The transcriptional landscape of the developing chick trigeminal ganglion.

The trigeminal ganglion is a critical structure in the peripheral nervous system, responsible for transmitting sensations of touch, pain, and temperature from craniofacial regions to the brain. Trigeminal ganglion development depends upon intrinsic cellular programming as well as extrinsic signals exchanged by diverse cell populations. With its complex anatomy and dual cellular origin from cranial placodes and neural crest cells, the trigeminal ganglion offers a rich context for examining diverse biological processes, including cell migration, fate determination, adhesion, and axon guidance.

Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis.

Introduction: Experiences of violence are important risk factors for worse outcome in people with mental health conditions; however, they are not routinely collected be mental health services, so their ascertainment depends on extraction from text fields with natural language processing (NLP) algorithms.

Methods: Applying previously developed neural network algorithms to routine mental healthcare records, we sought to describe the distribution of recorded violence victimisation by demographic and diagnostic characteristics. We ascertained recorded violence victimisation from the records of 60,021 patients receiving care from a large south London NHS mental healthcare provider during 2019.

The transcriptional landscape of the developing chick trigeminal ganglion.

The trigeminal ganglion is a critical structure in the peripheral nervous system, responsible for transmitting sensations of touch, pain, and temperature from craniofacial regions to the brain. Trigeminal ganglion development depends upon intrinsic cellular programming as well as extrinsic signals exchanged by diverse cell populations. With its complex anatomy and dual cellular origin from cranial placodes and neural crest cells, the trigeminal ganglion offers a rich context for examining diverse biological processes, including cell migration, fate determination, adhesion, and axon guidance.

Addressing bias in bagging and boosting regression models.

As artificial intelligence (AI) becomes widespread, there is increasing attention on investigating bias in machine learning (ML) models. Previous research concentrated on classification problems, with little emphasis on regression models. This paper presents an easy-to-apply and effective methodology for mitigating bias in bagging and boosting regression models, that is also applicable to any model trained through minimizing a differentiable loss function.

A traffic accident dataset for Chattanooga, Tennessee.

This publication presents an annotated accident dataset which fuses traffic data from radar detection sensors, weather condition data, and light condition data with traffic accident data (as illustrated in Fig. 1) in a format that is easy to process using machine learning tools, databases, or data workflows. The purpose of this data is to analyze, predict, and detect traffic patterns when accidents occur.