Challenge of superfund community relations

International Nuclear Information System (INIS)

Goldman, N.J.

1991-01-01

Conducting a community relations effort in a community which is home to a Superfund site is a formidable challenge. Any education press, however appropriate, quickly falls victim to doubt, mistrust of fears of the very public intended to be served by the effort. While each site is uniquely different, the issues raised by affected communities in one part of the country are strikingly similar to those raised in other parts. Those most involved must join those most affected in seeking meaningful solutions and in building the trust that is so vital in moving forward with Superfund

Case prediction in BPM systems : a research challenge

NARCIS (Netherlands)

Reijers, H.A.

2007-01-01

The capabilities of Business Process Management Systems (BPMS's) are continuously extended to increase the effectiveness of the management and enactment of business processes. This paper identifies the challenge of case prediction, which for a specific case under the control of a BPMS deals with the

Challenges of Predictability and Consistency in the First ...

African Journals Online (AJOL)

This article aims to investigate some features of Endemann's (1911) Wörterbuch der Sotho-Sprache (Dictionary of the Sotho language) with the focus on challenges of predictability and consistency in the lemmatization approach, the access alphabet, cross references and article treatments. The dictionary has hitherto ...

Airway management in Escobar syndrome: A formidable challenge

Directory of Open Access Journals (Sweden)

Shaji Mathew

2013-01-01

Full Text Available Escobar syndrome is a rare autosomal recessive disorder characterized by flexion joint and digit contractures, skin webbing, cleft palate, deformity of spine and cervical spine fusion. Associated difficult airway is mainly due to micrognathia, retrognathia, webbing of neck and limitation of the mouth opening and neck extension. We report a case of a 1 year old child with Escobar syndrome posted for bilateral hamstrings to quadriceps transfer. The child had adequate mouth opening with no evidence of cervical spine fusion, yet we faced difficulty in intubation which was ultimately overcome by securing a proseal laryngeal mask airway (PLMA and then by intubating with an endotracheal tube railroaded over a paediatric fibreoptic bronchoscope passed through the lumen of a PLMA.

Challenges in the Tracking and Prediction of Scheduled-Vehicle Journeys

DEFF Research Database (Denmark)

Jensen, Christian Søndergaard; Tiesyte, Dalia

2007-01-01

this type of knowledge with minimal cost. This paper characterizes the problem of real-time vehicle tracking using wireless communication, and of predicting the future status of the vehicles when their movements are restricted to given routes and when they follow schedules with a best effort. The paper...... discusses challenges related to tracking, to the prediction of future travel times, and to historical data analysis. It also suggests approaches to addressing the challenges.......A number of applications in areas such as logistics, cargo delivery, and collective transport involve the management of fleets of vehicles that are expected to travel along known routes according to fixed schedules. Due to road construction, accidents, and other unanticipated conditions...

Predictive analytics in mental health: applications, guidelines, challenges and perspectives.

Science.gov (United States)

Hahn, T; Nierenberg, A A; Whitfield-Gabrieli, S

2017-01-01

The emerging field of 'predictive analytics in mental health' has recently generated tremendous interest with the bold promise to revolutionize clinical practice in psychiatry paralleling similar developments in personalized and precision medicine. Here, we provide an overview of the key questions and challenges in the field, aiming to (1) propose general guidelines for predictive analytics projects in psychiatry, (2) provide a conceptual introduction to core aspects of predictive modeling technology, and (3) foster a broad and informed discussion involving all stakeholders including researchers, clinicians, patients, funding bodies and policymakers.

Highly accurate prediction of food challenge outcome using routinely available clinical data.

Science.gov (United States)

DunnGalvin, Audrey; Daly, Deirdre; Cullinane, Claire; Stenke, Emily; Keeton, Diane; Erlewyn-Lajeunesse, Mich; Roberts, Graham C; Lucas, Jane; Hourihane, Jonathan O'B

2011-03-01

Serum specific IgE or skin prick tests are less useful at levels below accepted decision points. We sought to develop and validate a model to predict food challenge outcome by using routinely collected data in a diverse sample of children considered suitable for food challenge. The proto-algorithm was generated by using a limited data set from 1 service (phase 1). We retrospectively applied, evaluated, and modified the initial model by using an extended data set in another center (phase 2). Finally, we prospectively validated the model in a blind study in a further group of children undergoing food challenge for peanut, milk, or egg in the second center (phase 3). Allergen-specific models were developed for peanut, egg, and milk. Phase 1 (N = 429) identified 5 clinical factors associated with diagnosis of food allergy by food challenge. In phase 2 (N = 289), we examined the predictive ability of 6 clinical factors: skin prick test, serum specific IgE, total IgE minus serum specific IgE, symptoms, sex, and age. In phase 3 (N = 70), 97% of cases were accurately predicted as positive and 94% as negative. Our model showed an advantage in clinical prediction compared with serum specific IgE only, skin prick test only, and serum specific IgE and skin prick test (92% accuracy vs 57%, and 81%, respectively). Our findings have implications for the improved delivery of food allergy-related health care, enhanced food allergy-related quality of life, and economized use of health service resources by decreasing the number of food challenges performed. Copyright © 2011 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

Severity of Illness and Adaptive Functioning Predict Quality of Care of Children Among Parents with Psychotic Disorders : A Confirmatory Factor Analysis

NARCIS (Netherlands)

Campbell, L.; Hanlon, M.--C.; Cherrie, G.; Harvey, C.; Stain, H. J.; Cohen, M.; van Ravenzwaaij, D.; Brown, S. D.

2018-01-01

OBJECTIVE: Parenthood is central to the personal and social identity of many people. For individuals with psychotic disorders, parenthood is often associated with formidable challenges. We aimed to identify predictors of adequate parenting among parents with psychotic disorders. METHODS: Data

Nuclear Structure Near the Drip Lines

International Nuclear Information System (INIS)

Nazarewicz, W.

1998-01-01

Experiments with beams of unstable nuclei will make it possible to look closely into many aspects of the nuclear many-body problem. Theoretically, exotic nuclei represent a formidable challenge for the nuclear many-body theories and their power to predict nuclear properties in nuclear terra incognita

Implementing electronic health care predictive analytics: considerations and challenges.

Science.gov (United States)

Amarasingham, Ruben; Patzer, Rachel E; Huesch, Marco; Nguyen, Nam Q; Xie, Bin

2014-07-01

The use of predictive modeling for real-time clinical decision making is increasingly recognized as a way to achieve the Triple Aim of improving outcomes, enhancing patients' experiences, and reducing health care costs. The development and validation of predictive models for clinical practice is only the initial step in the journey toward mainstream implementation of real-time point-of-care predictions. Integrating electronic health care predictive analytics (e-HPA) into the clinical work flow, testing e-HPA in a patient population, and subsequently disseminating e-HPA across US health care systems on a broad scale require thoughtful planning. Input is needed from policy makers, health care executives, researchers, and practitioners as the field evolves. This article describes some of the considerations and challenges of implementing e-HPA, including the need to ensure patients' privacy, establish a health system monitoring team to oversee implementation, incorporate predictive analytics into medical education, and make sure that electronic systems do not replace or crowd out decision making by physicians and patients. Project HOPE—The People-to-People Health Foundation, Inc.

A community computational challenge to predict the activity of pairs of compounds.

Science.gov (United States)

Bansal, Mukesh; Yang, Jichen; Karan, Charles; Menden, Michael P; Costello, James C; Tang, Hao; Xiao, Guanghua; Li, Yajuan; Allen, Jeffrey; Zhong, Rui; Chen, Beibei; Kim, Minsoo; Wang, Tao; Heiser, Laura M; Realubit, Ronald; Mattioli, Michela; Alvarez, Mariano J; Shen, Yao; Gallahan, Daniel; Singer, Dinah; Saez-Rodriguez, Julio; Xie, Yang; Stolovitzky, Gustavo; Califano, Andrea

2014-12-01

Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.

Challenges in microbial ecology: Building predictive understanding of community function and dynamics

DEFF Research Database (Denmark)

Widder, Stefanie; Allen, Rosalind J.; Pfeiffer, Thomas

2016-01-01

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly...... complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development...... is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved....

Oral rehabilitation of the cancer patient: A formidable challenge.

Science.gov (United States)

Petrovic, Ivana; Rosen, Evan B; Matros, Evan; Huryn, Joseph M; Shah, Jatin P

2018-05-03

Rehabilitation of oral functions following surgery on the jaws is a goal that is often difficult to achieve. Removable dentures supported by remaining teeth or gum are often unstable and seldom satisfactory. On the other hand, endosseous (dental) implants offer a mechanism to provide stability to the dentures. This review, discusses factors related to the tumor, patient, treatment, and physicians which impact upon the feasibility and success of dental implants in patients with oral cancer. © 2018 Wiley Periodicals, Inc.

Challenges in IC design for hearing aids

DEFF Research Database (Denmark)

Jørgensen, Ivan Harald Holger

2012-01-01

Designing modern hearing aids is a formidable challenge. The size of hearing aids is constantly decreasing, making them virtually invisible today. Still, as in all other modern electronics, more and more features are added to these devices driven by the development in modern IC technology....... The demands for performance and features at very low supply voltage and power consumption constantly prove a challenge to the physical design of hearing aids and not at least the design of the ICs for these. As a result of this all large hearing aid manufacturers use fully customized ASICs in their products...... to produce a competitive advantage. This presentation will give a brief insight into the hearing aid market and industry, a brief view of the historic development of hearing aids and an introduction to how a modern hearing is constructed showing the amplifier as the key component in the modern hearing aid...

The international gas markets. Of major changes and challenges for Europe

International Nuclear Information System (INIS)

Westphal, Kirsten

2014-01-01

Already in the 2010 edition of its World Energy Outlook the World Energy Agency noted an unprecedented degree of uncertainty surrounding the international energy markets. The rate of change in these markets is indeed stupendous, posing formidable tasks to business companies as well as the political leadership. The European gas markets face new challenges in protecting their security of supply which stem from the combined effects of the shift of LNG trade flows into the Pacific region, decreasing rates of home production and the ongoing transformation process within the EU.

Analysis of variability and predictability challenges of wind and solar power

NARCIS (Netherlands)

Haan, de J.E.S.; Virag, A.; Kling, W.L.

2013-01-01

In power systems, reserves are essential to ensure system security, certainly when challenges of predictability (inaccurate forecast) and variability (imperfect correlation of renewable generation and system load) are causing power imbalances. Different techniques can be used to size and allocate

Large Hadron Collider (LHC) phenomenology, operational challenges and theoretical predictions

CERN Document Server

Gilles, Abelin R

2013-01-01

The Large Hadron Collider (LHC) is the highest-energy particle collider ever constructed and is considered "one of the great engineering milestones of mankind." It was built by the European Organization for Nuclear Research (CERN) from 1998 to 2008, with the aim of allowing physicists to test the predictions of different theories of particle physics and high-energy physics, and particularly prove or disprove the existence of the theorized Higgs boson and of the large family of new particles predicted by supersymmetric theories. In this book, the authors study the phenomenology, operational challenges and theoretical predictions of LHC. Topics discussed include neutral and charged black hole remnants at the LHC; the modified statistics approach for the thermodynamical model of multiparticle production; and astroparticle physics and cosmology in the LHC era.

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Directory of Open Access Journals (Sweden)

Ramesh Rajagopalan

2017-11-01

Full Text Available Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems.

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Science.gov (United States)

Rajagopalan, Ramesh; Litvan, Irene; Jung, Tzyy-Ping

2017-11-01

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems.

The predictive value of bronchial histamine challenge in the diagnosis of bronchial asthma

DEFF Research Database (Denmark)

Madsen, F; Holstein-Rathlou, N H; Mosbech, H

1985-01-01

as asthmatics (n = 97) or non-asthmatics (n = 54). The diagnostic properties of the challenge were calculated using the statement of Baye. Considering PC20 values below 4.00 mg/ml as positive, the predictive value of a positive test was about 0.80 and the predictive value of a negative about 0.76. When PC20...

Using natural selection and optimization for smarter vegetation models - challenges and opportunities

Science.gov (United States)

Franklin, Oskar; Han, Wang; Dieckmann, Ulf; Cramer, Wolfgang; Brännström, Åke; Pietsch, Stephan; Rovenskaya, Elena; Prentice, Iain Colin

2017-04-01

Dynamic global vegetation models (DGVMs) are now indispensable for understanding the biosphere and for estimating the capacity of ecosystems to provide services. The models are continuously developed to include an increasing number of processes and to utilize the growing amounts of observed data becoming available. However, while the versatility of the models is increasing as new processes and variables are added, their accuracy suffers from the accumulation of uncertainty, especially in the absence of overarching principles controlling their concerted behaviour. We have initiated a collaborative working group to address this problem based on a 'missing law' - adaptation and optimization principles rooted in natural selection. Even though this 'missing law' constrains relationships between traits, and therefore can vastly reduce the number of uncertain parameters in ecosystem models, it has rarely been applied to DGVMs. Our recent research have shown that optimization- and trait-based models of gross primary production can be both much simpler and more accurate than current models based on fixed functional types, and that observed plant carbon allocations and distributions of plant functional traits are predictable with eco-evolutionary models. While there are also many other examples of the usefulness of these and other theoretical principles, it is not always straight-forward to make them operational in predictive models. In particular on longer time scales, the representation of functional diversity and the dynamical interactions among individuals and species presents a formidable challenge. Here we will present recent ideas on the use of adaptation and optimization principles in vegetation models, including examples of promising developments, but also limitations of the principles and some key challenges.

Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2

Science.gov (United States)

Athanasiou, Christina; Vasilakaki, Sofia; Dellis, Dimitris; Cournia, Zoe

2018-01-01

Computer-aided drug design has become an integral part of drug discovery and development in the pharmaceutical and biotechnology industry, and is nowadays extensively used in the lead identification and lead optimization phases. The drug design data resource (D3R) organizes challenges against blinded experimental data to prospectively test computational methodologies as an opportunity for improved methods and algorithms to emerge. We participated in Grand Challenge 2 to predict the crystallographic poses of 36 Farnesoid X Receptor (FXR)-bound ligands and the relative binding affinities for two designated subsets of 18 and 15 FXR-bound ligands. Here, we present our methodology for pose and affinity predictions and its evaluation after the release of the experimental data. For predicting the crystallographic poses, we used docking and physics-based pose prediction methods guided by the binding poses of native ligands. For FXR ligands with known chemotypes in the PDB, we accurately predicted their binding modes, while for those with unknown chemotypes the predictions were more challenging. Our group ranked #1st (based on the median RMSD) out of 46 groups, which submitted complete entries for the binding pose prediction challenge. For the relative binding affinity prediction challenge, we performed free energy perturbation (FEP) calculations coupled with molecular dynamics (MD) simulations. FEP/MD calculations displayed a high success rate in identifying compounds with better or worse binding affinity than the reference (parent) compound. Our studies suggest that when ligands with chemical precedent are available in the literature, binding pose predictions using docking and physics-based methods are reliable; however, predictions are challenging for ligands with completely unknown chemotypes. We also show that FEP/MD calculations hold predictive value and can nowadays be used in a high throughput mode in a lead optimization project provided that crystal structures of

Smart integrated microsystems: the energy efficiency challenge (Conference Presentation) (Plenary Presentation)

Science.gov (United States)

Benini, Luca

2017-06-01

The "internet of everything" envisions trillions of connected objects loaded with high-bandwidth sensors requiring massive amounts of local signal processing, fusion, pattern extraction and classification. From the computational viewpoint, the challenge is formidable and can be addressed only by pushing computing fabrics toward massive parallelism and brain-like energy efficiency levels. CMOS technology can still take us a long way toward this goal, but technology scaling is losing steam. Energy efficiency improvement will increasingly hinge on architecture, circuits, design techniques such as heterogeneous 3D integration, mixed-signal preprocessing, event-based approximate computing and non-Von-Neumann architectures for scalable acceleration.

Development of a prediction model of severe reaction in boiled egg challenges.

Science.gov (United States)

Sugiura, Shiro; Matsui, Teruaki; Nakagawa, Tomoko; Sasaki, Kemal; Nakata, Joon; Kando, Naoyuki; Ito, Komei

2016-07-01

We have proposed a new scoring system (Anaphylaxis SCoring Aichi: ASCA) for a quantitative evaluation of the anaphylactic reaction that is observed in an oral food challenge (OFC). Furthermore, the TS/Pro (Total Score of ASCA/cumulative protein dose) can be a marker to represent the overall severity of a food allergy. We aimed to develop a prediction model for a severe allergic reaction that is provoked in a boiled egg white challenge. We used two separate datasets to develop and validate the prediction model, respectively. The development dataset included 198 OFCs, that tested positive. The validation dataset prospectively included 140 consecutive OFCs, irrespective of the result. A 'severe reaction' was defined as a TS/Pro higher than 31 (the median score of the development dataset). A multivariate logistic regression analysis was performed to identify the factors associated with a severe reaction and develop the prediction model. The following four factors were independently associated with a severe reaction: ovomucoid specific IgE class (OM-sIgE: 0-6), aged 5 years or over, a complete avoidance of egg, and a total IgE prediction model. The model showed good discrimination in a receiver operating characteristic analysis; area under the curve (AUC) = 0.84 in development dataset, AUC = 0.85 in validation dataset. The prediction model significantly improved the AUC in both datasets compared to OM-sIgE alone. This simple scoring prediction model was useful for avoiding risky OFC. Copyright © 2016 Japanese Society of Allergology. Production and hosting by Elsevier B.V. All rights reserved.

Providing Equal Access to Education--A Formidable Challenge for China

Science.gov (United States)

Jones, Narelle; Maxwell, Bev; Palmer, Bill

1996-01-01

The People's Republic of China has seen a dramatic growth in its economy in recent years. It has a rapidly increasing population of approximately 1.2 billion people, though the population is still largely rural with about 80% of people living outside urban areas. China in its Education Law (1995) restated its commitment to universal education. In…

Prediction and validation of diffusion coefficients in a model drug delivery system using microsecond atomistic molecular dynamics simulation and vapour sorption analysis.

Science.gov (United States)

Forrey, Christopher; Saylor, David M; Silverstein, Joshua S; Douglas, Jack F; Davis, Eric M; Elabd, Yossef A

2014-10-14

Diffusion of small to medium sized molecules in polymeric medical device materials underlies a broad range of public health concerns related to unintended leaching from or uptake into implantable medical devices. However, obtaining accurate diffusion coefficients for such systems at physiological temperature represents a formidable challenge, both experimentally and computationally. While molecular dynamics simulation has been used to accurately predict the diffusion coefficients, D, of a handful of gases in various polymers, this success has not been extended to molecules larger than gases, e.g., condensable vapours, liquids, and drugs. We present atomistic molecular dynamics simulation predictions of diffusion in a model drug eluting system that represent a dramatic improvement in accuracy compared to previous simulation predictions for comparable systems. We find that, for simulations of insufficient duration, sub-diffusive dynamics can lead to dramatic over-prediction of D. We present useful metrics for monitoring the extent of sub-diffusive dynamics and explore how these metrics correlate to error in D. We also identify a relationship between diffusion and fast dynamics in our system, which may serve as a means to more rapidly predict diffusion in slowly diffusing systems. Our work provides important precedent and essential insights for utilizing atomistic molecular dynamics simulations to predict diffusion coefficients of small to medium sized molecules in condensed soft matter systems.

An ensemble based top performing approach for NCI-DREAM drug sensitivity prediction challenge.

Directory of Open Access Journals (Sweden)

Qian Wan

Full Text Available We consider the problem of predicting sensitivity of cancer cell lines to new drugs based on supervised learning on genomic profiles. The genetic and epigenetic characterization of a cell line provides observations on various aspects of regulation including DNA copy number variations, gene expression, DNA methylation and protein abundance. To extract relevant information from the various data types, we applied a random forest based approach to generate sensitivity predictions from each type of data and combined the predictions in a linear regression model to generate the final drug sensitivity prediction. Our approach when applied to the NCI-DREAM drug sensitivity prediction challenge was a top performer among 47 teams and produced high accuracy predictions. Our results show that the incorporation of multiple genomic characterizations lowered the mean and variance of the estimated bootstrap prediction error. We also applied our approach to the Cancer Cell Line Encyclopedia database for sensitivity prediction and the ability to extract the top targets of an anti-cancer drug. The results illustrate the effectiveness of our approach in predicting drug sensitivity from heterogeneous genomic datasets.

Methodological Challenges in Examining the Impact of Healthcare Predictive Analytics on Nursing-Sensitive Patient Outcomes.

Science.gov (United States)

Jeffery, Alvin D

2015-06-01

The expansion of real-time analytic abilities within current electronic health records has led to innovations in predictive modeling and clinical decision support systems. However, the ability of these systems to influence patient outcomes is currently unknown. Even though nurses are the largest profession within the healthcare workforce, little research has been performed to explore the impact of clinical decision support on their decisions and the patient outcomes associated with them. A scoping literature review explored the impact clinical decision support systems containing healthcare predictive analytics have on four nursing-sensitive patient outcomes (pressure ulcers, failure to rescue, falls, and infections). While many articles discussed variable selection and predictive model development/validation, only four articles examined the impact on patient outcomes. The novelty of predictive analytics and the inherent methodological challenges in studying clinical decision support impact are likely responsible for this paucity of literature. Major methodological challenges include (1) multilevel nature of intervention, (2) treatment fidelity, and (3) adequacy of clinicians' subsequent behavior. There is currently insufficient evidence to demonstrate efficacy of healthcare predictive analytics-enhanced clinical decision support systems on nursing-sensitive patient outcomes. Innovative research methods and a greater emphasis on studying this phenomenon are needed.

Predicting ionospheric scintillation: Recent advancements and future challenges

Science.gov (United States)

Carter, B. A.; Currie, J. L.; Terkildsen, M.; Bouya, Z.; Parkinson, M. L.

2017-12-01

Society greatly benefits from space-based infrastructure and technology. For example, signals from Global Navigation Satellite Systems (GNSS) are used across a wide range of industrial sectors; including aviation, mining, agriculture and finance. Current trends indicate that the use of these space-based technologies is likely to increase over the coming decades as the global economy becomes more technology-dependent. Space weather represents a key vulnerability to space-based technology, both in terms of the space environment effects on satellite infrastructure and the influence of the ionosphere on the radio signals used for satellite communications. In recent decades, the impact of the ionosphere on GNSS signals has re-ignited research interest into the equatorial ionosphere, particularly towards understanding Equatorial Plasma Bubbles (EPBs). EPBs are a dominant source of nighttime plasma irregularities in the low-latitude ionosphere, which can cause severe scintillation on GNSS signals and subsequent degradation on GNSS product quality. Currently, ionospheric scintillation event forecasts are not being routinely released by any space weather prediction agency around the world, but this is likely to change in the near future. In this contribution, an overview of recent efforts to develop a global ionospheric scintillation prediction capability within Australia will be given. The challenges in understanding user requirements for ionospheric scintillation predictions will be discussed. Next, the use of ground- and space-based datasets for the purpose of near-real time ionospheric scintillation monitoring will be explored. Finally, some modeling that has shown significant promise in transitioning towards an operational ionospheric scintillation forecasting system will be discussed.

Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges.

Science.gov (United States)

Amarasingham, Ruben; Audet, Anne-Marie J; Bates, David W; Glenn Cohen, I; Entwistle, Martin; Escobar, G J; Liu, Vincent; Etheredge, Lynn; Lo, Bernard; Ohno-Machado, Lucila; Ram, Sudha; Saria, Suchi; Schilling, Lisa M; Shahi, Anand; Stewart, Walter F; Steyerberg, Ewout W; Xie, Bin

2016-01-01

The recent explosion in available electronic health record (EHR) data is motivating a rapid expansion of electronic health care predictive analytic (e-HPA) applications, defined as the use of electronic algorithms that forecast clinical events in real time with the intent to improve patient outcomes and reduce costs. There is an urgent need for a systematic framework to guide the development and application of e-HPA to ensure that the field develops in a scientifically sound, ethical, and efficient manner. Building upon earlier frameworks of model development and utilization, we identify the emerging opportunities and challenges of e-HPA, propose a framework that enables us to realize these opportunities, address these challenges, and motivate e-HPA stakeholders to both adopt and continuously refine the framework as the applications of e-HPA emerge. To achieve these objectives, 17 experts with diverse expertise including methodology, ethics, legal, regulation, and health care delivery systems were assembled to identify emerging opportunities and challenges of e-HPA and to propose a framework to guide the development and application of e-HPA. The framework proposed by the panel includes three key domains where e-HPA differs qualitatively from earlier generations of models and algorithms (Data Barriers, Transparency, and ETHICS) and areas where current frameworks are insufficient to address the emerging opportunities and challenges of e-HPA (Regulation and Certification; and Education and Training). The following list of recommendations summarizes the key points of the framework: Data Barriers: Establish mechanisms within the scientific community to support data sharing for predictive model development and testing.Transparency: Set standards around e-HPA validation based on principles of scientific transparency and reproducibility. Develop both individual-centered and society-centered risk-benefit approaches to evaluate e-HPA.Regulation and Certification: Construct a

Phenotype prediction using regularized regression on genetic data in the DREAM5 Systems Genetics B Challenge.

Directory of Open Access Journals (Sweden)

Po-Ru Loh

Full Text Available A major goal of large-scale genomics projects is to enable the use of data from high-throughput experimental methods to predict complex phenotypes such as disease susceptibility. The DREAM5 Systems Genetics B Challenge solicited algorithms to predict soybean plant resistance to the pathogen Phytophthora sojae from training sets including phenotype, genotype, and gene expression data. The challenge test set was divided into three subcategories, one requiring prediction based on only genotype data, another on only gene expression data, and the third on both genotype and gene expression data. Here we present our approach, primarily using regularized regression, which received the best-performer award for subchallenge B2 (gene expression only. We found that despite the availability of 941 genotype markers and 28,395 gene expression features, optimal models determined by cross-validation experiments typically used fewer than ten predictors, underscoring the importance of strong regularization in noisy datasets with far more features than samples. We also present substantial analysis of the training and test setup of the challenge, identifying high variance in performance on the gold standard test sets.

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

Science.gov (United States)

Goldstein, Benjamin A; Navar, Ann Marie; Carter, Rickey E

2017-06-14

Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology.

Predicting the outcome of oral food challenges with hen's egg through skin test end-point titration.

Science.gov (United States)

Tripodi, S; Businco, A Di Rienzo; Alessandri, C; Panetta, V; Restani, P; Matricardi, P M

2009-08-01

Oral food challenge (OFC) is the diagnostic 'gold standard' of food allergies but it is laborious and time consuming. Attempts to predict a positive OFC through specific IgE assays or conventional skin tests so far gave suboptimal results. To test whether skin test with titration curves predict with enough confidence the outcome of an oral food challenge. Children (n=47; mean age 6.2 +/- 4.2 years) with suspected and diagnosed allergic reactions to hen's egg (HE) were examined through clinical history, physical examination, oral food challenge, conventional and end-point titrated skin tests with HE white extract and determination of serum specific IgE against HE white. Predictive decision points for a positive outcome of food challenges were calculated through receiver operating characteristic (ROC) analysis for HE white using IgE concentration, weal size and end-point titration (EPT). OFC was positive (Sampson's score >or=3) in 20/47 children (42.5%). The area under the ROC curve obtained with the EPT method was significantly bigger than the one obtained by measuring IgE-specific antibodies (0.99 vs. 0.83, P<0.05) and weal size (0.99 vs. 0.88, P<0.05). The extract's dilution that successfully discriminated a positive from a negative OFC (sensitivity 95%, specificity 100%) was 1 : 256, corresponding to a concentration of 5.9 microg/mL of ovotransferrin, 22.2 microg/mL of ovalbumin, and 1.4 microg/mL of lysozyme. EPT is a promising approach to optimize the use of skin prick tests and to predict the outcome of OFC with HE in children. Further studies are needed to test whether this encouraging finding can be extended to other populations and food allergens.

Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP+

Science.gov (United States)

Schindler, Christina; Rippmann, Friedrich; Kuhn, Daniel

2018-01-01

Physics-based free energy simulations have increasingly become an important tool for predicting binding affinity and the recent introduction of automated protocols has also paved the way towards a more widespread use in the pharmaceutical industry. The D3R 2016 Grand Challenge 2 provided an opportunity to blindly test the commercial free energy calculation protocol FEP+ and assess its performance relative to other affinity prediction methods. The present D3R free energy prediction challenge was built around two experimental data sets involving inhibitors of farnesoid X receptor (FXR) which is a promising anticancer drug target. The FXR binding site is predominantly hydrophobic with few conserved interaction motifs and strong induced fit effects making it a challenging target for molecular modeling and drug design. For both data sets, we achieved reasonable prediction accuracy (RMSD ≈ 1.4 kcal/mol, rank 3-4 according to RMSD out of 20 submissions) comparable to that of state-of-the-art methods in the field. Our D3R results boosted our confidence in the method and strengthen our desire to expand its applications in future in-house drug design projects.

Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP.

Science.gov (United States)

Schindler, Christina; Rippmann, Friedrich; Kuhn, Daniel

2018-01-01

Physics-based free energy simulations have increasingly become an important tool for predicting binding affinity and the recent introduction of automated protocols has also paved the way towards a more widespread use in the pharmaceutical industry. The D3R 2016 Grand Challenge 2 provided an opportunity to blindly test the commercial free energy calculation protocol FEP+ and assess its performance relative to other affinity prediction methods. The present D3R free energy prediction challenge was built around two experimental data sets involving inhibitors of farnesoid X receptor (FXR) which is a promising anticancer drug target. The FXR binding site is predominantly hydrophobic with few conserved interaction motifs and strong induced fit effects making it a challenging target for molecular modeling and drug design. For both data sets, we achieved reasonable prediction accuracy (RMSD ≈ 1.4 kcal/mol, rank 3-4 according to RMSD out of 20 submissions) comparable to that of state-of-the-art methods in the field. Our D3R results boosted our confidence in the method and strengthen our desire to expand its applications in future in-house drug design projects.

Modelling and prediction of radionuclide migration from shallow, subgrade nuclear waste facilities in arid environments

International Nuclear Information System (INIS)

Smith, A.; Ward, A.; Geldenhuis, S.

1986-01-01

Over the past fifteen years, prodigious efforts and significant advances have been made in methods of prediction of the migration rate of dissolved species in aqueous systems. Despite such work, there remain formidable obstacles in prediction of solute transport in the unsaturated zone over the long time periods necessarily related to the radionuclide bearing wastes. The objective of this paper is to consider the methods, issues and problems with the use of predictive solute transport models for radionuclide migration from nuclear waste disposal in arid environments, if and when engineering containment of the waste fails. Having considered the ability for long term solute prediction for a number of geological environments, the advantages of a disposal environment in which the solute transport process is diffusion controlled will be described

Pushing the size limit of de novo structure ensemble prediction guided by sparse SDSL-EPR restraints to 200 residues: The monomeric and homodimeric forms of BAX

Science.gov (United States)

Fischer, Axel W.; Bordignon, Enrica; Bleicken, Stephanie; García-Sáez, Ana J.; Jeschke, Gunnar; Meiler, Jens

2016-01-01

Structure determination remains a challenge for many biologically important proteins. In particular, proteins that adopt multiple conformations often evade crystallization in all biologically relevant states. Although computational de novo protein folding approaches often sample biologically relevant conformations, the selection of the most accurate model for different functional states remains a formidable challenge, in particular, for proteins with more than about 150 residues. Electron paramagnetic resonance (EPR) spectroscopy can obtain limited structural information for proteins in well-defined biological states and thereby assist in selecting biologically relevant conformations. The present study demonstrates that de novo folding methods are able to accurately sample the folds of 192-residue long soluble monomeric Bcl-2-associated X protein (BAX). The tertiary structures of the monomeric and homodimeric forms of BAX were predicted using the primary structure as well as 25 and 11 EPR distance restraints, respectively. The predicted models were subsequently compared to respective NMR/X-ray structures of BAX. EPR restraints improve the protein-size normalized root-mean-square-deviation (RMSD100) of the most accurate models with respect to the NMR/crystal structure from 5.9 Å to 3.9 Å and from 5.7 Å to 3.3 Å, respectively. Additionally, the model discrimination is improved, which is demonstrated by an improvement of the enrichment from 5% to 15% and from 13% to 21%, respectively. PMID:27129417

Groundwater and human development: challenges and opportunities in livelihoods and environment.

Science.gov (United States)

Shah, T

2005-01-01

At less than 1000 km3/year, the world's annual use of groundwater is 1.5% of renewable water resource but contributes a lion's share of water-induced human welfare. Global groundwater use however has increased manifold in the past 50 years; and the human race has never had to manage groundwater use on such a large scale. Sustaining the massive welfare gains groundwater development has created without ruining the resource is a key water challenge facing the world today. In exploring this challenge, we have focused a good deal on conditions of resource occurrence but less so on resource use. I offer a typology of five groundwater demand systems as Groundwater Socio-ecologies (GwSE), each embodying a unique pattern of interactions between socio-economic and ecological variables, and each facing a distinct groundwater governance challenge. During the past century, a growing corpus of experiential knowledge has accumulated in the industrialized world on managing groundwater in various uses and contexts. A daunting global groundwater issue today is to apply this knowledge intelligently to by far the more formidable challenge that has arisen in developing regions of Asia and Africa, where groundwater irrigation has evolved into a colossal anarchy supporting billions of livelihoods but threatening the resource itself.

Validation of the cephalosporin intradermal skin test for predicting immediate hypersensitivity: a prospective study with drug challenge.

Science.gov (United States)

Yoon, S-Y; Park, S Y; Kim, S; Lee, T; Lee, Y S; Kwon, H-S; Cho, Y S; Moon, H-B; Kim, T-B

2013-07-01

Cephalosporin is a major offending agent in terms of drug hypersensitivity along with penicillin. Cephalosporin intradermal skin tests (IDTs) have been widely used; however, their validity for predicting immediate hypersensitivity has not been studied. This study aimed to determine the predictive value of cephalosporin intradermal skin testing before administration of the drug. We prospectively conducted IDTs with four cephalosporins, one each of selected first-, second-, third-, or fourth-generation cephalosporins: ceftezol; cefotetan or cefamandole; ceftriaxone or cefotaxime; and flomoxef, respectively, as well as with penicillin G. After the skin test, whatever the result, one of the tested cephalosporins was administered intravenously and the patient was carefully observed. We recruited 1421 patients who required preoperative cephalosporins. Seventy-four patients (74/1421, 5.2%) were positive to at least one cephalosporin. However, none of responders had immediate hypersensitivity reactions after a challenge dose of the same or different cephalosporin, which were positive in the skin test. Four patients who suffered generalized urticaria and itching after challenge gave negative skin tests for the corresponding drug. The IDT for cephalosporin had a sensitivity of 0%, a specificity of 97.5%, a negative predictive value of 99.7%, and a positive predictive value (PPV) of 0%, when challenged with the same drugs that were positive in the skin test. Routine skin testing with a cephalosporin before its administration is not useful for predicting immediate hypersensitivity because of the extremely low sensitivity and PPV of the skin test (CRIS registration no. KCT0000455). © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

The evolution of sex roles in birds is related to adult sex ratio

OpenAIRE

Liker, András; Freckleton, Robert P.; Székely, Tamás

2013-01-01

Sex-role reversal represents a formidable challenge for evolutionary biologists, since it is not clear which ecological, life-history or social factors facilitated conventional sex roles (female care and male-male competition for mates) to be reversed (male care and female-female competition). Classic theories suggested ecological or life-history predictors of role reversal, but most studies failed to support these hypotheses. Recent theory however predicts that sex-role reversal should be dr...

Neural System Prediction and Identification Challenge

Directory of Open Access Journals (Sweden)

Ioannis eVlachos

2013-12-01

Full Text Available Can we infer the function of a biological neural network (BNN if we know the connectivity and activity of all its constituent neurons? This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC. We provide the connectivity and activity of all neurons and invite participants (i to infer the functions implemented (hard-wired in spiking neural networks (SNNs by stimulating and recording the activity of neurons and, (ii to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

Neural system prediction and identification challenge.

Science.gov (United States)

Vlachos, Ioannis; Zaytsev, Yury V; Spreizer, Sebastian; Aertsen, Ad; Kumar, Arvind

2013-01-01

Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons?This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

The international gas markets. Of major changes and challenges for Europe; Die internationalen Gasmaerkte. Von grossen Veraenderungen und Herausforderungen fuer Europa

Energy Technology Data Exchange (ETDEWEB)

Westphal, Kirsten [Deutsches Institut fuer Internationale Politik und Sicherheit, Berlin (Germany). Stiftung Wissenschaft und Politik (SWP), Forschungsgruppe Globale Fragen

2014-01-15

Already in the 2010 edition of its World Energy Outlook the World Energy Agency noted an unprecedented degree of uncertainty surrounding the international energy markets. The rate of change in these markets is indeed stupendous, posing formidable tasks to business companies as well as the political leadership. The European gas markets face new challenges in protecting their security of supply which stem from the combined effects of the shift of LNG trade flows into the Pacific region, decreasing rates of home production and the ongoing transformation process within the EU.

Tissue polarimetry: concepts, challenges, applications, and outlook.

Science.gov (United States)

Ghosh, Nirmalya; Vitkin, I Alex

2011-11-01

Polarimetry has a long and successful history in various forms of clear media. Driven by their biomedical potential, the use of the polarimetric approaches for biological tissue assessment has also recently received considerable attention. Specifically, polarization can be used as an effective tool to discriminate against multiply scattered light (acting as a gating mechanism) in order to enhance contrast and to improve tissue imaging resolution. Moreover, the intrinsic tissue polarimetry characteristics contain a wealth of morphological and functional information of potential biomedical importance. However, in a complex random medium-like tissue, numerous complexities due to multiple scattering and simultaneous occurrences of many scattering and polarization events present formidable challenges both in terms of accurate measurements and in terms of analysis of the tissue polarimetry signal. In order to realize the potential of the polarimetric approaches for tissue imaging and characterization/diagnosis, a number of researchers are thus pursuing innovative solutions to these challenges. In this review paper, we summarize these and other issues pertinent to the polarized light methodologies in tissues. Specifically, we discuss polarized light basics, Stokes-Muller formalism, methods of polarization measurements, polarized light modeling in turbid media, applications to tissue imaging, inverse analysis for polarimetric results quantification, applications to quantitative tissue assessment, etc.

Emerging and Neglected Infectious Diseases: Insights, Advances, and Challenges.

Science.gov (United States)

Nii-Trebi, Nicholas Israel

2017-01-01

Infectious diseases are a significant burden on public health and economic stability of societies all over the world. They have for centuries been among the leading causes of death and disability and presented growing challenges to health security and human progress. The threat posed by infectious diseases is further deepened by the continued emergence of new, unrecognized, and old infectious disease epidemics of global impact. Over the past three and half decades at least 30 new infectious agents affecting humans have emerged, most of which are zoonotic and their origins have been shown to correlate significantly with socioeconomic, environmental, and ecological factors. As these factors continue to increase, putting people in increased contact with the disease causing pathogens, there is concern that infectious diseases may continue to present a formidable challenge. Constant awareness and pursuance of effective strategies for controlling infectious diseases and disease emergence thus remain crucial. This review presents current updates on emerging and neglected infectious diseases and highlights the scope, dynamics, and advances in infectious disease management with particular focus on WHO top priority emerging infectious diseases (EIDs) and neglected tropical infectious diseases.

Challenges in Transitioning Research Data to Operations: The SPoRT Paradigm

Science.gov (United States)

Jedloved, Gary J.; Smith, Matt; McGrath, Kevin

2010-01-01

Established in 2002 to demonstrate the weather and forecasting application of real-time EOS measurements, the NASA Short-term Prediction Research and Transition (SPoRT) program has grown to be an end-to-end research to operations activity focused on the use of advanced NASA modeling and data assimilation approaches, nowcasting techniques, and unique high-resolution multispectral data from EOS satellites to improve short-term weather forecasts on a regional and local scale. With the ever-broadening application of real-time high resolution satellite data from current EOS and planned NPP, JPSS, and GOES-R sensors to weather forecast problems, significant challenges arise in the acquisition, delivery, and integration of the new capabilities into the decision making process of the operational weather community. For polar orbiting sensors such as MODIS, AIRS, VIIRS, and CRiS, the use of direct broadcast ground stations is key to the real-time delivery of the data and derived products in a timely fashion. With the ABI on the geostationary GOES-R satellite, the data volume will likely increase by a factor of 5- 10 from current data streams. However, the high data volume and limited bandwidth of end user facilities presents a formidable obstacle to timely access to the data. This challenge can be addressed through the use of subsetting techniques, innovative web services, and the judicious selection of data formats. Many of these approaches have been implemented by SPoRT for the delivery of real-time products to NWS forecast offices and other weather entities. Once available in decision support systems like AWIPS II, these new data and products must be integrated into existing and new displays that allow for the integration of the data with existing operational products in these systems. SPoRT is leading the way in demonstrating this enhanced capability. This paper will highlight the ways SPoRT is overcoming many of the challenges presented by the enormous data volumes of

Attracted to power: challenge/threat and promotion/prevention focus differentially predict the attractiveness of group power

Science.gov (United States)

Scholl, Annika; Sassenrath, Claudia; Sassenberg, Kai

2015-01-01

Depending on their motivation, individuals prefer different group contexts for social interactions. The present research sought to provide more insight into this relationship. More specifically, we tested how challenge/threat and a promotion/prevention focus predict attraction to groups with high- or low-power. As such, we examined differential outcomes of threat and prevention focus as well as challenge and promotion focus that have often been regarded as closely related. According to regulatory focus, individuals should prefer groups that they expect to “feel right” for them to join: Low-power groups should be more attractive in a prevention (than a promotion) focus, as these groups suggest security-oriented strategies, which fit a prevention focus. High-power groups should be more attractive in a promotion (rather than a prevention) focus, as these groups are associated with promotion strategies fitting a promotion focus (Sassenberg et al., 2007). In contrast, under threat (vs. challenge), groups that allow individuals to restore their (perceived) lack of control should be preferred: Low-power groups should be less attractive under threat (than challenge) because they provide low resources which threatened individuals already perceive as insufficient and high-power groups might be more attractive under threat (than under challenge), because their high resources allow individuals to restore control. Two experiments (N = 140) supported these predictions. The attractiveness of a group often depends on the motivation to engage in what fits (i.e., prefer a group that feels right in the light of one’s regulatory focus). However, under threat the striving to restore control (i.e., prefer a group allowing them to change the status quo under threat vs. challenge) overrides the fit effect, which may in turn guide individuals’ behavior in social interactions. PMID:25904887

D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies

Science.gov (United States)

Gaieb, Zied; Liu, Shuai; Gathiaka, Symon; Chiu, Michael; Yang, Huanwang; Shao, Chenghua; Feher, Victoria A.; Walters, W. Patrick; Kuhn, Bernd; Rudolph, Markus G.; Burley, Stephen K.; Gilson, Michael K.; Amaro, Rommie E.

2018-01-01

The Drug Design Data Resource (D3R) ran Grand Challenge 2 (GC2) from September 2016 through February 2017. This challenge was based on a dataset of structures and affinities for the nuclear receptor farnesoid X receptor (FXR), contributed by F. Hoffmann-La Roche. The dataset contained 102 IC50 values, spanning six orders of magnitude, and 36 high-resolution co-crystal structures with representatives of four major ligand classes. Strong global participation was evident, with 49 participants submitting 262 prediction submission packages in total. Procedurally, GC2 mimicked Grand Challenge 2015 (GC2015), with a Stage 1 subchallenge testing ligand pose prediction methods and ranking and scoring methods, and a Stage 2 subchallenge testing only ligand ranking and scoring methods after the release of all blinded co-crystal structures. Two smaller curated sets of 18 and 15 ligands were developed to test alchemical free energy methods. This overview summarizes all aspects of GC2, including the dataset details, challenge procedures, and participant results. We also consider implications for progress in the field, while highlighting methodological areas that merit continued development. Similar to GC2015, the outcome of GC2 underscores the pressing need for methods development in pose prediction, particularly for ligand scaffolds not currently represented in the Protein Data Bank (http://www.pdb.org), and in affinity ranking and scoring of bound ligands.

CAR T Cell Therapy for Glioblastoma: Recent Clinical Advances and Future Challenges.

Science.gov (United States)

Bagley, Stephen J; Desai, Arati S; Linette, Gerald P; June, Carl H; O'Rourke, Donald M

2018-03-02

In patients with certain hematologic malignancies, the use of autologous T cells genetically modified to express chimeric antigen receptors (CARs) has led to unprecedented clinical responses. Although progress in solid tumors has been elusive, recent clinical studies have demonstrated the feasibility and safety of CAR T cell therapy for glioblastoma. In addition, despite formidable barriers to T cell localization and effector function in glioblastoma, signs of efficacy have been observed in select patients. In this review, we begin with a discussion of established obstacles to systemic therapy in glioblastoma and how these may be overcome by CAR T cells. We continue with a summary of previously published CAR T cell trials in GBM, and end by outlining the key therapeutic challenges associated with the use of CAR T cells in this disease.

Leadership challenges in multinational medical peacekeeping operations: Lessons from UNIFIL Hospital.

Science.gov (United States)

Datta, Rakesh; Khanna, Sangeeta

2017-10-01

Commanding a military multinational and multilingual healthcare facility can be a formidable task with very little margin for error. The authors were in leadership positions of UNIFIL Hospital, unique in its diversity of both staff and clientele. Experience about the challenges faced and methods adopted to overcome them will be shared. Troops from diverse backgrounds differ in their competency, and also in their attitudinal approach to situations. It is imperative for the medical commanders to identify these differences, and work towards harnessing individual strengths to form a cohesive unit. Frequent rotation of team members and thereby difficulty in adapting to new environment makes the tasks more challenging. Challenges can be broadly categorized in those dealing with functional roles (providing medical support) and command and control issues. Linguistic challenges especially in situations where professionals have to work as a coordinated unit remains a major challenge. The threat of medical errors arising out of misunderstandings is very real. Gender sensitization is essential to avoid potential unpleasant situations. Interpersonal conflict can easily go out of hand. The leadership has to be more direct and deliberate relying less on hierarchy and more on direct communication. A strict enforcement of UN standards for equipment and competence, frequent joint medical drills help to overcome interoperability issues and develop mutual confidence. Leadership in multinational UN hospitals is a demanding task with its peculiar set of challenges. A systematic and deliberate approach focused on mutual respect, flexibility and direct leadership can help medical commanders in such situations.

Challenges predicting ligand-receptor interactions of promiscuous proteins: the nuclear receptor PXR.

Directory of Open Access Journals (Sweden)

Sean Ekins

2009-12-01

Full Text Available Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses. The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5alpha-androstan-3beta-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches.

A two-stage approach for improved prediction of residue contact maps

Directory of Open Access Journals (Sweden)

Pollastri Gianluca

2006-03-01

Full Text Available Abstract Background Protein topology representations such as residue contact maps are an important intermediate step towards ab initio prediction of protein structure. Although improvements have occurred over the last years, the problem of accurately predicting residue contact maps from primary sequences is still largely unsolved. Among the reasons for this are the unbalanced nature of the problem (with far fewer examples of contacts than non-contacts, the formidable challenge of capturing long-range interactions in the maps, the intrinsic difficulty of mapping one-dimensional input sequences into two-dimensional output maps. In order to alleviate these problems and achieve improved contact map predictions, in this paper we split the task into two stages: the prediction of a map's principal eigenvector (PE from the primary sequence; the reconstruction of the contact map from the PE and primary sequence. Predicting the PE from the primary sequence consists in mapping a vector into a vector. This task is less complex than mapping vectors directly into two-dimensional matrices since the size of the problem is drastically reduced and so is the scale length of interactions that need to be learned. Results We develop architectures composed of ensembles of two-layered bidirectional recurrent neural networks to classify the components of the PE in 2, 3 and 4 classes from protein primary sequence, predicted secondary structure, and hydrophobicity interaction scales. Our predictor, tested on a non redundant set of 2171 proteins, achieves classification performances of up to 72.6%, 16% above a base-line statistical predictor. We design a system for the prediction of contact maps from the predicted PE. Our results show that predicting maps through the PE yields sizeable gains especially for long-range contacts which are particularly critical for accurate protein 3D reconstruction. The final predictor's accuracy on a non-redundant set of 327 targets is 35

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Narayanan Manikandan

2016-01-01

Full Text Available Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing.

Science.gov (United States)

Tulabandhula, Theja; Rudin, Cynthia

2014-06-01

Our goal is to design a prediction and decision system for real-time use during a professional car race. In designing a knowledge discovery process for racing, we faced several challenges that were overcome only when domain knowledge of racing was carefully infused within statistical modeling techniques. In this article, we describe how we leveraged expert knowledge of the domain to produce a real-time decision system for tire changes within a race. Our forecasts have the potential to impact how racing teams can optimize strategy by making tire-change decisions to benefit their rank position. Our work significantly expands previous research on sports analytics, as it is the only work on analytical methods for within-race prediction and decision making for professional car racing.

Predicting anxious response to a social challenge: the predictive utility of the social interaction anxiety scale and the social phobia scale in a college population.

Science.gov (United States)

Gore, K L; Carter, M M; Parker, S

2002-06-01

Trait anxiety is believed to be a hierarchical construct composed of several lower-order factors (Adv. Behav. Res. Therapy, 15 (1993) 147; J. Anxiety Disorders, 9 (1995) 163). Assessment devices such as the Social Interaction Anxiety Scale, the Social Phobia Scale (SIAS and SPS; Behav. Res. Therapy, 36 (4) (1998) 455), and the Anxiety Sensitivity Index (ASI; Behav. Res. Therapy, 24 (1986) 1) are good measures of the presumably separate lower-order factors. This study compared the effectiveness of the SIAS, SPS, ASI-physical scale and STAI-T (State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press (1970)) as predictors of anxious response to a social challenge (asking an aloof confederate out on a date). Consistent with the hierarchical model of anxiety, the measures of trait anxiety were moderately correlated with each other and each was a significant predictor of anxious response. The specific measures of trait social anxiety were slightly better predictors of anxious response to the social challenge than was either the ASI-physical scale or the STAI-T. The results provide evidence of the predictive validity of these social trait measures and some support for their specificity in the prediction of anxious response to a social challenge.

Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort.

Science.gov (United States)

Gao, Ying-Duo; Hu, Yuan; Crespo, Alejandro; Wang, Deping; Armacost, Kira A; Fells, James I; Fradera, Xavier; Wang, Hongwu; Wang, Huijun; Sherborne, Brad; Verras, Andreas; Peng, Zhengwei

2018-01-01

The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort

Science.gov (United States)

Gao, Ying-Duo; Hu, Yuan; Crespo, Alejandro; Wang, Deping; Armacost, Kira A.; Fells, James I.; Fradera, Xavier; Wang, Hongwu; Wang, Huijun; Sherborne, Brad; Verras, Andreas; Peng, Zhengwei

2018-01-01

The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

Challenging homeostasis to define biomarkers for nutrition related health

NARCIS (Netherlands)

Ommen, van B.; Keijer, J.; Heil, S.G.; Kaput, J.

2009-01-01

A primary goal of nutrition research is to optimize health and prevent or delay disease. Biomarkers to quantify health optimization are needed since many if not most biomarkers are developed for diseases. Quantifying normal homeostasis and developing validated biomarkers are formidable tasks because

Research issues for radiation protection for man during prolonged spaceflight

International Nuclear Information System (INIS)

Conklin, J.J.; Hagan, M.P.

1987-01-01

Stassinopoulos has shown that for a 5-year period during solar maximum, the solar flare predictive model (SOLPRO) predicts four anomalously large solar flares with 89% confidence. When the solar flare hazard is added to the other radiation hazards in space, radiation poses a formidable challenge to providing a safe permanent presence in space. From this it is clear that there are many unknown questions about space radiation, particularly involving HZE particles and the interaction of other space stressors with radiation. Despite the challenge, the authors are optimistic that the problems can be solved. NASA has achieved an extraordinary record of radiation safety during the first 25 years of spaceflight. During the next 25 years in space, the radiobiological challenge will be significantly greater, but so will the rewards. There are many tools that can be applied with current and future technologies. It is their opinion that the problems will be solved, and they require only the commitment to solve them

Development of patient specific cardiovascular models predicting dynamics in response to orthostatic stress challenges

DEFF Research Database (Denmark)

Ottesen, Johnny T.

2013-01-01

Physiological realistic models of the controlled cardiovascular system are constructed and validated against clinical data. Special attention is paid to the control of blood pressure, cerebral blood flow velocity, and heart rate during postural challenges, including sit-to-stand and head-up tilt....... This study describes development of patient specific models, and how sensitivity analysis and nonlinear optimization methods can be used to predict patient specific characteristics when analyzed using experimental data. Finally, we discuss how a given model can be used to understand physiological changes...

Challenges of nuclear fusion

International Nuclear Information System (INIS)

Kunkel, W.B.

1987-01-01

After 30 years of research and development in many countries, the magnetic confinement fusion experiments finally seem to be getting close to the original first goal: the point of ''scientific break-even''. Plans are being made for a generation of experiments and tests with actual controlled thermonuclear fusion conditions. Therefore engineers and material scientists are hard at work to develop the required technology. In this paper the principal elements of a generic fusion reactor are described briefly to introduce the reader to the nature of the problems at hand. The main portion of the presentation summarises the recent advances made in this field and discusses the major issues that still need to be addressed in regard to materials and technology for fusion power. Specific examples are the problems of the first wall and other components that come into direct contact with the plasma, where both lifetime and plasma contamination are matters of concern. Equally challenging are the demands on structural materials and on the magnetic-field coils, particularly in connection with the neutron-radiation environment of fusion reactors. Finally, the role of ceramics must be considered, both for insulators and for fuel breeding purposes. It is evident that we still have a formidable task before us, but at this point none of the problems seem to be insoluble. (author)

Individual differences in maternal response to immune challenge predict offspring behavior: Contribution of environmental factors

Science.gov (United States)

Bronson, Stefanie L.; Ahlbrand, Rebecca; Horn, Paul S.; Kern, Joseph R.; Richtand, Neil M.

2011-01-01

Maternal infection during pregnancy elevates risk for schizophrenia and related disorders in offspring. Converging evidence suggests the maternal inflammatory response mediates the interaction between maternal infection, altered brain development, and behavioral outcome. The extent to which individual differences in the maternal response to immune challenge influence the development of these abnormalities is unknown. The present study investigated the impact of individual differences in maternal response to the viral mimic polyinosinic:polycytidylic acid (poly I:C) on offspring behavior. We observed significant variability in body weight alterations of pregnant rats induced by administration of poly I:C on gestational day 14. Furthermore, the presence or absence of maternal weight loss predicted MK-801 and amphetamine stimulated locomotor abnormalities in offspring. MK-801 stimulated locomotion was altered in offspring of all poly I:C treated dams; however, the presence or absence of maternal weight loss resulted in decreased and modestly increased locomotion, respectively. Adult offspring of poly I:C treated dams that lost weight exhibited significantly decreased amphetamine stimulated locomotion, while offspring of poly I:C treated dams without weight loss performed similarly to vehicle controls. Social isolation and increased maternal age predicted weight loss in response to poly I:C but not vehicle injection. In combination, these data identify environmental factors associated with the maternal response to immune challenge and functional outcome of offspring exposed to maternal immune activation. PMID:21255612

Modulation Formats for Beyond-100Gbps Ethernet Optical Links – A Review of Research

DEFF Research Database (Denmark)

Jensen, Jesper Bevensee; Iglesias Olmedo, Miguel; Tafur Monroy, Idelfonso

2013-01-01

The current increase in data-centers traffic and cloud-based services presents a formidable challenge for optical interconnects. We examine these challenges, and review recent breakthroughs in advanced modulation formats formats for intensity modulation - direct detection....

Predicting and detecting adverse drug reactions in old age: challenges and opportunities.

Science.gov (United States)

Mangoni, Arduino A

2012-05-01

Increased, often inappropriate, drug exposure, pharmacokinetic and pharmacodynamic changes, reduced homeostatic reserve and frailty increase the risk of adverse drug reactions (ADRs) in the older population, thereby imposing a significant public health burden. Predicting and diagnosing ADRs in old age presents significant challenges for the clinician, even when specific risk scoring systems are available. The picture is further compounded by the potential adverse impact of several drugs on more 'global' health indicators, for example, physical function and independence, and the fragmentation of care (e.g., increased number of treating doctors and care transitions) experienced by older patients during their clinical journey. The current knowledge of drug safety in old age is also curtailed by the lack of efficacy and safety data from pre-marketing studies. Moreover, little consideration is given to individual patients' experiences and reporting of specific ADRs, particularly in the presence of cognitive impairment. Pending additional data on these issues, the close review and monitoring of individual patients' drug prescribing, clinical status and biochemical parameters remain essential to predict and detect ADRs in old age. Recently developed strategies, for example, medication reconciliation and trigger tool methodology, have the potential for ADRs risk mitigation in this population. However, more information is required on their efficacy and applicability in different healthcare settings.

Predicting cyclohexane/water distribution coefficients for the SAMPL5 challenge using MOSCED and the SMD solvation model

Science.gov (United States)

Diaz-Rodriguez, Sebastian; Bozada, Samantha M.; Phifer, Jeremy R.; Paluch, Andrew S.

2016-11-01

We present blind predictions using the solubility parameter based method MOSCED submitted for the SAMPL5 challenge on calculating cyclohexane/water distribution coefficients at 298 K. Reference data to parameterize MOSCED was generated with knowledge only of chemical structure by performing solvation free energy calculations using electronic structure calculations in the SMD continuum solvent. To maintain simplicity and use only a single method, we approximate the distribution coefficient with the partition coefficient of the neutral species. Over the final SAMPL5 set of 53 compounds, we achieved an average unsigned error of 2.2± 0.2 log units (ranking 15 out of 62 entries), the correlation coefficient ( R) was 0.6± 0.1 (ranking 35), and 72± 6 % of the predictions had the correct sign (ranking 30). While used here to predict cyclohexane/water distribution coefficients at 298 K, MOSCED is broadly applicable, allowing one to predict temperature dependent infinite dilution activity coefficients in any solvent for which parameters exist, and provides a means by which an excess Gibbs free energy model may be parameterized to predict composition dependent phase-equilibrium.

Network discovery, characterization, and prediction : a grand challenge LDRD final report.

Energy Technology Data Exchange (ETDEWEB)

Kegelmeyer, W. Philip, Jr.

2010-11-01

This report is the final summation of Sandia's Grand Challenge LDRD project No.119351, 'Network Discovery, Characterization and Prediction' (the 'NGC') which ran from FY08 to FY10. The aim of the NGC, in a nutshell, was to research, develop, and evaluate relevant analysis capabilities that address adversarial networks. Unlike some Grand Challenge efforts, that ambition created cultural subgoals, as well as technical and programmatic ones, as the insistence on 'relevancy' required that the Sandia informatics research communities and the analyst user communities come to appreciate each others needs and capabilities in a very deep and concrete way. The NGC generated a number of technical, programmatic, and cultural advances, detailed in this report. There were new algorithmic insights and research that resulted in fifty-three refereed publications and presentations; this report concludes with an abstract-annotated bibliography pointing to them all. The NGC generated three substantial prototypes that not only achieved their intended goals of testing our algorithmic integration, but which also served as vehicles for customer education and program development. The NGC, as intended, has catalyzed future work in this domain; by the end it had already brought in, in new funding, as much funding as had been invested in it. Finally, the NGC knit together previously disparate research staff and user expertise in a fashion that not only addressed our immediate research goals, but which promises to have created an enduring cultural legacy of mutual understanding, in service of Sandia's national security responsibilities in cybersecurity and counter proliferation.

Challenges of implementing economic model predictive control strategy for buildings interacting with smart energy systems

DEFF Research Database (Denmark)

Zong, Yi; Böning, Georg Martin; Santos, Rui Mirra

2016-01-01

) strategy for energy management in smart buildings, which can act as active users interacting with smart energy systems. The challenges encountered during the implementation of EMPC for active demand side management are investigated in detail in this paper. A pilot testing study shows energy savings......When there is a high penetration of renewables in the energy system, it requires proactive control of large numbers of distributed demand response resources to maintain the system’s reliability and improve its operational economics. This paper presents the Economic Model Predictive Control (EMPC...

Impact of over-the-top broadcast applications of Racer® on onion weed control

Science.gov (United States)

The weed control challenges for onion production are formidable; however, these challenges are even greater for those considering organic crop production. Organic onion producers need organic herbicides that can effectively provide post-emergent weed control. Racer (registered trademark) is a poten...

Vinegar as a broadcast herbicide for spring-transplanted onions

Science.gov (United States)

The weed control challenges for onion production are formidable; however, these challenges are even greater for those considering organic crop production. Organic onion producers need additional organic herbicides that can effectively provide post-emergent weed control. Field research was conducted...

Grand challenges in developing a predictive understanding of global fire dynamics

Science.gov (United States)

Randerson, J. T.; Chen, Y.; Wiggins, E. B.; Andela, N.; Morton, D. C.; Veraverbeke, S.; van der Werf, G.

2017-12-01

High quality satellite observations of burned area and fire thermal anomalies over the past two decades have transformed our understanding of climate, ecosystem, and human controls on the spatial and temporal distribution of landscape fires. The satellite observations provide evidence for a rapid and widespread loss of fire from grassland and savanna ecosystems worldwide. Continued expansion of industrial agriculture suggests that observed declines in global burned area are likely to continue in future decades, with profound consequences for ecosystem function and the habitat of many endangered species. Satellite time series also highlight the importance of El Niño-Southern Oscillation and other climate modes as drivers of interannual variability. In many regions, lead times between climate indices and fire activity are considerable, enabling the development of early warning prediction systems for fire season severity. With the recent availability of high-resolution observations from Suomi NPP, Landsat 8, and Sentinel 2, the field of global fire ecology is poised to make even more significant breakthroughs over the next decade. With these new observations, it may be possible to reduce uncertainties in the spatial pattern of burned area by several fold. It is difficult to overstate the importance of these new data constraints for improving our understanding of fire impacts on human health and radiative forcing of climate change. A key research challenge in this context is to understand how the loss of global burned area will affect magnitude of the terrestrial carbon sink and trends in atmospheric composition. Advances in prognostic fire modeling will require new approaches linking agriculture with landscape fire dynamics. A critical need in this context is the development of predictive models of road networks and other drivers of land fragmentation, and a closer integration of fragmentation information with algorithms predicting fire spread. Concurrently, a better

Can oral challenge with balsam of Peru predict possible benefit from a low-balsam diet?

Science.gov (United States)

Veien, N K; Hattel, T; Laurberg, G

1996-06-01

Previous studies have shown that some patients sensitive to balsams and/or fragrances obtain long-term benefits by following a low-balsam diet, whereas others do not. This study was performed to determine whether a low-balsam diet was a helpful long-term treatment for selected patients sensitive to balsam of Peru and/or a perfume mixture and to determine whether oral challenge with balsam of Peru could predict which balsam-sensitive patients might benefit from a reduction in balsam intake. Questionnaires were sent to 46 patients with positive patch test results to balsam of Peru and/or a perfume mixture and chronic dermatitis of a morphology consistent with endogenous dermatitis who had experienced improvement after 1 to 2 months on a diet intended to reduce the intake of balsams. The questionnaires were mailed 1 to 3 years after the initiation of the diet treatment to inquire about a possible long-term benefit of the diet. Twenty-eight of the 46 patients stated in the questionnaire that they had long-term benefits from the diet treatment. These included 16 of 22 patients who had reacted to a placebo-controlled oral challenge with 1 g balsam of Peru, 3 of 10 who had no reaction or a placebo reaction to the oral challenge, and 9 of 14 who had not been challenged orally. The efficacy of the diet treatment was not correlated to whether the patient had patch test reactivity to either balsam of Peru, the perfume mixture, or both substances. Food items most commonly mentioned by patients as causing aggravation of their symptoms on at least three different occasions were wine, candy, chocolate, cinnamon, curry, citrus fruit, and flavorings. In its present form, the oral challenge procedure with balsam of Peru offers only limited assistance in selecting patients who are likely to benefit from diet treatment.

Herb-drug interactions: challenges and opportunities for improved predictions.

Science.gov (United States)

Brantley, Scott J; Argikar, Aneesh A; Lin, Yvonne S; Nagar, Swati; Paine, Mary F

2014-03-01

Supported by a usage history that predates written records and the perception that "natural" ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb-drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb-drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb-drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb-drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens.

Ensemble models on palaeoclimate to predict India's groundwater challenge

Directory of Open Access Journals (Sweden)

Partha Sarathi Datta

2013-09-01

Full Text Available In many parts of the world, freshwater crisis is largely due to increasing water consumption and pollution by rapidly growing population and aspirations for economic development, but, ascribed usually to the climate. However, limited understanding and knowledge gaps in the factors controlling climate and uncertainties in the climate models are unable to assess the probable impacts on water availability in tropical regions. In this context, review of ensemble models on δ18O and δD in rainfall and groundwater, 3H- and 14C- ages of groundwater and 14C- age of lakes sediments helped to reconstruct palaeoclimate and long-term recharge in the North-west India; and predict future groundwater challenge. The annual mean temperature trend indicates both warming/cooling in different parts of India in the past and during 1901–2010. Neither the GCMs (Global Climate Models nor the observational record indicates any significant change/increase in temperature and rainfall over the last century, and climate change during the last 1200 yrs BP. In much of the North-West region, deep groundwater renewal occurred from past humid climate, and shallow groundwater renewal from limited modern recharge over the past decades. To make water management to be more responsive to climate change, the gaps in the science of climate change need to be bridged.

Dynamic nature of epigenetic patterns observed during the Mars 520-d mission simulation

Data.gov (United States)

National Aeronautics and Space Administration — Interplanetary human spaceflight represents a formidable medical challenge but also provides a unique platform for investigating human adaptation to extreme...

OBSERVATIONS (NEW AND OLD ON THE ARMED FORCES IN THE THE ATLANTIC WORLD: CURRENT AND FUTURE THREATS AND CHALLENGES IN HISTORICAL PERSPECTIVE

Directory of Open Access Journals (Sweden)

FREDERICK M. NUNN

2017-12-01

Full Text Available Confrontations with threats and challenges to the existence of governments and societies have marked the flow of time throughout history. Present and future threats associated, as they are, with historical and traditional challenge are, in many places, of such complexity that they already require new forms of relations between civilian authorities and the leadership of professional institutions dedicated to defense and security. These new relations will have to be based on an understanding of the processes of democratization and globalization as much as on the complex nature of conflicts that affect the security, sovereignty and stability of 21st century nation-states. As formidable as they appear, these threats and challenges may be considered current reiterations of others that mark our histories. In the Atlantic World, nowhere more so than in Latin America, the literature of the military profession has always demonstrated the recognition of lessons of the past applied to problems of the presentation aid in preparation for the future. A principal reason for this is that said literature is in and of itself the product of civil-military collaborations.

Speeding up GW Calculations to Meet the Challenge of Large Scale Quasiparticle Predictions.

Science.gov (United States)

Gao, Weiwei; Xia, Weiyi; Gao, Xiang; Zhang, Peihong

2016-11-11

Although the GW approximation is recognized as one of the most accurate theories for predicting materials excited states properties, scaling up conventional GW calculations for large systems remains a major challenge. We present a powerful and simple-to-implement method that can drastically accelerate fully converged GW calculations for large systems, enabling fast and accurate quasiparticle calculations for complex materials systems. We demonstrate the performance of this new method by presenting the results for ZnO and MgO supercells. A speed-up factor of nearly two orders of magnitude is achieved for a system containing 256 atoms (1024 valence electrons) with a negligibly small numerical error of ±0.03 eV. Finally, we discuss the application of our method to the GW calculations for 2D materials.

Can an earthquake prediction and warning system be developed?

Science.gov (United States)

N.N, Ambraseys

1990-01-01

Natural disasters affect countries large and small, rich, or poor, whatever their political persuasion. The toll exacted by natural calamities each year drains the human and economic resources of every nation and stands as one of the formidable barriers to national, regional, and world development.

Herb–Drug Interactions: Challenges and Opportunities for Improved Predictions

Science.gov (United States)

Brantley, Scott J.; Argikar, Aneesh A.; Lin, Yvonne S.; Nagar, Swati

2014-01-01

Supported by a usage history that predates written records and the perception that “natural” ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb–drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb–drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb–drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb–drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens. PMID:24335390

Localizing Global Medicine: Challenges and Opportunities in Cervical Screening in an Indigenous Community in Ecuador.

Science.gov (United States)

Nugus, Peter; Désalliers, Julie; Morales, Juana; Graves, Lisa; Evans, Andrea; Macaulay, Ann C

2018-04-01

This participatory research study examines the tensions and opportunities in accessing allopathic medicine, or biomedicine, in the context of a cervical cancer screening program in a rural indigenous community of Northern Ecuador. Focusing on the influence of social networks, the article extends research on "re-appropriation" of biomedicine. It does so by recognizing two competing tensions expressed through social interactions: suspicion of allopathic medicine and the desire to maximize one's health. Semistructured individual interviews and focus groups were conducted with 28 women who had previously participated in a government-sponsored cervical screening program. From inductive thematic analysis, the article traces these women's active agency in navigating coherent paths of health. Despite drawing on social networks to overcome formidable challenges, the participants faced enduring system obstacles-the organizational effects of the networks of allopathic medicine. Such obstacles need to be understood to reconcile competing knowledge systems and improve health care access in underresourced communities.

A Data mining Technique for Analyzing and Predicting the success of Movie

Science.gov (United States)

Meenakshi, K.; Maragatham, G.; Agarwal, Neha; Ghosh, Ishitha

2018-04-01

In real world prediction models and mechanisms can be used to predict the success of a movie. The proposed work aims to develop a system based upon data mining techniques that may help in predicting the success of a movie in advance thereby reducing certain level of uncertainty. An attempt is made to predict the past as well as the future of movie for the purpose of business certainty or simply a theoretical condition in which decision making [the success of the movie] is without risk, because the decision maker [movie makers and stake holders] has all the information about the exact outcome of the decision, before he or she makes the decision [release of the movie]. With over two million spectators a day and films exported to over 100 countries, the impact of Bollywood film industry is formidable We gather a series of interesting facts and relationships using a variety of data mining techniques. In particular, we concentrate on attributes relevant to the success prediction of movies, such as whether any particular actors or actresses are likely to help a movie to succeed. The paper additionally reports on the techniques used, giving their implementation and utility. Additionally, we found some attention-grabbing facts, such as the budget of a movie isn't any indication of how well-rated it'll be, there's a downward trend within the quality of films over time, and also the director and actors/actresses involved in the movie.

The Challenges of Developing a Framework for Global Water Cycle Monitoring and Prediction (Alfred Wegener Medal Lecture)

Science.gov (United States)

Wood, Eric F.

2014-05-01

The Global Earth Observation System of Systems (GEOSS) Water Strategy ("From Observations to Decisions") recognizes that "water is essential for ensuring food and energy security, for facilitating poverty reduction and health security, and for the maintenance of ecosystems and biodiversity", and that water cycle data and observations are critical for improved water management and water security - especially in less developed regions. The GEOSS Water Strategy has articulated a number of goals for improved water management, including flood and drought preparedness, that include: (i) facilitating the use of Earth Observations for water cycle observations; (ii) facilitating the acquisition, processing, and distribution of data products needed for effective management; (iii) providing expertise, information systems, and datasets to the global, regional, and national water communities. There are several challenges that must be met to advance our capability to provide near real-time water cycle monitoring, early warning of hydrological hazards (floods and droughts) and risk assessment under climate change, regionally and globally. Current approaches to monitoring and predicting hydrological hazards are limited in many parts of the world, and especially in developing countries where national capacity is limited and monitoring networks are inadequate. This presentation describes the developments at Princeton University towards a seamless monitoring and prediction framework at all time scales that allows for consistent assessment of water variability from historic to current conditions, and from seasonal and decadal predictions to climate change projections. At the center of the framework is an experimental, global water cycle monitoring and seasonal forecast system that has evolved out of regional and continental systems for the US and Africa. The system is based on land surface hydrological modeling that is driven by satellite remote sensing precipitation to predict

The Chavez Corollary: The New Hegemony on the Block

National Research Council Canada - National Science Library

McLaughlin, James A

2008-01-01

.... With its huge oil revenues, Chavez's Venezuela has the economic collateral to be a formidable challenge to the United States in the region, which could potentially threaten U.S. national interests...

CDOCKER and lambda λ -dynamics for prospective prediction in D3R Grand Challenge 2

Science.gov (United States)

Ding, Xinqiang; Hayes, Ryan L.; Vilseck, Jonah Z.; Charles, Murchtricia K.; Brooks, Charles L.

2018-01-01

The opportunity to prospectively predict ligand bound poses and free energies of binding to the Farnesoid X Receptor in the D3R Grand Challenge 2 provided a useful exercise to evaluate CHARMM based docking (CDOCKER) and λ-dynamics methodologies for use in "real-world" applications in computer aided drug design. In addition to measuring their current performance, several recent methodological developments have been analyzed retrospectively to highlight best procedural practices in future applications. For pose prediction with CDOCKER, when the protein structure used for rigid receptor docking was close to the crystallographic holo structure, reliable poses were obtained. Benzimidazoles, with a known holo receptor structure, were successfully docked with an average RMSD of 0.97 Å. Other non-benzimidazole ligands displayed less accuracy largely because the receptor structures we chose for docking were too different from the experimental holo structures. However, retrospective analysis has shown that when these ligands were re-docked into their holo structures, the average RMSD dropped to 1.18 Å for all ligands. When sulfonamides and spiros were docked with the apo structure, which agrees more with their holo structure than the structures we chose, five out of six ligands were correctly docked. These docking results emphasize the need for flexible receptor docking approaches. For λ-dynamics techniques, including multisite λ-dynamics (MSλD), reasonable agreement with experiment was observed for the 33 ligands investigated; root mean square errors of 2.08 and 1.67 kcal/mol were obtained for free energy sets 1 and 2, respectively. Retrospectively, soft-core potentials, adaptive landscape flattening, and biasing potential replica exchange (BP-REX) algorithms were critical to model large substituent perturbations with sufficient precision and within restrictive timeframes, such as was required with participation in Grand Challenge 2. These developments, their

AFRREV STECH, Vol. 3(2) May, 2014

African Journals Online (AJOL)

Toshiba

2014-05-07

May 7, 2014 ... Department of Basic Sciences (Microbiology option). Benson Idahosa ..... a formidable challenge to commercial fresh fruit product operations from the farm to retail and .... Chicago‟s Restaurant and. Entertainment. Guide.

Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R Grand Challenge 2015.

Science.gov (United States)

Deng, Nanjie; Flynn, William F; Xia, Junchao; Vijayan, R S K; Zhang, Baofeng; He, Peng; Mentes, Ahmet; Gallicchio, Emilio; Levy, Ronald M

2016-09-01

We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein-ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate

A Grand Challenge for CMOS Scaling: Alternate Gate Dielectrics

Science.gov (United States)

Wallace, Robert M.

2001-03-01

Many materials systems are currently under consideration as potential replacements for SiO2 as the gate dielectric material for sub-0.13 um complementary metal oxide semiconductor (CMOS) technology. The prospect of replacing SiO2 is a formidable task because the alternate gate dielectric must provide many properties that are, at a minimum, comparable to those of SiO2 yet with a much higher permittivity. A systematic examination of the required performance of gate dielectrics suggests that the key properties to consider in the selection an alternative gate dielectric candidate are (a) permittivity, band gap and band alignment to silicon, (b) thermodynamic stability, (c) film morphology, (d) interface quality, (e) compatibility with the current or expected materials to be used in processing for CMOS devices, (f) process compatibility, and (g) reliability. Many dielectrics appear favorable in some of these areas, but very few materials are promising with respect to all of these guidelines. We will review the performance requirements for materials associated with CMOS scaling, the challenges associated with these requirements, and the state-of-the-art in current research for alternate gate dielectrics. The requirements for process integration compatibility are remarkably demanding, and any serious candidates will emerge only through continued, intensive investigation.

The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt

Directory of Open Access Journals (Sweden)

Cécile Viboud

2018-03-01

Full Text Available Infectious disease forecasting is gaining traction in the public health community; however, limited systematic comparisons of model performance exist. Here we present the results of a synthetic forecasting challenge inspired by the West African Ebola crisis in 2014–2015 and involving 16 international academic teams and US government agencies, and compare the predictive performance of 8 independent modeling approaches. Challenge participants were invited to predict 140 epidemiological targets across 5 different time points of 4 synthetic Ebola outbreaks, each involving different levels of interventions and “fog of war” in outbreak data made available for predictions. Prediction targets included 1–4 week-ahead case incidences, outbreak size, peak timing, and several natural history parameters. With respect to weekly case incidence targets, ensemble predictions based on a Bayesian average of the 8 participating models outperformed any individual model and did substantially better than a null auto-regressive model. There was no relationship between model complexity and prediction accuracy; however, the top performing models for short-term weekly incidence were reactive models with few parameters, fitted to a short and recent part of the outbreak. Individual model outputs and ensemble predictions improved with data accuracy and availability; by the second time point, just before the peak of the epidemic, estimates of final size were within 20% of the target. The 4th challenge scenario − mirroring an uncontrolled Ebola outbreak with substantial data reporting noise − was poorly predicted by all modeling teams. Overall, this synthetic forecasting challenge provided a deep understanding of model performance under controlled data and epidemiological conditions. We recommend such “peace time” forecasting challenges as key elements to improve coordination and inspire collaboration between modeling groups ahead of the next pandemic threat

Predicting the affinity of Farnesoid X Receptor ligands through a hierarchical ranking protocol: a D3R Grand Challenge 2 case study

Science.gov (United States)

Réau, Manon; Langenfeld, Florent; Zagury, Jean-François; Montes, Matthieu

2018-01-01

The Drug Design Data Resource (D3R) Grand Challenges are blind contests organized to assess the state-of-the-art methods accuracy in predicting binding modes and relative binding free energies of experimentally validated ligands for a given target. The second stage of the D3R Grand Challenge 2 (GC2) was focused on ranking 102 compounds according to their predicted affinity for Farnesoid X Receptor. In this task, our workflow was ranked 5th out of the 77 submissions in the structure-based category. Our strategy consisted in (1) a combination of molecular docking using AutoDock 4.2 and manual edition of available structures for binding poses generation using SeeSAR, (2) the use of HYDE scoring for pose selection, and (3) a hierarchical ranking using HYDE and MM/GBSA. In this report, we detail our pose generation and ligands ranking protocols and provide guidelines to be used in a prospective computer aided drug design program.

Response to challenging dose of x-rays as a predictive assay for molecular epidemiology

International Nuclear Information System (INIS)

Cebulska-Wasilewska, A.

2003-01-01

Human biomonitoring, as a tool to identify or quantify the potential risk from genotoxic exposures, has gained increasing interest especially in the areas of cancer risk assessment and diseases treatment. Chromosome aberrations resulting from direct DNA breakage or from inhibition of DNA repair or synthesis, measured in peripheral blood lymphocytes have been used in occupational health surveillance programs in order to assess risks from exposures. Many results in our human monitoring studies have shown influence of the environmental or occupational exposure on the cytogenetic damage detected in lymphocytes, confirming both, the association with adverse health outcome and the influence of life style related confounding factors. Susceptibility to the environmental agent actions was also evaluated in lymphocytes in the studies of variation between responses to the challenging dose of UV or X-rays followed by the evaluation of the repair capacity of the DNA damage induced by a challenging dose. The induced and residual DNA damage was analyzed with the use of SCGE assay. Susceptibility and repair capacities of healthy donors and cancer patients were compared. Studies have shown a good correlation between DNA damage induced in vivo or in vitro and cytogenetic measures. Results from studies on susceptibilities and repair competence performed in occupationally exposed and unexposed 475 healthy donors and patients with diagnosed cancer are discussed. Significantly lower efficiency of repair process was observed in cancer patients. The possible effects on repair competency of various occupational exposures and influence of the diet and other confounding factors is shown. Although in our preliminary studies comet assay failed to detect DNA damage repair disorders in a teratoma immature infant, though, prospective use of a challenging dose of radiation combined with the comet assay as a predictive assay is suggested and limitation discussed

Improving women's lives

International Development Research Centre (IDRC) Digital Library (Canada)

IDRC has supported poor women in develop- ing countries ... and business management. Thanks to ... to local levels has changed the face of gov- ... Although formidable challenges ... Technology helps Asian women balance family and work.

Theory and analysis of soft x-ray laser experiments

International Nuclear Information System (INIS)

Whitten, B.L.; Hazi, A.U.

1985-10-01

The atomic modeling of soft x-ray laser schemes presents a formidable challenge to the theorists - a challenge magnified by the recent successful experiments. A complex plasma environment with many ion species present must be simulated. Effects such as turbulence, time dependence, and radiation transport, which are very difficult to model accurately, may be important. We shall describe our efforts to model the recently demonstrated soft x-ray laser in collisionally pumped neon-like selenium, with emphasis on the ionization balance and excited state kinetics. The relative importance of various atomic processes, such as collisional excitation and dielectronic recombination, on the inversion kinetics will be demonstrated. We shall compare our models with experimental results and evaluate the success of this technique in predicting and analyzing the results of x-ray laser experiments. 22 refs., 5 figs., 3 tabs

Addressing the empathy deficit: beliefs about the malleability of empathy predict effortful responses when empathy is challenging.

Science.gov (United States)

Schumann, Karina; Zaki, Jamil; Dweck, Carol S

2014-09-01

Empathy is often thought to occur automatically. Yet, empathy frequently breaks down when it is difficult or distressing to relate to people in need, suggesting that empathy is often not felt reflexively. Indeed, the United States as a whole is said to be displaying an empathy deficit. When and why does empathy break down, and what predicts whether people will exert effort to experience empathy in challenging contexts? Across 7 studies, we found that people who held a malleable mindset about empathy (believing empathy can be developed) expended greater empathic effort in challenging contexts than did people who held a fixed theory (believing empathy cannot be developed). Specifically, a malleable theory of empathy--whether measured or experimentally induced--promoted (a) more self-reported effort to feel empathy when it is challenging (Study 1); (b) more empathically effortful responses to a person with conflicting views on personally important sociopolitical issues (Studies 2-4); (c) more time spent listening to the emotional personal story of a racial outgroup member (Study 5); and (d) greater willingness to help cancer patients in effortful, face-to-face ways (Study 6). Study 7 revealed a possible reason for this greater empathic effort in challenging contexts: a stronger interest in improving one's empathy. Together, these data suggest that people's mindsets powerfully affect whether they exert effort to empathize when it is needed most, and these data may represent a point of leverage in increasing empathic behaviors on a broad scale. 2014 APA, all rights reserved

Gene expression prediction by soft integration and the elastic net-best performance of the DREAM3 gene expression challenge.

Directory of Open Access Journals (Sweden)

Mika Gustafsson

Full Text Available BACKGROUND: To predict gene expressions is an important endeavour within computational systems biology. It can both be a way to explore how drugs affect the system, as well as providing a framework for finding which genes are interrelated in a certain process. A practical problem, however, is how to assess and discriminate among the various algorithms which have been developed for this purpose. Therefore, the DREAM project invited the year 2008 to a challenge for predicting gene expression values, and here we present the algorithm with best performance. METHODOLOGY/PRINCIPAL FINDINGS: We develop an algorithm by exploring various regression schemes with different model selection procedures. It turns out that the most effective scheme is based on least squares, with a penalty term of a recently developed form called the "elastic net". Key components in the algorithm are the integration of expression data from other experimental conditions than those presented for the challenge and the utilization of transcription factor binding data for guiding the inference process towards known interactions. Of importance is also a cross-validation procedure where each form of external data is used only to the extent it increases the expected performance. CONCLUSIONS/SIGNIFICANCE: Our algorithm proves both the possibility to extract information from large-scale expression data concerning prediction of gene levels, as well as the benefits of integrating different data sources for improving the inference. We believe the former is an important message to those still hesitating on the possibilities for computational approaches, while the latter is part of an important way forward for the future development of the field of computational systems biology.

Cultured meat from stem cells: challenges and prospects.

Science.gov (United States)

Post, Mark J

2012-11-01

As one of the alternatives for livestock meat production, in vitro culturing of meat is currently studied. The generation of bio-artificial muscles from satellite cells has been ongoing for about 15 years, but has never been used for generation of meat, while it already is a great source of animal protein. In order to serve as a credible alternative to livestock meat, lab or factory grown meat should be efficiently produced and should mimic meat in all of its physical sensations, such as visual appearance, smell, texture and of course, taste. This is a formidable challenge even though all the technologies to create skeletal muscle and fat tissue have been developed and tested. The efficient culture of meat will primarily depend on culture conditions such as the source of medium and its composition. Protein synthesis by cultured skeletal muscle cells should further be maximized by finding the optimal combination of biochemical and physical conditions for the cells. Many of these variables are known, but their interactions are numerous and need to be mapped. This involves a systematic, if not systems, approach. Given the urgency of the problems that the meat industry is facing, this endeavor is worth undertaking. As an additional benefit, culturing meat may provide opportunities for production of novel and healthier products. Copyright © 2012 Elsevier Ltd. All rights reserved.

Environmental Governance Challenges in Kiribati : An Agenda for Legal and Policy Responses

Directory of Open Access Journals (Sweden)

Dejo Olowu

2007-12-01

Full Text Available Since the global notion of environmental governance is principally about how to achieve the goals of environmental conservation and sustainable development, analysing approaches to environmental governance invariably requires critical study of the policies and structures in place that determine how power is exercised and how environmental decisions are made not only in the abstract context of internationalism but with particular regard to national situations. This essay examines the legal and policy frameworks regulating environmental protection and the conservation of biodiversity within the broader goal of effective environmental governance in Kiribati . Acknowledging that Kiribati encounters formidable challenges in institutional, normative and policy terms, this essay particularly deals with the issue of pollution and its long- and short-term implications for this nation of many atolls. While highlighting the existence of significant treaties, municipal laws and diverse policy mechanisms, this essay identifies gaps and weaknesses, making suggestions for their reform and enhancement. Recognising that the path to the future lies in the synergy of initiatives and inputs from the government, the people and all other stakeholders in the environmental well-being of Kiribati, this essay proffers some viable trajectories for strategic responses.

The bureaucratization of war: moral challenges exemplified by the covert lethal drone

Directory of Open Access Journals (Sweden)

Richard Adams

2013-12-01

Full Text Available This article interrogates the bureaucratization of war, incarnate in the covert lethal drone. Bureaucracies are criticized typically for their complexity, inefficiency, and inflexibility. This article is concerned with their moral indifference. It explores killing, which is so highly administered, so morally remote, and of such scale, that we acknowledge a covert lethal program. This is a bureaucratized program of assassination in contravention of critical human rights. In this article, this program is seen to compromise the advance of global justice. Moreover, the bureaucratization of lethal force is seen to dissolve democratic ideals from within. The bureaucracy isolates the citizens from lethal force applied in their name. People are killed, in the name of the State, but without conspicuous justification, or judicial review, and without informed public debate. This article gives an account of the risk associated with the bureaucratization of the State's lethal power. Exemplified by the covert drone, this is power with formidable reach. It is power as well, which requires great moral sensitivity. Considering the drone program, this article identifies challenges, which will become more prominent and pressing, as technology advances.

Rapid stabilization of thawing soils For enhanced vehicle mobility: a field demonstration project

Science.gov (United States)

1999-02-01

Thawing soil presents a formidable challenge for vehicle operations cross-country and on unsurfaced roads. To mitigate the problem, a variety of stabilization techniques were evaluated for their suitability for rapid employment to enhance military ve...

Organizing the Canadian nuclear industry to meet the challenge

International Nuclear Information System (INIS)

Lortie, Pierre.

1983-06-01

The CANDU reactor is struggling for a share of the dwindling reactor market against formidable and well-established competition. The Canadian nuclear industry has historically depended upon two crown corporations, Atomic Energy of Canada Ltd. and Ontario Hydro, which have taken the lead in designing and engineering the reactor. Crown corporations are not notably successful in marketing, however, and the time has come for the industry to organize itself in preparation for an aggressive export drive

The challenges of risk society for impact assessment

DEFF Research Database (Denmark)

Larsen, Sanne Vammen

2017-01-01

, the challenge of delivering assessments and predictions and the challenge of handling differences of opinion and debate. Through a case example of integration of climate change in strategic environmental assessment, the paper uses empirical evidence from a survey and a series of interviews to carry out......This paper takes its point of departure in Ulrich Beck’s theory of risk society and the aspects that characterise this society. The paper puts forward a hypothe- sis, on which theoretical challenges the characteristics of risk society pose to impact assessment as a decision support tool; namely...... a preliminary discussion of how the theoretical challenges are reflected in practice. The case study results show that the challenge of delivering assessments and predictions in a risk society is reflected in the current state of practice, while the challenge of handling differences of opinion and debate...

An examination of acquiescent response styles in cross-cultural research

NARCIS (Netherlands)

Fischer, R.; Fontaine, J.R.J.; van de Vijver, F.J.R.; van Hemert, D.A.; Gari, A.; Mylonas, K.

2009-01-01

Response styles constitute a formidable challenge for cross-cultural research. In this article, three different response styles are discussed (acquiescence, extremity scoring, and social desirability). Acquiescence responding (ARS) is then integrated into a larger classical test theoretical

A New Vision of Professional Development for University Teachers in Libya "It's Not an Event, It Is a Process"

Science.gov (United States)

Suwaed, Hameda; Rahouma, Wesam

2015-01-01

Being a university teacher in the Libya is most of the time described as a challenge. In the case of the current unstable situation in Libya, the task is formidable in many cases. This paper investigates the challenges encountered by Alzawia university teachers in four colleges. It attempts to answer the following questions: what are the…

Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation: application to the D3R Grand Challenge 2

Science.gov (United States)

Rifai, Eko Aditya; van Dijk, Marc; Vermeulen, Nico P. E.; Geerke, Daan P.

2018-01-01

Computational protein binding affinity prediction can play an important role in drug research but performing efficient and accurate binding free energy calculations is still challenging. In the context of phase 2 of the Drug Design Data Resource (D3R) Grand Challenge 2 we used our automated eTOX ALLIES approach to apply the (iterative) linear interaction energy (LIE) method and we evaluated its performance in predicting binding affinities for farnesoid X receptor (FXR) agonists. Efficiency was obtained by our pre-calibrated LIE models and molecular dynamics (MD) simulations at the nanosecond scale, while predictive accuracy was obtained for a small subset of compounds. Using our recently introduced reliability estimation metrics, we could classify predictions with higher confidence by featuring an applicability domain (AD) analysis in combination with protein-ligand interaction profiling. The outcomes of and agreement between our AD and interaction-profile analyses to distinguish and rationalize the performance of our predictions highlighted the relevance of sufficiently exploring protein-ligand interactions during training and it demonstrated the possibility to quantitatively and efficiently evaluate if this is achieved by using simulation data only.

Ensemble method for dengue prediction.

Science.gov (United States)

Buczak, Anna L; Baugher, Benjamin; Moniz, Linda J; Bagley, Thomas; Babin, Steven M; Guven, Erhan

2018-01-01

In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.e., week in which the maximum weekly number of cases occurred); and 3) total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date. Our approach used ensemble models created by combining three disparate types of component models: 1) two-dimensional Method of Analogues models incorporating both dengue and climate data; 2) additive seasonal Holt-Winters models with and without wavelet smoothing; and 3) simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations. Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week. The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.

Ensemble method for dengue prediction.

Directory of Open Access Journals (Sweden)

Anna L Buczak

Full Text Available In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico during four dengue seasons: 1 peak height (i.e., maximum weekly number of cases during a transmission season; 2 peak week (i.e., week in which the maximum weekly number of cases occurred; and 3 total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date.Our approach used ensemble models created by combining three disparate types of component models: 1 two-dimensional Method of Analogues models incorporating both dengue and climate data; 2 additive seasonal Holt-Winters models with and without wavelet smoothing; and 3 simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations.Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week.The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.

Challenges in pKa Predictions for Proteins: The case of Asp213 in Human Proteinase 3

Science.gov (United States)

Hajjar, Eric; Dejaegere, Annick; Reuter, Nathalie

2009-09-01

Knowledge of the protonation states of the ionizable residues in an enzyme is a prerequisite to an accurate description of its structure and mechanism. In practice, the use of the inappropriate protonation state for an amino acid in a molecular modeling computation (e.g., molecular dynamics simulation) is likely to lead to unrealistic results. Although methods using solvers of the linearized Poisson-Boltzmann equation have proven to yield accurate pKa predictions, they bear a number of limitations. They are quite demanding in terms of computational power and are sensitive to representation of the charges and their position (force field and protein conformation). Moreover they depend on the choice of a dielectric constant for the protein interior. In this manuscript, we describe the difficulties met when trying to predict the protonation state of a buried amino acid, located in a protein for which very little biochemical data is available. Such a case is highly representative of the challenges faced in theoretical biology studies. Proteinase 3 (PR3) is an enzyme involved in proteolytic events associated with inflammation. It is a potential target in the development of new anti-inflammatory therapeutic strategies. We report the results of pKa predictions of the aspartic acid 213 of PR3 with a FDPB solver. We probed the influence of the choice of the dielectric constant for the protein interior ɛp and the benefits of conformational sampling by molecular dynamics (MD) on the pKa prediction of this carboxylate group. Using only the FDPB calculations, we could not conclude on the protonation state of Asp213. MD simulations confronted to knowledge of the ligand-binding and reaction mechanism led us to decide on a protonated form of this aspartic acid. We also demonstrate that the use of the wrong protonation state leads to an unreliable structural model for PR3. pKa prediction with a fast empirical method yielded a pKa of 8.4 for Asp213, which is in agreement with our

Remote C−H Activation of Quinolines through Copper-Catalyzed Radical Cross-Coupling

KAUST Repository

Xu, Jun; Shen, Chao; Zhu, Xiaolei; Zhang, Pengfei; Ajitha, Manjaly John; Huang, Kuo-Wei; An, Zhongfu; Liu, Xiaogang

2016-01-01

Achieving site selectivity in carbon-hydrogen (C-H) functionalization reactions is a formidable challenge in organic chemistry. Herein, we report a novel approach to activating remote C-H bonds at the C5 position of 8-aminoquinoline through copper

Modules, networks and systems medicine for understanding disease and aiding diagnosis

DEFF Research Database (Denmark)

Gustafsson, Mika; Nestor, Colm E.; Zhang, Huan

2014-01-01

Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics dat...

Modules, networks and systems medicine for understanding disease and aiding diagnosis

NARCIS (Netherlands)

Gustafsson, Mika; Nestor, Colm E.; Zhang, Huan; Barabási, Albert-László; Baranzini, Sergio; Brunak, Sören; Chung, Kian Fan; Federoff, Howard J.; Gavin, Anne-Claude; Meehan, Richard R.; Picotti, Paola; Pujana, Miguel Àngel; Rajewsky, Nikolaus; Smith, Kenneth Gc; Sterk, Peter J.; Villoslada, Pablo; Benson, Mikael

2014-01-01

Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics data

2008 The Menace of HIV/AIDS

African Journals Online (AJOL)

Gbaje E.S

leave school earlier and work to support other siblings. The illiteracy level of ... claimed and still claiming men and women in .... HIV/AIDS. This disease remains a formidable barrier in .... to face the challenge posed by this HIV/AIDS menace ...

Guidelines for the use and interpretation of assays for monitoring cell death in higher eukaryotes

NARCIS (Netherlands)

Galluzzi, L.; Aaronson, S. A.; Abrams, J.; Alnemri, E. S.; Andrews, D. W.; Baehrecke, E. H.; Bazan, N. G.; Blagosklonny, M. V.; Blomgren, K.; Borner, C.; Bredesen, D. E.; Brenner, C.; Castedo, M.; Cidlowski, J. A.; Ciechanover, A.; Cohen, G. M.; de Laurenzi, V.; de Maria, R.; Deshmukh, M.; Dynlacht, B. D.; El-Deiry, W. S.; Flavell, R. A.; Fulda, S.; Garrido, C.; Golstein, P.; Gougeon, M.-L.; Green, D. R.; Gronemeyer, H.; Hajnóczky, G.; Hardwick, J. M.; Hengartner, M. O.; Ichijo, H.; Jäättelä, M.; Kepp, O.; Kimchi, A.; Klionsky, D. J.; Knight, R. A.; Kornbluth, S.; Kumar, S.; Levine, B.; Lipton, S. A.; Lugli, E.; Madeo, F.; Malorni, W.; Marine, J.-Cw; Martin, S. J.; Medema, J. P.; Mehlen, P.; Melino, G.; Moll, U. M.; Morselli, E.; Nagata, S.; Nicholson, D. W.; Nicotera, P.; Nuñez, G.; Oren, M.; Penninger, J.; Pervaiz, S.; Peter, M. E.; Piacentini, M.; Prehn, J. H. M.; Puthalakath, H.; Rabinovich, G. A.; Rizzuto, R.; Rodrigues, C. M. P.; Rubinsztein, D. C.; Rudel, T.; Scorrano, L.; Simon, H.-U.; Steller, H.; Tschopp, J.; Tsujimoto, Y.; Vandenabeele, P.; Vitale, I.; Vousden, K. H.; Youle, R. J.; Yuan, J.; Zhivotovsky, B.; Kroemer, G.

2009-01-01

Cell death is essential for a plethora of physiological processes, and its deregulation characterizes numerous human diseases. Thus, the in-depth investigation of cell death and its mechanisms constitutes a formidable challenge for fundamental and applied biomedical research, and has tremendous

Pathways to Structure-Property Relationships of Peptide-Materials Interfaces: Challenges in Predicting Molecular Structures.

Science.gov (United States)

Walsh, Tiffany R

2017-07-18

challenges in their successful application to model the biotic-abiotic interface, related to several factors. For instance, simulations require a plausible description of the chemistry and the physics of the interface, which comprises two very different states of matter, in the presence of liquid water. Also, it is essential that the conformational ensemble be comprehensively characterized under these conditions; this is especially challenging because intrinsically disordered peptides do not typically admit one single structure or set of structures. Moreover, a plausible structural model of the substrate is required, which may require a high level of detail, even for single-element materials such as Au surfaces or graphene. Developing and applying strategies to make credible predictions of the conformational ensemble of adsorbed peptides and using these to construct structure-property relationships of these interfaces have been the goals of our efforts. We have made substantial progress in developing interatomic potentials for these interfaces and adapting advanced conformational sampling approaches for these purposes. This Account summarizes our progress in the development and deployment of interfacial force fields and molecular simulation techniques for the purpose of elucidating these insights at biomolecule-materials interfaces, using examples from our laboratories ranging from noble-metal interfaces to graphitic substrates (including carbon nanotubes and graphene) and oxide materials (such as titania). In addition to the well-established application areas of plasmonic materials, biosensing, and the production of medical implant materials, we outline new directions for this field that have the potential to bring new advances in areas such as energy materials and regenerative medicine.

Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation

Science.gov (United States)

Grudinin, Sergei; Kadukova, Maria; Eisenbarth, Andreas; Marillet, Simon; Cazals, Frédéric

2016-09-01

The 2015 D3R Grand Challenge provided an opportunity to test our new model for the binding free energy of small molecules, as well as to assess our protocol to predict binding poses for protein-ligand complexes. Our pose predictions were ranked 3-9 for the HSP90 dataset, depending on the assessment metric. For the MAP4K dataset the ranks are very dispersed and equal to 2-35, depending on the assessment metric, which does not provide any insight into the accuracy of the method. The main success of our pose prediction protocol was the re-scoring stage using the recently developed Convex-PL potential. We make a thorough analysis of our docking predictions made with AutoDock Vina and discuss the effect of the choice of rigid receptor templates, the number of flexible residues in the binding pocket, the binding pocket size, and the benefits of re-scoring. However, the main challenge was to predict experimentally determined binding affinities for two blind test sets. Our affinity prediction model consisted of two terms, a pairwise-additive enthalpy, and a non pairwise-additive entropy. We trained the free parameters of the model with a regularized regression using affinity and structural data from the PDBBind database. Our model performed very well on the training set, however, failed on the two test sets. We explain the drawback and pitfalls of our model, in particular in terms of relative coverage of the test set by the training set and missed dynamical properties from crystal structures, and discuss different routes to improve it.

Confined-interface-directed synthesis of Palladium single-atom catalysts on graphene/amorphous carbon

DEFF Research Database (Denmark)

Xi, Jiangbo; Sun, Hongyu; Zhang, Zheye

2018-01-01

The maximized atomic efficiency of supported catalysts is highly desired in heterogeneous catalysis. Therefore, the design and development of active, stable, and atomic metal-based catalysts remains a formidable challenge. To tackle these problems, it is necessary to investigate the interaction b...

An in vitro and in vivo study of peptide-functionalized nanoparticles for brain targeting : The importance of selective blood–brain barrier uptake

NARCIS (Netherlands)

Bode, Gerard H.; Coué, G.M.J.P.C.; Freese, Christian; Pickl, Karin E.; Sanchez-Purrà, Maria; Albaiges, Berta; Borrós, Salvador; van Winden, Ewoud C.; Tziveleka, Leto Aikaterini; Sideratou, Zili; Engbersen, Johan F.J.; Singh, Smriti; Albrecht, Krystyna; Groll, Jürgen; Möller, Martin; Pötgens, Andy J.G.; Schmitz, Christoph; Fröhlich, Eleonore; Grandfils, Christian; Sinner, Frank M.; Kirkpatrick, C. James; Steinbusch, Harry W.M.; Frank, Hans Georg; Unger, Ronald E.; Martinez-Martinez, Pilar

2017-01-01

Targeted delivery of drugs across endothelial barriers remains a formidable challenge, especially in the case of the brain, where the blood–brain barrier severely limits entry of drugs into the central nervous system. Nanoparticle-mediated transport of peptide/protein-based drugs across endothelial

Detecting gravitational waves from accreting neutron stars

NARCIS (Netherlands)

Watts, A.L.; Krishnan, B.

2009-01-01

The gravitational waves emitted by neutron stars carry unique information about their structure and composition. Direct detection of these gravitational waves, however, is a formidable technical challenge. In a recent study we quantified the hurdles facing searches for gravitational waves from the

Small-bandgap semiconducting polymers with high near-infrared photoresponse

NARCIS (Netherlands)

Hendriks, K.H.; Li, W.; Wienk, M.M.; Janssen, R.A.J.

2014-01-01

Lowering the optical bandgap of conjugated polymers while maintaining a high efficiency for photoinduced charge transfer to suitable electron acceptors such as fullerene has remained a formidable challenge in the area of organic photovoltaics. Here we present the synthesis and application of a

Challenges in Downscaling Surge and Flooding Predictions Associated with Major Coastal Storm Events

Science.gov (United States)

Bowman, M. J.

2015-12-01

Coastal zone managers, elected officials and emergency planning personnel are continually seeking more reliable estimates of storm surge and inundation for better land use planning, the design, construction and operation of coastal defense systems, resilience evaluation and evacuation planning. Customers of modern regional weather and storm surge prediction models demand high resolution, speed, accuracy, with informative, interactive graphics and easy evaluation of potentially dangerous threats to life and property. These challenges continue to get more difficult as the demand for street-scale and even building-scale predictions increase. Fluctuations in sub-grid-scale wind and water velocities can lead to unsuspected, unanticipated and dangerous flooding in local communities. But how reliable and believable are these models given the inherent natural uncertainty and chaotic behavior in the underlying dynamics, which can lead to rapid and unexpected perturbations in the wind and pressure fields and hence coastal flooding? Traditionally this uncertainty has been quantified by the use of the ensemble method, where a suite of model runs are made with varying physics and initial conditions, presenting the mean and variance of the ensemble as the best metrics possible. But this assumes that each component is equally possible and is statistically independent of the others. But this is rarely true, although the "safety in numbers" approach is comforting to those faced with life and death decisions. An example of the ensemble method is presented for the trajectory of superstorm Sandy's storm center as it approached coastal New Jersey. If one were to ask the question "was Sandy a worst case scenario", the answer would be "no: small variations in the timing (vis-à-vis tide phase) and location of landfall could easily have led to an additional surge of +50 cm at The Battery NY with even more catastrophic consequences to those experienced".

Direct satellite TV - The 12-GHz challenge

Science.gov (United States)

Fawcette, J.

1982-02-01

Manufacturers in Japan and Europe are developing the hardware necessary for commercially feasible direct broadcast satellite TV, including high-frequency circuits and mini-dishes for spacecasting. US companies are lagging behind due to formidable regulatory and legal difficulties. The article focuses on efforts to develop simple, inexpensive receivers which will be able to convert 12-GHz satellite transmissions into high-quality TV images. Three basic receiver designs are being developed: the mixer-downcaster, microwave integrated circuits using FET-preamplifier front ends with transistors connected by bond-wires, and monolithic gallium arsenide integrated circuits. Several companies are on the verge of introducing commercialized receivers utilizing these different basic designs.

Predictive maintenance of maritime systems : models and challenges

NARCIS (Netherlands)

Tinga, T.; Tiddens, W.W.; Amoiralis, F.; Politis, M.; Cepin, Marko; Bris, Radim

2017-01-01

To reduce maintenance and logistic costs and increase the asset availability, a predictive maintenance concept for maritime systems is developed. In the present paper, the physics-of-failure based prognostic methods will be introduced, but also other issues related to the development and application

Threat ≠ prevention, challenge ≠ promotion: the impact of threat, challenge and regulatory focus on attention to negative stimuli.

Science.gov (United States)

Sassenberg, Kai; Sassenrath, Claudia; Fetterman, Adam K

2015-01-01

The purpose of the current experiment was to distinguish between the impact of strategic and affective forms of gain- and loss-related motivational states on the attention to negative stimuli. On the basis of the counter-regulation principle and regulatory focus theory, we predicted that individuals would attend more to negative than to neutral stimuli in a prevention focus and when experiencing challenge, but not in a promotion focus and under threat. In one experiment (N = 88) promotion, prevention, threat, or challenge states were activated through a memory task, and a subsequent dot probe task was administered. As predicted, those in the prevention focus and challenge conditions had an attentional bias towards negative words, but those in promotion and threat conditions did not. These findings provide support for the idea that strategic mindsets (e.g., regulatory focus) and hot emotional states (e.g., threat vs. challenge) differently affect the processing of affective stimuli.

Prediction Models and Decision Support: Chances and Challenges

NARCIS (Netherlands)

Kappen, T.H.

2015-01-01

A clinical prediction model can assist doctors in arriving at the most likely diagnosis or estimating the prognosis. By utilizing various patient- and disease-related properties, such models can yield objective estimations of the risk of a disease or the probability of a certain disease course for

High capacity photonic integrated switching circuits

NARCIS (Netherlands)

Albores Mejia, A.

2011-01-01

As the demand for high-capacity data transfer keeps increasing in high performance computing and in a broader range of system area networking environments; reconfiguring the strained networks at ever faster speeds with larger volumes of traffic has become a huge challenge. Formidable bottlenecks

Feasibility and Preliminary Outcomes of a Yoga and Mindfulness Intervention for School Teachers

Science.gov (United States)

Ancona, Matthew R.; Mendelson, Tamar

2014-01-01

Many public school teachers face formidable challenges, including overcrowded classrooms, limited administrative resources, and high numbers of students with behavioral and emotional problems. Mindfulness-based strategies are a potentially promising means of reducing teachers' stress and enhancing their ability to handle job demands effectively.…

Hemodynamic changes in systolic and diastolic function during isoproterenol challenge predicts symptomatic response to myectomy in hypertrophic cardiomyopathy with labile obstruction.

Science.gov (United States)

Prasad, Megha; Geske, Jeffrey B; Sorajja, Paul; Ommen, Steve R; Schaff, Hartzell V; Gersh, Bernard J; Nishimura, Rick A

2016-11-15

We aimed to assess the utility of changes in systolic and diastolic function by isoproterenol challenge in predicting symptom resolution post-myectomy in selected patients with hypertrophic cardiomyopathy (HCM) and labile obstruction. In a subset of symptomatic HCM patients without resting/provocable obstruction on noninvasive assessment, isoproterenol challenge during hemodynamic catheterization may elicit labile left ventricular outflow tract (LVOT) obstruction, and demonstrate the effect of obstruction on diastolic function. These changes may determine whether patients achieve complete symptom resolution post-myectomy. Between February 2003 and April 2009, 18 symptomatic HCM patients without LVOT obstruction on noninvasive testing underwent isoproterenol provocation and septal myectomy due to presence of provocable gradient and were followed for 4 (IQR 3-7) years. Thirteen (72.2%) had complete symptom resolution, while 5 (27.8%) had improved, but persistent symptoms. Those with provoked gradient >100 mm Hg or increase in left atrial pressure (LAP) with isoproterenol had symptom resolution. Symptomatic HCM patients without LVOT gradient on noninvasive testing may demonstrate labile obstruction with isoproterenol. With isoproterenol, patients with high LVOT gradient or increase in LAP concomitant with an increase in gradient achieved complete symptom resolution post-myectomy. Thus, improved diastolic filling as well as outflow gradient production in patients with HCM may predict symptom response to myectomy. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Parental overcontrol x OPRM1 genotype interaction predicts school-aged children's sympathetic nervous system activation in response to performance challenge.

Science.gov (United States)

Partington, Lindsey C; Borelli, Jessica L; Smiley, Patricia; Jarvik, Ella; Rasmussen, Hannah F; Seaman, Lauren C; Nurmi, Erika L

2018-04-26

Parental overcontrol (OC), the excessive regulation of a child's behavior, cognition, and emotion, is associated with the development of child anxiety. While studies have shown that genetic factors may increase sensitivity to stress, genetic vulnerability to parental OC has not been examined in anxiety etiology. A functional polymorphism in the mu opioid receptor OPRM1 (A118G, rs1799971) has been shown to impact stress reactivity. Using a community sample of children (N = 85, 9-12 years old), we examined the main and interactive effects of maternal OC and child OPRM1 genotype in predicting children's sympathetic nervous system reactivity during a performance stressor. Neither OC nor genotype predicted children's electrodermal activity (EDA); however, the interaction between OC and child genotype significantly predicted stress reactivity, as indexed by EDA, during the challenging task. Among children with the minor G-allele, higher maternal OC was associated with higher reactivity. In A homozygotes, maternal OC was not associated with EDA, suggesting a diathesis-stress pattern of gene x environment interaction. We discuss implications for anxiety etiology and intervention. Copyright © 2018 Elsevier Ltd. All rights reserved.

Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges, and Recommendations

Science.gov (United States)

Penny, Stephen G.; Akella, Santha; Buehner, Mark; Chevallier, Matthieu; Counillon, Francois; Draper, Clara; Frolov, Sergey; Fujii, Yosuke; Karspeck, Alicia; Kumar, Arun

2017-01-01

The purpose of this report is to identify fundamental issues for coupled data assimilation (CDA), such as gaps in science and limitations in forecasting systems, in order to provide guidance to the World Meteorological Organization (WMO) on how to facilitate more rapid progress internationally. Coupled Earth system modeling provides the opportunity to extend skillful atmospheric forecasts beyond the traditional two-week barrier by extracting skill from low-frequency state components such as the land, ocean, and sea ice. More generally, coupled models are needed to support seamless prediction systems that span timescales from weather, subseasonal to seasonal (S2S), multiyear, and decadal. Therefore, initialization methods are needed for coupled Earth system models, either applied to each individual component (called Weakly Coupled Data Assimilation - WCDA) or applied the coupled Earth system model as a whole (called Strongly Coupled Data Assimilation - SCDA). Using CDA, in which model forecasts and potentially the state estimation are performed jointly, each model domain benefits from observations in other domains either directly using error covariance information known at the time of the analysis (SCDA), or indirectly through flux interactions at the model boundaries (WCDA). Because the non-atmospheric domains are generally under-observed compared to the atmosphere, CDA provides a significant advantage over single-domain analyses. Next, we provide a synopsis of goals, challenges, and recommendations to advance CDA: Goals: (a) Extend predictive skill beyond the current capability of NWP (e.g. as demonstrated by improving forecast skill scores), (b) produce physically consistent initial conditions for coupled numerical prediction systems and reanalyses (including consistent fluxes at the domain interfaces), (c) make best use of existing observations by allowing observations from each domain to influence and improve the full earth system analysis, (d) develop a robust

Blood Brain Barrier: A Challenge for Effectual Therapy of Brain Tumors

OpenAIRE

Bhowmik, Arijit; Khan, Rajni; Ghosh, Mrinal Kanti

2015-01-01

Brain tumors are one of the most formidable diseases of mankind. They have only a fair to poor prognosis and high relapse rate. One of the major causes of extreme difficulty in brain tumor treatment is the presence of blood brain barrier (BBB). BBB comprises different molecular components and transport systems, which in turn create efflux machinery or hindrance for the entry of several drugs in brain. Thus, along with the conventional techniques, successful modification of drug delivery and n...

The Strategic Challenge of Capacity for German Decommissioning

International Nuclear Information System (INIS)

Thomauske, Bruno; Moloney, Barry; Charlier, Frank

2016-01-01

Full text of publication follows: Experience of decommissioning across the world has allowed the nuclear industry to develop and enhance most of the technologies required for safe and efficient dismantling of Nuclear Power Plants (NPPs). One strategic challenge confronting the industry now is how to scale up implementation to address the burgeoning demand for dismantling of full size NPPs during the period 2016-2040. The German decommissioning programme will provide early evidence of whether the European industry can rise to this strategic challenge. It is widely reported in the media that German utilities will spend some Euro 30-40 Bn decommissioning NPPs during the next 25 years. In total, 22 NPPs will progress through the typical three stage programme encompassing post operations, dismantling and site clearance, with a peak occurring in the 2020's. Politically, immediate dismantling is strongly preferred as the strategy for the NPPs, so there will be a surge in decommissioning expenditure starting as soon as 2017. A critical issue is whether the German nuclear industry has sufficient capacity to deliver the programme, and where utilities may seek participation by other European companies. Innovation may be required, perhaps at a non-technical level. The circumstances of the German market require a thorough understanding. While the market is apparently open and receptive to international participation, three factors make it hard for foreign companies to penetrate. The political and regulatory environment is tough and for many foreign companies difficult to understand quickly. Utilities are mostly pursuing self-perform decommissioning strategies to preserve employment for their skilled workforce, limiting scope for some contractors. Finally, an innovative and highly experienced German nuclear industry can present formidable competition. Yet, this industry does not possess all the capacity needed for the utilities' programmes. Risks for new entrants can

Cognitive and affective components of challenge and threat states.

Science.gov (United States)

Meijen, Carla; Jones, Marc V; McCarthy, Paul J; Sheffield, David; Allen, Mark S

2013-01-01

We explored the cognitive and affective components of the Theory of Challenge and Threat States in Athletes (TCTSA) using a cross-sectional design. One hundred and seventy-seven collegiate athletes indicated how they typically approached an important competition on measures of self-efficacy, perceived control, achievement goals, emotional states and interpretation of emotional states. Participants also indicated to what extent they typically perceived the important competition as a challenge and/or a threat. The results suggest that a perception of challenge was not predicted by any of the cognitive components. A perception of threat was positively predicted by avoidance goals and negatively predicted by self-efficacy and approach goals. Both challenge and threat had a positive relationship with anxiety. Practical implications of this study are that an avoidance orientation appeared to be related to potentially negative constructs such as anxiety, threat and dejection. The findings may suggest that practitioners and researchers should focus on reducing an avoidance orientation, however the results should be treated with caution in applied settings, as this study did not examine how the combination of constructs exactly influences sport performance. The results provided partial support for the TCTSA with stronger support for proposed relationships with threat rather than challenge states.

Oral mucosa grafts for urethral reconstruction | Mungadi | Annals of ...

African Journals Online (AJOL)

Background: Urethral reconstruction has continued to present formidable and enormous challenges for urologic, paediatric and plastic surgeons as diverse opinions have been expressed on the quality and type of ideal substitution material. This literature review is aimed at drawing attention of surgeons to the versatile ...

Thinking through the guitar : the sound-cell-texture chain

NARCIS (Netherlands)

Titre, Marlon

2013-01-01

Although the guitar has been part of the classical music tradition for centuries, writing for the guitar remains a formidable challenge for many composers. Where orchestral instruments have a long history of scoring guides that help composers develop their craft, the number of studies dedicated to

Restoring tropical forests on bauxite mined lands: lessons from the Brazilian Amazon

Science.gov (United States)

John A. Parrotta; Oliver H. Knowles

2001-01-01

Restoring self-sustaining tropical forest ecosystems on surface mined sites is a formidable challenge that requires the integration of proven reclamation techniques and reforestation strategies appropriate to specific site conditions, including landscape biodiversity patterns. Restorationists working in most tropical settings are usually hampered by lack of basic...

Shared Geospatial Metadata Repository for Ontario University Libraries: Collaborative Approaches

Science.gov (United States)

Forward, Erin; Leahey, Amber; Trimble, Leanne

2015-01-01

Successfully providing access to special collections of digital geospatial data in academic libraries relies upon complete and accurate metadata. Creating and maintaining metadata using specialized standards is a formidable challenge for libraries. The Ontario Council of University Libraries' Scholars GeoPortal project, which created a shared…

A systems approach for management of pests and pathogens of nursery crops

Science.gov (United States)

Jennifer L. Parke; Niklaus J. Grünwald

2012-01-01

Horticultural nurseries are heterogeneous and spatially complex agricultural systems, which present formidable challenges to management of diseases and pests. Moreover, nursery plants shipped interstate and internationally can serve as important vectors for pathogens and pests that threaten both agriculture and forestry. Current regulatory strategies to prevent this...

Prediction Of Low Birth Weight Using Low Glucose Challenge Test In Pregnant Women

Directory of Open Access Journals (Sweden)

Davari Tanha F

2005-07-01

Full Text Available Background: We conducted this study To find relationship between maternal glucose challenge test (GCT levels and fetal body weight (BW. Materials and Methods: We analyzed five hundred women with singleton pregnancy, who had GCT at 24-28 week during pregnancy. All of them had no history of hypertension and diabetes mellitus or other medical disease before pregnancy or during previous pregnancy, and all of them had weight gain appropriate with their pre pregnancy body mass index (BMI. Also nobody had history of drug abuse or smoking. In this descriptive–analytic survey, maternal age, gravidity, BMI,GCT level ,gestational age (GA , sex of neonate, rout of delivery , newborn weight and apgar score were evaluated .The student’s T-test and logistic regression were used for statistical analysis. We used Pearson coefficient and receiver operating characteristic (ROC curve and chi-square test for determination GCT threshold. Results: We found rate of small for gestational age (SGA in newborns statistically was significant, who their maternal GCT level was ≤ 80mg/dl, P value: 0/018, specificity 89%, sensitivity 58% and confidence interval: 95% (0/162-0/545. Conclusion: Low GCT level has association with SGA and can be used as a predictive test and may be an indication for dietary intervention.

The evolution of sex roles in birds is related to adult sex ratio.

Science.gov (United States)

Liker, András; Freckleton, Robert P; Székely, Tamás

2013-01-01

Sex-role reversal represents a formidable challenge for evolutionary biologists, since it is not clear which ecological, life-history or social factors facilitated conventional sex roles (female care and male-male competition for mates) to be reversed (male care and female-female competition). Classic theories suggested ecological or life-history predictors of role reversal, but most studies failed to support these hypotheses. Recent theory however predicts that sex-role reversal should be driven by male-biased adult sex ratio (ASR). Here we test this prediction for the first time using phylogenetic comparative analyses. Consistent with theory, both mating system and parental care are strongly related to ASR in shorebirds: conventional sex roles are exhibited by species with female-biased ASR, whereas sex-role reversal is associated with male-biased ASR. These results suggest that social environment has a strong influence on breeding systems and therefore revealing the causes of ASR variation in wild populations is essential for understanding sex role evolution.

Complexity and competition in appetitive and aversive neural circuits

Directory of Open Access Journals (Sweden)

Crista L. Barberini

2012-11-01

Full Text Available Decision-making often involves using sensory cues to predict possible rewarding or punishing reinforcement outcomes before selecting a course of action. Recent work has revealed complexity in how the brain learns to predict rewards and punishments. Analysis of neural signaling during and after learning in the amygdala and orbitofrontal cortex, two brain areas that process appetitive and aversive stimuli, reveals a dynamic relationship between appetitive and aversive circuits. Specifically, the relationship between signaling in appetitive and aversive circuits in these areas shifts as a function of learning. Furthermore, although appetitive and aversive circuits may often drive opposite behaviors – approaching or avoiding reinforcement depending upon its valence – these circuits can also drive similar behaviors, such as enhanced arousal or attention; these processes also may influence choice behavior. These data highlight the formidable challenges ahead in dissecting how appetitive and aversive neural circuits interact to produce a complex and nuanced range of behaviors.

A Formidable Task: Reflections on obtaining legal empirical evidence on human trafficking in Canada

OpenAIRE

Hayli Millar; Tamara O'Doherty; Katrin Roots

2017-01-01

This article explores the experiences, challenges and findings of two empirical research studies examining Canada’s legal efforts to combat human trafficking. The authors outline the methodologies of their respective studies and reflect on some of the difficulties they faced in obtaining empirical data on human trafficking court cases and legal proceedings. Ultimately, the authors found that Canadian trafficking case law developments are in their early stages with very few convictions, despit...

The computational challenges of Earth-system science.

Science.gov (United States)

O'Neill, Alan; Steenman-Clark, Lois

2002-06-15

The Earth system--comprising atmosphere, ocean, land, cryosphere and biosphere--is an immensely complex system, involving processes and interactions on a wide range of space- and time-scales. To understand and predict the evolution of the Earth system is one of the greatest challenges of modern science, with success likely to bring enormous societal benefits. High-performance computing, along with the wealth of new observational data, is revolutionizing our ability to simulate the Earth system with computer models that link the different components of the system together. There are, however, considerable scientific and technical challenges to be overcome. This paper will consider four of them: complexity, spatial resolution, inherent uncertainty and time-scales. Meeting these challenges requires a significant increase in the power of high-performance computers. The benefits of being able to make reliable predictions about the evolution of the Earth system should, on their own, amply repay this investment.

A Proposed Astronomy Learning Progression for Remote Telescope Observation

Science.gov (United States)

Slater, Timothy F.; Burrows, Andrea C.; French, Debbie A.; Sanchez, Richard A.; Tatge, Coty B.

2014-01-01

Providing meaningful telescope observing experiences for students who are deeply urban or distantly rural place-bound--or even daylight time-bound--has consistently presented a formidable challenge for astronomy educators. For nearly 2 decades, the Internet has promised unfettered access for large numbers of students to conduct remote telescope…

Axis I Screens and Suicide Risk in Jails: A Comparative Analysis

Science.gov (United States)

Harrison, Kimberly S.; Rogers, Richard

2007-01-01

Mental health professionals conducting screenings in jail settings face formidable challenges in identifying inmates at risk for major depression and suicide. Psychologists often rely on correctional staff to provide initial appraisals of those inmates requiring further evaluation. In a sample of 100 jail detainees, the effectiveness of two…

Confirmation of the trials and tribulations of vaping.

Science.gov (United States)

Budney, Alan J; Sargent, James D; Lee, Dustin C

2015-11-01

Responses to our article indicate consensus on the need for expedited scientific and regulatory action related to vaping of cannabis and other substances to curtail untoward public health impact and identify potential benefits. How to speed up science, increase knowledge and enact responsible regulatory policy poses a formidable challenge.

Decision-Making Amplification under Uncertainty: An Exploratory Study of Behavioral Similarity and Intelligent Decision Support Systems

Science.gov (United States)

Campbell, Merle Wayne

2013-01-01

Intelligent decision systems have the potential to support and greatly amplify human decision-making across a number of industries and domains. However, despite the rapid improvement in the underlying capabilities of these "intelligent" systems, increasing their acceptance as decision aids in industry has remained a formidable challenge.…

Prospects and challenges for urban application of biogas installations in Sub-Saharan Africa

International Nuclear Information System (INIS)

Gebreegziabher, Zenebe; Naik, Linus; Melamu, Rethabile; Balana, Bedru Babulo

2014-01-01

Cities around the world generate substantial quantities of municipal solid waste, including organic residues. These organic residues can be managed productively and given value, or they can simply be wasted. Municipal solid waste management is a serious environmental and public health concern in developing countries. In addition, collecting, transporting and disposing of municipal solid wastes presents formidable challenges to many developing country cities. It is believed that the problems are likely to become even more pronounced as the level and pace of urbanization continue to grow rapidly. Moreover, cost recovery is a serious problem of municipal solid waste management in many cities in the developing world. This paper considers how anaerobic digestion can give value to organic residues and help reduce the problem of municipal waste management. Biogas technology has the potential to work for the growing urban populations of Africa as both an energy source and a waste management (minimization) tool that can be utilized both at a small or large scale. In this paper we review the potential roles of biogas in urban applications. Specifically, we review organic waste treatment methods as well as opportunities and challenges for urban application of biogas installations and identify the critical conditions for success of biogas in urban applications. - Highlights: • We review the potential of biogas technologies in urban organic waste management. • We discuss anaerobic digestion for urban waste treatment and energy provision. • Urban biogas systems need technical, socioeconomic and environmental assessment. • Climatic, economic and biowaste conditions in SSA provide opportunities for biogas. • Research in local contexts and case studies provide evidence to support policy

Protracted speciation revitalizes the neutral theory of biodiversity

NARCIS (Netherlands)

Rosindell, James; Cornell, Stephen J.; Hubbell, Stephen P.; Etienne, Rampal S.

Understanding the maintenance and origin of biodiversity is a formidable task, yet many ubiquitous ecological patterns are predicted by a surprisingly simple and widely studied neutral model that ignores functional differences between species. However, this model assumes that new species arise

Predicting unpredictability

Science.gov (United States)

Davis, Steven J.

2018-04-01

Analysts and markets have struggled to predict a number of phenomena, such as the rise of natural gas, in US energy markets over the past decade or so. Research shows the challenge may grow because the industry — and consequently the market — is becoming increasingly volatile.

Studies on the phase diagram of boron employing a neural network potential

Energy Technology Data Exchange (ETDEWEB)

Morawietz, Tobias; Behler, Joerg [Lehrstuhl fuer Theoretische Chemie, Ruhr-Universitaet Bochum (Germany); Parrinello, Michele [Department of Chemistry and Applied Biosciences, ETH Zuerich (Switzerland)

2009-07-01

The crystalline phases of elemental boron have a structural complexity unique in the periodic table. The complex connection pattern of the icosahedral building blocks forms a formidable challenge for the construction of accurate but efficient potentials. We present a high-dimensional neural network potential for boron, which is based on first-principles calculations and can be systematically improved. The potential is several orders of magnitude faster to evaluate than the underlying density-functional theory calculations and allows to perform long molecular dynamics and metadynamics simulations of large system. By a stepwise refinement of the potential and an application of the potential in metadynamics simulations we show that starting from random atomic positions the structure of {alpha}-boron is predicted in agreement with experiment. Further, pressure-induced phase transitions of {alpha}-boron are discussed.

Promoting Student Transition Planning by Using a Self-Directed Summary of Performance

Science.gov (United States)

Morgan, Robert L.; Kupferman, Scott; Jex, Eliza; Preece, Heidi; Williams, Shannon

2017-01-01

Youth and young adults with disabilities who make the transition out of secondary settings face formidable odds. For example, they confront challenges in regard to attaining employment and becoming involved in postsecondary education. In many cases, their efforts are unsuccessful. One way to support the transition from school to employment or…

Using Quality Circles to Enhance Student Involvement and Course Quality in a Large Undergraduate Food Science and Human Nutrition Course

Science.gov (United States)

Schmidt, S. J.; Parmer, M. S.; Bohn, D. M.

2005-01-01

Large undergraduate classes are a challenge to manage, to engage, and to assess, yet such formidable classes can flourish when student participation is facilitated. One method of generating authentic student involvement is implementation of quality circles by means of a Student Feedback Committee (SFC), which is a volunteer problem-solving and…

Triggering of the detectors at LHC

Energy Technology Data Exchange (ETDEWEB)

Bock, R

1996-12-31

The future Large Hadron Collider to be built at CERN presents among other technological challengers formidable problem of real-time data analysis. This is done by algorithms at different levels, using very partial and local data to start with, and, at reduced rates, increasingly complete data sets and complex algorithms subsequently.

Dual Enrollment in Times of Financial Constraint: A Community College Perspective

Science.gov (United States)

Hockley, Lori White

2013-01-01

Community college leaders today must contend with a formidable challenge: dwindling state funding and declining resources. Increased enrollments without proportional increases in state and local financial support have placed colleges in the unenviable position of needing to do more with less--or in some cases, simply do less. Despite the…

Aural Skills: At the Juncture of Research in Early Reading and Music Literacy

Science.gov (United States)

Hansen, Dee; Milligan, Sarah A.

2012-01-01

Pressure on music educators to accommodate reading initiatives in their schools continues to challenge genuine music-learning experiences. Children are taken out of music classrooms for additional reading time, although mounting research informs us of the value of music as a formidable avenue for developing crucial auditory skills needed for…

Specific IgE for Fag e 3 Predicts Oral Buckwheat Food Challenge Test Results and Anaphylaxis: A Pilot Study.

Science.gov (United States)

Yanagida, Noriyuki; Sato, Sakura; Maruyama, Nobuyuki; Takahashi, Kyohei; Nagakura, Ken-Ichi; Ogura, Kiyotake; Asaumi, Tomoyuki; Ebisawa, Motohiro

2018-01-01

Buckwheat (BW) is the source of a life-threatening allergen. Fag e 3-specific serum IgE (sIgE) is more useful than BW-sIgE for diagnosis; however, it is unknown whether Fag e 3-sIgE can predict oral food challenge (OFC) results and anaphylaxis. This study aimed to clarify the efficacy of Fag e 3-sIgE in predicting OFC results and anaphylaxis. We conducted a retrospective review of BW- and Fag e 3-sIgE data obtained using the ImmunoCAP® assay system and fluorescent enzyme-linked immunosorbent assay from children who underwent OFC using 3,072 mg of BW protein between July 2006 and March 2014 at Sagamihara National Hospital, Kanagawa, Japan. We analyzed 60 patients aged 1.9-13.4 years (median 6.0 years); 20 (33%) showed objective symptoms upon BW OFC. The patients without symptoms had significantly lower Fag e 3-sIgE than those with non-anaphylactic (p tested factor that significantly predicted positive OFC results (odds ratio 8.93, 95% confidence interval 3.10-25.73, p < 0.001) and OFC-induced anaphylaxis (2.67, 1.12-6.35, p = 0.027). We suggest that a threshold Fag e 3-sIgE level of 18.0 kUE/L has 95% probability of provoking a positive reaction to BW. Fag e 3-sIgE predicted OFC results and OFC-induced anaphylaxis. We further emphasize paying careful attention to the risk of BW OFC-induced anaphylaxis. © 2018 The Author(s) Published by S. Karger AG, Basel.

Tools and Frameworks for Big Learning in Scala: Leveraging the Language for High Productivity and Performance

OpenAIRE

Miller, Heather; Haller, Philipp; Odersky, Martin

2011-01-01

Implementing machine learning algorithms for large data, such as the Web graph and social networks, is challenging. Even though much research has focused on making sequential algorithms more scalable, their running times continue to be prohibitively long. Meanwhile, parallelization remains a formidable challenge for this class of problems, despite frameworks like MapReduce which hide much of the associated complexity. We present three ongoing efforts within our team, previously presented at v...

Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015

Science.gov (United States)

Xu, Xianjin; Yan, Chengfei; Zou, Xiaoqin

2017-08-01

The growing number of protein-ligand complex structures, particularly the structures of proteins co-bound with different ligands, in the Protein Data Bank helps us tackle two major challenges in molecular docking studies: the protein flexibility and the scoring function. Here, we introduced a systematic strategy by using the information embedded in the known protein-ligand complex structures to improve both binding mode and binding affinity predictions. Specifically, a ligand similarity calculation method was employed to search a receptor structure with a bound ligand sharing high similarity with the query ligand for the docking use. The strategy was applied to the two datasets (HSP90 and MAP4K4) in recent D3R Grand Challenge 2015. In addition, for the HSP90 dataset, a system-specific scoring function (ITScore2_hsp90) was generated by recalibrating our statistical potential-based scoring function (ITScore2) using the known protein-ligand complex structures and the statistical mechanics-based iterative method. For the HSP90 dataset, better performances were achieved for both binding mode and binding affinity predictions comparing with the original ITScore2 and with ensemble docking. For the MAP4K4 dataset, although there were only eight known protein-ligand complex structures, our docking strategy achieved a comparable performance with ensemble docking. Our method for receptor conformational selection and iterative method for the development of system-specific statistical potential-based scoring functions can be easily applied to other protein targets that have a number of protein-ligand complex structures available to improve predictions on binding.

Recent Successes and Remaining Challenges in Predicting Phosphorus Loading to Surface Waters at Large Scales

Science.gov (United States)

Harrison, J.; Metson, G.; Beusen, A.

2017-12-01

Over the past century humans have greatly accelerated phosphorus (P) flows from land to aquatic ecosystems, causing eutrophication and associated effects such as harmful algal blooms and hypoxia. Effectively addressing this challenge requires understanding geographic and temporal distribution of aquatic P loading, knowledge of major controls on P loading, and the relative importance of various potential P sources. The Global (N)utrient (E)xport from (W)ater(S)heds) NEWS model and recent improvements and extensions of this modeling system can be used to generate this understanding. This presentation will focus on insights global NEWS models grant into past, present, and potential future P sources and sinks, with a focus on the world's large rivers. Early results suggest: 1) that while aquatic P loading is globally dominated by particulate forms, dissolved P can be locally dominant; 2) that P loading has increased substantially at the global scale, but unevenly between world regions, with hotspots in South and East Asia; 3) that P loading is likely to continue to increase globally, but decrease in certain regions that are actively pursuing proactive P management; and 4) that point sources, especially in urban centers, play an important (even dominant) role in determining loads of dissolved inorganic P. Despite these insights, substantial unexplained variance remains when model predictions and measurements are compared at global and regional scales, for example within the U.S. Disagreements between model predictions and measurements suggest opportunities for model improvement. In particular, explicit inclusion of soil characteristics and the concept of temporal P legacies in future iterations of NEWS (and other) models may help improve correspondence between models and measurements.

Data-Based Predictive Control with Multirate Prediction Step

Science.gov (United States)

Barlow, Jonathan S.

2010-01-01

Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.

Citrus water use in South Africa

CSIR Research Space (South Africa)

Vahrmeijer, JT

2012-09-01

Full Text Available scheduling is needed to justify the quantity of water needed for the production of citrus. Models, which are formidable tools to predict water use and crop performance, are therefore vital to provide accurate estimates of citrus water use across different...

Resilient Parenting of Preschool Children at Developmental Risk

Science.gov (United States)

Ellingsen, R.; Baker, B. L.; Blacher, J.; Crnic, K.

2014-01-01

Background: Given the great benefits of effective parenting to child development under normal circumstances, and the even greater benefits in the face of risk, it is important to understand why some parents manage to be effective in their interactions with their child despite facing formidable challenges. This study examined factors that promoted…

A Superannuated Physicist's Attempts to Master Music Theory: Resolving Cognitive Conflicts and a Paradigm Clash

Science.gov (United States)

Page-Shipp, Roy; van Niekerk, Caroline

2014-01-01

A sexagenarian retired physicist (the first author) set out, with the assistance of members of a university music department, to acquire some insight into Western music theory. For a lifelong singer and seasoned autodidact, this appeared to be a not too formidable challenge, yet he experienced significant difficulty in penetrating the music theory…

Debating Life on Mars: The Knowledge Integration Environment (KIE) in Varied School Settings.

Science.gov (United States)

Shear, Linda

Technology-enabled learning environments are beginning to come of age. Tools and frameworks are now available that have been shown to improve learning and are being deployed more widely in varied school settings. Teachers are now faced with the formidable challenge of integrating these promising new environments with the everyday context in which…

Energy efficiency optimum strategies for low carbon development in ...

International Development Research Centre (IDRC) Digital Library (Canada)

Women in rural areas are particularly affected by poor access to reliable energy sources such as electricity, thus forcing households to turn to biofuels such as wood and dung to cook, light, and warm their homes. This “energy poverty” remains one of the most formidable challenges to any progress in global development.

Electronic coarse graining enhances the predictive power of molecular simulation allowing challenges in water physics to be addressed

Science.gov (United States)

Cipcigan, Flaviu S.; Sokhan, Vlad P.; Crain, Jason; Martyna, Glenn J.

2016-12-01

One key factor that limits the predictive power of molecular dynamics simulations is the accuracy and transferability of the input force field. Force fields are challenged by heterogeneous environments, where electronic responses give rise to biologically important forces such as many-body polarisation and dispersion. The importance of polarisation in the condensed phase was recognised early on, as described by Cochran in 1959 [Philosophical Magazine 4 (1959) 1082-1086] [32]. Currently in molecular simulation, dispersion forces are treated at the two-body level and in the dipole limit, although the importance of three-body terms in the condensed phase was demonstrated by Barker in the 1980s [Phys. Rev. Lett. 57 (1986) 230-233] [72]. One approach for treating both polarisation and dispersion on an equal basis is to coarse grain the electrons surrounding a molecular moiety to a single quantum harmonic oscillator (cf. Hirschfelder, Curtiss and Bird 1954 [The Molecular Theory of Gases and Liquids (1954)] [37]). The approach, when solved in strong coupling beyond the dipole limit, gives a description of long-range forces that includes two- and many-body terms to all orders. In the last decade, the tools necessary to implement the strong coupling limit have been developed, culminating in a transferable model of water with excellent predictive power across the phase diagram. Transferability arises since the environment automatically identifies the important long range interactions, rather than the modeller through a limited set of expressions. Here, we discuss the role of electronic coarse-graining in predictive multiscale materials modelling and describe the first implementation of the method in a general purpose molecular dynamics software: QDO_MD.

The Challenge of Predicting the Occurrence of Intense Storms ...

Indian Academy of Sciences (India)

weather. 1. Introduction. Space weather prediction involves forecasting of the magnitude and the time of the ... they depend solely on interplanetary (IP) parameters, viz., the solar wind speed and the southward compo- ... about 30 to 60 minutes of warning time as it measures the solar wind properties at the L1 point. A longer ...

Scientific Grand Challenges: Challenges in Climate Change Science and the Role of Computing at the Extreme Scale

Energy Technology Data Exchange (ETDEWEB)

Khaleel, Mohammad A.; Johnson, Gary M.; Washington, Warren M.

2009-07-02

The U.S. Department of Energy (DOE) Office of Biological and Environmental Research (BER) in partnership with the Office of Advanced Scientific Computing Research (ASCR) held a workshop on the challenges in climate change science and the role of computing at the extreme scale, November 6-7, 2008, in Bethesda, Maryland. At the workshop, participants identified the scientific challenges facing the field of climate science and outlined the research directions of highest priority that should be pursued to meet these challenges. Representatives from the national and international climate change research community as well as representatives from the high-performance computing community attended the workshop. This group represented a broad mix of expertise. Of the 99 participants, 6 were from international institutions. Before the workshop, each of the four panels prepared a white paper, which provided the starting place for the workshop discussions. These four panels of workshop attendees devoted to their efforts the following themes: Model Development and Integrated Assessment; Algorithms and Computational Environment; Decadal Predictability and Prediction; Data, Visualization, and Computing Productivity. The recommendations of the panels are summarized in the body of this report.

Sites and Landforms: A Phase One Archaeological Sampling Survey at Camp Ripley, Morrison County, Minnesota

Science.gov (United States)

1988-12-31

research of Obadiah StoutI Bennett, an entreprenuer at the Chippewa townsite with an obvious knack for selling undeveloped lots to young women . Dr. Elden...proved a formidable challenge to settlement and agriculture. I The belief that former pine lands were of no value for farming (Anonymous 1894:12) did...cause him to slur his words and lose his train of thought. His was a challenging interview but well worth the time. Among the topics of conversation

Predictive systems ecology.

Science.gov (United States)

Evans, Matthew R; Bithell, Mike; Cornell, Stephen J; Dall, Sasha R X; Díaz, Sandra; Emmott, Stephen; Ernande, Bruno; Grimm, Volker; Hodgson, David J; Lewis, Simon L; Mace, Georgina M; Morecroft, Michael; Moustakas, Aristides; Murphy, Eugene; Newbold, Tim; Norris, K J; Petchey, Owen; Smith, Matthew; Travis, Justin M J; Benton, Tim G

2013-11-22

Human societies, and their well-being, depend to a significant extent on the state of the ecosystems that surround them. These ecosystems are changing rapidly usually in response to anthropogenic changes in the environment. To determine the likely impact of environmental change on ecosystems and the best ways to manage them, it would be desirable to be able to predict their future states. We present a proposal to develop the paradigm of predictive systems ecology, explicitly to understand and predict the properties and behaviour of ecological systems. We discuss the necessary and desirable features of predictive systems ecology models. There are places where predictive systems ecology is already being practised and we summarize a range of terrestrial and marine examples. Significant challenges remain but we suggest that ecology would benefit both as a scientific discipline and increase its impact in society if it were to embrace the need to become more predictive.

Protein docking prediction using predicted protein-protein interface

Directory of Open Access Journals (Sweden)

Li Bin

2012-01-01

Full Text Available Abstract Background Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. Results We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm, is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. Conclusion We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

Protein docking prediction using predicted protein-protein interface.

Science.gov (United States)

Li, Bin; Kihara, Daisuke

2012-01-10

Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

Constructivist Pedagogy for the Business Communication Classroom

Science.gov (United States)

Mathews, Minu

2007-01-01

Business education and learning has become formidable and challenging over the last few years. A traditional learning environment is bereft of active learning where students only try to memorise terms and concepts and is unable to apply them to the real corporate world. It was found in the business communication classes that students fail to…

"Hiding Our Snickers": "Weekly Mail" Journalists' Indirect Resistance in Apartheid South Africa

Science.gov (United States)

Trabold, Bryan

2006-01-01

In the mid- to late 1980s, the challenges facing the editors and journalists working for the South African antiapartheid newspaper, the "Weekly Mail," were formidable. In addition to the more than one hundred censorship laws already in place, the apartheid government had declared a series of states of emergency in a final and desperate…

Aetiology and treatment outcome of severe traumatic brain injuries ...

African Journals Online (AJOL)

Background: Severe traumatic brain injury (TBI) is a major challenge to the patient, the relatives, the care givers, and the society in general. The primary and secondary injuries, and the high metabolism are formidable stages of the injury, each capable of taking the life of the patient. The objectives were to determine the ...

How to Create "Thriller" PowerPoints[R] in the Classroom!

Science.gov (United States)

Berk, Ronald A.

2012-01-01

PowerPoint[R] presentations in academia have a reputation for being less than engaging in this era of learner-centered teaching. The Net Generation also presents a formidable challenge to using PowerPoint[R]. Although the research on the basic elements is rather sparse, the multimedia elements of movement, music, and videos have a stronger…

BIG DATA-Related Challenges and Opportunities in Earth System Modeling

Science.gov (United States)

Bamzai, A. S.

2012-12-01

Knowledge of the Earth's climate has increased immensely in recent decades, both through observational analysis and modeling. BIG DATA-related challenges emerge in our quest for understanding the variability and predictability of the climate and earth system on a range of time scales, as well as in our endeavor to improve predictive capability using state-of-the-science models. To enable further scientific discovery, bottlenecks in current paradigms need to be addressed. An overview of current NSF activities in Earth System Modeling with a focus on associated data-related challenges and opportunities, will be presented.

Prediction Models for Dynamic Demand Response

Energy Technology Data Exchange (ETDEWEB)

Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

2015-11-02

As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D²R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D²R, which we address in this paper. Our first contribution is the formal definition of D²R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D²R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D²R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D²R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D²R. Also, prediction models require just few days’ worth of data indicating that small amounts of

A systems biology approach to transcription factor binding site prediction.

Directory of Open Access Journals (Sweden)

Xiang Zhou

2010-03-01

Full Text Available The elucidation of mammalian transcriptional regulatory networks holds great promise for both basic and translational research and remains one the greatest challenges to systems biology. Recent reverse engineering methods deduce regulatory interactions from large-scale mRNA expression profiles and cross-species conserved regulatory regions in DNA. Technical challenges faced by these methods include distinguishing between direct and indirect interactions, associating transcription regulators with predicted transcription factor binding sites (TFBSs, identifying non-linearly conserved binding sites across species, and providing realistic accuracy estimates.We address these challenges by closely integrating proven methods for regulatory network reverse engineering from mRNA expression data, linearly and non-linearly conserved regulatory region discovery, and TFBS evaluation and discovery. Using an extensive test set of high-likelihood interactions, which we collected in order to provide realistic prediction-accuracy estimates, we show that a careful integration of these methods leads to significant improvements in prediction accuracy. To verify our methods, we biochemically validated TFBS predictions made for both transcription factors (TFs and co-factors; we validated binding site predictions made using a known E2F1 DNA-binding motif on E2F1 predicted promoter targets, known E2F1 and JUND motifs on JUND predicted promoter targets, and a de novo discovered motif for BCL6 on BCL6 predicted promoter targets. Finally, to demonstrate accuracy of prediction using an external dataset, we showed that sites matching predicted motifs for ZNF263 are significantly enriched in recent ZNF263 ChIP-seq data.Using an integrative framework, we were able to address technical challenges faced by state of the art network reverse engineering methods, leading to significant improvement in direct-interaction detection and TFBS-discovery accuracy. We estimated the accuracy

Prediction models : the right tool for the right problem

NARCIS (Netherlands)

Kappen, Teus H.; Peelen, Linda M.

2016-01-01

PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to

A Perspective on Smart Process Manufacturing Research Challenges for Process Systems Engineers

Directory of Open Access Journals (Sweden)

Ian David Lockhart Bogle

2017-04-01

Full Text Available The challenges posed by smart manufacturing for the process industries and for process systems engineering (PSE researchers are discussed in this article. Much progress has been made in achieving plant- and site-wide optimization, but benchmarking would give greater confidence. Technical challenges confronting process systems engineers in developing enabling tools and techniques are discussed regarding flexibility and uncertainty, responsiveness and agility, robustness and security, the prediction of mixture properties and function, and new modeling and mathematics paradigms. Exploiting intelligence from big data to drive agility will require tackling new challenges, such as how to ensure the consistency and confidentiality of data through long and complex supply chains. Modeling challenges also exist, and involve ensuring that all key aspects are properly modeled, particularly where health, safety, and environmental concerns require accurate predictions of small but critical amounts at specific locations. Environmental concerns will require us to keep a closer track on all molecular species so that they are optimally used to create sustainable solutions. Disruptive business models may result, particularly from new personalized products, but that is difficult to predict.

Improved predictions of nuclear data: A continued challenge in astrophysics

International Nuclear Information System (INIS)

Goriely, S.

2001-01-01

Although important effort has been devoted in the last decades to measure reaction cross sections and decay half-lives of interest in astrophysics, most of the nuclear astrophysics applications still require the use of theoretical predictions to estimate experimentally unknown rates. The nuclear ingredients to the reaction or weak interaction models should preferentially be estimated from microscopic or semi-microscopic global predictions based on sound and reliable nuclear models which, in turn, can compete with more phenomenological highly-parametrized models in the reproduction of experimental data. The latest developments made in deriving the nuclear inputs of relevance in astrophysics applications are reviewed. It mainly concerns nuclear structure properties (atomic masses, deformations, radii, etc...), nuclear level densities, nucleon and α-optical potentials, γ-ray and Gamow-Teller strength functions

Relating Climate and Enviornmental Stress to Conflict... or Cooperation?

Science.gov (United States)

Kelley, C. P.

2016-12-01

There are many factors which contribute to social unrest, including governance, economy, access to resources and others. As global climate change progresses, many regions and nations, particularly those that are most vulnerable and least resilient, will face increasing challenges with respect to water and food scarcity. Increasing population and demand for water, combined with declining access to groundwater, will greatly increase exisiting vulnerability. Syria, Yemen and other countries serve as examples of nations that have experienced increasing both environmental stress and conflict. The Syria case in particular has had clear global repercussions, most notably contributing to a global refugee crisis. However, there are also examples of nations that have experienced increasing environmental stress that instead demonstrated transboundary water cooperation rather than conflict. An important and emerging body of work is that which seeks to better understand and characterize real-time resilience and vulnerability in order to better mitigate the consequences of future regional climate change. Prediction of potential conflict is a formidable challenge, one that is highly complex and multivariate, operating on many different temporal and spatial scales.

Fire management ramifications of Hurricane Hugo

Science.gov (United States)

J. M. Saveland; D. D. Wade

1991-01-01

Hurricane Hugo passed over the Francis Marion National Forest on September 22, 1989, removing almost 75 percent of the overstory. The radically altered fuel bed presented new and formidable challenges to fire managers. Tractor-plows, the mainstay of fire suppression, were rendered ineffective. The specter of wind-driven escaped burns with no effective means of ground...

Increase in data capacity utilising dimensions of wavelength, space, time, polarisation and multilevel modulation using a single laser

DEFF Research Database (Denmark)

Clausen, Anders; Hu, Hao; Ye, Feihong

2015-01-01

Increasing the capacity of optical networks while have the objective of lowering the total consumed energy per bit is challenging. By exploiting several dimensions, i.e. wavelength, space, time, polarisation and multilevel modulation simultaneously, a single laser can offer formidable capacity pe...... performance with potentially reduced energy consumption per bit. Up to 43 Tbit/s has been demonstrated....

Stringent homology-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

Science.gov (United States)

Zhou, Hufeng; Gao, Shangzhi; Nguyen, Nam Ninh; Fan, Mengyuan; Jin, Jingjing; Liu, Bing; Zhao, Liang; Xiong, Geng; Tan, Min; Li, Shijun; Wong, Limsoon

2014-04-08

H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are essential for understanding the infection mechanism of the formidable pathogen M. tuberculosis H37Rv. Computational prediction is an important strategy to fill the gap in experimental H. sapiens-M. tuberculosis H37Rv PPI data. Homology-based prediction is frequently used in predicting both intra-species and inter-species PPIs. However, some limitations are not properly resolved in several published works that predict eukaryote-prokaryote inter-species PPIs using intra-species template PPIs. We develop a stringent homology-based prediction approach by taking into account (i) differences between eukaryotic and prokaryotic proteins and (ii) differences between inter-species and intra-species PPI interfaces. We compare our stringent homology-based approach to a conventional homology-based approach for predicting host-pathogen PPIs, based on cellular compartment distribution analysis, disease gene list enrichment analysis, pathway enrichment analysis and functional category enrichment analysis. These analyses support the validity of our prediction result, and clearly show that our approach has better performance in predicting H. sapiens-M. tuberculosis H37Rv PPIs. Using our stringent homology-based approach, we have predicted a set of highly plausible H. sapiens-M. tuberculosis H37Rv PPIs which might be useful for many of related studies. Based on our analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent homology-based approach, we have discovered several interesting properties which are reported here for the first time. We find that both host proteins and pathogen proteins involved in the host-pathogen PPIs tend to be hubs in their own intra-species PPI network. Also, both host and pathogen proteins involved in host-pathogen PPIs tend to have longer primary sequence, tend to have more domains, tend to be more hydrophilic, etc. And the protein domains from both

Climate prediction and predictability

Science.gov (United States)

Allen, Myles

2010-05-01

Climate prediction is generally accepted to be one of the grand challenges of the Geophysical Sciences. What is less widely acknowledged is that fundamental issues have yet to be resolved concerning the nature of the challenge, even after decades of research in this area. How do we verify or falsify a probabilistic forecast of a singular event such as anthropogenic warming over the 21st century? How do we determine the information content of a climate forecast? What does it mean for a modelling system to be "good enough" to forecast a particular variable? How will we know when models and forecasting systems are "good enough" to provide detailed forecasts of weather at specific locations or, for example, the risks associated with global geo-engineering schemes. This talk will provide an overview of these questions in the light of recent developments in multi-decade climate forecasting, drawing on concepts from information theory, machine learning and statistics. I will draw extensively but not exclusively from the experience of the climateprediction.net project, running multiple versions of climate models on personal computers.

Fathers' challenging parenting behavior prevents social anxiety development in their 4-year-old children: a longitudinal observational study.

Science.gov (United States)

Majdandžić, Mirjana; Möller, Eline L; de Vente, Wieke; Bögels, Susan M; van den Boom, Dymphna C

2014-02-01

Recent models on parenting propose different roles for fathers and mothers in the development of child anxiety. Specifically, it is suggested that fathers' challenging parenting behavior, in which the child is playfully encouraged to push her limits, buffers against child anxiety. In this longitudinal study, we explored whether the effect of challenging parenting on children's social anxiety differed between fathers and mothers. Fathers and mothers from 94 families were separately observed with their two children (44 % girls), aged 2 and 4 years at Time 1, in three structured situations involving one puzzle task and two games. Overinvolved and challenging parenting behavior were coded. Child social anxiety was measured by observing the child's response to a stranger at Time 1, and half a year later at Time 2, and by parental ratings. In line with predictions, father's challenging parenting behavior predicted less subsequent observed social anxiety of the 4-year-old child. Mothers' challenging behavior, however, predicted more observed social anxiety of the 4-year-old. Parents' overinvolvement at Time 1 did not predict change in observed social anxiety of the 4-year-old child. For the 2-year-old child, maternal and paternal parenting behavior did not predict subsequent social anxiety, but early social anxiety marginally did. Parent-rated social anxiety was predicted by previous parental ratings of social anxiety, and not by parenting behavior. Challenging parenting behavior appears to have favorable effects on observed 4-year-old's social anxiety when displayed by the father. Challenging parenting behavior emerges as an important focus for future research and interventions.

An analysis of challenging behavior, comorbid psychopathology, and Attention-Deficit/Hyperactivity Disorder in Fragile X Syndrome.

LENUS (Irish Health Repository)

Newman, Isabel

2015-03-01

The present study sought to investigate the relationship between challenging behavior, comorbid psychopathology, and Attention-Deficit\\/Hyperactivity Disorder (AD\\/HD) in Fragile X Syndrome (FRAX). Additionally, this study sought to examine how such disorders are predicted by gender, presence of autism spectrum disorder (ASD), and presence of intellectual disability (ID). A total of 47 children and adolescents with FRAX were assessed. Results revealed high levels of challenging behavior and AD\\/HD symptoms within the sample, with some participants exhibiting symptoms of comorbid psychopathology. Further analysis revealed that challenging behavior and comorbid psychopathology were positively correlated, with stereotypy correlating most strongly with comorbid psychopathology. In addition, ASD was found to predict challenging behavior, and gender was found to predict AD\\/HD symptoms. The implications of these findings are discussed.

Start-up fever

CERN Multimedia

2008-01-01

Unusually for the holiday season, the car parks are full, finding a table at lunch is a formidable challenge, and people can (more than ever) be found in their offices late into the night. All the evidence points to one thing… the most ambitious particle collider in the world is just a few weeks away from its first proton beam!

Antiracism Education? A Study of an Antiracism Workshop in Finland

Science.gov (United States)

Alemanji, Aminkeng Atabong; Mafi, Boby

2018-01-01

In doing antiracism education there is a risk that it can in effect reinforce the very racialisation it is supposed to fight against. This paradox becomes a formidable challenge given the ubiquity of race in contemporary ways of knowing and ways of being for both its subjects and its objects: more so in an era of "racism without race," a…

Boronic acid-based autoligation of nucleic acids

DEFF Research Database (Denmark)

Barbeyron, R.; Vasseur, J.-J.; Smietana, M.

2013-01-01

Abstract: The development of synthetic systems displaying dynamic and adaptive characteristics is a formidable challenge with wide applications from biotechnology to therapeutics. Recently, we described a dynamic and programmable nucleic acid-based system relying on the formation of reversible bo....... Evidence suggests that geometric and steric factors are key features for controlling the equilibria. Graphical Abstract: [Figure not available: see fulltext.]...

Countering Political Risk in Colonial India

DEFF Research Database (Denmark)

Lubinski, Christina; Giacomin, Valeria; Schnitzer, Klara

challenges, impacted the perception of racial lines of distinctions and re-casted the category “European business.” While internment was perceived and managed as a political risk, the case also shows that it created unexpected networking opportunities, generating a tight community of German businesspeople......Internment in so-called “enemy countries” was a frequent occurrence in the twentieth century and created significant obstacles for multinational enterprises (MNEs). This article focuses on German MNEs in India and shows how they addressed the formidable challenge of the internment...

Manufacturing Concepts of the Future – Upcoming Technologies Solving Upcoming Challenges

DEFF Research Database (Denmark)

Hadar, Ronen; Bilberg, Arne

concepts and technologies that are being developed today which may be used to solve manufacturing challenges in the future, such as: (self) reconfigurable manufacturing systems, (focused) flexible manufacturing systems, and AI inspired manufacturing. The paper will try to offer a critical point of view......This paper presents an examination of Western European manufacturers’ future challenges as can be predicted today. Some of the challenges analyzed in the paper are: globalization, individualism and customization and agility challenges. Hereafter, the paper presents a broad analysis on manufacturing...

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data.

Science.gov (United States)

Guinney, Justin; Wang, Tao; Laajala, Teemu D; Winner, Kimberly Kanigel; Bare, J Christopher; Neto, Elias Chaibub; Khan, Suleiman A; Peddinti, Gopal; Airola, Antti; Pahikkala, Tapio; Mirtti, Tuomas; Yu, Thomas; Bot, Brian M; Shen, Liji; Abdallah, Kald; Norman, Thea; Friend, Stephen; Stolovitzky, Gustavo; Soule, Howard; Sweeney, Christopher J; Ryan, Charles J; Scher, Howard I; Sartor, Oliver; Xie, Yang; Aittokallio, Tero; Zhou, Fang Liz; Costello, James C

2017-01-01

Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a

Predicting armed conflict: Time to adjust our expectations?

Science.gov (United States)

Cederman, Lars-Erik; Weidmann, Nils B

2017-02-03

This Essay provides an introduction to the general challenges of predicting political violence, particularly compared with predicting other types of events (such as earthquakes). What is possible? What is less realistic? We aim to debunk myths about predicting violence, as well as to illustrate the substantial progress in this field. Copyright © 2017, American Association for the Advancement of Science.

Formidable achievement through determination and pragmatism

International Nuclear Information System (INIS)

Anon.

1979-01-01

Anglo American Corporation's former chief consulting metallurgist Ewen Pinkney has a remarkable record of achievement in mining metallurgy and chemical engineering at mines all over Southern Africa despite the fact that he could not afford a university education. Just one feature of his career was the leading role he played in the establishment of the country's first big uranium plants. This as well as other features of his career are discussed

State of the art and challenges in sequence based T-cell epitope prediction

DEFF Research Database (Denmark)

Lundegaard, Claus; Hoof, Ilka; Lund, Ole

2010-01-01

Sequence based T-cell epitope predictions have improved immensely in the last decade. From predictions of peptide binding to major histocompatibility complex molecules with moderate accuracy, limited allele coverage, and no good estimates of the other events in the antigen-processing pathway, the...

Predicting E-commerce Consumer Behaviour Using Sparse Session Data

OpenAIRE

Thorrud, Thorstein Kaldahl; Myklatun, Øyvind

2015-01-01

This thesis research consumer behavior in an e-commerce domain by using a data set of sparse session data collected from an anonymous European e-commerce site. The goal is to predict whether a consumer session results in a purchase, and if so, which items are purchased. The data is supplied by the ACM Recommender System Challenge, which is a yearly challenge held by the ACM Recommender System Conference. Classification is used for predicting whether or not a session made a purchase, as w...

Predictive Systems Toxicology

KAUST Repository

Kiani, Narsis A.; Shang, Ming-Mei; Zenil, Hector; Tegner, Jesper

2018-01-01

In this review we address to what extent computational techniques can augment our ability to predict toxicity. The first section provides a brief history of empirical observations on toxicity dating back to the dawn of Sumerian civilization. Interestingly, the concept of dose emerged very early on, leading up to the modern emphasis on kinetic properties, which in turn encodes the insight that toxicity is not solely a property of a compound but instead depends on the interaction with the host organism. The next logical step is the current conception of evaluating drugs from a personalized medicine point-of-view. We review recent work on integrating what could be referred to as classical pharmacokinetic analysis with emerging systems biology approaches incorporating multiple omics data. These systems approaches employ advanced statistical analytical data processing complemented with machine learning techniques and use both pharmacokinetic and omics data. We find that such integrated approaches not only provide improved predictions of toxicity but also enable mechanistic interpretations of the molecular mechanisms underpinning toxicity and drug resistance. We conclude the chapter by discussing some of the main challenges, such as how to balance the inherent tension between the predictive capacity of models, which in practice amounts to constraining the number of features in the models versus allowing for rich mechanistic interpretability, i.e. equipping models with numerous molecular features. This challenge also requires patient-specific predictions on toxicity, which in turn requires proper stratification of patients as regards how they respond, with or without adverse toxic effects. In summary, the transformation of the ancient concept of dose is currently successfully operationalized using rich integrative data encoded in patient-specific models.

Predictive Systems Toxicology

KAUST Repository

Kiani, Narsis A.

2018-01-15

In this review we address to what extent computational techniques can augment our ability to predict toxicity. The first section provides a brief history of empirical observations on toxicity dating back to the dawn of Sumerian civilization. Interestingly, the concept of dose emerged very early on, leading up to the modern emphasis on kinetic properties, which in turn encodes the insight that toxicity is not solely a property of a compound but instead depends on the interaction with the host organism. The next logical step is the current conception of evaluating drugs from a personalized medicine point-of-view. We review recent work on integrating what could be referred to as classical pharmacokinetic analysis with emerging systems biology approaches incorporating multiple omics data. These systems approaches employ advanced statistical analytical data processing complemented with machine learning techniques and use both pharmacokinetic and omics data. We find that such integrated approaches not only provide improved predictions of toxicity but also enable mechanistic interpretations of the molecular mechanisms underpinning toxicity and drug resistance. We conclude the chapter by discussing some of the main challenges, such as how to balance the inherent tension between the predictive capacity of models, which in practice amounts to constraining the number of features in the models versus allowing for rich mechanistic interpretability, i.e. equipping models with numerous molecular features. This challenge also requires patient-specific predictions on toxicity, which in turn requires proper stratification of patients as regards how they respond, with or without adverse toxic effects. In summary, the transformation of the ancient concept of dose is currently successfully operationalized using rich integrative data encoded in patient-specific models.

Electronic coarse graining enhances the predictive power of molecular simulation allowing challenges in water physics to be addressed

Energy Technology Data Exchange (ETDEWEB)

Cipcigan, Flaviu S., E-mail: flaviu.cipcigan@ed.ac.uk [School of Physics and Astronomy, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD (United Kingdom); National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW (United Kingdom); Sokhan, Vlad P. [National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW (United Kingdom); Crain, Jason [School of Physics and Astronomy, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD (United Kingdom); National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW (United Kingdom); Martyna, Glenn J. [IBM T. J. Watson Research Center, Yorktown Heights, NY 10598 (United States)

2016-12-01

One key factor that limits the predictive power of molecular dynamics simulations is the accuracy and transferability of the input force field. Force fields are challenged by heterogeneous environments, where electronic responses give rise to biologically important forces such as many-body polarisation and dispersion. The importance of polarisation in the condensed phase was recognised early on, as described by Cochran in 1959 [Philosophical Magazine 4 (1959) 1082–1086] [32]. Currently in molecular simulation, dispersion forces are treated at the two-body level and in the dipole limit, although the importance of three-body terms in the condensed phase was demonstrated by Barker in the 1980s [Phys. Rev. Lett. 57 (1986) 230–233] [72]. One approach for treating both polarisation and dispersion on an equal basis is to coarse grain the electrons surrounding a molecular moiety to a single quantum harmonic oscillator (cf. Hirschfelder, Curtiss and Bird 1954 [The Molecular Theory of Gases and Liquids (1954)] [37]). The approach, when solved in strong coupling beyond the dipole limit, gives a description of long-range forces that includes two- and many-body terms to all orders. In the last decade, the tools necessary to implement the strong coupling limit have been developed, culminating in a transferable model of water with excellent predictive power across the phase diagram. Transferability arises since the environment automatically identifies the important long range interactions, rather than the modeler through a limited set of expressions. Here, we discuss the role of electronic coarse-graining in predictive multiscale materials modelling and describe the first implementation of the method in a general purpose molecular dynamics software: QDO-MD. - Highlights: • Electronic coarse graining unites many-body dispersion and polarisation beyond the dipole limit. • It consists of replacing the electrons of a molecule using a quantum harmonic oscillator, called a

Electronic coarse graining enhances the predictive power of molecular simulation allowing challenges in water physics to be addressed

International Nuclear Information System (INIS)

Cipcigan, Flaviu S.; Sokhan, Vlad P.; Crain, Jason; Martyna, Glenn J.

2016-01-01

One key factor that limits the predictive power of molecular dynamics simulations is the accuracy and transferability of the input force field. Force fields are challenged by heterogeneous environments, where electronic responses give rise to biologically important forces such as many-body polarisation and dispersion. The importance of polarisation in the condensed phase was recognised early on, as described by Cochran in 1959 [Philosophical Magazine 4 (1959) 1082–1086] [32]. Currently in molecular simulation, dispersion forces are treated at the two-body level and in the dipole limit, although the importance of three-body terms in the condensed phase was demonstrated by Barker in the 1980s [Phys. Rev. Lett. 57 (1986) 230–233] [72]. One approach for treating both polarisation and dispersion on an equal basis is to coarse grain the electrons surrounding a molecular moiety to a single quantum harmonic oscillator (cf. Hirschfelder, Curtiss and Bird 1954 [The Molecular Theory of Gases and Liquids (1954)] [37]). The approach, when solved in strong coupling beyond the dipole limit, gives a description of long-range forces that includes two- and many-body terms to all orders. In the last decade, the tools necessary to implement the strong coupling limit have been developed, culminating in a transferable model of water with excellent predictive power across the phase diagram. Transferability arises since the environment automatically identifies the important long range interactions, rather than the modeler through a limited set of expressions. Here, we discuss the role of electronic coarse-graining in predictive multiscale materials modelling and describe the first implementation of the method in a general purpose molecular dynamics software: QDO-MD. - Highlights: • Electronic coarse graining unites many-body dispersion and polarisation beyond the dipole limit. • It consists of replacing the electrons of a molecule using a quantum harmonic oscillator, called a

Toxicity challenges in environmental chemicals: Prediction of ...

Science.gov (United States)

Physiologically based pharmacokinetic (PBPK) models bridge the gap between in vitro assays and in vivo effects by accounting for the adsorption, distribution, metabolism, and excretion of xenobiotics, which is especially useful in the assessment of human toxicity. Quantitative structure-activity relationships (QSAR) serve as a vital tool for the high-throughput prediction of chemical-specific PBPK parameters, such as the fraction of a chemical unbound by plasma protein (Fub). The presented work explores the merit of utilizing experimental pharmaceutical Fub data for the construction of a universal QSAR model, in order to compensate for the limited range of high-quality experimental Fub data for environmentally relevant chemicals, such as pollutants, pesticides, and consumer products. Independent QSAR models were constructed with three machine-learning algorithms, k nearest neighbors (kNN), random forest (RF), and support vector machine (SVM) regression, from a large pharmaceutical training set (~1000) and assessed with independent test sets of pharmaceuticals (~200) and environmentally relevant chemicals in the ToxCast program (~400). Small descriptor sets yielded the optimal balance of model complexity and performance, providing insight into the biochemical factors of plasma protein binding, while preventing over fitting to the training set. Overlaps in chemical space between pharmaceutical and environmental compounds were considered through applicability of do

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

Science.gov (United States)

Vidyasagar, Mathukumalli

2015-01-01

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

Quasiparticle Approach to Molecules Interacting with Quantum Solvents.

Science.gov (United States)

Lemeshko, Mikhail

2017-03-03

Understanding the behavior of molecules interacting with superfluid helium represents a formidable challenge and, in general, requires approaches relying on large-scale numerical simulations. Here, we demonstrate that experimental data collected over the last 20 years provide evidence that molecules immersed in superfluid helium form recently predicted angulon quasiparticles [Phys. Rev. Lett. 114, 203001 (2015)PRLTAO0031-900710.1103/PhysRevLett.114.203001]. Most important, casting the many-body problem in terms of angulons amounts to a drastic simplification and yields effective molecular moments of inertia as straightforward analytic solutions of a simple microscopic Hamiltonian. The outcome of the angulon theory is in good agreement with experiment for a broad range of molecular impurities, from heavy to medium-mass to light species. These results pave the way to understanding molecular rotation in liquid and crystalline phases in terms of the angulon quasiparticle.

The CC-Bio Project: Studying the Effects of Climate Change on Quebec Biodiversity

Directory of Open Access Journals (Sweden)

Luc Vescovi

2010-11-01

Full Text Available Anticipating the effects of climate change on biodiversity is now critical for managing wild species and ecosystems. Climate change is a global driver and thus affects biodiversity globally. However, land-use planners and natural resource managers need regional or even local predictions. This provides scientists with formidable challenges given the poor documentation of biodiversity and its complex relationships with climate. We are approaching this problem in Quebec, Canada, through the CC-Bio Project (http://cc‑bio.uqar.ca/, using a boundary organization as a catalyst for team work involving climate modelers, biologists, naturalists, and biodiversity managers. In this paper we present the CC-Bio Project and its general approach, some preliminary results, the emerging hypothesis of the northern biodiversity paradox (a potential increase of biodiversity in northern ecosystems due to climate change, and an early assessment of the conservation implications generated by our team work.

A Formidable Task: Reflections on obtaining legal empirical evidence on human trafficking in Canada

Directory of Open Access Journals (Sweden)

Hayli Millar

2017-04-01

Full Text Available This article explores the experiences, challenges and findings of two empirical research studies examining Canada’s legal efforts to combat human trafficking. The authors outline the methodologies of their respective studies and reflect on some of the difficulties they faced in obtaining empirical data on human trafficking court cases and legal proceedings. Ultimately, the authors found that Canadian trafficking case law developments are in their early stages with very few convictions, despite a growing number of police-reported charges. The authors assert it is difficult to assess the efficacy and effects of Canadian anti-trafficking laws and policies due to the institutional and political limitations to collecting legal data in this highly politicised subject area. They conclude with five recommendations to increase the transparency of Canada’s public claims about its anti-trafficking enforcement efforts and call for more empirically-based law reform.

Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2

Science.gov (United States)

Baumgartner, Matthew P.; Evans, David A.

2018-01-01

Two of the major ongoing challenges in computational drug discovery are predicting the binding pose and affinity of a compound to a protein. The Drug Design Data Resource Grand Challenge 2 was developed to address these problems and to drive development of new methods. The challenge provided the 2D structures of compounds for which the organizers help blinded data in the form of 35 X-ray crystal structures and 102 binding affinity measurements and challenged participants to predict the binding pose and affinity of the compounds. We tested a number of pose prediction methods as part of the challenge; we found that docking methods that incorporate protein flexibility (Induced Fit Docking) outperformed methods that treated the protein as rigid. We also found that using binding pose metadynamics, a molecular dynamics based method, to score docked poses provided the best predictions of our methods with an average RMSD of 2.01 Å. We tested both structure-based (e.g. docking) and ligand-based methods (e.g. QSAR) in the affinity prediction portion of the competition. We found that our structure-based methods based on docking with Smina (Spearman ρ = 0.614), performed slightly better than our ligand-based methods (ρ = 0.543), and had equivalent performance with the other top methods in the competition. Despite the overall good performance of our methods in comparison to other participants in the challenge, there exists significant room for improvement especially in cases such as these where protein flexibility plays such a large role.

Challenges of Aircraft Design Integration

Science.gov (United States)

2003-03-01

predicted by the conceptual stick model and the full FEM of the Challenger wing without winglets . Advanced aerodynamic wing design methods To design wings...Piperni, E. Laurendeau Advanced Aerodynamics Bombardier Aerospace 400 CMte Vertu Road Dorval, Quebec, Canada, H4S 1Y9 Fassi.Kafyeke @notes.canadair.ca Tel...514) 855-7186 Abstract The design of a modern airplane brings together many disciplines: structures, aerodynamics , controls, systems, propulsion

PGT: A Statistical Approach to Prediction and Mechanism Design

Science.gov (United States)

Wolpert, David H.; Bono, James W.

One of the biggest challenges facing behavioral economics is the lack of a single theoretical framework that is capable of directly utilizing all types of behavioral data. One of the biggest challenges of game theory is the lack of a framework for making predictions and designing markets in a manner that is consistent with the axioms of decision theory. An approach in which solution concepts are distribution-valued rather than set-valued (i.e. equilibrium theory) has both capabilities. We call this approach Predictive Game Theory (or PGT). This paper outlines a general Bayesian approach to PGT. It also presents one simple example to illustrate the way in which this approach differs from equilibrium approaches in both prediction and mechanism design settings.

Predictive Biomarkers for Asthma Therapy.

Science.gov (United States)

Medrek, Sarah K; Parulekar, Amit D; Hanania, Nicola A

2017-09-19

Asthma is a heterogeneous disease characterized by multiple phenotypes. Treatment of patients with severe disease can be challenging. Predictive biomarkers are measurable characteristics that reflect the underlying pathophysiology of asthma and can identify patients that are likely to respond to a given therapy. This review discusses current knowledge regarding predictive biomarkers in asthma. Recent trials evaluating biologic therapies targeting IgE, IL-5, IL-13, and IL-4 have utilized predictive biomarkers to identify patients who might benefit from treatment. Other work has suggested that using composite biomarkers may offer enhanced predictive capabilities in tailoring asthma therapy. Multiple biomarkers including sputum eosinophil count, blood eosinophil count, fractional concentration of nitric oxide in exhaled breath (FeNO), and serum periostin have been used to identify which patients will respond to targeted asthma medications. Further work is needed to integrate predictive biomarkers into clinical practice.

Adventures in bridgehead substitution chemistry: synthesis of polycyclic polyprenylated acylphloroglucinols (PPAPs).

Science.gov (United States)

Simpkins, Nigel S

2013-02-04

The polycyclic polyprenylated acylphloroglucinol (PPAP) family of natural products includes important compounds with notable biological activities, such as garsubellin A, hyperforin and clusianone. The synthesis of these complex, bridged, highly oxidized and substituted systems presents a formidable challenge to synthetic chemists. This feature article describes how the use of unconventional bridgehead substitution chemistry has enabled the synthesis of these natural products and their analogues.

Genetic Localization of Foraging (For): A Major Gene for Larval Behavior in Drosophila Melanogaster

OpenAIRE

de-Belle, J. S.; Hilliker, A. J.; Sokolowski, M. B.

1989-01-01

Localizing genes for quantitative traits by conventional recombination mapping is a formidable challenge because environmental variation, minor genes, and genetic markers have modifying effects on continuously varying phenotypes. We describe ``lethal tagging,'' a method used in conjunction with deficiency mapping for localizing major genes associated with quantitative traits. Rover/sitter is a naturally occurring larval foraging polymorphism in Drosophila melanogaster which has a polygenic pa...

Data-aware remaining time prediction of business process instances

NARCIS (Netherlands)

Polato, M.; Sperduti, A.; Burattin, A.; Leoni, de M.

2014-01-01

Accurate prediction of the completion time of a business process instance would constitute a valuable tool when managing processes under service level agreement constraints. Such prediction, however, is a very challenging task. A wide variety of factors could influence the trend of a process

A predictive framework to understand forest responses to global change.

Science.gov (United States)

McMahon, Sean M; Dietze, Michael C; Hersh, Michelle H; Moran, Emily V; Clark, James S

2009-04-01

Forests are one of Earth's critical biomes. They have been shown to respond strongly to many of the drivers that are predicted to change natural systems over this century, including climate, introduced species, and other anthropogenic influences. Predicting how different tree species might respond to this complex of forces remains a daunting challenge for forest ecologists. Yet shifts in species composition and abundance can radically influence hydrological and atmospheric systems, plant and animal ranges, and human populations, making this challenge an important one to address. Forest ecologists have gathered a great deal of data over the past decades and are now using novel quantitative and computational tools to translate those data into predictions about the fate of forests. Here, after a brief review of the threats to forests over the next century, one of the more promising approaches to making ecological predictions is described: using hierarchical Bayesian methods to model forest demography and simulating future forests from those models. This approach captures complex processes, such as seed dispersal and mortality, and incorporates uncertainty due to unknown mechanisms, data problems, and parameter uncertainty. After describing the approach, an example by simulating drought for a southeastern forest is offered. Finally, there is a discussion of how this approach and others need to be cast within a framework of prediction that strives to answer the important questions posed to environmental scientists, but does so with a respect for the challenges inherent in predicting the future of a complex biological system.

Synthesis of User Needs for Arctic Sea Ice Predictions

Science.gov (United States)

Wiggins, H. V.; Turner-Bogren, E. J.; Sheffield Guy, L.

2017-12-01

Forecasting Arctic sea ice on sub-seasonal to seasonal scales in a changing Arctic is of interest to a diverse range of stakeholders. However, sea ice forecasting is still challenging due to high variability in weather and ocean conditions and limits to prediction capabilities; the science needs for observations and modeling are extensive. At a time of challenged science funding, one way to prioritize sea ice prediction efforts is to examine the information needs of various stakeholder groups. This poster will present a summary and synthesis of existing surveys, reports, and other literature that examines user needs for sea ice predictions. The synthesis will include lessons learned from the Sea Ice Prediction Network (a collaborative, multi-agency-funded project focused on seasonal Arctic sea ice predictions), the Sea Ice for Walrus Outlook (a resource for Alaska Native subsistence hunters and coastal communities, that provides reports on weather and sea ice conditions), and other efforts. The poster will specifically compare the scales and variables of sea ice forecasts currently available, as compared to what information is requested by various user groups.

Challenges of Managing Animals in Disasters in the U.S.

OpenAIRE

Heath, Sebastian; Linnabary, Robert

2015-01-01

Simple Summary This article describes common challenges to managing animals in disasters in the US, summarizes how some of these challenges are being met and makes recommendations on how to overcome others. Many predictable adverse situations affecting animals and their owners can be prevented when communities develop a comprehensive emergency management strategy that integrates animal care into planning, preparedness, mitigation, and recovery activities, as well as response. Abstract Common ...

Predictive value of IgE/IgG4 antibody ratio in children with egg allergy

Directory of Open Access Journals (Sweden)

Okamoto Shindou

2012-06-01

Full Text Available Abstract Background The aim of this study was to investigate the role of specific IgG4 antibodies to hen’s egg white and determine their utility as a marker for the outcome of oral challenge test in children sensitized to hen’s egg Methods The hen’s egg oral food challenge test was performed in 105 sensitized children without atopic dermatitis, and the titers of egg white-specific immunoglobulin G4 (IgG4 and immunoglobulin E (IgE antibodies were measured. To set the cut-off values of IgG4, IgE, and the IgE/IgG4 ratio for predicting positive results in oral challenges, receiver operating characteristic curves were plotted and the area under the curves (AUC were calculated. Results Sixty-four of 105 oral challenges with whole eggs were assessed as positive. The AUC for IgE, IgG4, and IgE/IgG4 for the prediction of positive results were 0.609, 0.724, and 0.847, respectively. Thus, the IgE/IgG4 ratio generated significantly higher specificity, sensitivity, positive predictive value (%, and negative predictive value (% than the individual IgE and IgG4. The negative predictive value of the IgE/IgG4 ratio was 90% at a value of 1. Conclusions We have demonstrated that the egg white-specific serum IgE/IgG4 ratio is important for predicting reactivity to egg during food challenges.

Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction.

Science.gov (United States)

Ak, Ronay; Fink, Olga; Zio, Enrico

2016-08-01

The increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria.

Requirements for guidelines systems: implementation challenges and lessons from existing software-engineering efforts.

Science.gov (United States)

Shah, Hemant; Allard, Raymond D; Enberg, Robert; Krishnan, Ganesh; Williams, Patricia; Nadkarni, Prakash M

2012-03-09

A large body of work in the clinical guidelines field has identified requirements for guideline systems, but there are formidable challenges in translating such requirements into production-quality systems that can be used in routine patient care. Detailed analysis of requirements from an implementation perspective can be useful in helping define sub-requirements to the point where they are implementable. Further, additional requirements emerge as a result of such analysis. During such an analysis, study of examples of existing, software-engineering efforts in non-biomedical fields can provide useful signposts to the implementer of a clinical guideline system. In addition to requirements described by guideline-system authors, comparative reviews of such systems, and publications discussing information needs for guideline systems and clinical decision support systems in general, we have incorporated additional requirements related to production-system robustness and functionality from publications in the business workflow domain, in addition to drawing on our own experience in the development of the Proteus guideline system (http://proteme.org). The sub-requirements are discussed by conveniently grouping them into the categories used by the review of Isern and Moreno 2008. We cite previous work under each category and then provide sub-requirements under each category, and provide example of similar work in software-engineering efforts that have addressed a similar problem in a non-biomedical context. When analyzing requirements from the implementation viewpoint, knowledge of successes and failures in related software-engineering efforts can guide implementers in the choice of effective design and development strategies.

Testing the predictive power of nuclear mass models

International Nuclear Information System (INIS)

Mendoza-Temis, J.; Morales, I.; Barea, J.; Frank, A.; Hirsch, J.G.; Vieyra, J.C. Lopez; Van Isacker, P.; Velazquez, V.

2008-01-01

A number of tests are introduced which probe the ability of nuclear mass models to extrapolate. Three models are analyzed in detail: the liquid drop model, the liquid drop model plus empirical shell corrections and the Duflo-Zuker mass formula. If predicted nuclei are close to the fitted ones, average errors in predicted and fitted masses are similar. However, the challenge of predicting nuclear masses in a region stabilized by shell effects (e.g., the lead region) is far more difficult. The Duflo-Zuker mass formula emerges as a powerful predictive tool

Blood DNA methylation biomarkers predict clinical reactivity in food-sensitized infants.

Science.gov (United States)

Martino, David; Dang, Thanh; Sexton-Oates, Alexandra; Prescott, Susan; Tang, Mimi L K; Dharmage, Shyamali; Gurrin, Lyle; Koplin, Jennifer; Ponsonby, Anne-Louise; Allen, Katrina J; Saffery, Richard

2015-05-01

The diagnosis of food allergy (FA) can be challenging because approximately half of food-sensitized patients are asymptomatic. Current diagnostic tests are excellent makers of sensitization but poor predictors of clinical reactivity. Thus oral food challenges (OFCs) are required to determine a patient's risk of reactivity. We sought to discover genomic biomarkers of clinical FA with utility for predicting food challenge outcomes. Genome-wide DNA methylation (DNAm) profiling was performed on blood mononuclear cells from volunteers who had undergone objective OFCs, concurrent skin prick tests, and specific IgE tests. Fifty-eight food-sensitized patients (aged 11-15 months) were assessed, half of whom were clinically reactive. Thirteen nonallergic control subjects were also assessed. Reproducibility was assessed in an additional 48 samples by using methylation data from an independent population of patients with clinical FA. Using a supervised learning approach, we discovered a DNAm signature of 96 CpG sites that predict clinical outcomes. Diagnostic scores were derived from these 96 methylation sites, and cutoffs were determined in a sensitivity analysis. Methylation biomarkers outperformed allergen-specific IgE and skin prick tests for predicting OFC outcomes. FA status was correctly predicted in the replication cohort with an accuracy of 79.2%. DNAm biomarkers with clinical utility for predicting food challenge outcomes are readily detectable in blood. The development of this technology in detailed follow-up studies will yield highly innovative diagnostic assays. Copyright © 2015 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Real-time prediction of respiratory motion based on local regression methods

International Nuclear Information System (INIS)

Ruan, D; Fessler, J A; Balter, J M

2007-01-01

Recent developments in modulation techniques enable conformal delivery of radiation doses to small, localized target volumes. One of the challenges in using these techniques is real-time tracking and predicting target motion, which is necessary to accommodate system latencies. For image-guided-radiotherapy systems, it is also desirable to minimize sampling rates to reduce imaging dose. This study focuses on predicting respiratory motion, which can significantly affect lung tumours. Predicting respiratory motion in real-time is challenging, due to the complexity of breathing patterns and the many sources of variability. We propose a prediction method based on local regression. There are three major ingredients of this approach: (1) forming an augmented state space to capture system dynamics, (2) local regression in the augmented space to train the predictor from previous observation data using semi-periodicity of respiratory motion, (3) local weighting adjustment to incorporate fading temporal correlations. To evaluate prediction accuracy, we computed the root mean square error between predicted tumor motion and its observed location for ten patients. For comparison, we also investigated commonly used predictive methods, namely linear prediction, neural networks and Kalman filtering to the same data. The proposed method reduced the prediction error for all imaging rates and latency lengths, particularly for long prediction lengths

Limits of predictability for large-scale urban vehicular mobility

OpenAIRE

Li, Yong; Jin, Depeng; Hui, Pan; Wang, Zhaocheng; Chen, Sheng

2014-01-01

Key challenges in vehicular transportation and communication systems are understanding vehicular mobility and utilizing mobility prediction, which are vital for both solving the congestion problem and helping to build efficient vehicular communication networking. Most of the existing works mainly focus on designing algorithms for mobility prediction and exploring utilization of these algorithms. However, the crucial questions of how much the mobility is predictable and how the mobility predic...

Microvascularity, blood flow and tissue structure at the subchondral plate using an X-ray fluorescence technique

International Nuclear Information System (INIS)

Muthuvelu, P.; Ellis, R.E.; Green, E.M.; Attenburrow, D.; Arkill, K.; Colridge, D.B.; Winlove, C.P.; Bradley, D.A.

2007-01-01

The measurement of blood flow and blood in bone and cartilaginous tissues is crucial to understanding of the development of various diseases, but it presents a formidable technical challenge. We have therefore developed a method based on the detection of metallized microspheres using X-ray fluorescence. This approach provides unrivalled sensitivity and spatial resolution and also allows us simultaneously to measure other markers of the metabolic status of the tissue. (author)

Moving Beyond the Stigma: Systematic Review of Video Games and Their Potential to Combat Obesity

OpenAIRE

Guy, Stacey; Ratzki-Leewing, Alexandria; Gwadry-Sridhar, Femida

2011-01-01

Increasing epidemic proportions of overweight children in the United States presents formidable challenges for education and healthcare. Given the popularity and pervasiveness of video gaming culture in North American children, the perfect opportunity arises to investigate the potential of video games to promote healthful behaviour. Our objective was to systematically review the literature for possible benefits of active and educational video games targeting diet and physical activity in chil...

US Accelerator R&D Program Toward Intensity Frontier Machines

Energy Technology Data Exchange (ETDEWEB)

Shiltsev, Vladimir [Fermilab

2016-09-15

The 2014 P5 report indicated the accelerator-based neutrino and rare decay physics research as a centerpiece of the US domestic HEP program. Operation, upgrade and development of the accelerators for the near-term and longer-term particle physics program at the Intensity Frontier face formidable challenges. Here we discuss key elements of the accelerator physics and technology R&D program toward future multi-MW proton accelerators.

Understanding the Nature and Reactivity of Residual Lignin for Improved Pulping and Bleaching Efficiency

Energy Technology Data Exchange (ETDEWEB)

Yuan-Zong Lai

2001-11-30

One of the most formidable challenges in kraft pulping to produce bleached chemical pulps is how to effectively remove the last 5-10% of lignin while maintaining the fiber quality. To avoid a severe fiber degradation, kraft pulping is usually terminated in the 25-30 kappa number range and then followed by an elementally chlorine free (ECF) or a totally chlorine free (TCF) bleaching sequence to reduce the environmental impacts.

Understanding the Nature and Reactivity of Residual Lignin for Improved Pulping and Bleaching Efficiency; FINAL

International Nuclear Information System (INIS)

Yuan-Zong Lai

2001-01-01

One of the most formidable challenges in kraft pulping to produce bleached chemical pulps is how to effectively remove the last 5-10% of lignin while maintaining the fiber quality. To avoid a severe fiber degradation, kraft pulping is usually terminated in the 25-30 kappa number range and then followed by an elementally chlorine free (ECF) or a totally chlorine free (TCF) bleaching sequence to reduce the environmental impacts

Prediction of human population responses to toxic compounds by a collaborative competition.

Science.gov (United States)

Eduati, Federica; Mangravite, Lara M; Wang, Tao; Tang, Hao; Bare, J Christopher; Huang, Ruili; Norman, Thea; Kellen, Mike; Menden, Michael P; Yang, Jichen; Zhan, Xiaowei; Zhong, Rui; Xiao, Guanghua; Xia, Menghang; Abdo, Nour; Kosyk, Oksana; Friend, Stephen; Dearry, Allen; Simeonov, Anton; Tice, Raymond R; Rusyn, Ivan; Wright, Fred A; Stolovitzky, Gustavo; Xie, Yang; Saez-Rodriguez, Julio

2015-09-01

The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.

Numerical prediction of slamming loads

DEFF Research Database (Denmark)

Seng, Sopheak; Jensen, Jørgen J; Pedersen, Preben T

2012-01-01

It is important to include the contribution of the slamming-induced response in the structural design of large vessels with a significant bow flare. At the same time it is a challenge to develop rational tools to determine the slamming-induced loads and the prediction of their occurrence. Today i...

Radiomic analysis in prediction of Human Papilloma Virus status.

Science.gov (United States)

Yu, Kaixian; Zhang, Youyi; Yu, Yang; Huang, Chao; Liu, Rongjie; Li, Tengfei; Yang, Liuqing; Morris, Jeffrey S; Baladandayuthapani, Veerabhadran; Zhu, Hongtu

2017-12-01

Human Papilloma Virus (HPV) has been associated with oropharyngeal cancer prognosis. Traditionally the HPV status is tested through invasive lab test. Recently, the rapid development of statistical image analysis techniques has enabled precise quantitative analysis of medical images. The quantitative analysis of Computed Tomography (CT) provides a non-invasive way to assess HPV status for oropharynx cancer patients. We designed a statistical radiomics approach analyzing CT images to predict HPV status. Various radiomics features were extracted from CT scans, and analyzed using statistical feature selection and prediction methods. Our approach ranked the highest in the 2016 Medical Image Computing and Computer Assisted Intervention (MICCAI) grand challenge: Oropharynx Cancer (OPC) Radiomics Challenge, Human Papilloma Virus (HPV) Status Prediction. Further analysis on the most relevant radiomic features distinguishing HPV positive and negative subjects suggested that HPV positive patients usually have smaller and simpler tumors.

Entering the New Millennium: Dilemmas in Arms Control

Energy Technology Data Exchange (ETDEWEB)

BROWN,JAMES

1999-11-01

The end of the Cold War finds the international community no longer divided into two opposing blocks. The concerns that the community now faces are becoming more fluid, less focused, and, in many ways, much less predictable. Issues of religion, ethnicity, and nationalism; the possible proliferation of Weapons of Mass Destruction; and the diffusion of technology and information processing throughout the world community have greatly changed the international security landscape in the last decade. Although our challenges appear formidable, the United Nations, State Parties, nongovernmental organizations, and the arms control community are moving to address and lessen these concerns through both formal and informal efforts. Many of the multilateral agreements (e.g., NPT, BWC, CWC, CTBT, MTCR), as well as the bilateral efforts that are taking place between Washington and Moscow employ confidence-building and transparency measures. These measures along with on-site inspection and other verification procedures lessen suspicion and distrust and reduce uncertainty, thus enhancing stability, confidence, and cooperation.

Entering the New Millennium: Dilemmas in Arms Control; TOPICAL

International Nuclear Information System (INIS)

BROWN, JAMES

1999-01-01

The end of the Cold War finds the international community no longer divided into two opposing blocks. The concerns that the community now faces are becoming more fluid, less focused, and, in many ways, much less predictable. Issues of religion, ethnicity, and nationalism; the possible proliferation of Weapons of Mass Destruction; and the diffusion of technology and information processing throughout the world community have greatly changed the international security landscape in the last decade. Although our challenges appear formidable, the United Nations, State Parties, nongovernmental organizations, and the arms control community are moving to address and lessen these concerns through both formal and informal efforts. Many of the multilateral agreements (e.g., NPT, BWC, CWC, CTBT, MTCR), as well as the bilateral efforts that are taking place between Washington and Moscow employ confidence-building and transparency measures. These measures along with on-site inspection and other verification procedures lessen suspicion and distrust and reduce uncertainty, thus enhancing stability, confidence, and cooperation

Seven challenges for neuroscience.

Science.gov (United States)

Markram, Henry

2013-01-01

Although twenty-first century neuroscience is a major scientific enterprise, advances in basic research have not yet translated into benefits for society. In this paper, I outline seven fundamental challenges that need to be overcome. First, neuroscience has to become "big science" - we need big teams with the resources and competences to tackle the big problems. Second, we need to create interlinked sets of data providing a complete picture of single areas of the brain at their different levels of organization with "rungs" linking the descriptions for humans and other species. Such "data ladders" will help us to meet the third challenge - the development of efficient predictive tools, enabling us to drastically increase the information we can extract from expensive experiments. The fourth challenge goes one step further: we have to develop novel hardware and software sufficiently powerful to simulate the brain. In the future, supercomputer-based brain simulation will enable us to make in silico manipulations and recordings, which are currently completely impossible in the lab. The fifth and sixth challenges are translational. On the one hand we need to develop new ways of classifying and simulating brain disease, leading to better diagnosis and more effective drug discovery. On the other, we have to exploit our knowledge to build new brain-inspired technologies, with potentially huge benefits for industry and for society. This leads to the seventh challenge. Neuroscience can indeed deliver huge benefits but we have to be aware of widespread social concern about our work. We need to recognize the fears that exist, lay them to rest, and actively build public support for neuroscience research. We have to set goals for ourselves that the public can recognize and share. And then we have to deliver on our promises. Only in this way, will we receive the support and funding we need.

Baseline Assessment and Prioritization Framework for IVHM Integrity Assurance Enabling Capabilities

Science.gov (United States)

Cooper, Eric G.; DiVito, Benedetto L.; Jacklin, Stephen A.; Miner, Paul S.

2009-01-01

Fundamental to vehicle health management is the deployment of systems incorporating advanced technologies for predicting and detecting anomalous conditions in highly complex and integrated environments. Integrated structural integrity health monitoring, statistical algorithms for detection, estimation, prediction, and fusion, and diagnosis supporting adaptive control are examples of advanced technologies that present considerable verification and validation challenges. These systems necessitate interactions between physical and software-based systems that are highly networked with sensing and actuation subsystems, and incorporate technologies that are, in many respects, different from those employed in civil aviation today. A formidable barrier to deploying these advanced technologies in civil aviation is the lack of enabling verification and validation tools, methods, and technologies. The development of new verification and validation capabilities will not only enable the fielding of advanced vehicle health management systems, but will also provide new assurance capabilities for verification and validation of current generation aviation software which has been implicated in anomalous in-flight behavior. This paper describes the research focused on enabling capabilities for verification and validation underway within NASA s Integrated Vehicle Health Management project, discusses the state of the art of these capabilities, and includes a framework for prioritizing activities.

Time-series prediction of shellfish farm closure: A comparison of alternatives

Directory of Open Access Journals (Sweden)

Ashfaqur Rahman

2014-08-01

Full Text Available Shellfish farms are closed for harvest when microbial pollutants are present. Such pollutants are typically present in rainfall runoff from various land uses in catchments. Experts currently use a number of observable parameters (river flow, rainfall, salinity as proxies to determine when to close farms. We have proposed using the short term historical rainfall data as a time-series prediction problem where we aim to predict the closure of shellfish farms based only on rainfall. Time-series event prediction consists of two steps: (i feature extraction, and (ii prediction. A number of data mining challenges exist for these scenarios: (i which feature extraction method best captures the rainfall pattern over successive days that leads to opening or closure of the farms?, (ii The farm closure events occur infrequently and this leads to a class imbalance problem; the question is what is the best way to deal with this problem? In this paper we have analysed and compared different combinations of balancing methods (under-sampling and over-sampling, feature extraction methods (cluster profile, curve fitting, Fourier Transform, Piecewise Aggregate Approximation, and Wavelet Transform and learning algorithms (neural network, support vector machine, k-nearest neighbour, decision tree, and Bayesian Network to predict closure events accurately considering the above data mining challenges. We have identified the best combination of techniques to accurately predict shellfish farm closure from rainfall, given the above data mining challenges.

Predicting_Systemic_Toxicity_Effects_ArchTox_2017_Data

Data.gov (United States)

U.S. Environmental Protection Agency — In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was...

Predicting the Future Contribution of Himalayan Debris-covered Glaciers to River Discharge: Advances and Challenges

Science.gov (United States)

Quincey, D. J.; Hubbard, B. P.; Klaar, M. J.; Miles, E.; Miles, K.; Rowan, A. V.; King, O.; Watson, C. S.

2017-12-01

The glaciers and snowfields of the Himalaya are the ultimate source for the many rivers that flow across the Asian subcontinent, but they are diminishing rapidly in the face of sustained climatic change. Predictions of how future river discharge may vary through space and time are hampered by two major knowledge gaps. First, simulations of glacier mass loss in high Asia are severely limited by data availability and assumptions made in the parameterisation of glacier models. Consequently, projections of glacier change vary widely; in Nepal for example, recent estimates of volumetric ice loss by AD2100 have ranged between 8% and 99%. A second major gap in knowledge lies in the coupling between glaciers and downstream areas, and specifically in quantifying the relative contributions of different sources to river flow. Although it is clear that ice and snow melt dominates flow for considerable distances downstream, how this contribution interacts with groundwater supplies with increasing distance from its source remains poorly understood. This presentation will review recent work that closes some of the knowledge gaps in understanding debris-covered glacier behaviour including new results from drilling work on the Khumbu Glacier in Nepal. Additionally, it will report on the outputs from an interdisciplinary study in the Annapurna region of Nepal, which is focussing specifically on disaggregating the relative contributions to flow using isotope-based hydrograph separations. It will finish by exploring the most likely drivers of future changes to water supply, including an evaluation of the impact of glacial lake development, and by identifying the main challenges for future related research.

Prediction of porosity of food materials during drying: Current challenges and directions.

Science.gov (United States)

Joardder, Mohammad U H; Kumar, C; Karim, M A

2017-07-18

Pore formation in food samples is a common physical phenomenon observed during dehydration processes. The pore evolution during drying significantly affects the physical properties and quality of dried foods. Therefore, it should be taken into consideration when predicting transport processes in the drying sample. Characteristics of pore formation depend on the drying process parameters, product properties and processing time. Understanding the physics of pore formation and evolution during drying will assist in accurately predicting the drying kinetics and quality of food materials. Researchers have been trying to develop mathematical models to describe the pore formation and evolution during drying. In this study, existing porosity models are critically analysed and limitations are identified. Better insight into the factors affecting porosity is provided, and suggestions are proposed to overcome the limitations. These include considerations of process parameters such as glass transition temperature, sample temperature, and variable material properties in the porosity models. Several researchers have proposed models for porosity prediction of food materials during drying. However, these models are either very simplistic or empirical in nature and failed to consider relevant significant factors that influence porosity. In-depth understanding of characteristics of the pore is required for developing a generic model of porosity. A micro-level analysis of pore formation is presented for better understanding, which will help in developing an accurate and generic porosity model.

Lifesaving emergency obstetric services are inadequate in south-west Ethiopia: a formidable challenge to reducing maternal mortality in Ethiopia.

Science.gov (United States)

Girma, Meseret; Yaya, Yaliso; Gebrehanna, Ewenat; Berhane, Yemane; Lindtjørn, Bernt

2013-11-04

Most maternal deaths take place during labour and within a few weeks after delivery. The availability and utilization of emergency obstetric care facilities is a key factor in reducing maternal mortality; however, there is limited evidence about how these institutions perform and how many people use emergency obstetric care facilities in rural Ethiopia. We aimed to assess the availability, quality, and utilization of emergency obstetric care services in the Gamo Gofa Zone of south-west Ethiopia. We conducted a retrospective review of three hospitals and 63 health centres in Gamo Gofa. Using a retrospective review, we recorded obstetric services, documents, cards, and registration books of mothers treated and served in the Gamo Gofa Zone health facilities between July 2009 and June 2010. There were three basic and two comprehensive emergency obstetric care qualifying facilities for the 1,740,885 people living in Gamo Gofa. The proportion of births attended by skilled attendants in the health facilities was 6.6% of expected births, though the variation was large. Districts with a higher proportion of midwives per capita, hospitals and health centres capable of doing emergency caesarean sections had higher institutional delivery rates. There were 521 caesarean sections (0.8% of 64,413 expected deliveries and 12.3% of 4,231 facility deliveries). We recorded 79 (1.9%) maternal deaths out of 4,231 deliveries and pregnancy-related admissions at institutions, most often because of post-partum haemorrhage (42%), obstructed labour (15%) and puerperal sepsis (15%). Remote districts far from the capital of the Zone had a lower proportion of institutional deliveries (4% of deliveries, much higher than the average 1.9%). Based on a population of 1.7 million people, there should be 14 basic and four comprehensive emergency obstetric care (EmOC) facilities in the Zone. Our study found that only three basic and two comprehensive EmOC service qualifying facilities serve this large population which is below the UN's minimum recommendation. The utilization of the existing facilities for delivery was also low, which is clearly inadequate to reduce maternal deaths to the MDG target.

Theory of neutrinoless double beta decay

CERN Document Server

Vergados, J.D.; Simkovic, F.

2012-01-01

Neutrinoless double beta decay, which is a very old and yet elusive process, is reviewed. Its observation will signal that lepton number is not conserved and the neutrinos are Majorana particles. More importantly it is our best hope for determining the absolute neutrino mass scale at the level of a few tens of meV. To achieve the last goal certain hurdles have to be overcome involving particle, nuclear and experimental physics. Nuclear physics is important for extracting the useful information from the data. One must accurately evaluate the relevant nuclear matrix elements, a formidable task. To this end, we review the sophisticated nuclear structure approaches recently been developed, which give confidence that the needed nuclear matrix elements can be reliably calculated. From an experimental point of view it is challenging, since the life times are long and one has to fight against formidable backgrounds. If a signal is found, it will be a tremendous accomplishment. Then, of course, the real task is going ...

Are attractive male crickets better able to pay the costs of an immune challenge?

Science.gov (United States)

Telemeco, Melissa S.C.; Bartholomay, Lyric C.

2015-01-01

Reproduction and immunity are fitness-related traits that trade-off with each other. Parasite-mediated theories of sexual selection suggest, however, that higher-quality males should suffer smaller costs to reproduction-related traits and behaviours (e.g., sexual display) from an immune challenge because these males possess more resources with which to deal with the challenge. We used Gryllus texensis field crickets to test the prediction that attractive males should better maintain the performance of fitness-related traits (e.g., calling effort) in the face of an immune challenge compared with unattractive males. We found no support for our original predictions. However, that immune activation causes attractive males to significantly increase their calling effort compared with unattractive males suggests that these males might terminally invest in order to compensate for decreased future reproduction. PMID:26713249

Trading network predicts stock price.

Science.gov (United States)

Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi

2014-01-16

Stock price prediction is an important and challenging problem for studying financial markets. Existing studies are mainly based on the time series of stock price or the operation performance of listed company. In this paper, we propose to predict stock price based on investors' trading behavior. For each stock, we characterize the daily trading relationship among its investors using a trading network. We then classify the nodes of trading network into three roles according to their connectivity pattern. Strong Granger causality is found between stock price and trading relationship indices, i.e., the fraction of trading relationship among nodes with different roles. We further predict stock price by incorporating these trading relationship indices into a neural network based on time series of stock price. Experimental results on 51 stocks in two Chinese Stock Exchanges demonstrate the accuracy of stock price prediction is significantly improved by the inclusion of trading relationship indices.

Deep Visual Attention Prediction

Science.gov (United States)

Wang, Wenguan; Shen, Jianbing

2018-05-01

In this work, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although Convolutional Neural Networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve CNN based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark datasets demonstrate our method yields state-of-the-art performance with competitive inference time.

Experimental approaches to predict allergenic potential of novel food

DEFF Research Database (Denmark)

Madsen, Charlotte Bernhard; Kroghsbo, Stine; Bøgh, Katrine Lindholm

2013-01-01

’t know under what circumstances oral tolerance develops. With all these unanswered questions, it is a big challenge to designan animal model that, with relatively few animals, is able to predict if a food protein is a potential allergen. An even larger challenge is to predict its potency, a prerequisite...... for risk evaluation.Attempts have been made to rank proteins according to their allergenic potency based on the magnitude of the IgE response in experimental animals. This ranking has not included abundance as a parameter. We may be able to predict potential allergenicity i.e. hazard but our lack......There are many unanswered questions relating to food allergy sensitization in humans. We don’t know under what circumstances sensitization takes place i.e. route (oral, dermal, respiratory), age, dose, frequencyof exposure, infection or by-stander effect of other allergens. In addition we don...

Gender and Professionalism in Law: The Challenge of (Women’s Biography

Directory of Open Access Journals (Sweden)

Mary Jane Mossman

2009-02-01

Full Text Available This paper explores the story of a woman who “created” her life in the law in the late nineteenth and early twentieth centuries. Although now almost unknown, Cornelia Sorabji achieved prominence as a woman pioneer in the legal profession, who provided legal services to women clients in northern India, the Purdahnashins. Sorabji’s experiences as a woman in law were often similar to the stories of other first women lawyers in a number of different jurisdictions at the end of the nineteenth century: all of these women had to overcome gender barriers to gain admission to the legal professions, and they were often the only woman in law in their jurisdictions for many years. Yet, as Sorabji’s story reveals, while ideas about gender and the culture of legal professionalism could present formidable barriers for aspiring women lawyers, these ideas sometimes intersected in paradoxical ways to offer new opportunities for women to become legal professionals. In exploring the impact of gender and legal professionalism on Sorabji’s legal work, the paper also suggests that her story presents a number of challenges and contradictions that may require new approaches to gender history so as to capture the complexity of stories about women lawyers. Cet article examine l’histoire d’une femme qui a «créé» sa vie dans le domaine du droit à la fin du dix-neuvième et au début du vingtième siècles. Quoique présentement presque inconnue, Cornelia Sorabji a acquis une certaine renommée comme femme pionnière dans la profession juridique qui offrait des services juridiques à des femmes clientes dans le nord de l’Inde, les Purdahnashins. Les expériences de Mme Sorabji en tant que femme dans le domaine du droit ressemblaient souvent aux récits d’autres premières femmes avocates sur un nombre d’autres territoires à la fin du dix-neuvième siècle : ces femmes devaient toutes surmonter des barrières sexistes pour être admises à la

Challenges and progress in predicting biological responses to incorporated radioactivity

International Nuclear Information System (INIS)

Howell, R. W.; Neti, P. V. S. V.; Pinto, M.; Gerashchenko, B. I.; Narra, V. R.; Azzam, E. I.

2006-01-01

Prediction of risks and therapeutic outcome in nuclear medicine largely rely on calculation of the absorbed dose. Absorbed dose specification is complex due to the wide variety of radiations emitted, non-uniform activity distribution, biokinetics, etc. Conventional organ absorbed dose estimates assumed that radioactivity is distributed uniformly throughout the organ. However, there have been dramatic improvements in dosimetry models that reflect the substructure of organs as well as tissue elements within them. These models rely on improved nuclear medicine imaging capabilities that facilitate determination of activity within voxels that represent tissue elements of ∼0.2-1 cm 3 . However, even these improved approaches assume that all cells within the tissue element receive the same dose. The tissue element may be comprised of a variety of cells having different radiosensitivities and different incorporated radioactivity. Furthermore, the extent to which non-uniform distributions of radioactivity within a small tissue element impact the absorbed dose distribution is strongly dependent on the number, type, and energy of the radiations emitted by the radionuclide. It is also necessary to know whether the dose to a given cell arises from radioactive decays within itself (self-dose) or decays in surrounding cells (cross-dose). Cellular response to self-dose can be considerably different than its response to cross-dose from the same radiopharmaceutical. Bystander effects can also play a role in the response. Evidence shows that even under conditions of 'uniform' distribution of radioactivity, a combination of organ dosimetry, voxel dosimetry and dosimetry at the cellular and multicellular levels can be required to predict response. (authors)

The economic effect and outcome of delaying oral food challenges.

Science.gov (United States)

Couch, Christopher; Franxman, Tim; Greenhawt, Matthew

2016-05-01

Food specific IgE (sIgE) is a useful marker to assess predictability of oral food challenge (OFC) outcome. A threshold of less than 2 kUA/L for peanut, egg, and milk has been proposed as a 50% negative predictive value at which patients may pass an OFC. To assess the economic effect and outcome of delaying OFCs. A retrospective analysis was performed for peanut, egg, and milk OFCs conducted between 2001 and 2012 at a tertiary food allergy referral center. Delayed OFC was defined as greater than 12 months from the time the sIgE level became less than 2 kUA/L. Time to OFC was explored in association with skin prick test result (wheal size), OFC outcome, and the economic effect of delay. Of 319 challenges, 173 OFCs were delayed (54.2%) by a mean time of 35.5 months (range, 13-123 months) vs a mean time of 4.2 months in the 146 challenges that were not delayed (P care system. Copyright © 2016 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Water challenges of the future; how scientific understanding can help

Science.gov (United States)

Young, G.

2012-04-01

Demands for water resources are diverse and are increasing as human populations grow and become more concentrated in urban areas and as economies develop. Water is essential for many uses including the basic human needs of food and the maintenance of good health, for many industries and the creation of electrical energy and as vital for the sustenance of the natural ecosystems on which all life is dependent. At the same time threats from water - floods, droughts - are increasing with these extreme events becoming more common and more intense in many regions of the world and as more people locate in flood- and drought-prone regions. In general, the challenges for water managers are thus becoming greater; managers not only are having to make increasingly difficult decisions regarding allocation of water resources between competing uses as demand outstrips supply, but they also have to take measures to protect societies from the ravages of extreme events. The intensity of the challenges facing water managers is not uniform throughout the world - many nations in the less developed world experiencing far greater problems than most highly developed nations - but the trend towards greater challenges is clear. Decision-makers, whether at the international, national, provincial or local level benefit from reliable information on water resources. They need information on the availability in quantity and quality of water from a variety of sources - surface waters, aquifers or from artificial sources such as re-cycling of wastewater and desalination techniques. Managers also need reliable predictions on water availability for the various uses to which water is put - such predictions are needed on time scales from weeks to decades to inform decision-making. Predictions are also needed on the probabilities of occurrence of extreme events. Thus hydrological scientists developing predictive models and working within a fast-changing world have much to contribute to the needs of

HEPEX - achievements and challenges!

Science.gov (United States)

Pappenberger, Florian; Ramos, Maria-Helena; Thielen, Jutta; Wood, Andy; Wang, Qj; Duan, Qingyun; Collischonn, Walter; Verkade, Jan; Voisin, Nathalie; Wetterhall, Fredrik; Vuillaume, Jean-Francois Emmanuel; Lucatero Villasenor, Diana; Cloke, Hannah L.; Schaake, John; van Andel, Schalk-Jan

2014-05-01

HEPEX is an international initiative bringing together hydrologists, meteorologists, researchers and end-users to develop advanced probabilistic hydrological forecast techniques for improved flood, drought and water management. HEPEX was launched in 2004 as an independent, cooperative international scientific activity. During the first meeting, the overarching goal was defined as: "to develop and test procedures to produce reliable hydrological ensemble forecasts, and to demonstrate their utility in decision making related to the water, environmental and emergency management sectors." The applications of hydrological ensemble predictions span across large spatio-temporal scales, ranging from short-term and localized predictions to global climate change and regional modeling. Within the HEPEX community, information is shared through its blog (www.hepex.org), meetings, testbeds and intercompaison experiments, as well as project reportings. Key questions of HEPEX are: * What adaptations are required for meteorological ensemble systems to be coupled with hydrological ensemble systems? * How should the existing hydrological ensemble prediction systems be modified to account for all sources of uncertainty within a forecast? * What is the best way for the user community to take advantage of ensemble forecasts and to make better decisions based on them? This year HEPEX celebrates its 10th year anniversary and this poster will present a review of the main operational and research achievements and challenges prepared by Hepex contributors on data assimilation, post-processing of hydrologic predictions, forecast verification, communication and use of probabilistic forecasts in decision-making. Additionally, we will present the most recent activities implemented by Hepex and illustrate how everyone can join the community and participate to the development of new approaches in hydrologic ensemble prediction.

Comparative effectiveness of 50g glucose challenge test and risk ...

African Journals Online (AJOL)

... of 50g glucose challenge test and risk factor based screening in detection of ... Mean maternal and gestational ages at recruitment were 30.8+1.2 years and ... Predictive Value, PPV - 20%) compared to risk factors only (PPV- 11.1%).

Blood Brain Barrier: A Challenge for Effectual Therapy of Brain Tumors

Directory of Open Access Journals (Sweden)

Arijit Bhowmik

2015-01-01

Full Text Available Brain tumors are one of the most formidable diseases of mankind. They have only a fair to poor prognosis and high relapse rate. One of the major causes of extreme difficulty in brain tumor treatment is the presence of blood brain barrier (BBB. BBB comprises different molecular components and transport systems, which in turn create efflux machinery or hindrance for the entry of several drugs in brain. Thus, along with the conventional techniques, successful modification of drug delivery and novel therapeutic strategies are needed to overcome this obstacle for treatment of brain tumors. In this review, we have elucidated some critical insights into the composition and function of BBB and along with it we have discussed the effective methods for delivery of drugs to the brain and therapeutic strategies overcoming the barrier.

Probability-based collaborative filtering model for predicting gene–disease associations

OpenAIRE

Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

2017-01-01

Background Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene–disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. Methods We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our mo...

Using predictive analytics and big data to optimize pharmaceutical outcomes.

Science.gov (United States)

Hernandez, Inmaculada; Zhang, Yuting

2017-09-15

The steps involved, the resources needed, and the challenges associated with applying predictive analytics in healthcare are described, with a review of successful applications of predictive analytics in implementing population health management interventions that target medication-related patient outcomes. In healthcare, the term big data typically refers to large quantities of electronic health record, administrative claims, and clinical trial data as well as data collected from smartphone applications, wearable devices, social media, and personal genomics services; predictive analytics refers to innovative methods of analysis developed to overcome challenges associated with big data, including a variety of statistical techniques ranging from predictive modeling to machine learning to data mining. Predictive analytics using big data have been applied successfully in several areas of medication management, such as in the identification of complex patients or those at highest risk for medication noncompliance or adverse effects. Because predictive analytics can be used in predicting different outcomes, they can provide pharmacists with a better understanding of the risks for specific medication-related problems that each patient faces. This information will enable pharmacists to deliver interventions tailored to patients' needs. In order to take full advantage of these benefits, however, clinicians will have to understand the basics of big data and predictive analytics. Predictive analytics that leverage big data will become an indispensable tool for clinicians in mapping interventions and improving patient outcomes. Copyright © 2017 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Bayesian Graphical Models for Genomewide Association Studies

OpenAIRE

Verzilli, Claudio J.; Stallard, Nigel; Whittaker, John C.

2006-01-01

As the extent of human genetic variation becomes more fully characterized, the research community is faced with the challenging task of using this information to dissect the heritable components of complex traits. Genomewide association studies offer great promise in this respect, but their analysis poses formidable difficulties. In this article, we describe a computationally efficient approach to mining genotype-phenotype associations that scales to the size of the data sets currently being ...

Proteomic analysis of post-translational modifications

DEFF Research Database (Denmark)

Mann, Matthias; Jensen, Ole N

2003-01-01

Post-translational modifications modulate the activity of most eukaryote proteins. Analysis of these modifications presents formidable challenges but their determination generates indispensable insight into biological function. Strategies developed to characterize individual proteins are now...... systematically applied to protein populations. The combination of function- or structure-based purification of modified 'subproteomes', such as phosphorylated proteins or modified membrane proteins, with mass spectrometry is proving particularly successful. To map modification sites in molecular detail, novel...

Hospice clinical experiences for nursing students: living to the fullest.

Science.gov (United States)

Spicer, Sherri; Heller, Rebecca; Troth, Sarah

2015-01-01

Preparing future nurses to provide appropriate care for patients and their families at the end of life can be a formidable challenge for nurse educators. Most nursing schools thread end-of-life concepts throughout the curriculum. Grand Canyon University includes a 40-hour hospice clinical as a component of a home healthcare practicum. Students' weekly written reflections reveal the depth of affective learning that occurs during this experience. Article includes hospice materials and resources.

Understanding Money Demand in the Transition from a Centrally Planned to a Market Economy

OpenAIRE

Delatte, Anne-Laure; Fouquau, Julien; Holz, Carsten

2013-01-01

Fundamental changes in institutions during the transition from a centrally planned to a market economy present a formidable challenge to monetary policy decision makers. For the case of China, we examine the institutional changes in the monetary system during the process of transition and develop money demand functions that reflect these institutional changes. We consider seasonal unit roots and estimate long run, equilibrium money demand functions, explicitly taking into consideration the ch...

Statistical Teleodynamics: Toward a Theory of Emergence.

Science.gov (United States)

Venkatasubramanian, Venkat

2017-10-24

The central scientific challenge of the 21st century is developing a mathematical theory of emergence that can explain and predict phenomena such as consciousness and self-awareness. The most successful research program of the 20th century, reductionism, which goes from the whole to parts, seems unable to address this challenge. This is because addressing this challenge inherently requires an opposite approach, going from parts to the whole. In addition, reductionism, by the very nature of its inquiry, typically does not concern itself with teleology or purposeful behavior. Modeling emergence, in contrast, requires the addressing of teleology. Together, these two requirements present a formidable challenge in developing a successful mathematical theory of emergence. In this article, I describe a new theory of emergence, called statistical teleodynamics, that addresses certain aspects of the general problem. Statistical teleodynamics is a mathematical framework that unifies three seemingly disparate domains-purpose-free entities in statistical mechanics, human engineered teleological systems in systems engineering, and nature-evolved teleological systems in biology and sociology-within the same conceptual formalism. This theory rests on several key conceptual insights, the most important one being the recognition that entropy mathematically models the concept of fairness in economics and philosophy and, equivalently, the concept of robustness in systems engineering. These insights help prove that the fairest inequality of income is a log-normal distribution, which will emerge naturally at equilibrium in an ideal free market society. Similarly, the theory predicts the emergence of the three classes of network organization-exponential, scale-free, and Poisson-seen widely in a variety of domains. Statistical teleodynamics is the natural generalization of statistical thermodynamics, the most successful parts-to-whole systems theory to date, but this generalization is

Neural activity to a partner's facial expression predicts self-regulation after conflict

Science.gov (United States)

Hooker, Christine I.; Gyurak, Anett; Verosky, Sara; Miyakawa, Asako; Ayduk, Özlem

2009-01-01

Introduction Failure to self-regulate after an interpersonal conflict can result in persistent negative mood and maladaptive behaviors. Research indicates that lateral prefrontal cortex (LPFC) activity is related to the regulation of emotional experience in response to lab-based affective challenges, such as viewing emotional pictures. This suggests that compromised LPFC function may be a risk-factor for mood and behavior problems after an interpersonal stressor. However, it remains unclear whether LPFC activity to a lab-based affective challenge predicts self-regulation in real-life. Method We investigated whether LPFC activity to a lab-based affective challenge (negative facial expressions of a partner) predicts self-regulation after a real-life affective challenge (interpersonal conflict). During an fMRI scan, healthy, adult participants in committed, dating relationships (N = 27) viewed positive, negative, and neutral facial expressions of their partners. In an online daily-diary, participants reported conflict occurrence, level of negative mood, rumination, and substance-use. Results LPFC activity in response to the lab-based affective challenge predicted self-regulation after an interpersonal conflict in daily life. When there was no interpersonal conflict, LPFC activity was not related to the change in mood or behavior the next day. However, when an interpersonal conflict did occur, ventral LPFC (VLPFC) activity predicted the change in mood and behavior the next day, such that lower VLPFC activity was related to higher levels of negative mood, rumination, and substance-use. Conclusions Low LPFC function may be a vulnerability and high LPFC function may be a protective factor for the development of mood and behavior problems after an interpersonal stressor. PMID:20004365

Neural activity to a partner's facial expression predicts self-regulation after conflict.

Science.gov (United States)

Hooker, Christine I; Gyurak, Anett; Verosky, Sara C; Miyakawa, Asako; Ayduk, Ozlem

2010-03-01

Failure to self-regulate after an interpersonal conflict can result in persistent negative mood and maladaptive behaviors. Research indicates that lateral prefrontal cortex (LPFC) activity is related to emotion regulation in response to laboratory-based affective challenges, such as viewing emotional pictures. This suggests that compromised LPFC function may be a risk factor for mood and behavior problems after an interpersonal conflict. However, it remains unclear whether LPFC activity to a laboratory-based affective challenge predicts self-regulation in real life. We investigated whether LPFC activity to a laboratory-based affective challenge (negative facial expressions of a partner) predicts self-regulation after a real-life affective challenge (interpersonal conflict). During a functional magnetic resonance imaging scan, healthy, adult participants in committed relationships (n = 27) viewed positive, negative, and neutral facial expressions of their partners. In a three-week online daily diary, participants reported conflict occurrence, level of negative mood, rumination, and substance use. LPFC activity in response to the laboratory-based affective challenge predicted self-regulation after an interpersonal conflict in daily life. When there was no interpersonal conflict, LPFC activity was not related to mood or behavior the next day. However, when an interpersonal conflict did occur, ventral LPFC (VLPFC) activity predicted mood and behavior the next day, such that lower VLPFC activity was related to higher levels of negative mood, rumination, and substance use. Low LPFC function may be a vulnerability and high LPFC function may be a protective factor for the development of mood and behavior problems after an interpersonal stressor. Copyright 2010 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Comprehensive adaptive mesh refinement in wrinkling prediction analysis

NARCIS (Netherlands)

Selman, A.; Meinders, Vincent T.; Huetink, Han; van den Boogaard, Antonius H.

2002-01-01

Discretisation errors indicator, contact free wrinkling and wrinkling with contact indicators are, in a challenging task, brought together and used in a comprehensive approach to wrinkling prediction analysis in thin sheet metal forming processes.

The economy-energy CO{sub 2} connection: a review of trends and challenges

Energy Technology Data Exchange (ETDEWEB)

Darmstadter, J. [Resources for the Future, Washington, DC (United States)

2001-07-01

Though highly aggregative and a straightforward arithmetic identity, a useful 'decomposition' of the change in CO{sub 2} emissions breaks out four constituent elements: (1) population, (2) GDP/person, (3) energy consumption/unit GDP, and (4) CO{sub 2} emissions/unit energy consumption. Other things equal, slower population growth means less growth in CO{sub 2} release, while higher GDP/capita signifies a greater volume of CO{sub 2} emitted. The energy/GDP ratio measures an economy's aggregate energy intensity, reflecting structural, technological and energy-use characteristics of society. The CO{sub 2}/energy element spotlights the effect of a changing mix of energy sources with varying carbon characteristics. This paper concentrates in particular on the 3rd and 4th components of this dissection. In the case of the energy/GDP ratio, the author examines the impact of energy price change on energy demand as well as the contribution of 'autonomous' technological advance. Electronic commerce injects a growing and conceivably significant factor into enhanced energy efficiency. In the case of the CO{sub 2}/energy ratio, such developments as increased use of natural gas in electric generation and - more conjecturally - use of renewables, are likely to prove important. The prospect of a sharp turnaround in the trend of US (and other industrial country) CO{sub 2} emissions and of at least moderate deceleration in the case of developing countries is found to constitute a formidable, but by no means hopeless, challenge. The deterrent effect of rising energy prices would appear to be at least one condition for that goal to be attainable. 15 refs., 2 tabs.

Using Predictability for Lexical Segmentation.

Science.gov (United States)

Çöltekin, Çağrı

2017-09-01

This study investigates a strategy based on predictability of consecutive sub-lexical units in learning to segment a continuous speech stream into lexical units using computational modeling and simulations. Lexical segmentation is one of the early challenges during language acquisition, and it has been studied extensively through psycholinguistic experiments as well as computational methods. However, despite strong empirical evidence, the explicit use of predictability of basic sub-lexical units in models of segmentation is underexplored. This paper presents an incremental computational model of lexical segmentation for exploring the usefulness of predictability for lexical segmentation. We show that the predictability cue is a strong cue for segmentation. Contrary to earlier reports in the literature, the strategy yields state-of-the-art segmentation performance with an incremental computational model that uses only this particular cue in a cognitively plausible setting. The paper also reports an in-depth analysis of the model, investigating the conditions affecting the usefulness of the strategy. Copyright © 2016 Cognitive Science Society, Inc.

Are attractive male crickets better able to pay the costs of an immune challenge?

Directory of Open Access Journals (Sweden)

Clint D. Kelly

2015-12-01

Full Text Available Reproduction and immunity are fitness-related traits that trade-off with each other. Parasite-mediated theories of sexual selection suggest, however, that higher-quality males should suffer smaller costs to reproduction-related traits and behaviours (e.g., sexual display from an immune challenge because these males possess more resources with which to deal with the challenge. We used Gryllus texensis field crickets to test the prediction that attractive males should better maintain the performance of fitness-related traits (e.g., calling effort in the face of an immune challenge compared with unattractive males. We found no support for our original predictions. However, that immune activation causes attractive males to significantly increase their calling effort compared with unattractive males suggests that these males might terminally invest in order to compensate for decreased future reproduction.

Daily Streamflow Predictions in an Ungauged Watershed in Northern California Using the Precipitation-Runoff Modeling System (PRMS): Calibration Challenges when nearby Gauged Watersheds are Hydrologically Dissimilar

Science.gov (United States)

Dhakal, A. S.; Adera, S.

2017-12-01

Accurate daily streamflow prediction in ungauged watersheds with sparse information is challenging. The ability of a hydrologic model calibrated using nearby gauged watersheds to predict streamflow accurately depends on hydrologic similarities between the gauged and ungauged watersheds. This study examines daily streamflow predictions using the Precipitation-Runoff Modeling System (PRMS) for the largely ungauged San Antonio Creek watershed, a 96 km2 sub-watershed of the Alameda Creek watershed in Northern California. The process-based PRMS model is being used to improve the accuracy of recent San Antonio Creek streamflow predictions generated by two empirical methods. Although San Antonio Creek watershed is largely ungauged, daily streamflow data exists for hydrologic years (HY) 1913 - 1930. PRMS was calibrated for HY 1913 - 1930 using streamflow data, modern-day land use and PRISM precipitation distribution, and gauged precipitation and temperature data from a nearby watershed. The PRMS model was then used to generate daily streamflows for HY 1996-2013, during which the watershed was ungauged, and hydrologic responses were compared to two nearby gauged sub-watersheds of Alameda Creek. Finally, the PRMS-predicted daily flows between HY 1996-2013 were compared to the two empirically-predicted streamflow time series: (1) the reservoir mass balance method and (2) correlation of historical streamflows from 80 - 100 years ago between San Antonio Creek and a nearby sub-watershed located in Alameda Creek. While the mass balance approach using reservoir storage and transfers is helpful for estimating inflows to the reservoir, large discrepancies in daily streamflow estimation can arise. Similarly, correlation-based predicted daily flows which rely on a relationship from flows collected 80-100 years ago may not represent current watershed hydrologic conditions. This study aims to develop a method of streamflow prediction in the San Antonio Creek watershed by examining PRMS

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.

Science.gov (United States)

Sieberts, Solveig K; Zhu, Fan; García-García, Javier; Stahl, Eli; Pratap, Abhishek; Pandey, Gaurav; Pappas, Dimitrios; Aguilar, Daniel; Anton, Bernat; Bonet, Jaume; Eksi, Ridvan; Fornés, Oriol; Guney, Emre; Li, Hongdong; Marín, Manuel Alejandro; Panwar, Bharat; Planas-Iglesias, Joan; Poglayen, Daniel; Cui, Jing; Falcao, Andre O; Suver, Christine; Hoff, Bruce; Balagurusamy, Venkat S K; Dillenberger, Donna; Neto, Elias Chaibub; Norman, Thea; Aittokallio, Tero; Ammad-Ud-Din, Muhammad; Azencott, Chloe-Agathe; Bellón, Víctor; Boeva, Valentina; Bunte, Kerstin; Chheda, Himanshu; Cheng, Lu; Corander, Jukka; Dumontier, Michel; Goldenberg, Anna; Gopalacharyulu, Peddinti; Hajiloo, Mohsen; Hidru, Daniel; Jaiswal, Alok; Kaski, Samuel; Khalfaoui, Beyrem; Khan, Suleiman Ali; Kramer, Eric R; Marttinen, Pekka; Mezlini, Aziz M; Molparia, Bhuvan; Pirinen, Matti; Saarela, Janna; Samwald, Matthias; Stoven, Véronique; Tang, Hao; Tang, Jing; Torkamani, Ali; Vert, Jean-Phillipe; Wang, Bo; Wang, Tao; Wennerberg, Krister; Wineinger, Nathan E; Xiao, Guanghua; Xie, Yang; Yeung, Rae; Zhan, Xiaowei; Zhao, Cheng; Greenberg, Jeff; Kremer, Joel; Michaud, Kaleb; Barton, Anne; Coenen, Marieke; Mariette, Xavier; Miceli, Corinne; Shadick, Nancy; Weinblatt, Michael; de Vries, Niek; Tak, Paul P; Gerlag, Danielle; Huizinga, Tom W J; Kurreeman, Fina; Allaart, Cornelia F; Louis Bridges, S; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M; Bridges, S Louis; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M

2016-08-23

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

Scientific and social challenges for the management of fire-prone wildland-urban interfaces

Science.gov (United States)

Gill, A. Malcolm; Stephens, Scott L.

2009-09-01

At their worst, fires at the rural-urban or wildland-urban interface cause tragic loss of human lives and homes, but mitigating these fire effects through management elicits many social and scientific challenges. This paper addresses four interconnected management challenges posed by socially disastrous landscape fires. The issues concern various assets (particularly houses, human life and biodiversity), fuel treatments, and fire and human behaviours. The topics considered are: 'asset protection zones'; 'defensible space' and urban fire spread in relation to house ignition and loss; 'stay-or-go' policy and the prediction of time available for safe egress and the possible conflict between the creation of defensible space and wildland management objectives. The first scientific challenge is to model the effective width of an asset protection zone of an urban area. The second is to consider the effect of vegetation around a house, potentially defensible space, on fire arrival at the structure. The third scientific challenge is to present stakeholders with accurate information on rates of spread, and where the fire front is located, so as to allow them to plan safe egress or preparation time in their particular circumstances. The fourth scientific challenge is to be able to predict the effects of fires on wildland species composition. Associated with each scientific challenge is a social challenge: for the first two scientific challenges the social challenge is to co-ordinate fuel management within and between the urban and rural or wildland sides of the interface. For the third scientific challenge, the social challenge is to be aware of, and appropriately use, fire danger information so that the potential for safe egress from a home can be estimated most accurately. Finally, the fourth social challenge is to for local residents of wildland-urban interfaces with an interest in biodiversity conservation to understand the effects of fire regimes on biodiversity, thereby

Scientific and social challenges for the management of fire-prone wildland-urban interfaces

International Nuclear Information System (INIS)

Gill, A Malcolm; Stephens, Scott L

2009-01-01

At their worst, fires at the rural-urban or wildland-urban interface cause tragic loss of human lives and homes, but mitigating these fire effects through management elicits many social and scientific challenges. This paper addresses four interconnected management challenges posed by socially disastrous landscape fires. The issues concern various assets (particularly houses, human life and biodiversity), fuel treatments, and fire and human behaviours. The topics considered are: 'asset protection zones'; 'defensible space' and urban fire spread in relation to house ignition and loss; 'stay-or-go' policy and the prediction of time available for safe egress and the possible conflict between the creation of defensible space and wildland management objectives. The first scientific challenge is to model the effective width of an asset protection zone of an urban area. The second is to consider the effect of vegetation around a house, potentially defensible space, on fire arrival at the structure. The third scientific challenge is to present stakeholders with accurate information on rates of spread, and where the fire front is located, so as to allow them to plan safe egress or preparation time in their particular circumstances. The fourth scientific challenge is to be able to predict the effects of fires on wildland species composition. Associated with each scientific challenge is a social challenge: for the first two scientific challenges the social challenge is to co-ordinate fuel management within and between the urban and rural or wildland sides of the interface. For the third scientific challenge, the social challenge is to be aware of, and appropriately use, fire danger information so that the potential for safe egress from a home can be estimated most accurately. Finally, the fourth social challenge is to for local residents of wildland-urban interfaces with an interest in biodiversity conservation to understand the effects of fire regimes on biodiversity, thereby

Big data from electronic health records for early and late translational cardiovascular research: challenges and potential.

Science.gov (United States)

Hemingway, Harry; Asselbergs, Folkert W; Danesh, John; Dobson, Richard; Maniadakis, Nikolaos; Maggioni, Aldo; van Thiel, Ghislaine J M; Cronin, Maureen; Brobert, Gunnar; Vardas, Panos; Anker, Stefan D; Grobbee, Diederick E; Denaxas, Spiros

2018-04-21

Cohorts of millions of people's health records, whole genome sequencing, imaging, sensor, societal and publicly available data present a rapidly expanding digital trace of health. We aimed to critically review, for the first time, the challenges and potential of big data across early and late stages of translational cardiovascular disease research. We sought exemplars based on literature reviews and expertise across the BigData@Heart Consortium. We identified formidable challenges including: data quality, knowing what data exist, the legal and ethical framework for their use, data sharing, building and maintaining public trust, developing standards for defining disease, developing tools for scalable, replicable science and equipping the clinical and scientific work force with new inter-disciplinary skills. Opportunities claimed for big health record data include: richer profiles of health and disease from birth to death and from the molecular to the societal scale; accelerated understanding of disease causation and progression, discovery of new mechanisms and treatment-relevant disease sub-phenotypes, understanding health and diseases in whole populations and whole health systems and returning actionable feedback loops to improve (and potentially disrupt) existing models of research and care, with greater efficiency. In early translational research we identified exemplars including: discovery of fundamental biological processes e.g. linking exome sequences to lifelong electronic health records (EHR) (e.g. human knockout experiments); drug development: genomic approaches to drug target validation; precision medicine: e.g. DNA integrated into hospital EHR for pre-emptive pharmacogenomics. In late translational research we identified exemplars including: learning health systems with outcome trials integrated into clinical care; citizen driven health with 24/7 multi-parameter patient monitoring to improve outcomes and population-based linkages of multiple EHR sources

Challenges in parameter identification of large structural dynamic systems

International Nuclear Information System (INIS)

Koh, C.G.

2001-01-01

In theory, it is possible to determine the parameters of a structural or mechanical system by subjecting it to some dynamic excitation and measuring the response. Considerable research has been carried out in this subject area known as the system identification over the past two decades. Nevertheless, the challenges associated with numerical convergence are still formidable when the system is large in terms of the number of degrees of freedom and number of unknowns. While many methods work for small systems, the convergence becomes difficult, if not impossible, for large systems. In this keynote lecture, both classical and non-classical system identification methods for dynamic testing and vibration-based inspection are discussed. For classical methods, the extended Kalman filter (EKF) approach is used. On this basis, a substructural identification method has been developed as a strategy to deal with large structural systems. This is achieved by reducing the problem size, thereby significantly improving the numerical convergence and efficiency. Two versions of this method are presented each with its own merits. A numerical example of frame structure with 20 unknown parameters is illustrated. For non-classical methods, the Genetic Algorithm (GA) is shown to be applicable with relative ease due to its 'forward analysis' nature. The computational time is, however, still enormous for large structural systems due to the combinatorial explosion problem. A model GA method has been developed to address this problem and tested with considerable success on a relatively large system of 50 degrees of freedom, accounting for input and output noise effects. An advantages of this GA-based identification method is that the objective function can be defined in response measured. Numerical studies show that the method is relatively robust, as it does in response measured. Numerical studies show that the method is relatively robust, as it dos not require good initial guess and the

Nuclear Explosion Monitoring Advances and Challenges

Science.gov (United States)

Baker, G. E.

2015-12-01

We address the state-of-the-art in areas important to monitoring, current challenges, specific efforts that illustrate approaches addressing shortcomings in capabilities, and additional approaches that might be helpful. The exponential increase in the number of events that must be screened as magnitude thresholds decrease presents one of the greatest challenges. Ongoing efforts to exploit repeat seismic events using waveform correlation, subspace methods, and empirical matched field processing holds as much "game-changing" promise as anything being done, and further efforts to develop and apply such methods efficiently are critical. Greater accuracy of travel time, signal loss, and full waveform predictions are still needed to better locate and discriminate seismic events. Important developments include methods to model velocities using multiple types of data; to model attenuation with better separation of source, path, and site effects; and to model focusing and defocusing of surface waves. Current efforts to model higher frequency full waveforms are likely to improve source characterization while more effective estimation of attenuation from ambient noise holds promise for filling in gaps. Censoring in attenuation modeling is a critical problem to address. Quantifying uncertainty of discriminants is key to their operational use. Efforts to do so for moment tensor (MT) inversion are particularly important, and fundamental progress on the statistics of MT distributions is the most important advance needed in the near term in this area. Source physics is seeing great progress through theoretical, experimental, and simulation studies. The biggest need is to accurately predict the effects of source conditions on seismic generation. Uniqueness is the challenge here. Progress will depend on studies that probe what distinguishes mechanisms, rather than whether one of many possible mechanisms is consistent with some set of observations.

Problems, challenges and promises: perspectives on precision medicine.

Science.gov (United States)

Duffy, David J

2016-05-01

The 'precision medicine (systems medicine)' concept promises to achieve a shift to future healthcare systems with a more proactive and predictive approach to medicine, where the emphasis is on disease prevention rather than the treatment of symptoms. The individualization of treatment for each patient will be at the centre of this approach, with all of a patient's medical data being computationally integrated and accessible. Precision medicine is being rapidly embraced by biomedical researchers, pioneering clinicians and scientific funding programmes in both the European Union (EU) and USA. Precision medicine is a key component of both Horizon 2020 (the EU Framework Programme for Research and Innovation) and the White House's Precision Medicine Initiative. Precision medicine promises to revolutionize patient care and treatment decisions. However, the participants in precision medicine are faced with a considerable central challenge. Greater volumes of data from a wider variety of sources are being generated and analysed than ever before; yet, this heterogeneous information must be integrated and incorporated into personalized predictive models, the output of which must be intelligible to non-computationally trained clinicians. Drawing primarily from the field of 'oncology', this article will introduce key concepts and challenges of precision medicine and some of the approaches currently being implemented to overcome these challenges. Finally, this article also covers the criticisms of precision medicine overpromising on its potential to transform patient care. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Computational challenges in atomic, molecular and optical physics.

Science.gov (United States)

Taylor, Kenneth T

2002-06-15

Six challenges are discussed. These are the laser-driven helium atom; the laser-driven hydrogen molecule and hydrogen molecular ion; electron scattering (with ionization) from one-electron atoms; the vibrational and rotational structure of molecules such as H(3)(+) and water at their dissociation limits; laser-heated clusters; and quantum degeneracy and Bose-Einstein condensation. The first four concern fundamental few-body systems where use of high-performance computing (HPC) is currently making possible accurate modelling from first principles. This leads to reliable predictions and support for laboratory experiment as well as true understanding of the dynamics. Important aspects of these challenges addressable only via a terascale facility are set out. Such a facility makes the last two challenges in the above list meaningfully accessible for the first time, and the scientific interest together with the prospective role for HPC in these is emphasized.

Questioning the Faith - Models and Prediction in Stream Restoration (Invited)

Science.gov (United States)

Wilcock, P.

2013-12-01

River management and restoration demand prediction at and beyond our present ability. Management questions, framed appropriately, can motivate fundamental advances in science, although the connection between research and application is not always easy, useful, or robust. Why is that? This presentation considers the connection between models and management, a connection that requires critical and creative thought on both sides. Essential challenges for managers include clearly defining project objectives and accommodating uncertainty in any model prediction. Essential challenges for the research community include matching the appropriate model to project duration, space, funding, information, and social constraints and clearly presenting answers that are actually useful to managers. Better models do not lead to better management decisions or better designs if the predictions are not relevant to and accepted by managers. In fact, any prediction may be irrelevant if the need for prediction is not recognized. The predictive target must be developed in an active dialog between managers and modelers. This relationship, like any other, can take time to develop. For example, large segments of stream restoration practice have remained resistant to models and prediction because the foundational tenet - that channels built to a certain template will be able to transport the supplied sediment with the available flow - has no essential physical connection between cause and effect. Stream restoration practice can be steered in a predictive direction in which project objectives are defined as predictable attributes and testable hypotheses. If stream restoration design is defined in terms of the desired performance of the channel (static or dynamic, sediment surplus or deficit), then channel properties that provide these attributes can be predicted and a basis exists for testing approximations, models, and predictions.

Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations.

Science.gov (United States)

Ou, Jian; Chen, Yongguang; Zhao, Feng; Liu, Jin; Xiao, Shunping

2017-03-19

The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity.

Prediction of selected Indian stock using a partitioning–interpolation based ARIMA–GARCH model

Directory of Open Access Journals (Sweden)

C. Narendra Babu

2015-07-01

Full Text Available Accurate long-term prediction of time series data (TSD is a very useful research challenge in diversified fields. As financial TSD are highly volatile, multi-step prediction of financial TSD is a major research problem in TSD mining. The two challenges encountered are, maintaining high prediction accuracy and preserving the data trend across the forecast horizon. The linear traditional models such as autoregressive integrated moving average (ARIMA and generalized autoregressive conditional heteroscedastic (GARCH preserve data trend to some extent, at the cost of prediction accuracy. Non-linear models like ANN maintain prediction accuracy by sacrificing data trend. In this paper, a linear hybrid model, which maintains prediction accuracy while preserving data trend, is proposed. A quantitative reasoning analysis justifying the accuracy of proposed model is also presented. A moving-average (MA filter based pre-processing, partitioning and interpolation (PI technique are incorporated by the proposed model. Some existing models and the proposed model are applied on selected NSE India stock market data. Performance results show that for multi-step ahead prediction, the proposed model outperforms the others in terms of both prediction accuracy and preserving data trend.

Grand Challenges in Music Information Research

OpenAIRE

Goto, Masataka

2012-01-01

This paper discusses some grand challenges in which music information research will impact our daily lives and our society in the future. Here, some fundamental questions are how to provide the best music for each person, how to predict music trends, how to enrich human-music relationships, how to evolve new music, and how to address environmental, energy issues by using music technologies. Our goal is to increase both attractiveness and social impacts of music information research in the fut...

A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

International Nuclear Information System (INIS)

Pousinho, H.M.I.; Mendes, V.M.F.; Catalao, J.P.S.

2011-01-01

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

Internet of Things, Challenges for Demand Side Management

Directory of Open Access Journals (Sweden)

Simona-Vasilica OPREA

2017-01-01

Full Text Available The adoption of any new product means also the apparition of new issues and challenges, and this is especially true when we talk about a mass adoption. The advent of Internet of Things (IoT devices will be, in the authors of this paper opinion, the largest and the fastest product adoption yet to be seen, as several early sources were predicting a volume of 50 billion IoT devices to be active by 2020 [1][2]. While later forecasts reduced the predicted amount to about 20-30 billion devices [3], even for such “reduced” number, demand side management issues are foreseeable, for the potential economic impact of IoT applications in 2025 will be between 3.9 and $11.1 trillion USD [4]. Not only that new patterns will emerge in energy consumption and Internet traffic, but we predict that the sheer amount of data produced by this quantity of IoT devices will give birth to a new sort of demand side management, the demand side management of IoT data. How will this work is yet to be seen but, at the current moment, one can at least identify the bits and pieces that will constitute it. This paper is intended to serve as short guide regarding the possible challenges raised by the adoption of IoT devices. The data types and structures, lifecycle and patterns will be briefly discussed throughout the following article.

Probabilistic Modeling and Visualization for Bankruptcy Prediction

DEFF Research Database (Denmark)

Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara

2017-01-01

In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful...... studies on bankruptcy detection, seldom probabilistic approaches were carried out. In this paper we assume a probabilistic point-of-view by applying Gaussian Processes (GP) in the context of bankruptcy prediction, comparing it against the Support Vector Machines (SVM) and the Logistic Regression (LR......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...

FERMILAB ACCELERATOR R&D PROGRAM TOWARDS INTENSITY FRONTIER ACCELERATORS : STATUS AND PROGRESS

Energy Technology Data Exchange (ETDEWEB)

Shiltsev, Vladimir [Fermilab

2016-11-15

The 2014 P5 report indicated the accelerator-based neutrino and rare decay physics research as a centrepiece of the US domestic HEP program at Fermilab. Operation, upgrade and development of the accelerators for the near- term and longer-term particle physics program at the Intensity Frontier face formidable challenges. Here we discuss key elements of the accelerator physics and technology R&D program toward future multi-MW proton accelerators and present its status and progress. INTENSITY FRONTIER ACCELERATORS

Walking with lions: why there is no role for captive-origin lions Panthera leo in species restoration

OpenAIRE

Hunter, Luke T.B.; White, Paula; Henschel, Philipp; Frank, Laurence; Burton, Cole; Loveridge, Andrew; Balme, Guy; Breitenmoser, Christine; Breitenmoser, Urs

2017-01-01

Despite formidable challenges and few successes in reintroducing large cats from captivity to the wild, the release of captives has widespread support from the general public and local governments, and continues to occur ad hoc. Commercial so-called lion Panthera leo encounter operations in Africa exemplify the issue, in which the captive breeding of the lion is linked to claims of reintroduction and broader conservation outcomes. In this article we assess the capacity of such programmes to c...

Knowledge about HIV/AIDS among secondary school students

OpenAIRE

Pratibha Gupta; Fatima Anjum; Pankaj Bhardwaj; J P Srivastav; Zeashan Haider Zaidi

2013-01-01

Background: HIV/AIDS has emerged as the single most formidable challenge to public health. School children of today are exposed to the risk of HIV/AIDS. Aims: The study was conducted to determine the knowledge among secondary school students regarding HIV/AIDS and provide suggestions for HIV/AIDS education in schools. Materials and Methods: A cross-sectional study was conducted among students of tenth to twelfth standard in the intermediate schools of Lucknow, India, from July to October 2011...

Mesoscopic Self-Assembly: A Shift to Complexity

Directory of Open Access Journals (Sweden)

Massimo eMastrangeli

2015-06-01

Full Text Available By focusing on the construction of thermodynamically stable structures, the self-assembly of mesoscopic systems has proven capable of formidable achievements in the bottom-up engineering of micro- and nanosystems. Yet, inspired by an analogous evolution in supramolecular chemistry, synthetic mesoscopic self-assembly may have a lot more ahead, within reach of a shift toward fully three-dimensional architectures, collective interactions of building blocks and kinetic control. All over these challenging fronts, complexity holds the key.

Ebola and Immune System

OpenAIRE

KOMENAN, Alexis

2016-01-01

Ebola hemorrhagic fever is a formidable disease whose surges always result in a high number of victims in sub-Saharan Africa. There is no official treatment against the virus, which makes the task of containment extremely delicate. However, the existence of survivors to the virus demonstrates curable nature of the disease and suggests the existence of favorable factors of immunity. The author examines these factors and their challenges and perspectives in the cure of the disease.

Strategic Management of Ecotourism: An Australian Perspective

OpenAIRE

John SAEE

2008-01-01

In 2002, the United Nations declared the International Year of Ecotourism, whose peak event was the World Ecotourism Summit, held in Quebec, Canada in May of that year. Ecotourism has since presented many formidable challenges including the following: many of the world’s natural areas remain under threat; there has been a further loss of biodiversity and resources for conservation remain inadequate; world tourism arrivals have grown by 23% and are forecast to double by 2020; climate change ha...

Improving runoff prediction using agronomical information in a cropped, loess covered catchment

NARCIS (Netherlands)

Lefrancq, Marie; Van Dijk, Paul; Jetten, Victor; Schwob, Matthieu; Payraudeau, Sylvain

2017-01-01

Predicting runoff hot spots and hot-moments within a headwater crop-catchment is of the utmost importance to reduce adverse effects on aquatic ecosystems by adapting land use management to control runoff. Reliable predictions of runoff patterns during a crop growing season remain challenging. This

Assessing Biobehavioural Self-Regulation and Coregulation in Early Childhood: The Parent-Child Challenge Task.

Science.gov (United States)

Lunkenheimer, Erika; Kemp, Christine J; Lucas-Thompson, Rachel G; Cole, Pamela M; Albrecht, Erin C

2017-01-01

Researchers have argued for more dynamic and contextually relevant measures of regulatory processes in interpersonal interactions. In response, we introduce and examine the effectiveness of a new task, the Parent-Child Challenge Task, designed to assess the self-regulation and coregulation of affect, goal-directed behavior, and physiology in parents and their preschoolers in response to an experimental perturbation. Concurrent and predictive validity was examined via relations with children's externalizing behaviors. Mothers used only their words to guide their 3-year-old children to complete increasingly difficult puzzles in order to win a prize ( N = 96). A challenge condition was initiated mid-way through the task with a newly introduced time limit. The challenge produced decreases in parental teaching and dyadic behavioral variability and increases in child negative affect and dyadic affective variability, measured by dynamic systems-based methods. Children rated lower on externalizing showed respiratory sinus arrhythmia (RSA) suppression in response to challenge, whereas those rated higher on externalizing showed RSA augmentation. Additionally, select task changes in affect, behavior, and physiology predicted teacher-rated externalizing behaviors four months later. Findings indicate the Parent-Child Challenge Task was effective in producing regulatory changes and suggest its utility in assessing biobehavioral self-regulation and coregulation in parents and their preschoolers.

Assessing the Predictability of Scheduled-Vehicle Travel Times

DEFF Research Database (Denmark)

Tiesyte, Dalia; Jensen, Christian Søndergaard

2009-01-01

One of the most desired and challenging services in collective transport systems is the real-time prediction of the near-future travel times of scheduled vehicles, especially public buses, thus improving the experience of the transportation users, who may be able to better schedule their travel......, and also enabling system operators to perform real-time monitoring. While travel-time prediction has been researched extensively during the past decade, the accuracies of existing techniques fall short of what is desired, and proposed mathematical prediction models are often not transferable to other...... systems because the properties of the travel-time-related data of vehicles are highly context-dependent, making the models difficult to fit. We propose a framework for evaluating various predictability types of the data independently of the model, and we also compare predictability analysis results...

Cervical mucus and serum estradiol as predictors of response to progestin challenge.

Science.gov (United States)

Rarick, L D; Shangold, M M; Ahmed, S W

1990-08-01

The present study was undertaken to assess the correlation between and relative predictive value of each of the following variables and progestin-induced withdrawal bleeding: cervical mucus appearance, serum E2 level, patient age, duration of amenorrhea, smoking and exercise habits, and body composition. Of 120 oligomenorrheic and amenorrheic women evaluated, only cervical mucus appearance and serum E2 level were significantly associated with response to progestin challenge. A multivariate logistical regression analysis showed cervical mucus to be the most predictive variable followed by serum E2 level. No absolute E2 level was found to discriminate between those who did and those who did not have withdrawal bleeding after progestin challenge. These data suggest that office examination of cervical mucus may be a useful indicator and guideline in planning therapy.

U.S. Geological Survey climate and land use change science strategy: a framework for understanding and responding to global change

Science.gov (United States)

Burkett, Virginia R.; Kirtland, David A.; Taylor, Ione L.; Belnap, Jayne; Cronin, Thomas M.; Dettinger, Michael D.; Frazier, Eldrich L.; Haines, John W.; Loveland, Thomas R.; Milly, Paul C.D.; ,; ,; ,; Robert, S.; Maule, Alec G.; McMahon, Gerard; Striegl, Robert G.

2013-01-01

The U.S. Geological Survey (USGS), a nonregulatory Federal science agency with national scope and responsibilities, is uniquely positioned to serve the Nation’s needs in understanding and responding to global change, including changes in climate, water availability, sea level, land use and land cover, ecosystems, and global biogeochemical cycles. Global change is among the most challenging and formidable issues confronting our Nation and society. Scientists agree that global environmental changes during this century will have far-reaching societal implications (Intergovernmental Panel on Climate Change, 2007; U.S. Global Change Research Program, 2009). In the face of these challenges, the Nation can benefit greatly by using natural science information in decisionmaking.

Metrology Needs for Predicting Concrete Pumpability

Directory of Open Access Journals (Sweden)

Myoungsung Choi

2015-01-01

Full Text Available With the increasing use of pumping to place concrete, the development and refinement of the industry practice to ensure successful concrete pumping are becoming important needs for the concrete construction industry. To date, research on concrete pumping has been largely limited to a few theses and research papers. The major obstacle to conduct research on concrete pumping is that it requires heavy equipment and large amounts of materials. Thus, developing realistic and simple measurement techniques and prediction tools is a financial and logistical challenge that is out of reach for small research labs and many private companies in the concrete construction industry. Moreover, because concrete pumping involves the flow of a complex fluid under pressure in a pipe, predicting its flow necessitates detailed knowledge of the rheological properties of concrete, which requires new measurement science. This paper summarizes the technical challenges associated with concrete pumping and the development in concrete pumping that have been published in the technical literature and identifies future research needed for the industry to develop best practices for ensuring successful concrete pumping in the field.

Upscaling solute transport in naturally fractured porous media with the continuous time random walk method

Energy Technology Data Exchange (ETDEWEB)

Geiger, S.; Cortis, A.; Birkholzer, J.T.

2010-04-01

Solute transport in fractured porous media is typically 'non-Fickian'; that is, it is characterized by early breakthrough and long tailing and by nonlinear growth of the Green function-centered second moment. This behavior is due to the effects of (1) multirate diffusion occurring between the highly permeable fracture network and the low-permeability rock matrix, (2) a wide range of advection rates in the fractures and, possibly, the matrix as well, and (3) a range of path lengths. As a consequence, prediction of solute transport processes at the macroscale represents a formidable challenge. Classical dual-porosity (or mobile-immobile) approaches in conjunction with an advection-dispersion equation and macroscopic dispersivity commonly fail to predict breakthrough of fractured porous media accurately. It was recently demonstrated that the continuous time random walk (CTRW) method can be used as a generalized upscaling approach. Here we extend this work and use results from high-resolution finite element-finite volume-based simulations of solute transport in an outcrop analogue of a naturally fractured reservoir to calibrate the CTRW method by extracting a distribution of retention times. This procedure allows us to predict breakthrough at other model locations accurately and to gain significant insight into the nature of the fracture-matrix interaction in naturally fractured porous reservoirs with geologically realistic fracture geometries.

Surgical Management of Giant Intracranial Meningioma: Operative Nuances, Challenges, and Outcome.

Science.gov (United States)

Narayan, Vinayak; Bir, Shyamal C; Mohammed, Nasser; Savardekar, Amey R; Patra, Devi Prasad; Nanda, Anil

2018-02-01

The giant intracranial meningioma (GIM) constitutes a different spectrum of brain tumors that invade the vital neurovascular structures, which makes the primary mode of treatment, surgery, a technically challenging one. The surgery for GIM is unique because of the large size of the tumor, prominent vascularity, entangling and limited visualization of various neurovascular structures, and severe cerebral edema. This study reports the authors surgical experience of 80 GIM cases, the operative challenges and surgical outcome. A retrospective analysis of 80 patients with histologically proven meningioma (≥5 cm) who underwent surgical treatment at Louisiana State University Health Sciences Center (Shreveport, Louisiana, USA) over a 20-year period (1995-2015) is presented. The clinical and radiologic data were collected from the hospital database. The tumors were categorized into histologic groups according to World Health Organization (WHO) classification. The relevant statistical analysis of the study was conducted using SPSS software, version 22.0. The study included 27 male patients (33.8%) and 53 female patients (66.3%). The mean age of the cohort was 56 years (56.3±16.1). The mean size of the tumor was 56.4 ±4 mm with a range from 50 mm to 84 mm. Skull base was the most common location of GIM (57 patients, 71.3%). Simpson grade 1 excision was achieved in 9 patients (11.3%), whereas grade 2 excision was achieved in 57 patients (71.3%); 80% of the tumors belonged to WHO grade 1. The operative mortality was seen in 4 patients (5%). Regression analysis showed that age, sex, location of the tumor, neuronavigation, Simpson grade of excision, and histology of tumor were the factors that significantly affected the recurrence-free survival (RFS). The surgery for GIM is unique in different ways. As surgery for GIM is formidable, radiologic characteristics can be useful adjuncts for planning an effective and safe surgical strategy. The factors such as young age, male sex

Chernobyl, Challenger and the numbers game

International Nuclear Information System (INIS)

Cannell, W.

1986-01-01

Probabilistic safety analysis or risk analysis, particularly with reference to reactor accidents, is explained. Errors in calculating risk can be traced to two basic causes. First and more important, an inadequate model of the plant, and second, the use of inaccurate component and system failure probabilities. The difficulty of knowing exactly how a plant will behave arises because under accident conditions the reactor is outside its normal operating conditions so that dependent failures can occur (unforeseen interactions between different systems within the reactor). Human liability and management procedures are also factors that can have a significant effect on the predicted risk. Risk analysis applied to the Challenger space shuttle is also considered. The conclusion is that risk analysis is essentially an intellectual construction. There is a credibility gap between prediction and experience if the two do not match. (UK)

A Standardized Evaluation System for Decadal Climate Prediction

Science.gov (United States)

Kadow, C.; Cubasch, U.

2012-12-01

The evaluation of decadal prediction systems is a scientific challenge as well as a technical challenge in the climate research. The major project MiKlip (www.fona-miklip.de) for medium-term climate prediction funded by the Federal Ministry of Education and Research in Germany (BMBF) has the aim to create a model system that can provide reliable decadal forecasts on climate and weather. The model system to be developed will be novel in several aspects, with great challenges for the methodology development. This concerns especially the determination of the initial conditions, the inclusion into the model of processes relevant to decadal predictions, the increase of the spatial resolution through regionalisation, the improvement or adjustment of statistical post-processing, and finally the synthesis and validation of the entire model system. Therefore, a standardized evaluation system will be part of the MiKlip system to validate it - developed by the project 'Integrated data and evaluation system for decadal scale prediction' (INTEGRATION). The presentation gives an overview of the different linkages of such a project, shows the different development stages and gives an outlook for users and possible end users in climate service. The technical interface combines all projects inside of MiKlip and invites them to participate in a common evaluation system. The system design and the validation strategy from a standalone tool in the beginning to a user friendly web based system using GRID technologies to an integrated part of the operational MiKlip system for industry and society will give the opportunity to enhance the MiKlip strategy. First results of different possibilities of such a system will be shown to present the scientific background through Taylor diagrams, ensemble skill scores and e.g. climatological means to show the usability and possibilities of MiKlip and the INTEGRATION project.

Differential Aging Trajectories of Modulation of Activation to Cognitive Challenge in APOE ε4 Groups: Reduced Modulation Predicts Poorer Cognitive Performance.

Science.gov (United States)

Foster, Chris M; Kennedy, Kristen M; Rodrigue, Karen M

2017-07-19

The present study was designed to investigate the effect of a genetic risk factor for Alzheimer's disease (AD), ApolipoproteinE ε4 (APOEε4), on the ability of the brain to modulate activation in response to cognitive challenge in a lifespan sample of healthy human adults. A community-based sample of 181 cognitively intact, healthy adults were recruited from the Dallas-Fort Worth metroplex. Thirty-one APOEε4+ individuals (48% women), derived from the parent sample, were matched based on sex, age, and years of education to 31 individuals who were APOEε4-negative (APOEε4-). Ages ranged from 20 to 86 years of age. Blood oxygen level-dependent functional magnetic resonance imaging was collected during the performance of a visuospatial distance judgment task with three parametric levels of difficulty. Multiple regression was used in a whole-brain analysis with age, APOE group, and their interaction predicting functional brain modulation in response to difficulty. Results revealed an interaction between age and APOE in a large cluster localized primarily to the bilateral precuneus. APOEε4- individuals exhibited age-invariant modulation in response to task difficulty, whereas APOEε4+ individuals showed age-related reduction of modulation in response to increasing task difficulty compared with ε4- individuals. Decreased modulation in response to cognitive challenge was associated with reduced task accuracy as well as poorer name-face associative memory performance. Findings suggest that APOEε4 is associated with a reduction in the ability of the brain to dynamically modulate in response to cognitive challenge. Coupled with a significant genetic risk factor for AD, changes in modulation may provide additional information toward identifying individuals potentially at risk for cognitive decline associated with preclinical AD. SIGNIFICANCE STATEMENT Understanding how risk factors for Alzheimer's disease (AD) affect brain function and cognition in healthy adult samples

Computational predictions of zinc oxide hollow structures

Science.gov (United States)

Tuoc, Vu Ngoc; Huan, Tran Doan; Thao, Nguyen Thi

2018-03-01

Nanoporous materials are emerging as potential candidates for a wide range of technological applications in environment, electronic, and optoelectronics, to name just a few. Within this active research area, experimental works are predominant while theoretical/computational prediction and study of these materials face some intrinsic challenges, one of them is how to predict porous structures. We propose a computationally and technically feasible approach for predicting zinc oxide structures with hollows at the nano scale. The designed zinc oxide hollow structures are studied with computations using the density functional tight binding and conventional density functional theory methods, revealing a variety of promising mechanical and electronic properties, which can potentially find future realistic applications.

78 FR 70303 - Announcement of Requirements and Registration for the Predict the Influenza Season Challenge

Science.gov (United States)

2013-11-25

... season would be very useful in planning vaccination campaigns, targeting resources and therefore reducing... contest. Information is not collected for commercial marketing. Registering through the Challenge.gov Web...

A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

Energy Technology Data Exchange (ETDEWEB)

Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal); Catalao, J.P.S. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Center for Innovation in Electrical and Energy Engineering, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon (Portugal)

2011-01-15

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches. (author)

Efficient Computation of Info-Gap Robustness for Finite Element Models

International Nuclear Information System (INIS)

Stull, Christopher J.; Hemez, Francois M.; Williams, Brian J.

2012-01-01

A recent research effort at LANL proposed info-gap decision theory as a framework by which to measure the predictive maturity of numerical models. Info-gap theory explores the trade-offs between accuracy, that is, the extent to which predictions reproduce the physical measurements, and robustness, that is, the extent to which predictions are insensitive to modeling assumptions. Both accuracy and robustness are necessary to demonstrate predictive maturity. However, conducting an info-gap analysis can present a formidable challenge, from the standpoint of the required computational resources. This is because a robustness function requires the resolution of multiple optimization problems. This report offers an alternative, adjoint methodology to assess the info-gap robustness of Ax = b-like numerical models solved for a solution x. Two situations that can arise in structural analysis and design are briefly described and contextualized within the info-gap decision theory framework. The treatments of the info-gap problems, using the adjoint methodology are outlined in detail, and the latter problem is solved for four separate finite element models. As compared to statistical sampling, the proposed methodology offers highly accurate approximations of info-gap robustness functions for the finite element models considered in the report, at a small fraction of the computational cost. It is noted that this report considers only linear systems; a natural follow-on study would extend the methodologies described herein to include nonlinear systems.

Antecedents of positive self-disclosure online: an empirical study of US college students’ Facebook usage

OpenAIRE

Chen H

2017-01-01

Hongliang Chen Department of Communication, Texas A&M University, College Station, TX, USA Abstract: This study investigates the factors predicting positive self-disclosure on social networking sites (SNSs). There is a formidable body of empirical research relating to online self-disclosure, but very few studies have assessed the antecedents of positive self-disclosure. To address this literature gap, the current study tests the effects of self-esteem, life satisfact...

Tone Noise Predictions for a Spacecraft Cabin Ventilation Fan Ingesting Distorted Inflow and the Challenges of Validation

Science.gov (United States)

Koch, L. Danielle; Shook, Tony D.; Astler, Douglas T.; Bittinger, Samantha A.

2012-01-01

A fan tone noise prediction code has been developed at NASA Glenn Research Center that is capable of estimating duct mode sound power levels for a fan ingesting distorted inflow. This code was used to predict the circumferential and radial mode sound power levels in the inlet and exhaust duct of an axial spacecraft cabin ventilation fan. Noise predictions at fan design rotational speed were generated. Three fan inflow conditions were studied: an undistorted inflow, a circumferentially symmetric inflow distortion pattern (cylindrical rods inserted radially into the flowpath at 15deg, 135deg, and 255deg), and a circumferentially asymmetric inflow distortion pattern (rods located at 15deg, 52deg and 173deg). Noise predictions indicate that tones are produced for the distorted inflow cases that are not present when the fan operates with an undistorted inflow. Experimental data are needed to validate these acoustic predictions, as well as the aerodynamic performance predictions. Given the aerodynamic design of the spacecraft cabin ventilation fan, a mechanical and electrical conceptual design study was conducted. Design features of a fan suitable for obtaining detailed acoustic and aerodynamic measurements needed to validate predictions are discussed.

Large-scale investigation of the parameters in response to Eimeria maxima challenge in broilers.

Science.gov (United States)

Hamzic, E; Bed'Hom, B; Juin, H; Hawken, R; Abrahamsen, M S; Elsen, J M; Servin, B; Pinard-van der Laan, M H; Demeure, O

2015-04-01

Coccidiosis, a parasitic disease of the intestinal tract caused by members of the genera Eimeria and Isospora, is one of the most common and costly diseases in chicken. The aims of this study were to assess the effect of the challenge and level of variability of measured parameters in chickens during the challenge with Eimeria maxima. Furthermore, this study aimed to investigate which parameters are the most relevant indicators of the health status. Finally, the study also aimed to estimate accuracy of prediction for traits that cannot be measured on large scale (such as intestinal lesion score and fecal oocyst count) using parameters that can easily be measured on all animals. The study was performed in 2 parts: a pilot challenge on 240 animals followed by a large-scale challenge on 2,024 animals. In both experiments, animals were challenged with 50,000 Eimeria maxima oocysts at 16 d of age. In the pilot challenge, all animals were measured for BW gain, plasma coloration, hematocrit, and rectal temperature and, in addition, a subset of 48 animals was measured for oocyst count and the intestinal lesion score. All animals from the second challenge were measured for BW gain, plasma coloration, and hematocrit whereas a subset of 184 animals was measured for intestinal lesion score, fecal oocyst count, blood parameters, and plasma protein content and composition. Most of the parameters measured were significantly affected by the challenge. Lesion scores for duodenum and jejunum (P Eimeria maxima. Prediction of intestinal lesion score and fecal oocyst count using the other parameters measured was not very precise (R2 Eimeria maxima has a strong genetic determinism, which may be improved by genetic selection.

Nonlinear predictive control in the LHC accelerator

CERN Document Server

Blanco, E; Cristea, S; Casas, J

2009-01-01

This paper describes the application of a nonlinear model-based control strategy in a real challenging process. A predictive controller based on a nonlinear model derived from physical relationships, mainly heat and mass balances, has been developed and commissioned in the inner triplet heat exchanger unit (IT-HXTU) of the large hadron collider (LHC) particle accelerator at European Center for Nuclear Research (CERN). The advanced regulation\\ maintains the magnets temperature at about 1.9 K. The development includes a constrained nonlinear state estimator with a receding horizon estimation procedure to improve the regulator predictions.

Challenges in design of zirconium alloy reactor components

International Nuclear Information System (INIS)

Kakodkar, Anil; Sinha, R.K.

1992-01-01

Zirconium alloy components used in core-internal assemblies of heavy water reactors have to be designed under constraints imposed by need to have minimum mass, limitations of fabrication, welding and joining techniques with this material, and unique mechanisms for degradation of the operating performance of these components. These constraints manifest as challenges for design and development when the size, shape and dimensions of the components and assemblies are unconventional or untried, or when one is aiming for maximization of service life of these components under severe operating conditions. A number of such challenges were successfully met during the development of core-internal components and assemblies of Dhruva reactor. Some of the then untried ideas which were developed and successfully implemented include use of electron beam welding, cold forming of hemispherical ends of reentrant cans, and a large variety of rolled joints of innovative designs. This experience provided the foundation for taking up and successfully completing several tasks relating to coolant channels, liquid poison channels and sparger channels for PHWRs and test sections for the in-pile loops of Dhruva reactor. For life prediction and safety assessment of coolant channels of PHWRs some analytical tools, notably, a computer code for prediction of creep limited life of coolant channels has been developed. Some of the future challenges include the development of easily replaceable coolant channels and also large diameter coolant channels for Advanced Heavy Water Reactor, and development of solutions to overcome deterioration of service life of coolant channels due to hydriding. (author). 5 refs., 13 figs., 1 tab

Real-time travel time prediction framework for departure time and route advice

NARCIS (Netherlands)

Calvert, S.C.; Snelder, M.; Bakri, T.; Heijligers, B.; Knoop, V.L.

2015-01-01

Heavily used urban networks remain a challenge for travel time prediction because traffic flow is rarely homogeneous and is also subject to a wide variety of disturbances. Various models, some of which use traffic flow theory and some of which are data driven, have been developed to predict traffic

Predictive models for the assessment of occupational exposure to chemicals: A new challenge for employers

Directory of Open Access Journals (Sweden)

Jan Piotr Gromiec

2013-10-01

Full Text Available Employers are obliged to carry out and document the risk associated with the use of chemical substances. The best but the most expensive method is to measure workplace concentrations of chemicals. At present no "measureless" method for risk assessment is available in Poland, but predictive models for such assessments have been developed in some countries. The purpose of this work is to review and evaluate the applicability of selected predictive methods for assessing occupational inhalation exposure and related risk to check the compliance with Occupational Exposure Limits (OELs, as well as the compliance with REACH obligations. Based on the literature data HSE COSHH Essentials, EASE, ECETOC TRA, Stoffenmanager, and EMKG-Expo-Tool were evaluated. The data on validation of predictive models were also examined. It seems that predictive models may be used as a useful method for Tier 1 assessment of occupational exposure by inhalation. Since the levels of exposure are frequently overestimated, they should be considered as "rational worst cases" for selection of proper control measures. Bearing in mind that the number of available exposure scenarios and PROC categories is limited, further validation by field surveys is highly recommended. Predictive models may serve as a good tool for preliminary risk assessment and selection of the most appropriate risk control measures in Polish small and medium size enterprises (SMEs providing that they are available in the Polish language. This also requires an extensive training of their future users. Med Pr 2013;64(5:699–716

Contrasting responses of leaf stomatal characteristics to climate change: a considerable challenge to predict carbon and water cycles.

Science.gov (United States)

Yan, Weiming; Zhong, Yangquanwei; Shangguan, Zhouping

2017-09-01

Stomata control the cycling of water and carbon between plants and the atmosphere; however, no consistent conclusions have been drawn regarding the response of stomatal frequency to climate change. Here, we conducted a meta-analysis of 1854 globally obtained data series to determine the response of stomatal frequency to climate change, which including four plant life forms (over 900 species), at altitudes ranging from 0 to 4500 m and over a time span of more than one hundred thousand years. Stomatal frequency decreased with increasing CO 2 concentration and increased with elevated temperature and drought stress; it was also dependent on the species and experimental conditions. The response of stomatal frequency to climate change showed a trade-off between stomatal control strategies and environmental factors, such as the CO 2 concentration, temperature, and soil water availability. Moreover, threshold effects of elevated CO 2 and temperature on stomatal frequency were detected, indicating that the response of stomatal density to increasing CO 2 concentration will decrease over the next few years. The results also suggested that the stomatal index may be more reliable than stomatal density for determination of the historic CO 2 concentration. Our findings indicate that the contrasting responses of stomata to climate change bring a considerable challenge in predicting future water and carbon cycles. © 2017 John Wiley & Sons Ltd.

Soft Computing Methods for Disulfide Connectivity Prediction.

Science.gov (United States)

Márquez-Chamorro, Alfonso E; Aguilar-Ruiz, Jesús S

2015-01-01

The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods.

Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction.

Science.gov (United States)

Soleimani, Hossein; Hensman, James; Saria, Suchi

2017-08-21

Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation methods, which are typically used for completing the data prior to event prediction, lack a principled mechanism to account for the uncertainty due to missingness. Alternatively, state-of-the-art joint modeling techniques can be used for jointly modeling the longitudinal and event data and compute event probabilities conditioned on the longitudinal observations. These approaches, however, make strong parametric assumptions and do not easily scale to multivariate signals with many observations. Our proposed approach consists of several key innovations. First, we develop a flexible and scalable joint model based upon sparse multiple-output Gaussian processes. Unlike state-of-the-art joint models, the proposed model can explain highly challenging structure including non-Gaussian noise while scaling to large data. Second, we derive an optimal policy for predicting events using the distribution of the event occurrence estimated by the joint model. The derived policy trades-off the cost of a delayed detection versus incorrect assessments and abstains from making decisions when the estimated event probability does not satisfy the derived confidence criteria. Experiments on a large dataset show that the proposed framework significantly outperforms state-of-the-art techniques in event prediction.

Challenges of model transferability to data-scarce regions (Invited)

Science.gov (United States)

Samaniego, L. E.

2013-12-01

Developing the ability to globally predict the movement of water on the land surface at spatial scales from 1 to 5 km constitute one of grand challenges in land surface modelling. Copying with this grand challenge implies that land surface models (LSM) should be able to make reliable predictions across locations and/or scales other than those used for parameter estimation. In addition to that, data scarcity and quality impose further difficulties in attaining reliable predictions of water and energy fluxes at the scales of interest. Current computational limitations impose also seriously limitations to exhaustively investigate the parameter space of LSM over large domains (e.g. greater than half a million square kilometers). Addressing these challenges require holistic approaches that integrate the best techniques available for parameter estimation, field measurements and remotely sensed data at their native resolutions. An attempt to systematically address these issues is the multiscale parameterisation technique (MPR) that links high resolution land surface characteristics with effective model parameters. This technique requires a number of pedo-transfer functions and a much fewer global parameters (i.e. coefficients) to be inferred by calibration in gauged basins. The key advantage of this technique is the quasi-scale independence of the global parameters which enables to estimate global parameters at coarser spatial resolutions and then to transfer them to (ungauged) areas and scales of interest. In this study we show the ability of this technique to reproduce the observed water fluxes and states over a wide range of climate and land surface conditions ranging from humid to semiarid and from sparse to dense forested regions. Results of transferability of global model parameters in space (from humid to semi-arid basins) and across scales (from coarser to finer) clearly indicate the robustness of this technique. Simulations with coarse data sets (e.g. EOBS

Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations

Directory of Open Access Journals (Sweden)

Jian Ou

2017-03-01

Full Text Available The extensive applications of multi-function radars (MFRs have presented a great challenge to the technologies of radar countermeasures (RCMs and electronic intelligence (ELINT. The recently proposed cognitive electronic warfare (CEW provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR. With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity.

Use of serologic tests to predict resistance to Canine distemper virus-induced disease in vaccinated dogs.

Science.gov (United States)

Jensen, Wayne A; Totten, Janet S; Lappin, Michael R; Schultz, Ronald D

2015-09-01

The objective of the current study was to determine whether detection of Canine distemper virus (CDV)-specific serum antibodies correlates with resistance to challenge with virulent virus. Virus neutralization (VN) assay results were compared with resistance to viral challenge in 2 unvaccinated Beagle puppies, 9 unvaccinated Beagle dogs (4.4-7.2 years of age), and 9 vaccinated Beagle dogs (3.7-4.7 years of age). Eight of 9 (89%) unvaccinated adult dogs exhibited clinical signs after virus challenge, and 1 (13%) dog died. As compared to adult dogs, the 2 unvaccinated puppies developed more severe clinical signs and either died or were euthanized after challenge. In contrast, no clinical signs were detected after challenge of the 9 adult vaccinated dogs with post-vaccination intervals of up to 4.4 years. In vaccinated dogs, the positive and negative predictive values of VN assay results for resistance to challenge were 100% and 0%, respectively. Results indicate that dogs vaccinated with modified live CDV can be protected from challenge for ≤4.4 years postvaccination and that detection of virus-specific antibodies is predictive of whether dogs are resistant to challenge with virulent virus. Results also indicate that CDV infection in unvaccinated dogs results in age-dependent morbidity and mortality. Knowledge of age-dependent morbidity and mortality, duration of vaccine-induced immunity, and the positive and negative predictive values of detection of virus-specific serum antibodies are useful in development of rational booster vaccination intervals for the prevention of CDV-mediated disease in adult dogs. © 2015 The Author(s).

Defending or Challenging the Status Quo: Position Effects on Biased Intergroup Perceptions

Directory of Open Access Journals (Sweden)

Emma A. Bäck

2014-05-01

Full Text Available The default ideological position is status quo maintaining, and challenging the status quo is associated with increased efforts and risks. Nonetheless, some people choose to challenge the status quo. Therefore, to challenge the status quo should imply a strong belief in one’s position as the correct one, and thus efforts may be undertaken to undermine the position of others. Study 1 (N = 311 showed that challengers undermined, by ascribing more externality and less rationality, the position of defenders to a larger extent than defenders did of challengers’ position. Studies 2 (N = 135 and 3 (N = 109 tested if these effects were driven by the implied minority status of the challenging position. Results revealed no effects of experimentally manipulated numerical status, but challengers were again more biased than defenders. Study 3 also revealed that challengers felt more negatively toward their opponents (possibly due to greater social identification with like-minded others, and these negative emotions in turn predicted biased attributions. Results are important as they add to the understanding of how intergroup conflict may arise, providing explanations for why challengers are less tolerant of others’ point of view.

Internationalising Indian Higher Education: Opportunities, Challenges and the Way Forward

Directory of Open Access Journals (Sweden)

Gautam Rajkhowa

2017-06-01

Full Text Available This paper examines the higher education system in India together with its status regarding internationalisation, and presents the case for the higher education sector in India to embrace internationalisation. Starting with an overview of the academic literature around the concepts of globalisation and internationalisation, and their interrelationship particularly in the context of higher education, the paper focuses on the specific issues of Indian higher education especially within the context of internationalisation. Reviewing the current landscape of the Indian higher education sector, the paper concludes that, in the context of a globally connected world, higher education in India is characterised by asymmetry in flows and unclear policies. Recommending that the internationalisation strategy focuses on the four strands of student and programme mobility; infrastructure and policy support; development of research capability; and the employment of technology as an enabler, the paper concludes that a clear approach to internationalisation would offer the potential to secure India a formidable global standing in higher education.

Semi-supervised prediction of gene regulatory networks using ...

Indian Academy of Sciences (India)

2015-09-28

Sep 28, 2015 ... Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging ... two types of methods differ primarily based on whether ..... negligible, allowing us to draw the qualitative conclusions .... research will be conducted to develop additional biologically.

Psoriasis prediction from genome-wide SNP profiles

Directory of Open Access Journals (Sweden)

Fang Xiangzhong

2011-01-01

Full Text Available Abstract Background With the availability of large-scale genome-wide association study (GWAS data, choosing an optimal set of SNPs for disease susceptibility prediction is a challenging task. This study aimed to use single nucleotide polymorphisms (SNPs to predict psoriasis from searching GWAS data. Methods Totally we had 2,798 samples and 451,724 SNPs. Process for searching a set of SNPs to predict susceptibility for psoriasis consisted of two steps. The first one was to search top 1,000 SNPs with high accuracy for prediction of psoriasis from GWAS dataset. The second one was to search for an optimal SNP subset for predicting psoriasis. The sequential information bottleneck (sIB method was compared with classical linear discriminant analysis(LDA for classification performance. Results The best test harmonic mean of sensitivity and specificity for predicting psoriasis by sIB was 0.674(95% CI: 0.650-0.698, while only 0.520(95% CI: 0.472-0.524 was reported for predicting disease by LDA. Our results indicate that the new classifier sIB performs better than LDA in the study. Conclusions The fact that a small set of SNPs can predict disease status with average accuracy of 68% makes it possible to use SNP data for psoriasis prediction.

Numerical simulation of turbulent combustion: Scientific challenges

Science.gov (United States)

Ren, ZhuYin; Lu, Zhen; Hou, LingYun; Lu, LiuYan

2014-08-01

Predictive simulation of engine combustion is key to understanding the underlying complicated physicochemical processes, improving engine performance, and reducing pollutant emissions. Critical issues as turbulence modeling, turbulence-chemistry interaction, and accommodation of detailed chemical kinetics in complex flows remain challenging and essential for high-fidelity combustion simulation. This paper reviews the current status of the state-of-the-art large eddy simulation (LES)/prob-ability density function (PDF)/detailed chemistry approach that can address the three challenging modelling issues. PDF as a subgrid model for LES is formulated and the hybrid mesh-particle method for LES/PDF simulations is described. Then the development need in micro-mixing models for the PDF simulations of turbulent premixed combustion is identified. Finally the different acceleration methods for detailed chemistry are reviewed and a combined strategy is proposed for further development.

Prediction of human gait trajectories during the SSP using a neuromusculoskeletal modeling: A challenge for parametric optimization.

Science.gov (United States)

Seyed, Mohammadali Rahmati; Mostafa, Rostami; Borhan, Beigzadeh

2018-04-27

The parametric optimization techniques have been widely employed to predict human gait trajectories; however, their applications to reveal the other aspects of gait are questionable. The aim of this study is to investigate whether or not the gait prediction model is able to justify the movement trajectories for the higher average velocities. A planar, seven-segment model with sixteen muscle groups was used to represent human neuro-musculoskeletal dynamics. At first, the joint angles, ground reaction forces (GRFs) and muscle activations were predicted and validated for normal average velocity (1.55 m/s) in the single support phase (SSP) by minimizing energy expenditure, which is subject to the non-linear constraints of the gait. The unconstrained system dynamics of extended inverse dynamics (USDEID) approach was used to estimate muscle activations. Then by scaling time and applying the same procedure, the movement trajectories were predicted for higher average velocities (from 2.07 m/s to 4.07 m/s) and compared to the pattern of movement with fast walking speed. The comparison indicated a high level of compatibility between the experimental and predicted results, except for the vertical position of the center of gravity (COG). It was concluded that the gait prediction model can be effectively used to predict gait trajectories for higher average velocities.

Presidents and health reform: from Franklin D. Roosevelt to Barack Obama.

Science.gov (United States)

Morone, James A

2010-06-01

The health care reforms that President Barack Obama signed into law in March 2010 were seventy-five years in the making. Since Franklin D. Roosevelt, U.S. presidents have struggled to enact national health care reform; most failed. This article explores the highly charged political landscape in which Obama maneuvered and the skills he brought to bear. It contrasts his accomplishments with the experiences of his Oval Office predecessors. Going forward, implementation poses formidable challenges for Democrats, Republicans, and the political process itself.

Duodenal Transection without Pancreatic Injury following Blunt Abdominal Trauma

OpenAIRE

Bankar, Sanket Subhash; Gosavi, Vikas S.; Hamid, Mohd.

2014-01-01

With the inventions of faster cars and even more faster motorbikes there is a worldwide increase in road traffic accidents, which has increased the incidence of blunt abdominal trauma but still duodenal injury following a blunt abdominal trauma is uncommon and can pose a formidable challenge to the surgeon and failure to manage it properly can result in devastating results. It may typically occur in isolation or with pancreatic injury. Here, we report a case of an isolated transection of the ...

Kernel-based whole-genome prediction of complex traits: a review.

Science.gov (United States)

Morota, Gota; Gianola, Daniel

2014-01-01

Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways), thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics.

Kernel-based whole-genome prediction of complex traits: a review

Directory of Open Access Journals (Sweden)

Gota eMorota

2014-10-01

Full Text Available Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways, thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics.

Predicting Player Churn In the Wild

DEFF Research Database (Denmark)

Hadiji, Fabian; Sifa, Rafet; Drachen, Anders

2014-01-01

Free-to-Play or "freemium" games represent a fundamental shift in the business models of the game industry, facilitated by the increasing use of online distribution platforms and the introduction of increasingly powerful mobile platforms. The ability of a game development company to analyze...... a crucial value, allowing developers to obtain data-driven insights to inform design, development and marketing strategies. One of the key challenges is modeling and predicting player churn. This paper presents the first cross-game study of churn prediction in Free-to-Play games. Churn in games is discussed...... and derive insights from behavioral telemetry is crucial to the success of these games which rely on in-game purchases and in-game advertising to generate revenue, and for the Company to remain competitive in a global marketplace. The ability to model, understand and predict future player behavior has...

A genome-wide gene function prediction resource for Drosophila melanogaster.

Directory of Open Access Journals (Sweden)

Han Yan

2010-08-01

Full Text Available Predicting gene functions by integrating large-scale biological data remains a challenge for systems biology. Here we present a resource for Drosophila melanogaster gene function predictions. We trained function-specific classifiers to optimize the influence of different biological datasets for each functional category. Our model predicted GO terms and KEGG pathway memberships for Drosophila melanogaster genes with high accuracy, as affirmed by cross-validation, supporting literature evidence, and large-scale RNAi screens. The resulting resource of prioritized associations between Drosophila genes and their potential functions offers a guide for experimental investigations.

Prediction of lake surface temperature using the air2water model: guidelines, challenges, and future perspectives

Directory of Open Access Journals (Sweden)

Sebastiano Piccolroaz

2016-04-01

Full Text Available Water temperature plays a primary role in controlling a wide range of physical, geochemical and ecological processes in lakes, with considerable influences on lake water quality and ecosystem functioning. Being able to reliably predict water temperature is therefore a desired goal, which stimulated the development of models of different type and complexity, ranging from simple regression-based models to more sophisticated process-based numerical models. However, both types of models suffer of some limitations: the first are not able to address some fundamental physical processes as e.g., thermal stratification, while the latter generally require a large amount of data in input, which are not always available. In this work, lake surface temperature is simulated by means of air2water, a hybrid physically-based/statistical model, which is able to provide a robust, predictive understanding of LST dynamics knowing air temperature only. This model showed performances that are comparable with those obtained by using process based models (a root mean square error on the order of 1°C, at daily scale, while retaining the simplicity and parsimony of regression-based models, thus making it a good candidate for long-term applications.The aim of the present work is to provide the reader with useful and practical guidelines for proper use of the air2water model and for critical analysis of results. Two case studies have been selected for the analysis: Lake Superior and Lake Erie. These are clear and emblematic examples of a deep and a shallow temperate lake characterized by markedly different thermal responses to external forcing, thus are ideal for making the results of the analysis the most general and comprehensive. Particular attention is paid to assessing the influence of missing data on model performance, and to evaluating when an observed time series is sufficiently informative for proper model calibration or, conversely, data are too scarce thus

Human reinforcement learning subdivides structured action spaces by learning effector-specific values

OpenAIRE

Gershman, Samuel J.; Pesaran, Bijan; Daw, Nathaniel D.

2009-01-01

Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable, due to the high dimensionality of the action space. An unresolved question is how neural systems for reinforcement learning – such as prediction error signals for action valuation associated with dopamine and the striatum – can cope with this “curse of dimensionality...

RNA-SSPT: RNA Secondary Structure Prediction Tools.

Science.gov (United States)

Ahmad, Freed; Mahboob, Shahid; Gulzar, Tahsin; Din, Salah U; Hanif, Tanzeela; Ahmad, Hifza; Afzal, Muhammad

2013-01-01

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.

Fired up or burned out? How developmental challenge differentially impacts leader behavior.

Science.gov (United States)

Courtright, Stephen H; Colbert, Amy E; Choi, Daejeong

2014-07-01

Leadership development research has largely drawn on experiential and enactive learning theories to explore the positive effects of developmental challenge on leaders. In contrast, we examined potential positive and negative effects of developmental challenge (i.e., challenging job assignments) on leader behavior through an alternative theoretical lens--transactional stress theory. We predicted, on one hand, that developmental challenge may be associated with higher leader engagement and transformational leadership behavior; however, developmental challenge also has the potential to be associated with higher leader emotional exhaustion and laissez-faire leadership behavior. We further proposed that leadership self-efficacy (LSE) moderates these potential effects of developmental challenge and helps explain why leaders react either positively or negatively to developmental challenge. We tested our hypotheses in a sample of 153 leaders and 631 direct reports at a Fortune 500 company. Findings supported positive relationships among developmental challenge, leader engagement, and transformational leadership. However, we also found support for significant relationships among developmental challenge, emotional exhaustion, and laissez-faire leadership. Additionally, leaders lower in LSE were more likely to encounter the negative effects of developmental challenge by experiencing increased emotional exhaustion and displaying laissez-faire leadership behaviors. Our study contributes to theory and practice by elucidating a "dark side" of developmental challenge, identifying LSE as a moderator of the negative effects of developmental challenge, identifying antecedents of transformational and laissez-faire leadership behaviors, and investigating demands and stress in leadership roles.

Prediction of heat-illness symptoms with the prediction of human vascular response in hot environment under resting condition.

Science.gov (United States)

Aggarwal, Yogender; Karan, Bhuwan Mohan; Das, Barsa Nand; Sinha, Rakesh Kumar

2008-04-01

The thermoregulatory control of human skin blood flow is vital to maintain the body heat storage during challenges of thermal homeostasis under heat stress. Whenever thermal homeostasis disturbed, the heat load exceeds heat dissipation capacity, which alters the cutaneous vascular responses along with other body physiological variables. Whole body skin blood flow has been calculated from the forearm blood flow. Present model has been designed using electronics circuit simulator (Multisim 8.0, National Instruments, USA), is to execute a series of predictive equations for early prediction of physiological parameters of young nude subjects during resting condition at various level of dry heat stress under almost still air to avoid causalities associated with hot environmental. The users can execute the model by changing the environmental temperature in degrees C and exposure time in minutes. The model would be able to predict and detect the changes in human vascular responses along with other physiological parameters and from this predicted values heat related-illness symptoms can be inferred.

Prediction of regulatory elements

DEFF Research Database (Denmark)

Sandelin, Albin

2008-01-01

Finding the regulatory mechanisms responsible for gene expression remains one of the most important challenges for biomedical research. A major focus in cellular biology is to find functional transcription factor binding sites (TFBS) responsible for the regulation of a downstream gene. As wet......-lab methods are time consuming and expensive, it is not realistic to identify TFBS for all uncharacterized genes in the genome by purely experimental means. Computational methods aimed at predicting potential regulatory regions can increase the efficiency of wet-lab experiments significantly. Here, methods...

Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

Science.gov (United States)

Khalilia, Mohammed; Choi, Myung; Henderson, Amelia; Iyengar, Sneha; Braunstein, Mark; Sun, Jimeng

2015-01-01

Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction.

Opportunities and Challenges of Multiplex Assays: A Machine Learning Perspective.

Science.gov (United States)

Chen, Junfang; Schwarz, Emanuel

2017-01-01

Multiplex assays that allow the simultaneous measurement of multiple analytes in small sample quantities have developed into a widely used technology. Their implementation spans across multiple assay systems and can provide readouts of similar quality as the respective single-plex measures, albeit at far higher throughput. Multiplex assay systems are therefore an important element for biomarker discovery and development strategies but analysis of the derived data can face substantial challenges that may limit the possibility of identifying meaningful biological markers. This chapter gives an overview of opportunities and challenges of multiplexed biomarker analysis, in particular from the perspective of machine learning aimed at identification of predictive biological signatures.

Challenges in Modeling Disturbance Regimes and Their Impacts in Arctic and Boreal Ecosystems (Invited)

Science.gov (United States)

McGuire, A. D.; Rupp, T. S.; Kurz, W.

2013-12-01

Disturbances in arctic and boreal terrestrial ecosystems influence services provided by these ecosystems to society. In particular, changes in disturbance regimes in northern latitudes have uncertain consequences for the climate system. A major challenge for the scientific community is to develop the capability to predict how the frequency, severity and resultant impacts of disturbance regimes will change in response to future changes in climate projected for northern high latitudes. Here we compare what is known about drivers and impacts of wildfire, phytophagous insect pests, and thermokarst disturbance to illustrate the complexities in predicting future changes in disturbance regimes and their impacts in arctic and boreal regions. Much of the research on predicting fire has relied on the use of drivers related to fire weather. However, changes in vegetation, such as increases in broadleaf species, associated with intensified fire regimes have the potential to influence future fire regimes through negative feedbacks associated with reduced flammability. Phytophagous insect outbreaks have affected substantial portions of the boreal region in the past, but frequently the range of the tree host is larger than the range of the insect. There is evidence that a number of insect species are expanding their range in response to climate change. Major challenges to predicting outbreaks of phytophagous insects include modeling the effects of climate change on insect growth and maturation, winter mortality, plant host health, the synchrony of insect life stages and plant host phenology, and changes in the ranges of insect pests. Moreover, Earth System Models often simplify the representation of vegetation characteristics, e.g. the use of plant functional types, providing insufficient detail to link to insect population models. Thermokarst disturbance occurs when the thawing of ice-rich permafrost results in substantial ground subsidence. In the boreal forest, thermokarst can

Population predictions for Seychelles warblers in novel environments

NARCIS (Netherlands)

Ridley, Jo; Komdeur, Jan; Richardson, David; Sutherland, William J.

2006-01-01

A major challenge for population ecology is to be able to predict population sizes in novel conditions, as in those following habitat loss or translocation. To do this successfully, we show here that it is necessary to understand the behavioral basis of dispersal decisions as they affect fitness.

Potential for western US seasonal snowpack prediction

Science.gov (United States)

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

2018-01-01

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

Prediction model of RSV-hospitalization in late preterm infants : An update and validation study

NARCIS (Netherlands)

Korsten, Koos; Blanken, Maarten O; Nibbelke, Elisabeth E; Moons, Karel G M; Bont, Louis

BACKGROUND: New vaccines and RSV therapeutics have been developed in the past decade. With approval of these new pharmaceuticals on the horizon, new challenges lie ahead in selecting the appropriate target population. We aimed to improve a previously published prediction model for prediction of

Prediction model of RSV-hospitalization in late preterm infants: An update and validation study

NARCIS (Netherlands)

Korsten, K.; Blanken, M.O.; Nibbelke, E.E.; Moons, K.G.; Bont, L.; Liem, K.D.; et al.,

2016-01-01

BACKGROUND: New vaccines and RSV therapeutics have been developed in the past decade. With approval of these new pharmaceuticals on the horizon, new challenges lie ahead in selecting the appropriate target population. We aimed to improve a previously published prediction model for prediction of

Unsupervised energy prediction in a smart grid context using reinforcement cross-buildings transfer learning

NARCIS (Netherlands)

Mocanu, E.; Nguyen, P.H.; Kling, W.L.; Gibescu, M.

2016-01-01

In a future Smart Grid context, increasing challenges in managing the stochastic local energy supply and demand are expected. This increased the need of more accurate energy prediction methods in order to support further complex decision-making processes. Although many methods aiming to predict the

A consensus approach for estimating the predictive accuracy of dynamic models in biology.

Science.gov (United States)

Villaverde, Alejandro F; Bongard, Sophia; Mauch, Klaus; Müller, Dirk; Balsa-Canto, Eva; Schmid, Joachim; Banga, Julio R

2015-04-01

Mathematical models that predict the complex dynamic behaviour of cellular networks are fundamental in systems biology, and provide an important basis for biomedical and biotechnological applications. However, obtaining reliable predictions from large-scale dynamic models is commonly a challenging task due to lack of identifiability. The present work addresses this challenge by presenting a methodology for obtaining high-confidence predictions from dynamic models using time-series data. First, to preserve the complex behaviour of the network while reducing the number of estimated parameters, model parameters are combined in sets of meta-parameters, which are obtained from correlations between biochemical reaction rates and between concentrations of the chemical species. Next, an ensemble of models with different parameterizations is constructed and calibrated. Finally, the ensemble is used for assessing the reliability of model predictions by defining a measure of convergence of model outputs (consensus) that is used as an indicator of confidence. We report results of computational tests carried out on a metabolic model of Chinese Hamster Ovary (CHO) cells, which are used for recombinant protein production. Using noisy simulated data, we find that the aggregated ensemble predictions are on average more accurate than the predictions of individual ensemble models. Furthermore, ensemble predictions with high consensus are statistically more accurate than ensemble predictions with large variance. The procedure provides quantitative estimates of the confidence in model predictions and enables the analysis of sufficiently complex networks as required for practical applications. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Predictive technologies: Can smart tools augment the brain’s predictive abilities?

Directory of Open Access Journals (Sweden)

Giovanni ePezzulo

2016-04-01

Full Text Available The ability of looking into the future – namely, the capacity of anticipating future states of the environment or of the body – represents a fundamental function of human (and animal brains. A goalkeeper who tries to guess the ball’s direction; a chess player who attempts to anticipate the opponent’s next move; or a man-in-love who tries to calculate what are the chances of her saying yes – in all these cases, people are simulating possible future states of the world, in order to maximize the success of their decisions or actions. Research in neuroscience is showing that our ability to predict the behaviour of physical or social phenomena is largely dependent on the brain’s ability to integrate current and past information to generate (probabilistic simulations of the future. But could predictive processing be augmented using advanced technologies? In this contribution, we discuss how computational technologies may be used to support, facilitate or enhance the prediction of future events, by considering exemplificative scenarios across different domains, from simpler sensorimotor decisions to more complex cognitive tasks. We also examine the key scientific and technical challenges that must be faced to turn this vision into reality.

Deep-Learning-Based Drug-Target Interaction Prediction.

Science.gov (United States)

Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

2017-04-07

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

Predicting the Outcome of NBA Playoffs Based on the Maximum Entropy Principle

OpenAIRE

Ge Cheng; Zhenyu Zhang; Moses Ntanda Kyebambe; Nasser Kimbugwe

2016-01-01

Predicting the outcome of National Basketball Association (NBA) matches poses a challenging problem of interest to the research community as well as the general public. In this article, we formalize the problem of predicting NBA game results as a classification problem and apply the principle of Maximum Entropy to construct an NBA Maximum Entropy (NBAME) model that fits to discrete statistics for NBA games, and then predict the outcomes of NBA playoffs using the model. Our results reveal that...

Link prediction with node clustering coefficient

Science.gov (United States)

Wu, Zhihao; Lin, Youfang; Wang, Jing; Gregory, Steve

2016-06-01

Predicting missing links in incomplete complex networks efficiently and accurately is still a challenging problem. The recently proposed Cannistrai-Alanis-Ravai (CAR) index shows the power of local link/triangle information in improving link-prediction accuracy. Inspired by the idea of employing local link/triangle information, we propose a new similarity index with more local structure information. In our method, local link/triangle structure information can be conveyed by clustering coefficient of common-neighbors directly. The reason why clustering coefficient has good effectiveness in estimating the contribution of a common-neighbor is that it employs links existing between neighbors of a common-neighbor and these links have the same structural position with the candidate link to this common-neighbor. In our experiments, three estimators: precision, AUP and AUC are used to evaluate the accuracy of link prediction algorithms. Experimental results on ten tested networks drawn from various fields show that our new index is more effective in predicting missing links than CAR index, especially for networks with low correlation between number of common-neighbors and number of links between common-neighbors.

Computational Prediction of Hot Spot Residues

Science.gov (United States)

Morrow, John Kenneth; Zhang, Shuxing

2013-01-01

Most biological processes involve multiple proteins interacting with each other. It has been recently discovered that certain residues in these protein-protein interactions, which are called hot spots, contribute more significantly to binding affinity than others. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of protein-protein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Using biological methods to determine which residues are hot spots can be costly and time consuming. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features, and have shown increasing predictive successes. Here we review the state of knowledge around protein-protein interactions, hot spots, and give an overview of multiple in silico prediction techniques of hot spot residues. PMID:22316154

A comparative analysis of soft computing techniques for gene prediction.

Science.gov (United States)

Goel, Neelam; Singh, Shailendra; Aseri, Trilok Chand

2013-07-01

The rapid growth of genomic sequence data for both human and nonhuman species has made analyzing these sequences, especially predicting genes in them, very important and is currently the focus of many research efforts. Beside its scientific interest in the molecular biology and genomics community, gene prediction is of considerable importance in human health and medicine. A variety of gene prediction techniques have been developed for eukaryotes over the past few years. This article reviews and analyzes the application of certain soft computing techniques in gene prediction. First, the problem of gene prediction and its challenges are described. These are followed by different soft computing techniques along with their application to gene prediction. In addition, a comparative analysis of different soft computing techniques for gene prediction is given. Finally some limitations of the current research activities and future research directions are provided. Copyright © 2013 Elsevier Inc. All rights reserved.

A fuzzy set preference model for market share analysis

Science.gov (United States)

Turksen, I. B.; Willson, Ian A.

1992-01-01

Consumer preference models are widely used in new product design, marketing management, pricing, and market segmentation. The success of new products depends on accurate market share prediction and design decisions based on consumer preferences. The vague linguistic nature of consumer preferences and product attributes, combined with the substantial differences between individuals, creates a formidable challenge to marketing models. The most widely used methodology is conjoint analysis. Conjoint models, as currently implemented, represent linguistic preferences as ratio or interval-scaled numbers, use only numeric product attributes, and require aggregation of individuals for estimation purposes. It is not surprising that these models are costly to implement, are inflexible, and have a predictive validity that is not substantially better than chance. This affects the accuracy of market share estimates. A fuzzy set preference model can easily represent linguistic variables either in consumer preferences or product attributes with minimal measurement requirements (ordinal scales), while still estimating overall preferences suitable for market share prediction. This approach results in flexible individual-level conjoint models which can provide more accurate market share estimates from a smaller number of more meaningful consumer ratings. Fuzzy sets can be incorporated within existing preference model structures, such as a linear combination, using the techniques developed for conjoint analysis and market share estimation. The purpose of this article is to develop and fully test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation), and how much to make (market share

Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

Science.gov (United States)

Zhao, Yingwen; Fu, Guangyuan; Wang, Jun; Guo, Maozu; Yu, Guoxian

2018-02-23

Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash. Copyright © 2018 Elsevier Inc. All rights reserved.

Fetal fibronectin in the prediction of preterm birth

NARCIS (Netherlands)

Bruijn, M.M.C.

2016-01-01

Accurate prediction of preterm birth is a big clinical challenge in obstetrics. Most of the women presenting with symptoms of preterm labour will not deliver within one week and the majority will even deliver at term. Correct discrimination between women with a high and a low risk to deliver on

Data Assimilation in the Solar Wind: Challenges and First Results.

Science.gov (United States)

Lang, Matthew; Browne, Philip; van Leeuwen, Peter Jan; Owens, Mathew

2017-11-01

Data assimilation (DA) is used extensively in numerical weather prediction (NWP) to improve forecast skill. Indeed, improvements in forecast skill in NWP models over the past 30 years have directly coincided with improvements in DA schemes. At present, due to data availability and technical challenges, DA is underused in space weather applications, particularly for solar wind prediction. This paper investigates the potential of advanced DA methods currently used in operational NWP centers to improve solar wind prediction. To develop the technical capability, as well as quantify the potential benefit, twin experiments are conducted to assess the performance of the Local Ensemble Transform Kalman Filter (LETKF) in the solar wind model ENLIL. Boundary conditions are provided by the Wang-Sheeley-Arge coronal model and synthetic observations of density, temperature, and momentum generated every 4.5 h at 0.6 AU. While in situ spacecraft observations are unlikely to be routinely available at 0.6 AU, these techniques can be applied to remote sensing of the solar wind, such as with Heliospheric Imagers or interplanetary scintillation. The LETKF can be seen to improve the state at the observation location and advect that improvement toward the Earth, leading to an improvement in forecast skill in near-Earth space for both the observed and unobserved variables. However, sharp gradients caused by the analysis of a single observation in space resulted in artificial wavelike structures being advected toward Earth. This paper is the first attempt to apply DA to solar wind prediction and provides the first in-depth analysis of the challenges and potential solutions.

Emission reduction in diesel hybrid commercial vehicles; Emissionsreduzierung bei NFZ mit Dieselhybridantrieb

Energy Technology Data Exchange (ETDEWEB)

Kuberczyk, Raffael; Koehler, Jochen; Blattner, Stefan [ZF Friedrichshafen AG, Friedrichshafen (Germany)

2013-03-01

As far as the commercial vehicle driveline is concerned, today's formidable challenges are combined with an equally compelling need for action - all geared to the aim of meeting stringent emissions standards which reduce fuel consumption and emissions. At the same time the costs for sophisticated new exhaust emissions systems should be reduced. In the following, engineers from ZF Friedrichshafen AG illustrate the advantages offered by diesel hybrid trucks - and why a comprehensive analysis of all saving potentials is required to meet the ambitious targets. (orig.)

Impact of Ultraviolet Light on Vitiligo.

Science.gov (United States)

Singh, Rasnik K

2017-01-01

Vitiligo is a disorder of the melanocytes that results in a dynamic spectrum of skin depigmentation. Its etiology is complex and multifactorial, with data supporting several different hypotheses. Given its prominent phenotype, vitiligo has a significant negative impact on quality of life. Coupled with the chronic and incurable nature of the disease, this presents a formidable treatment challenge. Several treatment modalities have been instituted over the years, with varying efficacy. This chapter focuses on the use of ultraviolet light in vitiligo as an established therapeutic option.

Taming Big Data Variety in the Earth Observing System Data and Information System

Science.gov (United States)

Lynnes, Christopher; Walter, Jeff

2015-01-01

Although the volume of the remote sensing data managed by the Earth Observing System Data and Information System is formidable, an oft-overlooked challenge is the variety of data. The diversity in satellite instruments, science disciplines and user communities drives cost as much or more as the data volume. Several strategies are used to tame this variety: data allocation to distinct centers of expertise; a common metadata repository for discovery, data format standards and conventions; and services that further abstract the variations in data.

Predicting the Remaining Useful Life of Rolling Element Bearings

DEFF Research Database (Denmark)

Hooghoudt, Jan Otto; Jantunen, E; Yi, Yang

2018-01-01

Condition monitoring of rolling element bearings is of vital importance in order to keep the industrial wheels running. In wind industry this is especially important due to the challenges in practical maintenance. The paper presents an attempt to improve the capability of prediction of remaining...

A large-scale evaluation of computational protein function prediction

NARCIS (Netherlands)

Radivojac, P.; Clark, W.T.; Oron, T.R.; Schnoes, A.M.; Wittkop, T.; Kourmpetis, Y.A.I.; Dijk, van A.D.J.; Friedberg, I.

2013-01-01

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be

Cofactory: Sequence-based prediction of cofactor specificity of Rossmann folds

DEFF Research Database (Denmark)

Geertz-Hansen, Henrik Marcus; Blom, Nikolaj; Feist, Adam

2014-01-01

Obtaining optimal cofactor balance to drive production is a challenge metabolically engineered microbial strains. To facilitate identification of heterologous enzymes with desirable altered cofactor requirements from native content, we have developed Cofactory, a method for prediction of enzyme...

Climate, ecosystems, and planetary futures: The challenge to predict life in Earth system models.

Science.gov (United States)

Bonan, Gordon B; Doney, Scott C

2018-02-02

Many global change stresses on terrestrial and marine ecosystems affect not only ecosystem services that are essential to humankind, but also the trajectory of future climate by altering energy and mass exchanges with the atmosphere. Earth system models, which simulate terrestrial and marine ecosystems and biogeochemical cycles, offer a common framework for ecological research related to climate processes; analyses of vulnerability, impacts, and adaptation; and climate change mitigation. They provide an opportunity to move beyond physical descriptors of atmospheric and oceanic states to societally relevant quantities such as wildfire risk, habitat loss, water availability, and crop, fishery, and timber yields. To achieve this, the science of climate prediction must be extended to a more multifaceted Earth system prediction that includes the biosphere and its resources. Copyright © 2018, American Association for the Advancement of Science.

Staphylococcus aureus bacteraemia in children: a formidable foe ...

African Journals Online (AJOL)

Staphylococcus aureus remains one of the most common causes of bacteraemia in children. In order to evade and overcome the immune responses of its host and any antimicrobial therapies aimed at destroying it, this organism, through various mechanisms, continues to evolve. Staphylococcus aureus bacteraemia is a ...

Staphylococcus aureus bacteraemia in children: a formidable foe

African Journals Online (AJOL)

Staphylococcus aureus bacteraemia is a systemic disease; and, multiple organ involvement should be .... damage.3. Few studies have investigated the epidemiology of SAB in South ... producing a multitude of virulence factors, exotoxins and.

SOS, the formidable strategy of bacteria against aggressions.

Science.gov (United States)

Baharoglu, Zeynep; Mazel, Didier

2014-11-01

The presence of an abnormal amount of single-stranded DNA in the bacterial cell constitutes a genotoxic alarm signal that induces the SOS response, a broad regulatory network found in most bacterial species to address DNA damage. The aim of this review was to point out that beyond being a repair process, SOS induction leads to a very strong but transient response to genotoxic stress, during which bacteria can rearrange and mutate their genome, induce several phenotypic changes through differential regulation of genes, and sometimes acquire characteristics that potentiate bacterial survival and adaptation to changing environments. We review here the causes and consequences of SOS induction, but also how this response can be modulated under various circumstances and how it is connected to the network of other important stress responses. In the first section, we review articles describing the induction of the SOS response at the molecular level. The second section discusses consequences of this induction in terms of DNA repair, changes in the genome and gene expression, and sharing of genomic information, with their effects on the bacteria's life and evolution. The third section is about the fine tuning of this response to fit with the bacteria's 'needs'. Finally, we discuss recent findings linking the SOS response to other stress responses. Under these perspectives, SOS can be perceived as a powerful bacterial strategy against aggressions. © 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

Predictive access control for distributed computation

DEFF Research Database (Denmark)

Yang, Fan; Hankin, Chris; Nielson, Flemming

2013-01-01

We show how to use aspect-oriented programming to separate security and trust issues from the logical design of mobile, distributed systems. The main challenge is how to enforce various types of security policies, in particular predictive access control policies — policies based on the future beh...... behavior of a program. A novel feature of our approach is that we can define policies concerning secondary use of data....

Model-based predictive control scheme for cost optimization and balancing services for supermarket refrigeration Systems

NARCIS (Netherlands)

Weerts, H.H.M.; Shafiei, S.E.; Stoustrup, J.; Izadi-Zamanabadi, R.; Boje, E.; Xia, X.

2014-01-01

A new formulation of model predictive control for supermarket refrigeration systems is proposed to facilitate the regulatory power services as well as energy cost optimization of such systems in the smart grid. Nonlinear dynamics existed in large-scale refrigeration plants challenges the predictive

Iterative near-term ecological forecasting: Needs, opportunities, and challenges

Science.gov (United States)

Dietze, Michael C.; Fox, Andrew; Beck-Johnson, Lindsay; Betancourt, Julio L.; Hooten, Mevin B.; Jarnevich, Catherine S.; Keitt, Timothy H.; Kenney, Melissa A.; Laney, Christine M.; Larsen, Laurel G.; Loescher, Henry W.; Lunch, Claire K.; Pijanowski, Bryan; Randerson, James T.; Read, Emily; Tredennick, Andrew T.; Vargas, Rodrigo; Weathers, Kathleen C.; White, Ethan P.

2018-01-01

Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges.

Science.gov (United States)

Dietze, Michael C; Fox, Andrew; Beck-Johnson, Lindsay M; Betancourt, Julio L; Hooten, Mevin B; Jarnevich, Catherine S; Keitt, Timothy H; Kenney, Melissa A; Laney, Christine M; Larsen, Laurel G; Loescher, Henry W; Lunch, Claire K; Pijanowski, Bryan C; Randerson, James T; Read, Emily K; Tredennick, Andrew T; Vargas, Rodrigo; Weathers, Kathleen C; White, Ethan P

2018-02-13

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

ACCEPT: Introduction of the Adverse Condition and Critical Event Prediction Toolbox

Science.gov (United States)

Martin, Rodney A.; Santanu, Das; Janakiraman, Vijay Manikandan; Hosein, Stefan

2015-01-01

The prediction of anomalies or adverse events is a challenging task, and there are a variety of methods which can be used to address the problem. In this paper, we introduce a generic framework developed in MATLAB (sup registered mark) called ACCEPT (Adverse Condition and Critical Event Prediction Toolbox). ACCEPT is an architectural framework designed to compare and contrast the performance of a variety of machine learning and early warning algorithms, and tests the capability of these algorithms to robustly predict the onset of adverse events in any time-series data generating systems or processes.

Pharmacogenetic approaches to the prediction of drug response

International Nuclear Information System (INIS)

Vesell, E.S.

1986-01-01

The following review of pharmacogenetic progress and methodology is offered to stimulate and suggest analogous studies on drugs of abuse. It is readily acknowledged that formidable methodological problems are posed by adapting to drugs of abuse these pharmacogenetic approaches based on the administration of single safe doses of various prescription drugs to normal subjects under carefully controlled environmental conditions. Results of similarly designed studies on drugs of abuse in addicts might be uninterpretable because of confounding by numerous environmental perturbations, including the smoking of cigarettes and/or marijuana, nutritional variations, and intake of other drugs such as ethanol. Ethical considerations render objectionable the administration to unaddicted subjects of drugs at dosage levels usually ingested by drug abusers. Other approaches would have to be taken in such normal subjects. Possibilities include administration of tracer doses of /sup 14/C- or /sup 13/C- labeled drugs or growth of normal cells in culture to investigate their pharmacokinetic and/or pharmacodynamic responses to various drugs of abuse

Limitations of diagnostic precision and predictive utility in the individual case: a challenge for forensic practice.

Science.gov (United States)

Cooke, David J; Michie, Christine

2010-08-01

Knowledge of group tendencies may not assist accurate predictions in the individual case. This has importance for forensic decision making and for the assessment tools routinely applied in forensic evaluations. In this article, we applied Monte Carlo methods to examine diagnostic agreement with different levels of inter-rater agreement given the distributional characteristics of PCL-R scores. Diagnostic agreement and score agreement were substantially less than expected. In addition, we examined the confidence intervals associated with individual predictions of violent recidivism. On the basis of empirical findings, statistical theory, and logic, we conclude that predictions of future offending cannot be achieved in the individual case with any degree of confidence. We discuss the problems identified in relation to the PCL-R in terms of the broader relevance to all instruments used in forensic decision making.

Maternal sensitivity and latency to positive emotion following challenge: pathways through effortful control.

Science.gov (United States)

Conway, Anne; McDonough, Susan C; Mackenzie, Michael; Miller, Alison; Dayton, Carolyn; Rosenblum, Katherine; Muzik, Maria; Sameroff, Arnold

2014-01-01

The ability to self-generate positive emotions is an important component of emotion regulation. In this study, we focus on children's latency to express positive emotions following challenging situations and assess whether this ability operates through early maternal sensitivity and children's effortful control. Longitudinal relations between maternal sensitivity, infant negative affect, effortful control, and latency to positive emotion following challenge were examined in 156 children who were 33 months of age. Structural equation models supported the hypothesis that maternal sensitivity during infancy predicted better effortful control and, in turn, shorter latencies to positive emotions following challenge at 33 months. Directions for future research are discussed. © 2014 Michigan Association for Infant Mental Health.

76 FR 41526 - Centennial Challenges 2011 Strong Tether Challenge

Science.gov (United States)

2011-07-14

... NATIONAL AERONAUTICS AND SPACE ADMINISTRATION [Notice (11-063)] Centennial Challenges 2011 Strong... scheduled and teams that wish to compete may register. Centennial Challenges is a program of prize... NASA Centennial Challenges Program please visit: http://www.nasa.gov/challenges . General questions and...

Nuclear power generation: challenge in the 1980s

International Nuclear Information System (INIS)

Eklund, S.A.

1981-01-01

In the lecture ''Nuclear power generation - challenge in the 1980s'', attempt is made to predict the events arising in 1980s on the basis of the data available in the International Atomic Energy Agency. By the term ''challenge'', emphasis is placed on the potentiality of nuclear power for solving the world energy problem. This is indicated clearly by nuclear power currently accounting for 8%, of the total power generation in the world. The explanation in the above connection with figures and tables is made, including geographical distribution of reactors, nuclear power generation and total power generation in various countries, future capacity of nuclear power generation, situation of reactor operation, future installation of nuclear power plants, uranium demand/supply situation, spent fuel storage, etc. Then, discussion and analysis are made on such problems as waste management, economy, safety, and safeguards. (J.P.N.)

Comparisons of Faulting-Based Pavement Performance Prediction Models

Directory of Open Access Journals (Sweden)

Weina Wang

2017-01-01

Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

Staff Judgements of Responsibility for the Challenging Behaviour of Adults with Intellectual Disabilities

Science.gov (United States)

Dagnan, D.; Cairns, M.

2005-01-01

This study examines the importance of staff judgements of responsibility for challenging behaviour in predicting their emotional and intended helping responses. Sixty-two carers completed questionnaires rating attributions of internality, stability and controllability, emotions of sympathy and anger, judgements of responsibility for the…

Do Implicit Attitudes Predict Actual Voting Behavior Particularly for Undecided Voters?

Science.gov (United States)

Friese, Malte; Smith, Colin Tucker; Plischke, Thomas; Bluemke, Matthias; Nosek, Brian A.

2012-01-01

The prediction of voting behavior of undecided voters poses a challenge to psychologists and pollsters. Recently, researchers argued that implicit attitudes would predict voting behavior particularly for undecided voters whereas explicit attitudes would predict voting behavior particularly for decided voters. We tested this assumption in two studies in two countries with distinct political systems in the context of real political elections. Results revealed that (a) explicit attitudes predicted voting behavior better than implicit attitudes for both decided and undecided voters, and (b) implicit attitudes predicted voting behavior better for decided than undecided voters. We propose that greater elaboration of attitudes produces stronger convergence between implicit and explicit attitudes resulting in better predictive validity of both, and less incremental validity of implicit over explicit attitudes for the prediction of voting behavior. However, greater incremental predictive validity of implicit over explicit attitudes may be associated with less elaboration. PMID:22952898

The legal and ethical concerns that arise from using complex predictive analytics in health care.

Science.gov (United States)

Cohen, I Glenn; Amarasingham, Ruben; Shah, Anand; Xie, Bin; Lo, Bernard

2014-07-01

Predictive analytics, or the use of electronic algorithms to forecast future events in real time, makes it possible to harness the power of big data to improve the health of patients and lower the cost of health care. However, this opportunity raises policy, ethical, and legal challenges. In this article we analyze the major challenges to implementing predictive analytics in health care settings and make broad recommendations for overcoming challenges raised in the four phases of the life cycle of a predictive analytics model: acquiring data to build the model, building and validating it, testing it in real-world settings, and disseminating and using it more broadly. For instance, we recommend that model developers implement governance structures that include patients and other stakeholders starting in the earliest phases of development. In addition, developers should be allowed to use already collected patient data without explicit consent, provided that they comply with federal regulations regarding research on human subjects and the privacy of health information. Project HOPE—The People-to-People Health Foundation, Inc.

78 FR 19742 - Centennial Challenges: 2014 Night Rover Challenge

Science.gov (United States)

2013-04-02

... NATIONAL AERONAUTICS AND SPACE ADMINISTRATION [Notice 13-032] Centennial Challenges: 2014 Night... Centennial Challenges 2014 Night Rover Challenge. SUMMARY: This notice is issued in accordance with 51 U.S.C.... Centennial Challenges is a program of prize competitions to stimulate innovation in technologies of interest...

Prediction of the 2004 national elections in South Africa

CSIR Research Space (South Africa)

Greben, JM

2005-03-01

Full Text Available of the model used. We compare the rate of convergence of our predictions towards the final results with the convergence of the actual results. We also comment on the special challenges and time pressures faced by a research team when it uses a scientific...

Deep-Learning-Based Approach for Prediction of Algal Blooms

Directory of Open Access Journals (Sweden)

Feng Zhang

2016-10-01

Full Text Available Algal blooms have recently become a critical global environmental concern which might put economic development and sustainability at risk. However, the accurate prediction of algal blooms remains a challenging scientific problem. In this study, a novel prediction approach for algal blooms based on deep learning is presented—a powerful tool to represent and predict highly dynamic and complex phenomena. The proposed approach constructs a five-layered model to extract detailed relationships between the density of phytoplankton cells and various environmental parameters. The algal blooms can be predicted by the phytoplankton density obtained from the output layer. A case study is conducted in coastal waters of East China using both our model and a traditional back-propagation neural network for comparison. The results show that the deep-learning-based model yields better generalization and greater accuracy in predicting algal blooms than a traditional shallow neural network does.

77 FR 70835 - Centennial Challenges 2013 Sample Return Robot Challenge

Science.gov (United States)

2012-11-27

... NATIONAL AERONAUTICS AND SPACE ADMINISTRATION Centennial Challenges 2013 Sample Return Robot... Challenge is scheduled and teams that wish to compete may register. Centennial Challenges is a program of... Challenge, please visit: http://challenge.wpi.edu . For general information on the NASA Centennial...

Energy Predictions 2011

Energy Technology Data Exchange (ETDEWEB)

NONE

2010-10-15

Even as the recession begins to subside, the energy sector is still likely to experience challenging conditions as we enter 2011. It should be remembered how very important a role energy plays in driving the global economy. Serving as a simple yet global and unified measure of economic recovery, it is oil's price range and the strength and sustainability of the recovery which will impact the ways in which all forms of energy are produced and consumed. The report aims for a closer insight into these predictions: What will happen with M and A (Mergers and Acquisitions) in the energy industry?; What are the prospects for renewables?; Will the water-energy nexus grow in importance?; How will technological leaps and bounds affect E and P (exploration and production) operations?; What about electric cars? This is the second year Deloitte's Global Energy and Resources Group has published its predictions for the year ahead. The report is based on in-depth interviews with clients, industry analysts, and senior energy practitioners from Deloitte member firms around the world.

Energy Predictions 2011

International Nuclear Information System (INIS)

2010-10-01

Even as the recession begins to subside, the energy sector is still likely to experience challenging conditions as we enter 2011. It should be remembered how very important a role energy plays in driving the global economy. Serving as a simple yet global and unified measure of economic recovery, it is oil's price range and the strength and sustainability of the recovery which will impact the ways in which all forms of energy are produced and consumed. The report aims for a closer insight into these predictions: What will happen with M and A (Mergers and Acquisitions) in the energy industry?; What are the prospects for renewables?; Will the water-energy nexus grow in importance?; How will technological leaps and bounds affect E and P (exploration and production) operations?; What about electric cars? This is the second year Deloitte's Global Energy and Resources Group has published its predictions for the year ahead. The report is based on in-depth interviews with clients, industry analysts, and senior energy practitioners from Deloitte member firms around the world.

Predicting the Potential Market for Electric Vehicles

DEFF Research Database (Denmark)

Jensen, Anders Fjendbo; Cherchi, Elisabetta; Mabit, Stefan Lindhard

2017-01-01

diffusion models in marketing research use fairly simple demand models. In this paper we discuss the problem of predicting market shares for new products and suggest a method that combines advanced choice models with a diffusion model to take into account that new products often need time to gain......Forecasting the potential demand for electric vehicles is a challenging task. Because most studies for new technologies rely on stated preference (SP) data, market share predictions will reflect shares in the SP data and not in the real market. Moreover, typical disaggregate demand models...... are suitable to forecast demand in relatively stable markets, but show limitations in the case of innovations. When predicting the market for new products it is crucial to account for the role played by innovation and how it penetrates the new market over time through a diffusion process. However, typical...

76 FR 56819 - Centennial Challenges 2012 Sample Return Robot Challenge

Science.gov (United States)

2011-09-14

... NATIONAL AERONAUTICS AND SPACE ADMINISTRATION [Notice (11-079)] Centennial Challenges 2012 Sample... Challenge is scheduled and teams that wish to compete may register. Centennial Challenges is a program of... Challenge, please visit: http://wp.wpi.edu/challenge/ . For general information on the NASA Centennial...

Improved part-of-speech prediction in suffix analysis.

Directory of Open Access Journals (Sweden)

Mario Fruzangohar

Full Text Available MOTIVATION: Predicting the part of speech (POS tag of an unknown word in a sentence is a significant challenge. This is particularly difficult in biomedicine, where POS tags serve as an input to training sophisticated literature summarization techniques, such as those based on Hidden Markov Models (HMM. Different approaches have been taken to deal with the POS tagger challenge, but with one exception--the TnT POS tagger--previous publications on POS tagging have omitted details of the suffix analysis used for handling unknown words. The suffix of an English word is a strong predictor of a POS tag for that word. As a pre-requisite for an accurate HMM POS tagger for biomedical publications, we present an efficient suffix prediction method for integration into a POS tagger. RESULTS: We have implemented a fully functional HMM POS tagger using experimentally optimised suffix based prediction. Our simple suffix analysis method, significantly outperformed the probability interpolation based TnT method. We have also shown how important suffix analysis can be for probability estimation of a known word (in the training corpus with an unseen POS tag; a common scenario with a small training corpus. We then integrated this simple method in our POS tagger and determined an optimised parameter set for both methods, which can help developers to optimise their current algorithm, based on our results. We also introduce the concept of counting methods in maximum likelihood estimation for the first time and show how counting methods can affect the prediction result. Finally, we describe how machine-learning techniques were applied to identify words, for which prediction of POS tags were always incorrect and propose a method to handle words of this type. AVAILABILITY AND IMPLEMENTATION: Java source code, binaries and setup instructions are freely available at http://genomes.sapac.edu.au/text_mining/pos_tagger.zip.

Deep learning and data assimilation for real-time production prediction in natural gas wells

NARCIS (Netherlands)

Loh, K.K.L.; Shoeibi Omrani, P.S.; Linden, R.J.P. van der

2018-01-01

The prediction of the gas production from mature gas wells, due to their complex end-of-life behavior, is challenging and crucial for operational decision making. In this paper, we apply a modified deep LSTM model for prediction of the gas flow rates in mature gas wells, including the uncertainties

Prediction Interval: What to Expect When You're Expecting … A Replication.

Directory of Open Access Journals (Sweden)

Jeffrey R Spence

Full Text Available A challenge when interpreting replications is determining whether the results of a replication "successfully" replicate the original study. Looking for consistency between two studies is challenging because individual studies are susceptible to many sources of error that can cause study results to deviate from each other and the population effect in unpredictable directions and magnitudes. In the current paper, we derive methods to compute a prediction interval, a range of results that can be expected in a replication due to chance (i.e., sampling error, for means and commonly used indexes of effect size: correlations and d-values. The prediction interval is calculable based on objective study characteristics (i.e., effect size of the original study and sample sizes of the original study and planned replication even when sample sizes across studies are unequal. The prediction interval provides an a priori method for assessing if the difference between an original and replication result is consistent with what can be expected due to sample error alone. We provide open-source software tools that allow researchers, reviewers, replicators, and editors to easily calculate prediction intervals.

Predicting sample lifetimes in creep fracture of heterogeneous materials

Science.gov (United States)

Koivisto, Juha; Ovaska, Markus; Miksic, Amandine; Laurson, Lasse; Alava, Mikko J.

2016-08-01

Materials flow—under creep or constant loads—and, finally, fail. The prediction of sample lifetimes is an important and highly challenging problem because of the inherently heterogeneous nature of most materials that results in large sample-to-sample lifetime fluctuations, even under the same conditions. We study creep deformation of paper sheets as one heterogeneous material and thus show how to predict lifetimes of individual samples by exploiting the "universal" features in the sample-inherent creep curves, particularly the passage to an accelerating creep rate. Using simulations of a viscoelastic fiber bundle model, we illustrate how deformation localization controls the shape of the creep curve and thus the degree of lifetime predictability.

Use of new scientific developments in regulatory risk assessments: challenges and opportunities.

Science.gov (United States)

Tarazona, Jose V

2013-07-01

Since the 1990s, science based ecological risk assessments constitute an essential tool for supporting decision making in the regulatory context. Using the European REACH Regulation as example, this article presents the challenges and opportunities for new scientific developments within the area of chemical control and environmental protection. These challenges can be sorted out in 3 main related topics (sets). In the short term, the challenges are directly associated with the regulatory requirements, required for facilitating a scientifically sound implementation of the different obligations for industry and authorities. It is important to mention that although the actual tools are different due to the regulatory requirements, the basic needs are still the same as those addressed in the early 1990s: understanding the ecological relevance of the predicted effects, including the uncertainty, and facilitating the link with the socio-economic assessment. The second set of challenges covers the opportunities for getting an added value from the regulatory efforts. The information compiled through REACH registration and notification processes is analyzed as source for new integrative developments for assessing the combined chemical risk at the regional level. Finally, the article discusses the challenge of inverting the process and developing risk assessment methods focusing on the receptor, the individual or ecosystem, instead of on the stressor or source. These approaches were limited in the past due to the lack of information, but the identification and dissemination of standard information, including uses, manufacturing sites, physical-chemical, environmental, ecotoxicological, and toxicological properties as well as operational conditions and risk management measures for thousands of chemicals, combined by the knowledge gathered through large scale monitoring programs and spatial information systems is generating new opportunities. The challenge is liking

Predicting Surface Runoff from Catchment to Large Region

Directory of Open Access Journals (Sweden)

Hongxia Li

2015-01-01

Full Text Available Predicting surface runoff from catchment to large region is a fundamental and challenging task in hydrology. This paper presents a comprehensive review for various studies conducted for improving runoff predictions from catchment to large region in the last several decades. This review summarizes the well-established methods and discusses some promising approaches from the following four research fields: (1 modeling catchment, regional and global runoff using lumped conceptual rainfall-runoff models, distributed hydrological models, and land surface models, (2 parameterizing hydrological models in ungauged catchments, (3 improving hydrological model structure, and (4 using new remote sensing precipitation data.

Detecting failure of climate predictions

Science.gov (United States)

Runge, Michael C.; Stroeve, Julienne C.; Barrett, Andrew P.; McDonald-Madden, Eve

2016-01-01

The practical consequences of climate change challenge society to formulate responses that are more suited to achieving long-term objectives, even if those responses have to be made in the face of uncertainty1, 2. Such a decision-analytic focus uses the products of climate science as probabilistic predictions about the effects of management policies3. Here we present methods to detect when climate predictions are failing to capture the system dynamics. For a single model, we measure goodness of fit based on the empirical distribution function, and define failure when the distribution of observed values significantly diverges from the modelled distribution. For a set of models, the same statistic can be used to provide relative weights for the individual models, and we define failure when there is no linear weighting of the ensemble models that produces a satisfactory match to the observations. Early detection of failure of a set of predictions is important for improving model predictions and the decisions based on them. We show that these methods would have detected a range shift in northern pintail 20 years before it was actually discovered, and are increasingly giving more weight to those climate models that forecast a September ice-free Arctic by 2055.

Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations

Science.gov (United States)

Mey, Antonia S. J. S.; Jiménez, Jordi Juárez; Michel, Julien

2018-01-01

The Drug Design Data Resource (D3R) consortium organises blinded challenges to address the latest advances in computational methods for ligand pose prediction, affinity ranking, and free energy calculations. Within the context of the second D3R Grand Challenge several blinded binding free energies predictions were made for two congeneric series of Farsenoid X Receptor (FXR) inhibitors with a semi-automated alchemical free energy calculation workflow featuring FESetup and SOMD software tools. Reasonable performance was observed in retrospective analyses of literature datasets. Nevertheless, blinded predictions on the full D3R datasets were poor due to difficulties encountered with the ranking of compounds that vary in their net-charge. Performance increased for predictions that were restricted to subsets of compounds carrying the same net-charge. Disclosure of X-ray crystallography derived binding modes maintained or improved the correlation with experiment in a subsequent rounds of predictions. The best performing protocols on D3R set1 and set2 were comparable or superior to predictions made on the basis of analysis of literature structure activity relationships (SAR)s only, and comparable or slightly inferior, to the best submissions from other groups.

Prediction of molecular crystal structures

International Nuclear Information System (INIS)

Beyer, Theresa

2001-01-01

The ab initio prediction of molecular crystal structures is a scientific challenge. Reliability of first-principle prediction calculations would show a fundamental understanding of crystallisation. Crystal structure prediction is also of considerable practical importance as different crystalline arrangements of the same molecule in the solid state (polymorphs)are likely to have different physical properties. A method of crystal structure prediction based on lattice energy minimisation has been developed in this work. The choice of the intermolecular potential and of the molecular model is crucial for the results of such studies and both of these criteria have been investigated. An empirical atom-atom repulsion-dispersion potential for carboxylic acids has been derived and applied in a crystal structure prediction study of formic, benzoic and the polymorphic system of tetrolic acid. As many experimental crystal structure determinations at different temperatures are available for the polymorphic system of paracetamol (acetaminophen), the influence of the variations of the molecular model on the crystal structure lattice energy minima, has also been studied. The general problem of prediction methods based on the assumption that the experimental thermodynamically stable polymorph corresponds to the global lattice energy minimum, is that more hypothetical low lattice energy structures are found within a few kJ mol -1 of the global minimum than are likely to be experimentally observed polymorphs. This is illustrated by the results for molecule I, 3-oxabicyclo(3.2.0)hepta-1,4-diene, studied for the first international blindtest for small organic crystal structures organised by the Cambridge Crystallographic Data Centre (CCDC) in May 1999. To reduce the number of predicted polymorphs, additional factors to thermodynamic criteria have to be considered. Therefore the elastic constants and vapour growth morphologies have been calculated for the lowest lattice energy

Prediction of molecular crystal structures

Energy Technology Data Exchange (ETDEWEB)

Beyer, Theresa

2001-07-01

The ab initio prediction of molecular crystal structures is a scientific challenge. Reliability of first-principle prediction calculations would show a fundamental understanding of crystallisation. Crystal structure prediction is also of considerable practical importance as different crystalline arrangements of the same molecule in the solid state (polymorphs)are likely to have different physical properties. A method of crystal structure prediction based on lattice energy minimisation has been developed in this work. The choice of the intermolecular potential and of the molecular model is crucial for the results of such studies and both of these criteria have been investigated. An empirical atom-atom repulsion-dispersion potential for carboxylic acids has been derived and applied in a crystal structure prediction study of formic, benzoic and the polymorphic system of tetrolic acid. As many experimental crystal structure determinations at different temperatures are available for the polymorphic system of paracetamol (acetaminophen), the influence of the variations of the molecular model on the crystal structure lattice energy minima, has also been studied. The general problem of prediction methods based on the assumption that the experimental thermodynamically stable polymorph corresponds to the global lattice energy minimum, is that more hypothetical low lattice energy structures are found within a few kJ mol{sup -1} of the global minimum than are likely to be experimentally observed polymorphs. This is illustrated by the results for molecule I, 3-oxabicyclo(3.2.0)hepta-1,4-diene, studied for the first international blindtest for small organic crystal structures organised by the Cambridge Crystallographic Data Centre (CCDC) in May 1999. To reduce the number of predicted polymorphs, additional factors to thermodynamic criteria have to be considered. Therefore the elastic constants and vapour growth morphologies have been calculated for the lowest lattice energy

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

Science.gov (United States)

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

2016-07-01

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

The relationship between challenging parenting behaviour and childhood anxiety disorders.

Science.gov (United States)

Lazarus, Rebecca S; Dodd, Helen F; Majdandžić, Mirjana; de Vente, Wieke; Morris, Talia; Byrow, Yulisha; Bögels, Susan M; Hudson, Jennifer L

2016-01-15

This research investigates the relationship between challenging parenting behaviour and childhood anxiety disorders proposed by Bögels and Phares (2008). Challenging parenting behaviour involves the playful encouragement of children to go beyond their own limits, and may decrease children's risk for anxiety (Bögels and Phares, 2008). Parents (n=164 mothers and 144 fathers) of 164 children aged between 3.4 and 4.8 years participated in the current study. A multi-method, multi-informant assessment of anxiety was used, incorporating data from diagnostic interviews as well as questionnaire measures. Parents completed self-report measures of their parenting behaviour (n=147 mothers and 138 fathers) and anxiety (n=154 mothers and 143 fathers). Mothers reported on their child's anxiety via questionnaire as well as diagnostic interview (n=156 and 164 respectively). Of these children, 74 met criteria for an anxiety disorder and 90 did not. Fathers engaged in challenging parenting behaviour more often than mothers. Both mothers' and fathers' challenging parenting behaviour was associated with lower report of child anxiety symptoms. However, only mothers' challenging parenting behaviour was found to predict child clinical anxiety diagnosis. Shared method variance from mothers confined the interpretation of these results. Moreover, due to study design, it is not possible to delineate cause and effect. The finding with respect to maternal challenging parenting behaviour was not anticipated, prompting replication of these results. Future research should investigate the role of challenging parenting behaviour by both caregivers as this may have implications for parenting interventions for anxious children. Crown Copyright © 2015. Published by Elsevier B.V. All rights reserved.

Toward a Predictive Understanding of Earth?s Microbiomes to Address 21st Century Challenges

OpenAIRE

Blaser, Martin J.; Cardon, Zoe G.; Cho, Mildred K.; Dangl, Jeffrey L.; Donohue, Timothy J.; Green, Jessica L.; Knight, Rob; Maxon, Mary E.; Northen, Trent R.; Pollard, Katherine S.; Brodie, Eoin L.

2016-01-01

ABSTRACT Microorganisms have shaped our planet and its inhabitants for over 3.5 billion?years. Humankind has had a profound influence on the biosphere, manifested as global climate and land use changes, and extensive urbanization in response to a growing population. The challenges we face to supply food, energy, and clean water while maintaining and improving the health of our population and ecosystems are significant. Given the extensive influence of microorganisms across our biosphere, we p...

Predicting the Outcome of NBA Playoffs Based on the Maximum Entropy Principle

Directory of Open Access Journals (Sweden)

Ge Cheng

2016-12-01

Full Text Available Predicting the outcome of National Basketball Association (NBA matches poses a challenging problem of interest to the research community as well as the general public. In this article, we formalize the problem of predicting NBA game results as a classification problem and apply the principle of Maximum Entropy to construct an NBA Maximum Entropy (NBAME model that fits to discrete statistics for NBA games, and then predict the outcomes of NBA playoffs using the model. Our results reveal that the model is able to predict the winning team with 74.4% accuracy, outperforming other classical machine learning algorithms that could only afford a maximum prediction accuracy of 70.6% in the experiments that we performed.

Geospatial Analytics in Retail Site Selection and Sales Prediction.

Science.gov (United States)

Ting, Choo-Yee; Ho, Chiung Ching; Yee, Hui Jia; Matsah, Wan Razali

2018-03-01

Studies have shown that certain features from geography, demography, trade area, and environment can play a vital role in retail site selection, largely due to the impact they asserted on retail performance. Although the relevant features could be elicited by domain experts, determining the optimal feature set can be intractable and labor-intensive exercise. The challenges center around (1) how to determine features that are important to a particular retail business and (2) how to estimate retail sales performance given a new location? The challenges become apparent when the features vary across time. In this light, this study proposed a nonintervening approach by employing feature selection algorithms and subsequently sales prediction through similarity-based methods. The results of prediction were validated by domain experts. In this study, data sets from different sources were transformed and aggregated before an analytics data set that is ready for analysis purpose could be obtained. The data sets included data about feature location, population count, property type, education status, and monthly sales from 96 branches of a telecommunication company in Malaysia. The finding suggested that (1) optimal retail performance can only be achieved through fulfillment of specific location features together with the surrounding trade area characteristics and (2) similarity-based method can provide solution to retail sales prediction.

Verification of target motion effects on SAR imagery using the Gotcha GMTI challenge dataset

Science.gov (United States)

Hack, Dan E.; Saville, Michael A.

2010-04-01

This paper investigates the relationship between a ground moving target's kinematic state and its SAR image. While effects such as cross-range offset, defocus, and smearing appear well understood, their derivations in the literature typically employ simplifications of the radar/target geometry and assume point scattering targets. This study adopts a geometrical model for understanding target motion effects in SAR imagery, termed the target migration path, and focuses on experimental verification of predicted motion effects using both simulated and empirical datasets based on the Gotcha GMTI challenge dataset. Specifically, moving target imagery is generated from three data sources: first, simulated phase history for a moving point target; second, simulated phase history for a moving vehicle derived from a simulated Mazda MPV X-band signature; and third, empirical phase history from the Gotcha GMTI challenge dataset. Both simulated target trajectories match the truth GPS target position history from the Gotcha GMTI challenge dataset, allowing direct comparison between all three imagery sets and the predicted target migration path. This paper concludes with a discussion of the parallels between the target migration path and the measurement model within a Kalman filtering framework, followed by conclusions.

Video Scene Parsing with Predictive Feature Learning

OpenAIRE

Jin, Xiaojie; Li, Xin; Xiao, Huaxin; Shen, Xiaohui; Lin, Zhe; Yang, Jimei; Chen, Yunpeng; Dong, Jian; Liu, Luoqi; Jie, Zequn; Feng, Jiashi; Yan, Shuicheng

2016-01-01

In this work, we address the challenging video scene parsing problem by developing effective representation learning methods given limited parsing annotations. In particular, we contribute two novel methods that constitute a unified parsing framework. (1) \\textbf{Predictive feature learning}} from nearly unlimited unlabeled video data. Different from existing methods learning features from single frame parsing, we learn spatiotemporal discriminative features by enforcing a parsing network to ...

A Copula Based Approach for Design of Multivariate Random Forests for Drug Sensitivity Prediction.

Science.gov (United States)

Haider, Saad; Rahman, Raziur; Ghosh, Souparno; Pal, Ranadip

2015-01-01

Modeling sensitivity to drugs based on genetic characterizations is a significant challenge in the area of systems medicine. Ensemble based approaches such as Random Forests have been shown to perform well in both individual sensitivity prediction studies and team science based prediction challenges. However, Random Forests generate a deterministic predictive model for each drug based on the genetic characterization of the cell lines and ignores the relationship between different drug sensitivities during model generation. This application motivates the need for generation of multivariate ensemble learning techniques that can increase prediction accuracy and improve variable importance ranking by incorporating the relationships between different output responses. In this article, we propose a novel cost criterion that captures the dissimilarity in the output response structure between the training data and node samples as the difference in the two empirical copulas. We illustrate that copulas are suitable for capturing the multivariate structure of output responses independent of the marginal distributions and the copula based multivariate random forest framework can provide higher accuracy prediction and improved variable selection. The proposed framework has been validated on genomics of drug sensitivity for cancer and cancer cell line encyclopedia database.

Lessons learned from participating in D3R 2016 Grand Challenge 2: compounds targeting the farnesoid X receptor

Science.gov (United States)

Duan, Rui; Xu, Xianjin; Zou, Xiaoqin

2018-01-01

D3R 2016 Grand Challenge 2 focused on predictions of binding modes and affinities for 102 compounds against the farnesoid X receptor (FXR). In this challenge, two distinct methods, a docking-based method and a template-based method, were employed by our team for the binding mode prediction. For the new template-based method, 3D ligand similarities were calculated for each query compound against the ligands in the co-crystal structures of FXR available in Protein Data Bank. The binding mode was predicted based on the co-crystal protein structure containing the ligand with the best ligand similarity score against the query compound. For the FXR dataset, the template-based method achieved a better performance than the docking-based method on the binding mode prediction. For the binding affinity prediction, an in-house knowledge-based scoring function ITScore2 and MM/PBSA approach were employed. Good performance was achieved for MM/PBSA, whereas the performance of ITScore2 was sensitive to ligand composition, e.g. the percentage of carbon atoms in the compounds. The sensitivity to ligand composition could be a clue for the further improvement of our knowledge-based scoring function.

Nodal quasi-particles of the high-Tc superconductors as carriers of heat

Directory of Open Access Journals (Sweden)

K. Behnia

2006-09-01

Full Text Available   In the quest for understanding correlated electrons, high-temperature superconductivity remains a formidable challenge and a source of insight. This paper briefly recalls the central achievement by the study of heat transport at low temperatures. At very low temperatures, nodal quasi-particles of the d-wave superconducting gap become the main carriers of heat. Their thermal conductivity is unaffected by disorder and reflects the fine structure of the superconducting gap. This finding had led to new openings in the exploration of other unconventional superconductors

SWOT analysis and revelation in traditional Chinese medicine internationalization.

Science.gov (United States)

Tang, Haitao; Huang, Wenlong; Ma, Jimei; Liu, Li

2018-01-01

Traditional Chinese medicine (TCM) is currently the best-preserved and most influential traditional medical system with the largest number of users worldwide. In recent years, the trend of TCM adoption has increased greatly, but the process of TCM internationalization has suffered from a series of setbacks for both internal and external reasons. Thus, the process of TCM internationalization faces formidable challenges, although it also has favourable opportunities. Using SWOT analysis, this paper investigates the strengths, weaknesses, opportunities and threats for TCM. These findings can serve as references for TCM enterprises with global ambitions.

Protein structure prediction using bee colony optimization metaheuristic

DEFF Research Database (Denmark)

Fonseca, Rasmus; Paluszewski, Martin; Winter, Pawel

2010-01-01

of the proteins structure, an energy potential and some optimization algorithm that ¿nds the structure with minimal energy. Bee Colony Optimization (BCO) is a relatively new approach to solving opti- mization problems based on the foraging behaviour of bees. Several variants of BCO have been suggested......Predicting the native structure of proteins is one of the most challenging problems in molecular biology. The goal is to determine the three-dimensional struc- ture from the one-dimensional amino acid sequence. De novo prediction algorithms seek to do this by developing a representation...... our BCO method to generate good solutions to the protein structure prediction problem. The results show that BCO generally ¿nds better solutions than simulated annealing which so far has been the metaheuristic of choice for this problem....

Observed Emotional and Behavioral Indicators of Motivation Predict School Readiness in Head Start Graduates

Science.gov (United States)

Berhenke, Amanda; Miller, Alison L.; Brown, Eleanor; Seifer, Ronald; Dickstein, Susan

2011-01-01

Emotions and behaviors observed during challenging tasks are hypothesized to be valuable indicators of young children's motivation, the assessment of which may be particularly important for children at risk for school failure. The current study demonstrated reliability and concurrent validity of a new observational assessment of motivation in young children. Head Start graduates completed challenging puzzle and trivia tasks during their kindergarten year. Children's emotion expression and task engagement were assessed based on their observed facial and verbal expressions and behavioral cues. Hierarchical regression analyses revealed that observed persistence and shame predicted teacher ratings of children's academic achievement, whereas interest, anxiety, pride, shame, and persistence predicted children's social skills and learning-related behaviors. Children's emotional and behavioral responses to challenge thus appeared to be important indicators of school success. Observation of such responses may be a useful and valid alternative to self-report measures of motivation at this age. PMID:21949599

VWPS: A Ventilator Weaning Prediction System with Artificial Intelligence

Science.gov (United States)

Chen, Austin H.; Chen, Guan-Ting

How to wean patients efficiently off mechanical ventilation continues to be a challenge for medical professionals. In this paper we have described a novel approach to the study of a ventilator weaning prediction system (VWPS). Firstly, we have developed and written three Artificial Neural Network (ANN) algorithms to predict a weaning successful rate based on the clinical data. Secondly, we have implemented two user-friendly weaning success rate prediction systems; the VWPS system and the BWAP system. Both systems could be used to help doctors objectively and effectively predict whether weaning is appropriate for patients based on the patients' clinical data. Our system utilizes the powerful processing abilities of MatLab. Thirdly, we have calculated the performance through measures such as sensitivity and accuracy for these three algorithms. The results show a very high sensitivity (around 80%) and accuracy (around 70%). To our knowledge, this is the first design approach of its kind to be used in the study of ventilator weaning success rate prediction.

Challenges and opportunities for improved understanding of regional climate dynamics

Science.gov (United States)

Collins, Matthew; Minobe, Shoshiro; Barreiro, Marcelo; Bordoni, Simona; Kaspi, Yohai; Kuwano-Yoshida, Akira; Keenlyside, Noel; Manzini, Elisa; O'Reilly, Christopher H.; Sutton, Rowan; Xie, Shang-Ping; Zolina, Olga

2018-01-01

Dynamical processes in the atmosphere and ocean are central to determining the large-scale drivers of regional climate change, yet their predictive understanding is poor. Here, we identify three frontline challenges in climate dynamics where significant progress can be made to inform adaptation: response of storms, blocks and jet streams to external forcing; basin-to-basin and tropical-extratropical teleconnections; and the development of non-linear predictive theory. We highlight opportunities and techniques for making immediate progress in these areas, which critically involve the development of high-resolution coupled model simulations, partial coupling or pacemaker experiments, as well as the development and use of dynamical metrics and exploitation of hierarchies of models.

External validation of prediction models for time to death in potential donors after circulatory death

NARCIS (Netherlands)

Kotsopoulos, A.M.M.; Böing-Messing, F.; Jansen, N.E.; Vos, P.; Abdo, W.F.

2018-01-01

Predicting time to death in controlled donation after circulatory death (cDCD) donors following withdrawal of life‐sustaining treatment (WLST) is important but poses a major challenge. The aim of this study is to determine factors predicting time to circulatory death within 60 minutes after WSLT and

Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.

Science.gov (United States)

Lampa, Samuel; Alvarsson, Jonathan; Spjuth, Ola

2016-01-01

Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.Graphical abstract.

First trimester prediction of maternal glycemic status.

Science.gov (United States)

Gabbay-Benziv, Rinat; Doyle, Lauren E; Blitzer, Miriam; Baschat, Ahmet A

2015-05-01

To predict gestational diabetes mellitus (GDM) or normoglycemic status using first trimester maternal characteristics. We used data from a prospective cohort study. First trimester maternal characteristics were compared between women with and without GDM. Association of these variables with sugar values at glucose challenge test (GCT) and subsequent GDM was tested to identify key parameters. A predictive algorithm for GDM was developed and receiver operating characteristics (ROC) statistics was used to derive the optimal risk score. We defined normoglycemic state, when GCT and all four sugar values at oral glucose tolerance test, whenever obtained, were normal. Using same statistical approach, we developed an algorithm to predict the normoglycemic state. Maternal age, race, prior GDM, first trimester BMI, and systolic blood pressure (SBP) were all significantly associated with GDM. Age, BMI, and SBP were also associated with GCT values. The logistic regression analysis constructed equation and the calculated risk score yielded sensitivity, specificity, positive predictive value, and negative predictive value of 85%, 62%, 13.8%, and 98.3% for a cut-off value of 0.042, respectively (ROC-AUC - area under the curve 0.819, CI - confidence interval 0.769-0.868). The model constructed for normoglycemia prediction demonstrated lower performance (ROC-AUC 0.707, CI 0.668-0.746). GDM prediction can be achieved during the first trimester encounter by integration of maternal characteristics and basic measurements while normoglycemic status prediction is less effective.

PSO-MISMO modeling strategy for multistep-ahead time series prediction.

Science.gov (United States)

Bao, Yukun; Xiong, Tao; Hu, Zhongyi

2014-05-01

Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.

Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation.

Directory of Open Access Journals (Sweden)

Frank Technow

Full Text Available Genomic selection, enabled by whole genome prediction (WGP methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (G×E, continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs attempt to represent the impact of functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of G×E and certain types of non-additive gene effects on the expressed phenotype. Approximate Bayesian computation (ABC, a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with synthetic data sets. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising and novel approach to improving prediction accuracy for some of the most challenging scenarios in plant breeding and applied genetics.

Profit through predictability: The MRF difference at optimax

Science.gov (United States)

Light, Brandon

2007-05-01

In the manufacturing business, there is one product that matters, money. Whether making shoelaces or aircraft carriers a business that doesn't also make a profit doesn't stay around long. Being able to predict operational expenses is critical to determining a product's sale price. Priced too high a product won't sell, too low profit goes away. In the business of precision optics manufacturing, predictability has been often impossible or had large error bars. Manufacturing unpredictability made setting price a challenge. What if predictability could improve by changing the polishing process? Would a predictable, deterministic process lead to profit? Optimax Systems has experienced exactly that. Incorporating Magnetorheological Finishing (MRF) into its finishing process, Optimax saw parts categorized financially as "high risk" become a routine product of higher quality, delivered on time and within budget. Using actual production figures, this presentation will show how much incorporating MRF reduced costs, improved output and increased quality all at the same time.

Announcing the Launch of CPTAC’s Proteogenomics DREAM Challenge | Office of Cancer Clinical Proteomics Research

Science.gov (United States)

This week, we are excited to announce the launch of the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) Proteogenomics Computational DREAM Challenge.  The aim of this Challenge is to encourage the generation of computational methods for extracting information from the cancer proteome and for linking those data to genomic and transcriptomic information.  The specific goals are to predict proteomic and phosphoproteomic data from other multiple data types including transcriptomics and genetics.

Predicting the Lifetimes of Nuclear Waste Containers

Science.gov (United States)

King, Fraser

2014-03-01

As for many aspects of the disposal of nuclear waste, the greatest challenge we have in the study of container materials is the prediction of the long-term performance over periods of tens to hundreds of thousands of years. Various methods have been used for predicting the lifetime of containers for the disposal of high-level waste or spent fuel in deep geological repositories. Both mechanical and corrosion-related failure mechanisms need to be considered, although until recently the interactions of mechanical and corrosion degradation modes have not been considered in detail. Failure from mechanical degradation modes has tended to be treated through suitable container design. In comparison, the inevitable loss of container integrity due to corrosion has been treated by developing specific corrosion models. The most important aspect, however, is to be able to justify the long-term predictions by demonstrating a mechanistic understanding of the various degradation modes.

Predicting Baseline for Analysis of Electricity Pricing

Energy Technology Data Exchange (ETDEWEB)

Kim, T. [Ulsan National Inst. of Science and Technology (Korea, Republic of); Lee, D. [Ulsan National Inst. of Science and Technology (Korea, Republic of); Choi, J. [Ulsan National Inst. of Science and Technology (Korea, Republic of); Spurlock, A. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, A. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Todd, A. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Wu, K. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2016-05-03

To understand the impact of new pricing structure on residential electricity demands, we need a baseline model that captures every factor other than the new price. The standard baseline is a randomized control group, however, a good control group is hard to design. This motivates us to devlop data-driven approaches. We explored many techniques and designed a strategy, named LTAP, that could predict the hourly usage years ahead. The key challenge in this process is that the daily cycle of electricity demand peaks a few hours after the temperature reaching its peak. Existing methods rely on the lagged variables of recent past usages to enforce this daily cycle. These methods have trouble making predictions years ahead. LTAP avoids this trouble by assuming the daily usage profile is determined by temperature and other factors. In a comparison against a well-designed control group, LTAP is found to produce accurate predictions.

Computational prediction of protein hot spot residues.

Science.gov (United States)

Morrow, John Kenneth; Zhang, Shuxing

2012-01-01

Most biological processes involve multiple proteins interacting with each other. It has been recently discovered that certain residues in these protein-protein interactions, which are called hot spots, contribute more significantly to binding affinity than others. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of protein-protein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Using biological methods to determine which residues are hot spots can be costly and time consuming. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features, and have shown increasing predictive successes. Here we review the state of knowledge around protein-protein interactions, hot spots, and give an overview of multiple in silico prediction techniques of hot spot residues.

Market Confidence Predicts Stock Price: Beyond Supply and Demand.

Directory of Open Access Journals (Sweden)

Xiao-Qian Sun

Full Text Available Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using cross-day trading behavior to characterize market confidence and to predict stock price.

Market Confidence Predicts Stock Price: Beyond Supply and Demand.

Science.gov (United States)

Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi; Zhang, Yuqing

2016-01-01

Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using cross-day trading behavior to characterize market confidence and to predict stock price.

Community challenges in biomedical text mining over 10 years: success, failure and the future.

Science.gov (United States)

Huang, Chung-Chi; Lu, Zhiyong

2016-01-01

One effective way to improve the state of the art is through competitions. Following the success of the Critical Assessment of protein Structure Prediction (CASP) in bioinformatics research, a number of challenge evaluations have been organized by the text-mining research community to assess and advance natural language processing (NLP) research for biomedicine. In this article, we review the different community challenge evaluations held from 2002 to 2014 and their respective tasks. Furthermore, we examine these challenge tasks through their targeted problems in NLP research and biomedical applications, respectively. Next, we describe the general workflow of organizing a Biomedical NLP (BioNLP) challenge and involved stakeholders (task organizers, task data producers, task participants and end users). Finally, we summarize the impact and contributions by taking into account different BioNLP challenges as a whole, followed by a discussion of their limitations and difficulties. We conclude with future trends in BioNLP challenge evaluations. Published by Oxford University Press 2015. This work is written by US Government employees and is in the public domain in the US.

Current Challenges in Cancer Treatment.

Science.gov (United States)

Zugazagoitia, Jon; Guedes, Cristiano; Ponce, Santiago; Ferrer, Irene; Molina-Pinelo, Sonia; Paz-Ares, Luis

2016-07-01

In this review, we highlight the current concepts and discuss some of the current challenges and future prospects in cancer therapy. We frequently use the example of lung cancer. We conducted a nonsystematic PubMed search, selecting the most comprehensive and relevant research articles, clinical trials, translational papers, and review articles on precision oncology and immuno-oncology. Papers were prioritized and selected based on their originality and potential clinical applicability. Two major revolutions have changed cancer treatment paradigms in the past few years: targeting actionable alterations in oncogene-driven cancers and immuno-oncology. Important challenges are still ongoing in both fields of cancer therapy. On the one hand, druggable genomic alterations are diverse and represent only small subsets of patients in certain tumor types, which limits testing their clinical impact in biomarker-driven clinical trials. Next-generation sequencing technologies are increasingly being implemented for molecular prescreening in clinical research, but issues regarding clinical interpretation of large genomic data make their wide clinical use difficult. Further, dealing with tumor heterogeneity and acquired resistance is probably the main limitation for the success of precision oncology. On the other hand, long-term survival benefits with immune checkpoint inhibitors (anti-programmed death cell protein-1/programmed death cell ligand-1[PD-1/L1] and anti-cytotoxic T lymphocyte antigen-4 monoclonal antibodies) are restricted to a minority of patients, and no predictive markers are yet robustly validated that could help us recognize these subsets and optimize treatment delivery and selection. To achieve long-term survival benefits, drug combinations targeting several molecular alterations or cancer hallmarks might be needed. This will probably be one of the most challenging but promising precision cancer treatment strategies in the future. Targeting single molecular

Matching phenotypes to whole genomes: Lessons learned from four iterations of the personal genome project community challenges.

Science.gov (United States)

Cai, Binghuang; Li, Biao; Kiga, Nikki; Thusberg, Janita; Bergquist, Timothy; Chen, Yun-Ching; Niknafs, Noushin; Carter, Hannah; Tokheim, Collin; Beleva-Guthrie, Violeta; Douville, Christopher; Bhattacharya, Rohit; Yeo, Hui Ting Grace; Fan, Jean; Sengupta, Sohini; Kim, Dewey; Cline, Melissa; Turner, Tychele; Diekhans, Mark; Zaucha, Jan; Pal, Lipika R; Cao, Chen; Yu, Chen-Hsin; Yin, Yizhou; Carraro, Marco; Giollo, Manuel; Ferrari, Carlo; Leonardi, Emanuela; Tosatto, Silvio C E; Bobe, Jason; Ball, Madeleine; Hoskins, Roger A; Repo, Susanna; Church, George; Brenner, Steven E; Moult, John; Gough, Julian; Stanke, Mario; Karchin, Rachel; Mooney, Sean D

2017-09-01

The advent of next-generation sequencing has dramatically decreased the cost for whole-genome sequencing and increased the viability for its application in research and clinical care. The Personal Genome Project (PGP) provides unrestricted access to genomes of individuals and their associated phenotypes. This resource enabled the Critical Assessment of Genome Interpretation (CAGI) to create a community challenge to assess the bioinformatics community's ability to predict traits from whole genomes. In the CAGI PGP challenge, researchers were asked to predict whether an individual had a particular trait or profile based on their whole genome. Several approaches were used to assess submissions, including ROC AUC (area under receiver operating characteristic curve), probability rankings, the number of correct predictions, and statistical significance simulations. Overall, we found that prediction of individual traits is difficult, relying on a strong knowledge of trait frequency within the general population, whereas matching genomes to trait profiles relies heavily upon a small number of common traits including ancestry, blood type, and eye color. When a rare genetic disorder is present, profiles can be matched when one or more pathogenic variants are identified. Prediction accuracy has improved substantially over the last 6 years due to improved methodology and a better understanding of features. © 2017 Wiley Periodicals, Inc.

Crowdsourcing for Challenging Technical Problems - It Works!

Science.gov (United States)

Davis, Jeffrey R.

2011-01-01

problems or challenges were posted through three different vendors: InnoCentive, yet2.com and TopCoder. The 20 internal challenges were conducted using the InnoCentive crowdsourcing platform designed for use internal to an organization and customized for NASA use, and promoted as NASA@Work. The results were significant. Of the seven InnoCentive external challenges, two full and five partial awards were made in complex technical areas such as predicting solar flares and long-duration food packaging.

Fusion Materials Research at Oak Ridge National Laboratory in Fiscal Year 2014

Energy Technology Data Exchange (ETDEWEB)

Wiffen, Frederick W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Noe, Susan P. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Snead, Lance Lewis [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2014-10-01

The realization of fusion energy is a formidable challenge with significant achievements resulting from close integration of the plasma physics and applied technology disciplines. Presently, the most significant technological challenge for the near-term experiments such as ITER, and next generation fusion power systems, is the inability of current materials and components to withstand the harsh fusion nuclear environment. The overarching goal of the ORNL fusion materials program is to provide the applied materials science support and understanding to underpin the ongoing DOE Office of Science fusion energy program while developing materials for fusion power systems. In doing so the program continues to be integrated both with the larger U.S. and international fusion materials communities, and with the international fusion design and technology communities.

78 FR 49296 - Centennial Challenges 2014 Sample Return Robot Challenge

Science.gov (United States)

2013-08-13

... NATIONAL AERONAUTICS AND SPACE ADMINISTRATION [Notice 13-093] Centennial Challenges 2014 Sample... Centennial Challenges 2014 Sample Return Robot Challenge. SUMMARY: This notice is issued in accordance with... compete may register. Centennial Challenges is a program of prize competitions to stimulate innovation in...

Living in the twilight zone : from regulation to retail

International Nuclear Information System (INIS)

Lambright, B.

1998-01-01

The electric power industry structure in the province of Alberta was discussed. One of the biggest challenges facing the industry is the transition from regulation to open retail competition. Deregulation in the industry began in 1996 with the Electric Utilities Act. In January 1999 the market will open for large industrial customers and full customer choice is to be in place by January 2001. The challenges that Alberta will face in moving to full customer choice was the special focus of this presentation. Adding to these formidable challenges is the fact that the Alberta market is small and is physically isolated from other electrical systems with very limited interconnections with other areas. In consequence, the system may be nearing a point of supply and demand balance for generation and needs some immediate market action to avoid capacity shortages. Alberta Power is tackling these challenges on three fronts: (1) the regulated wire business, (2) the deregulation framework, and (3) new competitive opportunities

Immunohistochemistry for predictive biomarkers in non-small cell lung cancer.

Science.gov (United States)

Mino-Kenudson, Mari

2017-10-01

In the era of targeted therapy, predictive biomarker testing has become increasingly important for non-small cell lung cancer. Of multiple predictive biomarker testing methods, immunohistochemistry (IHC) is widely available and technically less challenging, can provide clinically meaningful results with a rapid turn-around-time and is more cost efficient than molecular platforms. In fact, several IHC assays for predictive biomarkers have already been implemented in routine pathology practice. In this review, we will discuss: (I) the details of anaplastic lymphoma kinase (ALK) and proto-oncogene tyrosine-protein kinase ROS (ROS1) IHC assays including the performance of multiple antibody clones, pros and cons of IHC platforms and various scoring systems to design an optimal algorithm for predictive biomarker testing; (II) issues associated with programmed death-ligand 1 (PD-L1) IHC assays; (III) appropriate pre-analytical tissue handling and selection of optimal tissue samples for predictive biomarker IHC.

The Mock LISA Data Challenges: from Challenge 1B to Challenge 3

International Nuclear Information System (INIS)

Babak, Stanislav; Porter, Edward K; Gair, Jonathan; Baker, John G; Arnaud, Keith; Benacquista, Matthew J; Cornish, Neil J; Crowder, Jeff; Vallisneri, Michele; Cutler, Curt; Larson, Shane L; Plagnol, Eric; Vecchio, Alberto; Barack, Leor; Blaut, Arkadiusz; Fairhurst, Stephen; Harry, Ian; Gong Xuefei; Khurana, Deepak; Krolak, Andrzej

2008-01-01

The Mock LISA Data Challenges are a programme to demonstrate and encourage the development of LISA data-analysis capabilities, tools and techniques. At the time of this workshop, three rounds of challenges had been completed, and the next was about to start. In this paper we provide a critical analysis of the entries to the latest completed round, Challenge 1B. The entries confirm the consolidation of a range of data-analysis techniques for galactic and massive-black-hole binaries, and they include the first convincing examples of detection and parameter estimation of extreme-mass-ratio inspiral sources. In this paper we also introduce the next round, Challenge 3. Its data sets feature more realistic waveform models (e.g., galactic binaries may now chirp, and massive-black-hole binaries may precess due to spin interactions), as well as new source classes (bursts from cosmic strings, isotropic stochastic backgrounds) and more complicated nonsymmetric instrument noise

Selecting the minimum prediction base of historical data to perform 5-year predictions of the cancer burden: The GoF-optimal method.

Science.gov (United States)

Valls, Joan; Castellà, Gerard; Dyba, Tadeusz; Clèries, Ramon

2015-06-01

Predicting the future burden of cancer is a key issue for health services planning, where a method for selecting the predictive model and the prediction base is a challenge. A method, named here Goodness-of-Fit optimal (GoF-optimal), is presented to determine the minimum prediction base of historical data to perform 5-year predictions of the number of new cancer cases or deaths. An empirical ex-post evaluation exercise for cancer mortality data in Spain and cancer incidence in Finland using simple linear and log-linear Poisson models was performed. Prediction bases were considered within the time periods 1951-2006 in Spain and 1975-2007 in Finland, and then predictions were made for 37 and 33 single years in these periods, respectively. The performance of three fixed different prediction bases (last 5, 10, and 20 years of historical data) was compared to that of the prediction base determined by the GoF-optimal method. The coverage (COV) of the 95% prediction interval and the discrepancy ratio (DR) were calculated to assess the success of the prediction. The results showed that (i) models using the prediction base selected through GoF-optimal method reached the highest COV and the lowest DR and (ii) the best alternative strategy to GoF-optimal was the one using the base of prediction of 5-years. The GoF-optimal approach can be used as a selection criterion in order to find an adequate base of prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Implementing novel models of posttreatment care for cancer survivors: Enablers, challenges and recommendations.

Science.gov (United States)

Jefford, Michael; Kinnane, Nicole; Howell, Paula; Nolte, Linda; Galetakis, Spiridoula; Bruce Mann, Gregory; Naccarella, Lucio; Lai-Kwon, Julia; Simons, Katherine; Avery, Sharon; Thompson, Kate; Ashley, David; Haskett, Martin; Davies, Elise; Whitfield, Kathryn

2015-12-01

The American Society of Clinical Oncology and US Institute of Medicine emphasize the need to trial novel models of posttreatment care, and disseminate findings. In 2011, the Victorian State Government (Australia) established the Victorian Cancer Survivorship Program (VCSP), funding six 2-year demonstration projects, targeting end of initial cancer treatment. Projects considered various models, enrolling people of differing cancer types, age and residential areas. We sought to determine common enablers of success, as well as challenges/barriers. Throughout the duration of the projects, a formal "community of practice" met regularly to share experiences. Projects provided regular formal progress reports. An analysis framework was developed to synthesize key themes and identify critical enablers and challenges. Two external reviewers examined final project reports. Discussion with project teams clarified content. Survivors reported interventions to be acceptable, appropriate and effective. Strong clinical leadership was identified as a critical success factor. Workforce education was recognized as important. Partnerships with consumers, primary care and community organizations; risk stratified pathways with rapid re-access to specialist care; and early preparation for survivorship, self-management and shared care models supported positive project outcomes. Tailoring care to individual needs and predicted risks was supported. Challenges included: lack of valid assessment and prediction tools; limited evidence to support novel care models; workforce redesign; and effective engagement with community-based care and issues around survivorship terminology. The VCSP project outcomes have added to growing evidence around posttreatment care. Future projects should consider the identified enablers and challenges when designing and implementing survivorship care. © 2015 Wiley Publishing Asia Pty Ltd.

MU-LOC: A Machine-Learning Method for Predicting Mitochondrially Localized Proteins in Plants

DEFF Research Database (Denmark)

Zhang, Ning; Rao, R Shyama Prasad; Salvato, Fernanda

2018-01-01

-sequence or a multitude of internal signals. Compared with experimental approaches, computational predictions provide an efficient way to infer subcellular localization of a protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations. Consequently......, the performance of current tools can be improved with new data and new machine-learning methods. We present MU-LOC, a novel computational approach for large-scale prediction of plant mitochondrial proteins. We collected a comprehensive dataset of plant subcellular localization, extracted features including amino...

75 FR 47316 - Centennial Challenges 2010 Strong Tether Challenge

Science.gov (United States)

2010-08-05

... NATIONAL AERONAUTICS AND SPACE ADMINISTRATION Centennial Challenges 2010 Strong Tether Challenge... teams that wish to compete may register. Centennial Challenges is a program of prize competitions to..., please visit: http://www.spaceward.org/elevator2010-ts . For general information on the NASA Centennial...

Challenges in sensor development for the electric utility industry

Science.gov (United States)

Ward, Barry H.

1999-01-01

The electric utility industry is reducing operating costs in order to prepare for deregulation. The reduction in operating cost has meant a reduction in manpower. The ability to utilize remaining maintenance staff more effectively and to stay competitive in a deregulated environment has therefore become critical. In recent years, the industry has moved away from routine or periodic maintenance to predictive or condition based maintenance. This requires the assessment of equipment condition by frequent testing and inspection; a requirement that is incompatible with cost reduction. To overcome this dilemma, industry trends are toward condition monitoring, whereby the health of apparatus is monitored continuously. This requires the installation of sensors hr transducers on power equipment and the data taken forwarded to an intelligent device for further processing. These devices then analyze the data and make evaluations based on parameter levels or trends, in an attempt to predict possible deterioration. This continuous monitoring allows the electric utility to schedule maintenance on an as needed basis. The industry has been faced with many challenges in sensor design. The measurement of physical, chemical and electrical parameters under extreme conditions of electric fields, magnetic fields, temperature, corrosion, etc. is extensive. This paper will give an overview of these challenges and the solutions adopted for apparatus such as power transformers, circuit breakers, boilers, cables, batteries, and rotating machinery.

A Novel Model for Stock Price Prediction Using Hybrid Neural Network

Science.gov (United States)

Senapati, Manas Ranjan; Das, Sumanjit; Mishra, Sarojananda

2018-06-01

The foremost challenge for investors is to select stock price by analyzing financial data which is a menial task as of distort associated and massive pattern. Thereby, selecting stock poses one of the greatest difficulties for investors. Nowadays, prediction of financial market like stock market, exchange rate and share value are very challenging field of research. The prediction and scrutinization of stock price is also a potential area of research due to its vital significance in decision making by financial investors. This paper presents an intelligent and an optimal model for prophecy of stock market price using hybridization of Adaline Neural Network (ANN) and modified Particle Swarm Optimization (PSO). The connoted model hybrid of Adaline and PSO uses fluctuations of stock market as a factor and employs PSO to optimize and update weights of Adaline representation to depict open price of Bombay stock exchange. The prediction performance of the proposed model is compared with different representations like interval measurements, CMS-PSO and Bayesian-ANN. The result indicates that proposed scheme has an edge over all the juxtaposed schemes in terms of mean absolute percentage error.

DeepRain: ConvLSTM Network for Precipitation Prediction using Multichannel Radar Data

OpenAIRE

Kim, Seongchan; Hong, Seungkyun; Joh, Minsu; Song, Sa-kwang

2017-01-01

Accurate rainfall forecasting is critical because it has a great impact on people's social and economic activities. Recent trends on various literatures show that Deep Learning (Neural Network) is a promising methodology to tackle many challenging tasks. In this study, we introduce a brand-new data-driven precipitation prediction model called DeepRain. This model predicts the amount of rainfall from weather radar data, which is three-dimensional and four-channel data, using convolutional LSTM...

Bodily Attractiveness and Egalitarianism are Negatively Related in Males

Directory of Open Access Journals (Sweden)

Michael E. Price

2015-01-01

Full Text Available Ancestrally, relatively attractive individuals and relatively formidable males may have had reduced incentives to be egalitarian (i.e., to act in accordance with norms promoting social equality. If selection calibrated one's egalitarianism to one's attractiveness/formidability, then such people may exhibit reduced egalitarianism (“observed egalitarianism” and be perceived by others as less egalitarian (“perceived egalitarianism” in modern environments. To investigate, we created 3D body models of 125 participants to use both as a source of anthropometric measurements and as stimuli to obtain ratings of bodily attractiveness and perceived egalitarianism. We also measured observed egalitarianism (via an economic “dictator” game and indices of political egalitarianism (preference for socialism over capitalism and “equity sensitivity.” Results indicated higher egalitarianism levels in women than in men, and moderate-to-strong negative relationships between (a attractiveness and observed egalitarianism among men, (b attractiveness and perceived egalitarianism among both sexes, and (c formidability and perceived egalitarianism among men. We did not find support for two previously-reported findings: that observed egalitarianism and formidability are negatively related in men, and that wealth and formidability interact to explain variance in male egalitarianism. However, this lack of support may have been due to differences in variable measurement between our study and previous studies.

Shopping intention prediction using decision trees

OpenAIRE

Šebalj, Dario; Franjković, Jelena; Hodak, Kristina

2017-01-01

Introduction: The price is considered to be neglected marketing mix element due to the complexity of price management and sensitivity of customers on price changes. It pulls the fastest customer reactions to that change. Accordingly, the process of making shopping decisions can be very challenging for customer.Objective: The aim of this paper is to create a model that is able to predict shopping intention and classify respondents into one of the two categories, depending on whether they inten...

The Mock LISA Data Challenges: from challenge 3 to challenge 4

International Nuclear Information System (INIS)

Babak, Stanislav; Petiteau, Antoine; Robinson, Emma L; Baker, John G; McWilliams, Sean T; Arnaud, Keith A; Benacquista, Matthew J; Cornish, Neil J; Adams, Matt; Larson, Shane L; Mandel, Ilya; Porter, Edward K; Vallisneri, Michele; Cutler, Curt; Vecchio, Alberto; Blaut, Arkadiusz; Bridges, Michael; Feroz, Farhan; Cohen, Michael; Gair, Jonathan R.

2010-01-01

The Mock LISA Data Challenges are a program to demonstrate LISA data-analysis capabilities and to encourage their development. Each round of challenges consists of one or more datasets containing simulated instrument noise and gravitational waves from sources of undisclosed parameters. Participants analyze the datasets and report best-fit solutions for the source parameters. Here we present the results of the third challenge, issued in April 2008, which demonstrated the positive recovery of signals from chirping galactic binaries, from spinning supermassive-black-hole binaries (with optimal SNRs between ∼10 and 2000), from simultaneous extreme-mass-ratio inspirals (SNRs of 10-50), from cosmic-string-cusp bursts (SNRs of 10-100), and from a relatively loud isotropic background with Ω gw (f) ∼ 10 -11 , slightly below the LISA instrument noise.

The Mock LISA Data Challenges: from challenge 3 to challenge 4

Energy Technology Data Exchange (ETDEWEB)

Babak, Stanislav; Petiteau, Antoine; Robinson, Emma L [Max-Planck-Institut fuer Gravitationsphysik (Albert-Einstein-Institut), Am Muehlenberg 1, D-14476 Golm bei Potsdam (Germany); Baker, John G; McWilliams, Sean T; Arnaud, Keith A [Gravitational Astrophysics Laboratory, NASA Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771 (United States); Benacquista, Matthew J [Center for Gravitational Wave Astronomy, University of Texas at Brownsville, Brownsville, TX 78520 (United States); Cornish, Neil J; Adams, Matt [Department of Physics, Montana State University, Bozeman, MT 59717 (United States); Larson, Shane L [Department of Physics, Utah State University, Logan, UT 84322 (United States); Mandel, Ilya [Department of Physics and Astronomy, Northwestern University, Evanston, IL (United States); Porter, Edward K [APC, UMR 7164, University Paris 7 Denis Diderot, 10, rue Alice Domon et Leonie Duquet, 75025 Paris Cedex 13 (France); Vallisneri, Michele; Cutler, Curt [Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109 (United States); Vecchio, Alberto [School of Physics and Astronomy, University of Birmingham, Edgbaston, Birmingham B152TT (United Kingdom); Blaut, Arkadiusz [Institute of Theoretical Physics, University of Wroclaw, Wroclaw (Poland); Bridges, Michael; Feroz, Farhan [Astrophysics Group, Cavendish Laboratory, University of Cambridge, Cambridge CB30HE (United Kingdom); Cohen, Michael [Theoretical Astrophysics, California Institute of Technology, Pasadena, CA 91125 (United States); Gair, Jonathan R., E-mail: Michele.Vallisneri@jpl.nasa.go [Institute of Astronomy, University of Cambridge, Cambridge CB30HA (United Kingdom)

2010-04-21

The Mock LISA Data Challenges are a program to demonstrate LISA data-analysis capabilities and to encourage their development. Each round of challenges consists of one or more datasets containing simulated instrument noise and gravitational waves from sources of undisclosed parameters. Participants analyze the datasets and report best-fit solutions for the source parameters. Here we present the results of the third challenge, issued in April 2008, which demonstrated the positive recovery of signals from chirping galactic binaries, from spinning supermassive-black-hole binaries (with optimal SNRs between approx10 and 2000), from simultaneous extreme-mass-ratio inspirals (SNRs of 10-50), from cosmic-string-cusp bursts (SNRs of 10-100), and from a relatively loud isotropic background with OMEGA{sub gw}(f) approx 10{sup -11}, slightly below the LISA instrument noise.

Making smart social judgments takes time: infants' recruitment of goal information when generating action predictions.

Science.gov (United States)

Krogh-Jespersen, Sheila; Woodward, Amanda L

2014-01-01

Previous research has shown that young infants perceive others' actions as structured by goals. One open question is whether the recruitment of this understanding when predicting others' actions imposes a cognitive challenge for young infants. The current study explored infants' ability to utilize their knowledge of others' goals to rapidly predict future behavior in complex social environments and distinguish goal-directed actions from other kinds of movements. Fifteen-month-olds (N = 40) viewed videos of an actor engaged in either a goal-directed (grasping) or an ambiguous (brushing the back of her hand) action on a Tobii eye-tracker. At test, critical elements of the scene were changed and infants' predictive fixations were examined to determine whether they relied on goal information to anticipate the actor's future behavior. Results revealed that infants reliably generated goal-based visual predictions for the grasping action, but not for the back-of-hand behavior. Moreover, response latencies were longer for goal-based predictions than for location-based predictions, suggesting that goal-based predictions are cognitively taxing. Analyses of areas of interest indicated that heightened attention to the overall scene, as opposed to specific patterns of attention, was the critical indicator of successful judgments regarding an actor's future goal-directed behavior. These findings shed light on the processes that support "smart" social behavior in infants, as it may be a challenge for young infants to use information about others' intentions to inform rapid predictions.

Predictability of Extreme Precipitations Over the Conterminous us, 1949-2010

Science.gov (United States)

Jiang, M.; Felzer, B. S.

2015-12-01

Extreme precipitation plays an important role in regulating ecosystem services. Precipitation extremes vary in magnitude and duration both spatially and temporally, making it one of the most challenging climate variables to comprehend and predict. Using information theory, we provide an attempt to improve understanding of the predictability of extreme precipitation in the conterminous U.S. over the period of 1949-2010. We define predictability as the recurrent likelihood of patterns described by the measures of constancy and contingency, with the former describing the inter-annual variability and the latter describing the seasonality. This study shows that there are clear west-east contrasts of predictability over the U.S. landscape, with a generally decreasing gradient from the Northeast to the Southwest for intensity-based extremes and a generally increasing gradient from the West to the East for duration-based extremes. We further identify spatially heterogeneous patterns of temporal changes in predictability over the investigated timeframe. Finally, it is evident that constancy plays a heavier role in regulating predictability increases for both intensity and duration-based extremes and for predictability decreases for duration-based extremes, while contingency contributes equally with constancy to determining the decreases in predictability for intensity-based extremes.

Can We Envision a Bettor's Guide to Climate Prediction Markets?

Science.gov (United States)

Trexler, M.

2017-12-01

It's one thing to set up a climate prediction market, it's another to find enough informed traders to make the market work. Climate bets could range widely, from purely scientific or atmospheric metrics, to bets that involve the interplay of science, policy, economic, and behavioral outcomes. For a topic as complex and politicized as climate change, a Bettor's Guide to Climate Predictions could substantially expand and diversify the pool of individuals trading in the market, increasing both its liquidity and decision-support value. The Climate Web is an on-line and publically accessible Beta version of such a Bettor's Guide, implementing the knowledge management adage: "if only we knew what we know." The Climate Web not only curates the key literature, news coverage, and websites relating to more than 100 climate topics, from extreme event exceedance curves to climate economics to climate risk scenarios, it extracts and links together thousands of ideas and graphics across all of those topics. The Climate Web integrates the many disciplinary silos that characterize today's often dysfunctional climate policy conversations, allowing rapid cross-silo exploration and understanding. As a Bettor's Guide it would allow prediction market traders to better research and understand their potential bets, and to quickly survey key thinking and uncertainties relating to those bets. The availability of such a Bettor's Guide to Climate Predictions should make traders willing to place more bets than they otherwise would, and should facilitate higher quality betting. The presentation will introduce the knowledge management dimensions and challenges of climate prediction markets, and introduce the Climate Web as one solution to those challenges.

Handling imbalance data in churn prediction using combined SMOTE and RUS with bagging method

Science.gov (United States)

Pura Hartati, Eka; Adiwijaya; Arif Bijaksana, Moch

2018-03-01

Customer churn has become a significant problem and also a challenge for Telecommunication company such as PT. Telkom Indonesia. It is necessary to evaluate whether the big problems of churn customer and the company’s managements will make appropriate strategies to minimize the churn and retaining the customer. Churn Customer data which categorized churn Atas Permintaan Sendiri (APS) in this Company is an imbalance data, and this issue is one of the challenging tasks in machine learning. This study will investigate how is handling class imbalance in churn prediction using combined Synthetic Minority Over-Sampling (SMOTE) and Random Under-Sampling (RUS) with Bagging method for a better churn prediction performance’s result. The dataset that used is Broadband Internet data which is collected from Telkom Regional 6 Kalimantan. The research firstly using data preprocessing to balance the imbalanced dataset and also to select features by sampling technique SMOTE and RUS, and then building churn prediction model using Bagging methods and C4.5.

Predicting targeted drug combinations based on Pareto optimal patterns of coexpression network connectivity.

Science.gov (United States)

Penrod, Nadia M; Greene, Casey S; Moore, Jason H

2014-01-01

Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets. We have developed a framework to mine high-throughput transcriptomic data, based on differential coexpression and Pareto optimization, to investigate drug-induced tumor adaptation. We use this approach to identify tumor-essential genes as druggable candidates. We apply our method to a set of ER(+) breast tumor samples, collected before (n = 58) and after (n = 60) neoadjuvant treatment with the aromatase inhibitor letrozole, to prioritize genes as targets for combination therapy with letrozole treatment. We validate letrozole-induced tumor adaptation through coexpression and pathway analyses in an independent data set (n = 18). We find pervasive differential coexpression between the untreated and letrozole-treated tumor samples as evidence of letrozole-induced tumor adaptation. Based on patterns of coexpression, we identify ten genes as potential candidates for combination therapy with letrozole including EPCAM, a letrozole-induced essential gene and a target to which drugs have already been developed as cancer therapeutics. Through replication, we validate six letrozole-induced coexpression relationships and confirm the epithelial-to-mesenchymal transition as a process that is upregulated in the residual tumor samples following letrozole treatment. To derive the greatest benefit from molecularly targeted drugs it is critical to design combination

The Challenge Posed by Geomagnetic Activity to Electric Power Reliability: Evidence From England and Wales

Science.gov (United States)

Forbes, Kevin F.; St. Cyr, O. C.

2017-10-01

This paper addresses whether geomagnetic activity challenged the reliability of the electric power system during part of the declining phase of solar cycle 23. Operations by National Grid in England and Wales are examined over the period of 11 March 2003 through 31 March 2005. This paper examines the relationship between measures of geomagnetic activity and a metric of challenged electric power reliability known as the net imbalance volume (NIV). Measured in megawatt hours, NIV represents the sum of all energy deployments initiated by the system operator to balance the electric power system. The relationship between geomagnetic activity and NIV is assessed using a multivariate econometric model. The model was estimated using half-hour settlement data over the period of 11 March 2003 through 31 December 2004. The results indicate that geomagnetic activity had a demonstrable effect on NIV over the sample period. Based on the parameter estimates, out-of-sample predictions of NIV were generated for each half hour over the period of 1 January to 31 March 2005. Consistent with the existence of a causal relationship between geomagnetic activity and the electricity market imbalance, the root-mean-square error of the out-of-sample predictions of NIV is smaller; that is, the predictions are more accurate, when the statistically significant estimated effects of geomagnetic activity are included as drivers in the predictions.

How Predictive Analytics and Choice Architecture Can Improve Student Success

Science.gov (United States)

Denley, Tristan

2014-01-01

This article explores the challenges that students face in navigating the curricular structure of post-secondary degree programs, and how predictive analytics and choice architecture can play a role. It examines Degree Compass, a course recommendation system that successfully pairs current students with the courses that best fit their talents and…

International Space Station Bacteria Filter Element Post-Flight Testing and Service Life Prediction

Science.gov (United States)

Perry, J. L.; von Jouanne, R. G.; Turner, E. H.

2003-01-01

The International Space Station uses high efficiency particulate air (HEPA) filters to remove particulate matter from the cabin atmosphere. Known as Bacteria Filter Elements (BFEs), there are 13 elements deployed on board the ISS's U.S. Segment. The pre-flight service life prediction of 1 year for the BFEs is based upon performance engineering analysis of data collected during developmental testing that used a synthetic dust challenge. While this challenge is considered reasonable and conservative from a design perspective, an understanding of the actual filter loading is required to best manage the critical ISS Program resources. Thus testing was conducted on BFEs returned from the ISS to refine the service life prediction. Results from this testing and implications to ISS resource management are discussed. Recommendations for realizing significant savings to the ISS Program are presented.

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Directory of Open Access Journals (Sweden)

Hao Li

2017-01-01

Full Text Available Predicting the performance of solar water heater (SWH is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a high-performance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

Predicting climate change impacts on polar bear litter size.

Science.gov (United States)

Molnár, Péter K; Derocher, Andrew E; Klanjscek, Tin; Lewis, Mark A

2011-02-08

Predicting the ecological impacts of climate warming is critical for species conservation. Incorporating future warming into population models, however, is challenging because reproduction and survival cannot be measured for yet unobserved environmental conditions. In this study, we use mechanistic energy budget models and data obtainable under current conditions to predict polar bear litter size under future conditions. In western Hudson Bay, we predict climate warming-induced litter size declines that jeopardize population viability: ∼28% of pregnant females failed to reproduce for energetic reasons during the early 1990s, but 40-73% could fail if spring sea ice break-up occurs 1 month earlier than during the 1990s, and 55-100% if break-up occurs 2 months earlier. Simultaneously, mean litter size would decrease by 22-67% and 44-100%, respectively. The expected timeline for these declines varies with climate-model-specific sea ice predictions. Similar litter size declines may occur in over one-third of the global polar bear population.

Machine learning methods for metabolic pathway prediction

Directory of Open Access Journals (Sweden)

Karp Peter D

2010-01-01

Full Text Available Abstract Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations.

Machine learning methods for metabolic pathway prediction

Science.gov (United States)

2010-01-01

Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML) methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations. PMID:20064214

Wind farms production: Control and prediction

Science.gov (United States)

El-Fouly, Tarek Hussein Mostafa

Wind energy resources, unlike dispatchable central station generation, produce power dependable on external irregular source and that is the incident wind speed which does not always blow when electricity is needed. This results in the variability, unpredictability, and uncertainty of wind resources. Therefore, the integration of wind facilities to utility electrical grid presents a major challenge to power system operator. Such integration has significant impact on the optimum power flow, transmission congestion, power quality issues, system stability, load dispatch, and economic analysis. Due to the irregular nature of wind power production, accurate prediction represents the major challenge to power system operators. Therefore, in this thesis two novel models are proposed for wind speed and wind power prediction. One proposed model is dedicated to short-term prediction (one-hour ahead) and the other involves medium term prediction (one-day ahead). The accuracy of the proposed models is revealed by comparing their results with the corresponding values of a reference prediction model referred to as the persistent model. Utility grid operation is not only impacted by the uncertainty of the future production of wind farms, but also by the variability of their current production and how the active and reactive power exchange with the grid is controlled. To address this particular task, a control technique for wind turbines, driven by doubly-fed induction generators (DFIGs), is developed to regulate the terminal voltage by equally sharing the generated/absorbed reactive power between the rotor-side and the gridside converters. To highlight the impact of the new developed technique in reducing the power loss in the generator set, an economic analysis is carried out. Moreover, a new aggregated model for wind farms is proposed that accounts for the irregularity of the incident wind distribution throughout the farm layout. Specifically, this model includes the wake effect

An analysis of prediction skill of monthly mean climate variability

Energy Technology Data Exchange (ETDEWEB)

Kumar, Arun; Chen, Mingyue; Wang, Wanqiu [Climate Prediction Center, National Centers for Environmental Prediction (CPC/NCEP), Camp Springs, MD (United States)

2011-09-15

In this paper, lead-time and spatial dependence in skill for prediction of monthly mean climate variability is analyzed. The analysis is based on a set of extensive hindcasts from the Climate Forecast System at the National Centers for Environmental Prediction. The skill characteristics of initialized predictions is also compared with the AMIP simulations forced with the observed sea surface temperature (SST) to quantify the role of initial versus boundary conditions in the prediction of monthly means. The analysis is for prediction of monthly mean SST, precipitation, and 200-hPa height. The results show a rapid decay in skill with lead time for the atmospheric variables in the extratropical latitudes. Further, after a lead-time of approximately 30-40 days, the skill of monthly mean prediction is essentially a boundary forced problem, with SST anomalies in the tropical central/eastern Pacific playing a dominant role. Because of the larger contribution from the atmospheric internal variability to monthly time-averages (compared to seasonal averages), skill for monthly mean prediction associated with boundary forcing is also lower. The analysis indicates that the prospects of skillful prediction of monthly means may remain a challenging problem, and may be limited by inherent limits in predictability. (orig.)

Research on cross - Project software defect prediction based on transfer learning

Science.gov (United States)

Chen, Ya; Ding, Xiaoming

2018-04-01

According to the two challenges in the prediction of cross-project software defects, the distribution differences between the source project and the target project dataset and the class imbalance in the dataset, proposing a cross-project software defect prediction method based on transfer learning, named NTrA. Firstly, solving the source project data's class imbalance based on the Augmented Neighborhood Cleaning Algorithm. Secondly, the data gravity method is used to give different weights on the basis of the attribute similarity of source project and target project data. Finally, a defect prediction model is constructed by using Trad boost algorithm. Experiments were conducted using data, come from NASA and SOFTLAB respectively, from a published PROMISE dataset. The results show that the method has achieved good values of recall and F-measure, and achieved good prediction results.

Advanced Computational Modeling Approaches for Shock Response Prediction

Science.gov (United States)

Derkevorkian, Armen; Kolaini, Ali R.; Peterson, Lee

2015-01-01

Motivation: (1) The activation of pyroshock devices such as explosives, separation nuts, pin-pullers, etc. produces high frequency transient structural response, typically from few tens of Hz to several hundreds of kHz. (2) Lack of reliable analytical tools makes the prediction of appropriate design and qualification test levels a challenge. (3) In the past few decades, several attempts have been made to develop methodologies that predict the structural responses to shock environments. (4) Currently, there is no validated approach that is viable to predict shock environments overt the full frequency range (i.e., 100 Hz to 10 kHz). Scope: (1) Model, analyze, and interpret space structural systems with complex interfaces and discontinuities, subjected to shock loads. (2) Assess the viability of a suite of numerical tools to simulate transient, non-linear solid mechanics and structural dynamics problems, such as shock wave propagation.

Improving Saliency Models by Predicting Human Fixation Patches

KAUST Repository

Dubey, Rachit

2015-04-16

There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. Our proposed model detects sparse fixation patches with an accuracy of 84 % and eliminates non-fixation patches with an accuracy of 84 % demonstrating that low-level image features can indeed be used to short-list and identify human fixation patches. We then show how these detected fixation patches can be used as saliency priors for popular saliency models, thus, reducing false positives while maintaining true positives. Extensive experimental results show that our proposed approach allows state-of-the-art saliency methods to achieve better prediction performance on benchmark datasets.

Improving Saliency Models by Predicting Human Fixation Patches

KAUST Repository

Dubey, Rachit; Dave, Akshat; Ghanem, Bernard

2015-01-01

There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. Our proposed model detects sparse fixation patches with an accuracy of 84 % and eliminates non-fixation patches with an accuracy of 84 % demonstrating that low-level image features can indeed be used to short-list and identify human fixation patches. We then show how these detected fixation patches can be used as saliency priors for popular saliency models, thus, reducing false positives while maintaining true positives. Extensive experimental results show that our proposed approach allows state-of-the-art saliency methods to achieve better prediction performance on benchmark datasets.

Becoming Predictably Adaptable in Software Development

Directory of Open Access Journals (Sweden)

Michael Vakoc

2017-10-01

Full Text Available It’s difficult to state exact timelines in software development and it is even more difficult to say when features that users want will be delivered. We propose changes to current software development methodologies that enable companies to be predictably adaptable and deliver both on time and what customer asked for. We do so through research of current literature, interviews and personal experience working at an international company that builds products for millions of customers and is facing exactly the challenges described above.

Predicting effects of noncoding variants with deep learning-based sequence model.

Science.gov (United States)

Zhou, Jian; Troyanskaya, Olga G

2015-10-01

Identifying functional effects of noncoding variants is a major challenge in human genetics. To predict the noncoding-variant effects de novo from sequence, we developed a deep learning-based algorithmic framework, DeepSEA (http://deepsea.princeton.edu/), that directly learns a regulatory sequence code from large-scale chromatin-profiling data, enabling prediction of chromatin effects of sequence alterations with single-nucleotide sensitivity. We further used this capability to improve prioritization of functional variants including expression quantitative trait loci (eQTLs) and disease-associated variants.

Genome-based prediction of common diseases: Methodological considerations for future research

NARCIS (Netherlands)

A.C.J.W. Janssens (Cécile); P. Tikka-Kleemola (Päivi)

2009-01-01

textabstractThe translation of emerging genomic knowledge into public health and clinical care is one of the major challenges for the coming decades. At the moment, genome-based prediction of common diseases, such as type 2 diabetes, coronary heart disease and cancer, is still not informative. Our

The first mock data challenge for LISA Pathfinder

Energy Technology Data Exchange (ETDEWEB)

Monsky, A; Hewitson, M; Wanner, G; Nofrarias, M; Diepholz, I; Danzmann, K [Albert-Einstein-Institut, Max-Planck-Institut fuer Gravitationsphysik und Universitaet Hannover, 30167 Hannover (Germany); Ferraioli, L; Hueller, M; Cavalleri, A; Ciani, G; Dolesi, R [Dipartimento di Fisica, Universita di Trento and INFN, Gruppo Collegato di Trento, 38050 Povo, Trento (Italy); Grynagier, A [Institut fuer Flugmechanik und Flugregelung, 70569 Stuttgart (Germany); Armano, M [European Space Agency, ESAC, Villanueva de la Canada, 28692 Madrid (Spain); Benedetti, M [Dipartimento di Ingegneria dei Materiali e Tecnologie Industriali, Universita di Trento and INFN, Gruppo Collegato di Trento, Mesiano, Trento (Italy); Bogenstahl, J [Department of Physics and Astronomy, University of Glasgow, Glasgow (United Kingdom); Bortoluzzi, D; Bosetti, P; Cristofolini, I [Dipartimento di Ingegneria Meccanica e Strutturale, Universita di Trento and INFN, Gruppo Collegato di Trento, Mesiano, Trento (Italy); Brandt, N [Astrium GmbH, 88039 Friedrichshafen (Germany); Cruise, M, E-mail: anneke.monsky@aei.mpg.d [Department of Physics and Astronomy, University of Birmingham, Birmingham (United Kingdom)

2009-05-07

The data analysis of the LISA Technology Package (LTP) will comprise a series of discrete experiments, each focusing on a particular noise measurement or characterization of the instrument in various operating modes. Each of these experiments must be analysed and planned in advance of the mission because the results of a given experiment will have an impact on those that follow. As such, a series of mock data challenges (MDCs) will be developed and carried out with the aim of preparing the analysis tools and optimizing the various planned analyses. The first of these MDCs (MDC1) is a simplified treatment of the dynamics along the axis joining the two test masses onboard LISA Pathfinder. The validation of the dynamical model by predicting the spectra of the interferometer output data is shown, a prediction for the data analysis is calculated and, finally, several simulated interferometer data sets are analysed and calibrated to equivalent out-of-loop test mass acceleration.

Towards a system level understanding of non-model organisms sampled from the environment: a network biology approach.

Science.gov (United States)

Williams, Tim D; Turan, Nil; Diab, Amer M; Wu, Huifeng; Mackenzie, Carolynn; Bartie, Katie L; Hrydziuszko, Olga; Lyons, Brett P; Stentiford, Grant D; Herbert, John M; Abraham, Joseph K; Katsiadaki, Ioanna; Leaver, Michael J; Taggart, John B; George, Stephen G; Viant, Mark R; Chipman, Kevin J; Falciani, Francesco

2011-08-01

The acquisition and analysis of datasets including multi-level omics and physiology from non-model species, sampled from field populations, is a formidable challenge, which so far has prevented the application of systems biology approaches. If successful, these could contribute enormously to improving our understanding of how populations of living organisms adapt to environmental stressors relating to, for example, pollution and climate. Here we describe the first application of a network inference approach integrating transcriptional, metabolic and phenotypic information representative of wild populations of the European flounder fish, sampled at seven estuarine locations in northern Europe with different degrees and profiles of chemical contaminants. We identified network modules, whose activity was predictive of environmental exposure and represented a link between molecular and morphometric indices. These sub-networks represented both known and candidate novel adverse outcome pathways representative of several aspects of human liver pathophysiology such as liver hyperplasia, fibrosis, and hepatocellular carcinoma. At the molecular level these pathways were linked to TNF alpha, TGF beta, PDGF, AGT and VEGF signalling. More generally, this pioneering study has important implications as it can be applied to model molecular mechanisms of compensatory adaptation to a wide range of scenarios in wild populations.

Human Response to Emergency Warning

Science.gov (United States)

Sorensen, J.

2009-12-01

Almost every day people evacuate from their homes, businesses or other sites, even ships, in response to actual or predicted threats or hazards. Evacuation is the primary protective action utilized in large-scale emergencies such as hurricanes, floods, tornados, tsunamis, volcanic eruptions, or wildfires. Although often precautionary, protecting human lives by temporally relocating populations before or during times of threat remains a major emergency management strategy. One of the most formidable challenges facing emergency officials is evacuating residents for a fast-moving and largely unpredictable event such as a wildfire or a local tsunami. How to issue effective warnings to those at risk in time for residents to take appropriate action is an on-going problem. To do so, some communities have instituted advanced communications systems that include reverse telephone call-down systems or other alerting systems to notify at-risk residents of imminent threats. This presentation examines the effectiveness of using reverse telephone call-down systems for warning San Diego residents of wildfires in the October of 2007. This is the first systematic study conducted on this topic and is based on interviews with 1200 households in the evacuation areas.