Business Processes for the Crowd Computer

open7noKucherbaev, Pavel; Tranquillini, Stefano; Daniel, Florian; Casati, Fabio; Marchese, Maurizio; Brambilla, Marco; Fraternali, PieroKucherbaev, Pavel; Tranquillini, Stefano; Daniel, Florian; Casati, Fabio; Marchese, Maurizio; Brambilla, Marco; Fraternali, Pier

Antibody correlates of protection from SARS-CoV-2 reinfection prior to vaccination : a nested case-control within the SIREN study

Funding: This study was supported by the U.K. Health Security Agency, the U.K. Department of Health and Social Care (with contributions from the governments in Northern Ireland, Wales, and Scotland), the National Institute for Health Research, and grant from the UK Medical Research Council (grant number MR/W02067X/1). This work was supported by the Francis Crick Institute which receives its core funding from Cancer Research UK (CC2087, CC1283), the UK Medical Research Council (CC2087, CC1283), and the Wellcome Trust (CC2087, CC1283).Objectives To investigate serological differences between SARS-CoV-2 reinfection cases and contemporary controls, to identify antibody correlates of protection against reinfection. Methods We performed a case-control study, comparing reinfection cases with singly infected individuals pre-vaccination, matched by gender, age, region and timing of first infection. Serum samples were tested for anti-SARS-CoV-2 spike (anti-S), anti-SARS-CoV-2 nucleocapsid (anti-N), live virus microneutralisation (LV-N) and pseudovirus microneutralisation (PV-N). Results were analysed using fixed effect linear regression and fitted into conditional logistic regression models. Results We identified 23 cases and 92 controls. First infections occurred before November 2020; reinfections occurred before February 2021, pre-vaccination. Anti-S levels, LV-N and PV-N titres were significantly lower among cases; no difference was found for anti-N levels. Increasing anti-S levels were associated with reduced risk of reinfection (OR 0·63, CI 0·47-0·85), but no association for anti-N levels (OR 0·88, CI 0·73-1·05). Titres >40 were correlated with protection against reinfection for LV-N Wuhan (OR 0·02, CI 0·001–0·31) and LV-N Alpha (OR 0·07, CI 0·009–0·62). For PV-N, titres >100 were associated with protection against Wuhan (OR 0·14, CI 0·03–0·64) and Alpha (0·06, CI 0·008–0·40). Conclusions Before vaccination, protection against SARS-CoV-2 reinfection was directly correlated with anti-S levels, PV-N and LV-N titres, but not with anti-N levels. Detectable LV-N titres were sufficient for protection, whilst PV-N titres >100 were required for a protective effect. Trial registration number ISRCTN11041050Publisher PDFPeer reviewe

A GSM-based approach for monitoring cross-organization business processes using smart objects

The execution of cross-organization business processes often implies the exchange of physical goods without necessarily changing the ownership of such goods. Typical examples are logistic processes where goods are managed by shipping companies that are not the owner of the goods. To ensure that these goods are properly handled, while the service is executed, a monitoring system needs to be put in place. The goal of this paper is to propose a novel approach for monitoring physical goods while executing cross-organization business processes. The approach envisions the usage of Smart Objects attached to the physical goods, or to their containers. To this aim, an extension of the Guard-Stage-Milestone framework is proposed to allow the Smart Objects to monitor the process execution and take into account the limitations of their power and computational resources

k-Nearest neighbour local linear prediction of scalp EEG activity during intermittent photic stimulation

none5noThe characterization of the EEG response to photic stimulation (PS) is an important issue with significant clinical relevance. This study aims to quantify and map the complexity of the EEG during PS, where complexity is measured as the degree of unpredictability resulting from local linear prediction. EEG activity was recorded with eyes closed (EC) and eyes open (EO) during resting and PS at 5, 10, and 15 Hz in a group of 30 healthy subjects and in a case-report of a patient suffering from cerebral ischemia. The mean squared prediction error (MSPE) resulting from k-nearest neighbour local linear prediction was calculated in each condition as an index of EEG unpredictability. The linear or nonlinear nature of the system underlying EEG activity was evaluated quantifying MSPE as a function of the neighbourhood size during local linear prediction, and by surrogate data analysis as well. Unpredictability maps were obtained for each subject interpolating MSPE values over a schematic head representation. Results on healthy subjects evidenced: (i) the prevalence of linear mechanisms in the generation of EEG dynamics, (ii) the lower predictability of EO EEG, (iii) the desynchronization of oscillatory mechanisms during PS leading to increased EEG complexity, (iv) the entrainment of alpha rhythm during EC obtained by 10 Hz PS, and (v) differences of EEG predictability among different scalp regions. Ischemic patient showed different MSPE values in healthy and damaged regions. The EEG predictability decreased moving from the early acute stage to a stage of partial recovery. These results suggest that nonlinear prediction can be a useful tool to characterize EEG dynamics during PS protocols, and may consequently constitute a complement of quantitative EEG analysis in clinical applications.Erla, Silvia; Faes, Luca; Tranquillini, Enzo; Orrico, Daniele; Nollo, GiandomenicoErla, Silvia; Faes, Luca; Tranquillini, Enzo; Orrico, Daniele; Nollo, Giandomenic

Multivariate autoregressive model with instantaneous effects to improve brain connectivity estimation

Evaluation of brain connectivity in the frequency domain is based on prior multivariate autoregressive (MVAR) model identification from multichannel neurological time series. The MVAR model commonly used in neuroscience applications accounts only for lagged effects among the time series and forsakes instantaneous effects. However, zero-lag interactions are likely to occur among simultaneously recorded neural signals, and the impact of their exclusion on connectivity measures has not been investigated yet. In this study we propose the use of an extended MVAR model including instantaneous effects, and compare its performance to that of the traditional MVAR approach using the Partial Directed Coherence (PDC). We show by simulations that, in presence of zero-lag correlations, the PDC derived from traditional MVAR modeling may produce misleading frequency domain connectivity evaluation, and that in such situations the correct connectivity pattern is recovered using the extended MVAR model. Then we provide examples of multichannel EEG recordings in which instantaneous effects are found to be far from negligible, and thus extended MVAR modeling seems more suitable to elucidate direction and strength of the interactions among EEG rhythms

SmartCrowd: A Workflow Framework for Complex Crowdsourcing Tasks

Over the past decade, a number of frameworks have been introduced to support different crowdsourcing tasks. However, complex creative tasks have remained out of reach for workflow modeling. Unlike typical tasks, creative tasks are often interdependent, requiring human cognitive ability and team collaboration. The crowd workers are required not only to perform typical tasks, but also to participate in the analysis and manipulation of complex tasks, hence the number and execution order of tasks are unknown until runtime. Thus, it is difficult to model this kind of complex tasks by using existing workflow approaches. Therefore, we propose a workflow modeling approach based on state machine to design crowdsourcing model that can be translated into SCXML code and executed by an open source engine. This approach and engine are embodied in SmartCrowd. Through two evaluations, we found that SmartCrowd can provide support for complex crowdsourcing tasks, especially on creative tasks. Moreover, we introduce a set of basic design patterns, and by employing them to compose complex patterns, our framework can support more crowdsourcing research.</p