Lagere stikstofbemesting drukt melkproductie

Verlaging van de stikstofgift gaat echter ten koste van de grasproductie. Daardoor kunnen minder koeien per hectare worden gehouden

Controlled Quantum Secret Sharing

We present a new protocol in which a secret multiqubit quantum state $\ket{\Psi}$ is shared by $n$ players and $m$ controllers, where $\ket{\Psi}$ is the encoding state of a quantum secret sharing scheme. The players may be considered as field agents responsible for carrying out a task, using the secret information encrypted in $\ket{\Psi}$, while the controllers are superiors who decide if and when the task should be carried out and who to do it. Our protocol only requires ancillary Bell states and Bell-basis measurements.Comment: 6 pages, 0 figure, RevTeX4; published version with minor change

Multivariate random effects meta-analysis of diagnostic tests with multiple thresholds

Background. Bivariate random effects meta-analysis of diagnostic tests is becoming a well established approach when studies present one two-by-two table or one pair of sensitivity and specificity. When studies present multiple thresholds for test positivity, usually meta-analysts reduce the data to a two-by-two table or take one threshold value at a time and apply the well developed meta-analytic approaches. However, this approach does not fully exploi

Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data

This paper considers the problem of obtaining a dynamic prediction for 5-year failure free survival after bone marrow transplantation in ALL patients using data from the EBMT, the European Group for Blood and Marrow Transplantation. The paper compares the new landmark methodology as developed by the first author and the established multi-state modeling as described in a recent Tutorial in Biostatistics in Statistics in Medicine by the second author and colleagues. As expected the two approaches give similar results. The landmark methodology does not need complex modeling and leads to easy prediction rules. On the other hand, it does not give the insight in the biological processes as obtained for the multi-state model

The Use of Molecular Analyses in Voided Urine for the Assessment of Patients with Hematuria

Introduction:Patients presenting with painless hematuria form a large part of the urological patient population. In many cases, especially in younger patients, the cause of hematuria is harmless. Nonetheless, hematuria could be a symptom of malignant disease and hence most patients will be subject to cystoscopy. In this study, we aimed to develop a prediction model based on methylation markers in combination with clinical variables, in order to stratify patients with high risk for bladder cancer.Material and Methods:Patients (n=169) presenting with painless hematuria were included. 54 patients were diagnosed with bladder cancer. In the remaining 115 patients, the cause of hematuria was non-malignant. Urine samples were collected prior to cystoscopy. Urine DNA was analyzed for methylation of OSR1, SIM2, OTX1, MEIS1 and ONECUT2. Methylation percentages were calculated and were combined with clinical variables into a logistic regression model.Results:Logistic regression analysis based on the five methylation markers, age, gender and type of hematuria resulted in an area under the curve (AUC) of 0.88 and an optimism corrected AUC of 0.84 after internal validation by bootstrapping. Using a cut-off value of 0.307 allowed stratification of patients in a low-risk and high-risk group, resulting in a sensitivity of 82% (44/54) and a specificity of 82% (94/115). Most aggressive tumors were found in patients in the high-risk group. The addition of cytology to the prediction model, improved the AUC from 0.88 to 0.89, with a sensitivity and specificity of 85% (39/46) and 87% (80/92), retrospectively.Conclusions:This newly developed prediction model could be a helpful tool in risk stratification of patients presenting with painless hematuria. Accurate risk prediction might result in less extensive examination of low risk patients and thereby, reducing patient burden and costs. Further validation in a large prospective patient cohort is necessary to prove the true clinical value of this model

Comparison of Frequency of Periprocedural Myocardial Infarction in Patients With and Without Diabetes Mellitus to Those With Previously Unknown but Elevated Glycated Hemoglobin Levels (from the TWENTE Trial)

In patients without a history of diabetes mellitus, increased levels of glycated hemoglobin (HbA1c) are associated with higher cardiovascular risk. The relation between undetected diabetes and clinical outcome after percutaneous coronary intervention is unknown. To investigate whether these patients may have an increased risk of periprocedural myocardial infarction (PMI), the most frequent adverse event after percutaneous coronary intervention, we assessed patients of the TWENTE trial (a randomized, controlled, second-generation drug-eluting stent trial) in whom HbA1c data were available. Patients were classified as known diabetics or patients without a history of diabetes who were subdivided into undetected diabetics (HbA1c ≥6.5%) and nondiabetics (HbA1c <6.5%). Systematic measurement of cardiac biomarkers and electrocardiographic assessment were performed. One-year clinical outcome was also compared. Of 626 patients, 44 (7%) were undetected diabetics, 181 (29%) were known diabetics, and 401 (64%) were nondiabetics. In undetected diabetics the PMI rate was higher than in nondiabetics (13.6% vs 3.7%, p = 0.01) and known diabetics (13.6% vs 6.1%, p = 0.11). Multivariate analysis adjusting for covariates confirmed a significantly higher PMI risk in undetected diabetics compared to nondiabetics (odds ratio 6.13, 95% confidence interval 2.07 to 18.13, p = 0.001) and known diabetics (odds ratio 3.73, 95% confidence interval 1.17 to 11.89, p = 0.03). After 1 year, target vessel MI rate was significantly higher in undetected diabetics (p = 0.02) than in nondiabetics, which was related mainly to differences in PMI. Target vessel failure was numerically larger in unknown diabetics than in nondiabetics, but this difference did not reach statistical significance (13.6% vs 8.0%, p = 0.25). In conclusion, undetected diabetics were shown to have an increased risk of PMI

mlr3proba: An R Package for Machine Learning in Survival Analysis

As machine learning has become increasingly popular over the last few decades, so too has the number of machine learning interfaces for implementing these models. Whilst many R libraries exist for machine learning, very few offer extended support for survival analysis. This is problematic considering its importance in fields like medicine, bioinformatics, economics, engineering, and more. mlr3proba provides a comprehensive machine learning interface for survival analysis and connects with mlr3's general model tuning and benchmarking facilities to provide a systematic infrastructure for survival modeling and evaluation.Comment: Submitted to Bioinformatic

Antithrombotic therapy in patients undergoing TAVI: an overview of Dutch hospitals

To assess current antithrombotic treatment strategies in the Netherlands in patients undergoing transcatheter aortic valve implantation (TAVI). For every Dutch hospital performing TAVI (n = 14) an interventional cardiologist experienced in performing TAVI was interviewed concerning heparin, aspirin, thienopyridine and oral anticoagulation treatment in patients undergoing TAVI. The response rate was 100 %. In every centre, a protocol for antithrombotic treatment after TAVI was available. Aspirin was prescribed in all centres, concomitant clopidogrel was prescribed 13 of the 14 centres. Duration of concomitant clopidogrel was 3 months in over two-thirds of cases. In 2 centres, duration of concomitant clopidogrel was based upon type of prosthesis: 6 months versus 3 months for supra-annular and intra-annular prostheses, respectively. Leaning on a small basis of evidence and recommendations, the antithrombotic policy for patients undergoing TAVI is highly variable in the Netherlands. As a standardised regimen might further reduce haemorrhagic complications, large randomised clinical trials may help to establish the most appropriate approac

A family history of breast cancer will not predict female early onset breast cancer in a population-based setting

ABSTRACT: BACKGROUND: An increased risk of breast cancer for relatives of breast cancer patients has been demonstrated in many studies, and having a relative diagnosed with breast cancer at an early age is an indication for breast cancer screening. This indication has been derived from estimates based on data from cancer-prone families or from BRCA1/2 mutation families, and might be biased because BRCA1/2 mutations explain only a small proportion of the familial clustering of breast cancer. The aim of the current study was to determine the predictive value of a family history of cancer with regard to early onset of female breast cancer in a population based setting. METHODS: An unselected sample of 1,987 women with and without breast cancer was studied with regard to the age of diagnosis of breast cancer. RESULTS: The risk of early-onset breast cancer was increased when there were: (1) at least 2 cases of female breast cancer in first-degree relatives (yes/no; HR at age 30: 3.09; 95% CI: 128-7.44), (2) at least 2 cases of female breast cancer in first or second-degree relatives under the age of 50 (yes/no; HR at age 30: 3.36; 95% CI: 1.12-10.08), (3) at least 1 case of female breast cancer under the age of 40 in a first- or second-degree relative (yes/no; HR at age 30: 2.06; 95% CI: 0.83-5.12) and (4) any case of bilateral breast cancer (yes/no; HR at age 30: 3.47; 95%: 1.33-9.05). The positive predictive value of having 2 or more of these characteristics was 13% for breast cancer before the age of 70, 11% for breast cancer before the age of 50, and 1% for breast cancer before the age of 30. CONCLUSION: Applying family history related criteria in an unselected population could result in the screening of many women who will not develop breast cancer at an early age