Chronic treatment with fluoxetine up-regulates cellular BDNF mRNA expression in rat dopaminergic regions.

During the last few years several studies have highlighted the possibility that major depression can be characterized by a general reduction in brain plasticity and an increased vulnerability under challenging situations. Such dysfunction may be the consequence of reduced expression and function of proteins important for neuroplasticity such as brain-derived neurotrophic factor (BDNF). On this basis, by using a sensitive non-radioactive in-situ hybridization, we evaluated the effects of a chronic treatment with fluoxetine on BDNF expression within rat dopaminergic regions. In fact, besides the well-established role of the hippocampus, increasing evidence indicates that other brain regions may be involved in the pathophysiology of depression and consequently be relevant for the therapeutic action of antidepressant drugs. Our results indicate that 3 wk of fluoxetine administration up-regulates BDNF mRNA levels selectively within structures belonging to the meso-cortico-limbic pathway. The expression of the neurotrophin is significantly increased in the ventral tegmental area, prefrontal cortex, and shell region of the nucleus accumbens, whereas no changes were detected in the substantia nigra and striatum. Moreover, in agreement with previous studies, fluoxetine increased BDNF mRNA levels in the hippocampus, an effect that was limited to the cell bodies without any change in its dendritic targeting. These data show that chronic treatment with fluoxetine increases BDNF gene expression not only in limbic areas but also in dopaminergic regions, suggesting that such an effect may contribute to improve the function of the dopaminergic system in depressed subjects

Dual-mode wake-up nodes for IoT monitoring applications: Measurements and algorithms

Internet of Things (IoTs)-based monitoring applications usually involve large-scale deployments of battery-enabled sensor nodes providing measurements at regular intervals. In order to guarantee the service continuity over time, the energy-efficiency of the networked system should be maximized. In this paper, we address such issue via a combination of novel hardware/software solutions including new classes of Wake-up radio IoT Nodes (WuNs) and novel data- and hardware-driven network management algorithms. Three main contributions are provided. First, we present the design and prototype implementation of WuN nodes able to support two different energy-saving modes; such modes can be configured via software, and hence dynamically tuned. Second, we show by experimental measurements that the optimal policy strictly depends on the application requirements. Third, we move from the node design to the network design, and we devise proper orchestration algorithms which select both the optimal set of WuN to wake-up and the proper energy-saving mode for each WuN, so that the application lifetime is maximized, while the redundancy of correlated measurements is minimized. The proposed solutions are extensively evaluated via OMNeT++ simulations under different IoT scenarios and requirements of the monitoring applications

Could Inflammatory Indices and Metabolic Syndrome Predict the Risk of Cancer Development? Analysis from the Bagnacavallo Population Study

Background: Despite the robust data available on inflammatory indices (neutrophil lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), and systemic immune-inflammation index (SII)) and clinical outcome in oncological patients, their utility as a predictor of cancer incidence in the general population has not been reported in literature. Methods: The Bagnacavallo study was performed between October 2005 and March 2009. All citizens of Bagnacavallo (Ravenna, Emilia-Romagna, Italy) aged 30-60 years as of January 2005 were eligible and were invited by written letter to participate to the study. All participants underwent a detailed clinical history and physical examination following the model of the Dionysos Study. All blood values included in the analysis were obtained the day of physical examination. Cancer incidence data were obtained from the population-based Romagna Cancer Registry, which operates according to standard methods. The aim of this analysis was to examine the association between metabolic syndrome and baseline SII, NLR, and PLR levels, and the diagnosis of an invasive cancer in the Bagnacavallo study cohort. Results: At univariate analysis, metabolic syndrome was not associated with an increase of cancer incidence (HR 1.30; p = 0.155). High glucose (HR 1.49; p = 0.0.16), NLR HR 1.54, p = 0.002), PLR (HR 1.58, p = 0.001), and SII (HR 1.47, p = 0.006) were associated with an increase of cancer incidence. After adjusting for clinical covariates (smoking, physical activity, education, age, and gender) SII, PLR, and NLR remained independent prognostic factors for the prediction of cancer incidence. Conclusions: Inflammatory indices are promising, easy to perform, and inexpensive tools for identifying patients with higher risk of cancer in cancer-free population

Impact of Interdisciplinary Research on Planning, Running, and Managing Electromobility as a Smart Grid Extension

The smart grid is concerned with energy efficiency and with the environment, being a countermeasure against the territory devastations that may originate by the fossil fuel mining industry feeding the conventional power grids. This paper deals with the integration between the electromobility and the urban power distribution network in a smart grid framework, i.e., a multi-stakeholder and multi-Internet ecosystem (Internet of Information, Internet of Energy, and Internet of Things) with edge computing capabilities supported by cloud-level services and with clean mapping between the logical and physical entities involved and their stakeholders. In particular, this paper presents some of the results obtained by us in several European projects that refer to the development of a traffic and power network co-simulation tool for electro mobility planning, platforms for recharging services, and communication and service management architectures supporting interoperability and other qualities required for the implementation of the smart grid framework. For each contribution, this paper describes the inter-disciplinary characteristics of the proposed approaches

A self-adapting algorithm based on atmospheric pressure to localize indoor devices

Modern smartphones are nowadays equipped with a multitude of sensors, which extend their capabilities paving the way for a multitude of services. Among these, the ability to locate the device is exploited by many. While outdoor the GPS provides good accuracy, indoor localization is challenging to be performed with it, as buildings shadow the satellite signal. In particular, the barometric pressure sensor is often used to determine the altitude of the device from the ground floor, particularly for safety applications and indoor navigation. However, pressure changes during the day, and thus it is challenging to bind a static value to a specific altitude. In this work, we propose a self-adapting algorithm able to determine the height at which the device is in a building, by exploiting the barometric pressure. We implemented and tested our algorithm on an Android application, and we compared it against other techniques. We tested our proposal for three specific use-cases, and our results show the benefit of our proposal

Indoor Use of Gray and White Spaces: Another Look at Wireless Indoor Communication

Television (TV) white space (WS) constitutes a key technology to support the increasing worldwide growth of spectrum demand with several regulation standards that are already available for long-and medium-range communications. At the same time, estimations based on WS spectrum databases (SDBs) indicate that the availability of TVWS is often very limited in dense urban areas where spectrum resources are more needed. Therefore, the benefits provided by the utilization of TVWS have yet to be fully assessed. In this article, we rethink the utilization of TVWS in indoor communication environments through novel three-dimensional (3-D) spectrum-sharing mechanisms. Based on measurements that demonstrate the differences in terms of spectrum opportunities at different floors of the same building, we propose an underlay spectrum-sharing architecture to enable a per-building finegrained reuse of TV frequencies while protecting the operations of TV receivers in a neighborhood. We evaluate the effectiveness of the proposed spectrum-sharing architecture over several urban environments in Italy by taking into account many real characteristics of the scenarios. Our results demonstrate that through our architecture, more spectrum resources than what are reported in the SDB can be available for indoor scenarios, even in highly congested urban areas, paving the way to novel TVWS applications

Cognitive modulation and coding scheme adaptation for 802.11n and 802.11af networks

In recent years, wireless communication experienced a huge growth, and nowadays many services are built on top of it. Much of the user demands comes from indoor environments, in which obstructions like walls and floors decrease the received signal to levels not suitable for reliable and high speed communication. Recently, national regulators worldwide started to investigate the usage of TV bands, thanks to the switch from analog to digital TV. These regulations gave birth to different wireless standards to make use of this new opportunistic spectrum. In this paper, we show how different 802.11 standard behave in indoor environment. Namely, we analyze IEEE 802.11n networks in the 2.4 GHz band, and IEEE 802.11af in the TV band, by means of theoretical analisys and simulations. Then, we design an algorithm that leverages the use of the different IEEE 802.11 amendments, by monitoring the achievable data rates with respect to the packet-error-rate, and provide simulation results on its performance on different modeled scenarios

Indoor communication over TV gray spaces based on spectrum measurements

The spectrum scarcity is a known problem for a multitude of services. Several bands have been licensed, and nowadays it is difficult to find unused spectrum. Cognitive radio networks have been proposed as a possible solution to contrast the experienced spectrum scarcity. One case of particular interest come from the scarce utilization of TV frequencies, which form the so-called TV White Spaces. In this paper we investigate the utilization of occupied frequencies by secondary devices for indoor communication. We conduct spectrum measurements to quantify the availability of spectrum, and study how indoor communications could impact the DTV receiver. We show that this portions of spectrum, called gray spaces, can be utilized under certain circumstances, for example in highly populated areas, which is the scenario in which it is harder to find TV White Spaces. Simulation studies show the impact gray spaces can have on the available spectrum for opportunistic use

Benchmark Comparison of Commercially Available Systems for Particle Number Measurement

Measurement of particle number was introduced in the Euro 5/6 light duty vehicle emissions regulation. Due to the complex nature of combustion exhaust particles, and to transportation, transformation and deposition mechanisms, such type of measurement is particularly complex, and regression analysis is commonly used for the comparison of different measurement systems. This paper compares various commercial instruments, developing a correlation analysis focused on PN (Particle Number) measurement, and isolating the factors that mainly influence each measuring method. In particular, the experimental activity has been conducted to allow critical comparisons between measurement techniques that are imposed by current regulations and instruments that can be used also on the test cell. The paper presents the main results obtained by analyzing instruments based on different physical principles, and the effects of different sampling locations and different operating parameters. The main instruments that have been critically analyzed during this project are: Horiba MEXA 2000 SPCS Particle Counter installed on a CVS tunnel; AVL APC 489 installed directly on the exhaust gas flow; AVL Smart Sample 478 GEM 140 (Mini CVS tunnel) + AVL APC 489; Cambustion DMS 500 installed directly on the exhaust gas flow; AVL MicroSoot Sensor 483 installed directly on the exhaust gas flow. The tests have been carried out on a prototype vehicle equipped with a GDI engine, both under steady-state conditions and during the New European Driving Cycle (NEDC), while comparing the effects of different dilution factors, different engine calibration datasets, and different positions of the various instruments