Stunning of neutrophils accounts for the anti-inflammatory effects of clodronate liposomes.

Clodronate liposomes (Clo-Lip) have been widely used to deplete mononuclear phagocytes (MoPh) to study the function of these cells in vivo. Here, we revisited the effects of Clo-Lip together with genetic models of MoPh deficiency, revealing that Clo-Lip exert their anti-inflammatory effects independent of MoPh. Notably, not only MoPh but also polymorphonuclear neutrophils (PMN) ingested Clo-Lip in vivo, which resulted in their functional arrest. Adoptive transfer of PMN, but not of MoPh, reversed the anti-inflammatory effects of Clo-Lip treatment, indicating that stunning of PMN rather than depletion of MoPh accounts for the anti-inflammatory effects of Clo-Lip in vivo. Our data highlight the need for a critical revision of the current literature on the role of MoPh in inflammation.

Rotten Hazelnuts Prediction via Simulation Modeling-A Case Study on the Turkish Hazelnut Sector.

The quality defects of hazelnut fruits comprise changes in morphology and taste, and their intensity mainly depends on seasonal environmental conditions. The strongest off-flavor of hazelnuts is known as rotten defect, whose candidate causal agents are a complex of fungal pathogens, with Diaporthe as the dominant genus. Timely indications on the expected incidence of rotten defect would be essential for buyers to identify areas where hazelnut quality will be superior, other than being useful for farmers to have the timely indications of the risk of pathogens infection. Here, we propose a rotten defect forecasting model, and we apply it in the seven main hazelnut producing municipalities in Turkey. We modulate plant susceptibility to fungal infection according to simulated hazelnut phenology, and we reproduce the key components of the Diaporthe spp. epidemiological cycle via a process-based simulation model. A model sensitivity analysis has been performed under contrasting weather conditions to select most relevant parameters for calibration, which relied on weekly phenological observations and the post-harvest analyses of rotten incidence in the period 2016-2019, conducted in 22 orchards. The rotten simulation model reproduced rotten incidence data in calibration and validation datasets with a mean absolute error below 1.8%. The dataset used for model validation (321 additional sampling locations) has been characterized by large variability of rotten incidence, in turn contributing to decrease the correlation between reference and simulated data (R 2 = 0.4 and 0.21 in West and East Black Sea region, respectively). This denotes the key effect of other environmental and agronomic factors on rotten incidence, which are not yet taken into account by the predictive workflow and will be considered in further improvements. When applied in spatially distributed simulations, the model differentiated the rotten incidence across municipalities, and reproduced the interannual variability of rotten incidence. Our results confirmed that the rotten defect is strictly dependent on precipitation amount and timing, and that plant susceptibility is crucial to trigger fungal infections. Future steps will envisage the application of the rotten simulation model to other hazelnut producing regions, before being operationally used for in-season forecasting activities.

The HADES Yield Prediction System - A Case Study on the Turkish Hazelnut Sector.

Crop yield forecasting activities are essential to support decision making of farmers, private companies and public entities. While standard systems use georeferenced agro-climatic data as input to process-based simulation models, new trends entail the application of machine learning for yield prediction. In this paper we present HADES (HAzelnut yielD forEcaSt), a hazelnut yield prediction system, in which process-based modeling and machine learning techniques are hybridized and applied in Turkey. Official yields in the top hazelnut producing municipalities in 2004-2019 are used as reference data, whereas ground observations of phenology and weather data represent the main HADES inputs. A statistical analysis allows inferring the occurrence and magnitude of biennial bearing in official yields and is used to aid the calibration of a process-based hazelnut simulation model. Then, a Random Forest algorithm is deployed in regression mode using the outputs of the process-based model as predictors, together with information on hazelnut varieties, the presence of alternate bearing in the yield series, and agro-meteorological indicators. HADES predictive ability in calibration and validation was balanced, with relative root mean square error below 20%, and R2 and Nash-Sutcliffe modeling efficiency above 0.7 considering all municipalities together. HADES paves the way for a next-generation yield prediction system, to deliver timely and robust information and enhance the sustainability of the hazelnut sector across the globe.

Locally renewing resident synovial macrophages provide a protective barrier for the joint.

Macrophages are considered to contribute to chronic inflammatory diseases such as rheumatoid arthritis1. However, both the exact origin and the role of macrophages in inflammatory joint disease remain unclear. Here we use fate-mapping approaches in conjunction with three-dimensional light-sheet fluorescence microscopy and single-cell RNA sequencing to perform a comprehensive spatiotemporal analysis of the composition, origin and differentiation of subsets of macrophages within healthy and inflamed joints, and study the roles of these macrophages during arthritis. We find that dynamic membrane-like structures, consisting of a distinct population of CX3CR1+ tissue-resident macrophages, form an internal immunological barrier at the synovial lining and physically seclude the joint. These barrier-forming macrophages display features that are otherwise typical of epithelial cells, and maintain their numbers through a pool of locally proliferating CX3CR1- mononuclear cells that are embedded into the synovial tissue. Unlike recruited monocyte-derived macrophages, which actively contribute to joint inflammation, these epithelial-like CX3CR1+ lining macrophages restrict the inflammatory reaction by providing a tight-junction-mediated shield for intra-articular structures. Our data reveal an unexpected functional diversification among synovial macrophages and have important implications for the general role of macrophages in health and disease.

Discovery of potent and selective PPARα/δ dual antagonists and initial biological studies.

We previously published on the design and synthesis of novel, potent and selective PPARα antagonists suitable for either i.p. or oral in vivo administration for the potential treatment of cancer. Described herein is SAR for a subsequent program, where we set out to identify selective and potent PPARα/δ dual antagonist molecules. Emerging literature indicates that both PPARα and PPARδ antagonism may be helpful in curbing the proliferation of certain types of cancer. This dual antagonism could also be used to study PPARs in other settings. After testing for selective and dual potency, off-target counter screening, metabolic stability, oral bioavailability and associated toxicity, compound 11, the first reported PPARα/δ dual antagonist was chosen for more advanced preclinical evaluation.