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Abstract Background Air pollution is recognized as an emerging environmental risk factor for neurological diseases. Large-scale epidemiological studies associate traffic-related particulate matter (PM) with impaired cognitive functions and increased incidence of neurodegenerative diseases such as Alzheimer’s disease. Inhaled components of PM may directly invade the brain via the olfactory route, or act through peripheral system responses resulting in inflammation and oxidative stress in the brain. Microglia are the immune cells of the brain implicated in the progression of neurodegenerative diseases. However, it remains unknown how PM affects live human microglia. Results Here we show that two different PMs derived from exhausts of cars running on EN590 diesel or compressed natural gas (CNG) alter the function of human microglia-like cells in vitro. We exposed human induced pluripotent stem cell (iPSC)-derived microglia-like cells (iMGLs) to traffic related PMs and explored their functional responses. Lower concentrations of PMs ranging between 10 and 100 µg ml−1 increased microglial survival whereas higher concentrations became toxic over time. Both tested pollutants impaired microglial phagocytosis and increased secretion of a few proinflammatory cytokines with distinct patterns, compared to lipopolysaccharide induced responses. iMGLs showed pollutant dependent responses to production of reactive oxygen species (ROS) with CNG inducing and EN590 reducing ROS production. Conclusions Our study indicates that traffic-related air pollutants alter the function of human microglia and warrant further studies to determine whether these changes contribute to adverse effects in the brain and on cognition over time. This study demonstrates human iPSC-microglia as a valuable tool to study functional microglial responses to environmental agents.

Ring Oscillators (RO) are often used in true random number generators (TRNG). Their jittered clock signal, used as randomness source, originates from thermal and flicker noises. While thermal noise jitter is generally used as the main source of randomness, flicker noise jitter is not due to its autocorrelation. This work aims at qualitatively settling the issue of the influence of flicker noise in TRNGs, as its impact increases in newer technology nodes. For this, we built a RO behavioural model, which generates time series equivalent to a jittered RO signal. It is then used to generate the output of an elementary RO-TRNG. Despite general expectations, the autocorrelation inside the output bit stream is reduced when the amplitude of flicker noise increases. The model shows that this effect is caused by the sampling of the jittered signal by the second oscillator, which hides the behaviour of the absolute jitter, causes resetting of the perceived phase, and suppresses any memory effect. The inclusion of flicker noise as a legitimate noise source can increase the TRNG output bit rate by a factor of four for the same output entropy rate. This observation opens new perspectives towards more efficient stochastic models of the RO-TRNGs.

Most membrane proteins studies require the use of detergents, but because of the lack of a general, accurate and rapid method to quantify them, many uncertainties remain that hamper proper functional and structural data analyses. To solve this problem, we propose a method based on matrix-assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS) that allows quantification of pure or mixed detergents in complex with membrane proteins. We validated the method with a wide variety of detergents and membrane proteins. We automated the process, thereby allowing routine quantification for a broad spectrum of usage. As a first illustration, we show how to obtain information of the amount of detergent in complex with a membrane protein, essential for liposome or nanodiscs reconstitutions. Thanks to the method, we also show how to reliably and easily estimate the detergent corona diameter and select the smallest size, critical for favoring protein-protein contacts and triggering/promoting membrane protein crystallization, and to visualize the detergent belt for Cryo-EM studies.

Human cerebral organoids, derived from induced pluripotent stem cells, offer a unique in vitro research window to the development of the cerebral cortex. However, a key player in the developing brain, the microglia, do not natively emerge in cerebral organoids. Here we show that erythromyeloid progenitors (EMPs), differentiated from induced pluripotent stem cells, migrate to cerebral organoids, and mature into microglia-like cells and interact with synaptic material. Patch-clamp electrophysiological recordings show that the microglia-like population supported the emergence of more mature and diversified neuronal phenotypes displaying repetitive firing of action potentials, low-threshold spikes and synaptic activity, while multielectrode array recordings revealed spontaneous bursting activity and increased power of gamma-band oscillations upon pharmacological challenge with NMDA. To conclude, microglia-like cells within the organoids promote neuronal and network maturation and recapitulate some aspects of microglia-neuron co-development in vivo, indicating that cerebral organoids could be a useful biorealistic human in vitro platform for studying microglia-neuron interactions.

Recurrent neural networks are currently subject to intensive research efforts to solve temporal computing problems. Neuromorphic processors (NPs), composed of networked neuron and synapse circuit models, natively compute in time and offer an ultralow power solution particularly suited to emerging temporal edge-computing applications (wearable medical devices, for example). The most significant roadblock to addressing useful problems with neuromorphic hardware is the difficulty in maintaining healthy network dynamics in recurrent neural networks. In animal nervous systems, this is achieved via a multitude of adaptive homeostatic mechanisms which act over multiple time scales to counteract network instability induced via drift, component failure, or learning processes such as spike-timing dependent plasticity. One such mechanism is neuronal intrinsic plasticity (IP) where a neuron adapts its parameters which govern its excitability to fire around a target rate. The approach employed in state of the art NPs, based on a central volatile memory remotely setting model parameters, critically constrains parameter variety and bandwidth rendering realization of these essential mechanisms impossible. This paper demonstrates how reconfigurable nonvolatile resistive memories can be incorporated into neuron and synapse circuits allowing memory to be truly colocalized with the computational units in the computing fabric and facilitating the realization of massively parallel local plasticity mechanisms in neuromorphic hardware. Exploiting nonconventional programming operations of HfO2 based RRAM (stochastic SET and the RESET random variable), we propose a technologically plausible IP algorithm and demonstrate its use in the case of a recurrent neural network topology whereby the system self-organizes to sustain stable and healthy network dynamics around a target firing rate.