Istituto Italiano di Tecnologia

About: Istituto Italiano di Tecnologia is a facility organization based out in Genoa, Italy. It is known for research contribution in the topics: Humanoid robot & Robot. The organization has 4561 authors who have published 14595 publications receiving 437558 citations. The organization is also known as: Italian Institute of Technology & IIT.

Role of Distinct Natural Killer Cell Subsets in Anticancer Response.

Abstract: Natural killer (NK) cells, the prototypic member of innate lymphoid cells, are important effectors of anti-cancer immune response. These cells can survey and control tumor initiation due to their capability to recognize and kill malignant cells and to regulate the adaptive immune response via cytokines and chemokines release. However, several studies have shown that tumor infiltrating NK cells associated with advanced disease can have profound functional defects and display protumor activity. This evidence indicates that NK cell behaviour undergoes crucial alterations during cancer progression. Moreover, a further level of complexity is due to the extensive heterogeneity and plasticity of these lymphocytes, implying that different NK cell subsets, endowed with specific phenotypic and functional features, may be involved and play distinct roles in the tumor context. Accordingly, many studies reported the enrichment of selective NK cell subsets within tumor tissue, whereas the underlying mechanisms are not fully elucidated. A malignant microenvironment can significantly impact NK cell activity, by recruiting specific subpopulations and/or influencing their developmental programming or the acquisition of a mature phenotype; in particular, neoplastic, stroma and immune cells or tumor-derived factors take part in these processes. In this review, we will summarize and discuss the recently acquired knowledge on the possible contribution of distinct NK cell subsets in the control and/or progression of solid and haematological malignancies. Moreover, we will address emerging evidence regarding the role of different components of tumor microenvironment on shaping NK cell response.

FALKON: An Optimal Large Scale Kernel Method

Abstract: Kernel methods provide a principled way to perform non linear, nonparametric learning. They rely on solid functional analytic foundations and enjoy optimal statistical properties. However, at least in their basic form, they have limited applicability in large scale scenarios because of stringent computational requirements in terms of time and especially memory. In this paper, we take a substantial step in scaling up kernel methods, proposing FALKON, a novel algorithm that allows to efficiently process millions of points. FALKON is derived combining several algorithmic principles, namely stochastic subsampling, iterative solvers and preconditioning. Our theoretical analysis shows that optimal statistical accuracy is achieved requiring essentially $O(n)$ memory and $O(n\sqrt{n})$ time. An extensive experimental analysis on large scale datasets shows that, even with a single machine, FALKON outperforms previous state of the art solutions, which exploit parallel/distributed architectures.

Structure refinement using precession electron diffraction tomography and dynamical diffraction: tests on experimental data.

Abstract: The recently published method for the structure refinement from three-dimensional precession electron diffraction data using dynamical diffraction theory [Palatinus et al. (2015). Acta Cryst. A71, 235-244] has been applied to a set of experimental data sets from five different samples - Ni2Si, PrVO3, kaolinite, orthopyroxene and mayenite. The data were measured on different instruments and with variable precession angles. For each sample a reliable reference structure was available. A large series of tests revealed that the method provides structure models with an average error in atomic positions typically between 0.01 and 0.02 A. The obtained structure models are significantly more accurate than models obtained by refinement using kinematical approximation for the calculation of model intensities. The method also allows a reliable determination of site occupancies and determination of absolute structure. Based on the extensive tests, an optimal set of the parameters for the method is proposed.

BaAMPs: the database of biofilm-active antimicrobial peptides

Abstract: Antimicrobial peptides (AMPs) are increasingly being considered as novel agents against biofilms. The development of AMP-based anti-biofilm strategies strongly relies on the design of sequences optimized to target specific features of sessile bacterial/fungal communities. Although several AMP databases have been created and successfully exploited for AMP design, all of these use data collected on peptides tested against planktonic microorganisms. Here, an open-access, manually curated database of AMPs specifically assayed against microbial biofilms (BaAMPs) is presented for the first time. In collecting relevant data from the literature an effort was made to define a minimal standard set of essential information including, for each AMP, the microbial species and biofilm conditions against which it was tested, and the specific assay and peptide concentration used. The availability of these data in an organized framework will benefit anti-biofilm research and support the design of novel molecules active against biofilm. BaAMPs is accessible at http://www.baamps.it.