Nicola Romanò @nicolaromano@qoto.org

I've created a little schematic on basic Git/GitHub usage.
Feel free to reuse! (CC-BY-NC-4.0)

#git #github #versioncontrol #programming

OK... so you want to scam me into giving you personal data (and maybe a bit of money) for your journal... at least change your email address! 🤦‍♂️

I've recently picked up this great #book by Cathy O'Neil, "Weapons of Math Destruction"

https://books.google.co.uk/books?id=60n0DAAAQBAJ&newbks=0&printsec=frontcover&pg=PP1

An "old" book (2016) but still extremely if not more relevant than ever.

#bigdata #dataethics #modelling #maths

Very happy to share our newly published #article "Sex differences in pituitary corticotroph excitability".

It is well known that sex differences exist in stress-related disorders, with women having twice the lifetime rate of depression compared to men and most anxiety disorders.

Corticotroph cells in the pituitary gland are a key player in the generation of hormonal stress responses. However, their contribution to sexually differential responses of the stress axis (which might underlie differences in stress-related disorders) is very poorly understood.

We found sex differences in the electrical activity of these cells, which could be related to differences in their gene expression pattern.

These findings shed light on the cellular mechanisms underlying sex differences in stress responses, contributing to a better understanding of stress-related disorders and potential avenues for diagnosis and treatment.

#stress #pituitary #research #electrophysiology #physiology #corticotrophs #hpa #anxiety

https://www.frontiersin.org/articles/10.3389/fphys.2023.1205162/full

And a new blog post is here!
This time we will learn how to build a simple neural network in Python! 🤖
https://www.nicolaromano.net/data-thoughts/neural-network-in-python/

Keep watching this space as more posts are in the pipeline!

Are you interested in the topic? Let me know what you would like to hear next!

#neuralnetworks #ai #artificialintelligence #machinelearning #training #statistics #python

Following up from last week's post, here is the continuation of our journey onto neural networks! In this post I discuss the basics of neural network training. 🤖

https://www.nicolaromano.net/data-thoughts/training-neural-networks/

Keep watching this space as more posts are in the pipeline! Next up: make your first neural network in Python!

Are you interested in the topic? Let me know what you would like to hear next

#neuralnetworks #ai #artificialintelligence #machinelearning #training #statistics #python

After a long hiatus (because, life...) I have updated my blog!

I have been meaning to write about neural networks for a while, so here it is! Part 1 of ... a certain number😜

In this first episode, I will guide you through what is a neural network, with a bit of history on the topic!

https://www.nicolaromano.net/data-thoughts/intro-neural-networks-pt1/

Stay tuned because other posts will be showing up in the upcoming weeks... we'll see a bit more of the theory of how these networks are trained, and then we'll code our own network in #python!

#ai #machinelearning #neuralnetworks #statistics #artificialintelligence

Wow, we've reached new heights... just got a #spam email from a #predatory journal using anti spam-filter techniques straight out of the 90s... (which obviously work with the crappy spam filters our uni uses...)

#predatorypublishing

I have created a little R #Shiny interface to deconvolute #luciferase assay data.

This is based on Brown 2008 - Inferring gene expression dynamics from reporter protein levels (but does not include volume correction)

The app also reports initial rate, maximum and decay rate

It is available here, for anyone to enjoy, modify etc

https://github.com/nicolaromano/LucDeconvolution

#rstats #analysis #biology #transcription

Just finished reading this very interesting #review about #analysis of #scRNAseq data from different #species.

"Cross-Species Analysis of Single-Cell Transcriptomic Data" - Schafer - Front. Cell Dev. Biol 2019

https://www.frontiersin.org/articles/10.3389/fcell.2019.00175/full#B5

Just reading this very interesting paper on how many #replicates are needed for appropriate analysis of bulk #RNAseq data and comparing tools for DE gene expression.

#bioinformatics #article #goodread

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4878611/

From the abstract

With three biological replicates, nine of the 11 tools evaluated
found only 20%–40% of the significantly differentially expressed (SDE) genes identified with the full set of 42 clean replicates.
This rises to >85% for the subset of SDE genes changing in expression by more than fourfold. To achieve >85% for all SDE genes regardless of fold change requires more than 20 biological replicates.

I'd like to point out this great little table about reproducible research.

This is from a great resource "The Turing Way handbook to reproducible, ethical and collaborative data science", which you can find here: https://the-turing-way.netlify.app/welcome.html

I thought I would share this image, which I took a while ago, but that I really like! This is a #pituitary gland where cells expressing a protein called proopiomelanocortin (POMC for short) have been coloured in green. You can see two groups of cells; a very packed band in the middle and some sparse cells on the sides. The first are called melanotrophs, and they are important in determining skin and fur colour. The sparse cells on the sides are called corticotrophs (and that's what I am studying at the moment!) and they are important for the response to #stress . They secrete an #hormone called ACTH which stimulates the production of the stress hormone cortisol from the adrenal gland. At the moment we are trying to understand what happens to this cells after the body is exposed to stress for a prolonged amount of time. #scicomm #biology #science #physiology #microscopy