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Dimitrios Toumpanakis,1 Constantinos Glynos,1 Pinelopi Schoini,2 Vyronia Vassilakopoulou,1 Athanasia Chatzianastasiou,1 Maria Dettoraki,1 Eleftheria Mizi,1 Dionysios Tsoukalas,1 Fotis Perlikos,1 Christina Magkou,3 Andreas Papapetropoulos,4,5 Theodoros Vassilakopoulos1,6 1“Marianthi Simou” Applied Biomedical Research and Training Center, Medical School, National and Kapodistrian University of Athens, Evangelismos Hospital, Athens, Greece; 2 4th Respiratory Clinic, “Sotiria” General Hospital for Thoracic Diseases of Athens, Athens, Greece; 3Department of Pathology, Evangelismos Hospital, Athens, Greece; 4Biomedical Research Foundation of the Academy of Athens, Athens, Greece; 5Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Athens, Greece; 6Department of Physiology, Medical School, National and Kapodistrian University of Athens, Athens, GreeceCorrespondence: Dimitrios Toumpanakis, “Marianthi Simou” Applied Biomedical Research and Training Center, Medical School, National and Kapodistrian University of Athens, Evangelismos Hospital, 3rd Ploutarchou Street, 2nd Floor, Athens, 10675, Greece, Tel +302107235521, Email dtoumpanakis@yahoo.grIntroduction: Resistive breathing (RB) is characterized by forceful contractions of the inspiratory muscles, mainly the diaphragm, resulting in large negative intrathoracic pressure and mechanical stress imposed on the lung. We have shown that RB induces lung injury in healthy animals. Whether RB exerts additional injurious effects when added to pulmonary or extrapulmonary lung injury is unknown. Our aim was to study the synergistic effect of RB on lipopolysaccharide (LPS)-induced lung injury.Methods: C57BL/6 mice inhaled an LPS aerosol (10mg/3mL) or received an intraperitoneal injection of LPS (10 mg/kg). Mice were then anaesthetized, the trachea was surgically exposed, and a nylon band of a specified length was sutured around the trachea, to provoke a reduction of the surface area at 50%. RB through tracheal banding was applied for 24 hours. Respiratory system mechanics were measured, BAL was performed, and lung sections were evaluated for histological features of lung injury.Results: LPS inhalation increased BAL cellularity, mainly neutrophils (p < 0.001 to ctr), total protein and IL-6 in BAL (p < 0.001 and p < 0.001, respectively) and increased the lung injury score (p = 0.001). Lung mechanics were not altered. Adding RB to inhaled LPS further increased BAL cellularity (p < 0.001 to LPS inh.), total protein (p = 0.016), lung injury score (p = 0.001) and increased TNFa levels in BAL (p = 0.011). Intraperitoneal LPS increased BAL cellularity, mainly macrophages (p < 0.001 to ctr.), total protein levels (p = 0.017), decreased static compliance (p = 0.004) and increased lung injury score (p < 0.001). Adding RB further increased histological features of lung injury (p = 0.022 to LPS ip).Conclusion: Resistive breathing exerts synergistic injurious effects when combined with inhalational LPS-induced lung injury, while the additive effect on extrapulmonary lung injury is less prominent.Keywords: endotoxin, resistive breathing, lung, inflammation, injury

Endowing strain sensors with neuromorphic computing capabilities could permit the efficient processing of tactile information on the edge. The realization of such functionalities from a simple circuit without software processing holds promise for attaining skin-based perception. Here, leveraging the intrinsic neuronal plasticity of memristive neurons, various firing patterns induced by the applied strain were demonstrated. More specifically, tonic, bursting, transition from tonic to bursting, adaptive, and nociceptive activities were captured. The implementation of these patterns permits the facile translation of the analog pressure signals into digital spikes, attaining accurate perception of various tactile characteristics. The tactile sensory neuron consisting of an RC circuit was composed of a SiO2-based conductive bridge memristor exhibiting leaky integrate-and-fire properties and a Pt nanoparticles (NPs)-based strain sensor with a gauge factor of ∼270. A dense layer of Pt NPs was also used as the bottom electrode for the memristive element, yielding the manifestation of a threshold switching mode with a switching voltage of only ∼350 mV and an exceptional switching ratio of 107. Our work provides valuable insights for developing low power neurons with tactile feedback for prosthetics and robotics applications. [ABSTRACT FROM AUTHOR]

The development of disruptive artificial neural networks (ANNs) endowed with brain-inspired neuromorphic capabilities is emerging as a promising solution to deal with the challenges of the artificial intelligence era. The fabrication of robust and accurate ANNs is strongly associated with the design of new electronic devices. The intriguing properties of memristors render them suitable as building blocks within ANNs. However, the impact of the operating electrodes on the dynamics of the switching process and the relaxation effect remains elusive. It is, thus, apparent that a deep understanding of the underlying electrochemical metallization mechanism that affects the formation of the conductive filament is of great importance. Along these lines, in this work, the impact of various materials as inert electrodes (Pt NPs, ITO, n++ Si, TiN, and W) on tuning the switching mode of low power SiO2-based conductive bridge random access memory devices was systematically investigated. A comprehensive model was applied to interpret the threshold and bipolar switching patterns and shed light on the respective physical mechanisms. The model incorporated the different coefficients of thermal conductivity of the various materials and attempted to associate them with the Soret coefficient and the activation energy of thermophoresis to interpret the experimental outcomes. Our work provides valuable insight for the realization of memristive devices with tunable properties, which can be directly leveraged for implementing a variety of neuromorphic functionalities, such as synaptic plasticity and spike generation. [ABSTRACT FROM AUTHOR]

The resistive switching characteristics of forming free TiO2–x/TiO2–y memory devices containing Pt nanocrystals (NCs) beneath the top electrode were systematically investigated through experiments and numerical simulation insights. By embedding Pt nanocrystals, we have the possibility to narrow down the possible locations where the switching effect will evolve and thus significantly improve the inherent variability of the devices. Besides, the deployment of bilayer structures can tune the resistance levels, since the presence of the layer with low oxygen content (TiO2–y) acts practically as series resistance, limiting the operating currents and at the same time forcing the switching effect to evolve in the layer with the higher oxygen content (TiO2–z). A numerical model is implemented, in order to shed light into the origin of the SET/RESET transitions and illustrate the direct impact of NCs on the conducting filament (CF) shape and distribution of oxygen vacancies. It is demonstrated that a higher density of oxygen vacancies is created in the vicinity of NCs, which can directly impact the operating current values and the uniformity of the switching characteristics. The presence of NCs also facilitates the reduction of the operating voltages (∼3 V), and, as a result, it significantly improves power consumption, without sacrificing the switching ratio (∼103), temporal/spatial variability (σ/μ < 0.2), and pulse endurance (108 cycles) characteristics of our memory cells. Evidence about the impact of the NCs position within the material configuration are also presented. The direct impact of Pt NCs on the depression and potentiation characteristics of the synaptic weight denotes similarly the huge applicability of our approach to tune a wide range of resistive switching properties. [ABSTRACT FROM AUTHOR]

Reservoir computing (RC)-based neuromorphic applications exhibit extremely low power consumption, thus challenging the use of deep neural networks in terms of both consumption requirements and integration density. Under this perspective, this work focuses on the basic principles of RC systems. The ability of self-selective conductive-bridging random access memory devices to operate in two modes, namely, volatile and non-volatile, by regulating the applied voltage is first presented. We then investigate the relaxation time of these devices as a function of the applied amplitude and pulse duration, a critical step in determining the desired non-linearity by the reservoir. Moreover, we present an in-depth study of the impact of selecting the appropriate pulse-stream and its final effects on the total power consumption and recognition accuracy in a handwritten digit recognition application from the National Institute of Standards and Technology dataset. Finally, we conclude at the optimal pulse-stream of 3-bit, through the minimization of two cost criteria, with the total power remaining at 287 µW and simultaneously achieving 82.58% recognition accuracy upon the test set. [ABSTRACT FROM AUTHOR]