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Fabaceae

Fabaceae, also known as the legume family, is a large and diverse group of flowering plants that are widely distributed around the world.
This family includes important crops such as soybeans, peas, and lentils, as well as many ornamental and forage species.
Fabaceae plants are characterized by their unique fruit structures, known as legumes, which are often edible and nutritious.
The family is of great ecological and economic importance, with many species playing crucial roles in nitrogen fixation, soil enrichment, and providing food and other resources for humans and animals.
Researchers studying Fabaceae can utilize powerful tools like PubCompare.ai to optimize their work by enhancing reproducibility, acuracy, and research effeciency through AI-driven comparisions of protocols from literature, pre-prints, and patents.

Most cited protocols related to «Fabaceae»

1
Dietary Assessment Using FFQ and 24-hour Recalls
Cited 200 times
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
2
Dietary Indices for Plant-Based Diets
Cited 70 times
We created an overall plant-based diet index (PDI), a healthful plant-based diet index (hPDI), and an unhealthful plant-based diet index (uPDI). The procedure we used to create these indices is similar to the one used by Martínez-González et al. [13 (link)]; their “provegetarian food pattern” is similar in composition to our PDI. Frequencies of consumption of each food were converted into servings consumed per day. Then the number of servings of foods that belonged to each of 18 food groups were added up. The 18 food groups were created on the basis of nutrient and culinary similarities, within larger categories of animal foods and healthy and less healthy plant foods. We distinguished between healthy and less healthy plant foods using existing knowledge of associations of the foods with T2D, other outcomes (CVD, certain cancers), and intermediate conditions (obesity, hypertension, lipids, inflammation). Plant foods not clearly associated in one direction with several health outcomes, specifically alcoholic beverages, were not included in the indices. We also excluded margarine from the indices, as its fatty acid composition has changed over time from high trans fat to high unsaturated fat. We controlled for alcoholic beverages and margarine consumption in the analysis.
Healthy plant food groups included whole grains, fruits, vegetables, nuts, legumes, vegetable oils, and tea/coffee, whereas less healthy plant food groups included fruit juices, sugar-sweetened beverages, refined grains, potatoes, and sweets/desserts. Animal food groups included animal fats, dairy, eggs, fish/seafood, meat (poultry and red meat), and miscellaneous animal-based foods.
S1 Table details examples of foods constituting the food groups. The 18 food groups were divided into quintiles of consumption, and each quintile was assigned a score between 1 and 5. For PDI, participants received a score of 5 for each plant food group for which they were above the highest quintile of consumption, a score of 4 for each plant food group for which they were above the second highest quintile but below the highest quintile, and so on, with a score of 1 for consumption below the lowest quintile (positive scores). On the other hand, participants received a score of 1 for each animal food group for which they were above the highest quintile of consumption, a score of 2 for each animal food group for which they were between the highest and second highest quintiles, and so on, with a score of 5 for consumption below the lowest quintile (reverse scores). For hPDI, positive scores were given to healthy plant food groups, and reverse scores to less healthy plant food groups and animal food groups. Finally, for uPDI, positive scores were given to less healthy plant food groups, and reverse scores to healthy plant food groups and animal food groups. The 18 food group scores for an individual were summed to obtain the indices, with a theoretical range of 18 (lowest possible score) to 90 (highest possible score). The observed ranges at baseline were 24–85 (PDI), 28–86 (hPDI), and 27–90 (uPDI) across the cohorts. The indices were analyzed as deciles, with energy intake adjusted at the analysis stage.
Satija A., Bhupathiraju S.N., Rimm E.B., Spiegelman D., Chiuve S.E., Borgi L., Willett W.C., Manson J.E., Sun Q, & Hu F.B. (2016). Plant-Based Dietary Patterns and Incidence of Type 2 Diabetes in US Men and Women: Results from Three Prospective Cohort Studies. PLoS Medicine, 13(6), e1002039.
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Publication 2016
Alcoholic Beverages Animals Cereals Coffee Diet Eggs Fabaceae Fats Fats, Unsaturated Fatty Acids Feeds, Animal Fishes Food Fowls, Domestic Fruit Fruit Juices High Blood Pressures Inflammation Lipids Malignant Neoplasms Margarine Meat Nutrients Nuts Obesity Plants Plants, Edible Red Meat Seafood Solanum tuberosum Sugar-Sweetened Beverages Vegetable Oils Vegetables Whole Grains
3
Validating Food Frequency Questionnaire
Cited 65 times
Self-reported FFQs were designed to assess average food intake over the preceding year. A standard portion size and nine possible frequency of consumption responses, ranging from “never, or less than once per month” to “six or more times per day” were given for each food. Total energy and nutrient intake was calculated by summing up energy or nutrients from all foods. Previous validation studies in this cohort revealed good correlations between nutrients assessed by the FFQ and multiple weeks of food records completed over the preceding year10 . For example, correlation coefficients between 1986 FFQ and 4 weeks of diet records obtained in 1986 were 0.68 for saturated fat and 0.78 for crude fiber. The mean correlation coefficient between frequencies of intake of 55 foods assessed by two FFQ 12 months apart was 0.5710 , 11 (link).
The aMed score was adapted from the Mediterranean diet scale by Trichopoulou et al8 (link). Our components include vegetables (excluding potatoes), fruits, nuts, whole grains, legumes, fish, monounsaturated-to-saturated fat ratio, red and processed meats, and alcohol. Participants with intake above the median intake received 1 point for these categories; otherwise they received 0 points. Red and processed meat consumption below the median received 1 point. We assigned 1 point for alcohol intake between 5-15 g/d. This represents approximately one 12-oz can of regular beer, 5 oz of wine, or 1.5 oz of liquor. The possible score range for aMed was 0–9, with a higher score representing closer resemblance to the Mediterranean diet. Table 1 shows the intake of aMed components during the follow-up periods. Consumption of each food group was stable across time except for a trend toward a decrease in alcohol and red/processed meat intake.
Fung T.T., Rexrode K.M., Mantzoros C.S., Manson J.E., Willett W.C, & Hu F.B. (2009). Mediterranean diet and incidence and mortality of coronary heart disease and stroke in women. Circulation, 119(8), 1093-1100.
Publication 2009
Amniotic Fluid Beer Diet, Mediterranean Eating Ethanol Fabaceae Fibrosis Fishes Food Fruit Meat Nutrient Intake Nutrients Nuts Potato Red Meat Saturated Fatty Acid Vegetables Whole Grains Wine
4
Sequencing the Lotus japonicus Genome
Cited 27 times
Two types of sequencing approaches were combined to sequence the L. japonicus genome: clone-by-clone sequencing and shotgun sequencing of selected regions of the genome.
TAC/BAC clones were selected from the genomic libraries as seed points using the sequence information from ESTs and cDNA markers from L. japonicus and other legumes. The nucleotide sequence of each clone was determined according to the shotgun strategy with three to five times redundancy. A total of 1909 TAC/BAC clones, those newly sequenced in this study and those that had been sequenced previously,5 (link)–9 (link) were assembled into 954 scaffolds using the Paracel Genome Assembler (PGA; version 2.6.2, Paracel Co., 2002), followed by manual TAC/BAC end-pair scaffolding, resulting in high-quality genomic sequence (HGS) contigs.
In parallel, shotgun sequencing of a selected TAC mixture (STM) enriched in gene spaces and a whole genomic DNA from which highly repetitive and organelle genomic sequences were subtracted (selected genomic regions, SGRs) was carried out. The TAC clones, neither end sequence of which hit repetitive or organelle genomic sequences in the L. japonicus genome, were selected from the libraries, pooled, and subjected to shotgun sequencing. For the SGRs, a genomic library with an average insert size of 2.5 kb was generated using pBluescript SK− as the cloning vector. For subtraction, polymerase chain reaction (PCR)-amplified fragments of LjTR1 were biotinylated using Biotin-High Prime (Roche, Basel, Switzerland) and used as a driver in subtractive hybridization with the WGS library. The WGS library was single-stranded prior to hybridization by combined action of gene II and exonuclease III. Hybrids were removed using Dynabeads M-280 Streptavidin (Invitrogen, Carlsbad, CA, USA) and the remaining single-stranded WGS library was double-stranded using Klenow fragments (Takara Bio, Japan) and transformed into host E. coli ElectroTen-Blue (Agilent Technologies, Santa Clara, CA, USA).
A total of 808 816 reads from STM generated from 4603 TAC inserts and 847 513 SGR reads were assembled into a set of 109 986 contigs, 147 805 446 bp in length (selected genome assembly, SGA) by the Arachne assembler, version 2.01.11 (link) The SGA sequences were then subjected to assemble with the HGS, and finally, a total of 110 940 supercontigs with a total coverage of 315 073 275 tentative genomic sequence (TGS) bases were obtained.
Sato S., Nakamura Y., Kaneko T., Asamizu E., Kato T., Nakao M., Sasamoto S., Watanabe A., Ono A., Kawashima K., Fujishiro T., Katoh M., Kohara M., Kishida Y., Minami C., Nakayama S., Nakazaki N., Shimizu Y., Shinpo S., Takahashi C., Wada T., Yamada M., Ohmido N., Hayashi M., Fukui K., Baba T., Nakamichi T., Mori H, & Tabata S. (2008). Genome Structure of the Legume, Lotus japonicus. DNA Research: An International Journal for Rapid Publication of Reports on Genes and Genomes, 15(4), 227-239.
Publication 2008
Base Sequence Biotin Clone Cells Cloning Vectors Crossbreeding DNA, A-Form DNA, Complementary DNA Library DNA Polymerase I Escherichia coli exodeoxyribonuclease III Expressed Sequence Tags Fabaceae Genes Genome Genomic Library Hybrids M 280 Organelles Polymerase Chain Reaction Streptavidin Subtractive Hybridization Techniques
5
Automated Genome Annotation for Lotus japonicus
Cited 23 times
Tentative genomic sequence was subjected to gene prediction and modeling by the Kazusa Annotation PipelinE for Lotus japonicus (KAPSEL).5 (link) The KAPSEL employs ab initio gene-finding software and similarity searches in order to generate the elements for gene model production. The ab initio gene-finding software used in the pipeline includes GeneMark.hmm,24 (link) Genscan25 (link) and Grail26 (link) using the A. thaliana-trained matrix. Splice-site candidates were deduced by NetGene227 (link) and SplicePredictor.28 (link) The similarity searches to detect potential protein-coding exons were performed using the BLASTX function of BLAST against the UniProtKB database.29 (link) The assigned exon candidates were extracted from the original sequence library, then mapped on the TGS more precisely using the dps and nap programs in the program suite of the analysis and annotation tool (AAT) package.30 (link) Similarity searches of transcript sequences were performed by aligning the TGS against the Gene Indices31 (link) for legume species including L. japonicus, M. truncatula and Glycine max. The assigned transcript sequences were mapped on the TGS using the dds and gap2 programs in AAT to confirm working models of protein-encoding genes. As a result of the automated annotation process, a total of 19 848 partial and 10 951 complete models were assigned as protein-encoding genes in the TGS, except for those related to TEs. The 76.4-Mb sequences in the HGS were edited and annotated manually to ensure high-quality gene prediction.
The genes thus assigned were denoted by IDs with the clone (LjT**** for TACs and LjB**** for BACs) or contig (CM****) names followed by sequential numbers from one end to another. Of these, manually annotated genes on the HGS were followed by “.nc”, and others were followed by “.nd”. The genes assigned on the SGA sequences were denoted by IDs with the assemble consensus names (LjSGA_****) followed by sequential numbers from one end to another in the insert.
A global alignment of the genome sequences and ESTs was performed using the NEEDLE program32 (link),33 that is provided at the EMBOSS site (http://emboss.sourceforge.net/). To identify a possible TATA box-like motif for recognition by RNA polymerase II, a search against the plant cis-acting regulatory DNA elements (PLACE) database34 (link) (http://www.dna.affrc.go.jp/PLACE/) was carried out.
Sato S., Nakamura Y., Kaneko T., Asamizu E., Kato T., Nakao M., Sasamoto S., Watanabe A., Ono A., Kawashima K., Fujishiro T., Katoh M., Kohara M., Kishida Y., Minami C., Nakayama S., Nakazaki N., Shimizu Y., Shinpo S., Takahashi C., Wada T., Yamada M., Ohmido N., Hayashi M., Fukui K., Baba T., Nakamichi T., Mori H, & Tabata S. (2008). Genome Structure of the Legume, Lotus japonicus. DNA Research: An International Journal for Rapid Publication of Reports on Genes and Genomes, 15(4), 227-239.
Publication 2008
Arabidopsis thalianas Clone Cells DNA, A-Form DNA Library Exons Expressed Sequence Tags Fabaceae Gene Products, Protein Genes Genome Lotus japonicus Needles Plants Proteins RNA Polymerase II Sequence Alignment Soybeans TATA Box

Most recents protocols related to «Fabaceae»

1
Developing Galactose-Metabolizing Yeast Strains
Not available on PMC !

Example 1

A primary goal of the present invention was to develop yeast strains highly effective at metabolizing galactose. However, galactose-metabolizing yeasts are uncommon; yeasts typically prefer glucose, a carbohydrate source known to strongly suppress the expression of genes needed to metabolize other carbohydrates such as galactose (See Escalante-Chong et al., “Galactose metabolic genes in yeast respond to a ratio of galactose and glucose.” Proc Nat'l Acad Sci, USA 112:1636-41 (2015)). Wild type yeast strains may thus be prevented from utilizing any carbohydrates when glucose is present. Even if a yeast does have the capability to use other carbohydrate sources, it may occur late in the growth process, only after glucose has been completely depleted. Therefore, a particular type of galactose-metabolizing yeast was developed that degrades galactose in the presence of glucose. The present invention provides methods for adaptively evolving yeast according to this process.

To assess the ability of a yeast strain to degrade galactose, its growth was evaluated on media containing galactose in presence or absence of glucose. The following strains were tested: the commercially available strains Saccharomyces cerevisiae (N) (Natureland, Saccharomyces boulardii (SB) (Jarrow, Santa Fe Springs, CA), and Saccharomyces boulardii (B) (Biocodex, Redwood City, CA). Additional strains included in the screening were isolated from food containing large amounts of galactose such as dairy products and legumes stored at room temperature for over two weeks.

Cultures of various strains were initiated from a single colony on agar plates or from glycerol stocks, and grown in liquid YP medium (1% yeast extract, 2% peptone; Teknova) by incubation at 30° C. with agitation at 125 rpm (Murakami & Kaeberlein “Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells.” J Visual Exp (27) (2009)). Overnight yeast cultures initiated in duplicate in liquid YP medium were used as pre-cultures to initiate growth efficiency experiments in liquid CM (Synthetic Complete Minimal Medium, 0.5% Ammonium Sulfate, Teknova) containing 2% galactose alone as the sole carbon source, 2% glucose alone as the sole carbon source, or galactose and glucose. Culture growth of cultures set at 30° C. under static conditions was monitored over time by measuring optical density (OD) at 600 nm (OD600) using a spectrophotometer.

Growth—was evaluated for several strains. As illustrated in Table 1, one of the evolved clone exhibited the lowest doubling time, which remained at the same level independently of the carbohydrate source and growth conditions.

TABLE 1
Doubling Time of Yeast Strains under Static Growth Conditions
in Media Containing Galactose alone, Glucose alone,
and Galactose and Glucose.
Galactose +
GalactoseGlucoseGlucose
Avg.Avg.Avg.
Strains(h)SD(h)SD(h)SD
Y1_Parent4.570.034.430.054.400.01
Evolved Clone4.050.024.170.084.210.02
Strain N6.050.034.460.024.230.01
Strain SB7.790.064.680.004.750.03
Strain B8.850.024.960.024.630.01
Each data point represents the averages (Avg.) and standard deviation (SD) of quadruplicate values obtained for two independent cultures per strain.

US11865151B2. Live biotherapeutics for the treatment of carbohydrate disorders (2024-01-09). GUTSYBIO INC. [US]. Inventors: Genevieve Hansen [US].
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Patent 2024
43-63 Agar Carbohydrates Carbon Cellular Senescence Clone Cells Culture Media, Conditioned Dairy Products Fabaceae Food Galactose Gene Expression Genes Glucose Glycerin Growth Disorders hydrocortisone butyrate Natural Springs Parent Peptones Redwood Saccharomyces Saccharomyces boulardii Saccharomyces cerevisiae Strains Sulfate, Ammonium Vision Yeasts
2
Increasing Oil Content in Forage Crops
Not available on PMC !

Example 6

Oil content in the dicotyledonous plant species Trifolium repens (clover), a legume commonly used as a pasture species, was increased by expressing the combination of WRI1, DGAT and Oleosin genes in vegetative parts. The construct pJP3502 was used to transform T. repens by Agrobacterium-mediated transformation (Larkin et al., 1996). Briefly, the genetic construct pJP3502 was introduced into A. tumefaciens via a standard electroporation procedure. The binary vector also contained a 35S:NptII selectable marker gene within the T-DNA. The transformed Agrobacterium cells were grown on solid LB media supplemented with kanamycin (50 mg/L) and rifampicin (25 mg/L) and incubated at 28° C. for two days. A single colony was used to initiate a fresh culture. Following 48 hours vigorous culture, the Agrobacterium cells was used to treat T. repens (cv. Haifa) cotyledons that had been dissected from imbibed seed as described by Larkin et al. (1996). Following co-cultivation for three days the explants were exposed to 25 mg/L kanamycin to select transformed shoots and then transferred to rooting medium to form roots, before transfer to soil.

Six transformed plants containing the T-DNA from pJP3502 were obtained and transferred to soil in the glasshouse. Increased oil content was observed in the non-seed tissue of some of the plants, with one plant showing greater than 4-fold increase in TAG levels in the leaves. Such plants are useful as animal feed, for example by growing the plants in pastures, providing feed with an increased energy content per unit weight (energy density) and resulting in increased growth rates in the animals.

The construct pJP3502 is also used to transform other leguminous plants such as alfalfa (Medicago sativa) and barrel medic (Medicago truncatula) by the method of Wright et al. (2006) to obtain transgenic plants which have increased TAG content in vegetative parts. The transgenic plants are useful as pasture species or as hay or silage as a source of feed for animals such as, for example, cattle, sheep and horses, providing an increased energy density in the feed.

US11859193B2. Plants with modified traits (2024-01-02). Commonwealth Scientific and Industrial Research Organisation [AU]. Inventors: XueRong Zhou [AU], Qing Liu [AU], Anna El Tahchy [AU], Srinivas Belide [AU], Madeline Claire Mitchell [AU], Uday Kumar Divi [AU], Thomas Vanhercke [AU], James Robertson Petrie [AU], Surinder Pal Singh [AU], Allan Graham Green [AU].
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Patent 2024
Agrobacterium Alfalfa Animals Cattle Cells Cloning Vectors Cotyledon Domestic Sheep Electroporation Equus caballus Fabaceae Genes Kanamycin Magnoliopsida Markers, DNA Medicago truncatula Plant Embryos Plant Oils Plant Roots Plants Plants, Transgenic Reproduction Rifampin Silage Tissues Trifolium Trifolium repens
3
Determinants of Food Insecurity in Households
Percentages were used to report the distribution of categorized variables. The chi-square test was used to measure the association between the grouped variables in this study. In addition, multiple logistic regression was used to examine the adjusted relationships (odds ratios and 95% CI) between the study variables (household heads, parents’ education, parents’ job, household size, having a chronic disease, having a member of vulnerable groups, consumption of vegetables, fruits, meat, nuts and legumes, socioeconomic status, cost, food cost to income ratio, government financial assistance, covid-19-induced poverty) and food insecurity.
Variables were included in the model (p-value under 0.2 in the univariable analysis) if they significantly contributed to the model’s fitness using the stepwise selection method, and the final model was reported. P-value < 0.05 was considered significant. STATA14.0 software (Stata, College Station, TX, USA) was used for all the statistical analyses.
Joulaei H., Keshani P., Foroozanfar Z., Afrashteh S., Hosseinkhani Z., Mohsenpour M.A., Moghimi G, & Homayouni Meymandi A. (2023). Food insecurity status and its contributing factors in slums’ dwellers of southwest Iran, 2021: a cross-sectional study. Archives of Public Health, 81, 38.
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Publication 2023
COVID 19 Disease, Chronic Fabaceae Food Fruit Head of Household Households Meat Nuts Parent Vegetables
4
Agroforestry Crop Growth Monitoring Protocol

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Publication 2023
Agricultural Crops Cereals Crop, Avian Debility Fabaceae Genetic Heterogeneity Genitalia Olea Plants Trees
5
Evaluating Mediterranean Legume Crops in Agroforestry

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Publication 2023
Chickpea Crop, Avian Fabaceae Fertilization Food Fruit Infrared Rays Lentils Olea Olea europaea phosphoric anhydride superphosphate Trees Vicia faba

Top products related to «Fabaceae»

1
Icap 7000 duo by Thermo Fisher Scientific
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2
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The ImmunoCAP is a laboratory instrument used for in vitro allergen-specific IgE testing. It provides quantitative measurement of IgE antibodies to a wide range of allergens. The ImmunoCAP system utilizes fluorescent enzyme immunoassay technology to detect and measure IgE levels in patient samples.
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The DNeasy Plant Mini Kit is a lab equipment product designed for the isolation and purification of DNA from plant samples. It utilizes a silica-based membrane technology to extract and concentrate DNA effectively from a variety of plant materials.
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More about "Fabaceae"

The Fabaceae family, also known as the legume or pea family, is a diverse and economically important group of flowering plants.
This large family includes a variety of crops such as soybeans (Glycine max), peas (Pisum sativum), lentils (Lens culinaris), and many ornamental and forage species.
Fabaceae plants are characterized by their unique fruit structures called legumes, which are often edible and nutritious.
Researchers studying the Fabaceae family can utilize powerful tools like PubCompare.ai to optimize their work.
This AI-driven platform allows for enhanced reproducibility, accuracy, and research efficiency by enabling comparisons of protocols from literature, preprints, and patents.
By leveraging PubCompare.ai, scientists can locate the best protocols for their Fabaceae research, improving overall productivity and research outcomes.
The Fabaceae family plays a crucial role in nitrogen fixation and soil enrichment, making it of great ecological and economic importance.
Many Fabaceae species provide valuable resources for humans and animals, including food, animal feed, and other useful products.
When conducting Fabaceae research, scientists may utilize various techniques and tools such as the ICAP-7000 Duo, ImmunoCAP, SPSS version 25, DNeasy Plant Mini Kit, HiSeq 2000 platform, Whatman filter paper, Tween 20, Lugol's solution, Formic acid, and SPSS Statistics 25.
These resources can aid in the analysis, identification, and processing of Fabaceae samples and data.
By understanding the Fabaceae family and leveraging advanced tools like PubCompare.ai, researchers can enhance the reproducibility, accuracy, and efficiency of their studies, leading to more impactful discoveries and advancements in this important field of plant science.