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Resistance, tolerance, and persistence to antibiotics have mainly been studied at the level of a single microbial isolate. However, in recent years it has become evident that microbial interactions play a role in determining the success of antibiotic treatments, in particular by influencing the occurrence of persistence and tolerance within a population. Additionally, the challenge of resuscitation (the capability of a population to revive after antibiotic exposure) and pathogen clearance are strongly linked to the small size of the surviving population and to the presence of fluctuations in cell counts. Indeed, while large population dynamics can be considered deterministic, small populations are influenced by stochastic processes, making their behaviour less predictable. Our study argues that microbe-microbe interactions within a community affect the mode, tempo, and success of persister resuscitation and that these are further influenced by noise. To this aim, we developed a theoretical model of a three-member microbial community and analysed the role of cell-to-cell interactions on pathogen clearance, using both deterministic and stochastic simulations. Our findings highlight the importance of ecological interactions and population size fluctuations (and hence the underlying cellular mechanisms) in determining the resilience of microbial populations following antibiotic treatment.

Process-based Forest Models (PBFMs) offer the possibility to capture important spatial and temporal patterns of carbon fluxes and stocks in forests. Yet, their predictive capacity should be demonstrated not only at the stand-level but also in the context of broad spatial and temporal heterogeneity. We apply a stand scale PBFM (3D-CMCC-FEM) in a spatially explicit manner at 1 km resolution in southern Italy. We developed a methodology to initialize the model that includes information derived from the integration of Remote Sensing (RS) and the National Forest Inventory (NFI) data and regional forest maps to characterize structural features of the main forest species. Gross primary production (GPP) is simulated over 2005–2019 period and the model predictive capability of the model in simulating GPP is evaluated both aggregated as at species-level through multiple independent data sources based on different nature RS-based products. We show that the model is able to reproduce most of the spatial (~2800 km2) and temporal (32 years in total) patterns of the observed GPP at both seasonal, annual and interannual time scales, even at the species-level. These promising results open the possibility of confindently applying the 3D-CMCC-FEM to investigate the forests’ behaviour under climate and environmental variability over large areas across highly variable ecological and bio-geographical heterogeneity of the Mediterranean region.

The textile district of Prato (Italy) has developed a wastewater recycling system of considerable scale. The reclaimed wastewater is characterized by high levels of hardness (32 °F on average), which precludes its direct reuse in numerous wet textile processes (e.g., textile dyeing). Consequently, these companies utilize ion exchange resins for water softening. However, the regeneration of the resins results in an increased concentration of chlorides in the reclaimed wastewater that exceeds the limit set by Italian regulations for the reuse of water for irrigation purposes. The objective of this study is to investigate the potential of membrane filtration as an alternative method for removing hardness from water. Therefore, an industrial-scale ultrafiltration-nanofiltration (UF-NF) pilot plant was installed to test the rejection of hardness from the reclaimed wastewater. The experiment employed two types of NF membranes and three permeate fluxes (27, 35, and 38 L·m−2·h−1) for testing. The results demonstrated that the system could remove hardness with efficiencies exceeding 98% under all conditions tested. The experimental findings indicate that the UF-NF system has the potential to be employed as a post-treatment step to render the reclaimed wastewater suitable for all textile finishing processes and to expand the scope for reuse.

Over the last decade, the CO2 reduction reaction (CO2RR) has been increasingly exploited for the synthesis of high‐value raw materials in gaseous or liquid form, although no examples of CO2 fixation in nanoparticle systems have been demonstrated. Herein, CO2 fixation into solid nanomaterials by laser synthesis and processing of gold colloids in water, traditionally considered a green approach leading to ligand‐free nanoparticles without the formation of by‐products, is reported. If carbon monoxide‐rich gold nanoparticles are observable even after synthesis in deionized water, the presence of CO2 derivatives in alkaline water environment leads to C2 and C3 coupling with the production of carboxylic acids as a typical CO2RR fingerprint. While laser processing of preformed gold colloids is selective for C2 coupling, both C2 and C3 coupling to lactic acid are observed during pulsed laser ablation of a gold target. In the latter case, it is demonstrated that it is possible to synthesize photoluminescent organometallic nanocomposites in the blue spectral region with a quantum yield of about 20% under adequate experimental conditions. In this research, new pathways are offered to be explored in energetics, photonics, catalysis, and synthesis at the nanoscale.

A biochar from co-pyrolysis of a mixture of sawdust and biological sludge (70/30, w/w), providing a high environmental compatibility in terms of water leachable polycyclic aromatic hydrocarbons and inorganic elements, together with a remarkable surface area (389 m2/g), was integrated into laboratory-scale vertical-flow constructed wetlands (VF-CWs), planted with Phragmites australis and unplanted. Biochar-filled VF-CWs have been tested for 8 months for the refining of effluents from the tertiary clariflocculation stage of a wastewater treatment plant operating in a mixed domestic-industrial textile context, in comparison with systems filled with gravel. VF-CW influents and effluents were monitored for chemical oxygen demand (COD), nitrogen and phosphorus cycles, and absorbance values at 254 and 420 nm, the latter as rapid and reliable screening parameters of the removal of organic micropollutants containing aromatic moieties and/or chromophores. Biochar-based systems provided a statistically significant improvement in COD (Δ = 22%) and ammonia (Δ = 35%) removal, as well as in the reduction of UV–Vis absorbance values (Δ = 32–34% and Δ = 28% for 254 and 420 nm, respectively), compared to gravel-filled microcosms. The higher removal of organic was mainly attributed to the well-known adsorption properties of biochars, while for nitrogen the biological mechanisms seem to play a predominant role. HIGHLIGHTS Biochar from sawdust and sludge 70/30 (w/w) was produced and characterized.; This biochar was used as filling media of vertical-flow constructed wetlands (CWs).; Planted and unplanted CWs filled with biochar were compared to gravel-based CWs.; Organic matter, UV–Vis absorption, nitrogen and phosphorus cycles were monitored.; Biochar-based CWs showed 22–35% higher removal, depending on the parameter studied.;

ABSTRACTRelative soil water content (RSWC) is widely used to characterize the impact of water stress (WS) on vegetation. In bi-layer ecosystems, such as olive groves, this impact must be primarily estimated for the tree component, which, having greater rooting depth, responds more slowly to WS than understory grass. This complicates the application of methods for RSWC prediction, which must be properly adapted to consider the deeper soil layer influential on olive trees. The current study investigates the modification of a recently proposed RSWC simulation method based on a combination of meteorological and satellite-derived normalized difference vegetation index (NDVI) data. The application of the method to an olive grove in Central Italy requires the estimation of both weather and NDVI contributions affecting solely olive trees, which is carried out through the use of appropriate data processing techniques. The RSWC estimates obtained reasonably reproduce the ground RSWC observations referred to the 1 m soil layer, which are representative of the WS affecting olive trees (r2 = 0.795, RMSE = 0.15 and MBE = −0.09). The limits and prospects of this method are finally discussed with particular reference to the possible integration of the RSWC estimates within more complex ecosystem models.

A recently proposed modelling strategy predicts the net primary production (NPP) of forest ecosystems by combining the outputs of a NDVI-driven model, Modified C-Fix, and a bio-geochemical model, BIOME-BGC. This combination strategy takes into account the effects of forest disturbances but still assumes the presence of a mixture of differently aged trees. The application of this strategy to even-aged forests, therefore, requires a methodological advancement aimed at properly modifying the modelling of main ecosystem processes. In particular, the adaptation of the method to even-aged forests is based on the use of high-spatial-resolution airborne laser scanning (ALS) datasets, which yields green and woody biomass estimates that regulate the simulation of photosynthetic and respiratory processes, respectively. This approach was experimented in a Mediterranean study area, San Rossore Regional Park (Central Italy), which is covered by even-aged pine stands in different development phases. The modelling strategy is driven by MODIS NDVI images and meteorological data across five years (2011–2015), which are combined with estimates of forest canopy cover and height obtained from ALS data taken in 2015. This allows the production of stand NPP estimates, which, when converted into respective current annual increment (CAI) values, reasonably reproduce the age dependency of the available ground observations. The CAI estimates also show a highly significant correlation with these observations (r = 0.773) and moderate error levels (RMSE = 2.03 m3 ha−1 year−1, MBE = −0.45 m3 ha−1 year−1). These results confirm the potential of the modified simulation method to yield accurate high-spatial-resolution NPP estimates, which can offer valuable insights into C cycling and storage, in even-aged forests.

This paper describes a case study involving a revamping of a full-scale membrane bioreactor that treats landfill leachate and other liquid wastes. The main change was the introduction of nitritation/denitritation in alternating cycles instead of the classic denitrification/nitrification process, together with the installation of fine bubble diffusers, a reduction in the volume of the biological compartment, and an increase in the equalization volume. The most significant results were obtained for the biological compartment, with a decrease in the specific energy consumption of 46.6%. At the same time, the removal efficiency of COD, BOD, and TN substantially remained the same before and after plant revamping, while the removal efficiency of TP increased over the years, reaching an average value of almost 71%. Regarding the ultrafiltration unit, the specific flux (or permeability) was characterized by an increasing trend. At the same time, the specific energy consumption of this section decreased by 9.4%. These results led to the conclusion that the changes introduced with the revamp led to a more stable process, a reduction in membrane fouling, and important energy savings.

This work presents the design and implementation of an operational infrastructure for the monitoring of atmospheric parameters at sea through GNSS meteorology sensors installed on liners operating in the north-west Mediterranean Sea. A measurement system, capable of operationally and continuously providing the values of surface parameters, is implemented together with software procedures based on a float-PPP approach for estimating zenith path delay (ZPD) values. The values continuously registered over a three year period (2020–2022) from this infrastructure are compared with the data from a numerical meteorological reanalysis model (MERRA-2). The results clearly prove the ability of the system to estimate the ZPD from ship-based GNSS-meteo equipment, with the accuracy evaluated in terms of correlation and root mean square error reaching values between 0.94 and 0.65 and between 18.4 and 42.9 mm, these extreme values being from the best and worst performing installations, respectively. This offers a new perspective on the operational exploitation of GNSS signals over sea areas in climate and operational meteorological applications.

This study investigates the distinctive characteristics of iron oxide magnetic nanoparticles (mNPs) and their potential application in cancer therapy, focusing on melanoma. Three types of mNPs, pre-validated for safety, underwent molecular analysis to uncover the activated signaling pathways in melanoma cells. Using the Western blot technique, the study revealed that mNPs induce cytotoxicity, hinder proliferation through ERK1/2 dephosphorylation, and prompt proapoptotic effects, including DNA damage by inducing H2AX phosphorylation. Additionally, in vitro magnetic hyperthermia notably enhanced cellular damage in melanoma cells. Moreover, the quantification of intracellular iron levels through Inductively Coupled Plasma Mass Spectrometry (ICP-MS) analysis unveils the precise dosage required to induce cellular damage effectively. These compelling findings not only shed light on the therapeutic potential of mNPs in melanoma treatment but also open exciting avenues for future research, heralding a new era in the development of targeted and effective cancer therapies. Indeed, by discerning the effective dose, our approach becomes instrumental in optimizing the therapeutic utilization of iron oxide magnetic nanoparticles, enabling the induction of precisely targeted and controlled cellular responses.

A recent study has proposed and tested a semi-empirical method to estimate crop irrigation based on a water balance logic and Sentinel-2 Multi Spectral Instrument (MSI) NDVI imagery. The current paper aims at extending the same approach to the analysis of the main irrigation patterns occurred in Tuscany (Central Italy) during the 2000–2019 period. This operation was made possible by feeding the irrigation water (IW) estimation method with 250-m spatial resolution Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images. The results of this operation were first assessed versus various reference datasets available for the region; next, the annual maps of IW estimated for the 20 study years were analyzed at province scale in conjunction with relevant agricultural statistics. The use of MODIS in place of MSI images reduces the IW estimation accuracy irregularly at local scale, depending on the size and spatial arrangement of irrigated and non-irrigated fields; the reduction in accuracy is, however, marginal over relatively large areas. Irrigated crops are decreasing throughout most Tuscany provinces, while they are increasing in the most southern and driest province. The possible reasons and implications of these findings are finally discussed in relation to the main environmental issues affecting the region.