The Interplay between Autonomic Nervous System and Inflammation across Systemic Autoimmune Diseases

The autonomic nervous system (ANS) and the immune system are deeply interrelated. The ANS regulates both innate and adaptive immunity through the sympathetic and parasympathetic branches, and an imbalance in this system can determine an altered inflammatory response as typically observed in chronic conditions such as systemic autoimmune diseases. Rheumatoid arthritis, systemic lupus erythematosus, and systemic sclerosis all show a dysfunction of the ANS that is mutually related to the increase in inflammation and cardiovascular risk. Moreover, an interaction between ANS and the gut microbiota has direct effects on inflammation homeostasis. Recently vagal stimulation techniques have emerged as an unprecedented possibility to reduce ANS dysfunction, especially in chronic diseases characterized by pain and a decreased quality of life as well as in chronic inflammation.     

Molecular Interactions of Tannic Acid with Proteins Associated with SARS-CoV-2 Infectivity

The overall impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on our society is unprecedented. The identification of small natural ligands that could prevent the entry and/or replication of the coronavirus remains a pertinent approach to fight the coronavirus disease (COVID-19) pandemic. Previously, we showed that the phenolic compounds corilagin and 1,3,6-tri-O-galloyl-β-D-glucose (TGG) inhibit the interaction between the SARS-CoV-2 spike protein receptor binding domain (RBD) and angiotensin-converting enzyme 2 (ACE2), the SARS-CoV-2 target receptor on the cell membrane of the host organism. Building on these promising results, we now assess the effects of these phenolic ligands on two other crucial targets involved in SARS-CoV-2 cell entry and replication, respectively: transmembrane protease serine 2 (TMPRSS2) and 3-chymotrypsin like protease (3CLpro) inhibitors. Since corilagin, TGG, and tannic acid (TA) share many physicochemical and structural properties, we investigate the binding of TA to these targets. In this work, a combination of experimental methods (biochemical inhibition assays, surface plasmon resonance, and quartz crystal microbalance with dissipation monitoring) confirms the potential role of TA in the prevention of SARS-CoV-2 infectivity through the inhibition of extracellular RBD/ACE2 interactions and TMPRSS2 and 3CLpro activity. Moreover, molecular docking prediction followed by dynamic simulation and molecular mechanics Poisson–Boltzmann surface area (MMPBSA) free energy calculation also shows that TA binds to RBD, TMPRSS2, and 3CLpro with higher affinities than TGG and corilagin. Overall, these results suggest that naturally occurring TA is a promising candidate to prevent and inhibit the infectivity of SARS-CoV-2.     

The BDNF/TrkB Neurotrophin System in the Sensory Organs of Zebrafish

The brain-derived neurotrophic factor (BDNF) was discovered in the last century, and identified as a member of the neurotrophin family. BDNF shares approximately 50% of its amino acid with other neurotrophins such as NGF, NT-3 and NT-4/5, and its linear amino acid sequences in zebrafish (Danio rerio) and human are 91% identical. BDNF functions can be mediated by two categories of receptors: p75NTR and Trk. Intriguingly, BDNF receptors were highly conserved in the process of evolution, as were the other NTs’ receptors. In this review, we update current knowledge about the distribution and functions of the BDNF-TrkB system in the sensory organs of zebrafish. In fish, particularly in zebrafish, the distribution and functions of BDNF and TrkB in the brain have been widely studied. Both components of the system, associated or segregated, are also present outside the central nervous system, especially in sensory organs including the inner ear, lateral line system, retina, taste buds and olfactory epithelium.     

Synthesis and hydrogen adsorption properties of a new iron based porous metal-organic framework

A new metal-organic framework [Fe₃O(OOC-C₆H₄-COO)₃(H₂O)₃]Cl·(H₂O)_x was synthesized with a specific surface area of 2823 m²/g and a lattice parameter of 88.61 Å. Isostructural with MIL-101, this compound exhibits similar hydrogen adsorption properties, with maximum adsorption capacity of 5.1wt.% H at 77 K. The adsorption enthalpy of hydrogen for MIL-101 and ITIM-1 (MIL-101Fe) at zero coverage was calculated for a wide temperature range of 77 K ÷ 324 K, considering corrections for the variation of hydrogen gas entropy with the temperature. The resulted adsorption enthalpy is 9.4 kJ/mol for MIL-101, in excellent agreement with the value reported in literature from microcalorimetric measurements, and a value of 10.4 kJ/mol at zero coverage was obtained for ITIM-1 (MIL-101Fe).     

Polyetherimide-LaNi5 composite films for hydrogen storage applications

This study investigates the preparation of polyetherimide (PEI) – LaNi₅ composites films for hydrogen storage. Prior to the polymer addition, LaNi₅ was ball-milled at different conditions (250, 350, and 450 RPM) and annealed at 500 °C for 1 h under vacuum. The composites were produced with BM-LaNi₅-350 (PEI/LaNi₅-350) and annealed BM-LaNi₅-350 (PEI/LaNi₅-350-TT). Membranes were successfully produced through solvent casting assisted by an ultrasonic bath. The particles dispersion and the film morphology did not change after hydrogenation cycles. In the H₂ sorption experiments at 43 °C and 20 bar, the films stored H₂ without incubation time; both samples reached a capacity of ~0.6 wt%. The H₂ sorption kinetics of PEI/LaNi₅-350 was comparable to that of BM-LaNi₅-350, whereas PEI/LaNi₅-350-TT presented significantly slower kinetics. LaNi₅ oxidation was hindered by PEI, showing that it can be explored to improve metal hydrides air resistance. The results demonstrated that PEI films filled with LaNi₅ are promising materials for hydrogen storage.     

Proactive defragmentation in elastic optical networks under dynamic load conditions

The main weakness of elastic optical networks (EON), under dynamic traffic conditions, stems from spectrum fragmentation. A lot of research efforts have been dedicated during recent years to spectrum defragmentation. In this work, a thorough study about proactive defragmentation is carried out. Effects of the different defragmentation parameters on the EON performance are analyzed, and appropriate values of the defragmentation period, which guarantee suitable network performance while keeping the network control complexity at reasonable values, are obtained by means of extensive simulations. Benefit obtained by applying different defragmentation strategies, in terms of increase in the supported load at a given bandwidth blocking probability, is also reported. Different traffic conditions and network topologies are simulated to assess the validity of the obtained results.     

Fault Tolerance Mechanisms for FPGA-Based Regular Expression Matching

Traditional fault-tolerance techniques relying on spatial and temporal redundancy typically imply high power, delay, and area overheads. Cost-effective solutions often depend on system’s design and hardware platform at hand. Particularly for Field-Programmable Gate Arrays (FPGAs), soft errors on the configuration memory are a significant dependability threat. In this work, we present an extended and comprehensive fault tolerance mechanism especially suited for dealing with configuration faults on FPGA-based systems that must deal multiple failure modes. Each failure mode may present different criticality and probability of occurrence, and these properties are measured and exploited to provide low-cost solutions when compared to standard approaches such as triple modular redundancy. The exploited properties are typically found in critical monitoring systems that may trigger security- or safety-critical alarms and warnings in general. In such systems, failing to trigger an alarm when necessary is frequently regarded as more critical than providing an occasional false alarm. For instance, Regular Expression Matching (REM), a compute-intensive mechanism heavily used to perform Deep Packet Inspection in critical network applications, presents such properties, and it can be greatly accelerated by FPGAs to meet performance constraints in high-throughput networks. Therefore, we use FPGA-based REM engines as a case study to demonstrate the effectiveness of the proposed techniques. Additionally, a mutually-aware placement and scrubbing mechanism is introduced to reduce the repair time, improving the system reliability and availability. Experimental results show that the failure rate and the repair time can be reduced by 95 and 90% respectively while avoiding the costs of triplication.     

An information based feedback control for audio-motor binaural localization

In static scenarios, binaural sound localization is fundamentally limited by front-back ambiguity and distance non-observability. Over the past few years, “active” schemes have been shown to overcome these shortcomings, by combining spatial binaural cues with the motor commands of the sensor. In this context, given a Gaussian prior on the relative position to a source, this paper determines an admissible motion of a binaural head which leads, on average, to the one-step-ahead most informative audio-motor localization. To this aim, a constrained optimization problem is set up, which consists in maximizing the entropy of the next predicted measurement probability density function over a cylindric admissible set. The method is appraised through geometrical arguments, and validated in simulations and on real-life robotic experiments.