Ecotoxicity of basil (Ocimum Basilicum) extract in aquaculture feeds: Is it really eco-safe for the aquatic environment?

Plant extract and essential oils are gaining application in aquaculture, but data about their environmental impact are limited and their potential effects on aquatic organisms are largely unknown. For this study, ecotoxicity tests were performed under standardized conditions on fish feed supplemented with 3 % w/w of a basil supercritical extract (F1-BEO; substance A), F1-BEO extract (substance B), and fish feed without F1-BEO extract (substance C) on three model species of different trophic levels (bacteria, primary producer, primary consumer) considered representative for freshwater (Aliivibrio fischeri, Raphidocelis subcapitata, Daphnia magna) and marine (A. fischeri, Phaeodactylum tricornutum, Paracentrotus lividus) ecosystems. Ecotoxicological response was largely comparable within the same trophic level (whichever the ecosystem). EC50 was not calculable in the concentration range here tested (3.9–500 mg/L) for freshwater and marine microalgae, suggesting that none of the substances were toxic for primary producers. Reduction of A. fischeri bioluminescence at the tested concentration (0.5–10 mg/L) was observed only for substance A (EC50 9.53 mg/L and 9 mg/L for freshwater and marine ecosystems, respectively). Notably, in P. lividus embryotoxicity was higher for substances A (EC50 1.80 mg/L) and C (EC50 4.6 mg/L) than for substance B (EC50 7.10 mg/L), suggesting a toxic effect due to feed dissolution. In contrast, substance B was more toxic (EC50 0.34 mg/L) in D. magna than substances A (EC50 3.98 mg/L) and C (EC50 5.50 mg/L). Based on the Globally Harmonized System of Classification and Labelling of Chemicals, all substances were categorized Acute 2, except for substance A which was categorized Acute 1 for D. magna. Overall, the substances were found to be potentially toxic for an aquatic ecosystem, especially for primary consumer. Further study of plant extract and essential oils is needed to better understand their effects and fate on the aquatic environment

The economic value of a climate service for water irrigation. A case study for Castiglione District, Emilia-Romagna, Italy

The use of climate services to support decision makers in incorporating climate change adaptation in their practices is well established and widely recognized. Their role is particularly relevant in a climate sensitive sector like agriculture where they can provide evidence for the adoption of transformative solutions from seasonal to multi-decadal time scales. Adaptation solutions are often expensive and irreversible in the short/medium run. Accordingly, end users should have a reliable reference to make decisions. Here, we propose and apply a methodology, co-developed with service developers and a representative potential user, to assess the value of the IRRICLIME climate service, whose information is used to support decisions on climate smart irrigation investment by water planners in a sub-irrigation district in Italy. We quantify the value of the information provided by the climate service, that we consider the intrinsic value of the service, or the value of adaptation. We demonstrate that under three different climate change scenarios, the maximum potential value of IRRICLIME could range between 2,985 €/ha and 7,480 €/ha

Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets

Smart climate hydropower tool: A machine-learning seasonal forecasting climate service to support cost–benefit analysis of reservoir management

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets

Controversies in the Treatment of Peripheral T-cell Lymphoma.

Peripheral T-cell lymphomas are a heterogeneous group of rare diseases with an aggressive behavior and dismal prognosis. Their classification is complex and still evolving, and several biomolecular markers now help refine the prognosis of specific disease entities, although still have limited impact in tailoring the treatment. First-line treatment strategies can cure only a minority of patients and relapsed-refractory disease still represents the major cause of failure. Frontline autologous transplantation may have an impact in the consolidation of response; however, its role is still questioned as far as complete responses obtained after induction chemotherapy are concerned. Newer drugs are now being evaluated in clinical trials, but effective salvage strategies for those who experience treatment failures are lacking. Here we review and discuss the most controversial aspects of diagnosis and treatment of peripheral T-cell lymphomas

A novel POLR3A genotype leads to leukodystrophy type-7 in two siblings with unusually late age of onset

Background: Leukodystrophies are familial heterogeneous disorders primarily affecting the white matter, which are defined as hypomyelinating or demyelinating based on disease severity as assessed at MRI. Recently, a group of clinically overlapping hypomyelinating leukodystrophies (HL) has been associated with mutations in RNA polymerase III enzymes (Pol III) subunits. Case presentation: In this manuscript, we describe two Italian siblings carrying a novel POLR3A genotype. MRI imaging, genetic analysis, and clinical data led to diagnosing HL type 7. The female sibling, at the age of 34, is tetra-paretic and suffers from severe cognitive regression. She had a disease onset at the age of 19, characterized by slow and progressive cognitive impairment associated with gait disturbances and amenorrhea. The male sibling was diagnosed during an MRI carried out for cephalalgia at the age of 41. After 5 years, he developed mild cognitive impairment, dystonia with 4-limb hypotonia, and moderate dysmetria with balance and gait impairment. Conclusions: The present study provides the first evidence of unusually late age of onset in HL, describing two siblings with a novel POLR3A genotype which showed the first symptoms at the age of 41 and 19, respectively. This provides a powerful insight into clinical heterogeneity and genotype-phenotype correlation in POLR3A related HL

Work Ability in Healthcare: Vulnerable Groups and Organizational Factors

The recent pandemic, along with heavy workloads and staff shortages, has placed significant pressure on healthcare workers. Maintaining adequate work ability is vital for ensuring favorable working conditions, mitigating stress, preventing related illnesses, and safeguarding worker performance and patient safety. This article assesses the work ability and working conditions of healthcare professionals at the University Hospital of Modena through a questionnaire administered between August 1, 2022, and September 30, 2022, to identify vulnerable groups and organizational factors influencing work ability. Among workers with reduced work ability, the majority are over 45 years old and female, 52% are obese, 64% have 3 or more illnesses, 47% report a poor work-life balance, and 50% have at least one dependent adult. Work characteristics are also highlighted as relevant: supervisor support and cooperation with colleagues, autonomy in decision-making processes, participation in the improvement of work processes, possession of skills appropriate to the tasks required, are associated with high levels of work ability. Finally, nurses and nurses aides are associated with lower work ability. Emergency and medical wards are particularly critical in terms of work ability when gender and age differences are taken into account

An initial intercomparison of atmospheric and oceanic climatology for the ICE-5G and ICE-4G models of LGM paleotopography

This paper investigates the impact of the new ICE-5G paleotopography dataset for Last Glacial Maximum (LGM) conditions on a coupled model simulation of the thermal and dynamical state of the glacial atmosphere and on both land surface and sea surface conditions. The study is based upon coupled climate simulations performed with the ocean–atmosphere–sea ice model of intermediate-complexity Climate de Bilt-coupled large-scale ice–ocean (ECBilt-Clio) model. Four simulations focusing on the Last Glacial Maximum [21 000 calendar years before present (BP)] have been analyzed: a first simulation (LGM-4G) that employed the original ICE-4G ice sheet topography and albedo, and a second simulation (LGM-5G) that employed the newly constructed ice sheet topography, denoted ICE-5G, and its respective albedo. Intercomparison of the results obtained in these experiments demonstrates that the LGM-5G simulation delivers significantly enhanced cooling over Canada compared to the LGM-4G simulation whereas positive temperature anomalies are simulated over southern North America and the northern Atlantic. Moreover, introduction of the ICE-5G topography is shown to lead to a deceleration of the subtropical westerlies and to the development of an intensified ridge over North America, which has a profound effect upon the hydrological cycle. Additionally, two flat ice sheet experiments were carried out to investigate the impact of the ice sheet albedo on global climate. By comparing these experiments with the full LGM simulations, it becomes evident that the climate anomalies between LGM-5G and LGM-4G are mainly driven by changes of the earth’s topography

Genetics and clinical phenotype of Erdheim–Chester disease: A case report of constrictive pericarditis and a systematic review of the literature

Background: Erdheim–Chester disease (ECD) is a rare form of histiocytosis. An increasing number of genetic mutations have been associated with this syndrome, confirming its possible neoplastic origin. Recently, a connection between the BRAF mutational status and a specific phenotype was described; however, no studies have yet evaluated the correlations between other mutations and the clinical features of the disease. Objectives: This study aims to clarify the association between the clinical phenotype and genetic mutations identified in the neoplastic cell lines of ECD. Methods: We describe a case of ECD characterized by pericardial involvement and a KRAS mutation shared with chronic myelomonocytic leukemia. Hence, through a meta-analysis of individual participant data of all genetically and clinically described cases of ECD in the literature, we aimed to elucidate the association between its clinical phenotype and baseline genetic mutations. Results: Of the 760 studies screened, our review included 133 articles published from 2012 to April 2021. We identified 311 ECD patients whose genotype and phenotype were described. We found five main genes (BRAF, KRAS, NRAS, PIK3CA, and MAP2K1) whose mutation was reported at least three times. Mutation of BRAF led to a neurological disease (183 of 273 patients, 67%; p < 0.001); KRAS- and NRAS-mutated patients mainly showed cutaneous (five of six patients, 83.3%, p < 0.004) and pleural (four of nine patients, 44%, p = 0.002) involvement, respectively; PIK3CA was not associated with specific organ involvement; and MAP2K1 mutations caused the disease to primarily involve the peritoneum and retroperitoneum (4 of 11, 36.4%, p = 0.01). Conclusion: This work implies a possible influence of baseline mutation over the natural history of ECD, underscoring the importance of a thorough genetic analysis in all cases with the ultimate goal of identifying a possible targeted therapy for each patient