Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2018

Arslanovic Jasmina, Lovric Mario, Kern Roman

EATING DISORDERS IN SYNCHRONIZED SWIMMING

2018

Journal
The aim of the present study was to identify eating disorders in synchronized swimming and the level of distortion of the body image of synchronized swimming athletes. Synchronized swimming is sport in which modality is considered of risk for development of eating disorder. It is a Olympic sport where synchronized swimmers are competing in the range of age 13-15 years old and that ages are critical for every young woman (puberty). Also, the beauty of movement is associated to low body mass and judges include thinness in their final score. Eating disorders include anorexia nervosa, bulimia nervosa, binge eating symptoms, and other specified (or non-specified) feeding or eating disorders which are presenting serious issue. Twenty synchronized swimmers age 13-16 years old was studied and the group of twenty female water polo players was used for comparison with athletes. To test the presence of some symptoms of the eating disorders was used The Eating Attitudes Test: “Eat26” (Garner and Garfunkel, 1979). Comparison of results have shown that statistically significant difference exist between synchronized swimmers and female water polo players in the image of dissatisfaction, with pathological control of body weight of synchronized swimmers. Almost every sport demands certain nutrition and most of them certain weight, but that should be achieved with the help of expert team. It is possible that synchronized swimmers are skinny and strong, but only with certain nutrition which is individual for every swimmer.
2018

Babić Sanja, Barišić Josip, Stipaničev Draženka, Repec Siniša, Lovric Mario, Malev Olga, Čož-Rakovac Rozalindra, Klobučar GIV

Assessment of river sediment toxicity: Combining empirical zebrafish embryotoxicity testing with in silico toxicity characterization

Science of the Total Environment, Elsevier, 2018

Journal
Quantitative chemical analyses of 428 organic contaminants (OCs) confirmed the presence of 313 OCs in the sediment extracts from river Sava, Croatia. Pharmaceuticals were present in higher concentration than pesticides thus confirming their increasing threat to freshwater ecosystems. Toxicity evaluation of the sediment extracts from four locations (Jesenice, Rugvica, Galdovo and Lukavec) using zebrafish embryotoxicity test (ZET) accompanied with semi-quantitative histopathological analyses exhibited good correlation with cumulative number and concentrations of OCs at investigated sites (10,048.6, 15,222.8, 1,247.6, and 9,130.5 ng/g respectively) and proved its role as a good indicator of toxic potential of complex contaminant mixtures. Toxicity prediction of sediment extracts and sediment was assessed using Toxic unit (TU) approach and PBT (persistence, bioaccumulation and toxicity) ranking. Also, prior-knowledge informed chemical-gene interaction models were generated and graph mining approaches used to identify OCs and genes most likely to be influential in these mixtures. Predicted toxicity of sediment extracts (TUext) for sampled locations was similar to the results obtained by ZET and associated histopathology resulting in Rugvica sediment as being the most toxic, followed by Jesenice, Lukavec and Galdovo. Sediment TU (TUsed) favoured OCs with low octanol-water partition coefficient like herbicide glyphosate and antibiotics ciprofloxacin and sulfamethazine thus indicating locations containing higher concentrations of these OCs (Galdovo and Rugvica) as most toxic. Results suggest that comprehensive in silico sediment toxicity predictions advocate providing equal attention to organic contaminants with either very low or very high log Kow
2018

Lovric Mario

Molecular modeling of the quantitative structure activity relationship in Python – a tutorial (part I)

Journal of Chemists and Chemical Engineers, Croatian Society of Chemical Engineers, Zagreb, 2018

Journal
Today's data amount is significantly increasing. A strong buzzword in research nowadays is big data.Therefore the chemistry student has to be well prepared for the upcoming age where he does not only rule the laboratories but is a modeler and data scientist as well. This tutorial covers the very basics of molecular modeling and data handling with the use of Python and Jupyter Notebook. It is the first in a series aiming to cover the relevant topics in machine learning, QSAR and molecular modeling, as well as the basics of Python programming
2018

Lovric Mario, Krebs Sarah, Cemernek David, Kern Roman

BIG DATA IN INDUSTRIAL APPLICATION

XII Meeting of Young Chemical Engineers, Zagreb, Kroatien, 2018

Konferenz
The use of big data technologies has a deep impact on today’s research (Tetko et al., 2016) and industry (Li et al., n.d.), but also on public health (Khoury and Ioannidis, 2014) and economy (Einav and Levin, 2014). These technologies are particularly important for manufacturing sites, where complex processes are coupled with large amounts of data, for example in chemical and steel industry. This data originates from sensors, processes. and quality-testing. Typical application of these technologies is related to predictive maintenance and optimisation of production processes. Media makes the term “big data” a hot buzzword without going to deep into the topic. We noted a lack in user’s understanding of the technologies and techniques behind it, making the application of such technologies challenging. In practice the data is often unstructured (Gandomi and Haider, 2015) and a lot of resources are devoted to cleaning and preparation, but also to understanding causalities and relevance among features. The latter one requires domain knowledge, making big data projects not only challenging from a technical perspective, but also from a communication perspective. Therefore, there is a need to rethink the big data concept among researchers and manufacturing experts including topics like data quality, knowledge exchange and technology required. The scope of this presentation is to present the main pitfalls in applying big data technologies amongst users from industry, explain scaling principles in big data projects, and demonstrate common challenges in an industrial big data project
2018

Lovric Mario, Stipaničev Draženka , Repec Siniša , Malev Olga , Klobučar Göran

Combined toxic unit: Moving towards a multipath risk assessment strategy of organic contaminants in river sediment

The International Water Association, 2018

Konferenz
2018

Lovric Mario

Chemical outlier dataset

Zenodo, 2018

The objects are numbered. The Y-variable are boiling points. Other features are structural features of molecules. In the outlier column the outliers are assigned with a value of 1.The data is derived from a published chemical dataset on boiling point measurements [1] and from public data [2]. Features were generated by means of the RDKit Python library [3]. The dataset was infused with known outliers (~5%) based on significant structural differences, i.e. polar and non-polar molecules. Cherqaoui D., Villemin D. Use of a Neural Network to determine the Boiling Point of Alkanes. J CHEM SOC FARADAY TRANS. 1994;90(1):97–102. https://pubchem.ncbi.nlm.nih.gov/ RDKit: Open-source cheminformatics; http://www.rdkit.org
2018

Lovric Mario, Molero Perez Jose Manuel, Kern Roman

PySpark and RDKit: moving towards Big Data in QSAR

Journal of Chemical Information and Modelin, ACSPublication, 2018

Journal
We present an implementation of the cheminformatics toolkit RDKit in a distributed computing environment, Apache Hadoop. Together with the Apache Spark analytics engine, wrapped in PySpark, resources from commodity scalable hardware can be used for cheminformatic calculations and query operations with basic knowledge in Python coding and understanding of the RDD abstraction. A comparison of the computing acceleration in the Hadoop cluster is presented in two computation tasks of querying substructures and calculating molecular descriptors, as well as the source code for the PySpark-RDKit implementation
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