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

CDKL5 expression is modulated during neuronal development and its subcellular distribution is tightly regulated by the C-terminal tail

Mutations in the human X-linked cyclin-dependent kinase-like 5 (CDKL5) gene have been identified in patients with Rett syndrome (RTT), West syndrome, and X-linked infantile spasms, sharing the common feature of mental retardation and early seizures. CDKL5 is a rather uncharacterized kinase, but its involvement in RTT seems to be explained by the fact that it works upstream of MeCP2, the main cause of Rett syndrome. To understand the role of this kinase for nervous system functions and to address if molecular mechanisms are involved in regulating its distribution and activity, we studied the ontogeny of CDKL5 expression in developing mouse brains by immunostaining and Western blotting. The expression profile of CDKL5 was compared with that of MeCP2. The two proteins share a general expression profile in the adult mouse brain, but CDKL5 levels appear to be highly modulated at the regional level. Its expression is strongly induced in early postnatal stages, and in the adult brain CDKL5 is present in mature neurons, but not in astroglia. Interestingly, the presence of CDKL5 in the cell nucleus varies at the regional level of the adult brain and is developmentally regulated. CDKL5 shuttles between the cytoplasm and the nucleus and the C-terminal tail is involved in localizing the protein to the cytoplasm in a mechanism depending on active nuclear export. Accordingly, Rett derivatives containing disease-causing truncations of the C terminus are constitutively nuclear, suggesting that they might act as gain of function mutations in this cellular compartment

Arx acts as a regional key selector gene in the ventral telencephalon mainly through its transcriptional repression activity

AbstractThe homeobox-containing gene Arx is expressed during ventral telencephalon development and required for correct GABAergic interneuron tangential migration from the ganglionic eminences to the olfactory bulbs, cerebral cortex and striatum. Its human ortholog is associated with a variety of neurological clinical manifestations whose symptoms are compatible with the loss of cortical interneurons and altered basal ganglia-related activities. Herein, we report the identification of a number of genes whose expression is consistently altered in Arx mutant ganglionic eminences. Our analyses revealed a striking ectopic expression in the ganglionic eminences of several of these genes normally at most marginally expressed in the ventral telencephalon. Among them, Ebf3 was functionally analyzed. Thus, its ectopic expression in ventral telencephalon was found to prevent neuronal tangential migration. Further, we showed that Arx is sufficient to repress Ebf3 endogenous expression and that its silencing in Arx mutant tissues partially rescues tangential cell movement. Together, these data provide new insights into the molecular pathways regulated by Arx during telencephalon development

Long-term efficacy and safety of ibrutinib in the treatment of CLL patients: A real life experience

Ibrutinib has demonstrated a significant clinical impact in patients with de novo and relapsed/refractory chronic lymphocytic leukemia (CLL), even in cases with unfavorable cytogenetics and molecular markers. All CLL patients’ data treated at our Institute with ibrutinib have been retrospectively reviewed. Forty-six patients received ibrutinib either as frontline (10) or second or more advanced treatment (36). Five patients presented with TP53 mutations; 11 had the deletion of chromosome 17p; 17 displayed an unmutated immunoglobulin variable heavy chain status. The median number of cycles administered was 26. Among patients treated frontline, the best overall response rate (ORR) was 90.0%. In patients receiving ibrutinib as a second or later line ORR was 97.2%. Median progression-free survival was 28.8 and 21.1 months for patients treated frontline and as second/later line, respectively. Median overall survival was not reached for those treated frontline and resulted in 4.9 years for patients treated as second/later line. Grade 3–4 hematological toxicities were neutropenia, thrombocytopenia, and anemia. Grade 3–4 extrahematological toxicities included diarrhea, cutaneous rash, utero-vesical prolapse, vasculitis, and sepsis. Ibrutinib is effective and well tolerated in CLL. Responses obtained in a real-life setting are durable and the safety profile of the drug is favorable

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

Prognostic value of interim positron emission tomography in patients with peripheral T-cell lymphoma

The definition of the role of positron emission tomography (PET) in peripheral T-cell lymphomas (PTCLs) is still under investigation. The purpose of the present observational retrospective study was to assess the early prognostic value of PET after the first three cycles of therapy (PET+3), evaluating visual data in de novo PTCL patients treated in first line with standard chemotherapy and followed by both PET and computed tomography scan. Of 27 PET+3-negative patients, 19 also had a negative PET at the end of treatment (PET+6), whereas 8 of 27 had a positive final one; 6 of 7 PET+3-positive patients had a positive PET+6, whereas only 1 patient had a negative PET+6. Estimated overall survival plotted according to PET+3 results showed 78.6% for negative patients and 21.4% for positive patients at 88.7 months with a significant difference. Patients with negative PET+3 had superior progression-free survival of 72.6% compared with 16.7% of PET+3-positive patients. At the time of this analysis, 17 of 19 (89.5%) patients with negative PET+3 are in continuous complete response (CCR) and only 1 of 7 (14.2%) patients with positive PET+3 is still in CCR. In conclusion, our results indicate that positive PET+3 is predictive of a worse outcome in PTCL, and this significant statistical difference between the two curves could be clinically informative. Larger and prospective studies and harmonization of PET reading criteria are needed

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