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Chickens

Chickens are a domesticated avian species that are widely raised for their meat and eggs.
These gallinaceous birds are characterized by their feathered bodies, wings, and beaks.
Chickens play a vital role in the poultry industry, providing a significant source of protein and other nutrients for human consumption.
Their diverse breeds and production systems have been studied extensively, contributing to advancements in areas such as genetics, nutrition, and disease management.
Researching chickens can lead to improved agricultural practices, enhanced food security, and better animal welfare outcomes.

Most cited protocols related to «Chickens»

1
Comprehensive Update of miRDB miRNA Target Database
Since the first version of miRDB was established in 2008, thousands of novel miRNAs have been discovered. In addition, annotations of gene targets, especially concerning the identification of 3′-UTR sequences, have been significantly expanded. Thus, we have performed a major update on target prediction data by employing the up-to-date miRNA and target gene annotations. All miRNA sequences and annotations were downloaded from miRBase (version 21) in June 2014 (7 (link)). We have adopted the NCBI RefSeq database for identification of 3′-UTR sequences. In brief, RefSeq sequences were downloaded from NCBI's ftp site (8 (link)) and further parsed with the BioPerl program to obtain the 3′-UTR sequences of the transcripts. Target prediction was then performed with the MirTarget algorithm, which was developed by analyzing high-throughput expression profiling data in a support vector machine framework (9 (link)). Unlike most other prediction algorithms, MirTarget predicts both conserved and nonconserved gene targets by treating target site conservation as an important but non-required sequence feature. The robust performance of MirTarget has been extensively demonstrated. For example, a recent independent analysis shows that MirTarget has superior performance over other public algorithms for identifying miRNA-downregulated gene targets (10 (link)). In this miRDB update, we have also updated the MirTarget algorithm by including additional model training data, which were generated from miRNA-target pairs experimentally identified by RNA-seq (11 (link)). Details of the algorithmic improvement will be described elsewhere.
With updated genomic data and the MirTarget algorithm, we have performed genome-wide miRNA target prediction for all known transcripts (including all isoforms) from five species—human, mouse, rat, dog and chicken. In total, 2.1 million gene targets were predicted to be regulated by 6709 miRNAs in these five species. All the targets have a prediction score in the range of 50–100 as assigned by MirTarget, with a higher score representing more statistical confidence in the prediction result. Detailed statistics of the target prediction are presented in the miRDB website. All the target prediction data as well as the associated genomic annotations were imported into a backend MySQL database for web presentation. The users can search for precompiled results via miRDB web interface, using either miRNA or gene target search terms. Notably, the users have the flexibility of searching a single miRNA/gene target (Figure 1A), or a combination of multiple miRNAs/gene targets (Figure 1B). The users can download target prediction results for individual miRNAs or gene targets to a tab-delimited spreadsheet file via the Target Mining search interface. In addition, the users can download all precompiled target prediction data via the miRDB download page. A representative target prediction result retrieved from miRDB is presented in Figure 1C. There has been a major change in miRNA naming rules recently, resulting in multiple names describing the same miRNA (7 (link)). Thus, historical names for the same miRNAs are also presented in the result page. Data stored in miRDB are interconnected with the miRBase database (7 (link)). In each miRBase miRNA entry, there is a dynamic link directing to specific miRNA target prediction data in miRDB. The miRDB web interface and backend database are hosted by a Linux server at Washington University (http://mirdb.wustl.edu). Besides searching for target sites in 3′-UTR, the users may also locate unconventional target sites in the coding region or 5′-UTR via the Custom Prediction web interface.
Wong N, & Wang X. (2014). miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Research, 43(Database issue), D146-D152.
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Publication 2014
3' Untranslated Regions 5' Untranslated Regions Chickens Gene Annotation Genes Genes, vif Genome Homo sapiens MicroRNAs Mus Protein Isoforms RNA-Seq Term Birth
2
Dietary Assessment Using FFQ and 24-hour Recalls
The FFQ, originally developed for the TLGS, was a Willett-format questionnaire modified based on Iranian food items25 and contains questions about average consumption and frequency for 168 food items during the past year.7 The food items were chosen according to the most frequently consumed items in the national food consumption survey in Iran.25 Because different recipes are used for food preparation, the FFQ was based on food items rather than dishes, eg, beans, different meats and oils, and rice. Subjects indicated their food consumption frequencies on a daily basis (eg, for bread), weekly basis (eg, for rice and meat), monthly basis (eg, for fish), yearly basis (eg, for organ meats), or a never/seldom basis according to portion sizes that were provided in the FFQ. For each food item on the FFQ, a portion size was specified using USDA serving sizes (eg, bread, 1 slice; apple, 1 medium; dairy, 1 cup) whenever possible; if this was not possible, household measures (eg, beans, 1 tablespoon; chicken meat, 1 leg, breast, or wing; rice, 1 large, medium, or small plate) were chosen. Table 1shows food items and portion sizes used in the FFQ. Trained dietary interviewers with at least 3 of experience in the Nationwide Food Consumption Survey project25 or TLGS26 (link) administered the FFQs and 24-hour DRs during face-to-face interviews. The interviewer read out the food items on the FFQ, and recorded their serving size and frequency. The interview session took about 45 minutes. The interviewer for FFQ1 and FFQ2 was the same for each participant. Daily intakes of each food item were determined based on the consumption frequency multiplied by the portion size or household measure for each food item.27 The weight of seasonal foods, like some fruits, was estimated according to the number of seasons when each food was available.
Dietary data were also collected monthly by means of twelve 24-hour DRs that lasted for 20 minutes on average. For all subjects, 2 formal weekend day (Thursday and Friday in Iran) and 10 weekdays were recalled. All recall interviews were performed at subjects’ homes to better estimate the commonly used household measures and to limit the number of missing subjects. Detailed information about food preparation methods and recipe ingredients were considered by interviewers. To prevent subjects from intentionally altering their regular diets, participants were informed of the recall meetings with dietitians during the evening before the interview. All recalls were checked by investigators, and ambiguities were resolved with the subjects. Mixed dishes in 24-hour DRs were converted into their ingredients according to the subjects’ report on the amount of the food item consumed, thus taking into account variations in meal preparation recipes. For instance, broth or soup ingredients—usually vegetables (carrot or green beans), noodles, barley, etc.—differed according to subjects’ meal preparation. Because the only available Iranian food composition table (FCT)28 analyzes a very limited number of raw food items and nutrients, we used the USDA FCT29 as the main FCT; the Iranian FCT was used as an alternative for traditional Iranian food items, like kashk, which are not included in the USDA FCT.
The food items on the FFQ and DR were grouped according to their nutrient contents, based on other studies,30 (link) and modified according to our dietary patterns. Seventeen food groups were thus obtained, as follows: 1) whole grains, 2) refined grains, 3) potatoes, 4) dairy products, 5) vegetables, 6) fruits, 7) legumes, 8) meats, 9) nuts and seeds, 10) solid fat, 11) liquid oil, 12) tea and coffee, 13) salty snacks, 14) simple sugars, 15) honey and jams, 16) soft drinks, and 17) desserts and snacks (Table 1). The 168 food items on the FFQ were allocated to these 17 food groups, and the amounts in grams of each item were summed to obtain the daily intake of each food group.
Hosseini Esfahani F., Asghari G., Mirmiran P, & Azizi F. (2010). Reproducibility and Relative Validity of Food Group Intake in a Food Frequency Questionnaire Developed for the Tehran Lipid and Glucose Study. Journal of Epidemiology, 20(2), 150-158.
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Publication 2010
Barley Bread Breast Carrots Cereals Chickens Coffee Dairy Products Diet Dietitian Eating Fabaceae Face Fishes Food Fruit Honey Households Hyperostosis, Diffuse Idiopathic Skeletal Interviewers Meat Mental Recall Monosaccharides Nutrients Nuts Oryza sativa Plant Embryos Potato Raw Foods Snacks Sodium Chloride, Dietary Soft Drinks Vegetables Whole Grains
3
Transcriptional Profiling of Chicken Hearts and Human Huntington's Brains
Thirty hearts of chicken embryos of 3 days of development were isolated and separated into the five different compartments, i.e. sinus venosus (SV), atrium (A), atrioventricular canal (AVC), ventricle (V) and outflow tract (OFT). Post-mortem cortical brain tissue of eight control persons and 10 Huntington disease patients was obtained from Prof Dr R.A.C. Roos (Leiden University, the Netherlands). Total RNA was isolated using RNAeasy columns (Qiagen) according to the manufacturer's instructions. The total RNA was treated with DNase RQ1 (Promega) and the integrity of the RNA was checked using the BioAnalyzer and the Agilent RNA 6000 Nano kit (II). A 1–0.5 µg total RNA was converted into cDNA using an anchored poly-dT primer and the Superscript II (human samples) or III (chicken samples) Reverse transcription kit (Invitrogen).
Ruijter J.M., Ramakers C., Hoogaars W.M., Karlen Y., Bakker O., van den Hoff M.J, & Moorman A.F. (2009). Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucleic Acids Research, 37(6), e45.
Publication 2009
Autopsy Brain Cerebral Ventricles Chickens Common atrioventricular canal Cortex, Cerebral Deoxyribonucleases DNA, Complementary Embryonic Development Heart Heart Atrium Homo sapiens Huntington Disease Oligonucleotide Primers Patients Poly T Promega Reverse Transcription Sinuses, Nasal Tissues
4
Efficient Electroporation and Cryopreservation of Mouse ES Cells
The final targeting constructs were prepared for ES cell electroporation from 2 ml of culture (2X LB plus antibiotics) in 96-well format using the Qiagen Turboprep kit. Before electroporation, vectors were linearized with AsiSI and examined by gel electrophoresis. For most clones, the digested DNA migrated as a single high-molecular-mass band of the expected size (Supplementary Fig. 5). Occasionally, contaminating smaller molecular mass bands were also observed on the gel (DNA quality failures).
JM8 mouse ES cell lines derived from the C57BL/6N strain were grown either on a feeder layer of SNL6/7 fibroblasts (neomycin and/or puromycin resistant) or on gelatinized tissue culture plates16 (link). Both feeder-independent and feeder-dependent lines were maintained in Knockout DMEM (500 ml, Gibco) supplemented with 2 mM glutamine, 5 ml 100× β-mercaptoethanol (360 μl in 500 ml PBS, filter sterilized), 10–15% fetal calf serum respectively (Invitrogen) and 500 U ml−1 leukaemia-inhibitory factor (ESGRO, Millipore). Trypsin solution was prepared by adding 20 ml of 2.5% trypsin solution (Gibco) and 5 ml chicken serum (Gibco) to 500 ml filter-sterilized PBS containing 0.1 g EDTA (Sigma) and 0.5 g d-glucose (Sigma).
Electroporations of ES cells were carried out in a 25-well cuvette using the ECM 630 96-well electroporator /HT-200 automatic plate handler (BTX Harvard Apparatus; set at 700 V, 400 Ω, 25 μF). Immediately before electroporation, cell suspensions of ~1 × 107 cells and ~2 μg of linearized targeting vector DNA were mixed in a final volume of 120 μl PBS. Cells were seeded onto a 10-cm dish (with feeders or gelatin) and colonies were picked after 10 d of selection in 100 μg (active) per ml Geneticin (Invitrogen). To expand cells into duplicate wells for archiving and preparation of genomic DNA, confluent cultures of JM8 ES cells grown on feeder cells were washed twice with pre-warmed PBS and trypsinized for 15 min at 37 °C. Five volumes of pre-warmed media were added and the cells were gently dispersed by tituration and passed at a dilution of 1:4 into new plates containing feeder cells. Passage of cells grown on gelatinized plates was carried out in a similar manner except that the cells were trypsinized for 10 min and passed at a dilution of 1:6 into freshly gelatin-coated plates (0.1% gelatin, Sigma G1393). Culture medium was replaced daily and cells reached confluence 2 days after passage. To archive ES cell clones, trypsinized cells from confluent 96-well plates were transferred in 200 μl freezing medium (Knockout DMEM, 15% serum/ 10% DMSO) to 96-well cryovials (Matrix) and overlayed with sterile mineral oil. The cells were placed at −80 °C overnight and then transferred to liquid nitrogen.
Skarnes W.C., Rosen B., West A.P., Koutsourakis M., Bushell W., Iyer V., Mujica A.O., Thomas M., Harrow J., Cox T., Jackson D., Severin J., Biggs P., Fu J., Nefedov M., de Jong P.J., Stewart A.F, & Bradley A. (2011). A conditional knockout resource for the genome–wide study of mouse gene function. Nature, 474(7351), 337-342.
Publication 2011
2-Mercaptoethanol Antibiotics Cells Chickens Clone Cells Cloning Vectors Edetic Acid Electrophoresis Electroporation Embryonic Stem Cells Feeder Cell Layers Feeder Cells Fetal Bovine Serum Fibroblasts Gelatins Geneticin Genome Glucose Glutamine Hyperostosis, Diffuse Idiopathic Skeletal LIF protein, human Mus Neomycin Nitrogen Oil, Mineral PRSS2 protein, human Puromycin Serum Sterility, Reproductive Strains Sulfoxide, Dimethyl Technique, Dilution Tissues Trypsin
5
Curating Genome Browser's Data Ecosystem
A large number of datasets originate outside UCSC and contribute to the Genome Browser’s core idea of hosting as many high-quality resources as possible. In most cases, UCSC does not perform significant postprocessing or computation on the data, limiting intervention to converting file formats or other parsing tasks and quality-assurance review. Examples of this type of track include probe sets for commercially available microarrays, human copy-number variation (CNV) data from the Database of Genomic Variants (DGV) [25 (link)]; human dismorphology data from DECIPHER [26 (link)]; expression data for mouse and human from the GNF Expression Atlas [27 (link)]; and segmental duplication data for human, mouse, rat, dog and chicken [28 (link)].
The ENCODE project, for which the UCSC Genome Browser is the Data Coordination Center [29 (link)], presents a large number of functional annotations: including DNAse hypersensitivity sites, indicating open chromatin; histone marks, implicated in gene regulation; and gene expression levels from whole-genome RNA-seq experiments. These data, which are available on the human and mouse assemblies hg19 and mm9, are mapped across multiple cell lines. The resulting tracks represent tissue specificity and developmental mileposts (e.g. embryonic stem cells) for these elements. They can be displayed along with any other tracks on the same assembly, such as GenBank mRNAs or multispecies conservation.
A complete list of tracks available for any assembly can be found by visiting the Gateway page for any genome assembly (http://genome.ucsc.edu/cgi-bin/hgGateway) and clicking the button, ‘configure tracks and display’ or by simply inspecting the track controls beneath the main Browser graphic. The Track Search feature provides keyword lookup.
Examples of data tracks that do undergo further processing or filtering at UCSC include dbSNP [30 (link)] and OMIM (Online Mendelian Inheritance in Man) [31 (link)]. In these tracks, data from the providers are subdivided into categories to make them more useful to our users. For example, dbSNP data are presented in their entirety in one track, but three other tracks offer subsets: Common Single Nucleotide Polymorphisms (SNPs) (those with minor allele frequency >1%), Flagged SNPs (those identified in dbSNP as ‘clinical’—may be associated with disease, but use with caution!) and Multiple SNPs (those mapping to more than one genomic location).
Similarly, the OMIM data set has been filtered by UCSC to create three separate tracks, including one track of Allelic Variant SNPs that have phenotypic associations annotated by OMIM. These filtered sets are transmitted to OMIM for redistribution to their licensees. As always, details of how the filtering was done are available by clicking into an item or via the track configuration page.
Users may read about the filtering options available when using tracks by clicking on the small button to the left of the track in the Genome Browser image, or on the label in the track control area below the image. This configuration page gives users an opportunity to set colors and filters to suit themselves.
For users who do not know exactly which data set contains the information they seek, each data track is accompanied by a description outlining the rationale for the production of the data, implementation details, interpretation guidelines and references to the literature. All of this information is indexed and may be searched by keyword via the Track Search button beneath the Browser graphic. The result is a list of all tracks that have the search term in the documentation and a link to the track description.
Kuhn R.M., Haussler D, & Kent W.J. (2012). The UCSC genome browser and associated tools. Briefings in Bioinformatics, 14(2), 144-161.
Publication 2012
Alleles Cell Lines Chickens Chromatin Deoxyribonuclease I Embryonic Stem Cells Gene Expression Gene Expression Regulation Genome Genome, Human Histone Code Homo sapiens Hypersensitivity Microarray Analysis Mus Phenotype RNA, Messenger RNA-Seq Segmental Duplications, Genomic Single Nucleotide Polymorphism Tissue Specificity

Most recents protocols related to «Chickens»

1
Effect of Antibiotics and Nisin on Broiler Chick Growth
Not available on PMC !

Example 5

Three conditions were prepared, a AGP-containing feed (PC) obtained by adding antibiotics (lasalocid 0.05% by mass and avilamycin 0.01% by mass) to a standard feed, a PRB-supplemented feed (nisin (Lc)) supplemented with 2% of nisin A culture solution obtained by culturing Lactococcus lactis NCIMB 8780 in the same manner as in Example 4-1, and a AGP-free feed (standard feed only) (NC), and were administered to newborn chicks. Note that, for one condition, ten Cobb Broiler male newborn chicks were used, and the experiment was repeated three times to evaluate the body weight gain effect and feed conversion ratio of chickens. For the drug-free group (NC), a standard feed (ME 3160 kcal and CP 22% by mass without antibiotics used) was used. For the PC and nisin addition group, 2% by mass of the antibiotics (lasalocid and avilamycin) or nisin Z-containing liquid was added to the standard feed (ME 3160 kcal and CP 22% by mass), respectively.

TABLE 13
BWGFCR
Category1 w2 w1 W2 W
NC106.2 ± 3.0330.3 ± 7.8 1.19 ± 11.34 ± 0.02
PC*110.4 ± 4.7379.9 ± 10.01.08 ± 01.23 ± 0.02
Nisin (Lc)**111.1 ± 5.4352.4 ± 27.91.23 ± 21.35 ± 0.03
*Antibiotics: lasalocid 0.05% and avilamycin 0.01% added.
**For nisin, 2% Lactococcus lactis culture solution was added.

US11857596B2. Animal feed additive and animal feed (2024-01-02). AJINOMOTO CO., INC. [JP]. Inventors: Akinori Uehara [JP], Kazuki Nakagawa [JP], Mayumi Maekawa [JP].
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Patent 2024
Antibiotics, Antitubercular avilamycin Chickens Growth Disorders Infant, Newborn Lactococcus lactis Lasalocid Males nisin A nisin Z Pharmaceutical Preparations
2
Isolation of Canine Influenza H3N8 Viruses
Not available on PMC !

Example 8

Lung tissues from the 7 dogs were analyzed by quantitative real-time RT-PCR assays that detect the M gene of influenza type A and the H3 gene of canine H3N8 influenza A virus. The lungs from all 7 dogs were positive for both the influenza A M gene and the canine influenza H3 gene (Table 8). After 3 passages in MDCK cells, influenza A subtype H3N8 virus was isolated from the lungs of a shelter dog that died after 3 days of pneumonia. This virus was named A/canine/Jacksonville/05 (H3N8) (canine/Jax/05). After 2 passages in embryonated chicken eggs, influenza A subtype H3N8 virus was recovered from the lungs of the pet dog that also died after 3 days of pneumonia. This virus was named A/canine/Miami//05 (H3N8) (canine/Miami/05).

US11865172B2. Materials and methods for respiratory disease control in canines (2024-01-09). UNIVERSITY OF FLORIDA RESEARCH FOUNDATION, INC. [US], CORNELL RESEARCH FOUNDATION, INC. [US], The Government of The United States of America as represented by The Secretary of the Department of [US]. Inventors: Patti Cynthia Crawford [US], Paul J. Gibbs [US], Edward J. Dubovi [US], Ruben Omar Donis [US], Jacqueline Katz [US], Alexander I. Klimov [US], Nallakannu P. Lakshmanan [US], Melissa Anne Lum [US], Daniel Ghislena Emiel Goovaerts [NL], Mark William Mellencamp [US], Nancy J. Cox [US], William L. Castleman [US].
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Patent 2024
Biological Assay Canis familiaris Chickens Eggs Genes Influenza A virus isolation Lung Madin Darby Canine Kidney Cells Pneumonia Pneumonia, Viral Real-Time Polymerase Chain Reaction Tissues Virus Virus Vaccine, Influenza
3
Optimizing Receptor Expression
Not available on PMC !

Example 4

With a view to optimising expression of the receptor, the following were tested: (a) inclusion of a scaffold attachment region (SAR) into the cassette; (b) inclusion of chicken beta hemoglobin chromatin insulator (CHS4) into the 3′LTR and (c) codon optimization of the open reading frame (FIG. 6a). It was shown that inclusion of a SAR improved the nature of expression as did codon-optimization while the CHS4 had little effect (FIG. 6b). Combining SAR and codon-optimization improved expression additively (FIG. 6c)

US11879016B2. Chimeric antigen receptor (2024-01-23). AUTOLUS LIMITED [GB]. Inventors: Martin Pulé [GB], John Anderson [GB], Simon Thomas [GB].
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Patent 2024
Chickens Chromatin Codon hemoglobin B Matrix Attachment Regions
4
Serological Evidence of Canine Influenza Virus Circulation
Not available on PMC !

Example 5

To investigate whether a Canine/FL/04-like influenza virus had circulated among greyhound populations in Florida prior to the January 2004 outbreak, archival sera from 65 racing greyhounds were tested for the presence of antibodies to Canine/FL/04 using the HI and MN assays. There were no detectable antibodies in 33 dogs sampled from 1996 to 1999. Of 32 dogs sampled between 2000 and 2003, 9 were seropositive in both assays—1 in 2000, 2 in 2002, and 6 in 2003 (Table 5). The seropositive dogs were located at Florida tracks involved in outbreaks of respiratory disease of unknown etiology from 1999 to 2003, suggesting that a Canine/FL/04-like virus may have been the causative agent of those outbreaks. To investigate this possibility further, we examined archival tissues from greyhounds that died from hemorrhagic bronchopneumonia in March 2003. Lung homogenates inoculated into MDCK cells and chicken embryos from one dog yielded H3N8 influenza virus, termed A/Canine/Florida/242/2003 (Canine/FL/03). Sequence analysis of the complete genome of Canine/FL/03 revealed >99% identity to Canine/FL/04 (Table 4), indicating that Canine/FL/04-like viruses had infected greyhounds prior to 2004.

US11865172B2. Materials and methods for respiratory disease control in canines (2024-01-09). UNIVERSITY OF FLORIDA RESEARCH FOUNDATION, INC. [US], CORNELL RESEARCH FOUNDATION, INC. [US], The Government of The United States of America as represented by The Secretary of the Department of [US]. Inventors: Patti Cynthia Crawford [US], Paul J. Gibbs [US], Edward J. Dubovi [US], Ruben Omar Donis [US], Jacqueline Katz [US], Alexander I. Klimov [US], Nallakannu P. Lakshmanan [US], Melissa Anne Lum [US], Daniel Ghislena Emiel Goovaerts [NL], Mark William Mellencamp [US], Nancy J. Cox [US], William L. Castleman [US].
Full text: Click here
Patent 2024
Antibodies Biological Assay Bronchopneumonia Canis familiaris Chickens Disease Outbreaks Embryo Genome Hemorrhage Influenza Influenza A Virus, H3N8 Subtype Lung Madin Darby Canine Kidney Cells Orthomyxoviridae Population Group Respiration Disorders Respiratory Rate Sequence Analysis Serum Tissues Virus
5
Nitrogen Modulation of Endophytic Microbiome in Nematode-Parasitized Tomato
To reveal the association between RKN parasitism and the variation in endophytic nitrogen-fixing bacteria, seedlings of tomato cultivar cv Xinzhongshu No.4 were planted in Meloidogyne sp.-parasitized soils by supplying different nitrogen sources, in pot experiments carried out from June to August 2020. The soil used was collected from a nursery field with a 3-year nematode parasitism history. In total, 11 different inorganic or organic nitrogen compounds and two biofertilizers were selected for testing (Additional Table S9). Nitrogen sources were separately applied to each plot at 300 mg N/Kg soil after tomato seeding (keeping 5 tomato plants per pot out of 8–10 seeds sowed). The two biofertilizers were fresh chicken manure (fermented) and commercial chicken manure-based biofertilizer. Each nitrogen amendment treatment was performed with three replicates. Pot-planted tomato plants in soil without nematode parasitism history were used as positive control, using tomato plants in soil with nematode parasitism history but no nitrogen supplementation as negative control. At 55 days after seeding, tomato plants were harvested for the evaluation of RKN parasitism, quantifying the attack severity using the number of galls per plant [22 (link), 49 (link)]. Subsequently, root and/or gall samples were separately collected from healthy or nematode-parasitized tomato plants, as described above. Together, 57 samples (45 root, and 12 gall samples) were collected from healthy and nematode-parasitized tomato plants, including healthy control, parasitized control, and plants treated with 13 different nitrogen sources (Additional Table S9). Furthermore, community analysis for the effect of nitrogen supplement on root endophytic microbiota was performed, following the procedure described above.
Li Y., Lei S., Cheng Z., Jin L., Zhang T., Liang L.M., Cheng L., Zhang Q., Xu X., Lan C., Lu C., Mo M., Zhang K.Q., Xu J, & Tian B. (2023). Microbiota and functional analyses of nitrogen-fixing bacteria in root-knot nematode parasitism of plants. Microbiome, 11, 48.
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Publication 2023
Chickens Compounds, Nitrogen Dietary Supplements Endophytes Lycopersicon esculentum Meloidogyne Microbial Community Nematoda Nitrogen Nitrogen-13 Nitrogen-Fixing Bacteria Organic Chemicals Plant Embryos Plant Roots Plants Seedlings

Top products related to «Chickens»

1
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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
2
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Bovine serum albumin (BSA) is a common laboratory reagent derived from bovine blood plasma. It is a protein that serves as a stabilizer and blocking agent in various biochemical and immunological applications. BSA is widely used to maintain the activity and solubility of enzymes, proteins, and other biomolecules in experimental settings.
9
4 6 diamidino 2 phenylindole (dapi) by Thermo Fisher Scientific
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10
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DAPI is a fluorescent dye that binds strongly to adenine-thymine (A-T) rich regions in DNA. It is commonly used as a nuclear counterstain in fluorescence microscopy to visualize and locate cell nuclei.

More about "Chickens"

Chickens are a vital part of the poultry industry, providing a rich source of protein and other essential nutrients for human consumption.
These domesticated avian species, characterized by their feathered bodies, wings, and beaks, are widely raised for their meat and eggs.
The diverse breeds and production systems of chickens have been extensively studied, contributing to advancements in areas such as genetics, nutrition, and disease management.
Researching chickens can lead to improved agricultural practices, enhanced food security, and better animal welfare outcomes.
Techniques like FBS (Fetal Bovine Serum), DMEM (Dulbecco's Modified Eagle Medium), and antibodies like Ab13970 (Chicken anti-GFP) are commonly used in chicken-related studies.
Additionally, the use of Penicillin/Streptomycin, Alexa Fluor 488, TRIzol reagent, and Bovine Serum Albumin (BSA) can be important for various aspects of chicken research, such as cell culture, protein labeling, and RNA extraction.
The reproducibility of poultry research can be enhanced by utilizing AI-driven platforms like PubCompare.ai, which can help researchers locate the best chicken research protocols from literature, pre-prints, and patents.
This powerful tool can streamline the research workflow and optimize studies and product development related to this important avian species.
Whether you're interested in genetics, nutrition, or disease management, exploring the world of chickens can open up new possibilities for advancements in the poultry industry and beyond.
With the right tools and techniques, researchers can uncover valuable insights that contribute to improved agricultural practices, food security, and animal welfare.