In the present studies, we have found a fragment of amino acid sequence, called TFT motif, both in light-dependent protochlorophyllide oxidoreductase (LPOR) and in the L subunit of dark-operative (light-independent) protochlorophyllide oxidoreductases (DPOR). Amino acid residues of this motif shared similar physicochemical properties in both types of the enzymes. In the present paper, physicochemical properties of amino acid residues of this common motif, its spatial arrangement and a possible physiological role are being discussed. This is the first report when similarity between LPOR and DPOR, phylogenetically unrelated, but functionally redundant enzymes, is described., M. Gabruk ... [et al.]., and Obsahuje bibliografii
Článek popisuje postupnou změnu postoje vůči přístrojům a materiálům ve filosofií a historiografií vědy a konfrontuje současné snahy o přehodnocení materiální kultury vědy s koncepcí experimentálních systémů Hanse-Jörga Rheinbergera a epistemologických strojů Dona Ihde, This article outlines the gradually changing attitude towards instruments and materials in the philosophy and historiography of science and confronts contemporary revaluations of the material culture of science with Hans-Jörg Rheinberger’s concept of an experimental system and Don Ihde’s notion of an epistemology engine, and Tomáš Dvořák.
Physical exercise instruction sheets are difficult to understand. In general, considerable information is hidden in these types of instruction sheets, which also makes them difficult for machines to understand. Major missing information types include the source and destination location of a human movement. Here we present a Bayesian network to extract the implicit or missing information from typical exercise instruction sheets. We proposed two different kind of Bayesian networks which consists of three and four variables respectively. The network with three variable are designed to for single exercise instruction with single action or pose and the other one designed for single or multiple sentence with two actions or poses. The conditional probability table (CPT) is the backbone of the Bayesian network. At the start, the CPT is updated from our physical exercise instruction sheet corpus (PEISC). Keeping the Action and Bodypart fixed, we have developed our CPT using a unique approach, i.e., crowdsourcing, where we have developed a CPT update system using 13 different exercises consisting of 44 different exercise videos. Using this system based on the rating of a participant of the video the specific variable of that CPT is updated automatically in the Bayesian network. We also updated the Action variable, which consists of 14 different values (action verbs) using crowdsourcing with a human computation approach.