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Broccoli

Broccoli is a cultivar of the wild cabbage plant (Brassica oleracea) that is grown for its edible green flower heads.
It is a nutrient-dense vegetable that is rich in vitamins, minerals, and antioxidants.
Broccoli has a distinctive flavor and is commonly used in a variety of cuisines around the world.
It is a popular choice for its health benefits and can be prepared in numerous ways, such as steaming, roasting, or stir-frying.
Broccoli research is an important area of study, as it may offer potential health benefits and applications in medicinal and agricultural fields.
PubCompare.ai can help optimize your broccoli research by identifying the best protocols from literature, pre-prints, and patents, enhancing reproducibility and accuracy for your experiments.

Most cited protocols related to «Broccoli»

Dietary Intake Assessment in the Adventist Health Study-2

The 24 h dietary recalls were unannounced and obtained by telephone. A two-dimensional food portion visual (2D Food Portion Visual; Nutrition Consulting Enterprises, Framingham, MA, USA) was sent to each participant before the first recall to assist with portion size estimates. Trained research dietitians used standard probes and a multiple-pass approach methodology to collect detailed information on all foods, beverages and supplements consumed by each subject during the previous 24 h. Each recall interview was entered using Nutrition Data System for Research (NDS-R) version 4·06 or 5·0 (The Nutrition Coordinating Center, Minneapolis, MN, USA) and the conversation digitally recorded for subsequent quality check. An experienced research dietitian later evaluated randomly selected recall interviews ( ~5 %) and compared them with the recording, as a quality control measure.
The AHS-2 FFQ is a quantitative and comprehensive 22-page instrument consisting of 204 foods, fifty-four questions about food preparation and forty-six fields for open-ended questions. Frequency categories vary with food type to allow respondents to define their daily intake with greater specificity. Thus, the lowest category for most foods and beverages is never or rarely, and for cold cereals and vegetarian protein products, 1–3 per month. The highest frequency category for vegetables, soups, cereals, pasta, dressing, meats, fish, vegetarian protein products and soya milk is 2 or more per day; for nuts and seeds, 4 or more per day; for breads, eggs, dairy products, snacks and beverages, 6 or more per day. Portion sizes include three levels: standard,

\frac{1}{2}

or less, and

1 \frac{1}{2}

or more. Standard portions are based on serving sizes using familiar household units such as cup, tablespoon, slice, patty and others. Pictures of common foods or beverages typically served together were included with the questionnaire to assist subjects in estimating portion sizes. For example, to represent a standard portion, spaghetti, broccoli and steak are arranged on an 11-inch dinner plate, each food measured in a standard portion. Representations of

\frac{1}{2}

of standard and

1 \frac{1}{2}

times the standard portion size are provided as well, using the same foods and dinner plate.
The AHS-2 FFQ was designed to include foods commonly eaten by US Adventists and later modified to accommodate foods specific to black Adventists of US and Caribbean origin^{(6 (link))}. The questionnaire was sent to each subject, completed at home, and then mailed back to AHS-2. Respondents were asked to report on their intake over the previous one year. Upon receipt of the questionnaire, study personnel reviewed the questionnaire for completeness and as necessary followed-up by telephone to clarify any ambiguous or incomplete information.
Completed questionnaires were optically scanned using the NCS 5000i Image Scanner with ScanTools Plus software (Pearson NCS, Bloomington, MN, USA). Standardized processing of open-ended questions was done using the Food Write-In Processing software (Adventist Health Study-2, Loma Linda, CA, USA), a network-based application created in Microsoft® Access (Microsoft Corporation, Redmond, WA, USA).
Nutrient composition of foods reported from 24 h recalls and FFQ were based on the NDS-R 5·0_35 database (The Nutrition Coordinating Center), an analytic database of over 20 000 foods updated annually while maintaining nutrient profiles true to the version used for data collection^{(7 (link))}. Nutrient profiles of foods and supplements not found in the NDS database were obtained from the US Department of Agriculture, manufacturers, and the Caribbean Food and Nutrition Institute. Considerable attention was given to creating recipes for home-cooked vegetarian dishes (n > 500), home-made and commercial soya and nut milks (n > 180) and commercial meat analogues (n 309) frequently consumed among our study population. For the latter we contacted manufacturers or worked with a senior food technologist with experience in this industry, to create recipes.

Jaceldo-Siegl K., Knutsen S.F., Sabaté J., Beeson W.L., Chan J., Herring R.P., Butler T.L., Haddad E., Bennett H., Montgomery S., Sharma S.S., Oda K, & Fraser G.E. (2009). Validation of nutrient intake using an FFQ and repeated 24 h recalls in black and white subjects of the Adventist Health Study-2 (AHS-2). Public health nutrition, 13(6), 812-819.

Publication 2009

Attention Beverages Bread Broccoli Caribbean People Cereals Common Cold Conditioning, Psychology Dairy Products Diet Dietary Supplements Dietitian Eating Eggs Fishes Food Households Hyperostosis, Diffuse Idiopathic Skeletal Loma Meat Mental Recall Milk Nutrients Pastes Plant Embryos Proteins Snacks Soybeans Soy Milk Vegetables Vegetarians

Alternate Healthy Eating Index for Pregnancy

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Publication 2009

Allergens Bacon BAD protein, human Beef Broccoli Calcium, Dietary Diet Dietary Supplements Eating Ethanol Fibrosis Fishes Folate Food Fowls, Domestic Fruit Healthy Eating Index Iron liposomal amphotericin B Meat Micronutrients Nurses Nutrient Intake Nutrients Nuts Pork Pregnancy Red Meat Saturated Fatty Acid Soybeans Soy Proteins Tofu Vegetables Vitamins Woman

Harmonizing Dietary Assessment Methods

All data for both administrations of the PEDALS FFQ and the 4DEFD were entered into a Microsoft Excel Spreadsheet and were checked by a trained nutritionist. Food items from the 4DEFD were matched to items from the PEDALS FFQ. As an example, porridge from 4DEFD was assigned to the ’Breakfast cereals’ group in the FFQ. Mixed foods were each allocated a proportion of the meal and then assigned to their relevant food or food groups from the FFQ. For example, Teriyaki chicken sushi was considered as the two following food groups in the PEDALS FFQ: ‘Other meats’ and ‘Rice’. Also, if a child had a ham sandwich with carrot and cheese slices for lunch, this was considered as ‘Bread’, ‘Vegetables’, ‘Processed meat’ and ‘Cheese’ for comparison to the FFQ. For assigning the frequency to each of the food items from the 4DEFD, a frequency of ‘1’ was given to each food item, if it was consumed even more than once during a specific food occasion e.g., if a meal contained both carrots and broccoli. Frequency of intake of food and beverages from the FFQ were based on weekly frequencies, whereas, data from the 4DEFD were based on frequency over four days. Therefore, to provide comparable data, the 4DEFD data were converted into a weekly intake. As each 4DEFD included three weekdays and a weekend day, to provide an estimate of weighted average weekly frequencies, average frequency of food intake was divided by three and multiplied by five for weekdays and multiplied by two for weekend days.

Saeedi P., Skeaff S.A., Wong J.E, & Skidmore P.M. (2016). Reproducibility and Relative Validity of a Short Food Frequency Questionnaire in 9–10 Year-Old Children. Nutrients, 8(5), 271.

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Publication 2016

Beverages Bread Broccoli Carrots Cereals Cheese Chickens Child Daucus carota Eating Food Foot Meat Nutritionist Oryza sativa Vegetables

Comprehensive Dietary Assessment Protocol

The FFQ was designed to assess habitual diet over the past year, with emphasis on fish consumption and a traditional diet in the study population. Questions were asked about the intake of milk, coffee, orange juice, soft drinks, yoghurt, breakfast cereal, bread, fat on bread, toppings for open sandwiches (jam, cheeses, meat and fish products), fruit, vegetables, potatoes, rice, pasta, rice porridge, fish and fish products, shellfish, condiments and sauces for fish, meat and poultry, eggs, ice cream, cakes, desserts, chocolate, snacks, alcoholic beverages, and dietary supplements. Similar items were grouped together in blocks with question headings. The response options were predefined and listed in increasing order with check-boxes to facilitate completion and optical reading. For example, the items listed under the question "How often do you eat fruit?" were "apples/pears", "oranges", "bananas", and "other fruit" with the following options: "never/rarely", "1–3 per month", "1 per week", "2–4 per week", "5–6 per week", "1 per day", and "2+ per day". The first alternative for consumption frequencies was always "never/rarely", but the number of options ranged from 4 to 7 depending on the food. When convenient, the questions were phrased in terms of natural units, such as glasses (milk, fruit juice, soft drinks, and wine), cups (coffee), slices (bread), or number (eggs and potatoes). Separate questions about the usual amounts consumed were included for fat on bread, vegetables, fish and fish products, sauces and condiments for fish, meat and meat products, ice cream, chocolate, and cod liver oil supplements. The number of response options ranged from 3 to 5 with units in pieces, slices, decilitres, florets (broccoli and cauliflower), or spoonfuls. The dietary intake computations included a total of 132 questions in the FFQ (consumption frequencies = 91, types of fat used on bread = 7, amounts = 28, and time of year for the consumption of different species of fish = 6). A detailed list of the food items, including a specification of those with a separate amount question, can be found in Additional file 1. The original version of the test-retest FFQ is shown in Additional file 2.

Parr C.L., Veierød M.B., Laake P., Lund E, & Hjartåker A. (2006). Test-retest reproducibility of a food frequency questionnaire (FFQ) and estimated effects on disease risk in the Norwegian Women and Cancer Study (NOWAC). Nutrition Journal, 5, 4.

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Publication 2006

Alcoholic Beverages Banana Bread Broccoli Cacao Cauliflower Cereals Cheese Coffee Condiments Diet Dietary Supplements Eggs Eyeglasses Fishes Fish Products Food Fowls, Domestic Fruit Fruit Juices Ice Cream Meat Meat Products Milk, Cow's Oil, Cod Liver Oryza sativa Paste Pears Potato Shellfish Snacks Soft Drinks Vegetables Vision Wine Yogurt

Developing Sinitic Clubroot Differential Set

Four hosts from the Williams’ classification system, including “Badger Shipper,” “Jersey Queen,” “Laurentian,” and “Wilhelmsburger” were used in this study. The above four differential hosts and eleven CR inbred lines of Chinese cabbage were infected with 37 isolates for the selection of possible hosts to develop a new clubroot differential system, named the Sinitic clubroot differential (SCD) set (Table 1). An inbred line of Chinese cabbage, “BJN3-1,” was used as a clubroot-susceptible control. Isolate Collection and Plant Inoculation
In total, 132 field isolates collected from the infected roots of Chinese cabbage, canola, broccoli, or wild mustard from China and Korea were used in this study (Table S1). Of these, 37 (numbers 1–37) were used to inoculate to 4 hosts from Williams’ set (1966 ) and 12 Chinese cabbage inbred lines (Table 1), for the development of the clubroot differential set. The remaining 95 isolates were used to inoculate eight selected CR hosts (Table 2, H01–H08) to validate and evaluate the practicability, stability and extensibility of the developed SCD system. To accurately evaluate the pathotypes of P. brassicae, the resistance tests lasted from 2015 to 2019. All these materials were planted in 72-well multi-pots with 3 replications and maintained in a greenhouse at 20°C to 25°C under a 16-h photoperiod until inoculation with P. brassicae. Each replication contained 24 plants.
All field isolates were propagated with the “BJN3-1” line under controlled environments, and fresh galls were stored at −20°C for further use. Preparation of the resting spores of P. brassicae was according to Williams (1966) with minor modifications. Briefly, after the galls were ground in sterile distilled water with a homogenizer, the mixtures were filtered through 8 layers of cheesecloth. The resting spores were collected by centrifugation at 2,500g and quantified with a hemocytometer (Neubauer improved, Marienfeld, Germany). The concentration of resting spores was adjusted to 1 × 10⁷/ml, and 1 ml of the suspension was inoculated to the 5-day-old seedlings of each host. The potting mixture (Fanyu, Shenyang, China) was kept moist until 6 weeks after P. brassicae inoculation.

Pang W., Liang Y., Zhan Z., Li X, & Piao Z. (2020). Development of a Sinitic Clubroot Differential Set for the Pathotype Classification of Plasmodiophora brassicae. Frontiers in Plant Science, 11, 568771.

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Publication 2020

Badgers Brassica rapa Broccoli Cabbage Centrifugation Chinese DNA Replication Environment, Controlled LINE-1 Elements Plant Roots Plants Seedlings Spores Sterility, Reproductive Vaccination

Most recents protocols related to «Broccoli»

Dietary Zinc Intake Assessment in Indonesia

A semi-quantitative FFQ to estimate dietary Zn intake in the Indonesian population was developed, focusing particularly on pregnancy and the period of infancy. In an initial phase, participants filled out an online questionnaire to report their recollection of all foods consumed in the previous 24 h (Q-24 h) to gather information on foods commonly consumed in Indonesia. The food items gathered from the Q-24 h were used in the development of a FFQ to be used to estimate habitual Zn intake over a longer period (LFFQ). As this study focused on Zn intake, food items not captured through the Q-24 h but known to be good sources of Zn were added. The LFFQ comprised 82 food items.
A series of food photographs were produced to enable participants to estimate their usual food portion and were based on the recommendations of a previous study (22 (link)). Each food was presented as four portion sizes comprising 25%, 50%, 100% and 125% of a portion commonly consumed or portion on the package label of commercial products. Portions were measured out using an electrical scale (TANITA digital food scale). The amount of Zn in each food was obtained from USDA Nutrient Database for Standard Reference (United States Department of Agriculture 2013; https://data.nal.usda.gov/dataset/usda-national-nutrient-database-standard-reference-legacy-release), the Indonesian food database Nutrisurvey 2007 (http://www.nutrisurvey.de), or from previous studies (23 (link), 24 (link)). A plate or bowl containing the food was arranged together with a spoon and fork on each side. Food was photographed on a white background using a digital camera with a macro lens (Nikon 3100D) and photographs were printed at a size of 4 cm × 8 cm. In parallel, a shorter version of the FFQ (S-FFQ), which comprised fewer food items (28 items), was developed with the aim of reducing the required time for completion and thus pressure on the interviewer and participant during the clinic visit. To develop the S-FFQ, the number of food items was reduced by focusing on Zn-rich foods, such as red meat, offal, avocado, broccoli, spinach, grouping vegetables with lower Zn content, such as cabbage, carrot and lettuce, into a category of “other vegetables” and excluding items that were found to be rarely or never consumed by this population, such as brown rice, veal and pork. The L-FFQ and S-FFQ were compared with one and other and with a 3-day food record (Q3-d).
Both the L-FFQ and the S-FFQ consisted of five sections, which were: (1) personal information about child and parents, which included name, date of birth, birth weight, parents' educational background and occupation; (2) prenatal and birth history; (3) post-natal history, including feeding in the first six months, weaning age and foods, and consumption of food supplements; (4) retrospective record of foods consumed during pregnancy; (5) retrospective record of foods consumed by the child during infancy (from weaning up to age one year old); and (6) record of foods consumed by the child at the point of sampling. The S-FFQ is included as supplementary information.

Wahono N.A., Wakeling L.A., Dirks W., Banks D.A., Shepherd T.J., Ford D, & Valentine R.A. (2023). Use of zinc deposited in deciduous teeth as a retrospective measurement of dietary zinc exposure during early development. Frontiers in Oral Health, 4, 1119086.

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Publication 2023

Birth Weight Broccoli Cabbage Carrots Child Childbirth Clinic Visits Dietary Supplements Electricity Fingers Food Interviewers Lactuca sativa Lens, Crystalline Nutrients Oryza sativa Parent Persea americana Pork Pregnancy Pressure Red Meat Spinach Veal Vegetables

Comparing Greenhouse Gas Emissions and Costs of Ready Meals vs. Home-Cooked Dishes

We performed a secondary data analysis using the National Diet and Nutrition Survey (NDNS) nutrient database year 11 (2018/19)^{(15 –17 )}. The NDNS nutrient databank contains compositional data from the nearly 6000 foods, drinks and prepared dishes available in the UK, including home-cooked and ready meals. Of these, we selected all main course meals, chilled or frozen, that needed to be heated prior to consumption, sold within a container, and had an equivalent home-cooked version in the NDNS nutrient database. As a result, we included fifty-four main courses with data on nutrient profile, and on frequency of consumption over 4 d, in 444 participants (Table 1).

Table 1

Ready meal dishes, and home-cooked equivalent dishes, including frequency of consumption, GHGE and cost per dish

Ready meals				Home-cooked equivalent meals
Food name	Frequency*	GHGE†	Cost‡	Food name	Frequency*	GHGE†	Cost‡
Animal-based
Beef and potato pie	2	1543	0·51	Beef and potato pie 2 crusts	0	433	0·96
Beef stew and dumplings frozen or chilled ready meal	1	1400	0·87	Beef stew and dumplings	0	390	0·56
Beef stir fry with green peppers and black bean sauce	0	1280	1·36	Beef stir fry	0	1150	0·55
Minced beef pie purchased	4	1200	0·62	Minced beef pie top pastry	0	706	0·96
Steak pie, short crust, purchased	3	1200	0·84	Steak pie pastry top only	0	797	0·19
Cornish pasty purchased	10	1200	0·44	Cornish pasty homemade	0	508	0·14
Corned beef pasty purchased	0	1200	0·66	Corned beef pasty	0	508	0·14
Lamb bhuna purchased	0	1180	1·00	Lamb curry (no potatoes) with onions and curry pas	0	1019	1·20
Tagliatelle carbonara ready meal	1	1110	0·37	Spaghetti carbonara	0	431	0·74
Chilli con carne no rice ready meal	0	1070	0·37	Chilli con carne minced beef kid beans and tin tom	2	433	0·16
Cottage pie, frozen/chilled beef	4	1040	0·64	Cottage pie	0	285	0·22
Cottage pie, reduced fat, ready meal	0	1040	0·28	Cottage pie with lean minced beef, potatoes and butter	0	285	0·22
Lasagne beef, ready meal	5	1000	0·66	Lasagne homemade	1	497	0·24
Lasagne, reduced fat, ready meal	1	1000	0·66	Lasagne made with extra lean mince	0	497	0·24
Beef curry frozen/chilled ready meal no rice	1	900	0·25	Beef curry with cream or coconut sauce	0	242	0·32
Shepherd’s pie, lamb, ready meal	0	880	0·65	Shepherd’s pie homemade with minced lamb	0	485	0·48
Beef hot pot with pots ready meal	1	810	0·56	Beef hot pot made with stewing steak carrots cab	0	498	0·56
Moussaka ready meal chill/frozen/long life	1	670	0·87	Moussaka with aubergines homemade	0	678	0·36
Chicken curry frozen chilled no rice	3	670	0·2	Chicken curry homemade	6	553	0·62
Lamb hot pot with potatoes ready meal	0	670	0·87	Lamb hot pot	0	590	0·48
Lemon chicken	0	530	1·00	Lemon chicken – chicken breasts in sauce	1	463	1·25
Chicken chow mein ready meal	3	530	0·70	Chicken Chow Mein	3	401	0·87
Quiche, meat-based, Quiche Lorraine not low fat	12	491	0·58	Quiche Lorraine not wholemeal	0	611	0·52
Fishcakes, salmon, retail, coated in breadcrumbs, baked/grilled	5	460	0·74	Salmon fishcakes grilled	0	209	1·20
Smoked haddock chowder, for example M&S	0	460	0·44	Fish and seafood chowder	2	256	0·58
Tuna and pasta bake ready meal	0	460	0·68	Tuna and pasta bake	0	498	0·39
Sweet and sour pork frozen ready meal no rice	0	460	1·10	Sweet and sour pork	0	360	0·58
Chicken and sweetcorn soup	1	410	0·21	Chicken and veg soup with carrot potato and onion	0	147	0·19
Chicken pie frozen/chilled individual two crusts	7	400	0·37	Chicken pie 2 crusts	0	525	0·62
Chicken in white sauce ham mushroom and rice	0	400	0·85	Chicken and mushrooms in white wine sauce	1	507	0·53
Chicken and pasta bake with broccoli, low fat	0	400	0·68	Chicken and broccoli pasta bake	0	650	0·65
Chicken casserole chicken in tomato/gravy/sauce and vegetables	0	400	0·62	Chicken and vegetable casserole with olive oil	0	345	0·23
Fisherman’s pie (white fish) retail	2	400	0·5	Fisherman’s pie (potato based) with cod and prawns	0	265	0·96
Fisherman’s pie reduced calorie and fat retail	0	400	0·5	Fisherman’s pie with prawns and smoked haddock	0	265	0·96
Tuna and red pepper fish cakes	0	329	1·0	Tuna and potato fish cakes	0	215	0·21
Plant-based
Macaroni cheese ready meal low fat	4	1110	0·20	Macaroni cheese semi skim milk and reduced fat spread	1	407	0·25
Macaroni cheese purchased	3	1110	0·22	Macaroni cheese with butter and semi-skimmed milk	0	407	0·25
Broccoli and stilton soup, premium, chilled carton	0	1110	0·11	Broccoli and cheese soup homemade	0	203	0·18
Quiche, cheese and onion, purchased	13	491	0·58	Cheese and onion quiche homemade	0	479	0·38
Cheese and vegetable quiche purchased	1	491	0·57	Cheese and tomato quiche	1	476	0·38
Quiche, vegetable only, no cheese, purchased	1	491	0·58	Cauliflower and broccoli quiche	0	658	0·38
Mushroom soup, premium, chilled, carton	1	480	0·18	Homemade mushroom soup	0	107	0·18
Vegetable curry, ready meal, no rice	0	280	0·66	Vegetable curry	3	123	0·29
Ross veg chow mein stir-fried in olive oil	0	270	0·54	Vegetable Chow Mein	0	316	0·25
Vegetable lasagne purchased	0	260	0·87	Vegetable lasagne homemade	1	241	0·32
Vegetable bake purchased ready meal	0	260	0·37	Vegetable bake with carrots, broccoli, potatoes and cheese sauce	0	120	0·21
Cauliflower cheese: ready meal purchased standard	2	220	0·67	Cauliflower cheese (whole milk)	0	279	0·20
Cauliflower cheese: healthy range ready meal purchased	0	220	0·67	Cauliflower cheese with butter and semi-skimmed milk	0	279	0·20
Vegetable shepherd’s pie – purchased ready meal	0	220	0·27	Vegetable shepherd’s pie	0	265	0·21
Spinach and potato curry purchased or takeaway	1	180	0·66	Spinach and potato curry with tomatoes and onion	0	123	0·29
Vegetable soup carton	6	150	0·18	Soup vegetable	0	56	0·21
Carrot and coriander soup, purchased	2	150	0·12	Carrot and onion soup homemade	0	58	0·04
Cream of tomato soup, carton	1	150	0·25	Tomato soup with cream, homemade	0	110	0·19
Ratatouille frozen purchased	0	120	0·66	Ratatouille homemade	1	155	0·25

Frequency of consumption across all participant’s (n 444, NDNS 2018/2019) 4-d dietary recalls.

†

GHGE per 100 g of product up to supermarket shelf.

‡

Cost per 100 g of product.

Aceves-Martins M., Denton P, & de Roos B. (2023). Ready meals, especially those that are animal-based and cooked in an oven, have lower nutritional quality and higher greenhouse gas emissions and are more expensive than equivalent home-cooked meals. Public Health Nutrition, 26(3), 531-539.

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Publication 2023

Agaricales Allium cepa Animals Aubergine Beef Bell Pepper Breast Broccoli Butter Carrots Cheese Chickens Coconut Coriandrum sativum Daucus carota Diet Fishes Food Freezing Hyperostosis, Diffuse Idiopathic Skeletal liposomal amphotericin B Meat Mental Recall Milk, Cow's Nutrients Olives Onions Pastes Pork Potato Salmo salar Seafood SELL protein, human Tomatoes Vegetables Vigna umbellata Wine

Antifungal Efficacy of Silver Nanoparticles

Preliminary screening assay was performed to estimate inhibitory effect of AgNPs against 12 phytopathohenic fungi, namely Alternaria alternata IOR 1783 (isolated from kohlrabi), Botrytis cinerea IOR 1873 (isolated from tomato), Colletotrichum acutatum IOR 2153 (isolated from blueberry), Fusarium oxysporum IOR 342 (isolated from pine), Fusarium solani IOR 825 (isolated from parsley), Phoma lingam IOR 2284 (isolated from rape), Sclerotinia sclerotiorum IOR 2242 (isolated from broccoli), and oomycetes, such as Phytophthora cactorum IOR 1925 (isolated from strawberry), Phytophthora cryptogea IOR 2080 (isolated from Lawson cypress), Phytophthora megasperma IOR 404 (isolated from raspberry), Phytophthora plurivora IOR 2303 (isolated from Quercus petraea) using agar well-diffusion method (Magaldi et al., 2004 (link)), with some modifications. Briefly, fungal colonies grown on potato dextrose agar (PDA, Becton Dickinson) in Petri plates for 14 days at 26°C were washed with 10 ml of sterile distilled water to release fungal spores/sclerotia. Their suspensions were collected and filtered through a sterile cotton wool syringe filter to remove mycelia. The concentration of fungal spores/sclerotia were estimated using cell counting chamber (Brand, Germany) and diluted to adjust concentration of 10⁶ spores mL⁻¹. One milliliter of such suspension was added into 6 ml of sterile melted PDA and spread on the surface of sterile medium in Petri plates, as a second layer. Subsequently, the wells (Ø =5 mm) were cut in the inoculated plates using sterile cork borer and filled with 50 μl of AgNPs solution at concentration of 3 mg mL⁻¹. Then, inoculated plates were incubated for 7 days at 26°C and zones of inhibition of fungal growth around wells were measured in mm.

Trzcińska-Wencel J., Wypij M., Rai M, & Golińska P. (2023). Biogenic nanosilver bearing antimicrobial and antibiofilm activities and its potential for application in agriculture and industry. Frontiers in Microbiology, 14, 1125685.

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Publication 2023

Agar Alternaria alternata Biological Assay Blueberries Botrytis cinerea Brassica napus Broccoli Colletotrichum acutatum Cypress Diffusion Fungi Fusarium oxysporum Fusarium solani Glucose Gossypium Leptosphaeria maculans Lycopersicon esculentum Mycelium Oomycetes Petroselinum crispum Phytophthora Pinus Psychological Inhibition Quercus Raspberries Sclerotinia sclerotiorum Solanum tuberosum Spores Spores, Fungal Sterility, Reproductive Strawberries Syringes

Virtual Supermarket Shopping Experiment in VR

A supermarket environment was designed in Unreal Engine V4.1² and implemented in a VR HTC Vive 5.³ The VR supermarket was comparable to the supermarkets in Denmark in terms of appearance. Participants had two controllers (which appeared as hands in the VR) that they can use to choose the items and teleport through the VR. They could also walk (for one or two steps in the physical space), but we asked them to limit their movement in order to keep them in VR environments. All of the items in the supermarket were accessible and participants could choose whatever they see in the store through free navigation. We have allocated a budget of 250 Danish Kroner (DKK) (∼$35) for each participant to spend on these two shopping tasks. Therefore, participants were free to distribute their budget between these two shopping tasks and prevent the neurological effect of product price suggested by Tang and Song (2019) (link) in online context. We provided a list of products in the VR for the participants, which contained six items (Broccoli, Milk, Cheese, Soda, Cereal, and Chocolate). The range of the cost of these items was between 80 and 120 DKK. Participants need to purchase all of these items, which we call as planned purchase condition. With the remaining amount of budget, they could buy whatever they wanted in the store (Unplanned Purchase Condition). They could leave the environment as soon as they had bought the items they wanted. The number of planned purchases (items from the list) was fixed, and the number of unplanned purchases could be different among the subjects, as they were free to choose any item they wanted if it was within their budget. In Figure 1, the experimental procedure of the experiment is illustrated. In the upper side of the figure, the environment for the virtual supermarket is presented.
The EEG data were recorded during the whole shopping task. After mounting the VR and EEG cap, we instructed the participant on how to use the VR so they could start the experiment and navigate through the store. The list of the products that they needed to purchase was available for them by hitting a button on the controller. In the first phase, they were required to buy all the products from the list. Afterward, we informed them about the remaining budget. Then, they could start the second phase of the experiment. On average, participants spent 238.87 ± 85.57 s on the planned purchase condition, and 228.00 ± 107.20 s on the unplanned purchase condition. The time-points of each purchase (i.e., when the participants choose an item) have been recorded during the experiment.

Saffari F., Kakaria S., Bigné E., Bruni L.E., Zarei S, & Ramsøy T.Z. (2023). Motivation in the metaverse: A dual-process approach to consumer choices in a virtual reality supermarket. Frontiers in Neuroscience, 17, 1062980.

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Publication 2023

Broccoli Cacao Cereals Cheese Milk, Cow's Movement Physical Examination

Broccoli Sampling and Microbial Analysis

All procedures for sample collection and preparation were described precisely [8 (link), 31 (link)]. Briefly, 66 florets of broccoli were collected from 22 farms distributed in four regions of Jeju Island (six farms in Daejeong [A], five farms in Hallim [B], five farms in Jocheon [C], and six farms in Seongsan [D]) during harvest season from November 2014 to February 2015 (Additional file 1: Fig. S1). The florets were collected from immature plants grown for 8–9 weeks after seedlings planted in the soil (weight, 80–120 g) at one of each region, and mature plants grown for 15–16 weeks (250–400 g) at the remaining farms of each region. The Agricultural Research and Extension Services of Jeju reported that broccoli plants at five farms in region C were physically damaged by black rot and downy mildew in late 2014 before sampling. Retail samples (n = 40) were collected from ten local grocery stores of Jeju Island from November 2014 to January 2015, and November 2015. Three sections (approximately 10 g per section) were taken using a sterile knife and pooled to be 30 g per sample. Floret samples were mixed with 120 ml of 0.1% buffered peptone water (Difco, Becton Dickinson, Sparks, MD, USA), and sonicated for 10 min at maximum power (Powersonic420, Hwashin Technology, Republic of Korea). Microbial pellets were obtained by centrifugation at 16,000 × g at 4 °C. Agro-meteorological data (temperature, relative humidity, precipitation, insolation, wind speed, soil temperature and soil moisture) at the sampling date were provided by the Jeju Agricultural Research and Extension Services (https://ipm.agri.jeju.kr/develope/weather/weather0102.php). The weather observation stations are located within 3.4 ± 1.6 km (4.4 ± 0.6 km for region A, 3.2 ± 0.4 km for region B, 0.9 ± 0.6 km for region C, and 4.6 ± 1.0 km for region D) at the shortest direct distance from the local farms (Additional file 2: Table S1).

Kim M.S, & Park E.J. (2023). Composition and variability of core phyllosphere fungal mycobiota on field-grown broccoli. Environmental Microbiome, 18, 15.

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Publication 2023

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More about "Broccoli"

Broccoli, a member of the Brassica family, is a nutrient-dense cruciferous vegetable prized for its health benefits.
This cultivar of the wild cabbage plant (Brassica oleracea) is known for its distinctive green florets and earthy, slightly sweet flavor.
Broccoli is rich in vitamins, minerals, and antioxidants, making it a popular choice in cuisines around the world.
Broccoli research is an important area of study, as this versatile vegetable may offer potential applications in the medicinal and agricultural fields.
Scientists are exploring the therapeutic properties of broccoli compounds like sulforaphane, glucoraphanin, and gallic acid, which have been linked to anti-inflammatory, anti-cancer, and detoxification effects.
To optimize broccoli research, techniques like TRIzol reagent and SYBR Gold for RNA extraction and gene expression analysis, as well as the use of DMEM growth medium and disposable polypropylene columns, can be employed.
Chromatographic methods, such as C18 column separation, can be used to isolate and quantify specific broccoli phytochemicals like desulfoglucoraphanin.
PubCompare.ai is a powerful tool that can help researchers identify the best protocols and approaches from the literature, preprints, and patents, enhancing the reproducibility and accuracy of broccoli experiments.
By leveraging the insights and metadescription provided, scientists can streamline their broccoli research and uncover new, valuable insights about this nutritious and versatile vegetable.