Ensemble weather forecast post-processing with a flexible probabilistic neural network approach

Ensemble forecast post-processing is a necessary step in producing accurate probabilistic forecasts. Conventional post-processing methods operate by estimating the parameters of a parametric distribution, frequently on a per-location or per-lead-time basis. We propose a novel, neural network-based method, which produces forecasts for all locations and lead times, jointly. To relax the distributional assumption of many post-processing methods, our approach incorporates normalizing flows as flexible parametric distribution estimators. This enables us to model varying forecast distributions in a mathematically exact way. We demonstrate the effectiveness of our method in the context of the EUPPBench benchmark, where we conduct temperature forecast post-processing for stations in a sub-region of western Europe. We show that our novel method exhibits state-of-the-art performance on the benchmark, outclassing our previous, well-performing entry. Additionally, by providing a detailed comparison of three variants of our novel post-processing method, we elucidate the reasons why our method outperforms per-lead-time-based approaches and approaches with distributional assumptions

Improving the use of e-learning in health care curricula

E-learning is nowadays included in different health care curricula. However, this learning and teaching approach is still relatively underused in the health care study programmes. Hence, the goal of this article is to present three e-learning best practices in health care, which were validated in our previous projects/studies.First best practice, e-learning practice contents for the development of critical thinking, presents the approach which resulted in significant (p<0.001) raise in the number of discussion posts (+12.2%) for which the opinions and conclusions of the participants were justified with valid arguments. Furthermore, also the survey results showed that students achieve critical thinking skills with these contents. Second best practice, inclusion of the health care students in the development of e-contents, also resulted as a valid approach for improving the use of e-learning in health care curricula, which was confirmed by the expert opinion and e-contents final users. Finally, third best practice, our approach for evaluation and comparison of e-learning and traditional pedagogical elements value by health care students and teachers, was confirmed in the interview with the management as a valid approach that helps the management to identify whether previous elearning initiatives and activities were successful. Best practices, presented in this paper, represent the evidence that the use of modern information and communication technologies should be considered in the future as a serious requisite in health care study programmes. The broader community interested in the e-learning initiative should prepare a list of similar best practices, validate its effectiveness and prepare guidelines for their optimal implementation.

SMIXS: Novel efficient algorithm for non-parametric mixture regression-based clustering

We investigate a novel non-parametric regression-based clustering algorithm for longitudinal data analysis. Combining natural cubic splines with Gaussian mixture models (GMM), the algorithm can produce smooth cluster means that describe the underlying data well. However, there are some shortcomings in the algorithm: high computational complexity in the parameter estimation procedure and a numerically unstable variance estimator. Therefore, to further increase the usability of the method, we incorporated approaches to reduce its computational complexity, we developed a new, more stable variance estimator, and we developed a new smoothing parameter estimation procedure. We show that the developed algorithm, SMIXS, performs better than GMM on a synthetic dataset in terms of clustering and regression performance. We demonstrate the impact of the computational speed-ups, which we formally prove in the new framework. Finally, we perform a case study by using SMIXS to cluster vertical atmospheric measurements to determine different weather regimes

Investigation of adhesion and mechanical properties of human glioma cells by single cell force spectroscopy and atomic force microscopy.

Active cell migration and invasion is a peculiar feature of glioma that makes this tumor able to rapidly infiltrate into the surrounding brain tissue. In our recent work, we identified a novel class of glioma-associated-stem cells (defined as GASC for high-grade glioma--HG--and Gasc for low-grade glioma--LG) that, although not tumorigenic, act supporting the biological aggressiveness of glioma-initiating stem cells (defined as GSC for HG and Gsc for LG) favoring also their motility. Migrating cancer cells undergo considerable molecular and cellular changes by remodeling their cytoskeleton and cell interactions with surrounding environment. To get a better understanding about the role of the glioma-associated-stem cells in tumor progression, cell deformability and interactions between glioma-initiating stem cells and glioma-associated-stem cells were investigated. Adhesion of HG/LG-cancer cells on HG/LG-glioma-associated stem cells was studied by time-lapse microscopy, while cell deformability and cell-cell adhesion strengths were quantified by indentation measurements by atomic force microscopy and single cell force spectroscopy. Our results demonstrate that for both HG and LG glioma, cancer-initiating-stem cells are softer than glioma-associated-stem cells, in agreement with their neoplastic features. The adhesion strength of GSC on GASC appears to be significantly lower than that observed for Gsc on Gasc. Whereas, GSC spread and firmly adhere on Gasc with an adhesion strength increased as compared to that obtained on GASC. These findings highlight that the grade of glioma-associated-stem cells plays an important role in modulating cancer cell adhesion, which could affect glioma cell migration, invasion and thus cancer aggressiveness. Moreover this work provides evidence about the importance of investigating cell adhesion and elasticity for new developments in disease diagnostics and therapeutics

Glioma-associated stem cells: A novel class of tumor-supporting cells able to predict prognosis of human low-grade gliomas.

Background: Translational medicine aims at transferring advances in basic science research into new approaches for diagnosis and treatment of diseases. Low-grade gliomas (LGG) have a heterogeneous clinical behavior that can be only partially predicted employing current state-of-the-art markers, hindering the decision-making process. To deepen our comprehension on tumor heterogeneity, we dissected the mechanism of interaction between tumor cells and relevant components of the neoplastic environment, isolating, from LGG and high-grade gliomas (HGG), proliferating stem cell lines from both the glioma stroma and, where possible, the neoplasm. Methods and Findings: We isolated glioma-associated stem cells (GASC) from LGG (n=40) and HGG (n=73). GASC showed stem cell features, anchorage-independent growth, and supported the malignant properties of both A172 cells and human glioma-stem cells, mainly through the release of exosomes. Finally, starting from GASC obtained from HGG (n=13) and LGG (n=12) we defined a score, based on the expression of 9 GASC surface markers, whose prognostic value was assayed on 40 subsequent LGG-patients. At the multivariate Cox analysis, the GASC-based score was the only independent predictor of overall survival and malignant progression free-survival. Conclusions: The microenvironment of both LGG and HGG hosts non-tumorigenic multipotent stem cells that can increase in vitro the biological aggressiveness of glioma-initiating cells through the release of exosomes. The clinical importance of this finding is supported by the strong prognostic value associated with the characteristics of GASC. This patient-based approach can provide a groundbreaking method to predict prognosis and to exploit novel strategies that target the tumor stroma

Analysis of implementation of motor activities within additional professional assistance in kindergarten

The preschool period is a time of relatively quick and different changes at all areas of the development at the same time. Understanding of the development is therefore crucial for proper planning of activities in the kindergarten, which also includes additional professional assistance for children with special needs. In the first year of life, the motor development is in forefront due to learning on a concrete experiential level. Through motion, the child can research his body and the world around him and so for the first time gain optimal concrete experiences, which enable him to acquire knowledge and create new brain connections. With thoughtfully and carefully planned physical activity in the kindergarten (or at home) we can have an important positive impact on the quality and speed of the child’s integrated development. In children with special needs, which are already in the earliest period facing problems that significantly slow down their development, learning through concrete experiences, playing and motion, is even more important. The purpose of the thesis was to analyse the importance of selected material, substantive and personnel factors for the execution of motor activities in additional professional assistance classes in regular kindergarten departments. The results showed that there are no statistically significant connections between additional education from the field of motion and the quantity of the planned motor activities in additional professional assistance classes. Precisely the quantity of the planned motor activities does not depend on special factors. Half of the questioned providers of additional professional assistance devote more than a half of their time for motor activities and the other half of the questioned devote less than half of the joint time for assistance to children with special needs. The results show that the rate of the planned fine motor activities is greater than the rate of gross motor skills within the planned motor activities