Fincons Group (Italy)

Cytochrome P450 enzymes in ribosomally synthesized and post-translationally modified peptides (RiPPs) catalyze C-C, C-N, or C-O cross-linking reactions in the biosynthesis of biaryl cyclophane natural products. Here, we manually identified 127 homologous P450s linked to putative precursor peptides containing the YPW motif. Through in vivo functional studies in Escherichia coli, the newly identified enzyme MpoB from Micromonospora polyrhachis DSM 45886 was found to catalyze the formation of a cross-link between Tyr-C3 and Trp-N1 at the YPW motif. This result provides an additional toolkit for cross-linked peptide modification.

The original article [1] mistakenly presented each author's name with Given and Family Names inverted. The authors' names have since been amended.

The agricultural sector holds the greatest reduction potential to limit adverse effects of reactive nitrogen in the environment. Assessing the negative impacts of excessive release of reactive nitrogen into the biosphere requires spatially explicit information to capture e.g. hot spots of nitrogen surplus, nitrogen use efficiency, the impact on sensitive ecosystems or on ground/drinking water quality. The agricultural economic model CAPRI is one of the main tools applied by the European Commission for the ex-ante analysis of the impact of agricultural policies and agro-environmental legislation at regional level (NUTS2) in Europe. CAPRI builds on long-term time series of regional, national and international agricultural statistics (e.g. crop and livestock production, fertilizer use), market and trade data. Inputs (e.g. inorganic fertilizer) in CAPRI are explicitly linked to production which delivers the basis for connecting environmental indicators (e.g. nitrogen surplus) directly to individual activities. To provide the link between the agro-economic model CAPRI and the impact assessment of nitrogen use in agriculture on the environment at higher spatial resolution, we developed a procedure to disaggregate CAPRI regional data and provide maps of nitrogen input/output, crop and livestock production for the time series 2000 – 2018 at the level of Farm Structure Units (FSU) for 26 EU member states and the UK. The FSU are built by the spatial intersection of a 10 x 10 km2 INSPIRE compliant grid, the CAPRI NUTS2 region borders and the soil mapping units of the Harmonized World Soil Data Base (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009), having a median area of 12 km2 (minimum 1 km2, maximum 100 km2). The disaggregation procedure for 36 crop types and 18 livestock categories from the regional level to the FSU is driven by information from the gridded Farm Structure Survey (FSS, 2010) crop and livestock data at 10 x 10 km2 resolution, CORINE (2018) non-agricultural land cover shares, altitude and slope constraints for individual crops / crop classes derived from LUCAS survey data (https://ec.europa.eu/eurostat/web/lucas). Nitrogen inputs from mineral fertilizer and manure are disaggregated from the regional level to the FSU following the crops’ requirements. N input from atmospheric deposition and N supply by biological fixation is taken into account at FSU level. N from mineralization of soil organic matter could not be taken into account due to lack of data. N losses from volatilization and surface run-off, N removal by harvest, N in crop residues complete the N flow data set in agricultural areas at FSU level.

Remote rural areas are facing strong demographic and socioeconomic challenges as being far from a city can limit social and economic interaction opportunities. This study analyses the EU remote rural areas based on a set of selected indicators related to demographic, economic, accessibility, connectivity, biodiversity, land use and geographical context. Spatial cluster techniques revealed that half of the remote rural areas are in a disadvantaged situation because they performed poorly on most of the analysed indicators. Despite being remote, a few rural areas presented socioeconomic favourable conditions and high natural assets. This finding motivated our investigation to identify statistical differences between remote and non-remote areas by applying multivariate regression models. Our results showed that remote rural areas face major challenges in relation to transport infrastructures, internet speed, access to essential services, household income, population decline and ageing. On the contrary, their natural landscape and ecosystems, as well as agriculture and forestry can offer new opportunities to remote rural areas while contribute to the goals of the EU Green Deal.

Channel identification is a useful function to support wireless telecommunication operations because the knowledge of the radio frequency propagation channel characteristics can improve communication efficiency and robustness. In recent times, the application of machine learning (ML) algorithms to the problem of channel identification has been proposed in the literature. In particular, Deep Learning (DL) has demonstrated superior performance to ’shallow’ machine learning algorithms for many wireless communication functions. Inspired by the success of DL in literature, the authors in this paper apply Convolutional Neural Networks (CNN) to the problem of channel identification, which is still an emerging research area. CNN is a deep learning algorithm that has demonstrated superior performance to ML algorithms, in particular for image processing tasks. Because the digitized RF signal is a one-dimensional time series, different algorithms are applied to convert the time series to images using various Time Frequency Transform (TFT) including the CWTs, spectrogram, and Wigner Ville distribution. The images are then provided as input to the CNN. The approach is applied to a data set based on weather radar pulse signals generated in the laboratory of the author’s facilities on which different fading models are applied. These models are inspired by the tap-delay-line 3GPP configurations defined in the standards, but they have been customized with Nakagami-m fading distribution (3GPP-like fading models). The results show the superior performance of time–frequency CNN in comparison to 1D CNN for different values of Signal to Noise Ratio (SNR) in dB. In particular, the study shows that the Continuous Wavelet Transform (CWT) has the optimal performance in this data set, but the choice of the mother wavelet remains a problem to be solved (this is a well-known problem in the research literature). Then, this study also proposes an adaptive technique for the choice of the optimal mother wavelet, which is evaluated on the mentioned data set. The results show that the adaptive proposed approach is able to obtain the optimal performance for most of the SNR conditions.

Before urban flooding actually happens, weather forecasts with varying degrees of precision are available to emergency managers. In the aftermath of the event, authoritative information including Earth Observation (EO) data can be used to estimate precisely the flood extent, possibly after several hours. This study aims to determine how social media information can reduce the inherent uncertainty of the information in the immediate aftermath of an urban flood event. Specifically, the study investigates how to collect relevant social media images and to interpolate such data in order to create a map. The premise of the study is that social media platforms, when combined with digital surface models, can provide control points for creating a reliable near real-time estimate of the flood extent. In the study, we compared a flood extent map derived from social media with that derived from authoritative altimetry data during one of the worst floods to hit Venice, which occurred in November 2019. The results of the experiments show a good overall accuracy using several digital surface models. Given the global coverage of such models and the low resources required, we think the methodology proposed could be beneficial for emergency managers. Specifically, we describe how a flood extent map can be made available within 24 h, or even less, after urban flooding strikes a densely inhabited area, where data generated by the public are available.

Although exposure from Wi-Fi devices is reported to be far below the established regulatory limits, there are still concerns of safe use of such equipment because of the continuous evolution of the protocols and of the widespread use of Wireless Local Area Networks (WLANs). This paper describes an experimental analysis of Wi-Fi 6 transmissions and their respective electromagnetic field exposure levels during different conditions of operation (output power, frequency band, bandwidth) in the direction of maximum antenna gain of the devices under test while trying to achieve maximum traffic in the channel. The measurements were carried out in the controlled environment of an anechoic chamber. Apart from power density levels which were measured to be far below the reference levels, radiation patterns, spectrograms and time traces of the Wi-Fi signals were captured, and demodulation was performed. The results can provide valuable feedback on the exposure assessment of the sixth generation of Wi-Fi networks.

The application of last-generation spatial data modelling, integrating Earth Observation, population, economic and other spatially explicit data, enables insights into the sustainability of the global urbanisation processes with unprecedented detail, consistency, and international comparability. In this study, the land use efficiency indicator, as developed in the Sustainable Development Goals, is assessed globally for the first time at the level of Functional Urban Areas (FUAs). Each FUA includes the city and its commuting zone as inferred from statistical modelling of available spatial data. FUAs represent the economic area of influence of each urban centre. Hence, the analysis of land consumption within their boundary has significance in the fields of spatial planning and policy analyses as well as many other research areas. We utilize the boundaries of more than 9,000 FUAs to estimate the land use efficiency between 1990 and 2015, by using population and built-up area data extracted from the Global Human Settlement Layer. This analysis shows how, in the observed period, FUAs in low-income countries of the Global South evolved with rates of population growth surpassing the ones of land consumption. However, in almost all regions of the globe, more than half of the FUAs improved their land use efficiency in recent years (2000–2015) with respect to the previous decade (1990–2000). Our study concludes that the spatial expansion of urban areas within FUA boundaries is reducing compactness of settlements, and that settlements located within FUAs do not display higher land use efficiency than those outside FUAs.

Mandatory targets are set in Europe for Carbon Dioxide (CO2) emissions of light-duty vehicles. EU law recognises the potential of certain innovative technologies to contribute to reducing CO2 emissions. Vehicle systems and innovations are becoming increasingly complex, and the accurate quantification of their benefits increasingly difficult. The study investigates the potential of the CO2MPAS simulator to serve this purpose. Two innovative technologies were studied, Light-emitting diode (LED) lighting systems, efficient alternators (EA), and their combination. The model was validated on detailed test results from eight vehicles. A total of 452 passenger cars, for which test data were available, were subsequently simulated using CO2MPAS simulator. The mean simulated CO2 savings was 0.91gCO2/km (LED lights), 0.98 gCO2/km (EA), and 1.78 gCO2/km (combined). Results show that simulated CO2 savings were comparable to those calculated using the existing standardised method. For gasoline and diesel vehicles respectively, the difference in CO2 savings between simulated and existing method was 2.8% and 0.14% in the LED lights case, and 0.6% and 0.67% in the alternator case. In the combined case, the difference was calculated to be 1.7% and 0.34%. Similar approaches could be used in the future for accurately capturing the benefits of more complex technologies.