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Nowadays, several options are available to treat patients with conductive or mixed hearing loss. Whenever surgical intervention is not possible or contra-indicated, and amplification by a conventional hearing device (e.g., behind-the-ear device) is not feasible, then implantable hearing devices are an indispensable next option. Implantable bone-conduction devices and middle-ear implants have advantages but also limitations concerning complexity/invasiveness of the surgery, medical complications, and effectiveness. To counsel the patient, the clinician should have a good overview of the options with regard to safety and reliability as well as unequivocal technical performance data. The present consensus document is the outcome of an extensive iterative process including ENT specialists, audiologists, health-policy scientists, and representatives/technicians of the main companies in this field. This document should provide a first framework for procedures and technical characterization to enhance effective communication between these stakeholders, improving health care.
Land surface and hydrologic models (LSMs/HMs) are used at diverse spatial resolutions ranging from catchment-scale (1–10 km) to global-scale (over 50 km) applications. Applying the same model structure at different spatial scales requires that the model estimates similar fluxes independent of the chosen resolution, i.e., fulfills a flux-matching condition across scales. An analysis of state-of-the-art LSMs and HMs reveals that most do not have consistent hydrologic parameter fields. Multiple experiments with the mHM, Noah-MP, PCR-GLOBWB, and WaterGAP models demonstrate the pitfalls of deficient parameterization practices currently used in most operational models, which are insufficient to satisfy the flux-matching condition. These examples demonstrate that J. Dooge's 1982 statement on the unsolved problem of parameterization in these models remains true. Based on a review of existing parameter regionalization techniques, we postulate that the multiscale parameter regionalization (MPR) technique offers a practical and robust method that provides consistent (seamless) parameter and flux fields across scales. Herein, we develop a general model protocol to describe how MPR can be applied to a particular model and present an example application using the PCR-GLOBWB model. Finally, we discuss potential advantages and limitations of MPR in obtaining the seamless prediction of hydrological fluxes and states across spatial scales.
Land surface and hydrologic models (LSM/HM) are used at diverse spatial resolutions ranging from 1-10 km in catchment-scale applications to over 50 km in global-scale applications. Application of the same model structure at different spatial scales requires that the model estimates similar fluxes independent of the model resolution and fulfills a flux-matching condition across scales. An analysis of state-of-the-art LSMs and HMs reveals that most do not have consistent and realistic parameter fields for land surface geophysical properties. Multiple experiments with the mHM, Noah-MP, PCR-GLOBWB and WaterGAP models are conducted to demonstrate the pitfalls of poor parameterization practices currently used in most operational models, which are insufficient to satisfy the flux-matching condition. These examples demonstrate that J. Dooge’s 1982 statement on the unsolved problem of parameterization in these models remains true. We provide a short review of existing parameter regionalization techniques and discuss a method for obtaining seamless hydrological predictions of water fluxes and states across multiple spatial resolutions. The multiscale parameter regionalization (MPR) technique is a practical and robust method that provides consistent (seamless) parameter and flux fields across scales. A general model protocol is presented to describe how MPR can be applied to a specific model, with an example of this application using the PCR-GLOBWB model. Applying MPR to PCR-GLOBWB substantially improves the flux-matching condition. Estimation of evapotranspiration without MPR at 5 arcmin and 30 arcmin spatial resolutions for the Rhine river basin results in a difference of approximately 29%. Applying MPR reduce this difference to 9%. For total soil water, the differences without and with MPR are 25% and 7%, respectively.
Pancreatic cancer (PC) still remains a major cause of cancer-related death worldwide and alternative treatments are urgently required. A common problem of PC is the development of resistance against apoptosis that limits therapeutic success. Here we demonstrate that the prototypical Smac mimetic BV6 cooperates with the stimulator of interferon (IFN) genes (STING) ligand 2′,3′-cyclic guanosine monophosphate–adenosine monophosphate (2′3′-cGAMP) to trigger necroptosis in apoptosis-deficient PC cells. Pharmacological inhibition of key components of necroptosis signaling, such as receptor-interacting protein 1 (RIPK1), RIPK3, and mixed lineage kinase domain-like protein (MLKL), significantly rescues PC cells from 2′3′-cGAMP/BV6/zVAD.fmk-mediated cell death, suggesting the induction of necroptosis. Consistently, 2′3′-cGAMP/BV6 co-treatment promotes phosphorylation of MLKL. Furthermore, we show that 2′3′-cGAMP stimulates the production of type I IFNs, which cooperate with BV6 to trigger necroptosis in apoptosis-deficient settings. STING silencing via siRNA or CRISPR/Cas9-mediated gene knockout protects PC cells from 2′3′-cGAMP/BV6/zVAD.fmk-mediated cell death. Interestingly, we demonstrate that nuclear factor-κB (NF-κB), tumor necrosis factor-α (TNFα), and IFN-regulatory factor 1 (IRF1) signaling are involved in triggering 2′3′-cGAMP/BV6/zVAD.fmk-induced necroptosis. In conclusion, we show that activated STING and BV6 act together to exert antitumor effects on PC cells with important implications for the design of new PC treatment concepts.
Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha−1. Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.
Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, yet are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely-sensed biomass products and are undersampled by in-situ monitoring. Current global change threats emphasise the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we assess whether canopy height inferred from drone photogrammetry allows the estimation of aboveground biomass (AGB) across low-stature plant species sampled through a global site network. We found mean canopy height is strongly predictive of AGB across species, demonstrating standardised photogrammetric approaches are generalisable across growth forms and environmental settings. Biomass per-unit-of-height was similar within, but different among, plant functional types. We find drone-based photogrammetry allows for monitoring of AGB across large spatial extents and can advance understanding of understudied and vulnerable non-forested ecosystems across the globe.