Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model

Nitrous oxide (N2O) is the main biogenic greenhouse gas contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate therefore requires a capacity to predict N2O emissions in relation to environmental conditions and crop management. Biophysical models simulating the dynamics of carbon and nitrogen in agro-ecosystems have a unique potential to explore these relationships, but are fraught with high uncertainties in their parameters due to their variations over time and space. Here, we used a Bayesian approach to calibrate the parameters of the N2O submodel of the agro-ecosystem model CERES-EGC. The submodel simulates N2O emissions from the nitrification and denitrification processes, which are modelled as the product of a potential rate with three dimensionless factors related to soil water content, nitrogen content and temperature. These equations involve a total set of 15 parameters, four of which are site-specific and should be measured on site, while the other 11 are considered global, i.e. invariant over time and space. We first gathered prior information on the model parameters based on the literature review, and assigned them uniform probability distributions. A Bayesian method based on the Metropolis–Hastings algorithm was subsequently developed to update the parameter distributions against a database of seven different field-sites in France. Three parallel Markov chains were run to ensure a convergence of the algorithm. This site-specific calibration significantly reduced the spread in parameter distribution, and the uncertainty in the N2O simulations. The model’s root mean square error (RMSE) was also abated by 73% across the field sites compared to the prior parameterization. The Bayesian calibration was subsequently applied simultaneously to all data sets, to obtain better global estimates for the parameters initially deemed universal. This made it possible to reduce the RMSE by 33% on average, compared to the uncalibrated model. These global parameter values may be used to obtain more realistic estimates of N2O emissions from arable soils at regional or continental scales

Friend or Foe? Recent Strategies to Target Myeloid Cells in Cancer

The tumor microenvironment (TME) is a complex network of epithelial and stromal cells, wherein stromal components provide support to tumor cells during all stages of tumorigenesis. Among these stromal cell populations are myeloid cells, which are comprised mainly of tumor-associated macrophages (TAM), dendritic cells (DC), myeloid-derived suppressor cells (MDSC), and tumor-associated neutrophils (TAN). Myeloid cells play a major role in tumor growth through nurturing cancer stem cells by providing growth factors and metabolites, increasing angiogenesis, as well as promoting immune evasion through the creation of an immune-suppressive microenvironment. Immunosuppression in the TME is achieved by preventing critical anti-tumor immune responses by natural killer and T cells within the primary tumor and in metastatic niches. Therapeutic success in targeting myeloid cells in malignancies may prove to be an effective strategy to overcome chemotherapy and immunotherapy limitations. Current therapeutic approaches to target myeloid cells in various cancers include inhibition of their recruitment, alteration of function, or functional re-education to an antitumor phenotype to overcome immunosuppression. In this review, we describe strategies to target TAMs and MDSCs, consisting of single agent therapies, nanoparticle-targeted approaches and combination therapies including chemotherapy and immunotherapy. We also summarize recent molecular targets that are specific to myeloid cell populations in the TME, while providing a critical review of the limitations of current strategies aimed at targeting a single subtype of the myeloid cell compartment. The goal of this review is to provide the reader with an understanding of the critical role of myeloid cells in the TME and current therapeutic approaches including ongoing or recently completed clinical trials

Manifestation of pairing modes in nuclear collisions

We discuss the possible manifestation of pairing dynamics in nuclear collisions beyond the standard quasi-static treatment of pairing correlations. These involve solitonic excitations induced by pairing phase difference of colliding nuclei and pairing dynamic enhancement in the di-nuclear system formed by merging nuclei.Comment: 2 figures, 56th Zakopane Conference On Nuclear Physic

Real-time data acquisition and processing system for MHz repetition rate image sensors

An electro-optic detector is one of the diagnostic setups used in particle accelerators. It employs an electro-optic crystal to encode the longitudinal beam charge profile in the spectrum of a light pulse. The charge distribution is then reconstructed using data captured by a fast spectrometer. The measurement repetition rate should match or exceed the machine bunching frequency, which is often in the range of several MHz. A high-speed optical line detector (HOLD) is a linear camera designed for easy integration with scientific experiments. The use of modern FPGA circuits helps in the efficient collection and processing of data. The solution is based on Xilinx 7-Series FPGA circuits and implements a custom latency-optimized architecture utilizing the AXI4 family of interfaces. HOLD is one of the fastest line cameras in the world. Thanks to its hardware architecture and a powerful KALYPSO sensor from KIT, it outperforms the fastest comparable commercial devices

EcDBS1R4, an antimicrobial peptide effective against Escherichia coli with in vitro fusogenic ability

©2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/)Discovering antibiotic molecules able to hold the growing spread of antimicrobial resistance is one of the most urgent endeavors that public health must tackle. The case of Gram-negative bacterial pathogens is of special concern, as they are intrinsically resistant to many antibiotics, due to an outer membrane that constitutes an effective permeability barrier. Antimicrobial peptides (AMPs) have been pointed out as potential alternatives to conventional antibiotics, as their main mechanism of action is membrane disruption, arguably less prone to elicit resistance in pathogens. Here, we investigate the in vitro activity and selectivity of EcDBS1R4, a bioinspired AMP. To this purpose, we have used bacterial cells and model membrane systems mimicking both the inner and the outer membranes of Escherichia coli, and a variety of optical spectroscopic methodologies. EcDBS1R4 is effective against the Gram-negative E. coli, ineffective against the Gram-positive Staphylococcus aureus and noncytotoxic for human cells. EcDBS1R4 does not form stable pores in E. coli, as the peptide does not dissipate its membrane potential, suggesting an unusual mechanism of action. Interestingly, EcDBS1R4 promotes a hemi-fusion of vesicles mimicking the inner membrane of E. coli. This fusogenic ability of EcDBS1R4 requires the presence of phospholipids with a negative curvature and a negative charge. This finding suggests that EcDBS1R4 promotes a large lipid spatial reorganization able to reshape membrane curvature, with interesting biological implications herein discussed.This research was funded by Fundação para a Ciência e a Tecnologia—Ministério da Ciência, Tecnologia e Ensino Superior (FCT-MCTES, Portugal), Marie Skłodowska-Curie Research and Innovation Staff Exchange (MSCA-RISE, European Union) project INPACT (call H2020-MSCA-RISE-2014, grant agreement 644167), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Brazil), Fundação de Amparo a Pesquisa do Distrito Federal (FAPDF, Brazil) and Fundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul (FUNDECT, Brazil). M.M. and M.R.F. also acknowledge FCT-MCTES fellowships SPRH/BD/128290/2017 and SPRH/BD/100517/2014, respectively.info:eu-repo/semantics/publishedVersio

Mean-risk models using two risk measures: A multi-objective approach

This paper proposes a model for portfolio optimisation, in which distributions are characterised and compared on the basis of three statistics: the expected value, the variance and the CVaR at a specified confidence level. The problem is multi-objective and transformed into a single objective problem in which variance is minimised while constraints are imposed on the expected value and CVaR. In the case of discrete random variables, the problem is a quadratic program. The mean-variance (mean-CVaR) efficient solutions that are not dominated with respect to CVaR (variance) are particular efficient solutions of the proposed model. In addition, the model has efficient solutions that are discarded by both mean-variance and mean-CVaR models, although they may improve the return distribution. The model is tested on real data drawn from the FTSE 100 index. An analysis of the return distribution of the chosen portfolios is presented

What Does DALL-E 2 Know About Radiology?

Generative models, such as DALL-E 2 (OpenAI), could represent promising future tools for image generation, augmentation, and manipulation for artificial intelligence research in radiology, provided that these models have sufficient medical domain knowledge. Herein, we show that DALL-E 2 has learned relevant representations of x-ray images, with promising capabilities in terms of zero-shot text-to-image generation of new images, the continuation of an image beyond its original boundaries, and the removal of elements; however, its capabilities for the generation of images with pathological abnormalities (eg, tumors, fractures, and inflammation) or computed tomography, magnetic resonance imaging, or ultrasound images are still limited. The use of generative models for augmenting and generating radiological data thus seems feasible, even if the further fine-tuning and adaptation of these models to their respective domains are required first