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1. Regularity of Iterative Hairpin Completions of Crossing (2, 2)-Words.

Author

Shikishima-Tsuji, Kayoko

Subjects
ITERATIVE methods (Mathematics), BIOCHEMISTRY, DNA, PROGRAMMING languages, COMPUTER science, COMPUTER network resources
Abstract

Hairpin completion is a formal operation inspired from DNA biochemistry. It is known that the (one step) hairpin completion of a regular language is linear context-free, but not regular in general. Further, it is decidable whether the (one step) hairpin completion of a regular language is regular. However, it is an open question whether the iterated hairpin completion of a regular language is regular, even if it is a singleton. If the word is a non-crossing α-word, there are results, but for crossing words there are no results. In this paper, we give necessary and sufficient conditions that the iterated hairpin completion of a given crossing (2, 2)-α-word in is regular. [ABSTRACT FROM AUTHOR]

Published
2016
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3. celldeath: A tool for detection of cell death in transmitted light microscopy images by deep learning-based visual recognition

Author

Gustavo Sevlever, María Agustina Scarafía, Alan Miqueas Möbbs, Ariel Waisman, Santiago Gabriel Miriuka, Carlos Luzzani, Nelba Pérez, Sheila Castañeda, Alejandro La Greca, Lucía Natalia Moro, and Paula Melania Milone

Subjects
Computer science, Cell Lines, Transmitted light, Cancer Treatment, Apoptosis, Biochemistry, Machine Learning, Animal Cells, Medicine and Health Sciences, Microscopy, Multidisciplinary, Artificial neural network, Cell Death, Stem Cells, Nucleic acids, Oncology, Cell Processes, MCF-7 Cells, Medicine, Biological Cultures, Cellular Types, medicine.drug, Research Article, Pluripotency, Programmed cell death, Computer and Information Sciences, Cell Potency, Science, Research and Analysis Methods, Deep Learning, Artificial Intelligence, Image Interpretation, Computer-Assisted, medicine, Genetics, Humans, business.industry, Deep learning, Biology and Life Sciences, Cancers and Neoplasms, Pattern recognition, Cell Biology, DNA, Visual recognition, DNA damage, Stem cell line, Programming Languages, Artificial intelligence, business, Stem Cell Lines, Camptothecin
Abstract

Cell death experiments are routinely done in many labs around the world, these experiments are the backbone of many assays for drug development. Cell death detection is usually performed in many ways, and requires time and reagents. However, cell death is preceded by slight morphological changes in cell shape and texture. In this paper, we trained a neural network to classify cells undergoing cell death. We found that the network was able to highly predict cell death after one hour of exposure to camptothecin. Moreover, this prediction largely outperforms human ability. Finally, we provide a simple python tool that can broadly be used to detect cell death.

Published
2021