The Efficacy of Three Objective Systems for Identifying Beef Cuts That Can Be Guaranteed Tender

The objective of this study was to determine the accuracy of three objective systems (prototype BeefCam, colorimeter, and slice shear force) for identifying guaranteed tender beef. In Phase I, 308 carcasses (105 Top Choice, 101 Low Choice, and 102 Select) from two commercial plants were tested. In Phase II, 400 carcasses (200 rolled USDA Select and 200 rolled USDA Choice) from one commercial plant were tested. The three systems were evaluated based on progressive certification of the longissimus as “tender” in 10% increments (the best 10, 20, 30%, etc., certified as “tender” by each technology; 100% certification would mean no sorting for tenderness). In Phase I, the error (percentage of carcasses certified as tender that had Warner- Bratzler shear force of ≥ 5 kg at 14 d postmortem) for 100% certification using all carcasses was 14.1%. All certification levels up to 80% (slice shear force) and up to 70% (colorimeter) had less error (P \u3c 0.05) than 100% certification. Errors in all levels of certification by prototype BeefCam (13.8 to 9.7%) were not different (P \u3e 0.05) from 100% certification. In Phase I, the error for 100% certification for USDA Select carcasses was 30.7%. For Select carcasses, all slice shear force certification levels up to 60% (0 to 14.8%) had less error (P \u3c 0.05) than 100% certification. For Select carcasses, errors in all levels of certification by colorimeter (20.0 to 29.6%) and by BeefCam (27.5 to 31.4%) were not different (P \u3e 0.05) from 100% certification. In Phase II, the error for 100% certification for all carcasses was 9.3%. For all levels of slice shear force certification less than 90% (for all carcasses) or less than 80% (Select carcasses), errors in tenderness certification were less than (P \u3c 0.05) for 100% certification. In Phase II, for all carcasses or Select carcasses, colorimeter and prototype BeefCam certifications did not significantly reduce errors (P \u3e 0.05) compared to 100% certification. Thus, the direct measure of tenderness provided by slice shear force results in more accurate identification of “tender” beef carcasses than either of the indirect technologies, prototype BeefCam, or colorimeter, particularly for USDA Select carcasses. As tested in this study, slice shear force, but not the prototype BeefCam or colorimeter systems, accurately identified “tender” beef

Beef quality traits of Nellore, F1 Simmental × Nellore and F1 Angus × Nellore steers fed at the maintenance level or ad libitum with two concentrate levels in the diet

This trial was conducted to evaluate some beef quality attributes of Nellore, F1 Simmental × Nellore and F1 Angus × Nellore steers finished on feedlot. The effects of feeding regime and genetic group on shear force, thawing losses, cooking (leak + evaporation) losses, total losses and muscle fiber type, as well as carcass pH and temperature during 24 h of chilling were evaluated. There was a genetic group effect on shear force, where the beef from F1 Simmental × Nellore and F1 Angus × Nellore animals had lower values than Nellore animals. Beef of the animals fed the diets with 1% and 2% of body weight on concentrated lost more liquid than the meat of the animals fed at maintenance during thawing and when considering total losses. During cooking there was a difference among the feeding regimes for drip losses which were greater on the animals fed the diet of 1% of body weight on concentrate, followed by the 2% diet and, finally, by the animals fed at maintenance. The muscle of the Nellore steers had larger proportion of intermediate fibers and lower proportion of oxidative fibers than the crossbred animals. The proportion of glycolytic fibers was not influenced by genetic group. The Nellore animals had larger proportion of fibers of fast contraction and smaller proportion of fibers of slow contraction when compared with the crossbred animals. Feeding regime did not influence the proportion of muscular fibers or shear force. Nellore cattle produce tougher beef than crossbred Simmental × Nellore or Angus × Nellore, although all of them have the potential to produce an acceptable beef when slaughtered at young age. Feed restriction up to 90 days is not enough to cause modification on muscle fiber frequencies, then not affecting beef quality

Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015

Background Improving survival and extending the longevity of life for all populations requires timely, robust evidence on local mortality levels and trends. The Global Burden of Disease 2015 Study (GBD 2015) provides a comprehensive assessment of all-cause and cause-specific mortality for 249 causes in 195 countries and territories from 1980 to 2015. These results informed an in-depth investigation of observed and expected mortality patterns based on sociodemographic measures. Methods We estimated all-cause mortality by age, sex, geography, and year using an improved analytical approach originally developed for GBD 2013 and GBD 2010. Improvements included refinements to the estimation of child and adult mortality and corresponding uncertainty, parameter selection for under-5 mortality synthesis by spatiotemporal Gaussian process regression, and sibling history data processing. We also expanded the database of vital registration, survey, and census data to 14 294 geography–year datapoints. For GBD 2015, eight causes, including Ebola virus disease, were added to the previous GBD cause list for mortality. We used six modelling approaches to assess cause-specific mortality, with the Cause of Death Ensemble Model (CODEm) generating estimates for most causes. We used a series of novel analyses to systematically quantify the drivers of trends in mortality across geographies. First, we assessed observed and expected levels and trends of cause-specific mortality as they relate to the Socio-demographic Index (SDI), a summary indicator derived from measures of income per capita, educational attainment, and fertility. Second, we examined factors affecting total mortality patterns through a series of counterfactual scenarios, testing the magnitude by which population growth, population age structures, and epidemiological changes contributed to shifts in mortality. Finally, we attributed changes in life expectancy to changes in cause of death. We documented each step of the GBD 2015 estimation processes, as well as data sources, in accordance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). Findings Globally, life expectancy from birth increased from 61·7 years (95% uncertainty interval 61·4–61·9) in 1980 to 71·8 years (71·5–72·2) in 2015. Several countries in sub-Saharan Africa had very large gains in life expectancy from 2005 to 2015, rebounding from an era of exceedingly high loss of life due to HIV/AIDS. At the same time, many geographies saw life expectancy stagnate or decline, particularly for men and in countries with rising mortality from war or interpersonal violence. From 2005 to 2015, male life expectancy in Syria dropped by 11·3 years (3·7–17·4), to 62·6 years (56·5–70·2). Total deaths increased by 4·1% (2·6–5·6) from 2005 to 2015, rising to 55·8 million (54·9 million to 56·6 million) in 2015, but age-standardised death rates fell by 17·0% (15·8–18·1) during this time, underscoring changes in population growth and shifts in global age structures. The result was similar for non-communicable diseases (NCDs), with total deaths from these causes increasing by 14·1% (12·6–16·0) to 39·8 million (39·2 million to 40·5 million) in 2015, whereas age-standardised rates decreased by 13·1% (11·9–14·3). Globally, this mortality pattern emerged for several NCDs, including several types of cancer, ischaemic heart disease, cirrhosis, and Alzheimer's disease and other dementias. By contrast, both total deaths and age-standardised death rates due to communicable, maternal, neonatal, and nutritional conditions significantly declined from 2005 to 2015, gains largely attributable to decreases in mortality rates due to HIV/AIDS (42·1%, 39·1–44·6), malaria (43·1%, 34·7–51·8), neonatal preterm birth complications (29·8%, 24·8–34·9), and maternal disorders (29·1%, 19·3–37·1). Progress was slower for several causes, such as lower respiratory infections and nutritional deficiencies, whereas deaths increased for others, including dengue and drug use disorders. Age-standardised death rates due to injuries significantly declined from 2005 to 2015, yet interpersonal violence and war claimed increasingly more lives in some regions, particularly in the Middle East. In 2015, rotaviral enteritis (rotavirus) was the leading cause of under-5 deaths due to diarrhoea (146 000 deaths, 118 000–183 000) and pneumococcal pneumonia was the leading cause of under-5 deaths due to lower respiratory infections (393 000 deaths, 228 000–532 000), although pathogen-specific mortality varied by region. Globally, the effects of population growth, ageing, and changes in age-standardised death rates substantially differed by cause. Our analyses on the expected associations between cause-specific mortality and SDI show the regular shifts in cause of death composition and population age structure with rising SDI. Country patterns of premature mortality (measured as years of life lost [YLLs]) and how they differ from the level expected on the basis of SDI alone revealed distinct but highly heterogeneous patterns by region and country or territory. Ischaemic heart disease, stroke, and diabetes were among the leading causes of YLLs in most regions, but in many cases, intraregional results sharply diverged for ratios of observed and expected YLLs based on SDI. Communicable, maternal, neonatal, and nutritional diseases caused the most YLLs throughout sub-Saharan Africa, with observed YLLs far exceeding expected YLLs for countries in which malaria or HIV/AIDS remained the leading causes of early death. Interpretation At the global scale, age-specific mortality has steadily improved over the past 35 years; this pattern of general progress continued in the past decade. Progress has been faster in most countries than expected on the basis of development measured by the SDI. Against this background of progress, some countries have seen falls in life expectancy, and age-standardised death rates for some causes are increasing. Despite progress in reducing age-standardised death rates, population growth and ageing mean that the number of deaths from most non-communicable causes are increasing in most countries, putting increased demands on health systems. Funding Bill & Melinda Gates Foundation