A new color to be seen

Where is the color of what we see? Is it part of the object we see? Is it in the light from that object? Is it in our eyes, our retinas? Or is it in the brain?

All of the above, I believe, are where there is formation of what is ultimately termed a color, in many of the different senses of the term color, including color wavelength, color sensation and their many subtypes. Optical illusions can show us that there is more to color vision than light alone.

Perhaps, the retina in particular is where a particular color (not just its wavelengths) actually begins to become what we can see. The article below shows that selective stimulation of a population of cone cells in a way that is not offset by nearby cells not activated by the computer-controlled micro-laser can create a color that they report is noticeably different from any prior color sensation. The color still seems to fit in a color wheel in its shade (turquoise green) if not in its saturation.

This seems to work because our eyes have 3 cone cell types, and the light wavelengths for a given frequency tend to stimulate more than one type, for example unavoidably in usual function stimulating L cone cells with similar wavelengths as green-sensing M cone cells (see the graph of cell stimulation versus wavelength from Wikipedia below):

The ingenuity of the creators of this study's equipment is commendable, as that equipment allowed them to bypass the usual limitations of the eye's SML color scheme.

It's also clear that those who see this color have learned what a previously unknown shade of green looks like. This amounts to a kind of scientific confirmation of Frank Jackson's knowledge argument.

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ABSTRACT

        AU - James Fong
    
        AU - Hannah K. Doyle
    
        AU - Congli Wang
    
        AU - Alexandra E. Boehm
    
        AU - Sofie R. Herbeck
    
        AU - Vimal Prabhu Pandiyan
    
        AU - Brian P. Schmidt
    
        AU - Pavan Tiruveedhula
    
        AU - John E. Vanston
    
        AU - William S. Tuten
    
        AU - Ramkumar Sabesan
    
        AU - Austin Roorda
    
        AU - Ren Ng
    

TI - Novel color via stimulation of individual photoreceptors at population scale
PT - Journal Article
DP - 2025
TA - Science Advances
PG - eadu1052
VI - 11
IP - 16
AID - 10.1126/sciadv.adu1052 [doi]
    PMID - 40249825
    4099 - https://www.science.org/doi/abs/10.1126/sciadv.adu1052
4100 - https://www.science.org/doi/full/10.1126/sciadv.adu1052
SO - Science Advances 2025-04-18 11(16): eadu1052
AB - We introduce a principle, Oz, for displaying color imagery: directly controlling the human eye’s 
photoreceptor activity via cell-by-cell light delivery. Theoretically, novel colors are possible through 
bypassing the constraints set by the cone spectral sensitivities and activating M cone cells exclusively. 
In practice, we confirm a partial expansion of colorspace toward that theoretical ideal. Attempting to 
activate M cones exclusively is shown to elicit a color beyond the natural human gamut, formally measured 
with color matching by human subjects. They describe the color as blue-green of unprecedented saturation. 
Further experiments show that subjects perceive Oz colors in image and video form. The prototype targets 
laser microdoses to thousands of spectrally classified cones under fixational eye motion. These results 
are proof-of-principle for programmable control over individual photoreceptors at population scale. 
Image display by cell-by-cell retina stimulation, enabling colors impossible to see under natural viewing.

You are what you eat?

Well, maybe your microbiome is about 3% exactly what you ate, at least.

ABSTRACT

===========================================================

TITLE: Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome

AUTHORS: Niccolò Carlino, Aitor Blanco-Míguez, Michal Punčochář, Paul D. Cotter, Nicola Segata1, Edoardo Pasolli and many more...

DOI: doi: 10.1016/j.cell.2024.07.039

Published online in Cell, 2024/09/03

Highlights

With curated Food Metagenomic Data (FMD), we integrated and analyzed >2,500 food metagenomes.

Over 10,000 prokaryotic and eukaryotic MAGs uncover substantial food microbial diversity.

Food microbes account for up to an average of 3% of the adult gut microbiome.

Strain-level analysis highlights potential instances of food-to-gut microbe transmission.

Summary

Complex microbiomes are part of the food we eat and influence our own microbiome, but their diversity remains largely unexplored. Here, we generated the open access curatedFoodMetagenomicData (cFMD) resource by integrating 1,950 newly sequenced and 583 public food metagenomes. We produced 10,899 metagenome-assembled genomes spanning 1,036 prokaryotic and 108 eukaryotic species-level genome bins (SGBs), including 320 previously undescribed taxa. Food SGBs displayed significant microbial diversity within and between food categories. Extension to >20,000 human metagenomes revealed that food SGBs accounted on average for 3% of the adult gut microbiome. Strain-level analysis highlighted potential instances of food-to-gut transmission and intestinal colonization (e.g., Lacticaseibacillus paracasei) as well as SGBs with divergent genomic structures in food and humans (e.g., Streptococcus gallolyticus and Limosilactobabillus mucosae). The cFMD expands our knowledge on food microbiomes, their role in shaping the human microbiome, and supports future uses of metagenomics for food quality, safety, and authentication.

Risks for impaired post-stroke cognitive function

In a printed posted to the medRxiv preprint archive this month, I found a chart review of patients with stroke to determine factors (other than the stroke itself) which could predict cognitive impairment following the stroke admission to hospital.
The paper used patients admitted to Massachusetts General Hospital as their cases for which to form a risk factor formula, and then validated the formula against cases in a sister Boston hospital, Brighamm and Women’s Hospital.
The results are quoted as a table below:

The results found were that older age was the biggest overall risk factor (weighted for example as 4 for > 65, 8 for > 85), followed by those weighted 2: insurance of Medicare (a proxy for age over 65), mobility problems, delirium, peripheral vascular disease, and depression. Other factors were rated only at a weight of 1: these included Medicaid(a proxy for low income), prior falls, Parkinson’s, kidney disease, weight loss, and nursing home placement from the acute hospital stay.
Indeed, it seems that the factors found more significant, such as age, gait status, depression, delirium, vascular disease, are actually all also known from other, prospective studies to be risk factors for dementia in the general population, even in those without any stroke.
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ABSTRACT
Predicting post-stroke cognitive impairment using electronic health record data
Jeffrey M. Ashburner, PhD, MPH1,2; Yuchiao Chang, PhD1,2; Bianca Porneala, MS1; Sanjula D. Singh, MD, PhD3; Nirupama Yechoor, MD, MSc3; Jonathan M. Rosand, MD, MSc3; Daniel E. Singer, MD1,2; Christopher D. Anderson, MD, MMSc4; Steven J. Atlas, MD, MPH
medRxiv preprint: https://doi.org/10.1101/2024.02.02.24302240
Importance: Secondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification.
Objective: To develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records.
Design: Cohort study with patients enrolled between 2003-2016 with follow-up through 2022. Setting: Primary care practices affiliated with two academic medical centers.
Participants: Individuals 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003-2016 (development/internal validation cohort) or 2010-2022 (external validation cohort).
Exposures: Predictors of PSCI were ascertained from the electronic health record.
Main Outcome: The outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using ICD-9/10 codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included. Results: The analysis included 332 incident diagnoses of PSCI in the development cohort (n=3,741), and 161 and 128 incident diagnoses in the internal (n=1,925) and external (n=2,237) validation cohorts. The c-statistic for predicting PSCI was 0.731 (95% CI: 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-35 points) risk groups. The hazard ratios for incident PSCI were significantly different by risk categories in internal (High, HR: 6.2, 95% CI 4.1-9.3; Intermediate, HR 2.7, 95% CI: 1.8-4.1) and external (High, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR 2.8, 95% CI: 1.9-4.3) validation cohorts.
Conclusions and Relevance: Five-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts.
Key Words: post-stroke cognitive impairment, risk stratification, risk prediction

On medical AI in 2023

“To study persons is to study beings who only exist in, or are partly constituted by a certain language”. -- Charles Taylor, Sources of the Self

Large language model-based AI (LLM’s) are the epitome of what can be constituted by language alone. They can easily take isolated linguistic philosophy to its absurd extreme.

Unfortunately, this means there is a disconnect between a statement that is correct in the context of its LLM and the statements we want, which appropriately address a scientific or clinical context in the world outside of the LLM. This results in AI dialog replies that currently make medical advisory AI impossible to trust.

Current LLM base AI’s are masters of what Harry G. Frankfurt called “bullshit.” Until AI can distinguish and eliminate fictional or obsolete diagnoses and treatments within its model, separating them from those which help the patient, it cannot be trusted with any unsupervised role in patient care.

Mountain and City

Mauna Loa November-December 2022 Eruption Livestream

Life expectancy dropped much more with the Spanish flu in 1918 than COVID-19 so far.