Control and Local Measurement of the Spin Chemical Potential in a Magnetic Insulator

The spin chemical potential characterizes the tendency of spins to diffuse. Probing the spin chemical potential could provide insight into materials such as magnetic insulators and spin liquids and aid optimization of spintronic devices. Here, we introduce single-spin magnetometry as a generic platform for non-perturbative, nanoscale characterization of spin chemical potentials. We use this platform to investigate magnons in a magnetic insulator, surprisingly finding that the magnon chemical potential can be efficiently controlled by driving the system's ferromagnetic resonance. We introduce a symmetry-based two-fluid theory describing the underlying magnon processes, realize the first experimental determination of the local thermomagnonic torque, and illustrate the detection sensitivity using electrically controlled spin injection. Our results open the way for nanoscale control and imaging of spin transport in mesoscopic spin systems.Comment: 18 pages, 4 figure

HIV-infected T cells are migratory vehicles for viral dissemination

After host entry through mucosal surfaces, HIV-1 disseminates to lymphoid tissues to establish a generalized infection of the immune system. The mechanisms by which this virus spreads among permissive target cells locally during early stages of transmission, and systemically during subsequent dissemination are not known1. In vitro studies suggest that formation of virological synapses (VSs) during stable contacts between infected and uninfected T cells greatly increases the efficiency of viral transfer2. It is unclear, however, if T cell contacts are sufficiently stable in vivo to allow for functional synapse formation under the conditions of perpetual cell motility in epithelial3 and lymphoid tissues4. Here, using multiphoton intravital microscopy (MP-IVM), we examined the dynamic behavior of HIV-infected T cells in lymph nodes (LNs) of humanized mice. We found that most productively infected T cells migrated robustly, resulting in their even distribution throughout the LN cortex. A subset of infected cells formed multinucleated syncytia through HIV envelope (Env)-dependent cell fusion. Both uncoordinated motility of syncytia as well as adhesion to CD4+ LN cells led to the formation of long membrane tethers, increasing cell lengths to up to 10 times that of migrating uninfected T cells. Blocking the egress of migratory T cells from LNs into efferent lymph, and thus interrupting T cell recirculation, limited HIV dissemination and strongly reduced plasma viremia. Thus, we have found that HIV-infected T cells are motile, form syncytia, and establish tethering interactions that may facilitate cell-to-cell transmission through VSs. While their migration in LNs spreads infection locally, T cell recirculation through tissues is important for efficient systemic viral spread, suggesting new molecular targets to antagonize HIV infection

Nanometre-scale probing of spin waves using single-electron spins

Pushing the frontiers of condensed-matter magnetism requires the development of tools that provide real-space, few-nanometre-scale probing of correlated-electron magnetic excitations under ambient conditions. Here we present a practical approach to meet this challenge, using magnetometry based on single nitrogen-vacancy centres in diamond. We focus on spin-wave excitations in a ferromagnetic microdisc, and demonstrate local, quantitative and phase-sensitive detection of the spin-wave magnetic field at ~50 nm from the disc. We map the magnetic-field dependence of spin-wave excitations by detecting the associated local reduction in the disc’s longitudinal magnetization. In addition, we characterize the spin–noise spectrum by nitrogen-vacancy spin relaxometry, finding excellent agreement with a general analytical description of the stray fields produced by spin–spin correlations in a 2D magnetic system. These complementary measurement modalities pave the way towards imaging the local excitations of systems such as ferromagnets and antiferromagnets, skyrmions, atomically assembled quantum magnets, and spin ice.Physic

Exploiting Workflow Languages and Semantics for Validation of Security Policies in IoT Composite Services

Internet of Things (IoT) ecosystems are recently experiencing a significant growth in complexity. Most IoT applications in domains like healthcare, industry, automotive, and smart energy are composed of several interconnected subsystems that produce, collect, process, and exchange a huge amount of data, and that offer composite services to the end users based on these data. This scenario is exacerbated by the dynamism of the IoT device layer, which may be subject to structural or technological changes over time, to cope for example with the need for new sensing/actuation capabilities requirements or with technical issues. Due to the inherent sensitive nature of the data that is typically processed by IoT applications, security represents one of the primary issues to address. It is worth noting that each subsystem integrated within a composite IoT application may have different requirements and enforce different local security policies, and the policies that result globally enforced at the system level may not comply with the existing global requirements. In general, the analysis and validation of security properties in a composite IoT system represents a very complex task, made even more complex by the introduction of new laws and regulations during system life. To cope with the above issues, in this article, we propose a methodology that leverages both workflow languages and semantics in order to enable the validation of the security features offered by a composite IoT system, with the goal of verifying whether they match with global end-user policies and even with national and international laws and rules

A Novel COVID-19 Data Set and an Effective Deep Learning Approach for the De-Identification of Italian Medical Records

In the last years, the need to de-identify privacy-sensitive information within Electronic Health Records (EHRs) has become increasingly felt and extremely relevant to encourage the sharing and publication of their content in accordance with the restrictions imposed by both national and supranational privacy authorities. In the field of Natural Language Processing (NLP), several deep learning techniques for Named Entity Recognition (NER) have been applied to face this issue, significantly improving the effectiveness in identifying sensitive information in EHRs written in English. However, the lack of data sets in other languages has strongly limited their applicability and performance evaluation. To this aim, a new de-identification data set in Italian has been developed in this work, starting from the 115 COVID-19 EHRs provided by the Italian Society of Radiology (SIRM): 65 were used for training and development, the remaining 50 were used for testing. The data set was labelled following the guidelines of the i2b2 2014 de-identification track. As additional contribution, combined with the best performing Bi-LSTM + CRF sequence labeling architecture, a stacked word representation form, not yet experimented for the Italian clinical de-identification scenario, has been tested, based both on a contextualized linguistic model to manage word polysemy and its morpho-syntactic variations and on sub-word embeddings to better capture latent syntactic and semantic similarities. Finally, other cutting-edge approaches were compared with the proposed model, which achieved the best performance highlighting the goodness of the promoted approach