A kinetic error filtering mechanism for enzyme-free copying of nucleic acid sequences

Accurate copying of nucleic acid sequences is essential for self-replicating systems. Modern cells achieve error ratios as low as 10-9 with sophisticated enzymes capable of kinetic proofreading. In contrast, experiments probing enzyme-free copying of RNA and DNA as potential prebiotic replication processes find error ratios on the order of 10%. Given this low intrinsic copying fidelity, plausible scenarios for the spontaneous emergence of molecular evolution require an accuracy-enhancing mechanism. Here, we study a 'kinetic error filtering' scenario that dramatically boosts the likelihood of producing exact copies of nucleic acid sequences. The mechanism exploits the observation that initial errors in template-directed polymerization of both DNA and RNA are likely to trigger a cascade of consecutive errors and significantly stall downstream extension. We incorporate these characteristics into a mathematical model with experimentally estimated parameters, and leverage this model to probe to what extent accurate and faulty polymerization products can be kinetically discriminated. While limiting the time window for polymerization prevents completion of erroneous strands, resulting in a pool in which full-length products show an enhanced accuracy, this comes at the price of a concomitant reduction in yield. We show that this fidelity-yield trade-off can be circumvented via repeated copying attempts in cyclically varying environments such as the temperature cycles occurring naturally in the vicinity of hydrothermal systems. This setting could produce exact copies of sequences as long as 50mers within their lifetime, facilitating the emergence and maintenance of catalytically active oligonucleotides.

U-TAPIS: AUTOMATIC SPELLING FILTER AS AN EFFORT TO IMPROVE INDONESIAN LANGUAGE COMPETENCIES OF JOURNALISTIC STUDENTS

The problem of Indonesian language errors among students is of particular observation. This problem becomes an important concern for students majoring in journalism because one day the graduates will become journalists. A language error filtering application has been developed that can be used quickly and accurately in journalists’ work. This application, which involves statistical analysis, computational language, and artificial intelligence, is named U-Tapis. This study was aimed at finding out the feasibility and effectiveness measures of the U-Tapis model by focusing on the language of students’ journalistic works such as opinions, news items, and news articles. The study involved 30 students majoring in Journalism, a private university in Jakarta, Indonesia. It was found that the students’ error rate decreased after the use of the model. It can be concluded that, in addition to eligibility which reaches 92.31%, the U-Tapis application can help effectively increase students’ proficiency in the use of the Indonesian language.

MicroSEC: Sequence error filtering pipeline for formalin-fixed and paraffin-embedded samples

The clinical sequencing of tumors is usually performed on formalin-fixed, paraffin-embedded (FFPE) samples and results in many sequencing errors. Most of these errors are detected in chimeric reads caused by single-strand DNA molecules with microhomology. Our filtering pipeline, MicroSEC, focuses on the uneven distribution of mutations in each read and removes the sequencing errors in FFPE samples without eliminating the true mutations that are also detected in fresh frozen samples.

Orientation-Constrained System for Lamp Detection in Buildings Based on Computer Vision

Computer vision is used in this work to detect lighting elements in buildings with the goal of improving the accuracy of previous methods to provide a precise inventory of the location and state of lamps. Using the framework developed in our previous works, we introduce two new modifications to enhance the system: first, a constraint on the orientation of the detected poses in the optimization methods for both the initial and the refined estimates based on the geometric information of the building information modelling (BIM) model; second, an additional reprojection error filtering step to discard the erroneous poses introduced with the orientation restrictions, keeping the identification and localization errors low while greatly increasing the number of detections. These enhancements are tested in five different case studies with more than 30,000 images, with results showing improvements in the number of detections, the percentage of correct model and state identifications, and the distance between detections and reference positions.