Publications by authors named "A Ardalan"

The analysis of circulating tumour DNA (ctDNA) through minimally invasive liquid biopsies is promising for early multi-cancer detection and monitoring minimal residual disease. Most existing methods focus on targeted deep sequencing, but few integrate multiple data modalities. Here, we develop a methodology for ctDNA detection using deep (80x) whole-genome TET-Assisted Pyridine Borane Sequencing (TAPS), a less destructive approach than bisulphite sequencing, which permits the simultaneous analysis of genomic and methylomic data.

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Objectives: Breast cancer (BC) stands as the second-leading cause of female-specific cancer-related fatalities globally, necessitating comprehensive research to address its critical aspects. This study aimed to explore the time intervals between surgery and disease recurrence in BC patients and their survival utilizing various parametric and semi-parametric models.

Methods: After the examination of data collected from 2010 to 2021 at a BC Center in Tehran, Iran, 171 cases met the criteria for analysis out of 2246 datasets.

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Glyphosate is the most used herbicide on Earth. After a half-century of use we know only two biodegradative pathways, each of which appears to degrade glyphosate incidentally. One pathway begins with oxidation of glyphosate catalysed by glycine oxidase (GO).

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Lead halide perovskite and organic semiconductors are promising classes of materials for photodetector (PD) applications. State-of-the-art perovskite PDs have performance metrics exceeding silicon PDs in the visible. While organic semiconductors offer bandgap tunability due to their chemical design with detection extended into the near-infrared (NIR), perovskites are limited to the visible band and the first fraction of the NIR spectrum.

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The ability to prioritize among contents in working memory (WM) is critical for successful control of thought and behavior. Recent work has demonstrated that prioritization in WM can be implemented by representing different states of priority in different representational formats. Here, we explored the mechanisms underlying WM prioritization by simulating the double serial retrocuing task with recurrent neural networks.

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