Large-scale Signal Research Articles

A comparison of large-scale climate signals and the North American Multi-Model Ensemble (NMME) for drought prediction in China

Drought is an extreme natural disaster that can lead to huge socioeconomic losses. Drought prediction ahead of months is helpful for early drought warning and preparations. In this study, we developed a statistical model, two weighted dynamic models and a statistical-dynamic (hybrid) model for 1–6 month lead drought prediction in China. Specifically, statistical component refers to climate signals weighting by support vector regression (SVR), dynamic components consist of the ensemble mean (EM) and Bayesian model averaging (BMA) of the North American Multi-Model Ensemble (NMME) climatic models, and the hybrid part denotes a combination of statistical and dynamic components by assigning weights based on their historical performances. The results indicate that the statistical and hybrid models show better rainfall predictions than NMME-EM and NMME-BMA models, which have good predictability only in southern China. In the 2011 China winter-spring drought event, the statistical model well predicted the spatial extent and severity of drought nationwide, although the severity was underestimated in the mid-lower reaches of Yangtze River (MLRYR) region. The NMME-EM and NMME-BMA models largely overestimated rainfall in northern and western China in 2011 drought. In the 2013 China summer drought, the NMME-EM model forecasted the drought extent and severity in eastern China well, while the statistical and hybrid models falsely detected negative precipitation anomaly (NPA) in some areas. Model ensembles such as multiple statistical approaches, multiple dynamic models or multiple hybrid models for drought predictions were highlighted. These conclusions may be helpful for drought prediction and early drought warnings in China.

Evolutionary action and structural basis of the allosteric switch controlling \u03b22AR functional selectivity

Functional selectivity of G-protein-coupled receptors is believed to originate from ligand-specific conformations that activate only subsets of signaling effectors. In this study, to identify molecular motifs playing important roles in transducing ligand binding into distinct signaling responses, we combined in silico evolutionary lineage analysis and structure-guided site-directed mutagenesis with large-scale functional signaling characterization and non-negative matrix factorization clustering of signaling profiles. Clustering based on the signaling profiles of 28 variants of the β2-adrenergic receptor reveals three clearly distinct phenotypical clusters, showing selective impairments of either the Gi or βarrestin/endocytosis pathways with no effect on Gs activation. Robustness of the results is confirmed using simulation-based error propagation. The structural changes resulting from functionally biasing mutations centered around the DRY, NPxxY, and PIF motifs, selectively linking these micro-switches to unique signaling profiles. Our data identify different receptor regions that are important for the stabilization of distinct conformations underlying functional selectivity.

Petroleum Refinery Mesh Network Propagation Measurements

This communication presents the results of the first field measurement campaign to characterize the large- and small-scale propagation statistics of the peer-to-peer wireless link in an operating petroleum refinery. Results for ground level transmission and transmission between ground level and devices mounted 18 m from the ground on a distillation stack are included. The measurements characterize the 20-MHz $2 \times 1$ multiple-input, single-output channel at 2.45 GHz. Analysis of the measurement data reveals an environment with severe small-scale fading and multipath dispersion but very mild large-scale signal attenuation given the level of obstruction on the links.

Models of Models: A Translational Route for Cancer Treatment and Drug Development.

Every patient and every disease is different. Each patient therefore requires a personalized treatment approach. For technical reasons, a personalized approach is feasible for treatment strategies such as surgery, but not for drug-based therapy or drug development. The development of individual mechanistic models of the disease process in every patient offers the possibility of attaining truly personalized drug-based therapy and prevention. The concept of virtual clinical trials and the integrated use of in silico, in vitro, and in vivo models in preclinical development could lead to significant gains in efficiency and order of magnitude increases in the cost effectiveness of drug development and approval. We have developed mechanistic computational models of large-scale cellular signal transduction networks for prediction of drug effects and functional responses, based on patient-specific multi-level omics profiles. However, a major barrier to the use of such models in a clinical and developmental context is the reliability of predictions. Here we detail how the approach of using “models of models” has the potential to impact cancer treatment and drug development. We describe the iterative refinement process that leverages the flexibility of experimental systems to generate highly dimensional data, which can be used to train and validate computational model parameters and improve model predictions. In this way, highly optimized computational models with robust predictive capacity can be generated. Such models open up a number of opportunities for cancer drug treatment and development, from enhancing the design of experimental studies, reducing costs, and improving animal welfare, to increasing the translational value of results generated.

An annual proxy for the geomagnetic signal of magnetospheric currents on Earth based on observatory data from 1900–2010

We introduce the Annual Magnetospheric Currents index as long-term proxy for the geomagnetic signal of magnetospheric currents on Earth valid within the time span 1900–2010. Similar to the widely used disturbance storm time and ‘Ring Current’ indices, it is based on geomagnetic observatory data, but provides a realistic absolute level and uncertainty estimates. Crucial aspects to this end are the revision of observatory crustal biases as well as the implementation of a Bayesian inversion accounting for uncertainties in the main field estimate, both required for the index derivation. The observatory choice is based on a minimization of index variance during a reference period spanning 1960–2010. The new index is capable of correcting observatory time series from large-scale external signals in a user-friendly manner. At present the index is only available as annual mean values. An extension to hourly values for the same time span is in progress.

Changing climate shifts timing of European floods.

A warming climate is expected to have an impact on the magnitude and timing of river floods; however, no consistent large-scale climate change signal in observed flood magnitudes has been identified so far. We analyzed the timing of river floods in Europe over the past five decades, using a pan-European database from 4262 observational hydrometric stations, and found clear patterns of change in flood timing. Warmer temperatures have led to earlier spring snowmelt floods throughout northeastern Europe; delayed winter storms associated with polar warming have led to later winter floods around the North Sea and some sectors of the Mediterranean coast; and earlier soil moisture maxima have led to earlier winter floods in western Europe. Our results highlight the existence of a clear climate signal in flood observations at the continental scale.

OSL chronologies of paleoenvironmental dynamics recorded by loess-paleosol sequences from Europe: Case studies from the Rhine-Meuse area and the Neckar Basin

Loess-paleosol sequences (LPS) represent an important terrestrial archive for the reconstruction of the paleoenvironmental evolution during the Last Glacial cycle in Europe. In the Rhine-Meuse area and the southwestern Germany, there are only few numerical ages determined with state-of-the-art luminescence methods, which limits the robustness of established chronostratigraphies. This study presents a comparative dating approach using quartz and feldspar post-infrared (pIRIR290) optically stimulated luminescence (OSL) from fine- and medium-grained samples of five loess-paleosol sequences. Paleoenvironmental dynamics are reconstructed by high-resolution grain-size analysis.The OSL ages are evaluated as being robust and consistent with other records. Furthermore, we show that ages for hillslope deposits are sometimes overestimated due to low-frequency high-magnitude erosive processes. The integration of OSL ages, stratigraphy and high-resolution grain-size data contributes to a temporal more complete data record from Western and Central Europe. Secondly, we propose a combined stratigraphic model for LPS of the Last Glacial cycle, which is developed and discussed in the context of other key sites such as Havrincourt, Schwalbenberg II, Tönchesberg, Nussloch and Dolni Vĕstonice. Comparable chronologies and patterns in proxy data between sites with a large spatial distance suggest a possible semi-synchronous response to climatic changes in Europe. By combining our data with the Eifel-Laminated-Sediment-Archive (ELSA) and the NGRIP ice core, a relationship of loess archives and large-scale climatic signals is discussed and a proxydata based event-stratigraphy is developed. This model refines existing chronostratigraphic models and contributes to solve open discussion concerning the timing and demarcation of sedimentary units and sequences from for Western and Central Europe.

On the flexibility of block coordinate descent for large-scale optimization

We consider a large-scale minimization problem (not necessarily convex) with non-smooth separable convex penalty. Problems in this form widely arise in many modern large-scale machine learning and signal processing applications. In this paper, we present a new perspective towards the parallel Block Coordinate Descent (BCD) methods. Specifically we explicitly give a concept of so-called two-layered block variable updating loop for parallel BCD methods in modern computing environment comprised of multiple distributed computing nodes. The outer loop refers to the block variable updating assigned to distributed nodes, and the inner loop involves the updating step inside each node. Each loop allows to adopt either Jacobi or Gauss–Seidel update rule. In particular, we give detailed theoretical convergence analysis to two practical schemes: Jacobi/Gauss–Seidel and Gauss–Seidel/Jacobi that embodies two algorithms respectively. Our new perspective and behind theoretical results help devise parallel BCD algorithms in a principled fashion, which in turn lend them a flexible implementation for BCD methods suited to the parallel computing environment. The effectiveness of the algorithm framework is verified on the benchmark tasks of large-scale ℓ1 regularized sparse logistic regression and non-negative matrix factorization.

Growing-season precipitation since 1872 in the coastal area of subtropical southeast China reconstructed from tree rings and its relationship with the East Asian summer monsoon system

Tree rings from temperate zones of the world have provided abundant palaeo- ecological and paleo-hydroclimatic information. However, tree rings from subtropical to tropical regions remain relatively scarce, which greatly limit our fully understanding about the climate change issues. In the present work, tree-ring-width (TRW) measurements of Masson pine from Fujian province, the coastal area of subtropical southeast China were successfully crossdated and a TRW STD chronology was developed from 1854 to 2012. Significantly positive correlation was identified between the tree rings and April–November total precipitation (r=0.71, p<0.01). The reconstructed April–November precipitation exhibited two comparatively wet (1876–1886 and 1957–1962) and one comparatively dry (1986–2004) periods. An evident drying trend since 1959 was seen and it was mitigated after 1993. Most of the extreme low-precipitation years in the reconstruction were supported by the historical records. As revealed by the spatial correlation patterns, our precipitation reconstruction was also consistent with other hydroclimatic records along the coastal areas of southeast China, proving its ability to capture the large-scale hydrological signal in southeast China (mainly refers to the south of the middle-lower reaches of Yangtze River). The reconstructed precipitation showed significant correlation with the East Asian summer Monsoon (EASM) index. Moreover, it also indicated simultaneous variation with the monsoon precipitation in North China on a decadal scale, implying that growing season precipitation variations in both regions were influenced by the EASM strength. This work highlights the potential of using tree-ring width to reconstruct precipitation in subtropical southeast China, while the relevant issues about precipitation variation in this region is far from resolved.

A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems

This paper addresses large-scale urban transportation optimization problems with time-dependent continuous decision variables, a stochastic simulation-based objective function, and general analytical differentiable constraints. We propose a metamodel approach to address, in a computationally efficient way, these large-scale dynamic simulation-based optimization problems. We formulate an analytical dynamic network model that is used as part of the metamodel. The network model formulation combines ideas from transient queueing theory and traffic flow theory. The model is formulated as a system of equations. The model complexity is linear in the number of road links and is independent of the link space capacities. This makes it a scalable model suitable for the analysis of large-scale problems. The proposed dynamic metamodel approach is used to address a time-dependent large-scale traffic signal control problem for the city of Lausanne. Its performance is compared to that of a stationary metamodel approach. The proposed approach outperforms the stationary approach. This comparison illustrates the added value of providing the algorithm with analytical dynamic problem-specific structural information. The performance of a signal plan derived by the proposed approach is also compared to that of an existing signal plan for the city of Lausanne, and to that of a signal plan derived by a mainstream commercial signal control software. The proposed method can systematically identify signal plans with good performance. The online appendix is available at https://doi.org/10.1287/trsc.2016.0717 .

The bottom water exchange between the Singapore Strait and the West Johor Strait

As a part of the border between Singapore and Malaysia, the West Johor Strait (WJS) suffered newly from harmful algal blooms. There is no previous study showing the source of the nutrients in the WJS. This paper is investigating the possible water exchange between the water in the WJS and the bottom water in Singapore Strait.This paper adopts a two-level nesting atmosphere-ocean coupled models to downscale the global atmosphere-ocean model into the Singapore coastal water, keeping the large-scale and long-term ocean and climate circulation signals and the advantages of the high-resolution. Based on the high-resolution ocean circulation fields, a Lagrangian particle tracking model is used to trace the Singapore Strait's bottom water movement and the water mixing in the WJS.The results showed that the numerical models well resolved the Singapore coastal water regional circulation. There is a small but significant bottom water (1.25%) transport from the Singapore Strait to the WJS, which occurs from the southwest coastline of Singapore. The bottom water in the Singapore Strait prefers to enter the WJS during the spring tide and the flood period, and stay in Johor Strait for 6.4 days. The spring tide is the first-order factor for the water vertical mixing in the WJS, the wind is also very important for the vertical mixing especially in neap tide condition. An overall very important factor is the light perturbation. With the strongest vertical mixing of nutrients and bottom sediments due to the spring tide, the latter ones may inhibit the light penetration during the spring tide and reduce the algal bloom. The light penetration otherwise is greater during the neap tide, when the winds are the most important factor and hence favor the algal bloom. With the strongest wind in February and the longest permanence time in June and the sufficient nutrient supply in February and June, the most serious algal blooms may happen in February and June in the WJS.

On the evidence for large-scale galactic conformity in the local Universe

We re-examine the observational evidence for large-scale (4 Mpc) galactic conformity in the local Universe, as presented in Kauffmann et al. (2013). We show that a number of methodological features of their analysis act to produce a misleadingly high amplitude of the conformity signal. These include a weighting in favour of central galaxies in very high-density regions, the likely misclassification of satellite galaxies as centrals in the same high-density regions, and the use of medians to characterize bimodal distributions. We show that the large-scale conformity signal in Kauffmann et al. clearly originates from a very small number of central galaxies in the vicinity of just a few very massive clusters, whose effect is strongly amplified by the methodological issues that we have identified. Some of these 'centrals' are likely misclassified satellites, but some may be genuine centrals showing a real conformity effect. Regardless, this analysis suggests that conformity on 4 Mpc scales is best viewed as a relatively short-range effect (at the virial radius) associated with these very large neighbouring haloes, rather than a very long-range effect (at tens of virial radii) associated with the relatively low-mass haloes that host the nominal central galaxies in the analysis. A mock catalogue constructed from a recent semi-analytic model shows very similar conformity effects to the data when analysed in the same way, suggesting that there is no need to introduce new physical processes to explain galactic conformity on 4 Mpc scales.

Deep learning control for complex and large scale cloud systems

Deep learning attempts to model high level perceptions in data using deep graph representations and creating models to learn these representations from large-scale unlabeled signals. Efficient unsu...

A new prediction model for grain yield in Northeast China based on spring North Atlantic Oscillation and late-winter Bering Sea ice cover

Accurate estimations of grain output in the agriculturally important region of Northeast China are of great strategic significance for guaranteeing food security. New prediction models for maize and rice yields are built in this paper based on the spring North Atlantic Oscillation index and the Bering Sea ice cover index. The year-to-year increment is first forecasted and then the original yield value is obtained by adding the historical yield of the previous year. The multivariate linear prediction model of maize shows good predictive ability, with a low normalized root-mean-square error (NRMSE) of 13.9%, and the simulated yield accounts for 81% of the total variance of the observation. To improve the performance of the multivariate linear model, a combined forecasting model of rice is built by considering the weight of the predictors. The NRMSE of the model is 12.9% and the predicted rice yield explains 71% of the total variance. The corresponding cross-validation test and independent samples test further demonstrate the efficiency of the models. It is inferred that the statistical models established here by applying year-to-year increment approach could make rational prediction for the maize and rice yield in Northeast China before harvest. The present study may shed new light on yield prediction in advance by use of antecedent large-scale climate signals adequately.

Was the Little Ice Age more or less El Niño-like than the Medieval Climate Anomaly? Evidence from hydrological and temperature proxy data

Abstract. The El Niño–Southern Oscillation (ENSO) is the most important source of global climate variability on interannual timescales and has substantial environmental and socio-economic consequences. However, it is unclear how it interacts with large-scale climate states over longer (decadal to centennial) timescales. The instrumental ENSO record is too short for analysing long-term trends and variability and climate models are unable to accurately simulate past ENSO states. Proxy data are used to extend the record, but different proxy sources have produced dissimilar reconstructions of long-term ENSO-like climate change, with some evidence for a temperature–precipitation divergence in ENSO-like climate over the past millennium, in particular during the Medieval Climate Anomaly (MCA; AD ∼ 800–1300) and the Little Ice Age (LIA; AD ∼ 1400–1850). This throws into question the stability of the modern ENSO system and its links to the global climate, which has implications for future projections. Here we use a new statistical approach using weighting based on empirical orthogonal function (EOF) to create two new large-scale reconstructions of ENSO-like climate change derived independently from precipitation proxies and temperature proxies. The method is developed and validated using model-derived pseudo-proxy experiments that address the effects of proxy dating error, resolution, and noise to improve uncertainty estimations. We find no evidence that temperature and precipitation disagree over the ENSO-like state over the past millennium, but neither do they agree strongly. There is no statistically significant difference between the MCA and the LIA in either reconstruction. However, the temperature reconstruction suffers from a lack of high-quality proxy records located in ENSO-sensitive regions, which limits its ability to capture the large-scale ENSO signal. Further expansion of the palaeo-database and improvements to instrumental, satellite, and model representations of ENSO are needed to fully resolve the discrepancies found among proxy records and establish the long-term stability of this important mode of climatic variability.

On the mesoscale monitoring capability of Argo floats in the Mediterranean Sea

Abstract. In this work a simplified observing system simulation experiment (OSSE) approach is used to investigate which Argo design sampling in the Mediterranean Sea would be necessary to properly capture the mesoscale dynamics in this basin. The monitoring of the mesoscale features is not an initial objective of the Argo network. However, it is an interesting question from the perspective of future network extensions in order to improve the ocean state estimates. The true field used to conduct the OSSEs is provided by a specific altimetry-gridded merged product for the Mediterranean Sea. Synthetic observations were obtained by sub-sampling this Nature Run according to different configurations of the ARGO network. The observation errors required to perform the OSSEs were obtained through the comparison of sea level anomalies (SLAs) from altimetry and dynamic height anomalies (DHAs) computed from the real in situ Argo network. This analysis also contributes to validate satellite SLAs with an increased confidence. The simulation experiments show that a configuration similar to the current Argo array in the Mediterranean (with a spatial resolution of 2° × 2°) is only able to recover the large-scale signals of the basin. Increasing the spatial resolution to nearly 75 km × 75 km, allows the capture of most of the mesoscale signal in the basin and to retrieve the SLA field with a RMSE of 3 cm for spatial scales larger than 150 km, similar to those presently captured by the altimetry. This would represent a theoretical reduction of 40 % of the actual RMSE. Such a high-resolution Argo array composed of around 450 floats, cycling every 10 days, is expected to increase the actual network cost by approximately a factor of 6.

Variability, trends, and teleconnections of stream flows with large-scale climate signals in the Omo-Ghibe River Basin, Ethiopia.

This study assesses variability, trends, and teleconnections of stream flow with large-scale climate signals (global sea surface temperatures (SSTs)) for the Omo-Ghibe River Basin of Ethiopia. Fourteen hydrological indices of variability and extremes were defined from daily stream flow data series and analyzed for two common periods, which are 1972-2006 for 5 stations and 1982-2006 for 15 stations. The Mann-Kendall's test was used to detect trends at 0.05 significance level, and simple correlation analysis was applied to evaluate associations between the selected stream flow indices and SSTs. We found weak and mixed (upward and downward) trend signals for annual and wet (Kiremt) season flows. Indices generated for high-flow (flood) magnitudes showed the same weak trend signals. However, trend tests for flood frequencies and low-flow magnitudes showed little evidences of increasing change. It was also found that El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) are the major anomalies affecting stream flow variability in the Omo-Ghibe Basin. The strongest associations are observed between ENSO/Niño3.4 and the stream flow in August and September, mean Kiremt flow (July-September), and flood frequency (peak over threshold on average three peaks per year (POT3_Fre)). The findings of this study provide a general overview on the long-term stream flow variability and predictability of stream flows for the Omo-Ghibe River Basin.

A statistical model for brain networks inferred from large-scale electrophysiological signals.

Network science has been extensively developed to characterize the structural properties of complex systems, including brain networks inferred from neuroimaging data. As a result of the inference process, networks estimated from experimentally obtained biological data represent one instance of a larger number of realizations with similar intrinsic topology. A modelling approach is therefore needed to support statistical inference on the bottom-up local connectivity mechanisms influencing the formation of the estimated brain networks. Here, we adopted a statistical model based on exponential random graph models (ERGMs) to reproduce brain networks, or connectomes, estimated by spectral coherence between high-density electroencephalographic (EEG) signals. ERGMs are made up by different local graph metrics, whereas the parameters weight the respective contribution in explaining the observed network. We validated this approach in a dataset of N = 108 healthy subjects during eyes-open (EO) and eyes-closed (EC) resting-state conditions. Results showed that the tendency to form triangles and stars, reflecting clustering and node centrality, better explained the global properties of the EEG connectomes than other combinations of graph metrics. In particular, the synthetic networks generated by this model configuration replicated the characteristic differences found in real brain networks, with EO eliciting significantly higher segregation in the alpha frequency band (8-13 Hz) than EC. Furthermore, the fitted ERGM parameter values provided complementary information showing that clustering connections are significantly more represented from EC to EO in the alpha range, but also in the beta band (14-29 Hz), which is known to play a crucial role in cortical processing of visual input and externally oriented attention. Taken together, these findings support the current view of the functional segregation and integration of the brain in terms of modules and hubs, and provide a statistical approach to extract new information on the (re)organizational mechanisms in healthy and diseased brains.

Context-independent essential regulatory interactions for apoptosis and hypertrophy in the cardiac signaling network

Apoptosis and hypertrophy of cardiomyocytes are the primary causes of heart failure and are known to be regulated by complex interactions in the underlying intracellular signaling network. Previous experimental studies were successful in identifying some key signaling components, but most of the findings were confined to particular experimental conditions corresponding to specific cellular contexts. A question then arises as to whether there might be essential regulatory interactions that prevail across diverse cellular contexts. To address this question, we have constructed a large-scale cardiac signaling network by integrating previous experimental results and developed a mathematical model using normalized ordinary differential equations. Specific cellular contexts were reflected to different kinetic parameters sampled from random distributions. Through extensive computer simulations with various parameter distributions, we revealed the five most essential context-independent regulatory interactions (between: (1) αAR and Gαq, (2) IP3 and calcium, (3) epac and CaMK, (4) JNK and NFAT, and (5) p38 and NFAT) for hypertrophy and apoptosis that were consistently found over all our perturbation analyses. These essential interactions are expected to be the most promising therapeutic targets across a broad spectrum of individual conditions of heart failure patients.

A Primal Douglas–Rachford Splitting Method for the Constrained Minimization Problem in Compressive Sensing

Compressive sensing has achieved great success in many scientific research fields. It has revealed that sparse signals can be stably recovered from a small number of noisy measurements by solving the constrained convex $${\ell }_1$$ -minimization problem. In practice, a faster algorithm for solving this optimization problem is the key to compressive sensing. The Douglas–Rachford splitting method is a well-known operator splitting method that has been widely applied for solving a certain class of convex composite problems. In particular, its dual application results in the popular alternating direction method of multipliers (ADMM). In this paper, we reformulate the constrained convex $${\ell }_1$$ -minimization problem as a convex composite problem with a special structure and then apply the primal Douglas–Rachford splitting method to solve it. The computational cost of the developed algorithm in each iteration is dominated by the projection onto the constraint set. A fast and efficient method of computing the projection is proposed. Numerical results show that the developed algorithm performs better than the popular NESTA and LADMM (inexact ADMM) in terms of accuracy and run time for large-scale sparse signal recovery.