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Global water models (GWMs) simulate the terrestrial water cycle, on the global scale, and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modeling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how state-of-the-art GWMs are designed. We analyze water storage compartments, water flows, and human water use sectors included in 16 GWMs that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to further enhance model improvement, intercomparison, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Seven models used six compartments, while three models (JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPIHM) simulate only water used by humans for the irrigation sector. We conclude that even though hydrologic processes are often based on similar equations, in the end, these equations have been adjusted or have used different values for specific parameters or specific variables. Our results highlight that the predictive uncertainty of GWMs can be reduced through improvements of the existing hydrologic processes, implementation of new processes in the models, and high-quality input data.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
Background: Running is a popular sport with high injury rates. Although risk factors have intensively been investigated, synthesized knowledge about the differences in injury rates of female and male runners is scarce. Objective: To systematically investigate the differences in injury rates and characteristics between female and male runners. Methods: Database searches (PubMed, Web of Science, PEDro, SPORTDiscus) were conducted according to PRISMA guidelines using the keywords “running AND injur*”. Prospective studies reporting running related injury rates for both sexes were included. A random-effects meta-analysis was used to pool the risk ratios (RR) for the occurrence of injuries in female vs. male runners. Potential moderators (effect modifiers) were analysed using meta-regression. Results: After removal of duplicates, 12,215 articles were screened. Thirty-eight studies were included and the OR of 31 could be pooled in the quantitative analysis. The overall injury rate was 20.8 (95% CI 19.9–21.7) injuries per 100 female runners and 20.4 (95% CI 19.7–21.1) injuries per 100 male runners. Meta-analysis revealed no differences between sexes for overall injuries reported per 100 runners (RR 0.99, 95% CI 0.90–1.10, n = 24) and per hours or athlete exposure (RR 0.94, 95% CI 0.69–1.27, n = 6). Female sex was associated with a more frequent occurrence of bone stress injury (RR (for males) 0.52, 95% CI 0.36–0.76, n = 5) while male runners had higher risk for Achilles tendinopathies (RR 1. 86, 95% CI 1.25–2.79, n = 2). Meta-regression showed an association between a higher injury risk and competition distances of 10 km and shorter in female runners (RR 1.08, 95% CI 1.00–1.69). Conclusion: Differences between female and male runners in specific injury diagnoses should be considered in the development of individualised and sex-specific prevention and rehabilitation strategies to manage running-related injuries.
Truffle fungi are well known for their enticing aromas partially emitted by microbes colonizing truffle fruiting bodies. The identity and diversity of these microbes remain poorly investigated, because few studies have determined truffle-associated bacterial communities while considering only a small number of fruiting bodies. Hence, the factors driving the assembly of truffle microbiomes are yet to be elucidated. Here we investigated the bacterial community structure of more than 50 fruiting bodies of the black truffle Tuber aestivum in one French and one Swiss orchard using 16S rRNA gene amplicon high-throughput sequencing. Bacterial communities from truffles collected in both orchards shared their main dominant taxa: while 60% of fruiting bodies were dominated by α-Proteobacteria, in some cases the β-Proteobacteria or the Sphingobacteriia classes were the most abundant, suggesting that specific factors (i.e., truffle maturation and soil properties) shape differently truffle-associated microbiomes. We further attempted to assess the influence in truffle microbiome variation of factors related to collection season, truffle mating type, degree of maturation, and location within the truffle orchards. These factors had differential effects between the two truffle orchards, with season being the strongest predictor of community variation in the French orchard, and spatial location in the Swiss one. Surprisingly, genotype and fruiting body maturation did not have a significant effect on microbial community composition. In summary, our results show, regardless of the geographical location considered, the existence of heterogeneous bacterial communities within T. aestivum fruiting bodies that are dominated by three bacterial classes. They also indicate that factors shaping microbial communities within truffle fruiting bodies differ across local conditions.
Global water models (GWMs) simulate the terrestrial water cycle on the global scale and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modelling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how 16 state-of-the-art GWMs are designed. We analyse water storage compartments, water flows, and human water use sectors included in models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to enhance model intercomparison, improvement, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Six models used six compartments, while four models (DBH, JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water for the irrigation sector. We conclude that, even though hydrological processes are often based on similar equations for various processes, in the end these equations have been adjusted or models have used different values for specific parameters or specific variables. The similarities and differences found among the models analysed in this study are expected to enable us to reduce the uncertainty in multi-model ensembles, improve existing hydrological processes, and integrate new processes.
We present measurements of exclusive ensuremathπ+,0 and η production in pp reactions at 1.25GeV and 2.2GeV beam kinetic energy in hadron and dielectron channels. In the case of π+ and π0 , high-statistics invariant-mass and angular distributions are obtained within the HADES acceptance as well as acceptance-corrected distributions, which are compared to a resonance model. The sensitivity of the data to the yield and production angular distribution of Δ (1232) and higher-lying baryon resonances is shown, and an improved parameterization is proposed. The extracted cross-sections are of special interest in the case of pp → pp η , since controversial data exist at 2.0GeV; we find \ensuremathσ=0.142±0.022 mb. Using the dielectron channels, the π0 and η Dalitz decay signals are reconstructed with yields fully consistent with the hadronic channels. The electron invariant masses and acceptance-corrected helicity angle distributions are found in good agreement with model predictions.
Despite an increasing demand for Burgundy truffles (Tuber aestivum), gaps remain in our understanding of the fungus’ overall lifecycle and ecology. Here, we compile evidence from three independent surveys in Hungary and Switzerland. First, we measured the weight and maturity of 2,656 T. aestivum fruit bodies from a three-day harvest in August 2014 in a highly productive orchard in Hungary. All specimens ranging between 2 and 755 g were almost evenly distributed through five maturation classes. Then, we measured the weight and maturity of another 4,795 T. aestivum fruit bodies harvested on four occasions between June and October 2015 in the same truffière. Again, different maturation stages occurred at varying fruit body size and during the entire fruiting season. Finally, the predominantly unrelated weight and maturity of 81 T. aestivum fruit bodies from four fruiting seasons between 2010 and 2013 in Switzerland confirmed the Hungarian results. The spatiotemporal coexistence of 7,532 small-ripe and large-unripe T. aestivum, which accumulate to ~182 kg, differs from species-specific associations between the size and ripeness that have been reported for other mushrooms. Although size-independent truffle maturation stages may possibly relate to the perpetual belowground environment, the role of mycelial connectivity, soil property, microclimatology, as well as other abiotic factors and a combination thereof, is still unclear. Despite its massive sample size and proof of concept, this study, together with existing literature, suggests consideration of a wider ecological and biogeographical range, as well as the complex symbiotic fungus-host interaction, to further illuminate the hidden development of belowground truffle fruit bodies.
Pathogens possess the ability to adapt and survive in some host species but not in others–an ecological trait known as host tropism. Transmitted through ticks and carried mainly by mammals and birds, the Lyme disease (LD) bacterium is a well-suited model to study such tropism. Three main causative agents of LD, Borrelia burgdorferi, B. afzelii, and B. garinii, vary in host ranges through mechanisms eluding characterization. By feeding ticks infected with different Borrelia species, utilizing feeding chambers and live mice and quail, we found species-level differences in bacterial transmission. These differences localize on the tick blood meal, and specifically complement, a defense in vertebrate blood, and a polymorphic bacterial protein, CspA, which inactivates complement by binding to a host complement inhibitor, Factor H (FH). CspA selectively confers bacterial transmission to vertebrates that produce FH capable of allele-specific recognition. CspA is the only member of the Pfam54 gene family to exhibit host-specific FH-binding. Phylogenetic analyses revealed convergent evolution as the driver of such uniqueness, and that FH-binding likely emerged during the last glacial maximum. Our results identify a determinant of host tropism in Lyme disease infection, thus defining an evolutionary mechanism that shapes host-pathogen associations.
Introduction: Deep brain stimulation (DBS) has become a well-established treatment modality for a variety of conditions over the last decades. Multiple surgeries are an essential part in the postoperative course of DBS patients if nonrechargeable implanted pulse generators (IPGs) are applied. So far, the rate of subclinical infections in this field is unknown. In this prospective cohort study, we used sonication to evaluate possible microbial colonization of IPGs from replacement surgery. Methods: All consecutive patients undergoing IPG replacement between May 1, 2019 and November 15, 2020 were evaluated. The removed hardware was investigated using sonication to detect biofilm-associated bacteria. Demographic and clinical data were analyzed. Results: A total of 71 patients with a mean (±SD) of 64.5 ± 15.3 years were evaluated. In 23 of these (i.e., 32.4%) patients, a positive sonication culture was found. In total, 25 microorganisms were detected. The most common isolated microorganisms were Cutibacterium acnes (formerly known as Propionibacterium acnes) (68%) and coagulase-negative Staphylococci (28%). Within the follow-up period (5.2 ± 4.3 months), none of the patients developed a clinical manifest infection. Discussions/Conclusions: Bacterial colonization of IPGs without clinical signs of infection is common but does not lead to manifest infection. Further larger studies are warranted to clarify the impact of low-virulent pathogens in clinically asymptomatic patients.
The forest, savanna, and grassland biomes, and the transitions between them, are expected to undergo major changes in the future due to global climate change. Dynamic global vegetation models (DGVMs) are very useful for understanding vegetation dynamics under the present climate, and for predicting its changes under future conditions. However, several DGVMs display high uncertainty in predicting vegetation in tropical areas. Here we perform a comparative analysis of three different DGVMs (JSBACH, LPJ-GUESS-SPITFIRE and aDGVM) with regard to their representation of the ecological mechanisms and feedbacks that determine the forest, savanna, and grassland biomes, in an attempt to bridge the knowledge gap between ecology and global modeling. The outcomes of the models, which include different mechanisms, are compared to observed tree cover along a mean annual precipitation gradient in Africa. By drawing on the large number of recent studies that have delivered new insights into the ecology of tropical ecosystems in general, and of savannas in particular, we identify two main mechanisms that need improved representation in the examined DGVMs. The first mechanism includes water limitation to tree growth, and tree–grass competition for water, which are key factors in determining savanna presence in arid and semi-arid areas. The second is a grass–fire feedback, which maintains both forest and savanna presence in mesic areas. Grasses constitute the majority of the fuel load, and at the same time benefit from the openness of the landscape after fires, since they recover faster than trees. Additionally, these two mechanisms are better represented when the models also include tree life stages (adults and seedlings), and distinguish between fire-prone and shade-tolerant forest trees, and fire-resistant and shade-intolerant savanna trees. Including these basic elements could improve the predictive ability of the DGVMs, not only under current climate conditions but also and especially under future scenarios.