IntroductionBone marrow (BM) is an immunologically privileged site where activated autoantibody-producing B cells may survive for prolonged periods. We investigated the effect of rituximab (anti-CD20 mAb) in peripheral blood (PB) and BM B-cell and T-cell populations in active rheumatoid arthritis (RA) patients.MethodsActive RA patients received rituximab (1,000 mg) on days 1 and 15. PB (n = 11) and BM (n = 8) aspirates were collected at baseline and at 3 months. We assessed B-cell and T-cell populations using triple-color flow cytometry.ResultsRituximab therapy decreased PB (from a mean 2% to 0.9%, P = 0.022) but not BM (from 4.6% to 3.8%, P = 0.273) CD19+ B cells, associated with a significant reduction in the activated CD19+HLA-DR+ subset both in PB (from 55% to 19%, P = 0.007) and in BM (from 68% to 19%, P = 0.007). Response to rituximab was preceded by a significant decrease in PB and BM CD19+CD27+ memory B cells (P = 0.022). These effects were specific to rituximab since anti-TNF therapy did not reduce total or activated B cells. Rituximab therapy did not alter the number of activated CD4+HLA-DR+ and CD4+CD25+ T cells.ConclusionsRituximab therapy preferentially depletes activated CD19+HLA-DR+ B cells in the PB and BM of active RA patients. Clinical response to rituximab is associated with depletion of CD19+CD27+ memory B cells in PB and BM of RA patients.

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We describe a 62-year-old female with primary Sjögren syndrome and myopathy, severe osteoporosis, and vertebral fractures that were attributed to celiac disease. A year after the diagnosis, she developed a skin nodule on the extensor surface of her right elbow, which was due to an amyloid deposit of AA type. Amyloidosis, although relatively common in some chronic inflammatory diseases, has been uncommon in Sjögren syndrome or celiac disease. Visceral amyloid was not found in this patient.

Advances in the fields of networking, broadband communications and demand for high-fidelity low-latency last-mile communications have rendered as-efficient-as-possible relaying methods more necessary than ever. This paper investigates the possibility of the utilization of cellular-enabled drones as aerial base stations in next-generation cellular networks. Flying ad hoc networks (FANETs) acting as clusters of deployable relays for the on-demand extension of broadband connectivity constitute a promising scenario in the domain of next-generation high-availability communications. Matters of mobility, handover efficiency, energy availability, optimal positioning and node localization as well as respective multi-objective optimizations are discussed in detail, with their core ideas defining the structure of the work at hand. This paper examines improvements to the existing cellular network core to support novel use-cases and lower the operation costs of diverse ad hoc deployments.

TeraFlow proposes a new type of secure, cloudnative Software Defined Networking (SDN) controller that will radically advance the state-of-the-art in beyond 5G networks by introducing novel micro-services architecture, and provide revolutionary features for both flow management (service layer) and optical/microwave network equipment integration (infrastructure layer) by adapting new data models. TeraFlow will also incorporate security using Machine Learning (ML) and forensic evidence for multi-tenancy based on Distributed Ledgers. Finally, this new SDN controller shall be able to integrate with the current Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC) frameworks as well as to other networks. The target pool of TeraFlow stakeholders expands beyond the traditional telecom operators towards edge and hyperscale cloud providers.

The technological advancements of every era of human civilization owe themselves to the materials available at the time. Despite the substantial interest in the discovery of novel materials, materials research remains a very delicate and time‐exhaustive procedure. Over the last 30 years, ab initio computational methods based on density functional theory (DFT) have allowed researchers to explore materials with ease and expect above‐experiment measurement precision. However, DFT‐based detailed investigation of novel materials is generally computationally intensive and greatly time‐consuming. This review presents machine learning methods applied to DFT simulation data to uncover connections between material structure, chemical composition, and magnetization, a target property chosen for its great industrial demand. Models are developed that can partially circumvent the need for simulation, guiding researchers in the design of magnetic materials. Specifically, the Materials Project database is examined and it is concluded that Eu, Gd, Pu, Fe, Np, Mn, U, Cr, Co, and Ce are amongst the most common elements found in magnetic materials, and that materials of the same composition may have different magnetization depending on their space group. A neural network capable of predicting magnetization with a standard error of 8.3 × 10−3 μ B Å−3 is created.

The use of the mischmetal alloy, comprised of La and Ce in 1:3 ratio, as a partial substitute for Sm in the CaCu5-type structure is explored, as a means for...

Int5Gent targets the integration of innovative data plane technology building blocks under a flexible 5G network resource, slice and application orchestration framework, providing a complete 5G system platform for the validation of advance 5G services and Internet of Things (IoT) solutions. The platform can act as the enabler for the transition beyond the current 5G networking capabilities allowing novel and state-ofthe-art data transport and edge processing solutions to be evaluated under a cutting-edge network orchestration framework, with intelligent service allocation and management capabilities. A sample of the envisioned technologies include: flexible multi-Radio Access Technology (multi-RAT) baseband signal processing, millimeter Wave (mmWave)technology solutions at 60GHz and 150GHz bands, hardware-based edge processor with Time Sensitive Networking (TSN), Graphical Processing Unit (GPU)processing capabilities, and elastic Software Defined Networking (SDN)-based photonic data transport. The integration of the technology blocks is performed as part of an overall architecture that promotes edge processing and is orchestrated by a Network Function Virtualization Orchestrator (NFVO) compatible framework with edge node extensions at the network layer and an overlay vertical services application orchestrator at the user plane layer.