2,884 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Dissecting Extracellular Matrix Internalisation Mechanisms using Functional Genomics
Breast and ovarian malignancies account for one third of female cancers. The role of the stroma in supporting invasive growth in breast cancer has become clear. Breast cancer cells interact and respond to the cues from the surrounding extracellular matrix (ECM). Integrins are main cell adhesion receptors and key players in invasive migration by linking the ECM to the actin cytoskeleton. In addition, integrins mediate distinctive biochemical and biomechanical signals to support cancer invasion. The role of matrix proteases in promoting ECM degradation and cancer dissemination has been extensively studied; however, cancer cells possess additional means to support those processes, such as integrin-mediated ECM endocytosis and consequent degradation in the lysosomes. Internalisation of the extracellular matrix is upregulated in invasive breast cancer. Nonetheless, the mechanisms by which cancer cells regulate this process are poorly understood. We developed a high throughput pH sensitive system to detect ECM uptake. Here, we show that MDA-MB-231 breast cancer cells converge in macropinocytosis to internalise diverse ECM components and we confirm that this process is modulated by PAK1. To unravel which ECM components breast cancer cells internalise in a complex environment (namely, cell derived matrices), we performed mass spectrometry. Proteomic analysis identified Annexin A6, Collagen VI, Tenascin C and fibronectin, among other matrisome proteins, to be internalised by invasive breast cancer cells. Following ECM endocytosis, ECM is targeted for lysosomal degradation. To unravel the molecular mechanisms behind this process, we performed a trafficking screen and identified the AP3 complex, VAMP7, Arf1 and ARFGEF2. Our results suggest that the AP3 complex may regulate ECM-integrin delivery to lysosomes.
To gain more insight on the signalling pathways governing macropinocytosis in breast cancer cells, we performed a kinase and phosphatase screen that unravelled MAP3K1 and PPP2R1A, a subunit of protein phosphatase 2A (PP2A) as relevant regulators of ECM endocytosis. Furthermore, our data suggests that p38 mitogen-activated protein kinase (MAPK) activation upon binding to the ECM is required for ECM macropinocytosis. Outstandingly, inhibiting p38 MAPK led to profound changes in the ability of breast cancer cells to migrate in cell derived matrices. Previous work from the Rainero lab focused on characterising the receptors involved in ECM internalisation; α2β1 integrin was identified as the main regulator of ECM uptake in MDA-MB-231 cells. In particular, α2β1 integrin has been shown to activate p38 MAPK pathway. Taken together, we hypothesise that binding of ECM to α2β1 integrin results in the activation of PAK1 and MAP3K1, which in turn leads to ECM endocytosis. p38 MAPK activity may induce changes in actin polymerisation via PPP2R1A and/or focal adhesion turnover, which consequently promotes ECM macropinocytosis and invasive migration
Investigating the learning potential of the Second Quantum Revolution: development of an approach for secondary school students
In recent years we have witnessed important changes: the Second Quantum Revolution is in the spotlight of many countries, and it is creating a new generation of technologies.
To unlock the potential of the Second Quantum Revolution, several countries have launched strategic plans and research programs that finance and set the pace of research and development of these new technologies (like the Quantum Flagship, the National Quantum Initiative Act and so on).
The increasing pace of technological changes is also challenging science education and institutional systems, requiring them to help to prepare new generations of experts.
This work is placed within physics education research and contributes to the challenge by developing an approach and a course about the Second Quantum Revolution. The aims are to promote quantum literacy and, in particular, to value from a cultural and educational perspective the Second Revolution.
The dissertation is articulated in two parts. In the first, we unpack the Second Quantum Revolution from a cultural perspective and shed light on the main revolutionary aspects that are elevated to the rank of principles implemented in the design of a course for secondary school students, prospective and in-service teachers. The design process and the educational reconstruction of the activities are presented as well as the results of a pilot study conducted to investigate the impact of the approach on students' understanding and to gather feedback to refine and improve the instructional materials.
The second part consists of the exploration of the Second Quantum Revolution as a context to introduce some basic concepts of quantum physics. We present the results of an implementation with secondary school students to investigate if and to what extent external representations could play any role to promote students’ understanding and acceptance of quantum physics as a personal reliable description of the world
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
ACORN: Input Validation for Secure Aggregation
Secure aggregation enables a server to learn the sum of client-held vectors in a privacy-preserving way, and has been successfully applied to distributed statistical analysis and machine learning. In this paper, we both introduce a more efficient secure aggregation construction and extend secure aggregation by enabling input validation, in which the server can check that clients\u27 inputs satisfy required constraints such as , , and bounds. This prevents malicious clients from gaining disproportionate influence on the computed aggregated statistics or machine learning model.
Our new secure aggregation protocol improves the computational efficiency of the state-of-the-art protocol of Bell et al. (CCS 2020) both asymptotically and concretely: we show via experimental evaluation that it results in -X speedups in client computation in practical scenarios. Likewise, our extended protocol with input validation improves on prior work by more than X in terms of client communiation (with comparable computation costs). Compared to the base protocols without input validation, the extended protocols incur only X additional communication, and can process binary indicator vectors of length M, or 16-bit dense vectors of length K, in under s of computation per client
- …