Refine
Year of publication
Document Type
- Article (19)
Has Fulltext
- yes (19)
Is part of the Bibliography
- no (19)
Keywords
- Artificial intelligence (2)
- CT (2)
- Community ecology (2)
- Ecological networks (2)
- Ecology (2)
- Radiomics (2)
- Anemia (1)
- Angiography (1)
- Biomarkers (1)
- Blood (1)
Institute
Ecological networks are more sensitive to plant than to animal extinction under climate change
(2016)
Impacts of climate change on individual species are increasingly well documented, but we lack understanding of how these effects propagate through ecological communities. Here we combine species distribution models with ecological network analyses to test potential impacts of climate change on >700 plant and animal species in pollination and seed-dispersal networks from central Europe. We discover that animal species that interact with a low diversity of plant species have narrow climatic niches and are most vulnerable to climate change. In contrast, biotic specialization of plants is not related to climatic niche breadth and vulnerability. A simulation model incorporating different scenarios of species coextinction and capacities for partner switches shows that projected plant extinctions under climate change are more likely to trigger animal coextinctions than vice versa. This result demonstrates that impacts of climate change on biodiversity can be amplified via extinction cascades from plants to animals in ecological networks.
Background: MicroRNA-21 (miR-21) is up-regulated in tumor tissue of patients with malignant diseases, including hepatocellular carcinoma (HCC). Elevated concentrations of miR-21 have also been found in sera or plasma from patients with malignancies, rendering it an interesting candidate as serum/plasma marker for malignancies. Here we correlated serum miR-21 levels with clinical parameters in patients with different stages of chronic hepatitis C virus infection (CHC) and CHC-associated HCC.
Methodology/Principal Findings: 62 CHC patients, 29 patients with CHC and HCC and 19 healthy controls were prospectively enrolled. RNA was extracted from the sera and miR-21 as well as miR-16 levels were analyzed by quantitative real-time PCR; miR-21 levels (normalized by miR-16) were correlated with standard liver parameters, histological grading and staging of CHC. The data show that serum levels of miR-21 were elevated in patients with CHC compared to healthy controls (P<0.001); there was no difference between serum miR-21 in patients with CHC and CHC-associated HCC. Serum miR-21 levels correlated with histological activity index (HAI) in the liver (r = −0.494, P = 0.00002), alanine aminotransferase (ALT) (r = −0.309, P = 0.007), aspartate aminotransferase (r = −0.495, P = 0.000007), bilirubin (r = −0.362, P = 0.002), international normalized ratio (r = −0.338, P = 0.034) and γ-glutamyltransferase (r = −0.244, P = 0.034). Multivariate analysis revealed that ALT and miR-21 serum levels were independently associated with HAI. At a cut-off dCT of 1.96, miR-21 discriminated between minimal and mild-severe necroinflammation (AUC = 0.758) with a sensitivity of 53.3% and a specificity of 95.2%.
Conclusions/Significance: The serum miR-21 level is a marker for necroinflammatory activity, but does not differ between patients with HCV and HCV-induced HCC.
We demonstrate that momentum-dependent nuclear interactions (MDI) have a large effect on the dynamics and on the observables of high-energy heavy-ion collisions: A soft potential with MDI suppresses pion and kaon yields much more strongly than a local hard potential and results in transverse momenta intermediate between soft and hard local potentials. The collective-flow angles and the deuteron-to-proton ratios are rather insensitive to the MDI. Only simultaneous measurements of these observables can give clues on the nuclear equation of state at densities of interest for supernova collapse and neutron-star stability.
Stopping power and thermalization in relativistic heavy ion collisions is investigated employing the quantum molecular dynamics approach. For heavy systems stopping of the incoming nuclei is predicted, independent of the energy. The influence of the quantum effects and their increasing importance at low energies, is demonstrated by inspection of the mean free path of the nucleons and the n-n collision number. Classical models, which neglect these effects, overestimate the stopping and the thermalization as well as the collective flow and squeeze out. The sensitivity of the transverse and longitudinal momentum transfer to the in-medium cross section and to the pressure is investigated.
The quantum molecular dynamic method is used to study multifragmentation and fragment flow and their dependence on in-medium cross sections, momentum dependent interactions, and the nuclear equation of state, for collisions of 197Au+197Au and 93Nb+93Nb in the bombarding energy regime from 100 to 800A MeV. Time and impact parameter dependence of the fragment formation and their implications for the conjectured liquid-vapor phase transition are investigated. We find that the inclusive fragment mass distribution is independent of the equation of state and exhibits a power-law behavior Y(A)∼A-τ with an exponent τ≊-2.3. True multifragmentation events are found in central collisions for energies Elab∼30–200 MeV/nucleon. The associated light fragment (d,t,α) to proton ratios increase with the multiplicity of charged particles and decrease with energy, in agreement with recent experiments. The calculated absolute charged particle multiplicities, the multiplicities of intermediate mass (A>4) fragments, and their respective rapidity distributions do compare well with recent 4π data, but are quite insensitive to the equation of state. On the other hand, these quantities depend sensitively on the nucleon-nucleon scattering cross section, and can be used to determine σ experimentally. The transverse momentum flow of the complex fragments increases with the stiffness of the equation of state. Reduced (in-medium) n-n scattering cross sections reduce the fragment flow. Momentum dependent interactions increase the fragment flow. It is shown that the measured fragment flow at 200A MeV can be reproduced in the model. We find that also the increase of the px/A values with the fragment mass is in agreement with experiments. The calculated fragment flow is too small as compared to the plastic ball data, if a soft equation of state with in-medium corrections (momentum dependent interactions plus reduced cross sections) is employed. An alternative, most intriguing resolution of the puzzle about the stiffness of the equation of state could be an increase of the scattering cross sections due to precritical scattering in the vicinity of a phase transition.
Nuclear transport models including density- and momentum-dependent mean-field effects are compared to intranuclear-cascade models and tested on recent data on inclusive p-like cross sections for 800A-MeV La+La. We find a remarkable agreement between most model calculations but a systematic disagreement with the measured yield at 20°, possibly indicating a need for modification of nuclear transport properties at high densities.
Aim: The identification of the mechanisms determining spatial variation in biological diversity along elevational gradients is a central objective in ecology and biogeography. Here, we disentangle the direct and indirect effects of abiotic drivers (climatic conditions, and land use) and biotic drivers (vegetation structure and food resources) on functional diversity and composition of bird and bat assemblages along a tropical elevational gradient. Location: Southern slopes of Mt. Kilimanjaro, Tanzania, East Africa. Methods: We counted birds and recorded bat sonotypes on 58 plots distributed in near-natural and anthropogenically modified habitats from 700 to 4,600 m above sea level. For the recorded taxa, we compiled functional traits related to movement, foraging and body size from museum specimens and databases. Further, we recorded mean annual temperature, precipitation, vegetation complexity as well as the number of fruits, flowers, and insect biomass as measures of resource availability on each study site. Results: Using path analyses, we found similar responses of bird and bat functional diversity to the variation in abiotic and biotic drivers along the elevational gradient. In contrast, the functional composition of both taxa showed distinct responses to abiotic and biotic drivers. For both groups, direct temperature effects were most important, followed by resource availability, precipitation and vegetation complexity. Main Conclusions: Our findings indicate that physiological and metabolic constraints imposed by temperature and resource availability determine the functional diversity of bird and bat assemblages, whereas the composition of individual functional traits is driven by taxon-specific processes. Our study illustrates that distinct filtering mechanisms can result in similar patterns of functional diversity along broad environmental gradients. Such differences need to be taken into account when it comes to conserving the functional diversity of flying vertebrates on tropical mountains.
Background: To assess the potential of radiomic features to quantify components of blood in intraaortic vessels to non-invasively predict moderate-to-severe anemia in non-contrast enhanced CT scans. Methods: One hundred patients (median age, 69 years; range, 19–94 years) who received CT scans of the thoracolumbar spine and blood-testing for hemoglobin and hematocrit levels ± 24 h between 08/2018 and 11/2019 were retrospectively included. Intraaortic blood was segmented using a spherical volume of interest of 1 cm diameter with consecutive radiomic analysis applying PyRadiomics software. Feature selection was performed applying analysis of correlation and collinearity. The final feature set was obtained to differentiate moderate-to-severe anemia. Random forest machine learning was applied and predictive performance was assessed. A decision-tree was obtained to propose a cut-off value of CT Hounsfield units (HU). Results: High correlation with hemoglobin and hematocrit levels was shown for first-order radiomic features (p < 0.001 to p = 0.032). The top 3 features showed high correlation to hemoglobin values (p) and minimal collinearity (r) to the top ranked feature Median (p < 0.001), Energy (p = 0.002, r = 0.387), Minimum (p = 0.032, r = 0.437). Median (p < 0.001) and Minimum (p = 0.003) differed in moderate-to-severe anemia compared to non-anemic state. Median yielded superiority to the combination of Median and Minimum (p(AUC) = 0.015, p(precision) = 0.017, p(accuracy) = 0.612) in the predictive performance employing random forest analysis. A Median HU value ≤ 36.5 indicated moderate-to-severe anemia (accuracy = 0.90, precision = 0.80). Conclusions: First-order radiomic features correlate with hemoglobin levels and may be feasible for the prediction of moderate-to-severe anemia. High dimensional radiomic features did not aid augmenting the data in our exemplary use case of intraluminal blood component assessment.
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.