IRF3, IRF5 and IRF9 are transcription factors, which play distinct roles in the regulation of antiviral and inflammatory responses. The determinants that mediate IRF-specific enhancer selection are not fully understood. To uncover regions occupied predominantly by IRF3, IRF5 or IRF9, we performed ChIP-seq experiments in activated murine dendritic cells. The identified regions were analysed with respect to the enrichment of DNA motifs, the interferon-stimulated response element (ISRE) and ISRE half-site variants, and chromatin accessibility. Using a machine learning method, we investigated the predictability of IRF-dominance. We found that IRF5-dominant regions differed fundamentally from the IRF3- and IRF9-dominant regions: ISREs were rare, while the NFKB motif and special ISRE half-sites, such as 5′-GAGA-3′ and 5′-GACA-3′, were enriched. IRF3- and IRF9-dominant regions were characterized by the enriched ISRE motif and lower frequency of accessible chromatin. Enrichment analysis and the machine learning method uncovered the features that favour IRF3 or IRF9 dominancy (e.g. a tripartite form of ISRE and motifs for NF-κB for IRF3, and the GAS motif and certain ISRE variants for IRF9). This study contributes to our understanding of how IRF members, which bind overlapping sets of DNA sequences, can initiate signal-dependent responses without activating superfluous or harmful programmes.
Modeling and simulating movement of vehicles in established transportation infrastructures, especially in large urban road networks is an important task. It helps in understanding and handling traffic problems, optimizing traffic regulations and adapting the traffic management in real time for unexpected disaster events. A mathematically rigorous stochastic model that can be used for traffic analysis was proposed earlier by other researchers which is based on an interplay between graph and Markov chain theories. This model provides a transition probability matrix which describes the traffic’s dynamic with its unique stationary distribution of the vehicles on the road network. In this paper, a new parametrization is presented for this model by introducing the concept of two-dimensional stationary distribution which can handle the traffic’s dynamic together with the vehicles’ distribution. In addition, the weighted least squares estimation method is applied for estimating this new parameter matrix using trajectory data. In a case study, we apply our method on the Taxi Trajectory Prediction dataset and road network data from the OpenStreetMap project, both available publicly. To test our approach, we have implemented the proposed model in software. We have run simulations in medium and large scales and both the model and estimation procedure, based on artificial and real datasets, have been proved satisfactory and superior to the frequency based maximum likelihood method. In a real application, we have unfolded a stationary distribution on the map graph of Porto, based on the dataset. The approach described here combines techniques which, when used together to analyze traffic on large road networks, has not previously been reported.
BCI (Brain-Computer Interface) is a technology which goal is to create and manage a connection between the human brain and a computer with the help of EEG signals. In the last decade consumer-grade BCI devices became available thus giving opportunity to develop BCI applications outside of clinical settings. In this paper we use a device called NeuroSky MindWave Mobile. We investigate what type of information can be deducted from the data acquired from this device, and we evaluate whether it can help us in BCI applications. Our methods of processing the data involves feature extraction methods, and neural networks. Specifically, we make experiments with finding patterns in the data by binary and multiclass classification. With these methods we could detect sharp changes in the signal such as blinking patterns, but we could not extract more complex information successfully.
In this article, we introduce a new conception of a family of esport games called Samu Entropy to try to improve dataflow program graphs like the ones that are based on Google's TensorFlow. Currently, the Samu Entropy project specifies only requirements for new esport games to be developed with particular attention to the investigation of the relationship between esport and artificial intelligence. It is quite obvious that there is a very close and natural relationship between esport games and artificial intelligence. Furthermore, the project Samu Entropy focuses not only on using artificial intelligence, but on creating AI in a new way. We present a reference game called Face Battle that implements the Samu Entropy requirements.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.