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The evaluation of model performance is an essential part of hydrological modeling. However, leveraging the full information that performance criteria provide, requires a deep understanding of their properties. This Technical Note focuses on a rather counterintuitive aspect of the perhaps most widely used hydrological metric, the Nash-Sutcliffe Efficiency (NSE). Specifically, we demonstrate that the overall NSE of a dataset is not bounded by the NSEs of all its partitions. We term this phenomenon the 'Divide and Measure Nonconformity'. It follows naturally from the definition of the NSE, yet because modelers often subdivide datasets in a non-random way, the resulting behavior can have unintended consequences in practice. In this note we therefore discuss the implications of the 'Divide and Measure Nonconformity', examine its empirical and theoretical properties, and provide recommendations for modelers to avoid drawing misleading conclusions. [ABSTRACT FROM AUTHOR]

Numerous prognostic models have been proposed for ovarian cancer, extending from single serological factors to complex gene-expression signatures. Nonetheless, these models have not been routinely translated into clinical practice. We constructed a robust and readily calculable model for predicting surgical outcome and prognosis of ovarian cancer patients by exploiting commonly available clinico-pathological factors and three selected serum parameters. Serum CA125, human epididymis protein 4 (HE4) and mesothelin (MSL) were quantified by Lumipulse® G chemiluminescent enzyme immunoassay (Fujirebio) in a total of 342 serum samples from 190 ovarian cancer patients, including 152 paired pre- and post-operative samples. Detection of pre-operative HE4 and CA125 was the optimal marker combination for blood-based prediction of surgical outcome (AUC=0.86). We constructed a prognostic model, computed by serum levels of pre-operative CA125, post-operative HE4, post-operative MSL and surgical outcome. Prognostic performance of our model was superior to any of these parameters alone and was independent from BRCA1/2 mutational status. We subsequently transformed our model into a prognostic risk index, stratifying patients as "lower risk" or "higher risk". In "higher risk" patients, relapse or death was predicted with an AUC of 0.89 and they had a significantly shorter progression free survival (HR: 9.74; 95 % CI: 5.95–15.93; p<0.0001) and overall survival (HR: 5.62; 95 % CI: 3.16–9.99; p<0.0001) compared to "lower risk" patients. We present a robust predictive/prognostic model for ovarian cancer, which could readily be implemented into routine diagnostics in order to identify ovarian cancer patients at high risk of recurrence. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has played an increasing role in the hydrological sciences. In particular, certain types of time series modeling strategies are popular for rainfall–runoff modeling. A large majority of studies that use this type of model do not follow best practices, and there is one mistake in particular that is common: training deep learning models on small, homogeneous data sets (i.e., data from one or a small number of watersheds). In this position paper, we show that Long Short Term Memory (LSTM) streamflow models are best when trained with a large amount of hydrologically diverse data. [ABSTRACT FROM AUTHOR]

The nutritional adequacy of human milk (HM) from vegan/vegetarian mothers has been a matter of debate, and a variety of recommendations regarding the eligibility of these mothers as human milk donors exists. According to the latest evidence, HM from vegans/vegetarians is similar in its composition to that from omnivores, however, some differences may be observed regarding vitamin B12 and omega‐3 fatty acids concentrations. Maternal supplementation of these compounds has been proven effective in increasing their HM concentration. With this survey, we aimed to explore the practices currently in use in European human milk banks (HMBs) regarding potential donors following vegan/vegetarian diets. The online survey was distributed to European HMBs between January and July 2022. A total of 188 HMBs were contacted, and 118 replied (response rate 63%). Vegan and vegetarian mothers were recommended supplements of vitamin B12 to be accepted as donors in 27% and 26% of HMBs, respectively. Additional omega‐3 fatty acid supplementation was required in 8% HMBs. In the remaining HMBs, these mothers were either systematically excluded or included regardless of supplementation. The dosage of the recommended supplements was extremely variable. Fifty‐one percent of HMBs were following recommendations to guide their practice, national or local recommendations mainly. Great variability in European HMBs practices towards potential donors following vegan/vegetarian diets exists. Some of these practices can result in loss of donors and/or in potential nutritional deficiencies. Standardised evidence‐based recommendations on this issue and their implementation in daily HMB practice are needed. Key messages: There is significant variability among the practices of European human milk banks towards donors following a vegan or vegetarian diet.The majority of human milk banks follow no guidelines or local guidelines, rather than international ones.Some of the practices currently in use can result in the loss of potential donors and/or nutritional deficiencies for mothers, their babies, and potentially for recipients of donor human milk.Evidence‐based recommendations regarding the inclusion of these mothers in human milk donation and nutritional counselling are needed. [ABSTRACT FROM AUTHOR]

Simple Summary: We established an in vitro model for PARPi-resistant ovarian cancer by long-term olaparib exposure of either BRCA1-proficient or BRCA1-deficient cell lines. We describe that PARPi-resistant cells show a broad spectrum of cross-resistance toward other clinically relevant PARPis and chemotherapeutic drugs independently of the BRCA1-status of the parental cell lines. Using co-culture experiments, we studied the clonal dynamics of our in vitro model and showed that PARPi-resistant cells have a proliferative disadvantage over PARPi-sensitive cells under drug-free conditions, while they rapidly gained clonal dominance under selection pressure with olaparib, which can be blocked by pharmacological inhibition of ABC-transporter proteins. Conclusively, we present a well-characterized in vitro model of PARPi resistance that could be useful in dissecting mechanisms of PARPi resistance derived from a BRCA1-proficient or BRCA1-deficient background. Furthermore, our model will allow studying how experimental therapeutic drugs, such as novel PARPi-sensitizers, affect the clonal dynamics of PARPi-resistant cells. Overcoming PARPi resistance is a high clinical priority. We established and characterized comparative in vitro models of acquired PARPi resistance, derived from either a BRCA1-proficient or BRCA1-deficient isogenic background by long-term exposure to olaparib. While parental cell lines already exhibited a certain level of intrinsic activity of multidrug resistance (MDR) proteins, resulting PARPi-resistant cells from both models further converted toward MDR. In both models, the PARPi-resistant phenotype was shaped by (i) cross-resistance to other PARPis (ii) impaired susceptibility toward the formation of DNA-platinum adducts upon exposure to cisplatin, which could be reverted by the drug efflux inhibitors verapamil or diphenhydramine, and (iii) reduced PARP-trapping activity. However, the signature and activity of ABC-transporter expression and the cross-resistance spectra to other chemotherapeutic drugs considerably diverged between the BRCA1-proficient vs. BRCA1-deficient models. Using dual-fluorescence co-culture experiments, we observed that PARPi-resistant cells had a competitive disadvantage over PARPi-sensitive cells in a drug-free medium. However, they rapidly gained clonal dominance under olaparib selection pressure, which could be mitigated by the MRP1 inhibitor MK-751. Conclusively, we present a well-characterized in vitro model, which could be instrumental in dissecting mechanisms of PARPi resistance from HR-proficient vs. HR-deficient background and in studying clonal dynamics of PARPi-resistant cells in response to experimental drugs, such as novel olaparib-sensitizers. [ABSTRACT FROM AUTHOR]