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AbstractTropical fruit flies are considered among the most economically important invasive species detected in temperate areas of the United States and the European Union. Detections often trigger quarantine and eradication programs that are conducted without a holistic understanding of the threat posed. Weather-driven physiologically-based demographic models are used to estimate the geographic range, relative abundance, and threat posed by four tropical tephritid fruit flies (Mediterranean fruit fly, melon fly, oriental fruit fly, and Mexican fruit fly) in North and Central America, and the European-Mediterranean region under extant and climate change weather (RCP8.5 and A1B scenarios). Most temperate areas under tropical fruit fly propagule pressure have not been suitable for establishment, but suitability is predicted to increase in some areas with climate change. To meet this ongoing challenge, investments are needed to collect sound biological data to develop mechanistic models to predict the geographic range and relative abundance of these and other invasive species, and to put eradication policies on a scientific basis.

Plants are primary resources for oxygen and foods whose production is fundamental for our life. However, diseases and pests may interfere with plant growth and cause a significant reduction of both the quality and quantity of agriculture products. Increasing agricultural productivity is crucial for poverty reduction and food security improvements. For this reason, the 2030 Agenda for Sustainable Development gives a central role to agriculture by promoting a strong technological innovation for advancing sustainable practices at the plant level. To accomplish this aim, recently, wearable sensors and flexible electronics have been extended from humans to plants for measuring elongation, microclimate, and stressing factors that may affect the plant’s healthy growth. Unexpectedly, fiber Bragg gratings (FBGs), which are very popular in health monitoring applications ranging from civil infrastructures to the human body, are still overlooked for the agriculture sector. In this work, for the first time, plant wearables based on FBG technology are proposed for the continuous and simultaneous monitoring of plant growth and environmental parameters (i.e., temperature and humidity) in real settings. The promising results demonstrated the feasibility of FBG-based sensors to work in real situations by holding the promise to advance continuous and accurate plant health growth monitoring techniques.

AbstractIn recent years, we have been witnessing the widespread use of low-cost, increasingly high-performance Unmanned Aerial Vehicles, or UAVs, equipped with a large number of sensors capable of extracting detailed information on several scales and in an immediate manner. This study was motivated by the need to perform a geological survey in an area with difficult physical access, and to compare the results with those from conventional surveys. Here we used a Multirotor UAV equipped with a high definition RGB camera and the digital photogrammetry technique to reconstruct a three-dimensional model of the Selmun promontory, located in the northern part of the island of Malta (central Mediterranean Sea). In this area, the evident cliff retreat is linked to landslide processes involving the outcropping geological succession, characterized by the over position of stiff limestones on ductile clays. Such an instability process consists of a lateral spreading associated with toppling and fall of different-size rock blocks. Starting from the 3D model obtained from the UAV-photogrammetry, a digital geological-structural survey was performed in which we identified the spatial geometry of the fractures that characterize the area of the Selmun promontory by measuring strike, dip and dip direction of the fractures with semi-automatic digital tools. Furthermore, we were able to measure the size and volume of singularized rock masses as well as cracks, and their sizes were mapped in a GIS environment that contains a large number of digital structural measures. It is the first application of this type for the Maltese islands and the results obtained with this innovative digital methodology were then compared with those of the traditional field survey of the same area acquired during a previous campaign. This study demonstrated how the innovation of digital geological surveying lies in the possibility of mapping areas and geological features not detectable with traditional methods, mainly due to the high risk associated with the stability of the cliff or, more generally, the inaccessibility of some sites, therefore allowing the user to operate in safety and to detect in detail the most remote rocky outcrops.

A pervasive assessment of air quality in an urban or mobile scenario is paramount for personal or city-wide exposure reduction action design and implementation. The capability to deploy a high-resolution hybrid network of regulatory grade and low-cost fixed and mobile devices is a primary enabler for the development of such knowledge, both as a primary source of information and for validating high-resolution air quality predictive models. The capability of real-time and cumulative personal exposure monitoring is also considered a primary driver for exposome monitoring and future predictive medicine approaches. Leveraging on chemical sensing, machine learning, and Internet of Things (IoT) expertise, we developed an integrated architecture capable of meeting the demanding requirements of this challenging problem. A detailed account of the design, development, and validation procedures is reported here, along with the results of a two-year field validation effort.

SummaryEggplant (Solanum melongena L.) is an important horticultural crop and one of the most widely grown vegetables from the Solanaceae family. It was domesticated from a wild, prickly progenitor carrying small, round, non‐anthocyanic fruits. We obtained a novel, highly contiguous genome assembly of the eggplant ‘67/3’ reference line, by Hi‐C retrofitting of a previously released short read‐ and optical mapping‐based assembly. The sizes of the 12 chromosomes and the fraction of anchored genes in the improved assembly were comparable to those of a chromosome‐level assembly. We resequenced 23 accessions of S. melongena representative of the worldwide phenotypic, geographic, and genetic diversity of the species, and one each from the closely related species Solanum insanum and Solanum incanum. The eggplant pan‐genome contained approximately 51.5 additional megabases and 816 additional genes compared with the reference genome, while the pan‐plastome showed little genetic variation. We identified 53 selective sweeps related to fruit color, prickliness, and fruit shape in the nuclear genome, highlighting selection leading to the emergence of present‐day S. melongena cultivars from its wild ancestors. Candidate genes underlying the selective sweeps included a MYBL1 repressor and CHALCONE ISOMERASE (for fruit color), homologs of Arabidopsis GLABRA1 and GLABROUS INFLORESCENCE STEMS2 (for prickliness), and orthologs of tomato FW2.2, OVATE, LOCULE NUMBER/WUSCHEL, SUPPRESSOR OF OVATE, and CELL SIZE REGULATOR (for fruit size/shape), further suggesting that selection for the latter trait relied on a common set of orthologous genes in tomato and eggplant.

Air–sea heat fluxes are essential climate variables, required for understanding air–sea interactions, local, regional and global climate, the hydrological cycle and atmospheric and oceanic circulation. In situ measurements of fluxes over the ocean are sparse and model reanalysis and satellite data can provide estimates at different scales. The accuracy of such estimates is therefore essential to obtain a reliable description of the occurring phenomena and changes. In this work, air–sea radiative fluxes derived from the SEVIRI sensor onboard the MSG satellite and from ERA5 reanalysis have been compared to direct high quality measurements performed over a complete annual cycle at the ENEA oceanographic observatory, near the island of Lampedusa in the Central Mediterranean Sea. Our analysis reveals that satellite derived products overestimate in situ direct observations of the downwelling short-wave (bias of 6.1 W/m2) and longwave (bias of 6.6 W/m2) irradiances. ERA5 reanalysis data show a negligible positive bias (+1.0 W/m2) for the shortwave irradiance and a large negative bias (−17 W/m2) for the longwave irradiance with respect to in situ observations. ERA5 meteorological variables, which are needed to calculate the air–sea heat flux using bulk formulae, have been compared with in situ measurements made at the oceanographic observatory. The two meteorological datasets show a very good agreement, with some underestimate of the wind speed by ERA5 for high wind conditions. We investigated the impact of different determinations of heat fluxes on the near surface sea temperature (1 m depth), as determined by calculations with a one-dimensional numerical model, the General Ocean Turbulence Model (GOTM). The sensitivity of the model to the different forcing was measured in terms of differences with respect to in situ temperature measurements made during the period under investigation. All simulations reproduced the true seasonal cycle and all high frequency variabilities. The best results on the overall seasonal cycle were obtained when using meteorological variables in the bulk formulae formulations used by the model itself. The derived overall annual net heat flux values were between +1.6 and 40.4 W/m2, depending on the used dataset. The large variability obtained with different datasets suggests that current determinations of the heat flux components and, in particular, of the longwave irradiance, need to be improved. The ENEA oceanographic observatory provides a complete, long-term, high resolution time series of high quality in situ observations. In the future, more similar sites worldwide will be needed for model and satellite validations and to improve the determination of the air–sea exchange and the understanding of related processes.