Equivalence of Discrete Fracture Network and Porous Media Models by Hydraulic Tomography

Hydraulic tomography (HT) has emerged as a potentially viable method for mapping fractures in geologic media as demonstrated by recent studies. However, most of the studies adopted equivalent porous media (EPM) models to generate and invert hydraulic interference test data for HT. While these models assign significant different hydraulic properties to fractures and matrix, they may not fully capture the discrete nature of the fractures in the rocks. As a result, HT performance may have been overrated. To explore this issue, this study employed a discrete fracture network (DFN) model to simulate hydraulic interference tests. HT with the EPM model was then applied to estimate the distributions of hydraulic conductivity (K) and specific storage (S-s) of the DFN. Afterward, the estimated fields were used to predict the observed heads from DFN models, not used in the HT analysis (i.e., validation). Additionally, this study defined the spatial representative elementary volume (REV) of the fracture connectivity probability for the entire DFN dominant. The study showed that if this spatial REV exists, the DFN is deemed equivalent to EPM and vice versa. The hydraulic properties estimated by HT with an EPM model can then predict head fields satisfactorily over the entire DFN domain with limited monitoring wells. For a sparse DFN without this spatial REV, a dense observation network is needed. Nevertheless, HT is able to capture the dominant fractures.National Science and Technology Major Project of China [2017ZX05008-003-021]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDB10030601]; Youth Innovation Promotion Association of the Chinese Academy of Sciences [2016063]; US Civilain Research and Development Foundation (CRDF) under the award: Hydraulic tomography in shallow alluvial sediments: Nile River Valley, Egypt [DAA2-15-61224-1]; Global Expert award through Tianjin Normal University from the Thousand Talents Plan of Tianjin City6 month embargo; published online: 23 April 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Phase 2 study of RO4929097, a gamma‐secretase inhibitor, in metastatic melanoma: SWOG 0933

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110565/1/cncr29055.pd

Deep learning integrating scale conversion and pedo-transfer function to avoid potential errors in cross-scale transfer

Pedo-transfer functions (PTFs) relate soil/landscape static properties to a wide range of model inputs (e.g., soil hydraulic parameters) that are essential to soil hydrological modeling. Combining PTFs and hydrological models is a powerful strategy allowing the use of soil/landscape static properties for the generalization of large-scale modeling. However, since the spatial scales of soil hydraulic parameters required for model inputs and soil/landscape static properties are often not identical, cross-scale transfer is required, which can be a significant source of errors. Here, we investigate uncertainties in cross-scale transfer and develop an approach that avoids them. The proposed method uses the convolutional neural network (CNN) as a cross-scale transfer approach to directly map soil/landscape static properties to soil hydraulic parameters across different spatial scales. The proposed CNN approach is applied under two different estimation strategies to invert the hydraulic parameters of a soil-water balance model and subsequently the quality of the parameters is assessed. Both synthetical and real-world results around the conterminous United States indicate that in general the employed end-to-end strategy is superior to the two-step strategy. The CNN-based integrated model successfully reduces potential errors in cross-scale transfer and can be applied to other areas lacking information on hydraulic parameters or observations. The proposed method can be extended to improve parameter estimation in earth system models and enhance our understanding of key hydrological processes

Multi-product characterization of surface soil moisture drydowns in the UK

The persistence or memory of soil moisture (θ) after rainfall has substantial environmental implications. Much work has been done to study soil moisture drydown for in-situ and satellite data separately. In this work, we present a comparison of drydown characteristics across multiple UK soil moisture products, including satellite-merged (i.e. TCM), in-situ (i.e. COSMOS-UK), hydrological model (i.e. G2G), statistical model (i.e. SMUK) and land surface model (LSM) (i.e. CHESS) data. The drydown decay time scale (τ) for all gridded products are computed at an unprecedented resolution of 1-2 km, a scale relevant to weather and climate models. While their range of τ differ (except SMUK and CHESS are similar) due to differences such as sensing depths, their spatial patterns are correlated to land cover and soil types. We further analyse the occurrence of drydown events at COSMOS-UK sites. We show that soil moisture drydown regimes exhibit strong seasonal dependencies, whereby the soil dries out quicker in summer than winter. These seasonal dependencies are important to consider during model benchmarking and evaluation. We show that fitted τ based on COSMOS and LSM are well correlated, with a bias of lower τ for COSMOS. Our findings contribute to a growing body of literature to characterize τ, with the aim of developing a method to systematically validate model soil moisture products at a range of scales

The relative importance of head, flux, and prior information in hydraulic tomography analysis

Using cross-correlation analysis, we demonstrate that flux measurements at observation locations during hydraulic tomography (HT) surveys carry nonredundant information about heterogeneity that are complementary to head measurements at the same locations. We then hypothesize that a joint interpretation of head and flux data, even when the same observation network as head has been used, can enhance the resolution of HT estimates. Subsequently, we use numerical experiments to test this hypothesis and investigate the impact of flux conditioning and prior information (such as correlation lengths and initial mean models (i.e., uniform mean or distributed means)) on the HT estimates of a nonstationary, layered medium. We find that the addition of flux conditioning to HT analysis improves the estimates in all of the prior models tested. While prior information on geologic structures could be useful, its influence on the estimates reduces as more nonredundant data (i.e., flux) are used in the HT analysis. Lastly, recommendations for conducting HT surveys and analysis are presented

Spatial downscaling and gap-filling of SMAP soil moisture to high resolution using MODIS surface variables and machine learning approaches over ShanDian River Basin, China

High-resolution soil moisture (SM) information is essential for regional to global hydrological and agricultural applications. The Soil Moisture Active Passive (SMAP) offers daily global composites of SM at coarse-resolution 9 and 36 km, with data gaps limiting its local application to depict SM distribution in detail. To overcome the aforementioned problem, a downscaling and gap-filling novel approach was adopted, using random forest (RF) and artificial neural network (ANN) algorithms to downscale SMAP SM data, using land-surface variables from moderate-resolution imaging spectroradiometer (MODIS) onboard Aqua and Terra satellites from the years 2018 to 2019. Firstly, four combinations (RF+Aqua, RF+Terra, ANN+Aqua, and ANN+Terra) were developed. Each combination downscaled SMAP SM at a high resolution (1 km). These combinations were evaluated by using error matrices and in situ SM at different scales in the ShanDian River (SDR) Basin. The combination RF+Terra showed a better performance, with a low averaged unbiased root mean square error (ubRMSE) of 0.034 (Formula presented.) / (Formula presented.) and high averaged correlation (R) of 0.54 against the small-, medium-, and large-scale in situ SM. Secondly, the impact of various land covers was examined by using downscaled SMAP and in situ SM. Vegetation attenuation makes woodland more error-prone and less correlated than grassland and farmland. Finally, the RF+Terra and ANN+Terra combinations were selected for their higher accuracy in gap filling of downscaled SMAP SM. The gap-filled downscaled SMAP SM results were compared spatially with China Land Data Assimilation System (CLDAS) SM and in situ SM. The RF+Terra combination outcomes were more humid than ANN+Terra combination results in the SDR basin. Overall, the RF+Terra combination gap-filled data showed high R (0.40) and less ubRMSE (0.064 (Formula presented.) / (Formula presented.)) against in situ SM, which was close to CLDAS SM. This study showed that the proposed RF- and ANN-based downscaling methods have a potential to improve the spatial resolution and gap-filling of SMAP SM at a high resolution (1 km)

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

A High-Resolution Global Map of Soil Hydraulic Properties Produced by a Hierarchical Parameterization of a Physically Based Water Retention Model

A correct quantification of mass and energy exchange processes among Earth's land surface, groundwater, and atmosphere requires an accurate parameterization of soil hydraulic properties. Pedotransfer functions (PTFs) are useful in this regard because they estimate these otherwise difficult to obtain characteristics using texture and other ubiquitous soil data. Most PTFs estimate parameters of empirical hydraulic functions with modest accuracy. In a continued pursuit of improving global-scale PTF estimates, we evaluated whether improvements can be obtained when estimating parameters of hydraulic functions that make physically based assumptions. To this end, we developed a PTF that estimates the parameters of the Kosugi retention and hydraulic conductivity functions (Kosugi, 1994, , 1996, ), which explicitly assume a lognormal pore size distribution and apply the Young-Laplace equation to derive a corresponding pressure head distribution. Using a previously developed combination of machine learning and bootstrapping, the developed five hierarchical PTFs allow for estimates under practical data-poor to data-rich conditions. Using an independent global data set containing nearly 50,000 samples (118,000 retention points), we demonstrated that the new Kosugi-based PTFs outperformed two van Genuchten-based PTFs calibrated on the same data. The new PTFs were applied to a 1x1km(2) global map of texture and bulk density, thus producing maps of the parameters, field capacity, wilting point, plant available water, and associated uncertainties. Soil hydraulic parameters exhibit a much larger variability in the Northern Hemisphere than in the Southern Hemisphere, which is likely due to the geographical distribution of climate zones that affect weathering and sedimentation processes.National Natural Science Foundation of China [41807181]6 month embargo; published online: 6 November 2018This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]