RNA-SeqEZPZ: A Point-and-Click Pipeline for Comprehensive Transcriptomics Analysis with Interactive Visualizations.

RNA-Seq analysis has become a routine task in numerous genomic research labs, driven by the reduced cost of bulk RNA sequencing experiments. These generate billions of reads that require accurate, efficient, effective, and reproducible analysis. But the time required for comprehensive analysis remains a bottleneck.

Parents' Experiences of Raising 7- to 11-Year-Old Children With ADHD and Perception of a Proposed Parenting Program: A Qualitative Study.

Objective: To examine the experiences of Australian parents raising primary school-aged children with ADHD and gather feedback on a proposed ADHD parenting program.

Methods: Reflexive thematic analysis of semi-structured interviews undertaken with 11 Australian parents of 7- to 11-year-old children with ADHD. Interviews were conducted over Webex, audio recorded, transcribed verbatim, and analyzed in NVivo Ltd.

Machine learning techniques for the prediction of indoor gamma-ray dose rates - Strengths, weaknesses and implications for epidemiology.

We investigate methods that improve the estimation of indoor gamma ray dose rates at locations where measurements had not been made. These new predictions use a greater range of modelling techniques and larger variety of explanatory variables than our previous examinations of this subject. Specifically, we now employ three types of machine learning models in addition to the geostatistical, nearest neighbour and other earlier models.

Engineering an fgfr4 knockout zebrafish to study its role in development and disease.

Fibroblast growth factor receptor 4 (FGFR4) has a role in many biological processes, including lipid metabolism, tissue repair, and vertebrate development. In recent years, FGFR4 overexpression and activating mutations have been associated with numerous adult and pediatric cancers. As such, FGFR4 presents an opportunity for therapeutic targeting which is being pursued in clinical trials.

New dual inducible cellular model to investigate temporal control of oncogenic cooperating genes.

The study of cooperating genes in cancer can lead to mechanistic understanding and identifying potential therapeutic targets. To facilitate these types of studies, we developed a new dual-inducible system utilizing the tetracycline- and cumate-inducible systems driving HES3 and the PAX3::FOXO1 fusion-oncogene, respectively, as cooperating genes from fusion-positive rhabdomyosarcoma. With this model, we can independently induce expression of either HES3 or PAX3::FOXO1, as well as simultaneously induce expression of both genes.