Coupled Inversion of Hydraulic and Self-Potential Data from Transient Outflow Experiments to Estimate Soil Petrophysical Properties

Hydraulicproperties of soils could play an important role in affecting the partitioning of precipitation in the critical zone. In addition to traditional approaches, in the last two decades, many geophysical methods have been used to aid the hydrologic characterization and measurement of geological materials. In particular, the self-potential (SP) method shows great potential in these hydrogeophysical applications. The objective of this study is to evaluate whether the addition of SP data can improve the estimation of hydraulic properties of soils in an outflow experiment. A stochastic, coupled hydrogeophysical inversion was developed, in which the governing equations were solved using the finite volume method and the parameter estimation was conducted using a Bayesian approach associated with the Markov chain Monte Carlo technique. The results show that the addition of SP data in the inversion could reduce the uncertainty related to the estimated hydraulic parameters of soils and the length of the associated 95% confidence interval can be shortened by ∼1/3. It is also shown that the electrical properties of soils at saturated and unsaturated conditions may also be estimated from the outflow experiment when SP data are available. Compared with hydraulic parameters, the accuracy of the estimated electrical properties is slightly lower. Among them, the saturated streaming potential coupling coefficient Csat has the highest accuracy and lowest uncertainty since Csat directly influences the magnitude of SP signals. The accuracy of other electrical parameters is lower than that of Csat (and hydraulic parameters), and the associated uncertainty can be one order of magnitude larger

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Dynamic Cross-Correlations Analysis on Economic Policy Uncertainty and US Dollar Exchange Rate: AMF-DCCA Perspective

In this paper, we employ the multifractal detrended cross-correlation analysis (MF-DCCA) as the measurement instrument for the dynamic cross-correlation inspection between US economic policy uncertainty (EPU) index and US dollar exchange rate return (Ret). By calculating the cross-correlation statistics, we find mild acceptance of cross-correlation between EPU and Ret qualitatively. With further application of MF-DCCA methodology, we find strong power law cross-correlation existence within all scaling orders. Also, apparent persistence of cross-correlation has been discovered with significant Hurst exponents of all orders. Besides, we find that long-term cross-correlation demonstrates more persistence and higher degree of multifractality than those in the short term. Finally, we utilize the rolling window and binominal measurement analysis as revisits of the model. The results are consistent with model statements

Human endogenous retroviruses in viral disease and therapy

Abstract As retained sequences of ancient retroviruses, human endogenous retroviruses (HERVs) are widespread in the human genome. Although most HERVs have been inert throughout evolution, several HERVs are still functional and involved in various diseases, such as autoimmune diseases, cancer, neurological diseases and viral infection. Hence, certain types of therapies targeting HERVs or utilizing HERV tools have been developed. Here, we summarize recent findings on the role of HERVs in viral infection, including HERV dysregulation in different kinds of viral infections and the potential mechanisms of HERV‐mediated antiviral immunity. In addition, we provide an overview of HERV‐related therapies that directly target HERVs or are indirectly based on HERVs to improve the efficacy of vaccines and drugs

Origin and Deep Evolution of Human Endogenous Retroviruses in Pan-Primates

Human endogenous retroviruses (HERVs) are viral “fossils” in the human genome that originated from the ancient integration of exogenous retroviruses. Although HERVs have sporadically been reported in nonhuman primate genomes, their deep origination in pan-primates remains to be explored. Hence, based on the in silico genomic mining of full-length HERVs in 49 primates, we performed the largest systematic survey to date of the distribution, phylogeny, and functional predictions of HERVs. Most importantly, we obtained conclusive evidence of nonhuman origin for most contemporary HERVs. We found that various supergroups, including HERVW9, HUERSP, HSERVIII, HERVIPADP, HERVK, and HERVHF, were widely distributed in Strepsirrhini, Platyrrhini (New World monkeys) and Catarrhini (Old World monkeys and apes). We found that numerous HERVHFs are spread by vertical transmission within Catarrhini and one HERVHF was traced in 17 species, indicating its ancient nature. We also discovered that 164 HERVs were likely involved in genomic rearrangement and 107 HERVs were potentially coopted in the form of noncoding RNAs (ncRNAs) in humans. In summary, we provided comprehensive data on the deep origination of modern HERVs in pan-primates

Elastic-Impedance-Based Fluid/Porosity Term and Fracture Weaknesses Inversion in Transversely Isotropic Media with a Tilted Axis of Symmetry

The rock containing a set of tilted fractures is equivalent to a transversely isotropic (TTI) medium with a tilted axis of symmetry. To implement fluid identification and tilted fracture detection, we propose an inversion approach of utilizing seismic data to simultaneously estimate parameters that are sensitive to fluids and tilted fractures. We first derive a PP-wave reflection coefficient and elastic impedance (EI) in terms of the dip angle, fluid/porosity term, shear modulus, density, and fracture weaknesses, and we present numerical examples to demonstrate how the PP-wave reflection coefficient and EI vary with the dip angle. Based on the information of dip angle of fractures provided by geologic and well data, we propose a two-step inversion approach of utilizing azimuthal seismic data to estimate unknown parameters involving the fluid/porosity term and fracture weaknesses: (1) the constrained sparse spike inversion (CSSI) for azimuthally anisotropic EI data and (2) the estimation of unknown parameters with the low-frequency constrained regularization term. Synthetic and real data demonstrate that fluid and fracture parameters are reasonably estimated, which may help fluid identification and fracture characterization

Controllable Synthesis of Cu₂In₂ZnS₅ Nano/Microcrystals and Hierarchical Films and Applications in Dye-Sensitized Solar Cells

Cu₂­In₂­ZnS₅ microcrystals with controllable nanostructures are synthesized via a facile solvothermal method. The microcrystals consist of numerous nanorods packed along a preferred crystal orientation. The crystal size of Cu₂­In₂­ZnS₅ is tunable into the nanoscale. Cu₂­In₂­ZnS₅ nanocrystals are composed of nanoparticles with an average size of 4.2 nm. Moreover, microcrystals were assembled on a molybdenum substrate to form a Cu₂­In₂­ZnS₅ thin film with a hierarchical architecture. The hierarchical nanostructure benefits to increase the optical path, decrease the reflection to capture photons effectively, and provide multiple channels for directly transferring charges to the conducting substrate. The hierarchical thin films were exploited as counter electrodes of dye-sensitized solar cells, which enhanced the catalytic activity of the counter electrode. The power conversion efficiency reached up to 6.1%, comparable to that of the dye-sensitized solar cells with a Pt counter electrode