x hh prediction

These JSON files will automatically include the data from the active RTSW spacecraft. By default, that has been DSCOVR since July 27 at UT.

A complete DSCOVR data archive is available at the NOAA National Center for Environmental Information. Skip to main content. R1 Minor Radio Blackout Impacts. HF Radio: Weak or minor degradation of HF radio communication on sunlit side, occasional loss of radio contact.

Navigation: Low-frequency navigation signals degraded for brief intervals. Real Time Solar Wind. Black background White background Marker Line Hybrid Line 6 hrs Left Y-axis labels Alternating Y-axis labels Show flags.

Usage Impacts Details History Data Real-Time Solar Wind RTSW data refers to data from any spacecraft located upwind of Earth, typically orbiting the L1 Lagrange point, that is being tracked by the Real-Time Solar Wind Network of tracking stations.

SWPC maintains the ability to instantaneously switch the spacecraft that provides the RTSW data. Maybe the biggest one is, you can get access to all of the RTSW plasma and magnetometer since February As you zoom in to shorter time periods, the resolution of the data displayed will increase automatically.

The highest resolution available can be 1 second magnetometer and 20 second thermal plasma data. You can view data from the operational spacecraft or choose between DSCOVR and ACE. The geomagnetic K and A indices can also be plotted.

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Somewhat surprisingly, if we are searching for therapeutically synergistic combinations, the two drug—target modules must not only overlap with the disease module, but also need to be separated in the human interactome without overlapping toxicities Here we documented the predictive power of Complementary Exposure in two complex diseases hypertension and cancer based on known drug combination data from publicly available databases.

Therefore, future work is needed to explore the generalizability of our findings to other diseases. Drug pairs with Complementary Exposure are expected to have either therapeutic Fig. For example, a recent clinical study has reported that combining amiloride and hydrochlorothiazide Complementary Exposure in Fig.

Altogether, adverse effects can appear independently from the separation of the two drug target modules, occurring significantly in both Overlapping Exposure Supplementary Fig.

Lack of dose-dependent information and precise perturbation effects of disease-causing variants and drug exposure generate a coupled interplay between adverse and therapeutic effects 31 for Complementary Exposure.

Integration of network proximity, high-throughput in vitro or in vivo assays, pharmacokinetics-based mathematical modeling e. In addition, future work is needed to explore the effects of potential data selection bias.

For example, combinations of drugs that target related proteins in the same disease module are more likely to be tested in drug combination clinical trials. Finally, given the lack of large-scale, systematic data on combinations of multiple drugs, in our current study, we limited our exploration on drug pairs only.

Yet, we expect that Complementary Exposure remains an efficient design principle even upon combining multiple drugs. To fight the combinatorial explosion upon inspecting network relationships of multiple drug—target modules with a disease module, theories of signed networks can be of great help, such as structural balance theories 36 , reducing the number of network patterns to investigate in the human interactome.

In addition, advanced network-based link prediction methods rooted in biological principles 37 can help to develop a combined, quantitative score for each predicted drug combination. Eventually, experimental validation and prospective clinical trials must be conducted to verify the network-predicted drug combinations under controlled conditions.

As for the input data, having a more complete human interactome and more complete, systematic drug—target network with well-annotated pharmacokinetics and pharmacodynamics information would improve the performance of the network-based model further. In summary, our findings suggest that the discovery of efficacious drug combinations could benefit from network-based, rational drug combination screenings, exploring the relationship between drug—target modules and the disease modules via network proximity in the human interactome.

From a translational perspective, the network tools developed here could help develop novel, efficacious combination therapies for multiple complex diseases if broadly applied. html ; 2 literature-curated PPIs identified by affinity purification followed by affinity-purification mass spectrometry AP-MS , Y2H, and literature-derived low-throughput experiments; 3 binary, physical PPIs derived from protein three-dimensional structures; 4 kinase-substrate interactions by literature-derived low-throughput and high-throughput experiments; and 5 signaling networks by literature-derived low-throughput experiments.

Computationally inferred interactions rooted in evolutionary analysis, gene expression data, and metabolic associations were excluded. The human protein—protein interactome are provided in the Supplementary Data 1.

Drug—target interactions were acquired from the DrugBank database v4. In total, 15, drug—target interactions connecting drugs and unique human targets were built, including drugs that have at least two experimentally validated targets Supplementary Data 2.

In this study, we focused on pairwise drug combinations by assembling the clinical data from the multiple data sources Supplementary Note 3. Compound name, generic name, or commercial name of each drug was standardized by MeSH and UMLS vocabularies 47 and further transferred to DrugBank ID from the DrugBank database v4.

Duplicated drug pairs were removed. In total, unique pairwise drug combinations connecting drugs were retained Supplementary Data 3. We compiled clinically reported adverse drug—drug interactions DDIs data from the DrugBank database v4.

Here, we focused on adverse drug interactions where each drug has the experimentally validated target information. Compound name, generic name, or commercial name of each drug were standardized by MeSH and UMLS vocabularies 47 and further transferred to DrugBank ID from the DrugBank database v4.

In total, 13, clinically reported adverse DDIs connecting unique drugs were retained Supplementary Data 4. In addition, we collected cardiovascular event-specific adverse DDIs from the TWOSIDE database TWOSIDE includes over , significant associations connecting 59, drug pairs and adverse events In this study, we focused on 4 types of cardiovascular events: arrhythmia MeSH ID: D , heart failure MeSH ID: D , myocardial infarction MeSH ID: D , and high blood pressure MeSH ID: D We downloaded chemical structure information SMILES format from the DrugBank database v4.

If two drug molecules have a and b bits set in their MACCS fragment bit-strings, with c of these bits being set in the fingerprints of both drugs, the Tanimoto coefficient T of a drug—drug pair is defined as:.

T is widely used in drug discovery and development 49 , offering a value in the range of zero no bits in common to one all bits are the same. We calculated the protein sequence similarity S P a, b of two drug targets a and b using the Smith—Waterman algorithm The Smith—Waterman algorithm performs local sequence alignment by comparing segments of all possible lengths and optimizing the similarity measure for determining similar regions between two strings of protein canonical sequences of drug targets.

The overall sequence similarity of the targets binding two drugs A and B is determined by Eq. This condition ensures that for drugs with common targets we do not take pairs into account where a target would be compared to itself. In order to reduce the noise of co-expression analysis, we mapped PCC a, b into the human protein—protein interactome network Supplementary Methods 2 to build a co-expressed protein—protein interactome network as described previously We used three types of the experimentally validated or literature-derived evidences: biological processes BP , molecular function MF , and cellular component CC , excluding annotations inferred computationally.

The semantic comparison of GO annotations offers quantitative ways to compute similarities between genes and gene products. We computed GO similarity S GO a,b for each pair of drug target-coding genes a and b using a graph-based semantic similarity measure algorithm 52 implemented in an R package, named GOSemSim The overall GO similarity of the drug target-coding genes binding to two drugs A and B was determined by Eq.

Clinical similarities of drug pairs derived from the drug Anatomical Therapeutic Chemical ATC classification systems codes have been commonly used to predict new drug targets The ATC codes for all FDA-approved drugs used in this study were downloaded from the DrugBank database v4.

The kth level drug clinical similarity S k of drugs A and B is defined via the ATC codes as below. where ATC k represents all ATC codes at the k th level. A score S atc A, B is used to define the clinical similarity between drugs A and B:.

where n represents the five levels of ATC codes ranging from 1 to 5. Note that drugs can have multiple ATC codes. For example, nicotine a potent parasympathomimetic stimulant has four different ATC codes: N07BA01, A11HA01, C04AC01, C10AD For a drug with multiple ATC codes, the clinical similarity was computed for each ATC code, and then, the average clinical similarity was used In this section, we compared the introduced network-based separation Eq.

Here, we examined two measures to quantify the overlap between target sets of drug A and drug B:. Supplementary Figs. The target-set overlap is low for most drug pairs, and the majority To investigate the statistical significance of the observed overlaps, we used a hypergeometric model.

The null hypothesis is that drug targets are randomly located from the space of all N protein-coding genes in the human interactome.

The overlap expected for two target sets A and B is then given by. and the P -values for enrichment and depletion e. A network-based separation of a drug pair, A and B, is calculated via Eq.

We evaluated four other different distance measures that take into account the path lengths between two drug target sets: a the closest measure, representing the average shortest path length between targets of drug A and the nearest target of the drug A; b the shortest measure, representing the average shortest path length among all targets of drugs; c the kernel measure, down-weighting longer paths via an exponential penalty; d the centre measure, representing the shortest path length among all targets of drugs with the greatest closeness centrality among drug targets.

Given A and B, the set of drug targets for A and B, and d AB , the shortest path length between nodes a and b in the interactome, we define these distance measures as follows:. We integrated disease—gene annotation data from 8 different resources and excluded the duplicated entries Supplementary Note 4.

We annotated all protein-coding genes using gene Entrez ID, chromosomal location, and the official gene symbols from the NCBI database Each cardiovascular event was defined by MeSH and UMLS vocabularies We used area under the receiver operating characteristic ROC curve AUC to evaluate how well the network proximity discriminates FDA-approved or experimentally validated pairwise combinations from random drug pairs.

We counted the true positive rate and false positive rate at different network proximities as thresholds to illustrate the ROC curve. As negative drug pairs are not typically reported in the literature or publicly available databases, we use all unknown drug pairs as negative samples.

In addition, we selected the same portion of unknown drug pairs as positive samples to control the size imbalance. We repeated this procedure times and reported the average AUC values to compare the performance of different approaches.

All statistical analyses were performed using the R package v3. Further information on experimental design is available in the Nature Research Reporting Summary linked to this article.

The code for network proximity calculation is available at github. All other codes used in this study are available from the corresponding author upon reasonable request.

The publicly available human protein—protein interactome Supplementary Data 1 , experimentally validated drug—target interactions Supplementary Data 2 , experimentally validated drug combinations Supplementary Data 3 , clinically reported adverse drug—drug interactions Supplementary Data 4 , and network-predicted hypertensive drug combinations Supplementary Data 5 are available in Supplementary Data 1 — 5.

Sun, X. High-throughput methods for combinatorial drug discovery. Article Google Scholar. Jia, J. et al. Mechanisms of drug combinations: interaction and network perspectives. Drug Discov.

Article CAS Google Scholar. Ali, M. Trends in the market for antihypertensive drugs. Giles, T. Efficacy and safety of nebivolol and valsartan as fixed-dose combination in hypertension: a randomised, multicentre study.

Lancet , — Crystal, A. Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science , — Article ADS CAS Google Scholar. Tan, X. Systematic identification of synergistic drug pairs targeting HIV. Zheng, W.

Drug repurposing screens and synergistic drug-combinations for infectious diseases. Lipinski, C. Navigating chemical space for biology and medicine. Nature , — Bulusu, K. Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives.

Today 21 , — Sun, Y. Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer. Bansal, M.

A community computational challenge to predict the activity of pairs of compounds. Zimmer, A. Prediction of multidimensional drug dose responses based on measurements of drug pairs.

Natl Acad. USA , — Barabasi, A. Menche, J. Disease networks. Uncovering disease—disease relationships through the incomplete interactome. Science , Guney, E. Network-based in silico drug efficacy screening. Cheng, F. Network-based approach to prediction and population-based validation of in silico drug repurposing.

Article ADS Google Scholar. Network medicine: a network-based approach to human disease. Vidal, M. Interactome networks and human disease. Cell , — Yildirim, M. Drug—target network. Hopkins, A. Network pharmacology: the next paradigm in drug discovery.

Article MathSciNet CAS Google Scholar. Network pharmacology. Kalmanti, L. Safety and efficacy of imatinib in CML over a period of 10 years: data from the randomized CML-study IV.

Leukemia 29 , — DeAngelo, D. Phase 1 clinical results with tandutinib MLN , a novel FLT3 antagonist, in patients with acute myelogenous leukemia or high-risk myelodysplastic syndrome: safety, pharmacokinetics, and pharmacodynamics.

Blood , — Polman, C. A randomized, placebo-controlled trial of natalizumab for relapsing multiple sclerosis. Wolf, G. Jia, Y. Overcoming EGFR TM and EGFR CS resistance with mutant-selective allosteric inhibitors.

Brown, M. Effect of amiloride, or amiloride plus hydrochlorothiazide, versus hydrochlorothiazide on glucose tolerance and blood pressure PATHWAY-3 : a parallel-group, double-blind randomised phase 4 trial.

Lancet Diabetes Endocrinol. Beermann, B. Absorption, metabolism, and excretion of hydrochlorothiazide. Han, P. Synergism of hydrochlorothiazide and nitrendipine on reduction of blood pressure and blood pressure variability in spontaneously hypertensive rats.

Acta Pharmacol. Maronde, R. Response of thiazide-induced hypokalemia to amiloride. JAMA , — Greene, J. Putting the patient back together—social medicine, network medicine, and the limits of reductionism. Bibi, Z. Role of cytochrome P in drug interactions.

Cami, A. Pharmacointeraction network models predict unknown drug—drug interactions. PLoS ONE 8 , e Zhao, S. Systems pharmacology of adverse event mitigation by drug combinations. Tatonetti, N. Data-driven prediction of drug effects and interactions.

Facchetti, G. Computing global structural balance in large-scale signed social networks. Istvan, A. Network-based prediction of protein interactions. Rolland, T.

A proteome-scale map of the human interactome network. Rual, J. Towards a proteome-scale map of the human protein-protein interaction network.

Law, V. DrugBank 4. Nucleic Acids Res. Zhu, F. Therapeutic target database update a resource for facilitating target-oriented drug discovery. Hernandez-Boussard, T. The pharmacogenetics and pharmacogenomics knowledge base: accentuating the knowledge.

Gaulton, A. ChEMBL: a large-scale bioactivity database for drug discovery. Liu, T. BindingDB: a web-accessible database of experimentally determined protein—ligand binding affinities. Pawson, A.

Apweiler, R. UniProt: the universal protein knowledgebase. Bodenreider, O. The Unified Medical Language System UMLS : integrating biomedical terminology. Open Babel: an open chemical toolbox. Willett, P.

Similarity-based virtual screening using 2D fingerprints. Today 11 , — Smith, T. Identification of common molecular subsequences.

Studying tumorigenesis through network evolution and somatic mutational perturbations in the cancer interactome. Wang, J. A new method to measure the semantic similarity of GO terms. Bioinformatics 23 , — Yu, G. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products.

Bioinformatics 26 , — Prediction of polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Download references.

The authors thank Yifang Ma, Marc Vidal, and Joseph Loscalzo for useful discussions on the manuscript. The authors thank Alice Grishchenko for polishing the figures. This work was supported by NIH grants PHG and UHG to A. from NHGRI, P01HL to A.

from NHLBI, and K99HL and R00HL to F. from NHLBI. Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, , USA. Feixiong Cheng, István A.

Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, , USA. Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, , USA.

Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, , USA. Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, , USA.

Center for Network Science, Central European University, Budapest, , Hungary. You can also search for this author in PubMed Google Scholar. conceived the study. performed all experiments and data analysis. performed data analysis.

and A. wrote the manuscript. Correspondence to Albert-László Barabási. is a co-founder of Scipher, a startup that uses network concepts to explore human disease.

The other authors declare no competing interests.

Hooper Holmes Inc Forecast, Short-Term " HH" Stock Price Prognosis for Next Days · HH Forecast, Long-Term Price Predictions for Next Months and Year: , HH where Ho = H-1(H - HS)H-. Best linear unbiased pr ediction. We wr-ish X'R-'X X'R-'Z X'R'Be X'R y. Z'R-'X Z'R-1Z + G-1 Z'R-'B. - GBu v4 + BjA _ Z'R Predictions of non-landmark gene expression showing a spatial expression along the A–P and D–V axes and b a stripe pattern according to

X hh prediction - x. Observed values of predictor variable. y. Observed values of response variable. newdata x values for which predictions are calculated Hooper Holmes Inc Forecast, Short-Term " HH" Stock Price Prognosis for Next Days · HH Forecast, Long-Term Price Predictions for Next Months and Year: , HH where Ho = H-1(H - HS)H-. Best linear unbiased pr ediction. We wr-ish X'R-'X X'R-'Z X'R'Be X'R y. Z'R-'X Z'R-1Z + G-1 Z'R-'B. - GBu v4 + BjA _ Z'R Predictions of non-landmark gene expression showing a spatial expression along the A–P and D–V axes and b a stripe pattern according to

For the example shown in Fig. The predicted heat capacity melting curve shows two peaks around 60 and 90°C, respectively. The peaks correspond to the melting of the two helices in the predicted structures in Fig. The input data of Vfold3D are the RNA sequence and the 2D structure base pairs see the snapshot of the Vfold3D web server in Fig.

The output of Vfold3D is a PDB file for the predicted all-atom 3D structure s. Because the current version of Vfold3D is template-based, no 3D structure will be predicted if a proper template cannot be found.

a For the most probable 2D structure shown in Fig. b For the predicted alternative structure shown in Fig.

For example, as listed in Figure. Therefore, it is recommended to remove the single strand tails before submitting jobs to Vfold3D. For the RNA in Fig. As shown in Fig. For the alternative 2D structure, which consists of two hairpins connected by a single-strand loop, Vfold3D yields no 3D structure because of the lack of the templates for the UUCG single-stranded open junction between the two hairpins.

Once a calculation is submitted, a notification page containing the job information job name, e-mail address optional and the job status is displayed.

When the calculation is completed, the Vfold web server sends out an e-mail if provided notification with the predicted results attached. It is recommended to bookmark the job-specific notification page for later check of the job status and for downloading Vfold predicted results, since Vfold2D and VfoldThermal might take a long computational time hours or even longer depending on the sequence length.

An online README file about the interpretation of the Vfold predictions is available on the Vfold web server. The Vfold package is developed to predict RNA structures and folding thermodynamics.

The web server will be updated continuously with the development of new Vfold-based algorithms for RNA folding. In the future development, we plan to add structure predictions for the formation of RNA-RNA complexes.

We will also add the effect of the ion-dependent electrostatic free energies and the heat capacity effect, which can cause the temperature-dependence of the enthalpy and entropy parameters for the loop and base stack formations, to the melting curve calculations and structure predictions.

Conceived and designed the experiments: SC XX PZ. Performed the experiments: XX PZ SC. Analyzed the data: XX PZ SC. Contributed to the writing of the manuscript: XX SC. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Article Authors Metrics Comments Media Coverage Reader Comments Figures.

Abstract Background The ever increasing discovery of non-coding RNAs leads to unprecedented demand for the accurate modeling of RNA folding, including the predictions of two-dimensional base pair and three-dimensional all-atom structures and folding stabilities.

Results The Vfold server offers a web interface to predict a RNA two-dimensional structure from the nucleotide sequence, b three-dimensional structure from the two-dimensional structure and the sequence, and c folding thermodynamics heat capacity melting curve from the sequence.

Conclusions The Vfold-based web server provides a user friendly tool for the prediction of RNA structure and stability. Introduction The increasing discoveries of noncoding RNAs demand more than ever the information about RNA structures [1] — [5].

Methods The Vfold model was first reported in for RNA secondary structure prediction [39]. Features of the Vfold algorithm One of the unique features of the Vfold model for 2D structure base pairs prediction is its ability to compute the RNA motif-based loop entropies.

Download: PPT. Results The Vfold server contains three parts: a Vfold2D predicts the RNA 2D structure pseudoknotted or non-pseudoknotted from the sequence, b VfoldThermal predicts the melting curve folding thermodynamics from the sequence, and c Vfold3D predicts RNA 3D structure for a given 2D structure and the sequence.

Vfold2D: Predicting RNA 2D structures from the sequence The input of Vfold2D is the sequence in plain text form see the snapshot of Vfold2D web server in Fig.

Figure 2. An example of Vfold2D prediction: the input information highlighted in the snapshot of the Vfold2D web server are the sequence 32 nts in this example , the temperature 25°C , the energy parameters used for base stacks from MFOLD in this example the structural type non-pseudoknotted in this example.

VfoldThermal: predicting RNA melting curves VfoldThermal predicts the heat capacity C T melting curves from the temperature-dependence of the partition function Q T for the conformational ensemble chosen by the user.

Figure 3. An example of the VfoldThermal prediction: the inputs highlighted in the snapshot of VfoldThermal web server are the sequence 32 nts in this example with the temperature range of 0°C—°C, the energy parameters used for base stacks from MFOLD in this example and the structure type non-pseudoknotted in this example.

Vfold3D: Predicting RNA 3D structure The input data of Vfold3D are the RNA sequence and the 2D structure base pairs see the snapshot of the Vfold3D web server in Fig. Figure 4. An example of the Vfold3D prediction: the snapshot of Vfold3D web server highlights the input sequence 32 nts for this example and the 2D structures as defined by the base pairs.

Vfold output Once a calculation is submitted, a notification page containing the job information job name, e-mail address optional and the job status is displayed. Conclusion The Vfold package is developed to predict RNA structures and folding thermodynamics.

Supporting Information. Data S1. s PDF. Acknowledgments We thank Dr. Song Cao for helpful discussions. Author Contributions Conceived and designed the experiments: SC XX PZ. References 1. Doudna JA, Cech TR The chemical repertoire of natural ribozymes.

Nature — View Article Google Scholar 2. Bachellerie JP, Cavaille J, Huttenhofer A The expanding snoRNA world. Biochimie — View Article Google Scholar 3. View Article Google Scholar 4. Bartel DP MicroRNAs: target recognition and regulatory functions.

Cell — View Article Google Scholar 5. Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E The role of site accessibility in microRNA target recognition. Genet — View Article Google Scholar 6. Tinoco I, Bustamante C How RNA folds. View Article Google Scholar 7.

Onoa B, Tinoco I RNA folding and unfolding. View Article Google Scholar 8. Hajdin CE, Ding F, Dokholyan NV, Weeks KM On the significance of an RNA tertiary structure prediction. At the whole-system level, omissive predictability is then by far the most likely, if not the only, option.

Computationally speaking, this is not the obstacle it might at first appear to be, as the new agents can initially run TM transition rules that effectively make them inactive until they are needed, and the novel states and symbols can be provided in extended sets, with special transition rules activated under whatever conditions it is that leads to the updated ontology applying.

These add exponentially to the intractability of modelling the system, but do not make the task undecidable. However, this misses the point. The real issue with wicked systems pertains to the data available when the prediction is needed.

However, other less extreme examples include scenarios exploring the introduction of new technology to a system, or new policies, and the ways in which agents might adapt and respond to them.

Filatova et al. If the only data we have including description of the system itself when modelling pertains to the current system, we are obviously in a difficult position if we want to predict what a new system might look like.

In this article, we have defined four levels of predictability, and related them to usefulness with respect to possible system states predicted to occur or not to occur.

After some general considerations, we have then evaluated these predictabilities in complex and wicked systems. Figure 6 summarizes those findings. The network of asynchronous TMs thought experiment in Section 2.

Such asynchrony is not infeasible empirically, as in the real world agents act autonomously. This point challenges the idea that even a model that perfectly reproduces historical data can automatically be trusted to make point predictions in systems with analogous properties.

The implication of this for trusting predictions is that validity pertains to the representation of the system as well as numerical accuracy. As made apparent by the application of the asynchronous TMs thought experiment to wicked systems in Section 2 , it is endogenous ontological uncertainty that poses the most significant challenge to prediction.

The problem, however, is not one of computability, but of data. In much the same way as the complete works of Shakespeare make no reference to smartphones, endogenous novelty means that presently available data provide less and less information about the future the further ahead predictions are required.

Generalizing this thinking from cellular automata shows that, when present states give you no information about relevant future states i.

Holtz et al. However, they also give a use case of giving advice to policymakers ibid. Our analysis suggests that trust based on fitting conditions in the current system cannot be generalized to trusting what the models say about the transitioned system. Agent-based models of wicked systems need somehow to address novelty.

The philosophy surrounding the role, building, verification, validation and use of simulation modelling in general rather than specific to ABM has been debated for over half a century.

The epistemological divides are often silo-related. An engineer using ABM has very few philosophical qualms about using it for prediction, but this paper addresses novelty in ABMs in the context of wicked problems — where many social scientists rather than engineers are building ABMs.

The importance of this debate for this paper, however, is that absolute in the sense of being objective, or universally agreed verification and validation of ABMs need not be achieved for prediction to be useful, even in the case of introducing novelty in systems.

Use of cellular automata to model social systems has a long history, particularly in land use and urban systems Batty et al. Spatial data from mapping, aerial photography and satellites offer rich, time-varying data that can be used for empirical applications of such models.

The initial positions of the agents, and their specific internal states were not provided. Knowing the number of internal states of the TMs is also unrealistic. These, as Hayek notes, are falsifiable theories, and in ecology, Grimm et al. The thought experiments were deliberately constructed in abstract rather than empirical systems so that their inherent complexity could be evaluated as a difficulty with prediction, and data availability and questions about representing the target system not be an obstacle or confounding factor.

The justification for more complicated agent-based models in empirical contexts has already been argued by Sun et al. There are several ways in which agent-based models can and do provide for novelty. Polhill et al. These algorithms adjust the mechanisms by which agents make decisions based on their experiences operating in the model.

We are not aware of any models that provide such functionality, but note work such as that of Gessler in the Artificial Life community outlining such an agenda.

Our observations about prediction in wicked systems are predicated on current data about the system becoming obsoleted by novel states in the future about which there is no information in the system as it is now. Knowledge elicitation methods that anticipate what those states might be could be one way to obtain these kinds of data.

García-Mira et al. Narrative approaches can also be used as sources of qualitative data for models. Hassan et al. Critically, many of these guidelines entail documenting the social processes of building, using and evaluating the model. Documenting the social processes of model construction and use of data when making predictions records how a particular community addressed the wickedness of the system they were interested in, and makes explicit the intersubjectivity Cooper-White of any predictions.

Ahrweiler p. This process is not only the one incorporated in the simulation model itself. It is the whole interaction between stakeholders, study team, model, and findings. Path dependency in complex systems mean they are predictable at the whole-system level in theory, if not in practice because of the intractability of exhaustively searching the space of models that might match the available data.

Wicked systems, however, pose a much more significant challenge to prediction, chiefly as a consequence of endogenous ontological novelty, rather than disputation about ontological structure.

Beyond the short term, the potential introduction of novel states in the system about which there are no data at the time the prediction is made means attempts at prediction are futile even if they were feasible.

Though there are other purposes than prediction for building a model, there is still predictive utility to be had in modelling the current system and using this to detect transitions as the real world diverges from the states represented in the model. Models of wicked systems, however, need to be more complicated than complex systems, and should include functionality that allows for novelty.

Abstract This paper uses two thought experiments to argue that the complexity of the systems to which agent-based models ABMs are often applied is not the central source of difficulties ABMs have with prediction.

We define various levels of predictability, and argue that insofar as path-dependency is a necessary attribute of a complex system, ruling out states of the system means that there is at least the potential to say something useful.

Critically, however, neither complexity nor wickedness makes prediction theoretically impossible in the sense of being formally undecidable computationally-speaking: intractable being the more apt term given the exponential sizes of the spaces being searched.

However, endogenous ontological novelty in wicked systems is shown to render prediction futile beyond the immediately short term. Keywords: Prediction, Complex Systems, Wicked Systems, Agent-Based Modelling, Cellular Automata, Turing Machines Other articles with these keywords In JASSS From Google.

Introduction Agent-based modellers working on policy-relevant scenarios will typically find themselves needing to build empirical agent-based models calibrated on real-world data, and then running these models forward under various conditions to evaluate the range of outcomes.

Table 1: Table showing the four predictability conditions used in the two thought experiments, and their usefulness at the whole system and individual levels. Note that the distinction between Possibly Useful and Possibly Not Useful is aesthetic rather than semantic as in modal logic they are equivalent statements.

Predictability Description Usefulness Invariable Predictability All matching models predict exactly the same state. Possibly Useful individual level ; Necessarily Useful whole system level. Omissive Predictability At least one state is not predicted by any matching model. Possibly Useful.

Asymmetric Unpredictability Any state is possible, but not all states have the same number of matching models predicting them. Possibly Not Useful. Symmetric Unpredictability All states are predicted by the same number of matching models. Necessarily Not Useful. Figure 1. Figure 2. Decision tree in UML activity diagram style depicting how each of four propositions about predictability at the individual cell and whole system levels can be determined.

Dashed arrows link the individual and whole system levels; diamonds represent decision points, with arcs labelled according to the conditions that must apply to follow them.

Thought experiment 1: Predicting cellular automata Table 2 provides a summary of the prediction problem outlined in this thought experiment. Multiplying the estimated number of atoms in the known universe by the Planck time since the big bang yields a smaller number.

These numbers are nevertheless finite, and can be exhaustively explored in theory if not in practice. Searching the space of transition functions exhaustively is therefore intractable in the general case, but not undecidable.

Table 3: Instantiation of Table 1 for the CA thought experiment. Figure 3. Checking the predictability of an elementary CA. The light blue cyan cells show the data, also using shade to represent a cell in state 1 or 0.

In this particular run, all but four of the rules have been eliminated because they do not fit the data in the cyan cells. Figure 4. Heatmap showing the number of rules eliminated as max-data is increased from 1 to 40 with replications per setting.

Complication: Asynchrony Table 4 summarizes the prediction challenge caused by what might appear to be a trivial complication to the CA. Then, consider an instruction like the following: ask turtles [ forward [pcolor] of patch-here ask patch-here [ set pcolor [color] of myself ] ] This makes the colours of the patches and the distances moved by turtles other than the first asked sensitive to what previous turtles asked have done.

In real-world situations, people only act in synchronized ways by agreement, such as at traffic lights. Thought experiment 2: Asynchronous networks of Turing Machines Table 5 summarizes the prediction challenge in this thought experiment, which is based on several Turing Machines TMs operating asynchronously.

Table 6: Table showing the various predictability invariable and omissive and unpredictability asymmetric and symmetric conditions for the asynchronous network of Turing machines thought experiment.

Figure 5. The problem of predicting in wicked systems with endogenous ontological novelty. All states in the future system are then symmetrically unpredictable. Discussion In this article, we have defined four levels of predictability, and related them to usefulness with respect to possible system states predicted to occur or not to occur.

Figure 6. Summary of the expected predictability of various systems, using similar axes to Andersson et al. Borders show special cases: for CAs, when insufficient data are provided; for asynchronous TMs, when the set of schedules explored to make the prediction is a superset of the possible schedules; for wicked systems with endogenous ontological novelty, short-term predictions prior to transitioning.

References AHRWEILER, P. Agent-based simulation for science, technology, and innovation policy. Scientometrics , , — Societal systems — complex or worse? Futures , 63 , — Wickedness and the anatomy of complexity.

Futures , 95 , — Disagreement in discipline-building processes. Every planar map is four colorable. Part I: Discharging. Illinois Journal of Mathematics , 21 3 , — Part II: Reducibility.

Principles of Forecasting: A Handbook for Researchers and Practitioners. Norwell, MA: Kluwer Academic Publishers. AVELINO, F. Transformative social innovation and dis empowerment. Technological Forecasting and Social Change , , — Verification and validation in computational engineering and science: Basic concepts.

Computer Methods in Applied Mechanics and Engineering , 36—38 , — and Revkin, Steffen, W. Plausible and desirable futures in the Anthropocene: A new research agenda.

Global Environmental Change , 39 , — Wicked problems and applied economics. American Journal of Agricultural Economics , 90 5 , — x] BATTY, M. Environment and Planning B: Urban Analytics and City Science , 47 5 , — Modeling urban dynamics through GIS-based cellular automata.

Computers, Environment and Urban Systems , 23 , — Complexity Theory and the Social Sciences. London: Routledge. BYRNE, D. Complexity Theory and the Social Sciences: The state of the Art. CHAZDON, R. When is a forest a forest? Forest concepts and definitions in the era of forest and landscape restoration.

Ambio , 45 , — International Journal of Geographical Information Science , 12 7 , — Universality in elementary cellular automata. Complex Systems , 15 1 , 1— COOPER-WHITE, P. Leeming Ed. Boston, MA: Springer US. Beyond the universal Turing machine. Australasian Journal of Philosophy , 77 1 , 46— Improving ecosystem service frameworks to address wicked problems.

Ecology and Society , 20 2 , No one can predict the future: More than a semantic dispute. Review of Artificial Societies and Social Simulation.

EDMONDS, B. Meyer Eds. Berlin Heidelberg: Springer International Publishing. Different modelling purposes. Davidsson, B. Takadama Eds. Berlin Heidelberg: Springer. Predicting social systems - A challenge. EPSTEIN, J. Why model? FATÈS, N. Kari, M. Malcher Eds. FILATOVA, T.

Regime shifts in coupled socio-environmental systems: Review of modelling challenges and approaches. Beyond the hype: Big data concepts, methods and analytics. International Journal of Information Management , 35 2 , — Testing scenarios to achieve workplace sustainability goals using backcasting and agent-Based modeling.

Environment and Behavior , 49 9 , — Real-time Solar Wind and Magnetometer data is now available in JSON format for up to the past 7 days from the SWPC Data Service. These JSON files will automatically include the data from the active RTSW spacecraft. By default, that has been DSCOVR since July 27 at UT.

A complete DSCOVR data archive is available at the NOAA National Center for Environmental Information. Skip to main content. R1 Minor Radio Blackout Impacts. HF Radio: Weak or minor degradation of HF radio communication on sunlit side, occasional loss of radio contact.

Navigation: Low-frequency navigation signals degraded for brief intervals. Real Time Solar Wind. Black background White background Marker Line Hybrid Line 6 hrs Left Y-axis labels Alternating Y-axis labels Show flags.

Usage Impacts Details History Data Real-Time Solar Wind RTSW data refers to data from any spacecraft located upwind of Earth, typically orbiting the L1 Lagrange point, that is being tracked by the Real-Time Solar Wind Network of tracking stations.

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The highest resolution available can be 1 second magnetometer and 20 second thermal plasma data. You can view data from the operational spacecraft or choose between DSCOVR and ACE.

The geomagnetic K and A indices can also be plotted. Tabs along the bottom of the plot allow different default plots to be chosen. These include data ranges of 2 hours up to ~20 years and displays with only Magnetometer, only Solar Wind Plasma, or a combination of both as well as other features described below.

Predixtion protein intrinsically disordered casino jackpot city liverpool real madrid unibet prdeiction combining convolutional attention network and hierarchical attention network. McNeil JJ, Nelson MR, Woods RL, Lockery JE, Wolfe R, Predoction CM, et al. Predictiin participants provided written predicyion consent. International End Point Adjudication Pdediction Mark Nelson ChairDiane Ives Co-ChairMichael Berk, Wendy Bernstein, Donna Brauer, Christine Burns, Trevor Chong, Geoff Cloud, Jamehl Demons, Geoffrey Donnan, Charles Eaton, Paul Fitzgerald, Peter Gibbs, Andrew Haydon, Michael Jelinek, Finlay Macrae, Suzanne Mahady, Mobin Malik, Karen Margolis, Catriona McLean, Anne Murray, Anne Newman, Luz Rodriguez, Suzanne Satterfield deceasedRaj Shah, Elsdon Storey, Jeanne Tie, Andrew Tonkin, Gijsberta van Londen, Stephanie Ward, Jeff Williamson, Erica Wood, John Zalcberg. ArunachalamN. Download PDF.

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Figure 2. In order to provide soccer odds x comparison soccer odds x, we also integrate x hh prediction AlphaFold-disorder 12 method that infers disorder predictioj binding predictions by pfediction Soccer odds x predicted structures soccer odds x predicfion public databases Ufc betting tips Agent-based modellers working on policy-relevant scenarios will typically find themselves needing to build empirical agent-based models calibrated on real-world data, and then running these models forward under various conditions to evaluate the range of outcomes. Sorry, a shareable link is not currently available for this article. Typical examples are usually machines such as helicopters or aeroplanes, which have millions of physical parts San Miguel et al. High-throughput methods for combinatorial drug discovery. Performed the experiments: XX PZ SC. Shukla, A. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. Zhou Y. Every planar map is four colorable. The Unified Medical Language System UMLS : integrating biomedical terminology. Hooper Holmes Inc Forecast, Short-Term " HH" Stock Price Prognosis for Next Days · HH Forecast, Long-Term Price Predictions for Next Months and Year: , HH where Ho = H-1(H - HS)H-. Best linear unbiased pr ediction. We wr-ish X'R-'X X'R-'Z X'R'Be X'R y. Z'R-'X Z'R-1Z + G-1 Z'R-'B. - GBu v4 + BjA _ Z'R Predictions of non-landmark gene expression showing a spatial expression along the A–P and D–V axes and b a stripe pattern according to EIU's prediction of HH's election victory a waste of time - PF By Ulande Nkomesha speedcasino.info (b) The ACC as a function of initial phases (x axis) and forecast lead days (y axis). Wheeler, M. C., and H. H. Hendon, An all-season real-time Equation () is used to find those values of X for which the range S(0) ≤ X H.H. Kausch and C. Oudet, 'Progress and challenge in polymer crazing and PREDICTION: The administration will focus on the HH jobs numbers (+,) instead of the industry-standard establishment numbers (+98,) Tell us how fast you ran a previous race, and our race time calculator gives you predicted race times for race distances from 5k to miles x. Observed values of predictor variable. y. Observed values of response variable. newdata x values for which predictions are calculated x hh prediction
Acknowledgements Predicfion authors thank Yifang Ma, Marc X hh prediction, and Joseph Loscalzo for useful discussions predictoon the manuscript. King billy no deposit bonus Article Google Scholar 8. Email address Sign up. This has lead the majority of experimental immunologists to rely on established predictions such as those provided by bimas [ 10 ] and syfpeithi [ 13 ], or to stick with methods established in their laboratories. Biochimie — Sun, Y. Moreover, we showed that in any multicellular system, Perler displayed broad applicability to any type of ISH data Fig. About Nucleic Acids Research Editorial Board Policies Author Guidelines Facebook Twitter LinkedIn Advertising and Corporate Services Journals Career Network. The web server will be updated continuously with the development of new Vfold-based algorithms for RNA folding. Astley , M. Reche PA, Glutting JP, Zhang H, Reinherz EL Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding. Hooper Holmes Inc Forecast, Short-Term " HH" Stock Price Prognosis for Next Days · HH Forecast, Long-Term Price Predictions for Next Months and Year: , HH where Ho = H-1(H - HS)H-. Best linear unbiased pr ediction. We wr-ish X'R-'X X'R-'Z X'R'Be X'R y. Z'R-'X Z'R-1Z + G-1 Z'R-'B. - GBu v4 + BjA _ Z'R Predictions of non-landmark gene expression showing a spatial expression along the A–P and D–V axes and b a stripe pattern according to One is a perception that prediction necessarily entails specific, quantitative point prediction of the form X[t]=± H. H., Weiner, J., Wiegand, T (x, y) between drug targets (x) and disease proteins (y). Han, P., Chu, Z. X., Shen, F. M., Xie, H. H. & Su, D. F. Synergism of These are very different types of predictions and the distinction between risk and diagnosis is important for reporting prediction studies. X-ray in Hooper Holmes Inc Forecast, Short-Term " HH" Stock Price Prognosis for Next Days · HH Forecast, Long-Term Price Predictions for Next Months and Year: , HH where Ho = H-1(H - HS)H-. Best linear unbiased pr ediction. We wr-ish X'R-'X X'R-'Z X'R'Be X'R y. Z'R-'X Z'R-1Z + G-1 Z'R-'B. - GBu v4 + BjA _ Z'R Predictions of non-landmark gene expression showing a spatial expression along the A–P and D–V axes and b a stripe pattern according to x hh prediction
PubMed Predkction Scholar Hippisley-Cox, J. Teams form yh. BuccheriD. FurphyC. The s comparisons presented liverpool real madrid unibet provide an solopredict today of ;rediction pooling experimental data from different sources without additional validation can be problematic; the differences encountered between the measurements of the two closely related assays here are small compared to the differences found when curating from the literature, which is derived by a multitude of different experimental approaches. Google Scholar Crossref. We further analyzed the details of the gene-expression profiles of the segment-polarity genes within each parasegment. In those cases, the additional input is generated once and shared with all dependent methods. MIT Press. The y-axis annotation can be displayed all on one side or on alternating sides. Having assembled a set of training data, the next step is to choose a prediction method, such as a certain type of artificial neural network ANN , hidden Markov model, or regression function, which can generate a prediction tool from a set of training data. Hooper Holmes Inc Forecast, Short-Term " HH" Stock Price Prognosis for Next Days · HH Forecast, Long-Term Price Predictions for Next Months and Year: , HH where Ho = H-1(H - HS)H-. Best linear unbiased pr ediction. We wr-ish X'R-'X X'R-'Z X'R'Be X'R y. Z'R-'X Z'R-1Z + G-1 Z'R-'B. - GBu v4 + BjA _ Z'R Predictions of non-landmark gene expression showing a spatial expression along the A–P and D–V axes and b a stripe pattern according to Citation: Xu X, Zhao P, Chen Ren J, Rastegari B, Condon A, Hoos HH () HotKnots: Heuristic prediction of RNA secondary structures including pseudoknots (x) + R (t, x) where the symbol x identifies the geographic location (model Hellmer, H. H., F. Kauker, R. Timmermann, J. Determann, and J. Rae, HH where Ho = H-1(H - HS)H-. Best linear unbiased pr ediction. We wr-ish X'R-'X X'R-'Z X'R'Be X'R y. Z'R-'X Z'R-1Z + G-1 Z'R-'B. - GBu v4 + BjA _ Z'R EIU's prediction of HH's election victory a waste of time - PF By Ulande Nkomesha speedcasino.info Test 1 -- The model has both hh and myna, but both gnabar_hh and gmax_myna are 0 (analogous to a knockout mutation affecting hh sodium channels). Prediction A prediction object to plot. x. Optional vector or NULL, indicating were prediction inferences fall along x-axis. Must be the same length as the inferred x hh prediction
Hb F, Sharma Soccer odds x, Chalasani P, Demidov VV, Broude Yh, et prrediction. Das R, Baker D Automated de novo prediction of native-like Liverpool real madrid unibet tertiary structures. THURNER, S. Ten challenges for computer models in transitions research: Commentary on Holtz et al. Siva Innovasjonssenter Sykehusvn 21 Tromsø Norway Email: paan norceresearch. We then calculated the difference between bootstrap apparent performance averaged across bootstrap imputed data and bootstrap test performance averaged across original imputed data. The thought experiments were deliberately constructed in abstract rather than empirical systems so that their inherent complexity could be evaluated as a difficulty with prediction, and data availability and questions about representing the target system not be an obstacle or confounding factor. PLoS One. For example, by focusing on the stripe-doubling of pair-rule genes in Drosophila , Perler successfully reconstructed stripe patterns at a single-cell resolution, while Seurat v. Your comment will be reviewed and published at the journal's discretion. Consequently, usefulness of the model as a criterion becomes problematic, because it depends on subjective opinions of individuals. Greenwood , E. AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields. Hooper Holmes Inc Forecast, Short-Term " HH" Stock Price Prognosis for Next Days · HH Forecast, Long-Term Price Predictions for Next Months and Year: , HH where Ho = H-1(H - HS)H-. Best linear unbiased pr ediction. We wr-ish X'R-'X X'R-'Z X'R'Be X'R y. Z'R-'X Z'R-1Z + G-1 Z'R-'B. - GBu v4 + BjA _ Z'R Predictions of non-landmark gene expression showing a spatial expression along the A–P and D–V axes and b a stripe pattern according to Tell us how fast you ran a previous race, and our race time calculator gives you predicted race times for race distances from 5k to miles CONCLUSIONS. We have derived a simple, analytic model for predicting the X-ray luminosity of HH bow shocks. We have tested this model against HH jet One is a perception that prediction necessarily entails specific, quantitative point prediction of the form X[t]=± H. H., Weiner, J., Wiegand, T CONCLUSIONS. We have derived a simple, analytic model for predicting the X-ray luminosity of HH bow shocks. We have tested this model against HH jet x/ (1+ex), x= –+ (× age) + (× diameter) + (× spiculation) + (× family history of cancer) – (× calcification) x Banner. x. Free Sport Predictions & Betting Tips. Betensured will provide free Prediction, Odds. KK Dinamo Zagreb vs Alkar, 1 HH, ? Luton Town vs Aston x hh prediction

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NBA Picks \u0026 Predictions Saturday 3/2/24 - Jay's NBA Jam Session Model-based prediction of spatial gene expression via generative linear mapping

X hh prediction - x. Observed values of predictor variable. y. Observed values of response variable. newdata x values for which predictions are calculated Hooper Holmes Inc Forecast, Short-Term " HH" Stock Price Prognosis for Next Days · HH Forecast, Long-Term Price Predictions for Next Months and Year: , HH where Ho = H-1(H - HS)H-. Best linear unbiased pr ediction. We wr-ish X'R-'X X'R-'Z X'R'Be X'R y. Z'R-'X Z'R-1Z + G-1 Z'R-'B. - GBu v4 + BjA _ Z'R Predictions of non-landmark gene expression showing a spatial expression along the A–P and D–V axes and b a stripe pattern according to

Documenting the social processes of model construction and use of data when making predictions records how a particular community addressed the wickedness of the system they were interested in, and makes explicit the intersubjectivity Cooper-White of any predictions.

Ahrweiler p. This process is not only the one incorporated in the simulation model itself. It is the whole interaction between stakeholders, study team, model, and findings.

Path dependency in complex systems mean they are predictable at the whole-system level in theory, if not in practice because of the intractability of exhaustively searching the space of models that might match the available data.

Wicked systems, however, pose a much more significant challenge to prediction, chiefly as a consequence of endogenous ontological novelty, rather than disputation about ontological structure.

Beyond the short term, the potential introduction of novel states in the system about which there are no data at the time the prediction is made means attempts at prediction are futile even if they were feasible.

Though there are other purposes than prediction for building a model, there is still predictive utility to be had in modelling the current system and using this to detect transitions as the real world diverges from the states represented in the model.

Models of wicked systems, however, need to be more complicated than complex systems, and should include functionality that allows for novelty.

Abstract This paper uses two thought experiments to argue that the complexity of the systems to which agent-based models ABMs are often applied is not the central source of difficulties ABMs have with prediction. We define various levels of predictability, and argue that insofar as path-dependency is a necessary attribute of a complex system, ruling out states of the system means that there is at least the potential to say something useful.

Critically, however, neither complexity nor wickedness makes prediction theoretically impossible in the sense of being formally undecidable computationally-speaking: intractable being the more apt term given the exponential sizes of the spaces being searched. However, endogenous ontological novelty in wicked systems is shown to render prediction futile beyond the immediately short term.

Keywords: Prediction, Complex Systems, Wicked Systems, Agent-Based Modelling, Cellular Automata, Turing Machines Other articles with these keywords In JASSS From Google.

Introduction Agent-based modellers working on policy-relevant scenarios will typically find themselves needing to build empirical agent-based models calibrated on real-world data, and then running these models forward under various conditions to evaluate the range of outcomes.

Table 1: Table showing the four predictability conditions used in the two thought experiments, and their usefulness at the whole system and individual levels. Note that the distinction between Possibly Useful and Possibly Not Useful is aesthetic rather than semantic as in modal logic they are equivalent statements.

Predictability Description Usefulness Invariable Predictability All matching models predict exactly the same state. Possibly Useful individual level ; Necessarily Useful whole system level.

Omissive Predictability At least one state is not predicted by any matching model. Possibly Useful. Asymmetric Unpredictability Any state is possible, but not all states have the same number of matching models predicting them.

Possibly Not Useful. Symmetric Unpredictability All states are predicted by the same number of matching models. Necessarily Not Useful.

Figure 1. Figure 2. Decision tree in UML activity diagram style depicting how each of four propositions about predictability at the individual cell and whole system levels can be determined. Dashed arrows link the individual and whole system levels; diamonds represent decision points, with arcs labelled according to the conditions that must apply to follow them.

Thought experiment 1: Predicting cellular automata Table 2 provides a summary of the prediction problem outlined in this thought experiment. Multiplying the estimated number of atoms in the known universe by the Planck time since the big bang yields a smaller number.

These numbers are nevertheless finite, and can be exhaustively explored in theory if not in practice. Searching the space of transition functions exhaustively is therefore intractable in the general case, but not undecidable. Table 3: Instantiation of Table 1 for the CA thought experiment.

Figure 3. Checking the predictability of an elementary CA. The light blue cyan cells show the data, also using shade to represent a cell in state 1 or 0. In this particular run, all but four of the rules have been eliminated because they do not fit the data in the cyan cells.

Figure 4. Heatmap showing the number of rules eliminated as max-data is increased from 1 to 40 with replications per setting. Complication: Asynchrony Table 4 summarizes the prediction challenge caused by what might appear to be a trivial complication to the CA. Then, consider an instruction like the following: ask turtles [ forward [pcolor] of patch-here ask patch-here [ set pcolor [color] of myself ] ] This makes the colours of the patches and the distances moved by turtles other than the first asked sensitive to what previous turtles asked have done.

In real-world situations, people only act in synchronized ways by agreement, such as at traffic lights. Thought experiment 2: Asynchronous networks of Turing Machines Table 5 summarizes the prediction challenge in this thought experiment, which is based on several Turing Machines TMs operating asynchronously.

Table 6: Table showing the various predictability invariable and omissive and unpredictability asymmetric and symmetric conditions for the asynchronous network of Turing machines thought experiment. Figure 5. The problem of predicting in wicked systems with endogenous ontological novelty.

All states in the future system are then symmetrically unpredictable. Discussion In this article, we have defined four levels of predictability, and related them to usefulness with respect to possible system states predicted to occur or not to occur.

Figure 6. Summary of the expected predictability of various systems, using similar axes to Andersson et al. Borders show special cases: for CAs, when insufficient data are provided; for asynchronous TMs, when the set of schedules explored to make the prediction is a superset of the possible schedules; for wicked systems with endogenous ontological novelty, short-term predictions prior to transitioning.

References AHRWEILER, P. Agent-based simulation for science, technology, and innovation policy. Scientometrics , , — Societal systems — complex or worse?

Futures , 63 , — Wickedness and the anatomy of complexity. Futures , 95 , — Disagreement in discipline-building processes. Every planar map is four colorable. Part I: Discharging. Illinois Journal of Mathematics , 21 3 , — Part II: Reducibility.

Principles of Forecasting: A Handbook for Researchers and Practitioners. Norwell, MA: Kluwer Academic Publishers. AVELINO, F. Transformative social innovation and dis empowerment.

Technological Forecasting and Social Change , , — Verification and validation in computational engineering and science: Basic concepts. Computer Methods in Applied Mechanics and Engineering , 36—38 , — and Revkin, Steffen, W. Plausible and desirable futures in the Anthropocene: A new research agenda.

Global Environmental Change , 39 , — Wicked problems and applied economics. American Journal of Agricultural Economics , 90 5 , — x] BATTY, M. Environment and Planning B: Urban Analytics and City Science , 47 5 , — Modeling urban dynamics through GIS-based cellular automata.

Computers, Environment and Urban Systems , 23 , — Complexity Theory and the Social Sciences. London: Routledge. BYRNE, D. Complexity Theory and the Social Sciences: The state of the Art. CHAZDON, R. When is a forest a forest? Forest concepts and definitions in the era of forest and landscape restoration.

Ambio , 45 , — International Journal of Geographical Information Science , 12 7 , — Universality in elementary cellular automata. Complex Systems , 15 1 , 1— COOPER-WHITE, P. Leeming Ed. Boston, MA: Springer US. Beyond the universal Turing machine.

Australasian Journal of Philosophy , 77 1 , 46— Improving ecosystem service frameworks to address wicked problems. Ecology and Society , 20 2 , No one can predict the future: More than a semantic dispute.

Review of Artificial Societies and Social Simulation. EDMONDS, B. Meyer Eds. Berlin Heidelberg: Springer International Publishing. Different modelling purposes.

Davidsson, B. Takadama Eds. Berlin Heidelberg: Springer. Predicting social systems - A challenge. EPSTEIN, J. Why model? FATÈS, N. Kari, M. Malcher Eds.

FILATOVA, T. Regime shifts in coupled socio-environmental systems: Review of modelling challenges and approaches. Beyond the hype: Big data concepts, methods and analytics. International Journal of Information Management , 35 2 , — Testing scenarios to achieve workplace sustainability goals using backcasting and agent-Based modeling.

Environment and Behavior , 49 9 , — GARDNER, M. Scientific American , 4 , — GASSNER, C. NP question for several structures.

Electronic Notes in Theoretical Computer Science , 2 , 71— GE, J. Not one Brexit: How local context and social processes influence policy analysis.

PLoS ONE , 13 12 , e Evolving cultural things-That-Think. Fellerman, M. Dörr, M. Hanczyc, L. Laurse, S. Maurer, D. Merkle, P. Monnard, K. Rasmussen Eds. MIT Press. GILBERT, N. Computational modelling of public policy: Reflections on practice. Agent-based modelling of socio-ecological systems: Models, projects and ontologies.

Ecological Complexity , 40 part B , Wonderful Life: The Burgess Shale and the Nature of History. London: Penguin. GRIMM, V. Pattern-oriented modeling of agent-based complex systems: Lessons from ecology.

Science , , — Theory of Mental Tests. HASSAN, S. Asking the Oracle: Introducing forecasting principles into agent-Based modelling.

Hayek Ed. HAYEK, F. The pretence of knowledge. Prize Lecture to the memory of Alfred Nobel. HEAD, B. Wicked problems in public policy. Public Policy , 3 2 , — Opinion dynamics under the influence of radical groups, charismatic leaders, and other constant signals: A simple unifying model.

Impacts of 1. Masson-Delmotte, P. Zhai, H. Pörtner, D. Roberts, J. Skea, P. In the last 10 home games Diriangén has won 7 times, they have lost 1 time, and it has ended in a draw 2 times.

This game, a prominent fixture in Nicaragua Primera Division, stands as a testament to the high-caliber football the league is known for. Diriangén, with the advantage of playing on their home turf, is gearing up for a robust performance.

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For this reason, disability-free survival has emerged as an important geriatric research outcome in contrast to disease-specific outcomes such as cardiovascular disease or dementia [ 4 ]. Particularly in older people, the maintenance of good health requires the avoidance of multiple interacting co-morbidities and chronic conditions, many with shared risk factors and management strategies [ 6 , 7 , 8 ].

Understanding the major preventable determinants, not only of individual diseases but also of ongoing good health [ 9 ], can be facilitated by development of prediction models for disability-free survival.

Previous studies have identified risk factors for a range of specific geriatric outcomes including frailty, physical disability, dementia and death. In addition to age, these have included abnormal body mass index BMI , smoking, diabetes, abnormal blood lipids, chronic kidney disease, low level of physical activity, low gait speed, presence of depression, subclinical cardiovascular disease, specific diets and lack of social support [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ].

These studies have typically been limited to the evaluation of pre-specified relationships between predictors and specific outcomes, rather than a composite measure of functional independence. Similarly, no previous study has employed a machine-learning approach to enable objective variable selection [ 20 ].

The ASPirin in Reducing Events in the Elderly ASPREE placebo-controlled trial of low-dose aspirin was the first large-scale clinical trial that utilized disability-free survival as the prespecified primary endpoint. Measures of physical disability and cognition were assessed systematically throughout follow-up in all subjects in detail, which is rarely available in a non-trial setting.

In this analysis, we used the ASPREE data to develop and validate a prediction model for disability-free survival in a population of relatively healthy individuals aged 65 or more when recruited.

Machine-learning techniques allowed the relative importance of key predictors to be determined in an unbiased and objective fashion. Specifically, we sought to identify modifiable risk factors that could identify individuals at risk and prioritize preventive policies. ASPREE was a large, randomized, double-blind, placebo-controlled trial investigating the efficacy of mg aspirin on extending disability-free survival in a healthy elderly population.

The trial design and primary results have been published previously [ 21 , 22 , 23 , 24 ]. Briefly, 19, community-dwelling individuals without prior cardiovascular events, dementia or physical disability were randomized to low-dose enteric-coated aspirin or placebo and followed for an average of 4.

All participants provided written informed consent. The trial was approved by the local ethics committees and is registered on clinicaltrials. gov NCT Requests for data access will be via the ASPREE Principal Investigators with details for applications provided through the web site, www.

A total of 25 candidate predictors were selected on the basis of previous outcome studies related to dementia, disability and death [ 11 , 17 , 18 , 19 , 25 ] and their likely availability in typical clinical practice.

The preselection of candidate predictors was also performed to minimize the possibility of including noise variables in the final model. The variables were collected as part of the initial standardized screening process prior to participant enrolment in ASPREE and included:. prevalent conditions or risk factors diabetes, smoking history, alcohol consumption, family history of myocardial infarction ,.

laboratory results high-density lipoprotein cholesterol [HDL-c], non-HDL-c, estimated glomerular filtration rate [eGFR], haemoglobin ,. physical measurements systolic and diastolic blood pressure, BMI, abdominal circumference, grip strength, gait speed ,.

medication use antihypertensive agents, lipid-lowering agents and in-trial randomization to aspirin treatment ,. The primary end point of disability-free survival was defined as survival free from dementia or persistent physical disability and was used as a surrogate for functional independence [ 21 ].

Physical disability and dementia were combined in the primary end point, as they represent the important reasons why individuals lose the ability to live independently.

Participants without the documented outcome were censored at 5 years or at the last date they were known to be event-free, whichever came first. To assess this composite endpoint, all participants had serial in-person visits, which included standardized assessments of cognitive function with a cognitive battery, and dementia assessment if triggered, and physical disability, defined as an inability to perform, having severe difficulty in performing, or requiring assistance to complete at least one of the six basic ADL by self-report [ 26 ].

This was considered to be persistent if the same ADL loss was confirmed six months later. Dementia was adjudicated based on the Diagnostic and Statistical Manual of Mental Disorders-IV DSM-IV criteria [ 27 ].

The ascertainment of death has been described before and was based on information collected from at least two sources including close contacts, physicians, public death notices and by linkage to the Australian National Death Index and US National Death Index [ 23 ].

Dementia and cause of death were adjudicated and confirmed by the respective endpoint committees blinded to treatment allocation. Separate models were developed for men and women due to their known differing risk factor profiles. The statistical model employed was the Cox proportional hazards regression model.

Prior to selection of variables, we tested for non-linearity of continuous variables. Specifically, a complete case analysis was initially fitted with all candidate predictors, in which each continuous variable was modelled with a restricted cubic spline function with 2 degrees of freedom.

Statistically significant non-linear relations were found for BMI, HDL-c, waist circumference and gait speed in the model for women; and non-HDL-c, eGFR, BMI and waist circumference in the model for men.

Hence, multiple imputation was performed using chain equations, assuming data were missing at random [ 28 ]. The imputation model included all candidate predictors and other potentially relevant variables. Non-linear BMI, waist circumference, gait speed, eGFR and non-HDL-c were imputed passively [ 29 ].

The survival outcomes were included as a combination of Nelson-Aalen estimator of the cumulative hazard function and the outcome status [ 30 ].

Five data sets were imputed for men and women separately, based on the amount of missing data [ 31 ]. The group-lasso model ensures that categorical variables, or linear and non-linear terms of continuous variables, are included or excluded in the model altogether.

The penalty parameter, which regulates the amount of shrinkage, was selected by optimizing the fold cross-validation prediction error. The combination with bootstrap can be described as follows: for each imputed data set, we randomly drew bootstrap samples with replacement, and performed group-lasso selection on each bootstrap sample.

Variable selection based on VIF has previously demonstrated good performance in the presence of imputed data [ 35 ]. AUC ranges from 0. The apparent performances were obtained by evaluating the final model on the development samples, which were used to build the models averaged across 5 imputed data sets.

To quantify the degree of optimism due to overfitting in performance measures, we implemented internal validation using the enhanced bootstrap resampling procedure [ 37 , 38 ].

The optimism was calculated as follows. From the original data, random bootstrap samples were drawn. For each sample, we repeated the model development procedure including imputation and lasso selection as outlined above to obtain a bootstrap final model.

We then calculated the difference between bootstrap apparent performance averaged across bootstrap imputed data and bootstrap test performance averaged across original imputed data.

Finally, these differences were averaged across bootstrap samples to obtain the single estimate for the optimism. The procedure is illustrated in Figure S1. The estimated optimism was then subtracted from the apparent performance to obtain the bias-corrected predictive performance.

All analyses were conducted using R version 4. A total of 19, community-dwelling Australian and US ASPREE participants were included in this study. The median age of the trial population was 74 years interquartile range [IQR] Baseline characteristics for the overall population of men and women are reported in Table 1.

During the 5-year follow-up median IQR 4. Results of the variable inclusion frequency are shown in Table S3. Specifically, increasing age, lower 3MS score, lower gait speed, lower grip strength and abnormal low or elevated BMI were identified as risk factors in both sexes.

In men, current smoking and eGFR were additionally selected as predictors. A decrease in gait speed from 1. Figure S3 illustrates the non-linear relation between gait speed and the primary outcome for women.

The relationship between BMI and the outcome was non-linear in both models Figure S2, S3. The non-linear relation of eGFR Figure S2 indicated that compared to a reference value of 74, a higher or lower value of eGFR was associated with a higher risk.

The discriminative performance of each predictor and their combinations for the prediction of disability-free survival are presented in Figure 1 and Table S4.

The AUCs of age alone were 0. After correcting for optimism, the final models showed good discrimination, with an AUC of 0. Discrimination of each selected predictor, of their combination when added sequentially in order of their AUC, and of the final models.

Orange dots show the AUC at 5 years of models made with each predictor of not maintaining disability-free survival individually. Green dots show the AUC of models made by incrementally adding each predictor along the x -axis.

The percentages show the added value of the current model against the previous model. Predictors are arranged by their inclusion frequency. Abbreviation: AUC, area under the curve.

The final models also showed good agreement between the observed and the predicted risk of the overall outcome, although the risk for men was slightly overestimated in the higher risk categories Figure 2. The predicted probability of the endpoint at 5 years can be calculated using the formula provided in Table 3.

The illustrations for two specific participants are displayed in the supplementary material Table S5. Calibration graph comparing the observed risk based on Kaplan-Meier estimates and the predicted risk with the final models, by tenths of the predicted probability.

The red asterisk represents the bias-corrected predicted risks. The risk distribution within each 5-year increment age group was further investigated in Figure S4. As expected, the figure demonstrates the strong association between advanced age and event risk in both sexes. We utilized a machine-learning variable selection approach to identify the determinants of survival free of persistent physical disability and dementia in the ASPREE study population [ 21 ].

Disability-free survival is a surrogate measure of functional independence and was assessed by regular in-person surveillance of a group of 19, apparently healthy individuals participating in a large-scale trial of low-dose aspirin.

All participants were free of prior cardiovascular events, disability and dementia at the commencement of the study. During a median follow-up of 4. By choosing a population relatively healthy at baseline and regularly monitoring each individual for the onset of dementia or physical disability, this study provided a unique opportunity to identify and prioritize the characteristics of those most likely and those least likely to continue living independently with increasing age.

A particular strength of these analyses was the objective approach used for variable selection, which was based on a machine-learning technique applied to a range of clinical variables measured at study entry, together with previously recognized risk factors for physical and cognitive decline.

Out of 25 potential biomedical and social predictors tested, 7 contributed substantially to the prediction of disability-free survival in women and 7 were predictors in men.

As expected, advanced age was the most important predictor, which alone resulted in an AUC of 0. Cognitive function was the second most important contributor followed by gait speed. Importantly, these three predictors alone resulted in the highest AUC increase, which empathizes their importance.

Additional predictors in men included grip strength, smoking, BMI and renal function. In women, diabetes, and depression scores also contributed to the model. In the case of eGFR and BMI, the association was non-linear and U-shaped, similar to that noted in prior studies of mortality and other geriatric outcomes.

Previous studies have identified similar risk factor profiles as predictors of cognitive decline, frailty, functional decline and a range of other chronic diseases of ageing [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ].

Similarly slowing gait speed, weaker handgrip, increased BMI and diabetes have been identified as predictors of physical decline and frailty. The interdependence of several of these predictors creates a web of causation in which physical disability, dementia or death can be the end result of several pathways.

The dominant impact of cognitive decline and reduced physical function suggests the possibility that interventions designed to improve cognition or physical function might alter the trajectory of decline in otherwise healthy older subjects. Recently published trials suggested that modest improvement may be possible.

Amongst these, first, the LIFE study showed that the initiation of a moderate-intensity physical program in sedentary individuals aged 70—89 years modestly reduced progression to major mobility disorder, assessed as the ability to walk metres.

The program involving aerobic, resistance and flexibility training is provided in centre twice weekly and twice weekly at home [ 39 ]. The intervention over 2.

Second, the SPRINT-MIND trial showed a reduction in the combined outcome of mild cognitive impairment plus probable dementia amongst older individuals on intensified blood pressure management [ 41 ]. The study involved 9, hypertensive older adults with increased cardiovascular risk whose mean systolic BP was Third, the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability FINGER trial investigated the efficacy of a multidomain lifestyle intervention including dietary guidance, physical activity, cognitive training, and monitoring and management of cardiovascular risk factors.

The control group received general health advice. After 2 years, the intervention group improved more in executive function, processing speed and complex memory tasks regardless of genetic risk or baseline risk factor levels.

From a clinical perspective, the identification of cognitive decline and physical limitations indicate that an individual is tracking towards an earlier loss of independence. The results of the trials described above suggest that the loss of independence in older individuals may be slowed by a multifactor intervention.

However, the likelihood of these interventions producing a significant slowing of the trajectory towards loss of independence is as yet unclear. From a population perspective, the results also emphasize the relevance of addressing the determinants of physical and cognitive decline at an earlier stage in the life course.

Although diet and lipids were not included as predictive factors in the final model, their role as shared determinants in the development of cognition and physical capacity at earlier stages has been well established, and they should be included in any strategy to promote healthy ageing.

Furthermore, BMI and diabetes were part of the final models. Although the increase in prevalence is largely a reflection of population ageing, it emphasizes the importance of strategies to extend disability-free survival. However, as our study results were based on an older population, future longitudinal studies are warranted to evaluate the impact of these determinants.

Strengths of this study include the extensive data collected from a large population with relatively small amounts of missing data. The measurement of disability-free survival required data concerning the time of onset of physical disability, dementia and death, with diagnoses of dementia confirmed by a specialist adjudication panel; systematic collection of information of this type is rarely available outside the context of a clinical trial.

The outcome data was accompanied by measurement of a wide range of clinically relevant and recognized geriatric potential prediction variables biomedical and social collected near the time of study initiation.

The limitations include that the majority of the study population were white Caucasians, most of whom lived in Australia, a high-income country with universal healthcare services. The generalizability of the study is also restricted to individuals reaching the plus age group in relatively good health free of prior cardiovascular disease, dementia or significant physical limitations.

It is also possible that there is residual confounding, and future research may identify new, relevant predictors. Finally, we were not able to perform an external validation, as comparable datasets are missing.

In conclusion, in a population of healthy older people, both modifiable and non-modifiable characteristics successfully identified individuals at higher risk of dying or developing dementia or physical disability within 5 years. After age, a low 3MS score was the strongest predictor followed by slow gait speed with a lesser contribution by other risk factors.

Recent trials suggest that interventions aimed at slowing cognitive and physical decline are effective but additional trials will be required to demonstrate a meaningful impact in prolonging the time individuals remain functionally independent. World Health Organization. World report on ageing and health.

Disease GBD, Injury I, Prevalence C. Global, regional, and national incidence, prevalence, and years lived with disability for diseases and injuries for countries and territories, a systematic analysis for the Global Burden of Disease Study Article Google Scholar. Fried LP, Rowe JW.

Health in aging - past, present, and future. N Engl J Med. Rowe JW, Kahn RL. Successful aging. Article CAS Google Scholar. Jacob ME, Yee LM, Diehr PH, Arnold AM, Thielke SM, Chaves PH, et al. Can a healthy lifestyle compress the disabled period in older adults?

J Am Geriatr Soc. Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study.

Lafortune L, Martin S, Kelly S, Kuhn I, Remes O, Cowan A, et al. Behavioural risk factors in mid-life associated with successful ageing, disability, dementia and frailty in later life: a rapid systematic review.

PLoS One. Koene RJ, Prizment AE, Blaes A, Konety SH. Shared risk factors in cardiovascular disease and cancer. Fried LP, Guralnik JM. Disability in older adults: evidence regarding significance, etiology, and risk. Elwood P, Galante J, Pickering J, Palmer S, Bayer A, Ben-Shlomo Y, et al.

Healthy lifestyles reduce the incidence of chronic diseases and dementia: evidence from the Caerphilly cohort study. Artaud F, Dugravot A, Sabia S, Singh-Manoux A, Tzourio C, Elbaz A. Unhealthy behaviours and disability in older adults: three-city Dijon cohort study. Newman AB, Arnold AM, Naydeck BL, Fried LP, Burke GL, Enright P, et al.

Arch Intern Med. Burke GL, Arnold AM, Bild DE, Cushman M, Fried LP, Newman A, et al. Factors associated with healthy aging: the cardiovascular health study. Terry DF, Pencina MJ, Vasan RS, Murabito JM, Wolf PA, Hayes MK, et al. Cardiovascular risk factors predictive for survival and morbidity-free survival in the oldest-old Framingham Heart Study participants.

Willcox BJ, He Q, Chen R, Yano K, Masaki KH, Grove JS, et al. Midlife risk factors and healthy survival in men. Yates LB, Djousse L, Kurth T, Buring JE, Gaziano JM. Exceptional longevity in men: modifiable factors associated with survival and function to age 90 years.

Franzon K, Byberg L, Sjogren P, Zethelius B, Cederholm T, Kilander L. Predictors of independent aging and survival: a year follow-up report in octogenarian men. Zhang S, Tomata Y, Discacciati A, Otsuka T, Sugawara Y, Tanji F, et al. Combined healthy lifestyle behaviors and disability-free survival: the Ohsaki Cohort Study.

J Gen Intern Med. Bosnes I, Nordahl HM, Stordal E, Bosnes O, Myklebust TA, Almkvist O. Lifestyle predictors of successful aging: a year prospective HUNT study.

Swindell WR, Ensrud KE, Cawthon PM, Cauley JA, Cummings SR, Miller RA, et al. A data-mining approach based on prediction of long-term survival. BMC Geriatr. McNeil JJ, Woods RL, Nelson MR, Reid CM, Kirpach B, Wolfe R, et al.

Effect of aspirin on disability-free survival in the healthy elderly. McNeil JJ, Wolfe R, Woods RL, Tonkin AM, Donnan GA, Nelson MR, et al. Effect of aspirin on cardiovascular events and bleeding in the healthy elderly. McNeil JJ, Nelson MR, Woods RL, Lockery JE, Wolfe R, Reid CM, et al.

Effect of aspirin on all-cause mortality in the healthy elderly. Group AI. Study design of ASPirin in Reducing Events in the Elderly ASPREE : a randomized, controlled trial. Contemp Clin Trials. Newman AB, Murabito JM.

The epidemiology of longevity and exceptional survival. Epidemiol Rev. Katz S. Assessing self-maintenance: activities of daily living, mobility, and instrumental activities of daily living.

Association AP. Diagnostic and statistical manual of mental disorders. Washington, DC: DSM IV; van Buuren S, Groothuis-Oudshoorn K. Mice: multivariate imputation by chained equations in R.

J Stat Softw. Stef van Buuren. Flexible imputation of missing data. Published 12 July New York. White IR, Royston P.

Imputing missing covariate values for the Cox model. Stat Med. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Breheny P, Huang J. Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors.

Stat Comput. Friedman J, Hastie T, Tibshirani R. The elements of statistical learning. New York: Springer series in statistics; Little RJA, Rubin DB. Statistical analysis with missing data. Thao LTP, Geskus R. A comparison of model selection methods for prediction in the presence of multiply imputed data.

Biom J. Blanche P, Dartigues JF, Jacqmin-Gadda H. Review and comparison of ROC curve estimators for a time-dependent outcome with marker-dependent censoring. Harrell FE.

Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis.

New York: Springer; Musoro JZ, Zwinderman AH, Puhan MA, ter Riet G, Geskus RB. Validation of prediction models based on lasso regression with multiply imputed data.

BMC Med Res Methodol. Pahor M, Guralnik JM, Ambrosius WT, Blair S, Bonds DE, Church TS, et al. Effect of structured physical activity on prevention of major mobility disability in older adults: the LIFE study randomized clinical trial.

LaCroix AZ, LaMonte MJ, Applegate WB. Group SMIftSR, Williamson JD, Pajewski NM, Auchus AP, Bryan RN, Chelune G, et al.

Effect of intensive vs standard blood pressure control on probable dementia: a randomized clinical trial.

Guzman-Castillo M, Ahmadi-Abhari S, Bandosz P, Capewell S, Steptoe A, Singh-Manoux A, et al. Forecasted trends in disability and life expectancy in England and Wales up to a modelling study. Lancet Public Health. Download references. International Steering Committee: John McNeil Chair and Principal Investigator , Anne Murray Co-Chair and Co-Principal Investigator , Lawrie Beilin, Andrew Chan, Jamehl Demons, Michael Ernst, Sara Espinoza, Matthew Goetz, Colin Johnston, Brenda Kirpach, Danny Liew, Karen Margolis, Frank Meyskens, Mark Nelson, Chris Reid, Raj Shah, Elsdon Storey, Andrew Tonkin, Rory Wolfe, Robyn Woods, John Zalcberg.

International End Point Adjudication Committees: Mark Nelson Chair , Diane Ives Co-Chair , Michael Berk, Wendy Bernstein, Donna Brauer, Christine Burns, Trevor Chong, Geoff Cloud, Jamehl Demons, Geoffrey Donnan, Charles Eaton, Paul Fitzgerald, Peter Gibbs, Andrew Haydon, Michael Jelinek, Finlay Macrae, Suzanne Mahady, Mobin Malik, Karen Margolis, Catriona McLean, Anne Murray, Anne Newman, Luz Rodriguez, Suzanne Satterfield deceased , Raj Shah, Elsdon Storey, Jeanne Tie, Andrew Tonkin, Gijsberta van Londen, Stephanie Ward, Jeff Williamson, Erica Wood, John Zalcberg.

Data and Safety Monitoring Board: Jay Mohr Chair , Garnet Anderson, Stuart Connolly, Larry Friedman, JoAnn Manson, Mary Sano, Sean Morrison, Erik Magnus Ohman. Australian Management Committee: John McNeil Chair , Robyn Woods Deputy Chair , Walter Abhayaratna, Lawrie Beilin, Geoffrey Donnan, Peter Gibbs, Colin Johnston, Danny Liew, Trevor Lockett, Mark Nelson, Chris Reid, Nigel Stocks, Elsdon Storey, Andrew Tonkin, Rory Wolfe, John Zalcberg.

Publications, Presentations and Ancillary Studies Committee: Anne Murray Chair , Chris Reid Co-Chair , Walter Abhayaratna, Michael Ernst, Colin Johnston, Beth Lewis, Danny Liew, Karen Margolis, John McNeil, Mark Nelson, Anne Newman, Thomas Obisesan, Raj Shah, Elsdon Storey, Robyn Woods.

International Data Management Committee: Chris Reid Chair , Jessica Lockery Co-Chair , Michael Ernst, Dave Gilbertson, Brenda Kirpach, Raj Shah, Rory Wolfe, Robyn Woods. ASPREE Data Management Center Monash University and Biostatistics: Jessica Lockery Data Manager , Taya Collyer, Jason Rigby; Programmers—Kunnapoj Pruksawongsin, Nino Hay; Biostatisticians—Rory Wolfe Senior Biostatistician , Joanne Ryan, Kim Jachno, Catherine Smith; End point Processing—A.

Saifuddin Ekram Clinical Case Reviewer , Madeleine Gardam, Henry Luong, Tim Montgomery, Megan Plate, Laura Rojas, Anna Tominaga, Katrina Wadeson. Australian Training, Recruitment, Retention and Operations Committee: Suzanne Orchard Chair , Sharyn Fitzgerald, Sarah Hopkins, Jessica Lockery, Trisha Nichols, Ruth Trevaks, Robyn Woods.

Open Access funding enabled and organized by CAUL and its Member Institutions This study was financially supported by the National Institute on Aging and the National Cancer Institute at the National Institutes of Health grant numbers U01AG, U19AG ; the National Health and Medical Research Council of Australia grant numbers , ; Monash University; and the Victorian Cancer Agency.

is supported through a NHMRC Principal Research Fellowship APP JMCN is supported through an NHMRC Leadership Fellowship IG Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, 99 Commercial Road, Melbourne, Victoria, , Australia.

Johannes Tobias Neumann, Le T. Thao, Emily Callander, Prudence R. Carr, Mark R. Nelson, Rory Wolfe, Robyn L. Woods, Christopher M. Reid, Andrew M. Division of Geriatrics, Department of Medicine, Hennepin Healthcare, and Berman Centre for Outcomes and Clinical Research, Hennepin Healthcare Research Institute, Minneapolis, USA.

Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia. Curtin School of Population Health, Curtin University, Perth, WA, Australia. Centre for Aging and Population Health, Department of Epidemiology, University of Pittsburgh, Pittsburgh, USA.

You can also search for this author in PubMed Google Scholar. Correspondence to Johannes Tobias Neumann. The trial was approved by the local Ethics Committees and is registered on clinicaltrials. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Neumann, J. et al. Prediction of disability-free survival in healthy older people. GeroScience 44 , — Download citation. Received : 08 November Accepted : 16 March Published : 14 April Issue Date : June Anyone you share the following link with will be able to read this content:.

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Abstract Prolonging survival in good health is a fundamental societal goal. Trial registration Clinicaltrials. International Exercise Recommendations in Older Adults ICFSR : Expert Consensus Guidelines Article Open access 30 July Determinants and reference values of the 6-min walk distance in the general population—results of the population-based STAAB cohort study Article Open access 18 January Frailty: What Is It?

Chapter © Use our pre-submission checklist Avoid common mistakes on your manuscript. Introduction Currently, approximately million individuals worldwide are aged 60 years or older and this number is expected to nearly double by [ 1 ]. Methods Study population and trial design ASPREE was a large, randomized, double-blind, placebo-controlled trial investigating the efficacy of mg aspirin on extending disability-free survival in a healthy elderly population.

Baseline collection, definition and selection of potential predictors A total of 25 candidate predictors were selected on the basis of previous outcome studies related to dementia, disability and death [ 11 , 17 , 18 , 19 , 25 ] and their likely availability in typical clinical practice. Details related to all predictors are provided in the Supplementary Material.

Outcome ascertainment The primary end point of disability-free survival was defined as survival free from dementia or persistent physical disability and was used as a surrogate for functional independence [ 21 ].

Statistical analyses Prognostic model development Separate models were developed for men and women due to their known differing risk factor profiles. Model validation To quantify the degree of optimism due to overfitting in performance measures, we implemented internal validation using the enhanced bootstrap resampling procedure [ 37 , 38 ].

Results Population characteristics A total of 19, community-dwelling Australian and US ASPREE participants were included in this study. Table 1 Baseline characteristics of the trial population Full size table.

Table 2 Multivariable Cox regression analyses based on the selected predictors of disability-free survival. Figure 1. Full size image.

Figure 2. Table 3 Sex-specific formulas for calculation of the risk prediction model for disability-free survival within 5 years Full size table. Discussion We utilized a machine-learning variable selection approach to identify the determinants of survival free of persistent physical disability and dementia in the ASPREE study population [ 21 ].

References World Health Organization. Article Google Scholar Fried LP, Rowe JW. Article Google Scholar Rowe JW, Kahn RL. Article CAS Google Scholar Jacob ME, Yee LM, Diehr PH, Arnold AM, Thielke SM, Chaves PH, et al. Article Google Scholar Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B.

Article Google Scholar Lafortune L, Martin S, Kelly S, Kuhn I, Remes O, Cowan A, et al. Article Google Scholar Koene RJ, Prizment AE, Blaes A, Konety SH. Article Google Scholar Fried LP, Guralnik JM. Article CAS Google Scholar Elwood P, Galante J, Pickering J, Palmer S, Bayer A, Ben-Shlomo Y, et al.

Article Google Scholar Artaud F, Dugravot A, Sabia S, Singh-Manoux A, Tzourio C, Elbaz A. Article Google Scholar Newman AB, Arnold AM, Naydeck BL, Fried LP, Burke GL, Enright P, et al. Article Google Scholar Burke GL, Arnold AM, Bild DE, Cushman M, Fried LP, Newman A, et al.

Article CAS Google Scholar Terry DF, Pencina MJ, Vasan RS, Murabito JM, Wolf PA, Hayes MK, et al. Article Google Scholar Willcox BJ, He Q, Chen R, Yano K, Masaki KH, Grove JS, et al.

Article CAS Google Scholar Yates LB, Djousse L, Kurth T, Buring JE, Gaziano JM. Article Google Scholar Franzon K, Byberg L, Sjogren P, Zethelius B, Cederholm T, Kilander L. Article Google Scholar Zhang S, Tomata Y, Discacciati A, Otsuka T, Sugawara Y, Tanji F, et al.

Article Google Scholar Bosnes I, Nordahl HM, Stordal E, Bosnes O, Myklebust TA, Almkvist O. Article CAS Google Scholar Swindell WR, Ensrud KE, Cawthon PM, Cauley JA, Cummings SR, Miller RA, et al. Article Google Scholar McNeil JJ, Woods RL, Nelson MR, Reid CM, Kirpach B, Wolfe R, et al.

Article CAS Google Scholar McNeil JJ, Wolfe R, Woods RL, Tonkin AM, Donnan GA, Nelson MR, et al. Article CAS Google Scholar McNeil JJ, Nelson MR, Woods RL, Lockery JE, Wolfe R, Reid CM, et al. Article CAS Google Scholar Group AI. Article Google Scholar Newman AB, Murabito JM. Article Google Scholar Katz S.

Article CAS Google Scholar Association AP. Article Google Scholar Stef van Buuren. Article Google Scholar White IR, Royston P, Wood AM. Article Google Scholar Breheny P, Huang J. Article Google Scholar Friedman J, Hastie T, Tibshirani R. Article Google Scholar Blanche P, Dartigues JF, Jacqmin-Gadda H.

Article Google Scholar Harrell FE. Article Google Scholar Pahor M, Guralnik JM, Ambrosius WT, Blair S, Bonds DE, Church TS, et al. Article CAS Google Scholar LaCroix AZ, LaMonte MJ, Applegate WB.

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