In the event of a Foot and Mouth Disease (FMD) outbreak in the U.S., local, state, and federal authorities will implement a foreign animal disease emergency response plan restricting the pork supply chain movements and likely disrupting the continuity of the swine industry business. To minimize disruptions of the food supply while providing an effective response in an outbreak, it is necessary to ensure eradication strategies and risk management efforts are focused towards the most critical movements; those that are most necessary for business continuity and most likely to contribute to disease spread. This study recruited experts from production, harvest, retail, and allied pork industries to assess 30 common pork supply movements for their industry criticality. Movements spanned five categories: equipment, live animal production, genetics, harvest, and people. Experts were recruited via email to the American Association of Swine Veterinarians (AASV) mailing list and their assessments were collected via an online survey. For each of the thirty movements, experts were asked to rate the risk of FMD spread using a four-point scale, from no or slight risk of disease spread to high risk of disease spread. Then they were asked to estimate the time at which the restriction of each movement during an outbreak would have a significant negative consequence on business (e.g., high likelihood of bankruptcy, negative impact on animal welfare). These two facets of each movement were analyzed to provide an initial guide for prioritization of risk management efforts and resources to be better prepared in the event of a FMD outbreak in the US. The Data.csv file contains the raw survey responses (location information collected by Qualtrics has been removed). Information about the variables and value labels can be found in the DataDictionary.txt file. The data can be read into the Analysis_Code.R file to perform analysis described in the paper and to create a static version of the Movements.html graph. Survey.pdf contains the survey questions with relevant skip and display logic. Resources in this dataset:Resource Title: Risk prioritization of pork supply movements during an FMD outbreak in the US - Data and Materials. File Name: Web Page, url: https://conservancy.umn.edu/handle/11299/181833 Link to dataset in the Data Repository for the University of Minnesota (DRUM). The Data.csv file contains the raw survey responses (location information collected by Qualtrics has been removed). Information about the variables and value labels can be found in the DataDictionary.txt file. The data can be read into the Analysis_Code.R file to perform analysis described in the paper and to create a static version of the Movements.html graph. Survey.pdf contains the survey questions with relevant skip and display logic.

This dataset was created for the Wild Pig Management at the Jack and Laura Dangermond Preserve, a group project by Bren School of Environmental Science and Management at the University of California, Santa Barbara. The project includes a population study of wild pigs (Sus scrofa) and a cost analysis of three management scenarios. For the wild pig population study, this dataset contains pig count data created by tagging camera trap photos between October 2013 to August 2015 (01_WildPigPopulation_TimelapseData.csv), estimated abundance data by camera trap stations (02_WildPigPopulation_AbundanceByStation.csv), and spatial files of their interpolated distribution in the Preserve (03_WildPigPopulation_Distribution.zip). In the abundance dataset, April Check refers to the camera trap photos taken between 2013-10-23 and 2014-04-22 while September Check refers to the time period of 2014-04-24 to 2014-09-25. For cost analysis, this dataset provides spatial files of four proposed fencing areas (11_CostAnalysis_ProtectedAreasByFencing.zip), the costs of six fencing scenarios for Dangermond Preserve (12_CostAnalysis_DangermondFencingCost.csv), and cost and pig removal data gathered from seven case studies (13_CostAnalysis_CaseStudies.csv). Raw data used to generate the dataset were provided by The Nature Conservancy at Dangermond Preserve.

Methods Data collection:

Camera trap photos were collected by WRA and TNC from 2013-2014 using 38 camera trap stations. The data from camera traps were gathered once in April 2014 (April Check) and once in September 2014 (September Check). September Check also contains a few observations from June 2015 to August 2015 which were excluded in the analysis. Fencing cost data were provided by TNC. Fencing protected areas and footage were measured from spatial files.

Data processing:

Step 1. Classify camera trap photos into animals, humans, and vehicles in MegaDetector by Microsoft AI for Earth. This step allowed us to quickly pick out animal photos, which were narrowed down from ~400,000 images to ~250,000.

Step 2. Manually tag wild pigs (Sus scrofa) in Timelapse2. Timelapse2 takes the classification output from MegaDetector to filter out animal photos in the image set. From the camera trap photos, we collected data of date, time, temperature, geographic location, and the number of pigs in each image. See 01_WildPigPopulation_TimelapseData.csv for the result data.

Step 3. Estimate the number of pig groups at each camera trap station. We first defined one day as one visit and calculated the observed number of groups for each day at each camera trap from 01_WildPigPopulation_TimelapseData.csv. We used 5 minutes and 30 seconds to divide up groups. Then, we used the N-mixture model (unmarked package in R) to find the estimated number of pig groups.

Step 4. Estimate the pig abundance. We calculated a weighted average group size at each camera trap, then times it to the estimated number of pig groups to find the estimated abundance. The result of this step can be found in 02_WildPigPopulation_AbundanceByStation.csv. The overall density of wild pigs was estimated by looking at the extremes of the estimated abundance. We examined the sum of abundance, the maximum abundance per site, and consulted with experts to nail down a density of ~2 pigs/km2.

Step 5. Model wild pig distribution in Dangermond using kriging in R. We first detrended the spatial trend to meet the assumption of constant mean and variance through the study area. We determined the global trend to be removed by comparing first order and second-order polynomial fit. We then looked at the sample experimental variogram plot and fitted it with the Spherical model. Following that, we sent the fitted variogram to kriging interpolation, which used the localized pattern produced by sample data to compute the weights of neighboring pig counts. After kriging, we combined the output with the previously removed global trend to produce the final result of the interpolation. See the TIFF file in 03_WildPigPopulation_Distribution/ folder.

Step 6. Estimate total cost and cost per area protected under five fencing scenarios in the Dangermond Preserve. We first took measurements of the five proposed areas where TNC might install fences in ArcGIS (see shapefile in 11_CostAnalysis_ProtectedAreasByFencing/ folder). Given the measured area protected, we calculated the total costs of fencing, gates, and old fence removal (see 12_CostAnalysis_CasegermondFencingCost.csv).

Step 7. Examine cost efficiency from case studies. We took data of fencing costs, footage, acres, and the number of pigs removed from seven case studies and calculated the cost efficiency corresponding to the density of wild pigs. See 13_CostAnalysis_CaseStudies.csv.

For a more detailed methodology, please refer to Wild Pig Management at the Jack and Laura Dangermond Preserve Report on the website of Bren School or contact Shuhan Song to obtain access to our GitHub repository.

BackgroundThe occurrence of livestock-associated (LA) MRSA (ST398) in pig herds has emerged as a threat to occupational safety in many parts of the world. Recently, an outbreak of skin lesions due to MRSA occurred in workers at a pig farm in regional Australia and both the humans and pigs were shown to have a high prevalence of carriage of either the human-strain ST93 or porcine strain ST398. This study closely scrutinises this outbreak to determine factors associated with MRSA carriage amongst the workers.MethodsInformation on potential risk factors was collected from employees by means of a questionnaire. The carriage status of MRSA by workers was assessed by nasal swabs processed using standard laboratory techniques with confirmed isolates subjected to sequence typing. Associations between MRSA carriage in workers and their questionnaire responses were investigated using univariable and multivariable logistic regression.ResultsNasal carriage of MRSA was identified in 60% (31/52) of participants. Workers having contact with pigs had 24 times the odds of MRSA carriage compared to workers with no direct contact (OR 23.6; CI 5.2–172.8). In addition, the probability of MRSA carriage in workers was significantly (P < 0.001) associated with the number of hours in contact with pigs and each hour of contact-time per day increased the risk of MRSA carriage by 1.44 times (CI 1.14–1.96). These associations were significant (P < 0.001) for both strains, ST398 and ST93, present on this farm. Using a multivariable logistic regression model that incorporated human exposure to five different pig age groups (dry sows, farrowing, weaner, grower, and finisher) as fixed effects, a significant (P = 0.027) increased odds of MRSA carriage was found for persons working with farrowing sows compared with those who did not (OR 6.39, CI 1.23–39.36).ConclusionsThis study shows that workers in close contact with pigs on a pig farm where MRSA is present had a higher risk of MRSA carriage as the number of hours of direct contact with pigs increased. Since we have detected a significant association for the human-derived CA-MRSA ST93, similar to the pig-adapted LA-MRSA ST398, we consider ST93 as a potential occupational risk for piggery workers. The risk of MRSA carriage is greatest when working with the farrowing group; therefore, an emphasis is required on personal protective equipment while working in the farrowing house. The study has ramifications for the conduct of surveillance for MRSA in people exposed to pigs.

IntroductionNon-typhoidal Salmonella (NTS) is a major cause of gastroenteritis worldwide, often associated with meat consumption and meat processing. Research on NTS infection and circulating serovars in meat value chains in Uganda is limited. We aimed to establish NTS prevalence, antimicrobial resistance, and risk factors among slaughterhouse workers, and to identify potentially zoonotic serovars in the pork value chain.Material and methodsWe conducted a nationwide cross-sectional survey, collecting 364 stool samples from livestock slaughterhouse workers and 1,535 samples from the pork value chain: mesenteric lymph nodes, fecal samples, swabs of carcass splitting floor, cleaning water, meat handlers hand swabs, carcass swabs, raw pork, cooked pork, and mixed raw vegetables. Samples were cultured for isolation of NTS, and subsequently serotyped according to White–Kauffmann–Le Minor scheme. Antimicrobial resistance profiles were determined using tube microdilution and Sensititre® EUVSEC3® plates. Semi- structured questionnaires with 35 questions were used to collect data on demographics, work related risk factors and activities outside the slaughterhouse.Results and discussionOverall NTS prevalence was 19.2% (365/1899). Proportions at slaughter were; 46.7% in floor swabs, 30.5% in carcass swabs, 20.5% in pig faeces,19.2% in mesenteric lymph nodes,18.4% in hand swabs, 9.5% in water and 5.2% in slaughterhouse workers. At retail, proportions were 33.8% in pork chopping surface, 33.1% in raw pork, 18.9% in hand swabs, 4.0% in cooked pork and 0.7% in vegetables. Sixty-one serovars were identified, with significant overlap between humans and the pork value chain. Overall, zoonotic S. Zanzibar, monophasic serovars of S. subspecies salamae (II) and subspecies enterica (I), S. Typhimurium and S. Newport, were the most prevalent. S. Typhimurium was predominant in humans and exhibited multi-drug resistance. NTS infection was significantly associated with eating, drinking, or smoking while working (OR = 1.95, 95% CI: 0.67-2.90%, p = 0.004). The detected NTS serovars in slaughterhouse workers could be a potential indicator of circulating serovars in the general population. The persistent presence of NTS along the pork value chain highlights occurrence of cross-contamination and the potential for transmission to consumers and slaughterhouse workers. This emphasizes the need to reduce Salmonella prevalence on pig farms and improve hygiene and pork handling practices at slaughter and retail points.

This dataset contains 244 product entries across diverse categories, primarily focused on fresh produce (vegetables, herbs, fruits) and meat products (beef, poultry, pork, lamb, game meats).

Key Features: Structure:

id: Unique product identifier (372–615)

product_name: Name and often a specific variety (e.g., "Corn - Bi-Color (Mirai 301)")

product_price: Price in USD (mostly $100, with some exceptions like eggs, herbs, or bulk meat packs)

description: Detailed culinary/cooking notes, flavor profiles, and usage suggestions.

Categories:

Produce: Asparagus, beans, basil, beets, berries, broccoli, cabbage, carrots, mushrooms, herbs, etc.

Meats:

Beef (brisket, steaks, ground beef, organ meats)

Poultry (chicken, duck, turkey, goose)

Pork (bacon, sausages, ribs)

Lamb/Mutton (chops, shanks, roasts)

Game (rabbit, goat offal)

Use Cases:

Inventory management for grocery stores, farmers' markets, or meal-prep services.

Recipe/cooking app integration (e.g., pairing products with dish ideas).

Price analysis for agricultural or meat products.

Meat products include both standard cuts (e.g., ribeye, ground beef) and specialty items (tongue, liver, gizzards).

Descriptions emphasize culinary versatility (e.g., "perfect for grilling," "ideal for stir-fries").

Potential Audience: Food retailers, culinary apps, agricultural researchers, or meal-planning services. Let me know if you need further analysis! 🥦🥩

IntroductionIn Uganda, pig production is an important source of livelihood for many people and contributes to food security. African swine fever (ASF) is a major constraint to pig production in Uganda, threatening the food supply and sustainable livelihoods. Prevention of ASF primarily relies on good biosecurity practices along the pig value chain. Previous studies showed that biosecurity along the pig value chain and on farms in Uganda is poor. However, the biosecurity practices of pig farmers in ASF affected areas of Uganda and their opinions on on-farm ASF morbidity and mortality were previously not comprehensively characterized. The objectives of this study were to document pig farmers’ experiences with ASF in their farms and to describe the pig biosecurity practices in districts of Uganda that were highly affected by ASF.MethodsA total of 99 farmers were interviewed in five districts. Data were collected by way of triangulation through farmer interviews, field observations during the farmer interviews, and a survey of key informants. However, farmer interviews were considered the primary source of data for this study. Farmers’ biosecurity practices were scored using a biosecurity scoring algorithm.ResultsForty-one out of 96 (42.7%) farmers reported having pigs with ASF in the past 12 months. The level of pig farming experience (p = 0.0083) and herd size (p 

Introduction

Animal welfare is important because there are so many animals around the world suffering from being used for entertainment, food, medicine, fashion, scientific advancement, and as exotic pets. Every animal deserves to have a good life where they enjoy the benefits of the Five Domains.

About Dataset

We aim to reduce total suffering, society’s ability to reduce this in other animals – which feel pain, too – also matters.

This is especially true when we look at the numbers: every year, humans slaughter more than 80 billion land-based animals for farming alone. Most of these animals are raised in factory farms, often in painful and inhumane conditions.

Estimates for fish are more uncertain, but when we include them, these numbers more than double.

These numbers are large – but this also means that there are large opportunities to alleviate animal suffering by reducing the number of animals we use for food, science, cosmetics, and other industries and improving the living conditions of those we continue to raise.

On this page, you can find all of our data, and writing on animal welfare.

File 1: The estimated number of animal lives that go toward each kilogram of animal product purchased for retail sale, including direct deaths only. For example, the pork numbers include only the deaths of pigs slaughtered for food.

File 2: The estimated number of animal lives that go toward each kilogram of animal product purchased for retail sale, including direct and indirect deaths. For example, the pork numbers include the deaths of pigs slaughtered for food (direct) but also those who die pre-slaughter and feed fish given to those pigs (indirect).

File 3: The estimated quantity of edible meat produced per animal, measured in kilograms.

File 4: Different location on time span = 2013 - 2020

File 5: Share of hens in cages Share of hens housed in a barn or aviary Share of non-organic, free-range hens Share of organic, free-range hens Share of laying hens in unknown housing

File 6: Number of eggs from hens in organic, free-range farms Number of eggs from hens in non-organic, free-range farms Number of eggs from hens in barns Number of eggs from hens in (enriched) cages

File 7: Estimated number of farmed decapod crustaceans Estimated number of farmed decapod crustaceans (upper bound) Estimated number of decapod crustaceans (lower bound)

File 8: Estimated number of farmed fish Estimated number of farmed fish (upper bound) Estimated number of farmed fish (lower bound)

File 9: Share of cage-free eggs Share of all eggs that are produced in cage-free housing systems. This includes barns, pasture and free-range (non-organic and organic) eggs.

Lets diving in dataset and create some excellent notebook for visualization and types of prediction. So, Good luck.

By Hannah Ritchie, Pablo Rosado and Max Roser (Our world in data)

Monthly average retail prices for selected products, for Canada, provinces, Whitehorse and Yellowknife. Prices are presented for the current month and the previous four months. Prices are based on transaction data from Canadian retailers, and are presented in Canadian current dollars.

this graph was created in OurDataworld:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F5251a7628fd7daf343947c8d3d0c766e%2Fgraph1.png?generation=1718059501109889&alt=media" alt="">

An increasing number of people would describe their dietary habits as “flexitarian” or “reducitarian.” These are people who still eat meat and dairy but are trying to reduce their consumption, often for environmental or ethical reasons. In the UK, there are more flexitarians than vegans, vegetarians, and pescetarians (who only eat fish) combined.

These ethically-conscious consumers still have a choice to make: what types of meat should they eat to reduce their environmental impact and reduce animal welfare costs?

It’s tempting to assume that what’s good for the planet is also good for the animal, but unfortunately, this is not the case. These two goals are often in conflict. What’s better for animal welfare is often worse for the environment, and vice versa. This is true across different types of livestock (for example, beef versus chicken) and across different ways of raising a specific animal (caged versus free-range hens).

This trade-off is easily missed. How consumers navigate this dilemma will depend on their values and priorities, including other things such as cost, taste, and their relationship with farmers and communities.

In this article, I’ll present some of the research on the trade-offs between environmental protection and animal welfare so that you can decide what you want to do when faced with this trade-off. Swap a beef burger for a chicken one, and you’ll cut the carbon footprint of your dinner by around 80%.1 The problem, however, is that you’ll need to kill 200 times as many chickens as cows to get the same amount of meat. An average chicken might produce around 1.7 kilograms of meat, while a cow produces around 360 kilograms.

This is true for other types of livestock, too. In the chart below, I’ve shown each type of meat’s carbon footprint on the right and the number of animals killed to produce one tonne on the left. You can see the trade-off. Bigger animals — cows, pigs, and lambs — emit more greenhouse gases but produce much more meat per animal. Chicken and fish might have a low carbon footprint but are killed in much higher numbers.

The consequence is that many more smaller animals — chickens and fish — are slaughtered. As my colleague, Max Roser shows in another article, every day 200 million chickens and hundreds of millions of fish are killed, compared to several million pigs and sheep, and about 900,000 cows daily.

About Dataset

This dataset consists of a comprehensive collection of images of iconic dishes from the eight major Chinese cuisines. The dataset includes a total of 5,793 labeled images, meticulously curated and annotated to support image recognition and classification tasks. The dishes are carefully selected to represent the diversity and richness of Chinese culinary culture, making this dataset valuable for various machine learning and computer vision applications.

Dishes Included

1. Peking Duck (北京烤鸭) - A renowned Beijing specialty known for its crispy skin and tender meat.
2. Sweet and Sour Pork (糖醋里脊) - A popular dish featuring deep-fried pork pieces coated in a sweet and tangy sauce.
3. Mapo Tofu (麻婆豆腐) - A spicy Sichuan dish made with tofu, minced meat, and a variety of seasonings.
4. Yuxiang Shredded Pork (鱼香肉丝) - A Sichuan dish with shredded pork cooked in a fragrant sauce made from pickled chili, garlic, and ginger.
5. Husband and Wife Lung Slices (夫妻肺片) - A famous Sichuan cold dish made from sliced beef and ox tongue seasoned with chili oil.
6. Twice-Cooked Pork (回锅肉) - A classic Sichuan dish where pork belly is first boiled, then stir-fried with vegetables and spicy bean paste.
7. Kung Pao Chicken (宫保鸡丁) - A well-known dish from Sichuan cuisine, featuring diced chicken stir-fried with peanuts, vegetables, and chili peppers.
8. Saliva Chicken (口水鸡) - A spicy and flavorful Sichuan dish made with poached chicken, chili oil, and sesame sauce.
9. Soup Dumplings (小笼包) - Steamed dumplings filled with savory broth and meat, a specialty from Jiangsu cuisine.
10. Braised Pork Meatballs in Brown Sauce (红烧狮子头) - Large, tender meatballs braised in a rich brown sauce, popular in Jiangsu cuisine.
11. Dongpo Pork (东坡肉) - A famous Hangzhou dish featuring braised pork belly, known for its rich and succulent taste.
12. West Lake Vinegar Fish (西湖醋鱼) - A traditional Hangzhou dish with fish cooked in a sweet and sour sauce.
13. Buddha Jumps Over the Wall (佛跳墙) - A luxurious Fujian dish made with a variety of seafood, meats, and Chinese herbs.
14. Steamed Sea Bass (清蒸鲈鱼) - A classic Cantonese dish where sea bass is steamed with ginger, scallions, and soy sauce.
15. Fish with Pickled Cabbage and Chili (酸菜鱼) - A popular Sichuan dish with fish fillets cooked in a spicy and sour broth with pickled cabbage.
16. Boiled Fish with Sichuan Peppercorns (水煮鱼) - Another Sichuan favorite, featuring fish fillets in a numbing and spicy broth made with Sichuan peppercorns.

Data Collection and Annotation

This dataset features a well-structured annotation process to ensure high-quality labels. For each category, the first 100 images (001-100) were manually annotated, resulting in a total of 1,600 meticulously labeled images. These manually labeled images were then used to train a demo model, which automatically annotated the remaining images in the dataset. The auto-annotated images were subsequently reviewed and corrected by humans to ensure accuracy and consistency. This combined approach of manual and automated annotation guarantees precise labeling across all 5,793 images.

Usage

This dataset can be directly used for training ultralytics/YOLOv8 models. However, please note that you need to modify the "path:" in the dataset.yaml file. The train.txt, val.txt, and test.txt files have already been split in a ratio of 8:1:1, facilitating a streamlined workflow for model training and validation.

Disclaimer

As an individual, you may use this dataset for free. However, some images in the dataset may be subject to copyright restrictions. If you intend to use the dataset for commercial purposes, please be aware of these potential copyright issues.

Positive emotions can reduce disease susceptibility during infectious challenges in humans, and emerging evidence suggests similar effects in farm animals. Because play behaviour may support a positive emotional state in pigs, this study investigates whether rearing pigs with regular intermittent play opportunities enhances disease resilience when challenged with porcine reproductive and respiratory syndrome virus (PRRSV). Litters were assigned to either play (PLY; n = 5 L) or control (CON; n = 4 L) treatments at birth. In PLY, play was promoted with extra space and enrichment items for three hours daily from five days of age (doa). At weaning (25 ± 2 doa; mean ± SD), 28 pigs (14/treatment) were selected for a disease challenge, based on weight, sex, and sow. The pigs were transported to a disease containment facility and at 43 ± 2 doa (day 0 post-inoculation, DPI) inoculated with PRRSV. Skin lesions, blood, rectal temperature, clinical signs, body weight, and behaviour were collected pre- and post-inoculation. Play opportunities for PLY continued every other day until euthanasia of all pigs at 65 ± 2 doa (22 DPI). PLY pigs exhibited fewer skin lesions following transport and throughout the infection compared to CON. Although the viral load did not differ between treatments, PLY pigs had a lower probability of experiencing moderate and severe respiratory distress, with a shorter duration. PLY also performed better throughout the infection, showing higher ADG and greater feed efficiency. The immune response differed as well. PLY pigs had fewer monocytes on 8 DPI than CON, with levels returning to baseline by 21 DPI, whereas CON levels exceeded baseline. Regardless of day of infection, lymphocyte counts tended to be lower in PLY than in CON, and white blood cells and neutrophils were also lower, but only in slow-growing pigs. PLY pigs continued to play during the infection, demonstrating less sickness behaviour and emphasizing the rewarding properties of play. Results suggest that PLY pigs were less affected by PRRSV and developed increased resilience to PRRSV compared to CON. This study demonstrates that rearing pigs in an environment supporting positive experiences through provision of play opportunities can enhance resilience against common modern production challenges, underscoring the value of positive welfare in intensive pig farming. Methods Animal measures in detail, including tables, are described in the published paper. Citation: Steinerová K, Harding JC, Parker SE, Wilson HL, Nery Finatto A and Seddon YM (2024) Rearing pigs with play opportunities: the effects on disease resilience in pigs experimentally inoculated with PRRSV. Front. Vet. Sci. 11:1460993. doi: 10.3389/fvets.2024.1460993 Behaviour and skin lesions The duration of play (locomotor, social and object) and exploratory behaviour (Table 2) were scored with continuous sampling within the initial 10 min of the play sessions only in the PLY treatment at -2 (baseline), 3, 7, 11, 16 and 20 DPI. The scoring commenced after an experimenter exited the playpen and closed the gate. On the same days, to assess pig activity during the challenge, the frequency of active, inactive, and feeding behaviours (Table 2) in the PLY and CON treatments were assessed through instantaneous sampling within the first half of the play sessions at 5-min intervals (90 min, 18 scans/pig/DPI). Additionally, scans were collected at 10-min intervals when no play sessions were occurring for two 90-min periods without human presence in the pen, in the morning (between 7:30 – 9:30 AM) and evening (between 5:00 – 7:00 PM), totalling 180 min per day (18 scans/pig/DPI). In the chosen periods, human presence in the BSL2 was recorded only in the AM, and when it happened, an experimenter continued scanning but noted a person in the room. The pigs were individually marked with spray paint (Raidex GmbH, Dettingen/Erms, Germany) at least two hours before video recording. All behaviours were videotaped with Lorex cameras (4K Ultra HD IP Security Camera, Lorex Technology, Markham, ON, Canada) in the farrowing room (one camera/two neighbouring home pens), the nursery room (one camera/home pen, one camera/two playpens) and the BSL2 room (one camera/pen; Figure 1). The behaviours were scored from the video recordings by one experimenter using the Observer software XT14 (version 14.2.1127, Noldus, Leesburg, VA, USA). The experimenter could not be blinded to the treatments due to clear distinctions between treatments in the experimental set up (playpens in PLY) and restricted number of trained personnel allowed to access to the disease containment facility. Skin lesions were scored as a proxy measure of aggression (Turner et al., 2006) two days pre-weaning (age: D23 ± 2 (days); mean ± S.D.), one-day post-weaning (age: D26 ± 2), one day before transport (age: D33 ± 2), one day after transport (age: D35 ± 2), pre-inoculation (-2 DPI, age: D40 ± 2) and at the end of the trial (21 DPI, age: D64 ± 2). The body was divided into six regions: ears, face, front (neck, shoulders, and front legs), middle (the body after the shoulders up to the frontal tip of the hind legs), rear (the hind legs), and tail1. Each body region was scored individually and was assigned a score from 0 to 3: score 0 (none) = no lesions; score 1 (mild) = less than five superficial scratches; score 2 (moderate) = 5-10 superficial scratches and/or less than three deep wounds; score 3 (severe) = more than 10 superficial scratches and/or more than three deep wounds. A total body skin lesion score was calculated by summing all body region scores per pig and day (maximum score of 18/pig/day). One experimenter, who could not be blinded to the treatments, directly scored the skin lesions while standing outside the pen. Rectal temperature and body weight Rectal temperature (RT) was taken on 0 (baseline), 2, 4, 8, 13, 17, and 21 DPI using a digital thermometer with a flexible tip and a resolution of 0.1ºC. On 0 DPI, a baseline RT was measured pre-inoculation. However, due to technical difficulties with the thermometer, the initial baseline data were discarded, and the baseline RT was recorded three hours post-inoculation before the onset of detectable viremia (6-48 hours post-exposure; Zimmerman et al., 2019a) using a new thermometer that was used thereafter. The pigs were weighted on 0, 8, 13, 17, and 21 DPI on a digital scale with a resolution of 0.1 kg. The ADG post-inoculation was calculated between each subsequent weigh period per pig. Blood collection and clinical signs Blood (serum, EDTA) was collected from the jugular vein with the pig restrained in a supine position on -1, 2, 4, 8, 13, 17, and 21 DPI. Tubes with EDTA were gently inverted 8-10 times to ensure thorough mixing with the anti-coagulant and stored on ice. Rectal temperature, body weight, and blood were collected between 8 to 10 am in the aforementioned order. To prevent cross-contamination, the negative control pigs in the BSL1 were blood sampled (serum, EDTA), and weighted and their RT was collected on -1 (blood) or 0 (weight, RT), 13, and 21 DPI before the pigs in the BSL2. The pigs in the BSL2 were monitored for PRRSV clinical signs with scores assigned based on severity (0: not present, 4: severe) in the AM and PM. The negative control pigs in the BSL1 were monitored in the AM only by a separate team, from the first day in the acclimation period until 21 DPI. Monitored clinical signs included: respiratory distress (RD), coughing, responsiveness, appetite, colour of the skin, consistency of the faeces, body condition, and additionally lameness as a clinical sign not specific to PRRSV (see description in Table 2 in the suppl. mat.). Pen feed intake Pen feed intake was recorded in the PM pre-inoculation on -8, -5, and -1 DPI, and post-inoculation on 2, 6, 9, 13, 16, 20, and 21 DPI. Feed intake was divided into periods: pre-inoculation (DPI -8 to -1; 8 days), one-week post-inoculation (DPI 0 to 6; 7 days), second-week post-inoculation (DPI 7 to 13; 7 days), third-week post-inoculation (DPI 14 to 21; 8 days), from which the average feed intake per pig per day in a given period was calculated. Feed-to-gain ratio (F:G) was calculated per pen (total (from 0 to 21 DPI) feed intake per pen / total gain per pen) and averaged per treatment. Gross lung lesions At necropsy, lungs were rinsed with water and carefully placed on a tray, and their ventral and dorsal surface showing left and right cranial (CR), middle (M), caudal (CA), and accessory (A) lobes were photographed for later examination of pathomorphological changes. A consistent observer utilized a lung drawing from Halbur et al. (1995) to shade areas on the lobes exhibiting the colour change observed in the photographs. Lung lesions typical of interstitial pneumonia and differing in severity with colour ranging from tan to dark red and purple (Zimmerman et al., 2019b) were identified. A 9 mm by 9 mm grid was placed on the shaded lung drawing to calculate the proportion of the affected lobes (number of shaded grid squares (with a precision of ¾ of a square) / total number of grid squares). This proportion was then multiplied with a pre-defined score assigned to each lobe (ventral left and right – CR: 10, M: 10, CA: 25, A: 5; dorsal left and right – CR: 10, M: 10, CA: 30; Halbur et al. 1995), resulting in an estimate of the percentage of the affected lobe, and thereafter summed to determine the total affected area of the lungs. Other characteristics of the gross lung lesions, such as the consistency of the lungs (slightly firm to rubbery) (Zimmerman et al., 2019b) were not possible to record from the photographs. Lab analyses Immediately after the blood collection, whole blood (-1, 2, 4, 8, 13, 17, and 21 DPI) was submitted to Prairie Diagnostic Services (PDS) for a total count of white blood cells (WBC) and differential counts of lymphocytes, neutrophils, and

The pig is a well-known animal model used to investigate genetic and mechanistic aspects of human disease biology. They are particularly useful in the context of obesity and metabolic diseases because other widely used models (e.g. mice) do not completely recapitulate key pathophysiological features associated with these diseases in humans. Therefore, we established a F2 pig resource population (n = 564) designed to elucidate the genetics underlying obesity and metabolic phenotypes. Segregation of obesity traits was ensured by using breeds highly divergent with respect to obesity traits in the parental generation. Several obesity and metabolic phenotypes were recorded (n = 35) from birth to slaughter (242 ± 48 days), including body composition determined at about two months of age (63 ± 10 days) via dual-energy x-ray absorptiometry (DXA) scanning. All pigs were genotyped using Illumina Porcine 60k SNP Beadchip and a combined linkage disequilibrium-linkage analysis was used to identify genome-wide significant associations for collected phenotypes. We identified 229 QTLs which associated with adiposity- and metabolic phenotypes at genome-wide significant levels. Subsequently comparative analyses were performed to identify the extent of overlap between previously identified QTLs in both humans and pigs. The combined analysis of a large number of obesity phenotypes has provided insight in the genetic architecture of the molecular mechanisms underlying these traits indicating that QTLs underlying similar phenotypes are clustered in the genome. Our analyses have further confirmed that genetic heterogeneity is an inherent characteristic of obesity traits most likely caused by segregation or fixation of different variants of the individual components belonging to cellular pathways in different populations. Several important genes previously associated to obesity in human studies, along with novel genes were identified. Altogether, this study provides novel insight that may further the current understanding of the molecular mechanisms underlying human obesity.

Dataset listing all publications used in systematic mapping review. This file contains the dataset, a dictionary and values worksheets for the following publication:

Skin-Based Vaccination: A Systematic Mapping Review of the Types of Vaccines and Methods Used and Immunity and Protection Elicited in Pigs.

Vaccines 2023

Inés Có-Rives 1, Ann Ying-An Chen 1, Anne C Moore 1

PMID: 36851328

PMCID: PMC9962282

DOI: 10.3390/vaccines11020450

The advantages of skin-based vaccination include induction of strong immunity, dose-sparing, and ease of administration. Several technologies for skin-based immunisation in humans are being developed to maximise these key advantages. This route is more conventionally used in veterinary medicine. Skin-based vaccination of pigs is of high relevance due to their anatomical, physiological, and immunological similarities to humans, as well as being a source of zoonotic diseases and their livestock value. We conducted a systematic mapping review, focusing on vaccine-induced immunity and safety after the skin immunisation of pigs. Veterinary vaccines, specifically anti-viral vaccines, predominated in the literature. The safe and potent skin administration to pigs of adjuvanted vaccines, particularly emulsions, are frequently documented. Multiple methods of skin immunisation exist; however, there is a lack of consistent terminology and accurate descriptions of the route and device. Antibody responses, compared to other immune correlates, are most frequently reported. There is a lack of research on the underlying mechanisms of action and breadth of responses. Nevertheless, encouraging results, both in safety and immunogenicity, were observed after skin vaccination that were often comparable to or superior the intramuscular route. Further research in this area will underlie the development of enhanced skin vaccine strategies for pigs, other animals and humans.

Keywords: epicutaneous; epidermal; intradermal; needle-free; percutaneous; pig; skin; transcutaneous; transdermal; vaccine.

Chinese dumpling/JIAOZI is a traditional food in China and the best-known food in the world. During the Winter Solstice, one of the Chinese traditional festivals, Chinese families will get together and have dumpling parties. Today various frozen dumplings are sold online. They're filled with meat, fish, cheese, vegetables, and so on. I want to check how many types of dumpling fillings are there.

Gathering data for JD.com. It contains 7 brands, 92 types of fillings, 47 different ingredients.

p.s. Some names of filling are optimized to maintain data consistency, such as "白菜猪肉 " (cabbage with pork) is transformed to "猪肉白菜"(pork with cabbage )

Summary

Access

This data set is also available as a public data set on Mode Analytics as raindata.table_name. Mode provides a PostgreSQL interface for querying and dashboard creation.

Kansas City Barbeque Society

The Kansas City Barbeque Society hosts competitive BBQ events in the US and around the world. The KCBS trains and certifies barbeque judges to provide rigorous scoring at their contests. Millions of dollars in prizes are awarded annually to top teams.

This data set represents all of the competition results posted on the KCBS Events pages up to November of 2018.

Data Overview

This dataset includes four tables: competitions, teams, results and competition web pages.

Competitions

Each row is a KCBS competition with some dimensional data like the prize value, date and location

Results

Each row is a team's score and place rank within a category at a competition. The category 'overall' reflects their overall score and place rank at the competition.

Teams

Each row is a team uniquely identified by their lower-cased name string (name_key). Dimensional data is not broadly available for teams on the KCBS website but some can be derived from competition+result data (ie a guess at the team's 'home state').

Competition Web Pages

Further competition meta-data from the KCBS events pages including the URL and year/month/country slugs of the containing search page.

Competition Domain Knowledge

Competition Format

A standard KCBS competition consists of four rounds/categories: chicken, pork ribs, pork, and brisket. Judges are divided into tables with six judges at each table. Efforts are made to evenly distribute judges based on their level of experience (competitions judged).

Judging proceeds with one round for each category. In a standard competition the rounds are ordered: chicken, pork ribs, pork and brisket. Within a round, each table of judges receives entries from six teams. Efforts are made to have each team judged by a different table of judges in each category. Judging is blind.

Non-standard competitions may include additional categories or exclude main categories and may not calculate the overall score as a combination of the four main categories.

Scoring System

An entry is scored on three dimensions: appearance, taste and texture. Nine is 'excellent', eight is 'very good', seven is 'above average', six is 'average', five is 'below-average' and the list goes on. A one is a penalty, and can only be given after consulting a KCBS representative.

Judges are inclined to avoid scoring below a six unless there was an obvious problem with an entry. Teams spend substantial sums on supplies and judges, while volunteers, are eating for free. Judges are encouraged to fill out comment cards for flawed entries.

A team's final score in a single category is calculated by a sum of weighted judge scores with the lowest score thrown away. A team's overall score is (typically) the sum of their four category scores. Unweighted judge scores can be used to break ties, and if there is still a tie, the lowest score is brought back into the equation.

Score Range: Appearance: 0 : 9 Taste: 0 : 9 Tenderness: 0 : 9

Score Weighting

(appearance × .56) + (taste × 2.2972) + (tenderness × 1.1428)

Maximum Single Judge Score:

(9 × .56) + (9 × 2.2972) + (9 × 1.1428) = 36

Maximum Category Score:

(6 - 1) judges × 36 points = 180

Maximum Standard Overall Score:

4 categories × 180 points = 720

Here's The Difference Between Menudo, Afritada, Mechado, And Kaldereta Menudo, afritada, mechado, and kaldereta are all tomato sauce-based Pinoy ulam favorites, which can be confusing for a lot of people. Philip T. Hernandez of Davao Conyo even created a Ratatouille Filipino-dubbed parody to depict how baffling it can be for someone to prepare the said dishes. To end the confusion, we researched and asked real home cooks for the real difference between the four tomato sauce-based ulams. Here's a summary:

Menudo - This stew has small cuts of pork, chopped liver, potatoes, and garbanzo. https://images.summitmedia-digital.com/cosmo/images/2020/07/06/pork-menudo-recipe---yummy-ph-1594005717.jpg" alt=""> Afritada - May contain chicken or pork, potatoes, carrots, and green bell peppers. Its sauce isn't as thick as the others. https://images.summitmedia-digital.com/cosmo/images/2020/07/06/pork-afritada-recipe---yummy-ph-1594005714.jpg" alt=""> Mechado - This beef stew has thick tomato sauce with potatoes and carrots. https://images.summitmedia-digital.com/cosmo/images/2020/07/06/beef-mechado-recipe---yummy-ph-1594005713.jpg" alt=""> Kaldereta - It may have beef or goat meat. It also has potatoes, carrots, liver spread, and cheese. Some add chili for a kick of heat, too. https://images.summitmedia-digital.com/cosmo/images/2020/07/06/beef-kaldereta-recipe-with-olives---yummy-ph-1594005712.jpg" alt="">

REFERENCES: https://www.cosmo.ph/lifestyle/food-drink/difference-menudo-afritada-mechado-kaldereta-a1014-20200706

In recent years, the increase in awareness of antimicrobial resistance together with the societal demand of healthier meat products have driven attention to health-related traits in livestock production. Previous studies have reported medium to high heritabilities for these traits and described genomic regions associated with them. Despite its genetic component, health- and immunity-related traits are complex and its study by association analysis with genomic markers may be missing some information. To analyse multiple phenotypes and gene-by-gene interactions, systems biology approaches, such as the association weight matrix (AWM), allows combining genome wide association study results with network inference algorithms. The present study aimed to identify gene networks, key regulators and candidate genes associated to immunocompetence in pigs by integrating multiple health-related traits, enriched for innate immune phenotypes, using the AWM approach. The co-association network analysis unveiled a network comprised of 3,636 nodes (genes) and 451,407 edges (interactions), including a total of 246 regulators. From these, five genes (ARNT2, BRMS1L, MED12L, SUPT3H and TRIM25) were selected as key regulators as they were associated with the maximum number of genes with the minimum overlapping (1,827 genes in total). The five regulators were involved in pathways related to immunity such as lymphocyte differentiation and activation, platelet activation and degranulation, megakaryocyte differentiation, FcγR-mediated phagocytosis and response to nitric oxide, among others, but also in immunometabolism. Furthermore, we identified genes co-associated with the key regulators previously reported as candidate genes (e.g., ANGPT1, CD4, CD36, DOCK1, PDE4B, PRKCE, PTPRC and SH2B3) for immunity traits in humans and pigs, but also new candidate ones (e.g., ACSL3, CXADR, HBB, MMP12, PTPN6, WLS) that were not previously described. The co-association analysis revealed new regulators associated with health-related traits in pigs. This approach also identified gene-by-gene interactions and candidate genes involved in pathways related to cell fate and metabolic and immune functions. Our results shed new light in the regulatory mechanisms involved in pig immunity and reinforce the use of the pig as biomedical model.

Pigs (Sus scrofa) are an animal model for metabolic diseases in humans. Pork is an important source of fatty acids (FAs) in the human diet, as it is one of the most consumed meats worldwide. The effects of dietary inclusion of oils such as canola, fish, and soybean oils on pig gene expression are mostly unknown. Our objective was to evaluate FA composition, identify changes in gene expression in the liver of male pigs fed diets enriched with different FA profiles, and identify impacted metabolic pathways and gene networks to enlighten the biological mechanisms’ variation. Large White male pigs were randomly allocated to one of three diets with 18 pigs in each; all diets comprised a base of corn and soybean meal to which either 3% of soybean oil (SOY), 3% canola oil (CO), or 3% fish oil (FO) was added for a 98-day trial during the growing and finishing phases. RNA sequencing was performed on the liver samples of each animal by Illumina technology for differential gene expression analyses, using the R package DESeq2. The diets modified the FA profile, mainly in relation to polyunsaturated and saturated FAs. Comparing SOY vs. FO, 143 differentially expressed genes (DEGs) were identified as being associated with metabolism, metabolic and neurodegenerative disease pathways, inflammatory processes, and immune response networks. Comparing CO vs. SOY, 148 DEGs were identified, with pathways related to FA oxidation, regulation of lipid metabolism, and metabolic and neurodegenerative diseases. Our results help explain the behavior of genes with differential expression in metabolic pathways resulting from feeding different types of oils in pig diets.