Constrained self-organizing feature map to preserve feature extraction topology

In many classification problems, it is necessary to consider the specific location of an n-dimensional space from which features have been calculated. For example, considering the location of features extracted from specific areas of a two-dimensional space, as an image, could improve the understanding of a scene for a video surveillance system. In the same way, the same features extracted from different locations could mean different actions for a 3D HCI system. In this paper, we present a self-organizing feature map able to preserve the topology of locations of an n-dimensional space in which the vector of features have been extracted. The main contribution is to implicitly preserving the topology of the original space because considering the locations of the extracted features and their topology could ease the solution to certain problems. Specifically, the paper proposes the n-dimensional constrained self-organizing map preserving the input topology (nD-SOM-PINT). Features in adjacent areas of the n-dimensional space, used to extract the feature vectors, are explicitly in adjacent areas of the nD-SOM-PINT constraining the neural network structure and learning. As a study case, the neural network has been instantiate to represent and classify features as trajectories extracted from a sequence of images into a high level of semantic understanding. Experiments have been thoroughly carried out using the CAVIAR datasets (Corridor, Frontal and Inria) taken into account the global behaviour of an individual in order to validate the ability to preserve the topology of the two-dimensional space to obtain high-performance classification for trajectory classification in contrast of non-considering the location of features. Moreover, a brief example has been included to focus on validate the nD-SOM-PINT proposal in other domain than the individual trajectory. Results confirm the high accuracy of the nD-SOM-PINT outperforming previous methods aimed to classify the same datasets.

Human naïve regulatory T-cells feature high steady-state turnover and are maintained by IL-7

Naïve FoxP3-expressing regulatory T-cells (Tregs) are essential to control immune responses via continuous replenishment of the activated-Treg pool with thymus-committed suppressor cells. The mechanisms underlying naïve-Treg maintenance throughout life in face of the age-associated thymic involution remain unclear. We found that in adults thymectomized early in infancy the naïve-Treg pool is remarkably well preserved, in contrast to conventional naïve CD4 T-cells. Naïve-Tregs featured high levels of cycling and pro-survival markers, even in healthy individuals, and contrasted with other circulating naïve/memory CD4 T-cell subsets in terms of their strong γc-cytokine-dependent signaling, particularly in response to IL-7. Accordingly, ex-vivo stimulation of naïve-Tregs with IL-7 induced robust cytokine-dependent signaling, Bcl-2 expression, and phosphatidylinositol 3-kinase (PI3K)-dependent proliferation, whilst preserving naïve phenotype and suppressive capacity. Altogether, our data strongly implicate IL-7 in the thymus-independent long-term survival of functional naïve-Tregs, and highlight the potential of targeting the IL-7 pathway to modulate Tregs in different clinical settings.