Composition and nutritive value of starter diets (1–14 days).

Composition and nutritive value of starter diets (1–14 days).

<p>Composition and nutritive value of starter diets (1–14 days).</p>

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Xehetasun bibliografikoak
Egile nagusia:	Muhammad Rumman Aslam (22683482) (author)
Beste egile batzuk:	Bartosz Kierończyk (500220) (author), Piotr Szymkowiak (22683485) (author), Liliana Ciesielska (22683488) (author), Mateusz Rawski (500223) (author), Robert Mikuła (6595409) (author), Damian Józefiak (500219) (author)
Argitaratua:	2025
Gaiak:	Biochemistry Microbiology Molecular Biology Biotechnology Ecology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified various dietary fats selected animal fats lefse analysis revealed inhibiting opportunistic pathogens broilers responds similarly broiler chicken nutrition hermetia illucens </ eubacterium coprostanoligenes </ clostridia vadin </ eight dietary groups 10 replicate pens 011 ), enterobacteriaceae 003 ), rhodospirillales 001 ), rikenellaceae ), rapeseed oil bsfl significantly reduced bsfl positively affects including ph determination div >< p 025 ), peptococcaceae 35 days ), including bsfl fat 35 days 025 ), shigella </ alistipes </ pl ), 10 birds 048 ), 032 ), 018 ), bsfl treatment bsfl increased bsfl fat pkfd groups upper part uncultured genus study design study aimed sole source significant reduction randomly assigned ngs ). gut ph generation sequencing findings suggest detected among cecal microbiota cecal microbiome cecal environment
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Antzeko izenburuak

Effects of the inclusion of selected types of dietary fat in broiler chicken diets on the gastrointestinal tract pH.
nork: Muhammad Rumman Aslam (22683482)
Argitaratua: (2025)

Composition and nutritive value of grower diets (15–35 days).
nork: Muhammad Rumman Aslam (22683482)
Argitaratua: (2025)

Linear discriminant analysis effect size (LEfSe) at various taxonomic levels (kingdom (k), phylum (p), class (c), order (o), family (f), genus (g), and species (s)) for broiler chickens fed diets containing fats of plant, animal, and insect origin.
nork: Muhammad Rumman Aslam (22683482)
Argitaratua: (2025)

Linear discriminant analysis effect size (LEfSe) at various taxonomic levels (kingdom (k), phylum (p), class (c), order (o), family (f), genus (g), and species (s)) for broiler chickens fed diets containing fats of plant, animal, and insect origin.
nork: Muhammad Rumman Aslam (22683482)
Argitaratua: (2025)

Graphical representation of beta diversity analysis and bacterial abundance in broiler ceca.
nork: Muhammad Rumman Aslam (22683482)
Argitaratua: (2025)

(A) Venn diagram of the overlap among three groups (BSFL, plant oils, and animal fats); (B) UpSet plot representing the intersection of OTUs across eight experimental groups in the ceca of broiler chickens. BSFL – basal diet with 100% black soldier fly (<i>Hermetia illucens</i>) larval fat; plant oils (SO – basal diet with 100% soybean oil; RO – basal diet with 100% rapeseed oil; PO – basal diet with 100% palm oil; PKFD – basal diet with 100% palm kernel fat distillate); animal fats (PF – basal diet with 100% poultry fat; PL – basal diet with 100% pig lard; BT – basal diet with 100% beef tallow).
nork: Muhammad Rumman Aslam (22683482)
Argitaratua: (2025)

Diversity analysis of microbial communities in broiler ceca.
nork: Muhammad Rumman Aslam (22683482)
Argitaratua: (2025)

Table 6_Study on the differences of fat deposition in cattle-yak and yak based on transcriptomics and metabolomics.docx
nork: Lin Xiong (623256)
Argitaratua: (2025)

Image 1_Study on the differences of fat deposition in cattle-yak and yak based on transcriptomics and metabolomics.pdf
nork: Lin Xiong (623256)
Argitaratua: (2025)

Table 4_Study on the differences of fat deposition in cattle-yak and yak based on transcriptomics and metabolomics.xlsx
nork: Lin Xiong (623256)
Argitaratua: (2025)

Table 5_Study on the differences of fat deposition in cattle-yak and yak based on transcriptomics and metabolomics.xls
nork: Lin Xiong (623256)
Argitaratua: (2025)

Table 2_Study on the differences of fat deposition in cattle-yak and yak based on transcriptomics and metabolomics.xls
nork: Lin Xiong (623256)
Argitaratua: (2025)

Table 3_Study on the differences of fat deposition in cattle-yak and yak based on transcriptomics and metabolomics.xls
nork: Lin Xiong (623256)
Argitaratua: (2025)

Table 1_Study on the differences of fat deposition in cattle-yak and yak based on transcriptomics and metabolomics.xls
nork: Lin Xiong (623256)
Argitaratua: (2025)

Table 1_VPS35 D620N mutation impairs neurogenesis and promotes ferroptosis in Parkinson’s disease by using molecular docking, molecular dynamic simulation, and cellular model.docx
nork: Mei Jiang (192549)
Argitaratua: (2025)

Study characteristics of the subjects in the present study (n = 97).
nork: Jette Hinrichsen (22683252)
Argitaratua: (2025)

Relative bias of the App-results.
nork: Jette Hinrichsen (22683252)
Argitaratua: (2025)

Subject recruitment and Dropouts.
nork: Jette Hinrichsen (22683252)
Argitaratua: (2025)

Bland-Altman-Plots for the difference between App and 24-hour-recall on Days 1, 2, 3 and the mean over the three days for ‘Beverages’.
nork: Jette Hinrichsen (22683252)
Argitaratua: (2025)

Bland-Altman-Plots for the difference between App and 24-hour-recall on Days 1, 2, 3 and the mean over the three days for ‘Fats and oils’.
nork: Jette Hinrichsen (22683252)
Argitaratua: (2025)

Bland-Altman-Plots for the difference between App and 24-hour-recall on Days 1, 2, 3 and the mean over the three days for ‘Meat, sausage, fish and egg’.
nork: Jette Hinrichsen (22683252)
Argitaratua: (2025)

Bland-Altman-Plots for the difference between App and 24-hour-recall on Days 1, 2, 3 and the mean over the three days for ‘Milk and dairy’.
nork: Jette Hinrichsen (22683252)
Argitaratua: (2025)

Bland-Altman-Plots for the difference between App and 24-hour-recall on Days 1, 2, 3 and the mean over the three days for ‘Fruit and fruit juice’.
nork: Jette Hinrichsen (22683252)
Argitaratua: (2025)

Bland-Altman-Plots for the difference between App and 24-hour-recall on Days 1, 2, 3 and the mean over the three days for ‘Vegetables and salad’.
nork: Jette Hinrichsen (22683252)
Argitaratua: (2025)

Bland-Altman-Plots for the difference between App and 24-hour-recall on Days 1, 2, 3 and the mean over the three days for ‘Grain, grain products and potatoes’.
nork: Jette Hinrichsen (22683252)
Argitaratua: (2025)

Bland-Altman-Plots for the difference between App and 24-hour-recall on Days 1, 2, 3 and the mean over the three days for fat.
nork: Jette Hinrichsen (22683252)
Argitaratua: (2025)

Bland-Altman-Plots for the difference between App and 24-hour-recall on Days 1, 2, 3 and the mean over the three days for carbohydrates.
nork: Jette Hinrichsen (22683252)
Argitaratua: (2025)

Bland-Altman-Plots for the difference between App and 24-hour-recall on Days 1, 2, 3 and the mean over the three days for protein.
nork: Jette Hinrichsen (22683252)
Argitaratua: (2025)

Bland-Altman-Plots for the difference between App and 24-hour-recall on Days 1, 2, 3 and the mean over the three days for calories.
nork: Jette Hinrichsen (22683252)
Argitaratua: (2025)

Characteristics of patients infected with norovirus GII.
nork: Leera Kittigul (22676501)
Argitaratua: (2025)

Difference in viral loads in stool samples from norovirus GII-infected patients, grouped by genotype.
nork: Leera Kittigul (22676501)
Argitaratua: (2025)

Distribution of norovirus genotypes and P-types detected in patients with acute gastroenteritis attending two hospitals in Thailand.
nork: Leera Kittigul (22676501)
Argitaratua: (2025)

Comparison of hierarchical classifier performance among four machine learning models for the early-loss, intermediate-loss, and late/no-loss groups.
nork: Raiki Yoshimura (22676573)
Argitaratua: (2025)

Comparison of multi-class classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.
nork: Raiki Yoshimura (22676573)
Argitaratua: (2025)

Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.
nork: Raiki Yoshimura (22676573)
Argitaratua: (2025)

Comparison of performance of four machine learning models for long-term graft loss prediction.
nork: Raiki Yoshimura (22676573)
Argitaratua: (2025)

Comparison of prediction performance with previously reported metrics.
nork: Raiki Yoshimura (22676573)
Argitaratua: (2025)

Comparison of performance of four machine learning models for early graft loss prediction.
nork: Raiki Yoshimura (22676573)
Argitaratua: (2025)

Predicting stratified groups in the validation cohort: (A) Workflows for the multi-class classification and hierarchical classification models are illustrated.
nork: Raiki Yoshimura (22676573)
Argitaratua: (2025)

Stratification and characterization of groups showing different graft survival: (A) UMAP visualization of the stratified derivation cohort data based on the distance matrix from the RF clustering.
nork: Raiki Yoshimura (22676573)
Argitaratua: (2025)

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