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The ATL2MED demonstration experiment involved two autonomous surface vehicles from Saildrone Inc. (SD) which travelled a route from the eastern tropical North Atlantic to the Adriatic Sea between October 2019 and July 2020 (see Table A6). This 9-month experiment in a transition zone between the temperate and tropical belts represents a major challenge for the SD's operations. The sensors on board were exposed to varying degrees of degradation and biofouling depending on the geographical area and season, which led to a deterioration in the measurements. As a result, some maintenance measures were required during the mission. We address the difficulty of correcting the data during a period of COVID-19 restrictions, which significantly reduced the number of discrete samples planned for the SD salinity and dissolved oxygen validation. This article details alternative correction methods for salinity and dissolved oxygen. Due to the lack of in situ data, model products have been used to correct the salinity data acquired by the SD instruments, and then the resulting corrected salinity was validated with data from fixed ocean stations, gliders, and Argo floats. In addition, dissolved oxygen data acquired from the SD instruments after correction using air oxygen measurements were tested and found to be coherent with the variation in oxygen concentrations expected from changes in temperature and phytoplankton abundance (from chlorophyll a). The correction methods are relevant and useful in situations where validation capabilities are lacking, which was the case during the ATL2MED demonstration experiment. For future experiments, a more frequent sample collection would improve the data qualification and validation.

Breast metastasis originating from a primary lung tumor is exceedingly rare and can present challenges in distinguishing it from primary breast cancer. This case report discusses the management of a 64-year-old woman who initially presented with a nodule in her left breast. A biopsy revealed an infiltrating ductal carcinoma. Despite negative BRCA genetic testing, her significant family history of cancer and the presence of a newly detected right breast lesion led to a bilateral mastectomy. Post-operative imaging identified multiple hypodense nodules and a spiculated pulmonary nodule, necessitating further investigation. An endoscopic lung biopsy confirmed a primary pulmonary carcinoma with histological features similar to the breast carcinoma, suggesting the lung as the primary source. This case highlights the complexity of differentiating breast metastasis originating from a lung tumor and primary breast cancer. It underscores the importance of comprehensive diagnostic evaluations and the consideration of extramammary origins in metastatic cases. The findings emphasize the role of multidisciplinary teams in managing such rare and challenging cases and highlight the necessity for thorough and repeated assessments in atypical breast cancer presentations.

Autoimmune encephalitis (AE) is an immune-mediated condition that induces brain inflammation due to several neural-specific autoantibodies. The main triggering and predisposing factors are infections, genetics, the use of immune checkpoint inhibitors and tumors. We report a case of a 57-year-old male with a biopsy-confirmed Langerhans cell histiocytosis (LCH) and a concomitant anti-LGI1 encephalitis discussing a possible relationship in the pathogenesis of these phenomena. Only sporadic cases of AE developing in the context of histiocytic neoplasms have been described and there are no other reports on the relationship between LGI1-AE and LCH worldwide due to small number of cases.

IntroductionA potential overlap in symptoms between post-acute COVID-19 syndrome and post-COVID-19 vaccination syndrome has been noted. We report a paired description of patients presenting with similar manifestations involving the central (CNS) or peripheral nervous system (PNS) following SARS-CoV-2 infection or vaccination, suggesting that both may have triggered similar immune-mediated neurological disorders in the presence of anti-idiotype antibodies directed against the ACE2 protein.Patients and methodsFour patients exhibited overlapping neurological manifestations following SARS-CoV-2 infection or vaccination: radiculitis, Guillain–Barré syndrome, and MRI-negative myelitis, respectively, sharing positivity for anti-ACE2 antibodies. Autoantibodies against AQP-4, MOG, GlyR, GAD, and amphiphysin, onconeural antibodies for CNS syndromes, and anti-ganglioside antibodies for PNS syndromes tested negative in all patients.DiscussionAnti-idiotype antibodies against ACE2 have been detected in patients who recovered from COVID-19 infection, and it has been hypothesized that such antibodies may mediate adverse events following SARS-CoV-2 infection or vaccination, resulting in the activation of the immune system against cells expressing ACE2, such as neurons. Our data reveal clinically overlapping syndromes triggered by SARS-CoV-2 infection or vaccination, sharing positivity for anti-ACE2 antibodies. Their presence, in the absence of other classic autoimmune markers of CNS or PNS involvement, suggests that they might play an active role in the context of an aberrant immune response.ConclusionAnti-idiotype antibodies directed against ACE2 may be triggered by both SARS-CoV-2 infection and vaccination, possibly contributing to neurological autoimmune manifestations. Their pathogenic role, however, remains to be demonstrated in large-scale, more structured studies.

River inlets, deltas, and estuaries represent delicate ecosystems highly susceptible to climate change impacts. While significant progress has been made in understanding the morphodynamics of these environments in recent decades, the development of models still requires thorough testing and data integration. In this context, remote sensing emerges as a potent tool, providing crucial data and the ability to monitor temporal changes. In this paper, an integrated approach combining remote sensing and morphodynamic modeling is proposed to assess river systems comprehensively. By utilizing multispectral or RGB optical imagery from satellites or UAVs for river classification and remotely derived bathymetry, echo sounder data for ground truth, and photogrammetric modeling of emerged areas, we outline a procedure to create an integrated and continuous digital terrain model (DTM) of a riverbed, paying particular attention to the wet–dry interface. This method enables us to identify the river centerline, its width, and its slope variations. Additionally, by applying a linear morphodynamic model that considers the spatial variability of river morphology commonly found in estuarine environments, it is possible to predict the wavelength and migration rate of sediment bars. This approach has been successfully applied to recreate the DTM and monitor the morphodynamics of the seaward reach of the Roya River (Italy).

Ocean surface radiation measurement best practices have been developed as a first step to support the interoperability of radiation measurements across multiple ocean platforms and between land and ocean networks. This document describes the consensus by a working group of radiation measurement experts from land, ocean, and aircraft communities. The scope was limited to broadband shortwave (solar) and longwave (terrestrial infrared) surface irradiance measurements for quantification of the surface radiation budget. Best practices for spectral measurements for biological purposes like photosynthetically active radiation and ocean color are only mentioned briefly to motivate future interactions between the physical surface flux and biological radiation measurement communities. Topics discussed in these best practices include instrument selection, handling of sensors and installation, data quality monitoring, data processing, and calibration. It is recognized that platform and resource limitations may prohibit incorporating all best practices into all measurements and that spatial coverage is also an important motivator for expanding current networks. Thus, one of the key recommendations is to perform interoperability experiments that can help quantify the uncertainty of different practices and lay the groundwork for a multi-tiered global network with a mix of high-accuracy reference stations and lower-cost platforms and practices that can fill in spatial gaps.

Even though genetic resources represent a fundamental reservoir of options to achieve sustainable development goals in a changing world, they are overlooked in the policy agenda and severely threatened. The conservation of genetic resources relies on complementary in situ and ex situ approaches appropriately designed for each type of organism. Environmental and socioeconomic changes raise new challenges and opportunities for sustainable use and conservation of genetic resources. Aiming at a more integrated and adaptive approach, European scientists and genetic resources managers with long experience in the agricultural crop, animal and forestry domains joined their expertise to address three critical challenges: (1) how to adapt genetic resources conservation strategies to climate change, (2) how to promote in situ conservation strategies and (3) how can genetic resources conservation contribute to and benefit from agroecological systems. We present here 31 evidence-based statements and 88 key recommendations elaborated around these questions for policymakers, conservation actors and the scientific community. We anticipate that stakeholders in other genetic resources domains and biodiversity conservation actors across the globe will have interest in these crosscutting and multi-actor recommendations, which support several biodiversity conservation policies and practices.

Monitoring slow-moving landslides is a crucial task for socioeconomic risk prevention and/or mitigation. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is an advanced remote sensing method for monitoring ground deformation. In this research, PS-InSAR time series derived from COSMO-SkyMed (descending orbit) and Sentinel-1 (ascending orbit) are analyzed for a region in Central Apennines in Italy. The sequential turning point detection method (STPD) is implemented to detect the trend turning dates and their directions in the PS-InSAR time series within areas of interest susceptible to landslides. The monthly maps of significant turning points and their directions for years 2018, 2019, 2020, and 2021 are produced and classified for four Italian administrative regions, namely, Marche, Umbria, Abruzzo, and Lazio. Monthly global precipitation measurement (GPM) images at 0.1∘×0.1∘ spatial resolution and four local precipitation time series are also analyzed by STPD to investigate when the precipitation rate has changed and how they might have reactivated slow-moving landslides. Generally, a strong correlation (r≥0.7) is observed between GPM (satellite-based) and local precipitation (station-based) with similar STPD results. Marche and Abruzzo (the coastal regions) have an insignificant precipitation rate while Umbria and Lazio have a significant increase in precipitation from 2017 to 2023. The coastal regions also exhibit relatively lower precipitation amounts. The results indicate a strong correlation between the trend turning dates of the accumulated precipitation and displacement time series, especially for Lazio during summer and fall 2020, where relatively more significant precipitation rate of change is observed. The findings of this study may guide stakeholders and responsible authorities for risk management and mitigating damage to infrastructures.

Land Surface Temperature (LST) is an important climate factor for understanding the relationship between the land surface and atmosphere. Furthermore, LST is linked to soil moisture and evapotranspiration, which can potentially alter the severity and regime of wildfires, landslide-triggering precipitation thresholds, and others. In this paper, the monthly daytime and nighttime LST products of Moderate Resolution Imaging Spectroradiometer (MODIS) are employed for the period 2000–2023 in order to find areas that have been cooling or warming in a region of great interest in Central Italy, due to its complex geological and geomorphological settings and its recent seismic sequences and landslide events. The annual MODIS land cover images for 2001–2022 are also utilized to investigate the interconnection between LST and land cover change. The results of the non-parametric Mann–Kendall trend test and its associated Sen’s slope reveal a significant nighttime warming trend in the region, particularly in July, linked to forest and woodland expansion. Grasslands toward the coastline with low elevation (less than 500 m a.s.l.) have experienced significant heat waves during the summer, with an LST of more than 35 °C. A significant negative correlation between the elevation and LST is observed for each calendar month. In particular, the daytime and nighttime LST have more than 80% correlation with elevation during winter and summer, respectively. In addition, nighttime warming and gradual drainage are noticed in Lake Campotosto. The results of this study could be useful for wildfire and landslide susceptibility analyses and hazard management.

BackgroundThere is little information on the trajectory and developmental fate of Lin-CD34+DNAM-1bright CXCR4+ progenitors exiting bone marrow during systemic inflammation.ObjectiveTo study Lin-CD34+DNAM-1bright CXCR4+ cell circulation in cancer patients, to characterize their entry into involved lung tissue and to characterize their progenies.MethodsFlow cytometric analysis of PBMC from 18 patients with lung cancer on samples collected immediately before the first and the second treatment was performed to study Lin-CD34+DNAM-1bright CXCR4+ precursors. Precursors were purified (>99%) and cultured in vitro from all patients. Paired PBMC and tissue samples from patients undergoing tumor resection were analyzed by flow cytometry to assess tissue entry and compare phenotype and developmental potential of Lin-CD34+DNAM-1bright CXCR4+ cells in both compartments.ResultsSignificant circulation of Lin-CD34+DNAM-1bright CXCR4+ precursors was observed 20d after the first treatment. Precursors express CXC3CR1, CXCR3, CXCR1 consistent with travel towards inflamed tissues. Flowcytometric analysis of lung tissue samples showed precursor presence in all patients in tumor and neighboring uninvolved areas. Successful purification and in vitro culture from both blood and lung tissue generates a minor proportion of maturing NK cells (85%) of α/β T-progenies with innate-like phenotype expressing NKG2D,NKp30,DNAM-1. Innate-like maturing T-cells in vitro are cytotoxic, can be triggered via NKR/TCR co-stimulation and display broad spectrum Th1,Th2 and Th1/Th17 cytokine production.ConclusionIn advanced stage lung cancer CD34+DNAM-1brightCXCR4+ inflammatory precursors increase upon treatment, enter involved tissues, generate functional progenies and may thus represent an additional player contributing to immune balance in the highly SDF-1/CXCR4-biased pro-metastatic tumor microenvironment.

Fermentation biomasses can be defined as a complex mixture of different natural components and microbes, having biodegradable and organic waste as the primary source. Its correct characterization is crucial to have proper processing in fermentative units. Firstly, proximate analysis is done to retrieve the content of specific compounds in the mixture, such as fat, proteins, and carbohydrates. However, this is often not enough to achieve the sufficient precision, since some low-concentration species are not easily found through this methodology (i.e., sulfate compounds, ethanol, caproic acid). Consequently, ultimate analysis is performed to evaluate the exact amount of every element in the mixture. For biomass-based compounds, atoms content can be synthesized in carbon, hydrogen, oxygen, nitrogen, and sulfur. The total content of these elements is also known as CHONS. From this, it is possible to derive the exact amount of the relative species in the biomass. However, the experimental procedure for its determination is rather time and budget-consuming. On the other hand, the amount of data collected in the literature, from both experimental and industrial analysis, can be exploited to build a numerical model, based on the multivariate statistical analysis and machine learning principles that predict the CHONS content for every type of biomass. In this work, a data-driven model has been developed to achieve this aim, having as input a set of relevant variables. Consequently, a dataset has been built to gather all these data. The multivariate statistical technique of Canonical Correlation Analysis (CCA) is used to find 'hidden' correlations and predict CHON content for 27 different biomass types. In future research, machine learning techniques will be applied to compare the results obtained.