Second-generation antipsychotics (SGA) are multi-target agents widely used for the treatment of schizophrenia and bipolar disorder that also hold potential for the treatment of impaired emotional control, thanks to their diverse receptor profiles as well as their potential in modulating neuroadaptive changes in key brain regions. The aim of this study was thus to establish the ability of lurasidone, a novel SGA characterized by a multi-receptor signature, to modulate behavioural and molecular defects associated with a genetic model of impaired emotional control, namely serotonin transporter knockout (SERT KO) rats. At behavioural level, we found that chronic lurasidone treatment significantly increased fear extinction in SERT KO rats, but not in wild-type control animals. Moreover, at molecular level, lurasidone was able to normalize the reduced expression of the neurotrophin brain-derived neurotrophic factor in the prefrontal cortex of SERT KO rats, an effect that occurred through the regulation of specific neurotrophin transcripts (primarily exon VI). Furthermore, chronic lurasidone treatment was also able to restore the reduced expression of different GABAergic markers that is present in these animals. Our results show that lurasidone can improve emotional control in SERT KO rats, with a primary impact on the prefrontal cortex. The adaptive changes set in motion by repeated treatment with lurasidone may in fact contribute to the amelioration of functional capacities, closely associated with neuronal plasticity, which are deteriorated in patients with schizophrenia, bipolar disease and major depression.

This paper approaches the problem of canceling the disturbances due to RF interferences in P-band, airborne SAR missions. Two techniques are introduced: one exploits MUSIC to estimate the interferences' frequencies, and then performs notch filtering at that frequencies; whereas the other adaptively estimate the interference contributions and cancel them by means of in-phase subtraction. Both techniques have been successfully tested on the data acquired by the DLR E-SAR sensor over urban area

Despite the several sources of inaccuracy, commercial microwave links (CML) have been recently exploited to estimate the average rainfall intensity along the radio path from signal attenuation. Validating these measurements against “ground truth” from conventional rainfall sensors, as rain gauges, is a challenging issue due to the different spatial sampling involved. Here, we assess the performance of a network of CML as opportunistic rainfall sensors in a challenging mountainous environment located in Northern Italy. The benchmark dataset was provided by an operational network of rain gauges and by three disdrometers. Moreover, disdrometer data were used to establish an accurate relationship between path attenuation and rainfall intensity. A new method was developed for assessing CML: time series of rainfall occurrence and rainfall depth, representative of CML radio path, were derived from the nearby rain gauges and disdrometers and compared with the same quantities gathered from the CML. It turns out that, over the very short integration times considered (10 min), CML perform well in detecting rainfall, whereas quantitative rainfall estimates may have large discrepancies.

Abstract. Commercial microwave links (CMLs) can be used as opportunistic and unconventional rainfall sensors by converting the received signal level into path-averaged rainfall intensity. As the reliable reconstruction of the spatial distribution of rainfall is still a challenging issue in meteorology and hydrology, there is a widespread interest in integrating the precipitation estimates gathered by the ubiquitous CMLs with the conventional rainfall sensors, i.e. rain gauges (RGs) and weather radars. Here, we investigate the potential of a dense CML network for the estimation of river discharges via a semi-distributed hydrological model. The analysis is conducted in a peri-urban catchment, Lambro, located in northern Italy and covered by 50 links. A two-level comparison is made between CML- and RG-based outcomes, relying on 12 storm/flood events. First, rainfall data are spatially interpolated and assessed in a set of significant points of the catchment area. Rainfall depth values obtained from CMLs are definitively comparable with direct RG measurements, except for the spells of persistent light rain, probably due to the limited sensitivity of CMLs caused by the coarse quantization step of raw power data. Moreover, it is shown that, when changing the type of rainfall input, a new calibration of model parameters is required. In fact, after the recalibration of model parameters, CML-driven model performance is comparable with RG-driven performance, confirming that the exploitation of a CML network may be a great support to hydrological modelling in areas lacking a well-designed and dense traditional monitoring system.

The extrapolation of quantiles beyond or below the largest or smallest observation plays an important role in hydrological practice, design of hydraulic structures, water resources management, or risk assessment. Traditionally, extreme quantiles are obtained using parametric methods that require to make an a priori assumption about the distribution that generated the data. This approach has several limitations mainly when applied to the tails of the distribution. Semiparametric or nonparametric methods, on the other hand, allow more flexibility and they may overcome the problems of the parametric approach. Therefore, we present here a comparison between three selected semi/nonparametric methods, namely the methods of Hutson (Stat and Comput, 12(4):331–338, 2002) and Scholz (Nonparametric tail extrapolation. Tech. Rep. ISSTECH-95-014, Boeing Information and Support Services, Seattle, WA, United States of America, 1995) and kernel density estimation. While the first and third methods have already applications in hydrology, Scholz (Nonparametric tail extrapolation. Tech. Rep. ISSTECH-95-014, Boeing Information and Support Services, Seattle, WA, United States of America, 1995) is proposed in this context for the first time. After describing the methods and their applications in hydrology, we compare their performance for different sample lengths and return periods. We use synthetic samples extracted from four distributions whose maxima belong to the Gumbel, Weibull, and Fréchet domain of attraction. Then, the same methods are applied to a real precipitation dataset and compared with a parametric approach. Eventually, a detailed discussion of the results is presented to guide researchers in the choice of the most suitable method. None of the three methods, in fact, outperforms the others; performances, instead, vary greatly with distribution type, return period, and sample size.