Identifying predictive factors for long-term complications following button battery impactions: A case series and literature review.

PubMed

Eliason, Michael J; Melzer, Jonathan M; Winters, Jessica R; Gallagher, Thomas Q

2016-08-01

To complement a case series review of button battery impactions managed at our single military tertiary care center with a thorough literature review of laboratory research and clinical cases to develop a protocol to optimize patient care. Specifically, to identify predictive factors of long-term complications which can be used by the pediatric otolaryngologist to guide patient management after button battery impactions. A retrospective review of the Department of Defense's electronic medical record systems was conducted to identify patients with button battery ingestions and then characterize their treatment course. A thorough literature review complemented the lessons learned to identify potentially predictive clinical measures for long-term complications. Eight patients were identified as being treated for button battery impaction in the aerodigestive tract with two sustaining long-term complications. The median age of the patients treated was 33 months old and the median estimated time of impaction in the aerodigestive tract prior to removal was 10.5 h. Time of impaction, anatomic direction of the battery's negative pole, and identifying specific battery parameters were identified as factors that may be employed to predict sequelae. Based on case reviews, advancements in battery manufacturing, and laboratory research, there are distinct clinical factors that should be assessed at the time of initial therapy to guide follow-up management to minimize potential catastrophic sequelae of button battery ingestion. Published by Elsevier Ireland Ltd.

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.

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

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

Alves, Vinicius M.; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599; Muratov, Eugene

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putativemore » sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation. - Highlights: • It was compiled the largest publicly-available skin sensitization dataset. • Predictive QSAR models were developed for skin sensitization. • Developed models have higher prediction accuracy than OECD QSAR Toolbox. �

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

PubMed Central

Alves, Vinicius M.; Muratov, Eugene; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H.; Tropsha, Alexander

2015-01-01

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using random forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers were 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the ScoreCard database of possible skin or sense organ toxicants as primary candidates for experimental validation. PMID:25560674

Spontaneous swallowing frequency has potential to identify dysphagia in acute stroke.

PubMed

Crary, Michael A; Carnaby, Giselle D; Sia, Isaac; Khanna, Anna; Waters, Michael F

2013-12-01

Spontaneous swallowing frequency has been described as an index of dysphagia in various health conditions. This study evaluated the potential of spontaneous swallow frequency analysis as a screening protocol for dysphagia in acute stroke. In a cohort of 63 acute stroke cases, swallow frequency rates (swallows per minute [SPM]) were compared with stroke and swallow severity indices, age, time from stroke to assessment, and consciousness level. Mean differences in SPM were compared between patients with versus without clinically significant dysphagia. Receiver operating characteristic curve analysis was used to identify the optimal threshold in SPM, which was compared with a validated clinical dysphagia examination for identification of dysphagia cases. Time series analysis was used to identify the minimally adequate time period to complete spontaneous swallow frequency analysis. SPM correlated significantly with stroke and swallow severity indices but not with age, time from stroke onset, or consciousness level. Patients with dysphagia demonstrated significantly lower SPM rates. SPM differed by dysphagia severity. Receiver operating characteristic curve analysis yielded a threshold of SPM≤0.40 that identified dysphagia (per the criterion referent) with 0.96 sensitivity, 0.68 specificity, and 0.96 negative predictive value. Time series analysis indicated that a 5- to 10-minute sampling window was sufficient to calculate spontaneous swallow frequency to identify dysphagia cases in acute stroke. Spontaneous swallowing frequency presents high potential to screen for dysphagia in acute stroke without the need for trained, available personnel.

Spontaneous Swallowing Frequency [Has Potential to] Identify Dysphagia in Acute Stroke

PubMed Central

Carnaby, Giselle D; Sia, Isaac; Khanna, Anna; Waters, Michael

2014-01-01

Background and Purpose Spontaneous swallowing frequency has been described as an index of dysphagia in various health conditions. This study evaluated the potential of spontaneous swallow frequency analysis as a screening protocol for dysphagia in acute stroke. Methods In a cohort of 63 acute stroke cases swallow frequency rates (swallows per minute: SPM) were compared to stroke and swallow severity indices, age, time from stroke to assessment, and consciousness level. Mean differences in SPM were compared between patients with vs. without clinically significant dysphagia. ROC analysis was used to identify the optimal threshold in SPM which was compared to a validated clinical dysphagia examination for identification of dysphagia cases. Time series analysis was employed to identify the minimally adequate time period to complete spontaneous swallow frequency analysis. Results SPM correlated significantly with stroke and swallow severity indices but not with age, time from stroke onset, or consciousness level. Patients with dysphagia demonstrated significantly lower SPM rates. SPM differed by dysphagia severity. ROC analysis yielded a threshold of SPM ≤ 0.40 which identified dysphagia (per the criterion referent) with 0.96 sensitivity, 0.68 specificity, and 0.96 negative predictive value. Time series analysis indicated that a 5 to 10 minute sampling window was sufficient to calculate spontaneous swallow frequency to identify dysphagia cases in acute stroke. Conclusions Spontaneous swallowing frequency presents high potential to screen for dysphagia in acute stroke without the need for trained, available personnel. PMID:24149008

Seasonal Extratropical Storm Activity Potential Predictability and its Origins during the Cold Seasons

NASA Astrophysics Data System (ADS)

Pingree-Shippee, K. A.; Zwiers, F. W.; Atkinson, D. E.

2016-12-01

Extratropical cyclones (ETCs) often produce extreme hazardous weather conditions, such as high winds, blizzard conditions, heavy precipitation, and flooding, all of which can have detrimental socio-economic impacts. The North American east and west coastal regions are both strongly influenced by ETCs and, subsequently, land-based, coastal, and maritime economic sectors in Canada and the USA all experience strong adverse impacts from extratropical storm activity from time to time. Society would benefit if risks associated with ETCs and storm activity variability could be reliably predicted for the upcoming season. Skillful prediction would enable affected sectors to better anticipate, prepare for, manage, and respond to storm activity variability and the associated risks and impacts. In this study, the potential predictability of seasonal variations in extratropical storm activity is investigated using analysis of variance to provide quantitative and geographical observational evidence indicative of whether it may be possible to predict storm activity on the seasonal timescale. This investigation will also identify origins of the potential predictability using composite analysis and large-scale teleconnections (Southern Oscillation, Pacific Decadal Oscillation, and North Atlantic Oscillation), providing the basis upon which seasonal predictions can be developed. Seasonal potential predictability and its origins are investigated for the cold seasons (OND, NDJ, DJF, JFM) during the 1979-2015 time period using daily mean sea level pressure, absolute pressure tendency, and 10-m wind speed from the ECMWF ERA-Interim reanalysis as proxies for extratropical storm activity. Results indicate potential predictability of seasonal variations in storm activity in areas strongly influenced by ETCs and with origins in the investigated teleconnections. For instance, the North Pacific storm track has considerable potential predictability and with notable origins in the SO and PDO.

Rapid, Potentially Automatable, Method Extract Biomarkers for HPLC/ESI/MS/MS to Detect and Identify BW Agents

DTIC Science & Technology

1997-11-01

status can sometimes be reflected in the infectious potential or drug resistance of those pathogens. For example, in Mycobacterium tuberculosis ... Mycobacterium tuberculosis , its antibiotic resistance and prediction of pathogenicity amongst Mycobacterium spp. based on signature lipid biomarkers ...TITLE AND SUBTITLE Rapid, Potentially Automatable, Method Extract Biomarkers for HPLC/ESI/MS/MS to Detect and Identify BW Agents 5a. CONTRACT NUMBER 5b

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

Predicting Fish Growth Potential and Identifying Water Quality Constraints: A Spatially-Explicit Bioenergetics Approach

NASA Astrophysics Data System (ADS)

Budy, Phaedra; Baker, Matthew; Dahle, Samuel K.

2011-10-01

Anthropogenic impairment of water bodies represents a global environmental concern, yet few attempts have successfully linked fish performance to thermal habitat suitability and fewer have distinguished co-varying water quality constraints. We interfaced fish bioenergetics, field measurements, and Thermal Remote Imaging to generate a spatially-explicit, high-resolution surface of fish growth potential, and next employed a structured hypothesis to detect relationships among measures of fish performance and co-varying water quality constraints. Our thermal surface of fish performance captured the amount and spatial-temporal arrangement of thermally-suitable habitat for three focal species in an extremely heterogeneous reservoir, but interpretation of this pattern was initially confounded by seasonal covariation of water residence time and water quality. Subsequent path analysis revealed that in terms of seasonal patterns in growth potential, catfish and walleye responded to temperature, positively and negatively, respectively; crappie and walleye responded to eutrophy (negatively). At the high eutrophy levels observed in this system, some desired fishes appear to suffer from excessive cultural eutrophication within the context of elevated temperatures whereas others appear to be largely unaffected or even enhanced. Our overall findings do not lead to the conclusion that this system is degraded by pollution; however, they do highlight the need to use a sensitive focal species in the process of determining allowable nutrient loading and as integrators of habitat suitability across multiple spatial and temporal scales. We provide an integrated approach useful for quantifying fish growth potential and identifying water quality constraints on fish performance at spatial scales appropriate for whole-system management.

A new simplex chemometric approach to identify olive oil blends with potentially high traceability.

PubMed

Semmar, N; Laroussi-Mezghani, S; Grati-Kamoun, N; Hammami, M; Artaud, J

2016-10-01

Olive oil blends (OOBs) are complex matrices combining different cultivars at variable proportions. Although qualitative determinations of OOBs have been subjected to several chemometric works, quantitative evaluations of their contents remain poorly developed because of traceability difficulties concerning co-occurring cultivars. Around this question, we recently published an original simplex approach helping to develop predictive models of the proportions of co-occurring cultivars from chemical profiles of resulting blends (Semmar & Artaud, 2015). Beyond predictive model construction and validation, this paper presents an extension based on prediction errors' analysis to statistically define the blends with the highest predictability among all the possible ones that can be made by mixing cultivars at different proportions. This provides an interesting way to identify a priori labeled commercial products with potentially high traceability taking into account the natural chemical variability of different constitutive cultivars. Copyright © 2016 Elsevier Ltd. All rights reserved.

Sparse Bayesian Learning for Identifying Imaging Biomarkers in AD Prediction

PubMed Central

Shen, Li; Qi, Yuan; Kim, Sungeun; Nho, Kwangsik; Wan, Jing; Risacher, Shannon L.; Saykin, Andrew J.

2010-01-01

We apply sparse Bayesian learning methods, automatic relevance determination (ARD) and predictive ARD (PARD), to Alzheimer’s disease (AD) classification to make accurate prediction and identify critical imaging markers relevant to AD at the same time. ARD is one of the most successful Bayesian feature selection methods. PARD is a powerful Bayesian feature selection method, and provides sparse models that is easy to interpret. PARD selects the model with the best estimate of the predictive performance instead of choosing the one with the largest marginal model likelihood. Comparative study with support vector machine (SVM) shows that ARD/PARD in general outperform SVM in terms of prediction accuracy. Additional comparison with surface-based general linear model (GLM) analysis shows that regions with strongest signals are identified by both GLM and ARD/PARD. While GLM P-map returns significant regions all over the cortex, ARD/PARD provide a small number of relevant and meaningful imaging markers with predictive power, including both cortical and subcortical measures. PMID:20879451

Methods of identifying potential vanpool riders.

DOT National Transportation Integrated Search

1977-01-01

Identifying potential vanpool riders and matching them to form pools are fundamental tasks in the initiation of a vanpool program. The manner in which these tasks are done will determine the costs and benefits of the program. This report presents the...

Identify the dominant variables to predict stream water temperature

NASA Astrophysics Data System (ADS)

Chien, H.; Flagler, J.

2016-12-01

Stream water temperature is a critical variable controlling water quality and the health of aquatic ecosystems. Accurate prediction of water temperature and the assessment of the impacts of environmental variables on water temperature variation are critical for water resources management, particularly in the context of water quality and aquatic ecosystem sustainability. The objective of this study is to measure stream water temperature and air temperature and to examine the importance of streamflow on stream water temperature prediction. The measured stream water temperature and air temperature will be used to test two hypotheses: 1) streamflow is a relatively more important factor than air temperature in regulating water temperature, and 2) by combining air temperature and streamflow data stream water temperature can be more accurately estimated. Water and air temperature data loggers are placed at two USGS stream gauge stations #01362357and #01362370, located in the upper Esopus Creek watershed in Phonecia, NY. The ARIMA (autoregressive integrated moving average) time series model is used to analyze the measured water temperature data, identify the dominant environmental variables, and predict the water temperature with identified dominant variable. The preliminary results show that streamflow is not a significant variable in predicting stream water temperature at both USGS gauge stations. Daily mean air temperature is sufficient to predict stream water temperature at this site scale.

Automated identification and predictive tools to help identify high-risk heart failure patients: pilot evaluation.

PubMed

Evans, R Scott; Benuzillo, Jose; Horne, Benjamin D; Lloyd, James F; Bradshaw, Alejandra; Budge, Deborah; Rasmusson, Kismet D; Roberts, Colleen; Buckway, Jason; Geer, Norma; Garrett, Teresa; Lappé, Donald L

2016-09-01

Develop and evaluate an automated identification and predictive risk report for hospitalized heart failure (HF) patients. Dictated free-text reports from the previous 24 h were analyzed each day with natural language processing (NLP), to help improve the early identification of hospitalized patients with HF. A second application that uses an Intermountain Healthcare-developed predictive score to determine each HF patient's risk for 30-day hospital readmission and 30-day mortality was also developed. That information was included in an identification and predictive risk report, which was evaluated at a 354-bed hospital that treats high-risk HF patients. The addition of NLP-identified HF patients increased the identification score's sensitivity from 82.6% to 95.3% and its specificity from 82.7% to 97.5%, and the model's positive predictive value is 97.45%. Daily multidisciplinary discharge planning meetings are now based on the information provided by the HF identification and predictive report, and clinician's review of potential HF admissions takes less time compared to the previously used manual methodology (10 vs 40 min). An evaluation of the use of the HF predictive report identified a significant reduction in 30-day mortality and a significant increase in patient discharges to home care instead of to a specialized nursing facility. Using clinical decision support to help identify HF patients and automatically calculating their 30-day all-cause readmission and 30-day mortality risks, coupled with a multidisciplinary care process pathway, was found to be an effective process to improve HF patient identification, significantly reduce 30-day mortality, and significantly increase patient discharges to home care. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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.

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.

Sequence-based predictive modeling to identify cancerlectins

PubMed Central

Lai, Hong-Yan; Chen, Xin-Xin; Chen, Wei; Tang, Hua; Lin, Hao

2017-01-01

Lectins are a diverse type of glycoproteins or carbohydrate-binding proteins that have a wide distribution to various species. They can specially identify and exclusively bind to a certain kind of saccharide groups. Cancerlectins are a group of lectins that are closely related to cancer and play a major role in the initiation, survival, growth, metastasis and spread of tumor. Several computational methods have emerged to discriminate cancerlectins from non-cancerlectins, which promote the study on pathogenic mechanisms and clinical treatment of cancer. However, the predictive accuracies of most of these techniques are very limited. In this work, by constructing a benchmark dataset based on the CancerLectinDB database, a new amino acid sequence-based strategy for feature description was developed, and then the binomial distribution was applied to screen the optimal feature set. Ultimately, an SVM-based predictor was performed to distinguish cancerlectins from non-cancerlectins, and achieved an accuracy of 77.48% with AUC of 85.52% in jackknife cross-validation. The results revealed that our prediction model could perform better comparing with published predictive tools. PMID:28423655

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.

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

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.

Maximal Predictability Approach for Identifying the Right Descriptors for Electrocatalytic Reactions.

PubMed

Krishnamurthy, Dilip; Sumaria, Vaidish; Viswanathan, Venkatasubramanian

2018-02-01

Density functional theory (DFT) calculations are being routinely used to identify new material candidates that approach activity near fundamental limits imposed by thermodynamics or scaling relations. DFT calculations are associated with inherent uncertainty, which limits the ability to delineate materials (distinguishability) that possess high activity. Development of error-estimation capabilities in DFT has enabled uncertainty propagation through activity-prediction models. In this work, we demonstrate an approach to propagating uncertainty through thermodynamic activity models leading to a probability distribution of the computed activity and thereby its expectation value. A new metric, prediction efficiency, is defined, which provides a quantitative measure of the ability to distinguish activity of materials and can be used to identify the optimal descriptor(s) ΔG opt . We demonstrate the framework for four important electrochemical reactions: hydrogen evolution, chlorine evolution, oxygen reduction and oxygen evolution. Future studies could utilize expected activity and prediction efficiency to significantly improve the prediction accuracy of highly active material candidates.

Identifying and describing feelings and psychological flexibility predict mental health in men with HIV.

PubMed

Landstra, Jodie M B; Ciarrochi, Joseph; Deane, Frank P; Hillman, Richard J

2013-11-01

Difficulty identifying and describing feelings (DIDF) and psychological flexibility (PF) predict poor emotional adjustment. To examine the relationship between DIDF and PF and whether DIDF and low PF would put men undergoing cancer screening at risk for poor adjustment. Longitudinal self-report survey. Two hundred and one HIV-infected men who have sex with men participated in anal cancer screening at two time points over 14 weeks. Psychological flexibility was assessed by the Acceptance and Action Questionnaire II and DIDF by the Toronto Alexithymia Scale-20. We also measured depression, anxiety, stress (DASS) and health-related quality of life (QOL; SF-12). Both DIDF and PF were reliable predictors of mental health. When levels of baseline mental health were controlled, greater DIDF predicted increases in Time 2 depression, anxiety and stress and decreases in mental and physical QOL. The link between PF and mental health was entirely mediated by DIDF. Being chronically low in PF could lead to greater DIDF and thereby worse mental health. Having more PF promotes the ability to identify and differentiate the nuances of pleasant and unpleasant emotions, which enhances an individual's mental health. Intentionally enhancing men's ability to identify and describe feelings or PF may assist them to better manage a range of difficult life experiences such as health screenings and other potentially threatening information. © 2013 The British Psychological Society.

Evaluation of in silico tools to predict the skin sensitization potential of chemicals.

PubMed

Verheyen, G R; Braeken, E; Van Deun, K; Van Miert, S

2017-01-01

Public domain and commercial in silico tools were compared for their performance in predicting the skin sensitization potential of chemicals. The packages were either statistical based (Vega, CASE Ultra) or rule based (OECD Toolbox, Toxtree, Derek Nexus). In practice, several of these in silico tools are used in gap filling and read-across, but here their use was limited to make predictions based on presence/absence of structural features associated to sensitization. The top 400 ranking substances of the ATSDR 2011 Priority List of Hazardous Substances were selected as a starting point. Experimental information was identified for 160 chemically diverse substances (82 positive and 78 negative). The prediction for skin sensitization potential was compared with the experimental data. Rule-based tools perform slightly better, with accuracies ranging from 0.6 (OECD Toolbox) to 0.78 (Derek Nexus), compared with statistical tools that had accuracies ranging from 0.48 (Vega) to 0.73 (CASE Ultra - LLNA weak model). Combining models increased the performance, with positive and negative predictive values up to 80% and 84%, respectively. However, the number of substances that were predicted positive or negative for skin sensitization in both models was low. Adding more substances to the dataset will increase the confidence in the conclusions reached. The insights obtained in this evaluation are incorporated in a web database www.asopus.weebly.com that provides a potential end user context for the scope and performance of different in silico tools with respect to a common dataset of curated skin sensitization data.

Increasing potential predictability of Indian Summer monsoon active and break spells

NASA Astrophysics Data System (ADS)

Mani, N. J.; Goswami, B.

2009-12-01

An understanding of the limit on potential predictability is crucial for developing appropriate tools for extended range prediction of active/break spells of Indian summer monsoon (ISM). The global low frequency changes in climate modulate the annual cycle of the ISM and can influence the intrinsic predictability limit of the ISM intraseasonal oscillations (ISOs). Using 104 year (1901-2004) long daily rainfall data, the change in potential predictability of active and break spells are estimated by an empirical method. Using an ISO index based on 10-90 day filtered precipitation, Goswami and Xavier (2003)showed that the monsoon breaks are intrinsically more predictable (20-25 days) than the active conditions (10-15 days. In the present study, employing the same method in 15 year sliding windows, we found that the potential predictability of both active and break spells have undergone a rapid increase during the recent three decades. The potential predictability of active spells has shown an increase from 1 week to 2 weeks while that for break spells increased from 2 weeks to 3 weeks. This result is interesting and intriguing in the backdrop of recent finding that the potential predictability of monsoon weather has decreased substantially over the same period compared to earlier decades due to increased potential instability of the atmosphere. The possible role of internal dynamics and external forcing in producing this change has been explored. The variance among peak active/break conditions shows a steady decrease over the years, indicating a lesser event to event variability in the magnitude of ISO peak phases in recent years. The ISO predictability may be closely linked to the error energy cascading from the synoptic scales and the interaction between these scales. Computation of nonlinear kinetic energy exchange between synoptic and ISO scales in frequency domain, also support the notion of ineffectual influence of synoptic scale errors on the ISO scale

Potential predictability of Northern America surface temperature in AGCMs and CGCMs

NASA Astrophysics Data System (ADS)

Tang, Youmin; Chen, Dake; Yan, Xiaoqin

2015-07-01

In this study, the potential predictability of the Northern America (NA) surface air temperature (SAT) was explored using an information-based predictability framework and two multiple model ensemble products: a one-tier prediction by coupled models (T1), and a two-tier prediction by atmospheric models only (T2). Furthermore, the potential predictability was optimally decomposed into different modes for both T1 and T2, by extracting the most predictable structures. Emphasis was placed on the comparison of the predictability between T1 and T2. It was found that the potential predictability of the NA SAT is seasonal and spatially dependent in both T1 and T2. Higher predictability occurs in spring and winter and over the southeastern US and northwestern Canada. There is no significant difference of potential predictability between T1 and T2 for most areas of NA, although T1 has higher potential predictability than T2 in the southeastern US. Both T1 and T2 display similar most predictable components (PrCs) for the NA SAT, characterized by the inter-annual variability mode and the long-term trend mode. The first one is inherent to the tropical Pacific sea surface temperature forcing, such as the El Nino-Southern Oscillation, whereas the second one is closely associated with global warming. In general, the PrC modes can better characterize the predictability in T1 than in T2, in particular for the inter-annual variability mode in the fall. The prediction skill against observations is better measured by the PrC analysis than by principal component analysis for all seasons, indicating the stronger capability of PrCA in extracting prediction targets.

Using high frequency consumption data to identify demand response potential for solar energy integration

NASA Astrophysics Data System (ADS)

Jin, L.; Borgeson, S.; Fredman, D.; Hans, L.; Spurlock, A.; Todd, A.

2015-12-01

California's renewable portfolio standard (2012) requires the state to get 33% of its electricity from renewable sources by 2020. Increased share of variable renewable sources such as solar and wind in the California electricity system may require more grid flexibility to insure reliable power services. Such grid flexibility can be potentially provided by changes in end use electricity consumptions in response to grid conditions (demand-response). In the solar case, residential consumption in the late afternoon can be used as reserve capacity to balance the drop in solar generation. This study presents our initial attempt to identify, from a behavior perspective, residential demand response potentials in relation to solar ramp events using a data-driven approach. Based on hourly residential energy consumption data, we derive representative daily load shapes focusing on discretionary consumption with an innovative clustering analysis technique. We aggregate the representative load shapes into behavior groups in terms of the timing and rhythm of energy use in the context of solar ramp events. Households of different behavior groups that are active during hours with high solar ramp rates are identified for capturing demand response potential. Insights into the nature and predictability of response to demand-response programs are provided.

Predicting Recovery Potential for Individual Stroke Patients Increases Rehabilitation Efficiency.

PubMed

Stinear, Cathy M; Byblow, Winston D; Ackerley, Suzanne J; Barber, P Alan; Smith, Marie-Claire

2017-04-01

Several clinical measures and biomarkers are associated with motor recovery after stroke, but none are used to guide rehabilitation for individual patients. The objective of this study was to evaluate the implementation of upper limb predictions in stroke rehabilitation, by combining clinical measures and biomarkers using the Predict Recovery Potential (PREP) algorithm. Predictions were provided for patients in the implementation group (n=110) and withheld from the comparison group (n=82). Predictions guided rehabilitation therapy focus for patients in the implementation group. The effects of predictive information on clinical practice (length of stay, therapist confidence, therapy content, and dose) were evaluated. Clinical outcomes (upper limb function, impairment and use, independence, and quality of life) were measured 3 and 6 months poststroke. The primary clinical practice outcome was inpatient length of stay. The primary clinical outcome was Action Research Arm Test score 3 months poststroke. Length of stay was 1 week shorter for the implementation group (11 days; 95% confidence interval, 9-13 days) than the comparison group (17 days; 95% confidence interval, 14-21 days; P =0.001), controlling for upper limb impairment, age, sex, and comorbidities. Therapists were more confident ( P =0.004) and modified therapy content according to predictions for the implementation group ( P <0.05). The algorithm correctly predicted the primary clinical outcome for 80% of patients in both groups. There were no adverse effects of algorithm implementation on patient outcomes at 3 or 6 months poststroke. PREP algorithm predictions modify therapy content and increase rehabilitation efficiency after stroke without compromising clinical outcome. URL: http://anzctr.org.au. Unique identifier: ACTRN12611000755932. © 2017 American Heart Association, Inc.

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.

Evaluation of the Predictive Validity of Thermography in Identifying Extravasation With Intravenous Chemotherapy Infusions.

PubMed

Matsui, Yuko; Murayama, Ryoko; Tanabe, Hidenori; Oe, Makoto; Motoo, Yoshiharu; Wagatsuma, Takanori; Michibuchi, Michiko; Kinoshita, Sachiko; Sakai, Keiko; Konya, Chizuko; Sugama, Junko; Sanada, Hiromi

Early detection of extravasation is important, but conventional methods of detection lack objectivity and reliability. This study evaluated the predictive validity of thermography for identifying extravasation during intravenous antineoplastic therapy. Of 257 patients who received chemotherapy through peripheral veins, extravasation was identified in 26. Thermography was performed every 15 to 30 minutes during the infusions. Sensitivity, specificity, positive predictive value, and negative predictive value using thermography were 84.6%, 94.8%, 64.7%, and 98.2%, respectively. This study showed that thermography offers an accurate prediction of extravasation.

Evaluation of the Predictive Validity of Thermography in Identifying Extravasation With Intravenous Chemotherapy Infusions

PubMed Central

Murayama, Ryoko; Tanabe, Hidenori; Oe, Makoto; Motoo, Yoshiharu; Wagatsuma, Takanori; Michibuchi, Michiko; Kinoshita, Sachiko; Sakai, Keiko; Konya, Chizuko; Sugama, Junko; Sanada, Hiromi

2017-01-01

Early detection of extravasation is important, but conventional methods of detection lack objectivity and reliability. This study evaluated the predictive validity of thermography for identifying extravasation during intravenous antineoplastic therapy. Of 257 patients who received chemotherapy through peripheral veins, extravasation was identified in 26. Thermography was performed every 15 to 30 minutes during the infusions. Sensitivity, specificity, positive predictive value, and negative predictive value using thermography were 84.6%, 94.8%, 64.7%, and 98.2%, respectively. This study showed that thermography offers an accurate prediction of extravasation. PMID:29112585

Prediction of the in planta Phakopsora pachyrhizi secretome and potential effector families.

PubMed

de Carvalho, Mayra C da C G; Costa Nascimento, Leandro; Darben, Luana M; Polizel-Podanosqui, Adriana M; Lopes-Caitar, Valéria S; Qi, Mingsheng; Rocha, Carolina S; Carazzolle, Marcelo Falsarella; Kuwahara, Márcia K; Pereira, Goncalo A G; Abdelnoor, Ricardo V; Whitham, Steven A; Marcelino-Guimarães, Francismar C

2017-04-01

Asian soybean rust (ASR), caused by the obligate biotrophic fungus Phakopsora pachyrhizi, can cause losses greater than 80%. Despite its economic importance, there is no soybean cultivar with durable ASR resistance. In addition, the P. pachyrhizi genome is not yet available. However, the availability of other rust genomes, as well as the development of sample enrichment strategies and bioinformatics tools, has improved our knowledge of the ASR secretome and its potential effectors. In this context, we used a combination of laser capture microdissection (LCM), RNAseq and a bioinformatics pipeline to identify a total of 36 350 P. pachyrhizi contigs expressed in planta and a predicted secretome of 851 proteins. Some of the predicted secreted proteins had characteristics of candidate effectors: small size, cysteine rich, do not contain PFAM domains (except those associated with pathogenicity) and strongly expressed in planta. A comparative analysis of the predicted secreted proteins present in Pucciniales species identified new members of soybean rust and new Pucciniales- or P. pachyrhizi-specific families (tribes). Members of some families were strongly up-regulated during early infection, starting with initial infection through haustorium formation. Effector candidates selected from two of these families were able to suppress immunity in transient assays, and were localized in the plant cytoplasm and nuclei. These experiments support our bioinformatics predictions and show that these families contain members that have functions consistent with P. pachyrhizi effectors. © 2016 BSPP AND JOHN WILEY & SONS LTD.

Nightmares in the general population: identifying potential causal factors.

PubMed

Rek, Stephanie; Sheaves, Bryony; Freeman, Daniel

2017-09-01

Nightmares are inherently distressing, prevent restorative sleep, and are associated with a number of psychiatric problems, but have rarely been the subject of empirical study. Negative affect, linked to stressful events, is generally considered the key trigger of nightmares; hence nightmares have most often been considered in the context of post-traumatic stress disorder (PTSD). However, many individuals with heightened negative affect do not have nightmares. The objective of this study was to identify mechanistically plausible factors, beyond negative affect, that may explain why individuals experience nightmares. 846 participants from the UK general population completed an online survey about nightmare occurrence and severity (pre-occupation, distress, and impairment), negative affect, worry, depersonalisation, hallucinatory experiences, paranoia, alcohol use, sleep duration, physical activity levels, PTSD symptoms, and stressful life events. Associations of nightmares with the putative predictive factors were tested controlling for levels of negative affect. Analyses were also repeated controlling for levels of PTSD and the recent occurrence of stressful life events. Nightmare occurrence, adjusting for negative affect, was associated with higher levels of worry, depersonalisation, hallucinatory experiences, paranoia, and sleep duration (odds ratios 1.25-1.45). Nightmare severity, controlling for negative affect, was associated with higher levels of worry, depersonalisation, hallucinatory experiences, and paranoia (R 2 s: 0.33-0.39). Alcohol use and physical activity levels were not associated with nightmares. The study identifies a number of potential predictors of the occurrence and severity of nightmares. Causal roles require testing in future longitudinal, experimental, and treatment studies.

Identifying the potential long-term survivors among breast cancer patients with distant metastasis.

PubMed

Lee, E S; Jung, S Y; Kim, J Y; Kim, J J; Yoo, T K; Kim, Y G; Lee, K S; Lee, E S; Kim, E K; Min, J W; Han, W; Noh, D Y; Moon, H G

2016-05-01

We aimed to develop a prediction model to identify long-term survivors after developing distant metastasis from breast cancer. From the institution's database, we collected data of 547 patients who developed distant metastasis during their follow-ups. We developed a model that predicts the post-metastasis overall survival (PMOS) based on the clinicopathologic factors of the primary tumors and the characteristics of the distant metastasis. For validation, the survival data of 254 patients from four independent institutions were used. The median duration of the PMOS was 31.0 months. The characteristics of the initial primary tumor, such as tumor stage, hormone receptor status, and Ki-67 expression level, and the characteristics of the distant metastasis presentation including the duration of disease-free interval, the site of metastasis, and the presence of metastasis-related symptoms were independent prognostic factors determining the PMOS. The association between tumor stage and the PMOS was only seen in tumors with early relapses. The PMOS score, which was developed based on the above six factors, successfully identified patients with superior survival after metastasis. The median PMOS for patients with a PMOS score of <2 and for patients with a PMOS score of >5 were 71.0 and 12 months, respectively. The clinical significance of the PMOS score was further validated using independent multicenter datasets. We have developed a novel prediction model that can classify breast cancer patients with distant metastasis according to their survival after metastasis. Our model can be a valuable tool to identify long-term survivors who can be potential candidates for more intensive multidisciplinary approaches. Furthermore, our model can provide a more reliable survival information for both physicians and patients during their informed decision-making process. © The Author 2016. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All

Prediction of Reduction Potentials of Copper Proteins with Continuum Electrostatics and Density Functional Theory

PubMed Central

Fowler, Nicholas J.; Blanford, Christopher F.

2017-01-01

Abstract Blue copper proteins, such as azurin, show dramatic changes in Cu2+/Cu+ reduction potential upon mutation over the full physiological range. Hence, they have important functions in electron transfer and oxidation chemistry and have applications in industrial biotechnology. The details of what determines these reduction potential changes upon mutation are still unclear. Moreover, it has been difficult to model and predict the reduction potential of azurin mutants and currently no unique procedure or workflow pattern exists. Furthermore, high‐level computational methods can be accurate but are too time consuming for practical use. In this work, a novel approach for calculating reduction potentials of azurin mutants is shown, based on a combination of continuum electrostatics, density functional theory and empirical hydrophobicity factors. Our method accurately reproduces experimental reduction potential changes of 30 mutants with respect to wildtype within experimental error and highlights the factors contributing to the reduction potential change. Finally, reduction potentials are predicted for a series of 124 new mutants that have not yet been investigated experimentally. Several mutants are identified that are located well over 10 Å from the copper center that change the reduction potential by more than 85 mV. The work shows that secondary coordination sphere mutations mostly lead to long‐range electrostatic changes and hence can be modeled accurately with continuum electrostatics. PMID:28815759

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.

Plant water potential improves prediction of empirical stomatal models.

PubMed

Anderegg, William R L; Wolf, Adam; Arango-Velez, Adriana; Choat, Brendan; Chmura, Daniel J; Jansen, Steven; Kolb, Thomas; Li, Shan; Meinzer, Frederick; Pita, Pilar; Resco de Dios, Víctor; Sperry, John S; Wolfe, Brett T; Pacala, Stephen

2017-01-01

Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

HCV prevalence can predict HIV epidemic potential among people who inject drugs: mathematical modeling analysis.

PubMed

Akbarzadeh, Vajiheh; Mumtaz, Ghina R; Awad, Susanne F; Weiss, Helen A; Abu-Raddad, Laith J

2016-12-03

Hepatitis C virus (HCV) and HIV are both transmitted through percutaneous exposures among people who inject drugs (PWID). Ecological analyses on global epidemiological data have identified a positive association between HCV and HIV prevalence among PWID. Our objective was to demonstrate how HCV prevalence can be used to predict HIV epidemic potential among PWID. Two population-level models were constructed to simulate the evolution of HCV and HIV epidemics among PWID. The models described HCV and HIV parenteral transmission, and were solved both deterministically and stochastically. The modeling results provided a good fit to the epidemiological data describing the ecological HCV and HIV association among PWID. HCV was estimated to be eight times more transmissible per shared injection than HIV. A threshold HCV prevalence of 29.0% (95% uncertainty interval (UI): 20.7-39.8) and 46.5% (95% UI: 37.6-56.6) were identified for a sustainable HIV epidemic (HIV prevalence >1%) and concentrated HIV epidemic (HIV prevalence >5%), respectively. The association between HCV and HIV was further described with six dynamical regimes depicting the overlapping epidemiology of the two infections, and was quantified using defined and estimated measures of association. Modeling predictions across a wide range of HCV prevalence indicated overall acceptable precision in predicting HIV prevalence at endemic equilibrium. Modeling predictions were found to be robust with respect to stochasticity and behavioral and biological parameter uncertainty. In an illustrative application of the methodology, the modeling predictions of endemic HIV prevalence in Iran agreed with the scale and time course of the HIV epidemic in this country. Our results show that HCV prevalence can be used as a proxy biomarker of HIV epidemic potential among PWID, and that the scale and evolution of HIV epidemic expansion can be predicted with sufficient precision to inform HIV policy, programming, and resource

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.

Identifying potential academic leaders

PubMed Central

White, David; Krueger, Paul; Meaney, Christopher; Antao, Viola; Kim, Florence; Kwong, Jeffrey C.

2016-01-01

Objective To identify variables associated with willingness to undertake leadership roles among academic family medicine faculty. Design Web-based survey. Bivariate and multivariable analyses (logistic regression) were used to identify variables associated with willingness to undertake leadership roles. Setting Department of Family and Community Medicine at the University of Toronto in Ontario. Participants A total of 687 faculty members. Main outcome measures Variables related to respondents’ willingness to take on various academic leadership roles. Results Of all 1029 faculty members invited to participate in the survey, 687 (66.8%) members responded. Of the respondents, 596 (86.8%) indicated their level of willingness to take on various academic leadership roles. Multivariable analysis revealed that the predictors associated with willingness to take on leadership roles were as follows: pursuit of professional development opportunities (odds ratio [OR] 3.79, 95% CI 2.29 to 6.27); currently holding at least 1 leadership role (OR 5.37, 95% CI 3.38 to 8.53); a history of leadership training (OR 1.86, 95% CI 1.25 to 2.78); the perception that mentorship is important for one’s current role (OR 2.25, 95% CI 1.40 to 3.60); and younger age (OR 0.97, 95% CI 0.95 to 0.99). Conclusion Willingness to undertake new or additional leadership roles was associated with 2 variables related to leadership experiences, 2 variables related to perceptions of mentorship and professional development, and 1 demographic variable (younger age). Interventions that support opportunities in these areas might expand the pool and strengthen the academic leadership potential of faculty members. PMID:27331226

Mechanism-based classification of PAH mixtures to predict carcinogenic potential

DOE PAGES

Tilton, Susan C.; Siddens, Lisbeth K.; Krueger, Sharon K.; ...

2015-04-22

We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[ a]pyrene (BaP). Therefore, we developed a pathway based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[ def,p]chrysene (DBC), BaP or environmental PAH mixtures (Mix 1-3) following a two-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC>>BaP=Mix2=Mix3>Mix1=Control, based on statistical significance. Gene expression profilesmore » measured in skin of mice collected 12 h post-initiation were compared to tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (p<0.05) for DNA damage, apoptosis, response to chemical stimulus and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. As a result, these data further provide a ‘source-to outcome’ model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action based risk assessment could be employed for environmental PAH mixtures.« less

Using Helicopter Electromagnetic Surveys to Identify Potential Hazards at Mine Waste Impoundments

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

Hammack, R.W.

2008-01-01

In July 2003, helicopter electromagnetic surveys were conducted at 14 coal waste impoundments in southern West Virginia. The purpose of the surveys was to detect conditions that could lead to impoundment failure either by structural failure of the embankment or by the flooding of adjacent or underlying mine works. Specifically, the surveys attempted to: 1) identify saturated zones within the mine waste, 2) delineate filtrate flow paths through the embankment or into adjacent strata and receiving streams, and 3) identify flooded mine workings underlying or adjacent to the waste impoundment. Data from the helicopter surveys were processed to generate conductivity/depthmore » images. Conductivity/depth images were then spatially linked to georeferenced air photos or topographic maps for interpretation. Conductivity/depth images were found to provide a snapshot of the hydrologic conditions that exist within the impoundment. This information can be used to predict potential areas of failure within the embankment because of its ability to image the phreatic zone. Also, the electromagnetic survey can identify areas of unconsolidated slurry in the decant basin and beneath the embankment. Although shallow, flooded mineworks beneath the impoundment were identified by this survey, it cannot be assumed that electromagnetic surveys can detect all underlying mines. A preliminary evaluation of the data implies that helicopter electromagnetic surveys can provide a better understanding of the phreatic zone than the piezometer arrays that are typically used.« less

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.

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.

How accurately does the Brief Job Stress Questionnaire identify workers with or without potential psychological distress?

PubMed

Tsutsumi, Akizumi; Inoue, Akiomi; Eguchi, Hisashi

2017-07-27

The manual for the Japanese Stress Check Program recommends use of the Brief Job Stress Questionnaire (BJSQ) from among the program's instruments and proposes criteria for defining "high-stress" workers. This study aimed to examine how accurately the BJSQ identifies workers with or without potential psychological distress. We used an online survey to administer the BJSQ with a psychological distress scale (K6) to randomly selected workers (n=1,650). We conducted receiver operating characteristics curve analyses to estimate the screening performance of the cutoff points that the Stress Check Program manual recommends for the BJSQ. Prevalence of workers with potential psychological distress defined as K6 score ≥13 was 13%. Prevalence of "high-risk" workers defined using criteria recommended by the program manual was 16.7% for the original version of the BJSQ. The estimated values were as follows: sensitivity, 60.5%; specificity, 88.9%; Youden index, 0.504; positive predictive value, 47.3%; negative predictive value, 93.8%; positive likelihood ratio, 6.0; and negative likelihood ratio, 0.4. Analyses based on the simplified BJSQ indicated lower sensitivity compared with the original version, although we expected roughly the same screening performance for the best scenario using the original version. Our analyses in which psychological distress measured by K6 was set as the target condition indicate less than half of the identified "high-stress" workers warrant consideration for secondary screening for psychological distress.

Prediction of Reduction Potentials of Copper Proteins with Continuum Electrostatics and Density Functional Theory.

PubMed

Fowler, Nicholas J; Blanford, Christopher F; Warwicker, Jim; de Visser, Sam P

2017-11-02

Blue copper proteins, such as azurin, show dramatic changes in Cu 2+ /Cu + reduction potential upon mutation over the full physiological range. Hence, they have important functions in electron transfer and oxidation chemistry and have applications in industrial biotechnology. The details of what determines these reduction potential changes upon mutation are still unclear. Moreover, it has been difficult to model and predict the reduction potential of azurin mutants and currently no unique procedure or workflow pattern exists. Furthermore, high-level computational methods can be accurate but are too time consuming for practical use. In this work, a novel approach for calculating reduction potentials of azurin mutants is shown, based on a combination of continuum electrostatics, density functional theory and empirical hydrophobicity factors. Our method accurately reproduces experimental reduction potential changes of 30 mutants with respect to wildtype within experimental error and highlights the factors contributing to the reduction potential change. Finally, reduction potentials are predicted for a series of 124 new mutants that have not yet been investigated experimentally. Several mutants are identified that are located well over 10 Å from the copper center that change the reduction potential by more than 85 mV. The work shows that secondary coordination sphere mutations mostly lead to long-range electrostatic changes and hence can be modeled accurately with continuum electrostatics. © 2017 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

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

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

Identifying future scientists: predicting persistence into research training.

PubMed

McGee, Richard; Keller, Jill L

2007-01-01

This study used semistructured interviews and grounded theory to look for characteristics among college undergraduates that predicted persistence into Ph.D. and M.D./Ph.D. training. Participants in the summer undergraduate and postbaccalaureate research programs at the Mayo Clinic College of Medicine were interviewed at the start, near the end, and 8-12 months after their research experience. Of more than 200 themes considered, five characteristics predicted those students who went on to Ph.D. and M.D./Ph.D. training or to M.D. training intending to do research: 1) Curiosity to discover the unknown, 2) Enjoyment of problem solving, 3) A high level of independence, 4) The desire to help others indirectly through research, and 5) A flexible, minimally structured approach to the future. Web-based surveys with different students confirmed the high frequency of curiosity and/or problem solving as the primary reason students planned research careers. No evidence was found for differences among men, women, and minority and nonminority students. Although these results seem logical compared with successful scientists, their constancy, predictive capabilities, and sharp contrast to students who chose clinical medicine were striking. These results provide important insights into selection and motivation of potential biomedical scientists and the early experiences that will motivate them toward research careers.

Application of near infrared reflectance (NIR) spectroscopy to identify potential PSE meat.

PubMed

Li, Xiao; Feng, Fang; Gao, Runze; Wang, Lu; Qian, Ye; Li, Chunbao; Zhou, Guanghong

2016-07-01

Pale, soft and exudative (PSE) meat is a quality problem that causes a large economic loss to the pork industry. In the present work, near infrared (NIR) quantification and identification methods were used to investigate the feasibility of differentiating potential PSE meat from normal meat. NIR quantification models were developed to estimate meat pH and colour attributes (L*, a*, b*). Promising results were reported for prediction of muscle pH (R(2) CV  = 70.10%, RPDCV = 1.83) and L* (R(2) CV  = 77.18%, RPDCV = 1.91), but it is still hard to promote to practical application at this level. The Factorisation Method applied to NIR spectra could differentiate potential PSE meat from normal meat at 3 h post-mortem. Correlation analysis showed significant relationship between NIR data and LF-NMR T2 components that were indicative of water distribution and mobility in muscle. PSE meat had unconventionally faster energy metabolism than normal meat, which caused greater water mobility. NIR spectra coupled with the Factorisation Method could be a promising technology to identify potential PSE meat. The difference in the intensity of H2 O absorbance peaks between PSE and normal meat might be the basis of this identification method. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

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

Mechanism-Based Classification of PAH Mixtures to Predict Carcinogenic Potential.

PubMed

Tilton, Susan C; Siddens, Lisbeth K; Krueger, Sharon K; Larkin, Andrew J; Löhr, Christiane V; Williams, David E; Baird, William M; Waters, Katrina M

2015-07-01

We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[a]pyrene (BaP). Therefore, we developed a pathway-based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[def,p]chrysene (DBC), BaP, or environmental PAH mixtures (Mix 1-3) following a 2-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC > BaP = Mix2 = Mix3 > Mix1 = Control, based on statistical significance. Gene expression profiles measured in skin of mice collected 12 h post-initiation were compared with tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (P < .05) for DNA damage, apoptosis, response to chemical stimulus, and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. These data further provide a 'source-to-outcome' model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action-based risk assessment could be employed for environmental PAH mixtures. © The Author 2015. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Identifying and Assessing Gaps in Subseasonal to Seasonal Prediction Skill using the North American Multi-model Ensemble

NASA Astrophysics Data System (ADS)

Pegion, K.; DelSole, T. M.; Becker, E.; Cicerone, T.

2016-12-01

Predictability represents the upper limit of prediction skill if we had an infinite member ensemble and a perfect model. It is an intrinsic limit of the climate system associated with the chaotic nature of the atmosphere. Producing a forecast system that can make predictions very near to this limit is the ultimate goal of forecast system development. Estimates of predictability together with calculations of current prediction skill are often used to define the gaps in our prediction capabilities on subseasonal to seasonal timescales and to inform the scientific issues that must be addressed to build the next forecast system. Quantification of the predictability is also important for providing a scientific basis for relaying to stakeholders what kind of climate information can be provided to inform decision-making and what kind of information is not possible given the intrinsic predictability of the climate system. One challenge with predictability estimates is that different prediction systems can give different estimates of the upper limit of skill. How do we know which estimate of predictability is most representative of the true predictability of the climate system? Previous studies have used the spread-error relationship and the autocorrelation to evaluate the fidelity of the signal and noise estimates. Using a multi-model ensemble prediction system, we can quantify whether these metrics accurately indicate an individual model's ability to properly estimate the signal, noise, and predictability. We use this information to identify the best estimates of predictability for 2-meter temperature, precipitation, and sea surface temperature from the North American Multi-model Ensemble and compare with current skill to indicate the regions with potential for improving skill.

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 vapor-liquid phase equilibria with augmented ab initio interatomic potentials

NASA Astrophysics Data System (ADS)

Vlasiuk, Maryna; Sadus, Richard J.

2017-06-01

The ability of ab initio interatomic potentials to accurately predict vapor-liquid phase equilibria is investigated. Monte Carlo simulations are reported for the vapor-liquid equilibria of argon and krypton using recently developed accurate ab initio interatomic potentials. Seventeen interatomic potentials are studied, formulated from different combinations of two-body plus three-body terms. The simulation results are compared to either experimental or reference data for conditions ranging from the triple point to the critical point. It is demonstrated that the use of ab initio potentials enables systematic improvements to the accuracy of predictions via the addition of theoretically based terms. The contribution of three-body interactions is accounted for using the Axilrod-Teller-Muto plus other multipole contributions and the effective Marcelli-Wang-Sadus potentials. The results indicate that the predictive ability of recent interatomic potentials, obtained from quantum chemical calculations, is comparable to that of accurate empirical models. It is demonstrated that the Marcelli-Wang-Sadus potential can be used in combination with accurate two-body ab initio models for the computationally inexpensive and accurate estimation of vapor-liquid phase equilibria.

Predicting vapor-liquid phase equilibria with augmented ab initio interatomic potentials.

PubMed

Vlasiuk, Maryna; Sadus, Richard J

2017-06-28

The ability of ab initio interatomic potentials to accurately predict vapor-liquid phase equilibria is investigated. Monte Carlo simulations are reported for the vapor-liquid equilibria of argon and krypton using recently developed accurate ab initio interatomic potentials. Seventeen interatomic potentials are studied, formulated from different combinations of two-body plus three-body terms. The simulation results are compared to either experimental or reference data for conditions ranging from the triple point to the critical point. It is demonstrated that the use of ab initio potentials enables systematic improvements to the accuracy of predictions via the addition of theoretically based terms. The contribution of three-body interactions is accounted for using the Axilrod-Teller-Muto plus other multipole contributions and the effective Marcelli-Wang-Sadus potentials. The results indicate that the predictive ability of recent interatomic potentials, obtained from quantum chemical calculations, is comparable to that of accurate empirical models. It is demonstrated that the Marcelli-Wang-Sadus potential can be used in combination with accurate two-body ab initio models for the computationally inexpensive and accurate estimation of vapor-liquid phase equilibria.

Identifying Potential Kidney Donors Using Social Networking Websites

PubMed Central

Chang, Alexander; Anderson, Emily E.; Turner, Hang T.; Shoham, David; Hou, Susan H.; Grams, Morgan

2013-01-01

Social networking sites like Facebook may be a powerful tool for increasing rates of live kidney donation. They allow for wide dissemination of information and discussion, and could lessen anxiety associated with a face-to-face request for donation. However, sparse data exist on the use of social media for this purpose. We searched Facebook, the most popular social networking site, for publicly available English-language pages seeking kidney donors for a specific individual, abstracting information on the potential recipient, characteristics of the page itself, and whether potential donors were tested. In the 91 pages meeting inclusion criteria, the mean age of potential recipients was 37 (range: 2–69); 88% were U.S. residents. Other posted information included the individual’s photograph (76%), blood type (64%), cause of kidney disease (43%), and location (71%). Thirty-two percent of pages reported having potential donors tested, and 10% reported receiving a live donor kidney transplant. Those reporting donor testing shared more potential recipient characteristics, provided more information about transplantation, and had higher page traffic. Facebook is already being used to identify potential kidney donors. Future studies should focus on how to safely, ethically, and effectively use social networking sites to inform potential donors and potentially expand live kidney donation. PMID:23600791

Genome-Wide Locations of Potential Epimutations Associated with Environmentally Induced Epigenetic Transgenerational Inheritance of Disease Using a Sequential Machine Learning Prediction Approach.

PubMed

Haque, M Muksitul; Holder, Lawrence B; Skinner, Michael K

2015-01-01

Environmentally induced epigenetic transgenerational inheritance of disease and phenotypic variation involves germline transmitted epimutations. The primary epimutations identified involve altered differential DNA methylation regions (DMRs). Different environmental toxicants have been shown to promote exposure (i.e., toxicant) specific signatures of germline epimutations. Analysis of genomic features associated with these epimutations identified low-density CpG regions (<3 CpG / 100bp) termed CpG deserts and a number of unique DNA sequence motifs. The rat genome was annotated for these and additional relevant features. The objective of the current study was to use a machine learning computational approach to predict all potential epimutations in the genome. A number of previously identified sperm epimutations were used as training sets. A novel machine learning approach using a sequential combination of Active Learning and Imbalance Class Learner analysis was developed. The transgenerational sperm epimutation analysis identified approximately 50K individual sites with a 1 kb mean size and 3,233 regions that had a minimum of three adjacent sites with a mean size of 3.5 kb. A select number of the most relevant genomic features were identified with the low density CpG deserts being a critical genomic feature of the features selected. A similar independent analysis with transgenerational somatic cell epimutation training sets identified a smaller number of 1,503 regions of genome-wide predicted sites and differences in genomic feature contributions. The predicted genome-wide germline (sperm) epimutations were found to be distinct from the predicted somatic cell epimutations. Validation of the genome-wide germline predicted sites used two recently identified transgenerational sperm epimutation signature sets from the pesticides dichlorodiphenyltrichloroethane (DDT) and methoxychlor (MXC) exposure lineage F3 generation. Analysis of this positive validation data set

Identifying Future Scientists: Predicting Persistence into Research Training

PubMed Central

2007-01-01

This study used semistructured interviews and grounded theory to look for characteristics among college undergraduates that predicted persistence into Ph.D. and M.D./Ph.D. training. Participants in the summer undergraduate and postbaccalaureate research programs at the Mayo Clinic College of Medicine were interviewed at the start, near the end, and 8–12 months after their research experience. Of more than 200 themes considered, five characteristics predicted those students who went on to Ph.D. and M.D./Ph.D. training or to M.D. training intending to do research: 1) Curiosity to discover the unknown, 2) Enjoyment of problem solving, 3) A high level of independence, 4) The desire to help others indirectly through research, and 5) A flexible, minimally structured approach to the future. Web-based surveys with different students confirmed the high frequency of curiosity and/or problem solving as the primary reason students planned research careers. No evidence was found for differences among men, women, and minority and nonminority students. Although these results seem logical compared with successful scientists, their constancy, predictive capabilities, and sharp contrast to students who chose clinical medicine were striking. These results provide important insights into selection and motivation of potential biomedical scientists and the early experiences that will motivate them toward research careers. PMID:18056303

The prediction of airborne and structure-borne noise potential for a tire

NASA Astrophysics Data System (ADS)

Sakamoto, Nicholas Y.

Tire/pavement interaction noise is a major component of both exterior pass-by noise and vehicle interior noise. The current testing methods for ranking tires from loud to quiet require expensive equipment, multiple tires, and/or long experimental set-up and run times. If a laboratory based off-vehicle test could be used to identify the airborne and structure-borne potential of a tire from its dynamic characteristics, a relative ranking of a large group of tires could be performed at relatively modest expense. This would provide a smaller sample set of tires for follow-up testing and thus save expense for automobile OEMs. The focus of this research was identifying key noise features from a tire/pavement experiment. These results were compared against a stationary tire test in which the natural response of the tire to a forced input was measured. Since speed was identified as having some effect on the noise, an input function was also developed to allow the tires to be ranked at an appropriate speed. A relative noise model was used on a second sample set of tires to verify if the ranking could be used against interior vehicle measurements. While overall level analysis of the specified spectrum had mixed success, important noise generating features were identified, and the methods used could be improved to develop a standard off-vehicle test to predict a tire's noise potential.

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.

Accurate structure prediction of peptide–MHC complexes for identifying highly immunogenic antigens

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

Park, Min-Sun; Park, Sung Yong; Miller, Keith R.

2013-11-01

Designing an optimal HIV-1 vaccine faces the challenge of identifying antigens that induce a broad immune capacity. One factor to control the breadth of T cell responses is the surface morphology of a peptide–MHC complex. Here, we present an in silico protocol for predicting peptide–MHC structure. A robust signature of a conformational transition was identified during all-atom molecular dynamics, which results in a model with high accuracy. A large test set was used in constructing our protocol and we went another step further using a blind test with a wild-type peptide and two highly immunogenic mutants, which predicted substantial conformationalmore » changes in both mutants. The center residues at position five of the analogs were configured to be accessible to solvent, forming a prominent surface, while the residue of the wild-type peptide was to point laterally toward the side of the binding cleft. We then experimentally determined the structures of the blind test set, using high resolution of X-ray crystallography, which verified predicted conformational changes. Our observation strongly supports a positive association of the surface morphology of a peptide–MHC complex to its immunogenicity. Our study offers the prospect of enhancing immunogenicity of vaccines by identifying MHC binding immunogens.« less

Computational Prediction of the Immunomodulatory Potential of RNA Sequences.

PubMed

Nagpal, Gandharva; Chaudhary, Kumardeep; Dhanda, Sandeep Kumar; Raghava, Gajendra Pal Singh

2017-01-01

Advances in the knowledge of various roles played by non-coding RNAs have stimulated the application of RNA molecules as therapeutics. Among these molecules, miRNA, siRNA, and CRISPR-Cas9 associated gRNA have been identified as the most potent RNA molecule classes with diverse therapeutic applications. One of the major limitations of RNA-based therapeutics is immunotoxicity of RNA molecules as it may induce the innate immune system. In contrast, RNA molecules that are potent immunostimulators are strong candidates for use in vaccine adjuvants. Thus, it is important to understand the immunotoxic or immunostimulatory potential of these RNA molecules. The experimental techniques for determining immunostimulatory potential of siRNAs are time- and resource-consuming. To overcome this limitation, recently our group has developed a web-based server "imRNA" for predicting the immunomodulatory potential of RNA sequences. This server integrates a number of modules that allow users to perform various tasks including (1) generation of RNA analogs with reduced immunotoxicity, (2) identification of highly immunostimulatory regions in RNA sequence, and (3) virtual screening. This server may also assist users in the identification of minimum mutations required in a given RNA sequence to minimize its immunomodulatory potential that is required for designing RNA-based therapeutics. Besides, the server can be used for designing RNA-based vaccine adjuvants as it may assist users in the identification of mutations required for increasing immunomodulatory potential of a given RNA sequence. In summary, this chapter describes major applications of the "imRNA" server in designing RNA-based therapeutics and vaccine adjuvants (http://www.imtech.res.in/raghava/imrna/).

Release of genetically engineered insects: a framework to identify potential ecological effects

PubMed Central

David, Aaron S; Kaser, Joe M; Morey, Amy C; Roth, Alexander M; Andow, David A

2013-01-01

Genetically engineered (GE) insects have the potential to radically change pest management worldwide. With recent approvals of GE insect releases, there is a need for a synthesized framework to evaluate their potential ecological and evolutionary effects. The effects may occur in two phases: a transitory phase when the focal population changes in density, and a steady state phase when it reaches a new, constant density. We review potential effects of a rapid change in insect density related to population outbreaks, biological control, invasive species, and other GE organisms to identify a comprehensive list of potential ecological and evolutionary effects of GE insect releases. We apply this framework to the Anopheles gambiae mosquito – a malaria vector being engineered to suppress the wild mosquito population – to identify effects that may occur during the transitory and steady state phases after release. Our methodology reveals many potential effects in each phase, perhaps most notably those dealing with immunity in the transitory phase, and with pathogen and vector evolution in the steady state phase. Importantly, this framework identifies knowledge gaps in mosquito ecology. Identifying effects in the transitory and steady state phases allows more rigorous identification of the potential ecological effects of GE insect release. PMID:24198955

Subarachnoid hemorrhage admissions retrospectively identified using a prediction model

PubMed Central

McIntyre, Lauralyn; Fergusson, Dean; Turgeon, Alexis; dos Santos, Marlise P.; Lum, Cheemun; Chassé, Michaël; Sinclair, John; Forster, Alan; van Walraven, Carl

2016-01-01

Objective: To create an accurate prediction model using variables collected in widely available health administrative data records to identify hospitalizations for primary subarachnoid hemorrhage (SAH). Methods: A previously established complete cohort of consecutive primary SAH patients was combined with a random sample of control hospitalizations. Chi-square recursive partitioning was used to derive and internally validate a model to predict the probability that a patient had primary SAH (due to aneurysm or arteriovenous malformation) using health administrative data. Results: A total of 10,322 hospitalizations with 631 having primary SAH (6.1%) were included in the study (5,122 derivation, 5,200 validation). In the validation patients, our recursive partitioning algorithm had a sensitivity of 96.5% (95% confidence interval [CI] 93.9–98.0), a specificity of 99.8% (95% CI 99.6–99.9), and a positive likelihood ratio of 483 (95% CI 254–879). In this population, patients meeting criteria for the algorithm had a probability of 45% of truly having primary SAH. Conclusions: Routinely collected health administrative data can be used to accurately identify hospitalized patients with a high probability of having a primary SAH. This algorithm may allow, upon validation, an easy and accurate method to create validated cohorts of primary SAH from either ruptured aneurysm or arteriovenous malformation. PMID:27629096

Identified state-space prediction model for aero-optical wavefronts

NASA Astrophysics Data System (ADS)

Faghihi, Azin; Tesch, Jonathan; Gibson, Steve

2013-07-01

A state-space disturbance model and associated prediction filter for aero-optical wavefronts are described. The model is computed by system identification from a sequence of wavefronts measured in an airborne laboratory. Estimates of the statistics and flow velocity of the wavefront data are shown and can be computed from the matrices in the state-space model without returning to the original data. Numerical results compare velocity values and power spectra computed from the identified state-space model with those computed from the aero-optical data.

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.

Potential Predictability and Prediction Skill for Southern Peru Summertime Rainfall

NASA Astrophysics Data System (ADS)

WU, S.; Notaro, M.; Vavrus, S. J.; Mortensen, E.; Block, P. J.; Montgomery, R. J.; De Pierola, J. N.; Sanchez, C.

2016-12-01

The central Andes receive over 50% of annual climatological rainfall during the short period of January-March. This summertime rainfall exhibits strong interannual and decadal variability, including severe drought events that incur devastating societal impacts and cause agricultural communities and mining facilities to compete for limited water resources. An improved seasonal prediction skill of summertime rainfall would aid in water resource planning and allocation across the water-limited southern Peru. While various underlying mechanisms have been proposed by past studies for the drivers of interannual variability in summertime rainfall across southern Peru, such as the El Niño-Southern Oscillation (ENSO), Madden Julian Oscillation (MJO), and extratropical forcings, operational forecasts continue to be largely based on rudimentary ENSO-based indices, such as NINO3.4, justifying further exploration of predictive skill. In order to bridge this gap between the understanding of driving mechanisms and the operational forecast, we performed systematic studies on the predictability and prediction skill of southern Peru summertime rainfall by constructing statistical forecast models using best available weather station and reanalysis datasets. At first, by assuming the first two empirical orthogonal functions (EOFs) of summertime rainfall are predictable, the potential predictability skill was evaluated for southern Peru. Then, we constructed a simple regression model, based on the time series of tropical Pacific sea-surface temperatures (SSTs), and a more advanced Linear Inverse Model (LIM), based on the EOFs of tropical ocean SSTs and large-scale atmosphere variables from reanalysis. Our results show that the LIM model consistently outperforms the more rudimentary regression models on the forecast skill of domain averaged precipitation index and individual station indices. The improvement of forecast correlation skill ranges from 10% to over 200% for different

Probabilistic empirical prediction of seasonal climate: evaluation and potential applications

NASA Astrophysics Data System (ADS)

Dieppois, B.; Eden, J.; van Oldenborgh, G. J.

2017-12-01

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a new evaluation of an established empirical system used to predict seasonal climate across the globe. Forecasts for surface air temperature, precipitation and sea level pressure are produced by the KNMI Probabilistic Empirical Prediction (K-PREP) system every month and disseminated via the KNMI Climate Explorer (climexp.knmi.nl). K-PREP is based on multiple linear regression and built on physical principles to the fullest extent with predictive information taken from the global CO2-equivalent concentration, large-scale modes of variability in the climate system and regional-scale information. K-PREP seasonal forecasts for the period 1981-2016 will be compared with corresponding dynamically generated forecasts produced by operational forecast systems. While there are many regions of the world where empirical forecast skill is extremely limited, several areas are identified where K-PREP offers comparable skill to dynamical systems. We discuss two key points in the future development and application of the K-PREP system: (a) the potential for K-PREP to provide a more useful basis for reference forecasts than those based on persistence or climatology, and (b) the added value of including K-PREP forecast information in multi-model forecast products, at least for known regions of good skill. We also discuss the potential development of

Use of Event-Related Potentials to Identify Language and Reading Skills

ERIC Educational Resources Information Center

Molfese, Victoria J.; Molfese, Dennis L.; Beswick, Jennifer L.; Jacobi-Vessels, Jill; Molfese, Peter J.; Molnar, Andrew E.; Wagner, Mary C.; Haines, Brittany L.

2008-01-01

The extent to which oral language and emergent literacy skills are influenced by event-related potential measures of phonological processing was examined. Results revealed that event-related potential responses identify differences in letter naming but not receptive language skills.

An integrative strategy to identify the entire protein coding potential of prokaryotic genomes by proteogenomics.

PubMed

Omasits, Ulrich; Varadarajan, Adithi R; Schmid, Michael; Goetze, Sandra; Melidis, Damianos; Bourqui, Marc; Nikolayeva, Olga; Québatte, Maxime; Patrignani, Andrea; Dehio, Christoph; Frey, Juerg E; Robinson, Mark D; Wollscheid, Bernd; Ahrens, Christian H

2017-12-01

Accurate annotation of all protein-coding sequences (CDSs) is an essential prerequisite to fully exploit the rapidly growing repertoire of completely sequenced prokaryotic genomes. However, large discrepancies among the number of CDSs annotated by different resources, missed functional short open reading frames (sORFs), and overprediction of spurious ORFs represent serious limitations. Our strategy toward accurate and complete genome annotation consolidates CDSs from multiple reference annotation resources, ab initio gene prediction algorithms and in silico ORFs (a modified six-frame translation considering alternative start codons) in an integrated proteogenomics database (iPtgxDB) that covers the entire protein-coding potential of a prokaryotic genome. By extending the PeptideClassifier concept of unambiguous peptides for prokaryotes, close to 95% of the identifiable peptides imply one distinct protein, largely simplifying downstream analysis. Searching a comprehensive Bartonella henselae proteomics data set against such an iPtgxDB allowed us to unambiguously identify novel ORFs uniquely predicted by each resource, including lipoproteins, differentially expressed and membrane-localized proteins, novel start sites and wrongly annotated pseudogenes. Most novelties were confirmed by targeted, parallel reaction monitoring mass spectrometry, including unique ORFs and single amino acid variations (SAAVs) identified in a re-sequenced laboratory strain that are not present in its reference genome. We demonstrate the general applicability of our strategy for genomes with varying GC content and distinct taxonomic origin. We release iPtgxDBs for B. henselae , Bradyrhizobium diazoefficiens and Escherichia coli and the software to generate both proteogenomics search databases and integrated annotation files that can be viewed in a genome browser for any prokaryote. © 2017 Omasits et al.; Published by Cold Spring Harbor Laboratory Press.

Prediction markets and their potential role in biomedical research--a review.

PubMed

Pfeiffer, Thomas; Almenberg, Johan

2010-01-01

Predictions markets are marketplaces for trading contracts with payoffs that depend on the outcome of future events. Popular examples are markets on the outcome of presidential elections, where contracts pay $1 if a specific candidate wins the election and $0 if someone else wins. Contract prices on prediction markets can be interpreted as forecasts regarding the outcome of future events. Further attractive properties include the potential to aggregate private information, to generate and disseminate a consensus among the market participants, and to offer incentives for the acquisition of information. It has been argued that these properties might be valuable in the context of scientific research. In this review, we give an overview of key properties of prediction markets and discuss potential benefits for science. To illustrate these benefits for biomedical research, we discuss an example application in the context of decision making in research on the genetics of diseases. Moreover, some potential practical problems of prediction market application in science are discussed, and solutions are outlined. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Predicting missing links and identifying spurious links via likelihood analysis

NASA Astrophysics Data System (ADS)

Pan, Liming; Zhou, Tao; Lü, Linyuan; Hu, Chin-Kun

2016-03-01

Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms.

Predicting missing links and identifying spurious links via likelihood analysis

PubMed Central

Pan, Liming; Zhou, Tao; Lü, Linyuan; Hu, Chin-Kun

2016-01-01

Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms. PMID:26961965

Assessing urban potential flooding risk and identifying effective risk-reduction measures.

PubMed

Cherqui, Frédéric; Belmeziti, Ali; Granger, Damien; Sourdril, Antoine; Le Gauffre, Pascal

2015-05-01

Flood protection is one of the traditional functions of any drainage system, and it remains a major issue in many cities because of economic and health impact. Heavy rain flooding has been well studied and existing simulation software can be used to predict and improve level of protection. However, simulating minor flooding remains highly complex, due to the numerous possible causes related to operational deficiencies or negligent behaviour. According to the literature, causes of blockages vary widely from one case to another: it is impossible to provide utility managers with effective recommendations on how to improve the level of protection. It is therefore vital to analyse each context in order to define an appropriate strategy. Here we propose a method to represent and assess the flooding risk, using GIS and data gathered during operation and maintenance. Our method also identifies potential management responses. The approach proposed aims to provide decision makers with clear and comprehensible information. Our method has been successfully applied to the Urban Community of Bordeaux (France) on 4895 interventions related to flooding recorded during the 2009-2011 period. Results have shown the relative importance of different issues, such as human behaviour (grease, etc.) or operational deficiencies (roots, etc.), and lead to identify corrective and proactive. This study also confirms that blockages are not always directly due to the network itself and its deterioration. Many causes depend on environmental and operating conditions on the network and often require collaboration between municipal departments in charge of roads, green spaces, etc. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Clonal analyses and gene profiling identify genetic biomarkers of the thermogenic potential of human brown and white preadipocytes.

PubMed

Xue, Ruidan; Lynes, Matthew D; Dreyfuss, Jonathan M; Shamsi, Farnaz; Schulz, Tim J; Zhang, Hongbin; Huang, Tian Lian; Townsend, Kristy L; Li, Yiming; Takahashi, Hirokazu; Weiner, Lauren S; White, Andrew P; Lynes, Maureen S; Rubin, Lee L; Goodyear, Laurie J; Cypess, Aaron M; Tseng, Yu-Hua

2015-07-01

Targeting brown adipose tissue (BAT) content or activity has therapeutic potential for treating obesity and the metabolic syndrome by increasing energy expenditure. However, both inter- and intra-individual differences contribute to heterogeneity in human BAT and potentially to differential thermogenic capacity in human populations. Here we generated clones of brown and white preadipocytes from human neck fat and characterized their adipogenic and thermogenic differentiation. We combined an uncoupling protein 1 (UCP1) reporter system and expression profiling to define novel sets of gene signatures in human preadipocytes that could predict the thermogenic potential of the cells once they were maturated. Knocking out the positive UCP1 regulators, PREX1 and EDNRB, in brown preadipocytes using CRISPR-Cas9 markedly abolished the high level of UCP1 in brown adipocytes differentiated from the preadipocytes. Finally, we were able to prospectively isolate adipose progenitors with great thermogenic potential using the cell surface marker CD29. These data provide new insights into the cellular heterogeneity in human fat and offer potential biomarkers for identifying thermogenically competent preadipocytes.

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.

Identifying pollution sources and predicting urban air quality using ensemble learning methods

NASA Astrophysics Data System (ADS)

Singh, Kunwar P.; Gupta, Shikha; Rai, Premanjali

2013-12-01

In this study, principal components analysis (PCA) was performed to identify air pollution sources and tree based ensemble learning models were constructed to predict the urban air quality of Lucknow (India) using the air quality and meteorological databases pertaining to a period of five years. PCA identified vehicular emissions and fuel combustion as major air pollution sources. The air quality indices revealed the air quality unhealthy during the summer and winter. Ensemble models were constructed to discriminate between the seasonal air qualities, factors responsible for discrimination, and to predict the air quality indices. Accordingly, single decision tree (SDT), decision tree forest (DTF), and decision treeboost (DTB) were constructed and their generalization and predictive performance was evaluated in terms of several statistical parameters and compared with conventional machine learning benchmark, support vector machines (SVM). The DT and SVM models discriminated the seasonal air quality rendering misclassification rate (MR) of 8.32% (SDT); 4.12% (DTF); 5.62% (DTB), and 6.18% (SVM), respectively in complete data. The AQI and CAQI regression models yielded a correlation between measured and predicted values and root mean squared error of 0.901, 6.67 and 0.825, 9.45 (SDT); 0.951, 4.85 and 0.922, 6.56 (DTF); 0.959, 4.38 and 0.929, 6.30 (DTB); 0.890, 7.00 and 0.836, 9.16 (SVR) in complete data. The DTF and DTB models outperformed the SVM both in classification and regression which could be attributed to the incorporation of the bagging and boosting algorithms in these models. The proposed ensemble models successfully predicted the urban ambient air quality and can be used as effective tools for its management.

Identifying potential kidney donors using social networking web sites.

PubMed

Chang, Alexander; Anderson, Emily E; Turner, Hang T; Shoham, David; Hou, Susan H; Grams, Morgan

2013-01-01

Social networking sites like Facebook may be a powerful tool for increasing rates of live kidney donation. They allow for wide dissemination of information and discussion and could lessen anxiety associated with a face-to-face request for donation. However, sparse data exist on the use of social media for this purpose. We searched Facebook, the most popular social networking site, for publicly available English-language pages seeking kidney donors for a specific individual, abstracting information on the potential recipient, characteristics of the page itself, and whether potential donors were tested. In the 91 pages meeting inclusion criteria, the mean age of potential recipients was 37 (range: 2-69); 88% were US residents. Other posted information included the individual's photograph (76%), blood type (64%), cause of kidney disease (43%), and location (71%). Thirty-two percent of pages reported having potential donors tested, and 10% reported receiving a live-donor kidney transplant. Those reporting donor testing shared more potential recipient characteristics, provided more information about transplantation, and had higher page traffic. Facebook is already being used to identify potential kidney donors. Future studies should focus on how to safely, ethically, and effectively use social networking sites to inform potential donors and potentially expand live kidney donation. © 2013 John Wiley & Sons A/S.

Predictive values of diagnostic codes for identifying serious hypocalcemia and dermatologic adverse events among women with postmenopausal osteoporosis in a commercial health plan database.

PubMed

Wang, Florence T; Xue, Fei; Ding, Yan; Ng, Eva; Critchlow, Cathy W; Dore, David D

2018-04-10

Post-marketing safety studies of medicines often rely on administrative claims databases to identify adverse outcomes following drug exposure. Valid ascertainment of outcomes is essential for accurate results. We aim to quantify the validity of diagnostic codes for serious hypocalcemia and dermatologic adverse events from insurance claims data among women with postmenopausal osteoporosis (PMO). We identified potential cases of serious hypocalcemia and dermatologic events through ICD-9 diagnosis codes among women with PMO within claims from a large US healthcare insurer (June 2005-May 2010). A physician adjudicated potential hypocalcemic and dermatologic events identified from the primary position on emergency department (ED) or inpatient claims through medical record review. Positive predictive values (PPVs) and 95% confidence intervals (CIs) quantified the fraction of potential cases that were confirmed. Among 165,729 patients with PMO, medical charts were obtained for 40 of 55 (73%) potential hypocalcemia cases; 16 were confirmed (PPV 40%, 95% CI 25-57%). The PPV was higher for ED than inpatient claims (82 vs. 24%). Among 265 potential dermatologic events (primarily urticaria or rash), we obtained 184 (69%) charts and confirmed 128 (PPV 70%, 95% CI 62-76%). The PPV was higher for ED than inpatient claims (77 vs. 39%). Diagnostic codes for hypocalcemia and dermatologic events may be sufficient to identify events giving rise to emergency care, but are less accurate for identifying events within hospitalizations.

Pan-genome analysis of human gastric pathogen H. pylori: comparative genomics and pathogenomics approaches to identify regions associated with pathogenicity and prediction of potential core therapeutic targets.

PubMed

Ali, Amjad; Naz, Anam; Soares, Siomar C; Bakhtiar, Marriam; Tiwari, Sandeep; Hassan, Syed S; Hanan, Fazal; Ramos, Rommel; Pereira, Ulisses; Barh, Debmalya; Figueiredo, Henrique César Pereira; Ussery, David W; Miyoshi, Anderson; Silva, Artur; Azevedo, Vasco

2015-01-01

Helicobacter pylori is a human gastric pathogen implicated as the major cause of peptic ulcer and second leading cause of gastric cancer (~70%) around the world. Conversely, an increased resistance to antibiotics and hindrances in the development of vaccines against H. pylori are observed. Pan-genome analyses of the global representative H. pylori isolates consisting of 39 complete genomes are presented in this paper. Phylogenetic analyses have revealed close relationships among geographically diverse strains of H. pylori. The conservation among these genomes was further analyzed by pan-genome approach; the predicted conserved gene families (1,193) constitute ~77% of the average H. pylori genome and 45% of the global gene repertoire of the species. Reverse vaccinology strategies have been adopted to identify and narrow down the potential core-immunogenic candidates. Total of 28 nonhost homolog proteins were characterized as universal therapeutic targets against H. pylori based on their functional annotation and protein-protein interaction. Finally, pathogenomics and genome plasticity analysis revealed 3 highly conserved and 2 highly variable putative pathogenicity islands in all of the H. pylori genomes been analyzed.

Regulatory element-based prediction identifies new susceptibility regulatory variants for osteoporosis.

PubMed

Yao, Shi; Guo, Yan; Dong, Shan-Shan; Hao, Ruo-Han; Chen, Xiao-Feng; Chen, Yi-Xiao; Chen, Jia-Bin; Tian, Qing; Deng, Hong-Wen; Yang, Tie-Lin

2017-08-01

Despite genome-wide association studies (GWASs) have identified many susceptibility genes for osteoporosis, it still leaves a large part of missing heritability to be discovered. Integrating regulatory information and GWASs could offer new insights into the biological link between the susceptibility SNPs and osteoporosis. We generated five machine learning classifiers with osteoporosis-associated variants and regulatory features data. We gained the optimal classifier and predicted genome-wide SNPs to discover susceptibility regulatory variants. We further utilized Genetic Factors for Osteoporosis Consortium (GEFOS) and three in-house GWASs samples to validate the associations for predicted positive SNPs. The random forest classifier performed best among all machine learning methods with the F1 score of 0.8871. Using the optimized model, we predicted 37,584 candidate SNPs for osteoporosis. According to the meta-analysis results, a list of regulatory variants was significantly associated with osteoporosis after multiple testing corrections and contributed to the expression of known osteoporosis-associated protein-coding genes. In summary, combining GWASs and regulatory elements through machine learning could provide additional information for understanding the mechanism of osteoporosis. The regulatory variants we predicted will provide novel targets for etiology research and treatment of osteoporosis.

Computational Gene Expression Modeling Identifies Salivary Biomarker Analysis that Predict Oral Feeding Readiness in the Newborn

PubMed Central

Maron, Jill L.; Hwang, Jooyeon S.; Pathak, Subash; Ruthazer, Robin; Russell, Ruby L.; Alterovitz, Gil

2014-01-01

Objective To combine mathematical modeling of salivary gene expression microarray data and systems biology annotation with RT-qPCR amplification to identify (phase I) and validate (phase II) salivary biomarker analysis for the prediction of oral feeding readiness in preterm infants. Study design Comparative whole transcriptome microarray analysis from 12 preterm newborns pre- and post-oral feeding success was used for computational modeling and systems biology analysis to identify potential salivary transcripts associated with oral feeding success (phase I). Selected gene expression biomarkers (15 from computational modeling; 6 evidence-based; and 3 reference) were evaluated by RT-qPCR amplification on 400 salivary samples from successful (n=200) and unsuccessful (n=200) oral feeders (phase II). Genes, alone and in combination, were evaluated by a multivariate analysis controlling for sex and post-conceptional age (PCA) to determine the probability that newborns achieved successful oral feeding. Results Advancing post-conceptional age (p < 0.001) and female sex (p = 0.05) positively predicted an infant’s ability to feed orally. A combination of five genes, NPY2R (hunger signaling), AMPK (energy homeostasis), PLXNA1 (olfactory neurogenesis), NPHP4 (visual behavior) and WNT3 (facial development), in addition to PCA and sex, demonstrated good accuracy for determining feeding success (AUROC = 0.78). Conclusions We have identified objective and biologically relevant salivary biomarkers that noninvasively assess a newborn’s developing brain, sensory and facial development as they relate to oral feeding success. Understanding the mechanisms that underlie the development of oral feeding readiness through translational and computational methods may improve clinical decision making while decreasing morbidities and health care costs. PMID:25620512

Prediction of potential disease-associated microRNAs based on random walk.

PubMed

Xuan, Ping; Han, Ke; Guo, Yahong; Li, Jin; Li, Xia; Zhong, Yingli; Zhang, Zhaogong; Ding, Jian

2015-06-01

Identifying microRNAs associated with diseases (disease miRNAs) is helpful for exploring the pathogenesis of diseases. Because miRNAs fulfill function via the regulation of their target genes and because the current number of experimentally validated targets is insufficient, some existing methods have inferred potential disease miRNAs based on the predicted targets. It is difficult for these methods to achieve excellent performance due to the high false-positive and false-negative rates for the target prediction results. Alternatively, several methods have constructed a network composed of miRNAs based on their associated diseases and have exploited the information within the network to predict the disease miRNAs. However, these methods have failed to take into account the prior information regarding the network nodes and the respective local topological structures of the different categories of nodes. Therefore, it is essential to develop a method that exploits the more useful information to predict reliable disease miRNA candidates. miRNAs with similar functions are normally associated with similar diseases and vice versa. Therefore, the functional similarity between a pair of miRNAs is calculated based on their associated diseases to construct a miRNA network. We present a new prediction method based on random walk on the network. For the diseases with some known related miRNAs, the network nodes are divided into labeled nodes and unlabeled nodes, and the transition matrices are established for the two categories of nodes. Furthermore, different categories of nodes have different transition weights. In this way, the prior information of nodes can be completely exploited. Simultaneously, the various ranges of topologies around the different categories of nodes are integrated. In addition, how far the walker can go away from the labeled nodes is controlled by restarting the walking. This is helpful for relieving the negative effect of noisy data. For the diseases

Transcriptome-wide identification of Rauvolfia serpentina microRNAs and prediction of their potential targets.

PubMed

Prakash, Pravin; Rajakani, Raja; Gupta, Vikrant

2016-04-01

MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 19-24 nucleotides (nt) in length and considered as potent regulators of gene expression at transcriptional and post-transcriptional levels. Here we report the identification and characterization of 15 conserved miRNAs belonging to 13 families from Rauvolfia serpentina through in silico analysis of available nucleotide dataset. The identified mature R. serpentina miRNAs (rse-miRNAs) ranged between 20 and 22nt in length, and the average minimal folding free energy index (MFEI) value of rse-miRNA precursor sequences was found to be -0.815 kcal/mol. Using the identified rse-miRNAs as query, their potential targets were predicted in R. serpentina and other plant species. Gene Ontology (GO) annotation showed that predicted targets of rse-miRNAs include transcription factors as well as genes involved in diverse biological processes such as primary and secondary metabolism, stress response, disease resistance, growth, and development. Few rse-miRNAs were predicted to target genes of pharmaceutically important secondary metabolic pathways such as alkaloids and anthocyanin biosynthesis. Phylogenetic analysis showed the evolutionary relationship of rse-miRNAs and their precursor sequences to homologous pre-miRNA sequences from other plant species. The findings under present study besides giving first hand information about R. serpentina miRNAs and their targets, also contributes towards the better understanding of miRNA-mediated gene regulatory processes in plants. Copyright © 2015 Elsevier Ltd. All rights reserved.

Domain-Specific QSAR Model for Identifying Potential Estrogenic Activity of Phenols (ASCCT annual meeting)

EPA Science Inventory

Humans are potentially exposed to tens of thousands of man-made chemicals in the environment, some of which may mimic natural endocrine hormones and thus have the potential to be endocrine disruptors. Predictive in silico tools can be used to quickly and efficiently evaluate thes...

Using a watershed-centric approach to identify potentially impacted beaches

EPA Science Inventory

Beaches can be affected by a variety of contaminants. Of particular concern are beaches impacted by human fecal contamination and urban runoff. This poster demonstrates a methodology to identify potentially impacted beaches using Geographic Information Systems (GIS). Since h...

Trajectories of Substance Use Disorders in Youth: Identifying and Predicting Group Memberships

ERIC Educational Resources Information Center

Lee, Chih-Yuan S.; Winters, Ken C.; Wall, Melanie M.

2010-01-01

This study used latent class regression to identify latent trajectory classes based on individuals' diagnostic course of substance use disorders (SUDs) from late adolescence to early adulthood as well as to examine whether several psychosocial risk factors predicted the trajectory class membership. The study sample consisted of 310 individuals…

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

In silico prediction of potential chemical reactions mediated by human enzymes.

PubMed

Yu, Myeong-Sang; Lee, Hyang-Mi; Park, Aaron; Park, Chungoo; Ceong, Hyithaek; Rhee, Ki-Hyeong; Na, Dokyun

2018-06-13

Administered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms. We developed an in silico model to predict which of human enzymes including metabolic enzymes as well as CYP450 family can catalyze a given chemical compound. The prediction is based on the chemical and physical similarity between known enzyme substrates and a query chemical compound. Our in silico model was developed using multiple linear regression and the model showed high performance (AUC = 0.896) despite of the large number of enzymes. When evaluated on a test dataset, it also showed significantly high performance (AUC = 0.746). Interestingly, evaluation with literature data showed that our model can be used to predict not only enzymatic reactions but also drug conversion and enzyme inhibition. Our model was able to predict enzymatic reactions of a query molecule with a high accuracy. This may foster to discover new metabolic routes and to accelerate the computational development of drug candidates by enabling the prediction of the potential conversion of administered drugs into active or inactive forms.

Identifying developmental vascular disruptor compounds using a predictive signature and alternative toxicity models

EPA Science Inventory

Identifying Developmental Vascular Disruptor Compounds Using a Predictive Signature and Alternative Toxicity Models Presenting Author: Tamara Tal Affiliation: U.S. EPA/ORD/ISTD, RTP, NC, USA Chemically induced vascular toxicity during embryonic development can result in a wide...

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

Exploring the Potential of Predictive Analytics and Big Data in Emergency Care.

PubMed

Janke, Alexander T; Overbeek, Daniel L; Kocher, Keith E; Levy, Phillip D

2016-02-01

Clinical research often focuses on resource-intensive causal inference, whereas the potential of predictive analytics with constantly increasing big data sources remains largely unexplored. Basic prediction, divorced from causal inference, is much easier with big data. Emergency care may benefit from this simpler application of big data. Historically, predictive analytics have played an important role in emergency care as simple heuristics for risk stratification. These tools generally follow a standard approach: parsimonious criteria, easy computability, and independent validation with distinct populations. Simplicity in a prediction tool is valuable, but technological advances make it no longer a necessity. Emergency care could benefit from clinical predictions built using data science tools with abundant potential input variables available in electronic medical records. Patients' risks could be stratified more precisely with large pools of data and lower resource requirements for comparing each clinical encounter to those that came before it, benefiting clinical decisionmaking and health systems operations. The largest value of predictive analytics comes early in the clinical encounter, in which diagnostic and prognostic uncertainty are high and resource-committing decisions need to be made. We propose an agenda for widening the application of predictive analytics in emergency care. Throughout, we express cautious optimism because there are myriad challenges related to database infrastructure, practitioner uptake, and patient acceptance. The quality of routinely compiled clinical data will remain an important limitation. Complementing big data sources with prospective data may be necessary if predictive analytics are to achieve their full potential to improve care quality in the emergency department. Copyright © 2015 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

Prediction model of sinoatrial node field potential using high order partial least squares.

PubMed

Feng, Yu; Cao, Hui; Zhang, Yanbin

2015-01-01

High order partial least squares (HOPLS) is a novel data processing method. It is highly suitable for building prediction model which has tensor input and output. The objective of this study is to build a prediction model of the relationship between sinoatrial node field potential and high glucose using HOPLS. The three sub-signals of the sinoatrial node field potential made up the model's input. The concentration and the actuation duration of high glucose made up the model's output. The results showed that on the premise of predicting two dimensional variables, HOPLS had the same predictive ability and a lower dispersion degree compared with partial least squares (PLS).

[Predicting individual risk of high healthcare cost to identify complex chronic patients].

PubMed

Coderch, Jordi; Sánchez-Pérez, Inma; Ibern, Pere; Carreras, Marc; Pérez-Berruezo, Xavier; Inoriza, José M

2014-01-01

To develop a predictive model for the risk of high consumption of healthcare resources, and assess the ability of the model to identify complex chronic patients. A cross-sectional study was performed within a healthcare management organization by using individual data from 2 consecutive years (88,795 people). The dependent variable consisted of healthcare costs above the 95th percentile (P95), including all services provided by the organization and pharmaceutical consumption outside of the institution. The predictive variables were age, sex, morbidity-based on clinical risk groups (CRG)-and selected data from previous utilization (use of hospitalization, use of high-cost drugs in ambulatory care, pharmaceutical expenditure). A univariate descriptive analysis was performed. We constructed a logistic regression model with a 95% confidence level and analyzed sensitivity, specificity, positive predictive values (PPV), and the area under the ROC curve (AUC). Individuals incurring costs >P95 accumulated 44% of total healthcare costs and were concentrated in ACRG3 (aggregated CRG level 3) categories related to multiple chronic diseases. All variables were statistically significant except for sex. The model had a sensitivity of 48.4% (CI: 46.9%-49.8%), specificity of 97.2% (CI: 97.0%-97.3%), PPV of 46.5% (CI: 45.0%-47.9%), and an AUC of 0.897 (CI: 0.892 to 0.902). High consumption of healthcare resources is associated with complex chronic morbidity. A model based on age, morbidity, and prior utilization is able to predict high-cost risk and identify a target population requiring proactive care. Copyright © 2013 SESPAS. Published by Elsevier Espana. All rights reserved.

Integrative analyses of miRNA and proteomics identify potential biological pathways associated with onset of pulmonary fibrosis in the bleomycin rat model

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

Fukunaga, Satoki; Environmental Health Science Laboratory, Sumitomo Chemical Co., Ltd., 3-1-98 Kasugade-Naka, Konohana-ku, Osaka 554-8558; Kakehashi, Anna

To determine miRNAs and their predicted target proteins regulatory networks which are potentially involved in onset of pulmonary fibrosis in the bleomycin rat model, we conducted integrative miRNA microarray and iTRAQ-coupled LC-MS/MS proteomic analyses, and evaluated the significance of altered biological functions and pathways. We observed that alterations of miRNAs and proteins are associated with the early phase of bleomycin-induced pulmonary fibrosis, and identified potential target pairs by using ingenuity pathway analysis. Using the data set of these alterations, it was demonstrated that those miRNAs, in association with their predicted target proteins, are potentially involved in canonical pathways reflective ofmore » initial epithelial injury and fibrogenic processes, and biofunctions related to induction of cellular development, movement, growth, and proliferation. Prediction of activated functions suggested that lung cells acquire proliferative, migratory, and invasive capabilities, and resistance to cell death especially in the very early phase of bleomycin-induced pulmonary fibrosis. The present study will provide new insights for understanding the molecular pathogenesis of idiopathic pulmonary fibrosis. - Highlights: • We analyzed bleomycin-induced pulmonary fibrosis in the rat. • Integrative analyses of miRNA microarray and proteomics were conducted. • We determined the alterations of miRNAs and their potential target proteins. • The alterations may control biological functions and pathways in pulmonary fibrosis. • Our result may provide new insights of pulmonary fibrosis.« less

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.

Hemisphere Differences in Speech-Sound Event-Related Potentials in Intensive Care Neonates: Associations and Predictive Value for Development in Infancy

PubMed Central

Maitre, Nathalie L.; Slaughter, James C.; Aschner, Judy L.; Key, Alexandra P.

2014-01-01

Neurodevelopmental delays in intensive care neonates are common but difficult to predict. In children, hemisphere differences in cortical processing of speech are predictive of cognitive performance. We hypothesized that hemisphere differences in auditory event-related potentials in intensive care neonates are predictive of neurodevelopment in infancy, even in those born preterm. Event-related potentials to speech sounds were prospectively recorded in 57 infants (gestational age 24–40 weeks) prior to discharge. The Developmental Assessment of Young Children was performed at 6 and 12 months. Hemisphere differences in mean amplitudes increased with postnatal age (P < .01) but not with gestational age. Greater hemisphere differences were associated with improved communication and cognitive scores at 6 and 12 months, but decreased in significance at 12 months after adjusting for socioeconomic and clinical factors. Auditory cortical responses can be used in intensive care neonates to help identify infants at higher risk for delays in infancy. PMID:23864588

Predicting the global warming potential of agro-ecosystems

NASA Astrophysics Data System (ADS)

Lehuger, S.; Gabrielle, B.; Larmanou, E.; Laville, P.; Cellier, P.; Loubet, B.

2007-04-01

Nitrous oxide, carbon dioxide and methane are the main biogenic greenhouse gases (GHG) contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate thus requires a capacity to predict the net exchanges of these gases in an integrated manner, as related to environmental conditions and crop management. Here, we used two year-round data sets from two intensively-monitored cropping systems in northern France to test the ability of the biophysical crop model CERES-EGC to simulate GHG exchanges at the plot-scale. The experiments involved maize and rapeseed crops on a loam and rendzina soils, respectively. The model was subsequently extrapolated to predict CO2 and N2O fluxes over an entire crop rotation. Indirect emissions (IE) arising from the production of agricultural inputs and from cropping operations were also added to the final GWP. One experimental site (involving a wheat-maize-barley rotation on a loamy soil) was a net source of GHG with a GWP of 350 kg CO2-C eq ha-1 yr-1, of which 75% were due to IE and 25% to direct N2O emissions. The other site (involving an oilseed rape-wheat-barley rotation on a rendzina) was a net sink of GHG for -250 kg CO2-C eq ha-1 yr-1, mainly due to a higher predicted C sequestration potential and C return from crops. Such modelling approach makes it possible to test various agronomic management scenarios, in order to design productive agro-ecosystems with low global warming impact.

Bioinformatics prediction of siRNAs as potential antiviral agents against dengue viruses

PubMed Central

Villegas-Rosales, Paula M; Méndez-Tenorio, Alfonso; Ortega-Soto, Elizabeth; Barrón, Blanca L

2012-01-01

Dengue virus (DENV 1-4) represents the major emerging arthropod-borne viral infection in the world. Currently, there is neither an available vaccine nor a specific treatment. Hence, there is a need of antiviral drugs for these viral infections; we describe the prediction of short interfering RNA (siRNA) as potential therapeutic agents against the four DENV serotypes. Our strategy was to carry out a series of multiple alignments using ClustalX program to find conserved sequences among the four DENV serotype genomes to obtain a consensus sequence for siRNAs design. A highly conserved sequence among the four DENV serotypes, located in the encoding sequence for NS4B and NS5 proteins was found. A total of 2,893 complete DENV genomes were downloaded from the NCBI, and after a depuration procedure to identify identical sequences, 220 complete DENV genomes were left. They were edited to select the NS4B and NS5 sequences, which were aligned to obtain a consensus sequence. Three different servers were used for siRNA design, and the resulting siRNAs were aligned to identify the most prevalent sequences. Three siRNAs were chosen, one targeted the genome region that codifies for NS4B protein and the other two; the region for NS5 protein. Predicted secondary structure for DENV genomes was used to demonstrate that the siRNAs were able to target the viral genome forming double stranded structures, necessary to activate the RNA silencing machinery. PMID:22829722

BFH-OST, a new predictive screening tool for identifying osteoporosis in postmenopausal Han Chinese women

PubMed Central

Ma, Zhao; Yang, Yong; Lin, JiSheng; Zhang, XiaoDong; Meng, Qian; Wang, BingQiang; Fei, Qi

2016-01-01

Purpose To develop a simple new clinical screening tool to identify primary osteoporosis by dual-energy X-ray absorptiometry (DXA) in postmenopausal women and to compare its validity with the Osteoporosis Self-Assessment Tool for Asians (OSTA) in a Han Chinese population. Methods A cross-sectional study was conducted, enrolling 1,721 community-dwelling postmenopausal Han Chinese women. All the subjects completed a structured questionnaire and had their bone mineral density measured using DXA. Using logistic regression analysis, we assessed the ability of numerous potential risk factors examined in the questionnaire to identify women with osteoporosis. Based on this analysis, we build a new predictive model, the Beijing Friendship Hospital Osteoporosis Self-Assessment Tool (BFH-OST). Receiver operating characteristic curves were generated to compare the validity of the new model and OSTA in identifying postmenopausal women at increased risk of primary osteoporosis as defined according to the World Health Organization criteria. Results At screening, it was found that of the 1,721 subjects with DXA, 22.66% had osteoporosis and a further 47.36% had osteopenia. Of the items screened in the questionnaire, it was found that age, weight, height, body mass index, personal history of fracture after the age of 45 years, history of fragility fracture in either parent, current smoking, and consumption of three of more alcoholic drinks per day were all predictive of osteoporosis. However, age at menarche and menopause, years since menopause, and number of pregnancies and live births were irrelevant in this study. The logistic regression analysis and item reduction yielded a final tool (BFH-OST) based on age, body weight, height, and history of fracture after the age of 45 years. The BFH-OST index (cutoff =9.1), which performed better than OSTA, had a sensitivity of 73.6% and a specificity of 72.7% for identifying osteoporosis, with an area under the receiver operating

Development of computational fluid dynamics--habitat suitability (CFD-HSI) models to identify potential passage--Challenge zones for migratory fishes in the Penobscot River

USGS Publications Warehouse

Haro, Alexander J.; Dudley, Robert W.; Chelminski, Michael

2012-01-01

A two-dimensional computational fluid dynamics-habitat suitability (CFD–HSI) model was developed to identify potential zones of shallow depth and high water velocity that may present passage challenges for five anadromous fish species in the Penobscot River, Maine, upstream from two existing dams and as a result of the proposed future removal of the dams. Potential depth-challenge zones were predicted for larger species at the lowest flow modeled in the dam-removal scenario. Increasing flows under both scenarios increased the number and size of potential velocity-challenge zones, especially for smaller species. This application of the two-dimensional CFD–HSI model demonstrated its capabilities to estimate the potential effects of flow and hydraulic alteration on the passage of migratory fish.

Identifying influential data points in hydrological model calibration and their impact on streamflow predictions

NASA Astrophysics Data System (ADS)

Wright, David; Thyer, Mark; Westra, Seth

2015-04-01

Highly influential data points are those that have a disproportionately large impact on model performance, parameters and predictions. However, in current hydrological modelling practice the relative influence of individual data points on hydrological model calibration is not commonly evaluated. This presentation illustrates and evaluates several influence diagnostics tools that hydrological modellers can use to assess the relative influence of data. The feasibility and importance of including influence detection diagnostics as a standard tool in hydrological model calibration is discussed. Two classes of influence diagnostics are evaluated: (1) computationally demanding numerical "case deletion" diagnostics; and (2) computationally efficient analytical diagnostics, based on Cook's distance. These diagnostics are compared against hydrologically orientated diagnostics that describe changes in the model parameters (measured through the Mahalanobis distance), performance (objective function displacement) and predictions (mean and maximum streamflow). These influence diagnostics are applied to two case studies: a stage/discharge rating curve model, and a conceptual rainfall-runoff model (GR4J). Removing a single data point from the calibration resulted in differences to mean flow predictions of up to 6% for the rating curve model, and differences to mean and maximum flow predictions of up to 10% and 17%, respectively, for the hydrological model. When using the Nash-Sutcliffe efficiency in calibration, the computationally cheaper Cook's distance metrics produce similar results to the case-deletion metrics at a fraction of the computational cost. However, Cooks distance is adapted from linear regression with inherit assumptions on the data and is therefore less flexible than case deletion. Influential point detection diagnostics show great potential to improve current hydrological modelling practices by identifying highly influential data points. The findings of this

Identifying predictive features in drug response using machine learning: opportunities and challenges.

PubMed

Vidyasagar, Mathukumalli

2015-01-01

This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

On the importance of identifying, characterizing, and predicting fundamental phenomena towards microbial electrochemistry applications.

PubMed

Torres, César Iván

2014-06-01

The development of microbial electrochemistry research toward technological applications has increased significantly in the past years, leading to many process configurations. This short review focuses on the need to identify and characterize the fundamental phenomena that control the performance of microbial electrochemical cells (MXCs). Specifically, it discusses the importance of recent efforts to discover and characterize novel microorganisms for MXC applications, as well as recent developments to understand transport limitations in MXCs. As we increase our understanding of how MXCs operate, it is imperative to continue modeling efforts in order to effectively predict their performance, design efficient MXC technologies, and implement them commercially. Thus, the success of MXC technologies largely depends on the path of identifying, understanding, and predicting fundamental phenomena that determine MXC performance. Copyright © 2013 Elsevier Ltd. All rights reserved.

Multivariate linear regression analysis to identify general factors for quantitative predictions of implant stability quotient values

PubMed Central

Huang, Hairong; Xu, Zanzan; Shao, Xianhong; Wismeijer, Daniel; Sun, Ping; Wang, Jingxiao

2017-01-01

Objectives This study identified potential general influencing factors for a mathematical prediction of implant stability quotient (ISQ) values in clinical practice. Methods We collected the ISQ values of 557 implants from 2 different brands (SICace and Osstem) placed by 2 surgeons in 336 patients. Surgeon 1 placed 329 SICace implants, and surgeon 2 placed 113 SICace implants and 115 Osstem implants. ISQ measurements were taken at T1 (immediately after implant placement) and T2 (before dental restoration). A multivariate linear regression model was used to analyze the influence of the following 11 candidate factors for stability prediction: sex, age, maxillary/mandibular location, bone type, immediate/delayed implantation, bone grafting, insertion torque, I-stage or II-stage healing pattern, implant diameter, implant length and T1-T2 time interval. Results The need for bone grafting as a predictor significantly influenced ISQ values in all three groups at T1 (weight coefficients ranging from -4 to -5). In contrast, implant diameter consistently influenced the ISQ values in all three groups at T2 (weight coefficients ranging from 3.4 to 4.2). Other factors, such as sex, age, I/II-stage implantation and bone type, did not significantly influence ISQ values at T2, and implant length did not significantly influence ISQ values at T1 or T2. Conclusions These findings provide a rational basis for mathematical models to quantitatively predict the ISQ values of implants in clinical practice. PMID:29084260

Discovering Potential Pathogens among Fungi Identified as Nonsporulating Molds▿

PubMed Central

Pounder, June I.; Simmon, Keith E.; Barton, Claudia A.; Hohmann, Sheri L.; Brandt, Mary E.; Petti, Cathy A.

2007-01-01

Fungal infections are increasing, particularly among immunocompromised hosts, and a rapid diagnosis is essential to initiate antifungal therapy. Often fungi cannot be identified by conventional methods and are classified as nonsporulating molds (NSM).We sequenced internal transcribed spacer regions from 50 cultures of NSM and found 16 potential pathogens that can be associated with clinical disease. In selected clinical settings, identification of NSM could prove valuable and have an immediate impact on patient management. PMID:17135442

Clinical prediction model to identify vulnerable patients in ambulatory surgery: towards optimal medical decision-making.

PubMed

Mijderwijk, Herjan; Stolker, Robert Jan; Duivenvoorden, Hugo J; Klimek, Markus; Steyerberg, Ewout W

2016-09-01

Ambulatory surgery patients are at risk of adverse psychological outcomes such as anxiety, aggression, fatigue, and depression. We developed and validated a clinical prediction model to identify patients who were vulnerable to these psychological outcome parameters. We prospectively assessed 383 mixed ambulatory surgery patients for psychological vulnerability, defined as the presence of anxiety (state/trait), aggression (state/trait), fatigue, and depression seven days after surgery. Three psychological vulnerability categories were considered-i.e., none, one, or multiple poor scores, defined as a score exceeding one standard deviation above the mean for each single outcome according to normative data. The following determinants were assessed preoperatively: sociodemographic (age, sex, level of education, employment status, marital status, having children, religion, nationality), medical (heart rate and body mass index), and psychological variables (self-esteem and self-efficacy), in addition to anxiety, aggression, fatigue, and depression. A prediction model was constructed using ordinal polytomous logistic regression analysis, and bootstrapping was applied for internal validation. The ordinal c-index (ORC) quantified the discriminative ability of the model, in addition to measures for overall model performance (Nagelkerke's R (2) ). In this population, 137 (36%) patients were identified as being psychologically vulnerable after surgery for at least one of the psychological outcomes. The most parsimonious and optimal prediction model combined sociodemographic variables (level of education, having children, and nationality) with psychological variables (trait anxiety, state/trait aggression, fatigue, and depression). Model performance was promising: R (2)  = 30% and ORC = 0.76 after correction for optimism. This study identified a substantial group of vulnerable patients in ambulatory surgery. The proposed clinical prediction model could allow healthcare

Proteomics-based approach identified differentially expressed proteins with potential roles in endometrial carcinoma.

PubMed

Li, Zhengyu; Min, Wenjiao; Huang, Canhua; Bai, Shujun; Tang, Minghai; Zhao, Xia

2010-01-01

We used proteomic approaches to identify altered expressed proteins in endometrial carcinoma, with the aim of discovering potential biomarkers or therapeutic targets for endometrial carcinoma. The global proteins extracted from endometrial carcinoma and normal endometrial tissues were separated by 2-dimensional electrophoresis and analyzed with PDQuest (Bio-Rad, Hercules, Calif) software. The differentially expressed spots were identified by mass spectrometry and searched against NCBInr protein database. Those proteins with potential roles were confirmed by Western blotting and immunohistochemical assays. Ninety-nine proteins were identified by mass spectrometry, and a cluster diagram analysis indicated that these proteins were involved in metabolism, cell transformation, protein folding, translation and modification, proliferation and apoptosis, signal transduction, cytoskeleton, and so on. In confirmatory immunoblotting and immunohistochemical analyses, overexpressions of epidermal fatty acid-binding protein, calcyphosine, and cyclophilin A were also observed in endometrial carcinoma tissues, which were consistent with the proteomic results. Our results suggested that these identified proteins, including epidermal fatty acid-binding protein, calcyphosine, and cyclophilin A, might be of potential values in the studies of endometrial carcinogenesis or investigations of diagnostic biomarkers or treatment targets for endometrial carcinoma.

In Silico Repositioning-Chemogenomics Strategy Identifies New Drugs with Potential Activity against Multiple Life Stages of Schistosoma mansoni

PubMed Central

Neves, Bruno J.; Braga, Rodolpho C.; Bezerra, José C. B.; Cravo, Pedro V. L.; Andrade, Carolina H.

2015-01-01

Morbidity and mortality caused by schistosomiasis are serious public health problems in developing countries. Because praziquantel is the only drug in therapeutic use, the risk of drug resistance is a concern. In the search for new schistosomicidal drugs, we performed a target-based chemogenomics screen of a dataset of 2,114 proteins to identify drugs that are approved for clinical use in humans that may be active against multiple life stages of Schistosoma mansoni. Each of these proteins was treated as a potential drug target, and its amino acid sequence was used to interrogate three databases: Therapeutic Target Database (TTD), DrugBank and STITCH. Predicted drug-target interactions were refined using a combination of approaches, including pairwise alignment, conservation state of functional regions and chemical space analysis. To validate our strategy, several drugs previously shown to be active against Schistosoma species were correctly predicted, such as clonazepam, auranofin, nifedipine, and artesunate. We were also able to identify 115 drugs that have not yet been experimentally tested against schistosomes and that require further assessment. Some examples are aprindine, gentamicin, clotrimazole, tetrabenazine, griseofulvin, and cinnarizine. In conclusion, we have developed a systematic and focused computer-aided approach to propose approved drugs that may warrant testing and/or serve as lead compounds for the design of new drugs against schistosomes. PMID:25569258

Exome sequencing of a large family identifies potential candidate genes contributing risk to bipolar disorder.

PubMed

Zhang, Tianxiao; Hou, Liping; Chen, David T; McMahon, Francis J; Wang, Jen-Chyong; Rice, John P

2018-03-01

Bipolar disorder is a mental illness with lifetime prevalence of about 1%. Previous genetic studies have identified multiple chromosomal linkage regions and candidate genes that might be associated with bipolar disorder. The present study aimed to identify potential susceptibility variants for bipolar disorder using 6 related case samples from a four-generation family. A combination of exome sequencing and linkage analysis was performed to identify potential susceptibility variants for bipolar disorder. Our study identified a list of five potential candidate genes for bipolar disorder. Among these five genes, GRID1(Glutamate Receptor Delta-1 Subunit), which was previously reported to be associated with several psychiatric disorders and brain related traits, is particularly interesting. Variants with functional significance in this gene were identified from two cousins in our bipolar disorder pedigree. Our findings suggest a potential role for these genes and the related rare variants in the onset and development of bipolar disorder in this one family. Additional research is needed to replicate these findings and evaluate their patho-biological significance. Copyright © 2017 Elsevier B.V. All rights reserved.

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

Hot spot analysis applied to identify ecosystem services potential in Lithuania

NASA Astrophysics Data System (ADS)

Pereira, Paulo; Depellegrin, Daniel; Misiune, Ieva

2016-04-01

Hot spot analysis are very useful to identify areas with similar characteristics. This is important for a sustainable use of the territory, since we can identify areas that need to be protected, or restored. This is a great advantage in terms of land use planning and management, since we can allocate resources, reduce the economical costs and do a better intervention in the landscape. Ecosystem services (ES) are different according land use. Since landscape is very heterogeneous, it is of major importance understand their spatial pattern and where are located the areas that provide better ES and the others that provide less services. The objective of this work is to use hot-spot analysis to identify areas with the most valuable ES in Lithuania. CORINE land-cover (CLC) of 2006 was used as the main spatial information. This classification uses a grid of 100 m resolution and extracted a total of 31 land use types. ES ranking was carried out based on expert knowledge. They were asked to evaluate the ES potential of each different CLC from 0 (no potential) to 5 (very high potential). Hot spot analysis were evaluated using the Getis-ord test, which identifies cluster analysis available in ArcGIS toolbox. This tool identifies areas with significantly high low values and significant high values at a p level of 0.05. In this work we used hot spot analysis to assess the distribution of providing, regulating cultural and total (sum of the previous 3) ES. The Z value calculated from Getis-ord was used to statistical analysis to access the clusters of providing, regulating cultural and total ES. ES with high Z value show that they have a high number of cluster areas with high potential of ES. The results showed that the Z-score was significantly different among services (Kruskal Wallis ANOVA =834. 607, p<0.001). The Z score of providing services (0.096±2.239) were significantly higher than the total (0.093±2.045), cultural (0.080±1.979) and regulating (0.076±1.961). These

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.

EEG potentials predict upcoming emergency brakings during simulated driving

NASA Astrophysics Data System (ADS)

Haufe, Stefan; Treder, Matthias S.; Gugler, Manfred F.; Sagebaum, Max; Curio, Gabriel; Blankertz, Benjamin

2011-10-01

Emergency braking assistance has the potential to prevent a large number of car crashes. State-of-the-art systems operate in two stages. Basic safety measures are adopted once external sensors indicate a potential upcoming crash. If further activity at the brake pedal is detected, the system automatically performs emergency braking. Here, we present the results of a driving simulator study indicating that the driver's intention to perform emergency braking can be detected based on muscle activation and cerebral activity prior to the behavioural response. Identical levels of predictive accuracy were attained using electroencephalography (EEG), which worked more quickly than electromyography (EMG), and using EMG, which worked more quickly than pedal dynamics. A simulated assistance system using EEG and EMG was found to detect emergency brakings 130 ms earlier than a system relying only on pedal responses. At 100 km h-1 driving speed, this amounts to reducing the braking distance by 3.66 m. This result motivates a neuroergonomic approach to driving assistance. Our EEG analysis yielded a characteristic event-related potential signature that comprised components related to the sensory registration of a critical traffic situation, mental evaluation of the sensory percept and motor preparation. While all these components should occur often during normal driving, we conjecture that it is their characteristic spatio-temporal superposition in emergency braking situations that leads to the considerable prediction performance we observed.

EEG potentials predict upcoming emergency brakings during simulated driving.

PubMed

Haufe, Stefan; Treder, Matthias S; Gugler, Manfred F; Sagebaum, Max; Curio, Gabriel; Blankertz, Benjamin

2011-10-01

Emergency braking assistance has the potential to prevent a large number of car crashes. State-of-the-art systems operate in two stages. Basic safety measures are adopted once external sensors indicate a potential upcoming crash. If further activity at the brake pedal is detected, the system automatically performs emergency braking. Here, we present the results of a driving simulator study indicating that the driver's intention to perform emergency braking can be detected based on muscle activation and cerebral activity prior to the behavioural response. Identical levels of predictive accuracy were attained using electroencephalography (EEG), which worked more quickly than electromyography (EMG), and using EMG, which worked more quickly than pedal dynamics. A simulated assistance system using EEG and EMG was found to detect emergency brakings 130 ms earlier than a system relying only on pedal responses. At 100 km h(-1) driving speed, this amounts to reducing the braking distance by 3.66 m. This result motivates a neuroergonomic approach to driving assistance. Our EEG analysis yielded a characteristic event-related potential signature that comprised components related to the sensory registration of a critical traffic situation, mental evaluation of the sensory percept and motor preparation. While all these components should occur often during normal driving, we conjecture that it is their characteristic spatio-temporal superposition in emergency braking situations that leads to the considerable prediction performance we observed.

Predicting the Risk of Clostridium difficile Infection upon Admission: A Score to Identify Patients for Antimicrobial Stewardship Efforts.

PubMed

Kuntz, Jennifer L; Smith, David H; Petrik, Amanda F; Yang, Xiuhai; Thorp, Micah L; Barton, Tracy; Barton, Karen; Labreche, Matthew; Spindel, Steven J; Johnson, Eric S

2016-01-01

Increasing morbidity and health care costs related to Clostridium difficile infection (CDI) have heightened interest in methods to identify patients who would most benefit from interventions to mitigate the likelihood of CDI. To develop a risk score that can be calculated upon hospital admission and used by antimicrobial stewards, including pharmacists and clinicians, to identify patients at risk for CDI who would benefit from enhanced antibiotic review and patient education. We assembled a cohort of Kaiser Permanente Northwest patients with a hospital admission from July 1, 2005, through December 30, 2012, and identified CDI in the six months following hospital admission. Using Cox regression, we constructed a score to identify patients at high risk for CDI on the basis of preadmission characteristics. We calculated and plotted the observed six-month CDI risk for each decile of predicted risk. We identified 721 CDIs following 54,186 hospital admissions-a 6-month incidence of 13.3 CDIs/1000 patient admissions. Patients with the highest predicted risk of CDI had an observed incidence of 53 CDIs/1000 patient admissions. The score differentiated between patients who do and do not develop CDI, with values for the extended C-statistic of 0.75. Predicted risk for CDI agreed closely with observed risk. Our risk score accurately predicted six-month risk for CDI using preadmission characteristics. Accurate predictions among the highest-risk patient subgroups allow for the identification of patients who could be targeted for and who would likely benefit from review of inpatient antibiotic use or enhanced educational efforts at the time of discharge planning.

Father involvement: Identifying and predicting family members' shared and unique perceptions.

PubMed

Dyer, W Justin; Day, Randal D; Harper, James M

2014-08-01

Father involvement research has typically not recognized that reports of involvement contain at least two components: 1 reflecting a view of father involvement that is broadly recognized in the family, and another reflecting each reporter's unique perceptions. Using a longitudinal sample of 302 families, this study provides a first examination of shared and unique views of father involvement (engagement and warmth) from the perspectives of fathers, children, and mothers. This study also identifies influences on these shared and unique perspectives. Father involvement reports were obtained when the child was 12 and 14 years old. Mother reports overlapped more with the shared view than father or child reports. This suggests the mother's view may be more in line with broadly recognized father involvement. Regarding antecedents, for fathers' unique view, a compensatory model partially explains results; that is, negative aspects of family life were positively associated with fathers' unique view. Children's unique view of engagement may partially reflect a sentiment override with father antisocial behaviors being predictive. Mothers' unique view of engagement was predicted by father and mother work hours and her unique view of warmth was predicted by depression and maternal gatekeeping. Taken, together finding suggests a far more nuanced view of father involvement should be considered.

Can Predictive Modeling Identify Head and Neck Oncology Patients at Risk for Readmission?

PubMed

Manning, Amy M; Casper, Keith A; Peter, Kay St; Wilson, Keith M; Mark, Jonathan R; Collar, Ryan M

2018-05-01

Objective Unplanned readmission within 30 days is a contributor to health care costs in the United States. The use of predictive modeling during hospitalization to identify patients at risk for readmission offers a novel approach to quality improvement and cost reduction. Study Design Two-phase study including retrospective analysis of prospectively collected data followed by prospective longitudinal study. Setting Tertiary academic medical center. Subjects and Methods Prospectively collected data for patients undergoing surgical treatment for head and neck cancer from January 2013 to January 2015 were used to build predictive models for readmission within 30 days of discharge using logistic regression, classification and regression tree (CART) analysis, and random forests. One model (logistic regression) was then placed prospectively into the discharge workflow from March 2016 to May 2016 to determine the model's ability to predict which patients would be readmitted within 30 days. Results In total, 174 admissions had descriptive data. Thirty-two were excluded due to incomplete data. Logistic regression, CART, and random forest predictive models were constructed using the remaining 142 admissions. When applied to 106 consecutive prospective head and neck oncology patients at the time of discharge, the logistic regression model predicted readmissions with a specificity of 94%, a sensitivity of 47%, a negative predictive value of 90%, and a positive predictive value of 62% (odds ratio, 14.9; 95% confidence interval, 4.02-55.45). Conclusion Prospectively collected head and neck cancer databases can be used to develop predictive models that can accurately predict which patients will be readmitted. This offers valuable support for quality improvement initiatives and readmission-related cost reduction in head and neck cancer care.

Identifying potential impact of lead contamination using a geographic information system

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

Bocco, G.; Sanchez, R.

1997-01-01

The main objective of this research was to identify the potential hazards associated with lead contamination from fixed sources in the city of Tijuana. An exploratory model is presented that describes the potential polluting sources as well as the exposed universe. The results of the analysis provide a clear picture of the geographic distribution of hazards areas for potential lead pollution in Tijuana. The findings are indicative of the dramatic consequences of rapid industrialization and urbanization in a city where there have not been significant planning efforts to mitigate the negative effects of this growth. The approach followed helps tomore » narrow the universe of potential pollution sources, which can help to direct attention, research priorities, and resources to the most critical areas. 16 refs.« less

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.

SVM-Based Prediction of Propeptide Cleavage Sites in Spider Toxins Identifies Toxin Innovation in an Australian Tarantula

PubMed Central

Wong, Emily S. W.; Hardy, Margaret C.; Wood, David; Bailey, Timothy; King, Glenn F.

2013-01-01

Spider neurotoxins are commonly used as pharmacological tools and are a popular source of novel compounds with therapeutic and agrochemical potential. Since venom peptides are inherently toxic, the host spider must employ strategies to avoid adverse effects prior to venom use. It is partly for this reason that most spider toxins encode a protective proregion that upon enzymatic cleavage is excised from the mature peptide. In order to identify the mature toxin sequence directly from toxin transcripts, without resorting to protein sequencing, the propeptide cleavage site in the toxin precursor must be predicted bioinformatically. We evaluated different machine learning strategies (support vector machines, hidden Markov model and decision tree) and developed an algorithm (SpiderP) for prediction of propeptide cleavage sites in spider toxins. Our strategy uses a support vector machine (SVM) framework that combines both local and global sequence information. Our method is superior or comparable to current tools for prediction of propeptide sequences in spider toxins. Evaluation of the SVM method on an independent test set of known toxin sequences yielded 96% sensitivity and 100% specificity. Furthermore, we sequenced five novel peptides (not used to train the final predictor) from the venom of the Australian tarantula Selenotypus plumipes to test the accuracy of the predictor and found 80% sensitivity and 99.6% 8-mer specificity. Finally, we used the predictor together with homology information to predict and characterize seven groups of novel toxins from the deeply sequenced venom gland transcriptome of S. plumipes, which revealed structural complexity and innovations in the evolution of the toxins. The precursor prediction tool (SpiderP) is freely available on ArachnoServer (http://www.arachnoserver.org/spiderP.html), a web portal to a comprehensive relational database of spider toxins. All training data, test data, and scripts used are available from the Spider

Identifying the necessary and sufficient number of risk factors for predicting academic failure.

PubMed

Lucio, Robert; Hunt, Elizabeth; Bornovalova, Marina

2012-03-01

Identifying the point at which individuals become at risk for academic failure (grade point average [GPA] < 2.0) involves an understanding of which and how many factors contribute to poor outcomes. School-related factors appear to be among the many factors that significantly impact academic success or failure. This study focused on 12 school-related factors. Using a thorough 5-step process, we identified which unique risk factors place one at risk for academic failure. Academic engagement, academic expectations, academic self-efficacy, homework completion, school relevance, school safety, teacher relationships (positive relationship), grade retention, school mobility, and school misbehaviors (negative relationship) were uniquely related to GPA even after controlling for all relevant covariates. Next, a receiver operating characteristic curve was used to determine a cutoff point for determining how many risk factors predict academic failure (GPA < 2.0). Results yielded a cutoff point of 2 risk factors for predicting academic failure, which provides a way for early identification of individuals who are at risk. Further implications of these findings are discussed. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Potential Predictability of U.S. Summer Climate with "Perfect" Soil Moisture

NASA Technical Reports Server (NTRS)

Yang, Fanglin; Kumar, Arun; Lau, K.-M.

2004-01-01

The potential predictability of surface-air temperature and precipitation over the United States continent was assessed for a GCM forced by observed sea surface temperatures and an estimate of observed ground soil moisture contents. The latter was obtained by substituting the GCM simulated precipitation, which is used to drive the GCM's land-surface component, with observed pentad-mean precipitation at each time step of the model's integration. With this substitution, the simulated soil moisture correlates well with an independent estimate of observed soil moisture in all seasons over the entire US continent. Significant enhancements on the predictability of surface-air temperature and precipitation were found in boreal late spring and summer over the US continent. Anomalous pattern correlations of precipitation and surface-air temperature over the US continent in the June-July-August season averaged for the 1979-2000 period increased from 0.01 and 0.06 for the GCM simulations without precipitation substitution to 0.23 and 0.3 1, respectively, for the simulations with precipitation substitution. Results provide an estimate for the limits of potential predictability if soil moisture variability is to be perfectly predicted. However, this estimate may be model dependent, and needs to be substantiated by other modeling groups.

Many amino acid substitution variants identified in DNA repair genes during human population screenings are predicted to impact protein function

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

Xi, T; Jones, I M; Mohrenweiser, H W

2003-11-03

Over 520 different amino acid substitution variants have been previously identified in the systematic screening of 91 human DNA repair genes for sequence variation. Two algorithms were employed to predict the impact of these amino acid substitutions on protein activity. Sorting Intolerant From Tolerant (SIFT) classified 226 of 508 variants (44%) as ''Intolerant''. Polymorphism Phenotyping (PolyPhen) classed 165 of 489 amino acid substitutions (34%) as ''Probably or Possibly Damaging''. Another 9-15% of the variants were classed as ''Potentially Intolerant or Damaging''. The results from the two algorithms are highly associated, with concordance in predicted impact observed for {approx}62% of themore » variants. Twenty one to thirty one percent of the variant proteins are predicted to exhibit reduced activity by both algorithms. These variants occur at slightly lower individual allele frequency than do the variants classified as ''Tolerant'' or ''Benign''. Both algorithms correctly predicted the impact of 26 functionally characterized amino acid substitutions in the APE1 protein on biochemical activity, with one exception. It is concluded that a substantial fraction of the missense variants observed in the general human population are functionally relevant. These variants are expected to be the molecular genetic and biochemical basis for the associations of reduced DNA repair capacity phenotypes with elevated cancer risk.« less

A hadoop-based method to predict potential effective drug combination.

PubMed

Sun, Yifan; Xiong, Yi; Xu, Qian; Wei, Dongqing

2014-01-01

Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request.

A Hadoop-Based Method to Predict Potential Effective Drug Combination

PubMed Central

Xiong, Yi; Xu, Qian; Wei, Dongqing

2014-01-01

Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request. PMID:25147789

HumanMethylation450K Array–Identified Biomarkers Predict Tumour Recurrence/Progression at Initial Diagnosis of High-risk Non-muscle Invasive Bladder Cancer

PubMed Central

Kitchen, Mark O; Bryan, Richard T; Emes, Richard D; Luscombe, Christopher J; Cheng, KK; Zeegers, Maurice P; James, Nicholas D; Gommersall, Lyndon M; Fryer, Anthony A

2018-01-01

Background: High-risk non-muscle invasive bladder cancer (HR-NMIBC) is a clinically unpredictable disease. Despite clinical risk estimation tools, many patients are undertreated with intra-vesical therapies alone, whereas others may be over-treated with early radical surgery. Molecular biomarkers, particularly DNA methylation, have been reported as predictive of tumour/patient outcomes in numerous solid organ and haematologic malignancies; however, there are few reports in HR-NMIBC and none using genome-wide array assessment. We therefore sought to identify novel DNA methylation markers of HR-NMIBC clinical outcomes that might predict tumour behaviour at initial diagnosis and help guide patient management. Patients and methods: A total of 21 primary initial diagnosis HR-NMIBC tumours were analysed by Illumina HumanMethylation450 BeadChip arrays and subsequently bisulphite Pyrosequencing. In all, 7 had not recurred at 1 year after resection and 14 had recurred and/or progressed despite intra-vesical BCG. A further independent cohort of 32 HR-NMIBC tumours (17 no recurrence and 15 recurrence and/or progression despite BCG) were also assessed by bisulphite Pyrosequencing. Results: Array analyses identified 206 CpG loci that segregated non-recurrent HR-NMIBC tumours from clinically more aggressive recurrence/progression tumours. Hypermethylation of CpG cg11850659 and hypomethylation of CpG cg01149192 in combination predicted HR-NMIBC recurrence and/or progression within 1 year of diagnosis with 83% sensitivity, 79% specificity, and 83% positive and 79% negative predictive values. Conclusions: This is the first genome-wide DNA methylation analysis of a unique HR-NMIBC tumour cohort encompassing known 1-year clinical outcomes. Our analyses identified potential novel epigenetic markers that could help guide individual patient management in this clinically unpredictable disease. PMID:29343995

Identifying dyslexia in adults: an iterative method using the predictive value of item scores and self-report questions.

PubMed

Tamboer, Peter; Vorst, Harrie C M; Oort, Frans J

2014-04-01

Methods for identifying dyslexia in adults vary widely between studies. Researchers have to decide how many tests to use, which tests are considered to be the most reliable, and how to determine cut-off scores. The aim of this study was to develop an objective and powerful method for diagnosing dyslexia. We took various methodological measures, most of which are new compared to previous methods. We used a large sample of Dutch first-year psychology students, we considered several options for exclusion and inclusion criteria, we collected as many cognitive tests as possible, we used six independent sources of biographical information for a criterion of dyslexia, we compared the predictive power of discriminant analyses and logistic regression analyses, we used both sum scores and item scores as predictor variables, we used self-report questions as predictor variables, and we retested the reliability of predictions with repeated prediction analyses using an adjusted criterion. We were able to identify 74 dyslexic and 369 non-dyslexic students. For 37 students, various predictions were too inconsistent for a final classification. The most reliable predictions were acquired with item scores and self-report questions. The main conclusion is that it is possible to identify dyslexia with a high reliability, although the exact nature of dyslexia is still unknown. We therefore believe that this study yielded valuable information for future methods of identifying dyslexia in Dutch as well as in other languages, and that this would be beneficial for comparing studies across countries.

Evoked potentials recorded during routine EEG predict outcome after perinatal asphyxia.

PubMed

Nevalainen, Päivi; Marchi, Viviana; Metsäranta, Marjo; Lönnqvist, Tuula; Toiviainen-Salo, Sanna; Vanhatalo, Sampsa; Lauronen, Leena

2017-07-01

To evaluate the added value of somatosensory (SEPs) and visual evoked potentials (VEPs) recorded simultaneously with routine EEG in early outcome prediction of newborns with hypoxic-ischemic encephalopathy under modern intensive care. We simultaneously recorded multichannel EEG, median nerve SEPs, and flash VEPs during the first few postnatal days in 50 term newborns with hypoxic-ischemic encephalopathy. EEG background was scored into five grades and the worst two grades were considered to indicate poor cerebral recovery. Evoked potentials were classified as absent or present. Clinical outcome was determined from the medical records at a median age of 21months. Unfavorable outcome included cerebral palsy, severe mental retardation, severe epilepsy, or death. The accuracy of outcome prediction was 98% with SEPs compared to 90% with EEG. EEG alone always predicted unfavorable outcome when it was inactive (n=9), and favorable outcome when it was normal or only mildly abnormal (n=17). However, newborns with moderate or severe EEG background abnormality could have either favorable or unfavorable outcome, which was correctly predicted by SEP in all but one newborn (accuracy in this subgroup 96%). Absent VEPs were always associated with an inactive EEG, and an unfavorable outcome. However, presence of VEPs did not guarantee a favorable outcome. SEPs accurately predict clinical outcomes in newborns with hypoxic-ischemic encephalopathy and improve the EEG-based prediction particularly in those newborns with severely or moderately abnormal EEG findings. SEPs should be added to routine EEG recordings for early bedside assessment of newborns with hypoxic-ischemic encephalopathy. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets.

PubMed

Fletez-Brant, Christopher; Lee, Dongwon; McCallion, Andrew S; Beer, Michael A

2013-07-01

Massively parallel sequencing technologies have made the generation of genomic data sets a routine component of many biological investigations. For example, Chromatin immunoprecipitation followed by sequence assays detect genomic regions bound (directly or indirectly) by specific factors, and DNase-seq identifies regions of open chromatin. A major bottleneck in the interpretation of these data is the identification of the underlying DNA sequence code that defines, and ultimately facilitates prediction of, these transcription factor (TF) bound or open chromatin regions. We have recently developed a novel computational methodology, which uses a support vector machine (SVM) with kmer sequence features (kmer-SVM) to identify predictive combinations of short transcription factor-binding sites, which determine the tissue specificity of these genomic assays (Lee, Karchin and Beer, Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011; 21:2167-80). This regulatory information can (i) give confidence in genomic experiments by recovering previously known binding sites, and (ii) reveal novel sequence features for subsequent experimental testing of cooperative mechanisms. Here, we describe the development and implementation of a web server to allow the broader research community to independently apply our kmer-SVM to analyze and interpret their genomic datasets. We analyze five recently published data sets and demonstrate how this tool identifies accessory factors and repressive sequence elements. kmer-SVM is available at http://kmersvm.beerlab.org.

kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets

PubMed Central

Fletez-Brant, Christopher; Lee, Dongwon; McCallion, Andrew S.; Beer, Michael A.

2013-01-01

Massively parallel sequencing technologies have made the generation of genomic data sets a routine component of many biological investigations. For example, Chromatin immunoprecipitation followed by sequence assays detect genomic regions bound (directly or indirectly) by specific factors, and DNase-seq identifies regions of open chromatin. A major bottleneck in the interpretation of these data is the identification of the underlying DNA sequence code that defines, and ultimately facilitates prediction of, these transcription factor (TF) bound or open chromatin regions. We have recently developed a novel computational methodology, which uses a support vector machine (SVM) with kmer sequence features (kmer-SVM) to identify predictive combinations of short transcription factor-binding sites, which determine the tissue specificity of these genomic assays (Lee, Karchin and Beer, Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011; 21:2167–80). This regulatory information can (i) give confidence in genomic experiments by recovering previously known binding sites, and (ii) reveal novel sequence features for subsequent experimental testing of cooperative mechanisms. Here, we describe the development and implementation of a web server to allow the broader research community to independently apply our kmer-SVM to analyze and interpret their genomic datasets. We analyze five recently published data sets and demonstrate how this tool identifies accessory factors and repressive sequence elements. kmer-SVM is available at http://kmersvm.beerlab.org. PMID:23771147

Potential impact of initialization on decadal predictions as assessed for CMIP5 models

NASA Astrophysics Data System (ADS)

Branstator, Grant; Teng, Haiyan

2012-06-01

To investigate the potential for initialization to improve decadal range predictions, we quantify the initial value predictability of upper 300 m temperature in the two northern ocean basins for 12 models from Coupled Model Intercomparison Project phase 5 (CMIP5), and we contrast it with the forced predictability in Representative Concentration Pathways (RCP) 4.5 climate change projections. We use a recently introduced method that produces predictability estimates from long control runs. Many initial states are considered, and we find on average 1) initialization has the potential to improve skill in the first 5 years in the North Pacific and the first 9 years in the North Atlantic, and 2) the impact from initialization becomes secondary compared to the impact of RCP4.5 forcing after 6 1/2 and 8 years in the two basins, respectively. Model-to-model and spatial variations in these limits are, however, substantial.

Genetic regulation of gene expression in the lung identifies CST3 and CD22 as potential causal genes for airflow obstruction.

PubMed

Lamontagne, Maxime; Timens, Wim; Hao, Ke; Bossé, Yohan; Laviolette, Michel; Steiling, Katrina; Campbell, Joshua D; Couture, Christian; Conti, Massimo; Sherwood, Karen; Hogg, James C; Brandsma, Corry-Anke; van den Berge, Maarten; Sandford, Andrew; Lam, Stephen; Lenburg, Marc E; Spira, Avrum; Paré, Peter D; Nickle, David; Sin, Don D; Postma, Dirkje S

2014-11-01

COPD is a complex chronic disease with poorly understood pathogenesis. Integrative genomic approaches have the potential to elucidate the biological networks underlying COPD and lung function. We recently combined genome-wide genotyping and gene expression in 1111 human lung specimens to map expression quantitative trait loci (eQTL). To determine causal associations between COPD and lung function-associated single nucleotide polymorphisms (SNPs) and lung tissue gene expression changes in our lung eQTL dataset. We evaluated causality between SNPs and gene expression for three COPD phenotypes: FEV(1)% predicted, FEV(1)/FVC and COPD as a categorical variable. Different models were assessed in the three cohorts independently and in a meta-analysis. SNPs associated with a COPD phenotype and gene expression were subjected to causal pathway modelling and manual curation. In silico analyses evaluated functional enrichment of biological pathways among newly identified causal genes. Biologically relevant causal genes were validated in two separate gene expression datasets of lung tissues and bronchial airway brushings. High reliability causal relations were found in SNP-mRNA-phenotype triplets for FEV(1)% predicted (n=169) and FEV(1)/FVC (n=80). Several genes of potential biological relevance for COPD were revealed. eQTL-SNPs upregulating cystatin C (CST3) and CD22 were associated with worse lung function. Signalling pathways enriched with causal genes included xenobiotic metabolism, apoptosis, protease-antiprotease and oxidant-antioxidant balance. By using integrative genomics and analysing the relationships of COPD phenotypes with SNPs and gene expression in lung tissue, we identified CST3 and CD22 as potential causal genes for airflow obstruction. This study also augmented the understanding of previously described COPD pathways. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Identifying black swans in NextGen: predicting human performance in off-nominal conditions.

PubMed

Wickens, Christopher D; Hooey, Becky L; Gore, Brian F; Sebok, Angelia; Koenicke, Corey S

2009-10-01

The objective is to validate a computational model of visual attention against empirical data--derived from a meta-analysis--of pilots' failure to notice safety-critical unexpected events. Many aircraft accidents have resulted, in part, because of failure to notice nonsalient unexpected events outside of foveal vision, illustrating the phenomenon of change blindness. A model of visual noticing, N-SEEV (noticing-salience, expectancy, effort, and value), was developed to predict these failures. First, 25 studies that reported objective data on miss rate for unexpected events in high-fidelity cockpit simulations were identified, and their miss rate data pooled across five variables (phase of flight, event expectancy, event location, presence of a head-up display, and presence of a highway-in-the-sky display). Second, the parameters of the N-SEEV model were tailored to mimic these dichotomies. The N-SEEV model output predicted variance in the obtained miss rate (r = .73). The individual miss rates of all six dichotomous conditions were predicted within 14%, and four of these were predicted within 7%. The N-SEEV model, developed on the basis of an independent data set, was able to successfully predict variance in this safety-critical measure of pilot response to abnormal circumstances, as collected from the literature. As new technology and procedures are envisioned for the future airspace, it is important to predict if these may compromise safety in terms of pilots' failing to notice unexpected events. Computational models such as N-SEEV support cost-effective means of making such predictions.

Spatial analysis of plague in California: niche modeling predictions of the current distribution and potential response to climate change

PubMed Central

Holt, Ashley C; Salkeld, Daniel J; Fritz, Curtis L; Tucker, James R; Gong, Peng

2009-01-01

Background Plague, caused by the bacterium Yersinia pestis, is a public and wildlife health concern in California and the western United States. This study explores the spatial characteristics of positive plague samples in California and tests Maxent, a machine-learning method that can be used to develop niche-based models from presence-only data, for mapping the potential distribution of plague foci. Maxent models were constructed using geocoded seroprevalence data from surveillance of California ground squirrels (Spermophilus beecheyi) as case points and Worldclim bioclimatic data as predictor variables, and compared and validated using area under the receiver operating curve (AUC) statistics. Additionally, model results were compared to locations of positive and negative coyote (Canis latrans) samples, in order to determine the correlation between Maxent model predictions and areas of plague risk as determined via wild carnivore surveillance. Results Models of plague activity in California ground squirrels, based on recent climate conditions, accurately identified case locations (AUC of 0.913 to 0.948) and were significantly correlated with coyote samples. The final models were used to identify potential plague risk areas based on an ensemble of six future climate scenarios. These models suggest that by 2050, climate conditions may reduce plague risk in the southern parts of California and increase risk along the northern coast and Sierras. Conclusion Because different modeling approaches can yield substantially different results, care should be taken when interpreting future model predictions. Nonetheless, niche modeling can be a useful tool for exploring and mapping the potential response of plague activity to climate change. The final models in this study were used to identify potential plague risk areas based on an ensemble of six future climate scenarios, which can help public managers decide where to allocate surveillance resources. In addition, Maxent

Predicting Energy Consumption for Potential Effective Use in Hybrid Vehicle Powertrain Management Using Driver Prediction

NASA Astrophysics Data System (ADS)

Magnuson, Brian

A proof-of-concept software-in-the-loop study is performed to assess the accuracy of predicted net and charge-gaining energy consumption for potential effective use in optimizing powertrain management of hybrid vehicles. With promising results of improving fuel efficiency of a thermostatic control strategy for a series, plug-ing, hybrid-electric vehicle by 8.24%, the route and speed prediction machine learning algorithms are redesigned and implemented for real- world testing in a stand-alone C++ code-base to ingest map data, learn and predict driver habits, and store driver data for fast startup and shutdown of the controller or computer used to execute the compiled algorithm. Speed prediction is performed using a multi-layer, multi-input, multi- output neural network using feed-forward prediction and gradient descent through back- propagation training. Route prediction utilizes a Hidden Markov Model with a recurrent forward algorithm for prediction and multi-dimensional hash maps to store state and state distribution constraining associations between atomic road segments and end destinations. Predicted energy is calculated using the predicted time-series speed and elevation profile over the predicted route and the road-load equation. Testing of the code-base is performed over a known road network spanning 24x35 blocks on the south hill of Spokane, Washington. A large set of training routes are traversed once to add randomness to the route prediction algorithm, and a subset of the training routes, testing routes, are traversed to assess the accuracy of the net and charge-gaining predicted energy consumption. Each test route is traveled a random number of times with varying speed conditions from traffic and pedestrians to add randomness to speed prediction. Prediction data is stored and analyzed in a post process Matlab script. The aggregated results and analysis of all traversals of all test routes reflect the performance of the Driver Prediction algorithm. The

Predictive Validity of Curriculum-Embedded Measures on Outcomes of Kindergarteners Identified as At Risk for Reading Difficulty

ERIC Educational Resources Information Center

Oslund, Eric L.; Hagan-Burke, Shanna; Simmons, Deborah C.; Clemens, Nathan H.; Simmons, Leslie E.; Taylor, Aaron B.; Kwok, Oi-man; Coyne, Michael D.

2017-01-01

This study examined the predictive validity of formative assessments embedded in a Tier 2 intervention curriculum for kindergarten students identified as at risk for reading difficulty. We examined when (i.e., months during the school year) measures could predict reading outcomes gathered at the end of kindergarten and whether the predictive…

Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study.

PubMed

Rastegar-Mojarad, Majid; Liu, Hongfang; Nambisan, Priya

2016-06-16

Drug repurposing (defined as discovering new indications for existing drugs) could play a significant role in drug development, especially considering the declining success rates of developing novel drugs. Typically, new indications for existing medications are identified by accident. However, new technologies and a large number of available resources enable the development of systematic approaches to identify and validate drug-repurposing candidates. Patients today report their experiences with medications on social media and reveal side effects as well as beneficial effects of those medications. Our aim was to assess the feasibility of using patient reviews from social media to identify potential candidates for drug repurposing. We retrieved patient reviews of 180 medications from an online forum, WebMD. Using dictionary-based and machine learning approaches, we identified disease names in the reviews. Several publicly available resources were used to exclude comments containing known indications and adverse drug effects. After manually reviewing some of the remaining comments, we implemented a rule-based system to identify beneficial effects. The dictionary-based system and machine learning system identified 2178 and 6171 disease names respectively in 64,616 patient comments. We provided a list of 10 common patterns that patients used to report any beneficial effects or uses of medication. After manually reviewing the comments tagged by our rule-based system, we identified five potential drug repurposing candidates. To our knowledge, this is the first study to consider using social media data to identify drug-repurposing candidates. We found that even a rule-based system, with a limited number of rules, could identify beneficial effect mentions in patient comments. Our preliminary study shows that social media has the potential to be used in drug repurposing.

Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes

NASA Astrophysics Data System (ADS)

Oh, Jung Hun; Kerns, Sarah; Ostrer, Harry; Powell, Simon N.; Rosenstein, Barry; Deasy, Joseph O.

2017-02-01

The biological cause of clinically observed variability of normal tissue damage following radiotherapy is poorly understood. We hypothesized that machine/statistical learning methods using single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) would identify groups of patients of differing complication risk, and furthermore could be used to identify key biological sources of variability. We developed a novel learning algorithm, called pre-conditioned random forest regression (PRFR), to construct polygenic risk models using hundreds of SNPs, thereby capturing genomic features that confer small differential risk. Predictive models were trained and validated on a cohort of 368 prostate cancer patients for two post-radiotherapy clinical endpoints: late rectal bleeding and erectile dysfunction. The proposed method results in better predictive performance compared with existing computational methods. Gene ontology enrichment analysis and protein-protein interaction network analysis are used to identify key biological processes and proteins that were plausible based on other published studies. In conclusion, we confirm that novel machine learning methods can produce large predictive models (hundreds of SNPs), yielding clinically useful risk stratification models, as well as identifying important underlying biological processes in the radiation damage and tissue repair process. The methods are generally applicable to GWAS data and are not specific to radiotherapy endpoints.

Identifying and Predicting Profiles of Medical Noncompliance: Pediatric Caregivers' Antibiotic Stewardship.

PubMed

Smith, Rachel A; Kim, Youllee; M'Ikanatha, Nkuchia M

2018-05-14

Sometimes compliance with medical recommendations is problematic. We investigated pediatric caregivers' (N = 606) patterns of noncompliance with antibiotic stewardship based on the obstacle hypothesis. We tested predictors of noncompliance framed by the obstacle hypothesis, dissonance theory, and psychological reactance. The results revealed four profiles of caregivers' stewardship: one marked by compliance (Stewards) and three marked by types of noncompliance (Stockers, Persuaders, and Dissenters). The covariate analysis showed that, although psychological reactance predicted being noncompliant, it was types of obstacles and discrepant experiences that predicted caregivers' patterns of noncompliance with antibiotic stewardship. Campaign planning often focuses on identifying the belief most associated with the targeted outcome, such as compliance. Noncompliance research, however, points out that persuaders may be successful to the extent to which they anticipate obstacles to compliance and address them in their influence attempts. A shift from medical noncompliance to patient engagement also affords an opportunity to consider how some recommendations create obstacles for others and to find positive ways to embrace conflicting needs, tensions, and reasons for refusal in order to promote collective goals.

[Potential distribution of Panax ginseng and its predicted responses to climate change.

PubMed

Zhao, Ze Fang; Wei, Hai Yan; Guo, Yan Long; Gu, Wei

2016-11-18

This study utilized Panax ginseng as the research object. Based on BioMod2 platform, with species presence data and 22 climatic variables, the potential geographic distribution of P. ginseng under the current conditions in northeast China was simulated with ten species distribution model. And then with the receiver-operating characteristic curve (ROC) as weights, we build an ensemble model, which integrated the results of 10 models, using the ensemble model, the future distributions of P. ginseng were also projected for the periods 2050s and 2070s under the climate change scenarios of RCP 8.5, RCP 6, RCP 4.5 and RCP 2.6 emission scenarios described in the Special Report on Emissions Scenarios (SRES) of IPCC (Intergovernmental Panel on Climate Change). The results showed that for the entire region of study area, under the present climatic conditions, 10.4% of the areas were identified as suitable habitats, which were mainly located in northeast Changbai Mountains area and the southeastern region of the Xiaoxing'an Mountains. The model simulations indicated that the suitable habitats would have a relatively significant change under the different climate change scenarios, and generally the range of suitable habitats would be a certain degree of decrease. Meanwhile, the goodness-of-fit, predicted ranges, and weights of explanatory variables was various for each model. And according to the goodness-of-fit, Maxent had the highest model performance, and GAM, RF and ANN were followed, while SRE had the lowest prediction accuracy. In this study we established an ensemble model, which could improve the accuracy of the existing species distribution models, and optimization of species distribution prediction results.

Identifying the bleeding trauma patient: predictive factors for massive transfusion in an Australasian trauma population.

PubMed

Hsu, Jeremy Ming; Hitos, Kerry; Fletcher, John P

2013-09-01

Military and civilian data would suggest that hemostatic resuscitation results in improved outcomes for exsanguinating patients. However, identification of those patients who are at risk of significant hemorrhage is not clearly defined. We attempted to identify factors that would predict the need for massive transfusion (MT) in an Australasian trauma population, by comparing those trauma patients who did receive massive transfusion with those who did not. Between 1985 and 2010, 1,686 trauma patients receiving at least 1 U of packed red blood cells were identified from our prospectively maintained trauma registry. Demographic, physiologic, laboratory, injury, and outcome variables were reviewed. Univariate analysis determined significant factors between those who received MT and those who did not. A predictive multivariate logistic regression model with backward conditional stepwise elimination was used for MT risk. Statistical analysis was performed using SPSS PASW. MT patients had a higher pulse rate, lower Glasgow Coma Scale (GCS) score, lower systolic blood pressure, lower hemoglobin level, higher Injury Severity Score (ISS), higher international normalized ratio (INR), and longer stay. Initial logistic regression identified base deficit (BD), INR, and hemoperitoneum at laparotomy as independent predictive variables. After assigning cutoff points of BD being greater than 5 and an INR of 1.5 or greater, a further model was created. A BD greater than 5 and either INR of 1.5 or greater or hemoperitoneum was associated with 51 times increase in MT risk (odds ratio, 51.6; 95% confidence interval, 24.9-95.8). The area under the receiver operating characteristic curve for the model was 0.859. From this study, a combination of BD, INR, and hemoperitoneum has demonstrated good predictability for MT. This tool may assist in the determination of those patients who might benefit from hemostatic resuscitation. Prognostic study, level III.

Testing predictions of the quantum landscape multiverse 2: the exponential inflationary potential

NASA Astrophysics Data System (ADS)

Di Valentino, Eleonora; Mersini-Houghton, Laura

2017-03-01

The 2015 Planck data release tightened the region of the allowed inflationary models. Inflationary models with convex potentials have now been ruled out since they produce a large tensor to scalar ratio. Meanwhile the same data offers interesting hints on possible deviations from the standard picture of CMB perturbations. Here we revisit the predictions of the theory of the origin of the universe from the landscape multiverse for the case of exponential inflation, for two reasons: firstly to check the status of the anomalies associated with this theory, in the light of the recent Planck data; secondly, to search for a counterexample whereby new physics modifications may bring convex inflationary potentials, thought to have been ruled out, back into the region of potentials allowed by data. Using the exponential inflation as an example of convex potentials, we find that the answer to both tests is positive: modifications to the perturbation spectrum and to the Newtonian potential of the universe originating from the quantum entanglement, bring the exponential potential, back within the allowed region of current data; and, the series of anomalies previously predicted in this theory, is still in good agreement with current data. Hence our finding for this convex potential comes at the price of allowing for additional thermal relic particles, equivalently dark radiation, in the early universe.

Identifying potential selective fluorescent probes for cancer-associated protein carbonic anhydrase IX using a computational approach.

PubMed

Kamstra, Rhiannon L; Floriano, Wely B

2014-11-01

Carbonic anhydrase IX (CAIX) is a biomarker for tumor hypoxia. Fluorescent inhibitors of CAIX have been used to study hypoxic tumor cell lines. However, these inhibitor-based fluorescent probes may have a therapeutic effect that is not appropriate for monitoring treatment efficacy. In the search for novel fluorescent probes that are not based on known inhibitors, a database of 20,860 fluorescent compounds was virtually screened against CAIX using hierarchical virtual ligand screening (HierVLS). The screening database contained 14,862 compounds tagged with the ATTO680 fluorophore plus an additional 5998 intrinsically fluorescent compounds. Overall ranking of compounds to identify hit molecular probe candidates utilized a principal component analysis (PCA) approach. Four potential binding sites, including the catalytic site, were identified within the structure of the protein and targeted for virtual screening. Available sequence information for 23 carbonic anhydrase isoforms was used to prioritize the four sites based on the estimated "uniqueness" of each site in CAIX relative to the other isoforms. A database of 32 known inhibitors and 478 decoy compounds was used to validate the methodology. A receiver-operating characteristic (ROC) analysis using the first principal component (PC1) as predictive score for the validation database yielded an area under the curve (AUC) of 0.92. AUC is interpreted as the probability that a binder will have a better score than a non-binder. The use of first component analysis of binding energies for multiple sites is a novel approach for hit selection. The very high prediction power for this approach increases confidence in the outcome from the fluorescent library screening. Ten of the top scoring candidates for isoform-selective putative binding sites are suggested for future testing as fluorescent molecular probe candidates. Copyright © 2014 Elsevier Inc. All rights reserved.

Identifying High Academic Potential in Australian Aboriginal Children Using Dynamic Testing

ERIC Educational Resources Information Center

Chaffey, Graham W.; Bailey, Stan B.; Vine, Ken W.

2015-01-01

The primary purpose of this study was to determine the effectiveness of dynamic testing as a method for identifying high academic potential in Australian Aboriginal children. The 79 participating Aboriginal children were drawn from Years 3-5 in rural schools in northern New South Wales. The dynamic testing method used in this study involved a…

Predicting the distributions of Egypt's medicinal plants and their potential shifts under future climate change.

PubMed

Kaky, Emad; Gilbert, Francis

2017-01-01

Climate change is one of the most difficult of challenges to conserving biodiversity, especially for countries with few data on the distributions of their taxa. Species distribution modelling is a modern approach to the assessment of the potential effects of climate change on biodiversity, with the great advantage of being robust to small amounts of data. Taking advantage of a recently validated dataset, we use the medicinal plants of Egypt to identify hotspots of diversity now and in the future by predicting the effect of climate change on the pattern of species richness using species distribution modelling. Then we assess how Egypt's current Protected Area network is likely to perform in protecting plants under climate change. The patterns of species richness show that in most cases the A2a 'business as usual' scenario was more harmful than the B2a 'moderate mitigation' scenario. Predicted species richness inside Protected Areas was higher than outside under all scenarios, indicating that Egypt's PAs are well placed to help conserve medicinal plants.

Genomic Models of Short-Term Exposure Accurately Predict Long-Term Chemical Carcinogenicity and Identify Putative Mechanisms of Action

PubMed Central

Gusenleitner, Daniel; Auerbach, Scott S.; Melia, Tisha; Gómez, Harold F.; Sherr, David H.; Monti, Stefano

2014-01-01

Background Despite an overall decrease in incidence of and mortality from cancer, about 40% of Americans will be diagnosed with the disease in their lifetime, and around 20% will die of it. Current approaches to test carcinogenic chemicals adopt the 2-year rodent bioassay, which is costly and time-consuming. As a result, fewer than 2% of the chemicals on the market have actually been tested. However, evidence accumulated to date suggests that gene expression profiles from model organisms exposed to chemical compounds reflect underlying mechanisms of action, and that these toxicogenomic models could be used in the prediction of chemical carcinogenicity. Results In this study, we used a rat-based microarray dataset from the NTP DrugMatrix Database to test the ability of toxicogenomics to model carcinogenicity. We analyzed 1,221 gene-expression profiles obtained from rats treated with 127 well-characterized compounds, including genotoxic and non-genotoxic carcinogens. We built a classifier that predicts a chemical's carcinogenic potential with an AUC of 0.78, and validated it on an independent dataset from the Japanese Toxicogenomics Project consisting of 2,065 profiles from 72 compounds. Finally, we identified differentially expressed genes associated with chemical carcinogenesis, and developed novel data-driven approaches for the molecular characterization of the response to chemical stressors. Conclusion Here, we validate a toxicogenomic approach to predict carcinogenicity and provide strong evidence that, with a larger set of compounds, we should be able to improve the sensitivity and specificity of the predictions. We found that the prediction of carcinogenicity is tissue-dependent and that the results also confirm and expand upon previous studies implicating DNA damage, the peroxisome proliferator-activated receptor, the aryl hydrocarbon receptor, and regenerative pathology in the response to carcinogen exposure. PMID:25058030

Using Click Chemistry to Identify Potential Drug Targets in Plasmodium

DTIC Science & Technology

2015-04-01

step of the Plasmodium mammalian cycle . Inhibiting this step can block malaria at an early step. However, few anti-malarials target liver infection...points in the life cycle of malaria parasites. PLoS Biol 12: e1001806. 2. Falae A, Combe A, Amaladoss A, Carvalho T, Menard R, et al. (2010) Role of...AWARD NUMBER: W81XWH-13-1-0429 TITLE: Using "Click Chemistry" to Identify Potential Drug Targets in Plasmodium PRINCIPAL INVESTIGATOR: Dr. Purnima

Climate matching as a tool for predicting potential North American spread of Brown Treesnakes

USGS Publications Warehouse

Rodda, Gordon H.; Reed, Robert N.; Jarnevich, Catherine S.; Witmer, G.W.; Pitt, W. C.; Fagerstone, K.A.

2007-01-01

Climate matching identifies extralimital destinations that could be colonized by a potential invasive species on the basis of similarity to climates found in the species’ native range. Climate is a proxy for the factors that determine whether a population will reproduce enough to offset mortality. Previous climate matching models (e.g., Genetic Algorithm for Rule-set Prediction [GARP]) for brown treesnakes (Boiga irregularis) were unsatisfactory, perhaps because the models failed to allow different combinations of climate attributes to influence a species’ range limits in different parts of the range. Therefore, we explored the climate space described by bivariate parameters of native range temperature and rainfall, allowing up to two months of aestivation in the warmer portions of the range, or four months of hibernation in temperate climes. We found colonization area to be minimally sensitive to assumptions regarding hibernation temperature thresholds. Although brown treesnakes appear to be limited by dry weather in the interior of Australia, aridity rarely limits potential distribution in most of the world. Potential colonization area in North America is limited primarily by cold. Climatically suitable portions of the United States (US) mainland include the Central Valley of California, mesic patches in the Southwest, and the southeastern coastal plain from Texas to Virginia.

The Impact of Climate Change on Indigenous Arabica Coffee (Coffea arabica): Predicting Future Trends and Identifying Priorities

PubMed Central

Gole, Tadesse Woldemariam; Baena, Susana

2012-01-01

Precise modelling of the influence of climate change on Arabica coffee is limited; there are no data available for indigenous populations of this species. In this study we model the present and future predicted distribution of indigenous Arabica, and identify priorities in order to facilitate appropriate decision making for conservation, monitoring and future research. Using distribution data we perform bioclimatic modelling and examine future distribution with the HadCM3 climate model for three emission scenarios (A1B, A2A, B2A) over three time intervals (2020, 2050, 2080). The models show a profoundly negative influence on indigenous Arabica. In a locality analysis the most favourable outcome is a c. 65% reduction in the number of pre-existing bioclimatically suitable localities, and at worst an almost 100% reduction, by 2080. In an area analysis the most favourable outcome is a 38% reduction in suitable bioclimatic space, and the least favourable a c. 90% reduction, by 2080. Based on known occurrences and ecological tolerances of Arabica, bioclimatic unsuitability would place populations in peril, leading to severe stress and a high risk of extinction. This study establishes a fundamental baseline for assessing the consequences of climate change on wild populations of Arabica coffee. Specifically, it: (1) identifies and categorizes localities and areas that are predicted to be under threat from climate change now and in the short- to medium-term (2020–2050), representing assessment priorities for ex situ conservation; (2) identifies ‘core localities’ that could have the potential to withstand climate change until at least 2080, and therefore serve as long-term in situ storehouses for coffee genetic resources; (3) provides the location and characterization of target locations (populations) for on-the-ground monitoring of climate change influence. Arabica coffee is confimed as a climate sensitivite species, supporting data and inference that existing

Effect of foam on temperature prediction and heat recovery potential from biological wastewater treatment.

PubMed

Corbala-Robles, L; Volcke, E I P; Samijn, A; Ronsse, F; Pieters, J G

2016-05-15

Heat is an important resource in wastewater treatment plants (WWTPs) which can be recovered. A prerequisite to determine the theoretical heat recovery potential is an accurate heat balance model for temperature prediction. The insulating effect of foam present on the basin surface and its influence on temperature prediction were assessed in this study. Experiments were carried out to characterize the foam layer and its insulating properties. A refined dynamic temperature prediction model, taking into account the effect of foam, was set up. Simulation studies for a WWTP treating highly concentrated (manure) wastewater revealed that the foam layer had a significant effect on temperature prediction (3.8 ± 0.7 K over the year) and thus on the theoretical heat recovery potential (30% reduction when foam is not considered). Seasonal effects on the individual heat losses and heat gains were assessed. Additionally, the effects of the critical basin temperature above which heat is recovered, foam thickness, surface evaporation rate reduction and the non-absorbed solar radiation on the theoretical heat recovery potential were evaluated. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2015-11-01

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

Identifying Effective Signals to Predict Deleted and Suspended Accounts on Twitter across Languages

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

Volkova, Svitlana; Bell, Eric B.

Social networks have an ephemerality to them where accounts and messages are constantly being edited, deleted, or marked as private. This continuous change comes from concerns around privacy, a potential desire for deception, and spam-like behavior. In this study we analyze multiple large datasets of thousands of active and deleted Twitter accounts to produce a series of predictive features for the removal or shutdown of an account. We have selected these accounts from speakers of three languages -- Russian, Spanish, and English to evaluate if speakers of various languages behave differently with regards to deleting accounts. We find that unlikemore » previously used profile and network features, the discourse of deleted vs. active accounts forms the basis for highly accurate account deletion prediction. More precisely, we observed that the presence of a certain set of terms in user tweets leads to a higher likelihood for that user's account deletion. We show that the predictive power of profile, language, affect, and network features is not consistent across speakers of the three evaluated languages.« less

[Prediction of potential geographic distribution of Lyme disease in Qinghai province with Maximum Entropy model].

PubMed

Zhang, Lin; Hou, Xuexia; Liu, Huixin; Liu, Wei; Wan, Kanglin; Hao, Qin

2016-01-01

To predict the potential geographic distribution of Lyme disease in Qinghai by using Maximum Entropy model (MaxEnt). The sero-diagnosis data of Lyme disease in 6 counties (Huzhu, Zeku, Tongde, Datong, Qilian and Xunhua) and the environmental and anthropogenic data including altitude, human footprint, normalized difference vegetation index (NDVI) and temperature in Qinghai province since 1990 were collected. By using the data of Huzhu Zeku and Tongde, the prediction of potential distribution of Lyme disease in Qinghai was conducted with MaxEnt. The prediction results were compared with the human sero-prevalence of Lyme disease in Datong, Qilian and Xunhua counties in Qinghai. Three hot spots of Lyme disease were predicted in Qinghai, which were all in the east forest areas. Furthermore, the NDVI showed the most important role in the model prediction, followed by human footprint. Datong, Qilian and Xunhua counties were all in eastern Qinghai. Xunhua was in hot spot areaⅡ, Datong was close to the north of hot spot area Ⅲ, while Qilian with lowest sero-prevalence of Lyme disease was not in the hot spot areas. The data were well modeled in MaxEnt (Area Under Curve=0.980). The actual distribution of Lyme disease in Qinghai was in consistent with the results of the model prediction. MaxEnt could be used in predicting the potential distribution patterns of Lyme disease. The distribution of vegetation and the range and intensity of human activity might be related with Lyme disease distribution.

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

PubMed

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

2018-06-01

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

Impact Assessment of Mikania Micrantha on Land Cover and Maxent Modeling to Predict its Potential Invasion Sites

NASA Astrophysics Data System (ADS)

Baidar, T.; Shrestha, A. B.; Ranjit, R.; Adhikari, R.; Ghimire, S.; Shrestha, N.

2017-05-01

Mikania micrantha is one of the major invasive alien plant species in tropical moist forest regions of Asia including Nepal. Recently, this weed is spreading at an alarming rate in Chitwan National Park (CNP) and threatening biodiversity. This paper aims to assess the impacts of Mikania micrantha on different land cover and to predict potential invasion sites in CNP using Maxent model. Primary data for this were presence point coordinates and perceived Mikania micrantha cover collected through systematic random sampling technique. Rapideye image, Shuttle Radar Topographic Mission data and bioclimatic variables were acquired as secondary data. Mikania micrantha distribution maps were prepared by overlaying the presence points on image classified by object based image analysis. The overall accuracy of classification was 90 % with Kappa coefficient 0.848. A table depicting the number of sample points in each land cover with respective Mikania micrantha coverage was extracted from the distribution maps to show the impact. The riverine forest was found to be the most affected land cover with 85.98 % presence points and sal forest was found to be very less affected with only 17.02 % presence points. Maxent modeling predicted the areas near the river valley as the potential invasion sites with statistically significant Area Under the Receiver Operating Curve (AUC) value of 0.969. Maximum temperature of warmest month and annual precipitation were identified as the predictor variables that contribute the most to Mikania micrantha's potential distribution.

Potential Seasonal Predictability for Winter Storms over Europe

NASA Astrophysics Data System (ADS)

Wild, Simon; Befort, Daniel J.; Leckebusch, Gregor C.

2017-04-01

Reliable seasonal forecasts of strong extra-tropical cyclones and windstorms would have great social and economical benefits, as these events are the most costly natural hazards over Europe. In a previous study we have shown good agreement of spatial climatological distributions of extra-tropical cyclones and wind storms in state-of-the-art multi-member seasonal prediction systems with reanalysis. We also found significant seasonal prediction skill of extra-tropical cyclones and windstorms affecting numerous European countries. We continue this research by investigating the mechanisms and precursor conditions (primarily over the North Atlantic) on a seasonal time scale leading to enhanced extra-tropical cyclone activity and winter storm frequency over Europe. Our results regarding mechanisms show that an increased surface temperature gradient at the western edge of the North Atlantic can be related to enhanced winter storm frequency further downstream causing for example a greater number of storms over the British Isles, as observed in winter 2013-14.The so-called "Horseshoe Index", a SST tripole anomaly pattern over the North Atlantic in the summer months can also cause a higher number of winter storms over Europe in the subsequent winter. We will show results of AMIP-type sensitivity experiments using an AGCM (ECHAM5), supporting this hypothesis. Finally we will analyse whether existing seasonal forecast systems are able to capture these identified mechanisms and precursor conditions affecting the models' seasonal prediction skill.

Predicting hepatocellular carcinoma through cross-talk genes identified by risk pathways

PubMed Central

Shao, Zhuo; Huo, Diwei; Zhang, Denan; Xie, Hongbo; Yang, Jingbo; Liu, Qiuqi; Chen, Xiujie

2018-01-01

Hepatocellular carcinoma (HCC) is the most frequent type of liver cancer with poor survival rate and high mortality. Despite efforts on the mechanism of HCC, new molecular markers are needed for exact diagnosis, evaluation and treatment. Here, we combined transcriptome of HCC with networks and pathways to identify reliable molecular markers. Through integrating 249 differentially expressed genes with syncretic protein interaction networks, we constructed a HCC-specific network, from which we further extracted 480 pivotal genes. Based on the cross-talk between the enriched pathways of the pivotal genes, we finally identified a HCC signature of 45 genes, which could accurately distinguish HCC patients with normal individuals and reveal the prognosis of HCC patients. Among these 45 genes, 15 showed dysregulated expression patterns and a part have been reported to be associated with HCC and/or other cancers. These findings suggested that our identified 45 gene signature could be potential and valuable molecular markers for diagnosis and evaluation of HCC. PMID:29765536

Structured methods for identifying and correcting potential human errors in aviation operations

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

Nelson, W.R.

1997-10-01

Human errors have been identified as the source of approximately 60% of the incidents and accidents that occur in commercial aviation. It can be assumed that a very large number of human errors occur in aviation operations, even though in most cases the redundancies and diversities built into the design of aircraft systems prevent the errors from leading to serious consequences. In addition, when it is acknowledged that many system failures have their roots in human errors that occur in the design phase, it becomes apparent that the identification and elimination of potential human errors could significantly decrease the risksmore » of aviation operations. This will become even more critical during the design of advanced automation-based aircraft systems as well as next-generation systems for air traffic management. Structured methods to identify and correct potential human errors in aviation operations have been developed and are currently undergoing testing at the Idaho National Engineering and Environmental Laboratory (INEEL).« less

Identifying Potential Mechanisms Enabling Acidophily in the Ammonia-Oxidizing Archaeon "Candidatus Nitrosotalea devanaterra".

PubMed

Lehtovirta-Morley, Laura E; Sayavedra-Soto, Luis A; Gallois, Nicolas; Schouten, Stefan; Stein, Lisa Y; Prosser, James I; Nicol, Graeme W

2016-05-01

Ammonia oxidation is the first and rate-limiting step in nitrification and is dominated by two distinct groups of microorganisms in soil: ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB). AOA are often more abundant than AOB and dominate activity in acid soils. The mechanism of ammonia oxidation under acidic conditions has been a long-standing paradox. While high rates of ammonia oxidation are frequently measured in acid soils, cultivated ammonia oxidizers grew only at near-neutral pH when grown in standard laboratory culture. Although a number of mechanisms have been demonstrated to enable neutrophilic AOB growth at low pH in the laboratory, these have not been demonstrated in soil, and the recent cultivation of the obligately acidophilic ammonia oxidizer "Candidatus Nitrosotalea devanaterra" provides a more parsimonious explanation for the observed high rates of activity. Analysis of the sequenced genome, transcriptional activity, and lipid content of "Ca Nitrosotalea devanaterra" reveals that previously proposed mechanisms used by AOB for growth at low pH are not essential for archaeal ammonia oxidation in acidic environments. Instead, the genome indicates that "Ca Nitrosotalea devanaterra" contains genes encoding both a predicted high-affinity substrate acquisition system and potential pH homeostasis mechanisms absent in neutrophilic AOA. Analysis of mRNA revealed that candidate genes encoding the proposed homeostasis mechanisms were all expressed during acidophilic growth, and lipid profiling by high-performance liquid chromatography-mass spectrometry (HPLC-MS) demonstrated that the membrane lipids of "Ca Nitrosotalea devanaterra" were not dominated by crenarchaeol, as found in neutrophilic AOA. This study for the first time describes a genome of an obligately acidophilic ammonia oxidizer and identifies potential mechanisms enabling this unique phenotype for future biochemical characterization. Copyright © 2016 Lehtovirta-Morley et al.

Potential of DNA sequences to identify zoanthids (Cnidaria: Zoantharia).

PubMed

Sinniger, Frederic; Reimer, James D; Pawlowski, Jan

2008-12-01

The order Zoantharia is known for its chaotic taxonomy and difficult morphological identification. One method that potentially could help for examining such troublesome taxa is DNA barcoding, which identifies species using standard molecular markers. The mitochondrial cytochrome oxidase subunit I (COI) has been utilized to great success in groups such as birds and insects; however, its applicability in many other groups is controversial. Recently, some studies have suggested that barcoding is not applicable to anthozoans. Here, we examine the use of COI and mitochondrial 16S ribosomal DNA for zoanthid identification. Despite the absence of a clear barcoding gap, our results show that for most of 54 zoanthid samples, both markers could separate samples to the species, or species group, level, particularly when easily accessible ecological or distributional data were included. Additionally, we have used the short V5 region of mt 16S rDNA to identify eight old (13 to 50 years old) museum samples. We discuss advantages and disadvantages of COI and mt 16S rDNA as barcodes for Zoantharia, and recommend that either one or both of these markers be considered for zoanthid identification in the future.

Interannual Variability, Global Teleconnection, and Potential Predictability Associated with the Asian Summer Monsoon

NASA Technical Reports Server (NTRS)

Lau, K. M.; Kim, K. M.; Li, J. Y.

2001-01-01

In this Chapter, aspects of global teleconnections associated with the interannual variability of the Asian summer monsoon (ASM) are discussed. The basic differences in the basic dynamics of the South Asian Monsoon and the East Asian monsoon, and their implications on global linkages are discussed. Two teleconnection modes linking ASM variability to summertime precipitation over the continental North America were identified. These modes link regional circulation and precipitation anomalies over East Asia and continental North America, via coupled atmosphere-ocean variations over the North Pacific. The first mode has a large zonally symmetrical component and appears to be associated with subtropical jetstream variability and the second mode with Rossby wave dispersion. Both modes possess strong sea surface temperature (SST) expressions in the North Pacific. Results show that the two teleconnection modes may have its origin in intrinsic modes of sea surface temperature variability in the extratropical oceans, which are forced in part by atmospheric variability and in part by air-sea interaction. The potential predictability of the ASM associated with SST variability in different ocean basins is explored using a new canonical ensemble correlation prediction scheme. It is found that SST anomalies in tropical Pacific, i.e., El Nino, is the most dominant forcing for the ASM, especially over the maritime continent and eastern Australia. SST anomalies in the India Ocean may trump the influence from El Nino in western Australia and western maritime continent. Both El Nino, and North Pacific SSTs contribute to monsoon precipitation anomalies over Japan, southern Korea, northern and central China. By optimizing SST variability signals from the world ocean basins using CEC, the overall predictability of ASM can be substantially improved.

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

Use of NMR and NMR Prediction Software to Identify Components in Red Bull Energy Drinks

ERIC Educational Resources Information Center

Simpson, Andre J.; Shirzadi, Azadeh; Burrow, Timothy E.; Dicks, Andrew P.; Lefebvre, Brent; Corrin, Tricia

2009-01-01

A laboratory experiment designed as part of an upper-level undergraduate analytical chemistry course is described. Students investigate two popular soft drinks (Red Bull Energy Drink and sugar-free Red Bull Energy Drink) by NMR spectroscopy. With assistance of modern NMR prediction software they identify and quantify major components in each…

PREVAPORATION PERFORMANCE PREDICTION SOFTWARE

EPA Science Inventory

The Pervaporation, Performance, Prediction Software and Database (PPPS&D) computer software program is currently being developed within the USEPA, NRMRL. The purpose of the PPPS&D program is to educate and assist potential users in identifying opportunities for using pervaporati...

Development and Validation of a Clinic-Based Prediction Tool to Identify Female Athletes at High Risk for Anterior Cruciate Ligament Injury

PubMed Central

Myer, Gregory D.; Ford, Kevin R.; Khoury, Jane; Succop, Paul; Hewett, Timothy E.

2012-01-01

Background Prospective measures of high knee abduction moment (KAM) during landing identify female athletes at high risk for anterior cruciate ligament injury. Laboratory-based measurements demonstrate 90% accuracy in prediction of high KAM. Clinic-based prediction algorithms that employ correlates derived from laboratory-based measurements also demonstrate high accuracy for prediction of high KAM mechanics during landing. Hypotheses Clinic-based measures derived from highly predictive laboratory-based models are valid for the accurate prediction of high KAM status, and simultaneous measurements using laboratory-based and clinic-based techniques highly correlate. Study Design Cohort study (diagnosis); Level of evidence, 2. Methods One hundred female athletes (basketball, soccer, volleyball players) were tested using laboratory-based measures to confirm the validity of identified laboratory-based correlate variables to clinic-based measures included in a prediction algorithm to determine high KAM status. To analyze selected clinic-based surrogate predictors, another cohort of 20 female athletes was simultaneously tested with both clinic-based and laboratory-based measures. Results The prediction model (odds ratio: 95% confidence interval), derived from laboratory-based surrogates including (1) knee valgus motion (1.59: 1.17-2.16 cm), (2) knee flexion range of motion (0.94: 0.89°-1.00°), (3) body mass (0.98: 0.94-1.03 kg), (4) tibia length (1.55: 1.20-2.07 cm), and (5) quadriceps-to-hamstrings ratio (1.70: 0.48%-6.0%), predicted high KAM status with 84% sensitivity and 67% specificity (P < .001). Clinic-based techniques that used a calibrated physician’s scale, a standard measuring tape, standard camcorder, ImageJ software, and an isokinetic dynamometer showed high correlation (knee valgus motion, r = .87; knee flexion range of motion, r = .95; and tibia length, r = .98) to simultaneous laboratory-based measurements. Body mass and quadriceps-to-hamstrings ratio

Identifying Pre-Service Teachers' Beliefs about Teaching EFL and Their Potential Changes

ERIC Educational Resources Information Center

Suárez Flórez, Sergio Andrés; Basto Basto, Edwin Arley

2017-01-01

This study aims at identifying pre-service teachers' beliefs about teaching English as a foreign language and tracking their potential changes throughout the teaching practicum. Participants were two pre-service teachers in their fifth year of their Bachelor of Arts in Foreign Languages program in a public university in Colombia. Data were…

Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions

PubMed Central

Musleh, Basam; García, Fernando; Otamendi, Javier; Armingol, José Mª; de la Escalera, Arturo

2010-01-01

The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle. PMID:22163639

Identifying and tracking pedestrians based on sensor fusion and motion stability predictions.

PubMed

Musleh, Basam; García, Fernando; Otamendi, Javier; Armingol, José Maria; de la Escalera, Arturo

2010-01-01

The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle.

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.

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…

AP® Potential Predicted by PSAT/NMSQT® Scores Using Logistic Regression. Statistical Report 2014-1

ERIC Educational Resources Information Center

Zhang, Xiuyuan; Patel, Priyank; Ewing, Maureen

2014-01-01

AP Potential™ is an educational guidance tool that uses PSAT/NMSQT® scores to identify students who have the potential to do well on one or more Advanced Placement® (AP®) Exams. Students identified as having AP potential, perhaps students who would not have been otherwise identified, should consider enrolling in the corresponding AP course if they…

Automatically Identifying and Predicting Unplanned Wind Turbine Stoppages Using SCADA and Alarms System Data: Case Study and Results

NASA Astrophysics Data System (ADS)

Leahy, Kevin; Gallagher, Colm; Bruton, Ken; O'Donovan, Peter; O'Sullivan, Dominic T. J.

2017-11-01

Using 10-minute wind turbine SCADA data for fault prediction offers an attractive way of gaining additional prognostic capabilities without needing to invest in extra hardware. To use these data-driven methods effectively, the historical SCADA data must be labelled with the periods when the turbine was in faulty operation as well the sub-system the fault was attributed to. Manually identifying faults using maintenance logs can be effective, but is also highly time consuming and tedious due to the disparate nature of these logs across manufacturers, operators and even individual maintenance events. Turbine alarm systems can help to identify these periods, but the sheer volume of alarms and false positives generated makes analysing them on an individual basis ineffective. In this work, we present a new method for automatically identifying historical stoppages on the turbine using SCADA and alarms data. Each stoppage is associated with either a fault in one of the turbine’s sub-systems, a routine maintenance activity, a grid-related event or a number of other categories. This is then checked against maintenance logs for accuracy and the labelled data fed into a classifier for predicting when these stoppages will occur. Results show that the automated labelling process correctly identifies each type of stoppage, and can be effectively used for SCADA-based prediction of turbine faults.

Inflationary predictions of double-well, Coleman-Weinberg, and hilltop potentials with non-minimal coupling

NASA Astrophysics Data System (ADS)

Bostan, Nilay; Güleryüz, Ömer; Nefer Şenoğuz, Vedat

2018-05-01

We discuss how the non-minimal coupling ξphi2R between the inflaton and the Ricci scalar affects the predictions of single field inflation models where the inflaton has a non-zero vacuum expectation value (VEV) v after inflation. We show that, for inflaton values both above the VEV and below the VEV during inflation, under certain conditions the inflationary predictions become approximately the same as the predictions of the Starobinsky model. We then analyze inflation with double-well and Coleman-Weinberg potentials in detail, displaying the regions in the v-ξ plane for which the spectral index ns and the tensor-to-scalar ratio r values are compatible with the current observations. r is always larger than 0.002 in these regions. Finally, we consider the effect of ξ on small field inflation (hilltop) potentials.

Potential for western US seasonal snowpack prediction

USGS Publications Warehouse

Kapnick, Sarah B.; Yang, Xiaosong; Vecchi, Gabriel A.; Delworth, Thomas L.; Gudgel, Rich; Malyshev, Sergey; Milly, Paul C. D.; Shevliakova, Elena; Underwood, Seth; Margulis, Steven A.

2018-01-01

Western US snowpack—snow that accumulates on the ground in the mountains—plays a critical role in regional hydroclimate and water supply, with 80% of snowmelt runoff being used for agriculture. While climate projections provide estimates of snowpack loss by the end of th ecentury and weather forecasts provide predictions of weather conditions out to 2 weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), crucial for regional agricultural decisions (e.g., plant choice and quantity). Seasonal predictions with climate models first took the form of El Niño predictions 3 decades ago, with hydroclimate predictions emerging more recently. While the field has been focused on single-season predictions (3 months or less), we are now poised to advance our predictions beyond this timeframe. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate the feasibility of seasonal snowpack predictions and quantify the limits of predictive skill 8 month sin advance. This physically based dynamic system outperforms observation-based statistical predictions made on July 1 for March snowpack everywhere except the southern Sierra Nevada, a region where prediction skill is nonexistent for every predictor presently tested. Additionally, in the absence of externally forced negative trends in snowpack, narrow maritime mountain ranges with high hydroclimate variability pose a challenge for seasonal prediction in our present system; natural snowpack variability may inherently be unpredictable at this timescale. This work highlights present prediction system successes and gives cause for optimism for developing seasonal predictions for societal needs.

Obtaining subjects' consent to publish identifying personal information: current practices and identifying potential issues.

PubMed

Yoshida, Akiko; Dowa, Yuri; Murakami, Hiromi; Kosugi, Shinji

2013-11-25

In studies publishing identifying personal information, obtaining consent is regarded as necessary, as it is impossible to ensure complete anonymity. However, current journal practices around specific points to consider when obtaining consent, the contents of consent forms and how consent forms are managed have not yet been fully examined. This study was conducted to identify potential issues surrounding consent to publish identifying personal information. Content analysis was carried out on instructions for authors and consent forms developed by academic journals in four fields (as classified by Journal Citation Reports): medicine general and internal, genetics and heredity, pediatrics, and psychiatry. An online questionnaire survey of editors working for journals that require the submission of consent forms was also conducted. Instructions for authors were reviewed for 491 academic journals (132 for medicine general and internal, 147 for genetics and heredity, 100 for pediatrics, and 112 for psychiatry). Approximately 40% (203: 74 for medicine general and internal, 31 for genetics and heredity, 58 for pediatrics, and 40 for psychiatry) stated that subject consent was necessary. The submission of consent forms was required by 30% (154) of the journals studied, and 10% (50) provided their own consent forms for authors to use. Two journals mentioned that the possible effects of publication on subjects should be considered. Many journal consent forms mentioned the difficulties in ensuring complete anonymity of subjects, but few addressed the study objective, the subjects' right to refuse consent and the withdrawal of consent. The main reason for requiring the submission of consent forms was to confirm that consent had been obtained. Approximately 40% of journals required subject consent to be obtained. However, differences were observed depending on the fields. Specific considerations were not always documented. There is a need to address issues around the study

Obtaining subjects’ consent to publish identifying personal information: current practices and identifying potential issues

PubMed Central

2013-01-01

Background In studies publishing identifying personal information, obtaining consent is regarded as necessary, as it is impossible to ensure complete anonymity. However, current journal practices around specific points to consider when obtaining consent, the contents of consent forms and how consent forms are managed have not yet been fully examined. This study was conducted to identify potential issues surrounding consent to publish identifying personal information. Methods Content analysis was carried out on instructions for authors and consent forms developed by academic journals in four fields (as classified by Journal Citation Reports): medicine general and internal, genetics and heredity, pediatrics, and psychiatry. An online questionnaire survey of editors working for journals that require the submission of consent forms was also conducted. Results Instructions for authors were reviewed for 491 academic journals (132 for medicine general and internal, 147 for genetics and heredity, 100 for pediatrics, and 112 for psychiatry). Approximately 40% (203: 74 for medicine general and internal, 31 for genetics and heredity, 58 for pediatrics, and 40 for psychiatry) stated that subject consent was necessary. The submission of consent forms was required by 30% (154) of the journals studied, and 10% (50) provided their own consent forms for authors to use. Two journals mentioned that the possible effects of publication on subjects should be considered. Many journal consent forms mentioned the difficulties in ensuring complete anonymity of subjects, but few addressed the study objective, the subjects’ right to refuse consent and the withdrawal of consent. The main reason for requiring the submission of consent forms was to confirm that consent had been obtained. Conclusion Approximately 40% of journals required subject consent to be obtained. However, differences were observed depending on the fields. Specific considerations were not always documented. There is a need

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

Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia II. Weather-based prediction systems perform comparably to early detection systems in identifying times for interventions.

PubMed

Teklehaimanot, Hailay D; Schwartz, Joel; Teklehaimanot, Awash; Lipsitch, Marc

2004-11-19

Timely and accurate information about the onset of malaria epidemics is essential for effective control activities in epidemic-prone regions. Early warning methods that provide earlier alerts (usually by the use of weather variables) may permit control measures to interrupt transmission earlier in the epidemic, perhaps at the expense of some level of accuracy. Expected case numbers were modeled using a Poisson regression with lagged weather factors in a 4th-degree polynomial distributed lag model. For each week, the numbers of malaria cases were predicted using coefficients obtained using all years except that for which the prediction was being made. The effectiveness of alerts generated by the prediction system was compared against that of alerts based on observed cases. The usefulness of the prediction system was evaluated in cold and hot districts. The system predicts the overall pattern of cases well, yet underestimates the height of the largest peaks. Relative to alerts triggered by observed cases, the alerts triggered by the predicted number of cases performed slightly worse, within 5% of the detection system. The prediction-based alerts were able to prevent 10-25% more cases at a given sensitivity in cold districts than in hot ones. The prediction of malaria cases using lagged weather performed well in identifying periods of increased malaria cases. Weather-derived predictions identified epidemics with reasonable accuracy and better timeliness than early detection systems; therefore, the prediction of malarial epidemics using weather is a plausible alternative to early detection systems.

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

Proteomic Analysis of Saliva Identifies Potential Biomarkers for Orthodontic Tooth Movement

PubMed Central

Ellias, Mohd Faiz; Zainal Ariffin, Shahrul Hisham; Karsani, Saiful Anuar; Abdul Rahman, Mariati; Senafi, Shahidan; Megat Abdul Wahab, Rohaya

2012-01-01

Orthodontic treatment has been shown to induce inflammation, followed by bone remodelling in the periodontium. These processes trigger the secretion of various proteins and enzymes into the saliva. This study aims to identify salivary proteins that change in expression during orthodontic tooth movement. These differentially expressed proteins can potentially serve as protein biomarkers for the monitoring of orthodontic treatment and tooth movement. Whole saliva from three healthy female subjects were collected before force application using fixed appliance and at 14 days after 0.014′′ Niti wire was applied. Salivary proteins were resolved using two-dimensional gel electrophoresis (2DE) over a pH range of 3–10, and the resulting proteome profiles were compared. Differentially expressed protein spots were then identified by MALDI-TOF/TOF tandem mass spectrometry. Nine proteins were found to be differentially expressed; however, only eight were identified by MALDI-TOF/TOF. Four of these proteins—Protein S100-A9, immunoglobulin J chain, Ig alpha-1 chain C region, and CRISP-3—have known roles in inflammation and bone resorption. PMID:22919344

Pharmacophore modeling and virtual screening to identify potential RET kinase inhibitors.

PubMed

Shih, Kuei-Chung; Shiau, Chung-Wai; Chen, Ting-Shou; Ko, Ching-Huai; Lin, Chih-Lung; Lin, Chun-Yuan; Hwang, Chrong-Shiong; Tang, Chuan-Yi; Chen, Wan-Ru; Huang, Jui-Wen

2011-08-01

Chemical features based 3D pharmacophore model for REarranged during Transfection (RET) tyrosine kinase were developed by using a training set of 26 structurally diverse known RET inhibitors. The best pharmacophore hypothesis, which identified inhibitors with an associated correlation coefficient of 0.90 between their experimental and estimated anti-RET values, contained one hydrogen-bond acceptor, one hydrogen-bond donor, one hydrophobic, and one ring aromatic features. The model was further validated by a testing set, Fischer's randomization test, and goodness of hit (GH) test. We applied this pharmacophore model to screen NCI database for potential RET inhibitors. The hits were docked to RET with GOLD and CDOCKER after filtering by Lipinski's rules. Ultimately, 24 molecules were selected as potential RET inhibitors for further investigation. Copyright © 2011 Elsevier Ltd. All rights reserved.

Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo

PubMed Central

Fontaine, Bertrand; Peña, José Luis; Brette, Romain

2014-01-01

Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo. PMID:24722397

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...

Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

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

Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov; Cross, Kevin P.

Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describemore » the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive performance.« less

Time-saving impact of an algorithm to identify potential surgical site infections.

PubMed

Knepper, B C; Young, H; Jenkins, T C; Price, C S

2013-10-01

To develop and validate a partially automated algorithm to identify surgical site infections (SSIs) using commonly available electronic data to reduce manual chart review. Retrospective cohort study of patients undergoing specific surgical procedures over a 4-year period from 2007 through 2010 (algorithm development cohort) or over a 3-month period from January 2011 through March 2011 (algorithm validation cohort). A single academic safety-net hospital in a major metropolitan area. Patients undergoing at least 1 included surgical procedure during the study period. Procedures were identified in the National Healthcare Safety Network; SSIs were identified by manual chart review. Commonly available electronic data, including microbiologic, laboratory, and administrative data, were identified via a clinical data warehouse. Algorithms using combinations of these electronic variables were constructed and assessed for their ability to identify SSIs and reduce chart review. The most efficient algorithm identified in the development cohort combined microbiologic data with postoperative procedure and diagnosis codes. This algorithm resulted in 100% sensitivity and 85% specificity. Time savings from the algorithm was almost 600 person-hours of chart review. The algorithm demonstrated similar sensitivity on application to the validation cohort. A partially automated algorithm to identify potential SSIs was highly sensitive and dramatically reduced the amount of manual chart review required of infection control personnel during SSI surveillance.

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.

40 CFR Table 5 to Subpart Jj of... - List of VHAP of Potential Concern Identified by Industry

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 10 2010-07-01 2010-07-01 false List of VHAP of Potential Concern Identified by Industry 5 Table 5 to Subpart JJ of Part 63 Protection of Environment ENVIRONMENTAL PROTECTION.... 63, Subpt. JJ, Table 5 Table 5 to Subpart JJ of Part 63—List of VHAP of Potential Concern Identified...

Characterization of potential mineralization in Afghanistan: four permissive areas identified using imaging spectroscopy data

USGS Publications Warehouse

King, Trude V.V.; Berger, Byron R.; Johnson, Michaela R.

2014-01-01

As part of the U.S. Geological Survey and Department of Defense Task Force for Business and Stability Operations natural resources revitalization activities in Afghanistan, four permissive areas for mineralization, Bamyan 1, Farah 1, Ghazni 1, and Ghazni 2, have been identified using imaging spectroscopy data. To support economic development, the areas of potential mineralization were selected on the occurrence of selected mineral assemblages mapped using the HyMap™ data (kaolinite, jarosite, hydrated silica, chlorite, epidote, iron-bearing carbonate, buddingtonite, dickite, and alunite) that may be indicative of past mineralization processes in areas with limited or no previous mineral resource studies. Approximately 30 sites were initially determined to be candidates for areas of potential mineralization. Additional criteria and material used to refine the selection and prioritization process included existing geologic maps, Landsat Thematic Mapper data, and published literature. The HyMapTM data were interpreted in the context of the regional geologic and tectonic setting and used the presence of alteration mineral assemblages to identify areas with the potential for undiscovered mineral resources. Further field-sampling, mapping, and supporting geochemical analyses are necessary to fully substantiate and verify the specific deposit types in the four areas of potential mineralization.

Predicting protein thermal stability changes upon point mutations using statistical potentials: Introducing HoTMuSiC.

PubMed

Pucci, Fabrizio; Bourgeas, Raphaël; Rooman, Marianne

2016-03-18

The accurate prediction of the impact of an amino acid substitution on the thermal stability of a protein is a central issue in protein science, and is of key relevance for the rational optimization of various bioprocesses that use enzymes in unusual conditions. Here we present one of the first computational tools to predict the change in melting temperature ΔTm upon point mutations, given the protein structure and, when available, the melting temperature Tm of the wild-type protein. The key ingredients of our model structure are standard and temperature-dependent statistical potentials, which are combined with the help of an artificial neural network. The model structure was chosen on the basis of a detailed thermodynamic analysis of the system. The parameters of the model were identified on a set of more than 1,600 mutations with experimentally measured ΔTm. The performance of our method was tested using a strict 5-fold cross-validation procedure, and was found to be significantly superior to that of competing methods. We obtained a root mean square deviation between predicted and experimental ΔTm values of 4.2 °C that reduces to 2.9 °C when ten percent outliers are removed. A webserver-based tool is freely available for non-commercial use at soft.dezyme.com.

Prediction of chronic post-operative pain: pre-operative DNIC testing identifies patients at risk.

PubMed

Yarnitsky, David; Crispel, Yonathan; Eisenberg, Elon; Granovsky, Yelena; Ben-Nun, Alon; Sprecher, Elliot; Best, Lael-Anson; Granot, Michal

2008-08-15

Surgical and medical procedures, mainly those associated with nerve injuries, may lead to chronic persistent pain. Currently, one cannot predict which patients undergoing such procedures are 'at risk' to develop chronic pain. We hypothesized that the endogenous analgesia system is key to determining the pattern of handling noxious events, and therefore testing diffuse noxious inhibitory control (DNIC) will predict susceptibility to develop chronic post-thoracotomy pain (CPTP). Pre-operative psychophysical tests, including DNIC assessment (pain reduction during exposure to another noxious stimulus at remote body area), were conducted in 62 patients, who were followed 29.0+/-16.9 weeks after thoracotomy. Logistic regression revealed that pre-operatively assessed DNIC efficiency and acute post-operative pain intensity were two independent predictors for CPTP. Efficient DNIC predicted lower risk of CPTP, with OR 0.52 (0.33-0.77 95% CI, p=0.0024), i.e., a 10-point numerical pain scale (NPS) reduction halves the chance to develop chronic pain. Higher acute pain intensity indicated OR of 1.80 (1.28-2.77, p=0.0024) predicting nearly a double chance to develop chronic pain for each 10-point increase. The other psychophysical measures, pain thresholds and supra-threshold pain magnitudes, did not predict CPTP. For prediction of acute post-operative pain intensity, DNIC efficiency was not found significant. Effectiveness of the endogenous analgesia system obtained at a pain-free state, therefore, seems to reflect the individual's ability to tackle noxious events, identifying patients 'at risk' to develop post-intervention chronic pain. Applying this diagnostic approach before procedures that might generate pain may allow individually tailored pain prevention and management, which may substantially reduce suffering.

A predicted protein interactome identifies conserved global networks and disease resistance subnetworks in maize

PubMed Central

Musungu, Bryan; Bhatnagar, Deepak; Brown, Robert L.; Fakhoury, Ahmad M.; Geisler, Matt

2015-01-01

Interactomes are genome-wide roadmaps of protein-protein interactions. They have been produced for humans, yeast, the fruit fly, and Arabidopsis thaliana and have become invaluable tools for generating and testing hypotheses. A predicted interactome for Zea mays (PiZeaM) is presented here as an aid to the research community for this valuable crop species. PiZeaM was built using a proven method of interologs (interacting orthologs) that were identified using both one-to-one and many-to-many orthology between genomes of maize and reference species. Where both maize orthologs occurred for an experimentally determined interaction in the reference species, we predicted a likely interaction in maize. A total of 49,026 unique interactions for 6004 maize proteins were predicted. These interactions are enriched for processes that are evolutionarily conserved, but include many otherwise poorly annotated proteins in maize. The predicted maize interactions were further analyzed by comparing annotation of interacting proteins, including different layers of ontology. A map of pairwise gene co-expression was also generated and compared to predicted interactions. Two global subnetworks were constructed for highly conserved interactions. These subnetworks showed clear clustering of proteins by function. Another subnetwork was created for disease response using a bait and prey strategy to capture interacting partners for proteins that respond to other organisms. Closer examination of this subnetwork revealed the connectivity between biotic and abiotic hormone stress pathways. We believe PiZeaM will provide a useful tool for the prediction of protein function and analysis of pathways for Z. mays researchers and is presented in this paper as a reference tool for the exploration of protein interactions in maize. PMID:26089837

Identifying external nutrient reduction requirements and potential in the hypereutrophic Lake Taihu Basin, China.

PubMed

Peng, Jiao-Ting; Zhu, Xiao-Dong; Sun, Xiang; Song, Xiao-Wei

2018-04-01

Reducing external nutrient loads is the first step for controlling eutrophication. Here, we identified external nutrient reduction requirements and potential of strategies for achieving reductions to remediate a eutrophic water body, Lake Taihu, China. A mass balance approach based on the entire lake was used to identify nutrient reduction requirements; an empirical export coefficient approach was introduced to estimate the nutrient reduction potential of the overall program on integrated regulation of Taihu Lake Basin (hereafter referred to as the "Guideline"). Reduction requirements included external total nitrogen (TN) and total phosphorus (TP) loads, which should be reduced by 41-55 and 25-50%, respectively, to prevent nutrient accumulation in Lake Taihu and to meet the planned water quality targets. In 2010, which is the most seriously polluted calendar year during the 2008-2014 period, the nutrient reduction requirements were estimated to be 36,819 tons of N and 2442 tons of P, and the potential nutrient reduction strategies would reduce approximately 25,821 tons of N and 3024 tons of P. Since there is a net N remaining in the reduction requirements, it should be the focus and deserves more attention in identifying external nutrient reduction strategies. Moreover, abatement measures outlined in the Guideline with high P reduction potential required large monetary investments. Achieving TP reduction requirement using the cost-effective strategy costs about 80.24 million USD. The design of nutrient reduction strategies should be enacted according to regional and sectoral differences and the cost-effectiveness of abatement measures.

What’s the risk? Identifying potential human pathogens within grey-headed flying foxes faeces

PubMed Central

Galbraith, Penelope; Coutts, Scott; Prosser, Toby; Boyce, John; McCarthy, David T.

2018-01-01

Pteropus poliocephalus (grey-headed flying foxes) are recognised vectors for a range of potentially fatal human pathogens. However, to date research has primarily focused on viral disease carriage, overlooking bacterial pathogens, which also represent a significant human disease risk. The current study applied 16S rRNA amplicon sequencing, community analysis and a multi-tiered database OTU picking approach to identify faecal-derived zoonotic bacteria within two colonies of P. poliocephalus from Victoria, Australia. Our data show that sequences associated with Enterobacteriaceae (62.8% ± 24.7%), Pasteurellaceae (19.9% ± 25.7%) and Moraxellaceae (9.4% ± 11.8%) dominate flying fox faeces. Further colony specific differences in bacterial faecal colonisation patterns were also identified. In total, 34 potential pathogens, representing 15 genera, were identified. However, species level definition was only possible for Clostridium perfringens, which likely represents a low infectious risk due to the low proportion observed within the faeces and high infectious dose required for transmission. In contrast, sequences associated with other pathogenic species clusters such as Haemophilus haemolyticus-H. influenzae and Salmonella bongori-S. enterica, were present at high proportions in the faeces, and due to their relatively low infectious doses and modes of transmissions, represent a greater potential human disease risk. These analyses of the microbial community composition of Pteropus poliocephalus have significantly advanced our understanding of the potential bacterial disease risk associated with flying foxes and should direct future epidemiological and quantitative microbial risk assessments to further define the health risks presented by these animals. PMID:29360880

Researchers identify potential therapeutic targets for a rare childhood cancer | Center for Cancer Research

Cancer.gov

CCR researchers have identified the mechanism behind a rare but extremely aggressive childhood cancer called alveolar rhabdomyosarcoma (ARMS) and have pinpointed a potential drug target for its treatment. Learn more...

Surveillance methods for identifying, characterizing, and monitoring tobacco products: potential reduced exposure products as an example

PubMed Central

O’Connor, Richard J.; Cummings, K. Michael; Rees, Vaughan W.; Connolly, Gregory N.; Norton, Kaila J.; Sweanor, David; Parascandola, Mark; Hatsukami, Dorothy K.; Shields, Peter G.

2015-01-01

Tobacco products are widely sold and marketed, yet integrated data systems for identifying, tracking, and characterizing products are lacking. Tobacco manufacturers recently have developed potential reduction exposure products (PREPs) with implied or explicit health claims. Currently, a systematic approach for identifying, defining, and evaluating PREPs sold at the local, state or national levels in the US has not been developed. Identifying, characterizing, and monitoring new tobacco products could be greatly enhanced with a responsive surveillance system. This paper critically reviews available surveillance data sources for identifying and tracking tobacco products, including PREPs, evaluating strengths and weaknesses of potential data sources in light of their reliability and validity. Absent regulations mandating disclosure of product-specific information, it is likely that public health officials will need to rely on a variety of imperfect data sources to help identify, characterize, and monitor tobacco products, including PREPs. PMID:19959680

Powder diffraction and crystal structure prediction identify four new coumarin polymorphs

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

Shtukenberg, Alexander G.; Zhu, Qiang; Carter, Damien J.

Coumarin, a simple, commodity chemical isolated from beans in 1820, has, to date, only yielded one solid state structure. Here, we report a rich polymorphism of coumarin grown from the melt. Four new metastable forms were identified and their crystal structures were solved using a combination of computational crystal structure prediction algorithms and X-ray powder diffraction. With five crystal structures, coumarin has become one of the few rigid molecules showing extensive polymorphism at ambient conditions. We demonstrate the crucial role of advanced electronic structure calculations including many-body dispersion effects for accurate ranking of the stability of coumarin polymorphs and themore » need to account for anharmonic vibrational contributions to their free energy. As such, coumarin is a model system for studying weak intermolecular interactions, crystallization mechanisms, and kinetic effects.« less

Powder diffraction and crystal structure prediction identify four new coumarin polymorphs

DOE PAGES

Shtukenberg, Alexander G.; Zhu, Qiang; Carter, Damien J.; ...

2017-05-15

Coumarin, a simple, commodity chemical isolated from beans in 1820, has, to date, only yielded one solid state structure. Here, we report a rich polymorphism of coumarin grown from the melt. Four new metastable forms were identified and their crystal structures were solved using a combination of computational crystal structure prediction algorithms and X-ray powder diffraction. With five crystal structures, coumarin has become one of the few rigid molecules showing extensive polymorphism at ambient conditions. We demonstrate the crucial role of advanced electronic structure calculations including many-body dispersion effects for accurate ranking of the stability of coumarin polymorphs and themore » need to account for anharmonic vibrational contributions to their free energy. As such, coumarin is a model system for studying weak intermolecular interactions, crystallization mechanisms, and kinetic effects.« less

Predicting the Spatial Distribution of Aspen Growth Potential in the Upper Great Lakes Region

Treesearch

Eric J. Gustafson; Sue M. Lietz; John L. Wright

2003-01-01

One way to increase aspen yields is to produce aspen on sites where aspen growth potential is highest. Aspen growth rates are typically predicted using site index, but this is impractical for landscape-level assessments. We tested the hypothesis that aspen growth can be predicted from site and climate variables and generated a model to map the spatial variability of...

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.

Prediction of the potential global distribution for Biomphalaria straminea, an intermediate host for Schistosoma mansoni

PubMed Central

Yang, Ya; Cheng, Wanting; Wu, Xiaoying; Huang, Shaoyu; Deng, Zhuohui; Zeng, Xin; Yang, Yu; Wu, Zhongdao; Chen, Yue; Zhou, Yibiao; Jiang, Qingwu

2018-01-01

Background Schistosomiasis is a snail-borne parasitic disease and is endemic in many tropical and subtropical countries. Biomphalaria straminea, an intermediate host for Schistosoma mansoni, is native to the southeastern part of South America and has established in other regions of South America, Central America and southern China during the last decades. S. mansoni is endemic in Africa, the Middle East, South America and the Caribbean. Knowledge of the potential global distribution of this snail is essential for risk assessment, monitoring, disease prevention and control. Methods and findings A comprehensive database of cross-continental occurrence for B. straminea was compiled to construct ecological models. We used several approaches to investigate the distribution of B. straminea, including direct comparison of climatic conditions, principal component analysis and niche overlap analyses to detect niche shifts. We also investigated the impacts of bioclimatic and human factors, and then used the bioclimatic and footprint layers to predict the potential distribution of B. straminea at global scale. We detected niche shifts accompanying the invasions of B. straminea in the Americas and China. The introduced populations had enlarged its habitats to subtropical regions where annual mean temperature is relatively low. Annual mean temperature, isothermality and temperature seasonality were identified as most important climatic features for the occurrence of B. straminea. Additionally, human factors improved the model prediction (P<0.001). Our model showed that under current climate conditions the snail should mostly be confined to the tropic and subtropic regions, including South America, Central America, Sub-Saharan Africa and Southeast Asia. Conclusions Our results confirmed that niche shifts took place in the invasions of B. straminea, in which bioclimatic and human factors played an important role. Our model predicted the global distribution of B. straminea based

Profiling of the Tox21 Chemical Collection for Mitochondrial Function to Identify Compounds that Acutely Decrease Mitochondrial Membrane Potential

PubMed Central

Attene-Ramos, Matias S.; Huang, Ruili; Michael, Sam; Witt, Kristine L.; Richard, Ann; Tice, Raymond R.; Simeonov, Anton; Austin, Christopher P.

2014-01-01

Background: Mitochondrial dysfunction has been implicated in the pathogenesis of a variety of disorders including cancer, diabetes, and neurodegenerative and cardiovascular diseases. Understanding whether different environmental chemicals and druglike molecules impact mitochondrial function represents an initial step in predicting exposure-related toxicity and defining a possible role for such compounds in the onset of various diseases. Objectives: We sought to identify individual chemicals and general structural features associated with changes in mitochondrial membrane potential (MMP). Methods: We used a multiplexed [two end points in one screen; MMP and adenosine triphosphate (ATP) content] quantitative high throughput screening (qHTS) approach combined with informatics tools to screen the Tox21 library of 10,000 compounds (~ 8,300 unique chemicals) at 15 concentrations each in triplicate to identify chemicals and structural features that are associated with changes in MMP in HepG2 cells. Results: Approximately 11% of the compounds (913 unique compounds) decreased MMP after 1 hr of treatment without affecting cell viability (ATP content). In addition, 309 compounds decreased MMP over a concentration range that also produced measurable cytotoxicity [half maximal inhibitory concentration (IC50) in MMP assay/IC50 in viability assay ≤ 3; p < 0.05]. More than 11% of the structural clusters that constitute the Tox21 library (76 of 651 clusters) were significantly enriched for compounds that decreased the MMP. Conclusions: Our multiplexed qHTS approach allowed us to generate a robust and reliable data set to evaluate the ability of thousands of drugs and environmental compounds to decrease MMP. The use of structure-based clustering analysis allowed us to identify molecular features that are likely responsible for the observed activity. Citation: Attene-Ramos MS, Huang R, Michael S, Witt KL, Richard A, Tice RR, Simeonov A, Austin CP, Xia M. 2015. Profiling of the Tox

Nurses' Assessment of Rehabilitation Potential and Prediction of Functional Status at Discharge from Inpatient Rehabilitation

ERIC Educational Resources Information Center

Myers, Jamie S.; Grigsby, Jim; Teel, Cynthia S.; Kramer, Andrew M.

2009-01-01

The goals of this study were to evaluate the accuracy of nurses' predictions of rehabilitation potential in older adults admitted to inpatient rehabilitation facilities and to ascertain whether the addition of a measure of executive cognitive function would enhance predictive accuracy. Secondary analysis was performed on prospective data collected…

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.

Identifying Potential Mechanisms Enabling Acidophily in the Ammonia-Oxidizing Archaeon “Candidatus Nitrosotalea devanaterra”

PubMed Central

Sayavedra-Soto, Luis A.; Gallois, Nicolas; Schouten, Stefan; Stein, Lisa Y.; Prosser, James I.; Nicol, Graeme W.

2016-01-01

Ammonia oxidation is the first and rate-limiting step in nitrification and is dominated by two distinct groups of microorganisms in soil: ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB). AOA are often more abundant than AOB and dominate activity in acid soils. The mechanism of ammonia oxidation under acidic conditions has been a long-standing paradox. While high rates of ammonia oxidation are frequently measured in acid soils, cultivated ammonia oxidizers grew only at near-neutral pH when grown in standard laboratory culture. Although a number of mechanisms have been demonstrated to enable neutrophilic AOB growth at low pH in the laboratory, these have not been demonstrated in soil, and the recent cultivation of the obligately acidophilic ammonia oxidizer “Candidatus Nitrosotalea devanaterra” provides a more parsimonious explanation for the observed high rates of activity. Analysis of the sequenced genome, transcriptional activity, and lipid content of “Ca. Nitrosotalea devanaterra” reveals that previously proposed mechanisms used by AOB for growth at low pH are not essential for archaeal ammonia oxidation in acidic environments. Instead, the genome indicates that “Ca. Nitrosotalea devanaterra” contains genes encoding both a predicted high-affinity substrate acquisition system and potential pH homeostasis mechanisms absent in neutrophilic AOA. Analysis of mRNA revealed that candidate genes encoding the proposed homeostasis mechanisms were all expressed during acidophilic growth, and lipid profiling by high-performance liquid chromatography–mass spectrometry (HPLC-MS) demonstrated that the membrane lipids of “Ca. Nitrosotalea devanaterra” were not dominated by crenarchaeol, as found in neutrophilic AOA. This study for the first time describes a genome of an obligately acidophilic ammonia oxidizer and identifies potential mechanisms enabling this unique phenotype for future biochemical characterization. PMID:26896134

Identifying high risk medications causing potential drug-drug interactions in outpatients: A prescription database study based on an online surveillance system.

PubMed

Toivo, T M; Mikkola, J A V; Laine, K; Airaksinen, M

2016-01-01

Drug-drug interactions (DDIs) are a significant cause for adverse drug events (ADEs). DDIs are often predictable and preventable, but their prevention and management require systematic service development. Most DDI studies focus on interaction rates in hospitalized patients. Less is known of DDIs in outpatients, particularly how community pharmacists could contribute to DDI management by applying their surveillance systems for identifying high-risk medications. The study was related to the implementation of the first online DDI surveillance system in Finnish community pharmacies. The goal was to demonstrate how community pharmacies can utilize their prospective surveillance system 1) for identifying high risk medications causing potential DDIs in outpatients, 2) for collaborative service development with local physicians, and 3) for academic risk management research purposes. All DDI alerts given by the online surveillance system were collected during a one-month period in 16 out of 17 University Pharmacy outlets in Finland, covering approximately 10% of the national outpatient prescription volume. The surveillance system was based on the FASS database, which categorizes DDIs into four classes (A-D) according to their clinical significance. Potential drug-drug DDIs were analyzed for 276,891 dispensed community pharmacy prescriptions. Potential DDIs were associated with 10.8%, or 31,110 of these prescriptions. Clinically significant interaction alerts categorized as FASS classes D (most severe, should be avoided) and C (clinically significant but controllable) were associated with 0.5% and 7.0% of the prescriptions, respectively. Methotrexate and warfarin had the highest risk of causing potentially serious (class D) interactions. These interaction alerts were most frequently between methotrexate and NSAIDs and warfarin and NSAIDs. In general, NSAIDs were the most commonly interacting drugs in this study. This study demonstrates that community pharmacies can actively

Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees.

PubMed

Amirabadizadeh, Alireza; Nezami, Hossein; Vaughn, Michael G; Nakhaee, Samaneh; Mehrpour, Omid

2018-05-12

Substance abuse exacts considerable social and health care burdens throughout the world. The aim of this study was to create a prediction model to better identify risk factors for drug use. A prospective cross-sectional study was conducted in South Khorasan Province, Iran. Of the total of 678 eligible subjects, 70% (n: 474) were randomly selected to provide a training set for constructing decision tree and multiple logistic regression (MLR) models. The remaining 30% (n: 204) were employed in a holdout sample to test the performance of the decision tree and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the testing set. Independent variables were selected from demographic characteristics and history of drug use. For the decision tree model, the sensitivity and specificity for identifying people at risk for drug abuse were 66% and 75%, respectively, while the MLR model was somewhat less effective at 60% and 73%. Key independent variables in the analyses included first substance experience, age at first drug use, age, place of residence, history of cigarette use, and occupational and marital status. While study findings are exploratory and lack generalizability they do suggest that the decision tree model holds promise as an effective classification approach for identifying risk factors for drug use. Convergent with prior research in Western contexts is that age of drug use initiation was a critical factor predicting a substance use disorder.

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.

Identifying Future Drinkers: Behavioral Analysis of Monkeys Initiating Drinking to Intoxication is Predictive of Future Drinking Classification.

PubMed

Baker, Erich J; Walter, Nicole A R; Salo, Alex; Rivas Perea, Pablo; Moore, Sharon; Gonzales, Steven; Grant, Kathleen A

2017-03-01

The Monkey Alcohol Tissue Research Resource (MATRR) is a repository and analytics platform for detailed data derived from well-documented nonhuman primate (NHP) alcohol self-administration studies. This macaque model has demonstrated categorical drinking norms reflective of human drinking populations, resulting in consumption pattern classifications of very heavy drinking (VHD), heavy drinking (HD), binge drinking (BD), and low drinking (LD) individuals. Here, we expand on previous findings that suggest ethanol drinking patterns during initial drinking to intoxication can reliably predict future drinking category assignment. The classification strategy uses a machine-learning approach to examine an extensive set of daily drinking attributes during 90 sessions of induction across 7 cohorts of 5 to 8 monkeys for a total of 50 animals. A Random Forest classifier is employed to accurately predict categorical drinking after 12 months of self-administration. Predictive outcome accuracy is approximately 78% when classes are aggregated into 2 groups, "LD and BD" and "HD and VHD." A subsequent 2-step classification model distinguishes individual LD and BD categories with 90% accuracy and between HD and VHD categories with 95% accuracy. Average 4-category classification accuracy is 74%, and provides putative distinguishing behavioral characteristics between groupings. We demonstrate that data derived from the induction phase of this ethanol self-administration protocol have significant predictive power for future ethanol consumption patterns. Importantly, numerous predictive factors are longitudinal, measuring the change of drinking patterns through 3 stages of induction. Factors during induction that predict future heavy drinkers include being younger at the time of first intoxication and developing a shorter latency to first ethanol drink. Overall, this analysis identifies predictive characteristics in future very heavy drinkers that optimize intoxication, such as having

Identifying Potential Ventilator Auto-Triggering Among Organ Procurement Organization Referrals.

PubMed

Henry, Nicholas R; Russian, Christopher J; Nespral, Joseph

2016-06-01

Ventilator auto-trigger is the delivery of an assisted mechanical ventilated breath over the set ventilator frequency in the absence of a spontaneous inspiratory effort and can be caused by inappropriate ventilator trigger sensitivity. Ventilator auto-trigger can be misinterpreted as a spontaneous breath and has the potential to delay or prevent brain death testing and confuse health-care professionals and/or patient families. To determine the frequency of organ donor referrals from 1 Organ Procurement Organization (OPO) that could benefit from an algorithm designed to assist organ recovery coordinators to identify and correct ventilator auto-triggering. This retrospective analysis evaluated documentation of organ donor referrals from 1 OPO in central Texas during the 2013 calendar year that resulted in the withdrawal of care by the patient's family and the recovery of organs. The frequency of referrals that presented with absent brain stem reflexes except for additional respirations over the set ventilator rate was determined to assess for the need of the proposed algorithm. Documentation of 672 organ procurement organization referrals was evaluated. Documentation from 42 referrals that resulted in the withdrawal of care and 21 referrals that resulted in the recovery of organs were identified with absent brain stem reflexes except for spontaneous respirations on the mechanical ventilator. As a result, an algorithm designed to identify and correct ventilator auto-trigger could have been used 63 times during the 2013 calendar year. © 2016, NATCO.

Identifying potential dropouts from college physics classes

NASA Astrophysics Data System (ADS)

Wollman, Warren; Lawrenz, Frances

Hudson and Rottman (1981) established that mathematics ability is probably a secondary factor influencing dropout from college physics courses. Other factors remain to be found for predicting who will drop out or at least have difficulty with the course. When mathematics ability is coupled with general indicators of performance (total GPA and ACT natural science), prediction of performance for those who complete the course is substantially improved. Moreover, discriminant analyses reveal who will have at least some difficulty, but not who will drop out. The problem of isolating specific weaknesses of students who have difficulty persists. Physics achievement appears to depend on mathematics ability only to the extent that students possess the ability to utilize mathematics knowledge for solving physics problems. Identification of the specific aspects of this ability as well as the specific deficiencies leading to dropout should be the object of future research. For the present, interviews might be more revealing than group testing methods.

Predicting climate-driven regime shifts versus rebound potential in coral reefs.

PubMed

Graham, Nicholas A J; Jennings, Simon; MacNeil, M Aaron; Mouillot, David; Wilson, Shaun K

2015-02-05

Climate-induced coral bleaching is among the greatest current threats to coral reefs, causing widespread loss of live coral cover. Conditions under which reefs bounce back from bleaching events or shift from coral to algal dominance are unknown, making it difficult to predict and plan for differing reef responses under climate change. Here we document and predict long-term reef responses to a major climate-induced coral bleaching event that caused unprecedented region-wide mortality of Indo-Pacific corals. Following loss of >90% live coral cover, 12 of 21 reefs recovered towards pre-disturbance live coral states, while nine reefs underwent regime shifts to fleshy macroalgae. Functional diversity of associated reef fish communities shifted substantially following bleaching, returning towards pre-disturbance structure on recovering reefs, while becoming progressively altered on regime shifting reefs. We identified threshold values for a range of factors that accurately predicted ecosystem response to the bleaching event. Recovery was favoured when reefs were structurally complex and in deeper water, when density of juvenile corals and herbivorous fishes was relatively high and when nutrient loads were low. Whether reefs were inside no-take marine reserves had no bearing on ecosystem trajectory. Although conditions governing regime shift or recovery dynamics were diverse, pre-disturbance quantification of simple factors such as structural complexity and water depth accurately predicted ecosystem trajectories. These findings foreshadow the likely divergent but predictable outcomes for reef ecosystems in response to climate change, thus guiding improved management and adaptation.

Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals.

PubMed

Trisciuzzi, Daniela; Alberga, Domenico; Mansouri, Kamel; Judson, Richard; Novellino, Ettore; Mangiatordi, Giuseppe Felice; Nicolotti, Orazio

2017-11-27

We present a practical and easy-to-run in silico workflow exploiting a structure-based strategy making use of docking simulations to derive highly predictive classification models of the androgenic potential of chemicals. Models were trained on a high-quality chemical collection comprising 1689 curated compounds made available within the CoMPARA consortium from the US Environmental Protection Agency and were integrated with a two-step applicability domain whose implementation had the effect of improving both the confidence in prediction and statistics by reducing the number of false negatives. Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from the Protein Data Bank) was associated with the best performing structure-based classification model. Three validation sets comprising each 2590 compounds extracted by the DUD-E collection were used to challenge model performance and the effectiveness of Applicability Domain implementation. Next, the 2PNU model was applied to screen and prioritize two collections of chemicals. The first is a small pool of 12 representative androgenic compounds that were accurately classified based on outstanding rationale at the molecular level. The second is a large external blind set of 55450 chemicals with potential for human exposure. We show how the use of molecular docking provides highly interpretable models and can represent a real-life option as an alternative nontesting method for predictive toxicology.

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.

Newer Approaches to Identify Potential Untoward Effects in Functional Foods.

PubMed

Marone, Palma Ann; Birkenbach, Victoria L; Hayes, A Wallace

2016-01-01

Globalization has greatly accelerated the numbers and variety of food and beverage products available worldwide. The exchange among greater numbers of countries, manufacturers, and products in the United States and worldwide has necessitated enhanced quality measures for nutritional products for larger populations increasingly reliant on functionality. These functional foods, those that provide benefit beyond basic nutrition, are increasingly being used for their potential to alleviate food insufficiency while enhancing quality and longevity of life. In the United States alone, a steady import increase of greater than 15% per year or 24 million shipments, over 70% products of which are food related, is regulated under the Food and Drug Administration (FDA). This unparalleled growth has resulted in the need for faster, cheaper, and better safety and efficacy screening methods in the form of harmonized guidelines and recommendations for product standardization. In an effort to meet this need, the in vitro toxicology testing market has similarly grown with an anticipatory 15% increase between 2010 and 2015 of US$1.3 to US$2.7 billion. Although traditionally occupying a small fraction of the market behind pharmaceuticals and cosmetic/household products, the scope of functional food testing, including additives/supplements, ingredients, residues, contact/processing, and contaminants, is potentially expansive. Similarly, as functional food testing has progressed, so has the need to identify potential adverse factors that threaten the safety and quality of these products. © The Author(s) 2015.

A New Scheme to Characterize and Identify Protein Ubiquitination Sites.

PubMed

Nguyen, Van-Nui; Huang, Kai-Yao; Huang, Chien-Hsun; Lai, K Robert; Lee, Tzong-Yi

2017-01-01

Protein ubiquitination, involving the conjugation of ubiquitin on lysine residue, serves as an important modulator of many cellular functions in eukaryotes. Recent advancements in proteomic technology have stimulated increasing interest in identifying ubiquitination sites. However, most computational tools for predicting ubiquitination sites are focused on small-scale data. With an increasing number of experimentally verified ubiquitination sites, we were motivated to design a predictive model for identifying lysine ubiquitination sites for large-scale proteome dataset. This work assessed not only single features, such as amino acid composition (AAC), amino acid pair composition (AAPC) and evolutionary information, but also the effectiveness of incorporating two or more features into a hybrid approach to model construction. The support vector machine (SVM) was applied to generate the prediction models for ubiquitination site identification. Evaluation by five-fold cross-validation showed that the SVM models learned from the combination of hybrid features delivered a better prediction performance. Additionally, a motif discovery tool, MDDLogo, was adopted to characterize the potential substrate motifs of ubiquitination sites. The SVM models integrating the MDDLogo-identified substrate motifs could yield an average accuracy of 68.70 percent. Furthermore, the independent testing result showed that the MDDLogo-clustered SVM models could provide a promising accuracy (78.50 percent) and perform better than other prediction tools. Two cases have demonstrated the effective prediction of ubiquitination sites with corresponding substrate motifs.

The Near-Earth Object Human Space Flight Accessible Targets Study (NHATS) List of Near-Earth Asteroids: Identifying Potential Targets for Future Exploration

NASA Astrophysics Data System (ADS)

Abell, Paul; Barbee, B. W.; Mink, R. G.; Adamo, D. R.; Alberding, C. M.; Mazanek, D. D.; Johnson, L. N.; Yeomans, D. K.; Chodas, P. W.; Chamberlin, A. B.; Benner, L. A. M.; Drake, B. G.; Friedensen, V. P.

2012-10-01

Introduction: Much attention has recently been focused on human exploration of near-Earth asteroids (NEAs). Detailed planning for deep space exploration and identification of potential NEA targets for human space flight requires selecting objects from the growing list of known NEAs. NASA therefore initiated the Near-Earth Object Human Space Flight Accessible Target Study (NHATS), which uses dynamical trajectory performance constraints to identify potentially accessible NEAs. Accessibility Criteria: Future NASA human space flight capability is being defined while the Orion Multi-Purpose Crew Vehicle and Space Launch System are under development. Velocity change and mission duration are two of the most critical factors in any human spaceflight endeavor, so the most accessible NEAs tend to be those with orbits similar to Earth’s. To be classified as NHATS-compliant, a NEA must offer at least one round-trip trajectory solution satisfying purposely inclusive constraints, including total mission change in velocity ≤ 12 km/s, mission duration ≤ 450 days (with at least 8 days at the NEA), Earth departure between Jan 1, 2015 and Dec 31, 2040, Earth departure C3 ≤ 60 km2/s2, and Earth return atmospheric entry speed ≤ 12 km/s. Monitoring and Updates: The NHATS list of potentially accessible targets is continuously updated as NEAs are discovered and orbit solutions for known NEAs are improved. The current list of accessible NEAs identified as potentially viable for future human exploration under the NHATS criteria is available to the international community via a website maintained by NASA’s NEO Program Office (http://neo.jpl.nasa.gov/nhats/). This website also lists predicted optical and radar observing opportunities for each NHATS-compliant NEA to facilitate acquisition of follow-up observations. Conclusions: This list of NEAs will be useful for analyzing robotic mission opportunities, identifying optimal round trip human space flight trajectories, and

77 FR 41406 - Evaluation of In Vitro Tests for Identifying Eye Injury Hazard Potential of Chemicals and...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-13

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Evaluation of In Vitro Tests for Identifying Eye Injury...-animal testing strategies proposed for identifying eye injury hazard potential of chemicals and products... Panel and submission of data from substances tested in in vitro tests for identifying eye injury hazard...

Identifying DNA-binding proteins using structural motifs and the electrostatic potential

PubMed Central

Shanahan, Hugh P.; Garcia, Mario A.; Jones, Susan; Thornton, Janet M.

2004-01-01

Robust methods to detect DNA-binding proteins from structures of unknown function are important for structural biology. This paper describes a method for identifying such proteins that (i) have a solvent accessible structural motif necessary for DNA-binding and (ii) a positive electrostatic potential in the region of the binding region. We focus on three structural motifs: helix–turn-helix (HTH), helix–hairpin–helix (HhH) and helix–loop–helix (HLH). We find that the combination of these variables detect 78% of proteins with an HTH motif, which is a substantial improvement over previous work based purely on structural templates and is comparable to more complex methods of identifying DNA-binding proteins. Similar true positive fractions are achieved for the HhH and HLH motifs. We see evidence of wide evolutionary diversity for DNA-binding proteins with an HTH motif, and much smaller diversity for those with an HhH or HLH motif. PMID:15356290

A potential prognostic long non-coding RNA signature to predict metastasis-free survival of breast cancer patients.

PubMed

Sun, Jie; Chen, Xihai; Wang, Zhenzhen; Guo, Maoni; Shi, Hongbo; Wang, Xiaojun; Cheng, Liang; Zhou, Meng

2015-11-09

Long non-coding RNAs (lncRNAs) have been implicated in a variety of biological processes, and dysregulated lncRNAs have demonstrated potential roles as biomarkers and therapeutic targets for cancer prognosis and treatment. In this study, by repurposing microarray probes, we analyzed lncRNA expression profiles of 916 breast cancer patients from the Gene Expression Omnibus (GEO). Nine lncRNAs were identified to be significantly associated with metastasis-free survival (MFS) in the training dataset of 254 patients using the Cox proportional hazards regression model. These nine lncRNAs were then combined to form a single prognostic signature for predicting metastatic risk in breast cancer patients that was able to classify patients in the training dataset into high- and low-risk subgroups with significantly different MFSs (median 2.4 years versus 3.0 years, log-rank test p < 0.001). This nine-lncRNA signature was similarly effective for prognosis in a testing dataset and two independent datasets. Further analysis showed that the predictive ability of the signature was independent of clinical variables, including age, ER status, ESR1 status and ERBB2 status. Our results indicated that lncRNA signature could be a useful prognostic marker to predict metastatic risk in breast cancer patients and may improve upon our understanding of the molecular mechanisms underlying breast cancer metastasis.

Identifying potential recommendation domains for conservation agriculture in Ethiopia, Kenya, and Malawi.

PubMed

Tesfaye, Kindie; Jaleta, Moti; Jena, Pradyot; Mutenje, Munyaradzi

2015-02-01

Conservation agriculture (CA) is being promoted as an option for reducing soil degradation, conserving water, enhancing crop productivity, and maintaining yield stability. However, CA is a knowledge- and technology-intensive practice, and may not be feasible or may not perform better than conventional agriculture under all conditions and farming systems. Using high resolution (≈1 km(2)) biophysical and socioeconomic geospatial data, this study identified potential recommendation domains (RDs) for CA in Ethiopia, Kenya, and Malawi. The biophysical variables used were soil texture, surface slope, and rainfall while the socioeconomic variables were market access and human and livestock population densities. Based on feasibility and comparative performance of CA over conventional agriculture, the biophysical and socioeconomic factors were first used to classify cultivated areas into three biophysical and three socioeconomic potential domains, respectively. Combinations of biophysical and socioeconomic domains were then used to develop potential RDs for CA based on adoption potential within the cultivated areas. About 39, 12, and 5% of the cultivated areas showed high biophysical and socioeconomic potential while 50, 39, and 21% of the cultivated areas showed high biophysical and medium socioeconomic potential for CA in Malawi, Kenya, and Ethiopia, respectively. The results indicate considerable acreages of land with high CA adoption potential in the mixed crop-livestock systems of the studied countries. However, there are large differences among countries depending on biophysical and socio-economic conditions. The information generated in this study could be used for targeting CA and prioritizing CA-related agricultural research and investment priorities in the three countries.

Identifying Potential Recommendation Domains for Conservation Agriculture in Ethiopia, Kenya, and Malawi

NASA Astrophysics Data System (ADS)

Tesfaye, Kindie; Jaleta, Moti; Jena, Pradyot; Mutenje, Munyaradzi

2015-02-01

Conservation agriculture (CA) is being promoted as an option for reducing soil degradation, conserving water, enhancing crop productivity, and maintaining yield stability. However, CA is a knowledge- and technology-intensive practice, and may not be feasible or may not perform better than conventional agriculture under all conditions and farming systems. Using high resolution (≈1 km2) biophysical and socioeconomic geospatial data, this study identified potential recommendation domains (RDs) for CA in Ethiopia, Kenya, and Malawi. The biophysical variables used were soil texture, surface slope, and rainfall while the socioeconomic variables were market access and human and livestock population densities. Based on feasibility and comparative performance of CA over conventional agriculture, the biophysical and socioeconomic factors were first used to classify cultivated areas into three biophysical and three socioeconomic potential domains, respectively. Combinations of biophysical and socioeconomic domains were then used to develop potential RDs for CA based on adoption potential within the cultivated areas. About 39, 12, and 5 % of the cultivated areas showed high biophysical and socioeconomic potential while 50, 39, and 21 % of the cultivated areas showed high biophysical and medium socioeconomic potential for CA in Malawi, Kenya, and Ethiopia, respectively. The results indicate considerable acreages of land with high CA adoption potential in the mixed crop-livestock systems of the studied countries. However, there are large differences among countries depending on biophysical and socio-economic conditions. The information generated in this study could be used for targeting CA and prioritizing CA-related agricultural research and investment priorities in the three countries.

Systems to identify potentially inappropriate prescribing in people with advanced dementia: a systematic review.

PubMed

Disalvo, Domenica; Luckett, Tim; Agar, Meera; Bennett, Alexandra; Davidson, Patricia Mary

2016-05-31

Systems for identifying potentially inappropriate medications in older adults are not immediately transferrable to advanced dementia, where the management goal is palliation. The aim of the systematic review was to identify and synthesise published systems and make recommendations for identifying potentially inappropriate prescribing in advanced dementia. Studies were included if published in a peer-reviewed English language journal and concerned with identifying the appropriateness or otherwise of medications in advanced dementia or dementia and palliative care. The quality of each study was rated using the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) checklist. Synthesis was narrative due to heterogeneity among designs and measures. Medline (OVID), CINAHL, the Cochrane Database of Systematic Reviews (2005 - August 2014) and AMED were searched in October 2014. Reference lists of relevant reviews and included articles were searched manually. Eight studies were included, all of which were scored a high quality using the STROBE checklist. Five studies used the same system developed by the Palliative Excellence in Alzheimer Care Efforts (PEACE) Program. One study used number of medications as an index, and two studies surveyed health professionals' opinions on appropriateness of specific medications in different clinical scenarios. Future research is needed to develop and validate systems with clinical utility for improving safety and quality of prescribing in advanced dementia. Systems should account for individual clinical context and distinguish between deprescribing and initiation of medications.

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

NASA Technical Reports Server (NTRS)

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

2010-01-01

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

An entropy and viscosity corrected potential method for rotor performance prediction

NASA Technical Reports Server (NTRS)

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

1988-01-01

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

Prediction of Nursing Workload in Hospital.

PubMed

Fiebig, Madlen; Hunstein, Dirk; Bartholomeyczik, Sabine

2018-01-01

A dissertation project at the Witten/Herdecke University [1] is investigating which (nursing sensitive) patient characteristics are suitable for predicting a higher or lower degree of nursing workload. For this research project four predictive modelling methods were selected. In a first step, SUPPORT VECTOR MACHINE, RANDOM FOREST, and GRADIENT BOOSTING were used to identify potential predictors from the nursing sensitive patient characteristics. The results were compared via FEATURE IMPORTANCE. To predict nursing workload the predictors identified in step 1 were modelled using MULTINOMIAL LOGISTIC REGRESSION. First results from the data mining process will be presented. A prognostic determination of nursing workload can be used not only as a basis for human resource planning in hospital, but also to respond to health policy issues.

Towards prediction of heatwaves based on the complementary relationship between actual and potential evaporation - energy partitioning and hydrologic attributes

NASA Astrophysics Data System (ADS)

Or, D.; Aminzadeh, M.; Roderick, M. L.

2017-12-01

Prediction of extreme climate events such as heatwaves that are characterized by prolonged periods of high air temperatures (accompanied by low precipitation and high radiation) provides an opportunity to potentially mitigate the associated environmental, social and economic impacts. Vegetation may respond to these extreme conditions by reducing evaporative flux either due to soil water depletion or inability to meet the atmospheric evaporative demand (high canopy resistance). We implement a newly generalized Complementary Relationship (CR) for spatially heterogeneous land surfaces to predict the actual evaporation from drying landscapes covered by different vegetation types (i.e., grassland and forest). A strong correlation between air temperature and sensible heat flux anomalies identified from FLUXNET network data suggests that abrupt changes in sensible heat flux above climatological means can serve as indicators for predicting the onset of a heatwave. We thus capitalize on the inherent coupling between evaporative and sensible heat fluxes linked to moisture availability within the CR framework to predict rapid increase in regional sensible heat flux associated with soil drying (low precipitation) or with extreme evaporative demand (high radiation) while soil moisture is not limiting. The proposed approach evaluated using FLUXNET datasets provides an energy constraint framework based on the CR concept to obtain new insights into the onset of heatwaves and climate extremes such as regional droughts.

The Potential of Tropospheric Gradients for Regional Precipitation Prediction

NASA Astrophysics Data System (ADS)

Boisits, Janina; Möller, Gregor; Wittmann, Christoph; Weber, Robert

2017-04-01

Changes of temperature and humidity in the neutral atmosphere cause variations in tropospheric path delays and tropospheric gradients. By estimating zenith wet delays (ZWD) and gradients using a GNSS reference station network the obtained time series provide information about spatial and temporal variations of water vapour in the atmosphere. Thus, GNSS-based tropospheric parameters can contribute to the forecast of regional precipitation events. In a recently finalized master thesis at TU Wien the potential of tropospheric gradients for weather prediction was investigated. Therefore, ZWD and gradient time series at selected GNSS reference stations were compared to precipitation data over a period of six months (April to September 2014). The selected GNSS stations form two test areas within Austria. All required meteorological data was provided by the Central Institution for Meteorology and Geodynamics (ZAMG). Two characteristics in ZWD and gradient time series can be anticipated in case of an approaching weather front. First, an induced asymmetry in tropospheric delays results in both, an increased magnitude of the gradient and in gradients pointing towards the weather front. Second, an increase in ZWD reflects the increased water vapour concentration right before a precipitation event. To investigate these characteristics exemplary test events were processed. On the one hand, the sequence of the anticipated increase in ZWD at each GNSS station obtained by cross correlation of the time series indicates the direction of the approaching weather front. On the other hand, the corresponding peak in gradient time series allows the deduction of the direction of movement as well. To verify the results precipitation data from ZAMG was used. It can be deduced, that tropospheric gradients show high potential for predicting precipitation events. While ZWD time series rather indicate the orientation of the air mass boundary, gradients rather indicate the direction of movement

A Psychoevolutionary Approach to Identifying Preferred Nature Scenes With Potential to Provide Restoration From Stress.

PubMed

Thake, Carol L; Bambling, Matthew; Edirippulige, Sisira; Marx, Eric

2017-10-01

Research supports therapeutic use of nature scenes in healthcare settings, particularly to reduce stress. However, limited literature is available to provide a cohesive guide for selecting scenes that may provide optimal therapeutic effect. This study produced and tested a replicable process for selecting nature scenes with therapeutic potential. Psychoevolutionary theory informed the construction of the Importance for Survival Scale (IFSS), and its usefulness for identifying scenes that people generally prefer to view and that hold potential to reduce stress was tested. Relationships between Importance for Survival (IFS), preference, and restoration were tested. General community participants ( N = 20 males, 20 females; M age = 48 years) Q-sorted sets of landscape photographs (preranked by the researcher in terms of IFS using the IFSS) from most to least preferred, and then completed the Short-Version Revised Restoration Scale in response to viewing a selection of the scenes. Results showed significant positive relationships between IFS and each of scene preference (large effect), and restoration potential (medium effect), as well as between scene preference and restoration potential across the levels of IFS (medium effect), and for individual participants and scenes (large effect). IFS was supported as a framework for identifying nature scenes that people will generally prefer to view and that hold potential for restoration from emotional distress; however, greater therapeutic potential may be expected when people can choose which of the scenes they would prefer to view. Evidence for the effectiveness of the IFSS was produced.

Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets

DOE PAGES

Schulze, Kornelius; Imbeaud, Sandrine; Letouzé, Eric; ...

2015-03-30

Our genomic analyses promise to improve tumor characterization to optimize personalized treatment for patients with hepatocellular carcinoma (HCC). Exome sequencing analysis of 243 liver tumors identified mutational signatures associated with specific risk factors, mainly combined alcohol and tobacco consumption and exposure to aflatoxin B1. We identified 161 putative driver genes associated with 11 recurrently altered pathways. Associations of mutations defined 3 groups of genes related to risk factors and centered on CTNNB1 (alcohol), TP53 (hepatitis B virus, HBV) and AXIN1. These analyses according to tumor stage progression identified TERT promoter mutation as an early event, whereasFGF3, FGF4, FGF19 or CCND1more » amplification and TP53 and CDKN2A alterations appeared at more advanced stages in aggressive tumors. In 28% of the tumors, we identified genetic alterations potentially targetable by US Food and Drug Administration (FDA)–approved drugs. Finally, we identified risk factor–specific mutational signatures and defined the extensive landscape of altered genes and pathways in HCC, which will be useful to design clinical trials for targeted therapy.« less

Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets

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

Schulze, Kornelius; Imbeaud, Sandrine; Letouzé, Eric

Our genomic analyses promise to improve tumor characterization to optimize personalized treatment for patients with hepatocellular carcinoma (HCC). Exome sequencing analysis of 243 liver tumors identified mutational signatures associated with specific risk factors, mainly combined alcohol and tobacco consumption and exposure to aflatoxin B1. We identified 161 putative driver genes associated with 11 recurrently altered pathways. Associations of mutations defined 3 groups of genes related to risk factors and centered on CTNNB1 (alcohol), TP53 (hepatitis B virus, HBV) and AXIN1. These analyses according to tumor stage progression identified TERT promoter mutation as an early event, whereasFGF3, FGF4, FGF19 or CCND1more » amplification and TP53 and CDKN2A alterations appeared at more advanced stages in aggressive tumors. In 28% of the tumors, we identified genetic alterations potentially targetable by US Food and Drug Administration (FDA)–approved drugs. Finally, we identified risk factor–specific mutational signatures and defined the extensive landscape of altered genes and pathways in HCC, which will be useful to design clinical trials for targeted therapy.« less

Computational Models for Prediction of Yeast Strain Potential for Winemaking from Phenotypic Profiles

PubMed Central

Umek, Lan; Fonseca, Elza; Drumonde-Neves, João; Dequin, Sylvie; Zupan, Blaz; Schuller, Dorit

2013-01-01

Saccharomyces cerevisiae strains from diverse natural habitats harbour a vast amount of phenotypic diversity, driven by interactions between yeast and the respective environment. In grape juice fermentations, strains are exposed to a wide array of biotic and abiotic stressors, which may lead to strain selection and generate naturally arising strain diversity. Certain phenotypes are of particular interest for the winemaking industry and could be identified by screening of large number of different strains. The objective of the present work was to use data mining approaches to identify those phenotypic tests that are most useful to predict a strain's potential for winemaking. We have constituted a S. cerevisiae collection comprising 172 strains of worldwide geographical origins or technological applications. Their phenotype was screened by considering 30 physiological traits that are important from an oenological point of view. Growth in the presence of potassium bisulphite, growth at 40°C, and resistance to ethanol were mostly contributing to strain variability, as shown by the principal component analysis. In the hierarchical clustering of phenotypic profiles the strains isolated from the same wines and vineyards were scattered throughout all clusters, whereas commercial winemaking strains tended to co-cluster. Mann-Whitney test revealed significant associations between phenotypic results and strain's technological application or origin. Naïve Bayesian classifier identified 3 of the 30 phenotypic tests of growth in iprodion (0.05 mg/mL), cycloheximide (0.1 µg/mL) and potassium bisulphite (150 mg/mL) that provided most information for the assignment of a strain to the group of commercial strains. The probability of a strain to be assigned to this group was 27% using the entire phenotypic profile and increased to 95%, when only results from the three tests were considered. Results show the usefulness of computational approaches to simplify strain selection

Evaluating predictive modeling's potential to improve teleretinal screening participation in urban safety net clinics.

PubMed

Ogunyemi, Omolola; Teklehaimanot, Senait; Patty, Lauren; Moran, Erin; George, Sheba

2013-01-01

Screening guidelines for diabetic patients recommend yearly eye examinations to detect diabetic retinopathy and other forms of diabetic eye disease. However, annual screening rates for retinopathy in US urban safety net settings remain low. Using data gathered from a study of teleretinal screening in six urban safety net clinics, we assessed whether predictive modeling could be of value in identifying patients at risk of developing retinopathy. We developed and examined the accuracy of two predictive modeling approaches for diabetic retinopathy in a sample of 513 diabetic individuals, using routinely available clinical variables from retrospective medical record reviews. Bayesian networks and radial basis function (neural) networks were learned using ten-fold cross-validation. The predictive models were modestly predictive with the best model having an AUC of 0.71. Using routinely available clinical variables to predict patients at risk of developing retinopathy and to target them for annual eye screenings may be of some usefulness to safety net clinics.

Ultrasonication as a potential tool to predict solute crystallization in freeze-concentrates.

PubMed

Ragoonanan, Vishard; Suryanarayanan, Raj

2014-06-01

We hypothesize that ultrasonication can accelerate solute crystallization in freeze-concentrates. Our objective is to demonstrate ultrasonication as a potential predictive tool for evaluating physical stability of excipients in frozen solutions. The crystallization tendencies of lyoprotectants (trehalose, sucrose), carboxylic acid buffers (citric, tartaric, malic, and acetic) and an amino acid buffer (histidine HCl) were studied. Aqueous solutions of buffers, lyoprotectants and mixtures of the two were cooled from room temperature to -20°C and sonicated to induce solute crystallization. The crystallized phases were identified by X-ray diffractometry (laboratory or synchrotron source). Sonication accelerated crystallization of trehalose dihydrate in frozen trehalose solutions. Sonication also enhanced solute crystallization in tartaric (200 mM; pH 5), citric (200 mM pH 4) and malic (200 mM; pH 4) acid buffers. At lower buffer concentrations, longer annealing times following sonication were required to facilitate solute crystallization. The time for crystallization of histidine HCl progressively increased as a function of sucrose concentration. The insonation period required to effect crystallization also increased with sucrose concentration. Sonication can substantially accelerate solute crystallization in the freeze-concentrate. Ultrasonication may be useful in assessing the crystallization tendency of formulation constituents used in long term frozen storage and freeze-drying.

Vitiligo blood transcriptomics provides new insights into disease mechanisms and identifies potential novel therapeutic targets.

PubMed

Dey-Rao, Rama; Sinha, Animesh A

2017-01-28

Significant gaps remain regarding the pathomechanisms underlying the autoimmune response in vitiligo (VL), where the loss of self-tolerance leads to the targeted killing of melanocytes. Specifically, there is incomplete information regarding alterations in the systemic environment that are relevant to the disease state. We undertook a genome-wide profiling approach to examine gene expression in the peripheral blood of VL patients and healthy controls in the context of our previously published VL-skin gene expression profile. We used several in silico bioinformatics-based analyses to provide new insights into disease mechanisms and suggest novel targets for future therapy. Unsupervised clustering methods of the VL-blood dataset demonstrate a "disease-state"-specific set of co-expressed genes. Ontology enrichment analysis of 99 differentially expressed genes (DEGs) uncovers a down-regulated immune/inflammatory response, B-Cell antigen receptor (BCR) pathways, apoptosis and catabolic processes in VL-blood. There is evidence for both type I and II interferon (IFN) playing a role in VL pathogenesis. We used interactome analysis to identify several key blood associated transcriptional factors (TFs) from within (STAT1, STAT6 and NF-kB), as well as "hidden" (CREB1, MYC, IRF4, IRF1, and TP53) from the dataset that potentially affect disease pathogenesis. The TFs overlap with our reported lesional-skin transcriptional circuitry, underscoring their potential importance to the disease. We also identify a shared VL-blood and -skin transcriptional "hot spot" that maps to chromosome 6, and includes three VL-blood dysregulated genes (PSMB8, PSMB9 and TAP1) described as potential VL-associated genetic susceptibility loci. Finally, we provide bioinformatics-based support for prioritizing dysregulated genes in VL-blood or skin as potential therapeutic targets. We examined the VL-blood transcriptome in context with our (previously published) VL-skin transcriptional profile to address

Do resting brain dynamics predict oddball evoked-potential?

PubMed Central

2011-01-01

Background The oddball paradigm is widely applied to the investigation of cognitive function in neuroscience and in neuropsychiatry. Whether cortical oscillation in the resting state can predict the elicited oddball event-related potential (ERP) is still not clear. This study explored the relationship between resting electroencephalography (EEG) and oddball ERPs. The regional powers of 18 electrodes across delta, theta, alpha and beta frequencies were correlated with the amplitude and latency of N1, P2, N2 and P3 components of oddball ERPs. A multivariate analysis based on partial least squares (PLS) was applied to further examine the spatial pattern revealed by multiple correlations. Results Higher synchronization in the resting state, especially at the alpha spectrum, is associated with higher neural responsiveness and faster neural propagation, as indicated by the higher amplitude change of N1/N2 and shorter latency of P2. None of the resting quantitative EEG indices predict P3 latency and amplitude. The PLS analysis confirms that the resting cortical dynamics which explains N1/N2 amplitude and P2 latency does not show regional specificity, indicating a global property of the brain. Conclusions This study differs from previous approaches by relating dynamics in the resting state to neural responsiveness in the activation state. Our analyses suggest that the neural characteristics carried by resting brain dynamics modulate the earlier/automatic stage of target detection. PMID:22114868

Event-related potentials during word mapping to object shape predict toddlers' vocabulary size

PubMed Central

Borgström, Kristina; Torkildsen, Janne von Koss; Lindgren, Magnus

2015-01-01

What role does attention to different object properties play in early vocabulary development? This longitudinal study using event-related potentials in combination with behavioral measures investigated 20- and 24-month-olds' (n = 38; n = 34; overlapping n = 24) ability to use object shape and object part information in word-object mapping. The N400 component was used to measure semantic priming by images containing shape or detail information. At 20 months, the N400 to words primed by object shape varied in topography and amplitude depending on vocabulary size, and these differences predicted productive vocabulary size at 24 months. At 24 months, when most of the children had vocabularies of several hundred words, the relation between vocabulary size and the N400 effect in a shape context was weaker. Detached object parts did not function as word primes regardless of age or vocabulary size, although the part-objects were identified behaviorally. The behavioral measure, however, also showed relatively poor recognition of the part-objects compared to the shape-objects. These three findings provide new support for the link between shape recognition and early vocabulary development. PMID:25762957

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib.

PubMed

Li, Bin; Shin, Hyunjin; Gulbekyan, Georgy; Pustovalova, Olga; Nikolsky, Yuri; Hope, Andrew; Bessarabova, Marina; Schu, Matthew; Kolpakova-Hart, Elona; Merberg, David; Dorner, Andrew; Trepicchio, William L

2015-01-01

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug's known mechanism of action. Also, the models predict each drug's potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets.

Identifying Potential Collapse Features Under Highways

DOT National Transportation Integrated Search

2003-01-01

In 1994, subsidence features were identified on Interstate 70 in eastern Ohio. These : features were caused by collapse of old mine workings beneath the highway. An attempt : was made to delineate these features using geophysical methods with no avai...

Use of multi-criteria decision analysis to identify potentially dangerous glacial lakes.

PubMed

Kougkoulos, Ioannis; Cook, Simon J; Jomelli, Vincent; Clarke, Leon; Symeonakis, Elias; Dortch, Jason M; Edwards, Laura A; Merad, Myriam

2018-04-15

Glacial Lake Outburst Floods (GLOFs) represent a significant threat in deglaciating environments, necessitating the development of GLOF hazard and risk assessment procedures. Here, we outline a Multi-Criteria Decision Analysis (MCDA) approach that can be used to rapidly identify potentially dangerous lakes in regions without existing tailored GLOF risk assessments, where a range of glacial lake types exist, and where field data are sparse or non-existent. Our MCDA model (1) is desk-based and uses freely and widely available data inputs and software, and (2) allows the relative risk posed by a range of glacial lake types to be assessed simultaneously within any region. A review of the factors that influence GLOF risk, combined with the strict rules of criteria selection inherent to MCDA, has allowed us to identify 13 exhaustive, non-redundant, and consistent risk criteria. We use our MCDA model to assess the risk of 16 extant glacial lakes and 6 lakes that have already generated GLOFs, and found that our results agree well with previous studies. For the first time in GLOF risk assessment, we employed sensitivity analyses to test the strength of our model results and assumptions, and to identify lakes that are sensitive to the criteria and risk thresholds used. A key benefit of the MCDA method is that sensitivity analyses are readily undertaken. Overall, these sensitivity analyses lend support to our model, although we suggest that further work is required to determine the relative importance of assessment criteria, and the thresholds that determine the level of risk for each criterion. As a case study, the tested method was then applied to 25 potentially dangerous lakes in the Bolivian Andes, where GLOF risk is poorly understood; 3 lakes are found to pose 'medium' or 'high' risk, and require further detailed investigation. Copyright © 2017 Elsevier B.V. All rights reserved.

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

On the potential use of radar-derived information in operational numerical weather prediction

NASA Technical Reports Server (NTRS)

Mcpherson, R. D.

1986-01-01

Estimates of requirements likely to be levied on a new observing system for mesoscale meteonology are given. Potential observing systems for mesoscale numerical weather prediction are discussed. Thermodynamic profiler radiometers, infrared radiometer atmospheric sounders, Doppler radar wind profilers and surveillance radar, and moisture profilers are among the instruments described.

Identifying potential collapse features under highways.

DOT National Transportation Integrated Search

2003-03-01

In 1994, subsidence features were identified on Interstate 70 in eastern Ohio. These features were caused by collapse of old mine workings beneath the highway. An attempt was made to delineate these features using geophysical methods with no avail. T...

Identifying Future Disease Hot Spots: Infectious Disease Vulnerability Index.

PubMed

Moore, Melinda; Gelfeld, Bill; Okunogbe, Adeyemi; Paul, Christopher

2017-06-01

Recent high-profile outbreaks, such as Ebola and Zika, have illustrated the transnational nature of infectious diseases. Countries that are most vulnerable to such outbreaks might be higher priorities for technical support. RAND created the Infectious Disease Vulnerability Index to help U.S. government and international agencies identify these countries and thereby inform programming to preemptively help mitigate the spread and effects of potential transnational outbreaks. The authors employed a rigorous methodology to identify the countries most vulnerable to disease outbreaks. They conducted a comprehensive review of relevant literature to identify factors influencing infectious disease vulnerability. Using widely available data, the authors created an index for identifying potentially vulnerable countries and then ranked countries by overall vulnerability score. Policymakers should focus on the 25 most-vulnerable countries with an eye toward a potential "disease belt" in the Sahel region of Africa. The infectious disease vulnerability scores for several countries were better than what would have been predicted on the basis of economic status alone. This suggests that low-income countries can overcome economic challenges and become more resilient to public health challenges, such as infectious disease outbreaks.

Potential of the Thermal Infrared Wavelength Region to predict semi-arid Soil Surface Properties for Remote Sensing Monitoring

NASA Astrophysics Data System (ADS)

Eisele, Andreas; Chabrillat, Sabine; Lau, Ian; Hecker, Christoph; Hewson, Robert; Carter, Dan; Wheaton, Buddy; Ong, Cindy; Cudahy, Thomas John; Kaufmann, Hermann

2014-05-01

Digital soil mapping with the means of passive remote sensing basically relies on the soils' spectral characteristics and an appropriate atmospheric window, where electromagnetic radiation transmits without significant attenuation. Traditionally the atmospheric window in the solar-reflective wavelength region (visible, VIS: 0.4 - 0.7 μm; near infrared, NIR: 0.7 - 1.1 μm; shortwave infrared, SWIR: 1.1 - 2.5 μm) has been used to quantify soil surface properties. However, spectral characteristics of semi-arid soils, typically have a coarse quartz rich texture and iron coatings that can limit the prediction of soil surface properties. In this study we investigated the potential of the atmospheric window in the thermal wavelength region (long wave infrared, LWIR: 8 - 14 μm) to predict soil surface properties such as the grain size distribution (texture) and the organic carbon content (SOC) for coarse-textured soils from the Australian wheat belt region. This region suffers soil loss due to wind erosion processes and large scale monitoring techniques, such as remote sensing, is urgently required to observe the dynamic changes of such soil properties. The coarse textured sandy soils of the investigated area require methods, which can measure the special spectral response of the quartz dominated mineralogy with iron oxide enriched grain coatings. By comparison, the spectroscopy using the solar-reflective region has limitations to discriminate such arid soil mineralogy and associated coatings. Such monitoring is important for observing potential desertification trends associated with coarsening of topsoil texture and reduction in SOC. In this laboratory study we identified the relevant LWIR wavelengths to predict these soil surface properties. The results showed the ability of multivariate analyses methods (PLSR) to predict these soil properties from the soil's spectral signature, where the texture parameters (clay and sand content) could be predicted well in the models

Challenges of predicting the potential distribution of a slow-spreading invader: a habitat suitability map for an invasive riparian tree

USGS Publications Warehouse

Jarnevich, Catherine S.; Reynolds, Lindsay V.

2011-01-01

Understanding the potential spread of invasive species is essential for land managers to prevent their establishment and restore impacted habitat. Habitat suitability modeling provides a tool for researchers and managers to understand the potential extent of invasive species spread. Our goal was to use habitat suitability modeling to map potential habitat of the riparian plant invader, Russian olive (Elaeagnus angustifolia). Russian olive has invaded riparian habitat across North America and is continuing to expand its range. We compiled 11 disparate datasets for Russian olive presence locations (n = 1,051 points and 139 polygons) in the western US and used Maximum entropy (Maxent) modeling to develop two habitat suitability maps for Russian olive in the western United States: one with coarse-scale water data and one with fine-scale water data. Our models were able to accurately predict current suitable Russian olive habitat (Coarse model: training AUC = 0.938, test AUC = 0.907; Fine model: training AUC = 0.923, test AUC = 0.885). Distance to water was the most important predictor for Russian olive presence in our coarse-scale water model, but it was only the fifth most important variable in the fine-scale model, suggesting that when water bodies are considered on a fine scale, Russian olive does not necessarily rely on water. Our model predicted that Russian olive has suitable habitat further west from its current distribution, expanding into the west coast and central North America. Our methodology proves useful for identifying potential future areas of invasion. Model results may be influenced by locations of cultivated individuals and sampling bias. Further study is needed to examine the potential for Russian olive to invade beyond its current range. Habitat suitability modeling provides an essential tool for enhancing our understanding of invasive species spread.

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

Evaluation of SARs for the prediction of eye irritation/corrosion potential: structural inclusion rules in the BfR decision support system.

PubMed

Tsakovska, I; Saliner, A Gallegos; Netzeva, T; Pavan, M; Worth, A P

2007-01-01

The proposed REACH regulation within the European Union (EU) aims to minimise the number of laboratory animals used for human hazard and risk assessment while ensuring adequate protection of human health and the environment. One way to achieve this goal is to develop non-testing methods, such as (quantitative) structure-activity relationships ([Q]SARs), suitable for identifying toxicological hazard from chemical structure and physicochemical properties alone. A database containing data submitted within the EU New Chemicals Notification procedure was compiled by the German Bundesinstitut für Risikobewertung (BfR). On the basis of these data, the BfR built a decision support system (DSS) for the prediction of several toxicological endpoints. For the prediction of eye irritation and corrosion potential, the DSS contains 31 physicochemical exclusion rules evaluated previously by the European Chemicals Bureau (ECB), and 27 inclusion rules that define structural alerts potentially responsible for eye irritation and/or corrosion. This work summarises the results of a study carried out by the ECB to assess the performance of the BfR structural rulebase. The assessment included: (a) evaluation of the structural alerts by using the training set of 1341 substances with experimental data for eye irritation and corrosion; and (b) external validation by using an independent test set of 199 chemicals. Recommendations are made for the further development of the structural rules in order to increase the overall predictivity of the DSS.

The useful potential of using existing data to uniquely identify predictable wind events and regimes, part 1

NASA Technical Reports Server (NTRS)

Trettel, D. W.; Aquino, J. T.; Piazza, T. R.; Taylor, L. E.; Trask, D. C.

1982-01-01

Correlations between standard meteorological data and wind power generation potential were developed. Combined with appropriate wind forecasts, these correlations can be useful to load dispatchers to supplement conventional energy sources. Hourly wind data were analyzed for four sites, each exhibiting a unique physiography. These sites are Amarillo, Texas; Ludington, Michigan; Montauk Point, New York; and San Gorgonio, California. Synoptic weather maps and tables are presented to illustrate various wind 'regimes' at these sites.

High-Risk Carotid Plaques Identified by CT-Angiogram can Predict Acute Myocardial Infarction

PubMed Central

Mosleh, Wassim; Adib, Keenan; Natdanai, Punnanithinont; Carmona-Rubio, Andres; Karki, Roshan; Paily, Jacienta; Ahmed, Mohamed Abdel-Aal; Vakkalanka, Sujit; Madam, Narasa; Gudleski, Gregory D; Chung, Charles; Sharma, Umesh C

2016-01-01

Purpose Prior studies identified the incremental value of non-invasive imaging by CT-angiogram (CTA) to detect high-risk coronary atherosclerotic plaques. Due to their superficial locations, larger calibers and motion-free imaging, the carotid arteries provide the best anatomic access for the non-invasive characterization of atherosclerotic plaques. We aim to assess the ability of predicting obstructive coronary artery disease (CAD) or acute myocardial infarction (MI) based on high-risk carotid plaque features identified by CTA. Methods We retrospectively examined carotid CTAs of 492 patients that presented with acute stroke to characterize the atherosclerotic plaques of the carotid arteries and examined development of acute MI and obstructive CAD within 12-months. Carotid lesions were defined in terms of calcifications (large or speckled), presence of low-attenuation plaques, positive remodeling, and presence of napkin ring sign (NRS). Adjusted relative risks were calculated for each plaque features. Results Patients with speckled (<3mm) calcifications and/or larger calcifications on CTA had a higher risk of developing an MI and/or obstructive CAD within one year compared to patients without [adjusted RR of 7.51, 95%CI 1.26 to 73.42, P= 0.001]. Patients with low-attenuation plaques on CTA had a higher risk of developing an MI and/or obstructive CAD within one year than patients without [adjusted RR of 2.73, 95%CI 1.19 to 8.50, P= 0.021]. Presence of carotid calcifications and low-attenuation plaques also portended higher sensitivity (100% and 79.17%, respectively) for the development of acute MI. Conclusions Presence of carotid calcifications and low-attenuation plaques can predict the risk of developing acute MI and/or obstructive CAD within 12-months. Given their high sensitivity, their absence can reliably exclude 12-month events. PMID:27866279

High-risk carotid plaques identified by CT-angiogram can predict acute myocardial infarction.

PubMed

Mosleh, Wassim; Adib, Keenan; Natdanai, Punnanithinont; Carmona-Rubio, Andres; Karki, Roshan; Paily, Jacienta; Ahmed, Mohamed Abdel-Aal; Vakkalanka, Sujit; Madam, Narasa; Gudleski, Gregory D; Chung, Charles; Sharma, Umesh C

2017-04-01

Prior studies identified the incremental value of non-invasive imaging by CT-angiogram (CTA) to detect high-risk coronary atherosclerotic plaques. Due to their superficial locations, larger calibers and motion-free imaging, the carotid arteries provide the best anatomic access for the non-invasive characterization of atherosclerotic plaques. We aim to assess the ability of predicting obstructive coronary artery disease (CAD) or acute myocardial infarction (MI) based on high-risk carotid plaque features identified by CTA. We retrospectively examined carotid CTAs of 492 patients that presented with acute stroke to characterize the atherosclerotic plaques of the carotid arteries and examined development of acute MI and obstructive CAD within 12-months. Carotid lesions were defined in terms of calcifications (large or speckled), presence of low-attenuation plaques, positive remodeling, and presence of napkin ring sign. Adjusted relative risks were calculated for each plaque features. Patients with speckled (<3 mm) calcifications and/or larger calcifications on CTA had a higher risk of developing an MI and/or obstructive CAD within 1 year compared to patients without (adjusted RR of 7.51, 95%CI 1.26-73.42, P = 0.001). Patients with low-attenuation plaques on CTA had a higher risk of developing an MI and/or obstructive CAD within 1 year than patients without (adjusted RR of 2.73, 95%CI 1.19-8.50, P = 0.021). Presence of carotid calcifications and low-attenuation plaques also portended higher sensitivity (100 and 79.17%, respectively) for the development of acute MI. Presence of carotid calcifications and low-attenuation plaques can predict the risk of developing acute MI and/or obstructive CAD within 12-months. Given their high sensitivity, their absence can reliably exclude 12-month events.

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.

Meta-analysis of the predictive value of DNA aneuploidy in malignant transformation of oral potentially malignant disorders.

PubMed

Alaizari, Nader A; Sperandio, Marcelo; Odell, Edward W; Peruzzo, Daiane; Al-Maweri, Sadeq A

2018-02-01

DNA aneuploidy is an imbalance of chromosomal DNA content that has been highlighted as a predictor of biological behavior and risk of malignant transformation. To date, DNA aneuploidy in oral potentially malignant diseases (OPMD) has been shown to correlate strongly with severe dysplasia and high-risk lesions that appeared non-dysplastic can be identified by ploidy analysis. Nevertheless, the prognostic value of DNA aneuploidy in predicting malignant transformation of OPMD remains to be validated. The aim of this meta-analysis was to assess the role of DNA aneuploidy in predicting malignant transformation in OPMD. The questions addressed were (i) Is DNA aneuploidy a useful marker to predict malignant transformation in OPMD? (ii) Is DNA diploidy a useful negative marker of malignant transformation in OPMD? These questions were addressed using the PECO method. Five studies assessing aneuploidy as a risk marker of malignant change were pooled into the meta-analysis. Aneuploidy was found to be associated with a 3.12-fold increased risk to progress into cancer (RR=3.12, 95% CI 1.86-5.24). Based on the five studies meta-analyzed, "no malignant progression" was more likely to occur in DNA diploid OPMD by 82% when compared to aneuploidy (RR=0.18, 95% CI 0.08-0.41). In conclusion, aneuploidy is a useful marker of malignant transformation in OPMD, although a diploid result should be interpreted with caution. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Onset dynamics of action potentials in rat neocortical neurons and identified snail neurons: quantification of the difference.

PubMed

Volgushev, Maxim; Malyshev, Aleksey; Balaban, Pavel; Chistiakova, Marina; Volgushev, Stanislav; Wolf, Fred

2008-04-09

The generation of action potentials (APs) is a key process in the operation of nerve cells and the communication between neurons. Action potentials in mammalian central neurons are characterized by an exceptionally fast onset dynamics, which differs from the typically slow and gradual onset dynamics seen in identified snail neurons. Here we describe a novel method of analysis which provides a quantitative measure of the onset dynamics of action potentials. This method captures the difference between the fast, step-like onset of APs in rat neocortical neurons and the gradual, exponential-like AP onset in identified snail neurons. The quantitative measure of the AP onset dynamics, provided by the method, allows us to perform quantitative analyses of factors influencing the dynamics.

Onset Dynamics of Action Potentials in Rat Neocortical Neurons and Identified Snail Neurons: Quantification of the Difference

PubMed Central

Volgushev, Maxim; Malyshev, Aleksey; Balaban, Pavel; Chistiakova, Marina; Volgushev, Stanislav; Wolf, Fred

2008-01-01

The generation of action potentials (APs) is a key process in the operation of nerve cells and the communication between neurons. Action potentials in mammalian central neurons are characterized by an exceptionally fast onset dynamics, which differs from the typically slow and gradual onset dynamics seen in identified snail neurons. Here we describe a novel method of analysis which provides a quantitative measure of the onset dynamics of action potentials. This method captures the difference between the fast, step-like onset of APs in rat neocortical neurons and the gradual, exponential-like AP onset in identified snail neurons. The quantitative measure of the AP onset dynamics, provided by the method, allows us to perform quantitative analyses of factors influencing the dynamics. PMID:18398478

Potential ligand-binding residues in rat olfactory receptors identified by correlated mutation analysis

NASA Technical Reports Server (NTRS)

Singer, M. S.; Oliveira, L.; Vriend, G.; Shepherd, G. M.

1995-01-01

A family of G-protein-coupled receptors is believed to mediate the recognition of odor molecules. In order to identify potential ligand-binding residues, we have applied correlated mutation analysis to receptor sequences from the rat. This method identifies pairs of sequence positions where residues remain conserved or mutate in tandem, thereby suggesting structural or functional importance. The analysis supported molecular modeling studies in suggesting several residues in positions that were consistent with ligand-binding function. Two of these positions, dominated by histidine residues, may play important roles in ligand binding and could confer broad specificity to mammalian odor receptors. The presence of positive (overdominant) selection at some of the identified positions provides additional evidence for roles in ligand binding. Higher-order groups of correlated residues were also observed. Each group may interact with an individual ligand determinant, and combinations of these groups may provide a multi-dimensional mechanism for receptor diversity.

Genomic analysis of adult B-ALL identifies potential markers of shorter survival.

PubMed

Patel, Shiven; Mason, Clinton C; Glenn, Martha J; Paxton, Christian N; South, Sara T; Cessna, Melissa H; Asch, Julie; Cobain, Erin F; Bixby, Dale L; Smith, Lauren B; Reshmi, Shalini; Gastier-Foster, Julie M; Schiffman, Joshua D; Miles, Rodney R

2017-05-01

B lymphoblastic leukemia (B-ALL) in adults has a higher risk of relapse and lower long-term survival than pediatric B-ALL, but data regarding genetic prognostic biomarkers are much more limited for adult patients. We identified 70 adult B-ALL patients from three institutions and performed genome-wide analysis via single nucleotide polymorphism (SNP) arrays on DNA isolated from their initial diagnostic sample and, when available, relapse bone marrow specimens to identify recurring copy number alterations (CNA). As B-cell developmental genes play a crucial role in this leukemia, we assessed such for recurrent deletions in diagnostic and relapse samples. We confirmed previous findings that the most prevalent deletions of these genes occur in CDKN2A, IKZF1, and PAX5, with several others at lower frequencies. Of the 16 samples having paired diagnostic and relapse samples, 5 showed new deletions in these recurrent B-cell related genes and 8 showed abolishment. Deletion of EBF1 heralded a significant negative prognostic impact on relapse free survival in univariate and multivariate analyses. The combination of both a CDKN2A/B deletion and an IKZF1 alteration (26% of cases) also showed a trend toward predicting worse overall survival compared to having only one or neither of these deletions. These findings add to the understanding of genomic influences on this comparably understudied disease cohort that upon further validation may help identify patients who would benefit from upfront treatment intensification. Copyright © 2017 Elsevier Ltd. All rights reserved.

Approaches to predicting potential impacts of climate change on forest disease: an example with Armillaria root disease

Treesearch

Ned B. Klopfenstein; Mee-Sook Kim; John W. Hanna; Bryce A. Richardson; John E. Lundquist

2009-01-01

Predicting climate change influences on forest diseases will foster forest management practices that minimize adverse impacts of diseases. Precise locations of accurately identified pathogens and hosts must be documented and spatially referenced to determine which climatic factors influence species distribution. With this information, bioclimatic models can predict the...

Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density

PubMed Central

Coronnello, Claudia; Hartmaier, Ryan; Arora, Arshi; Huleihel, Luai; Pandit, Kusum V.; Bais, Abha S.; Butterworth, Michael; Kaminski, Naftali; Stormo, Gary D.; Oesterreich, Steffi; Benos, Panayiotis V.

2012-01-01

MicroRNAs (miRNAs) are post-transcriptional regulators that bind to their target mRNAs through base complementarity. Predicting miRNA targets is a challenging task and various studies showed that existing algorithms suffer from high number of false predictions and low to moderate overlap in their predictions. Until recently, very few algorithms considered the dynamic nature of the interactions, including the effect of less specific interactions, the miRNA expression level, and the effect of combinatorial miRNA binding. Addressing these issues can result in a more accurate miRNA:mRNA modeling with many applications, including efficient miRNA-related SNP evaluation. We present a novel thermodynamic model based on the Fermi-Dirac equation that incorporates miRNA expression in the prediction of target occupancy and we show that it improves the performance of two popular single miRNA target finders. Modeling combinatorial miRNA targeting is a natural extension of this model. Two other algorithms show improved prediction efficiency when combinatorial binding models were considered. ComiR (Combinatorial miRNA targeting), a novel algorithm we developed, incorporates the improved predictions of the four target finders into a single probabilistic score using ensemble learning. Combining target scores of multiple miRNAs using ComiR improves predictions over the naïve method for target combination. ComiR scoring scheme can be used for identification of SNPs affecting miRNA binding. As proof of principle, ComiR identified rs17737058 as disruptive to the miR-488-5p:NCOA1 interaction, which we confirmed in vitro. We also found rs17737058 to be significantly associated with decreased bone mineral density (BMD) in two independent cohorts indicating that the miR-488-5p/NCOA1 regulatory axis is likely critical in maintaining BMD in women. With increasing availability of comprehensive high-throughput datasets from patients ComiR is expected to become an essential tool for mi

Seasonal predictability of Arctic Sea Ice: assessing its limits and potential in a GCM and implications for observations.

NASA Astrophysics Data System (ADS)

Blanchard-Wrigglesworth, E.

2012-12-01

Arctic sea ice has exhibited a dramatic decrease both in area and thickness over the recent decades, particularly during the summer months. This decrease has led to growing interest in the potential predictability of summer sea ice, spurred in part by the socioeconomic implications. Here we present results of several parallel experiments designed to assess and understand the limits and potential for seasonal predictability of Arctic sea ice, with an emphasis on the summer minimum. Building on our experience from the SEARCH Outlook, we present results of a coupled general circulation model (GCM) hindcast simulation of Arctic summer sea ice variability for the satellite period (1979-present). These are initialized with spring sea ice volume anomalies obtained from a modelling and assimilation system, considered to be a close representation of reality. We show that there is significant predictability, yet the stochastic forcing imparted mainly by the atmosphere can lead to large errors in the hindcast. The model, however, can simulate anomalous runs that lie beyond a Gaussian distribution. Additionally, we investigate the regional characteristics of predictability and its links to sea ice dynamics and the spatio-temporal behavior of sea ice anomalies. We show a distinct difference between models. Unfortunately, observational data of thickness are not yet detailed enough to assess the models. Our results indicate the potential for detailed ice thickness observations in improving regional predictability. Finally, we discuss the importance of experiment design in predictability experiments, and show that predictions made with models that have a large mean state bias in sea ice require a careful initialization in order to fully capture all initial value predictability.

Predictive performance and inter-laboratory reproducibility in assessing eye irritation potential of water- and oil-soluble mixtures using the Short Time Exposure test method.

PubMed

Abo, Takayuki; Hilberer, Allison; Behle-Wagner, Christine; Watanabe, Mika; Cameron, David; Kirst, Annette; Nukada, Yuko; Yuki, Takuo; Araki, Daisuke; Sakaguchi, Hitoshi; Itagaki, Hiroshi

2018-04-01

The Short Time Exposure (STE) test method is an alternative method for assessing eye irritation potential using Statens Seruminstitut Rabbit Cornea cells and has been adopted as test guideline 491 by the Organisation for Economic Co-operation and Development. Its good predictive performance in identifying the Globally Harmonized System (GHS) No Category (NC) or Irritant Category has been demonstrated in evaluations of water-soluble substances, oil-soluble substances, and water-soluble mixtures. However, the predictive performance for oil-soluble mixtures was not evaluated. Twenty-four oil-soluble mixtures were evaluated using the STE test method. The GHS NC or Irritant Category of 22 oil-soluble mixtures were consistent with that of a Reconstructed human Cornea-like Epithelium (RhCE) test method. Inter-laboratory reproducibility was then confirmed using 20 water- and oil-soluble mixtures blind-coded. The concordance in GHS NC or Irritant Category among four laboratories was 90%-100%. In conclusion, the concordance in comparison with the results of RhCE test method using 24 oil-soluble mixtures and inter-laboratory reproducibility using 20 water- and oil-soluble mixtures blind-coded were good, indicating that the STE test method is a suitable alternative for predicting the eye irritation potential of both substances and mixtures. Copyright © 2018 Elsevier Ltd. All rights reserved.

Identifying protein phosphorylation sites with kinase substrate specificity on human viruses.

PubMed

Bretaña, Neil Arvin; Lu, Cheng-Tsung; Chiang, Chiu-Yun; Su, Min-Gang; Huang, Kai-Yao; Lee, Tzong-Yi; Weng, Shun-Long

2012-01-01

Viruses infect humans and progress inside the body leading to various diseases and complications. The phosphorylation of viral proteins catalyzed by host kinases plays crucial regulatory roles in enhancing replication and inhibition of normal host-cell functions. Due to its biological importance, there is a desire to identify the protein phosphorylation sites on human viruses. However, the use of mass spectrometry-based experiments is proven to be expensive and labor-intensive. Furthermore, previous studies which have identified phosphorylation sites in human viruses do not include the investigation of the responsible kinases. Thus, we are motivated to propose a new method to identify protein phosphorylation sites with its kinase substrate specificity on human viruses. The experimentally verified phosphorylation data were extracted from virPTM--a database containing 301 experimentally verified phosphorylation data on 104 human kinase-phosphorylated virus proteins. In an attempt to investigate kinase substrate specificities in viral protein phosphorylation sites, maximal dependence decomposition (MDD) is employed to cluster a large set of phosphorylation data into subgroups containing significantly conserved motifs. The experimental human phosphorylation sites are collected from Phospho.ELM, grouped according to its kinase annotation, and compared with the virus MDD clusters. This investigation identifies human kinases such as CK2, PKB, CDK, and MAPK as potential kinases for catalyzing virus protein substrates as confirmed by published literature. Profile hidden Markov model is then applied to learn a predictive model for each subgroup. A five-fold cross validation evaluation on the MDD-clustered HMMs yields an average accuracy of 84.93% for Serine, and 78.05% for Threonine. Furthermore, an independent testing data collected from UniProtKB and Phospho.ELM is used to make a comparison of predictive performance on three popular kinase-specific phosphorylation site

Identifying Potential Regions of Copy Number Variation for Bipolar Disorder

PubMed Central

Chen, Yi-Hsuan; Lu, Ru-Band; Hung, Hung; Kuo, Po-Hsiu

2014-01-01

Bipolar disorder is a complex psychiatric disorder with high heritability, but its genetic determinants are still largely unknown. Copy number variation (CNV) is one of the sources to explain part of the heritability. However, it is a challenge to estimate discrete values of the copy numbers using continuous signals calling from a set of markers, and to simultaneously perform association testing between CNVs and phenotypic outcomes. The goal of the present study is to perform a series of data filtering and analysis procedures using a DNA pooling strategy to identify potential CNV regions that are related to bipolar disorder. A total of 200 normal controls and 200 clinically diagnosed bipolar patients were recruited in this study, and were randomly divided into eight control and eight case pools. Genome-wide genotyping was employed using Illumina Human Omni1-Quad array with approximately one million markers for CNV calling. We aimed at setting a series of criteria to filter out the signal noise of marker data and to reduce the chance of false-positive findings for CNV regions. We first defined CNV regions for each pool. Potential CNV regions were reported based on the different patterns of CNV status between cases and controls. Genes that were mapped into the potential CNV regions were examined with association testing, Gene Ontology enrichment analysis, and checked with existing literature for their associations with bipolar disorder. We reported several CNV regions that are related to bipolar disorder. Two CNV regions on chromosome 11 and 22 showed significant signal differences between cases and controls (p < 0.05). Another five CNV regions on chromosome 6, 9, and 19 were overlapped with results in previous CNV studies. Experimental validation of two CNV regions lent some support to our reported findings. Further experimental and replication studies could be designed for these selected regions. PMID:27605030

Evaluating predictive modeling’s potential to improve teleretinal screening participation in urban safety net clinics

PubMed Central

Ogunyemi, Omolola; Teklehaimanot, Senait; Patty, Lauren; Moran, Erin; George, Sheba

2013-01-01

Introduction Screening guidelines for diabetic patients recommend yearly eye examinations to detect diabetic retinopathy and other forms of diabetic eye disease. However, annual screening rates for retinopathy in US urban safety net settings remain low. Methods Using data gathered from a study of teleretinal screening in six urban safety net clinics, we assessed whether predictive modeling could be of value in identifying patients at risk of developing retinopathy. We developed and examined the accuracy of two predictive modeling approaches for diabetic retinopathy in a sample of 513 diabetic individuals, using routinely available clinical variables from retrospective medical record reviews. Bayesian networks and radial basis function (neural) networks were learned using ten-fold cross-validation. Results The predictive models were modestly predictive with the best model having an AUC of 0.71. Discussion Using routinely available clinical variables to predict patients at risk of developing retinopathy and to target them for annual eye screenings may be of some usefulness to safety net clinics. PMID:23920536

Interplanetary Parameters Leading to Relativistic Electron Enhancement and Persistent Depletion Events at Geosynchronous Orbit and Potential for Prediction

NASA Astrophysics Data System (ADS)

Pinto, Victor A.; Kim, Hee-Jeong; Lyons, Larry R.; Bortnik, Jacob

2018-02-01

We have identified 61 relativistic electron enhancement events and 21 relativistic electron persistent depletion events during 1996 to 2006 from the Geostationary Operational Environmental Satellite (GOES) 8 and 10 using data from the Energetic Particle Sensor (EPS) >2 MeV fluxes. We then performed a superposed epoch time analysis of the events to find the characteristic solar wind parameters that determine the occurrence of such events, using the OMNI database. We found that there are clear differences between the enhancement events and the persistent depletion events, and we used these to establish a set of threshold values in solar wind speed, proton density and interplanetary magnetic field (IMF) Bz that can potentially be useful to predict sudden increases in flux. Persistent depletion events are characterized by a low solar wind speed, a sudden increase in proton density that remains elevated for a few days, and a northward turning of IMF Bz shortly after the depletion starts. We have also found that all relativistic electron enhancement or persistent depletion events occur when some geomagnetic disturbance is present, either a coronal mass ejection or a corotational interaction region; however, the storm index, SYM-H, does not show a strong connection with relativistic electron enhancement events or persistent depletion events. We have tested a simple threshold method for predictability of relativistic electron enhancement events using data from GOES 11 for the years 2007-2010 and found that around 90% of large increases in electron fluxes can be identified with this method.

7 CFR 170.6 - How are potential market participants identified for the USDA Farmers Market?

Code of Federal Regulations, 2013 CFR

2013-01-01

... the USDA Farmers Market? 170.6 Section 170.6 Agriculture Regulations of the Department of Agriculture... AGRICULTURE (CONTINUED) MISCELLANEOUS MARKETING PRACTICES UNDER THE AGRICULTURAL MARKETING ACT OF 1946 USDA FARMERS MARKET § 170.6 How are potential market participants identified for the USDA Farmers Market...

7 CFR 170.6 - How are potential market participants identified for the USDA Farmers Market?

Code of Federal Regulations, 2011 CFR

2011-01-01

... the USDA Farmers Market? 170.6 Section 170.6 Agriculture Regulations of the Department of Agriculture... AGRICULTURE (CONTINUED) MISCELLANEOUS MARKETING PRACTICES UNDER THE AGRICULTURAL MARKETING ACT OF 1946 USDA FARMERS MARKET § 170.6 How are potential market participants identified for the USDA Farmers Market...

7 CFR 170.6 - How are potential market participants identified for the USDA Farmers Market?

Code of Federal Regulations, 2012 CFR

2012-01-01

... the USDA Farmers Market? 170.6 Section 170.6 Agriculture Regulations of the Department of Agriculture... AGRICULTURE (CONTINUED) MISCELLANEOUS MARKETING PRACTICES UNDER THE AGRICULTURAL MARKETING ACT OF 1946 USDA FARMERS MARKET § 170.6 How are potential market participants identified for the USDA Farmers Market...

7 CFR 170.6 - How are potential market participants identified for the USDA Farmers Market?

Code of Federal Regulations, 2014 CFR

2014-01-01

... the USDA Farmers Market? 170.6 Section 170.6 Agriculture Regulations of the Department of Agriculture... AGRICULTURE (CONTINUED) MISCELLANEOUS MARKETING PRACTICES UNDER THE AGRICULTURAL MARKETING ACT OF 1946 USDA FARMERS MARKET § 170.6 How are potential market participants identified for the USDA Farmers Market...

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…

Identifying Future Scientists: Predicting Persistence into Research Training

ERIC Educational Resources Information Center

McGee, Richard; Keller, Jill L.

2007-01-01

This study used semistructured interviews and grounded theory to look for characteristics among college undergraduates that predicted persistence into Ph.D. and M.D./Ph.D. training. Participants in the summer undergraduate and postbaccalaureate research programs at the Mayo Clinic College of Medicine were interviewed at the start, near the end,…

Using SCOPE to identify potential regulatory motifs in coregulated genes.

PubMed

Martyanov, Viktor; Gross, Robert H

2011-05-31

SCOPE is an ensemble motif finder that uses three component algorithms in parallel to identify potential regulatory motifs by over-representation and motif position preference. Each component algorithm is optimized to find a different kind of motif. By taking the best of these three approaches, SCOPE performs better than any single algorithm, even in the presence of noisy data. In this article, we utilize a web version of SCOPE to examine genes that are involved in telomere maintenance. SCOPE has been incorporated into at least two other motif finding programs and has been used in other studies. The three algorithms that comprise SCOPE are BEAM, which finds non-degenerate motifs (ACCGGT), PRISM, which finds degenerate motifs (ASCGWT), and SPACER, which finds longer bipartite motifs (ACCnnnnnnnnGGT). These three algorithms have been optimized to find their corresponding type of motif. Together, they allow SCOPE to perform extremely well. Once a gene set has been analyzed and candidate motifs identified, SCOPE can look for other genes that contain the motif which, when added to the original set, will improve the motif score. This can occur through over-representation or motif position preference. Working with partial gene sets that have biologically verified transcription factor binding sites, SCOPE was able to identify most of the rest of the genes also regulated by the given transcription factor. Output from SCOPE shows candidate motifs, their significance, and other information both as a table and as a graphical motif map. FAQs and video tutorials are available at the SCOPE web site which also includes a "Sample Search" button that allows the user to perform a trial run. Scope has a very friendly user interface that enables novice users to access the algorithm's full power without having to become an expert in the bioinformatics of motif finding. As input, SCOPE can take a list of genes, or FASTA sequences. These can be entered in browser text fields, or read from

T-Epitope Designer: A HLA-peptide binding prediction server.

PubMed

Kangueane, Pandjassarame; Sakharkar, Meena Kishore

2005-05-15

The current challenge in synthetic vaccine design is the development of a methodology to identify and test short antigen peptides as potential T-cell epitopes. Recently, we described a HLA-peptide binding model (using structural properties) capable of predicting peptides binding to any HLA allele. Consequently, we have developed a web server named T-EPITOPE DESIGNER to facilitate HLA-peptide binding prediction. The prediction server is based on a model that defines peptide binding pockets using information gleaned from X-ray crystal structures of HLA-peptide complexes, followed by the estimation of peptide binding to binding pockets. Thus, the prediction server enables the calculation of peptide binding to HLA alleles. This model is superior to many existing methods because of its potential application to any given HLA allele whose sequence is clearly defined. The web server finds potential application in T cell epitope vaccine design. http://www.bioinformation.net/ted/

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

Parameter identifiability and regional calibration for reservoir inflow prediction

NASA Astrophysics Data System (ADS)

Kolberg, Sjur; Engeland, Kolbjørn; Tøfte, Lena S.; Bruland, Oddbjørn

2013-04-01

The large hydropower producer Statkraft is currently testing regional, distributed models for operational reservoir inflow prediction. The need for simultaneous forecasts and consistent updating in a large number of catchments supports the shift from catchment-oriented to regional models. Low-quality naturalized inflow series in the reservoir catchments further encourages the use of donor catchments and regional simulation for calibration purposes. MCMC based parameter estimation (the Dream algorithm; Vrugt et al, 2009) is adapted to regional parameter estimation, and implemented within the open source ENKI framework. The likelihood is based on the concept of effectively independent number of observations, spatially as well as in time. Marginal and conditional (around an optimum) parameter distributions for each catchment may be extracted, even though the MCMC algorithm itself is guided only by the regional likelihood surface. Early results indicate that the average performance loss associated with regional calibration (difference in Nash-Sutcliffe R2 between regionally and locally optimal parameters) is in the range of 0.06. The importance of the seasonal snow storage and melt in Norwegian mountain catchments probably contributes to the high degree of similarity among catchments. The evaluation continues for several regions, focusing on posterior parameter uncertainty and identifiability. Vrugt, J. A., C. J. F. ter Braak, C. G. H. Diks, B. A. Robinson, J. M. Hyman and D. Higdon: Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling. Int. J. of nonlinear sciences and numerical simulation 10, 3, 273-290, 2009.

Prediction of preservative sensitization potential using surface marker CD86 and/or CD54 expression on human cell line, THP-1.

PubMed

Sakaguchi, Hitoshi; Miyazawa, Masaaki; Yoshida, Yukiko; Ito, Yuichi; Suzuki, Hiroyuki

2007-02-01

Preservatives are important components in many products, but have a history of purported allergy. Several assays [e.g., guinea pig maximization test (GPMT), local lymph node assay (LLNA)] are used to evaluate allergy potential of preservatives. We recently developed the human Cell Line Activation Test (h-CLAT), an in vitro skin sensitization test using human THP-1 cells. This test evaluates the augmentation of CD86 and CD54 expression, which are key events in the sensitization process, as an indicator of allergy following treatment with test chemical. Earlier, we found that a sub-toxic concentration was needed for the up-regulation of surface marker expression. In this study, we further evaluate the capability of h-CLAT to predict allergy potential using eight preservatives. Cytotoxicity was determined using propidium iodide with flow cytometry analysis and five doses that produce a 95, 85, 75, 65, and 50% cell viability were selected. If a material did not have any cytotoxicity at the highest technical dose (HTD), five doses are set using serial 1.3 dilutions of the HTD. The test materials used were six known allergic preservatives (e.g., methylchloroisothiazolinone/methylisothiazolinone, formaldehyde), and two non-allergic preservatives (methylparaben and 4-hydroxybenzoic acid). All allergic preservatives augmented CD86 and/or CD54 expression, indicating h-CLAT correctly identified the allergens. No augmentation was observed with the non-allergic preservatives; also correctly identified by h-CLAT. In addition, we report two threshold concentrations that may be used to categorize skin sensitization potency like the LLNA estimated concentration that yield a three-fold stimulation (EC3) value. These corresponding values are the estimated concentration which gives a relative fluorescence intensity (RFI) = 150 for CD86 and an RFI = 200 for CD54. These data suggest that h-CLAT, using THP-1 cells, may be able to predict the allergy potential of preservatives and

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib

PubMed Central

Li, Bin; Shin, Hyunjin; Gulbekyan, Georgy; Pustovalova, Olga; Nikolsky, Yuri; Hope, Andrew; Bessarabova, Marina; Schu, Matthew; Kolpakova-Hart, Elona; Merberg, David; Dorner, Andrew; Trepicchio, William L.

2015-01-01

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug’s known mechanism of action. Also, the models predict each drug’s potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets. PMID:26107615

Characterization of silver nanoparticles in selected consumer products and its relevance for predicting children's potential exposures.

PubMed

Tulve, Nicolle S; Stefaniak, Aleksandr B; Vance, Marina E; Rogers, Kim; Mwilu, Samuel; LeBouf, Ryan F; Schwegler-Berry, Diane; Willis, Robert; Thomas, Treye A; Marr, Linsey C

2015-05-01

Due to their antifungal, antibacterial, antiviral, and antimicrobial properties, silver nanoparticles (AgNPs) are used in consumer products intended for use by children or in the home. Children may be especially affected by the normal use of consumer products because of their physiological functions, developmental stage, and activities and behaviors. Despite much research to date, children's potential exposures to AgNPs are not well characterized. Our objectives were to characterize selected consumer products containing AgNPs and to use the data to estimate a child's potential non-dietary ingestion exposure. We identified and cataloged 165 consumer products claiming to contain AgNPs that may be used by or near children or found in the home. Nineteen products (textile, liquid, plastic) were selected for further analysis. We developed a tiered analytical approach to determine silver content, form (particulate or ionic), size, morphology, agglomeration state, and composition. Silver was detected in all products except one sippy cup body. Among products in a given category, silver mass contributions were highly variable and not always uniformly distributed within products, highlighting the need to sample multiple areas of a product. Electron microscopy confirmed the presence of AgNPs. Using this data, a child's potential non-dietary ingestion exposure to AgNPs when drinking milk formula from a sippy cup is 1.53 μg Ag/kg. Additional research is needed to understand the number and types of consumer products containing silver and the concentrations of silver in these products in order to more accurately predict children's potential aggregate and cumulative exposures to AgNPs. Published by Elsevier GmbH.

Kidney disease models: tools to identify mechanisms and potential therapeutic targets

PubMed Central

Bao, Yin-Wu; Yuan, Yuan; Chen, Jiang-Hua; Lin, Wei-Qiang

2018-01-01

Acute kidney injury (AKI) and chronic kidney disease (CKD) are worldwide public health problems affecting millions of people and have rapidly increased in prevalence in recent years. Due to the multiple causes of renal failure, many animal models have been developed to advance our understanding of human nephropathy. Among these experimental models, rodents have been extensively used to enable mechanistic understanding of kidney disease induction and progression, as well as to identify potential targets for therapy. In this review, we discuss AKI models induced by surgical operation and drugs or toxins, as well as a variety of CKD models (mainly genetically modified mouse models). Results from recent and ongoing clinical trials and conceptual advances derived from animal models are also explored. PMID:29515089

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

EPA Science Inventory

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

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

Application of a GIS-/remote sensing-based approach for predicting groundwater potential zones using a multi-criteria data mining methodology.

PubMed

Mogaji, Kehinde Anthony; Lim, Hwee San

2017-07-01

This study integrates the application of Dempster-Shafer-driven evidential belief function (DS-EBF) methodology with remote sensing and geographic information system techniques to analyze surface and subsurface data sets for the spatial prediction of groundwater potential in Perak Province, Malaysia. The study used additional data obtained from the records of the groundwater yield rate of approximately 28 bore well locations. The processed surface and subsurface data produced sets of groundwater potential conditioning factors (GPCFs) from which multiple surface hydrologic and subsurface hydrogeologic parameter thematic maps were generated. The bore well location inventories were partitioned randomly into a ratio of 70% (19 wells) for model training to 30% (9 wells) for model testing. Application results of the DS-EBF relationship model algorithms of the surface- and subsurface-based GPCF thematic maps and the bore well locations produced two groundwater potential prediction (GPP) maps based on surface hydrologic and subsurface hydrogeologic characteristics which established that more than 60% of the study area falling within the moderate-high groundwater potential zones and less than 35% falling within the low potential zones. The estimated uncertainty values within the range of 0 to 17% for the predicted potential zones were quantified using the uncertainty algorithm of the model. The validation results of the GPP maps using relative operating characteristic curve method yielded 80 and 68% success rates and 89 and 53% prediction rates for the subsurface hydrogeologic factor (SUHF)- and surface hydrologic factor (SHF)-based GPP maps, respectively. The study results revealed that the SUHF-based GPP map accurately delineated groundwater potential zones better than the SHF-based GPP map. However, significant information on the low degree of uncertainty of the predicted potential zones established the suitability of the two GPP maps for future development of

PREDICT-PD: An online approach to prospectively identify risk indicators of Parkinson's disease.

PubMed

Noyce, Alastair J; R'Bibo, Lea; Peress, Luisa; Bestwick, Jonathan P; Adams-Carr, Kerala L; Mencacci, Niccolo E; Hawkes, Christopher H; Masters, Joseph M; Wood, Nicholas; Hardy, John; Giovannoni, Gavin; Lees, Andrew J; Schrag, Anette

2017-02-01

A number of early features can precede the diagnosis of Parkinson's disease (PD). To test an online, evidence-based algorithm to identify risk indicators of PD in the UK population. Participants aged 60 to 80 years without PD completed an online survey and keyboard-tapping task annually over 3 years, and underwent smell tests and genotyping for glucocerebrosidase (GBA) and leucine-rich repeat kinase 2 (LRRK2) mutations. Risk scores were calculated based on the results of a systematic review of risk factors and early features of PD, and individuals were grouped into higher (above 15th centile), medium, and lower risk groups (below 85th centile). Previously defined indicators of increased risk of PD ("intermediate markers"), including smell loss, rapid eye movement-sleep behavior disorder, and finger-tapping speed, and incident PD were used as outcomes. The correlation of risk scores with intermediate markers and movement of individuals between risk groups was assessed each year and prospectively. Exploratory Cox regression analyses with incident PD as the dependent variable were performed. A total of 1323 participants were recruited at baseline and >79% completed assessments each year. Annual risk scores were correlated with intermediate markers of PD each year and baseline scores were correlated with intermediate markers during follow-up (all P values < 0.001). Incident PD diagnoses during follow-up were significantly associated with baseline risk score (hazard ratio = 4.39, P = .045). GBA variants or G2019S LRRK2 mutations were found in 47 participants, and the predictive power for incident PD was improved by the addition of genetic variants to risk scores. The online PREDICT-PD algorithm is a unique and simple method to identify indicators of PD risk. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder

Predicting and analyzing DNA-binding domains using a systematic approach to identifying a set of informative physicochemical and biochemical properties

PubMed Central

2011-01-01

Background Existing methods of predicting DNA-binding proteins used valuable features of physicochemical properties to design support vector machine (SVM) based classifiers. Generally, selection of physicochemical properties and determination of their corresponding feature vectors rely mainly on known properties of binding mechanism and experience of designers. However, there exists a troublesome problem for designers that some different physicochemical properties have similar vectors of representing 20 amino acids and some closely related physicochemical properties have dissimilar vectors. Results This study proposes a systematic approach (named Auto-IDPCPs) to automatically identify a set of physicochemical and biochemical properties in the AAindex database to design SVM-based classifiers for predicting and analyzing DNA-binding domains/proteins. Auto-IDPCPs consists of 1) clustering 531 amino acid indices in AAindex into 20 clusters using a fuzzy c-means algorithm, 2) utilizing an efficient genetic algorithm based optimization method IBCGA to select an informative feature set of size m to represent sequences, and 3) analyzing the selected features to identify related physicochemical properties which may affect the binding mechanism of DNA-binding domains/proteins. The proposed Auto-IDPCPs identified m=22 features of properties belonging to five clusters for predicting DNA-binding domains with a five-fold cross-validation accuracy of 87.12%, which is promising compared with the accuracy of 86.62% of the existing method PSSM-400. For predicting DNA-binding sequences, the accuracy of 75.50% was obtained using m=28 features, where PSSM-400 has an accuracy of 74.22%. Auto-IDPCPs and PSSM-400 have accuracies of 80.73% and 82.81%, respectively, applied to an independent test data set of DNA-binding domains. Some typical physicochemical properties discovered are hydrophobicity, secondary structure, charge, solvent accessibility, polarity, flexibility, normalized Van Der

The Health Informatics Trial Enhancement Project (HITE): Using routinely collected primary care data to identify potential participants for a depression trial

PubMed Central

2010-01-01

Background Recruitment to clinical trials can be challenging. We identified anonymous potential participants to an existing pragmatic randomised controlled depression trial to assess the feasibility of using routinely collected data to identify potential trial participants. We discuss the strengths and limitations of this approach, assess its potential value, report challenges and ethical issues encountered. Methods Swansea University's Health Information Research Unit's Secure Anonymised Information Linkage (SAIL) database of routinely collected health records was interrogated, using Structured Query Language (SQL). Read codes were used to create an algorithm of inclusion/exclusion criteria with which to identify suitable anonymous participants. Two independent clinicians rated the eligibility of the potential participants' identified. Inter-rater reliability was assessed using the kappa statistic and inter-class correlation. Results The study population (N = 37263) comprised all adults registered at five general practices in Swansea UK. Using the algorithm 867 anonymous potential participants were identified. The sensitivity and specificity results > 0.9 suggested a high degree of accuracy from the algorithm. The inter-rater reliability results indicated strong agreement between the confirming raters. The Intra Class Correlation Coefficient (Cronbach's Alpha) > 0.9, suggested excellent agreement and Kappa coefficient > 0.8; almost perfect agreement. Conclusions This proof of concept study showed that routinely collected primary care data can be used to identify potential participants for a pragmatic randomised controlled trial of folate augmentation of antidepressant therapy for the treatment of depression. Further work will be needed to assess generalisability to other conditions and settings and the inclusion of this approach to support Electronic Enhanced Recruitment (EER). PMID:20398303

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.

National variety trials identify clones with high potential

USDA-ARS?s Scientific Manuscript database

Quality potato varieties are the backbone of a strong potato industry. Variety trials have been used to identify promising new varieties for well over a century. Trials are repeated and information collected over many years in order to confidently identify lines that may be well suited for productio...

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

PubMed

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

2004-01-01

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

Omen: identifying potential spear-phishing targets before the email is sent.

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

Wendt, Jeremy Daniel.

2013-07-01

We present the results of a two year project focused on a common social engineering attack method called "spear phishing". In a spear phishing attack, the user receives an email with information specifically focused on the user. This email contains either a malware-laced attachment or a link to download the malware that has been disguised as a useful program. Spear phishing attacks have been one of the most effective avenues for attackers to gain initial entry into a target network. This project focused on a proactive approach to spear phishing. To create an effective, user-specific spear phishing email, the attackermore » must research the intended recipient. We believe that much of the information used by the attacker is provided by the target organization's own external website. Thus when researching potential targets, the attacker leaves signs of his research in the webserver's logs. We created tools and visualizations to improve cybersecurity analysts' abilities to quickly understand a visitor's visit patterns and interests. Given these suspicious visitors and log-parsing tools, analysts can more quickly identify truly suspicious visitors, search for potential spear-phishing targeted users, and improve security around those users before the spear phishing email is sent.« less

Prediction of chemo-response in serous ovarian cancer.

PubMed

Gonzalez Bosquet, Jesus; Newtson, Andreea M; Chung, Rebecca K; Thiel, Kristina W; Ginader, Timothy; Goodheart, Michael J; Leslie, Kimberly K; Smith, Brian J

2016-10-19

Nearly one-third of serous ovarian cancer (OVCA) patients will not respond to initial treatment with surgery and chemotherapy and die within one year of diagnosis. If patients who are unlikely to respond to current standard therapy can be identified up front, enhanced tumor analyses and treatment regimens could potentially be offered. Using the Cancer Genome Atlas (TCGA) serous OVCA database, we previously identified a robust molecular signature of 422-genes associated with chemo-response. Our objective was to test whether this signature is an accurate and sensitive predictor of chemo-response in serous OVCA. We first constructed prediction models to predict chemo-response using our previously described 422-gene signature that was associated with response to treatment in serous OVCA. Performance of all prediction models were measured with area under the curves (AUCs, a measure of the model's accuracy) and their respective confidence intervals (CIs). To optimize the prediction process, we determined which elements of the signature most contributed to chemo-response prediction. All prediction models were replicated and validated using six publicly available independent gene expression datasets. The 422-gene signature prediction models predicted chemo-response with AUCs of ~70 %. Optimization of prediction models identified the 34 most important genes in chemo-response prediction. These 34-gene models had improved performance, with AUCs approaching 80 %. Both 422-gene and 34-gene prediction models were replicated and validated in six independent datasets. These prediction models serve as the foundation for the future development and implementation of a diagnostic tool to predict response to chemotherapy for serous OVCA patients.

Aqua/Aura Updated Inclination Adjust Maneuver Performance Prediction Model

NASA Technical Reports Server (NTRS)

Boone, Spencer

2017-01-01

This presentation will discuss the updated Inclination Adjust Maneuver (IAM) performance prediction model that was developed for Aqua and Aura following the 2017 IAM series. This updated model uses statistical regression methods to identify potential long-term trends in maneuver parameters, yielding improved predictions when re-planning past maneuvers. The presentation has been reviewed and approved by Eric Moyer, ESMO Deputy Project Manager.

Potential Distribution Predicted for Rhynchophorus ferrugineus in China under Different Climate Warming Scenarios.

PubMed

Ge, Xuezhen; He, Shanyong; Wang, Tao; Yan, Wei; Zong, Shixiang

2015-01-01

As the primary pest of palm trees, Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae) has caused serious harm to palms since it first invaded China. The present study used CLIMEX 1.1 to predict the potential distribution of R. ferrugineus in China according to both current climate data (1981-2010) and future climate warming estimates based on simulated climate data for the 2020s (2011-2040) provided by the Tyndall Center for Climate Change Research (TYN SC 2.0). Additionally, the Ecoclimatic Index (EI) values calculated for different climatic conditions (current and future, as simulated by the B2 scenario) were compared. Areas with a suitable climate for R. ferrugineus distribution were located primarily in central China according to the current climate data, with the northern boundary of the distribution reaching to 40.1°N and including Tibet, north Sichuan, central Shaanxi, south Shanxi, and east Hebei. There was little difference in the potential distribution predicted by the four emission scenarios according to future climate warming estimates. The primary prediction under future climate warming models was that, compared with the current climate model, the number of highly favorable habitats would increase significantly and expand into northern China, whereas the number of both favorable and marginally favorable habitats would decrease. Contrast analysis of EI values suggested that climate change and the density of site distribution were the main effectors of the changes in EI values. These results will help to improve control measures, prevent the spread of this pest, and revise the targeted quarantine areas.

Potential Distribution Predicted for Rhynchophorus ferrugineus in China under Different Climate Warming Scenarios

PubMed Central

Ge, Xuezhen; He, Shanyong; Wang, Tao; Yan, Wei; Zong, Shixiang

2015-01-01

As the primary pest of palm trees, Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae) has caused serious harm to palms since it first invaded China. The present study used CLIMEX 1.1 to predict the potential distribution of R. ferrugineus in China according to both current climate data (1981–2010) and future climate warming estimates based on simulated climate data for the 2020s (2011–2040) provided by the Tyndall Center for Climate Change Research (TYN SC 2.0). Additionally, the Ecoclimatic Index (EI) values calculated for different climatic conditions (current and future, as simulated by the B2 scenario) were compared. Areas with a suitable climate for R. ferrugineus distribution were located primarily in central China according to the current climate data, with the northern boundary of the distribution reaching to 40.1°N and including Tibet, north Sichuan, central Shaanxi, south Shanxi, and east Hebei. There was little difference in the potential distribution predicted by the four emission scenarios according to future climate warming estimates. The primary prediction under future climate warming models was that, compared with the current climate model, the number of highly favorable habitats would increase significantly and expand into northern China, whereas the number of both favorable and marginally favorable habitats would decrease. Contrast analysis of EI values suggested that climate change and the density of site distribution were the main effectors of the changes in EI values. These results will help to improve control measures, prevent the spread of this pest, and revise the targeted quarantine areas. PMID:26496438

TH-B-BRC-01: How to Identify and Resolve Potential Clinical Errors

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

Das, I.

2016-06-15

Radiation treatment consists of a chain of events influenced by the quality of machine operation, beam data commissioning, machine calibration, patient specific data, simulation, treatment planning, imaging and treatment delivery. There is always a chance that the clinical medical physicist may make or fail to detect an error in one of the events that may impact on the patient’s treatment. In the clinical scenario, errors may be systematic and, without peer review, may have a low detectability because they are not part of routine QA procedures. During treatment, there might be errors on machine that needs attention. External reviews ofmore » some of the treatment delivery components by independent reviewers, like IROC, can detect errors, but may not be timely. The goal of this session is to help junior clinical physicists identify potential errors as well as the approach of quality assurance to perform a root cause analysis to find and eliminate an error and to continually monitor for errors. A compilation of potential errors will be presented by examples of the thought process required to spot the error and determine the root cause. Examples may include unusual machine operation, erratic electrometer reading, consistent lower electron output, variation in photon output, body parts inadvertently left in beam, unusual treatment plan, poor normalization, hot spots etc. Awareness of the possibility and detection of error in any link of the treatment process chain will help improve the safe and accurate delivery of radiation to patients. Four experts will discuss how to identify errors in four areas of clinical treatment. D. Followill, NIH grant CA 180803.« less

Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction.

PubMed

Daberdaku, Sebastian; Ferrari, Carlo

2018-02-06

The correct determination of protein-protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein-Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction

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.

An equivalent potential vorticity theory applied to the analysis and prediction of severe storm dynamics

NASA Technical Reports Server (NTRS)

Paine, D. A.; Kaplan, M. L.

1976-01-01

Potential vorticity theory is developed in a description of an equivalent potential temperature topography, and a new theory suited to the description of scale interaction is elaborated. Macroscale triggering of ageostrophic flow fields at the mesoscale, in turn leading to release of convective instability along narrow zones at the microscale, is examined. Correlation of appreciable decrease in potential vorticity with such phenomena as cumulonimbi, tornados, and duststorms is examined. The relevance of a multiscale energy-momentum cascade in numerical prediction of severe mesoscale and microscale phenomena from radiosonde data is reviewed. Hypotheses for mesoscale dynamics are constructed.

An Australasian model license reassessment procedure for identifying potentially unsafe drivers.

PubMed

Fildes, Brian N; Charlton, Judith; Pronk, Nicola; Langford, Jim; Oxley, Jennie; Koppel, Sjaanie

2008-08-01

Most licensing jurisdictions in Australia currently employ age-based assessment programs as a means to manage older driver safety, yet available evidence suggests that these programs have no safety benefits. This paper describes a community referral-based model license re assessment procedure for identifying and assessing potentially unsafe drivers. While the model was primarily developed for assessing older driver fitness to drive, it could be applicable to other forms of driver impairment associated with increased crash risk. It includes a three-tier process of assessment, involving the use of validated and relevant assessment instruments. A case is argued that this process is a more systematic, transparent and effective process for managing older driver safety and thus more likely to be widely acceptable to the target community and licensing authorities than age-based practices.

The more from East-Asian, the better: risk prediction of colorectal cancer risk by GWAS-identified SNPs among Japanese.

PubMed

Abe, Makiko; Ito, Hidemi; Oze, Isao; Nomura, Masatoshi; Ogawa, Yoshihiro; Matsuo, Keitaro

2017-12-01

Little is known about the difference of genetic predisposition for CRC between ethnicities; however, many genetic traits common to colorectal cancer have been identified. This study investigated whether more SNPs identified in GWAS in East Asian population could improve the risk prediction of Japanese and explored possible application of genetic risk groups as an instrument of the risk communication. 558 Patients histologically verified colorectal cancer and 1116 first-visit outpatients were included for derivation study, and 547 cases and 547 controls were for replication study. Among each population, we evaluated prediction models for the risk of CRC that combined the genetic risk group based on SNPs from GWASs in European-population and a similarly developed model adding SNPs from GWASs in East Asian-population. We examined whether adding East Asian-specific SNPs would improve the discrimination. Six SNPs (rs6983267, rs4779584, rs4444235, rs9929218, rs10936599, rs16969681) from 23 SNPs by European-based GWAS and five SNPs (rs704017, rs11196172, rs10774214, rs647161, rs2423279) among ten SNPs by Asian-based GWAS were selected in CRC risk prediction model. Compared with a 6-SNP-based model, an 11-SNP model including Asian GWAS-SNPs showed improved discrimination capacity in Receiver operator characteristic analysis. A model with 11 SNPs resulted in statistically significant improvement in both derivation (P = 0.0039) and replication studies (P = 0.0018) compared with six SNP model. We estimated cumulative risk of CRC by using genetic risk group based on 11 SNPs and found that the cumulative risk at age 80 is approximately 13% in the high-risk group while 6% in the low-risk group. We constructed a more efficient CRC risk prediction model with 11 SNPs including newly identified East Asian-based GWAS SNPs (rs704017, rs11196172, rs10774214, rs647161, rs2423279). Risk grouping based on 11 SNPs depicted lifetime difference of CRC risk. This might be useful for

Protein model quality assessment prediction by combining fragment comparisons and a consensus Cα contact potential

PubMed Central

Zhou, Hongyi; Skolnick, Jeffrey

2009-01-01

In this work, we develop a fully automated method for the quality assessment prediction of protein structural models generated by structure prediction approaches such as fold recognition servers, or ab initio methods. The approach is based on fragment comparisons and a consensus Cα contact potential derived from the set of models to be assessed and was tested on CASP7 server models. The average Pearson linear correlation coefficient between predicted quality and model GDT-score per target is 0.83 for the 98 targets which is better than those of other quality assessment methods that participated in CASP7. Our method also outperforms the other methods by about 3% as assessed by the total GDT-score of the selected top models. PMID:18004783

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.

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.

Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease.

PubMed

Emdin, Connor A; Khera, Amit V; Chaffin, Mark; Klarin, Derek; Natarajan, Pradeep; Aragam, Krishna; Haas, Mary; Bick, Alexander; Zekavat, Seyedeh M; Nomura, Akihiro; Ardissino, Diego; Wilson, James G; Schunkert, Heribert; McPherson, Ruth; Watkins, Hugh; Elosua, Roberto; Bown, Matthew J; Samani, Nilesh J; Baber, Usman; Erdmann, Jeanette; Gupta, Namrata; Danesh, John; Chasman, Daniel; Ridker, Paul; Denny, Joshua; Bastarache, Lisa; Lichtman, Judith H; D'Onofrio, Gail; Mattera, Jennifer; Spertus, John A; Sheu, Wayne H-H; Taylor, Kent D; Psaty, Bruce M; Rich, Stephen S; Post, Wendy; Rotter, Jerome I; Chen, Yii-Der Ida; Krumholz, Harlan; Saleheen, Danish; Gabriel, Stacey; Kathiresan, Sekar

2018-04-24

Less than 3% of protein-coding genetic variants are predicted to result in loss of protein function through the introduction of a stop codon, frameshift, or the disruption of an essential splice site; however, such predicted loss-of-function (pLOF) variants provide insight into effector transcript and direction of biological effect. In >400,000 UK Biobank participants, we conduct association analyses of 3759 pLOF variants with six metabolic traits, six cardiometabolic diseases, and twelve additional diseases. We identified 18 new low-frequency or rare (allele frequency < 5%) pLOF variant-phenotype associations. pLOF variants in the gene GPR151 protect against obesity and type 2 diabetes, in the gene IL33 against asthma and allergic disease, and in the gene IFIH1 against hypothyroidism. In the gene PDE3B, pLOF variants associate with elevated height, improved body fat distribution and protection from coronary artery disease. Our findings prioritize genes for which pharmacologic mimics of pLOF variants may lower risk for disease.

Integrated Molecular Profiling of Human Gastric Cancer Identifies DDR2 as a Potential Regulator of Peritoneal Dissemination.

PubMed

Kurashige, Junji; Hasegawa, Takanori; Niida, Atsushi; Sugimachi, Keishi; Deng, Niantao; Mima, Kosuke; Uchi, Ryutaro; Sawada, Genta; Takahashi, Yusuke; Eguchi, Hidetoshi; Inomata, Masashi; Kitano, Seigo; Fukagawa, Takeo; Sasako, Mitsuru; Sasaki, Hiroki; Sasaki, Shin; Mori, Masaki; Yanagihara, Kazuyoshi; Baba, Hideo; Miyano, Satoru; Tan, Patrick; Mimori, Koshi

2016-03-03

Peritoneal dissemination is the most frequent, incurable metastasis occurring in patients with advanced gastric cancer (GC). However, molecular mechanisms driving peritoneal dissemination still remain poorly understood. Here, we aimed to provide novel insights into the molecular mechanisms that drive the peritoneal dissemination of GC. We performed combined expression analysis with in vivo-selected metastatic cell lines and samples from 200 GC patients to identify driver genes of peritoneal dissemination. The driver-gene functions associated with GC dissemination were examined using a mouse xenograft model. We identified a peritoneal dissemination-associated expression signature, whose profile correlated with those of genes related to development, focal adhesion, and the extracellular matrix. Among the genes comprising the expression signature, we identified that discoidin-domain receptor 2 (DDR2) as a potential regulator of peritoneal dissemination. The DDR2 was upregulated by the loss of DNA methylation and that DDR2 knockdown reduced peritoneal metastasis in a xenograft model. Dasatinib, an inhibitor of the DDR2 signaling pathway, effectively suppressed peritoneal dissemination. DDR2 was identified as a driver gene for GC dissemination from the combined expression signature and can potentially serve as a novel therapeutic target for inhibiting GC peritoneal dissemination.

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

PubMed

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

NASA Astrophysics Data System (ADS)

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

The potential predictive value of circulating immune cell ratio and tumor marker in atezolizumab treated advanced non-small cell lung cancer patients.

PubMed

Zhuo, Minglei; Chen, Hanxiao; Zhang, Tianzhuo; Yang, Xue; Zhong, Jia; Wang, Yuyan; An, Tongtong; Wu, Meina; Wang, Ziping; Huang, Jing; Zhao, Jun

2018-05-04

The PD-L1 antibody atezolizumab has shown promising efficacy in patients with advanced non-small cell lung cancer. But the predictive marker of clinical benefit has not been identified. This study aimed to search for potential predictive factors in circulating blood of patients receiving atezolizumab. Ten patients diagnosed with advanced non-small cell lung cancer were enrolled in this open-label observing study. Circulating immune cells and plasma tumor markers were examined in peripheral blood from these patients before and after atezolizumab treatment respectively. Relation between changes in circulating factors and anti-tumor efficacy were analyzed. Blood routine test showed that atezolizumab therapy induced slightly elevation of white blood cells count generally. The lymphocyte ratio was increased slightly in disease controlled patients but decreased prominently in disease progressed patients in response to atezolizumab therapy. Flow cytometric analysis revealed changes in percentage of various immune cell types, including CD4+ T cell, CD8+ T cell, myeloid-derived suppressor cell, regulatory T cell and PD-1 expressing T cell after atezolizumab. Levels of plasma tumor marker CEA, CA125 and CA199 were also altered after anti-PD-L1 therapy. In comparison with baseline, the disease progressed patients showed sharp increase in tumor marker levels, while those disease controlled patients were seen with decreased regulatory T cell and myeloid-derived suppressor cell ratios. The circulating immune cell ratios and plasma tumor marker levels were related with clinical efficacy of atezolizumab therapy. These factors could be potential predictive marker for anti-PD-L1 therapy in advanced non-small cell lung cancer.

Identifying Anomalies in Gravitational Lens Time Delays

NASA Astrophysics Data System (ADS)

Congdon, Arthur B.; Keeton, Charles R.; Nordgren, C. Erik

2010-02-01

We examine the ability of gravitational lens time delays to reveal complex structure in lens potentials. In a previous paper, we predicted how the time delay between the bright pair of images in a "fold" lens scales with the image separation, for smooth lens potentials. Here we show that the proportionality constant increases with the quadrupole moment of the lens potential, and depends only weakly on the position of the source along the caustic. We use Monte Carlo simulations to determine the range of time delays that can be produced by realistic smooth lens models consisting of isothermal ellipsoid galaxies with tidal shear. We can then identify outliers as "time delay anomalies." We find evidence for anomalies in close image pairs in the cusp lenses RX J1131 - 1231 and B1422+231. The anomalies in RX J1131 - 1231 provide strong evidence for substructure in the lens potential, while at this point the apparent anomalies in B1422+231 mainly indicate that the time delay measurements need to be improved. We also find evidence for time delay anomalies in larger-separation image pairs in the fold lenses, B1608+656 and WFI 2033 - 4723, and the cusp lens RX J0911+0551. We suggest that these anomalies are caused by some combination of substructure and a complex lens environment. Finally, to assist future monitoring campaigns we use our smooth models with shear to predict the time delays for all known four-image lenses.

A spatial modeling approach to identify potential butternut restoration sites in Mammoth Cave National Park

USGS Publications Warehouse

Thompson, L.M.; Van Manen, F.T.; Schlarbaum, S.E.; DePoy, M.

2006-01-01

Incorporation of disease resistance is nearly complete for several important North American hardwood species threatened by exotic fungal diseases. The next important step toward species restoration would be to develop reliable tools to delineate ideal restoration sites on a landscape scale. We integrated spatial modeling and remote sensing techniques to delineate potential restoration sites for Butternut (Juglans cinerea L.) trees, a hardwood species being decimated by an exotic fungus, in Mammoth Cave National Park (MCNP), Kentucky. We first developed a multivariate habitat model to determine optimum Butternut habitats within MCNP. Habitat characteristics of 54 known Butternut locations were used in combination with eight topographic and land use data layers to calculate an index of habitat suitability based on Mahalanobis distance (D2). We used a bootstrapping technique to test the reliability of model predictions. Based on a threshold value for the D2 statistic, 75.9% of the Butternut locations were correctly classified, indicating that the habitat model performed well. Because Butternut seedlings require extensive amounts of sunlight to become established, we used canopy cover data to refine our delineation of favorable areas for Butternut restoration. Areas with the most favorable conditions to establish Butternut seedlings were limited to 291.6 ha. Our study provides a useful reference on the amount and location of favorable Butternut habitat in MCNP and can be used to identify priority areas for future Butternut restoration. Given the availability of relevant habitat layers and accurate location records, our approach can be applied to other tree species and areas. ?? 2006 Society for Ecological Restoration International.

Identifying geographic hot spots of reassortment in a multipartite plant virus

PubMed Central

Savory, Fiona R; Varma, Varun; Ramakrishnan, Uma

2014-01-01

Reassortment between different species or strains plays a key role in the evolution of multipartite plant viruses and can have important epidemiological implications. Identifying geographic locations where reassortant lineages are most likely to emerge could be a valuable strategy for informing disease management and surveillance efforts. We developed a predictive framework to identify potential geographic hot spots of reassortment based upon spatially explicit analyses of genome constellation diversity. To demonstrate the utility of this approach, we examined spatial variation in the potential for reassortment among Cardamom bushy dwarf virus (CBDV; Nanoviridae, Babuvirus) isolates in Northeast India. Using sequence data corresponding to six discrete genome components for 163 CBDV isolates, a quantitative measure of genome constellation diversity was obtained for locations across the sampling region. Two key areas were identified where viruses with highly distinct genome constellations cocirculate, and these locations were designated as possible geographic hot spots of reassortment, where novel reassortant lineages could emerge. Our study demonstrates that the potential for reassortment can be spatially dependent in multipartite plant viruses and highlights the use of evolutionary analyses to identify locations which could be actively managed to facilitate the prevention of outbreaks involving novel reassortant strains. PMID:24944570

Using the integrative model of behavioral prediction to identify promising message strategies to promote healthy sleep behavior among college students.

PubMed

Robbins, Rebecca; Niederdeppe, Jeff

2015-01-01

This research used the Integrative Model of Behavioral Prediction (IMBP) to examine cognitive predictors of intentions to engage in healthy sleep behavior among a population of college students. In doing so, we identify promising message strategies to increase healthy sleep behavior during college. In Phase 1, members of a small sample of undergraduates (n = 31) were asked to describe their beliefs about expected outcomes, norms, and perceived behavioral control associated with sleep on an open-ended questionnaire. We analyzed these qualitative responses to create a closed-ended survey about sleep-related attitudes, perceived norms, control beliefs, behavioral intentions, and behavior. In Phase 2, a larger sample of undergraduate students (n = 365) completed the survey. Attitudes and perceived behavioral control were the strongest predictors of both intentions to engage in sleep behavior and self-reported sleep behavior. Control beliefs associated with time management and stress also had substantial room to change, suggesting their potential as message strategies to better promote healthy sleep behavior in college. We conclude with a broader discussion of the study's implications for message design and intervention.

Potential of the octanol-water partition coefficient (logP) to predict the dermal penetration behaviour of amphiphilic compounds in aqueous solutions.

PubMed

Korinth, Gintautas; Wellner, Tanja; Schaller, Karl Heinz; Drexler, Hans

2012-11-23

Aqueous amphiphilic compounds may exhibit enhanced skin penetration compared with neat compounds. Conventional models do not predict this percutaneous penetration behaviour. We investigated the potential of the octanol-water partition coefficient (logP) to predict dermal fluxes for eight compounds applied neat and as 50% aqueous solutions in diffusion cell experiments using human skin. Data for seven other compounds were accessed from literature. In total, seven glycol ethers, three alcohols, two glycols, and three other chemicals were considered. Of these 15 compounds, 10 penetrated faster through the skin as aqueous solutions than as neat compounds. The other five compounds exhibited larger fluxes as neat applications. For 13 of the 15 compounds, a consistent relationship was identified between the percutaneous penetration behaviour and the logP. Compared with the neat applications, positive logP were associated with larger fluxes for eight of the diluted compounds, and negative logP were associated with smaller fluxes for five of the diluted compounds. Our study demonstrates that decreases or enhancements in dermal penetration upon aqueous dilution can be predicted for many compounds from the sign of logP (i.e., positive or negative). This approach may be suitable as a first approximation in risk assessments of dermal exposure. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Prediction of plant lncRNA by ensemble machine learning classifiers.

PubMed

Simopoulos, Caitlin M A; Weretilnyk, Elizabeth A; Golding, G Brian

2018-05-02

In plants, long non-protein coding RNAs are believed to have essential roles in development and stress responses. However, relative to advances on discerning biological roles for long non-protein coding RNAs in animal systems, this RNA class in plants is largely understudied. With comparatively few validated plant long non-coding RNAs, research on this potentially critical class of RNA is hindered by a lack of appropriate prediction tools and databases. Supervised learning models trained on data sets of mostly non-validated, non-coding transcripts have been previously used to identify this enigmatic RNA class with applications largely focused on animal systems. Our approach uses a training set comprised only of empirically validated long non-protein coding RNAs from plant, animal, and viral sources to predict and rank candidate long non-protein coding gene products for future functional validation. Individual stochastic gradient boosting and random forest classifiers trained on only empirically validated long non-protein coding RNAs were constructed. In order to use the strengths of multiple classifiers, we combined multiple models into a single stacking meta-learner. This ensemble approach benefits from the diversity of several learners to effectively identify putative plant long non-coding RNAs from transcript sequence features. When the predicted genes identified by the ensemble classifier were compared to those listed in GreeNC, an established plant long non-coding RNA database, overlap for predicted genes from Arabidopsis thaliana, Oryza sativa and Eutrema salsugineum ranged from 51 to 83% with the highest agreement in Eutrema salsugineum. Most of the highest ranking predictions from Arabidopsis thaliana were annotated as potential natural antisense genes, pseudogenes, transposable elements, or simply computationally predicted hypothetical protein. Due to the nature of this tool, the model can be updated as new long non-protein coding transcripts are

IDENTIFYING AND PREDICTING DIVING PLUME BEHAVIOR AT GROUNDWATER SITES CONTAINING MTBE: PART 1 SUPPLEMENTAL FUNDING FOR ACTIVITIES IN FY 2002

EPA Science Inventory

This work will complete work began under Identifying and Predicting Plume Diving Behavior at Groundwater Sites Containing MTBE: Part 1. As of September 2001, ORD Staff and ORD Contractors have characterized dividing MTBE plumes at Spring Green, Wisconsin; Milford, Michigan; and ...

Quantitative Profiling Identifies Potential Regulatory Proteins Involved in Development from Dauer Stage to L4 Stage in Caenorhabditis elegans.

PubMed

Kim, Sunhee; Lee, Hyoung-Joo; Hahm, Jeong-Hoon; Jeong, Seul-Ki; Park, Don-Ha; Hancock, William S; Paik, Young-Ki

2016-02-05

When Caenorhabditis elegans encounters unfavorable growth conditions, it enters the dauer stage, an alternative L3 developmental period. A dauer larva resumes larval development to the normal L4 stage by uncharacterized postdauer reprogramming (PDR) when growth conditions become more favorable. During this transition period, certain heterochronic genes involved in controlling the proper sequence of developmental events are known to act, with their mutations suppressing the Muv (multivulva) phenotype in C. elegans. To identify the specific proteins in which the Muv phenotype is highly suppressed, quantitative proteomic analysis with iTRAQ labeling of samples obtained from worms at L1 + 30 h (for continuous development [CD]) and dauer recovery +3 h (for postdauer development [PD]) was carried out to detect changes in protein abundance in the CD and PD states of both N2 and lin-28(n719). Of the 1661 unique proteins identified with a < 1% false discovery rate at the peptide level, we selected 58 proteins exhibiting ≥2-fold up-regulation or ≥2-fold down-regulation in the PD state and analyzed the Gene Ontology terms. RNAi assays against 15 selected up-regulated genes showed that seven genes were predicted to be involved in higher Muv phenotype (p < 0.05) in lin-28(n791), which is not seen in N2. Specifically, two genes, K08H10.1 and W05H9.1, displayed not only the highest rate (%) of Muv phenotype in the RNAi assay but also the dauer-specific mRNA expression, indicating that these genes may be required for PDR, leading to the very early onset of dauer recovery. Thus, our proteomic approach identifies and quantitates the regulatory proteins potentially involved in PDR in C. elegans, which safeguards the overall lifecycle in response to environmental changes.

Identifying the location of fire refuges in wet forest ecosystems.

PubMed

Berry, Laurence E; Driscoll, Don A; Stein, John A; Blanchard, Wade; Banks, Sam C; Bradstock, Ross A; Lindenmayer, David B

2015-12-01

The increasing frequency of large, high-severity fires threatens the survival of old-growth specialist fauna in fire-prone forests. Within topographically diverse montane forests, areas that experience less severe or fewer fires compared with those prevailing in the landscape may present unique resource opportunities enabling old-growth specialist fauna to survive. Statistical landscape models that identify the extent and distribution of potential fire refuges may assist land managers to incorporate these areas into relevant biodiversity conservation strategies. We used a case study in an Australian wet montane forest to establish how predictive fire simulation models can be interpreted as management tools to identify potential fire refuges. We examined the relationship between the probability of fire refuge occurrence as predicted by an existing fire refuge model and fire severity experienced during a large wildfire. We also examined the extent to which local fire severity was influenced by fire severity in the surrounding landscape. We used a combination of statistical approaches, including generalized linear modeling, variogram analysis, and receiver operating characteristics and area under the curve analysis (ROC AUC). We found that the amount of unburned habitat and the factors influencing the retention and location of fire refuges varied with fire conditions. Under extreme fire conditions, the distribution of fire refuges was limited to only extremely sheltered, fire-resistant regions of the landscape. During extreme fire conditions, fire severity patterns were largely determined by stochastic factors that could not be predicted by the model. When fire conditions were moderate, physical landscape properties appeared to mediate fire severity distribution. Our study demonstrates that land managers can employ predictive landscape fire models to identify the broader climatic and spatial domain within which fire refuges are likely to be present. It is essential

Cell host response to infection with novel human coronavirus EMC predicts potential antivirals and important differences with SARS coronavirus.

PubMed

Josset, Laurence; Menachery, Vineet D; Gralinski, Lisa E; Agnihothram, Sudhakar; Sova, Pavel; Carter, Victoria S; Yount, Boyd L; Graham, Rachel L; Baric, Ralph S; Katze, Michael G

2013-04-30

A novel human coronavirus (HCoV-EMC) was recently identified in the Middle East as the causative agent of a severe acute respiratory syndrome (SARS) resembling the illness caused by SARS coronavirus (SARS-CoV). Although derived from the CoV family, the two viruses are genetically distinct and do not use the same receptor. Here, we investigated whether HCoV-EMC and SARS-CoV induce similar or distinct host responses after infection of a human lung epithelial cell line. HCoV-EMC was able to replicate as efficiently as SARS-CoV in Calu-3 cells and similarly induced minimal transcriptomic changes before 12 h postinfection. Later in infection, HCoV-EMC induced a massive dysregulation of the host transcriptome, to a much greater extent than SARS-CoV. Both viruses induced a similar activation of pattern recognition receptors and the interleukin 17 (IL-17) pathway, but HCoV-EMC specifically down-regulated the expression of several genes within the antigen presentation pathway, including both type I and II major histocompatibility complex (MHC) genes. This could have an important impact on the ability of the host to mount an adaptive host response. A unique set of 207 genes was dysregulated early and permanently throughout infection with HCoV-EMC, and was used in a computational screen to predict potential antiviral compounds, including kinase inhibitors and glucocorticoids. Overall, HCoV-EMC and SARS-CoV elicit distinct host gene expression responses, which might impact in vivo pathogenesis and could orient therapeutic strategies against that emergent virus. Identification of a novel coronavirus causing fatal respiratory infection in humans raises concerns about a possible widespread outbreak of severe respiratory infection similar to the one caused by SARS-CoV. Using a human lung epithelial cell line and global transcriptomic profiling, we identified differences in the host response between HCoV-EMC and SARS-CoV. This enables rapid assessment of viral properties and the

Applying psychological theories to evidence-based clinical practice: identifying factors predictive of placing preventive fissure sealants.

PubMed

Bonetti, Debbie; Johnston, Marie; Clarkson, Jan E; Grimshaw, Jeremy; Pitts, Nigel B; Eccles, Martin; Steen, Nick; Thomas, Ruth; Maclennan, Graeme; Glidewell, Liz; Walker, Anne

2010-04-08

Psychological models are used to understand and predict behaviour in a wide range of settings, but have not been consistently applied to health professional behaviours, and the contribution of differing theories is not clear. This study explored the usefulness of a range of models to predict an evidence-based behaviour -- the placing of fissure sealants. Measures were collected by postal questionnaire from a random sample of general dental practitioners (GDPs) in Scotland. Outcomes were behavioural simulation (scenario decision-making), and behavioural intention. Predictor variables were from the Theory of Planned Behaviour (TPB), Social Cognitive Theory (SCT), Common Sense Self-regulation Model (CS-SRM), Operant Learning Theory (OLT), Implementation Intention (II), Stage Model, and knowledge (a non-theoretical construct). Multiple regression analysis was used to examine the predictive value of each theoretical model individually. Significant constructs from all theories were then entered into a 'cross theory' stepwise regression analysis to investigate their combined predictive value. Behavioural simulation - theory level variance explained was: TPB 31%; SCT 29%; II 7%; OLT 30%. Neither CS-SRM nor stage explained significant variance. In the cross theory analysis, habit (OLT), timeline acute (CS-SRM), and outcome expectancy (SCT) entered the equation, together explaining 38% of the variance. Behavioural intention - theory level variance explained was: TPB 30%; SCT 24%; OLT 58%, CS-SRM 27%. GDPs in the action stage had significantly higher intention to place fissure sealants. In the cross theory analysis, habit (OLT) and attitude (TPB) entered the equation, together explaining 68% of the variance in intention. The study provides evidence that psychological models can be useful in understanding and predicting clinical behaviour. Taking a theory-based approach enables the creation of a replicable methodology for identifying factors that may predict clinical behaviour

Procalcitonin as a potential predicting factor for prognosis in bacterial meningitis.

PubMed

Park, Bong Soo; Kim, Si Eun; Park, Si Hyung; Kim, Jinseung; Shin, Kyong Jin; Ha, Sam Yeol; Park, JinSe; Kim, Sung Eun; Lee, Byung In; Park, Kang Min

2017-02-01

We investigated the potential role of serum procalcitonin in differentiating bacterial meningitis from viral meningitis, and in predicting the prognosis in patients with bacterial meningitis. This was a retrospective study of 80 patients with bacterial meningitis (13 patients died). In addition, 58 patients with viral meningitis were included as the disease control groups for comparison. The serum procalcitonin level was measured in all patients at admission. Differences in demographic and laboratory data, including the procalcitonin level, were analyzed between the groups. We used the mortality rate during hospitalization as a marker of prognosis in patients with bacterial meningitis. Multiple logistic regression analysis showed that high serum levels of procalcitonin (>0.12ng/mL) were an independently significant variable for differentiating bacterial meningitis from viral meningitis. The risk of having bacterial meningitis with high serum levels of procalcitonin was at least 6 times higher than the risk of having viral meningitis (OR=6.76, 95% CI: 1.84-24.90, p=0.004). In addition, we found that high levels of procalcitonin (>7.26ng/mL) in the blood were an independently significant predictor for death in patients with bacterial meningitis. The risk of death in patients with bacterial meningitis with high serum levels of procalcitonin may be at least 9 times higher than those without death (OR=9.09, 95% CI: 1.74-47.12, p=0.016). We found that serum procalcitonin is a useful marker for differentiating bacterial meningitis from viral meningitis, and it is also a potential predicting factor for prognosis in patients with bacterial meningitis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Identifying a parsimonious model for predicting academic achievement in undergraduate medical education: A confirmatory factor analysis

PubMed Central

Ali, Syeda Kauser; Baig, Lubna Ansari; Violato, Claudio; Zahid, Onaiza

2017-01-01

Objectives: This study was conducted to adduce evidence of validity for admissions tests and processes and for identifying a parsimonious model that predicts students’ academic achievement in Medical College. Methods: Psychometric study done on admission data and assessment scores for five years of medical studies at Aga Khan University Medical College, Pakistan using confirmatory factor analysis (CFA) and structured equation modeling (SEM). Sample included 276 medical students admitted in 2003, 2004 and 2005. Results: The SEM supported the existence of covariance between verbal reasoning, science and clinical knowledge for predicting achievement in medical school employing Maximum Likelihood (ML) estimations (n=112). Fit indices: χ2 (21) = 59.70, p =<.0001; CFI=.873; RMSEA = 0.129; SRMR = 0.093. Conclusions: This study shows that in addition to biology and chemistry which have been traditionally used as major criteria for admission to medical colleges in Pakistan; mathematics has proven to be a better predictor for higher achievements in medical college. PMID:29067063

Identifying a parsimonious model for predicting academic achievement in undergraduate medical education: A confirmatory factor analysis.

PubMed

Ali, Syeda Kauser; Baig, Lubna Ansari; Violato, Claudio; Zahid, Onaiza

2017-01-01

This study was conducted to adduce evidence of validity for admissions tests and processes and for identifying a parsimonious model that predicts students' academic achievement in Medical College. Psychometric study done on admission data and assessment scores for five years of medical studies at Aga Khan University Medical College, Pakistan using confirmatory factor analysis (CFA) and structured equation modeling (SEM). Sample included 276 medical students admitted in 2003, 2004 and 2005. The SEM supported the existence of covariance between verbal reasoning, science and clinical knowledge for predicting achievement in medical school employing Maximum Likelihood (ML) estimations (n=112). Fit indices: χ 2 (21) = 59.70, p =<.0001; CFI=.873; RMSEA = 0.129; SRMR = 0.093. This study shows that in addition to biology and chemistry which have been traditionally used as major criteria for admission to medical colleges in Pakistan; mathematics has proven to be a better predictor for higher achievements in medical college.

Computationally identified novel agonists for GPRC6A

PubMed Central

Ye, Ruisong; Hwang, Dong-Jin; Miller, Duane D.; Smith, Jeremy C.; Baudry, Jerome; Quarles, L. Darryl

2018-01-01

New insights into G protein coupled receptor regulation of glucose metabolism by β-cells, skeletal muscle and liver hepatocytes identify GPRC6A as a potential therapeutic target for treating type 2 diabetes mellitus (T2D). Activating GPRC6A with a small molecule drug represents a potential paradigm-shifting opportunity to make significant strides in regulating glucose homeostasis by simultaneously correcting multiple metabolic derangements that underlie T2D, including abnormalities in β-cell proliferation and insulin secretion and peripheral insulin resistance. Using a computational, structure-based high-throughput screening approach, we identified novel tri-phenyl compounds predicted to bind to the venus fly trap (VFT) and 7-transmembrane (7-TM) domains of GPRC6A. Experimental testing found that these compounds dose-dependently stimulated GPRC6A signaling in a heterologous cell expression system. Additional chemical modifications and functional analysis identified one tri-phenyl lead compound, DJ-V-159 that demonstrated the greatest potency in stimulating insulin secretion in β-cells and lowering serum glucose in wild-type mice. Collectively, these studies show that GPRC6A is a “druggable” target for developing chemical probes to treat T2DM. PMID:29684031

Characterization of silver nanoparticles in selected consumer products and its relevance for predicting children’s potential exposures

PubMed Central

Tulve, Nicolle S.; Stefaniak, Aleksandr B.; Vance, Marina E.; Rogers, Kim; Mwilu, Samuel; LeBouf, Ryan F.; Schwegler-Berry, Diane; Willis, Robert; Thomas, Treye A.; Marr, Linsey C.

2015-01-01

Due to their antifungal, antibacterial, antiviral, and antimicrobial properties, silver nanoparticles (AgNPs) are used in consumer products intended for use by children or in the home. Children may be especially affected by the normal use of consumer products because of their physiological functions, developmental stage, and activities and behaviors. Despite much research to date, children’s potential exposures to AgNPs are not well characterized. Our objectives were to characterize selected consumer products containing AgNPs and to use the data to estimate a child’s potential non-dietary ingestion exposure. We identified and cataloged 165 consumer products claiming to contain AgNPs that may be used by or near children or found in the home. Nineteen products (textile, liquid, plastic) were selected for further analysis. We developed a tiered analytical approach to determine silver content, form (particulate or ionic), size, morphology, agglomeration state, and composition. Silver was detected in all products except one sippy cup body. Among products in a given category, silver mass contributions were highly variable and not always uniformly distributed within products, highlighting the need to sample multiple areas of a product. Electron microscopy confirmed the presence of AgNPs. Using this data, a child’s potential non-dietary ingestion exposure to AgNPs when drinking milk formula from a sippy cup is 1.53 μg Ag/kg. Additional research is needed to understand the number and types of consumer products containing silver and the concentrations of silver in these products in order to more accurately predict children’s potential aggregate and cumulative exposures to AgNPs. PMID:25747543

Testing predictions of the quantum landscape multiverse 1: the Starobinsky inflationary potential

NASA Astrophysics Data System (ADS)

Di Valentino, Eleonora; Mersini-Houghton, Laura

2017-03-01

The 2015 Planck data release has placed tight constraints on the allowed class of inflationary models. The current data favors concave downwards inflationary potentials while offering interesting hints on possible deviations from the standard picture of CMB perturbations. We here test the predictions of the theory of the origin of the universe from the landscape multiverse, against the most recent Planck data, for the case of concave downwards inflationary potentials, such as the Starobinsky model of inflation. By considering the quantum entanglement correction of the multiverse, we can place a lower limit on the local `SUSY breaking' scale b > 1.2 × 107 GeV at 95% c.l. from Planck TT+lowTEB. We find that this limit is consistent with the range for b that allows the landscape multiverse to explain a serie of anomalies present in the current data.

The potential predictability of fire danger provided by ECMWF forecast

NASA Astrophysics Data System (ADS)

Di Giuseppe, Francesca

2017-04-01

The European Forest Fire Information System (EFFIS), is currently being developed in the framework of the Copernicus Emergency Management Services to monitor and forecast fire danger in Europe. The system provides timely information to civil protection authorities in 38 nations across Europe and mostly concentrates on flagging regions which might be at high danger of spontaneous ignition due to persistent drought. The daily predictions of fire danger conditions are based on the US Forest Service National Fire Danger Rating System (NFDRS), the Canadian forest service Fire Weather Index Rating System (FWI) and the Australian McArthur (MARK-5) rating systems. Weather forcings are provided in real time by the European Centre for Medium range Weather Forecasts (ECMWF) forecasting system. The global system's potential predictability is assessed using re-analysis fields as weather forcings. The Global Fire Emissions Database (GFED4) provides 11 years of observed burned areas from satellite measurements and is used as a validation dataset. The fire indices implemented are good predictors to highlight dangerous conditions. High values are correlated with observed fire and low values correspond to non observed events. A more quantitative skill evaluation was performed using the Extremal Dependency Index which is a skill score specifically designed for rare events. It revealed that the three indices were more skilful on a global scale than the random forecast to detect large fires. The performance peaks in the boreal forests, in the Mediterranean, the Amazon rain-forests and southeast Asia. The skill-scores were then aggregated at country level to reveal which nations could potentiallty benefit from the system information in aid of decision making and fire control support. Overall we found that fire danger modelling based on weather forecasts, can provide reasonable predictability over large parts of the global landmass.

A mouse diversity panel approach reveals the potential for clinical kidney injury due to DB289 not predicted by classical rodent models.

PubMed

Harrill, Alison H; Desmet, Kristina D; Wolf, Kristina K; Bridges, Arlene S; Eaddy, J Scott; Kurtz, C Lisa; Hall, J Ed; Paine, Mary F; Tidwell, Richard R; Watkins, Paul B

2012-12-01

DB289 is the first oral drug shown in clinical trials to have efficacy in treating African trypanosomiasis (African sleeping sickness). Mild liver toxicity was noted but was not treatment limiting. However, development of DB289 was terminated when several treated subjects developed severe kidney injury, a liability not predicted from preclinical testing. We tested the hypothesis that the kidney safety liability of DB289 would be detected in a mouse diversity panel (MDP) comprised of 34 genetically diverse inbred mouse strains. MDP mice received 10 days of oral treatment with DB289 or vehicle and classical renal biomarkers blood urea nitrogen (BUN) and serum creatinine (sCr), as well as urine biomarkers of kidney injury were measured. While BUN and sCr remained within reference ranges, marked elevations were observed for kidney injury molecule-1 (KIM-1) in the urine of sensitive mouse strains. KIM-1 elevations were not always coincident with elevations in alanine aminotransferase (ALT), suggesting that renal injury was not linked to hepatic injury. Genome-wide association analyses of KIM-1 elevations indicated that genes participating in cholesterol and lipid biosynthesis and transport, oxidative stress, and cytokine release may play a role in DB289 renal injury. Taken together, the data resulting from this study highlight the utility of using an MDP to predict clinically relevant toxicities, to identify relevant toxicity biomarkers that may translate into the clinic, and to identify potential mechanisms underlying toxicities. In addition, the sensitive mouse strains identified in this study may be useful in screening next-in-class compounds for renal injury.

Multiple machine learning based descriptive and predictive workflow for the identification of potential PTP1B inhibitors.

PubMed

Chandra, Sharat; Pandey, Jyotsana; Tamrakar, Akhilesh Kumar; Siddiqi, Mohammad Imran

2017-01-01

In insulin and leptin signaling pathway, Protein-Tyrosine Phosphatase 1B (PTP1B) plays a crucial controlling role as a negative regulator, which makes it an attractive therapeutic target for both Type-2 Diabetes (T2D) and obesity. In this work, we have generated classification models by using the inhibition data set of known PTP1B inhibitors to identify new inhibitors of PTP1B utilizing multiple machine learning techniques like naïve Bayesian, random forest, support vector machine and k-nearest neighbors, along with structural fingerprints and selected molecular descriptors. Several models from each algorithm have been constructed and optimized, with the different combination of molecular descriptors and structural fingerprints. For the training and test sets, most of the predictive models showed more than 90% of overall prediction accuracies. The best model was obtained with support vector machine approach and has Matthews Correlation Coefficient of 0.82 for the external test set, which was further employed for the virtual screening of Maybridge small compound database. Five compounds were subsequently selected for experimental assay. Out of these two compounds were found to inhibit PTP1B with significant inhibitory activity in in-vitro inhibition assay. The structural fragments which are important for PTP1B inhibition were identified by naïve Bayesian method and can be further exploited to design new molecules around the identified scaffolds. The descriptive and predictive modeling strategy applied in this study is capable of identifying PTP1B inhibitors from the large compound libraries. Copyright © 2016 Elsevier Inc. All rights reserved.

Genetic studies of plasma analytes identify novel potential biomarkers for several complex traits

PubMed Central

Deming, Yuetiva; Xia, Jian; Cai, Yefei; Lord, Jenny; Del-Aguila, Jorge L.; Fernandez, Maria Victoria; Carrell, David; Black, Kathleen; Budde, John; Ma, ShengMei; Saef, Benjamin; Howells, Bill; Bertelsen, Sarah; Bailey, Matthew; Ridge, Perry G.; Hefti, Franz; Fillit, Howard; Zimmerman, Earl A.; Celmins, Dzintra; Brown, Alice D.; Carrillo, Maria; Fleisher, Adam; Reeder, Stephanie; Trncic, Nadira; Burke, Anna; Tariot, Pierre; Reiman, Eric M.; Chen, Kewei; Sabbagh, Marwan N.; Beiden, Christine M.; Jacobson, Sandra A.; Sirrel, Sherye A.; Doody, Rachelle S.; Villanueva-Meyer, Javier; Chowdhury, Munir; Rountree, Susan; Dang, Mimi; Kowall, Neil; Killiany, Ronald; Budson, Andrew E.; Norbash, Alexander; Johnson, Patricia Lynn; Green, Robert C.; Marshall, Gad; Johnson, Keith A.; Sperling, Reisa A.; Snyder, Peter; Salloway, Stephen; Malloy, Paul; Correia, Stephen; Bernick, Charles; Munic, Donna; Stern, Yaakov; Honig, Lawrence S.; Bell, Karen L.; Relkin, Norman; Chaing, Gloria; Ravdin, Lisa; Paul, Steven; Flashman, Laura A.; Seltzer, Marc; Hynes, Mary L.; Santulli, Robert B.; Bates, Vernice; Capote, Horacio; Rainka, Michelle; Friedl, Karl; Murali Doraiswamy, P.; Petrella, Jeffrey R.; Borges-Neto, Salvador; James, Olga; Wong, Terence; Coleman, Edward; Schwartz, Adam; Cellar, Janet S.; Levey, Allan L.; Lah, James J.; Behan, Kelly; Scott Turner, Raymond; Johnson, Kathleen; Reynolds, Brigid; Pearlson, Godfrey D.; Blank, Karen; Anderson, Karen; Obisesan, Thomas O.; Wolday, Saba; Allard, Joanne; Lerner, Alan; Ogrocki, Paula; Tatsuoka, Curtis; Fatica, Parianne; Farlow, Martin R.; Saykin, Andrew J.; Foroud, Tatiana M.; Shen, Li; Faber, Kelly; Kim, Sungeun; Nho, Kwangsik; Marie Hake, Ann; Matthews, Brandy R.; Brosch, Jared R.; Herring, Scott; Hunt, Cynthia; Albert, Marilyn; Onyike, Chiadi; D’Agostino, Daniel; Kielb, Stephanie; Graff-Radford, Neill R; Parfitt, Francine; Kendall, Tracy; Johnson, Heather; Petersen, Ronald; Jack, Clifford R.; Bernstein, Matthew; Borowski, Bret; Gunter, Jeff; Senjem, Matt; Vemuri, Prashanthi; Jones, David; Kantarci, Kejal; Ward, Chad; Mason, Sara S.; Albers, Colleen S.; Knopman, David; Johnson, Kris; Chertkow, Howard; Hosein, Chris; Mintzer, Jacob; Spicer, Kenneth; Bachman, David; Grossman, Hillel; Mitsis, Effie; Pomara, Nunzio; Hernando, Raymundo; Sarrael, Antero; Potter, William; Buckholtz, Neil; Hsiao, John; Kittur, Smita; Galvin, James E.; Cerbone, Brittany; Michel, Christina A.; Pogorelec, Dana M.; Rusinek, Henry; de Leon, Mony J; Glodzik, Lidia; De Santi, Susan; Johnson, Nancy; Chuang-Kuo; Kerwin, Diana; Bonakdarpour, Borna; Weintraub, Sandra; Grafman, Jordan; Lipowski, Kristine; Mesulam, Marek-Marsel; Scharre, Douglas W.; Kataki, Maria; Adeli, Anahita; Kaye, Jeffrey; Quinn, Joseph; Silbert, Lisa; Lind, Betty; Carter, Raina; Dolen, Sara; Borrie, Michael; Lee, T-Y; Bartha, Rob; Martinez, Walter; Villena, Teresa; Sadowsky, Carl; Khachaturian, Zaven; Ott, Brian R.; Querfurth, Henry; Tremont, Geoffrey; Frank, Richard; Fleischman, Debra; Arfanakis, Konstantinos; Shah, Raj C.; deToledo-Morrell, Leyla; Sorensen, Greg; Finger, Elizabeth; Pasternack, Stephen; Rachinsky, Irina; Drost, Dick; Rogers, John; Kertesz, Andrew; Furst, Ansgar J.; Chad, Stevan; Yesavage, Jerome; Taylor, Joy L.; Lane, Barton; Rosen, Allyson; Tinklenberg, Jared; Black, Sandra; Stefanovic, Bojana; Caldwell, Curtis; Robin Hsiung, Ging-Yuek; Mudge, Benita; Assaly, Michele; Fox, Nick; Schultz, Susan K.; Boles Ponto, Laura L.; Shim, Hyungsub; Ekstam Smith, Karen; Burns, Jeffrey M.; Swerdlow, Russell H.; Brooks, William M.; Marson, Daniel; Griffith, Randall; Clark, David; Geldmacher, David; Brockington, John; Roberson, Erik; Natelson Love, Marissa; DeCarli, Charles; Carmichael, Owen; Olichney, John; Maillard, Pauline; Fletcher, Evan; Nguyen, Dana; Preda, Andrian; Potkin, Steven; Mulnard, Ruth A.; Thai, Gaby; McAdams-Ortiz, Catherine; Landau, Susan; Jagust, William; Apostolova, Liana; Tingus, Kathleen; Woo, Ellen; Silverman, Daniel H.S.; Lu, Po H.; Bartzokis, George; Thompson, Paul; Donohue, Michael; Thomas, Ronald G.; Walter, Sarah; Gessert, Devon; Brewer, James; Vanderswag, Helen; Sather, Tamie; Jiminez, Gus; Balasubramanian, Archana B.; Mason, Jennifer; Sim, Iris; Aisen, Paul; Davis, Melissa; Morrison, Rosemary; Harvey, Danielle; Thal, Lean; Beckett, Laurel; Neylan, Thomas; Finley, Shannon; Weiner, Michael W.; Hayes, Jacqueline; Rosen, Howard J.; Miller, Bruce L.; Perry, David; Massoglia, Dino; Brawman-Mentzer, Olga; Schuff, Norbert; Smith, Charles D.; Hardy, Peter; Sinha, Partha; Oates, Elizabeth; Conrad, Gary; Koeppe, Robert A.; Lord, Joanne L.; Heidebrink, Judith L.; Arnold, Steven E.; Karlawish, Jason H.; Wolk, David; Clark, Christopher M.; Trojanowki, John Q.; Shaw, Leslie M.; Lee, Virginia; Korecka, Magdalena; Figurski, Michal; Toga, Arthur W.; Crawford, Karen; Neu, Scott; Schneider, Lon S.; Pawluczyk, Sonia; Beccera, Mauricio; Teodoro, Liberty; Spann, Bryan M.; Womack, Kyle; Mathews, Dana; Quiceno, Mary; Foster, Norm; Montine, Tom; Fruehling, J. Jay; Harding, Sandra; Johnson, Sterling; Asthana, Sanjay; Carlsson, Cynthia M.; Petrie, Eric C.; Peskind, Elaine; Li, Gail; Porsteinsson, Anton P.; Goldstein, Bonnie S.; Martin, Kim; Makino, Kelly M.; Ismail, M. Saleem; Brand, Connie; Smith, Amanda; Ashok Raj, Balebail; Fargher, Kristin; Kuller, Lew; Mathis, Chet; Ann Oakley, Mary; Lopez, Oscar L.; Simpson, Donna M.; Sink, Kaycee M.; Gordineer, Leslie; Williamson, Jeff D.; Garg, Pradeep; Watkins, Franklin; Cairns, Nigel J.; Raichle, Marc; Morris, John C.; Householder, Erin; Taylor-Reinwald, Lisa; Holtzman, David; Ances, Beau; Carroll, Maria; Creech, Mary L.; Franklin, Erin; Mintun, Mark A.; Schneider, Stacy; Oliver, Angela; Duara, Ranjan; Varon, Daniel; Greig, Maria T.; Roberts, Peggy; Varma, Pradeep; MacAvoy, Martha G.; Carson, Richard E.; van Dyck, Christopher H.; Davies, Peter; Holtzman, David; Morris, John C.; Bales, Kelly; Pickering, Eve H.; Lee, Jin-Moo; Heitsch, Laura; Kauwe, John; Goate, Alison; Piccio, Laura; Cruchaga, Carlos

2016-01-01

Genome-wide association studies of 146 plasma protein levels in 818 individuals revealed 56 genome-wide significant associations (28 novel) with 47 analytes. Loci associated with plasma levels of 39 proteins tested have been previously associated with various complex traits such as heart disease, inflammatory bowel disease, Type 2 diabetes, and multiple sclerosis. These data suggest that these plasma protein levels may constitute informative endophenotypes for these complex traits. We found three potential pleiotropic genes: ABO for plasma SELE and ACE levels, FUT2 for CA19-9 and CEA plasma levels, and APOE for ApoE and CRP levels. We also found multiple independent signals in loci associated with plasma levels of ApoH, CA19-9, FetuinA, IL6r, and LPa. Our study highlights the power of biological traits for genetic studies to identify genetic variants influencing clinically relevant traits, potential pleiotropic effects, and complex disease associations in the same locus.

Simulation and Prediction of the Drug-Drug Interaction Potential of Naloxegol by Physiologically Based Pharmacokinetic Modeling.

PubMed

Zhou, D; Bui, K; Sostek, M; Al-Huniti, N

2016-05-01

Naloxegol, a peripherally acting μ-opioid receptor antagonist for the treatment of opioid-induced constipation, is a substrate for cytochrome P450 (CYP) 3A4/3A5 and the P-glycoprotein (P-gp) transporter. By integrating in silico, preclinical, and clinical pharmacokinetic (PK) findings, minimal and full physiologically based pharmacokinetic (PBPK) models were developed to predict the drug-drug interaction (DDI) potential for naloxegol. The models reasonably predicted the observed changes in naloxegol exposure with ketoconazole (increase of 13.1-fold predicted vs. 12.9-fold observed), diltiazem (increase of 2.8-fold predicted vs. 3.4-fold observed), rifampin (reduction of 76% predicted vs. 89% observed), and quinidine (increase of 1.2-fold predicted vs. 1.4-fold observed). The moderate CYP3A4 inducer efavirenz was predicted to reduce naloxegol exposure by ∼50%, whereas weak CYP3A inhibitors were predicted to minimally affect exposure. In summary, the PBPK models reasonably estimated interactions with various CYP3A modulators and can be used to guide dosing in clinical practice when naloxegol is coadministered with such agents. © 2016 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Potentially preventable infant and child deaths identified at autopsy; findings and implications.

PubMed

Bamber, Andrew R; Mifsud, William; Wolfe, Ingrid; Cass, Hilary; Pryce, Jeremy; Malone, Marian; Sebire, Neil J

2015-09-01

The purpose of the study was to determine the proportion of pediatric deaths investigated by HM Coronial autopsy which were potentially preventable deaths due to treatable natural disease, and what implications such findings may have for health policies to reduce their occurrence. A retrospective study of 1779 autopsies of individuals between 7 days and 14 years of age requested by HM Coroner, taking place in one specialist pediatric autopsy center, was undertaken. Cases were included if they involved a definite natural disease process in which appropriate recognition and treatment was likely to have affected their outcome. Strict criteria were used and cases were excluded where the individual had any longstanding condition which might have predisposed them to, or altered the recognition of, acute illness, or its response to therapy. Almost 8% (134/1779) of the study group were potentially preventable deaths as a result of natural disease, the majority occurring in children younger than 2 years of age. Most individuals reported between 1 and 7 days of symptoms before their death, and the majority had sought medical advice during this period, including from general practitioners within working hours, and hospital emergency departments. Of those who had sought medical attention, around one-third had done so more than once (28%, 15/53). Sepsis and pneumonia accounted for the majority of deaths (46 and 34% respectively), with all infections (sepsis, pneumonia and meningitis) accounting for 110/134 (82%). Around 10% of pediatric deaths referred to HM Coroner are potentially preventable, being the result of treatable natural acute illnesses. In many cases medical advice had been sought during the final illness. The results highlight how a review of autopsy data can identify significant findings with the potential to reduce mortality, and the importance of centralized investigation and reporting of pediatric deaths.

The availability of hydrogeologic data associated with areas identified by the US Geological Survey as experiencing potentially induced seismicity resulting from subsurface injection

NASA Astrophysics Data System (ADS)

Barnes, Caitlin; Halihan, Todd

2018-05-01

A critical need exists for site-specific hydrogeologic data in order to determine potential hazards of induced seismicity and to manage risk. By 2015, the United States Geological Survey (USGS) had identified 17 locations in the USA that are experiencing an increase in seismicity, which may be potentially induced through industrial subsurface injection. These locations span across seven states, which vary in geological setting, industrial exposure and seismic history. Comparing the research across the 17 locations revealed patterns for addressing induced seismicity concerns, despite the differences between geographical locations. Most induced seismicity studies evaluate geologic structure and seismic data from areas experiencing changes in seismic activity levels, but the inherent triggering mechanism is the transmission of hydraulic pressure pulses. This research conducted a systematic review of whether data are available in these locations to generate accurate hydrogeologic predictions, which could aid in managing seismicity. After analyzing peer-reviewed research within the 17 locations, this research confirms a lack of site-specific hydrogeologic data availability for at-risk areas. Commonly, formation geology data are available for these sites, but hydraulic parameters for the seismically active injection and basement zones are not available to researchers conducting peer-reviewed research. Obtaining hydrogeologic data would lead to better risk management for injection areas and provide additional scientific evidential support for determining a potentially induced seismic area.

Identifying and predicting subgroups of information needs among cancer patients: an initial study using latent class analysis.

PubMed

Neumann, Melanie; Wirtz, Markus; Ernstmann, Nicole; Ommen, Oliver; Längler, Alfred; Edelhäuser, Friedrich; Scheffer, Christian; Tauschel, Diethard; Pfaff, Holger

2011-08-01

Understanding how the information needs of cancer patients (CaPts) vary is important because met information needs affect health outcomes and CaPts' satisfaction. The goals of the study were to identify subgroups of CaPts based on self-reported cancer- and treatment-related information needs and to determine whether subgroups could be predicted on the basis of selected sociodemographic, clinical and clinician-patient relationship variables. Three hundred twenty-three CaPts participated in a survey using the "Cancer Patients Information Needs" scale, which is a new tool for measuring cancer-related information needs. The number of information need subgroups and need profiles within each subgroup was identified using latent class analysis (LCA). Multinomial logistic regression was applied to predict class membership. LCA identified a model of five subgroups exhibiting differences in type and extent of CaPts' unmet information needs: a subgroup with "no unmet needs" (31.4% of the sample), two subgroups with "high level of psychosocial unmet information needs" (27.0% and 12.0%), a subgroup with "high level of purely medical unmet information needs" (16.0%) and a subgroup with "high level of medical and psychosocial unmet information needs" (13.6%). An assessment of sociodemographic and clinical characteristics revealed that younger CaPts and CaPts' requiring psychological support seem to belong to subgroups with a higher level of unmet information needs. However, the most significant predictor for the subgroups with unmet information needs is a good clinician-patient relationship, i.e. subjective perception of high level of trust in and caring attention from nurses together with high degree of physician empathy seems to be predictive for inclusion in the subgroup with no unmet information needs. The results of our study can be used by oncology nurses and physicians to increase their awareness of the complexity and heterogeneity of information needs among CaPts and of

Action prediction based on anticipatory brain potentials during simulated driving.

PubMed

Khaliliardali, Zahra; Chavarriaga, Ricardo; Gheorghe, Lucian Andrei; Millán, José del R

2015-12-01

The ability of an automobile to infer the driver's upcoming actions directly from neural signals could enrich the interaction of the car with its driver. Intelligent vehicles fitted with an on-board brain-computer interface able to decode the driver's intentions can use this information to improve the driving experience. In this study we investigate the neural signatures of anticipation of specific actions, namely braking and accelerating. We investigated anticipatory slow cortical potentials in electroencephalogram recorded from 18 healthy participants in a driving simulator using a variant of the contingent negative variation (CNV) paradigm with Go and No-go conditions: count-down numbers followed by 'Start'/'Stop' cue. We report decoding performance before the action onset using a quadratic discriminant analysis classifier based on temporal features. (i) Despite the visual and driving related cognitive distractions, we show the presence of anticipatory event related potentials locked to the stimuli onset similar to the widely reported CNV signal (with an average peak value of -8 μV at electrode Cz). (ii) We demonstrate the discrimination between cases requiring to perform an action upon imperative subsequent stimulus (Go condition, e.g. a 'Red' traffic light) versus events that do not require such action (No-go condition; e.g. a 'Yellow' light); with an average single trial classification performance of 0.83 ± 0.13 for braking and 0.79 ± 0.12 for accelerating (area under the curve). (iii) We show that the centro-medial anticipatory potentials are observed as early as 320 ± 200 ms before the action with a detection rate of 0.77 ± 0.12 in offline analysis. We show for the first time the feasibility of predicting the driver's intention through decoding anticipatory related potentials during simulated car driving with high recognition rates.

Action prediction based on anticipatory brain potentials during simulated driving

NASA Astrophysics Data System (ADS)

Khaliliardali, Zahra; Chavarriaga, Ricardo; Gheorghe, Lucian Andrei; Millán, José del R.

2015-12-01

Objective. The ability of an automobile to infer the driver’s upcoming actions directly from neural signals could enrich the interaction of the car with its driver. Intelligent vehicles fitted with an on-board brain-computer interface able to decode the driver’s intentions can use this information to improve the driving experience. In this study we investigate the neural signatures of anticipation of specific actions, namely braking and accelerating. Approach. We investigated anticipatory slow cortical potentials in electroencephalogram recorded from 18 healthy participants in a driving simulator using a variant of the contingent negative variation (CNV) paradigm with Go and No-go conditions: count-down numbers followed by ‘Start’/‘Stop’ cue. We report decoding performance before the action onset using a quadratic discriminant analysis classifier based on temporal features. Main results. (i) Despite the visual and driving related cognitive distractions, we show the presence of anticipatory event related potentials locked to the stimuli onset similar to the widely reported CNV signal (with an average peak value of -8 μV at electrode Cz). (ii) We demonstrate the discrimination between cases requiring to perform an action upon imperative subsequent stimulus (Go condition, e.g. a ‘Red’ traffic light) versus events that do not require such action (No-go condition; e.g. a ‘Yellow’ light); with an average single trial classification performance of 0.83 ± 0.13 for braking and 0.79 ± 0.12 for accelerating (area under the curve). (iii) We show that the centro-medial anticipatory potentials are observed as early as 320 ± 200 ms before the action with a detection rate of 0.77 ± 0.12 in offline analysis. Significance. We show for the first time the feasibility of predicting the driver’s intention through decoding anticipatory related potentials during simulated car driving with high recognition rates.

The Effectiveness of Predict-Observe-Explain Tasks in Diagnosing Students' Understanding of Science and in Identifying Their Levels of Achievement.

ERIC Educational Resources Information Center

Liew, Chong-Wah; Treagust, David F.

This study involves action research to explore the effectiveness of the Predict-Observe-Explain (POE) technique in diagnosing students' understanding of science and identifying their levels of achievement. A multidimensional interpretive framework is used to interpret students' understanding of science. The research methodology incorporated…

A case study for the integration of predictive mineral potential maps

NASA Astrophysics Data System (ADS)

Lee, Saro; Oh, Hyun-Joo; Heo, Chul-Ho; Park, Inhye

2014-09-01

This study aims to elaborate on the mineral potential maps using various models and verify the accuracy for the epithermal gold (Au) — silver (Ag) deposits in a Geographic Information System (GIS) environment assuming that all deposits shared a common genesis. The maps of potential Au and Ag deposits were produced by geological data in Taebaeksan mineralized area, Korea. The methodological framework consists of three main steps: 1) identification of spatial relationships 2) quantification of such relationships and 3) combination of multiple quantified relationships. A spatial database containing 46 Au-Ag deposits was constructed using GIS. The spatial association between training deposits and 26 related factors were identified and quantified by probabilistic and statistical modelling. The mineral potential maps were generated by integrating all factors using the overlay method and recombined afterwards using the likelihood ratio model. They were verified by comparison with test mineral deposit locations. The verification revealed that the combined mineral potential map had the greatest accuracy (83.97%), whereas it was 72.24%, 65.85%, 72.23% and 71.02% for the likelihood ratio, weight of evidence, logistic regression and artificial neural network models, respectively. The mineral potential map can provide useful information for the mineral resource development.

Cheap and Nasty? The Potential Perils of Using Management Costs to Identify Global Conservation Priorities

PubMed Central

McCreless, Erin; Visconti, Piero; Carwardine, Josie; Wilcox, Chris; Smith, Robert J.

2013-01-01

The financial cost of biodiversity conservation varies widely around the world and such costs should be considered when identifying countries to best focus conservation investments. Previous global prioritizations have been based on global models for protected area management costs, but this metric may be related to other factors that negatively influence the effectiveness and social impacts of conservation. Here we investigate such relationships and first show that countries with low predicted costs are less politically stable. Local support and capacity can mitigate the impacts of such instability, but we also found that these countries have less civil society involvement in conservation. Therefore, externally funded projects in these countries must rely on government agencies for implementation. This can be problematic, as our analyses show that governments in countries with low predicted costs score poorly on indices of corruption, bureaucratic quality and human rights. Taken together, our results demonstrate that using national-level estimates for protected area management costs to set global conservation priorities is simplistic, as projects in apparently low-cost countries are less likely to succeed and more likely to have negative impacts on people. We identify the need for an improved approach to develop global conservation cost metrics that better capture the true costs of avoiding or overcoming such problems. Critically, conservation scientists must engage with practitioners to better understand and implement context-specific solutions. This approach assumes that measures of conservation costs, like measures of conservation value, are organization specific, and would bring a much-needed focus on reducing the negative impacts of conservation to develop projects that benefit people and biodiversity. PMID:24260502

Prediction of Host-Derived miRNAs with the Potential to Target PVY in Potato Plants

PubMed Central

Iqbal, Muhammad S.; Hafeez, Muhammad N.; Wattoo, Javed I.; Ali, Arfan; Sharif, Muhammad N.; Rashid, Bushra; Tabassum, Bushra; Nasir, Idrees A.

2016-01-01

Potato virus Y has emerged as a threatening problem in all potato growing areas around the globe. PVY reduces the yield and quality of potato cultivars. During the last 30 years, significant genetic changes in PVY strains have been observed with an increased incidence associated with crop damage. In the current study, computational approaches were applied to predict Potato derived miRNA targets in the PVY genome. The PVY genome is approximately 9 thousand nucleotides, which transcribes the following 6 genes:CI, NIa, NIb-Pro, HC-Pro, CP, and VPg. A total of 343 mature miRNAs were retrieved from the miRBase database and were examined for their target sequences in PVY genes using the minimum free energy (mfe), minimum folding energy, sequence complementarity and mRNA-miRNA hybridization approaches. The identified potato miRNAs against viral mRNA targets have antiviral activities, leading to translational inhibition by mRNA cleavage and/or mRNA blockage. We found 86 miRNAs targeting the PVY genome at 151 different sites. Moreover, only 36 miRNAs potentially targeted the PVY genome at 101 loci. The CI gene of the PVY genome was targeted by 32 miRNAs followed by the complementarity of 26, 19, 18, 16, and 13 miRNAs. Most importantly, we found 5 miRNAs (miR160a-5p, miR7997b, miR166c-3p, miR399h, and miR5303d) that could target the CI, NIa, NIb-Pro, HC-Pro, CP, and VPg genes of PVY. The predicted miRNAs can be used for the development of PVY-resistant potato crops in the future. PMID:27683585

Can we use genetic and genomic approaches to identify candidate animals for targeted selective treatment.

PubMed

Laurenson, Yan C S M; Kyriazakis, Ilias; Bishop, Stephen C

2013-10-18

Estimated breeding values (EBV) for faecal egg count (FEC) and genetic markers for host resistance to nematodes may be used to identify resistant animals for selective breeding programmes. Similarly, targeted selective treatment (TST) requires the ability to identify the animals that will benefit most from anthelmintic treatment. A mathematical model was used to combine the concepts and evaluate the potential of using genetic-based methods to identify animals for a TST regime. EBVs obtained by genomic prediction were predicted to be the best determinant criterion for TST in terms of the impact on average empty body weight and average FEC, whereas pedigree-based EBVs for FEC were predicted to be marginally worse than using phenotypic FEC as a determinant criterion. Whilst each method has financial implications, if the identification of host resistance is incorporated into a wider genomic selection indices or selective breeding programmes, then genetic or genomic information may be plausibly included in TST regimes. Copyright © 2013 Elsevier B.V. All rights reserved.

Intraoperative changes in transcranial motor evoked potentials and somatosensory evoked potentials predicting outcome in children with intramedullary spinal cord tumors.

PubMed

Cheng, Jason S; Ivan, Michael E; Stapleton, Christopher J; Quinones-Hinojosa, Alfredo; Gupta, Nalin; Auguste, Kurtis I

2014-06-01

Intraoperative dorsal column mapping, transcranial motor evoked potentials (TcMEPs), and somatosensory evoked potentials (SSEPs) have been used in adults to assist with the resection of intramedullary spinal cord tumors (IMSCTs) and to predict postoperative motor deficits. The authors sought to determine whether changes in MEP and SSEP waveforms would similarly predict postoperative motor deficits in children. The authors reviewed charts and intraoperative records for children who had undergone resection for IMSCTs as well as dorsal column mapping and TcMEP and SSEP monitoring. Motor evoked potential data were supplemented with electromyography data obtained using a Kartush microstimulator (Medtronic Inc.). Motor strength was graded using the Medical Research Council (MRC) scale during the preoperative, immediate postoperative, and follow-up periods. Reductions in SSEPs were documented after mechanical traction, in response to maneuvers with the cavitational ultrasonic surgical aspirator (CUSA), or both. Data from 12 patients were analyzed. Three lesions were encountered in the cervical and 7 in the thoracic spinal cord. Two patients had lesions of the cervicomedullary junction and upper spinal cord. Intraoperative MEP changes were noted in half of the patients. In these cases, normal polyphasic signals converted to biphasic signals, and these changes correlated with a loss of 1-2 grades in motor strength. One patient lost MEP signals completely and recovered strength to MRC Grade 4/5. The 2 patients with high cervical lesions showed neither intraoperative MEP changes nor motor deficits postoperatively. Dorsal columns were mapped in 7 patients, and the midline was determined accurately in all 7. Somatosensory evoked potentials were decreased in 7 patients. Two patients each had 2 SSEP decreases in response to traction intraoperatively but had no new sensory findings postoperatively. Another 2 patients had 3 traction-related SSEP decreases intraoperatively, and both

Automatically identifying health outcome information in MEDLINE records.

PubMed

Demner-Fushman, Dina; Few, Barbara; Hauser, Susan E; Thoma, George

2006-01-01

Understanding the effect of a given intervention on the patient's health outcome is one of the key elements in providing optimal patient care. This study presents a methodology for automatic identification of outcomes-related information in medical text and evaluates its potential in satisfying clinical information needs related to health care outcomes. An annotation scheme based on an evidence-based medicine model for critical appraisal of evidence was developed and used to annotate 633 MEDLINE citations. Textual, structural, and meta-information features essential to outcome identification were learned from the created collection and used to develop an automatic system. Accuracy of automatic outcome identification was assessed in an intrinsic evaluation and in an extrinsic evaluation, in which ranking of MEDLINE search results obtained using PubMed Clinical Queries relied on identified outcome statements. The accuracy and positive predictive value of outcome identification were calculated. Effectiveness of the outcome-based ranking was measured using mean average precision and precision at rank 10. Automatic outcome identification achieved 88% to 93% accuracy. The positive predictive value of individual sentences identified as outcomes ranged from 30% to 37%. Outcome-based ranking improved retrieval accuracy, tripling mean average precision and achieving 389% improvement in precision at rank 10. Preliminary results in outcome-based document ranking show potential validity of the evidence-based medicine-model approach in timely delivery of information critical to clinical decision support at the point of service.

Positive selection moments identify potential functional residues in human olfactory receptors

NASA Technical Reports Server (NTRS)

Singer, M. S.; Weisinger-Lewin, Y.; Lancet, D.; Shepherd, G. M.

1996-01-01

Correlated mutation analysis and molecular models of olfactory receptors have provided evidence that residues in the transmembrane domains form a binding pocket for odor ligands. As an independent test of these results, we have calculated positive selection moments for the alpha-helical sixth transmembrane domain (TM6) of human olfactory receptors. The moments can be used to identify residues that have been preferentially affected by positive selection and are thus likely to interact with odor ligands. The results suggest that residue 622, which is commonly a serine or threonine, could form critical H-bonds. In some receptors a dual-serine subsite, formed by residues 622 and 625, could bind hydroxyl determinants on odor ligands. The potential importance of these residues is further supported by site-directed mutagenesis in the beta-adrenergic receptor. The findings should be of practical value for future physiological studies, binding assays, and site-directed mutagenesis.

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

Development and Validation of an Algorithm to Identify Planned Readmissions From Claims Data.

PubMed

Horwitz, Leora I; Grady, Jacqueline N; Cohen, Dorothy B; Lin, Zhenqiu; Volpe, Mark; Ngo, Chi K; Masica, Andrew L; Long, Theodore; Wang, Jessica; Keenan, Megan; Montague, Julia; Suter, Lisa G; Ross, Joseph S; Drye, Elizabeth E; Krumholz, Harlan M; Bernheim, Susannah M

2015-10-01

It is desirable not to include planned readmissions in readmission measures because they represent deliberate, scheduled care. To develop an algorithm to identify planned readmissions, describe its performance characteristics, and identify improvements. Consensus-driven algorithm development and chart review validation study at 7 acute-care hospitals in 2 health systems. For development, all discharges qualifying for the publicly reported hospital-wide readmission measure. For validation, all qualifying same-hospital readmissions that were characterized by the algorithm as planned, and a random sampling of same-hospital readmissions that were characterized as unplanned. We calculated weighted sensitivity and specificity, and positive and negative predictive values of the algorithm (version 2.1), compared to gold standard chart review. In consultation with 27 experts, we developed an algorithm that characterizes 7.8% of readmissions as planned. For validation we reviewed 634 readmissions. The weighted sensitivity of the algorithm was 45.1% overall, 50.9% in large teaching centers and 40.2% in smaller community hospitals. The weighted specificity was 95.9%, positive predictive value was 51.6%, and negative predictive value was 94.7%. We identified 4 minor changes to improve algorithm performance. The revised algorithm had a weighted sensitivity 49.8% (57.1% at large hospitals), weighted specificity 96.5%, positive predictive value 58.7%, and negative predictive value 94.5%. Positive predictive value was poor for the 2 most common potentially planned procedures: diagnostic cardiac catheterization (25%) and procedures involving cardiac devices (33%). An administrative claims-based algorithm to identify planned readmissions is feasible and can facilitate public reporting of primarily unplanned readmissions. © 2015 Society of Hospital Medicine.

[Prediction of Promoter Motifs in Virophages].

PubMed

Gong, Chaowen; Zhou, Xuewen; Pan, Yingjie; Wang, Yongjie

2015-07-01

Virophages have crucial roles in ecosystems and are the transport vectors of genetic materials. To shed light on regulation and control mechanisms in virophage--host systems as well as evolution between virophages and their hosts, the promoter motifs of virophages were predicted on the upstream regions of start codons using an analytical tool for prediction of promoter motifs: Multiple EM for Motif Elicitation. Seventeen potential promoter motifs were identified based on the E-value, location, number and length of promoters in genomes. Sputnik and zamilon motif 2 with AT-rich regions were distributed widely on genomes, suggesting that these motifs may be associated with regulation of the expression of various genes. Motifs containing the TCTA box were predicted to be late promoter motif in mavirus; motifs containing the ATCT box were the potential late promoter motif in the Ace Lake mavirus . AT-rich regions were identified on motif 2 in the Organic Lake virophage, motif 3 in Yellowstone Lake virophage (YSLV)1 and 2, motif 1 in YSLV3, and motif 1 and 2 in YSLV4, respectively. AT-rich regions were distributed widely on the genomes of virophages. All of these motifs may be promoter motifs of virophages. Our results provide insights into further exploration of temporal expression of genes in virophages as well as associations between virophages and giant viruses.

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.

IDENTIFYING ANOMALIES IN GRAVITATIONAL LENS TIME DELAYS

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

Congdon, Arthur B.; Keeton, Charles R.; Nordgren, C. Erik, E-mail: acongdon@jpl.nasa.go, E-mail: keeton@physics.rutgers.ed, E-mail: nordgren@sas.upenn.ed

2010-02-01

We examine the ability of gravitational lens time delays to reveal complex structure in lens potentials. In a previous paper, we predicted how the time delay between the bright pair of images in a 'fold' lens scales with the image separation, for smooth lens potentials. Here we show that the proportionality constant increases with the quadrupole moment of the lens potential, and depends only weakly on the position of the source along the caustic. We use Monte Carlo simulations to determine the range of time delays that can be produced by realistic smooth lens models consisting of isothermal ellipsoid galaxiesmore » with tidal shear. We can then identify outliers as 'time delay anomalies'. We find evidence for anomalies in close image pairs in the cusp lenses RX J1131 - 1231 and B1422+231. The anomalies in RX J1131 - 1231 provide strong evidence for substructure in the lens potential, while at this point the apparent anomalies in B1422+231 mainly indicate that the time delay measurements need to be improved. We also find evidence for time delay anomalies in larger-separation image pairs in the fold lenses, B1608+656 and WFI 2033 - 4723, and the cusp lens RX J0911+0551. We suggest that these anomalies are caused by some combination of substructure and a complex lens environment. Finally, to assist future monitoring campaigns we use our smooth models with shear to predict the time delays for all known four-image lenses.« less

Potential use of multispectral imaging technology to identify moisture content and water-holding capacity in cooked pork sausages.

PubMed

Ma, Fei; Zhang, Bin; Wang, Wu; Li, Peijun; Niu, Xiangli; Chen, Conggui; Zheng, Lei

2018-03-01

The traditional detection methods for moisture content (MC) and water-holding capacity (WHC) in cooked pork sausages (CPS) are destructive, time consuming, require skilled personnel and are not suitable for online industry applications. The goal of this work was to explore the potential of multispectral imaging (MSI) in combination with multivariate analysis for the identification of MC and WHC in CPS. Spectra and textures of 156 CPS treated by six salt concentrations (0-2.5%) were analyzed using different calibration models to find the most optimal results of predicting MC and WHC in CPS. By using the fused data of spectra and textures, partial least squares regression models performed well for determining the MC and WHC, with a correlation coefficient (r) of 0.949 and 0.832, respectively. Additionally, their spatial distribution in CPS could be visualized via applying prediction equations to transfer each pixel in the image. Results of satisfactory detection and visualization of the MC and WHC showed that MSI has the potential to serve as a rapid and non-destructive method for use in sausage industry. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

miR Profiling Identifies Cyclin-Dependent Kinase 6 Downregulation as a Potential Mechanism of Acquired Cisplatin Resistance in Non-Small-Cell Lung Carcinoma.

PubMed

Bar, Jair; Gorn-Hondermann, Ivan; Moretto, Patricia; Perkins, Theodore J; Niknejad, Nima; Stewart, David J; Goss, Glenwood D; Dimitroulakos, Jim

2015-11-01

To identify the mechanisms of cisplatin resistance, global microRNA (miR) expression was tested. The expression of miR-145 was consistently higher in resistant cells. The expression of cyclin-dependent kinase 6 (CDK6), a potential target of miR-145, was lower in resistant cells, and inhibition of CDK4/6 protected cells from cisplatin. Cell cycle inhibition, currently being tested in clinical trials, might be antagonistic to cisplatin and other cytotoxic drugs. Non-small-cell lung cancer (NSCLC) is the leading cause of cancer-related death. Platinum-based chemotherapeutic drugs are the most active agents in treating advanced disease. Resistance to these drugs is common and multifactorial; insight into the molecular mechanisms involved will likely enhance efficacy. A set of NSCLC platinum-resistant sublines was created from the Calu6 cell line. Cell viability was quantified using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. Differentially expressed microRNAs (miRs) in these lines were identified using Affymetrix miR arrays. The potential genes targeted by these miRs were searched using the TargetScan algorithm. The expression levels of miRs and mRNA were tested using real-time polymerase chain reaction. miR-145 was reproducibly elevated in all the resistant sublines tested; however, modulation of miR-145 levels alone in these cells did not affect their response to cisplatin. A potential target of miR-145 is cyclin-dependent kinase 6 (CDK6), an important regulator of cell proliferation. The mRNA and protein levels of CDK6 were both downregulated in the resistant sublines. An inhibitor of CDK4/6 (PD0332991) protected parental NSCLC cells from cisplatin cytotoxicity. In the present study, we identified miRs differentially expressed in cisplatin-resistant cell lines, including miR-145. A predicted target of miR-145 is CDK6, and its expression was found to be downregulated in the resistant sublines, although not directly by miR-145. Inhibition

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

Computational prediction of host-pathogen protein-protein interactions.

PubMed

Dyer, Matthew D; Murali, T M; Sobral, Bruno W

2007-07-01

Infectious diseases such as malaria result in millions of deaths each year. An important aspect of any host-pathogen system is the mechanism by which a pathogen can infect its host. One method of infection is via protein-protein interactions (PPIs) where pathogen proteins target host proteins. Developing computational methods that identify which PPIs enable a pathogen to infect a host has great implications in identifying potential targets for therapeutics. We present a method that integrates known intra-species PPIs with protein-domain profiles to predict PPIs between host and pathogen proteins. Given a set of intra-species PPIs, we identify the functional domains in each of the interacting proteins. For every pair of functional domains, we use Bayesian statistics to assess the probability that two proteins with that pair of domains will interact. We apply our method to the Homo sapiens-Plasmodium falciparum host-pathogen system. Our system predicts 516 PPIs between proteins from these two organisms. We show that pairs of human proteins we predict to interact with the same Plasmodium protein are close to each other in the human PPI network and that Plasmodium pairs predicted to interact with same human protein are co-expressed in DNA microarray datasets measured during various stages of the Plasmodium life cycle. Finally, we identify functionally enriched sub-networks spanned by the predicted interactions and discuss the plausibility of our predictions. Supplementary data are available at http://staff.vbi.vt.edu/dyermd/publications/dyer2007a.html. Supplementary data are available at Bioinformatics online.

Biomedical Informatics Approaches to Identifying Drug-Drug Interactions: Application to Insulin Secretagogues

PubMed Central

Han, Xu; Chiang, ChienWei; Leonard, Charles E.; Bilker, Warren B.; Brensinger, Colleen M.; Li, Lang; Hennessy, Sean

2017-01-01

Background Drug-drug interactions with insulin secretagogues are associated with increased risk of serious hypoglycemia in patients with type 2 diabetes. We aimed to systematically screen for drugs that interact with the five most commonly used secretagogues―glipizide, glyburide, glimepiride, repaglinide, and nateglinide―to cause serious hypoglycemia. Methods We screened 400 drugs frequently co-prescribed with the secretagogues as candidate interacting precipitants. We first predicted the drug–drug interaction potential based on the pharmacokinetics of each secretagogue–precipitant pair. We then performed pharmacoepidemiologic screening for each secretagogue of interest, and for metformin as a negative control, using an administrative claims database and the self-controlled case series design. The overall rate ratios (RRs) and those for four predefined risk periods were estimated using Poisson regression. The RRs were adjusted for multiple estimation using semi-Bayes method, and then adjusted for metformin results to distinguish native effects of the precipitant from a drug–drug interaction. Results We predicted 34 pharmacokinetic drug–drug interactions with the secretagogues, nine moderate and 25 weak. There were 140 and 61 secretagogue–precipitant pairs associated with increased rates of serious hypoglycemia before and after the metformin adjustment, respectively. The results from pharmacokinetic prediction correlated poorly with those from pharmacoepidemiologic screening. Conclusions The self-controlled case series design has the potential to be widely applicable to screening for drug–drug interactions that lead to adverse outcomes identifiable in healthcare databases. Coupling pharmacokinetic prediction with pharmacoepidemiologic screening did not notably improve the ability to identify drug–drug interactions in this case. PMID:28169935

Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions

PubMed Central

2013-01-01

Background Subunit vaccines based on recombinant proteins have been effective in preventing infectious diseases and are expected to meet the demands of future vaccine development. Computational approach, especially reverse vaccinology (RV) method has enormous potential for identification of protein vaccine candidates (PVCs) from a proteome. The existing protective antigen prediction software and web servers have low prediction accuracy leading to limited applications for vaccine development. Besides machine learning techniques, those software and web servers have considered only protein’s adhesin-likeliness as criterion for identification of PVCs. Several non-adhesin functional classes of proteins involved in host-pathogen interactions and pathogenesis are known to provide protection against bacterial infections. Therefore, knowledge of bacterial pathogenesis has potential to identify PVCs. Results A web server, Jenner-Predict, has been developed for prediction of PVCs from proteomes of bacterial pathogens. The web server targets host-pathogen interactions and pathogenesis by considering known functional domains from protein classes such as adhesin, virulence, invasin, porin, flagellin, colonization, toxin, choline-binding, penicillin-binding, transferring-binding, fibronectin-binding and solute-binding. It predicts non-cytosolic proteins containing above domains as PVCs. It also provides vaccine potential of PVCs in terms of their possible immunogenicity by comparing with experimentally known IEDB epitopes, absence of autoimmunity and conservation in different strains. Predicted PVCs are prioritized so that only few prospective PVCs could be validated experimentally. The performance of web server was evaluated against known protective antigens from diverse classes of bacteria reported in Protegen database and datasets used for VaxiJen server development. The web server efficiently predicted known vaccine candidates reported from Streptococcus pneumoniae and

Metabolomic profiling to identify potential serum biomarkers for schizophrenia and risperidone action.

PubMed

Xuan, Jiekun; Pan, Guihua; Qiu, Yunping; Yang, Lun; Su, Mingming; Liu, Yumin; Chen, Jian; Feng, Guoyin; Fang, Yiru; Jia, Wei; Xing, Qinghe; He, Lin

2011-12-02

Despite recent advances in understanding the pathophysiology of schizophrenia and the mechanisms of antipsychotic drug action, the development of biomarkers for diagnosis and therapeutic monitoring in schizophrenia remains challenging. Metabolomics provides a powerful approach to discover diagnostic and therapeutic biomarkers by analyzing global changes in an individual's metabolic profile in response to pathophysiological stimuli or drug intervention. In this study, we performed gas chromatography-mass spectrometry based metabolomic profiling in serum of unmedicated schizophrenic patients before and after an 8-week risperidone monotherapy, to detect potential biomarkers associated with schizophrenia and risperidone treatment. Twenty-two marker metabolites contributing to the complete separation of schizophrenic patients from matched healthy controls were identified, with citrate, palmitic acid, myo-inositol, and allantoin exhibiting the best combined classification performance. Twenty marker metabolites contributing to the complete separation between posttreatment and pretreatment patients were identified, with myo-inositol, uric acid, and tryptophan showing the maximum combined classification performance. Metabolic pathways including energy metabolism, antioxidant defense systems, neurotransmitter metabolism, fatty acid biosynthesis, and phospholipid metabolism were found to be disturbed in schizophrenic patients and partially normalized following risperidone therapy. Further study of these metabolites may facilitate the development of noninvasive biomarkers and more efficient therapeutic strategies for schizophrenia.

Assessing predictive services' 7-day fire potential outlook

Treesearch

Karin Riley; Crystal Stonesifer; Dave Calkin; Haiganoush Preisler

2015-01-01

The Predictive Services program was created under the National Wildfire Coordinating Group in 2001 to address the need for long- and short-term decision support information for fire managers and operations personnel. The primary mission of Predictive Services is to integrate fire weather, fire danger, and resource availability to enable strategic fire suppression...

Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.

PubMed

Ring, Caroline L; Pearce, Robert G; Setzer, R Woodrow; Wetmore, Barbara A; Wambaugh, John F

2017-09-01

The thousands of chemicals present in the environment (USGAO, 2013) must be triaged to identify priority chemicals for human health risk research. Most chemicals have little of the toxicokinetic (TK) data that are necessary for relating exposures to tissue concentrations that are believed to be toxic. Ongoing efforts have collected limited, in vitro TK data for a few hundred chemicals. These data have been combined with biomonitoring data to estimate an approximate margin between potential hazard and exposure. The most "at risk" 95th percentile of adults have been identified from simulated populations that are generated either using standard "average" adult human parameters or very specific cohorts such as Northern Europeans. To better reflect the modern U.S. population, we developed a population simulation using physiologies based on distributions of demographic and anthropometric quantities from the most recent U.S. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES) data. This allowed incorporation of inter-individual variability, including variability across relevant demographic subgroups. Variability was analyzed with a Monte Carlo approach that accounted for the correlation structure in physiological parameters. To identify portions of the U.S. population that are more at risk for specific chemicals, physiologic variability was incorporated within an open-source high-throughput (HT) TK modeling framework. We prioritized 50 chemicals based on estimates of both potential hazard and exposure. Potential hazard was estimated from in vitro HT screening assays (i.e., the Tox21 and ToxCast programs). Bioactive in vitro concentrations were extrapolated to doses that produce equivalent concentrations in body tissues using a reverse dosimetry approach in which generic TK models are parameterized with: 1) chemical-specific parameters derived from in vitro measurements and predicted from chemical structure; and 2) with

Prospective Large-Scale Field Study Generates Predictive Model Identifying Major Contributors to Colony Losses

PubMed Central

Kielmanowicz, Merav Gleit; Inberg, Alex; Lerner, Inbar Maayan; Golani, Yael; Brown, Nicholas; Turner, Catherine Louise; Hayes, Gerald J. R.; Ballam, Joan M.

2015-01-01

Over the last decade, unusually high losses of colonies have been reported by beekeepers across the USA. Multiple factors such as Varroa destructor, bee viruses, Nosema ceranae, weather, beekeeping practices, nutrition, and pesticides have been shown to contribute to colony losses. Here we describe a large-scale controlled trial, in which different bee pathogens, bee population, and weather conditions across winter were monitored at three locations across the USA. In order to minimize influence of various known contributing factors and their interaction, the hives in the study were not treated with antibiotics or miticides. Additionally, the hives were kept at one location and were not exposed to potential stress factors associated with migration. Our results show that a linear association between load of viruses (DWV or IAPV) in Varroa and bees is present at high Varroa infestation levels (>3 mites per 100 bees). The collection of comprehensive data allowed us to draw a predictive model of colony losses and to show that Varroa destructor, along with bee viruses, mainly DWV replication, contributes to approximately 70% of colony losses. This correlation further supports the claim that insufficient control of the virus-vectoring Varroa mite would result in increased hive loss. The predictive model also indicates that a single factor may not be sufficient to trigger colony losses, whereas a combination of stressors appears to impact hive health. PMID:25875764

Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns.

PubMed

Liu, Jin; Liao, Xuhong; Xia, Mingrui; He, Yong

2018-02-01

The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the "chronnectome"). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a "fingerprint" of the brain. Here, we employed multiband resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time-window dynamic network analysis approach to systematically examine individual time-varying properties of the chronnectome. We revealed stable and remarkable individual variability in three dynamic characteristics of brain connectivity (i.e., strength, stability, and variability), which was mainly distributed in three higher order cognitive systems (i.e., default mode, dorsal attention, and fronto-parietal) and in two primary systems (i.e., visual and sensorimotor). Intriguingly, the spatial patterns of these dynamic characteristics of brain connectivity could successfully identify individuals with high accuracy and could further significantly predict individual higher cognitive performance (e.g., fluid intelligence and executive function), which was primarily contributed by the higher order cognitive systems. Together, our findings highlight that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease. © 2017 Wiley Periodicals, Inc.

Human oocyte developmental potential is predicted by mechanical properties within hours after fertilization

PubMed Central

Yanez, Livia Z.; Han, Jinnuo; Behr, Barry B.; Pera, Renee A. Reijo; Camarillo, David B.

2016-01-01

The causes of embryonic arrest during pre-implantation development are poorly understood. Attempts to correlate patterns of oocyte gene expression with successful embryo development have been hampered by the lack of reliable and nondestructive predictors of viability at such an early stage. Here we report that zygote viscoelastic properties can predict blastocyst formation in humans and mice within hours after fertilization, with >90% precision, 95% specificity and 75% sensitivity. We demonstrate that there are significant differences between the transcriptomes of viable and non-viable zygotes, especially in expression of genes important for oocyte maturation. In addition, we show that low-quality oocytes may undergo insufficient cortical granule release and zona-hardening, causing altered mechanics after fertilization. Our results suggest that embryo potential is largely determined by the quality and maturation of the oocyte before fertilization, and can be predicted through a minimally invasive mechanical measurement at the zygote stage. PMID:26904963

Causal network analysis of head and neck keloid tissue identifies potential master regulators.

PubMed

Garcia-Rodriguez, Laura; Jones, Lamont; Chen, Kang Mei; Datta, Indrani; Divine, George; Worsham, Maria J

2016-10-01

To generate novel insights and hypotheses in keloid development from potential master regulators. Prospective cohort. Six fresh keloid and six normal skin samples from 12 anonymous donors were used in a prospective cohort study. Genome-wide profiling was done previously on the cohort using the Infinium HumanMethylation450 BeadChip (Illumina, San Diego, CA). The 190 statistically significant CpG islands between keloid and normal tissue mapped to 152 genes (P < .05). The top 10 statistically significant genes (VAMP5, ACTR3C, GALNT3, KCNAB2, LRRC61, SCML4, SYNGR1, TNS1, PLEKHG5, PPP1R13-α, false discovery rate <.015) were uploaded into the Ingenuity Pathway Analysis software's Causal Network Analysis (QIAGEN, Redwood City, CA). To reflect expected gene expression direction in the context of methylation changes, the inverse of the methylation ratio from keloid versus normal tissue was used for the analysis. Causal Network Analysis identified disease-specific master regulator molecules based on downstream differentially expressed keloid-specific genes and expected directionality of expression (hypermethylated vs. hypomethylated). Causal Network Analysis software identified four hierarchical networks that included four master regulators (pyroxamide, tributyrin, PRKG2, and PENK) and 19 intermediate regulators. Causal Network Analysis of differentiated methylated gene data of keloid versus normal skin demonstrated four causal networks with four master regulators. These hierarchical networks suggest potential driver roles for their downstream keloid gene targets in the pathogenesis of the keloid phenotype, likely triggered due to perturbation/injury to normal tissue. NA Laryngoscope, 126:E319-E324, 2016. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

Microarray Meta-Analysis Identifies Acute Lung Injury Biomarkers in Donor Lungs That Predict Development of Primary Graft Failure in Recipients

PubMed Central

Haitsma, Jack J.; Furmli, Suleiman; Masoom, Hussain; Liu, Mingyao; Imai, Yumiko; Slutsky, Arthur S.; Beyene, Joseph; Greenwood, Celia M. T.; dos Santos, Claudia

2012-01-01

Objectives To perform a meta-analysis of gene expression microarray data from animal studies of lung injury, and to identify an injury-specific gene expression signature capable of predicting the development of lung injury in humans. Methods We performed a microarray meta-analysis using 77 microarray chips across six platforms, two species and different animal lung injury models exposed to lung injury with or/and without mechanical ventilation. Individual gene chips were classified and grouped based on the strategy used to induce lung injury. Effect size (change in gene expression) was calculated between non-injurious and injurious conditions comparing two main strategies to pool chips: (1) one-hit and (2) two-hit lung injury models. A random effects model was used to integrate individual effect sizes calculated from each experiment. Classification models were built using the gene expression signatures generated by the meta-analysis to predict the development of lung injury in human lung transplant recipients. Results Two injury-specific lists of differentially expressed genes generated from our meta-analysis of lung injury models were validated using external data sets and prospective data from animal models of ventilator-induced lung injury (VILI). Pathway analysis of gene sets revealed that both new and previously implicated VILI-related pathways are enriched with differentially regulated genes. Classification model based on gene expression signatures identified in animal models of lung injury predicted development of primary graft failure (PGF) in lung transplant recipients with larger than 80% accuracy based upon injury profiles from transplant donors. We also found that better classifier performance can be achieved by using meta-analysis to identify differentially-expressed genes than using single study-based differential analysis. Conclusion Taken together, our data suggests that microarray analysis of gene expression data allows for the detection of �

Topographic models for predicting malaria vector breeding habitats: potential tools for vector control managers.

PubMed

Nmor, Jephtha C; Sunahara, Toshihiko; Goto, Kensuke; Futami, Kyoko; Sonye, George; Akweywa, Peter; Dida, Gabriel; Minakawa, Noboru

2013-01-16

varied. Drains, foot-prints, puddles and swamp habitat types were most predictable. Both SRTM and ASTER models had similar predictive potentials, which were sufficiently accurate to predict vector habitats. The free availability of these DEMs suggests that topographic predictive models could be widely used by vector control managers in Africa to complement malaria control strategies.

Integrating virtual screening and biochemical experimental approach to identify potential anti-cancer agents from drug databank.

PubMed

Deka, Suman Jyoti; Roy, Ashalata; Manna, Debasis; Trivedi, Vishal

2018-06-01

Chemical libraries constitute a reservoir of pharmacophoric molecules to identify potent anti-cancer agents. Virtual screening of heterocyclic compound library in conjugation with the agonist-competition assay, toxicity-carcinogenicity analysis, and string-based structural searches enabled us to identify several drugs as potential anti-cancer agents targeting protein kinase C (PKC) as a target. Molecular modeling study indicates that Cinnarizine fits well within the PKC C2 domain and exhibits extensive interaction with the protein residues. Molecular dynamics simulation of PKC-Cinnarizine complex at different temperatures (300, 325, 350, 375, and 400[Formula: see text]K) confirms that Cinnarizine fits nicely into the C2 domain and forms a stable complex. The drug Cinnarizine was found to bind PKC with a dissociation constant Kd of [Formula: see text]M. The breast cancer cells stimulated with Cinnarizine causes translocation of PKC-[Formula: see text] to the plasma membrane as revealed by immunoblotting and immunofluorescence studies. Cinnarizine also dose dependently reduced the viability of MDAMB-231 and MCF-7 breast cancer cells with an IC[Formula: see text] of [Formula: see text] and [Formula: see text]g/mL, respectively. It is due to the disturbance of cell cycle of breast cancer cells with reduction of S-phase and accumulation of cells in G1-phase. It disturbs mitochondrial membrane potentials to release cytochrome C into the cytosol and activates caspase-3 to induce apoptosis in cancer cells. The cell death was due to induction of apoptosis involving mitochondrial pathway. Hence, the current study has assigned an additional role to Cinnarizine as an activator of PKC and potentials of the approach to identify new molecules for anti-cancer therapy. Thus, in silico screening along with biochemical experimentation is a robust approach to assign additional roles to the drugs present in the databank for anti-cancer therapy.

Ecological Model to Predict Potential Habitats of Oncomelania hupensis, the Intermediate Host of Schistosoma japonicum in the Mountainous Regions, China.

PubMed

Zhu, Hong-Ru; Liu, Lu; Zhou, Xiao-Nong; Yang, Guo-Jing

2015-01-01

Schistosomiasis japonica is a parasitic disease that remains endemic in seven provinces in the People's Republic of China (P.R. China). One of the most important measures in the process of schistosomiasis elimination in P.R. China is control of Oncomelania hupensis, the unique intermediate host snail of Schistosoma japonicum. Compared with plains/swamp and lake regions, the hilly/mountainous regions of schistosomiasis endemic areas are more complicated, which makes the snail survey difficult to conduct precisely and efficiently. There is a pressing call to identify the snail habitats of mountainous regions in an efficient and cost-effective manner. Twelve out of 56 administrative villages distributed with O. hupensis in Eryuan, Yunnan Province, were randomly selected to set up the ecological model. Thirty out of the rest of 78 villages (villages selected for building model were excluded from the villages for validation) in Eryuan and 30 out of 89 villages in Midu, Yunnan Province were selected via a chessboard method for model validation, respectively. Nine-year-average Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) as well as Digital Elevation Model (DEM) covering Eryuan and Midu were extracted from MODIS and ASTER satellite images, respectively. Slope, elevation and the distance from every village to its nearest stream were derived from DEM. Suitable survival environment conditions for snails were defined by comparing historical snail presence data and remote sensing derived images. According to the suitable conditions for snails, environment factors, i.e. NDVI, LST, elevation, slope and the distance from every village to its nearest stream, were integrated into an ecological niche model to predict O. hupensis potential habitats in Eryuan and Midu. The evaluation of the model was assessed by comparing the model prediction and field investigation. Then, the consistency rate of model validation was calculated in Eryuan and Midu

High-Energy Polarization: Scientific Potential and Model Predictions

DOE PAGES

Zhang, Haocheng

2017-07-28

Understanding magnetic field strength and morphology is very important for studying astrophysical jets. Polarization signatures have been a standard way to probe the jet magnetic field. Radio and optical polarization monitoring programs have been very successful in studying the space- and time-dependent jet polarization behaviors. A new era is now arriving with high-energy polarimetry. X-ray and γ-ray polarimetry can probe the most active jet regions with the most efficient particle acceleration. This new opportunity will make a strong impact on our current understanding of jet systems. Here, this article summarizes the scientific potential and current model predictions for X-ray andmore » γ-ray polarization of astrophysical jets. In particular, we discuss the advantages of using high-energy polarimetry to constrain several important problems in the jet physics, including the jet radiation mechanisms, particle acceleration mechanisms, and jet kinetic and magnetic energy composition. Here we take blazars as a study case, but the general approach can be similarly applied to other astrophysical jets. We conclude that by comparing combined magnetohydrodynamics (MHD), particle transport, and polarization-dependent radiation transfer simulations with multi-wavelength time-dependent radiation and polarization observations, we will obtain the strongest constraints and the best knowledge of jet physics.« less

High-Energy Polarization: Scientific Potential and Model Predictions

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

Zhang, Haocheng

Understanding magnetic field strength and morphology is very important for studying astrophysical jets. Polarization signatures have been a standard way to probe the jet magnetic field. Radio and optical polarization monitoring programs have been very successful in studying the space- and time-dependent jet polarization behaviors. A new era is now arriving with high-energy polarimetry. X-ray and γ-ray polarimetry can probe the most active jet regions with the most efficient particle acceleration. This new opportunity will make a strong impact on our current understanding of jet systems. Here, this article summarizes the scientific potential and current model predictions for X-ray andmore » γ-ray polarization of astrophysical jets. In particular, we discuss the advantages of using high-energy polarimetry to constrain several important problems in the jet physics, including the jet radiation mechanisms, particle acceleration mechanisms, and jet kinetic and magnetic energy composition. Here we take blazars as a study case, but the general approach can be similarly applied to other astrophysical jets. We conclude that by comparing combined magnetohydrodynamics (MHD), particle transport, and polarization-dependent radiation transfer simulations with multi-wavelength time-dependent radiation and polarization observations, we will obtain the strongest constraints and the best knowledge of jet physics.« less

Prediction for Intravenous Immunoglobulin Resistance by Using Weighted Genetic Risk Score Identified From Genome-Wide Association Study in Kawasaki Disease.

PubMed

Kuo, Ho-Chang; Wong, Henry Sung-Ching; Chang, Wei-Pin; Chen, Ben-Kuen; Wu, Mei-Shin; Yang, Kuender D; Hsieh, Kai-Sheng; Hsu, Yu-Wen; Liu, Shih-Feng; Liu, Xiao; Chang, Wei-Chiao

2017-10-01

Intravenous immunoglobulin (IVIG) is the treatment of choice in Kawasaki disease (KD). IVIG is used to prevent cardiovascular complications related to KD. However, a proportion of KD patients have persistent fever after IVIG treatment and are defined as IVIG resistant. To develop a risk scoring system based on genetic markers to predict IVIG responsiveness in KD patients, a total of 150 KD patients (126 IVIG responders and 24 IVIG nonresponders) were recruited for this study. A genome-wide association analysis was performed to compare the 2 groups and identified risk alleles for IVIG resistance. A weighted genetic risk score was calculated by the natural log of the odds ratio multiplied by the number of risk alleles. Eleven single-nucleotide polymorphisms were identified by genome-wide association study. The KD patients were categorized into 3 groups based on their calculated weighted genetic risk score. Results indicated a significant association between weighted genetic risk score (groups 3 and 4 versus group 1) and the response to IVIG (Fisher's exact P value 4.518×10 - 03 and 8.224×10 - 10 , respectively). This is the first weighted genetic risk score study based on a genome-wide association study in KD. The predictive model integrated the additive effects of all 11 single-nucleotide polymorphisms to provide a prediction of the responsiveness to IVIG. © 2017 The Authors.

Identifying Best Practices for and Utilities of the Pharmacy Curriculum Outcome Assessment Examination.

PubMed

Mok, Timothy Y; Romanelli, Frank

2016-12-25

Objective. A review was conducted to determine implementation strategies, utilities, score interpretation, and limitations of the Pharmacy Curriculum Outcome Assessment (PCOA) examination. Methods. Articles were identified through the PubMed and American Journal of Pharmaceutical Education , and International Pharmaceutical Abstracts databases using the following terms: "Pharmacy Curriculum Outcomes Assessment," "pharmacy comprehensive examination," and "curricular assessment." Studies containing information regarding implementation, utility, and predictive values for US student pharmacists, curricula, and/or PGY1/PGY2 residents were included. Publications from the Academic Medicine Journal , the Accreditation Council for Pharmacy Education (ACPE), and the American Association of Colleges of Pharmacy (ACCP) were included for background information and comparison of predictive utilities of comprehensive examinations in medicine. Results. Ten PCOA and nine residency-related publications were identified. Based on published information, the PCOA may be best used as an additional tool to identify knowledge gaps for third-year student pharmacists. Conclusion. Administering the PCOA to students after they have completed their didactic coursework may yield scores that reflect student knowledge. Predictive utility regarding the North American Pharmacy Licensure Examination (NAPLEX) and potential applications is limited, and more research is required to determine ways to use the PCOA.

Human Pluripotent Stem Cell-Based Assay Predicts Developmental Toxicity Potential of ToxCast Chemicals (ACT meeting)

EPA Science Inventory

Worldwide initiatives to screen for toxicity potential among the thousands of chemicals currently in use require inexpensive and high-throughput in vitro models to meet their goals. The devTOX quickPredict platform is an in vitro human pluripotent stem cell-based assay used to as...

Development and Validation of a Predictive Model to Identify Individuals Likely to Have Undiagnosed Chronic Obstructive Pulmonary Disease Using an Administrative Claims Database.

PubMed

Moretz, Chad; Zhou, Yunping; Dhamane, Amol D; Burslem, Kate; Saverno, Kim; Jain, Gagan; Devercelli, Giovanna; Kaila, Shuchita; Ellis, Jeffrey J; Hernandez, Gemzel; Renda, Andrew

2015-12-01

Despite the importance of early detection, delayed diagnosis of chronic obstructive pulmonary disease (COPD) is relatively common. Approximately 12 million people in the United States have undiagnosed COPD. Diagnosis of COPD is essential for the timely implementation of interventions, such as smoking cessation programs, drug therapies, and pulmonary rehabilitation, which are aimed at improving outcomes and slowing disease progression. To develop and validate a predictive model to identify patients likely to have undiagnosed COPD using administrative claims data. A predictive model was developed and validated utilizing a retro-spective cohort of patients with and without a COPD diagnosis (cases and controls), aged 40-89, with a minimum of 24 months of continuous health plan enrollment (Medicare Advantage Prescription Drug [MAPD] and commercial plans), and identified between January 1, 2009, and December 31, 2012, using Humana's claims database. Stratified random sampling based on plan type (commercial or MAPD) and index year was performed to ensure that cases and controls had a similar distribution of these variables. Cases and controls were compared to identify demographic, clinical, and health care resource utilization (HCRU) characteristics associated with a COPD diagnosis. Stepwise logistic regression (SLR), neural networking, and decision trees were used to develop a series of models. The models were trained, validated, and tested on randomly partitioned subsets of the sample (Training, Validation, and Test data subsets). Measures used to evaluate and compare the models included area under the curve (AUC); index of the receiver operating characteristics (ROC) curve; sensitivity, specificity, positive predictive value (PPV); and negative predictive value (NPV). The optimal model was selected based on AUC index on the Test data subset. A total of 50,880 cases and 50,880 controls were included, with MAPD patients comprising 92% of the study population. Compared

An integrated in vitro and in vivo high throughput screen identifies treatment leads for ependymoma

PubMed Central

Atkinson, Jennifer M.; Shelat, Anang A.; Carcaboso, Angel Montero; Kranenburg, Tanya A.; Arnold, Alexander; Boulos, Nidal; Wright, Karen; Johnson, Robert A.; Poppleton, Helen; Mohankumar, Kumarasamypet M.; Feau, Clementine; Phoenix, Timothy; Gibson, Paul; Zhu, Liqin; Tong, Yiai; Eden, Chris; Ellison, David W.; Priebe, Waldemar; Koul, Dimpy; Yung, W. K. Alfred; Gajjar, Amar; Stewart, Clinton F.; Guy, R. Kip; Gilbertson, Richard J.

2011-01-01

Summary Using a mouse model of ependymoma—a chemoresistant brain tumor—we combined multi-cell high-throughput screening (HTS), kinome-wide binding assays, and in vivo efficacy studies, to identify potential treatments with predicted toxicity against neural stem cells (NSC). We identified kinases within the insulin signaling pathway and centrosome cycle as regulators of ependymoma cell proliferation, and their corresponding inhibitors as potential therapies. FDA approved drugs not currently used to treat ependymoma were also identified that posses selective toxicity against ependymoma cells relative to normal NSCs both in vitro and in vivo e.g., 5-fluoruracil. Our comprehensive approach advances understanding of the biology and treatment of ependymoma including the discovery of several treatment leads for immediate clinical translation. PMID:21907928

Predictive Accuracy of Sweep Frequency Impedance Technology in Identifying Conductive Conditions in Newborns.

PubMed

Aithal, Venkatesh; Kei, Joseph; Driscoll, Carlie; Murakoshi, Michio; Wada, Hiroshi

2018-02-01

Diagnosing conductive conditions in newborns is challenging for both audiologists and otolaryngologists. Although high-frequency tympanometry (HFT), acoustic stapedial reflex tests, and wideband absorbance measures are useful diagnostic tools, there is performance measure variability in their detection of middle ear conditions. Additional diagnostic sensitivity and specificity measures gained through new technology such as sweep frequency impedance (SFI) measures may assist in the diagnosis of middle ear dysfunction in newborns. The purpose of this study was to determine the test performance of SFI to predict the status of the outer and middle ear in newborns against commonly used reference standards. Automated auditory brainstem response (AABR), HFT (1000 Hz), transient evoked otoacoustic emission (TEOAE), distortion product otoacoustic emission (DPOAE), and SFI tests were administered to the study sample. A total of 188 neonates (98 males and 90 females) with a mean gestational age of 39.4 weeks were included in the sample. Mean age at the time of testing was 44.4 hr. Diagnostic accuracy of SFI was assessed in terms of its ability to identify conductive conditions in neonates when compared with nine different reference standards (including four single tests [AABR, HFT, TEOAE, and DPOAE] and five test batteries [HFT + DPOAE, HFT + TEOAE, DPOAE + TEOAE, DPOAE + AABR, and TEOAE + AABR]), using receiver operating characteristic (ROC) analysis and traditional test performance measures such as sensitivity and specificity. The test performance of SFI against the test battery reference standard of HFT + DPOAE and single reference standard of HFT was high with an area under the ROC curve (AROC) of 0.87 and 0.82, respectively. Although the HFT + DPOAE test battery reference standard performed better than the HFT reference standard in predicting middle ear conductive conditions in neonates, the difference in AROC was not significant. Further analysis revealed that the

Application of comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry method to identify potential biomarkers of perinatal asphyxia in a non-human primate model.

PubMed

Beckstrom, Andrew C; Humston, Elizabeth M; Snyder, Laura R; Synovec, Robert E; Juul, Sandra E

2011-04-08

Perinatal asphyxia is a leading cause of brain injury in infants, occurring in 2-4 per 1000 live births. The clinical response to asphyxia is variable and difficult to predict with current diagnostic tests. Reliable biomarkers are needed to help predict the timing and severity of asphyxia, as well as response to treatment. Two-dimensional gas chromatography-time-of-flight-mass spectrometry (GC×GC-TOFMS) was used herein, in conjunction with chemometric data analysis approaches for metabolomic analysis in order to identify significant metabolites affected by birth asphyxia. Blood was drawn before and after 15 or 18 min of cord occlusion in a Macaca nemestrina model of perinatal asphyxia. Postnatal samples were drawn at 5 min of age (n=20 subjects). Metabolomic profiles of asphyxiated animals were compared to four controls delivered at comparable gestational age. Fifty metabolites with the greatest change pre- to post-asphyxia were identified and quantified. The metabolic profile of post-asphyxia samples showed marked variability compared to the pre-asphyxia samples. Fifteen of the 50 metabolites showed significant elevation in response to asphyxia, ten of which remained significant upon comparison to the control animals. This metabolomic analysis confirmed lactate and creatinine as markers of asphyxia and discovered new metabolites including succinic acid and malate (intermediates in the Krebs cycle) and arachidonic acid (a brain fatty acid and inflammatory marker) as potential biomarkers. GC×GC-TOFMS coupled with chemometric data analysis are useful tools to identify acute biomarkers of brain injury. Further study is needed to correlate these metabolites with severity of disease, and response to treatment. Copyright © 2011 Elsevier B.V. All rights reserved.

Application of Comprehensive Two-Dimensional Gas Chromatography with Time-of-Flight Mass Spectrometry Method to Identify Potential Biomarkers of Perinatal Asphyxia in a Non-human Primate Model

PubMed Central

Beckstrom, Andrew C.; Humston, Elizabeth M.; Snyder, Laura R.; Synovec, Robert E.; Juul, Sandra E.

2011-01-01

Perinatal asphyxia is a leading cause of brain injury in infants, occurring in 2–4 per 1000 live births. The clinical response to asphyxia is variable and difficult to predict with current diagnostic tests. Reliable biomarkers are needed to help predict the timing and severity of asphyxia, as well as response to treatment. Two-dimensional gas chromatography-time-of-flight-mass spectrometry (GC x GC-TOFMS) was used herein, in conjunction with chemometric data analysis approaches for metabolomic analysis in order to identify significant metabolites affected by birth asphyxia. Blood was drawn before and after 15 or 18 minutes of cord occlusion in a Macaca nemestrina model of perinatal asphyxia. Postnatal samples were drawn at 5 minutes of age (n=20 subjects). Metabolomic profiles of asphyxiated animals were compared to four controls delivered at comparable gestational age. Fifty metabolites with the greatest change pre- to post-asphyxia were identified and quantified. The metabolic profile of post-asphyxia samples showed marked variability compared to the pre-asphyxia samples. Fifteen of the 50 metabolites showed significant elevation in response to asphyxia, ten of which remained significant upon comparison to the control animals. This metabolomic analysis confirmed lactate and creatinine as markers of asphyxia and discovered new metabolites including succinic acid and malate (intermediates in the Krebs cycle) and arachidonic acid (a brain fatty acid and inflammatory marker) as potential biomarkers. GC × GC-TOFMS coupled with chemometric data analysis are useful tools to identify acute biomarkers of brain injury. Further study is needed to correlate these metabolites with severity of disease, and response to treatment. PMID:21353677

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.

Comparative study of Saccharomyces cerevisiae wine strains to identify potential marker genes correlated to desiccation stress tolerance.

PubMed

Capece, Angela; Votta, Sonia; Guaragnella, Nicoletta; Zambuto, Marianna; Romaniello, Rossana; Romano, Patrizia

2016-05-01

The most diffused formulation of starter for winemaking is active dry yeast (ADY). ADYs production process is essentially characterized by air-drying stress, a combination of several stresses, including thermal, hyperosmotic and oxidative and cell capacity to counteract such multiple stresses will determine its survival. The molecular mechanisms underlying cell stress response to desiccation have been mostly studied in laboratory and commercial yeast strains, but a growing interest is currently developing for indigenous yeast strains which represent a valuable and alternative source of genetic and molecular biodiversity to be exploited. In this work, a comparative study of different Saccharomyces cerevisiae indigenous wine strains, previously selected for their technological traits, has been carried out to identify potentially relevant genes involved in desiccation stress tolerance. Cell viability was evaluated along desiccation treatment and gene expression was analyzed by real-time PCR before and during the stress. Our data show that the observed differences in individual strain sensitivity to desiccation stress could be associated to specific gene expression over time. In particular, either the basal or the stress-induced mRNA levels of certain genes, such as HSP12, SSA3, TPS1, TPS2, CTT1 and SOD1, result tightly correlated to the strain survival advantage. This study provides a reliable and sensitive method to predict desiccation stress tolerance of indigenous wine yeast strains which could be preliminary to biotechnological applications. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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

New approach to predict photoallergic potentials of chemicals based on murine local lymph node assay.

PubMed

Maeda, Yosuke; Hirosaki, Haruka; Yamanaka, Hidenori; Takeyoshi, Masahiro

2018-05-23

Photoallergic dermatitis, caused by pharmaceuticals and other consumer products, is a very important issue in human health. However, S10 guidelines of the International Conference on Harmonization do not recommend the existing prediction methods for photoallergy because of their low predictability in human cases. We applied local lymph node assay (LLNA), a reliable, quantitative skin sensitization prediction test, to develop a new photoallergy prediction method. This method involves a three-step approach: (1) ultraviolet (UV) absorption analysis; (2) determination of no observed adverse effect level for skin phototoxicity based on LLNA; and (3) photoallergy evaluation based on LLNA. Photoallergic potential of chemicals was evaluated by comparing lymph node cell proliferation among groups treated with chemicals with minimal effect levels of skin sensitization and skin phototoxicity under UV irradiation (UV+) or non-UV irradiation (UV-). A case showing significant difference (P < .05) in lymph node cell proliferation rates between UV- and UV+ groups was considered positive for photoallergic reaction. After testing 13 chemicals, seven human photoallergens tested positive and the other six, with no evidence of causing photoallergic dermatitis or UV absorption, tested negative. Among these chemicals, both doxycycline hydrochloride and minocycline hydrochloride were tetracycline antibiotics with different photoallergic properties, and the new method clearly distinguished between the photoallergic properties of these chemicals. These findings suggested high predictability of our method; therefore, it is promising and effective in predicting human photoallergens. Copyright © 2018 John Wiley & Sons, Ltd.

Using Remote Sensing and High-Resolution Digital Elevation Models to Identify Potential Erosional Hotspots Along River Channels During High Discharge Storm Events

NASA Astrophysics Data System (ADS)

Orland, E. D.; Amidon, W. H.

2017-12-01

As global warming intensifies, large precipitation events and associated floods are becoming increasingly common. Channel adjustments during floods can occur by both erosion and deposition of sediment, often damaging infrastructure in the process. There is thus a need for predictive models that can help managers identify river reaches that are most prone to adjustment during storms. Because rivers in post-glacial landscapes often flow over a mixture of bedrock and alluvial substrates, the identification of bedrock vs. alluvial channel reaches is an important first step in predicting vulnerability to channel adjustment during flood events, especially because bedrock channels are unlikely to adjust significantly, even during floods. This study develops a semi-automated approach to predicting channel substrate using a high-resolution LiDAR-derived digital elevation model (DEM). The study area is the Middlebury River in Middlebury, VT-a well-studied watershed with a wide variety of channel substrates, including reaches with documented channel adjustments during recent flooding events. Multiple metrics were considered for reference—such as channel width and drainage area—but the study utilized channel slope as a key parameter for identifying morphological variations within the Middlebury River. Using data extracted from the DEM, a power law was fit to selected slope and drainage area values for each branch in order to model idealized slope-drainage area relationships, which were then compared with measured slope-drainage area relationships. Differences in measured slope minus predicted slope (called delta-slope) are shown to help predict river channel substrate. Compared with field observations, higher delta-slope values correlate with more stable, boulder rich channels or bedrock gorges; conversely the lowest delta-slope values correlate with flat, sediment rich alluvial channels. The delta-slope metric thus serves as a reliable first-order predictor of channel

Identifying Key Issues and Potential Solutions for Integrated Arrival, Departure, Surface Operations by Surveying Stakeholder Preferences

NASA Technical Reports Server (NTRS)

Aponso, Bimal; Coppenbarger, Richard A.; Jung, Yoon; Quon, Leighton; Lohr, Gary; O’Connor, Neil; Engelland, Shawn

2015-01-01

NASA's Aeronautics Research Mission Directorate (ARMD) collaborates with the FAA and industry to provide concepts and technologies that enhance the transition to the next-generation air-traffic management system (NextGen). To facilitate this collaboration, ARMD has a series of Airspace Technology Demonstration (ATD) sub-projects that develop, demonstrate, and transitions NASA technologies and concepts for implementation in the National Airspace System (NAS). The second of these sub-projects, ATD-2, is focused on the potential benefits to NAS stakeholders of integrated arrival, departure, surface (IADS) operations. To determine the project objectives and assess the benefits of a potential solution, NASA surveyed NAS stakeholders to understand the existing issues in arrival, departure, and surface operations, and the perceived benefits of better integrating these operations. NASA surveyed a broad cross-section of stakeholders representing the airlines, airports, air-navigation service providers, and industry providers of NAS tools. The survey indicated that improving the predictability of flight times (schedules) could improve efficiency in arrival, departure, and surface operations. Stakeholders also mentioned the need for better strategic and tactical information on traffic constraints as well as better information sharing and a coupled collaborative planning process that allows stakeholders to coordinate IADS operations. To assess the impact of a potential solution, NASA sketched an initial departure scheduling concept and assessed its viability by surveying a select group of stakeholders for a second time. The objective of the departure scheduler was to enable flights to move continuously from gate to cruise with minimal interruption in a busy metroplex airspace environment using strategic and tactical scheduling enhanced by collaborative planning between airlines and service providers. The stakeholders agreed that this departure concept could improve schedule

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.

USE OF qRTPCR TO IDENTIFY POTENTIAL BIOMARKERS OF BROMATE EXPOSURE IN F344 MALE RAT KIDNEYS

EPA Science Inventory

Potassium bromate (KBrO3) is a drinking water disinfection by-product that is nephrotoxic and carcinogenic. To identify potential biomarkers of carcinogenicity, male F344 rats were chronically exposed to a carcinogenic dose (400mg/l) of KBrO3 in their drinking water. Kidneys were...

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.

Predicting spatial distribution of postfire debris flows and potential consequences for native trout in headwater streams

USGS Publications Warehouse

Sedell, Edwin R; Gresswell, Bob; McMahon, Thomas E.

2015-01-01

Habitat fragmentation and degradation and invasion of nonnative species have restricted the distribution of native trout. Many trout populations are limited to headwater streams where negative effects of predicted climate change, including reduced stream flow and increased risk of catastrophic fires, may further jeopardize their persistence. Headwater streams in steep terrain are especially susceptible to disturbance associated with postfire debris flows, which have led to local extirpation of trout populations in some systems. We conducted a reach-scale spatial analysis of debris-flow risk among 11 high-elevation watersheds of the Colorado Rocky Mountains occupied by isolated populations of Colorado River Cutthroat Trout (Oncorhynchus clarkii pleuriticus). Stream reaches at high risk of disturbance by postfire debris flow were identified with the aid of a qualitative model based on 4 primary initiating and transport factors (hillslope gradient, flow accumulation pathways, channel gradient, and valley confinement). This model was coupled with a spatially continuous survey of trout distributions in these stream networks to assess the predicted extent of trout population disturbances related to debris flows. In the study systems, debris-flow potential was highest in the lower and middle reaches of most watersheds. Colorado River Cutthroat Trout occurred in areas of high postfire debris-flow risk, but they were never restricted to those areas. Postfire debris flows could extirpate trout from local reaches in these watersheds, but trout populations occupy refugia that should allow recolonization of interconnected, downstream reaches. Specific results of our study may not be universally applicable, but our risk assessment approach can be applied to assess postfire debris-flow risk for stream reaches in other watersheds.

Prospective assessment of a gene signature potentially predictive of clinical benefit in metastatic melanoma patients following MAGE-A3 immunotherapeutic (PREDICT).

PubMed

Saiag, P; Gutzmer, R; Ascierto, P A; Maio, M; Grob, J-J; Murawa, P; Dreno, B; Ross, M; Weber, J; Hauschild, A; Rutkowski, P; Testori, A; Levchenko, E; Enk, A; Misery, L; Vanden Abeele, C; Vojtek, I; Peeters, O; Brichard, V G; Therasse, P

2016-10-01

Genomic profiling of tumor tissue may aid in identifying predictive or prognostic gene signatures (GS) in some cancers. Retrospective gene expression profiling of melanoma and non-small-cell lung cancer led to the characterization of a GS associated with clinical benefit, including improved overall survival (OS), following immunization with the MAGE-A3 immunotherapeutic. The goal of the present study was to prospectively evaluate the predictive value of the previously characterized GS. An open-label prospective phase II trial ('PREDICT') in patients with MAGE-A3-positive unresectable stage IIIB-C/IV-M1a melanoma. Of 123 subjects who received the MAGE-A3 immunotherapeutic, 71 (58.7%) displayed the predictive GS (GS+). The 1-year OS rate was 83.1%/83.3% in the GS+/GS- populations. The rate of progression-free survival at 12 months was 5.8%/4.1% in GS+/GS- patients. The median time-to-treatment failure was 2.7/2.4 months (GS+/GS-). There was one complete response (GS-) and two partial responses (GS+). The MAGE-A3 immunotherapeutic was similarly immunogenic in both populations and had a clinically acceptable safety profile. Treatment of patients with MAGE-A3-positive unresectable stage IIIB-C/IV-M1a melanoma with the MAGE-A3 immunotherapeutic demonstrated an overall 1-year OS rate of 83.5%. GS- and GS+ patients had similar 1-year OS rates, indicating that in this study, GS was not predictive of outcome. Unexpectedly, the objective response rate was lower in this study than in other studies carried out in the same setting with the MAGE-A3 immunotherapeutic. Investigation of a GS to predict clinical benefit to adjuvant MAGE-A3 immunotherapeutic treatment is ongoing in another melanoma study.This study is registered at www.clinicatrials.gov NCT00942162. © The Author 2016. Published by Oxford University Press on behalf of the European Society for Medical Oncology.

Potential Use of Deep-ocean Bottom Temperatures Measured by NOAA's Operational DART Systems in Identifying Long-term Climate Trends

NASA Astrophysics Data System (ADS)

Eble, M. C.; Mungov, G.

2016-12-01

The National Oceanic and Atmospheric Administration holds more than 3200 months of ocean bottom temperature time series recorded by the network of Deep-ocean Assessment and Reporting of Tsunami (DART) developed for reporting bottom pressure in support of tsunami detection and warning. Measurements were made at more than 40 locations sited predominately within the Pacific Ocean, with a lesser number made at sites in the North Atlantic. Since 2003, time series data were collected at isolated locations and at those in close proximity to sites measuring other ocean and atmosphere parameters (e.g. TAO/TRITON), at depths ranging from approximately 1800 m to nearly 6000 m. A full 2300 months of temperature measurements were made by DART systems deployed at or below 4000 m. These time series offer the potential of narrowing a gap in the global ocean observing system (OceanObs09) by exposing long-term climate trends at more locations in the ocean deep then now available. The potential held by these data given specific limitations is the focus of this investigation. Limitations stem from the primary function of temperature being to correct pressure. The sole function of temperature has historically been to provide a correction to pressure so little attention has been paid to optimizing temperature measurements for long-term investigations. Temperature counts, like those of pressure, are converted to engineering units using calibration coefficients supplied by the manufacturer but temperatures are only coarsely calibrated. In addition, the response of each pressure transducer to ocean pressure varies by deployment and location. Time series of DART temperature frequency counts recorded during specific single deployments were processed and analyzed to identify consistent trends and explore methodologies that could be adopted in order to improve the utility (NOAA, 2014) of DART temperature measurements for climate studies. These data represent a vast amount of untapped

An event-related potential paradigm for identifying (rare negative) attitude stimuli that people intentionally misreport.

PubMed

Crites, Stephen L; Mojica, Andrew J; Corral, Guadalupe; Taylor, Jennifer H

2010-09-01

This experiment explored whether a late positive potential (LPP) of the event-related brain potential is useful for examining attitudes that people attempt to conceal. Participants identified a set of liked, neutral, and disliked people and viewed sequences consisting of either names or pictures of these people. Disliked people appeared rarely among liked people, and participants either: (1) always accurately reported their negative attitudes toward the people; (2) misreported negative attitudes as positive when they saw a picture of a disliked person; or (3) misreported negative attitudes as positive when they saw a name of a disliked person. Rare negative stimuli evoked a larger-amplitude LPP than frequent positive stimuli. Misreporting attitudes significantly reduced the amplitude difference between rare negative and frequent positive stimuli, though it remained significant.

Improving the Accuracy of a Heliocentric Potential (HCP) Prediction Model for the Aviation Radiation Dose

NASA Astrophysics Data System (ADS)

Hwang, Junga; Yoon, Kyoung-Won; Jo, Gyeongbok; Noh, Sung-Jun

2016-12-01

The space radiation dose over air routes including polar routes should be carefully considered, especially when space weather shows sudden disturbances such as coronal mass ejections (CMEs), flares, and accompanying solar energetic particle events. We recently established a heliocentric potential (HCP) prediction model for real-time operation of the CARI-6 and CARI-6M programs. Specifically, the HCP value is used as a critical input value in the CARI-6/6M programs, which estimate the aviation route dose based on the effective dose rate. The CARI-6/6M approach is the most widely used technique, and the programs can be obtained from the U.S. Federal Aviation Administration (FAA). However, HCP values are given at a one month delay on the FAA official webpage, which makes it difficult to obtain real-time information on the aviation route dose. In order to overcome this critical limitation regarding the time delay for space weather customers, we developed a HCP prediction model based on sunspot number variations (Hwang et al. 2015). In this paper, we focus on improvements to our HCP prediction model and update it with neutron monitoring data. We found that the most accurate method to derive the HCP value involves (1) real-time daily sunspot assessments, (2) predictions of the daily HCP by our prediction algorithm, and (3) calculations of the resultant daily effective dose rate. Additionally, we also derived the HCP prediction algorithm in this paper by using ground neutron counts. With the compensation stemming from the use of ground neutron count data, the newly developed HCP prediction model was improved.

Using Deep Learning for Compound Selectivity Prediction.

PubMed

Zhang, Ruisheng; Li, Juan; Lu, Jingjing; Hu, Rongjing; Yuan, Yongna; Zhao, Zhili

2016-01-01

Compound selectivity prediction plays an important role in identifying potential compounds that bind to the target of interest with high affinity. However, there is still short of efficient and accurate computational approaches to analyze and predict compound selectivity. In this paper, we propose two methods to improve the compound selectivity prediction. We employ an improved multitask learning method in Neural Networks (NNs), which not only incorporates both activity and selectivity for other targets, but also uses a probabilistic classifier with a logistic regression. We further improve the compound selectivity prediction by using the multitask learning method in Deep Belief Networks (DBNs) which can build a distributed representation model and improve the generalization of the shared tasks. In addition, we assign different weights to the auxiliary tasks that are related to the primary selectivity prediction task. In contrast to other related work, our methods greatly improve the accuracy of the compound selectivity prediction, in particular, using the multitask learning in DBNs with modified weights obtains the best performance.

12-Chemokine Gene Signature Identifies Lymph Node-like Structures in Melanoma: Potential for Patient Selection for Immunotherapy?

NASA Astrophysics Data System (ADS)

Messina, Jane L.; Fenstermacher, David A.; Eschrich, Steven; Qu, Xiaotao; Berglund, Anders E.; Lloyd, Mark C.; Schell, Michael J.; Sondak, Vernon K.; Weber, Jeffrey S.; Mulé, James J.

2012-10-01

We have interrogated a 12-chemokine gene expression signature (GES) on genomic arrays of 14,492 distinct solid tumors and show broad distribution across different histologies. We hypothesized that this 12-chemokine GES might accurately predict a unique intratumoral immune reaction in stage IV (non-locoregional) melanoma metastases. The 12-chemokine GES predicted the presence of unique, lymph node-like structures, containing CD20+ B cell follicles with prominent areas of CD3+ T cells (both CD4+ and CD8+ subsets). CD86+, but not FoxP3+, cells were present within these unique structures as well. The direct correlation between the 12-chemokine GES score and the presence of unique, lymph nodal structures was also associated with better overall survival of the subset of melanoma patients. The use of this novel 12-chemokine GES may reveal basic information on in situ mechanisms of the anti-tumor immune response, potentially leading to improvements in the identification and selection of melanoma patients most suitable for immunotherapy.

Identifying Factors that Most Strongly Predict Aircraft Reliability Behavior

DTIC Science & Technology

2013-06-01

time to perform a specific airlift mission or category of missions based on all pertinent operational and logistical factors.” ( Randall , 2004, p. 64...resources are contingent upon the demand and airfield environment. ( Randall , 2004) The challenge with researching and predicting MC rates is its...Departmental Publishing Office. http://www.e- publishing.af.mil/shared/media/epubs/AFDD3-17.pdf McClave JT, Benson PG, Sincich TS, (2011). Statistics for

Validation of pathological grading systems for predicting metastatic potential in pheochromocytoma and paraganglioma

PubMed Central

Koh, Jung-Min; Ahn, Seong Hee; Kim, Hyeonmok; Kim, Beom-Jun; Sung, Tae-Yon; Kim, Young Hoon; Hong, Suck Joon; Song, Dong Eun

2017-01-01

Purpose The Grading system for Adrenal Pheochromocytoma and Paraganglioma (GAPP) was proposed for predicting the metastatic potential of pheochromocytoma and paraganglioma to overcome the limitations of the Pheochromocytoma of the Adrenal Scaled Score (PASS). However, to date, no study validating the GAPP has been conducted, and previous studies did not include mutations in the succinate dehydrogenase type B (SDHB) gene in the score calculation. In this retrospective cohort study, we validated the prediction ability of GAPP and assessed whether it would be improved by inclusion of the loss of SDHB immunohistochemical staining. Methods We divided the tumors into non-metastatic and metastatic groups based on the presence of synchronous or metachronous metastases. The GAPP score and PASS at the initial operation were measured. Moreover, we combined some GAPP parameters with the immunohistochemical staining of SDHB to obtain a modified GAPP (M-GAPP) score. Results Metastasis occurred in 15/72 (20.8%) patients, with a mean follow-up of 43.5 months. Loss of SDHB staining was more frequent (P = 0.044) in the metastatic group. The GAPP score (P = 0.006), PASS (P = 0.003), and M-GAPP score (P<0.001) were all higher in the metastatic group. Twelve of 40 (30.0%) moderately or poorly differentiated tumors, as defined by the GAPP score, and 12/34 (35.3%) tumors with a PASS ≥4 were metastatic. Conversely, 10/19 (52.6%) tumors with an M-GAPP score ≥3 were metastatic. The area under the curve of the M-GAPP score (0.822) was significantly higher than that of the GAPP (0.728) (P = 0.012), but similar to that of the PASS (0.753) (P = 0.411). The GAPP (P = 0.032) and M-GAPP scores (P = 0.040), but not PASS (P = 0.200), negatively correlated with metastasis-free survival. Conclusion The GAPP was validated, and M-GAPP, a combination of some GAPP parameters and loss of SDHB staining, might be useful for the prediction of the metastatic potential of pheochromocytoma and paraganglioma

Identifying at-risk employees: A behavioral model for predicting potential insider threats

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

Greitzer, Frank L.; Kangas, Lars J.; Noonan, Christine F.

A psychosocial model was developed to assess an employee’s behavior associated with an increased risk of insider abuse. The model is based on case studies and research literature on factors/correlates associated with precursor behavioral manifestations of individuals committing insider crimes. In many of these crimes, managers and other coworkers observed that the offenders had exhibited signs of stress, disgruntlement, or other issues, but no alarms were raised. Barriers to using such psychosocial indicators include the inability to recognize the signs and the failure to record the behaviors so that they could be assessed by a person experienced in psychosocial evaluations.more » We have developed a model using a Bayesian belief network with the help of human resources staff, experienced in evaluating behaviors in staff. We conducted an experiment to assess its agreement with human resources and management professionals, with positive results. If implemented in an operational setting, the model would be part of a set of management tools for employee assessment that can raise an alarm about employees who pose higher insider threat risks. In separate work, we combine this psychosocial model’s assessment with computer workstation behavior to raise the efficacy of recognizing an insider crime in the making.« less

Structural insights into Cydia pomonella pheromone binding protein 2 mediated prediction of potentially active semiochemicals

NASA Astrophysics Data System (ADS)

Tian, Zhen; Liu, Jiyuan; Zhang, Yalin

2016-03-01

Given the advantages of behavioral disruption application in pest control and the damage of Cydia pomonella, due progresses have not been made in searching active semiochemicals for codling moth. In this research, 31 candidate semiochemicals were ranked for their binding potential to Cydia pomonella pheromone binding protein 2 (CpomPBP2) by simulated docking, and this sorted result was confirmed by competitive binding assay. This high predicting accuracy of virtual screening led to the construction of a rapid and viable method for semiochemicals searching. By reference to binding mode analyses, hydrogen bond and hydrophobic interaction were suggested to be two key factors in determining ligand affinity, so is the length of molecule chain. So it is concluded that semiochemicals of appropriate chain length with hydroxyl group or carbonyl group at one head tended to be favored by CpomPBP2. Residues involved in binding with each ligand were pointed out as well, which were verified by computational alanine scanning mutagenesis. Progress made in the present study helps establish an efficient method for predicting potentially active compounds and prepares for the application of high-throughput virtual screening in searching semiochemicals by taking insights into binding mode analyses.

Structural insights into Cydia pomonella pheromone binding protein 2 mediated prediction of potentially active semiochemicals

PubMed Central

Tian, Zhen; Liu, Jiyuan; Zhang, Yalin

2016-01-01

Given the advantages of behavioral disruption application in pest control and the damage of Cydia pomonella, due progresses have not been made in searching active semiochemicals for codling moth. In this research, 31 candidate semiochemicals were ranked for their binding potential to Cydia pomonella pheromone binding protein 2 (CpomPBP2) by simulated docking, and this sorted result was confirmed by competitive binding assay. This high predicting accuracy of virtual screening led to the construction of a rapid and viable method for semiochemicals searching. By reference to binding mode analyses, hydrogen bond and hydrophobic interaction were suggested to be two key factors in determining ligand affinity, so is the length of molecule chain. So it is concluded that semiochemicals of appropriate chain length with hydroxyl group or carbonyl group at one head tended to be favored by CpomPBP2. Residues involved in binding with each ligand were pointed out as well, which were verified by computational alanine scanning mutagenesis. Progress made in the present study helps establish an efficient method for predicting potentially active compounds and prepares for the application of high-throughput virtual screening in searching semiochemicals by taking insights into binding mode analyses. PMID:26928635

Potential Coastal Pumped Hydroelectric Energy Storage Locations Identified using GIS-based Topographic Analysis

NASA Astrophysics Data System (ADS)

Parsons, R.; Barnhart, C. J.; Benson, S. M.

2013-12-01

Large-scale electrical energy storage could accommodate variable, weather dependent energy resources such as wind and solar. Pumped hydroelectric energy storage (PHS) and compressed energy storage area (CAES) have life cycle energy and financial costs that are an order of magnitude lower than conventional electrochemical storage technologies. However PHS and CAES storage technologies require specific geologic conditions. Conventional PHS requires an upper and lower reservoir separated by at least 100 m of head, but no more than 10 km in horizontal distance. Conventional PHS also impacts fresh water supplies, riparian ecosystems, and hydrologic environments. A PHS facility that uses the ocean as the lower reservoir benefits from a smaller footprint, minimal freshwater impact, and the potential to be located near off shore wind resources and population centers. Although technologically nascent, today one coastal PHS facility exists. The storage potential for coastal PHS is unknown. Can coastal PHS play a significant role in augmenting future power grids with a high faction of renewable energy supply? In this study we employ GIS-based topographic analysis to quantify the coastal PHS potential of several geographic locations, including California, Chile and Peru. We developed automated techniques that seek local topographic minima in 90 m spatial resolution shuttle radar topography mission (SRTM) digital elevation models (DEM) that satisfy the following criteria conducive to PHS: within 10 km from the sea; minimum elevation 150 m; maximum elevation 1000 m. Preliminary results suggest the global potential for coastal PHS could be very significant. For example, in northern Chile we have identified over 60 locations that satisfy the above criteria. Two of these locations could store over 10 million cubic meters of water or several GWh of energy. We plan to report a global database of candidate coastal PHS locations and to estimate their energy storage capacity.

20170921 - An evaluation of selected (Q)SARs/expert systems for the Prediction of Skin Sensitization Potential (ASCCT)

EPA Science Inventory

Predictive testing to characterize substances for their skin sensitization potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximization Test (GPMT). In recent years, EU regulations have provided a strong incentiv...

Transcriptome analysis of cattle muscle identifies potential markers for skeletal muscle growth rate and major cell types.

PubMed

Guo, Bing; Greenwood, Paul L; Cafe, Linda M; Zhou, Guanghong; Zhang, Wangang; Dalrymple, Brian P

2015-03-13

This study aimed to identify markers for muscle growth rate and the different cellular contributors to cattle muscle and to link the muscle growth rate markers to specific cell types. The expression of two groups of genes in the longissimus muscle (LM) of 48 Brahman steers of similar age, significantly enriched for "cell cycle" and "ECM (extracellular matrix) organization" Gene Ontology (GO) terms was correlated with average daily gain/kg liveweight (ADG/kg) of the animals. However, expression of the same genes was only partly related to growth rate across a time course of postnatal LM development in two cattle genotypes, Piedmontese x Hereford (high muscling) and Wagyu x Hereford (high marbling). The deposition of intramuscular fat (IMF) altered the relationship between the expression of these genes and growth rate. K-means clustering across the development time course with a large set of genes (5,596) with similar expression profiles to the ECM genes was undertaken. The locations in the clusters of published markers of different cell types in muscle were identified and used to link clusters of genes to the cell type most likely to be expressing them. Overall correspondence between published cell type expression of markers and predicted major cell types of expression in cattle LM was high. However, some exceptions were identified: expression of SOX8 previously attributed to muscle satellite cells was correlated with angiogenesis. Analysis of the clusters and cell types suggested that the "cell cycle" and "ECM" signals were from the fibro/adipogenic lineage. Significant contributions to these signals from the muscle satellite cells, angiogenic cells and adipocytes themselves were not as strongly supported. Based on the clusters and cell type markers, sets of five genes predicted to be representative of fibro/adipogenic precursors (FAPs) and endothelial cells, and/or ECM remodelling and angiogenesis were identified. Gene sets and gene markers for the analysis of