who had stepped in excrement (demonstrating the law of contagion). Results are interpreted as indicating that PA promotes experiential processing. Implications for the psychology of nonrational beliefs and behaviors are discussed. ((c) 2007 APA, all rights reserved).

Comparing GIS-based habitat models for applications in EIA and SEA

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

Gontier, Mikael, E-mail: gontier@kth.s; Moertberg, Ulla, E-mail: mortberg@kth.s; Balfors, Berit, E-mail: balfors@kth.s

Land use changes, urbanisation and infrastructure developments in particular, cause fragmentation of natural habitats and threaten biodiversity. Tools and measures must be adapted to assess and remedy the potential effects on biodiversity caused by human activities and developments. Within physical planning, environmental impact assessment (EIA) and strategic environmental assessment (SEA) play important roles in the prediction and assessment of biodiversity-related impacts from planned developments. However, adapted prediction tools to forecast and quantify potential impacts on biodiversity components are lacking. This study tested and compared four different GIS-based habitat models and assessed their relevance for applications in environmental assessment. The modelsmore » were implemented in the Stockholm region in central Sweden and applied to data on the crested tit (Parus cristatus), a sedentary bird species of coniferous forest. All four models performed well and allowed the distribution of suitable habitats for the crested tit in the Stockholm region to be predicted. The models were also used to predict and quantify habitat loss for two regional development scenarios. The study highlighted the importance of model selection in impact prediction. Criteria that are relevant for the choice of model for predicting impacts on biodiversity were identified and discussed. Finally, the importance of environmental assessment for the preservation of biodiversity within the general frame of biodiversity conservation is emphasised.« less

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.

Molecular dynamics, flexible docking, virtual screening, ADMET predictions, and molecular interaction field studies to design novel potential MAO-B inhibitors.

PubMed

Braun, Glaucia H; Jorge, Daniel M M; Ramos, Henrique P; Alves, Raquel M; da Silva, Vinicius B; Giuliatti, Silvana; Sampaio, Suley Vilela; Taft, Carlton A; Silva, Carlos H T P

2008-02-01

Monoamine oxidase is a flavoenzyme bound to the mitochondrial outer membranes of the cells, which is responsible for the oxidative deamination of neurotransmitter and dietary amines. It has two distinct isozymic forms, designated MAO-A and MAO-B, each displaying different substrate and inhibitor specificities. They are the well-known targets for antidepressant, Parkinson's disease, and neuroprotective drugs. Elucidation of the x-ray crystallographic structure of MAO-B has opened the way for the molecular modeling studies. In this work we have used molecular modeling, density functional theory with correlation, virtual screening, flexible docking, molecular dynamics, ADMET predictions, and molecular interaction field studies in order to design new molecules with potential higher selectivity and enzymatic inhibitory activity over MAO-B.

A GIS model predicting potential distributions of a lineage: a test case on hermit spiders (Nephilidae: Nephilengys).

PubMed

Năpăruş, Magdalena; Kuntner, Matjaž

2012-01-01

Although numerous studies model species distributions, these models are almost exclusively on single species, while studies of evolutionary lineages are preferred as they by definition study closely related species with shared history and ecology. Hermit spiders, genus Nephilengys, represent an ecologically important but relatively species-poor lineage with a globally allopatric distribution. Here, we model Nephilengys global habitat suitability based on known localities and four ecological parameters. We geo-referenced 751 localities for the four most studied Nephilengys species: N. cruentata (Africa, New World), N. livida (Madagascar), N. malabarensis (S-SE Asia), and N. papuana (Australasia). For each locality we overlaid four ecological parameters: elevation, annual mean temperature, annual mean precipitation, and land cover. We used linear backward regression within ArcGIS to select two best fit parameters per species model, and ModelBuilder to map areas of high, moderate and low habitat suitability for each species within its directional distribution. For Nephilengys cruentata suitable habitats are mid elevation tropics within Africa (natural range), a large part of Brazil and the Guianas (area of synanthropic spread), and even North Africa, Mediterranean, and Arabia. Nephilengys livida is confined to its known range with suitable habitats being mid-elevation natural and cultivated lands. Nephilengys malabarensis, however, ranges across the Equator throughout Asia where the model predicts many areas of high ecological suitability in the wet tropics. Its directional distribution suggests the species may potentially spread eastwards to New Guinea where the suitable areas of N. malabarensis largely surpass those of the native N. papuana, a species that prefers dry forests of Australian (sub)tropics. Our model is a customizable GIS tool intended to predict current and future potential distributions of globally distributed terrestrial lineages. Its predictive potential may be tested in foreseeing species distribution shifts due to habitat destruction and global climate change.

A GIS Model Predicting Potential Distributions of a Lineage: A Test Case on Hermit Spiders (Nephilidae: Nephilengys)

PubMed Central

Năpăruş, Magdalena; Kuntner, Matjaž

2012-01-01

Background Although numerous studies model species distributions, these models are almost exclusively on single species, while studies of evolutionary lineages are preferred as they by definition study closely related species with shared history and ecology. Hermit spiders, genus Nephilengys, represent an ecologically important but relatively species-poor lineage with a globally allopatric distribution. Here, we model Nephilengys global habitat suitability based on known localities and four ecological parameters. Methodology/Principal Findings We geo-referenced 751 localities for the four most studied Nephilengys species: N. cruentata (Africa, New World), N. livida (Madagascar), N. malabarensis (S-SE Asia), and N. papuana (Australasia). For each locality we overlaid four ecological parameters: elevation, annual mean temperature, annual mean precipitation, and land cover. We used linear backward regression within ArcGIS to select two best fit parameters per species model, and ModelBuilder to map areas of high, moderate and low habitat suitability for each species within its directional distribution. For Nephilengys cruentata suitable habitats are mid elevation tropics within Africa (natural range), a large part of Brazil and the Guianas (area of synanthropic spread), and even North Africa, Mediterranean, and Arabia. Nephilengys livida is confined to its known range with suitable habitats being mid-elevation natural and cultivated lands. Nephilengys malabarensis, however, ranges across the Equator throughout Asia where the model predicts many areas of high ecological suitability in the wet tropics. Its directional distribution suggests the species may potentially spread eastwards to New Guinea where the suitable areas of N. malabarensis largely surpass those of the native N. papuana, a species that prefers dry forests of Australian (sub)tropics. Conclusions Our model is a customizable GIS tool intended to predict current and future potential distributions of globally distributed terrestrial lineages. Its predictive potential may be tested in foreseeing species distribution shifts due to habitat destruction and global climate change. PMID:22238692

Diagnostic Accuracy of Somatosensory Evoked Potential Monitoring in Evaluating Neurological Complications During Endovascular Aneurysm Treatment.

PubMed

Ares, William J; Grandhi, Ramesh M; Panczykowski, David M; Weiner, Gregory M; Thirumala, Parthasarathy; Habeych, Miguel E; Crammond, Donald J; Horowitz, Michael B; Jankowitz, Brian T; Jadhav, Ashutosh; Jovin, Tudor G; Ducruet, Andrew F; Balzer, Jeffrey

2018-02-01

Somatosensory evoked potential (SSEP) monitoring is used extensively for early detection and prevention of neurological complications in patients undergoing many different neurosurgical procedures. However, the predictive ability of SSEP monitoring during endovascular treatment of cerebral aneurysms is not well detailed. To evaluate the performance of intraoperative SSEP in the prediction postprocedural neurological deficits (PPNDs) after coil embolization of intracranial aneurysms. This population-based cohort study included patients ≥18 years of age undergoing intracranial aneurysm embolization with concurrent SSEP monitoring between January 2006 and August 2012. The ability of SSEP to predict PPNDs was analyzed by multiple regression analyses and assessed by the area under the receiver operating characteristic curve. In a population of 888 patients, SSEP changes occurred in 8.6% (n = 77). Twenty-eight patients (3.1%) suffered PPNDs. A 50% to 99% loss in SSEP waveform was associated with a 20-fold increase in risk of PPND; a total loss of SSEP waveform, regardless of permanence, was associated with a greater than 200-fold risk of PPND. SSEPs displayed very good predictive ability for PPND, with an area under the receiver operating characteristic curve of 0.84 (95% CI 0.76-0.92). This study supports the predictive ability of SSEPs for the detection of PPNDs. The magnitude and persistence of SSEP changes is clearly associated with the development of PPNDs. The utility of SSEP monitoring in detecting ischemia may provide an opportunity for neurointerventionalists to respond to changes intraoperatively to mitigate the potential for PPNDs. Copyright © 2017 by the Congress of Neurological Surgeons

Past speculations of the future: a review of the methods used for forecasting emerging health technologies.

PubMed

Doos, Lucy; Packer, Claire; Ward, Derek; Simpson, Sue; Stevens, Andrew

2016-03-10

Forecasting can support rational decision-making around the introduction and use of emerging health technologies and prevent investment in technologies that have limited long-term potential. However, forecasting methods need to be credible. We performed a systematic search to identify the methods used in forecasting studies to predict future health technologies within a 3-20-year timeframe. Identification and retrospective assessment of such methods potentially offer a route to more reliable prediction. Systematic search of the literature to identify studies reported on methods of forecasting in healthcare. People are not needed in this study. The authors searched MEDLINE, EMBASE, PsychINFO and grey literature sources, and included articles published in English that reported their methods and a list of identified technologies. Studies reporting methods used to predict future health technologies within a 3-20-year timeframe with an identified list of individual healthcare technologies. Commercially sponsored reviews, long-term futurology studies (with over 20-year timeframes) and speculative editorials were excluded. 15 studies met our inclusion criteria. Our results showed that the majority of studies (13/15) consulted experts either alone or in combination with other methods such as literature searching. Only 2 studies used more complex forecasting tools such as scenario building. The methodological fundamentals of formal 3-20-year prediction are consistent but vary in details. Further research needs to be conducted to ascertain if the predictions made were accurate and whether accuracy varies by the methods used or by the types of technologies identified. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Visual perception and regulatory conflict: motivation and physiology influence distance perception.

PubMed

Cole, Shana; Balcetis, Emily; Zhang, Sam

2013-02-01

Regulatory conflict can emerge when people experience a strong motivation to act on goals but a conflicting inclination to withhold action because physical resources available, or physiological potentials, are low. This study demonstrated that distance perception is biased in ways that theory suggests assists in managing this conflict. Participants estimated the distance to a target location. Individual differences in physiological potential measured via waist-to-hip ratio interacted with manipulated motivational states to predict visual perception. Among people low in physiological potential and likely to experience regulatory conflict, the environment appeared easier to traverse when motivation was strong compared with weak. Among people high in potential and less likely to experience conflict, perception was not predicted by motivational strength. The role of motivated distance perception in self-regulation is discussed. 2013 APA, all rights reserved

Computational Study on New Natural Compound Inhibitors of Pyruvate Dehydrogenase Kinases

PubMed Central

Zhou, Xiaoli; Yu, Shanshan; Su, Jing; Sun, Liankun

2016-01-01

Pyruvate dehydrogenase kinases (PDKs) are key enzymes in glucose metabolism, negatively regulating pyruvate dehyrogenase complex (PDC) activity through phosphorylation. Inhibiting PDKs could upregulate PDC activity and drive cells into more aerobic metabolism. Therefore, PDKs are potential targets for metabolism related diseases, such as cancers and diabetes. In this study, a series of computer-aided virtual screening techniques were utilized to discover potential inhibitors of PDKs. Structure-based screening using Libdock was carried out following by ADME (adsorption, distribution, metabolism, excretion) and toxicity prediction. Molecular docking was used to analyze the binding mechanism between these compounds and PDKs. Molecular dynamic simulation was utilized to confirm the stability of potential compound binding. From the computational results, two novel natural coumarins compounds (ZINC12296427 and ZINC12389251) from the ZINC database were found binding to PDKs with favorable interaction energy and predicted to be non-toxic. Our study provide valuable information of PDK-coumarins binding mechanisms in PDK inhibitor-based drug discovery. PMID:26959013

Computational Study on New Natural Compound Inhibitors of Pyruvate Dehydrogenase Kinases.

PubMed

Zhou, Xiaoli; Yu, Shanshan; Su, Jing; Sun, Liankun

2016-03-04

Pyruvate dehydrogenase kinases (PDKs) are key enzymes in glucose metabolism, negatively regulating pyruvate dehyrogenase complex (PDC) activity through phosphorylation. Inhibiting PDKs could upregulate PDC activity and drive cells into more aerobic metabolism. Therefore, PDKs are potential targets for metabolism related diseases, such as cancers and diabetes. In this study, a series of computer-aided virtual screening techniques were utilized to discover potential inhibitors of PDKs. Structure-based screening using Libdock was carried out following by ADME (adsorption, distribution, metabolism, excretion) and toxicity prediction. Molecular docking was used to analyze the binding mechanism between these compounds and PDKs. Molecular dynamic simulation was utilized to confirm the stability of potential compound binding. From the computational results, two novel natural coumarins compounds (ZINC12296427 and ZINC12389251) from the ZINC database were found binding to PDKs with favorable interaction energy and predicted to be non-toxic. Our study provide valuable information of PDK-coumarins binding mechanisms in PDK inhibitor-based drug discovery.

New Thiazolyl-triazole Schiff Bases: Synthesis and Evaluation of the Anti-Candida Potential.

PubMed

Stana, Anca; Enache, Alexandra; Vodnar, Dan Cristian; Nastasă, Cristina; Benedec, Daniela; Ionuț, Ioana; Login, Cezar; Marc, Gabriel; Oniga, Ovidiu; Tiperciuc, Brîndușa

2016-11-22

In the context of the dangerous phenomenon of fungal resistance to the available therapies, we present here the chemical synthesis of a new series of thiazolyl-triazole Schiff bases B1 - B15 , which were in vitro assessed for their anti- Candida potential. Compound B10 was found to be more potent against Candida spp. when compared with the reference drugs Fluconazole and Ketoconazole. A docking study of the newly synthesized Schiff bases was performed, and results showed good binding affinity in the active site of co-crystallized Itraconazole-lanosterol 14α-demethylase isolated from Saccharomyces cerevisiae . An in silico ADMET (absorption, distribution, metabolism, excretion, toxicity) study was done in order to predict some pharmacokinetic and pharmacotoxicological properties. The Schiff bases showed good drug-like properties. The results of in vitro anti- Candida activity, a docking study and ADMET prediction revealed that the newly synthesized compounds have potential anti- Candida activity and evidenced the most active derivative, B10 , which can be further optimized as a lead compound.

Prediction of the developmental toxicity hazard potential of halogenated drinking water disinfection by-products tested by the in vitro hydra assay

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

Fu, L.J.; Johnson, E.M.; Newman, L.M.

A series of seven randomly selected potential halogenated water disinfection by-products were evaluated in vitro by the hydra assay to determine their developmental toxicity hazard potential. For six of the chemicals tested by this assay (dibromoacetonitrile; trichloroacetonitrile; 2-chlorophenol; 2,4,6-trichlorophenol; trichloroacetic acid; dichloroacetone) it was predicted that they would be generally equally toxic to both adult and embryonic mammals when studied by means of standard developmental toxicity teratology tests. However, the potential water disinfection by-product chloroacetic acid (CA) was determined to be over eight times more toxic to the embryonic developmental portion of the assay than it was to the adults.more » Because of this potential selectivity, CA is a high-priority item for developmental toxicity tests in pregnant mammals to confirm or refute its apparent unique developmental hazard potential and/or to establish a NOAEL by the route of most likely human exposure.« less

cisMEP: an integrated repository of genomic epigenetic profiles and cis-regulatory modules in Drosophila

PubMed Central

2014-01-01

Background Cis-regulatory modules (CRMs), or the DNA sequences required for regulating gene expression, play the central role in biological researches on transcriptional regulation in metazoan species. Nowadays, the systematic understanding of CRMs still mainly resorts to computational methods due to the time-consuming and small-scale nature of experimental methods. But the accuracy and reliability of different CRM prediction tools are still unclear. Without comparative cross-analysis of the results and combinatorial consideration with extra experimental information, there is no easy way to assess the confidence of the predicted CRMs. This limits the genome-wide understanding of CRMs. Description It is known that transcription factor binding and epigenetic profiles tend to determine functions of CRMs in gene transcriptional regulation. Thus integration of the genome-wide epigenetic profiles with systematically predicted CRMs can greatly help researchers evaluate and decipher the prediction confidence and possible transcriptional regulatory functions of these potential CRMs. However, these data are still fragmentary in the literatures. Here we performed the computational genome-wide screening for potential CRMs using different prediction tools and constructed the pioneer database, cisMEP (cis-regulatory module epigenetic profile database), to integrate these computationally identified CRMs with genomic epigenetic profile data. cisMEP collects the literature-curated TFBS location data and nine genres of epigenetic data for assessing the confidence of these potential CRMs and deciphering the possible CRM functionality. Conclusions cisMEP aims to provide a user-friendly interface for researchers to assess the confidence of different potential CRMs and to understand the functions of CRMs through experimentally-identified epigenetic profiles. The deposited potential CRMs and experimental epigenetic profiles for confidence assessment provide experimentally testable hypotheses for the molecular mechanisms of metazoan gene regulation. We believe that the information deposited in cisMEP will greatly facilitate the comparative usage of different CRM prediction tools and will help biologists to study the modular regulatory mechanisms between different TFs and their target genes. PMID:25521507

Potential theory of radiation

NASA Technical Reports Server (NTRS)

Chiu, Huei-Huang

1989-01-01

A theoretical method is being developed by which the structure of a radiation field can be predicted by a radiation potential theory, similar to a classical potential theory. The introduction of a scalar potential is justified on the grounds that the spectral intensity vector is irrotational. The vector is also solenoidal in the limits of a radiation field in complete radiative equilibrium or in a vacuum. This method provides an exact, elliptic type equation that will upgrade the accuracy and the efficiency of the current CFD programs required for the prediction of radiation and flow fields. A number of interesting results emerge from the present study. First, a steady state radiation field exhibits an optically modulated inverse square law distribution character. Secondly, the unsteady radiation field is structured with two conjugate scalar potentials. Each is governed by a Klein-Gordon equation with a frictional force and a restoring force. This steady potential field structure and the propagation of radiation potentials are consistent with the well known results of classical electromagnetic theory. The extension of the radiation potential theory for spray combustion and hypersonic flow is also recommended.

Ensemble Cannonical Correlation Prediction of Seasonal Precipitation Over the US

NASA Technical Reports Server (NTRS)

Lau, William K. M.; Kim, Kyu-Myong; Shen, Samuel; Einaudi, Franco (Technical Monitor)

2001-01-01

This paper presents preliminary results of an ensemble cannonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate downscaling studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into nonoverlapping sectors. The cannonical SST modes for each sector are used as the predictor for the ensemble hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all regions of the US and for all seasonal compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible for enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduced the spring predictability barrier over all regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and regional regional data. Moreover, the ECC forecasts can be applied to other climate subsystems and, in conjunction with further diagnostic or model studies will enables a better understanding of the dynamic links between climate variations and precipitation, not only for the US, but also for other regions of the world.

Trp zipper folding kinetics by molecular dynamics and temperature-jump spectroscopy

PubMed Central

Snow, Christopher D.; Qiu, Linlin; Du, Deguo; Gai, Feng; Hagen, Stephen J.; Pande, Vijay S.

2004-01-01

We studied the microsecond folding dynamics of three β hairpins (Trp zippers 1–3, TZ1–TZ3) by using temperature-jump fluorescence and atomistic molecular dynamics in implicit solvent. In addition, we studied TZ2 by using time-resolved IR spectroscopy. By using distributed computing, we obtained an aggregate simulation time of 22 ms. The simulations included 150, 212, and 48 folding events at room temperature for TZ1, TZ2, and TZ3, respectively. The all-atom optimized potentials for liquid simulations (OPLSaa) potential set predicted TZ1 and TZ2 properties well; the estimated folding rates agreed with the experimentally determined folding rates and native conformations were the global potential-energy minimum. The simulations also predicted reasonable unfolding activation enthalpies. This work, directly comparing large simulated folding ensembles with multiple spectroscopic probes, revealed both the surprising predictive ability of current models as well as their shortcomings. Specifically, for TZ1–TZ3, OPLS for united atom models had a nonnative free-energy minimum, and the folding rate for OPLSaa TZ3 was sensitive to the initial conformation. Finally, we characterized the transition state; all TZs fold by means of similar, native-like transition-state conformations. PMID:15020773

Trp zipper folding kinetics by molecular dynamics and temperature-jump spectroscopy

NASA Astrophysics Data System (ADS)

Snow, Christopher D.; Qiu, Linlin; Du, Deguo; Gai, Feng; Hagen, Stephen J.; Pande, Vijay S.

2004-03-01

We studied the microsecond folding dynamics of three hairpins (Trp zippers 1-3, TZ1-TZ3) by using temperature-jump fluorescence and atomistic molecular dynamics in implicit solvent. In addition, we studied TZ2 by using time-resolved IR spectroscopy. By using distributed computing, we obtained an aggregate simulation time of 22 ms. The simulations included 150, 212, and 48 folding events at room temperature for TZ1, TZ2, and TZ3, respectively. The all-atom optimized potentials for liquid simulations (OPLSaa) potential set predicted TZ1 and TZ2 properties well; the estimated folding rates agreed with the experimentally determined folding rates and native conformations were the global potential-energy minimum. The simulations also predicted reasonable unfolding activation enthalpies. This work, directly comparing large simulated folding ensembles with multiple spectroscopic probes, revealed both the surprising predictive ability of current models as well as their shortcomings. Specifically, for TZ1-TZ3, OPLS for united atom models had a nonnative free-energy minimum, and the folding rate for OPLSaa TZ3 was sensitive to the initial conformation. Finally, we characterized the transition state; all TZs fold by means of similar, native-like transition-state conformations.

Temperature-Dependent Development, Cold Tolerance, and Potential Distribution of Cricotopus lebetis (Diptera: Chironomidae), a Tip Miner of Hydrilla verticillata (Hydrocharitaceae)

PubMed Central

Stratman, Karen N.; Overholt, William A.; Cuda, James P.; Mukherjee, A.; Diaz, R.; Netherland, Michael D.; Wilson, Patrick C.

2014-01-01

Abstract A chironomid midge, Cricotopus lebetis (Sublette) (Diptera: Chironomidae), was discovered attacking the apical meristems of Hydrilla verticillata (L.f. Royle) in Crystal River, Citrus Co., Florida in 1992. The larvae mine the stems of H. verticillata and cause basal branching and stunting of the plant. Temperature-dependent development, cold tolerance, and the potential distribution of the midge were investigated. The results of the temperature-dependent development study showed that optimal temperatures for larval development were between 20 and 30°C, and these data were used to construct a map of the potential number of generations per year of C. lebetis in Florida. Data from the cold tolerance study, in conjunction with historical weather data, were used to generate a predicted distribution of C. lebetis in the United States. A distribution was also predicted using an ecological niche modeling approach by characterizing the climate at locations where C. lebetis is known to occur and then finding other locations with similar climate. The distributions predicted using the two modeling approaches were not significantly different and suggested that much of the southeastern United States was climatically suitable for C. lebetis . PMID:25347841

Dynamic assessment of word learning skills of pre-school children with primary language impairment.

PubMed

Camilleri, Bernard; Law, James

2014-10-01

Dynamic assessment has been shown to have considerable theoretical and clinical significance in the assessment of socially disadvantaged and culturally and linguistically diverse children. In this study it is used to enhance assessment of pre-school children with primary language impairment. The purpose of the study was to determine whether a dynamic assessment (DA) has the potential to enhance the predictive capacity of a static measure of receptive vocabulary in pre-school children. Forty pre-school children were assessed using the static British Picture Vocabulary Scale (BPVS), a DA of word learning potential and an assessment of non-verbal cognitive ability. Thirty-seven children were followed up 6 months later and re-assessed using the BPVS. Although the predictive capacity of the static measure was found to be substantial, the DA increased this significantly especially for children with static scores below the 25th centile. The DA of children's word learning has the potential to add value to the static assessment of the child with low language skills, to predict subsequent receptive vocabulary skills and to increase the chance of correctly identifying children in need of ongoing support.

Predicting the safety and efficacy of buffer therapy to raise tumour pHe: an integrative modelling study

PubMed Central

Martin, N K; Robey, I F; Gaffney, E A; Gillies, R J; Gatenby, R A; Maini, P K

2012-01-01

Background: Clinical positron emission tomography imaging has demonstrated the vast majority of human cancers exhibit significantly increased glucose metabolism when compared with adjacent normal tissue, resulting in an acidic tumour microenvironment. Recent studies demonstrated reducing this acidity through systemic buffers significantly inhibits development and growth of metastases in mouse xenografts. Methods: We apply and extend a previously developed mathematical model of blood and tumour buffering to examine the impact of oral administration of bicarbonate buffer in mice, and the potential impact in humans. We recapitulate the experimentally observed tumour pHe effect of buffer therapy, testing a model prediction in vivo in mice. We parameterise the model to humans to determine the translational safety and efficacy, and predict patient subgroups who could have enhanced treatment response, and the most promising combination or alternative buffer therapies. Results: The model predicts a previously unseen potentially dangerous elevation in blood pHe resulting from bicarbonate therapy in mice, which is confirmed by our in vivo experiments. Simulations predict limited efficacy of bicarbonate, especially in humans with more aggressive cancers. We predict buffer therapy would be most effectual: in elderly patients or individuals with renal impairments; in combination with proton production inhibitors (such as dichloroacetate), renal glomular filtration rate inhibitors (such as non-steroidal anti-inflammatory drugs and angiotensin-converting enzyme inhibitors), or with an alternative buffer reagent possessing an optimal pK of 7.1–7.2. Conclusion: Our mathematical model confirms bicarbonate acts as an effective agent to raise tumour pHe, but potentially induces metabolic alkalosis at the high doses necessary for tumour pHe normalisation. We predict use in elderly patients or in combination with proton production inhibitors or buffers with a pK of 7.1–7.2 is most promising. PMID:22382688

Evaluation of the TBET model for potential improvement of southern P indices

USDA-ARS?s Scientific Manuscript database

Due to a shortage of available phosphorus (P) loss data sets, simulated data from a quantitative P transport model could be used to evaluate a P-index. However, the model would need to accurately predict the P loss data sets that are available. The objective of this study was to compare predictions ...

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…

Predictable and unpredictable modes of seasonal mean precipitation over Northeast China

NASA Astrophysics Data System (ADS)

Ying, Kairan; Frederiksen, Carsten S.; Zhao, Tianbao; Zheng, Xiaogu; Xiong, Zhe; Yi, Xue; Li, Chunxiang

2018-04-01

This study investigates the patterns of interannual variability that arise from the potentially predictable (slow) and unpredictable (intraseasonal) components of seasonal mean precipitation over Northeast (NE) China, using observations from a network of 162 meteorological stations for the period 1961-2014. A variance decomposition method is applied to identify the sources of predictability, as well as the sources of prediction uncertainty, for January-February-March (JFM), April-May-June (AMJ), July-August-September (JAS) and October-November-December (OND). The averaged potential predictability (ratio of slow to total variance) of NE China precipitation has the highest value of 0.32 during JAS and lowest value of 0.1 in AMJ. Possible sources of seasonal prediction for the leading predictable precipitation EOF modes come from the SST anomalies in the Japan Sea, as well as the North Atlantic during JFM, the Indian Ocean SST in AMJ, and the eastern tropical Pacific SST in JAS and OND. The prolonged linear trend, which is seen in the principal component time series of the leading predictable mode in JFM and OND, may also serve as a source of predictability. The Polar-Eurasia and Northern Annular Mode atmospheric teleconnection patterns are closely connected with the leading and the second predictable mode of JAS, respectively. The Hadley cell circulation is closely related to the leading predictable mode of OND. The leading/second unpredictable precipitation modes for all these four seasons show a similar monopole/dipole structure, and can be largely attributed to the intraseasonal variabilities of the atmosphere.

Microscopic optical model potential based on a Dirac Brueckner Hartree Fock approach and the relevant uncertainty analysis

NASA Astrophysics Data System (ADS)

Xu, Ruirui; Ma, Zhongyu; Muether, Herbert; van Dalen, E. N. E.; Liu, Tinjin; Zhang, Yue; Zhang, Zhi; Tian, Yuan

2017-09-01

A relativistic microscopic optical model potential, named CTOM, for nucleon-nucleus scattering is investigated in the framework of Dirac-Brueckner-Hartree-Fock approach. The microscopic feature of CTOM is guaranteed through rigorously adopting the isospin dependent DBHF calculation within the subtracted T matrix scheme. In order to verify its prediction power, a global study n, p+ A scattering are carried out. The predicted scattering observables coincide with experimental data within a good accuracy over a broad range of targets and a large region of energies only with two free items, namely the free-range factor t in the applied improved local density approximation and minor adjustments of the scalar and vector potentials in the low-density region. In addition, to estimate the uncertainty of the theoretical results, the deterministic simple least square approach is preliminarily employed to derive the covariance of predicted angular distributions, which is also briefly contained in this paper.

Potential Hydrodynamic Loads on Coastal Bridges in the Greater New York Area due to Extreme Storm Surge and Wave

DOT National Transportation Integrated Search

2018-04-18

This project makes a computer modeling study on vulnerability of coastal bridges in New York City (NYC) metropolitan region to storm surges and waves. Prediction is made for potential surges and waves in the region and consequent hydrodynamic load an...

Long-term bed degradation in Maryland streams (phase 2) : Blue Ridge and Western Piedmont provinces.

DOT National Transportation Integrated Search

2012-03-01

Estimation of potential long-term down-cutting of the stream bed is necessary for evaluation and design of bridges for scour and culverts for fish passage. The purpose of this study has been to improve predictions of this potential long-term bed degr...

Medial Frontal Event-Related Potentials and Reward Prediction: Do Responses Matter?

ERIC Educational Resources Information Center

Martin, Laura E.; Potts, Geoffrey F.

2011-01-01

Medial frontal event-related potentials (ERPs) following rewarding feedback index outcome evaluation. The majority of studies examining the feedback related medial frontal negativity (MFN) employ active tasks during which participants' responses impact their feedback, however, the MFN has been elicited during passive tasks. Many of the studies…

Predictive Endocrine Testing in the 21st Century Using In Vitro Assays of Estrogen Receptor Signaling Responses

EPA Science Inventory

Thousands of environmental chemicals are subject to regulatory review for their potential to be endocrine disruptors (ED). In vitro high-throughput screening (HTS) assays have emerged as a potential tool for prioritizing chemicals for ED-related whole-animal tests. In this study,...

Utilizing Dental Electronic Health Records Data to Predict Risk for Periodontal Disease.

PubMed

Thyvalikakath, Thankam P; Padman, Rema; Vyawahare, Karnali; Darade, Pratiksha; Paranjape, Rhucha

2015-01-01

Periodontal disease is a major cause for tooth loss and adversely affects individuals' oral health and quality of life. Research shows its potential association with systemic diseases like diabetes and cardiovascular disease, and social habits such as smoking. This study explores mining potential risk factors from dental electronic health records to predict and display patients' contextualized risk for periodontal disease. We retrieved relevant risk factors from structured and unstructured data on 2,370 patients who underwent comprehensive oral examinations at the Indiana University School of Dentistry, Indianapolis, IN, USA. Predicting overall risk and displaying relationships between risk factors and their influence on the patient's oral and general health can be a powerful educational and disease management tool for patients and clinicians at the point of care.

Predicting selective drug targets in cancer through metabolic networks

PubMed Central

Folger, Ori; Jerby, Livnat; Frezza, Christian; Gottlieb, Eyal; Ruppin, Eytan; Shlomi, Tomer

2011-01-01

The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome-scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled. PMID:21694718

Prediction of large negative shaded-side spacecraft potentials

NASA Technical Reports Server (NTRS)

Prokopenko, S. M. L.; Laframboise, J. G.

1977-01-01

A calculation by Knott, for the floating potential of a spherically symmetric synchronous-altitude satellite in eclipse, was adapted to provide simple calculations of upper bounds on negative potentials which may be achieved by electrically isolated shaded surfaces on spacecraft in sunlight. Large (approximately 60 percent) increases in predicted negative shaded-side potentials are obtained. To investigate effective potential barrier or angular momentum selection effects due to the presence of less negative sunlit-side or adjacent surface potentials, these expressions were replaced by the ion random current, which is a lower bound for convex surfaces when such effects become very severe. Further large increases in predicted negative potentials were obtained, amounting to a doubling in some cases.

Predicted Distribution of Visceral Leishmaniasis Vectors (Diptera: Psychodidae; Phlebotominae) in Iran: A Niche Model Study.

PubMed

Hanafi-Bojd, A A; Rassi, Y; Yaghoobi-Ershadi, M R; Haghdoost, A A; Akhavan, A A; Charrahy, Z; Karimi, A

2015-12-01

Visceral leishmaniasis (VL) is an important vector-borne disease in Iran. Till now, Leishmania infantum has been detected from five species of sand flies in the country including Phlebotomus kandelakii, Phlebotomus major s.l., Phlebotomus perfiliewi, Phlebotomus alexandri and Phlebotomus tobbi. Also, Phlebotomus keshishiani was found to be infected with Leishmania parasites. This study aimed at predicting the probable niches and distribution of vectors of visceral leishmaniasis in Iran. Data on spatial distribution studies of sand flies were obtained from Iranian database on sand flies. Sample points were included in data from faunistic studies on sand flies conducted during 1995-2013. MaxEnt software was used to predict the appropriate ecological niches for given species, using climatic and topographical data. Distribution maps were prepared and classified in ArcGIS to find main ecological niches of the vectors and hot spots for VL transmission in Iran. Phlebotomus kandelakii, Ph. major s.l. and Ph. alexandri seem to have played a more important role in VL transmission in Iran, so this study focuses on them. Representations of MaxEnt model for probability of distribution of the studied sand flies showed high contribution of climatological and topographical variables to predict the potential distribution of three vector species. Isothermality was found to be an environmental variable with the highest gain when used in isolation for Ph. kandelakii and Ph. major s.l., while for Ph. alexandri, the most effective variable was precipitation of the coldest quarter. The results of this study present the first prediction on distribution of sand fly vectors of VL in Iran. The predicted distributions were matched with the disease-endemic areas in the country, while it was found that there were some unaffected areas with the potential transmission. More comprehensive studies are recommended on the ecology and vector competence of VL vectors in the country. © 2015 Blackwell Verlag GmbH.

Childhood maltreatment and adulthood poor sleep quality: a longitudinal study.

PubMed

Abajobir, Amanuel A; Kisely, Steve; Williams, Gail; Strathearn, Lane; Najman, Jake M

2017-08-01

Available evidence from cross-sectional studies suggests that childhood maltreatment may be associated with a range of sleep disorders. However, these studies have not controlled for potential individual-, familial- and environmental-level confounders. To determine the association between childhood maltreatment and lower sleep quality after adjusting for potential confounders. Data for the present study were obtained from a pre-birth cohort study of 3778 young adults (52.6% female) of the Mater Hospital-University of Queensland Study of Pregnancy follow up at a mean age of 20.6 years. The Mater Hospital-University of Queensland Study of Pregnancy is a prospective Australian pre-birth cohort study of mothers consecutively recruited during their first obstetric clinic visit at Brisbane's Mater Hospital in 1981-1983. Participants completed the Pittsburgh Sleep Quality Index at the 21-year follow up. We linked this dataset to agency-recorded substantiated cases of childhood maltreatment. A series of separate logistic regression models was used to test whether childhood maltreatment predicted lower sleep quality after adjustment for selected confounders. Substantiated physical abuse significantly predicted lower sleep quality in males. Single and multiple forms of childhood maltreatment, including age of maltreatment and number of substantiations, did not predict lower sleep quality in either gender in both crude and adjusted models. Not being married, living in a residential problem area, cigarette smoking and internalising were significantly associated with lower sleep quality in a fully adjusted model for the male-female combined sample. Childhood maltreatment does not appear to predict young adult poor sleep quality, with the exception of physical abuse for males. While childhood maltreatment has been found to predict a range of mental health problems, childhood maltreatment does not appear to predict sleep problems occurring in young adults. Poor sleep quality was accounted for by concurrent social disadvantage, cigarette smoking and internalising. © 2017 Royal Australasian College of Physicians.

Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.

PubMed

Liu, Guang-Hui; Shen, Hong-Bin; Yu, Dong-Jun

2016-04-01

Accurately predicting protein-protein interaction sites (PPIs) is currently a hot topic because it has been demonstrated to be very useful for understanding disease mechanisms and designing drugs. Machine-learning-based computational approaches have been broadly utilized and demonstrated to be useful for PPI prediction. However, directly applying traditional machine learning algorithms, which often assume that samples in different classes are balanced, often leads to poor performance because of the severe class imbalance that exists in the PPI prediction problem. In this study, we propose a novel method for improving PPI prediction performance by relieving the severity of class imbalance using a data-cleaning procedure and reducing predicted false positives with a post-filtering procedure: First, a machine-learning-based data-cleaning procedure is applied to remove those marginal targets, which may potentially have a negative effect on training a model with a clear classification boundary, from the majority samples to relieve the severity of class imbalance in the original training dataset; then, a prediction model is trained on the cleaned dataset; finally, an effective post-filtering procedure is further used to reduce potential false positive predictions. Stringent cross-validation and independent validation tests on benchmark datasets demonstrated the efficacy of the proposed method, which exhibits highly competitive performance compared with existing state-of-the-art sequence-based PPIs predictors and should supplement existing PPI prediction methods.

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.

Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs.

PubMed

Liu, Mei; Wu, Yonghui; Chen, Yukun; Sun, Jingchun; Zhao, Zhongming; Chen, Xue-wen; Matheny, Michael Edwin; Xu, Hua

2012-06-01

Adverse drug reaction (ADR) is one of the major causes of failure in drug development. Severe ADRs that go undetected until the post-marketing phase of a drug often lead to patient morbidity. Accurate prediction of potential ADRs is required in the entire life cycle of a drug, including early stages of drug design, different phases of clinical trials, and post-marketing surveillance. Many studies have utilized either chemical structures or molecular pathways of the drugs to predict ADRs. Here, the authors propose a machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drug's chemical structures and biological properties, including protein targets and pathway information. A large-scale study was conducted to predict 1385 known ADRs of 832 approved drugs, and five machine-learning algorithms for this task were compared. This evaluation, based on a fivefold cross-validation, showed that the support vector machine algorithm outperformed the others. Of the three types of information, phenotypic data were the most informative for ADR prediction. When biological and phenotypic features were added to the baseline chemical information, the ADR prediction model achieved significant improvements in area under the curve (from 0.9054 to 0.9524), precision (from 43.37% to 66.17%), and recall (from 49.25% to 63.06%). Most importantly, the proposed model successfully predicted the ADRs associated with withdrawal of rofecoxib and cerivastatin. The results suggest that phenotypic information on drugs is valuable for ADR prediction. Moreover, they demonstrate that different models that combine chemical, biological, or phenotypic information can be built from approved drugs, and they have the potential to detect clinically important ADRs in both preclinical and post-marketing phases.

Predicting local field potentials with recurrent neural networks.

PubMed

Kim, Louis; Harer, Jacob; Rangamani, Akshay; Moran, James; Parks, Philip D; Widge, Alik; Eskandar, Emad; Dougherty, Darin; Chin, Sang Peter

2016-08-01

We present a Recurrent Neural Network using LSTM (Long Short Term Memory) that is capable of modeling and predicting Local Field Potentials. We train and test the network on real data recorded from epilepsy patients. We construct networks that predict multi-channel LFPs for 1, 10, and 100 milliseconds forward in time. Our results show that prediction using LSTM outperforms regression when predicting 10 and 100 millisecond forward in time.

"I Wanna Be the Very Best!" Agreeableness and Perseverance Predict Sustained Playing to Pokémon Go: A Longitudinal Study.

PubMed

Lalot, Fanny; Zerhouni, Oulmann; Pinelli, Mathieu

2017-10-01

The smartphone game Pokémon Go™ has attracted much scientific attention regarding its potential health-related outcomes. Most studies, however, limited their investigation to short-term outcomes. The aim of the present study is to investigate the role of personality traits in predicting sustained playing to the game on a 6-month period as well as related health outcomes in terms of distance walked per day. Pokémon Go players from 10 countries were recruited through social media and answered an online questionnaire. At Phase I (August 2016), 402 participants provided their game statistics and filled an extensive personality inventory (six main personality traits, impulsivity, need for cognition, need for closure, competitiveness, and self-efficacy). At Phase II (December 2016), 151 participants indicated whether they were still playing or not and provided updated game statistics. No personality traits predicted the distance walked by the players. However, the probability of still being playing the game at Phase II was positively predicted by three personality traits: agreeableness, perseverance, and premeditation. Distance walked per day significantly decreased between Phases I and II but remained substantial. This study identified three personality traits that predicted sustained playing and thus potentially higher game-related physical activity in the long run. In comparison with prior work, this study goes a step forward by (i) investigating personality traits underlying use of the game and related health outcomes, and (ii) providing longitudinal data concerning the use of the game. Findings open new perspectives for the development of other exergames.

Role of N-Methyl-D-Aspartate Receptors in Action-Based Predictive Coding Deficits in Schizophrenia.

PubMed

Kort, Naomi S; Ford, Judith M; Roach, Brian J; Gunduz-Bruce, Handan; Krystal, John H; Jaeger, Judith; Reinhart, Robert M G; Mathalon, Daniel H

2017-03-15

Recent theoretical models of schizophrenia posit that dysfunction of the neural mechanisms subserving predictive coding contributes to symptoms and cognitive deficits, and this dysfunction is further posited to result from N-methyl-D-aspartate glutamate receptor (NMDAR) hypofunction. Previously, by examining auditory cortical responses to self-generated speech sounds, we demonstrated that predictive coding during vocalization is disrupted in schizophrenia. To test the hypothesized contribution of NMDAR hypofunction to this disruption, we examined the effects of the NMDAR antagonist, ketamine, on predictive coding during vocalization in healthy volunteers and compared them with the effects of schizophrenia. In two separate studies, the N1 component of the event-related potential elicited by speech sounds during vocalization (talk) and passive playback (listen) were compared to assess the degree of N1 suppression during vocalization, a putative measure of auditory predictive coding. In the crossover study, 31 healthy volunteers completed two randomly ordered test days, a saline day and a ketamine day. Event-related potentials during the talk/listen task were obtained before infusion and during infusion on both days, and N1 amplitudes were compared across days. In the case-control study, N1 amplitudes from 34 schizophrenia patients and 33 healthy control volunteers were compared. N1 suppression to self-produced vocalizations was significantly and similarly diminished by ketamine (Cohen's d = 1.14) and schizophrenia (Cohen's d = .85). Disruption of NMDARs causes dysfunction in predictive coding during vocalization in a manner similar to the dysfunction observed in schizophrenia patients, consistent with the theorized contribution of NMDAR hypofunction to predictive coding deficits in schizophrenia. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Reprint of: Initial uncertainty impacts statistical learning in sound sequence processing.

PubMed

Todd, Juanita; Provost, Alexander; Whitson, Lisa; Mullens, Daniel

2018-05-18

This paper features two studies confirming a lasting impact of first learning on how subsequent experience is weighted in early relevance-filtering processes. In both studies participants were exposed to sequences of sound that contained a regular pattern on two different timescales. Regular patterning in sound is readily detected by the auditory system and used to form "prediction models" that define the most likely properties of sound to be encountered in a given context. The presence and strength of these prediction models is inferred from changes in automatically elicited components of auditory evoked potentials. Both studies employed sound sequences that contained both a local and longer-term pattern. The local pattern was defined by a regular repeating pure tone occasionally interrupted by a rare deviating tone (p=0.125) that was physically different (a 30msvs. 60ms duration difference in one condition and a 1000Hz vs. 1500Hz frequency difference in the other). The longer-term pattern was defined by the rate at which the two tones alternated probabilities (i.e., the tone that was first rare became common and the tone that was first common became rare). There was no task related to the tones and participants were asked to ignore them while focussing attention on a movie with subtitles. Auditory-evoked potentials revealed long lasting modulatory influences based on whether the tone was initially encountered as rare and unpredictable or common and predictable. The results are interpreted as evidence that probability (or indeed predictability) assigns a differential information-value to the two tones that in turn affects the extent to which prediction models are updated and imposed. These effects are exposed for both common and rare occurrences of the tones. The studies contribute to a body of work that reveals that probabilistic information is not faithfully represented in these early evoked potentials and instead exposes that predictability (or conversely uncertainty) may trigger value-based learning modulations even in task-irrelevant incidental learning. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Can we predict age at natural menopause using ovarian reserve tests or mother's age at menopause? A systematic literature review.

PubMed

Depmann, Martine; Broer, Simone L; van der Schouw, Yvonne T; Tehrani, Fahimeh R; Eijkemans, Marinus J; Mol, Ben W; Broekmans, Frank J

2016-02-01

This review aimed to appraise data on prediction of age at natural menopause (ANM) based on antimüllerian hormone (AMH), antral follicle count (AFC), and mother's ANM to evaluate clinical usefulness and to identify directions for further research. We conducted three systematic reviews of the literature to identify studies of menopause prediction based on AMH, AFC, or mother's ANM, corrected for baseline age. Six studies selected in the search for AMH all consistently demonstrated AMH as being capable of predicting ANM (hazard ratio, 5.6-9.2). The sole study reporting on mother's ANM indicated that AMH was capable of predicting ANM (hazard ratio, 9.1-9.3). Two studies provided analyses of AFC and yielded conflicting results, making this marker less strong. AMH is currently the most promising marker for ANM prediction. The predictive capacity of mother's ANM demonstrated in a single study makes this marker a promising contributor to AMH for menopause prediction. Models, however, do not predict the extremes of menopause age very well and have wide prediction interval. These markers clearly need improvement before they can be used for individual prediction of menopause in the clinical setting. Moreover, potential limitations for such use include variations in AMH assays used and a lack of correction for factors or diseases affecting AMH levels or ANM. Future studies should include women of a broad age range (irrespective of cycle regularity) and should base predictions on repeated AMH measurements. Furthermore, currently unknown candidate predictors need to be identified.

Computer prediction of three-dimensional potential flow fields in which aircraft propellers operate: Computer program description and users manual

NASA Technical Reports Server (NTRS)

Jumper, S. J.

1979-01-01

A method was developed for predicting the potential flow velocity field at the plane of a propeller operating under the influence of a wing-fuselage-cowl or nacelle combination. A computer program was written which predicts the three dimensional potential flow field. The contents of the program, its input data, and its output results are described.

Identification of Histone Deacetylase (HDAC) as a drug target against MRSA via interolog method of protein-protein interaction prediction.

PubMed

Uddin, Reaz; Tariq, Syeda Sumayya; Azam, Syed Sikander; Wadood, Abdul; Moin, Syed Tarique

2017-08-30

Patently, Protein-Protein Interactions (PPIs) lie at the core of significant biological functions and make the foundation of host-pathogen relationships. Hence, the current study is aimed to use computational biology techniques to predict host-pathogen Protein-Protein Interactions (HP-PPIs) between MRSA and Humans as potential drug targets ultimately proposing new possible inhibitors against them. As a matter of fact this study is based on the Interolog method which implies that homologous proteins retain their ability to interact. A distant homolog approach based on Interolog method was employed to speculate MRSA protein homologs in Humans using PSI-BLAST. In addition the protein interaction partners of these homologs as listed in Database of Interacting Proteins (DIP) were predicted to interact with MRSA as well. Moreover, a direct approach using BLAST was also applied so as to attain further confidence in the strategy. Consequently, the common HP-PPIs predicted by both approaches are suggested as potential drug targets (22%) whereas, the unique HP-PPIs estimated only through distant homolog approach are presented as novel drug targets (12%). Furthermore, the most repeated entry in our results was found to be MRSA Histone Deacetylase (HDAC) which was then modeled using SWISS-MODEL. Eventually, small molecules from ZINC, selected randomly, were docked against HDAC using Auto Dock and are suggested as potential binders (inhibitors) based on their energetic profiles. Thus the current study provides basis for further in-depth analysis of such data which not only include MRSA but other deadly pathogens as well. Copyright © 2017 Elsevier B.V. All rights reserved.

Bioenvironmental and radiological-safety feasibility studies, Atlantic-Pacific Interoceanic Canal. Phase 2, Freshwater ecology: Final report

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

Templeton, W.L.; Dean, J.M.; Watson, D.G.

1968-06-28

The purpose of this program is to conduct studies in the freshwater environment to acquire data needed to evaluate and predict the potential radiation hazards to human populations in the defined regions of proposed nuclear excavations in the Republics of Panama and Colombia. The results of the field surveys conducted in Phase II are presented in this report. Specifically, the data describes the elemental composition of the major components of the ecosystem, and reports the calculated stable element concentration factors for the major food organisms. This data provides baseline values from which predictions can be made of the potential maximummore » radionuclide intake by populations using this resource.« less

Different personal propensities among scientists relate to deeper vs. broader knowledge contributions

PubMed Central

Bateman, Thomas S.; Hess, Andrew M.

2015-01-01

Scientific journal publications, and their contributions to knowledge, can be described by their depth (specialized, domain-specific knowledge extensions) and breadth (topical scope, including spanning multiple knowledge domains). Toward generating hypotheses about how scientists’ personal dispositions would uniquely predict deeper vs. broader contributions to the literature, we assumed that conducting broader studies is generally viewed as less attractive (e.g., riskier) than conducting deeper studies. Study 1 then supported our assumptions: the scientists surveyed considered a hypothetical broader study, compared with an otherwise-comparable deeper study, to be riskier, a less-significant opportunity, and of lower potential importance; they further reported being less likely to pursue it and, in a forced choice, most chose to work on the deeper study. In Study 2, questionnaire measures of medical researchers’ personal dispositions and 10 y of PubMed data indicating their publications’ topical coverage revealed how dispositions differentially predict depth vs. breadth. Competitiveness predicted depth positively, whereas conscientiousness predicted breadth negatively. Performance goal orientation predicted depth but not breadth, and learning goal orientation contrastingly predicted breadth but not depth. Openness to experience positively predicted both depth and breadth. Exploratory work behavior (the converse of applying and exploiting one’s current knowledge) predicted breadth positively and depth negatively. Thus, this research distinguishes depth and breadth of published knowledge contributions, and provides new insights into how scientists’ personal dispositions influence research processes and products. PMID:25733900

Different personal propensities among scientists relate to deeper vs. broader knowledge contributions.

PubMed

Bateman, Thomas S; Hess, Andrew M

2015-03-24

Scientific journal publications, and their contributions to knowledge, can be described by their depth (specialized, domain-specific knowledge extensions) and breadth (topical scope, including spanning multiple knowledge domains). Toward generating hypotheses about how scientists' personal dispositions would uniquely predict deeper vs. broader contributions to the literature, we assumed that conducting broader studies is generally viewed as less attractive (e.g., riskier) than conducting deeper studies. Study 1 then supported our assumptions: the scientists surveyed considered a hypothetical broader study, compared with an otherwise-comparable deeper study, to be riskier, a less-significant opportunity, and of lower potential importance; they further reported being less likely to pursue it and, in a forced choice, most chose to work on the deeper study. In Study 2, questionnaire measures of medical researchers' personal dispositions and 10 y of PubMed data indicating their publications' topical coverage revealed how dispositions differentially predict depth vs. breadth. Competitiveness predicted depth positively, whereas conscientiousness predicted breadth negatively. Performance goal orientation predicted depth but not breadth, and learning goal orientation contrastingly predicted breadth but not depth. Openness to experience positively predicted both depth and breadth. Exploratory work behavior (the converse of applying and exploiting one's current knowledge) predicted breadth positively and depth negatively. Thus, this research distinguishes depth and breadth of published knowledge contributions, and provides new insights into how scientists' personal dispositions influence research processes and products.

The CRH1 Antagonist GSK561679 Increases Human Fear But Not Anxiety as Assessed by Startle

PubMed Central

Grillon, Christian; Hale, Elizabeth; Lieberman, Lynne; Davis, Andrew; Pine, Daniel S; Ernst, Monique

2015-01-01

Fear to predictable threat and anxiety to unpredictable threat reflect distinct processes mediated by different brain structures, the central nucleus of the amygdala and the bed nucleus of the stria terminalis (BNST), respectively. This study tested the hypothesis that the corticotropin-releasing factor (CRF1) antagonist GSK561679 differentially reduces anxiety but increases fear in humans. A total of 31 healthy females received each of four treatments: placebo, 50 mg GSK561679 (low-GSK), 400 mg GSK561679 (high-GSK), and 1 mg alprazolam in a crossover design. Participants were exposed to three conditions during each of the four treatments. The three conditions included one in which predictable aversive shocks were signaled by a cue, a second during which shocks were administered unpredictably, and a third condition without shock. Fear and anxiety were assessed using the acoustic startle reflex. High-GSK had no effect on startle potentiation during unpredictable threat (anxiety) but increased startle potentiation during the predictable condition (fear). Low-GSK did not affect startle potentiation across conditions. Consistent with previous findings, alprazolam reduced startle potentiation during unpredictable threat but not during predictable threat. The increased fear by high-GSK replicates animal findings and suggests a lift of the inhibitory effect of the BNST on the amygdala by the CRF1 antagonist. PMID:25430779

Enzyme/non-enzyme discrimination and prediction of enzyme active site location using charge-based methods.

PubMed

Bate, Paul; Warwicker, Jim

2004-07-02

Calculations of charge interactions complement analysis of a characterised active site, rationalising pH-dependence of activity and transition state stabilisation. Prediction of active site location through large DeltapK(a)s or electrostatic strain is relevant for structural genomics. We report a study of ionisable groups in a set of 20 enzymes, finding that false positives obscure predictive potential. In a larger set of 156 enzymes, peaks in solvent-space electrostatic properties are calculated. Both electric field and potential match well to active site location. The best correlation is found with electrostatic potential calculated from uniform charge density over enzyme volume, rather than from assignment of a standard atom-specific charge set. Studying a shell around each molecule, for 77% of enzymes the potential peak is within that 5% of the shell closest to the active site centre, and 86% within 10%. Active site identification by largest cleft, also with projection onto a shell, gives 58% of enzymes for which the centre of the largest cleft lies within 5% of the active site, and 70% within 10%. Dielectric boundary conditions emphasise clefts in the uniform charge density method, which is suited to recognition of binding pockets embedded within larger clefts. The variation of peak potential with distance from active site, and comparison between enzyme and non-enzyme sets, gives an optimal threshold distinguishing enzyme from non-enzyme. We find that 87% of the enzyme set exceeds the threshold as compared to 29% of the non-enzyme set. Enzyme/non-enzyme homologues, "structural genomics" annotated proteins and catalytic/non-catalytic RNAs are studied in this context.

[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.

Prediction of severe thunderstorms over Sriharikota Island by using the WRF-ARW operational model

NASA Astrophysics Data System (ADS)

Papa Rao, G.; Rajasekhar, M.; Pushpa Saroja, R.; Sreeshna, T.; Rajeevan, M.; Ramakrishna, S. S. V. S.

2016-05-01

Operational short range prediction of Meso-scale thunderstorms for Sriharikota(13.7°N ,80.18°E) has been performed using two nested domains 27 & 9Km configuration of Weather Research & Forecasting-Advanced Research Weather Model (WRF- ARW V3.4).Thunderstorm is a Mesoscale system with spatial scale of few kilometers to a couple of 100 kilometers and time scale of less than an one hour to several hours, which produces heavy rain, lightning, thunder, surface wind squalls and down-bursts. Numerical study of Thunderstorms at Sriharikota and its neighborhood have been discussed with its antecedent thermodynamic stability indices and Parameters that are usually favorable for the development of convective instability based on WRF ARW model predictions. Instability is a prerequisite for the occurrence of severe weather, the greater the instability, the greater will be the potential of thunderstorm. In the present study, K Index, Total totals Index (TTI), Convective Available Potential Energy (CAPE), Convective Inhibition Energy (CINE), Lifted Index (LI), Precipitable Water (PW), etc. are the instability indices used for the short range prediction of thunderstorms. In this study we have made an attempt to estimate the skill of WRF ARW predictability and diagnosed three thunderstorms that occurred during the late evening to late night of 31st July, 20th September and 2nd October of 2015 over Sriharikota Island which are validated with Local Electric Field Mill (EFM), rainfall observations and Chennai Doppler Weather Radar products. The model predicted thermodynamic indices (CAPE, CINE, K Index, LI, TTI and PW) over Sriharikota which act as good indicators for severe thunderstorm activity.

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.

PubMed

Alghamdi, Manal; Al-Mallah, Mouaz; Keteyian, Steven; Brawner, Clinton; Ehrman, Jonathan; Sakr, Sherif

2017-01-01

Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE). The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naïve Bayes Tree, Random Forest, and Logistic Model Tree) and achieved high accuracy of prediction (AUC = 0.92). The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.

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

Combining a Spatial Model and Demand Forecasts to Map Future Surface Coal Mining in Appalachia

PubMed Central

Strager, Michael P.; Strager, Jacquelyn M.; Evans, Jeffrey S.; Dunscomb, Judy K.; Kreps, Brad J.; Maxwell, Aaron E.

2015-01-01

Predicting the locations of future surface coal mining in Appalachia is challenging for a number of reasons. Economic and regulatory factors impact the coal mining industry and forecasts of future coal production do not specifically predict changes in location of future coal production. With the potential environmental impacts from surface coal mining, prediction of the location of future activity would be valuable to decision makers. The goal of this study was to provide a method for predicting future surface coal mining extents under changing economic and regulatory forecasts through the year 2035. This was accomplished by integrating a spatial model with production demand forecasts to predict (1 km2) gridded cell size land cover change. Combining these two inputs was possible with a ratio which linked coal extraction quantities to a unit area extent. The result was a spatial distribution of probabilities allocated over forecasted demand for the Appalachian region including northern, central, southern, and eastern Illinois coal regions. The results can be used to better plan for land use alterations and potential cumulative impacts. PMID:26090883

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.

Shame and guilt as shared vulnerability factors: Shame, but not guilt, prospectively predicts both social anxiety and bulimic symptoms.

PubMed

Levinson, Cheri A; Byrne, Meghan; Rodebaugh, Thomas L

2016-08-01

Social anxiety disorder (SAD) and bulimia nervosa (BN) are highly comorbid. However, little is known about the shared vulnerability factors that prospectively predict both SA and BN symptoms. Two potential factors that have not yet been tested are shame and guilt. In the current study we tested if shame and guilt were shared vulnerability factors for SA and BN symptoms. Women (N=300) completed measures of SA symptoms, BN symptoms, state shame and guilt, and trait negative affect at two time points, two months apart. Utilizing structural equation modeling we tested a cross-sectional and prospective model of SA and BN vulnerability. We found that shame prospectively predicted both SA and BN symptoms. We did not find that guilt prospectively predicted SA or BN symptoms. However, higher levels of both BN and SA symptoms predicted increased guilt over time. We found support for shame as a shared prospective vulnerability factor between BN and SA symptoms. Interventions that focus on decreasing shame could potentially alleviate symptoms of BN and SA in one protocol. Copyright © 2016 Elsevier Ltd. All rights reserved.

The potential of non-invasive pre- and post-mortem carcass measurements to predict the contribution of carcass components to slaughter yield of guinea pigs.

PubMed

Barba, Lida; Sánchez-Macías, Davinia; Barba, Iván; Rodríguez, Nibaldo

2018-06-01

Guinea pig meat consumption is increasing exponentially worldwide. The evaluation of the contribution of carcass components to carcass quality potentially can allow for the estimation of the value added to food animal origin and make research in guinea pigs more practicable. The aim of this study was to propose a methodology for modelling the contribution of different carcass components to the overall carcass quality of guinea pigs by using non-invasive pre- and post mortem carcass measurements. The selection of predictors was developed through correlation analysis and statistical significance; whereas the prediction models were based on Multiple Linear Regression. The prediction results showed higher accuracy in the prediction of carcass component contribution expressed in grams, compared to when expressed as a percentage of carcass quality components. The proposed prediction models can be useful for the guinea pig meat industry and research institutions by using non-invasive and time- and cost-efficient carcass component measuring techniques. Copyright © 2018 Elsevier Ltd. All rights reserved.

Rejection sensitivity prospectively predicts increased rumination.

PubMed

Pearson, Katherine A; Watkins, Edward R; Mullan, Eugene G

2011-10-01

Converging research findings indicate that rumination is correlated with a specific maladaptive interpersonal style encapsulating submissive (overly-accommodating, non-assertive and self-sacrificing) behaviours, and an attachment orientation characterised by rejection sensitivity. This study examined the prospective longitudinal relationship between rumination, the submissive interpersonal style, and rejection sensitivity by comparing two alternative hypotheses: (a) the submissive interpersonal style and rejection sensitivity prospectively predict increased rumination; (b) rumination prospectively predicts the submissive interpersonal style and rejection sensitivity. Currently depressed (n = 22), previously depressed (n = 42) and never depressed (n = 28) individuals completed self-report measures assessing depressive rumination and key psychosocial measures of interpersonal style and behaviours, at baseline and again six months later. Baseline rejection sensitivity prospectively predicted increased rumination six months later, after statistically controlling for baseline rumination, gender and depression. Baseline rumination did not predict the submissive interpersonal style or rejection sensitivity. The results provide a first step towards delineating a potential casual relationship between rejection sensitivity and rumination, and suggest the potential value of clinical assessment and intervention for both rejection sensitivity and rumination in individuals who present with either difficulty. Copyright © 2011 Elsevier Ltd. All rights reserved.

Assessing forest vulnerability and the potential distribution of pine beetles under current and future climate scenarios in the Interior West of the US

USGS Publications Warehouse

Evangelista, P.H.; Kumar, S.; Stohlgren, T.J.; Young, N.E.

2011-01-01

The aim of our study was to estimate forest vulnerability and potential distribution of three bark beetles (Curculionidae: Scolytinae) under current and projected climate conditions for 2020 and 2050. Our study focused on the mountain pine beetle (Dendroctonus ponderosae), western pine beetle (Dendroctonus brevicomis), and pine engraver (Ips pini). This study was conducted across eight states in the Interior West of the US covering approximately 2.2millionkm2 and encompassing about 95% of the Rocky Mountains in the contiguous US. Our analyses relied on aerial surveys of bark beetle outbreaks that occurred between 1991 and 2008. Occurrence points for each species were generated within polygons created from the aerial surveys. Current and projected climate scenarios were acquired from the WorldClim database and represented by 19 bioclimatic variables. We used Maxent modeling technique fit with occurrence points and current climate data to model potential beetle distributions and forest vulnerability. Three available climate models, each having two emission scenarios, were modeled independently and results averaged to produce two predictions for 2020 and two predictions for 2050 for each analysis. Environmental parameters defined by current climate models were then used to predict conditions under future climate scenarios, and changes in different species' ranges were calculated. Our results suggested that the potential distribution for bark beetles under current climate conditions is extensive, which coincides with infestation trends observed in the last decade. Our results predicted that suitable habitats for the mountain pine beetle and pine engraver beetle will stabilize or decrease under future climate conditions, while habitat for the western pine beetle will continue to increase over time. The greatest increase in habitat area was for the western pine beetle, where one climate model predicted a 27% increase by 2050. In contrast, the predicted habitat of the mountain pine beetle from another climate model suggested a decrease in habitat areas as great as 46% by 2050. Generally, 2020 and 2050 models that tested the three climate scenarios independently had similar trends, though one climate scenario for the western pine beetle produced contrasting results. Ranges for all three species of bark beetles shifted considerably geographically suggesting that some host species may become more vulnerable to beetle attack in the future, while others may have a reduced risk over time. ?? 2011 Elsevier B.V.

Transcriptome profiles in sarcoidosis and their potential role in disease prediction.

PubMed

Schupp, Jonas C; Vukmirovic, Milica; Kaminski, Naftali; Prasse, Antje

2017-09-01

Sarcoidosis is a systemic disease defined by the presence of nonnecrotizing granuloma in the absence of any known cause. Although the heterogeneity of sarcoidosis is well characterized clinically, the transcriptome of sarcoidosis and underlying molecular mechanisms are not. The signal of all transcripts, small and long noncoding RNAs, can be detected using microarrays or RNA-Sequencing. Analyzing the transcriptome of tissues that are directly affected by granulomas is of great importance to understand biology of the disease and may be predictive of disease and treatment outcome. Multiple genome wide expression studies performed on sarcoidosis affected tissues were published in the last 11 years. Published studies focused on differences in gene expression between sarcoidosis vs. control tissues, stable vs. progressive sarcoidosis, as well as sarcoidosis vs. other diseases. Strikingly, all these transcriptomics data confirm the key role of TH1 immune response in sarcoidosis and particularly of interferon-γ (IFN-γ) and type I IFN-driven signaling pathways. The steps toward transcriptomics of sarcoidosis in precision medicine highlight the potentials of this approach. Large prospective follow-up studies are required to identify signatures predictive of disease progression and outcome.

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.

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.; Zhao, H. Q.; Yang, Q. C.; Yang, Z. P.

2014-06-01

The aim of the present study is to evaluate the potential ecological risk and 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 the order of ER(Cd) > ER(Pb) > ER(Cu) > ER(Cr) > ER(Zn) have been obtained, which showed that Cd was the most important factor leading to risk. Based on the Cd pollution history, the cumulative acceleration and cumulative rate of Cd were estimated, then the fixed number of years exceeding the 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 metals and the relationship between sampling points and variables. These findings provided some useful insights for making appropriate management strategies to prevent or decrease heavy-metal pollution around a coal gangue dump in the Yangcaogou coal mine and other similar areas elsewhere.

Structure, stability, and properties of the trans peroxo nitrate radical: the importance of nondynamic correlation.

PubMed

Dutta, Achintya Kumar; Dar, Manzoor; Vaval, Nayana; Pal, Sourav

2014-02-27

We report a comparative single-reference and multireference coupled-cluster investigation on the structure, potential energy surface, and IR spectroscopic properties of the trans peroxo nitrate radical, one of the key intermediates in stratospheric NOX chemistry. The previous single-reference ab initio studies predicted an unbound structure for the trans peroxo nitrate radical. However, our Fock space multireference coupled-cluster calculation confirms a bound structure for the trans peroxo nitrate radical, in accordance with the experimental results reported earlier. Further, the analysis of the potential energy surface in FSMRCC method indicates a well-behaved minima, contrary to the shallow minima predicted by the single-reference coupled-cluster method. The harmonic force field analysis, of various possible isomers of peroxo nitrate also reveals that only the trans structure leads to the experimentally observed IR peak at 1840 cm(-1). The present study highlights the critical importance of nondynamic correlation in predicting the structure and properties of high-energy stratospheric NOx radicals.

Identification of consensus biomarkers for predicting non-genotoxic hepatocarcinogens

PubMed Central

Huang, Shan-Han; Tung, Chun-Wei

2017-01-01

The assessment of non-genotoxic hepatocarcinogens (NGHCs) is currently relying on two-year rodent bioassays. Toxicogenomics biomarkers provide a potential alternative method for the prioritization of NGHCs that could be useful for risk assessment. However, previous studies using inconsistently classified chemicals as the training set and a single microarray dataset concluded no consensus biomarkers. In this study, 4 consensus biomarkers of A2m, Ca3, Cxcl1, and Cyp8b1 were identified from four large-scale microarray datasets of the one-day single maximum tolerated dose and a large set of chemicals without inconsistent classifications. Machine learning techniques were subsequently applied to develop prediction models for NGHCs. The final bagging decision tree models were constructed with an average AUC performance of 0.803 for an independent test. A set of 16 chemicals with controversial classifications were reclassified according to the consensus biomarkers. The developed prediction models and identified consensus biomarkers are expected to be potential alternative methods for prioritization of NGHCs for further experimental validation. PMID:28117354

Childhood Maltreatment Predicts Allostatic Load in Adulthood

PubMed Central

Widom, Cathy Spatz; Horan, Jacqueline; Brzustowicz, Linda

2015-01-01

Childhood maltreatment has been linked to numerous negative health outcomes. However, few studies have examined mediating processes using longitudinal designs or objectively measured biological data. This study sought to determine whether child abuse and neglect predicts allostatic load (a composite indicator of accumulated stress-induced biological risk) and to examine potential mediators. Using a prospective cohort design, children (ages 0-11) with documented cases of abuse and neglect were matched with non-maltreated children and followed up into adulthood with in-person interviews and a medical status exam (mean age 41). Allostatic load was assessed with nine physical health indicators. Child abuse and neglect predicted allostatic load, controlling for age, sex, and race. The direct effect of child abuse and neglect persisted despite the introduction of potential mediators of internalizing and externalizing problems in adolescence and social support and risky lifestyle in middle adulthood. These findings reveal the long-term impact of childhood abuse and neglect on physical health over 30 years later. PMID:25700779

Compare the Difference of B-cell Epitopes of EgAgB1 and EgAgB3 Proteins Selected through Bioinformatic Analysis

NASA Astrophysics Data System (ADS)

An, Mengting; Zhang, Fengbo; Zhu, Yuejie; Zhao, Xiao; Ding, Jianbing

2018-01-01

Cystic echinococcosis, as a zoonosis, seriously endangers humans and animals, so early diagnosis of this disease is particularly important. Therefore, this study is to predict B-cell epitopes of EgAgB1 and EgAgB3 proteins by bioinformatics software. B-cell epitopes of EgAgB1 and EgAgB3 proteins are predicted using DNAStar and IEDB software. The results suggest that there are two potential B-cell epitopes in EgAgB1, which located in the 8-15 and 31-37 amino acid residue segments. And two potential B-cell epitopes in EgAgB2, located in the 20∼27 and 47∼53 amino acid residue segments. This study predicted the B-cell epitopes of EgAgB1 and EgAgB3 proteins, which laid the foundation for the early diagnosis of Cystic echinococcosis.

Metagenomic Functional Potential Predicts Degradation Rates of a Model Organophosphorus Xenobiotic in Pesticide Contaminated Soils

PubMed Central

Jeffries, Thomas C.; Rayu, Smriti; Nielsen, Uffe N.; Lai, Kaitao; Ijaz, Ali; Nazaries, Loic; Singh, Brajesh K.

2018-01-01

Chemical contamination of natural and agricultural habitats is an increasing global problem and a major threat to sustainability and human health. Organophosphorus (OP) compounds are one major class of contaminant and can undergo microbial degradation, however, no studies have applied system-wide ecogenomic tools to investigate OP degradation or use metagenomics to understand the underlying mechanisms of biodegradation in situ and predict degradation potential. Thus, there is a lack of knowledge regarding the functional genes and genomic potential underpinning degradation and community responses to contamination. Here we address this knowledge gap by performing shotgun sequencing of community DNA from agricultural soils with a history of pesticide usage and profiling shifts in functional genes and microbial taxa abundance. Our results showed two distinct groups of soils defined by differing functional and taxonomic profiles. Degradation assays suggested that these groups corresponded to the organophosphorus degradation potential of soils, with the fastest degrading community being defined by increases in transport and nutrient cycling pathways and enzymes potentially involved in phosphorus metabolism. This was against a backdrop of taxonomic community shifts potentially related to contamination adaptation and reflecting the legacy of exposure. Overall our results highlight the value of using holistic system-wide metagenomic approaches as a tool to predict microbial degradation in the context of the ecology of contaminated habitats. PMID:29515526

Reduced frontal theta oscillations indicate altered crossmodal prediction error processing in schizophrenia

PubMed Central

Keil, Julian; Balz, Johanna; Gallinat, Jürgen; Senkowski, Daniel

2016-01-01

Our brain generates predictions about forthcoming stimuli and compares predicted with incoming input. Failures in predicting events might contribute to hallucinations and delusions in schizophrenia (SZ). When a stimulus violates prediction, neural activity that reflects prediction error (PE) processing is found. While PE processing deficits have been reported in unisensory paradigms, it is unknown whether SZ patients (SZP) show altered crossmodal PE processing. We measured high-density electroencephalography and applied source estimation approaches to investigate crossmodal PE processing generated by audiovisual speech. In SZP and healthy control participants (HC), we used an established paradigm in which high- and low-predictive visual syllables were paired with congruent or incongruent auditory syllables. We examined crossmodal PE processing in SZP and HC by comparing differences in event-related potentials and neural oscillations between incongruent and congruent high- and low-predictive audiovisual syllables. In both groups event-related potentials between 206 and 250 ms were larger in high- compared with low-predictive syllables, suggesting intact audiovisual incongruence detection in the auditory cortex of SZP. The analysis of oscillatory responses revealed theta-band (4–7 Hz) power enhancement in high- compared with low-predictive syllables between 230 and 370 ms in the frontal cortex of HC but not SZP. Thus aberrant frontal theta-band oscillations reflect crossmodal PE processing deficits in SZ. The present study suggests a top-down multisensory processing deficit and highlights the role of dysfunctional frontal oscillations for the SZ psychopathology. PMID:27358314

Income reliably predicts daily sadness, but not happiness: A replication and extension of Kushlev, Dunn, & Lucas (2015)

PubMed Central

Hudson, Nathan W.; Lucas, Richard E.; Donnellan, M. Brent; Kushlev, Kostadin

2017-01-01

Kushlev, Dunn, and Lucas (2015) found that income predicts less daily sadness—but not greater happiness—among Americans. The present study used longitudinal data from an approximately representative German sample to replicate and extend these findings. Our results largely replicated Kushlev and colleagues’: income predicted less daily sadness (albeit with a smaller effect size), but was unrelated to happiness. Moreover, the association between income and sadness could not be explained by demographics, stress, or daily time-use. Extending Kushlev and colleagues’ findings, new analyses indicated that only between-persons variance in income (but not within-persons variance) predicted daily sadness—perhaps because there was relatively little within-persons variance in income. Finally, income predicted less daily sadness and worry, but not less anger or frustration—potentially suggesting that income predicts less “internalizing” but not less “externalizing” negative emotions. Together, our study and Kushlev and colleagues’ provide evidence that income robustly predicts select daily negative emotions—but not positive ones. PMID:29250303

Application of mid-infrared spectroscopy to the prediction of maturity and sensory texture attributes of cheddar cheese.

PubMed

Fagan, C C; O'Donnell, C P; O'Callaghan, D J; Downey, G; Sheehan, E M; Delahunty, C M; Everard, C; Guinee, T P; Howard, V

2007-04-01

The objective of this study was to determine the potential of mid-infrared spectroscopy in conjunction with partial least squares (PLS) regression to predict various quality parameters in cheddar cheese. Cheddar cheeses (n= 24) were manufactured and stored at 8 degrees C for 12 mo. Mid-infrared spectra (640 to 4000/cm) were recorded after 4, 6, 9, and 12 mo storage. At 4, 6, and 9 mo, the water-soluble nitrogen (WSN) content of the samples was determined and the samples were also evaluated for 11 sensory texture attributes using descriptive sensory analysis. The mid-infrared spectra were subjected to a number of pretreatments, and predictive models were developed for all parameters. Age was predicted using scatter-corrected, 1st derivative spectra with a root mean square error of cross-validation (RMSECV) of 1 mo, while WSN was predicted using 1st derivative spectra (RMSECV = 2.6%). The sensory texture attributes most successfully predicted were rubbery, crumbly, chewy, and massforming. These attributes were modeled using 2nd derivative spectra and had corresponding RMSECV values in the range of 2.5 to 4.2 on a scale of 0 to 100. It was concluded that mid-infrared spectroscopy has the potential to predict age, WSN, and several sensory texture attributes of cheddar cheese.

Prediction of remission of depression with clinical variables, neuropsychological performance, and serotonergic/dopaminergic gene polymorphisms.

PubMed

Gudayol-Ferré, Esteve; Herrera-Guzmán, Ixchel; Camarena, Beatriz; Cortés-Penagos, Carlos; Herrera-Abarca, Jorge E; Martínez-Medina, Patricia; Asbun-Bojalil, Juan; Lira-Islas, Yuridia; Reyes-Ponce, Celia; Guàrdia-Olmos, Joan

2012-11-01

The aim of our work is to study the possible role of clinical variables, neuropsychological performance, and the 5HTTLPR, rs25531, and val108/58Met COMT polymorphisms on the prediction of depression remission after 12 weeks' treatment with fluoxetine. These variables have been studied as potential predictors of depression remission, but they present poor prognostic sensitivity and specificity by themselves. Seventy-two depressed patients were genotyped according to the aforementioned polymorphisms and were clinically and neuropsychologically assessed before a 12-week fluxetine treatment. Only the La allele of rs25531 polymorphism and the GG and AA forms of the val 108/158 Met polymorphism predict major depressive disorder remission after 12 weeks' treatment with fluoxetine. None of the clinical and neuropsychological variables studied predicted remission. Our results suggest that clinical and neuropsychological variables can initially predict early response to fluoxetine and mask the predictive role of genetic variables; but in remission, where clinical and neuropsychological symptoms associated with depression tend to disappear thanks to the treatment administered, the polymorphisms studied are the only variables in our model capable of predicting remission. However, placebo effects that are difficult to control require cautious interpretation of the results.

Don't panic: interpretation bias is predictive of new onsets of panic disorder.

PubMed

Woud, Marcella L; Zhang, Xiao Chi; Becker, Eni S; McNally, Richard J; Margraf, Jürgen

2014-01-01

Psychological models of panic disorder postulate that interpretation of ambiguous material as threatening is an important maintaining factor for the disorder. However, demonstrations of whether such a bias predicts onset of panic disorder are missing. In the present study, we used data from the Dresden Prediction Study, in which a epidemiologic sample of young German women was tested at two time points approximately 17 months apart, allowing the study of biased interpretation as a potential risk factor. At time point one, participants completed an Interpretation Questionnaire including two types of ambiguous scenarios: panic-related and general threat-related. Analyses revealed that a panic-related interpretation bias predicted onset of panic disorder, even after controlling for two established risk factors: anxiety sensitivity and fear of bodily sensations. This is the first prospective study demonstrating the incremental validity of interpretation bias as a predictor of panic disorder onset. Copyright © 2013 Elsevier Ltd. All rights reserved.

Predicting Drug-Target Interactions With Multi-Information Fusion.

PubMed

Peng, Lihong; Liao, Bo; Zhu, Wen; Li, Zejun; Li, Keqin

2017-03-01

Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most models only consider chemical structures and protein sequences, and other models are oversimplified. Moreover, datasets used for analysis contain only true-positive interactions, and experimentally validated negative samples are unavailable. To overcome these limitations, we developed a semi-supervised based learning framework called NormMulInf through collaborative filtering theory by using labeled and unlabeled interaction information. The proposed method initially determines similarity measures, such as similarities among samples and local correlations among the labels of the samples, by integrating biological information. The similarity information is then integrated into a robust principal component analysis model, which is solved using augmented Lagrange multipliers. Experimental results on four classes of drug-target interaction networks suggest that the proposed approach can accurately classify and predict drug-target interactions. Part of the predicted interactions are reported in public databases. The proposed method can also predict possible targets for new drugs and can be used to determine whether atropine may interact with alpha1B- and beta1- adrenergic receptors. Furthermore, the developed technique identifies potential drugs for new targets and can be used to assess whether olanzapine and propiomazine may target 5HT2B. Finally, the proposed method can potentially address limitations on studies of multitarget drugs and multidrug targets.

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.

Small hydropower spot prediction using SWAT and a diversion algorithm, case study: Upper Citarum Basin

NASA Astrophysics Data System (ADS)

Kardhana, Hadi; Arya, Doni Khaira; Hadihardaja, Iwan K.; Widyaningtyas, Riawan, Edi; Lubis, Atika

2017-11-01

Small-Scale Hydropower (SHP) had been important electric energy power source in Indonesia. Indonesia is vast countries, consists of more than 17.000 islands. It has large fresh water resource about 3 m of rainfall and 2 m of runoff. Much of its topography is mountainous, remote but abundant with potential energy. Millions of people do not have sufficient access to electricity, some live in the remote places. Recently, SHP development was encouraged for energy supply of the places. Development of global hydrology data provides opportunity to predict distribution of hydropower potential. In this paper, we demonstrate run-of-river type SHP spot prediction tool using SWAT and a river diversion algorithm. The use of Soil and Water Assessment Tool (SWAT) with input of CFSR (Climate Forecast System Re-analysis) of 10 years period had been implemented to predict spatially distributed flow cumulative distribution function (CDF). A simple algorithm to maximize potential head of a location by a river diversion expressing head race and penstock had been applied. Firm flow and power of the SHP were estimated from the CDF and the algorithm. The tool applied to Upper Citarum River Basin and three out of four existing hydropower locations had been well predicted. The result implies that this tool is able to support acceleration of SHP development at earlier phase.

A population-based survey in Australia of men's and women's perceptions of genetic risk and predictive genetic testing and implications for primary care.

PubMed

Taylor, S

2011-01-01

Community attitudes research regarding genetic issues is important when contemplating the potential value and utilisation of predictive testing for common diseases in mainstream health services. This article aims to report population-based attitudes and discuss their relevance to integrating genetic services in primary health contexts. Men's and women's attitudes were investigated via population-based omnibus telephone survey in Queensland, Australia. Randomly selected adults (n = 1,230) with a mean age of 48.8 years were interviewed regarding perceptions of genetic determinants of health; benefits of genetic testing that predict 'certain' versus 'probable' future illness; and concern, if any, regarding potential misuse of genetic test information. Most (75%) respondents believed genetic factors significantly influenced health status; 85% regarded genetic testing positively although attitudes varied with age. Risk-based information was less valued than certainty-based information, but women valued risk information significantly more highly than men. Respondents reported 'concern' (44%) and 'no concern' (47%) regarding potential misuse of genetic information. This study contributes important population-based data as most research has involved selected individuals closely impacted by genetic disorders. While community attitudes were positive regarding genetic testing, genetic literacy is important to establish. The nature of gender differences regarding risk perception merits further study and has policy and service implications. Community concern about potential genetic discrimination must be addressed if health benefits of testing are to be maximised. Larger questions remain in scientific, policy, service delivery, and professional practice domains before predictive testing for common disorders is efficacious in mainstream health care. Copyright © 2011 S. Karger AG, Basel.

Relationship of physical fitness test results and hockey playing potential in elite-level ice hockey players.

PubMed

Burr, Jaime F; Jamnik, Roni K; Baker, Joseph; Macpherson, Alison; Gledhill, Norman; McGuire, E J

2008-09-01

The primary purpose of this study was to determine the fitness variables with the highest capability for predicting hockey playing potential at the elite level as determined by entry draft selection order. We also examined the differences associated with the predictive abilities of the test components among playing positions. The secondary purpose of this study was to update the physiological profile of contemporary hockey players including positional differences. Fitness test results conducted by our laboratory at the National Hockey League Entry Draft combine were compared with draft selection order on a total of 853 players. Regression models revealed peak anaerobic power output to be important for higher draft round selection in all positions; however, the degree of importance of this measurement varied with playing position. The body index, which is a composite score of height, lean mass, and muscular development, was similarly important in all models, with differing influence by position. Removal of the goalies' data increased predictive capacity, suggesting that talent identification using physical fitness testing of this sort may be more appropriate for skating players. Standing long jump was identified as a significant predictor variable for forwards and defense and could be a useful surrogate for assessing overall hockey potential. Significant differences exist between the physiological profiles of current players based on playing position. There are also positional differences in the relative importance of anthropometric and fitness measures of off-ice hockey tests in relation to draft order. Physical fitness measures and anthropometric data are valuable in helping predict hockey playing potential. Emphasis on anthropometry should be used when comparing elite-level forwards, whereas peak anaerobic power and fatigue rate are more useful for differentiating between defense.

Impacts of Climate Change on the Global Invasion Potential of the African Clawed Frog Xenopus laevis

PubMed Central

Ihlow, Flora; Courant, Julien; Secondi, Jean; Herrel, Anthony; Rebelo, Rui; Measey, G. John; Lillo, Francesco; De Villiers, F. André; Vogt, Solveig; De Busschere, Charlotte; Backeljau, Thierry; Rödder, Dennis

2016-01-01

By altering or eliminating delicate ecological relationships, non-indigenous species are considered a major threat to biodiversity, as well as a driver of environmental change. Global climate change affects ecosystems and ecological communities, leading to changes in the phenology, geographic ranges, or population abundance of several species. Thus, predicting the impacts of global climate change on the current and future distribution of invasive species is an important subject in macroecological studies. The African clawed frog (Xenopus laevis), native to South Africa, possesses a strong invasion potential and populations have become established in numerous countries across four continents. The global invasion potential of X. laevis was assessed using correlative species distribution models (SDMs). SDMs were computed based on a comprehensive set of occurrence records covering South Africa, North America, South America and Europe and a set of nine environmental predictors. Models were built using both a maximum entropy model and an ensemble approach integrating eight algorithms. The future occurrence probabilities for X. laevis were subsequently computed using bioclimatic variables for 2070 following four different IPCC scenarios. Despite minor differences between the statistical approaches, both SDMs predict the future potential distribution of X. laevis, on a global scale, to decrease across all climate change scenarios. On a continental scale, both SDMs predict decreasing potential distributions in the species’ native range in South Africa, as well as in the invaded areas in North and South America, and in Australia where the species has not been introduced. In contrast, both SDMs predict the potential range size to expand in Europe. Our results suggest that all probability classes will be equally affected by climate change. New regional conditions may promote new invasions or the spread of established invasive populations, especially in France and Great Britain. PMID:27248830

Impacts of Climate Change on the Global Invasion Potential of the African Clawed Frog Xenopus laevis.

PubMed

Ihlow, Flora; Courant, Julien; Secondi, Jean; Herrel, Anthony; Rebelo, Rui; Measey, G John; Lillo, Francesco; De Villiers, F André; Vogt, Solveig; De Busschere, Charlotte; Backeljau, Thierry; Rödder, Dennis

2016-01-01

By altering or eliminating delicate ecological relationships, non-indigenous species are considered a major threat to biodiversity, as well as a driver of environmental change. Global climate change affects ecosystems and ecological communities, leading to changes in the phenology, geographic ranges, or population abundance of several species. Thus, predicting the impacts of global climate change on the current and future distribution of invasive species is an important subject in macroecological studies. The African clawed frog (Xenopus laevis), native to South Africa, possesses a strong invasion potential and populations have become established in numerous countries across four continents. The global invasion potential of X. laevis was assessed using correlative species distribution models (SDMs). SDMs were computed based on a comprehensive set of occurrence records covering South Africa, North America, South America and Europe and a set of nine environmental predictors. Models were built using both a maximum entropy model and an ensemble approach integrating eight algorithms. The future occurrence probabilities for X. laevis were subsequently computed using bioclimatic variables for 2070 following four different IPCC scenarios. Despite minor differences between the statistical approaches, both SDMs predict the future potential distribution of X. laevis, on a global scale, to decrease across all climate change scenarios. On a continental scale, both SDMs predict decreasing potential distributions in the species' native range in South Africa, as well as in the invaded areas in North and South America, and in Australia where the species has not been introduced. In contrast, both SDMs predict the potential range size to expand in Europe. Our results suggest that all probability classes will be equally affected by climate change. New regional conditions may promote new invasions or the spread of established invasive populations, especially in France and Great Britain.

Predicting wildfire ignitions, escapes, and large fire activity using Predictive Service’s 7-Day Fire Potential Outlook in the western USA

Treesearch

Karin L. Riley; Crystal Stonesifer; Haiganoush Preisler; Dave Calkin

2014-01-01

Can fire potential forecasts assist with pre-positioning of fire suppression resources, which could result in a cost savings to the United States government? Here, we present a preliminary assessment of the 7-Day Fire Potential Outlook forecasts made by the Predictive Services program. We utilized historical fire occurrence data and archived forecasts to assess how...

[Prediction of ETA oligopeptides antagonists from Glycine max based on in silico proteolysis].

PubMed

Qiao, Lian-Sheng; Jiang, Lu-di; Luo, Gang-Gang; Lu, Fang; Chen, Yan-Kun; Wang, Ling-Zhi; Li, Gong-Yu; Zhang, Yan-Ling

2017-02-01

Oligopeptides are one of the the key pharmaceutical effective constituents of traditional Chinese medicine(TCM). Systematic study on composition and efficacy of TCM oligopeptides is essential for the analysis of material basis and mechanism of TCM. In this study, the potential anti-hypertensive oligopeptides from Glycine max and their endothelin receptor A (ETA) antagonistic activity were discovered and predicted based on in silico technologies.Main protein sequences of G. max were collected and oligopeptides were obtained using in silico gastrointestinal tract proteolysis. Then, the pharmacophore of ETA antagonistic peptides was constructed and included one hydrophobic feature, one ionizable negative feature, one ring aromatic feature and five excluded volumes. Meanwhile, three-dimensional structure of ETA was developed by homology modeling methods for further docking studies. According to docking analysis and consensus score, the key amino acid of GLN165 was identified for ETA antagonistic activity. And 27 oligopeptides from G. max were predicted as the potential ETA antagonists by pharmacophore and docking studies.In silico proteolysis could be used to analyze the protein sequences from TCM. According to combination of in silico proteolysis and molecular simulation, the biological activities of oligopeptides could be predicted rapidly based on the known TCM protein sequence. It might provide the methodology basis for rapidly and efficiently implementing the mechanism analysis of TCM oligopeptides. Copyright© by the Chinese Pharmaceutical Association.

Predictors of dropout from internet-based self-help cognitive behavioral therapy for insomnia.

PubMed

Yeung, Wing-Fai; Chung, Ka-Fai; Ho, Fiona Yan-Yee; Ho, Lai-Ming

2015-10-01

Dropout from self-help cognitive-behavioral therapy for insomnia (CBT-I) potentially diminishes therapeutic effect and poses clinical concern. We analyzed the characteristics of subjects who did not complete a 6-week internet-based CBT-I program. Receiver operator characteristics (ROC) analysis was used to identify potential variables and cutoff for predicting dropout among 207 participants with self-report insomnia 3 or more nights per week for at least 3 months randomly assigned to self-help CBT-I with telephone support (n = 103) and self-help CBT-I (n = 104). Seventy-two participants (34.4%) did not complete all 6 sessions, while 42 of the 72 (56.9%) dropped out prior to the fourth session. Significant predictors of non-completion are total sleep time (TST) ≥ 6.82 h, Hospital Anxiety and Depression Scale depression score ≥ 9 and Insomnia Severity Index score < 13 at baseline in this ranking order. Only TST ≥ 5.92 h predicts early dropout. Longer TST and less severe insomnia predict dropout in this study of self-help CBT-I, in contrast to shorter TST as a predictor in 2 studies of face-to-face CBT-I, while greater severity of depression predicts dropout in both this study and a study of face-to-face CBT-I. Strategies for minimizing dropout from internet-based CBT-I are discussed. Copyright © 2015 Elsevier Ltd. All rights reserved.

An analytical bond-order potential for carbon

DOE PAGES

Zhou, Xiaowang; Ward, Donald K.; Foster, Michael E.

2015-05-27

Carbon is the most widely studied material today because it exhibits special properties not seen in any other materials when in nano dimensions such as nanotube and graphene. Reduction of material defects created during synthesis has become critical to realize the full potential of carbon structures. Molecular dynamics (MD) simulations, in principle, allow defect formation mechanisms to be studied with high fidelity, and can, therefore, help guide experiments for defect reduction. Such MD simulations must satisfy a set of stringent requirements. First, they must employ an interatomic potential formalism that is transferable to a variety of carbon structures. Second, themore » potential needs to be appropriately parameterized to capture the property trends of important carbon structures, in particular, diamond, graphite, graphene, and nanotubes. The potential must predict the crystalline growth of the correct phases during direct MD simulations of synthesis to achieve a predictive simulation of defect formation. An unlimited number of structures not included in the potential parameterization are encountered, thus the literature carbon potentials are often not sufficient for growth simulations. We have developed an analytical bond order potential for carbon, and have made it available through the public MD simulation package LAMMPS. We also demonstrate that our potential reasonably captures the property trends of important carbon phases. As a result, stringent MD simulations convincingly show that our potential accounts not only for the crystalline growth of graphene, graphite, and carbon nanotubes but also for the transformation of graphite to diamond at high pressure.« less

An analytical bond-order potential for carbon.

PubMed

Zhou, X W; Ward, D K; Foster, M E

2015-09-05

Carbon is the most widely studied material today because it exhibits special properties not seen in any other materials when in nano dimensions such as nanotube and graphene. Reduction of material defects created during synthesis has become critical to realize the full potential of carbon structures. Molecular dynamics (MD) simulations, in principle, allow defect formation mechanisms to be studied with high fidelity, and can, therefore, help guide experiments for defect reduction. Such MD simulations must satisfy a set of stringent requirements. First, they must employ an interatomic potential formalism that is transferable to a variety of carbon structures. Second, the potential needs to be appropriately parameterized to capture the property trends of important carbon structures, in particular, diamond, graphite, graphene, and nanotubes. Most importantly, the potential must predict the crystalline growth of the correct phases during direct MD simulations of synthesis to achieve a predictive simulation of defect formation. Because an unlimited number of structures not included in the potential parameterization are encountered, the literature carbon potentials are often not sufficient for growth simulations. We have developed an analytical bond order potential for carbon, and have made it available through the public MD simulation package LAMMPS. We demonstrate that our potential reasonably captures the property trends of important carbon phases. Stringent MD simulations convincingly show that our potential accounts not only for the crystalline growth of graphene, graphite, and carbon nanotubes but also for the transformation of graphite to diamond at high pressure. © 2015 Wiley Periodicals, Inc.

Developing a methodology to predict oak wilt distribution using classification tree analysis

Treesearch

Marla C. Downing; Vernon L. Thomas; Robin M. Reich

2006-01-01

Oak wilt (Ceratocystis fagacearum), a fungal disease that causes some species of oak trees to wilt and die rapidly, is a threat to oak forested resources in 22 states in the United States. We developed a methodology for predicting the Potential Distribution of Oak Wilt (PDOW) using Anoka County, Minnesota as our study area. The PDOW utilizes GIS; the...

Prediction of transverse shrinkages of young-growth Sitka spruce (Picea sitchensis) and western hemlock (Tsuga heterophylla) with ultrasonic measurements

Treesearch

Turker Dundar; Xiping Wang; Robert J. Ross

2013-01-01

The objective of this study was to examine the potential of acoustic measurement as a rapid and nondestructive method to predict the dimensional stability of young-growth Sitka spruce and western hemlock. Ultrasonic velocity, peak energy, specific gravity, and radial and tangential shrinkages were measured on twenty-four 25- x

Measurement and prediction of post-fire erosion at the hillslope scale, Colorado Front Range

Treesearch

Juan de Dios Benavides-Solorio; Lee H. MacDonald

2005-01-01

Post-fire soil erosion is of considerable concern because of the potential decline in site productivity and adverse effects on downstream resources. For the Colorado Front Range there is a paucity of post-fire erosion data and a corresponding lack of predictive models. This study measured hillslope-scale sediment production rates and site characteristics for three wild...

[Application of near infrared reflectance spectroscopy to predict meat chemical compositions: a review].

PubMed

Tao, Lin-Li; Yang, Xiu-Juan; Deng, Jun-Ming; Zhang, Xi

2013-11-01

In contrast to conventional methods for the determination of meat chemical composition, near infrared reflectance spectroscopy enables rapid, simple, secure and simultaneous assessment of numerous meat properties. The present review focuses on the use of near infrared reflectance spectroscopy to predict meat chemical compositions. The potential of near infrared reflectance spectroscopy to predict crude protein, intramuscular fat, fatty acid, moisture, ash, myoglobin and collagen of beef, pork, chicken and lamb is reviewed. This paper discusses existing questions and reasons in the current research. According to the published results, although published results vary considerably, they suggest that near-infrared reflectance spectroscopy shows a great potential to replace the expensive and time-consuming chemical analysis of meat composition. In particular, under commercial conditions where simultaneous measurements of different chemical components are required, near infrared reflectance spectroscopy is expected to be the method of choice. The majority of studies selected feature-related wavelengths using principal components regression, developed the calibration model using partial least squares and modified partial least squares, and estimated the prediction accuracy by means of cross-validation using the same sample set previously used for the calibration. Meat fatty acid composition predicted by near-infrared spectroscopy and non-destructive prediction and visualization of chemical composition in meat using near-infrared hyperspectral imaging and multivariate regression are the hot studying field now. On the other hand, near infrared reflectance spectroscopy shows great difference for predicting different attributes of meat quality which are closely related to the selection of calibration sample set, preprocessing of near-infrared spectroscopy and modeling approach. Sample preparation also has an important effect on the reliability of NIR prediction; in particular, lack of homogeneity of the meat samples influenced the accuracy of estimation of chemical components. In general the predicting results of intramuscular fat, fatty acid and moisture are best, the predicting results of crude protein and myoglobin are better, while the predicting results of ash and collagen are less accurate.

Context conditioning and behavioral avoidance in a virtual reality environment: effect of predictability.

PubMed

Grillon, Christian; Baas, Johanna M P; Cornwell, Brian; Johnson, Linda

2006-10-01

Sustained anxiety can be modeled using context conditioning, which can be studied in a virtual reality environment. Unpredictable stressors increase context conditioning in animals. This study examined context conditioning to predictable and unpredictable shocks in humans using behavioral avoidance, potentiated startle, and subjective reports of anxiety. Subjects were guided through three virtual rooms (no-shock, predictable, unpredictable contexts). Eight-sec duration colored lights served as conditioned stimuli (CS). During acquisition, no shock was administered in the no-shock context. Shocks were paired with the CS in the predictable context and were administered randomly in the unpredictable context. No shock was administered during extinction. Startle stimuli were delivered during CS and between CS to assess cued and context conditioning, respectively. To assess avoidance, subjects freely navigated into two of the three contexts to retrieve money. Startle between CS was potentiated in the unpredictable context compared to the two other contexts. Following acquisition, subjects showed a strong preference for the no-shock context and avoidance of the unpredictable context. Consistent with animal data, context conditioning is increased by unpredictability. These data support virtual reality as a tool to extend research on physiological and behavioral signs of fear and anxiety in humans.

Phylogenomic detection and functional prediction of genes potentially important for plant meiosis.

PubMed

Zhang, Luoyan; Kong, Hongzhi; Ma, Hong; Yang, Ji

2018-02-15

Meiosis is a specialized type of cell division necessary for sexual reproduction in eukaryotes. A better understanding of the cytological procedures of meiosis has been achieved by comprehensive cytogenetic studies in plants, while the genetic mechanisms regulating meiotic progression remain incompletely understood. The increasing accumulation of complete genome sequences and large-scale gene expression datasets has provided a powerful resource for phylogenomic inference and unsupervised identification of genes involved in plant meiosis. By integrating sequence homology and expression data, 164, 131, 124 and 162 genes potentially important for meiosis were identified in the genomes of Arabidopsis thaliana, Oryza sativa, Selaginella moellendorffii and Pogonatum aloides, respectively. The predicted genes were assigned to 45 meiotic GO terms, and their functions were related to different processes occurring during meiosis in various organisms. Most of the predicted meiotic genes underwent lineage-specific duplication events during plant evolution, with about 30% of the predicted genes retaining only a single copy in higher plant genomes. The results of this study provided clues to design experiments for better functional characterization of meiotic genes in plants, promoting the phylogenomic approach to the evolutionary dynamics of the plant meiotic machineries. Copyright © 2017 Elsevier B.V. All rights reserved.

Systems-based biological concordance and predictive reproducibility of gene set discovery methods in cardiovascular disease.

PubMed

Azuaje, Francisco; Zheng, Huiru; Camargo, Anyela; Wang, Haiying

2011-08-01

The discovery of novel disease biomarkers is a crucial challenge for translational bioinformatics. Demonstration of both their classification power and reproducibility across independent datasets are essential requirements to assess their potential clinical relevance. Small datasets and multiplicity of putative biomarker sets may explain lack of predictive reproducibility. Studies based on pathway-driven discovery approaches have suggested that, despite such discrepancies, the resulting putative biomarkers tend to be implicated in common biological processes. Investigations of this problem have been mainly focused on datasets derived from cancer research. We investigated the predictive and functional concordance of five methods for discovering putative biomarkers in four independently-generated datasets from the cardiovascular disease domain. A diversity of biosignatures was identified by the different methods. However, we found strong biological process concordance between them, especially in the case of methods based on gene set analysis. With a few exceptions, we observed lack of classification reproducibility using independent datasets. Partial overlaps between our putative sets of biomarkers and the primary studies exist. Despite the observed limitations, pathway-driven or gene set analysis can predict potentially novel biomarkers and can jointly point to biomedically-relevant underlying molecular mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

Electrostatic potential of B-DNA: effect of interionic correlations.

PubMed Central

Gavryushov, S; Zielenkiewicz, P

1998-01-01

Modified Poisson-Boltzmann (MPB) equations have been numerically solved to study ionic distributions and mean electrostatic potentials around a macromolecule of arbitrarily complex shape and charge distribution. Results for DNA are compared with those obtained by classical Poisson-Boltzmann (PB) calculations. The comparisons were made for 1:1 and 2:1 electrolytes at ionic strengths up to 1 M. It is found that ion-image charge interactions and interionic correlations, which are neglected by the PB equation, have relatively weak effects on the electrostatic potential at charged groups of the DNA. The PB equation predicts errors in the long-range electrostatic part of the free energy that are only approximately 1.5 kJ/mol per nucleotide even in the case of an asymmetrical electrolyte. In contrast, the spatial correlations between ions drastically affect the electrostatic potential at significant separations from the macromolecule leading to a clearly predicted effect of charge overneutralization. PMID:9826596

Levels of cystathionine gamma lyase production by Geotrichum candidum in synthetic media and correlation with the presence of sulphur flavours in cheese.

PubMed

Gente, Stéphanie; La Carbona, Stéphanie; Guéguen, Micheline

2007-03-10

Geotrichum candidum is a cheese-ripening agent with the potential to produce sulphur flavour compounds in soft cheeses. We aimed to develop an alternative test for predicting the aromatic (sulphur flavours) potential of G. candidum strains in soft cheese. Twelve strains of G. candidum with different levels of demethiolase activity (determined by a chemical method) in YEL-met (yeast extract, lactate methionine) medium were studied. We investigated cgl (cystathionine gamma lyase) gene expression after culture in three media - YEL-met, casamino acid and curd media - and then carried out sensory analysis on a Camembert cheese matrix. We found no correlation between demethiolase activity in vitro and cgl gene expression. Sensory analysis (detection of sulphur flavours) identified different aromatic profiles linked to cgl expression, but not to demethiolase activity. The RT-PCR technique described here is potentially useful for predicting the tendency of a given strain of G. candidum to develop sulphur flavours in cheese matrix. This is the first demonstration that an in vitro molecular approach could be used as a predictive test for evaluating the potential of G. candidum strains to generate sulphur compounds in situ (Camembert cheese matrix).

Little pigeons can carry great messages: potential distribution and ecology of Uranotaenia (Pseudoficalbia) unguiculata Edwards, 1913 (Diptera: Culicidae), a lesser-known mosquito species from the Western Palaearctic.

PubMed

Filatov, Serhii

2017-10-10

Uranotaenia unguiculata is a Palaearctic mosquito species with poorly known distribution and ecology. This study is aimed at filling the gap in our understanding of the species potential distribution and its environmental requirements through a species distribution modelling (SDM) exercise. Furthermore, aspects of the mosquito ecology that may be relevant to the epidemiology of certain zoonotic vector-borne diseases in Europe are discussed. A maximum entropy (Maxent) modelling approach has been applied to predict the potential distribution of Ur. unguiculata in the Western Palaearctic. Along with the high accuracy and predictive power, the model reflects well the known species distribution and predicts as highly suitable some areas where the occurrence of the species is hitherto unknown. To our knowledge, the potential distribution of a mosquito species from the genus Uranotaenia is modelled for the first time. Provided that Ur. unguiculata is a widely-distributed species, and some pathogens of zoonotic concern have been detected in this mosquito on several occasions, the question regarding its host associations and possible epidemiological role warrants further investigation.

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

McLaughlin, E.; Gupta, S.

This project mainly involves a molecular dynamics and Monte Carlo study of the effect of molecular shape on thermophysical properties of bulk fluids with an emphasis on the aromatic hydrocarbon liquids. In this regard we have studied the modeling, simulation methodologies, and predictive and correlating methods for thermodynamic properties of fluids of nonspherical molecules. In connection with modeling we have studied the use of anisotropic site-site potentials, through a modification of the Gay-Berne Gaussian overlap potential, to successfully model the aromatic rings after adding the necessary electrostatic moments. We have also shown these interaction sites should be located at themore » geometric centers of the chemical groups. In connection with predictive methods, we have shown two perturbation type theories to work well for fluids modeled using one-center anisotropic potentials and the possibility exists for extending these to anisotropic site-site models. In connection with correlation methods, we have studied, through simulations, the effect of molecular shape on the attraction term in the generalized van der Waals equation of state for fluids of nonspherical molecules and proposed a possible form which is to be studied further. We have successfully studied the vector and parallel processing aspects of molecular simulations for fluids of nonspherical molecules.« less

Tissue Chips to aid drug development and modeling for rare diseases

PubMed Central

Low, Lucie A.; Tagle, Danilo A.

2016-01-01

Introduction The technologies used to design, create and use microphysiological systems (MPS, “tissue chips” or “organs-on-chips”) have progressed rapidly in the last 5 years, and validation studies of the functional relevance of these platforms to human physiology, and response to drugs for individual model organ systems, are well underway. These studies are paving the way for integrated multi-organ systems that can model diseases and predict drug efficacy and toxicology of multiple organs in real-time, improving the potential for diagnostics and development of novel treatments of rare diseases in the future. Areas covered This review will briefly summarize the current state of tissue chip research and highlight model systems where these microfabricated (or bioengineered) devices are already being used to screen therapeutics, model disease states, and provide potential treatments in addition to helping elucidate the basic molecular and cellular phenotypes of rare diseases. Expert opinion Microphysiological systems hold great promise and potential for modeling rare disorders, as well as for their potential use to enhance the predictive power of new drug therapeutics, plus potentially increase the statistical power of clinical trials while removing the inherent risks of these trials in rare disease populations. PMID:28626620

Long-term bed degradation in Maryland streams (phase 3, part I) : urban streams in the Piedmont Plateau province.

DOT National Transportation Integrated Search

2014-05-01

Estimation of potential long-term down-cutting of the stream bed is necessary for evaluation and design of bridges for scour and culverts for fish passage. The purpose of this study has been to improve predictions of this potential long-term bed degr...

On Explaining Language Shift: Sociology or Social Psychology of Language?

ERIC Educational Resources Information Center

Maitz, Peter

2011-01-01

This study investigates the potentials and limits of sociolinguistic research on language shift. Starting from a position that the ultimate goal of the research must be to create a general theory of language shift of predictive power, the author examines the explanatory potential of current mainstream research methodology now regarded as canonical…

Neural Reactivity to Angry Faces Predicts Treatment Response in Pediatric Anxiety.

PubMed

Bunford, Nora; Kujawa, Autumn; Fitzgerald, Kate D; Swain, James E; Hanna, Gregory L; Koschmann, Elizabeth; Simpson, David; Connolly, Sucheta; Monk, Christopher S; Phan, K Luan

2017-02-01

Although cognitive-behavioral psychotherapy (CBT) and pharmacotherapy are evidence-based treatments for pediatric anxiety, many youth with anxiety disorders fail to respond to these treatments. Given limitations of clinical measures in predicting treatment response, identifying neural predictors is timely. In this study, 35 anxious youth (ages 7-19 years) completed an emotional face-matching task during which the late positive potential (LPP), an event-related potential (ERP) component that indexes sustained attention towards emotional stimuli, was measured. Following the ERP measurement, youth received CBT or selective serotonin reuptake inhibitor (SSRI) treatment, and the LPP was examined as a predictor of treatment response. Findings indicated that, accounting for pre-treatment anxiety severity, neural reactivity to emotional faces predicted anxiety severity post- CBT and SSRI treatment such that enhanced electrocortical response to angry faces was associated with better treatment response. An enhanced LPP to angry faces may predict treatment response insofar as it may reflect greater emotion dysregulation or less avoidance and/or enhanced engagement with environmental stimuli in general, including with treatment.

Charged Particle Detection: Potential of Love Wave Acoustic Devices

NASA Astrophysics Data System (ADS)

Pedrick, Michael; Tittmann, Bernhard

2006-03-01

An investigation of the dependence of film density on group and phase velocities in a Love Wave Device shows potential for acoustic-based charged particle detection (CPD). Exposure of an ion sensitive photoresist to charged particles causes localized changes in density through either scission or cross-linking. A theoretical model was developed to study ion fluence effects on Love Wave sensitivity based on: ion energy, effective density changes, layer thickness and mode selection. The model is based on a Poly(Methyl Methacralate) (PMMA) film deposited on a Quartz substrate. The effect of Helium ion fluence on the properties of PMMA has previously been studied. These guidelines were used as an initial basis for the prediction of helium ion detection in a PMMA layer. Procedures for experimental characterization of ion effects on the material properties of PMMA are reviewed. Techniques for experimental validation of the predicted velocity shifts are discussed. A Love Wave Device for CPD could potentially provide a cost-effective alternative to semiconductor or photo-based counterparts. The potential for monitoring ion implantation effects on material properties is also discussed.

Role of learning potential in cognitive remediation: Construct and predictive validity.

PubMed

Davidson, Charlie A; Johannesen, Jason K; Fiszdon, Joanna M

2016-03-01

The construct, convergent, discriminant, and predictive validity of Learning Potential (LP) was evaluated in a trial of cognitive remediation for adults with schizophrenia-spectrum disorders. LP utilizes a dynamic assessment approach to prospectively estimate an individual's learning capacity if provided the opportunity for specific related learning. LP was assessed in 75 participants at study entry, of whom 41 completed an eight-week cognitive remediation (CR) intervention, and 22 received treatment-as-usual (TAU). LP was assessed in a "test-train-test" verbal learning paradigm. Incremental predictive validity was assessed as the degree to which LP predicted memory skill acquisition above and beyond prediction by static verbal learning ability. Examination of construct validity confirmed that LP scores reflected use of trained semantic clustering strategy. LP scores correlated with executive functioning and education history, but not other demographics or symptom severity. Following the eight-week active phase, TAU evidenced little substantial change in skill acquisition outcomes, which related to static baseline verbal learning ability but not LP. For the CR group, LP significantly predicted skill acquisition in domains of verbal and visuospatial memory, but not auditory working memory. Furthermore, LP predicted skill acquisition incrementally beyond relevant background characteristics, symptoms, and neurocognitive abilities. Results suggest that LP assessment can significantly improve prediction of specific skill acquisition with cognitive training, particularly for the domain assessed, and thereby may prove useful in individualization of treatment. Published by Elsevier B.V.

Seasonal to interannual Arctic sea ice predictability in current global climate models

NASA Astrophysics Data System (ADS)

Tietsche, S.; Day, J. J.; Guemas, V.; Hurlin, W. J.; Keeley, S. P. E.; Matei, D.; Msadek, R.; Collins, M.; Hawkins, E.

2014-02-01

We establish the first intermodel comparison of seasonal to interannual predictability of present-day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state-of-the-art global climate models. For Arctic sea ice extent and volume, there is potential predictive skill for lead times of up to 3 years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. Sea ice concentration errors are largest in the marginal ice zone, and in winter they are almost zero away from the ice edge. Sea ice thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by sea ice advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate.

Potential and limits to unravel the genetic architecture and predict the variation of Fusarium head blight resistance in European winter wheat (Triticum aestivum L.).

PubMed

Jiang, Y; Zhao, Y; Rodemann, B; Plieske, J; Kollers, S; Korzun, V; Ebmeyer, E; Argillier, O; Hinze, M; Ling, J; Röder, M S; Ganal, M W; Mette, M F; Reif, J C

2015-03-01

Genome-wide mapping approaches in diverse populations are powerful tools to unravel the genetic architecture of complex traits. The main goals of our study were to investigate the potential and limits to unravel the genetic architecture and to identify the factors determining the accuracy of prediction of the genotypic variation of Fusarium head blight (FHB) resistance in wheat (Triticum aestivum L.) based on data collected with a diverse panel of 372 European varieties. The wheat lines were phenotyped in multi-location field trials for FHB resistance and genotyped with 782 simple sequence repeat (SSR) markers, and 9k and 90k single-nucleotide polymorphism (SNP) arrays. We applied genome-wide association mapping in combination with fivefold cross-validations and observed surprisingly high accuracies of prediction for marker-assisted selection based on the detected quantitative trait loci (QTLs). Using a random sample of markers not selected for marker-trait associations revealed only a slight decrease in prediction accuracy compared with marker-based selection exploiting the QTL information. The same picture was confirmed in a simulation study, suggesting that relatedness is a main driver of the accuracy of prediction in marker-assisted selection of FHB resistance. When the accuracy of prediction of three genomic selection models was contrasted for the three marker data sets, no significant differences in accuracies among marker platforms and genomic selection models were observed. Marker density impacted the accuracy of prediction only marginally. Consequently, genomic selection of FHB resistance can be implemented most cost-efficiently based on low- to medium-density SNP arrays.

Trends in improving the embryonic stem cell test (EST): an overview.

PubMed

Buesen, Roland; Visan, Anke; Genschow, Elke; Slawik, Birgitta; Spielmann, Horst; Seiler, Andrea

2004-01-01

The embryonic stem cell test (EST) is an in vitro assay that has been developed to assess the teratogenic and embryotoxic potential of drugs and chemicals. It is based on the capacity of murine ES cells (cell line D3) to differentiate into contracting myocardial cells under specific cell culture conditions. The appearance of beating cardiomyocytes in embryoid body (EB) outgrowths is used as a toxicological endpoint to assess the embryotoxic potential of a test substance. Applying linear analysis of discriminance, a biostatistical prediction model (PM) was developed to assign test chemicals to three classes of embryotoxicity. In an international validation study the EST predicted the embryotoxic potential of chemicals and drugs with the same reliability as two other in vitro embryotoxicity tests, which employed embryonic cells and tissues from pregnant animals. In a joint research project with German pharmaceutical companies we have successfully improved the EST by establishing molecular endpoints of differentiation in cultured ES cells. The quantification of cardiac-specific protein expression by intracellular flow cytometry has been studied in the presence of chemicals of different embryotoxic potential. The results obtained using molecular endpoints specific for differentiated cardiomyocytes employing FACS (fluorescence-activated cell sorting) analysis will be presented in comparison to the validated endpoint - the microscopic analysis of beating areas. FACS analysis provides a more objective endpoint for predicting the embryotoxic potential of chemicals than the validated method. Furthermore, flow cytometry promises to be suitable for high-throughput screening systems (HTS). In addition, our partners from the joint project have improved the EST by developing protocols that stimulate differentiation of ES cells into neural and endothelial cells, chondrocytes and osteoblasts, because some substances might have embryotoxic effects on specific cell-types other than cardiomyocytes. These protocols have been successfully established at ZEBET and in the participating laboratories. Additionally, molecular endpoints have been established for the detection of specific differentiation pathways. Furthermore, new prediction models (PMs) have been developed using single endpoints of the EST.

The Emergence of Precision Urologic Oncology: A Collaborative Review on Biomarker-driven Therapeutics.

PubMed

Barbieri, Christopher E; Chinnaiyan, Arul M; Lerner, Seth P; Swanton, Charles; Rubin, Mark A

2017-02-01

Biomarker-driven cancer therapy, also referred to as precision oncology, has received increasing attention for its promise of improving patient outcomes by defining subsets of patients more likely to respond to various therapies. In this collaborative review article, we examine recent literature regarding biomarker-driven therapeutics in urologic oncology, to better define the state of the field, explore the current evidence supporting utility of this approach, and gauge potential for the future. We reviewed relevant literature, with a particular focus on recent studies about targeted therapy, predictors of response, and biomarker development. The recent advances in molecular profiling have led to a rapid expansion of potential biomarkers and predictive information for patients with urologic malignancies. Across disease states, distinct molecular subtypes of cancers have been identified, with the potential to inform choices of management strategy. Biomarkers predicting response to standard therapies (such as platinum-based chemotherapy) are emerging. In several malignancies (particularly renal cell carcinoma and castration-resistant prostate cancer), targeted therapy against commonly altered signaling pathways has emerged as standard of care. Finally, targeted therapy against alterations present in rare patients (less than 2%) across diseases has the potential to drastically alter patterns of care and choices of therapeutic options. Precision medicine has the highest potential to impact the care of patients. Prospective studies in the setting of clinical trials and standard of care therapy will help define reliable predictive biomarkers and new therapeutic targets leading to real improvement in patient outcomes. Precision oncology uses molecular information (DNA and RNA) from the individual and the tumor to match the right patient with the right treatment. Tremendous strides have been made in defining the molecular underpinnings of urologic malignancies and understanding how these predict response to treatment-this represents the future of urologic oncology. Copyright © 2016 European Association of Urology. Published by Elsevier B.V. All rights reserved.

The Emergence of Precision Urologic Oncology: A Collaborative Review on Biomarker-driven Therapeutics

PubMed Central

Barbieri, Christopher E.; Chinnaiyan, Arul M.; Lerner, Seth P.; Swanton, Charles; Rubin, Mark A.

2016-01-01

Context Biomarker-driven cancer therapy, also referred to as precision oncology, has received increasing attention for its promise of improving patient outcomes by defining subsets of patients more likely to respond to various therapies. Objective In this collaborative review article, we examine recent literature regarding biomarker-driven therapeutics in urologic oncology, to better define the state of the field, explore the current evidence supporting utility of this approach, and gauge potential for the future. Evidence acquisition We reviewed relevant literature, with a particular focus on recent studies about targeted therapy, predictors of response, and biomarker development. Evidence synthesis The recent advances in molecular profiling have led to a rapid expansion of potential biomarkers and predictive information for patients with urologic malignancies. Across disease states, distinct molecular subtypes of cancers have been identified, with the potential to inform choices of management strategy. Biomarkers predicting response to standard therapies (such as platinum-based chemotherapy) are emerging. In several malignancies (particularly renal cell carcinoma and castration-resistant prostate cancer), targeted therapy against commonly altered signaling pathways has emerged as standard of care. Finally, targeted therapy against alterations present in rare patients (less than 2%) across diseases has the potential to drastically alter patterns of care and choices of therapeutic options. Conclusions Precision medicine has the highest potential to impact the care of patients. Prospective studies in the setting of clinical trials and standard of care therapy will help define reliable predictive biomarkers and new therapeutic targets leading to real improvement in patient outcomes. Patient summary Precision oncology uses molecular information (DNA and RNA) from the individual and the tumor to match the right patient with the right treatment. Tremendous strides have been made in defining the molecular underpinnings of urologic malignancies and understanding how these predict response to treatment—this represents the future of urologic oncology. PMID:27567210

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.

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.

PubMed

Zhao, Linlin; Wang, Wenyi; Sedykh, Alexander; Zhu, Hao

2017-06-30

Numerous chemical data sets have become available for quantitative structure-activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting.

Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts

NASA Astrophysics Data System (ADS)

Tommasi, Desiree; Stock, Charles A.; Hobday, Alistair J.; Methot, Rick; Kaplan, Isaac C.; Eveson, J. Paige; Holsman, Kirstin; Miller, Timothy J.; Gaichas, Sarah; Gehlen, Marion; Pershing, Andrew; Vecchi, Gabriel A.; Msadek, Rym; Delworth, Tom; Eakin, C. Mark; Haltuch, Melissa A.; Séférian, Roland; Spillman, Claire M.; Hartog, Jason R.; Siedlecki, Samantha; Samhouri, Jameal F.; Muhling, Barbara; Asch, Rebecca G.; Pinsky, Malin L.; Saba, Vincent S.; Kapnick, Sarah B.; Gaitan, Carlos F.; Rykaczewski, Ryan R.; Alexander, Michael A.; Xue, Yan; Pegion, Kathleen V.; Lynch, Patrick; Payne, Mark R.; Kristiansen, Trond; Lehodey, Patrick; Werner, Francisco E.

2017-03-01

Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months to decades, before highlighting a range of pioneering case studies using climate predictions to inform LMR decisions. The success of these case studies suggests that many additional applications are possible. Progress, however, is limited by observational and modeling challenges. Priority developments include strengthening of the mechanistic linkages between climate and marine resource responses, development of LMR models able to explicitly represent such responses, integration of climate driven LMR dynamics in the multi-driver context within which marine resources exist, and improved prediction of ecosystem-relevant variables at the fine regional scales at which most marine resource decisions are made. While there are fundamental limits to predictability, continued advances in these areas have considerable potential to make LMR managers and industry decision more resilient to climate variability and help sustain valuable resources. Concerted dialog between scientists, LMR managers and industry is essential to realizing this potential.

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do

PubMed Central

2017-01-01

Numerous chemical data sets have become available for quantitative structure–activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting. PMID:28691113

Viscous/potential flow about multi-element two-dimensional and infinite-span swept wings: Theory and experiment

NASA Technical Reports Server (NTRS)

Olson, L. E.; Dvorak, F. A.

1975-01-01

The viscous subsonic flow past two-dimensional and infinite-span swept multi-component airfoils is studied theoretically and experimentally. The computerized analysis is based on iteratively coupled boundary layer and potential flow analysis. The method, which is restricted to flows with only slight separation, gives surface pressure distribution, chordwise and spanwise boundary layer characteristics, lift, drag, and pitching moment for airfoil configurations with up to four elements. Merging confluent boundary layers are treated. Theoretical predictions are compared with an exact theoretical potential flow solution and with experimental measures made in the Ames 40- by 80-Foot Wind Tunnel for both two-dimensional and infinite-span swept wing configurations. Section lift characteristics are accurately predicted for zero and moderate sweep angles where flow separation effects are negligible.

Evaluating models of climate and forest vegetation

NASA Technical Reports Server (NTRS)

Clark, James S.

1992-01-01

Understanding how the biosphere may respond to increasing trace gas concentrations in the atmosphere requires models that contain vegetation responses to regional climate. Most of the processes ecologists study in forests, including trophic interactions, nutrient cycling, and disturbance regimes, and vital components of the world economy, such as forest products and agriculture, will be influenced in potentially unexpected ways by changing climate. These vegetation changes affect climate in the following ways: changing C, N, and S pools; trace gases; albedo; and water balance. The complexity of the indirect interactions among variables that depend on climate, together with the range of different space/time scales that best describe these processes, make the problems of modeling and prediction enormously difficult. These problems of predicting vegetation response to climate warming and potential ways of testing model predictions are the subjects of this chapter.

Technical Report Series on Global Modeling and Data Assimilation. Volume 13; Interannual Variability and Potential Predictability in Reanalysis Products

NASA Technical Reports Server (NTRS)

Min, Wei; Schubert, Siegfried D.; Suarez, Max J. (Editor)

1997-01-01

The Data Assimilation Office (DAO) at Goddard Space Flight Center and the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) have produced multi-year global assimilations of historical data employing fixed analysis systems. These "reanalysis" products are ideally suited for studying short-term climatic variations. The availability of multiple reanalysis products also provides the opportunity to examine the uncertainty in the reanalysis data. The purpose of this document is to provide an updated estimate of seasonal and interannual variability based on the DAO and NCEP/NCAR reanalyses for the 15-year period 1980-1995. Intercomparisons of the seasonal means and their interannual variations are presented for a variety of prognostic and diagnostic fields. In addition, atmospheric potential predictability is re-examined employing selected DAO reanalysis variables.

Experimental validation of predicted cancer genes using FRET

NASA Astrophysics Data System (ADS)

Guala, Dimitri; Bernhem, Kristoffer; Ait Blal, Hammou; Jans, Daniel; Lundberg, Emma; Brismar, Hjalmar; Sonnhammer, Erik L. L.

2018-07-01

Huge amounts of data are generated in genome wide experiments, designed to investigate diseases with complex genetic causes. Follow up of all potential leads produced by such experiments is currently cost prohibitive and time consuming. Gene prioritization tools alleviate these constraints by directing further experimental efforts towards the most promising candidate targets. Recently a gene prioritization tool called MaxLink was shown to outperform other widely used state-of-the-art prioritization tools in a large scale in silico benchmark. An experimental validation of predictions made by MaxLink has however been lacking. In this study we used Fluorescence Resonance Energy Transfer, an established experimental technique for detection of protein-protein interactions, to validate potential cancer genes predicted by MaxLink. Our results provide confidence in the use of MaxLink for selection of new targets in the battle with polygenic diseases.

Measuring Gambling Reinforcers, Over Consumption and Fallacies: The Psychometric Properties and Predictive Validity of the Jonsson-Abbott Scale.

PubMed

Jonsson, Jakob; Abbott, Max W; Sjöberg, Anders; Carlbring, Per

2017-01-01

Traditionally, gambling and problem gambling research relies on cross-sectional and retrospective designs. This has compromised identification of temporal relationships and causal inference. To overcome these problems a new questionnaire, the Jonsson-Abbott Scale (JAS), was developed and used in a large, prospective, general population study, The Swedish Longitudinal Gambling Study (Swelogs). The JAS has 11 items and seeks to identify early indicators, examine relationships between indicators and assess their capacity to predict future problem progression. The aims of the study were to examine psychometric properties of the JAS (internal consistency and dimensionality) and predictive validity with respect to increased gambling risk and problem gambling onset. The results are based on repeated interviews with 3818 participants. The response rate from the initial baseline wave was 74%. The original sample consisted of a random, stratified selection from the Swedish population register aged between 16 and 84. The results indicate an acceptable fit of a three-factor solution in a confirmatory factor analysis with 'Over consumption,' 'Gambling fallacies,' and 'Reinforcers' as factors. Reinforcers, Over consumption and Gambling fallacies were significant predictors of gambling risk potential and Gambling fallacies and Over consumption were significant predictors of problem gambling onset (incident cases) at 12 month follow up. When controlled for risk potential measured at baseline, the predictor Over consumption was not significant for gambling risk potential at follow up. For incident cases, Gambling fallacies and Over consumption remained significant when controlled for risk potential. Implications of the results for the development of problem gambling, early detection, prevention, and future research are discussed.

Assessing the Risk of Invasion by Tephritid Fruit Flies: Intraspecific Divergence Matters

PubMed Central

Godefroid, Martin; Cruaud, Astrid; Rossi, Jean-Pierre; Rasplus, Jean-Yves

2015-01-01

Widely distributed species often show strong phylogeographic structure, with lineages potentially adapted to different biotic and abiotic conditions. The success of an invasion process may thus depend on the intraspecific identity of the introduced propagules. However, pest risk analyses are usually performed without accounting for intraspecific diversity. In this study, we developed bioclimatic models using MaxEnt and boosted regression trees approaches, to predict the potential distribution in Europe of six economically important Tephritid pests (Ceratitis fasciventris (Bezzi), Bactrocera oleae (Rossi), Anastrepha obliqua (Macquart), Anastrepha fraterculus (Wiedemann), Rhagoletis pomonella (Walsh) and Bactrocera cucurbitae (Coquillet)). We considered intraspecific diversity in our risk analyses by independently modeling the distributions of conspecific lineages. The six species displayed different potential distributions in Europe. A strong signal of intraspecific climate envelope divergence was observed in most species. In some cases, conspecific lineages differed strongly in potential distributions suggesting that taxonomic resolution should be accounted for in pest risk analyses. No models (lineage- and species-based approaches) predicted high climatic suitability in the entire invaded range of B. oleae—the only species whose intraspecific identity of invading populations has been elucidated—in California. Host availability appears to play the most important role in shaping the geographic range of this specialist pest. However, climatic suitability values predicted by species-based models are correlated with population densities of B. oleae globally reported in California. Our study highlights how classical taxonomic boundaries may lead to under- or overestimation of the potential pest distributions and encourages accounting for intraspecific diversity when assessing the risk of biological invasion. PMID:26274582

A predictive model of iron oxide nanoparticles flocculation tuning Z-potential in aqueous environment for biological application

NASA Astrophysics Data System (ADS)

Baldassarre, Francesca; Cacciola, Matteo; Ciccarella, Giuseppe

2015-09-01

Iron oxide nanoparticles are the most used magnetic nanoparticles in biomedical and biotechnological field because of their nontoxicity respect to the other metals. The investigation of iron oxide nanoparticles behaviour in aqueous environment is important for the biological applications in terms of polydispersity, mobility, cellular uptake and response to the external magnetic field. Iron oxide nanoparticles tend to agglomerate in aqueous solutions; thus, the stabilisation and aggregation could be modified tuning the colloids physical proprieties. Surfactants or polymers are often used to avoid agglomeration and increase nanoparticles stability. We have modelled and synthesised iron oxide nanoparticles through a co-precipitation method, in order to study the influence of surfactants and coatings on the aggregation state. Thus, we compared experimental results to simulation model data. The change of Z-potential and the clusters size were determined by Dynamic Light Scattering. We developed a suitable numerical model to predict the flocculation. The effects of Volume Mean Diameter and fractal dimension were explored in the model. We obtained the trend of these parameters tuning the Z-potential. These curves matched with the experimental results and confirmed the goodness of the model. Subsequently, we exploited the model to study the influence of nanoparticles aggregation and stability by Z-potential and external magnetic field. The highest Z-potential is reached up with a small external magnetic influence, a small aggregation and then a high suspension stability. Thus, we obtained a predictive model of Iron oxide nanoparticles flocculation that will be exploited for the nanoparticles engineering and experimental setup of bioassays.

Establishment of a novel experimental protocol for drug-induced seizure liability screening based on a locomotor activity assay in zebrafish.

PubMed

Koseki, Naoteru; Deguchi, Jiro; Yamashita, Akihito; Miyawaki, Izuru; Funabashi, Hitoshi

2014-08-01

As drug-induced seizures have severe impact on drug development, evaluating seizure induction potential of candidate drugs at the early stages of drug discovery is important. A novel assay system using zebrafish has attracted interest as a high throughput toxicological in vivo assay system, and we tried to establish an experimental method for drug-induced seizure liability on the basis of locomotor activity in zebrafish. We monitored locomotor activity at high-speed movement (> 20 mm/sec) for 60 min immediately after exposure, and assessed seizure liability potential in some drugs using locomotor activity. However this experimental procedure was not sufficient for predicting seizures because the potential of several drugs with demonstrated seizure potential in mammals was not detected. We, therefore, added other parameters for locomotor activity such as extending exposure time or conducting flashlight stimulation (10 Hz) which is a known seizure induction stimulus, and these additional parameters improved seizure potential detection in some drugs. The validation study using the improved methodology was used to assess 52 commercially available drugs, and the prediction rate was approximately 70%. The experimental protocol established in this present study is considered useful for seizure potential screening during early stages of drug discovery.

Identification of potential platelet alloantigens in the Equidae family by comparison of gene sequences encoding major platelet membrane glycoproteins.

PubMed

Boudreaux, Mary K; Humphries, Drew M

2013-12-01

Platelet alloantigens in horses may play an important role in the development of neonatal alloimmune thrombocytopenia (NAIT). The objective of this study was to evaluate genes encoding major platelet glycoproteins within the Equidae family in an effort to identify potential alloantigens. DNA was isolated from blood samples obtained from Equidae family members, including a Holsteiner-Oldenburg cross, a Quarter horse, a donkey, and a Plains zebra (Equus burchelli). Gene sequences encoding equine platelet membrane glycoproteins IIb, IIIa (integrin subunits αIIb and β3), Ia (integrin subunit α2), and Ibα were determined using PCR. Gene sequences were compared to the equine genome available on GenBank. Polymorphisms that would be predicted to result in amino acid changes on platelet surfaces were documented and compared with known alloantigenic sites documented on human platelets. Amino acid differences were predicted based on nucleotide sequences for all 4 genes. Nine differences were documented for αIIb, 5 differences were documented for β3, 7 differences were documented for α2, and 16 differences were documented for Ibα outside the macroglycopeptide region. This study represents the first effort at identifying potential platelet alloantigens in members of the Equidae Family based on evaluation of gene sequences. The data obtained form the groundwork for identifying potential platelet alloantigens involved in transfusion reactions and neonatal alloimmune thrombocytopenia (NAIT). More work is required to determine whether the predicted amino acid differences documented in this study play a role in alloimmunity, and whether other polymorphisms not detected in this study are present that may result in alloimmunity. © 2013 American Society for Veterinary Clinical Pathology.

Bringing modeling to the masses: A web based system to predict potential species distributions

USGS Publications Warehouse

Graham, Jim; Newman, Greg; Kumar, Sunil; Jarnevich, Catherine S.; Young, Nick; Crall, Alycia W.; Stohlgren, Thomas J.; Evangelista, Paul

2010-01-01

Predicting current and potential species distributions and abundance is critical for managing invasive species, preserving threatened and endangered species, and conserving native species and habitats. Accurate predictive models are needed at local, regional, and national scales to guide field surveys, improve monitoring, and set priorities for conservation and restoration. Modeling capabilities, however, are often limited by access to software and environmental data required for predictions. To address these needs, we built a comprehensive web-based system that: (1) maintains a large database of field data; (2) provides access to field data and a wealth of environmental data; (3) accesses values in rasters representing environmental characteristics; (4) runs statistical spatial models; and (5) creates maps that predict the potential species distribution. The system is available online at www.niiss.org, and provides web-based tools for stakeholders to create potential species distribution models and maps under current and future climate scenarios.

Imbalanced target prediction with pattern discovery on clinical data repositories.

PubMed

Chan, Tak-Ming; Li, Yuxi; Chiau, Choo-Chiap; Zhu, Jane; Jiang, Jie; Huo, Yong

2017-04-20

Clinical data repositories (CDR) have great potential to improve outcome prediction and risk modeling. However, most clinical studies require careful study design, dedicated data collection efforts, and sophisticated modeling techniques before a hypothesis can be tested. We aim to bridge this gap, so that clinical domain users can perform first-hand prediction on existing repository data without complicated handling, and obtain insightful patterns of imbalanced targets for a formal study before it is conducted. We specifically target for interpretability for domain users where the model can be conveniently explained and applied in clinical practice. We propose an interpretable pattern model which is noise (missing) tolerant for practice data. To address the challenge of imbalanced targets of interest in clinical research, e.g., deaths less than a few percent, the geometric mean of sensitivity and specificity (G-mean) optimization criterion is employed, with which a simple but effective heuristic algorithm is developed. We compared pattern discovery to clinically interpretable methods on two retrospective clinical datasets. They contain 14.9% deaths in 1 year in the thoracic dataset and 9.1% deaths in the cardiac dataset, respectively. In spite of the imbalance challenge shown on other methods, pattern discovery consistently shows competitive cross-validated prediction performance. Compared to logistic regression, Naïve Bayes, and decision tree, pattern discovery achieves statistically significant (p-values < 0.01, Wilcoxon signed rank test) favorable averaged testing G-means and F1-scores (harmonic mean of precision and sensitivity). Without requiring sophisticated technical processing of data and tweaking, the prediction performance of pattern discovery is consistently comparable to the best achievable performance. Pattern discovery has demonstrated to be robust and valuable for target prediction on existing clinical data repositories with imbalance and noise. The prediction results and interpretable patterns can provide insights in an agile and inexpensive way for the potential formal studies.

How Accurately Do Consecutive Cohort Audits Predict Phase III Multisite Clinical Trial Recruitment in Palliative Care?

PubMed

McCaffrey, Nikki; Fazekas, Belinda; Cutri, Natalie; Currow, David C

2016-04-01

Audits have been proposed for estimating possible recruitment rates to randomized controlled trials (RCTs), but few studies have compared audit data with subsequent recruitment rates. To compare the accuracy of estimates of potential recruitment from a retrospective consecutive cohort audit of actual participating sites and recruitment to four Phase III multisite clinical RCTs. The proportion of potentially eligible study participants estimated from an inpatient chart review of people with life-limiting illnesses referred to six Australian specialist palliative care services was compared with recruitment data extracted from study prescreening information from three sites that participated fully in four Palliative Care Clinical Studies Collaborative RCTs. The predominant reasons for ineligibility in the audit and RCTs were analyzed. The audit overestimated the proportion of people referred to the palliative care services who could participate in the RCTs (pain 17.7% vs. 1.2%, delirium 5.8% vs. 0.6%, anorexia 5.1% vs. 0.8%, and bowel obstruction 2.8% vs. 0.5%). Approximately 2% of the referral base was potentially eligible for these effectiveness studies. Ineligibility for general criteria (language, cognition, and geographic proximity) varied between studies, whereas the reasons for exclusion were similar between the audit and pain and anorexia studies but not for delirium or bowel obstruction. The retrospective consecutive case note audit in participating sites did not predict realistic recruitment rates, mostly underestimating the impact of study-specific inclusion criteria. These findings have implications for the applicability of the results of RCTs. Prospective pilot studies are more likely to predict actual recruitment. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Magnitude and Temporal Variability of Inter-stimulus EEG Modulate the Linear Relationship Between Laser-Evoked Potentials and Fast-Pain Perception

PubMed Central

Li, Linling; Huang, Gan; Lin, Qianqian; Liu, Jia; Zhang, Shengli; Zhang, Zhiguo

2018-01-01

The level of pain perception is correlated with the magnitude of pain-evoked brain responses, such as laser-evoked potentials (LEP), across trials. The positive LEP-pain relationship lays the foundation for pain prediction based on single-trial LEP, but cross-individual pain prediction does not have a good performance because the LEP-pain relationship exhibits substantial cross-individual difference. In this study, we aim to explain the cross-individual difference in the LEP-pain relationship using inter-stimulus EEG (isEEG) features. The isEEG features (root mean square as magnitude and mean square successive difference as temporal variability) were estimated from isEEG data (at full band and five frequency bands) recorded between painful stimuli. A linear model was fitted to investigate the relationship between pain ratings and LEP response for fast-pain trials on a trial-by-trial basis. Then the correlation between isEEG features and the parameters of LEP-pain model (slope and intercept) was evaluated. We found that the magnitude and temporal variability of isEEG could modulate the parameters of an individual's linear LEP-pain model for fast-pain trials. Based on this, we further developed a new individualized fast-pain prediction scheme, which only used training individuals with similar isEEG features as the test individual to train the fast-pain prediction model, and obtained improved accuracy in cross-individual fast-pain prediction. The findings could help elucidate the neural mechanism of cross-individual difference in pain experience and the proposed fast-pain prediction scheme could be potentially used as a practical and feasible pain prediction method in clinical practice. PMID:29904336

Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models.

PubMed

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. © 2012 Diabetes Technology Society.

CYP3A4 substrate selection and substitution in the prediction of potential drug-drug interactions.

PubMed

Galetin, Aleksandra; Ito, Kiyomi; Hallifax, David; Houston, J Brian

2005-07-01

The complexity of in vitro kinetic phenomena observed for CYP3A4 substrates (homo- or heterotropic cooperativity) confounds the prediction of drug-drug interactions, and an evaluation of alternative and/or pragmatic approaches and substrates is needed. The current study focused on the utility of the three most commonly used CYP3A4 in vitro probes for the prediction of 26 reported in vivo interactions with azole inhibitors (increase in area under the curve ranged from 1.2 to 24, 50% in the range of potent inhibition). In addition to midazolam, testosterone, and nifedipine, quinidine was explored as a more "pragmatic" substrate due to its kinetic properties and specificity toward CYP3A4 in comparison with CYP3A5. Ki estimates obtained in human liver microsomes under standardized in vitro conditions for each of the four probes were used to determine the validity of substrate substitution in CYP3A4 drug-drug interaction prediction. Detailed inhibitor-related (microsomal binding, depletion over incubation time) and substrate-related factors (cooperativity, contribution of other metabolic pathways, or renal excretion) were incorporated in the assessment of the interaction potential. All four CYP3A4 probes predicted 69 to 81% of the interactions with azoles within 2-fold of the mean in vivo value. Comparison of simple and multisite mechanistic models and interaction prediction accuracy for each of the in vitro probes indicated that midazolam and quinidine in vitro data provided the best assessment of a potential interaction, with the lowest bias and the highest precision of the prediction. Further investigations with a wider range of inhibitors are required to substantiate these findings.

Hypoglycemia Early Alarm Systems Based on Recursive Autoregressive Partial Least Squares Models

PubMed Central

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Background Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. Methods A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Results Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. Conclusions The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. PMID:23439179

Study of the Correlation between the Performances of Lunar Vehicle Wheels Predicted by the Nepean Wheeled Vehicle Performance Model and Test Data

NASA Technical Reports Server (NTRS)

Wong, J. Y.; Asnani, V. M.

2008-01-01

This paper describes the results of a study of the correlation between the performances of wheels for lunar vehicles predicted using the Nepean wheeled vehicle performance model (NWVPM), developed under the auspices of Vehicle Systems Development Corporation, Ottawa, Canada, and the corresponding test data presented in Performance evaluation of wheels for lunar vehicles , Technical Report M-70-2, prepared for George C. Marshall Space Flight Center, National Aeronautics and Space Administration (NASA), USA, by the US Army Engineer Waterways Experiment Station (WES). The NWVPM was originally developed for design and performance evaluation of terrestrial off-road wheeled vehicles. The purpose of this study is to assess the potential of the NWVPM for evaluating wheel candidates for the new generation of extra-terrestrial vehicles. Two versions of a wire-mesh wheel and a hoop-spring wheel, which were considered as candidates for lunar roving vehicles for the NASA Apollo program in the late 1960s, together with a pneumatic wheel were examined in this study. The tractive performances of these wheels and of a 464 test vehicle with the pneumatic wheels on air-dry sand were predicted using the NWVPM and compared with the corresponding test data obtained under Earth s gravity and previously documented in the above-named report. While test data on wheel or vehicle performances obtained under Earth s gravity may not necessarily be representative of those on extra-terrestrial bodies, because of the differences in gravity and in environmental conditions, such as atmospheric pressure, it is still a valid approach to use test data obtained under Earth s gravity to evaluate the predictive capability of the NWVPM and its potential applications to predicting wheel or wheeled rover performances on extra-terrestrial bodies. Results of this study show that, using the ratio (P20/W) of the drawbar pull to normal load at 20 per cent slip as a performance indicator, there is a reasonable correlation between the predictions and experimental data. This indicates that the NWVPM has the potential as an engineering tool for evaluating wheel candidates for a future generation of extra-terrestrial vehicles, provided that appropriate input data are available.

An empirical approach to improving tidal predictions using recent real-time tide gauge data

NASA Astrophysics Data System (ADS)

Hibbert, Angela; Royston, Samantha; Horsburgh, Kevin J.; Leach, Harry

2014-05-01

Classical harmonic methods of tidal prediction are often problematic in estuarine environments due to the distortion of tidal fluctuations in shallow water, which results in a disparity between predicted and observed sea levels. This is of particular concern in the Bristol Channel, where the error associated with tidal predictions is potentially greater due to an unusually large tidal range of around 12m. As such predictions are fundamental to the short-term forecasting of High Water (HW) extremes, it is vital that alternative solutions are found. In a pilot study, using a year-long observational sea level record from the Port of Avonmouth in the Bristol Channel, the UK National Tidal and Sea Level Facility (NTSLF) tested the potential for reducing tidal prediction errors, using three alternatives to the Harmonic Method of tidal prediction. The three methods evaluated were (1) the use of Artificial Neural Network (ANN) models, (2) the Species Concordance technique and (3) a simple empirical procedure for correcting Harmonic Method High Water predictions based upon a few recent observations (referred to as the Empirical Correction Method). This latter method was then successfully applied to sea level records from an additional 42 of the 45 tide gauges that comprise the UK Tide Gauge Network. Consequently, it is to be incorporated into the operational systems of the UK Coastal Monitoring and Forecasting Partnership in order to improve short-term sea level predictions for the UK and in particular, the accurate estimation of HW extremes.

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.

Potential uncertainty reduction in model-averaged benchmark dose estimates informed by an additional dose study.

PubMed

Shao, Kan; Small, Mitchell J

2011-10-01

A methodology is presented for assessing the information value of an additional dosage experiment in existing bioassay studies. The analysis demonstrates the potential reduction in the uncertainty of toxicity metrics derived from expanded studies, providing insights for future studies. Bayesian methods are used to fit alternative dose-response models using Markov chain Monte Carlo (MCMC) simulation for parameter estimation and Bayesian model averaging (BMA) is used to compare and combine the alternative models. BMA predictions for benchmark dose (BMD) are developed, with uncertainty in these predictions used to derive the lower bound BMDL. The MCMC and BMA results provide a basis for a subsequent Monte Carlo analysis that backcasts the dosage where an additional test group would have been most beneficial in reducing the uncertainty in the BMD prediction, along with the magnitude of the expected uncertainty reduction. Uncertainty reductions are measured in terms of reduced interval widths of predicted BMD values and increases in BMDL values that occur as a result of this reduced uncertainty. The methodology is illustrated using two existing data sets for TCDD carcinogenicity, fitted with two alternative dose-response models (logistic and quantal-linear). The example shows that an additional dose at a relatively high value would have been most effective for reducing the uncertainty in BMA BMD estimates, with predicted reductions in the widths of uncertainty intervals of approximately 30%, and expected increases in BMDL values of 5-10%. The results demonstrate that dose selection for studies that subsequently inform dose-response models can benefit from consideration of how these models will be fit, combined, and interpreted. © 2011 Society for Risk Analysis.

Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills.

PubMed

Brandstätter, Christian; Laner, David; Prantl, Roman; Fellner, Johann

2014-12-01

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed. This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables. The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills. Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies. Copyright © 2014 Elsevier Ltd. All rights reserved.

Self-reported clothing size as a proxy measure for body size.

PubMed

Hughes, Laura A E; Schouten, Leo J; Goldbohm, R Alexandra; van den Brandt, Piet A; Weijenberg, Matty P

2009-09-01

Few studies have considered the potential utility of clothing size as a predictor of diseases associated with body weight. We used data on weight-stable men and women from a subcohort of the Netherlands Cohort Study to assess the correlation of clothing size with other anthropometric variables. Cox regression using the case-cohort approach was performed to establish whether clothing size can predict cancer risk after 13.3 years of follow-up, and if additionally considering body mass index (BMI) in the model improves the prediction. Trouser and skirt size correlated well with circumference measurements. Skirt size predicted endometrial cancer risk, and this effect was slightly attenuated when BMI was added to the model. Trouser size predicted risk of renal cell carcinoma, regardless of whether BMI was in the model. Clothing size appears to predict cancer risk independently of BMI, suggesting that clothing size is a useful measure to consider in epidemiologic studies when waist circumference is not available.

Potential role of salinity in ENSO and MJO predictions

NASA Astrophysics Data System (ADS)

Zhu, J.; Kumar, A.; Murtugudde, R. G.; Xie, P.

2017-12-01

Studies have suggested that ocean salinity can vary in response to ENSO and MJO. For example, during an El Niño event, sea surface salinity decreases in the western and central equatorial Pacific, as a result of zonal advection of low salinity water by anomalous eastward surface currents, and to a lesser extent as a result of a rainfall excess associated with atmospheric convection and warm water displacements. However, the effect of salinity on ENSO and MJO evolutions and their forecasts has been less explored. In this analysis, we explored the potential role of salinity in ENSO and MJO predictions by conducting sensitivity experiments with NCEP CFSv2. Firstly, two forecasts experiments are conducted to explore its effect on ENSO predictions, in which the interannual variability of salinity in the ocean initial states is either included or excluded. Comparisons suggested that the salinity variability is essential to correctly forecast the 2007/08 La Niña starting from April 2007. With realistic salinity initial states, the tendency to decay of the subsurface cold condition during the spring and early summer 2007 was interrupted by positive salinity anomalies in the upper central Pacific, which working together with the Bjerknes positive feedback, contributed to the development of the La Niña event. Our study suggests that ENSO forecasts will benefit from more accurate sustained salinity observations having large-scale spatial coverage. We also assessed the potential role of salinity in MJO by evaluating a long coupled free run that has a relatively realistic MJO simulation and a set of predictability experiment, both based on CFSv2. Diagnostics of the free run suggest that, while the intraseasonal SST variations lead convections by a quarter cycle, they are almost in phase only with changes in barrier layer thickness, thereby suggesting an active role of salinity on SST. Its effect on MJO predictions is further explored by controlling the surface salinity feedback during the predictability experiments.

Potential Impacts of Climate Change on Native Plant Distributions in the Falkland Islands

PubMed Central

Upson, Rebecca; Williams, Jennifer J.; Wilkinson, Tim P.; Maclean, Ilya M. D.; McAdam, Jim H.; Moat, Justin F.

2016-01-01

The Falkland Islands are predicted to experience up to 2.2°C rise in mean annual temperature over the coming century, greater than four times the rate over the last century. Our study investigates likely vulnerabilities of a suite of range-restricted species whose distributions are associated with archipelago-wide climatic variation. We used present day climate maps calibrated using local weather data, 2020–2080 climate predictions from regional climate models, non-climate variables derived from a digital terrain model and a comprehensive database on local plant distributions. Weighted mean ensemble models were produced to assess changes in range sizes and overlaps between the current range and protected areas network. Target species included three globally threatened Falkland endemics, Nassauvia falklandica, Nastanthus falklandicus and Plantago moorei; and two nationally threatened species, Acaena antarctica and Blechnum cordatum. Our research demonstrates that temperature increases predicted for the next century have the potential to significantly alter plant distributions across the Falklands. Upland species, in particular, were found to be highly vulnerable to climate change impacts. No known locations of target upland species or the southwestern species Plantago moorei are predicted to remain environmentally suitable in the face of predicted climate change. We identify potential refugia for these species and associated gaps in the current protected areas network. Species currently restricted to the milder western parts of the archipelago are broadly predicted to expand their ranges under warmer temperatures. Our results emphasise the importance of implementing suitable adaptation strategies to offset climate change impacts, particularly site management. There is an urgent need for long-term monitoring and artificial warming experiments; the results of this study will inform the selection of the most suitable locations for these. Results are also helping inform management recommendations for the Falkland Islands Government who seek to better conserve their biodiversity and meet commitments to multi-lateral environmental agreements. PMID:27880846

Potential Impacts of Climate Change on Native Plant Distributions in the Falkland Islands.

PubMed

Upson, Rebecca; Williams, Jennifer J; Wilkinson, Tim P; Clubbe, Colin P; Maclean, Ilya M D; McAdam, Jim H; Moat, Justin F

2016-01-01

The Falkland Islands are predicted to experience up to 2.2°C rise in mean annual temperature over the coming century, greater than four times the rate over the last century. Our study investigates likely vulnerabilities of a suite of range-restricted species whose distributions are associated with archipelago-wide climatic variation. We used present day climate maps calibrated using local weather data, 2020-2080 climate predictions from regional climate models, non-climate variables derived from a digital terrain model and a comprehensive database on local plant distributions. Weighted mean ensemble models were produced to assess changes in range sizes and overlaps between the current range and protected areas network. Target species included three globally threatened Falkland endemics, Nassauvia falklandica, Nastanthus falklandicus and Plantago moorei; and two nationally threatened species, Acaena antarctica and Blechnum cordatum. Our research demonstrates that temperature increases predicted for the next century have the potential to significantly alter plant distributions across the Falklands. Upland species, in particular, were found to be highly vulnerable to climate change impacts. No known locations of target upland species or the southwestern species Plantago moorei are predicted to remain environmentally suitable in the face of predicted climate change. We identify potential refugia for these species and associated gaps in the current protected areas network. Species currently restricted to the milder western parts of the archipelago are broadly predicted to expand their ranges under warmer temperatures. Our results emphasise the importance of implementing suitable adaptation strategies to offset climate change impacts, particularly site management. There is an urgent need for long-term monitoring and artificial warming experiments; the results of this study will inform the selection of the most suitable locations for these. Results are also helping inform management recommendations for the Falkland Islands Government who seek to better conserve their biodiversity and meet commitments to multi-lateral environmental agreements.

Effects of psychosocial work factors on lifestyle changes: a cohort study.

PubMed

Allard, Karin Olofsson; Thomsen, Jane Frølund; Mikkelsen, Sigurd; Rugulies, Reiner; Mors, Ole; Kærgaard, Anette; Kolstad, Henrik A; Kaerlev, Linda; Andersen, Johan Hviid; Hansen, Ase Marie; Bonde, Jens Peter

2011-12-01

To evaluate the effect of the demand-control-support model, the effort-reward imbalance model, and emotional demands on smoking, alcohol consumption, physical activity, and body mass index. This is a 2-year prospective cohort study of 3224 public sector employees. Measures were assessed with questionnaires. Multiple regression analyses were used to predict changes in lifestyle factors. Low reward predicted smoking, low-decision latitude predicted being inactive, and high demands predicted high-alcohol consumption but only for men at follow-up even after controlling for potential confounders. There were no other significant findings in the expected direction except for some of the confounders. We found only limited and inconsistent support for the hypothesis that a poor psychosocial work environment is associated with an adverse lifestyle.

A NEW LOG EVALUATION METHOD TO APPRAISE MESAVERDE RE-COMPLETION OPPORTUNITIES

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

Albert Greer

2003-09-11

Artificial intelligence tools, fuzzy logic and neural networks were used to evaluate the potential of the behind pipe Mesaverde formation in BMG's Mancos formation wells. A fractal geostatistical mapping algorithm was also used to predict Mesaverde production. Additionally, a conventional geological study was conducted. To date one Mesaverde completion has been performed. The Janet No.3 Mesaverde completion was non-economic. Both the AI method and the geostatistical methods predicted the failure of the Janet No.3. The Gavilan No.1 in the Mesaverde was completed during the course of the study and was an extremely good well. This well was not included inmore » the statistical dataset. The AI method predicted very good production while the fractal map predicted a poor producer.« less

The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

DOE PAGES

Boer, George J.; Smith, Douglas M.; Cassou, Christophe; ...

2016-01-01

The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Predictionmore » (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours. Furthermore, groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.« less

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.

Combine experimental and theoretical investigation on an alkaloid-Dimethylisoborreverine

NASA Astrophysics Data System (ADS)

Singh, Swapnil; Singh, Harshita; Karthick, T.; Agarwal, Parag; Erande, Rohan D.; Dethe, Dattatraya H.; Tandon, Poonam

2016-01-01

A combined experimental (FT-IR, 1H and 13C NMR) and theoretical approach is used to study the structure and properties of antimalarial drug dimethylisoborreverine (DMIB). Conformational analysis, has been performed by plotting one dimensional potential energy curve that was computed using density functional theory (DFT) with B3LYP/6-31G method and predicted conformer A1 as the most stable conformer. After full geometry optimization, harmonic wavenumbers were computed for conformer A1 at the DFT/B3LYP/6-311++G(d,P) level. A complete vibrational assignment of all the vibrational modes have been performed on the bases of the potential energy distribution (PED) and theoretical results were found to be in good agreement with the observed data. To predict the solvent effect, the UV-Vis spectra were calculated in different solvents by polarizable continuum model using TD-DFT method. Molecular docking studies were performed to test the biological activity of the sample using SWISSDOCK web server and Hex 8.0.0 software. The molecular electrostatic potential (MESP) was plotted to identify the reactive sites of the molecule. Natural bond orbital (NBO) analysis was performed to get a deep insight of intramolecular charge transfer. Thermodynamical parameters were calculated to predict the direction of chemical reaction.

Temperature-dependent development, cold tolerance, and potential distribution of Cricotopus lebetis (Diptera: Chironomidae), a tip miner of Hydrilla verticillata (Hydrocharitaceae).

PubMed

Stratman, Karen N; Overholt, William A; Cuda, James P; Mukherjee, A; Diaz, R; Netherland, Michael D; Wilson, Patrick C

2014-10-15

A chironomid midge, Cricotopus lebetis (Sublette) (Diptera: Chironomidae), was discovered attacking the apical meristems of Hydrilla verticillata (L.f. Royle) in Crystal River, Citrus Co., Florida in 1992. The larvae mine the stems of H. verticillata and cause basal branching and stunting of the plant. Temperature-dependent development, cold tolerance, and the potential distribution of the midge were investigated. The results of the temperature-dependent development study showed that optimal temperatures for larval development were between 20 and 30°C, and these data were used to construct a map of the potential number of generations per year of C. lebetis in Florida. Data from the cold tolerance study, in conjunction with historical weather data, were used to generate a predicted distribution of C. lebetis in the United States. A distribution was also predicted using an ecological niche modeling approach by characterizing the climate at locations where C. lebetis is known to occur and then finding other locations with similar climate. The distributions predicted using the two modeling approaches were not significantly different and suggested that much of the southeastern United States was climatically suitable for C. lebetis. © The Author 2014. Published by Oxford University Press on behalf of the Entomological Society of America.

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.

The influence of weather on migraine – are migraine attacks predictable?

PubMed Central

Hoffmann, Jan; Schirra, Tonio; Lo, Hendra; Neeb, Lars; Reuter, Uwe; Martus, Peter

2015-01-01

Objective The study aimed at elucidating a potential correlation between specific meteorological variables and the prevalence and intensity of migraine attacks as well as exploring a potential individual predictability of a migraine attack based on meteorological variables and their changes. Methods Attack prevalence and intensity of 100 migraineurs were correlated with atmospheric pressure, relative air humidity, and ambient temperature in 4-h intervals over 12 consecutive months. For each correlation, meteorological parameters at the time of the migraine attack as well as their variation within the preceding 24 h were analyzed. For migraineurs showing a positive correlation, logistic regression analysis was used to assess the predictability of a migraine attack based on meteorological information. Results In a subgroup of migraineurs, a significant weather sensitivity could be observed. In contrast, pooled analysis of all patients did not reveal a significant association. An individual prediction of a migraine attack based on meteorological data was not possible, mainly as a result of the small prevalence of attacks. Interpretation The results suggest that only a subgroup of migraineurs is sensitive to specific weather conditions. Our findings may provide an explanation as to why previous studies, which commonly rely on a pooled analysis, show inconclusive results. The lack of individual attack predictability indicates that the use of preventive measures based on meteorological conditions is not feasible. PMID:25642431

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.

In Silico Identification of Bioremediation Potential: Carbamazepine and Other Recalcitrant Personal Care Products.

PubMed

Aukema, Kelly G; Escalante, Diego E; Maltby, Meghan M; Bera, Asim K; Aksan, Alptekin; Wackett, Lawrence P

2017-01-17

Emerging contaminants are principally personal care products not readily removed by conventional wastewater treatment and, with an increasing reliance on water recycling, become disseminated in drinking water supplies. Carbamazepine, a widely used neuroactive pharmaceutical, increasingly escapes wastewater treatment and is found in potable water. In this study, a mechanism is proposed by which carbamazepine resists biodegradation, and a previously unknown microbial biodegradation was predicted computationally. The prediction identified biphenyl dioxygenase from Paraburkholderia xenovorans LB400 as the best candidate enzyme for metabolizing carbamazepine. The rate of degradation described here is 40 times greater than the best reported rates. The metabolites cis-10,11-dihydroxy-10,11-dihydrocarbamazepine and cis-2,3-dihydroxy-2,3-dihydrocarbamazepine were demonstrated with the native organism and a recombinant host. The metabolites are considered nonharmful and mitigate the generation of carcinogenic acridine products known to form when advanced oxidation methods are used in water treatment. Other recalcitrant personal care products were subjected to prediction by the Pathway Prediction System and tested experimentally with P. xenovorans LB400. It was shown to biodegrade structurally diverse compounds. Predictions indicated hydrolase or oxygenase enzymes catalyzed the initial reactions. This study highlights the potential for using the growing body of enzyme-structural and genomic information with computational methods to rapidly identify enzymes and microorganisms that biodegrade emerging contaminants.

On the use and potential use of seasonal to decadal climate predictions for decision-making in Europe

NASA Astrophysics Data System (ADS)

Soares, Marta Bruno; Dessai, Suraje

2014-05-01

The need for climate information to help inform decision-making in sectors susceptible to climate events and impacts is widely recognised. In Europe, developments in the science and models underpinning the study of climate variability and change have led to an increased interest in seasonal to decadal climate predictions (S2DCP). While seasonal climate forecasts are now routinely produced operationally by a number of centres around the world, decadal climate predictions are still in its infancy restricted to the realm of research. Contrary to other regions of the world, where the use of these types of forecasts, particularly at seasonal timescales, has been pursued in recent years due to higher levels of predictability, little is known about the uptake and climate information needs of end-users regarding S2DCP in Europe. To fill this gap we conducted in-depth interviews with experts and decision-makers across a range of European sectors, a workshop with European climate services providers, and a systematic literature review on the use of S2DCP in Europe. This study is part of the EUropean Provision Of Regional Impact Assessment on a Seasonal-to-decadal timescale (EUPORIAS) project which aims to develop semi-operational prototypes of impact prediction systems in Europe on seasonal to decadal timescales. We found that the emerging landscape of users and potential users of S2DCP in Europe is complex and heterogeneous. Differences in S2DCP information needs across and within organisations and sectors are largely underpinned by factors such as the institutional and regulatory context of the organisations, the plethora of activities and decision-making processes involved, the level of expertise and capacity of the users, and the availability of resources within the organisations. In addition, although the use of S2DCP across Europe is still fairly limited, particular sectors such as agriculture, health, energy, water, (re)insurance, and transport are taking the lead on the use of seasonal forecasts. The potential to use decadal predictions across European sectors was also noted although these are currently not used due to the limitations of the science and the experimental nature of existing predictions. Despite the limited use of these types of climate predictions there is a general understanding that information on the uncertainty of such predictions is a fundamental component of S2DCP although approaches for dealing with such uncertainty also tend to differ across organisations. Perceived barriers to the uptake of these types of climate predictions are mainly associated with low skill and reliability of the models but also with other factors such as relevance, usability, and accessibility of S2DCP by end-users. Potential solutions to overcome such barriers include the potential to explore existing 'windows of opportunity' in Europe, improve current understanding of users' needs, and increase accessibility and awareness of users to available S2DCP in Europe. This paper will present findings from our analysis and consider some of the broader issues raised by the emergence of S2DCP for climate services in Europe.

Callous-Unemotional Traits Predict Self-Reported Offending in Adolescent Boys: The Mediating Role of Delinquent Peers and the Moderating Role of Parenting Practices

ERIC Educational Resources Information Center

Ray, James V.; Frick, Paul J.; Thornton, Laura C.; Wall Myers, Tina D.; Steinberg, Laurence; Cauffman, Elizabeth

2017-01-01

Research has only recently begun to examine how callous-unemotional (CU) traits interact with contextual factors to predict delinquent behavior. The current study attempts to explain the well-established link between CU traits and offending by testing the potential mediating and moderating roles of 2 critical contextual factors: peer delinquency…

Prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy: a systematic review and external validation study.

PubMed

Hilkens, N A; Algra, A; Greving, J P

2016-01-01

ESSENTIALS: Prediction models may help to identify patients at high risk of bleeding on antiplatelet therapy. We identified existing prediction models for bleeding and validated them in patients with cerebral ischemia. Five prediction models were identified, all of which had some methodological shortcomings. Performance in patients with cerebral ischemia was poor. Background Antiplatelet therapy is widely used in secondary prevention after a transient ischemic attack (TIA) or ischemic stroke. Bleeding is the main adverse effect of antiplatelet therapy and is potentially life threatening. Identification of patients at increased risk of bleeding may help target antiplatelet therapy. This study sought to identify existing prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy and evaluate their performance in patients with cerebral ischemia. We systematically searched PubMed and Embase for existing prediction models up to December 2014. The methodological quality of the included studies was assessed with the CHARMS checklist. Prediction models were externally validated in the European Stroke Prevention Study 2, comprising 6602 patients with a TIA or ischemic stroke. We assessed discrimination and calibration of included prediction models. Five prediction models were identified, of which two were developed in patients with previous cerebral ischemia. Three studies assessed major bleeding, one studied intracerebral hemorrhage and one gastrointestinal bleeding. None of the studies met all criteria of good quality. External validation showed poor discriminative performance, with c-statistics ranging from 0.53 to 0.64 and poor calibration. A limited number of prediction models is available that predict intracranial hemorrhage or major bleeding in patients on antiplatelet therapy. The methodological quality of the models varied, but was generally low. Predictive performance in patients with cerebral ischemia was poor. In order to reliably predict the risk of bleeding in patients with cerebral ischemia, development of a prediction model according to current methodological standards is needed. © 2015 International Society on Thrombosis and Haemostasis.

Development of a generally applicable morphokinetic algorithm capable of predicting the implantation potential of embryos transferred on Day 3

PubMed Central

Petersen, Bjørn Molt; Boel, Mikkel; Montag, Markus; Gardner, David K.

2016-01-01

STUDY QUESTION Can a generally applicable morphokinetic algorithm suitable for Day 3 transfers of time-lapse monitored embryos originating from different culture conditions and fertilization methods be developed for the purpose of supporting the embryologist's decision on which embryo to transfer back to the patient in assisted reproduction? SUMMARY ANSWER The algorithm presented here can be used independently of culture conditions and fertilization method and provides predictive power not surpassed by other published algorithms for ranking embryos according to their blastocyst formation potential. WHAT IS KNOWN ALREADY Generally applicable algorithms have so far been developed only for predicting blastocyst formation. A number of clinics have reported validated implantation prediction algorithms, which have been developed based on clinic-specific culture conditions and clinical environment. However, a generally applicable embryo evaluation algorithm based on actual implantation outcome has not yet been reported. STUDY DESIGN, SIZE, DURATION Retrospective evaluation of data extracted from a database of known implantation data (KID) originating from 3275 embryos transferred on Day 3 conducted in 24 clinics between 2009 and 2014. The data represented different culture conditions (reduced and ambient oxygen with various culture medium strategies) and fertilization methods (IVF, ICSI). The capability to predict blastocyst formation was evaluated on an independent set of morphokinetic data from 11 218 embryos which had been cultured to Day 5. PARTICIPANTS/MATERIALS, SETTING, METHODS The algorithm was developed by applying automated recursive partitioning to a large number of annotation types and derived equations, progressing to a five-fold cross-validation test of the complete data set and a validation test of different incubation conditions and fertilization methods. The results were expressed as receiver operating characteristics curves using the area under the curve (AUC) to establish the predictive strength of the algorithm. MAIN RESULTS AND THE ROLE OF CHANCE By applying the here developed algorithm (KIDScore), which was based on six annotations (the number of pronuclei equals 2 at the 1-cell stage, time from insemination to pronuclei fading at the 1-cell stage, time from insemination to the 2-cell stage, time from insemination to the 3-cell stage, time from insemination to the 5-cell stage and time from insemination to the 8-cell stage) and ranking the embryos in five groups, the implantation potential of the embryos was predicted with an AUC of 0.650. On Day 3 the KIDScore algorithm was capable of predicting blastocyst development with an AUC of 0.745 and blastocyst quality with an AUC of 0.679. In a comparison of blastocyst prediction including six other published algorithms and KIDScore, only KIDScore and one more algorithm surpassed an algorithm constructed on conventional Alpha/ESHRE consensus timings in terms of predictive power. LIMITATIONS, REASONS FOR CAUTION Some morphological assessments were not available and consequently three of the algorithms in the comparison were not used in full and may therefore have been put at a disadvantage. Algorithms based on implantation data from Day 3 embryo transfers require adjustments to be capable of predicting the implantation potential of Day 5 embryo transfers. The current study is restricted by its retrospective nature and absence of live birth information. Prospective Randomized Controlled Trials should be used in future studies to establish the value of time-lapse technology and morphokinetic evaluation. WIDER IMPLICATIONS OF THE FINDINGS Algorithms applicable to different culture conditions can be developed if based on large data sets of heterogeneous origin. STUDY FUNDING/COMPETING INTEREST(S) This study was funded by Vitrolife A/S, Denmark and Vitrolife AB, Sweden. B.M.P.’s company BMP Analytics is performing consultancy for Vitrolife A/S. M.B. is employed at Vitrolife A/S. M.M.’s company ilabcomm GmbH received honorarium for consultancy from Vitrolife AB. D.K.G. received research support from Vitrolife AB. PMID:27609980

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.

Molecular design of new aggrecanases-2 inhibitors.

PubMed

Shan, Zhi Jie; Zhai, Hong Lin; Huang, Xiao Yan; Li, Li Na; Zhang, Xiao Yun

2013-10-01

Aggrecanases-2 is a very important potential drug target for the treatment of osteoarthritis. In this study, a series of known aggrecanases-2 inhibitors was analyzed by the technologies of three-dimensional quantitative structure-activity relationships (3D-QSAR) and molecular docking. Two 3D-QSAR models, which based on comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods, were established. Molecular docking was employed to explore the details of the interaction between inhibitors and aggrecanases-2 protein. According to the analyses for these models, several new potential inhibitors with higher activity predicted were designed, and were supported by the simulation of molecular docking. This work propose the fast and effective approach to design and prediction for new potential inhibitors, and the study of the interaction mechanism provide a better understanding for the inhibitors binding into the target protein, which will be useful for the structure-based drug design and modifications. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

Efficient prediction of human protein-protein interactions at a global scale.

PubMed

Schoenrock, Andrew; Samanfar, Bahram; Pitre, Sylvain; Hooshyar, Mohsen; Jin, Ke; Phillips, Charles A; Wang, Hui; Phanse, Sadhna; Omidi, Katayoun; Gui, Yuan; Alamgir, Md; Wong, Alex; Barrenäs, Fredrik; Babu, Mohan; Benson, Mikael; Langston, Michael A; Green, James R; Dehne, Frank; Golshani, Ashkan

2014-12-10

Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

Prediction and theoretical characterization of p-type organic semiconductor crystals for field-effect transistor applications.

PubMed

Atahan-Evrenk, Sule; Aspuru-Guzik, Alán

2014-01-01

The theoretical prediction and characterization of the solid-state structure of organic semiconductors has tremendous potential for the discovery of new high performance materials. To date, the theoretical analysis mostly relied on the availability of crystal structures obtained through X-ray diffraction. However, the theoretical prediction of the crystal structures of organic semiconductor molecules remains a challenge. This review highlights some of the recent advances in the determination of structure-property relationships of the known organic semiconductor single-crystals and summarizes a few available studies on the prediction of the crystal structures of p-type organic semiconductors for transistor applications.

SELF-BLM: Prediction of drug-target interactions via self-training SVM.

PubMed

Keum, Jongsoo; Nam, Hojung

2017-01-01

Predicting drug-target interactions is important for the development of novel drugs and the repositioning of drugs. To predict such interactions, there are a number of methods based on drug and target protein similarity. Although these methods, such as the bipartite local model (BLM), show promise, they often categorize unknown interactions as negative interaction. Therefore, these methods are not ideal for finding potential drug-target interactions that have not yet been validated as positive interactions. Thus, here we propose a method that integrates machine learning techniques, such as self-training support vector machine (SVM) and BLM, to develop a self-training bipartite local model (SELF-BLM) that facilitates the identification of potential interactions. The method first categorizes unlabeled interactions and negative interactions among unknown interactions using a clustering method. Then, using the BLM method and self-training SVM, the unlabeled interactions are self-trained and final local classification models are constructed. When applied to four classes of proteins that include enzymes, G-protein coupled receptors (GPCRs), ion channels, and nuclear receptors, SELF-BLM showed the best performance for predicting not only known interactions but also potential interactions in three protein classes compare to other related studies. The implemented software and supporting data are available at https://github.com/GIST-CSBL/SELF-BLM.

Predicting and mapping potential Whooping Crane stopover habitat to guide site selection for wind energy projects.

PubMed

Belaire, J Amy; Kreakie, Betty J; Keitt, Timothy; Minor, Emily

2014-04-01

Migratory stopover habitats are often not part of planning for conservation or new development projects. We identified potential stopover habitats within an avian migratory flyway and demonstrated how this information can guide the site-selection process for new development. We used the random forests modeling approach to map the distribution of predicted stopover habitat for the Whooping Crane (Grus americana), an endangered species whose migratory flyway overlaps with an area where wind energy development is expected to become increasingly important. We then used this information to identify areas for potential wind power development in a U.S. state within the flyway (Nebraska) that minimize conflicts between Whooping Crane stopover habitat and the development of clean, renewable energy sources. Up to 54% of our study area was predicted to be unsuitable as Whooping Crane stopover habitat and could be considered relatively low risk for conflicts between Whooping Cranes and wind energy development. We suggest that this type of analysis be incorporated into the habitat conservation planning process in areas where incidental take permits are being considered for Whooping Cranes or other species of concern. Field surveys should always be conducted prior to construction to verify model predictions and understand baseline conditions. © 2013 Society for Conservation Biology.

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.

New Secondary Batteries Utilizing Electronically Conductive Polypyrrole Cathode. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Yeu, Taewhan

1991-01-01

To gain a better understanding of the dynamic behavior in electronically conducting polypyrroles and to provide guidance toward designs of new secondary batteries based on these polymers, two mathematical models are developed; one for the potentiostatically controlled switching behavior of polypyrrole film, and one for the galvanostatically controlled charge/discharge behavior of lithium/polypyrrole secondary battery cell. The first model is used to predict the profiles of electrolyte concentrations, charge states, and electrochemical potentials within the thin polypyrrole film during switching process as functions of applied potential and position. Thus, the detailed mechanisms of charge transport and electrochemical reaction can be understood. Sensitivity analysis is performed for independent parameters, describing the physical and electrochemical characteristic of polypyrrole film, to verify their influences on the model performance. The values of independent parameters are estimated by comparing model predictions with experimental data obtained from identical conditions. The second model is used to predict the profiles of electrolyte concentrations, charge state, and electrochemical potentials within the battery system during charge and discharge processes as functions of time and position. Energy and power densities are estimated from model predictions and compared with existing battery systems. The independent design criteria on the charge and discharge performance of the cell are provided by studying the effects of design parameters.

Perioperative Non-Invasive Indocyanine Green-Clearance Testing to Predict Postoperative Outcome after Liver Resection

PubMed Central

Haegele, Stefanie; Reiter, Silvia; Wanek, David; Offensperger, Florian; Pereyra, David; Stremitzer, Stefan; Fleischmann, Edith; Brostjan, Christine; Gruenberger, Thomas; Starlinger, Patrick

2016-01-01

Background Postoperative liver dysfunction may lead to morbidity and mortality after liver resection. Preoperative liver function assessment is critical to identify preexisting liver dysfunction in patients prior to resection. The aim of this study was to evaluate the predictive potential of perioperative indocyanine green (ICG)-clearance testing to prevent postoperative liver dysfunction and morbidity using standardized outcome parameters in a routine Western-clinical-setting. Study Design 137 patients undergoing partial hepatectomy between 2011 and 2013, at the general hospital of Vienna, were included. ICG-clearance was recorded one day prior to surgery as well as on the first and fifth postoperative day. Postoperative liver dysfunction was defined according to the International Study Group of Liver Surgery and evaluation of morbidity was based on the Dindo-Clavien classification. Statistical analyses were based on non-parametric tests. Results Preoperative reduced ICG—plasma disappearance rate (PDR) as well as increased ICG—retention rate at 15 min (R15) were able to significantly predict postoperative liver dysfunction (Area under the curve = PDR: 0.716, P = 0.018; R15: 0.719, P = 0.016). Furthermore, PDR <17%/min. or R15 >8%, were able to accurately predict postoperative complications prior to surgery. In addition to this, ICG-clearance on postoperative day 1 comparably predicted postoperative liver dysfunction (Area under the curve = PDR: 0.895; R15: 0.893; both P <0.001), specifically, PDR <10%/min or R15 >20% on postoperative day 1 predicted poor postoperative outcome. Conclusion PDR and R15 may represent useful parameters to distinguish preoperative high and low risk patients in a Western collective as well as on postoperative day 1, to identify patients who require closer monitoring for potential complications. PMID:27812143

Sex similarities and differences in risk factors for recurrence of major depression.

PubMed

van Loo, Hanna M; Aggen, Steven H; Gardner, Charles O; Kendler, Kenneth S

2017-11-27

Major depression (MD) occurs about twice as often in women as in men, but it is unclear whether sex differences subsist after disease onset. This study aims to elucidate potential sex differences in rates and risk factors for MD recurrence, in order to improve prediction of course of illness and understanding of its underlying mechanisms. We used prospective data from a general population sample (n = 653) that experienced a recent episode of MD. A diverse set of potential risk factors for recurrence of MD was analyzed using Cox models subject to elastic net regularization for males and females separately. Accuracy of the prediction models was tested in same-sex and opposite-sex test data. Additionally, interactions between sex and each of the risk factors were investigated to identify potential sex differences. Recurrence rates and the impact of most risk factors were similar for men and women. For both sexes, prediction models were highly multifactorial including risk factors such as comorbid anxiety, early traumas, and family history. Some subtle sex differences were detected: for men, prediction models included more risk factors concerning characteristics of the depressive episode and family history of MD and generalized anxiety, whereas for women, models included more risk factors concerning early and recent adverse life events and socioeconomic problems. No prominent sex differences in risk factors for recurrence of MD were found, potentially indicating similar disease maintaining mechanisms for both sexes. Course of MD is a multifactorial phenomenon for both males and females.

Clinical correlates of graph theory findings in temporal lobe epilepsy.

PubMed

Haneef, Zulfi; Chiang, Sharon

2014-11-01

Temporal lobe epilepsy (TLE) is considered a brain network disorder, additionally representing the most common form of pharmaco-resistant epilepsy in adults. There is increasing evidence that seizures in TLE arise from abnormal epileptogenic networks, which extend beyond the clinico-radiologically determined epileptogenic zone and may contribute to the failure rate of 30-50% following epilepsy surgery. Graph theory allows for a network-based representation of TLE brain networks using several neuroimaging and electrophysiologic modalities, and has potential to provide clinicians with clinically useful biomarkers for diagnostic and prognostic purposes. We performed a review of the current state of graph theory findings in TLE as they pertain to localization of the epileptogenic zone, prediction of pre- and post-surgical seizure frequency and cognitive performance, and monitoring cognitive decline in TLE. Although different neuroimaging and electrophysiologic modalities have yielded occasionally conflicting results, several potential biomarkers have been characterized for identifying the epileptogenic zone, pre-/post-surgical seizure prediction, and assessing cognitive performance. For localization, graph theory measures of centrality have shown the most potential, including betweenness centrality, outdegree, and graph index complexity, whereas for prediction of seizure frequency, measures of synchronizability have shown the most potential. The utility of clustering coefficient and characteristic path length for assessing cognitive performance in TLE is also discussed. Future studies integrating data from multiple modalities and testing predictive models are needed to clarify findings and develop graph theory for its clinical utility. Copyright © 2014 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Clinical correlates of graph theory findings in temporal lobe epilepsy

PubMed Central

Haneef, Zulfi; Chiang, Sharon

2014-01-01

Purpose Temporal lobe epilepsy (TLE) is considered a brain network disorder, additionally representing the most common form of pharmaco-resistant epilepsy in adults. There is increasing evidence that seizures in TLE arise from abnormal epileptogenic networks, which extend beyond the clinico-radiologically determined epileptogenic zone and may contribute to the failure rate of 30–50% following epilepsy surgery. Graph theory allows for a network-based representation of TLE brain networks using several neuroimaging and electrophysiologic modalities, and has potential to provide clinicians with clinically useful biomarkers for diagnostic and prognostic purposes. Methods We performed a review of the current state of graph theory findings in TLE as they pertain to localization of the epileptogenic zone, prediction of pre- and post-surgical seizure frequency and cognitive performance, and monitoring cognitive decline in TLE. Results Although different neuroimaging and electrophysiologic modalities have yielded occasionally conflicting results, several potential biomarkers have been characterized for identifying the epileptogenic zone, pre-/post-surgical seizure prediction, and assessing cognitive performance. For localization, graph theory measures of centrality have shown the most potential, including betweenness centrality, outdegree, and graph index complexity, whereas for prediction of seizure frequency, measures of synchronizability have shown the most potential. The utility of clustering coefficient and characteristic path length for assessing cognitive performance in TLE is also discussed. Conclusions Future studies integrating data from multiple modalities and testing predictive models are needed to clarify findings and develop graph theory for its clinical utility. PMID:25127370

Anxiety and Depression Symptom Dimensions Demonstrate Unique Relationships with the Startle Reflex in Anticipation of Unpredictable Threat in 8 to 14 Year-Old Girls.

PubMed

Nelson, Brady D; Hajcak, Greg

2017-02-01

There is growing evidence that heightened sensitivity to unpredictability is a core mechanism of anxiety disorders. In adults, multiple anxiety disorders have been associated with a heightened startle reflex in anticipation of unpredictable threat. Child and adolescent anxiety has been linked to an increased startle reflex across baseline, safety, and threat conditions. However, it is unclear whether anxiety in youth is related to the startle reflex as a function of threat predictability. In a sample of 90 8 to 14 year-old girls, the present study examined the association between anxiety symptom dimensions and startle potentiation during a no, predictable, and unpredictable threat task. Depression symptom dimensions were also examined given their high comorbidity with anxiety and mixed relationship with the startle reflex and sensitivity to unpredictability. To assess current symptoms, participants completed the self-report Screen for Child Anxiety Related Emotional Disorders and Children's Depression Inventory. Results indicated that social phobia symptoms were associated with heightened startle potentiation in anticipation of unpredictable threat and attenuated startle potentiation in anticipation of predictable threat. Negative mood and negative self-esteem symptoms were associated with attenuated and heightened startle potentiation in anticipation of unpredictable threat, respectively. All results remained significant after controlling for the other symptom dimensions. The present study provides initial evidence that anxiety and depression symptom dimensions demonstrate unique associations with the startle reflex in anticipation of unpredictable threat in children and adolescents.

Anxiety and Depression Symptom Dimensions Demonstrate Unique Relationships with the Startle Reflex in Anticipation of Unpredictable Threat in 8 to 14 Year-Old Girls

PubMed Central

Nelson, Brady D.; Hajcak, Greg

2016-01-01

There is growing evidence that heightened sensitivity to unpredictability is a core mechanism of anxiety disorders. In adults, multiple anxiety disorders have been associated with a heightened startle reflex in anticipation of unpredictable threat. Child and adolescent anxiety has been linked to an increased startle reflex across baseline, safety, and threat conditions. However, it is unclear whether anxiety in youth is related to the startle reflex as a function of threat predictability. In a sample of 90 8 to 14 year-old girls, the present study examined the association between anxiety symptom dimensions and startle potentiation during a no, predictable, and unpredictable threat task. Depression symptom dimensions were also examined given their high comorbidity with anxiety and mixed relationship with the startle reflex and sensitivity to unpredictability. To assess current symptoms, participants completed the self-report Screen for Child Anxiety Related Emotional Disorders and Children’s Depression Inventory. Results indicated that social phobia symptoms were associated with heightened startle potentiation in anticipation of unpredictable threat and attenuated startle potentiation in anticipation of predictable threat. Negative mood and negative self-esteem symptoms were associated with attenuated and heightened startle potentiation in anticipation of unpredictable threat, respectively. All results remained significant after controlling for the other symptom dimensions. The present study provides initial evidence that anxiety and depression symptom dimensions demonstrate unique associations with the startle reflex in anticipation of unpredictable threat in children and adolescents. PMID:27224989

A Probabilistic Tsunami Hazard Study of the Auckland Region, Part II: Inundation Modelling and Hazard Assessment

NASA Astrophysics Data System (ADS)

Lane, E. M.; Gillibrand, P. A.; Wang, X.; Power, W.

2013-09-01

Regional source tsunamis pose a potentially devastating hazard to communities and infrastructure on the New Zealand coast. But major events are very uncommon. This dichotomy of infrequent but potentially devastating hazards makes realistic assessment of the risk challenging. Here, we describe a method to determine a probabilistic assessment of the tsunami hazard by regional source tsunamis with an "Average Recurrence Interval" of 2,500-years. The method is applied to the east Auckland region of New Zealand. From an assessment of potential regional tsunamigenic events over 100,000 years, the inundation of the Auckland region from the worst 100 events is modelled using a hydrodynamic model and probabilistic inundation depths on a 2,500-year time scale were determined. Tidal effects on the potential inundation were included by coupling the predicted wave heights with the probability density function of tidal heights at the inundation site. Results show that the more exposed northern section of the east coast and outer islands in the Hauraki Gulf face the greatest hazard from regional tsunamis in the Auckland region. Incorporating tidal effects into predictions of inundation reduced the predicted hazard compared to modelling all the tsunamis arriving at high tide giving a more accurate hazard assessment on the specified time scale. This study presents the first probabilistic analysis of dynamic modelling of tsunami inundation for the New Zealand coast and as such provides the most comprehensive assessment of tsunami inundation of the Auckland region from regional source tsunamis available to date.

QSPR models for half-wave reduction potential of steroids: a comparative study between feature selection and feature extraction from subsets of or entire set of descriptors.

PubMed

Hemmateenejad, Bahram; Yazdani, Mahdieh

2009-02-16

Steroids are widely distributed in nature and are found in plants, animals, and fungi in abundance. A data set consists of a diverse set of steroids have been used to develop quantitative structure-electrochemistry relationship (QSER) models for their half-wave reduction potential. Modeling was established by means of multiple linear regression (MLR) and principle component regression (PCR) analyses. In MLR analysis, the QSPR models were constructed by first grouping descriptors and then stepwise selection of variables from each group (MLR1) and stepwise selection of predictor variables from the pool of all calculated descriptors (MLR2). Similar procedure was used in PCR analysis so that the principal components (or features) were extracted from different group of descriptors (PCR1) and from entire set of descriptors (PCR2). The resulted models were evaluated using cross-validation, chance correlation, application to prediction reduction potential of some test samples and accessing applicability domain. Both MLR approaches represented accurate results however the QSPR model found by MLR1 was statistically more significant. PCR1 approach produced a model as accurate as MLR approaches whereas less accurate results were obtained by PCR2 approach. In overall, the correlation coefficients of cross-validation and prediction of the QSPR models resulted from MLR1, MLR2 and PCR1 approaches were higher than 90%, which show the high ability of the models to predict reduction potential of the studied steroids.

Male partners' attachment styles as predictors of women's coerced first sexual intercourse in Chinese college students' dating relationships.

PubMed

He, Shanshan; Tsang, Sandra

2014-01-01

Attachment theory has great potential to help our understanding of the apparent contradiction between violence and intimacy. Yet very few studies applied this theory to explain or predict sexual coercion in the context of intimate relationships. This study examined the relation between male partners' attachment styles and women's coerced first sexual intercourse in dating relationships. There were 927 valid questionnaires collected by purposive snowball sampling in five main cities in China to college students who were currently in a romantic relationship. Results showed that in both male and female samples, male partners' anxious attachment style were significantly and positively predicted emotional manipulation coercive tactics. In the female sample, male partners' two attachment styles (anxious and avoidant) positively predicted violence threat tactics, and male partners' avoidant attachment style positively predicted defection threat tactics. The research hypothesis of this study has been successfully supported, and implications and limitations were discussed.

Does teacher evaluation based on student performance predict motivation, well-being, and ill-being?

PubMed

Cuevas, Ricardo; Ntoumanis, Nikos; Fernandez-Bustos, Juan G; Bartholomew, Kimberley

2018-06-01

This study tests an explanatory model based on self-determination theory, which posits that pressure experienced by teachers when they are evaluated based on their students' academic performance will differentially predict teacher adaptive and maladaptive motivation, well-being, and ill-being. A total of 360 Spanish physical education teachers completed a multi-scale inventory. We found support for a structural equation model that showed that perceived pressure predicted teacher autonomous motivation negatively, predicted amotivation positively, and was unrelated to controlled motivation. In addition, autonomous motivation predicted vitality positively and exhaustion negatively, whereas controlled motivation and amotivation predicted vitality negatively and exhaustion positively. Amotivation significantly mediated the relation between pressure and vitality and between pressure and exhaustion. The results underline the potential negative impact of pressure felt by teachers due to this type of evaluation on teacher motivation and psychological health. Copyright © 2018 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Fan Noise Prediction with Applications to Aircraft System Noise Assessment

NASA Technical Reports Server (NTRS)

Nark, Douglas M.; Envia, Edmane; Burley, Casey L.

2009-01-01

This paper describes an assessment of current fan noise prediction tools by comparing measured and predicted sideline acoustic levels from a benchmark fan noise wind tunnel test. Specifically, an empirical method and newly developed coupled computational approach are utilized to predict aft fan noise for a benchmark test configuration. Comparisons with sideline noise measurements are performed to assess the relative merits of the two approaches. The study identifies issues entailed in coupling the source and propagation codes, as well as provides insight into the capabilities of the tools in predicting the fan noise source and subsequent propagation and radiation. In contrast to the empirical method, the new coupled computational approach provides the ability to investigate acoustic near-field effects. The potential benefits/costs of these new methods are also compared with the existing capabilities in a current aircraft noise system prediction tool. The knowledge gained in this work provides a basis for improved fan source specification in overall aircraft system noise studies.

MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.

PubMed

Chen, Xing; Niu, Ya-Wei; Wang, Guang-Hui; Yan, Gui-Ying

2017-12-12

Recently, as the research of microRNA (miRNA) continues, there are plenty of experimental evidences indicating that miRNA could be associated with various human complex diseases development and progression. Hence, it is necessary and urgent to pay more attentions to the relevant study of predicting diseases associated miRNAs, which may be helpful for effective prevention, diagnosis and treatment of human diseases. Especially, constructing computational methods to predict potential miRNA-disease associations is worthy of more studies because of the feasibility and effectivity. In this work, we developed a novel computational model of multiple kernels learning-based Kronecker regularized least squares for MiRNA-disease association prediction (MKRMDA), which could reveal potential miRNA-disease associations by automatically optimizing the combination of multiple kernels for disease and miRNA. MKRMDA obtained AUCs of 0.9040 and 0.8446 in global and local leave-one-out cross validation, respectively. Meanwhile, MKRMDA achieved average AUCs of 0.8894 ± 0.0015 in fivefold cross validation. Furthermore, we conducted three different kinds of case studies on some important human cancers for further performance evaluation. In the case studies of colonic cancer, esophageal cancer and lymphoma based on known miRNA-disease associations in HMDDv2.0 database, 76, 94 and 88% of the corresponding top 50 predicted miRNAs were confirmed by experimental reports, respectively. In another two kinds of case studies for new diseases without any known associated miRNAs and diseases only with known associations in HMDDv1.0 database, the verified ratios of two different cancers were 88 and 94%, respectively. All the results mentioned above adequately showed the reliable prediction ability of MKRMDA. We anticipated that MKRMDA could serve to facilitate further developments in the field and the follow-up investigations by biomedical researchers.

Single Subject Prediction of Brain Disorders in Neuroimaging: Promises and Pitfalls

PubMed Central

Arbabshirani, Mohammad R.; Plis, Sergey; Sui, Jing; Calhoun, Vince D.

2016-01-01

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there are extensive evidences showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead. PMID:27012503

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

PubMed

Arbabshirani, Mohammad R; Plis, Sergey; Sui, Jing; Calhoun, Vince D

2017-01-15

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead. Copyright © 2016 Elsevier Inc. All rights reserved.

Prediction during language comprehension: benefits, costs, and ERP components.

PubMed

Van Petten, Cyma; Luka, Barbara J

2012-02-01

Because context has a robust influence on the processing of subsequent words, the idea that readers and listeners predict upcoming words has attracted research attention, but prediction has fallen in and out of favor as a likely factor in normal comprehension. We note that the common sense of this word includes both benefits for confirmed predictions and costs for disconfirmed predictions. The N400 component of the event-related potential (ERP) reliably indexes the benefits of semantic context. Evidence that the N400 is sensitive to the other half of prediction--a cost for failure--is largely absent from the literature. This raises the possibility that "prediction" is not a good description of what comprehenders do. However, it need not be the case that the benefits and costs of prediction are evident in a single ERP component. Research outside of language processing indicates that late positive components of the ERP are very sensitive to disconfirmed predictions. We review late positive components elicited by words that are potentially more or less predictable from preceding sentence context. This survey suggests that late positive responses to unexpected words are fairly common, but that these consist of two distinct components with different scalp topographies, one associated with semantically incongruent words and one associated with congruent words. We conclude with a discussion of the possible cognitive correlates of these distinct late positivities and their relationships with more thoroughly characterized ERP components, namely the P300, P600 response to syntactic errors, and the "old/new effect" in studies of recognition memory. Copyright © 2011 Elsevier B.V. All rights reserved.

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.

Long-term bed degradation in Maryland streams (Phase III Part 2) : urban streams in the Piedmont Plateau Province : research report : final report.

DOT National Transportation Integrated Search

2017-02-01

Estimation of potential long-term down-cutting of the stream bed is necessary for evaluation and design of bridges for scour and culverts for fish passage. The purpose of this study has been to improve predictions of this potential long-term bed degr...

∑s-∑s as a di-baryonic molecule

NASA Astrophysics Data System (ADS)

Rathaud, D. P.; Rai, Ajay Kumar

2018-05-01

We study the ∑s-∑s, as a possible di-baryonic molecule in the potential model framework. We approximated the binding mechanism mainly as One Boson Exchange (QBE) plus screen type Yukawa potential. We predict the ∑s-∑s bound state molecule with I(Jp)=0(0+) and 0(1+) possible quantum numbers.

Potential of ultrasonic pulse velocity for evaluating the dimensional stability of oak and chestnut wood

Treesearch

Turker Dundar; Xiping Wang; Nusret As; Erkan Avci

2016-01-01

The objective of this study was to examine the potential of ultrasonic velocity as a rapid and nondestructive method to predict the dimensional stability of oak (Quercus petraea (Mattuschka) Lieblein) and chestnut (Castanea sativa Mill.) that are commonly used in flooring industry. Ultrasonic velocity, specific gravity, and radial, tangential and volumetric shrinkages...

Chemical structure-based predictive model for methanogenic anaerobic biodegradation potential.

PubMed

Meylan, William; Boethling, Robert; Aronson, Dallas; Howard, Philip; Tunkel, Jay

2007-09-01

Many screening-level models exist for predicting aerobic biodegradation potential from chemical structure, but anaerobic biodegradation generally has been ignored by modelers. We used a fragment contribution approach to develop a model for predicting biodegradation potential under methanogenic anaerobic conditions. The new model has 37 fragments (substructures) and classifies a substance as either fast or slow, relative to the potential to be biodegraded in the "serum bottle" anaerobic biodegradation screening test (Organization for Economic Cooperation and Development Guideline 311). The model correctly classified 90, 77, and 91% of the chemicals in the training set (n = 169) and two independent validation sets (n = 35 and 23), respectively. Accuracy of predictions of fast and slow degradation was equal for training-set chemicals, but fast-degradation predictions were less accurate than slow-degradation predictions for the validation sets. Analysis of the signs of the fragment coefficients for this and the other (aerobic) Biowin models suggests that in the context of simple group contribution models, the majority of positive and negative structural influences on ultimate degradation are the same for aerobic and methanogenic anaerobic biodegradation.

Neuroprediction, Violence, and the Law: Setting the Stage

PubMed Central

Bibas, Stephanos; Grafton, Scott; Kiehl, Kent A.; Mansfield, Andrew; Sinnott-Armstrong, Walter; Gazzaniga, Michael

2014-01-01

In this paper, our goal is to (a) survey some of the legal contexts within which violence risk assessment already plays a prominent role, (b) explore whether developments in neuroscience could potentially be used to improve our ability to predict violence, and (c) discuss whether neuropredictive models of violence create any unique legal or moral problems above and beyond the well worn problems already associated with prediction more generally. In “Violence Risk Assessment and the Law”, we briefly examine the role currently played by predictions of violence in three high stakes legal contexts: capital sentencing (“Violence Risk Assessment and Capital Sentencing”), civil commitment hearings (“Violence Risk Assessment and Civil Commitment”), and “sexual predator” statutes (“Violence Risk Assessment and Sexual Predator Statutes”). In “Clinical vs. Actuarial Violence Risk Assessment”, we briefly examine the distinction between traditional clinical methods of predicting violence and more recently developed actuarial methods, exemplified by the Classification of Violence Risk (COVR) software created by John Monahan and colleagues as part of the MacArthur Study of Mental Disorder and Violence [1]. In “The Neural Correlates of Psychopathy”, we explore what neuroscience currently tells us about the neural correlates of violence, using the recent neuroscientific research on psychopathy as our focus. We also discuss some recent advances in both data collection (“Cutting-Edge Data Collection: Genetically Informed Neuroimaging”) and data analysis (“Cutting-Edge Data Analysis: Pattern Classification”) that we believe will play an important role when it comes to future neuroscientific research on violence. In “The Potential Promise of Neuroprediction”, we discuss whether neuroscience could potentially be used to improve our ability to predict future violence. Finally, in “The Potential Perils of Neuroprediction”, we explore some potential evidentiary (“Evidentiary Issues”), constitutional (“Constitutional Issues”), and moral (“Moral Issues”) issues that may arise in the context of the neuroprediction of violence. PMID:25083168

Parent-based diagnosis of ADHD is as accurate as a teacher-based diagnosis of ADHD.

PubMed

Bied, Adam; Biederman, Joseph; Faraone, Stephen

2017-04-01

To review the literature evaluating the psychometric properties of parent and teacher informants relative to a gold-standard ADHD diagnosis in pediatric populations. We included studies that included both a parent and teacher informant, a gold-standard diagnosis, and diagnostic accuracy metrics. Potential confounds were evaluated. We also assessed the 'OR' and the 'AND' rules for combining informant reports. Eight articles met inclusion criteria. The diagnostic accuracy for predicting gold standard ADHD diagnoses did not differ between parents and teachers. Sample size, sample type, participant drop-out, participant age, participant gender, geographic area of the study, and date of study publication were assessed as potential confounds. Parent and teachers both yielded moderate to good diagnostic accuracy for ADHD diagnoses. Parent reports were statistically indistinguishable from those of teachers. The predictive features of the 'OR' and 'AND' rules are useful in evaluating approaches to better integrating information from these informants.

Coupling Radar Rainfall to Hydrological Models for Water Abstraction Management

NASA Astrophysics Data System (ADS)

Asfaw, Alemayehu; Shucksmith, James; Smith, Andrea; MacDonald, Ken

2015-04-01

The impacts of climate change and growing water use are likely to put considerable pressure on water resources and the environment. In the UK, a reform to surface water abstraction policy has recently been proposed which aims to increase the efficiency of using available water resources whilst minimising impacts on the aquatic environment. Key aspects to this reform include the consideration of dynamic rather than static abstraction licensing as well as introducing water trading concepts. Dynamic licensing will permit varying levels of abstraction dependent on environmental conditions (i.e. river flow and quality). The practical implementation of an effective dynamic abstraction strategy requires suitable flow forecasting techniques to inform abstraction asset management. Potentially the predicted availability of water resources within a catchment can be coupled to predicted demand and current storage to inform a cost effective water resource management strategy which minimises environmental impacts. The aim of this work is to use a historical analysis of UK case study catchment to compare potential water resource availability using modelled dynamic abstraction scenario informed by a flow forecasting model, against observed abstraction under a conventional abstraction regime. The work also demonstrates the impacts of modelling uncertainties on the accuracy of predicted water availability over range of forecast lead times. The study utilised a conceptual rainfall-runoff model PDM - Probability-Distributed Model developed by Centre for Ecology & Hydrology - set up in the Dove River catchment (UK) using 1km2 resolution radar rainfall as inputs and 15 min resolution gauged flow data for calibration and validation. Data assimilation procedures are implemented to improve flow predictions using observed flow data. Uncertainties in the radar rainfall data used in the model are quantified using artificial statistical error model described by Gaussian distribution and propagated through the model to assess its influence on the forecasted flow uncertainty. Furthermore, the effects of uncertainties at different forecast lead times on potential abstraction strategies are assessed. The results show that over a 10 year period, an average of approximately 70 ML/d of potential water is missed in the study catchment under a convention abstraction regime. This indicates a considerable potential for the use of flow forecasting models to effectively implement advanced abstraction management and more efficiently utilize available water resources in the study catchment.

Predicting cerulean warbler habitat use in the Cumberland Mountains of Tennessee

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

Buehler, D.A.; Welton, M.J.; Beachy, T.A.

2006-12-15

We developed a habitat model to predict cerulean warbler (Dendroica cerulea) habitat availability in the Cumberland Mountains of eastern Tennessee. We used 7 remotely sensed vegetation and topographic landform explanatory variables and known locations of territorial male cerulean warblers mapped in 2003 as the response variable to develop a Mahalanobis distance statistic model of potential habitat. We evaluated the accuracy of the model based on field surveys for ceruleans during the 2004 breeding season. The model performed well with an 80% correct classification of cerulean presence based on the validation data, although prediction of absence was only 54% correct. Wemore » extrapolated from potential habitat to cerulean abundance based on density estimates from territory mapping on 8 20-ha plots in 2005. Over the 200,000-ha study area, we estimated there were 80,584 ha of potential habitat, capable of supporting about 36,500 breeding pairs. We applied the model to the 21,609-ha state-owned Royal Blue Wildlife Management Area to evaluate the potential effects of coal surface mining as one example of a potential conflict between land use and cerulean warbler conservation. Our models suggest coal surface mining could remove 2,954 ha of cerulean habitat on Royal Blue Wildlife Management Area and could displace 2,540 breeding pairs (23% of the Royal Blue population). A comprehensive conservation strategy is needed to address potential and realized habitat loss and degradation on the breeding grounds, during migration, and on the wintering grounds.« less

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.

Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation.

PubMed

Meertens, Linda J E; van Montfort, Pim; Scheepers, Hubertina C J; van Kuijk, Sander M J; Aardenburg, Robert; Langenveld, Josje; van Dooren, Ivo M A; Zwaan, Iris M; Spaanderman, Marc E A; Smits, Luc J M

2018-04-17

Prediction models may contribute to personalized risk-based management of women at high risk of spontaneous preterm delivery. Although prediction models are published frequently, often with promising results, external validation generally is lacking. We performed a systematic review of prediction models for the risk of spontaneous preterm birth based on routine clinical parameters. Additionally, we externally validated and evaluated the clinical potential of the models. Prediction models based on routinely collected maternal parameters obtainable during first 16 weeks of gestation were eligible for selection. Risk of bias was assessed according to the CHARMS guidelines. We validated the selected models in a Dutch multicenter prospective cohort study comprising 2614 unselected pregnant women. Information on predictors was obtained by a web-based questionnaire. Predictive performance of the models was quantified by the area under the receiver operating characteristic curve (AUC) and calibration plots for the outcomes spontaneous preterm birth <37 weeks and <34 weeks of gestation. Clinical value was evaluated by means of decision curve analysis and calculating classification accuracy for different risk thresholds. Four studies describing five prediction models fulfilled the eligibility criteria. Risk of bias assessment revealed a moderate to high risk of bias in three studies. The AUC of the models ranged from 0.54 to 0.67 and from 0.56 to 0.70 for the outcomes spontaneous preterm birth <37 weeks and <34 weeks of gestation, respectively. A subanalysis showed that the models discriminated poorly (AUC 0.51-0.56) for nulliparous women. Although we recalibrated the models, two models retained evidence of overfitting. The decision curve analysis showed low clinical benefit for the best performing models. This review revealed several reporting and methodological shortcomings of published prediction models for spontaneous preterm birth. Our external validation study indicated that none of the models had the ability to predict spontaneous preterm birth adequately in our population. Further improvement of prediction models, using recent knowledge about both model development and potential risk factors, is necessary to provide an added value in personalized risk assessment of spontaneous preterm birth. © 2018 The Authors Acta Obstetricia et Gynecologica Scandinavica published by John Wiley & Sons Ltd on behalf of Nordic Federation of Societies of Obstetrics and Gynecology (NFOG).

Comparison of Measured and Predicted Bioconcentration Estimates of Pharmaceuticals in Fish Plasma and Prediction of Chronic Risk.

PubMed

Nallani, Gopinath; Venables, Barney; Constantine, Lisa; Huggett, Duane

2016-05-01

Evaluation of the environmental risk of human pharmaceuticals is now a mandatory component in all new drug applications submitted for approval in EU. With >3000 drugs currently in use, it is not feasible to test each active ingredient, so prioritization is key. A recent review has listed nine prioritization approaches including the fish plasma model (FPM). The present paper focuses on comparison of measured and predicted fish plasma bioconcentration factors (BCFs) of four common over-the-counter/prescribed pharmaceuticals: norethindrone (NET), ibuprofen (IBU), verapamil (VER) and clozapine (CLZ). The measured data were obtained from the earlier published fish BCF studies. The measured BCF estimates of NET, IBU, VER and CLZ were 13.4, 1.4, 0.7 and 31.2, while the corresponding predicted BCFs (based log Kow at pH 7) were 19, 1.0, 7.6 and 30, respectively. These results indicate that the predicted BCFs matched well the measured values. The BCF estimates were used to calculate the human: fish plasma concentration ratios of each drug to predict potential risk to fish. The plasma ratio results show the following order of risk potential for fish: NET > CLZ > VER > IBU. The FPM has value in prioritizing pharmaceutical products for ecotoxicological assessments.

Conformational B-cell epitopes prediction from sequences using cost-sensitive ensemble classifiers and spatial clustering.

PubMed

Zhang, Jian; Zhao, Xiaowei; Sun, Pingping; Gao, Bo; Ma, Zhiqiang

2014-01-01

B-cell epitopes are regions of the antigen surface which can be recognized by certain antibodies and elicit the immune response. Identification of epitopes for a given antigen chain finds vital applications in vaccine and drug research. Experimental prediction of B-cell epitopes is time-consuming and resource intensive, which may benefit from the computational approaches to identify B-cell epitopes. In this paper, a novel cost-sensitive ensemble algorithm is proposed for predicting the antigenic determinant residues and then a spatial clustering algorithm is adopted to identify the potential epitopes. Firstly, we explore various discriminative features from primary sequences. Secondly, cost-sensitive ensemble scheme is introduced to deal with imbalanced learning problem. Thirdly, we adopt spatial algorithm to tell which residues may potentially form the epitopes. Based on the strategies mentioned above, a new predictor, called CBEP (conformational B-cell epitopes prediction), is proposed in this study. CBEP achieves good prediction performance with the mean AUC scores (AUCs) of 0.721 and 0.703 on two benchmark datasets (bound and unbound) using the leave-one-out cross-validation (LOOCV). When compared with previous prediction tools, CBEP produces higher sensitivity and comparable specificity values. A web server named CBEP which implements the proposed method is available for academic use.

Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal

PubMed Central

Lewis, Jesse S.; Farnsworth, Matthew L.; Burdett, Chris L.; Theobald, David M.; Gray, Miranda; Miller, Ryan S.

2017-01-01

Biotic and abiotic factors are increasingly acknowledged to synergistically shape broad-scale species distributions. However, the relative importance of biotic and abiotic factors in predicting species distributions is unclear. In particular, biotic factors, such as predation and vegetation, including those resulting from anthropogenic land-use change, are underrepresented in species distribution modeling, but could improve model predictions. Using generalized linear models and model selection techniques, we used 129 estimates of population density of wild pigs (Sus scrofa) from 5 continents to evaluate the relative importance, magnitude, and direction of biotic and abiotic factors in predicting population density of an invasive large mammal with a global distribution. Incorporating diverse biotic factors, including agriculture, vegetation cover, and large carnivore richness, into species distribution modeling substantially improved model fit and predictions. Abiotic factors, including precipitation and potential evapotranspiration, were also important predictors. The predictive map of population density revealed wide-ranging potential for an invasive large mammal to expand its distribution globally. This information can be used to proactively create conservation/management plans to control future invasions. Our study demonstrates that the ongoing paradigm shift, which recognizes that both biotic and abiotic factors shape species distributions across broad scales, can be advanced by incorporating diverse biotic factors. PMID:28276519

Predictive probability methods for interim monitoring in clinical trials with longitudinal outcomes.

PubMed

Zhou, Ming; Tang, Qi; Lang, Lixin; Xing, Jun; Tatsuoka, Kay

2018-04-17

In clinical research and development, interim monitoring is critical for better decision-making and minimizing the risk of exposing patients to possible ineffective therapies. For interim futility or efficacy monitoring, predictive probability methods are widely adopted in practice. Those methods have been well studied for univariate variables. However, for longitudinal studies, predictive probability methods using univariate information from only completers may not be most efficient, and data from on-going subjects can be utilized to improve efficiency. On the other hand, leveraging information from on-going subjects could allow an interim analysis to be potentially conducted once a sufficient number of subjects reach an earlier time point. For longitudinal outcomes, we derive closed-form formulas for predictive probabilities, including Bayesian predictive probability, predictive power, and conditional power and also give closed-form solutions for predictive probability of success in a future trial and the predictive probability of success of the best dose. When predictive probabilities are used for interim monitoring, we study their distributions and discuss their analytical cutoff values or stopping boundaries that have desired operating characteristics. We show that predictive probabilities utilizing all longitudinal information are more efficient for interim monitoring than that using information from completers only. To illustrate their practical application for longitudinal data, we analyze 2 real data examples from clinical trials. Copyright © 2018 John Wiley & Sons, Ltd.

Electroviscous Effects in Ceramic Nanofiltration Membranes.

PubMed

Farsi, Ali; Boffa, Vittorio; Christensen, Morten Lykkegaard

2015-11-16

Membrane permeability and salt rejection of a γ-alumina nanofiltration membrane were studied and modeled for different salt solutions. Salt rejection was predicted by using the Donnan-steric pore model, in which the extended Nernst-Planck equation was applied to predict ion transport through the pores. The solvent flux was modeled by using the Hagen-Poiseuille equation by introducing electroviscosity instead of bulk viscosity. γ-Alumina particles were used for ζ-potential measurements. The ζ-potential measurements show that monovalent ions did not adsorb on the γ-alumina surface, whereas divalent ions were highly adsorbed. Thus, for divalent ions, the model was modified, owing to pore shrinkage caused by ion adsorption. The ζ-potential lowered the membrane permeability, especially for membranes with a pore radius lower than 3 nm, a ζ-potential higher than 20 mV, and an ionic strength lower than 0.01 m. The rejection model showed that, for a pore radius lower than 3 nm and for solutions with ionic strengths lower than 0.01 m, there is an optimum ζ-potential for rejection, because of the concurrent effects of electromigration and convection. Hence, the model can be used as a prediction tool to optimize membrane perm-selectivity by designing a specific pore size and surface charge for application at specific ionic strengths and pH levels. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Spontaneous Decoding of the Timing and Content of Human Object Perception from Cortical Surface Recordings Reveals Complementary Information in the Event-Related Potential and Broadband Spectral Change

PubMed Central

Miller, Kai J.; Schalk, Gerwin; Hermes, Dora; Ojemann, Jeffrey G.; Rao, Rajesh P. N.

2016-01-01

The link between object perception and neural activity in visual cortical areas is a problem of fundamental importance in neuroscience. Here we show that electrical potentials from the ventral temporal cortical surface in humans contain sufficient information for spontaneous and near-instantaneous identification of a subject’s perceptual state. Electrocorticographic (ECoG) arrays were placed on the subtemporal cortical surface of seven epilepsy patients. Grayscale images of faces and houses were displayed rapidly in random sequence. We developed a template projection approach to decode the continuous ECoG data stream spontaneously, predicting the occurrence, timing and type of visual stimulus. In this setting, we evaluated the independent and joint use of two well-studied features of brain signals, broadband changes in the frequency power spectrum of the potential and deflections in the raw potential trace (event-related potential; ERP). Our ability to predict both the timing of stimulus onset and the type of image was best when we used a combination of both the broadband response and ERP, suggesting that they capture different and complementary aspects of the subject’s perceptual state. Specifically, we were able to predict the timing and type of 96% of all stimuli, with less than 5% false positive rate and a ~20ms error in timing. PMID:26820899

DNDO Report: Predicting Solar Modulation Potentials for Modeling Cosmic Background Radiation

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

Behne, Patrick Alan

The modeling of the detectability of special nuclear material (SNM) at ports and border crossings requires accurate knowledge of the background radiation at those locations. Background radiation originates from two main sources, cosmic and terrestrial. Cosmic background is produced by high-energy galactic cosmic rays (GCR) entering the atmosphere and inducing a cascade of particles that eventually impact the earth’s surface. The solar modulation potential represents one of the primary inputs to modeling cosmic background radiation. Usosokin et al. formally define solar modulation potential as “the mean energy loss [per unit charge] of a cosmic ray particle inside the heliosphere…” Modulationmore » potential, a function of elevation, location, and time, shares an inverse relationship with cosmic background radiation. As a result, radiation detector thresholds require adjustment to account for differing background levels, caused partly by differing solar modulations. Failure to do so can result in higher rates of false positives and failed detection of SNM for low and high levels of solar modulation potential, respectively. This study focuses on solar modulation’s time dependence, and seeks the best method to predict modulation for future dates using Python. To address the task of predicting future solar modulation, we utilize both non-linear least squares sinusoidal curve fitting and cubic spline interpolation. This material will be published in transactions of the ANS winter meeting of November, 2016.« less

Perfectionistic concerns predict increases in adolescents' anxiety symptoms: a three-wave longitudinal study.

PubMed

Damian, Lavinia E; Negru-Subtirica, Oana; Stoeber, Joachim; Băban, Adriana

2017-09-01

Although perfectionism has been proposed to be a risk factor for the development of anxiety, research on perfectionism and anxiety symptoms in adolescents is scarce and inconclusive. The aim of the present study was to investigate whether the two higher-order dimensions of perfectionism - perfectionistic strivings and perfectionistic concerns - predict the development and maintenance of anxiety symptoms. An additional aim of the present study was to examine potential reciprocal effects of anxiety symptoms predicting increases in perfectionism. The study used a longitudinal design with three waves spaced 4-5 months apart. A non-clinical sample of 489 adolescents aged 12-19 years completed a paper-and-pencil questionnaire. As expected, results showed a positive effect from perfectionistic concerns to anxiety symptoms, but the effect was restricted to middle-to-late adolescents (16-19 years old): Perfectionistic concerns predicted longitudinal increases in adolescents' anxiety symptoms, whereas perfectionistic strivings did not. Furthermore, anxiety symptoms did not predict increases in perfectionism. Implications for the understanding of the relationship between perfectionism and anxiety symptoms are discussed.

An empirical potential for simulating vacancy clusters in tungsten.

PubMed

Mason, D R; Nguyen-Manh, D; Becquart, C S

2017-12-20

We present an empirical interatomic potential for tungsten, particularly well suited for simulations of vacancy-type defects. We compare energies and structures of vacancy clusters generated with the empirical potential with an extensive new database of values computed using density functional theory, and show that the new potential predicts low-energy defect structures and formation energies with high accuracy. A significant difference to other popular embedded-atom empirical potentials for tungsten is the correct prediction of surface energies. Interstitial properties and short-range pairwise behaviour remain similar to the Ackford-Thetford potential on which it is based, making this potential well-suited to simulations of microstructural evolution following irradiation damage cascades. Using atomistic kinetic Monte Carlo simulations, we predict vacancy cluster dissociation in the range 1100-1300 K, the temperature range generally associated with stage IV recovery.

The effect of panic disorder versus anxiety sensitivity on event-related potentials during anticipation of threat.

PubMed

Stevens, Elizabeth S; Weinberg, Anna; Nelson, Brady D; Meissel, Emily E E; Shankman, Stewart A

2018-03-01

Attention-related abnormalities are key components of the abnormal defensive responding observed in panic disorder (PD). Although behavioral studies have found aberrant attentional biases towards threat in PD, psychophysiological studies have been mixed. Predictability of threat, an important feature of threat processing, may have contributed to these mixed findings. Additionally, anxiety sensitivity, a dimensional trait associated with PD, may yield stronger associations with cognitive processes than categorical diagnoses of PD. In this study, 171 participants with PD and/or depression and healthy controls completed a task that differentiated anticipation of predictable vs. unpredictable shocks, while startle eyeblink and event-related potentials (ERPs [N100, P300]) were recorded. In all participants, relative to the control condition, probe N100 was enhanced to both predictable and unpredictable threat, whereas P300 suppression was unique to predictable threat. Probe N100, but not P300, was associated with startle eyeblink during both threatening conditions, and was strongest for unpredictable threat. PD was not associated with ERPs, but anxiety sensitivity (physical concerns) was positively associated with probe N100 (indicating reduced responding) in the unpredictable condition independent of PD diagnosis. Vulnerability to panic-related psychopathology may be characterized by aberrant early processing of threat, which may be especially evident during anticipation of unpredictable threats. Copyright © 2017 Elsevier Ltd. All rights reserved.

Earthquake prediction; new studies yield promising results

USGS Publications Warehouse

Robinson, R.

1974-01-01

On Agust 3, 1973, a small earthquake (magnitude 2.5) occurred near Blue Mountain Lake in the Adirondack region of northern New York State. This seemingly unimportant event was of great significance, however, because it was predicted. Seismologsits at the Lamont-Doherty geologcal Observatory of Columbia University accurately foretold the time, place, and magnitude of the event. Their prediction was based on certain pre-earthquake processes that are best explained by a hypothesis known as "dilatancy," a concept that has injected new life and direction into the science of earthquake prediction. Although much mroe reserach must be accomplished before we can expect to predict potentially damaging earthquakes with any degree of consistency, results such as this indicate that we are on a promising road. 

Recent NASA Research on Aerodynamic Modeling of Post-Stall and Spin Dynamics of Large Transport Airplanes

NASA Technical Reports Server (NTRS)

Murch, Austin M.; Foster, John V.

2007-01-01

A simulation study was conducted to investigate aerodynamic modeling methods for prediction of post-stall flight dynamics of large transport airplanes. The research approach involved integrating dynamic wind tunnel data from rotary balance and forced oscillation testing with static wind tunnel data to predict aerodynamic forces and moments during highly dynamic departure and spin motions. Several state-of-the-art aerodynamic modeling methods were evaluated and predicted flight dynamics using these various approaches were compared. Results showed the different modeling methods had varying effects on the predicted flight dynamics and the differences were most significant during uncoordinated maneuvers. Preliminary wind tunnel validation data indicated the potential of the various methods for predicting steady spin motions.

A decision-analytic approach to predict state regulation of hydraulic fracturing.

PubMed

Linkov, Igor; Trump, Benjamin; Jin, David; Mazurczak, Marcin; Schreurs, Miranda

2014-01-01

The development of horizontal drilling and hydraulic fracturing methods has dramatically increased the potential for the extraction of previously unrecoverable natural gas. Nonetheless, the potential risks and hazards associated with such technologies are not without controversy and are compounded by frequently changing information and an uncertain landscape of international politics and laws. Where each nation has its own energy policies and laws, predicting how a state with natural gas reserves that require hydraulic fracturing will regulate the industry is of paramount importance for potential developers and extractors. We present a method for predicting hydraulic fracturing decisions using multiple-criteria decision analysis. The case study evaluates the decisions of five hypothetical countries with differing political, social, environmental, and economic priorities, choosing among four policy alternatives: open hydraulic fracturing, limited hydraulic fracturing, completely banned hydraulic fracturing, and a cap and trade program. The result is a model that identifies the preferred policy alternative for each archetypal country and demonstrates the sensitivity the decision to particular metrics. Armed with such information, observers can predict each country's likely decisions related to natural gas exploration as more data become available or political situations change. Decision analysis provides a method to manage uncertainty and address forecasting concerns where rich and objective data may be lacking. For the case of hydraulic fracturing, the various political pressures and extreme uncertainty regarding the technology's risks and benefits serve as a prime platform to demonstrate how decision analysis can be used to predict future behaviors.

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.

Characterization of Initial Parameter Information for Lifetime Prediction of Electronic Devices.

PubMed

Li, Zhigang; Liu, Boying; Yuan, Mengxiong; Zhang, Feifei; Guo, Jiaqiang

2016-01-01

Newly manufactured electronic devices are subject to different levels of potential defects existing among the initial parameter information of the devices. In this study, a characterization of electromagnetic relays that were operated at their optimal performance with appropriate and steady parameter values was performed to estimate the levels of their potential defects and to develop a lifetime prediction model. First, the initial parameter information value and stability were quantified to measure the performance of the electronics. In particular, the values of the initial parameter information were estimated using the probability-weighted average method, whereas the stability of the parameter information was determined by using the difference between the extrema and end points of the fitting curves for the initial parameter information. Second, a lifetime prediction model for small-sized samples was proposed on the basis of both measures. Finally, a model for the relationship of the initial contact resistance and stability over the lifetime of the sampled electromagnetic relays was proposed and verified. A comparison of the actual and predicted lifetimes of the relays revealed a 15.4% relative error, indicating that the lifetime of electronic devices can be predicted based on their initial parameter information.

Characterization of Initial Parameter Information for Lifetime Prediction of Electronic Devices

PubMed Central

Li, Zhigang; Liu, Boying; Yuan, Mengxiong; Zhang, Feifei; Guo, Jiaqiang

2016-01-01

Newly manufactured electronic devices are subject to different levels of potential defects existing among the initial parameter information of the devices. In this study, a characterization of electromagnetic relays that were operated at their optimal performance with appropriate and steady parameter values was performed to estimate the levels of their potential defects and to develop a lifetime prediction model. First, the initial parameter information value and stability were quantified to measure the performance of the electronics. In particular, the values of the initial parameter information were estimated using the probability-weighted average method, whereas the stability of the parameter information was determined by using the difference between the extrema and end points of the fitting curves for the initial parameter information. Second, a lifetime prediction model for small-sized samples was proposed on the basis of both measures. Finally, a model for the relationship of the initial contact resistance and stability over the lifetime of the sampled electromagnetic relays was proposed and verified. A comparison of the actual and predicted lifetimes of the relays revealed a 15.4% relative error, indicating that the lifetime of electronic devices can be predicted based on their initial parameter information. PMID:27907188

Ferromagnetism in two-dimensional hole-doped SnO

NASA Astrophysics Data System (ADS)

Houssa, M.; Iordanidou, K.; Pourtois, G.; Afanas'ev, V. V.; Stesmans, A.

2018-05-01

Hole-doped monolayer SnO has been recently predicted to be a ferromagnetic material, for a hole density typically above 5x1013/cm2. The possibility to induce a hole-doped stable ferromagnetic order in this two-dimensional material, either by intrinsic or extrinsic defects, is theoretically studied, using first-principles simulations. Sn vacancies and Sn vacancy-hydrogen complexes are predicted to be shallow acceptors, with relatively low formation energies in SnO monolayers grown under O-rich conditions. These defects produce spin-polarized gap states near the valence band-edge, potentially stabilizing the ferromagnetic order in 2D SnO. Hole-doping resulting from substitutional doping is also investigated. Among the considered possible dopants, As, substituting O, is predicted to produce shallow spin-polarized gap states near the valence band edge, also potentially resulting in a stable ferromagnetic order in SnO monolayers.

Machine Learning Estimates of Natural Product Conformational Energies

PubMed Central

Rupp, Matthias; Bauer, Matthias R.; Wilcken, Rainer; Lange, Andreas; Reutlinger, Michael; Boeckler, Frank M.; Schneider, Gisbert

2014-01-01

Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor of vacuolar-type ATPase, from the myxobacterium Archangium gephyra as an example. Our model estimates energies of new conformations by exploiting information from previous calculations via Gaussian process regression. Predictive variance is used to assess whether a conformation is in the interpolation region, allowing a controlled trade-off between prediction accuracy and computational speed-up. For energies of relaxed conformations at the density functional level of theory (implicit solvent, DFT/BLYP-disp3/def2-TZVP), mean absolute errors of less than 1 kcal/mol were achieved. The study demonstrates that predictive machine learning models can be developed for structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of larger molecular structures. PMID:24453952

Seasonal-to-Interannual Precipitation Variability and Predictability in a Coupled Land-Atmosphere System

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Suarez, M. J.; Heiser, M.

1998-01-01

In an earlier GCM study, we showed that interactive land surface processes generally contribute more to continental precipitation variance than do variable sea surface temperatures (SSTs). A new study extends this result through an analysis of 16-member ensembles of multi-decade GCM simulations. We can now show that in many regions, although land processes determine the amplitude of the interannual precipitation anomalies, variable SSTs nevertheless control their timing. The GCM data can be processed into indices that describe geographical variations in (1) the potential for seasonal-to-interannual prediction, and (2) the extent to which the predictability relies on the proper representation of land-atmosphere feedback.

Hemolytic potential of hydrodynamic cavitation.

PubMed

Chambers, S D; Bartlett, R H; Ceccio, S L

2000-08-01

The purpose of this study was to determine the hemolytic potentials of discrete bubble cavitation and attached cavitation. To generate controlled cavitation events, a venturigeometry hydrodynamic device, called a Cavitation Susceptibility Meter (CSM), was constructed. A comparison between the hemolytic potential of discrete bubble cavitation and attached cavitation was investigated with a single-pass flow apparatus and a recirculating flow apparatus, both utilizing the CSM. An analytical model, based on spherical bubble dynamics, was developed for predicting the hemolysis caused by discrete bubble cavitation. Experimentally, discrete bubble cavitation did not correlate with a measurable increase in plasma-free hemoglobin (PFHb), as predicted by the analytical model. However, attached cavitation did result in significant PFHb generation. The rate of PFHb generation scaled inversely with the Cavitation number at a constant flow rate, suggesting that the size of the attached cavity was the dominant hemolytic factor.

Auditory and visual P300 evoked potentials do not predict response to valproate treatment of aggression in patients with borderline and antisocial personality disorders.

PubMed

Reeves, Roy R; Struve, Frederick A; Patrick, Gloria

2005-01-01

In this study of patients with borderline personality disorder (BPD) or antisocial personality disorder (ASPD) hospitalized because of aggressive behavior, auditory and visual P300 evoked potentials were obtained prior to treatment with valproate. Eight ASPD patients (8 males, 0 females) and 11 BPD patients (2 males, 9 females) showed improvement, while in 7 patients with ASPD (7 males, 0 females) and 10 patients with BPD (2 males, 8 females), aggression was not improved. Differences in auditory and visual P300 latencies and amplitudes were not significant for either diagnosis, or for both diagnoses combined. These findings suggest that auditory or visual P300 evoked potentials may not be useful for predicting response of aggressive behavior to valproate treatment in patients with BPD or ASPD.

Prediction of movement intention using connectivity within motor-related network: An electrocorticography study.

PubMed

Kang, Byeong Keun; Kim, June Sic; Ryun, Seokyun; Chung, Chun Kee

2018-01-01

Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user's intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention.

Ensemble Canonical Correlation Prediction of Seasonal Precipitation Over the United States: Raising the Bar for Dynamical Model Forecasts

NASA Technical Reports Server (NTRS)

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

2001-01-01

This paper presents preliminary results of an ensemble canonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate downscaling studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into non-overlapping sectors. The canonical SST modes for each sector are used as the predictor for the ensemble hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all the regions of the US in every season compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible to the enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduces the spring predictability barrier over all the regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and additional local observations. The enhanced ECC forecast skill provides a new benchmark for evaluating dynamical model forecasts.

Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy.

PubMed

Asadi, Hamed; Dowling, Richard; Yan, Bernard; Mitchell, Peter

2014-01-01

Stroke is a major cause of death and disability. Accurately predicting stroke outcome from a set of predictive variables may identify high-risk patients and guide treatment approaches, leading to decreased morbidity. Logistic regression models allow for the identification and validation of predictive variables. However, advanced machine learning algorithms offer an alternative, in particular, for large-scale multi-institutional data, with the advantage of easily incorporating newly available data to improve prediction performance. Our aim was to design and compare different machine learning methods, capable of predicting the outcome of endovascular intervention in acute anterior circulation ischaemic stroke. We conducted a retrospective study of a prospectively collected database of acute ischaemic stroke treated by endovascular intervention. Using SPSS®, MATLAB®, and Rapidminer®, classical statistics as well as artificial neural network and support vector algorithms were applied to design a supervised machine capable of classifying these predictors into potential good and poor outcomes. These algorithms were trained, validated and tested using randomly divided data. We included 107 consecutive acute anterior circulation ischaemic stroke patients treated by endovascular technique. Sixty-six were male and the mean age of 65.3. All the available demographic, procedural and clinical factors were included into the models. The final confusion matrix of the neural network, demonstrated an overall congruency of ∼ 80% between the target and output classes, with favourable receiving operative characteristics. However, after optimisation, the support vector machine had a relatively better performance, with a root mean squared error of 2.064 (SD: ± 0.408). We showed promising accuracy of outcome prediction, using supervised machine learning algorithms, with potential for incorporation of larger multicenter datasets, likely further improving prediction. Finally, we propose that a robust machine learning system can potentially optimise the selection process for endovascular versus medical treatment in the management of acute stroke.

The identification of high potential archers based on fitness and motor ability variables: A Support Vector Machine approach.

PubMed

Taha, Zahari; Musa, Rabiu Muazu; P P Abdul Majeed, Anwar; Alim, Muhammad Muaz; Abdullah, Mohamad Razali

2018-02-01

Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low and high-performance athletes. The present study classified and predicted high and low-potential archers from a set of fitness and motor ability variables trained on different SVMs kernel algorithms. 50 youth archers with the mean age and standard deviation of 17.0 ± 0.6 years drawn from various archery programmes completed a six arrows shooting score test. Standard fitness and ability measurements namely hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were also recorded. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the performance variables tested. SVM models with linear, quadratic, cubic, fine RBF, medium RBF, as well as the coarse RBF kernel functions, were trained based on the measured performance variables. The HACA clustered the archers into high-potential archers (HPA) and low-potential archers (LPA), respectively. The linear, quadratic, cubic, as well as the medium RBF kernel functions models, demonstrated reasonably excellent classification accuracy of 97.5% and 2.5% error rate for the prediction of the HPA and the LPA. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from a combination of the selected few measured fitness and motor ability performance variables examined which would consequently save cost, time and effort during talent identification programme. Copyright © 2017 Elsevier B.V. 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).

CoMFA and CoMSIA studies on C-aryl glucoside SGLT2 inhibitors as potential anti-diabetic agents.

PubMed

Vyas, V K; Bhatt, H G; Patel, P K; Jalu, J; Chintha, C; Gupta, N; Ghate, M

2013-01-01

SGLT2 has become a target of therapeutic interest in diabetes research. CoMFA and CoMSIA studies were performed on C-aryl glucoside SGLT2 inhibitors (180 analogues) as potential anti-diabetic agents. Three different alignment strategies were used for the compounds. The best CoMFA and CoMSIA models were obtained by means of Distill rigid body alignment of training and test sets, and found statistically significant with cross-validated coefficients (q²) of 0.602 and 0.618, respectively, and conventional coefficients (r²) of 0.905 and 0.902, respectively. Both models were validated by a test set of 36 compounds giving satisfactory predicted correlation coefficients (r² pred) of 0.622 and 0.584 for CoMFA and CoMSIA models, respectively. A comparison was made with earlier 3D QSAR study on SGLT2 inhibitors, which shows that our 3D QSAR models are better than earlier models to predict good inhibitory activity. CoMFA and CoMSIA models generated in this work can provide useful information to design new compounds and helped in prediction of activity prior to synthesis.

Provocative work experiences predict the acquired capability for suicide in physicians.

PubMed

Fink-Miller, Erin L

2015-09-30

The interpersonal psychological theory of suicidal behavior (IPTS) offers a potential means to explain suicide in physicians. The IPTS posits three necessary and sufficient precursors to death by suicide: thwarted belongingness, perceived burdensomeness, and acquired capability. The present study sought to examine whether provocative work experiences unique to physicians (e.g., placing sutures, withdrawing life support) would predict levels of acquired capability, while controlling for gender and painful and provocative experiences outside the work environment. Data were obtained from 376 of 7723 recruited physicians. Study measures included the Acquired Capability for Suicide Scale, the Interpersonal Needs Questionnaire, the Painful and Provocative Events Scale, and the Life Events Scale-Medical Doctors Version. Painful and provocative events outside of work predicted acquired capability (β=0.23, t=3.82, p<0.001, f(2)=0.09) as did provocative work experiences (β=0.12, t=2.05, p<0.05, f(2)=0.07). This represents the first study assessing the potential impact of unique work experiences on suicidality in physicians. Limitations include over-representation of Caucasian participants, limited representation from various specialties of medicine, and lack of information regarding individual differences. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Print-specific multimodal brain activation in kindergarten improves prediction of reading skills in second grade.

PubMed

Bach, Silvia; Richardson, Ulla; Brandeis, Daniel; Martin, Ernst; Brem, Silvia

2013-11-15

Children who are poor readers usually experience troublesome school careers and consequently often suffer from secondary emotional and behavioural problems. Early identification and prediction of later reading problems thus are critical in order to start targeted interventions for those children with an elevated risk for emerging reading problems. In this study, behavioural precursors of reading were assessed in nineteen (aged 6.4 ± 0.3 years) non-reading kindergarteners before training letter-speech sound associations with a computerized game (Graphogame) for eight weeks. The training aimed to introduce the basic principles of letter-speech sound correspondences and to initialize the sensitization of specific brain areas to print. Event-related potentials (ERP) and functional magnetic resonance imaging (fMRI) data were recorded during an explicit word/symbol processing task after the training. Reading skills were assessed two years later in second grade. The focus of this study was on clarifying whether electrophysiological and fMRI data of kindergarten children significantly improve prediction of future reading skills in 2nd grade over behavioural data alone. Based on evidence from previous studies demonstrating the importance of initial print sensitivity in the left occipito-temporal visual word form system (VWFS) for learning to read, the first pronounced difference in processing words compared to symbols in the ERP, an occipito-temporal negativity (N1: 188-281 ms) along with the corresponding functional activation in the left occipito-temporal VWFS were defined as potential predictors. ERP and fMRI data in kindergarteners significantly improved the prediction of reading skills in 2nd grade over behavioural data alone. Together with the behavioural measures they explained up to 88% of the variance. An additional discriminant analysis revealed a remarkably high accuracy in classifying normal (n=11) and poor readers (n=6). Due to the key limitation of the study, i.e. the small group sizes, the results of our prediction analyses should be interpreted with caution and regarded as preliminary despite cross-validation. Nevertheless our results indicate the potential of combining neuroimaging and behavioural measures to improve prediction at an early stage, when literacy skills are acquired and interventions are most beneficial. Copyright © 2013 Elsevier Inc. All rights reserved.

Structural and functional predictors of regional peak pressures under the foot during walking.

PubMed

Morag, E; Cavanagh, P R

1999-04-01

The objective of this study was to identify structural and functional factors which are predictors of peak pressure underneath the human foot during walking. Peak plantar pressure during walking and eight data sets of structural and functional measures were collected on 55 asymptomatic subjects between 20 and 70 yr. A best subset regression approach was used to establish models which predicted peak regional pressure under the foot. Potential predictor variables were chosen from physical characteristics, anthropometric data, passive range of motion (PROM), measurements from standardized weight bearing foot radiographs, mechanical properties of the plantar soft tissue, stride parameters, foot motion in 3D, and EMG during walking. Peak pressure values under the rearfoot, midfoot, MTH1, and hallux were measured. Heel pressure was a function of linear kinematics, longitudinal arch structure, thickness of plantar soft tissue, and age. Midfoot pressure prediction was dominated by arch structure, while MTH1 pressure was a function of radiographic measurements, talo-crural joint motion, and gastrocnemius activity. Hallux pressure was a function of structural measures and MTP1 joint motion. Foot structure and function predicted only approximately 50% of the variance in peak pressure, although the relative contributions in different anatomical regions varied dramatically. Structure was dominant in predicting peak pressure under the midfoot and MTH1, while both structure and function were important at the heel and hallux. The predictive models developed in this study give insight into potential etiological factors associated with elevated plantar pressure. They also provide direction for future studies designed to reduce elevated pressure in "at-risk" patients.

Using existing data to predict and quantify the risks of GM forage to a population of a non-target invertebrate species: a New Zealand case study.

PubMed

O'Callaghan, Maureen; Soboleva, Tanya K; Barratt, Barbara I P

2010-01-01

Determining the effects of genetically modified (GM) crops on non-target organisms is essential as many non-target species provide important ecological functions. However, it is simply not possible to collect field data on more than a few potential non-target species present in the receiving environment of a GM crop. While risk assessment must be rigorous, new approaches are necessary to improve the efficiency of the process. Utilisation of published information and existing data on the phenology and population dynamics of test species in the field can be combined with limited amounts of experimental biosafety data to predict possible outcomes on species persistence. This paper presents an example of an approach where data from laboratory experiments and field studies on phenology are combined using predictive modelling. Using the New Zealand native weevil species Nicaeana cervina as a case study, we could predict that oviposition rates of the weevil feeding on a GM ryegrass could be reduced by up to 30% without threat to populations of the weevil in pastoral ecosystems. In addition, an experimentally established correlation between feeding level and oviposition led to the prediction that a consistent reduction in feeding of 50% or higher indicated a significant risk to the species and could potentially lead to local extinctions. This approach to biosafety risk assessment, maximising the use of pre-existing field and laboratory data on non-target species, can make an important contribution to informed decision-making by regulatory authorities and developers of new technologies. © ISBR, EDP Sciences, 2011.

A predictive data-driven framework for endocrine prioritization: a triazole fungicide case study.

PubMed

Paul Friedman, Katie; Papineni, Sabitha; Marty, M Sue; Yi, Kun Don; Goetz, Amber K; Rasoulpour, Reza J; Kwiatkowski, Pat; Wolf, Douglas C; Blacker, Ann M; Peffer, Richard C

2016-10-01

The US Environmental Protection Agency Endocrine Disruptor Screening Program (EDSP) is a tiered screening approach to determine the potential for a chemical to interact with estrogen, androgen, or thyroid hormone systems and/or perturb steroidogenesis. Use of high-throughput screening (HTS) to predict hazard and exposure is shifting the EDSP approach to (1) prioritization of chemicals for further screening; and (2) targeted use of EDSP Tier 1 assays to inform specific data needs. In this work, toxicology data for three triazole fungicides (triadimefon, propiconazole, and myclobutanil) were evaluated, including HTS results, EDSP Tier 1 screening (and other scientifically relevant information), and EPA guideline mammalian toxicology study data. The endocrine-related bioactivity predictions from HTS and information that satisfied the EDSP Tier 1 requirements were qualitatively concordant. Current limitations in the available HTS battery for thyroid and steroidogenesis pathways were mitigated by inclusion of guideline toxicology studies in this analysis. Similar margins (3-5 orders of magnitude) were observed between HTS-predicted human bioactivity and exposure values and between in vivo mammalian bioactivity and EPA chronic human exposure estimates for these products' registered uses. Combined HTS hazard and human exposure predictions suggest low priority for higher-tiered endocrine testing of these triazoles. Comparison with the mammalian toxicology database indicated that this HTS-based prioritization would have been protective for any potential in vivo effects that form the basis of current risk assessment for these chemicals. This example demonstrates an effective, human health protective roadmap for EDSP evaluation of pesticide active ingredients via prioritization using HTS and guideline toxicology information.

A predictive data-driven framework for endocrine prioritization: a triazole fungicide case study

PubMed Central

Paul Friedman, Katie; Papineni, Sabitha; Marty, M. Sue; Yi, Kun Don; Goetz, Amber K.; Rasoulpour, Reza J.; Kwiatkowski, Pat; Wolf, Douglas C.; Blacker, Ann M.; Peffer, Richard C.

2016-01-01

Abstract The US Environmental Protection Agency Endocrine Disruptor Screening Program (EDSP) is a tiered screening approach to determine the potential for a chemical to interact with estrogen, androgen, or thyroid hormone systems and/or perturb steroidogenesis. Use of high-throughput screening (HTS) to predict hazard and exposure is shifting the EDSP approach to (1) prioritization of chemicals for further screening; and (2) targeted use of EDSP Tier 1 assays to inform specific data needs. In this work, toxicology data for three triazole fungicides (triadimefon, propiconazole, and myclobutanil) were evaluated, including HTS results, EDSP Tier 1 screening (and other scientifically relevant information), and EPA guideline mammalian toxicology study data. The endocrine-related bioactivity predictions from HTS and information that satisfied the EDSP Tier 1 requirements were qualitatively concordant. Current limitations in the available HTS battery for thyroid and steroidogenesis pathways were mitigated by inclusion of guideline toxicology studies in this analysis. Similar margins (3–5 orders of magnitude) were observed between HTS-predicted human bioactivity and exposure values and between in vivo mammalian bioactivity and EPA chronic human exposure estimates for these products’ registered uses. Combined HTS hazard and human exposure predictions suggest low priority for higher-tiered endocrine testing of these triazoles. Comparison with the mammalian toxicology database indicated that this HTS-based prioritization would have been protective for any potential in vivo effects that form the basis of current risk assessment for these chemicals. This example demonstrates an effective, human health protective roadmap for EDSP evaluation of pesticide active ingredients via prioritization using HTS and guideline toxicology information. PMID:27347635

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.

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

Boer, George J.; Smith, Douglas M.; Cassou, Christophe

The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Predictionmore » (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours. Furthermore, groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.« less

The scent fingerprint of hepatocarcinoma: in-vitro metastasis prediction with volatile organic compounds (VOCs)

PubMed Central

Amal, Haitham; Ding, Lu; Liu, Bin-bin; Tisch, Ulrike; Xu, Zhen-qin; Shi, Da-you; Zhao, Yan; Chen, Jie; Sun, Rui-xia; Liu, Hu; Ye, Sheng-Long; Tang, Zhao-you; Haick, Hossam

2012-01-01

Background: Hepatocellular carcinoma (HCC) is a common and aggressive form of cancer. Due to a high rate of postoperative recurrence, the prognosis for HCC is poor. Subclinical metastasis is the major cause of tumor recurrence and patient mortality. Currently, there is no reliable prognostic method of invasion. Aim: To investigate the feasibility of fingerprints of volatile organic compounds (VOCs) for the in-vitro prediction of metastasis. Methods: Headspace gases were collected from 36 cell cultures (HCC with high and low metastatic potential and normal cells) and analyzed using nanomaterial-based sensors. Predictive models were built by employing discriminant factor analysis pattern recognition, and the classification success was determined using leave-one-out cross-validation. The chemical composition of each headspace sample was studied using gas chromatography coupled with mass spectrometry (GC-MS). Results: Excellent discrimination was achieved using the nanomaterial-based sensors between (i) all HCC and normal controls; (ii) low metastatic HCC and normal controls; (iii) high metastatic HCC and normal controls; and (iv) high and low HCC. Several HCC-related VOCs that could be associated with biochemical cellular processes were identified through GC-MS analysis. Conclusion: The presented results constitute a proof-of-concept for the in-vitro prediction of the metastatic potential of HCC from VOC fingerprints using nanotechnology. Further studies on a larger number of more diverse cell cultures are needed to evaluate the robustness of the VOC patterns. These findings could benefit the development of a fast and potentially inexpensive laboratory test for subclinical HCC metastasis. PMID:22888249

Can species traits predict the susceptibility of riverine fish to water resource development? An Australian case study.

PubMed

Rolls, Robert J; Sternberg, David

2015-06-01

Water resource developments alter riverine environments by disrupting longitudinal connectivity, transforming lotic habitats, and modifying in-stream hydraulic conditions. Effective management of anthropogenic disturbances therefore requires an understanding of the range of potential ecosystem effects and the inherent traits symptomatic of elevated vulnerability to disturbance. Using 42 riverine fish native to South Eastern Australia as a case study, we quantified six morphological, behavioral, and life-history traits to classify species into groups reflecting potential differences in their response to ecosystem changes as a result of water resource development. Classification analysis identified five strategies based on fish life-history dispersal requirements, climbing potential, and habitat preference. These strategies in turn highlight the potential species at risk from the separate impacts of water resource development and inform management decisions to mitigate those risks. Swimming ability did not contribute to distinguishing species into functional groups, likely due to methodological inconsistencies in quantifying swimming performance that may ultimately hinder the ability of fish passage facilities to function within the physical capabilities of species at risk of habitat fragmentation. This study improves our ability to predict the performance of groups of species at risk from the multiple environmental changes imposed by humans and goes beyond broad-scale dispersal requirements as a predictor of individual species response.

Potential assessment of genome-wide association study and genomic selection in Japanese pear Pyrus pyrifolia

PubMed Central

Iwata, Hiroyoshi; Hayashi, Takeshi; Terakami, Shingo; Takada, Norio; Sawamura, Yutaka; Yamamoto, Toshiya

2013-01-01

Although the potential of marker-assisted selection (MAS) in fruit tree breeding has been reported, bi-parental QTL mapping before MAS has hindered the introduction of MAS to fruit tree breeding programs. Genome-wide association studies (GWAS) are an alternative to bi-parental QTL mapping in long-lived perennials. Selection based on genomic predictions of breeding values (genomic selection: GS) is another alternative for MAS. This study examined the potential of GWAS and GS in pear breeding with 76 Japanese pear cultivars to detect significant associations of 162 markers with nine agronomic traits. We applied multilocus Bayesian models accounting for ordinal categorical phenotypes for GWAS and GS model training. Significant associations were detected at harvest time, black spot resistance and the number of spurs and two of the associations were closely linked to known loci. Genome-wide predictions for GS were accurate at the highest level (0.75) in harvest time, at medium levels (0.38–0.61) in resistance to black spot, firmness of flesh, fruit shape in longitudinal section, fruit size, acid content and number of spurs and at low levels (<0.2) in all soluble solid content and vigor of tree. Results suggest the potential of GWAS and GS for use in future breeding programs in Japanese pear. PMID:23641189

Endogenous Antiangiogenic Factors in Chronic Kidney Disease: Potential Biomarkers of Progression.

PubMed

Tanabe, Katsuyuki; Sato, Yasufumi; Wada, Jun

2018-06-24

Chronic kidney disease (CKD) is a major global health problem. Unless intensive intervention is initiated, some patients can rapidly progress to end-stage kidney disease. However, it is often difficult to predict renal outcomes using conventional laboratory tests in individuals with CKD. Therefore, many researchers have been searching for novel biomarkers to predict the progression of CKD. Angiogenesis is involved in physiological and pathological processes in the kidney and is regulated by the balance between a proangiogenic factor, vascular endothelial growth factor (VEGF)-A, and various endogenous antiangiogenic factors. In recent reports using genetically engineered mice, the roles of these antiangiogenic factors in the pathogenesis of kidney disease have become increasingly clear. In addition, recent clinical studies have demonstrated associations between circulating levels of antiangiogenic factors and renal dysfunction in CKD patients. In this review, we summarize recent advances in the study of representative endogenous antiangiogenic factors, including soluble fms-related tyrosine kinase 1, soluble endoglin, pigment epithelium-derived factor, VEGF-A 165 b, endostatin, and vasohibin-1, in associations with kidney diseases and discuss their predictive potentials as biomarkers of progression of CKD.

GBFEL-TIE (Ground-Based Free Electron Laser Technology Experiment) sample survey on White Sands Missile Range, New Mexico: The NASA, Stallion, and Orogrande Alternatives. Final report

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

Seaman, T.J.; Doleman, W.H.

1988-09-30

Three locations on White Sands Missile Range, New Mexico, are under consideration as alternatives for the proposed Ground-Based Free-Electron Laser Technology Integration Experiment (GBFEL-TIE). The study conducted jointly by Prewitt and Associates, Inc., and the Office of Contract Archeology, was designed to provide input into the GBFEL-TIE Draft Environmental Impact Statement concerning the potential impact of the proposed project on cultural resources in each of the alternatives. The input consists of a series of predictions based on data gathered from two sources: (1) a cultural resource sample survey (15%) of two alternatives conducted as part of this study, and (2)more » from a previous survey of the third alternative. A predictive model was devleoped and applied using these data that estimated the potential impact of the GBFEL-TIE facility on the cultural resources within each alternative. The predictions indicate that the NASA alternatives, by far, the least favorable location for the facility followed by the Orogrande and Stallion Alternatives.« less

Predicting risk in patients with acetaminophen overdose

PubMed Central

James, Laura P.; Gill, Prit; Simpson, Pippa

2014-01-01

Acetaminophen (APAP) overdose is a very common cause of drug overdose and acute liver failure in the US and Europe. Mechanism-based biomarkers of APAP toxicity have the potential to improve the clinical management of patients with large dose ingestions of APAP. The current approach to the management of APAP toxicity is limited by imprecise and time-constrained risk assessments and late-stage markers of liver injury. A recent study of “low-risk” APAP overdose patients who all received treatment with N-acetylcysteine, found that cell-death biomarkers were more sensitive than alanine aminotransferase (ALT) and APAP concentrations in predicting the development of acute liver injury. The data suggest a potential role for new biomarkers to identify “low risk” patients following APAP overdose. However, a practical and ethical consideration that complicates predictive biomarker research in this area is the clinical need to deliver antidote treatment within 10 hours of APAP overdose. The treatment effect and time-dependent nature of N-acetylcysteine treatment must be considered in future “predictive” toxicology studies of APAP-induced liver injury. PMID:23984999

CD147/EMMPRIN overexpression and prognosis in cancer: A systematic review and meta-analysis

PubMed Central

Xin, Xiaoyan; Zeng, Xianqin; Gu, Huajian; Li, Min; Tan, Huaming; Jin, Zhishan; Hua, Teng; Shi, Rui; Wang, Hongbo

2016-01-01

CD147/EMMPRIN (extracellular matrix metalloproteinase inducer) plays an important role in tumor progression and a number of studies have suggested that it is an indicator of tumor prognosis. This current meta-analysis systematically reevaluated the predictive potential of CD147/EMMPRIN in various cancers. We searched PubMed and Embase databases to screen the literature. Fixed-effect and random-effect meta-analytical techniques were used to correlate CD147 expression with outcome measures. A total of 53 studies that included 68 datasets were eligible for inclusion in the final analysis. We found a significant association between CD147/EMMPRIN overexpression and adverse tumor outcomes, such as overall survival, disease-specific survival, progression-free survival, metastasis-free survival or recurrence-free survival, irrespective of the model analysis. In addition, CD147/EMMPRIN overexpression predicted a high risk for chemotherapy drugs resistance. CD147/EMMPRIN is a central player in tumor progression and predicts a poor prognosis, including in patients who have received chemo-radiotherapy. Our results provide the evidence that CD147/EMMPRIN could be a potential therapeutic target for cancers. PMID:27608940

Predicting the In-Hospital Responsiveness to Treatment of Alcoholics. Social Factors as Predictors of Outcome. Brain Damage as a Factor in Treatment Outcome of Chronic Alcoholic Patients.

ERIC Educational Resources Information Center

Mascia, George V.; And Others

The authors attempt to locate predictor variables associated with the outcome of alcoholic treatment programs. Muscia's study focuses on the predictive potential of: (1) response to a GSR conditioning procedure; (2) several personality variables; and (3) age and IQ measures. Nine variables, reflecting diverse perspectives, were selected as a basis…

Logistic Regression Analyses for Predicting Clinically Important Differences in Motor Capacity, Motor Performance, and Functional Independence after Constraint-Induced Therapy in Children with Cerebral Palsy

ERIC Educational Resources Information Center

Wang, Tien-ni; Wu, Ching-yi; Chen, Chia-ling; Shieh, Jeng-yi; Lu, Lu; Lin, Keh-chung

2013-01-01

Given the growing evidence for the effects of constraint-induced therapy (CIT) in children with cerebral palsy (CP), there is a need for investigating the characteristics of potential participants who may benefit most from this intervention. This study aimed to establish predictive models for the effects of pediatric CIT on motor and functional…

HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction

PubMed Central

Zhang, Xu; You, Zhu-Hong; Huang, Yu-An; Yan, Gui-Ying

2016-01-01

Recently, microRNAs (miRNAs) have drawn more and more attentions because accumulating experimental studies have indicated miRNA could play critical roles in multiple biological processes as well as the development and progression of human complex diseases. Using the huge number of known heterogeneous biological datasets to predict potential associations between miRNAs and diseases is an important topic in the field of biology, medicine, and bioinformatics. In this study, considering the limitations in the previous computational methods, we developed the computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally verified miRNA-disease associations into a heterogeneous graph. HGIMDA obtained AUCs of 0.8781 and 0.8077 based on global and local leave-one-out cross validation, respectively. Furthermore, HGIMDA was applied to three important human cancers for performance evaluation. As a result, 90% (Colon Neoplasms), 88% (Esophageal Neoplasms) and 88% (Kidney Neoplasms) of top 50 predicted miRNAs are confirmed by recent experiment reports. Furthermore, HGIMDA could be effectively applied to new diseases and new miRNAs without any known associations, which overcome the important limitations of many previous computational models. PMID:27533456

HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction.

PubMed

Chen, Xing; Yan, Chenggang Clarence; Zhang, Xu; You, Zhu-Hong; Huang, Yu-An; Yan, Gui-Ying

2016-10-04

Recently, microRNAs (miRNAs) have drawn more and more attentions because accumulating experimental studies have indicated miRNA could play critical roles in multiple biological processes as well as the development and progression of human complex diseases. Using the huge number of known heterogeneous biological datasets to predict potential associations between miRNAs and diseases is an important topic in the field of biology, medicine, and bioinformatics. In this study, considering the limitations in the previous computational methods, we developed the computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally verified miRNA-disease associations into a heterogeneous graph. HGIMDA obtained AUCs of 0.8781 and 0.8077 based on global and local leave-one-out cross validation, respectively. Furthermore, HGIMDA was applied to three important human cancers for performance evaluation. As a result, 90% (Colon Neoplasms), 88% (Esophageal Neoplasms) and 88% (Kidney Neoplasms) of top 50 predicted miRNAs are confirmed by recent experiment reports. Furthermore, HGIMDA could be effectively applied to new diseases and new miRNAs without any known associations, which overcome the important limitations of many previous computational models.

Asian Monsoons: Variability, Predictability, and Sensitivity to External Forcing

NASA Technical Reports Server (NTRS)

Yang, Song; Lau, K.-M.

1999-01-01

In this study, we have addressed the interannual variations of Asian monsoons including both broad-scale and regional monsoon components. Particular attention is devoted to the identities of the South China Sea monsoon and Indian monsoon. We use CPC Merged Analysis of Precipitation and NCEP reanalyses to define regional monsoon indices and to depict the various monsoons. Parallel modeling studies have also been carried out to assess the role of boundary forcing and the potential predictability of the monsoons. Each monsoon is characterized by its unique features. While the South Asian monsoon represents a classical monsoon in which anomalous circulation is governed by Rossby-wave dynamics, the Southeast Asian monsoon symbolizes a "hybrid" monsoon that features multi-cellular meridional circulation over eastern Asia. The broad-scale Asian monsoon links to the basin-wide atmospheric circulation over the Indian-Pacific oceans. Both SST and land surface processes are important for determining the variations of all monsoons. For the broad-scale monsoon, SST anomalies are more important than land surface processes. For regional monsoons, however, land surface processes may become equally important. Both observation and model shows that the broad-scale monsoon is potentially more predictable than regional monsoons, and that the Southeast Asian monsoon may possess higher predictability than the South Asian monsoon.

Asian Monsoons: Variability, Predictability, and Sensitivity to External Forcing

NASA Technical Reports Server (NTRS)

Yang, Song; Lau, K.-M.; Kim, K.-M.

1999-01-01

In this study, we have addressed the interannual variations of Asian monsoons including both broad-scale and regional monsoon components. Particular attention is devoted to the identities of the South China Sea monsoon and Indian monsoon. We use CPC Merged Analysis of Precipitation and NCEP reanalyses to define regional monsoon indices and to depict the various monsoons. Parallel modeling studies have also been carried out to assess the potential predictability of the broad-scale and regional monsoons. Each monsoon is characterized by its unique features. While the South Asian monsoon represents a classical monsoon in which anomalous circulation is governed by Rossby-wave dynamics, the Southeast Asian monsoon symbolizes a "hybrid" monsoon that features multi-cellular meridional circulation over eastern Asia. The broad-scale Asian monsoon links to the basin-wide atmospheric circulation over the Indian-Pacific oceans. Both Sea Surface Temperatures (SST) and land surface processes are important for determining the variations of all monsoons. For the broad-scale monsoon, SST anomalies are more important than land surface processes. However, for regional monsoons, land surface processes may become equally important. Both observation and model shows that the broad-scale monsoon is potentially more predictable than regional monsoons, and that the Southeast Asian monsoon may possess higher predictability than the South Asian monsoon.

Development of brief versions of the Wechsler Intelligence Scale for schizophrenia: considerations of the structure and predictability of intelligence.

PubMed

Sumiyoshi, Chika; Uetsuki, Miki; Suga, Motomu; Kasai, Kiyoto; Sumiyoshi, Tomiki

2013-12-30

Short forms (SF) of the Wechsler Intelligence Scale have been developed to enhance its practicality. However, only a few studies have addressed the Wechsler Intelligence Scale Revised (WAIS-R) SFs based on data from patients with schizophrenia. The current study was conducted to develop the WAIS-R SFs for these patients based on the intelligence structure and predictability of the Full IQ (FIQ). Relations to demographic and clinical variables were also examined on selecting plausible subtests. The WAIS-R was administered to 90 Japanese patients with schizophrenia. Exploratory factor analysis (EFA) and multiple regression analysis were conducted to find potential subtests. EFA extracted two dominant factors corresponding to Verbal IQ and Performance IQ measures. Subtests with higher factor loadings on those factors were initially nominated. Regression analysis was carried out to reach the model containing all the nominated subtests. The optimality of the potential subtests included in that model was evaluated from the perspectives of the representativeness of intelligence structure, FIQ predictability, and the relation with demographic and clinical variables. Taken together, the dyad of Vocabulary and Block Design was considered to be the most optimal WAIS-R SF for patients with schizophrenia, reflecting both intelligence structure and FIQ predictability. © 2013 Elsevier Ireland Ltd. All rights reserved.

Wearable physiological sensors and real-time algorithms for detection of acute mountain sickness.

PubMed

Muza, Stephen R

2018-03-01

This is a minireview of potential wearable physiological sensors and algorithms (process and equations) for detection of acute mountain sickness (AMS). Given the emerging status of this effort, the focus of the review is on the current clinical assessment of AMS, known risk factors (environmental, demographic, and physiological), and current understanding of AMS pathophysiology. Studies that have examined a range of physiological variables to develop AMS prediction and/or detection algorithms are reviewed to provide insight and potential technological roadmaps for future development of real-time physiological sensors and algorithms to detect AMS. Given the lack of signs and nonspecific symptoms associated with AMS, development of wearable physiological sensors and embedded algorithms to predict in the near term or detect established AMS will be challenging. Prior work using [Formula: see text], HR, or HRv has not provided the sensitivity and specificity for useful application to predict or detect AMS. Rather than using spot checks as most prior studies have, wearable systems that continuously measure SpO 2 and HR are commercially available. Employing other statistical modeling approaches such as general linear and logistic mixed models or time series analysis to these continuously measured variables is the most promising approach for developing algorithms that are sensitive and specific for physiological prediction or detection of AMS.

Measuring Gambling Reinforcers, Over Consumption and Fallacies: The Psychometric Properties and Predictive Validity of the Jonsson-Abbott Scale

PubMed Central

Jonsson, Jakob; Abbott, Max W.; Sjöberg, Anders; Carlbring, Per

2017-01-01

Traditionally, gambling and problem gambling research relies on cross-sectional and retrospective designs. This has compromised identification of temporal relationships and causal inference. To overcome these problems a new questionnaire, the Jonsson-Abbott Scale (JAS), was developed and used in a large, prospective, general population study, The Swedish Longitudinal Gambling Study (Swelogs). The JAS has 11 items and seeks to identify early indicators, examine relationships between indicators and assess their capacity to predict future problem progression. The aims of the study were to examine psychometric properties of the JAS (internal consistency and dimensionality) and predictive validity with respect to increased gambling risk and problem gambling onset. The results are based on repeated interviews with 3818 participants. The response rate from the initial baseline wave was 74%. The original sample consisted of a random, stratified selection from the Swedish population register aged between 16 and 84. The results indicate an acceptable fit of a three-factor solution in a confirmatory factor analysis with ‘Over consumption,’ ‘Gambling fallacies,’ and ‘Reinforcers’ as factors. Reinforcers, Over consumption and Gambling fallacies were significant predictors of gambling risk potential and Gambling fallacies and Over consumption were significant predictors of problem gambling onset (incident cases) at 12 month follow up. When controlled for risk potential measured at baseline, the predictor Over consumption was not significant for gambling risk potential at follow up. For incident cases, Gambling fallacies and Over consumption remained significant when controlled for risk potential. Implications of the results for the development of problem gambling, early detection, prevention, and future research are discussed. PMID:29085320

Leptin levels in patients with anorexia nervosa following day/inpatient treatment do not predict weight 1 year post-referral.

PubMed

Seitz, Jochen; Bühren, Katharina; Biemann, Ronald; Timmesfeld, Nina; Dempfle, Astrid; Winter, Sibylle Maria; Egberts, Karin; Fleischhaker, Christian; Wewetzer, Christoph; Herpertz-Dahlmann, Beate; Hebebrand, Johannes; Föcker, Manuel

2016-09-01

Elevated serum leptin levels following rapid therapeutically induced weight gain in anorexia nervosa (AN) patients are discussed as a potential biomarker for renewed weight loss as a result of leptin-related suppression of appetite and increased energy expenditure. This study aims to analyze the predictive value of leptin levels at discharge as well as the average rate of weight gain during inpatient or day patient treatment for body weight at 1-year follow-up. 121 patients were recruited from the longitudinal Anorexia Nervosa Day patient versus Inpatient (ANDI) trial. Serum leptin levels were analyzed at referral and discharge. A multiple linear regression analysis to predict age-adjusted body mass index (BMI-SDS) at 1-year follow-up was performed. Leptin levels, the average rate of weight gain, premorbid BMI-SDS, BMI-SDS at referral, age and illness duration were included as independent variables. Neither leptin levels at discharge nor rate of weight gain significantly predicted BMI-SDS at 1-year follow-up explaining only 1.8 and 0.4 % of the variance, respectively. According to our results, leptin levels at discharge and average rate of weight gain did not exhibit any value in predicting weight at 1-year follow-up in our longitudinal observation study of adolescent patients with AN. Thus, research should focus on other potential factors to predict weight at follow-up. As elevated leptin levels and average rate of weight gain did not pose a risk for reduced weight, we found no evidence for the beneficial effect of slow refeeding in patients with acute AN.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-01-20

Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health-care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-02-01

Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 Stichting European Society for Clinical Investigation Journal Foundation.

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-01-06

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)

PubMed Central

Reitsma, Johannes B.; Altman, Douglas G.; Moons, Karel G.M.

2015-01-01

Background— Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. Methods— The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. Results— The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. Conclusions— To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). PMID:25561516

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement

PubMed Central

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-01-01

Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health-care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). PMID:25562432

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-02-01

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 Royal College of Obstetricians and Gynaecologists.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. The TRIPOD Group.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-01-13

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 The Authors.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-01-06

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-02-01

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). Copyright © 2015 Elsevier Inc. All rights reserved.

Psychosocial work environment factors and weight change: a prospective study among Danish health care workers.

PubMed

Gram Quist, Helle; Christensen, Ulla; Christensen, Karl Bang; Aust, Birgit; Borg, Vilhelm; Bjorner, Jakob B

2013-01-17

Lifestyle variables may serve as important intermediate factors between psychosocial work environment and health outcomes. Previous studies, focussing on work stress models have shown mixed and weak results in relation to weight change. This study aims to investigate psychosocial factors outside the classical work stress models as potential predictors of change in body mass index (BMI) in a population of health care workers. A cohort study, with three years follow-up, was conducted among Danish health care workers (3982 women and 152 men). Logistic regression analyses examined change in BMI (more than +/- 2 kg/m(2)) as predicted by baseline psychosocial work factors (work pace, workload, quality of leadership, influence at work, meaning of work, predictability, commitment, role clarity, and role conflicts) and five covariates (age, cohabitation, physical work demands, type of work position and seniority). Among women, high role conflicts predicted weight gain, while high role clarity predicted both weight gain and weight loss. Living alone also predicted weight gain among women, while older age decreased the odds of weight gain. High leadership quality predicted weight loss among men. Associations were generally weak, with the exception of quality of leadership, age, and cohabitation. This study of a single occupational group suggested a few new risk factors for weight change outside the traditional work stress models.

Quantifying social development in autism.

PubMed

Volkmar, F R; Carter, A; Sparrow, S S; Cicchetti, D V

1993-05-01

This study was concerned with the development of quantitative measures of social development in autism. Multiple regression equations predicting social, communicative, and daily living skills on the Vineland Adaptive Behavior Scales were derived from a large, normative sample and applied to groups of autistic and nonautistic, developmentally disordered children. Predictive models included either mental or chronological age and other relevant variables. Social skills in the autistic group were more than two standard deviations below those predicted by their mental age; an index derived from the ratio of actual to predicted social skills correctly classified 94% of the autistic and 92% of the nonautistic, developmentally disordered cases. The findings are consistent with the idea that social disturbance is central in the definition of autism. The approach used in this study has potential advantages for providing more precise measures of social development in autism.

Value of supervised learning events in predicting doctors in difficulty.

PubMed

Patel, Mumtaz; Agius, Steven; Wilkinson, Jack; Patel, Leena; Baker, Paul

2016-07-01

In the UK, supervised learning events (SLE) replaced traditional workplace-based assessments for foundation-year trainees in 2012. A key element of SLEs was to incorporate trainee reflection and assessor feedback in order to drive learning and identify training issues early. Few studies, however, have investigated the value of SLEs in predicting doctors in difficulty. This study aimed to identify principles that would inform understanding about how and why SLEs work or not in identifying doctors in difficulty (DiD). A retrospective case-control study of North West Foundation School trainees' electronic portfolios was conducted. Cases comprised all known DiD. Controls were randomly selected from the same cohort. Free-text supervisor comments from each SLE were assessed for the four domains defined in the General Medical Council's Good Medical Practice Guidelines and each scored blindly for level of concern using a three-point ordinal scale. Cumulative scores for each SLE were then analysed quantitatively for their predictive value of actual DiD. A qualitative thematic analysis was also conducted. The prevalence of DiD in this sample was 6.5%. Receiver operator characteristic curve analysis showed that Team Assessment of Behaviour (TAB) was the only SLE strongly predictive of actual DiD status. The Educational Supervisor Report (ESR) was also strongly predictive of DiD status. Fisher's test showed significant associations of TAB and ESR for both predicted and actual DiD status and also the health and performance subtypes. None of the other SLEs showed significant associations. Qualitative data analysis revealed inadequate completion and lack of constructive, particularly negative, feedback. This indicated that SLEs were not used to their full potential. TAB and the ESR are strongly predictive of DiD. However, SLEs are not being used to their full potential, and the quality of completion of reports on SLEs and feedback needs to be improved in order to better identify and manage DiD. © 2016 John Wiley & Sons Ltd.

Dynamic substrate preferences predict metabolic properties of a simple microbial consortium

DOE PAGES

Erbilgin, Onur; Bowen, Benjamin P.; Kosina, Suzanne M.; ...

2017-01-23

Mixed cultures of different microbial species are increasingly being used to carry out a specific biochemical function in lieu of engineering a single microbe to do the same t ask. However, knowing how different species' metabolisms will integrate to reach a desired outcome is a difficult problem that has been studied in great detail using steady-state models. However, many biotechnological processes, as well as natural habitats, represent a more dynamic system. Examining how individual species use resources in their growth medium or environment (exometabolomics) over time in batch culture conditions can provide rich phenotypic data that encompasses regulation and transporters,more » creating an opportunity to integrate the data into a predictive model of resource use by a mixed community. Here we use exometabolomic profiling to examine the time-varying substrate depletion from a mixture of 19 amino acids and glucose by two Pseudomonas and one Bacillus species isolated from ground water. Contrary to studies in model organisms, we found surprisingly few correlations between resource preferences and maximal growth rate or biomass composition. We then modeled patterns of substrate depletion, and used these models to examine if substrate usage preferences and substrate depletion kinetics of individual isolates can be used to predict the metabolism of a co-culture of the isolates. We found that most of the substrates fit the model predictions, except for glucose and histidine, which were depleted more slowly than predicted, and proline, glycine, glutamate, lysine and arginine, which were all consumed significantly faster. Our results indicate that a significant portion of a model community's overall metabolism can be predicted based on the metabolism of the individuals. Based on the nature of our model, the resources that significantly deviate from the prediction highlight potential metabolic pathways affected by species-species interactions, which when further studied can potentially be used to modulate microbial community structure and/or function.« less

Neural networks for dimensionality reduction of fluorescence spectra and prediction of drinking water disinfection by-products.

PubMed

Peleato, Nicolas M; Legge, Raymond L; Andrews, Robert C

2018-06-01

The use of fluorescence data coupled with neural networks for improved predictability of drinking water disinfection by-products (DBPs) was investigated. Novel application of autoencoders to process high-dimensional fluorescence data was related to common dimensionality reduction techniques of parallel factors analysis (PARAFAC) and principal component analysis (PCA). The proposed method was assessed based on component interpretability as well as for prediction of organic matter reactivity to formation of DBPs. Optimal prediction accuracies on a validation dataset were observed with an autoencoder-neural network approach or by utilizing the full spectrum without pre-processing. Latent representation by an autoencoder appeared to mitigate overfitting when compared to other methods. Although DBP prediction error was minimized by other pre-processing techniques, PARAFAC yielded interpretable components which resemble fluorescence expected from individual organic fluorophores. Through analysis of the network weights, fluorescence regions associated with DBP formation can be identified, representing a potential method to distinguish reactivity between fluorophore groupings. However, distinct results due to the applied dimensionality reduction approaches were observed, dictating a need for considering the role of data pre-processing in the interpretability of the results. In comparison to common organic measures currently used for DBP formation prediction, fluorescence was shown to improve prediction accuracies, with improvements to DBP prediction best realized when appropriate pre-processing and regression techniques were applied. The results of this study show promise for the potential application of neural networks to best utilize fluorescence EEM data for prediction of organic matter reactivity. Copyright © 2018 Elsevier Ltd. All rights reserved.

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EPA Pesticide Factsheets

High-throughput screening (HTS) technologies, along with efforts to improve public access to chemical toxicity information resources and to systematize older toxicity studies, have the potential to significantly improve predictive capabilities in toxicology. Internet Resource

Analysis of the regional MiKlip decadal prediction system over Europe: skill, added value of regionalization, and ensemble size dependeny

NASA Astrophysics Data System (ADS)

Reyers, Mark; Moemken, Julia; Pinto, Joaquim; Feldmann, Hendrik; Kottmeier, Christoph; MiKlip Module-C Team

2017-04-01

Decadal climate predictions can provide a useful basis for decision making support systems for the public and private sectors. Several generations of decadal hindcasts and predictions have been generated throughout the German research program MiKlip. Together with the global climate predictions computed with MPI-ESM, the regional climate model (RCM) COSMO-CLM is used for regional downscaling by MiKlip Module-C. The RCMs provide climate information on spatial and temporal scales closer to the needs of potential users. In this study, two downscaled hindcast generations are analysed (named b0 and b1). The respective global generations are both initialized by nudging them towards different reanalysis anomaly fields. An ensemble of five starting years (1961, 1971, 1981, 1991, and 2001), each comprising ten ensemble members, is used for both generations in order to quantify the regional decadal prediction skill for precipitation and near-surface temperature and wind speed over Europe. All datasets (including hindcasts, observations, reanalysis, and historical MPI-ESM runs) are pre-processed in an analogue manner by (i) removing the long-term trend and (ii) re-gridding to a common grid. Our analysis shows that there is potential for skillful decadal predictions over Europe in the regional MiKlip ensemble, but the skill is not systematic and depends on the PRUDENCE region and the variable. Further, the differences between the two hindcast generations are mostly small. As we used detrended time series, the predictive skill found in our study can probably attributed to reasonable predictions of anomalies which are associated with the natural climate variability. In a sensitivity study, it is shown that the results may strongly change when the long-term trend is kept in the datasets, as here the skill of predicting the long-term trend (e.g. for temperature) also plays a major role. The regionalization of the global ensemble provides an added value for decadal predictions for some complex regions like the Mediterranean and Iberian Peninsula, while for other regions no systematic improvement is found. A clear dependence of the performance of the regional MiKlip system on the ensemble size is detected. For all variables in both hindcast generations, the skill increases when the ensemble is enlarged. The results indicate that a number of ten members is an appropriate ensemble size for decadal predictions over Europe.

Future C loss in mid-latitude mineral soils: climate change exceeds land use mitigation potential in France

PubMed Central

Meersmans, Jeroen; Arrouays, Dominique; Van Rompaey, Anton J. J.; Pagé, Christian; De Baets, Sarah; Quine, Timothy A.

2016-01-01

Many studies have highlighted significant interactions between soil C reservoir dynamics and global climate and environmental change. However, in order to estimate the future soil organic carbon sequestration potential and related ecosystem services well, more spatially detailed predictions are needed. The present study made detailed predictions of future spatial evolution (at 250 m resolution) of topsoil SOC driven by climate change and land use change for France up to the year 2100 by taking interactions between climate, land use and soil type into account. We conclude that climate change will have a much bigger influence on future SOC losses in mid-latitude mineral soils than land use change dynamics. Hence, reducing CO2 emissions will be crucial to prevent further loss of carbon from our soils. PMID:27808169

Future C loss in mid-latitude mineral soils: climate change exceeds land use mitigation potential in France.

PubMed

Meersmans, Jeroen; Arrouays, Dominique; Van Rompaey, Anton J J; Pagé, Christian; De Baets, Sarah; Quine, Timothy A

2016-11-03

Many studies have highlighted significant interactions between soil C reservoir dynamics and global climate and environmental change. However, in order to estimate the future soil organic carbon sequestration potential and related ecosystem services well, more spatially detailed predictions are needed. The present study made detailed predictions of future spatial evolution (at 250 m resolution) of topsoil SOC driven by climate change and land use change for France up to the year 2100 by taking interactions between climate, land use and soil type into account. We conclude that climate change will have a much bigger influence on future SOC losses in mid-latitude mineral soils than land use change dynamics. Hence, reducing CO 2 emissions will be crucial to prevent further loss of carbon from our soils.

Mapping Oil and Gas Development Potential in the US Intermountain West and Estimating Impacts to Species

PubMed Central

Copeland, Holly E.; Doherty, Kevin E.; Naugle, David E.; Pocewicz, Amy; Kiesecker, Joseph M.

2009-01-01

Background Many studies have quantified the indirect effect of hydrocarbon-based economies on climate change and biodiversity, concluding that a significant proportion of species will be threatened with extinction. However, few studies have measured the direct effect of new energy production infrastructure on species persistence. Methodology/Principal Findings We propose a systematic way to forecast patterns of future energy development and calculate impacts to species using spatially-explicit predictive modeling techniques to estimate oil and gas potential and create development build-out scenarios by seeding the landscape with oil and gas wells based on underlying potential. We illustrate our approach for the greater sage-grouse (Centrocercus urophasianus) in the western US and translate the build-out scenarios into estimated impacts on sage-grouse. We project that future oil and gas development will cause a 7–19 percent decline from 2007 sage-grouse lek population counts and impact 3.7 million ha of sagebrush shrublands and 1.1 million ha of grasslands in the study area. Conclusions/Significance Maps of where oil and gas development is anticipated in the US Intermountain West can be used by decision-makers intent on minimizing impacts to sage-grouse. This analysis also provides a general framework for using predictive models and build-out scenarios to anticipate impacts to species. These predictive models and build-out scenarios allow tradeoffs to be considered between species conservation and energy development prior to implementation. PMID:19826472

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.

Metamemory prediction accuracy for simple prospective and retrospective memory tasks in 5-year-old children.

PubMed

Kvavilashvili, Lia; Ford, Ruth M

2014-11-01

It is well documented that young children greatly overestimate their performance on tests of retrospective memory (RM), but the current investigation is the first to examine children's prediction accuracy for prospective memory (PM). Three studies were conducted, each testing a different group of 5-year-olds. In Study 1 (N=46), participants were asked to predict their success in a simple event-based PM task (remembering to convey a message to a toy mole if they encountered a particular picture during a picture-naming activity). Before naming the pictures, children listened to either a reminder story or a neutral story. Results showed that children were highly accurate in their PM predictions (78% accuracy) and that the reminder story appeared to benefit PM only in children who predicted they would remember the PM response. In Study 2 (N=80), children showed high PM prediction accuracy (69%) regardless of whether the cue was specific or general and despite typical overoptimism regarding their performance on a 10-item RM task using item-by-item prediction. Study 3 (N=35) showed that children were prone to overestimate RM even when asked about their ability to recall a single item-the mole's unusual name. In light of these findings, we consider possible reasons for children's impressive PM prediction accuracy, including the potential involvement of future thinking in performance predictions and PM. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

Site relationships and black walnut height growth in natural stands in eastern Kansas

Treesearch

Wayne A. Geyer; Felix, Jr. Ponder

2004-01-01

Prediction of forestland productivity is needed for proper species selection in tree planting. By relating site quality to site and soil characteristics, potential productivity can be estimated for non-forested areas. Our study measured the growth potential of black walnut in natural stands in southeastern Kansas. We looked at over 200 stands on unglaciated soils....

Long-term potential and actual evapotranspiration of two different forests on the Atlantic Coastal Plain

Treesearch

Devendra Amatya; S. Tian; Z. Dai; Ge Sun

2016-01-01

A reliable estimate of potential evapotranspiration (PET) for a forest ecosystem is critical in ecohydrologic modeling related with water supply, vegetation dynamics, and climate change and yet is a challenging task due to its complexity. Based on long-term on-site measured hydro-climatic data and predictions from earlier validated hydrologic modeling studies...

Family Factors Predicting Categories of Suicide Risk

ERIC Educational Resources Information Center

Randell, Brooke P.; Wang, Wen-Ling; Herting, Jerald R.; Eggert, Leona L.

2006-01-01

We compared family risk and protective factors among potential high school dropouts with and without suicide-risk behaviors (SRB) and examined the extent to which these factors predict categories of SRB. Subjects were randomly selected from among potential dropouts in 14 high schools. Based upon suicide-risk status, 1,083 potential high school…

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

Modifiable pathways in Alzheimer's disease: Mendelian randomisation analysis.

PubMed

Larsson, Susanna C; Traylor, Matthew; Malik, Rainer; Dichgans, Martin; Burgess, Stephen; Markus, Hugh S

2017-12-06

To determine which potentially modifiable risk factors, including socioeconomic, lifestyle/dietary, cardiometabolic, and inflammatory factors, are associated with Alzheimer's disease. Mendelian randomisation study using genetic variants associated with the modifiable risk factors as instrumental variables. International Genomics of Alzheimer's Project. 17 008 cases of Alzheimer's disease and 37 154 controls. Odds ratio of Alzheimer's per genetically predicted increase in each modifiable risk factor estimated with Mendelian randomisation analysis. This study included analyses of 24 potentially modifiable risk factors. A Bonferroni corrected threshold of P=0.002 was considered to be significant, and P<0.05 was considered suggestive of evidence for a potential association. Genetically predicted educational attainment was significantly associated with Alzheimer's. The odds ratios were 0.89 (95% confidence interval 0.84 to 0.93; P=2.4×10 -6 ) per year of education completed and 0.74 (0.63 to 0.86; P=8.0×10 -5 ) per unit increase in log odds of having completed college/university. The correlated trait intelligence had a suggestive association with Alzheimer's (per genetically predicted 1 SD higher intelligence: 0.73, 0.57 to 0.93; P=0.01). There was suggestive evidence for potential associations between genetically predicted higher quantity of smoking (per 10 cigarettes a day: 0.69, 0.49 to 0.99; P=0.04) and 25-hydroxyvitamin D concentrations (per 20% higher levels: 0.92, 0.85 to 0.98; P=0.01) and lower odds of Alzheimer's and between higher coffee consumption (per one cup a day: 1.26, 1.05 to 1.51; P=0.01) and higher odds of Alzheimer's. Genetically predicted alcohol consumption, serum folate, serum vitamin B 12 , homocysteine, cardiometabolic factors, and C reactive protein were not associated with Alzheimer's disease. These results provide support that higher educational attainment is associated with a reduced risk of Alzheimer's disease. 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.

A comparative study of scramjet injection strategies for high Mach numbers flows

NASA Technical Reports Server (NTRS)

Riggins, D. W.; Mcclinton, C. R.; Rogers, R. C.; Bittner, R. D.

1992-01-01

A simple method for predicting the axial distribution of supersonic combustor thrust potential is described. A complementary technique for illustrating the spatial evolution and distribution of thrust potential and loss mechanisms in reacting flows is developed. Wall jet cases and swept ramp injector cases for Mach 17 and Mach 13.5 flight enthalpy inflow conditions are numerically modeled and analyzed using these techniques. The visualization of thrust potential in the combustor for the various cases examined provides a unique tool for increasing understanding of supersonic combustor performance potential.

Report on the sixth blind test of organic crystal structure prediction methods

PubMed Central

Reilly, Anthony M.; Cooper, Richard I.; Adjiman, Claire S.; Bhattacharya, Saswata; Boese, A. Daniel; Brandenburg, Jan Gerit; Bygrave, Peter J.; Bylsma, Rita; Campbell, Josh E.; Car, Roberto; Case, David H.; Chadha, Renu; Cole, Jason C.; Cosburn, Katherine; Cuppen, Herma M.; Curtis, Farren; Day, Graeme M.; DiStasio Jr, Robert A.; Dzyabchenko, Alexander; van Eijck, Bouke P.; Elking, Dennis M.; van den Ende, Joost A.; Facelli, Julio C.; Ferraro, Marta B.; Fusti-Molnar, Laszlo; Gatsiou, Christina-Anna; Gee, Thomas S.; de Gelder, René; Ghiringhelli, Luca M.; Goto, Hitoshi; Grimme, Stefan; Guo, Rui; Hofmann, Detlef W. M.; Hoja, Johannes; Hylton, Rebecca K.; Iuzzolino, Luca; Jankiewicz, Wojciech; de Jong, Daniël T.; Kendrick, John; de Klerk, Niek J. J.; Ko, Hsin-Yu; Kuleshova, Liudmila N.; Li, Xiayue; Lohani, Sanjaya; Leusen, Frank J. J.; Lund, Albert M.; Lv, Jian; Ma, Yanming; Marom, Noa; Masunov, Artëm E.; McCabe, Patrick; McMahon, David P.; Meekes, Hugo; Metz, Michael P.; Misquitta, Alston J.; Mohamed, Sharmarke; Monserrat, Bartomeu; Needs, Richard J.; Neumann, Marcus A.; Nyman, Jonas; Obata, Shigeaki; Oberhofer, Harald; Oganov, Artem R.; Orendt, Anita M.; Pagola, Gabriel I.; Pantelides, Constantinos C.; Pickard, Chris J.; Podeszwa, Rafal; Price, Louise S.; Price, Sarah L.; Pulido, Angeles; Read, Murray G.; Reuter, Karsten; Schneider, Elia; Schober, Christoph; Shields, Gregory P.; Singh, Pawanpreet; Sugden, Isaac J.; Szalewicz, Krzysztof; Taylor, Christopher R.; Tkatchenko, Alexandre; Tuckerman, Mark E.; Vacarro, Francesca; Vasileiadis, Manolis; Vazquez-Mayagoitia, Alvaro; Vogt, Leslie; Wang, Yanchao; Watson, Rona E.; de Wijs, Gilles A.; Yang, Jack; Zhu, Qiang; Groom, Colin R.

2016-01-01

The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and ‘best practices’ for performing CSP calculations. All of the targets, apart from a single potentially disordered Z′ = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms. PMID:27484368

Neural Reactivity to Angry Faces Predicts Treatment Response in Pediatric Anxiety

PubMed Central

Kujawa, Autumn; Fitzgerald, Kate D.; Swain, James E.; Hanna, Gregory L.; Koschmann, Elizabeth; Simpson, David; Connolly, Sucheta; Monk, Christopher S.; Phan, K. Luan

2018-01-01

Although cognitive-behavioral psychotherapy (CBT) and pharmacotherapy are evidence-based treatments for pediatric anxiety, many youth with anxiety disorders fail to respond to these treatments. Given limitations of clinical measures in predicting treatment response, identifying neural predictors is timely. In this study, 35 anxious youth (ages 7–19 years) completed an emotional face-matching task during which the late positive potential (LPP), an event-related potential (ERP) component that indexes sustained attention towards emotional stimuli, was measured. Following the ERP measurement, youth received CBT or selective serotonin reuptake inhibitor (SSRI) treatment, and the LPP was examined as a predictor of treatment response. Findings indicated that, accounting for pre-treatment anxiety severity, neural reactivity to emotional faces predicted anxiety severity post-CBT and SSRI treatment such that enhanced electrocortical response to angry faces was associated with better treatment response. An enhanced LPP to angry faces may predict treatment response insofar as it may reflect greater emotion dysregulation or less avoidance and/or enhanced engagement with environmental stimuli in general, including with treatment. PMID:27255517

Predicting meat quality traits of ovine m. semimembranosus, both fresh and following freezing and thawing, using a hand held Raman spectroscopic device.

PubMed

Fowler, Stephanie M; Schmidt, Heinar; van de Ven, Remy; Wynn, Peter; Hopkins, David L

2015-10-01

Complementary studies were conducted to determine the potential for a Raman spectroscopic hand held device to predict meat quality traits of fresh lamb m. semimembranosus (topside) after ageing and freezing/thawing. Spectra were collected from 80 fresh muscles at 24h and 5d PM, another 80 muscles were measured at 24h, 5d and following freezing/thawing. Shear force, cooking loss, sarcomere length, colour, particle size, collagen content, pH24, pHu, purge and thaw loss were also measured. Results indicated a potential to predict pHu (R(2)cv=0.59), pH24 (R(2)cv=0.48) and purge (R(2)cv=0.42) using spectra collected 24h PM. L* could be predicted using spectra collected 24h (R(2)cv=0.33) or 5d PM (R(2)cv=0.33). This suggests that Raman spectroscopy is suited to identifying carcases which deviate from the normal metabolic processes and related meat quality traits. Copyright © 2015. Published by Elsevier Ltd.

PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory.

PubMed

Xue, Yu; Li, Ao; Wang, Lirong; Feng, Huanqing; Yao, Xuebiao

2006-03-20

As a reversible and dynamic post-translational modification (PTM) of proteins, phosphorylation plays essential regulatory roles in a broad spectrum of the biological processes. Although many studies have been contributed on the molecular mechanism of phosphorylation dynamics, the intrinsic feature of substrates specificity is still elusive and remains to be delineated. In this work, we present a novel, versatile and comprehensive program, PPSP (Prediction of PK-specific Phosphorylation site), deployed with approach of Bayesian decision theory (BDT). PPSP could predict the potential phosphorylation sites accurately for approximately 70 PK (Protein Kinase) groups. Compared with four existing tools Scansite, NetPhosK, KinasePhos and GPS, PPSP is more accurate and powerful than these tools. Moreover, PPSP also provides the prediction for many novel PKs, say, TRK, mTOR, SyK and MET/RON, etc. The accuracy of these novel PKs are also satisfying. Taken together, we propose that PPSP could be a potentially powerful tool for the experimentalists who are focusing on phosphorylation substrates with their PK-specific sites identification. Moreover, the BDT strategy could also be a ubiquitous approach for PTMs, such as sumoylation and ubiquitination, etc.

A systematic study of chemogenomics of carbohydrates.

PubMed

Gu, Jiangyong; Luo, Fang; Chen, Lirong; Yuan, Gu; Xu, Xiaojie

2014-03-04

Chemogenomics focuses on the interactions between biologically active molecules and protein targets for drug discovery. Carbohydrates are the most abundant compounds in natural products. Compared with other drugs, the carbohydrate drugs show weaker side effects. Searching for multi-target carbohydrate drugs can be regarded as a solution to improve therapeutic efficacy and safety. In this work, we collected 60 344 carbohydrates from the Universal Natural Products Database (UNPD) and explored the chemical space of carbohydrates by principal component analysis. We found that there is a large quantity of potential lead compounds among carbohydrates. Then we explored the potential of carbohydrates in drug discovery by using a network-based multi-target computational approach. All carbohydrates were docked to 2389 target proteins. The most potential carbohydrates for drug discovery and their indications were predicted based on a docking score-weighted prediction model. We also explored the interactions between carbohydrates and target proteins to find the pathological networks, potential drug candidates and new indications.

Universality of multi-field α-attractors

NASA Astrophysics Data System (ADS)

Achúcarro, Ana; Kallosh, Renata; Linde, Andrei; Wang, Dong-Gang; Welling, Yvette

2018-04-01

We study a particular version of the theory of cosmological α-attractors with α=1/3, in which both the dilaton (inflaton) field and the axion field are light during inflation. The kinetic terms in this theory originate from maximal Script N=4 superconformal symmetry and from maximal Script N=8 supergravity. We show that because of the underlying hyperbolic geometry of the moduli space in this theory, it exhibits double attractor behavior: their cosmological predictions are stable not only with respect to significant modifications of the dilaton potential, but also with respect to significant modifications of the axion potential: nssimeq1‑2/N, rsimeq4/N2. We also show that the universality of predictions extends to other values of α lesssim Script O(1) with general two-field potentials that may or may not have an embedding in supergravity. Our results support the idea that inflation involving multiple, not stabilized, light fields on a hyperbolic manifold may be compatible with current observational constraints for a broad class of potentials.

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.

Prediction of the P-leaching potential of arable soils in areas with high livestock densities*

PubMed Central

Werner, Wilfried; Trimborn, Manfred; Pihl, Uwe

2006-01-01

Due to long-term positive P-balances many surface soils in areas with high livestock density in Germany are oversupplied with available P, creating a potential for vertical P losses by leaching. In extensive studies to characterize the endangering of ground water to P pollution by chemical soil parameters it is shown that the available P content and the P concentration of the soil solution in the deeper soil layers, as indicators of the P-leaching potential, cannot be satisfactorily predicted from the available P content of the topsoils. The P equilibrium concentration in the soil solution directly above ground water table or the pipe drainage system highly depends on the relative saturation of the P-sorption capacity in this layer. A saturation index of <20% normally corresponds with P equilibrium concentrations of <0.2 mg P/L. Phytoremediation may reduce the P leaching potential of P-enriched soils only over a very long period. PMID:16773724

Dysfunctional attitudes and poor problem solving skills predict hopelessness in major depression.

PubMed

Cannon, B; Mulroy, R; Otto, M W; Rosenbaum, J F; Fava, M; Nierenberg, A A

1999-09-01

Hopelessness is a significant predictor of suicidality, but not all depressed patients feel hopeless. If clinicians can predict hopelessness, they may be able to identify those patients at risk of suicide and focus interventions on factors associated with hopelessness. In this study, we examined potential predictors of hopelessness in a sample of depressed outpatients. In this study, we examined potential demographic, diagnostic, and symptom predictors of hopelessness in a sample of 138 medication-free outpatients (73 women and 65 men) with a primary diagnosis of major depression. The significance of predictors was evaluated in both simple and multiple regression analyses. Consistent with previous studies, we found no significant associations between demographic and diagnostic variables and greater hopelessness. Hopelessness was significantly associated with greater depression severity, poor problem solving abilities as assessed by the Problem Solving Inventory, and each of two measures of dysfunctional cognitions (the Dysfunctional Attitudes Scale and the Cognitions Questionnaire). In a stepwise multiple regression equation, however, only dysfunctional cognitions and poor problem solving offered non-redundant prediction of hopelessness scores, and accounted for 20% of the variance in these scores. This study is based on depressed patients entering into an outpatient treatment protocol. All analyses were correlational in nature, and no causal links can be concluded. Our findings, identifying clinical correlates of hopelessness, provide clinicians with potential additional targets for assessment and treatment of suicidal risk. In particular, clinical attention to dysfunctional attitudes and problem solving skills may be important for further reduction of hopelessness and perhaps suicidal risk.

Dynamic dual-energy chest radiography: a potential tool for lung tissue motion monitoring and kinetic study

PubMed Central

Xu, Tong; Ducote, Justin L.; Wong, Jerry T.; Molloi, Sabee

2011-01-01

Dual-energy chest radiography has the potential to provide better diagnosis of lung disease by removing the bone signal from the image. Dynamic dual-energy radiography is now possible with the introduction of digital flat panel detectors. The purpose of this study is to evaluate the feasibility of using dynamic dual-energy chest radiography for functional lung imaging and tumor motion assessment. The dual energy system used in this study can acquire up to 15 frame of dual-energy images per second. A swine animal model was mechanically ventilated and imaged using the dual-energy system. Sequences of soft-tissue images were obtained using dual-energy subtraction. Time subtracted soft-tissue images were shown to be able to provide information on regional ventilation. Motion tracking of a lung anatomic feature (a branch of pulmonary artery) was performed based on an image cross-correlation algorithm. The tracking precision was found to be better than 1 mm. An adaptive correlation model was established between the above tracked motion and an external surrogate signal (temperature within the tracheal tube). This model is used to predict lung feature motion using the continuous surrogate signal and low frame rate dual-energy images (0.1 to 3.0 frames /sec). The average RMS error of the prediction was (1.1 ± 0.3) mm. The dynamic dual-energy was shown to be potentially useful for lung functional imaging such as regional ventilation and kinetic studies. It can also be used for lung tumor motion assessment and prediction during radiation therapy. PMID:21285477