Models to predict nitrogen excretion from beef cattle fed a wide range of diets compiled from South America.

The objective of this meta-analysis was to develop and evaluate models for predicting nitrogen (N) excretion in feces, urine, and manure in beef cattle in South America. The study incorporated a total of 1,116 individual observations of N excretion in feces and 939 individual observations of N excretion in feces and in urine (g/d), representing a diverse range of diets, animal genotypes, and management conditions in South America. The dataset also included data on dry matter intake (DMI; kg/d) and nitrogen intake (NI; g/d), concentrations of dietary components, as well as average daily gain (ADG; g/d) and average body weight (BW; kg). Models were derived using linear mixed-effects regression with a random intercept for the study. Fecal N excretion was positively associated with DMI, NI, nonfibrous carbohydrates, average BW, and ADG and negatively associated with EE and CP concentration in the diet. The univariate model predicting fecal N excretion based on DMI (model 1) performed slightly better than the univariate model, which used NI as a predictor variable (model 2) with a root mean square error (RMSE) of 38.0 vs. 39.2%, the RMSE-observations SD ratio (RSR) of 0.81 vs. 0.84, and concordance correlation coefficient (CCC) of 0.53 vs. 0.50, respectively. Models predicting urinary N excretion were less accurate than those derived to predict fecal N excretion, with an average RMSE of 43.7% vs. 37.0%, respectively. Urinary and manure N excretion were positively associated with DMI, NI, CP, average BW, and ADG and negatively associated with neutral detergent fiber concentration in the diet. As opposed to fecal N excretion, the univariate model predicting urinary N excretion using NI (model 10) performed slightly better than the univariate model using DMI (model 9) as predictor variable with an RMSE of 36.0% vs. 39.7%, RSR 0.85 vs. 0.93, and CCC of 0.43 vs. 0.29, respectively. The models developed in this study are applicable for predicting N excretion in beef cattle across a broad spectrum of dietary compositions and animal genotypes in South America. The univariate model using DMI as a predictor is recommended for fecal N prediction, while the univariate model using NI is recommended for predicting urinary and manure N excretion because the use of more complex models resulted in little to no benefits. However, it may be more useful to consider more complex models that incorporate nutrient intakes and diet composition for decision-making when N excretion is a factor to be considered. Three extant equations evaluated in this study have the potential to be used in tropical conditions typical of South America to predict fecal N excretion with good precision and accuracy. However, none of the extant equations are recommended for predicting urine or manure N excretion because of their high RMSE, and low precision and accuracy.

Alterations of rumen and fecal microbiome in growing beef and dairy steers fed rumen-protected Capsicum oleoresin.

The microbiome has been linked to animal health and productivity, and thus, modulating animal microbiomes is becoming of increasing interest. Antimicrobial growth promoters (AGP) were once a common technology used to modulate the microbiome, but regulation and consumer pressure have decreased AGP use in food animals. One alternative to antimicrobial growth promoters are phytotherapeutics, compounds derived from plants. Capsaicin is a compound from the Capsicum genus, which includes chili peppers. Capsaicin has antimicrobial properties and could be used to manipulate the gastrointestinal microbiome of cattle. Both the rumen and fecal microbiomes are essential to cattle health and production, and modulation of either microbiome can affect both cattle health and productivity. We hypothesized that the addition of rumen-protected capsaicin to the diet of cattle would alter the composition of the fecal microbiome, but not the rumen microbiome. To determine the impact of rumen-protected capsaicin in cattle, four Holstein and four Angus steers were fed rumen-protected Capsicum oleoresin at 0 (Control), 5, 10, or 15 mg kg-1 diet dry matter. Cattle were fed in treatment groups in a 4 × 4 Latin Square design with a 21-d adaptation phase and a 7-d sample collection phase. Rumen samples were collected on day 22 at 0-, 2-, 6-, 12-, and 18-h post-feeding, and fecal swabs were collected on the last day of sample collection, day 28, within 1 h of feeding. Sequencing data of the 16s rRNA gene was analyzed using the dada2 pipeline and taxa were assigned using the SILVA database. No differences were observed in alpha diversity among fecal or rumen samples for either breed (P > 0.08) and no difference between groups was detected for either breed in rumen samples or for Angus steers in fecal samples (P > 0.42). There was a difference in beta diversity between treatments in fecal samples of Holstein steers (P < 0.01), however, a pairwise comparison of the treatment groups suggests no difference between treatments after adjusting for multiple comparisons. Therefore, we were unable to observe substantial overall variation in the rumen or fecal microbiomes of steers due to increasing concentrations of rumen-protected capsaicin. We do, however, see a trend toward increased concentrations of capsaicin influencing the fecal microbiome structure of Holstein steers despite this lack of significance.

The microbiome is the collection of microbes present in an animal's body and has been discovered to be directly connected to animal health and productivity. In production animals, such as feedlot cattle, the microbiome can be modulated by antimicrobials to promote growth, but increasing consumer pressure to reduce antimicrobial use has producers seeking alternatives. Capsaicin is a phytotherapeutic derived from chili peppers that can be used to modulate the microbiome due to its antimicrobial properties. Eight steers were fed rumen-protected Capsicum oleoresin to determine its effect on average daily gain. In addition, rumen and fecal samples were collected for microbiome testing. No differences were detected in the rumen microbiomes between cattle fed capsaicin (treatment) or those that received no capsaicin (control). While no overall effect was observed on the fecal microbiome of cattle fed different doses of capsaicin or control, we did observe changes in fecal beta diversity due to capsaicin treatment in Holstein steers fed greater doses. The fecal microbiome structure of Holsteins fed greater dosages of capsaicin differed from those fed control or low doses, as observed by the presence of two distinct clusters. This observation suggests an impact of greater doses of capsaicin treatment on microbiome structure.

Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy.

Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin's concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.

A global dataset of enteric methane mitigation experiments with lactating and non-lactating dairy cows conducted from 1963 to 2022.

A dataset of descriptive information was compiled from 213 peer-reviewed scientific publications that focused on dairy cow experiments and measured enteric methane emissions. This dataset was primarily based on the bibliography used by Arndt et al. (2022), with the addition of studies conducted from 2019 to 2022. The articles were identified for inclusion in the dataset using the "Web of Science Core Collection" database, using various combinations of search terms related to methane, dairy, cattle, rumen, ruminant, energy balance, energy metabolism, energy partitioning, and enteric emissions. For inclusion in the dataset, studies had to be written in English and provide information on enteric methane emission, as well as report feed dry matter intake along with measures of variance. Both continuous and crossover design studies were included, resulting in a comprehensive dataset with 797 records (rows) and 162 variables (columns). The variables cover various aspects such as publication information, experimental design, animal description, methane measurement method, and diet nutrient composition. Additionally, when available, the dataset includes treatment means and measures of variance for feed dry matter intake, rumen fermentation parameters, nutrient digestibility, nitrogen excretion, milk yield, milk components, as well as enteric methane, carbon dioxide, and hydrogen emissions. Researchers can use this dataset to assess the effectiveness of different enteric methane mitigation strategies and their impact on milk yield and other essential dairy cow nutrition and performance variables. Furthermore, it offers the opportunity to explore potential interactions between nutrients and feed additives.

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries.

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d-1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg-1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data.

The effect of 3-nitrooxypropanol, a potent methane inhibitor, on ruminal microbial gene expression profiles in dairy cows.

BACKGROUND: Enteric methane emissions from dairy cows are an environmental problem as well as a gross feed energy loss to the animal. Methane is generated in the rumen by methanogenic archaea from hydrogen (H2) + carbon dioxide and from H2 + methanol or methylamines. The methanogenic substrates are provided by non-methanogens during feed fermentation. Methane mitigation approaches have yielded variable results, partially due to an incomplete understanding of the contribution of hydrogenotrophic and methylotrophic archaea to methanogenesis. Research indicates that 3-nitrooxypropanol (3-NOP) reduces enteric methane formation in dairy cows by inhibiting methyl-coenzyme M reductase (MCR), the enzyme responsible for methane formation. The purpose of this study was to utilize metagenomic and metatranscriptomic approaches to investigate the effect of 3-NOP on the rumen microbiome and to determine the fate of H2 that accumulates less than expected under inhibited methanogenesis. RESULTS: The inhibitor 3-NOP was more inhibitory on Methanobrevibacter species than methanol-utilizing Methanosphaera and tended to reduce the gene expression of MCR. Under inhibited methanogenesis by 3-NOP, fluctuations in H2 concentrations were accompanied by changes in the expression of [FeFe] hydrogenases in H2-producing bacteria to regulate the amount of H2 production. No previously reported alternative H2 sinks increased under inhibited methanogenesis except for a significant increase in gene expression of enzymes involved in the butyrate pathway. CONCLUSION: By taking a metatranscriptomic approach, this study provides novel insights on the contribution of methylotrophic methanogens to total methanogenesis and regulation of H2 metabolism under normal and inhibited methanogenesis by 3-NOP in the rumen. Video Abstract.

Field-tuned ferroquadrupolar quantum phase transition in the insulator TmVO4.

We report results of low-temperature heat-capacity, magnetocaloric-effect, and neutron-diffraction measurements of TmVO4, an insulator that undergoes a continuous ferroquadrupolar phase transition associated with local partially filled 4f orbitals of the thulium (Tm[Formula: see text]) ions. The ferroquadrupolar transition, a realization of Ising nematicity, can be tuned to a quantum critical point by using a magnetic field oriented along the c axis of the tetragonal crystal lattice, which acts as an effective transverse field for the Ising-nematic order. In small magnetic fields, the thermal phase transition can be well described by using a semiclassical mean-field treatment of the transverse-field Ising model. However, in higher magnetic fields, closer to the field-tuned quantum phase transition, subtle deviations from this semiclassical behavior are observed, which are consistent with expectations of quantum fluctuations. Although the phase transition is driven by the local 4f degrees of freedom, the crystal lattice still plays a crucial role, both in terms of mediating the interactions between the local quadrupoles and in determining the critical scaling exponents, even though the phase transition itself can be described via mean field. In particular, bilinear coupling of the nematic order parameter to acoustic phonons changes the spatial and temporal fluctuations of the former in a fundamental way, resulting in different critical behavior of the nematic transverse-field Ising model, as compared to the usual case of the magnetic transverse-field Ising model. Our results establish TmVO4 as a model material and electronic nematicity as a paradigmatic example for quantum criticality in insulators.

Full adoption of the most effective strategies to mitigate methane emissions by ruminants can help meet the 1.5 °C target by 2030 but not 2050.

To meet the 1.5 °C target, methane (CH4) from ruminants must be reduced by 11 to 30% by 2030 and 24 to 47% by 2050 compared to 2010 levels. A meta-analysis identified strategies to decrease product-based (PB; CH4 per unit meat or milk) and absolute (ABS) enteric CH4 emissions while maintaining or increasing animal productivity (AP; weight gain or milk yield). Next, the potential of different adoption rates of one PB or one ABS strategy to contribute to the 1.5 °C target was estimated. The database included findings from 430 peer-reviewed studies, which reported 98 mitigation strategies that can be classified into three categories: animal and feed management, diet formulation, and rumen manipulation. A random-effects meta-analysis weighted by inverse variance was carried out. Three PB strategies­namely, increasing feeding level, decreasing grass maturity, and decreasing dietary forage-to-concentrate ratio­decreased CH4 per unit meat or milk by on average 12% and increased AP by a median of 17%. Five ABS strategies­namely CH4 inhibitors, tanniferous forages, electron sinks, oils and fats, and oilseeds­decreased daily methane by on average 21%. Globally, only 100% adoption of the most effective PB and ABS strategies can meet the 1.5 °C target by 2030 but not 2050, because mitigation effects are offset by projected increases in CH4 due to increasing milk and meat demand. Notably, by 2030 and 2050, low- and middle-income countries may not meet their contribution to the 1.5 °C target for this same reason, whereas high-income countries could meet their contributions due to only a minor projected increase in enteric CH4 emissions.

Nitrogen excretion from beef cattle fed a wide range of diets compiled in an intercontinental dataset: a meta-analysis.

Manure N from cattle contributes to nitrate leaching, nitrous oxide, and ammonia emissions. Measurement of manure N outputs on commercial beef cattle operations is laborious, expensive, and impractical; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were to 1) collate an international dataset of N excretion in feces and urine based on individual observations from beef cattle; 2) determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and 3) develop robust and reliable N excretion prediction models based on individual observation from beef cattle consuming various diets. A meta-analysis based on individual beef data from different experiments was carried out from a raw dataset including 1,004 observations from 33 experiments collected from 5 research institutes in Europe (n = 3), North America (n = 1), and South America (n = 1). A sequential approach was taken in developing models of increasing complexity by incrementally adding significant variables that affected fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models with experiment as a random effect. Simple models including dry matter intake (DMI) were better at predicting fecal N excretion than those using only dietary nutrient composition or body weight (BW). Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI. A model including DMI and dietary component concentrations led to the most robust prediction of fecal and urinary N excretion, generating root mean square prediction errors as a percentage of the observed mean values of 25.0% for feces and 25.6% for urine. Complex total manure N excretion models based on BW and dietary component concentrations led to the lowest prediction errors of about 14.6%. In conclusion, several models to predict N excretion already exist, but the ones developed in this study are based on individual observations encompassing larger variability than the previous developed models. In addition, models that include information on DMI or N intake are required for accurate prediction of fecal, urinary, and total manure N excretion. In the absence of intake data, equations have poor performance as compared with equations based on intake and dietary component concentrations.

China's low-emission pathways toward climate-neutral livestock production for animal-derived foods.

Animal-derived food production accounts for one-third of global anthropogenic greenhouse gas (GHG) emissions. Diet followed in China is ranked as low-carbon emitting (i.e., 0.21 t CO2-eq per capita in 2018, ranking at 145th of 168 countries) due to the low average animal-derived food consumption rate, and preferential consumption of animal-derived foods with lower GHG emissions (i.e., pork and eggs versus beef and milk). However, the projected increase in GHG emissions from livestock production poses great challenges for achieving China's "carbon neutrality" pledge. We propose that the livestock sector in China may achieve "climate neutrality" with net-zero warming around 2050 by implementing healthy diet and mitigation strategies to control enteric methane emissions.

Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database.

Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH4 measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH4 emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g d-1) and yield [g kg-1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH4 production from LAC dairy cows. Extant equations were not accurate in predicting CH4 yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries.

Using Structural Equation Modeling to Understand Interactions Between Bacterial and Archaeal Populations and Volatile Fatty Acid Proportions in the Rumen.

Microbial syntrophy (obligate metabolic mutualism) is the hallmark of energy-constrained anaerobic microbial ecosystems. For example, methanogenic archaea and fermenting bacteria coexist by interspecies hydrogen transfer in the complex microbial ecosystem in the foregut of ruminants; however, these synergistic interactions between different microbes in the rumen are seldom investigated. We hypothesized that certain bacteria and archaea interact and form specific microbial cohorts in the rumen. To this end, we examined the total (DNA-based) and potentially metabolically active (cDNA-based) bacterial and archaeal communities in rumen samples of dairy cows collected at different times in a 24 h period. Notably, we found the presence of distinct bacterial and archaeal networks showing potential metabolic interactions that were correlated with molar proportions of specific volatile fatty acids (VFAs). We employed hypothesis-driven structural equation modeling to test the significance of and to quantify the extent of these relationships between bacteria-archaea-VFAs in the rumen. Furthermore, we demonstrated that these distinct microbial networks were host-specific and differed between cows indicating a natural variation in specific microbial networks in the rumen of dairy cows. This study provides new insights on potential microbial metabolic interactions in anoxic environments that have broader applications in methane mitigation, energy conservation, and agricultural production.

Nonalcoholic Fatty Liver Disease and Diabetes Mellitus Are Associated With Post-Transjugular Intrahepatic Portosystemic Shunt Renal Dysfunction: An Advancing Liver Therapeutic Approaches Group Study.

Transjugular intrahepatic portosystemic shunt (TIPS) is an effective intervention for portal hypertensive complications, but its effect on renal function is not well characterized. Here we describe renal function and characteristics associated with renal dysfunction at 30 days post-TIPS. Adults with cirrhosis who underwent TIPS at 9 hospitals in the United States from 2010 to 2015 were included. We defined "post-TIPS renal dysfunction" as a change in estimated glomerular filtration rate (ΔeGFR) ≤-15 and eGFR ≤ 60 mL/min/1.73 m2 or new renal replacement therapy (RRT) at day 30. We identified the characteristics associated with post-TIPS renal dysfunction by logistic regression and evaluated survival using adjusted competing risk regressions. Of the 673 patients, the median age was 57 years, 38% of the patients were female, 26% had diabetes mellitus, and the median MELD-Na was 17. After 30 days post-TIPS, 66 (10%) had renal dysfunction, of which 23 (35%) required new RRT. Patients with post-TIPS renal dysfunction, compared with those with stable renal function, were more likely to have nonalcoholic fatty liver disease (NAFLD; 33% versus 17%; P = 0.01) and comorbid diabetes mellitus (42% versus 24%; P = 0.001). Multivariate logistic regressions showed NAFLD (odds ratio [OR], 2.04; 95% confidence interval [CI], 1.00-4.17; P = 0.05), serum sodium (Na; OR, 1.06 per mEq/L; 95% CI, 1.01-1.12; P = 0.03), and diabetes mellitus (OR, 2.04; 95% CI, 1.16-3.61; P = 0.01) were associated with post-TIPS renal dysfunction. Competing risk regressions showed that those with post-TIPS renal dysfunction were at a higher subhazard of death (subhazard ratio, 1.74; 95% CI, 1.18-2.56; P = 0.01). In this large, multicenter cohort, we found NAFLD, diabetes mellitus, and baseline Na associated with post-TIPS renal dysfunction. This study suggests that patients with NAFLD and diabetes mellitus undergoing TIPS evaluation may require additional attention to cardiac and renal comorbidities before proceeding with the procedure.

Outcomes of Liver Transplantation Among Older Recipients With Nonalcoholic Steatohepatitis in a Large Multicenter US Cohort: the Re-Evaluating Age Limits in Transplantation Consortium.

The liver transplantation (LT) population is aging, with the need for transplant being driven by the growing prevalence of nonalcoholic steatohepatitis (NASH). Older LT recipients with NASH may be at an increased risk for adverse outcomes after LT. Our objective is to characterize outcomes in these recipients in a large multicenter cohort. All primary LT recipients ≥65 years from 2010 to 2016 at 13 centers in the Re-Evaluating Age Limits in Transplantation (REALT) consortium were included. Of 1023 LT recipients, 226 (22.1%) were over 70 years old, and 207 (20.2%) had NASH. Compared with other LT recipients, NASH recipients were older (68.0 versus 67.3 years), more likely to be female (47.3% versus 32.8%), White (78.3% versus 68.0%), Hispanic (12.1% versus 9.2%), and had higher Model for End-Stage Liver Disease-sodium (21 versus 18) at LT (P < 0.05 for all). Specific cardiac risk factors including diabetes with or without chronic complications (69.6%), hypertension (66.3%), hyperlipidemia (46.3%), coronary artery disease (36.7%), and moderate-to-severe renal disease (44.4%) were highly prevalent among NASH LT recipients. Graft survival among NASH patients was 90.3% at 1 year and 82.4% at 3 years compared with 88.9% at 1 year and 80.4% at 3 years for non-NASH patients (log-rank P = 0.58 and P = 0.59, respectively). Within 1 year after LT, the incidence of graft rejection (17.4%), biliary strictures (20.9%), and solid organ cancers (4.9%) were comparable. Rates of cardiovascular (CV) complications, renal failure, and infection were also similar in both groups. We observed similar posttransplant morbidity and mortality outcomes for NASH and non-NASH LT recipients. Certain CV risk factors were more prevalent in this population, although posttransplant outcomes within 1 year including CV events and renal failure were similar to non-NASH LT recipients.

Comparison of Two Sampling Techniques for Evaluating Ruminal Fermentation and Microbiota in the Planktonic Phase of Rumen Digesta in Dairy Cows.

The objective of this experiment was to compare ruminal fluid samples collected through rumen cannula (RC) or using an oral stomach tube (ST) for measurement of ruminal fermentation and microbiota variables. Six ruminally cannulated lactating Holstein cows fed a standard diet were used in the study. Rumen samples were collected at 0, 2, 4, 6, 8, and 12 h after the morning feeding on two consecutive days using both RC and ST techniques. Samples were filtered through two layers of cheesecloth and the filtered ruminal fluid was used for further analysis. Compared with RC, ST samples had 7% greater pH; however, the pattern in pH change after feeding was similar between sampling methods. Total volatile fatty acids (VFA), acetate and propionate concentrations in ruminal fluid were on average 23% lower for ST compared with RC. There were no differences between RC and ST in VFA molar proportions (except for isobutyrate), ammonia and dissolved hydrogen (dH2) concentrations, or total protozoa counts, and there were no interactions between sampling technique and time of sampling. Bacterial ASV richness was higher in ST compared with RC samples; however, no differences were observed for Shannon diversity. Based on Permanova analysis, bacterial community composition was influenced by sampling method and there was an interaction between sampling method and time of sampling. A core microbiota comprised of Prevotella, S24-7, unclassified Bacteroidales and unclassified Clostridiales, Butyrivibrio, unclassified Lachnospiraceae, unclassified Ruminococcaceae, Ruminococcus, and Sharpea was present in both ST and RC samples, although their relative abundance varied and was influenced by an interaction between sampling time and sampling method. Overall, our results suggest that ruminal fluid samples collected using ST (at 180 to 200 cm depth) are not representative of rumen pH, absolute values of VFA concentrations, or bacterial communities >2 h post-feeding when compared to samples of ruminal fluid collected using RC. However, ST can be a feasible sampling technique if the purpose is to study molar proportions of VFA, protozoa counts, dH2, and ammonia concentrations.

Key Considerations for the Use of Seaweed to Reduce Enteric Methane Emissions From Cattle.

Enteric methane emissions are the single largest source of direct greenhouse gas emissions (GHG) in beef and dairy value chains and a substantial contributor to anthropogenic methane emissions globally. In late 2019, the World Wildlife Fund (WWF), the Advanced Research Projects Agency-Energy (ARPA-E) and the Foundation for Food and Agriculture Research (FFAR) convened approximately 50 stakeholders representing research and production of seaweeds, animal feeds, dairy cattle, and beef and dairy foods to discuss challenges and opportunities associated with the use of seaweed-based ingredients to reduce enteric methane emissions. This Perspective article describes the considerations identified by the workshop participants and suggests next steps for the further development and evaluation of seaweed-based feed ingredients as enteric methane mitigants. Although numerous compounds derived from sources other than seaweed have been identified as having enteric methane mitigation potential, these mitigants are outside the scope of this article.