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Vegetables

Vegetables are edible parts of plants, typically the roots, stems, leaves, flowers, or fruits.
They are a diverse group of plant-based foods that are low in calories and high in vitamins, minerals, and fiber.
Vegetables play a crucial role in a healthy, balanced diet and can help reduce the risk of chronic diseases such as heart disease, diabetes, and certain types of cancer.
This MeSH term encompasses a wide variety of vegetable types, including leafy greens, root vegedatas, cruciferous vegetables, and more.
Researchers in the field of plant-based nutrition can use this term to locate relevant studies and information on the health benefits and culinary applications of different vegetables.

Most cited protocols related to «Vegetables»

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 TLGS^{26 (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

Qualitative Synthesis of Children's Perspectives on Healthy Eating

In our children and healthy eating review, we originally planned to extract and synthesise study findings according to our review questions regarding the barriers to, and facilitators of, healthy eating amongst children. It soon became apparent, however, that few study findings addressed these questions directly and it appeared that we were in danger of ending up with an empty synthesis. We were also concerned about imposing the a priori framework implied by our review questions onto study findings without allowing for the possibility that a different or modified framework may be a better fit. We therefore temporarily put our review questions to one side and started from the study findings themselves to conduct an thematic analysis.
There were eight relevant qualitative studies examining children's views of healthy eating. We entered the verbatim findings of these studies into our database. Three reviewers then independently coded each line of text according to its meaning and content. Figure 1 illustrates this line-by-line coding using our specialist reviewing software, EPPI-Reviewer, which includes a component designed to support thematic synthesis. The text which was taken from the report of the primary study is on the left and codes were created inductively to capture the meaning and content of each sentence. Codes could be structured, either in a tree form (as shown in the figure) or as 'free' codes – without a hierarchical structure.
The use of line-by-line coding enabled us to undertake what has been described as one of the key tasks in the synthesis of qualitative research: the translation of concepts from one study to another [32 (link),55 ]. However, this process may not be regarded as a simple one of translation. As we coded each new study we added to our 'bank' of codes and developed new ones when necessary. As well as translating concepts between studies, we had already begun the process of synthesis (For another account of this process, see Doyle [[39 (link)], p331]). Every sentence had at least one code applied, and most were categorised using several codes (e.g. 'children prefer fruit to vegetables' or 'why eat healthily?'). Before completing this stage of the synthesis, we also examined all the text which had a given code applied to check consistency of interpretation and to see whether additional levels of coding were needed. (In grounded theory this is termed 'axial' coding; see Fisher [55 ] for further discussion of the application of axial coding in research synthesis.) This process created a total of 36 initial codes. For example, some of the text we coded as "bad food = nice, good food = awful" from one study [56 (link)] were:
'All the things that are bad for you are nice and all the things that are good for you are awful.' (Boys, year 6) [[56 (link)], p74]
'All adverts for healthy stuff go on about healthy things. The adverts for unhealthy things tell you how nice they taste.' [[56 (link)], p75]
Some children reported throwing away foods they knew had been put in because they were 'good for you' and only ate the crisps and chocolate. [[56 (link)], p75]
Reviewers looked for similarities and differences between the codes in order to start grouping them into a hierarchical tree structure. New codes were created to capture the meaning of groups of initial codes. This process resulted in a tree structure with several layers to organize a total of 12 descriptive themes (Figure 2). For example, the first layer divided the 12 themes into whether they were concerned with children's understandings of healthy eating or influences on children's food choice. The above example, about children's preferences for food, was placed in both areas, since the findings related both to children's reactions to the foods they were given, and to how they behaved when given the choice over what foods they might eat. A draft summary of the findings across the studies organized by the 12 descriptive themes was then written by one of the review authors. Two other review authors commented on this draft and a final version was agreed.

Thomas J, & Harden A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8, 45.

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Publication 2008

Anabolism Boys Child Chocolate Food Fruit Taste Trees Vegetables

Mediterranean Diet Adherence Assessment

At baseline, registered dietitians completed a 14-item Mediterranean Diet adherence screener (Table 1) in a face-to-face interview with the participant [21] (link), [27] –[29] (link). The dietitians had been previously trained and certified to implement the PREDIMED protocol and had been hired to work full-time for the trial. The 14-item tool was developed in a Spanish case-control study of myocardial infarction [30] (link), where the best cut-off points for discriminating between cases and controls were selected for each food or food group. With this first step, 9 of the 14 items were obtained [31] (link). Five additional items that were felt to be especially relevant to assess adherence to the traditional Mediterranean diet were subsequently added. Two of these items used short questions to inquire on food habits: Do you use olive oil as the principal source of fat for cooking? and Do you prefer to eat chicken, turkey or rabbit instead of beef, pork, hamburgers or sausages? The other 3 items inquired on frequency of consumption of nuts, soda drinks and a typical Mediterranean sauce (“sofrito”): How many times do you consume nuts per week? How many carbonated and/or sugar-sweetened beverages do you consume per day? How many times per week do you consume boiled vegetables, pasta, rice, or other dishes with a sauce (“sofrito”) of tomato, garlic, onion, or leeks sauteed in olive oil?[26] (link).
The baseline 14-item questionnaire (Table 1) was the primary measure used in this study to appraise adherence of participants to the Mediterranean diet. In addition, a full-length 137-item validated FFQ [32] (link) was also administered to all participants. We obtained information about total energy intake and alcohol intake (only with descriptive purposes) from this FFQ. In the validation study, the score obtained with brief 14-item questionnaire correlated significantly with that obtained from the full-length FFQ score (Pearson correlation coefficient (r) = 0.52; intraclass correlation coefficient = 0.51). Associations in the anticipated directions for the different dietary intakes reported on the FFQ were found [26] (link). Significant inverse correlations of the 14-item tool with fasting glucose, total:HDL cholesterol ratio, triglycerides and the 10-y estimated coronary artery disease risk also supported the validity of this brief Mediterranean diet adherence screener [26] (link).
Also a general medical questionnaire, and the validated Spanish version of the Minnesota Leisure-Time Physical Activity Questionnaire [33] (link)–[34] (link) were collected by the dietitians in the personal interview with each participant [21] (link). Weight, height and WC were directly measured by registered nurses who had been previously trained and certified to implement the PREDIMED protocol and were hired to work full-time for this trial, as previously described [21] (link), [27] –[29] (link). The WHtR was calculated as WC divided by height, both in centimeters.

Martínez-González M.A., García-Arellano A., Toledo E., Salas-Salvadó J., Buil-Cosiales P., Corella D., Covas M.I., Schröder H., Arós F., Gómez-Gracia E., Fiol M., Ruiz-Gutiérrez V., Lapetra J., Lamuela-Raventos R.M., Serra-Majem L., Pintó X., Muñoz M.A., Wärnberg J., Ros E, & Estruch R. (2012). A 14-Item Mediterranean Diet Assessment Tool and Obesity Indexes among High-Risk Subjects: The PREDIMED Trial. PLoS ONE, 7(8), e43134.

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Publication 2012

Allium cepa Beef Chickens Coronary Arteriosclerosis Diet, Mediterranean Dietitian Face Feelings Food Garlic Glucose High Density Lipoprotein Cholesterol Hispanic or Latino Hyperostosis, Diffuse Idiopathic Skeletal Leeks Myocardial Infarction Nuts Oil, Olive Oryza sativa Pastes Physical Examination Pork Rabbits Registered Nurse Sugar-Sweetened Beverages Tomatoes Triglycerides Vegetables

Dietary Indices for Plant-Based Diets

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

Validating Food Frequency Questionnaire

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

Most recents protocols related to «Vegetables»

Renewable Paraffinic Fuel Analysis

Not available on PMC !

Example 1

A renewable paraffinic product was produced by heavily cracking hydrodeoxygenation and isomerisation of feedstock mixture of vegetable and animal fat origin. This product was analysed using various analysis methods (Table 2).

TABLE 2

Analysed renewable paraffinic product.

AnalysisMethodUnitValue

Freezing pointIP529° C. −42.0

DensityASTM kg/m3753.0

D4052

Weighted average NM490—12.0

carbon number

% carbon number 14-17NM490wt-%30.5

T10 (° C.) cut-off temperatureASTM D86° C. 168.5

T90 (° C.) cut-off temperatureASTM D86° C. 245.5

Final boiling pointASTM D86° C. 256.0

The analysed product in Table 2 fulfils the freezing point of jet fuel specification, but the freezing point is not exceptionally low.

Example 4

Another renewable paraffinic product produced by hydrodeoxygenation and isomerisation of another feedstock mixture of vegetable and animal fat origin is further directed to a fractionation unit. In the fractionation unit, the renewable paraffinic product is divided into two fractions. Lighter of the fractions containing 80 wt-% of the original renewable paraffinic product is re-analysed using various analysis methods (Table 5).

TABLE 5

Analysed renewable paraffinic product.

AnalysisMethodUnitValue

Freezing pointIP529° C.−50.9

DensityASTM kg/m3770.1

D4052

Weighted average NM490—14.7

carbon number

% carbon number 14-17NM490wt-%73.6

T10 (° C.) cut-off temperatureASTM D86° C.191.9

T90 (° C.) cut-off temperatureASTM D86° C.276.6

Final boiling pointASTM D86° C.283.1

This product also fulfils all requirements of a high-quality renewable aviation fuels. From the analysis results it can be seen that despite the fact that the density of the paraffinic composition is over 768 kg/m³(measured 770.1 kg/m³) the freezing point (measured −50.9° C.) is significantly lower than the freezing point of the product of comparative example 1.

It will be obvious to a person skilled in the art that, as the technology advances, the inventive concept can be implemented in various ways. The invention and its embodiments are not limited to the examples described above but may vary within the scope of the claims.

US11859143B2. Hydrocarbon composition (2024-01-02). Neste Oyj [FI]. Inventors: Kati Sandberg [FI], Väinö Sippola [FI], Janne Suppula [FI], Jesse Vilja [FI].

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Patent 2024

Animals Carbon-14 Carbon-17 Fractionation, Chemical Hydrocarbons jet fuel A Light Paraffin Vegetables Vision

Microbial Seed Treatments for Enhanced Crop Performance

Not available on PMC !

Example 2

A. Seed Treatment with Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the isolated microbe as a seed coating, the corn will be planted and cultivated in the standard manner.

A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiably higher biomass than the control corn plants.

The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.

The biomass from the treated plants may equate to about a 1 bushel per acre increase over the controls, or a 2 bushel per acre increase, or a 3 bushel per acre increase, or a 4 bushel per acre increase, or a 5 bushel per acre increase, or more.

In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.

B. Seed Treatment with Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the microbial consortium as a seed coating, the corn will be planted and cultivated in the standard manner.

A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiably higher biomass than the control corn plants.

The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.

In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.

C. Treatment with Agricultural Composition Comprising Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiably higher biomass than the control corn plants.

The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.

In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.

D. Treatment with Agricultural Composition Comprising Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiably higher biomass than the control corn plants.

The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.

In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.

A. Seed Treatment with Isolated Microbe

A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.

The drought tolerance and/or water use efficiency can be based on any number of standard tests from the art, e.g leaf water retention, turgor loss point, rate of photosynthesis, leaf color and other phenotypic indications of drought stress, yield performance, and various root morphological and growth patterns.

B. Seed Treatment with Microbial Consortia

A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.

C. Treatment with Agricultural Composition Comprising Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the with the agricultural composition will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.

D. Treatment with Agricultural Composition Comprising Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

A. Seed Treatment with Isolated Microbe

A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.

The nitrogen use efficiency can be quantified by recording a measurable change in any of the main nitrogen metabolic pool sizes in the assimilation pathways (e.g., a measurable change in one or more of the following: nitrate, nitrite, ammonia, glutamic acid, aspartic acid, glutamine, asparagine, lysine, leucine, threonine, methionine, glycine, tryptophan, tyrosine, total protein content of a plant part, total nitrogen content of a plant part, and/or chlorophyll content), or where the treated plant is shown to provide the same or elevated biomass or harvestable yield at lower nitrogen fertilization levels compared to the control plant, or where the treated plant is shown to provide elevated biomass or harvestable yields at the same nitrogen fertilization levels compared to a control plant.

B. Seed Treatment with Microbial Consortia

A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.

C. Treatment with Agricultural Composition Comprising Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.

D. Treatment with Agricultural Composition Comprising Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

The inoculants were prepared from isolates grown as spread plates on R2A incubated at 25° C. for 48 to 72 hours. Colonies were harvested by blending with sterile distilled water (SDW) which was then transferred into sterile containers. Serial dilutions of the harvested cells were plated and incubated at 25° C. for 24 hours to estimate the number of colony forming units (CFU) in each suspension. Dilutions were prepared using individual isolates or blends of isolates (consortia) to deliver 1×10⁵cfu/microbe/seed and seeds inoculated by either imbibition in the liquid suspension or by overtreatment with 5% vegetable gum and oil.

Seeds corresponding to the plants of table 15 were planted within 24 to 48 hours of treatment in agricultural soil, potting media or inert growing media. Plants were grown in small pots (28 mL to 200 mL) in either a controlled environment or in a greenhouse. Chamber photoperiod was set to 16 hours for all experiments on all species. Air temperature was typically maintained between 22-24° C.

Unless otherwise stated, all plants were watered with tap water 2 to 3 times weekly. Growth conditions were varied according to the trait of interest and included manipulation of applied fertilizer, watering regime and salt stress as follows:

- Low N—seeds planted in soil potting media or inert growing media with no applied N fertilizer
- Moderate N—seeds planted in soil or growing media supplemented with commercial N fertilizer to equivalent of 135 kg/ha applied N
- Insol P—seeds planted in potting media or inert growth substrate and watered with quarter strength Pikovskaya's liquid medium containing tri-calcium phosphate as the only form phosphate fertilizer.
- Cold Stress—seeds planted in soil, potting media or inert growing media and incubated at 10° C. for one week before being transferred to the plant growth room.
- Salt stress—seeds planted in soil, potting media or inert growing media and watered with a solution containing between 100 to 200 mg/L NaCl.

Untreated (no applied microbe) controls were prepared for each experiment. Plants were randomized on trays throughout the growth environment. Between 10 and 30 replicate plants were prepared for each treatment in each experiment. Phenotypes were measured during early vegetative growth, typically before the V3 developmental stage and between 3 and 6 weeks after sowing. Foliage was cut and weighed. Roots were washed, blotted dry and weighed. Results indicate performance of treatments against the untreated control.

TABLE 15

StrainShootRoot

Microbe sp.IDCropAssayIOC (%)IOC (%)

Bosea thiooxidans123EfficacyEfficacy

overall100%100%

Bosea thiooxidans54522WheatEarly vigor - insol P30-40 —

Bosea thiooxidans54522RyegrassEarly vigor50-60 50-60

Bosea thiooxidans54522RyegrassEarly vigor - moderate P0-100-10

Duganella violaceinigra111EfficacyEfficacy

overall100%100%

Duganella violaceinigra66361TomatoEarly vigor0-100-10

Duganella violaceinigra66361TomatoEarly vigor30-40 40-50

Duganella violaceinigra66361TomatoEarly vigor20-30 20-30

Herbaspirillum huttiense222Efficacy—

overall100%

Herbaspirillum huttiense54487WheatEarly vigor - insol P30-40 —

Herbaspirillum huttiense60507MaizeEarly vigor - salt stress0-100-10

Janthinobacterium sp.222Efficacy—

Overall100%

Janthinobacterium sp.54456WheatEarly vigor - insol P30-40 —

Janthinobacterium sp.54456WheatEarly vigor - insol P0-10—

Janthinobacterium sp.63491RyegrassEarly vigor - drought0-100-10

stress

Massilia niastensis112EfficacyEfficacy

overall80%80%

Massilia niastensis55184WheatEarly vigor - salt stress0-1020-30

Massilia niastensis55184WinterEarly vigor - cold stress0-1010-20

wheat

Massilia niastensis55184WinterEarly vigor - cold stress20-30 20-30

wheat

Massilia niastensis55184WinterEarly vigor - cold stress10-20 10-20

wheat

Massilia niastensis55184WinterEarly vigor - cold stress<0<0

wheat

Novosphingobium rosa211EfficacyEfficacy

overall100%100%

Novosphingobium rosa65589MaizeEarly vigor - cold stress0-100-10

Novosphingobium rosa65619MaizeEarly vigor - cold stress0-100-10

Paenibacillus amylolyticus111EfficacyEfficacy

overall100%100%

Paenibacillus amylolyticus66316TomatoEarly vigor0-100-10

Paenibacillus amylolyticus66316TomatoEarly vigor10-20 10-20

Paenibacillus amylolyticus66316TomatoEarly vigor0-100-10

Pantoea agglomerans323EfficacyEfficacy

33%50%

Pantoea agglomerans54499WheatEarly vigor - insol P40-50 —

Pantoea agglomerans57547MaizeEarly vigor - low N<00-10

Pantoea vagans55529MaizeEarly vigor<0<0

(formerly P. agglomerans)

Polaromonas ginsengisoli111EfficacyEfficacy

66%100%

Polaromonas ginsengisoli66373TomatoEarly vigor0-100-10

Polaromonas ginsengisoli66373TomatoEarly vigor20-30 30-40

Polaromonas ginsengisoli66373TomatoEarly vigor<010-20

Pseudomonas fluorescens122Efficacy—

100%

Pseudomonas fluorescens54480WheatEarly vigor - insol P>100 —

Pseudomonas fluorescens56530MaizeEarly vigor - moderate N0-10—

Rahnella aquatilis334EfficacyEfficacy

80%63%

Rahnella aquatilis56532MaizeEarly vigor - moderate N10-20 —

Rahnella aquatilis56532MaizeEarly vigor - moderate N0-100-10

Rahnella aquatilis56532WheatEarly vigor - cold stress0-1010-20

Rahnella aquatilis56532WheatEarly vigor - cold stress<00-10

Rahnella aquatilis56532WheatEarly vigor - cold stress10-20 <0

Rahnella aquatilis57157RyegrassEarly vigor<0—

Rahnella aquatilis57157MaizeEarly vigor - low N0-100-10

Rahnella aquatilis57157MaizeEarly vigor - low N0-10<0

Rahnella aquatilis58013MaizeEarly vigor0-1010-20

Rahnella aquatilis58013MaizeEarly vigor - low N0-10<0

Rhodococcus erythropolis313Efficacy—

66%

Rhodococcus erythropolis54093MaizeEarly vigor - low N40-50 —

Rhodococcus erythropolis54299MaizeEarly vigor - insol P>100 —

Rhodococcus erythropolis54299MaizeEarly vigor<0<0

Stenotrophomonas chelatiphaga611EfficacyEfficacy

60%60%

Stenotrophomonas chelatiphaga54952MaizeEarly vigor0-100-10

Stenotrophomonas chelatiphaga47207MaizeEarly vigor<0 0

Stenotrophomonas chelatiphaga64212MaizeEarly vigor0-1010-20

Stenotrophomonas chelatiphaga64208MaizeEarly vigor0-100-10

Stenotrophomonas chelatiphaga58264MaizeEarly vigor<0<0

Stenotrophomonas maltophilia612EfficacyEfficacy

43%66%

Stenotrophomonas maltophilia54073MaizeEarly vigor - low N50-60 —

Stenotrophomonas maltophilia54073MaizeEarly vigor<00-10

Stenotrophomonas maltophilia56181MaizeEarly vigor0-10<0

Stenotrophomonas maltophilia54999MaizeEarly vigor0-100-10

Stenotrophomonas maltophilia54850MaizeEarly vigor 00-10

Stenotrophomonas maltophilia54841MaizeEarly vigor<00-10

Stenotrophomonas maltophilia46856MaizeEarly vigor<0<0

Stenotrophomonas rhizophila811EfficacyEfficacy

12.5%37.5%

Stenotrophomonas rhizophila50839MaizeEarly vigor<0<0

Stenotrophomonas rhizophila48183MaizeEarly vigor<0<0

Stenotrophomonas rhizophila45125MaizeEarly vigor<0<0

Stenotrophomonas rhizophila46120MaizeEarly vigor<00-10

Stenotrophomonas rhizophila46012MaizeEarly vigor<0<0

Stenotrophomonas rhizophila51718MaizeEarly vigor0-100-10

Stenotrophomonas rhizophila66478MaizeEarly vigor<0<0

Stenotrophomonas rhizophila65303MaizeEarly vigor<00-10

Stenotrophomonas terrae221EfficacyEfficacy

50%50%

Stenotrophomonas terrae68741MaizeEarly vigor<0<0

Stenotrophomonas terrae68599MaizeEarly vigor<00-10

Stenotrophomonas terrae68599Capsicum *Early vigor20-30 20-30

Stenotrophomonas terrae68741Capsicum *Early vigor10-20 20-30

The data presented in table 15 describes the efficacy with which a microbial species or strain can change a phenotype of interest relative to a control run in the same experiment. Phenotypes measured were shoot fresh weight and root fresh weight for plants growing either in the absence of presence of a stress (assay). For each microbe species, an overall efficacy score indicates the percentage of times a strain of that species increased a both shoot and root fresh weight in independent evaluations. For each species, the specifics of each independent assay is given, providing a strain ID (strain) and the crop species the assay was performed on (crop). For each independent assay the percentage increase in shoot and root fresh weight over the controls is given.

US11871752B2. Agriculturally beneficial microbes, microbial compositions, and consortia (2024-01-16). BIOCONSORTIA, INC. [US]. Inventors: Peter Wigley [NZ], Susan Turner [US], Caroline George [NZ], Thomas Williams [US], Kelly Roberts [US], Graham Hymus [US], Kelvin Lau [NZ].

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Patent 2024

Ammonia Asparagine Aspartic Acid Biological Assay Bosea thiooxidans Calcium Phosphates Capsicum Cells Chlorophyll Cold Shock Stress Cold Temperature Crop, Avian Dietary Fiber DNA Replication Droughts Drought Tolerance Embryophyta Environment, Controlled Farmers Fertilization Glutamic Acid Glutamine Glycine Growth Disorders Herbaspirillum Herbaspirillum huttiense Leucine Lolium Lycopersicon esculentum Lysine Maize Massilia niastensis Methionine Microbial Consortia Nitrates Nitrites Nitrogen Novosphingobium rosa Paenibacillus Paenibacillus amylolyticus Pantoea agglomerans Pantoea vagans Phenotype Phosphates Photosynthesis Plant Development Plant Embryos Plant Leaves Plant Proteins Plant Roots Plants Polaromonas ginsengisoli Pseudoduganella violaceinigra Pseudomonas Pseudomonas fluorescens Rahnella Rahnella aquatilis Retention (Psychology) Rhodococcus erythropolis Rosa Salt Stress Sodium Chloride Sodium Chloride, Dietary Stenotrophomonas chelatiphaga Stenotrophomonas maltophilia Stenotrophomonas rhizophila Stenotrophomonas terrae Sterility, Reproductive Strains Technique, Dilution Threonine Triticum aestivum Tryptophan Tyrosine Vegetables Zea mays

Root Microbiome Shifts in Nematode-Infected Plants

To assess the effects of Meloidogyne spp. on root-associated microbiota, rhizosphere soil and root samples of healthy and parasitized plants were collected in Shunchang County of Fujian province, China (26° 38′–27° 121′ N, 117° 29′–118° 14′ E) in June 2016 (Additional Table S1). Samples of three vegetables, tomato (Solanum lycopersicum), lettuce (Lactuca sativa L. var. ramosa Hort.), and celery (Apium graveolens L.), were collected from a vegetable farm, monitored for RKN parasitism for at least 5 years before sample collection [67 ]. The field prevalence of RKN for the three crops were approximately 30–50%. Two perennial plants, Snakegourd fruit (Trichosanthes kirilowii Maxim.) and citrus (Citrus reticulata Blanco), attacked by RKN for at least 2 years (severe parasitism, with > 75% roots with galls, and swollen by > 75%), were separately collected from orchards with RKN. The collected lettuce and celery roots showed a low RKN parasitism symptom (less than one third roots with galls), and tomato root with a moderate RKN parasitism symptom (more than half of roots with galls) (Additional Table S1). At least three replicated healthy or nematode-parasitized plants were sampled for each plant species. The collected plants were used to separate rhizosphere soil and root samples for the 16S rRNA gene-based high-throughput sequencing and bacterial community analysis (Additional Table S1).

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

Agricultural Crops Apium graveolens Apium graveolens var. dulce Bacteria Citrus Citrus reticulata Fruit Genes Lactuca sativa Lycopersicon esculentum Meloidogyne Microbial Community Nematoda Plant Roots Plants Rhizosphere RNA, Ribosomal, 16S Specimen Collection Trichosanthes kirilowii Vegetables

Biodigester Waste Characterization

The digestate was collected from a full-scale
biomethane plant operating in the Lombardy Region (northern Italy).
The anaerobic digestion input was a mix of manure and vegetables as
reported in Table S1. Samples of whole
digestate (liquid and solid phases) were collected at the end of the
AD and stored at −20 °C until use.

Bulgari D., Alias C., Peron G., Ribaudo G., Gianoncelli A., Savino S., Boureghda H., Bouznad Z., Monti E, & Gobbi E. (2023). Solid-State Fermentation of Trichoderma spp.: A New Way to Valorize the Agricultural Digestate and Produce Value-Added Bioproducts. Journal of Agricultural and Food Chemistry, 71(9), 3994-4004.

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Publication 2023

Digestion Plants Vegetables

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

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More about "Vegetables"

Vegetables are a diverse group of plant-based foods that are low in calories and high in essential nutrients like vitamins, minerals, and fiber.
This category encompasses a wide variety of veggdata types, including leafy greens, root vegedatas, cruciferous vegetables, and more.
Consuming a diet rich in vegetables has been shown to play a crucial role in maintaining a healthy, balanced lifestyle and can help reduce the risk of chronic diseases such as heart disease, diabetes, and certain types of cancer.
Researchers in the field of plant-based nutrition can utilize the MeSH term "Vegetables" to locate relevant studies and information on the culinary applications and health benefits of different vegetable varieties.
This term encompasses the edible parts of plants, typically the roots, stems, leaves, flowers, or fruits.
When conducting research on vegetables, scientists may also encounter terms like SAS version 9.4, SAS 9.4, SAS software, Purina Monkey Chow, Whatman No. 1 filter paper, Acetonitrile, Formic acid, Stata 15, and Stata 14.
These tools and materials may be used in various aspects of the research process, such as data analysis, sample preparation, and protocol optimization.
PubComapre.ai's AI-powered protocol optimization can help researchers in the vegedata field enhance the reproducibility of their studies.
By locating protocols from literature, preprints, and patents, and using AI-driven comparisons, researchers can identify the best protocols and products for their specific research needs, ultimately improving the quality and reliability of their findings.