Risk Factors and Biomarkers of Age-Related Macular Degeneration

PubMed Central

Lambert, Nathan G.; Singh, Malkit K.; ElShelmani, Hanan; Mansergh, Fiona C.; Wride, Michael A.; Padilla, Maximilian; Keegan, David; Hogg, Ruth E.; Ambati, Balamurali K.

2016-01-01

A biomarker can be a substance or structure measured in body parts, fluids or products that can affect or predict disease incidence. As age-related macular degeneration (AMD) is the leading cause of blindness in the developed world, much research and effort has been invested in the identification of different biomarkers to predict disease incidence, identify at risk individuals, elucidate causative pathophysiological etiologies, guide screening, monitoring and treatment parameters, and predict disease outcomes. To date, a host of genetic, environmental, proteomic, and cellular targets have been identified as both risk factors and potential biomarkers for AMD. Despite this, their use has been confined to research settings and has not yet crossed into the clinical arena. A greater understanding of these factors and their use as potential biomarkers for AMD can guide future research and clinical practice. This article will discuss known risk factors and novel, potential biomarkers of AMD in addition to their application in both academic and clinical settings. PMID:27156982

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 identified and functionally verified. This ensemble classifier is an accurate tool that can be used to rank long non-protein coding RNA predictions for use in conjunction with gene expression studies. Selection of plant transcripts with a high potential for regulatory roles as long non-protein coding RNAs will advance research in the elucidation of long non-protein coding RNA function.

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

PubMed

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

2014-06-07

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

Preference assessments in the zoo: Keeper and staff predictions of enrichment preferences across species.

PubMed

Mehrkam, Lindsay R; Dorey, Nicole R

2015-01-01

Environmental enrichment is widely used in the management of zoo animals, and is an essential strategy for increasing the behavioral welfare of these populations. It may be difficult, however, to identify potentially effective enrichment strategies that are also cost-effective and readily available. An animal's preference for a potential enrichment item may be a reliable predictor of whether that individual will reliably interact with that item, and subsequently enable staff to evaluate the effects of that enrichment strategy. The aim of the present study was to assess the utility of preference assessments for identifying potential enrichment items across six different species--each representing a different taxonomic group. In addition, we evaluated the agreement between zoo personnel's predictions of animals' enrichment preferences and stimuli selected via a preference assessment. Five out of six species (nine out of 11 individuals) exhibited clear, systematic preferences for specific stimuli. Similarities in enrichment preferences were observed among all individuals of primates, whereas individuals within ungulate and avian species displayed individual differences in enrichment preferences. Overall, zoo personnel, regardless of experience level, were significantly more accurate at predicting least-preferred stimuli than most-preferred stimuli across species, and tended to make the same predictions for all individuals within a species. Preference assessments may therefore be a useful, efficient husbandry strategy for identifying viable enrichment items at both the individual and species levels. © 2015 Wiley Periodicals, Inc.

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...

A Python Analytical Pipeline to Identify Prohormone Precursors and Predict Prohormone Cleavage Sites

PubMed Central

Southey, Bruce R.; Sweedler, Jonathan V.; Rodriguez-Zas, Sandra L.

2008-01-01

Neuropeptides and hormones are signaling molecules that support cell–cell communication in the central nervous system. Experimentally characterizing neuropeptides requires significant efforts because of the complex and variable processing of prohormone precursor proteins into neuropeptides and hormones. We demonstrate the power and flexibility of the Python language to develop components of an bioinformatic analytical pipeline to identify precursors from genomic data and to predict cleavage as these precursors are en route to the final bioactive peptides. We identified 75 precursors in the rhesus genome, predicted cleavage sites using support vector machines and compared the rhesus predictions to putative assignments based on homology to human sequences. The correct classification rate of cleavage using the support vector machines was over 97% for both human and rhesus data sets. The functionality of Python has been important to develop and maintain NeuroPred (http://neuroproteomics.scs.uiuc.edu/neuropred.html), a user-centered web application for the neuroscience community that provides cleavage site prediction from a wide range of models, precision and accuracy statistics, post-translational modifications, and the molecular mass of potential peptides. The combined results illustrate the suitability of the Python language to implement an all-inclusive bioinformatics approach to predict neuropeptides that encompasses a large number of interdependent steps, from scanning genomes for precursor genes to identification of potential bioactive neuropeptides. PMID:19169350

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

PubMed Central

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

2016-01-01

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

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

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

PubMed Central

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

2017-01-01

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

Impacts of climate change and renewable energy development on habitat of an endemic squirrel, Xerospermophilus mohavensis, in the Mojave Desert, USA

USGS Publications Warehouse

Inman, Richard D.; Esque, Todd C.; Nussear, Kenneth E.; Leitner, Philip; Matocq, Marjorie D.; Weisberg, Peter J.; Dilts, Thomas E.

2016-01-01

Predicting changes in species distributions under a changing climate is becoming widespread with the use of species distribution models (SDMs). The resulting predictions of future potential habitat can be cast in light of planned land use changes, such as urban expansion and energy development to identify areas with potential conflict. However, SDMs rarely incorporate an understanding of dispersal capacity, and therefore assume unlimited dispersal in potential range shifts under uncertain climate futures. We use SDMs to predict future distributions of the Mojave ground squirrel, Xerospermophilus mohavensis Merriam, and incorporate partial dispersal models informed by field data on juvenile dispersal to assess projected impact of climate change and energy development on future distributions of X. mohavensis. Our models predict loss of extant habitat, but also concurrent gains of new habitat under two scenarios of future climate change. Under the B1 emissions scenario- a storyline describing a convergent world with emphasis on curbing greenhouse gas emissions- our models predicted losses of up to 64% of extant habitat by 2080, while under the increased greenhouse gas emissions of the A2 scenario, we suggest losses of 56%. New potential habitat may become available to X. mohavensis, thereby offsetting as much as 6330 km2 (50%) of the current habitat lost. Habitat lost due to planned energy development was marginal compared to habitat lost from changing climates, but disproportionately affected current habitat. Future areas of overlap in potential habitat between the two climate change scenarios are identified and discussed in context of proposed energy development.

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/

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...

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.

Metrics for Diagnosing Undersampling in Monte Carlo Tally Estimates

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

Perfetti, Christopher M.; Rearden, Bradley T.

This study explored the potential of using Markov chain convergence diagnostics to predict the prevalence and magnitude of biases due to undersampling in Monte Carlo eigenvalue and flux tally estimates. Five metrics were applied to two models of pressurized water reactor fuel assemblies and their potential for identifying undersampling biases was evaluated by comparing the calculated test metrics with known biases in the tallies. Three of the five undersampling metrics showed the potential to accurately predict the behavior of undersampling biases in the responses examined in this study.

Temporal effects in trend prediction: identifying the most popular nodes in the future.

PubMed

Zhou, Yanbo; Zeng, An; Wang, Wei-Hong

2015-01-01

Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes' recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail.

Temporal Effects in Trend Prediction: Identifying the Most Popular Nodes in the Future

PubMed Central

Zhou, Yanbo; Zeng, An; Wang, Wei-Hong

2015-01-01

Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes’ recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail. PMID:25806810

A Systematic Evaluation of Analogs for the Read-across Prediction of Estrogenicity (SOT)

EPA Science Inventory

Read-across is a data gap filling technique widely used within category and analog approaches to predict a biological property for a target data-poor chemical using known information from similar (source analog) chemical(s). Potential source analogs are typically identified base...

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

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

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.

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

PubMed

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

2016-03-10

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

A Systematic Evaluation of Analogs and Automated Read-across Prediction of Estrogenicity (QSAR2016)

EPA Science Inventory

Read-across is a data gap filling technique widely used within category and analog approaches to predict a biological property for a data-poor (target) chemical using known information from similar (source analog) chemical(s). Potential source analogs are typically identified ba...

Predicting exotic earthworm distribution in the northern Great Lakes region

Treesearch

Lindsey M. Shartell; Erik A. Lilleskov; Andrew J. Storer

2013-01-01

Identifying influences of earthworm invasion and distribution in the northern Great Lakes is an important step in predicting the potential extent and impact of earthworms across the region. The occurrence of earthworm signs, indicating presence in general, and middens, indicating presence of Lumbricus terrestris exclusively, in the Huron Mountains...

The ToxCast Pathway Database for Identifying Toxicity Signatures and Potential Modes of Action from Chemical Screening Data

EPA Science Inventory

The U.S. Environmental Protection Agency (EPA), through its ToxCast program, is developing predictive toxicity approaches that will use in vitro high-throughput screening (HTS), high-content screening (HCS) and toxicogenomic data to predict in vivo toxicity phenotypes. There are ...

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

PubMed

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

2009-11-01

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

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.

Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements.

PubMed

Lan, Hui; Carson, Rachel; Provart, Nicholas J; Bonner, Anthony J

2007-09-21

Arabidopsis thaliana is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these genes is currently unknown. The purpose of this study is to infer gene function in Arabidopsis using machine-learning algorithms applied to large-scale gene expression data sets, with the goal of identifying genes that are potentially involved in plant response to abiotic stress. Using in house and publicly available data, we assembled a large set of gene expression measurements for A. thaliana. Using those genes of known function, we first evaluated and compared the ability of basic machine-learning algorithms to predict which genes respond to stress. Predictive accuracy was measured using ROC50 and precision curves derived through cross validation. To improve accuracy, we developed a method for combining these classifiers using a weighted-voting scheme. The combined classifier was then trained on genes of known function and applied to genes of unknown function, identifying genes that potentially respond to stress. Visual evidence corroborating the predictions was obtained using electronic Northern analysis. Three of the predicted genes were chosen for biological validation. Gene knockout experiments confirmed that all three are involved in a variety of stress responses. The biological analysis of one of these genes (At1g16850) is presented here, where it is shown to be necessary for the normal response to temperature and NaCl. Supervised learning methods applied to large-scale gene expression measurements can be used to predict gene function. However, the ability of basic learning methods to predict stress response varies widely and depends heavily on how much dimensionality reduction is used. Our method of combining classifiers can improve the accuracy of such predictions - in this case, predictions of genes involved in stress response in plants - and it effectively chooses the appropriate amount of dimensionality reduction automatically. The method provides a useful means of identifying genes in A. thaliana that potentially respond to stress, and we expect it would be useful in other organisms and for other gene functions.

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

PubMed

Shateri, Hamed; Cronin, Duane S

2015-01-01

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

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.

Proteins associated with EGFR-TKIs resistance in patients with non-small cell lung cancer revealed by mass spectrometry.

PubMed

Yang, Shaoyu; Chen, Xueqin; Pan, Yuelong; Yu, Jiekai; Li, Xin; Ma, Shenglin

2016-11-01

The present study aimed to identify potential serum biomarkers for predicting the clinical outcomes of patients with advanced non-small cell lung cancer (NSCLC) treated with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR‑TKIs). A total of 61 samples were collected and analyzed using the integrated approach of magnetic bead‑based weak cation exchange chromatography and matrix‑assisted laser desorption/ionization‑time of flight‑mass spectrometry. The Zhejiang University Protein Chip Data Analysis system was used to identify the protein spectra of patients that are resistant and sensitive to EGFR‑TKIs. Furthermore, a support vector machine was used to construct a predictive model with high accuracy. The model was trained using 46 samples and tested with the remaining 15 samples. In addition, the ExPASy Bioinformatics Resource Portal was used to search potential candidate proteins for peaks in the predictive model. Seven mass/charge (m/z) peaks at 3,264, 9,156, 9,172, 3,964, 9,451, 4,295 and 3,983 Da, were identified as significantly different peaks between the EGFR‑TKIs sensitive and resistant groups. A predictive model was generated with three protein peaks at 3,264, 9,451 and 4,295 Da (m/z). This three‑peak model was capable of distinguishing EGFR‑TKIs resistant patients from sensitive patients with a specificity of 80% and a sensitivity of 80.77%. Furthermore, in a blind test, this model exhibited a high specificity (80%) and a high sensitivity (90%). Apelin, TYRO protein tyrosine kinase‑binding protein and big endothelin‑1 may be potential candidates for the proteins identified with an m/z of 3,264, 9,451 and 4,295 Da, respectively. The predictive model used in the present study may provide an improved understanding of the pathogenesis of NSCLC, and may provide insights for the development of TKI treatment plans tailored to specific patients.

Visual but not motor processes predict simple visuomotor reaction time of badminton players.

PubMed

Hülsdünker, Thorben; Strüder, Heiko K; Mierau, Andreas

2018-03-01

The athlete's brain exhibits significant functional adaptations that facilitate visuomotor reaction performance. However, it is currently unclear if the same neurophysiological processes that differentiate athletes from non-athletes also determine performance within a homogeneous group of athletes. This information can provide valuable help for athletes and coaches aiming to optimize existing training regimes. Therefore, this study aimed to identify the neurophysiological correlates of visuomotor reaction performance in a group of skilled athletes. In 36 skilled badminton athletes, electroencephalography (EEG) was used to investigate pattern reversal and motion onset visual-evoked potentials (VEPs) as well as visuomotor reaction time (VMRT) during a simple reaction task. Stimulus-locked and response-locked event-related potentials (ERPs) in visual and motor regions as well as the onset of muscle activation (EMG onset) were determined. Correlation and multiple regression analyses identified the neurophysiological parameters predicting EMG onset and VMRT. For pattern reversal stimuli, the P100 latency and age best predicted EMG onset (r = 0.43; p = .003) and VMRT (r = 0.62; p = .001). In the motion onset experiment, EMG onset (r = 0.80; p < .001) and VMRT (r = 0.78; p < .001) were predicted by N2 latency and age. In both conditions, cortical potentials in motor regions were not correlated with EMG onset or VMRT. It is concluded that previously identified neurophysiological parameters differentiating athletes from non-athletes do not necessarily determine performance within a homogeneous group of athletes. Specifically, the speed of visual perception/processing predicts EMG onset and VMRT in skilled badminton players while motor-related processes, although differentiating athletes from non-athletes, are not associated simple with visuomotor reaction performance.

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.

Identifying and exploiting genes that potentiate the evolution of antibiotic resistance.

PubMed

Gifford, Danna R; Furió, Victoria; Papkou, Andrei; Vogwill, Tom; Oliver, Antonio; MacLean, R Craig

2018-06-01

There is an urgent need to develop novel approaches for predicting and preventing the evolution of antibiotic resistance. Here, we show that the ability to evolve de novo resistance to a clinically important β-lactam antibiotic, ceftazidime, varies drastically across the genus Pseudomonas. This variation arises because strains possessing the ampR global transcriptional regulator evolve resistance at a high rate. This does not arise because of mutations in ampR. Instead, this regulator potentiates evolution by allowing mutations in conserved peptidoglycan biosynthesis genes to induce high levels of β-lactamase expression. Crucially, blocking this evolutionary pathway by co-administering ceftazidime with the β-lactamase inhibitor avibactam can be used to eliminate pathogenic P. aeruginosa populations before they can evolve resistance. In summary, our study shows that identifying potentiator genes that act as evolutionary catalysts can be used to both predict and prevent the evolution of antibiotic resistance.

The Perfect Burrow, but for What? Identifying Local Habitat Conditions Promoting the Presence of the Host and Vector Species in the Kazakh Plague System

PubMed Central

Wilschut, Liesbeth; Addink, Elisabeth; Ageyev, Vladimir; Yeszhanov, Aidyn; Sapozhnikov, Valerij; Belayev, Alexander; Davydova, Tania; Eagle, Sally; Begon, Mike

2015-01-01

Introduction The wildlife plague system in the Pre-Balkhash desert of Kazakhstan has been a subject of study for many years. Much progress has been made in generating a method of predicting outbreaks of the disease (infection by the gram negative bacterium Yersinia pestis) but existing methods are not yet accurate enough to inform public health planning. The present study aimed to identify characteristics of individual mammalian host (Rhombomys opimus) burrows related to and potentially predictive of the presence of R.opimus and the dominant flea vectors (Xenopsylla spp.). Methods Over four seasons, burrow characteristics, their current occupancy status, and flea and tick burden of the occupants were recorded in the field. A second data set was generated of long term occupancy trends by recording the occupancy status of specific burrows over multiple occasions. Generalised linear mixed models were constructed to identify potential burrow properties predictive of either occupancy or flea burden. Results At the burrow level, it was identified that a burrow being occupied by Rhombomys, and remaining occupied, were both related to the characteristics of the sediment in which the burrow was constructed. The flea burden of Rhombomys in a burrow was found to be related to the tick burden. Further larger scale properties were also identified as being related to both Rhombomys and flea presence, including latitudinal position and the season. Conclusions Therefore, in advancing our current predictions of plague in Kazakhstan, we must consider the landscape at this local level to increase our accuracy in predicting the dynamics of gerbil and flea populations. Furthermore this demonstrates that in other zoonotic systems, it may be useful to consider the distribution and location of suitable habitat for both host and vector species at this fine scale to accurately predict future epizootics. PMID:26325073

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.

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.

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

PubMed

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

2017-09-01

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

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

PubMed Central

2014-01-01

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

Conformational B-cell epitopes prediction from sequences using cost-sensitive ensemble classifiers and spatial clustering.

PubMed

Zhang, Jian; Zhao, Xiaowei; Sun, Pingping; Gao, Bo; Ma, Zhiqiang

2014-01-01

B-cell epitopes are regions of the antigen surface which can be recognized by certain antibodies and elicit the immune response. Identification of epitopes for a given antigen chain finds vital applications in vaccine and drug research. Experimental prediction of B-cell epitopes is time-consuming and resource intensive, which may benefit from the computational approaches to identify B-cell epitopes. In this paper, a novel cost-sensitive ensemble algorithm is proposed for predicting the antigenic determinant residues and then a spatial clustering algorithm is adopted to identify the potential epitopes. Firstly, we explore various discriminative features from primary sequences. Secondly, cost-sensitive ensemble scheme is introduced to deal with imbalanced learning problem. Thirdly, we adopt spatial algorithm to tell which residues may potentially form the epitopes. Based on the strategies mentioned above, a new predictor, called CBEP (conformational B-cell epitopes prediction), is proposed in this study. CBEP achieves good prediction performance with the mean AUC scores (AUCs) of 0.721 and 0.703 on two benchmark datasets (bound and unbound) using the leave-one-out cross-validation (LOOCV). When compared with previous prediction tools, CBEP produces higher sensitivity and comparable specificity values. A web server named CBEP which implements the proposed method is available for academic use.

How ecology shapes exploitation: a framework to predict the behavioural response of human and animal foragers along exploration-exploitation trade-offs.

PubMed

Monk, Christopher T; Barbier, Matthieu; Romanczuk, Pawel; Watson, James R; Alós, Josep; Nakayama, Shinnosuke; Rubenstein, Daniel I; Levin, Simon A; Arlinghaus, Robert

2018-06-01

Understanding how humans and other animals behave in response to changes in their environments is vital for predicting population dynamics and the trajectory of coupled social-ecological systems. Here, we present a novel framework for identifying emergent social behaviours in foragers (including humans engaged in fishing or hunting) in predator-prey contexts based on the exploration difficulty and exploitation potential of a renewable natural resource. A qualitative framework is introduced that predicts when foragers should behave territorially, search collectively, act independently or switch among these states. To validate it, we derived quantitative predictions from two models of different structure: a generic mathematical model, and a lattice-based evolutionary model emphasising exploitation and exclusion costs. These models independently identified that the exploration difficulty and exploitation potential of the natural resource controls the social behaviour of resource exploiters. Our theoretical predictions were finally compared to a diverse set of empirical cases focusing on fisheries and aquatic organisms across a range of taxa, substantiating the framework's predictions. Understanding social behaviour for given social-ecological characteristics has important implications, particularly for the design of governance structures and regulations to move exploited systems, such as fisheries, towards sustainability. Our framework provides concrete steps in this direction. © 2018 John Wiley & Sons Ltd/CNRS.

Towards a general framework for predicting threat status of data-deficient species from phylogenetic, spatial and environmental information.

PubMed

Jetz, Walter; Freckleton, Robert P

2015-02-19

In taxon-wide assessments of threat status many species remain not included owing to lack of data. Here, we present a novel spatial-phylogenetic statistical framework that uses a small set of readily available or derivable characteristics, including phylogenetically imputed body mass and remotely sensed human encroachment, to provide initial baseline predictions of threat status for data-deficient species. Applied to assessed mammal species worldwide, the approach effectively identifies threatened species and predicts the geographical variation in threat. For the 483 data-deficient species, the models predict highly elevated threat, with 69% 'at-risk' species in this set, compared with 22% among assessed species. This results in 331 additional potentially threatened mammals, with elevated conservation importance in rodents, bats and shrews, and countries like Colombia, Sulawesi and the Philippines. These findings demonstrate the future potential for combining phylogenies and remotely sensed data with species distributions to identify species and regions of conservation concern. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

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

PubMed Central

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

2015-01-01

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

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

PubMed

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

2015-09-28

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

Co-acting gene networks predict TRAIL responsiveness of tumour cells with high accuracy.

PubMed

O'Reilly, Paul; Ortutay, Csaba; Gernon, Grainne; O'Connell, Enda; Seoighe, Cathal; Boyce, Susan; Serrano, Luis; Szegezdi, Eva

2014-12-19

Identification of differentially expressed genes from transcriptomic studies is one of the most common mechanisms to identify tumor biomarkers. This approach however is not well suited to identify interaction between genes whose protein products potentially influence each other, which limits its power to identify molecular wiring of tumour cells dictating response to a drug. Due to the fact that signal transduction pathways are not linear and highly interlinked, the biological response they drive may be better described by the relative amount of their components and their functional relationships than by their individual, absolute expression. Gene expression microarray data for 109 tumor cell lines with known sensitivity to the death ligand cytokine tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) was used to identify genes with potential functional relationships determining responsiveness to TRAIL-induced apoptosis. The machine learning technique Random Forest in the statistical environment "R" with backward elimination was used to identify the key predictors of TRAIL sensitivity and differentially expressed genes were identified using the software GeneSpring. Gene co-regulation and statistical interaction was assessed with q-order partial correlation analysis and non-rejection rate. Biological (functional) interactions amongst the co-acting genes were studied with Ingenuity network analysis. Prediction accuracy was assessed by calculating the area under the receiver operator curve using an independent dataset. We show that the gene panel identified could predict TRAIL-sensitivity with a very high degree of sensitivity and specificity (AUC=0·84). The genes in the panel are co-regulated and at least 40% of them functionally interact in signal transduction pathways that regulate cell death and cell survival, cellular differentiation and morphogenesis. Importantly, only 12% of the TRAIL-predictor genes were differentially expressed highlighting the importance of functional interactions in predicting the biological response. The advantage of co-acting gene clusters is that this analysis does not depend on differential expression and is able to incorporate direct- and indirect gene interactions as well as tissue- and cell-specific characteristics. This approach (1) identified a descriptor of TRAIL sensitivity which performs significantly better as a predictor of TRAIL sensitivity than any previously reported gene signatures, (2) identified potential novel regulators of TRAIL-responsiveness and (3) provided a systematic view highlighting fundamental differences between the molecular wiring of sensitive and resistant cell types.

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

PubMed

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

2016-01-01

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

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

PubMed Central

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

2016-01-01

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

Prediction of Safety Margin and Optimization of Dosing Protocol for a Novel Antibiotic using Quantitative Systems Pharmacology Modeling.

PubMed

Woodhead, Jeffrey L; Paech, Franziska; Maurer, Martina; Engelhardt, Marc; Schmitt-Hoffmann, Anne H; Spickermann, Jochen; Messner, Simon; Wind, Mathias; Witschi, Anne-Therese; Krähenbühl, Stephan; Siler, Scott Q; Watkins, Paul B; Howell, Brett A

2018-06-07

Elevations of liver enzymes have been observed in clinical trials with BAL30072, a novel antibiotic. In vitro assays have identified potential mechanisms for the observed hepatotoxicity, including electron transport chain (ETC) inhibition and reactive oxygen species (ROS) generation. DILIsym, a quantitative systems pharmacology (QSP) model of drug-induced liver injury, has been used to predict the likelihood that each mechanism explains the observed toxicity. DILIsym was also used to predict the safety margin for a novel BAL30072 dosing scheme; it was predicted to be low. DILIsym was then used to recommend potential modifications to this dosing scheme; weight-adjusted dosing and a requirement to assay plasma alanine aminotransferase (ALT) daily and stop dosing as soon as ALT increases were observed improved the predicted safety margin of BAL30072 and decreased the predicted likelihood of severe injury. This research demonstrates a potential application for QSP modeling in improving the safety profile of candidate drugs. © 2018 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Value of supervised learning events in predicting doctors in difficulty.

PubMed

Patel, Mumtaz; Agius, Steven; Wilkinson, Jack; Patel, Leena; Baker, Paul

2016-07-01

In the UK, supervised learning events (SLE) replaced traditional workplace-based assessments for foundation-year trainees in 2012. A key element of SLEs was to incorporate trainee reflection and assessor feedback in order to drive learning and identify training issues early. Few studies, however, have investigated the value of SLEs in predicting doctors in difficulty. This study aimed to identify principles that would inform understanding about how and why SLEs work or not in identifying doctors in difficulty (DiD). A retrospective case-control study of North West Foundation School trainees' electronic portfolios was conducted. Cases comprised all known DiD. Controls were randomly selected from the same cohort. Free-text supervisor comments from each SLE were assessed for the four domains defined in the General Medical Council's Good Medical Practice Guidelines and each scored blindly for level of concern using a three-point ordinal scale. Cumulative scores for each SLE were then analysed quantitatively for their predictive value of actual DiD. A qualitative thematic analysis was also conducted. The prevalence of DiD in this sample was 6.5%. Receiver operator characteristic curve analysis showed that Team Assessment of Behaviour (TAB) was the only SLE strongly predictive of actual DiD status. The Educational Supervisor Report (ESR) was also strongly predictive of DiD status. Fisher's test showed significant associations of TAB and ESR for both predicted and actual DiD status and also the health and performance subtypes. None of the other SLEs showed significant associations. Qualitative data analysis revealed inadequate completion and lack of constructive, particularly negative, feedback. This indicated that SLEs were not used to their full potential. TAB and the ESR are strongly predictive of DiD. However, SLEs are not being used to their full potential, and the quality of completion of reports on SLEs and feedback needs to be improved in order to better identify and manage DiD. © 2016 John Wiley & Sons Ltd.

Serum-circulating miRNAs predict neuroblastoma progression in mouse model of high-risk metastatic disease.

PubMed

Ramraj, Satish Kumar; Aravindan, Sheeja; Somasundaram, Dinesh Babu; Herman, Terence S; Natarajan, Mohan; Aravindan, Natarajan

2016-04-05

Circulating miRNAs have momentous clinical relevance as prognostic biomarkers and in the progression of solid tumors. Recognizing novel candidates of neuroblastoma-specific circulating miRNAs would allow us to identify potential prognostic biomarkers that could predict the switch from favorable to high-risk metastatic neuroblastoma (HR-NB). Utilizing mouse models of favorable and HR-NB and whole miRnome profiling, we identified high serum levels of 34 and low levels of 46 miRNAs in animals with HR-NB. Preferential sequence homology exclusion of mouse miRNAs identified 25 (11 increased; 14 decreased) human-specific prognostic marker candidates, of which, 21 were unique to HR-NB. miRNA QPCR validated miRnome profile. Target analysis defined the candidate miRNAs' signal transduction flow-through and demonstrated their converged roles in tumor progression. miRNA silencing studies verified the function of select miRNAs on the translation of at least 14 target proteins. Expressions of critical targets that correlate tumor progression in tissue of multifarious organs identify the orchestration of HR-NB. Significant (>10 fold) increase in serum levels of miR-381, miR-548h, and miR-580 identify them as potential prognostic markers for neuroblastoma progression. For the first time, we identified serum-circulating miRNAs that predict the switch from favorable to HR-NB and, further imply that these miRNAs could play a functional role in tumor progression.

Matrix factorization-based data fusion for the prediction of lncRNA-disease associations.

PubMed

Fu, Guangyuan; Wang, Jun; Domeniconi, Carlotta; Yu, Guoxian

2018-05-01

Long non-coding RNAs (lncRNAs) play crucial roles in complex disease diagnosis, prognosis, prevention and treatment, but only a small portion of lncRNA-disease associations have been experimentally verified. Various computational models have been proposed to identify lncRNA-disease associations by integrating heterogeneous data sources. However, existing models generally ignore the intrinsic structure of data sources or treat them as equally relevant, while they may not be. To accurately identify lncRNA-disease associations, we propose a Matrix Factorization based LncRNA-Disease Association prediction model (MFLDA in short). MFLDA decomposes data matrices of heterogeneous data sources into low-rank matrices via matrix tri-factorization to explore and exploit their intrinsic and shared structure. MFLDA can select and integrate the data sources by assigning different weights to them. An iterative solution is further introduced to simultaneously optimize the weights and low-rank matrices. Next, MFLDA uses the optimized low-rank matrices to reconstruct the lncRNA-disease association matrix and thus to identify potential associations. In 5-fold cross validation experiments to identify verified lncRNA-disease associations, MFLDA achieves an area under the receiver operating characteristic curve (AUC) of 0.7408, at least 3% higher than those given by state-of-the-art data fusion based computational models. An empirical study on identifying masked lncRNA-disease associations again shows that MFLDA can identify potential associations more accurately than competing models. A case study on identifying lncRNAs associated with breast, lung and stomach cancers show that 38 out of 45 (84%) associations predicted by MFLDA are supported by recent biomedical literature and further proves the capability of MFLDA in identifying novel lncRNA-disease associations. MFLDA is a general data fusion framework, and as such it can be adopted to predict associations between other biological entities. The source code for MFLDA is available at: http://mlda.swu.edu.cn/codes.php? name = MFLDA. gxyu@swu.edu.cn. Supplementary data are available at Bioinformatics online.

Strategies to design clinical studies to identify predictive biomarkers in cancer research.

PubMed

Perez-Gracia, Jose Luis; Sanmamed, Miguel F; Bosch, Ana; Patiño-Garcia, Ana; Schalper, Kurt A; Segura, Victor; Bellmunt, Joaquim; Tabernero, Josep; Sweeney, Christopher J; Choueiri, Toni K; Martín, Miguel; Fusco, Juan Pablo; Rodriguez-Ruiz, Maria Esperanza; Calvo, Alfonso; Prior, Celia; Paz-Ares, Luis; Pio, Ruben; Gonzalez-Billalabeitia, Enrique; Gonzalez Hernandez, Alvaro; Páez, David; Piulats, Jose María; Gurpide, Alfonso; Andueza, Mapi; de Velasco, Guillermo; Pazo, Roberto; Grande, Enrique; Nicolas, Pilar; Abad-Santos, Francisco; Garcia-Donas, Jesus; Castellano, Daniel; Pajares, María J; Suarez, Cristina; Colomer, Ramon; Montuenga, Luis M; Melero, Ignacio

2017-02-01

The discovery of reliable biomarkers to predict efficacy and toxicity of anticancer drugs remains one of the key challenges in cancer research. Despite its relevance, no efficient study designs to identify promising candidate biomarkers have been established. This has led to the proliferation of a myriad of exploratory studies using dissimilar strategies, most of which fail to identify any promising targets and are seldom validated. The lack of a proper methodology also determines that many anti-cancer drugs are developed below their potential, due to failure to identify predictive biomarkers. While some drugs will be systematically administered to many patients who will not benefit from them, leading to unnecessary toxicities and costs, others will never reach registration due to our inability to identify the specific patient population in which they are active. Despite these drawbacks, a limited number of outstanding predictive biomarkers have been successfully identified and validated, and have changed the standard practice of oncology. In this manuscript, a multidisciplinary panel reviews how those key biomarkers were identified and, based on those experiences, proposes a methodological framework-the DESIGN guidelines-to standardize the clinical design of biomarker identification studies and to develop future research in this pivotal field. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data.

PubMed

Braaksma, Machtelt; Martens-Uzunova, Elena S; Punt, Peter J; Schaap, Peter J

2010-10-19

The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation. A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified. We were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions.

An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data

PubMed Central

2010-01-01

Background The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation. Results A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified. Conclusions We were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions. PMID:20959013

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

PubMed

Saqr, Mohammed; Fors, Uno; Tedre, Matti

2017-07-01

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

Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger.

PubMed

Wright, James C; Sugden, Deana; Francis-McIntyre, Sue; Riba-Garcia, Isabel; Gaskell, Simon J; Grigoriev, Igor V; Baker, Scott E; Beynon, Robert J; Hubbard, Simon J

2009-02-04

Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide prima facie evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced Aspergillus niger fungal genome from the Joint Genome Institutes (JGI) and another predicted protein set from another A.niger sequence. Tandem mass spectra (MS/MS) were acquired from 1d gel electrophoresis bands and searched against all available gene models using Average Peptide Scoring (APS) and reverse database searching to produce confident identifications at an acceptable false discovery rate (FDR). 405 identified peptide sequences were mapped to 214 different A.niger genomic loci to which 4093 predicted gene models clustered, 2872 of which contained the mapped peptides. Interestingly, 13 (6%) of these loci either had no preferred predicted gene model or the genome annotators' chosen "best" model for that genomic locus was not found to be the most parsimonious match to the identified peptides. The peptides identified also boosted confidence in predicted gene structures spanning 54 introns from different gene models. This work highlights the potential of integrating experimental proteomics data into genomic annotation pipelines much as expressed sequence tag (EST) data has been. A comparison of the published genome from another strain of A.niger sequenced by DSM showed that a number of the gene models or proteins with proteomics evidence did not occur in both genomes, further highlighting the utility of the method.

Prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy: a systematic review and external validation study.

PubMed

Hilkens, N A; Algra, A; Greving, J P

2016-01-01

ESSENTIALS: Prediction models may help to identify patients at high risk of bleeding on antiplatelet therapy. We identified existing prediction models for bleeding and validated them in patients with cerebral ischemia. Five prediction models were identified, all of which had some methodological shortcomings. Performance in patients with cerebral ischemia was poor. Background Antiplatelet therapy is widely used in secondary prevention after a transient ischemic attack (TIA) or ischemic stroke. Bleeding is the main adverse effect of antiplatelet therapy and is potentially life threatening. Identification of patients at increased risk of bleeding may help target antiplatelet therapy. This study sought to identify existing prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy and evaluate their performance in patients with cerebral ischemia. We systematically searched PubMed and Embase for existing prediction models up to December 2014. The methodological quality of the included studies was assessed with the CHARMS checklist. Prediction models were externally validated in the European Stroke Prevention Study 2, comprising 6602 patients with a TIA or ischemic stroke. We assessed discrimination and calibration of included prediction models. Five prediction models were identified, of which two were developed in patients with previous cerebral ischemia. Three studies assessed major bleeding, one studied intracerebral hemorrhage and one gastrointestinal bleeding. None of the studies met all criteria of good quality. External validation showed poor discriminative performance, with c-statistics ranging from 0.53 to 0.64 and poor calibration. A limited number of prediction models is available that predict intracranial hemorrhage or major bleeding in patients on antiplatelet therapy. The methodological quality of the models varied, but was generally low. Predictive performance in patients with cerebral ischemia was poor. In order to reliably predict the risk of bleeding in patients with cerebral ischemia, development of a prediction model according to current methodological standards is needed. © 2015 International Society on Thrombosis and Haemostasis.

An application of statistics to comparative metagenomics

PubMed Central

Rodriguez-Brito, Beltran; Rohwer, Forest; Edwards, Robert A

2006-01-01

Background Metagenomics, sequence analyses of genomic DNA isolated directly from the environments, can be used to identify organisms and model community dynamics of a particular ecosystem. Metagenomics also has the potential to identify significantly different metabolic potential in different environments. Results Here we use a statistical method to compare curated subsystems, to predict the physiology, metabolism, and ecology from metagenomes. This approach can be used to identify those subsystems that are significantly different between metagenome sequences. Subsystems that were overrepresented in the Sargasso Sea and Acid Mine Drainage metagenome when compared to non-redundant databases were identified. Conclusion The methodology described herein applies statistics to the comparisons of metabolic potential in metagenomes. This analysis reveals those subsystems that are more, or less, represented in the different environments that are compared. These differences in metabolic potential lead to several testable hypotheses about physiology and metabolism of microbes from these ecosystems. PMID:16549025

An application of statistics to comparative metagenomics.

PubMed

Rodriguez-Brito, Beltran; Rohwer, Forest; Edwards, Robert A

2006-03-20

Metagenomics, sequence analyses of genomic DNA isolated directly from the environments, can be used to identify organisms and model community dynamics of a particular ecosystem. Metagenomics also has the potential to identify significantly different metabolic potential in different environments. Here we use a statistical method to compare curated subsystems, to predict the physiology, metabolism, and ecology from metagenomes. This approach can be used to identify those subsystems that are significantly different between metagenome sequences. Subsystems that were overrepresented in the Sargasso Sea and Acid Mine Drainage metagenome when compared to non-redundant databases were identified. The methodology described herein applies statistics to the comparisons of metabolic potential in metagenomes. This analysis reveals those subsystems that are more, or less, represented in the different environments that are compared. These differences in metabolic potential lead to several testable hypotheses about physiology and metabolism of microbes from these ecosystems.

Applying Sequential Analytic Methods to Self-Reported Information to Anticipate Care Needs.

PubMed

Bayliss, Elizabeth A; Powers, J David; Ellis, Jennifer L; Barrow, Jennifer C; Strobel, MaryJo; Beck, Arne

2016-01-01

Identifying care needs for newly enrolled or newly insured individuals is important under the Affordable Care Act. Systematically collected patient-reported information can potentially identify subgroups with specific care needs prior to service use. We conducted a retrospective cohort investigation of 6,047 individuals who completed a 10-question needs assessment upon initial enrollment in Kaiser Permanente Colorado (KPCO), a not-for-profit integrated delivery system, through the Colorado State Individual Exchange. We used responses from the Brief Health Questionnaire (BHQ), to develop a predictive model for cost for receiving care in the top 25 percent, then applied cluster analytic techniques to identify different high-cost subpopulations. Per-member, per-month cost was measured from 6 to 12 months following BHQ response. BHQ responses significantly predictive of high-cost care included self-reported health status, functional limitations, medication use, presence of 0-4 chronic conditions, self-reported emergency department (ED) use during the prior year, and lack of prior insurance. Age, gender, and deductible-based insurance product were also predictive. The largest possible range of predicted probabilities of being in the top 25 percent of cost was 3.5 percent to 96.4 percent. Within the top cost quartile, examples of potentially actionable clusters of patients included those with high morbidity, prior utilization, depression risk and financial constraints; those with high morbidity, previously uninsured individuals with few financial constraints; and relatively healthy, previously insured individuals with medication needs. Applying sequential predictive modeling and cluster analytic techniques to patient-reported information can identify subgroups of individuals within heterogeneous populations who may benefit from specific interventions to optimize initial care delivery.

Identification of consensus biomarkers for predicting non-genotoxic hepatocarcinogens

PubMed Central

Huang, Shan-Han; Tung, Chun-Wei

2017-01-01

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

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

ERIC Educational Resources Information Center

McCombs, Barbara L.; Dobrovolny, Jacqueline L.

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

Machine learning and hurdle models for improving regional predictions of stream water acid neutralizing capacity

Treesearch

Nicholas A. Povak; Paul F. Hessburg; Keith M. Reynolds; Timothy J. Sullivan; Todd C. McDonnell; R. Brion Salter

2013-01-01

In many industrialized regions of the world, atmospherically deposited sulfur derived from industrial, nonpoint air pollution sources reduces stream water quality and results in acidic conditions that threaten aquatic resources. Accurate maps of predicted stream water acidity are an essential aid to managers who must identify acid-sensitive streams, potentially...

A Brief Actuarial Assessment for the Prediction of Wife Assault Recidivism: The Ontario Domestic Assault Risk Assessment

ERIC Educational Resources Information Center

Hilton, N. Zoe; Harris, Grant T.; Rice, Marnie E.; Lang, Carol; Cormier, Catherine A.; Lines, Kathryn J.

2004-01-01

An actuarial assessment to predict male-to-female marital violence was constructed from a pool of potential predictors in a sample of 589 offenders identified in police records and followed up for an average of almost 5 years. Archival information in several domains (offender characteristics, domestic violence history, nondomestic criminal…

The Eighth Grade CRCT as a Predictive Measure of Student Success on the Ninth Grade EOCT

ERIC Educational Resources Information Center

Body, Matthew

2013-01-01

Student performance on high stakes testing in secondary education has contributed to the need for students' testing potential to be identified before entering high school. There is evidence to suggest that a greater understanding of how earlier test scores predict later test scores will help educators and school officials increase student…

The Earth Science Vision

NASA Technical Reports Server (NTRS)

Schoeberl, Mark; Rychekewkitsch, Michael; Andrucyk, Dennis; McConaughy, Gail; Meeson, Blanche; Hildebrand, Peter; Einaudi, Franco (Technical Monitor)

2000-01-01

NASA's Earth Science Enterprise's long range vision is to enable the development of a national proactive environmental predictive capability through targeted scientific research and technological innovation. Proactive environmental prediction means the prediction of environmental events and their secondary consequences. These consequences range from disasters and disease outbreak to improved food production and reduced transportation, energy and insurance costs. The economic advantage of this predictive capability will greatly outweigh the cost of development. Developing this predictive capability requires a greatly improved understanding of the earth system and the interaction of the various components of that system. It also requires a change in our approach to gathering data about the earth and a change in our current methodology in processing that data including its delivery to the customers. And, most importantly, it requires a renewed partnership between NASA and its sister agencies. We identify six application themes that summarize the potential of proactive environmental prediction. We also identify four technology themes that articulate our approach to implementing proactive environmental prediction.

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

PubMed Central

Isewon, Itunuoluwa; Aromolaran, Olufemi; Oladipupo, Olufunke

2018-01-01

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

Systems biology as a conceptual framework for research in family medicine; use in predicting response to influenza vaccination.

PubMed

Majnarić-Trtica, Ljiljana; Vitale, Branko

2011-10-01

To introduce systems biology as a conceptual framework for research in family medicine, based on empirical data from a case study on the prediction of influenza vaccination outcomes. This concept is primarily oriented towards planning preventive interventions and includes systematic data recording, a multi-step research protocol and predictive modelling. Factors known to affect responses to influenza vaccination include older age, past exposure to influenza viruses, and chronic diseases; however, constructing useful prediction models remains a challenge, because of the need to identify health parameters that are appropriate for general use in modelling patients' responses. The sample consisted of 93 patients aged 50-89 years (median 69), with multiple medical conditions, who were vaccinated against influenza. Literature searches identified potentially predictive health-related parameters, including age, gender, diagnoses of the main chronic ageing diseases, anthropometric measures, and haematological and biochemical tests. By applying data mining algorithms, patterns were identified in the data set. Candidate health parameters, selected in this way, were then combined with information on past influenza virus exposure to build the prediction model using logistic regression. A highly significant prediction model was obtained, indicating that by using a systems biology approach it is possible to answer unresolved complex medical uncertainties. Adopting this systems biology approach can be expected to be useful in identifying the most appropriate target groups for other preventive programmes.

Where the wild things are: Predicting hotspots of seabird aggregations in the California Current System

USGS Publications Warehouse

Nur, N.; Jahncke, J.; Herzog, M.P.; Howar, J.; Hyrenbach, K.D.; Zamon, J.E.; Ainley, D.G.; Wiens, J.A.; Morgan, K.; Balance, L.T.; Stralberg, D.

2011-01-01

Marine Protected Areas (MPAs) provide an important tool for conservation of marine ecosystems. To be most effective, these areas should be strategically located in a manner that supports ecosystem function. To inform marine spatial planning and support strategic establishment of MPAs within the California Current System, we identified areas predicted to support multispecies aggregations of seabirds ("hotspot????). We developed habitat-association models for 16 species using information from at-sea observations collected over an 11-year period (1997-2008), bathymetric data, and remotely sensed oceanographic data for an area from north of Vancouver Island, Canada, to the USA/Mexico border and seaward 600 km from the coast. This approach enabled us to predict distribution and abundance of seabirds even in areas of few or no surveys. We developed single-species predictive models using a machine-learning algorithm: bagged decision trees. Single-species predictions were then combined to identify potential hotspots of seabird aggregation, using three criteria: (1) overall abundance among species, (2) importance of specific areas ("core area????) to individual species, and (3) predicted persistence of hotspots across years. Model predictions were applied to the entire California Current for four seasons (represented by February, May, July, and October) in each of 11 years. Overall, bathymetric variables were often important predictive variables, whereas oceanographic variables derived from remotely sensed data were generally less important. Predicted hotspots often aligned with currently protected areas (e.g., National Marine Sanctuaries), but we also identified potential hotspots in Northern California/Southern Oregon (from Cape Mendocino to Heceta Bank), Southern California (adjacent to the Channel Islands), and adjacent to Vancouver Island, British Columbia, that are not currently included in protected areas. Prioritization and identification of multispecies hotspots will depend on which group of species is of highest management priority. Modeling hotspots at a broad spatial scale can contribute to MPA site selection, particularly if complemented by fine-scale information for focal areas. ?? 2011 by the Ecological Society of America.

A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis

PubMed Central

Noren, David P.; Long, Byron L.; Norel, Raquel; Rrhissorrakrai, Kahn; Hess, Kenneth; Hu, Chenyue Wendy; Bisberg, Alex J.; Schultz, Andre; Engquist, Erik; Liu, Li; Lin, Xihui; Chen, Gregory M.; Xie, Honglei; Hunter, Geoffrey A. M.; Norman, Thea; Friend, Stephen H.; Stolovitzky, Gustavo; Kornblau, Steven; Qutub, Amina A.

2016-01-01

Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response. PMID:27351836

Assessment of the potential utility of different regions of Streptococcus uberis adhesion molecule (SUAM) for mastitis subunit vaccine development.

PubMed

Perrig, Melina Soledad; Veaute, Carolina; Renna, María Sol; Pujato, Nazarena; Calvinho, Luis; Marcipar, Iván; Barbagelata, María Sol

2017-04-01

Streptococcus uberis is one of the most prevalent pathogens causing clinical and subclinical mastitis worldwide. Among bacterial factors involved in intramammary infections caused by this organism, S. uberis adhesion molecule (SUAM) is one of the main virulence factors identified. This molecule is involved in S. uberis internalization to mammary epithelial cells through lactoferrin (Lf) binding. The objective of this study was to evaluate SUAM properties as a potential subunit vaccine component for prevention of S. uberis mastitis. B epitope prediction analysis of SUAM sequence was used to identify potentially immunogenic regions. Since these regions were detected all along the gene, this criterion did not allow selecting a specific region as a potential immunogen. Hence, four fractions of SUAM (-1fr, 2fr, 3fr and 4fr), comprising most of the protein, were cloned and expressed. Every fraction elicited a humoral immune response in mice as predicted by bioinformatics analysis. SUAM-1fr generated antibodies with the highest recognition ability towards SUAM native protein. Moreover, antibodies against SUAM-1fr produced the highest proportion of internalization inhibition of S. uberis to mammary epithelial cells. In conclusion, SUAM immunogenic and functionally relevant regions were identified and allowed to propose SUAM-1fr as a potential candidate for a subunit vaccine for S. uberis mastitis prevention. Copyright © 2017 Elsevier Ltd. All rights reserved.

The medication appropriateness index at 20: where it started, where it has been, and where it may be going.

PubMed

Hanlon, Joseph T; Schmader, Kenneth E

2013-11-01

Potentially inappropriate prescribing for older adults is a major public health concern. While there are multiple measures of potentially inappropriate prescribing, the medication appropriateness index (MAI) is one of the most common implicit approaches published in the scientific literature. The objective of this narrative review is to describe findings regarding the MAI's reliability, comparison of the MAI with other quality measures of potentially inappropriate prescribing, its predictive validity with important health outcomes, and its responsiveness to change within the framework of randomized controlled trials. A search restricted to English-language literature involving humans aged 65+ years from January 1992 to June 2013 was conducted using MEDLINE and EMBASE databases using the search term 'medication appropriateness index'. A manual search of the reference lists from identified articles and the authors' article files, book chapters, and recent reviews was conducted to identify additional articles. A total of 26 articles were identified for inclusion in this narrative review. The main findings were that the MAI has acceptable inter- and intra-rater reliability, it more frequently detects potentially inappropriate prescribing than a commonly used set of explicit criteria, it predicts adverse health outcomes, and it is able to demonstrate the positive impact of interventions to improve this public health problem. We conclude that the MAI may serve as a valuable tool for measuring potentially inappropriate prescribing in older adults.

Parameter selection for and implementation of a web-based decision-support tool to predict extubation outcome in premature infants.

PubMed

Mueller, Martina; Wagner, Carol L; Annibale, David J; Knapp, Rebecca G; Hulsey, Thomas C; Almeida, Jonas S

2006-03-01

Approximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census. A five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN. CEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0-1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data originally used for the development of the prediction tool. State-of-the-art, machine-learning methods can be employed for the development of sophisticated tools to aid clinicians' decisions. We identified numerous variables considered relevant for extubation decisions for mechanically ventilated premature infants with RDS. We then developed a web-based decision-support tool for clinicians which can be made widely available and potentially improve patient care world wide.

Altered expression of four miRNA (miR-1238-3p, miR-202-3p, miR-630 and miR-766-3p) and their potential targets in peripheral blood from vitiligo patients.

PubMed

Shang, Zhiwei; Li, Hongwen

2017-10-01

Vitiligo is an acquired skin disease with pigmentary disorder. Autoimmune destruction of melanocytes is thought to be major factor in the etiology of vitiligo. miRNA-based regulators of gene expression have been reported to play crucial roles in autoimmune disease. Therefore, we attempt to profile the miRNA expressions and predict their potential targets, assessing the biological functions of differentially expressed miRNA. Total RNA was extracted from peripheral blood of vitiligo (experimental group, n = 5) and non-vitiligo (control group, n = 5) age-matched patients. Samples were hybridized to a miRNA array. Box, scatter and principal component analysis plots were performed, followed by unsupervised hierarchical clustering analysis to classify the samples. Quantitative reverse transcription polymerase chain reaction (RT-PCR) was conducted for validation of microarray data. Three different databases, TargetScan, PITA and microRNA.org, were used to predict the potential target genes. Gene ontology (GO) annotation and pathway analysis were performed to assess the potential functions of predicted genes of identified miRNA. A total of 100 (29 upregulated and 71 downregulated) miRNA were filtered by volcano plot analysis. Four miRNA were validated by quantitative RT-PCR as significantly downregulated in the vitiligo group. The functions of predicted target genes associated with differentially expressed miRNA were assessed by GO analysis, showing that the GO term with most significantly enriched target genes was axon guidance, and that the axon guidance pathway was most significantly correlated with these miRNA. In conclusion, we identified four downregulated miRNA in vitiligo and assessed the potential functions of target genes related to these differentially expressed miRNA. © 2017 Japanese Dermatological Association.

Mammalian genomic regulatory regions predicted by utilizing human genomics, transcriptomics, and epigenetics data

PubMed Central

Nguyen, Quan H; Tellam, Ross L; Naval-Sanchez, Marina; Porto-Neto, Laercio R; Barendse, William; Reverter, Antonio; Hayes, Benjamin; Kijas, James; Dalrymple, Brian P

2018-01-01

Abstract Genome sequences for hundreds of mammalian species are available, but an understanding of their genomic regulatory regions, which control gene expression, is only beginning. A comprehensive prediction of potential active regulatory regions is necessary to functionally study the roles of the majority of genomic variants in evolution, domestication, and animal production. We developed a computational method to predict regulatory DNA sequences (promoters, enhancers, and transcription factor binding sites) in production animals (cows and pigs) and extended its broad applicability to other mammals. The method utilizes human regulatory features identified from thousands of tissues, cell lines, and experimental assays to find homologous regions that are conserved in sequences and genome organization and are enriched for regulatory elements in the genome sequences of other mammalian species. Importantly, we developed a filtering strategy, including a machine learning classification method, to utilize a very small number of species-specific experimental datasets available to select for the likely active regulatory regions. The method finds the optimal combination of sensitivity and accuracy to unbiasedly predict regulatory regions in mammalian species. Furthermore, we demonstrated the utility of the predicted regulatory datasets in cattle for prioritizing variants associated with multiple production and climate change adaptation traits and identifying potential genome editing targets. PMID:29618048

Adjusted hospital death rates: a potential screen for quality of medical care.

PubMed

Dubois, R W; Brook, R H; Rogers, W H

1987-09-01

Increased economic pressure on hospitals has accelerated the need to develop a screening tool for identifying hospitals that potentially provide poor quality care. Based upon data from 93 hospitals and 205,000 admissions, we used a multiple regression model to adjust the hospitals crude death rate. The adjustment process used age, origin of patient from the emergency department or nursing home, and a hospital case mix index based on DRGs (diagnostic related groups). Before adjustment, hospital death rates ranged from 0.3 to 5.8 per 100 admissions. After adjustment, hospital death ratios ranged from 0.36 to 1.36 per 100 (actual death rate divided by predicted death rate). Eleven hospitals (12 per cent) were identified where the actual death rate exceeded the predicted death rate by more than two standard deviations. In nine hospitals (10 per cent), the predicted death rate exceeded the actual death rate by a similar statistical margin. The 11 hospitals with higher than predicted death rates may provide inadequate quality of care or have uniquely ill patient populations. The adjusted death rate model needs to be validated and generalized before it can be used routinely to screen hospitals. However, the remaining large differences in observed versus predicted death rates lead us to believe that important differences in hospital performance may exist.

Mammalian genomic regulatory regions predicted by utilizing human genomics, transcriptomics, and epigenetics data.

PubMed

Nguyen, Quan H; Tellam, Ross L; Naval-Sanchez, Marina; Porto-Neto, Laercio R; Barendse, William; Reverter, Antonio; Hayes, Benjamin; Kijas, James; Dalrymple, Brian P

2018-03-01

Genome sequences for hundreds of mammalian species are available, but an understanding of their genomic regulatory regions, which control gene expression, is only beginning. A comprehensive prediction of potential active regulatory regions is necessary to functionally study the roles of the majority of genomic variants in evolution, domestication, and animal production. We developed a computational method to predict regulatory DNA sequences (promoters, enhancers, and transcription factor binding sites) in production animals (cows and pigs) and extended its broad applicability to other mammals. The method utilizes human regulatory features identified from thousands of tissues, cell lines, and experimental assays to find homologous regions that are conserved in sequences and genome organization and are enriched for regulatory elements in the genome sequences of other mammalian species. Importantly, we developed a filtering strategy, including a machine learning classification method, to utilize a very small number of species-specific experimental datasets available to select for the likely active regulatory regions. The method finds the optimal combination of sensitivity and accuracy to unbiasedly predict regulatory regions in mammalian species. Furthermore, we demonstrated the utility of the predicted regulatory datasets in cattle for prioritizing variants associated with multiple production and climate change adaptation traits and identifying potential genome editing targets.

Potentially Inappropriate Prescribing in Disabled Older Patients with Chronic Diseases: A Screening Tool of Older Persons' Potentially Inappropriate Prescriptions versus Beers 2012 Criteria.

PubMed

Yang, Po-Jen; Lee, Yuan-Ti; Tzeng, Shu-Ling; Lee, Huei-Chao; Tsai, Chin-Feng; Chen, Chun-Chieh; Chen, Shiuan-Chih; Lee, Meng-Chih

2015-01-01

To evaluate the prescription of potentially inappropriate medications (PIM), using the Screening Tool of Older Persons' potentially inappropriate Prescriptions (STOPP) and Beers criteria, to disabled older people. One hundred and forty-one patients aged ≥65 years with Barthel scale scores ≤60 and a regular intake of medication for chronic diseases at Chung Shan Medical University Hospital from July to December 2012 were included, and their medical records were reviewed. Comprehensive patient information was extracted from the patients' medical notes. The STOPP and Beers 2012 criteria were used separately to identify PIM, and logistic regression analyses were performed to identify risk factors for PIM. The optimal cutoff for the number of medications prescribed for predicting PIM was estimated using the Youden index. Of the 141 patients, 94 (66.7%) and 94 (66.7%) had at least one PIM identified by the STOPP and Beers criteria, respectively. In multivariate analysis, PIM identified by the Beers criteria were associated with the prescription of multiple medications (p = 0.013) and the presence of psychiatric diseases (p < 0.001), whereas PIM identified by the STOPP criteria were only associated with the prescription of multiple medications (p = 0.008). The optimal cutoff for the number of medications prescribed for predicting PIM by using the STOPP or Beers criteria was 6. After adjustment for covariates, patients prescribed ≥6 medications had a significantly higher risk of PIM, identified using the STOPP or Beers criteria, compared to patients prescribed <6 medications (both p < 0.05). This study revealed a high frequency of PIM in disabled older patients with chronic diseases, particularly those prescribed ≥6 medications. © 2015 S. Karger AG, Basel.

Potentially Inappropriate Prescribing in Disabled Older Patients with Chronic Diseases: A Screening Tool of Older Persons' Potentially Inappropriate Prescriptions versus Beers 2012 Criteria

PubMed Central

Yang, Po-Jen; Lee, Yuan-Ti; Tzeng, Shu-Ling; Lee, Huei-Chao; Tsai, Chin-Feng; Chen, Chun-Chieh; Chen, Shiuan-Chih; Lee, Meng-Chih

2015-01-01

Objective To evaluate the prescription of potentially inappropriate medications (PIM), using the Screening Tool of Older Persons' potentially inappropriate Prescriptions (STOPP) and Beers criteria, to disabled older people. Subjects and Methods One hundred and forty-one patients aged ≥65 years with Barthel scale scores ≤60 and a regular intake of medication for chronic diseases at Chung Shan Medical University Hospital from July to December 2012 were included, and their medical records were reviewed. Comprehensive patient information was extracted from the patients' medical notes. The STOPP and Beers 2012 criteria were used separately to identify PIM, and logistic regression analyses were performed to identify risk factors for PIM. The optimal cutoff for the number of medications prescribed for predicting PIM was estimated using the Youden index. Results Of the 141 patients, 94 (66.7%) and 94 (66.7%) had at least one PIM identified by the STOPP and Beers criteria, respectively. In multivariate analysis, PIM identified by the Beers criteria were associated with the prescription of multiple medications (p = 0.013) and the presence of psychiatric diseases (p < 0.001), whereas PIM identified by the STOPP criteria were only associated with the prescription of multiple medications (p = 0.008). The optimal cutoff for the number of medications prescribed for predicting PIM by using the STOPP or Beers criteria was 6. After adjustment for covariates, patients prescribed ≥6 medications had a significantly higher risk of PIM, identified using the STOPP or Beers criteria, compared to patients prescribed <6 medications (both p < 0.05). Conclusion This study revealed a high frequency of PIM in disabled older patients with chronic diseases, particularly those prescribed ≥6 medications. PMID:26279164

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

The ecological niche and reciprocal prediction of the disjunct distribution of an invasive species: the example of Ailanthus altissima

Treesearch

Thomas P. Albright; Hao Chen; Lijun Chen; Qinfeng Guo

2010-01-01

Knowledge of the ecological niches of invasive species in native and introduced ranges can inform management as well as ecological and evolutionary theory. Here, we identified and compared factors associated with the distribution of an invasive tree, Ailanthus altissima, in both its native Chinese and introduced US ranges and predicted potential US...

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

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

PubMed Central

Sharafoddini, Anis; Dubin, Joel A

2017-01-01

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

Identification of Conserved and Potentially Regulatory Small RNAs in Heterocystous Cyanobacteria.

PubMed

Brenes-Álvarez, Manuel; Olmedo-Verd, Elvira; Vioque, Agustín; Muro-Pastor, Alicia M

2016-01-01

Small RNAs (sRNAs) are a growing class of non-protein-coding transcripts that participate in the regulation of virtually every aspect of bacterial physiology. Heterocystous cyanobacteria are a group of photosynthetic organisms that exhibit multicellular behavior and developmental alternatives involving specific transcriptomes exclusive of a given physiological condition or even a cell type. In the context of our ongoing effort to understand developmental decisions in these organisms we have undertaken an approach to the global identification of sRNAs. Using differential RNA-Seq we have previously identified transcriptional start sites for the model heterocystous cyanobacterium Nostoc sp. PCC 7120. Here we combine this dataset with a prediction of Rho-independent transcriptional terminators and an analysis of phylogenetic conservation of potential sRNAs among 89 available cyanobacterial genomes. In contrast to predictive genome-wide approaches, the use of an experimental dataset comprising all active transcriptional start sites (differential RNA-Seq) facilitates the identification of bona fide sRNAs. The output of our approach is a dataset of predicted potential sRNAs in Nostoc sp. PCC 7120, with different degrees of phylogenetic conservation across the 89 cyanobacterial genomes analyzed. Previously described sRNAs appear among the predicted sRNAs, demonstrating the performance of the algorithm. In addition, new predicted sRNAs are now identified that can be involved in regulation of different aspects of cyanobacterial physiology, including adaptation to nitrogen stress, the condition that triggers differentiation of heterocysts (specialized nitrogen-fixing cells). Transcription of several predicted sRNAs that appear exclusively in the genomes of heterocystous cyanobacteria is experimentally verified by Northern blot. Cell-specific transcription of one of these sRNAs, NsiR8 (nitrogen stress-induced RNA 8), in developing heterocysts is also demonstrated.

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.

Sequence-Based Prediction of RNA-Binding Residues in Proteins.

PubMed

Walia, Rasna R; El-Manzalawy, Yasser; Honavar, Vasant G; Dobbs, Drena

2017-01-01

Identifying individual residues in the interfaces of protein-RNA complexes is important for understanding the molecular determinants of protein-RNA recognition and has many potential applications. Recent technical advances have led to several high-throughput experimental methods for identifying partners in protein-RNA complexes, but determining RNA-binding residues in proteins is still expensive and time-consuming. This chapter focuses on available computational methods for identifying which amino acids in an RNA-binding protein participate directly in contacting RNA. Step-by-step protocols for using three different web-based servers to predict RNA-binding residues are described. In addition, currently available web servers and software tools for predicting RNA-binding sites, as well as databases that contain valuable information about known protein-RNA complexes, RNA-binding motifs in proteins, and protein-binding recognition sites in RNA are provided. We emphasize sequence-based methods that can reliably identify interfacial residues without the requirement for structural information regarding either the RNA-binding protein or its RNA partner.

Sequence-Based Prediction of RNA-Binding Residues in Proteins

PubMed Central

Walia, Rasna R.; EL-Manzalawy, Yasser; Honavar, Vasant G.; Dobbs, Drena

2017-01-01

Identifying individual residues in the interfaces of protein–RNA complexes is important for understanding the molecular determinants of protein–RNA recognition and has many potential applications. Recent technical advances have led to several high-throughput experimental methods for identifying partners in protein–RNA complexes, but determining RNA-binding residues in proteins is still expensive and time-consuming. This chapter focuses on available computational methods for identifying which amino acids in an RNA-binding protein participate directly in contacting RNA. Step-by-step protocols for using three different web-based servers to predict RNA-binding residues are described. In addition, currently available web servers and software tools for predicting RNA-binding sites, as well as databases that contain valuable information about known protein–RNA complexes, RNA-binding motifs in proteins, and protein-binding recognition sites in RNA are provided. We emphasize sequence-based methods that can reliably identify interfacial residues without the requirement for structural information regarding either the RNA-binding protein or its RNA partner. PMID:27787829

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.

Identifying Potential Norovirus Epidemics in China via Internet Surveillance

PubMed Central

Chen, Bin; Jiang, Tao; Cai, Gaofeng; Jiang, Zhenggang; Chen, Yongdi; Wang, Zhengting; Gu, Hua; Chai, Chengliang

2017-01-01

Background Norovirus is a common virus that causes acute gastroenteritis worldwide, but a monitoring system for norovirus is unavailable in China. Objective We aimed to identify norovirus epidemics through Internet surveillance and construct an appropriate model to predict potential norovirus infections. Methods The norovirus-related data of a selected outbreak in Jiaxing Municipality, Zhejiang Province of China, in 2014 were collected from immediate epidemiological investigation, and the Internet search volume, as indicated by the Baidu Index, was acquired from the Baidu search engine. All correlated search keywords in relation to norovirus were captured, screened, and composited to establish the composite Baidu Index at different time lags by Spearman rank correlation. The optimal model was chosen and possibly predicted maps in Zhejiang Province were presented by ArcGIS software. Results The combination of two vital keywords at a time lag of 1 day was ultimately identified as optimal (ρ=.924, P<.001). The exponential curve model was constructed to fit the trend of this epidemic, suggesting that a one-unit increase in the mean composite Baidu Index contributed to an increase of norovirus infections by 2.15 times during the outbreak. In addition to Jiaxing Municipality, Hangzhou Municipality might have had some potential epidemics in the study time from the predicted model. Conclusions Although there are limitations with early warning and unavoidable biases, Internet surveillance may be still useful for the monitoring of norovirus epidemics when a monitoring system is unavailable. PMID:28790023

Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger

PubMed Central

Wright, James C; Sugden, Deana; Francis-McIntyre, Sue; Riba-Garcia, Isabel; Gaskell, Simon J; Grigoriev, Igor V; Baker, Scott E; Beynon, Robert J; Hubbard, Simon J

2009-01-01

Background Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide prima facie evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced Aspergillus niger fungal genome from the Joint Genome Institutes (JGI) and another predicted protein set from another A.niger sequence. Tandem mass spectra (MS/MS) were acquired from 1d gel electrophoresis bands and searched against all available gene models using Average Peptide Scoring (APS) and reverse database searching to produce confident identifications at an acceptable false discovery rate (FDR). Results 405 identified peptide sequences were mapped to 214 different A.niger genomic loci to which 4093 predicted gene models clustered, 2872 of which contained the mapped peptides. Interestingly, 13 (6%) of these loci either had no preferred predicted gene model or the genome annotators' chosen "best" model for that genomic locus was not found to be the most parsimonious match to the identified peptides. The peptides identified also boosted confidence in predicted gene structures spanning 54 introns from different gene models. Conclusion This work highlights the potential of integrating experimental proteomics data into genomic annotation pipelines much as expressed sequence tag (EST) data has been. A comparison of the published genome from another strain of A.niger sequenced by DSM showed that a number of the gene models or proteins with proteomics evidence did not occur in both genomes, further highlighting the utility of the method. PMID:19193216

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%.

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome

PubMed Central

Low, Yen S; Caster, Ola; Bergvall, Tomas; Fourches, Denis; Zang, Xiaoling; Norén, G Niklas; Rusyn, Ivan; Edwards, Ralph

2016-01-01

Objective Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models. Materials and Methods Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan). Results We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%–81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature. Discussion Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts. Conclusions We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations. PMID:26499102

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.

In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database

NASA Astrophysics Data System (ADS)

Dai, Shao-Xing; Li, Wen-Xing; Han, Fei-Fei; Guo, Yi-Cheng; Zheng, Jun-Juan; Liu, Jia-Qian; Wang, Qian; Gao, Yue-Dong; Li, Gong-Hua; Huang, Jing-Fei

2016-05-01

There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database by using cheminformatics. We first predicted 5278 anti-cancer compounds from TCM database. The top 346 compounds were highly potent active in the 60 cell lines test. Similarity analysis revealed that 75% of the 5278 compounds are highly similar to the approved anti-cancer drugs. Based on the predicted anti-cancer compounds, we identified 57 anti-cancer plants by activity enrichment. The identified plants are widely distributed in 46 genera and 28 families, which broadens the scope of the anti-cancer drug screening. Finally, we constructed a network of predicted anti-cancer plants and approved drugs based on the above results. The network highlighted the supportive role of the predicted plant in the development of anti-cancer drug and suggested different molecular anti-cancer mechanisms of the plants. Our study suggests that the predicted compounds and plants from TCM database offer an attractive starting point and a broader scope to mine for potential anti-cancer agents.

In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database.

PubMed

Dai, Shao-Xing; Li, Wen-Xing; Han, Fei-Fei; Guo, Yi-Cheng; Zheng, Jun-Juan; Liu, Jia-Qian; Wang, Qian; Gao, Yue-Dong; Li, Gong-Hua; Huang, Jing-Fei

2016-05-05

There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database by using cheminformatics. We first predicted 5278 anti-cancer compounds from TCM database. The top 346 compounds were highly potent active in the 60 cell lines test. Similarity analysis revealed that 75% of the 5278 compounds are highly similar to the approved anti-cancer drugs. Based on the predicted anti-cancer compounds, we identified 57 anti-cancer plants by activity enrichment. The identified plants are widely distributed in 46 genera and 28 families, which broadens the scope of the anti-cancer drug screening. Finally, we constructed a network of predicted anti-cancer plants and approved drugs based on the above results. The network highlighted the supportive role of the predicted plant in the development of anti-cancer drug and suggested different molecular anti-cancer mechanisms of the plants. Our study suggests that the predicted compounds and plants from TCM database offer an attractive starting point and a broader scope to mine for potential anti-cancer agents.

In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database

PubMed Central

Dai, Shao-Xing; Li, Wen-Xing; Han, Fei-Fei; Guo, Yi-Cheng; Zheng, Jun-Juan; Liu, Jia-Qian; Wang, Qian; Gao, Yue-Dong; Li, Gong-Hua; Huang, Jing-Fei

2016-01-01

There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database by using cheminformatics. We first predicted 5278 anti-cancer compounds from TCM database. The top 346 compounds were highly potent active in the 60 cell lines test. Similarity analysis revealed that 75% of the 5278 compounds are highly similar to the approved anti-cancer drugs. Based on the predicted anti-cancer compounds, we identified 57 anti-cancer plants by activity enrichment. The identified plants are widely distributed in 46 genera and 28 families, which broadens the scope of the anti-cancer drug screening. Finally, we constructed a network of predicted anti-cancer plants and approved drugs based on the above results. The network highlighted the supportive role of the predicted plant in the development of anti-cancer drug and suggested different molecular anti-cancer mechanisms of the plants. Our study suggests that the predicted compounds and plants from TCM database offer an attractive starting point and a broader scope to mine for potential anti-cancer agents. PMID:27145869

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.

Hard sphere perturbation theory for fluids with soft-repulsive-core potentials

NASA Astrophysics Data System (ADS)

Ben-Amotz, Dor; Stell, George

2004-03-01

The thermodynamic properties of fluids with very soft repulsive-core potentials, resembling those of some liquid metals, are predicted with unprecedented accuracy using a new first-order thermodynamic perturbation theory. This theory is an extension of Mansoori-Canfield/Rasaiah-Stell (MCRS) perturbation theory, obtained by including a configuration integral correction recently identified by Mon, who evaluated it by computer simulation. In this work we derive an analytic expression for Mon's correction in terms of the radial distribution function of the soft-core fluid, g0(r), approximated using Lado's self-consistent extension of Weeks-Chandler-Andersen (WCA) theory. Comparisons with WCA and MCRS predictions show that our new extended-MCRS theory outperforms other first-order theories when applied to fluids with very soft inverse-power potentials (n⩽6), and predicts free energies that are within 0.3kT of simulation results up to the fluid freezing point.

Can cell kinetic parameters predict the response of tumours to radiotherapy?

PubMed

McNally, N J

1989-11-01

Three potential predictive assays of the repopulation component in tumour response to therapy are considered. (1) The DNA index can easily be measured. It is of prognostic value for cancers of certain sites, aneuploidy being a bad prognostic indicator. It is not strictly an indicator of cell proliferation. (2) The in vitro labelling index is of predictive value in early stage operable breast cancer and in head and neck cancer. In the former a high pretreatment labelling index can identify patients who could benefit from adjuvant chemotherapy. (3) The tumour potential doubling time (Tpot) can be measured rapidly following in vivo labelling with bromodeoxyuridine or iododeoxyuridine. We have measured Tpot in over 100 solid tumours with a success rate of about 75 per cent. Nearly 50 per cent of the tumours have a pre-treatment potential doubling time of 5 days or less. These would be suitable candidates for accelerated fractionation.

Landscapes for Energy and Wildlife: Conservation Prioritization for Golden Eagles across Large Spatial Scales

PubMed Central

Tack, Jason D.; Fedy, Bradley C.

2015-01-01

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development. PMID:26262876

Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales

USGS Publications Warehouse

Tack, Jason D.; Fedy, Bradley C.

2015-01-01

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.

Landscapes for Energy and Wildlife: Conservation Prioritization for Golden Eagles across Large Spatial Scales.

PubMed

Tack, Jason D; Fedy, Bradley C

2015-01-01

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.

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 prediction tools. In the independent testing, the high sensitivity and specificity of the proposed method demonstrate the predictive effectiveness of the identified substrate motifs and the importance of investigating potential kinases for viral protein phosphorylation sites.

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

PubMed

Boudreaux, Mary K; Humphries, Drew M

2013-12-01

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

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

PubMed

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

2017-07-03

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

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

PubMed Central

Chen, Xing; Yan, Gui-Ying

2017-01-01

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

Application of Functional Use Predictions to Aid in Structure ...

EPA Pesticide Factsheets

Humans are potentially exposed to thousands of anthropogenic chemicals in commerce. Recent work has shown that the bulk of this exposure may occur in near-field indoor environments (e.g., home, school, work, etc.). Advances in suspect screening analyses (SSA) now allow an improved understanding of the chemicals present in these environments. However, due to the nature of suspect screening techniques, investigators are often left with chemical formula predictions, with the possibility of many chemical structures matching to each formula. Here, newly developed quantitative structure-use relationship (QSUR) models are used to identify potential exposure sources for candidate structures. Previously, a suspect screening workflow was introduced and applied to house dust samples collected from the U.S. Department of Housing and Urban Development’s American Healthy Homes Survey (AHHS) [Rager, et al., Env. Int. 88 (2016)]. This workflow utilized the US EPA’s Distributed Structure-Searchable Toxicity (DSSTox) Database to link identified molecular features to molecular formulas, and ultimately chemical structures. Multiple QSUR models were applied to support the evaluation of candidate structures. These QSURs predict the likelihood of a chemical having a functional use commonly associated with consumer products having near-field use. For 3,228 structures identified as possible chemicals in AHHS house dust samples, we were able to obtain the required descriptors to appl

Can currently available non-animal methods detect pre and pro-haptens relevant for skin sensitization?

PubMed

Patlewicz, Grace; Casati, Silvia; Basketter, David A; Asturiol, David; Roberts, David W; Lepoittevin, Jean-Pierre; Worth, Andrew P; Aschberger, Karin

2016-12-01

Predictive testing to characterize substances for their skin sensitization potential has historically been based on animal tests such as the Local Lymph Node Assay (LLNA). In recent years, regulations in the cosmetics and chemicals sectors have provided strong impetus to develop non-animal alternatives. Three test methods have undergone OECD validation: the direct peptide reactivity assay (DPRA), the KeratinoSens™ and the human Cell Line Activation Test (h-CLAT). Whilst these methods perform relatively well in predicting LLNA results, a concern raised is their ability to predict chemicals that need activation to be sensitizing (pre- or pro-haptens). This current study reviewed an EURL ECVAM dataset of 127 substances for which information was available in the LLNA and three non-animal test methods. Twenty eight of the sensitizers needed to be activated, with the majority being pre-haptens. These were correctly identified by 1 or more of the test methods. Six substances were categorized exclusively as pro-haptens, but were correctly identified by at least one of the cell-based assays. The analysis here showed that skin metabolism was not likely to be a major consideration for assessing sensitization potential and that sensitizers requiring activation could be identified correctly using one or more of the current non-animal methods. Published by Elsevier Inc.

A Feedback-Controlled Mandibular Positioner Identifies Individuals With Sleep Apnea Who Will Respond to Oral Appliance Therapy.

PubMed

Remmers, John E; Topor, Zbigniew; Grosse, Joshua; Vranjes, Nikola; Mosca, Erin V; Brant, Rollin; Bruehlmann, Sabina; Charkhandeh, Shouresh; Zareian Jahromi, Seyed Abdolali

2017-07-15

Mandibular protruding oral appliances represent a potentially important therapy for obstructive sleep apnea (OSA). However, their clinical utility is limited by a less-than-ideal efficacy rate and uncertainty regarding an efficacious mandibular position, pointing to the need for a tool to assist in delivery of the therapy. The current study assesses the ability to prospectively identify therapeutic responders and determine an efficacious mandibular position. Individuals (n = 202) with OSA participated in a blinded, 2-part investigation. A system for identifying therapeutic responders was developed in part 1 (n = 149); the predictive accuracy of this system was prospectively evaluated on a new population in part 2 (n = 53). Each participant underwent a 2-night, in-home feedback-controlled mandibular positioner (FCMP) test, followed by treatment with a custom oral appliance and an outcome study with the oral appliance in place. A machine learning classification system was trained to predict therapeutic outcome on data obtained from FCMP studies on part 1 participants. The accuracy of this trained system was then evaluated on part 2 participants by examining the agreement between prospectively predicted outcome and observed outcome. A predicted efficacious mandibular position was derived from each FCMP study. Predictive accuracy was as follows: sensitivity 85%; specificity 93%; positive predictive value 97%; and negative predictive value 72%. Of participants correctly predicted to respond to therapy, the predicted mandibular protrusive position proved efficacious in 86% of cases. An unattended, in-home FCMP test prospectively identifies individuals with OSA who will respond to oral appliance therapy and provides an efficacious mandibular position. The trial that this study reports on is registered on www.clinicaltrials.gov, ID NCT03011762, study name: Feasibility and Predictive Accuracy of an In-Home Computer Controlled Mandibular Positioner in Identifying Favourable Candidates for Oral Appliance Therapy. © 2017 American Academy of Sleep Medicine

The Use of Simulation to Reduce the Domain of "Black Swans" with Application to Hurricane Impacts to Power Systems.

PubMed

Berner, Christine L; Staid, Andrea; Flage, Roger; Guikema, Seth D

2017-10-01

Recently, the concept of black swans has gained increased attention in the fields of risk assessment and risk management. Different types of black swans have been suggested, distinguishing between unknown unknowns (nothing in the past can convincingly point to its occurrence), unknown knowns (known to some, but not to relevant analysts), or known knowns where the probability of occurrence is judged as negligible. Traditional risk assessments have been questioned, as their standard probabilistic methods may not be capable of predicting or even identifying these rare and extreme events, thus creating a source of possible black swans. In this article, we show how a simulation model can be used to identify previously unknown potentially extreme events that if not identified and treated could occur as black swans. We show that by manipulating a verified and validated model used to predict the impacts of hazards on a system of interest, we can identify hazard conditions not previously experienced that could lead to impacts much larger than any previous level of impact. This makes these potential black swan events known and allows risk managers to more fully consider them. We demonstrate this method using a model developed to evaluate the effect of hurricanes on energy systems in the United States; we identify hurricanes with potentially extreme impacts, storms well beyond what the historic record suggests is possible in terms of impacts. © 2016 Society for Risk Analysis.

Family and school environmental predictors of sleep bruxism in children.

PubMed

Rossi, Debora; Manfredini, Daniele

2013-01-01

To identify potential predictors of self-reported sleep bruxism (SB) within children's family and school environments. A total of 65 primary school children (55.4% males, mean age 9.3 ± 1.9 years) were administered a 10-item questionnaire investigating the prevalence of self-reported SB as well as nine family and school-related potential bruxism predictors. Regression analyses were performed to assess the correlation between the potential predictors and SB. A positive answer to the self-reported SB item was endorsed by 18.8% of subjects, with no sex differences. Multiple variable regression analysis identified a final model showing that having divorced parents and not falling asleep easily were the only two weak predictors of self-reported SB. The percentage of explained variance for SB by the final multiple regression model was 13.3% (Nagelkerke's R² = 0.133). While having a high specificity and a good negative predictive value, the model showed unacceptable sensitivity and positive predictive values. The resulting accuracy to predict the presence of self-reported SB was 73.8%. The present investigation suggested that, among family and school-related matters, having divorced parents and not falling asleep easily were two predictors, even if weak, of a child's self-report of SB.

In Silico Prediction and Experimental Confirmation of HA Residues Conferring Enhanced Human Receptor Specificity of H5N1 Influenza A Viruses.

PubMed

Schmier, Sonja; Mostafa, Ahmed; Haarmann, Thomas; Bannert, Norbert; Ziebuhr, John; Veljkovic, Veljko; Dietrich, Ursula; Pleschka, Stephan

2015-06-19

Newly emerging influenza A viruses (IAV) pose a major threat to human health by causing seasonal epidemics and/or pandemics, the latter often facilitated by the lack of pre-existing immunity in the general population. Early recognition of candidate pandemic influenza viruses (CPIV) is of crucial importance for restricting virus transmission and developing appropriate therapeutic and prophylactic strategies including effective vaccines. Often, the pandemic potential of newly emerging IAV is only fully recognized once the virus starts to spread efficiently causing serious disease in humans. Here, we used a novel phylogenetic algorithm based on the informational spectrum method (ISM) to identify potential CPIV by predicting mutations in the viral hemagglutinin (HA) gene that are likely to (differentially) affect critical interactions between the HA protein and target cells from bird and human origin, respectively. Predictions were subsequently validated by generating pseudotyped retrovirus particles and genetically engineered IAV containing these mutations and characterizing potential effects on virus entry and replication in cells expressing human and avian IAV receptors, respectively. Our data suggest that the ISM-based algorithm is suitable to identify CPIV among IAV strains that are circulating in animal hosts and thus may be a new tool for assessing pandemic risks associated with specific strains.

In Silico Prediction and Experimental Confirmation of HA Residues Conferring Enhanced Human Receptor Specificity of H5N1 Influenza A Viruses

NASA Astrophysics Data System (ADS)

Schmier, Sonja; Mostafa, Ahmed; Haarmann, Thomas; Bannert, Norbert; Ziebuhr, John; Veljkovic, Veljko; Dietrich, Ursula; Pleschka, Stephan

2015-06-01

Newly emerging influenza A viruses (IAV) pose a major threat to human health by causing seasonal epidemics and/or pandemics, the latter often facilitated by the lack of pre-existing immunity in the general population. Early recognition of candidate pandemic influenza viruses (CPIV) is of crucial importance for restricting virus transmission and developing appropriate therapeutic and prophylactic strategies including effective vaccines. Often, the pandemic potential of newly emerging IAV is only fully recognized once the virus starts to spread efficiently causing serious disease in humans. Here, we used a novel phylogenetic algorithm based on the informational spectrum method (ISM) to identify potential CPIV by predicting mutations in the viral hemagglutinin (HA) gene that are likely to (differentially) affect critical interactions between the HA protein and target cells from bird and human origin, respectively. Predictions were subsequently validated by generating pseudotyped retrovirus particles and genetically engineered IAV containing these mutations and characterizing potential effects on virus entry and replication in cells expressing human and avian IAV receptors, respectively. Our data suggest that the ISM-based algorithm is suitable to identify CPIV among IAV strains that are circulating in animal hosts and thus may be a new tool for assessing pandemic risks associated with specific strains.

4D offline PET-based treatment verification in scanned ion beam therapy: a phantom study

NASA Astrophysics Data System (ADS)

Kurz, Christopher; Bauer, Julia; Unholtz, Daniel; Richter, Daniel; Stützer, Kristin; Bert, Christoph; Parodi, Katia

2015-08-01

At the Heidelberg Ion-Beam Therapy Center, patient irradiation with scanned proton and carbon ion beams is verified by offline positron emission tomography (PET) imaging: the {β+} -activity measured within the patient is compared to a prediction calculated on the basis of the treatment planning data in order to identify potential delivery errors. Currently, this monitoring technique is limited to the treatment of static target structures. However, intra-fractional organ motion imposes considerable additional challenges to scanned ion beam radiotherapy. In this work, the feasibility and potential of time-resolved (4D) offline PET-based treatment verification with a commercial full-ring PET/CT (x-ray computed tomography) device are investigated for the first time, based on an experimental campaign with moving phantoms. Motion was monitored during the gated beam delivery as well as the subsequent PET acquisition and was taken into account in the corresponding 4D Monte-Carlo simulations and data evaluation. Under the given experimental conditions, millimeter agreement between the prediction and measurement was found. Dosimetric consequences due to the phantom motion could be reliably identified. The agreement between PET measurement and prediction in the presence of motion was found to be similar as in static reference measurements, thus demonstrating the potential of 4D PET-based treatment verification for future clinical applications.

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

PubMed

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

2016-02-01

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

A Spatial Modeling Approach to Predicting the Secondary Spread of Invasive Species Due to Ballast Water Discharge

PubMed Central

Sieracki, Jennifer L.; Bossenbroek, Jonathan M.; Chadderton, W. Lindsay

2014-01-01

Ballast water in ships is an important contributor to the secondary spread of invasive species in the Laurentian Great Lakes. Here, we use a model previously created to determine the role ballast water management has played in the secondary spread of viral hemorrhagic septicemia virus (VHSV) to identify the future spread of one current and two potential invasive species in the Great Lakes, the Eurasian Ruffe (Gymnocephalus cernuus), killer shrimp (Dikerogammarus villosus), and golden mussel (Limnoperna fortunei), respectively. Model predictions for Eurasian Ruffe have been used to direct surveillance efforts within the Great Lakes and DNA evidence of ruffe presence was recently reported from one of three high risk port localities identified by our model. Predictions made for killer shrimp and golden mussel suggest that these two species have the potential to become rapidly widespread if introduced to the Great Lakes, reinforcing the need for proactive ballast water management. The model used here is flexible enough to be applied to any species capable of being spread by ballast water in marine or freshwater ecosystems. PMID:25470822

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.

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.

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.

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.

Network pharmacology analysis of the anti-cancer pharmacological mechanisms of Ganoderma lucidum extract with experimental support using Hepa1-6-bearing C57 BL/6 mice.

PubMed

Zhao, Ruo-Lin; He, Yu-Min

2018-01-10

Ganoderma lucidum (GL) is an oriental medical fungus, which was used to prevent and treat many diseases. Previously, the effective compounds of Ganoderma lucidum extract (GLE) were extracted from two kinds of GL, [Ganoderma lucidum (Leyss. Ex Fr.) Karst.] and [Ganoderma sinense Zhao, Xu et Zhang], which have been used for adjuvant anti-cancer clinical therapy for more than 20 years. However, its concrete active compounds and its regulation mechanisms on tumor are unclear. In this study, we aimed to identify the main active compounds from GLE and to investigate its anti-cancer mechanisms via drug-target biological network construction and prediction. The main active compounds of GLE were identified by HPLC, EI-MS and NMR, and the compounds related targets were predicted using docking program. To investigate the functions of GL holistically, the active compounds of GL and related targets were predicted based on four public databases. Subsequently, the Identified-Compound-Target network and Predicted-Compound-Target network were constructed respectively, and they were overlapped to detect the hub potential targets in both networks. Furthermore, the qRT-PCR and western-blot assays were used to validate the expression levels of target genes in GLE treated Hepa1-6-bearing C57 BL/6 mice. In our work, 12 active compounds of GLE were identified, including Ganoderic acid A, Ganoderenic acid A, Ganoderic acid B, Ganoderic acid H, Ganoderic acid C2, Ganoderenic acid D, Ganoderic acid D, Ganoderenic acid G, Ganoderic acid Y, Kaemferol, Genistein and Ergosterol. Using the docking program, 20 targets were mapped to 12 compounds of GLE. Furthermore, 122 effective active compounds of GL and 116 targets were holistically predicted using public databases. Compare with the Identified-Compound-Target network and Predicted-Compound-Target network, 6 hub targets were screened, including AR, CHRM2, ESR1, NR3C1, NR3C2 and PGR, which was considered as potential markers and might play important roles in the process of GLE treatment. GLE effectively inhibited tumor growth in Hepa1-6-bearing C57 BL/6 mice. Finally, consistent with the results of qRT-PCR data, the results of western-blot assay demonstrated the expression levels of PGR and ESR1 were up-regulated, as well as the expression levels of NR3C2 and AR were down-regulated, while the change of NR3C1 and CHRM2 had no statistical significance. The results indicated that these 4 hub target genes, including NR3C2, AR, ESR1 and PGR, might act as potential markers to evaluate the curative effect of GLE treatment in tumor. And, the combined data provide preliminary study of the pharmacological mechanisms of GLE, which may be a promising potential therapeutic and chemopreventative candidate for anti-cancer. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

A Market-Basket Approach to Predict the Acute Aquatic Toxicity of Munitions and Energetic Materials.

PubMed

Burgoon, Lyle D

2016-06-01

An ongoing challenge in chemical production, including the production of insensitive munitions and energetics, is the ability to make predictions about potential environmental hazards early in the process. To address this challenge, a quantitative structure activity relationship model was developed to predict acute fathead minnow toxicity of insensitive munitions and energetic materials. Computational predictive toxicology models like this one may be used to identify and prioritize environmentally safer materials early in their development. The developed model is based on the Apriori market-basket/frequent itemset mining approach to identify probabilistic prediction rules using chemical atom-pairs and the lethality data for 57 compounds from a fathead minnow acute toxicity assay. Lethality data were discretized into four categories based on the Globally Harmonized System of Classification and Labelling of Chemicals. Apriori identified toxicophores for categories two and three. The model classified 32 of the 57 compounds correctly, with a fivefold cross-validation classification rate of 74 %. A structure-based surrogate approach classified the remaining 25 chemicals correctly at 48 %. This result is unsurprising as these 25 chemicals were fairly unique within the larger set.

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

PubMed

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

2017-01-17

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

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 allocation.

Development of a Salmonella screening tool for consumer complaint-based foodborne illness surveillance systems.

PubMed

Li, John; Maclehose, Rich; Smith, Kirk; Kaehler, Dawn; Hedberg, Craig

2011-01-01

Foodborne illness surveillance based on consumer complaints detects outbreaks by finding common exposures among callers, but this process is often difficult. Laboratory testing of ill callers could also help identify potential outbreaks. However, collection of stool samples from all callers is not feasible. Methods to help screen calls for etiology are needed to increase the efficiency of complaint surveillance systems and increase the likelihood of detecting foodborne outbreaks caused by Salmonella. Data from the Minnesota Department of Health foodborne illness surveillance database (2000 to 2008) were analyzed. Complaints with identified etiologies were examined to create a predictive model for Salmonella. Bootstrap methods were used to internally validate the model. Seventy-one percent of complaints in the foodborne illness database with known etiologies were due to norovirus. The predictive model had a good discriminatory ability to identify Salmonella calls. Three cutoffs for the predictive model were tested: one that maximized sensitivity, one that maximized specificity, and one that maximized predictive ability, providing sensitivities and specificities of 32 and 96%, 100 and 54%, and 89 and 72%, respectively. Development of a predictive model for Salmonella could help screen calls for etiology. The cutoff that provided the best predictive ability for Salmonella corresponded to a caller reporting diarrhea and fever with no vomiting, and five or fewer people ill. Screening calls for etiology would help identify complaints for further follow-up and result in identifying Salmonella cases that would otherwise go unconfirmed; in turn, this could lead to the identification of more outbreaks.

[Prediction of intra-abdominal hypertension risk in patients with acute colonic obstruction under epidural analgesia].

PubMed

Stakanov, A V; Potseluev, E A; Musaeva, T S

2013-01-01

Purpose of the study was to identify prediction possibility of direct current potential level for intra-abdominal hypertension risk in patients with acute colonic obstruction under preoperative epidural analgesia. Prospective analysis of the preoperative period was carried out in 140 patients with acute colonic obstruction caused by colon cancer. Relations between preoperative level of permanent capacity and risk of intra-abdominal hypertension was identified Direct current potential level is an independent predictor of intra-abdominal hypertension. Diagnostic significance increases from first to fifth hour of preoperative period according to AUROC data from 0.821 to 0.905 and calibration 6.9 (p > 0.37) and 4.7 (p > 0.54) by Hosmer-Lemeshou criteria. The use of epidural analgesia in the complex intensive preoperative preparation is pathogenically justified. It reduces intra-abdominal hypertension in patients with acute colonic obstruction.

Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology

PubMed Central

Hsin, Kun-Yi; Ghosh, Samik; Kitano, Hiroaki

2013-01-01

Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate. PMID:24391846

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.

Drug target identification using network analysis: Taking active components in Sini decoction as an example

NASA Astrophysics Data System (ADS)

Chen, Si; Jiang, Hailong; Cao, Yan; Wang, Yun; Hu, Ziheng; Zhu, Zhenyu; Chai, Yifeng

2016-04-01

Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

Drug target identification using network analysis: Taking active components in Sini decoction as an example

PubMed Central

Chen, Si; Jiang, Hailong; Cao, Yan; Wang, Yun; Hu, Ziheng; Zhu, Zhenyu; Chai, Yifeng

2016-01-01

Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound. PMID:27095146

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 model results were significantly correlated with coyote samples, indicating that carnivore surveillance programs will continue to be important for tracking the response of plague to future climate conditions. PMID:19558717

Robust global identifiability theory using potentials--Application to compartmental models.

PubMed

Wongvanich, N; Hann, C E; Sirisena, H R

2015-04-01

This paper presents a global practical identifiability theory for analyzing and identifying linear and nonlinear compartmental models. The compartmental system is prolonged onto the potential jet space to formulate a set of input-output equations that are integrals in terms of the measured data, which allows for robust identification of parameters without requiring any simulation of the model differential equations. Two classes of linear and non-linear compartmental models are considered. The theory is first applied to analyze the linear nitrous oxide (N2O) uptake model. The fitting accuracy of the identified models from differential jet space and potential jet space identifiability theories is compared with a realistic noise level of 3% which is derived from sensor noise data in the literature. The potential jet space approach gave a match that was well within the coefficient of variation. The differential jet space formulation was unstable and not suitable for parameter identification. The proposed theory is then applied to a nonlinear immunological model for mastitis in cows. In addition, the model formulation is extended to include an iterative method which allows initial conditions to be accurately identified. With up to 10% noise, the potential jet space theory predicts the normalized population concentration infected with pathogens, to within 9% of the true curve. Copyright © 2015 Elsevier Inc. All rights reserved.

International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: rationale and protocol.

PubMed

Williams, Leanne M; Rush, A John; Koslow, Stephen H; Wisniewski, Stephen R; Cooper, Nicholas J; Nemeroff, Charles B; Schatzberg, Alan F; Gordon, Evian

2011-01-05

Clinically useful treatment moderators of Major Depressive Disorder (MDD) have not yet been identified, though some baseline predictors of treatment outcome have been proposed. The aim of iSPOT-D is to identify pretreatment measures that predict or moderate MDD treatment response or remission to escitalopram, sertraline or venlafaxine; and develop a model that incorporates multiple predictors and moderators. The International Study to Predict Optimized Treatment - in Depression (iSPOT-D) is a multi-centre, international, randomized, prospective, open-label trial. It is enrolling 2016 MDD outpatients (ages 18-65) from primary or specialty care practices (672 per treatment arm; 672 age-, sex- and education-matched healthy controls). Study-eligible patients are antidepressant medication (ADM) naïve or willing to undergo a one-week wash-out of any non-protocol ADM, and cannot have had an inadequate response to protocol ADM. Baseline assessments include symptoms; distress; daily function; cognitive performance; electroencephalogram and event-related potentials; heart rate and genetic measures. A subset of these baseline assessments are repeated after eight weeks of treatment. Outcomes include the 17-item Hamilton Rating Scale for Depression (primary) and self-reported depressive symptoms, social functioning, quality of life, emotional regulation, and side-effect burden (secondary). Participants may then enter a naturalistic telephone follow-up at weeks 12, 16, 24 and 52. The first half of the sample will be used to identify potential predictors and moderators, and the second half to replicate and confirm. First enrolment was in December 2008, and is ongoing. iSPOT-D evaluates clinical and biological predictors of treatment response in the largest known sample of MDD collected worldwide. International Study to Predict Optimised Treatment - in Depression (iSPOT-D) ClinicalTrials.gov Identifier: NCT00693849. URL: http://clinicaltrials.gov/ct2/show/NCT00693849?term=International+Study+to+Predict+Optimized+Treatment+for+Depression&rank=1

Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach.

PubMed

Nielsen, Morten; Lundegaard, Claus; Worning, Peder; Hvid, Christina Sylvester; Lamberth, Kasper; Buus, Søren; Brunak, Søren; Lund, Ole

2004-06-12

Prediction of which peptides will bind a specific major histocompatibility complex (MHC) constitutes an important step in identifying potential T-cell epitopes suitable as vaccine candidates. MHC class II binding peptides have a broad length distribution complicating such predictions. Thus, identifying the correct alignment is a crucial part of identifying the core of an MHC class II binding motif. In this context, we wish to describe a novel Gibbs motif sampler method ideally suited for recognizing such weak sequence motifs. The method is based on the Gibbs sampling method, and it incorporates novel features optimized for the task of recognizing the binding motif of MHC classes I and II. The method locates the binding motif in a set of sequences and characterizes the motif in terms of a weight-matrix. Subsequently, the weight-matrix can be applied to identifying effectively potential MHC binding peptides and to guiding the process of rational vaccine design. We apply the motif sampler method to the complex problem of MHC class II binding. The input to the method is amino acid peptide sequences extracted from the public databases of SYFPEITHI and MHCPEP and known to bind to the MHC class II complex HLA-DR4(B1*0401). Prior identification of information-rich (anchor) positions in the binding motif is shown to improve the predictive performance of the Gibbs sampler. Similarly, a consensus solution obtained from an ensemble average over suboptimal solutions is shown to outperform the use of a single optimal solution. In a large-scale benchmark calculation, the performance is quantified using relative operating characteristics curve (ROC) plots and we make a detailed comparison of the performance with that of both the TEPITOPE method and a weight-matrix derived using the conventional alignment algorithm of ClustalW. The calculation demonstrates that the predictive performance of the Gibbs sampler is higher than that of ClustalW and in most cases also higher than that of the TEPITOPE method.

Complete nucleotide sequence of the freshwater unicellular cyanobacterium Synechococcus elongatus PCC 6301 chromosome: gene content and organization.

PubMed

Sugita, Chieko; Ogata, Koretsugu; Shikata, Masamitsu; Jikuya, Hiroyuki; Takano, Jun; Furumichi, Miho; Kanehisa, Minoru; Omata, Tatsuo; Sugiura, Masahiro; Sugita, Mamoru

2007-01-01

The entire genome of the unicellular cyanobacterium Synechococcus elongatus PCC 6301 (formerly Anacystis nidulans Berkeley strain 6301) was sequenced. The genome consisted of a circular chromosome 2,696,255 bp long. A total of 2,525 potential protein-coding genes, two sets of rRNA genes, 45 tRNA genes representing 42 tRNA species, and several genes for small stable RNAs were assigned to the chromosome by similarity searches and computer predictions. The translated products of 56% of the potential protein-coding genes showed sequence similarities to experimentally identified and predicted proteins of known function, and the products of 35% of the genes showed sequence similarities to the translated products of hypothetical genes. The remaining 9% of genes lacked significant similarities to genes for predicted proteins in the public DNA databases. Some 139 genes coding for photosynthesis-related components were identified. Thirty-seven genes for two-component signal transduction systems were also identified. This is the smallest number of such genes identified in cyanobacteria, except for marine cyanobacteria, suggesting that only simple signal transduction systems are found in this strain. The gene arrangement and nucleotide sequence of Synechococcus elongatus PCC 6301 were nearly identical to those of a closely related strain Synechococcus elongatus PCC 7942, except for the presence of a 188.6 kb inversion. The sequences as well as the gene information shown in this paper are available in the Web database, CYORF (http://www.cyano.genome.jp/).

[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.

PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides.

PubMed

Islam, S M Ashiqul; Sajed, Tanvir; Kearney, Christopher Michel; Baker, Erich J

2015-07-05

Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides includes a tri-disulfide domain arrangement that offers toxicity and high stability. Sequential tri-disulfide connectivity variants create highly compact disulfide folds capable of withstanding a variety of environmental stresses. Their combination of toxicity and stability make these peptides remarkably valuable for their potential as bio-insecticides, antimicrobial peptides and peptide drug candidates. However, the wide sequence variation, sources and modalities of group members impose serious limitations on our ability to rapidly identify potential members. As a result, there is a need for automated high-throughput member classification approaches that leverage their demonstrated tertiary and functional homology. We developed an SVM-based model to predict sequential tri-disulfide peptide (STP) toxins from peptide sequences. One optimized model, called PredSTP, predicted STPs from training set with sensitivity, specificity, precision, accuracy and a Matthews correlation coefficient of 94.86%, 94.11%, 84.31%, 94.30% and 0.86, respectively, using 200 fold cross validation. The same model outperforms existing prediction approaches in three independent out of sample testsets derived from PDB. PredSTP can accurately identify a wide range of cystine stabilized peptide toxins directly from sequences in a species-agnostic fashion. The ability to rapidly filter sequences for potential bioactive peptides can greatly compress the time between peptide identification and testing structural and functional properties for possible antimicrobial and insecticidal candidates. A web interface is freely available to predict STP toxins from http://crick.ecs.baylor.edu/.

Clinical prediction models for mortality and functional outcome following ischemic stroke: A systematic review and meta-analysis

PubMed Central

Crayton, Elise; Wolfe, Charles; Douiri, Abdel

2018-01-01

Objective We aim to identify and critically appraise clinical prediction models of mortality and function following ischaemic stroke. Methods Electronic databases, reference lists, citations were searched from inception to September 2015. Studies were selected for inclusion, according to pre-specified criteria and critically appraised by independent, blinded reviewers. The discrimination of the prediction models was measured by the area under the curve receiver operating characteristic curve or c-statistic in random effects meta-analysis. Heterogeneity was measured using I2. Appropriate appraisal tools and reporting guidelines were used in this review. Results 31395 references were screened, of which 109 articles were included in the review. These articles described 66 different predictive risk models. Appraisal identified poor methodological quality and a high risk of bias for most models. However, all models precede the development of reporting guidelines for prediction modelling studies. Generalisability of models could be improved, less than half of the included models have been externally validated(n = 27/66). 152 predictors of mortality and 192 predictors and functional outcome were identified. No studies assessing ability to improve patient outcome (model impact studies) were identified. Conclusions Further external validation and model impact studies to confirm the utility of existing models in supporting decision-making is required. Existing models have much potential. Those wishing to predict stroke outcome are advised to build on previous work, to update and adapt validated models to their specific contexts opposed to designing new ones. PMID:29377923

Predicting selective drug targets in cancer through metabolic networks

PubMed Central

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

2011-01-01

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

Computational Approaches for Identifying Adverse Outcome Pathways

EPA Science Inventory

Adverse Outcome Pathways (AOPs) provide a framework for organizing toxicity information to improve predictions of the potential adverse impact of environment stressors on humans or wildlife populations, but these benefits are currently limited by the small number of AOPs currentl...

New ways of looking at sector demand and sector alerts

DOT National Transportation Integrated Search

2011-03-31

This report presents the latest results of research conducted at the Volpe Center on improving air traffic demand predictions and enhancing the Traffic Flow Management System (TFMS) Monitor/Alert function for identifying potential congestion at Natio...

RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4.

PubMed

Zaretzki, Jed; Bergeron, Charles; Rydberg, Patrik; Huang, Tao-wei; Bennett, Kristin P; Breneman, Curt M

2011-07-25

This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as "metabolophores": A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set. The resulting pathway-independent (O-dealkylation vs N-oxidation vs Csp(3) hydroxylation, etc.), isozyme-specific regioselectivity model may be used to predict potential metabolic liabilities. In the present work, cross-validated RS-Predictor models were generated for a set of 394 substrates of CYP 3A4 as a proof-of-principle for the method. Rank aggregation was then employed to merge independently generated predictions for each substrate into a single consensus prediction. The resulting consensus RS-Predictor models were shown to reliably identify at least one observed site of metabolism in the top two rank-positions on 78% of the substrates. Comparisons between RS-Predictor and previously described regioselectivity prediction methods reveal new insights into how in silico metabolite prediction methods should be compared.

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.

Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking.

PubMed

Sasse, Alexander; de Vries, Sjoerd J; Schindler, Christina E M; de Beauchêne, Isaure Chauvot; Zacharias, Martin

2017-01-01

Protein-protein docking protocols aim to predict the structures of protein-protein complexes based on the structure of individual partners. Docking protocols usually include several steps of sampling, clustering, refinement and re-scoring. The scoring step is one of the bottlenecks in the performance of many state-of-the-art protocols. The performance of scoring functions depends on the quality of the generated structures and its coupling to the sampling algorithm. A tool kit, GRADSCOPT (GRid Accelerated Directly SCoring OPTimizing), was designed to allow rapid development and optimization of different knowledge-based scoring potentials for specific objectives in protein-protein docking. Different atomistic and coarse-grained potentials can be created by a grid-accelerated directly scoring dependent Monte-Carlo annealing or by a linear regression optimization. We demonstrate that the scoring functions generated by our approach are similar to or even outperform state-of-the-art scoring functions for predicting near-native solutions. Of additional importance, we find that potentials specifically trained to identify the native bound complex perform rather poorly on identifying acceptable or medium quality (near-native) solutions. In contrast, atomistic long-range contact potentials can increase the average fraction of near-native poses by up to a factor 2.5 in the best scored 1% decoys (compared to existing scoring), emphasizing the need of specific docking potentials for different steps in the docking protocol.

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

Variability and predictability of decadal mean temperature and precipitation over China in the CCSM4 last millennium simulation

NASA Astrophysics Data System (ADS)

Ying, Kairan; Frederiksen, Carsten S.; Zheng, Xiaogu; Lou, Jiale; Zhao, Tianbao

2018-02-01

The modes of variability that arise from the slow-decadal (potentially predictable) and intra-decadal (unpredictable) components of decadal mean temperature and precipitation over China are examined, in a 1000 year (850-1850 AD) experiment using the CCSM4 model. Solar variations, volcanic aerosols, orbital forcing, land use, and greenhouse gas concentrations provide the main forcing and boundary conditions. The analysis is done using a decadal variance decomposition method that identifies sources of potential decadal predictability and uncertainty. The average potential decadal predictabilities (ratio of slow-to-total decadal variance) are 0.62 and 0.37 for the temperature and rainfall over China, respectively, indicating that the (multi-)decadal variations of temperature are dominated by slow-decadal variability, while precipitation is dominated by unpredictable decadal noise. Possible sources of decadal predictability for the two leading predictable modes of temperature are the external radiative forcing, and the combined effects of slow-decadal variability of the Arctic oscillation (AO) and the Pacific decadal oscillation (PDO), respectively. Combined AO and PDO slow-decadal variability is associated also with the leading predictable mode of precipitation. External radiative forcing as well as the slow-decadal variability of PDO are associated with the second predictable rainfall mode; the slow-decadal variability of Atlantic multi-decadal oscillation (AMO) is associated with the third predictable precipitation mode. The dominant unpredictable decadal modes are associated with intra-decadal/inter-annual phenomena. In particular, the El Niño-Southern Oscillation and the intra-decadal variability of the AMO, PDO and AO are the most important sources of prediction uncertainty.

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

Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening

PubMed Central

Phillips, Michael; Bauer, Thomas L.; Cataneo, Renee N.; Lebauer, Cassie; Mundada, Mayur; Pass, Harvey I.; Ramakrishna, Naren; Rom, William N.; Vallières, Eric

2015-01-01

Background Breath volatile organic compounds (VOCs) have been reported as biomarkers of lung cancer, but it is not known if biomarkers identified in one group can identify disease in a separate independent cohort. Also, it is not known if combining breath biomarkers with chest CT has the potential to improve the sensitivity and specificity of lung cancer screening. Methods Model-building phase (unblinded): Breath VOCs were analyzed with gas chromatography mass spectrometry in 82 asymptomatic smokers having screening chest CT, 84 symptomatic high-risk subjects with a tissue diagnosis, 100 without a tissue diagnosis, and 35 healthy subjects. Multiple Monte Carlo simulations identified breath VOC mass ions with greater than random diagnostic accuracy for lung cancer, and these were combined in a multivariate predictive algorithm. Model-testing phase (blinded validation): We analyzed breath VOCs in an independent cohort of similar subjects (n = 70, 51, 75 and 19 respectively). The algorithm predicted discriminant function (DF) values in blinded replicate breath VOC samples analyzed independently at two laboratories (A and B). Outcome modeling: We modeled the expected effects of combining breath biomarkers with chest CT on the sensitivity and specificity of lung cancer screening. Results Unblinded model-building phase. The algorithm identified lung cancer with sensitivity 74.0%, specificity 70.7% and C-statistic 0.78. Blinded model-testing phase: The algorithm identified lung cancer at Laboratory A with sensitivity 68.0%, specificity 68.4%, C-statistic 0.71; and at Laboratory B with sensitivity 70.1%, specificity 68.0%, C-statistic 0.70, with linear correlation between replicates (r = 0.88). In a projected outcome model, breath biomarkers increased the sensitivity, specificity, and positive and negative predictive values of chest CT for lung cancer when the tests were combined in series or parallel. Conclusions Breath VOC mass ion biomarkers identified lung cancer in a separate independent cohort, in a blinded replicated study. Combining breath biomarkers with chest CT could potentially improve the sensitivity and specificity of lung cancer screening. Trial Registration ClinicalTrials.gov NCT00639067 PMID:26698306

Predicting drug court outcome among amphetamine-using participants.

PubMed

Wu, Lora J; Altshuler, Sandra J; Short, Robert A; Roll, John M

2012-06-01

Amphetamine use and abuse carry with it substantial social costs. Although there is a perception that amphetamine users are more difficult to treat than other substance users, drug courts have been used to effectively address drug-related crimes and hold the potential to lessen the impact of amphetamine abuse through efficacious treatment and rehabilitation. The objective of this study was to identify predictors of drug court outcome among amphetamine-using participants. A drug court database was obtained (N = 540) and amphetamine-using participants (n= 341) identified. Multivariate binary regression models run for the amphetamine-using participants identified being employed and being a parent as predictive of successful completion of the program, whereas being sanctioned to jail during the program was inversely related to program completion. Copyright © 2012 Elsevier Inc. All rights reserved.

ToxCast: Using high throughput screening to identify profiles of biological activity

EPA Science Inventory

ToxCast, the United States Environmental Protection Agency’s chemical prioritization research program, is developing methods for utilizing computational chemistry and bioactivity profiling to predict potential for toxicity and prioritize limited testing resources (www.epa.gov/toc...

Applications of high throughput screening to identify profiles of biological activity

EPA Science Inventory

ToxCast, the United States Environmental Protection Agency’s chemical prioritization research program, is developing methods for utilizing computational chemistry and bioactivity profiling to predict potential for toxicity and prioritize limited testing resources (www.epa.gov/toc...

Multivariate Models for Prediction of Human Skin Sensitization Hazard.

EPA Science Inventory

One of the lnteragency Coordinating Committee on the Validation of Alternative Method's (ICCVAM) top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensiti...

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

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.

Potential Compounds for Oral Cancer Treatment: Resveratrol, Nimbolide, Lovastatin, Bortezomib, Vorinostat, Berberine, Pterostilbene, Deguelin, Andrographolide, and Colchicine

PubMed Central

Bundela, Saurabh; Sharma, Anjana; Bisen, Prakash S.

2015-01-01

Oral cancer is one of the main causes of cancer-related deaths in South-Asian countries. There are very limited treatment options available for oral cancer. Research endeavors focused on discovery and development of novel therapies for oral cancer, is necessary to control the ever rising oral cancer related mortalities. We mined the large pool of compounds from the publicly available compound databases, to identify potential therapeutic compounds for oral cancer. Over 84 million compounds were screened for the possible anti-cancer activity by custom build SVM classifier. The molecular targets of the predicted anti-cancer compounds were mined from reliable sources like experimental bioassays studies associated with the compound, and from protein-compound interaction databases. Therapeutic compounds from DrugBank, and a list of natural anti-cancer compounds derived from literature mining of published studies, were used for building partial least squares regression model. The regression model thus built, was used for the estimation of oral cancer specific weights based on the molecular targets. These weights were used to compute scores for screening the predicted anti-cancer compounds for their potential to treat oral cancer. The list of potential compounds was annotated with corresponding physicochemical properties, cancer specific bioactivity evidences, and literature evidences. In all, 288 compounds with the potential to treat oral cancer were identified in the current study. The majority of the compounds in this list are natural products, which are well-tolerated and have minimal side-effects compared to the synthetic counterparts. Some of the potential therapeutic compounds identified in the current study are resveratrol, nimbolide, lovastatin, bortezomib, vorinostat, berberine, pterostilbene, deguelin, andrographolide, and colchicine. PMID:26536350

Prioritizing human pharmaceuticals for ecological risks in the freshwater environment of Korea.

PubMed

Ji, Kyunghee; Han, Eun Jeong; Back, Sunhyoung; Park, Jeongim; Ryu, Jisung; Choi, Kyungho

2016-04-01

Pharmaceutical residues are potential threats to aquatic ecosystems. Because more than 3000 active pharmaceutical ingredients (APIs) are in use, identifying high-priority pharmaceuticals is important for developing appropriate management options. Priority pharmaceuticals may vary by geographical region, because their occurrence levels can be influenced by demographic, societal, and regional characteristics. In the present study, the authors prioritized human pharmaceuticals of potential ecological risk in the Korean water environment, based on amount of use, biological activity, and regional hydrologic characteristics. For this purpose, the authors estimated the amounts of annual production of 695 human APIs in Korea. Then derived predicted environmental concentrations, using 2 approaches, to develop an initial candidate list of target pharmaceuticals. Major antineoplastic drugs and hormones were added in the initial candidate list regardless of their production amount because of their high biological activity potential. The predicted no effect concentrations were derived for those pharmaceuticals based on ecotoxicity information available in the literature or by model prediction. Priority lists of human pharmaceuticals were developed based on ecological risks and availability of relevant information. Those priority APIs identified include acetaminophen, clarithromycin, ciprofloxacin, ofloxacin, metformin, and norethisterone. Many of these pharmaceuticals have been neither adequately monitored nor assessed for risks in Korea. Further efforts are needed to improve these lists and to develop management decisions for these compounds in Korean water. © 2015 SETAC.

Toward prediction of alkane/water partition coefficients.

PubMed

Toulmin, Anita; Wood, J Matthew; Kenny, Peter W

2008-07-10

Partition coefficients were measured for 47 compounds in the hexadecane/water ( P hxd) and 1-octanol/water ( P oct) systems. Some types of hydrogen bond acceptor presented by these compounds to the partitioning systems are not well represented in the literature of alkane/water partitioning. The difference, DeltalogP, between logP oct and logP hxd is a measure of the hydrogen bonding potential of a molecule and is identified as a target for predictive modeling. Minimized molecular electrostatic potential ( V min) was shown to be an effective predictor of the contribution of hydrogen bond acceptors to DeltalogP. Carbonyl oxygen atoms were found to be stronger hydrogen bond acceptors for their electrostatic potential than heteroaromatic nitrogen or oxygen bound to hypervalent sulfur or nitrogen. Values of V min calculated for hydrogen-bonded complexes were used to explore polarization effects. Predicted logP hxd and DeltalogP were shown to be more effective than logP oct for modeling brain penetration for a data set of 18 compounds.

User-Friendly Predictive Modeling of Greenhouse Gas (GHG) Fluxes and Carbon Storage in Tidal Wetlands

NASA Astrophysics Data System (ADS)

Ishtiaq, K. S.; Abdul-Aziz, O. I.

2015-12-01

We developed user-friendly empirical models to predict instantaneous fluxes of CO2 and CH4 from coastal wetlands based on a small set of dominant hydro-climatic and environmental drivers (e.g., photosynthetically active radiation, soil temperature, water depth, and soil salinity). The dominant predictor variables were systematically identified by applying a robust data-analytics framework on a wide range of possible environmental variables driving wetland greenhouse gas (GHG) fluxes. The method comprised of a multi-layered data-analytics framework, including Pearson correlation analysis, explanatory principal component and factor analyses, and partial least squares regression modeling. The identified dominant predictors were finally utilized to develop power-law based non-linear regression models to predict CO2 and CH4 fluxes under different climatic, land use (nitrogen gradient), tidal hydrology and salinity conditions. Four different tidal wetlands of Waquoit Bay, MA were considered as the case study sites to identify the dominant drivers and evaluate model performance. The study sites were dominated by native Spartina Alterniflora and characterized by frequent flooding and high saline conditions. The model estimated the potential net ecosystem carbon balance (NECB) both in gC/m2 and metric tonC/hectare by up-scaling the instantaneous predicted fluxes to the growing season and accounting for the lateral C flux exchanges between the wetlands and estuary. The entire model was presented in a single Excel spreadsheet as a user-friendly ecological engineering tool. The model can aid the development of appropriate GHG offset protocols for setting monitoring plans for tidal wetland restoration and maintenance projects. The model can also be used to estimate wetland GHG fluxes and potential carbon storage under various IPCC climate change and sea level rise scenarios; facilitating an appropriate management of carbon stocks in tidal wetlands and their incorporation into a potential carbon market.

The number of discharge medications predicts thirty-day hospital readmission: a cohort study.

PubMed

Picker, David; Heard, Kevin; Bailey, Thomas C; Martin, Nathan R; LaRossa, Gina N; Kollef, Marin H

2015-07-23

Hospital readmission occurs often and is difficult to predict. Polypharmacy has been identified as a potential risk factor for hospital readmission. However, the overall impact of the number of discharge medications on hospital readmission is still undefined. To determine whether the number of discharge medications is predictive of thirty-day readmission using a retrospective cohort study design performed at Barnes-Jewish Hospital from January 15, 2013 to May 9, 2013. The primary outcome assessed was thirty-day hospital readmission. We also assessed potential predictors of thirty-day readmission to include the number of discharge medications. The final cohort had 5507 patients of which 1147 (20.8 %) were readmitted within thirty days of their hospital discharge date. The number of discharge medications was significantly greater for patients having a thirty-day readmission compared to those without a thirty-day readmission (7.2 ± 4.1 medications [7.0 medications (4.0 medications, 10.0 medications)] versus 6.0 ± 3.9 medications [6.0 medications (3.0 medications, 9.0 medications)]; P < 0.001). There was a statistically significant association between increasing numbers of discharge medications and the prevalence of thirty-day hospital readmission (P < 0.001). Multiple logistic regression identified more than six discharge medications to be independently associated with thirty-day readmission (OR, 1.26; 95 % CI, 1.17-1.36; P = 0.003). Other independent predictors of thirty-day readmission were: more than one emergency department visit in the previous six months, a minimum hemoglobin value less than or equal to 9 g/dL, presence of congestive heart failure, peripheral vascular disease, cirrhosis, and metastatic cancer. A risk score for thirty-day readmission derived from the logistic regression model had good predictive accuracy (AUROC = 0.661 [95 % CI, 0.643-0.679]). The number of discharge medications is associated with the prevalence of thirty-day hospital readmission. A risk score, that includes the number of discharge medications, accurately predicts patients at risk for thirty-day readmission. Our findings suggest that relatively simple and accessible parameters can identify patients at high risk for hospital readmission potentially distinguishing such individuals for interventions to minimize readmissions.

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

PubMed Central

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

2016-01-01

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

Using individualized predictive disease modeling to identify patients with the potential to benefit from a disease management program for diabetes mellitus.

PubMed

Weber, Christian; Neeser, Kurt

2006-08-01

Diabetes is an increasing health problem, but efforts to handle this pandemic by disease management programs (DMP) have shown conflicting results. Our hypothesis is that, in addition to a program's content and setting, the choice of the right patients is crucial to a program's efficacy and effectiveness. We used individualized predictive disease modeling (IPDM) on a cohort of 918 patients with type 2 diabetes to identify those patients with the greatest potential to benefit from inclusion in a DMP. A portion of the patients (4.7%) did not have even a theoretical potential for an increase in life expectancy and would therefore be unlikely to benefit from a DMP. Approximately 16.1% had an increase in life expectancy of less than half a year. Stratification of the entire cohort by surrogate parameters like preventable 10-year costs or gain in life expectancy was much more effective than stratification by classical clinical parameters such as high HbA1c level. Preventable costs increased up to 50.6% (or 1,010 per patient (1 = US dollars 1.28), p < 0.01) and life expectancy increased up to 54.8% (or 2.3 years, p < 0.01). IPDM is a valuable strategy to identify those patients with the greatest potential to avoid diabetes-related complications and thus can improve the overall effectiveness and efficacy of DMPs for diabetes mellitus.

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

PubMed

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

2017-01-01

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

Progress and challenges in bioinformatics approaches for enhancer identification

PubMed Central

Kleftogiannis, Dimitrios; Kalnis, Panos

2016-01-01

Enhancers are cis-acting DNA elements that play critical roles in distal regulation of gene expression. Identifying enhancers is an important step for understanding distinct gene expression programs that may reflect normal and pathogenic cellular conditions. Experimental identification of enhancers is constrained by the set of conditions used in the experiment. This requires multiple experiments to identify enhancers, as they can be active under specific cellular conditions but not in different cell types/tissues or cellular states. This has opened prospects for computational prediction methods that can be used for high-throughput identification of putative enhancers to complement experimental approaches. Potential functions and properties of predicted enhancers have been catalogued and summarized in several enhancer-oriented databases. Because the current methods for the computational prediction of enhancers produce significantly different enhancer predictions, it will be beneficial for the research community to have an overview of the strategies and solutions developed in this field. In this review, we focus on the identification and analysis of enhancers by bioinformatics approaches. First, we describe a general framework for computational identification of enhancers, present relevant data types and discuss possible computational solutions. Next, we cover over 30 existing computational enhancer identification methods that were developed since 2000. Our review highlights advantages, limitations and potentials, while suggesting pragmatic guidelines for development of more efficient computational enhancer prediction methods. Finally, we discuss challenges and open problems of this topic, which require further consideration. PMID:26634919

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.

The development and testing of a skin tear risk assessment tool.

PubMed

Newall, Nelly; Lewin, Gill F; Bulsara, Max K; Carville, Keryln J; Leslie, Gavin D; Roberts, Pam A

2017-02-01

The aim of the present study is to develop a reliable and valid skin tear risk assessment tool. The six characteristics identified in a previous case control study as constituting the best risk model for skin tear development were used to construct a risk assessment tool. The ability of the tool to predict skin tear development was then tested in a prospective study. Between August 2012 and September 2013, 1466 tertiary hospital patients were assessed at admission and followed up for 10 days to see if they developed a skin tear. The predictive validity of the tool was assessed using receiver operating characteristic (ROC) analysis. When the tool was found not to have performed as well as hoped, secondary analyses were performed to determine whether a potentially better performing risk model could be identified. The tool was found to have high sensitivity but low specificity and therefore have inadequate predictive validity. Secondary analysis of the combined data from this and the previous case control study identified an alternative better performing risk model. The tool developed and tested in this study was found to have inadequate predictive validity. The predictive validity of an alternative, more parsimonious model now needs to be tested. © 2015 Medicalhelplines.com Inc and John Wiley & Sons Ltd.

SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction.

PubMed

Boniecki, Michal J; Lach, Grzegorz; Dawson, Wayne K; Tomala, Konrad; Lukasz, Pawel; Soltysinski, Tomasz; Rother, Kristian M; Bujnicki, Janusz M

2016-04-20

RNA molecules play fundamental roles in cellular processes. Their function and interactions with other biomolecules are dependent on the ability to form complex three-dimensional (3D) structures. However, experimental determination of RNA 3D structures is laborious and challenging, and therefore, the majority of known RNAs remain structurally uncharacterized. Here, we present SimRNA: a new method for computational RNA 3D structure prediction, which uses a coarse-grained representation, relies on the Monte Carlo method for sampling the conformational space, and employs a statistical potential to approximate the energy and identify conformations that correspond to biologically relevant structures. SimRNA can fold RNA molecules using only sequence information, and, on established test sequences, it recapitulates secondary structure with high accuracy, including correct prediction of pseudoknots. For modeling of complex 3D structures, it can use additional restraints, derived from experimental or computational analyses, including information about secondary structure and/or long-range contacts. SimRNA also can be used to analyze conformational landscapes and identify potential alternative structures. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Mutagenicity in a Molecule: Identification of Core Structural Features of Mutagenicity Using a Scaffold Analysis

PubMed Central

Hsu, Kuo-Hsiang; Su, Bo-Han; Tu, Yi-Shu; Lin, Olivia A.; Tseng, Yufeng J.

2016-01-01

With advances in the development and application of Ames mutagenicity in silico prediction tools, the International Conference on Harmonisation (ICH) has amended its M7 guideline to reflect the use of such prediction models for the detection of mutagenic activity in early drug safety evaluation processes. Since current Ames mutagenicity prediction tools only focus on functional group alerts or side chain modifications of an analog series, these tools are unable to identify mutagenicity derived from core structures or specific scaffolds of a compound. In this study, a large collection of 6512 compounds are used to perform scaffold tree analysis. By relating different scaffolds on constructed scaffold trees with Ames mutagenicity, four major and one minor novel mutagenic groups of scaffold are identified. The recognized mutagenic groups of scaffold can serve as a guide for medicinal chemists to prevent the development of potentially mutagenic therapeutic agents in early drug design or development phases, by modifying the core structures of mutagenic compounds to form non-mutagenic compounds. In addition, five series of substructures are provided as recommendations, for direct modification of potentially mutagenic scaffolds to decrease associated mutagenic activities. PMID:26863515

Gankyrin is a predictive and oncogenic factor in well-differentiated and dedifferentiated liposarcoma.

PubMed

Hwang, Ju-Ae; Yang, Heung-Mo; Hong, Doo-Pyo; Joo, Sung-Yeon; Choi, Yoon-La; Park, Joo-Hung; Lazar, Alexander J; Pollock, Raphael E; Lev, Dina; Kim, Sung Joo

2014-10-15

Liposarcoma is one of the most common histologic types of soft tissue sarcoma and is frequently an aggressive cancer with poor outcome. Hence, alternative approaches other than surgical excision are necessary to improve treatment of well-differentiated/dedifferentiated liposarcoma (WDLPS/DDLPS). For this reason, we performed a two-dimensional gel electrophoresis (2-DE) and matrix-assisted laser desorption/ionization-time of flight mass spectrometry/mass spectrometry (MALDI-TOF/MS) analysis to identify new factors for WDLPS and DDLPS. Among the selected candidate proteins, gankyrin, known to be an oncoprotein, showed a significantly high level of expression pattern and inversely low expression of p53/p21 in WDLPS and DDLPS tissues, suggesting possible utility as a new predictive factor. Moreover, inhibition of gankyrin not only led to reduction of in vitro cell growth ability including cell proliferation, colony-formation, and migration, but also in vivo DDLPS cell tumorigenesis, perhaps via downregulation of the p53 tumor suppressor gene and its p21 target and also reduction of AKT/mTOR signal activation. This study identifies gankyrin, for the first time, as new potential predictive and oncogenic factor of WDLPS and DDLPS, suggesting the potential for service as a future LPS therapeutic approach.

Multivariate Models for Prediction of Skin Sensitization Hazard in Humans

EPA Science Inventory

One of ICCVAM’s highest priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary for a substance to elicit a skin sensitization reaction suggests that no single alternative me...

Applications of high throughput screening to identify profiles of biological activity relevant to carcinogenesis

EPA Science Inventory

ToxCast, the United States Environmental Protection Agency’s chemical prioritization research program, is developing methods for utilizing computational chemistry and bioactivity profiling to predict potential for toxicity and prioritize limited testing resources (www.epa.gov/toc...

Proposing alerts for pre and pro-haptens (QSAR2016)

EPA Science Inventory

Predictive testing to identify and characterise substances for their skin sensitisation potential has historically been based on animal tests such as the Local Lymph Node Assay (LLNA). In recent years, regulations in the cosmetics and chemicals sectors has provided a strong impe...

Multivariate Models for Prediction of Human Skin Sensitization Hazard

EPA Science Inventory

One of ICCVAM’s top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary for a substance to elicit a skin sensitization reaction suggests that no single alternative method...

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.

Circulating microRNAs as novel biomarkers of ALK-positive nonsmall cell lung cancer and predictors of response to crizotinib therapy.

PubMed

Li, Liang-Liang; Qu, Li-Li; Fu, Han-Jiang; Zheng, Xiao-Fei; Tang, Chuan-Hao; Li, Xiao-Yan; Chen, Jian; Wang, Wei-Xia; Yang, Shao-Xing; Wang, Lin; Zhao, Guan-Hua; Lv, Pan-Pan; Zhang, Min; Lei, Yang-Yang; Qin, Hai-Feng; Wang, Hong; Gao, Hong-Jun; Liu, Xiao-Qing

2017-07-11

Circulating microRNAs are potential diagnostic and predictive biomarkers, but have not been investigated for patients with anaplastic lymphoma kinase (ALK)-positive lung cancer. In this exploratory study, we sought to identify potential plasma biomarkers for ALK-positive non-small cell lung cancer (NSCLC). A microRNA microarray was used to select ALK-related microRNAs in ALK-positive NSCLC (n = 3), ALK-negative NSCLC (n = 3), and healthy subjects (n = 3). Plasma levels of 21 microRNAs were differentially expressed for ALK-positive and ALK-negative NSCLC, including 14 down-regulated and 7 up-regulated microRNAs. We also identified 5s rRNA as the most stable endogenous control gene using geNorm and NormFinder algorithms. Candidate microRNAs in plasma from ALK-positive (n = 41) and ALK-negative NSCLC patients (n = 32) were quantified using real-time reverse transcriptase quantitative polymerase chain reaction. The expression levels of miR-28-5p, miR-362-5p, and miR-660-5p were all down-regulated in ALK-positive NSCLC, compared with ALK-negative NSCLC. The areas under the receiver operating characteristic curves of miR-28-5p, miR-362-5p, miR-660-5p, and 3-microRNAs panel were 0.873, 0.673, 0.760, and 0.876, respectively. The positive predictive values of miR-28-5p, miR-362-5p, and miR-660-5p were 96.43%, 80.77%, and 83.87%, respectively. Increased plasma levels of miR-660-5p after crizotinib treatment predicted good tumor response (p = 0.012). The pre-crizotinib levels of miR-362-5p were significantly associated with progression-free survival (p = 0.015). Thus, in this preliminary investigation, we identified a potential panel of 3 microRNAs for distinguishing between patients with ALK-positive and ALK-negative NSCLC. We also identified miR-660-5p and miR-362-5p as potential predictors for response to crizotinib treatment.

Circulating microRNAs as novel biomarkers of ALK-positive non-small cell lung cancer and predictors of response to crizotinib therapy

PubMed Central

Fu, Han-Jiang; Zheng, Xiao-Fei; Tang, Chuan-Hao; Li, Xiao-Yan; Chen, Jian; Wang, Wei-Xia; Yang, Shao-Xing; Wang, Lin; Zhao, Guan-Hua; Lv, Pan-Pan; Zhang, Min; Lei, Yang-Yang; Qin, Hai-Feng; Wang, Hong; Gao, Hong-Jun; Liu, Xiao-Qing

2017-01-01

Circulating microRNAs are potential diagnostic and predictive biomarkers, but have not been investigated for patients with anaplastic lymphoma kinase (ALK)-positive lung cancer. In this exploratory study, we sought to identify potential plasma biomarkers for ALK-positive non-small cell lung cancer (NSCLC). A microRNA microarray was used to select ALK-related microRNAs in ALK-positive NSCLC (n = 3), ALK-negative NSCLC (n = 3), and healthy subjects (n = 3). Plasma levels of 21 microRNAs were differentially expressed for ALK-positive and ALK-negative NSCLC, including 14 down-regulated and 7 up-regulated microRNAs. We also identified 5s rRNA as the most stable endogenous control gene using geNorm and NormFinder algorithms. Candidate microRNAs in plasma from ALK-positive (n = 41) and ALK-negative NSCLC patients (n = 32) were quantified using real-time reverse transcriptase quantitative polymerase chain reaction. The expression levels of miR-28-5p, miR-362-5p, and miR-660-5p were all down-regulated in ALK-positive NSCLC, compared with ALK-negative NSCLC. The areas under the receiver operating characteristic curves of miR-28-5p, miR-362-5p, miR-660-5p, and 3-microRNAs panel were 0.873, 0.673, 0.760, and 0.876, respectively. The positive predictive values of miR-28-5p, miR-362-5p, and miR-660-5p were 96.43%, 80.77%, and 83.87%, respectively. Increased plasma levels of miR-660-5p after crizotinib treatment predicted good tumor response (p = 0.012). The pre-crizotinib levels of miR-362-5p were significantly associated with progression-free survival (p = 0.015). Thus, in this preliminary investigation, we identified a potential panel of 3 microRNAs for distinguishing between patients with ALK-positive and ALK-negative NSCLC. We also identified miR-660-5p and miR-362-5p as potential predictors for response to crizotinib treatment. PMID:28514730

Remote Health Monitoring Outcome Success Prediction Using Baseline and First Month Intervention Data.

PubMed

Alshurafa, Nabil; Sideris, Costas; Pourhomayoun, Mohammad; Kalantarian, Haik; Sarrafzadeh, Majid; Eastwood, Jo-Ann

2017-03-01

Remote health monitoring (RHM) systems are becoming more widely adopted by clinicians and hospitals to remotely monitor and communicate with patients while optimizing clinician time, decreasing hospital costs, and improving quality of care. In the Women's heart health study (WHHS), we developed Wanda-cardiovascular disease (CVD), where participants received healthy lifestyle education followed by six months of technology support and reinforcement. Wanda-CVD is a smartphone-based RHM system designed to assist participants in reducing identified CVD risk factors through wireless coaching using feedback and prompts as social support. Many participants benefitted from this RHM system. In response to the variance in participants' success, we developed a framework to identify classification schemes that predicted successful and unsuccessful participants. We analyzed both contextual baseline features and data from the first month of intervention such as activity, blood pressure, and questionnaire responses transmitted through the smartphone. A prediction tool can aid clinicians and scientists in identifying participants who may optimally benefit from the RHM system. Targeting therapies could potentially save healthcare costs, clinician, and participant time and resources. Our classification scheme yields RHM outcome success predictions with an F-measure of 91.9%, and identifies behaviors during the first month of intervention that help determine outcome success. We also show an improvement in prediction by using intervention-based smartphone data. Results from the WHHS study demonstrates that factors such as the variation in first month intervention response to the consumption of nuts, beans, and seeds in the diet help predict patient RHM protocol outcome success in a group of young Black women ages 25-45.

Analyzing gene expression profiles in dilated cardiomyopathy via bioinformatics methods.

PubMed

Wang, Liming; Zhu, L; Luan, R; Wang, L; Fu, J; Wang, X; Sui, L

2016-10-10

Dilated cardiomyopathy (DCM) is characterized by ventricular dilatation, and it is a common cause of heart failure and cardiac transplantation. This study aimed to explore potential DCM-related genes and their underlying regulatory mechanism using methods of bioinformatics. The gene expression profiles of GSE3586 were downloaded from Gene Expression Omnibus database, including 15 normal samples and 13 DCM samples. The differentially expressed genes (DEGs) were identified between normal and DCM samples using Limma package in R language. Pathway enrichment analysis of DEGs was then performed. Meanwhile, the potential transcription factors (TFs) and microRNAs (miRNAs) of these DEGs were predicted based on their binding sequences. In addition, DEGs were mapped to the cMap database to find the potential small molecule drugs. A total of 4777 genes were identified as DEGs by comparing gene expression profiles between DCM and control samples. DEGs were significantly enriched in 26 pathways, such as lymphocyte TarBase pathway and androgen receptor signaling pathway. Furthermore, potential TFs (SP1, LEF1, and NFAT) were identified, as well as potential miRNAs (miR-9, miR-200 family, and miR-30 family). Additionally, small molecules like isoflupredone and trihexyphenidyl were found to be potential therapeutic drugs for DCM. The identified DEGs (PRSS12 and FOXG1), potential TFs, as well as potential miRNAs, might be involved in DCM.

Analyzing gene expression profiles in dilated cardiomyopathy via bioinformatics methods

PubMed Central

Wang, Liming; Zhu, L.; Luan, R.; Wang, L.; Fu, J.; Wang, X.; Sui, L.

2016-01-01

Dilated cardiomyopathy (DCM) is characterized by ventricular dilatation, and it is a common cause of heart failure and cardiac transplantation. This study aimed to explore potential DCM-related genes and their underlying regulatory mechanism using methods of bioinformatics. The gene expression profiles of GSE3586 were downloaded from Gene Expression Omnibus database, including 15 normal samples and 13 DCM samples. The differentially expressed genes (DEGs) were identified between normal and DCM samples using Limma package in R language. Pathway enrichment analysis of DEGs was then performed. Meanwhile, the potential transcription factors (TFs) and microRNAs (miRNAs) of these DEGs were predicted based on their binding sequences. In addition, DEGs were mapped to the cMap database to find the potential small molecule drugs. A total of 4777 genes were identified as DEGs by comparing gene expression profiles between DCM and control samples. DEGs were significantly enriched in 26 pathways, such as lymphocyte TarBase pathway and androgen receptor signaling pathway. Furthermore, potential TFs (SP1, LEF1, and NFAT) were identified, as well as potential miRNAs (miR-9, miR-200 family, and miR-30 family). Additionally, small molecules like isoflupredone and trihexyphenidyl were found to be potential therapeutic drugs for DCM. The identified DEGs (PRSS12 and FOXG1), potential TFs, as well as potential miRNAs, might be involved in DCM. PMID:27737314

Incidence and prognostic factors for postoperative frozen shoulder after shoulder surgery: a prospective cohort study.

PubMed

Koorevaar, Rinco C T; Van't Riet, Esther; Ipskamp, Marcel; Bulstra, Sjoerd K

2017-03-01

Frozen shoulder is a potential complication after shoulder surgery. It is a clinical condition that is often associated with marked disability and can have a profound effect on the patient's quality of life. The incidence, etiology, pathology and prognostic factors of postoperative frozen shoulder after shoulder surgery are not known. The purpose of this explorative study was to determine the incidence of postoperative frozen shoulder after various operative shoulder procedures. A second aim was to identify prognostic factors for postoperative frozen shoulder after shoulder surgery. 505 consecutive patients undergoing elective shoulder surgery were included in this prospective cohort study. Follow-up was 6 months after surgery. A prediction model was developed to identify prognostic factors for postoperative frozen shoulder after shoulder surgery using the TRIPOD guidelines. We nominated five potential predictors: gender, diabetes mellitus, type of physiotherapy, arthroscopic surgery and DASH score. Frozen shoulder was identified in 11% of the patients after shoulder surgery and was more common in females (15%) than in males (8%). Frozen shoulder was encountered after all types of operative procedures. A prediction model based on four variables (diabetes mellitus, specialized shoulder physiotherapy, arthroscopic surgery and DASH score) discriminated reasonably well with an AUC of 0.712. Postoperative frozen shoulder is a serious complication after shoulder surgery, with an incidence of 11%. Four prognostic factors were identified for postoperative frozen shoulder: diabetes mellitus, arthroscopic surgery, specialized shoulder physiotherapy and DASH score. The combination of these four variables provided a prediction rule for postoperative frozen shoulder with reasonable fit. Level II, prospective cohort study.

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

PubMed Central

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

2016-01-01

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

Human Biomarker Discovery and Predictive Models for Disease Progression for Idiopathic Pneumonia Syndrome Following Allogeneic Stem Cell Transplantation*

PubMed Central

Schlatzer, Daniela M.; Dazard, Jean-Eudes; Ewing, Rob M.; Ilchenko, Serguei; Tomcheko, Sara E.; Eid, Saada; Ho, Vincent; Yanik, Greg; Chance, Mark R.; Cooke, Kenneth R.

2012-01-01

Allogeneic hematopoietic stem cell transplantation (SCT) is the only curative therapy for many malignant and nonmalignant conditions. Idiopathic pneumonia syndrome (IPS) is a frequently fatal complication that limits successful outcomes. Preclinical models suggest that IPS represents an immune mediated attack on the lung involving elements of both the adaptive and the innate immune system. However, the etiology of IPS in humans is less well understood. To explore the disease pathway and uncover potential biomarkers of disease, we performed two separate label-free, proteomics experiments defining the plasma protein profiles of allogeneic SCT patients with IPS. Samples obtained from SCT recipients without complications served as controls. The initial discovery study, intended to explore the disease pathway in humans, identified a set of 81 IPS-associated proteins. These data revealed similarities between the known IPS pathways in mice and the condition in humans, in particular in the acute phase response. In addition, pattern recognition pathways were judged to be significant as a function of development of IPS, and from this pathway we chose the lipopolysaccaharide-binding protein (LBP) protein as a candidate molecular diagnostic for IPS, and verified its increase as a function of disease using an ELISA assay. In a separately designed study, we identified protein-based classifiers that could predict, at day 0 of SCT, patients who: 1) progress to IPS and 2) respond to cytokine neutralization therapy. Using cross-validation strategies, we built highly predictive classifier models of both disease progression and therapeutic response. In sum, data generated in this report confirm previous clinical and experimental findings, provide new insights into the pathophysiology of IPS, identify potential molecular classifiers of the condition, and uncover a set of markers potentially of interest for patient stratification as a basis for individualized therapy. PMID:22337588

Evaluating Candidate Principal Surrogate Endpoints

PubMed Central

Gilbert, Peter B.; Hudgens, Michael G.

2009-01-01

Summary Frangakis and Rubin (2002, Biometrics 58, 21–29) proposed a new definition of a surrogate endpoint (a “principal” surrogate) based on causal effects. We introduce an estimand for evaluating a principal surrogate, the causal effect predictiveness (CEP) surface, which quantifies how well causal treatment effects on the biomarker predict causal treatment effects on the clinical endpoint. Although the CEP surface is not identifiable due to missing potential outcomes, it can be identified by incorporating a baseline covariate(s) that predicts the biomarker. Given case–cohort sampling of such a baseline predictor and the biomarker in a large blinded randomized clinical trial, we develop an estimated likelihood method for estimating the CEP surface. This estimation assesses the “surrogate value” of the biomarker for reliably predicting clinical treatment effects for the same or similar setting as the trial. A CEP surface plot provides a way to compare the surrogate value of multiple biomarkers. The approach is illustrated by the problem of assessing an immune response to a vaccine as a surrogate endpoint for infection. PMID:18363776

Assessing Bleeding Risk in Patients Taking Anticoagulants

PubMed Central

Shoeb, Marwa; Fang, Margaret C.

2013-01-01

Anticoagulant medications are commonly used for the prevention and treatment of thromboembolism. Although highly effective, they are also associated with significant bleeding risks. Numerous individual clinical factors have been linked to an increased risk of hemorrhage, including older age, anemia, and renal disease. To help quantify hemorrhage risk for individual patients, a number of clinical risk prediction tools have been developed. These risk prediction tools differ in how they were derived and how they identify and weight individual risk factors. At present, their ability to effective predict anticoagulant-associated hemorrhage remains modest. Use of risk prediction tools to estimate bleeding in clinical practice is most influential when applied to patients at the lower spectrum of thromboembolic risk, when the risk of hemorrhage will more strongly affect clinical decisions about anticoagulation. Using risk tools may also help counsel and inform patients about their potential risk for hemorrhage while on anticoagulants, and can identify patients who might benefit from more careful management of anticoagulation. PMID:23479259

Gypsum crystals observed in experimental and natural sea ice

NASA Astrophysics Data System (ADS)

Geilfus, N.-X.; Galley, R. J.; Cooper, M.; Halden, N.; Hare, A.; Wang, F.; Søgaard, D. H.; Rysgaard, S.

2013-12-01

gypsum has been predicted to precipitate in sea ice, it has never been observed. Here we provide the first report on gypsum precipitation in both experimental and natural sea ice. Crystals were identified by X-ray diffraction analysis. Based on their apparent distinguishing characteristics, the gypsum crystals were identified as being authigenic. The FREeZing CHEMistry (FREZCHEM) model results support our observations of both gypsum and ikaite precipitation at typical in situ sea ice temperatures and confirms the "Gitterman pathway" where gypsum is predicted to precipitate. The occurrence of authigenic gypsum in sea ice during its formation represents a new observation of precipitate formation and potential marine deposition in polar seas.

Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria.

PubMed

Vishnepolsky, Boris; Gabrielian, Andrei; Rosenthal, Alex; Hurt, Darrell E; Tartakovsky, Michael; Managadze, Grigol; Grigolava, Maya; Makhatadze, George I; Pirtskhalava, Malak

2018-05-29

Antimicrobial peptides (AMPs) have been identified as a potential new class of anti-infectives for drug development. There are a lot of computational methods that try to predict AMPs. Most of them can only predict if a peptide will show any antimicrobial potency, but to the best of our knowledge, there are no tools which can predict antimicrobial potency against particular strains. Here we present a predictive model of linear AMPs being active against particular Gram-negative strains relying on a semi-supervised machine-learning approach with a density-based clustering algorithm. The algorithm can well distinguish peptides active against particular strains from others which may also be active but not against the considered strain. The available AMP prediction tools cannot carry out this task. The prediction tool based on the algorithm suggested herein is available on https://dbaasp.org.

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.

EuGI: a novel resource for studying genomic islands to facilitate horizontal gene transfer detection in eukaryotes.

PubMed

Clasen, Frederick Johannes; Pierneef, Rian Ewald; Slippers, Bernard; Reva, Oleg

2018-05-03

Genomic islands (GIs) are inserts of foreign DNA that have potentially arisen through horizontal gene transfer (HGT). There are evidences that GIs can contribute significantly to the evolution of prokaryotes. The acquisition of GIs through HGT in eukaryotes has, however, been largely unexplored. In this study, the previously developed GI prediction tool, SeqWord Gene Island Sniffer (SWGIS), is modified to predict GIs in eukaryotic chromosomes. Artificial simulations are used to estimate ratios of predicting false positive and false negative GIs by inserting GIs into different test chromosomes and performing the SWGIS v2.0 algorithm. Using SWGIS v2.0, GIs are then identified in 36 fungal, 22 protozoan and 8 invertebrate genomes. SWGIS v2.0 predicts GIs in large eukaryotic chromosomes based on the atypical nucleotide composition of these regions. Averages for predicting false negative and false positive GIs were 20.1% and 11.01% respectively. A total of 10,550 GIs were identified in 66 eukaryotic species with 5299 of these GIs coding for at least one functional protein. The EuGI web-resource, freely accessible at http://eugi.bi.up.ac.za , was developed that allows browsing the database created from identified GIs and genes within GIs through an interactive and visual interface. SWGIS v2.0 along with the EuGI database, which houses GIs identified in 66 different eukaryotic species, and the EuGI web-resource, provide the first comprehensive resource for studying HGT in eukaryotes.

Predictive models for fish assemblages in eastern USA streams: implications for assessing biodiversity

USGS Publications Warehouse

Meador, Michael R.; Carlisle, Daren M.

2009-01-01

Management and conservation of aquatic systems require the ability to assess biological conditions and identify changes in biodiversity. Predictive models for fish assemblages were constructed to assess biological condition and changes in biodiversity for streams sampled in the eastern United States as part of the U.S. Geological Survey's National Water Quality Assessment Program. Separate predictive models were developed for northern and southern regions. Reference sites were designated using land cover and local professional judgment. Taxonomic completeness was quantified based on the ratio of the number of observed native fish species expected to occur to the number of expected native fish species. Models for both regions accurately predicted fish species composition at reference sites with relatively high precision and low bias. In general, species that occurred less frequently than expected (decreasers) tended to prefer riffle areas and larger substrates, such as gravel and cobble, whereas increaser species (occurring more frequently than expected) tended to prefer pools, backwater areas, and vegetated and sand substrates. In the north, the percentage of species identified as increasers and the percentage identified as decreasers were equal, whereas in the south nearly two-thirds of the species examined were identified as decreasers. Predictive models of fish species can provide a standardized indicator for consistent assessments of biological condition at varying spatial scales and critical information for an improved understanding of fish species that are potentially at risk of loss with changing water quality conditions.

Predicting when biliary excretion of parent drug is a major route of elimination in humans.

PubMed

Hosey, Chelsea M; Broccatelli, Fabio; Benet, Leslie Z

2014-09-01

Biliary excretion is an important route of elimination for many drugs, yet measuring the extent of biliary elimination is difficult, invasive, and variable. Biliary elimination has been quantified for few drugs with a limited number of subjects, who are often diseased patients. An accurate prediction of which drugs or new molecular entities are significantly eliminated in the bile may predict potential drug-drug interactions, pharmacokinetics, and toxicities. The Biopharmaceutics Drug Disposition Classification System (BDDCS) characterizes significant routes of drug elimination, identifies potential transporter effects, and is useful in understanding drug-drug interactions. Class 1 and 2 drugs are primarily eliminated in humans via metabolism and will not exhibit significant biliary excretion of parent compound. In contrast, class 3 and 4 drugs are primarily excreted unchanged in the urine or bile. Here, we characterize the significant elimination route of 105 orally administered class 3 and 4 drugs. We introduce and validate a novel model, predicting significant biliary elimination using a simple classification scheme. The model is accurate for 83% of 30 drugs collected after model development. The model corroborates the observation that biliarily eliminated drugs have high molecular weights, while demonstrating the necessity of considering route of administration and extent of metabolism when predicting biliary excretion. Interestingly, a predictor of potential metabolism significantly improves predictions of major elimination routes of poorly metabolized drugs. This model successfully predicts the major elimination route for poorly permeable/poorly metabolized drugs and may be applied prior to human dosing.

Understanding Transcription Factor Regulation by Integrating Gene Expression and DNase I Hypersensitive Sites.

PubMed

Wang, Guohua; Wang, Fang; Huang, Qian; Li, Yu; Liu, Yunlong; Wang, Yadong

2015-01-01

Transcription factors are proteins that bind to DNA sequences to regulate gene transcription. The transcription factor binding sites are short DNA sequences (5-20 bp long) specifically bound by one or more transcription factors. The identification of transcription factor binding sites and prediction of their function continue to be challenging problems in computational biology. In this study, by integrating the DNase I hypersensitive sites with known position weight matrices in the TRANSFAC database, the transcription factor binding sites in gene regulatory region are identified. Based on the global gene expression patterns in cervical cancer HeLaS3 cell and HelaS3-ifnα4h cell (interferon treatment on HeLaS3 cell for 4 hours), we present a model-based computational approach to predict a set of transcription factors that potentially cause such differential gene expression. Significantly, 6 out 10 predicted functional factors, including IRF, IRF-2, IRF-9, IRF-1 and IRF-3, ICSBP, belong to interferon regulatory factor family and upregulate the gene expression levels responding to the interferon treatment. Another factor, ISGF-3, is also a transcriptional activator induced by interferon alpha. Using the different transcription factor binding sites selected criteria, the prediction result of our model is consistent. Our model demonstrated the potential to computationally identify the functional transcription factors in gene regulation.

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

PubMed

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

2017-02-01

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

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.

In vivo serial MRI-based models and statistical methods to quantify sensitivity and specificity of mechanical predictors for carotid plaque rupture: location and beyond.

PubMed

Wu, Zheyang; Yang, Chun; Tang, Dalin

2011-06-01

It has been hypothesized that mechanical risk factors may be used to predict future atherosclerotic plaque rupture. Truly predictive methods for plaque rupture and methods to identify the best predictor(s) from all the candidates are lacking in the literature. A novel combination of computational and statistical models based on serial magnetic resonance imaging (MRI) was introduced to quantify sensitivity and specificity of mechanical predictors to identify the best candidate for plaque rupture site prediction. Serial in vivo MRI data of carotid plaque from one patient was acquired with follow-up scan showing ulceration. 3D computational fluid-structure interaction (FSI) models using both baseline and follow-up data were constructed and plaque wall stress (PWS) and strain (PWSn) and flow maximum shear stress (FSS) were extracted from all 600 matched nodal points (100 points per matched slice, baseline matching follow-up) on the lumen surface for analysis. Each of the 600 points was marked "ulcer" or "nonulcer" using follow-up scan. Predictive statistical models for each of the seven combinations of PWS, PWSn, and FSS were trained using the follow-up data and applied to the baseline data to assess their sensitivity and specificity using the 600 data points for ulcer predictions. Sensitivity of prediction is defined as the proportion of the true positive outcomes that are predicted to be positive. Specificity of prediction is defined as the proportion of the true negative outcomes that are correctly predicted to be negative. Using probability 0.3 as a threshold to infer ulcer occurrence at the prediction stage, the combination of PWS and PWSn provided the best predictive accuracy with (sensitivity, specificity) = (0.97, 0.958). Sensitivity and specificity given by PWS, PWSn, and FSS individually were (0.788, 0.968), (0.515, 0.968), and (0.758, 0.928), respectively. The proposed computational-statistical process provides a novel method and a framework to assess the sensitivity and specificity of various risk indicators and offers the potential to identify the optimized predictor for plaque rupture using serial MRI with follow-up scan showing ulceration as the gold standard for method validation. While serial MRI data with actual rupture are hard to acquire, this single-case study suggests that combination of multiple predictors may provide potential improvement to existing plaque assessment schemes. With large-scale patient studies, this predictive modeling process may provide more solid ground for rupture predictor selection strategies and methods for image-based plaque vulnerability assessment.

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

PubMed

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

2016-09-19

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

Machine Learning Approaches for Predicting Human Skin Sensitization Hazard

EPA Science Inventory

One of ICCVAM’s top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary for a substance to elicit a skin sensitization reaction suggests that no single in chemico, in vit...

The auditory brain-stem response to complex sounds: a potential biomarker for guiding treatment of psychosis.

PubMed

Tarasenko, Melissa A; Swerdlow, Neal R; Makeig, Scott; Braff, David L; Light, Gregory A

2014-01-01

Cognitive deficits limit psychosocial functioning in schizophrenia. For many patients, cognitive remediation approaches have yielded encouraging results. Nevertheless, therapeutic response is variable, and outcome studies consistently identify individuals who respond minimally to these interventions. Biomarkers that can assist in identifying patients likely to benefit from particular forms of cognitive remediation are needed. Here, we describe an event-related potential (ERP) biomarker - the auditory brain-stem response (ABR) to complex sounds (cABR) - that appears to be particularly well-suited for predicting response to at least one form of cognitive remediation that targets auditory information processing. Uniquely, the cABR quantifies the fidelity of sound encoded at the level of the brainstem and midbrain. This ERP biomarker has revealed auditory processing abnormalities in various neurodevelopmental disorders, correlates with functioning across several cognitive domains, and appears to be responsive to targeted auditory training. We present preliminary cABR data from 18 schizophrenia patients and propose further investigation of this biomarker for predicting and tracking response to cognitive interventions.

Social identity threat motivates science-discrediting online comments.

PubMed

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

2015-01-01

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

Social Identity Threat Motivates Science-Discrediting Online Comments

PubMed Central

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

2015-01-01

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

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.

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.

An enhanced genome-scale metabolic reconstruction of Streptomyces clavuligerus identifies novel strain improvement strategies.

PubMed

Toro, León; Pinilla, Laura; Avignone-Rossa, Claudio; Ríos-Estepa, Rigoberto

2018-05-01

In this work, we expanded and updated a genome-scale metabolic model of Streptomyces clavuligerus. The model includes 1021 genes and 1494 biochemical reactions; genome-reaction information was curated and new features related to clavam metabolism and to the biomass synthesis equation were incorporated. The model was validated using experimental data from the literature and simulations were performed to predict cellular growth and clavulanic acid biosynthesis. Flux balance analysis (FBA) showed that limiting concentrations of phosphate and an excess of ammonia accumulation are unfavorable for growth and clavulanic acid biosynthesis. The evaluation of different objective functions for FBA showed that maximization of ATP yields the best predictions for cellular behavior in continuous cultures, while the maximization of growth rate provides better predictions for batch cultures. Through gene essentiality analysis, 130 essential genes were found using a limited in silico media, while 100 essential genes were identified in amino acid-supplemented media. Finally, a strain design was carried out to identify candidate genes to be overexpressed or knocked out so as to maximize antibiotic biosynthesis. Interestingly, potential metabolic engineering targets, identified in this study, have not been tested experimentally.

Identification and validation of Asteraceae miRNAs by the expressed sequence tag analysis.

PubMed

Monavar Feshani, Aboozar; Mohammadi, Saeed; Frazier, Taylor P; Abbasi, Abbas; Abedini, Raha; Karimi Farsad, Laleh; Ehya, Farveh; Salekdeh, Ghasem Hosseini; Mardi, Mohsen

2012-02-10

MicroRNAs (miRNAs) are small non-coding RNA molecules that play a vital role in the regulation of gene expression. Despite their identification in hundreds of plant species, few miRNAs have been identified in the Asteraceae, a large family that comprises approximately one tenth of all flowering plants. In this study, we used the expressed sequence tag (EST) analysis to identify potential conserved miRNAs and their putative target genes in the Asteraceae. We applied quantitative Real-Time PCR (qRT-PCR) to confirm the expression of eight potential miRNAs in Carthamus tinctorius and Helianthus annuus. We also performed qRT-PCR analysis to investigate the differential expression pattern of five newly identified miRNAs during five different cotyledon growth stages in safflower. Using these methods, we successfully identified and characterized 151 potentially conserved miRNAs, belonging to 26 miRNA families, in 11 genus of Asteraceae. EST analysis predicted that the newly identified conserved Asteraceae miRNAs target 130 total protein-coding ESTs in sunflower and safflower, as well as 433 additional target genes in other plant species. We experimentally confirmed the existence of seven predicted miRNAs, (miR156, miR159, miR160, miR162, miR166, miR396, and miR398) in safflower and sunflower seedlings. We also observed that five out of eight miRNAs are differentially expressed during cotyledon development. Our results indicate that miRNAs may be involved in the regulation of gene expression during seed germination and the formation of the cotyledons in the Asteraceae. The findings of this study might ultimately help in the understanding of miRNA-mediated gene regulation in important crop species. Copyright © 2011 Elsevier B.V. All rights reserved.

Disinfection by-products (DBPs) in drinking water and predictive models for their occurrence: a review.

PubMed

Sadiq, Rehan; Rodriguez, Manuel J

2004-04-05

Disinfection for drinking water reduces the risk of pathogenic infection but may pose chemical threat to human health due to disinfection residues and their by-products (DBPs) when the organic and inorganic precursors are present in water. More than 250 DBPs have been identified, but the behavioural profile of only approximately 20 DBPs are adequately known. In the last 2 decades, many modelling attempts have been made to predict the occurrence of DBPs in drinking water. Models have been developed based on data generated in laboratory-scaled and field-scaled investigations. The objective of this paper is to review DBPs predictive models, identify their advantages and limitations, and examine their potential applications as decision-making tools for water treatment analysis, epidemiological studies and regulatory concerns. The paper concludes with a discussion about the future research needs in this area.

Metabotypes with properly functioning mitochondria and anti-inflammation predict extended productive life span in dairy cows

PubMed Central

Huber, K.; Dänicke, S.; Rehage, J.; Sauerwein, H.; Otto, W.; Rolle-Kampczyk, U.; von Bergen, M.

2016-01-01

The failure to adapt metabolism to the homeorhetic demands of lactation is considered as a main factor in reducing the productive life span of dairy cows. The so far defined markers of production performance and metabolic health in dairy cows do not predict the length of productive life span satisfyingly. This study aimed to identify novel pathways and biomarkers related to productive life in dairy cows by means of (targeted) metabolomics. In a longitudinal study from 42 days before up to 100 days after parturition, we identified metabolites such as long-chain acylcarnitines and biogenic amines associated with extended productive life spans. These metabolites are mainly secreted by the liver and depend on the functionality of hepatic mitochondria. The concentrations of biogenic amines and some acylcarnitines differed already before the onset of lactation thus indicating their predictive potential for continuation or early ending of productive life. PMID:27089826

Metabotypes with properly functioning mitochondria and anti-inflammation predict extended productive life span in dairy cows.

PubMed

Huber, K; Dänicke, S; Rehage, J; Sauerwein, H; Otto, W; Rolle-Kampczyk, U; von Bergen, M

2016-04-19

The failure to adapt metabolism to the homeorhetic demands of lactation is considered as a main factor in reducing the productive life span of dairy cows. The so far defined markers of production performance and metabolic health in dairy cows do not predict the length of productive life span satisfyingly. This study aimed to identify novel pathways and biomarkers related to productive life in dairy cows by means of (targeted) metabolomics. In a longitudinal study from 42 days before up to 100 days after parturition, we identified metabolites such as long-chain acylcarnitines and biogenic amines associated with extended productive life spans. These metabolites are mainly secreted by the liver and depend on the functionality of hepatic mitochondria. The concentrations of biogenic amines and some acylcarnitines differed already before the onset of lactation thus indicating their predictive potential for continuation or early ending of productive life.

Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder.

PubMed

Bartsch, Georg; Mitra, Anirban P; Mitra, Sheetal A; Almal, Arpit A; Steven, Kenneth E; Skinner, Donald G; Fry, David W; Lenehan, Peter F; Worzel, William P; Cote, Richard J

2016-02-01

Due to the high recurrence risk of nonmuscle invasive urothelial carcinoma it is crucial to distinguish patients at high risk from those with indolent disease. In this study we used a machine learning algorithm to identify the genes in patients with nonmuscle invasive urothelial carcinoma at initial presentation that were most predictive of recurrence. We used the genes in a molecular signature to predict recurrence risk within 5 years after transurethral resection of bladder tumor. Whole genome profiling was performed on 112 frozen nonmuscle invasive urothelial carcinoma specimens obtained at first presentation on Human WG-6 BeadChips (Illumina®). A genetic programming algorithm was applied to evolve classifier mathematical models for outcome prediction. Cross-validation based resampling and gene use frequencies were used to identify the most prognostic genes, which were combined into rules used in a voting algorithm to predict the sample target class. Key genes were validated by quantitative polymerase chain reaction. The classifier set included 21 genes that predicted recurrence. Quantitative polymerase chain reaction was done for these genes in a subset of 100 patients. A 5-gene combined rule incorporating a voting algorithm yielded 77% sensitivity and 85% specificity to predict recurrence in the training set, and 69% and 62%, respectively, in the test set. A singular 3-gene rule was constructed that predicted recurrence with 80% sensitivity and 90% specificity in the training set, and 71% and 67%, respectively, in the test set. Using primary nonmuscle invasive urothelial carcinoma from initial occurrences genetic programming identified transcripts in reproducible fashion, which were predictive of recurrence. These findings could potentially impact nonmuscle invasive urothelial carcinoma management. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Physical deposit measures and commercial potential: The case of titanium-bearing heavy-mineral deposits

USGS Publications Warehouse

Attanasi, E.D.; DeYoung, J.H.

1988-01-01

Physical measures of mineral deposit characteristics, such as grade and tonnage, long have been used in both subjective and analytic models to predict favorability of areas for the occurrence of mineral deposits of particular types. After a deposit has been identified, however, the explorationist must decide whether to continue data collection, begin an economic feasibility study, or abandon the prospect. The decision maker can estimate the probability that a deposit will be commercial by examining physical measures. The amount of sampling data required before such a probability estimate can be considered reliable can be determined. A logit probability model estimated from onshore titanium-bearing heavy-mineral deposit data identifies and quantifies the relative influence of a deposit's physical measures on the chances of the deposit becoming commercial. A principal conclusion that can be drawn from the analysis is that, along with a measure of deposit size, the characteristics most important in predicting commercial potential are grades of the constituent minerals. Total heavy-mineral-bearing sand grade or even total titanium grade (without data on constituent mineral grades) are poor predictors of the deposit's commercial potential. ?? 1988 International Association for Mathematical Geology.

Computational Identification and Functional Predictions of Long Noncoding RNA in Zea mays

PubMed Central

Boerner, Susan; McGinnis, Karen M.

2012-01-01

Background Computational analysis of cDNA sequences from multiple organisms suggests that a large portion of transcribed DNA does not code for a functional protein. In mammals, noncoding transcription is abundant, and often results in functional RNA molecules that do not appear to encode proteins. Many long noncoding RNAs (lncRNAs) appear to have epigenetic regulatory function in humans, including HOTAIR and XIST. While epigenetic gene regulation is clearly an essential mechanism in plants, relatively little is known about the presence or function of lncRNAs in plants. Methodology/Principal Findings To explore the connection between lncRNA and epigenetic regulation of gene expression in plants, a computational pipeline using the programming language Python has been developed and applied to maize full length cDNA sequences to identify, classify, and localize potential lncRNAs. The pipeline was used in parallel with an SVM tool for identifying ncRNAs to identify the maximal number of ncRNAs in the dataset. Although the available library of sequences was small and potentially biased toward protein coding transcripts, 15% of the sequences were predicted to be noncoding. Approximately 60% of these sequences appear to act as precursors for small RNA molecules and may function to regulate gene expression via a small RNA dependent mechanism. ncRNAs were predicted to originate from both genic and intergenic loci. Of the lncRNAs that originated from genic loci, ∼20% were antisense to the host gene loci. Conclusions/Significance Consistent with similar studies in other organisms, noncoding transcription appears to be widespread in the maize genome. Computational predictions indicate that maize lncRNAs may function to regulate expression of other genes through multiple RNA mediated mechanisms. PMID:22916204

Assessing exposure to transformation products of soil-applied organic contaminants in surface water: comparison of model predictions and field data.

PubMed

Kern, Susanne; Singer, Heinz; Hollender, Juliane; Schwarzenbach, René P; Fenner, Kathrin

2011-04-01

Transformation products (TPs) of chemicals released to soil, for example, pesticides, are regularly detected in surface and groundwater with some TPs even dominating observed pesticide levels. Given the large number of TPs potentially formed in the environment, straightforward prioritization methods based on available data and simple, evaluative models are required to identify TPs with a high aquatic exposure potential. While different such methods exist, none of them has so far been systematically evaluated against field data. Using a dynamic multimedia, multispecies model for TP prioritization, we compared the predicted relative surface water exposure potential of pesticides and their TPs with experimental data for 16 pesticides and 46 TPs measured in a small river draining a Swiss agricultural catchment. Twenty TPs were determined quantitatively using solid-phase extraction liquid chromatography mass spectrometry (SPE-LC-MS/MS), whereas the remaining 26 TPs could only be detected qualitatively because of the lack of analytical reference standards. Accordingly, the two sets of TPs were used for quantitative and qualitative model evaluation, respectively. Quantitative comparison of predicted with measured surface water exposure ratios for 20 pairs of TPs and parent pesticides indicated agreement within a factor of 10, except for chloridazon-desphenyl and chloridazon-methyl-desphenyl. The latter two TPs were found to be present in elevated concentrations during baseflow conditions and in groundwater samples across Switzerland, pointing toward high concentrations in exfiltrating groundwater. A simple leaching relationship was shown to qualitatively agree with the observed baseflow concentrations and to thus be useful in identifying TPs for which the simple prioritization model might underestimate actual surface water concentrations. Application of the model to the 26 qualitatively analyzed TPs showed that most of those TPs categorized as exhibiting a high aquatic exposure potential could be confirmed to be present in the majority of water samples investigated. On the basis of these results, we propose a generally applicable, model-based approach to identify those TPs of soil-applied organic contaminants that exhibit a high aquatic exposure potential to prioritize them for higher-tier, experimental investigations.

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 that within these envelopes, forest is protected from logging, roads, and other developments so that the ecological processes related to the establishment and subsequent use of fire refuges are maintained.

Syndromic Algorithms for Detection of Gambiense Human African Trypanosomiasis in South Sudan

PubMed Central

Palmer, Jennifer J.; Surur, Elizeous I.; Goch, Garang W.; Mayen, Mangar A.; Lindner, Andreas K.; Pittet, Anne; Kasparian, Serena; Checchi, Francesco; Whitty, Christopher J. M.

2013-01-01

Background Active screening by mobile teams is considered the best method for detecting human African trypanosomiasis (HAT) caused by Trypanosoma brucei gambiense but the current funding context in many post-conflict countries limits this approach. As an alternative, non-specialist health care workers (HCWs) in peripheral health facilities could be trained to identify potential cases who need testing based on their symptoms. We explored the predictive value of syndromic referral algorithms to identify symptomatic cases of HAT among a treatment-seeking population in Nimule, South Sudan. Methodology/Principal Findings Symptom data from 462 patients (27 cases) presenting for a HAT test via passive screening over a 7 month period were collected to construct and evaluate over 14,000 four item syndromic algorithms considered simple enough to be used by peripheral HCWs. For comparison, algorithms developed in other settings were also tested on our data, and a panel of expert HAT clinicians were asked to make referral decisions based on the symptom dataset. The best performing algorithms consisted of three core symptoms (sleep problems, neurological problems and weight loss), with or without a history of oedema, cervical adenopathy or proximity to livestock. They had a sensitivity of 88.9–92.6%, a negative predictive value of up to 98.8% and a positive predictive value in this context of 8.4–8.7%. In terms of sensitivity, these out-performed more complex algorithms identified in other studies, as well as the expert panel. The best-performing algorithm is predicted to identify about 9/10 treatment-seeking HAT cases, though only 1/10 patients referred would test positive. Conclusions/Significance In the absence of regular active screening, improving referrals of HAT patients through other means is essential. Systematic use of syndromic algorithms by peripheral HCWs has the potential to increase case detection and would increase their participation in HAT programmes. The algorithms proposed here, though promising, should be validated elsewhere. PMID:23350005

Perioperative Non-Invasive Indocyanine Green-Clearance Testing to Predict Postoperative Outcome after Liver Resection

PubMed Central

Haegele, Stefanie; Reiter, Silvia; Wanek, David; Offensperger, Florian; Pereyra, David; Stremitzer, Stefan; Fleischmann, Edith; Brostjan, Christine; Gruenberger, Thomas; Starlinger, Patrick

2016-01-01

Background Postoperative liver dysfunction may lead to morbidity and mortality after liver resection. Preoperative liver function assessment is critical to identify preexisting liver dysfunction in patients prior to resection. The aim of this study was to evaluate the predictive potential of perioperative indocyanine green (ICG)-clearance testing to prevent postoperative liver dysfunction and morbidity using standardized outcome parameters in a routine Western-clinical-setting. Study Design 137 patients undergoing partial hepatectomy between 2011 and 2013, at the general hospital of Vienna, were included. ICG-clearance was recorded one day prior to surgery as well as on the first and fifth postoperative day. Postoperative liver dysfunction was defined according to the International Study Group of Liver Surgery and evaluation of morbidity was based on the Dindo-Clavien classification. Statistical analyses were based on non-parametric tests. Results Preoperative reduced ICG—plasma disappearance rate (PDR) as well as increased ICG—retention rate at 15 min (R15) were able to significantly predict postoperative liver dysfunction (Area under the curve = PDR: 0.716, P = 0.018; R15: 0.719, P = 0.016). Furthermore, PDR <17%/min. or R15 >8%, were able to accurately predict postoperative complications prior to surgery. In addition to this, ICG-clearance on postoperative day 1 comparably predicted postoperative liver dysfunction (Area under the curve = PDR: 0.895; R15: 0.893; both P <0.001), specifically, PDR <10%/min or R15 >20% on postoperative day 1 predicted poor postoperative outcome. Conclusion PDR and R15 may represent useful parameters to distinguish preoperative high and low risk patients in a Western collective as well as on postoperative day 1, to identify patients who require closer monitoring for potential complications. PMID:27812143

Predicting Climate-sensitive Infectious Diseases: Development of a Federal Science Plan and the Path Forward

NASA Astrophysics Data System (ADS)

Trtanj, J.; Balbus, J. M.; Brown, C.; Shimamoto, M. M.

2017-12-01

The transmission and spread of infectious diseases, especially vector-borne diseases, water-borne diseases and zoonosis, are influenced by short and long-term climate factors, in conjunction with numerous other drivers. Public health interventions, including vaccination, vector control programs, and outreach campaigns could be made more effective if the geographic range and timing of increased disease risk could be more accurately targeted, and high risk areas and populations identified. While some progress has been made in predictive modeling for transmission of these diseases using climate and weather data as inputs, they often still start after the first case appears, the skill of those models remains limited, and their use by public health officials infrequent. And further, predictions with lead times of weeks, months or seasons are even rarer, yet the value of acting early holds the potential to save more lives, reduce cost and enhance both economic and national security. Information on high-risk populations and areas for infectious diseases is also potentially useful for the federal defense and intelligence communities as well. The US Global Change Research Program, through its Interagency Group on Climate Change and Human Health (CCHHG), has put together a science plan that pulls together federal scientists and programs working on predictive modeling of climate-sensitive diseases, and draws on academic and other partners. Through a series of webinars and an in-person workshop, the CCHHG has convened key federal and academic stakeholders to assess the current state of science and develop an integrated science plan to identify data and observation systems needs as well as a targeted research agenda for enhancing predictive modeling. This presentation will summarize the findings from this effort and engage AGU members on plans and next steps to improve predictive modeling for infectious diseases.

Plant MetGenMAP: an integrative analysis system for plant systems biology

USDA-ARS?s Scientific Manuscript database

We have developed a web-based system, Plant MetGenMAP, which can identify significantly altered biochemical pathways and highly affected biological processes, predict functional roles of pathway genes, and potential pathway-related regulatory motifs from transcript and metabolite profile datasets. P...

Can currently available non-animal methods detect pre and pro-haptens? (QSAR2016)

EPA Science Inventory

Predictive testing to identify and characterise substances for their skin sensitisation potential has historically been based on animal tests such as the Local Lymph Node Assay (LLNA). In recent years, regulations in the cosmetics and chemicals sectors has provided a strong impe...

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

PubMed

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

2007-03-10

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

Running non-minimal inflation with stabilized inflaton potential

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

Okada, Nobuchika; Raut, Digesh

In the context of the Higgs model involving gauge and Yukawa interactions with the spontaneous gauge symmetry breaking, we consider λφ4 inflation with non- minimal gravitational coupling, where the Higgs field is identified as the inflaton. Since the inflaton quartic coupling is very small, once quantum corrections through the gauge and Yukawa interactions are taken into account, the inflaton effective potential most likely becomes unstable. Furthermore, in order to avoid this problem, we need to impose stability conditions on the effective inflaton potential, which lead to not only non-trivial relations amongst the particle mass spectrum of the model, but alsomore » correlations between the inflationary predictions and the mass spectrum. For reasons of concrete discussion, we investigate the minimal B - L extension of the standard model with identification of the B - L Higgs field as the inflaton. The stability conditions for the inflaton effective potential fix the mass ratio amongst the B - L gauge boson, the right-handed neutrinos and the inflaton. This mass ratio also correlates with the inflationary predictions. So, if the B - L gauge boson and the right-handed neutrinos are discovered in the future, their observed mass ratio provides constraints on the inflationary predictions.« less

Running non-minimal inflation with stabilized inflaton potential

DOE PAGES

Okada, Nobuchika; Raut, Digesh

2017-04-18

In the context of the Higgs model involving gauge and Yukawa interactions with the spontaneous gauge symmetry breaking, we consider λφ4 inflation with non- minimal gravitational coupling, where the Higgs field is identified as the inflaton. Since the inflaton quartic coupling is very small, once quantum corrections through the gauge and Yukawa interactions are taken into account, the inflaton effective potential most likely becomes unstable. Furthermore, in order to avoid this problem, we need to impose stability conditions on the effective inflaton potential, which lead to not only non-trivial relations amongst the particle mass spectrum of the model, but alsomore » correlations between the inflationary predictions and the mass spectrum. For reasons of concrete discussion, we investigate the minimal B - L extension of the standard model with identification of the B - L Higgs field as the inflaton. The stability conditions for the inflaton effective potential fix the mass ratio amongst the B - L gauge boson, the right-handed neutrinos and the inflaton. This mass ratio also correlates with the inflationary predictions. So, if the B - L gauge boson and the right-handed neutrinos are discovered in the future, their observed mass ratio provides constraints on the inflationary predictions.« less

Prediction and analysis of promiscuous T cell-epitopes derived from the vaccine candidate antigens of Leishmania donovani binding to MHC class-II alleles using in silico approach.

PubMed

Kashyap, Manju; Jaiswal, Varun; Farooq, Umar

2017-09-01

Visceral leishmaniasis is a dreadful infectious disease and caused by the intracellular protozoan parasites, Leishmania donovani and Leishmania infantum. Despite extensive efforts for developing effective prophylactic vaccine, still no vaccine is available against leishmaniasis. However, advancement in immunoinformatics methods generated new dimension in peptide based vaccine development. The present study was aimed to identify T-cell epitopes from the vaccine candidate antigens like Lipophosphogylcan-3(LPG-3) and Nucleoside hydrolase (NH) from the L. donovani using in silico methods. Available best tools were used for the identification of promiscuous peptides for MHC class-II alleles. A total of 34 promiscuous peptides from LPG-3, 3 from NH were identified on the basis of their 100% binding affinity towards all six HLA alleles, taken in this study. These peptides were further checked computationally to know their IFN-γ and IL4 inducing potential and nine peptides were identified. Peptide binding interactions with predominant HLA alleles were done by docking. Out of nine docked promiscuous peptides, only two peptides (QESRILRVIKKKLVR, RILRVIKKKLVRKTL), from LPG-3 and one peptide (FDKFWCLVIDALKRI) from NH showed lowest binding energy with all six alleles. These promiscuous T-cell epitopes were predicted on the basis of their antigenicity, hydrophobicity, potential immune response and docking scores. The immunogenicity of predicted promiscuous peptides might be used for subunit vaccine development with immune-modulating adjuvants. Copyright © 2017 Elsevier B.V. All rights reserved.

Exploring patterns of recurrent melanoma in Northeast Scotland to inform the introduction a digital self-examination intervention

PubMed Central

2014-01-01

Background Melanoma incidence is growing and more people require follow-up to detect recurrent melanoma quickly. Those detecting their own recurrent melanoma appear to have the best prognosis, so total skin self examination (TSSE) is advocated, but practice is suboptimal. A digital intervention to support TSSE has potential but it is not clear which patient groups could benefit most. The aim of this study was to explore cutaneous melanoma recurrence patterns between 1991 and 2012 in Northeast Scotland. The objectives were to: determine how recurrent melanomas were detected during the period; explore factors potentially predictive of mode of recurrence detection; identify groups least likely to detect their own recurrent melanoma and with most potential to benefit from digital TSSE support. Methods Pathology records were used to identify those with a potential recurrent melanoma of any type (local, regional and distant). Following screening of potential cases available secondary care-held records were subsequently scrutinised. Data was collected on demographics and clinical characteristics of the initial and recurrent melanoma. Data were handled in Microsoft Excel and transported into SPSS 20.0 for statistical analysis. Factors predicting detection at interval or scheduled follow-up were explored using univariate techniques, with potentially influential factors combined in a multivariate binary logistic model to adjust for confounding. Results 149 potential recurrences were identified from the pathology database held at Aberdeen Royal Infirmary. Reliable data could be obtained on 94 cases of recurrent melanoma of all types. 30 recurrences (31.9%) were found by doctors at follow-up, and 64 (68.1%) in the interval between visits, usually by the patient themselves. Melanoma recurrences of all types occurring within one-year were significantly more likely to be found at follow-up visits, and this remained so following adjustment for other factors that could be used to target digital TSSE support. Conclusions A digital intervention should be offered to all newly diagnosed patients. This group could benefit most from optimal TSSE practice. PMID:24612627

Predictive Modelling for Fisheries Management in the Colombian Amazon

NASA Astrophysics Data System (ADS)

Beal, Jacob; Bennett, Sara

A group of Colombian indigenous communities and Amacayacu National Park are cooperating to make regulations for sustainable use of their shared natural resources, especially the fish populations. To aid this effort, we are modeling the interactions among these communities and their ecosystem with the objective of predicting the stability of regulations, identifying potential failure modes, and guiding investment of scarce resources. The goal is to improve the probability of actually achieving fair, sustainable and community-managed subsistence fishing in the region.

Significance of DNA Replication Licensing Proteins (MCM2, MCM5 and CDC6), p16 and p63 as Markers of Premalignant Lesions of the Uterine Cervix: Its Usefulness to Predict Malignant Potential

PubMed Central

Saritha, VN; Veena, VS; Krishna, KM Jagathnath; Somanathan, Thara; Sujathan, K

2018-01-01

Cervical cancer continues to be a leading cancer among women in many parts of the world. Nation-wide screening with the Pap smear has not been implemented in India due to the lack of adequately trained cytologists. Identification of biomarkers to predict malignant potential of the identified low risk lesions is essential to avoid excessive retesting and follow up. The current study analyzed the expression patterns of DNA replication licensing proteins, proliferation inhibitor protein p16INK4A and tumor suppresser protein p63 in cervical tissues and smears to assess the ability of these proteins to predict progression. Methods: Cervical smears and corresponding tissues were immunostained using mouse monoclonal antibodies against MCM2, MCM5, CDC6, p16 and p63. Smears were treated with a non-ionic surfactant sodium deoxycholate prior to immuno-cytochemistry. The standard ABC method of immunohistochemistry was performed using DAB as the chromogen. The immunostained samples were scored on a 0-3+ scale and staining patterns of smears were compared with those of tissue sections. Sensitivity and specificity for each of these markers were calculated taking histopathology as the gold standard. Result: All the markers were positive in malignant and dysplastic cells. MCM protein expression was found to be up-regulated in LSIL, HSIL and in malignancies to a greater extent than p16 as well as p63. CDC6 protein was preferentially expressed in high grade lesions and in invasive squamous cell carcinomas. A progressive increase in the expression of DNA replication licensing proteins in accordance with the grades of cervical intraepithelial lesion suggests these markers as significant to predict malignant potential of low grade lesions in cervical smears. Conclusion: MCMs and CDC6 can be applied as biomarkers to predict malignant potential of low grade lesions identified in screening programmes and retesting / follow up might be confined to those with high risk lesions alone so that overuse of resources can be safely avoided. PMID:29373905

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.

Global-local methodologies and their application to nonlinear analysis

NASA Technical Reports Server (NTRS)

Noor, Ahmed K.

1989-01-01

An assessment is made of the potential of different global-local analysis strategies for predicting the nonlinear and postbuckling responses of structures. Two postbuckling problems of composite panels are used as benchmarks and the application of different global-local methodologies to these benchmarks is outlined. The key elements of each of the global-local strategies are discussed and future research areas needed to realize the full potential of global-local methodologies are identified.

Factors affecting exits from homelessness among persons with serious mental illness and substance use disorders

PubMed Central

Gabrielian, Sonya; Bromley, Elizabeth; Hellemann, Gerhard S.; Kern, Robert S.; Goldenson, Nicholas I.; Danley, Megan E.; Young, Alexander S.

2015-01-01

Objective We sought to understand the housing trajectories of homeless consumers with serious mental illness (SMI) and co-occurring substance use disorders (SUD) and to identify factors that best-predicted achievement of independent housing. Methods Using administrative data, we identified homeless persons with SMI and SUD admitted to a residential rehabilitation program from 12/2008-11/2011. On a random sample (n=36), we assessed a range of potential predictors of housing outcomes, including symptoms, cognition, and social/community supports. We used the Residential Time-Line Follow-Back (TLFB) Inventory to gather housing histories since exiting rehabilitation and identify housing outcomes. We used recursive partitioning to identify variables that best-differentiated participants by these outcomes. Results We identified three housing trajectories: stable housing (n=14); unstable housing (n=15); and continuously engaged in housing services (n=7). Using recursive partitioning, two variables (symbol digit modalities test (SDMT), a neurocognitive speed of processing measure and Behavior and Symptom Identification Scale (BASIS)-relationships subscale, which quantifies symptoms affecting relationships) were sufficient to capture information provided by 26 predictors to classify participants by housing outcome. Participants predicted to continuously engage in services had impaired processing speeds (SDMT score<32.5). Among consumers with SDMT score≥32.5, those predicted to achieve stable housing had fewer interpersonal symptoms (BASIS-relationships score<0.81) than those predicted to have unstable housing. This model explains 57% of this sample's variability and 14% of this population's variability in housing outcomes. Conclusion As cognition and symptoms influencing relationships predicted housing outcomes for homeless adults with SMI and SUD, cognitive and social skills trainings may be useful for this population. PMID:25919839

Factors affecting exits from homelessness among persons with serious mental illness and substance use disorders.

PubMed

Gabrielian, Sonya; Bromley, Elizabeth; Hellemann, Gerhard S; Kern, Robert S; Goldenson, Nicholas I; Danley, Megan E; Young, Alexander S

2015-04-01

We sought to understand the housing trajectories of homeless consumers with serious mental illness (SMI) and co-occurring substance use disorders (SUD) and to identify factors that best predicted achievement of independent housing. Using administrative data, we identified homeless persons with SMI and SUD admitted to a residential rehabilitation program from December 2008 to November 2011. Our primary outcome measure was independent housing status. On a random sample (N = 36), we assessed a range of potential predictors of housing outcomes, including symptoms, cognition, and social/community supports. We used the Residential Time-Line Follow-Back (TLFB) Inventory to gather housing histories since exiting rehabilitation and to identify housing outcomes. We used Recursive Partitioning (RP) to identify variables that best differentiated participants by these outcomes. We identified 3 housing trajectories: stable housing (n = 14), unstable housing (n = 15), and continuously engaged in housing services (n = 7). In RP analysis, 2 variables (Symbol Digit Modalities Test [SDMT], a neurocognitive speed of processing measure, and Behavior and Symptom Identification Scale [BASIS-24] Relationships subscale, which quantifies symptoms affecting relationships) were sufficient to capture information provided by 26 predictors to classify participants by housing outcome. Participants predicted to continuously engage in services had impaired processing speeds (SDMT score < 32.5). Among consumers with SDMT score ≥ 32.5, those predicted to achieve stable housing had fewer interpersonal symptoms (BASIS-24 Relationships subscale score < 0.81) than those predicted to have unstable housing. This model explains 57% of this sample's variability and 14% of this population's variability in housing outcomes. Because cognition and symptoms influencing relationships predicted housing outcomes for homeless adults with SMI and SUD, cognitive and social skills training may be useful for this population. © Copyright 2015 Physicians Postgraduate Press, Inc.

Analysis of predicted B and T-cell epitopes in Der p 23, allergen from Dermatophagoides pteronyssinus.

PubMed

Fanuel, Songwe; Tabesh, Saeideh; Sadroddiny, Esmaeil; Kardar, Gholam Ali

2017-01-01

House dust mite (HDM) allergy is the leading cause of IgE-mediated hypersensitivity. Therefore identifying potential epitopes in the Dermatophagoide pteronyssinus 23 (Der p 23), a major house dust mite allergen will aid in the development of therapeutic vaccines and diagnostic kits for HDM allergy. Experimental methods of epitope discovery have been widely exploited for the mapping of potential allergens. This study sought to use immunoinformatic methods to analyze the structure of Der p 23 for potential immunoreactive B and T-cell epitopes that could be useful for AIT and allergy diagnosis. We retrieved a Der p 23 allergen sequence from Genbank database and then analyzed it using a combination of web-based sequence analysis tools including the Immune Epitope Database (IEDB), Protparam, BCPREDS, ABCpred, BepiPred, Bcepred among others to predict the physiochemical properties and epitope spectra of the Der p 23 allergen. We then built 3D models of the predicted B-cell epitopes, T cell epitopes and Der p 23 for sequence structure homology analysis. Our results identified peptides 'TRWNEDE', 'TVHPTTTEQPDDK', and 'NDDDPTT' as immunogenic linear B-cell epitopes while 'CPSRFGYFADPKDPH' and 'CPGNTRWNEDEETCT' were found to be the most suitable T-cell epitopes that interacted well with a large number of MHC II alleles. Both epitopes had high population coverage as well as showing a 100% conservancy. These five Der p 23 epitopes are useful for AIT vaccines and HDM allergy diagnosis development.

Predicting response to EGFR inhibitors in metastatic colorectal cancer: current practice and future directions.

PubMed

Shankaran, Veena; Obel, Jennifer; Benson, Al B

2010-01-01

The identification of KRAS mutational status as a predictive marker of response to antibodies against the epidermal growth factor receptor (EGFR) has been one of the most significant and practice-changing recent advances in colorectal cancer research. Recently, data suggesting a potential role for other markers (including BRAF mutations, loss of phosphatase and tension homologue deleted on chromosome ten expression, and phosphatidylinositol-3-kinase-AKT pathway mutations) in predicting response to anti-EGFR therapy have emerged. Ongoing clinical trials and correlative analyses are essential to definitively identify predictive markers and develop therapeutic strategies for patients who may not derive benefit from anti-EGFR therapy. This article reviews recent clinical trials supporting the predictive role of KRAS, recent changes to clinical guidelines and pharmaceutical labeling, investigational predictive molecular markers, and newer clinical trials targeting patients with mutated KRAS.

High Proliferation Predicts Pathological Complete Response to Neoadjuvant Chemotherapy in Early Breast Cancer

PubMed Central

Lluch, Ana; Ribelles, Nuria; Anton-Torres, Antonio; Sanchez-Rovira, Pedro; Albanell, Joan; Calvo, Lourdes; García-Asenjo, Jose Antonio Lopez; Palacios, Jose; Chacon, Jose Ignacio; Ruiz, Amparo; De la Haba-Rodriguez, Juan; Segui-Palmer, Miguel A.; Cirauqui, Beatriz; Margeli, Mireia; Plazaola, Arrate; Barnadas, Agusti; Casas, Maribel; Caballero, Rosalia; Carrasco, Eva; Rojo, Federico

2016-01-01

Background. In the neoadjuvant setting, changes in the proliferation marker Ki67 are associated with primary endocrine treatment efficacy, but its value as a predictor of response to chemotherapy is still controversial. Patients and Methods. We analyzed 262 patients with centralized basal Ki67 immunohistochemical evaluation derived from 4 GEICAM (Spanish Breast Cancer Group) clinical trials of neoadjuvant chemotherapy for breast cancer. The objective was to identify the optimal threshold for Ki67 using the receiver-operating characteristic curve method to maximize its predictive value for chemotherapy benefit. We also evaluated the predictive role of the defined Ki67 cutoffs for molecular subtypes defined by estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2). Results. A basal Ki67 cutpoint of 50% predicted pathological complete response (pCR). Patients with Ki67 >50% achieved a pCR rate of 40% (36 of 91) versus a pCR rate of 19% in patients with Ki67 ≤50% (33 of 171) (p = .0004). Ki67 predictive value was especially relevant in ER-HER2− and ER-HER2+ patients (pCR rates of 42% and 64%, respectively, in patients with Ki67 >50% versus 15% and 45%, respectively, in patients with Ki67 ≤50%; p = .0337 and .3238, respectively). Both multivariate analyses confirmed the independent predictive value of the Ki67 cutpoint of 50%. Conclusion. Basal Ki67 proliferation index >50% should be considered an independent predictive factor for pCR reached after neoadjuvant chemotherapy, suggesting that cell proliferation is a phenomenon closely related to chemosensitivity. These findings could help to identify a group of patients with a potentially favorable long-term prognosis. Implications for Practice: The use of basal Ki67 status as a predictive factor of chemotherapy benefit could facilitate the identification of a patient subpopulation with high probability of achieving pathological complete response when treated with primary chemotherapy, and thus with a potentially favorable long-term prognosis. PMID:26786263

Distinctive Behaviors of Druggable Proteins in Cellular Networks

PubMed Central

Workman, Paul; Al-Lazikani, Bissan

2015-01-01

The interaction environment of a protein in a cellular network is important in defining the role that the protein plays in the system as a whole, and thus its potential suitability as a drug target. Despite the importance of the network environment, it is neglected during target selection for drug discovery. Here, we present the first systematic, comprehensive computational analysis of topological, community and graphical network parameters of the human interactome and identify discriminatory network patterns that strongly distinguish drug targets from the interactome as a whole. Importantly, we identify striking differences in the network behavior of targets of cancer drugs versus targets from other therapeutic areas and explore how they may relate to successful drug combinations to overcome acquired resistance to cancer drugs. We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. We also make available full predictions for 13,345 proteins to aid target selection for drug discovery. All target predictions are available through canSAR.icr.ac.uk. Underlying data and tools are available at https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/. PMID:26699810

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

PubMed

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

2016-02-01

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

Treatment-Related Predictive and Prognostic Factors in Trimodality Approach in Stage IIIA/N2 Non-Small Cell Lung Cancer.

PubMed

Jeremić, Branislav; Casas, Francesc; Dubinsky, Pavol; Gomez-Caamano, Antonio; Čihorić, Nikola; Videtic, Gregory; Igrutinovic, Ivan

2018-01-01

While there are no established pretreatment predictive and prognostic factors in patients with stage IIIA/pN2 non-small cell lung cancer (NSCLC) indicating a benefit to surgery as a part of trimodality approach, little is known about treatment-related predictive and prognostic factors in this setting. A literature search was conducted to identify possible treatment-related predictive and prognostic factors for patients for whom trimodality approach was reported on. Overall survival was the primary endpoint of this study. Of 30 identified studies, there were two phase II studies, 5 "prospective" studies, and 23 retrospective studies. No study was found which specifically looked at treatment-related predictive factors of improved outcomes in trimodality treatment. Of potential treatment-related prognostic factors, the least frequently analyzed factors among 30 available studies were overall pathologic stage after preoperative treatment and UICC downstaging. Evaluation of treatment response before surgery and by pathologic tumor stage after induction therapy were analyzed in slightly more than 40% of studies and found not to influence survival. More frequently studied factors-resection status, degree of tumor regression, and pathologic nodal stage after induction therapy as well as the most frequently studied factor, the treatment (in almost 75% studies)-showed no discernible impact on survival, due to conflicting results. Currently, it is impossible to identify any treatment-related predictive or prognostic factors for selecting surgery in the treatment of patients with stage IIIA/pN2 NSCLC.

Automated confidence ranked classification of randomized controlled trial articles: an aid to evidence-based medicine

PubMed Central

Smalheiser, Neil R; McDonagh, Marian S; Yu, Clement; Adams, Clive E; Davis, John M; Yu, Philip S

2015-01-01

Objective: For many literature review tasks, including systematic review (SR) and other aspects of evidence-based medicine, it is important to know whether an article describes a randomized controlled trial (RCT). Current manual annotation is not complete or flexible enough for the SR process. In this work, highly accurate machine learning predictive models were built that include confidence predictions of whether an article is an RCT. Materials and Methods: The LibSVM classifier was used with forward selection of potential feature sets on a large human-related subset of MEDLINE to create a classification model requiring only the citation, abstract, and MeSH terms for each article. Results: The model achieved an area under the receiver operating characteristic curve of 0.973 and mean squared error of 0.013 on the held out year 2011 data. Accurate confidence estimates were confirmed on a manually reviewed set of test articles. A second model not requiring MeSH terms was also created, and performs almost as well. Discussion: Both models accurately rank and predict article RCT confidence. Using the model and the manually reviewed samples, it is estimated that about 8000 (3%) additional RCTs can be identified in MEDLINE, and that 5% of articles tagged as RCTs in Medline may not be identified. Conclusion: Retagging human-related studies with a continuously valued RCT confidence is potentially more useful for article ranking and review than a simple yes/no prediction. The automated RCT tagging tool should offer significant savings of time and effort during the process of writing SRs, and is a key component of a multistep text mining pipeline that we are building to streamline SR workflow. In addition, the model may be useful for identifying errors in MEDLINE publication types. The RCT confidence predictions described here have been made available to users as a web service with a user query form front end at: http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/RCT_Tagger.cgi. PMID:25656516

Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis.

PubMed

Papa, Ester; van der Wal, Leon; Arnot, Jon A; Gramatica, Paola

2014-02-01

Bioaccumulation in fish is a function of competing rates of chemical uptake and elimination. For hydrophobic organic chemicals bioconcentration, bioaccumulation and biomagnification potential are high and the biotransformation rate constant is a key parameter. Few measured biotransformation rate constant data are available compared to the number of chemicals that are being evaluated for bioaccumulation hazard and for exposure and risk assessment. Three new Quantitative Structure-Activity Relationships (QSARs) for predicting whole body biotransformation half-lives (HLN) in fish were developed and validated using theoretical molecular descriptors that seek to capture structural characteristics of the whole molecule and three data set splitting schemes. The new QSARs were developed using a minimal number of theoretical descriptors (n=9) and compared to existing QSARs developed using fragment contribution methods that include up to 59 descriptors. The predictive statistics of the models are similar thus further corroborating the predictive performance of the different QSARs; Q(2)ext ranges from 0.75 to 0.77, CCCext ranges from 0.86 to 0.87, RMSE in prediction ranges from 0.56 to 0.58. The new QSARs provide additional mechanistic insights into the biotransformation capacity of organic chemicals in fish by including whole molecule descriptors and they also include information on the domain of applicability for the chemical of interest. Advantages of consensus modeling for improving overall prediction and minimizing false negative errors in chemical screening assessments, for identifying potential sources of residual error in the empirical HLN database, and for identifying structural features that are not well represented in the HLN dataset to prioritize future testing needs are illustrated. © 2013.

Clinical application of a systems model of apoptosis execution for the prediction of colorectal cancer therapy responses and personalisation of therapy.

PubMed

Hector, Suzanne; Rehm, Markus; Schmid, Jasmin; Kehoe, Joan; McCawley, Niamh; Dicker, Patrick; Murray, Frank; McNamara, Deborah; Kay, Elaine W; Concannon, Caoimhin G; Huber, Heinrich J; Prehn, Jochen H M

2012-05-01

Key to the clinical management of colorectal cancer is identifying tools which aid in assessing patient prognosis and determining more effective and personalised treatment strategies. We evaluated whether an experimental systems biology strategy which analyses the susceptibility of cancer cells to undergo caspase activation can be exploited to predict patient responses to 5-fluorouracil-based chemotherapy and to case-specifically identify potential alternative targeted treatments to reactivate apoptosis. We quantified five essential apoptosis-regulating proteins (Pro-Caspases 3 and 9, APAF-1, SMAC and XIAP) in samples of Stage II (n = 13) and III (n=17) tumour and normal colonic (n = 8) tissue using absolute quantitative immunoblotting and employed systems simulations of apoptosis signalling to predict the susceptibility of tumour cells to execute apoptosis. Additional systems analyses assessed the efficacy of novel apoptosis-inducing therapeutics such as XIAP antagonists, proteasome inhibitors and Pro-Caspase-3-activating compounds in restoring apoptosis execution in apoptosis-incompetent tumours. Comparisons of caspase activity profiles demonstrated that the likelihood of colorectal tumours to undergo apoptosis decreases with advancing disease stage. Systems-level analysis correctly predicted positive or negative outcome in 85% (p=0.004) of colorectal cancer patients receiving 5-fluorouracil based chemotherapy and significantly outperformed common uni- and multi-variate statistical approaches. Modelling of individual patient responses to novel apoptosis-inducing therapeutics revealed markedly different inter-individual responses. Our study represents the first proof-of-concept example demonstrating the significant clinical potential of systems biology-based approaches for predicting patient outcome and responsiveness to novel targeted treatment paradigms.

Multivariate Models for Prediction of Human Skin Sensitization Hazard

PubMed Central

Strickland, Judy; Zang, Qingda; Paris, Michael; Lehmann, David M.; Allen, David; Choksi, Neepa; Matheson, Joanna; Jacobs, Abigail; Casey, Warren; Kleinstreuer, Nicole

2016-01-01

One of ICCVAM’s top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays—the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT), and KeratinoSens™ assay—six physicochemical properties, and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches, logistic regression (LR) and support vector machine (SVM), to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three LR and three SVM) with the highest accuracy (92%) used: (1) DPRA, h-CLAT, and read-across; (2) DPRA, h-CLAT, read-across, and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens, and log P. The models performed better at predicting human skin sensitization hazard than the murine local lymph node assay (accuracy = 88%), any of the alternative methods alone (accuracy = 63–79%), or test batteries combining data from the individual methods (accuracy = 75%). These results suggest that computational methods are promising tools to effectively identify potential human skin sensitizers without animal testing. PMID:27480324

Applying an extended theory of planned behaviour to predict breakfast consumption in adolescents.

PubMed

Kennedy, S; Davies, E L; Ryan, L; Clegg, M E

2017-05-01

Breakfast skipping increases during adolescence and is associated with lower levels of physical activity and weight gain. Theory-based interventions promoting breakfast consumption in adolescents report mixed findings, potentially because of limited research identifying which determinants to target. This study aimed to: (i) utilise the Theory of Planned Behaviour (TPB) to identify the relative contribution of attitudes (affective, cognitive and behavioural) to predict intention to eat breakfast and breakfast consumption in adolescents and (ii) determine whether demographic factors moderate the relationship between TPB variables, intention and behaviour. Questionnaires were completed by 434 students (mean 14±0.9 years) measuring breakfast consumption (0-2, 3-6 or 7 days), physical activity levels and TPB measures. Data were analysed by breakfast frequency and demographics using hierarchical and multinomial regression analyses. Breakfast was consumed everyday by 57% of students, with boys more likely to eat a regular breakfast, report higher activity levels and report more positive attitudes towards breakfast than girls (P<0.001). The TPB predicted 58% of the variation in intentions. Overall, the model was predictive of breakfast behaviours (P<0.001), but the relative contribution of TPB constructs varied depending on breakfast frequency. Interactions between gender and intentions were significant when comparing 0-2- and 3-6-day breakfast eaters only highlighting a stronger intention-behaviour relationship for girls. Findings confirm that the TPB is a successful model for predicting breakfast intentions and behaviours in adolescents. The potential for a direct effect of attitudes on behaviours should be considered in the implementation and design of breakfast interventions.

Predictable and unpredictable modes of seasonal mean precipitation over Northeast China

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Modeling intrinsic potential for beaver (Castor canadensis) habitat to inform restoration and climate change adaptation

PubMed Central

Pollock, Michael M.; Schilling, Jason W.; Olden, Julian D.; Lawler, Joshua J.; Torgersen, Christian E.

2018-01-01

Through their dam-building activities and subsequent water storage, beaver have the potential to restore riparian ecosystems and offset some of the predicted effects of climate change by modulating streamflow. Thus, it is not surprising that reintroducing beaver to watersheds from which they have been extirpated is an often-used restoration and climate-adaptation strategy. Identifying sites for reintroduction, however, requires detailed information about habitat factors—information that is not often available at broad spatial scales. Here we explore the potential for beaver relocation throughout the Snohomish River Basin in Washington, USA with a model that identifies some of the basic building blocks of beaver habitat suitability and does so by relying solely on remotely sensed data. More specifically, we developed a generalized intrinsic potential model that draws on remotely sensed measures of stream gradient, stream width, and valley width to identify where beaver could become established if suitable vegetation were to be present. Thus, the model serves as a preliminary screening tool that can be applied over relatively large extents. We applied the model to 5,019 stream km and assessed the ability of the model to correctly predict beaver habitat by surveying for beavers in 352 stream reaches. To further assess the potential for relocation, we assessed land ownership, use, and land cover in the landscape surrounding stream reaches with varying levels of intrinsic potential. Model results showed that 33% of streams had moderate or high intrinsic potential for beaver habitat. We found that no site that was classified as having low intrinsic potential had any sign of beavers and that beaver were absent from nearly three quarters of potentially suitable sites, indicating that there are factors preventing the local population from occupying these areas. Of the riparian areas around streams with high intrinsic potential for beaver, 38% are on public lands and 17% are on large tracts of privately-owned timber land. Thus, although there are a large number of areas that could be suitable for relocation and restoration using beavers, current land use patterns may substantially limit feasibility in these areas. PMID:29489853

Modeling intrinsic potential for beaver (Castor canadensis) habitat to inform restoration and climate change adaptation

USGS Publications Warehouse

Dittbrenner, Benjamin J.; Pollack, Michael M.; Schilling, Jason W.; Olden, Julian D.; Lawler, Joshua J.; Torgersen, Christian E.

2018-01-01

Through their dam-building activities and subsequent water storage, beaver have the potential to restore riparian ecosystems and offset some of the predicted effects of climate change by modulating streamflow. Thus, it is not surprising that reintroducing beaver to watersheds from which they have been extirpated is an often-used restoration and climate-adaptation strategy. Identifying sites for reintroduction, however, requires detailed information about habitat factors—information that is not often available at broad spatial scales. Here we explore the potential for beaver relocation throughout the Snohomish River Basin in Washington, USA with a model that identifies some of the basic building blocks of beaver habitat suitability and does so by relying solely on remotely sensed data. More specifically, we developed a generalized intrinsic potential model that draws on remotely sensed measures of stream gradient, stream width, and valley width to identify where beaver could become established if suitable vegetation were to be present. Thus, the model serves as a preliminary screening tool that can be applied over relatively large extents. We applied the model to 5,019 stream km and assessed the ability of the model to correctly predict beaver habitat by surveying for beavers in 352 stream reaches. To further assess the potential for relocation, we assessed land ownership, use, and land cover in the landscape surrounding stream reaches with varying levels of intrinsic potential. Model results showed that 33% of streams had moderate or high intrinsic potential for beaver habitat. We found that no site that was classified as having low intrinsic potential had any sign of beavers and that beaver were absent from nearly three quarters of potentially suitable sites, indicating that there are factors preventing the local population from occupying these areas. Of the riparian areas around streams with high intrinsic potential for beaver, 38% are on public lands and 17% are on large tracts of privately-owned timber land. Thus, although there are a large number of areas that could be suitable for relocation and restoration using beavers, current land use patterns may substantially limit feasibility in these areas.

Modeling intrinsic potential for beaver (Castor canadensis) habitat to inform restoration and climate change adaptation.

PubMed

Dittbrenner, Benjamin J; Pollock, Michael M; Schilling, Jason W; Olden, Julian D; Lawler, Joshua J; Torgersen, Christian E

2018-01-01

Through their dam-building activities and subsequent water storage, beaver have the potential to restore riparian ecosystems and offset some of the predicted effects of climate change by modulating streamflow. Thus, it is not surprising that reintroducing beaver to watersheds from which they have been extirpated is an often-used restoration and climate-adaptation strategy. Identifying sites for reintroduction, however, requires detailed information about habitat factors-information that is not often available at broad spatial scales. Here we explore the potential for beaver relocation throughout the Snohomish River Basin in Washington, USA with a model that identifies some of the basic building blocks of beaver habitat suitability and does so by relying solely on remotely sensed data. More specifically, we developed a generalized intrinsic potential model that draws on remotely sensed measures of stream gradient, stream width, and valley width to identify where beaver could become established if suitable vegetation were to be present. Thus, the model serves as a preliminary screening tool that can be applied over relatively large extents. We applied the model to 5,019 stream km and assessed the ability of the model to correctly predict beaver habitat by surveying for beavers in 352 stream reaches. To further assess the potential for relocation, we assessed land ownership, use, and land cover in the landscape surrounding stream reaches with varying levels of intrinsic potential. Model results showed that 33% of streams had moderate or high intrinsic potential for beaver habitat. We found that no site that was classified as having low intrinsic potential had any sign of beavers and that beaver were absent from nearly three quarters of potentially suitable sites, indicating that there are factors preventing the local population from occupying these areas. Of the riparian areas around streams with high intrinsic potential for beaver, 38% are on public lands and 17% are on large tracts of privately-owned timber land. Thus, although there are a large number of areas that could be suitable for relocation and restoration using beavers, current land use patterns may substantially limit feasibility in these areas.

New Methods for Estimating Seasonal Potential Climate Predictability

NASA Astrophysics Data System (ADS)

Feng, Xia

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

Genome-wide analysis of regulatory proteases sequences identified through bioinformatics data mining in Taenia solium.

PubMed

Yan, Hong-Bin; Lou, Zhong-Zi; Li, Li; Brindley, Paul J; Zheng, Yadong; Luo, Xuenong; Hou, Junling; Guo, Aijiang; Jia, Wan-Zhong; Cai, Xuepeng

2014-06-04

Cysticercosis remains a major neglected tropical disease of humanity in many regions, especially in sub-Saharan Africa, Central America and elsewhere. Owing to the emerging drug resistance and the inability of current drugs to prevent re-infection, identification of novel vaccines and chemotherapeutic agents against Taenia solium and related helminth pathogens is a public health priority. The T. solium genome and the predicted proteome were reported recently, providing a wealth of information from which new interventional targets might be identified. In order to characterize and classify the entire repertoire of protease-encoding genes of T. solium, which act fundamental biological roles in all life processes, we analyzed the predicted proteins of this cestode through a combination of bioinformatics tools. Functional annotation was performed to yield insights into the signaling processes relevant to the complex developmental cycle of this tapeworm and to highlight a suite of the proteases as potential intervention targets. Within the genome of this helminth parasite, we identified 200 open reading frames encoding proteases from five clans, which correspond to 1.68% of the 11,902 protein-encoding genes predicted to be present in its genome. These proteases include calpains, cytosolic, mitochondrial signal peptidases, ubiquitylation related proteins, and others. Many not only show significant similarity to proteases in the Conserved Domain Database but have conserved active sites and catalytic domains. KEGG Automatic Annotation Server (KAAS) analysis indicated that ~60% of these proteases share strong sequence identities with proteins of the KEGG database, which are involved in human disease, metabolic pathways, genetic information processes, cellular processes, environmental information processes and organismal systems. Also, we identified signal peptides and transmembrane helices through comparative analysis with classes of important regulatory proteases. Phylogenetic analysis using Bayes approach provided support for inferring functional divergence among regulatory cysteine and serine proteases. Numerous putative proteases were identified for the first time in T. solium, and important regulatory proteases have been predicted. This comprehensive analysis not only complements the growing knowledge base of proteolytic enzymes, but also provides a platform from which to expand knowledge of cestode proteases and to explore their biochemistry and potential as intervention targets.

Identification, Expression Analysis, and Target Prediction of Flax Genotroph MicroRNAs Under Normal and Nutrient Stress Conditions

PubMed Central

Melnikova, Nataliya V.; Dmitriev, Alexey A.; Belenikin, Maxim S.; Koroban, Nadezhda V.; Speranskaya, Anna S.; Krinitsina, Anastasia A.; Krasnov, George S.; Lakunina, Valentina A.; Snezhkina, Anastasiya V.; Sadritdinova, Asiya F.; Kishlyan, Natalya V.; Rozhmina, Tatiana A.; Klimina, Kseniya M.; Amosova, Alexandra V.; Zelenin, Alexander V.; Muravenko, Olga V.; Bolsheva, Nadezhda L.; Kudryavtseva, Anna V.

2016-01-01

Cultivated flax (Linum usitatissimum L.) is an important plant valuable for industry. Some flax lines can undergo heritable phenotypic and genotypic changes (LIS-1 insertion being the most common) in response to nutrient stress and are called plastic lines. Offspring of plastic lines, which stably inherit the changes, are called genotrophs. MicroRNAs (miRNAs) are involved in a crucial regulatory mechanism of gene expression. They have previously been assumed to take part in nutrient stress response and can, therefore, participate in genotroph formation. In the present study, we performed high-throughput sequencing of small RNAs (sRNAs) extracted from flax plants grown under normal, phosphate deficient and nutrient excess conditions to identify miRNAs and evaluate their expression. Our analysis revealed expression of 96 conserved miRNAs from 21 families in flax. Moreover, 475 novel potential miRNAs were identified for the first time, and their targets were predicted. However, none of the identified miRNAs were transcribed from LIS-1. Expression of seven miRNAs (miR168, miR169, miR395, miR398, miR399, miR408, and lus-miR-N1) with up- or down-regulation under nutrient stress (on the basis of high-throughput sequencing data) was evaluated on extended sampling using qPCR. Reference gene search identified ETIF3H and ETIF3E genes as most suitable for this purpose. Down-regulation of novel potential lus-miR-N1 and up-regulation of conserved miR399 were revealed under the phosphate deficient conditions. In addition, the negative correlation of expression of lus-miR-N1 and its predicted target, ubiquitin-activating enzyme E1 gene, as well as, miR399 and its predicted target, ubiquitin-conjugating enzyme E2 gene, was observed. Thus, in our study, miRNAs expressed in flax plastic lines and genotrophs were identified and their expression and expression of their targets was evaluated using high-throughput sequencing and qPCR for the first time. These data provide new insights into nutrient stress response regulation in plastic flax cultivars. PMID:27092149

How to determine an optimal threshold to classify real-time crash-prone traffic conditions?

PubMed

Yang, Kui; Yu, Rongjie; Wang, Xuesong; Quddus, Mohammed; Xue, Lifang

2018-08-01

One of the proactive approaches in reducing traffic crashes is to identify hazardous traffic conditions that may lead to a traffic crash, known as real-time crash prediction. Threshold selection is one of the essential steps of real-time crash prediction. And it provides the cut-off point for the posterior probability which is used to separate potential crash warnings against normal traffic conditions, after the outcome of the probability of a crash occurring given a specific traffic condition on the basis of crash risk evaluation models. There is however a dearth of research that focuses on how to effectively determine an optimal threshold. And only when discussing the predictive performance of the models, a few studies utilized subjective methods to choose the threshold. The subjective methods cannot automatically identify the optimal thresholds in different traffic and weather conditions in real application. Thus, a theoretical method to select the threshold value is necessary for the sake of avoiding subjective judgments. The purpose of this study is to provide a theoretical method for automatically identifying the optimal threshold. Considering the random effects of variable factors across all roadway segments, the mixed logit model was utilized to develop the crash risk evaluation model and further evaluate the crash risk. Cross-entropy, between-class variance and other theories were employed and investigated to empirically identify the optimal threshold. And K-fold cross-validation was used to validate the performance of proposed threshold selection methods with the help of several evaluation criteria. The results indicate that (i) the mixed logit model can obtain a good performance; (ii) the classification performance of the threshold selected by the minimum cross-entropy method outperforms the other methods according to the criteria. This method can be well-behaved to automatically identify thresholds in crash prediction, by minimizing the cross entropy between the original dataset with continuous probability of a crash occurring and the binarized dataset after using the thresholds to separate potential crash warnings against normal traffic conditions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Do Emotional and Behavioural Difficulties in Primary School Predict Adolescent Victimisation Trajectories?

ERIC Educational Resources Information Center

Lester, Leanne; Cross, Donna

2014-01-01

Chronic victimisation in adolescence is a traumatic experience with potential negative long-term health consequences. Given that victimisation has been shown to increase over the transition from primary to secondary school, longitudinal data from 1810 students transitioning from primary to secondary school were used to identify victimisation…

Hispanic-Serving Institutions: Patterns, Predictions, and Implications for Informing Policy Discussions

ERIC Educational Resources Information Center

Torres, Vasti; Zerquera, Desiree

2012-01-01

This article seeks to identify and assess the readiness of "Potential" Hispanic-Serving Institutions (HSIs)--institutions located within Latino communities projected to increase the number of Latino/a high school graduates. Institutions are described based on evaluation of institutional missions, planning documents, programs, and marketing…

Use of causative variants and SNP weighting in a single-step GBLUP context

USDA-ARS?s Scientific Manuscript database

Much effort has been recently put into identifying causative quantitative trait nucleotides (QTN) in animal breeding, aiming genomic prediction. Among the genomic methods available, single-step GBLUP (ssGBLUP) became the choice because of its simplicity and potentially higher accuracy. When QTN are ...

Implications of genome-wide association studies in cancer therapeutics.

PubMed

Patel, Jai N; McLeod, Howard L; Innocenti, Federico

2013-09-01

Genome wide association studies (GWAS) provide an agnostic approach to identifying potential genetic variants associated with disease susceptibility, prognosis of survival and/or predictive of drug response. Although these techniques are costly and interpretation of study results is challenging, they do allow for a more unbiased interrogation of the entire genome, resulting in the discovery of novel genes and understanding of novel biological associations. This review will focus on the implications of GWAS in cancer therapy, in particular germ-line mutations, including findings from major GWAS which have identified predictive genetic loci for clinical outcome and/or toxicity. Lessons and challenges in cancer GWAS are also discussed, including the need for functional analysis and replication, as well as future perspectives for biological and clinical utility. Given the large heterogeneity in response to cancer therapeutics, novel methods of identifying mechanisms and biology of variable drug response and ultimately treatment individualization will be indispensable. © 2013 The British Pharmacological Society.

A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests

PubMed Central

2011-01-01

Background Allergic contact dermatitis is an inflammatory skin disease that affects a significant proportion of the population. This disease is caused by an adverse immune response towards chemical haptens, and leads to a substantial economic burden for society. Current test of sensitizing chemicals rely on animal experimentation. New legislations on the registration and use of chemicals within pharmaceutical and cosmetic industries have stimulated significant research efforts to develop alternative, human cell-based assays for the prediction of sensitization. The aim is to replace animal experiments with in vitro tests displaying a higher predictive power. Results We have developed a novel cell-based assay for the prediction of sensitizing chemicals. By analyzing the transcriptome of the human cell line MUTZ-3 after 24 h stimulation, using 20 different sensitizing chemicals, 20 non-sensitizing chemicals and vehicle controls, we have identified a biomarker signature of 200 genes with potent discriminatory ability. Using a Support Vector Machine for supervised classification, the prediction performance of the assay revealed an area under the ROC curve of 0.98. In addition, categorizing the chemicals according to the LLNA assay, this gene signature could also predict sensitizing potency. The identified markers are involved in biological pathways with immunological relevant functions, which can shed light on the process of human sensitization. Conclusions A gene signature predicting sensitization, using a human cell line in vitro, has been identified. This simple and robust cell-based assay has the potential to completely replace or drastically reduce the utilization of test systems based on experimental animals. Being based on human biology, the assay is proposed to be more accurate for predicting sensitization in humans, than the traditional animal-based tests. PMID:21824406

A multilateral modelling of Youth Soccer Performance Index (YSPI)

NASA Astrophysics Data System (ADS)

Bisyri Husin Musawi Maliki, Ahmad; Razali Abdullah, Mohamad; Juahir, Hafizan; Abdullah, Farhana; Ain Shahirah Abdullah, Nurul; Muazu Musa, Rabiu; Musliha Mat-Rasid, Siti; Adnan, Aleesha; Azura Kosni, Norlaila; Muhamad, Wan Siti Amalina Wan; Afiqah Mohamad Nasir, Nur

2018-04-01

This study aims to identify the most dominant factors that influencing performance of soccer player and to predict group performance for soccer players. A total of 184 of youth soccer players from Malaysia sport school and six soccer academy encompasses as respondence of the study. Exploratory factor analysis (EFA) and Confirmatory factor analysis (CFA) were computed to identify the most dominant factors whereas reducing the initial 26 parameters with recommended >0.5 of factor loading. Meanwhile, prediction of the soccer performance was predicted by regression model. CFA revealed that sit and reach, vertical jump, VO2max, age, weight, height, sitting height, calf circumference (cc), medial upper arm circumference (muac), maturation, bicep, triceps, subscapular, suprailiac, 5M, 10M, and 20M speed were the most dominant factors. Further index analysis forming Youth Soccer Performance Index (YSPI) resulting by categorizing three groups namely, high, moderate, and low. The regression model for this study was significant set as p < 0.001 and R2 is 0.8222 which explained that the model contributed a total of 82% prediction ability to predict the whole set of the variables. The significant parameters in contributing prediction of YSPI are discussed. As a conclusion, the precision of the prediction models by integrating a multilateral factor reflecting for predicting potential soccer player and hopefully can create a competitive soccer games.

Predicting environmental mitigation requirements for hydropower projects through the integration of biophysical and socio-political geographies

DOE PAGES

Bevelhimer, Mark S.; DeRolph, Christopher R.; Schramm, Michael P.

2016-06-06

Uncertainty about environmental mitigation needs at existing and proposed hydropower projects makes it difficult for stakeholders to minimize environmental impacts. Hydropower developers and operators desire tools to better anticipate mitigation requirements, while natural resource managers and regulators need tools to evaluate different mitigation scenarios and order effective mitigation. Here we sought to examine the feasibility of using a suite of multidisciplinary explanatory variables within a spatially explicit modeling framework to fit predictive models for future environmental mitigation requirements at hydropower projects across the conterminous U.S. Using a database comprised of mitigation requirements from more than 300 hydropower project licenses, wemore » were able to successfully fit models for nearly 50 types of environmental mitigation and to apply the predictive models to a set of more than 500 non-powered dams identified as having hydropower potential. The results demonstrate that mitigation requirements have been a result of a range of factors, from biological and hydrological to political and cultural. Furthermore, project developers can use these models to inform cost projections and design considerations, while regulators can use the models to more quickly identify likely environmental issues and potential solutions, hopefully resulting in more timely and more effective decisions on environmental mitigation.« less

Proteome-wide search for functional motifs altered in tumors: Prediction of nuclear export signals inactivated by cancer-related mutations

PubMed Central

Prieto, Gorka; Fullaondo, Asier; Rodríguez, Jose A.

2016-01-01

Large-scale sequencing projects are uncovering a growing number of missense mutations in human tumors. Understanding the phenotypic consequences of these alterations represents a formidable challenge. In silico prediction of functionally relevant amino acid motifs disrupted by cancer mutations could provide insight into the potential impact of a mutation, and guide functional tests. We have previously described Wregex, a tool for the identification of potential functional motifs, such as nuclear export signals (NESs), in proteins. Here, we present an improved version that allows motif prediction to be combined with data from large repositories, such as the Catalogue of Somatic Mutations in Cancer (COSMIC), and to be applied to a whole proteome scale. As an example, we have searched the human proteome for candidate NES motifs that could be altered by cancer-related mutations included in the COSMIC database. A subset of the candidate NESs identified was experimentally tested using an in vivo nuclear export assay. A significant proportion of the selected motifs exhibited nuclear export activity, which was abrogated by the COSMIC mutations. In addition, our search identified a cancer mutation that inactivates the NES of the human deubiquitinase USP21, and leads to the aberrant accumulation of this protein in the nucleus. PMID:27174732

Predicting environmental mitigation requirements for hydropower projects through the integration of biophysical and socio-political geographies.

PubMed

DeRolph, Christopher R; Schramm, Michael P; Bevelhimer, Mark S

2016-10-01

Uncertainty about environmental mitigation needs at existing and proposed hydropower projects makes it difficult for stakeholders to minimize environmental impacts. Hydropower developers and operators desire tools to better anticipate mitigation requirements, while natural resource managers and regulators need tools to evaluate different mitigation scenarios and order effective mitigation. Here we sought to examine the feasibility of using a suite of multi-faceted explanatory variables within a spatially explicit modeling framework to fit predictive models for future environmental mitigation requirements at hydropower projects across the conterminous U.S. Using a database comprised of mitigation requirements from more than 300 hydropower project licenses, we were able to successfully fit models for nearly 50 types of environmental mitigation and to apply the predictive models to a set of more than 500 non-powered dams identified as having hydropower potential. The results demonstrate that mitigation requirements are functions of a range of factors, from biophysical to socio-political. Project developers can use these models to inform cost projections and design considerations, while regulators can use the models to more quickly identify likely environmental issues and potential solutions, hopefully resulting in more timely and more effective decisions on environmental mitigation. Copyright © 2016 Elsevier B.V. All rights reserved.

Gankyrin is a predictive and oncogenic factor in well-differentiated and dedifferentiated liposarcoma

PubMed Central

Hong, Doo-Pyo; Joo, Sung-Yeon; Choi, Yoon-La; Park, Joo-Hung; Lazar, Alexander J.; Pollock, Raphael E.; Lev, Dina; Kim, Sung Joo

2014-01-01

Liposarcoma is one of the most common histologic types of soft tissue sarcoma and is frequently an aggressive cancer with poor outcome. Hence, alternative approaches other than surgical excision are necessary to improve treatment of well-differentiated/dedifferentiated liposarcoma (WDLPS/DDLPS). For this reason, we performed a two-dimensional gel electrophoresis (2-DE) and matrix-assisted laser desorption/ionization-time of flight mass spectrometry/mass spectrometry (MALDI-TOF/MS) analysis to identify new factors for WDLPS and DDLPS. Among the selected candidate proteins, gankyrin, known to be an oncoprotein, showed a significantly high level of expression pattern and inversely low expression of p53/p21 in WDLPS and DDLPS tissues, suggesting possible utility as a new predictive factor. Moreover, inhibition of gankyrin not only led to reduction of in vitro cell growth ability including cell proliferation, colony-formation, and migration, but also in vivo DDLPS cell tumorigenesis, perhaps via downregulation of the p53 tumor suppressor gene and its p21 target and also reduction of AKT/mTOR signal activation. This study identifies gankyrin, for the first time, as new potential predictive and oncogenic factor of WDLPS and DDLPS, suggesting the potential for service as a future LPS therapeutic approach. PMID:25238053

Predicting environmental mitigation requirements for hydropower projects through the integration of biophysical and socio-political geographies

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

Bevelhimer, Mark S.; DeRolph, Christopher R.; Schramm, Michael P.

Uncertainty about environmental mitigation needs at existing and proposed hydropower projects makes it difficult for stakeholders to minimize environmental impacts. Hydropower developers and operators desire tools to better anticipate mitigation requirements, while natural resource managers and regulators need tools to evaluate different mitigation scenarios and order effective mitigation. Here we sought to examine the feasibility of using a suite of multidisciplinary explanatory variables within a spatially explicit modeling framework to fit predictive models for future environmental mitigation requirements at hydropower projects across the conterminous U.S. Using a database comprised of mitigation requirements from more than 300 hydropower project licenses, wemore » were able to successfully fit models for nearly 50 types of environmental mitigation and to apply the predictive models to a set of more than 500 non-powered dams identified as having hydropower potential. The results demonstrate that mitigation requirements have been a result of a range of factors, from biological and hydrological to political and cultural. Furthermore, project developers can use these models to inform cost projections and design considerations, while regulators can use the models to more quickly identify likely environmental issues and potential solutions, hopefully resulting in more timely and more effective decisions on environmental mitigation.« less

Antipsychotic therapeutic drug monitoring: psychiatrists’ attitudes and factors predicting likely future use

PubMed Central

Law, Suzanne; Haddad, Peter M.; Chaudhry, Imran B.; Husain, Nusrat; Drake, Richard J.; Flanagan, Robert J.; David, Anthony S.

2015-01-01

Background: This study aimed to explore predictive factors for future use of therapeutic drug monitoring (TDM) and to further examine psychiatrists’ current prescribing practices and perspectives regarding antipsychotic TDM using plasma concentrations. Method: A cross-sectional study for consultant psychiatrists using a postal questionnaire was conducted in north-west England. Data were combined with those of a previous London-based study and principal axis factor analysis was conducted to identify predictors of future use of TDM. Results: Most of the 181 participants (82.9%, 95% confidence interval 76.7–87.7%) agreed that ‘if TDM for antipsychotics were readily available, I would use it’. Factor analysis identified five factors from the original 35 items regarding TDM. Four of the factors significantly predicted likely future use of antipsychotic TDM and together explained 40% of the variance in a multivariate linear regression model. Likely future use increased with positive attitudes and expectations, and decreased with potential barriers, negative attitudes and negative expectations. Scientific perspectives of TDM and psychiatrist characteristics were not significant predictors. Conclusion: Most senior psychiatrists indicated that they would use antipsychotic TDM if available. However, psychiatrists’ attitudes and expectations and the potential barriers need to be addressed, in addition to the scientific evidence, before widespread use of antipsychotic TDM is likely in clinical practice. PMID:26301077

Predicting Nonspecific Ion Binding Using DelPhi

PubMed Central

Petukh, Marharyta; Zhenirovskyy, Maxim; Li, Chuan; Li, Lin; Wang, Lin; Alexov, Emil

2012-01-01

Ions are an important component of the cell and affect the corresponding biological macromolecules either via direct binding or as a screening ion cloud. Although some ion binding is highly specific and frequently associated with the function of the macromolecule, other ions bind to the protein surface nonspecifically, presumably because the electrostatic attraction is strong enough to immobilize them. Here, we test such a scenario and demonstrate that experimentally identified surface-bound ions are located at a potential that facilitates binding, which indicates that the major driving force is the electrostatics. Without taking into consideration geometrical factors and structural fluctuations, we show that ions tend to be bound onto the protein surface at positions with strong potential but with polarity opposite to that of the ion. This observation is used to develop a method that uses a DelPhi-calculated potential map in conjunction with an in-house-developed clustering algorithm to predict nonspecific ion-binding sites. Although this approach distinguishes only the polarity of the ions, and not their chemical nature, it can predict nonspecific binding of positively or negatively charged ions with acceptable accuracy. One can use the predictions in the Poisson-Boltzmann approach by placing explicit ions in the predicted positions, which in turn will reduce the magnitude of the local potential and extend the limits of the Poisson-Boltzmann equation. In addition, one can use this approach to place the desired number of ions before conducting molecular-dynamics simulations to neutralize the net charge of the protein, because it was shown to perform better than standard screened Coulomb canned routines, or to predict ion-binding sites in proteins. This latter is especially true for proteins that are involved in ion transport, because such ions are loosely bound and very difficult to detect experimentally. PMID:22735539

Metabolomic profiling in the prediction of gestational diabetes mellitus

PubMed Central

Huynh, Jennifer; Xiong, Grace; Lee, Hang; Wenger, Julia; Clish, Clary; Nathan, David; Thadhani, Ravi; Gerszten, Robert

2015-01-01

Aims/hypothesis Metabolomic profiling in populations with impaired glucose tolerance has revealed that branched chain and aromatic amino acids (BCAAs) are predictive of type 2 diabetes. Because gestational diabetes mellitus (GDM) shares pathophysiological similarities with type 2 diabetes, the metabolite profile predictive of type 2 diabetes could potentially identify women who will develop GDM. Methods We conducted a nested case–control study of 18- to 40-year-old women who participated in the Massachusetts General Hospital Obstetrical Maternal Study between 1998 and 2007. Participants were enrolled during their first trimester of a singleton pregnancy and fasting serum samples were collected. The women were followed throughout pregnancy and identified as having GDM or normal glucose tolerance (NGT) in the third trimester. Women with GDM (n=96) were matched to women with NGT (n=96) by age, BMI, gravidity and parity. Liquid chromatography–mass spectrometry was used to measure the levels of 91 metabolites. Results Data analyses revealed the following characteristics (mean±SD): age 32.8±4.4 years, BMI 28.3±5.6 kg/m2, gravidity 2±1 and parity 1±1. Six metabolites (anthranilic acid, alanine, glutamate, creatinine, allantoin and serine) were identified as having significantly different levels between the two groups in conditional logistic regression analyses (p<0.05). The levels of the BCAAs did not differ significantly between GDM and NGT. Conclusions/interpretation Metabolic markers identified as being predictive of type 2 diabetes may not have the same predictive power for GDM. However, further study in a racially/ethnically diverse population-based cohort is necessary. PMID:25748329

Market inefficiency identified by both single and multiple currency trends

NASA Astrophysics Data System (ADS)

Tokár, T.; Horváth, D.

2012-11-01

Many studies have shown that there are good reasons to claim very low predictability of currency returns; nevertheless, the deviations from true randomness exist which have potential predictive and prognostic power [J. James, Simple trend-following strategies in currency trading, Quantitative finance 3 (2003) C75-C77]. We analyze the local trends which are of the main focus of the technical analysis. In this article we introduced various statistical quantities examining role of single temporal discretized trend or multitude of grouped trends corresponding to different time delays. Our specific analysis based predominantly on Euro-dollar currency pair data at the one minute frequency suggests the importance of cumulative nonrandom effect of trends on the potential forecasting performance.

Status of the DOE/NASA critical gas turbine research and technology project

NASA Technical Reports Server (NTRS)

Clark, J. S.

1980-01-01

Activities performed in order to provide an R&T data base for utility gas turbine systems burning coal-derived fuels are described. Experiments were run to determine the corrosivity effects of trace metal contaminants (and potential fuel additives) on gas turbine materials and these results were correlated in a corrosion-life prediction model. Actual fuels were burned in a burner rig hot corrosion test to verify the model. A deposition prediction model was assembled and compared with results of actual coal-derived fuel deposition tests. Thermal barrier coatings were tested to determine their potential for protecting gas turbine hardware from the corrosive contaminants. Several coatings were identified with significantly improved spallation-resistance (and, hence, corrosion resistance).

Could machine learning improve the prediction of pelvic nodal status of prostate cancer patients? Preliminary results of a pilot study.

PubMed

De Bari, B; Vallati, M; Gatta, R; Simeone, C; Girelli, G; Ricardi, U; Meattini, I; Gabriele, P; Bellavita, R; Krengli, M; Cafaro, I; Cagna, E; Bunkheila, F; Borghesi, S; Signor, M; Di Marco, A; Bertoni, F; Stefanacci, M; Pasinetti, N; Buglione, M; Magrini, S M

2015-07-01

We tested and compared performances of Roach formula, Partin tables and of three Machine Learning (ML) based algorithms based on decision trees in identifying N+ prostate cancer (PC). 1,555 cN0 and 50 cN+ PC were analyzed. Results were also verified on an independent population of 204 operated cN0 patients, with a known pN status (187 pN0, 17 pN1 patients). ML performed better, also when tested on the surgical population, with accuracy, specificity, and sensitivity ranging between 48-86%, 35-91%, and 17-79%, respectively. ML potentially allows better prediction of the nodal status of PC, potentially allowing a better tailoring of pelvic irradiation.

Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation.

PubMed

Noecker, Cecilia; Eng, Alexander; Srinivasan, Sujatha; Theriot, Casey M; Young, Vincent B; Jansson, Janet K; Fredricks, David N; Borenstein, Elhanan

2016-01-01

Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites' abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in health and disease. Studies characterizing both the taxonomic composition and metabolic profile of various microbial communities are becoming increasingly common, yet new computational methods are needed to integrate and interpret these data in terms of known biological mechanisms. Here, we introduce an analytical framework to link species composition and metabolite measurements, using a simple model to predict the effects of community ecology on metabolite concentrations and evaluating whether these predictions agree with measured metabolomic profiles. We find that a surprisingly large proportion of metabolite variation in the vaginal microbiome can be predicted based on species composition (including dramatic shifts associated with disease), identify putative mechanisms underlying these predictions, and evaluate the roles of individual bacterial species and genes. Analysis of gut microbiome data using this framework recovers similar community metabolic trends. This framework lays the foundation for model-based multi-omic integrative studies, ultimately improving our understanding of microbial community metabolism.

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

Noecker, Cecilia; Eng, Alexander; Srinivasan, Sujatha

ABSTRACT Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community.more » Our framework then compares variation in predicted metabolic potential with variation in measured metabolites’ abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in health and disease. IMPORTANCEStudies characterizing both the taxonomic composition and metabolic profile of various microbial communities are becoming increasingly common, yet new computational methods are needed to integrate and interpret these data in terms of known biological mechanisms. Here, we introduce an analytical framework to link species composition and metabolite measurements, using a simple model to predict the effects of community ecology on metabolite concentrations and evaluating whether these predictions agree with measured metabolomic profiles. We find that a surprisingly large proportion of metabolite variation in the vaginal microbiome can be predicted based on species composition (including dramatic shifts associated with disease), identify putative mechanisms underlying these predictions, and evaluate the roles of individual bacterial species and genes. Analysis of gut microbiome data using this framework recovers similar community metabolic trends. This framework lays the foundation for model-based multi-omic integrative studies, ultimately improving our understanding of microbial community metabolism.« less

Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation

PubMed Central

Noecker, Cecilia; Eng, Alexander; Srinivasan, Sujatha; Theriot, Casey M.; Young, Vincent B.; Jansson, Janet K.; Fredricks, David N.

2016-01-01

ABSTRACT Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites’ abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in health and disease. IMPORTANCE Studies characterizing both the taxonomic composition and metabolic profile of various microbial communities are becoming increasingly common, yet new computational methods are needed to integrate and interpret these data in terms of known biological mechanisms. Here, we introduce an analytical framework to link species composition and metabolite measurements, using a simple model to predict the effects of community ecology on metabolite concentrations and evaluating whether these predictions agree with measured metabolomic profiles. We find that a surprisingly large proportion of metabolite variation in the vaginal microbiome can be predicted based on species composition (including dramatic shifts associated with disease), identify putative mechanisms underlying these predictions, and evaluate the roles of individual bacterial species and genes. Analysis of gut microbiome data using this framework recovers similar community metabolic trends. This framework lays the foundation for model-based multi-omic integrative studies, ultimately improving our understanding of microbial community metabolism. PMID:27239563

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.

High-Throughput Sequencing Reveals Diverse Sets of Conserved, Nonconserved, and Species-Specific miRNAs in Jute

PubMed Central

Islam, Md. Tariqul; Ferdous, Ahlan Sabah; Najnin, Rifat Ara; Sarker, Suprovath Kumar; Khan, Haseena

2015-01-01

MicroRNAs play a pivotal role in regulating a broad range of biological processes, acting by cleaving mRNAs or by translational repression. A group of plant microRNAs are evolutionarily conserved; however, others are expressed in a species-specific manner. Jute is an agroeconomically important fibre crop; nonetheless, no practical information is available for microRNAs in jute to date. In this study, Illumina sequencing revealed a total of 227 known microRNAs and 17 potential novel microRNA candidates in jute, of which 164 belong to 23 conserved families and the remaining 63 belong to 58 nonconserved families. Among a total of 81 identified microRNA families, 116 potential target genes were predicted for 39 families and 11 targets were predicted for 4 among the 17 identified novel microRNAs. For understanding better the functions of microRNAs, target genes were analyzed by Gene Ontology and their pathways illustrated by KEGG pathway analyses. The presence of microRNAs identified in jute was validated by stem-loop RT-PCR followed by end point PCR and qPCR for randomly selected 20 known and novel microRNAs. This study exhaustively identifies microRNAs and their target genes in jute which will ultimately pave the way for understanding their role in this crop and other crops. PMID:25861616

Preliminary evaluation of factors associated with premature trial closure and feasibility of accrual benchmarks in phase III oncology trials.

PubMed

Schroen, Anneke T; Petroni, Gina R; Wang, Hongkun; Gray, Robert; Wang, Xiaofei F; Cronin, Walter; Sargent, Daniel J; Benedetti, Jacqueline; Wickerham, Donald L; Djulbegovic, Benjamin; Slingluff, Craig L

2010-08-01

A major challenge for randomized phase III oncology trials is the frequent low rates of patient enrollment, resulting in high rates of premature closure due to insufficient accrual. We conducted a pilot study to determine the extent of trial closure due to poor accrual, feasibility of identifying trial factors associated with sufficient accrual, impact of redesign strategies on trial accrual, and accrual benchmarks designating high failure risk in the clinical trials cooperative group (CTCG) setting. A subset of phase III trials opened by five CTCGs between August 1991 and March 2004 was evaluated. Design elements, experimental agents, redesign strategies, and pretrial accrual assessment supporting accrual predictions were abstracted from CTCG documents. Percent actual/predicted accrual rate averaged per month was calculated. Trials were categorized as having sufficient or insufficient accrual based on reason for trial termination. Analyses included univariate and bivariate summaries to identify potential trial factors associated with accrual sufficiency. Among 40 trials from one CTCG, 21 (52.5%) trials closed due to insufficient accrual. In 82 trials from five CTCGs, therapeutic trials accrued sufficiently more often than nontherapeutic trials (59% vs 27%, p = 0.05). Trials including pretrial accrual assessment more often achieved sufficient accrual than those without (67% vs 47%, p = 0.08). Fewer exclusion criteria, shorter consent forms, other CTCG participation, and trial design simplicity were not associated with achieving sufficient accrual. Trials accruing at a rate much lower than predicted (<35% actual/predicted accrual rate) were consistently closed due to insufficient accrual. This trial subset under-represents certain experimental modalities. Data sources do not allow accounting for all factors potentially related to accrual success. Trial closure due to insufficient accrual is common. Certain trial design factors appear associated with attaining sufficient accrual. Defining accrual benchmarks for early trial termination or redesign is feasible, but better accrual prediction methods are critically needed. Future studies should focus on identifying trial factors that allow more accurate accrual predictions and strategies that can salvage open trials experiencing slow accrual.

Preliminary evaluation of factors associated with premature trial closure and feasibility of accrual benchmarks in phase III oncology trials

PubMed Central

Schroen, Anneke T; Petroni, Gina R; Wang, Hongkun; Gray, Robert; Wang, Xiaofei F; Cronin, Walter; Sargent, Daniel J; Benedetti, Jacqueline; Wickerham, Donald L; Djulbegovic, Benjamin; Slingluff, Craig L

2014-01-01

Background A major challenge for randomized phase III oncology trials is the frequent low rates of patient enrollment, resulting in high rates of premature closure due to insufficient accrual. Purpose We conducted a pilot study to determine the extent of trial closure due to poor accrual, feasibility of identifying trial factors associated with sufficient accrual, impact of redesign strategies on trial accrual, and accrual benchmarks designating high failure risk in the clinical trials cooperative group (CTCG) setting. Methods A subset of phase III trials opened by five CTCGs between August 1991 and March 2004 was evaluated. Design elements, experimental agents, redesign strategies, and pretrial accrual assessment supporting accrual predictions were abstracted from CTCG documents. Percent actual/predicted accrual rate averaged per month was calculated. Trials were categorized as having sufficient or insufficient accrual based on reason for trial termination. Analyses included univariate and bivariate summaries to identify potential trial factors associated with accrual sufficiency. Results Among 40 trials from one CTCG, 21 (52.5%) trials closed due to insufficient accrual. In 82 trials from five CTCGs, therapeutic trials accrued sufficiently more often than nontherapeutic trials (59% vs 27%, p = 0.05). Trials including pretrial accrual assessment more often achieved sufficient accrual than those without (67% vs 47%, p = 0.08). Fewer exclusion criteria, shorter consent forms, other CTCG participation, and trial design simplicity were not associated with achieving sufficient accrual. Trials accruing at a rate much lower than predicted (<35% actual/predicted accrual rate) were consistently closed due to insufficient accrual. Limitations This trial subset under-represents certain experimental modalities. Data sources do not allow accounting for all factors potentially related to accrual success. Conclusion Trial closure due to insufficient accrual is common. Certain trial design factors appear associated with attaining sufficient accrual. Defining accrual benchmarks for early trial termination or redesign is feasible, but better accrual prediction methods are critically needed. Future studies should focus on identifying trial factors that allow more accurate accrual predictions and strategies that can salvage open trials experiencing slow accrual. PMID:20595245

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.

Toward the definition of a peptidome signature and protease profile in chronic periodontitis.

PubMed

Trindade, Fábio; Amado, Francisco; Oliveira-Silva, Rui P; Daniel-da-Silva, Ana L; Ferreira, Rita; Klein, Julie; Faria-Almeida, Ricardo; Gomes, Pedro S; Vitorino, Rui

2015-10-01

Chronic periodontitis (CP) is a complex immuno-inflammatory disease that results from preestablished gingivitis. We investigated potential differences in salivary peptidome in health and CP. Saliva was collected from nine CP patients and ten healthy subjects, from which five CP and five healthy were enriched following endoProteoFASP approach, separated and identified by nanoHPLC-MALDI-TOF/TOF. Protease prediction was carried out in silico with Proteasix. Parallel gelatin and collagen (I) zymographies were performed to study proteolytic activity in CP. An association of CP with increased gelatinolytic and collagenolytic activity was observed, which is mainly attributed to metalloproteases, remarkably MMP9. Protease prediction revealed distinct protease profiles in CP and in health. Peptidomic data corroborated the inflammatory status, and demonstrated that intact histatin 1 may play an important role in the defense response against oral pathogens. The application of the endoProteoFASP approach to study the salivary peptidome of CP subjects resulted in the identification of eight surrogate peptide markers, which may be used in multiplex to identify CP. These peptides belong to acidic PRP and to P-B peptide. Particularly, P-B peptide fragments exhibited domains with potential predicted antimicrobial activity, corroborating an antimicrobial function. The comparison between the salivary peptidome obtained by control and CP samples showed a specific association of eight peptides to CP, with remarkable predicted antimicrobial activity, which should be further validated in studies with large number of subjects. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Cryogenic Model Materials

NASA Technical Reports Server (NTRS)

Kimmel, W. M.; Kuhn, N. S.; Berry, R. F.; Newman, J. A.

2001-01-01

An overview and status of current activities seeking alternatives to 200 grade 18Ni Steel CVM alloy for cryogenic wind tunnel models is presented. Specific improvements in material selection have been researched including availability, strength, fracture toughness and potential for use in transonic wind tunnel testing. Potential benefits from utilizing damage tolerant life-prediction methods, recently developed fatigue crack growth codes and upgraded NDE methods are also investigated. Two candidate alloys are identified and accepted for cryogenic/transonic wind tunnel models and hardware.

Global-local methodologies and their application to nonlinear analysis. [for structural postbuckling study

NASA Technical Reports Server (NTRS)

Noor, Ahmed K.

1986-01-01

An assessment is made of the potential of different global-local analysis strategies for predicting the nonlinear and postbuckling responses of structures. Two postbuckling problems of composite panels are used as benchmarks and the application of different global-local methodologies to these benchmarks is outlined. The key elements of each of the global-local strategies are discussed and future research areas needed to realize the full potential of global-local methodologies are identified.

A field guide for rapid assessment of post-wildfire recovery potential in sagebrush and pinon-juniper ecosystems in the Great Basin: Evaluating resilience to disturbance and resistance to invasive annual grasses and predicting vegetation response

Treesearch

Richard F. Miller; Jeanne C. Chambers; Mike Pellant

2015-01-01

This field guide provides a framework for rapidly evaluating post-fire resilience to disturbance, or recovery potential, and resistance to invasive annual grasses, and for determining the need and suitability of the burned area for seeding. The framework identifies six primary components that largely determine resilience to disturbance, resistance to invasive grasses,...

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

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

PubMed Central

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

2017-01-01

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

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

PubMed

Callery, O; Healy, M G

2017-10-15

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

The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach.

PubMed

Hindumathi, V; Kranthi, T; Rao, S B; Manimaran, P

2014-06-01

With rapidly changing technology, prediction of candidate genes has become an indispensable task in recent years mainly in the field of biological research. The empirical methods for candidate gene prioritization that succors to explore the potential pathway between genetic determinants and complex diseases are highly cumbersome and labor intensive. In such a scenario predicting potential targets for a disease state through in silico approaches are of researcher's interest. The prodigious availability of protein interaction data coupled with gene annotation renders an ease in the accurate determination of disease specific candidate genes. In our work we have prioritized the cervix related cancer candidate genes by employing Csaba Ortutay and his co-workers approach of identifying the candidate genes through graph theoretical centrality measures and gene ontology. With the advantage of the human protein interaction data, cervical cancer gene sets and the ontological terms, we were able to predict 15 novel candidates for cervical carcinogenesis. The disease relevance of the anticipated candidate genes was corroborated through a literature survey. Also the presence of the drugs for these candidates was detected through Therapeutic Target Database (TTD) and DrugMap Central (DMC) which affirms that they may be endowed as potential drug targets for cervical cancer.

Global Invasion of Lantana camara: Has the Climatic Niche Been Conserved across Continents?

PubMed Central

Duarte, Milén; Bustamante, Ramiro O.; Lampo, Margarita; Velásquez, Grisel; Sharma, Gyan P.; García-Rangel, Shaenandhoa

2014-01-01

Lantana camara, a native plant from tropical America, is considered one of the most harmful invasive species worldwide. Several studies have identified potentially invasible areas under scenarios of global change, on the assumption that niche is conserved during the invasion process. Recent studies, however, suggest that many invasive plants do not conserve their niches. Using Principal Components Analyses (PCA), we tested the hypothesis of niche conservatism for L. camara by comparing its native niche in South America with its expressed niche in Africa, Australia and India. Using MaxEnt, the estimated niche for the native region was projected onto each invaded region to generate potential distributions there. Our results demonstrate that while L. camara occupied subsets of its original native niche in Africa and Australia, in India its niche shifted significantly. There, 34% of the occurrences were detected in warmer habitats nonexistent in its native range. The estimated niche for India was also projected onto Africa and Australia to identify other vulnerable areas predicted from the observed niche shift detected in India. As a result, new potentially invasible areas were identified in central Africa and southern Australia. Our findings do not support the hypothesis of niche conservatism for the invasion of L. camara. The mechanisms that allow this species to expand its niche need to be investigated in order to improve our capacity to predict long-term geographic changes in the face of global climatic changes. PMID:25343481

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.

Transient traceability analysis of land carbon storage dynamics: procedures and its application to two forest ecosystems

NASA Astrophysics Data System (ADS)

Jiang, L.; Shi, Z.; Xia, J.; Liang, J.; Lu, X.; Wang, Y.; Luo, Y.

2017-12-01

Uptake of anthropogenically emitted carbon (C) dioxide by terrestrial ecosystem is critical for determining future climate. However, Earth system models project large uncertainties in future C storage. To help identify sources of uncertainties in model predictions, this study develops a transient traceability framework to trace components of C storage dynamics. Transient C storage (X) can be decomposed into two components, C storage capacity (Xc) and C storage potential (Xp). Xc is the maximum C amount that an ecosystem can potentially store and Xp represents the internal capacity of an ecosystem to equilibrate C input and output for a network of pools. Xc is co-determined by net primary production (NPP) and residence time (𝜏N), with the latter being determined by allocation coefficients, transfer coefficients, environmental scalar, and exit rate. Xp is the product of redistribution matrix (𝜏ch) and net ecosystem exchange. We applied this framework to two contrasting ecosystems, Duke Forest and Harvard Forest with an ecosystem model. This framework helps identify the mechanisms underlying the responses of carbon cycling in the two forests to climate change. The temporal trajectories of X are similar between the two ecosystems. Using this framework, we found that two different mechanisms leading to the similar trajectory. This framework has potential to reveal mechanisms behind transient C storage in response to various global change factors. It can also identify sources of uncertainties in predicted transient C storage across models and can therefore be useful for model intercomparison.

A correlational and predictive study of creativity and personality of college students.

PubMed

Sanz de Acedo Baquedano, María Teresa; Sanz de Acedo Lizarraga, María Luisa

2012-11-01

The goals of this study were to examine the relationship between creativity and personality, to identify what personality variables better predict creativity, and to determine whether significant differences exist among them in relation to gender. The research was conducted with a sample of 87 students at the Universidad Pública de Navarra, Spain. We administered the Creative Intelligence Test (CREA), which provides a cognitive measure for creativity and the Situational Personality Questionnaire (SPQ), which is composed of 15 personality features. Positive and significant correlations between creativity and independence, cognitive control, and tolerance personality scales were found. Negative and significant correlations between creativity and anxious, dominant, and aggressive personalities were also found. Moreover, four personality variables that positively predicted creativity (efficacy, independence, cognitive control, and integrity-honesty) and another four that negatively predicted creativity (emotional stability, anxiety, dominance, and leadership) were identified. The results did not show significant differences in creativity and personality in relation to gender, except in self-concept and in social adjustment. In conclusion, the results from this study can potentially be used to expand the types of features that support creative personalities.

Bioinformatic prediction and in vivo validation of residue-residue interactions in human proteins

NASA Astrophysics Data System (ADS)

Jordan, Daniel; Davis, Erica; Katsanis, Nicholas; Sunyaev, Shamil

2014-03-01

Identifying residue-residue interactions in protein molecules is important for understanding both protein structure and function in the context of evolutionary dynamics and medical genetics. Such interactions can be difficult to predict using existing empirical or physical potentials, especially when residues are far from each other in sequence space. Using a multiple sequence alignment of 46 diverse vertebrate species we explore the space of allowed sequences for orthologous protein families. Amino acid changes that are known to damage protein function allow us to identify specific changes that are likely to have interacting partners. We fit the parameters of the continuous-time Markov process used in the alignment to conclude that these interactions are primarily pairwise, rather than higher order. Candidates for sites under pairwise epistasis are predicted, which can then be tested by experiment. We report the results of an initial round of in vivo experiments in a zebrafish model that verify the presence of multiple pairwise interactions predicted by our model. These experimentally validated interactions are novel, distant in sequence, and are not readily explained by known biochemical or biophysical features.

Predicting Fog in the Nocturnal Boundary Layer

NASA Astrophysics Data System (ADS)

Izett, Jonathan; van de Wiel, Bas; Baas, Peter; van der Linden, Steven; van Hooft, Antoon; Bosveld, Fred

2017-04-01

Fog is a global phenomenon that presents a hazard to navigation and human safety, resulting in significant economic impacts for air and shipping industries as well as causing numerous road traffic accidents. Accurate prediction of fog events, however, remains elusive both in terms of timing and occurrence itself. Statistical methods based on set threshold criteria for key variables such as wind speed have been developed, but high rates of correct prediction of fog events still lead to similarly high "false alarms" when the conditions appear favourable, but no fog forms. Using data from the CESAR meteorological observatory in the Netherlands, we analyze specific cases and perform statistical analyses of event climatology, in order to identify the necessary conditions for correct prediction of fog. We also identify potential "missing ingredients" in current analysis that could help to reduce the number of false alarms. New variables considered include the indicators of boundary layer stability, as well as the presence of aerosols conducive to droplet formation. The poster presents initial findings of new research as well as plans for continued research.

A Proposed Method to Predict Preterm Birth Using Clinical Data, Standard Maternal Serum Screening, and Cholesterol

PubMed Central

ALLEMAN, Brandon W.; SMITH, Amanda R.; BYERS, Heather M.; BEDELL, Bruce; RYCKMAN, Kelli K.; MURRAY, Jeffrey C.; BOROWSKI, Kristi S.

2013-01-01

Objective To create a predictive model for preterm birth (PTB) from available clinical data and serum analytes. Study Design Serum analytes, routine pregnancy screening plus cholesterol and corresponding health information were linked to birth certificate data for a cohort of 2699 Iowa women with serum sampled in the first and second trimester. Stepwise logistic regression was used to select the best predictive model for PTB. Results Serum screening markers remained significant predictors of PTB even after controlling for maternal characteristics. The best predictive model included maternal characteristics, first trimester total cholesterol (TC), TC change between trimesters and second trimester alpha-fetoprotein and inhibin A. The model showed better discriminatory ability than PTB history alone and performed similarly in subgroups of women without past PTB. Conclusions Using clinical and serum screening data a potentially useful predictor of PTB was constructed. Validation and replication in other populations, and incorporation of other measures that identify PTB risk, like cervical length, can be a step towards identifying additional women who may benefit from new or currently available interventions. PMID:23500456

External forcing as a metronome for Atlantic multidecadal variability

NASA Astrophysics Data System (ADS)

Otterå, Odd Helge; Bentsen, Mats; Drange, Helge; Suo, Lingling

2010-10-01

Instrumental records, proxy data and climate modelling show that multidecadal variability is a dominant feature of North Atlantic sea-surface temperature variations, with potential impacts on regional climate. To understand the observed variability and to gauge any potential for climate predictions it is essential to identify the physical mechanisms that lead to this variability, and to explore the spatial and temporal characteristics of multidecadal variability modes. Here we use a coupled ocean-atmosphere general circulation model to show that the phasing of the multidecadal fluctuations in the North Atlantic during the past 600 years is, to a large degree, governed by changes in the external solar and volcanic forcings. We find that volcanoes play a particularly important part in the phasing of the multidecadal variability through their direct influence on tropical sea-surface temperatures, on the leading mode of northern-hemisphere atmosphere circulation and on the Atlantic thermohaline circulation. We suggest that the implications of our findings for decadal climate prediction are twofold: because volcanic eruptions cannot be predicted a decade in advance, longer-term climate predictability may prove challenging, whereas the systematic post-eruption changes in ocean and atmosphere may hold promise for shorter-term climate prediction.

Development of a Response Surface Thermal Model for Orion Mated to the International Space Station

NASA Technical Reports Server (NTRS)

Miller, Stephen W.; Meier, Eric J.

2010-01-01

A study was performed to determine if a Design of Experiments (DOE)/Response Surface Methodology could be applied to on-orbit thermal analysis and produce a set of Response Surface Equations (RSE) that accurately predict vehicle temperatures. The study used an integrated thermal model of the International Space Station and the Orion Outer mold line model. Five separate factors were identified for study: yaw, pitch, roll, beta angle, and the environmental parameters. Twenty external Orion temperatures were selected as the responses. A DOE case matrix of 110 runs was developed. The data from these cases were analyzed to produce an RSE for each of the temperature responses. The initial agreement between the engineering data and the RSE predictions was encouraging, although many RSEs had large uncertainties on their predictions. Fourteen verification cases were developed to test the predictive powers of the RSEs. The verification showed mixed results with some RSE predicting temperatures matching the engineering data within the uncertainty bands, while others had very large errors. While this study to not irrefutably prove that the DOE/RSM approach can be applied to on-orbit thermal analysis, it does demonstrate that technique has the potential to predict temperatures. Additional work is needed to better identify the cases needed to produce the RSEs

Complete fold annotation of the human proteome using a novel structural feature space.

PubMed

Middleton, Sarah A; Illuminati, Joseph; Kim, Junhyong

2017-04-13

Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this method by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.

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

Middleton, Sarah A.; Illuminati, Joseph; Kim, Junhyong

Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this methodmore » by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Finally, our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.« less

Complete fold annotation of the human proteome using a novel structural feature space

PubMed Central

Middleton, Sarah A.; Illuminati, Joseph; Kim, Junhyong

2017-01-01

Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this method by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families. PMID:28406174

Proteomic analysis of human dental cementum and alveolar bone.

PubMed

Salmon, Cristiane R; Tomazela, Daniela M; Ruiz, Karina Gonzales Silvério; Foster, Brian L; Paes Leme, Adriana Franco; Sallum, Enilson Antonio; Somerman, Martha J; Nociti, Francisco H

2013-10-08

Dental cementum (DC) is a bone-like tissue covering the tooth root and responsible for attaching the tooth to the alveolar bone (AB) via the periodontal ligament (PDL). Studies have unsuccessfully tried to identify factors specific to DC versus AB, in an effort to better understand DC development and regeneration. The present study aimed to use matched human DC and AB samples (n=7) to generate their proteomes for comparative analysis. Bone samples were harvested from tooth extraction sites, whereas DC samples were obtained from the apical root portion of extracted third molars. Samples were denatured, followed by protein extraction reduction, alkylation and digestion for analysis by nanoAcquity HPLC system and LTQ-FT Ultra. Data analysis demonstrated that a total of 318 proteins were identified in AB and DC. In addition to shared proteins between these tissues, 105 and 83 proteins exclusive to AB or DC were identified, respectively. This is the first report analyzing the proteomic composition of human DC matrix and identifying putative unique and enriched proteins in comparison to alveolar bone. These findings may provide novel insights into developmental differences between DC and AB, and identify candidate biomarkers that may lead to more efficient and predictable therapies for periodontal regeneration. Periodontal disease is a highly prevalent disease affecting the world population, which involves breakdown of the tooth supporting tissues, the periodontal ligament, alveolar bone, and dental cementum. The lack of knowledge on specific factors that differentiate alveolar bone and dental cementum limits the development of more efficient and predictable reconstructive therapies. In order to better understand cementum development and potentially identify factors to improve therapeutic outcomes, we took the unique approach of using matched patient samples of dental cementum and alveolar bone to generate and compare a proteome list for each tissue. A potential biomarker for dental cementum was identified, superoxide dismutase 3 (SOD3), which is found in cementum and cementum-associated cells in mouse, pig, and human tissues. These findings may provide novel insights into developmental differences between alveolar bone and dental cementum, and represent the basis for improved and more predictable therapies. © 2013.

Neural Reactivity to Angry Faces Predicts Treatment Response in Pediatric Anxiety

PubMed Central

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

2018-01-01

Although cognitive-behavioral psychotherapy (CBT) and pharmacotherapy are evidence-based treatments for pediatric anxiety, many youth with anxiety disorders fail to respond to these treatments. Given limitations of clinical measures in predicting treatment response, identifying neural predictors is timely. In this study, 35 anxious youth (ages 7–19 years) completed an emotional face-matching task during which the late positive potential (LPP), an event-related potential (ERP) component that indexes sustained attention towards emotional stimuli, was measured. Following the ERP measurement, youth received CBT or selective serotonin reuptake inhibitor (SSRI) treatment, and the LPP was examined as a predictor of treatment response. Findings indicated that, accounting for pre-treatment anxiety severity, neural reactivity to emotional faces predicted anxiety severity post-CBT and SSRI treatment such that enhanced electrocortical response to angry faces was associated with better treatment response. An enhanced LPP to angry faces may predict treatment response insofar as it may reflect greater emotion dysregulation or less avoidance and/or enhanced engagement with environmental stimuli in general, including with treatment. PMID:27255517

Predicting meat quality traits of ovine m. semimembranosus, both fresh and following freezing and thawing, using a hand held Raman spectroscopic device.

PubMed

Fowler, Stephanie M; Schmidt, Heinar; van de Ven, Remy; Wynn, Peter; Hopkins, David L

2015-10-01

Complementary studies were conducted to determine the potential for a Raman spectroscopic hand held device to predict meat quality traits of fresh lamb m. semimembranosus (topside) after ageing and freezing/thawing. Spectra were collected from 80 fresh muscles at 24h and 5d PM, another 80 muscles were measured at 24h, 5d and following freezing/thawing. Shear force, cooking loss, sarcomere length, colour, particle size, collagen content, pH24, pHu, purge and thaw loss were also measured. Results indicated a potential to predict pHu (R(2)cv=0.59), pH24 (R(2)cv=0.48) and purge (R(2)cv=0.42) using spectra collected 24h PM. L* could be predicted using spectra collected 24h (R(2)cv=0.33) or 5d PM (R(2)cv=0.33). This suggests that Raman spectroscopy is suited to identifying carcases which deviate from the normal metabolic processes and related meat quality traits. Copyright © 2015. Published by Elsevier Ltd.

Combining micro-RNA and protein sequencing to detect robust biomarkers for Graves' disease and orbitopathy.

PubMed

Zhang, Lei; Masetti, Giulia; Colucci, Giuseppe; Salvi, Mario; Covelli, Danila; Eckstein, Anja; Kaiser, Ulrike; Draman, Mohd Shazli; Muller, Ilaria; Ludgate, Marian; Lucini, Luigi; Biscarini, Filippo

2018-05-30

Graves' Disease (GD) is an autoimmune condition in which thyroid-stimulating antibodies (TRAB) mimic thyroid-stimulating hormone function causing hyperthyroidism. 5% of GD patients develop inflammatory Graves' orbitopathy (GO) characterized by proptosis and attendant sight problems. A major challenge is to identify which GD patients are most likely to develop GO and has relied on TRAB measurement. We screened sera/plasma from 14 GD, 19 GO and 13 healthy controls using high-throughput proteomics and miRNA sequencing (Illumina's HiSeq2000 and Agilent-6550 Funnel quadrupole-time-of-flight mass spectrometry) to identify potential biomarkers for diagnosis or prognosis evaluation. Euclidean distances and differential expression (DE) based on miRNA and protein quantification were analysed by multidimensional scaling (MDS) and multinomial regression respectively. We detected 3025 miRNAs and 1886 proteins and MDS revealed good separation of the 3 groups. Biomarkers were identified by combined DE and Lasso-penalized predictive models; accuracy of predictions was 0.86 (±0:18), and 5 miRNA and 20 proteins were found including Zonulin, Alpha-2 macroglobulin, Beta-2 glycoprotein 1 and Fibronectin. Functional analysis identified relevant metabolic pathways, including hippo signaling, bacterial invasion of epithelial cells and mRNA surveillance. Proteomic and miRNA analyses, combined with robust bioinformatics, identified circulating biomarkers applicable to diagnose GD, predict GO disease status and optimize patient management.

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

PubMed

Jian, Xueqiu; Boerwinkle, Eric; Liu, Xiaoming

2014-12-16

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

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 physiological parameters for a virtual population. For risk-based prioritization of chemicals, predicted bioactive equivalent doses were compared to demographic-specific inferences of exposure rates that were based on NHANES urinary analyte biomonitoring data. The inclusion of NHANES-derived inter-individual variability decreased predicted bioactive equivalent doses by 12% on average for the total population when compared to previous methods. However, for some combinations of chemical and demographic groups the margin was reduced by as much as three quarters. This TK modeling framework allows targeted risk prioritization of chemicals for demographic groups of interest, including potentially sensitive life stages and subpopulations. Published by Elsevier Ltd.

Identifying Best Practices for and Utilities of the Pharmacy Curriculum Outcome Assessment Examination

PubMed Central

Romanelli, Frank

2016-01-01

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. PMID:28179712

Use of Linear Prediction Uncertainty Analysis to Guide Conditioning of Models Simulating Surface-Water/Groundwater Interactions

NASA Astrophysics Data System (ADS)

Hughes, J. D.; White, J.; Doherty, J.

2011-12-01

Linear prediction uncertainty analysis in a Bayesian framework was applied to guide the conditioning of an integrated surface water/groundwater model that will be used to predict the effects of groundwater withdrawals on surface-water and groundwater flows. Linear prediction uncertainty analysis is an effective approach for identifying (1) raw and processed data most effective for model conditioning prior to inversion, (2) specific observations and periods of time critically sensitive to specific predictions, and (3) additional observation data that would reduce model uncertainty relative to specific predictions. We present results for a two-dimensional groundwater model of a 2,186 km2 area of the Biscayne aquifer in south Florida implicitly coupled to a surface-water routing model of the actively managed canal system. The model domain includes 5 municipal well fields withdrawing more than 1 Mm3/day and 17 operable surface-water control structures that control freshwater releases from the Everglades and freshwater discharges to Biscayne Bay. More than 10 years of daily observation data from 35 groundwater wells and 24 surface water gages are available to condition model parameters. A dense parameterization was used to fully characterize the contribution of the inversion null space to predictive uncertainty and included bias-correction parameters. This approach allows better resolution of the boundary between the inversion null space and solution space. Bias-correction parameters (e.g., rainfall, potential evapotranspiration, and structure flow multipliers) absorb information that is present in structural noise that may otherwise contaminate the estimation of more physically-based model parameters. This allows greater precision in predictions that are entirely solution-space dependent, and reduces the propensity for bias in predictions that are not. Results show that application of this analysis is an effective means of identifying those surface-water and groundwater data, both raw and processed, that minimize predictive uncertainty, while simultaneously identifying the maximum solution-space dimensionality of the inverse problem supported by the data.

AutoSite: an automated approach for pseudo-ligands prediction—from ligand-binding sites identification to predicting key ligand atoms

PubMed Central

Ravindranath, Pradeep Anand; Sanner, Michel F.

2016-01-01

Motivation: The identification of ligand-binding sites from a protein structure facilitates computational drug design and optimization, and protein function assignment. We introduce AutoSite: an efficient software tool for identifying ligand-binding sites and predicting pseudo ligand corresponding to each binding site identified. Binding sites are reported as clusters of 3D points called fills in which every point is labelled as hydrophobic or as hydrogen bond donor or acceptor. From these fills AutoSite derives feature points: a set of putative positions of hydrophobic-, and hydrogen-bond forming ligand atoms. Results: We show that AutoSite identifies ligand-binding sites with higher accuracy than other leading methods, and produces fills that better matches the ligand shape and properties, than the fills obtained with a software program with similar capabilities, AutoLigand. In addition, we demonstrate that for the Astex Diverse Set, the feature points identify 79% of hydrophobic ligand atoms, and 81% and 62% of the hydrogen acceptor and donor hydrogen ligand atoms interacting with the receptor, and predict 81.2% of water molecules mediating interactions between ligand and receptor. Finally, we illustrate potential uses of the predicted feature points in the context of lead optimization in drug discovery projects. Availability and Implementation: http://adfr.scripps.edu/AutoDockFR/autosite.html Contact: sanner@scripps.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27354702

Insilico profiling of microRNAs in Korean ginseng (Panax ginseng Meyer)

PubMed Central

Mathiyalagan, Ramya; Subramaniyam, Sathiyamoorthy; Natarajan, Sathishkumar; Kim, Yeon Ju; Sun, Myung Suk; Kim, Se Young; Kim, Yu-Jin; Yang, Deok Chun

2013-01-01

MicroRNAs (miRNAs) are a class of recently discovered non-coding small RNA molecules, on average approximately 21 nucleotides in length, which underlie numerous important biological roles in gene regulation in various organisms. The miRNA database (release 18) has 18,226 miRNAs, which have been deposited from different species. Although miRNAs have been identified and validated in many plant species, no studies have been reported on discovering miRNAs in Panax ginseng Meyer, which is a traditionally known medicinal plant in oriental medicine, also known as Korean ginseng. It has triterpene ginseng saponins called ginsenosides, which are responsible for its various pharmacological activities. Predicting conserved miRNAs by homology-based analysis with available expressed sequence tag (EST) sequences can be powerful, if the species lacks whole genome sequence information. In this study by using the EST based computational approach, 69 conserved miRNAs belonging to 44 miRNA families were identified in Korean ginseng. The digital gene expression patterns of predicted conserved miRNAs were analyzed by deep sequencing using small RNA sequences of flower buds, leaves, and lateral roots. We have found that many of the identified miRNAs showed tissue specific expressions. Using the insilico method, 346 potential targets were identified for the predicted 69 conserved miRNAs by searching the ginseng EST database, and the predicted targets were mainly involved in secondary metabolic processes, responses to biotic and abiotic stress, and transcription regulator activities, as well as a variety of other metabolic processes. PMID:23717176

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

PubMed

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

2013-01-01

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

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

PubMed Central

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

2013-01-01

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

Efficacy of adjuvant chemotherapy for non-small cell lung cancer assessed by metastatic potential associated with ACTN4.

PubMed

Miura, Nami; Kamita, Masahiro; Kakuya, Takanori; Fujiwara, Yutaka; Tsuta, Koji; Shiraishi, Hideaki; Takeshita, Fumitaka; Ochiya, Takahiro; Shoji, Hirokazu; Huang, Wilber; Ohe, Yuichiro; Yamada, Tesshi; Honda, Kazufumi

2016-05-31

Although several clinical trials have demonstrated the benefits of platinum-combined adjuvant chemotherapy for resected non-small cell lung cancer (NSCLC), predictive biomarkers for the efficacy of such therapy have not yet been identified. Selection of patients with high metastatic ability in the early stage of non-small cell lung cancer (NSCLC) has the potential to predict clinical benefit of adjuvant chemotherapy (ADJ).In order to develop a predictive biomarker for efficacy of ADJ, we reanalyzed patient data using a public database enrolled by JBR.10, which was a clinical trial to probe the clinical benefits of ADJ in stage-IB/II patients with NSCLC. The patients who were enrolled by JBR.10 were classified into 2 subgroups according to expression of the ACTN4 transcript: ACTN4 positive (ACTN4 (+)) and ACTN4 negative (ACTN4 (-)). In the ACTN4 (+) group, overall survival (OS) was significantly higher in the ADJ subgroup compared with the observation subgroup (OBS), indicating a significant survival benefit of ADJ. However, no difference in OS was found between ADJ and OBS groups in ACTN4 (-). Although ACTN4 expression level did not correlate with the chemosensitivity of cancer cell lines for cytotoxic drugs, the metastatic potential of A549 lung adenocarcinoma cells was significantly reduced by ACTN4 shRNA in in vitro assays and in an animal transplantation model. The clinical and preclinical data suggested that ACTN4 is a potential predictive biomarker for efficacy of ADJ in stage-IB/II patients with NSCLC, by reflecting the metastatic potential of tumor cells.

Consumption and Response Output as a Function of Unit Price: Manipulation of Cost and Benefit Components

ERIC Educational Resources Information Center

Delmendo, Xeres; Borrero, John C.; Beauchamp, Kenneth L.; Francisco, Monica T.

2009-01-01

We conducted preference assessments with 4 typically developing children to identify potential reinforcers and assessed the reinforcing efficacy of those stimuli. Next, we tested two predictions of economic theory: that overall consumption (reinforcers obtained) would decrease as the unit price (response requirement per reinforcer) increased and…

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

USDA-ARS?s Scientific Manuscript database

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

Proteome Profiling Reveals Potential Toxicity and Detoxification Pathways Following Exposure of BEAS-2B Cells to Engineered Nanoparticle Titanium Dioxide

EPA Science Inventory

Identification of toxicity pathways linked to chemical -exposure is critical for a better understanding of biological effects of the exposure, toxic mechanisms, and for enhancement of the prediction of chemical toxicity and adverse health outcomes. To identify toxicity pathways a...

Unresolved Loss, a Risk Factor for Offspring, Predicts Event-Related Potential Responses to Death-Related Imagery

ERIC Educational Resources Information Center

Bahm, Naomi I. Gribneau; Simon-Thomas, Emiliana R.; Main, Mary; Hesse, Erik

2017-01-01

This study investigates whether individual differences in attachment status can be detected by electrophysiological responses to loss-themed pictures. The Adult Attachment Interview (AAI) was used to identify discourse/reasoning lapses during the discussion of loss experiences via death that place speakers in the Unresolved/disorganized AAI…

Development of an in vitro Hepatocyte Model to Investigate Chemical Mode of Action

EPA Science Inventory

There is a clear need to identify and characterize the potential of liver in vitro models that can be used to replace animals for mode of action analysis. Our goal is to use in vitro models for mode of action prediction which recapitulate critical cellular processes underlying in...

Stress, Behavior and Health: Developing a Model for Predicting Post-Deployment Morbidity, Mortality and Other Adverse Outcomes

DTIC Science & Technology

1999-07-01

related health outcomes and risk factors for adverse health events. A secondary effort is to link self-reports of stress and subsequent increases in...Gulf War era veterans, information critical to the interpretation of postwar differences between these two groups . We have identified potential

Photosynthetic Potential Of Laurel Oak Seedlings Following Canopy Manipulation

Treesearch

K.W. McLeod

2004-01-01

Abstract The theory of forest gap dynamics predicts that replacement individuals are those that can most fully use the light environment of a gap. Along the Coosawhatchie River in South Carolina, 12 canopy gaps were identified in a bottomland hardwood forest dominated by laurel oak (Quercus laurifolia Michaux). Each gap was...

Feed-forward and generalized regression neural networks in modeling feeding behavior of pigs in the grow-finishing phase

USDA-ARS?s Scientific Manuscript database

Feeding patterns in group-housed grow-finishing pigs have been investigated for use in management decisions, identifying sick animals, and determining genetic differences within a herd. Development of models to predict swine feeding behaviour has been limited due the large number of potential enviro...

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

USDA-ARS?s Scientific Manuscript database

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

Outliers: A Potential Data Problem.

ERIC Educational Resources Information Center

Douzenis, Cordelia; Rakow, Ernest A.

Outliers, extreme data values relative to others in a sample, may distort statistics that assume internal levels of measurement and normal distribution. The outlier may be a valid value or an error. Several procedures are available for identifying outliers, and each may be applied to errors of prediction from the regression lines for utility in a…

A SYSTEMS APPROACH TO CHARACTERIZING AND PREDICTING THYROID TOXICITY USING AN AMPHIBIAN MODEL

EPA Science Inventory

The EPA was recently mandated to evaluate the potential effects of chemicals on endocrine function and has identified Xenopus as a model organism to use as the basis for a thyroid disruption screening assay. The main objective of this work is to develop a hypothalamic-pituitary-t...

Teacher- and School-Level Predictors of Teacher Efficacy and Burnout: Identifying Potential Areas for Support

ERIC Educational Resources Information Center

Pas, Elise T.; Bradshaw, Catherine P.; Hershfeldt, Patricia A.

2012-01-01

Although several studies relate low teacher efficacy and high burnout to the quality of instruction and students' academic achievement, there has been limited research examining factors that predict teacher efficacy and burnout. The current study employed a longitudinal, multilevel modeling approach to examine the influence of teacher- and…

Comparative evaluation of the drug interaction screening programs MediQ and ID PHARMA CHECK in neurological inpatients.

PubMed

Zorina, Olesya I; Haueis, Patrick; Semmler, Alexander; Marti, Isabelle; Gonzenbach, Roman R; Guzek, Markus; Kullak-Ublick, Gerd A; Weller, Michael; Russmann, Stefan

2012-08-01

The comparative evaluation of clinical decision support software (CDSS) programs regarding their sensitivity and positive predictive value for the identification of clinically relevant drug interactions. In this research, we used a cross-sectional study that identified potential drug interactions using the CDSS MediQ and the ID PHARMA CHECK in 484 neurological inpatients. Interactions were reclassified according to the Zurich Interaction System, a multidimensional classification that incorporates the Operational Classification of Drug Interactions. In 484 patients with 2812 prescriptions, MediQ and ID PHARMA CHECK generated a total of 1759 and 1082 alerts, respectively. MediQ identified 658 unique potentially interacting combinations, 8 classified as "high danger," 164 as "average danger," and 486 as "low danger." ID PHARMA CHECK detected 336 combinations assigned to one or several of 12 risk and management categories. Altogether, both CDSS issued alerts relating to 808 unique potentially interacting combinations. According to the Zurich Interaction System, 6 of these were contraindicated, 25 were provisionally contraindicated, 190 carried a conditional risk, and 587 had a minimal risk of adverse events. The positive predictive value for alerts having at least a conditional risk was 0.24 for MediQ and 0.48 for ID PHARMA CHECK. CDSS showed major differences in the identification and grading of interactions, and many interactions were only identified by one of the two CDSS. For both programs, only a small proportion of all identified interactions appeared clinically relevant, and the selected display of alerts that imply management changes is a key issue in the further development and local setup of such programs. Copyright © 2012 John Wiley & Sons, Ltd.

Binding site and affinity prediction of general anesthetics to protein targets using docking.

PubMed

Liu, Renyu; Perez-Aguilar, Jose Manuel; Liang, David; Saven, Jeffery G

2012-05-01

The protein targets for general anesthetics remain unclear. A tool to predict anesthetic binding for potential binding targets is needed. In this study, we explored whether a computational method, AutoDock, could serve as such a tool. High-resolution crystal data of water-soluble proteins (cytochrome C, apoferritin, and human serum albumin), and a membrane protein (a pentameric ligand-gated ion channel from Gloeobacter violaceus [GLIC]) were used. Isothermal titration calorimetry (ITC) experiments were performed to determine anesthetic affinity in solution conditions for apoferritin. Docking calculations were performed using DockingServer with the Lamarckian genetic algorithm and the Solis and Wets local search method (http://www.dockingserver.com/web). Twenty general anesthetics were docked into apoferritin. The predicted binding constants were compared with those obtained from ITC experiments for potential correlations. In the case of apoferritin, details of the binding site and their interactions were compared with recent cocrystallization data. Docking calculations for 6 general anesthetics currently used in clinical settings (isoflurane, sevoflurane, desflurane, halothane, propofol, and etomidate) with known 50% effective concentration (EC(50)) values were also performed in all tested proteins. The binding constants derived from docking experiments were compared with known EC(50) values and octanol/water partition coefficients for the 6 general anesthetics. All 20 general anesthetics docked unambiguously into the anesthetic binding site identified in the crystal structure of apoferritin. The binding constants for 20 anesthetics obtained from the docking calculations correlate significantly with those obtained from ITC experiments (P = 0.04). In the case of GLIC, the identified anesthetic binding sites in the crystal structure are among the docking predicted binding sites, but not the top ranked site. Docking calculations suggest a most probable binding site located in the extracellular domain of GLIC. The predicted affinities correlated significantly with the known EC(50) values for the 6 frequently used anesthetics in GLIC for the site identified in the experimental crystal data (P = 0.006). However, predicted affinities in apoferritin, human serum albumin, and cytochrome C did not correlate with these 6 anesthetics' known experimental EC(50) values. A weak correlation between the predicted affinities and the octanol/water partition coefficients was observed for the sites in GLIC. We demonstrated that anesthetic binding sites and relative affinities can be predicted using docking calculations in an automatic docking server (AutoDock) for both water-soluble and membrane proteins. Correlation of predicted affinity and EC(50) for 6 frequently used general anesthetics was only observed in GLIC, a member of a protein family relevant to anesthetic mechanism.

Binding Site and Affinity Prediction of General Anesthetics to Protein Targets Using Docking

PubMed Central

Liu, Renyu; Perez-Aguilar, Jose Manuel; Liang, David; Saven, Jeffery G.

2012-01-01

Background The protein targets for general anesthetics remain unclear. A tool to predict anesthetic binding for potential binding targets is needed. In this study, we explore whether a computational method, AutoDock, could serve as such a tool. Methods High-resolution crystal data of water soluble proteins (cytochrome C, apoferritin and human serum albumin), and a membrane protein (a pentameric ligand-gated ion channel from Gloeobacter violaceus, GLIC) were used. Isothermal titration calorimetry (ITC) experiments were performed to determine anesthetic affinity in solution conditions for apoferritin. Docking calculations were performed using DockingServer with the Lamarckian genetic algorithm and the Solis and Wets local search method (https://www.dockingserver.com/web). Twenty general anesthetics were docked into apoferritin. The predicted binding constants are compared with those obtained from ITC experiments for potential correlations. In the case of apoferritin, details of the binding site and their interactions were compared with recent co-crystallization data. Docking calculations for six general anesthetics currently used in clinical settings (isoflurane, sevoflurane, desflurane, halothane, propofol, and etomidate) with known EC50 were also performed in all tested proteins. The binding constants derived from docking experiments were compared with known EC50s and octanol/water partition coefficients for the six general anesthetics. Results All 20 general anesthetics docked unambiguously into the anesthetic binding site identified in the crystal structure of apoferritin. The binding constants for 20 anesthetics obtained from the docking calculations correlate significantly with those obtained from ITC experiments (p=0.04). In the case of GLIC, the identified anesthetic binding sites in the crystal structure are among the docking predicted binding sites, but not the top ranked site. Docking calculations suggest a most probable binding site located in the extracellular domain of GLIC. The predicted affinities correlated significantly with the known EC50s for the six commonly used anesthetics in GLIC for the site identified in the experimental crystal data (p=0.006). However, predicted affinities in apoferritin, human serum albumin, and cytochrome C did not correlate with these six anesthetics’ known experimental EC50s. A weak correlation between the predicted affinities and the octanol/water partition coefficients was observed for the sites in GLIC. Conclusion We demonstrated that anesthetic binding sites and relative affinities can be predicted using docking calculations in an automatic docking server (Autodock) for both water soluble and membrane proteins. Correlation of predicted affinity and EC50 for six commonly used general anesthetics was only observed in GLIC, a member of a protein family relevant to anesthetic mechanism. PMID:22392968

Prediction of preterm birth in twin gestations using biophysical and biochemical tests

PubMed Central

Conde-Agudelo, Agustin; Romero, Roberto

2018-01-01

The objective of this study was to determine the performance of biophysical and biochemical tests for the prediction of preterm birth in both asymptomatic and symptomatic women with twin gestations. We identified a total of 19 tests proposed to predict preterm birth, mainly in asymptomatic women. In these women, a single measurement of cervical length with transvaginal ultrasound before 25 weeks of gestation appears to be a good test to predict preterm birth. Its clinical potential is enhanced by the evidence that vaginal progesterone administration in asymptomatic women with twin gestations and a short cervix reduces neonatal morbidity and mortality associated with spontaneous preterm delivery. Other tests proposed for the early identification of asymptomatic women at increased risk of preterm birth showed minimal to moderate predictive accuracy. None of the tests evaluated in this review meet the criteria to be considered clinically useful to predict preterm birth among patients with an episode of preterm labor. However, a negative cervicovaginal fetal fibronectin test could be useful in identifying women who are not at risk for delivering within the next week, which could avoid unnecessary hospitalization and treatment. This review underscores the need to develop accurate tests for predicting preterm birth in twin gestations. Moreover, the use of interventions in these patients based on test results should be associated with the improvement of perinatal outcomes. PMID:25072736

Prediction of preterm birth in twin gestations using biophysical and biochemical tests.

PubMed

Conde-Agudelo, Agustin; Romero, Roberto

2014-12-01

The objective of this study was to determine the performance of biophysical and biochemical tests for the prediction of preterm birth in both asymptomatic and symptomatic women with twin gestations. We identified a total of 19 tests proposed to predict preterm birth, mainly in asymptomatic women. In these women, a single measurement of cervical length with transvaginal ultrasound before 25 weeks of gestation appears to be a good test to predict preterm birth. Its clinical potential is enhanced by the evidence that vaginal progesterone administration in asymptomatic women with twin gestations and a short cervix reduces neonatal morbidity and mortality associated with spontaneous preterm delivery. Other tests proposed for the early identification of asymptomatic women at increased risk of preterm birth showed minimal to moderate predictive accuracy. None of the tests evaluated in this review meet the criteria to be considered clinically useful to predict preterm birth among patients with an episode of preterm labor. However, a negative cervicovaginal fetal fibronectin test could be useful in identifying women who are not at risk for delivering within the next week, which could avoid unnecessary hospitalization and treatment. This review underscores the need to develop accurate tests for predicting preterm birth in twin gestations. Moreover, the use of interventions in these patients based on test results should be associated with the improvement of perinatal outcomes. Copyright © 2014. Published by Elsevier Inc.

The Auditory Brain-Stem Response to Complex Sounds: A Potential Biomarker for Guiding Treatment of Psychosis

PubMed Central

Tarasenko, Melissa A.; Swerdlow, Neal R.; Makeig, Scott; Braff, David L.; Light, Gregory A.

2014-01-01

Cognitive deficits limit psychosocial functioning in schizophrenia. For many patients, cognitive remediation approaches have yielded encouraging results. Nevertheless, therapeutic response is variable, and outcome studies consistently identify individuals who respond minimally to these interventions. Biomarkers that can assist in identifying patients likely to benefit from particular forms of cognitive remediation are needed. Here, we describe an event-related potential (ERP) biomarker – the auditory brain-stem response (ABR) to complex sounds (cABR) – that appears to be particularly well-suited for predicting response to at least one form of cognitive remediation that targets auditory information processing. Uniquely, the cABR quantifies the fidelity of sound encoded at the level of the brainstem and midbrain. This ERP biomarker has revealed auditory processing abnormalities in various neurodevelopmental disorders, correlates with functioning across several cognitive domains, and appears to be responsive to targeted auditory training. We present preliminary cABR data from 18 schizophrenia patients and propose further investigation of this biomarker for predicting and tracking response to cognitive interventions. PMID:25352811

Machine learning algorithms for the prediction of hERG and CYP450 binding in drug development.

PubMed

Klon, Anthony E

2010-07-01

The cost of developing new drugs is estimated at approximately $1 billion; the withdrawal of a marketed compound due to toxicity can result in serious financial loss for a pharmaceutical company. There has been a greater interest in the development of in silico tools that can identify compounds with metabolic liabilities before they are brought to market. The two largest classes of machine learning (ML) models, which will be discussed in this review, have been developed to predict binding to the human ether-a-go-go related gene (hERG) ion channel protein and the various CYP isoforms. Being able to identify potentially toxic compounds before they are made would greatly reduce the number of compound failures and the costs associated with drug development. This review summarizes the state of modeling hERG and CYP binding towards this goal since 2003 using ML algorithms. A wide variety of ML algorithms that are comparable in their overall performance are available. These ML methods may be applied regularly in discovery projects to flag compounds with potential metabolic liabilities.

Grouping like catchments: A novel means to compare 40+ watersheds in the Northeastern U.S.

NASA Astrophysics Data System (ADS)

Shaw, S. B.; Walter, M.; Marjerison, R. D.

2008-12-01

One difficulty in understanding the effect of multi-scale patterns in watersheds comes from finding a concise way to identify and compare features across many basins. Comparing raw data (i.e. discharge time series) requires one to account for highly variable climate drivers while extracting meaningful metrics from the data series. Comparing model parameters imposes model assumptions that may obscure fundamental differences, potentially making it an exercise in comparing calibration factors. As a possible middle ground, we have found that the probability of a given basin-wide runoff response can be predicted by combining rainfall frequency with 1. a curve establishing a relationship between basin storage and base flow and 2. the baseflow flow-duration curve. In addition to providing a means to predict runoff, these curves succinctly present empirical runoff-response information, allowing ready graphical comparison of multiple watersheds. From 40+ watersheds throughout the Northeastern U.S., we demonstrate the potential to group watersheds and identify critical hydrologic features, providing particular insight into the influence of land use patterns as well as basin scale.

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

Predicting intraoperative feasibility of combined TES-mMEP and cSSEP monitoring during scoliosis surgery based on preoperative neurophysiological assessment.

PubMed

Azabou, Eric; Manel, Véronique; Abelin-Genevois, Kariman; Andre-Obadia, Nathalie; Cunin, Vincent; Garin, Christophe; Kohler, Remi; Berard, Jérôme; Ulkatan, Sedat

2014-07-01

Combined monitoring of muscle motor evoked potentials elicited by transcranial electric stimulation (TES-mMEP) and cortical somatosensory evoked potentials (cSSEPs) is safe and effective for spinal cord monitoring during scoliosis surgery. However, TES-mMEP/cSSEP is not always feasible. Predictors of feasibility would help to plan the monitoring strategy. To identify predictors of the feasibility of TES-mMEP/cSSEP during scoliosis surgery. Prospective cohort study in a clinical neurophysiology unit and pediatric orthopedic department of a French university hospital. A total of 103 children aged 2 to 19 years scheduled for scoliosis surgery. Feasibility rate of intraoperative TES-mMEP/cSSEP monitoring. All patients underwent a preoperative neurological evaluation and preoperative mMEP and cSSEP recordings at both legs. For each factor associated with feasibility, we computed sensitivity, specificity, positive predictive value (PPV), and negative predictive value. A decision tree was designed. Presence of any of the following factors was associated with 100% feasibility, 100% specificity, and 100% PPV: idiopathic scoliosis, normal preoperative neurological findings, and normal preoperative mMEP and cSSEP recordings. Feasibility was 0% in the eight patients with no recordable mMEPs or cSSEPs during preoperative testing. A decision tree involving three screening steps can be used to identify patients in whom intraoperative TES-mMEP/cSSEP is feasible. Preoperative neurological and neurophysiological assessments are helpful for identifying patients who can be successfully monitored by TES-mMEP/cSSEP during scoliosis surgery. Copyright © 2014 Elsevier Inc. All rights reserved.

Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD

PubMed Central

Galatzer-Levy, I R; Ma, S; Statnikov, A; Yehuda, R; Shalev, A Y

2017-01-01

To date, studies of biological risk factors have revealed inconsistent relationships with subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the use of data analytic tools that are ill equipped for modeling the complex interactions between biological and environmental factors that underlay post-traumatic psychopathology. Further, using symptom-based diagnostic status as the group outcome overlooks the inherent heterogeneity of PTSD, potentially contributing to failures to replicate. To examine the potential yield of novel analytic tools, we reanalyzed data from a large longitudinal study of individuals identified following trauma in the general emergency room (ER) that failed to find a linear association between cortisol response to traumatic events and subsequent PTSD. First, latent growth mixture modeling empirically identified trajectories of post-traumatic symptoms, which then were used as the study outcome. Next, support vector machines with feature selection identified sets of features with stable predictive accuracy and built robust classifiers of trajectory membership (area under the receiver operator characteristic curve (AUC)=0.82 (95% confidence interval (CI)=0.80–0.85)) that combined clinical, neuroendocrine, psychophysiological and demographic information. Finally, graph induction algorithms revealed a unique path from childhood trauma via lower cortisol during ER admission, to non-remitting PTSD. Traditional general linear modeling methods then confirmed the newly revealed association, thereby delineating a specific target population for early endocrine interventions. Advanced computational approaches offer innovative ways for uncovering clinically significant, non-shared biological signals in heterogeneous samples. PMID:28323285

Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD.

PubMed

Galatzer-Levy, I R; Ma, S; Statnikov, A; Yehuda, R; Shalev, A Y

2017-03-21

To date, studies of biological risk factors have revealed inconsistent relationships with subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the use of data analytic tools that are ill equipped for modeling the complex interactions between biological and environmental factors that underlay post-traumatic psychopathology. Further, using symptom-based diagnostic status as the group outcome overlooks the inherent heterogeneity of PTSD, potentially contributing to failures to replicate. To examine the potential yield of novel analytic tools, we reanalyzed data from a large longitudinal study of individuals identified following trauma in the general emergency room (ER) that failed to find a linear association between cortisol response to traumatic events and subsequent PTSD. First, latent growth mixture modeling empirically identified trajectories of post-traumatic symptoms, which then were used as the study outcome. Next, support vector machines with feature selection identified sets of features with stable predictive accuracy and built robust classifiers of trajectory membership (area under the receiver operator characteristic curve (AUC)=0.82 (95% confidence interval (CI)=0.80-0.85)) that combined clinical, neuroendocrine, psychophysiological and demographic information. Finally, graph induction algorithms revealed a unique path from childhood trauma via lower cortisol during ER admission, to non-remitting PTSD. Traditional general linear modeling methods then confirmed the newly revealed association, thereby delineating a specific target population for early endocrine interventions. Advanced computational approaches offer innovative ways for uncovering clinically significant, non-shared biological signals in heterogeneous samples.

Hypoxia Sensing in Plants: On a Quest for Ion Channels as Putative Oxygen Sensors.

PubMed

Wang, Feifei; Chen, Zhong-Hua; Shabala, Sergey

2017-07-01

Over 17 million km2 of land is affected by soil flooding every year, resulting in substantial yield losses and jeopardizing food security across the globe. A key step in resolving this problem and creating stress-tolerant cultivars is an understanding of the mechanisms by which plants sense low-oxygen stress. In this work, we review the current knowledge about the oxygen-sensing and signaling pathway in mammalian and plant systems and postulate the potential role of ion channels as putative oxygen sensors in plant roots. We first discuss the definition and requirements for the oxygen sensor and the difference between sensing and signaling. We then summarize the literature and identify several known candidates for oxygen sensing in the mammalian literature. This includes transient receptor potential (TRP) channels; K+-permeable channels (Kv, BK and TASK); Ca2+ channels (RyR and TPC); and various chemo- and reactive oxygen species (ROS)-dependent oxygen sensors. Identified key oxygen-sensing domains (PAS, GCS, GAF and PHD) in mammalian systems are used to predict the potential plant counterparts in Arabidopsis. Finally, the sequences of known mammalian ion channels with reported roles in oxygen sensing were employed to BLAST the Arabidopsis genome for the candidate genes. Several plasma membrane and tonoplast ion channels (such as TPC, AKT and KCO) and oxygen domain-containing proteins with predicted oxygen-sensing ability were identified and discussed. We propose a testable model for potential roles of ion channels in plant hypoxia sensing. © The Author 2017. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Predicted deep-sea coral habitat suitability for the U.S. West coast.

PubMed

Guinotte, John M; Davies, Andrew J

2014-01-01

Regional scale habitat suitability models provide finer scale resolution and more focused predictions of where organisms may occur. Previous modelling approaches have focused primarily on local and/or global scales, while regional scale models have been relatively few. In this study, regional scale predictive habitat models are presented for deep-sea corals for the U.S. West Coast (California, Oregon and Washington). Model results are intended to aid in future research or mapping efforts and to assess potential coral habitat suitability both within and outside existing bottom trawl closures (i.e. Essential Fish Habitat (EFH)) and identify suitable habitat within U.S. National Marine Sanctuaries (NMS). Deep-sea coral habitat suitability was modelled at 500 m×500 m spatial resolution using a range of physical, chemical and environmental variables known or thought to influence the distribution of deep-sea corals. Using a spatial partitioning cross-validation approach, maximum entropy models identified slope, temperature, salinity and depth as important predictors for most deep-sea coral taxa. Large areas of highly suitable deep-sea coral habitat were predicted both within and outside of existing bottom trawl closures and NMS boundaries. Predicted habitat suitability over regional scales are not currently able to identify coral areas with pin point accuracy and probably overpredict actual coral distribution due to model limitations and unincorporated variables (i.e. data on distribution of hard substrate) that are known to limit their distribution. Predicted habitat results should be used in conjunction with multibeam bathymetry, geological mapping and other tools to guide future research efforts to areas with the highest probability of harboring deep-sea corals. Field validation of predicted habitat is needed to quantify model accuracy, particularly in areas that have not been sampled.

Predicted Deep-Sea Coral Habitat Suitability for the U.S. West Coast

PubMed Central

Guinotte, John M.; Davies, Andrew J.

2014-01-01

Regional scale habitat suitability models provide finer scale resolution and more focused predictions of where organisms may occur. Previous modelling approaches have focused primarily on local and/or global scales, while regional scale models have been relatively few. In this study, regional scale predictive habitat models are presented for deep-sea corals for the U.S. West Coast (California, Oregon and Washington). Model results are intended to aid in future research or mapping efforts and to assess potential coral habitat suitability both within and outside existing bottom trawl closures (i.e. Essential Fish Habitat (EFH)) and identify suitable habitat within U.S. National Marine Sanctuaries (NMS). Deep-sea coral habitat suitability was modelled at 500 m×500 m spatial resolution using a range of physical, chemical and environmental variables known or thought to influence the distribution of deep-sea corals. Using a spatial partitioning cross-validation approach, maximum entropy models identified slope, temperature, salinity and depth as important predictors for most deep-sea coral taxa. Large areas of highly suitable deep-sea coral habitat were predicted both within and outside of existing bottom trawl closures and NMS boundaries. Predicted habitat suitability over regional scales are not currently able to identify coral areas with pin point accuracy and probably overpredict actual coral distribution due to model limitations and unincorporated variables (i.e. data on distribution of hard substrate) that are known to limit their distribution. Predicted habitat results should be used in conjunction with multibeam bathymetry, geological mapping and other tools to guide future research efforts to areas with the highest probability of harboring deep-sea corals. Field validation of predicted habitat is needed to quantify model accuracy, particularly in areas that have not been sampled. PMID:24759613

Insights on activation enthalpy for non-Schmid slip in body-centered cubic metals

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

Hale, Lucas M.; Lim, Hojun; Zimmerman, Jonathan A.

2014-12-18

We use insights gained from atomistic simulation to develop an activation enthalpy model for dislocation slip in body-centered cubic iron. Furthermore, using a classical potential that predicts dislocation core stabilities consistent with ab initio predictions, we quantify the non-Schmid stress-dependent effects of slip. The kink-pair activation enthalpy is evaluated and a model is identified as a function of the general stress state. Thus, our model enlarges the applicability of the classic Kocks activation enthalpy model to materials with non-Schmid behavior.

Precision medicine and precision therapeutics: hedgehog signaling pathway, basal cell carcinoma and beyond.

PubMed

Mohan, Shalini V; Chang, Anne Lynn S

2014-06-01

Precision medicine and precision therapeutics is currently in its infancy with tremendous potential to improve patient care by better identifying individuals at risk for skin cancer and predict tumor responses to treatment. This review focuses on the Hedgehog signaling pathway, its critical role in the pathogenesis of basal cell carcinoma, and the emergence of targeted treatments for advanced basal cell carcinoma. Opportunities to utilize precision medicine are outlined, such as molecular profiling to predict basal cell carcinoma response to targeted therapy and to inform therapeutic decisions.

Dysfunctional attitudes and poor problem solving skills predict hopelessness in major depression.

PubMed

Cannon, B; Mulroy, R; Otto, M W; Rosenbaum, J F; Fava, M; Nierenberg, A A

1999-09-01

Hopelessness is a significant predictor of suicidality, but not all depressed patients feel hopeless. If clinicians can predict hopelessness, they may be able to identify those patients at risk of suicide and focus interventions on factors associated with hopelessness. In this study, we examined potential predictors of hopelessness in a sample of depressed outpatients. In this study, we examined potential demographic, diagnostic, and symptom predictors of hopelessness in a sample of 138 medication-free outpatients (73 women and 65 men) with a primary diagnosis of major depression. The significance of predictors was evaluated in both simple and multiple regression analyses. Consistent with previous studies, we found no significant associations between demographic and diagnostic variables and greater hopelessness. Hopelessness was significantly associated with greater depression severity, poor problem solving abilities as assessed by the Problem Solving Inventory, and each of two measures of dysfunctional cognitions (the Dysfunctional Attitudes Scale and the Cognitions Questionnaire). In a stepwise multiple regression equation, however, only dysfunctional cognitions and poor problem solving offered non-redundant prediction of hopelessness scores, and accounted for 20% of the variance in these scores. This study is based on depressed patients entering into an outpatient treatment protocol. All analyses were correlational in nature, and no causal links can be concluded. Our findings, identifying clinical correlates of hopelessness, provide clinicians with potential additional targets for assessment and treatment of suicidal risk. In particular, clinical attention to dysfunctional attitudes and problem solving skills may be important for further reduction of hopelessness and perhaps suicidal risk.

Role of DNA secondary structures in fragile site breakage along human chromosome 10

PubMed Central

Dillon, Laura W.; Pierce, Levi C. T.; Ng, Maggie C. Y.; Wang, Yuh-Hwa

2013-01-01

The formation of alternative DNA secondary structures can result in DNA breakage leading to cancer and other diseases. Chromosomal fragile sites, which are regions of the genome that exhibit chromosomal breakage under conditions of mild replication stress, are predicted to form stable DNA secondary structures. DNA breakage at fragile sites is associated with regions that are deleted, amplified or rearranged in cancer. Despite the correlation, unbiased examination of the ability to form secondary structures has not been evaluated in fragile sites. Here, using the Mfold program, we predict potential DNA secondary structure formation on the human chromosome 10 sequence, and utilize this analysis to compare fragile and non-fragile DNA. We found that aphidicolin (APH)-induced common fragile sites contain more sequence segments with potential high secondary structure-forming ability, and these segments clustered more densely than those in non-fragile DNA. Additionally, using a threshold of secondary structure-forming ability, we refined legitimate fragile sites within the cytogenetically defined boundaries, and identified potential fragile regions within non-fragile DNA. In vitro detection of alternative DNA structure formation and a DNA breakage cell assay were used to validate the computational predictions. Many of the regions identified by our analysis coincide with genes mutated in various diseases and regions of copy number alteration in cancer. This study supports the role of DNA secondary structures in common fragile site instability, provides a systematic method for their identification and suggests a mechanism by which DNA secondary structures can lead to human disease. PMID:23297364

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

PubMed

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

2018-03-06

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

Molecular epidemiology, and possible real-world applications in breast cancer.

PubMed

Ito, Hidemi; Matsuo, Keitaro

2016-01-01

Gene-environment interaction, a key idea in molecular epidemiology, has enabled the development of personalized medicine. This concept includes personalized prevention. While genome-wide association studies have identified a number of genetic susceptibility loci in breast cancer risk, however, the application of this knowledge to practical prevention is still underway. Here, we briefly review the history of molecular epidemiology and its progress in breast cancer epidemiology. We then introduce our experience with the trial combination of GWAS-identified loci and well-established lifestyle and reproductive risk factors in the risk prediction of breast cancer. Finally, we report our exploration of the cumulative risk of breast cancer based on this risk prediction model as a potential tool for individual risk communication, including genetic risk factors and gene-environment interaction with obesity.

Weighing the Evidence in Peters' Rule: Does Neuronal Morphology Predict Connectivity?

PubMed

Rees, Christopher L; Moradi, Keivan; Ascoli, Giorgio A

2017-02-01

Although the importance of network connectivity is increasingly recognized, identifying synapses remains challenging relative to the routine characterization of neuronal morphology. Thus, researchers frequently employ axon-dendrite colocations as proxies of potential connections. This putative equivalence, commonly referred to as Peters' rule, has been recently studied at multiple levels and scales, fueling passionate debates regarding its validity. Our critical literature review identifies three conceptually distinct but often confused applications: inferring neuron type circuitry, predicting synaptic contacts among individual cells, and estimating synapse numbers within neuron pairs. Paradoxically, at the originally proposed cell-type level, Peters' rule remains largely untested. Leveraging Hippocampome.org, we validate and refine the relationship between axonal-dendritic colocations and synaptic circuits, clarifying the interpretation of existing and forthcoming data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Sex similarities and differences in risk factors for recurrence of major depression.

PubMed

van Loo, Hanna M; Aggen, Steven H; Gardner, Charles O; Kendler, Kenneth S

2017-11-27

Major depression (MD) occurs about twice as often in women as in men, but it is unclear whether sex differences subsist after disease onset. This study aims to elucidate potential sex differences in rates and risk factors for MD recurrence, in order to improve prediction of course of illness and understanding of its underlying mechanisms. We used prospective data from a general population sample (n = 653) that experienced a recent episode of MD. A diverse set of potential risk factors for recurrence of MD was analyzed using Cox models subject to elastic net regularization for males and females separately. Accuracy of the prediction models was tested in same-sex and opposite-sex test data. Additionally, interactions between sex and each of the risk factors were investigated to identify potential sex differences. Recurrence rates and the impact of most risk factors were similar for men and women. For both sexes, prediction models were highly multifactorial including risk factors such as comorbid anxiety, early traumas, and family history. Some subtle sex differences were detected: for men, prediction models included more risk factors concerning characteristics of the depressive episode and family history of MD and generalized anxiety, whereas for women, models included more risk factors concerning early and recent adverse life events and socioeconomic problems. No prominent sex differences in risk factors for recurrence of MD were found, potentially indicating similar disease maintaining mechanisms for both sexes. Course of MD is a multifactorial phenomenon for both males and females.

Clinical correlates of graph theory findings in temporal lobe epilepsy.

PubMed

Haneef, Zulfi; Chiang, Sharon

2014-11-01

Temporal lobe epilepsy (TLE) is considered a brain network disorder, additionally representing the most common form of pharmaco-resistant epilepsy in adults. There is increasing evidence that seizures in TLE arise from abnormal epileptogenic networks, which extend beyond the clinico-radiologically determined epileptogenic zone and may contribute to the failure rate of 30-50% following epilepsy surgery. Graph theory allows for a network-based representation of TLE brain networks using several neuroimaging and electrophysiologic modalities, and has potential to provide clinicians with clinically useful biomarkers for diagnostic and prognostic purposes. We performed a review of the current state of graph theory findings in TLE as they pertain to localization of the epileptogenic zone, prediction of pre- and post-surgical seizure frequency and cognitive performance, and monitoring cognitive decline in TLE. Although different neuroimaging and electrophysiologic modalities have yielded occasionally conflicting results, several potential biomarkers have been characterized for identifying the epileptogenic zone, pre-/post-surgical seizure prediction, and assessing cognitive performance. For localization, graph theory measures of centrality have shown the most potential, including betweenness centrality, outdegree, and graph index complexity, whereas for prediction of seizure frequency, measures of synchronizability have shown the most potential. The utility of clustering coefficient and characteristic path length for assessing cognitive performance in TLE is also discussed. Future studies integrating data from multiple modalities and testing predictive models are needed to clarify findings and develop graph theory for its clinical utility. Copyright © 2014 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Clinical correlates of graph theory findings in temporal lobe epilepsy

PubMed Central

Haneef, Zulfi; Chiang, Sharon

2014-01-01

Purpose Temporal lobe epilepsy (TLE) is considered a brain network disorder, additionally representing the most common form of pharmaco-resistant epilepsy in adults. There is increasing evidence that seizures in TLE arise from abnormal epileptogenic networks, which extend beyond the clinico-radiologically determined epileptogenic zone and may contribute to the failure rate of 30–50% following epilepsy surgery. Graph theory allows for a network-based representation of TLE brain networks using several neuroimaging and electrophysiologic modalities, and has potential to provide clinicians with clinically useful biomarkers for diagnostic and prognostic purposes. Methods We performed a review of the current state of graph theory findings in TLE as they pertain to localization of the epileptogenic zone, prediction of pre- and post-surgical seizure frequency and cognitive performance, and monitoring cognitive decline in TLE. Results Although different neuroimaging and electrophysiologic modalities have yielded occasionally conflicting results, several potential biomarkers have been characterized for identifying the epileptogenic zone, pre-/post-surgical seizure prediction, and assessing cognitive performance. For localization, graph theory measures of centrality have shown the most potential, including betweenness centrality, outdegree, and graph index complexity, whereas for prediction of seizure frequency, measures of synchronizability have shown the most potential. The utility of clustering coefficient and characteristic path length for assessing cognitive performance in TLE is also discussed. Conclusions Future studies integrating data from multiple modalities and testing predictive models are needed to clarify findings and develop graph theory for its clinical utility. PMID:25127370

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.

Metaphylogenomic and potential functionality of the limpet Patella pellucida's gastrointestinal tract microbiome.

PubMed

Dudek, Magda; Adams, Jessica; Swain, Martin; Hegarty, Matthew; Huws, Sharon; Gallagher, Joe

2014-10-20

This study investigated the microbial diversity associated with the digestive tract of the seaweed grazing marine limpet Patella pellucida. Using a modified indirect DNA extraction protocol and performing metagenomic profiling based on specific prokaryotic marker genes, the abundance of bacterial groups was identified from the analyzed metagenome. The members of three significantly abundant phyla of Proteobacteria, Firmicutes and Bacteroidetes were characterized through the literature and their predicted functions towards the host, as well as potential applications in the industrial environment assessed.

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

Shiraishi, Satomi; Moore, Kevin L., E-mail: kevinmoore@ucsd.edu

Purpose: To demonstrate knowledge-based 3D dose prediction for external beam radiotherapy. Methods: Using previously treated plans as training data, an artificial neural network (ANN) was trained to predict a dose matrix based on patient-specific geometric and planning parameters, such as the closest distance (r) to planning target volume (PTV) and organ-at-risks (OARs). Twenty-three prostate and 43 stereotactic radiosurgery/radiotherapy (SRS/SRT) cases with at least one nearby OAR were studied. All were planned with volumetric-modulated arc therapy to prescription doses of 81 Gy for prostate and 12–30 Gy for SRS. Using these clinically approved plans, ANNs were trained to predict dose matrixmore » and the predictive accuracy was evaluated using the dose difference between the clinical plan and prediction, δD = D{sub clin} − D{sub pred}. The mean (〈δD{sub r}〉), standard deviation (σ{sub δD{sub r}}), and their interquartile range (IQR) for the training plans were evaluated at a 2–3 mm interval from the PTV boundary (r{sub PTV}) to assess prediction bias and precision. Initially, unfiltered models which were trained using all plans in the cohorts were created for each treatment site. The models predict approximately the average quality of OAR sparing. Emphasizing a subset of plans that exhibited superior to the average OAR sparing during training, refined models were created to predict high-quality rectum sparing for prostate and brainstem sparing for SRS. Using the refined model, potentially suboptimal plans were identified where the model predicted further sparing of the OARs was achievable. Replans were performed to test if the OAR sparing could be improved as predicted by the model. Results: The refined models demonstrated highly accurate dose distribution prediction. For prostate cases, the average prediction bias for all voxels irrespective of organ delineation ranged from −1% to 0% with maximum IQR of 3% over r{sub PTV} ∈ [ − 6, 30] mm. The average prediction error was less than 10% for the same r{sub PTV} range. For SRS cases, the average prediction bias ranged from −0.7% to 1.5% with maximum IQR of 5% over r{sub PTV} ∈ [ − 4, 32] mm. The average prediction error was less than 8%. Four potentially suboptimal plans were identified for each site and subsequent replanning demonstrated improved sparing of rectum and brainstem. Conclusions: The study demonstrates highly accurate knowledge-based 3D dose predictions for radiotherapy plans.« less

Biomarkers of PTSD: military applications and considerations.

PubMed

Lehrner, Amy; Yehuda, Rachel

2014-01-01

Although there are no established biomarkers for posttraumatic stress disorder (PTSD) as yet, biological investigations of PTSD have made progress identifying the pathophysiology of PTSD. Given the biological and clinical complexity of PTSD, it is increasingly unlikely that a single biomarker of disease will be identified. Rather, investigations will more likely identify different biomarkers that indicate the presence of clinically significant PTSD symptoms, associate with risk for PTSD following trauma exposure, and predict or identify recovery. While there has been much interest in PTSD biomarkers, there has been less discussion of their potential clinical applications, and of the social, legal, and ethical implications of such biomarkers. This article will discuss possible applications of PTSD biomarkers, including the social, legal, and ethical implications of such biomarkers, with an emphasis on military applications. Literature on applications of PTSD biomarkers and on potential ethical and legal implications will be reviewed. Biologically informed research findings hold promise for prevention, assessment, treatment planning, and the development of prophylactic and treatment interventions. As with any biological indicator of disorder, there are potentially positive and negative clinical, social, legal, and ethical consequences of using such biomarkers. Potential clinical applications of PTSD biomarkers hold promise for clinicians, patients, and employers. The search for biomarkers of PTSD should occur in tandem with an interdisciplinary discussion regarding the potential implications of applying biological findings in clinical and employment settings.

Comprehensive Analyses of Ventricular Myocyte Models Identify Targets Exhibiting Favorable Rate Dependence

PubMed Central

Bugana, Marco; Severi, Stefano; Sobie, Eric A.

2014-01-01

Reverse rate dependence is a problematic property of antiarrhythmic drugs that prolong the cardiac action potential (AP). The prolongation caused by reverse rate dependent agents is greater at slow heart rates, resulting in both reduced arrhythmia suppression at fast rates and increased arrhythmia risk at slow rates. The opposite property, forward rate dependence, would theoretically overcome these parallel problems, yet forward rate dependent (FRD) antiarrhythmics remain elusive. Moreover, there is evidence that reverse rate dependence is an intrinsic property of perturbations to the AP. We have addressed the possibility of forward rate dependence by performing a comprehensive analysis of 13 ventricular myocyte models. By simulating populations of myocytes with varying properties and analyzing population results statistically, we simultaneously predicted the rate-dependent effects of changes in multiple model parameters. An average of 40 parameters were tested in each model, and effects on AP duration were assessed at slow (0.2 Hz) and fast (2 Hz) rates. The analysis identified a variety of FRD ionic current perturbations and generated specific predictions regarding their mechanisms. For instance, an increase in L-type calcium current is FRD when this is accompanied by indirect, rate-dependent changes in slow delayed rectifier potassium current. A comparison of predictions across models identified inward rectifier potassium current and the sodium-potassium pump as the two targets most likely to produce FRD AP prolongation. Finally, a statistical analysis of results from the 13 models demonstrated that models displaying minimal rate-dependent changes in AP shape have little capacity for FRD perturbations, whereas models with large shape changes have considerable FRD potential. This can explain differences between species and between ventricular cell types. Overall, this study provides new insights, both specific and general, into the determinants of AP duration rate dependence, and illustrates a strategy for the design of potentially beneficial antiarrhythmic drugs. PMID:24675446

Comprehensive analyses of ventricular myocyte models identify targets exhibiting favorable rate dependence.

PubMed

Cummins, Megan A; Dalal, Pavan J; Bugana, Marco; Severi, Stefano; Sobie, Eric A

2014-03-01

Reverse rate dependence is a problematic property of antiarrhythmic drugs that prolong the cardiac action potential (AP). The prolongation caused by reverse rate dependent agents is greater at slow heart rates, resulting in both reduced arrhythmia suppression at fast rates and increased arrhythmia risk at slow rates. The opposite property, forward rate dependence, would theoretically overcome these parallel problems, yet forward rate dependent (FRD) antiarrhythmics remain elusive. Moreover, there is evidence that reverse rate dependence is an intrinsic property of perturbations to the AP. We have addressed the possibility of forward rate dependence by performing a comprehensive analysis of 13 ventricular myocyte models. By simulating populations of myocytes with varying properties and analyzing population results statistically, we simultaneously predicted the rate-dependent effects of changes in multiple model parameters. An average of 40 parameters were tested in each model, and effects on AP duration were assessed at slow (0.2 Hz) and fast (2 Hz) rates. The analysis identified a variety of FRD ionic current perturbations and generated specific predictions regarding their mechanisms. For instance, an increase in L-type calcium current is FRD when this is accompanied by indirect, rate-dependent changes in slow delayed rectifier potassium current. A comparison of predictions across models identified inward rectifier potassium current and the sodium-potassium pump as the two targets most likely to produce FRD AP prolongation. Finally, a statistical analysis of results from the 13 models demonstrated that models displaying minimal rate-dependent changes in AP shape have little capacity for FRD perturbations, whereas models with large shape changes have considerable FRD potential. This can explain differences between species and between ventricular cell types. Overall, this study provides new insights, both specific and general, into the determinants of AP duration rate dependence, and illustrates a strategy for the design of potentially beneficial antiarrhythmic drugs.

Clinical implications of omics and systems medicine: focus on predictive and individualized treatment.

PubMed

Benson, M

2016-03-01

Many patients with common diseases do not respond to treatment. This is a key challenge to modern health care, which causes both suffering and enormous costs. One important reason for the lack of treatment response is that common diseases are associated with altered interactions between thousands of genes, in combinations that differ between subgroups of patients who do or do not respond to a given treatment. Such subgroups, or even distinct disease entities, have been described recently in asthma, diabetes, autoimmune diseases and cancer. High-throughput techniques (omics) allow identification and characterization of such subgroups or entities. This may have important clinical implications, such as identification of diagnostic markers for individualized medicine, as well as new therapeutic targets for patients who do not respond to existing drugs. For example, whole-genome sequencing may be applied to more accurately guide treatment of neurodevelopmental diseases, or to identify drugs specifically targeting mutated genes in cancer. A study published in 2015 showed that 28% of hepatocellular carcinomas contained mutated genes that potentially could be targeted by drugs already approved by the US Food and Drug Administration. A translational study, which is described in detail, showed how combined omics, computational, functional and clinical studies could identify and validate a novel diagnostic and therapeutic candidate gene in allergy. Another important clinical implication is the identification of potential diagnostic markers and therapeutic targets for predictive and preventative medicine. By combining computational and experimental methods, early disease regulators may be identified and potentially used to predict and treat disease before it becomes symptomatic. Systems medicine is an emerging discipline, which may contribute to such developments through combining omics with computational, functional and clinical studies. The aims of this review are to provide a brief introduction to systems medicine and discuss how it may contribute to the clinical implementation of individualized treatment, using clinically relevant examples. © 2015 The Association for the Publication of the Journal of Internal Medicine.

Expression profiling in canine osteosarcoma: identification of biomarkers and pathways associated with outcome

PubMed Central

2010-01-01

Background Osteosarcoma (OSA) spontaneously arises in the appendicular skeleton of large breed dogs and shares many physiological and molecular biological characteristics with human OSA. The standard treatment for OSA in both species is amputation or limb-sparing surgery, followed by chemotherapy. Unfortunately, OSA is an aggressive cancer with a high metastatic rate. Characterization of OSA with regard to its metastatic potential and chemotherapeutic resistance will improve both prognostic capabilities and treatment modalities. Methods We analyzed archived primary OSA tissue from dogs treated with limb amputation followed by doxorubicin or platinum-based drug chemotherapy. Samples were selected from two groups: dogs with disease free intervals (DFI) of less than 100 days (n = 8) and greater than 300 days (n = 7). Gene expression was assessed with Affymetrix Canine 2.0 microarrays and analyzed with a two-tailed t-test. A subset of genes was confirmed using qRT-PCR and used in classification analysis to predict prognosis. Systems-based gene ontology analysis was conducted on genes selected using a standard J5 metric. The genes identified using this approach were converted to their human homologues and assigned to functional pathways using the GeneGo MetaCore platform. Results Potential biomarkers were identified using gene expression microarray analysis and 11 differentially expressed (p < 0.05) genes were validated with qRT-PCR (n = 10/group). Statistical classification models using the qRT-PCR profiles predicted patient outcomes with 100% accuracy in the training set and up to 90% accuracy upon stratified cross validation. Pathway analysis revealed alterations in pathways associated with oxidative phosphorylation, hedgehog and parathyroid hormone signaling, cAMP/Protein Kinase A (PKA) signaling, immune responses, cytoskeletal remodeling and focal adhesion. Conclusions This profiling study has identified potential new biomarkers to predict patient outcome in OSA and new pathways that may be targeted for therapeutic intervention. PMID:20860831

A comparison between the clinical significance and growth mixture modelling early change methods at predicting negative outcomes.

PubMed

Flood, Nicola; Page, Andrew; Hooke, Geoff

2018-05-03

Routine outcome monitoring benefits treatment by identifying potential no change and deterioration. The present study compared two methods of identifying early change and their ability to predict negative outcomes on self-report symptom and wellbeing measures. 1467 voluntary day patients participated in a 10-day group Cognitive Behaviour Therapy (CBT) program and completed the symptom and wellbeing measures daily. Early change, as defined by (a) the clinical significance method and (b) longitudinal modelling, was compared on each measure. Early change, as defined by the simpler clinical significance method, was superior at predicting negative outcomes than longitudinal modelling. The longitudinal modelling method failed to detect a group of deteriorated patients, and agreement between the early change methods and the final unchanged outcome was higher for the clinical significance method. Therapists could use the clinical significance early change method during treatment to alert them of patients at risk for negative outcomes, which in turn could allow therapists to prevent those negative outcomes from occurring.

Sleep duration predicts behavioral and neural differences in adult speech sound learning.

PubMed

Earle, F Sayako; Landi, Nicole; Myers, Emily B

2017-01-01

Sleep is important for memory consolidation and contributes to the formation of new perceptual categories. This study examined sleep as a source of variability in typical learners' ability to form new speech sound categories. We trained monolingual English speakers to identify a set of non-native speech sounds at 8PM, and assessed their ability to identify and discriminate between these sounds immediately after training, and at 8AM on the following day. We tracked sleep duration overnight, and found that light sleep duration predicted gains in identification performance, while total sleep duration predicted gains in discrimination ability. Participants obtained an average of less than 6h of sleep, pointing to the degree of sleep deprivation as a potential factor. Behavioral measures were associated with ERP indexes of neural sensitivity to the learned contrast. These results demonstrate that the relative success in forming new perceptual categories depends on the duration of post-training sleep. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Sleep duration predicts behavioral and neural differences in adult speech sound learning

PubMed Central

Earle, F. Sayako; Landi, Nicole; Myers, Emily B.

2016-01-01

Sleep is important for memory consolidation and contributes to the formation of new perceptual categories. This study examined sleep as a source of variability in typical learners’ ability to form new speech sound categories. We trained monolingual English speakers to identify a set of non-native speech sounds at 8PM, and assessed their ability to identify and discriminate between these sounds immediately after training, and at 8AM on the following day. We tracked sleep duration overnight, and found that light sleep duration predicted gains in identification performance, while total sleep duration predicted gains in discrimination ability. Participants obtained an average of less than 6 hours of sleep, pointing to the degree of sleep deprivation as a potential factor. Behavioral measures were associated with ERP indexes of neural sensitivity to the learned contrast. These results demonstrate that the relative success in forming new perceptual categories depends on the duration of post-training sleep. PMID:27793703

Identifying Childhood Characteristics that Underlie Pre-Morbid Risk for Substance Use Disorders: Socialization and Boldness

PubMed Central

Hicks, Brian M.; Iacono, William G.; McGue, Matt

2013-01-01

Utilizing a longitudinal twin study (N = 2510), we identified the child characteristics present prior to initiation of substance use that best predicted later substance use disorders. Two independent traits accounted for the majority of pre-morbid risk: socialization (conformity to rules and conventional values) and boldness (sociability and social assurance, stress resilience, and thrill seeking). Low socialization was associated with disruptive behavior disorders, parental externalizing disorders, and environmental adversity, and exhibited moderate genetic (.45) and shared environmental influences (.30). Boldness was highly heritable (.71) and associated with less internalizing distress and environmental adversity. Together, these traits exhibited robust associations with adolescent and young adult substance use disorders (R = .48 and .50, respectively), and incremental prediction over disruptive behavior disorders, parental externalizing disorders, and environmental adversity. Results were replicated in an independent sample. Socialization and boldness offer a novel conceptualization of underlying risk for substance use disorders that has the potential to improve prediction and theory with implications for basic research, prevention, and intervention. PMID:24280373

Identifying childhood characteristics that underlie premorbid risk for substance use disorders: socialization and boldness.

PubMed

Hicks, Brian M; Iacono, William G; McGue, Matt

2014-02-01

We utilized a longitudinal twin study (N = 2,510) to identify the child characteristics present prior to initiation of substance use that best predicted later substance use disorders. Two independent traits accounted for the majority of premorbid risk: socialization (conformity to rules and conventional values) and boldness (sociability and social assurance, stress resilience, and thrill seeking). Low socialization was associated with disruptive behavior disorders, parental externalizing disorders, and environmental adversity and exhibited moderate genetic (0.45) and shared environmental influences (0.30). Boldness was highly heritable (0.71) and associated with less internalizing distress and environmental adversity. In combination, these traits exhibited robust associations with adolescent and young adult substance use disorders (R = .48 and .50, respectively) and incremental prediction over disruptive behavior disorders, parental externalizing disorders, and environmental adversity. The results were replicated in an independent sample. Socialization and boldness offer a novel conceptualization of underlying risk for substance use disorders that has the potential to improve prediction and theory with implications for basic research, prevention, and intervention.

Prediction of sub-surface 37 Ar concentrations at locations in the Northwestern United States

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

Fritz, Bradley G.; Aalseth, Craig E.; Back, Henning O.

The Comprehensive Nuclear Test-Ban Treaty, which is intended to prevent nuclear weapon testing, includes a verification regime, which provides monitoring to identify potential nuclear testing. The presence of elevated 37Ar is one way to identify subsurface nuclear testing. However, the naturally occurring formation of 37Ar in the subsurface adds a complicating factor. Prediction of the naturally occurring concentration of 37Ar can help to determine if a measured 37Ar concentration is elevated. The naturally occurring 37Ar background concentration has been shown to vary between less than 1 mBq/m3 to greater than 100 mBq/m3 (Riedmann and Purtschert 2011). Here, we evaluate amore » model for predicting the average concentration of 37Ar at any depth under transient barometric pressures, and compare it with measurements. This model is shown to compare favorably with concentrations of 37Ar measured at multiple locations in the Northwestern United States.« less

Prediction of PTSD in police officers after six months--a prospective study.

PubMed

Schütte, Nils; Bär, Olaf; Weiss, Udo; Heuft, Gereon

2012-11-01

The aim of this prospective study was to explore the predictors for the development of PTSD in police officers six months after encountering situations of a potentially traumatic nature. Fifty-nine police officers were studied immediately after the event (T1) and six months later (T2). At T2 PTSD was assessed using the Structured Clinical Interview for DSM-IV (SCID-I). PTSD was predicted by intrusions (Impact of Event Scale-Revised; IES-R), the impairment scale (is), global assessment of functioning scale (GAF), gender, age and sense of coherence scale (SOC). The diagnosis of an acute stress disorder (ASD) at T1 had a high specificity for identifying PTSD at T2. The strongest predictor for the development of PTSD was found to be the factor intrusions. Contrary to our expectations, age was not a significant predictive factor for PTSD. Thus, acute stress disorder (ASD) and a high degree of intrusions experienced immediately after a traumatic incident helped to identify early police officers at risk of developing chronic PTSD.

Predictive polymer modeling reveals coupled fluctuations in chromosome conformation and transcription

PubMed Central

Giorgetti, Luca; Galupa, Rafael; Nora, Elphège P.; Piolot, Tristan; Lam, France; Dekker, Job; Tiana, Guido; Heard, Edith

2015-01-01

Summary A new level of chromosome organization, Topologically Associating Domains (TADs), was recently uncovered by chromosome-confirmation-capture (3C) techniques. To explore TAD structure and function, we developed a polymer model that can extract the full repertoire of chromatin conformations within TADs from population-based 3C data. This model predicts actual physical distances and to what extent chromosomal contacts vary between cells. It also identifies interactions within single TADs that stabilize boundaries between TADs and allows us to identify and genetically validate key structural elements within TADs. Combining the model’s predictions with high-resolution DNA FISH and quantitative RNA FISH for TADs within the X-inactivation center (Xic), we dissect the relationship between transcription and spatial proximity to cis-regulatory elements. We demonstrate that contacts between potential regulatory elements occur in the context of fluctuating structures rather than stable loops and propose that such fluctuations may contribute to asymmetric expression in the Xic during X inactivation. PMID:24813616

Enhanced charging kinetics of porous electrodes: surface conduction as a short-circuit mechanism.

PubMed

Mirzadeh, Mohammad; Gibou, Frederic; Squires, Todd M

2014-08-29

We use direct numerical simulations of the Poisson-Nernst-Planck equations to study the charging kinetics of porous electrodes and to evaluate the predictive capabilities of effective circuit models, both linear and nonlinear. The classic transmission line theory of de Levie holds for general electrode morphologies, but only at low applied potentials. Charging dynamics are slowed appreciably at high potentials, yet not as significantly as predicted by the nonlinear transmission line model of Biesheuvel and Bazant. We identify surface conduction as a mechanism which can effectively "short circuit" the high-resistance electrolyte in the bulk of the pores, thus accelerating the charging dynamics and boosting power densities. Notably, the boost in power density holds only for electrode morphologies with continuous conducting surfaces in the charging direction.

Utility of metabolic profiling of serum in the diagnosis of pregnancy complications.

PubMed

Powell, Katie L; Carrozzi, Anthony; Stephens, Alexandre S; Tasevski, Vitomir; Morris, Jonathan M; Ashton, Anthony W; Dona, Anthony C

2018-06-01

Currently there are no clinical screening tests available to identify pregnancies at risk of developing preeclampsia (PET) and/or intrauterine growth restriction (IUGR), both of which are associated with abnormal placentation. Metabolic profiling is now a stable analytical platform used in many laboratories and has successfully been used to identify biomarkers associated with various pathological states. We used nuclear magnetic resonance spectroscopy (NMR) to metabolically profile serum samples collected from 143 pregnant women at 26-41 weeks gestation with pregnancy outcomes of PET, IUGR, PET IUGR or small for gestational age (SGA) that were age-matched to normal pre/term pregnancies. Spectral analysis found no difference in the measured metabolites from normal term, pre-term and SGA samples, and of 25 identified metabolites, only glutamate was marginally different between groups. Of the identified metabolites, 3-methylhistidine, creatinine, acetyl groups and acetate, were determined to be independent predictors of PET and produced area under the curves (AUC) = 0.938 and 0.936 for the discovery and validation sets. Only 3-hydroxybutyrate was determined to be an independent predictor of IUGR, however the model had low predictive power (AUC = 0.623 and 0.581 for the discovery and validation sets). A sub-panel of metabolites had strong predictive power for identifying PET samples in a validation dataset, however prediction of IUGR was more difficult using the identified metabolites. NMR based metabolomics can identify metabolites strongly associated with disease and has the potential to be useful in developing early clinical screening tests for at risk pregnancies. Copyright © 2018 Elsevier Ltd. All rights reserved.

Spatial patterns of high Aedes aegypti oviposition activity in northwestern Argentina.

PubMed

Estallo, Elizabet Lilia; Más, Guillermo; Vergara-Cid, Carolina; Lanfri, Mario Alberto; Ludueña-Almeida, Francisco; Scavuzzo, Carlos Marcelo; Introini, María Virginia; Zaidenberg, Mario; Almirón, Walter Ricardo

2013-01-01

In Argentina, dengue has affected mainly the Northern provinces, including Salta. The objective of this study was to analyze the spatial patterns of high Aedes aegypti oviposition activity in San Ramón de la Nueva Orán, northwestern Argentina. The location of clusters as hot spot areas should help control programs to identify priority areas and allocate their resources more effectively. Oviposition activity was detected in Orán City (Salta province) using ovitraps, weekly replaced (October 2005-2007). Spatial autocorrelation was measured with Moran's Index and depicted through cluster maps to identify hot spots. Total egg numbers were spatially interpolated and a classified map with Ae. aegypti high oviposition activity areas was performed. Potential breeding and resting (PBR) sites were geo-referenced. A logistic regression analysis of interpolated egg numbers and PBR location was performed to generate a predictive mapping of mosquito oviposition activity. Both cluster maps and predictive map were consistent, identifying in central and southern areas of the city high Ae. aegypti oviposition activity. A logistic regression model was successfully developed to predict Ae. aegypti oviposition activity based on distance to PBR sites, with tire dumps having the strongest association with mosquito oviposition activity. A predictive map reflecting probability of oviposition activity was produced. The predictive map delimitated an area of maximum probability of Ae. aegypti oviposition activity in the south of Orán city where tire dumps predominate. The overall fit of the model was acceptable (ROC=0.77), obtaining 99% of sensitivity and 75.29% of specificity. Distance to tire dumps is inversely associated with high mosquito activity, allowing us to identify hot spots. These methodologies are useful for prevention, surveillance, and control of tropical vector borne diseases and might assist National Health Ministry to focus resources more effectively.

BioFuelDB: a database and prediction server of enzymes involved in biofuels production.

PubMed

Chaudhary, Nikhil; Gupta, Ankit; Gupta, Sudheer; Sharma, Vineet K

2017-01-01

In light of the rapid decrease in fossils fuel reserves and an increasing demand for energy, novel methods are required to explore alternative biofuel production processes to alleviate these pressures. A wide variety of molecules which can either be used as biofuels or as biofuel precursors are produced using microbial enzymes. However, the common challenges in the industrial implementation of enzyme catalysis for biofuel production are the unavailability of a comprehensive biofuel enzyme resource, low efficiency of known enzymes, and limited availability of enzymes which can function under extreme conditions in the industrial processes. We have developed a comprehensive database of known enzymes with proven or potential applications in biofuel production through text mining of PubMed abstracts and other publicly available information. A total of 131 enzymes with a role in biofuel production were identified and classified into six enzyme classes and four broad application categories namely 'Alcohol production', 'Biodiesel production', 'Fuel Cell' and 'Alternate biofuels'. A prediction tool 'Benz' was developed to identify and classify novel homologues of the known biofuel enzyme sequences from sequenced genomes and metagenomes. 'Benz' employs a hybrid approach incorporating HMMER 3.0 and RAPSearch2 programs to provide high accuracy and high speed for prediction. Using the Benz tool, 153,754 novel homologues of biofuel enzymes were identified from 23 diverse metagenomic sources. The comprehensive data of curated biofuel enzymes, their novel homologs identified from diverse metagenomes, and the hybrid prediction tool Benz are presented as a web server which can be used for the prediction of biofuel enzymes from genomic and metagenomic datasets. The database and the Benz tool is publicly available at http://metabiosys.iiserb.ac.in/biofueldb& http://metagenomics.iiserb.ac.in/biofueldb.

Predicting Displaceable Water Sites Using Mixed-Solvent Molecular Dynamics.

PubMed

Graham, Sarah E; Smith, Richard D; Carlson, Heather A

2018-02-26

Water molecules are an important factor in protein-ligand binding. Upon binding of a ligand with a protein's surface, waters can either be displaced by the ligand or may be conserved and possibly bridge interactions between the protein and ligand. Depending on the specific interactions made by the ligand, displacing waters can yield a gain in binding affinity. The extent to which binding affinity may increase is difficult to predict, as the favorable displacement of a water molecule is dependent on the site-specific interactions made by the water and the potential ligand. Several methods have been developed to predict the location of water sites on a protein's surface, but the majority of methods are not able to take into account both protein dynamics and the interactions made by specific functional groups. Mixed-solvent molecular dynamics (MixMD) is a cosolvent simulation technique that explicitly accounts for the interaction of both water and small molecule probes with a protein's surface, allowing for their direct competition. This method has previously been shown to identify both active and allosteric sites on a protein's surface. Using a test set of eight systems, we have developed a method using MixMD to identify conserved and displaceable water sites. Conserved sites can be determined by an occupancy-based metric to identify sites which are consistently occupied by water even in the presence of probe molecules. Conversely, displaceable water sites can be found by considering the sites which preferentially bind probe molecules. Furthermore, the inclusion of six probe types allows the MixMD method to predict which functional groups are capable of displacing which water sites. The MixMD method consistently identifies sites which are likely to be nondisplaceable and predicts the favorable displacement of water sites that are known to be displaced upon ligand binding.

Metabolomic profiling in the prediction of gestational diabetes mellitus.

PubMed

Bentley-Lewis, Rhonda; Huynh, Jennifer; Xiong, Grace; Lee, Hang; Wenger, Julia; Clish, Clary; Nathan, David; Thadhani, Ravi; Gerszten, Robert

2015-06-01

Metabolomic profiling in populations with impaired glucose tolerance has revealed that branched chain and aromatic amino acids (BCAAs) are predictive of type 2 diabetes. Because gestational diabetes mellitus (GDM) shares pathophysiological similarities with type 2 diabetes, the metabolite profile predictive of type 2 diabetes could potentially identify women who will develop GDM. We conducted a nested case-control study of 18- to 40-year-old women who participated in the Massachusetts General Hospital Obstetrical Maternal Study between 1998 and 2007. Participants were enrolled during their first trimester of a singleton pregnancy and fasting serum samples were collected. The women were followed throughout pregnancy and identified as having GDM or normal glucose tolerance (NGT) in the third trimester. Women with GDM (n = 96) were matched to women with NGT (n = 96) by age, BMI, gravidity and parity. Liquid chromatography-mass spectrometry was used to measure the levels of 91 metabolites. Data analyses revealed the following characteristics (mean ± SD): age 32.8 ± 4.4 years, BMI 28.3 ± 5.6 kg/m(2), gravidity 2 ± 1 and parity 1 ± 1. Six metabolites (anthranilic acid, alanine, glutamate, creatinine, allantoin and serine) were identified as having significantly different levels between the two groups in conditional logistic regression analyses (p < 0.05). The levels of the BCAAs did not differ significantly between GDM and NGT. Metabolic markers identified as being predictive of type 2 diabetes may not have the same predictive power for GDM. However, further study in a racially/ethnically diverse population-based cohort is necessary.

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

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.

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

DOE PAGES

Wang, Pin; Wang, Yunshan; Hang, Bo; ...

2016-07-11

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A networkmore » was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.« less

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

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

Wang, Pin; Wang, Yunshan; Hang, Bo

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A networkmore » was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.« less

A decision-analytic approach to predict state regulation of hydraulic fracturing.

PubMed

Linkov, Igor; Trump, Benjamin; Jin, David; Mazurczak, Marcin; Schreurs, Miranda

2014-01-01

The development of horizontal drilling and hydraulic fracturing methods has dramatically increased the potential for the extraction of previously unrecoverable natural gas. Nonetheless, the potential risks and hazards associated with such technologies are not without controversy and are compounded by frequently changing information and an uncertain landscape of international politics and laws. Where each nation has its own energy policies and laws, predicting how a state with natural gas reserves that require hydraulic fracturing will regulate the industry is of paramount importance for potential developers and extractors. We present a method for predicting hydraulic fracturing decisions using multiple-criteria decision analysis. The case study evaluates the decisions of five hypothetical countries with differing political, social, environmental, and economic priorities, choosing among four policy alternatives: open hydraulic fracturing, limited hydraulic fracturing, completely banned hydraulic fracturing, and a cap and trade program. The result is a model that identifies the preferred policy alternative for each archetypal country and demonstrates the sensitivity the decision to particular metrics. Armed with such information, observers can predict each country's likely decisions related to natural gas exploration as more data become available or political situations change. Decision analysis provides a method to manage uncertainty and address forecasting concerns where rich and objective data may be lacking. For the case of hydraulic fracturing, the various political pressures and extreme uncertainty regarding the technology's risks and benefits serve as a prime platform to demonstrate how decision analysis can be used to predict future behaviors.

Pre-clinical methods for detecting the hypersensitivity potential of pharmaceuticals: regulatory considerations.

PubMed

Hastings, K L

2001-02-02

Immune-based systemic hypersensitivities account for a significant number of adverse drug reactions. There appear to be no adequate nonclinical models to predict systemic hypersensitivity to small molecular weight drugs. Although there are very good methods for detecting drugs that can induce contact sensitization, these have not been successfully adapted for prediction of systemic hypersensitivity. Several factors have made the development of adequate models difficult. The term systemic hypersensitivity encompases many discrete immunopathologies. Each type of immunopathology presumably is the result of a specific cluster of immunologic and biochemical phenomena. Certainly other factors, such as genetic predisposition, metabolic idiosyncrasies, and concomitant diseases, further complicate the problem. Therefore, it may be difficult to find common mechanisms upon which to construct adequate models to predict specific types of systemic hypersensitivity reactions. There is some reason to hope, however, that adequate methods could be developed for at least identifying drugs that have the potential to produce signs indicative of a general hazard for immune-based reactions.

VAN method of short-term earthquake prediction shows promise

NASA Astrophysics Data System (ADS)

Uyeda, Seiya

Although optimism prevailed in the 1970s, the present consensus on earthquake prediction appears to be quite pessimistic. However, short-term prediction based on geoelectric potential monitoring has stood the test of time in Greece for more than a decade [VarotsosandKulhanek, 1993] Lighthill, 1996]. The method used is called the VAN method.The geoelectric potential changes constantly due to causes such as magnetotelluric effects, lightning, rainfall, leakage from manmade sources, and electrochemical instabilities of electrodes. All of this noise must be eliminated before preseismic signals are identified, if they exist at all. The VAN group apparently accomplished this task for the first time. They installed multiple short (100-200m) dipoles with different lengths in both north-south and east-west directions and long (1-10 km) dipoles in appropriate orientations at their stations (one of their mega-stations, Ioannina, for example, now has 137 dipoles in operation) and found that practically all of the noise could be eliminated by applying a set of criteria to the data.

Relationship between cardiac quiescent periods derived from seismocardiography and echocardiography.

PubMed

Wick, Carson A; Inan, Omer T; Bhatti, Pamela; Tridandapani, Srini

2015-08-01

The seismocardiogram (SCG) is a measure of chest wall acceleration due to cardiac motion that could potentially supplement the electrocardiogram (ECG) to more reliably predict cardiac quiescence. Accurate prediction is critical for modalities requiring minimal motion during imaging data acquisition, such as cardiac computed tomography (CT) and magnetic resonance imaging (MRI). For seven healthy subjects, SCG and B-mode echocardiography were used to identify quiescent periods on a beat-by-beat basis. Quiescent periods were detected as time intervals when the magnitude of the velocity signals calculated from SCG and echocardiography were less than a specified threshold. The quiescent periods detected from SCG were compared to those detected from B-mode echocardiography. The quiescent periods of the SCG were found to occur before those detected by echocardiography. A linear relationship between the delay from SCG- to echocardiography-detected phases with respect to heart rate was found. This delay could potentially be used to predict cardiac quiescence from SCG-observed quiescence for use with cardiac imaging modalities such as CT and MRI.

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.

Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification.

PubMed

Wang, Hsin-Wei; Lin, Ya-Chi; Pai, Tun-Wen; Chang, Hao-Teng

2011-01-01

Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%).

The future of planetary defense

NASA Astrophysics Data System (ADS)

Mainzer, A.

2017-04-01

Asteroids and comets have impacted Earth in the past and will do so in the future. While the frequency of impacts is reasonably well understood on geologic timescales, it is difficult to predict the next sizeable impact on human timescales by extrapolation from population statistics alone. Fortunately, by identifying and tracking individual objects, we can make precise predictions of any potential close encounters with Earth. As more advance notice is provided, the range of possible mitigation options expands. While the chance of an impact is very small, the potential consequences can be severe, meaning that sensible risk reduction measures should be undertaken. By implementing surveys, the risk of an unforeseen impact can be greatly reduced: the first step is finding the objects. Fortunately, the worldwide community of professional and amateur astronomers has made significant progress in discovering large near-Earth objects (NEOs). More than 95% of NEOs capable of causing global devastation (objects larger than 1 km in diameter) have been discovered, and none of these pose an impact hazard in the near future. Infrastructure is in place to link observations and compute close approaches in real time. Interagency and international collaborations have been undertaken to strengthen cooperative efforts to plan potential mitigation and civil defense campaigns. Yet much remains to be done. Approximately 70% of NEOs larger than 140 m (large enough to cause severe regional damage) remain undiscovered. With the existing surveys, it will take decades to identify the rest. Progress can be accelerated by undertaking new surveys with improved sensitivity.