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This paper proposes an M-quantile regression approach to address the heterogeneity of the housing market in a modern European city. We show how M-quantile modelling is a rich and flexible tool for empirical market price data analysis, allowing us to obtain a robust estimation of the hedonic price function whilst accounting for different sources of heterogeneity in market prices. The suggested methodology can generally be used to analyse nonlinear interactions between prices and predictors. In particular, we develop a spatial semiparametric M-quantile model to capture both the potential nonlinear effects of the cultural environment on pricing and spatial trends. In both cases, nonlinearity is introduced into the model using appropriate bases functions. We show how the implicit price associated with the variable that measures cultural amenities can be determined in this semiparametric framework. Our findings show that the effect of several housing attributes and urban amenities differs significantly across the response distribution, suggesting that buyers of lower-priced properties behave differently than buyers of higher-priced properties.

Spatial data are becoming increasingly accessible to urban scientists, but these data are often prone to measurement error. Motivated by the analysis of the Milan (Italy) apartment market heterogeneity, we propose a semiparametric approach to adjust for the presence of measurement error in the covariates when estimating M-quantile regression. The M-quantile approach helps explain the heterogeneity across individual units, preserving robustness and efficiency in the estimates. The model’s parameters are estimated within a penalised likelihood framework and an analytical expression is proposed to estimate standard errors. Asymptotic properties of estimates are also provided.

Wind turbine partial-load controllers have evolved from simple static nonlinear function implementations to more advanced dynamic controller structures. Such dynamic control schemes have the potential to improve power production performance in realistic environmental conditions and allow for a more granular trade-off between loads and energy capture. The control structure generally consists of a wind speed estimator (WSE) combined with a controller aiming to track the commanded tip-speed ratio (TSR) reference. The performance and resulting closed-loop system stability are however highly dependent on the accuracy of the internal model in the WSE-TSR tracking scheme. Therefore, developing learning algorithms to calibrate the internal model is of particular interest. Previous works have proposed such algorithms; however, they all rely on the availability of (rotor-effective) wind speed measurements. For the first time, this paper proposes an excitation-based learning algorithm that exploits the closed-loop dynamic structure of the WSE-TSR tracking scheme. This algorithm calibrates the internal model without the need for wind speed measurements. Analysis and simulations show that the proposed algorithm corrects for model uncertainties in the form of magnitude scaling errors under ideal constant and realistic turbulent wind conditions., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Mulders, Wind Energy, Team Jan-Willem van Wingerden, Team Riccardo Ferrari

Background: Following the increased survival of patients with metastatic melanoma thanks to immunotherapy and targeted therapy, neoadjuvant approaches are being investigated to address the unmet needs of unresponsive and intolerant patients. We aim to investigate the efficacy of neoadjuvant plus adjuvant combined or sequenced vemurafenib, cobimetinib and atezolizumab in patients with high-risk, resectable BRAF-mutated and wild-type melanoma. Methods: The study is a phase II, open-label, randomized non-comparative trial in patients with stage IIIB/C/D surgically resectable, BRAF-mutated and wild-type melanoma, with three possible treatments: (1) vemurafenib 960 mg twice daily from day 1 to 42; (2) vemurafenib 720 mg twice daily from day 1 to 42; (3) cobimetinib 60 mg once daily from day 1 to 21 and from day 29 to 42; and (4) atezolizumab 840 mg for two cycles (day 22 and day 43). Patients will be randomized to three different arms: A) BRAF-mutated patients will receive over 6 weeks (1) + (3); B) BRAF-mutated patients will receive over 6 weeks (2) + (3) + (4); C) BRAF wild-type patients will receive over 6 weeks (3) + (4). All patients will also receive atezolizumab 1200 mg every 3 weeks for 17 cycles after surgery and after a second screening period (up to 6 weeks). Discussion: Neoadjuvant therapy for regional metastases may improve operability and outcomes and facilitate the identification of biomarkers that can guide further lines of treatment. Patients with clinical stage III melanoma may especially benefit from neoadjuvant treatment, as the outcomes of surgery alone are very poor. It is expected that the combination of neoadjuvant and adjuvant treatment may reduce the incidence of relapse and improve survival. Clinical trial registration: eudract.ema.europa.eu/protocol.htm, identifier 2018-004841-17.

Background: The incidence of cutaneous melanoma is increasing in Italy, in parallel with the implementation of gene panels. Therefore, a revision of national genetic assessment criteria for hereditary melanoma may be needed. The aim of this study was to identify predictors of susceptibility variants in the largest prospective cohort of Italian high-risk melanoma cases studied to date. Materials and methods: From 25 Italian centers, we recruited 1044 family members and germline sequenced 940 cutaneous melanoma index cases through a shared gene panel, which included the following genes: CDKN2A, CDK4, BAP1, POT1, ACD, TERF2IP, MITF and ATM. We assessed detection rate according to familial status, region of origin, number of melanomas and presence and type of non-melanoma tumors. Results: The overall detection rate was 9.47% (5.53% analyzing CDKN2A alone), ranging from 5.14% in sporadic multiple melanoma cases (spoMPM) with two cutaneous melanomas to 13.9% in familial cases with at least three affected members. Three or more cutaneous melanomas in spoMPM cases, pancreatic cancer and region of origin predicted germline status [odds ratio (OR) = 3.23, 3.15, 2.43, P < 0.05]. Conversely, age > 60 years was a negative independent predictor (OR = 0.13, P = 0.008), and was the age category with the lowest detection rate, especially for CDKN2A. Detection rate was 19% when cutaneous melanoma and pancreatic cancer clustered together. Conclusions: Gene panel doubled the detection rate given by CDKN2A alone. National genetic testing criteria may need a revision, especially regarding age cut-off (60) in the absence of strong family history, pancreatic cancer and/or a high number of cutaneous melanomas. Keywords: CDKN2A; gene panel; germline; melanoma; predictors; susceptibility.

The authors regret that some Author information was incorrectly given in the published article. Author Manuel Zamparini was missed in the Author list, and the listed affiliation for Paola Queirolo was incorrect. The full and correct details for the Authors are: Paola Queirolo Division of Melanoma, Sarcomas and Rare Tumors, IEO, European Institute of Oncology, IRCCS, Milan, Italy Manuel Zamparini University of Brescia, Department of Medical-Surgical Specialties, Radiological Sciences and Public Health, Medical Oncology, ASST-Spedali Civili, Brescia, Lombardia, ItalyThe Authors also note that values in the first column of Table 4 were mistakenly reversed for ‘Antibiotic intake (within 1 month before therapy)’ and ‘Haemoglobin’. The correct table in full is given here.

Is it safe to perform controlled ovarian stimulation (COS) for fertility preservation before starting anticancer therapies or ART after treatments in young breast cancer patients?, SCOPUS: re.j, info:eu-repo/semantics/published

We consider maximum likelihood estimation of the Latent Class (LC) model, which is formulated through individual discrete latent variables. We explore tempering techniques to overcome the problem of multimodality of the log-likelihood function. A Tempered Expectation-Maximization (T-EM) algorithm is proposed, which can adequately explore the parameter space and reach the global maximum more frequently than the standard EM algorithm. We assess the performance of the proposed approach by a Monte Carlo simulation study and an application based on data about anxiety and depression in oncological patients.

We propose a hidden Markov model for longitudinal multivariate continuous responses, accounting for missing data under the missing at random assumption. Maximum likelihood estimation of this model is carried out through the Expectation-Maximization algorithm. To address the problem of dimensionality reduction, we develop a greedy search algorithm based on the Bayesian Information Criterion. We illustrate the proposal through a dataset collected by the World Bank and UNESCO Institute for Statistics on the basis of which we dynamically cluster countries according to the selected variables observed during the period 2000-2017.

In 2019, the European Union registered its seventh consecutive year of economic growth, but significant differences among the European Union countries are still present. In this context, competitiveness is an important element of human-centric and sustainable economic progress. Focusing on this concept, this study proposes an approach to track the dynamics of competitive growth in a set of member countries. After selecting some economic indicators for a 6-years period from 2014 to 2019, a time trajectory showing the dynamics of innovation and digitalization for each country has been plotted. This graphical analysis allows to extract some evidences about different paths to competitiveness within the European Union.

As recently stated by EUROSTAT, Small and Medium Enterprises (SMEs) play a crucial role in the economy of European Union (EU). This is particularly true for Italy that has one of the largest share of SMEs in the Euro zone. In such a context, the assessment of the Italian firms creditworthiness is a priority, especially in the early stage of a SMEs life. The aim of this work is to explore the effectiveness of the Feedforward Neural Network for Italian firms defaults prediction in comparison with standard linear models (Logistic and Probit). Our analysis considers the Italian manufacturing SMEs in a short time span (2016-2017). Results, obtained via two different sampling strategies, are not clear-cut.

The objective of credit scoring is to develop accurate rule of classification that aids to distinguish between good and bad clients. In this context, also Statistical Learning (SL) techniques have been explored, for building models that estimate the clients’ probability of insolvency. Although there are some encouraging results in literature, two main issues makes this classification task very hard: (i) high imbalance ratio between the two groups in the target variable and (ii) the effect of hyperparameter settings on overall performance. In this work, Bayesian Optimization (BO) is used to optimize the hyperparameters of a cost sensitive eXtreme Gradient Boosting (XGBoost) model. Experimental results reveal that the proposed solution is a promising starting point for future development

Questo lavoro studia la stratificazione sociale dei lavoratori stranieri in Italia con una prospettiva centrata sulla classe sociale: è così possibile analizzare la trasmissione delle disuguaglianze e la mobilità sociale (sia inter- che intragenerazionale) dal paese di origine a quello di destinazione. Si ipotizza che bassa origine sociale e posizione di classe nel paese d’origine siano associate a scarsi esiti occupazionali (Hyp 1) e a ridotte possibilità di mobilità ascendente (Hyp 2) nel paese di destinazione. I risultati sottolineano una chiara riproduzione di classe negli strati più bassi della struttura occupazionale. Inoltre, la mobilità occupazionale verso l’upper class (ma non verso la piccola borghesia) è più realizzabile per chi ha già un’elevata posizione sociale, mentre chi ha un basso status socio-economico incontra difficoltà a migliorare la propria posizione anche nel paese ospitante., In this work we aim at studying the social stratification of foreign workers in Italy adopting a class-based approach: this perspective allows us to shed light on the transmission of inequality and social mobility (both inter- and intra-generational) from country of origin to destination. We formulate the hypotheses that a low social origin and class position in home country is associated both with poor occupational achievement (Hyp 1) and with scarce chances of upward social mobility (Hyp 2) in the host country. Our results underline a clear class reproduction in the lowest strata of the occupational structure. Furthermore, occupational mobility to upper class (but not to petty bourgeoisie) is easier for who already has a high social position, while who lies at the bottom of the social structure is prevented from improving his\her position also in the host country.

Optimal individualized treatment estimation in single or multi-stage clinical trials is a breakthrough for personalized medicine. In this context, statistical methodologies can significantly improve the estimation over model-based methods. Furthermore it can help in selecting important variables in high-dimensional settings. In this work, we investigate the performance of an hybrid approach that combine Sequential Advantage Selection (SAS) method and Q-learning. In addition, Q-functions are estimated with linear regression model, random forest and neural network.

Background: Cutaneous squamous cell carcinoma (cSCC) has an overall favourable outcome, except for patients with an advanced stage disease. The programmed death protein-1 (PD-1) inhibitor cemiplimab has been approved for use in advanced cSCC. We report clinical outcomes from the named patient programme-compassionate use of cemiplimab for patients with advanced cSCC in Italy. Methods: This is a retrospective, observational, multicentre study. We analysed medical records of patients with advanced cSCC treated with cemiplimab between May 2019 and February 2020 in 17 referral Italian centres. We assessed the safety profile according to the Common Terminology Criteria for Adverse Events, version 5.0 (CTCAE v 5.0), the clinical activity in terms of response rate, clinical benefit and duration of response and baseline clinical-pathologic characteristics associated with response. Results: 131 patients were included, with a median age of 79 years. Of them, 9.2% had a concurrent chronic lymphoproliferative disease and 8.5% a concomitant autoimmune disease. Some 42.7% of the total patients had at least one treatment-related adverse events (AEs); out of above, 9.2% had grade 3–4 adverse events, and there were two fatal adverse events. The overall response rate (ORR) was 58%, and the disease control rate (DCR) was 71.7%. Cutaneous squamous cell carcinomas (cSCCs) arising on the head and neck area (p = 0.007) and haemoglobin values in normal range (p = 0.034) were significantly associated with a better response, while cSCCs on the genitalia (p = 0.041), treatment with any systemic antibiotic within 1 month of cemiplimab initiation (p = 0.012), performance status ≥1 (p = 0.012), chronic corticosteroids therapy (p = 0.038), previous radiation therapy to lymph nodes (p = 0.052) and previous chemotherapy (p = 0.0020) were significantly associated with a worse response. Conclusions: Our real-world study showed safety and effectiveness results comparable to those obtained in clinical t

Clustering methods have typically found their application when dealing with continuous data. However, in many modern applications data consist of multiple categorical variables with no natural ordering. In the heuristic framework the problem of clustering these data is tackled by introducing suitable distances. In this work, we develop a model-based approach for clustering categorical data with nominal scale. Our approach is based on a mixture of distributions defined via the Hamming distance between categorical vectors. Maximum likelihood inference is delivered through an expectation-maximization algorithm. A simulation study is carried out to illustrate the proposed approach.

We propose a method to test dependence or conditional dependence be- tween parts of the domain of functional data. The tests are based on permutation procedure that tests if suitable blocks of the covariance or precision matrix of ba- sis expansion coefficients are equal to zero. We show that the procedure is able to identify the true structure of conditional dependence.

Nella stima robusta di un cluster weighted model, lo statistico deve fare molte scelte: specificare la forma dei cluster nelle variabili esplicative, assumere (o meno) varianza uguale per gli errori nelle linee di regressione e impostare i valori degli iper-parametri per la stima robusta, per evitare la distorsione generata da valori anomali e contaminazione. L’iper-parametro più delicato da specificare è la percentuale di trimming, ovvero la quantità di dati da escludere nella stima per garantirne l’affidabilità. In questo lavoro introduciamo specifici strumenti diagnostici per aiutare il professionista, o lo scienziato che ha bisogno di classificare i dati, a compiere una scelta ragionata a riguardo di tale iper-parametro, anche in base ad una prima esplorazione dello spazio delle soluzioni., In a robust approach to model fitting for the cluster weighted model, many choices are to be made by the statistician: specifying the shape of the clusters in the explanatory variables, assuming (or not) equal variance for the errors in the regression lines, and setting hyper-parameter values for the robust estimation to be protected from outliers and contamination. The most delicate hyper-parameter to specify is perhaps the percentage of trimming, or the amount of data to be excluded from the estimate, to ensure reliable inference. In this work we introduce diagnostic tools to help the professional, or the scientist who needs to group the data, to make an educated choice about this hyper-parameter, after a first exploration of the resulting model space.

Using the ‘Social Condition and Integration of Foreign Citizen’ survey (2011-2012) for Italy and the ‘Trajectoires et Origines’ survey (2008-2009) for France, we examined differences in the need for informal childcare and parental preferences in childcare (grandparents, other family and extra-family members) between migrants in Italy and France according to family demographic and socio-economic characteristics, migrants’ background and contextual factors. Results suggest that migrants’ choices and preferences are strongly affected by the household composition, mother occupational status and migrants’ country of origin. Furthermore, our results highlight the role played by the destination country, which can shape migrants’ choices and options. The use of informal childcare is higher for migrants in Italy than in France, even among the same origin areas.

In observational studies evaluating the treatment effect on a given out- come, the treated and untreated subjects may be highly unbalanced in their observed covariates, and these differences can lead to biased estimates of treatment effects. Propensity score is popular tool to reduce this bias. In this work we propose to esti- mate the propensity score by using Bayesian Networks as alternative to conventional logistic regression. Based on it, we develop an inferential methodology to evaluate the treatment effect. In simulation study, our proposed approach resulted in the best performance.

In a standard classification framework, a discriminating rule is usually built from a trustworthy set of labeled units. In this context, test observations will be automatically classified as to have arisen from one of the known groups encountered in the training set, without the possibility of detecting previously unseen classes. To overcome this limitation, an adaptive semi-parametric Bayesian classifier is introduced for modeling the test units, where robust knowledge is extracted from the training set and incorporated within the priors’ model specification. A successful application of the proposed approach in a real-world problem is addressed.

Several contributions to the recent literature have shown that supervised learning is greatly enhanced when only the most relevant features are selected for building the discrimination rule. Unfortunately, outliers and wrongly labelled units may undermine the determination of relevant predictors, and almost no dedicated methodologies have been developed to face this issue. In the present paper, we introduce a new robust variable selection approach, that embeds a classifier within a greedy-forward procedure. An experiment on synthetic data is provided, to under- line the benefits of the proposed method in comparison with non-robust solutions.

Bissiri et al. (2016) present a general Bayesian approach where the like- lihood is replaced more generally by a loss function, which is used to derive the posterior distribution from the prior. In this paper, we exploit this idea for learning the structure of undirected graphical models over discrete variables.

Aim: To assess safety and efficacy of vismodegib in the Italian cohort from the SafeTy Events in VIsmodEgib study. Materials & methods: Data from Italian patients with locally advanced basal cell carcinoma (laBCC) or metastatic BCC were analyzed. Results: Among 182 Italian patients, adverse events occurred with similar incidence to the overall population. Overall response rate was 67.1% in laBCC, 20% in metastatic BCC; complete response rate was 33.1% overall and 37.4% in laBCC. Median time to response was 2 months in complete responders versus 3.6 months overall. Quality of life improved from baseline. Conclusion: In the Italian cohort of STEVIE, vismodegib showed a safety profile consistent with the whole population; older age did not affect safety or efficacy. ClinicalTrials.gov registration: NCT0136766.

The contribution of recently established or candidate susceptibility genes to melanoma missing heritability has yet to be determined. Multigene panel testing could increase diagnostic yield and better define the role of candidate genes. We characterized 273 CDKN2A/ARF and CDK4-negative probands through a custom-designed targeted gene panel that included CDKN2A/ARF, CDK4, ACD, BAP1, MITF, POT1, TERF2IP, ATM, and PALB2. Co-segregation, loss of heterozygosity (LOH)/protein expression analysis, and splicing characterization were performed to improve variant classification. We identified 16 (5.9%) pathogenic and likely pathogenic variants in established high/medium penetrance cutaneous melanoma susceptibility genes (BAP1, POT1, ACD, MITF, and TERF2IP), including two novel variants in BAP1 and 4 in POT1. We also found four deleterious and five likely deleterious variants in ATM (3.3%). Thus, including potentially deleterious variants in ATM increased the diagnostic yield to about 9%. Inclusion of rare variants of uncertain significance would increase the overall detection yield to 14%. At least 10% of melanoma missing heritability may be explained through panel testing in our population. To our knowledge, this is the highest frequency of putative ATM deleterious variants reported in melanoma families, suggesting a possible role in melanoma susceptibility, which needs further investigation.

Objective/Purpose: The aryl hydrocarbon receptor (AHR) pathway plays a critical role in the biology of Growth Hormone (GH)-secreting pituitary tumor (somatotropinoma). Germline rs2066853 AHR variant was found to be more frequent among acromegaly patients and associated with a more severe disease with larger invasive somatropinoma, and with resistance to somatostatin analogs treatment in patients living in polluted areas. However, no somatic changes in AHR gene have been reported so far in acromegaly patients. On that basis, the aim of the study was to assess at the somatic level the AHR gene status encompassing exon 10 region, also because of the high rate of variants found in this genomic region. Methods: A cohort of 13 patients aged 20–76 years with biochemical, clinical and histological diagnosis of somatotropinoma was studied. DNA and RNA from pituitary tumor histological samples have been extracted and analyzed by PCR and direct sequencing for AHR gene variants, and compared with corresponding patients’ germline DNA as well as normal pituitary tissue as reference control. Results: A degenerated letter codes in the region corresponding to AHR exon 10 (c.1239-c.2056) was detected in somatotropinomas-derived DNA but not in that of matched germline and pituitary normal tissue. By multiple PCR and sequencing analysis, we observed amplification only before codon 1246 and after codon 1254, confirming the presence of a tumor-restricted somatic deletion in the 5’ upstream region of AHR exon 10. Analysis of PCR-amplified cDNA revealed a wildtype sequence of exon 9 and 10 in normal pituitary tissue, and a wildtype sequence of exon 9 and 10 up to codon 1246 and no sequence after the deletion region (c.1246-c.1254) in 6 out of 9 tumor samples. Patients carrying the germline rs2066853 AHR variant showed no somatic LOH at the corresponding genetic locus. Conclusion: This is the first demonstration of a recurrent somatic deletion in the exon 10 of the AHR gene in somatotropino

Objective: To estimate the prevalence of Pisa syndrome (PS) in patients with Parkinson disease (PD) and to assess the association between PS and demographic and clinical variables. Methods: In this multicenter cross-sectional study, consecutive outpatients with PD attending 21 movement disorders Italian tertiary centers were enrolled and underwent standardized clinical evaluation. PS was defined as trunk lateral deviation 10. Patients with PD were compared according to the presence of PS for several demographic and clinical variables. Results: Among 1,631 enrolled patients with PD, PS was detected in 143 patients (8.8%, 95% confidence interval 7.4%-10.3%). Patients with PS were older, had lower body mass index, longer disease duration, higher disease stages, and poorer quality of life. Falls were more frequent in the PS group as well as occurrence of "veering gait" (i.e., the progressive deviation toward one side when patient walked forward and backward with eyes closed). Patients with PS received higher daily levodopa equivalent daily dose and were more likely to be treated with combination of levodopa and dopamine agonists. Osteoporosis and arthrosis were significantly the most frequent associatedmedical conditions in patients with PS. Multiple explanatory variable logistic regression models confirmed the association of PSwith the following variables: Hoehn and Yahr stage, ongoing combined treatment with levodopa and dopamine agonist, associated medical conditions, and presence of veering gait. Conclusions: Our results suggest that PS is a relatively frequent and often disabling complication in PD, especially in the advanced disease stages. The association is dependent on a number of potentially relevant demographic and clinical variables.

Background and Objectives Electrochemotherapy (ECT) is technique for local control of skin metastasis. This study is primarily aimed at assessing the clinical activity of ECT in a prospective cohort of patients, and evaluating the association between primary tumor histology, number of metastatic lesions and size of tumor deposits and objective response rate. Methods and Results Thirty-nine patients with skin metastases from melanoma and other tumors underwent intravenous bleomycin ECT with palliative intent. No serious adverse events (SAE) or CTC grade 3 or 4 were observed. Overall response rate (ORR) was 66.6%. Response rate significantly correlated at univariate analysis both with the number (< or >10) and the size (< or >2 cm) of metastases. No relationship was observed for the histology of primary tumor. At multivariate analysis the size of the nodules under 2 cm was associated with a positive response and the correlation was statistically significant. Conclusions ORR was 66.6% and all patients obtained a subjective clinical benefit from the treatment with minimal side effects. The most suitable patients were the ones with nodules <2 cm. J. Surg. Oncol. 2014 109:270-274. © 2013 Wiley Periodicals, Inc.