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The occurrence of river floods is strongly related to specific climatic conditions that favor extreme precipitation events leading to catchment saturation. Although the impact of precipitation and temperature patterns on river flows is a well discussed topic in hydrology, few studies have focused on the relationship between peak discharges and standard Climate Change Indices (ETCCDI) of precipitation and temperature, widely used in climate research. It is of interest to evaluate whether these indices are relevant for characterizing and predicting floods in the Alpine area. In this study, a correlation analysis of the ETCCDI indices annual time series and annual maximum flows is presented for the Piedmont Region, in North-Western Italy. Spearman’s rank correlation is used to determine which ETCCDI indices are temporally correlated with maximum discharges, allowing to hypothesize which climate drivers better explain the interannual variability of floods. Moreover, the influence of climate (decadal) variability on the tendency of annual maximum discharges is examined by spatially correlating temporal trends of climate indices with temporal trends of the discharge series in the last twenty years, calculated using the Theil-Sen slope estimator. Results highlight that, while extreme precipitation indices are highly correlated with extreme discharges at the annual timescale, with different indices that are consistent with catchment size, the decadal tendencies of extreme discharges may be better explained by the decadal tendencies of the total annual precipitation over the study area. This suggests that future projections of the annual precipitation available from climate models simulations, whose reliability is higher compared to precipitation extremes, may be used as covariates for non-stationary flood frequency analysis.

Large scale modelling is becoming increasingly important in hydrology, particularly to characterize and quantify changes in the hydrological regime, whose drivers are typically large-scale phenomena, up to the global scale (e.g., climate change). This can be done with distributed models by estimating spatially consistent model parameters i.e. parameters having a functional relationship with catchment characteristics. In this study we adopt the newly developed PArameter Set Shuffling (PASS) approach, based on a machine learning decision tree algorithm, for the regional calibration of the TUWmodel over North-Western Italy. The method exploits observed patterns of locally calibrated parameters and catchment (climatic and geomorphological) descriptors, to derive functional relationships between the variables. The calibration procedure is performed by including snow cover information, as captured by MODIS datasets, in the model efficiency function. The results show that the PASS regionalization procedure allows to obtain very good regional model efficiencies, without significant loss of performance when moving from training to test catchments and from calibration to verification period, confirming the robustness of the methodology. We also highlight that using snow information in the calibration procedure is helpful to obtain spatially consistent model parameters for this study area. In the spirit of “obtaining good results for the right reasons”, this should be a preferred approach when performing the regional calibration of distributed hydrologic models over mountainous regions.

ObjectiveThe signal transducer and activator of transcription 3 (STAT3) gain-of-function (GOF) syndrome (STAT3-GOF) is an inborn error of immunity (IEI) characterized by diverse manifestations of immune dysregulation that necessitate systemic immunomodulatory treatment. The blockade of the interleukin-6 receptor and/or the inhibition of the Janus kinases has been commonly employed to treat diverse STAT3-GOF-associated manifestations. However, evidence on long-term treatment outcome, especially in the case of adult patients, is scarce.MethodsClinical data, including laboratory findings and medical imaging, were collected from all seven patients, diagnosed with STAT3-GOF, who have been treated at the Hannover University School, focusing on those who received a Janus kinase (JAK) inhibitor (JAKi). Previously published cases of STAT3-GOF patients who received a JAKi were evaluated, focusing on reported treatment efficacy with respect to diverse STAT3-GOF-associated manifestations of immune dysregulation and safety.ResultsFive out of seven patients diagnosed with STAT3-GOF were treated with a JAKi, each for a different indication. Including these patients, outcomes of JAKi treatment have been reported for a total of 41 patients. Treatment with a JAKi led to improvement of diverse autoimmune, inflammatory, or lymphoproliferative manifestations of STAT3-GOF and a therapeutic benefit could be documented for all except two patients. Considering all reported manifestations of immune dysregulation in each patient, complete remission was achieved in 10/41 (24.4%) treated patients.ConclusionsJAKi treatment improved diverse manifestations of immune dysregulation in the majority of STAT3-GOF patients, representing a promising therapeutic approach. Long-term follow-up data are needed to evaluate possible risks of prolonged treatment with a JAKi.

Abstract SNURPORTIN-1, encoded by SNUPN, plays a central role in the nuclear import of spliceosomal small nuclear ribonucleoproteins. However, its physiological function remains unexplored. In this study, we investigate 18 children from 15 unrelated families who present with atypical muscular dystrophy and neurological defects. Nine hypomorphic SNUPN biallelic variants, predominantly clustered in the last coding exon, are ascertained to segregate with the disease. We demonstrate that mutant SPN1 failed to oligomerize leading to cytoplasmic aggregation in patients’ primary fibroblasts and CRISPR/Cas9-mediated mutant cell lines. Additionally, mutant nuclei exhibit defective spliceosomal maturation and breakdown of Cajal bodies. Transcriptome analyses reveal splicing and mRNA expression dysregulation, particularly in sarcolemmal components, causing disruption of cytoskeletal organization in mutant cells and patient muscle tissues. Our findings establish SNUPN deficiency as the genetic etiology of a previously unrecognized subtype of muscular dystrophy and provide robust evidence of the role of SPN1 for muscle homeostasis.

New technologies in genetic diagnostics have revolutionized the understanding and management of rare diseases. This review highlights the significant advances and latest developments in genetic diagnostics in inborn errors of immunity (IEI), which encompass a diverse group of disorders characterized by defects in the immune system, leading to increased susceptibility to infections, autoimmunity, autoinflammatory diseases, allergies, and malignancies. Various diagnostic approaches, including targeted gene sequencing panels, whole exome sequencing, whole genome sequencing, RNA sequencing, or proteomics, have enabled the identification of causative genetic variants of rare diseases. These technologies not only facilitated the accurate diagnosis of IEI but also provided valuable insights into the underlying molecular mechanisms. Emerging technologies, currently mainly used in research, such as optical genome mapping, single cell sequencing or the application of artificial intelligence will allow even more insights in the aetiology of hereditary immune defects in the near future. The integration of genetic diagnostics into clinical practice significantly impacts patient care. Genetic testing enables early diagnosis, facilitating timely interventions and personalized treatment strategies. Additionally, establishing a genetic diagnosis is necessary for genetic counselling and prognostic assessments. Identifying specific genetic variants associated with inborn errors of immunity also paved the way for the development of targeted therapies and novel therapeutic approaches. This review emphasizes the challenges related with genetic diagnosis of rare diseases and provides future directions, specifically focusing on IEI. Despite the tremendous progress achieved over the last years, several obstacles remain or have become even more important due to the increasing amount of genetic data produced for each patient. This includes, first and foremost, the interpretation of variants of unknown significance (VUS) in known IEI genes and of variants in genes of unknown significance (GUS). Although genetic diagnostics have significantly contributed to the understanding and management of IEI and other rare diseases, further research, exchange between experts from different clinical disciplines, data integration and the establishment of comprehensive guidelines are crucial to tackle the remaining challenges and maximize the potential of genetic diagnostics in the field of rare diseases, such as IEI.

Quantification of cortisol concentration in hair has become a promising conservation tool for non-invasive monitoring of “stress” in wild populations, yet this method needs to be carefully validated for each species. The goals of the study were: • Immunologically validate two methods (study 1 and 2 respectively) to extract and quantify cortisol in the hair of wild Alpine marmots. • Compare the amount of cortisol extracted from hair samples using two methods i.e. cut into fine pieces (study 1) and hair samples pulverized using a ball mill (study 2). • Determine the extent to which methods in study 2 could provide individual specific hair cortisol (HC) measures when samples were taken from the same body location. Within and between individual variations in HC levels were examined from multiple hair samples from 14 subjects in study 2. We evaluated if inter-individual variations in HC levels could be explained by sex and age.At least twice the amount of cortisol was obtained per g/hair when samples were pulverized in a ball mill prior to extraction compared to when cut into pieces. Our methods demonstrated intra-individual consistency in HC at a given time point: inter-individual variation in HC was three times larger than within individual variance. Sex and age did not impact HC levels.

The long-term comparison between simulated and observed spectrally resolved outgoing longwave radiation (OLR) can represent a stringent test for the direct verification and improvement of general circulation models (GCMs), which are regularly tuned by adjusting parameters related to subgrid processes not explicitly represented in the model to constrain the integrated OLR energy fluxes to observed values. However, a good agreement between simulated and observed integrated OLR fluxes may be obtained from the cancellation of opposite-in-sign systematic errors localized in specific spectral ranges. Since the mid-2000s, stable hyperspectral observations of the mid-infrared region (667 to 2750 cm−1) of the Earth emission spectrum have been provided by different sensors (e.g. AIRS, IASI and CrIS). Furthermore, the FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) mission, selected to be the ninth ESA Earth Explorer, will measure, starting from 2027, the terrestrial radiation emitted to space at the top of the atmosphere (TOA) from 100 to 1600 cm−1, filling the observational gap in the far-infrared (FIR) region, from 100 to 667 cm−1. In this work, in anticipation of FORUM measurements, we compare Infrared Atmospheric Sounding Interferometer (IASI) Metop-A observations to radiances simulated on the basis of the atmospheric fields predicted by the EC-Earth Global Climate Model (version 3.3.3) in clear-sky conditions. To simulate spectra based on the atmospheric and surface state provided by the climate model, the radiative transfer model σ-IASI has been integrated in the Cloud Feedback Model Intercomparison Project (COSP) package. Therefore, online simulations, provided by the EC-Earth model equipped with the new COSP–σ-IASI module, have been performed in clear-sky conditions with prescribed sea surface temperature and sea ice concentration, every 6 h, over a time frame consistent with the availability of IASI data. Systematic comparisons between observed and simulated brightness temperature (BT) have been performed in 10 cm−1 spectral intervals, on a global scale over the ocean, with a specific focus on the latitudinal belt between 30∘ S and 30∘ N. The analysis has shown a warm BT bias of about 3.5 K in the core of the CO2 absorption band and a cold BT bias of approximately 1 K in the wing of the CO2 band, due to a positive temperature bias in the stratosphere and a negative temperature bias in the middle troposphere of the climate model, respectively. Finally, considering a warm BT bias in the rotational–vibrational water vapour band, we have highlighted a dry bias of the water vapour concentration in the upper troposphere of the model.

Climate warming in mountain regions is resulting in glacier shrinking, seasonal snow cover reduction, and changes in the amount and seasonality of meltwater runoff, with consequences on water availability. Droughts are expected to become more severe in the future with economical and environmental losses both locally and downstream. Effective adaptation strategies involve multiple timescales, and seasonal forecasts can help in the optimization of the available snow and water resources with a lead time of several months. We developed a prototype to generate seasonal forecasts of snow depth and snow water equivalent with a starting date of 1 November and a lead time of 7 months, so up to 31 May of the following year. The prototype has been co-designed with end users in the field of water management, hydropower production and mountain ski tourism, meeting their needs in terms of indicators, time resolution of the forecasts and visualization of the forecast outputs. In this paper we present the modelling chain, based on the seasonal forecasts of the ECMWF and Météo-France seasonal prediction systems, made available through the Copernicus Climate Change Service (C3S) Climate Data Store. Seasonal forecasts of precipitation, near-surface air temperature, radiative fluxes, wind and relative humidity are bias-corrected and downscaled to three sites in the Western Italian Alps and finally used as input for the physically based multi-layer snow model SNOWPACK. Precipitation is bias-corrected with a quantile mapping method using ERA5 reanalysis as a reference and then downscaled with the RainFARM stochastic procedure in order to allow an estimate of uncertainties due to the downscaling method. The impacts of precipitation bias adjustment and downscaling on the forecast skill have been investigated. The skill of the prototype in predicting the deviation of monthly snow depth with respect to the normal conditions from November to May in each season of the hindcast period 1995–2015 is demonstrated using both deterministic and probabilistic metrics. Forecast skills are determined with respect to a simple forecasting method based on the climatology, and station measurements are used as reference data. The prototype shows good skills at predicting the tercile category, i.e. snow depth below and above normal, in the winter (lead times: 2–3–4 months) and spring (lead times: 5–6–7 months) ahead: snow depth is predicted with higher accuracy (Brier skill score) and higher discrimination (area under the relative operating characteristics (ROC) curve skill score) with respect to a simple forecasting method based on the climatology. Ensemble mean monthly snow depth forecasts are significantly correlated with observations not only at short lead times of 1 and 2 months (November and December) but also at lead times of 5 and 6 months (March and April) when employing the ECMWFS5 forcing. Moreover the prototype shows skill at predicting extremely dry seasons, i.e. seasons with snow depth below the 10th percentile, while skills at predicting snow depth above the 90th percentile are model-, station- and score-dependent. The bias correction of precipitation forecasts is essential in the case of large biases in the global seasonal forecast system (MFS6) to reconstruct a realistic snow depth climatology; however, no remarkable differences are found among the skill scores when the precipitation input is bias-corrected, downscaled, or bias-corrected and downscaled, compared to the case in which raw data are employed, suggesting that skill scores are weakly sensitive to the treatment of the precipitation input.

IntroductionRare genetic diseases are a major cause for severe illness in children. Whole exome sequencing (WES) is a powerful tool for identifying genetic causes of rare diseases. For a better and faster assessment of the vast number of variants that are identified in the index patient in WES, parental sequencing can be applied (“trio WES”).MethodsWe assessed the diagnostic rate of routine trio WES including analysis of copy number variants in 224 pediatric patients during an evaluation period of three years.ResultsTrio WES provided a diagnosis in 67 (30%) of all 224 analysed children. The turnaround time of trio WES analysis has been reduced significantly from 41 days in 2019 to 23 days in 2021. Copy number variants could be identified to be causative in 10 cases (4.5%), underlying the importance of copy number variant analysis. Variants in three genes which were previously not associated with a clinical condition (GAD1, TMEM222 and ZNFX1) were identified using the matching tool GeneMatcher and were part of the first description of a new syndrome.DiscussionTrio WES has proven to have a high diagnostic yield and to shorten the process of identifying the correct diagnosis in paediatric patients. Re-evaluation of all 224 trio WES 1–3 years after initial analysis did not establish new diagnoses. Initiating (trio) WES as a first-tier diagnostics including copy number variant detection should be considered as early as possible, especially for children treated in ICU, if a monogenetic disease is suspected.

Whole-exome sequencing has expedited the diagnostic work-up of primary ciliary dyskinesia (PCD), when used in addition to clinical phenotype and nasal nitric oxide. However, it reveals variants of uncertain significance (VUS) in established PCD genes or (likely) pathogenic variants in genes of uncertain significance in approximately 30% of tested individuals. We aimed to assess genotype–phenotype correlations in adults with bronchiectasis, clinical suspicion of PCD, and inconclusive whole-exome sequencing results using transmission electron microscopy (TEM) and ciliary image averaging by the PCD Detect software. We recruited 16 patients with VUS in CCDC39, CCDC40, CCDC103, DNAH5, DNAH5/CCDC40, DNAH8/HYDIN, DNAH11, and DNAI1 as well as variants in the PCD candidate genes DNAH1, DNAH7, NEK10, and NME5. We found normal ciliary ultrastructure in eight patients with VUS in CCDC39, DNAH1, DNAH7, DNAH8/HYDIN, DNAH11, and DNAI1. In six patients with VUS in CCDC40, CCDC103, DNAH5, and DNAI1, we identified a corresponding ultrastructural hallmark defect. In one patient with homozygous variant in NME5, we detected a central complex defect supporting clinical relevance. Using TEM as a targeted approach, we established important genotype–phenotype correlations and definite PCD in a considerable proportion of patients. Overall, the PCD Detect software proved feasible in support of TEM.

Monogenic autoinflammatory diseases (AID) encompass a growing group of inborn errors of the innate immune system causing unprovoked or exaggerated systemic inflammation. Diagnosis of monogenic AID requires an accurate description of the patients’ phenotype, and the identification of highly penetrant genetic variants in single genes is pivotal. We performed whole exome sequencing (WES) of 125 pediatric patients with suspected monogenic AID in a routine genetic diagnostic setting. Datasets were analyzed in a step-wise approach to identify the most feasible diagnostic strategy. First, we analyzed a virtual gene panel including 13 genes associated with known AID and, if no genetic diagnosis was established, we then analyzed a virtual panel including 542 genes published by the International Union of Immunological Societies associated including all known inborn error of immunity (IEI). Subsequently, WES data was analyzed without pre-filtering for known AID/IEI genes. Analyzing 13 genes yielded a definite diagnosis in 16.0% (n = 20). The diagnostic yield was increased by analyzing 542 genes to 20.8% (n = 26). Importantly, expanding the analysis to WES data did not increase the diagnostic yield in our cohort, neither in single WES analysis, nor in trio-WES analysis. The study highlights that the cost- and time-saving analysis of virtual gene panels is sufficient to rapidly confirm the differential diagnosis in pediatric patients with AID. WES data or trio-WES data analysis as a first-tier diagnostic analysis in patients with suspected monogenic AID is of limited benefit.