Search results for "artificial"
showing 10 items of 7394 documents
Modeling Chronic Toxicity: A Comparison of Experimental Variability With (Q)SAR/Read-Across Predictions
2018
This study compares the accuracy of (Q)SAR/read-across predictions with the experimental variability of chronic lowest-observed-adverse-effect levels (LOAELs) from in vivo experiments. We could demonstrate that predictions of the lazy structure-activity relationships (lazar) algorithm within the applicability domain of the training data have the same variability as the experimental training data. Predictions with a lower similarity threshold (i.e., a larger distance from the applicability domain) are also significantly better than random guessing, but the errors to be expected are higher and a manual inspection of prediction results is highly recommended.
Establishing and validating a new source analysis method using phase.
2017
Electroencephalogram (EEG) measures the brain oscillatory activity non-invasively. The localization of deep brain generators of the electric fields is essential for understanding neuronal function in healthy humans and for damasking specific regions that cause abnormal activity in patients with neurological disorders. The aim of this study was to test whether the phase estimation from scalp data can be reliably used to identify the number of dipoles in source analyses. The steps performed included: i) modeling different phasic oscillatory signals using auto-regressive processes at a particular frequency, ii) simulation of two different noises, namely white and colored noise, having differen…
Single nucleotide polymorphisms in A4GALT spur extra products of the human Gb3/CD77 synthase and underlie the P1PK blood group system.
2018
Contrary to the mainstream blood group systems, P1PK continues to puzzle and generate controversies over its molecular background. The P1PK system comprises three glycosphingolipid antigens: Pk, P1 and NOR, all synthesised by a glycosyltransferase called Gb3/CD77 synthase. The Pk antigen is present in most individuals, whereas P1 frequency is lesser and varies regionally, thus underlying two common phenotypes: P1, if the P1 antigen is present, and P2, when P1 is absent. Null and NOR phenotypes are extremely rare. To date, several single nucleotide polymorphisms (SNPs) have been proposed to predict the P1/P2 status, but it has not been clear how important they are in general and in relation …
Molecular detection of Borrelia burgdorferi sensu lato – An analytical comparison of real-time PCR protocols from five different Scandinavian laborat…
2017
Introduction Lyme borreliosis (LB) is the most common tick transmitted disease in Europe. The diagnosis of LB today is based on the patient A s medical history, clinical presentation and laboratory findings. The laboratory diagnostics are mainly based on antibody detection, but in certain conditions molecular detection by polymerase chain reaction (PCR) may serve as a complement. Aim The purpose of this study was to evaluate the analytical sensitivity, analytical specificity and concordance of eight different real-time PCR methods at five laboratories in Sweden, Norway and Denmark. Method Each participating laboratory was asked to analyse three different sets of samples (reference panels; a…
Response to I. Batinic-Haberle et al.
2016
Letter to the editor.-- et al.
View images with unprecedented resolution in integral microscopy
2018
Integral microscopy is a novel technique that allows the simultaneous capture of multiple perspective images of microscopic samples. This feature is achieved at the cost of a significant reduction of the spatial resolution. In fact, it is assumed that in the best cases the resolution is reduced by a factor that is not smaller than ten, what poses a hard drawback to the utility of the technique. However, to the best of our knowledge, this resolution limitation has never been researched rigorously. For this reason, the aim of this paper is to explore the real limitations in resolution of integral microscopy and to obtain optically, without the need of any image-processing algorithm, perspecti…
Computational modeling of bicuspid aortopathy: Towards personalized risk strategies.
2019
This paper describes current advances on the application of in-silico for the understanding of bicuspid aortopathy and future perspectives of this technology on routine clinical care. This includes the impact that artificial intelligence can provide to develop computer-based clinical decision support system and that wearable sensors can offer to remotely monitor high-risk bicuspid aortic valve (BAV) patients. First, we discussed the benefit of computational modeling by providing tangible examples of in-silico software products based on computational fluid-dynamic (CFD) and finite-element method (FEM) that are currently transforming the way we diagnose and treat cardiovascular diseases. Then…
Automatic sleep scoring: A deep learning architecture for multi-modality time series
2020
Background: Sleep scoring is an essential but time-consuming process, and therefore automatic sleep scoring is crucial and urgent to help address the growing unmet needs for sleep research. This paper aims to develop a versatile deep-learning architecture to automate sleep scoring using raw polysomnography recordings. Method: The model adopts a linear function to address different numbers of inputs, thereby extending model applications. Two-dimensional convolution neural networks are used to learn features from multi-modality polysomnographic signals, a “squeeze and excitation” block to recalibrate channel-wise features, together with a long short-term memory module to exploit long-range co…
On the structural connectivity of large-scale models of brain networks at cellular level
2021
AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the …
Mechanical ventilation alters the development of staphylococcus aureus pneumonia in rabbit
2016
Ventilator-associated pneumonia (VAP) is common during mechanical ventilation (MV). Beside obvious deleterious effects on muco-ciliary clearance, MV could adversely shift the host immune response towards a pro-inflammatory pattern through toll-like receptor (TLRs) up-regulation. We tested this hypothesis in a rabbit model of Staphylococcus aureus VAP. Pneumonia was caused by airway challenge with S. aureus, in either spontaneously breathing (SB) or MV rabbits (n = 13 and 17, respectively). Pneumonia assessment regarding pulmonary and systemic bacterial burden, as well as inflammatory response was done 8 and 24 hours after S. aureus challenge. In addition, ex vivo stimulations of whole blood…