Brno University of Technology

About: Brno University of Technology is a education organization based out in Brno, Czechia. It is known for research contribution in the topics: Fracture mechanics & Filter (video). The organization has 6339 authors who have published 15226 publications receiving 194088 citations. The organization is also known as: Vysoké učení technické v Brně & BUT.

LAMP-on-a-chip: Revising microfluidic platforms for loop-mediated DNA amplification

Abstract: Nucleic acid amplification for the detection of infectious diseases, food pathogens, or assessment of genetic disorders require a laboratory setting with specialized equipment and technical expertise. Isothermal deoxyribonucleic acid amplification methods, such as loop-mediated isothermal amplification (LAMP), exhibit characteristics ideal for point-of-care (POC) applications, since their instrumentation is simpler in comparison with the standard method of polymerase chain reaction. Other key advantages of LAMP are robustness and the production of pyrophosphate in the presence of the target gene, enabling to detect the reaction products using the naked eye. Polymerase inhibitors, presented in clinical samples, do not affect the amplification process, making LAMP suitable for a simple sample-to-answer diagnostic systems with simplified sample preparation. In this review, we discuss the trends in miniaturized LAMP techniques, such as microfluidic, paper-based, and digital with their advantages and disadvantages, especially for POC applications alongside our opinion of the future development of miniaturized LAMP.

Succession and replacement of bacterial populations in the caecum of egg laying hens over their whole life.

Abstract: In this study we characterised the development of caecal microbiota in egg laying hens over their commercial production lifespan, from the day of hatching until 60 weeks of age. Using pyrosequencing of V3/V4 variable regions of 16S rRNA genes for microbiota characterisation, we were able to define 4 different stages of caecal microbiota development. The first stage lasted for the first week of life and was characterised by a high prevalence of Enterobacteriaceae (phylum Proteobacteria). The second stage lasted from week 2 to week 4 and was characterised by nearly an absolute dominance of Lachnospiraceae and Ruminococcaceae (both phylum Firmicutes). The third stage lasted from month 2 to month 6 and was characterised by the succession of Firmicutes at the expense of Bacteroidetes. The fourth stage was typical for adult hens in full egg production aged 7 months or more and was characterised by a constant ratio of Bacteroidetes and Firmicutes formed by equal numbers of the representatives of both phyla.

Semi-supervised training of Deep Neural Networks

Abstract: In this paper we search for an optimal strategy for semi-supervised Deep Neural Network (DNN) training. We assume that a small part of the data is transcribed, while the majority of the data is untranscribed. We explore self-training strategies with data selection based on both the utterance-level and frame-level confidences. Further on, we study the interactions between semi-supervised frame-discriminative training and sequence-discriminative sMBR training. We found it beneficial to reduce the disproportion in amounts of transcribed and untranscribed data by including the transcribed data several times, as well as to do a frame-selection based on per-frame confidences derived from confusion in a lattice. For the experiments, we used the Limited language pack condition for the Surprise language task (Vietnamese) from the IARPA Babel program. The absolute Word Error Rate (WER) improvement for frame cross-entropy training is 2.2%, this corresponds to WER recovery of 36% when compared to the identical system, where the DNN is built on the fully transcribed data.