Seasonal River Discharge Forecasting Using Support Vector Regression: A Case Study in the Italian Alps

In this contribution we analyze the performance of a monthly river discharge forecasting model with a Support Vector Regression (SVR) technique in a European alpine area. We considered as predictors the discharges of the antecedent months, snow-covered area (SCA), and meteorological and climatic variables for 14 catchments in South Tyrol (Northern Italy), as well as the long-term average discharge of the month of prediction, also regarded as a benchmark. Forecasts at a six-month lead time tend to perform no better than the benchmark, with an average 33% relative root mean square error (RMSE%) on test samples. However, at one month lead time, RMSE% was 22%, a non-negligible improvement over the benchmark; moreover, the SVR model reduces the frequency of higher errors associated with anomalous months. Predictions with a lead time of three months show an intermediate performance between those at one and six months lead time. Among the considered predictors, SCA alone reduces RMSE% to 6% and 5% compared to using monthly discharges only, for a lead time equal to one and three months, respectively, whereas meteorological parameters bring only minor improvements. The model also outperformed a simpler linear autoregressive model, and yielded the lowest volume error in forecasting with one month lead time, while at longer lead times the differences compared to the benchmarks are negligible. Our results suggest that although an SVR model may deliver better forecasts than its simpler linear alternatives, long lead-time hydrological forecasting in Alpine catchments remains a challenge. Catchment state variables may play a bigger role than catchment input variables; hence a focus on characterizing seasonal catchment storage—Rather than seasonal weather forecasting—Could be key for improving our predictive capacity.JRC.H.1-Water Resource

Efficient characterisation of large deviations using population dynamics

We consider population dynamics as implemented by the cloning algorithm for analysis of large deviations of time-averaged quantities. We use the simple symmetric exclusion process with periodic boundary conditions as a prototypical example and investigate the convergence of the results with respect to the algorithmic parameters, focussing on the dynamical phase transition between homogeneous and inhomogeneous states, where convergence is relatively difficult to achieve. We discuss how the performance of the algorithm can be optimised, and how it can be efficiently exploited on parallel computing platforms

A Novel Data Fusion Technique for Snow Cover Retrieval

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a novel data fusion technique for improving the snow cover monitoring for a mesoscale Alpine region, in particular in those areas where two information sources disagree. The presented methodological innovation consists in the integration of remote-sensing data products and the numerical simulation results by means of a machine learning classifier (support vector machine), capable to extract information from their quality measures. This differs from the existing approaches where remote sensing is only used for model tuning or data assimilation. The technique has been tested to generate a time series of about 1300 snow maps for the period between October 2012 and July 2016. The results show an average agreement between the fused product and the reference ground data of 96%, compared to 90% of the moderate-resolution imaging spectroradiometer (MODIS) data product and 92% of the numerical model simulation. Moreover, one of the most important results is observed from the analysis of snow cover area (SCA) time series, where the fused product seems to overcome the well-known underestimation of snow in forest of the MODIS product, by accurately reproducing the SCA peaks of winter season

Contribution to the ecology of the Italian hare (Lepus corsicanus)

the italian hare (Lepus corsicanus) is endemic to Central-Southern Italy and Sicily, classified as vulnerable due to habitat alterations, low density and fragmented populations and ecological competition with the sympatric european hare (Lepus europaeus). Despite this status, only few and local studies have explored its ecological features. We provided some key traits of the ecological niche of the italian hare as well as its potential distribution in the italian peninsula. All data derived from genetically validated presences. We generated a habitat suitability model using maximum entropy distribution model for the italian hare and its main competitor, the european hare. the dietary habits were obtained for the italian hare with DnA metabarcoding and High-throughput Sequencing on faecal pellets. The most relevant environmental variables affecting the potential distribution of the italian hare are shared with the european hare, suggesting a potential competition. the variation in the observed altitudinal distribution is statistically significant between the two species.The diet of the Italian hare all year around includes 344 plant taxa accounted by 62 families. The Fagaceae, Fabaceae, Poaceae, Rosaceae and Solanaceae (counts > 20,000) represented the 90.22% of the total diet. Fabaceae (60.70%) and Fagaceae (67.47%) were the most abundant plant items occurring in the Spring/Summer and Autumn/Winter diets, respectively. the Spring/Summer diet showed richness (N = 266) and diversity index values (Shannon: 2.329, Evenness: 0.03858, Equitability: 0.4169) higher than the Autumn/Winter diet (N = 199, Shannon: 1.818, Evenness: 0.03096, Equitability: 0.3435). Our contribution adds important information to broaden the knowledge on the environmental (spatial and trophic) requirements of the Italian hare, representing effective support for fitting management actions in conservation planning

. Double-microcatheter technique through tortuous anatomy for coil embolization of a saccular splenic aneurysm: a technical report

We report on a case of an asymptomatic splenic artery aneurysm (SAA) with a large neck in a 53-year-old female with an extreme vessel tortuosity which was treated with a Double Microcatheter Technique. This endovascular procedure consists of embolization of the aneurysm using detachable coils with no application of any glue, stent or balloon. At the end of procedure, no complications occurred. At the three-month follow-up an MRI showed the aneurysm’s complete exclusion and patency of the splenic artery

Lmx1a-Dependent Activation of miR-204/211 Controls the Timing of Nurr1-Mediated Dopaminergic Differentiation

The development of midbrain dopaminergic (DA) neurons requires a fine temporal and spatial regulation of a very specific gene expression program. Here, we report that during mouse brain development, the microRNA (miR-) 204/211 is present at a high level in a subset of DA precursors expressing the transcription factor Lmx1a, an early determinant for DA-commitment, but not in more mature neurons expressing Th or Pitx3. By combining different in vitro model systems of DA differentiation, we show that the levels of Lmx1a influence the expression of miR-204/211. Using published transcriptomic data, we found a significant enrichment of miR-204/211 target genes in midbrain dopaminergic neurons where Lmx1a was selectively deleted at embryonic stages. We further demonstrated that miR-204/211 controls the timing of the DA differentiation by directly downregulating the expression of Nurr1, a late DA differentiation master gene. Thus, our data indicate the Lmx1a-miR-204/211-Nurr1 axis as a key component in the cascade of events that ultimately lead to mature midbrain dopaminergic neurons differentiation and point to miR-204/211 as the molecular switch regulating the timing of Nurr1 expression

Effect of antihypertensive treatments on insulin signalling in lympho-monocytes of essential hypertensive patients: a pilot study.

It was previously demonstrated that metabolic syndrome in humans is associated with an impairment of insulin signalling in circulating mononuclear cells. At least in animal models of hypertension, angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARB) may correct alterations of insulin signalling in the skeletal muscle. In the first study, we investigated the effects of a 3-month treatment with an ARB with additional PPARγ agonist activity, telmisartan, or with a dihydropyridine calcium channel blocker, nifedipine, on insulin signalling in patients with mild-moderate essential hypertension. Insulin signalling was evaluated in mononuclear cells by isolating them through Ficoll-Paque density gradient centrifugation and protein analysis by Western Blot. An increased expression of mTOR and of phosphorylated (active) mTOR (p-mTOR) was observed in patients treated with telmisartan, but not in those treated with nifedipine, while both treatments increased the cellular expression of glucose transporter type 4 (GLUT-4). We also investigated the effects of antihypertensive treatment with two drug combinations on insulin signalling and oxidative stress. Twenty essential hypertensive patients were included in the study and treated for 4 weeks with lercanidipine. Then they were treated for 6 months with lercanidipine + enalapril or lercanidipine + hydrochlorothiazide. An increased expression of insulin receptor, GLUT-4 and an increased activation of p70S6K1 were observed during treatment with lercanidipine + enalapril but not with lercanidipine + hydrochlorothiazide. In conclusion, telmisartan and nifedipine are both effective in improving insulin signalling in human hypertension; however, telmisartan seems to have broader effects. The combination treatment lercanidipine + enalapril seems to be more effective than lercanidipine + hydrochlorothiazide in activating insulin signalling in human lympho-monocytes

A score that predicts aquaporin-4-IgG positivity in patients with longitudinally extensive transverse myelitis

Background: Longitudinally extensive transverse myelitis (LETM) associated with aquaporin-4 autoantibodies (AQP4-IgG) can cause severe disability. Early diagnosis and prompt treatment are critical to prevent relapses. We describe a novel score based on clinical and neuroimaging characteristics that predicts AQP4-IgG positivity in patients with LETM. Methods: Patients were enrolled both retrospectively and prospectively from multiple Italian centers. Clinical and neuroimaging characteristics of AQP4-IgG positive and negative patients were compared through univariate and multivariate analysis. Results: Sixty-six patients were included. Twenty-seven (41%) were AQP4-IgG positive and median age at onset was 45.5 years old (range 19-81, interquartile range 24). Female sex (odds ratio [OR] 17.9; 95% confidence interval [CI] 2.6-381.9; p=0.014), tonic spasms (OR 45.6; CI 3.1-2197; p=0.017) and lesion hypointensity on T1-weighted images (OR 52.9; CI 6.8-1375; p=0.002) were independently associated with AQP4-IgG positivity. The Aquaporin-4-IgG positivity in Myelitis (AIM) score predicted AQP4-IgG positivity with 85% sensitivity and 95% specificity. Positive and negative likelihood ratio were 16.6 and 0.2 respectively. The inter-rater and intra-rater agreement in the score application were both excellent. Conclusions: The AIM score predicts AQP4-IgG positivity with good sensitivity and specificity in patients with a first episode of LETM. The score may assist clinicians in early diagnosis and treatment of AQP4-IgG positive LETM

Investigación de E. coli O157 y de Salmonella spp en bovinos de un feedlot de la provincia de Buenos Aires

La gestión ambiental apropiada en producciones intensivas requiere identificar las áreas de riesgo para controlar o reducir sus efectos sobre la contaminación. En feedlot, la contaminación localizada de suelos y aguas subterráneas y superficiales, emergente de la acumulación de deyecciones y movimiento de efluentes, constituye el área de mayor riesgo ambiental. Los residuos ganaderos son portadores de poblaciones microbianas y la industria frigorífica contribuye hasta en un 30% a la contaminación de las aguas. Escherichia coli productor de toxina Shiga (STEC) y Samonella spp., son bacterias patógenas asociadas a infección humana por consumo de alimentos contaminados.Trabajo publicado en Acta Bioquímica Clínica Latinoamericana; no. 52, supl. 2, parte II, diciembre de 2018.Universidad Nacional de La Plat

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients