The circadian clock regulates diverse physiological processes by maintaining a 24-h gene expression pattern. Genetic and environmental cues that disrupt normal clock rhythms can lead to cancer, yet the extent to which this effect is controlled by the cancer cells versus non-malignant cells in the tumor microenvironment (TME) is not clear. Here we set out to address this question, by selective manipulation of circadian clock genes in the TME. In two different mouse models of cancer we find that expression of the core clock gene
Per2
in the TME is crucial for tumor initiation and metastatic colonization, whereas another core gene,
Per1
, is dispensable. We further show that loss of
Per2
in the TME leads to significant transcriptional changes in response to cancer cell introduction. These changes may contribute to a tumor-suppressive microenvironment. Thus, our work unravels an unexpected protumorigenic role for the core clock gene
Per2
in the TME, with potential implications for therapeutic dosing strategies and treatment regimens.
In this paper, we consider a simple road intersection with traffic light control and suggest a queueing model for the traffic flow in the intersection. The suggested model implements the well-known queue with state-dependent departure rates. Using this model, we define optimal state-dependent scheduling of the traffic lights in the intersection and consider its properties. Activity of the model is illustrated by numerical simulations. It is demonstrated that in practical conditions the suggested scheduling of the traffic lights allows the prevention of traffic jams in the intersection and resolves vehicles queues with reasonable waiting times in the crossing lanes.
In the paper we present a simple algorithm for unsupervised classification of given items by a group of agents. The purpose of the algorithm is to provide fast and computationally light solutions of classification tasks by the randomly chosen agents. The algorithm follows basic techniques of plurality voting and combinatorial stable matching and does not use additional assumptions or information about the levels of the agents' expertise. Performance of the suggested algorithm is illustrated by its application to simulated and real-world datasets, and it was demonstrated that the algorithm provides close to correct classifications. The obtained solutions can be used both separately and as initial classifications in more complicated algorithms.
The article presents a library of MATLAB functions that implement the widely used algorithms of outlier detection. The library includes the outlier tests for univariate and multivariate data sets with an approximately normal distribution. The software library is accompanied by a brief review of the methods for detecting and treating outliers.
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