Wheat
It is a member of the grass family and is cultivated extensively for its edible seeds, which are used to make flour, bread, pasta, and other food products.
Wheat is a versatile crop that can be grown in a variety of climates and soils, and it is an important source of carbohydrates, protein, fiber, and other nutrients.
Reseachers are constantly striving to improve wheat yields, quality, and resilience through advanced techniques like AI-driven protocol comparison.
Tools like PubCompare.ai can help optimize wheat research by locating the best protocols from literature, pre-prints, and patents, and enhancing reproducibility and accuaracy.
Most cited protocols related to «Wheat»
Prior to phenotype derivation, we removed individuals who were pregnant, had kidney disease as defined by ICD10 codes, or a cancer diagnosis within the last year (field 40005). The UKB FFQ consists of quantitative continuous variables (i.e., field 1289, tablespoons of cooked vegetables per day), ordinal non-quantitative variables depending on overall daily/weekly frequency (i.e., field 1329, overall oily fish intake), food types (i.e., milk, spread, bread, cereal, or coffee), or foods never eaten (field 6144, dairy, eggs, sugar, and wheat). Supplementary Data
To collect individual dietary data, each household member was asked to report all food consumed over the previous 24 h for each of the three days, whether at home or away from home. Interviewers recorded the types and amounts of food consumed at each meal during the previous day. The amount of food in each dish was estimated from the household inventory and the proportion of each dish consumed was reported by each person. Household food inventory was used to collect information of household level food intake and to further estimate the individual salt and oil intake. Extreme dietary data is based on the judgment of the interviewers. For example, if a person reported an intake of 2 kg of rice a day, this was regarded as extreme. Detailed dietary data collection is described elsewhere [14 (link),17 ]. As the present study does not include calculation of salt and oil intake, we used data of 24 h recall over three consecutive days at the individual level for the analysis. The three-day recall method used in this study has a high correlation with the household food inventory method for each food group (e.g., correlation coefficient was 0.84 for rice, 0.84 for wheat) [19 ].
The food groups included were based on a food system developed specifically for CHNS and Chinese Food Composition Table [20 (link)]. Initially, 33 food groups were included. As some food items were consumed by less than 5% of participants, food intakes were further collapsed into 27 food groups based on similarity of nutritional profiles. The 27 food groups are: rice; wheat flour and wheat noodles; wheat buns and bread; corn and coarse grains; deep-fried wheat; starchy roots and tubers; pork; red meat; organ meat; processed meats; poultry and game; fish and seafood; milk; eggs and egg products; fresh legumes; legume products; dried legumes; fresh vegetables, non-leafy; fresh vegetables, leafy; pickled, salted or canned vegetables; dried vegetables; cakes; fruits; nuts and seeds; beer; liquor and fast food.
Mean consumption of each food group per day was calculated from dietary data, as liang (Chinese ounce, 1 liang = 50 g). Mean consumption of alcoholic beverages, soft drink, and tea was calculated from questionnaire responses. Respondents were asked “do you drink any kind of alcoholic beverage (beer or liquor)?”, and were asked further questions on drinking frequency, types and quantity consumed in a week. Also, participants were asked “do you normally drink tea?” and “do you drink soft drinks or sugared fruit drinks?” Further questions on drinking frequency and number of cups consumed per day (a cup is approximately 240 mL) were asked. Energy intake was calculated by CHNS based on Chinese Food Composition Table [21 ].
Most recents protocols related to «Wheat»
Example 22
Pasta was made from pasta dough comprising the following ingredients:
The pasta dough was mixed, kneaded and pressed through a pasta maker according to the recipe and formed into individual pasta noodles. The pasta noodles were placed into boiling water and cooked according to the recipe until soft and tender but not sticky. The pasta was comparable or superior in quality and taste compared to pasta made using traditional all-purpose or cake flour and had superior nutritional profile. The pasta had superior texture compared to conventional pasta made using all-purpose flour instead of the composite wheat-MCT flour.
Example 19
Muffins were made from muffin dough comprising the following ingredients:
The muffins were made in a muffin baking pan containing wells into which muffin dough in muffin cups were placed and baked at ordinary temperature and time in an oven according to the recipe. The muffins were comparable or superior in quality and taste compared to muffins made using traditional all-purpose or cake flour and have superior nutrition profile. The muffins were lighter, fluffier and more moist compared to conventional muffins made using all-purpose flour instead of the composite wheat-MCT flour.
The feeding trial lasted for 8 weeks. Then, all fish in each tank were weighed after 24 h of food deprivation, and 4 fish per tank were randomly sampled and frozen at -20°C for whole-body composition analysis. The rest of the fish continued refeeding for one more week; 3 fish/tank were randomly sampled at 6 h after the last meal. They were anesthetized by MS-222 (100 mg L−1 tricaine methane sulfonate, Argent Chemical Laboratories Inc., Redmond, WA, USA). After that, blood was collected and then centrifuged at 3000 g, 4°C, 15 min, to obtain the plasma; fresh liver, middle intestine, and muscle (biopsies) of the fish were separated on the ice, immediately frozen in liquid nitrogen, and then kept at -80°C for further analysis.
Food waste components were further adjusted for analysis of carbohydrate effects on the larval bioconversion process. Three substrates were set as 100% food waste (FW100 CM0), 60% food waste and 40% corn meal (FW60 CM40), and 20% food waste and 80% corn meal (FW20 CM80). Percentages of each component were based on their wet weight. The FW100 CM0 group was the food waste treatment carried out above. The FW60 CM40 and FW20 CM80 groups were performed in the same manner as the experiments above, except that the waste components and sampling time points were adjusted. The food waste was still the university canteen waste, whereas the corn meal was prepared by mixing corn meal flour and water in a 3:7 ratio and cooking in a rice cooker for 0.5 h. When the substrates were mixed thoroughly, 21 parallel boxes were prepared for the FW60 CM40 and FW20 CM80 groups, respectively. The sampling time points were set as Days 3, 5, 7, 9,11, 13, and 15. At each time point, triplicate boxes were collected, and the larvae and frass were manually separated and weighted. The larvae were further analyzed for body FA contents and compositions, and the frass samples were further determined for the properties of reducing carbohydrates.
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More about "Wheat"
This versatile crop can be cultivated in various climates and soil types, making it an important source of carbohydrates, protein, fiber, and other essential nutrients.
Researchers are constantly seeking to improve wheat yields, quality, and resilience through advanced techniques like AI-driven protocol comparison.
Tools like PubCompare.ai revolutionize wheat research by utilizing artificial intelligence to locate the best protocols from literature, pre-prints, and patents.
This optimizes wheat research by enhancing reproducibility and accuracy, ultimately contributing to the advancement of this crucial crop.
Wheat is often associated with other related terms and concepts, such as ImmunoCAP, Pepsin, ImmunoCAP system, Trypsin, Gliadin from wheat, ImmunoCAP Phadiatop Infant, Wheat gliadin, Phadiatop, and Phadiatop Combi®.
These terms and technologies play a role in the analysis, understanding, and management of wheat-related properties and interactions.
By leveraging the power of AI and comprehensive protocol comparison, researchers can unlock new insights and optimize their approaches to wheat research, ultimately benefiting the global population that relies on this versatile and essential cereal grain.