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The rare decay 𝜂′→𝜋+𝜋−𝑒+𝑒− is studied using a sample of 1.3×109 𝐽/𝜓 events collected with the BESIII detector at BEPCII in 2009 and 2012. The branching fraction is measured with improved precision to be (2.42±0.05stat±0.08syst)×10−3. Due to the inclusion of new data, this result supersedes the last BESIII result on this branching fraction. In addition, the 𝐶𝑃-violating asymmetry in the angle between the decay planes of the 𝜋+𝜋−-pair and the 𝑒+𝑒−-pair is investigated. A measurable value would indicate physics beyond the standard model; the result is 𝒜𝐶𝑃=(2.9±3.7stat±1.1syst)%, which is consistent with the standard model expectation of no 𝐶𝑃-violation. The precision is comparable to the asymmetry measurement in the 𝐾0𝐿→𝜋+𝜋−𝑒+𝑒− decay where the observed (14±2)% effect is driven by a standard model mechanism.
Search for the reaction channel e⁺e⁻ → ηcηπ⁺π⁻ at center-of-mass energies from 4.23 to 4.60 GeV
(2021)
Using data collected with the BESIII detector operating at the Beijing Electron Positron Collider, we search for the process 𝑒+𝑒−→𝜂𝑐𝜂𝜋+𝜋−. The search is performed using five large datasets recorded at center-of-mass energies of 4.23, 4.26, 4.36, 4.42, and 4.60 GeV. The 𝜂𝑐 meson is reconstructed in 16 exclusive decay modes. No signal is observed in the 𝜂𝑐 mass region at any center-of-mass energy. The upper limits on the reaction cross sections are determined to be 6.2, 10.8, 27.6, 22.6 and 23.7 pb at the 90% confidence level at the center-of-mass energies listed above.
Background: Bone age (BA) assessment performed by artificial intelligence (AI) is of growing interest due to improved accuracy, precision and time efficiency in daily routine. The aim of this study was to investigate the accuracy and efficiency of a novel AI software version for automated BA assessment in comparison to the Greulich-Pyle method.
Methods: Radiographs of 514 patients were analysed in this retrospective study. Total BA was assessed independently by three blinded radiologists applying the GP method and by the AI software. Overall and gender-specific BA assessment results, as well as reading times of both approaches, were compared, while the reference BA was defined by two blinded experienced paediatric radiologists in consensus by application of the Greulich-Pyle method.
Results: Mean absolute deviation (MAD) and root mean square deviation (RSMD) were significantly lower between AI-derived BA and reference BA (MAD 0.34 years, RSMD 0.38 years) than between reader-calculated BA and reference BA (MAD 0.79 years, RSMD 0.89 years; p < 0.001). The correlation between AI-derived BA and reference BA (r = 0.99) was significantly higher than between reader-calculated BA and reference BA (r = 0.90; p < 0.001). No statistical difference was found in reader agreement and correlation analyses regarding gender (p = 0.241). Mean reading times were reduced by 87% using the AI system.
Conclusions: A novel AI software enabled highly accurate automated BA assessment. It may improve efficiency in clinical routine by reducing reading times without compromising the accuracy compared with the Greulich-Pyle method.
Objectives: To compare dual-energy CT (DECT) and MRI for assessing presence and extent of traumatic bone marrow edema (BME) and fracture line depiction in acute vertebral fractures. Methods: Eighty-eight consecutive patients who underwent dual-source DECT and 3-T MRI of the spine were retrospectively analyzed. Five radiologists assessed all vertebrae for presence and extent of BME and for identification of acute fracture lines on MRI and, after 12 weeks, on DECT series. Additionally, image quality, image noise, and diagnostic confidence for overall diagnosis of acute vertebral fracture were assessed. Quantitative analysis of CT numbers was performed by a sixth radiologist. Two radiologists analyzed MRI and grayscale DECT series to define the reference standard. Results: For assessing BME presence and extent, DECT showed high sensitivity (89% and 84%, respectively) and specificity (98% in both), and similarly high diagnostic confidence compared to MRI (2.30 vs. 2.32; range 0–3) for the detection of BME (p = .72). For evaluating acute fracture lines, MRI achieved high specificity (95%), moderate sensitivity (76%), and a significantly lower diagnostic confidence compared to DECT (2.42 vs. 2.62, range 0–3) (p < .001). A cutoff value of − 0.43 HU provided a sensitivity of 89% and a specificity of 90% for diagnosing BME, with an overall AUC of 0.96. Conclusions: DECT and MRI provide high diagnostic confidence and image quality for assessing acute vertebral fractures. While DECT achieved high overall diagnostic accuracy in the analysis of BME presence and extent, MRI provided moderate sensitivity and lower confidence for evaluating fracture lines.
BACKGROUND: The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements.
RESULTS: The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF).
CONCLUSION: Here we describe a new, efficient method for the analysis of sparse and heterogeneous time course data with high detection sensitivity and transparency. It is implemented as R package TTCA (transcript time course analysis) and can be installed from the Comprehensive R Archive Network, CRAN. The source code is provided with the Additional file 1.
Species’ functional traits set the blueprint for pair-wise interactions in ecological networks. Yet, it is unknown to what extent the functional diversity of plant and animal communities controls network assembly along environmental gradients in real-world ecosystems. Here we address this question with a unique dataset of mutualistic bird–fruit, bird–flower and insect–flower interaction networks and associated functional traits of 200 plant and 282 animal species sampled along broad climate and land-use gradients on Mt. Kilimanjaro. We show that plant functional diversity is mainly limited by precipitation, while animal functional diversity is primarily limited by temperature. Furthermore, shifts in plant and animal functional diversity along the elevational gradient control the niche breadth and partitioning of the respective other trophic level. These findings reveal that climatic constraints on the functional diversity of either plants or animals determine the relative importance of bottom-up and top-down control in plant–animal interaction networks.
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.