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Abstract Eurasia, home to ~70% of global population, is characterized by (semi-)arid climate. Water scarcity in the mid-latitude Eurasia (MLE) has been exacerbated by a consistent decline in terrestrial water storage (TWS), attributed primarily to human activities. However, the atmospheric mechanisms behind such TWS decline remain unclear. Here, we investigate teleconnections between drying in low-latitude North Atlantic Ocean (LNATO) and TWS depletions across MLE. We elucidate mechanistic linkages and detecte high correlations between decreased TWS in MLE and the decreased precipitation-minus-evapotranspiration (PME) in LNATO. TWS in MLE declines by ~257% during 2003-2017 due to northeastward propagation of PME deficit following two distinct seasonal landfalling routes during January-May and June-January. The same mechanism reduces TWS during 2031-2050 by ~107% and ~447% under scenarios SSP245 and SSP585, respectively. Our findings highlight the risk of increased future water scarcity across MLE caused by large-scale climatic drivers, compounding the impacts of human activities. 

EmrE is anEscherichia colimultidrug efflux pump and member of the small multidrug resistance (SMR) family that transports drugs as a homodimer by harnessing energy from the proton motive force. SMR family transporters contain a conserved glutamate residue in transmembrane 1 (Glu14 in EmrE) that is required for binding protons and drugs. Yet the mechanism underlying proton-coupled transport by the two glutamate residues in the dimer remains unresolved. Here, we used NMR spectroscopy to determine acid dissociation constants (pK_a) for wild-type EmrE and heterodimers containing one or two Glu14 residues in the dimer. For wild-type EmrE, we measured chemical shifts of the carboxyl side chain of Glu14 using solid-state NMR in lipid bilayers and obtained unambiguous evidence on the existence of asymmetric protonation states. Subsequent measurements of pK_avalues for heterodimers with a single Glu14 residue showed no significant differences from heterodimers with two Glu14 residues, supporting a model where the two Glu14 residues have independent pK_avalues and are not electrostatically coupled. These insights support a transport pathway with well-defined protonation states in each monomer of the dimer, including a preferred cytoplasmic-facing state where Glu14 is deprotonated in monomer A and protonated in monomer B under pH conditions in the cytoplasm ofE. coli. Our findings also lead to a model, hop-free exchange, which proposes how exchangers with conformation-dependent pK_avalues reduce proton leakage. This model is relevant to the SMR family and transporters comprised of inverted repeat domains. 

We present P5, a web-based visualization toolkit that combines declarative visualization grammar and GPU computing for progressive data analysis and visualization. To interactively analyze and explore big data, progressive analytics and visualization methods have recently emerged. Progressive visualizations of incrementally refining results have the advantages of allowing users to steer the analysis process and make early decisions. P5 leverages declarative grammar for specifying visualization designs and exploits GPU computing to accelerate progressive data processing and rendering. The declarative specifications can be modified during progressive processing to create different visualizations for analyzing the intermediate results. To enable user interactions for progressive data analysis, P5 utilizes the GPU to automatically aggregate and index data based on declarative interaction specifications to facilitate effective interactive visualization. We demonstrate the effectiveness and usefulness of P5 through a variety of example applications and several performance benchmark tests. 

Abstract This study assesses the predictive skill of eight North American Multimodel Ensemble (NMME) models in predicting the Indian Ocean dipole (IOD). We find that the forecasted ensemble-mean IOD–El Niño–Southern Oscillation (ENSO) relationship deteriorates away from the observed relationship with increasing lead time, which might be one reason that limits the IOD predictive skill in coupled models. We are able to improve the IOD predictive skill using a recently developed stochastic dynamical model (SDM) forced by forecasted ENSO conditions. The results are consistent with the previous result that operational IOD predictability beyond persistence at lead times beyond one season is mostly controlled by ENSO predictability and the signal-to-noise ratio of the Indo-Pacific climate system. The multimodel ensemble (MME) investigated here is found to be of superior skill compared to each individual model at most lead times. Importantly, the skill of the SDM IOD predictions forced with forecasted ENSO conditions were either similar or better than those of the MME IOD forecasts. Moreover, the SDM forced with observed ENSO conditions exhibits significantly higher IOD prediction skill than the MME at longer lead times, suggesting the large potential skill increase that could be achieved by improving operational ENSO forecasts. We find that both cold and warm biases of the predicted Niño-3.4 index may cause false alarms of negative and positive IOD events, respectively, in NMME models. Many false alarms for IOD forecasts at lead times longer than one season in the original forecasts disappear or are significantly reduced in the SDM forced by forecasted ENSO conditions.