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Browsing by Author "Feng, Xin"

Now showing 1 - 4 of 4
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    Antibiotic resistance genes in the faeces of dairy cows following short-term therapeutic and prophylactic antibiotic administration
    Feng, Xin; Chambers, Lindsey R.; Knowlton, Katharine F. (2019-12-06)
    The objective of the research was to quantify three antibiotic resistance genes (tetQ, cfxA and mefA) in the faeces of dairy cows following therapeutic and prophylactic antibiotic treatments. Manure collected from dairy cows treated with either no antibiotic, pirlimycin hydrochloride (PIRL), ceftiofur crystalline free acid (CCFA) or cephapirin benzathine (CEPH) were submitted to quantitative PCR analysis. No treatment effects on the abundance of the tetQ and cfxA were observed. There was a trend for the abundance of the mefA to be increased in cows treated with PIRL (P = 0.07). Overall, the results showed no difference of measured three ARGs from cows receiving different antibiotics. Considering the limited scope of our investigation, further investigation is needed to provide more information on ARGs excretion from cows that received therapeutic and prophylactic antibiotic treatment.
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    In vivo and modeling approaches to improve prediction of phosphorus availability in ruminants
    Feng, Xin (Virginia Tech, 2015-06-04)
    Improving prediction of P availability necessitates understanding of P digestion and absorption mechanism in ruminants. Greater knowledge of the interaction of P with other nutrients and the utilization of dietary P in the digestive tract will improve our ability to optimize P feeding and reduce P runoff in agricultural areas. In vivo experiments were performed and the data were used to reparamterize a model regarding P digestion and metabolism. The interaction of P and iron was investigated in lactating dairy cows by infusing 0, 200, 500, or 1250 mg/d Fe (equivalent to 0, 2, 5, or 12.5 mg Fe/L in drinking water) in the form of ferrous lactate solution into the abomasum of lactating cows. Phosphorus absorption was not negatively influenced by abomasally infused ferrous lactate, and the highest infusion (1250 mg Fe/d) approximates a drinking water iron content far above that found in most samples from the field. In the second study the effects of dietary P intake on intestinal P absorption was evaluated in eight growing Holstein steers fitted with permanent duodenal and ileal cannulas. Diets varying in P content (0.15%, 0.27%, 0.36% and 0.45%, DM basis) were fed , and increasing P intake increased the quantity of P absorbed from the small intestine linearly without affecting the absorption efficiency (mean = 59.6%). Only a small portion of P absorption occurred in large intestine and this was not affected by dietary P concentration. An absence of change of salivary P secretion at low dietary P suggested rumen function was prioritized during short-term P deficiency. Finally the data from these experiments along with four other studies were used to parameterize the P digestion and metabolism model of Hill et al. (2008) to provide a better understanding of the digestion and metabolism of P fractions in cattle. The data used were adequate to parameterize the digestive elements of the model with good precision, and the model structure appears to be appropriate with no significant mean or slope bias. The resulting model could be used to derive P bio-availabilities of commonly used feedstuffs in cattle production. Although the model explained the data used with no apparent bias, this does not guarantee that the model parameters are valid for all conditions. Additional data are needed to evaluate this model in a wider range of scenarios.
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    The iPlant collaborative: cyberinfrastructure for plant biology
    Goff, Stephen A.; Vaughn, Matthew; McKay, Sheldon; Lyons, Eric; Stapleton, Ann E.; Gessler, Damian; Matasci, Naim; Wang, Liya; Hanlon, Matthew; Lenards, Andrew; Muir, Andy; Merchant, Nirav; Lowry, Sonya; Mock, Stephen; Helmus, Matthew R.; Kubach, Adam; Narro, Martha; Hopkins, Nicole; Micklos, David; Hilgert, Uwe; Gonzales, Michael; Jordan, Chris; Skidmore, Edwin; Dooley, Rion; Cazes, John; McLay, Robert; Lu, Zhenyuan; Pasternak, Shiran; Koesterke, Lars; Piel, William H.; Grene, Ruth; Noutsos, Christos; Gendler, Karla; Feng, Xin; Tang, Chunlao; Lent, Monica; Kim, Seung-Jin; Kvilekval, Kristian; Manjunath, B. S.; Tannen, Val; Stamatakis, Alexandros; Sanderson, Michael; Welch, Stephen M.; Cranston, Karen A.; Soltis, Pamela S.; Soltis, Douglas E.; O'Meara, Brian; Ane, Cecile; Brutnell, Tom; Kleibenstein, Daniel J.; White, Jeffery W.; Leebens-Mack, James H.; Donoghue, Michael J.; Spalding, Edgar P.; Vision, Todd J.; Myers, Christopher R.; Lowenthal, David K.; Enquist, Brian J.; Boyle, Brad; Akoglu, Ali; Andrews, Greg; Ram, Sudha; Ware, Doreen; Stein, Lincoln; Stanzione, Dan (Frontiers, 2011)
    The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services.
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    An Update on Protein and Amino Acid Nutrition
    Hanigan, Mark D.; White, Robin R.; Arriola Apelo, Sebastian I.; Aguilar, Michelle; Castro, Juan; Estes, Kari; Myers, Adelyn; Feng, Xin (Virginia Tech. Department of Dairy Science, 2017-02-16)
    Summary - AA are very, very important! - Representation of effects is complicated - Multiple AA - Energy - Hormones - Integrated response - Nyet on the barrel with broken staves - Can’t be done by guess and by golly - Excellent modeling progress - USDA funding was renewed - Look for a new model soon in theaters near you - Upgrade your optimizer skills