Ugotavljanje indikatorskih bakterij fekalnega onesnaženja in prisotnosti vrste Escherichia coli, ki tvori encime β-laktamaze v črni vodi

The aim of this study was to identify and quantify faecal indicator bacteria in blackwater collected from a source separation unit and determine the amount of E. coli isolates resistant to antimicrobials and their potential to produce extended spectrum β-lactamases (ESβLs) and metallo-β-lactamases (MβLs), which hydrolyse the most important antibiotics used in clinical practice. Most of the isolates were resistant to amoxicillin with clavulanic acid (36.4 %), followed by ticarcillin with clavulanic acid (22.7 %) and tetracycline (18.2 %). ESβL-producing genes blaCTX-M and blaTEM were found in three (13.6 %) and four (18.2 %) E. coli strains, respectively, while MβL genes were found in two (9.1 %). By separating at source, this pilot study clearly shows that gastrointestinal bacteria of healthy people can be an important source of antibiotic resistance released into the environment through wastewaters. One way to prevent that is to treat wastewater with a combination of TiO2, UV light, or ozone, as successful methods to remove resistant bacteria and prevent their spread in the environment.V vzorcih črne vode, ki je ena od frakcij odpadne vode, smo ugotavljali prisotnost in število fekalnih indikatorskih bakterij, vključno z bakterijo Escherichia coli (E. coli). Pri osamljenih sevih E. coli smo ugotavljali njihovo odpornost proti izbranim antibiotikom in njihov potencial za tvorbo nekaterih β-laktamaz razširjenega spektra in metalo-β-laktamaz. Preizkušeni sevi so bili najpogosteje odporni proti amoksicilinu s klavulansko kislino (36,4 %), tikarcilinu s klavulansko kislino (22,7 %) in tetraciklinu (18,2 %). Nukleotidne sekvence za blaCTX-M in blaTEM smo našli pri treh (13,6 %) in štirih (18,2 %) sevih, medtem ko smo gene za izbrane metalo-β-laktamaze ugotovili pri dveh (9,1 %) sevih E. coli. Pilotna študija, z ločevanjem odpadne vode na viru nastanka, kaže, da so bakterije v prebavnem traktu zdravih ljudi lahko pomemben vir prenosa odpornosti proti antibiotikom v okolju preko odpadne vode. Eden izmed načinov za preprečevanje širjenja odpornosti proti antibiotikom je čiščenje odpadne vode z uporabo kombinacije TiO2, UV svetlobe in ozona, ki so se pokazale kot uspešne metode za odstranjevanje bakterij, odpornih proti antibiotikom

Slovene corpus for general relation extraction SloREL 1.1

The SloREL corpus contains annotations for training relation extraction models on Slovene documents. It contains documents from Slovene Wikipedia with annotated entities and relations. We constructed the annotations using a semi-supervised process based on linking the documents to the WikiData knowledge graph. The corpus contains 244,437 sentences from Slovene Wikipedia pages. We also provide 896 additional sentences collected from the 24ur.com news website with annotated and linked entities, which do not contain annotated relations and are meant for additional testing of the models. The entities in our corpus are linked to the entities in the WikiData knowledge graph which is useful for models that take advantage of additional knowledge from a knowledge graph. Altogether the corpus comprises 245,333 sentences with 813,952 relations and 1,616,193 entities. The corpus comprises of multiple documents: - schema-definition.xsd: defines the structure of the xml documents containing relation annotations. - SloREL/train.xml: training portion of the SloREL corpus containing Wikipedia documents - SloREL/test.xml: testing portion of the SloREL corpus containing Wikipedia documents - SloREL/validation.xml: validation portion of the SloREL corpus containing Wikipedia documents - 24ur/24ur.xml: additional sentences from the 24ur.com news articles Changes in version 1.1: We fixed the mislabeled relation types that were present in the previous version of the dataset. We also rearranged the archive to make the structure more understandable

Slovene corpus for general relation extraction SloREL 1.0

The SloREL corpus contains annotations for training relation extraction models on Slovene documents. It contains documents from Slovene Wikipedia with annotated entities and relations. We constructed the annotations using a semi-supervised process based on linking the documents to the WikiData knowledge graph. The corpus contains 244,437 sentences from Slovene Wikipedia pages. We also provide 896 additional sentences collected from the 24ur.com news website with annotated and linked entities, which do not contain annotated relations and are meant for additional testing of the models. The entities in our corpus are linked to the entities in the WikiData knowledge graph which is useful for models that take advantage of additional knowledge from a knowledge graph. All together the corpus comprises 245,333 sentences with 813,952 relations and 1,616,193 entities. The corpus comprises of multiple documents: - schema-definition.xsd: defines the structure of the xml documents containing relation annotations. - wikipedia-train.xml: training portion of the wikipedia corpus - wikipedia-test.xml: testing portion of the wikipedia corpus - wikipedia-validation.xml: validation portion of the wikipedia corpus - 24ur.xml: additional sentences from the 24ur.com news article

Treatment and re-use of raw blackwater by Chlorella vulgaris-based system

In this study, we examined a Chlorella vulgaris-based system as a potential solution to change liquid waste, such as blackwater, into valuable products for agriculture while protecting waters from pollution without technical demanding pre-treatment. To evaluate the possibility of nutrient removal and biomass production from raw blackwater, four blackwater dilutions were tested at lab-scale: 50%, 30%, 20%, and 10%. The results showed that even the less diluted raw blackwater was a suitable growth medium for microalgae C. vulgaris. As expected, the optimum conditions were observed in 10% blackwater with the highest growth rate (0.265 d$^{−1}$) and a nutrient removal efficiency of 99.6% for ammonium and 33.7% for phosphate. However, the highest biomass productivity (5.581 mg chlorophyll-a L$^{−1}$ d$^{−1}$) and total biomass (332.82 mg dry weight L$^{−1}$) were achieved in 50% blackwater together with the highest chemical oxygen demand removal (81%) as a result of the highest nutrient content and thus prolonged growth phase. The results suggested that the dilution factor of 0.5 followed by microalgae cultivation with a hydraulic retention time of 14 days could offer the highest biomass production for the potential use in agriculture and, in parallel, a way to treat raw blackwater from source-separation sanitation systems

Orange: data mining toolbox in Python

Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part, which features interactive data analysis and component-based assembly of data mining procedures. In the selection and design of components, we focus on the flexibility of their reuse: our principal intention is to let the user write simple and clear scripts in Python, which build upon C++ implementations of computationally-intensive tasks. Orange is intended both for experienced users and programmers, as well as for students of data mining