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Weigh-In-Motion (WIM) data have been collected by state departments of transportation (DOT) in the U.S. and are anticipated to grow as state DOTs expand the number of WIM sites in order to better manage transportation infrastructure and enhance mobility. Traditional approaches for monitoring the vehicle weight measured in WIM systems include conducting statistical tests between two datasets obtained from two calibration visits. Depending on the frequency of visits, these traditional approaches are ineffective or resource-demanding for identifying calibration needs. Excessive vehicle-weight drifts exceeding 10% are usually indicative of poor performance by WIM systems. However, it has been difficult to consistently monitor such performance due to the sheer amount of data. In Georgia, the number of WIM sites have expanded from 12 to 29 in the past 3 years. This paper proposes a deep-learning-based temporal prediction approach for modeling sequential data and monitoring the time-history of the live loads imposed on roads and bridges. In total, 29 WIM sites in Georgia are analyzed to examine the effectiveness of a proposed temporal prediction approach for evaluating observed live loads. This study finds that the Jensen–Shannon divergence method is more effective than statistical difference tests, particularly when screening for live load anomalies. It is concluded that a LSTM neural network is able to capture temporal dynamics underlying the sequential load patterns observed in the WIM data and serves as an effective model for consistently monitoring the performance of WIM systems over time.

Data collected using sensors plays an essential role in active bridge health monitoring. When analyzing a large number of bridges in the U.S., the National Bridge Inventory data as been widely used. Yet, the database does not provide information about live loads, one of the most indeterminate variables for monitoring bridges. Such asymmetric information can lead to an adverse selection problem in making maintenance, rehabilitation, and repair decisions. This study proposes a data-driven reliability analysis to assess probabilities of bridge failure by synthesizing NBI data and Weigh-In-Motion (WIM) data for a large number of bridges in Georgia. On the resistance side, tree ensemble methods are employed to support the hypothesis that the NBI operating load rating represents the distribution of bridge resistance capacities which change over time. On the loading side, the live load distribution is derived from field data collected using WIM sensors. Our results show that the proposed WIM data-enabled reliability analysis substantially enhances information symmetry and provides a reliability index that supports monitoring of bridge conditions, depending on live loads and load-carrying capacities.

BACKGROUND: Individuals respond differently to dietary intake leading to different associations between diet and traits. Most studies have investigated large cohorts without subgrouping them.OBJECTIVE: The purpose was to identify non-uniform associations between diets and anthropometric traits that appeared to be in conflict with one another across subgroups.DESIGN: We used a cohort comprising 43,790 women and men, the Danish Diet, Cancer and Health study, which includes a baseline examination at age 50-64 years and a follow-up about 5 years later. The baseline examination involved anthropometrics, body fat percentage, a food frequency questionnaire and information on lifestyle. From the questionnaire data we computed association rules between the intake of food groups and changes in waist circumference and body weight. Using association rule mining on subgroups and gender-specific cohorts, we identified non-uniform associations. The two gender-specific cohorts were stratified into subgroups using a non-linear, self-organizing map based method.RESULTS: We found 22 and 7 cases of conflicting rules in 8 participant subgroups for different anthropometric traits in women and men, respectively. For example, in a subgroup of women moderate waist loss was associated with a dietary pattern characterized by low intake in both cabbages and wine, in conflict with the association trends of both dietary factors in the female cohort. The finding of more conflicting rules in women suggests that inter-individual differences in response to dietary intake are stronger in women than in men.CONCLUSIONS: This combined stratification and association discovery approach revealed epidemiological relationships between dietary factors and changes in anthropometric traits in subgroups that take food group interactions into account. Conflicting rules adds an additional layer of complexity that should be integrated into the study of these relationships, for example in relation to genotypes.

Many of the world's major freshwater aquifers are being exploited unsustainably, with some projected to approach environmentally unsafe drawdown limits within the 21st century. Given that aquifer depletion tends to occur in important crop producing regions, the prospect of running dry poses a significant threat to global food security. Here we use the Global Change Assessment Model (GCAM) to explore the response of land use and agriculture sectors to severe constraints on global water resources. We simulate a scenario in which a number of important groundwater aquifers become depleted to the point where further water withdrawal is unviable, either due to excessive extraction costs or environmental limits being reached. Results are then benchmarked against a scenario that neglects constraints on water withdrawals. We find that groundwater depletion and associated water price increases drive two distinct responses in the agriculture sector: an expansion of rain fed agriculture, and a shift in irrigated crop production toward regions with cheaper water resources. Losses in crop production are most pronounced in water stressed regions where groundwater is being depleted unsustainably to meet irrigation demands-namely northwest India, Pakistan, the Middle East, western United States, Mexico, and Central Asia. While these results highlight substantial risks for the affected regional agricultural economies, we show that modest changes in irrigation and location of crop growth, in a world with frictionless trade, could ensure global food demands are met despite severe water constraints.

Five new scenarios, or Shared Socioeconomic Pathways (SSPs), have been developed, spanning a range of challenges to mitigation and challenges to adaptation. The Shared Socioeconomic Pathway 4 (SSP4), “Inequality” or “A Road Divided,” is one of these scenarios, characterized by low challenges to mitigation and high challenges to adaptation. We describe, in quantitative terms, the SSP4 as implemented by the Global Change Assessment Model (GCAM), the marker model for this scenario. We use demographic and economic assumptions, in combination with technology and non-climate policy assumptions to develop a quantitative representation of energy, land-use and land-cover, and emissions consistent with the SSP4 narrative. The scenario is one with stark differences within and across regions. High-income regions prosper, continuing to increase their demand for energy and food. Electrification increases in these regions, with the increased generation being met by nuclear and renewables. Low-income regions, however, stagnate due to limited economic growth. Growth in total consumption is dominated by increases in population, not increases in per capita consumption. Due to failures in energy access policies, these regions continue to depend on traditional biofuels, leading to high pollutant emissions. Declining dependence on fossil fuels in all regions means that total radiative forcing absent the inclusion of mitigation or impacts only reaches 6.4 W m−2 in 2100, making this a world with relatively low challenges to mitigation. We explore the effects of mitigation effort on the SSP4 world, finding that the imposition of a carbon price has a varied effect across regions. In particular, the SSP4 mitigation scenarios are characterized by afforestation in the high-income regions and deforestation in the low-income regions. Furthermore, we find that the SSP4 is a world with low challenges to mitigation, but only to a point due to incomplete mitigation of land-related emissions.

Projections of building electricity consumption are used in multiple fields and for a variety of purposes, from energy utility investment decisions to global climate assessments. Existing approaches to modeling building electricity consumption span a range of structural methodologies, spatial resolutions, and temporal scales, potentially leading to divergent projections. This paper compares how two models with different structures and resolutions respond to a common set of population and climate drivers in the western U.S. The BEND model simulates hourly residential and commercial building electricity consumption at the county scale by weighting the results of simulations of thousands of representative buildings. In contrast, the projected electricity demand in GCAM-USA is determined for each state, year, sector (residential/commercial), and service (e.g., heating, cooling, and others), based on population, income, technology, energy prices, and average annual climate. This paper aggregates the two models’ results to a common resolution: annual residential and commercial building electricity consumption by state. Both models show similar responses to future projected population change and climate change, with population change having the larger impact between the two. Differences are primarily due to how the models capture changes in the aggregate energy efficiency of the building stock as it evolves over time.

BackgroundSurveying the scientific literature is an important part of early drug discovery; and with the ever-increasing amount of biomedical publications it is imperative to focus on the most interesting articles. Here we present a project that highlights new understanding (e.g. recently discovered modes of action) and identifies potential drug targets, via a novel, data-driven text mining approach to score type 2 diabetes (T2D) relevance. We focused on monitoring trends and jumps in T2D relevance to help us be timely informed of important breakthroughs.MethodsWe extracted over 7 million n-grams from PubMed abstracts and then clustered around 240,000 linked to T2D into almost 50,000 T2D relevant 'semantic concepts'. To score papers, we weighted the concepts based on co-mentioning with core T2D proteins. A protein's T2D relevance was determined by combining the scores of the papers mentioning it in the five preceding years. Each week all proteins were ranked according to their T2D relevance. Furthermore, the historical distribution of changes in rank from one week to the next was used to calculate the significance of a change in rank by T2D relevance for each protein.ResultsWe show that T2D relevant papers, even those not mentioning T2D explicitly, were prioritised by relevant semantic concepts. Well known T2D proteins were therefore enriched among the top scoring proteins. Our 'high jumpers' identified important past developments in the apprehension of how certain key proteins relate to T2D, indicating that our method will make us aware of future breakthroughs. In summary, this project facilitated keeping up with current T2D research by repeatedly providing short lists of potential novel targets into our early drug discovery pipeline.

China, the world's largest electricity consumer, has strong motivations to deploy nuclear power due to increasing electricity demand and environmental concerns. However, there are social, institutional, and technical barriers to future nuclear expansion. This paper provides an updated assessment of nuclear energy potential in China with analyses at both national and provincial levels and compares results with previous studies. It integrates unit-level information into integrated assessment modelling and scenarios and considers the interactions among different socioeconomic pathways, technology options, climate policies, and social/political concerns on inland siting. Among all factors explored, climate policies have the most significant impact on accelerating nuclear technology deployment. Inland siting of nuclear power plants increases total capacity, but the impact is limited without climate policy. The share of annual nuclear generation across scenarios/studies is unlikely to rise above 11% by 2030 and 30% by 2050 of total Chinese electricity generation. A dramatic increase in total nuclear power generation in China – around 4000 TWh of nuclear generation by 2050 – is only possible in scenarios where China sees electricity demand increase substantially, sticks to and tightens its current Paris Accord commitments, achieves low costs of nuclear technologies, and allows inland siting of nuclear power plants.

This paper describes GCAM v5.1, an open source model that represents the linkages between energy, water, land, climate, and economic systems. GCAM is a market equilibrium model, is global in scope, and operates from 1990 to 2100 in five-year time steps. It can be used to examine, for example, how changes in population, income, or technology cost might alter crop production, energy demand, or water withdrawals, or how changes in one region's demand for energy affect energy, water, and land in other regions. This paper describes the model, including its assumptions, inputs, and outputs. We then use eleven scenarios, varying socioeconomic and climate policy assumptions, to illustrate the results from the model. The resulting scenarios illustrate a wide range of potential future energy, water, and land uses. We compare the results from GCAM v5.1 to historical data and to future scenario simulations from earlier versions of GCAM and from other models. Finally, we provide information on how to obtain the model.

The integrated Earth system model (iESM) has been developed as a new tool for projecting the joint human/climate system. The iESM is based upon coupling an integrated assessment model (IAM) and an Earth system model (ESM) into a common modeling infrastructure. IAMs are the primary tool for describing the human–Earth system, including the sources of global greenhouse gases (GHGs) and short-lived species (SLS), land use and land cover change (LULCC), and other resource-related drivers of anthropogenic climate change. ESMs are the primary scientific tools for examining the physical, chemical, and biogeochemical impacts of human-induced changes to the climate system. The iESM project integrates the economic and human-dimension modeling of an IAM and a fully coupled ESM within a single simulation system while maintaining the separability of each model if needed. Both IAM and ESM codes are developed and used by large communities and have been extensively applied in recent national and international climate assessments. By introducing heretofore-omitted feedbacks between natural and societal drivers, we can improve scientific understanding of the human–Earth system dynamics. Potential applications include studies of the interactions and feedbacks leading to the timing, scale, and geographic distribution of emissions trajectories and other human influences, corresponding climate effects, and the subsequent impacts of a changing climate on human and natural systems. This paper describes the formulation, requirements, implementation, testing, and resulting functionality of the first version of the iESM released to the global climate community.

Despite the well-recognized role of water in transmitting climate impacts to some of the growth drivers of the economy, the water sector has been largely ignored in climate change deliberations. The impacts are projected to vary regionally, and are likely to include changes in average hydroclimate patterns (precipitation, surface runoff, and stream flow), as well as increases in the probability of extreme events. Climate shocks are likely to impose higher costs than gradual changes in climate averages. Prudent management of water resources will be pivotal in addressing the climate challenge—both for adapting to the effects of climate change as well as for meeting global greenhouse gas mitigation goals. The precise consequences of climate change on the hydrological cycle are uncertain, which makes adaptation especially challenging. Uncertainty regarding impacts is partly a consequence of the limitations of climate models. Despite improvements in climate science, the Global Circulation Models developed to project climate futures generate a wide range of projections that often disagree on both the direction and magnitude of precipitation changes. Furthermore, these models have not been designed to predict changes in the hydrological cycle and lack the precision required for planning and managing water resources. In addition to this, changes in the hydrological cycle imply that future water systems may not resemble the past (non-stationarity), so historic trends as used in engineering designs, no longer serve as a reliable guide for assessing and managing future risks. This study presents an investigation of the impacts of climate change on water resources throughout the world, and specific effects on water dependent sectors of the economy such as urban, energy, and agriculture. The results can be used to illustrate the centrality of water in achieving global climate change goals.

The EU has established an aggressive portfolio with explicit near-term targets for 2020 – to reduce GHG emissions by 20%, rising to 30% if the conditions are right, to increase the share of renewable energy to 20%, and to make a 20% improvement in energy efficiency – intended to be the first step in a long-term strategy to limit climate forcing. The effectiveness and cost of extending these measures in time are considered along with the ambition and propagation to the rest of the world. Numerical results are reported and analysed for the contribution of the portfolio's various elements through a set of sensitivity experiments. It is found that the hypothetical programme leads to very substantial reductions in GHG emissions, dramatic increases in use of electricity, and substantial changes in land-use including reduced deforestation, but at the expense of higher food prices. The GHG emissions reductions are driven primarily by the direct limits. Although the carbon price is lower under the hypothetical pro...

The increasing data requirements of complex models demand robust, reproducible, and transparent systems to track and prepare models’ inputs. Here we describe version 1.0 of the gcamdata R package that processes raw inputs to produce the hundreds of XML files needed by the GCAM integrated human-earth systems model. It features extensive functional and unit testing, data tracing and visualization, and enforces metadata, documentation, and flexibility in its component data-processing subunits. Although this package is specific to GCAM, many of its structural pieces and approaches should be broadly applicable to, and reusable by, other complex model/data systems aiming to improve transparency, reproducibility, and flexibility. Funding statement: Primary support for this work was provided by the U.S. Department of Energy, Office of Science, as part of research in Multi-Sector Dynamics, Earth and Environmental System Modeling Program. Additional support was provided by the U.S. Department of Energy Offices of Fossil Energy, Nuclear Energy, and Energy Efficiency and Renewable Energy and the U.S. Environmental Protection Agency.

Integrated assessment models have been extensively used to examine long term energy and greenhouse emissions trajectories, but these models have rarely been examined with respect to their ability to capture historical dynamics. We evaluate the USA buildings component of the Global Change Assessment Model by calibrating the model for 1990 and running forward to 2095. Results for 1995, 2000, 2005 and 2010 are compared to historical estimates at the level of fuel, service, and US states. We highlight that the creation of a historical evaluation dataset is one of the foremost challenges in the evaluation process. This model set-up matches the reported growth of residential floorspace but slightly overestimates heating service demand and underestimates the growth in miscellaneous plug loads. While cooling service appears to be underestimated in 2005, we find little evidence for a systematic bias for other years. The apparently rapid growth of some demands in the commercial sector was also not captured. While these differences carry through into future projections, the overall character of these projections is not changed. These results highlight the difficulty of determining the relationship between energy service demand, which is an unobserved variable, and its drivers when there is limited historical data.

ChemProt is a publicly available compilation of chemical-protein-disease annotation resources that enables the study of systems pharmacology for a small molecule across multiple layers of complexity from molecular to clinical levels. In this third version, ChemProt has been updated to more than 1.7 million compounds with 7.8 million bioactivity measurements for 19 504 proteins. Here, we report the implementation of global pharmacological heatmap, supporting a user-friendly navigation of chemogenomics space. This facilitates the visualization and selection of chemicals that share similar structural properties. In addition, the user has the possibility to search by compound, target, pathway, disease and clinical effect. Genetic variations associated to target proteins were integrated, making it possible to plan pharmacogenetic studies and to suggest human response variability to drug. Finally, Quantitative Structure-Activity Relationship models for 850 proteins having sufficient data were implemented, enabling secondary pharmacological profiling predictions from molecular structure.Database URL: http://potentia.cbs.dtu.dk/ChemProt/.

Systems pharmacology is an emergent area that studies drug action across multiple scales of complexity, from molecular and cellular to tissue and organism levels. There is a critical need to develop network-based approaches to integrate the growing body of chemical biology knowledge with network biology. Here, we report ChemProt, a disease chemical biology database, which is based on a compilation of multiple chemical-protein annotation resources, as well as disease-associated protein-protein interactions (PPIs). We assembled more than 700,000 unique chemicals with biological annotation for 30,578 proteins. We gathered over 2-million chemical-protein interactions, which were integrated in a quality scored human PPI network of 428,429 interactions. The PPI network layer allows for studying disease and tissue specificity through each protein complex. ChemProt can assist in the in silico evaluation of environmental chemicals, natural products and approved drugs, as well as the selection of new compounds based on their activity profile against most known biological targets, including those related to adverse drug events. Results from the disease chemical biology database associate citalopram, an antidepressant, with osteogenesis imperfect and leukemia and bisphenol A, an endocrine disruptor, with certain types of cancer, respectively. The server can be accessed at http://www.cbs.dtu.dk/services/ChemProt/.

ChemProt-2.0 (http://www.cbs.dtu.dk/services/ChemProt-2.0) is a public available compilation of multiple chemical-protein annotation resources integrated with diseases and clinical outcomes information. The database has been updated to > 1.15 million compounds with 5.32 millions bioactivity measurements for 15 290 proteins. Each protein is linked to quality-scored human protein-protein interactions data based on more than half a million interactions, for studying diseases and biological outcomes (diseases, pathways and GO terms) through protein complexes. In ChemProt-2.0, therapeutic effects as well as adverse drug reactions have been integrated allowing for suggesting proteins associated to clinical outcomes. New chemical structure fingerprints were computed based on the similarity ensemble approach. Protein sequence similarity search was also integrated to evaluate the promiscuity of proteins, which can help in the prediction of off-target effects. Finally, the database was integrated into a visual interface that enables navigation of the pharmacological space for small molecules. Filtering options were included in order to facilitate and to guide dynamic search of specific queries.

Denne afhandling præsenterer arbejde udført på Center for Biologisk Sekvens Analyse, Danmarks Tekniske Universitet. Afhandlingen indeholder fire artikler, der beskriver integration af store data mængder samt metoder til forudsigelse af bivirkninger.Kapitel 2 præsenterer ChemProt, en ny sygdom-kemikalie-biologi database. ChemProt integrerer forskellige kemikalie-protein annotations resourcer for sygdoms associerede proteiner og protein-protein interaktions data. ChemProt er udviklet til at assistere in silico evaluering af miljø kemikalier, natur produkter og godkendte lægemidler, samt understøtte udvælgelsen af nye kemiske stoffer baseret på deres aktivitets profiler imod biologiske targets. Den seneste opdatering af ChemProt databasen inkluderer en ny visuel interface, som muliggør let navigering i det farmakologiske rum. Ydermere, er der implementeret nye søgemetoder for kemikalie, protein, sygdom og bivirknings data.Kapitel 3 præsenterer to artikler, der fremviser anvendelsen af system kemisk-biologiske tilgange til at forstå og modelere bivirknings data. Den første tilgang anvender automatiske lærings metoder til at gruppere bivirkninger, kemiske stoffer, proteiner og kliniske resultater i netværk. Dette arbejde demonstrerer styrken i en strategi der anvender klinisk data mining i samarbejde med kemikalie biologi for at reducere søge mulighederne og støtte identificeringen af nye virkemidler. Den anden artikel beskrevet i kapitel 3 skitserer et høj confidens bivirkning-medikament interaktions datasæt.På baggrund af placebo kontrollerede studier fra DailyMed estimerede vi at kun ca. 20% af lægemiddel bivirkningerne er signifikante. Ved hjælp af ChemProt databsen parrede vi kemiske stoffer med deres biologiske targets og påførte en scoring funktion for at fange de hyppigt forekommende bivirkning-protein associationer. Derefter lavede vi en kemisk biologisk model, som kunne forudsige bivirkninger hos 55% af de 133 lægemidler I SIDER databasen. Yderligere validering blev udført på tilbagetrukne lægemidler fra DrugBank og mange af deres bivirkninger blev bekræftet gennem litteratur søgning. Dette arbejde demonstrerer vigtigheden af at anvende høj confidens lægemiddel-bivirknings data til at decifrere små molekylers effekt på mennesker.Opsummeret, denne afhandling præsenterer system kemisk biologiske tilgange som kan støtte identificeringen af små molekylers kliniske effekter via integration af store data mængder. Disse tilgange er også med til at bane vejen for en række studier indenfor kemogenomik, polyfarmakologi og system kemisk biologi. This thesis presents the work carried out at the Center for Biological Sequence Analysis, Technical University of Denmark. The thesis includes four articles describing large-scale data integration and methods for the prediction of drug side-effects.Chapter 2 presents ChemProt, a novel disease chemical biology database. ChemProt integrates different chemical-protein annotation resources for disease-associated proteins and protein-protein interaction data. ChemProt is developed to assist in silico evaluation of environmental chemicals, natural products and approved drugs, as well as to aid the selection of new compounds based on their activity profiles against biological targets. The latest update of ChemProt database includes a new visual interface, which enables easy navigation through the pharmacological space. Additionally, new search methods for chemical, protein, disease and side-effect data have been implemented.Chapter 3 presents two articles that showcase the application of systems chemical biology approaches to understand and model drug side-effect data. The first approach applies machine learning methods to cluster side-effects, drugs, proteins and clinical outcomes in networks. This work demonstrates the power of a strategy that uses clinical data mining in association with chemical biology in order to reduce the search space and aid identification of novel drug actions. The second article described in chapter 3 outlines a high confidence side-effect-drug interaction dataset.We estimated based on the placebo-controlled studies from DailyMed that only approximately 20% of the drug-side-effect associations are significant. With the ChemProt database we linked drugs with their biological targets and applied a scoring function in order to capture frequently encountered side-effect-protein associations. We then built a computational chemical biology model, which revealed side-effect predictive capabilities for 55% of the 133 drugs in the SIDER database. Further validation was performed on withdrawn drugs stored in DrugBank and many side-effects were confirmed through literature search. This work demonstrates the importance of using high-confidence drug-side-effect data in deciphering the effect of small molecules in humans.In summary, this thesis presents computational systems chemical biology approaches that can help identify clinical effects of small molecules through large-scale data integration. These approaches also serve to pave the way into a variety of chemogenomics, polypharmacology and systems chemical biology studies.

Finding new uses for old drugs is a strategy embraced by the pharmaceutical industry, with increasing participation from the academic sector. Drug repurposing efforts focus on identifying novel modes of action, but not in a systematic manner. With intensive data mining and curation, we aim to apply bio- and cheminformatics tools using the DRUGS database, containing 3,837 unique small molecules annotated on 1,750 proteins. These are likely to serve as drug targets and antitargets (i.e., associated with side effects, SE). The academic community, the pharmaceutical sector and clinicians alike could benefit from an integrated, semantic-web compliant computer-aided drug repurposing (CADR) effort, one that would enable deep data mining of associations between approved drugs (D), targets (T), clinical outcomes (CO) and SE. We report preliminary results from text mining and multivariate statistics, based on 7,684 approved drug labels, ADL (Dailymed) via text mining. From the ADL corresponding to 988 unique drugs, the "adverse reactions" section was mapped onto 174 SE, then clustered via principal component analysis into a 5x5 self-organizing map that was integrated into a Cytoscape network of SE-D-T-CO. This type of data can be used to streamline drug repurposing and may result in novel insights that can lead to the identification of novel drug actions.