Université libre de Bruxelles

About: Université libre de Bruxelles is a education organization based out in Brussels, Belgium. It is known for research contribution in the topics: Population & Breast cancer. The organization has 24974 authors who have published 56969 publications receiving 2084303 citations. The organization is also known as: ULB.

Immunological mechanisms of vaccine-induced protection against COVID-19 in humans.

Abstract: Most COVID-19 vaccines are designed to elicit immune responses, ideally neutralizing antibodies (NAbs), against the SARS-CoV-2 spike protein. Several vaccines, including mRNA, adenoviral-vectored, protein subunit and whole-cell inactivated virus vaccines, have now reported efficacy in phase III trials and have received emergency approval in many countries. The two mRNA vaccines approved to date show efficacy even after only one dose, when non-NAbs and moderate T helper 1 cell responses are detectable, but almost no NAbs. After a single dose, the adenovirus vaccines elicit polyfunctional antibodies that are capable of mediating virus neutralization and of driving other antibody-dependent effector functions, as well as potent T cell responses. These data suggest that protection may require low levels of NAbs and might involve other immune effector mechanisms including non-NAbs, T cells and innate immune mechanisms. Identifying the mechanisms of protection as well as correlates of protection is crucially important to inform further vaccine development and guide the use of licensed COVID-19 vaccines worldwide.

Cross-modal bias and perceptual fusion with auditory-visual spatial discordance

Abstract: Investigations of situations involving spatial discordance between auditory and visual data which can otherwise be attributed to a common origin have revealed two main phenomena:cross-modal bias andperceptual fusion (or ventriloquism). The focus of the present study is the relationship between these two. The question asked was whether bias occurred only with fusion, as is predicted by some accounts of reactions to discordance, among them those based on cuesubstitution. The approach consisted of having subjects, on each trial, both point to signals in one modality in the presence of conflicting signals in the other modality and produce same-different origin judgments. To avoid the confounding of immediate effects with cumulative adaptation, which was allowed in most previous studies, the direction and amplitude of discordance was varied randomly from trial to trial. Experiment 1, which was a pilot study, showed that both visual bias of auditory localization and auditory bias of visual localization can be observed under such conditions. Experiment 2, which addressed the main question, used a method which controls for the selection involved in separating fusion from no-fusion trials and showed that the attraction of auditory localization by conflicting visual inputs occurs even when fusion is not reported. This result is inconsistent with purely postperceptual views of cross-modal interactions. The question could not be answered for auditory bias of visual localization, which, although significant, was very small in Experiment 1 and fell below significance under the conditions of Experiment 2.

Machine Learning Strategies for Time Series Forecasting

Abstract: The increasing availability of large amounts of historical data and the need of performing accurate forecasting of future behavior in several scientific and applied domains demands the definition of robust and efficient techniques able to infer from observations the stochastic dependency between past and future. The forecasting domain has been influenced, from the 1960s on, by linear statistical methods such as ARIMA models. More recently, machine learning models have drawn attention and have established themselves as serious contenders to classical statistical models in the forecasting community. This chapter presents an overview of machine learning techniques in time series forecasting by focusing on three aspects: the formalization of one-step forecasting problems as supervised learning tasks, the discussion of local learning techniques as an effective tool for dealing with temporal data and the role of the forecasting strategy when we move from one-step to multiple-step forecasting.

Bell’s inequalities versus teleportation: What is nonlocality?

Abstract: The nonlocality responsible for violations of Bell's inequalities is not equivalent to that used in teleportation, although they probably are two aspects of the same physical property. There are mixed states which do not violate any Bell type inequality, but still can be used for teleportation.