Tallinn University of Technology

About: Tallinn University of Technology is a education organization based out in Tallinn, Estonia. It is known for research contribution in the topics: European union & Computer science. The organization has 3688 authors who have published 10313 publications receiving 145058 citations. The organization is also known as: Tallinn Technical University & Tallinna Tehnikaülikool.

DOME: Recommendations for supervised machine learning validation in biology

Abstract: Modern biology frequently relies on machine learning to provide predictions and improve decision processes. There have been recent calls for more scrutiny on machine learning performance and possible limitations. Here we present a set of community-wide recommendations aiming to help establish standards of supervised machine learning validation in biology. Adopting a structured methods description for machine learning based on data, optimization, model, evaluation (DOME) will aim to help both reviewers and readers to better understand and assess the performance and limitations of a method or outcome. The recommendations are formulated as questions to anyone wishing to pursue implementation of a machine learning algorithm. Answers to these questions can be easily included in the supplementary material of published papers.

Hospital wastewater treatment with pilot-scale pulsed corona discharge for removal of pharmaceutical residues

Abstract: In recent years, accumulation of pharmaceutical compounds in the environment has been an issue of growing concern. Conventional wastewater treatment has limited effectiveness with many pharmaceuticals at concentrations of ppb or ppt scale. An intuitive solution would be to treat the pharmaceuticals-contaminated wastewaters at the source sites before dilution in sewer networks. Health institutions with concentrated drug consumption provide logical point sources for pharmaceuticals entering the sewage. This paper describes the pilot-scale removal of a wide range of pharmaceuticals from real wastewaters via gas-phase pulsed corona discharge oxidation. The process was studied for raw sewage from a public hospital and for biologically treated wastewater of a health-care institute. The non-selective oxidation of the observed pharmaceuticals (32 compounds) was effective at reasonable energy cost: 87-% reduction in residual pharmaceuticals (excluding biodegradable caffeine) from raw sewage was attained with 1 kWh m−3 from the raw sewage and 100% removal was achieved for biologically treated wastewater at only 0.5 kWh m−3. The impact for affected aquatic environments upon the present solution would be a dramatically reduced load of pharmaceutical accumulation.

Application of Activated Carbon Derived from Seed Shells of Jatropha curcas for Decontamination of Zearalenone Mycotoxin.

Abstract: In the present study, activated carbon (AC) was derived from seed shells of Jatropha curcas and applied to decontaminate the zearalenone (ZEA) mycotoxin. The AC of J. curcas (ACJC) was prepared by ZnCl2 chemical activation method and its crystalline structure was determined by X-ray diffraction analysis. The crystalline graphitic nature of ACJC was confirmed from the Raman spectroscopy. Scanning electron microscope showed the porous surface morphology of the ACJC surface with high pore density and presence of elemental carbon was identified from the energy dispersive X-ray analysis. From Brunauer-Emmett-Teller (BET) analysis, SBET, micropore area, and average pore diameter of ACJC were calculated as 822.78 (m2/g), 255.36 (m2/g), and 8.5980 (A), respectively. The adsorption of ZEA by ACJC was accomplished with varying contact time, concentration of ZEA and ACJC, and pH of media. The ACJC has adsorbed the ZEA over a short period of time and adsorption of ZEA was dependent on the dose of ACJC. The effect of different pH on adsorption of ZEA by ACJC was not much effective. Desorption studies confirmed that adsorption of ZEA by ACJC was stable. The adsorption isotherm of ZEA by ACJC was well fitted with Langmuir model rather than Freundlich and concluded the homogeneous process of sorption. The maximum adsorption of ZEA by ACJC was detected as 23.14 μg/mg. Finally, adsorption property of ACJC was utilized to establish ACJC as an antidote against ZEA-induced toxicity under in vitro in neuro-2a cells. The percentage of live cells was high in cells treated together with a combination of ZEA and ACJC compared to ZEA treated cells. In a similar way, ΔΨM was not dropped in cells exposed to combination of ACJC and ZEA compared to ZEA treated cells. Furthermore, cells treated with a combination of ZEA and ACJC exhibited lower level of intracellular reactive oxygen species and caspase-3 compared to ZEA treated cells. These in vitro studies concluded that ACJC has successfully protected the cells from ZEA-induced toxicity by lowering the availability of ZEA in media as a result of adsorption of ZEA. The study concluded that ACJC was a potent decontaminating agent for ZEA and could be used as an antidote against ZEA-induced toxicity.

Minimally-Supervised Morphological Segmentation using Adaptor Grammars

Abstract: This paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation. We compare three training methods: unsupervised training, semi-supervised training, and a novel model selection method. In the model selection method, we train unsupervised Adaptor Grammars using an over-articulated metagrammar , then use a small labelled data set to select which potential morph boundaries identified by the meta-grammar should be returned in the final output. We evaluate on five languages and show that semi-supervised training provides a boost over unsupervised training, while the model selection method yields the best average results over all languages and is competitive with state-of-the-art semi-supervised systems. Moreover, this method provides the potential to tune performance according to different evaluation metrics or downstream tasks.