Explore the vast range of titles available.

Recent climate warming has fueled interest into climate-driven range shifts of tree species. A common approach to detect range shifts is to compare the divergent occurrences between juvenile and adult trees along environmental gradients using static data. Divergent occurrences between life stages can, however, also be caused by ontogenetic effects. These include shifts of the viable environmental conditions throughout development ('ontogenetic niche shift') as well as demographic dependencies that constrain the possible occurrence of subsequent life stages. Whether ontogenetic effects are an important driver of divergent occurrences between juvenile and adult trees along large-scale climatic gradients is largely unknown. It is, however, critical in evaluating whether impacts of environmental change can be inferred from static data on life stage occurrences. Here, we first show theoretically, using a two-life stage simulation model, how both temporal range shift and ontogenetic effects can lead to similar divergent occurrences between adults and juveniles (juvenile divergence). We further demonstrate that juvenile divergence can unambiguously be attributed to ontogenetic effects, when juveniles diverge from adults in opposite direction to their temporal shift along the environmental gradient. Second, to empirically test whether ontogenetic effects are an important driver of divergent occurrences across Europe, we use repeated national forest inventories from Sweden, Germany and Spain to assess juvenile divergence and temporal shift for 40 tree species along large-scale climatic gradients. About half of the species-country combinations had significant juvenile divergences along heat sum and water availability gradients. Only a quarter of the tree species had significant detectable temporal shifts within the observation period. Furthermore, significant juvenile divergences were frequently associated with opposite temporal shifts, indicating that ontogenetic effects are a relevant cause of divergent occurrences between life stages. Our study furthers the understanding of ontogenetic effects and challenges the practice of inferring climate change impacts from static data.

In forest communities, light competition is a key process for community assembly. Species' differences in seedling and sapling tolerance to shade cast by overstory trees is thought to determine species composition at late‐successional stages. Most forests are distant from these late‐successional equilibria, impeding a formal evaluation of their potential species composition. To extrapolate competitive equilibria from short‐term data, we therefore introduce the JAB model, a parsimonious dynamic model with interacting size‐structured populations, which focuses on sapling demography including the tolerance to overstory competition. We apply the JAB model to a two‐“species” system from temperate European forests, that is, the shade‐tolerant species Fagus sylvatica L. and the group of all other competing species. Using Bayesian calibration with prior information from external Slovakian national forest inventory (NFI) data, we fit the JAB model to short time series from the German NFI. We use the posterior estimates of demographic rates to extrapolate that F. sylvatica will be the predominant species in 94% of the competitive equilibria, despite only predominating in 24% of the initial states. We further simulate counterfactual equilibria with parameters switched between species to assess the role of different demographic processes for competitive equilibria. These simulations confirm the hypothesis that the higher shade tolerance of F. sylvatica saplings is key for its long‐term predominance. Our results highlight the importance of demographic differences in early life stages for tree species assembly in forest communities.

Universidad de Alcalá

Systems with artificial intelligence components, so-called AI-based systems, have gained considerable attention recently. However, many organizations have issues with achieving production readiness with such systems. As a means to improve certain software quality attributes and to address frequently occurring problems, design patterns represent proven solution blueprints. While new patterns for AI-based systems are emerging, existing patterns have also been adapted to this new context. The goal of this study is to provide an overview of design patterns for AI-based systems, both new and adapted ones. We want to collect and categorize patterns, and make them accessible for researchers and practitioners. To this end, we first performed a multivocal literature review (MLR) to collect design patterns used with AI-based systems. We then integrated the created pattern collection into a web-based pattern repository to make the patterns browsable and easy to find. As a result, we selected 51 resources (35 white and 16 gray ones), from which we extracted 70 unique patterns used for AI-based systems. Among these are 34 new patterns and 36 traditional ones that have been adapted to this context. Popular pattern categories include \"architecture\" (25 patterns), \"deployment\" (16), \"implementation\" (9), or \"security & safety\" (9). While some patterns with four or more mentions already seem established, the majority of patterns have only been mentioned once or twice (51 patterns). Our results in this emerging field can be used by researchers as a foundation for follow-up studies and by practitioners to discover relevant patterns for informing the design of AI-based systems.

Understanding how species abundances are driven by biotic interactions along environmental gradients is a fundamental question in ecology. For abundances at competitive equilibria in Central European forests, a classical ecological theory formulated by Ellenberg (1963) predicts that beech (Fagus sylvatica L.) outcompetes other tree species within a mesic range of soil pH and water levels, while other species prevail under less favorable conditions. While the theory is generally accepted in forest ecology, only certain aspects of it have been substantiated by empirical evidence. Moreover, the demographic processes driving the turnover from beech to other tree species at the extremes of the soil gradients remain mostly unexplained. To address this, we inversely calibrated a parsimonious forest model (JAB model) with a sapling stage and interacting populations with short time series of observed tree abundances from the German national forest inventory. By modelling how demographic rates vary along pH and soil water gradients, we were able to test the prediction that beech naturally predominates only at favorable soil conditions. Moreover, we tested with simulations how the environmental response of demographic rates explains Fagus' changing relative abundance along the soil gradients. Our results largely confirm that Fagus outcompetes other species in a central environmental range. Environmental change of Fagus' relative abundance is primarily explained by environmental variation of its net basal area increment, followed by its competition response at the overstory and at the sapling stage. We found that even though sapling tolerance to shading is the primary mechanism for Fagus predominance, it only plays a secondary role for the environmental variation of its relative abundance. Synthesis: By inverse calibration of a forest population model with demographic rates that respond to the environment, we confirm the predictions of Ellenberg's classical, albeit only partially-evidenced, theory on F. sylvatica's predominance in Central European forests. Furthermore, for the first time, we substantiate the theory by elucidating how the environmental variation in species composition is based in demographic processes. This demonstrates that our approach can be utilized to predict distributions of interacting species and to explain the dynamics between species, as influenced by their environment.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://datadryad.org/stash/dataset/doi:10.5061/dryad.3ffbg79pv

The dynamics and phenotypes of intratumoral myeloid cells during tumor progression are poorly understood. Here we define myeloid cellular states in gliomas by longitudinal single-cell profiling and demonstrate their strict control by the tumor genotype: in isocitrate dehydrogenase (IDH)-mutant tumors, differentiation of infiltrating myeloid cells is blocked, resulting in an immature phenotype. In late-stage gliomas, monocyte-derived macrophages drive tolerogenic alignment of the microenvironment, thus preventing T cell response. We define the IDH-dependent tumor education of infiltrating macrophages to be causally related to a complex re-orchestration of tryptophan metabolism, resulting in activation of the aryl hydrocarbon receptor. We further show that the altered metabolism of IDH-mutant gliomas maintains this axis in bystander cells and that pharmacological inhibition of tryptophan metabolism can reverse immunosuppression. In conclusion, we provide evidence of a glioma genotype-dependent intratumoral network of resident and recruited myeloid cells and identify tryptophan metabolism as a target for immunotherapy of IDH-mutant tumors.

Intrinsic malignant brain tumors, such as glioblastomas are frequently resistant to immune checkpoint blockade (ICB) with few hypermutated glioblastomas showing response. Modeling patient-individual resistance is challenging due to the lack of predictive biomarkers and limited accessibility of tissue for serial biopsies. Here, we investigate resistance mechanisms to anti-PD-1 and anti-CTLA-4 therapy in syngeneic hypermutated experimental gliomas and show a clear dichotomy and acquired immune heterogeneity in ICB-responder and non-responder tumors. We made use of this dichotomy to establish a radiomic signature predicting tumor regression after pseudoprogression induced by ICB therapy based on serial magnetic resonance imaging. We provide evidence that macrophage-driven ICB resistance is established by CD4 T cell suppression and T reg expansion in the tumor microenvironment via the PD-L1/PD-1/CD80 axis. These findings uncover an unexpected heterogeneity of response to ICB in strictly syngeneic tumors and provide a rationale for targeting PD-L1-expressing tumor-associated macrophages to overcome resistance to ICB. Modeling patient-individual resistance to immunotherapy is challenging. Here, the authors use a syngeneic experimental hypermutated orthotopic glioma model to define radiological and biological features that can predict or explain the mechanistic differences between responders and non-responders to immunotherapy.

Background Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder linked to substantial cardiovascular, pulmonary and metabolic morbidity. Recent observations have raised the question that OSA may also contribute to cancer development. This study aimed to evaluate the association between OSA and the 5-year incidence of cancer, cardiometabolic, pulmonary and metabolic disorders in a large real-world hospitalized population. Methods A retrospective analysis of the TriNetX research database was conducted. Two cohorts of 1,524,190 adults with and without OSA, matched for age and sex, were analyzed for incident cancer, chronic ischemic heart disease, chronic obstructive pulmonary disease (COPD), asthma, overweight/obesity, and diabetes mellitus. Risk estimates, risk differences, risk ratios (RR), and odds ratios were calculated; cancer risk was additionally analyzed using Kaplan–Meier curves and log-rank tests. Significance was indicated by p  < 0.05. Results OSA was associated with significantly higher 5-year incidences of all predefined outcomes, including cancer (RR 1.40), chronic ischemic heart disease (RR 1.36), COPD (RR 1.64), asthma (RR 1.96), diabetes mellitus (RR 1.51), and overweight/obesity (RR 2.70) ( p  < 0.001). Conclusions In this large real-world analysis, OSA was associated with increased 5-year risks of incident cancer and major cardiovascular, pulmonary, and metabolic diseases. The magnitude of cancer risk was comparable to that of non-malignant chronic conditions, supporting the concept of OSA as a systemic disorder characterized by multisystem morbidity.

To apply the MB (multiband) excitation and blipped-CAIPI (blipped-controlled aliasing in parallel imaging) techniques in a spin and gradient-echo (SAGE) EPI sequence to improve the slice coverage for vessel architecture imaging (VAI). Both MB excitation and blipped-CAIPI with in-plane parallel imaging were incorporated into a gradient-echo (GE)/spin-echo (SE) EPI sequence for simultaneous tracking of the dynamic MR signal changes in both GE and SE contrasts after the injection of contrast agent. MB and singleband (SB) excitation were compared using a 20-channel head coil at 3 Tesla, and high-resolution MB VAI could be performed in 32 glioma patients. Whole-brain covered high resolution VAI can be achieved after applying multiband excitation with a factor of 2 and in-plane parallel imaging with a factor of 3. The quality of the images resulting from MB acceleration was comparable to those from the SB method: images were reconstructed without any loss of spatial resolution or severe distortions. In addition, MB and SB signal-to-noise ratios (SNR) were similar. A relative low g-factor induced from the MB acceleration method was achieved after using a blipped-CAIPI technique (1.35 for GE and 1.33 for SE imaging). Performing quantitative VAI, we found that, among all VAI parametric maps, microvessel type indicator (MTI), distance map (I) and vascular-induced bolus peak-time shift (VIPS) were highly correlated. Likewise, VAI parametric maps of slope, slope length and short axis were highly correlated. Multiband accelerated SAGE successfully doubles the number of readout slices in the same measurement time when compared to conventional readout sequences. The corresponding VAI parametric maps provide insights into the complexity and heterogeneity of vascular changes in glioma.

Abstract Context Due to the heterogenous clinical symptoms and deficits, the diagnosis of diabetic polyneuropathy (DPN) is still difficult in clinical routines, leading to increased morbidity and mortality. Objective We studied the correlation of phase angle (PhA) of bioelectrical impedance analysis (BIA) with clinical, laboratory, and physical markers of DPN to evaluate PhA as a possible diagnostic method for DPN. Materials and methods In this cross-sectional observational study as part of the Heidelberg Study on Diabetes and Complications, we examined 104 healthy individuals and 205 patients with type 2 diabetes mellitus (T2D), among which 63 had DPN. The PhA was calculated from multifrequency BIA. Nerve conduction studies, quantitative sensory testing (QST) and diffusion-weighted magnetic resonance neurography to determine fractional anisotropy (FA) reflecting peripheral nerve integrity were performed. Results T2D patients with DPN had lower PhA values (5.71 ± 0.10) compared to T2D patients without DPN (6.07 ± 0.08, P = .007, + 6.1%) and healthy controls (6.18 ± 0.08, P < .001, + 7.9%). Confounder-adjusted analyses showed correlations of the PhA with conduction velocities and amplitudes of the peroneal (β=.28; β=.31, P < .001) and tibial nerves (β=.28; β=.32, P < .001), Z-scores of QST (thermal detection β=.30, P < .05) and the FA (β=.60, P < .001). Receiver-operating characteristic analysis showed similar performance of PhA in comparison to the mentioned diagnostic methods. Conclusion The study shows that PhA is, in comparison to other test systems used, at least an equally good and much easier to handle investigator-independent marker for detection of DPN.