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Navigating nitrogen sustainability with microbiome-associated phenotypes Crop microbiomes promote plant health through various mechanisms, including nutrient provisioning. However, agriculture neglected the importance of th…

Navigating #nitrogen sustainability with #microbiome-associated #phenotypes

www.sciencedirect.com/science/arti...

#PlantScience @cp-trendsplantsci.bsky.social @cellpress.bsky.social @microbioblog.bsky.social @microbiome.bsky.social @plantmicrobiol.bsky.social @apcmicrobiomeirel.bsky.social

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New #OpenAccess research in #RESEcolEnt

Reduced #Fecundity associated with #Wolbachia infection in a neotropical Drosophilid
doi.org/10.1111/een.70051

#Drosophila #Phenotypes
@sheborg.bsky.social @robwilsonmncn.bsky.social @callomac.bsky.social @wileyecology.bsky.social

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📌JANUARY 2026 ISSUE

💊 #DOAC in #dialysis patients: -60% RR of #ICH

🩸 #TissueFactor #Thrombin Generation: #Hypercoagulability in #Obesity

🫀 #Phenotypes Associated with Increased Risk in #AtrialFibrillation Patients: The COOL-AF Registry

Read more at: tinyurl.com/yfrtaw7n

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Variation is everywhere. 🦗

#intraspecificvariation
#colorvariation
#phenotypes
#insects

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The phenotypic spaces of Arabidopsis thaliana F2s vs. wild accessions. The results shown correspond to one of the 10 hypervolume calculations. Left: Trait hypervolumes based on the first three principal component analysis (PCA) dimensions (with opaque dots representing observed data points, and semitransparent small dots representing uniformly distributed random points generated by the algorithm used for hypervolume computation). Hypervolumes with size equivalent to the mean of the 100 hypervolumes generated by resampling per group (F2s vs. accessions) are shown. Right: Trait variation within the phenotypic spaces of A. thaliana F2s (blue) and wild accessions (red) considering the first three PCA dimensions of trait hypervolumes. Hypervolume centroids, observed data points, and uniformly distributed random points generated by the algorithm used for hypervolume computation are depicted by large circles, opaque dots, and semitransparent small dots, respectively. Hypervolumes with size equivalent to the mean of the 100 hypervolumes generated by resampling per group (F2s vs. accessions) are shown. LA, leaf area; LDMC, leaf dry matter content; LNC, leaf nitrogen content; SLA, specific leaf area.

The phenotypic spaces of Arabidopsis thaliana F2s vs. wild accessions. The results shown correspond to one of the 10 hypervolume calculations. Left: Trait hypervolumes based on the first three principal component analysis (PCA) dimensions (with opaque dots representing observed data points, and semitransparent small dots representing uniformly distributed random points generated by the algorithm used for hypervolume computation). Hypervolumes with size equivalent to the mean of the 100 hypervolumes generated by resampling per group (F2s vs. accessions) are shown. Right: Trait variation within the phenotypic spaces of A. thaliana F2s (blue) and wild accessions (red) considering the first three PCA dimensions of trait hypervolumes. Hypervolume centroids, observed data points, and uniformly distributed random points generated by the algorithm used for hypervolume computation are depicted by large circles, opaque dots, and semitransparent small dots, respectively. Hypervolumes with size equivalent to the mean of the 100 hypervolumes generated by resampling per group (F2s vs. accessions) are shown. LA, leaf area; LDMC, leaf dry matter content; LNC, leaf nitrogen content; SLA, specific leaf area.

Why do we observe some plant #phenotypes but not others? This study explores the multivariate #PhenotypicSpace of a widely distributed #plant species to reveal how interplays between population history & natural selection shape phenotypic diversity in #Arabidopsis @plosbiology.org 🧪 plos.io/43Z8IWi

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The phenotypic spaces of Arabidopsis thaliana F2s vs. wild accessions. The results shown correspond to one of the 10 hypervolume calculations. Left: Trait hypervolumes based on the first three principal component analysis (PCA) dimensions (with opaque dots representing observed data points, and semitransparent small dots representing uniformly distributed random points generated by the algorithm used for hypervolume computation). Hypervolumes with size equivalent to the mean of the 100 hypervolumes generated by resampling per group (F2s vs. accessions) are shown. Right: Trait variation within the phenotypic spaces of A. thaliana F2s (blue) and wild accessions (red) considering the first three PCA dimensions of trait hypervolumes. Hypervolume centroids, observed data points, and uniformly distributed random points generated by the algorithm used for hypervolume computation are depicted by large circles, opaque dots, and semitransparent small dots, respectively. Hypervolumes with size equivalent to the mean of the 100 hypervolumes generated by resampling per group (F2s vs. accessions) are shown. LA, leaf area; LDMC, leaf dry matter content; LNC, leaf nitrogen content; SLA, specific leaf area.

The phenotypic spaces of Arabidopsis thaliana F2s vs. wild accessions. The results shown correspond to one of the 10 hypervolume calculations. Left: Trait hypervolumes based on the first three principal component analysis (PCA) dimensions (with opaque dots representing observed data points, and semitransparent small dots representing uniformly distributed random points generated by the algorithm used for hypervolume computation). Hypervolumes with size equivalent to the mean of the 100 hypervolumes generated by resampling per group (F2s vs. accessions) are shown. Right: Trait variation within the phenotypic spaces of A. thaliana F2s (blue) and wild accessions (red) considering the first three PCA dimensions of trait hypervolumes. Hypervolume centroids, observed data points, and uniformly distributed random points generated by the algorithm used for hypervolume computation are depicted by large circles, opaque dots, and semitransparent small dots, respectively. Hypervolumes with size equivalent to the mean of the 100 hypervolumes generated by resampling per group (F2s vs. accessions) are shown. LA, leaf area; LDMC, leaf dry matter content; LNC, leaf nitrogen content; SLA, specific leaf area.

Why do we observe some plant #phenotypes but not others? This study explores the multivariate #PhenotypicSpace of a widely distributed #plant species to reveal how interplays between population history & natural selection shape phenotypic diversity in #Arabidopsis @plosbiology.org 🧪 plos.io/43Z8IWi

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🌟 BlueSky trending hashtags (1h):

#booksky #birds #nature #art #photography #naturephotography #plant #bookreview #pnw #stripes #birdoftheday #furryart #phenotypicspace #phenotypes #arabidopsis #gamedev #visualnovel #whidbeyisland #sony #birdphotograpy

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🔥 BlueSky trending hashtags (30m):

#booksky #birds #nature #art #photography #naturephotography #plant #bookreview #pnw #stripes #birdoftheday #furryart #phenotypicspace #phenotypes #arabidopsis #gamedev #visualnovel #whidbeyisland #sony #birdphotograpy

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🚀 BlueSky trending hashtags (15m):

#booksky #birds #nature #art #photography #naturephotography #plant #bookreview #pnw #stripes #birdoftheday #furryart #phenotypicspace #phenotypes #arabidopsis #gamedev #visualnovel #whidbeyisland #sony #birdphotograpy

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The phenotypic spaces of Arabidopsis thaliana F2s vs. wild accessions. The results shown correspond to one of the 10 hypervolume calculations. Left: Trait hypervolumes based on the first three principal component analysis (PCA) dimensions (with opaque dots representing observed data points, and semitransparent small dots representing uniformly distributed random points generated by the algorithm used for hypervolume computation). Hypervolumes with size equivalent to the mean of the 100 hypervolumes generated by resampling per group (F2s vs. accessions) are shown. Right: Trait variation within the phenotypic spaces of A. thaliana F2s (blue) and wild accessions (red) considering the first three PCA dimensions of trait hypervolumes. Hypervolume centroids, observed data points, and uniformly distributed random points generated by the algorithm used for hypervolume computation are depicted by large circles, opaque dots, and semitransparent small dots, respectively. Hypervolumes with size equivalent to the mean of the 100 hypervolumes generated by resampling per group (F2s vs. accessions) are shown. LA, leaf area; LDMC, leaf dry matter content; LNC, leaf nitrogen content; SLA, specific leaf area.

The phenotypic spaces of Arabidopsis thaliana F2s vs. wild accessions. The results shown correspond to one of the 10 hypervolume calculations. Left: Trait hypervolumes based on the first three principal component analysis (PCA) dimensions (with opaque dots representing observed data points, and semitransparent small dots representing uniformly distributed random points generated by the algorithm used for hypervolume computation). Hypervolumes with size equivalent to the mean of the 100 hypervolumes generated by resampling per group (F2s vs. accessions) are shown. Right: Trait variation within the phenotypic spaces of A. thaliana F2s (blue) and wild accessions (red) considering the first three PCA dimensions of trait hypervolumes. Hypervolume centroids, observed data points, and uniformly distributed random points generated by the algorithm used for hypervolume computation are depicted by large circles, opaque dots, and semitransparent small dots, respectively. Hypervolumes with size equivalent to the mean of the 100 hypervolumes generated by resampling per group (F2s vs. accessions) are shown. LA, leaf area; LDMC, leaf dry matter content; LNC, leaf nitrogen content; SLA, specific leaf area.

Why do we observe some plant #phenotypes but not others? This study explores the multivariate #PhenotypicSpace of a widely distributed #plant species to reveal how interplays between population history & natural selection shape phenotypic diversity in #Arabidopsis @plosbiology.org 🧪 plos.io/43Z8IWi

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1 - 5:
💪 Define #timing and #dose of rehabilitation and nutrition
🧬 Understand underlying #biological mechanisms
🧍‍♀️ Identify patient #phenotypes for personalised care
🫀 Adapt rehab and #nutrition across recovery phases
🫶 Identify priorities of #persons with lived experience

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Pathomechanistic #Phenotypes of #Cirrhosis

Dalila Costa🇵🇹 Jonel Trebicka🇩🇪 Cristina Ripoll🇪🇸 Richard Moreau🇫🇷 @rajivjalan.bsky.social🇬🇧 Reiberger🇦🇹

📈 Portal Hypertension #PH & 🔥 Inflammation #SI coexist in pts w/ cirrhosis

💡review on PH-SI interplay in distinct cirrhosis stages
@easlnews.bsky.social

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#Genes #variants #phenotypes, oh my! @ebi.embl.org Human Genomics Team services presented at #CCG2025:

🔍@gene2phenotype.bsky.social (G2P) - accelerating genomic medicine w/ high-confidence, evidence-based gene-disease models www.ebi.ac.uk/gene2phenoty...

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Read Maision et al's article:
Utilization of Computable #Phenotypes in Electronic Health Record Research: A Review and Case Study in #AtopicDermatitis
doi.org/10.1016/j.ji...

#DermatologyJournal #dermatology #dermsky #dermatologists #dermresearch #dermscience #dermatologyresearch

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Huge congrats to @seandreu.bsky.social and the whole team! 🥳 So glad to have been a part of this exciting work that focused on the links between gut #microbial #genetic variation at the #strain level and a whole slew of host #phenotypes and geography. Read more @cellpress.bsky.social!
#metagenomics

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VIDEO: AI may help deepen understanding of obesity phenotypes NATIONAL HARBOR, Md. — AI is “up and coming” across health care, and obesity medicine is no exception, according to Angela Fitch, MD, FACP, MFOMA. In this video, Fitch, chief medical...

VIDEO: #AI may help deepen understanding of obesity #phenotypes www.healio.com/news/primary...
#MedSky #AImedicine #AI #HealthAI #Healthcare #MedTech

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1.🎤 9:00–10:00

We start strong with Petr Tureček's New Investigator Plenary:

“Error, Inspiration, and the Dynamics of Variance in Cultural Transmission.”

Cultural evolution fans, this one's for you! #EHBEA2025

@turecek.bsky.social
#CulturalTransmission #Phenotypes

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🧵2/4: But how to preserve data? #NIH flagship Database of #Genotypes and #Phenotypes #dbGaP allows for data to be restricted to nonprofit use only, preserving scientific benefit of #23andMe data as well as corp value.

But #doge #RIFs #today will make this even harder, hurting everyone everywhere.

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Identifying obesity phenotypes: The role of personality,... : Saudi Journal of Obesity Inventory), appetitive traits (the Adult Eating Behavior Questionnaire), hyperpalatable food consumption, and body mass index (BMI). Participants and Procedure: The research employed a nonexperimen...

Personas c/sobrepeso u obesidad tienen una asociación heterogénea con los fenotipos del peso y factores psicológicos y conductuales, cuya definición puede beneficiar el procedo diagnóstico y el diseño de intervenciones personalizadas obesity #phenotypes #personality journals.lww.com/sjob/fulltex...

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Comparative Study Between Cognitive Phenotypes of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Multiple Sclerosis Objective: Cognitive impairments are one of the most common and disabling symptoms associated with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Here, we address the possibility of a sp...

🇫🇷 Comparative Study Between Cognitive Phenotypes of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Multiple Sclerosis
www.mdpi.com/2075-4418/15... #ME/CFS vs #MS #Phenotypes

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#Fungi as you have #never #seen #before.

I have been working for years on #FungalGalaxies, an exploration into the most #extraordinary and intimate #phenotypes within the fungal kingdom, a #manifesto of #biodiversity that will unite #science and #art in an unprecedented way. Follow me for more 🍄

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In response to these challenges, a research team from Beijing University of Technology and The University of Tokyo has developed EasyDAM_V4, a pioneering AI-driven approach, published in Horticulture Research.
#Phenotypes #AI-driven approach
Details: doi.org/10.1093/hr/uhae007

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🌿 Let us introduce our research group Systems Biology & Mathematical Modelling: Led by Zoran Nikoloski, this group explores plant #phenotypes & #genotypes using #machinelearning, #AI and #mathematicalmodelling 💻. Their aim? Predict plant development in future climate conditions 🌡️!

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OSF

Exploring #nonergodicity in #chronicpain challenges the notion of static #phenotypes or fixed pain categories. Pain doesn’t fit neatly into boxes—it’s dynamic and uniquely #individual.

#painscience #physio #physicaltherapy #medicine #neuroscience #medicine #idontfitinabox

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Genetic risk factors for long-COVID uncovered in a large multi-ethnic study Researchers identified genetic loci and phenotypes linked to long-COVID in the largest multi-ethnic GWAS, revealing increased risk factors for chronic conditions like fatigue, depression, and fibromya...

Genetic risk factors for long-COVID uncovered in a large multi-ethnic study 🔬🧬🌍 www.news-medical.net/news/2024101... #LongCOVID #Genetics #GWAS #Fatigue #Depression #Fibromyalgia #COVID19 #ImmuneSystem #Phenotypes #ChronicDisease @medrxivpreprint.bsky.social

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IOB
"Our results found that cave-adapted populations do share certain anatomical features; however, they generally did not support the hypothesis of a conserved craniofacial phenotype across caves..."
Holtz & Albertson
doi.org/10.1093/iob/...

#phenotypes #science #biology #fish

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My article on optimization of self- or parent-reported psychiatric phenotypes in longitudinal studies is now online in the Journal of Child Psychology and Psychiatry. #Psychiatry #Phenotypes #Academia #HigherEducation #ChildPsychology #ChildPsychiatry

onlinelibrary.wiley.com/share/author... 🧠🧪

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image from
https://beespotter.org/topics/bio/Bombus/impatiens/index.html

image from https://beespotter.org/topics/bio/Bombus/impatiens/index.html

Bee reads to start the week:
ICB
Impacts of Early-Life Experience on #Bee #Phenotypes & Fitness
by Rittschof et al
doi.org/10.1093/icb/...
IOB
The Effect of Pollen Diet Composition and Quantity on Diapause Survival & Performance in an Annual Pollinator
by Treanore et al
doi.org/10.1093/iob/...

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Mechanisms of #molecular convergence #evolution across #bees and #wasps with rudimentary societies ... uncovered sets of #genes with conserved expression patterns among reproductive and non-reproductive #phenotypes across species....

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