Fine-Tuning Nonhomogeneous Regression for Probabilistic Precipitation Forecasts: Unanimous Predictions, Heavy Tails, and Link Functions

Raw ensemble forecasts of precipitation amounts and their forecast uncertainty have large errors, especially in mountainous regions where the modeled topography in the numerical weather prediction model and real topography differ most. Therefore, statistical postprocessing is typically applied to obtain automatically corrected weather forecasts. This study applies the nonhomogenous regression framework as a state-of-the-art ensemble postprocessing technique to predict a full forecast distribution and improves its forecast performance with three statistical refinements. First of all, a novel split-type approach effectively accounts for unanimous zero precipitation predictions of the global ensemble model of the ECMWF. Additionally, the statistical model uses a censored logistic distribution to deal with the heavy tails of precipitation amounts. Finally, it is investigated which are the most suitable link functions for the optimization of regression coefficients for the scale parameter. These three refinements are tested for 10 stations in a small area of the European Alps for lead times from +24 to +144 h and accumulation periods of 24 and 6 h. Together, they improve probabilistic forecasts for precipitation amounts as well as the probability of precipitation events over the default postprocessing method. The improvements are largest for the shorter accumulation periods and shorter lead times, where the information of unanimous ensemble predictions is more important. </jats:p

A Sorting Hat For Clusters. Dynamic Provisioning of Compute Nodes for Colocated Large Scale Computational Research Infrastructures

Current large scale computational research infrastructures are composed of multitudes of compute nodes fitted with similar or identical hardware. For practical purposes, the deployment of the software operating environment to each compute node is done in an automated fashion. If a data centre hosts more than one of these systems – for example cloud and HPC clusters – it is beneficial to use the same provisioning method for all of them. The uniform provisioning approach unifies administration of the various systems and allows flexible dedication and reconfiguration of computational resources. In particular, we will highlight the requirements on the underlying network infrastructure for unified remote boot but segregated service operations. Building upon this, we will present the Boot Selection Service, allowing for the addition, removal or rededication of a node to a given research infrastructure with a simple reconfiguration

Occurrence of different Cacopsylla species in apple orchards in South Tyrol (Italy) and detection of apple proliferation phytoplasma in Cacopsylla melanoneura and Cacopsylla picta: (Hemiptera: Psylloidea)

Preventing the diffusion of phytoplasma associated diseases until now is based mainly on indirect control measurements against the transmitting insect vectors. Apple proliferation, one of the economically most important pests in European apple cultivation is caused by the apple proliferation (AP) phytoplasma (‘Candidatus Phytoplasma mali’), which is spread by the psyllids Cacopsylla (C.) picta (Foerster, 1848) and C. melanoneura (Foerster, 1848). Current control measures primarily comprise treatments against these AP phytoplasma transmitting vectors. The surveillance of C. picta and C. melanoneura population dynamics, as well as the determination of their infection rate in the field are crucial prerequisites to develop suitable and appropriate strategies to limit further spread of AP phytoplasma. Furthermore, the analysis of the species composition of the genus Cacopsylla present in apple orchards provides important information about the presence of other insect vectors potentially involved in spreading AP or other diseases. During an intensive monitoring program realized in the valleys of Val Venosta and Burggraviato (South Tyrol, Italy), the hotspots of apple proliferation epidemics, over 13,000 Cacopsylla individuals were captured and the occurrence of 16 species of the genus Cacopsylla was confirmed. The presence of C.&nbsp;picta was recorded in more than 50% of the investigated apple orchards and the natural infection rate of this vector was about 21% in a three-year average. Conversely, C. melanoneura was confirmed in more than 90% of the investigated sites but its low infection rate of about 1 % further supports that it plays a rather secondary role in spreading AP phytoplasma in South Tyrol

Insights from a Murine Aortic Transplantation Model

Transplant vasculopathy (TV) represents a major obstacle to long-term graft survival and correlates with severity of ischemia reperfusion injury (IRI). Donor administration of the nitric oxide synthases (NOS) co-factor tetrahydrobiopterin has been shown to prevent IRI. Herein, we analysed whether tetrahydrobiopterin is also involved in TV development. Using a fully allogeneic mismatched (BALB/c to C57BL/6) murine aortic transplantation model grafts subjected to long cold ischemia time developed severe TV with intimal hyperplasia (α-smooth muscle actin positive cells in the neointima) and endothelial activation (increased P-selectin expression). Donor pretreatment with tetrahydrobiopterin significantly minimised these changes resulting in only marginal TV development. Severe TV observed in the non-treated group was associated with increased protein oxidation and increased occurrence of endothelial NOS monomers in the aortic grafts already during graft procurement. Tetrahydrobiopterin supplementation of the donor prevented all these early oxidative changes in the graft. Non-treated allogeneic grafts without cold ischemia time and syngeneic grafts did not develop any TV. We identified early protein oxidation and impaired endothelial NOS homodimer formation as plausible mechanistic explanation for the crucial role of IRI in triggering TV in transplanted aortic grafts. Therefore, targeting endothelial NOS in the donor represents a promising strategy to minimise TV

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Bayesian Optimization of a Laser-Plasma Accelerator

Generating high-quality laser-plasma accelerated electron beams requires carefully balancing a plethora of physical effects and is therefore challenging—both conceptually and in experiments. Here, we use Bayesian optimization of key laser and plasma parameters to flatten the longitudinal phase space of an ionization-injected electron bunch via optimal beam loading. We first study the concept with particle-in-cell simulations and then demonstrate it in experiments. Starting from an arbitrary set point, the plasma accelerator autonomously tunes the beam energy spread to the subpercent level at 254 MeV and 4.7 pC/MeV spectral density. Finally, we study a robust regime, which improves the stability of the laser-plasma accelerator and delivers sub-five-percent rms energy spread beams for 90% of all shots