Search

Epilepsy is one of the most prevalent neurological diseases. Electroencephalography (EEG) is an essential tool for diagnosing and detecting epilepsy. In this paper, we aimed to provide an accurate system for classifying epileptic EEG signals using an information fusion-based approach. To this effect, an innovative feature-level fusion methodology was presented. First, utilizing the theory of information, the EEG signals were characterized by two similarity indicators, including correntropy and Cauchy–Schwarz divergence. Then, different combining modes, including, stacking, adding, weighted sum, and maximum rules were used to combine the information of both similarity measures. Concurrently, a novel algorithm was also proposed to define the weights of each feature based on the information gain ratio (IGR). Finally, the probabilistic neural network and the k-nearest neighbor were implemented to detect the epileptic conditions. The EEGs of the Bonn database were used to appraise the framework. Promising classification rates were obtained for epilepsy detection. The results revealed the potent ability of the fusion approach based on maximum IGR in the improvement of the classifier performances. [ABSTRACT FROM AUTHOR]

The access to goods and their allocation (who gets what, when, where and how) is one of the analytical problems of the 2009 Earth System Governance (ESG) Science and Implementation Plan (Biermann, in: Earth System Governance project report, IHDP no. 1, International Human Dimensions Program, Bonn). The 2009 Science Plan distinguishes three dimensions of access: fair outcomes, adequate pathways and effective reallocation. This paper answers the question: how have scholars used concepts related to access and allocation in their research related to international aid, trade and investment. In other words, what have we learnt from the environmental governance literature about who gets what, when and why in the areas of international trade, aid or investment? To date there has not been an attempt to synthesise the findings of this research and this is the gap that this paper addresses. The paper first highlights the key themes in the literature on access and allocation from a justice perspective. It analyses the literature of the ESG and related communities over the last 10 years on international aid, trade, and investment, drawing out the important issues and findings. This study found that most papers applied empirical case study methodologies, highlighted how governance deficits contributed to environmental injustice and were normatively premised on the need to solve the particular and case based problems of global inequality related to resource use and distribution. However, existing studies fell short of identifying the conceptual preconditions and ideal parameters for fair access and allocation of environmental resources in international economic systems. Future research is needed on the extent, nature and causes of the underlying normative and structural elements of the international economic systems that entrench unfair access and allocation of environmental goods and services; on the nature and protagonists of environmental rights and duties and on the elements of governance that may guarantee fair access and allocation in international trade, aid and investment. [ABSTRACT FROM AUTHOR]

In this paper, we propose and evaluate Epilepsy-Net, a collection of deep learning EEG signal processing tools to detect epileptic seizures against non-epileptic seizures without any handcrafted features extraction. In the Epilepsy-Net model, the 1D-convolutional neural networks (CNN), the recurrent neural network (RNN) and the attention mechanism are combined, where each algorithm is represented by the ResNet and Inception, the gated recurrent unit and the convolutional block attention module respectively; without any handcrafted features. To the best of our knowledge, Epilepsy-Net is the first EEG signal processing work to detect epileptic seizures by combining the attention mechanism with the Inception deep network algorithm.We validate our Epilepsy-Net through several large public EEG signal datasets. The results of our experiments show that the proposed attention deep learning approach is an effective tool for epilepsy detection using EEG signals with high accuracy of 100%, 99.05% and 98.22% for the Bonn EEG dataset, variant of the Bonn EEG dataset, and CHB-MIT dataset, respectively. [ABSTRACT FROM AUTHOR]

The traditional visual simultaneous localization and mapping (SLAM) systems have a high dependence on static world assumption, which makes them easy to fail to track in dynamic environments. In this paper, we propose a real-time dynamic visual SLAM system (RTDSLAM) based on ORB-SLAM3 to realize accurate pose estimation of the camera in indoor dynamic environments. We regard the static objects in the environments as a complete virtual rigid body and add two motion removal modules to handle the dynamic feature points without the aid of the camera's ego motion. The geometry-based motion removal module utilizes the point correlations and the structural invariance of rigid body to detect sparse dynamic feature points between two keyframes, and the clustering of depth images helps find the complete dynamic regions. Meanwhile, the template-based motion removal module uses template matching to fast track the known moving objects between ordinary frames. The dynamic feature points located on moving objects are removed and only static feature points are reserved for pose estimation. We evaluate our system on public TUM and Bonn datasets, and the comparison with state-of-the-art dynamic visual SLAM systems shows our advantages both in runtime and the accuracy of pose estimation. Besides, the test in real-world scenes shows the effectiveness of our system in dynamic environments. [ABSTRACT FROM AUTHOR]

In recent days, local binary pattern and their variants plays a vital role in classification of EEG signals. Hence, in this paper a novel method for classification of EEG signals in accordance with local binary pattern is proposed. Initially, the EEG signal with 9 points is considered and then the average of the signal points existing at various distances such as 1, 2 and 3 are computed. Then, the computed average value for an EEG segment is compared with the center signal point of an EEG segment and thus yields the binary value 0 or 1. Later, the majority rule is employed resulting in 8 bit binary pattern. Also, the average of EEG segment is compared with center point. The decimal equivalent of the generated binary code is referred as the proposed label. Later, the histogram is generated involving the proposed label and then this histogram are used as the feature for an EEG signal. The experimental results on Bonn and Freiburg dataset shows that the proposed method achieves 75.81% and 94.43%, 72.43% and 89.30% in sensitivity and specificity, respectively. Also, the performance is evaluated in terms of classification accuracy with KNN and SVMclassifier by changing the training and testing data. The results indicate that the proposed method could distinguish the seizure and seizure free EEG signals evidently. [ABSTRACT FROM AUTHOR]

Polarimetric weather radars offer a wealth of new information compared to conventional technology, not only to enhance quantitative precipitation estimation, warnings, and short-term forecasts, but also to improve our understanding of precipitation generating processes and their representation in numerical weather prediction models. To support such research opportunities, this paper describes an open-access dataset between 2014–2019 collected by the polarimetric Doppler X-band weather radar in Bonn (BoXPol), western Germany. To complement this dataset, the technical radar characteristics, scanning strategy and the best-practice for radar data processing are detailed. In addition, an investigation of radar calibration is presented. Reflectivity measurements from the Dual-frequency Precipitation Radar operating on the core satellite of the Global Precipitation Mission are compared to those of BoXPol to provide absolute calibration offsets with the dataset. The Relative Calibration Adjustment technique is applied to identify stable calibration periods. The absolute calibration of differential reflectivity is determined using the vertical scan and provided with the BoxPol dataset. Measurement(s) Radar backscattering of precipitation Technology Type(s) Polarimetric Doppler X-band weather radar [ABSTRACT FROM AUTHOR]

Epilepsy is a psychosocial neurological disorder, which emerges as a major threat to public health. In this age of the internet of things, the smart diagnosis of epilepsy has gained huge research attention with machine learning-based seizure detection in cloud-fog assisted environments. The present paper also proposes a cloud-fog integrated smart neurocare approach, which performs a temporal analysis of raw electroencephalogram (EEG) signals using deep learning to detect the occurrence of epileptic seizures. This patient-independent approach makes use of single-channel EEG signals to achieve real-time and computationally efficient seizure detection at fog layer devices. It employs a maximum variance-based channel selection procedure to select only one channel of raw scalp EEG signals, followed by their filtering and segmentation into various short-duration temporal segments. To analyse EEG patterns, these segments are further fed to the proposed models of convolutional neural network, recurrent neural network and stacked autoencoder deep learning classifiers. The performance analysis through simulation results evidently reveals that the proposed convolutional neural network-based temporal analysis approach performs better than other approaches. It realises an optimum accuracy of 96.43%, sensitivity of 100% and specificity of 93.33% for 30s duration EEG segments of CHB-MIT dataset and achieves 100% accuracy, sensitivity and specificity values for EEG segments of Bonn dataset for 23.6s EEG segments. Thus, the proposed convolutional neural network-based approach emerges as an appropriate method for rapid and accurate detection of epileptic seizures in fog-cloud integrated environment. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM), as one of the core prerequisite technologies for intelligent mobile robots, has attracted much attention in recent years. However, the traditional SLAM systems rely on the static environment assumption, which becomes unstable for the dynamic environment and further limits the real-world practical applications. To deal with the problem, this paper presents a dynamic-environment-robust visual SLAM system named YOLO-SLAM. In YOLO-SLAM, a lightweight object detection network named Darknet19-YOLOv3 is designed, which adopts a low-latency backbone to accelerate and generate essential semantic information for the SLAM system. Then, a new geometric constraint method is proposed to filter dynamic features in the detecting areas, where dynamic features can be distinguished by utilizing the depth difference with Random Sample Consensus (RANSAC). YOLO-SLAM composes the object detection approach and the geometric constraint method in a tightly coupled manner, which is able to effectively reduce the impact of dynamic objects. Experiments are conducted on the challenging dynamic sequences of TUM dataset and Bonn dataset to evaluate the performance of YOLO-SLAM. The results demonstrate that the RMSE index of absolute trajectory error can be significantly reduced to 98.13% compared with ORB-SLAM2 and 51.28% compared with DS-SLAM, indicating that YOLO-SLAM is able to effectively improve stability and accuracy in the highly dynamic environment. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) have dominated as the best performers on almost all computer vision tasks over the past several years. However, it remains a major challenge to deploy these powerful DCNNs in resource-limited environments, such as embedded devices and smartphones. To this end, 1-bit CNNs have emerged as a feasible solution as they are much more resource-efficient. Unfortunately, they often suffer from a significant performance drop compared to their full-precision counterparts. In this paper, we propose a novel Bayesian Optimized compact 1-bit CNNs (BONNs) model, which has the advantage of Bayesian learning, to improve the performance of 1-bit CNNs significantly. BONNs incorporate the prior distributions of full-precision kernels, features, and filters into a Bayesian framework to construct 1-bit CNNs in a comprehensive end-to-end manner. The proposed Bayesian learning algorithms are well-founded and used to optimize the network simultaneously in different kernels, features, and filters, which largely improves the compactness and capacity of 1-bit CNNs. We further introduce a new Bayesian learning-based pruning method for 1-bit CNNs, which significantly increases the model efficiency with very competitive performance. This enables our method to be used in a variety of practical scenarios. Extensive experiments on the ImageNet, CIFAR, and LFW datasets show that BONNs achieve the best in classification performance compared to a variety of state-of-the-art 1-bit CNN models. In particular, BONN achieves a strong generalization performance on the object detection task. [ABSTRACT FROM AUTHOR]

Various research groups are working on the automated detection of epileptic seizures using Electroencephalogram (EEG) data. EEG waveforms are composed of distinct bands of frequencies. Most of the researchers have used a wide range of frequencies or every frequency band of EEG for detection process of epileptic seizures to obtain high accuracy. However, not all frequency bins contain relevant information about seizures, thereby degrading the performance of the detection system. This paper demonstrates the suitability of only delta band (0.5–4 Hz) for the detection of seizures due to epilepsy. The work has been performed in four stages: (1) Short-time Fourier transform (STFT) of EEG data, (2) extraction of delta band from the time–frequency (t–f) plane, (3) calculation of four statistical features (4) performance analysis using Random Forest (RF) classifier. The proposed methodology achieved an average accuracy, specificity and sensitivity of 99.6%, 99.5% and 99.67% respectively between persons suffering from epilepsy and healthy people on Bonn EEG dataset. Proposed work is computationally efficient as it uses only single band which results in small data computation. Its detection time is very short (< 0.5 s) which makes it suitable for real-time clinical application. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals, generated during the neuron firing, are an effective way of predicting such seizure and it is used widely in recent days for classifying and predicting seizure activity. But EEG signals generated during an epileptic seizure are highly nonstationary and dynamic in nature and contain very crucial information about the state of the brain. Due to this randomness, the accuracy of analysis of EEG data by conventional and visual methods is reduced drastically. This paper aims at enhancing epilepsy seizure detection using deep learning models with an FPGA implementation of the short-time Fourier transform block. Detection of seizure has been achieved in the following stages: (1) time–frequency analysis of EEG segments using STFT; (2) extraction of frequency bands and features of interest; and (3) seizure detection using convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM). For this work, the Bonn EEG dataset has been used. The maximum error of ~ 0.13% was encountered while the comparison of STFT output generated via proposed hardware architecture vs the output generated via simulation was done. The average classification accuracy of 93.9% and 97.2% was achieved by CNN and Bi-LSTM models, respectively, considering all frequency bands for epileptic and non-epileptic patients. [ABSTRACT FROM AUTHOR]

The reaction γ p → p π 0 η has been studied with the CBELSA/TAPS detector at the electron stretcher accelerator ELSA in Bonn for incident photon energies from threshold up to 3.1 GeV. This paper has been motivated by the recently claimed observation of a narrow structure in the M N η invariant mass distribution at a mass of 1678 MeV / c 2 . The existence of this structure cannot be confirmed in the present work. Instead, for E γ = 1400–1500 MeV and the cut M p π 0 ≤ 1190 MeV / c 2 a statistically significant structure in the M p η invariant mass distribution near 1700 MeV / c 2 is observed with a width of Γ ≈ 35 MeV / c 2 while the mass resolution is σ res = 5 MeV / c 2 . Increasing the incident photon energy from 1420 to 1540 MeV this structure shifts in mass from ≈ 1700 MeV / c 2 to ≈ 1725 MeV / c 2 ; the width increases to about 50 MeV / c 2 and decreases thereafter. The cross section associated with this structure reaches a maximum of ≈ 100 nb around E γ ≈ 1490 MeV (W ≈ 1920 MeV), which coincides with the p a 0 threshold. Three scenarios are discussed which might be the origin of this structure in the M p η invariant mass distribution. The most likely interpretation is that it is due to a triangular singularity in the γ p → p a 0 → p π 0 η reaction. [ABSTRACT FROM AUTHOR]

Bessenrodt and Ono's work on additive and multiplicative properties of the partition function and DeSalvo and Pak's paper on the log-concavity of the partition function have generated many beautiful theorems and conjectures. In January 2020, the first author gave a lecture at the MPIM in Bonn on a conjecture of Chern–Fu–Tang, and presented an extension (joint work with Neuhauser) involving polynomials. Partial results have been announced. Bringmann, Kane, Rolen, and Tripp provided complete proof of the Chern–Fu–Tang conjecture, following advice from Ono to utilize a recently provided exact formula for the fractional partition functions. They also proved a large proportion of Heim–Neuhauser's conjecture, which is the polynomization of Chern–Fu–Tang's conjecture. We prove several cases, not covered by Bringmann et. al. Finally, we lay out a general approach for proving the conjecture. [ABSTRACT FROM AUTHOR]

Neural network (NN) finds role in variety of applications due to combined effect of feature extraction and classification availability in deep learning algorithms. In this paper, we have chosen SVM, logistic regression machine learning algorithms and NN for EEG signal classification. Two-layer LSTM and four-layer improved NN deep learning algorithms are proposed to improve the performance in EEG classification. Novelty lies in one-dimensional gradient descent activation functions with radial basis operations used in the initial layers of improved NN which help in achieving better performance. Statistical features namely mean, standard deviation, kurtosis and skewness are extracted for input EEG collected from Bonn database and then applied for various classification techniques. Accuracy, precision, recall and F1 score are the performance metrics used for analyzing the algorithms. Improved NN and LSTM give better performance compared to all other architectures. The simulations are carried out with variety of activation functions, optimizers and loss models to analyze the performance using Python in keras. [ABSTRACT FROM AUTHOR]

In this paper, a new epileptic seizure detection method using fuzzy-rules-based sub-band specific features and layered directed acyclic graph support vector machine (LDAG-SVM) is proposed for classification of electroencephalogram (EEG) signals. Wavelet transformation is used to decompose the input EEG signals into various sub-bands. The nonlinear features, namely approximate entropy, largest Lyapunov exponent and correlation dimension, are extracted from each sub-band. In this proposed work, sub-band specific feature subset that is reduced in size and capable of discriminating samples is selected by employing fuzzy rules. For classification purpose, a new LDAG-SVM is used for detecting epileptic seizure. Every sub-band has its own characteristics. If appropriate features which characterize the specific sub-band are selected, then the classification accuracy is improved and computational complexity is reduced. The important advantage of the fuzzy logic is its close relation to human thinking. Due to the lengthy record and intra-professional variability, automation of epileptic detection is inevitable. Fuzzy rules are the natural choice of employing human expertise to build machine learning system. Performances of the proposed methods are evaluated using two different benchmark EEG datasets, namely Bonn and CHB-MIT. The performance measures such as classification accuracy, sensitivity, specificity, execution time and receiver operating characteristics are used to measure and analyze the performances of the proposed classifier. The proposed LDAG-SVM with fuzzy-rules-based selected sub-band specific features provides better performance in terms of improved classification accuracy with reduced execution time compared to existing methods. [ABSTRACT FROM AUTHOR]

The double-spin-polarization observable E for γ → p → → p π 0 has been measured with the CEBAF Large Acceptance Spectrometer (CLAS) at photon beam energies E γ from 0.367 to 2.173 GeV (corresponding to center-of-mass energies from 1.240 to 2.200 GeV ) for pion center-of-mass angles, cos θ π 0 c. m. , between - 0.86 and 0.82. These new CLAS measurements cover a broader energy range and have smaller uncertainties compared to previous CBELSA data and provide an important independent check on systematics. These measurements are compared to predictions as well as new global fits from The George Washington University, Mainz, and Bonn-Gatchina groups. Their inclusion in multipole analyses will allow us to refine our understanding of the single-pion production contribution to the Gerasimov-Drell-Hearn sum rule and improve the determination of resonance properties, which will be presented in a future publication. [ABSTRACT FROM AUTHOR]

A precise identification of the epileptogenic focus in the brain plays a significant role in treating patients suffering from pharmacoresistant focal epilepsy. Various machine learning techniques have been developed recently to aid neurologists in accurate diagnosis for epileptic patients. In this work, the epileptogenic region is identified by detecting focal (F) EEG signals using Fourier-based signal decomposition scheme. The F and non-focal (NF) EEG signals are segregated into various Fourier intrinsic band functions (FIBFs), obtained by dividing the entire bandwidth of the signal into uniform frequency bands, using discrete cosine transform (DCT)-based filter bank. Six features, namely variance, interquartile range, complexity, kurtosis, range, and mean frequency, are calculated from FIBFs of the original EEG signals. In addition to this, complexity, mean frequency, and interquartile range are also extracted from the first derivative of the EEG signals. To reduce computational complexity, the Wilcoxon rank-sum statistical test is employed to check the discriminating ability of the obtained features. Applying a support vector machine (SVM) classifier with tenfold cross-validation scheme on publicly available Bern-Barcelona dataset, the proposed method achieved an average classification accuracy of 99.44% considering 50 signals each from both the categories. The proposed model is also validated on Bonn EEG dataset, and an average accuracy of 99.64% is obtained while discriminating between seizure and non-seizure EEG signals. This method produces better classification results than the existing state-of-the-art algorithms and can be easily implemented in real-time systems. [ABSTRACT FROM AUTHOR]

The coefficient of static friction between solids normally increases with the time they have remained in static contact before the measurement. This phenomenon, known as frictional aging, is at the origin of the difference between static and dynamic friction coefficients but has remained difficult to understand. It is usually attributed to a slow expansion of the area of atomic contact as the interface changes under pressure. This is however challenging to quantify as surfaces have roughness at all length scales. In addition, friction is not always proportional to the contact area. Here we show that the normalized stress relaxation of the surface asperities during frictional contact with a hard substrate is the same as that of the bulk material, regardless of the asperities' size or degree of compression. This result enables us to predict the frictional aging of rough interfaces based on the bulk material properties of two typical polymers: polypropylene and polytetrafluoroethylene. Increase of friction between two solid surfaces in stationary contact over time, known as frictional aging, has been widely observed. Farain and Bonn show that, regardless of surface roughness or degree of compression, the normalized stress relaxation of microcontacts is the same as that of bulk material. [ABSTRACT FROM AUTHOR]

Epileptic seizure is a chronic central nervous system disorder which is characterized by transient event of abnormal and excessive neuronal discharge in the brain. Electroencephalogram (EEG) records the neurological activity in the brain which is commonly used for the identification of epilepsy. However, visual screening of EEG recordings is time consuming, tedious and subjective. Hence, automatic seizure detection becomes urgent in clinical application. Relevant technique in this filed has been developed rapidly with the employment of machine learning and signal processing tools. In this study, we propose a novel method for automatic epileptic EEG identification based upon dual Q-factor signal decomposition derived from tunable Q-factor wavelet transform (TQWT), fast and adaptive multivariate empirical mode decomposition (FA-MVEMD) and neural networks. First, EEG signals are decomposed into high- and low-resonance components using both high-Q-factor and low-Q-factor TQWT jointly. Second, FA-MVEMD decomposes the high- and low-Q-factor components into scale-aligned intrinsic mode components (IMFs). The first two IMFs of the high- and low-Q-factor components are extracted, which contain most of the EEG signals' energy and are considered to be the predominant IMFs. The properties associated with the nonlinear EEG system dynamics are preserved in the predominant IMFs which demonstrate significant difference in EEG system dynamics between normal (healthy), interictal and ictal EEG signals. Third, neural networks are then used to model, identify and classify EEG system dynamics with the predominant IMFs as the input. Finally, experiments on Bonn EEG database are included to assess the effectiveness of the proposed method. The overall average accuracy for binary classification, ternary classification and five-class classification is reported to be 98.54 % , 99.18 % and 97.84 % , respectively, by using tenfold cross-validation. In comparison with other state-of-the-art methods, our algorithm illustrates superior performance, which can serve as a potential candidate for the automatic EEG seizure detection in the clinical application. [ABSTRACT FROM AUTHOR]

EEG signal classification is a key technology for EEG signal processing and identification systems. Dictionary learning has shown excellent performance due to its sparse representation and learning capability. Usually dictionary learning requires sufficient labeled EEG signals to build classification models, and assumes that the data distribution of training and test signals are the same. However, in new EEG signal domain, often only a small amount of signals are labeled, and more are not labeled. At the same time, data dynamicity, confounding factors and strong interclass similarity also seriously disrupt the performance of EEG signal classifier. To this end, a geometric preserving transfer discriminative dictionary learning method called GPTDDL is developed for cross-domain EEG signal classification. Through projected signals of different domains to the common subspace, a shared discriminative dictionary is obtained, which explores the geometric structure information by graph Laplacian regularization and discriminative information by principal component analysis regularization. Benefiting from the discriminative information transferred from source domain, the discriminability of the learned sparse coding of target domain is strengthened. GPTDDL integrates this idea into the framework of LC-KSVD, and learns the subspace and dictionary learning parameters in an iterative strategy. The experimental results on Bonn EEG signal dataset demonstrate the validity of the GPTDDL method. [ABSTRACT FROM AUTHOR]

Highlights the 12th Congress of the International Association for Neo-Latin Studies, held on August 3-9, 2003 in Bonn, Germany. Theme; Attendees; Programs and activities; Keynote speakers.

Scope, historical overview and perspectives: Athineos Philippu, Department of Pharmacology and Toxicology, University of Innsbruck, Austria The eighth pharmacologic-historical Forum was held online in 2022 in Bonn during the Meeting of the DGPT. In this forum the personalities of Hans Dengler, Paul Martini, Manfred Göthert, and Rudolf Buchheim were honoured by describing their lives and scientific achievements. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ornithology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The Glasgow coma scale (GCS) was introduced as a scoring system for patients with impaired consciousness after traumatic brain injury (TBI). Since, it has become the worldwide standard in TBI assessment. The GCS has repeatedly been criticized for its several failures to reflect verbal reaction in intubated patients, and to test brain stem reflexes. Recently, the full outline of unresponsiveness (FOUR) score was introduced, which is composed of four clinically distinct categories of evaluation: eye reaction, motor function, brainstem reflexes and respiratory pattern. This study aims to validate the FOUR score in neurosurgical patients. Methods: FOUR score and GCS were assessed in a consecutive series of neurosurgical patients with severely impaired consciousness (GCS < 9). Their correlation with the 30-day Glasgow outcome score (GOS) was compared. Patients admitted for TBI, spontaneous intracranial hemorrhage (intracerebral hemorrhage, aneurysmal subarachnoid hemorrhage, cerebellar hemorrhage), or malignant middle cerebral artery infarction were included. Results: We assessed a total of 101 patients (mean age = 64y, SD = 36.1y). The area under the curve (AUC) for mortality was 0.768 ( P = 0.0001) for the FOUR Score, and 0.699 ( P = 0.001) for the GCS. For poor outcome (GOS = 2-3) the FOUR score AUC was 0.683 ( P = 0.018), the GCS AUC was 0.682 ( P = 0.019). The FOUR score value for favorable outcome (GOS = 4-5) was 0.748 ( P = 0.001), the corresponding GCS value was 0.704 ( P = 0.002). Conclusions: The FOUR score was more robust than the GCS in predicting mortality after 30 days in neurosurgical patients with severely impaired consciousness. There was no relevant difference in predicting poor and good outcome. [ABSTRACT FROM AUTHOR]

Within the one-nucleon exchange approximation, collinear kinematics and invariant-amplitude method with the one-nucleon-exchange assumption, the elastic deuteron–proton scattering is considered. Calculations of polarisation characteristics of T 20 , κ 0 , T 20 + 2 2 κ 0 vs. k, C yy , K y y , K xz y for the deuteron wave function in coordinate representation for the Argonne v18 potential, are demonstrated. Experimental data are better described by theoretical predictions using the invariant-amplitude method than by the impulse approximation and in collinear kinematics. The obtained theoretical values of the tensor analysing power T 20 and the polarisation transfer coefficient κ 0 in the choice of the deuteron wave function for the fss2 baryon–baryon model, Bonn one-boson-exchange potential, Argonne v18 and Paris potentials, are in good agreement with the experimental data of leading collaborations and reviews. [ABSTRACT FROM AUTHOR]

The quasifree γ → d → π 0 n (p) photon beam asymmetry, Σ , has been measured at photon energies, E γ , from 390 to 610 MeV, corresponding to center of mass energy from 1.271 to 1.424 GeV, for the first time. The data were collected in the A2 hall of the MAMI electron beam facility with the Crystal Ball and TAPS calorimeters covering pion center-of-mass angles from 49 ∘ to 148 ∘ . In this kinematic region, polarization observables are sensitive to contributions from the Δ (1232) and N(1440) resonances. The extracted values of Σ have been compared to predictions based on partial-wave analyses (PWAs) of the existing pion photoproduction database. Our comparison includes the SAID, MAID and Bonn–Gatchina analyses; while a revised SAID fit, including the new Σ measurements, has also been performed. In addition, isospin symmetry is examined as a way to predict π 0 n photoproduction observables, based on fits to published data in the channels π 0 p , π + n and π - p . [ABSTRACT FROM AUTHOR]

To estimate the seroprevalence and temporal course of SARS-CoV-2 neutralizing antibodies, we embedded a multi-tiered seroprevalence survey within an ongoing community-based cohort study in Bonn, Germany. We first assessed anti-SARS-CoV-2 immunoglobulin G levels with an immunoassay, followed by confirmatory testing of borderline and positive test results with a recombinant spike-based immunofluorescence assay and a plaque reduction neutralization test (PRNT). Those with a borderline or positive immunoassay result were retested after 4 to 5 months. At baseline, 4771 persons participated (88% response rate). Between April 24th and June 30th, 2020, seroprevalence was 0.97% (95% CI: 0.72−1.30) by immunoassay and 0.36% (95% CI: 0.21−0.61) when considering only those with two additional positive confirmatory tests. Importantly, about 20% of PRNT+ individuals lost their neutralizing antibodies within five months. Here, we show that neutralizing antibodies are detectable in only one third of those with a positive immunoassay result, and wane relatively quickly. Population-based studies of SARS-CoV-2 seroprevalence are needed to understand levels of immunity and antibody dynamics. Here, the authors show that the seroprevalence in Bonn, Germany was low (<1%) following the first epidemic wave, and that neutralising antibodies waned within a few months. [ABSTRACT FROM AUTHOR]

This article seeks to explain the complicated relationship between human rights and the rule of law in a country that is unable or unwilling to advance the rule of law. Islamic principles and Afghan legal traditions complicates the already complex relationship between the rule of law, human rights and impunity. The rule of law and human rights are two sides of the same principle, the freedom to live in dignity. The rule of law and human rights therefore have an indivisible and intrinsic relationship. The Afghan government's promotion of impunity, for warlords and others accused of war crimes, hampers the advancement of rule of law and human rights and negatively affects the path of reforms in the area of human rights and rule of law in Afghanistan. [ABSTRACT FROM AUTHOR]

The BGOOD experiment at the ELSA facility in Bonn has been commissioned within the framework of an international collaboration. The experiment pursues a systematic investigation of non-strange and strange meson photoproduction, in particular t-channel processes at low momentum transfer. The setup uniquely combines a central almost 4 π acceptance BGO crystal calorimeter with a large aperture forward magnetic spectrometer providing excellent detection of both neutral and charged particles, complementary to other setups such as Crystal Barrel, Crystal Ball, LEPS and CLAS. [ABSTRACT FROM AUTHOR]

Haemophilia A (HA) is caused by a lack or reduced amount of factor VIII protein (FVIII). About one-third of patients with non-severe HA carrying specific missense mutations show discrepant results between FVIII activity (FVIII:C), measured by one-stage or chromogenic two-stage assays. The aim of this study was to elucidate the mechanism underlying the assay discrepancy in vitro and in silico. Thirteen missense mutations in the Factor 8-gene associated with discrepant results in patients were transiently expressed. FVIII:C of the mutations was determined using two one-stage assays (FVIII:C1st, FVIII:CBonn) and a two-stage chromogenic assay (FVIII:Cchr). Furthermore, thrombin generation test (TGT) and in silico analysis were performed to investigate the haemostatic potential as well as the structural impact of the variants, respectively. For the majority (9/13) of the analysed mutations, the discrepancy was confirmed. Moreover, we established a modified TGT protocol for in vitro characterization of FVIII. Hence, TGT parameters were significantly impaired in the group of variants associated with higher chromogenic values. Additionally, in silico analysis revealed the impact of the mutations on FVIII protein structure leading to assay discrepancy. Moreover, the data shows that also among one-stage clotting assays, assay discrepancy is observed. Our results show that for the majority of mutations, application of a global assay like TGT method could help to improve diagnosis or correct assessment of the severity of HA. [ABSTRACT FROM AUTHOR]

Our ability to represent temporal, spatial, and numerical information is critical for understanding the world around us. Given the prominence of quantitative representations in the natural world, numerous cognitive, neurobiological, and developmental models have been proposed as a means of describing how we track quantity. One prominent theory posits that time, space, and number are represented by a common magnitude system, or a common neural locus (i.e., Bonn & Cantlon in Cognitive Neuropsychology, 29(1/2), 149–173, 2012; Cantlon, Platt, & Brannon in Trends in Cognitive Sciences, 13(2), 83–91, 2009; Meck & Church in Animal Behavior Processes, 9(3), 320, 1983; Walsh in Trends in Cognitive Sciences, 7(11), 483–488, 2003). Despite numerous similarities in representations of time, space, and number, an increasing body of literature reveals striking dissociations in how each quantity is processed, particularly later in development. These findings have led many researchers to consider the possibility that separate systems may be responsible for processing each quantity. This review will analyze evidence in favor of a common magnitude system, particularly in infancy, which will be tempered by counter evidence, the majority of which comes from experiments with children and adult participants. After reviewing the current data, we argue that although the common magnitude system may account for quantity representations in infancy, the data do not provide support for this system throughout the life span. We also identify future directions for the field and discuss the likelihood of the developmental divergence model of quantity representation, like that of Newcombe (Ecological Psychology, 2, 147–157, 2014), as a more plausible account of quantity development. [ABSTRACT FROM AUTHOR]

The article discusses the perspectives and activities of the different international agencies regarding harnessing technical and vocational education and training (TVET) for sustainable development. It offers an overview of TVET issues tackled in the in the programme of the United Nations Educational, Scientific and Cultural Organization's International Experts Meeting Learning for Work, Citizenship and Sustainability held in Bonn, Germany from October 25-28, 2004.

The reaction ?p?K0S+ was measured in the photon energy range from threshold up to 2.6 GeV with the SAPHIR detector at the electron stretcher facility, ELSA, in Bonn. Results are presented on the reaction cross-section and the polarization of the S+ as a function of the kaon production angle in the centre-of-mass system, cosTc.m.K, and the photon energy. The cross-section is lower and varies less with photon energy and kaon production angle than that of ?p?K+S0. The S+ is polarized predominantly at cosTc.m.K ˜ 0. The data presented here are more precise than previous ones obtained with SAPHIR and extend the photon energy range to higher values. They are compared to isobar model calculations. [ABSTRACT FROM AUTHOR]

The quickly increasing number of spatio-temporal applications in fields like environmental monitoring, geology and mobile communication is a new challenge to the development of geodatabases. However, the query functionality of today’s geo-information systems is still limited to the thematic attributes of spatial objects and to spatial 2-D objects. This article reports on GeoToolKit, an object-oriented geo-database kernel system developed at Bonn University to support 3-D/4-D geological applications. GeoToolKit is not a GIS-in-a-box package, but rather a library of C ++ classes that allows the incorporation of spatio-temporal functionality within an application. Being a component toolkit, it encourages the development and deployment of re-usable and open software. The history, concepts and implementation of GeoToolKit are discussed in detail. Performance tests underline the practicability of the concepts. Extensions to and experiences with GeoToolKit applications like GeoStore, GeoWeb and WellStore are presented. Finally, we give an outlook on our future research introducing GeoToolKit as a 3-D/4-D database component within a network of distributed and mobile geo-information services. [ABSTRACT FROM AUTHOR]

The article focuses on the study of Neanderthals by palaeoanthropologists. Across the Europe, the researchers are scrambling to unearth fresh bones of Neanderthals that might yield intact genetic material. On July 21-26, 2006, the scientists will gather in Bonn, Germany to mark the 150th anniversary of the discovery of Homo neanderthalensis and to debate all aspects of Neanderthal life. The Institute of Human Paleaentology will unveil its plans to sequence an entire Neanderthal genome.

Information about the meeting on global warming by International climate negotiator in Bonn, Germany in June 2009 is presented. Topics include the various provisions related to climate change , global greenhouse-gas emission, and reduction of emission. The meeting featured various country leaders including Todd Stern and John Holdren.