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Indoor locations with limited air exchange can easily be contaminated by harmful volatile compounds. Thus, is of great interest to monitor the distribution of chemicals indoors to reduce associated risks. To this end, we introduce a monitoring system based on a Machine Learning approach that processes the information delivered by a low-cost wearable VOC sensor incorporated in a Wireless Sensor Network (WSN). The WSN includes fixed anchor nodes necessary for the localization of mobile devices. The localization of mobile sensor units is the main challenge for indoor applications. Yes. The localization of mobile devices was performed by analyzing the RSSIs with machine learning algorithms aimed at localizing the emitting source in a predefined map. Tests performed on a 120 m 2 meandered indoor location showed a localization accuracy greater than 99%. The WSN, equipped with a commercial metal oxide semiconductor gas sensor, was used to map the distribution of ethanol from a point-like source. The sensor signal correlated with the actual ethanol concentration as measured by a PhotoIonization Detector (PID), demonstrating the simultaneous detection and localization of the VOC source.

The appraisal of stress in plants is of great relevance in agriculture and any time the transport of living plants is involved. Wireless sensor networks (WSNs) are an optimal solution to simultaneously monitor a large number of plants in a mostly automatic way. A number of sensors are readily available to monitor indicators that are likely related to stress. The most common of them include the levels of total volatile compounds and CO 2 together with common physical parameters such as temperature, relative humidity, and illumination, which are known to affect plants' behavior. Recent progress in microsensors and communication technologies, such as the LoRa protocol, makes it possible to design sensor nodes of high sensitivity where power consumption, transmitting distances, and costs are optimized. In this paper, the design of a WSN dedicated to plant stress monitoring is described. The nodes have been tested on European privet ( Ligustrum Jonandrum ) kept in completely different conditions in order to induce opposite level of stress. The results confirmed the relationship between the release of total Volatile Organic Compounds (VOCs) and the environmental conditions. A machine learning model based on recursive neural networks demonstrates that total VOCs can be estimated from the measure of the environmental parameters.

Mutations in the voltage-gated sodium channel SCN1A are responsible for a number of seizure disorders including Generalized Epilepsy with Febrile Seizures Plus (GEFS+) and Severe Myoclonic Epilepsy of Infancy (SMEI). To determine the effects of SCN1A mutations on channel function in vivo, we generated a bacterial artificial chromosome (BAC) transgenic mouse model that expresses the human SCN1A GEFS+ mutation, R1648H. Mice with the R1648H mutation exhibit a more severe response to the proconvulsant kainic acid compared with mice expressing a control Scn1a transgene. Electrophysiological analysis of dissociated neurons from mice with the R1648H mutation reveal delayed recovery from inactivation and increased use-dependent inactivation only in inhibitory bipolar neurons, as well as a hyperpolarizing shift in the voltage dependence of inactivation only in excitatory pyramidal neurons. These results demonstrate that the effects of SCN1A mutations are cell type-dependent and that the R1648H mutation specifically leads to a reduction in interneuron excitability.

It is a common clinical observation that stress is accompanied by dysfunction of the hypothalamic-pituitary-ovarian axis, and there is mounting experimental evidence that CRH, the principal regulator of ACTH release and the central coordinator of the stress response, is able to suppress gonadal function by inhibiting hypothalamic GnRH release. Recently, it has been shown that immunoreactive CRH, CRH messenger RNA, and CRH receptors are also present in the ovary. This prompted us to examine the role of CRH on ovarian function. To accomplish this, we studied the effects of this neuropeptide on estrogen production and cAMP intracellular content from rat granulosa and human granulosa-luteal cells. We also evaluated the activity of the enzyme aromatase by measuring the production of tritiated water from homogenates of cultured rat granulosa cells. CRH inhibited FSH-stimulated estrogen production from rat granulosa cells in a dose-dependent fashion. The maximal effect was achieved at a concentration of 10(-8) M, which suppressed estrogen production by about 30%. Low concentrations of CRH (10(-10) M), incapable of modulating maximal estrogen production in response to FSH, provoked a right-ward shift of the estrogen dose-response curve to FSH. CRH (10(-8) M) suppressed the production of tritiated water (equivalent to estrogen production) from homogenates of rat granulosa cells incubated with a half-maximal concentration of FSH. Basal estrogen production by human granulosa-luteal cells was also inhibited by CRH at a concentration of 10(-10) M. The maximal effect was achieved with a concentration of 10(-8) M, which lowered estrogen production by 25%. The CRH receptor antagonist alpha-helical CRH-(9-41) antagonized the inhibitory effect of CRH on estrogen production from rat granulosa and human granulosa-luteal cells, whereas alone it had no effect. CRH did not have any effect on the intracellular cAMP content of rat granulosa and human granulosa-luteal cells. In conclusion, these results suggest that CRH is able to suppress estrogen production from rat and human granulosa cells in vitro. This effect seems to be linked to inhibition of aromatase activity in the rat and is independent of cAMP generation. We speculate that CRH may also interfere with hypothalamic-pituitary-gonadal axis function by acting directly at the ovarian level.