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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